From 01aeb1cd308744a97cd9b0ce26728a166483ff49 Mon Sep 17 00:00:00 2001 From: Vladimir Mandic Date: Tue, 12 Jan 2021 08:24:00 -0500 Subject: [PATCH] fix safari incopatibility --- config.js | 4 +- dist/demo-browser-index.js | 22 +- dist/demo-browser-index.js.map | 4 +- dist/demo-browser-index.json | 8 +- dist/human.esm-nobundle.js | 6 +- dist/human.esm-nobundle.js.map | 6 +- dist/human.esm.js | 22 +- dist/human.esm.js.map | 4 +- dist/human.esm.json | 18 +- dist/human.js | 22 +- dist/human.js.map | 4 +- dist/human.json | 18 +- dist/human.node-gpu.js | 6 +- dist/human.node-gpu.js.map | 6 +- dist/human.node.js | 6 +- dist/human.node.js.map | 6 +- dist/human.node.json | 231 +++++----- models/blazepose.bin | Bin 3257752 -> 6802836 bytes models/blazepose.json | 763 ++++++++++++++++++++++----------- src/blazepose/blazepose.js | 197 +-------- src/blazepose/detect.js | 295 ------------- src/blazepose/helpers.js | 160 ------- src/blazepose/main.js | 374 ---------------- src/human.js | 63 ++- wiki | 2 +- 25 files changed, 776 insertions(+), 1471 deletions(-) delete mode 100644 src/blazepose/detect.js delete mode 100644 src/blazepose/helpers.js delete mode 100644 src/blazepose/main.js diff --git a/config.js b/config.js index 6b653ab9..1a39d5e5 100644 --- a/config.js +++ b/config.js @@ -144,14 +144,14 @@ export default { modelType: 'MobileNet', // Human includes MobileNet version, but you can switch to ResNet }, - pose: { + pose: { // TBD: not currently in use enabled: false, scoreThreshold: 0.6, // threshold for deciding when to remove boxes based on score // in non-maximum suppression iouThreshold: 0.3, // threshold for deciding whether boxes overlap too much // in non-maximum suppression modelPath: '../models/blazepose.json', - inputSize: 128, // fixed value + inputSize: 256, // fixed value }, hand: { diff --git a/dist/demo-browser-index.js b/dist/demo-browser-index.js index 0afac6a7..542b3b30 100644 --- a/dist/demo-browser-index.js +++ b/dist/demo-browser-index.js @@ -5,19 +5,19 @@ author: ' */ -var __create=Object.create,__defProp=Object.defineProperty,__getProtoOf=Object.getPrototypeOf,__hasOwnProp=Object.prototype.hasOwnProperty,__getOwnPropNames=Object.getOwnPropertyNames,__getOwnPropDesc=Object.getOwnPropertyDescriptor,__markAsModule=target=>__defProp(target,"__esModule",{value:!0}),__commonJS=(callback,module)=>()=>(module||(module={exports:{}},callback(module.exports,module)),module.exports),__export=(target,all4)=>{__markAsModule(target);for(var name in all4)__defProp(target,name,{get:all4[name],enumerable:!0})},__exportStar=(target,module,desc)=>{if(__markAsModule(target),module&&typeof module=="object"||typeof module=="function")for(let key of __getOwnPropNames(module))!__hasOwnProp.call(target,key)&&key!=="default"&&__defProp(target,key,{get:()=>module[key],enumerable:!(desc=__getOwnPropDesc(module,key))||desc.enumerable});return target},__toModule=module=>module&&module.__esModule?module:__exportStar(__defProp(module!=null?__create(__getProtoOf(module)):{},"default",{value:module,enumerable:!0}),module),require_blazeface=__commonJS(exports=>{var NUM_LANDMARKS=6;function generateAnchors2(inputSize){let spec={strides:[inputSize/16,inputSize/8],anchors:[2,6]},anchors2=[];for(let i=0;i{box.startEndTensor.dispose(),box.startPoint.dispose(),box.endPoint.dispose()},createBox=startEndTensor=>({startEndTensor,startPoint:slice(startEndTensor,[0,0],[-1,2]),endPoint:slice(startEndTensor,[0,2],[-1,2])}),scaleBox=(box,factors)=>{let starts=mul(box.startPoint,factors),ends=mul(box.endPoint,factors),newCoordinates=concat2d([starts,ends],1);return createBox(newCoordinates)};function decodeBounds(boxOutputs,anchors2,inputSize){let boxStarts=slice(boxOutputs,[0,1],[-1,2]),centers=add2(boxStarts,anchors2),boxSizes=slice(boxOutputs,[0,3],[-1,2]),boxSizesNormalized=div(boxSizes,inputSize),centersNormalized=div(centers,inputSize),halfBoxSize=div(boxSizesNormalized,2),starts=sub(centersNormalized,halfBoxSize),ends=add2(centersNormalized,halfBoxSize),startNormalized=mul(starts,inputSize),endNormalized=mul(ends,inputSize);return concat2d([startNormalized,endNormalized],1)}function scaleBoxFromPrediction(face3,scaleFactor){return tidy(()=>{let box=face3.box?face3.box:face3;return scaleBox(box,scaleFactor).startEndTensor.squeeze()})}var BlazeFaceModel=class{constructor(model2,config3){this.blazeFaceModel=model2,this.width=config3.face.detector.inputSize,this.height=config3.face.detector.inputSize,this.anchorsData=generateAnchors2(config3.face.detector.inputSize),this.anchors=tensor2d(this.anchorsData),this.inputSize=tensor1d([this.width,this.height]),this.config=config3,this.scaleFaces=.8}async getBoundingBoxes(inputImage){if(!inputImage||inputImage.isDisposedInternal||inputImage.shape.length!==4||inputImage.shape[1]<1||inputImage.shape[2]<1)return null;let[detectedOutputs,boxes,scores]=tidy(()=>{let resizedImage=inputImage.resizeBilinear([this.width,this.height]),normalizedImage=sub(resizedImage.div(127.5),1),batchedPrediction=this.blazeFaceModel.predict(normalizedImage),prediction;if(Array.isArray(batchedPrediction)){let sorted=batchedPrediction.sort((a,b)=>a.size-b.size),concat384=concat([sorted[0],sorted[2]],2),concat512=concat([sorted[1],sorted[3]],2);prediction=concat([concat512,concat384],1).squeeze(0)}else prediction=batchedPrediction.squeeze();let decodedBounds=decodeBounds(prediction,this.anchors,this.inputSize),logits=slice(prediction,[0,0],[-1,1]),scoresOut=sigmoid(logits).squeeze();return[prediction,decodedBounds,scoresOut]}),boxIndicesTensor=await image.nonMaxSuppressionAsync(boxes,scores,this.config.face.detector.maxFaces,this.config.face.detector.iouThreshold,this.config.face.detector.scoreThreshold),boxIndices=boxIndicesTensor.arraySync();boxIndicesTensor.dispose();let boundingBoxes=boxIndices.map(boxIndex=>slice(boxes,[boxIndex,0],[1,-1])).map(boundingBox=>{let vals=boundingBox.arraySync();return boundingBox.dispose(),vals}),scoresVal=scores.dataSync(),annotatedBoxes=[];for(let i=0;ithis.config.face.detector.minConfidence){let box=createBox(boundingBoxes[i]),anchor=this.anchorsData[boxIndex],landmarks=tidy(()=>slice(detectedOutputs,[boxIndex,NUM_LANDMARKS-1],[1,-1]).squeeze().reshape([NUM_LANDMARKS,-1]));annotatedBoxes.push({box,landmarks,anchor,confidence})}}return detectedOutputs.dispose(),boxes.dispose(),scores.dispose(),detectedOutputs.dispose(),{boxes:annotatedBoxes,scaleFactor:[inputImage.shape[2]/this.width,inputImage.shape[1]/this.height]}}async estimateFaces(input2){let{boxes,scaleFactor}=await this.getBoundingBoxes(input2),faces=[];for(let face3 of boxes){let landmarkData=face3.landmarks.arraySync(),scaledBox=scaleBoxFromPrediction(face3,scaleFactor),boxData=scaleBox.arraySync(),probabilityData=face3.probability.arraySync(),anchor=face3.anchor,[scaleFactorX,scaleFactorY]=scaleFactor,scaledLandmarks=landmarkData.map(landmark=>[(landmark[0]+anchor[0])*scaleFactorX,(landmark[1]+anchor[1])*scaleFactorY]),normalizedFace={topLeft:boxData.slice(0,2),bottomRight:boxData.slice(2),landmarks:scaledLandmarks,probability:probabilityData};disposeBox(face3.box),face3.landmarks.dispose(),face3.probability.dispose(),scaledBox.dispose(),faces.push(normalizedFace)}return faces}};async function load3(config3){let blazeface=await loadGraphModel(config3.face.detector.modelPath,{fromTFHub:config3.face.detector.modelPath.includes("tfhub.dev")}),model2=new BlazeFaceModel(blazeface,config3);return log(`load model: ${config3.face.detector.modelPath.match(/\/(.*)\./)[1]}`),model2}exports.load=load3,exports.BlazeFaceModel=BlazeFaceModel,exports.disposeBox=disposeBox}),require_box=__commonJS(exports=>{function scaleBoxCoordinates2(box,factor){let startPoint=[box.startPoint[0]*factor[0],box.startPoint[1]*factor[1]],endPoint=[box.endPoint[0]*factor[0],box.endPoint[1]*factor[1]];return{startPoint,endPoint}}exports.scaleBoxCoordinates=scaleBoxCoordinates2;function getBoxSize2(box){return[Math.abs(box.endPoint[0]-box.startPoint[0]),Math.abs(box.endPoint[1]-box.startPoint[1])]}exports.getBoxSize=getBoxSize2;function getBoxCenter2(box){return[box.startPoint[0]+(box.endPoint[0]-box.startPoint[0])/2,box.startPoint[1]+(box.endPoint[1]-box.startPoint[1])/2]}exports.getBoxCenter=getBoxCenter2;function cutBoxFromImageAndResize2(box,image3,cropSize){let h=image3.shape[1],w=image3.shape[2],boxes=[[box.startPoint[1]/h,box.startPoint[0]/w,box.endPoint[1]/h,box.endPoint[0]/w]];return image.cropAndResize(image3,boxes,[0],cropSize)}exports.cutBoxFromImageAndResize=cutBoxFromImageAndResize2;function enlargeBox2(box,factor=1.5){let center=getBoxCenter2(box),size=getBoxSize2(box),newHalfSize=[factor*size[0]/2,factor*size[1]/2],startPoint=[center[0]-newHalfSize[0],center[1]-newHalfSize[1]],endPoint=[center[0]+newHalfSize[0],center[1]+newHalfSize[1]];return{startPoint,endPoint,landmarks:box.landmarks}}exports.enlargeBox=enlargeBox2;function squarifyBox2(box){let centers=getBoxCenter2(box),size=getBoxSize2(box),halfSize=Math.max(...size)/2,startPoint=[centers[0]-halfSize,centers[1]-halfSize],endPoint=[centers[0]+halfSize,centers[1]+halfSize];return{startPoint,endPoint,landmarks:box.landmarks}}exports.squarifyBox=squarifyBox2}),require_util=__commonJS(exports=>{exports.IDENTITY_MATRIX=[[1,0,0],[0,1,0],[0,0,1]];function normalizeRadians2(angle){return angle-2*Math.PI*Math.floor((angle+Math.PI)/(2*Math.PI))}exports.normalizeRadians=normalizeRadians2;function computeRotation2(point1,point2){let radians=Math.PI/2-Math.atan2(-(point2[1]-point1[1]),point2[0]-point1[0]);return normalizeRadians2(radians)}exports.computeRotation=computeRotation2;function radToDegrees(rad){return rad*180/Math.PI}exports.radToDegrees=radToDegrees;function buildTranslationMatrix2(x,y){return[[1,0,x],[0,1,y],[0,0,1]]}function dot5(v1,v2){let product=0;for(let i=0;i{var 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,378,379,397,288,361,454,356,70,63,105,66,107,336,296,334,293,300,168,6,195,4,98,97,2,326,327,33,160,158,133,153,144,362,385,387,263,373,380,57,40,37,0,267,270,287,321,314,17,84,91,78,81,13,311,308,402,14,178],VTX33=[33,133,362,263,1,62,308,159,145,386,374,6,102,331,2,13,14,70,105,107,336,334,300,54,10,284,50,280,234,454,58,288,152],VTX7=[33,133,362,263,1,78,308];exports.MESH_ANNOTATIONS=MESH_ANNOTATIONS,exports.MESH_TO_IRIS_INDICES_MAP=MESH_TO_IRIS_INDICES_MAP,exports.TRI468=TRI468,exports.TRI68=TRI68,exports.TRI33=TRI33,exports.TRI7=TRI7,exports.UV468=UV468,exports.UV68=VTX68.map(x=>UV468[x]),exports.UV33=VTX33.map(x=>UV468[x]),exports.UV7=VTX7.map(x=>UV468[x])}),require_facepipeline=__commonJS(exports=>{var bounding=__toModule(require_box()),util=__toModule(require_util()),coords2=__toModule(require_coords()),LANDMARKS_COUNT=468,MESH_MOUTH_INDEX=13,MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES=[MESH_MOUTH_INDEX,coords2.MESH_ANNOTATIONS.midwayBetweenEyes[0]],BLAZEFACE_MOUTH_INDEX=3,BLAZEFACE_NOSE_INDEX=2,BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES=[BLAZEFACE_MOUTH_INDEX,BLAZEFACE_NOSE_INDEX],LEFT_EYE_OUTLINE=coords2.MESH_ANNOTATIONS.leftEyeLower0,LEFT_EYE_BOUNDS=[LEFT_EYE_OUTLINE[0],LEFT_EYE_OUTLINE[LEFT_EYE_OUTLINE.length-1]],RIGHT_EYE_OUTLINE=coords2.MESH_ANNOTATIONS.rightEyeLower0,RIGHT_EYE_BOUNDS=[RIGHT_EYE_OUTLINE[0],RIGHT_EYE_OUTLINE[RIGHT_EYE_OUTLINE.length-1]],IRIS_UPPER_CENTER_INDEX=3,IRIS_LOWER_CENTER_INDEX=4,IRIS_IRIS_INDEX=71,IRIS_NUM_COORDINATES=76;function replaceRawCoordinates(rawCoords,newCoords,prefix,keys){for(let i=0;i[scaleFactor[0]*(coord[0]-this.meshWidth/2),scaleFactor[1]*(coord[1]-this.meshHeight/2),coord[2]]),coordsRotationMatrix=angle!==0?util.buildRotationMatrix(angle,[0,0]):util.IDENTITY_MATRIX,coordsRotated=angle!==0?coordsScaled.map(coord=>[...util.rotatePoint(coord,coordsRotationMatrix),coord[2]]):coordsScaled,inverseRotationMatrix=angle!==0?util.invertTransformMatrix(rotationMatrix):util.IDENTITY_MATRIX,boxCenter=[...bounding.getBoxCenter({startPoint:box.startPoint,endPoint:box.endPoint}),1];return coordsRotated.map(coord=>[coord[0]+util.dot(boxCenter,inverseRotationMatrix[0]),coord[1]+util.dot(boxCenter,inverseRotationMatrix[1]),coord[2]])}getLeftToRightEyeDepthDifference(rawCoords){let leftEyeZ=rawCoords[LEFT_EYE_BOUNDS[0]][2],rightEyeZ=rawCoords[RIGHT_EYE_BOUNDS[0]][2];return leftEyeZ-rightEyeZ}getEyeBox(rawCoords,face3,eyeInnerCornerIndex,eyeOuterCornerIndex,flip=!1){let box=bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex],rawCoords[eyeOuterCornerIndex]]),this.irisEnlarge)),boxSize=bounding.getBoxSize(box),crop=image.cropAndResize(face3,[[box.startPoint[1]/this.meshHeight,box.startPoint[0]/this.meshWidth,box.endPoint[1]/this.meshHeight,box.endPoint[0]/this.meshWidth]],[0],[this.irisSize,this.irisSize]);return flip&&(crop=image.flipLeftRight(crop)),{box,boxSize,crop}}getEyeCoords(eyeData,eyeBox,eyeBoxSize,flip=!1){let eyeRawCoords=[];for(let i=0;i{let z=averageZ;return i===2?z=upperCenterZ:i===4&&(z=lowerCenterZ),[coord[0],coord[1],z]})}async predict(input2,config3){let useFreshBox=!1,detector;if((this.skipped===0||this.skipped>config3.face.detector.skipFrames||!config3.face.mesh.enabled||!config3.videoOptimized)&&(detector=await this.boundingBoxDetector.getBoundingBoxes(input2),this.skipped=0),config3.videoOptimized&&this.skipped++,detector&&detector.boxes&&detector.boxes.length>0&&(!config3.face.mesh.enabled||detector.boxes.length!==this.detectedFaces&&this.detectedFaces!==config3.face.detector.maxFaces)){this.storedBoxes=[],this.detectedFaces=0;for(let possible of detector.boxes)this.storedBoxes.push({startPoint:possible.box.startPoint.dataSync(),endPoint:possible.box.endPoint.dataSync(),landmarks:possible.landmarks,confidence:possible.confidence});this.storedBoxes.length>0&&(useFreshBox=!0)}if(useFreshBox){if(!detector||!detector.boxes||detector.boxes.length===0)return this.storedBoxes=[],this.detectedFaces=0,null;for(let i=0;i{prediction.box.startPoint.dispose(),prediction.box.endPoint.dispose(),prediction.landmarks.dispose()});let results=tidy(()=>this.storedBoxes.map((box,i)=>{let face3,angle=0,rotationMatrix;if(config3.face.detector.rotation){let[indexOfMouth,indexOfForehead]=box.landmarks.length>=LANDMARKS_COUNT?MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES:BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES;angle=util.computeRotation(box.landmarks[indexOfMouth],box.landmarks[indexOfForehead]);let faceCenter=bounding.getBoxCenter({startPoint:box.startPoint,endPoint:box.endPoint}),faceCenterNormalized=[faceCenter[0]/input2.shape[2],faceCenter[1]/input2.shape[1]],rotatedImage=image.rotateWithOffset(input2,angle,0,faceCenterNormalized);rotationMatrix=util.buildRotationMatrix(-angle,faceCenter),face3=bounding.cutBoxFromImageAndResize({startPoint:box.startPoint,endPoint:box.endPoint},rotatedImage,[this.meshHeight,this.meshWidth]).div(255)}else{rotationMatrix=util.IDENTITY_MATRIX;let cloned=input2.clone();face3=bounding.cutBoxFromImageAndResize({startPoint:box.startPoint,endPoint:box.endPoint},cloned,[this.meshHeight,this.meshWidth]).div(255)}if(!config3.face.mesh.enabled)return{coords:null,box,faceConfidence:null,confidence:box.confidence,image:face3};let[,confidence,contourCoords]=this.meshDetector.predict(face3),confidenceVal=confidence.dataSync()[0];if(confidenceVala!==null),this.detectedFaces=results.length,results}calculateLandmarksBoundingBox(landmarks){let xs=landmarks.map(d=>d[0]),ys=landmarks.map(d=>d[1]),startPoint=[Math.min(...xs),Math.min(...ys)],endPoint=[Math.max(...xs),Math.max(...ys)];return{startPoint,endPoint,landmarks}}};exports.Pipeline=Pipeline}),require_facemesh=__commonJS(exports=>{var blazeface=__toModule(require_blazeface()),facepipeline=__toModule(require_facepipeline()),coords2=__toModule(require_coords()),MediaPipeFaceMesh=class{constructor(blazeFace,blazeMeshModel,irisModel,config3){this.facePipeline=new facepipeline.Pipeline(blazeFace,blazeMeshModel,irisModel,config3),this.config=config3}async estimateFaces(input2,config3){let predictions=await this.facePipeline.predict(input2,config3),results=[];for(let prediction of predictions||[]){if(prediction.isDisposedInternal)continue;let mesh=prediction.coords?prediction.coords.arraySync():null,meshRaw=prediction.rawCoords,annotations={};if(mesh&&mesh.length>0)for(let key of Object.keys(coords2.MESH_ANNOTATIONS))annotations[key]=coords2.MESH_ANNOTATIONS[key].map(index=>mesh[index]);let boxRaw=config3.face.mesh.returnRawData&&prediction.box?{topLeft:prediction.box.startPoint,bottomRight:prediction.box.endPoint}:null,box=prediction.box?[Math.max(0,prediction.box.startPoint[0]),Math.max(0,prediction.box.startPoint[1]),Math.min(input2.shape[2],prediction.box.endPoint[0])-prediction.box.startPoint[0],Math.min(input2.shape[1],prediction.box.endPoint[1])-prediction.box.startPoint[1]]:0;results.push({confidence:prediction.confidence||0,box,mesh,boxRaw,meshRaw,annotations,image:prediction.image?clone(prediction.image):null}),prediction.coords&&prediction.coords.dispose(),prediction.image&&prediction.image.dispose()}return results}},faceModels=[null,null,null];async function load3(config3){faceModels=await Promise.all([!faceModels[0]&&config3.face.enabled?blazeface.load(config3):null,!faceModels[1]&&config3.face.mesh.enabled?loadGraphModel(config3.face.mesh.modelPath,{fromTFHub:config3.face.mesh.modelPath.includes("tfhub.dev")}):null,!faceModels[2]&&config3.face.iris.enabled?loadGraphModel(config3.face.iris.modelPath,{fromTFHub:config3.face.iris.modelPath.includes("tfhub.dev")}):null]);let faceMesh=new MediaPipeFaceMesh(faceModels[0],faceModels[1],faceModels[2],config3);return config3.face.mesh.enabled&&log(`load model: ${config3.face.mesh.modelPath.match(/\/(.*)\./)[1]}`),config3.face.iris.enabled&&log(`load model: ${config3.face.iris.modelPath.match(/\/(.*)\./)[1]}`),faceMesh}exports.load=load3,exports.MediaPipeFaceMesh=MediaPipeFaceMesh,exports.triangulation=coords2.TRI468}),require_profile=__commonJS(exports=>{var profileData={};function profile3(name,data2){if(!data2||!data2.kernels)return;let maxResults=5,time2=data2.kernels.filter(a=>a.kernelTimeMs>0).reduce((a,b)=>a+=b.kernelTimeMs,0),slowest=data2.kernels.map((a,i)=>(a.id=i,a)).filter(a=>a.kernelTimeMs>0).sort((a,b)=>b.kernelTimeMs-a.kernelTimeMs),largest=data2.kernels.map((a,i)=>(a.id=i,a)).filter(a=>a.totalBytesSnapshot>0).sort((a,b)=>b.totalBytesSnapshot-a.totalBytesSnapshot);slowest.length>maxResults&&(slowest.length=maxResults),largest.length>maxResults&&(largest.length=maxResults);let res={newBytes:data2.newBytes,newTensors:data2.newTensors,peakBytes:data2.peakBytes,numKernelOps:data2.kernels.length,timeKernelOps:time2,slowestKernelOps:slowest,largestKernelOps:largest};profileData[name]=res,log("Human profiler",name,res)}exports.run=profile3}),require_age=__commonJS(exports=>{var profile3=__toModule(require_profile()),models2={},last={age:0},skipped=Number.MAX_SAFE_INTEGER;async function load3(config3){return models2.age||(models2.age=await loadGraphModel(config3.face.age.modelPath),log(`load model: ${config3.face.age.modelPath.match(/\/(.*)\./)[1]}`)),models2.age}async function predict3(image3,config3){return models2.age?skipped0?(skipped++,last):(config3.videoOptimized?skipped=0:skipped=Number.MAX_SAFE_INTEGER,new Promise(async resolve=>{let resize=image.resizeBilinear(image3,[config3.face.age.inputSize,config3.face.age.inputSize],!1),enhance=mul(resize,[255]);dispose(resize);let ageT,obj={};if(!config3.profile)config3.face.age.enabled&&(ageT=await models2.age.predict(enhance));else{let profileAge=config3.face.age.enabled?await profile(()=>models2.age.predict(enhance)):{};ageT=profileAge.result.clone(),profileAge.result.dispose(),profile3.run("age",profileAge)}if(enhance.dispose(),ageT){let data2=ageT.dataSync();obj.age=Math.trunc(10*data2[0])/10}ageT.dispose(),last=obj,resolve(obj)})):null}exports.predict=predict3,exports.load=load3}),require_gender=__commonJS(exports=>{var profile3=__toModule(require_profile()),models2={},last={gender:""},skipped=Number.MAX_SAFE_INTEGER,alternative=!1,rgb=[.2989,.587,.114];async function load3(config3){return models2.gender||(models2.gender=await loadGraphModel(config3.face.gender.modelPath),alternative=models2.gender.inputs[0].shape[3]===1,log(`load model: ${config3.face.gender.modelPath.match(/\/(.*)\./)[1]}`)),models2.gender}async function predict3(image3,config3){return models2.gender?skipped{let resize=image.resizeBilinear(image3,[config3.face.gender.inputSize,config3.face.gender.inputSize],!1),enhance;alternative?enhance=tidy(()=>{let[red,green,blue]=split(resize,3,3),redNorm=mul(red,rgb[0]),greenNorm=mul(green,rgb[1]),blueNorm=mul(blue,rgb[2]);return addN([redNorm,greenNorm,blueNorm]).sub(.5).mul(2)}):enhance=mul(resize,[255]),dispose(resize);let genderT,obj={};if(!config3.profile)config3.face.gender.enabled&&(genderT=await models2.gender.predict(enhance));else{let profileGender=config3.face.gender.enabled?await profile(()=>models2.gender.predict(enhance)):{};genderT=profileGender.result.clone(),profileGender.result.dispose(),profile3.run("gender",profileGender)}if(enhance.dispose(),genderT){let data2=genderT.dataSync();if(alternative){let confidence=Math.trunc(100*Math.abs(data2[0]-data2[1]))/100;confidence>config3.face.gender.minConfidence&&(obj.gender=data2[0]>data2[1]?"female":"male",obj.confidence=confidence)}else{let confidence=Math.trunc(200*Math.abs(data2[0]-.5))/100;confidence>config3.face.gender.minConfidence&&(obj.gender=data2[0]<=.5?"female":"male",obj.confidence=Math.min(.99,confidence))}}genderT.dispose(),last=obj,resolve(obj)})):null}exports.predict=predict3,exports.load=load3}),require_emotion=__commonJS(exports=>{var profile3=__toModule(require_profile()),annotations=["angry","disgust","fear","happy","sad","surprise","neutral"],models2={},last=[],skipped=Number.MAX_SAFE_INTEGER,rgb=[.2989,.587,.114],scale2=1;async function load3(config3){return models2.emotion||(models2.emotion=await loadGraphModel(config3.face.emotion.modelPath),log(`load model: ${config3.face.emotion.modelPath.match(/\/(.*)\./)[1]}`)),models2.emotion}async function predict3(image3,config3){return models2.emotion?skipped0?(skipped++,last):(config3.videoOptimized?skipped=0:skipped=Number.MAX_SAFE_INTEGER,new Promise(async resolve=>{let resize=image.resizeBilinear(image3,[config3.face.emotion.inputSize,config3.face.emotion.inputSize],!1),[red,green,blue]=split(resize,3,3);resize.dispose();let redNorm=mul(red,rgb[0]),greenNorm=mul(green,rgb[1]),blueNorm=mul(blue,rgb[2]);red.dispose(),green.dispose(),blue.dispose();let grayscale=addN([redNorm,greenNorm,blueNorm]);redNorm.dispose(),greenNorm.dispose(),blueNorm.dispose();let normalize=tidy(()=>grayscale.sub(.5).mul(2));grayscale.dispose();let obj=[];if(config3.face.emotion.enabled){let data2;if(config3.profile){let profileData=await profile(()=>models2.emotion.predict(normalize));data2=profileData.result.dataSync(),profileData.result.dispose(),profile3.run("emotion",profileData)}else{let emotionT=await models2.emotion.predict(normalize);data2=emotionT.dataSync(),dispose(emotionT)}for(let i=0;iconfig3.face.emotion.minConfidence&&obj.push({score:Math.min(.99,Math.trunc(100*scale2*data2[i])/100),emotion:annotations[i]});obj.sort((a,b)=>b.score-a.score)}normalize.dispose(),last=obj,resolve(obj)})):null}exports.predict=predict3,exports.load=load3}),require_embedding=__commonJS(exports=>{var profile3=__toModule(require_profile()),models2={};async function load3(config3){return models2.embedding||(models2.embedding=await loadGraphModel(config3.face.embedding.modelPath),log(`load model: ${config3.face.embedding.modelPath.match(/\/(.*)\./)[1]}`)),models2.embedding}function simmilarity2(embedding1,embedding22){if((embedding1==null?void 0:embedding1.length)!==(embedding22==null?void 0:embedding22.length))return 0;let order=2,distance=10*embedding1.map((val,i)=>val-embedding22[i]).reduce((dist,diff)=>dist+diff**order,0)**(1/order);return Math.trunc(1e3*(1-distance))/1e3}async function predict3(image3,config3){return models2.embedding?new Promise(async resolve=>{let resize=image.resizeBilinear(image3,[config3.face.embedding.inputSize,config3.face.embedding.inputSize],!1),data2=[];if(config3.face.embedding.enabled)if(config3.profile){let profileData=await profile(()=>models2.embedding.predict({img_inputs:resize}));data2=[...profileData.result.dataSync()],profileData.result.dispose(),profile3.run("emotion",profileData)}else{let embeddingT=await models2.embedding.predict({img_inputs:resize});data2=[...embeddingT.dataSync()],dispose(embeddingT)}resize.dispose(),resolve(data2)}):null}exports.predict=predict3,exports.simmilarity=simmilarity2,exports.load=load3}),require_modelBase=__commonJS(exports=>{var imageNetMean=[-123.15,-115.9,-103.06];function nameOutputResultsMobileNet(results){let[offsets,heatmap,displacementFwd,displacementBwd]=results;return{offsets,heatmap,displacementFwd,displacementBwd}}function nameOutputResultsResNet(results){let[displacementFwd,displacementBwd,offsets,heatmap]=results;return{offsets,heatmap,displacementFwd,displacementBwd}}var BaseModel=class{constructor(model2){this.model=model2}predict(input2,config3){return tidy(()=>{let asBatch=(config3.body.modelType==="ResNet"?input2.toFloat().add(imageNetMean):input2.toFloat().div(127.5).sub(1)).expandDims(0),results3d=this.model.predict(asBatch).map(y=>y.squeeze([0])),namedResults=config3.body.modelType==="ResNet"?nameOutputResultsResNet(results3d):nameOutputResultsMobileNet(results3d);return{heatmapScores:namedResults.heatmap.sigmoid(),offsets:namedResults.offsets,displacementFwd:namedResults.displacementFwd,displacementBwd:namedResults.displacementBwd}})}dispose(){this.model.dispose()}};exports.BaseModel=BaseModel}),require_heapSort=__commonJS(exports=>{function half(k){return Math.floor(k/2)}var MaxHeap=class{constructor(maxSize,getElementValue){this.priorityQueue=new Array(maxSize),this.numberOfElements=-1,this.getElementValue=getElementValue}enqueue(x){this.priorityQueue[++this.numberOfElements]=x,this.swim(this.numberOfElements)}dequeue(){let max6=this.priorityQueue[0];return this.exchange(0,this.numberOfElements--),this.sink(0),this.priorityQueue[this.numberOfElements+1]=null,max6}empty(){return this.numberOfElements===-1}size(){return this.numberOfElements+1}all(){return this.priorityQueue.slice(0,this.numberOfElements+1)}max(){return this.priorityQueue[0]}swim(k){for(;k>0&&this.less(half(k),k);)this.exchange(k,half(k)),k=half(k)}sink(k){for(;2*k<=this.numberOfElements;){let j=2*k;if(j{var heapSort=__toModule(require_heapSort());function scoreIsMaximumInLocalWindow(keypointId,score,heatmapY,heatmapX,localMaximumRadius,scores){let[height,width]=scores.shape,localMaximum=!0,yStart=Math.max(heatmapY-localMaximumRadius,0),yEnd=Math.min(heatmapY+localMaximumRadius+1,height);for(let yCurrent=yStart;yCurrentscore){localMaximum=!1;break}if(!localMaximum)break}return localMaximum}function buildPartWithScoreQueue(scoreThreshold,localMaximumRadius,scores){let[height,width,numKeypoints]=scores.shape,queue=new heapSort.MaxHeap(height*width*numKeypoints,({score})=>score);for(let heatmapY=0;heatmapY{exports.partNames=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],exports.NUM_KEYPOINTS=exports.partNames.length,exports.partIds=exports.partNames.reduce((result,jointName,i)=>(result[jointName]=i,result),{});var connectedPartNames=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]];exports.connectedPartIndices=connectedPartNames.map(([jointNameA,jointNameB])=>[exports.partIds[jointNameA],exports.partIds[jointNameB]]),exports.poseChain=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]],exports.partChannels=["left_face","right_face","right_upper_leg_front","right_lower_leg_back","right_upper_leg_back","left_lower_leg_front","left_upper_leg_front","left_upper_leg_back","left_lower_leg_back","right_feet","right_lower_leg_front","left_feet","torso_front","torso_back","right_upper_arm_front","right_upper_arm_back","right_lower_arm_back","left_lower_arm_front","left_upper_arm_front","left_upper_arm_back","left_lower_arm_back","right_hand","right_lower_arm_front","left_hand"]}),require_vectors=__commonJS(exports=>{var kpt=__toModule(require_keypoints());function getOffsetPoint(y,x,keypoint,offsets){return{y:offsets.get(y,x,keypoint),x:offsets.get(y,x,keypoint+kpt.NUM_KEYPOINTS)}}exports.getOffsetPoint=getOffsetPoint;function getImageCoords(part,outputStride,offsets){let{heatmapY,heatmapX,id:keypoint}=part,{y,x}=getOffsetPoint(heatmapY,heatmapX,keypoint,offsets);return{x:part.heatmapX*outputStride+x,y:part.heatmapY*outputStride+y}}exports.getImageCoords=getImageCoords;function fillArray(element,size){let result=new Array(size);for(let i=0;imax6?max6:a}exports.clamp=clamp2;function squaredDistance(y1,x1,y2,x2){let dy=y2-y1,dx=x2-x1;return dy*dy+dx*dx}exports.squaredDistance=squaredDistance;function addVectors(a,b){return{x:a.x+b.x,y:a.y+b.y}}exports.addVectors=addVectors;function clampVector(a,min6,max6){return{y:clamp2(a.y,min6,max6),x:clamp2(a.x,min6,max6)}}exports.clampVector=clampVector}),require_decoders=__commonJS(exports=>{var kpt=__toModule(require_keypoints());function getPointsConfidence(heatmapScores,heatMapCoords){let numKeypoints=heatMapCoords.shape[0],result=new Float32Array(numKeypoints);for(let keypoint=0;keypointheatMapCoordsBuffer.toTensor().mul(scalar(outputStride,"int32")).toFloat().add(getOffsetVectors(heatMapCoordsBuffer,offsetsBuffer)))}exports.getOffsetPoints=getOffsetPoints;function mod4(a,b){return tidy(()=>{let floored=a.div(scalar(b,"int32"));return a.sub(floored.mul(scalar(b,"int32")))})}function argmax2d(inputs){let[height,width,depth]=inputs.shape;return tidy(()=>{let coords2=inputs.reshape([height*width,depth]).argMax(0),yCoords=coords2.div(scalar(width,"int32")).expandDims(1),xCoords=mod4(coords2,width).expandDims(1);return concat([yCoords,xCoords],1)})}exports.argmax2d=argmax2d}),require_decodePose=__commonJS(exports=>{var keypoints=__toModule(require_keypoints()),vectors=__toModule(require_vectors()),decoders=__toModule(require_decoders()),parentChildrenTuples=keypoints.poseChain.map(([parentJoinName,childJoinName])=>[keypoints.partIds[parentJoinName],keypoints.partIds[childJoinName]]),parentToChildEdges=parentChildrenTuples.map(([,childJointId])=>childJointId),childToParentEdges=parentChildrenTuples.map(([parentJointId])=>parentJointId);function getDisplacement(edgeId,point,displacements){let numEdges=displacements.shape[2]/2;return{y:displacements.get(point.y,point.x,edgeId),x:displacements.get(point.y,point.x,numEdges+edgeId)}}function getStridedIndexNearPoint(point,outputStride,height,width){return{y:vectors.clamp(Math.round(point.y/outputStride),0,height-1),x:vectors.clamp(Math.round(point.x/outputStride),0,width-1)}}function traverseToTargetKeypoint(edgeId,sourceKeypoint,targetKeypointId,scoresBuffer,offsets,outputStride,displacements,offsetRefineStep=2){let[height,width]=scoresBuffer.shape,sourceKeypointIndices=getStridedIndexNearPoint(sourceKeypoint.position,outputStride,height,width),displacement=getDisplacement(edgeId,sourceKeypointIndices,displacements),targetKeypoint=vectors.addVectors(sourceKeypoint.position,displacement);for(let i=0;i=0;--edge){let sourceKeypointId=parentToChildEdges[edge],targetKeypointId=childToParentEdges[edge];instanceKeypoints[sourceKeypointId]&&!instanceKeypoints[targetKeypointId]&&(instanceKeypoints[targetKeypointId]=traverseToTargetKeypoint(edge,instanceKeypoints[sourceKeypointId],targetKeypointId,scores,offsets,outputStride,displacementsBwd))}for(let edge=0;edge(totalScore+=score,{position:{y:offsetPointsBuffer.get(i,0),x:offsetPointsBuffer.get(i,1)},part:keypoints.partNames[i],score})),filteredKeypoints=instanceKeypoints.filter(kpt=>kpt.score>config3.body.scoreThreshold);return heatmapValues.dispose(),offsetPoints.dispose(),{keypoints:filteredKeypoints,score:totalScore/instanceKeypoints.length}}exports.decodeSinglePose=decodeSinglePose}),require_decodeMultiple=__commonJS(exports=>{var buildParts=__toModule(require_buildParts()),decodePose=__toModule(require_decodePose()),vectors=__toModule(require_vectors()),kLocalMaximumRadius=1;function withinNmsRadiusOfCorrespondingPoint(poses,squaredNmsRadius,{x,y},keypointId){return poses.some(({keypoints})=>{let correspondingKeypoint=keypoints[keypointId].position;return vectors.squaredDistance(y,x,correspondingKeypoint.y,correspondingKeypoint.x)<=squaredNmsRadius})}function getInstanceScore(existingPoses,squaredNmsRadius,instanceKeypoints){return instanceKeypoints.reduce((result,{position,score},keypointId)=>(withinNmsRadiusOfCorrespondingPoint(existingPoses,squaredNmsRadius,position,keypointId)||(result+=score),result),0)/instanceKeypoints.length}function decodeMultiplePoses(scoresBuffer,offsetsBuffer,displacementsFwdBuffer,displacementsBwdBuffer,config3){let poses=[],queue=buildParts.buildPartWithScoreQueue(config3.body.scoreThreshold,kLocalMaximumRadius,scoresBuffer),squaredNmsRadius=config3.body.nmsRadius^2;for(;poses.lengthconfig3.body.scoreThreshold&&poses.push({keypoints,score})}return poses}exports.decodeMultiplePoses=decodeMultiplePoses}),require_util2=__commonJS(exports=>{var kpt=__toModule(require_keypoints());function eitherPointDoesntMeetConfidence(a,b,minConfidence){return a(eitherPointDoesntMeetConfidence(keypoints[leftJoint].score,keypoints[rightJoint].score,minConfidence)||result.push([keypoints[leftJoint],keypoints[rightJoint]]),result),[])}exports.getAdjacentKeyPoints=getAdjacentKeyPoints;var{NEGATIVE_INFINITY,POSITIVE_INFINITY}=Number;function getBoundingBox(keypoints){return keypoints.reduce(({maxX,maxY,minX,minY},{position:{x,y}})=>({maxX:Math.max(maxX,x),maxY:Math.max(maxY,y),minX:Math.min(minX,x),minY:Math.min(minY,y)}),{maxX:NEGATIVE_INFINITY,maxY:NEGATIVE_INFINITY,minX:POSITIVE_INFINITY,minY:POSITIVE_INFINITY})}exports.getBoundingBox=getBoundingBox;function getBoundingBoxPoints(keypoints){let{minX,minY,maxX,maxY}=getBoundingBox(keypoints);return[{x:minX,y:minY},{x:maxX,y:minY},{x:maxX,y:maxY},{x:minX,y:maxY}]}exports.getBoundingBoxPoints=getBoundingBoxPoints;async function toTensorBuffers3D(tensors){return Promise.all(tensors.map(tensor2=>tensor2.buffer()))}exports.toTensorBuffers3D=toTensorBuffers3D;function scalePose(pose,scaleY,scaleX){return{score:pose.score,keypoints:pose.keypoints.map(({score,part,position})=>({score,part,position:{x:position.x*scaleX,y:position.y*scaleY}}))}}exports.scalePose=scalePose;function resizeTo(image3,[targetH,targetW]){let input2=image3.squeeze(0),resized=input2.resizeBilinear([targetH,targetW]);return input2.dispose(),resized}exports.resizeTo=resizeTo;function scaleAndFlipPoses(poses,[height,width],[inputResolutionHeight,inputResolutionWidth]){return poses.map(pose=>scalePose(pose,height/inputResolutionHeight,width/inputResolutionWidth))}exports.scaleAndFlipPoses=scaleAndFlipPoses}),require_modelPoseNet=__commonJS(exports=>{var modelBase=__toModule(require_modelBase()),decodeMultiple=__toModule(require_decodeMultiple()),decodePose=__toModule(require_decodePose()),util=__toModule(require_util2());async function estimateMultiple(input2,res,config3){return new Promise(async resolve=>{let height=input2.shape[1],width=input2.shape[2],allTensorBuffers=await util.toTensorBuffers3D([res.heatmapScores,res.offsets,res.displacementFwd,res.displacementBwd]),scoresBuffer=allTensorBuffers[0],offsetsBuffer=allTensorBuffers[1],displacementsFwdBuffer=allTensorBuffers[2],displacementsBwdBuffer=allTensorBuffers[3],poses=await decodeMultiple.decodeMultiplePoses(scoresBuffer,offsetsBuffer,displacementsFwdBuffer,displacementsBwdBuffer,config3),scaled=util.scaleAndFlipPoses(poses,[height,width],[config3.body.inputSize,config3.body.inputSize]);resolve(scaled)})}async function estimateSingle(input2,res,config3){return new Promise(async resolve=>{let height=input2.shape[1],width=input2.shape[2],poses=[await decodePose.decodeSinglePose(res.heatmapScores,res.offsets,config3)],scaled=util.scaleAndFlipPoses(poses,[height,width],[config3.body.inputSize,config3.body.inputSize]);resolve(scaled)})}var PoseNet=class{constructor(model2){this.baseModel=model2}async estimatePoses(input2,config3){let resized=util.resizeTo(input2,[config3.body.inputSize,config3.body.inputSize]),res=this.baseModel.predict(resized,config3),poses=config3.body.maxDetections<2?await estimateSingle(input2,res,config3):await estimateMultiple(input2,res,config3);return res.heatmapScores.dispose(),res.offsets.dispose(),res.displacementFwd.dispose(),res.displacementBwd.dispose(),resized.dispose(),poses}dispose(){this.baseModel.dispose()}};exports.PoseNet=PoseNet;async function load3(config3){let model2=await loadGraphModel(config3.body.modelPath),mobilenet=new modelBase.BaseModel(model2);return log(`load model: ${config3.body.modelPath.match(/\/(.*)\./)[1]}`),new PoseNet(mobilenet)}exports.load=load3}),require_posenet=__commonJS(exports=>{var modelPoseNet=__toModule(require_modelPoseNet()),keypoints=__toModule(require_keypoints()),util=__toModule(require_util2());exports.load=modelPoseNet.load,exports.PoseNet=modelPoseNet.PoseNet,exports.partChannels=keypoints.partChannels,exports.partIds=keypoints.partIds,exports.partNames=keypoints.partNames,exports.poseChain=keypoints.poseChain,exports.getAdjacentKeyPoints=util.getAdjacentKeyPoints,exports.getBoundingBox=util.getBoundingBox,exports.getBoundingBoxPoints=util.getBoundingBoxPoints,exports.scaleAndFlipPoses=util.scaleAndFlipPoses,exports.scalePose=util.scalePose}),require_handdetector=__commonJS(exports=>{var HandDetector=class{constructor(model2,inputSize,anchorsAnnotated){this.model=model2,this.anchors=anchorsAnnotated.map(anchor=>[anchor.x_center,anchor.y_center]),this.anchorsTensor=tensor2d(this.anchors),this.inputSizeTensor=tensor1d([inputSize,inputSize]),this.doubleInputSizeTensor=tensor1d([inputSize*2,inputSize*2])}normalizeBoxes(boxes){return tidy(()=>{let boxOffsets=slice(boxes,[0,0],[-1,2]),boxSizes=slice(boxes,[0,2],[-1,2]),boxCenterPoints=add2(div(boxOffsets,this.inputSizeTensor),this.anchorsTensor),halfBoxSizes=div(boxSizes,this.doubleInputSizeTensor),startPoints=mul(sub(boxCenterPoints,halfBoxSizes),this.inputSizeTensor),endPoints=mul(add2(boxCenterPoints,halfBoxSizes),this.inputSizeTensor);return concat2d([startPoints,endPoints],1)})}normalizeLandmarks(rawPalmLandmarks,index){return tidy(()=>{let landmarks=add2(div(rawPalmLandmarks.reshape([-1,7,2]),this.inputSizeTensor),this.anchors[index]);return mul(landmarks,this.inputSizeTensor)})}async getBoxes(input2,config3){let batched=this.model.predict(input2),predictions=batched.squeeze();batched.dispose();let scoresT=tidy(()=>sigmoid(slice(predictions,[0,0],[-1,1])).squeeze()),scores=scoresT.dataSync(),rawBoxes=slice(predictions,[0,1],[-1,4]),boxes=this.normalizeBoxes(rawBoxes);rawBoxes.dispose();let filteredT=await image.nonMaxSuppressionAsync(boxes,scores,config3.hand.maxHands,config3.hand.iouThreshold,config3.hand.scoreThreshold),filtered=filteredT.arraySync();scoresT.dispose(),filteredT.dispose();let hands=[];for(let index of filtered)if(scores[index]>=config3.hand.minConfidence){let matchingBox=slice(boxes,[index,0],[1,-1]),rawPalmLandmarks=slice(predictions,[index,5],[1,14]),palmLandmarks=tidy(()=>this.normalizeLandmarks(rawPalmLandmarks,index).reshape([-1,2]));rawPalmLandmarks.dispose(),hands.push({box:matchingBox,palmLandmarks,confidence:scores[index]})}return predictions.dispose(),boxes.dispose(),hands}async estimateHandBounds(input2,config3){let inputHeight=input2.shape[1],inputWidth=input2.shape[2],image3=tidy(()=>input2.resizeBilinear([config3.hand.inputSize,config3.hand.inputSize]).div(127.5).sub(1)),predictions=await this.getBoxes(image3,config3);image3.dispose();let hands=[];if(!predictions||predictions.length===0)return hands;for(let prediction of predictions){let boxes=prediction.box.dataSync(),startPoint=boxes.slice(0,2),endPoint=boxes.slice(2,4),palmLandmarks=prediction.palmLandmarks.arraySync();prediction.box.dispose(),prediction.palmLandmarks.dispose(),hands.push(scaleBoxCoordinates({startPoint,endPoint,palmLandmarks,confidence:prediction.confidence},[inputWidth/config3.hand.inputSize,inputHeight/config3.hand.inputSize]))}return hands}};exports.HandDetector=HandDetector}),require_handpipeline=__commonJS(exports=>{var PALM_BOX_ENLARGE_FACTOR=5,HAND_BOX_ENLARGE_FACTOR=1.65,PALM_LANDMARK_IDS=[0,5,9,13,17,1,2],PALM_LANDMARKS_INDEX_OF_PALM_BASE=0,PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE=2,HandPipeline=class{constructor(handDetector,landmarkDetector,inputSize){this.handDetector=handDetector,this.landmarkDetector=landmarkDetector,this.inputSize=inputSize,this.storedBoxes=[],this.skipped=0,this.detectedHands=0}getBoxForPalmLandmarks(palmLandmarks,rotationMatrix){let rotatedPalmLandmarks=palmLandmarks.map(coord=>rotatePoint([...coord,1],rotationMatrix)),boxAroundPalm=this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);return enlargeBox(squarifyBox(boxAroundPalm),PALM_BOX_ENLARGE_FACTOR)}getBoxForHandLandmarks(landmarks){let boundingBox=this.calculateLandmarksBoundingBox(landmarks),boxAroundHand=enlargeBox(squarifyBox(boundingBox),HAND_BOX_ENLARGE_FACTOR);boxAroundHand.palmLandmarks=[];for(let i=0;i[scaleFactor[0]*(coord[0]-this.inputSize/2),scaleFactor[1]*(coord[1]-this.inputSize/2),scaleFactor[2]*coord[2]]),coordsRotationMatrix=buildRotationMatrix(angle,[0,0]),coordsRotated=coordsScaled.map(coord=>[...rotatePoint(coord,coordsRotationMatrix),coord[2]]),inverseRotationMatrix=invertTransformMatrix(rotationMatrix),boxCenter=[...getBoxCenter(box2),1],originalBoxCenter=[dot4(boxCenter,inverseRotationMatrix[0]),dot4(boxCenter,inverseRotationMatrix[1])];return coordsRotated.map(coord=>[coord[0]+originalBoxCenter[0],coord[1]+originalBoxCenter[1],coord[2]])}async estimateHands(image3,config3){let useFreshBox=!1,boxes;(this.skipped===0||this.skipped>config3.hand.skipFrames||!config3.hand.landmarks||!config3.videoOptimized)&&(boxes=await this.handDetector.estimateHandBounds(image3,config3),this.skipped=0),config3.videoOptimized&&this.skipped++,boxes&&boxes.length>0&&(boxes.length!==this.detectedHands&&this.detectedHands!==config3.hand.maxHands||!config3.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...boxes],this.storedBoxes.length>0&&(useFreshBox=!0));let hands=[];for(let i=0;i=config3.hand.minConfidence){let keypointsReshaped=reshape(keypoints,[-1,3]),rawCoords=keypointsReshaped.arraySync();keypoints.dispose(),keypointsReshaped.dispose();let coords2=this.transformRawCoords(rawCoords,newBox,angle,rotationMatrix),nextBoundingBox=this.getBoxForHandLandmarks(coords2);this.storedBoxes[i]=nextBoundingBox;let result={landmarks:coords2,confidence,box:{topLeft:nextBoundingBox.startPoint,bottomRight:nextBoundingBox.endPoint}};hands.push(result)}else this.storedBoxes[i]=null;keypoints.dispose()}else{let enlarged=enlargeBox(squarifyBox(currentBox),HAND_BOX_ENLARGE_FACTOR),result={confidence:currentBox.confidence,box:{topLeft:enlarged.startPoint,bottomRight:enlarged.endPoint}};hands.push(result)}}return this.storedBoxes=this.storedBoxes.filter(a=>a!==null),this.detectedHands=hands.length,hands}calculateLandmarksBoundingBox(landmarks){let xs=landmarks.map(d=>d[0]),ys=landmarks.map(d=>d[1]),startPoint=[Math.min(...xs),Math.min(...ys)],endPoint=[Math.max(...xs),Math.max(...ys)];return{startPoint,endPoint}}};exports.HandPipeline=HandPipeline}),require_anchors=__commonJS(exports=>{exports.anchors=[{w:1,h:1,x_center:.015625,y_center:.015625},{w:1,h:1,x_center:.015625,y_center:.015625},{w:1,h:1,x_center:.046875,y_center:.015625},{w:1,h:1,x_center:.046875,y_center:.015625},{w:1,h:1,x_center:.078125,y_center:.015625},{w:1,h:1,x_center:.078125,y_center:.015625},{w:1,h:1,x_center:.109375,y_center:.015625},{w:1,h:1,x_center:.109375,y_center:.015625},{w:1,h:1,x_center:.140625,y_center:.015625},{w:1,h:1,x_center:.140625,y_center:.015625},{w:1,h:1,x_center:.171875,y_center:.015625},{w:1,h:1,x_center:.171875,y_center:.015625},{w:1,h:1,x_center:.203125,y_center:.015625},{w:1,h:1,x_center:.203125,y_center:.015625},{w:1,h:1,x_center:.234375,y_center:.015625},{w:1,h:1,x_center:.234375,y_center:.015625},{w:1,h:1,x_center:.265625,y_center:.015625},{w:1,h:1,x_center:.265625,y_center:.015625},{w:1,h:1,x_center:.296875,y_center:.015625},{w:1,h:1,x_center:.296875,y_center:.015625},{w:1,h:1,x_center:.328125,y_center:.015625},{w:1,h:1,x_center:.328125,y_center:.015625},{w:1,h:1,x_center:.359375,y_center:.015625},{w:1,h:1,x_center:.359375,y_center:.015625},{w:1,h:1,x_center:.390625,y_center:.015625},{w:1,h:1,x_center:.390625,y_center:.015625},{w:1,h:1,x_center:.421875,y_center:.015625},{w:1,h:1,x_center:.421875,y_center:.015625},{w:1,h:1,x_center:.453125,y_center:.015625},{w:1,h:1,x_center:.453125,y_center:.015625},{w:1,h:1,x_center:.484375,y_center:.015625},{w:1,h:1,x_center:.484375,y_center:.015625},{w:1,h:1,x_center:.515625,y_center:.015625},{w:1,h:1,x_center:.515625,y_center:.015625},{w:1,h:1,x_center:.546875,y_center:.015625},{w:1,h:1,x_center:.546875,y_center:.015625},{w:1,h:1,x_center:.578125,y_center:.015625},{w:1,h:1,x_center:.578125,y_center:.015625},{w:1,h:1,x_center:.609375,y_center:.015625},{w:1,h:1,x_center:.609375,y_center:.015625},{w:1,h:1,x_center:.640625,y_center:.015625},{w:1,h:1,x_center:.640625,y_center:.015625},{w:1,h:1,x_center:.671875,y_center:.015625},{w:1,h:1,x_center:.671875,y_center:.015625},{w:1,h:1,x_center:.703125,y_center:.015625},{w:1,h:1,x_center:.703125,y_center:.015625},{w:1,h:1,x_center:.734375,y_center:.015625},{w:1,h:1,x_center:.734375,y_center:.015625},{w:1,h:1,x_center:.765625,y_center:.015625},{w:1,h:1,x_center:.765625,y_center:.015625},{w:1,h:1,x_center:.796875,y_center:.015625},{w:1,h:1,x_center:.796875,y_center:.015625},{w:1,h:1,x_center:.828125,y_center:.015625},{w:1,h:1,x_center:.828125,y_center:.015625},{w:1,h:1,x_center:.859375,y_center:.015625},{w:1,h:1,x_center:.859375,y_center:.015625},{w:1,h:1,x_center:.890625,y_center:.015625},{w:1,h:1,x_center:.890625,y_center:.015625},{w:1,h:1,x_center:.921875,y_center:.015625},{w:1,h:1,x_center:.921875,y_center:.015625},{w:1,h:1,x_center:.953125,y_center:.015625},{w:1,h:1,x_center:.953125,y_center:.015625},{w:1,h:1,x_center:.984375,y_center:.015625},{w:1,h:1,x_center:.984375,y_center:.015625},{w:1,h:1,x_center:.015625,y_center:.046875},{w:1,h:1,x_center:.015625,y_center:.046875},{w:1,h:1,x_center:.046875,y_center:.046875},{w:1,h:1,x_center:.046875,y_center:.046875},{w:1,h:1,x_center:.078125,y_center:.046875},{w:1,h:1,x_center:.078125,y_center:.046875},{w:1,h:1,x_center:.109375,y_center:.046875},{w:1,h:1,x_center:.109375,y_center:.046875},{w:1,h:1,x_center:.140625,y_center:.046875},{w:1,h:1,x_center:.140625,y_center:.046875},{w:1,h:1,x_center:.171875,y_center:.046875},{w:1,h:1,x_center:.171875,y_center:.046875},{w:1,h:1,x_center:.203125,y_center:.046875},{w:1,h:1,x_center:.203125,y_center:.046875},{w:1,h:1,x_center:.234375,y_center:.046875},{w:1,h:1,x_center:.234375,y_center:.046875},{w:1,h:1,x_center:.265625,y_center:.046875},{w:1,h:1,x_center:.265625,y_center:.046875},{w:1,h:1,x_center:.296875,y_center:.046875},{w:1,h:1,x_center:.296875,y_center:.046875},{w:1,h:1,x_center:.328125,y_center:.046875},{w:1,h:1,x_center:.328125,y_center:.046875},{w:1,h:1,x_center:.359375,y_center:.046875},{w:1,h:1,x_center:.359375,y_center:.046875},{w:1,h:1,x_center:.390625,y_center:.046875},{w:1,h:1,x_center:.390625,y_center:.046875},{w:1,h:1,x_center:.421875,y_center:.046875},{w:1,h:1,x_center:.421875,y_center:.046875},{w:1,h:1,x_center:.453125,y_center:.046875},{w:1,h:1,x_center:.453125,y_center:.046875},{w:1,h:1,x_center:.484375,y_center:.046875},{w:1,h:1,x_center:.484375,y_center:.046875},{w:1,h:1,x_center:.515625,y_center:.046875},{w:1,h:1,x_center:.515625,y_center:.046875},{w:1,h:1,x_center:.546875,y_center:.046875},{w:1,h:1,x_center:.546875,y_center:.046875},{w:1,h:1,x_center:.578125,y_center:.046875},{w:1,h:1,x_center:.578125,y_center:.046875},{w:1,h:1,x_center:.609375,y_center:.046875},{w:1,h:1,x_center:.609375,y_center:.046875},{w:1,h:1,x_center:.640625,y_center:.046875},{w:1,h:1,x_center:.640625,y_center:.046875},{w:1,h:1,x_center:.671875,y_center:.046875},{w:1,h:1,x_center:.671875,y_cent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HandPose(handPipeline);return config3.hand.enabled&&log(`load model: ${config3.hand.detector.modelPath.match(/\/(.*)\./)[1]}`),config3.hand.landmarks&&log(`load model: ${config3.hand.skeleton.modelPath.match(/\/(.*)\./)[1]}`),handPose}exports.load=load3}),require_gesture=__commonJS(exports=>{exports.body=res=>{if(!res)return[];let gestures=[];for(let i=0;ia.part==="leftWrist"),rightWrist=res[i].keypoints.find(a=>a.part==="rightWrist"),nose=res[i].keypoints.find(a=>a.part==="nose");nose&&leftWrist&&rightWrist&&leftWrist.position.ya.part==="leftShoulder"),rightShoulder=res[i].keypoints.find(a=>a.part==="rightShoulder");leftShoulder&&rightShoulder&&gestures.push({body:i,gesture:`leaning ${leftShoulder.position.y>rightShoulder.position.y?"left":"right"}`})}return gestures},exports.face=res=>{if(!res)return[];let gestures=[];for(let i=0;i0){let eyeFacing=res[i].mesh[35][2]-res[i].mesh[263][2];Math.abs(eyeFacing)<10?gestures.push({face:i,gesture:"facing camera"}):gestures.push({face:i,gesture:`facing ${eyeFacing<0?"right":"left"}`}),Math.abs(res[i].mesh[374][1]-res[i].mesh[386][1])/Math.abs(res[i].mesh[443][1]-res[i].mesh[450][1])<.2&&gestures.push({face:i,gesture:"blink left eye"}),Math.abs(res[i].mesh[145][1]-res[i].mesh[159][1])/Math.abs(res[i].mesh[223][1]-res[i].mesh[230][1])<.2&&gestures.push({face:i,gesture:"blink right eye"});let mouthOpen=Math.min(100,500*Math.abs(res[i].mesh[13][1]-res[i].mesh[14][1])/Math.abs(res[i].mesh[10][1]-res[i].mesh[152][1]));mouthOpen>10&&gestures.push({face:i,gesture:`mouth ${Math.trunc(mouthOpen)}% open`});let chinDepth=res[i].mesh[152][2];Math.abs(chinDepth)>10&&gestures.push({face:i,gesture:`head ${chinDepth<0?"up":"down"}`})}return gestures},exports.iris=res=>{if(!res)return[];let gestures=[];for(let i=0;i{if(!res)return[];let gestures=[];for(let i=0;i0){let closest=fingers.reduce((best,a)=>best.position[2]best.position[1]{var WebGLProgram=function(gl,vertexSource,fragmentSource){let _collect=function(source,prefix,collection){let r=new RegExp("\\b"+prefix+" \\w+ (\\w+)","ig");source.replace(r,(match,name)=>(collection[name]=0,match))},_compile=function(source,type){let shader=gl.createShader(type);if(gl.shaderSource(shader,source),gl.compileShader(shader),!gl.getShaderParameter(shader,gl.COMPILE_STATUS))throw new Error("Filter: GL compile failed",gl.getShaderInfoLog(shader));return shader};this.uniform={},this.attribute={};let _vsh=_compile(vertexSource,gl.VERTEX_SHADER),_fsh=_compile(fragmentSource,gl.FRAGMENT_SHADER);if(this.id=gl.createProgram(),gl.attachShader(this.id,_vsh),gl.attachShader(this.id,_fsh),gl.linkProgram(this.id),!gl.getProgramParameter(this.id,gl.LINK_STATUS))throw new Error("Filter: GL link failed",gl.getProgramInfoLog(this.id));gl.useProgram(this.id),_collect(vertexSource,"attribute",this.attribute);for(let a in this.attribute)this.attribute[a]=gl.getAttribLocation(this.id,a);_collect(vertexSource,"uniform",this.uniform),_collect(fragmentSource,"uniform",this.uniform);for(let u in this.uniform)this.uniform[u]=gl.getUniformLocation(this.id,u)},WebGLImageFilter=function(params){params||(params={});let _drawCount=0,_sourceTexture=null,_lastInChain=!1,_currentFramebufferIndex=-1,_tempFramebuffers=[null,null],_filterChain=[],_width=-1,_height=-1,_vertexBuffer=null,_currentProgram=null,_canvas=params.canvas||document.createElement("canvas"),_shaderProgramCache={},gl=_canvas.getContext("webgl");if(!gl)throw new Error("Filter: getContext() failed");this.addFilter=function(name){let args=Array.prototype.slice.call(arguments,1),filter=_filter[name];_filterChain.push({func:filter,args})},this.reset=function(){_filterChain=[]},this.apply=function(image3){if(_resize(image3.width,image3.height),_drawCount=0,_sourceTexture||(_sourceTexture=gl.createTexture()),gl.bindTexture(gl.TEXTURE_2D,_sourceTexture),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_WRAP_S,gl.CLAMP_TO_EDGE),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_WRAP_T,gl.CLAMP_TO_EDGE),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_MIN_FILTER,gl.NEAREST),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_MAG_FILTER,gl.NEAREST),gl.texImage2D(gl.TEXTURE_2D,0,gl.RGBA,gl.RGBA,gl.UNSIGNED_BYTE,image3),_filterChain.length===0)return _draw(),_canvas;for(let i=0;i<_filterChain.length;i++){_lastInChain=i===_filterChain.length-1;let f=_filterChain[i];f.func.apply(this,f.args||[])}return _canvas};let _resize=function(width,height){if(!(width===_width&&height===_height)){if(_canvas.width=width,_width=width,_canvas.height=height,_height=height,!_vertexBuffer){let vertices=new Float32Array([-1,-1,0,1,1,-1,1,1,-1,1,0,0,-1,1,0,0,1,-1,1,1,1,1,1,0]);_vertexBuffer=gl.createBuffer(),gl.bindBuffer(gl.ARRAY_BUFFER,_vertexBuffer),gl.bufferData(gl.ARRAY_BUFFER,vertices,gl.STATIC_DRAW),gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL,!0)}gl.viewport(0,0,_width,_height),_tempFramebuffers=[null,null]}},_getTempFramebuffer=function(index){return _tempFramebuffers[index]=_tempFramebuffers[index]||_createFramebufferTexture(_width,_height),_tempFramebuffers[index]},_createFramebufferTexture=function(width,height){let fbo=gl.createFramebuffer();gl.bindFramebuffer(gl.FRAMEBUFFER,fbo);let renderbuffer=gl.createRenderbuffer();gl.bindRenderbuffer(gl.RENDERBUFFER,renderbuffer);let texture=gl.createTexture();return gl.bindTexture(gl.TEXTURE_2D,texture),gl.texImage2D(gl.TEXTURE_2D,0,gl.RGBA,width,height,0,gl.RGBA,gl.UNSIGNED_BYTE,null),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_MAG_FILTER,gl.LINEAR),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_MIN_FILTER,gl.LINEAR),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_WRAP_S,gl.CLAMP_TO_EDGE),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_WRAP_T,gl.CLAMP_TO_EDGE),gl.framebufferTexture2D(gl.FRAMEBUFFER,gl.COLOR_ATTACHMENT0,gl.TEXTURE_2D,texture,0),gl.bindTexture(gl.TEXTURE_2D,null),gl.bindFramebuffer(gl.FRAMEBUFFER,null),{fbo,texture}},_draw=function(flags){var _a,_b;let source=null,target=null,flipY=!1;_drawCount===0?source=_sourceTexture:source=(_a=_getTempFramebuffer(_currentFramebufferIndex))==null?void 0:_a.texture,_drawCount++,_lastInChain&&!(flags&DRAW.INTERMEDIATE)?(target=null,flipY=_drawCount%2==0):(_currentFramebufferIndex=(_currentFramebufferIndex+1)%2,target=(_b=_getTempFramebuffer(_currentFramebufferIndex))==null?void 0:_b.fbo),gl.bindTexture(gl.TEXTURE_2D,source),gl.bindFramebuffer(gl.FRAMEBUFFER,target),gl.uniform1f(_currentProgram.uniform.flipY,flipY?-1:1),gl.drawArrays(gl.TRIANGLES,0,6)},_compileShader=function(fragmentSource){if(_shaderProgramCache[fragmentSource])return _currentProgram=_shaderProgramCache[fragmentSource],gl.useProgram(_currentProgram.id),_currentProgram;_currentProgram=new WebGLProgram(gl,SHADER.VERTEX_IDENTITY,fragmentSource);let floatSize=Float32Array.BYTES_PER_ELEMENT,vertSize=4*floatSize;return gl.enableVertexAttribArray(_currentProgram.attribute.pos),gl.vertexAttribPointer(_currentProgram.attribute.pos,2,gl.FLOAT,!1,vertSize,0*floatSize),gl.enableVertexAttribArray(_currentProgram.attribute.uv),gl.vertexAttribPointer(_currentProgram.attribute.uv,2,gl.FLOAT,!1,vertSize,2*floatSize),_shaderProgramCache[fragmentSource]=_currentProgram,_currentProgram},DRAW={INTERMEDIATE:1},SHADER={};SHADER.VERTEX_IDENTITY=["precision highp float;","attribute vec2 pos;","attribute vec2 uv;","varying vec2 vUv;","uniform float flipY;","void main(void) {","vUv = uv;","gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);","}"].join(` +var __create=Object.create,__defProp=Object.defineProperty,__getProtoOf=Object.getPrototypeOf,__hasOwnProp=Object.prototype.hasOwnProperty,__getOwnPropNames=Object.getOwnPropertyNames,__getOwnPropDesc=Object.getOwnPropertyDescriptor,__markAsModule=target=>__defProp(target,"__esModule",{value:!0}),__commonJS=(callback,module)=>()=>(module||(module={exports:{}},callback(module.exports,module)),module.exports),__export=(target,all4)=>{__markAsModule(target);for(var name in all4)__defProp(target,name,{get:all4[name],enumerable:!0})},__exportStar=(target,module,desc)=>{if(__markAsModule(target),module&&typeof module=="object"||typeof module=="function")for(let key of __getOwnPropNames(module))!__hasOwnProp.call(target,key)&&key!=="default"&&__defProp(target,key,{get:()=>module[key],enumerable:!(desc=__getOwnPropDesc(module,key))||desc.enumerable});return target},__toModule=module=>module&&module.__esModule?module:__exportStar(__defProp(module!=null?__create(__getProtoOf(module)):{},"default",{value:module,enumerable:!0}),module),require_blazeface=__commonJS(exports=>{var NUM_LANDMARKS=6;function generateAnchors(inputSize){let spec={strides:[inputSize/16,inputSize/8],anchors:[2,6]},anchors=[];for(let i=0;i{box.startEndTensor.dispose(),box.startPoint.dispose(),box.endPoint.dispose()},createBox=startEndTensor=>({startEndTensor,startPoint:slice(startEndTensor,[0,0],[-1,2]),endPoint:slice(startEndTensor,[0,2],[-1,2])}),scaleBox=(box,factors)=>{let starts=mul(box.startPoint,factors),ends=mul(box.endPoint,factors),newCoordinates=concat2d([starts,ends],1);return createBox(newCoordinates)};function decodeBounds(boxOutputs,anchors,inputSize){let boxStarts=slice(boxOutputs,[0,1],[-1,2]),centers=add2(boxStarts,anchors),boxSizes=slice(boxOutputs,[0,3],[-1,2]),boxSizesNormalized=div(boxSizes,inputSize),centersNormalized=div(centers,inputSize),halfBoxSize=div(boxSizesNormalized,2),starts=sub(centersNormalized,halfBoxSize),ends=add2(centersNormalized,halfBoxSize),startNormalized=mul(starts,inputSize),endNormalized=mul(ends,inputSize);return concat2d([startNormalized,endNormalized],1)}function scaleBoxFromPrediction(face3,scaleFactor){return tidy(()=>{let box=face3.box?face3.box:face3;return scaleBox(box,scaleFactor).startEndTensor.squeeze()})}var BlazeFaceModel=class{constructor(model2,config2){this.blazeFaceModel=model2,this.width=config2.face.detector.inputSize,this.height=config2.face.detector.inputSize,this.anchorsData=generateAnchors(config2.face.detector.inputSize),this.anchors=tensor2d(this.anchorsData),this.inputSize=tensor1d([this.width,this.height]),this.config=config2,this.scaleFaces=.8}async getBoundingBoxes(inputImage){if(!inputImage||inputImage.isDisposedInternal||inputImage.shape.length!==4||inputImage.shape[1]<1||inputImage.shape[2]<1)return null;let[detectedOutputs,boxes,scores]=tidy(()=>{let resizedImage=inputImage.resizeBilinear([this.width,this.height]),normalizedImage=sub(resizedImage.div(127.5),1),batchedPrediction=this.blazeFaceModel.predict(normalizedImage),prediction;if(Array.isArray(batchedPrediction)){let sorted=batchedPrediction.sort((a,b)=>a.size-b.size),concat384=concat([sorted[0],sorted[2]],2),concat512=concat([sorted[1],sorted[3]],2);prediction=concat([concat512,concat384],1).squeeze(0)}else prediction=batchedPrediction.squeeze();let decodedBounds=decodeBounds(prediction,this.anchors,this.inputSize),logits=slice(prediction,[0,0],[-1,1]),scoresOut=sigmoid(logits).squeeze();return[prediction,decodedBounds,scoresOut]}),boxIndicesTensor=await image.nonMaxSuppressionAsync(boxes,scores,this.config.face.detector.maxFaces,this.config.face.detector.iouThreshold,this.config.face.detector.scoreThreshold),boxIndices=boxIndicesTensor.arraySync();boxIndicesTensor.dispose();let boundingBoxes=boxIndices.map(boxIndex=>slice(boxes,[boxIndex,0],[1,-1])).map(boundingBox=>{let vals=boundingBox.arraySync();return boundingBox.dispose(),vals}),scoresVal=scores.dataSync(),annotatedBoxes=[];for(let i=0;ithis.config.face.detector.minConfidence){let box=createBox(boundingBoxes[i]),anchor=this.anchorsData[boxIndex],landmarks=tidy(()=>slice(detectedOutputs,[boxIndex,NUM_LANDMARKS-1],[1,-1]).squeeze().reshape([NUM_LANDMARKS,-1]));annotatedBoxes.push({box,landmarks,anchor,confidence})}}return detectedOutputs.dispose(),boxes.dispose(),scores.dispose(),detectedOutputs.dispose(),{boxes:annotatedBoxes,scaleFactor:[inputImage.shape[2]/this.width,inputImage.shape[1]/this.height]}}async estimateFaces(input2){let{boxes,scaleFactor}=await this.getBoundingBoxes(input2),faces=[];for(let face3 of boxes){let landmarkData=face3.landmarks.arraySync(),scaledBox=scaleBoxFromPrediction(face3,scaleFactor),boxData=scaleBox.arraySync(),probabilityData=face3.probability.arraySync(),anchor=face3.anchor,[scaleFactorX,scaleFactorY]=scaleFactor,scaledLandmarks=landmarkData.map(landmark=>[(landmark[0]+anchor[0])*scaleFactorX,(landmark[1]+anchor[1])*scaleFactorY]),normalizedFace={topLeft:boxData.slice(0,2),bottomRight:boxData.slice(2),landmarks:scaledLandmarks,probability:probabilityData};disposeBox(face3.box),face3.landmarks.dispose(),face3.probability.dispose(),scaledBox.dispose(),faces.push(normalizedFace)}return faces}};async function load3(config2){let blazeface=await loadGraphModel(config2.face.detector.modelPath,{fromTFHub:config2.face.detector.modelPath.includes("tfhub.dev")}),model2=new BlazeFaceModel(blazeface,config2);return log(`load model: ${config2.face.detector.modelPath.match(/\/(.*)\./)[1]}`),model2}exports.load=load3,exports.BlazeFaceModel=BlazeFaceModel,exports.disposeBox=disposeBox}),require_box=__commonJS(exports=>{function scaleBoxCoordinates2(box,factor){let startPoint=[box.startPoint[0]*factor[0],box.startPoint[1]*factor[1]],endPoint=[box.endPoint[0]*factor[0],box.endPoint[1]*factor[1]];return{startPoint,endPoint}}exports.scaleBoxCoordinates=scaleBoxCoordinates2;function getBoxSize2(box){return[Math.abs(box.endPoint[0]-box.startPoint[0]),Math.abs(box.endPoint[1]-box.startPoint[1])]}exports.getBoxSize=getBoxSize2;function getBoxCenter2(box){return[box.startPoint[0]+(box.endPoint[0]-box.startPoint[0])/2,box.startPoint[1]+(box.endPoint[1]-box.startPoint[1])/2]}exports.getBoxCenter=getBoxCenter2;function cutBoxFromImageAndResize2(box,image3,cropSize){let h=image3.shape[1],w=image3.shape[2],boxes=[[box.startPoint[1]/h,box.startPoint[0]/w,box.endPoint[1]/h,box.endPoint[0]/w]];return image.cropAndResize(image3,boxes,[0],cropSize)}exports.cutBoxFromImageAndResize=cutBoxFromImageAndResize2;function enlargeBox2(box,factor=1.5){let center=getBoxCenter2(box),size=getBoxSize2(box),newHalfSize=[factor*size[0]/2,factor*size[1]/2],startPoint=[center[0]-newHalfSize[0],center[1]-newHalfSize[1]],endPoint=[center[0]+newHalfSize[0],center[1]+newHalfSize[1]];return{startPoint,endPoint,landmarks:box.landmarks}}exports.enlargeBox=enlargeBox2;function squarifyBox2(box){let centers=getBoxCenter2(box),size=getBoxSize2(box),halfSize=Math.max(...size)/2,startPoint=[centers[0]-halfSize,centers[1]-halfSize],endPoint=[centers[0]+halfSize,centers[1]+halfSize];return{startPoint,endPoint,landmarks:box.landmarks}}exports.squarifyBox=squarifyBox2}),require_util=__commonJS(exports=>{exports.IDENTITY_MATRIX=[[1,0,0],[0,1,0],[0,0,1]];function normalizeRadians2(angle){return angle-2*Math.PI*Math.floor((angle+Math.PI)/(2*Math.PI))}exports.normalizeRadians=normalizeRadians2;function computeRotation2(point1,point2){let radians=Math.PI/2-Math.atan2(-(point2[1]-point1[1]),point2[0]-point1[0]);return normalizeRadians2(radians)}exports.computeRotation=computeRotation2;function radToDegrees(rad){return rad*180/Math.PI}exports.radToDegrees=radToDegrees;function buildTranslationMatrix2(x,y){return[[1,0,x],[0,1,y],[0,0,1]]}function dot5(v1,v2){let product=0;for(let i=0;i{var 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,378,379,397,288,361,454,356,70,63,105,66,107,336,296,334,293,300,168,6,195,4,98,97,2,326,327,33,160,158,133,153,144,362,385,387,263,373,380,57,40,37,0,267,270,287,321,314,17,84,91,78,81,13,311,308,402,14,178],VTX33=[33,133,362,263,1,62,308,159,145,386,374,6,102,331,2,13,14,70,105,107,336,334,300,54,10,284,50,280,234,454,58,288,152],VTX7=[33,133,362,263,1,78,308];exports.MESH_ANNOTATIONS=MESH_ANNOTATIONS,exports.MESH_TO_IRIS_INDICES_MAP=MESH_TO_IRIS_INDICES_MAP,exports.TRI468=TRI468,exports.TRI68=TRI68,exports.TRI33=TRI33,exports.TRI7=TRI7,exports.UV468=UV468,exports.UV68=VTX68.map(x=>UV468[x]),exports.UV33=VTX33.map(x=>UV468[x]),exports.UV7=VTX7.map(x=>UV468[x])}),require_facepipeline=__commonJS(exports=>{var bounding=__toModule(require_box()),util=__toModule(require_util()),coords2=__toModule(require_coords()),LANDMARKS_COUNT=468,MESH_MOUTH_INDEX=13,MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES=[MESH_MOUTH_INDEX,coords2.MESH_ANNOTATIONS.midwayBetweenEyes[0]],BLAZEFACE_MOUTH_INDEX=3,BLAZEFACE_NOSE_INDEX=2,BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES=[BLAZEFACE_MOUTH_INDEX,BLAZEFACE_NOSE_INDEX],LEFT_EYE_OUTLINE=coords2.MESH_ANNOTATIONS.leftEyeLower0,LEFT_EYE_BOUNDS=[LEFT_EYE_OUTLINE[0],LEFT_EYE_OUTLINE[LEFT_EYE_OUTLINE.length-1]],RIGHT_EYE_OUTLINE=coords2.MESH_ANNOTATIONS.rightEyeLower0,RIGHT_EYE_BOUNDS=[RIGHT_EYE_OUTLINE[0],RIGHT_EYE_OUTLINE[RIGHT_EYE_OUTLINE.length-1]],IRIS_UPPER_CENTER_INDEX=3,IRIS_LOWER_CENTER_INDEX=4,IRIS_IRIS_INDEX=71,IRIS_NUM_COORDINATES=76;function replaceRawCoordinates(rawCoords,newCoords,prefix,keys){for(let i=0;i[scaleFactor[0]*(coord[0]-this.meshWidth/2),scaleFactor[1]*(coord[1]-this.meshHeight/2),coord[2]]),coordsRotationMatrix=angle!==0?util.buildRotationMatrix(angle,[0,0]):util.IDENTITY_MATRIX,coordsRotated=angle!==0?coordsScaled.map(coord=>[...util.rotatePoint(coord,coordsRotationMatrix),coord[2]]):coordsScaled,inverseRotationMatrix=angle!==0?util.invertTransformMatrix(rotationMatrix):util.IDENTITY_MATRIX,boxCenter=[...bounding.getBoxCenter({startPoint:box.startPoint,endPoint:box.endPoint}),1];return coordsRotated.map(coord=>[coord[0]+util.dot(boxCenter,inverseRotationMatrix[0]),coord[1]+util.dot(boxCenter,inverseRotationMatrix[1]),coord[2]])}getLeftToRightEyeDepthDifference(rawCoords){let leftEyeZ=rawCoords[LEFT_EYE_BOUNDS[0]][2],rightEyeZ=rawCoords[RIGHT_EYE_BOUNDS[0]][2];return leftEyeZ-rightEyeZ}getEyeBox(rawCoords,face3,eyeInnerCornerIndex,eyeOuterCornerIndex,flip=!1){let box=bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex],rawCoords[eyeOuterCornerIndex]]),this.irisEnlarge)),boxSize=bounding.getBoxSize(box),crop=image.cropAndResize(face3,[[box.startPoint[1]/this.meshHeight,box.startPoint[0]/this.meshWidth,box.endPoint[1]/this.meshHeight,box.endPoint[0]/this.meshWidth]],[0],[this.irisSize,this.irisSize]);return flip&&(crop=image.flipLeftRight(crop)),{box,boxSize,crop}}getEyeCoords(eyeData,eyeBox,eyeBoxSize,flip=!1){let eyeRawCoords=[];for(let i=0;i{let z=averageZ;return i===2?z=upperCenterZ:i===4&&(z=lowerCenterZ),[coord[0],coord[1],z]})}async predict(input2,config2){let useFreshBox=!1,detector;if((this.skipped===0||this.skipped>config2.face.detector.skipFrames||!config2.face.mesh.enabled||!config2.videoOptimized)&&(detector=await this.boundingBoxDetector.getBoundingBoxes(input2),this.skipped=0),config2.videoOptimized&&this.skipped++,detector&&detector.boxes&&detector.boxes.length>0&&(!config2.face.mesh.enabled||detector.boxes.length!==this.detectedFaces&&this.detectedFaces!==config2.face.detector.maxFaces)){this.storedBoxes=[],this.detectedFaces=0;for(let possible of detector.boxes)this.storedBoxes.push({startPoint:possible.box.startPoint.dataSync(),endPoint:possible.box.endPoint.dataSync(),landmarks:possible.landmarks,confidence:possible.confidence});this.storedBoxes.length>0&&(useFreshBox=!0)}if(useFreshBox){if(!detector||!detector.boxes||detector.boxes.length===0)return this.storedBoxes=[],this.detectedFaces=0,null;for(let i=0;i{prediction.box.startPoint.dispose(),prediction.box.endPoint.dispose(),prediction.landmarks.dispose()});let results=tidy(()=>this.storedBoxes.map((box,i)=>{let face3,angle=0,rotationMatrix;if(config2.face.detector.rotation){let[indexOfMouth,indexOfForehead]=box.landmarks.length>=LANDMARKS_COUNT?MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES:BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES;angle=util.computeRotation(box.landmarks[indexOfMouth],box.landmarks[indexOfForehead]);let faceCenter=bounding.getBoxCenter({startPoint:box.startPoint,endPoint:box.endPoint}),faceCenterNormalized=[faceCenter[0]/input2.shape[2],faceCenter[1]/input2.shape[1]],rotatedImage=image.rotateWithOffset(input2,angle,0,faceCenterNormalized);rotationMatrix=util.buildRotationMatrix(-angle,faceCenter),face3=bounding.cutBoxFromImageAndResize({startPoint:box.startPoint,endPoint:box.endPoint},rotatedImage,[this.meshHeight,this.meshWidth]).div(255)}else{rotationMatrix=util.IDENTITY_MATRIX;let cloned=input2.clone();face3=bounding.cutBoxFromImageAndResize({startPoint:box.startPoint,endPoint:box.endPoint},cloned,[this.meshHeight,this.meshWidth]).div(255)}if(!config2.face.mesh.enabled)return{coords:null,box,faceConfidence:null,confidence:box.confidence,image:face3};let[,confidence,contourCoords]=this.meshDetector.predict(face3),confidenceVal=confidence.dataSync()[0];if(confidenceVala!==null),this.detectedFaces=results.length,results}calculateLandmarksBoundingBox(landmarks){let xs=landmarks.map(d=>d[0]),ys=landmarks.map(d=>d[1]),startPoint=[Math.min(...xs),Math.min(...ys)],endPoint=[Math.max(...xs),Math.max(...ys)];return{startPoint,endPoint,landmarks}}};exports.Pipeline=Pipeline}),require_facemesh=__commonJS(exports=>{var blazeface=__toModule(require_blazeface()),facepipeline=__toModule(require_facepipeline()),coords2=__toModule(require_coords()),MediaPipeFaceMesh=class{constructor(blazeFace,blazeMeshModel,irisModel,config2){this.facePipeline=new facepipeline.Pipeline(blazeFace,blazeMeshModel,irisModel,config2),this.config=config2}async estimateFaces(input2,config2){let predictions=await this.facePipeline.predict(input2,config2),results=[];for(let prediction of predictions||[]){if(prediction.isDisposedInternal)continue;let mesh=prediction.coords?prediction.coords.arraySync():null,meshRaw=prediction.rawCoords,annotations={};if(mesh&&mesh.length>0)for(let key of Object.keys(coords2.MESH_ANNOTATIONS))annotations[key]=coords2.MESH_ANNOTATIONS[key].map(index=>mesh[index]);let boxRaw=config2.face.mesh.returnRawData&&prediction.box?{topLeft:prediction.box.startPoint,bottomRight:prediction.box.endPoint}:null,box=prediction.box?[Math.max(0,prediction.box.startPoint[0]),Math.max(0,prediction.box.startPoint[1]),Math.min(input2.shape[2],prediction.box.endPoint[0])-prediction.box.startPoint[0],Math.min(input2.shape[1],prediction.box.endPoint[1])-prediction.box.startPoint[1]]:0;results.push({confidence:prediction.confidence||0,box,mesh,boxRaw,meshRaw,annotations,image:prediction.image?clone(prediction.image):null}),prediction.coords&&prediction.coords.dispose(),prediction.image&&prediction.image.dispose()}return results}},faceModels=[null,null,null];async function load3(config2){faceModels=await Promise.all([!faceModels[0]&&config2.face.enabled?blazeface.load(config2):null,!faceModels[1]&&config2.face.mesh.enabled?loadGraphModel(config2.face.mesh.modelPath,{fromTFHub:config2.face.mesh.modelPath.includes("tfhub.dev")}):null,!faceModels[2]&&config2.face.iris.enabled?loadGraphModel(config2.face.iris.modelPath,{fromTFHub:config2.face.iris.modelPath.includes("tfhub.dev")}):null]);let faceMesh=new MediaPipeFaceMesh(faceModels[0],faceModels[1],faceModels[2],config2);return config2.face.mesh.enabled&&log(`load model: ${config2.face.mesh.modelPath.match(/\/(.*)\./)[1]}`),config2.face.iris.enabled&&log(`load model: ${config2.face.iris.modelPath.match(/\/(.*)\./)[1]}`),faceMesh}exports.load=load3,exports.MediaPipeFaceMesh=MediaPipeFaceMesh,exports.triangulation=coords2.TRI468}),require_profile=__commonJS(exports=>{var profileData={};function profile3(name,data2){if(!data2||!data2.kernels)return;let maxResults=5,time2=data2.kernels.filter(a=>a.kernelTimeMs>0).reduce((a,b)=>a+=b.kernelTimeMs,0),slowest=data2.kernels.map((a,i)=>(a.id=i,a)).filter(a=>a.kernelTimeMs>0).sort((a,b)=>b.kernelTimeMs-a.kernelTimeMs),largest=data2.kernels.map((a,i)=>(a.id=i,a)).filter(a=>a.totalBytesSnapshot>0).sort((a,b)=>b.totalBytesSnapshot-a.totalBytesSnapshot);slowest.length>maxResults&&(slowest.length=maxResults),largest.length>maxResults&&(largest.length=maxResults);let res={newBytes:data2.newBytes,newTensors:data2.newTensors,peakBytes:data2.peakBytes,numKernelOps:data2.kernels.length,timeKernelOps:time2,slowestKernelOps:slowest,largestKernelOps:largest};profileData[name]=res,log("Human profiler",name,res)}exports.run=profile3}),require_age=__commonJS(exports=>{var profile3=__toModule(require_profile()),models2={},last={age:0},skipped=Number.MAX_SAFE_INTEGER;async function load3(config2){return models2.age||(models2.age=await loadGraphModel(config2.face.age.modelPath),log(`load model: ${config2.face.age.modelPath.match(/\/(.*)\./)[1]}`)),models2.age}async function predict3(image3,config2){return models2.age?skipped0?(skipped++,last):(config2.videoOptimized?skipped=0:skipped=Number.MAX_SAFE_INTEGER,new Promise(async resolve=>{let resize=image.resizeBilinear(image3,[config2.face.age.inputSize,config2.face.age.inputSize],!1),enhance=mul(resize,[255]);dispose(resize);let ageT,obj={};if(!config2.profile)config2.face.age.enabled&&(ageT=await models2.age.predict(enhance));else{let profileAge=config2.face.age.enabled?await profile(()=>models2.age.predict(enhance)):{};ageT=profileAge.result.clone(),profileAge.result.dispose(),profile3.run("age",profileAge)}if(enhance.dispose(),ageT){let data2=ageT.dataSync();obj.age=Math.trunc(10*data2[0])/10}ageT.dispose(),last=obj,resolve(obj)})):null}exports.predict=predict3,exports.load=load3}),require_gender=__commonJS(exports=>{var profile3=__toModule(require_profile()),models2={},last={gender:""},skipped=Number.MAX_SAFE_INTEGER,alternative=!1,rgb=[.2989,.587,.114];async function load3(config2){return models2.gender||(models2.gender=await loadGraphModel(config2.face.gender.modelPath),alternative=models2.gender.inputs[0].shape[3]===1,log(`load model: ${config2.face.gender.modelPath.match(/\/(.*)\./)[1]}`)),models2.gender}async function predict3(image3,config2){return models2.gender?skipped{let resize=image.resizeBilinear(image3,[config2.face.gender.inputSize,config2.face.gender.inputSize],!1),enhance;alternative?enhance=tidy(()=>{let[red,green,blue]=split(resize,3,3),redNorm=mul(red,rgb[0]),greenNorm=mul(green,rgb[1]),blueNorm=mul(blue,rgb[2]);return addN([redNorm,greenNorm,blueNorm]).sub(.5).mul(2)}):enhance=mul(resize,[255]),dispose(resize);let genderT,obj={};if(!config2.profile)config2.face.gender.enabled&&(genderT=await models2.gender.predict(enhance));else{let profileGender=config2.face.gender.enabled?await profile(()=>models2.gender.predict(enhance)):{};genderT=profileGender.result.clone(),profileGender.result.dispose(),profile3.run("gender",profileGender)}if(enhance.dispose(),genderT){let data2=genderT.dataSync();if(alternative){let confidence=Math.trunc(100*Math.abs(data2[0]-data2[1]))/100;confidence>config2.face.gender.minConfidence&&(obj.gender=data2[0]>data2[1]?"female":"male",obj.confidence=confidence)}else{let confidence=Math.trunc(200*Math.abs(data2[0]-.5))/100;confidence>config2.face.gender.minConfidence&&(obj.gender=data2[0]<=.5?"female":"male",obj.confidence=Math.min(.99,confidence))}}genderT.dispose(),last=obj,resolve(obj)})):null}exports.predict=predict3,exports.load=load3}),require_emotion=__commonJS(exports=>{var profile3=__toModule(require_profile()),annotations=["angry","disgust","fear","happy","sad","surprise","neutral"],models2={},last=[],skipped=Number.MAX_SAFE_INTEGER,rgb=[.2989,.587,.114],scale2=1;async function load3(config2){return models2.emotion||(models2.emotion=await loadGraphModel(config2.face.emotion.modelPath),log(`load model: ${config2.face.emotion.modelPath.match(/\/(.*)\./)[1]}`)),models2.emotion}async function predict3(image3,config2){return models2.emotion?skipped0?(skipped++,last):(config2.videoOptimized?skipped=0:skipped=Number.MAX_SAFE_INTEGER,new Promise(async resolve=>{let resize=image.resizeBilinear(image3,[config2.face.emotion.inputSize,config2.face.emotion.inputSize],!1),[red,green,blue]=split(resize,3,3);resize.dispose();let redNorm=mul(red,rgb[0]),greenNorm=mul(green,rgb[1]),blueNorm=mul(blue,rgb[2]);red.dispose(),green.dispose(),blue.dispose();let grayscale=addN([redNorm,greenNorm,blueNorm]);redNorm.dispose(),greenNorm.dispose(),blueNorm.dispose();let normalize=tidy(()=>grayscale.sub(.5).mul(2));grayscale.dispose();let obj=[];if(config2.face.emotion.enabled){let data2;if(config2.profile){let profileData=await profile(()=>models2.emotion.predict(normalize));data2=profileData.result.dataSync(),profileData.result.dispose(),profile3.run("emotion",profileData)}else{let emotionT=await models2.emotion.predict(normalize);data2=emotionT.dataSync(),dispose(emotionT)}for(let i=0;iconfig2.face.emotion.minConfidence&&obj.push({score:Math.min(.99,Math.trunc(100*scale2*data2[i])/100),emotion:annotations[i]});obj.sort((a,b)=>b.score-a.score)}normalize.dispose(),last=obj,resolve(obj)})):null}exports.predict=predict3,exports.load=load3}),require_embedding=__commonJS(exports=>{var profile3=__toModule(require_profile()),models2={};async function load3(config2){return models2.embedding||(models2.embedding=await loadGraphModel(config2.face.embedding.modelPath),log(`load model: ${config2.face.embedding.modelPath.match(/\/(.*)\./)[1]}`)),models2.embedding}function simmilarity2(embedding1,embedding22){if((embedding1==null?void 0:embedding1.length)!==(embedding22==null?void 0:embedding22.length))return 0;let order=2,distance=10*embedding1.map((val,i)=>val-embedding22[i]).reduce((dist,diff)=>dist+diff**order,0)**(1/order);return Math.trunc(1e3*(1-distance))/1e3}async function predict3(image3,config2){return models2.embedding?new Promise(async resolve=>{let resize=image.resizeBilinear(image3,[config2.face.embedding.inputSize,config2.face.embedding.inputSize],!1),data2=[];if(config2.face.embedding.enabled)if(config2.profile){let profileData=await profile(()=>models2.embedding.predict({img_inputs:resize}));data2=[...profileData.result.dataSync()],profileData.result.dispose(),profile3.run("emotion",profileData)}else{let embeddingT=await models2.embedding.predict({img_inputs:resize});data2=[...embeddingT.dataSync()],dispose(embeddingT)}resize.dispose(),resolve(data2)}):null}exports.predict=predict3,exports.simmilarity=simmilarity2,exports.load=load3}),require_modelBase=__commonJS(exports=>{var imageNetMean=[-123.15,-115.9,-103.06];function nameOutputResultsMobileNet(results){let[offsets,heatmap,displacementFwd,displacementBwd]=results;return{offsets,heatmap,displacementFwd,displacementBwd}}function nameOutputResultsResNet(results){let[displacementFwd,displacementBwd,offsets,heatmap]=results;return{offsets,heatmap,displacementFwd,displacementBwd}}var BaseModel=class{constructor(model2){this.model=model2}predict(input2,config2){return tidy(()=>{let asBatch=(config2.body.modelType==="ResNet"?input2.toFloat().add(imageNetMean):input2.toFloat().div(127.5).sub(1)).expandDims(0),results3d=this.model.predict(asBatch).map(y=>y.squeeze([0])),namedResults=config2.body.modelType==="ResNet"?nameOutputResultsResNet(results3d):nameOutputResultsMobileNet(results3d);return{heatmapScores:namedResults.heatmap.sigmoid(),offsets:namedResults.offsets,displacementFwd:namedResults.displacementFwd,displacementBwd:namedResults.displacementBwd}})}dispose(){this.model.dispose()}};exports.BaseModel=BaseModel}),require_heapSort=__commonJS(exports=>{function half(k){return Math.floor(k/2)}var MaxHeap=class{constructor(maxSize,getElementValue){this.priorityQueue=new Array(maxSize),this.numberOfElements=-1,this.getElementValue=getElementValue}enqueue(x){this.priorityQueue[++this.numberOfElements]=x,this.swim(this.numberOfElements)}dequeue(){let max6=this.priorityQueue[0];return this.exchange(0,this.numberOfElements--),this.sink(0),this.priorityQueue[this.numberOfElements+1]=null,max6}empty(){return this.numberOfElements===-1}size(){return this.numberOfElements+1}all(){return this.priorityQueue.slice(0,this.numberOfElements+1)}max(){return this.priorityQueue[0]}swim(k){for(;k>0&&this.less(half(k),k);)this.exchange(k,half(k)),k=half(k)}sink(k){for(;2*k<=this.numberOfElements;){let j=2*k;if(j{var heapSort=__toModule(require_heapSort());function scoreIsMaximumInLocalWindow(keypointId,score,heatmapY,heatmapX,localMaximumRadius,scores){let[height,width]=scores.shape,localMaximum=!0,yStart=Math.max(heatmapY-localMaximumRadius,0),yEnd=Math.min(heatmapY+localMaximumRadius+1,height);for(let yCurrent=yStart;yCurrentscore){localMaximum=!1;break}if(!localMaximum)break}return localMaximum}function buildPartWithScoreQueue(scoreThreshold,localMaximumRadius,scores){let[height,width,numKeypoints]=scores.shape,queue=new heapSort.MaxHeap(height*width*numKeypoints,({score})=>score);for(let heatmapY=0;heatmapY{exports.partNames=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],exports.NUM_KEYPOINTS=exports.partNames.length,exports.partIds=exports.partNames.reduce((result,jointName,i)=>(result[jointName]=i,result),{});var connectedPartNames=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]];exports.connectedPartIndices=connectedPartNames.map(([jointNameA,jointNameB])=>[exports.partIds[jointNameA],exports.partIds[jointNameB]]),exports.poseChain=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]],exports.partChannels=["left_face","right_face","right_upper_leg_front","right_lower_leg_back","right_upper_leg_back","left_lower_leg_front","left_upper_leg_front","left_upper_leg_back","left_lower_leg_back","right_feet","right_lower_leg_front","left_feet","torso_front","torso_back","right_upper_arm_front","right_upper_arm_back","right_lower_arm_back","left_lower_arm_front","left_upper_arm_front","left_upper_arm_back","left_lower_arm_back","right_hand","right_lower_arm_front","left_hand"]}),require_vectors=__commonJS(exports=>{var kpt=__toModule(require_keypoints());function getOffsetPoint(y,x,keypoint,offsets){return{y:offsets.get(y,x,keypoint),x:offsets.get(y,x,keypoint+kpt.NUM_KEYPOINTS)}}exports.getOffsetPoint=getOffsetPoint;function getImageCoords(part,outputStride,offsets){let{heatmapY,heatmapX,id:keypoint}=part,{y,x}=getOffsetPoint(heatmapY,heatmapX,keypoint,offsets);return{x:part.heatmapX*outputStride+x,y:part.heatmapY*outputStride+y}}exports.getImageCoords=getImageCoords;function fillArray(element,size){let result=new Array(size);for(let i=0;imax6?max6:a}exports.clamp=clamp2;function squaredDistance(y1,x1,y2,x2){let dy=y2-y1,dx=x2-x1;return dy*dy+dx*dx}exports.squaredDistance=squaredDistance;function addVectors(a,b){return{x:a.x+b.x,y:a.y+b.y}}exports.addVectors=addVectors;function clampVector(a,min6,max6){return{y:clamp2(a.y,min6,max6),x:clamp2(a.x,min6,max6)}}exports.clampVector=clampVector}),require_decoders=__commonJS(exports=>{var kpt=__toModule(require_keypoints());function getPointsConfidence(heatmapScores,heatMapCoords){let numKeypoints=heatMapCoords.shape[0],result=new Float32Array(numKeypoints);for(let keypoint=0;keypointheatMapCoordsBuffer.toTensor().mul(scalar(outputStride,"int32")).toFloat().add(getOffsetVectors(heatMapCoordsBuffer,offsetsBuffer)))}exports.getOffsetPoints=getOffsetPoints;function mod4(a,b){return tidy(()=>{let floored=a.div(scalar(b,"int32"));return a.sub(floored.mul(scalar(b,"int32")))})}function argmax2d(inputs){let[height,width,depth]=inputs.shape;return tidy(()=>{let coords2=inputs.reshape([height*width,depth]).argMax(0),yCoords=coords2.div(scalar(width,"int32")).expandDims(1),xCoords=mod4(coords2,width).expandDims(1);return concat([yCoords,xCoords],1)})}exports.argmax2d=argmax2d}),require_decodePose=__commonJS(exports=>{var keypoints=__toModule(require_keypoints()),vectors=__toModule(require_vectors()),decoders=__toModule(require_decoders()),parentChildrenTuples=keypoints.poseChain.map(([parentJoinName,childJoinName])=>[keypoints.partIds[parentJoinName],keypoints.partIds[childJoinName]]),parentToChildEdges=parentChildrenTuples.map(([,childJointId])=>childJointId),childToParentEdges=parentChildrenTuples.map(([parentJointId])=>parentJointId);function getDisplacement(edgeId,point,displacements){let numEdges=displacements.shape[2]/2;return{y:displacements.get(point.y,point.x,edgeId),x:displacements.get(point.y,point.x,numEdges+edgeId)}}function getStridedIndexNearPoint(point,outputStride,height,width){return{y:vectors.clamp(Math.round(point.y/outputStride),0,height-1),x:vectors.clamp(Math.round(point.x/outputStride),0,width-1)}}function traverseToTargetKeypoint(edgeId,sourceKeypoint,targetKeypointId,scoresBuffer,offsets,outputStride,displacements,offsetRefineStep=2){let[height,width]=scoresBuffer.shape,sourceKeypointIndices=getStridedIndexNearPoint(sourceKeypoint.position,outputStride,height,width),displacement=getDisplacement(edgeId,sourceKeypointIndices,displacements),targetKeypoint=vectors.addVectors(sourceKeypoint.position,displacement);for(let i=0;i=0;--edge){let sourceKeypointId=parentToChildEdges[edge],targetKeypointId=childToParentEdges[edge];instanceKeypoints[sourceKeypointId]&&!instanceKeypoints[targetKeypointId]&&(instanceKeypoints[targetKeypointId]=traverseToTargetKeypoint(edge,instanceKeypoints[sourceKeypointId],targetKeypointId,scores,offsets,outputStride,displacementsBwd))}for(let edge=0;edge(totalScore+=score,{position:{y:offsetPointsBuffer.get(i,0),x:offsetPointsBuffer.get(i,1)},part:keypoints.partNames[i],score})),filteredKeypoints=instanceKeypoints.filter(kpt=>kpt.score>config2.body.scoreThreshold);return heatmapValues.dispose(),offsetPoints.dispose(),{keypoints:filteredKeypoints,score:totalScore/instanceKeypoints.length}}exports.decodeSinglePose=decodeSinglePose}),require_decodeMultiple=__commonJS(exports=>{var buildParts=__toModule(require_buildParts()),decodePose=__toModule(require_decodePose()),vectors=__toModule(require_vectors()),kLocalMaximumRadius=1;function withinNmsRadiusOfCorrespondingPoint(poses,squaredNmsRadius,{x,y},keypointId){return poses.some(({keypoints})=>{let correspondingKeypoint=keypoints[keypointId].position;return vectors.squaredDistance(y,x,correspondingKeypoint.y,correspondingKeypoint.x)<=squaredNmsRadius})}function getInstanceScore(existingPoses,squaredNmsRadius,instanceKeypoints){return instanceKeypoints.reduce((result,{position,score},keypointId)=>(withinNmsRadiusOfCorrespondingPoint(existingPoses,squaredNmsRadius,position,keypointId)||(result+=score),result),0)/instanceKeypoints.length}function decodeMultiplePoses(scoresBuffer,offsetsBuffer,displacementsFwdBuffer,displacementsBwdBuffer,config2){let poses=[],queue=buildParts.buildPartWithScoreQueue(config2.body.scoreThreshold,kLocalMaximumRadius,scoresBuffer),squaredNmsRadius=config2.body.nmsRadius^2;for(;poses.lengthconfig2.body.scoreThreshold&&poses.push({keypoints,score})}return poses}exports.decodeMultiplePoses=decodeMultiplePoses}),require_util2=__commonJS(exports=>{var kpt=__toModule(require_keypoints());function eitherPointDoesntMeetConfidence(a,b,minConfidence){return a(eitherPointDoesntMeetConfidence(keypoints[leftJoint].score,keypoints[rightJoint].score,minConfidence)||result.push([keypoints[leftJoint],keypoints[rightJoint]]),result),[])}exports.getAdjacentKeyPoints=getAdjacentKeyPoints;var{NEGATIVE_INFINITY,POSITIVE_INFINITY}=Number;function getBoundingBox(keypoints){return keypoints.reduce(({maxX,maxY,minX,minY},{position:{x,y}})=>({maxX:Math.max(maxX,x),maxY:Math.max(maxY,y),minX:Math.min(minX,x),minY:Math.min(minY,y)}),{maxX:NEGATIVE_INFINITY,maxY:NEGATIVE_INFINITY,minX:POSITIVE_INFINITY,minY:POSITIVE_INFINITY})}exports.getBoundingBox=getBoundingBox;function getBoundingBoxPoints(keypoints){let{minX,minY,maxX,maxY}=getBoundingBox(keypoints);return[{x:minX,y:minY},{x:maxX,y:minY},{x:maxX,y:maxY},{x:minX,y:maxY}]}exports.getBoundingBoxPoints=getBoundingBoxPoints;async function toTensorBuffers3D(tensors){return Promise.all(tensors.map(tensor2=>tensor2.buffer()))}exports.toTensorBuffers3D=toTensorBuffers3D;function scalePose(pose,scaleY,scaleX){return{score:pose.score,keypoints:pose.keypoints.map(({score,part,position})=>({score,part,position:{x:position.x*scaleX,y:position.y*scaleY}}))}}exports.scalePose=scalePose;function resizeTo(image3,[targetH,targetW]){let input2=image3.squeeze(0),resized=input2.resizeBilinear([targetH,targetW]);return input2.dispose(),resized}exports.resizeTo=resizeTo;function scaleAndFlipPoses(poses,[height,width],[inputResolutionHeight,inputResolutionWidth]){return poses.map(pose=>scalePose(pose,height/inputResolutionHeight,width/inputResolutionWidth))}exports.scaleAndFlipPoses=scaleAndFlipPoses}),require_modelPoseNet=__commonJS(exports=>{var modelBase=__toModule(require_modelBase()),decodeMultiple=__toModule(require_decodeMultiple()),decodePose=__toModule(require_decodePose()),util=__toModule(require_util2());async function estimateMultiple(input2,res,config2){return new Promise(async resolve=>{let height=input2.shape[1],width=input2.shape[2],allTensorBuffers=await util.toTensorBuffers3D([res.heatmapScores,res.offsets,res.displacementFwd,res.displacementBwd]),scoresBuffer=allTensorBuffers[0],offsetsBuffer=allTensorBuffers[1],displacementsFwdBuffer=allTensorBuffers[2],displacementsBwdBuffer=allTensorBuffers[3],poses=await decodeMultiple.decodeMultiplePoses(scoresBuffer,offsetsBuffer,displacementsFwdBuffer,displacementsBwdBuffer,config2),scaled=util.scaleAndFlipPoses(poses,[height,width],[config2.body.inputSize,config2.body.inputSize]);resolve(scaled)})}async function estimateSingle(input2,res,config2){return new Promise(async resolve=>{let height=input2.shape[1],width=input2.shape[2],poses=[await decodePose.decodeSinglePose(res.heatmapScores,res.offsets,config2)],scaled=util.scaleAndFlipPoses(poses,[height,width],[config2.body.inputSize,config2.body.inputSize]);resolve(scaled)})}var PoseNet=class{constructor(model2){this.baseModel=model2}async estimatePoses(input2,config2){let resized=util.resizeTo(input2,[config2.body.inputSize,config2.body.inputSize]),res=this.baseModel.predict(resized,config2),poses=config2.body.maxDetections<2?await estimateSingle(input2,res,config2):await estimateMultiple(input2,res,config2);return res.heatmapScores.dispose(),res.offsets.dispose(),res.displacementFwd.dispose(),res.displacementBwd.dispose(),resized.dispose(),poses}dispose(){this.baseModel.dispose()}};exports.PoseNet=PoseNet;async function load3(config2){let model2=await loadGraphModel(config2.body.modelPath),mobilenet=new modelBase.BaseModel(model2);return log(`load model: ${config2.body.modelPath.match(/\/(.*)\./)[1]}`),new PoseNet(mobilenet)}exports.load=load3}),require_posenet=__commonJS(exports=>{var modelPoseNet=__toModule(require_modelPoseNet()),keypoints=__toModule(require_keypoints()),util=__toModule(require_util2());exports.load=modelPoseNet.load,exports.PoseNet=modelPoseNet.PoseNet,exports.partChannels=keypoints.partChannels,exports.partIds=keypoints.partIds,exports.partNames=keypoints.partNames,exports.poseChain=keypoints.poseChain,exports.getAdjacentKeyPoints=util.getAdjacentKeyPoints,exports.getBoundingBox=util.getBoundingBox,exports.getBoundingBoxPoints=util.getBoundingBoxPoints,exports.scaleAndFlipPoses=util.scaleAndFlipPoses,exports.scalePose=util.scalePose}),require_handdetector=__commonJS(exports=>{var HandDetector=class{constructor(model2,inputSize,anchorsAnnotated){this.model=model2,this.anchors=anchorsAnnotated.map(anchor=>[anchor.x_center,anchor.y_center]),this.anchorsTensor=tensor2d(this.anchors),this.inputSizeTensor=tensor1d([inputSize,inputSize]),this.doubleInputSizeTensor=tensor1d([inputSize*2,inputSize*2])}normalizeBoxes(boxes){return tidy(()=>{let boxOffsets=slice(boxes,[0,0],[-1,2]),boxSizes=slice(boxes,[0,2],[-1,2]),boxCenterPoints=add2(div(boxOffsets,this.inputSizeTensor),this.anchorsTensor),halfBoxSizes=div(boxSizes,this.doubleInputSizeTensor),startPoints=mul(sub(boxCenterPoints,halfBoxSizes),this.inputSizeTensor),endPoints=mul(add2(boxCenterPoints,halfBoxSizes),this.inputSizeTensor);return concat2d([startPoints,endPoints],1)})}normalizeLandmarks(rawPalmLandmarks,index){return tidy(()=>{let landmarks=add2(div(rawPalmLandmarks.reshape([-1,7,2]),this.inputSizeTensor),this.anchors[index]);return mul(landmarks,this.inputSizeTensor)})}async getBoxes(input2,config2){let batched=this.model.predict(input2),predictions=batched.squeeze();batched.dispose();let scoresT=tidy(()=>sigmoid(slice(predictions,[0,0],[-1,1])).squeeze()),scores=scoresT.dataSync(),rawBoxes=slice(predictions,[0,1],[-1,4]),boxes=this.normalizeBoxes(rawBoxes);rawBoxes.dispose();let filteredT=await image.nonMaxSuppressionAsync(boxes,scores,config2.hand.maxHands,config2.hand.iouThreshold,config2.hand.scoreThreshold),filtered=filteredT.arraySync();scoresT.dispose(),filteredT.dispose();let hands=[];for(let index of filtered)if(scores[index]>=config2.hand.minConfidence){let matchingBox=slice(boxes,[index,0],[1,-1]),rawPalmLandmarks=slice(predictions,[index,5],[1,14]),palmLandmarks=tidy(()=>this.normalizeLandmarks(rawPalmLandmarks,index).reshape([-1,2]));rawPalmLandmarks.dispose(),hands.push({box:matchingBox,palmLandmarks,confidence:scores[index]})}return predictions.dispose(),boxes.dispose(),hands}async estimateHandBounds(input2,config2){let inputHeight=input2.shape[1],inputWidth=input2.shape[2],image3=tidy(()=>input2.resizeBilinear([config2.hand.inputSize,config2.hand.inputSize]).div(127.5).sub(1)),predictions=await this.getBoxes(image3,config2);image3.dispose();let hands=[];if(!predictions||predictions.length===0)return hands;for(let prediction of predictions){let boxes=prediction.box.dataSync(),startPoint=boxes.slice(0,2),endPoint=boxes.slice(2,4),palmLandmarks=prediction.palmLandmarks.arraySync();prediction.box.dispose(),prediction.palmLandmarks.dispose(),hands.push(scaleBoxCoordinates({startPoint,endPoint,palmLandmarks,confidence:prediction.confidence},[inputWidth/config2.hand.inputSize,inputHeight/config2.hand.inputSize]))}return hands}};exports.HandDetector=HandDetector}),require_handpipeline=__commonJS(exports=>{var PALM_BOX_ENLARGE_FACTOR=5,HAND_BOX_ENLARGE_FACTOR=1.65,PALM_LANDMARK_IDS=[0,5,9,13,17,1,2],PALM_LANDMARKS_INDEX_OF_PALM_BASE=0,PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE=2,HandPipeline=class{constructor(handDetector,landmarkDetector,inputSize){this.handDetector=handDetector,this.landmarkDetector=landmarkDetector,this.inputSize=inputSize,this.storedBoxes=[],this.skipped=0,this.detectedHands=0}getBoxForPalmLandmarks(palmLandmarks,rotationMatrix){let rotatedPalmLandmarks=palmLandmarks.map(coord=>rotatePoint([...coord,1],rotationMatrix)),boxAroundPalm=this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);return enlargeBox(squarifyBox(boxAroundPalm),PALM_BOX_ENLARGE_FACTOR)}getBoxForHandLandmarks(landmarks){let boundingBox=this.calculateLandmarksBoundingBox(landmarks),boxAroundHand=enlargeBox(squarifyBox(boundingBox),HAND_BOX_ENLARGE_FACTOR);boxAroundHand.palmLandmarks=[];for(let i=0;i[scaleFactor[0]*(coord[0]-this.inputSize/2),scaleFactor[1]*(coord[1]-this.inputSize/2),scaleFactor[2]*coord[2]]),coordsRotationMatrix=buildRotationMatrix(angle,[0,0]),coordsRotated=coordsScaled.map(coord=>[...rotatePoint(coord,coordsRotationMatrix),coord[2]]),inverseRotationMatrix=invertTransformMatrix(rotationMatrix),boxCenter=[...getBoxCenter(box2),1],originalBoxCenter=[dot4(boxCenter,inverseRotationMatrix[0]),dot4(boxCenter,inverseRotationMatrix[1])];return coordsRotated.map(coord=>[coord[0]+originalBoxCenter[0],coord[1]+originalBoxCenter[1],coord[2]])}async estimateHands(image3,config2){let useFreshBox=!1,boxes;(this.skipped===0||this.skipped>config2.hand.skipFrames||!config2.hand.landmarks||!config2.videoOptimized)&&(boxes=await this.handDetector.estimateHandBounds(image3,config2),this.skipped=0),config2.videoOptimized&&this.skipped++,boxes&&boxes.length>0&&(boxes.length!==this.detectedHands&&this.detectedHands!==config2.hand.maxHands||!config2.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...boxes],this.storedBoxes.length>0&&(useFreshBox=!0));let hands=[];for(let i=0;i=config2.hand.minConfidence){let keypointsReshaped=reshape(keypoints,[-1,3]),rawCoords=keypointsReshaped.arraySync();keypoints.dispose(),keypointsReshaped.dispose();let coords2=this.transformRawCoords(rawCoords,newBox,angle,rotationMatrix),nextBoundingBox=this.getBoxForHandLandmarks(coords2);this.storedBoxes[i]=nextBoundingBox;let result={landmarks:coords2,confidence,box:{topLeft:nextBoundingBox.startPoint,bottomRight:nextBoundingBox.endPoint}};hands.push(result)}else this.storedBoxes[i]=null;keypoints.dispose()}else{let enlarged=enlargeBox(squarifyBox(currentBox),HAND_BOX_ENLARGE_FACTOR),result={confidence:currentBox.confidence,box:{topLeft:enlarged.startPoint,bottomRight:enlarged.endPoint}};hands.push(result)}}return this.storedBoxes=this.storedBoxes.filter(a=>a!==null),this.detectedHands=hands.length,hands}calculateLandmarksBoundingBox(landmarks){let xs=landmarks.map(d=>d[0]),ys=landmarks.map(d=>d[1]),startPoint=[Math.min(...xs),Math.min(...ys)],endPoint=[Math.max(...xs),Math.max(...ys)];return{startPoint,endPoint}}};exports.HandPipeline=HandPipeline}),require_anchors=__commonJS(exports=>{exports.anchors=[{w:1,h:1,x_center:.015625,y_center:.015625},{w:1,h:1,x_center:.015625,y_center:.015625},{w:1,h:1,x_center:.046875,y_center:.015625},{w:1,h:1,x_center:.046875,y_center:.015625},{w:1,h:1,x_center:.078125,y_center:.015625},{w:1,h:1,x_center:.078125,y_center:.015625},{w:1,h:1,x_center:.109375,y_center:.015625},{w:1,h:1,x_center:.109375,y_center:.015625},{w:1,h:1,x_center:.140625,y_center:.015625},{w:1,h:1,x_center:.140625,y_center:.015625},{w:1,h:1,x_center:.171875,y_center:.015625},{w:1,h:1,x_center:.171875,y_center:.015625},{w:1,h:1,x_center:.203125,y_center:.015625},{w:1,h:1,x_center:.203125,y_center:.015625},{w:1,h:1,x_center:.234375,y_center:.015625},{w:1,h:1,x_center:.234375,y_center:.015625},{w:1,h:1,x_center:.265625,y_center:.015625},{w:1,h:1,x_center:.265625,y_center:.015625},{w:1,h:1,x_center:.296875,y_center:.015625},{w:1,h:1,x_center:.296875,y_center:.015625},{w:1,h:1,x_center:.328125,y_center:.015625},{w:1,h:1,x_center:.328125,y_center:.015625},{w:1,h:1,x_center:.359375,y_center:.015625},{w:1,h:1,x_center:.359375,y_center:.015625},{w:1,h:1,x_center:.390625,y_center:.015625},{w:1,h:1,x_center:.390625,y_center:.015625},{w:1,h:1,x_center:.421875,y_center:.015625},{w:1,h:1,x_center:.421875,y_center:.015625},{w:1,h:1,x_center:.453125,y_center:.015625},{w:1,h:1,x_center:.453125,y_center:.015625},{w:1,h:1,x_center:.484375,y_center:.015625},{w:1,h:1,x_center:.484375,y_center:.015625},{w:1,h:1,x_center:.515625,y_center:.015625},{w:1,h:1,x_center:.515625,y_center:.015625},{w:1,h:1,x_center:.546875,y_center:.015625},{w:1,h:1,x_center:.546875,y_center:.015625},{w:1,h:1,x_center:.578125,y_center:.015625},{w:1,h:1,x_center:.578125,y_center:.015625},{w:1,h:1,x_center:.609375,y_center:.015625},{w:1,h:1,x_center:.609375,y_center:.015625},{w:1,h:1,x_center:.640625,y_center:.015625},{w:1,h:1,x_center:.640625,y_center:.015625},{w:1,h:1,x_center:.671875,y_center:.015625},{w:1,h:1,x_center:.671875,y_center:.015625},{w:1,h:1,x_center:.703125,y_center:.015625},{w:1,h:1,x_center:.703125,y_center:.015625},{w:1,h:1,x_center:.734375,y_center:.015625},{w:1,h:1,x_center:.734375,y_center:.015625},{w:1,h:1,x_center:.765625,y_center:.015625},{w:1,h:1,x_center:.765625,y_center:.015625},{w:1,h:1,x_center:.796875,y_center:.015625},{w:1,h:1,x_center:.796875,y_center:.015625},{w:1,h:1,x_center:.828125,y_center:.015625},{w:1,h:1,x_center:.828125,y_center:.015625},{w:1,h:1,x_center:.859375,y_center:.015625},{w:1,h:1,x_center:.859375,y_center:.015625},{w:1,h:1,x_center:.890625,y_center:.015625},{w:1,h:1,x_center:.890625,y_center:.015625},{w:1,h:1,x_center:.921875,y_center:.015625},{w:1,h:1,x_center:.921875,y_center:.015625},{w:1,h:1,x_center:.953125,y_center:.015625},{w:1,h:1,x_center:.953125,y_center:.015625},{w:1,h:1,x_center:.984375,y_center:.015625},{w:1,h:1,x_center:.984375,y_center:.015625},{w:1,h:1,x_center:.015625,y_center:.046875},{w:1,h:1,x_center:.015625,y_center:.046875},{w:1,h:1,x_center:.046875,y_center:.046875},{w:1,h:1,x_center:.046875,y_center:.046875},{w:1,h:1,x_center:.078125,y_center:.046875},{w:1,h:1,x_center:.078125,y_center:.046875},{w:1,h:1,x_center:.109375,y_center:.046875},{w:1,h:1,x_center:.109375,y_center:.046875},{w:1,h:1,x_center:.140625,y_center:.046875},{w:1,h:1,x_center:.140625,y_center:.046875},{w:1,h:1,x_center:.171875,y_center:.046875},{w:1,h:1,x_center:.171875,y_center:.046875},{w:1,h:1,x_center:.203125,y_center:.046875},{w:1,h:1,x_center:.203125,y_center:.046875},{w:1,h:1,x_center:.234375,y_center:.046875},{w:1,h:1,x_center:.234375,y_center:.046875},{w:1,h:1,x_center:.265625,y_center:.046875},{w:1,h:1,x_center:.265625,y_center:.046875},{w:1,h:1,x_center:.296875,y_center:.046875},{w:1,h:1,x_center:.296875,y_center:.046875},{w:1,h:1,x_center:.328125,y_center:.046875},{w:1,h:1,x_center:.328125,y_center:.046875},{w:1,h:1,x_center:.359375,y_center:.046875},{w:1,h:1,x_center:.359375,y_center:.046875},{w:1,h:1,x_center:.390625,y_center:.046875},{w:1,h:1,x_center:.390625,y_center:.046875},{w:1,h:1,x_center:.421875,y_center:.046875},{w:1,h:1,x_center:.421875,y_center:.046875},{w:1,h:1,x_center:.453125,y_center:.046875},{w:1,h:1,x_center:.453125,y_center:.046875},{w:1,h:1,x_center:.484375,y_center:.046875},{w:1,h:1,x_center:.484375,y_center:.046875},{w:1,h:1,x_center:.515625,y_center:.046875},{w:1,h:1,x_center:.515625,y_center:.046875},{w:1,h:1,x_center:.546875,y_center:.046875},{w:1,h:1,x_center:.546875,y_center:.046875},{w:1,h:1,x_center:.578125,y_center:.046875},{w:1,h:1,x_center:.578125,y_center:.046875},{w:1,h:1,x_center:.609375,y_center:.046875},{w:1,h:1,x_center:.609375,y_center:.046875},{w:1,h:1,x_center:.640625,y_center:.046875},{w:1,h:1,x_center:.640625,y_center:.046875},{w:1,h:1,x_center:.671875,y_center:.046875},{w:1,h:1,x_center:.671875,y_cent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HandPose(handPipeline);return config2.hand.enabled&&log(`load model: ${config2.hand.detector.modelPath.match(/\/(.*)\./)[1]}`),config2.hand.landmarks&&log(`load model: ${config2.hand.skeleton.modelPath.match(/\/(.*)\./)[1]}`),handPose}exports.load=load3}),require_gesture=__commonJS(exports=>{exports.body=res=>{if(!res)return[];let gestures=[];for(let i=0;ia.part==="leftWrist"),rightWrist=res[i].keypoints.find(a=>a.part==="rightWrist"),nose=res[i].keypoints.find(a=>a.part==="nose");nose&&leftWrist&&rightWrist&&leftWrist.position.ya.part==="leftShoulder"),rightShoulder=res[i].keypoints.find(a=>a.part==="rightShoulder");leftShoulder&&rightShoulder&&gestures.push({body:i,gesture:`leaning ${leftShoulder.position.y>rightShoulder.position.y?"left":"right"}`})}return gestures},exports.face=res=>{if(!res)return[];let gestures=[];for(let i=0;i0){let eyeFacing=res[i].mesh[35][2]-res[i].mesh[263][2];Math.abs(eyeFacing)<10?gestures.push({face:i,gesture:"facing camera"}):gestures.push({face:i,gesture:`facing ${eyeFacing<0?"right":"left"}`}),Math.abs(res[i].mesh[374][1]-res[i].mesh[386][1])/Math.abs(res[i].mesh[443][1]-res[i].mesh[450][1])<.2&&gestures.push({face:i,gesture:"blink left eye"}),Math.abs(res[i].mesh[145][1]-res[i].mesh[159][1])/Math.abs(res[i].mesh[223][1]-res[i].mesh[230][1])<.2&&gestures.push({face:i,gesture:"blink right eye"});let mouthOpen=Math.min(100,500*Math.abs(res[i].mesh[13][1]-res[i].mesh[14][1])/Math.abs(res[i].mesh[10][1]-res[i].mesh[152][1]));mouthOpen>10&&gestures.push({face:i,gesture:`mouth ${Math.trunc(mouthOpen)}% open`});let chinDepth=res[i].mesh[152][2];Math.abs(chinDepth)>10&&gestures.push({face:i,gesture:`head ${chinDepth<0?"up":"down"}`})}return gestures},exports.iris=res=>{if(!res)return[];let gestures=[];for(let i=0;i{if(!res)return[];let gestures=[];for(let i=0;i0){let closest=fingers.reduce((best,a)=>best.position[2]best.position[1]{var WebGLProgram=function(gl,vertexSource,fragmentSource){let _collect=function(source,prefix,collection){let r=new RegExp("\\b"+prefix+" \\w+ (\\w+)","ig");source.replace(r,(match,name)=>(collection[name]=0,match))},_compile=function(source,type){let shader=gl.createShader(type);if(gl.shaderSource(shader,source),gl.compileShader(shader),!gl.getShaderParameter(shader,gl.COMPILE_STATUS))throw new Error("Filter: GL compile failed",gl.getShaderInfoLog(shader));return shader};this.uniform={},this.attribute={};let _vsh=_compile(vertexSource,gl.VERTEX_SHADER),_fsh=_compile(fragmentSource,gl.FRAGMENT_SHADER);if(this.id=gl.createProgram(),gl.attachShader(this.id,_vsh),gl.attachShader(this.id,_fsh),gl.linkProgram(this.id),!gl.getProgramParameter(this.id,gl.LINK_STATUS))throw new Error("Filter: GL link failed",gl.getProgramInfoLog(this.id));gl.useProgram(this.id),_collect(vertexSource,"attribute",this.attribute);for(let a in this.attribute)this.attribute[a]=gl.getAttribLocation(this.id,a);_collect(vertexSource,"uniform",this.uniform),_collect(fragmentSource,"uniform",this.uniform);for(let u in this.uniform)this.uniform[u]=gl.getUniformLocation(this.id,u)},WebGLImageFilter=function(params){params||(params={});let _drawCount=0,_sourceTexture=null,_lastInChain=!1,_currentFramebufferIndex=-1,_tempFramebuffers=[null,null],_filterChain=[],_width=-1,_height=-1,_vertexBuffer=null,_currentProgram=null,_canvas=params.canvas||document.createElement("canvas"),_shaderProgramCache={},gl=_canvas.getContext("webgl");if(!gl)throw new Error("Filter: getContext() failed");this.addFilter=function(name){let args=Array.prototype.slice.call(arguments,1),filter=_filter[name];_filterChain.push({func:filter,args})},this.reset=function(){_filterChain=[]},this.apply=function(image3){if(_resize(image3.width,image3.height),_drawCount=0,_sourceTexture||(_sourceTexture=gl.createTexture()),gl.bindTexture(gl.TEXTURE_2D,_sourceTexture),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_WRAP_S,gl.CLAMP_TO_EDGE),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_WRAP_T,gl.CLAMP_TO_EDGE),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_MIN_FILTER,gl.NEAREST),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_MAG_FILTER,gl.NEAREST),gl.texImage2D(gl.TEXTURE_2D,0,gl.RGBA,gl.RGBA,gl.UNSIGNED_BYTE,image3),_filterChain.length===0)return _draw(),_canvas;for(let i=0;i<_filterChain.length;i++){_lastInChain=i===_filterChain.length-1;let f=_filterChain[i];f.func.apply(this,f.args||[])}return _canvas};let _resize=function(width,height){if(!(width===_width&&height===_height)){if(_canvas.width=width,_width=width,_canvas.height=height,_height=height,!_vertexBuffer){let vertices=new Float32Array([-1,-1,0,1,1,-1,1,1,-1,1,0,0,-1,1,0,0,1,-1,1,1,1,1,1,0]);_vertexBuffer=gl.createBuffer(),gl.bindBuffer(gl.ARRAY_BUFFER,_vertexBuffer),gl.bufferData(gl.ARRAY_BUFFER,vertices,gl.STATIC_DRAW),gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL,!0)}gl.viewport(0,0,_width,_height),_tempFramebuffers=[null,null]}},_getTempFramebuffer=function(index){return _tempFramebuffers[index]=_tempFramebuffers[index]||_createFramebufferTexture(_width,_height),_tempFramebuffers[index]},_createFramebufferTexture=function(width,height){let fbo=gl.createFramebuffer();gl.bindFramebuffer(gl.FRAMEBUFFER,fbo);let renderbuffer=gl.createRenderbuffer();gl.bindRenderbuffer(gl.RENDERBUFFER,renderbuffer);let texture=gl.createTexture();return gl.bindTexture(gl.TEXTURE_2D,texture),gl.texImage2D(gl.TEXTURE_2D,0,gl.RGBA,width,height,0,gl.RGBA,gl.UNSIGNED_BYTE,null),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_MAG_FILTER,gl.LINEAR),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_MIN_FILTER,gl.LINEAR),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_WRAP_S,gl.CLAMP_TO_EDGE),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_WRAP_T,gl.CLAMP_TO_EDGE),gl.framebufferTexture2D(gl.FRAMEBUFFER,gl.COLOR_ATTACHMENT0,gl.TEXTURE_2D,texture,0),gl.bindTexture(gl.TEXTURE_2D,null),gl.bindFramebuffer(gl.FRAMEBUFFER,null),{fbo,texture}},_draw=function(flags){var _a,_b;let source=null,target=null,flipY=!1;_drawCount===0?source=_sourceTexture:source=(_a=_getTempFramebuffer(_currentFramebufferIndex))==null?void 0:_a.texture,_drawCount++,_lastInChain&&!(flags&DRAW.INTERMEDIATE)?(target=null,flipY=_drawCount%2==0):(_currentFramebufferIndex=(_currentFramebufferIndex+1)%2,target=(_b=_getTempFramebuffer(_currentFramebufferIndex))==null?void 0:_b.fbo),gl.bindTexture(gl.TEXTURE_2D,source),gl.bindFramebuffer(gl.FRAMEBUFFER,target),gl.uniform1f(_currentProgram.uniform.flipY,flipY?-1:1),gl.drawArrays(gl.TRIANGLES,0,6)},_compileShader=function(fragmentSource){if(_shaderProgramCache[fragmentSource])return _currentProgram=_shaderProgramCache[fragmentSource],gl.useProgram(_currentProgram.id),_currentProgram;_currentProgram=new WebGLProgram(gl,SHADER.VERTEX_IDENTITY,fragmentSource);let floatSize=Float32Array.BYTES_PER_ELEMENT,vertSize=4*floatSize;return gl.enableVertexAttribArray(_currentProgram.attribute.pos),gl.vertexAttribPointer(_currentProgram.attribute.pos,2,gl.FLOAT,!1,vertSize,0*floatSize),gl.enableVertexAttribArray(_currentProgram.attribute.uv),gl.vertexAttribPointer(_currentProgram.attribute.uv,2,gl.FLOAT,!1,vertSize,2*floatSize),_shaderProgramCache[fragmentSource]=_currentProgram,_currentProgram},DRAW={INTERMEDIATE:1},SHADER={};SHADER.VERTEX_IDENTITY=["precision highp float;","attribute vec2 pos;","attribute vec2 uv;","varying vec2 vUv;","uniform float flipY;","void main(void) {","vUv = uv;","gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);","}"].join(` `),SHADER.FRAGMENT_IDENTITY=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","void main(void) {","gl_FragColor = texture2D(texture, vUv);","}"].join(` `);let _filter={};_filter.colorMatrix=function(matrix){let m=new Float32Array(matrix);m[4]/=255,m[9]/=255,m[14]/=255,m[19]/=255;let shader=m[18]===1&&m[3]===0&&m[8]===0&&m[13]===0&&m[15]===0&&m[16]===0&&m[17]===0&&m[19]===0?_filter.colorMatrix.SHADER.WITHOUT_ALPHA:_filter.colorMatrix.SHADER.WITH_ALPHA,program=_compileShader(shader);gl.uniform1fv(program.uniform.m,m),_draw()},_filter.colorMatrix.SHADER={},_filter.colorMatrix.SHADER.WITH_ALPHA=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","uniform float m[20];","void main(void) {","vec4 c = texture2D(texture, vUv);","gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];","gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];","gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];","gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];","}"].join(` `),_filter.colorMatrix.SHADER.WITHOUT_ALPHA=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","uniform float m[20];","void main(void) {","vec4 c = texture2D(texture, vUv);","gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];","gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];","gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];","gl_FragColor.a = c.a;","}"].join(` `),_filter.brightness=function(brightness){let b=(brightness||0)+1;_filter.colorMatrix([b,0,0,0,0,0,b,0,0,0,0,0,b,0,0,0,0,0,1,0])},_filter.saturation=function(amount){let x=(amount||0)*2/3+1,y=(x-1)*-.5;_filter.colorMatrix([x,y,y,0,0,y,x,y,0,0,y,y,x,0,0,0,0,0,1,0])},_filter.desaturate=function(){_filter.saturation(-1)},_filter.contrast=function(amount){let v=(amount||0)+1,o=-128*(v-1);_filter.colorMatrix([v,0,0,0,o,0,v,0,0,o,0,0,v,0,o,0,0,0,1,0])},_filter.negative=function(){_filter.contrast(-2)},_filter.hue=function(rotation){rotation=(rotation||0)/180*Math.PI;let cos4=Math.cos(rotation),sin4=Math.sin(rotation),lumR=.213,lumG=.715,lumB=.072;_filter.colorMatrix([lumR+cos4*(1-lumR)+sin4*-lumR,lumG+cos4*-lumG+sin4*-lumG,lumB+cos4*-lumB+sin4*(1-lumB),0,0,lumR+cos4*-lumR+sin4*.143,lumG+cos4*(1-lumG)+sin4*.14,lumB+cos4*-lumB+sin4*-.283,0,0,lumR+cos4*-lumR+sin4*-(1-lumR),lumG+cos4*-lumG+sin4*lumG,lumB+cos4*(1-lumB)+sin4*lumB,0,0,0,0,0,1,0])},_filter.desaturateLuminance=function(){_filter.colorMatrix([.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,0,0,0,1,0])},_filter.sepia=function(){_filter.colorMatrix([.393,.7689999,.18899999,0,0,.349,.6859999,.16799999,0,0,.272,.5339999,.13099999,0,0,0,0,0,1,0])},_filter.brownie=function(){_filter.colorMatrix([.5997023498159715,.34553243048391263,-.2708298674538042,0,47.43192855600873,-.037703249837783157,.8609577587992641,.15059552388459913,0,-36.96841498319127,.24113635128153335,-.07441037908422492,.44972182064877153,0,-7.562075277591283,0,0,0,1,0])},_filter.vintagePinhole=function(){_filter.colorMatrix([.6279345635605994,.3202183420819367,-.03965408211312453,0,9.651285835294123,.02578397704808868,.6441188644374771,.03259127616149294,0,7.462829176470591,.0466055556782719,-.0851232987247891,.5241648018700465,0,5.159190588235296,0,0,0,1,0])},_filter.kodachrome=function(){_filter.colorMatrix([1.1285582396593525,-.3967382283601348,-.03992559172921793,0,63.72958762196502,-.16404339962244616,1.0835251566291304,-.05498805115633132,0,24.732407896706203,-.16786010706155763,-.5603416277695248,1.6014850761964943,0,35.62982807460946,0,0,0,1,0])},_filter.technicolor=function(){_filter.colorMatrix([1.9125277891456083,-.8545344976951645,-.09155508482755585,0,11.793603434377337,-.3087833385928097,1.7658908555458428,-.10601743074722245,0,-70.35205161461398,-.231103377548616,-.7501899197440212,1.847597816108189,0,30.950940869491138,0,0,0,1,0])},_filter.polaroid=function(){_filter.colorMatrix([1.438,-.062,-.062,0,0,-.122,1.378,-.122,0,0,-.016,-.016,1.483,0,0,0,0,0,1,0])},_filter.shiftToBGR=function(){_filter.colorMatrix([0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0])},_filter.convolution=function(matrix){let m=new Float32Array(matrix),pixelSizeX=1/_width,pixelSizeY=1/_height,program=_compileShader(_filter.convolution.SHADER);gl.uniform1fv(program.uniform.m,m),gl.uniform2f(program.uniform.px,pixelSizeX,pixelSizeY),_draw()},_filter.convolution.SHADER=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","uniform vec2 px;","uniform float m[9];","void main(void) {","vec4 c11 = texture2D(texture, vUv - px);","vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y));","vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y));","vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) );","vec4 c22 = texture2D(texture, vUv);","vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) );","vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) );","vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) );","vec4 c33 = texture2D(texture, vUv + px );","gl_FragColor = ","c11 * m[0] + c12 * m[1] + c22 * m[2] +","c21 * m[3] + c22 * m[4] + c23 * m[5] +","c31 * m[6] + c32 * m[7] + c33 * m[8];","gl_FragColor.a = c22.a;","}"].join(` `),_filter.detectEdges=function(){_filter.convolution.call(this,[0,1,0,1,-4,1,0,1,0])},_filter.sobelX=function(){_filter.convolution.call(this,[-1,0,1,-2,0,2,-1,0,1])},_filter.sobelY=function(){_filter.convolution.call(this,[-1,-2,-1,0,0,0,1,2,1])},_filter.sharpen=function(amount){let a=amount||1;_filter.convolution.call(this,[0,-1*a,0,-1*a,1+4*a,-1*a,0,-1*a,0])},_filter.emboss=function(size){let s=size||1;_filter.convolution.call(this,[-2*s,-1*s,0,-1*s,1,1*s,0,1*s,2*s])},_filter.blur=function(size){let blurSizeX=size/7/_width,blurSizeY=size/7/_height,program=_compileShader(_filter.blur.SHADER);gl.uniform2f(program.uniform.px,0,blurSizeY),_draw(DRAW.INTERMEDIATE),gl.uniform2f(program.uniform.px,blurSizeX,0),_draw()},_filter.blur.SHADER=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","uniform vec2 px;","void main(void) {","gl_FragColor = vec4(0.0);","gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;","gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;","gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;","gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;","gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;","gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;","gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;","gl_FragColor += texture2D(texture, vUv )*0.159576912161;","gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x, 1.0*px.y))*0.147308056121;","gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x, 2.0*px.y))*0.115876621105;","gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x, 3.0*px.y))*0.0776744219933;","gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x, 4.0*px.y))*0.0443683338718;","gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x, 5.0*px.y))*0.0215963866053;","gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794;","gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265;","}"].join(` `),_filter.pixelate=function(size){let blurSizeX=size/_width,blurSizeY=size/_height,program=_compileShader(_filter.pixelate.SHADER);gl.uniform2f(program.uniform.size,blurSizeX,blurSizeY),_draw()},_filter.pixelate.SHADER=["precision highp float;","varying vec2 vUv;","uniform vec2 size;","uniform sampler2D texture;","vec2 pixelate(vec2 coord, vec2 size) {","return floor( coord / size ) * size;","}","void main(void) {","gl_FragColor = vec4(0.0);","vec2 coord = pixelate(vUv, size);","gl_FragColor += texture2D(texture, coord);","}"].join(` -`)};exports.Canvas=WebGLImageFilter}),require_image=__commonJS(exports=>{var fxImage=__toModule(require_imagefx()),inCanvas=null,outCanvas=null;function process3(input2,config3){let tensor2;if(input2 instanceof Tensor)tensor2=clone(input2);else{let originalWidth=input2.naturalWidth||input2.videoWidth||input2.width||input2.shape&&input2.shape[1]>0,originalHeight=input2.naturalHeight||input2.videoHeight||input2.height||input2.shape&&input2.shape[2]>0,targetWidth=originalWidth,targetHeight=originalHeight;if(config3.filter.width>0?targetWidth=config3.filter.width:config3.filter.height>0&&(targetWidth=originalWidth*(config3.filter.height/originalHeight)),config3.filter.height>0?targetHeight=config3.filter.height:config3.filter.width>0&&(targetHeight=originalHeight*(config3.filter.width/originalWidth)),!targetWidth||!targetHeight)return log("Human: invalid input",input2),null;(!inCanvas||inCanvas.width!==targetWidth||inCanvas.height!==targetHeight)&&(inCanvas=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(targetWidth,targetHeight):document.createElement("canvas"),inCanvas.width!==targetWidth&&(inCanvas.width=targetWidth),inCanvas.height!==targetHeight&&(inCanvas.height=targetHeight));let ctx=inCanvas.getContext("2d");if(input2 instanceof ImageData?ctx.putImageData(input2,0,0):ctx.drawImage(input2,0,0,originalWidth,originalHeight,0,0,inCanvas.width,inCanvas.height),config3.filter.enabled){if((!this.fx||!outCanvas||inCanvas.width!==outCanvas.width||inCanvas.height!==outCanvas.height)&&(outCanvas=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(inCanvas.width,inCanvas.height):document.createElement("canvas"),outCanvas.width!==inCanvas.width&&(outCanvas.width=inCanvas.width),outCanvas.height!==inCanvas.height&&(outCanvas.height=inCanvas.height),this.fx=ENV.flags.IS_BROWSER?new fxImage.Canvas({canvas:outCanvas}):null),!this.fx)return inCanvas;this.fx.reset(),this.fx.addFilter("brightness",config3.filter.brightness),config3.filter.contrast!==0&&this.fx.addFilter("contrast",config3.filter.contrast),config3.filter.sharpness!==0&&this.fx.addFilter("sharpen",config3.filter.sharpness),config3.filter.blur!==0&&this.fx.addFilter("blur",config3.filter.blur),config3.filter.saturation!==0&&this.fx.addFilter("saturation",config3.filter.saturation),config3.filter.hue!==0&&this.fx.addFilter("hue",config3.filter.hue),config3.filter.negative&&this.fx.addFilter("negative"),config3.filter.sepia&&this.fx.addFilter("sepia"),config3.filter.vintage&&this.fx.addFilter("brownie"),config3.filter.sepia&&this.fx.addFilter("sepia"),config3.filter.kodachrome&&this.fx.addFilter("kodachrome"),config3.filter.technicolor&&this.fx.addFilter("technicolor"),config3.filter.polaroid&&this.fx.addFilter("polaroid"),config3.filter.pixelate!==0&&this.fx.addFilter("pixelate",config3.filter.pixelate),this.fx.apply(inCanvas)}else outCanvas=inCanvas;let pixels;if(outCanvas.data){let shape=[outCanvas.height,outCanvas.width,3];pixels=tensor3d(outCanvas.data,shape,"int32")}else if(config3.backend==="webgl"||outCanvas instanceof ImageData)pixels=browser_exports.fromPixels(outCanvas);else{let tempCanvas=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(targetWidth,targetHeight):document.createElement("canvas");tempCanvas.width=targetWidth,tempCanvas.height=targetHeight;let tempCtx=tempCanvas.getContext("2d");tempCtx==null||tempCtx.drawImage(outCanvas,0,0);let data2=tempCtx==null?void 0:tempCtx.getImageData(0,0,targetWidth,targetHeight);pixels=browser_exports.fromPixels(data2)}let casted=pixels.toFloat();tensor2=casted.expandDims(0),pixels.dispose(),casted.dispose()}return{tensor:tensor2,canvas:config3.filter.return?outCanvas:null}}exports.process=process3});function log(...msg){let dt=new Date,ts=`${dt.getHours().toString().padStart(2,"0")}:${dt.getMinutes().toString().padStart(2,"0")}:${dt.getSeconds().toString().padStart(2,"0")}.${dt.getMilliseconds().toString().padStart(3,"0")}`;msg&&console.log(ts,"Human:",...msg)}var 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impl}):this.xor4096=impl})(exports,typeof module=="object"&&module,typeof define=="function"&&define)}),require_tychei=__commonJS2((exports,module)=>{(function(global2,module2,define2){function XorGen(seed){var me=this,strseed="";me.next=function(){var b=me.b,c=me.c,d=me.d,a=me.a;return b=b<<25^b>>>7^c,c=c-d|0,d=d<<24^d>>>8^a,a=a-b|0,me.b=b=b<<20^b>>>12^c,me.c=c=c-d|0,me.d=d<<16^c>>>16^a,me.a=a-b|0},me.a=0,me.b=0,me.c=2654435769|0,me.d=1367130551,seed===Math.floor(seed)?(me.a=seed/4294967296|0,me.b=seed|0):strseed+=seed;for(var k=0;k>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state&&(typeof state=="object"&©(state,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.tychei=impl})(exports,typeof module=="object"&&module,typeof define=="function"&&define)}),require_crypto=__commonJS2(()=>{}),require_seedrandom=__commonJS2((exports,module)=>{(function(pool3,math){var global2=this,width=256,chunks=6,digits=52,rngname="random",startdenom=math.pow(width,chunks),significance=math.pow(2,digits),overflow=significance*2,mask=width-1,nodecrypto;function seedrandom5(seed,options,callback){var key=[];options=options==!0?{entropy:!0}:options||{};var shortseed=mixkey(flatten4(options.entropy?[seed,tostring(pool3)]:seed==null?autoseed():seed,3),key),arc4=new ARC4(key),prng=function(){for(var n=arc4.g(chunks),d=startdenom,x=0;n=overflow;)n/=2,d/=2,x>>>=1;return(n+x)/d};return prng.int32=function(){return arc4.g(4)|0},prng.quick=function(){return arc4.g(4)/4294967296},prng.double=prng,mixkey(tostring(arc4.S),pool3),(options.pass||callback||function(prng2,seed2,is_math_call,state){return state&&(state.S&©(state,arc4),prng2.state=function(){return copy(arc4,{})}),is_math_call?(math[rngname]=prng2,seed2):prng2})(prng,shortseed,"global"in options?options.global:this==math,options.state)}math["seed"+rngname]=seedrandom5;function ARC4(key){var t,keylen=key.length,me=this,i=0,j=me.i=me.j=0,s=me.S=[];for(keylen||(key=[keylen++]);i{var alea5=require_alea(),xor128=require_xor128(),xorwow=require_xorwow(),xorshift7=require_xorshift7(),xor4096=require_xor4096(),tychei=require_tychei(),sr=require_seedrandom();sr.alea=alea5,sr.xor128=xor128,sr.xorwow=xorwow,sr.xorshift7=xorshift7,sr.xor4096=xor4096,sr.tychei=tychei,module.exports=sr}),require_alea2=__commonJS2((exports,module)=>{(function(global2,module2,define2){function Alea(seed){var me=this,mash=Mash();me.next=function(){var t=2091639*me.s0+me.c*23283064365386963e-26;return me.s0=me.s1,me.s1=me.s2,me.s2=t-(me.c=t|0)},me.c=1,me.s0=mash(" "),me.s1=mash(" "),me.s2=mash(" "),me.s0-=mash(seed),me.s0<0&&(me.s0+=1),me.s1-=mash(seed),me.s1<0&&(me.s1+=1),me.s2-=mash(seed),me.s2<0&&(me.s2+=1),mash=null}function copy(f,t){return t.c=f.c,t.s0=f.s0,t.s1=f.s1,t.s2=f.s2,t}function impl(seed,opts){var xg=new Alea(seed),state=opts&&opts.state,prng=xg.next;return prng.int32=function(){return xg.next()*4294967296|0},prng.double=function(){return prng()+(prng()*2097152|0)*11102230246251565e-32},prng.quick=prng,state&&(typeof state=="object"&©(state,xg),prng.state=function(){return copy(xg,{})}),prng}function Mash(){var n=4022871197,mash=function(data2){data2=data2.toString();for(var i=0;i>>0,h-=n,h*=n,n=h>>>0,h-=n,n+=h*4294967296}return(n>>>0)*23283064365386963e-26};return mash}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.alea=impl})(exports,typeof module=="object"&&module,typeof define=="function"&&define)}),require_xor1282=__commonJS2((exports,module)=>{(function(global2,module2,define2){function XorGen(seed){var me=this,strseed="";me.x=0,me.y=0,me.z=0,me.w=0,me.next=function(){var t=me.x^me.x<<11;return me.x=me.y,me.y=me.z,me.z=me.w,me.w^=me.w>>>19^t^t>>>8},seed===(seed|0)?me.x=seed:strseed+=seed;for(var k=0;k>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state&&(typeof state=="object"&©(state,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.xor128=impl})(exports,typeof module=="object"&&module,typeof define=="function"&&define)}),require_xorwow2=__commonJS2((exports,module)=>{(function(global2,module2,define2){function XorGen(seed){var me=this,strseed="";me.next=function(){var t=me.x^me.x>>>2;return me.x=me.y,me.y=me.z,me.z=me.w,me.w=me.v,(me.d=me.d+362437|0)+(me.v=me.v^me.v<<4^(t^t<<1))|0},me.x=0,me.y=0,me.z=0,me.w=0,me.v=0,seed===(seed|0)?me.x=seed:strseed+=seed;for(var k=0;k>>4),me.next()}function copy(f,t){return t.x=f.x,t.y=f.y,t.z=f.z,t.w=f.w,t.v=f.v,t.d=f.d,t}function impl(seed,opts){var xg=new XorGen(seed),state=opts&&opts.state,prng=function(){return(xg.next()>>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state&&(typeof state=="object"&©(state,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.xorwow=impl})(exports,typeof module=="object"&&module,typeof define=="function"&&define)}),require_xorshift72=__commonJS2((exports,module)=>{(function(global2,module2,define2){function XorGen(seed){var me=this;me.next=function(){var X=me.x,i=me.i,t,v,w;return t=X[i],t^=t>>>7,v=t^t<<24,t=X[i+1&7],v^=t^t>>>10,t=X[i+3&7],v^=t^t>>>3,t=X[i+4&7],v^=t^t<<7,t=X[i+7&7],t=t^t<<13,v^=t^t<<9,X[i]=v,me.i=i+1&7,v};function init2(me2,seed2){var j,w,X=[];if(seed2===(seed2|0))w=X[0]=seed2;else for(seed2=""+seed2,j=0;j0;--j)me2.next()}init2(me,seed)}function copy(f,t){return t.x=f.x.slice(),t.i=f.i,t}function impl(seed,opts){seed==null&&(seed=+new Date);var xg=new XorGen(seed),state=opts&&opts.state,prng=function(){return(xg.next()>>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state&&(state.x&©(state,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.xorshift7=impl})(exports,typeof module=="object"&&module,typeof define=="function"&&define)}),require_xor40962=__commonJS2((exports,module)=>{(function(global2,module2,define2){function XorGen(seed){var me=this;me.next=function(){var w=me.w,X=me.X,i=me.i,t,v;return me.w=w=w+1640531527|0,v=X[i+34&127],t=X[i=i+1&127],v^=v<<13,t^=t<<17,v^=v>>>15,t^=t>>>12,v=X[i]=v^t,me.i=i,v+(w^w>>>16)|0};function init2(me2,seed2){var t,v,i,j,w,X=[],limit=128;for(seed2===(seed2|0)?(v=seed2,seed2=null):(seed2=seed2+"\0",v=0,limit=Math.max(limit,seed2.length)),i=0,j=-32;j>>15,v^=v<<4,v^=v>>>13,j>=0&&(w=w+1640531527|0,t=X[j&127]^=v+w,i=t==0?i+1:0);for(i>=128&&(X[(seed2&&seed2.length||0)&127]=-1),i=127,j=4*128;j>0;--j)v=X[i+34&127],t=X[i=i+1&127],v^=v<<13,t^=t<<17,v^=v>>>15,t^=t>>>12,X[i]=v^t;me2.w=w,me2.X=X,me2.i=i}init2(me,seed)}function copy(f,t){return t.i=f.i,t.w=f.w,t.X=f.X.slice(),t}function impl(seed,opts){seed==null&&(seed=+new Date);var xg=new XorGen(seed),state=opts&&opts.state,prng=function(){return(xg.next()>>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state&&(state.X&©(state,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.xor4096=impl})(exports,typeof module=="object"&&module,typeof define=="function"&&define)}),require_tychei2=__commonJS2((exports,module)=>{(function(global2,module2,define2){function XorGen(seed){var me=this,strseed="";me.next=function(){var b=me.b,c=me.c,d=me.d,a=me.a;return b=b<<25^b>>>7^c,c=c-d|0,d=d<<24^d>>>8^a,a=a-b|0,me.b=b=b<<20^b>>>12^c,me.c=c=c-d|0,me.d=d<<16^c>>>16^a,me.a=a-b|0},me.a=0,me.b=0,me.c=2654435769|0,me.d=1367130551,seed===Math.floor(seed)?(me.a=seed/4294967296|0,me.b=seed|0):strseed+=seed;for(var k=0;k>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state&&(typeof state=="object"&©(state,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.tychei=impl})(exports,typeof module=="object"&&module,typeof define=="function"&&define)}),require_seedrandom3=__commonJS2((exports,module)=>{(function(pool3,math){var global2=this,width=256,chunks=6,digits=52,rngname="random",startdenom=math.pow(width,chunks),significance=math.pow(2,digits),overflow=significance*2,mask=width-1,nodecrypto;function seedrandom5(seed,options,callback){var key=[];options=options==!0?{entropy:!0}:options||{};var shortseed=mixkey(flatten4(options.entropy?[seed,tostring(pool3)]:seed==null?autoseed():seed,3),key),arc4=new ARC4(key),prng=function(){for(var n=arc4.g(chunks),d=startdenom,x=0;n=overflow;)n/=2,d/=2,x>>>=1;return(n+x)/d};return prng.int32=function(){return arc4.g(4)|0},prng.quick=function(){return arc4.g(4)/4294967296},prng.double=prng,mixkey(tostring(arc4.S),pool3),(options.pass||callback||function(prng2,seed2,is_math_call,state){return state&&(state.S&©(state,arc4),prng2.state=function(){return copy(arc4,{})}),is_math_call?(math[rngname]=prng2,seed2):prng2})(prng,shortseed,"global"in options?options.global:this==math,options.state)}math["seed"+rngname]=seedrandom5;function ARC4(key){var t,keylen=key.length,me=this,i=0,j=me.i=me.j=0,s=me.S=[];for(keylen||(key=[keylen++]);i{var alea5=require_alea2(),xor128=require_xor1282(),xorwow=require_xorwow2(),xorshift7=require_xorshift72(),xor4096=require_xor40962(),tychei=require_tychei2(),sr=require_seedrandom3();sr.alea=alea5,sr.xor128=xor128,sr.xorwow=xorwow,sr.xorshift7=xorshift7,sr.xor4096=xor4096,sr.tychei=tychei,module.exports=sr}),require_path=__commonJS2(()=>{}),require_worker_threads=__commonJS2(()=>{}),require_perf_hooks=__commonJS2(()=>{}),require_tfjs_backend_wasm_threaded_simd=__commonJS2((exports,module)=>{var WasmBackendModuleThreadedSimd=function(){var _scriptDir=typeof document!="undefined"&&document.currentScript?document.currentScript.src:void 0;return typeof __filename!="undefined"&&(_scriptDir=_scriptDir||__filename),function(WasmBackendModuleThreadedSimd2){WasmBackendModuleThreadedSimd2=WasmBackendModuleThreadedSimd2||{};function GROWABLE_HEAP_I8(){return wasmMemory.buffer!=buffer2&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAP8}function GROWABLE_HEAP_U8(){return wasmMemory.buffer!=buffer2&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAPU8}function GROWABLE_HEAP_I32(){return wasmMemory.buffer!=buffer2&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAP32}function GROWABLE_HEAP_U32(){return wasmMemory.buffer!=buffer2&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAPU32}function GROWABLE_HEAP_F64(){return wasmMemory.buffer!=buffer2&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAPF64}var Module=typeof WasmBackendModuleThreadedSimd2!="undefined"?WasmBackendModuleThreadedSimd2:{},_scriptDir2=void 0,moduleOverrides={},key;for(key in Module)Module.hasOwnProperty(key)&&(moduleOverrides[key]=Module[key]);var arguments_=[],thisProgram="./this.program",quit_=function(status2,toThrow){throw toThrow},ENVIRONMENT_IS_WEB=!1,ENVIRONMENT_IS_WORKER=!1,ENVIRONMENT_IS_NODE=!1,ENVIRONMENT_IS_SHELL=!1;ENVIRONMENT_IS_WEB=typeof window=="object",ENVIRONMENT_IS_WORKER=typeof importScripts=="function",ENVIRONMENT_IS_NODE=typeof process=="object"&&typeof process.versions=="object"&&typeof process.versions.node=="string",ENVIRONMENT_IS_SHELL=!ENVIRONMENT_IS_WEB&&!ENVIRONMENT_IS_NODE&&!ENVIRONMENT_IS_WORKER;var ENVIRONMENT_IS_PTHREAD=Module.ENVIRONMENT_IS_PTHREAD||!1;ENVIRONMENT_IS_PTHREAD&&(buffer2=Module.buffer,DYNAMIC_BASE=Module.DYNAMIC_BASE,DYNAMICTOP_PTR=Module.DYNAMICTOP_PTR);var scriptDirectory="";function locateFile(path){return Module.locateFile?Module.locateFile(path,scriptDirectory):scriptDirectory+path}var read_,readAsync,readBinary,setWindowTitle,nodeFS,nodePath;if(ENVIRONMENT_IS_NODE){ENVIRONMENT_IS_WORKER?scriptDirectory=require_path().dirname(scriptDirectory)+"/":scriptDirectory=__dirname+"/",read_=function(filename,binary){return nodeFS||(nodeFS=require("fs")),nodePath||(nodePath=require_path()),filename=nodePath.normalize(filename),nodeFS.readFileSync(filename,binary?null:"utf8")},readBinary=function(filename){var ret=read_(filename,!0);return ret.buffer||(ret=new Uint8Array(ret)),assert3(ret.buffer),ret},process.argv.length>1&&(thisProgram=process.argv[1].replace(/\\/g,"/")),arguments_=process.argv.slice(2),process.on("uncaughtException",function(ex){if(!(ex instanceof ExitStatus))throw ex}),process.on("unhandledRejection",abort),quit_=function(status2){process.exit(status2)},Module.inspect=function(){return"[Emscripten Module object]"};var nodeWorkerThreads;try{nodeWorkerThreads=require_worker_threads()}catch(e){throw console.error('The "worker_threads" module is not supported in this node.js build - perhaps a newer version is needed?'),e}Worker=nodeWorkerThreads.Worker}else ENVIRONMENT_IS_SHELL?(typeof read!="undefined"&&(read_=function(f){return read(f)}),readBinary=function(f){var data2;return typeof readbuffer=="function"?new Uint8Array(readbuffer(f)):(data2=read(f,"binary"),assert3(typeof data2=="object"),data2)},typeof scriptArgs!="undefined"?arguments_=scriptArgs:typeof arguments!="undefined"&&(arguments_=arguments),typeof quit=="function"&&(quit_=function(status2){quit(status2)}),typeof print!="undefined"&&(typeof console=="undefined"&&(console={}),console.log=print,console.warn=console.error=typeof printErr!="undefined"?printErr:print)):(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&(ENVIRONMENT_IS_WORKER?scriptDirectory=self.location.href:document.currentScript&&(scriptDirectory=document.currentScript.src),_scriptDir2&&(scriptDirectory=_scriptDir2),scriptDirectory.indexOf("blob:")!==0?scriptDirectory=scriptDirectory.substr(0,scriptDirectory.lastIndexOf("/")+1):scriptDirectory="",ENVIRONMENT_IS_NODE?(read_=function(filename,binary){return nodeFS||(nodeFS=require("fs")),nodePath||(nodePath=require_path()),filename=nodePath.normalize(filename),nodeFS.readFileSync(filename,binary?null:"utf8")},readBinary=function(filename){var ret=read_(filename,!0);return ret.buffer||(ret=new Uint8Array(ret)),assert3(ret.buffer),ret}):(read_=function(url){var xhr=new XMLHttpRequest;return xhr.open("GET",url,!1),xhr.send(null),xhr.responseText},ENVIRONMENT_IS_WORKER&&(readBinary=function(url){var xhr=new XMLHttpRequest;return xhr.open("GET",url,!1),xhr.responseType="arraybuffer",xhr.send(null),new Uint8Array(xhr.response)}),readAsync=function(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open("GET",url,!0),xhr.responseType="arraybuffer",xhr.onload=function(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()},xhr.onerror=onerror,xhr.send(null)}),setWindowTitle=function(title){document.title=title});ENVIRONMENT_IS_NODE&&typeof performance=="undefined"&&(performance=require_perf_hooks().performance);var out=Module.print||console.log.bind(console),err=Module.printErr||console.warn.bind(console);for(key in moduleOverrides)moduleOverrides.hasOwnProperty(key)&&(Module[key]=moduleOverrides[key]);moduleOverrides=null,Module.arguments&&(arguments_=Module.arguments),Module.thisProgram&&(thisProgram=Module.thisProgram),Module.quit&&(quit_=Module.quit);var Atomics_load=Atomics.load,Atomics_store=Atomics.store,Atomics_compareExchange=Atomics.compareExchange,wasmBinary;Module.wasmBinary&&(wasmBinary=Module.wasmBinary);var noExitRuntime;Module.noExitRuntime&&(noExitRuntime=Module.noExitRuntime),typeof WebAssembly!="object"&&err("no native wasm support detected");var wasmMemory,wasmTable=new WebAssembly.Table({initial:169,maximum:169+0,element:"anyfunc"}),wasmModule,threadInfoStruct=0,selfThreadId=0,ABORT=!1,EXITSTATUS=0;function assert3(condition,text){condition||abort("Assertion failed: "+text)}function getCFunc(ident){var func2=Module["_"+ident];return assert3(func2,"Cannot call unknown function "+ident+", make sure it is exported"),func2}function ccall(ident,returnType,argTypes,args,opts){var toC={string:function(str2){var ret2=0;if(str2!=null&&str2!==0){var len=(str2.length<<2)+1;ret2=stackAlloc(len),stringToUTF8(str2,ret2,len)}return ret2},array:function(arr){var ret2=stackAlloc(arr.length);return writeArrayToMemory(arr,ret2),ret2}};function convertReturnValue(ret2){return returnType==="string"?UTF8ToString(ret2):returnType==="boolean"?Boolean(ret2):ret2}var func2=getCFunc(ident),cArgs=[],stack2=0;if(args)for(var i=0;i=endIdx);){var u0=heap[idx++];if(!u0)return str2;if(!(u0&128)){str2+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str2+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224?u0=(u0&15)<<12|u1<<6|u2:u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63,u0<65536)str2+=String.fromCharCode(u0);else{var ch=u0-65536;str2+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}return str2}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(GROWABLE_HEAP_U8(),ptr,maxBytesToRead):""}function stringToUTF8Array(str2,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;for(var startIdx=outIdx,endIdx=outIdx+maxBytesToWrite-1,i=0;i=55296&&u<=57343){var u1=str2.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6,heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12,heap[outIdx++]=128|u>>6&63,heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18,heap[outIdx++]=128|u>>12&63,heap[outIdx++]=128|u>>6&63,heap[outIdx++]=128|u&63}}return heap[outIdx]=0,outIdx-startIdx}function stringToUTF8(str2,outPtr,maxBytesToWrite){return stringToUTF8Array(str2,GROWABLE_HEAP_U8(),outPtr,maxBytesToWrite)}function lengthBytesUTF8(str2){for(var len=0,i=0;i=55296&&u<=57343&&(u=65536+((u&1023)<<10)|str2.charCodeAt(++i)&1023),u<=127?++len:u<=2047?len+=2:u<=65535?len+=3:len+=4}return len}function writeArrayToMemory(array2,buffer3){GROWABLE_HEAP_I8().set(array2,buffer3)}var WASM_PAGE_SIZE=65536;function alignUp(x,multiple){return x%multiple>0&&(x+=multiple-x%multiple),x}var buffer2,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer2=buf,Module.HEAP8=HEAP8=new Int8Array(buf),Module.HEAP16=HEAP16=new Int16Array(buf),Module.HEAP32=HEAP32=new Int32Array(buf),Module.HEAPU8=HEAPU8=new Uint8Array(buf),Module.HEAPU16=HEAPU16=new Uint16Array(buf),Module.HEAPU32=HEAPU32=new Uint32Array(buf),Module.HEAPF32=HEAPF32=new Float32Array(buf),Module.HEAPF64=HEAPF64=new Float64Array(buf)}var STACK_BASE=5256464,STACKTOP=STACK_BASE,STACK_MAX=13584,DYNAMIC_BASE=5256464,DYNAMICTOP_PTR=12656,INITIAL_INITIAL_MEMORY=Module.INITIAL_MEMORY||16777216;if(ENVIRONMENT_IS_PTHREAD)wasmMemory=Module.wasmMemory,buffer2=Module.buffer;else if(Module.wasmMemory)wasmMemory=Module.wasmMemory;else if(wasmMemory=new WebAssembly.Memory({initial:INITIAL_INITIAL_MEMORY/WASM_PAGE_SIZE,maximum:2147483648/WASM_PAGE_SIZE,shared:!0}),!(wasmMemory.buffer instanceof SharedArrayBuffer))throw err("requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag"),ENVIRONMENT_IS_NODE&&console.log("(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)"),Error("bad memory");wasmMemory&&(buffer2=wasmMemory.buffer),INITIAL_INITIAL_MEMORY=buffer2.byteLength,updateGlobalBufferAndViews(buffer2),ENVIRONMENT_IS_PTHREAD||(GROWABLE_HEAP_I32()[DYNAMICTOP_PTR>>2]=DYNAMIC_BASE);function callRuntimeCallbacks(callbacks2){for(;callbacks2.length>0;){var callback=callbacks2.shift();if(typeof callback=="function"){callback(Module);continue}var func2=callback.func;typeof func2=="number"?callback.arg===void 0?Module.dynCall_v(func2):Module.dynCall_vi(func2,callback.arg):func2(callback.arg===void 0?null:callback.arg)}}var __ATPRERUN__=[],__ATINIT__=[],__ATMAIN__=[],__ATEXIT__=[],__ATPOSTRUN__=[],runtimeInitialized=!1;ENVIRONMENT_IS_PTHREAD&&(runtimeInitialized=!0);function preRun(){if(!ENVIRONMENT_IS_PTHREAD){if(Module.preRun)for(typeof Module.preRun=="function"&&(Module.preRun=[Module.preRun]);Module.preRun.length;)addOnPreRun(Module.preRun.shift());callRuntimeCallbacks(__ATPRERUN__)}}function initRuntime(){runtimeInitialized=!0,callRuntimeCallbacks(__ATINIT__)}function preMain(){ENVIRONMENT_IS_PTHREAD||callRuntimeCallbacks(__ATMAIN__)}function postRun(){if(!ENVIRONMENT_IS_PTHREAD){if(Module.postRun)for(typeof Module.postRun=="function"&&(Module.postRun=[Module.postRun]);Module.postRun.length;)addOnPostRun(Module.postRun.shift());callRuntimeCallbacks(__ATPOSTRUN__)}}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var Math_ceil=Math.ceil,Math_floor=Math.floor,runDependencies=0,runDependencyWatcher=null,dependenciesFulfilled=null;function addRunDependency(id){assert3(!ENVIRONMENT_IS_PTHREAD,"addRunDependency cannot be used in a pthread worker"),runDependencies++,Module.monitorRunDependencies&&Module.monitorRunDependencies(runDependencies)}function removeRunDependency(id){if(runDependencies--,Module.monitorRunDependencies&&Module.monitorRunDependencies(runDependencies),runDependencies==0&&(runDependencyWatcher!==null&&(clearInterval(runDependencyWatcher),runDependencyWatcher=null),dependenciesFulfilled)){var callback=dependenciesFulfilled;dependenciesFulfilled=null,callback()}}Module.preloadedImages={},Module.preloadedAudios={};function abort(what){throw Module.onAbort&&Module.onAbort(what),ENVIRONMENT_IS_PTHREAD&&console.error("Pthread aborting at "+new Error().stack),what+="",out(what),err(what),ABORT=!0,EXITSTATUS=1,what="abort("+what+"). Build with -s ASSERTIONS=1 for more info.",new WebAssembly.RuntimeError(what)}function hasPrefix(str2,prefix){return String.prototype.startsWith?str2.startsWith(prefix):str2.indexOf(prefix)===0}var dataURIPrefix="data:application/octet-stream;base64,";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix="file://";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile="tfjs-backend-wasm-threaded-simd.wasm";isDataURI(wasmBinaryFile)||(wasmBinaryFile=locateFile(wasmBinaryFile));function getBinary(){try{if(wasmBinary)return new Uint8Array(wasmBinary);if(readBinary)return readBinary(wasmBinaryFile);throw"both async and sync fetching of the wasm failed"}catch(err2){abort(err2)}}function getBinaryPromise(){return!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&typeof fetch=="function"&&!isFileURI(wasmBinaryFile)?fetch(wasmBinaryFile,{credentials:"same-origin"}).then(function(response){if(!response.ok)throw"failed to load wasm binary file at '"+wasmBinaryFile+"'";return response.arrayBuffer()}).catch(function(){return getBinary()}):new Promise(function(resolve,reject){resolve(getBinary())})}function createWasm(){var info={a:asmLibraryArg};function receiveInstance(instance2,module2){var exports3=instance2.exports;if(Module.asm=exports3,wasmModule=module2,!ENVIRONMENT_IS_PTHREAD){var numWorkersToLoad=PThread.unusedWorkers.length;PThread.unusedWorkers.forEach(function(w){PThread.loadWasmModuleToWorker(w,function(){--numWorkersToLoad||removeRunDependency("wasm-instantiate")})})}}ENVIRONMENT_IS_PTHREAD||addRunDependency("wasm-instantiate");function receiveInstantiatedSource(output){receiveInstance(output.instance,output.module)}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err("failed to asynchronously prepare wasm: "+reason),abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming=="function"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch=="function")fetch(wasmBinaryFile,{credentials:"same-origin"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err("wasm streaming compile failed: "+reason),err("falling back to ArrayBuffer instantiation"),instantiateArrayBuffer(receiveInstantiatedSource)})});else return instantiateArrayBuffer(receiveInstantiatedSource)}if(Module.instantiateWasm)try{var exports2=Module.instantiateWasm(info,receiveInstance);return exports2}catch(e){return err("Module.instantiateWasm callback failed with error: "+e),!1}return instantiateAsync(),{}}var ASM_CONSTS={};function initPthreadsJS(){PThread.initRuntime()}ENVIRONMENT_IS_PTHREAD||__ATINIT__.push({func:function(){___wasm_call_ctors()}});var __pthread_ptr=0,__pthread_is_main_runtime_thread=0,__pthread_is_main_browser_thread=0;function __register_pthread_ptr(pthreadPtr,isMainBrowserThread,isMainRuntimeThread){pthreadPtr=pthreadPtr|0,isMainBrowserThread=isMainBrowserThread|0,isMainRuntimeThread=isMainRuntimeThread|0,__pthread_ptr=pthreadPtr,__pthread_is_main_browser_thread=isMainBrowserThread,__pthread_is_main_runtime_thread=isMainRuntimeThread}Module.__register_pthread_ptr=__register_pthread_ptr;var ERRNO_CODES={EPERM:63,ENOENT:44,ESRCH:71,EINTR:27,EIO:29,ENXIO:60,E2BIG:1,ENOEXEC:45,EBADF:8,ECHILD:12,EAGAIN:6,EWOULDBLOCK:6,ENOMEM:48,EACCES:2,EFAULT:21,ENOTBLK:105,EBUSY:10,EEXIST:20,EXDEV:75,ENODEV:43,ENOTDIR:54,EISDIR:31,EINVAL:28,ENFILE:41,EMFILE:33,ENOTTY:59,ETXTBSY:74,EFBIG:22,ENOSPC:51,ESPIPE:70,EROFS:69,EMLINK:34,EPIPE:64,EDOM:18,ERANGE:68,ENOMSG:49,EIDRM:24,ECHRNG:106,EL2NSYNC:156,EL3HLT:107,EL3RST:108,ELNRNG:109,EUNATCH:110,ENOCSI:111,EL2HLT:112,EDEADLK:16,ENOLCK:46,EBADE:113,EBADR:114,EXFULL:115,ENOANO:104,EBADRQC:103,EBADSLT:102,EDEADLOCK:16,EBFONT:101,ENOSTR:100,ENODATA:116,ETIME:117,ENOSR:118,ENONET:119,ENOPKG:120,EREMOTE:121,ENOLINK:47,EADV:122,ESRMNT:123,ECOMM:124,EPROTO:65,EMULTIHOP:36,EDOTDOT:125,EBADMSG:9,ENOTUNIQ:126,EBADFD:127,EREMCHG:128,ELIBACC:129,ELIBBAD:130,ELIBSCN:131,ELIBMAX:132,ELIBEXEC:133,ENOSYS:52,ENOTEMPTY:55,ENAMETOOLONG:37,ELOOP:32,EOPNOTSUPP:138,EPFNOSUPPORT:139,ECONNRESET:15,ENOBUFS:42,EAFNOSUPPORT:5,EPROTOTYPE:67,ENOTSOCK:57,ENOPROTOOPT:50,ESHUTDOWN:140,ECONNREFUSED:14,EADDRINUSE:3,ECONNABORTED:13,ENETUNREACH:40,ENETDOWN:38,ETIMEDOUT:73,EHOSTDOWN:142,EHOSTUNREACH:23,EINPROGRESS:26,EALREADY:7,EDESTADDRREQ:17,EMSGSIZE:35,EPROTONOSUPPORT:66,ESOCKTNOSUPPORT:137,EADDRNOTAVAIL:4,ENETRESET:39,EISCONN:30,ENOTCONN:53,ETOOMANYREFS:141,EUSERS:136,EDQUOT:19,ESTALE:72,ENOTSUP:138,ENOMEDIUM:148,EILSEQ:25,EOVERFLOW:61,ECANCELED:11,ENOTRECOVERABLE:56,EOWNERDEAD:62,ESTRPIPE:135},__main_thread_futex_wait_address=13568;function _emscripten_futex_wake(addr,count2){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&!0||count2<0)return-28;if(count2==0)return 0;count2>=2147483647&&(count2=Infinity);var mainThreadWaitAddress=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2),mainThreadWoken=0;if(mainThreadWaitAddress==addr){var loadedAddr=Atomics.compareExchange(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,mainThreadWaitAddress,0);if(loadedAddr==mainThreadWaitAddress&&(--count2,mainThreadWoken=1,count2<=0))return 1}var ret=Atomics.notify(GROWABLE_HEAP_I32(),addr>>2,count2);if(ret>=0)return ret+mainThreadWoken;throw"Atomics.notify returned an unexpected value "+ret}Module._emscripten_futex_wake=_emscripten_futex_wake;function __kill_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw"Internal Error! _kill_thread() can only ever be called from main application thread!";if(!pthread_ptr)throw"Internal Error! Null pthread_ptr in _kill_thread!";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];pthread.worker.terminate(),PThread.freeThreadData(pthread),PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(pthread.worker),1),pthread.worker.pthread=void 0}function __cancel_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw"Internal Error! _cancel_thread() can only ever be called from main application thread!";if(!pthread_ptr)throw"Internal Error! Null pthread_ptr in _cancel_thread!";var pthread=PThread.pthreads[pthread_ptr];pthread.worker.postMessage({cmd:"cancel"})}function __cleanup_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw"Internal Error! _cleanup_thread() can only ever be called from main application thread!";if(!pthread_ptr)throw"Internal Error! Null pthread_ptr in _cleanup_thread!";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];if(pthread){var worker2=pthread.worker;PThread.returnWorkerToPool(worker2)}}var PThread={MAIN_THREAD_ID:1,mainThreadInfo:{schedPolicy:0,schedPrio:0},unusedWorkers:[],runningWorkers:[],initRuntime:function(){__register_pthread_ptr(PThread.mainThreadBlock,!ENVIRONMENT_IS_WORKER,1),_emscripten_register_main_browser_thread_id(PThread.mainThreadBlock)},initMainThreadBlock:function(){for(var pthreadPoolSize=8,i=0;i>2]=PThread.mainThreadBlock;var headPtr=PThread.mainThreadBlock+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;for(var tlsMemory=13056,i=0;i<128;++i)GROWABLE_HEAP_U32()[tlsMemory/4+i]=0;Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+104>>2,tlsMemory),Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+40>>2,PThread.mainThreadBlock),Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+44>>2,42)},initWorker:function(){},pthreads:{},exitHandlers:null,setThreadStatus:function(){},runExitHandlers:function(){if(PThread.exitHandlers!==null){for(;PThread.exitHandlers.length>0;)PThread.exitHandlers.pop()();PThread.exitHandlers=null}ENVIRONMENT_IS_PTHREAD&&threadInfoStruct&&___pthread_tsd_run_dtors()},threadExit:function(exitCode){var tb=_pthread_self();tb&&(Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,exitCode),Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1),Atomics.store(GROWABLE_HEAP_U32(),tb+60>>2,1),Atomics.store(GROWABLE_HEAP_U32(),tb+64>>2,0),PThread.runExitHandlers(),_emscripten_futex_wake(tb+0,2147483647),__register_pthread_ptr(0,0,0),threadInfoStruct=0,ENVIRONMENT_IS_PTHREAD&&postMessage({cmd:"exit"}))},threadCancel:function(){PThread.runExitHandlers(),Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+4>>2,-1),Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+0>>2,1),_emscripten_futex_wake(threadInfoStruct+0,2147483647),threadInfoStruct=selfThreadId=0,__register_pthread_ptr(0,0,0),postMessage({cmd:"cancelDone"})},terminateAllThreads:function(){for(var t in PThread.pthreads){var pthread=PThread.pthreads[t];pthread&&pthread.worker&&PThread.returnWorkerToPool(pthread.worker)}PThread.pthreads={};for(var i=0;i>2];GROWABLE_HEAP_I32()[pthread.threadInfoStruct+104>>2]=0,_free(tlsMemory),_free(pthread.threadInfoStruct)}pthread.threadInfoStruct=0,pthread.allocatedOwnStack&&pthread.stackBase&&_free(pthread.stackBase),pthread.stackBase=0,pthread.worker&&(pthread.worker.pthread=null)}},returnWorkerToPool:function(worker2){delete PThread.pthreads[worker2.pthread.thread],PThread.unusedWorkers.push(worker2),PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker2),1),PThread.freeThreadData(worker2.pthread),worker2.pthread=void 0},receiveObjectTransfer:function(data2){},loadWasmModuleToWorker:function(worker2,onFinishedLoading){worker2.onmessage=function(e){var d=e.data,cmd=d.cmd;if(worker2.pthread&&(PThread.currentProxiedOperationCallerThread=worker2.pthread.threadInfoStruct),d.targetThread&&d.targetThread!=_pthread_self()){var thread=PThread.pthreads[d.targetThread];thread?thread.worker.postMessage(e.data,d.transferList):console.error('Internal error! Worker sent a message "'+cmd+'" to target pthread '+d.targetThread+", but that thread no longer exists!"),PThread.currentProxiedOperationCallerThread=void 0;return}if(cmd==="processQueuedMainThreadWork")_emscripten_main_thread_process_queued_calls();else if(cmd==="spawnThread")__spawn_thread(e.data);else if(cmd==="cleanupThread")__cleanup_thread(d.thread);else if(cmd==="killThread")__kill_thread(d.thread);else if(cmd==="cancelThread")__cancel_thread(d.thread);else if(cmd==="loaded")worker2.loaded=!0,onFinishedLoading&&onFinishedLoading(worker2),worker2.runPthread&&(worker2.runPthread(),delete worker2.runPthread);else if(cmd==="print")out("Thread "+d.threadId+": "+d.text);else if(cmd==="printErr")err("Thread "+d.threadId+": "+d.text);else if(cmd==="alert")alert("Thread "+d.threadId+": "+d.text);else if(cmd==="exit"){var detached=worker2.pthread&&Atomics.load(GROWABLE_HEAP_U32(),worker2.pthread.thread+68>>2);detached&&PThread.returnWorkerToPool(worker2)}else cmd==="cancelDone"?PThread.returnWorkerToPool(worker2):cmd==="objectTransfer"?PThread.receiveObjectTransfer(e.data):e.data.target==="setimmediate"?worker2.postMessage(e.data):err("worker sent an unknown command "+cmd);PThread.currentProxiedOperationCallerThread=void 0},worker2.onerror=function(e){err("pthread sent an error! "+e.filename+":"+e.lineno+": "+e.message)},ENVIRONMENT_IS_NODE&&(worker2.on("message",function(data2){worker2.onmessage({data:data2})}),worker2.on("error",function(data2){worker2.onerror(data2)}),worker2.on("exit",function(data2){console.log("worker exited - TODO: update the worker queue?")})),worker2.postMessage({cmd:"load",urlOrBlob:Module.mainScriptUrlOrBlob||_scriptDir2,wasmMemory,wasmModule,DYNAMIC_BASE,DYNAMICTOP_PTR})},allocateUnusedWorker:function(){var pthreadMainJs=locateFile("tfjs-backend-wasm-threaded-simd.worker.js");PThread.unusedWorkers.push(new Worker(pthreadMainJs))},getNewWorker:function(){return PThread.unusedWorkers.length==0&&(PThread.allocateUnusedWorker(),PThread.loadWasmModuleToWorker(PThread.unusedWorkers[0])),PThread.unusedWorkers.length>0?PThread.unusedWorkers.pop():null},busySpinWait:function(msecs){for(var t=performance.now()+msecs;performance.now()>2]=value,value}function _atexit(func2,arg){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(1,1,func2,arg);__ATEXIT__.unshift({func:func2,arg})}function __emscripten_notify_thread_queue(targetThreadId,mainThreadId){if(targetThreadId==mainThreadId)postMessage({cmd:"processQueuedMainThreadWork"});else if(ENVIRONMENT_IS_PTHREAD)postMessage({targetThread:targetThreadId,cmd:"processThreadQueue"});else{var pthread=PThread.pthreads[targetThreadId],worker2=pthread&&pthread.worker;if(!worker2)return;worker2.postMessage({cmd:"processThreadQueue"})}return 1}function _abort(){abort()}function _emscripten_conditional_set_current_thread_status(expectedStatus,newStatus){expectedStatus=expectedStatus|0,newStatus=newStatus|0}function _emscripten_futex_wait(addr,val,timeout){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&!0)return-28;if(ENVIRONMENT_IS_WORKER){var ret=Atomics.wait(GROWABLE_HEAP_I32(),addr>>2,val,timeout);if(ret==="timed-out")return-73;if(ret==="not-equal")return-6;if(ret==="ok")return 0;throw"Atomics.wait returned an unexpected value "+ret}else{var loadedVal=Atomics.load(GROWABLE_HEAP_I32(),addr>>2);if(val!=loadedVal)return-6;var tNow=performance.now(),tEnd=tNow+timeout;Atomics.store(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,addr);for(var ourWaitAddress=addr;addr==ourWaitAddress;){if(tNow=performance.now(),tNow>tEnd)return-73;_emscripten_main_thread_process_queued_calls(),addr=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2)}return 0}}function _emscripten_is_main_browser_thread(){return __pthread_is_main_browser_thread|0}function _emscripten_is_main_runtime_thread(){return __pthread_is_main_runtime_thread|0}function _emscripten_memcpy_big(dest,src,num){GROWABLE_HEAP_U8().copyWithin(dest,src,src+num)}function _emscripten_num_logical_cores(){return navigator.hardwareConcurrency}function _emscripten_proxy_to_main_thread_js(index,sync){for(var numCallArgs=arguments.length-2,stack2=stackSave(),args=stackAlloc(numCallArgs*8),b=args>>3,i=0;i>3]),buf+=8):(buf=buf+3&~3,args.push(GROWABLE_HEAP_I32()[buf>>2]),buf+=4);return args}function _emscripten_receive_on_main_thread_js(index,numCallArgs,args){_emscripten_receive_on_main_thread_js_callArgs.length=numCallArgs;for(var b=args>>3,i=0;i>>16),updateGlobalBufferAndViews(wasmMemory.buffer),1}catch(e){}}function _emscripten_resize_heap(requestedSize){requestedSize=requestedSize>>>0;var oldSize=_emscripten_get_heap_size();if(requestedSize<=oldSize)return!1;var PAGE_MULTIPLE=65536,maxHeapSize=2147483648;if(requestedSize>maxHeapSize)return!1;for(var minHeapSize=16777216,cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(minHeapSize,requestedSize,overGrownHeapSize),PAGE_MULTIPLE)),replacement=emscripten_realloc_buffer(newSize);if(replacement)return!0}return!1}var JSEvents={keyEvent:0,mouseEvent:0,wheelEvent:0,uiEvent:0,focusEvent:0,deviceOrientationEvent:0,deviceMotionEvent:0,fullscreenChangeEvent:0,pointerlockChangeEvent:0,visibilityChangeEvent:0,touchEvent:0,previousFullscreenElement:null,previousScreenX:null,previousScreenY:null,removeEventListenersRegistered:!1,removeAllEventListeners:function(){for(var i=JSEvents.eventHandlers.length-1;i>=0;--i)JSEvents._removeHandler(i);JSEvents.eventHandlers=[],JSEvents.deferredCalls=[]},registerRemoveEventListeners:function(){JSEvents.removeEventListenersRegistered||(__ATEXIT__.push(JSEvents.removeAllEventListeners),JSEvents.removeEventListenersRegistered=!0)},deferredCalls:[],deferCall:function(targetFunction,precedence,argsList){function arraysHaveEqualContent(arrA,arrB){if(arrA.length!=arrB.length)return!1;for(var i2 in arrA)if(arrA[i2]!=arrB[i2])return!1;return!0}for(var i in JSEvents.deferredCalls){var call=JSEvents.deferredCalls[i];if(call.targetFunction==targetFunction&&arraysHaveEqualContent(call.argsList,argsList))return}JSEvents.deferredCalls.push({targetFunction,precedence,argsList}),JSEvents.deferredCalls.sort(function(x,y){return x.precedence>2]=eventTypeId,GROWABLE_HEAP_I32()[varargs+4>>2]=eventData,GROWABLE_HEAP_I32()[varargs+8>>2]=userData,_emscripten_async_queue_on_thread_(targetThread,637534208,eventHandlerFunc,eventData,varargs),stackRestore(stackTop)},getTargetThreadForEventCallback:function(targetThread){switch(targetThread){case 1:return 0;case 2:return PThread.currentProxiedOperationCallerThread;default:return targetThread}},getNodeNameForTarget:function(target){return target?target==window?"#window":target==screen?"#screen":target&&target.nodeName?target.nodeName:"":""},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function stringToNewUTF8(jsString){var length=lengthBytesUTF8(jsString)+1,cString=_malloc(length);return stringToUTF8(jsString,cString,length),cString}function _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height){var stackTop=stackSave(),varargs=stackAlloc(12),targetCanvasPtr=0;targetCanvas&&(targetCanvasPtr=stringToNewUTF8(targetCanvas)),GROWABLE_HEAP_I32()[varargs>>2]=targetCanvasPtr,GROWABLE_HEAP_I32()[varargs+4>>2]=width,GROWABLE_HEAP_I32()[varargs+8>>2]=height,_emscripten_async_queue_on_thread_(targetThread,657457152,0,targetCanvasPtr,varargs),stackRestore(stackTop)}function _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,targetCanvas,width,height){targetCanvas=targetCanvas?UTF8ToString(targetCanvas):"",_emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height)}function __maybeCStringToJsString(cString){return cString>2?UTF8ToString(cString):cString}var specialHTMLTargets=[0,typeof document!="undefined"?document:0,typeof window!="undefined"?window:0];function __findEventTarget(target){target=__maybeCStringToJsString(target);var domElement=specialHTMLTargets[target]||(typeof document!="undefined"?document.querySelector(target):void 0);return domElement}function __findCanvasEventTarget(target){return __findEventTarget(target)}function _emscripten_set_canvas_element_size_calling_thread(target,width,height){var canvas=__findCanvasEventTarget(target);if(!canvas)return-4;if(canvas.canvasSharedPtr&&(GROWABLE_HEAP_I32()[canvas.canvasSharedPtr>>2]=width,GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+4>>2]=height),canvas.offscreenCanvas||!canvas.controlTransferredOffscreen){canvas.offscreenCanvas&&(canvas=canvas.offscreenCanvas);var autoResizeViewport=!1;if(canvas.GLctxObject&&canvas.GLctxObject.GLctx){var prevViewport=canvas.GLctxObject.GLctx.getParameter(2978);autoResizeViewport=prevViewport[0]===0&&prevViewport[1]===0&&prevViewport[2]===canvas.width&&prevViewport[3]===canvas.height}canvas.width=width,canvas.height=height,autoResizeViewport&&canvas.GLctxObject.GLctx.viewport(0,0,width,height)}else if(canvas.canvasSharedPtr){var targetThread=GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+8>>2];return _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,target,width,height),1}else return-4;return 0}function _emscripten_set_canvas_element_size_main_thread(target,width,height){return ENVIRONMENT_IS_PTHREAD?_emscripten_proxy_to_main_thread_js(2,1,target,width,height):_emscripten_set_canvas_element_size_calling_thread(target,width,height)}function _emscripten_set_canvas_element_size(target,width,height){var canvas=__findCanvasEventTarget(target);return canvas?_emscripten_set_canvas_element_size_calling_thread(target,width,height):_emscripten_set_canvas_element_size_main_thread(target,width,height)}function _emscripten_set_current_thread_status(newStatus){newStatus=newStatus|0}function _emscripten_set_thread_name(threadId,name){threadId=threadId|0,name=name|0}function __webgl_enable_ANGLE_instanced_arrays(ctx){var ext=ctx.getExtension("ANGLE_instanced_arrays");if(ext)return ctx.vertexAttribDivisor=function(index,divisor){ext.vertexAttribDivisorANGLE(index,divisor)},ctx.drawArraysInstanced=function(mode,first,count2,primcount){ext.drawArraysInstancedANGLE(mode,first,count2,primcount)},ctx.drawElementsInstanced=function(mode,count2,type,indices,primcount){ext.drawElementsInstancedANGLE(mode,count2,type,indices,primcount)},1}function __webgl_enable_OES_vertex_array_object(ctx){var ext=ctx.getExtension("OES_vertex_array_object");if(ext)return ctx.createVertexArray=function(){return ext.createVertexArrayOES()},ctx.deleteVertexArray=function(vao){ext.deleteVertexArrayOES(vao)},ctx.bindVertexArray=function(vao){ext.bindVertexArrayOES(vao)},ctx.isVertexArray=function(vao){return ext.isVertexArrayOES(vao)},1}function __webgl_enable_WEBGL_draw_buffers(ctx){var ext=ctx.getExtension("WEBGL_draw_buffers");if(ext)return ctx.drawBuffers=function(n,bufs){ext.drawBuffersWEBGL(n,bufs)},1}var GL={counter:1,lastError:0,buffers:[],mappedBuffers:{},programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},currentContext:null,offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,init:function(){for(var miniTempFloatBuffer=new Float32Array(GL.MINI_TEMP_BUFFER_SIZE),i=0;i>2]:-1;source+=UTF8ToString(GROWABLE_HEAP_I32()[string+i*4>>2],len<0?void 0:len)}return source},createContext:function(canvas,webGLContextAttributes){var ctx=canvas.getContext("webgl",webGLContextAttributes);if(!ctx)return 0;var handle=GL.registerContext(ctx,webGLContextAttributes);return handle},registerContext:function(ctx,webGLContextAttributes){var handle=_malloc(8);GROWABLE_HEAP_I32()[handle+4>>2]=_pthread_self();var context={handle,attributes:webGLContextAttributes,version:webGLContextAttributes.majorVersion,GLctx:ctx};return ctx.canvas&&(ctx.canvas.GLctxObject=context),GL.contexts[handle]=context,(typeof webGLContextAttributes.enableExtensionsByDefault=="undefined"||webGLContextAttributes.enableExtensionsByDefault)&&GL.initExtensions(context),handle},makeContextCurrent:function(contextHandle){return GL.currentContext=GL.contexts[contextHandle],Module.ctx=GLctx=GL.currentContext&&GL.currentContext.GLctx,!(contextHandle&&!GLctx)},getContext:function(contextHandle){return GL.contexts[contextHandle]},deleteContext:function(contextHandle){GL.currentContext===GL.contexts[contextHandle]&&(GL.currentContext=null),typeof JSEvents=="object"&&JSEvents.removeAllHandlersOnTarget(GL.contexts[contextHandle].GLctx.canvas),GL.contexts[contextHandle]&&GL.contexts[contextHandle].GLctx.canvas&&(GL.contexts[contextHandle].GLctx.canvas.GLctxObject=void 0),_free(GL.contexts[contextHandle].handle),GL.contexts[contextHandle]=null},initExtensions:function(context){if(context||(context=GL.currentContext),!context.initExtensionsDone){context.initExtensionsDone=!0;var GLctx2=context.GLctx;__webgl_enable_ANGLE_instanced_arrays(GLctx2),__webgl_enable_OES_vertex_array_object(GLctx2),__webgl_enable_WEBGL_draw_buffers(GLctx2),GLctx2.disjointTimerQueryExt=GLctx2.getExtension("EXT_disjoint_timer_query");var automaticallyEnabledExtensions=["OES_texture_float","OES_texture_half_float","OES_standard_derivatives","OES_vertex_array_object","WEBGL_compressed_texture_s3tc","WEBGL_depth_texture","OES_element_index_uint","EXT_texture_filter_anisotropic","EXT_frag_depth","WEBGL_draw_buffers","ANGLE_instanced_arrays","OES_texture_float_linear","OES_texture_half_float_linear","EXT_blend_minmax","EXT_shader_texture_lod","EXT_texture_norm16","WEBGL_compressed_texture_pvrtc","EXT_color_buffer_half_float","WEBGL_color_buffer_float","EXT_sRGB","WEBGL_compressed_texture_etc1","EXT_disjoint_timer_query","WEBGL_compressed_texture_etc","WEBGL_compressed_texture_astc","EXT_color_buffer_float","WEBGL_compressed_texture_s3tc_srgb","EXT_disjoint_timer_query_webgl2","WEBKIT_WEBGL_compressed_texture_pvrtc"],exts=GLctx2.getSupportedExtensions()||[];exts.forEach(function(ext){automaticallyEnabledExtensions.indexOf(ext)!=-1&&GLctx2.getExtension(ext)})}},populateUniformTable:function(program){for(var p2=GL.programs[program],ptable=GL.programInfos[program]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1},utable=ptable.uniforms,numUniforms=GLctx.getProgramParameter(p2,35718),i=0;i>2;contextAttributes.alpha=!!GROWABLE_HEAP_I32()[a+(0>>2)],contextAttributes.depth=!!GROWABLE_HEAP_I32()[a+(4>>2)],contextAttributes.stencil=!!GROWABLE_HEAP_I32()[a+(8>>2)],contextAttributes.antialias=!!GROWABLE_HEAP_I32()[a+(12>>2)],contextAttributes.premultipliedAlpha=!!GROWABLE_HEAP_I32()[a+(16>>2)],contextAttributes.preserveDrawingBuffer=!!GROWABLE_HEAP_I32()[a+(20>>2)];var powerPreference=GROWABLE_HEAP_I32()[a+(24>>2)];contextAttributes.powerPreference=__emscripten_webgl_power_preferences[powerPreference],contextAttributes.failIfMajorPerformanceCaveat=!!GROWABLE_HEAP_I32()[a+(28>>2)],contextAttributes.majorVersion=GROWABLE_HEAP_I32()[a+(32>>2)],contextAttributes.minorVersion=GROWABLE_HEAP_I32()[a+(36>>2)],contextAttributes.enableExtensionsByDefault=GROWABLE_HEAP_I32()[a+(40>>2)],contextAttributes.explicitSwapControl=GROWABLE_HEAP_I32()[a+(44>>2)],contextAttributes.proxyContextToMainThread=GROWABLE_HEAP_I32()[a+(48>>2)],contextAttributes.renderViaOffscreenBackBuffer=GROWABLE_HEAP_I32()[a+(52>>2)];var canvas=__findCanvasEventTarget(target);if(!canvas)return-4;if(contextAttributes.explicitSwapControl)return-1;var contextHandle=GL.createContext(canvas,contextAttributes);return contextHandle}function _emscripten_webgl_create_context(a0,a12){return _emscripten_webgl_do_create_context(a0,a12)}var PATH={splitPath:function(filename){var splitPathRe=/^(\/?|)([\s\S]*?)((?:\.{1,2}|[^\/]+?|)(\.[^.\/]*|))(?:[\/]*)$/;return splitPathRe.exec(filename).slice(1)},normalizeArray:function(parts,allowAboveRoot){for(var up=0,i=parts.length-1;i>=0;i--){var last=parts[i];last==="."?parts.splice(i,1):last===".."?(parts.splice(i,1),up++):up&&(parts.splice(i,1),up--)}if(allowAboveRoot)for(;up;up--)parts.unshift("..");return parts},normalize:function(path){var isAbsolute=path.charAt(0)==="/",trailingSlash=path.substr(-1)==="/";return path=PATH.normalizeArray(path.split("/").filter(function(p2){return!!p2}),!isAbsolute).join("/"),!path&&!isAbsolute&&(path="."),path&&trailingSlash&&(path+="/"),(isAbsolute?"/":"")+path},dirname:function(path){var result=PATH.splitPath(path),root=result[0],dir=result[1];return!root&&!dir?".":(dir&&(dir=dir.substr(0,dir.length-1)),root+dir)},basename:function(path){if(path==="/")return"/";var lastSlash=path.lastIndexOf("/");return lastSlash===-1?path:path.substr(lastSlash+1)},extname:function(path){return PATH.splitPath(path)[3]},join:function(){var paths=Array.prototype.slice.call(arguments,0);return PATH.normalize(paths.join("/"))},join2:function(l,r){return PATH.normalize(l+"/"+r)}},SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer3=SYSCALLS.buffers[stream];curr===0||curr===10?((stream===1?out:err)(UTF8ArrayToString(buffer3,0)),buffer3.length=0):buffer3.push(curr)},varargs:void 0,get:function(){SYSCALLS.varargs+=4;var ret=GROWABLE_HEAP_I32()[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){return ENVIRONMENT_IS_PTHREAD?_emscripten_proxy_to_main_thread_js(3,1,fd):0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(4,1,fd,offset_low,offset_high,whence,newOffset)}function _fd_write(fd,iov,iovcnt,pnum){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(5,1,fd,iov,iovcnt,pnum);for(var num=0,i=0;i>2],len=GROWABLE_HEAP_I32()[iov+(i*8+4)>>2],j=0;j>2]=num,0}function _pthread_cleanup_pop(execute2){var routine=PThread.exitHandlers.pop();execute2&&routine()}function _pthread_cleanup_push(routine,arg){PThread.exitHandlers===null&&(PThread.exitHandlers=[]),PThread.exitHandlers.push(function(){dynCall_vi(routine,arg)})}function __spawn_thread(threadParams){if(ENVIRONMENT_IS_PTHREAD)throw"Internal Error! _spawn_thread() can only ever be called from main application thread!";var worker2=PThread.getNewWorker();if(worker2.pthread!==void 0)throw"Internal error!";if(!threadParams.pthread_ptr)throw"Internal error, no pthread ptr!";PThread.runningWorkers.push(worker2);for(var tlsMemory=_malloc(128*4),i=0;i<128;++i)GROWABLE_HEAP_I32()[tlsMemory+i*4>>2]=0;var stackHigh=threadParams.stackBase+threadParams.stackSize,pthread=PThread.pthreads[threadParams.pthread_ptr]={worker:worker2,stackBase:threadParams.stackBase,stackSize:threadParams.stackSize,allocatedOwnStack:threadParams.allocatedOwnStack,thread:threadParams.pthread_ptr,threadInfoStruct:threadParams.pthread_ptr},tis=pthread.threadInfoStruct>>2;Atomics.store(GROWABLE_HEAP_U32(),tis+(0>>2),0),Atomics.store(GROWABLE_HEAP_U32(),tis+(4>>2),0),Atomics.store(GROWABLE_HEAP_U32(),tis+(8>>2),0),Atomics.store(GROWABLE_HEAP_U32(),tis+(68>>2),threadParams.detached),Atomics.store(GROWABLE_HEAP_U32(),tis+(104>>2),tlsMemory),Atomics.store(GROWABLE_HEAP_U32(),tis+(48>>2),0),Atomics.store(GROWABLE_HEAP_U32(),tis+(40>>2),pthread.threadInfoStruct),Atomics.store(GROWABLE_HEAP_U32(),tis+(44>>2),42),Atomics.store(GROWABLE_HEAP_U32(),tis+(108>>2),threadParams.stackSize),Atomics.store(GROWABLE_HEAP_U32(),tis+(84>>2),threadParams.stackSize),Atomics.store(GROWABLE_HEAP_U32(),tis+(80>>2),stackHigh),Atomics.store(GROWABLE_HEAP_U32(),tis+(108+8>>2),stackHigh),Atomics.store(GROWABLE_HEAP_U32(),tis+(108+12>>2),threadParams.detached),Atomics.store(GROWABLE_HEAP_U32(),tis+(108+20>>2),threadParams.schedPolicy),Atomics.store(GROWABLE_HEAP_U32(),tis+(108+24>>2),threadParams.schedPrio);var global_libc=_emscripten_get_global_libc(),global_locale=global_libc+40;Atomics.store(GROWABLE_HEAP_U32(),tis+(176>>2),global_locale),worker2.pthread=pthread;var msg={cmd:"run",start_routine:threadParams.startRoutine,arg:threadParams.arg,threadInfoStruct:threadParams.pthread_ptr,selfThreadId:threadParams.pthread_ptr,parentThreadId:threadParams.parent_pthread_ptr,stackBase:threadParams.stackBase,stackSize:threadParams.stackSize};worker2.runPthread=function(){msg.time=performance.now(),worker2.postMessage(msg,threadParams.transferList)},worker2.loaded&&(worker2.runPthread(),delete worker2.runPthread)}function _pthread_getschedparam(thread,policy,schedparam){if(!policy&&!schedparam)return ERRNO_CODES.EINVAL;if(!thread)return err("pthread_getschedparam called with a null thread pointer!"),ERRNO_CODES.ESRCH;var self2=GROWABLE_HEAP_I32()[thread+12>>2];if(self2!==thread)return err("pthread_getschedparam attempted on thread "+thread+", which does not point to a valid thread, or does not exist anymore!"),ERRNO_CODES.ESRCH;var schedPolicy=Atomics.load(GROWABLE_HEAP_U32(),thread+108+20>>2),schedPrio=Atomics.load(GROWABLE_HEAP_U32(),thread+108+24>>2);return policy&&(GROWABLE_HEAP_I32()[policy>>2]=schedPolicy),schedparam&&(GROWABLE_HEAP_I32()[schedparam>>2]=schedPrio),0}function _pthread_self(){return __pthread_ptr|0}Module._pthread_self=_pthread_self;function _pthread_create(pthread_ptr,attr,start_routine,arg){if(typeof SharedArrayBuffer=="undefined")return err("Current environment does not support SharedArrayBuffer, pthreads are not available!"),6;if(!pthread_ptr)return err("pthread_create called with a null thread pointer!"),28;var transferList=[],error=0;if(ENVIRONMENT_IS_PTHREAD&&(transferList.length===0||error))return _emscripten_sync_run_in_main_thread_4(687865856,pthread_ptr,attr,start_routine,arg);if(error)return error;var stackSize=0,stackBase=0,detached=0,schedPolicy=0,schedPrio=0;if(attr){stackSize=GROWABLE_HEAP_I32()[attr>>2],stackSize+=81920,stackBase=GROWABLE_HEAP_I32()[attr+8>>2],detached=GROWABLE_HEAP_I32()[attr+12>>2]!==0;var inheritSched=GROWABLE_HEAP_I32()[attr+16>>2]===0;if(inheritSched){var prevSchedPolicy=GROWABLE_HEAP_I32()[attr+20>>2],prevSchedPrio=GROWABLE_HEAP_I32()[attr+24>>2],parentThreadPtr=PThread.currentProxiedOperationCallerThread?PThread.currentProxiedOperationCallerThread:_pthread_self();_pthread_getschedparam(parentThreadPtr,attr+20,attr+24),schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2],schedPrio=GROWABLE_HEAP_I32()[attr+24>>2],GROWABLE_HEAP_I32()[attr+20>>2]=prevSchedPolicy,GROWABLE_HEAP_I32()[attr+24>>2]=prevSchedPrio}else schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2],schedPrio=GROWABLE_HEAP_I32()[attr+24>>2]}else stackSize=2097152;var allocatedOwnStack=stackBase==0;allocatedOwnStack?stackBase=_memalign(16,stackSize):(stackBase-=stackSize,assert3(stackBase>0));for(var threadInfoStruct2=_malloc(232),i=0;i<232>>2;++i)GROWABLE_HEAP_U32()[(threadInfoStruct2>>2)+i]=0;GROWABLE_HEAP_I32()[pthread_ptr>>2]=threadInfoStruct2,GROWABLE_HEAP_I32()[threadInfoStruct2+12>>2]=threadInfoStruct2;var headPtr=threadInfoStruct2+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var threadParams={stackBase,stackSize,allocatedOwnStack,schedPolicy,schedPrio,detached,startRoutine:start_routine,pthread_ptr:threadInfoStruct2,parent_pthread_ptr:_pthread_self(),arg,transferList};return ENVIRONMENT_IS_PTHREAD?(threadParams.cmd="spawnThread",postMessage(threadParams,transferList)):__spawn_thread(threadParams),0}function _roundf(d){return d=+d,d>=0?+Math_floor(d+.5):+Math_ceil(d-.5)}function _sysconf(name){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(6,1,name);switch(name){case 30:return 16384;case 85:var maxHeapSize=2147483648;return maxHeapSize/16384;case 132:case 133:case 12:case 137:case 138:case 15:case 235:case 16:case 17:case 18:case 19:case 20:case 149:case 13:case 10:case 236:case 153:case 9:case 21:case 22:case 159:case 154:case 14:case 77:case 78:case 139:case 80:case 81:case 82:case 68:case 67:case 164:case 11:case 29:case 47:case 48:case 95:case 52:case 51:case 46:case 79:return 200809;case 27:case 246:case 127:case 128:case 23:case 24:case 160:case 161:case 181:case 182:case 242:case 183:case 184:case 243:case 244:case 245:case 165:case 178:case 179:case 49:case 50:case 168:case 169:case 175:case 170:case 171:case 172:case 97:case 76:case 32:case 173:case 35:return-1;case 176:case 177:case 7:case 155:case 8:case 157:case 125:case 126:case 92:case 93:case 129:case 130:case 131:case 94:case 91:return 1;case 74:case 60:case 69:case 70:case 4:return 1024;case 31:case 42:case 72:return 32;case 87:case 26:case 33:return 2147483647;case 34:case 1:return 47839;case 38:case 36:return 99;case 43:case 37:return 2048;case 0:return 2097152;case 3:return 65536;case 28:return 32768;case 44:return 32767;case 75:return 16384;case 39:return 1e3;case 89:return 700;case 71:return 256;case 40:return 255;case 2:return 100;case 180:return 64;case 25:return 20;case 5:return 16;case 6:return 6;case 73:return 4;case 84:return typeof navigator=="object"&&navigator.hardwareConcurrency||1}return setErrNo(28),-1}ENVIRONMENT_IS_PTHREAD?PThread.initWorker():PThread.initMainThreadBlock();var GLctx;GL.init();var proxiedFunctionTable=[null,_atexit,_emscripten_set_canvas_element_size_main_thread,_fd_close,_fd_seek,_fd_write,_sysconf],asmLibraryArg={e:___assert_fail,r:___call_main,w:__emscripten_notify_thread_queue,a:_abort,l:_emscripten_conditional_set_current_thread_status,d:_emscripten_futex_wait,c:_emscripten_futex_wake,h:_emscripten_get_now,g:_emscripten_is_main_browser_thread,x:_emscripten_is_main_runtime_thread,q:_emscripten_memcpy_big,B:_emscripten_num_logical_cores,t:_emscripten_receive_on_main_thread_js,A:_emscripten_resize_heap,u:_emscripten_set_canvas_element_size,k:_emscripten_set_current_thread_status,s:_emscripten_set_thread_name,v:_emscripten_webgl_create_context,m:_fd_close,o:_fd_seek,i:_fd_write,p:initPthreadsJS,memory:wasmMemory||Module.wasmMemory,y:_pthread_cleanup_pop,z:_pthread_cleanup_push,j:_pthread_create,b:_pthread_self,f:_roundf,n:_sysconf,table:wasmTable},asm=createWasm();Module.asm=asm;var 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Uint8Array?"int32":isNumber(values)?"float32":isString(values)?"string":isBoolean(values)?"bool":"float32"}function isFunction(f){return!!(f&&f.constructor&&f.call&&f.apply)}function nearestDivisor(size,start){for(let i=start;i=0;--i)strides[i]=strides[i+1]*shape[i+1];return strides}function createNestedArray(offset,shape,a){let ret=new Array;if(shape.length===1){let d=shape[0];for(let i=0;iacc*c);for(let i=0;iacc*c);if(size===0)return[];if(size!==a.length)throw new Error(`[${shape}] does not match the input size ${a.length}.`);return createNestedArray(0,shape,a)}function makeOnesTypedArray(size,dtype){let array2=makeZerosTypedArray(size,dtype);for(let i=0;iprev*curr,1);if(dtype==null||dtype==="float32")return toNestedArray(shape,new Float32Array(size));if(dtype==="int32")return toNestedArray(shape,new Int32Array(size));if(dtype==="bool")return toNestedArray(shape,new Uint8Array(size));throw new Error(`Unknown data type ${dtype}`)}function assertNonNegativeIntegerDimensions(shape){shape.forEach(dimSize=>{assert(Number.isInteger(dimSize)&&dimSize>=0,()=>`Tensor must have a shape comprised of positive integers but got shape [${shape}].`)})}function locToIndex(locs,rank,strides){if(rank===0)return 0;if(rank===1)return locs[0];let index=locs[locs.length-1];for(let i=0;i{let[key,value]=keyValue.split(":");this.urlFlags[key]=parseValue(key,value)})}};function getQueryParams(queryString){let params={};return queryString.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g,(s,...t)=>(decodeParam(params,t[0],t[1]),t.join("="))),params}function decodeParam(params,name,value){params[decodeURIComponent(name)]=decodeURIComponent(value||"")}function parseValue(flagName,value){if(value=value.toLowerCase(),value==="true"||value==="false")return value==="true";if(`${+value}`===value)return+value;throw new Error(`Could not parse value flag value ${value} for flag ${flagName}.`)}function env(){return ENV}var ENV=null;function setEnvironmentGlobal(environment){ENV=environment}var globalNameSpace;function getGlobalNamespace(){if(globalNameSpace==null){let ns;if(typeof window!="undefined")ns=window;else if(typeof global!="undefined")ns=global;else if(typeof process!="undefined")ns=process;else if(typeof self!="undefined")ns=self;else throw new Error("Could not find a global object");globalNameSpace=ns}return globalNameSpace}function getGlobalMap(){let ns=getGlobalNamespace();return ns._tfGlobals==null&&(ns._tfGlobals=new Map),ns._tfGlobals}function getGlobal(key,init2){let globalMap=getGlobalMap();if(globalMap.has(key))return globalMap.get(key);{let singleton=init2();return globalMap.set(key,singleton),globalMap.get(key)}}var Abs="Abs",Acos="Acos",Acosh="Acosh",Add="Add",AddN="AddN",All="All",Any="Any",ArgMax="ArgMax",ArgMin="ArgMin",Asin="Asin",Asinh="Asinh",Atan="Atan",Atanh="Atanh",Atan2="Atan2",AvgPool="AvgPool",AvgPoolGrad="AvgPoolGrad",AvgPool3D="AvgPool3D",AvgPool3DGrad="AvgPool3DGrad",BatchMatMul="BatchMatMul",BatchToSpaceND="BatchToSpaceND",Bincount="Bincount",BroadcastTo="BroadcastTo",Cast="Cast",Ceil="Ceil",ClipByValue="ClipByValue",Complex="Complex",ComplexAbs="ComplexAbs",Concat="Concat",Conv2D="Conv2D",Conv2DBackpropFilter="Conv2DBackpropFilter",Conv2DBackpropInput="Conv2DBackpropInput",Conv3D="Conv3D",Conv3DBackpropFilterV2="Conv3DBackpropFilterV2",Conv3DBackpropInputV2="Conv3DBackpropInputV2",Cos="Cos",Cosh="Cosh",Cumsum="Cumsum",CropAndResize="CropAndResize",DenseBincount="DenseBincount",DepthToSpace="DepthToSpace",DepthwiseConv2dNative="DepthwiseConv2dNative",DepthwiseConv2dNativeBackpropFilter="DepthwiseConv2dNativeBackpropFilter",DepthwiseConv2dNativeBackpropInput="DepthwiseConv2dNativeBackpropInput",Diag="Diag",Dilation2D="Dilation2D",Dilation2DBackpropInput="Dilation2DBackpropInput",Dilation2DBackpropFilter="Dilation2DBackpropFilter",RealDiv="RealDiv",Elu="Elu",EluGrad="EluGrad",Erf="Erf",Equal="Equal",Exp="Exp",ExpandDims="ExpandDims",Expm1="Expm1",FFT="FFT",Fill="Fill",FlipLeftRight="FlipLeftRight",Floor="Floor",FloorDiv="FloorDiv",FusedBatchNorm="FusedBatchNorm",GatherV2="GatherV2",GatherNd="GatherNd",Greater="Greater",GreaterEqual="GreaterEqual",Identity="Identity",IFFT="IFFT",Imag="Imag",IsFinite="IsFinite",IsInf="IsInf",IsNan="IsNan",LeakyRelu="LeakyRelu",Less="Less",LessEqual="LessEqual",LinSpace="LinSpace",Log="Log",Log1p="Log1p",LogicalAnd="LogicalAnd",LogicalNot="LogicalNot",LogicalOr="LogicalOr",LogSoftmax="LogSoftmax",LRN="LRN",LRNGrad="LRNGrad",Max="Max",Maximum="Maximum",MaxPool="MaxPool",MaxPoolGrad="MaxPoolGrad",MaxPool3D="MaxPool3D",MaxPool3DGrad="MaxPool3DGrad",MaxPoolWithArgmax="MaxPoolWithArgmax",Mean="Mean",Min="Min",Minimum="Minimum",MirrorPad="MirrorPad",Mod="Mod",Multinomial="Multinomial",Multiply="Multiply",Neg="Neg",NotEqual="NotEqual",NonMaxSuppressionV3="NonMaxSuppressionV3",NonMaxSuppressionV4="NonMaxSuppressionV4",NonMaxSuppressionV5="NonMaxSuppressionV5",OnesLike="OnesLike",OneHot="OneHot",Pack="Pack",PadV2="PadV2",Pool="Pool",Pow="Pow",Prelu="Prelu",Prod="Prod",Range="Range",Real="Real",Reciprocal="Reciprocal",Relu="Relu",Reshape="Reshape",ResizeNearestNeighbor="ResizeNearestNeighbor",ResizeNearestNeighborGrad="ResizeNearestNeighborGrad",ResizeBilinear="ResizeBilinear",ResizeBilinearGrad="ResizeBilinearGrad",Relu6="Relu6",Reverse="Reverse",Round="Round",Rsqrt="Rsqrt",ScatterNd="ScatterNd",Select="Select",Selu="Selu",Slice="Slice",Sin="Sin",Sinh="Sinh",Sign="Sign",Sigmoid="Sigmoid",Softplus="Softplus",Sqrt="Sqrt",Sum="Sum",SpaceToBatchND="SpaceToBatchND",SplitV="SplitV",Softmax="Softmax",SquaredDifference="SquaredDifference",Square="Square",Sub="Sub",SparseToDense="SparseToDense",StridedSlice="StridedSlice",Tan="Tan",Tanh="Tanh",Tile="Tile",TopK="TopK",Transpose="Transpose",Unique="Unique",Unpack="Unpack",UnsortedSegmentSum="UnsortedSegmentSum",ZerosLike="ZerosLike",Step="Step",FromPixels="FromPixels",RotateWithOffset="RotateWithOffset",_FusedMatMul="_FusedMatMul",FusedConv2D="FusedConv2D",FusedDepthwiseConv2D="FusedDepthwiseConv2D",kernelRegistry=getGlobal("kernelRegistry",()=>new Map),gradRegistry=getGlobal("gradRegistry",()=>new Map);function getKernel(kernelName,backendName){let key=makeKey(kernelName,backendName);return kernelRegistry.get(key)}function getGradient(kernelName){return gradRegistry.get(kernelName)}function getKernelsForBackend(backendName){let it=kernelRegistry.entries(),result=[];for(;;){let{done,value}=it.next();if(done)break;let[key,config3]=value,[backend22]=key.split("_");backend22===backendName&&result.push(config3)}return result}function registerKernel(config3){let{kernelName,backendName}=config3,key=makeKey(kernelName,backendName);kernelRegistry.has(key)&&console.warn(`The kernel '${kernelName}' for backend '${backendName}' is already registered`),kernelRegistry.set(key,config3)}function registerGradient(config3){let{kernelName}=config3;gradRegistry.has(kernelName)&&env().getBool("DEBUG")&&console.warn(`Overriding the gradient for '${kernelName}'`),gradRegistry.set(kernelName,config3)}function unregisterKernel(kernelName,backendName){let key=makeKey(kernelName,backendName);if(!kernelRegistry.has(key))throw new Error(`The kernel '${kernelName}' for backend '${backendName}' is not registered`);kernelRegistry.delete(key)}function unregisterGradient(kernelName){if(!gradRegistry.has(kernelName))throw new Error(`The gradient '${kernelName}' for backend is not registered`);gradRegistry.delete(kernelName)}function copyRegisteredKernels(registeredBackendName,newBackendName){getKernelsForBackend(registeredBackendName).forEach(kernelConfig=>{let newKernelConfig=Object.assign({},kernelConfig,{backendName:newBackendName});registerKernel(newKernelConfig)})}function makeKey(kernelName,backendName){return`${backendName}_${kernelName}`}var util_exports={};__export2(util_exports,{arraysEqual:()=>arraysEqual,assert:()=>assert,assertNonNegativeIntegerDimensions:()=>assertNonNegativeIntegerDimensions,assertNonNull:()=>assertNonNull,assertShapesMatch:()=>assertShapesMatch,bytesFromStringArray:()=>bytesFromStringArray,bytesPerElement:()=>bytesPerElement,checkConversionForErrors:()=>checkConversionForErrors,clamp:()=>clamp,computeStrides:()=>computeStrides,createScalarValue:()=>createScalarValue,createShuffledIndices:()=>createShuffledIndices,decodeString:()=>decodeString,distSquared:()=>distSquared,encodeString:()=>encodeString,fetch:()=>fetch2,flatten:()=>flatten,getArrayFromDType:()=>getArrayFromDType,getTypedArrayFromDType:()=>getTypedArrayFromDType,hasEncodingLoss:()=>hasEncodingLoss,indexToLoc:()=>indexToLoc,inferDtype:()=>inferDtype,inferFromImplicitShape:()=>inferFromImplicitShape,isBoolean:()=>isBoolean,isFunction:()=>isFunction,isInt:()=>isInt,isNumber:()=>isNumber,isPromise:()=>isPromise,isScalarShape:()=>isScalarShape,isString:()=>isString,isTypedArray:()=>isTypedArray,isValidDtype:()=>isValidDtype,locToIndex:()=>locToIndex,makeOnesTypedArray:()=>makeOnesTypedArray,makeZerosNestedTypedArray:()=>makeZerosNestedTypedArray,makeZerosTypedArray:()=>makeZerosTypedArray,nearestDivisor:()=>nearestDivisor,nearestLargerEven:()=>nearestLargerEven,now:()=>now,parseAxisParam:()=>parseAxisParam,randUniform:()=>randUniform,repeatedTry:()=>repeatedTry,rightPad:()=>rightPad,shuffle:()=>shuffle,sizeFromShape:()=>sizeFromShape,sizeToSquarishShape:()=>sizeToSquarishShape,squeezeShape:()=>squeezeShape,sum:()=>sum,tanh:()=>tanh,toNestedArray:()=>toNestedArray,toTypedArray:()=>toTypedArray});function createScalarValue(value,dtype){return dtype==="string"?encodeString(value):toTypedArray([value],dtype)}function noConversionNeeded(a,dtype){return a instanceof Float32Array&&dtype==="float32"||a instanceof Int32Array&&dtype==="int32"||a instanceof Uint8Array&&dtype==="bool"}function toTypedArray(a,dtype){if(dtype==="string")throw new Error("Cannot convert a string[] to a TypedArray");if(Array.isArray(a)&&(a=flatten(a)),env().getBool("DEBUG")&&checkConversionForErrors(a,dtype),noConversionNeeded(a,dtype))return a;if(dtype==null||dtype==="float32"||dtype==="complex64")return new Float32Array(a);if(dtype==="int32")return new Int32Array(a);if(dtype==="bool"){let bool=new Uint8Array(a.length);for(let i=0;i{outputs=f()},timer=this.backendTimer.time(holdResultWrapperFn);if(env().getBool("CHECK_COMPUTATION_FOR_ERRORS"))for(let i=0;i{checkComputationForErrors(tensorVals,output.dtype,kernelName)})}return{kernelName,outputs,inputs,timeMs:timer.then(timing=>timing.kernelMs),extraInfo:timer.then(timing=>timing.getExtraProfileInfo!=null?timing.getExtraProfileInfo():"")}}logKernelProfile(kernelProfile){let{kernelName,outputs,timeMs,inputs,extraInfo}=kernelProfile;outputs.forEach(result=>{Promise.all([result.data(),timeMs,extraInfo]).then(valueContainer=>{this.logger.logKernelProfile(kernelName,result,valueContainer[0],valueContainer[1],inputs,valueContainer[2])})})}};function checkComputationForErrors(vals,dtype,kernelName){if(dtype!=="float32")return!1;for(let i=0;i0?inputShape:""} `}}console.log(`%c${paddedName} %c${time2} %c${rank}D ${shape} %c${size} %c${inputShapesDescription} %c${extraInfo}`,"font-weight:bold","color:red","color:blue","color: orange","color: green","color: steelblue")}};function getFilteredNodesXToY(tape,xs,y){let tensorsFromX={},nodesFromX={};for(let i=0;itensorsFromX[output.id]=!0),anyInputFromX=!0,nodesFromX[node.id]=!0;break}if(anyInputFromX)break}}let tensorsLeadToY={};tensorsLeadToY[y.id]=!0;let nodesToY={};for(let i=tape.length-1;i>=0;i--){let node=tape[i],nodeInputs=node.inputs;for(let j=0;j=0;i--){let node=filteredTape[i],dys=[];if(node.outputs.forEach(o=>{let gradTensor=tensorAccumulatedGradientMap[o.id];gradTensor!=null?dys.push(gradTensor):dys.push(null)}),node.gradient==null)throw new Error(`Cannot compute gradient: gradient function not found for ${node.kernelName}.`);let inputGradients=node.gradient(dys);for(let inputName in node.inputs){if(!(inputName in inputGradients))throw new Error(`Cannot backprop through input ${inputName}. Available gradients found: ${Object.keys(inputGradients)}.`);let dx=tidy2(()=>inputGradients[inputName]());if(dx.dtype!=="float32")throw new Error(`Error in gradient for op ${node.kernelName}. The gradient of input ${inputName} must have 'float32' dtype, but has '${dx.dtype}'`);let x=node.inputs[inputName];if(!arraysEqual(dx.shape,x.shape))throw new Error(`Error in gradient for op ${node.kernelName}. The gradient of input '${inputName}' has shape '${dx.shape}', which does not match the shape of the input '${x.shape}'`);if(tensorAccumulatedGradientMap[x.id]==null)tensorAccumulatedGradientMap[x.id]=dx;else{let curGradient=tensorAccumulatedGradientMap[x.id];tensorAccumulatedGradientMap[x.id]=add5(curGradient,dx),curGradient.dispose()}}}}var FORMAT_LIMIT_NUM_VALS=20,FORMAT_NUM_FIRST_LAST_VALS=3,FORMAT_NUM_SIG_DIGITS=7;function tensorToString(vals,shape,dtype,verbose){let strides=computeStrides(shape),padPerCol=computeMaxSizePerColumn(vals,shape,dtype,strides),rank=shape.length,valsLines=subTensorToString(vals,shape,dtype,strides,padPerCol),lines=["Tensor"];return verbose&&(lines.push(` dtype: ${dtype}`),lines.push(` rank: ${rank}`),lines.push(` shape: [${shape}]`),lines.push(" values:")),lines.push(valsLines.map(l=>" "+l).join(` +`)};exports.Canvas=WebGLImageFilter}),require_image=__commonJS(exports=>{var fxImage=__toModule(require_imagefx()),inCanvas=null,outCanvas=null;function process3(input2,config2){let tensor2;if(input2 instanceof Tensor)tensor2=clone(input2);else{let originalWidth=input2.naturalWidth||input2.videoWidth||input2.width||input2.shape&&input2.shape[1]>0,originalHeight=input2.naturalHeight||input2.videoHeight||input2.height||input2.shape&&input2.shape[2]>0,targetWidth=originalWidth,targetHeight=originalHeight;if(config2.filter.width>0?targetWidth=config2.filter.width:config2.filter.height>0&&(targetWidth=originalWidth*(config2.filter.height/originalHeight)),config2.filter.height>0?targetHeight=config2.filter.height:config2.filter.width>0&&(targetHeight=originalHeight*(config2.filter.width/originalWidth)),!targetWidth||!targetHeight)return log("Human: invalid input",input2),null;(!inCanvas||inCanvas.width!==targetWidth||inCanvas.height!==targetHeight)&&(inCanvas=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(targetWidth,targetHeight):document.createElement("canvas"),inCanvas.width!==targetWidth&&(inCanvas.width=targetWidth),inCanvas.height!==targetHeight&&(inCanvas.height=targetHeight));let ctx=inCanvas.getContext("2d");if(input2 instanceof ImageData?ctx.putImageData(input2,0,0):ctx.drawImage(input2,0,0,originalWidth,originalHeight,0,0,inCanvas.width,inCanvas.height),config2.filter.enabled){if((!this.fx||!outCanvas||inCanvas.width!==outCanvas.width||inCanvas.height!==outCanvas.height)&&(outCanvas=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(inCanvas.width,inCanvas.height):document.createElement("canvas"),outCanvas.width!==inCanvas.width&&(outCanvas.width=inCanvas.width),outCanvas.height!==inCanvas.height&&(outCanvas.height=inCanvas.height),this.fx=ENV.flags.IS_BROWSER?new fxImage.Canvas({canvas:outCanvas}):null),!this.fx)return inCanvas;this.fx.reset(),this.fx.addFilter("brightness",config2.filter.brightness),config2.filter.contrast!==0&&this.fx.addFilter("contrast",config2.filter.contrast),config2.filter.sharpness!==0&&this.fx.addFilter("sharpen",config2.filter.sharpness),config2.filter.blur!==0&&this.fx.addFilter("blur",config2.filter.blur),config2.filter.saturation!==0&&this.fx.addFilter("saturation",config2.filter.saturation),config2.filter.hue!==0&&this.fx.addFilter("hue",config2.filter.hue),config2.filter.negative&&this.fx.addFilter("negative"),config2.filter.sepia&&this.fx.addFilter("sepia"),config2.filter.vintage&&this.fx.addFilter("brownie"),config2.filter.sepia&&this.fx.addFilter("sepia"),config2.filter.kodachrome&&this.fx.addFilter("kodachrome"),config2.filter.technicolor&&this.fx.addFilter("technicolor"),config2.filter.polaroid&&this.fx.addFilter("polaroid"),config2.filter.pixelate!==0&&this.fx.addFilter("pixelate",config2.filter.pixelate),this.fx.apply(inCanvas)}else outCanvas=inCanvas;let pixels;if(outCanvas.data){let shape=[outCanvas.height,outCanvas.width,3];pixels=tensor3d(outCanvas.data,shape,"int32")}else if(config2.backend==="webgl"||outCanvas instanceof ImageData)pixels=browser_exports.fromPixels(outCanvas);else{let tempCanvas=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(targetWidth,targetHeight):document.createElement("canvas");tempCanvas.width=targetWidth,tempCanvas.height=targetHeight;let tempCtx=tempCanvas.getContext("2d");tempCtx==null||tempCtx.drawImage(outCanvas,0,0);let data2=tempCtx==null?void 0:tempCtx.getImageData(0,0,targetWidth,targetHeight);pixels=browser_exports.fromPixels(data2)}let casted=pixels.toFloat();tensor2=casted.expandDims(0),pixels.dispose(),casted.dispose()}return{tensor:tensor2,canvas:config2.filter.return?outCanvas:null}}exports.process=process3});function log(...msg){let dt=new Date,ts=`${dt.getHours().toString().padStart(2,"0")}:${dt.getMinutes().toString().padStart(2,"0")}:${dt.getSeconds().toString().padStart(2,"0")}.${dt.getMilliseconds().toString().padStart(3,"0")}`;msg&&console.log(ts,"Human:",...msg)}var tfjs_esm_exports={};__export(tfjs_esm_exports,{Abs:()=>Abs,Acos:()=>Acos,Acosh:()=>Acosh,AdadeltaOptimizer:()=>AdadeltaOptimizer,AdagradOptimizer:()=>AdagradOptimizer,AdamOptimizer:()=>AdamOptimizer,AdamaxOptimizer:()=>AdamaxOptimizer,Add:()=>Add,AddN:()=>AddN,All:()=>All,Any:()=>Any,ArgMax:()=>ArgMax,ArgMin:()=>ArgMin,Asin:()=>Asin,Asinh:()=>Asinh,Atan:()=>Atan,Atan2:()=>Atan2,Atanh:()=>Atanh,AvgPool:()=>AvgPool,AvgPool3D:()=>AvgPool3D,AvgPool3DGrad:()=>AvgPool3DGrad,AvgPoolGrad:()=>AvgPoolGrad,BackendWasm:()=>BackendWasm,BatchMatMul:()=>BatchMatMul,BatchToSpaceND:()=>BatchToSpaceND,Bincount:()=>Bincount,BroadcastTo:()=>BroadcastTo,Callback:()=>Callback,CallbackList:()=>CallbackList,Cast:()=>Cast,Ceil:()=>Ceil,ClipByValue:()=>ClipByValue,Complex:()=>Complex,ComplexAbs:()=>ComplexAbs,Concat:()=>Concat,Conv2D:()=>Conv2D,Conv2DBackpropFilter:()=>Conv2DBackpropFilter,Conv2DBackpropInput:()=>Conv2DBackpropInput,Conv3D:()=>Conv3D,Conv3DBackpropFilterV2:()=>Conv3DBackpropFilterV2,Conv3DBackpropInputV2:()=>Conv3DBackpropInputV2,Cos:()=>Cos,Cosh:()=>Cosh,CropAndResize:()=>CropAndResize,Cumsum:()=>Cumsum,CustomCallback:()=>CustomCallback,DataStorage:()=>DataStorage,DenseBincount:()=>DenseBincount,DepthToSpace:()=>DepthToSpace,DepthwiseConv2dNative:()=>DepthwiseConv2dNative,DepthwiseConv2dNativeBackpropFilter:()=>DepthwiseConv2dNativeBackpropFilter,DepthwiseConv2dNativeBackpropInput:()=>DepthwiseConv2dNativeBackpropInput,Diag:()=>Diag,Dilation2D:()=>Dilation2D,Dilation2DBackpropFilter:()=>Dilation2DBackpropFilter,Dilation2DBackpropInput:()=>Dilation2DBackpropInput,ENV:()=>ENV,EarlyStopping:()=>EarlyStopping,Elu:()=>Elu,EluGrad:()=>EluGrad,Environment:()=>Environment,Equal:()=>Equal,Erf:()=>Erf,Exp:()=>Exp,ExpandDims:()=>ExpandDims,Expm1:()=>Expm1,FFT:()=>FFT,Fill:()=>Fill,FlipLeftRight:()=>FlipLeftRight,Floor:()=>Floor,FloorDiv:()=>FloorDiv,FromPixels:()=>FromPixels,FusedBatchNorm:()=>FusedBatchNorm,FusedConv2D:()=>FusedConv2D,FusedDepthwiseConv2D:()=>FusedDepthwiseConv2D,GPGPUContext:()=>GPGPUContext,GatherNd:()=>GatherNd,GatherV2:()=>GatherV2,GraphModel:()=>GraphModel,Greater:()=>Greater,GreaterEqual:()=>GreaterEqual,History:()=>History,IFFT:()=>IFFT,Identity:()=>Identity,Imag:()=>Imag,InputSpec:()=>InputSpec,IsFinite:()=>IsFinite,IsInf:()=>IsInf,IsNan:()=>IsNan,KernelBackend:()=>KernelBackend,LRN:()=>LRN,LRNGrad:()=>LRNGrad,LayerVariable:()=>LayerVariable,LayersModel:()=>LayersModel,LeakyRelu:()=>LeakyRelu,Less:()=>Less,LessEqual:()=>LessEqual,LinSpace:()=>LinSpace,Log:()=>Log,Log1p:()=>Log1p,LogSoftmax:()=>LogSoftmax,LogicalAnd:()=>LogicalAnd,LogicalNot:()=>LogicalNot,LogicalOr:()=>LogicalOr,MathBackendCPU:()=>MathBackendCPU,MathBackendWebGL:()=>MathBackendWebGL,Max:()=>Max,MaxPool:()=>MaxPool,MaxPool3D:()=>MaxPool3D,MaxPool3DGrad:()=>MaxPool3DGrad,MaxPoolGrad:()=>MaxPoolGrad,MaxPoolWithArgmax:()=>MaxPoolWithArgmax,Maximum:()=>Maximum,Mean:()=>Mean,Min:()=>Min,Minimum:()=>Minimum,MirrorPad:()=>MirrorPad,Mod:()=>Mod,MomentumOptimizer:()=>MomentumOptimizer,Multinomial:()=>Multinomial,Multiply:()=>Multiply,Neg:()=>Neg,NonMaxSuppressionV3:()=>NonMaxSuppressionV3,NonMaxSuppressionV4:()=>NonMaxSuppressionV4,NonMaxSuppressionV5:()=>NonMaxSuppressionV5,NotEqual:()=>NotEqual,OP_SCOPE_SUFFIX:()=>OP_SCOPE_SUFFIX,OneHot:()=>OneHot,OnesLike:()=>OnesLike,Optimizer:()=>Optimizer,Pack:()=>Pack,PadV2:()=>PadV2,Pool:()=>Pool,Pow:()=>Pow,Prelu:()=>Prelu,Prod:()=>Prod,RMSPropOptimizer:()=>RMSPropOptimizer,RNN:()=>RNN,Range:()=>Range,Rank:()=>Rank,Real:()=>Real,RealDiv:()=>RealDiv,Reciprocal:()=>Reciprocal,Reduction:()=>Reduction,Relu:()=>Relu,Relu6:()=>Relu6,Reshape:()=>Reshape,ResizeBilinear:()=>ResizeBilinear,ResizeBilinearGrad:()=>ResizeBilinearGrad,ResizeNearestNeighbor:()=>ResizeNearestNeighbor,ResizeNearestNeighborGrad:()=>ResizeNearestNeighborGrad,Reverse:()=>Reverse,RotateWithOffset:()=>RotateWithOffset,Round:()=>Round,Rsqrt:()=>Rsqrt,SGDOptimizer:()=>SGDOptimizer,ScatterNd:()=>ScatterNd,Select:()=>Select,Selu:()=>Selu,Sequential:()=>Sequential,Sigmoid:()=>Sigmoid,Sign:()=>Sign,Sin:()=>Sin,Sinh:()=>Sinh,Slice:()=>Slice,Softmax:()=>Softmax,Softplus:()=>Softplus,SpaceToBatchND:()=>SpaceToBatchND,SparseToDense:()=>SparseToDense,SplitV:()=>SplitV,Sqrt:()=>Sqrt,Square:()=>Square,SquaredDifference:()=>SquaredDifference,Step:()=>Step,StridedSlice:()=>StridedSlice,Sub:()=>Sub,Sum:()=>Sum,SymbolicTensor:()=>SymbolicTensor,Tan:()=>Tan,Tanh:()=>Tanh,Tensor:()=>Tensor,TensorBuffer:()=>TensorBuffer,Tile:()=>Tile,TopK:()=>TopK,Transpose:()=>Transpose,Unique:()=>Unique,Unpack:()=>Unpack,UnsortedSegmentSum:()=>UnsortedSegmentSum,Variable:()=>Variable,ZerosLike:()=>ZerosLike,_FusedMatMul:()=>_FusedMatMul,abs:()=>abs,acos:()=>acos,acosh:()=>acosh,add:()=>add2,addN:()=>addN,addStrict:()=>addStrict,all:()=>all,any:()=>any,argMax:()=>argMax,argMin:()=>argMin,asin:()=>asin,asinh:()=>asinh,atan:()=>atan,atan2:()=>atan2,atanh:()=>atanh,avgPool:()=>avgPool,avgPool3d:()=>avgPool3d,backend:()=>backend,backend_util:()=>backend_util_exports,basicLSTMCel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define=="function"&&define)}),require_crypto=__commonJS2(()=>{}),require_seedrandom=__commonJS2((exports,module)=>{(function(pool3,math){var global2=this,width=256,chunks=6,digits=52,rngname="random",startdenom=math.pow(width,chunks),significance=math.pow(2,digits),overflow=significance*2,mask=width-1,nodecrypto;function seedrandom5(seed,options,callback){var key=[];options=options==!0?{entropy:!0}:options||{};var shortseed=mixkey(flatten4(options.entropy?[seed,tostring(pool3)]:seed==null?autoseed():seed,3),key),arc4=new ARC4(key),prng=function(){for(var n=arc4.g(chunks),d=startdenom,x=0;n=overflow;)n/=2,d/=2,x>>>=1;return(n+x)/d};return prng.int32=function(){return arc4.g(4)|0},prng.quick=function(){return arc4.g(4)/4294967296},prng.double=prng,mixkey(tostring(arc4.S),pool3),(options.pass||callback||function(prng2,seed2,is_math_call,state){return state&&(state.S&©(state,arc4),prng2.state=function(){return copy(arc4,{})}),is_math_call?(math[rngname]=prng2,seed2):prng2})(prng,shortseed,"global"in options?options.global:this==math,options.state)}math["seed"+rngname]=seedrandom5;function ARC4(key){var t,keylen=key.length,me=this,i=0,j=me.i=me.j=0,s=me.S=[];for(keylen||(key=[keylen++]);i{var alea5=require_alea(),xor128=require_xor128(),xorwow=require_xorwow(),xorshift7=require_xorshift7(),xor4096=require_xor4096(),tychei=require_tychei(),sr=require_seedrandom();sr.alea=alea5,sr.xor128=xor128,sr.xorwow=xorwow,sr.xorshift7=xorshift7,sr.xor4096=xor4096,sr.tychei=tychei,module.exports=sr}),require_alea2=__commonJS2((exports,module)=>{(function(global2,module2,define2){function Alea(seed){var me=this,mash=Mash();me.next=function(){var t=2091639*me.s0+me.c*23283064365386963e-26;return me.s0=me.s1,me.s1=me.s2,me.s2=t-(me.c=t|0)},me.c=1,me.s0=mash(" "),me.s1=mash(" "),me.s2=mash(" "),me.s0-=mash(seed),me.s0<0&&(me.s0+=1),me.s1-=mash(seed),me.s1<0&&(me.s1+=1),me.s2-=mash(seed),me.s2<0&&(me.s2+=1),mash=null}function copy(f,t){return t.c=f.c,t.s0=f.s0,t.s1=f.s1,t.s2=f.s2,t}function impl(seed,opts){var xg=new Alea(seed),state=opts&&opts.state,prng=xg.next;return prng.int32=function(){return xg.next()*4294967296|0},prng.double=function(){return prng()+(prng()*2097152|0)*11102230246251565e-32},prng.quick=prng,state&&(typeof state=="object"&©(state,xg),prng.state=function(){return copy(xg,{})}),prng}function Mash(){var n=4022871197,mash=function(data2){data2=data2.toString();for(var i=0;i>>0,h-=n,h*=n,n=h>>>0,h-=n,n+=h*4294967296}return(n>>>0)*23283064365386963e-26};return mash}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.alea=impl})(exports,typeof module=="object"&&module,typeof define=="function"&&define)}),require_xor1282=__commonJS2((exports,module)=>{(function(global2,module2,define2){function XorGen(seed){var me=this,strseed="";me.x=0,me.y=0,me.z=0,me.w=0,me.next=function(){var t=me.x^me.x<<11;return me.x=me.y,me.y=me.z,me.z=me.w,me.w^=me.w>>>19^t^t>>>8},seed===(seed|0)?me.x=seed:strseed+=seed;for(var k=0;k>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state&&(typeof state=="object"&©(state,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.xor128=impl})(exports,typeof module=="object"&&module,typeof define=="function"&&define)}),require_xorwow2=__commonJS2((exports,module)=>{(function(global2,module2,define2){function XorGen(seed){var me=this,strseed="";me.next=function(){var t=me.x^me.x>>>2;return me.x=me.y,me.y=me.z,me.z=me.w,me.w=me.v,(me.d=me.d+362437|0)+(me.v=me.v^me.v<<4^(t^t<<1))|0},me.x=0,me.y=0,me.z=0,me.w=0,me.v=0,seed===(seed|0)?me.x=seed:strseed+=seed;for(var k=0;k>>4),me.next()}function copy(f,t){return t.x=f.x,t.y=f.y,t.z=f.z,t.w=f.w,t.v=f.v,t.d=f.d,t}function impl(seed,opts){var xg=new XorGen(seed),state=opts&&opts.state,prng=function(){return(xg.next()>>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state&&(typeof state=="object"&©(state,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.xorwow=impl})(exports,typeof module=="object"&&module,typeof define=="function"&&define)}),require_xorshift72=__commonJS2((exports,module)=>{(function(global2,module2,define2){function XorGen(seed){var me=this;me.next=function(){var X=me.x,i=me.i,t,v,w;return t=X[i],t^=t>>>7,v=t^t<<24,t=X[i+1&7],v^=t^t>>>10,t=X[i+3&7],v^=t^t>>>3,t=X[i+4&7],v^=t^t<<7,t=X[i+7&7],t=t^t<<13,v^=t^t<<9,X[i]=v,me.i=i+1&7,v};function init2(me2,seed2){var j,w,X=[];if(seed2===(seed2|0))w=X[0]=seed2;else for(seed2=""+seed2,j=0;j0;--j)me2.next()}init2(me,seed)}function copy(f,t){return t.x=f.x.slice(),t.i=f.i,t}function impl(seed,opts){seed==null&&(seed=+new Date);var xg=new XorGen(seed),state=opts&&opts.state,prng=function(){return(xg.next()>>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state&&(state.x&©(state,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.xorshift7=impl})(exports,typeof module=="object"&&module,typeof define=="function"&&define)}),require_xor40962=__commonJS2((exports,module)=>{(function(global2,module2,define2){function XorGen(seed){var me=this;me.next=function(){var w=me.w,X=me.X,i=me.i,t,v;return me.w=w=w+1640531527|0,v=X[i+34&127],t=X[i=i+1&127],v^=v<<13,t^=t<<17,v^=v>>>15,t^=t>>>12,v=X[i]=v^t,me.i=i,v+(w^w>>>16)|0};function init2(me2,seed2){var t,v,i,j,w,X=[],limit=128;for(seed2===(seed2|0)?(v=seed2,seed2=null):(seed2=seed2+"\0",v=0,limit=Math.max(limit,seed2.length)),i=0,j=-32;j>>15,v^=v<<4,v^=v>>>13,j>=0&&(w=w+1640531527|0,t=X[j&127]^=v+w,i=t==0?i+1:0);for(i>=128&&(X[(seed2&&seed2.length||0)&127]=-1),i=127,j=4*128;j>0;--j)v=X[i+34&127],t=X[i=i+1&127],v^=v<<13,t^=t<<17,v^=v>>>15,t^=t>>>12,X[i]=v^t;me2.w=w,me2.X=X,me2.i=i}init2(me,seed)}function copy(f,t){return t.i=f.i,t.w=f.w,t.X=f.X.slice(),t}function impl(seed,opts){seed==null&&(seed=+new Date);var xg=new XorGen(seed),state=opts&&opts.state,prng=function(){return(xg.next()>>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state&&(state.X&©(state,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.xor4096=impl})(exports,typeof module=="object"&&module,typeof define=="function"&&define)}),require_tychei2=__commonJS2((exports,module)=>{(function(global2,module2,define2){function XorGen(seed){var me=this,strseed="";me.next=function(){var b=me.b,c=me.c,d=me.d,a=me.a;return b=b<<25^b>>>7^c,c=c-d|0,d=d<<24^d>>>8^a,a=a-b|0,me.b=b=b<<20^b>>>12^c,me.c=c=c-d|0,me.d=d<<16^c>>>16^a,me.a=a-b|0},me.a=0,me.b=0,me.c=2654435769|0,me.d=1367130551,seed===Math.floor(seed)?(me.a=seed/4294967296|0,me.b=seed|0):strseed+=seed;for(var k=0;k>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state&&(typeof state=="object"&©(state,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.tychei=impl})(exports,typeof module=="object"&&module,typeof define=="function"&&define)}),require_seedrandom3=__commonJS2((exports,module)=>{(function(pool3,math){var global2=this,width=256,chunks=6,digits=52,rngname="random",startdenom=math.pow(width,chunks),significance=math.pow(2,digits),overflow=significance*2,mask=width-1,nodecrypto;function seedrandom5(seed,options,callback){var key=[];options=options==!0?{entropy:!0}:options||{};var shortseed=mixkey(flatten4(options.entropy?[seed,tostring(pool3)]:seed==null?autoseed():seed,3),key),arc4=new ARC4(key),prng=function(){for(var n=arc4.g(chunks),d=startdenom,x=0;n=overflow;)n/=2,d/=2,x>>>=1;return(n+x)/d};return prng.int32=function(){return arc4.g(4)|0},prng.quick=function(){return arc4.g(4)/4294967296},prng.double=prng,mixkey(tostring(arc4.S),pool3),(options.pass||callback||function(prng2,seed2,is_math_call,state){return state&&(state.S&©(state,arc4),prng2.state=function(){return copy(arc4,{})}),is_math_call?(math[rngname]=prng2,seed2):prng2})(prng,shortseed,"global"in options?options.global:this==math,options.state)}math["seed"+rngname]=seedrandom5;function ARC4(key){var t,keylen=key.length,me=this,i=0,j=me.i=me.j=0,s=me.S=[];for(keylen||(key=[keylen++]);i{var alea5=require_alea2(),xor128=require_xor1282(),xorwow=require_xorwow2(),xorshift7=require_xorshift72(),xor4096=require_xor40962(),tychei=require_tychei2(),sr=require_seedrandom3();sr.alea=alea5,sr.xor128=xor128,sr.xorwow=xorwow,sr.xorshift7=xorshift7,sr.xor4096=xor4096,sr.tychei=tychei,module.exports=sr}),require_path=__commonJS2(()=>{}),require_worker_threads=__commonJS2(()=>{}),require_perf_hooks=__commonJS2(()=>{}),require_tfjs_backend_wasm_threaded_simd=__commonJS2((exports,module)=>{var WasmBackendModuleThreadedSimd=function(){var _scriptDir=typeof document!="undefined"&&document.currentScript?document.currentScript.src:void 0;return typeof __filename!="undefined"&&(_scriptDir=_scriptDir||__filename),function(WasmBackendModuleThreadedSimd2){WasmBackendModuleThreadedSimd2=WasmBackendModuleThreadedSimd2||{};function GROWABLE_HEAP_I8(){return wasmMemory.buffer!=buffer2&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAP8}function GROWABLE_HEAP_U8(){return wasmMemory.buffer!=buffer2&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAPU8}function GROWABLE_HEAP_I32(){return wasmMemory.buffer!=buffer2&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAP32}function GROWABLE_HEAP_U32(){return wasmMemory.buffer!=buffer2&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAPU32}function GROWABLE_HEAP_F64(){return wasmMemory.buffer!=buffer2&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAPF64}var Module=typeof WasmBackendModuleThreadedSimd2!="undefined"?WasmBackendModuleThreadedSimd2:{},_scriptDir2=void 0,moduleOverrides={},key;for(key in Module)Module.hasOwnProperty(key)&&(moduleOverrides[key]=Module[key]);var arguments_=[],thisProgram="./this.program",quit_=function(status2,toThrow){throw toThrow},ENVIRONMENT_IS_WEB=!1,ENVIRONMENT_IS_WORKER=!1,ENVIRONMENT_IS_NODE=!1,ENVIRONMENT_IS_SHELL=!1;ENVIRONMENT_IS_WEB=typeof window=="object",ENVIRONMENT_IS_WORKER=typeof importScripts=="function",ENVIRONMENT_IS_NODE=typeof process=="object"&&typeof process.versions=="object"&&typeof process.versions.node=="string",ENVIRONMENT_IS_SHELL=!ENVIRONMENT_IS_WEB&&!ENVIRONMENT_IS_NODE&&!ENVIRONMENT_IS_WORKER;var ENVIRONMENT_IS_PTHREAD=Module.ENVIRONMENT_IS_PTHREAD||!1;ENVIRONMENT_IS_PTHREAD&&(buffer2=Module.buffer,DYNAMIC_BASE=Module.DYNAMIC_BASE,DYNAMICTOP_PTR=Module.DYNAMICTOP_PTR);var scriptDirectory="";function locateFile(path){return Module.locateFile?Module.locateFile(path,scriptDirectory):scriptDirectory+path}var read_,readAsync,readBinary,setWindowTitle,nodeFS,nodePath;if(ENVIRONMENT_IS_NODE){ENVIRONMENT_IS_WORKER?scriptDirectory=require_path().dirname(scriptDirectory)+"/":scriptDirectory=__dirname+"/",read_=function(filename,binary){return nodeFS||(nodeFS=require("fs")),nodePath||(nodePath=require_path()),filename=nodePath.normalize(filename),nodeFS.readFileSync(filename,binary?null:"utf8")},readBinary=function(filename){var ret=read_(filename,!0);return ret.buffer||(ret=new Uint8Array(ret)),assert3(ret.buffer),ret},process.argv.length>1&&(thisProgram=process.argv[1].replace(/\\/g,"/")),arguments_=process.argv.slice(2),process.on("uncaughtException",function(ex){if(!(ex instanceof ExitStatus))throw ex}),process.on("unhandledRejection",abort),quit_=function(status2){process.exit(status2)},Module.inspect=function(){return"[Emscripten Module object]"};var nodeWorkerThreads;try{nodeWorkerThreads=require_worker_threads()}catch(e){throw console.error('The "worker_threads" module is not supported in this node.js build - perhaps a newer version is needed?'),e}Worker=nodeWorkerThreads.Worker}else ENVIRONMENT_IS_SHELL?(typeof read!="undefined"&&(read_=function(f){return read(f)}),readBinary=function(f){var data2;return typeof readbuffer=="function"?new Uint8Array(readbuffer(f)):(data2=read(f,"binary"),assert3(typeof data2=="object"),data2)},typeof scriptArgs!="undefined"?arguments_=scriptArgs:typeof arguments!="undefined"&&(arguments_=arguments),typeof quit=="function"&&(quit_=function(status2){quit(status2)}),typeof print!="undefined"&&(typeof console=="undefined"&&(console={}),console.log=print,console.warn=console.error=typeof printErr!="undefined"?printErr:print)):(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&(ENVIRONMENT_IS_WORKER?scriptDirectory=self.location.href:document.currentScript&&(scriptDirectory=document.currentScript.src),_scriptDir2&&(scriptDirectory=_scriptDir2),scriptDirectory.indexOf("blob:")!==0?scriptDirectory=scriptDirectory.substr(0,scriptDirectory.lastIndexOf("/")+1):scriptDirectory="",ENVIRONMENT_IS_NODE?(read_=function(filename,binary){return nodeFS||(nodeFS=require("fs")),nodePath||(nodePath=require_path()),filename=nodePath.normalize(filename),nodeFS.readFileSync(filename,binary?null:"utf8")},readBinary=function(filename){var ret=read_(filename,!0);return ret.buffer||(ret=new Uint8Array(ret)),assert3(ret.buffer),ret}):(read_=function(url){var xhr=new XMLHttpRequest;return xhr.open("GET",url,!1),xhr.send(null),xhr.responseText},ENVIRONMENT_IS_WORKER&&(readBinary=function(url){var xhr=new XMLHttpRequest;return xhr.open("GET",url,!1),xhr.responseType="arraybuffer",xhr.send(null),new Uint8Array(xhr.response)}),readAsync=function(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open("GET",url,!0),xhr.responseType="arraybuffer",xhr.onload=function(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()},xhr.onerror=onerror,xhr.send(null)}),setWindowTitle=function(title){document.title=title});ENVIRONMENT_IS_NODE&&typeof performance=="undefined"&&(performance=require_perf_hooks().performance);var out=Module.print||console.log.bind(console),err=Module.printErr||console.warn.bind(console);for(key in moduleOverrides)moduleOverrides.hasOwnProperty(key)&&(Module[key]=moduleOverrides[key]);moduleOverrides=null,Module.arguments&&(arguments_=Module.arguments),Module.thisProgram&&(thisProgram=Module.thisProgram),Module.quit&&(quit_=Module.quit);var Atomics_load=Atomics.load,Atomics_store=Atomics.store,Atomics_compareExchange=Atomics.compareExchange,wasmBinary;Module.wasmBinary&&(wasmBinary=Module.wasmBinary);var noExitRuntime;Module.noExitRuntime&&(noExitRuntime=Module.noExitRuntime),typeof WebAssembly!="object"&&err("no native wasm support detected");var wasmMemory,wasmTable=new WebAssembly.Table({initial:169,maximum:169+0,element:"anyfunc"}),wasmModule,threadInfoStruct=0,selfThreadId=0,ABORT=!1,EXITSTATUS=0;function assert3(condition,text){condition||abort("Assertion failed: "+text)}function getCFunc(ident){var func2=Module["_"+ident];return assert3(func2,"Cannot call unknown function "+ident+", make sure it is exported"),func2}function ccall(ident,returnType,argTypes,args,opts){var toC={string:function(str2){var ret2=0;if(str2!=null&&str2!==0){var len=(str2.length<<2)+1;ret2=stackAlloc(len),stringToUTF8(str2,ret2,len)}return ret2},array:function(arr){var ret2=stackAlloc(arr.length);return writeArrayToMemory(arr,ret2),ret2}};function convertReturnValue(ret2){return returnType==="string"?UTF8ToString(ret2):returnType==="boolean"?Boolean(ret2):ret2}var func2=getCFunc(ident),cArgs=[],stack2=0;if(args)for(var i=0;i=endIdx);){var u0=heap[idx++];if(!u0)return str2;if(!(u0&128)){str2+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str2+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224?u0=(u0&15)<<12|u1<<6|u2:u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63,u0<65536)str2+=String.fromCharCode(u0);else{var ch=u0-65536;str2+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}return str2}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(GROWABLE_HEAP_U8(),ptr,maxBytesToRead):""}function stringToUTF8Array(str2,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;for(var startIdx=outIdx,endIdx=outIdx+maxBytesToWrite-1,i=0;i=55296&&u<=57343){var u1=str2.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6,heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12,heap[outIdx++]=128|u>>6&63,heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18,heap[outIdx++]=128|u>>12&63,heap[outIdx++]=128|u>>6&63,heap[outIdx++]=128|u&63}}return heap[outIdx]=0,outIdx-startIdx}function stringToUTF8(str2,outPtr,maxBytesToWrite){return stringToUTF8Array(str2,GROWABLE_HEAP_U8(),outPtr,maxBytesToWrite)}function lengthBytesUTF8(str2){for(var len=0,i=0;i=55296&&u<=57343&&(u=65536+((u&1023)<<10)|str2.charCodeAt(++i)&1023),u<=127?++len:u<=2047?len+=2:u<=65535?len+=3:len+=4}return len}function writeArrayToMemory(array2,buffer3){GROWABLE_HEAP_I8().set(array2,buffer3)}var WASM_PAGE_SIZE=65536;function alignUp(x,multiple){return x%multiple>0&&(x+=multiple-x%multiple),x}var buffer2,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer2=buf,Module.HEAP8=HEAP8=new Int8Array(buf),Module.HEAP16=HEAP16=new Int16Array(buf),Module.HEAP32=HEAP32=new Int32Array(buf),Module.HEAPU8=HEAPU8=new Uint8Array(buf),Module.HEAPU16=HEAPU16=new Uint16Array(buf),Module.HEAPU32=HEAPU32=new Uint32Array(buf),Module.HEAPF32=HEAPF32=new Float32Array(buf),Module.HEAPF64=HEAPF64=new Float64Array(buf)}var STACK_BASE=5256464,STACKTOP=STACK_BASE,STACK_MAX=13584,DYNAMIC_BASE=5256464,DYNAMICTOP_PTR=12656,INITIAL_INITIAL_MEMORY=Module.INITIAL_MEMORY||16777216;if(ENVIRONMENT_IS_PTHREAD)wasmMemory=Module.wasmMemory,buffer2=Module.buffer;else if(Module.wasmMemory)wasmMemory=Module.wasmMemory;else if(wasmMemory=new WebAssembly.Memory({initial:INITIAL_INITIAL_MEMORY/WASM_PAGE_SIZE,maximum:2147483648/WASM_PAGE_SIZE,shared:!0}),!(wasmMemory.buffer instanceof SharedArrayBuffer))throw err("requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag"),ENVIRONMENT_IS_NODE&&console.log("(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)"),Error("bad memory");wasmMemory&&(buffer2=wasmMemory.buffer),INITIAL_INITIAL_MEMORY=buffer2.byteLength,updateGlobalBufferAndViews(buffer2),ENVIRONMENT_IS_PTHREAD||(GROWABLE_HEAP_I32()[DYNAMICTOP_PTR>>2]=DYNAMIC_BASE);function callRuntimeCallbacks(callbacks2){for(;callbacks2.length>0;){var callback=callbacks2.shift();if(typeof callback=="function"){callback(Module);continue}var func2=callback.func;typeof func2=="number"?callback.arg===void 0?Module.dynCall_v(func2):Module.dynCall_vi(func2,callback.arg):func2(callback.arg===void 0?null:callback.arg)}}var __ATPRERUN__=[],__ATINIT__=[],__ATMAIN__=[],__ATEXIT__=[],__ATPOSTRUN__=[],runtimeInitialized=!1;ENVIRONMENT_IS_PTHREAD&&(runtimeInitialized=!0);function preRun(){if(!ENVIRONMENT_IS_PTHREAD){if(Module.preRun)for(typeof Module.preRun=="function"&&(Module.preRun=[Module.preRun]);Module.preRun.length;)addOnPreRun(Module.preRun.shift());callRuntimeCallbacks(__ATPRERUN__)}}function initRuntime(){runtimeInitialized=!0,callRuntimeCallbacks(__ATINIT__)}function preMain(){ENVIRONMENT_IS_PTHREAD||callRuntimeCallbacks(__ATMAIN__)}function postRun(){if(!ENVIRONMENT_IS_PTHREAD){if(Module.postRun)for(typeof Module.postRun=="function"&&(Module.postRun=[Module.postRun]);Module.postRun.length;)addOnPostRun(Module.postRun.shift());callRuntimeCallbacks(__ATPOSTRUN__)}}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var Math_ceil=Math.ceil,Math_floor=Math.floor,runDependencies=0,runDependencyWatcher=null,dependenciesFulfilled=null;function addRunDependency(id){assert3(!ENVIRONMENT_IS_PTHREAD,"addRunDependency cannot be used in a pthread worker"),runDependencies++,Module.monitorRunDependencies&&Module.monitorRunDependencies(runDependencies)}function removeRunDependency(id){if(runDependencies--,Module.monitorRunDependencies&&Module.monitorRunDependencies(runDependencies),runDependencies==0&&(runDependencyWatcher!==null&&(clearInterval(runDependencyWatcher),runDependencyWatcher=null),dependenciesFulfilled)){var callback=dependenciesFulfilled;dependenciesFulfilled=null,callback()}}Module.preloadedImages={},Module.preloadedAudios={};function abort(what){throw Module.onAbort&&Module.onAbort(what),ENVIRONMENT_IS_PTHREAD&&console.error("Pthread aborting at "+new Error().stack),what+="",out(what),err(what),ABORT=!0,EXITSTATUS=1,what="abort("+what+"). Build with -s ASSERTIONS=1 for more info.",new WebAssembly.RuntimeError(what)}function hasPrefix(str2,prefix){return String.prototype.startsWith?str2.startsWith(prefix):str2.indexOf(prefix)===0}var dataURIPrefix="data:application/octet-stream;base64,";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix="file://";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile="tfjs-backend-wasm-threaded-simd.wasm";isDataURI(wasmBinaryFile)||(wasmBinaryFile=locateFile(wasmBinaryFile));function getBinary(){try{if(wasmBinary)return new Uint8Array(wasmBinary);if(readBinary)return readBinary(wasmBinaryFile);throw"both async and sync fetching of the wasm failed"}catch(err2){abort(err2)}}function getBinaryPromise(){return!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&typeof fetch=="function"&&!isFileURI(wasmBinaryFile)?fetch(wasmBinaryFile,{credentials:"same-origin"}).then(function(response){if(!response.ok)throw"failed to load wasm binary file at '"+wasmBinaryFile+"'";return response.arrayBuffer()}).catch(function(){return getBinary()}):new Promise(function(resolve,reject){resolve(getBinary())})}function createWasm(){var info={a:asmLibraryArg};function receiveInstance(instance2,module2){var exports3=instance2.exports;if(Module.asm=exports3,wasmModule=module2,!ENVIRONMENT_IS_PTHREAD){var numWorkersToLoad=PThread.unusedWorkers.length;PThread.unusedWorkers.forEach(function(w){PThread.loadWasmModuleToWorker(w,function(){--numWorkersToLoad||removeRunDependency("wasm-instantiate")})})}}ENVIRONMENT_IS_PTHREAD||addRunDependency("wasm-instantiate");function receiveInstantiatedSource(output){receiveInstance(output.instance,output.module)}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err("failed to asynchronously prepare wasm: "+reason),abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming=="function"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch=="function")fetch(wasmBinaryFile,{credentials:"same-origin"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err("wasm streaming compile failed: "+reason),err("falling back to ArrayBuffer instantiation"),instantiateArrayBuffer(receiveInstantiatedSource)})});else return instantiateArrayBuffer(receiveInstantiatedSource)}if(Module.instantiateWasm)try{var exports2=Module.instantiateWasm(info,receiveInstance);return exports2}catch(e){return err("Module.instantiateWasm callback failed with error: "+e),!1}return instantiateAsync(),{}}var ASM_CONSTS={};function initPthreadsJS(){PThread.initRuntime()}ENVIRONMENT_IS_PTHREAD||__ATINIT__.push({func:function(){___wasm_call_ctors()}});var __pthread_ptr=0,__pthread_is_main_runtime_thread=0,__pthread_is_main_browser_thread=0;function __register_pthread_ptr(pthreadPtr,isMainBrowserThread,isMainRuntimeThread){pthreadPtr=pthreadPtr|0,isMainBrowserThread=isMainBrowserThread|0,isMainRuntimeThread=isMainRuntimeThread|0,__pthread_ptr=pthreadPtr,__pthread_is_main_browser_thread=isMainBrowserThread,__pthread_is_main_runtime_thread=isMainRuntimeThread}Module.__register_pthread_ptr=__register_pthread_ptr;var ERRNO_CODES={EPERM:63,ENOENT:44,ESRCH:71,EINTR:27,EIO:29,ENXIO:60,E2BIG:1,ENOEXEC:45,EBADF:8,ECHILD:12,EAGAIN:6,EWOULDBLOCK:6,ENOMEM:48,EACCES:2,EFAULT:21,ENOTBLK:105,EBUSY:10,EEXIST:20,EXDEV:75,ENODEV:43,ENOTDIR:54,EISDIR:31,EINVAL:28,ENFILE:41,EMFILE:33,ENOTTY:59,ETXTBSY:74,EFBIG:22,ENOSPC:51,ESPIPE:70,EROFS:69,EMLINK:34,EPIPE:64,EDOM:18,ERANGE:68,ENOMSG:49,EIDRM:24,ECHRNG:106,EL2NSYNC:156,EL3HLT:107,EL3RST:108,ELNRNG:109,EUNATCH:110,ENOCSI:111,EL2HLT:112,EDEADLK:16,ENOLCK:46,EBADE:113,EBADR:114,EXFULL:115,ENOANO:104,EBADRQC:103,EBADSLT:102,EDEADLOCK:16,EBFONT:101,ENOSTR:100,ENODATA:116,ETIME:117,ENOSR:118,ENONET:119,ENOPKG:120,EREMOTE:121,ENOLINK:47,EADV:122,ESRMNT:123,ECOMM:124,EPROTO:65,EMULTIHOP:36,EDOTDOT:125,EBADMSG:9,ENOTUNIQ:126,EBADFD:127,EREMCHG:128,ELIBACC:129,ELIBBAD:130,ELIBSCN:131,ELIBMAX:132,ELIBEXEC:133,ENOSYS:52,ENOTEMPTY:55,ENAMETOOLONG:37,ELOOP:32,EOPNOTSUPP:138,EPFNOSUPPORT:139,ECONNRESET:15,ENOBUFS:42,EAFNOSUPPORT:5,EPROTOTYPE:67,ENOTSOCK:57,ENOPROTOOPT:50,ESHUTDOWN:140,ECONNREFUSED:14,EADDRINUSE:3,ECONNABORTED:13,ENETUNREACH:40,ENETDOWN:38,ETIMEDOUT:73,EHOSTDOWN:142,EHOSTUNREACH:23,EINPROGRESS:26,EALREADY:7,EDESTADDRREQ:17,EMSGSIZE:35,EPROTONOSUPPORT:66,ESOCKTNOSUPPORT:137,EADDRNOTAVAIL:4,ENETRESET:39,EISCONN:30,ENOTCONN:53,ETOOMANYREFS:141,EUSERS:136,EDQUOT:19,ESTALE:72,ENOTSUP:138,ENOMEDIUM:148,EILSEQ:25,EOVERFLOW:61,ECANCELED:11,ENOTRECOVERABLE:56,EOWNERDEAD:62,ESTRPIPE:135},__main_thread_futex_wait_address=13568;function _emscripten_futex_wake(addr,count2){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&!0||count2<0)return-28;if(count2==0)return 0;count2>=2147483647&&(count2=Infinity);var mainThreadWaitAddress=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2),mainThreadWoken=0;if(mainThreadWaitAddress==addr){var loadedAddr=Atomics.compareExchange(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,mainThreadWaitAddress,0);if(loadedAddr==mainThreadWaitAddress&&(--count2,mainThreadWoken=1,count2<=0))return 1}var ret=Atomics.notify(GROWABLE_HEAP_I32(),addr>>2,count2);if(ret>=0)return ret+mainThreadWoken;throw"Atomics.notify returned an unexpected value "+ret}Module._emscripten_futex_wake=_emscripten_futex_wake;function __kill_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw"Internal Error! _kill_thread() can only ever be called from main application thread!";if(!pthread_ptr)throw"Internal Error! Null pthread_ptr in _kill_thread!";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];pthread.worker.terminate(),PThread.freeThreadData(pthread),PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(pthread.worker),1),pthread.worker.pthread=void 0}function __cancel_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw"Internal Error! _cancel_thread() can only ever be called from main application thread!";if(!pthread_ptr)throw"Internal Error! Null pthread_ptr in _cancel_thread!";var pthread=PThread.pthreads[pthread_ptr];pthread.worker.postMessage({cmd:"cancel"})}function __cleanup_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw"Internal Error! _cleanup_thread() can only ever be called from main application thread!";if(!pthread_ptr)throw"Internal Error! Null pthread_ptr in _cleanup_thread!";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];if(pthread){var worker2=pthread.worker;PThread.returnWorkerToPool(worker2)}}var PThread={MAIN_THREAD_ID:1,mainThreadInfo:{schedPolicy:0,schedPrio:0},unusedWorkers:[],runningWorkers:[],initRuntime:function(){__register_pthread_ptr(PThread.mainThreadBlock,!ENVIRONMENT_IS_WORKER,1),_emscripten_register_main_browser_thread_id(PThread.mainThreadBlock)},initMainThreadBlock:function(){for(var pthreadPoolSize=8,i=0;i>2]=PThread.mainThreadBlock;var headPtr=PThread.mainThreadBlock+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;for(var tlsMemory=13056,i=0;i<128;++i)GROWABLE_HEAP_U32()[tlsMemory/4+i]=0;Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+104>>2,tlsMemory),Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+40>>2,PThread.mainThreadBlock),Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+44>>2,42)},initWorker:function(){},pthreads:{},exitHandlers:null,setThreadStatus:function(){},runExitHandlers:function(){if(PThread.exitHandlers!==null){for(;PThread.exitHandlers.length>0;)PThread.exitHandlers.pop()();PThread.exitHandlers=null}ENVIRONMENT_IS_PTHREAD&&threadInfoStruct&&___pthread_tsd_run_dtors()},threadExit:function(exitCode){var tb=_pthread_self();tb&&(Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,exitCode),Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1),Atomics.store(GROWABLE_HEAP_U32(),tb+60>>2,1),Atomics.store(GROWABLE_HEAP_U32(),tb+64>>2,0),PThread.runExitHandlers(),_emscripten_futex_wake(tb+0,2147483647),__register_pthread_ptr(0,0,0),threadInfoStruct=0,ENVIRONMENT_IS_PTHREAD&&postMessage({cmd:"exit"}))},threadCancel:function(){PThread.runExitHandlers(),Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+4>>2,-1),Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+0>>2,1),_emscripten_futex_wake(threadInfoStruct+0,2147483647),threadInfoStruct=selfThreadId=0,__register_pthread_ptr(0,0,0),postMessage({cmd:"cancelDone"})},terminateAllThreads:function(){for(var t in PThread.pthreads){var pthread=PThread.pthreads[t];pthread&&pthread.worker&&PThread.returnWorkerToPool(pthread.worker)}PThread.pthreads={};for(var i=0;i>2];GROWABLE_HEAP_I32()[pthread.threadInfoStruct+104>>2]=0,_free(tlsMemory),_free(pthread.threadInfoStruct)}pthread.threadInfoStruct=0,pthread.allocatedOwnStack&&pthread.stackBase&&_free(pthread.stackBase),pthread.stackBase=0,pthread.worker&&(pthread.worker.pthread=null)}},returnWorkerToPool:function(worker2){delete PThread.pthreads[worker2.pthread.thread],PThread.unusedWorkers.push(worker2),PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker2),1),PThread.freeThreadData(worker2.pthread),worker2.pthread=void 0},receiveObjectTransfer:function(data2){},loadWasmModuleToWorker:function(worker2,onFinishedLoading){worker2.onmessage=function(e){var d=e.data,cmd=d.cmd;if(worker2.pthread&&(PThread.currentProxiedOperationCallerThread=worker2.pthread.threadInfoStruct),d.targetThread&&d.targetThread!=_pthread_self()){var thread=PThread.pthreads[d.targetThread];thread?thread.worker.postMessage(e.data,d.transferList):console.error('Internal error! Worker sent a message "'+cmd+'" to target pthread '+d.targetThread+", but that thread no longer exists!"),PThread.currentProxiedOperationCallerThread=void 0;return}if(cmd==="processQueuedMainThreadWork")_emscripten_main_thread_process_queued_calls();else if(cmd==="spawnThread")__spawn_thread(e.data);else if(cmd==="cleanupThread")__cleanup_thread(d.thread);else if(cmd==="killThread")__kill_thread(d.thread);else if(cmd==="cancelThread")__cancel_thread(d.thread);else if(cmd==="loaded")worker2.loaded=!0,onFinishedLoading&&onFinishedLoading(worker2),worker2.runPthread&&(worker2.runPthread(),delete worker2.runPthread);else if(cmd==="print")out("Thread "+d.threadId+": "+d.text);else if(cmd==="printErr")err("Thread "+d.threadId+": "+d.text);else if(cmd==="alert")alert("Thread "+d.threadId+": "+d.text);else if(cmd==="exit"){var detached=worker2.pthread&&Atomics.load(GROWABLE_HEAP_U32(),worker2.pthread.thread+68>>2);detached&&PThread.returnWorkerToPool(worker2)}else cmd==="cancelDone"?PThread.returnWorkerToPool(worker2):cmd==="objectTransfer"?PThread.receiveObjectTransfer(e.data):e.data.target==="setimmediate"?worker2.postMessage(e.data):err("worker sent an unknown command "+cmd);PThread.currentProxiedOperationCallerThread=void 0},worker2.onerror=function(e){err("pthread sent an error! "+e.filename+":"+e.lineno+": "+e.message)},ENVIRONMENT_IS_NODE&&(worker2.on("message",function(data2){worker2.onmessage({data:data2})}),worker2.on("error",function(data2){worker2.onerror(data2)}),worker2.on("exit",function(data2){console.log("worker exited - TODO: update the worker queue?")})),worker2.postMessage({cmd:"load",urlOrBlob:Module.mainScriptUrlOrBlob||_scriptDir2,wasmMemory,wasmModule,DYNAMIC_BASE,DYNAMICTOP_PTR})},allocateUnusedWorker:function(){var pthreadMainJs=locateFile("tfjs-backend-wasm-threaded-simd.worker.js");PThread.unusedWorkers.push(new Worker(pthreadMainJs))},getNewWorker:function(){return PThread.unusedWorkers.length==0&&(PThread.allocateUnusedWorker(),PThread.loadWasmModuleToWorker(PThread.unusedWorkers[0])),PThread.unusedWorkers.length>0?PThread.unusedWorkers.pop():null},busySpinWait:function(msecs){for(var t=performance.now()+msecs;performance.now()>2]=value,value}function _atexit(func2,arg){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(1,1,func2,arg);__ATEXIT__.unshift({func:func2,arg})}function __emscripten_notify_thread_queue(targetThreadId,mainThreadId){if(targetThreadId==mainThreadId)postMessage({cmd:"processQueuedMainThreadWork"});else if(ENVIRONMENT_IS_PTHREAD)postMessage({targetThread:targetThreadId,cmd:"processThreadQueue"});else{var pthread=PThread.pthreads[targetThreadId],worker2=pthread&&pthread.worker;if(!worker2)return;worker2.postMessage({cmd:"processThreadQueue"})}return 1}function _abort(){abort()}function _emscripten_conditional_set_current_thread_status(expectedStatus,newStatus){expectedStatus=expectedStatus|0,newStatus=newStatus|0}function _emscripten_futex_wait(addr,val,timeout){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&!0)return-28;if(ENVIRONMENT_IS_WORKER){var ret=Atomics.wait(GROWABLE_HEAP_I32(),addr>>2,val,timeout);if(ret==="timed-out")return-73;if(ret==="not-equal")return-6;if(ret==="ok")return 0;throw"Atomics.wait returned an unexpected value "+ret}else{var loadedVal=Atomics.load(GROWABLE_HEAP_I32(),addr>>2);if(val!=loadedVal)return-6;var tNow=performance.now(),tEnd=tNow+timeout;Atomics.store(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,addr);for(var ourWaitAddress=addr;addr==ourWaitAddress;){if(tNow=performance.now(),tNow>tEnd)return-73;_emscripten_main_thread_process_queued_calls(),addr=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2)}return 0}}function _emscripten_is_main_browser_thread(){return __pthread_is_main_browser_thread|0}function _emscripten_is_main_runtime_thread(){return __pthread_is_main_runtime_thread|0}function _emscripten_memcpy_big(dest,src,num){GROWABLE_HEAP_U8().copyWithin(dest,src,src+num)}function _emscripten_num_logical_cores(){return navigator.hardwareConcurrency}function _emscripten_proxy_to_main_thread_js(index,sync){for(var numCallArgs=arguments.length-2,stack2=stackSave(),args=stackAlloc(numCallArgs*8),b=args>>3,i=0;i>3]),buf+=8):(buf=buf+3&~3,args.push(GROWABLE_HEAP_I32()[buf>>2]),buf+=4);return args}function _emscripten_receive_on_main_thread_js(index,numCallArgs,args){_emscripten_receive_on_main_thread_js_callArgs.length=numCallArgs;for(var b=args>>3,i=0;i>>16),updateGlobalBufferAndViews(wasmMemory.buffer),1}catch(e){}}function _emscripten_resize_heap(requestedSize){requestedSize=requestedSize>>>0;var oldSize=_emscripten_get_heap_size();if(requestedSize<=oldSize)return!1;var PAGE_MULTIPLE=65536,maxHeapSize=2147483648;if(requestedSize>maxHeapSize)return!1;for(var minHeapSize=16777216,cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(minHeapSize,requestedSize,overGrownHeapSize),PAGE_MULTIPLE)),replacement=emscripten_realloc_buffer(newSize);if(replacement)return!0}return!1}var JSEvents={keyEvent:0,mouseEvent:0,wheelEvent:0,uiEvent:0,focusEvent:0,deviceOrientationEvent:0,deviceMotionEvent:0,fullscreenChangeEvent:0,pointerlockChangeEvent:0,visibilityChangeEvent:0,touchEvent:0,previousFullscreenElement:null,previousScreenX:null,previousScreenY:null,removeEventListenersRegistered:!1,removeAllEventListeners:function(){for(var i=JSEvents.eventHandlers.length-1;i>=0;--i)JSEvents._removeHandler(i);JSEvents.eventHandlers=[],JSEvents.deferredCalls=[]},registerRemoveEventListeners:function(){JSEvents.removeEventListenersRegistered||(__ATEXIT__.push(JSEvents.removeAllEventListeners),JSEvents.removeEventListenersRegistered=!0)},deferredCalls:[],deferCall:function(targetFunction,precedence,argsList){function arraysHaveEqualContent(arrA,arrB){if(arrA.length!=arrB.length)return!1;for(var i2 in arrA)if(arrA[i2]!=arrB[i2])return!1;return!0}for(var i in JSEvents.deferredCalls){var call=JSEvents.deferredCalls[i];if(call.targetFunction==targetFunction&&arraysHaveEqualContent(call.argsList,argsList))return}JSEvents.deferredCalls.push({targetFunction,precedence,argsList}),JSEvents.deferredCalls.sort(function(x,y){return x.precedence>2]=eventTypeId,GROWABLE_HEAP_I32()[varargs+4>>2]=eventData,GROWABLE_HEAP_I32()[varargs+8>>2]=userData,_emscripten_async_queue_on_thread_(targetThread,637534208,eventHandlerFunc,eventData,varargs),stackRestore(stackTop)},getTargetThreadForEventCallback:function(targetThread){switch(targetThread){case 1:return 0;case 2:return PThread.currentProxiedOperationCallerThread;default:return targetThread}},getNodeNameForTarget:function(target){return target?target==window?"#window":target==screen?"#screen":target&&target.nodeName?target.nodeName:"":""},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function stringToNewUTF8(jsString){var length=lengthBytesUTF8(jsString)+1,cString=_malloc(length);return stringToUTF8(jsString,cString,length),cString}function _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height){var stackTop=stackSave(),varargs=stackAlloc(12),targetCanvasPtr=0;targetCanvas&&(targetCanvasPtr=stringToNewUTF8(targetCanvas)),GROWABLE_HEAP_I32()[varargs>>2]=targetCanvasPtr,GROWABLE_HEAP_I32()[varargs+4>>2]=width,GROWABLE_HEAP_I32()[varargs+8>>2]=height,_emscripten_async_queue_on_thread_(targetThread,657457152,0,targetCanvasPtr,varargs),stackRestore(stackTop)}function _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,targetCanvas,width,height){targetCanvas=targetCanvas?UTF8ToString(targetCanvas):"",_emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height)}function __maybeCStringToJsString(cString){return cString>2?UTF8ToString(cString):cString}var specialHTMLTargets=[0,typeof document!="undefined"?document:0,typeof window!="undefined"?window:0];function __findEventTarget(target){target=__maybeCStringToJsString(target);var domElement=specialHTMLTargets[target]||(typeof document!="undefined"?document.querySelector(target):void 0);return domElement}function __findCanvasEventTarget(target){return __findEventTarget(target)}function _emscripten_set_canvas_element_size_calling_thread(target,width,height){var canvas=__findCanvasEventTarget(target);if(!canvas)return-4;if(canvas.canvasSharedPtr&&(GROWABLE_HEAP_I32()[canvas.canvasSharedPtr>>2]=width,GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+4>>2]=height),canvas.offscreenCanvas||!canvas.controlTransferredOffscreen){canvas.offscreenCanvas&&(canvas=canvas.offscreenCanvas);var autoResizeViewport=!1;if(canvas.GLctxObject&&canvas.GLctxObject.GLctx){var prevViewport=canvas.GLctxObject.GLctx.getParameter(2978);autoResizeViewport=prevViewport[0]===0&&prevViewport[1]===0&&prevViewport[2]===canvas.width&&prevViewport[3]===canvas.height}canvas.width=width,canvas.height=height,autoResizeViewport&&canvas.GLctxObject.GLctx.viewport(0,0,width,height)}else if(canvas.canvasSharedPtr){var targetThread=GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+8>>2];return _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,target,width,height),1}else return-4;return 0}function _emscripten_set_canvas_element_size_main_thread(target,width,height){return ENVIRONMENT_IS_PTHREAD?_emscripten_proxy_to_main_thread_js(2,1,target,width,height):_emscripten_set_canvas_element_size_calling_thread(target,width,height)}function _emscripten_set_canvas_element_size(target,width,height){var canvas=__findCanvasEventTarget(target);return canvas?_emscripten_set_canvas_element_size_calling_thread(target,width,height):_emscripten_set_canvas_element_size_main_thread(target,width,height)}function _emscripten_set_current_thread_status(newStatus){newStatus=newStatus|0}function _emscripten_set_thread_name(threadId,name){threadId=threadId|0,name=name|0}function __webgl_enable_ANGLE_instanced_arrays(ctx){var ext=ctx.getExtension("ANGLE_instanced_arrays");if(ext)return ctx.vertexAttribDivisor=function(index,divisor){ext.vertexAttribDivisorANGLE(index,divisor)},ctx.drawArraysInstanced=function(mode,first,count2,primcount){ext.drawArraysInstancedANGLE(mode,first,count2,primcount)},ctx.drawElementsInstanced=function(mode,count2,type,indices,primcount){ext.drawElementsInstancedANGLE(mode,count2,type,indices,primcount)},1}function __webgl_enable_OES_vertex_array_object(ctx){var ext=ctx.getExtension("OES_vertex_array_object");if(ext)return ctx.createVertexArray=function(){return ext.createVertexArrayOES()},ctx.deleteVertexArray=function(vao){ext.deleteVertexArrayOES(vao)},ctx.bindVertexArray=function(vao){ext.bindVertexArrayOES(vao)},ctx.isVertexArray=function(vao){return ext.isVertexArrayOES(vao)},1}function __webgl_enable_WEBGL_draw_buffers(ctx){var ext=ctx.getExtension("WEBGL_draw_buffers");if(ext)return ctx.drawBuffers=function(n,bufs){ext.drawBuffersWEBGL(n,bufs)},1}var GL={counter:1,lastError:0,buffers:[],mappedBuffers:{},programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},currentContext:null,offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,init:function(){for(var miniTempFloatBuffer=new Float32Array(GL.MINI_TEMP_BUFFER_SIZE),i=0;i>2]:-1;source+=UTF8ToString(GROWABLE_HEAP_I32()[string+i*4>>2],len<0?void 0:len)}return source},createContext:function(canvas,webGLContextAttributes){var ctx=canvas.getContext("webgl",webGLContextAttributes);if(!ctx)return 0;var handle=GL.registerContext(ctx,webGLContextAttributes);return handle},registerContext:function(ctx,webGLContextAttributes){var handle=_malloc(8);GROWABLE_HEAP_I32()[handle+4>>2]=_pthread_self();var context={handle,attributes:webGLContextAttributes,version:webGLContextAttributes.majorVersion,GLctx:ctx};return ctx.canvas&&(ctx.canvas.GLctxObject=context),GL.contexts[handle]=context,(typeof webGLContextAttributes.enableExtensionsByDefault=="undefined"||webGLContextAttributes.enableExtensionsByDefault)&&GL.initExtensions(context),handle},makeContextCurrent:function(contextHandle){return GL.currentContext=GL.contexts[contextHandle],Module.ctx=GLctx=GL.currentContext&&GL.currentContext.GLctx,!(contextHandle&&!GLctx)},getContext:function(contextHandle){return GL.contexts[contextHandle]},deleteContext:function(contextHandle){GL.currentContext===GL.contexts[contextHandle]&&(GL.currentContext=null),typeof JSEvents=="object"&&JSEvents.removeAllHandlersOnTarget(GL.contexts[contextHandle].GLctx.canvas),GL.contexts[contextHandle]&&GL.contexts[contextHandle].GLctx.canvas&&(GL.contexts[contextHandle].GLctx.canvas.GLctxObject=void 0),_free(GL.contexts[contextHandle].handle),GL.contexts[contextHandle]=null},initExtensions:function(context){if(context||(context=GL.currentContext),!context.initExtensionsDone){context.initExtensionsDone=!0;var GLctx2=context.GLctx;__webgl_enable_ANGLE_instanced_arrays(GLctx2),__webgl_enable_OES_vertex_array_object(GLctx2),__webgl_enable_WEBGL_draw_buffers(GLctx2),GLctx2.disjointTimerQueryExt=GLctx2.getExtension("EXT_disjoint_timer_query");var automaticallyEnabledExtensions=["OES_texture_float","OES_texture_half_float","OES_standard_derivatives","OES_vertex_array_object","WEBGL_compressed_texture_s3tc","WEBGL_depth_texture","OES_element_index_uint","EXT_texture_filter_anisotropic","EXT_frag_depth","WEBGL_draw_buffers","ANGLE_instanced_arrays","OES_texture_float_linear","OES_texture_half_float_linear","EXT_blend_minmax","EXT_shader_texture_lod","EXT_texture_norm16","WEBGL_compressed_texture_pvrtc","EXT_color_buffer_half_float","WEBGL_color_buffer_float","EXT_sRGB","WEBGL_compressed_texture_etc1","EXT_disjoint_timer_query","WEBGL_compressed_texture_etc","WEBGL_compressed_texture_astc","EXT_color_buffer_float","WEBGL_compressed_texture_s3tc_srgb","EXT_disjoint_timer_query_webgl2","WEBKIT_WEBGL_compressed_texture_pvrtc"],exts=GLctx2.getSupportedExtensions()||[];exts.forEach(function(ext){automaticallyEnabledExtensions.indexOf(ext)!=-1&&GLctx2.getExtension(ext)})}},populateUniformTable:function(program){for(var p2=GL.programs[program],ptable=GL.programInfos[program]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1},utable=ptable.uniforms,numUniforms=GLctx.getProgramParameter(p2,35718),i=0;i>2;contextAttributes.alpha=!!GROWABLE_HEAP_I32()[a+(0>>2)],contextAttributes.depth=!!GROWABLE_HEAP_I32()[a+(4>>2)],contextAttributes.stencil=!!GROWABLE_HEAP_I32()[a+(8>>2)],contextAttributes.antialias=!!GROWABLE_HEAP_I32()[a+(12>>2)],contextAttributes.premultipliedAlpha=!!GROWABLE_HEAP_I32()[a+(16>>2)],contextAttributes.preserveDrawingBuffer=!!GROWABLE_HEAP_I32()[a+(20>>2)];var powerPreference=GROWABLE_HEAP_I32()[a+(24>>2)];contextAttributes.powerPreference=__emscripten_webgl_power_preferences[powerPreference],contextAttributes.failIfMajorPerformanceCaveat=!!GROWABLE_HEAP_I32()[a+(28>>2)],contextAttributes.majorVersion=GROWABLE_HEAP_I32()[a+(32>>2)],contextAttributes.minorVersion=GROWABLE_HEAP_I32()[a+(36>>2)],contextAttributes.enableExtensionsByDefault=GROWABLE_HEAP_I32()[a+(40>>2)],contextAttributes.explicitSwapControl=GROWABLE_HEAP_I32()[a+(44>>2)],contextAttributes.proxyContextToMainThread=GROWABLE_HEAP_I32()[a+(48>>2)],contextAttributes.renderViaOffscreenBackBuffer=GROWABLE_HEAP_I32()[a+(52>>2)];var canvas=__findCanvasEventTarget(target);if(!canvas)return-4;if(contextAttributes.explicitSwapControl)return-1;var contextHandle=GL.createContext(canvas,contextAttributes);return contextHandle}function _emscripten_webgl_create_context(a0,a12){return _emscripten_webgl_do_create_context(a0,a12)}var PATH={splitPath:function(filename){var splitPathRe=/^(\/?|)([\s\S]*?)((?:\.{1,2}|[^\/]+?|)(\.[^.\/]*|))(?:[\/]*)$/;return splitPathRe.exec(filename).slice(1)},normalizeArray:function(parts,allowAboveRoot){for(var up=0,i=parts.length-1;i>=0;i--){var last=parts[i];last==="."?parts.splice(i,1):last===".."?(parts.splice(i,1),up++):up&&(parts.splice(i,1),up--)}if(allowAboveRoot)for(;up;up--)parts.unshift("..");return parts},normalize:function(path){var isAbsolute=path.charAt(0)==="/",trailingSlash=path.substr(-1)==="/";return path=PATH.normalizeArray(path.split("/").filter(function(p2){return!!p2}),!isAbsolute).join("/"),!path&&!isAbsolute&&(path="."),path&&trailingSlash&&(path+="/"),(isAbsolute?"/":"")+path},dirname:function(path){var result=PATH.splitPath(path),root=result[0],dir=result[1];return!root&&!dir?".":(dir&&(dir=dir.substr(0,dir.length-1)),root+dir)},basename:function(path){if(path==="/")return"/";var lastSlash=path.lastIndexOf("/");return lastSlash===-1?path:path.substr(lastSlash+1)},extname:function(path){return PATH.splitPath(path)[3]},join:function(){var paths=Array.prototype.slice.call(arguments,0);return PATH.normalize(paths.join("/"))},join2:function(l,r){return PATH.normalize(l+"/"+r)}},SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer3=SYSCALLS.buffers[stream];curr===0||curr===10?((stream===1?out:err)(UTF8ArrayToString(buffer3,0)),buffer3.length=0):buffer3.push(curr)},varargs:void 0,get:function(){SYSCALLS.varargs+=4;var ret=GROWABLE_HEAP_I32()[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){return ENVIRONMENT_IS_PTHREAD?_emscripten_proxy_to_main_thread_js(3,1,fd):0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(4,1,fd,offset_low,offset_high,whence,newOffset)}function _fd_write(fd,iov,iovcnt,pnum){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(5,1,fd,iov,iovcnt,pnum);for(var num=0,i=0;i>2],len=GROWABLE_HEAP_I32()[iov+(i*8+4)>>2],j=0;j>2]=num,0}function _pthread_cleanup_pop(execute2){var routine=PThread.exitHandlers.pop();execute2&&routine()}function _pthread_cleanup_push(routine,arg){PThread.exitHandlers===null&&(PThread.exitHandlers=[]),PThread.exitHandlers.push(function(){dynCall_vi(routine,arg)})}function __spawn_thread(threadParams){if(ENVIRONMENT_IS_PTHREAD)throw"Internal Error! _spawn_thread() can only ever be called from main application thread!";var worker2=PThread.getNewWorker();if(worker2.pthread!==void 0)throw"Internal error!";if(!threadParams.pthread_ptr)throw"Internal error, no pthread ptr!";PThread.runningWorkers.push(worker2);for(var tlsMemory=_malloc(128*4),i=0;i<128;++i)GROWABLE_HEAP_I32()[tlsMemory+i*4>>2]=0;var stackHigh=threadParams.stackBase+threadParams.stackSize,pthread=PThread.pthreads[threadParams.pthread_ptr]={worker:worker2,stackBase:threadParams.stackBase,stackSize:threadParams.stackSize,allocatedOwnStack:threadParams.allocatedOwnStack,thread:threadParams.pthread_ptr,threadInfoStruct:threadParams.pthread_ptr},tis=pthread.threadInfoStruct>>2;Atomics.store(GROWABLE_HEAP_U32(),tis+(0>>2),0),Atomics.store(GROWABLE_HEAP_U32(),tis+(4>>2),0),Atomics.store(GROWABLE_HEAP_U32(),tis+(8>>2),0),Atomics.store(GROWABLE_HEAP_U32(),tis+(68>>2),threadParams.detached),Atomics.store(GROWABLE_HEAP_U32(),tis+(104>>2),tlsMemory),Atomics.store(GROWABLE_HEAP_U32(),tis+(48>>2),0),Atomics.store(GROWABLE_HEAP_U32(),tis+(40>>2),pthread.threadInfoStruct),Atomics.store(GROWABLE_HEAP_U32(),tis+(44>>2),42),Atomics.store(GROWABLE_HEAP_U32(),tis+(108>>2),threadParams.stackSize),Atomics.store(GROWABLE_HEAP_U32(),tis+(84>>2),threadParams.stackSize),Atomics.store(GROWABLE_HEAP_U32(),tis+(80>>2),stackHigh),Atomics.store(GROWABLE_HEAP_U32(),tis+(108+8>>2),stackHigh),Atomics.store(GROWABLE_HEAP_U32(),tis+(108+12>>2),threadParams.detached),Atomics.store(GROWABLE_HEAP_U32(),tis+(108+20>>2),threadParams.schedPolicy),Atomics.store(GROWABLE_HEAP_U32(),tis+(108+24>>2),threadParams.schedPrio);var global_libc=_emscripten_get_global_libc(),global_locale=global_libc+40;Atomics.store(GROWABLE_HEAP_U32(),tis+(176>>2),global_locale),worker2.pthread=pthread;var msg={cmd:"run",start_routine:threadParams.startRoutine,arg:threadParams.arg,threadInfoStruct:threadParams.pthread_ptr,selfThreadId:threadParams.pthread_ptr,parentThreadId:threadParams.parent_pthread_ptr,stackBase:threadParams.stackBase,stackSize:threadParams.stackSize};worker2.runPthread=function(){msg.time=performance.now(),worker2.postMessage(msg,threadParams.transferList)},worker2.loaded&&(worker2.runPthread(),delete worker2.runPthread)}function _pthread_getschedparam(thread,policy,schedparam){if(!policy&&!schedparam)return ERRNO_CODES.EINVAL;if(!thread)return err("pthread_getschedparam called with a null thread pointer!"),ERRNO_CODES.ESRCH;var self2=GROWABLE_HEAP_I32()[thread+12>>2];if(self2!==thread)return err("pthread_getschedparam attempted on thread "+thread+", which does not point to a valid thread, or does not exist anymore!"),ERRNO_CODES.ESRCH;var schedPolicy=Atomics.load(GROWABLE_HEAP_U32(),thread+108+20>>2),schedPrio=Atomics.load(GROWABLE_HEAP_U32(),thread+108+24>>2);return policy&&(GROWABLE_HEAP_I32()[policy>>2]=schedPolicy),schedparam&&(GROWABLE_HEAP_I32()[schedparam>>2]=schedPrio),0}function _pthread_self(){return __pthread_ptr|0}Module._pthread_self=_pthread_self;function _pthread_create(pthread_ptr,attr,start_routine,arg){if(typeof SharedArrayBuffer=="undefined")return err("Current environment does not support SharedArrayBuffer, pthreads are not available!"),6;if(!pthread_ptr)return err("pthread_create called with a null thread pointer!"),28;var transferList=[],error=0;if(ENVIRONMENT_IS_PTHREAD&&(transferList.length===0||error))return _emscripten_sync_run_in_main_thread_4(687865856,pthread_ptr,attr,start_routine,arg);if(error)return error;var stackSize=0,stackBase=0,detached=0,schedPolicy=0,schedPrio=0;if(attr){stackSize=GROWABLE_HEAP_I32()[attr>>2],stackSize+=81920,stackBase=GROWABLE_HEAP_I32()[attr+8>>2],detached=GROWABLE_HEAP_I32()[attr+12>>2]!==0;var inheritSched=GROWABLE_HEAP_I32()[attr+16>>2]===0;if(inheritSched){var prevSchedPolicy=GROWABLE_HEAP_I32()[attr+20>>2],prevSchedPrio=GROWABLE_HEAP_I32()[attr+24>>2],parentThreadPtr=PThread.currentProxiedOperationCallerThread?PThread.currentProxiedOperationCallerThread:_pthread_self();_pthread_getschedparam(parentThreadPtr,attr+20,attr+24),schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2],schedPrio=GROWABLE_HEAP_I32()[attr+24>>2],GROWABLE_HEAP_I32()[attr+20>>2]=prevSchedPolicy,GROWABLE_HEAP_I32()[attr+24>>2]=prevSchedPrio}else schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2],schedPrio=GROWABLE_HEAP_I32()[attr+24>>2]}else stackSize=2097152;var allocatedOwnStack=stackBase==0;allocatedOwnStack?stackBase=_memalign(16,stackSize):(stackBase-=stackSize,assert3(stackBase>0));for(var threadInfoStruct2=_malloc(232),i=0;i<232>>2;++i)GROWABLE_HEAP_U32()[(threadInfoStruct2>>2)+i]=0;GROWABLE_HEAP_I32()[pthread_ptr>>2]=threadInfoStruct2,GROWABLE_HEAP_I32()[threadInfoStruct2+12>>2]=threadInfoStruct2;var headPtr=threadInfoStruct2+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var threadParams={stackBase,stackSize,allocatedOwnStack,schedPolicy,schedPrio,detached,startRoutine:start_routine,pthread_ptr:threadInfoStruct2,parent_pthread_ptr:_pthread_self(),arg,transferList};return ENVIRONMENT_IS_PTHREAD?(threadParams.cmd="spawnThread",postMessage(threadParams,transferList)):__spawn_thread(threadParams),0}function _roundf(d){return d=+d,d>=0?+Math_floor(d+.5):+Math_ceil(d-.5)}function _sysconf(name){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(6,1,name);switch(name){case 30:return 16384;case 85:var maxHeapSize=2147483648;return maxHeapSize/16384;case 132:case 133:case 12:case 137:case 138:case 15:case 235:case 16:case 17:case 18:case 19:case 20:case 149:case 13:case 10:case 236:case 153:case 9:case 21:case 22:case 159:case 154:case 14:case 77:case 78:case 139:case 80:case 81:case 82:case 68:case 67:case 164:case 11:case 29:case 47:case 48:case 95:case 52:case 51:case 46:case 79:return 200809;case 27:case 246:case 127:case 128:case 23:case 24:case 160:case 161:case 181:case 182:case 242:case 183:case 184:case 243:case 244:case 245:case 165:case 178:case 179:case 49:case 50:case 168:case 169:case 175:case 170:case 171:case 172:case 97:case 76:case 32:case 173:case 35:return-1;case 176:case 177:case 7:case 155:case 8:case 157:case 125:case 126:case 92:case 93:case 129:case 130:case 131:case 94:case 91:return 1;case 74:case 60:case 69:case 70:case 4:return 1024;case 31:case 42:case 72:return 32;case 87:case 26:case 33:return 2147483647;case 34:case 1:return 47839;case 38:case 36:return 99;case 43:case 37:return 2048;case 0:return 2097152;case 3:return 65536;case 28:return 32768;case 44:return 32767;case 75:return 16384;case 39:return 1e3;case 89:return 700;case 71:return 256;case 40:return 255;case 2:return 100;case 180:return 64;case 25:return 20;case 5:return 16;case 6:return 6;case 73:return 4;case 84:return typeof navigator=="object"&&navigator.hardwareConcurrency||1}return setErrNo(28),-1}ENVIRONMENT_IS_PTHREAD?PThread.initWorker():PThread.initMainThreadBlock();var GLctx;GL.init();var proxiedFunctionTable=[null,_atexit,_emscripten_set_canvas_element_size_main_thread,_fd_close,_fd_seek,_fd_write,_sysconf],asmLibraryArg={e:___assert_fail,r:___call_main,w:__emscripten_notify_thread_queue,a:_abort,l:_emscripten_conditional_set_current_thread_status,d:_emscripten_futex_wait,c:_emscripten_futex_wake,h:_emscripten_get_now,g:_emscripten_is_main_browser_thread,x:_emscripten_is_main_runtime_thread,q:_emscripten_memcpy_big,B:_emscripten_num_logical_cores,t:_emscripten_receive_on_main_thread_js,A:_emscripten_resize_heap,u:_emscripten_set_canvas_element_size,k:_emscripten_set_current_thread_status,s:_emscripten_set_thread_name,v:_emscripten_webgl_create_context,m:_fd_close,o:_fd_seek,i:_fd_write,p:initPthreadsJS,memory:wasmMemory||Module.wasmMemory,y:_pthread_cleanup_pop,z:_pthread_cleanup_push,j:_pthread_create,b:_pthread_self,f:_roundf,n:_sysconf,table:wasmTable},asm=createWasm();Module.asm=asm;var 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Uint8Array?"int32":isNumber(values)?"float32":isString(values)?"string":isBoolean(values)?"bool":"float32"}function isFunction(f){return!!(f&&f.constructor&&f.call&&f.apply)}function nearestDivisor(size,start){for(let i=start;i=0;--i)strides[i]=strides[i+1]*shape[i+1];return strides}function createNestedArray(offset,shape,a){let ret=new Array;if(shape.length===1){let d=shape[0];for(let i=0;iacc*c);for(let i=0;iacc*c);if(size===0)return[];if(size!==a.length)throw new Error(`[${shape}] does not match the input size ${a.length}.`);return createNestedArray(0,shape,a)}function makeOnesTypedArray(size,dtype){let array2=makeZerosTypedArray(size,dtype);for(let i=0;iprev*curr,1);if(dtype==null||dtype==="float32")return toNestedArray(shape,new Float32Array(size));if(dtype==="int32")return toNestedArray(shape,new Int32Array(size));if(dtype==="bool")return toNestedArray(shape,new Uint8Array(size));throw new Error(`Unknown data type ${dtype}`)}function assertNonNegativeIntegerDimensions(shape){shape.forEach(dimSize=>{assert(Number.isInteger(dimSize)&&dimSize>=0,()=>`Tensor must have a shape comprised of positive integers but got shape [${shape}].`)})}function locToIndex(locs,rank,strides){if(rank===0)return 0;if(rank===1)return locs[0];let index=locs[locs.length-1];for(let i=0;i{let[key,value]=keyValue.split(":");this.urlFlags[key]=parseValue(key,value)})}};function getQueryParams(queryString){let params={};return queryString.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g,(s,...t)=>(decodeParam(params,t[0],t[1]),t.join("="))),params}function decodeParam(params,name,value){params[decodeURIComponent(name)]=decodeURIComponent(value||"")}function parseValue(flagName,value){if(value=value.toLowerCase(),value==="true"||value==="false")return value==="true";if(`${+value}`===value)return+value;throw new Error(`Could not parse value flag value ${value} for flag ${flagName}.`)}function env(){return ENV}var ENV=null;function setEnvironmentGlobal(environment){ENV=environment}var globalNameSpace;function getGlobalNamespace(){if(globalNameSpace==null){let ns;if(typeof window!="undefined")ns=window;else if(typeof global!="undefined")ns=global;else if(typeof process!="undefined")ns=process;else if(typeof self!="undefined")ns=self;else throw new Error("Could not find a global object");globalNameSpace=ns}return globalNameSpace}function getGlobalMap(){let ns=getGlobalNamespace();return ns._tfGlobals==null&&(ns._tfGlobals=new Map),ns._tfGlobals}function getGlobal(key,init2){let globalMap=getGlobalMap();if(globalMap.has(key))return globalMap.get(key);{let singleton=init2();return globalMap.set(key,singleton),globalMap.get(key)}}var Abs="Abs",Acos="Acos",Acosh="Acosh",Add="Add",AddN="AddN",All="All",Any="Any",ArgMax="ArgMax",ArgMin="ArgMin",Asin="Asin",Asinh="Asinh",Atan="Atan",Atanh="Atanh",Atan2="Atan2",AvgPool="AvgPool",AvgPoolGrad="AvgPoolGrad",AvgPool3D="AvgPool3D",AvgPool3DGrad="AvgPool3DGrad",BatchMatMul="BatchMatMul",BatchToSpaceND="BatchToSpaceND",Bincount="Bincount",BroadcastTo="BroadcastTo",Cast="Cast",Ceil="Ceil",ClipByValue="ClipByValue",Complex="Complex",ComplexAbs="ComplexAbs",Concat="Concat",Conv2D="Conv2D",Conv2DBackpropFilter="Conv2DBackpropFilter",Conv2DBackpropInput="Conv2DBackpropInput",Conv3D="Conv3D",Conv3DBackpropFilterV2="Conv3DBackpropFilterV2",Conv3DBackpropInputV2="Conv3DBackpropInputV2",Cos="Cos",Cosh="Cosh",Cumsum="Cumsum",CropAndResize="CropAndResize",DenseBincount="DenseBincount",DepthToSpace="DepthToSpace",DepthwiseConv2dNative="DepthwiseConv2dNative",DepthwiseConv2dNativeBackpropFilter="DepthwiseConv2dNativeBackpropFilter",DepthwiseConv2dNativeBackpropInput="DepthwiseConv2dNativeBackpropInput",Diag="Diag",Dilation2D="Dilation2D",Dilation2DBackpropInput="Dilation2DBackpropInput",Dilation2DBackpropFilter="Dilation2DBackpropFilter",RealDiv="RealDiv",Elu="Elu",EluGrad="EluGrad",Erf="Erf",Equal="Equal",Exp="Exp",ExpandDims="ExpandDims",Expm1="Expm1",FFT="FFT",Fill="Fill",FlipLeftRight="FlipLeftRight",Floor="Floor",FloorDiv="FloorDiv",FusedBatchNorm="FusedBatchNorm",GatherV2="GatherV2",GatherNd="GatherNd",Greater="Greater",GreaterEqual="GreaterEqual",Identity="Identity",IFFT="IFFT",Imag="Imag",IsFinite="IsFinite",IsInf="IsInf",IsNan="IsNan",LeakyRelu="LeakyRelu",Less="Less",LessEqual="LessEqual",LinSpace="LinSpace",Log="Log",Log1p="Log1p",LogicalAnd="LogicalAnd",LogicalNot="LogicalNot",LogicalOr="LogicalOr",LogSoftmax="LogSoftmax",LRN="LRN",LRNGrad="LRNGrad",Max="Max",Maximum="Maximum",MaxPool="MaxPool",MaxPoolGrad="MaxPoolGrad",MaxPool3D="MaxPool3D",MaxPool3DGrad="MaxPool3DGrad",MaxPoolWithArgmax="MaxPoolWithArgmax",Mean="Mean",Min="Min",Minimum="Minimum",MirrorPad="MirrorPad",Mod="Mod",Multinomial="Multinomial",Multiply="Multiply",Neg="Neg",NotEqual="NotEqual",NonMaxSuppressionV3="NonMaxSuppressionV3",NonMaxSuppressionV4="NonMaxSuppressionV4",NonMaxSuppressionV5="NonMaxSuppressionV5",OnesLike="OnesLike",OneHot="OneHot",Pack="Pack",PadV2="PadV2",Pool="Pool",Pow="Pow",Prelu="Prelu",Prod="Prod",Range="Range",Real="Real",Reciprocal="Reciprocal",Relu="Relu",Reshape="Reshape",ResizeNearestNeighbor="ResizeNearestNeighbor",ResizeNearestNeighborGrad="ResizeNearestNeighborGrad",ResizeBilinear="ResizeBilinear",ResizeBilinearGrad="ResizeBilinearGrad",Relu6="Relu6",Reverse="Reverse",Round="Round",Rsqrt="Rsqrt",ScatterNd="ScatterNd",Select="Select",Selu="Selu",Slice="Slice",Sin="Sin",Sinh="Sinh",Sign="Sign",Sigmoid="Sigmoid",Softplus="Softplus",Sqrt="Sqrt",Sum="Sum",SpaceToBatchND="SpaceToBatchND",SplitV="SplitV",Softmax="Softmax",SquaredDifference="SquaredDifference",Square="Square",Sub="Sub",SparseToDense="SparseToDense",StridedSlice="StridedSlice",Tan="Tan",Tanh="Tanh",Tile="Tile",TopK="TopK",Transpose="Transpose",Unique="Unique",Unpack="Unpack",UnsortedSegmentSum="UnsortedSegmentSum",ZerosLike="ZerosLike",Step="Step",FromPixels="FromPixels",RotateWithOffset="RotateWithOffset",_FusedMatMul="_FusedMatMul",FusedConv2D="FusedConv2D",FusedDepthwiseConv2D="FusedDepthwiseConv2D",kernelRegistry=getGlobal("kernelRegistry",()=>new Map),gradRegistry=getGlobal("gradRegistry",()=>new Map);function getKernel(kernelName,backendName){let key=makeKey(kernelName,backendName);return kernelRegistry.get(key)}function getGradient(kernelName){return gradRegistry.get(kernelName)}function getKernelsForBackend(backendName){let it=kernelRegistry.entries(),result=[];for(;;){let{done,value}=it.next();if(done)break;let[key,config2]=value,[backend22]=key.split("_");backend22===backendName&&result.push(config2)}return result}function registerKernel(config2){let{kernelName,backendName}=config2,key=makeKey(kernelName,backendName);kernelRegistry.has(key)&&console.warn(`The kernel '${kernelName}' for backend '${backendName}' is already registered`),kernelRegistry.set(key,config2)}function registerGradient(config2){let{kernelName}=config2;gradRegistry.has(kernelName)&&env().getBool("DEBUG")&&console.warn(`Overriding the gradient for '${kernelName}'`),gradRegistry.set(kernelName,config2)}function unregisterKernel(kernelName,backendName){let key=makeKey(kernelName,backendName);if(!kernelRegistry.has(key))throw new Error(`The kernel '${kernelName}' for backend '${backendName}' is not registered`);kernelRegistry.delete(key)}function unregisterGradient(kernelName){if(!gradRegistry.has(kernelName))throw new Error(`The gradient '${kernelName}' for backend is not registered`);gradRegistry.delete(kernelName)}function copyRegisteredKernels(registeredBackendName,newBackendName){getKernelsForBackend(registeredBackendName).forEach(kernelConfig=>{let newKernelConfig=Object.assign({},kernelConfig,{backendName:newBackendName});registerKernel(newKernelConfig)})}function makeKey(kernelName,backendName){return`${backendName}_${kernelName}`}var util_exports={};__export2(util_exports,{arraysEqual:()=>arraysEqual,assert:()=>assert,assertNonNegativeIntegerDimensions:()=>assertNonNegativeIntegerDimensions,assertNonNull:()=>assertNonNull,assertShapesMatch:()=>assertShapesMatch,bytesFromStringArray:()=>bytesFromStringArray,bytesPerElement:()=>bytesPerElement,checkConversionForErrors:()=>checkConversionForErrors,clamp:()=>clamp,computeStrides:()=>computeStrides,createScalarValue:()=>createScalarValue,createShuffledIndices:()=>createShuffledIndices,decodeString:()=>decodeString,distSquared:()=>distSquared,encodeString:()=>encodeString,fetch:()=>fetch2,flatten:()=>flatten,getArrayFromDType:()=>getArrayFromDType,getTypedArrayFromDType:()=>getTypedArrayFromDType,hasEncodingLoss:()=>hasEncodingLoss,indexToLoc:()=>indexToLoc,inferDtype:()=>inferDtype,inferFromImplicitShape:()=>inferFromImplicitShape,isBoolean:()=>isBoolean,isFunction:()=>isFunction,isInt:()=>isInt,isNumber:()=>isNumber,isPromise:()=>isPromise,isScalarShape:()=>isScalarShape,isString:()=>isString,isTypedArray:()=>isTypedArray,isValidDtype:()=>isValidDtype,locToIndex:()=>locToIndex,makeOnesTypedArray:()=>makeOnesTypedArray,makeZerosNestedTypedArray:()=>makeZerosNestedTypedArray,makeZerosTypedArray:()=>makeZerosTypedArray,nearestDivisor:()=>nearestDivisor,nearestLargerEven:()=>nearestLargerEven,now:()=>now,parseAxisParam:()=>parseAxisParam,randUniform:()=>randUniform,repeatedTry:()=>repeatedTry,rightPad:()=>rightPad,shuffle:()=>shuffle,sizeFromShape:()=>sizeFromShape,sizeToSquarishShape:()=>sizeToSquarishShape,squeezeShape:()=>squeezeShape,sum:()=>sum,tanh:()=>tanh,toNestedArray:()=>toNestedArray,toTypedArray:()=>toTypedArray});function createScalarValue(value,dtype){return dtype==="string"?encodeString(value):toTypedArray([value],dtype)}function noConversionNeeded(a,dtype){return a instanceof Float32Array&&dtype==="float32"||a instanceof Int32Array&&dtype==="int32"||a instanceof Uint8Array&&dtype==="bool"}function toTypedArray(a,dtype){if(dtype==="string")throw new Error("Cannot convert a string[] to a TypedArray");if(Array.isArray(a)&&(a=flatten(a)),env().getBool("DEBUG")&&checkConversionForErrors(a,dtype),noConversionNeeded(a,dtype))return a;if(dtype==null||dtype==="float32"||dtype==="complex64")return new Float32Array(a);if(dtype==="int32")return new Int32Array(a);if(dtype==="bool"){let bool=new Uint8Array(a.length);for(let i=0;i{outputs=f()},timer=this.backendTimer.time(holdResultWrapperFn);if(env().getBool("CHECK_COMPUTATION_FOR_ERRORS"))for(let i=0;i{checkComputationForErrors(tensorVals,output.dtype,kernelName)})}return{kernelName,outputs,inputs,timeMs:timer.then(timing=>timing.kernelMs),extraInfo:timer.then(timing=>timing.getExtraProfileInfo!=null?timing.getExtraProfileInfo():"")}}logKernelProfile(kernelProfile){let{kernelName,outputs,timeMs,inputs,extraInfo}=kernelProfile;outputs.forEach(result=>{Promise.all([result.data(),timeMs,extraInfo]).then(valueContainer=>{this.logger.logKernelProfile(kernelName,result,valueContainer[0],valueContainer[1],inputs,valueContainer[2])})})}};function checkComputationForErrors(vals,dtype,kernelName){if(dtype!=="float32")return!1;for(let i=0;i0?inputShape:""} `}}console.log(`%c${paddedName} %c${time2} %c${rank}D ${shape} %c${size} %c${inputShapesDescription} %c${extraInfo}`,"font-weight:bold","color:red","color:blue","color: orange","color: green","color: steelblue")}};function getFilteredNodesXToY(tape,xs,y){let tensorsFromX={},nodesFromX={};for(let i=0;itensorsFromX[output.id]=!0),anyInputFromX=!0,nodesFromX[node.id]=!0;break}if(anyInputFromX)break}}let tensorsLeadToY={};tensorsLeadToY[y.id]=!0;let nodesToY={};for(let i=tape.length-1;i>=0;i--){let node=tape[i],nodeInputs=node.inputs;for(let j=0;j=0;i--){let node=filteredTape[i],dys=[];if(node.outputs.forEach(o=>{let gradTensor=tensorAccumulatedGradientMap[o.id];gradTensor!=null?dys.push(gradTensor):dys.push(null)}),node.gradient==null)throw new Error(`Cannot compute gradient: gradient function not found for ${node.kernelName}.`);let inputGradients=node.gradient(dys);for(let inputName in node.inputs){if(!(inputName in inputGradients))throw new Error(`Cannot backprop through input ${inputName}. Available gradients found: ${Object.keys(inputGradients)}.`);let dx=tidy2(()=>inputGradients[inputName]());if(dx.dtype!=="float32")throw new Error(`Error in gradient for op ${node.kernelName}. The gradient of input ${inputName} must have 'float32' dtype, but has '${dx.dtype}'`);let x=node.inputs[inputName];if(!arraysEqual(dx.shape,x.shape))throw new Error(`Error in gradient for op ${node.kernelName}. The gradient of input '${inputName}' has shape '${dx.shape}', which does not match the shape of the input '${x.shape}'`);if(tensorAccumulatedGradientMap[x.id]==null)tensorAccumulatedGradientMap[x.id]=dx;else{let curGradient=tensorAccumulatedGradientMap[x.id];tensorAccumulatedGradientMap[x.id]=add5(curGradient,dx),curGradient.dispose()}}}}var FORMAT_LIMIT_NUM_VALS=20,FORMAT_NUM_FIRST_LAST_VALS=3,FORMAT_NUM_SIG_DIGITS=7;function tensorToString(vals,shape,dtype,verbose){let strides=computeStrides(shape),padPerCol=computeMaxSizePerColumn(vals,shape,dtype,strides),rank=shape.length,valsLines=subTensorToString(vals,shape,dtype,strides,padPerCol),lines=["Tensor"];return verbose&&(lines.push(` dtype: ${dtype}`),lines.push(` rank: ${rank}`),lines.push(` shape: [${shape}]`),lines.push(" values:")),lines.push(valsLines.map(l=>" "+l).join(` `)),lines.join(` `)}function computeMaxSizePerColumn(vals,shape,dtype,strides){let n=sizeFromShape(shape),numCols=strides[strides.length-1],padPerCol=new Array(numCols).fill(0),rank=shape.length,valuesOrTuples=dtype==="complex64"?createComplexTuples(vals):vals;if(rank>1)for(let row=0;rowFORMAT_LIMIT_NUM_VALS){let firstValsSize=FORMAT_NUM_FIRST_LAST_VALS*storagePerElement,firstVals=Array.from(vals.slice(0,firstValsSize)),lastVals=Array.from(vals.slice((size-FORMAT_NUM_FIRST_LAST_VALS)*storagePerElement,size*storagePerElement));return dtype==="complex64"&&(firstVals=createComplexTuples(firstVals),lastVals=createComplexTuples(lastVals)),["["+firstVals.map((x,i)=>valToString(x,padPerCol[i],dtype)).join(", ")+", ..., "+lastVals.map((x,i)=>valToString(x,padPerCol[size-FORMAT_NUM_FIRST_LAST_VALS+i],dtype)).join(", ")+"]"]}let displayVals=dtype==="complex64"?createComplexTuples(vals):Array.from(vals);return["["+displayVals.map((x,i)=>valToString(x,padPerCol[i],dtype)).join(", ")+"]"]}let subshape=shape.slice(1),substrides=strides.slice(1),stride=strides[0]*storagePerElement,lines=[];if(size>FORMAT_LIMIT_NUM_VALS){for(let i=0;i`Length of values '${n}' does not match the size inferred by the shape '${this.size}'.`)}if(dtype==="complex64")throw new Error("complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).");this.values=values||getArrayFromDType(dtype,this.size),this.strides=computeStrides(shape)}set(value,...locs){locs.length===0&&(locs=[0]),assert(locs.length===this.rank,()=>`The number of provided coordinates (${locs.length}) must match the rank (${this.rank})`);let index=this.locToIndex(locs);this.values[index]=value}get(...locs){locs.length===0&&(locs=[0]);let i=0;for(let loc of locs){if(loc<0||loc>=this.shape[i]){let msg=`Requested out of range element at ${locs}. Buffer shape=${this.shape}`;throw new Error(msg)}i++}let index=locs[locs.length-1];for(let i2=0;i2decodeString(b))}catch(_a){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}}return data2}dataSync(){this.throwIfDisposed();let data2=trackerFn().readSync(this.dataId);if(this.dtype==="string")try{return data2.map(b=>decodeString(b))}catch(_a){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}return data2}async bytes(){this.throwIfDisposed();let data2=await trackerFn().read(this.dataId);return this.dtype==="string"?data2:new Uint8Array(data2.buffer)}dispose(){this.isDisposed||(trackerFn().disposeTensor(this),this.isDisposedInternal=!0)}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(verbose=!1){return opHandler.print(this,verbose)}clone(){return this.throwIfDisposed(),opHandler.clone(this)}toString(verbose=!1){let vals=this.dataSync();return tensorToString(vals,this.shape,this.dtype,verbose)}cast(dtype){return this.throwIfDisposed(),opHandler.cast(this,dtype)}variable(trainable=!0,name,dtype){return this.throwIfDisposed(),trackerFn().makeVariable(this,trainable,name,dtype)}};Object.defineProperty(Tensor,Symbol.hasInstance,{value:instance2=>!!instance2&&instance2.data!=null&&instance2.dataSync!=null&&instance2.throwIfDisposed!=null});var Variable=class extends Tensor{constructor(initialValue,trainable,name,tensorId){super(initialValue.shape,initialValue.dtype,initialValue.dataId,tensorId);this.trainable=trainable,this.name=name}assign(newValue){if(newValue.dtype!==this.dtype)throw new Error(`dtype of the new value (${newValue.dtype}) and previous value (${this.dtype}) must match`);if(!arraysEqual(newValue.shape,this.shape))throw new Error(`shape of the new value (${newValue.shape}) and previous value (${this.shape}) must match`);trackerFn().disposeTensor(this),this.dataId=newValue.dataId,trackerFn().incRef(this,null)}dispose(){trackerFn().disposeVariable(this),this.isDisposedInternal=!0}};Object.defineProperty(Variable,Symbol.hasInstance,{value:instance2=>instance2 instanceof Tensor&&instance2.assign!=null&&instance2.assign instanceof Function});var tensor_util_exports={};__export2(tensor_util_exports,{assertTypesMatch:()=>assertTypesMatch,getTensorsInContainer:()=>getTensorsInContainer,isTensorInList:()=>isTensorInList,makeTypesMatch:()=>makeTypesMatch});var Rank;(function(Rank2){Rank2.R0="R0",Rank2.R1="R1",Rank2.R2="R2",Rank2.R3="R3",Rank2.R4="R4",Rank2.R5="R5",Rank2.R6="R6"})(Rank||(Rank={}));var UpcastInt32AndMap;(function(UpcastInt32AndMap2){UpcastInt32AndMap2.float32="float32",UpcastInt32AndMap2.int32="int32",UpcastInt32AndMap2.bool="int32",UpcastInt32AndMap2.complex64="complex64"})(UpcastInt32AndMap||(UpcastInt32AndMap={}));var UpcastBoolAndMap;(function(UpcastBoolAndMap2){UpcastBoolAndMap2.float32="float32",UpcastBoolAndMap2.int32="int32",UpcastBoolAndMap2.bool="bool",UpcastBoolAndMap2.complex64="complex64"})(UpcastBoolAndMap||(UpcastBoolAndMap={}));var UpcastFloat32AndMap;(function(UpcastFloat32AndMap2){UpcastFloat32AndMap2.float32="float32",UpcastFloat32AndMap2.int32="float32",UpcastFloat32AndMap2.bool="float32",UpcastFloat32AndMap2.complex64="complex64"})(UpcastFloat32AndMap||(UpcastFloat32AndMap={}));var UpcastComplex64AndMap;(function(UpcastComplex64AndMap2){UpcastComplex64AndMap2.float32="complex64",UpcastComplex64AndMap2.int32="complex64",UpcastComplex64AndMap2.bool="complex64",UpcastComplex64AndMap2.complex64="complex64"})(UpcastComplex64AndMap||(UpcastComplex64AndMap={}));var upcastTypeMap={float32:UpcastFloat32AndMap,int32:UpcastInt32AndMap,bool:UpcastBoolAndMap,complex64:UpcastComplex64AndMap};function upcastType(typeA,typeB){if(typeA==="string"||typeB==="string"){if(typeA==="string"&&typeB==="string")return"string";throw new Error(`Can not upcast ${typeA} with ${typeB}`)}return upcastTypeMap[typeA][typeB]}function sumOutType(type){return upcastType(type,"int32")}function makeTypesMatch(a,b){if(a.dtype===b.dtype)return[a,b];let dtype=upcastType(a.dtype,b.dtype);return[a.cast(dtype),b.cast(dtype)]}function assertTypesMatch(a,b){assert(a.dtype===b.dtype,()=>`The dtypes of the first(${a.dtype}) and second(${b.dtype}) input must match`)}function isTensorInList(tensor2,tensorList){return tensorList.some(x=>x.id===tensor2.id)}function getTensorsInContainer(result){let list=[],seen=new Set;return walkTensorContainer(result,list,seen),list}function walkTensorContainer(container,list,seen){if(container==null)return;if(container instanceof Tensor){list.push(container);return}if(!isIterable(container))return;let iterable=container;for(let k in iterable){let val=iterable[k];seen.has(val)||(seen.add(val),walkTensorContainer(val,list,seen))}}function isIterable(obj){return Array.isArray(obj)||typeof obj=="object"}var EngineState=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(k=>k.name)))}}}dispose(){for(let variableName in this.registeredVariables)this.registeredVariables[variableName].dispose()}},Engine=class{constructor(ENV5){this.ENV=ENV5,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new EngineState}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let sortedBackends=this.getSortedBackends();for(let i=0;i{kernel.setupFunc!=null&&kernel.setupFunc(this.backendInstance)})}disposeRegisteredKernels(backendName){getKernelsForBackend(backendName).forEach(kernel=>{kernel.disposeFunc!=null&&kernel.disposeFunc(this.registry[backendName])})}initializeBackend(backendName){let registryFactoryEntry=this.registryFactory[backendName];if(registryFactoryEntry==null)throw new Error(`Cannot initialize backend ${backendName}, no registration found.`);try{let backend22=registryFactoryEntry.factory();if(backend22&&!(backend22 instanceof KernelBackend)&&typeof backend22.then=="function"){let promiseId=++this.pendingBackendInitId,success=backend22.then(backendInstance=>promiseId(promiseIdthis.registryFactory[b].priority-this.registryFactory[a].priority)}initializeBackendsAndReturnBest(){let sortedBackends=this.getSortedBackends();for(let i=0;ithis.startScope(name),()=>this.endScope(result),()=>(result=fn(),result instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),result))}scopedRun(start,end,f){start();try{let res=f();return end(),res}catch(ex){throw end(),ex}}nextTensorId(){return Engine.nextTensorId++}nextVariableId(){return Engine.nextVariableId++}clone(x){let y=this.makeTensorFromDataId(x.dataId,x.shape,x.dtype),inputs={x},grad2=dy=>({x:()=>{let dtype="float32",gradInputs={x:dy},attrs={dtype};return ENGINE.runKernelFunc(backend22=>backend22.cast(dy,dtype),gradInputs,null,Cast,attrs)}}),saved=[];return this.addTapeNode(this.state.activeScope.name,inputs,[y],grad2,saved,{}),y}runKernel(kernelName,inputs,attrs,inputsToSave,outputsToSave){let forwardFunc=null,backwardsFunc=null;return this.runKernelFunc(forwardFunc,inputs,backwardsFunc,kernelName,attrs,inputsToSave,outputsToSave)}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(kernelName,numDataIdsBefore,outInfos){let numDataIdsAfter=this.backend.numDataIds(),numOutputDataIds=0;outInfos.forEach(info=>{numOutputDataIds+=info.dtype==="complex64"?3:1});let numMoves=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],dataIdsLeaked=numDataIdsAfter-numDataIdsBefore-numOutputDataIds-numMoves;if(dataIdsLeaked>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${dataIdsLeaked} data ids) after running '${kernelName}'`)}runKernelFunc(forwardFunc,inputs,backwardsFunc,kernelName,attrs,inputsToSave,outputsToSave){let outputs,saved=[],isTapeOn=this.isTapeOn();kernelName==null&&(kernelName=this.state.activeScope!=null?this.state.activeScope.name:"");let startingBytecount=this.state.numBytes,startingNumTensors=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let kernelFunc3;this.backendName==null&&this.backend;let kernel=getKernel(kernelName,this.backendName),out;if(kernel!=null)kernelFunc3=()=>{let numDataIdsBefore=this.backend.numDataIds();out=kernel.kernelFunc({inputs,attrs,backend:this.backend});let outInfos=Array.isArray(out)?out:[out];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(kernelName,numDataIdsBefore,outInfos);let outTensors=outInfos.map(outInfo=>{if(outInfo.rank!=null)return outInfo;let{dataId,shape,dtype}=outInfo;return this.makeTensorFromDataId(dataId,shape,dtype)});if(isTapeOn){let tensorsToSave=this.getTensorsForGradient(kernelName,inputs,outTensors);if(tensorsToSave==null){outputsToSave==null&&(outputsToSave=[]);let outsToSave=outTensors.filter((_,i)=>outputsToSave[i]);tensorsToSave=(inputsToSave||[]).slice().concat(outsToSave)}saved=this.saveTensorsForBackwardMode(tensorsToSave)}return outTensors};else{if(forwardFunc==null)throw new Error(`Error running ${kernelName}: Neither modular kernel nor forward func passed`);let saveFunc=tensors=>{!isTapeOn||(saved=tensors.map(tensor2=>this.keep(this.clone(tensor2))))};kernelFunc3=()=>{let numDataIdsBefore=this.backend.numDataIds();out=this.tidy(()=>forwardFunc(this.backend,saveFunc));let outs=Array.isArray(out)?out:[out];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(kernelName,numDataIdsBefore,outs),outs}}let kernelProfile;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?outputs=kernelFunc3():(kernelProfile=this.profiler.profileKernel(kernelName,inputs,()=>kernelFunc3()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(kernelProfile),outputs=kernelProfile.outputs)}),isTapeOn&&this.addTapeNode(kernelName,inputs,outputs,backwardsFunc,saved,attrs),this.state.profiling&&this.state.activeProfile.kernels.push({name:kernelName,bytesAdded:this.state.numBytes-startingBytecount,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-startingNumTensors,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(inputs).map(key=>inputs[key]!=null?inputs[key].shape:null),outputShapes:outputs.map(item=>item.shape),kernelTimeMs:kernelProfile.timeMs,extraInfo:kernelProfile.extraInfo}),Array.isArray(out)?outputs:outputs[0]}saveTensorsForBackwardMode(tensors){return tensors.map(tensor2=>this.keep(this.clone(tensor2)))}getTensorsForGradient(kernelName,inputs,outputs){let gradConfig=getGradient(kernelName);if(gradConfig!=null){let inputsToSave=gradConfig.inputsToSave||[],outputsToSave=gradConfig.outputsToSave||[],inputTensorsToSave;gradConfig.saveAllInputs?(assert(Array.isArray(inputs),()=>"saveAllInputs is true, expected inputs to be an array."),inputTensorsToSave=Object.keys(inputs).map(key=>inputs[key])):inputTensorsToSave=inputsToSave.map(inputName=>inputs[inputName]);let outputTensorsToSave=outputs.filter((_,i)=>outputsToSave[i]);return inputTensorsToSave.concat(outputTensorsToSave)}return null}makeTensor(values,shape,dtype,backend22){if(values==null)throw new Error("Values passed to engine.makeTensor() are null");dtype=dtype||"float32",backend22=backend22||this.backend;let backendVals=values;dtype==="string"&&isString(values[0])&&(backendVals=values.map(d=>encodeString(d)));let dataId=backend22.write(backendVals,shape,dtype),t=new Tensor(shape,dtype,dataId,this.nextTensorId());if(this.incRef(t,backend22),dtype==="string"){let info=this.state.tensorInfo.get(dataId),newBytes=bytesFromStringArray(backendVals);this.state.numBytes+=newBytes-info.bytes,info.bytes=newBytes}return t}makeTensorFromDataId(dataId,shape,dtype,backend22){dtype=dtype||"float32";let t=new Tensor(shape,dtype,dataId,this.nextTensorId());return this.incRef(t,backend22),t}makeVariable(initialValue,trainable=!0,name,dtype){name=name||this.nextVariableId().toString(),dtype!=null&&dtype!==initialValue.dtype&&(initialValue=initialValue.cast(dtype));let v=new Variable(initialValue,trainable,name,this.nextTensorId());if(this.state.registeredVariables[v.name]!=null)throw new Error(`Variable with name ${v.name} was already registered`);return this.state.registeredVariables[v.name]=v,this.incRef(v,this.backend),v}incRef(a,backend22){let refCount=this.state.tensorInfo.has(a.dataId)?this.state.tensorInfo.get(a.dataId).refCount:0;if(this.state.numTensors++,a.dtype==="string"&&this.state.numStringTensors++,refCount===0){this.state.numDataBuffers++;let bytes=0;a.dtype!=="complex64"&&a.dtype!=="string"&&(bytes=a.size*bytesPerElement(a.dtype)),this.state.tensorInfo.set(a.dataId,{backend:backend22||this.backend,dtype:a.dtype,shape:a.shape,bytes,refCount:0}),this.state.numBytes+=bytes}this.state.tensorInfo.get(a.dataId).refCount++,a instanceof Variable||this.track(a)}disposeTensor(a){if(!this.state.tensorInfo.has(a.dataId))return;this.state.numTensors--,a.dtype==="string"&&this.state.numStringTensors--;let info=this.state.tensorInfo.get(a.dataId);info.refCount<=1?(a.dtype!=="complex64"&&(this.state.numBytes-=info.bytes),this.state.numDataBuffers--,info.backend.disposeData(a.dataId),this.state.tensorInfo.delete(a.dataId)):this.state.tensorInfo.get(a.dataId).refCount--}disposeVariables(){for(let varName in this.state.registeredVariables){let v=this.state.registeredVariables[varName];this.disposeVariable(v)}}disposeVariable(v){this.disposeTensor(v),this.state.registeredVariables[v.name]!=null&&delete this.state.registeredVariables[v.name]}memory(){let info=this.backend.memory();return info.numTensors=this.state.numTensors,info.numDataBuffers=this.state.numDataBuffers,info.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(info.unreliable=!0,info.reasons==null&&(info.reasons=[]),info.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),info}async profile(query){this.state.profiling=!0;let startBytes=this.state.numBytes,startNumTensors=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await query(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(d=>d.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-startBytes,this.state.activeProfile.newTensors=this.state.numTensors-startNumTensors;for(let kernel of this.state.activeProfile.kernels)kernel.kernelTimeMs=await kernel.kernelTimeMs,kernel.extraInfo=await kernel.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(kernelName,inputs,outputs,gradientsFunc,saved,attrs){let tapeNode={id:this.state.nextTapeNodeId++,kernelName,inputs,outputs,saved},gradConfig=getGradient(kernelName);gradConfig!=null&&(gradientsFunc=gradConfig.gradFunc),gradientsFunc!=null&&(tapeNode.gradient=dys=>(dys=dys.map((dy,i)=>{if(dy==null){let output=outputs[i],vals=makeZerosTypedArray(output.size,output.dtype);return this.makeTensor(vals,output.shape,output.dtype)}return dy}),gradientsFunc(dys.length>1?dys:dys[0],saved,attrs))),this.state.activeTape.push(tapeNode)}keep(result){return result.kept=!0,result}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(name){let scopeInfo={track:[],name:"unnamed scope",id:this.state.nextScopeId++};name&&(scopeInfo.name=name),this.state.scopeStack.push(scopeInfo),this.state.activeScope=scopeInfo}endScope(result){let tensorsToTrackInParent=getTensorsInContainer(result),tensorsToTrackInParentSet=new Set(tensorsToTrackInParent.map(t=>t.id));for(let i=0;i{!tensor2.kept&&tensor2.scopeId===oldScope.id&&this.track(tensor2)})}gradients(f,xs,dy,allowNoGradients=!1){if(assert(xs.length>0,()=>"gradients() received an empty list of xs."),dy!=null&&dy.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${dy.dtype}'`);let y=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",f));assert(y instanceof Tensor,()=>"The result y returned by f() must be a tensor.");let filteredTape=getFilteredNodesXToY(this.state.activeTape,xs,y);if(!allowNoGradients&&filteredTape.length===0&&xs.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.");return this.tidy("backward",()=>{let accumulatedGradientMap={};accumulatedGradientMap[y.id]=dy==null?ones(y.shape):dy,backpropagateGradients(accumulatedGradientMap,filteredTape,f2=>this.tidy(f2),add);let grads2=xs.map(x=>accumulatedGradientMap[x.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(node=>{for(let tensor2 of node.saved)tensor2.dispose()}),this.state.activeTape=null),{value:y,grads:grads2}})}customGrad(f){return assert(isFunction(f),()=>"The f passed in customGrad(f) must be a function."),(...inputs)=>{assert(inputs.every(t=>t instanceof Tensor),()=>"The args passed in customGrad(f)(x1, x2,...) must all be tensors");let res,inputMap={};return inputs.forEach((input2,i)=>{inputMap[i]=input2}),this.runKernelFunc((_,save)=>(res=f(...inputs,save),assert(res.value instanceof Tensor,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),assert(isFunction(res.gradFunc),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."),res.value),inputMap,(dy,saved)=>{let gradRes=res.gradFunc(dy,saved),grads2=Array.isArray(gradRes)?gradRes:[gradRes];assert(grads2.length===inputs.length,()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...)."),assert(grads2.every(t=>t instanceof Tensor),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.");let gradMap={};return grads2.forEach((grad2,i)=>{gradMap[i]=()=>grad2}),gradMap})}}readSync(dataId){return this.state.tensorInfo.get(dataId).backend.readSync(dataId)}read(dataId){return this.state.tensorInfo.get(dataId).backend.read(dataId)}async time(query){let start=now(),timingInfo=await this.backend.time(query);return timingInfo.wallMs=now()-start,timingInfo}track(result){return this.state.activeScope!=null&&(result.scopeId=this.state.activeScope.id,this.state.activeScope.track.push(result)),result}get registeredVariables(){return this.state.registeredVariables}reset(){this.pendingBackendInitId++,this.state.dispose(),this.ENV.reset(),this.state=new EngineState;for(let backendName in this.registry)this.disposeRegisteredKernels(backendName),this.registry[backendName].dispose(),delete this.registry[backendName];this.backendName=null,this.backendInstance=null,this.pendingBackendInit=null}};Engine.nextTensorId=0;Engine.nextVariableId=0;function ones(shape){let values=makeOnesTypedArray(sizeFromShape(shape),"float32");return ENGINE.makeTensor(values,shape,"float32")}function getOrMakeEngine(){let ns=getGlobalNamespace();if(ns._tfengine==null){let environment=new Environment(ns);ns._tfengine=new Engine(environment)}return setEnvironmentGlobal(ns._tfengine.ENV),setTensorTracker(()=>ns._tfengine),ns._tfengine}var ENGINE=getOrMakeEngine();function add(a,b){let inputs={a,b};return ENGINE.runKernel(Add,inputs)}var device_util_exports={};__export2(device_util_exports,{isBrowser:()=>isBrowser,isMobile:()=>isMobile});function _isNavigatorDefined(){return typeof navigator!="undefined"&&navigator!=null}function isMobile(){if(_isNavigatorDefined()){let a=navigator.userAgent||navigator.vendor||window.opera;return/(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i.test(a)||/1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\-m|r |s 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is ON. The output of every math call will be downloaded to CPU and checked for NaNs. This significantly impacts performance.")});ENV2.registerFlag("IS_BROWSER",()=>isBrowser());ENV2.registerFlag("IS_NODE",()=>typeof process!="undefined"&&typeof process.versions!="undefined"&&typeof process.versions.node!="undefined");ENV2.registerFlag("IS_CHROME",()=>typeof navigator!="undefined"&&navigator!=null&&navigator.userAgent!=null&&/Chrome/.test(navigator.userAgent)&&/Google Inc/.test(navigator.vendor));ENV2.registerFlag("PROD",()=>!1);ENV2.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY",()=>ENV2.getBool("DEBUG"));ENV2.registerFlag("DEPRECATION_WARNINGS_ENABLED",()=>!0);ENV2.registerFlag("IS_TEST",()=>!1);ENV2.registerFlag("CHECK_COMPUTATION_FOR_ERRORS",()=>!0);function inferShape(val,dtype){let firstElem=val;if(isTypedArray(val))return dtype==="string"?[]:[val.length];if(!Array.isArray(val))return[];let shape=[];for(;Array.isArray(firstElem)||isTypedArray(firstElem)&&dtype!=="string";)shape.push(firstElem.length),firstElem=firstElem[0];return Array.isArray(val)&&env().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY")&&deepAssertShapeConsistency(val,shape,[]),shape}function deepAssertShapeConsistency(val,shape,indices){if(indices=indices||[],!Array.isArray(val)&&!isTypedArray(val)){assert(shape.length===0,()=>`Element arr[${indices.join("][")}] is a primitive, but should be an array/TypedArray of ${shape[0]} elements`);return}assert(shape.length>0,()=>`Element arr[${indices.join("][")}] should be a primitive, but is an array of ${val.length} elements`),assert(val.length===shape[0],()=>`Element arr[${indices.join("][")}] should have ${shape[0]} elements, but has ${val.length} elements`);let subShape=shape.slice(1);for(let i=0;i=0&&(inferredDtype=parseAsDtype),assertDtype(parseAsDtype,inferredDtype,argName,functionName),x==null||!isTypedArray(x)&&!Array.isArray(x)&&typeof x!="number"&&typeof x!="boolean"&&typeof x!="string"){let type=x==null?"null":x.constructor.name;throw new Error(`Argument '${argName}' passed to '${functionName}' must be a Tensor or TensorLike, but got '${type}'`)}let inferredShape=inferShape(x,inferredDtype);!isTypedArray(x)&&!Array.isArray(x)&&(x=[x]);let values=inferredDtype!=="string"?toTypedArray(x,inferredDtype):flatten(x,[],!0);return ENGINE.makeTensor(values,inferredShape,inferredDtype)}function convertToTensorArray(arg,argName,functionName,parseAsDtype="numeric"){if(!Array.isArray(arg))throw new Error(`Argument ${argName} passed to ${functionName} must be a \`Tensor[]\` or \`TensorLike[]\``);return arg.map((t,i)=>convertToTensor(t,`${argName}[${i}]`,functionName,parseAsDtype))}var OP_SCOPE_SUFFIX="__op";function op(f){let keys=Object.keys(f);if(keys.length!==1)throw new Error(`Please provide an object with a single key (operation name) mapping to a function. Got an object with ${keys.length} keys.`);let opName=keys[0],fn=f[opName];opName.endsWith("_")&&(opName=opName.substring(0,opName.length-1)),opName=opName+OP_SCOPE_SUFFIX;let f2=(...args)=>{ENGINE.startScope(opName);try{let result=fn(...args);return isPromise(result)&&console.error("Cannot return a Promise inside of tidy."),ENGINE.endScope(result),result}catch(ex){throw ENGINE.endScope(null),ex}};return Object.defineProperty(f2,"name",{value:opName,configurable:!0}),f2}function complex_(real4,imag4){let $real=convertToTensor(real4,"real","complex"),$imag=convertToTensor(imag4,"imag","complex");assertShapesMatch($real.shape,$imag.shape,`real and imag shapes, ${$real.shape} and ${$imag.shape}, must match in call to tf.complex().`);let inputs={real:$real,imag:$imag};return ENGINE.runKernel(Complex,inputs)}var complex=op({complex_});function makeTensor(values,shape,inferredShape,dtype){if(dtype==null&&(dtype=inferDtype(values)),dtype==="complex64")throw new Error("Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).");if(!isTypedArray(values)&&!Array.isArray(values)&&typeof values!="number"&&typeof values!="boolean"&&typeof values!="string")throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray");if(shape!=null){assertNonNegativeIntegerDimensions(shape);let providedSize=sizeFromShape(shape),inferredSize=sizeFromShape(inferredShape);assert(providedSize===inferredSize,()=>`Based on the provided shape, [${shape}], the tensor should have ${providedSize} values but has ${inferredSize}`);for(let i=0;i`Error creating a new Tensor. Inferred shape (${inferredShape}) does not match the provided shape (${shape}). `)}}return!isTypedArray(values)&&!Array.isArray(values)&&(values=[values]),shape=shape||inferredShape,values=dtype!=="string"?toTypedArray(values,dtype):flatten(values,[],!0),ENGINE.makeTensor(values,shape,dtype)}function tensor(values,shape,dtype){let inferredShape=inferShape(values,dtype);return makeTensor(values,shape,inferredShape,dtype)}var DTYPE_VALUE_SIZE_MAP={float32:4,float16:2,int32:4,uint16:2,uint8:1,bool:1,complex64:8},NUM_BYTES_STRING_LENGTH=4;async function encodeWeights(tensors,group){let specs=[],dataPromises=[],names=Array.isArray(tensors)?tensors.map(tensor2=>tensor2.name):Object.keys(tensors);for(let i=0;i{let vals=await t.bytes(),totalNumBytes=vals.reduce((p2,c)=>p2+c.length,0)+NUM_BYTES_STRING_LENGTH*vals.length,bytes=new Uint8Array(totalNumBytes),offset=0;for(let i2=0;i2{if(totalByteLength+=x.byteLength,normalizedXs.push(x.byteLength===x.buffer.byteLength?x:new x.constructor(x)),!(x instanceof Float32Array||x instanceof Int32Array||x instanceof Uint8Array))throw new Error(`Unsupported TypedArray subtype: ${x.constructor.name}`)});let y=new Uint8Array(totalByteLength),offset=0;return normalizedXs.forEach(x=>{y.set(new Uint8Array(x.buffer),offset),offset+=x.byteLength}),y.buffer}var useNodeBuffer=typeof Buffer!="undefined"&&(typeof Blob=="undefined"||typeof atob=="undefined"||typeof btoa=="undefined");function stringByteLength(str2){return useNodeBuffer?Buffer.byteLength(str2):new Blob([str2]).size}function arrayBufferToBase64String(buffer2){if(useNodeBuffer)return Buffer.from(buffer2).toString("base64");let buf=new Uint8Array(buffer2),s="";for(let i=0,l=buf.length;i{totalByteLength+=buffer2.byteLength});let temp=new Uint8Array(totalByteLength),offset=0;return buffers.forEach(buffer2=>{temp.set(new Uint8Array(buffer2),offset),offset+=buffer2.byteLength}),temp.buffer}function basename(path){let SEPARATOR="/";for(path=path.trim();path.endsWith(SEPARATOR);)path=path.slice(0,path.length-1);let items=path.split(SEPARATOR);return items[items.length-1]}function getModelArtifactsInfoForJSON(modelArtifacts){if(modelArtifacts.modelTopology instanceof ArrayBuffer)throw new Error("Expected JSON model topology, received ArrayBuffer.");return{dateSaved:new Date,modelTopologyType:"JSON",modelTopologyBytes:modelArtifacts.modelTopology==null?0:stringByteLength(JSON.stringify(modelArtifacts.modelTopology)),weightSpecsBytes:modelArtifacts.weightSpecs==null?0:stringByteLength(JSON.stringify(modelArtifacts.weightSpecs)),weightDataBytes:modelArtifacts.weightData==null?0:modelArtifacts.weightData.byteLength}}function computeFloat16MantisaTable(){let convertMantissa=i=>{let m=i<<13,e=0;for(;(m&8388608)==0;)e-=8388608,m<<=1;return m&=~8388608,e+=947912704,m|e},mantisaTable=new Uint32Array(2048);mantisaTable[0]=0;for(let i=1;i<1024;i++)mantisaTable[i]=convertMantissa(i);for(let i=1024;i<2048;i++)mantisaTable[i]=939524096+(i-1024<<13);return mantisaTable}function computeFloat16ExponentTable(){let exponentTable=new Uint32Array(64);exponentTable[0]=0,exponentTable[31]=1199570944,exponentTable[32]=2147483648,exponentTable[63]=3347054592;for(let i=1;i<31;i++)exponentTable[i]=i<<23;for(let i=33;i<63;i++)exponentTable[i]=2147483648+(i-32<<23);return exponentTable}function computeFloat16OffsetTable(){let offsetTable=new Uint32Array(64);for(let i=0;i<64;i++)offsetTable[i]=1024;return offsetTable[0]=offsetTable[32]=0,offsetTable}function getFloat16Decoder(){let mantisaTable=computeFloat16MantisaTable(),exponentTable=computeFloat16ExponentTable(),offsetTable=computeFloat16OffsetTable();return quantizedArray=>{let buffer2=new ArrayBuffer(4*quantizedArray.length),bufferUint32View=new Uint32Array(buffer2);for(let index=0;index>10]+(float16Bits&1023)]+exponentTable[float16Bits>>10];bufferUint32View[index]=float32Bits}return new Float32Array(buffer2)}}var IORouterRegistry=class{constructor(){this.saveRouters=[],this.loadRouters=[]}static getInstance(){return IORouterRegistry.instance==null&&(IORouterRegistry.instance=new IORouterRegistry),IORouterRegistry.instance}static registerSaveRouter(saveRouter){IORouterRegistry.getInstance().saveRouters.push(saveRouter)}static registerLoadRouter(loadRouter){IORouterRegistry.getInstance().loadRouters.push(loadRouter)}static getSaveHandlers(url){return IORouterRegistry.getHandlers(url,"save")}static getLoadHandlers(url,loadOptions){return IORouterRegistry.getHandlers(url,"load",loadOptions)}static getHandlers(url,handlerType,loadOptions){let validHandlers=[];return(handlerType==="load"?IORouterRegistry.getInstance().loadRouters:IORouterRegistry.getInstance().saveRouters).forEach(router=>{let handler=router(url,loadOptions);handler!==null&&validHandlers.push(handler)}),validHandlers}},registerSaveRouter=loudRouter=>IORouterRegistry.registerSaveRouter(loudRouter),registerLoadRouter=loudRouter=>IORouterRegistry.registerLoadRouter(loudRouter),getSaveHandlers=url=>IORouterRegistry.getSaveHandlers(url),getLoadHandlers=(url,loadOptions)=>IORouterRegistry.getLoadHandlers(url,loadOptions),DATABASE_NAME="tensorflowjs",DATABASE_VERSION=1,MODEL_STORE_NAME="models_store",INFO_STORE_NAME="model_info_store";function getIndexedDBFactory(){if(!env().getBool("IS_BROWSER"))throw new Error("Failed to obtain IndexedDB factory because the current environmentis not a web browser.");let theWindow=typeof window=="undefined"?self:window,factory=theWindow.indexedDB||theWindow.mozIndexedDB||theWindow.webkitIndexedDB||theWindow.msIndexedDB||theWindow.shimIndexedDB;if(factory==null)throw new Error("The current browser does not appear to support IndexedDB.");return factory}function setUpDatabase(openRequest){let db=openRequest.result;db.createObjectStore(MODEL_STORE_NAME,{keyPath:"modelPath"}),db.createObjectStore(INFO_STORE_NAME,{keyPath:"modelPath"})}var BrowserIndexedDB=class{constructor(modelPath){if(this.indexedDB=getIndexedDBFactory(),modelPath==null||!modelPath)throw new Error("For IndexedDB, modelPath must not be null, undefined or empty.");this.modelPath=modelPath}async save(modelArtifacts){if(modelArtifacts.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");return this.databaseAction(this.modelPath,modelArtifacts)}async load(){return this.databaseAction(this.modelPath)}databaseAction(modelPath,modelArtifacts){return new Promise((resolve,reject)=>{let openRequest=this.indexedDB.open(DATABASE_NAME,DATABASE_VERSION);openRequest.onupgradeneeded=()=>setUpDatabase(openRequest),openRequest.onsuccess=()=>{let db=openRequest.result;if(modelArtifacts==null){let modelTx=db.transaction(MODEL_STORE_NAME,"readonly"),getRequest=modelTx.objectStore(MODEL_STORE_NAME).get(this.modelPath);getRequest.onsuccess=()=>{if(getRequest.result==null)return db.close(),reject(new Error(`Cannot find model with path '${this.modelPath}' in IndexedDB.`));resolve(getRequest.result.modelArtifacts)},getRequest.onerror=error=>(db.close(),reject(getRequest.error)),modelTx.oncomplete=()=>db.close()}else{let modelArtifactsInfo=getModelArtifactsInfoForJSON(modelArtifacts),infoTx=db.transaction(INFO_STORE_NAME,"readwrite"),infoStore=infoTx.objectStore(INFO_STORE_NAME),putInfoRequest=infoStore.put({modelPath:this.modelPath,modelArtifactsInfo}),modelTx;putInfoRequest.onsuccess=()=>{modelTx=db.transaction(MODEL_STORE_NAME,"readwrite");let putModelRequest=modelTx.objectStore(MODEL_STORE_NAME).put({modelPath:this.modelPath,modelArtifacts,modelArtifactsInfo});putModelRequest.onsuccess=()=>resolve({modelArtifactsInfo}),putModelRequest.onerror=error=>{infoStore=infoTx.objectStore(INFO_STORE_NAME);let deleteInfoRequest=infoStore.delete(this.modelPath);deleteInfoRequest.onsuccess=()=>(db.close(),reject(putModelRequest.error)),deleteInfoRequest.onerror=error2=>(db.close(),reject(putModelRequest.error))}},putInfoRequest.onerror=error=>(db.close(),reject(putInfoRequest.error)),infoTx.oncomplete=()=>{modelTx==null?db.close():modelTx.oncomplete=()=>db.close()}}},openRequest.onerror=error=>reject(openRequest.error)})}};BrowserIndexedDB.URL_SCHEME="indexeddb://";var indexedDBRouter=url=>env().getBool("IS_BROWSER")&&!Array.isArray(url)&&url.startsWith(BrowserIndexedDB.URL_SCHEME)?browserIndexedDB(url.slice(BrowserIndexedDB.URL_SCHEME.length)):null;IORouterRegistry.registerSaveRouter(indexedDBRouter);IORouterRegistry.registerLoadRouter(indexedDBRouter);function browserIndexedDB(modelPath){return new BrowserIndexedDB(modelPath)}function maybeStripScheme(key){return key.startsWith(BrowserIndexedDB.URL_SCHEME)?key.slice(BrowserIndexedDB.URL_SCHEME.length):key}var BrowserIndexedDBManager=class{constructor(){this.indexedDB=getIndexedDBFactory()}async listModels(){return new Promise((resolve,reject)=>{let openRequest=this.indexedDB.open(DATABASE_NAME,DATABASE_VERSION);openRequest.onupgradeneeded=()=>setUpDatabase(openRequest),openRequest.onsuccess=()=>{let db=openRequest.result,tx=db.transaction(INFO_STORE_NAME,"readonly"),getAllInfoRequest=tx.objectStore(INFO_STORE_NAME).getAll();getAllInfoRequest.onsuccess=()=>{let out={};for(let item of getAllInfoRequest.result)out[item.modelPath]=item.modelArtifactsInfo;resolve(out)},getAllInfoRequest.onerror=error=>(db.close(),reject(getAllInfoRequest.error)),tx.oncomplete=()=>db.close()},openRequest.onerror=error=>reject(openRequest.error)})}async removeModel(path){return path=maybeStripScheme(path),new Promise((resolve,reject)=>{let openRequest=this.indexedDB.open(DATABASE_NAME,DATABASE_VERSION);openRequest.onupgradeneeded=()=>setUpDatabase(openRequest),openRequest.onsuccess=()=>{let db=openRequest.result,infoTx=db.transaction(INFO_STORE_NAME,"readwrite"),infoStore=infoTx.objectStore(INFO_STORE_NAME),getInfoRequest=infoStore.get(path),modelTx;getInfoRequest.onsuccess=()=>{if(getInfoRequest.result==null)return db.close(),reject(new Error(`Cannot find model with path '${path}' in IndexedDB.`));{let deleteInfoRequest=infoStore.delete(path),deleteModelData=()=>{modelTx=db.transaction(MODEL_STORE_NAME,"readwrite");let deleteModelRequest=modelTx.objectStore(MODEL_STORE_NAME).delete(path);deleteModelRequest.onsuccess=()=>resolve(getInfoRequest.result.modelArtifactsInfo),deleteModelRequest.onerror=error=>reject(getInfoRequest.error)};deleteInfoRequest.onsuccess=deleteModelData,deleteInfoRequest.onerror=error=>(deleteModelData(),db.close(),reject(getInfoRequest.error))}},getInfoRequest.onerror=error=>(db.close(),reject(getInfoRequest.error)),infoTx.oncomplete=()=>{modelTx==null?db.close():modelTx.oncomplete=()=>db.close()}},openRequest.onerror=error=>reject(openRequest.error)})}},PATH_SEPARATOR="/",PATH_PREFIX="tensorflowjs_models",INFO_SUFFIX="info",MODEL_TOPOLOGY_SUFFIX="model_topology",WEIGHT_SPECS_SUFFIX="weight_specs",WEIGHT_DATA_SUFFIX="weight_data",MODEL_METADATA_SUFFIX="model_metadata";function getModelKeys(path){return{info:[PATH_PREFIX,path,INFO_SUFFIX].join(PATH_SEPARATOR),topology:[PATH_PREFIX,path,MODEL_TOPOLOGY_SUFFIX].join(PATH_SEPARATOR),weightSpecs:[PATH_PREFIX,path,WEIGHT_SPECS_SUFFIX].join(PATH_SEPARATOR),weightData:[PATH_PREFIX,path,WEIGHT_DATA_SUFFIX].join(PATH_SEPARATOR),modelMetadata:[PATH_PREFIX,path,MODEL_METADATA_SUFFIX].join(PATH_SEPARATOR)}}function getModelPathFromKey(key){let items=key.split(PATH_SEPARATOR);if(items.length<3)throw new Error(`Invalid key format: ${key}`);return items.slice(1,items.length-1).join(PATH_SEPARATOR)}function maybeStripScheme2(key){return key.startsWith(BrowserLocalStorage.URL_SCHEME)?key.slice(BrowserLocalStorage.URL_SCHEME.length):key}var BrowserLocalStorage=class{constructor(modelPath){if(!env().getBool("IS_BROWSER")||typeof window=="undefined"||typeof window.localStorage=="undefined")throw new Error("The current environment does not support local storage.");if(this.LS=window.localStorage,modelPath==null||!modelPath)throw new Error("For local storage, modelPath must not be null, undefined or empty.");this.modelPath=modelPath,this.keys=getModelKeys(this.modelPath)}async save(modelArtifacts){if(modelArtifacts.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");{let topology=JSON.stringify(modelArtifacts.modelTopology),weightSpecs=JSON.stringify(modelArtifacts.weightSpecs),modelArtifactsInfo=getModelArtifactsInfoForJSON(modelArtifacts);try{this.LS.setItem(this.keys.info,JSON.stringify(modelArtifactsInfo)),this.LS.setItem(this.keys.topology,topology),this.LS.setItem(this.keys.weightSpecs,weightSpecs),this.LS.setItem(this.keys.weightData,arrayBufferToBase64String(modelArtifacts.weightData));let result={format:modelArtifacts.format,generatedBy:modelArtifacts.generatedBy,convertedBy:modelArtifacts.convertedBy};return modelArtifacts.signature!=null&&(result.signature=modelArtifacts.signature),modelArtifacts.userDefinedMetadata!=null&&(result.userDefinedMetadata=modelArtifacts.userDefinedMetadata),modelArtifacts.modelInitializer!=null&&(result.modelInitializer=modelArtifacts.modelInitializer),this.LS.setItem(this.keys.modelMetadata,JSON.stringify(result)),{modelArtifactsInfo}}catch(err){throw this.LS.removeItem(this.keys.info),this.LS.removeItem(this.keys.topology),this.LS.removeItem(this.keys.weightSpecs),this.LS.removeItem(this.keys.weightData),this.LS.removeItem(this.keys.modelMetadata),new Error(`Failed to save model '${this.modelPath}' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes=${modelArtifactsInfo.modelTopologyBytes}, weightSpecsBytes=${modelArtifactsInfo.weightSpecsBytes}, weightDataBytes=${modelArtifactsInfo.weightDataBytes}.`)}}}async load(){let info=JSON.parse(this.LS.getItem(this.keys.info));if(info==null)throw new Error(`In local storage, there is no model with name '${this.modelPath}'`);if(info.modelTopologyType!=="JSON")throw new Error("BrowserLocalStorage does not support loading non-JSON model topology yet.");let out={},topology=JSON.parse(this.LS.getItem(this.keys.topology));if(topology==null)throw new Error(`In local storage, the topology of model '${this.modelPath}' is missing.`);out.modelTopology=topology;let weightSpecs=JSON.parse(this.LS.getItem(this.keys.weightSpecs));if(weightSpecs==null)throw new Error(`In local storage, the weight specs of model '${this.modelPath}' are missing.`);out.weightSpecs=weightSpecs;let metadataString=this.LS.getItem(this.keys.modelMetadata);if(metadataString!=null){let metadata=JSON.parse(metadataString);out.format=metadata.format,out.generatedBy=metadata.generatedBy,out.convertedBy=metadata.convertedBy,metadata.signature!=null&&(out.signature=metadata.signature),metadata.userDefinedMetadata!=null&&(out.userDefinedMetadata=metadata.userDefinedMetadata),metadata.modelInitializer!=null&&(out.modelInitializer=metadata.modelInitializer)}let weightDataBase64=this.LS.getItem(this.keys.weightData);if(weightDataBase64==null)throw new Error(`In local storage, the binary weight values of model '${this.modelPath}' are missing.`);return out.weightData=base64StringToArrayBuffer(weightDataBase64),out}};BrowserLocalStorage.URL_SCHEME="localstorage://";var localStorageRouter=url=>env().getBool("IS_BROWSER")&&!Array.isArray(url)&&url.startsWith(BrowserLocalStorage.URL_SCHEME)?browserLocalStorage(url.slice(BrowserLocalStorage.URL_SCHEME.length)):null;IORouterRegistry.registerSaveRouter(localStorageRouter);IORouterRegistry.registerLoadRouter(localStorageRouter);function browserLocalStorage(modelPath){return new BrowserLocalStorage(modelPath)}var BrowserLocalStorageManager=class{constructor(){assert(env().getBool("IS_BROWSER"),()=>"Current environment is not a web browser"),assert(typeof window=="undefined"||typeof window.localStorage!="undefined",()=>"Current browser does not appear to support localStorage"),this.LS=window.localStorage}async listModels(){let out={},prefix=PATH_PREFIX+PATH_SEPARATOR,suffix=PATH_SEPARATOR+INFO_SUFFIX;for(let i=0;i"scheme must not be undefined or null."),scheme.endsWith(URL_SCHEME_SUFFIX)&&(scheme=scheme.slice(0,scheme.indexOf(URL_SCHEME_SUFFIX))),assert(scheme.length>0,()=>"scheme must not be an empty string.");let registry=ModelStoreManagerRegistry.getInstance();assert(registry.managers[scheme]==null,()=>`A model store manager is already registered for scheme '${scheme}'.`),registry.managers[scheme]=manager}static getManager(scheme){let manager=this.getInstance().managers[scheme];if(manager==null)throw new Error(`Cannot find model manager for scheme '${scheme}'`);return manager}static getSchemes(){return Object.keys(this.getInstance().managers)}};function parseURL(url){if(url.indexOf(URL_SCHEME_SUFFIX)===-1)throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${ModelStoreManagerRegistry.getSchemes().join(",")}`);return{scheme:url.split(URL_SCHEME_SUFFIX)[0],path:url.split(URL_SCHEME_SUFFIX)[1]}}async function cloneModelInternal(sourceURL,destURL,deleteSource=!1){assert(sourceURL!==destURL,()=>`Old path and new path are the same: '${sourceURL}'`);let loadHandlers=IORouterRegistry.getLoadHandlers(sourceURL);assert(loadHandlers.length>0,()=>`Copying failed because no load handler is found for source URL ${sourceURL}.`),assert(loadHandlers.length<2,()=>`Copying failed because more than one (${loadHandlers.length}) load handlers for source URL ${sourceURL}.`);let loadHandler=loadHandlers[0],saveHandlers=IORouterRegistry.getSaveHandlers(destURL);assert(saveHandlers.length>0,()=>`Copying failed because no save handler is found for destination URL ${destURL}.`),assert(saveHandlers.length<2,()=>`Copying failed because more than one (${loadHandlers.length}) save handlers for destination URL ${destURL}.`);let saveHandler=saveHandlers[0],sourceScheme=parseURL(sourceURL).scheme,sourcePath=parseURL(sourceURL).path,sameMedium=sourceScheme===parseURL(sourceURL).scheme,modelArtifacts=await loadHandler.load();deleteSource&&sameMedium&&await ModelStoreManagerRegistry.getManager(sourceScheme).removeModel(sourcePath);let saveResult=await saveHandler.save(modelArtifacts);return deleteSource&&!sameMedium&&await ModelStoreManagerRegistry.getManager(sourceScheme).removeModel(sourcePath),saveResult.modelArtifactsInfo}async function listModels(){let schemes=ModelStoreManagerRegistry.getSchemes(),out={};for(let scheme of schemes){let schemeOut=await ModelStoreManagerRegistry.getManager(scheme).listModels();for(let path in schemeOut){let url=scheme+URL_SCHEME_SUFFIX+path;out[url]=schemeOut[path]}}return out}async function removeModel(url){let schemeAndPath=parseURL(url);return ModelStoreManagerRegistry.getManager(schemeAndPath.scheme).removeModel(schemeAndPath.path)}async function copyModel(sourceURL,destURL){return cloneModelInternal(sourceURL,destURL,!1)}async function moveModel(sourceURL,destURL){return cloneModelInternal(sourceURL,destURL,!0)}var PlatformBrowser=class{fetch(path,init2){return fetch(path,init2)}now(){return performance.now()}encode(text,encoding){if(encoding!=="utf-8"&&encoding!=="utf8")throw new Error(`Browser's encoder only supports utf-8, but got ${encoding}`);return this.textEncoder==null&&(this.textEncoder=new TextEncoder),this.textEncoder.encode(text)}decode(bytes,encoding){return new TextDecoder(encoding).decode(bytes)}};if(env().get("IS_BROWSER")){env().setPlatform("browser",new PlatformBrowser);try{ModelStoreManagerRegistry.registerManager(BrowserLocalStorage.URL_SCHEME,new BrowserLocalStorageManager)}catch(err){}try{ModelStoreManagerRegistry.registerManager(BrowserIndexedDB.URL_SCHEME,new BrowserIndexedDBManager)}catch(err){}}var getNodeFetch={importFetch:()=>require_browser()},systemFetch,PlatformNode=class{constructor(){this.util=require("util"),this.textEncoder=new this.util.TextEncoder}fetch(path,requestInits){return env().global.fetch!=null?env().global.fetch(path,requestInits):(systemFetch==null&&(systemFetch=getNodeFetch.importFetch()),systemFetch(path,requestInits))}now(){let time2=process.hrtime();return time2[0]*1e3+time2[1]/1e6}encode(text,encoding){if(encoding!=="utf-8"&&encoding!=="utf8")throw new Error(`Node built-in encoder only supports utf-8, but got ${encoding}`);return this.textEncoder.encode(text)}decode(bytes,encoding){return bytes.length===0?"":new this.util.TextDecoder(encoding).decode(bytes)}};env().get("IS_NODE")&&env().setPlatform("node",new PlatformNode);function buffer(shape,dtype="float32",values){return dtype=dtype||"float32",assertNonNegativeIntegerDimensions(shape),new TensorBuffer(shape,dtype,values)}function cast_(x,dtype){let $x=convertToTensor(x,"x","cast");if(!isValidDtype(dtype))throw new Error(`Failed to cast to unknown dtype ${dtype}`);if(dtype==="string"&&$x.dtype!=="string"||dtype!=="string"&&$x.dtype==="string")throw new Error("Only strings can be casted to strings");let inputs={x:$x},attrs={dtype};return ENGINE.runKernel(Cast,inputs,attrs)}var cast=op({cast_});function clone_(x){let inputs={x:convertToTensor(x,"x","clone","string_or_numeric")};return ENGINE.runKernel(Identity,inputs)}var clone=op({clone_});function print2(x,verbose=!1){console.log(x.toString(verbose))}getOrMakeEngine();var opHandler2={buffer,cast,clone,print:print2};setOpHandler(opHandler2);var io_exports={};__export2(io_exports,{browserFiles:()=>browserFiles,browserHTTPRequest:()=>browserHTTPRequest,concatenateArrayBuffers:()=>concatenateArrayBuffers,copyModel:()=>copyModel,decodeWeights:()=>decodeWeights,encodeWeights:()=>encodeWeights,fromMemory:()=>fromMemory,getLoadHandlers:()=>getLoadHandlers,getModelArtifactsInfoForJSON:()=>getModelArtifactsInfoForJSON,getSaveHandlers:()=>getSaveHandlers,http:()=>http,isHTTPScheme:()=>isHTTPScheme,listModels:()=>listModels,loadWeights:()=>loadWeights,moveModel:()=>moveModel,registerLoadRouter:()=>registerLoadRouter,registerSaveRouter:()=>registerSaveRouter,removeModel:()=>removeModel,weightsLoaderFactory:()=>weightsLoaderFactory,withSaveHandler:()=>withSaveHandler});var DEFAULT_FILE_NAME_PREFIX="model",DEFAULT_JSON_EXTENSION_NAME=".json",DEFAULT_WEIGHT_DATA_EXTENSION_NAME=".weights.bin";function defer(f){return new Promise(resolve=>setTimeout(resolve)).then(f)}var BrowserDownloads=class{constructor(fileNamePrefix){if(!env().getBool("IS_BROWSER"))throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");fileNamePrefix.startsWith(BrowserDownloads.URL_SCHEME)&&(fileNamePrefix=fileNamePrefix.slice(BrowserDownloads.URL_SCHEME.length)),(fileNamePrefix==null||fileNamePrefix.length===0)&&(fileNamePrefix=DEFAULT_FILE_NAME_PREFIX),this.modelTopologyFileName=fileNamePrefix+DEFAULT_JSON_EXTENSION_NAME,this.weightDataFileName=fileNamePrefix+DEFAULT_WEIGHT_DATA_EXTENSION_NAME}async save(modelArtifacts){if(typeof document=="undefined")throw new Error("Browser downloads are not supported in this environment since `document` is not present");let weightsURL=window.URL.createObjectURL(new Blob([modelArtifacts.weightData],{type:"application/octet-stream"}));if(modelArtifacts.modelTopology instanceof ArrayBuffer)throw new Error("BrowserDownloads.save() does not support saving model topology in binary formats yet.");{let weightsManifest=[{paths:["./"+this.weightDataFileName],weights:modelArtifacts.weightSpecs}],modelTopologyAndWeightManifest={modelTopology:modelArtifacts.modelTopology,format:modelArtifacts.format,generatedBy:modelArtifacts.generatedBy,convertedBy:modelArtifacts.convertedBy,weightsManifest};modelArtifacts.signature!=null&&(modelTopologyAndWeightManifest.signature=modelArtifacts.signature),modelArtifacts.userDefinedMetadata!=null&&(modelTopologyAndWeightManifest.userDefinedMetadata=modelArtifacts.userDefinedMetadata),modelArtifacts.modelInitializer!=null&&(modelTopologyAndWeightManifest.modelInitializer=modelArtifacts.modelInitializer);let modelTopologyAndWeightManifestURL=window.URL.createObjectURL(new Blob([JSON.stringify(modelTopologyAndWeightManifest)],{type:"application/json"})),jsonAnchor=this.jsonAnchor==null?document.createElement("a"):this.jsonAnchor;if(jsonAnchor.download=this.modelTopologyFileName,jsonAnchor.href=modelTopologyAndWeightManifestURL,await defer(()=>jsonAnchor.dispatchEvent(new MouseEvent("click"))),modelArtifacts.weightData!=null){let weightDataAnchor=this.weightDataAnchor==null?document.createElement("a"):this.weightDataAnchor;weightDataAnchor.download=this.weightDataFileName,weightDataAnchor.href=weightsURL,await defer(()=>weightDataAnchor.dispatchEvent(new MouseEvent("click")))}return{modelArtifactsInfo:getModelArtifactsInfoForJSON(modelArtifacts)}}}};BrowserDownloads.URL_SCHEME="downloads://";var BrowserFiles=class{constructor(files){if(files==null||files.length<1)throw new Error(`When calling browserFiles, at least 1 file is required, but received ${files}`);this.files=files}async load(){let jsonFile=this.files[0],weightFiles=this.files.slice(1);return new Promise((resolve,reject)=>{let jsonReader=new FileReader;jsonReader.onload=event=>{let modelJSON=JSON.parse(event.target.result),modelTopology=modelJSON.modelTopology;if(modelTopology==null){reject(new Error(`modelTopology field is missing from file ${jsonFile.name}`));return}weightFiles.length===0&&resolve({modelTopology});let weightsManifest=modelJSON.weightsManifest;if(weightsManifest==null){reject(new Error(`weightManifest field is missing from file ${jsonFile.name}`));return}let pathToFile;try{pathToFile=this.checkManifestAndWeightFiles(weightsManifest,weightFiles)}catch(err){reject(err);return}let weightSpecs=[],paths=[],perFileBuffers=[];weightsManifest.forEach(weightsGroup=>{weightsGroup.paths.forEach(path=>{paths.push(path),perFileBuffers.push(null)}),weightSpecs.push(...weightsGroup.weights)}),weightsManifest.forEach(weightsGroup=>{weightsGroup.paths.forEach(path=>{let weightFileReader=new FileReader;weightFileReader.onload=event2=>{let weightData=event2.target.result,index=paths.indexOf(path);if(perFileBuffers[index]=weightData,perFileBuffers.indexOf(null)===-1){let result={modelTopology,weightSpecs,weightData:concatenateArrayBuffers(perFileBuffers),format:modelJSON.format,generatedBy:modelJSON.generatedBy,convertedBy:modelJSON.convertedBy};modelJSON.signature!=null&&(result.signature=modelJSON.signature),modelJSON.userDefinedMetadata!=null&&(result.userDefinedMetadata=modelJSON.userDefinedMetadata),modelJSON.modelInitializer!=null&&(result.modelInitializer=modelJSON.modelInitializer),resolve(result)}},weightFileReader.onerror=error=>reject(`Failed to weights data from file of path '${path}'.`),weightFileReader.readAsArrayBuffer(pathToFile[path])})})},jsonReader.onerror=error=>reject(`Failed to read model topology and weights manifest JSON from file '${jsonFile.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`),jsonReader.readAsText(jsonFile)})}checkManifestAndWeightFiles(manifest,files){let basenames=[],fileNames=files.map(file=>basename(file.name)),pathToFile={};for(let group of manifest)group.paths.forEach(path=>{let pathBasename=basename(path);if(basenames.indexOf(pathBasename)!==-1)throw new Error(`Duplicate file basename found in weights manifest: '${pathBasename}'`);if(basenames.push(pathBasename),fileNames.indexOf(pathBasename)===-1)throw new Error(`Weight file with basename '${pathBasename}' is not provided.`);pathToFile[path]=files[fileNames.indexOf(pathBasename)]});if(basenames.length!==files.length)throw new Error(`Mismatch in the number of files in weights manifest (${basenames.length}) and the number of weight files provided (${files.length}).`);return pathToFile}},browserDownloadsRouter=url=>env().getBool("IS_BROWSER")&&!Array.isArray(url)&&url.startsWith(BrowserDownloads.URL_SCHEME)?browserDownloads(url.slice(BrowserDownloads.URL_SCHEME.length)):null;IORouterRegistry.registerSaveRouter(browserDownloadsRouter);function browserDownloads(fileNamePrefix="model"){return new BrowserDownloads(fileNamePrefix)}function browserFiles(files){return new BrowserFiles(files)}function monitorPromisesProgress(promises,onProgress,startFraction,endFraction){checkPromises(promises),startFraction=startFraction==null?0:startFraction,endFraction=endFraction==null?1:endFraction,checkFraction(startFraction,endFraction);let resolvedPromise=0,registerMonitor=promise=>(promise.then(value=>{let fraction=startFraction+ ++resolvedPromise/promises.length*(endFraction-startFraction);return onProgress(fraction),value}),promise);function checkPromises(promises2){assert(promises2!=null&&Array.isArray(promises2)&&promises2.length>0,()=>"promises must be a none empty array")}function checkFraction(startFraction2,endFraction2){assert(startFraction2>=0&&startFraction2<=1,()=>`Progress fraction must be in range [0, 1], but got startFraction ${startFraction2}`),assert(endFraction2>=0&&endFraction2<=1,()=>`Progress fraction must be in range [0, 1], but got endFraction ${endFraction2}`),assert(endFraction2>=startFraction2,()=>`startFraction must be no more than endFraction, but got startFraction ${startFraction2} and endFraction ${endFraction2}`)}return Promise.all(promises.map(registerMonitor))}async function loadWeightsAsArrayBuffer(fetchURLs,loadOptions){loadOptions==null&&(loadOptions={});let fetchFunc=loadOptions.fetchFunc==null?env().platform.fetch:loadOptions.fetchFunc,requests=fetchURLs.map(fetchURL=>fetchFunc(fetchURL,loadOptions.requestInit,{isBinary:!0})),fetchStartFraction=0,fetchEndFraction=.5,bufferPromises=(loadOptions.onProgress==null?await Promise.all(requests):await monitorPromisesProgress(requests,loadOptions.onProgress,fetchStartFraction,fetchEndFraction)).map(response=>response.arrayBuffer()),bufferStartFraction=.5,bufferEndFraction=1;return loadOptions.onProgress==null?await Promise.all(bufferPromises):await monitorPromisesProgress(bufferPromises,loadOptions.onProgress,bufferStartFraction,bufferEndFraction)}async function loadWeights(manifest,filePathPrefix="",weightNames,requestInit){return weightsLoaderFactory(fetchUrls=>loadWeightsAsArrayBuffer(fetchUrls,{requestInit}))(manifest,filePathPrefix,weightNames)}function weightsLoaderFactory(fetchWeightsFunction){return async(manifest,filePathPrefix="",weightNames)=>{let groupIndicesToFetchMap=manifest.map(()=>!1),groupWeightsToFetch={},weightsFound=weightNames!=null?weightNames.map(()=>!1):[],allManifestWeightNames=[];if(manifest.forEach((manifestGroupConfig,groupIndex)=>{let groupOffset=0;manifestGroupConfig.weights.forEach(weightsEntry=>{let rawDtype="quantization"in weightsEntry?weightsEntry.quantization.dtype:weightsEntry.dtype,weightsBytes=DTYPE_VALUE_SIZE_MAP[rawDtype]*sizeFromShape(weightsEntry.shape),enqueueWeightsForFetchingFn=()=>{groupIndicesToFetchMap[groupIndex]=!0,groupWeightsToFetch[groupIndex]==null&&(groupWeightsToFetch[groupIndex]=[]),groupWeightsToFetch[groupIndex].push({manifestEntry:weightsEntry,groupOffset,sizeBytes:weightsBytes})};weightNames!=null?weightNames.forEach((weightName,weightIndex)=>{weightName===weightsEntry.name&&(enqueueWeightsForFetchingFn(),weightsFound[weightIndex]=!0)}):enqueueWeightsForFetchingFn(),allManifestWeightNames.push(weightsEntry.name),groupOffset+=weightsBytes})}),!weightsFound.every(found=>found)){let weightsNotFound=weightNames.filter((_,i)=>!weightsFound[i]);throw new Error(`Could not find weights in manifest with names: ${weightsNotFound.join(", ")}. -Manifest JSON has weights with names: ${allManifestWeightNames.join(", ")}.`)}let groupIndicesToFetch=groupIndicesToFetchMap.reduce((accumulator,shouldFetch,i)=>(shouldFetch&&accumulator.push(i),accumulator),[]),fetchUrls=[];groupIndicesToFetch.forEach(i=>{manifest[i].paths.forEach(filepath=>{let fetchUrl=filePathPrefix+(filePathPrefix.endsWith("/")?"":"/")+filepath;fetchUrls.push(fetchUrl)})});let buffers=await fetchWeightsFunction(fetchUrls),weightsTensorMap={},bufferIndexOffset=0;return groupIndicesToFetch.forEach(i=>{let numBuffers=manifest[i].paths.length,groupBytes=0;for(let i2=0;i2{let byteBuffer=groupBuffer.slice(weightsEntry.groupOffset,weightsEntry.groupOffset+weightsEntry.sizeBytes),nameToTensorMap=decodeWeights(byteBuffer,[weightsEntry.manifestEntry]);for(let name in nameToTensorMap)weightsTensorMap[name]=nameToTensorMap[name]}),bufferIndexOffset+=numBuffers}),weightsTensorMap}}var OCTET_STREAM_MIME_TYPE="application/octet-stream",JSON_TYPE="application/json",HTTPRequest=class{constructor(path,loadOptions){if(this.DEFAULT_METHOD="POST",loadOptions==null&&(loadOptions={}),this.weightPathPrefix=loadOptions.weightPathPrefix,this.onProgress=loadOptions.onProgress,this.weightUrlConverter=loadOptions.weightUrlConverter,loadOptions.fetchFunc!=null?(assert(typeof loadOptions.fetchFunc=="function",()=>"Must pass a function that matches the signature of `fetch` (see https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)"),this.fetch=loadOptions.fetchFunc):this.fetch=env().platform.fetch,assert(path!=null&&path.length>0,()=>"URL path for http must not be null, undefined or empty."),Array.isArray(path)&&assert(path.length===2,()=>`URL paths for http must have a length of 2, (actual length is ${path.length}).`),this.path=path,loadOptions.requestInit!=null&&loadOptions.requestInit.body!=null)throw new Error("requestInit is expected to have no pre-existing body, but has one.");this.requestInit=loadOptions.requestInit||{}}async save(modelArtifacts){if(modelArtifacts.modelTopology instanceof ArrayBuffer)throw new Error("BrowserHTTPRequest.save() does not support saving model topology in binary formats yet.");let init2=Object.assign({method:this.DEFAULT_METHOD},this.requestInit);init2.body=new FormData;let weightsManifest=[{paths:["./model.weights.bin"],weights:modelArtifacts.weightSpecs}],modelTopologyAndWeightManifest={modelTopology:modelArtifacts.modelTopology,format:modelArtifacts.format,generatedBy:modelArtifacts.generatedBy,convertedBy:modelArtifacts.convertedBy,weightsManifest};modelArtifacts.signature!=null&&(modelTopologyAndWeightManifest.signature=modelArtifacts.signature),modelArtifacts.userDefinedMetadata!=null&&(modelTopologyAndWeightManifest.userDefinedMetadata=modelArtifacts.userDefinedMetadata),modelArtifacts.modelInitializer!=null&&(modelTopologyAndWeightManifest.modelInitializer=modelArtifacts.modelInitializer),init2.body.append("model.json",new Blob([JSON.stringify(modelTopologyAndWeightManifest)],{type:JSON_TYPE}),"model.json"),modelArtifacts.weightData!=null&&init2.body.append("model.weights.bin",new Blob([modelArtifacts.weightData],{type:OCTET_STREAM_MIME_TYPE}),"model.weights.bin");let response=await this.fetch(this.path,init2);if(response.ok)return{modelArtifactsInfo:getModelArtifactsInfoForJSON(modelArtifacts),responses:[response]};throw new Error(`BrowserHTTPRequest.save() failed due to HTTP response status ${response.status}.`)}async load(){let modelConfigRequest=await this.fetch(this.path,this.requestInit);if(!modelConfigRequest.ok)throw new Error(`Request to ${this.path} failed with status code ${modelConfigRequest.status}. Please verify this URL points to the model JSON of the model to load.`);let modelConfig;try{modelConfig=await modelConfigRequest.json()}catch(e){let message=`Failed to parse model JSON of response from ${this.path}.`;throw this.path.endsWith(".pb")?message+=" Your path contains a .pb file extension. Support for .pb models have been removed in TensorFlow.js 1.0 in favor of .json models. You can re-convert your Python TensorFlow model using the TensorFlow.js 1.0 conversion scripts or you can convert your.pb models with the 'pb2json'NPM script in the tensorflow/tfjs-converter repository.":message+=" Please make sure the server is serving valid JSON for this request.",new Error(message)}let modelTopology=modelConfig.modelTopology,weightsManifest=modelConfig.weightsManifest,generatedBy=modelConfig.generatedBy,convertedBy=modelConfig.convertedBy,format=modelConfig.format,signature=modelConfig.signature,userDefinedMetadata=modelConfig.userDefinedMetadata;if(modelTopology==null&&weightsManifest==null)throw new Error(`The JSON from HTTP path ${this.path} contains neither model topology or manifest for weights.`);let weightSpecs,weightData;weightsManifest!=null&&([weightSpecs,weightData]=await this.loadWeights(weightsManifest));let artifacts={modelTopology,weightSpecs,weightData,generatedBy,convertedBy,format};signature!=null&&(artifacts.signature=signature),userDefinedMetadata!=null&&(artifacts.userDefinedMetadata=userDefinedMetadata);let initializer=modelConfig.modelInitializer;return initializer&&(artifacts.modelInitializer=initializer),artifacts}async loadWeights(weightsManifest){let weightPath=Array.isArray(this.path)?this.path[1]:this.path,[prefix,suffix]=parseUrl(weightPath),pathPrefix=this.weightPathPrefix||prefix,weightSpecs=[];for(let entry of weightsManifest)weightSpecs.push(...entry.weights);let fetchURLs=[],urlPromises=[];for(let weightsGroup of weightsManifest)for(let path of weightsGroup.paths)this.weightUrlConverter!=null?urlPromises.push(this.weightUrlConverter(path)):fetchURLs.push(pathPrefix+path+suffix);this.weightUrlConverter&&fetchURLs.push(...await Promise.all(urlPromises));let buffers=await loadWeightsAsArrayBuffer(fetchURLs,{requestInit:this.requestInit,fetchFunc:this.fetch,onProgress:this.onProgress});return[weightSpecs,concatenateArrayBuffers(buffers)]}};HTTPRequest.URL_SCHEME_REGEX=/^https?:\/\//;function parseUrl(url){let lastSlash=url.lastIndexOf("/"),lastSearchParam=url.lastIndexOf("?"),prefix=url.substring(0,lastSlash),suffix=lastSearchParam>lastSlash?url.substring(lastSearchParam):"";return[prefix+"/",suffix]}function isHTTPScheme(url){return url.match(HTTPRequest.URL_SCHEME_REGEX)!=null}var httpRouter=(url,loadOptions)=>{if(typeof fetch=="undefined"&&(loadOptions==null||loadOptions.fetchFunc==null))return null;{let isHTTP=!0;if(Array.isArray(url)?isHTTP=url.every(urlItem=>isHTTPScheme(urlItem)):isHTTP=isHTTPScheme(url),isHTTP)return http(url,loadOptions)}return null};IORouterRegistry.registerSaveRouter(httpRouter);IORouterRegistry.registerLoadRouter(httpRouter);function http(path,loadOptions){return new HTTPRequest(path,loadOptions)}function browserHTTPRequest(path,loadOptions){return http(path,loadOptions)}var PassthroughLoader=class{constructor(modelArtifacts){this.modelArtifacts=modelArtifacts}async load(){return this.modelArtifacts}},PassthroughSaver=class{constructor(saveHandler){this.saveHandler=saveHandler}async save(modelArtifacts){return this.saveHandler(modelArtifacts)}};function fromMemory(modelArtifacts,weightSpecs,weightData,trainingConfig){return arguments.length===1?modelArtifacts.modelTopology!=null||modelArtifacts.weightSpecs!=null?new PassthroughLoader(modelArtifacts):(console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."),new PassthroughLoader({modelTopology:modelArtifacts})):(console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."),new PassthroughLoader({modelTopology:modelArtifacts,weightSpecs,weightData,trainingConfig}))}function withSaveHandler(saveHandler){return new PassthroughSaver(saveHandler)}var math_exports={};__export2(math_exports,{confusionMatrix:()=>confusionMatrix});function matMul_(a,b,transposeA=!1,transposeB=!1){let $a=convertToTensor(a,"a","matMul"),$b=convertToTensor(b,"b","matMul");[$a,$b]=makeTypesMatch($a,$b);let inputs={a:$a,b:$b},attrs={transposeA,transposeB};return ENGINE.runKernel(BatchMatMul,inputs,attrs)}var matMul=op({matMul_});function oneHot_(indices,depth,onValue=1,offValue=0){if(depth<2)throw new Error(`Error in oneHot: depth must be >=2, but it is ${depth}`);let inputs={indices:convertToTensor(indices,"indices","oneHot","int32")},attrs={depth,onValue,offValue};return ENGINE.runKernel(OneHot,inputs,attrs)}var oneHot=op({oneHot_});function transpose_(x,perm){let $x=convertToTensor(x,"x","transpose");if(perm==null&&(perm=$x.shape.map((s,i)=>i).reverse()),assert($x.rank===perm.length,()=>`Error in transpose: rank of input ${$x.rank} must match length of perm ${perm}.`),perm.forEach(axis=>{assert(axis>=0&&axis<$x.rank,()=>`All entries in 'perm' must be between 0 and ${$x.rank-1} but got ${perm}`)}),$x.rank<=1)return $x.clone();let inputs={x:$x},attrs={perm};return ENGINE.runKernel(Transpose,inputs,attrs)}var transpose=op({transpose_});function confusionMatrix_(labels,predictions,numClasses){let $labels=convertToTensor(labels,"labels","confusionMatrix"),$predictions=convertToTensor(predictions,"predictions","confusionMatrix");assert(numClasses==null||numClasses>0&&Number.isInteger(numClasses),()=>`If provided, numClasses must be a positive integer, but got ${numClasses}`),assert($labels.rank===1,()=>`Expected the rank of labels to be 1, but got ${$labels.rank}`),assert($predictions.rank===1,()=>`Expected the rank of predictions to be 1, but got ${$predictions.rank}`),assert($labels.shape[0]===$predictions.shape[0],()=>`Mismatch in the number of examples: ${$labels.shape[0]} vs. ${$predictions.shape[0]}. Labels and predictions should have the same number of elements.`),assert(numClasses>0&&Number.isInteger(numClasses),()=>`numClasses is required to be a positive integer, but got ${numClasses}`);let oneHotLabels=oneHot(cast($labels,"int32"),numClasses),oneHotPredictions=oneHot(cast($predictions,"int32"),numClasses),oneHotLabelsT=transpose(oneHotLabels),product=matMul(oneHotLabelsT,oneHotPredictions);return cast(product,"int32")}var confusionMatrix=op({confusionMatrix_}),browser_exports={};__export2(browser_exports,{fromPixels:()=>fromPixels,toPixels:()=>toPixels});function tensor3d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==3)throw new Error("tensor3d() requires shape to have three numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==3&&inferredShape.length!==1)throw new Error("tensor3d() requires values to be number[][][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor3d() requires shape to be provided when `values` are a flat array");return makeTensor(values,shape,inferredShape,dtype)}var fromPixels2DContext;function fromPixels_(pixels,numChannels=3){if(numChannels>4)throw new Error("Cannot construct Tensor with more than 4 channels from pixels.");if(pixels==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let isPixelData=!1,isImageData=!1,isVideo=!1,isImage=!1,isCanvasLike=!1,isImageBitmap=!1;if(pixels.data instanceof Uint8Array)isPixelData=!0;else if(typeof ImageData!="undefined"&&pixels instanceof ImageData)isImageData=!0;else if(typeof HTMLVideoElement!="undefined"&&pixels instanceof HTMLVideoElement)isVideo=!0;else if(typeof HTMLImageElement!="undefined"&&pixels instanceof HTMLImageElement)isImage=!0;else if(pixels.getContext!=null)isCanvasLike=!0;else if(typeof ImageBitmap!="undefined"&&pixels instanceof ImageBitmap)isImageBitmap=!0;else throw new Error(`pixels passed to tf.browser.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData in browser, or OffscreenCanvas, ImageData in webworker or {data: Uint32Array, width: number, height: number}, but was ${pixels.constructor.name}`);if(isVideo){let HAVE_CURRENT_DATA_READY_STATE=2;if(isVideo&&pixels.readyState element.")}if(getKernel(FromPixels,ENGINE.backendName)!=null){let inputs={pixels},attrs={numChannels};return ENGINE.runKernel(FromPixels,inputs,attrs)}let[width,height]=isVideo?[pixels.videoWidth,pixels.videoHeight]:[pixels.width,pixels.height],vals;isCanvasLike?vals=pixels.getContext("2d").getImageData(0,0,width,height).data:isImageData||isPixelData?vals=pixels.data:(isImage||isVideo||isImageBitmap)&&(fromPixels2DContext==null&&(fromPixels2DContext=document.createElement("canvas").getContext("2d")),fromPixels2DContext.canvas.width=width,fromPixels2DContext.canvas.height=height,fromPixels2DContext.drawImage(pixels,0,0,width,height),vals=fromPixels2DContext.getImageData(0,0,width,height).data);let values;if(numChannels===4)values=new Int32Array(vals);else{let numPixels=width*height;values=new Int32Array(numPixels*numChannels);for(let i=0;i4||depth===2)throw new Error(`toPixels only supports depth of size 1, 3 or 4 but got ${depth}`);if($img.dtype!=="float32"&&$img.dtype!=="int32")throw new Error(`Unsupported type for toPixels: ${$img.dtype}. Please use float32 or int32 tensors.`);let data2=await $img.data(),multiplier=$img.dtype==="float32"?255:1,bytes=new Uint8ClampedArray(width*height*4);for(let i=0;i1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${value}.`)}else if($img.dtype==="int32"&&(value<0||value>255))throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${value}.`);depth===1?(rgba[0]=value*multiplier,rgba[1]=value*multiplier,rgba[2]=value*multiplier):rgba[d]=value*multiplier}let j=i*4;bytes[j+0]=Math.round(rgba[0]),bytes[j+1]=Math.round(rgba[1]),bytes[j+2]=Math.round(rgba[2]),bytes[j+3]=Math.round(rgba[3])}if(canvas!=null){canvas.width=width,canvas.height=height;let ctx=canvas.getContext("2d"),imageData=new ImageData(bytes,width,height);ctx.putImageData(imageData,0,0)}return $img!==img&&$img.dispose(),bytes}var fromPixels=op({fromPixels_}),gather_nd_util_exports={};__export2(gather_nd_util_exports,{prepareAndValidate:()=>prepareAndValidate});function prepareAndValidate(tensor2,indices){let tensorRank=tensor2.shape.length,indicesRank=indices.shape.length;if(tensorRank<1)throw new Error(`tf.gatherND() expects the input to be rank 1 or higher, but the rank was ${tensorRank}.`);if(indicesRank<1)throw new Error(`tf.gatherND() expects the indices to be rank 1 or higher, but the rank was ${indicesRank}.`);if(indices.dtype!=="int32")throw new Error(`tf.gatherND() expects the indices to be int32 type, but the dtype was ${indices.dtype}.`);if(indices.shape[indicesRank-1]>tensorRank)throw new Error(`index innermost dimension length must be <= tensor rank; saw: ${indices.shape[indicesRank-1]} vs. ${tensorRank}`);if(sizeFromShape(tensor2.shape)===0)throw new Error(`Requested more than 0 entries, but input is empty. Input shape: ${tensor2.shape}.`);let indicesShape=indices.shape,sliceRank=indicesShape[indicesShape.length-1],nResult=1;for(let i=0;istride/sliceSize),1].slice(0,sliceRank);return[resultShape,nResult,sliceSize,strides]}var scatter_nd_util_exports={};__export2(scatter_nd_util_exports,{calculateShapes:()=>calculateShapes,validateInput:()=>validateInput,validateUpdateShape:()=>validateUpdateShape});function validateUpdateShape(shape,indices,updates){let sliceDim=indices.rank>1?indices.shape[indices.rank-1]:1,batchDim=indices.rank>1?indices.rank-1:1,shapeError=`Must have updates.shape = indices.shape[:batchDim] + shape[sliceDim:], got updates.shape: ${updates.shape}, indices.shape: ${indices.shape}, shape: ${shape}, sliceDim: ${sliceDim}, and batchDim: ${batchDim}.`;if(updates.rank1?indices.shape[indicesRank-1]:1,totalNd=shape.length,sliceSize=1;for(let i=sliceRank;iassertParamsValid,computeFlatOffset:()=>computeFlatOffset,computeOutShape:()=>computeOutShape,getNormalizedAxes:()=>getNormalizedAxes,isSliceContinous:()=>isSliceContinous,maskToAxes:()=>maskToAxes,parseSliceParams:()=>parseSliceParams,sliceInfo:()=>sliceInfo,startForAxis:()=>startForAxis,startIndicesWithElidedDims:()=>startIndicesWithElidedDims,stopForAxis:()=>stopForAxis,stopIndicesWithElidedDims:()=>stopIndicesWithElidedDims,stridesForAxis:()=>stridesForAxis,stridesWithElidedDims:()=>stridesWithElidedDims});function assertParamsValid(input2,begin,size){let inputRank=input2.shape.length;assert(inputRank===begin.length,()=>`Error in slice${inputRank}D: Length of begin ${begin} must match the rank of the array (${inputRank}).`),assert(inputRank===size.length,()=>`Error in slice${inputRank}D: Length of size ${size} must match the rank of the array (${inputRank}).`);for(let i=0;i`Error in slice${inputRank}D: begin[${i}] + size[${i}] (${begin[i]+size[i]}) would overflow input.shape[${i}] (${input2.shape[i]})`)}function maskToAxes(mask){let axes=[],axis=0;for(;mask>0;)mask&1&&axes.push(axis),mask/=2,axis++;return axes}function computeOutShape(begin,end,strides){let size=[];for(let axis=0;axis0){let fullIndex=ellipsisAxes[0],numElidedAxes=numInterpolatedAxes+1;normalizedBegin=startIndicesWithElidedDims(beginMask,fullIndex,numElidedAxes,begin,inputShape),normalizedEnd=stopIndicesWithElidedDims(endMask,fullIndex,numElidedAxes,end,inputShape),normalizedStrides=stridesWithElidedDims(strides,fullIndex,numElidedAxes,inputShape)}else for(let axis=0;axis-1)newIndices[axis]=0;else{let originalAxis=unnormalizeAxis(ellipsisInsertionIndex,numElidedAxes,axis),originalValue=originalBegin[originalAxis];beginMask&1<-1)newIndices[axis]=Number.MAX_SAFE_INTEGER;else{let originalAxis=unnormalizeAxis(ellipsisInsertionIndex,numElidedAxes,axis),originalValue=originalEnd[originalAxis];endMask&1<0?start=Number.MIN_SAFE_INTEGER:start=Number.MAX_SAFE_INTEGER);let axisSize=inputShape[axis];return start<0&&(start+=axisSize),start=clamp(0,start,axisSize-1),start}function stopForAxis(endMask,stopIndices,strides,inputShape,axis,ellipsisMask){let stop=stopIndices[axis],stride=strides[axis]||1;(endMask&1<0?stop=Number.MAX_SAFE_INTEGER:stop=Number.MIN_SAFE_INTEGER);let axisSize=inputShape[axis];return stop<0&&(stop+=axisSize),stride>0?stop=clamp(0,stop,axisSize):stop=clamp(-1,stop,axisSize-1),stop}function isSliceContinous(shape,begin,size){let firstNonOneAxis=size.length;for(let i=0;i1){firstNonOneAxis=i;break}for(let i=firstNonOneAxis+1;i0||size[i]!==shape[i])return!1;return!0}function computeFlatOffset(begin,strides){let flatOffset=begin.length>0?begin[begin.length-1]:1;for(let i=0;i{assert(d!==-1,()=>"slice() does not support negative begin indexing.")});let size_;return size==null?size_=new Array(xRank).fill(-1):typeof size=="number"?size_=[size,...new Array(xRank-1).fill(-1)]:size.lengthd>=0?d:(assert(d===-1,()=>`Negative size values should be exactly -1 but got ${d} for the slice() size at index ${i}.`),x.shape[i]-begin_[i])),[begin_,size_]}function sliceInfo(xShape,begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask){let $begin=begin.slice(),$end=end.slice(),$strides=strides;strides==null&&($strides=new Array($begin.length));let ellipsisAxes=maskToAxes(ellipsisMask);if(ellipsisAxes.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(ellipsisMask!==0&&newAxisMask!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(ellipsisMask!==0&&shrinkAxisMask!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");let numInterpolatedAxes=xShape.length-$begin.length,expandAxes=maskToAxes(newAxisMask),newShape=xShape.slice();expandAxes.forEach(axis=>{$begin[axis]=0,$end[axis]=1,newShape.splice(axis,0,1)});let{begin:normalizedBegin,end:normalizedEnd,strides:normalizedStrides}=getNormalizedAxes(newShape,ellipsisAxes,numInterpolatedAxes,$begin,$end,$strides,beginMask,endMask,ellipsisMask);$begin=normalizedBegin,$end=normalizedEnd,$strides=normalizedStrides;let shrinkAxes=maskToAxes(shrinkAxisMask);shrinkAxes.forEach(axis=>{$end[axis]=$begin[axis]+1,$strides[axis]=1});let size=computeOutShape($begin,$end,$strides),outShape=size.filter((_,axis)=>shrinkAxes.indexOf(axis)===-1);return{nonStrided:$strides.every(v=>v===1),$begin,$end,$strides,size,newShape,outShape}}var serialization_exports={};__export2(serialization_exports,{Serializable:()=>Serializable,SerializationMap:()=>SerializationMap,registerClass:()=>registerClass});var Serializable=class{getClassName(){return this.constructor.className}static fromConfig(cls,config3){return new cls(config3)}},SerializationMap=class{constructor(){this.classNameMap={}}static getMap(){return SerializationMap.instance==null&&(SerializationMap.instance=new SerializationMap),SerializationMap.instance}static register(cls){SerializationMap.getMap().classNameMap[cls.className]=[cls,cls.fromConfig]}};function registerClass(cls){assert(cls.className!=null,()=>"Class being registered does not have the static className property defined."),assert(typeof cls.className=="string",()=>"className is required to be a string, but got type "+typeof cls.className),assert(cls.className.length>0,()=>"Class being registered has an empty-string as its className, which is disallowed."),SerializationMap.register(cls)}var test_util_exports={};__export2(test_util_exports,{TEST_EPSILON_FLOAT16:()=>TEST_EPSILON_FLOAT16,encodeStrings:()=>encodeStrings,expectArrayBuffersEqual:()=>expectArrayBuffersEqual,expectArraysClose:()=>expectArraysClose,expectArraysEqual:()=>expectArraysEqual,expectNumbersClose:()=>expectNumbersClose,expectPromiseToFail:()=>expectPromiseToFail,expectValuesInRange:()=>expectValuesInRange,testEpsilon:()=>testEpsilon});var TEST_EPSILON_FLOAT32=.001,TEST_EPSILON_FLOAT16=.1;function expectArraysClose(actual,expected,epsilon3){return epsilon3==null&&(epsilon3=testEpsilon()),expectArraysPredicate(actual,expected,(a,b)=>areClose(a,b,epsilon3))}function testEpsilon(){return ENGINE.backend.floatPrecision()===32?TEST_EPSILON_FLOAT32:TEST_EPSILON_FLOAT16}function expectArraysPredicate(actual,expected,predicate){let checkClassType=!0;if((isTypedArray(actual)||isTypedArray(expected))&&(checkClassType=!1),isTypedArray(actual)&&isTypedArray(expected)&&(checkClassType=!0),checkClassType){let aType=actual.constructor.name,bType=expected.constructor.name;if(aType!==bType)throw new Error(`Arrays are of different type. Actual: ${aType}. Expected: ${bType}`)}if(Array.isArray(actual)&&Array.isArray(expected)){let actualShape=inferShape(actual),expectedShape=inferShape(expected);if(!arraysEqual(actualShape,expectedShape))throw new Error(`Arrays have different shapes. Actual: [${actualShape}]. Expected: [${expectedShape}]`)}let actualFlat=isTypedArray(actual)?actual:flatten(actual),expectedFlat=isTypedArray(expected)?expected:flatten(expected);if(actualFlat.length!==expectedFlat.length)throw new Error(`Arrays have different lengths actual: ${actualFlat.length} vs expected: ${expectedFlat.length}. +Manifest JSON has weights with names: ${allManifestWeightNames.join(", ")}.`)}let groupIndicesToFetch=groupIndicesToFetchMap.reduce((accumulator,shouldFetch,i)=>(shouldFetch&&accumulator.push(i),accumulator),[]),fetchUrls=[];groupIndicesToFetch.forEach(i=>{manifest[i].paths.forEach(filepath=>{let fetchUrl=filePathPrefix+(filePathPrefix.endsWith("/")?"":"/")+filepath;fetchUrls.push(fetchUrl)})});let buffers=await fetchWeightsFunction(fetchUrls),weightsTensorMap={},bufferIndexOffset=0;return groupIndicesToFetch.forEach(i=>{let numBuffers=manifest[i].paths.length,groupBytes=0;for(let i2=0;i2{let byteBuffer=groupBuffer.slice(weightsEntry.groupOffset,weightsEntry.groupOffset+weightsEntry.sizeBytes),nameToTensorMap=decodeWeights(byteBuffer,[weightsEntry.manifestEntry]);for(let name in nameToTensorMap)weightsTensorMap[name]=nameToTensorMap[name]}),bufferIndexOffset+=numBuffers}),weightsTensorMap}}var OCTET_STREAM_MIME_TYPE="application/octet-stream",JSON_TYPE="application/json",HTTPRequest=class{constructor(path,loadOptions){if(this.DEFAULT_METHOD="POST",loadOptions==null&&(loadOptions={}),this.weightPathPrefix=loadOptions.weightPathPrefix,this.onProgress=loadOptions.onProgress,this.weightUrlConverter=loadOptions.weightUrlConverter,loadOptions.fetchFunc!=null?(assert(typeof loadOptions.fetchFunc=="function",()=>"Must pass a function that matches the signature of `fetch` (see https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)"),this.fetch=loadOptions.fetchFunc):this.fetch=env().platform.fetch,assert(path!=null&&path.length>0,()=>"URL path for http must not be null, undefined or empty."),Array.isArray(path)&&assert(path.length===2,()=>`URL paths for http must have a length of 2, (actual length is ${path.length}).`),this.path=path,loadOptions.requestInit!=null&&loadOptions.requestInit.body!=null)throw new Error("requestInit is expected to have no pre-existing body, but has one.");this.requestInit=loadOptions.requestInit||{}}async save(modelArtifacts){if(modelArtifacts.modelTopology instanceof ArrayBuffer)throw new Error("BrowserHTTPRequest.save() does not support saving model topology in binary formats yet.");let init2=Object.assign({method:this.DEFAULT_METHOD},this.requestInit);init2.body=new FormData;let weightsManifest=[{paths:["./model.weights.bin"],weights:modelArtifacts.weightSpecs}],modelTopologyAndWeightManifest={modelTopology:modelArtifacts.modelTopology,format:modelArtifacts.format,generatedBy:modelArtifacts.generatedBy,convertedBy:modelArtifacts.convertedBy,weightsManifest};modelArtifacts.signature!=null&&(modelTopologyAndWeightManifest.signature=modelArtifacts.signature),modelArtifacts.userDefinedMetadata!=null&&(modelTopologyAndWeightManifest.userDefinedMetadata=modelArtifacts.userDefinedMetadata),modelArtifacts.modelInitializer!=null&&(modelTopologyAndWeightManifest.modelInitializer=modelArtifacts.modelInitializer),init2.body.append("model.json",new Blob([JSON.stringify(modelTopologyAndWeightManifest)],{type:JSON_TYPE}),"model.json"),modelArtifacts.weightData!=null&&init2.body.append("model.weights.bin",new Blob([modelArtifacts.weightData],{type:OCTET_STREAM_MIME_TYPE}),"model.weights.bin");let response=await this.fetch(this.path,init2);if(response.ok)return{modelArtifactsInfo:getModelArtifactsInfoForJSON(modelArtifacts),responses:[response]};throw new Error(`BrowserHTTPRequest.save() failed due to HTTP response status ${response.status}.`)}async load(){let modelConfigRequest=await this.fetch(this.path,this.requestInit);if(!modelConfigRequest.ok)throw new Error(`Request to ${this.path} failed with status code ${modelConfigRequest.status}. Please verify this URL points to the model JSON of the model to load.`);let modelConfig;try{modelConfig=await modelConfigRequest.json()}catch(e){let message=`Failed to parse model JSON of response from ${this.path}.`;throw this.path.endsWith(".pb")?message+=" Your path contains a .pb file extension. Support for .pb models have been removed in TensorFlow.js 1.0 in favor of .json models. You can re-convert your Python TensorFlow model using the TensorFlow.js 1.0 conversion scripts or you can convert your.pb models with the 'pb2json'NPM script in the tensorflow/tfjs-converter repository.":message+=" Please make sure the server is serving valid JSON for this request.",new Error(message)}let modelTopology=modelConfig.modelTopology,weightsManifest=modelConfig.weightsManifest,generatedBy=modelConfig.generatedBy,convertedBy=modelConfig.convertedBy,format=modelConfig.format,signature=modelConfig.signature,userDefinedMetadata=modelConfig.userDefinedMetadata;if(modelTopology==null&&weightsManifest==null)throw new Error(`The JSON from HTTP path ${this.path} contains neither model topology or manifest for weights.`);let weightSpecs,weightData;weightsManifest!=null&&([weightSpecs,weightData]=await this.loadWeights(weightsManifest));let artifacts={modelTopology,weightSpecs,weightData,generatedBy,convertedBy,format};signature!=null&&(artifacts.signature=signature),userDefinedMetadata!=null&&(artifacts.userDefinedMetadata=userDefinedMetadata);let initializer=modelConfig.modelInitializer;return initializer&&(artifacts.modelInitializer=initializer),artifacts}async loadWeights(weightsManifest){let weightPath=Array.isArray(this.path)?this.path[1]:this.path,[prefix,suffix]=parseUrl(weightPath),pathPrefix=this.weightPathPrefix||prefix,weightSpecs=[];for(let entry of weightsManifest)weightSpecs.push(...entry.weights);let fetchURLs=[],urlPromises=[];for(let weightsGroup of weightsManifest)for(let path of weightsGroup.paths)this.weightUrlConverter!=null?urlPromises.push(this.weightUrlConverter(path)):fetchURLs.push(pathPrefix+path+suffix);this.weightUrlConverter&&fetchURLs.push(...await Promise.all(urlPromises));let buffers=await loadWeightsAsArrayBuffer(fetchURLs,{requestInit:this.requestInit,fetchFunc:this.fetch,onProgress:this.onProgress});return[weightSpecs,concatenateArrayBuffers(buffers)]}};HTTPRequest.URL_SCHEME_REGEX=/^https?:\/\//;function parseUrl(url){let lastSlash=url.lastIndexOf("/"),lastSearchParam=url.lastIndexOf("?"),prefix=url.substring(0,lastSlash),suffix=lastSearchParam>lastSlash?url.substring(lastSearchParam):"";return[prefix+"/",suffix]}function isHTTPScheme(url){return url.match(HTTPRequest.URL_SCHEME_REGEX)!=null}var httpRouter=(url,loadOptions)=>{if(typeof fetch=="undefined"&&(loadOptions==null||loadOptions.fetchFunc==null))return null;{let isHTTP=!0;if(Array.isArray(url)?isHTTP=url.every(urlItem=>isHTTPScheme(urlItem)):isHTTP=isHTTPScheme(url),isHTTP)return http(url,loadOptions)}return null};IORouterRegistry.registerSaveRouter(httpRouter);IORouterRegistry.registerLoadRouter(httpRouter);function http(path,loadOptions){return new HTTPRequest(path,loadOptions)}function browserHTTPRequest(path,loadOptions){return http(path,loadOptions)}var PassthroughLoader=class{constructor(modelArtifacts){this.modelArtifacts=modelArtifacts}async load(){return this.modelArtifacts}},PassthroughSaver=class{constructor(saveHandler){this.saveHandler=saveHandler}async save(modelArtifacts){return this.saveHandler(modelArtifacts)}};function fromMemory(modelArtifacts,weightSpecs,weightData,trainingConfig){return arguments.length===1?modelArtifacts.modelTopology!=null||modelArtifacts.weightSpecs!=null?new PassthroughLoader(modelArtifacts):(console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."),new PassthroughLoader({modelTopology:modelArtifacts})):(console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."),new PassthroughLoader({modelTopology:modelArtifacts,weightSpecs,weightData,trainingConfig}))}function withSaveHandler(saveHandler){return new PassthroughSaver(saveHandler)}var math_exports={};__export2(math_exports,{confusionMatrix:()=>confusionMatrix});function matMul_(a,b,transposeA=!1,transposeB=!1){let $a=convertToTensor(a,"a","matMul"),$b=convertToTensor(b,"b","matMul");[$a,$b]=makeTypesMatch($a,$b);let inputs={a:$a,b:$b},attrs={transposeA,transposeB};return ENGINE.runKernel(BatchMatMul,inputs,attrs)}var matMul=op({matMul_});function oneHot_(indices,depth,onValue=1,offValue=0){if(depth<2)throw new Error(`Error in oneHot: depth must be >=2, but it is ${depth}`);let inputs={indices:convertToTensor(indices,"indices","oneHot","int32")},attrs={depth,onValue,offValue};return ENGINE.runKernel(OneHot,inputs,attrs)}var oneHot=op({oneHot_});function transpose_(x,perm){let $x=convertToTensor(x,"x","transpose");if(perm==null&&(perm=$x.shape.map((s,i)=>i).reverse()),assert($x.rank===perm.length,()=>`Error in transpose: rank of input ${$x.rank} must match length of perm ${perm}.`),perm.forEach(axis=>{assert(axis>=0&&axis<$x.rank,()=>`All entries in 'perm' must be between 0 and ${$x.rank-1} but got ${perm}`)}),$x.rank<=1)return $x.clone();let inputs={x:$x},attrs={perm};return ENGINE.runKernel(Transpose,inputs,attrs)}var transpose=op({transpose_});function confusionMatrix_(labels,predictions,numClasses){let $labels=convertToTensor(labels,"labels","confusionMatrix"),$predictions=convertToTensor(predictions,"predictions","confusionMatrix");assert(numClasses==null||numClasses>0&&Number.isInteger(numClasses),()=>`If provided, numClasses must be a positive integer, but got ${numClasses}`),assert($labels.rank===1,()=>`Expected the rank of labels to be 1, but got ${$labels.rank}`),assert($predictions.rank===1,()=>`Expected the rank of predictions to be 1, but got ${$predictions.rank}`),assert($labels.shape[0]===$predictions.shape[0],()=>`Mismatch in the number of examples: ${$labels.shape[0]} vs. ${$predictions.shape[0]}. Labels and predictions should have the same number of elements.`),assert(numClasses>0&&Number.isInteger(numClasses),()=>`numClasses is required to be a positive integer, but got ${numClasses}`);let oneHotLabels=oneHot(cast($labels,"int32"),numClasses),oneHotPredictions=oneHot(cast($predictions,"int32"),numClasses),oneHotLabelsT=transpose(oneHotLabels),product=matMul(oneHotLabelsT,oneHotPredictions);return cast(product,"int32")}var confusionMatrix=op({confusionMatrix_}),browser_exports={};__export2(browser_exports,{fromPixels:()=>fromPixels,toPixels:()=>toPixels});function tensor3d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==3)throw new Error("tensor3d() requires shape to have three numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==3&&inferredShape.length!==1)throw new Error("tensor3d() requires values to be number[][][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor3d() requires shape to be provided when `values` are a flat array");return makeTensor(values,shape,inferredShape,dtype)}var fromPixels2DContext;function fromPixels_(pixels,numChannels=3){if(numChannels>4)throw new Error("Cannot construct Tensor with more than 4 channels from pixels.");if(pixels==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let isPixelData=!1,isImageData=!1,isVideo=!1,isImage=!1,isCanvasLike=!1,isImageBitmap=!1;if(pixels.data instanceof Uint8Array)isPixelData=!0;else if(typeof ImageData!="undefined"&&pixels instanceof ImageData)isImageData=!0;else if(typeof HTMLVideoElement!="undefined"&&pixels instanceof HTMLVideoElement)isVideo=!0;else if(typeof HTMLImageElement!="undefined"&&pixels instanceof HTMLImageElement)isImage=!0;else if(pixels.getContext!=null)isCanvasLike=!0;else if(typeof ImageBitmap!="undefined"&&pixels instanceof ImageBitmap)isImageBitmap=!0;else throw new Error(`pixels passed to tf.browser.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData in browser, or OffscreenCanvas, ImageData in webworker or {data: Uint32Array, width: number, height: number}, but was ${pixels.constructor.name}`);if(isVideo){let HAVE_CURRENT_DATA_READY_STATE=2;if(isVideo&&pixels.readyState element.")}if(getKernel(FromPixels,ENGINE.backendName)!=null){let inputs={pixels},attrs={numChannels};return ENGINE.runKernel(FromPixels,inputs,attrs)}let[width,height]=isVideo?[pixels.videoWidth,pixels.videoHeight]:[pixels.width,pixels.height],vals;isCanvasLike?vals=pixels.getContext("2d").getImageData(0,0,width,height).data:isImageData||isPixelData?vals=pixels.data:(isImage||isVideo||isImageBitmap)&&(fromPixels2DContext==null&&(fromPixels2DContext=document.createElement("canvas").getContext("2d")),fromPixels2DContext.canvas.width=width,fromPixels2DContext.canvas.height=height,fromPixels2DContext.drawImage(pixels,0,0,width,height),vals=fromPixels2DContext.getImageData(0,0,width,height).data);let values;if(numChannels===4)values=new Int32Array(vals);else{let numPixels=width*height;values=new Int32Array(numPixels*numChannels);for(let i=0;i4||depth===2)throw new Error(`toPixels only supports depth of size 1, 3 or 4 but got ${depth}`);if($img.dtype!=="float32"&&$img.dtype!=="int32")throw new Error(`Unsupported type for toPixels: ${$img.dtype}. Please use float32 or int32 tensors.`);let data2=await $img.data(),multiplier=$img.dtype==="float32"?255:1,bytes=new Uint8ClampedArray(width*height*4);for(let i=0;i1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${value}.`)}else if($img.dtype==="int32"&&(value<0||value>255))throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${value}.`);depth===1?(rgba[0]=value*multiplier,rgba[1]=value*multiplier,rgba[2]=value*multiplier):rgba[d]=value*multiplier}let j=i*4;bytes[j+0]=Math.round(rgba[0]),bytes[j+1]=Math.round(rgba[1]),bytes[j+2]=Math.round(rgba[2]),bytes[j+3]=Math.round(rgba[3])}if(canvas!=null){canvas.width=width,canvas.height=height;let ctx=canvas.getContext("2d"),imageData=new ImageData(bytes,width,height);ctx.putImageData(imageData,0,0)}return $img!==img&&$img.dispose(),bytes}var fromPixels=op({fromPixels_}),gather_nd_util_exports={};__export2(gather_nd_util_exports,{prepareAndValidate:()=>prepareAndValidate});function prepareAndValidate(tensor2,indices){let tensorRank=tensor2.shape.length,indicesRank=indices.shape.length;if(tensorRank<1)throw new Error(`tf.gatherND() expects the input to be rank 1 or higher, but the rank was ${tensorRank}.`);if(indicesRank<1)throw new Error(`tf.gatherND() expects the indices to be rank 1 or higher, but the rank was ${indicesRank}.`);if(indices.dtype!=="int32")throw new Error(`tf.gatherND() expects the indices to be int32 type, but the dtype was ${indices.dtype}.`);if(indices.shape[indicesRank-1]>tensorRank)throw new Error(`index innermost dimension length must be <= tensor rank; saw: ${indices.shape[indicesRank-1]} vs. ${tensorRank}`);if(sizeFromShape(tensor2.shape)===0)throw new Error(`Requested more than 0 entries, but input is empty. Input shape: ${tensor2.shape}.`);let indicesShape=indices.shape,sliceRank=indicesShape[indicesShape.length-1],nResult=1;for(let i=0;istride/sliceSize),1].slice(0,sliceRank);return[resultShape,nResult,sliceSize,strides]}var scatter_nd_util_exports={};__export2(scatter_nd_util_exports,{calculateShapes:()=>calculateShapes,validateInput:()=>validateInput,validateUpdateShape:()=>validateUpdateShape});function validateUpdateShape(shape,indices,updates){let sliceDim=indices.rank>1?indices.shape[indices.rank-1]:1,batchDim=indices.rank>1?indices.rank-1:1,shapeError=`Must have updates.shape = indices.shape[:batchDim] + shape[sliceDim:], got updates.shape: ${updates.shape}, indices.shape: ${indices.shape}, shape: ${shape}, sliceDim: ${sliceDim}, and batchDim: ${batchDim}.`;if(updates.rank1?indices.shape[indicesRank-1]:1,totalNd=shape.length,sliceSize=1;for(let i=sliceRank;iassertParamsValid,computeFlatOffset:()=>computeFlatOffset,computeOutShape:()=>computeOutShape,getNormalizedAxes:()=>getNormalizedAxes,isSliceContinous:()=>isSliceContinous,maskToAxes:()=>maskToAxes,parseSliceParams:()=>parseSliceParams,sliceInfo:()=>sliceInfo,startForAxis:()=>startForAxis,startIndicesWithElidedDims:()=>startIndicesWithElidedDims,stopForAxis:()=>stopForAxis,stopIndicesWithElidedDims:()=>stopIndicesWithElidedDims,stridesForAxis:()=>stridesForAxis,stridesWithElidedDims:()=>stridesWithElidedDims});function assertParamsValid(input2,begin,size){let inputRank=input2.shape.length;assert(inputRank===begin.length,()=>`Error in slice${inputRank}D: Length of begin ${begin} must match the rank of the array (${inputRank}).`),assert(inputRank===size.length,()=>`Error in slice${inputRank}D: Length of size ${size} must match the rank of the array (${inputRank}).`);for(let i=0;i`Error in slice${inputRank}D: begin[${i}] + size[${i}] (${begin[i]+size[i]}) would overflow input.shape[${i}] (${input2.shape[i]})`)}function maskToAxes(mask){let axes=[],axis=0;for(;mask>0;)mask&1&&axes.push(axis),mask/=2,axis++;return axes}function computeOutShape(begin,end,strides){let size=[];for(let axis=0;axis0){let fullIndex=ellipsisAxes[0],numElidedAxes=numInterpolatedAxes+1;normalizedBegin=startIndicesWithElidedDims(beginMask,fullIndex,numElidedAxes,begin,inputShape),normalizedEnd=stopIndicesWithElidedDims(endMask,fullIndex,numElidedAxes,end,inputShape),normalizedStrides=stridesWithElidedDims(strides,fullIndex,numElidedAxes,inputShape)}else for(let axis=0;axis-1)newIndices[axis]=0;else{let originalAxis=unnormalizeAxis(ellipsisInsertionIndex,numElidedAxes,axis),originalValue=originalBegin[originalAxis];beginMask&1<-1)newIndices[axis]=Number.MAX_SAFE_INTEGER;else{let originalAxis=unnormalizeAxis(ellipsisInsertionIndex,numElidedAxes,axis),originalValue=originalEnd[originalAxis];endMask&1<0?start=Number.MIN_SAFE_INTEGER:start=Number.MAX_SAFE_INTEGER);let axisSize=inputShape[axis];return start<0&&(start+=axisSize),start=clamp(0,start,axisSize-1),start}function stopForAxis(endMask,stopIndices,strides,inputShape,axis,ellipsisMask){let stop=stopIndices[axis],stride=strides[axis]||1;(endMask&1<0?stop=Number.MAX_SAFE_INTEGER:stop=Number.MIN_SAFE_INTEGER);let axisSize=inputShape[axis];return stop<0&&(stop+=axisSize),stride>0?stop=clamp(0,stop,axisSize):stop=clamp(-1,stop,axisSize-1),stop}function isSliceContinous(shape,begin,size){let firstNonOneAxis=size.length;for(let i=0;i1){firstNonOneAxis=i;break}for(let i=firstNonOneAxis+1;i0||size[i]!==shape[i])return!1;return!0}function computeFlatOffset(begin,strides){let flatOffset=begin.length>0?begin[begin.length-1]:1;for(let i=0;i{assert(d!==-1,()=>"slice() does not support negative begin indexing.")});let size_;return size==null?size_=new Array(xRank).fill(-1):typeof size=="number"?size_=[size,...new Array(xRank-1).fill(-1)]:size.lengthd>=0?d:(assert(d===-1,()=>`Negative size values should be exactly -1 but got ${d} for the slice() size at index ${i}.`),x.shape[i]-begin_[i])),[begin_,size_]}function sliceInfo(xShape,begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask){let $begin=begin.slice(),$end=end.slice(),$strides=strides;strides==null&&($strides=new Array($begin.length));let ellipsisAxes=maskToAxes(ellipsisMask);if(ellipsisAxes.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(ellipsisMask!==0&&newAxisMask!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(ellipsisMask!==0&&shrinkAxisMask!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");let numInterpolatedAxes=xShape.length-$begin.length,expandAxes=maskToAxes(newAxisMask),newShape=xShape.slice();expandAxes.forEach(axis=>{$begin[axis]=0,$end[axis]=1,newShape.splice(axis,0,1)});let{begin:normalizedBegin,end:normalizedEnd,strides:normalizedStrides}=getNormalizedAxes(newShape,ellipsisAxes,numInterpolatedAxes,$begin,$end,$strides,beginMask,endMask,ellipsisMask);$begin=normalizedBegin,$end=normalizedEnd,$strides=normalizedStrides;let shrinkAxes=maskToAxes(shrinkAxisMask);shrinkAxes.forEach(axis=>{$end[axis]=$begin[axis]+1,$strides[axis]=1});let size=computeOutShape($begin,$end,$strides),outShape=size.filter((_,axis)=>shrinkAxes.indexOf(axis)===-1);return{nonStrided:$strides.every(v=>v===1),$begin,$end,$strides,size,newShape,outShape}}var serialization_exports={};__export2(serialization_exports,{Serializable:()=>Serializable,SerializationMap:()=>SerializationMap,registerClass:()=>registerClass});var Serializable=class{getClassName(){return this.constructor.className}static fromConfig(cls,config2){return new cls(config2)}},SerializationMap=class{constructor(){this.classNameMap={}}static getMap(){return SerializationMap.instance==null&&(SerializationMap.instance=new SerializationMap),SerializationMap.instance}static register(cls){SerializationMap.getMap().classNameMap[cls.className]=[cls,cls.fromConfig]}};function registerClass(cls){assert(cls.className!=null,()=>"Class being registered does not have the static className property defined."),assert(typeof cls.className=="string",()=>"className is required to be a string, but got type "+typeof cls.className),assert(cls.className.length>0,()=>"Class being registered has an empty-string as its className, which is disallowed."),SerializationMap.register(cls)}var test_util_exports={};__export2(test_util_exports,{TEST_EPSILON_FLOAT16:()=>TEST_EPSILON_FLOAT16,encodeStrings:()=>encodeStrings,expectArrayBuffersEqual:()=>expectArrayBuffersEqual,expectArraysClose:()=>expectArraysClose,expectArraysEqual:()=>expectArraysEqual,expectNumbersClose:()=>expectNumbersClose,expectPromiseToFail:()=>expectPromiseToFail,expectValuesInRange:()=>expectValuesInRange,testEpsilon:()=>testEpsilon});var TEST_EPSILON_FLOAT32=.001,TEST_EPSILON_FLOAT16=.1;function expectArraysClose(actual,expected,epsilon3){return epsilon3==null&&(epsilon3=testEpsilon()),expectArraysPredicate(actual,expected,(a,b)=>areClose(a,b,epsilon3))}function testEpsilon(){return ENGINE.backend.floatPrecision()===32?TEST_EPSILON_FLOAT32:TEST_EPSILON_FLOAT16}function expectArraysPredicate(actual,expected,predicate){let checkClassType=!0;if((isTypedArray(actual)||isTypedArray(expected))&&(checkClassType=!1),isTypedArray(actual)&&isTypedArray(expected)&&(checkClassType=!0),checkClassType){let aType=actual.constructor.name,bType=expected.constructor.name;if(aType!==bType)throw new Error(`Arrays are of different type. Actual: ${aType}. Expected: ${bType}`)}if(Array.isArray(actual)&&Array.isArray(expected)){let actualShape=inferShape(actual),expectedShape=inferShape(expected);if(!arraysEqual(actualShape,expectedShape))throw new Error(`Arrays have different shapes. Actual: [${actualShape}]. Expected: [${expectedShape}]`)}let actualFlat=isTypedArray(actual)?actual:flatten(actual),expectedFlat=isTypedArray(expected)?expected:flatten(expected);if(actualFlat.length!==expectedFlat.length)throw new Error(`Arrays have different lengths actual: ${actualFlat.length} vs expected: ${expectedFlat.length}. Actual: ${actualFlat}. Expected: ${expectedFlat}.`);for(let i=0;i`Dimension size must be evenly divisible by ${blockSize*blockSize} but is ${inputDepth} for depthToSpace with input shape ${$x.shape}`);let inputs={x:$x},attrs={blockSize,dataFormat};return ENGINE.runKernel(DepthToSpace,inputs,attrs)}var depthToSpace=op({depthToSpace_});function depthwiseConv2d_(x,filter,strides,pad3,dataFormat="NHWC",dilations=[1,1],dimRoundingMode){let $x=convertToTensor(x,"x","depthwiseConv2d"),$filter=convertToTensor(filter,"filter","depthwiseConv2d"),x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in depthwiseConv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert($filter.rank===4,()=>`Error in depthwiseConv2d: filter must be rank 4, but got rank ${$filter.rank}.`),assert(x4D.shape[3]===$filter.shape[2],()=>`Error in depthwiseConv2d: number of input channels (${x4D.shape[3]}) must match the inChannels dimension in filter ${$filter.shape[2]}.`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={x:x4D,filter:$filter},attrs={strides,pad:pad3,dataFormat,dilations,dimRoundingMode},res=ENGINE.runKernel(DepthwiseConv2dNative,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var depthwiseConv2d=op({depthwiseConv2d_});function diag_(x){let inputs={x:convertToTensor(x,"x","diag")};return ENGINE.runKernel(Diag,inputs)}var diag=op({diag_});function dilation2d_(x,filter,strides,pad3,dilations=[1,1],dataFormat="NHWC"){let $x=convertToTensor(x,"x","dilation2d"),$filter=convertToTensor(filter,"filter","dilation2d");assert($x.rank===3||$x.rank===4,()=>`Error in dilation2d: input must be rank 3 or 4, but got rank ${$x.rank}.`),assert($filter.rank===3,()=>`Error in dilation2d: filter must be rank 3, but got rank ${$filter.rank}.`),assert(dataFormat==="NHWC",()=>`Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${dataFormat}`);let x4D=$x,reshapedTo4D=!1;$x.rank===3&&(x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]]),reshapedTo4D=!0);let inputs={x:x4D,filter:$filter},attrs={strides,pad:pad3,dilations},res=ENGINE.runKernel(Dilation2D,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var dilation2d=op({dilation2d_});function getBroadcastDims(inShape,outShape){let inRank=inShape.length,dims=[];for(let i=0;i1&&a===1&&dims.unshift(dim)}return dims}function getReductionAxes(inShape,outShape){let result=[];for(let i=0;i1)&&result.unshift(outAxis)}return result}function assertAndGetBroadcastShape(shapeA,shapeB){let result=[],l=Math.max(shapeA.length,shapeB.length);for(let i=0;i"The first dimension of `a` must match the size of `condition`."),$condition.rank!==1&&assertShapesMatch($condition.shape,$broadcastedB.shape,"Error in where: ");let inputs={condition:$condition,t:$broadcastedA,e:$broadcastedB};return ENGINE.runKernel(Select,inputs)}var where=op({where_});function zerosLike_(x){let inputs={x:convertToTensor(x,"x","zerosLike")};return ENGINE.runKernel(ZerosLike,inputs)}var zerosLike=op({zerosLike_});function divNoNan_(a,b){let $a=convertToTensor(a,"a","div"),$b=convertToTensor(b,"b","div");[$a,$b]=makeTypesMatch($a,$b);let divResult=div($a,$b),zeros4=zerosLike(divResult),bEqualsZero=equal($b,zeros4);return where(bEqualsZero,zeros4,divResult)}var divNoNan=op({divNoNan_});function dot_(t1,t2){let $t1=convertToTensor(t1,"t1","dot"),$t2=convertToTensor(t2,"t2","dot");assert(($t1.rank===1||$t1.rank===2)&&($t2.rank===1||$t2.rank===2),()=>`Error in dot: inputs must all be rank 1 or 2, but got ranks ${$t1.rank} and ${$t2.rank}.`);let t1Inner=$t1.rank===1?$t1.size:$t1.shape[1],t2Inner=$t2.rank===1?$t2.size:$t2.shape[0];if(assert(t1Inner===t2Inner,()=>`Error in dot: inner dimensions of inputs must match, but got ${t1Inner} and ${t2Inner}.`),$t1.rank===1&&$t2.rank===1){let t12D=reshape($t1,[1,-1]),t22D=reshape($t2,[-1,1]),t1t2=matMul(t12D,t22D);return reshape(t1t2,[])}else if($t1.rank===1&&$t2.rank===2){let t12D=reshape($t1,[1,-1]),t22D=reshape($t2,[$t2.shape[0],$t2.shape[1]]),t1t2=matMul(t12D,t22D);return reshape(t1t2,[t1t2.size])}else if($t1.rank===2&&$t2.rank===1){let t22D=reshape($t2,[-1,1]),t1t2=matMul($t1,t22D);return reshape(t1t2,[t1t2.size])}else{let t22D=reshape($t2,[$t2.shape[0],$t2.shape[1]]);return matMul($t1,t22D)}}var dot=op({dot_});function elu_(x){let inputs={x:convertToTensor(x,"x","elu")};return ENGINE.runKernel(Elu,inputs)}var elu=op({elu_});function erf_(x){let $x=convertToTensor(x,"x","erf");assert($x.dtype==="int32"||$x.dtype==="float32",()=>"Input dtype must be `int32` or `float32`."),$x.dtype==="int32"&&($x=cast($x,"float32"));let inputs={x:$x};return ENGINE.runKernel(Erf,inputs)}var erf=op({erf_});function exp_(x){let inputs={x:convertToTensor(x,"x","exp")};return ENGINE.runKernel(Exp,inputs)}var exp=op({exp_});function expandDims_(x,axis=0){let $x=convertToTensor(x,"x","expandDims","string_or_numeric");assert(axis<=$x.rank,()=>"Axis must be <= rank of the tensor");let inputs={input:$x},attrs={dim:axis};return ENGINE.runKernel(ExpandDims,inputs,attrs)}var expandDims=op({expandDims_});function expm1_(x){let inputs={x:convertToTensor(x,"x","expm1")};return ENGINE.runKernel(Expm1,inputs)}var expm1=op({expm1_});function tile_(x,reps){let $x=convertToTensor(x,"x","tile","string_or_numeric");assert($x.rank===reps.length,()=>`Error in transpose: rank of input ${$x.rank} must match length of reps ${reps}.`);let inputs={x:$x},attrs={reps};return ENGINE.runKernel(Tile,inputs,attrs)}var tile=op({tile_});function eye_(numRows,numColumns,batchShape,dtype="float32"){numColumns==null&&(numColumns=numRows);let buff=buffer([numRows,numColumns],dtype),n=numRows<=numColumns?numRows:numColumns;for(let i=0;i`Error in localResponseNormalization: x must be rank 3 or 4 but got rank ${$x.rank}.`),assert(isInt(depthRadius),()=>`Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${depthRadius}.`);let x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]]));let inputs={x:x4D},attrs={depthRadius,bias,alpha,beta},res=ENGINE.runKernel(LRN,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var localResponseNormalization=op({localResponseNormalization_});function log_(x){let inputs={x:convertToTensor(x,"x","log")};return ENGINE.runKernel(Log,inputs)}var log2=op({log_});function log1p_(x){let inputs={x:convertToTensor(x,"x","log1p")};return ENGINE.runKernel(Log1p,inputs)}var log1p=op({log1p_});function grad(f){return assert(isFunction(f),()=>"The f passed in grad(f) must be a function"),(x,dy)=>{let $x=convertToTensor(x,"x","tf.grad","string_or_numeric"),$dy=dy!=null?convertToTensor(dy,"dy","tf.grad"):null;return ENGINE.tidy(()=>{let{value,grads:grads2}=ENGINE.gradients(()=>f($x),[$x],$dy);return $dy!=null&&assertShapesMatch(value.shape,$dy.shape,"The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)"),checkGrads(grads2),grads2[0]})}}function grads(f){return assert(isFunction(f),()=>"The f passed in grads(f) must be a function"),(args,dy)=>{assert(Array.isArray(args),()=>"The args passed in grads(f)(args) must be an array of `Tensor`s or `TensorLike`s");let $args=convertToTensorArray(args,"args","tf.grads","string_or_numeric"),$dy=dy!=null?convertToTensor(dy,"dy","tf.grads"):null;return ENGINE.tidy(()=>{let{value,grads:grads2}=ENGINE.gradients(()=>f(...$args),$args,$dy);return $dy!=null&&assertShapesMatch(value.shape,$dy.shape,"The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),checkGrads(grads2),grads2})}}function valueAndGrad(f){return assert(isFunction(f),()=>"The f passed in valueAndGrad(f) must be a function"),(x,dy)=>{assert(x instanceof Tensor,()=>"The x passed in valueAndGrad(f)(x) must be a tensor"),assert(dy==null||dy instanceof Tensor,()=>"The dy passed in valueAndGrad(f)(x, dy) must be a tensor");let{grads:grads2,value}=ENGINE.gradients(()=>f(x),[x],dy);return checkGrads(grads2),{grad:grads2[0],value}}}function valueAndGrads(f){return assert(isFunction(f),()=>"The f passed in valueAndGrads(f) must be a function"),(args,dy)=>{assert(Array.isArray(args)&&args.every(arg=>arg instanceof Tensor),()=>"The args passed in valueAndGrads(f)(args) must be array of tensors"),assert(dy==null||dy instanceof Tensor,()=>"The dy passed in valueAndGrads(f)(args, dy) must be a tensor");let res=ENGINE.gradients(()=>f(...args),args,dy);return dy!=null&&assertShapesMatch(res.value.shape,dy.shape,"The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),checkGrads(res.grads),res}}function variableGrads(f,varList){assert(isFunction(f),()=>"The f passed in variableGrads(f) must be a function"),assert(varList==null||Array.isArray(varList)&&varList.every(v=>v instanceof Variable),()=>"The varList passed in variableGrads(f, varList) must be an array of variables");let specifiedVarList=varList!=null;if(!specifiedVarList){varList=[];for(let varName in ENGINE.registeredVariables)varList.push(ENGINE.registeredVariables[varName])}let specifiedNonTrainable=specifiedVarList?varList.filter(variable2=>!variable2.trainable):null,originalVarCount=varList.length;varList=varList.filter(variable2=>variable2.trainable),assert(varList.length>0,()=>`variableGrads() expects at least one of the input variables to be trainable, but none of the ${originalVarCount} variables is trainable.`);let allowNoGradients=!0,{value,grads:grads2}=ENGINE.gradients(f,varList,null,allowNoGradients);assert(grads2.some(g=>g!=null),()=>"Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize()."),assert(value.rank===0,()=>`The f passed in variableGrads(f) must return a scalar, but it returned a rank-${value.rank} tensor`);let namedGrads={};return varList.forEach((v,i)=>{grads2[i]!=null&&(namedGrads[v.name]=grads2[i])}),specifiedNonTrainable!=null&&specifiedNonTrainable.forEach(v=>namedGrads[v.name]=null),{value,grads:namedGrads}}function customGrad(f){return ENGINE.customGrad(f)}function checkGrads(grads2){if(grads2.filter(g=>g==null).length>0)throw new Error(`Cannot compute gradient of y=f(x) with respect to x. Make sure that - the f you passed encloses all operations that lead from x to y.`)}function neg_(x){let inputs={x:convertToTensor(x,"x","neg")};return ENGINE.runKernel(Neg,inputs)}var neg=op({neg_});function softplus_(x){let inputs={x:convertToTensor(x,"x","softplus")};return ENGINE.runKernel(Softplus,inputs)}var softplus=op({softplus_});function logSigmoid_(x){let $x=convertToTensor(x,"x","logSigmoid");return customGrad(x2=>({value:neg(softplus(neg(x2))),gradFunc:dy=>mul(dy,sigmoid(neg(x2)))}))($x)}var logSigmoid=op({logSigmoid_});function max_(x,axis=null,keepDims=!1){let inputs={x:convertToTensor(x,"x","max")},attrs={reductionIndices:axis,keepDims};return ENGINE.runKernel(Max,inputs,attrs)}var max=op({max_});function sub_(a,b){let $a=convertToTensor(a,"a","sub"),$b=convertToTensor(b,"b","sub");[$a,$b]=makeTypesMatch($a,$b);let inputs={a:$a,b:$b};return ENGINE.runKernel(Sub,inputs)}var sub=op({sub_});function sum_(x,axis=null,keepDims=!1){let $x=convertToTensor(x,"x","sum");$x.dtype==="bool"&&($x=cast($x,"int32"));let inputs={x:$x},attrs={axis,keepDims};return ENGINE.runKernel(Sum,inputs,attrs)}var sum2=op({sum_});function logSoftmax_(logits,axis=-1){let $logits=convertToTensor(logits,"logits","logSoftmax");if(axis===-1&&(axis=$logits.rank-1),axis!==$logits.rank-1)throw Error(`Log Softmax along a non-last dimension is not yet supported. Logits was rank ${$logits.rank} and axis was ${axis}`);return customGrad((logits2,save)=>{let keepDims=!0,xMax=max(logits2,axis,!0),shifted=sub(logits2,xMax),value=sub(cast(shifted,"float32"),log2(sum2(exp(shifted),axis,keepDims)));return save([value]),{value,gradFunc:(dy,saved)=>{let[value2]=saved,keepDims2=!0,softmax6=exp(value2);return sub(dy,mul(sum2(dy,axis,keepDims2),softmax6))}}})($logits)}var logSoftmax=op({logSoftmax_});function axesAreInnerMostDims(axes,rank){for(let i=0;iaShape[dim]);return[outShape,reduceShape]}function expandShapeToKeepDim(shape,axes){let reduceSubShape=axes.map(x=>1);return combineLocations(shape,reduceSubShape,axes)}function assertAxesAreInnerMostDims(msg,axes,rank){assert(axesAreInnerMostDims(axes,rank),()=>`${msg} supports only inner-most axes for now. Got axes ${axes} and rank-${rank} input.`)}function getAxesPermutation(axes,rank){if(axesAreInnerMostDims(axes,rank))return null;let result=[];for(let i=0;iresult.push(axis)),result}function getUndoAxesPermutation(axes){return axes.map((axis,i)=>[i,axis]).sort((a,b)=>a[1]-b[1]).map(x=>x[0])}function getInnerMostAxes(numAxes,rank){let res=[];for(let i=rank-numAxes;i`Error in maxPool: input must be rank 4 but got rank ${x4D.rank}.`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in maxPool: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={x:x4D},attrs={filterSize,strides,pad:pad3,dimRoundingMode},res=ENGINE.runKernel(MaxPool,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var maxPool=op({maxPool_});function maxPool3d_(x,filterSize=[1,1,1],strides,pad3,dimRoundingMode,dataFormat="NDHWC",dilations){dilations==null?dilations=[1,1,1]:deprecationWarn("dilations is deprecated, this field will be gone in v3.0.0.");let $x=convertToTensor(x,"x","maxPool3d"),x5D=$x,reshapedTo5D=!1;$x.rank===4&&(reshapedTo5D=!0,x5D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2],$x.shape[3]])),assert(x5D.rank===5,()=>`Error in maxPool3d: x must be rank 5 but got rank ${x5D.rank}.`),assert(dataFormat==="NDHWC",()=>`Error in maxPool3d: Only NDHWC is currently supported, but got dataFormat of ${dataFormat}`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in maxPool3d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in maxPool3d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={x:x5D},attrs={filterSize,strides,pad:pad3,dimRoundingMode,dataFormat,dilations},res=ENGINE.runKernel(MaxPool3D,inputs,attrs);return reshapedTo5D?reshape(res,[res.shape[1],res.shape[2],res.shape[3],res.shape[4]]):res}var maxPool3d=op({maxPool3d_});function maxPoolWithArgmax_(x,filterSize,strides,pad3,includeBatchInIndex=!1){let inputs={x:convertToTensor(x,"x","maxPoolWithArgmax")},attrs={filterSize,strides,pad:pad3,includeBatchInIndex},result=ENGINE.runKernel(MaxPoolWithArgmax,inputs,attrs);return{result:result[0],indexes:result[1]}}var maxPoolWithArgmax=op({maxPoolWithArgmax_});function maximum_(a,b){let $a=convertToTensor(a,"a","maximum"),$b=convertToTensor(b,"b","maximum");[$a,$b]=makeTypesMatch($a,$b),$a.dtype==="bool"&&($a=cast($a,"int32"),$b=cast($b,"int32")),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(Maximum,inputs)}var maximum=op({maximum_});function mean_(x,axis=null,keepDims=!1){let inputs={x:convertToTensor(x,"x","mean")},attrs={axis,keepDims};return ENGINE.runKernel(Mean,inputs,attrs)}var mean=op({mean_});function min_(x,axis=null,keepDims=!1){let inputs={x:convertToTensor(x,"x","min")},attrs={axis,keepDims};return ENGINE.runKernel(Min,inputs,attrs)}var min=op({min_});function minimum_(a,b){let $a=convertToTensor(a,"a","minimum"),$b=convertToTensor(b,"b","minimum");[$a,$b]=makeTypesMatch($a,$b),$a.dtype==="bool"&&($a=cast($a,"int32"),$b=cast($b,"int32")),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(Minimum,inputs)}var minimum=op({minimum_});function mirrorPad_(x,paddings,mode){assert(mode==="reflect"||mode==="symmetric",()=>`Invalid mode. Mode must be either reflect or symmetric. Got ${mode}.`);let $x=convertToTensor(x,"x","mirrorPad");if($x.rank===0)throw new Error("mirrorPad(scalar) is not defined. Pass non-scalar to mirrorPad");assert(paddings.length===$x.rank,()=>`Padding doesn't match input. Must be ${$x.rank}. Got ${paddings.length}.`);let shapeOffset=mode==="reflect"?1:0;for(let i=0;i<$x.rank;i++)assert(paddings[i].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),assert(paddings[i][0]>=0&&paddings[i][0]<=$x.shape[i]-shapeOffset&&paddings[i][1]>=0&&paddings[i][1]<=$x.shape[i]-shapeOffset,()=>`Padding in dimension ${i} cannot be greater than or equal to ${$x.shape[i]-shapeOffset} or less than 0 for input of shape ${$x.shape}`);let attrs={paddings,mode},inputs={x:$x};return ENGINE.runKernel(MirrorPad,inputs,attrs)}var mirrorPad=op({mirrorPad_});function mod_(a,b){let $a=convertToTensor(a,"a","mod"),$b=convertToTensor(b,"b","mod");[$a,$b]=makeTypesMatch($a,$b);let inputs={a:$a,b:$b};return ENGINE.runKernel(Mod,inputs)}var mod=op({mod_});function square_(x){let $x=convertToTensor(x,"x","square"),attrs={};return ENGINE.runKernel("Square",{x:$x},attrs)}var square=op({square_});function moments_(x,axis=null,keepDims=!1){x=convertToTensor(x,"x","moments");let axes=parseAxisParam(axis,x.shape),xMean=mean(x,axes,keepDims),keepDimsShape=xMean.shape;keepDims||(keepDimsShape=expandShapeToKeepDim(xMean.shape,axes));let devSquared=square(sub(cast(x,"float32"),reshape(xMean,keepDimsShape))),variance=mean(devSquared,axes,keepDims);return{mean:xMean,variance}}var moments=op({moments_});function multiRNNCell_(lstmCells,data2,c,h){let $data=convertToTensor(data2,"data","multiRNNCell"),$c=convertToTensorArray(c,"c","multiRNNCell"),$h=convertToTensorArray(h,"h","multiRNNCell"),input2=$data,newStates=[];for(let i=0;i2)throw new Error(`Rank of probabilities must be 1 or 2, but is ${origRank}`);seed=seed||Math.random();let inputs={logits:origRank===1?reshape($logits,[1,-1]):$logits},attrs={numSamples,seed,normalized},res=ENGINE.runKernel(Multinomial,inputs,attrs);return origRank===1?reshape(res,[res.size]):res}var multinomial=op({multinomial_});function notEqual_(a,b){let $a=convertToTensor(a,"a","notEqual"),$b=convertToTensor(b,"b","notEqual");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(NotEqual,inputs)}var notEqual=op({notEqual_});function zeros(shape,dtype="float32"){if(dtype==="complex64"){let real4=zeros(shape,"float32"),imag4=zeros(shape,"float32");return complex(real4,imag4)}let values=makeZerosTypedArray(sizeFromShape(shape),dtype);return ENGINE.makeTensor(values,shape,dtype)}function ones2(shape,dtype="float32"){if(dtype==="complex64"){let real4=ones2(shape,"float32"),imag4=zeros(shape,"float32");return complex(real4,imag4)}let values=makeOnesTypedArray(sizeFromShape(shape),dtype);return ENGINE.makeTensor(values,shape,dtype)}function onesLike_(x){let inputs={x:convertToTensor(x,"x","onesLike")};return ENGINE.runKernel(OnesLike,inputs)}var onesLike=op({onesLike_});function outerProduct_(v1,v2){let $v1=convertToTensor(v1,"v1","outerProduct"),$v2=convertToTensor(v2,"v2","outerProduct");assert($v1.rank===1&&$v2.rank===1,()=>`Error in outerProduct: inputs must be rank 1, but got ranks ${$v1.rank} and ${$v2.rank}.`);let v12D=reshape($v1,[-1,1]),v22D=reshape($v2,[1,-1]);return matMul(v12D,v22D)}var outerProduct=op({outerProduct_});function pad_(x,paddings,constantValue=0){let $x=convertToTensor(x,"x","pad");if($x.rank===0)throw new Error("pad(scalar) is not defined. Pass non-scalar to pad");let attrs={paddings,constantValue},inputs={x:$x};return ENGINE.runKernel(PadV2,inputs,attrs)}var pad=op({pad_});function pad1d_(x,paddings,constantValue=0){return assert(paddings.length===2,()=>"Invalid number of paddings. Must be length of 2."),pad(x,[paddings],constantValue)}var pad1d=op({pad1d_});function pad2d_(x,paddings,constantValue=0){return assert(paddings.length===2&&paddings[0].length===2&&paddings[1].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),pad(x,paddings,constantValue)}var pad2d=op({pad2d_});function pad3d_(x,paddings,constantValue=0){return assert(paddings.length===3&&paddings[0].length===2&&paddings[1].length===2&&paddings[2].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),pad(x,paddings,constantValue)}var pad3d=op({pad3d_});function pad4d_(x,paddings,constantValue=0){return assert(paddings.length===4&&paddings[0].length===2&&paddings[1].length===2&&paddings[2].length===2&&paddings[3].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),pad(x,paddings,constantValue)}var pad4d=op({pad4d_});function spaceToBatchND_(x,blockShape,paddings){let $x=convertToTensor(x,"x","spaceToBatchND");assert($x.rank>=1+blockShape.length,()=>`input rank ${$x.rank} should be > than [blockShape] ${blockShape.length}`),assert(paddings.length===blockShape.length,()=>`paddings.shape[0] ${paddings.length} must be equal to [blockShape] ${blockShape.length}`),assert($x.shape.reduce((a,b,i)=>i>0&&i<=blockShape.length?a&&(b+paddings[i-1][0]+paddings[i-1][1])%blockShape[i-1]==0:a,!0),()=>`input spatial dimensions ${$x.shape.slice(1)} with paddings ${paddings.toString()} must be divisible by blockShapes ${blockShape.toString()}`);let inputs={x:$x},attrs={blockShape,paddings};return ENGINE.runKernel(SpaceToBatchND,inputs,attrs)}var spaceToBatchND=op({spaceToBatchND_});function pool_(input2,windowShape,poolingType,pad3,dilations,strides){dilations==null&&(dilations=[1,1]),strides==null&&(strides=1),pad3===0&&(pad3="valid");let $x=convertToTensor(input2,"x","maxPool"),x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in pool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);let convInfo=computePool2DInfo(x4D.shape,windowShape,strides,dilations,pad3),dilation=[convInfo.dilationHeight,convInfo.dilationWidth],basePadding;pad3==="same"?basePadding=withSpaceToBatchBasePaddings([convInfo.filterHeight,convInfo.filterWidth],dilation):basePadding=[[0,0],[0,0]];let isDilationOne=dilation[0]===1&&dilation[1]===1,[adjustedPadding,adjustedCrops]=requiredSpaceToBatchPaddings([convInfo.inHeight,convInfo.inWidth],dilation,basePadding),convertedPad=isDilationOne?pad3:"valid",convertedX=isDilationOne?x4D:spaceToBatchND(x4D,dilation,adjustedPadding),y=(poolingType==="avg"?()=>avgPool(convertedX,windowShape,strides,convertedPad):()=>maxPool(convertedX,windowShape,strides,convertedPad))(),res=isDilationOne?y:batchToSpaceND(y,dilation,adjustedCrops);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}function requiredSpaceToBatchPaddings(inputShape,blockShape,basePadding){let padStart=basePadding.map(b=>b[0]),origPadEnd=basePadding.map(b=>b[1]),fullInputShape=inputShape.concat(padStart,origPadEnd),padEndExtra=blockShape.map((b,i)=>(b-fullInputShape[i]%b)%b),padEnd=origPadEnd.map((s,i)=>s+padEndExtra[i]),paddings=blockShape.map((_,i)=>[padStart[i],padEnd[i]]),crops=blockShape.map((_,i)=>[0,padEndExtra[i]]);return[paddings,crops]}function withSpaceToBatchBasePaddings(filterShape,dilation){let padExtraShape=filterShape.map((s,i)=>s+(s-1)*(dilation[i]-1)).map(s=>s-1),padExtraStart=padExtraShape.map(s=>Math.floor(s/2)),padExtraEnd=padExtraShape.map((s,i)=>s-padExtraStart[i]);return padExtraShape.map((_,i)=>[padExtraStart[i],padExtraEnd[i]])}var pool=op({pool_});function pow_(base2,exp4){let $base=convertToTensor(base2,"base","pow"),$exp=convertToTensor(exp4,"exp","pow");[$base,$exp]=makeTypesMatch($base,$exp);let inputs={a:$base,b:$exp};return ENGINE.runKernel(Pow,inputs)}var pow=op({pow_});function prelu_(x,alpha){let $x=convertToTensor(x,"x","prelu"),$alpha=convertToTensor(alpha,"alpha","prelu"),inputs={x:$x,alpha:$alpha};return ENGINE.runKernel(Prelu,inputs)}var prelu=op({prelu_});function prod_(x,axis=null,keepDims=!1){let $x=convertToTensor(x,"x","prod");$x.dtype==="bool"&&($x=cast($x,"int32"));let inputs={x:$x},attrs={axis,keepDims};return ENGINE.runKernel(Prod,inputs,attrs)}var prod=op({prod_});function rand_(shape,randFunction,dtype){let size=sizeFromShape(shape),values=null;if(dtype==null||dtype==="float32")values=new Float32Array(size);else if(dtype==="int32")values=new Int32Array(size);else if(dtype==="bool")values=new Uint8Array(size);else throw new Error(`Unknown data type ${dtype}`);for(let i=0;i=1||s===0);let mul2=Math.sqrt(-2*Math.log(s)/s);resultX=this.mean+this.stdDev*v1*mul2,resultY=this.mean+this.stdDev*v2*mul2,(!this.truncated||this.isValidTruncated(resultX))&&(isValid=!0)}return(!this.truncated||this.isValidTruncated(resultY))&&(this.nextVal=this.convertValue(resultY)),this.convertValue(resultX)}convertValue(value){return this.dtype==null||this.dtype==="float32"?value:Math.round(value)}isValidTruncated(value){return value<=this.upper&&value>=this.lower}},RandGamma=class{constructor(alpha,beta,dtype,seed){this.alpha=alpha,this.beta=1/beta,this.dtype=dtype;let seedValue=seed||Math.random();this.randu=seedrandom.alea(seedValue.toString()),this.randn=new MPRandGauss(0,1,dtype,!1,this.randu()),alpha<1?this.d=alpha+2/3:this.d=alpha-1/3,this.c=1/Math.sqrt(9*this.d)}nextValue(){let x2,v0,v1,x,u,v;for(;;){do x=this.randn.nextValue(),v=1+this.c*x;while(v<=0);if(v*=v*v,x2=x*x,v0=1-.331*x2*x2,v1=.5*x2+this.d*(1-v+Math.log(v)),u=this.randu(),uthis.dtype==null||this.dtype==="float32",this.min=min6,this.range=max6-min6,this.dtype=dtype,seed==null&&(seed=Math.random()),typeof seed=="number"&&(seed=seed.toString()),!this.canReturnFloat()&&this.range<=1)throw new Error(`The difference between ${min6} - ${max6} <= 1 and dtype is not float`);this.random=seedrandom.alea(seed)}convertValue(value){return this.canReturnFloat()?value:Math.round(value)}nextValue(){return this.convertValue(this.min+this.range*this.random())}};function randomGamma_(shape,alpha,beta=1,dtype="float32",seed){if(beta==null&&(beta=1),dtype==null&&(dtype="float32"),dtype!=="float32"&&dtype!=="int32")throw new Error(`Unsupported data type ${dtype}`);let rgamma=new RandGamma(alpha,beta,dtype,seed),res=buffer(shape,dtype);for(let i=0;i`Error in reverse1D: x must be rank 1 but got rank ${$x.rank}.`),reverse($x,0)}var reverse1d=op({reverse1d_});function reverse2d_(x,axis){let $x=convertToTensor(x,"x","reverse");return assert($x.rank===2,()=>`Error in reverse2D: x must be rank 2 but got rank ${$x.rank}.`),reverse($x,axis)}var reverse2d=op({reverse2d_});function reverse3d_(x,axis){let $x=convertToTensor(x,"x","reverse");return assert($x.rank===3,()=>`Error in reverse3D: x must be rank 3 but got rank ${$x.rank}.`),reverse($x,axis)}var reverse3d=op({reverse3d_});function reverse4d_(x,axis){let $x=convertToTensor(x,"x","reverse");return assert($x.rank===4,()=>`Error in reverse4D: x must be rank 4 but got rank ${$x.rank}.`),reverse($x,axis)}var reverse4d=op({reverse4d_});function round_(x){let inputs={x:convertToTensor(x,"x","round")};return ENGINE.runKernel(Round,inputs)}var round2=op({round_});function rsqrt_(x){let inputs={x:convertToTensor(x,"x","rsqrt")};return ENGINE.runKernel(Rsqrt,inputs)}var rsqrt=op({rsqrt_});function scalar(value,dtype){if((isTypedArray(value)&&dtype!=="string"||Array.isArray(value))&&dtype!=="complex64")throw new Error("Error creating a new Scalar: value must be a primitive (number|boolean|string)");if(dtype==="string"&&isTypedArray(value)&&!(value instanceof Uint8Array))throw new Error("When making a scalar from encoded string, the value must be `Uint8Array`.");return makeTensor(value,[],[],dtype)}function selu_(x){let inputs={x:convertToTensor(x,"x","selu")};return ENGINE.runKernel(Selu,inputs)}var selu=op({selu_});function separableConv2d_(x,depthwiseFilter,pointwiseFilter,strides,pad3,dilation=[1,1],dataFormat="NHWC"){let $x=convertToTensor(x,"x","separableConv2d"),$depthwiseFilter=convertToTensor(depthwiseFilter,"depthwiseFilter","separableConv2d"),$pointwiseFilter=convertToTensor(pointwiseFilter,"pointwiseFilter","separableConv2d"),x4D=$x,reshapedTo4D=!1;if($x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),dataFormat==="NCHW")throw new Error("separableConv2d currently does not support dataFormat NCHW; only NHWC is supported");assert(x4D.rank===4,()=>`Error in separableConv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert($depthwiseFilter.rank===4,()=>`Error in separableConv2d: depthwise filter must be rank 4, but got rank ${$depthwiseFilter.rank}.`),assert($pointwiseFilter.rank===4,()=>`Error in separableConv2d: pointwise filter must be rank 4, but got rank ${$depthwiseFilter.rank}.`),assert($pointwiseFilter.shape[0]===1,()=>`Error in separableConv2d: the first dimension of pointwise filter must be 1, but got ${$pointwiseFilter.shape[0]}.`),assert($pointwiseFilter.shape[1]===1,()=>`Error in separableConv2d: the second dimension of pointwise filter must be 1, but got ${$pointwiseFilter.shape[1]}.`);let inChannels=$depthwiseFilter.shape[2],channelMultiplier=$depthwiseFilter.shape[3];assert($pointwiseFilter.shape[2]===inChannels*channelMultiplier,()=>`Error in separableConv2d: the third dimension of pointwise filter must be ${inChannels*channelMultiplier}, but got ${$pointwiseFilter.shape[2]}.`);let depthwise=depthwiseConv2d(x4D,$depthwiseFilter,strides,pad3,dataFormat,dilation),res=conv2d(depthwise,$pointwiseFilter,1,"valid",dataFormat);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var separableConv2d=op({separableConv2d_});async function setdiff1dAsync_(x,y){let $x=convertToTensor(x,"x","setdiff1d"),$y=convertToTensor(y,"y","setdiff1d");assert($x.dtype===$y.dtype,()=>`x and y should have the same dtype, but got x (${$x.dtype}) and y (${$y.dtype}).`),assert($x.rank===1,()=>`x should be 1D tensor, but got x (${$x.shape}).`),assert($y.rank===1,()=>`y should be 1D tensor, but got y (${$y.shape}).`);let xVals=await $x.data(),yVals=await $y.data(),ySet=new Set(yVals),outputSize=0;for(let i=0;i`slice1d expects a rank-1 tensor, but got a rank-${$x.rank} tensor`),slice($x,[begin],[size])}var slice1d=op({slice1d_});function slice2d_(x,begin,size){let $x=convertToTensor(x,"x","slice2d");return assert($x.rank===2,()=>`slice2d expects a rank-2 tensor, but got a rank-${$x.rank} tensor`),slice($x,begin,size)}var slice2d=op({slice2d_});function slice3d_(x,begin,size){let $x=convertToTensor(x,"x","slice3d");return assert($x.rank===3,()=>`slice3d expects a rank-3 tensor, but got a rank-${$x.rank} tensor`),slice($x,begin,size)}var slice3d=op({slice3d_});function slice4d_(x,begin,size){let $x=convertToTensor(x,"x","slice4d");return assert($x.rank===4,()=>`slice4d expects a rank-4 tensor, but got a rank-${$x.rank} tensor`),slice($x,begin,size)}var slice4d=op({slice4d_});function softmax_(logits,dim=-1){let $logits=convertToTensor(logits,"logits","softmax","float32");if(dim===-1&&(dim=$logits.rank-1),dim!==$logits.rank-1)throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${$logits.rank} and dim was ${dim}`);let inputs={logits:$logits},attrs={dim};return ENGINE.runKernel(Softmax,inputs,attrs)}var softmax=op({softmax_});function fft_(input2){assert(input2.dtype==="complex64",()=>`The dtype for tf.spectral.fft() must be complex64 but got ${input2.dtype}.`);let inputs={input:input2};return ENGINE.runKernel(FFT,inputs)}var fft=op({fft_});function ifft_(input2){assert(input2.dtype==="complex64",()=>`The dtype for tf.spectral.ifft() must be complex64 but got ${input2.dtype}.`);let inputs={input:input2};return ENGINE.runKernel(IFFT,inputs)}var ifft=op({ifft_});function irfft_(input2){let innerDimensionSize=input2.shape[input2.shape.length-1],batch=input2.size/innerDimensionSize,ret;if(innerDimensionSize<=2){let complexInput=reshape(input2,[batch,innerDimensionSize]);ret=ifft(complexInput)}else{let outputShape=[batch,2*(innerDimensionSize-1)],realInput=reshape(real(input2),[batch,innerDimensionSize]),imagInput=reshape(imag(input2),[batch,innerDimensionSize]),realConjugate=reverse(slice(realInput,[0,1],[batch,innerDimensionSize-2]),1),imagConjugate=mul(reverse(slice(imagInput,[0,1],[batch,innerDimensionSize-2]),1),scalar(-1)),r=concat([realInput,realConjugate],1),i=concat([imagInput,imagConjugate],1),complexInput=reshape(complex(r,i),[outputShape[0],outputShape[1]]);ret=ifft(complexInput)}if(ret=real(ret),input2.rank===3&&input2.shape[0]!==0){let temp=ret,batch2=input2.shape[0];ret=reshape(ret,[batch2,ret.shape[0]/batch2,ret.shape[1]]),temp.dispose()}return ret}var irfft=op({irfft_});function split_(x,numOrSizeSplits,axis=0){let inputs={x:convertToTensor(x,"x","split")},attr={numOrSizeSplits,axis};return ENGINE.runKernel(SplitV,inputs,attr)}var split=op({split_});function rfft_(input2,fftLength){assert(input2.dtype==="float32",()=>`The dtype for rfft() must be real value but got ${input2.dtype}`);let innerDimensionSize=input2.shape[input2.shape.length-1],batch=input2.size/innerDimensionSize,adjustedInput;if(fftLength!=null&&fftLength0),size=input2.shape.map(v=>v);size[input2.shape.length-1]=fftLength,adjustedInput=slice(input2,begin,size),innerDimensionSize=fftLength}else if(fftLength!=null&&fftLength>innerDimensionSize){let zerosShape=input2.shape.map(v=>v);zerosShape[input2.shape.length-1]=fftLength-innerDimensionSize,adjustedInput=concat([input2,zeros(zerosShape)],input2.shape.length-1),innerDimensionSize=fftLength}else adjustedInput=input2;let zerosInput=zerosLike(adjustedInput),complexInput=reshape(complex(adjustedInput,zerosInput),[batch,innerDimensionSize]),ret=fft(complexInput),half=Math.floor(innerDimensionSize/2)+1,realValues=real(ret),imagValues=imag(ret),realComplexConjugate=split(realValues,[half,innerDimensionSize-half],realValues.shape.length-1),imagComplexConjugate=split(imagValues,[half,innerDimensionSize-half],imagValues.shape.length-1),outputShape=adjustedInput.shape.slice();return outputShape[adjustedInput.shape.length-1]=half,reshape(complex(realComplexConjugate[0],imagComplexConjugate[0]),outputShape)}var rfft=op({rfft_});function sqrt_(x){let inputs={x:convertToTensor(x,"x","sqrt")};return ENGINE.runKernel(Sqrt,inputs)}var sqrt=op({sqrt_});function squaredDifference_(a,b){let $a=convertToTensor(a,"a","squaredDifference"),$b=convertToTensor(b,"b","squaredDifference");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b},attrs={};return ENGINE.runKernel(SquaredDifference,inputs,attrs)}var squaredDifference=op({squaredDifference_});function squeeze_(x,axis){let $x=convertToTensor(x,"x","squeeze");return reshape($x,squeezeShape($x.shape,axis).newShape)}var squeeze=op({squeeze_});function stack_(tensors,axis=0){let $tensors=convertToTensorArray(tensors,"tensors","stack","string_or_numeric");assert($tensors.length>=1,()=>"Pass at least one tensor to tf.stack"),$tensors.length>0&&assert(axis<=$tensors[0].rank,()=>"Axis must be <= rank of the tensor");let inputs=$tensors,attrs={axis};return ENGINE.runKernel(Pack,inputs,attrs)}var stack=op({stack_});function step_(x,alpha=0){let inputs={x:convertToTensor(x,"x","step")},attrs={alpha};return ENGINE.runKernel(Step,inputs,attrs)}var step=op({step_});function stridedSlice_(x,begin,end,strides,beginMask=0,endMask=0,ellipsisMask=0,newAxisMask=0,shrinkAxisMask=0){let inputs={x:convertToTensor(x,"x","stridedSlice")},attrs={begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask};return ENGINE.runKernel(StridedSlice,inputs,attrs)}var stridedSlice=op({stridedSlice_});function tan_(x){let inputs={x:convertToTensor(x,"x","tan")};return ENGINE.runKernel(Tan,inputs)}var tan=op({tan_});function tensor1d(values,dtype){assertNonNull(values);let inferredShape=inferShape(values,dtype);if(inferredShape.length!==1)throw new Error("tensor1d() requires values to be a flat/TypedArray");return makeTensor(values,null,inferredShape,dtype)}function tensor2d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==2)throw new Error("tensor2d() requires shape to have two numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==2&&inferredShape.length!==1)throw new Error("tensor2d() requires values to be number[][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor2d() requires shape to be provided when `values` are a flat/TypedArray");return makeTensor(values,shape,inferredShape,dtype)}function tensor4d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==4)throw new Error("tensor4d() requires shape to have four numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==4&&inferredShape.length!==1)throw new Error("tensor4d() requires values to be number[][][][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor4d() requires shape to be provided when `values` are a flat array");return makeTensor(values,shape,inferredShape,dtype)}function tensor5d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==5)throw new Error("tensor5d() requires shape to have five numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==5&&inferredShape.length!==1)throw new Error("tensor5d() requires values to be number[][][][][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor5d() requires shape to be provided when `values` are a flat array");return makeTensor(values,shape,inferredShape,dtype)}function tensor6d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==6)throw new Error("tensor6d() requires shape to have six numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==6&&inferredShape.length!==1)throw new Error("tensor6d() requires values to be number[][][][][][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor6d() requires shape to be provided when `values` are a flat array");return shape=shape||inferredShape,makeTensor(values,shape,inferredShape,dtype)}function topk_(x,k=1,sorted=!0){let $x=convertToTensor(x,"x","topk");if($x.rank===0)throw new Error("topk() expects the input to be of rank 1 or higher");let lastDim=$x.shape[$x.shape.length-1];if(k>lastDim)throw new Error(`'k' passed to topk() must be <= the last dimension (${lastDim}) but got ${k}`);let inputs={x:$x},attrs={k,sorted},[values,indices]=ENGINE.runKernel(TopK,inputs,attrs);return{values,indices}}var topk=op({topk_});function truncatedNormal_(shape,mean4=0,stdDev=1,dtype,seed){if(dtype!=null&&dtype==="bool")throw new Error("Unsupported data type $ { dtype }");let randGauss=new MPRandGauss(mean4,stdDev,dtype,!0,seed),res=buffer(shape,dtype);for(let i=0;i0,()=>"The input tensor must be at least 1D");let inputs={x:$x},attrs={axis},[values,indices]=ENGINE.runKernel(Unique,inputs,attrs);return{values,indices}}var unique=op({unique_});function unsortedSegmentSum_(x,segmentIds,numSegments){let $x=convertToTensor(x,"x","unsortedSegmentSum"),$segmentIds=convertToTensor(segmentIds,"segmentIds","unsortedSegmentSum","int32");assert(isInt(numSegments),()=>"numSegments must be of dtype int");let inputs={x:$x,segmentIds:$segmentIds},attrs={numSegments};return ENGINE.runKernel(UnsortedSegmentSum,inputs,attrs)}var unsortedSegmentSum=op({unsortedSegmentSum_});function unstack_(x,axis=0){let $x=convertToTensor(x,"x","unstack","string_or_numeric");assert(axis>=-$x.shape.length&&axis<$x.shape.length,()=>`Axis = ${axis} is not in [-${$x.shape.length}, ${$x.shape.length})`);let inputs={value:$x},attrs={axis};return ENGINE.runKernel(Unpack,inputs,attrs)}var unstack=op({unstack_});function variable(initialValue,trainable=!0,name,dtype){return ENGINE.makeVariable(initialValue,trainable,name,dtype)}function whereImpl(condShape,condVals){let indices=[];for(let i=0;i0,()=>"mask cannot be scalar"),assertShapesMatch(tensorShape.slice(axisFrom,axisFrom+maskDim),$mask.shape,"mask's shape must match the first K dimensions of tensor's shape,");let leadingSize=1;for(let i=axisFrom;i"Shape mismatch in v and x");let one=scalar(1),oneMinusDecay=sub(one,$decay),update=mul(sub($x,$v),oneMinusDecay);if(zeroDebias){assert(step4!=null,()=>"When using zeroDebias: true, step is required.");let $step=convertToTensor(step4,"step","movingAverage");update=div(update,sub(one,pow($decay,$step)))}return add2($v,update)}var movingAverage=op({movingAverage_});function scatterND_(indices,updates,shape){let $indices=convertToTensor(indices,"indices","scatterND","int32"),$updates=convertToTensor(updates,"updates","scatterND");validateInput($updates,$indices,shape);let inputs={indices:$indices,updates:$updates},attrs={shape};return ENGINE.runKernel(ScatterNd,inputs,attrs)}var scatterND=op({scatterND_});function validateInput2(sparseIndices,sparseValues,outputShape,defaultValues){if(sparseIndices.dtype!=="int32")throw new Error(`tf.sparseToDense() expects the indices to be int32 type, but the dtype was ${sparseIndices.dtype}.`);if(sparseIndices.rank>2)throw new Error(`sparseIndices should be a scalar, vector, or matrix, but got shape ${sparseIndices.shape}.`);let numElems=sparseIndices.rank>0?sparseIndices.shape[0]:1,numDims=sparseIndices.rank>1?sparseIndices.shape[1]:1;if(outputShape.length!==numDims)throw new Error(`outputShape has incorrect number of elements:, ${outputShape.length}, should be: ${numDims}.`);let numValues=sparseValues.size;if(!(sparseValues.rank===0||sparseValues.rank===1&&numValues===numElems))throw new Error(`sparseValues has incorrect shape ${sparseValues.shape}, should be [] or [${numElems}]`);if(sparseValues.dtype!==defaultValues.dtype)throw new Error("sparseValues.dtype must match defaultValues.dtype")}function sparseToDense_(sparseIndices,sparseValues,outputShape,defaultValue=0){let $sparseIndices=convertToTensor(sparseIndices,"sparseIndices","sparseToDense","int32"),$sparseValues=convertToTensor(sparseValues,"sparseValues","sparseToDense"),$defaultValue=convertToTensor(defaultValue,"defaultValue","sparseToDense",$sparseValues.dtype);validateInput2($sparseIndices,$sparseValues,outputShape,$defaultValue);let inputs={sparseIndices:$sparseIndices,sparseValues:$sparseValues,defaultValue:$defaultValue},attrs={outputShape};return ENGINE.runKernel(SparseToDense,inputs,attrs)}var sparseToDense=op({sparseToDense_});function gatherND_(x,indices){let $indices=convertToTensor(indices,"indices","gatherND","int32"),inputs={params:convertToTensor(x,"x","gatherND"),indices:$indices};return ENGINE.runKernel(GatherNd,inputs)}var gatherND=op({gatherND_});function getNoiseShape(x,noiseShape){if(noiseShape==null)return x.shape.slice();if(arraysEqual(x.shape,noiseShape))return noiseShape;if(x.shape.length===noiseShape.length){let newDimension=[];for(let i=0;i`x has to be a floating point tensor since it's going to be scaled, but got a ${$x.dtype} tensor instead.`),assert(rate>=0&&rate<1,()=>`rate must be a float in the range [0, 1), but got ${rate}.`),rate===0)return x instanceof Tensor?$x.clone():$x;let $noiseShape=getNoiseShape($x,noiseShape),keepProb=1-rate,multiplier=div(floor(add2(randomUniform($noiseShape,0,1,"float32",seed),keepProb)),keepProb);return mul($x,multiplier)}var dropout=op({dropout_});function enclosingPowerOfTwo(value){return Math.floor(Math.pow(2,Math.ceil(Math.log(value)/Math.log(2))))}function cosineWindow(windowLength,a,b){let even=1-windowLength%2,newValues=new Float32Array(windowLength);for(let i=0;i1,()=>`inTopK() expects the predictions to be of rank 2 or higher, but got ${$predictions.rank}`),assert($predictions.rank-1===$targets.rank,()=>`predictions rank should be 1 larger than targets rank, but got predictions rank ${$predictions.rank} and targets rank ${$targets.rank}`),assertShapesMatch($predictions.shape.slice(0,$predictions.shape.length-1),$targets.shape,"predictions's shape should be align with the targets' shape, except the last dimension.");let lastDim=$predictions.shape[$predictions.shape.length-1];assert(k>0&&k<=lastDim,()=>`'k' passed to inTopK() must be > 0 && <= the predictions last dimension (${lastDim}), but got ${k}`);let predictionsVals=await $predictions.data(),targetsVals=await $targets.data(),[batch,size]=[predictionsVals.length/lastDim,lastDim],precision3=getTypedArrayFromDType("bool",batch);for(let b=0;bb2.value-a.value),precision3[b]=0;for(let i=0;iconv2d2,depthwiseConv2d:()=>depthwiseConv2d2,matMul:()=>matMul2});function conv2DBackpropFilter_(x,dy,filterShape,strides,pad3,dataFormat="NHWC",dimRoundingMode){let x4D=x;x.rank===3&&(x4D=reshape(x,[1,x.shape[0],x.shape[1],x.shape[2]]));let dy4D=dy;dy4D.rank===3&&(dy4D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2]])),assert(x4D.rank===4,()=>`Error in conv2dDerFilter: input must be rank 4, but got shape ${x4D.shape}.`),assert(dy4D.rank===4,()=>`Error in conv2dDerFilter: dy must be rank 4, but got shape ${dy4D.shape}.`),assert(filterShape.length===4,()=>`Error in conv2dDerFilter: filterShape must be length 4, but got ${filterShape}.`);let inDepth=dataFormat==="NHWC"?x4D.shape[3]:x4D.shape[1],outDepth=dataFormat==="NHWC"?dy4D.shape[3]:dy4D.shape[1];assert(inDepth===filterShape[2],()=>`Error in conv2dDerFilter: depth of input ${inDepth}) must match input depth in filter (${filterShape[2]}.`),assert(outDepth===filterShape[3],()=>`Error in conv2dDerFilter: depth of dy (${outDepth}) must match output depth for filter (${filterShape[3]}).`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in conv2dDerFilter: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={x:x4D,dy:dy4D},attrs={strides,pad:pad3,dataFormat,dimRoundingMode,filterShape};return ENGINE.runKernel(Conv2DBackpropFilter,inputs,attrs)}var conv2DBackpropFilter=op({conv2DBackpropFilter_});function getFusedDyActivation(dy,y,activation2){if(activation2==null||activation2==="linear")return dy;if(activation2==="relu")return mul(dy,step(y));throw new Error(`Cannot compute gradient for fused activation ${activation2}.`)}function getFusedBiasGradient(bias,dyActivation){let res=dyActivation,reduceAxes=getReductionAxes(bias.shape,dyActivation.shape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,bias.shape)}function applyActivation(x,activation2,preluActivationWeights,leakyreluAlpha){if(activation2==="linear")return x;if(activation2==="relu")return relu(x);if(activation2==="elu")return elu(x);if(activation2==="relu6")return relu6(x);if(activation2==="prelu")return prelu(x,preluActivationWeights);if(activation2==="leakyrelu")return leakyRelu(x,leakyreluAlpha);throw new Error(`Unknown fused activation ${activation2}.`)}var shouldFuse=(gradientDepth,activation2)=>!(gradientDepth>0)||activation2==="linear";function fusedConv2d_({x,filter,strides,pad:pad3,dataFormat="NHWC",dilations=[1,1],dimRoundingMode,bias,activation:activation2="linear",preluActivationWeights,leakyreluAlpha}){if(activation2=activation2||"linear",shouldFuse(ENGINE.state.gradientDepth,activation2)===!1){let result=conv2d(x,filter,strides,pad3,dataFormat,dilations,dimRoundingMode);return bias!=null&&(result=add2(result,bias)),applyActivation(result,activation2,preluActivationWeights,leakyreluAlpha)}let $x=convertToTensor(x,"x","conv2d"),$filter=convertToTensor(filter,"filter","conv2d"),x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in fused conv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert($filter.rank===4,()=>`Error in fused conv2d: filter must be rank 4, but got rank ${$filter.rank}.`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in fused conv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`),assert(x4D.shape[3]===$filter.shape[2],()=>`Error in conv2d: depth of input (${x4D.shape[3]}) must match input depth for filter ${$filter.shape[2]}.`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),assert(dataFormat==="NHWC",()=>`Error in conv2d: got dataFormat of ${dataFormat} but only NHWC is currently supported.`);let convInfo=computeConv2DInfo(x4D.shape,$filter.shape,strides,dilations,pad3,dimRoundingMode),$bias;bias!=null&&($bias=convertToTensor(bias,"bias","fused conv2d"),[$bias]=makeTypesMatch($bias,$x),assertAndGetBroadcastShape(convInfo.outShape,$bias.shape));let $preluActivationWeights;preluActivationWeights!=null&&($preluActivationWeights=convertToTensor(preluActivationWeights,"prelu weights","fused conv2d"));let grad2=(dy,saved)=>{let[$filter2,x4D2,y,$bias2]=saved,dyActivation=getFusedDyActivation(dy,y,activation2);assert(tupleValuesAreOne(dilations),()=>`Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`);let xDer=conv2DBackpropInput(x4D2.shape,dyActivation,$filter2,strides,pad3),filterDer=conv2DBackpropFilter(x4D2,dyActivation,$filter2.shape,strides,pad3),der=[xDer,filterDer];if($bias2!=null){let biasDer=getFusedBiasGradient($bias2,dyActivation);der.push(biasDer)}return der},inputs={x:x4D,filter:$filter,bias:$bias,preluActivationWeights:$preluActivationWeights},attrs={strides,pad:pad3,dataFormat,dilations,dimRoundingMode,activation:activation2,leakyreluAlpha};return bias==null?customGrad((x4D2,filter2,save)=>{let res=ENGINE.runKernel(FusedConv2D,inputs,attrs);return save([filter2,x4D2,res]),reshapedTo4D&&(res=reshape(res,[res.shape[1],res.shape[2],res.shape[3]])),{value:res,gradFunc:grad2}})(x4D,$filter):customGrad((x4D2,filter2,bias2,save)=>{let res=ENGINE.runKernel(FusedConv2D,inputs,attrs);return save([filter2,x4D2,res,bias2]),reshapedTo4D&&(res=reshape(res,[res.shape[1],res.shape[2],res.shape[3]])),{value:res,gradFunc:grad2}})(x4D,$filter,$bias)}var conv2d2=op({fusedConv2d_});function depthwiseConv2dNativeBackpropFilter_(x,dy,filterShape,strides,pad3,dilations=[1,1],dimRoundingMode){let x4D=x;x.rank===3&&(x4D=reshape(x,[1,x.shape[0],x.shape[1],x.shape[2]]));let dy4D=dy;dy4D.rank===3&&(dy4D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2]]));let inputs={x:x4D,dy:dy4D},attrs={strides,pad:pad3,dimRoundingMode,dilations,filterShape};return ENGINE.runKernel(DepthwiseConv2dNativeBackpropFilter,inputs,attrs)}var depthwiseConv2dNativeBackpropFilter=op({depthwiseConv2dNativeBackpropFilter_});function depthwiseConv2dNativeBackpropInput_(xShape,dy,filter,strides,pad3,dilations=[1,1],dimRoundingMode){let dy4D=dy,reshapedTo4D=!1;dy.rank===3&&(reshapedTo4D=!0,dy4D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2]]));let inputs={dy:dy4D,filter},attrs={strides,pad:pad3,dimRoundingMode,dilations,inputShape:xShape},res=ENGINE.runKernel(DepthwiseConv2dNativeBackpropInput,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var depthwiseConv2dNativeBackpropInput=op({depthwiseConv2dNativeBackpropInput_});function fusedDepthwiseConv2d_({x,filter,strides,pad:pad3,dataFormat="NHWC",dilations=[1,1],dimRoundingMode,bias,activation:activation2="linear",preluActivationWeights,leakyreluAlpha}){if(shouldFuse(ENGINE.state.gradientDepth,activation2)===!1){let result=depthwiseConv2d(x,filter,strides,pad3,dataFormat,dilations,dimRoundingMode);return bias!=null&&(result=add2(result,bias)),applyActivation(result,activation2,preluActivationWeights,leakyreluAlpha)}let $x=convertToTensor(x,"x","depthwiseConv2d"),$filter=convertToTensor(filter,"filter","depthwiseConv2d"),x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in fused depthwiseConv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert($filter.rank===4,()=>`Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${$filter.rank}.`),assert(x4D.shape[3]===$filter.shape[2],()=>`Error in fused depthwiseConv2d: number of input channels (${x4D.shape[3]}) must match the inChannels dimension in filter ${$filter.shape[2]}.`),dilations==null&&(dilations=[1,1]),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in fused depthwiseConv2d: pad must be an integer when using dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let convInfo=computeConv2DInfo(x4D.shape,$filter.shape,strides,dilations,pad3,dimRoundingMode,!0),$bias;bias!=null&&($bias=convertToTensor(bias,"bias","fused conv2d"),[$bias]=makeTypesMatch($bias,$x),assertAndGetBroadcastShape(convInfo.outShape,$bias.shape));let $preluActivationWeights;preluActivationWeights!=null&&($preluActivationWeights=convertToTensor(preluActivationWeights,"prelu weights","fused depthwiseConv2d"));let grad2=(dy,saved)=>{assert(tupleValuesAreOne(dilations),()=>`Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${dilations}'`);let[$filter2,x4D2,y,bias2]=saved,dyActivation=getFusedDyActivation(dy,y,activation2),xDer=depthwiseConv2dNativeBackpropInput(x4D2.shape,dyActivation,$filter2,strides,pad3,dilations,dimRoundingMode),filterDer=depthwiseConv2dNativeBackpropFilter(x4D2,dyActivation,$filter2.shape,strides,pad3,dilations,dimRoundingMode);if(bias2!=null){let biasDer=getFusedBiasGradient($bias,dyActivation);return[xDer,filterDer,biasDer]}return[xDer,filterDer]},inputs={x:x4D,filter:$filter,bias:$bias,preluActivationWeights:$preluActivationWeights},attrs={strides,pad:pad3,dataFormat,dilations,dimRoundingMode,activation:activation2,leakyreluAlpha};return bias==null?customGrad((x4D2,filter2,save)=>{let res=ENGINE.runKernel(FusedDepthwiseConv2D,inputs,attrs);return save([filter2,x4D2,res]),reshapedTo4D&&(res=reshape(res,[res.shape[1],res.shape[2],res.shape[3]])),{value:res,gradFunc:grad2}})(x4D,$filter):customGrad((x4D2,filter2,bias2,save)=>{let res=ENGINE.runKernel(FusedDepthwiseConv2D,inputs,attrs);return save([filter2,x4D2,res,bias2]),reshapedTo4D&&(res=reshape(res,[res.shape[1],res.shape[2],res.shape[3]])),{value:res,gradFunc:grad2}})(x4D,$filter,$bias)}var depthwiseConv2d2=op({fusedDepthwiseConv2d_});function fusedMatMul_({a,b,transposeA=!1,transposeB=!1,bias,activation:activation2="linear",preluActivationWeights,leakyreluAlpha}){if(shouldFuse(ENGINE.state.gradientDepth,activation2)===!1){let result=matMul(a,b,transposeA,transposeB);return bias!=null&&(result=add2(result,bias)),applyActivation(result,activation2,preluActivationWeights,leakyreluAlpha)}let $a=convertToTensor(a,"a","fused matMul"),$b=convertToTensor(b,"b","fused matMul");[$a,$b]=makeTypesMatch($a,$b);let innerShapeA=transposeA?$a.shape[$a.rank-2]:$a.shape[$a.rank-1],innerShapeB=transposeB?$b.shape[$b.rank-1]:$b.shape[$b.rank-2],outerShapeA=transposeA?$a.shape[$a.rank-1]:$a.shape[$a.rank-2],outerShapeB=transposeB?$b.shape[$b.rank-2]:$b.shape[$b.rank-1],outerDimsA=$a.shape.slice(0,-2),outerDimsB=$b.shape.slice(0,-2),batchDimA=sizeFromShape(outerDimsA),batchDimB=sizeFromShape(outerDimsB);assert($a.rank>=2&&$b.rank>=2&&$a.rank===$b.rank,()=>`Error in fused matMul: inputs must have the same rank of at least 2, got ranks ${$a.rank} and ${$b.rank}.`),assert(arraysEqual(outerDimsA,outerDimsB),()=>`Error in fused matMul: outer dimensions (${outerDimsA}) and (${outerDimsB}) of Tensors with shapes ${$a.shape} and ${$b.shape} must match.`),assert(innerShapeA===innerShapeB,()=>`Error in fused matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${$a.shape} and ${$b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);let outShape=$a.shape.slice(0,-2).concat([outerShapeA,outerShapeB]),a3D=transposeA?reshape($a,[batchDimA,innerShapeA,outerShapeA]):reshape($a,[batchDimA,outerShapeA,innerShapeA]),b3D=transposeB?reshape($b,[batchDimB,outerShapeB,innerShapeB]):reshape($b,[batchDimB,innerShapeB,outerShapeB]),$bias;bias!=null&&($bias=convertToTensor(bias,"bias","fused matMul"),[$bias]=makeTypesMatch($bias,$a),assertAndGetBroadcastShape(outShape,$bias.shape));let $preluActivationWeights;preluActivationWeights!=null&&($preluActivationWeights=convertToTensor(preluActivationWeights,"prelu weights","fused matMul"));let grad2=(dy,saved)=>{let[a3D2,b3D2,y,$bias2]=saved,dyActivation=getFusedDyActivation(reshape(dy,y.shape),y,activation2),aDer,bDer;if(!transposeA&&!transposeB?(aDer=matMul(dyActivation,b3D2,!1,!0),bDer=matMul(a3D2,dyActivation,!0,!1)):!transposeA&&transposeB?(aDer=matMul(dyActivation,b3D2,!1,!1),bDer=matMul(dyActivation,a3D2,!0,!1)):transposeA&&!transposeB?(aDer=matMul(b3D2,dyActivation,!1,!0),bDer=matMul(a3D2,dyActivation,!1,!1)):(aDer=matMul(b3D2,dyActivation,!0,!0),bDer=matMul(dyActivation,a3D2,!0,!0)),bias!=null){let biasDer=getFusedBiasGradient($bias2,dyActivation);return[aDer,bDer,biasDer]}else return[aDer,bDer]},inputs={a:a3D,b:b3D,bias:$bias,preluActivationWeights:$preluActivationWeights},attrs={transposeA,transposeB,activation:activation2,leakyreluAlpha};return bias==null?customGrad((a3D2,b3D2,save)=>{let res=ENGINE.runKernel(_FusedMatMul,inputs,attrs);return save([a3D2,b3D2,res]),{value:reshape(res,outShape),gradFunc:grad2}})(a3D,b3D):customGrad((a3D2,b3D2,$bias2,save)=>{let res=ENGINE.runKernel(_FusedMatMul,inputs,attrs);return save([a3D2,b3D2,res,$bias2]),{value:reshape(res,outShape),gradFunc:grad2}})(a3D,b3D,$bias)}var matMul2=op({fusedMatMul_});function hammingWindow_(windowLength){return cosineWindow(windowLength,.54,.46)}var hammingWindow=op({hammingWindow_});function hannWindow_(windowLength){return cosineWindow(windowLength,.5,.5)}var hannWindow=op({hannWindow_});function frame_(signal2,frameLength,frameStep,padEnd=!1,padValue=0){let start=0,output=[];for(;start+frameLength<=signal2.size;)output.push(slice(signal2,start,frameLength)),start+=frameStep;if(padEnd)for(;start`Error in cropAndResize: image must be rank 4,but got rank ${$image.rank}.`),assert($boxes.rank===2&&$boxes.shape[1]===4,()=>`Error in cropAndResize: boxes must be have size [${numBoxes},4] but had shape ${$boxes.shape}.`),assert($boxInd.rank===1&&$boxInd.shape[0]===numBoxes,()=>`Error in cropAndResize: boxInd must be have size [${numBoxes}] but had shape ${$boxes.shape}.`),assert(cropSize.length===2,()=>`Error in cropAndResize: cropSize must be of length 2, but got length ${cropSize.length}.`),assert(cropSize[0]>=1&&cropSize[1]>=1,()=>`cropSize must be atleast [1,1], but was ${cropSize}`),assert(method==="bilinear"||method==="nearest",()=>`method must be bilinear or nearest, but was ${method}`);let inputs={image:$image,boxes:$boxes,boxInd:$boxInd},attrs={method,extrapolationValue,cropSize};return ENGINE.runKernel(CropAndResize,inputs,attrs)}var cropAndResize=op({cropAndResize_});function flipLeftRight_(image3){let $image=convertToTensor(image3,"image","flipLeftRight","float32");assert($image.rank===4,()=>`Error in flipLeftRight: image must be rank 4,but got rank ${$image.rank}.`);let inputs={image:$image};return ENGINE.runKernel(FlipLeftRight,inputs,{})}var flipLeftRight=op({flipLeftRight_});function rotateWithOffset_(image3,radians,fillValue=0,center=.5){let $image=convertToTensor(image3,"image","rotateWithOffset","float32");assert($image.rank===4,()=>`Error in rotateWithOffset: image must be rank 4,but got rank ${$image.rank}.`);let inputs={image:$image},attrs={radians,fillValue,center};return ENGINE.runKernel(RotateWithOffset,inputs,attrs)}var rotateWithOffset=op({rotateWithOffset_});function nonMaxSuppSanityCheck(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma){iouThreshold==null&&(iouThreshold=.5),scoreThreshold==null&&(scoreThreshold=Number.NEGATIVE_INFINITY),softNmsSigma==null&&(softNmsSigma=0);let numBoxes=boxes.shape[0];return maxOutputSize=Math.min(maxOutputSize,numBoxes),assert(0<=iouThreshold&&iouThreshold<=1,()=>`iouThreshold must be in [0, 1], but was '${iouThreshold}'`),assert(boxes.rank===2,()=>`boxes must be a 2D tensor, but was of rank '${boxes.rank}'`),assert(boxes.shape[1]===4,()=>`boxes must have 4 columns, but 2nd dimension was ${boxes.shape[1]}`),assert(scores.rank===1,()=>"scores must be a 1D tensor"),assert(scores.shape[0]===numBoxes,()=>`scores has incompatible shape with boxes. Expected ${numBoxes}, but was ${scores.shape[0]}`),assert(0<=softNmsSigma&&softNmsSigma<=1,()=>`softNmsSigma must be in [0, 1], but was '${softNmsSigma}'`),{maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma}}function nonMaxSuppression_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppression"),$scores=convertToTensor(scores,"scores","nonMaxSuppression"),inputs=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold);maxOutputSize=inputs.maxOutputSize,iouThreshold=inputs.iouThreshold,scoreThreshold=inputs.scoreThreshold;let attrs={maxOutputSize,iouThreshold,scoreThreshold};return ENGINE.runKernel(NonMaxSuppressionV3,{boxes:$boxes,scores:$scores},attrs)}var nonMaxSuppression=op({nonMaxSuppression_});function binaryInsert(arr,element,comparator){let index=binarySearch(arr,element,comparator),insertionPoint=index<0?-(index+1):index;arr.splice(insertionPoint,0,element)}function binarySearch(arr,target,comparator){return binarySearch_(arr,target,comparator||defaultComparator)}function defaultComparator(a,b){return a>b?1:a>>1);let compareResult=comparator(target,arr[middle]);compareResult>0?left=middle+1:(right=middle,found=!compareResult)}return found?left:-left-1}function nonMaxSuppressionV3Impl(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold){return nonMaxSuppressionImpl_(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,0)}function nonMaxSuppressionV4Impl(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize){return nonMaxSuppressionImpl_(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,0,!1,padToMaxOutputSize,!0)}function nonMaxSuppressionV5Impl(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma){return nonMaxSuppressionImpl_(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma,!0)}function nonMaxSuppressionImpl_(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma,returnScoresTensor=!1,padToMaxOutputSize=!1,returnValidOutputs=!1){let candidates=[];for(let i=0;iscoreThreshold&&candidates.push({score:scores[i],boxIndex:i,suppressBeginIndex:0});candidates.sort(ascendingComparator);let scale2=softNmsSigma>0?-.5/softNmsSigma:0,selectedIndices=[],selectedScores=[];for(;selectedIndices.length0;){let candidate=candidates.pop(),{score:originalScore,boxIndex,suppressBeginIndex}=candidate;if(originalScore=suppressBeginIndex;--j){let iou=intersectionOverUnion(boxes,boxIndex,selectedIndices[j]);if(iou>=iouThreshold){ignoreCandidate=!0;break}if(candidate.score=candidate.score*suppressWeight(iouThreshold,scale2,iou),candidate.score<=scoreThreshold)break}candidate.suppressBeginIndex=selectedIndices.length,ignoreCandidate||(candidate.score===originalScore?(selectedIndices.push(boxIndex),selectedScores.push(candidate.score)):candidate.score>scoreThreshold&&binaryInsert(candidates,candidate,ascendingComparator))}let validOutputs=selectedIndices.length,elemsToPad=maxOutputSize-validOutputs;padToMaxOutputSize&&elemsToPad>0&&(selectedIndices.push(...new Array(elemsToPad).fill(0)),selectedScores.push(...new Array(elemsToPad).fill(0)));let result={selectedIndices};return returnScoresTensor&&(result.selectedScores=selectedScores),returnValidOutputs&&(result.validOutputs=validOutputs),result}function intersectionOverUnion(boxes,i,j){let iCoord=boxes.subarray(i*4,i*4+4),jCoord=boxes.subarray(j*4,j*4+4),yminI=Math.min(iCoord[0],iCoord[2]),xminI=Math.min(iCoord[1],iCoord[3]),ymaxI=Math.max(iCoord[0],iCoord[2]),xmaxI=Math.max(iCoord[1],iCoord[3]),yminJ=Math.min(jCoord[0],jCoord[2]),xminJ=Math.min(jCoord[1],jCoord[3]),ymaxJ=Math.max(jCoord[0],jCoord[2]),xmaxJ=Math.max(jCoord[1],jCoord[3]),areaI=(ymaxI-yminI)*(xmaxI-xminI),areaJ=(ymaxJ-yminJ)*(xmaxJ-xminJ);if(areaI<=0||areaJ<=0)return 0;let intersectionYmin=Math.max(yminI,yminJ),intersectionXmin=Math.max(xminI,xminJ),intersectionYmax=Math.min(ymaxI,ymaxJ),intersectionXmax=Math.min(xmaxI,xmaxJ),intersectionArea=Math.max(intersectionYmax-intersectionYmin,0)*Math.max(intersectionXmax-intersectionXmin,0);return intersectionArea/(areaI+areaJ-intersectionArea)}function suppressWeight(iouThreshold,scale2,iou){let weight=Math.exp(scale2*iou*iou);return iou<=iouThreshold?weight:0}function ascendingComparator(c1,c2){return c1.score-c2.score||c1.score===c2.score&&c2.boxIndex-c1.boxIndex}async function nonMaxSuppressionAsync_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppressionAsync"),$scores=convertToTensor(scores,"scores","nonMaxSuppressionAsync"),inputs=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold);maxOutputSize=inputs.maxOutputSize,iouThreshold=inputs.iouThreshold,scoreThreshold=inputs.scoreThreshold;let boxesAndScores=await Promise.all([$boxes.data(),$scores.data()]),boxesVals=boxesAndScores[0],scoresVals=boxesAndScores[1],{selectedIndices}=nonMaxSuppressionV3Impl(boxesVals,scoresVals,maxOutputSize,iouThreshold,scoreThreshold);return $boxes!==boxes&&$boxes.dispose(),$scores!==scores&&$scores.dispose(),tensor1d(selectedIndices,"int32")}var nonMaxSuppressionAsync=nonMaxSuppressionAsync_;function nonMaxSuppressionWithScore_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY,softNmsSigma=0){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppression"),$scores=convertToTensor(scores,"scores","nonMaxSuppression"),params=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma);maxOutputSize=params.maxOutputSize,iouThreshold=params.iouThreshold,scoreThreshold=params.scoreThreshold,softNmsSigma=params.softNmsSigma;let inputs={boxes:$boxes,scores:$scores},attrs={maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma},result=ENGINE.runKernel(NonMaxSuppressionV5,inputs,attrs);return{selectedIndices:result[0],selectedScores:result[1]}}var nonMaxSuppressionWithScore=op({nonMaxSuppressionWithScore_});async function nonMaxSuppressionWithScoreAsync_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY,softNmsSigma=0){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppressionAsync"),$scores=convertToTensor(scores,"scores","nonMaxSuppressionAsync"),params=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma);maxOutputSize=params.maxOutputSize,iouThreshold=params.iouThreshold,scoreThreshold=params.scoreThreshold,softNmsSigma=params.softNmsSigma;let boxesAndScores=await Promise.all([$boxes.data(),$scores.data()]),boxesVals=boxesAndScores[0],scoresVals=boxesAndScores[1],{selectedIndices,selectedScores}=nonMaxSuppressionV5Impl(boxesVals,scoresVals,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma);return $boxes!==boxes&&$boxes.dispose(),$scores!==scores&&$scores.dispose(),{selectedIndices:tensor1d(selectedIndices,"int32"),selectedScores:tensor1d(selectedScores)}}var nonMaxSuppressionWithScoreAsync=nonMaxSuppressionWithScoreAsync_;function nonMaxSuppressionPadded_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY,padToMaxOutputSize=!1){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppression"),$scores=convertToTensor(scores,"scores","nonMaxSuppression"),params=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold,null),$maxOutputSize=params.maxOutputSize,$iouThreshold=params.iouThreshold,$scoreThreshold=params.scoreThreshold,inputs={boxes:$boxes,scores:$scores},attrs={maxOutputSize:$maxOutputSize,iouThreshold:$iouThreshold,scoreThreshold:$scoreThreshold,padToMaxOutputSize},result=ENGINE.runKernel(NonMaxSuppressionV4,inputs,attrs);return{selectedIndices:result[0],validOutputs:result[1]}}var nonMaxSuppressionPadded=op({nonMaxSuppressionPadded_});async function nonMaxSuppressionPaddedAsync_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY,padToMaxOutputSize=!1){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppressionAsync"),$scores=convertToTensor(scores,"scores","nonMaxSuppressionAsync"),params=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold,null),$maxOutputSize=params.maxOutputSize,$iouThreshold=params.iouThreshold,$scoreThreshold=params.scoreThreshold,[boxesVals,scoresVals]=await Promise.all([$boxes.data(),$scores.data()]),{selectedIndices,validOutputs}=nonMaxSuppressionV4Impl(boxesVals,scoresVals,$maxOutputSize,$iouThreshold,$scoreThreshold,padToMaxOutputSize);return $boxes!==boxes&&$boxes.dispose(),$scores!==scores&&$scores.dispose(),{selectedIndices:tensor1d(selectedIndices,"int32"),validOutputs:scalar(validOutputs,"int32")}}var nonMaxSuppressionPaddedAsync=nonMaxSuppressionPaddedAsync_;function resizeBilinear_(images,size,alignCorners=!1,halfPixelCenters=!1){let $images=convertToTensor(images,"images","resizeBilinear");assert($images.rank===3||$images.rank===4,()=>`Error in resizeBilinear: x must be rank 3 or 4, but got rank ${$images.rank}.`),assert(size.length===2,()=>`Error in resizeBilinear: new shape must 2D, but got shape ${size}.`),assert(halfPixelCenters===!1||alignCorners===!1,()=>"Error in resizeBilinear: If halfPixelCenters is true, alignCorners must be false.");let batchImages=$images,reshapedTo4D=!1;$images.rank===3&&(reshapedTo4D=!0,batchImages=reshape($images,[1,$images.shape[0],$images.shape[1],$images.shape[2]]));let[]=size,inputs={images:batchImages},attrs={alignCorners,halfPixelCenters,size},res=ENGINE.runKernel(ResizeBilinear,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var resizeBilinear=op({resizeBilinear_});function resizeNearestNeighbor_(images,size,alignCorners=!1,halfPixelCenters=!1){let $images=convertToTensor(images,"images","resizeNearestNeighbor");assert($images.rank===3||$images.rank===4,()=>`Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${$images.rank}.`),assert(size.length===2,()=>`Error in resizeNearestNeighbor: new shape must 2D, but got shape ${size}.`),assert($images.dtype==="float32"||$images.dtype==="int32",()=>"`images` must have `int32` or `float32` as dtype"),assert(halfPixelCenters===!1||alignCorners===!1,()=>"Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false.");let batchImages=$images,reshapedTo4D=!1;$images.rank===3&&(reshapedTo4D=!0,batchImages=reshape($images,[1,$images.shape[0],$images.shape[1],$images.shape[2]]));let[]=size,inputs={images:batchImages},attrs={alignCorners,halfPixelCenters,size},res=ENGINE.runKernel(ResizeNearestNeighbor,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var resizeNearestNeighbor=op({resizeNearestNeighbor_});function bandPart_(a,numLower,numUpper){assert(numLower%1==0,()=>`bandPart(): numLower must be an integer, got ${numLower}.`),assert(numUpper%1==0,()=>`bandPart(): numUpper must be an integer, got ${numUpper}.`);let $a=convertToTensor(a,"a","bandPart");assert($a.rank>=2,()=>`bandPart(): Rank must be at least 2, got ${$a.rank}.`);let shape=$a.shape,[M,N]=$a.shape.slice(-2);if(!(numLower<=M))throw new Error(`bandPart(): numLower (${numLower}) must not be greater than the number of rows (${M}).`);if(!(numUpper<=N))throw new Error(`bandPart(): numUpper (${numUpper}) must not be greater than the number of columns (${N}).`);numLower<0&&(numLower=M),numUpper<0&&(numUpper=N);let i=reshape(range(0,M,1,"int32"),[-1,1]),j=range(0,N,1,"int32"),ij=sub(i,j),inBand=logicalAnd(lessEqual(ij,scalar(+numLower,"int32")),greaterEqual(ij,scalar(-numUpper,"int32"))),zero=zeros([M,N],$a.dtype);return reshape(stack(unstack(reshape($a,[-1,M,N])).map(mat=>where(inBand,mat,zero))),shape)}var bandPart=op({bandPart_});function gramSchmidt_(xs){let inputIsTensor2D;if(Array.isArray(xs)){inputIsTensor2D=!1,assert(xs!=null&&xs.length>0,()=>"Gram-Schmidt process: input must not be null, undefined, or empty");let dim=xs[0].shape[0];for(let i=1;i`Gram-Schmidt: Non-unique lengths found in the input vectors: (${xs[i].shape[0]} vs. ${dim})`)}else inputIsTensor2D=!0,xs=split(xs,xs.shape[0],0).map(x=>squeeze(x,[0]));assert(xs.length<=xs[0].shape[0],()=>`Gram-Schmidt: Number of vectors (${xs.length}) exceeds number of dimensions (${xs[0].shape[0]}).`);let ys=[],xs1d=xs;for(let i=0;i{let x=xs1d[i];if(i>0)for(let j=0;j=2,()=>`qr() requires input tensor to have a rank >= 2, but got rank ${x.rank}`),x.rank===2)return qr2d(x,fullMatrices);{let outerDimsProd=x.shape.slice(0,x.shape.length-2).reduce((value,prev)=>value*prev),x2ds=unstack(reshape(x,[outerDimsProd,x.shape[x.shape.length-2],x.shape[x.shape.length-1]]),0),q2ds=[],r2ds=[];x2ds.forEach(x2d=>{let[q2d,r2d]=qr2d(x2d,fullMatrices);q2ds.push(q2d),r2ds.push(r2d)});let q=reshape(stack(q2ds,0),x.shape),r=reshape(stack(r2ds,0),x.shape);return[q,r]}}function qr2d(x,fullMatrices=!1){return ENGINE.tidy(()=>{assert(x.shape.length===2,()=>`qr2d() requires a 2D Tensor, but got a ${x.shape.length}D Tensor.`);let m=x.shape[0],n=x.shape[1],q=eye(m),r=clone(x),one2D=tensor2d([[1]],[1,1]),w=clone(one2D),iters=m>=n?n:m;for(let j=0;j{let rjEnd1=slice(r,[j,j],[m-j,1]),normX=norm(rjEnd1),rjj=slice(r,[j,j],[1,1]),s=where(greater(rjj,0),tensor2d([[-1]]),tensor2d([[1]])),u1=sub(rjj,mul(s,normX)),wPre=div(rjEnd1,u1);wPre.shape[0]===1?w=clone(one2D):w=concat([one2D,slice(wPre,[1,0],[wPre.shape[0]-1,wPre.shape[1]])],0);let tau=neg(div(matMul(s,u1),normX)),rjEndAll=slice(r,[j,0],[m-j,n]),tauTimesW=mul(tau,w),wT=transpose(w);if(j===0)r=sub(rjEndAll,matMul(tauTimesW,matMul(wT,rjEndAll)));else{let rTimesTau=sub(rjEndAll,matMul(tauTimesW,matMul(wT,rjEndAll)));r=concat([slice(r,[0,0],[j,n]),rTimesTau],0)}let tawTimesWT=transpose(tauTimesW),qAllJEnd=slice(q,[0,j],[m,q.shape[1]-j]);if(j===0)q=sub(qAllJEnd,matMul(matMul(qAllJEnd,w),tawTimesWT));else{let qTimesTau=sub(qAllJEnd,matMul(matMul(qAllJEnd,w),tawTimesWT));q=concat([slice(q,[0,0],[m,j]),qTimesTau],1)}return[w,r,q]}),dispose([rTemp,wTemp,qTemp])}return!fullMatrices&&m>n&&(q=slice(q,[0,0],[m,n]),r=slice(r,[0,0],[n,n])),[q,r]})}var qr=op({qr_}),Reduction;(function(Reduction2){Reduction2[Reduction2.NONE=0]="NONE",Reduction2[Reduction2.MEAN=1]="MEAN",Reduction2[Reduction2.SUM=2]="SUM",Reduction2[Reduction2.SUM_BY_NONZERO_WEIGHTS=3]="SUM_BY_NONZERO_WEIGHTS"})(Reduction||(Reduction={}));function computeWeightedLoss_(losses4,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $losses=convertToTensor(losses4,"losses","computeWeightedLoss"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","computeWeightedLoss"));let weightedLoss=$weights==null?$losses:mul($losses,$weights);if(reduction2===Reduction.NONE)return weightedLoss;if(reduction2===Reduction.SUM)return sum2(weightedLoss);if(reduction2===Reduction.MEAN){if($weights==null)return mean(weightedLoss);{let broadcastFactor=$losses.size/$weights.size,result=div(sum2(weightedLoss),sum2($weights));return broadcastFactor>1?div(result,scalar(broadcastFactor)):result}}if(reduction2===Reduction.SUM_BY_NONZERO_WEIGHTS){if($weights==null)return div(sum2(weightedLoss),scalar($losses.size));{let broadcastedWeights=mul($weights,ones2($losses.shape)),numNonZeros=cast(sum2(notEqual(broadcastedWeights,scalar(0))),"float32");return div(sum2(weightedLoss),numNonZeros)}}throw Error(`Unknown reduction: ${reduction2}`)}var computeWeightedLoss=op({computeWeightedLoss_});function absoluteDifference_(labels,predictions,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","absoluteDifference"),$predictions=convertToTensor(predictions,"predictions","absoluteDifference"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","absoluteDifference")),assertShapesMatch($labels.shape,$predictions.shape,"Error in absoluteDifference: ");let losses4=abs(sub($labels,$predictions));return computeWeightedLoss(losses4,$weights,reduction2)}var absoluteDifference=op({absoluteDifference_});function cosineDistance_(labels,predictions,axis,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","cosineDistance"),$predictions=convertToTensor(predictions,"predictions","cosineDistance"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","cosineDistance")),assertShapesMatch($labels.shape,$predictions.shape,"Error in cosineDistance: ");let one=scalar(1),losses4=sub(one,sum2(mul($labels,$predictions),axis,!0));return computeWeightedLoss(losses4,$weights,reduction2)}var cosineDistance=op({cosineDistance_});function hingeLoss_(labels,predictions,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","hingeLoss"),$predictions=convertToTensor(predictions,"predictions","hingeLoss"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","hingeLoss")),assertShapesMatch($labels.shape,$predictions.shape,"Error in hingeLoss: ");let one=scalar(1);$labels=sub(mul(scalar(2),$labels),one);let losses4=relu(sub(one,mul($labels,$predictions)));return computeWeightedLoss(losses4,$weights,reduction2)}var hingeLoss=op({hingeLoss_});function huberLoss_(labels,predictions,weights,delta=1,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","huberLoss"),$predictions=convertToTensor(predictions,"predictions","huberLoss"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","huberLoss")),assertShapesMatch($labels.shape,$predictions.shape,"Error in huberLoss: ");let deltaScalar=scalar(delta),error=abs(sub($predictions,$labels)),quadratic=minimum(error,deltaScalar),linear=sub(error,quadratic),losses4=add2(mul(scalar(.5),square(quadratic)),mul(deltaScalar,linear));return computeWeightedLoss(losses4,$weights,reduction2)}var huberLoss=op({huberLoss_});function logLoss_(labels,predictions,weights,epsilon3=1e-7,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","logLoss"),$predictions=convertToTensor(predictions,"predictions","logLoss"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","logLoss")),assertShapesMatch($labels.shape,$predictions.shape,"Error in logLoss: ");let one=scalar(1),epsilonScalar=scalar(epsilon3),l13=neg(mul($labels,log2(add2($predictions,epsilonScalar)))),l23=mul(sub(one,$labels),log2(add2(sub(one,$predictions),epsilonScalar))),losses4=sub(l13,l23);return computeWeightedLoss(losses4,$weights,reduction2)}var logLoss=op({logLoss_});function meanSquaredError_(labels,predictions,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","meanSquaredError"),$predictions=convertToTensor(predictions,"predictions","meanSquaredError"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","meanSquaredError")),assertShapesMatch($labels.shape,$predictions.shape,"Error in meanSquaredError: ");let losses4=squaredDifference($labels,$predictions);return computeWeightedLoss(losses4,$weights,reduction2)}var meanSquaredError=op({meanSquaredError_});function sigmoidCrossEntropyWithLogits_(labels,logits){let $labels=convertToTensor(labels,"labels","sigmoidCrossEntropyWithLogits"),$logits=convertToTensor(logits,"logits","sigmoidCrossEntropyWithLogits");assertShapesMatch($labels.shape,$logits.shape,"Error in sigmoidCrossEntropyWithLogits: ");let maxOutput=relu($logits),outputXTarget=mul($logits,$labels),sigmoidOutput=log1p(exp(neg(abs($logits))));return add2(sub(maxOutput,outputXTarget),sigmoidOutput)}function sigmoidCrossEntropy_(multiClassLabels,logits,weights,labelSmoothing=0,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $multiClassLabels=convertToTensor(multiClassLabels,"multiClassLabels","sigmoidCrossEntropy"),$logits=convertToTensor(logits,"logits","sigmoidCrossEntropy"),$weights=null;if(weights!=null&&($weights=convertToTensor(weights,"weights","sigmoidCrossEntropy")),assertShapesMatch($multiClassLabels.shape,$logits.shape,"Error in sigmoidCrossEntropy: "),labelSmoothing>0){let labelSmoothingScalar=scalar(labelSmoothing),one=scalar(1),half=scalar(.5);$multiClassLabels=add2(mul($multiClassLabels,sub(one,labelSmoothingScalar)),mul(half,labelSmoothingScalar))}let losses4=sigmoidCrossEntropyWithLogits_($multiClassLabels,$logits);return computeWeightedLoss(losses4,$weights,reduction2)}var sigmoidCrossEntropy=op({sigmoidCrossEntropy_});function softmaxCrossEntropyWithLogits_(labels,logits,dim=-1){if(dim===-1&&(dim=logits.rank-1),dim!==logits.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${logits.rank} and dim was ${dim}`);return customGrad((labels2,logits2,save)=>{let lse=logSumExp(logits2,[dim],!0),logResult=sub(cast(logits2,"float32"),lse);save([labels2,logResult]);let costVector=neg(mul(logResult,labels2));return{value:sum2(costVector,[dim]),gradFunc:(dy,saved)=>{let[labels3,logResult2]=saved,dyShape=expandShapeToKeepDim(dy.shape,[dim]);return[mul(reshape(dy,dyShape),sub(cast(labels3,"float32"),exp(logResult2))),mul(reshape(dy,dyShape),sub(exp(logResult2),cast(labels3,"float32")))]}}})(labels,logits)}function softmaxCrossEntropy_(onehotLabels,logits,weights,labelSmoothing=0,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $onehotLabels=convertToTensor(onehotLabels,"onehotLabels","softmaxCrossEntropy"),$logits=convertToTensor(logits,"logits","softmaxCrossEntropy"),$weights=null;if(weights!=null&&($weights=convertToTensor(weights,"weights","softmaxCrossEntropy")),assertShapesMatch($onehotLabels.shape,$logits.shape,"Error in softmaxCrossEntropy: "),labelSmoothing>0){let labelSmoothingScalar=scalar(labelSmoothing),one=scalar(1),numClasses=scalar($onehotLabels.shape[1]);$onehotLabels=add2(mul($onehotLabels,sub(one,labelSmoothingScalar)),div(labelSmoothingScalar,numClasses))}let losses4=softmaxCrossEntropyWithLogits_($onehotLabels,$logits);return computeWeightedLoss(losses4,$weights,reduction2)}var softmaxCrossEntropy=op({softmaxCrossEntropy_}),spectral={fft,ifft,rfft,irfft},signal={hammingWindow,hannWindow,frame,stft},image={flipLeftRight,resizeNearestNeighbor,resizeBilinear,rotateWithOffset,cropAndResize,nonMaxSuppression,nonMaxSuppressionAsync,nonMaxSuppressionWithScore,nonMaxSuppressionWithScoreAsync,nonMaxSuppressionPadded,nonMaxSuppressionPaddedAsync},linalg={bandPart,gramSchmidt,qr},losses={absoluteDifference,computeWeightedLoss,cosineDistance,hingeLoss,huberLoss,logLoss,meanSquaredError,sigmoidCrossEntropy,softmaxCrossEntropy},Optimizer=class extends Serializable{minimize(f,returnCost=!1,varList){let{value,grads:grads2}=this.computeGradients(f,varList);if(varList!=null){let gradArray=varList.map(v=>({name:v.name,tensor:grads2[v.name]}));this.applyGradients(gradArray)}else this.applyGradients(grads2);return dispose(grads2),returnCost?value:(value.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(f,varList){return variableGrads(f,varList)}dispose(){this.iterations_!=null&&dispose(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:scalar(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(weightValues){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(weightValues){return this.iterations_=(await weightValues[0].tensor.data())[0],weightValues.slice(1)}};Object.defineProperty(Optimizer,Symbol.hasInstance,{value:instance2=>instance2.minimize!=null&&instance2.computeGradients!=null&&instance2.applyGradients!=null});var AdadeltaOptimizer=class extends Optimizer{constructor(learningRate,rho,epsilon3=null){super();this.learningRate=learningRate,this.rho=rho,this.epsilon=epsilon3,this.accumulatedGrads=[],this.accumulatedUpdates=[],epsilon3==null&&(this.epsilon=ENGINE.backend.epsilon())}applyGradients(variableGradients){(Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients)).forEach((name,i)=>{let value=ENGINE.registeredVariables[name],trainable=!1;this.accumulatedGrads[i]==null&&(this.accumulatedGrads[i]={originalName:`${name}/accum_grad`,variable:tidy(()=>zerosLike(value).variable(trainable))}),this.accumulatedUpdates[i]==null&&(this.accumulatedUpdates[i]={originalName:`${name}/accum_var`,variable:tidy(()=>zerosLike(value).variable(trainable))});let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;let accumulatedGrad=this.accumulatedGrads[i].variable,accumulatedUpdate=this.accumulatedUpdates[i].variable;tidy(()=>{let newAccumulatedGrad=add2(mul(accumulatedGrad,this.rho),mul(square(gradient),1-this.rho)),updates=mul(div(sqrt(add2(accumulatedUpdate,this.epsilon)),sqrt(add2(accumulatedGrad,this.epsilon))),gradient),newAccumulatedUpdate=add2(mul(accumulatedUpdate,this.rho),mul(square(updates),1-this.rho));accumulatedGrad.assign(newAccumulatedGrad),accumulatedUpdate.assign(newAccumulatedUpdate);let newValue=add2(mul(updates,-this.learningRate),value);value.assign(newValue)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(dispose(this.accumulatedGrads.map(v=>v.variable)),dispose(this.accumulatedUpdates.map(v=>v.variable)))}async getWeights(){let variables=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(variables.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues);let variableCount=weightValues.length/2,trainable=!1;this.accumulatedGrads=weightValues.slice(0,variableCount).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})),this.accumulatedUpdates=weightValues.slice(variableCount,variableCount*2).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(cls,config3){return new cls(config3.learningRate,config3.rho,config3.epsilon)}};AdadeltaOptimizer.className="Adadelta";registerClass(AdadeltaOptimizer);var AdagradOptimizer=class extends Optimizer{constructor(learningRate,initialAccumulatorValue=.1){super();this.learningRate=learningRate,this.initialAccumulatorValue=initialAccumulatorValue,this.accumulatedGrads=[]}applyGradients(variableGradients){(Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients)).forEach((name,i)=>{let value=ENGINE.registeredVariables[name];if(this.accumulatedGrads[i]==null){let trainable=!1;this.accumulatedGrads[i]={originalName:`${name}/accumulator`,variable:tidy(()=>fill(value.shape,this.initialAccumulatorValue).variable(trainable))}}let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;let accumulatedGrad=this.accumulatedGrads[i].variable;tidy(()=>{let newAccumulatedGrad=add2(accumulatedGrad,square(gradient));accumulatedGrad.assign(newAccumulatedGrad);let newValue=add2(mul(div(gradient,sqrt(add2(newAccumulatedGrad,ENGINE.backend.epsilon()))),-this.learningRate),value);value.assign(newValue)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&dispose(this.accumulatedGrads.map(v=>v.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues);let trainable=!1;this.accumulatedGrads=weightValues.map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(cls,config3){return new cls(config3.learningRate,config3.initialAccumulatorValue)}};AdagradOptimizer.className="Adagrad";registerClass(AdagradOptimizer);var AdamOptimizer=class extends Optimizer{constructor(learningRate,beta1,beta2,epsilon3=null){super();this.learningRate=learningRate,this.beta1=beta1,this.beta2=beta2,this.epsilon=epsilon3,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],tidy(()=>{this.accBeta1=scalar(beta1).variable(),this.accBeta2=scalar(beta2).variable()}),epsilon3==null&&(this.epsilon=ENGINE.backend.epsilon())}applyGradients(variableGradients){let varNames=Array.isArray(variableGradients)?variableGradients.map(v=>v.name):Object.keys(variableGradients);tidy(()=>{let oneMinusAccBeta1=sub(1,this.accBeta1),oneMinusAccBeta2=sub(1,this.accBeta2);varNames.forEach((name,i)=>{let value=ENGINE.registeredVariables[name],trainable=!1;this.accumulatedFirstMoment[i]==null&&(this.accumulatedFirstMoment[i]={originalName:`${name}/m`,variable:tidy(()=>zerosLike(value).variable(trainable))}),this.accumulatedSecondMoment[i]==null&&(this.accumulatedSecondMoment[i]={originalName:`${name}/v`,variable:tidy(()=>zerosLike(value).variable(trainable))});let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;let firstMoment=this.accumulatedFirstMoment[i].variable,secondMoment=this.accumulatedSecondMoment[i].variable,newFirstMoment=add2(mul(firstMoment,this.beta1),mul(gradient,1-this.beta1)),newSecondMoment=add2(mul(secondMoment,this.beta2),mul(square(gradient),1-this.beta2)),biasCorrectedFirstMoment=div(newFirstMoment,oneMinusAccBeta1),biasCorrectedSecondMoment=div(newSecondMoment,oneMinusAccBeta2);firstMoment.assign(newFirstMoment),secondMoment.assign(newSecondMoment);let newValue=add2(mul(div(biasCorrectedFirstMoment,add2(sqrt(biasCorrectedSecondMoment),this.epsilon)),-this.learningRate),value);value.assign(newValue)}),this.accBeta1.assign(mul(this.accBeta1,this.beta1)),this.accBeta2.assign(mul(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&dispose(this.accumulatedFirstMoment.map(v=>v.variable)),this.accumulatedSecondMoment!=null&&dispose(this.accumulatedSecondMoment.map(v=>v.variable))}async getWeights(){let variables=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(variables.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues),tidy(()=>{this.accBeta1.assign(pow(this.beta1,this.iterations_+1)),this.accBeta2.assign(pow(this.beta2,this.iterations_+1))});let variableCount=weightValues.length/2,trainable=!1;this.accumulatedFirstMoment=weightValues.slice(0,variableCount).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})),this.accumulatedSecondMoment=weightValues.slice(variableCount,variableCount*2).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(cls,config3){return new cls(config3.learningRate,config3.beta1,config3.beta2,config3.epsilon)}};AdamOptimizer.className="Adam";registerClass(AdamOptimizer);var AdamaxOptimizer=class extends Optimizer{constructor(learningRate,beta1,beta2,epsilon3=null,decay=0){super();this.learningRate=learningRate,this.beta1=beta1,this.beta2=beta2,this.epsilon=epsilon3,this.decay=decay,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],tidy(()=>{this.iteration=scalar(0).variable(),this.accBeta1=scalar(beta1).variable()}),epsilon3==null&&(this.epsilon=ENGINE.backend.epsilon())}applyGradients(variableGradients){let variableNames=Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients);tidy(()=>{let oneMinusAccBeta1=sub(1,this.accBeta1),lr=div(-this.learningRate,add2(mul(this.iteration,this.decay),1));variableNames.forEach((name,i)=>{let value=ENGINE.registeredVariables[name],trainable=!1;this.accumulatedFirstMoment[i]==null&&(this.accumulatedFirstMoment[i]={originalName:`${name}/m`,variable:zerosLike(value).variable(trainable)}),this.accumulatedWeightedInfNorm[i]==null&&(this.accumulatedWeightedInfNorm[i]={originalName:`${name}/v`,variable:zerosLike(value).variable(trainable)});let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;let firstMoment=this.accumulatedFirstMoment[i].variable,weightedInfNorm=this.accumulatedWeightedInfNorm[i].variable,newFirstMoment=add2(mul(firstMoment,this.beta1),mul(gradient,1-this.beta1)),ut0=mul(weightedInfNorm,this.beta2),ut1=abs(gradient),newWeightedInfNorm=maximum(ut0,ut1);firstMoment.assign(newFirstMoment),weightedInfNorm.assign(newWeightedInfNorm);let newValue=add2(mul(div(lr,oneMinusAccBeta1),div(newFirstMoment,add2(newWeightedInfNorm,this.epsilon))),value);value.assign(newValue)}),this.iteration.assign(add2(this.iteration,1)),this.accBeta1.assign(mul(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&dispose(this.accumulatedFirstMoment.map(v=>v.variable)),this.accumulatedWeightedInfNorm!=null&&dispose(this.accumulatedWeightedInfNorm.map(v=>v.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(weightValues){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(cls,config3){return new cls(config3.learningRate,config3.beta1,config3.beta2,config3.epsilon,config3.decay)}};AdamaxOptimizer.className="Adamax";registerClass(AdamaxOptimizer);var SGDOptimizer=class extends Optimizer{constructor(learningRate){super();this.learningRate=learningRate,this.setLearningRate(learningRate)}applyGradients(variableGradients){(Array.isArray(variableGradients)?variableGradients.map(v=>v.name):Object.keys(variableGradients)).forEach((name,i)=>{let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;let value=ENGINE.registeredVariables[name];tidy(()=>{let newValue=add2(mul(this.c,gradient),value);value.assign(newValue)})}),this.incrementIterations()}setLearningRate(learningRate){this.learningRate=learningRate,this.c!=null&&this.c.dispose(),this.c=keep(scalar(-learningRate))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(weightValues){if(weightValues=await this.extractIterations(weightValues),weightValues.length!==0)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(cls,config3){return new cls(config3.learningRate)}};SGDOptimizer.className="SGD";registerClass(SGDOptimizer);var MomentumOptimizer=class extends SGDOptimizer{constructor(learningRate,momentum,useNesterov=!1){super(learningRate);this.learningRate=learningRate,this.momentum=momentum,this.useNesterov=useNesterov,this.accumulations=[],this.m=scalar(this.momentum)}applyGradients(variableGradients){(Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients)).forEach((name,i)=>{let value=ENGINE.registeredVariables[name];if(this.accumulations[i]==null){let trainable=!1;this.accumulations[i]={originalName:`${name}/momentum`,variable:tidy(()=>zerosLike(value).variable(trainable))}}let accumulation=this.accumulations[i].variable,gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];gradient!=null&&tidy(()=>{let newValue,newAccumulation=add2(mul(this.m,accumulation),gradient);this.useNesterov?newValue=add2(mul(this.c,add2(gradient,mul(newAccumulation,this.m))),value):newValue=add2(mul(this.c,newAccumulation),value),accumulation.assign(newAccumulation),value.assign(newValue)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&dispose(this.accumulations.map(v=>v.variable))}setMomentum(momentum){this.momentum=momentum}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues);let trainable=!1;this.accumulations=weightValues.map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(cls,config3){return new cls(config3.learningRate,config3.momentum,config3.useNesterov)}};MomentumOptimizer.className="Momentum";registerClass(MomentumOptimizer);var RMSPropOptimizer=class extends Optimizer{constructor(learningRate,decay=.9,momentum=0,epsilon3=null,centered=!1){super();if(this.learningRate=learningRate,this.decay=decay,this.momentum=momentum,this.epsilon=epsilon3,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=centered,epsilon3==null&&(this.epsilon=ENGINE.backend.epsilon()),learningRate==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(variableGradients){(Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients)).forEach((name,i)=>{let value=ENGINE.registeredVariables[name],trainable=!1;this.accumulatedMeanSquares[i]==null&&(this.accumulatedMeanSquares[i]={originalName:`${name}/rms`,variable:tidy(()=>zerosLike(value).variable(trainable))}),this.accumulatedMoments[i]==null&&(this.accumulatedMoments[i]={originalName:`${name}/momentum`,variable:tidy(()=>zerosLike(value).variable(trainable))}),this.accumulatedMeanGrads[i]==null&&this.centered&&(this.accumulatedMeanGrads[i]={originalName:`${name}/mg`,variable:tidy(()=>zerosLike(value).variable(trainable))});let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;let accumulatedMeanSquare=this.accumulatedMeanSquares[i].variable,accumulatedMoments=this.accumulatedMoments[i].variable;tidy(()=>{let newAccumulatedMeanSquare=add2(mul(accumulatedMeanSquare,this.decay),mul(square(gradient),1-this.decay));if(this.centered){let accumulatedMeanGrad=this.accumulatedMeanGrads[i].variable,newAccumulatedMeanGrad=add2(mul(accumulatedMeanGrad,this.decay),mul(gradient,1-this.decay)),gradContribution=div(mul(gradient,this.learningRate),sqrt(sub(newAccumulatedMeanSquare,add2(square(newAccumulatedMeanGrad),this.epsilon)))),newAccumulatedMoments=add2(mul(accumulatedMoments,this.momentum),gradContribution);accumulatedMeanSquare.assign(newAccumulatedMeanSquare),accumulatedMeanGrad.assign(newAccumulatedMeanGrad),accumulatedMoments.assign(newAccumulatedMoments);let newValue=sub(value,newAccumulatedMoments);value.assign(newValue)}else{let newAccumulatedMeanSquare2=add2(mul(accumulatedMeanSquare,this.decay),mul(square(gradient),1-this.decay)),newAccumulatedMoments=add2(mul(accumulatedMoments,this.momentum),div(mul(gradient,this.learningRate),sqrt(add2(newAccumulatedMeanSquare2,this.epsilon))));accumulatedMeanSquare.assign(newAccumulatedMeanSquare2),accumulatedMoments.assign(newAccumulatedMoments);let newValue=sub(value,newAccumulatedMoments);value.assign(newValue)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&dispose(this.accumulatedMeanSquares.map(v=>v.variable)),this.accumulatedMeanGrads!=null&&this.centered&&dispose(this.accumulatedMeanGrads.map(v=>v.variable)),this.accumulatedMoments!=null&&dispose(this.accumulatedMoments.map(v=>v.variable))}async getWeights(){let variables=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&variables.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(variables.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues);let variableCount=this.centered?weightValues.length/3:weightValues.length/2,trainable=!1;this.accumulatedMeanSquares=weightValues.slice(0,variableCount).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})),this.accumulatedMoments=weightValues.slice(variableCount,variableCount*2).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})),this.centered&&(this.accumulatedMeanGrads=weightValues.slice(variableCount*2,variableCount*3).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(cls,config3){return new cls(config3.learningRate,config3.decay,config3.momentum,config3.epsilon,config3.centered)}};RMSPropOptimizer.className="RMSProp";registerClass(RMSPropOptimizer);var OptimizerConstructors=class{static sgd(learningRate){return new SGDOptimizer(learningRate)}static momentum(learningRate,momentum,useNesterov=!1){return new MomentumOptimizer(learningRate,momentum,useNesterov)}static rmsprop(learningRate,decay=.9,momentum=0,epsilon3=null,centered=!1){return new RMSPropOptimizer(learningRate,decay,momentum,epsilon3,centered)}static adam(learningRate=.001,beta1=.9,beta2=.999,epsilon3=null){return new AdamOptimizer(learningRate,beta1,beta2,epsilon3)}static adadelta(learningRate=.001,rho=.95,epsilon3=null){return new AdadeltaOptimizer(learningRate,rho,epsilon3)}static adamax(learningRate=.002,beta1=.9,beta2=.999,epsilon3=null,decay=0){return new AdamaxOptimizer(learningRate,beta1,beta2,epsilon3,decay)}static adagrad(learningRate,initialAccumulatorValue=.1){return new AdagradOptimizer(learningRate,initialAccumulatorValue)}},train={sgd:OptimizerConstructors.sgd,momentum:OptimizerConstructors.momentum,adadelta:OptimizerConstructors.adadelta,adagrad:OptimizerConstructors.adagrad,rmsprop:OptimizerConstructors.rmsprop,adamax:OptimizerConstructors.adamax,adam:OptimizerConstructors.adam},delayCallback=(()=>typeof requestAnimationFrame!="undefined"?requestAnimationFrame:typeof setImmediate!="undefined"?setImmediate:f=>f())();function nextFrame(){return new Promise(resolve=>delayCallback(()=>resolve()))}var backend_util_exports={};__export2(backend_util_exports,{ERF_A1:()=>ERF_A1,ERF_A2:()=>ERF_A2,ERF_A3:()=>ERF_A3,ERF_A4:()=>ERF_A4,ERF_A5:()=>ERF_A5,ERF_P:()=>ERF_P,PARALLELIZE_THRESHOLD:()=>PARALLELIZE_THRESHOLD,SELU_SCALE:()=>SELU_SCALE,SELU_SCALEALPHA:()=>SELU_SCALEALPHA,applyActivation:()=>applyActivation,assertAndGetBroadcastShape:()=>assertAndGetBroadcastShape,assertAxesAreInnerMostDims:()=>assertAxesAreInnerMostDims,assertParamsConsistent:()=>assertParamsConsistent,assignToTypedArray:()=>assignToTypedArray,axesAreInnerMostDims:()=>axesAreInnerMostDims,calculateShapes:()=>calculateShapes,castTensor:()=>castTensor,combineLocations:()=>combineLocations,complexWithEvenIndex:()=>complexWithEvenIndex,complexWithOddIndex:()=>complexWithOddIndex,computeConv2DInfo:()=>computeConv2DInfo,computeConv3DInfo:()=>computeConv3DInfo,computeDefaultPad:()=>computeDefaultPad,computeDilation2DInfo:()=>computeDilation2DInfo,computeOptimalWindowSize:()=>computeOptimalWindowSize,computeOutAndReduceShapes:()=>computeOutAndReduceShapes,computeOutShape:()=>computeOutShape2,computePool2DInfo:()=>computePool2DInfo,computePool3DInfo:()=>computePool3DInfo,convertConv2DDataFormat:()=>convertConv2DDataFormat,eitherStridesOrDilationsAreOne:()=>eitherStridesOrDilationsAreOne,expandShapeToKeepDim:()=>expandShapeToKeepDim,exponent:()=>exponent,exponents:()=>exponents,fromStringArrayToUint8:()=>fromStringArrayToUint8,fromUint8ToStringArray:()=>fromUint8ToStringArray,getAxesPermutation:()=>getAxesPermutation,getBroadcastDims:()=>getBroadcastDims,getComplexWithIndex:()=>getComplexWithIndex,getFusedBiasGradient:()=>getFusedBiasGradient,getFusedDyActivation:()=>getFusedDyActivation,getImageCenter:()=>getImageCenter,getInnerMostAxes:()=>getInnerMostAxes,getPermuted:()=>getPermuted,getReductionAxes:()=>getReductionAxes,getReshaped:()=>getReshaped,getReshapedPermuted:()=>getReshapedPermuted,getSliceBeginCoords:()=>getSliceBeginCoords,getSliceSize:()=>getSliceSize,getUndoAxesPermutation:()=>getUndoAxesPermutation,log:()=>log22,mergeRealAndImagArrays:()=>mergeRealAndImagArrays,prepareAndValidate:()=>prepareAndValidate,prepareSplitSize:()=>prepareSplitSize,reshapeTensor:()=>reshapeTensor,segment_util:()=>segment_util_exports,shouldFuse:()=>shouldFuse,slice_util:()=>slice_util_exports,splitRealAndImagArrays:()=>splitRealAndImagArrays,tupleValuesAreOne:()=>tupleValuesAreOne,upcastType:()=>upcastType,validateInput:()=>validateInput,validateUpdateShape:()=>validateUpdateShape,warn:()=>warn});function assertParamsConsistent(shapes,axis){let rank=shapes[0].length;shapes.forEach((shape,i)=>{assert(shape.length===rank,()=>`Error in concat${rank}D: rank of tensors[${i}] must be the same as the rank of the rest (${rank})`)}),assert(axis>=0&&axis`Error in concat${rank}D: axis must be between 0 and ${rank-1}.`);let firstShape=shapes[0];shapes.forEach((shape,i)=>{for(let r=0;r`Error in concat${rank}D: Shape of tensors[${i}] (${shape}) does not match the shape of the rest (${firstShape}) along the non-concatenated axis ${i}.`)})}function computeOutShape2(shapes,axis){let outputShape=shapes[0].slice();for(let i=1;i=blockShapeRank*2+1||i%2==1?permutedAfterBatch.push(i):permutedBeforeBatch.push(i);permuted.push(...permutedBeforeBatch),permuted.push(0),permuted.push(...permutedAfterBatch)}return permuted}function getReshapedPermuted(inputShape,blockShape,prod5,batchToSpace=!0){let reshapedPermuted=[];batchToSpace?reshapedPermuted.push(inputShape[0]/prod5):reshapedPermuted.push(inputShape[0]*prod5);for(let i=1;i"Number of splits must evenly divide the axis."),splitSizes=new Array(numOrSizeSplits).fill(x.shape[axis]/numOrSizeSplits);else{let numOfNegs=numOrSizeSplits.reduce((count2,value)=>(value===-1&&(count2+=1),count2),0);assert(numOfNegs<=1,()=>"There should be only one negative value in split array.");let negIndex=numOrSizeSplits.indexOf(-1);if(negIndex!==-1){let total=numOrSizeSplits.reduce((a,b)=>b>0?a+b:a);numOrSizeSplits[negIndex]=x.shape[axis]-total}assert(x.shape[axis]===numOrSizeSplits.reduce((a,b)=>a+b),()=>"The sum of sizes must match the size of the axis dimension."),splitSizes=numOrSizeSplits}return splitSizes}var segment_util_exports={};__export2(segment_util_exports,{collectGatherOpShapeInfo:()=>collectGatherOpShapeInfo,computeOutShape:()=>computeOutShape3,segOpComputeOptimalWindowSize:()=>segOpComputeOptimalWindowSize});function segOpComputeOptimalWindowSize(inSize,numSegments){let done=!1,res;for(inSize<=PARALLELIZE_THRESHOLD?(res=inSize,done=!0):res=nearestDivisor(inSize,Math.floor(Math.sqrt(inSize)));!done;)res>numSegments||res===inSize?done=!0:res=nearestDivisor(inSize,res+1);return res}function computeOutShape3(aShape,axis,numSegments){let outShape=[],rank=aShape.length;for(let dim=0;dimindicesRank))throw new Error(`Expect batchDims in the range of [-${indicesRank}, ${indicesRank}], but got ${batchDims}`);if(batchDims<0&&(batchDims+=indicesRank),batchDims>xRank)throw new Error(`batchDims (${batchDims}) must be less than rank(x) ( + the f you passed encloses all operations that lead from x to y.`)}function neg_(x){let inputs={x:convertToTensor(x,"x","neg")};return ENGINE.runKernel(Neg,inputs)}var neg=op({neg_});function softplus_(x){let inputs={x:convertToTensor(x,"x","softplus")};return ENGINE.runKernel(Softplus,inputs)}var softplus=op({softplus_});function logSigmoid_(x){let $x=convertToTensor(x,"x","logSigmoid");return customGrad(x2=>({value:neg(softplus(neg(x2))),gradFunc:dy=>mul(dy,sigmoid(neg(x2)))}))($x)}var logSigmoid=op({logSigmoid_});function max_(x,axis=null,keepDims=!1){let inputs={x:convertToTensor(x,"x","max")},attrs={reductionIndices:axis,keepDims};return ENGINE.runKernel(Max,inputs,attrs)}var max=op({max_});function sub_(a,b){let $a=convertToTensor(a,"a","sub"),$b=convertToTensor(b,"b","sub");[$a,$b]=makeTypesMatch($a,$b);let inputs={a:$a,b:$b};return ENGINE.runKernel(Sub,inputs)}var sub=op({sub_});function sum_(x,axis=null,keepDims=!1){let $x=convertToTensor(x,"x","sum");$x.dtype==="bool"&&($x=cast($x,"int32"));let inputs={x:$x},attrs={axis,keepDims};return ENGINE.runKernel(Sum,inputs,attrs)}var sum2=op({sum_});function logSoftmax_(logits,axis=-1){let $logits=convertToTensor(logits,"logits","logSoftmax");if(axis===-1&&(axis=$logits.rank-1),axis!==$logits.rank-1)throw Error(`Log Softmax along a non-last dimension is not yet supported. Logits was rank ${$logits.rank} and axis was ${axis}`);return customGrad((logits2,save)=>{let keepDims=!0,xMax=max(logits2,axis,!0),shifted=sub(logits2,xMax),value=sub(cast(shifted,"float32"),log2(sum2(exp(shifted),axis,keepDims)));return save([value]),{value,gradFunc:(dy,saved)=>{let[value2]=saved,keepDims2=!0,softmax6=exp(value2);return sub(dy,mul(sum2(dy,axis,keepDims2),softmax6))}}})($logits)}var logSoftmax=op({logSoftmax_});function axesAreInnerMostDims(axes,rank){for(let i=0;iaShape[dim]);return[outShape,reduceShape]}function expandShapeToKeepDim(shape,axes){let reduceSubShape=axes.map(x=>1);return combineLocations(shape,reduceSubShape,axes)}function assertAxesAreInnerMostDims(msg,axes,rank){assert(axesAreInnerMostDims(axes,rank),()=>`${msg} supports only inner-most axes for now. Got axes ${axes} and rank-${rank} input.`)}function getAxesPermutation(axes,rank){if(axesAreInnerMostDims(axes,rank))return null;let result=[];for(let i=0;iresult.push(axis)),result}function getUndoAxesPermutation(axes){return axes.map((axis,i)=>[i,axis]).sort((a,b)=>a[1]-b[1]).map(x=>x[0])}function getInnerMostAxes(numAxes,rank){let res=[];for(let i=rank-numAxes;i`Error in maxPool: input must be rank 4 but got rank ${x4D.rank}.`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in maxPool: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={x:x4D},attrs={filterSize,strides,pad:pad3,dimRoundingMode},res=ENGINE.runKernel(MaxPool,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var maxPool=op({maxPool_});function maxPool3d_(x,filterSize=[1,1,1],strides,pad3,dimRoundingMode,dataFormat="NDHWC",dilations){dilations==null?dilations=[1,1,1]:deprecationWarn("dilations is deprecated, this field will be gone in v3.0.0.");let $x=convertToTensor(x,"x","maxPool3d"),x5D=$x,reshapedTo5D=!1;$x.rank===4&&(reshapedTo5D=!0,x5D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2],$x.shape[3]])),assert(x5D.rank===5,()=>`Error in maxPool3d: x must be rank 5 but got rank ${x5D.rank}.`),assert(dataFormat==="NDHWC",()=>`Error in maxPool3d: Only NDHWC is currently supported, but got dataFormat of ${dataFormat}`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in maxPool3d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in maxPool3d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={x:x5D},attrs={filterSize,strides,pad:pad3,dimRoundingMode,dataFormat,dilations},res=ENGINE.runKernel(MaxPool3D,inputs,attrs);return reshapedTo5D?reshape(res,[res.shape[1],res.shape[2],res.shape[3],res.shape[4]]):res}var maxPool3d=op({maxPool3d_});function maxPoolWithArgmax_(x,filterSize,strides,pad3,includeBatchInIndex=!1){let inputs={x:convertToTensor(x,"x","maxPoolWithArgmax")},attrs={filterSize,strides,pad:pad3,includeBatchInIndex},result=ENGINE.runKernel(MaxPoolWithArgmax,inputs,attrs);return{result:result[0],indexes:result[1]}}var maxPoolWithArgmax=op({maxPoolWithArgmax_});function maximum_(a,b){let $a=convertToTensor(a,"a","maximum"),$b=convertToTensor(b,"b","maximum");[$a,$b]=makeTypesMatch($a,$b),$a.dtype==="bool"&&($a=cast($a,"int32"),$b=cast($b,"int32")),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(Maximum,inputs)}var maximum=op({maximum_});function mean_(x,axis=null,keepDims=!1){let inputs={x:convertToTensor(x,"x","mean")},attrs={axis,keepDims};return ENGINE.runKernel(Mean,inputs,attrs)}var mean=op({mean_});function min_(x,axis=null,keepDims=!1){let inputs={x:convertToTensor(x,"x","min")},attrs={axis,keepDims};return ENGINE.runKernel(Min,inputs,attrs)}var min=op({min_});function minimum_(a,b){let $a=convertToTensor(a,"a","minimum"),$b=convertToTensor(b,"b","minimum");[$a,$b]=makeTypesMatch($a,$b),$a.dtype==="bool"&&($a=cast($a,"int32"),$b=cast($b,"int32")),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(Minimum,inputs)}var minimum=op({minimum_});function mirrorPad_(x,paddings,mode){assert(mode==="reflect"||mode==="symmetric",()=>`Invalid mode. Mode must be either reflect or symmetric. Got ${mode}.`);let $x=convertToTensor(x,"x","mirrorPad");if($x.rank===0)throw new Error("mirrorPad(scalar) is not defined. Pass non-scalar to mirrorPad");assert(paddings.length===$x.rank,()=>`Padding doesn't match input. Must be ${$x.rank}. Got ${paddings.length}.`);let shapeOffset=mode==="reflect"?1:0;for(let i=0;i<$x.rank;i++)assert(paddings[i].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),assert(paddings[i][0]>=0&&paddings[i][0]<=$x.shape[i]-shapeOffset&&paddings[i][1]>=0&&paddings[i][1]<=$x.shape[i]-shapeOffset,()=>`Padding in dimension ${i} cannot be greater than or equal to ${$x.shape[i]-shapeOffset} or less than 0 for input of shape ${$x.shape}`);let attrs={paddings,mode},inputs={x:$x};return ENGINE.runKernel(MirrorPad,inputs,attrs)}var mirrorPad=op({mirrorPad_});function mod_(a,b){let $a=convertToTensor(a,"a","mod"),$b=convertToTensor(b,"b","mod");[$a,$b]=makeTypesMatch($a,$b);let inputs={a:$a,b:$b};return ENGINE.runKernel(Mod,inputs)}var mod=op({mod_});function square_(x){let $x=convertToTensor(x,"x","square"),attrs={};return ENGINE.runKernel("Square",{x:$x},attrs)}var square=op({square_});function moments_(x,axis=null,keepDims=!1){x=convertToTensor(x,"x","moments");let axes=parseAxisParam(axis,x.shape),xMean=mean(x,axes,keepDims),keepDimsShape=xMean.shape;keepDims||(keepDimsShape=expandShapeToKeepDim(xMean.shape,axes));let devSquared=square(sub(cast(x,"float32"),reshape(xMean,keepDimsShape))),variance=mean(devSquared,axes,keepDims);return{mean:xMean,variance}}var moments=op({moments_});function multiRNNCell_(lstmCells,data2,c,h){let $data=convertToTensor(data2,"data","multiRNNCell"),$c=convertToTensorArray(c,"c","multiRNNCell"),$h=convertToTensorArray(h,"h","multiRNNCell"),input2=$data,newStates=[];for(let i=0;i2)throw new Error(`Rank of probabilities must be 1 or 2, but is ${origRank}`);seed=seed||Math.random();let inputs={logits:origRank===1?reshape($logits,[1,-1]):$logits},attrs={numSamples,seed,normalized},res=ENGINE.runKernel(Multinomial,inputs,attrs);return origRank===1?reshape(res,[res.size]):res}var multinomial=op({multinomial_});function notEqual_(a,b){let $a=convertToTensor(a,"a","notEqual"),$b=convertToTensor(b,"b","notEqual");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(NotEqual,inputs)}var notEqual=op({notEqual_});function zeros(shape,dtype="float32"){if(dtype==="complex64"){let real4=zeros(shape,"float32"),imag4=zeros(shape,"float32");return complex(real4,imag4)}let values=makeZerosTypedArray(sizeFromShape(shape),dtype);return ENGINE.makeTensor(values,shape,dtype)}function ones2(shape,dtype="float32"){if(dtype==="complex64"){let real4=ones2(shape,"float32"),imag4=zeros(shape,"float32");return complex(real4,imag4)}let values=makeOnesTypedArray(sizeFromShape(shape),dtype);return ENGINE.makeTensor(values,shape,dtype)}function onesLike_(x){let inputs={x:convertToTensor(x,"x","onesLike")};return ENGINE.runKernel(OnesLike,inputs)}var onesLike=op({onesLike_});function outerProduct_(v1,v2){let $v1=convertToTensor(v1,"v1","outerProduct"),$v2=convertToTensor(v2,"v2","outerProduct");assert($v1.rank===1&&$v2.rank===1,()=>`Error in outerProduct: inputs must be rank 1, but got ranks ${$v1.rank} and ${$v2.rank}.`);let v12D=reshape($v1,[-1,1]),v22D=reshape($v2,[1,-1]);return matMul(v12D,v22D)}var outerProduct=op({outerProduct_});function pad_(x,paddings,constantValue=0){let $x=convertToTensor(x,"x","pad");if($x.rank===0)throw new Error("pad(scalar) is not defined. Pass non-scalar to pad");let attrs={paddings,constantValue},inputs={x:$x};return ENGINE.runKernel(PadV2,inputs,attrs)}var pad=op({pad_});function pad1d_(x,paddings,constantValue=0){return assert(paddings.length===2,()=>"Invalid number of paddings. Must be length of 2."),pad(x,[paddings],constantValue)}var pad1d=op({pad1d_});function pad2d_(x,paddings,constantValue=0){return assert(paddings.length===2&&paddings[0].length===2&&paddings[1].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),pad(x,paddings,constantValue)}var pad2d=op({pad2d_});function pad3d_(x,paddings,constantValue=0){return assert(paddings.length===3&&paddings[0].length===2&&paddings[1].length===2&&paddings[2].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),pad(x,paddings,constantValue)}var pad3d=op({pad3d_});function pad4d_(x,paddings,constantValue=0){return assert(paddings.length===4&&paddings[0].length===2&&paddings[1].length===2&&paddings[2].length===2&&paddings[3].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),pad(x,paddings,constantValue)}var pad4d=op({pad4d_});function spaceToBatchND_(x,blockShape,paddings){let $x=convertToTensor(x,"x","spaceToBatchND");assert($x.rank>=1+blockShape.length,()=>`input rank ${$x.rank} should be > than [blockShape] ${blockShape.length}`),assert(paddings.length===blockShape.length,()=>`paddings.shape[0] ${paddings.length} must be equal to [blockShape] ${blockShape.length}`),assert($x.shape.reduce((a,b,i)=>i>0&&i<=blockShape.length?a&&(b+paddings[i-1][0]+paddings[i-1][1])%blockShape[i-1]==0:a,!0),()=>`input spatial dimensions ${$x.shape.slice(1)} with paddings ${paddings.toString()} must be divisible by blockShapes ${blockShape.toString()}`);let inputs={x:$x},attrs={blockShape,paddings};return ENGINE.runKernel(SpaceToBatchND,inputs,attrs)}var spaceToBatchND=op({spaceToBatchND_});function pool_(input2,windowShape,poolingType,pad3,dilations,strides){dilations==null&&(dilations=[1,1]),strides==null&&(strides=1),pad3===0&&(pad3="valid");let $x=convertToTensor(input2,"x","maxPool"),x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in pool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);let convInfo=computePool2DInfo(x4D.shape,windowShape,strides,dilations,pad3),dilation=[convInfo.dilationHeight,convInfo.dilationWidth],basePadding;pad3==="same"?basePadding=withSpaceToBatchBasePaddings([convInfo.filterHeight,convInfo.filterWidth],dilation):basePadding=[[0,0],[0,0]];let isDilationOne=dilation[0]===1&&dilation[1]===1,[adjustedPadding,adjustedCrops]=requiredSpaceToBatchPaddings([convInfo.inHeight,convInfo.inWidth],dilation,basePadding),convertedPad=isDilationOne?pad3:"valid",convertedX=isDilationOne?x4D:spaceToBatchND(x4D,dilation,adjustedPadding),y=(poolingType==="avg"?()=>avgPool(convertedX,windowShape,strides,convertedPad):()=>maxPool(convertedX,windowShape,strides,convertedPad))(),res=isDilationOne?y:batchToSpaceND(y,dilation,adjustedCrops);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}function requiredSpaceToBatchPaddings(inputShape,blockShape,basePadding){let padStart=basePadding.map(b=>b[0]),origPadEnd=basePadding.map(b=>b[1]),fullInputShape=inputShape.concat(padStart,origPadEnd),padEndExtra=blockShape.map((b,i)=>(b-fullInputShape[i]%b)%b),padEnd=origPadEnd.map((s,i)=>s+padEndExtra[i]),paddings=blockShape.map((_,i)=>[padStart[i],padEnd[i]]),crops=blockShape.map((_,i)=>[0,padEndExtra[i]]);return[paddings,crops]}function withSpaceToBatchBasePaddings(filterShape,dilation){let padExtraShape=filterShape.map((s,i)=>s+(s-1)*(dilation[i]-1)).map(s=>s-1),padExtraStart=padExtraShape.map(s=>Math.floor(s/2)),padExtraEnd=padExtraShape.map((s,i)=>s-padExtraStart[i]);return padExtraShape.map((_,i)=>[padExtraStart[i],padExtraEnd[i]])}var pool=op({pool_});function pow_(base2,exp4){let $base=convertToTensor(base2,"base","pow"),$exp=convertToTensor(exp4,"exp","pow");[$base,$exp]=makeTypesMatch($base,$exp);let inputs={a:$base,b:$exp};return ENGINE.runKernel(Pow,inputs)}var pow=op({pow_});function prelu_(x,alpha){let $x=convertToTensor(x,"x","prelu"),$alpha=convertToTensor(alpha,"alpha","prelu"),inputs={x:$x,alpha:$alpha};return ENGINE.runKernel(Prelu,inputs)}var prelu=op({prelu_});function prod_(x,axis=null,keepDims=!1){let $x=convertToTensor(x,"x","prod");$x.dtype==="bool"&&($x=cast($x,"int32"));let inputs={x:$x},attrs={axis,keepDims};return ENGINE.runKernel(Prod,inputs,attrs)}var prod=op({prod_});function rand_(shape,randFunction,dtype){let size=sizeFromShape(shape),values=null;if(dtype==null||dtype==="float32")values=new Float32Array(size);else if(dtype==="int32")values=new Int32Array(size);else if(dtype==="bool")values=new Uint8Array(size);else throw new Error(`Unknown data type ${dtype}`);for(let i=0;i=1||s===0);let mul2=Math.sqrt(-2*Math.log(s)/s);resultX=this.mean+this.stdDev*v1*mul2,resultY=this.mean+this.stdDev*v2*mul2,(!this.truncated||this.isValidTruncated(resultX))&&(isValid=!0)}return(!this.truncated||this.isValidTruncated(resultY))&&(this.nextVal=this.convertValue(resultY)),this.convertValue(resultX)}convertValue(value){return this.dtype==null||this.dtype==="float32"?value:Math.round(value)}isValidTruncated(value){return value<=this.upper&&value>=this.lower}},RandGamma=class{constructor(alpha,beta,dtype,seed){this.alpha=alpha,this.beta=1/beta,this.dtype=dtype;let seedValue=seed||Math.random();this.randu=seedrandom.alea(seedValue.toString()),this.randn=new MPRandGauss(0,1,dtype,!1,this.randu()),alpha<1?this.d=alpha+2/3:this.d=alpha-1/3,this.c=1/Math.sqrt(9*this.d)}nextValue(){let x2,v0,v1,x,u,v;for(;;){do x=this.randn.nextValue(),v=1+this.c*x;while(v<=0);if(v*=v*v,x2=x*x,v0=1-.331*x2*x2,v1=.5*x2+this.d*(1-v+Math.log(v)),u=this.randu(),uthis.dtype==null||this.dtype==="float32",this.min=min6,this.range=max6-min6,this.dtype=dtype,seed==null&&(seed=Math.random()),typeof seed=="number"&&(seed=seed.toString()),!this.canReturnFloat()&&this.range<=1)throw new Error(`The difference between ${min6} - ${max6} <= 1 and dtype is not float`);this.random=seedrandom.alea(seed)}convertValue(value){return this.canReturnFloat()?value:Math.round(value)}nextValue(){return this.convertValue(this.min+this.range*this.random())}};function randomGamma_(shape,alpha,beta=1,dtype="float32",seed){if(beta==null&&(beta=1),dtype==null&&(dtype="float32"),dtype!=="float32"&&dtype!=="int32")throw new Error(`Unsupported data type ${dtype}`);let rgamma=new RandGamma(alpha,beta,dtype,seed),res=buffer(shape,dtype);for(let i=0;i`Error in reverse1D: x must be rank 1 but got rank ${$x.rank}.`),reverse($x,0)}var reverse1d=op({reverse1d_});function reverse2d_(x,axis){let $x=convertToTensor(x,"x","reverse");return assert($x.rank===2,()=>`Error in reverse2D: x must be rank 2 but got rank ${$x.rank}.`),reverse($x,axis)}var reverse2d=op({reverse2d_});function reverse3d_(x,axis){let $x=convertToTensor(x,"x","reverse");return assert($x.rank===3,()=>`Error in reverse3D: x must be rank 3 but got rank ${$x.rank}.`),reverse($x,axis)}var reverse3d=op({reverse3d_});function reverse4d_(x,axis){let $x=convertToTensor(x,"x","reverse");return assert($x.rank===4,()=>`Error in reverse4D: x must be rank 4 but got rank ${$x.rank}.`),reverse($x,axis)}var reverse4d=op({reverse4d_});function round_(x){let inputs={x:convertToTensor(x,"x","round")};return ENGINE.runKernel(Round,inputs)}var round2=op({round_});function rsqrt_(x){let inputs={x:convertToTensor(x,"x","rsqrt")};return ENGINE.runKernel(Rsqrt,inputs)}var rsqrt=op({rsqrt_});function scalar(value,dtype){if((isTypedArray(value)&&dtype!=="string"||Array.isArray(value))&&dtype!=="complex64")throw new Error("Error creating a new Scalar: value must be a primitive (number|boolean|string)");if(dtype==="string"&&isTypedArray(value)&&!(value instanceof Uint8Array))throw new Error("When making a scalar from encoded string, the value must be `Uint8Array`.");return makeTensor(value,[],[],dtype)}function selu_(x){let inputs={x:convertToTensor(x,"x","selu")};return ENGINE.runKernel(Selu,inputs)}var selu=op({selu_});function separableConv2d_(x,depthwiseFilter,pointwiseFilter,strides,pad3,dilation=[1,1],dataFormat="NHWC"){let $x=convertToTensor(x,"x","separableConv2d"),$depthwiseFilter=convertToTensor(depthwiseFilter,"depthwiseFilter","separableConv2d"),$pointwiseFilter=convertToTensor(pointwiseFilter,"pointwiseFilter","separableConv2d"),x4D=$x,reshapedTo4D=!1;if($x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),dataFormat==="NCHW")throw new Error("separableConv2d currently does not support dataFormat NCHW; only NHWC is supported");assert(x4D.rank===4,()=>`Error in separableConv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert($depthwiseFilter.rank===4,()=>`Error in separableConv2d: depthwise filter must be rank 4, but got rank ${$depthwiseFilter.rank}.`),assert($pointwiseFilter.rank===4,()=>`Error in separableConv2d: pointwise filter must be rank 4, but got rank ${$depthwiseFilter.rank}.`),assert($pointwiseFilter.shape[0]===1,()=>`Error in separableConv2d: the first dimension of pointwise filter must be 1, but got ${$pointwiseFilter.shape[0]}.`),assert($pointwiseFilter.shape[1]===1,()=>`Error in separableConv2d: the second dimension of pointwise filter must be 1, but got ${$pointwiseFilter.shape[1]}.`);let inChannels=$depthwiseFilter.shape[2],channelMultiplier=$depthwiseFilter.shape[3];assert($pointwiseFilter.shape[2]===inChannels*channelMultiplier,()=>`Error in separableConv2d: the third dimension of pointwise filter must be ${inChannels*channelMultiplier}, but got ${$pointwiseFilter.shape[2]}.`);let depthwise=depthwiseConv2d(x4D,$depthwiseFilter,strides,pad3,dataFormat,dilation),res=conv2d(depthwise,$pointwiseFilter,1,"valid",dataFormat);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var separableConv2d=op({separableConv2d_});async function setdiff1dAsync_(x,y){let $x=convertToTensor(x,"x","setdiff1d"),$y=convertToTensor(y,"y","setdiff1d");assert($x.dtype===$y.dtype,()=>`x and y should have the same dtype, but got x (${$x.dtype}) and y (${$y.dtype}).`),assert($x.rank===1,()=>`x should be 1D tensor, but got x (${$x.shape}).`),assert($y.rank===1,()=>`y should be 1D tensor, but got y (${$y.shape}).`);let xVals=await $x.data(),yVals=await $y.data(),ySet=new Set(yVals),outputSize=0;for(let i=0;i`slice1d expects a rank-1 tensor, but got a rank-${$x.rank} tensor`),slice($x,[begin],[size])}var slice1d=op({slice1d_});function slice2d_(x,begin,size){let $x=convertToTensor(x,"x","slice2d");return assert($x.rank===2,()=>`slice2d expects a rank-2 tensor, but got a rank-${$x.rank} tensor`),slice($x,begin,size)}var slice2d=op({slice2d_});function slice3d_(x,begin,size){let $x=convertToTensor(x,"x","slice3d");return assert($x.rank===3,()=>`slice3d expects a rank-3 tensor, but got a rank-${$x.rank} tensor`),slice($x,begin,size)}var slice3d=op({slice3d_});function slice4d_(x,begin,size){let $x=convertToTensor(x,"x","slice4d");return assert($x.rank===4,()=>`slice4d expects a rank-4 tensor, but got a rank-${$x.rank} tensor`),slice($x,begin,size)}var slice4d=op({slice4d_});function softmax_(logits,dim=-1){let $logits=convertToTensor(logits,"logits","softmax","float32");if(dim===-1&&(dim=$logits.rank-1),dim!==$logits.rank-1)throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${$logits.rank} and dim was ${dim}`);let inputs={logits:$logits},attrs={dim};return ENGINE.runKernel(Softmax,inputs,attrs)}var softmax=op({softmax_});function fft_(input2){assert(input2.dtype==="complex64",()=>`The dtype for tf.spectral.fft() must be complex64 but got ${input2.dtype}.`);let inputs={input:input2};return ENGINE.runKernel(FFT,inputs)}var fft=op({fft_});function ifft_(input2){assert(input2.dtype==="complex64",()=>`The dtype for tf.spectral.ifft() must be complex64 but got ${input2.dtype}.`);let inputs={input:input2};return ENGINE.runKernel(IFFT,inputs)}var ifft=op({ifft_});function irfft_(input2){let innerDimensionSize=input2.shape[input2.shape.length-1],batch=input2.size/innerDimensionSize,ret;if(innerDimensionSize<=2){let complexInput=reshape(input2,[batch,innerDimensionSize]);ret=ifft(complexInput)}else{let outputShape=[batch,2*(innerDimensionSize-1)],realInput=reshape(real(input2),[batch,innerDimensionSize]),imagInput=reshape(imag(input2),[batch,innerDimensionSize]),realConjugate=reverse(slice(realInput,[0,1],[batch,innerDimensionSize-2]),1),imagConjugate=mul(reverse(slice(imagInput,[0,1],[batch,innerDimensionSize-2]),1),scalar(-1)),r=concat([realInput,realConjugate],1),i=concat([imagInput,imagConjugate],1),complexInput=reshape(complex(r,i),[outputShape[0],outputShape[1]]);ret=ifft(complexInput)}if(ret=real(ret),input2.rank===3&&input2.shape[0]!==0){let temp=ret,batch2=input2.shape[0];ret=reshape(ret,[batch2,ret.shape[0]/batch2,ret.shape[1]]),temp.dispose()}return ret}var irfft=op({irfft_});function split_(x,numOrSizeSplits,axis=0){let inputs={x:convertToTensor(x,"x","split")},attr={numOrSizeSplits,axis};return ENGINE.runKernel(SplitV,inputs,attr)}var split=op({split_});function rfft_(input2,fftLength){assert(input2.dtype==="float32",()=>`The dtype for rfft() must be real value but got ${input2.dtype}`);let innerDimensionSize=input2.shape[input2.shape.length-1],batch=input2.size/innerDimensionSize,adjustedInput;if(fftLength!=null&&fftLength0),size=input2.shape.map(v=>v);size[input2.shape.length-1]=fftLength,adjustedInput=slice(input2,begin,size),innerDimensionSize=fftLength}else if(fftLength!=null&&fftLength>innerDimensionSize){let zerosShape=input2.shape.map(v=>v);zerosShape[input2.shape.length-1]=fftLength-innerDimensionSize,adjustedInput=concat([input2,zeros(zerosShape)],input2.shape.length-1),innerDimensionSize=fftLength}else adjustedInput=input2;let zerosInput=zerosLike(adjustedInput),complexInput=reshape(complex(adjustedInput,zerosInput),[batch,innerDimensionSize]),ret=fft(complexInput),half=Math.floor(innerDimensionSize/2)+1,realValues=real(ret),imagValues=imag(ret),realComplexConjugate=split(realValues,[half,innerDimensionSize-half],realValues.shape.length-1),imagComplexConjugate=split(imagValues,[half,innerDimensionSize-half],imagValues.shape.length-1),outputShape=adjustedInput.shape.slice();return outputShape[adjustedInput.shape.length-1]=half,reshape(complex(realComplexConjugate[0],imagComplexConjugate[0]),outputShape)}var rfft=op({rfft_});function sqrt_(x){let inputs={x:convertToTensor(x,"x","sqrt")};return ENGINE.runKernel(Sqrt,inputs)}var sqrt=op({sqrt_});function squaredDifference_(a,b){let $a=convertToTensor(a,"a","squaredDifference"),$b=convertToTensor(b,"b","squaredDifference");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b},attrs={};return ENGINE.runKernel(SquaredDifference,inputs,attrs)}var squaredDifference=op({squaredDifference_});function squeeze_(x,axis){let $x=convertToTensor(x,"x","squeeze");return reshape($x,squeezeShape($x.shape,axis).newShape)}var squeeze=op({squeeze_});function stack_(tensors,axis=0){let $tensors=convertToTensorArray(tensors,"tensors","stack","string_or_numeric");assert($tensors.length>=1,()=>"Pass at least one tensor to tf.stack"),$tensors.length>0&&assert(axis<=$tensors[0].rank,()=>"Axis must be <= rank of the tensor");let inputs=$tensors,attrs={axis};return ENGINE.runKernel(Pack,inputs,attrs)}var stack=op({stack_});function step_(x,alpha=0){let inputs={x:convertToTensor(x,"x","step")},attrs={alpha};return ENGINE.runKernel(Step,inputs,attrs)}var step=op({step_});function stridedSlice_(x,begin,end,strides,beginMask=0,endMask=0,ellipsisMask=0,newAxisMask=0,shrinkAxisMask=0){let inputs={x:convertToTensor(x,"x","stridedSlice")},attrs={begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask};return ENGINE.runKernel(StridedSlice,inputs,attrs)}var stridedSlice=op({stridedSlice_});function tan_(x){let inputs={x:convertToTensor(x,"x","tan")};return ENGINE.runKernel(Tan,inputs)}var tan=op({tan_});function tensor1d(values,dtype){assertNonNull(values);let inferredShape=inferShape(values,dtype);if(inferredShape.length!==1)throw new Error("tensor1d() requires values to be a flat/TypedArray");return makeTensor(values,null,inferredShape,dtype)}function tensor2d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==2)throw new Error("tensor2d() requires shape to have two numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==2&&inferredShape.length!==1)throw new Error("tensor2d() requires values to be number[][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor2d() requires shape to be provided when `values` are a flat/TypedArray");return makeTensor(values,shape,inferredShape,dtype)}function tensor4d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==4)throw new Error("tensor4d() requires shape to have four numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==4&&inferredShape.length!==1)throw new Error("tensor4d() requires values to be number[][][][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor4d() requires shape to be provided when `values` are a flat array");return makeTensor(values,shape,inferredShape,dtype)}function tensor5d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==5)throw new Error("tensor5d() requires shape to have five numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==5&&inferredShape.length!==1)throw new Error("tensor5d() requires values to be number[][][][][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor5d() requires shape to be provided when `values` are a flat array");return makeTensor(values,shape,inferredShape,dtype)}function tensor6d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==6)throw new Error("tensor6d() requires shape to have six numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==6&&inferredShape.length!==1)throw new Error("tensor6d() requires values to be number[][][][][][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor6d() requires shape to be provided when `values` are a flat array");return shape=shape||inferredShape,makeTensor(values,shape,inferredShape,dtype)}function topk_(x,k=1,sorted=!0){let $x=convertToTensor(x,"x","topk");if($x.rank===0)throw new Error("topk() expects the input to be of rank 1 or higher");let lastDim=$x.shape[$x.shape.length-1];if(k>lastDim)throw new Error(`'k' passed to topk() must be <= the last dimension (${lastDim}) but got ${k}`);let inputs={x:$x},attrs={k,sorted},[values,indices]=ENGINE.runKernel(TopK,inputs,attrs);return{values,indices}}var topk=op({topk_});function truncatedNormal_(shape,mean4=0,stdDev=1,dtype,seed){if(dtype!=null&&dtype==="bool")throw new Error("Unsupported data type $ { dtype }");let randGauss=new MPRandGauss(mean4,stdDev,dtype,!0,seed),res=buffer(shape,dtype);for(let i=0;i0,()=>"The input tensor must be at least 1D");let inputs={x:$x},attrs={axis},[values,indices]=ENGINE.runKernel(Unique,inputs,attrs);return{values,indices}}var unique=op({unique_});function unsortedSegmentSum_(x,segmentIds,numSegments){let $x=convertToTensor(x,"x","unsortedSegmentSum"),$segmentIds=convertToTensor(segmentIds,"segmentIds","unsortedSegmentSum","int32");assert(isInt(numSegments),()=>"numSegments must be of dtype int");let inputs={x:$x,segmentIds:$segmentIds},attrs={numSegments};return ENGINE.runKernel(UnsortedSegmentSum,inputs,attrs)}var unsortedSegmentSum=op({unsortedSegmentSum_});function unstack_(x,axis=0){let $x=convertToTensor(x,"x","unstack","string_or_numeric");assert(axis>=-$x.shape.length&&axis<$x.shape.length,()=>`Axis = ${axis} is not in [-${$x.shape.length}, ${$x.shape.length})`);let inputs={value:$x},attrs={axis};return ENGINE.runKernel(Unpack,inputs,attrs)}var unstack=op({unstack_});function variable(initialValue,trainable=!0,name,dtype){return ENGINE.makeVariable(initialValue,trainable,name,dtype)}function whereImpl(condShape,condVals){let indices=[];for(let i=0;i0,()=>"mask cannot be scalar"),assertShapesMatch(tensorShape.slice(axisFrom,axisFrom+maskDim),$mask.shape,"mask's shape must match the first K dimensions of tensor's shape,");let leadingSize=1;for(let i=axisFrom;i"Shape mismatch in v and x");let one=scalar(1),oneMinusDecay=sub(one,$decay),update=mul(sub($x,$v),oneMinusDecay);if(zeroDebias){assert(step4!=null,()=>"When using zeroDebias: true, step is required.");let $step=convertToTensor(step4,"step","movingAverage");update=div(update,sub(one,pow($decay,$step)))}return add2($v,update)}var movingAverage=op({movingAverage_});function scatterND_(indices,updates,shape){let $indices=convertToTensor(indices,"indices","scatterND","int32"),$updates=convertToTensor(updates,"updates","scatterND");validateInput($updates,$indices,shape);let inputs={indices:$indices,updates:$updates},attrs={shape};return ENGINE.runKernel(ScatterNd,inputs,attrs)}var scatterND=op({scatterND_});function validateInput2(sparseIndices,sparseValues,outputShape,defaultValues){if(sparseIndices.dtype!=="int32")throw new Error(`tf.sparseToDense() expects the indices to be int32 type, but the dtype was ${sparseIndices.dtype}.`);if(sparseIndices.rank>2)throw new Error(`sparseIndices should be a scalar, vector, or matrix, but got shape ${sparseIndices.shape}.`);let numElems=sparseIndices.rank>0?sparseIndices.shape[0]:1,numDims=sparseIndices.rank>1?sparseIndices.shape[1]:1;if(outputShape.length!==numDims)throw new Error(`outputShape has incorrect number of elements:, ${outputShape.length}, should be: ${numDims}.`);let numValues=sparseValues.size;if(!(sparseValues.rank===0||sparseValues.rank===1&&numValues===numElems))throw new Error(`sparseValues has incorrect shape ${sparseValues.shape}, should be [] or [${numElems}]`);if(sparseValues.dtype!==defaultValues.dtype)throw new Error("sparseValues.dtype must match defaultValues.dtype")}function sparseToDense_(sparseIndices,sparseValues,outputShape,defaultValue=0){let $sparseIndices=convertToTensor(sparseIndices,"sparseIndices","sparseToDense","int32"),$sparseValues=convertToTensor(sparseValues,"sparseValues","sparseToDense"),$defaultValue=convertToTensor(defaultValue,"defaultValue","sparseToDense",$sparseValues.dtype);validateInput2($sparseIndices,$sparseValues,outputShape,$defaultValue);let inputs={sparseIndices:$sparseIndices,sparseValues:$sparseValues,defaultValue:$defaultValue},attrs={outputShape};return ENGINE.runKernel(SparseToDense,inputs,attrs)}var sparseToDense=op({sparseToDense_});function gatherND_(x,indices){let $indices=convertToTensor(indices,"indices","gatherND","int32"),inputs={params:convertToTensor(x,"x","gatherND"),indices:$indices};return ENGINE.runKernel(GatherNd,inputs)}var gatherND=op({gatherND_});function getNoiseShape(x,noiseShape){if(noiseShape==null)return x.shape.slice();if(arraysEqual(x.shape,noiseShape))return noiseShape;if(x.shape.length===noiseShape.length){let newDimension=[];for(let i=0;i`x has to be a floating point tensor since it's going to be scaled, but got a ${$x.dtype} tensor instead.`),assert(rate>=0&&rate<1,()=>`rate must be a float in the range [0, 1), but got ${rate}.`),rate===0)return x instanceof Tensor?$x.clone():$x;let $noiseShape=getNoiseShape($x,noiseShape),keepProb=1-rate,multiplier=div(floor(add2(randomUniform($noiseShape,0,1,"float32",seed),keepProb)),keepProb);return mul($x,multiplier)}var dropout=op({dropout_});function enclosingPowerOfTwo(value){return Math.floor(Math.pow(2,Math.ceil(Math.log(value)/Math.log(2))))}function cosineWindow(windowLength,a,b){let even=1-windowLength%2,newValues=new Float32Array(windowLength);for(let i=0;i1,()=>`inTopK() expects the predictions to be of rank 2 or higher, but got ${$predictions.rank}`),assert($predictions.rank-1===$targets.rank,()=>`predictions rank should be 1 larger than targets rank, but got predictions rank ${$predictions.rank} and targets rank ${$targets.rank}`),assertShapesMatch($predictions.shape.slice(0,$predictions.shape.length-1),$targets.shape,"predictions's shape should be align with the targets' shape, except the last dimension.");let lastDim=$predictions.shape[$predictions.shape.length-1];assert(k>0&&k<=lastDim,()=>`'k' passed to inTopK() must be > 0 && <= the predictions last dimension (${lastDim}), but got ${k}`);let predictionsVals=await $predictions.data(),targetsVals=await $targets.data(),[batch,size]=[predictionsVals.length/lastDim,lastDim],precision3=getTypedArrayFromDType("bool",batch);for(let b=0;bb2.value-a.value),precision3[b]=0;for(let i=0;iconv2d2,depthwiseConv2d:()=>depthwiseConv2d2,matMul:()=>matMul2});function conv2DBackpropFilter_(x,dy,filterShape,strides,pad3,dataFormat="NHWC",dimRoundingMode){let x4D=x;x.rank===3&&(x4D=reshape(x,[1,x.shape[0],x.shape[1],x.shape[2]]));let dy4D=dy;dy4D.rank===3&&(dy4D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2]])),assert(x4D.rank===4,()=>`Error in conv2dDerFilter: input must be rank 4, but got shape ${x4D.shape}.`),assert(dy4D.rank===4,()=>`Error in conv2dDerFilter: dy must be rank 4, but got shape ${dy4D.shape}.`),assert(filterShape.length===4,()=>`Error in conv2dDerFilter: filterShape must be length 4, but got ${filterShape}.`);let inDepth=dataFormat==="NHWC"?x4D.shape[3]:x4D.shape[1],outDepth=dataFormat==="NHWC"?dy4D.shape[3]:dy4D.shape[1];assert(inDepth===filterShape[2],()=>`Error in conv2dDerFilter: depth of input ${inDepth}) must match input depth in filter (${filterShape[2]}.`),assert(outDepth===filterShape[3],()=>`Error in conv2dDerFilter: depth of dy (${outDepth}) must match output depth for filter (${filterShape[3]}).`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in conv2dDerFilter: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={x:x4D,dy:dy4D},attrs={strides,pad:pad3,dataFormat,dimRoundingMode,filterShape};return ENGINE.runKernel(Conv2DBackpropFilter,inputs,attrs)}var conv2DBackpropFilter=op({conv2DBackpropFilter_});function getFusedDyActivation(dy,y,activation2){if(activation2==null||activation2==="linear")return dy;if(activation2==="relu")return mul(dy,step(y));throw new Error(`Cannot compute gradient for fused activation ${activation2}.`)}function getFusedBiasGradient(bias,dyActivation){let res=dyActivation,reduceAxes=getReductionAxes(bias.shape,dyActivation.shape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,bias.shape)}function applyActivation(x,activation2,preluActivationWeights,leakyreluAlpha){if(activation2==="linear")return x;if(activation2==="relu")return relu(x);if(activation2==="elu")return elu(x);if(activation2==="relu6")return relu6(x);if(activation2==="prelu")return prelu(x,preluActivationWeights);if(activation2==="leakyrelu")return leakyRelu(x,leakyreluAlpha);throw new Error(`Unknown fused activation ${activation2}.`)}var shouldFuse=(gradientDepth,activation2)=>!(gradientDepth>0)||activation2==="linear";function fusedConv2d_({x,filter,strides,pad:pad3,dataFormat="NHWC",dilations=[1,1],dimRoundingMode,bias,activation:activation2="linear",preluActivationWeights,leakyreluAlpha}){if(activation2=activation2||"linear",shouldFuse(ENGINE.state.gradientDepth,activation2)===!1){let result=conv2d(x,filter,strides,pad3,dataFormat,dilations,dimRoundingMode);return bias!=null&&(result=add2(result,bias)),applyActivation(result,activation2,preluActivationWeights,leakyreluAlpha)}let $x=convertToTensor(x,"x","conv2d"),$filter=convertToTensor(filter,"filter","conv2d"),x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in fused conv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert($filter.rank===4,()=>`Error in fused conv2d: filter must be rank 4, but got rank ${$filter.rank}.`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in fused conv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`),assert(x4D.shape[3]===$filter.shape[2],()=>`Error in conv2d: depth of input (${x4D.shape[3]}) must match input depth for filter ${$filter.shape[2]}.`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),assert(dataFormat==="NHWC",()=>`Error in conv2d: got dataFormat of ${dataFormat} but only NHWC is currently supported.`);let convInfo=computeConv2DInfo(x4D.shape,$filter.shape,strides,dilations,pad3,dimRoundingMode),$bias;bias!=null&&($bias=convertToTensor(bias,"bias","fused conv2d"),[$bias]=makeTypesMatch($bias,$x),assertAndGetBroadcastShape(convInfo.outShape,$bias.shape));let $preluActivationWeights;preluActivationWeights!=null&&($preluActivationWeights=convertToTensor(preluActivationWeights,"prelu weights","fused conv2d"));let grad2=(dy,saved)=>{let[$filter2,x4D2,y,$bias2]=saved,dyActivation=getFusedDyActivation(dy,y,activation2);assert(tupleValuesAreOne(dilations),()=>`Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`);let xDer=conv2DBackpropInput(x4D2.shape,dyActivation,$filter2,strides,pad3),filterDer=conv2DBackpropFilter(x4D2,dyActivation,$filter2.shape,strides,pad3),der=[xDer,filterDer];if($bias2!=null){let biasDer=getFusedBiasGradient($bias2,dyActivation);der.push(biasDer)}return der},inputs={x:x4D,filter:$filter,bias:$bias,preluActivationWeights:$preluActivationWeights},attrs={strides,pad:pad3,dataFormat,dilations,dimRoundingMode,activation:activation2,leakyreluAlpha};return bias==null?customGrad((x4D2,filter2,save)=>{let res=ENGINE.runKernel(FusedConv2D,inputs,attrs);return save([filter2,x4D2,res]),reshapedTo4D&&(res=reshape(res,[res.shape[1],res.shape[2],res.shape[3]])),{value:res,gradFunc:grad2}})(x4D,$filter):customGrad((x4D2,filter2,bias2,save)=>{let res=ENGINE.runKernel(FusedConv2D,inputs,attrs);return save([filter2,x4D2,res,bias2]),reshapedTo4D&&(res=reshape(res,[res.shape[1],res.shape[2],res.shape[3]])),{value:res,gradFunc:grad2}})(x4D,$filter,$bias)}var conv2d2=op({fusedConv2d_});function depthwiseConv2dNativeBackpropFilter_(x,dy,filterShape,strides,pad3,dilations=[1,1],dimRoundingMode){let x4D=x;x.rank===3&&(x4D=reshape(x,[1,x.shape[0],x.shape[1],x.shape[2]]));let dy4D=dy;dy4D.rank===3&&(dy4D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2]]));let inputs={x:x4D,dy:dy4D},attrs={strides,pad:pad3,dimRoundingMode,dilations,filterShape};return ENGINE.runKernel(DepthwiseConv2dNativeBackpropFilter,inputs,attrs)}var depthwiseConv2dNativeBackpropFilter=op({depthwiseConv2dNativeBackpropFilter_});function depthwiseConv2dNativeBackpropInput_(xShape,dy,filter,strides,pad3,dilations=[1,1],dimRoundingMode){let dy4D=dy,reshapedTo4D=!1;dy.rank===3&&(reshapedTo4D=!0,dy4D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2]]));let inputs={dy:dy4D,filter},attrs={strides,pad:pad3,dimRoundingMode,dilations,inputShape:xShape},res=ENGINE.runKernel(DepthwiseConv2dNativeBackpropInput,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var depthwiseConv2dNativeBackpropInput=op({depthwiseConv2dNativeBackpropInput_});function fusedDepthwiseConv2d_({x,filter,strides,pad:pad3,dataFormat="NHWC",dilations=[1,1],dimRoundingMode,bias,activation:activation2="linear",preluActivationWeights,leakyreluAlpha}){if(shouldFuse(ENGINE.state.gradientDepth,activation2)===!1){let result=depthwiseConv2d(x,filter,strides,pad3,dataFormat,dilations,dimRoundingMode);return bias!=null&&(result=add2(result,bias)),applyActivation(result,activation2,preluActivationWeights,leakyreluAlpha)}let $x=convertToTensor(x,"x","depthwiseConv2d"),$filter=convertToTensor(filter,"filter","depthwiseConv2d"),x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in fused depthwiseConv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert($filter.rank===4,()=>`Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${$filter.rank}.`),assert(x4D.shape[3]===$filter.shape[2],()=>`Error in fused depthwiseConv2d: number of input channels (${x4D.shape[3]}) must match the inChannels dimension in filter ${$filter.shape[2]}.`),dilations==null&&(dilations=[1,1]),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in fused depthwiseConv2d: pad must be an integer when using dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let convInfo=computeConv2DInfo(x4D.shape,$filter.shape,strides,dilations,pad3,dimRoundingMode,!0),$bias;bias!=null&&($bias=convertToTensor(bias,"bias","fused conv2d"),[$bias]=makeTypesMatch($bias,$x),assertAndGetBroadcastShape(convInfo.outShape,$bias.shape));let $preluActivationWeights;preluActivationWeights!=null&&($preluActivationWeights=convertToTensor(preluActivationWeights,"prelu weights","fused depthwiseConv2d"));let grad2=(dy,saved)=>{assert(tupleValuesAreOne(dilations),()=>`Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${dilations}'`);let[$filter2,x4D2,y,bias2]=saved,dyActivation=getFusedDyActivation(dy,y,activation2),xDer=depthwiseConv2dNativeBackpropInput(x4D2.shape,dyActivation,$filter2,strides,pad3,dilations,dimRoundingMode),filterDer=depthwiseConv2dNativeBackpropFilter(x4D2,dyActivation,$filter2.shape,strides,pad3,dilations,dimRoundingMode);if(bias2!=null){let biasDer=getFusedBiasGradient($bias,dyActivation);return[xDer,filterDer,biasDer]}return[xDer,filterDer]},inputs={x:x4D,filter:$filter,bias:$bias,preluActivationWeights:$preluActivationWeights},attrs={strides,pad:pad3,dataFormat,dilations,dimRoundingMode,activation:activation2,leakyreluAlpha};return bias==null?customGrad((x4D2,filter2,save)=>{let res=ENGINE.runKernel(FusedDepthwiseConv2D,inputs,attrs);return save([filter2,x4D2,res]),reshapedTo4D&&(res=reshape(res,[res.shape[1],res.shape[2],res.shape[3]])),{value:res,gradFunc:grad2}})(x4D,$filter):customGrad((x4D2,filter2,bias2,save)=>{let res=ENGINE.runKernel(FusedDepthwiseConv2D,inputs,attrs);return save([filter2,x4D2,res,bias2]),reshapedTo4D&&(res=reshape(res,[res.shape[1],res.shape[2],res.shape[3]])),{value:res,gradFunc:grad2}})(x4D,$filter,$bias)}var depthwiseConv2d2=op({fusedDepthwiseConv2d_});function fusedMatMul_({a,b,transposeA=!1,transposeB=!1,bias,activation:activation2="linear",preluActivationWeights,leakyreluAlpha}){if(shouldFuse(ENGINE.state.gradientDepth,activation2)===!1){let result=matMul(a,b,transposeA,transposeB);return bias!=null&&(result=add2(result,bias)),applyActivation(result,activation2,preluActivationWeights,leakyreluAlpha)}let $a=convertToTensor(a,"a","fused matMul"),$b=convertToTensor(b,"b","fused matMul");[$a,$b]=makeTypesMatch($a,$b);let innerShapeA=transposeA?$a.shape[$a.rank-2]:$a.shape[$a.rank-1],innerShapeB=transposeB?$b.shape[$b.rank-1]:$b.shape[$b.rank-2],outerShapeA=transposeA?$a.shape[$a.rank-1]:$a.shape[$a.rank-2],outerShapeB=transposeB?$b.shape[$b.rank-2]:$b.shape[$b.rank-1],outerDimsA=$a.shape.slice(0,-2),outerDimsB=$b.shape.slice(0,-2),batchDimA=sizeFromShape(outerDimsA),batchDimB=sizeFromShape(outerDimsB);assert($a.rank>=2&&$b.rank>=2&&$a.rank===$b.rank,()=>`Error in fused matMul: inputs must have the same rank of at least 2, got ranks ${$a.rank} and ${$b.rank}.`),assert(arraysEqual(outerDimsA,outerDimsB),()=>`Error in fused matMul: outer dimensions (${outerDimsA}) and (${outerDimsB}) of Tensors with shapes ${$a.shape} and ${$b.shape} must match.`),assert(innerShapeA===innerShapeB,()=>`Error in fused matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${$a.shape} and ${$b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);let outShape=$a.shape.slice(0,-2).concat([outerShapeA,outerShapeB]),a3D=transposeA?reshape($a,[batchDimA,innerShapeA,outerShapeA]):reshape($a,[batchDimA,outerShapeA,innerShapeA]),b3D=transposeB?reshape($b,[batchDimB,outerShapeB,innerShapeB]):reshape($b,[batchDimB,innerShapeB,outerShapeB]),$bias;bias!=null&&($bias=convertToTensor(bias,"bias","fused matMul"),[$bias]=makeTypesMatch($bias,$a),assertAndGetBroadcastShape(outShape,$bias.shape));let $preluActivationWeights;preluActivationWeights!=null&&($preluActivationWeights=convertToTensor(preluActivationWeights,"prelu weights","fused matMul"));let grad2=(dy,saved)=>{let[a3D2,b3D2,y,$bias2]=saved,dyActivation=getFusedDyActivation(reshape(dy,y.shape),y,activation2),aDer,bDer;if(!transposeA&&!transposeB?(aDer=matMul(dyActivation,b3D2,!1,!0),bDer=matMul(a3D2,dyActivation,!0,!1)):!transposeA&&transposeB?(aDer=matMul(dyActivation,b3D2,!1,!1),bDer=matMul(dyActivation,a3D2,!0,!1)):transposeA&&!transposeB?(aDer=matMul(b3D2,dyActivation,!1,!0),bDer=matMul(a3D2,dyActivation,!1,!1)):(aDer=matMul(b3D2,dyActivation,!0,!0),bDer=matMul(dyActivation,a3D2,!0,!0)),bias!=null){let biasDer=getFusedBiasGradient($bias2,dyActivation);return[aDer,bDer,biasDer]}else return[aDer,bDer]},inputs={a:a3D,b:b3D,bias:$bias,preluActivationWeights:$preluActivationWeights},attrs={transposeA,transposeB,activation:activation2,leakyreluAlpha};return bias==null?customGrad((a3D2,b3D2,save)=>{let res=ENGINE.runKernel(_FusedMatMul,inputs,attrs);return save([a3D2,b3D2,res]),{value:reshape(res,outShape),gradFunc:grad2}})(a3D,b3D):customGrad((a3D2,b3D2,$bias2,save)=>{let res=ENGINE.runKernel(_FusedMatMul,inputs,attrs);return save([a3D2,b3D2,res,$bias2]),{value:reshape(res,outShape),gradFunc:grad2}})(a3D,b3D,$bias)}var matMul2=op({fusedMatMul_});function hammingWindow_(windowLength){return cosineWindow(windowLength,.54,.46)}var hammingWindow=op({hammingWindow_});function hannWindow_(windowLength){return cosineWindow(windowLength,.5,.5)}var hannWindow=op({hannWindow_});function frame_(signal2,frameLength,frameStep,padEnd=!1,padValue=0){let start=0,output=[];for(;start+frameLength<=signal2.size;)output.push(slice(signal2,start,frameLength)),start+=frameStep;if(padEnd)for(;start`Error in cropAndResize: image must be rank 4,but got rank ${$image.rank}.`),assert($boxes.rank===2&&$boxes.shape[1]===4,()=>`Error in cropAndResize: boxes must be have size [${numBoxes},4] but had shape ${$boxes.shape}.`),assert($boxInd.rank===1&&$boxInd.shape[0]===numBoxes,()=>`Error in cropAndResize: boxInd must be have size [${numBoxes}] but had shape ${$boxes.shape}.`),assert(cropSize.length===2,()=>`Error in cropAndResize: cropSize must be of length 2, but got length ${cropSize.length}.`),assert(cropSize[0]>=1&&cropSize[1]>=1,()=>`cropSize must be atleast [1,1], but was ${cropSize}`),assert(method==="bilinear"||method==="nearest",()=>`method must be bilinear or nearest, but was ${method}`);let inputs={image:$image,boxes:$boxes,boxInd:$boxInd},attrs={method,extrapolationValue,cropSize};return ENGINE.runKernel(CropAndResize,inputs,attrs)}var cropAndResize=op({cropAndResize_});function flipLeftRight_(image3){let $image=convertToTensor(image3,"image","flipLeftRight","float32");assert($image.rank===4,()=>`Error in flipLeftRight: image must be rank 4,but got rank ${$image.rank}.`);let inputs={image:$image};return ENGINE.runKernel(FlipLeftRight,inputs,{})}var flipLeftRight=op({flipLeftRight_});function rotateWithOffset_(image3,radians,fillValue=0,center=.5){let $image=convertToTensor(image3,"image","rotateWithOffset","float32");assert($image.rank===4,()=>`Error in rotateWithOffset: image must be rank 4,but got rank ${$image.rank}.`);let inputs={image:$image},attrs={radians,fillValue,center};return ENGINE.runKernel(RotateWithOffset,inputs,attrs)}var rotateWithOffset=op({rotateWithOffset_});function nonMaxSuppSanityCheck(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma){iouThreshold==null&&(iouThreshold=.5),scoreThreshold==null&&(scoreThreshold=Number.NEGATIVE_INFINITY),softNmsSigma==null&&(softNmsSigma=0);let numBoxes=boxes.shape[0];return maxOutputSize=Math.min(maxOutputSize,numBoxes),assert(0<=iouThreshold&&iouThreshold<=1,()=>`iouThreshold must be in [0, 1], but was '${iouThreshold}'`),assert(boxes.rank===2,()=>`boxes must be a 2D tensor, but was of rank '${boxes.rank}'`),assert(boxes.shape[1]===4,()=>`boxes must have 4 columns, but 2nd dimension was ${boxes.shape[1]}`),assert(scores.rank===1,()=>"scores must be a 1D tensor"),assert(scores.shape[0]===numBoxes,()=>`scores has incompatible shape with boxes. Expected ${numBoxes}, but was ${scores.shape[0]}`),assert(0<=softNmsSigma&&softNmsSigma<=1,()=>`softNmsSigma must be in [0, 1], but was '${softNmsSigma}'`),{maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma}}function nonMaxSuppression_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppression"),$scores=convertToTensor(scores,"scores","nonMaxSuppression"),inputs=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold);maxOutputSize=inputs.maxOutputSize,iouThreshold=inputs.iouThreshold,scoreThreshold=inputs.scoreThreshold;let attrs={maxOutputSize,iouThreshold,scoreThreshold};return ENGINE.runKernel(NonMaxSuppressionV3,{boxes:$boxes,scores:$scores},attrs)}var nonMaxSuppression=op({nonMaxSuppression_});function binaryInsert(arr,element,comparator){let index=binarySearch(arr,element,comparator),insertionPoint=index<0?-(index+1):index;arr.splice(insertionPoint,0,element)}function binarySearch(arr,target,comparator){return binarySearch_(arr,target,comparator||defaultComparator)}function defaultComparator(a,b){return a>b?1:a>>1);let compareResult=comparator(target,arr[middle]);compareResult>0?left=middle+1:(right=middle,found=!compareResult)}return found?left:-left-1}function nonMaxSuppressionV3Impl(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold){return nonMaxSuppressionImpl_(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,0)}function nonMaxSuppressionV4Impl(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize){return nonMaxSuppressionImpl_(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,0,!1,padToMaxOutputSize,!0)}function nonMaxSuppressionV5Impl(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma){return nonMaxSuppressionImpl_(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma,!0)}function nonMaxSuppressionImpl_(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma,returnScoresTensor=!1,padToMaxOutputSize=!1,returnValidOutputs=!1){let candidates=[];for(let i=0;iscoreThreshold&&candidates.push({score:scores[i],boxIndex:i,suppressBeginIndex:0});candidates.sort(ascendingComparator);let scale2=softNmsSigma>0?-.5/softNmsSigma:0,selectedIndices=[],selectedScores=[];for(;selectedIndices.length0;){let candidate=candidates.pop(),{score:originalScore,boxIndex,suppressBeginIndex}=candidate;if(originalScore=suppressBeginIndex;--j){let iou=intersectionOverUnion(boxes,boxIndex,selectedIndices[j]);if(iou>=iouThreshold){ignoreCandidate=!0;break}if(candidate.score=candidate.score*suppressWeight(iouThreshold,scale2,iou),candidate.score<=scoreThreshold)break}candidate.suppressBeginIndex=selectedIndices.length,ignoreCandidate||(candidate.score===originalScore?(selectedIndices.push(boxIndex),selectedScores.push(candidate.score)):candidate.score>scoreThreshold&&binaryInsert(candidates,candidate,ascendingComparator))}let validOutputs=selectedIndices.length,elemsToPad=maxOutputSize-validOutputs;padToMaxOutputSize&&elemsToPad>0&&(selectedIndices.push(...new Array(elemsToPad).fill(0)),selectedScores.push(...new Array(elemsToPad).fill(0)));let result={selectedIndices};return returnScoresTensor&&(result.selectedScores=selectedScores),returnValidOutputs&&(result.validOutputs=validOutputs),result}function intersectionOverUnion(boxes,i,j){let iCoord=boxes.subarray(i*4,i*4+4),jCoord=boxes.subarray(j*4,j*4+4),yminI=Math.min(iCoord[0],iCoord[2]),xminI=Math.min(iCoord[1],iCoord[3]),ymaxI=Math.max(iCoord[0],iCoord[2]),xmaxI=Math.max(iCoord[1],iCoord[3]),yminJ=Math.min(jCoord[0],jCoord[2]),xminJ=Math.min(jCoord[1],jCoord[3]),ymaxJ=Math.max(jCoord[0],jCoord[2]),xmaxJ=Math.max(jCoord[1],jCoord[3]),areaI=(ymaxI-yminI)*(xmaxI-xminI),areaJ=(ymaxJ-yminJ)*(xmaxJ-xminJ);if(areaI<=0||areaJ<=0)return 0;let intersectionYmin=Math.max(yminI,yminJ),intersectionXmin=Math.max(xminI,xminJ),intersectionYmax=Math.min(ymaxI,ymaxJ),intersectionXmax=Math.min(xmaxI,xmaxJ),intersectionArea=Math.max(intersectionYmax-intersectionYmin,0)*Math.max(intersectionXmax-intersectionXmin,0);return intersectionArea/(areaI+areaJ-intersectionArea)}function suppressWeight(iouThreshold,scale2,iou){let weight=Math.exp(scale2*iou*iou);return iou<=iouThreshold?weight:0}function ascendingComparator(c1,c2){return c1.score-c2.score||c1.score===c2.score&&c2.boxIndex-c1.boxIndex}async function nonMaxSuppressionAsync_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppressionAsync"),$scores=convertToTensor(scores,"scores","nonMaxSuppressionAsync"),inputs=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold);maxOutputSize=inputs.maxOutputSize,iouThreshold=inputs.iouThreshold,scoreThreshold=inputs.scoreThreshold;let boxesAndScores=await Promise.all([$boxes.data(),$scores.data()]),boxesVals=boxesAndScores[0],scoresVals=boxesAndScores[1],{selectedIndices}=nonMaxSuppressionV3Impl(boxesVals,scoresVals,maxOutputSize,iouThreshold,scoreThreshold);return $boxes!==boxes&&$boxes.dispose(),$scores!==scores&&$scores.dispose(),tensor1d(selectedIndices,"int32")}var nonMaxSuppressionAsync=nonMaxSuppressionAsync_;function nonMaxSuppressionWithScore_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY,softNmsSigma=0){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppression"),$scores=convertToTensor(scores,"scores","nonMaxSuppression"),params=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma);maxOutputSize=params.maxOutputSize,iouThreshold=params.iouThreshold,scoreThreshold=params.scoreThreshold,softNmsSigma=params.softNmsSigma;let inputs={boxes:$boxes,scores:$scores},attrs={maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma},result=ENGINE.runKernel(NonMaxSuppressionV5,inputs,attrs);return{selectedIndices:result[0],selectedScores:result[1]}}var nonMaxSuppressionWithScore=op({nonMaxSuppressionWithScore_});async function nonMaxSuppressionWithScoreAsync_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY,softNmsSigma=0){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppressionAsync"),$scores=convertToTensor(scores,"scores","nonMaxSuppressionAsync"),params=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma);maxOutputSize=params.maxOutputSize,iouThreshold=params.iouThreshold,scoreThreshold=params.scoreThreshold,softNmsSigma=params.softNmsSigma;let boxesAndScores=await Promise.all([$boxes.data(),$scores.data()]),boxesVals=boxesAndScores[0],scoresVals=boxesAndScores[1],{selectedIndices,selectedScores}=nonMaxSuppressionV5Impl(boxesVals,scoresVals,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma);return $boxes!==boxes&&$boxes.dispose(),$scores!==scores&&$scores.dispose(),{selectedIndices:tensor1d(selectedIndices,"int32"),selectedScores:tensor1d(selectedScores)}}var nonMaxSuppressionWithScoreAsync=nonMaxSuppressionWithScoreAsync_;function nonMaxSuppressionPadded_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY,padToMaxOutputSize=!1){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppression"),$scores=convertToTensor(scores,"scores","nonMaxSuppression"),params=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold,null),$maxOutputSize=params.maxOutputSize,$iouThreshold=params.iouThreshold,$scoreThreshold=params.scoreThreshold,inputs={boxes:$boxes,scores:$scores},attrs={maxOutputSize:$maxOutputSize,iouThreshold:$iouThreshold,scoreThreshold:$scoreThreshold,padToMaxOutputSize},result=ENGINE.runKernel(NonMaxSuppressionV4,inputs,attrs);return{selectedIndices:result[0],validOutputs:result[1]}}var nonMaxSuppressionPadded=op({nonMaxSuppressionPadded_});async function nonMaxSuppressionPaddedAsync_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY,padToMaxOutputSize=!1){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppressionAsync"),$scores=convertToTensor(scores,"scores","nonMaxSuppressionAsync"),params=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold,null),$maxOutputSize=params.maxOutputSize,$iouThreshold=params.iouThreshold,$scoreThreshold=params.scoreThreshold,[boxesVals,scoresVals]=await Promise.all([$boxes.data(),$scores.data()]),{selectedIndices,validOutputs}=nonMaxSuppressionV4Impl(boxesVals,scoresVals,$maxOutputSize,$iouThreshold,$scoreThreshold,padToMaxOutputSize);return $boxes!==boxes&&$boxes.dispose(),$scores!==scores&&$scores.dispose(),{selectedIndices:tensor1d(selectedIndices,"int32"),validOutputs:scalar(validOutputs,"int32")}}var nonMaxSuppressionPaddedAsync=nonMaxSuppressionPaddedAsync_;function resizeBilinear_(images,size,alignCorners=!1,halfPixelCenters=!1){let $images=convertToTensor(images,"images","resizeBilinear");assert($images.rank===3||$images.rank===4,()=>`Error in resizeBilinear: x must be rank 3 or 4, but got rank ${$images.rank}.`),assert(size.length===2,()=>`Error in resizeBilinear: new shape must 2D, but got shape ${size}.`),assert(halfPixelCenters===!1||alignCorners===!1,()=>"Error in resizeBilinear: If halfPixelCenters is true, alignCorners must be false.");let batchImages=$images,reshapedTo4D=!1;$images.rank===3&&(reshapedTo4D=!0,batchImages=reshape($images,[1,$images.shape[0],$images.shape[1],$images.shape[2]]));let[]=size,inputs={images:batchImages},attrs={alignCorners,halfPixelCenters,size},res=ENGINE.runKernel(ResizeBilinear,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var resizeBilinear=op({resizeBilinear_});function resizeNearestNeighbor_(images,size,alignCorners=!1,halfPixelCenters=!1){let $images=convertToTensor(images,"images","resizeNearestNeighbor");assert($images.rank===3||$images.rank===4,()=>`Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${$images.rank}.`),assert(size.length===2,()=>`Error in resizeNearestNeighbor: new shape must 2D, but got shape ${size}.`),assert($images.dtype==="float32"||$images.dtype==="int32",()=>"`images` must have `int32` or `float32` as dtype"),assert(halfPixelCenters===!1||alignCorners===!1,()=>"Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false.");let batchImages=$images,reshapedTo4D=!1;$images.rank===3&&(reshapedTo4D=!0,batchImages=reshape($images,[1,$images.shape[0],$images.shape[1],$images.shape[2]]));let[]=size,inputs={images:batchImages},attrs={alignCorners,halfPixelCenters,size},res=ENGINE.runKernel(ResizeNearestNeighbor,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var resizeNearestNeighbor=op({resizeNearestNeighbor_});function bandPart_(a,numLower,numUpper){assert(numLower%1==0,()=>`bandPart(): numLower must be an integer, got ${numLower}.`),assert(numUpper%1==0,()=>`bandPart(): numUpper must be an integer, got ${numUpper}.`);let $a=convertToTensor(a,"a","bandPart");assert($a.rank>=2,()=>`bandPart(): Rank must be at least 2, got ${$a.rank}.`);let shape=$a.shape,[M,N]=$a.shape.slice(-2);if(!(numLower<=M))throw new Error(`bandPart(): numLower (${numLower}) must not be greater than the number of rows (${M}).`);if(!(numUpper<=N))throw new Error(`bandPart(): numUpper (${numUpper}) must not be greater than the number of columns (${N}).`);numLower<0&&(numLower=M),numUpper<0&&(numUpper=N);let i=reshape(range(0,M,1,"int32"),[-1,1]),j=range(0,N,1,"int32"),ij=sub(i,j),inBand=logicalAnd(lessEqual(ij,scalar(+numLower,"int32")),greaterEqual(ij,scalar(-numUpper,"int32"))),zero=zeros([M,N],$a.dtype);return reshape(stack(unstack(reshape($a,[-1,M,N])).map(mat=>where(inBand,mat,zero))),shape)}var bandPart=op({bandPart_});function gramSchmidt_(xs){let inputIsTensor2D;if(Array.isArray(xs)){inputIsTensor2D=!1,assert(xs!=null&&xs.length>0,()=>"Gram-Schmidt process: input must not be null, undefined, or empty");let dim=xs[0].shape[0];for(let i=1;i`Gram-Schmidt: Non-unique lengths found in the input vectors: (${xs[i].shape[0]} vs. ${dim})`)}else inputIsTensor2D=!0,xs=split(xs,xs.shape[0],0).map(x=>squeeze(x,[0]));assert(xs.length<=xs[0].shape[0],()=>`Gram-Schmidt: Number of vectors (${xs.length}) exceeds number of dimensions (${xs[0].shape[0]}).`);let ys=[],xs1d=xs;for(let i=0;i{let x=xs1d[i];if(i>0)for(let j=0;j=2,()=>`qr() requires input tensor to have a rank >= 2, but got rank ${x.rank}`),x.rank===2)return qr2d(x,fullMatrices);{let outerDimsProd=x.shape.slice(0,x.shape.length-2).reduce((value,prev)=>value*prev),x2ds=unstack(reshape(x,[outerDimsProd,x.shape[x.shape.length-2],x.shape[x.shape.length-1]]),0),q2ds=[],r2ds=[];x2ds.forEach(x2d=>{let[q2d,r2d]=qr2d(x2d,fullMatrices);q2ds.push(q2d),r2ds.push(r2d)});let q=reshape(stack(q2ds,0),x.shape),r=reshape(stack(r2ds,0),x.shape);return[q,r]}}function qr2d(x,fullMatrices=!1){return ENGINE.tidy(()=>{assert(x.shape.length===2,()=>`qr2d() requires a 2D Tensor, but got a ${x.shape.length}D Tensor.`);let m=x.shape[0],n=x.shape[1],q=eye(m),r=clone(x),one2D=tensor2d([[1]],[1,1]),w=clone(one2D),iters=m>=n?n:m;for(let j=0;j{let rjEnd1=slice(r,[j,j],[m-j,1]),normX=norm(rjEnd1),rjj=slice(r,[j,j],[1,1]),s=where(greater(rjj,0),tensor2d([[-1]]),tensor2d([[1]])),u1=sub(rjj,mul(s,normX)),wPre=div(rjEnd1,u1);wPre.shape[0]===1?w=clone(one2D):w=concat([one2D,slice(wPre,[1,0],[wPre.shape[0]-1,wPre.shape[1]])],0);let tau=neg(div(matMul(s,u1),normX)),rjEndAll=slice(r,[j,0],[m-j,n]),tauTimesW=mul(tau,w),wT=transpose(w);if(j===0)r=sub(rjEndAll,matMul(tauTimesW,matMul(wT,rjEndAll)));else{let rTimesTau=sub(rjEndAll,matMul(tauTimesW,matMul(wT,rjEndAll)));r=concat([slice(r,[0,0],[j,n]),rTimesTau],0)}let tawTimesWT=transpose(tauTimesW),qAllJEnd=slice(q,[0,j],[m,q.shape[1]-j]);if(j===0)q=sub(qAllJEnd,matMul(matMul(qAllJEnd,w),tawTimesWT));else{let qTimesTau=sub(qAllJEnd,matMul(matMul(qAllJEnd,w),tawTimesWT));q=concat([slice(q,[0,0],[m,j]),qTimesTau],1)}return[w,r,q]}),dispose([rTemp,wTemp,qTemp])}return!fullMatrices&&m>n&&(q=slice(q,[0,0],[m,n]),r=slice(r,[0,0],[n,n])),[q,r]})}var qr=op({qr_}),Reduction;(function(Reduction2){Reduction2[Reduction2.NONE=0]="NONE",Reduction2[Reduction2.MEAN=1]="MEAN",Reduction2[Reduction2.SUM=2]="SUM",Reduction2[Reduction2.SUM_BY_NONZERO_WEIGHTS=3]="SUM_BY_NONZERO_WEIGHTS"})(Reduction||(Reduction={}));function computeWeightedLoss_(losses4,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $losses=convertToTensor(losses4,"losses","computeWeightedLoss"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","computeWeightedLoss"));let weightedLoss=$weights==null?$losses:mul($losses,$weights);if(reduction2===Reduction.NONE)return weightedLoss;if(reduction2===Reduction.SUM)return sum2(weightedLoss);if(reduction2===Reduction.MEAN){if($weights==null)return mean(weightedLoss);{let broadcastFactor=$losses.size/$weights.size,result=div(sum2(weightedLoss),sum2($weights));return broadcastFactor>1?div(result,scalar(broadcastFactor)):result}}if(reduction2===Reduction.SUM_BY_NONZERO_WEIGHTS){if($weights==null)return div(sum2(weightedLoss),scalar($losses.size));{let broadcastedWeights=mul($weights,ones2($losses.shape)),numNonZeros=cast(sum2(notEqual(broadcastedWeights,scalar(0))),"float32");return div(sum2(weightedLoss),numNonZeros)}}throw Error(`Unknown reduction: ${reduction2}`)}var computeWeightedLoss=op({computeWeightedLoss_});function absoluteDifference_(labels,predictions,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","absoluteDifference"),$predictions=convertToTensor(predictions,"predictions","absoluteDifference"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","absoluteDifference")),assertShapesMatch($labels.shape,$predictions.shape,"Error in absoluteDifference: ");let losses4=abs(sub($labels,$predictions));return computeWeightedLoss(losses4,$weights,reduction2)}var absoluteDifference=op({absoluteDifference_});function cosineDistance_(labels,predictions,axis,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","cosineDistance"),$predictions=convertToTensor(predictions,"predictions","cosineDistance"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","cosineDistance")),assertShapesMatch($labels.shape,$predictions.shape,"Error in cosineDistance: ");let one=scalar(1),losses4=sub(one,sum2(mul($labels,$predictions),axis,!0));return computeWeightedLoss(losses4,$weights,reduction2)}var cosineDistance=op({cosineDistance_});function hingeLoss_(labels,predictions,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","hingeLoss"),$predictions=convertToTensor(predictions,"predictions","hingeLoss"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","hingeLoss")),assertShapesMatch($labels.shape,$predictions.shape,"Error in hingeLoss: ");let one=scalar(1);$labels=sub(mul(scalar(2),$labels),one);let losses4=relu(sub(one,mul($labels,$predictions)));return computeWeightedLoss(losses4,$weights,reduction2)}var hingeLoss=op({hingeLoss_});function huberLoss_(labels,predictions,weights,delta=1,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","huberLoss"),$predictions=convertToTensor(predictions,"predictions","huberLoss"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","huberLoss")),assertShapesMatch($labels.shape,$predictions.shape,"Error in huberLoss: ");let deltaScalar=scalar(delta),error=abs(sub($predictions,$labels)),quadratic=minimum(error,deltaScalar),linear=sub(error,quadratic),losses4=add2(mul(scalar(.5),square(quadratic)),mul(deltaScalar,linear));return computeWeightedLoss(losses4,$weights,reduction2)}var huberLoss=op({huberLoss_});function logLoss_(labels,predictions,weights,epsilon3=1e-7,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","logLoss"),$predictions=convertToTensor(predictions,"predictions","logLoss"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","logLoss")),assertShapesMatch($labels.shape,$predictions.shape,"Error in logLoss: ");let one=scalar(1),epsilonScalar=scalar(epsilon3),l13=neg(mul($labels,log2(add2($predictions,epsilonScalar)))),l23=mul(sub(one,$labels),log2(add2(sub(one,$predictions),epsilonScalar))),losses4=sub(l13,l23);return computeWeightedLoss(losses4,$weights,reduction2)}var logLoss=op({logLoss_});function meanSquaredError_(labels,predictions,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","meanSquaredError"),$predictions=convertToTensor(predictions,"predictions","meanSquaredError"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","meanSquaredError")),assertShapesMatch($labels.shape,$predictions.shape,"Error in meanSquaredError: ");let losses4=squaredDifference($labels,$predictions);return computeWeightedLoss(losses4,$weights,reduction2)}var meanSquaredError=op({meanSquaredError_});function sigmoidCrossEntropyWithLogits_(labels,logits){let $labels=convertToTensor(labels,"labels","sigmoidCrossEntropyWithLogits"),$logits=convertToTensor(logits,"logits","sigmoidCrossEntropyWithLogits");assertShapesMatch($labels.shape,$logits.shape,"Error in sigmoidCrossEntropyWithLogits: ");let maxOutput=relu($logits),outputXTarget=mul($logits,$labels),sigmoidOutput=log1p(exp(neg(abs($logits))));return add2(sub(maxOutput,outputXTarget),sigmoidOutput)}function sigmoidCrossEntropy_(multiClassLabels,logits,weights,labelSmoothing=0,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $multiClassLabels=convertToTensor(multiClassLabels,"multiClassLabels","sigmoidCrossEntropy"),$logits=convertToTensor(logits,"logits","sigmoidCrossEntropy"),$weights=null;if(weights!=null&&($weights=convertToTensor(weights,"weights","sigmoidCrossEntropy")),assertShapesMatch($multiClassLabels.shape,$logits.shape,"Error in sigmoidCrossEntropy: "),labelSmoothing>0){let labelSmoothingScalar=scalar(labelSmoothing),one=scalar(1),half=scalar(.5);$multiClassLabels=add2(mul($multiClassLabels,sub(one,labelSmoothingScalar)),mul(half,labelSmoothingScalar))}let losses4=sigmoidCrossEntropyWithLogits_($multiClassLabels,$logits);return computeWeightedLoss(losses4,$weights,reduction2)}var sigmoidCrossEntropy=op({sigmoidCrossEntropy_});function softmaxCrossEntropyWithLogits_(labels,logits,dim=-1){if(dim===-1&&(dim=logits.rank-1),dim!==logits.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${logits.rank} and dim was ${dim}`);return customGrad((labels2,logits2,save)=>{let lse=logSumExp(logits2,[dim],!0),logResult=sub(cast(logits2,"float32"),lse);save([labels2,logResult]);let costVector=neg(mul(logResult,labels2));return{value:sum2(costVector,[dim]),gradFunc:(dy,saved)=>{let[labels3,logResult2]=saved,dyShape=expandShapeToKeepDim(dy.shape,[dim]);return[mul(reshape(dy,dyShape),sub(cast(labels3,"float32"),exp(logResult2))),mul(reshape(dy,dyShape),sub(exp(logResult2),cast(labels3,"float32")))]}}})(labels,logits)}function softmaxCrossEntropy_(onehotLabels,logits,weights,labelSmoothing=0,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $onehotLabels=convertToTensor(onehotLabels,"onehotLabels","softmaxCrossEntropy"),$logits=convertToTensor(logits,"logits","softmaxCrossEntropy"),$weights=null;if(weights!=null&&($weights=convertToTensor(weights,"weights","softmaxCrossEntropy")),assertShapesMatch($onehotLabels.shape,$logits.shape,"Error in softmaxCrossEntropy: "),labelSmoothing>0){let labelSmoothingScalar=scalar(labelSmoothing),one=scalar(1),numClasses=scalar($onehotLabels.shape[1]);$onehotLabels=add2(mul($onehotLabels,sub(one,labelSmoothingScalar)),div(labelSmoothingScalar,numClasses))}let losses4=softmaxCrossEntropyWithLogits_($onehotLabels,$logits);return computeWeightedLoss(losses4,$weights,reduction2)}var softmaxCrossEntropy=op({softmaxCrossEntropy_}),spectral={fft,ifft,rfft,irfft},signal={hammingWindow,hannWindow,frame,stft},image={flipLeftRight,resizeNearestNeighbor,resizeBilinear,rotateWithOffset,cropAndResize,nonMaxSuppression,nonMaxSuppressionAsync,nonMaxSuppressionWithScore,nonMaxSuppressionWithScoreAsync,nonMaxSuppressionPadded,nonMaxSuppressionPaddedAsync},linalg={bandPart,gramSchmidt,qr},losses={absoluteDifference,computeWeightedLoss,cosineDistance,hingeLoss,huberLoss,logLoss,meanSquaredError,sigmoidCrossEntropy,softmaxCrossEntropy},Optimizer=class extends Serializable{minimize(f,returnCost=!1,varList){let{value,grads:grads2}=this.computeGradients(f,varList);if(varList!=null){let gradArray=varList.map(v=>({name:v.name,tensor:grads2[v.name]}));this.applyGradients(gradArray)}else this.applyGradients(grads2);return dispose(grads2),returnCost?value:(value.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(f,varList){return variableGrads(f,varList)}dispose(){this.iterations_!=null&&dispose(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:scalar(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(weightValues){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(weightValues){return this.iterations_=(await weightValues[0].tensor.data())[0],weightValues.slice(1)}};Object.defineProperty(Optimizer,Symbol.hasInstance,{value:instance2=>instance2.minimize!=null&&instance2.computeGradients!=null&&instance2.applyGradients!=null});var AdadeltaOptimizer=class extends Optimizer{constructor(learningRate,rho,epsilon3=null){super();this.learningRate=learningRate,this.rho=rho,this.epsilon=epsilon3,this.accumulatedGrads=[],this.accumulatedUpdates=[],epsilon3==null&&(this.epsilon=ENGINE.backend.epsilon())}applyGradients(variableGradients){(Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients)).forEach((name,i)=>{let value=ENGINE.registeredVariables[name],trainable=!1;this.accumulatedGrads[i]==null&&(this.accumulatedGrads[i]={originalName:`${name}/accum_grad`,variable:tidy(()=>zerosLike(value).variable(trainable))}),this.accumulatedUpdates[i]==null&&(this.accumulatedUpdates[i]={originalName:`${name}/accum_var`,variable:tidy(()=>zerosLike(value).variable(trainable))});let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;let accumulatedGrad=this.accumulatedGrads[i].variable,accumulatedUpdate=this.accumulatedUpdates[i].variable;tidy(()=>{let newAccumulatedGrad=add2(mul(accumulatedGrad,this.rho),mul(square(gradient),1-this.rho)),updates=mul(div(sqrt(add2(accumulatedUpdate,this.epsilon)),sqrt(add2(accumulatedGrad,this.epsilon))),gradient),newAccumulatedUpdate=add2(mul(accumulatedUpdate,this.rho),mul(square(updates),1-this.rho));accumulatedGrad.assign(newAccumulatedGrad),accumulatedUpdate.assign(newAccumulatedUpdate);let newValue=add2(mul(updates,-this.learningRate),value);value.assign(newValue)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(dispose(this.accumulatedGrads.map(v=>v.variable)),dispose(this.accumulatedUpdates.map(v=>v.variable)))}async getWeights(){let variables=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(variables.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues);let variableCount=weightValues.length/2,trainable=!1;this.accumulatedGrads=weightValues.slice(0,variableCount).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})),this.accumulatedUpdates=weightValues.slice(variableCount,variableCount*2).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(cls,config2){return new cls(config2.learningRate,config2.rho,config2.epsilon)}};AdadeltaOptimizer.className="Adadelta";registerClass(AdadeltaOptimizer);var AdagradOptimizer=class extends Optimizer{constructor(learningRate,initialAccumulatorValue=.1){super();this.learningRate=learningRate,this.initialAccumulatorValue=initialAccumulatorValue,this.accumulatedGrads=[]}applyGradients(variableGradients){(Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients)).forEach((name,i)=>{let value=ENGINE.registeredVariables[name];if(this.accumulatedGrads[i]==null){let trainable=!1;this.accumulatedGrads[i]={originalName:`${name}/accumulator`,variable:tidy(()=>fill(value.shape,this.initialAccumulatorValue).variable(trainable))}}let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;let accumulatedGrad=this.accumulatedGrads[i].variable;tidy(()=>{let newAccumulatedGrad=add2(accumulatedGrad,square(gradient));accumulatedGrad.assign(newAccumulatedGrad);let newValue=add2(mul(div(gradient,sqrt(add2(newAccumulatedGrad,ENGINE.backend.epsilon()))),-this.learningRate),value);value.assign(newValue)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&dispose(this.accumulatedGrads.map(v=>v.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues);let trainable=!1;this.accumulatedGrads=weightValues.map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(cls,config2){return new cls(config2.learningRate,config2.initialAccumulatorValue)}};AdagradOptimizer.className="Adagrad";registerClass(AdagradOptimizer);var AdamOptimizer=class extends Optimizer{constructor(learningRate,beta1,beta2,epsilon3=null){super();this.learningRate=learningRate,this.beta1=beta1,this.beta2=beta2,this.epsilon=epsilon3,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],tidy(()=>{this.accBeta1=scalar(beta1).variable(),this.accBeta2=scalar(beta2).variable()}),epsilon3==null&&(this.epsilon=ENGINE.backend.epsilon())}applyGradients(variableGradients){let varNames=Array.isArray(variableGradients)?variableGradients.map(v=>v.name):Object.keys(variableGradients);tidy(()=>{let oneMinusAccBeta1=sub(1,this.accBeta1),oneMinusAccBeta2=sub(1,this.accBeta2);varNames.forEach((name,i)=>{let value=ENGINE.registeredVariables[name],trainable=!1;this.accumulatedFirstMoment[i]==null&&(this.accumulatedFirstMoment[i]={originalName:`${name}/m`,variable:tidy(()=>zerosLike(value).variable(trainable))}),this.accumulatedSecondMoment[i]==null&&(this.accumulatedSecondMoment[i]={originalName:`${name}/v`,variable:tidy(()=>zerosLike(value).variable(trainable))});let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;let firstMoment=this.accumulatedFirstMoment[i].variable,secondMoment=this.accumulatedSecondMoment[i].variable,newFirstMoment=add2(mul(firstMoment,this.beta1),mul(gradient,1-this.beta1)),newSecondMoment=add2(mul(secondMoment,this.beta2),mul(square(gradient),1-this.beta2)),biasCorrectedFirstMoment=div(newFirstMoment,oneMinusAccBeta1),biasCorrectedSecondMoment=div(newSecondMoment,oneMinusAccBeta2);firstMoment.assign(newFirstMoment),secondMoment.assign(newSecondMoment);let newValue=add2(mul(div(biasCorrectedFirstMoment,add2(sqrt(biasCorrectedSecondMoment),this.epsilon)),-this.learningRate),value);value.assign(newValue)}),this.accBeta1.assign(mul(this.accBeta1,this.beta1)),this.accBeta2.assign(mul(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&dispose(this.accumulatedFirstMoment.map(v=>v.variable)),this.accumulatedSecondMoment!=null&&dispose(this.accumulatedSecondMoment.map(v=>v.variable))}async getWeights(){let variables=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(variables.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues),tidy(()=>{this.accBeta1.assign(pow(this.beta1,this.iterations_+1)),this.accBeta2.assign(pow(this.beta2,this.iterations_+1))});let variableCount=weightValues.length/2,trainable=!1;this.accumulatedFirstMoment=weightValues.slice(0,variableCount).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})),this.accumulatedSecondMoment=weightValues.slice(variableCount,variableCount*2).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(cls,config2){return new cls(config2.learningRate,config2.beta1,config2.beta2,config2.epsilon)}};AdamOptimizer.className="Adam";registerClass(AdamOptimizer);var AdamaxOptimizer=class extends Optimizer{constructor(learningRate,beta1,beta2,epsilon3=null,decay=0){super();this.learningRate=learningRate,this.beta1=beta1,this.beta2=beta2,this.epsilon=epsilon3,this.decay=decay,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],tidy(()=>{this.iteration=scalar(0).variable(),this.accBeta1=scalar(beta1).variable()}),epsilon3==null&&(this.epsilon=ENGINE.backend.epsilon())}applyGradients(variableGradients){let variableNames=Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients);tidy(()=>{let oneMinusAccBeta1=sub(1,this.accBeta1),lr=div(-this.learningRate,add2(mul(this.iteration,this.decay),1));variableNames.forEach((name,i)=>{let value=ENGINE.registeredVariables[name],trainable=!1;this.accumulatedFirstMoment[i]==null&&(this.accumulatedFirstMoment[i]={originalName:`${name}/m`,variable:zerosLike(value).variable(trainable)}),this.accumulatedWeightedInfNorm[i]==null&&(this.accumulatedWeightedInfNorm[i]={originalName:`${name}/v`,variable:zerosLike(value).variable(trainable)});let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;let firstMoment=this.accumulatedFirstMoment[i].variable,weightedInfNorm=this.accumulatedWeightedInfNorm[i].variable,newFirstMoment=add2(mul(firstMoment,this.beta1),mul(gradient,1-this.beta1)),ut0=mul(weightedInfNorm,this.beta2),ut1=abs(gradient),newWeightedInfNorm=maximum(ut0,ut1);firstMoment.assign(newFirstMoment),weightedInfNorm.assign(newWeightedInfNorm);let newValue=add2(mul(div(lr,oneMinusAccBeta1),div(newFirstMoment,add2(newWeightedInfNorm,this.epsilon))),value);value.assign(newValue)}),this.iteration.assign(add2(this.iteration,1)),this.accBeta1.assign(mul(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&dispose(this.accumulatedFirstMoment.map(v=>v.variable)),this.accumulatedWeightedInfNorm!=null&&dispose(this.accumulatedWeightedInfNorm.map(v=>v.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(weightValues){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(cls,config2){return new cls(config2.learningRate,config2.beta1,config2.beta2,config2.epsilon,config2.decay)}};AdamaxOptimizer.className="Adamax";registerClass(AdamaxOptimizer);var SGDOptimizer=class extends Optimizer{constructor(learningRate){super();this.learningRate=learningRate,this.setLearningRate(learningRate)}applyGradients(variableGradients){(Array.isArray(variableGradients)?variableGradients.map(v=>v.name):Object.keys(variableGradients)).forEach((name,i)=>{let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;let value=ENGINE.registeredVariables[name];tidy(()=>{let newValue=add2(mul(this.c,gradient),value);value.assign(newValue)})}),this.incrementIterations()}setLearningRate(learningRate){this.learningRate=learningRate,this.c!=null&&this.c.dispose(),this.c=keep(scalar(-learningRate))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(weightValues){if(weightValues=await this.extractIterations(weightValues),weightValues.length!==0)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(cls,config2){return new cls(config2.learningRate)}};SGDOptimizer.className="SGD";registerClass(SGDOptimizer);var MomentumOptimizer=class extends SGDOptimizer{constructor(learningRate,momentum,useNesterov=!1){super(learningRate);this.learningRate=learningRate,this.momentum=momentum,this.useNesterov=useNesterov,this.accumulations=[],this.m=scalar(this.momentum)}applyGradients(variableGradients){(Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients)).forEach((name,i)=>{let value=ENGINE.registeredVariables[name];if(this.accumulations[i]==null){let trainable=!1;this.accumulations[i]={originalName:`${name}/momentum`,variable:tidy(()=>zerosLike(value).variable(trainable))}}let accumulation=this.accumulations[i].variable,gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];gradient!=null&&tidy(()=>{let newValue,newAccumulation=add2(mul(this.m,accumulation),gradient);this.useNesterov?newValue=add2(mul(this.c,add2(gradient,mul(newAccumulation,this.m))),value):newValue=add2(mul(this.c,newAccumulation),value),accumulation.assign(newAccumulation),value.assign(newValue)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&dispose(this.accumulations.map(v=>v.variable))}setMomentum(momentum){this.momentum=momentum}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues);let trainable=!1;this.accumulations=weightValues.map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(cls,config2){return new cls(config2.learningRate,config2.momentum,config2.useNesterov)}};MomentumOptimizer.className="Momentum";registerClass(MomentumOptimizer);var RMSPropOptimizer=class extends Optimizer{constructor(learningRate,decay=.9,momentum=0,epsilon3=null,centered=!1){super();if(this.learningRate=learningRate,this.decay=decay,this.momentum=momentum,this.epsilon=epsilon3,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=centered,epsilon3==null&&(this.epsilon=ENGINE.backend.epsilon()),learningRate==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(variableGradients){(Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients)).forEach((name,i)=>{let value=ENGINE.registeredVariables[name],trainable=!1;this.accumulatedMeanSquares[i]==null&&(this.accumulatedMeanSquares[i]={originalName:`${name}/rms`,variable:tidy(()=>zerosLike(value).variable(trainable))}),this.accumulatedMoments[i]==null&&(this.accumulatedMoments[i]={originalName:`${name}/momentum`,variable:tidy(()=>zerosLike(value).variable(trainable))}),this.accumulatedMeanGrads[i]==null&&this.centered&&(this.accumulatedMeanGrads[i]={originalName:`${name}/mg`,variable:tidy(()=>zerosLike(value).variable(trainable))});let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;let accumulatedMeanSquare=this.accumulatedMeanSquares[i].variable,accumulatedMoments=this.accumulatedMoments[i].variable;tidy(()=>{let newAccumulatedMeanSquare=add2(mul(accumulatedMeanSquare,this.decay),mul(square(gradient),1-this.decay));if(this.centered){let accumulatedMeanGrad=this.accumulatedMeanGrads[i].variable,newAccumulatedMeanGrad=add2(mul(accumulatedMeanGrad,this.decay),mul(gradient,1-this.decay)),gradContribution=div(mul(gradient,this.learningRate),sqrt(sub(newAccumulatedMeanSquare,add2(square(newAccumulatedMeanGrad),this.epsilon)))),newAccumulatedMoments=add2(mul(accumulatedMoments,this.momentum),gradContribution);accumulatedMeanSquare.assign(newAccumulatedMeanSquare),accumulatedMeanGrad.assign(newAccumulatedMeanGrad),accumulatedMoments.assign(newAccumulatedMoments);let newValue=sub(value,newAccumulatedMoments);value.assign(newValue)}else{let newAccumulatedMeanSquare2=add2(mul(accumulatedMeanSquare,this.decay),mul(square(gradient),1-this.decay)),newAccumulatedMoments=add2(mul(accumulatedMoments,this.momentum),div(mul(gradient,this.learningRate),sqrt(add2(newAccumulatedMeanSquare2,this.epsilon))));accumulatedMeanSquare.assign(newAccumulatedMeanSquare2),accumulatedMoments.assign(newAccumulatedMoments);let newValue=sub(value,newAccumulatedMoments);value.assign(newValue)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&dispose(this.accumulatedMeanSquares.map(v=>v.variable)),this.accumulatedMeanGrads!=null&&this.centered&&dispose(this.accumulatedMeanGrads.map(v=>v.variable)),this.accumulatedMoments!=null&&dispose(this.accumulatedMoments.map(v=>v.variable))}async getWeights(){let variables=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&variables.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(variables.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues);let variableCount=this.centered?weightValues.length/3:weightValues.length/2,trainable=!1;this.accumulatedMeanSquares=weightValues.slice(0,variableCount).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})),this.accumulatedMoments=weightValues.slice(variableCount,variableCount*2).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})),this.centered&&(this.accumulatedMeanGrads=weightValues.slice(variableCount*2,variableCount*3).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(cls,config2){return new cls(config2.learningRate,config2.decay,config2.momentum,config2.epsilon,config2.centered)}};RMSPropOptimizer.className="RMSProp";registerClass(RMSPropOptimizer);var OptimizerConstructors=class{static sgd(learningRate){return new SGDOptimizer(learningRate)}static momentum(learningRate,momentum,useNesterov=!1){return new MomentumOptimizer(learningRate,momentum,useNesterov)}static rmsprop(learningRate,decay=.9,momentum=0,epsilon3=null,centered=!1){return new RMSPropOptimizer(learningRate,decay,momentum,epsilon3,centered)}static adam(learningRate=.001,beta1=.9,beta2=.999,epsilon3=null){return new AdamOptimizer(learningRate,beta1,beta2,epsilon3)}static adadelta(learningRate=.001,rho=.95,epsilon3=null){return new AdadeltaOptimizer(learningRate,rho,epsilon3)}static adamax(learningRate=.002,beta1=.9,beta2=.999,epsilon3=null,decay=0){return new AdamaxOptimizer(learningRate,beta1,beta2,epsilon3,decay)}static adagrad(learningRate,initialAccumulatorValue=.1){return new AdagradOptimizer(learningRate,initialAccumulatorValue)}},train={sgd:OptimizerConstructors.sgd,momentum:OptimizerConstructors.momentum,adadelta:OptimizerConstructors.adadelta,adagrad:OptimizerConstructors.adagrad,rmsprop:OptimizerConstructors.rmsprop,adamax:OptimizerConstructors.adamax,adam:OptimizerConstructors.adam},delayCallback=(()=>typeof requestAnimationFrame!="undefined"?requestAnimationFrame:typeof setImmediate!="undefined"?setImmediate:f=>f())();function nextFrame(){return new Promise(resolve=>delayCallback(()=>resolve()))}var backend_util_exports={};__export2(backend_util_exports,{ERF_A1:()=>ERF_A1,ERF_A2:()=>ERF_A2,ERF_A3:()=>ERF_A3,ERF_A4:()=>ERF_A4,ERF_A5:()=>ERF_A5,ERF_P:()=>ERF_P,PARALLELIZE_THRESHOLD:()=>PARALLELIZE_THRESHOLD,SELU_SCALE:()=>SELU_SCALE,SELU_SCALEALPHA:()=>SELU_SCALEALPHA,applyActivation:()=>applyActivation,assertAndGetBroadcastShape:()=>assertAndGetBroadcastShape,assertAxesAreInnerMostDims:()=>assertAxesAreInnerMostDims,assertParamsConsistent:()=>assertParamsConsistent,assignToTypedArray:()=>assignToTypedArray,axesAreInnerMostDims:()=>axesAreInnerMostDims,calculateShapes:()=>calculateShapes,castTensor:()=>castTensor,combineLocations:()=>combineLocations,complexWithEvenIndex:()=>complexWithEvenIndex,complexWithOddIndex:()=>complexWithOddIndex,computeConv2DInfo:()=>computeConv2DInfo,computeConv3DInfo:()=>computeConv3DInfo,computeDefaultPad:()=>computeDefaultPad,computeDilation2DInfo:()=>computeDilation2DInfo,computeOptimalWindowSize:()=>computeOptimalWindowSize,computeOutAndReduceShapes:()=>computeOutAndReduceShapes,computeOutShape:()=>computeOutShape2,computePool2DInfo:()=>computePool2DInfo,computePool3DInfo:()=>computePool3DInfo,convertConv2DDataFormat:()=>convertConv2DDataFormat,eitherStridesOrDilationsAreOne:()=>eitherStridesOrDilationsAreOne,expandShapeToKeepDim:()=>expandShapeToKeepDim,exponent:()=>exponent,exponents:()=>exponents,fromStringArrayToUint8:()=>fromStringArrayToUint8,fromUint8ToStringArray:()=>fromUint8ToStringArray,getAxesPermutation:()=>getAxesPermutation,getBroadcastDims:()=>getBroadcastDims,getComplexWithIndex:()=>getComplexWithIndex,getFusedBiasGradient:()=>getFusedBiasGradient,getFusedDyActivation:()=>getFusedDyActivation,getImageCenter:()=>getImageCenter,getInnerMostAxes:()=>getInnerMostAxes,getPermuted:()=>getPermuted,getReductionAxes:()=>getReductionAxes,getReshaped:()=>getReshaped,getReshapedPermuted:()=>getReshapedPermuted,getSliceBeginCoords:()=>getSliceBeginCoords,getSliceSize:()=>getSliceSize,getUndoAxesPermutation:()=>getUndoAxesPermutation,log:()=>log22,mergeRealAndImagArrays:()=>mergeRealAndImagArrays,prepareAndValidate:()=>prepareAndValidate,prepareSplitSize:()=>prepareSplitSize,reshapeTensor:()=>reshapeTensor,segment_util:()=>segment_util_exports,shouldFuse:()=>shouldFuse,slice_util:()=>slice_util_exports,splitRealAndImagArrays:()=>splitRealAndImagArrays,tupleValuesAreOne:()=>tupleValuesAreOne,upcastType:()=>upcastType,validateInput:()=>validateInput,validateUpdateShape:()=>validateUpdateShape,warn:()=>warn});function assertParamsConsistent(shapes,axis){let rank=shapes[0].length;shapes.forEach((shape,i)=>{assert(shape.length===rank,()=>`Error in concat${rank}D: rank of tensors[${i}] must be the same as the rank of the rest (${rank})`)}),assert(axis>=0&&axis`Error in concat${rank}D: axis must be between 0 and ${rank-1}.`);let firstShape=shapes[0];shapes.forEach((shape,i)=>{for(let r=0;r`Error in concat${rank}D: Shape of tensors[${i}] (${shape}) does not match the shape of the rest (${firstShape}) along the non-concatenated axis ${i}.`)})}function computeOutShape2(shapes,axis){let outputShape=shapes[0].slice();for(let i=1;i=blockShapeRank*2+1||i%2==1?permutedAfterBatch.push(i):permutedBeforeBatch.push(i);permuted.push(...permutedBeforeBatch),permuted.push(0),permuted.push(...permutedAfterBatch)}return permuted}function getReshapedPermuted(inputShape,blockShape,prod5,batchToSpace=!0){let reshapedPermuted=[];batchToSpace?reshapedPermuted.push(inputShape[0]/prod5):reshapedPermuted.push(inputShape[0]*prod5);for(let i=1;i"Number of splits must evenly divide the axis."),splitSizes=new Array(numOrSizeSplits).fill(x.shape[axis]/numOrSizeSplits);else{let numOfNegs=numOrSizeSplits.reduce((count2,value)=>(value===-1&&(count2+=1),count2),0);assert(numOfNegs<=1,()=>"There should be only one negative value in split array.");let negIndex=numOrSizeSplits.indexOf(-1);if(negIndex!==-1){let total=numOrSizeSplits.reduce((a,b)=>b>0?a+b:a);numOrSizeSplits[negIndex]=x.shape[axis]-total}assert(x.shape[axis]===numOrSizeSplits.reduce((a,b)=>a+b),()=>"The sum of sizes must match the size of the axis dimension."),splitSizes=numOrSizeSplits}return splitSizes}var segment_util_exports={};__export2(segment_util_exports,{collectGatherOpShapeInfo:()=>collectGatherOpShapeInfo,computeOutShape:()=>computeOutShape3,segOpComputeOptimalWindowSize:()=>segOpComputeOptimalWindowSize});function segOpComputeOptimalWindowSize(inSize,numSegments){let done=!1,res;for(inSize<=PARALLELIZE_THRESHOLD?(res=inSize,done=!0):res=nearestDivisor(inSize,Math.floor(Math.sqrt(inSize)));!done;)res>numSegments||res===inSize?done=!0:res=nearestDivisor(inSize,res+1);return res}function computeOutShape3(aShape,axis,numSegments){let outShape=[],rank=aShape.length;for(let dim=0;dimindicesRank))throw new Error(`Expect batchDims in the range of [-${indicesRank}, ${indicesRank}], but got ${batchDims}`);if(batchDims<0&&(batchDims+=indicesRank),batchDims>xRank)throw new Error(`batchDims (${batchDims}) must be less than rank(x) ( ${xRank}).`);if(axisdecodeString(val))}catch(err){throw new Error(`Failed to decode encoded string bytes into utf-8, error: ${err}`)}}function fromStringArrayToUint8(strings){return strings.map(s=>encodeString(s))}var kernel_impls_exports={};__export2(kernel_impls_exports,{nonMaxSuppressionV3Impl:()=>nonMaxSuppressionV3Impl,nonMaxSuppressionV4Impl:()=>nonMaxSuppressionV4Impl,nonMaxSuppressionV5Impl:()=>nonMaxSuppressionV5Impl,whereImpl:()=>whereImpl});var absGradConfig={kernelName:Abs,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(dy,step(cast(x,"float32"),-1))}}},acosGradConfig={kernelName:Acos,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>{let a=square(cast(x,"float32")),b=sqrt(sub(scalar(1),a));return neg(div(dy,b))}}}},acoshGradConfig={kernelName:Acosh,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>{let a=sqrt(sub(square(cast(x,"float32")),1));return div(dy,a)}}}},addGradConfig={kernelName:Add,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,outShape=assertAndGetBroadcastShape(a.shape,b.shape);return{a:()=>{let res=dy,reduceAxes=getReductionAxes(a.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,a.shape)},b:()=>{let res=dy,reduceAxes=getReductionAxes(b.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,b.shape)}}}},addNGradConfig={kernelName:AddN,saveAllInputs:!0,gradFunc:(dy,saved)=>{let ders={};return saved.forEach((_,i)=>{ders[i]=()=>dy.clone()}),ders}},argMaxGradConfig={kernelName:ArgMax,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>zerosLike(x)}}},argMinGradConfig={kernelName:ArgMin,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>zerosLike(x)}}},asinGradConfig={kernelName:Asin,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div(dy,sqrt(sub(scalar(1),square(cast(x,"float32")))))}}},asinhGradConfig={kernelName:Asinh,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>{let a=sqrt(add2(scalar(1),square(cast(x,"float32"))));return div(dy,a)}}}},atan2GradConfig={kernelName:Atan2,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,outShape=assertAndGetBroadcastShape(a.shape,b.shape);return{a:()=>{let d=add2(square(a),square(b)),res=mul(dy,div(b,d)),reduceAxes=getReductionAxes(a.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,a.shape)},b:()=>{let d=add2(square(a),square(b)),res=neg(mul(dy,div(a,d))),reduceAxes=getReductionAxes(b.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,b.shape)}}}},atanGradConfig={kernelName:Atan,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div(dy,add2(square(cast(x,"float32")),1))}}},atanhGradConfig={kernelName:Atanh,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div(dy,sub(scalar(1),square(cast(x,"float32"))))}}};function avgPool3dGrad_(dy,input2,filterSize,strides,dilations=[1,1,1],pad3,dimRoundingMode){let $dy=convertToTensor(dy,"dy","avgPool3dGrad"),$input=convertToTensor(input2,"input","avgPool3dGrad"),dy5D=$dy,input5D=$input,reshapedTo5D=!1;$input.rank===4&&(reshapedTo5D=!0,dy5D=reshape($dy,[1,$dy.shape[0],$dy.shape[1],$dy.shape[2],$dy.shape[3]]),input5D=reshape($input,[1,$input.shape[0],$input.shape[1],$input.shape[2],$input.shape[3]])),assert(dy5D.rank===5,()=>`Error in avgPool3dGrad: dy must be rank 5 but got rank ${dy5D.rank}.`),assert(input5D.rank===5,()=>`Error in avgPool3dGrad: input must be rank 5 but got rank ${input5D.rank}.`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in avgPool3dGrad: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in avgPool3dGrad: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={dy:dy5D,input:input5D},attrs={filterSize,strides,dilations,pad:pad3,dimRoundingMode},res=ENGINE.runKernel(AvgPool3DGrad,inputs,attrs);return reshapedTo5D?reshape(res,[res.shape[1],res.shape[2],res.shape[3],res.shape[4]]):res}var avgPool3dGrad=op({avgPool3dGrad_}),avgPool3DGradConfig={kernelName:AvgPool3D,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{filterSize,strides,dilations,pad:pad3,dimRoundingMode}=attrs,$dilations=dilations==null?[1,1,1]:dilations;return{x:()=>avgPool3dGrad(dy,x,filterSize,strides,$dilations,pad3,dimRoundingMode)}}};function avgPoolGrad_(dy,input2,filterSize,strides,pad3){let $dy=convertToTensor(dy,"dy","avgPoolGrad"),$input=convertToTensor(input2,"input","avgPoolGrad");assert($input.rank===$dy.rank,()=>`Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`);let input4D=$input,dy4D=$dy,reshapedTo4D=!1;$input.rank===3&&(reshapedTo4D=!0,input4D=reshape($input,[1,$input.shape[0],$input.shape[1],$input.shape[2]]),dy4D=reshape($dy,[1,$dy.shape[0],$dy.shape[1],$dy.shape[2]])),assert(dy4D.rank===4,()=>`Error in avgPoolGrad: dy must be rank 4 but got rank ${dy4D.rank}.`),assert(input4D.rank===4,()=>`Error in avgPoolGrad: input must be rank 4 but got rank ${input4D.rank}.`);let inputs={dy:dy4D,input:input4D},attrs={filterSize,strides,pad:pad3},res=ENGINE.runKernel(AvgPoolGrad,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var avgPoolGrad=op({avgPoolGrad_}),avgPoolGradConfig={kernelName:AvgPool,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{filterSize,strides,pad:pad3}=attrs;return{x:()=>avgPoolGrad(dy,x,filterSize,strides,pad3)}}},batchMatMulGradConfig={kernelName:BatchMatMul,inputsToSave:["a","b"],gradFunc:(dy,saved,attrs)=>{let[a,b]=saved,{transposeA,transposeB}=attrs;return!transposeA&&!transposeB?{a:()=>matMul(dy,b,!1,!0),b:()=>matMul(a,dy,!0,!1)}:!transposeA&&transposeB?{a:()=>matMul(dy,b,!1,!1),b:()=>matMul(dy,a,!0,!1)}:transposeA&&!transposeB?{a:()=>matMul(b,dy,!1,!0),b:()=>matMul(a,dy,!1,!1)}:{a:()=>matMul(b,dy,!0,!0),b:()=>matMul(dy,a,!0,!0)}}},batchToSpaceNDGradConfig={kernelName:BatchToSpaceND,gradFunc:(dy,saved,attrs)=>{let{blockShape,crops}=attrs;return{x:()=>spaceToBatchND(dy,blockShape,crops)}}},broadcastToGradConfig={kernelName:BroadcastTo,gradFunc:(dy,saved,attrs)=>{let broadCastToAttrs=attrs,inputShape=broadCastToAttrs.inputShape,outputShape=broadCastToAttrs.shape,reps=Array.from(outputShape);for(let i=inputShape.length-1;i>=0;i--)if(inputShape[i]===outputShape[i])reps[i]=1;else if(inputShape[i]!==1)throw new Error(`broadcastTo(): [${inputShape}] cannot be broadcast to [${outputShape}].`);let axes=[];for(let i=0;i1&&axes.push(i);return{x:()=>sum2(dy,axes,!0)}}},castGradConfig={kernelName:Cast,gradFunc:dy=>({x:()=>dy.clone()})},ceilGradConfig={kernelName:Ceil,gradFunc:dy=>({x:()=>zerosLike(dy)})},clipByValueGradConfig={kernelName:ClipByValue,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{clipValueMin,clipValueMax}=attrs;return{x:()=>where(logicalAnd(greaterEqual(x,clipValueMin),lessEqual(x,clipValueMax)),dy,zerosLike(dy))}}},complexAbsGradConfig={kernelName:ComplexAbs,inputsToSave:["x"],gradFunc:absGradConfig.gradFunc},concatGradConfig={kernelName:Concat,saveAllInputs:!0,gradFunc:(dy,saved,attrs)=>{let shapes=saved.map(t=>t.shape),{axis}=attrs,$axis=parseAxisParam(axis,saved[0].shape)[0],sizeSplits=shapes.map(s=>s[$axis]);return split(dy,sizeSplits,$axis).map(t=>()=>t)}},conv2DGradConfig={kernelName:Conv2D,inputsToSave:["x","filter"],gradFunc:(dy,saved,attrs)=>{let[x4D,$filter]=saved,{dilations,strides,pad:pad3,dataFormat}=attrs;return assert(tupleValuesAreOne(dilations),()=>`Error in gradient of conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`),{x:()=>conv2DBackpropInput(x4D.shape,dy,$filter,strides,pad3,dataFormat),filter:()=>conv2DBackpropFilter(x4D,dy,$filter.shape,strides,pad3,dataFormat)}}},conv2DBackpropInputGradConfig={kernelName:Conv2DBackpropInput,inputsToSave:["dy","filter"],gradFunc:(ddx,saved,attrs)=>{let[dy,filter]=saved,{strides,pad:pad3,dataFormat,dimRoundingMode}=attrs;return{dy:()=>conv2d(ddx,filter,strides,pad3,dataFormat,1,dimRoundingMode),filter:()=>conv2DBackpropFilter(ddx,dy,filter.shape,strides,pad3,dataFormat,dimRoundingMode)}}};function conv3DBackpropFilter_(x,dy,filterShape,strides,pad3){let x5D=x;x.rank===4&&(x5D=reshape(x,[1,x.shape[0],x.shape[1],x.shape[2],x.shape[3]]));let dy5D=dy;dy5D.rank===4&&(dy5D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2],dy.shape[3]])),assert(x5D.rank===5,()=>`Error in conv3dDerFilter: input must be rank 5, but got shape ${x5D.shape}.`),assert(dy5D.rank===5,()=>`Error in conv3dDerFilter: dy must be rank 5, but got shape ${dy5D.shape}.`),assert(filterShape.length===5,()=>`Error in conv3dDerFilter: filterShape must be length 5, but got ${filterShape}.`),assert(x5D.shape[4]===filterShape[3],()=>`Error in conv3dDerFilter: depth of input ${x5D.shape[4]}) must match input depth in filter (${filterShape[3]}.`),assert(dy5D.shape[4]===filterShape[4],()=>`Error in conv3dDerFilter: depth of dy (${dy5D.shape[4]}) must match output depth for filter (${filterShape[4]}).`);let inputs={x:x5D,dy:dy5D},attrs={strides,pad:pad3,filterShape};return ENGINE.runKernel(Conv3DBackpropFilterV2,inputs,attrs)}var conv3DBackpropFilter=op({conv3DBackpropFilter_}),conv3DGradConfig={kernelName:Conv3D,inputsToSave:["x","filter"],gradFunc:(dy,saved,attrs)=>{let{dilations,strides,pad:pad3}=attrs;assert(tupleValuesAreOne(dilations),()=>`Error in gradient of conv3D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`);let[x5D,$filter]=saved;return{x:()=>conv3DBackpropInput(x5D.shape,dy,$filter,strides,pad3),filter:()=>conv3DBackpropFilter(x5D,dy,$filter.shape,strides,pad3)}}},cosGradConfig={kernelName:Cos,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(neg(sin(cast(x,"float32"))),dy)}}},coshGradConfig={kernelName:Cosh,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(sinh(cast(x,"float32")),dy)}}},cumsumGradConfig={kernelName:Cumsum,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{axis,exclusive,reverse:reverse5}=attrs;return{x:()=>{let permutation=getAxesPermutation([axis],x.rank),out=cumsum(dy,axis,exclusive,!reverse5);return permutation!=null&&(out=transpose(out,permutation)),out}}}},depthwiseConv2dNativeGradConfig={kernelName:DepthwiseConv2dNative,inputsToSave:["x","filter"],gradFunc:(dy,saved,attrs)=>{let{dilations,strides,pad:pad3,dimRoundingMode}=attrs,$dilations=dilations==null?[1,1]:dilations;assert(tupleValuesAreOne($dilations),()=>`Error in gradient of depthwiseConv2dNative: dilation rates greater than 1 are not yet supported. Got dilations '${$dilations}'`);let[x,filter]=saved;return assert(x.rank===4,()=>`Error in gradient of depthwiseConv2dNative: input must be rank 4, but got rank ${x.rank}.`),assert(filter.rank===4,()=>`Error in gradient of depthwiseConv2dNative: filter must be rank 4, but got rank ${filter.rank}.`),assert(x.shape[3]===filter.shape[2],()=>`Error in gradient of depthwiseConv2d: number of input channels (${x.shape[3]}) must match the inChannels dimension in filter ${filter.shape[2]}.`),assert(eitherStridesOrDilationsAreOne(strides,$dilations),()=>`Error in gradient of depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${$dilations}'.`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`),{x:()=>depthwiseConv2dNativeBackpropInput(x.shape,dy,filter,strides,pad3,dilations,dimRoundingMode),filter:()=>depthwiseConv2dNativeBackpropFilter(x,dy,filter.shape,strides,pad3,dilations,dimRoundingMode)}}},dilation2dGradConfig={kernelName:Dilation2D,inputsToSave:["x","filter"],gradFunc:(dy,saved,attrs)=>{let[x,filter]=saved,inputInputs={x,filter,dy},filterInputs={x,filter,dy};return{x:()=>ENGINE.runKernel(Dilation2DBackpropInput,inputInputs,attrs),filter:()=>ENGINE.runKernel(Dilation2DBackpropFilter,filterInputs,attrs)}}},eluGradConfig={kernelName:Elu,outputsToSave:[!0],gradFunc:(dy,saved)=>{let[y]=saved,inputs={dy,y};return{x:()=>ENGINE.runKernel(EluGrad,inputs)}}},erfGradConfig={kernelName:Erf,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved,a=mul(exp(neg(square(x))),2/Math.sqrt(Math.PI));return{x:()=>mul(dy,a)}}},expGradConfig={kernelName:Exp,outputsToSave:[!0],gradFunc:(dy,saved)=>{let[y]=saved;return{x:()=>mul(dy,y)}}},expandDimsGradConfig={kernelName:ExpandDims,inputsToSave:["input"],gradFunc:(dy,saved)=>{let[input2]=saved;return{input:()=>reshape(dy,input2.shape)}}},expm1GradConfig={kernelName:Expm1,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(dy,exp(x))}}},floorGradConfig={kernelName:Floor,gradFunc:dy=>({x:()=>zerosLike(dy)})},floorDivGradConfig={kernelName:FloorDiv,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,outShape=assertAndGetBroadcastShape(a.shape,b.shape);return{a:()=>{let res=div(dy,cast(b,"float32")),reduceAxes=getReductionAxes(a.shape,outShape);return reduceAxes.length>0?reshape(sum2(res,reduceAxes),a.shape):res},b:()=>{let res=mul(dy,cast(a,"float32")),reduceAxes=getReductionAxes(b.shape,outShape);reduceAxes.length>0&&(res=reshape(sum2(res,reduceAxes),b.shape));let tmp=square(b);return neg(div(res,cast(tmp,"float32")))}}}},fusedBatchNormGradConfig={kernelName:FusedBatchNorm,inputsToSave:["x","mean","variance","scale"],gradFunc:(dy,saved,attrs)=>{let{varianceEpsilon}=attrs,[x,mean4,variance,scale2]=saved,scaleValue=scale2==null?scalar(1):scale2,reductionAxes=getReductionAxes(mean4.shape,x.shape),tileShape=[];if(mean4.rank===1){for(let i=0;imean4.rank===1?reshape(mul(mul(dy,tile(reshape(oneOverSqrtVariance,[1,1,1,mean4.shape[0]]),tileShape)),scaleValue),x.shape):reshape(mul(mul(dy,oneOverSqrtVariance),scaleValue),x.shape),mean:()=>{let meanDer=mul(mul(oneOverSqrtVariance,scalar(-1)),dyTimesScaleValue);return mean4.rank===1&&(meanDer=sum2(meanDer,reductionAxes)),reshape(meanDer,mean4.shape)},variance:()=>{let varianceDer=mul(mul(minusHalfRCube,xMinusMean),dyTimesScaleValue);return mean4.rank===1&&(varianceDer=sum2(varianceDer,reductionAxes)),reshape(varianceDer,mean4.shape)},scale:()=>{let xMinusMean2TimesRsqrt=mul(xMinusMean,oneOverSqrtVariance),scaleDer=mul(dy,xMinusMean2TimesRsqrt);return mean4.rank===1&&(scaleDer=sum2(scaleDer,reductionAxes)),reshape(scaleDer,mean4.shape)},offset:()=>{let offsetDer=dy;return mean4.rank===1&&(offsetDer=sum2(offsetDer,reductionAxes)),reshape(offsetDer,mean4.shape)}}}},gatherGradConfig={kernelName:GatherV2,inputsToSave:["x","indices"],gradFunc:(dy,saved,attrs)=>{let[x,indices]=saved,{axis}=attrs,parsedAxis=parseAxisParam(axis,x.shape)[0];return{x:()=>{let paramsShape=x.shape,indicesSize=indices.size,outerShape=paramsShape.slice(0,parsedAxis),outerDims=outerShape.length,innerShape=paramsShape.slice(axis,paramsShape.length).slice(1),innerDims=innerShape.length,outerAxesIndices=arrayRange(0,outerDims),innerAxesIndices=arrayRange(outerDims+1,outerDims+1+innerDims),valuesShape=arrayConcat([outerShape,[indicesSize],innerShape]),values=reshape(dy,valuesShape),reshapedIndices=reshape(indices,[indicesSize]),transposeDims=arrayConcat([[outerDims],outerAxesIndices,innerAxesIndices]),valuesTranspose=transpose(values,transposeDims),paramsGrad=unsortedSegmentSum(valuesTranspose,reshapedIndices,x.shape[parsedAxis]),invertTransposeDims=getUndoAxesPermutation(transposeDims);return paramsGrad=transpose(paramsGrad,invertTransposeDims),paramsGrad},indices:()=>indices}}};function arrayRange(start,stop){let result=[];for(let i=start;i{let[a,b]=saved;return{a:()=>zerosLike(a),b:()=>zerosLike(b)}}},identityGradConfig={kernelName:Identity,gradFunc:dy=>({x:()=>cast(dy,"float32")})},isFiniteGradConfig={kernelName:IsFinite,gradFunc:dy=>({x:()=>zerosLike(dy)})},isInfGradConfig={kernelName:IsInf,gradFunc:dy=>({x:()=>zerosLike(dy)})},isNanGradConfig={kernelName:IsNan,gradFunc:dy=>({x:()=>zerosLike(dy)})},leakyReluGradConfig={kernelName:LeakyRelu,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{alpha}=attrs,mask=greater(x,0);return{x:()=>where(mask,dy,mul(dy,alpha))}}},log1pGradConfig={kernelName:Log1p,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div(dy,add2(x,1))}}},logGradConfig={kernelName:Log,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div(dy,cast(x,"float32"))}}},logSoftmaxGradConfig={kernelName:LogSoftmax,inputsToSave:[],outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let[value]=saved,{axis}=attrs;return{logits:()=>{let keepDims=!0,softmax6=exp(value);return sub(dy,mul(sum2(dy,axis,keepDims),softmax6))}}}};function localResponseNormalizationBackprop_(x,y,dy,depthRadius=5,bias=1,alpha=1,beta=.5){let inputs={x,y,dy},attrs={depthRadius,bias,alpha,beta};return ENGINE.runKernel(LRNGrad,inputs,attrs)}var localResponseNormalizationBackprop=op({localResponseNormalizationBackprop_}),lrnGradConfig={kernelName:LRN,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let[x,y]=saved,{depthRadius,bias,alpha,beta}=attrs;return{x:()=>localResponseNormalizationBackprop(x,y,dy,depthRadius,bias,alpha,beta)}}};function gradForMinAndMax(dy,y,xOrig,origAxes){return y.rankmul(dy,cast(equal(xOrig,y),dy.dtype))}}var maxGradConfig={kernelName:Max,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let maxAttrs=attrs,{reductionIndices}=maxAttrs,x=saved[0],y=saved[1],origAxes=parseAxisParam(reductionIndices,x.shape),maxGrad=gradForMinAndMax(dy,y,x,origAxes);return{x:()=>maxGrad.x()}}},maximumGradConfig={kernelName:Maximum,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved;return{a:()=>mul(dy,cast(greaterEqual(a,b),"float32")),b:()=>mul(dy,cast(less(a,b),"float32"))}}};function maxPool3dGrad_(dy,input2,output,filterSize,strides,dilations=[1,1,1],pad3,dimRoundingMode){let $dy=convertToTensor(dy,"dy","maxPool3dGrad"),$input=convertToTensor(input2,"input","maxPool3dGrad"),$output=convertToTensor(output,"output","maxPool3dGrad"),dy5D=$dy,input5D=$input,output5D=$output,reshapedTo5D=!1;$input.rank===4&&(reshapedTo5D=!0,dy5D=reshape($dy,[1,$dy.shape[0],$dy.shape[1],$dy.shape[2],$dy.shape[3]]),input5D=reshape($input,[1,$input.shape[0],$input.shape[1],$input.shape[2],$input.shape[3]]),output5D=reshape($output,[1,$output.shape[0],$output.shape[1],$output.shape[2],$output.shape[3]])),assert(dy5D.rank===5,()=>`Error in maxPool3dGrad: dy must be rank 5 but got rank ${dy5D.rank}.`),assert(input5D.rank===5,()=>`Error in maxPool3dGrad: input must be rank 5 but got rank ${input5D.rank}.`),assert(output5D.rank===5,()=>`Error in maxPool3dGrad: output must be rank 5 but got rank ${output5D.rank}.`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in maxPool3dGrad: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in maxPool3dGrad: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={dy:dy5D,input:input5D,output:output5D},attrs={filterSize,strides,dilations,pad:pad3,dimRoundingMode},res=ENGINE.runKernel(MaxPool3DGrad,inputs,attrs);return reshapedTo5D?reshape(res,[res.shape[1],res.shape[2],res.shape[3],res.shape[4]]):res}var maxPool3dGrad=op({maxPool3dGrad_}),maxPool3DGradConfig={kernelName:MaxPool3D,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let[x,y]=saved,{filterSize,strides,dilations,pad:pad3,dimRoundingMode}=attrs,$dilations=dilations==null?[1,1,1]:dilations;return{x:()=>maxPool3dGrad(dy,x,y,filterSize,strides,$dilations,pad3,dimRoundingMode)}}};function maxPoolGrad_(dy,input2,output,filterSize,strides,pad3,dimRoundingMode){let $dy=convertToTensor(dy,"dy","maxPoolGrad"),$input=convertToTensor(input2,"input","maxPoolGrad"),$output=convertToTensor(output,"output","maxPoolGrad");assert($input.rank===$dy.rank,()=>`Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`),assert($dy.rank===4,()=>`Error in maxPoolGrad: dy must be rank 4 but got rank ${$dy.rank}.`),assert($input.rank===4,()=>`Error in maxPoolGrad: input must be rank 4 but got rank ${$input.rank}.`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in maxPoolGrad: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={dy:$dy,input:$input,output:$output},attrs={filterSize,strides,pad:pad3,dimRoundingMode};return ENGINE.runKernel(MaxPoolGrad,inputs,attrs)}var maxPoolGrad=op({maxPoolGrad_}),maxPoolGradConfig={kernelName:MaxPool,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let[x,y]=saved,{filterSize,strides,pad:pad3}=attrs;return{x:()=>maxPoolGrad(dy,x,y,filterSize,strides,pad3)}}},meanGradConfig={kernelName:Mean,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{axis}=attrs,axes=parseAxisParam(axis,x.shape),reduceShape=computeOutAndReduceShapes(x.shape,axes)[1],reduceSize=sizeFromShape(reduceShape);return{x:()=>{let expandedDyShape=x.shape.slice();axes.forEach(axis2=>{expandedDyShape[axis2]=1});let expandedDy=reshape(dy,expandedDyShape);return div(mul(expandedDy,ones2(x.shape,"float32")),reduceSize)}}}},minGradConfig={kernelName:Min,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let minAttrs=attrs,{axis}=minAttrs,[x,y]=saved,origAxes=parseAxisParam(axis,x.shape),minGrad=gradForMinAndMax(dy,y,x,origAxes);return{x:()=>minGrad.x()}}},minimumGradConfig={kernelName:Minimum,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved;return{a:()=>mul(dy,cast(lessEqual(a,b),"float32")),b:()=>mul(dy,cast(greater(a,b),"float32"))}}},mirrorPadGradConfig={kernelName:MirrorPad,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let x=saved[0],{paddings}=attrs,begin=paddings.map(p2=>p2[0]);return{x:()=>slice(dy,begin,x.shape)}}},modGradConfig={kernelName:Mod,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,outShape=assertAndGetBroadcastShape(a.shape,b.shape);return{a:()=>{let reduceAxes=getReductionAxes(a.shape,outShape);return reduceAxes.length>0?reshape(sum2(dy,reduceAxes),a.shape):dy},b:()=>{let res=mul(dy,neg(floor(div(a,b)))),reduceAxes=getReductionAxes(b.shape,outShape);return reduceAxes.length>0?reshape(sum2(res,reduceAxes),b.shape):res}}}},multiplyGradConfig={kernelName:Multiply,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,outShape=assertAndGetBroadcastShape(a.shape,b.shape);return{a:()=>{let res=mul(dy,cast(b,"float32")),reduceAxes=getReductionAxes(a.shape,outShape);return reduceAxes.length>0?reshape(sum2(res,reduceAxes),a.shape):res},b:()=>{let res=mul(dy,cast(a,"float32")),reduceAxes=getReductionAxes(b.shape,outShape);return reduceAxes.length>0?reshape(sum2(res,reduceAxes),b.shape):res}}}},negGradConfig={kernelName:Neg,gradFunc:dy=>({x:()=>neg(dy)})},oneHotGradConfig={kernelName:OneHot,inputsToSave:["indices"],gradFunc:(dy,saved)=>{let indices=saved[0];return{indices:()=>zeros(indices.shape,"float32")}}},onesLikeGradConfig={kernelName:OnesLike,gradFunc:dy=>({x:()=>zerosLike(dy)})},packGradConfig={kernelName:Pack,saveAllInputs:!0,gradFunc:(dy,saved,attrs)=>{let{axis}=attrs;return unstack(dy,axis).map(t=>()=>t)}},padV2GradConfig={kernelName:PadV2,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let x=saved[0],{paddings}=attrs,begin=paddings.map(p2=>p2[0]);return{x:()=>slice(dy,begin,x.shape)}}},powGradConfig={kernelName:Pow,inputsToSave:["a","b"],outputsToSave:[!0],gradFunc:(dy,saved)=>{let[a,b,y]=saved,base2=a,exp4=b,outShape=assertAndGetBroadcastShape(base2.shape,exp4.shape);return{a:()=>{let expFloat=cast(exp4,"float32"),res=mul(dy,mul(expFloat,pow(base2,sub(expFloat,scalar(1))))),reduceAxes=getReductionAxes(base2.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,base2.shape)},b:()=>{let condition=greater(base2,0),logBase=where(condition,log2(base2),zerosLike(base2)),res=mul(dy,mul(y,logBase)),reduceAxes=getReductionAxes(exp4.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,exp4.shape)}}}},preluGradConfig={kernelName:Prelu,inputsToSave:["x","alpha"],gradFunc:(dy,saved)=>{let[x,alpha]=saved,mask=greater(x,0);return{x:()=>where(mask,dy,mul(dy,alpha)),alpha:()=>{let res=where(mask,zerosLike(dy),mul(dy,x)),reduceAxes=getReductionAxes(alpha.shape,dy.shape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,alpha.shape)}}}},divGradConfig={kernelName:RealDiv,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,outShape=assertAndGetBroadcastShape(a.shape,b.shape);return{a:()=>{let res=div(dy,cast(b,"float32")),reduceAxes=getReductionAxes(a.shape,outShape);return reduceAxes.length>0?reshape(sum2(res,reduceAxes),a.shape):res},b:()=>{let res=mul(dy,cast(a,"float32")),reduceAxes=getReductionAxes(b.shape,outShape);reduceAxes.length>0&&(res=reshape(sum2(res,reduceAxes),b.shape));let tmp=square(b);return neg(div(res,cast(tmp,"float32")))}}}},reciprocalGradConfig={kernelName:Reciprocal,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div(dy,neg(square(x)))}}},relu6GradConfig={kernelName:Relu6,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved,mask=mul(lessEqual(x,6),step(x));return{x:()=>mul(dy,cast(mask,"float32"))}}},reluGradConfig={kernelName:Relu,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(dy,cast(step(x),"float32"))}}},reshapeGradConfig={kernelName:Reshape,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>reshape(dy,x.shape)}}},resizeBilinearGradConfig={kernelName:ResizeBilinear,inputsToSave:["images"],gradFunc:(dy,saved,attrs)=>{let[images]=saved,inputs={dy,images};return{images:()=>ENGINE.runKernel(ResizeBilinearGrad,inputs,attrs)}}},resizeNearestNeighborGradConfig={kernelName:ResizeNearestNeighbor,inputsToSave:["images"],gradFunc:(dy,saved,attrs)=>{let[images]=saved,inputs={dy,images};return{images:()=>ENGINE.runKernel(ResizeNearestNeighborGrad,inputs,attrs)}}},reverseGradConfig={kernelName:Reverse,gradFunc:(dy,saved,attrs)=>{let{dims}=attrs,axes=parseAxisParam(dims,dy.shape);return{x:()=>reverse(dy,axes)}}},roundGradConfig={kernelName:Round,gradFunc:dy=>({x:()=>zerosLike(dy)})},rsqrtGradConfig={kernelName:Rsqrt,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>neg(div(dy,mul(pow(x,1.5),2)))}}},selectGradConfig={kernelName:Select,inputsToSave:["condition"],gradFunc:(dy,saved)=>{let[condition]=saved;return{condition:()=>cast(zerosLike(condition),"float32"),t:()=>mul(dy,cast(condition,dy.dtype)),e:()=>mul(dy,cast(logicalNot(condition),dy.dtype))}}},seluGradConfig={kernelName:Selu,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>{let mask=greater(x,scalar(0)),scaleAlpha2=scalar(SELU_SCALEALPHA),scale2=scalar(SELU_SCALE),greaterThanZeroDer=mul(dy,scale2),lessEqualZeroDer=mul(mul(dy,scaleAlpha2),exp(cast(x,"float32")));return where(mask,greaterThanZeroDer,lessEqualZeroDer)}}}},sigmoidGradConfig={kernelName:Sigmoid,outputsToSave:[!0],gradFunc:(dy,saved)=>{let[y]=saved;return{x:()=>mul(dy,mul(y,sub(scalar(1),y)))}}},signGradConfig={kernelName:Sign,gradFunc:dy=>({x:()=>zerosLike(dy)})},sinGradConfig={kernelName:Sin,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(cos(cast(x,"float32")),dy)}}},sinhGradConfig={kernelName:Sinh,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(cosh(cast(x,"float32")),dy)}}},sliceGradConfig={kernelName:Slice,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{begin,size}=attrs,inputShape=x.shape,[begin_,size_]=parseSliceParams(x,begin,size),paddings=[];for(let i=0;ipad(dy,paddings)}}},softmaxGradConfig={kernelName:Softmax,outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let[y]=saved,{dim}=attrs,keepDims=!0,dyTimesY=mul(dy,y);return{logits:()=>sub(dyTimesY,mul(sum2(dyTimesY,[dim],keepDims),y))}}},softplusGradConfig={kernelName:Softplus,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(dy,sigmoid(x))}}},spaceToBatchNDGradConfig={kernelName:SpaceToBatchND,gradFunc:(dy,saved,attrs)=>{let{blockShape,paddings}=attrs;return{x:()=>batchToSpaceND(dy,blockShape,paddings)}}},splitVGradConfig={kernelName:SplitV,gradFunc:(dy,saved,attrs)=>{let{axis}=attrs;return{x:()=>concat(dy,axis)}}},sqrtGradConfig={kernelName:Sqrt,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div(dy,mul(sqrt(cast(x,"float32")),2))}}},squareGradConfig={kernelName:Square,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(dy,mul(cast(x,"float32"),2))}}},squaredDifferenceGradConfig={kernelName:SquaredDifference,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,two=scalar(2);return{a:()=>mul(dy,mul(two,sub(a,b))),b:()=>mul(dy,mul(two,sub(b,a)))}}},stepGradConfig={kernelName:Step,gradFunc:dy=>({x:()=>zerosLike(dy)})},subGradConfig={kernelName:Sub,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,outShape=assertAndGetBroadcastShape(a.shape,b.shape);return{a:()=>{let res=dy,reduceAxes=getReductionAxes(a.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,a.shape)},b:()=>{let res=dy,reduceAxes=getReductionAxes(b.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(neg(res),b.shape)}}}},sumGradConfig={kernelName:Sum,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,expandedDyShape=x.shape.slice(),{axis}=attrs;parseAxisParam(axis,x.shape).forEach(axis2=>{expandedDyShape[axis2]=1});let expandedDy=reshape(dy,expandedDyShape),derX=mul(expandedDy,ones2(x.shape,"float32"));return{x:()=>derX}}},tanGradConfig={kernelName:Tan,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div(dy,square(cos(x)))}}},tanhGradConfig={kernelName:Tanh,outputsToSave:[!0],gradFunc:(dy,saved)=>{let[y]=saved;return{x:()=>mul(sub(scalar(1),square(y)),dy)}}},tileGradConfig={kernelName:Tile,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{reps}=attrs;return{x:()=>{let xGrad=zerosLike(x);if(x.rank===1)for(let i=0;i{let transposeAttrs=attrs,{perm}=transposeAttrs,undoPerm=getUndoAxesPermutation(perm);return{x:()=>transpose(dy,undoPerm)}}},unpackGradConfig={kernelName:Unpack,gradFunc:(dy,saved,attrs)=>{let unpackAttrs=attrs,{axis}=unpackAttrs;return{value:()=>stack(dy,axis)}}},unsortedSegmentSumGradConfig={kernelName:UnsortedSegmentSum,inputsToSave:["segmentIds"],gradFunc:(dy,saved)=>{let[segmentIds]=saved;return{x:()=>gatherDropNegatives(dy,segmentIds)}}};function gatherDropNegatives(x,indices){let zeroClippedIndices=maximum(indices,zerosLike(indices)),gathered=gather(x,zeroClippedIndices),isPositive=greaterEqual(indices,scalar(0,"int32")),numIters=gathered.rank-isPositive.rank;for(let i=0;i({x:()=>zerosLike(dy)})},gradConfigs=[absGradConfig,acosGradConfig,acoshGradConfig,addGradConfig,addNGradConfig,argMaxGradConfig,argMinGradConfig,asinGradConfig,asinhGradConfig,atan2GradConfig,atanGradConfig,atanhGradConfig,avgPool3DGradConfig,avgPoolGradConfig,batchMatMulGradConfig,batchToSpaceNDGradConfig,broadcastToGradConfig,castGradConfig,ceilGradConfig,clipByValueGradConfig,complexAbsGradConfig,concatGradConfig,conv2DBackpropInputGradConfig,conv2DGradConfig,conv3DGradConfig,cosGradConfig,coshGradConfig,cumsumGradConfig,depthwiseConv2dNativeGradConfig,dilation2dGradConfig,divGradConfig,eluGradConfig,erfGradConfig,expGradConfig,expandDimsGradConfig,expm1GradConfig,floorDivGradConfig,floorGradConfig,fusedBatchNormGradConfig,gatherGradConfig,greaterEqualGradConfig,identityGradConfig,isFiniteGradConfig,isInfGradConfig,isNanGradConfig,leakyReluGradConfig,log1pGradConfig,logGradConfig,logSoftmaxGradConfig,lrnGradConfig,maxGradConfig,maxGradConfig,maximumGradConfig,maxPool3DGradConfig,maxPoolGradConfig,meanGradConfig,minGradConfig,minimumGradConfig,mirrorPadGradConfig,modGradConfig,multiplyGradConfig,negGradConfig,oneHotGradConfig,onesLikeGradConfig,packGradConfig,padV2GradConfig,padV2GradConfig,powGradConfig,preluGradConfig,reciprocalGradConfig,relu6GradConfig,reluGradConfig,reshapeGradConfig,resizeBilinearGradConfig,resizeNearestNeighborGradConfig,reverseGradConfig,roundGradConfig,rsqrtGradConfig,selectGradConfig,seluGradConfig,sigmoidGradConfig,signGradConfig,sinGradConfig,sinhGradConfig,sliceGradConfig,softmaxGradConfig,softplusGradConfig,spaceToBatchNDGradConfig,spaceToBatchNDGradConfig,splitVGradConfig,splitVGradConfig,sqrtGradConfig,squaredDifferenceGradConfig,squareGradConfig,stepGradConfig,subGradConfig,sumGradConfig,tanGradConfig,tanhGradConfig,tileGradConfig,transposeGradConfig,unpackGradConfig,unsortedSegmentSumGradConfig,zerosLikeGradConfig];for(let gradientConfig of gradConfigs)registerGradient(gradientConfig);Tensor.prototype.abs=function(){return this.throwIfDisposed(),abs(this)};Tensor.prototype.acos=function(){return this.throwIfDisposed(),acos(this)};Tensor.prototype.acosh=function(){return this.throwIfDisposed(),acosh(this)};Tensor.prototype.addStrict=function(x){return this.throwIfDisposed(),addStrict(this,x)};Tensor.prototype.add=function(b){return this.throwIfDisposed(),add2(this,b)};Tensor.prototype.all=function(axis,keepDims){return this.throwIfDisposed(),all(this,axis,keepDims)};Tensor.prototype.any=function(axis,keepDims){return this.throwIfDisposed(),any(this,axis,keepDims)};Tensor.prototype.argMax=function(axis){return this.throwIfDisposed(),argMax(this,axis)};Tensor.prototype.argMin=function(axis){return this.throwIfDisposed(),argMin(this,axis)};Tensor.prototype.asScalar=function(){return this.throwIfDisposed(),assert(this.size===1,()=>"The array must have only 1 element."),reshape(this,[])};Tensor.prototype.asType=function(dtype){return this.throwIfDisposed(),cast(this,dtype)};Tensor.prototype.as1D=function(){return this.throwIfDisposed(),reshape(this,[this.size])};Tensor.prototype.as2D=function(rows,columns){return this.throwIfDisposed(),reshape(this,[rows,columns])};Tensor.prototype.as3D=function(rows,columns,depth){return this.throwIfDisposed(),reshape(this,[rows,columns,depth])};Tensor.prototype.as4D=function(rows,columns,depth,depth2){return this.throwIfDisposed(),reshape(this,[rows,columns,depth,depth2])};Tensor.prototype.as5D=function(rows,columns,depth,depth2,depth3){return this.throwIfDisposed(),reshape(this,[rows,columns,depth,depth2,depth3])};Tensor.prototype.asin=function(){return this.throwIfDisposed(),asin(this)};Tensor.prototype.asinh=function(){return this.throwIfDisposed(),asinh(this)};Tensor.prototype.atan=function(){return this.throwIfDisposed(),atan(this)};Tensor.prototype.atan2=function(b){return this.throwIfDisposed(),atan2(this,b)};Tensor.prototype.atanh=function(){return this.throwIfDisposed(),atanh(this)};Tensor.prototype.avgPool=function(filterSize,strides,pad3,dimRoundingMode){return this.throwIfDisposed(),avgPool(this,filterSize,strides,pad3,dimRoundingMode)};Tensor.prototype.batchToSpaceND=function(blockShape,crops){return this.throwIfDisposed(),batchToSpaceND(this,blockShape,crops)};Tensor.prototype.batchNorm=function(mean4,variance,offset,scale2,varianceEpsilon){return this.throwIfDisposed(),batchNorm(this,mean4,variance,offset,scale2,varianceEpsilon)};Tensor.prototype.broadcastTo=function(shape){return this.throwIfDisposed(),broadcastTo(this,shape)};Tensor.prototype.cast=function(dtype){return this.throwIfDisposed(),cast(this,dtype)};Tensor.prototype.ceil=function(){return this.throwIfDisposed(),ceil(this)};Tensor.prototype.clipByValue=function(min6,max6){return this.throwIfDisposed(),clipByValue(this,min6,max6)};Tensor.prototype.concat=function(x,axis){return this.throwIfDisposed(),x instanceof Tensor&&(x=[x]),concat([this,...x],axis)};Tensor.prototype.conv1d=function(filter,stride,pad3,dataFormat,dilation,dimRoundingMode){return this.throwIfDisposed(),conv1d(this,filter,stride,pad3,dataFormat,dilation,dimRoundingMode)};Tensor.prototype.conv2dTranspose=function(filter,outputShape,strides,pad3,dimRoundingMode){return this.throwIfDisposed(),conv2dTranspose(this,filter,outputShape,strides,pad3,dimRoundingMode)};Tensor.prototype.conv2d=function(filter,strides,pad3,dataFormat,dilations,dimRoundingMode){return this.throwIfDisposed(),conv2d(this,filter,strides,pad3,dataFormat,dilations,dimRoundingMode)};Tensor.prototype.cos=function(){return this.throwIfDisposed(),cos(this)};Tensor.prototype.cosh=function(){return this.throwIfDisposed(),cosh(this)};Tensor.prototype.cumsum=function(axis,exclusive,reverse5){return this.throwIfDisposed(),cumsum(this,axis,exclusive,reverse5)};Tensor.prototype.depthToSpace=function(blockSize,dataFormat){return this.throwIfDisposed(),depthToSpace(this,blockSize,dataFormat)};Tensor.prototype.depthwiseConv2D=function(filter,strides,pad3,dataFormat,dilations,dimRoundingMode){return deprecationWarn("depthwiseConv2D is deprecated, use depthwiseConv2d instead"),this.throwIfDisposed(),depthwiseConv2d(this,filter,strides,pad3,dataFormat,dilations,dimRoundingMode)};Tensor.prototype.depthwiseConv2d=function(filter,strides,pad3,dataFormat,dilations,dimRoundingMode){return this.throwIfDisposed(),depthwiseConv2d(this,filter,strides,pad3,dataFormat,dilations,dimRoundingMode)};Tensor.prototype.dilation2d=function(filter,strides,pad3,dilations,dataFormat){return this.throwIfDisposed(),dilation2d(this,filter,strides,pad3,dilations,dataFormat)};Tensor.prototype.divNoNan=function(b){return this.throwIfDisposed(),divNoNan(this,b)};Tensor.prototype.divStrict=function(x){return this.throwIfDisposed(),divStrict(this,x)};Tensor.prototype.div=function(b){return this.throwIfDisposed(),div(this,b)};Tensor.prototype.dot=function(b){return this.throwIfDisposed(),dot(this,b)};Tensor.prototype.elu=function(){return this.throwIfDisposed(),elu(this)};Tensor.prototype.equalStrict=function(x){return this.throwIfDisposed(),equalStrict(this,x)};Tensor.prototype.equal=function(b){return this.throwIfDisposed(),equal(this,b)};Tensor.prototype.erf=function(){return this.throwIfDisposed(),erf(this)};Tensor.prototype.exp=function(){return this.throwIfDisposed(),exp(this)};Tensor.prototype.expandDims=function(axis){return this.throwIfDisposed(),expandDims(this,axis)};Tensor.prototype.expm1=function(){return this.throwIfDisposed(),expm1(this)};Tensor.prototype.fft=function(){return this.throwIfDisposed(),fft(this)};Tensor.prototype.flatten=function(){return this.throwIfDisposed(),reshape(this,[this.size])};Tensor.prototype.floor=function(){return this.throwIfDisposed(),floor(this)};Tensor.prototype.floorDiv=function(b){return this.throwIfDisposed(),floorDiv(this,b)};Tensor.prototype.gather=function(indices,axis){return this.throwIfDisposed(),gather(this,indices,axis)};Tensor.prototype.greaterEqualStrict=function(x){return this.throwIfDisposed(),greaterEqualStrict(this,x)};Tensor.prototype.greaterEqual=function(b){return this.throwIfDisposed(),greaterEqual(this,b)};Tensor.prototype.greaterStrict=function(x){return this.throwIfDisposed(),greaterStrict(this,x)};Tensor.prototype.greater=function(b){return this.throwIfDisposed(),greater(this,b)};Tensor.prototype.ifft=function(){return this.throwIfDisposed(),ifft(this)};Tensor.prototype.irfft=function(){return this.throwIfDisposed(),irfft(this)};Tensor.prototype.isFinite=function(){return this.throwIfDisposed(),isFinite2(this)};Tensor.prototype.isInf=function(){return this.throwIfDisposed(),isInf(this)};Tensor.prototype.isNaN=function(){return this.throwIfDisposed(),isNaN2(this)};Tensor.prototype.leakyRelu=function(alpha){return this.throwIfDisposed(),leakyRelu(this,alpha)};Tensor.prototype.lessEqualStrict=function(x){return this.throwIfDisposed(),lessEqualStrict(this,x)};Tensor.prototype.lessEqual=function(b){return this.throwIfDisposed(),lessEqual(this,b)};Tensor.prototype.lessStrict=function(x){return this.throwIfDisposed(),lessStrict(this,x)};Tensor.prototype.less=function(b){return this.throwIfDisposed(),less(this,b)};Tensor.prototype.localResponseNormalization=function(depthRadius,bias,alpha,beta){return this.throwIfDisposed(),localResponseNormalization(this,depthRadius,bias,alpha,beta)};Tensor.prototype.logSigmoid=function(){return this.throwIfDisposed(),logSigmoid(this)};Tensor.prototype.logSoftmax=function(axis){return this.throwIfDisposed(),logSoftmax(this,axis)};Tensor.prototype.logSumExp=function(axis,keepDims){return this.throwIfDisposed(),logSumExp(this,axis,keepDims)};Tensor.prototype.log=function(){return this.throwIfDisposed(),log2(this)};Tensor.prototype.log1p=function(){return this.throwIfDisposed(),log1p(this)};Tensor.prototype.logicalAnd=function(b){return this.throwIfDisposed(),logicalAnd(this,b)};Tensor.prototype.logicalNot=function(){return this.throwIfDisposed(),logicalNot(this)};Tensor.prototype.logicalOr=function(b){return this.throwIfDisposed(),logicalOr(this,b)};Tensor.prototype.logicalXor=function(b){return this.throwIfDisposed(),logicalXor(this,b)};Tensor.prototype.matMul=function(b,transposeA,transposeB){return this.throwIfDisposed(),matMul(this,b,transposeA,transposeB)};Tensor.prototype.maxPool=function(filterSize,strides,pad3,dimRoundingMode){return this.throwIfDisposed(),maxPool(this,filterSize,strides,pad3,dimRoundingMode)};Tensor.prototype.max=function(axis,keepDims){return this.throwIfDisposed(),max(this,axis,keepDims)};Tensor.prototype.maximumStrict=function(x){return this.throwIfDisposed(),maximumStrict(this,x)};Tensor.prototype.maximum=function(b){return this.throwIfDisposed(),maximum(this,b)};Tensor.prototype.mean=function(axis,keepDims){return this.throwIfDisposed(),mean(this,axis,keepDims)};Tensor.prototype.min=function(axis,keepDims){return this.throwIfDisposed(),min(this,axis,keepDims)};Tensor.prototype.minimumStrict=function(x){return this.throwIfDisposed(),minimumStrict(this,x)};Tensor.prototype.minimum=function(b){return this.throwIfDisposed(),minimum(this,b)};Tensor.prototype.mirrorPad=function(paddings,mode){return this.throwIfDisposed(),mirrorPad(this,paddings,mode)};Tensor.prototype.modStrict=function(x){return this.throwIfDisposed(),modStrict(this,x)};Tensor.prototype.mod=function(b){return this.throwIfDisposed(),mod(this,b)};Tensor.prototype.mulStrict=function(x){return this.throwIfDisposed(),mulStrict(this,x)};Tensor.prototype.mul=function(b){return this.throwIfDisposed(),mul(this,b)};Tensor.prototype.neg=function(){return this.throwIfDisposed(),neg(this)};Tensor.prototype.norm=function(ord,axis,keepDims){return this.throwIfDisposed(),norm(this,ord,axis,keepDims)};Tensor.prototype.notEqualStrict=function(x){return this.throwIfDisposed(),notEqualStrict(this,x)};Tensor.prototype.notEqual=function(b){return this.throwIfDisposed(),notEqual(this,b)};Tensor.prototype.oneHot=function(depth,onValue=1,offValue=0){return this.throwIfDisposed(),oneHot(this,depth,onValue,offValue)};Tensor.prototype.onesLike=function(){return this.throwIfDisposed(),onesLike(this)};Tensor.prototype.pad=function(paddings,constantValue){return this.throwIfDisposed(),pad(this,paddings,constantValue)};Tensor.prototype.pool=function(windowShape,poolingType,padding,dilationRate,strides){return this.throwIfDisposed(),pool(this,windowShape,poolingType,padding,dilationRate,strides)};Tensor.prototype.powStrict=function(exp4){return this.throwIfDisposed(),powStrict(this,exp4)};Tensor.prototype.pow=function(exp4){return this.throwIfDisposed(),pow(this,exp4)};Tensor.prototype.prelu=function(alpha){return this.throwIfDisposed(),prelu(this,alpha)};Tensor.prototype.prod=function(axis,keepDims){return this.throwIfDisposed(),prod(this,axis,keepDims)};Tensor.prototype.reciprocal=function(){return this.throwIfDisposed(),reciprocal(this)};Tensor.prototype.relu=function(){return this.throwIfDisposed(),relu(this)};Tensor.prototype.relu6=function(){return this.throwIfDisposed(),relu6(this)};Tensor.prototype.reshapeAs=function(x){return this.throwIfDisposed(),reshape(this,x.shape)};Tensor.prototype.reshape=function(shape){return this.throwIfDisposed(),reshape(this,shape)};Tensor.prototype.resizeBilinear=function(newShape2D,alignCorners,halfPixelCenters){return this.throwIfDisposed(),resizeBilinear(this,newShape2D,alignCorners,halfPixelCenters)};Tensor.prototype.resizeNearestNeighbor=function(newShape2D,alignCorners,halfFloatCenters){return this.throwIfDisposed(),resizeNearestNeighbor(this,newShape2D,alignCorners,halfFloatCenters)};Tensor.prototype.reverse=function(axis){return this.throwIfDisposed(),reverse(this,axis)};Tensor.prototype.rfft=function(){return this.throwIfDisposed(),rfft(this)};Tensor.prototype.round=function(){return this.throwIfDisposed(),round2(this)};Tensor.prototype.rsqrt=function(){return this.throwIfDisposed(),rsqrt(this)};Tensor.prototype.selu=function(){return this.throwIfDisposed(),selu(this)};Tensor.prototype.separableConv2d=function(depthwiseFilter,pointwiseFilter,strides,pad3,dilation,dataFormat){return this.throwIfDisposed(),separableConv2d(this,depthwiseFilter,pointwiseFilter,strides,pad3,dilation,dataFormat)};Tensor.prototype.sigmoid=function(){return this.throwIfDisposed(),sigmoid(this)};Tensor.prototype.sign=function(){return this.throwIfDisposed(),sign(this)};Tensor.prototype.sin=function(){return this.throwIfDisposed(),sin(this)};Tensor.prototype.sinh=function(){return this.throwIfDisposed(),sinh(this)};Tensor.prototype.slice=function(begin,size){return this.throwIfDisposed(),slice(this,begin,size)};Tensor.prototype.softmax=function(dim){return this.throwIfDisposed(),softmax(this,dim)};Tensor.prototype.softplus=function(){return this.throwIfDisposed(),softplus(this)};Tensor.prototype.spaceToBatchND=function(blockShape,paddings){return this.throwIfDisposed(),spaceToBatchND(this,blockShape,paddings)};Tensor.prototype.split=function(numOrSizeSplits,axis){return this.throwIfDisposed(),split(this,numOrSizeSplits,axis)};Tensor.prototype.sqrt=function(){return this.throwIfDisposed(),sqrt(this)};Tensor.prototype.square=function(){return this.throwIfDisposed(),square(this)};Tensor.prototype.squaredDifference=function(b){return this.throwIfDisposed(),squaredDifference(this,b)};Tensor.prototype.squaredDifferenceStrict=function(x){return this.throwIfDisposed(),squaredDifferenceStrict(this,x)};Tensor.prototype.squeeze=function(axis){return this.throwIfDisposed(),squeeze(this,axis)};Tensor.prototype.stack=function(x,axis){this.throwIfDisposed();let tensorsToBeStacked=x instanceof Tensor?[this,x]:[this,...x];return stack(tensorsToBeStacked,axis)};Tensor.prototype.step=function(alpha){return this.throwIfDisposed(),step(this,alpha)};Tensor.prototype.stridedSlice=function(begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask){return this.throwIfDisposed(),stridedSlice(this,begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask)};Tensor.prototype.subStrict=function(x){return this.throwIfDisposed(),subStrict(this,x)};Tensor.prototype.sub=function(b){return this.throwIfDisposed(),sub(this,b)};Tensor.prototype.sum=function(axis,keepDims){return this.throwIfDisposed(),sum2(this,axis,keepDims)};Tensor.prototype.tan=function(){return this.throwIfDisposed(),tan(this)};Tensor.prototype.tanh=function(){return this.throwIfDisposed(),tanh2(this)};Tensor.prototype.tile=function(reps){return this.throwIfDisposed(),tile(this,reps)};Tensor.prototype.toBool=function(){return this.throwIfDisposed(),cast(this,"bool")};Tensor.prototype.toFloat=function(){return this.throwIfDisposed(),cast(this,"float32")};Tensor.prototype.toInt=function(){return this.throwIfDisposed(),cast(this,"int32")};Tensor.prototype.topk=function(k,sorted){return this.throwIfDisposed(),topk(this,k,sorted)};Tensor.prototype.transpose=function(perm){return this.throwIfDisposed(),transpose(this,perm)};Tensor.prototype.unique=function(axis){return this.throwIfDisposed(),unique(this,axis)};Tensor.prototype.unsortedSegmentSum=function(segmentIds,numSegments){return this.throwIfDisposed(),unsortedSegmentSum(this,segmentIds,numSegments)};Tensor.prototype.unstack=function(axis){return this.throwIfDisposed(),unstack(this,axis)};Tensor.prototype.where=function(condition,x){return this.throwIfDisposed(),where(condition,this,x)};Tensor.prototype.zerosLike=function(){return this.throwIfDisposed(),zerosLike(this)};function assertNotComplex(tensor2,opName){Array.isArray(tensor2)||(tensor2=[tensor2]),tensor2.forEach(t=>{t!=null&&util_exports.assert(t.dtype!=="complex64",()=>`${opName} does not support complex64 tensors in the CPU backend.`)})}var whereImpl2=kernel_impls_exports.whereImpl,MathBackendCPU=class extends KernelBackend{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new DataStorage(this,engine())}write(values,shape,dtype){this.firstUse&&(this.firstUse=!1,env().get("IS_NODE")&&backend_util_exports.warn(` ============================ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, which binds to TensorFlow C++, by running npm i @tensorflow/tfjs-node, or npm i @tensorflow/tfjs-node-gpu if you have CUDA. Then call require('@tensorflow/tfjs-node'); (-gpu suffix for CUDA) at the start of your program. Visit https://github.com/tensorflow/tfjs-node for more details. @@ -4040,19 +4040,19 @@ return a / b;`,DIV_PACKED=` } setOutput(${returnValue}); } - `}};function unsortedSegmentSum3(args){let{inputs,backend:backend22,attrs}=args,{x,segmentIds}=inputs,{numSegments}=attrs,xRank=x.shape.length,toDispose=[],axis=0,permutation=backend_util_exports.getAxesPermutation([axis],xRank),permutedX=x;permutation!=null&&(permutedX=transpose3({inputs:{x},backend:backend22,attrs:{perm:permutation}}),toDispose.push(permutedX),axis=backend_util_exports.getInnerMostAxes(1,xRank)[0]);let outShape=backend_util_exports.segment_util.computeOutShape(permutedX.shape,axis,numSegments),inSize=util_exports.sizeFromShape([permutedX.shape[axis]]),a2D=reshape3({inputs:{x:permutedX},backend:backend22,attrs:{shape:[-1,inSize]}});toDispose.push(a2D);let outputDType=sumOutType(x.dtype),segOpCompute=(x2,segOpType,segmentIds2,dtype,numSegments2)=>{let batchSize=x2.shape[0],inSize2=x2.shape[1],windowSize=backend_util_exports.segment_util.segOpComputeOptimalWindowSize(inSize2,numSegments2),segOpInfo={windowSize,inSize:inSize2,batchSize,numSegments:numSegments2},program=new SegmentOpProgram(segOpInfo,segOpType),output=backend22.compileAndRun(program,[x2,segmentIds2],dtype);if(toDispose.push(output),output.shape[1]===numSegments2)return output;let rangeInfo=range3({backend:backend22,attrs:{start:0,stop:numSegments2,step:1,dtype:"float32"}}),tileInfo=tile3({inputs:{x:rangeInfo},backend:backend22,attrs:{reps:[inSize2/windowSize]}});return toDispose.push(rangeInfo),toDispose.push(tileInfo),segOpCompute(output,segOpType,tileInfo,dtype,numSegments2)},segOpResult=segOpCompute(a2D,"unsortedSegmentSum",segmentIds,outputDType,numSegments),reshaped=reshape3({inputs:{x:segOpResult},backend:backend22,attrs:{shape:outShape}}),result=reshaped;if(permutation!=null){toDispose.push(reshaped);let perm=backend_util_exports.getUndoAxesPermutation(permutation);result=transpose3({inputs:{x:result},backend:backend22,attrs:{perm}})}return toDispose.forEach(t=>backend22.disposeIntermediateTensorInfo(t)),result}var unsortedSegmentSumConfig2={kernelName:UnsortedSegmentSum,backendName:"webgl",kernelFunc:unsortedSegmentSum3},kernelConfigs2=[LRNConfig,LRNGradConfig,_fusedMatMulConfig2,absConfig2,acosConfig2,acoshConfig2,addConfig2,addNConfig2,allConfig2,anyConfig2,argMaxConfig2,argMinConfig2,asinConfig2,asinhConfig2,atan2Config2,atanConfig2,atanhConfig2,avgPool3DConfig2,avgPoolConfig2,avgPoolGrad3DConfig,avgPoolGradConfig3,batchMatMulConfig2,batchNormConfig2,batchToSpaceNDConfig2,bincountConfig2,castConfig2,ceilConfig2,clipByValueConfig,complexAbsConfig2,complexConfig2,concatConfig2,conv2DBackpropFilterConfig2,conv2DBackpropInputConfig2,conv2DConfig2,conv3DBackpropFilterV2Config2,conv3DBackpropInputConfig,conv3DConfig2,cosConfig2,coshConfig2,cropAndResizeConfig2,cumsumConfig2,denseBincountConfig2,depthToSpaceConfig2,depthwiseConv2dNativeBackpropFilterConfig2,depthwiseConv2dNativeBackpropInputConfig2,depthwiseConv2dNativeConfig2,diagConfig2,dilation2DConfig,eluConfig2,eluGradConfig3,equalConfig2,erfConfig2,expConfig2,expandDimsConfig2,expm1Config2,fftConfig2,fillConfig2,flipLeftRightConfig2,floorConfig2,floorDivConfig2,fromPixelsConfig,fusedConv2DConfig2,fusedDepthwiseConv2DConfig2,gatherNdConfig2,gatherV2Config2,greaterConfig2,greaterEqualConfig2,identityConfig2,ifftConfig2,imagConfig2,isFiniteConfig2,isInfConfig2,isNaNConfig2,leakyReluConfig2,lessConfig2,lessEqualConfig2,linSpaceConfig2,log1pConfig2,logConfig2,logicalAndConfig2,logicalNotConfig2,logicalOrConfig2,maxConfig2,maxPool3DConfig2,maxPoolConfig2,maxPoolGrad3DConfig,maxPoolGradConfig3,maxPoolWithArgmaxConfig2,maximumConfig2,meanConfig2,minConfig2,minimumConfig2,mirrorPadConfig2,modConfig2,multinomialConfig2,multiplyConfig2,negConfig2,nonMaxSuppressionV3Config2,nonMaxSuppressionV4Config2,nonMaxSuppressionV5Config2,notEqualConfig2,oneHotConfig2,onesLikeConfig2,packConfig2,padV2Config2,powConfig2,preluConfig2,prodConfig2,rangeConfig2,realConfig2,realDivConfig2,reciprocalConfig2,relu6Config2,reluConfig2,reshapeConfig2,resizeBilinearConfig2,resizeBilinearGradConfig3,resizeNearestNeighborConfig2,resizeNearestNeighborGradConfig3,reverseConfig2,rotateWithOffsetConfig2,roundConfig2,rsqrtConfig2,scatterNdConfig2,selectConfig2,seluConfig2,sigmoidConfig2,signConfig2,sinConfig2,sinhConfig2,sliceConfig2,softmaxConfig2,softplusConfig2,spaceToBatchNDConfig2,sparseToDenseConfig2,splitVConfig2,sqrtConfig2,squareConfig2,squaredDifferenceConfig2,stepConfig2,stridedSliceConfig2,subConfig2,sumConfig2,tanConfig2,tanhConfig2,tileConfig2,topKConfig2,transposeConfig2,uniqueConfig2,unpackConfig2,unsortedSegmentSumConfig2,zerosLikeConfig2];for(let kernelConfig of kernelConfigs2)registerKernel(kernelConfig);var CppDType;(function(CppDType2){CppDType2[CppDType2.float32=0]="float32",CppDType2[CppDType2.int32=1]="int32",CppDType2[CppDType2.bool=2]="bool",CppDType2[CppDType2.string=3]="string",CppDType2[CppDType2.complex64=4]="complex64"})(CppDType||(CppDType={}));var FusableActivation;(function(FusableActivation2){FusableActivation2[FusableActivation2.linear=0]="linear",FusableActivation2[FusableActivation2.relu=1]="relu",FusableActivation2[FusableActivation2.relu6=2]="relu6",FusableActivation2[FusableActivation2.prelu=3]="prelu",FusableActivation2[FusableActivation2.leakyrelu=4]="leakyrelu"})(FusableActivation||(FusableActivation={}));var wasmFusedMatMul;function setup(backend22){wasmFusedMatMul=backend22.wasm.cwrap(_FusedMatMul,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function fusedBatchMatMul(args){let{inputs,backend:backend22,attrs}=args,{a,b,bias,preluActivationWeights}=inputs;if(a.dtype!=="float32"||b.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA,transposeB,activation:activation2,leakyreluAlpha}=attrs,aId=backend22.dataIdMap.get(a.dataId).id,bId=backend22.dataIdMap.get(b.dataId).id,biasId=0;if(bias!=null){let biasData=backend22.dataIdMap.get(bias.dataId);if(biasData.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${biasData.shape.length}.`);biasId=biasData.id}let preluActivationWeightsId=preluActivationWeights==null?0:backend22.dataIdMap.get(preluActivationWeights.dataId).id,fusedActivation=FusableActivation[activation2];if(fusedActivation==null)throw new Error(`${activation2} activation not yet supported for FusedConv2D in the wasm backend.`);let leftDim=transposeA?a.shape[2]:a.shape[1],rightDim=transposeB?b.shape[1]:b.shape[2],batchDim=a.shape[0],out=backend22.makeOutput([batchDim,leftDim,rightDim],a.dtype),outId=backend22.dataIdMap.get(out.dataId).id,aShapeBytes=new Uint8Array(new Int32Array(a.shape).buffer),bShapeBytes=new Uint8Array(new Int32Array(b.shape).buffer);return wasmFusedMatMul(aId,aShapeBytes,a.shape.length,bId,bShapeBytes,b.shape.length,transposeA,transposeB,fusedActivation,biasId,preluActivationWeightsId,leakyreluAlpha||0,outId),out}var fusedMatMulConfig={kernelName:_FusedMatMul,backendName:"wasm",setupFunc:setup,kernelFunc:fusedBatchMatMul};function createUnaryKernelConfig(kernelName){let wasmFunc9;function setupFunc3(backend22){wasmFunc9=backend22.wasm.cwrap(kernelName,null,["number","number"])}function kernelFunc3(args){let{backend:backend22,inputs:{x}}=args,xId=backend22.dataIdMap.get(x.dataId).id,out=backend22.makeOutput(x.shape,x.dtype),outId=backend22.dataIdMap.get(out.dataId).id;return util_exports.sizeFromShape(out.shape)===0||wasmFunc9(xId,outId),out}return{kernelName,backendName:"wasm",setupFunc:setupFunc3,kernelFunc:kernelFunc3}}var absConfig3=createUnaryKernelConfig(Abs);function createBinaryKernelConfig(kernelName,supportsFullBroadcast17,dtype){let wasmFunc9;function setupFunc3(backend22){wasmFunc9=backend22.wasm.cwrap(kernelName,null,["number","array","number","number","array","number","number","number"])}function kernelFunc3(args){let{backend:backend22,inputs}=args,{a,b}=inputs,aId=backend22.dataIdMap.get(a.dataId).id,bId=backend22.dataIdMap.get(b.dataId).id,outputType=dtype!=null?dtype:a.dtype,newShape=backend_util_exports.assertAndGetBroadcastShape(a.shape,b.shape),out=backend22.makeOutput(newShape,outputType);if(util_exports.sizeFromShape(newShape)===0)return out;let aShapeBytes=new Uint8Array(new Int32Array(a.shape).buffer),bShapeBytes=new Uint8Array(new Int32Array(b.shape).buffer),outId=backend22.dataIdMap.get(out.dataId).id,kernelFunc4=()=>wasmFunc9(aId,aShapeBytes,a.shape.length,bId,bShapeBytes,b.shape.length,CppDType[a.dtype],outId);if(supportsFullBroadcast17&&a.dtype==="float32")return kernelFunc4(),out;let aBroadcastDims=backend_util_exports.getBroadcastDims(a.shape,newShape),bBroadcastDims=backend_util_exports.getBroadcastDims(b.shape,newShape),loopsOverAllOfA=aBroadcastDims.every((v,i)=>v===i),loopsOverAllOfB=bBroadcastDims.every((v,i)=>v===i);if(loopsOverAllOfA&&loopsOverAllOfB)return kernelFunc4(),out;throw new Error(`Broadcasting along outer dims is not yet supported for ${a.dtype} ${kernelName}.`)}return{kernelName,backendName:"wasm",setupFunc:setupFunc3,kernelFunc:kernelFunc3}}var supportsFullBroadcast=!0,addConfig3=createBinaryKernelConfig(Add,supportsFullBroadcast),wasmFunc;function setupFunc(backend22){wasmFunc=backend22.wasm.cwrap(AddN,null,["array","number","number","number"])}function addn(args){let{inputs,backend:backend22}=args,out=backend22.makeOutput(inputs[0].shape,inputs[0].dtype);if(util_exports.sizeFromShape(out.shape)===0)return out;let inputIds=inputs.map(x=>backend22.dataIdMap.get(x.dataId).id),inputIdsBytes=new Uint8Array(new Int32Array(inputIds).buffer),outId=backend22.dataIdMap.get(out.dataId).id;return wasmFunc(inputIdsBytes,inputIds.length,CppDType[out.dtype],outId),out}var addNConfig3={kernelName:AddN,backendName:"wasm",setupFunc,kernelFunc:addn};function identity3(args){let{inputs:{x},backend:backend22}=args,out=backend22.makeOutput(x.shape,x.dtype),inVals=backend22.typedArrayFromHeap(x);return backend22.typedArrayFromHeap(out).set(inVals),out}var identityConfig3={kernelName:Identity,backendName:"wasm",kernelFunc:identity3},wasmTranspose;function setup2(backend22){wasmTranspose=backend22.wasm.cwrap(Transpose,null,["number","array","number","number","number","array","number"])}function transpose4(args){let{inputs,backend:backend22,attrs}=args,[reducedShape,perm]=removeOneSizeDims(inputs.x.shape,attrs.perm),permIsNoOp=!0;for(let i=0;i=i&&(minValIdx===-1||newPerm[minValIdx]>newPerm[j])&&(minValIdx=j);newPerm[minValIdx]=i}return[newShape,newPerm]}var transposeConfig3={kernelName:Transpose,backendName:"wasm",kernelFunc:transpose4,setupFunc:setup2};function permuteAxesAndTranspose(x,axis,backend22){let xShape=x.shape,xRank=x.shape.length,originalAxes=util_exports.parseAxisParam(axis,xShape),axes=originalAxes,permutedAxes=backend_util_exports.getAxesPermutation(axes,xRank),xTransposed=null,inputWasTransposed=!1;if(permutedAxes!=null){let newShape=new Array(xRank);for(let i=0;i`new shape: ${$shape}, old shape: ${x.shape}. New shape and old shape must have the same number of elements.`),{dataId:x.dataId,shape:$shape,dtype:x.dtype}}var reshapeConfig3={kernelName:Reshape,backendName:"wasm",kernelFunc:reshape4},wasmBatchMatMul;function setup5(backend22){wasmBatchMatMul=backend22.wasm.cwrap(BatchMatMul,null,["number","array","number","number","array","number","number","number","number"])}function batchMatMul3(args){let{inputs,backend:backend22,attrs}=args,{a,b}=inputs,{transposeA,transposeB}=attrs;if(a.dtype!=="float32"||b.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let aRank=a.shape.length,bRank=b.shape.length,innerShapeA=transposeA?a.shape[aRank-2]:a.shape[aRank-1],innerShapeB=transposeB?b.shape[bRank-1]:b.shape[bRank-2],outerShapeA=transposeA?a.shape[aRank-1]:a.shape[aRank-2],outerShapeB=transposeB?b.shape[bRank-2]:b.shape[bRank-1],outerDimsA=a.shape.slice(0,-2),outerDimsB=b.shape.slice(0,-2),batchDimA=util_exports.sizeFromShape(outerDimsA),batchDimB=util_exports.sizeFromShape(outerDimsB),batchDimsCompatible=batchDimA===batchDimB||batchDimA===1||batchDimB===1;util_exports.assert(aRank>=2&&bRank>=2&&batchDimsCompatible,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${outerDimsA}) and (${outerDimsB}).`);let outShape=(batchDimA>batchDimB?a.shape.slice(0,-2):b.shape.slice(0,-2)).concat([outerShapeA,outerShapeB]);util_exports.assert(innerShapeA===innerShapeB,()=>`Error in matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${a.shape} and ${b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);let a3dShape=transposeA?[batchDimA,innerShapeA,outerShapeA]:[batchDimA,outerShapeA,innerShapeA],b3dShape=transposeB?[batchDimB,outerShapeB,innerShapeB]:[batchDimB,innerShapeB,outerShapeB],a3d=reshape4({inputs:{x:a},backend:backend22,attrs:{shape:a3dShape}}),b3d=reshape4({inputs:{x:b},backend:backend22,attrs:{shape:b3dShape}}),a3dId=backend22.dataIdMap.get(a3d.dataId).id,b3dId=backend22.dataIdMap.get(b3d.dataId).id,leftDim=transposeA?a3d.shape[2]:a3d.shape[1],rightDim=transposeB?b3d.shape[1]:b3d.shape[2],batchDim=Math.max(batchDimA,batchDimB),out=backend22.makeOutput([batchDim,leftDim,rightDim],a3d.dtype),outId=backend22.dataIdMap.get(out.dataId).id,aShapeBytes=new Uint8Array(new Int32Array(a3d.shape).buffer),bShapeBytes=new Uint8Array(new Int32Array(b3d.shape).buffer);return wasmBatchMatMul(a3dId,aShapeBytes,a3d.shape.length,b3dId,bShapeBytes,b3d.shape.length,transposeA,transposeB,outId),out.shape=outShape,out}var batchMatMulConfig3={kernelName:BatchMatMul,backendName:"wasm",setupFunc:setup5,kernelFunc:batchMatMul3};function cast4(args){let{inputs:{x},attrs:{dtype},backend:backend22}=args,out=backend22.makeOutput(x.shape,dtype),inVals=backend22.typedArrayFromHeap(x);return backend22.typedArrayFromHeap(out).set(inVals),out}var castConfig3={kernelName:Cast,backendName:"wasm",kernelFunc:cast4},wasmClip;function setup6(backend22){wasmClip=backend22.wasm.cwrap(ClipByValue,null,["number","number","number","number"])}function clip2(args){let{inputs,backend:backend22,attrs}=args,{x}=inputs,{clipValueMin,clipValueMax}=attrs,xId=backend22.dataIdMap.get(x.dataId).id,out=backend22.makeOutput(x.shape,x.dtype),outId=backend22.dataIdMap.get(out.dataId).id;return wasmClip(xId,clipValueMin,clipValueMax,outId),out}var clipByValueConfig2={kernelName:ClipByValue,backendName:"wasm",setupFunc:setup6,kernelFunc:clip2};function concat4(args){let{inputs,backend:backend22}=args,axis=util_exports.parseAxisParam(args.attrs.axis,inputs[0].shape)[0],outShape=backend_util_exports.computeOutShape(inputs.map(t=>t.shape),axis),$inputs=inputs.filter(t=>util_exports.sizeFromShape(t.shape)>0);if($inputs.length===1)return identity3({inputs:{x:$inputs[0]},backend:backend22});let out=backend22.makeOutput(outShape,inputs[0].dtype);if(util_exports.sizeFromShape(outShape)===0)return out;let shapes=$inputs.map(t=>t.shape);if(backend_util_exports.assertParamsConsistent(shapes,axis),$inputs[0].dtype==="string"){let inputs2D=$inputs.map(t=>{let innerSize=util_exports.sizeFromShape(t.shape.slice(axis));return reshape4({inputs:{x:t},backend:backend22,attrs:{shape:[-1,innerSize]}})}),inputsValShapes=inputs2D.map(t=>({vals:backend22.readSync(t.dataId),shape:t.shape}));outShape=backend_util_exports.computeOutShape(inputs2D.map(t=>t.shape),1);let simplyConcat=inputs2D[0].shape[0]===1,outVals2=concatImpl(inputsValShapes,outShape,inputs[0].dtype,simplyConcat),finalOutShape=backend_util_exports.computeOutShape($inputs.map(t=>t.shape),axis);out.shape=finalOutShape;let outData=backend22.dataIdMap.get(out.dataId);return outData.stringBytes=backend_util_exports.fromStringArrayToUint8(outVals2),out}let batchDim=util_exports.sizeFromShape($inputs[0].shape.slice(0,axis)),sumInnerDims=0,innerDims=$inputs.map(input2=>{let innerDim=util_exports.sizeFromShape(input2.shape.slice(axis));return sumInnerDims+=innerDim,innerDim}),inVals=$inputs.map(input2=>backend22.typedArrayFromHeap(input2)),outVals=backend22.typedArrayFromHeap(out);for(let b=0;b`cumsum does not support ${x.dtype} tensors in the WASM backend`);let permutation=backend_util_exports.getAxesPermutation([axis],xRank),permutedX=x;permutation!==null&&(permutedX=transpose4({inputs:{x},attrs:{perm:permutation},backend:backend22}));let permutedAxis=backend_util_exports.getInnerMostAxes(1,xRank)[0];backend_util_exports.assertAxesAreInnerMostDims("cumsum",[permutedAxis],xRank);let permutedOut=backend22.makeOutput(permutedX.shape,permutedX.dtype),finalDim=permutedX.shape[permutedAxis],permutedXId=backend22.dataIdMap.get(permutedX.dataId).id,permutedOutId=backend22.dataIdMap.get(permutedOut.dataId).id;wasmCumsum(permutedXId,exclusive?1:0,reverse5?1:0,finalDim,permutedOutId,CppDType[x.dtype]);let out=permutedOut;if(permutation!==null){let undoPermutation=backend_util_exports.getUndoAxesPermutation(permutation);out=transpose4({inputs:{x:permutedOut},attrs:{perm:undoPermutation},backend:backend22}),backend22.disposeData(permutedX.dataId),backend22.disposeData(permutedOut.dataId)}return out}var cumsumConfig3={kernelName:Cumsum,backendName:"wasm",setupFunc:setup10,kernelFunc:cumsum4},wasmDepthToSpace;function setup11(backend22){wasmDepthToSpace=backend22.wasm.cwrap(DepthToSpace,null,["number","number","number","array","number","array","array","number","number"])}function depthToSpace4(args){let{backend:backend22,inputs,attrs}=args,{x}=inputs,{blockSize,dataFormat}=attrs;util_exports.assert(blockSize>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);let batchSize=x.shape[0],inputHeight=dataFormat==="NHWC"?x.shape[1]:x.shape[2],inputWidth=dataFormat==="NHWC"?x.shape[2]:x.shape[3],inputDepth=dataFormat==="NHWC"?x.shape[3]:x.shape[1],outputHeight=inputHeight*blockSize,outputWidth=inputWidth*blockSize,outputDepth=inputDepth/(blockSize*blockSize),outputShape=dataFormat==="NHWC"?[batchSize,outputHeight,outputWidth,outputDepth]:[batchSize,outputDepth,outputHeight,outputWidth],out=backend22.makeOutput(outputShape,"float32"),xId=backend22.dataIdMap.get(x.dataId).id,xStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(x.shape)).buffer),outputShapeBytes=new Uint8Array(new Int32Array(outputShape).buffer),outStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(outputShape)).buffer),outId=backend22.dataIdMap.get(out.dataId).id;return wasmDepthToSpace(xId,blockSize,dataFormat==="NHWC"?1:0,xStridesBytes,x.shape.length-1,outputShapeBytes,outStridesBytes,outputShape.length,outId),out}var depthToSpaceConfig3={kernelName:DepthToSpace,backendName:"wasm",setupFunc:setup11,kernelFunc:depthToSpace4},wasmDepthwiseConv2d;function setup12(backend22){wasmDepthwiseConv2d=backend22.wasm.cwrap(DepthwiseConv2dNative,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function depthwiseConv2d3(args){let{inputs,attrs,backend:backend22}=args,{x,filter}=inputs,xId=backend22.dataIdMap.get(x.dataId).id,filterId=backend22.dataIdMap.get(filter.dataId).id,{strides,dilations,pad:pad3,dimRoundingMode}=attrs,$dilations=dilations==null?[1,1]:dilations,convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,$dilations,pad3,dimRoundingMode,!0),filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,inputChannels=convInfo.inChannels,outputChannels=convInfo.outChannels,isSamePad=convInfo.padInfo.type==="SAME"?1:0;if(convInfo.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);let out=backend22.makeOutput(convInfo.outShape,"float32"),outId=backend22.dataIdMap.get(out.dataId).id;return wasmDepthwiseConv2d(xId,x.shape[0],x.shape[1],x.shape[2],filterId,filterHeight,filterWidth,padTop,padRight,padBottom,padLeft,isSamePad,dilationHeight,dilationWidth,strideHeight,strideWidth,inputChannels,outputChannels,outId),out}var depthwiseConv2dNativeConfig3={kernelName:DepthwiseConv2dNative,backendName:"wasm",setupFunc:setup12,kernelFunc:depthwiseConv2d3},supportsFullBroadcast2=!1,equalConfig3=createBinaryKernelConfig(Equal,supportsFullBroadcast2,"bool"),expConfig3=createUnaryKernelConfig(Exp);function expandDims4(args){let{inputs,attrs,backend:backend22}=args,{input:input2}=inputs,{dim}=attrs,inputRank=input2.shape.length,newShape=input2.shape.slice(),$dim=dim;return dim<0&&(util_exports.assert(-(inputRank+1)<=dim,()=>`Axis must be in the interval [${-(inputRank+1)}, ${inputRank}]`),$dim=inputRank+dim+1),newShape.splice($dim,0,1),reshape4({inputs:{x:input2},backend:backend22,attrs:{shape:newShape}})}var expandDimsConfig3={kernelName:ExpandDims,backendName:"wasm",kernelFunc:expandDims4};function fill4(args){let{attrs:{shape,value,dtype},backend:backend22}=args,out=backend22.makeOutput(shape,dtype);return backend22.typedArrayFromHeap(out).fill(value),out}var fillConfig3={kernelName:Fill,backendName:"wasm",kernelFunc:fill4},wasmFlipLeftRight;function setup13(backend22){wasmFlipLeftRight=backend22.wasm.cwrap(FlipLeftRight,null,["number","number","number","number","number","number"])}function flipLeftRight2(args){let{inputs,backend:backend22}=args,{image:image3}=inputs,out=backend22.makeOutput(image3.shape,image3.dtype),imageId=backend22.dataIdMap.get(image3.dataId).id,outId=backend22.dataIdMap.get(out.dataId).id,[batch,imageHeight,imageWidth,numChannels]=image3.shape;return wasmFlipLeftRight(imageId,batch,imageHeight,imageWidth,numChannels,outId),out}var flipLeftRightConfig3={kernelName:FlipLeftRight,backendName:"wasm",kernelFunc:flipLeftRight2,setupFunc:setup13},floorConfig3=createUnaryKernelConfig(Floor),supportsFullBroadcast3=!1,floorDivConfig3=createBinaryKernelConfig(FloorDiv,supportsFullBroadcast3),wasmBatchNorm;function setup14(backend22){wasmBatchNorm=backend22.wasm.cwrap(FusedBatchNorm,null,["number","number","number","number","number","number","number"])}function fusedBatchNorm(args){let{backend:backend22,inputs,attrs}=args,{varianceEpsilon}=attrs,{x,mean:mean4,variance,offset,scale:scale2}=inputs,xId=backend22.dataIdMap.get(x.dataId).id,meanId=backend22.dataIdMap.get(mean4.dataId).id,varianceId=backend22.dataIdMap.get(variance.dataId).id,offsetId=offset!=null?backend22.dataIdMap.get(offset.dataId).id:0,scaleId=scale2!=null?backend22.dataIdMap.get(scale2.dataId).id:0,out=backend22.makeOutput(x.shape,x.dtype);if(util_exports.sizeFromShape(x.shape)===0)return out;let outId=backend22.dataIdMap.get(out.dataId).id;return wasmBatchNorm(xId,meanId,varianceId,offsetId,scaleId,varianceEpsilon,outId),out}var fusedBatchNormConfig={kernelName:FusedBatchNorm,backendName:"wasm",setupFunc:setup14,kernelFunc:fusedBatchNorm},wasmFusedConv2d;function setup15(backend22){wasmFusedConv2d=backend22.wasm.cwrap(FusedConv2D,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function fusedConv2d2(args){let{inputs,attrs,backend:backend22}=args,{x,filter,bias,preluActivationWeights}=inputs,{strides,pad:pad3,dilations,dataFormat,dimRoundingMode,activation:activation2,leakyreluAlpha}=attrs,convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,dilations,pad3,dimRoundingMode),fusedActivation=FusableActivation[activation2];if(fusedActivation==null)throw new Error(`${activation2} activation not yet supported for FusedConv2D in the wasm backend.`);let xId=backend22.dataIdMap.get(x.dataId).id,filterId=backend22.dataIdMap.get(filter.dataId).id,outputChannels=convInfo.outChannels,biasId=0;if(bias!=null){let biasData=backend22.dataIdMap.get(bias.dataId);if(biasData.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${biasData.shape.length}.`);if(biasData.shape[0]!==outputChannels)throw new Error(`FusedConv2D bias shape (${biasData.shape}) does not match the number of output channels (${outputChannels})`);biasId=biasData.id}let filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,inputChannels=convInfo.inChannels,isSamePad=convInfo.padInfo.type==="SAME"?1:0,batchSize=convInfo.batchSize,inHeight=convInfo.inHeight,inWidth=convInfo.inWidth;if(dataFormat!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${dataFormat}'. Please use 'NHWC'.`);let out=backend22.makeOutput(convInfo.outShape,"float32"),outId=backend22.dataIdMap.get(out.dataId).id,preluActivationWeightsId=preluActivationWeights==null?0:backend22.dataIdMap.get(preluActivationWeights.dataId).id;return wasmFusedConv2d(xId,batchSize,inHeight,inWidth,filterId,filterHeight,filterWidth,biasId,padTop,padRight,padBottom,padLeft,isSamePad,dilationHeight,dilationWidth,strideHeight,strideWidth,inputChannels,outputChannels,fusedActivation,preluActivationWeightsId,leakyreluAlpha||0,outId),out}var fusedConv2DConfig3={kernelName:FusedConv2D,backendName:"wasm",setupFunc:setup15,kernelFunc:fusedConv2d2},wasmFusedDepthwiseConv2d;function setup16(backend22){wasmFusedDepthwiseConv2d=backend22.wasm.cwrap(FusedDepthwiseConv2D,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function fusedDepthwiseConv2d(args){let{inputs,attrs,backend:backend22}=args,{x,filter,bias,preluActivationWeights}=inputs,{strides,pad:pad3,dilations,dataFormat,dimRoundingMode,activation:activation2,leakyreluAlpha}=attrs,convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,dilations,pad3,dimRoundingMode,!0),fusedActivation=FusableActivation[activation2];if(fusedActivation==null)throw new Error(`${activation2} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let xId=backend22.dataIdMap.get(x.dataId).id,filterId=backend22.dataIdMap.get(filter.dataId).id,outputChannels=convInfo.outChannels,biasId=0;if(bias!=null){let biasData=backend22.dataIdMap.get(bias.dataId);if(biasData.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${biasData.shape.length}.`);if(biasData.shape[0]!==outputChannels)throw new Error(`FusedDepthwiseConv2D bias shape (${biasData.shape}) does not match the number of output channels (${outputChannels})`);biasId=biasData.id}let filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,inputChannels=convInfo.inChannels,isSamePad=convInfo.padInfo.type==="SAME"?1:0,batchSize=convInfo.batchSize,inHeight=convInfo.inHeight,inWidth=convInfo.inWidth;if(dataFormat!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${dataFormat}'. Please use 'NHWC'.`);let out=backend22.makeOutput(convInfo.outShape,"float32"),outId=backend22.dataIdMap.get(out.dataId).id,preluActivationWeightsId=preluActivationWeights==null?0:backend22.dataIdMap.get(preluActivationWeights.dataId).id;return wasmFusedDepthwiseConv2d(xId,batchSize,inHeight,inWidth,filterId,filterHeight,filterWidth,biasId,padTop,padRight,padBottom,padLeft,isSamePad,dilationHeight,dilationWidth,strideHeight,strideWidth,inputChannels,outputChannels,fusedActivation,preluActivationWeightsId,leakyreluAlpha||0,outId),out}var fusedDepthwiseConv2DConfig3={kernelName:FusedDepthwiseConv2D,backendName:"wasm",setupFunc:setup16,kernelFunc:fusedDepthwiseConv2d},wasmGatherNd;function setup17(backend22){wasmGatherNd=backend22.wasm.cwrap(GatherNd,null,["number","number","number","number","number","number","array","number"])}function gatherNd3(args){let{backend:backend22,inputs}=args,{params,indices}=inputs,[resultShape,numSlices,sliceSize,strides]=gather_nd_util_exports.prepareAndValidate(params,indices),out=backend22.makeOutput(resultShape,params.dtype);if(numSlices===0)return out;let indicesShape=indices.shape,sliceRank=indicesShape[indicesShape.length-1],xId=backend22.dataIdMap.get(params.dataId).id,indicesId=backend22.dataIdMap.get(indices.dataId).id,stridesBytes=new Uint8Array(new Int32Array(strides).buffer),outId=backend22.dataIdMap.get(out.dataId).id;return wasmGatherNd(xId,CppDType[params.dtype],indicesId,numSlices,sliceRank,sliceSize,stridesBytes,outId),out}var gatherNdConfig3={kernelName:GatherNd,backendName:"wasm",setupFunc:setup17,kernelFunc:gatherNd3},wasmGather;function setup18(backend22){wasmGather=backend22.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function gatherV23(args){let{backend:backend22,inputs,attrs}=args,{x,indices}=inputs,{axis,batchDims}=attrs,parsedAxis=util_exports.parseAxisParam(axis,x.shape)[0],shapeInfo=backend_util_exports.segment_util.collectGatherOpShapeInfo(x,indices,parsedAxis,batchDims),flattenX=reshape4({inputs:{x},attrs:{shape:[shapeInfo.batchSize,shapeInfo.outerSize,shapeInfo.dimSize,shapeInfo.sliceSize]},backend:backend22}),indicesSize=util_exports.sizeFromShape(indices.shape),flattenIndex=reshape4({inputs:{x:indices},attrs:{shape:[shapeInfo.batchSize,indicesSize/shapeInfo.batchSize]},backend:backend22}),flattenOutputShape=[shapeInfo.batchSize,shapeInfo.outerSize,indicesSize/shapeInfo.batchSize,shapeInfo.sliceSize],out=backend22.makeOutput(flattenOutputShape,x.dtype);if(util_exports.sizeFromShape(x.shape)===0)return out;let stridesSize=flattenX.shape.length-1,xId=backend22.dataIdMap.get(flattenX.dataId).id,indicesId=backend22.dataIdMap.get(flattenIndex.dataId).id,outId=backend22.dataIdMap.get(out.dataId).id,xStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(flattenX.shape)).buffer),outStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(flattenOutputShape)).buffer);return wasmGather(xId,CppDType[x.dtype],xStridesBytes,stridesSize,indicesId,shapeInfo.batchSize,outStridesBytes,outId),out.shape=shapeInfo.outputShape,out}var gatherV2Config3={kernelName:GatherV2,backendName:"wasm",setupFunc:setup18,kernelFunc:gatherV23},supportsFullBroadcast4=!1,greaterConfig3=createBinaryKernelConfig(Greater,supportsFullBroadcast4,"bool"),supportsFullBroadcast5=!1,greaterEqualConfig3=createBinaryKernelConfig(GreaterEqual,supportsFullBroadcast5,"bool"),wasmFunc3;function setupFunc2(backend22){wasmFunc3=backend22.wasm.cwrap(LeakyRelu,null,["number","number","number"])}function leakyRelu4(args){let{inputs:{x},attrs:{alpha},backend:backend22}=args,xId=backend22.dataIdMap.get(x.dataId).id,out=backend22.makeOutput(x.shape,x.dtype);if(util_exports.sizeFromShape(x.shape)!==0){let outId=backend22.dataIdMap.get(out.dataId).id;wasmFunc3(xId,alpha,outId)}return out}var leakyReluConfig3={kernelName:LeakyRelu,backendName:"wasm",setupFunc:setupFunc2,kernelFunc:leakyRelu4},supportsFullBroadcast6=!1,lessConfig3=createBinaryKernelConfig(Less,supportsFullBroadcast6,"bool"),supportsFullBroadcast7=!1,lessEqualConfig3=createBinaryKernelConfig(LessEqual,supportsFullBroadcast7,"bool"),logConfig3=createUnaryKernelConfig(Log),supportsFullBroadcast8=!1,logicalAndConfig3=createBinaryKernelConfig(LogicalAnd,supportsFullBroadcast8,"bool"),wasmMax;function setup19(backend22){wasmMax=backend22.wasm.cwrap(Max,null,["number, number, number"])}function max4(args){let{backend:backend22,inputs,attrs}=args,{reductionIndices:axis,keepDims}=attrs,{x}=inputs,inputId=backend22.dataIdMap.get(x.dataId).id,input2=x,{transposed,axes,originalAxes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend22);if(inputWasTransposed){let transposedId=backend22.dataIdMap.get(transposed.dataId).id;input2=transposed,inputId=transposedId}let inputRank=input2.shape.length;backend_util_exports.assertAxesAreInnerMostDims("max",axes,inputRank);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(input2.shape,axes),reduceSize=util_exports.sizeFromShape(reduceShape),out=backend22.makeOutput(outShape,x.dtype);if(util_exports.sizeFromShape(input2.shape)!==0){let outId=backend22.dataIdMap.get(out.dataId).id;wasmMax(inputId,reduceSize,outId)}if(inputWasTransposed&&backend22.disposeData(transposed.dataId),keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(out.shape,originalAxes);out.shape=newShape}return out}var maxConfig3={kernelName:Max,backendName:"wasm",setupFunc:setup19,kernelFunc:max4},supportsFullBroadcast9=!1,maximumConfig3=createBinaryKernelConfig(Maximum,supportsFullBroadcast9),wasmMaxPool;function setup20(backend22){wasmMaxPool=backend22.wasm.cwrap(MaxPool,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function maxPool4(args){let{inputs,attrs,backend:backend22}=args,x=inputs.x,xId=backend22.dataIdMap.get(x.dataId).id,{filterSize,strides,pad:pad3,dimRoundingMode}=attrs,convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,1,pad3,dimRoundingMode),filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,inputChannels=convInfo.inChannels,outputChannels=convInfo.outChannels;if(convInfo.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);let out=backend22.makeOutput(convInfo.outShape,"float32"),outId=backend22.dataIdMap.get(out.dataId).id;return wasmMaxPool(xId,x.shape[0],x.shape[1],x.shape[2],filterHeight,filterWidth,padTop,padRight,padBottom,padLeft,dilationHeight,dilationWidth,strideHeight,strideWidth,inputChannels,outputChannels,outId),out}var maxPoolConfig3={kernelName:MaxPool,backendName:"wasm",setupFunc:setup20,kernelFunc:maxPool4},wasmMean;function setup21(backend22){wasmMean=backend22.wasm.cwrap(Mean,null,["number, number, number"])}function mean3(args){let{backend:backend22,inputs,attrs}=args,{axis,keepDims}=attrs,{x}=inputs,xId=backend22.dataIdMap.get(x.dataId).id,inputId=xId,input2=x,{transposed,axes,originalAxes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend22),reductionAxes=axes;if(inputWasTransposed){let transposedId=backend22.dataIdMap.get(transposed.dataId).id;transposedId!==xId&&(input2=transposed,inputId=transposedId,reductionAxes=backend_util_exports.getInnerMostAxes(reductionAxes.length,input2.shape.length))}backend_util_exports.assertAxesAreInnerMostDims("mean",reductionAxes,input2.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(input2.shape,reductionAxes),reduceSize=util_exports.sizeFromShape(reduceShape),castedInput=input2;input2.dtype!=="float32"&&(castedInput=cast4({backend:backend22,inputs:{x:input2},attrs:{dtype:"float32"}}),inputId=backend22.dataIdMap.get(castedInput.dataId).id);let out=backend22.makeOutput(outShape,"float32");if(util_exports.sizeFromShape(input2.shape)!==0){let outId=backend22.dataIdMap.get(out.dataId).id;wasmMean(inputId,reduceSize,outId)}if(inputWasTransposed&&backend22.disposeData(transposed.dataId),keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(out.shape,originalAxes);out.shape=newShape}return input2.dtype!=="float32"&&backend22.disposeData(castedInput.dataId),out}var meanConfig3={kernelName:Mean,backendName:"wasm",setupFunc:setup21,kernelFunc:mean3},wasmMin;function setup22(backend22){wasmMin=backend22.wasm.cwrap(Min,null,["number, number, number"])}function min4(args){let{backend:backend22,inputs,attrs}=args,{axis,keepDims}=attrs,{x}=inputs,xId=backend22.dataIdMap.get(x.dataId).id,inputId=xId,input2=x,{transposed,axes,originalAxes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend22);if(inputWasTransposed){let transposedId=backend22.dataIdMap.get(transposed.dataId).id;transposedId!==xId&&(input2=transposed,inputId=transposedId)}let inputRank=input2.shape.length;backend_util_exports.assertAxesAreInnerMostDims("min",axes,inputRank);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(input2.shape,axes),reduceSize=util_exports.sizeFromShape(reduceShape),out=backend22.makeOutput(outShape,input2.dtype);if(util_exports.sizeFromShape(input2.shape)!==0){let outId=backend22.dataIdMap.get(out.dataId).id;wasmMin(inputId,reduceSize,outId)}if(inputWasTransposed&&backend22.disposeData(transposed.dataId),keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(out.shape,originalAxes);out.shape=newShape}return out}var minConfig3={kernelName:Min,backendName:"wasm",setupFunc:setup22,kernelFunc:min4},supportsFullBroadcast10=!1,minimumConfig3=createBinaryKernelConfig(Minimum,supportsFullBroadcast10),supportsFullBroadcast11=!0,multiplyConfig3=createBinaryKernelConfig(Multiply,supportsFullBroadcast11),negConfig3=createUnaryKernelConfig(Neg);function parseResultStruct(backend22,resOffset){let result=new Int32Array(backend22.wasm.HEAPU8.buffer,resOffset,4),pSelectedIndices=result[0],selectedSize=result[1],pSelectedScores=result[2],pValidOutputs=result[3];return backend22.wasm._free(resOffset),{pSelectedIndices,selectedSize,pSelectedScores,pValidOutputs}}var wasmFunc4;function setup23(backend22){wasmFunc4=backend22.wasm.cwrap(NonMaxSuppressionV3,"number",["number","number","number","number","number"])}function kernelFunc(args){let{backend:backend22,inputs,attrs}=args,{iouThreshold,maxOutputSize,scoreThreshold}=attrs,{boxes,scores}=inputs,boxesId=backend22.dataIdMap.get(boxes.dataId).id,scoresId=backend22.dataIdMap.get(scores.dataId).id,resOffset=wasmFunc4(boxesId,scoresId,maxOutputSize,iouThreshold,scoreThreshold),{pSelectedIndices,selectedSize,pSelectedScores,pValidOutputs}=parseResultStruct(backend22,resOffset);return backend22.wasm._free(pSelectedScores),backend22.wasm._free(pValidOutputs),backend22.makeOutput([selectedSize],"int32",pSelectedIndices)}var nonMaxSuppressionV3Config3={kernelName:NonMaxSuppressionV3,backendName:"wasm",setupFunc:setup23,kernelFunc},wasmFunc5;function setup24(backend22){wasmFunc5=backend22.wasm.cwrap(NonMaxSuppressionV4,"number",["number","number","number","number","number","bool"])}function nonMaxSuppressionV43(args){let{backend:backend22,inputs,attrs}=args,{iouThreshold,maxOutputSize,scoreThreshold,padToMaxOutputSize}=attrs,{boxes,scores}=inputs,boxesId=backend22.dataIdMap.get(boxes.dataId).id,scoresId=backend22.dataIdMap.get(scores.dataId).id,resOffset=wasmFunc5(boxesId,scoresId,maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize),{pSelectedIndices,selectedSize,pSelectedScores,pValidOutputs}=parseResultStruct(backend22,resOffset);backend22.wasm._free(pSelectedScores);let selectedIndicesTensor=backend22.makeOutput([selectedSize],"int32",pSelectedIndices),validOutputsTensor=backend22.makeOutput([],"int32",pValidOutputs);return[selectedIndicesTensor,validOutputsTensor]}var nonMaxSuppressionV4Config3={kernelName:NonMaxSuppressionV4,backendName:"wasm",setupFunc:setup24,kernelFunc:nonMaxSuppressionV43},wasmFunc6;function setup25(backend22){wasmFunc6=backend22.wasm.cwrap(NonMaxSuppressionV5,"number",["number","number","number","number","number","number"])}function kernelFunc2(args){let{backend:backend22,inputs,attrs}=args,{iouThreshold,maxOutputSize,scoreThreshold,softNmsSigma}=attrs,{boxes,scores}=inputs,boxesId=backend22.dataIdMap.get(boxes.dataId).id,scoresId=backend22.dataIdMap.get(scores.dataId).id,resOffset=wasmFunc6(boxesId,scoresId,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma),{pSelectedIndices,selectedSize,pSelectedScores,pValidOutputs}=parseResultStruct(backend22,resOffset);backend22.wasm._free(pValidOutputs);let selectedIndicesTensor=backend22.makeOutput([selectedSize],"int32",pSelectedIndices),selectedScoresTensor=backend22.makeOutput([selectedSize],"float32",pSelectedScores);return[selectedIndicesTensor,selectedScoresTensor]}var nonMaxSuppressionV5Config3={kernelName:NonMaxSuppressionV5,backendName:"wasm",setupFunc:setup25,kernelFunc:kernelFunc2},supportsFullBroadcast12=!1,notEqualConfig3=createBinaryKernelConfig(NotEqual,supportsFullBroadcast12,"bool"),wasmOneHot;function setup26(backend22){wasmOneHot=backend22.wasm.cwrap(OneHot,null,["number","number","number","number","number"])}function oneHot4(args){let{inputs,backend:backend22,attrs}=args,{indices}=inputs,{depth,onValue,offValue}=attrs,out=backend22.makeOutput([...indices.shape,depth],"int32"),outId=backend22.dataIdMap.get(out.dataId).id,indicesId=backend22.dataIdMap.get(indices.dataId).id;return wasmOneHot(indicesId,depth,onValue,offValue,outId),out}var oneHotConfig3={kernelName:OneHot,backendName:"wasm",setupFunc:setup26,kernelFunc:oneHot4};function onesLike4(args){let{inputs:{x},backend:backend22}=args,out=backend22.makeOutput(x.shape,x.dtype);return backend22.typedArrayFromHeap(out).fill(1),out}var onesLikeConfig3={kernelName:OnesLike,backendName:"wasm",kernelFunc:onesLike4};function pack3(args){let{inputs,backend:backend22,attrs}=args,{axis}=attrs;if(inputs.length===1)return expandDims4({inputs:{input:inputs[0]},backend:backend22,attrs:{dim:axis}});let shape=inputs[0].shape,dtype=inputs[0].dtype;inputs.forEach(t=>{util_exports.assertShapesMatch(shape,t.shape,"All tensors passed to stack must have matching shapes"),util_exports.assert(dtype===t.dtype,()=>"All tensors passed to stack must have matching dtypes")});let expandedTensors=inputs.map(t=>expandDims4({inputs:{input:t},backend:backend22,attrs:{dim:axis}}));return concat4({inputs:expandedTensors,backend:backend22,attrs:{axis}})}var packConfig3={kernelName:Pack,backendName:"wasm",kernelFunc:pack3},wasmPadV2;function setup27(backend22){wasmPadV2=backend22.wasm.cwrap(PadV2,null,["number","array","number","number","array","array","number","number"])}function pad2(args){let{inputs:{x},backend:backend22,attrs:{paddings,constantValue}}=args,outShape=paddings.map((p2,i)=>p2[0]+x.shape[i]+p2[1]),xId=backend22.dataIdMap.get(x.dataId).id,out=backend22.makeOutput(outShape,x.dtype),outId=backend22.dataIdMap.get(out.dataId).id,xShapeBytes=new Uint8Array(new Int32Array(x.shape).buffer),prePaddingsFlat=paddings.map(padTuple=>padTuple[0]),postPaddingsFlat=paddings.map(padTuple=>padTuple[1]),prePaddingsBytes=new Uint8Array(new Int32Array(prePaddingsFlat).buffer),postPaddingsBytes=new Uint8Array(new Int32Array(postPaddingsFlat).buffer);return wasmPadV2(xId,xShapeBytes,x.shape.length,CppDType[x.dtype],prePaddingsBytes,postPaddingsBytes,constantValue,outId),out}var padV2Config3={kernelName:PadV2,backendName:"wasm",kernelFunc:pad2,setupFunc:setup27},supportsFullBroadcast13=!1,powConfig3=createBinaryKernelConfig(Pow,supportsFullBroadcast13),wasmPrelu;function setup28(backend22){wasmPrelu=backend22.wasm.cwrap(Prelu,null,["number","number","number"])}function prelu4(args){let{inputs,backend:backend22}=args,{x,alpha}=inputs,xId=backend22.dataIdMap.get(x.dataId).id,weightsId=backend22.dataIdMap.get(alpha.dataId).id,out=backend22.makeOutput(x.shape,"float32"),outId=backend22.dataIdMap.get(out.dataId).id;return wasmPrelu(xId,weightsId,outId),out}var preluConfig3={kernelName:Prelu,backendName:"wasm",setupFunc:setup28,kernelFunc:prelu4},wasmProd;function setup29(backend22){wasmProd=backend22.wasm.cwrap(Prod,null,["number","number","number","number"])}function prod4(args){let{backend:backend22,inputs,attrs}=args,{axis,keepDims}=attrs,{x}=inputs,xId=backend22.dataIdMap.get(x.dataId).id,inputId=xId,input2=x,{transposed,axes,originalAxes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend22),reductionAxes=axes;if(inputWasTransposed){let transposedId=backend22.dataIdMap.get(transposed.dataId).id;transposedId!==xId&&(input2=transposed,inputId=transposedId,reductionAxes=backend_util_exports.getInnerMostAxes(reductionAxes.length,input2.shape.length))}backend_util_exports.assertAxesAreInnerMostDims("prod",reductionAxes,input2.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(input2.shape,reductionAxes),reduceSize=util_exports.sizeFromShape(reduceShape),out=backend22.makeOutput(outShape,input2.dtype);if(util_exports.sizeFromShape(input2.shape)!==0){let outId=backend22.dataIdMap.get(out.dataId).id;wasmProd(inputId,reduceSize,CppDType[out.dtype],outId)}if(inputWasTransposed&&backend22.disposeData(transposed.dataId),keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(out.shape,originalAxes);out.shape=newShape}return out}var prodConfig3={kernelName:Prod,backendName:"wasm",setupFunc:setup29,kernelFunc:prod4},range4=args=>{let{backend:backend22,attrs}=args,{start,stop,step:step4,dtype}=attrs,values=rangeImpl(start,stop,step4,dtype),out=backend22.makeOutput([values.length],dtype);return backend22.typedArrayFromHeap(out).set(values),out},rangeConfig3={kernelName:Range,backendName:"wasm",kernelFunc:range4},supportsFullBroadcast14=!0,realDivConfig3=createBinaryKernelConfig(RealDiv,supportsFullBroadcast14),reluConfig3=createUnaryKernelConfig(Relu),relu6Config3=createUnaryKernelConfig(Relu6),wasmResizeBilinear;function setup30(backend22){wasmResizeBilinear=backend22.wasm.cwrap(ResizeBilinear,null,["number","number","number","number","number","number","number","number","number","number"])}function resizeBilinear4(args){let{backend:backend22,inputs,attrs}=args,{images}=inputs,{alignCorners,halfPixelCenters,size}=attrs,[newHeight,newWidth]=size,[batch,oldHeight,oldWidth,numChannels]=images.shape,outShape=[batch,newHeight,newWidth,numChannels],xData=backend22.dataIdMap.get(images.dataId),castedData;xData.dtype!=="float32"&&(castedData=cast4({backend:backend22,inputs:{x:images},attrs:{dtype:"float32"}}),xData=backend22.dataIdMap.get(castedData.dataId));let xId=xData.id,out=backend22.makeOutput(outShape,"float32");if(util_exports.sizeFromShape(images.shape)===0)return out;let outId=backend22.dataIdMap.get(out.dataId).id;return wasmResizeBilinear(xId,batch,oldHeight,oldWidth,numChannels,newHeight,newWidth,alignCorners?1:0,halfPixelCenters?1:0,outId),castedData!=null&&backend22.disposeData(castedData.dataId),out}var resizeBilinearConfig3={kernelName:ResizeBilinear,backendName:"wasm",setupFunc:setup30,kernelFunc:resizeBilinear4},wasmReverse;function setup31(backend22){wasmReverse=backend22.wasm.cwrap(Reverse,null,["number","array","number","array","number","number"])}function reverse4(args){let{inputs,backend:backend22,attrs}=args,{x}=inputs,{dims}=attrs,axes=util_exports.parseAxisParam(dims,x.shape);if(x.shape.length===0)return identity3({inputs:{x},backend:backend22});let out=backend22.makeOutput(x.shape,x.dtype),xId=backend22.dataIdMap.get(x.dataId).id,outId=backend22.dataIdMap.get(out.dataId).id,axesBytes=new Uint8Array(new Int32Array(axes).buffer),outShapeBytes=new Uint8Array(new Int32Array(x.shape).buffer);return wasmReverse(xId,axesBytes,axes.length,outShapeBytes,x.shape.length,outId),reshape4({inputs:{x:out},attrs:{shape:x.shape},backend:backend22})}var reverseConfig3={kernelName:Reverse,backendName:"wasm",kernelFunc:reverse4,setupFunc:setup31},wasmRotate;function setup32(backend22){wasmRotate=backend22.wasm.cwrap(RotateWithOffset,null,["number","number","number","number","number","number","number","number","array","number","number"])}function rotateWithOffset2(args){let{inputs,backend:backend22,attrs}=args,{image:image3}=inputs,{radians,fillValue,center}=attrs,out=backend22.makeOutput(image3.shape,image3.dtype),imageId=backend22.dataIdMap.get(image3.dataId).id,outId=backend22.dataIdMap.get(out.dataId).id,[batch,imageHeight,imageWidth,numChannels]=image3.shape,[centerX,centerY]=backend_util_exports.getImageCenter(center,imageHeight,imageWidth),fillIsBlack=fillValue===0,fullOpacityValue=255,fillValues2=typeof fillValue=="number"?[fillValue,fillValue,fillValue,fillIsBlack?0:fullOpacityValue]:[...fillValue,fullOpacityValue],fillBytes=new Uint8Array(new Int32Array(fillValues2).buffer);return wasmRotate(imageId,batch,imageHeight,imageWidth,numChannels,radians,centerX,centerY,fillBytes,fillValues2.length,outId),out}var rotateWithOffsetConfig3={kernelName:RotateWithOffset,backendName:"wasm",kernelFunc:rotateWithOffset2,setupFunc:setup32},roundConfig3=createUnaryKernelConfig(Round),rsqrtConfig3=createUnaryKernelConfig(Rsqrt),wasmScatterNd;function setup33(backend22){wasmScatterNd=backend22.wasm.cwrap(ScatterNd,null,["number","number","number","number","number","number","array","number","number"])}function scatterNd3(args){let{backend:backend22,inputs,attrs}=args,{indices,updates}=inputs,{shape}=attrs,out=backend22.makeOutput(shape,updates.dtype);if(util_exports.sizeFromShape(shape)===0)return out;let{sliceRank,numUpdates,sliceSize,strides,outputSize}=scatter_nd_util_exports.calculateShapes(updates,indices,shape),indicesId=backend22.dataIdMap.get(indices.dataId).id,updatesId=backend22.dataIdMap.get(updates.dataId).id,stridesBytes=new Uint8Array(new Int32Array(strides).buffer),outId=backend22.dataIdMap.get(out.dataId).id;return wasmScatterNd(indicesId,updatesId,CppDType[updates.dtype],sliceRank,numUpdates,sliceSize,stridesBytes,outputSize,outId),out}var scatterNdConfig3={kernelName:ScatterNd,backendName:"wasm",setupFunc:setup33,kernelFunc:scatterNd3},wasmSelect;function setup34(backend22){wasmSelect=backend22.wasm.cwrap("SelectV2",null,["number","number","number","number","number"])}function select3(args){let{inputs,backend:backend22}=args,{condition,t,e}=inputs,conditionId=backend22.dataIdMap.get(condition.dataId).id,tId=backend22.dataIdMap.get(t.dataId).id,eId=backend22.dataIdMap.get(e.dataId).id,out=backend22.makeOutput(t.shape,t.dtype),outId=backend22.dataIdMap.get(out.dataId).id,cRank=condition.shape.length,tRank=t.shape.length,offset=cRank===0||cRank>1||tRank===1?1:util_exports.sizeFromShape(t.shape.slice(1));return wasmSelect(conditionId,tId,eId,offset,outId),out}var selectConfig3={kernelName:Select,backendName:"wasm",kernelFunc:select3,setupFunc:setup34},wasmFunc7;function setup35(backend22){wasmFunc7=backend22.wasm.cwrap(Sigmoid,null,["number","number"])}function sigmoid4(args){let{backend:backend22,inputs:{x}}=args,xId=backend22.dataIdMap.get(x.dataId).id,out=backend22.makeOutput(x.shape,x.dtype),outId=backend22.dataIdMap.get(out.dataId).id;return util_exports.sizeFromShape(out.shape)===0||wasmFunc7(xId,outId),out}var sigmoidConfig3={kernelName:"Sigmoid",backendName:"wasm",setupFunc:setup35,kernelFunc:sigmoid4},sinConfig3=createUnaryKernelConfig(Sin);function slice4(args){let{inputs:{x},attrs:{begin,size},backend:backend22}=args,[begin_,size_]=slice_util_exports.parseSliceParams(x,begin,size),isContinous=slice_util_exports.isSliceContinous(x.shape,begin_,size_),xVals=backend22.readSync(x.dataId),out=backend22.makeOutput(size_,x.dtype),xStrides=util_exports.computeStrides(x.shape),outData=backend22.dataIdMap.get(out.dataId);if(isContinous){let flatOffset=slice_util_exports.computeFlatOffset(begin_,xStrides);return x.dtype==="string"?outData.stringBytes=xVals.slice(flatOffset,flatOffset+util_exports.sizeFromShape(size_)):backend22.typedArrayFromHeap(out).set(xVals.subarray(flatOffset,flatOffset+util_exports.sizeFromShape(size_))),out}if(x.dtype==="string"){let res=sliceImpl(xVals,begin_,size_,x.shape,x.dtype);return outData.stringBytes=res,out}let outVals=backend22.typedArrayFromHeap(out),rank=x.shape.length;if(rank===2)slice2d2(xVals,xStrides[0],outVals,begin_,size_);else if(rank===3)slice3d2(xVals,xStrides[0],xStrides[1],outVals,begin_,size_);else if(rank===4)slice4d2(xVals,xStrides[0],xStrides[1],xStrides[2],outVals,begin_,size_);else{let res=sliceImpl(xVals,begin_,size_,x.shape,x.dtype);outVals.set(res)}return out}function slice2d2(xVals,xStride,outVals,begin,size){let outOffset=0,beginI=begin[0],beginJ=begin[1],endI=beginI+size[0];for(let i=beginI;i{let xSliceSize=[...size];xSliceSize[$axis]=s;let xSlice=slice4({inputs:{x},attrs:{begin,size:xSliceSize},backend:backend22});return begin[$axis]+=s,xSlice})}var splitVConfig3={kernelName:SplitV,backendName:"wasm",kernelFunc:splitV3},sqrtConfig3=createUnaryKernelConfig(Sqrt),squareConfig3=createUnaryKernelConfig(Square),supportsFullBroadcast15=!0,squaredDifferenceConfig3=createBinaryKernelConfig(SquaredDifference,supportsFullBroadcast15),wasmStridedSlice;function setup37(backend22){wasmStridedSlice=backend22.wasm.cwrap(StridedSlice,null,["number","array","number","array","array","array","array","array","number","number"])}function stridedSlice4(args){let{backend:backend22,inputs,attrs}=args,{x}=inputs,{begin,end,strides}=attrs;strides==null&&(strides=new Array(begin.length));let{beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask}=attrs,ellipsisAxes=backend_util_exports.slice_util.maskToAxes(ellipsisMask);if(ellipsisAxes.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(ellipsisMask!==0&&newAxisMask!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(ellipsisMask!==0&&shrinkAxisMask!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");let numInterpolatedAxes=x.shape.length-begin.length,expandAxes=backend_util_exports.slice_util.maskToAxes(newAxisMask),newShape=x.shape.slice();expandAxes.forEach(axis=>{begin[axis]=0,end[axis]=1,newShape.splice(axis,0,1)});let xReshaped=reshape4({inputs:{x},attrs:{shape:newShape},backend:backend22}),{begin:normalizedBegin,end:normalizedEnd,strides:normalizedStrides}=backend_util_exports.slice_util.getNormalizedAxes(xReshaped.shape,ellipsisAxes,numInterpolatedAxes,begin,end,strides,beginMask,endMask,ellipsisMask);begin=normalizedBegin,end=normalizedEnd,strides=normalizedStrides;let shrinkAxes=backend_util_exports.slice_util.maskToAxes(shrinkAxisMask);shrinkAxes.forEach(axis=>{end[axis]=begin[axis]+1,strides[axis]=1});let size=backend_util_exports.slice_util.computeOutShape(begin,end,strides),outShape=size.filter((_,axis)=>shrinkAxes.indexOf(axis)===-1);if(strides.every(v=>v===1)){let xSliced=slice4({inputs:{x},attrs:{begin,size},backend:backend22});return reshape4({inputs:{x:xSliced},attrs:{shape:outShape},backend:backend22})}let out=backend22.makeOutput(outShape,"float32");if(!outShape.some(axis=>axis===0)){let xId=backend22.dataIdMap.get(xReshaped.dataId).id,xStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(xReshaped.shape)).buffer),beginBytes=new Uint8Array(new Int32Array(begin).buffer),endBytes=new Uint8Array(new Int32Array(end).buffer),stridesBytes=new Uint8Array(new Int32Array(strides).buffer),outputShapeBytes=new Uint8Array(new Int32Array(outShape).buffer),outStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(outShape)).buffer),outId=backend22.dataIdMap.get(out.dataId).id;wasmStridedSlice(xId,xStridesBytes,xReshaped.shape.length,beginBytes,endBytes,stridesBytes,outputShapeBytes,outStridesBytes,outShape.length,outId)}return reshape4({inputs:{x:out},attrs:{shape:outShape},backend:backend22})}var stridedSliceConfig3={kernelName:StridedSlice,backendName:"wasm",setupFunc:setup37,kernelFunc:stridedSlice4},supportsFullBroadcast16=!0,subConfig3=createBinaryKernelConfig(Sub,supportsFullBroadcast16),wasmSum;function setup38(backend22){wasmSum=backend22.wasm.cwrap(Sum,null,["number, number, number"])}function sum5(args){let{backend:backend22,inputs,attrs}=args,{axis,keepDims}=attrs,{x}=inputs,xId=backend22.dataIdMap.get(x.dataId).id,inputId=xId,input2=x,{transposed,axes,originalAxes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend22),reductionAxes=axes;if(inputWasTransposed){let transposedId=backend22.dataIdMap.get(transposed.dataId).id;transposedId!==xId&&(input2=transposed,inputId=transposedId,reductionAxes=backend_util_exports.getInnerMostAxes(reductionAxes.length,input2.shape.length))}backend_util_exports.assertAxesAreInnerMostDims("sum",reductionAxes,input2.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(input2.shape,reductionAxes),reduceSize=util_exports.sizeFromShape(reduceShape),out=backend22.makeOutput(outShape,input2.dtype);if(util_exports.sizeFromShape(input2.shape)!==0){let outId=backend22.dataIdMap.get(out.dataId).id;wasmSum(inputId,reduceSize,outId)}if(inputWasTransposed&&backend22.disposeData(transposed.dataId),keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(out.shape,originalAxes);out.shape=newShape}return out}var sumConfig3={kernelName:Sum,backendName:"wasm",setupFunc:setup38,kernelFunc:sum5},tanhConfig3=createUnaryKernelConfig(Tanh),wasmTile;function setup39(backend22){wasmTile=backend22.wasm.cwrap(Tile,null,["number","array","number","array","number","number"])}function tile4(args){let{inputs,backend:backend22,attrs}=args,{x}=inputs,xId=backend22.dataIdMap.get(x.dataId).id,{reps}=attrs,newShape=new Array(x.shape.length);for(let i=0;i{let{x}=inputs,{k,sorted}=attrs,xId=backend22.dataIdMap.get(x.dataId).id,xShapeBytes=new Uint8Array(new Int32Array(x.shape).buffer),outputShape=x.shape.slice();outputShape[outputShape.length-1]=k;let outValues=backend22.makeOutput(outputShape,x.dtype),outValuesId=backend22.dataIdMap.get(outValues.dataId).id,outIndices=backend22.makeOutput(outputShape,"int32"),outIndicesId=backend22.dataIdMap.get(outIndices.dataId).id;return wasmTopK(xId,xShapeBytes,x.shape.length,CppDType[x.dtype],k,sorted,outValuesId,outIndicesId),[outValues,outIndices]},topKConfig3={kernelName:TopK,backendName:"wasm",setupFunc:setup40,kernelFunc:topk2};function unpack3(args){let{inputs,backend:backend22,attrs}=args,{value}=inputs,{axis}=attrs;axis<0&&(axis+=value.shape.length);let numOutputs=value.shape[axis],rank=value.shape.length,outShape=new Array(rank-1),outIndex=0;for(let i=0;i({dataId,dtype,shape:outShape}))}var unpackConfig3={kernelName:Unpack,backendName:"wasm",kernelFunc:unpack3};function zerosLike4(args){let{inputs:{x},backend:backend22}=args,out=backend22.makeOutput(x.shape,x.dtype);return backend22.typedArrayFromHeap(out).fill(0),out}var zerosLikeConfig3={kernelName:ZerosLike,backendName:"wasm",kernelFunc:zerosLike4},kernelConfigs3=[absConfig3,addConfig3,addNConfig3,argMaxConfig3,avgPoolConfig3,batchMatMulConfig3,castConfig3,clipByValueConfig2,concatConfig3,conv2DConfig3,conv2DBackpropInputConfig3,cosConfig3,cropAndResizeConfig3,cumsumConfig3,depthToSpaceConfig3,depthwiseConv2dNativeConfig3,equalConfig3,expConfig3,expandDimsConfig3,fillConfig3,flipLeftRightConfig3,floorConfig3,floorDivConfig3,fusedMatMulConfig,fusedBatchNormConfig,fusedConv2DConfig3,fusedDepthwiseConv2DConfig3,gatherNdConfig3,gatherV2Config3,greaterConfig3,greaterEqualConfig3,identityConfig3,leakyReluConfig3,lessConfig3,lessEqualConfig3,logConfig3,logicalAndConfig3,maxConfig3,maximumConfig3,maxPoolConfig3,meanConfig3,minConfig3,minimumConfig3,multiplyConfig3,negConfig3,nonMaxSuppressionV3Config3,nonMaxSuppressionV4Config3,nonMaxSuppressionV5Config3,notEqualConfig3,oneHotConfig3,onesLikeConfig3,packConfig3,padV2Config3,powConfig3,preluConfig3,prodConfig3,rangeConfig3,realDivConfig3,reluConfig3,relu6Config3,reshapeConfig3,resizeBilinearConfig3,reverseConfig3,rotateWithOffsetConfig3,rsqrtConfig3,roundConfig3,scatterNdConfig3,selectConfig3,sigmoidConfig3,sinConfig3,sliceConfig3,softmaxConfig3,splitVConfig3,sqrtConfig3,squareConfig3,squaredDifferenceConfig3,stridedSliceConfig3,subConfig3,sumConfig3,tanhConfig3,tileConfig3,topKConfig3,transposeConfig3,unpackConfig3,zerosLikeConfig3];for(let kernelConfig of kernelConfigs3)registerKernel(kernelConfig);var ENV4=env();ENV4.registerFlag("WASM_HAS_SIMD_SUPPORT",async()=>WebAssembly.validate(new Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,2,1,0,10,9,1,7,0,65,0,253,15,26,11])));ENV4.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT",async()=>{if(ENV4.get("IS_NODE"))return!1;try{return new MessageChannel().port1.postMessage(new SharedArrayBuffer(1)),WebAssembly.validate(new Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,2,1,0,5,4,1,3,1,1,10,11,1,9,0,65,0,254,16,2,0,26,11]))}catch(e){return!1}});var import_tfjs_backend_wasm_threaded_simd=__toModule2(require_tfjs_backend_wasm_threaded_simd()),wasmWorkerContents='var threadInfoStruct=0;var selfThreadId=0;var parentThreadId=0;var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(" ");console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(" ");postMessage({cmd:"alert",text:text,threadId:selfThreadId})}var err=threadPrintErr;this.alert=threadAlert;Module["instantiateWasm"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module["wasmModule"],info);Module["wasmModule"]=null;receiveInstance(instance);return instance.exports};this.onmessage=function(e){try{if(e.data.cmd==="load"){Module["DYNAMIC_BASE"]=e.data.DYNAMIC_BASE;Module["DYNAMICTOP_PTR"]=e.data.DYNAMICTOP_PTR;Module["wasmModule"]=e.data.wasmModule;Module["wasmMemory"]=e.data.wasmMemory;Module["buffer"]=Module["wasmMemory"].buffer;Module["ENVIRONMENT_IS_PTHREAD"]=true;if(typeof e.data.urlOrBlob==="string"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}Module=WasmBackendModuleThreadedSimd(Module);postMessage({"cmd":"loaded"})}else if(e.data.cmd==="objectTransfer"){Module["PThread"].receiveObjectTransfer(e.data)}else if(e.data.cmd==="run"){Module["__performance_now_clock_drift"]=performance.now()-e.data.time;threadInfoStruct=e.data.threadInfoStruct;Module["__register_pthread_ptr"](threadInfoStruct,0,0);selfThreadId=e.data.selfThreadId;parentThreadId=e.data.parentThreadId;var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module["establishStackSpace"](top,max);Module["_emscripten_tls_init"]();Module["PThread"].receiveObjectTransfer(e.data);Module["PThread"].setThreadStatus(Module["_pthread_self"](),1);try{var result=Module["dynCall_ii"](e.data.start_routine,e.data.arg);if(!Module["getNoExitRuntime"]())Module["PThread"].threadExit(result)}catch(ex){if(ex==="Canceled!"){Module["PThread"].threadCancel()}else if(ex!="unwind"){Atomics.store(Module["HEAPU32"],threadInfoStruct+4>>2,ex instanceof Module["ExitStatus"]?ex.status:-2);Atomics.store(Module["HEAPU32"],threadInfoStruct+0>>2,1);Module["_emscripten_futex_wake"](threadInfoStruct+0,2147483647);if(!(ex instanceof Module["ExitStatus"]))throw ex}}}else if(e.data.cmd==="cancel"){if(threadInfoStruct){Module["PThread"].threadCancel()}}else if(e.data.target==="setimmediate"){}else if(e.data.cmd==="processThreadQueue"){if(threadInfoStruct){Module["_emscripten_current_thread_process_queued_calls"]()}}else{err("worker.js received unknown command "+e.data.cmd);err(e.data)}}catch(ex){err("worker.js onmessage() captured an uncaught exception: "+ex);if(ex.stack)err(ex.stack);throw ex}};if(typeof process==="object"&&typeof process.versions==="object"&&typeof process.versions.node==="string"){self={location:{href:__filename}};var onmessage=this.onmessage;var nodeWorkerThreads=require("worker_threads");Worker=nodeWorkerThreads.Worker;var parentPort=nodeWorkerThreads.parentPort;parentPort.on("message",function(data){onmessage({data:data})});var nodeFS=require("fs");var nodeRead=function(filename){return nodeFS.readFileSync(filename,"utf8")};function globalEval(x){global.require=require;global.Module=Module;eval.call(null,x)}importScripts=function(f){globalEval(nodeRead(f))};postMessage=function(msg){parentPort.postMessage(msg)};if(typeof performance==="undefined"){performance={now:function(){return Date.now()}}}}',import_tfjs_backend_wasm=__toModule2(require_tfjs_backend_wasm()),BackendWasm=class extends KernelBackend{constructor(wasm){super();this.wasm=wasm,this.dataIdNextNumber=1,this.wasm.tfjs.init(),this.dataIdMap=new DataStorage(this,engine())}write(values,shape,dtype){let dataId={};return this.move(dataId,values,shape,dtype),dataId}numDataIds(){return this.dataIdMap.numDataIds()}async time(f){let start=util_exports.now();return f(),{kernelMs:util_exports.now()-start}}move(dataId,values,shape,dtype){let id=this.dataIdNextNumber++;if(dtype==="string"){let stringBytes=values;this.dataIdMap.set(dataId,{id,stringBytes,shape,dtype,memoryOffset:null});return}let size=util_exports.sizeFromShape(shape),numBytes=size*util_exports.bytesPerElement(dtype),memoryOffset=this.wasm._malloc(numBytes);this.dataIdMap.set(dataId,{id,memoryOffset,shape,dtype}),this.wasm.tfjs.registerTensor(id,size,memoryOffset),values!=null&&this.wasm.HEAPU8.set(new Uint8Array(values.buffer,values.byteOffset,numBytes),memoryOffset)}async read(dataId){return this.readSync(dataId)}readSync(dataId){let{memoryOffset,dtype,shape,stringBytes}=this.dataIdMap.get(dataId);if(dtype==="string")return stringBytes;let bytes=this.wasm.HEAPU8.slice(memoryOffset,memoryOffset+util_exports.sizeFromShape(shape)*util_exports.bytesPerElement(dtype));return typedArrayFromBuffer(bytes.buffer,dtype)}disposeData(dataId){let data2=this.dataIdMap.get(dataId);this.wasm._free(data2.memoryOffset),this.wasm.tfjs.disposeData(data2.id),this.dataIdMap.delete(dataId)}floatPrecision(){return 32}getMemoryOffset(dataId){return this.dataIdMap.get(dataId).memoryOffset}dispose(){this.wasm.tfjs.dispose(),this.wasm=null}memory(){return{unreliable:!1}}makeOutput(shape,dtype,memoryOffset){let dataId;if(memoryOffset==null)dataId=this.write(null,shape,dtype);else{dataId={};let id=this.dataIdNextNumber++;this.dataIdMap.set(dataId,{id,memoryOffset,shape,dtype});let size=util_exports.sizeFromShape(shape);this.wasm.tfjs.registerTensor(id,size,memoryOffset)}return{dataId,shape,dtype}}typedArrayFromHeap({shape,dtype,dataId}){let buffer2=this.wasm.HEAPU8.buffer,{memoryOffset}=this.dataIdMap.get(dataId),size=util_exports.sizeFromShape(shape);switch(dtype){case"float32":return new Float32Array(buffer2,memoryOffset,size);case"int32":return new Int32Array(buffer2,memoryOffset,size);case"bool":return new Uint8Array(buffer2,memoryOffset,size);default:throw new Error(`Unknown dtype ${dtype}`)}}};function createInstantiateWasmFunc(path){return(imports,callback)=>(util_exports.fetch(path,{credentials:"same-origin"}).then(response=>{response.ok||imports.env.a(`failed to load wasm binary file at '${path}'`),response.arrayBuffer().then(binary=>{WebAssembly.instantiate(binary,imports).then(output=>{callback(output.instance)})})}),{})}function getPathToWasmBinary(simdSupported,threadsSupported,wasmModuleFolder){if(wasmPath!=null)return wasmPath;let path="tfjs-backend-wasm.wasm";return simdSupported&&threadsSupported?path="tfjs-backend-wasm-threaded-simd.wasm":simdSupported&&(path="tfjs-backend-wasm-simd.wasm"),wasmFileMap!=null&&wasmFileMap[path]!=null?wasmFileMap[path]:wasmModuleFolder+path}async function init(){let[simdSupported,threadsSupported]=await Promise.all([env().getAsync("WASM_HAS_SIMD_SUPPORT"),env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]);return new Promise((resolve,reject)=>{let factoryConfig={};factoryConfig.locateFile=(path,prefix)=>{if(path.endsWith(".worker.js")){let response=wasmWorkerContents,blob=new Blob([response],{type:"application/javascript"});return URL.createObjectURL(blob)}return path.endsWith(".wasm")?getPathToWasmBinary(simdSupported,threadsSupported,wasmPathPrefix!=null?wasmPathPrefix:prefix):prefix+path},customFetch&&(factoryConfig.instantiateWasm=createInstantiateWasmFunc(getPathToWasmBinary(simdSupported,threadsSupported,wasmPathPrefix!=null?wasmPathPrefix:"")));let wasm;threadsSupported&&simdSupported&&wasmPath==null?(wasm=import_tfjs_backend_wasm_threaded_simd.default(factoryConfig),wasm.mainScriptUrlOrBlob=new Blob(["var WasmBackendModuleThreadedSimd = "+import_tfjs_backend_wasm_threaded_simd.default.toString()],{type:"text/javascript"})):wasm=import_tfjs_backend_wasm.default(factoryConfig);let voidReturnType=null;wasm.tfjs={init:wasm.cwrap("init",null,[]),registerTensor:wasm.cwrap("register_tensor",null,["number","number","number"]),disposeData:wasm.cwrap("dispose_data",voidReturnType,["number"]),dispose:wasm.cwrap("dispose",voidReturnType,[])};let initialized=!1;wasm.onRuntimeInitialized=()=>{initialized=!0,initAborted=!1,resolve({wasm})},wasm.onAbort=()=>{initialized||initAborted||(initAborted=!0,reject({message:"Make sure the server can serve the `.wasm` file relative to the bundled js file. For more details see https://github.com/tensorflow/tfjs/blob/master/tfjs-backend-wasm/README.md#using-bundlers"}))}})}function typedArrayFromBuffer(buffer2,dtype){switch(dtype){case"float32":return new Float32Array(buffer2);case"int32":return new Int32Array(buffer2);case"bool":return new Uint8Array(buffer2);default:throw new Error(`Unknown dtype ${dtype}`)}}var wasmBinaryNames=["tfjs-backend-wasm.wasm","tfjs-backend-wasm-simd.wasm","tfjs-backend-wasm-threaded-simd.wasm"],wasmPath=null,wasmPathPrefix=null,wasmFileMap={},initAborted=!1,customFetch=!1;function setWasmPath(path,usePlatformFetch=!1){if(deprecationWarn("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release."),initAborted)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`");wasmPath=path,customFetch=usePlatformFetch}function setWasmPaths(prefixOrFileMap,usePlatformFetch=!1){if(initAborted)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPaths()` before you call `tf.setBackend()` or `tf.ready()`");if(typeof prefixOrFileMap=="string")wasmPathPrefix=prefixOrFileMap;else{wasmFileMap=prefixOrFileMap;let missingPaths=wasmBinaryNames.filter(name=>wasmFileMap[name]==null);if(missingPaths.length>0)throw new Error(`There were no entries found for the following binaries: ${missingPaths.join(",")}. Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`)}customFetch=usePlatformFetch}var version9="2.8.3",WASM_PRIORITY=2;registerBackend("wasm",async()=>{let{wasm}=await init();return new BackendWasm(wasm)},WASM_PRIORITY);var exports_constraints_exports={};__export2(exports_constraints_exports,{maxNorm:()=>maxNorm,minMaxNorm:()=>minMaxNorm,nonNeg:()=>nonNeg,unitNorm:()=>unitNorm});var _epsilon;function epsilon2(){return _epsilon==null&&(_epsilon=backend().epsilon()),_epsilon}function imageDataFormat(){return"channelsLast"}var AttributeError=class extends Error{constructor(message){super(message);Object.setPrototypeOf(this,AttributeError.prototype)}},RuntimeError=class extends Error{constructor(message){super(message);Object.setPrototypeOf(this,RuntimeError.prototype)}},ValueError=class extends Error{constructor(message){super(message);Object.setPrototypeOf(this,ValueError.prototype)}},NotImplementedError=class extends Error{constructor(message){super(message);Object.setPrototypeOf(this,NotImplementedError.prototype)}},AssertionError=class extends Error{constructor(message){super(message);Object.setPrototypeOf(this,AssertionError.prototype)}},IndexError=class extends Error{constructor(message){super(message);Object.setPrototypeOf(this,IndexError.prototype)}};function pyListRepeat(value,numValues){if(Array.isArray(value)){let newArray=[];for(let i=0;ip1.toUpperCase())}var _GLOBAL_CUSTOM_OBJECTS={};function serializeKerasObject(instance2){if(instance2==null)return null;let dict={};return dict.className=instance2.getClassName(),dict.config=instance2.getConfig(),dict}function convertNDArrayScalarsInConfig(config3){if(!(config3==null||typeof config3!="object"))if(Array.isArray(config3))config3.forEach(configItem=>convertNDArrayScalarsInConfig(configItem));else{let fields=Object.keys(config3);for(let field of fields){let value=config3[field];value!=null&&typeof value=="object"&&(!Array.isArray(value)&&value.type==="ndarray"&&typeof value.value=="number"?config3[field]=value.value:convertNDArrayScalarsInConfig(value))}}}function deserializeKerasObject(identifier,moduleObjects={},customObjects={},printableModuleName="object",fastWeightInit=!1){if(typeof identifier=="string"){let functionName=identifier,fn;if(functionName in customObjects)fn=customObjects[functionName];else if(functionName in _GLOBAL_CUSTOM_OBJECTS)fn=_GLOBAL_CUSTOM_OBJECTS[functionName];else if(fn=moduleObjects[functionName],fn==null)throw new ValueError(`Unknown ${printableModuleName}: ${identifier}. This may be due to one of the following reasons: + `}};function unsortedSegmentSum3(args){let{inputs,backend:backend22,attrs}=args,{x,segmentIds}=inputs,{numSegments}=attrs,xRank=x.shape.length,toDispose=[],axis=0,permutation=backend_util_exports.getAxesPermutation([axis],xRank),permutedX=x;permutation!=null&&(permutedX=transpose3({inputs:{x},backend:backend22,attrs:{perm:permutation}}),toDispose.push(permutedX),axis=backend_util_exports.getInnerMostAxes(1,xRank)[0]);let outShape=backend_util_exports.segment_util.computeOutShape(permutedX.shape,axis,numSegments),inSize=util_exports.sizeFromShape([permutedX.shape[axis]]),a2D=reshape3({inputs:{x:permutedX},backend:backend22,attrs:{shape:[-1,inSize]}});toDispose.push(a2D);let outputDType=sumOutType(x.dtype),segOpCompute=(x2,segOpType,segmentIds2,dtype,numSegments2)=>{let batchSize=x2.shape[0],inSize2=x2.shape[1],windowSize=backend_util_exports.segment_util.segOpComputeOptimalWindowSize(inSize2,numSegments2),segOpInfo={windowSize,inSize:inSize2,batchSize,numSegments:numSegments2},program=new SegmentOpProgram(segOpInfo,segOpType),output=backend22.compileAndRun(program,[x2,segmentIds2],dtype);if(toDispose.push(output),output.shape[1]===numSegments2)return output;let rangeInfo=range3({backend:backend22,attrs:{start:0,stop:numSegments2,step:1,dtype:"float32"}}),tileInfo=tile3({inputs:{x:rangeInfo},backend:backend22,attrs:{reps:[inSize2/windowSize]}});return toDispose.push(rangeInfo),toDispose.push(tileInfo),segOpCompute(output,segOpType,tileInfo,dtype,numSegments2)},segOpResult=segOpCompute(a2D,"unsortedSegmentSum",segmentIds,outputDType,numSegments),reshaped=reshape3({inputs:{x:segOpResult},backend:backend22,attrs:{shape:outShape}}),result=reshaped;if(permutation!=null){toDispose.push(reshaped);let perm=backend_util_exports.getUndoAxesPermutation(permutation);result=transpose3({inputs:{x:result},backend:backend22,attrs:{perm}})}return toDispose.forEach(t=>backend22.disposeIntermediateTensorInfo(t)),result}var unsortedSegmentSumConfig2={kernelName:UnsortedSegmentSum,backendName:"webgl",kernelFunc:unsortedSegmentSum3},kernelConfigs2=[LRNConfig,LRNGradConfig,_fusedMatMulConfig2,absConfig2,acosConfig2,acoshConfig2,addConfig2,addNConfig2,allConfig2,anyConfig2,argMaxConfig2,argMinConfig2,asinConfig2,asinhConfig2,atan2Config2,atanConfig2,atanhConfig2,avgPool3DConfig2,avgPoolConfig2,avgPoolGrad3DConfig,avgPoolGradConfig3,batchMatMulConfig2,batchNormConfig2,batchToSpaceNDConfig2,bincountConfig2,castConfig2,ceilConfig2,clipByValueConfig,complexAbsConfig2,complexConfig2,concatConfig2,conv2DBackpropFilterConfig2,conv2DBackpropInputConfig2,conv2DConfig2,conv3DBackpropFilterV2Config2,conv3DBackpropInputConfig,conv3DConfig2,cosConfig2,coshConfig2,cropAndResizeConfig2,cumsumConfig2,denseBincountConfig2,depthToSpaceConfig2,depthwiseConv2dNativeBackpropFilterConfig2,depthwiseConv2dNativeBackpropInputConfig2,depthwiseConv2dNativeConfig2,diagConfig2,dilation2DConfig,eluConfig2,eluGradConfig3,equalConfig2,erfConfig2,expConfig2,expandDimsConfig2,expm1Config2,fftConfig2,fillConfig2,flipLeftRightConfig2,floorConfig2,floorDivConfig2,fromPixelsConfig,fusedConv2DConfig2,fusedDepthwiseConv2DConfig2,gatherNdConfig2,gatherV2Config2,greaterConfig2,greaterEqualConfig2,identityConfig2,ifftConfig2,imagConfig2,isFiniteConfig2,isInfConfig2,isNaNConfig2,leakyReluConfig2,lessConfig2,lessEqualConfig2,linSpaceConfig2,log1pConfig2,logConfig2,logicalAndConfig2,logicalNotConfig2,logicalOrConfig2,maxConfig2,maxPool3DConfig2,maxPoolConfig2,maxPoolGrad3DConfig,maxPoolGradConfig3,maxPoolWithArgmaxConfig2,maximumConfig2,meanConfig2,minConfig2,minimumConfig2,mirrorPadConfig2,modConfig2,multinomialConfig2,multiplyConfig2,negConfig2,nonMaxSuppressionV3Config2,nonMaxSuppressionV4Config2,nonMaxSuppressionV5Config2,notEqualConfig2,oneHotConfig2,onesLikeConfig2,packConfig2,padV2Config2,powConfig2,preluConfig2,prodConfig2,rangeConfig2,realConfig2,realDivConfig2,reciprocalConfig2,relu6Config2,reluConfig2,reshapeConfig2,resizeBilinearConfig2,resizeBilinearGradConfig3,resizeNearestNeighborConfig2,resizeNearestNeighborGradConfig3,reverseConfig2,rotateWithOffsetConfig2,roundConfig2,rsqrtConfig2,scatterNdConfig2,selectConfig2,seluConfig2,sigmoidConfig2,signConfig2,sinConfig2,sinhConfig2,sliceConfig2,softmaxConfig2,softplusConfig2,spaceToBatchNDConfig2,sparseToDenseConfig2,splitVConfig2,sqrtConfig2,squareConfig2,squaredDifferenceConfig2,stepConfig2,stridedSliceConfig2,subConfig2,sumConfig2,tanConfig2,tanhConfig2,tileConfig2,topKConfig2,transposeConfig2,uniqueConfig2,unpackConfig2,unsortedSegmentSumConfig2,zerosLikeConfig2];for(let kernelConfig of kernelConfigs2)registerKernel(kernelConfig);var CppDType;(function(CppDType2){CppDType2[CppDType2.float32=0]="float32",CppDType2[CppDType2.int32=1]="int32",CppDType2[CppDType2.bool=2]="bool",CppDType2[CppDType2.string=3]="string",CppDType2[CppDType2.complex64=4]="complex64"})(CppDType||(CppDType={}));var FusableActivation;(function(FusableActivation2){FusableActivation2[FusableActivation2.linear=0]="linear",FusableActivation2[FusableActivation2.relu=1]="relu",FusableActivation2[FusableActivation2.relu6=2]="relu6",FusableActivation2[FusableActivation2.prelu=3]="prelu",FusableActivation2[FusableActivation2.leakyrelu=4]="leakyrelu"})(FusableActivation||(FusableActivation={}));var wasmFusedMatMul;function setup(backend22){wasmFusedMatMul=backend22.wasm.cwrap(_FusedMatMul,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function fusedBatchMatMul(args){let{inputs,backend:backend22,attrs}=args,{a,b,bias,preluActivationWeights}=inputs;if(a.dtype!=="float32"||b.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA,transposeB,activation:activation2,leakyreluAlpha}=attrs,aId=backend22.dataIdMap.get(a.dataId).id,bId=backend22.dataIdMap.get(b.dataId).id,biasId=0;if(bias!=null){let biasData=backend22.dataIdMap.get(bias.dataId);if(biasData.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${biasData.shape.length}.`);biasId=biasData.id}let preluActivationWeightsId=preluActivationWeights==null?0:backend22.dataIdMap.get(preluActivationWeights.dataId).id,fusedActivation=FusableActivation[activation2];if(fusedActivation==null)throw new Error(`${activation2} activation not yet supported for FusedConv2D in the wasm backend.`);let leftDim=transposeA?a.shape[2]:a.shape[1],rightDim=transposeB?b.shape[1]:b.shape[2],batchDim=a.shape[0],out=backend22.makeOutput([batchDim,leftDim,rightDim],a.dtype),outId=backend22.dataIdMap.get(out.dataId).id,aShapeBytes=new Uint8Array(new Int32Array(a.shape).buffer),bShapeBytes=new Uint8Array(new Int32Array(b.shape).buffer);return wasmFusedMatMul(aId,aShapeBytes,a.shape.length,bId,bShapeBytes,b.shape.length,transposeA,transposeB,fusedActivation,biasId,preluActivationWeightsId,leakyreluAlpha||0,outId),out}var fusedMatMulConfig={kernelName:_FusedMatMul,backendName:"wasm",setupFunc:setup,kernelFunc:fusedBatchMatMul};function createUnaryKernelConfig(kernelName){let wasmFunc9;function setupFunc3(backend22){wasmFunc9=backend22.wasm.cwrap(kernelName,null,["number","number"])}function kernelFunc3(args){let{backend:backend22,inputs:{x}}=args,xId=backend22.dataIdMap.get(x.dataId).id,out=backend22.makeOutput(x.shape,x.dtype),outId=backend22.dataIdMap.get(out.dataId).id;return util_exports.sizeFromShape(out.shape)===0||wasmFunc9(xId,outId),out}return{kernelName,backendName:"wasm",setupFunc:setupFunc3,kernelFunc:kernelFunc3}}var absConfig3=createUnaryKernelConfig(Abs);function createBinaryKernelConfig(kernelName,supportsFullBroadcast17,dtype){let wasmFunc9;function setupFunc3(backend22){wasmFunc9=backend22.wasm.cwrap(kernelName,null,["number","array","number","number","array","number","number","number"])}function kernelFunc3(args){let{backend:backend22,inputs}=args,{a,b}=inputs,aId=backend22.dataIdMap.get(a.dataId).id,bId=backend22.dataIdMap.get(b.dataId).id,outputType=dtype!=null?dtype:a.dtype,newShape=backend_util_exports.assertAndGetBroadcastShape(a.shape,b.shape),out=backend22.makeOutput(newShape,outputType);if(util_exports.sizeFromShape(newShape)===0)return out;let aShapeBytes=new Uint8Array(new Int32Array(a.shape).buffer),bShapeBytes=new Uint8Array(new Int32Array(b.shape).buffer),outId=backend22.dataIdMap.get(out.dataId).id,kernelFunc4=()=>wasmFunc9(aId,aShapeBytes,a.shape.length,bId,bShapeBytes,b.shape.length,CppDType[a.dtype],outId);if(supportsFullBroadcast17&&a.dtype==="float32")return kernelFunc4(),out;let aBroadcastDims=backend_util_exports.getBroadcastDims(a.shape,newShape),bBroadcastDims=backend_util_exports.getBroadcastDims(b.shape,newShape),loopsOverAllOfA=aBroadcastDims.every((v,i)=>v===i),loopsOverAllOfB=bBroadcastDims.every((v,i)=>v===i);if(loopsOverAllOfA&&loopsOverAllOfB)return kernelFunc4(),out;throw new Error(`Broadcasting along outer dims is not yet supported for ${a.dtype} ${kernelName}.`)}return{kernelName,backendName:"wasm",setupFunc:setupFunc3,kernelFunc:kernelFunc3}}var supportsFullBroadcast=!0,addConfig3=createBinaryKernelConfig(Add,supportsFullBroadcast),wasmFunc;function setupFunc(backend22){wasmFunc=backend22.wasm.cwrap(AddN,null,["array","number","number","number"])}function addn(args){let{inputs,backend:backend22}=args,out=backend22.makeOutput(inputs[0].shape,inputs[0].dtype);if(util_exports.sizeFromShape(out.shape)===0)return out;let inputIds=inputs.map(x=>backend22.dataIdMap.get(x.dataId).id),inputIdsBytes=new Uint8Array(new Int32Array(inputIds).buffer),outId=backend22.dataIdMap.get(out.dataId).id;return wasmFunc(inputIdsBytes,inputIds.length,CppDType[out.dtype],outId),out}var addNConfig3={kernelName:AddN,backendName:"wasm",setupFunc,kernelFunc:addn};function identity3(args){let{inputs:{x},backend:backend22}=args,out=backend22.makeOutput(x.shape,x.dtype),inVals=backend22.typedArrayFromHeap(x);return backend22.typedArrayFromHeap(out).set(inVals),out}var identityConfig3={kernelName:Identity,backendName:"wasm",kernelFunc:identity3},wasmTranspose;function setup2(backend22){wasmTranspose=backend22.wasm.cwrap(Transpose,null,["number","array","number","number","number","array","number"])}function transpose4(args){let{inputs,backend:backend22,attrs}=args,[reducedShape,perm]=removeOneSizeDims(inputs.x.shape,attrs.perm),permIsNoOp=!0;for(let i=0;i=i&&(minValIdx===-1||newPerm[minValIdx]>newPerm[j])&&(minValIdx=j);newPerm[minValIdx]=i}return[newShape,newPerm]}var transposeConfig3={kernelName:Transpose,backendName:"wasm",kernelFunc:transpose4,setupFunc:setup2};function permuteAxesAndTranspose(x,axis,backend22){let xShape=x.shape,xRank=x.shape.length,originalAxes=util_exports.parseAxisParam(axis,xShape),axes=originalAxes,permutedAxes=backend_util_exports.getAxesPermutation(axes,xRank),xTransposed=null,inputWasTransposed=!1;if(permutedAxes!=null){let newShape=new Array(xRank);for(let i=0;i`new shape: ${$shape}, old shape: ${x.shape}. New shape and old shape must have the same number of elements.`),{dataId:x.dataId,shape:$shape,dtype:x.dtype}}var reshapeConfig3={kernelName:Reshape,backendName:"wasm",kernelFunc:reshape4},wasmBatchMatMul;function setup5(backend22){wasmBatchMatMul=backend22.wasm.cwrap(BatchMatMul,null,["number","array","number","number","array","number","number","number","number"])}function batchMatMul3(args){let{inputs,backend:backend22,attrs}=args,{a,b}=inputs,{transposeA,transposeB}=attrs;if(a.dtype!=="float32"||b.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let aRank=a.shape.length,bRank=b.shape.length,innerShapeA=transposeA?a.shape[aRank-2]:a.shape[aRank-1],innerShapeB=transposeB?b.shape[bRank-1]:b.shape[bRank-2],outerShapeA=transposeA?a.shape[aRank-1]:a.shape[aRank-2],outerShapeB=transposeB?b.shape[bRank-2]:b.shape[bRank-1],outerDimsA=a.shape.slice(0,-2),outerDimsB=b.shape.slice(0,-2),batchDimA=util_exports.sizeFromShape(outerDimsA),batchDimB=util_exports.sizeFromShape(outerDimsB),batchDimsCompatible=batchDimA===batchDimB||batchDimA===1||batchDimB===1;util_exports.assert(aRank>=2&&bRank>=2&&batchDimsCompatible,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${outerDimsA}) and (${outerDimsB}).`);let outShape=(batchDimA>batchDimB?a.shape.slice(0,-2):b.shape.slice(0,-2)).concat([outerShapeA,outerShapeB]);util_exports.assert(innerShapeA===innerShapeB,()=>`Error in matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${a.shape} and ${b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);let a3dShape=transposeA?[batchDimA,innerShapeA,outerShapeA]:[batchDimA,outerShapeA,innerShapeA],b3dShape=transposeB?[batchDimB,outerShapeB,innerShapeB]:[batchDimB,innerShapeB,outerShapeB],a3d=reshape4({inputs:{x:a},backend:backend22,attrs:{shape:a3dShape}}),b3d=reshape4({inputs:{x:b},backend:backend22,attrs:{shape:b3dShape}}),a3dId=backend22.dataIdMap.get(a3d.dataId).id,b3dId=backend22.dataIdMap.get(b3d.dataId).id,leftDim=transposeA?a3d.shape[2]:a3d.shape[1],rightDim=transposeB?b3d.shape[1]:b3d.shape[2],batchDim=Math.max(batchDimA,batchDimB),out=backend22.makeOutput([batchDim,leftDim,rightDim],a3d.dtype),outId=backend22.dataIdMap.get(out.dataId).id,aShapeBytes=new Uint8Array(new Int32Array(a3d.shape).buffer),bShapeBytes=new Uint8Array(new Int32Array(b3d.shape).buffer);return wasmBatchMatMul(a3dId,aShapeBytes,a3d.shape.length,b3dId,bShapeBytes,b3d.shape.length,transposeA,transposeB,outId),out.shape=outShape,out}var batchMatMulConfig3={kernelName:BatchMatMul,backendName:"wasm",setupFunc:setup5,kernelFunc:batchMatMul3};function cast4(args){let{inputs:{x},attrs:{dtype},backend:backend22}=args,out=backend22.makeOutput(x.shape,dtype),inVals=backend22.typedArrayFromHeap(x);return backend22.typedArrayFromHeap(out).set(inVals),out}var castConfig3={kernelName:Cast,backendName:"wasm",kernelFunc:cast4},wasmClip;function setup6(backend22){wasmClip=backend22.wasm.cwrap(ClipByValue,null,["number","number","number","number"])}function clip2(args){let{inputs,backend:backend22,attrs}=args,{x}=inputs,{clipValueMin,clipValueMax}=attrs,xId=backend22.dataIdMap.get(x.dataId).id,out=backend22.makeOutput(x.shape,x.dtype),outId=backend22.dataIdMap.get(out.dataId).id;return wasmClip(xId,clipValueMin,clipValueMax,outId),out}var clipByValueConfig2={kernelName:ClipByValue,backendName:"wasm",setupFunc:setup6,kernelFunc:clip2};function concat4(args){let{inputs,backend:backend22}=args,axis=util_exports.parseAxisParam(args.attrs.axis,inputs[0].shape)[0],outShape=backend_util_exports.computeOutShape(inputs.map(t=>t.shape),axis),$inputs=inputs.filter(t=>util_exports.sizeFromShape(t.shape)>0);if($inputs.length===1)return identity3({inputs:{x:$inputs[0]},backend:backend22});let out=backend22.makeOutput(outShape,inputs[0].dtype);if(util_exports.sizeFromShape(outShape)===0)return out;let shapes=$inputs.map(t=>t.shape);if(backend_util_exports.assertParamsConsistent(shapes,axis),$inputs[0].dtype==="string"){let inputs2D=$inputs.map(t=>{let innerSize=util_exports.sizeFromShape(t.shape.slice(axis));return reshape4({inputs:{x:t},backend:backend22,attrs:{shape:[-1,innerSize]}})}),inputsValShapes=inputs2D.map(t=>({vals:backend22.readSync(t.dataId),shape:t.shape}));outShape=backend_util_exports.computeOutShape(inputs2D.map(t=>t.shape),1);let simplyConcat=inputs2D[0].shape[0]===1,outVals2=concatImpl(inputsValShapes,outShape,inputs[0].dtype,simplyConcat),finalOutShape=backend_util_exports.computeOutShape($inputs.map(t=>t.shape),axis);out.shape=finalOutShape;let outData=backend22.dataIdMap.get(out.dataId);return outData.stringBytes=backend_util_exports.fromStringArrayToUint8(outVals2),out}let batchDim=util_exports.sizeFromShape($inputs[0].shape.slice(0,axis)),sumInnerDims=0,innerDims=$inputs.map(input2=>{let innerDim=util_exports.sizeFromShape(input2.shape.slice(axis));return sumInnerDims+=innerDim,innerDim}),inVals=$inputs.map(input2=>backend22.typedArrayFromHeap(input2)),outVals=backend22.typedArrayFromHeap(out);for(let b=0;b`cumsum does not support ${x.dtype} tensors in the WASM backend`);let permutation=backend_util_exports.getAxesPermutation([axis],xRank),permutedX=x;permutation!==null&&(permutedX=transpose4({inputs:{x},attrs:{perm:permutation},backend:backend22}));let permutedAxis=backend_util_exports.getInnerMostAxes(1,xRank)[0];backend_util_exports.assertAxesAreInnerMostDims("cumsum",[permutedAxis],xRank);let permutedOut=backend22.makeOutput(permutedX.shape,permutedX.dtype),finalDim=permutedX.shape[permutedAxis],permutedXId=backend22.dataIdMap.get(permutedX.dataId).id,permutedOutId=backend22.dataIdMap.get(permutedOut.dataId).id;wasmCumsum(permutedXId,exclusive?1:0,reverse5?1:0,finalDim,permutedOutId,CppDType[x.dtype]);let out=permutedOut;if(permutation!==null){let undoPermutation=backend_util_exports.getUndoAxesPermutation(permutation);out=transpose4({inputs:{x:permutedOut},attrs:{perm:undoPermutation},backend:backend22}),backend22.disposeData(permutedX.dataId),backend22.disposeData(permutedOut.dataId)}return out}var cumsumConfig3={kernelName:Cumsum,backendName:"wasm",setupFunc:setup10,kernelFunc:cumsum4},wasmDepthToSpace;function setup11(backend22){wasmDepthToSpace=backend22.wasm.cwrap(DepthToSpace,null,["number","number","number","array","number","array","array","number","number"])}function depthToSpace4(args){let{backend:backend22,inputs,attrs}=args,{x}=inputs,{blockSize,dataFormat}=attrs;util_exports.assert(blockSize>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);let batchSize=x.shape[0],inputHeight=dataFormat==="NHWC"?x.shape[1]:x.shape[2],inputWidth=dataFormat==="NHWC"?x.shape[2]:x.shape[3],inputDepth=dataFormat==="NHWC"?x.shape[3]:x.shape[1],outputHeight=inputHeight*blockSize,outputWidth=inputWidth*blockSize,outputDepth=inputDepth/(blockSize*blockSize),outputShape=dataFormat==="NHWC"?[batchSize,outputHeight,outputWidth,outputDepth]:[batchSize,outputDepth,outputHeight,outputWidth],out=backend22.makeOutput(outputShape,"float32"),xId=backend22.dataIdMap.get(x.dataId).id,xStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(x.shape)).buffer),outputShapeBytes=new Uint8Array(new Int32Array(outputShape).buffer),outStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(outputShape)).buffer),outId=backend22.dataIdMap.get(out.dataId).id;return wasmDepthToSpace(xId,blockSize,dataFormat==="NHWC"?1:0,xStridesBytes,x.shape.length-1,outputShapeBytes,outStridesBytes,outputShape.length,outId),out}var depthToSpaceConfig3={kernelName:DepthToSpace,backendName:"wasm",setupFunc:setup11,kernelFunc:depthToSpace4},wasmDepthwiseConv2d;function setup12(backend22){wasmDepthwiseConv2d=backend22.wasm.cwrap(DepthwiseConv2dNative,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function depthwiseConv2d3(args){let{inputs,attrs,backend:backend22}=args,{x,filter}=inputs,xId=backend22.dataIdMap.get(x.dataId).id,filterId=backend22.dataIdMap.get(filter.dataId).id,{strides,dilations,pad:pad3,dimRoundingMode}=attrs,$dilations=dilations==null?[1,1]:dilations,convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,$dilations,pad3,dimRoundingMode,!0),filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,inputChannels=convInfo.inChannels,outputChannels=convInfo.outChannels,isSamePad=convInfo.padInfo.type==="SAME"?1:0;if(convInfo.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);let out=backend22.makeOutput(convInfo.outShape,"float32"),outId=backend22.dataIdMap.get(out.dataId).id;return wasmDepthwiseConv2d(xId,x.shape[0],x.shape[1],x.shape[2],filterId,filterHeight,filterWidth,padTop,padRight,padBottom,padLeft,isSamePad,dilationHeight,dilationWidth,strideHeight,strideWidth,inputChannels,outputChannels,outId),out}var depthwiseConv2dNativeConfig3={kernelName:DepthwiseConv2dNative,backendName:"wasm",setupFunc:setup12,kernelFunc:depthwiseConv2d3},supportsFullBroadcast2=!1,equalConfig3=createBinaryKernelConfig(Equal,supportsFullBroadcast2,"bool"),expConfig3=createUnaryKernelConfig(Exp);function expandDims4(args){let{inputs,attrs,backend:backend22}=args,{input:input2}=inputs,{dim}=attrs,inputRank=input2.shape.length,newShape=input2.shape.slice(),$dim=dim;return dim<0&&(util_exports.assert(-(inputRank+1)<=dim,()=>`Axis must be in the interval [${-(inputRank+1)}, ${inputRank}]`),$dim=inputRank+dim+1),newShape.splice($dim,0,1),reshape4({inputs:{x:input2},backend:backend22,attrs:{shape:newShape}})}var expandDimsConfig3={kernelName:ExpandDims,backendName:"wasm",kernelFunc:expandDims4};function fill4(args){let{attrs:{shape,value,dtype},backend:backend22}=args,out=backend22.makeOutput(shape,dtype);return backend22.typedArrayFromHeap(out).fill(value),out}var fillConfig3={kernelName:Fill,backendName:"wasm",kernelFunc:fill4},wasmFlipLeftRight;function setup13(backend22){wasmFlipLeftRight=backend22.wasm.cwrap(FlipLeftRight,null,["number","number","number","number","number","number"])}function flipLeftRight2(args){let{inputs,backend:backend22}=args,{image:image3}=inputs,out=backend22.makeOutput(image3.shape,image3.dtype),imageId=backend22.dataIdMap.get(image3.dataId).id,outId=backend22.dataIdMap.get(out.dataId).id,[batch,imageHeight,imageWidth,numChannels]=image3.shape;return wasmFlipLeftRight(imageId,batch,imageHeight,imageWidth,numChannels,outId),out}var flipLeftRightConfig3={kernelName:FlipLeftRight,backendName:"wasm",kernelFunc:flipLeftRight2,setupFunc:setup13},floorConfig3=createUnaryKernelConfig(Floor),supportsFullBroadcast3=!1,floorDivConfig3=createBinaryKernelConfig(FloorDiv,supportsFullBroadcast3),wasmBatchNorm;function setup14(backend22){wasmBatchNorm=backend22.wasm.cwrap(FusedBatchNorm,null,["number","number","number","number","number","number","number"])}function fusedBatchNorm(args){let{backend:backend22,inputs,attrs}=args,{varianceEpsilon}=attrs,{x,mean:mean4,variance,offset,scale:scale2}=inputs,xId=backend22.dataIdMap.get(x.dataId).id,meanId=backend22.dataIdMap.get(mean4.dataId).id,varianceId=backend22.dataIdMap.get(variance.dataId).id,offsetId=offset!=null?backend22.dataIdMap.get(offset.dataId).id:0,scaleId=scale2!=null?backend22.dataIdMap.get(scale2.dataId).id:0,out=backend22.makeOutput(x.shape,x.dtype);if(util_exports.sizeFromShape(x.shape)===0)return out;let outId=backend22.dataIdMap.get(out.dataId).id;return wasmBatchNorm(xId,meanId,varianceId,offsetId,scaleId,varianceEpsilon,outId),out}var fusedBatchNormConfig={kernelName:FusedBatchNorm,backendName:"wasm",setupFunc:setup14,kernelFunc:fusedBatchNorm},wasmFusedConv2d;function setup15(backend22){wasmFusedConv2d=backend22.wasm.cwrap(FusedConv2D,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function fusedConv2d2(args){let{inputs,attrs,backend:backend22}=args,{x,filter,bias,preluActivationWeights}=inputs,{strides,pad:pad3,dilations,dataFormat,dimRoundingMode,activation:activation2,leakyreluAlpha}=attrs,convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,dilations,pad3,dimRoundingMode),fusedActivation=FusableActivation[activation2];if(fusedActivation==null)throw new Error(`${activation2} activation not yet supported for FusedConv2D in the wasm backend.`);let xId=backend22.dataIdMap.get(x.dataId).id,filterId=backend22.dataIdMap.get(filter.dataId).id,outputChannels=convInfo.outChannels,biasId=0;if(bias!=null){let biasData=backend22.dataIdMap.get(bias.dataId);if(biasData.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${biasData.shape.length}.`);if(biasData.shape[0]!==outputChannels)throw new Error(`FusedConv2D bias shape (${biasData.shape}) does not match the number of output channels (${outputChannels})`);biasId=biasData.id}let filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,inputChannels=convInfo.inChannels,isSamePad=convInfo.padInfo.type==="SAME"?1:0,batchSize=convInfo.batchSize,inHeight=convInfo.inHeight,inWidth=convInfo.inWidth;if(dataFormat!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${dataFormat}'. Please use 'NHWC'.`);let out=backend22.makeOutput(convInfo.outShape,"float32"),outId=backend22.dataIdMap.get(out.dataId).id,preluActivationWeightsId=preluActivationWeights==null?0:backend22.dataIdMap.get(preluActivationWeights.dataId).id;return wasmFusedConv2d(xId,batchSize,inHeight,inWidth,filterId,filterHeight,filterWidth,biasId,padTop,padRight,padBottom,padLeft,isSamePad,dilationHeight,dilationWidth,strideHeight,strideWidth,inputChannels,outputChannels,fusedActivation,preluActivationWeightsId,leakyreluAlpha||0,outId),out}var fusedConv2DConfig3={kernelName:FusedConv2D,backendName:"wasm",setupFunc:setup15,kernelFunc:fusedConv2d2},wasmFusedDepthwiseConv2d;function setup16(backend22){wasmFusedDepthwiseConv2d=backend22.wasm.cwrap(FusedDepthwiseConv2D,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function fusedDepthwiseConv2d(args){let{inputs,attrs,backend:backend22}=args,{x,filter,bias,preluActivationWeights}=inputs,{strides,pad:pad3,dilations,dataFormat,dimRoundingMode,activation:activation2,leakyreluAlpha}=attrs,convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,dilations,pad3,dimRoundingMode,!0),fusedActivation=FusableActivation[activation2];if(fusedActivation==null)throw new Error(`${activation2} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let xId=backend22.dataIdMap.get(x.dataId).id,filterId=backend22.dataIdMap.get(filter.dataId).id,outputChannels=convInfo.outChannels,biasId=0;if(bias!=null){let biasData=backend22.dataIdMap.get(bias.dataId);if(biasData.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${biasData.shape.length}.`);if(biasData.shape[0]!==outputChannels)throw new Error(`FusedDepthwiseConv2D bias shape (${biasData.shape}) does not match the number of output channels (${outputChannels})`);biasId=biasData.id}let filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,inputChannels=convInfo.inChannels,isSamePad=convInfo.padInfo.type==="SAME"?1:0,batchSize=convInfo.batchSize,inHeight=convInfo.inHeight,inWidth=convInfo.inWidth;if(dataFormat!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${dataFormat}'. Please use 'NHWC'.`);let out=backend22.makeOutput(convInfo.outShape,"float32"),outId=backend22.dataIdMap.get(out.dataId).id,preluActivationWeightsId=preluActivationWeights==null?0:backend22.dataIdMap.get(preluActivationWeights.dataId).id;return wasmFusedDepthwiseConv2d(xId,batchSize,inHeight,inWidth,filterId,filterHeight,filterWidth,biasId,padTop,padRight,padBottom,padLeft,isSamePad,dilationHeight,dilationWidth,strideHeight,strideWidth,inputChannels,outputChannels,fusedActivation,preluActivationWeightsId,leakyreluAlpha||0,outId),out}var fusedDepthwiseConv2DConfig3={kernelName:FusedDepthwiseConv2D,backendName:"wasm",setupFunc:setup16,kernelFunc:fusedDepthwiseConv2d},wasmGatherNd;function setup17(backend22){wasmGatherNd=backend22.wasm.cwrap(GatherNd,null,["number","number","number","number","number","number","array","number"])}function gatherNd3(args){let{backend:backend22,inputs}=args,{params,indices}=inputs,[resultShape,numSlices,sliceSize,strides]=gather_nd_util_exports.prepareAndValidate(params,indices),out=backend22.makeOutput(resultShape,params.dtype);if(numSlices===0)return out;let indicesShape=indices.shape,sliceRank=indicesShape[indicesShape.length-1],xId=backend22.dataIdMap.get(params.dataId).id,indicesId=backend22.dataIdMap.get(indices.dataId).id,stridesBytes=new Uint8Array(new Int32Array(strides).buffer),outId=backend22.dataIdMap.get(out.dataId).id;return wasmGatherNd(xId,CppDType[params.dtype],indicesId,numSlices,sliceRank,sliceSize,stridesBytes,outId),out}var gatherNdConfig3={kernelName:GatherNd,backendName:"wasm",setupFunc:setup17,kernelFunc:gatherNd3},wasmGather;function setup18(backend22){wasmGather=backend22.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function gatherV23(args){let{backend:backend22,inputs,attrs}=args,{x,indices}=inputs,{axis,batchDims}=attrs,parsedAxis=util_exports.parseAxisParam(axis,x.shape)[0],shapeInfo=backend_util_exports.segment_util.collectGatherOpShapeInfo(x,indices,parsedAxis,batchDims),flattenX=reshape4({inputs:{x},attrs:{shape:[shapeInfo.batchSize,shapeInfo.outerSize,shapeInfo.dimSize,shapeInfo.sliceSize]},backend:backend22}),indicesSize=util_exports.sizeFromShape(indices.shape),flattenIndex=reshape4({inputs:{x:indices},attrs:{shape:[shapeInfo.batchSize,indicesSize/shapeInfo.batchSize]},backend:backend22}),flattenOutputShape=[shapeInfo.batchSize,shapeInfo.outerSize,indicesSize/shapeInfo.batchSize,shapeInfo.sliceSize],out=backend22.makeOutput(flattenOutputShape,x.dtype);if(util_exports.sizeFromShape(x.shape)===0)return out;let stridesSize=flattenX.shape.length-1,xId=backend22.dataIdMap.get(flattenX.dataId).id,indicesId=backend22.dataIdMap.get(flattenIndex.dataId).id,outId=backend22.dataIdMap.get(out.dataId).id,xStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(flattenX.shape)).buffer),outStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(flattenOutputShape)).buffer);return wasmGather(xId,CppDType[x.dtype],xStridesBytes,stridesSize,indicesId,shapeInfo.batchSize,outStridesBytes,outId),out.shape=shapeInfo.outputShape,out}var gatherV2Config3={kernelName:GatherV2,backendName:"wasm",setupFunc:setup18,kernelFunc:gatherV23},supportsFullBroadcast4=!1,greaterConfig3=createBinaryKernelConfig(Greater,supportsFullBroadcast4,"bool"),supportsFullBroadcast5=!1,greaterEqualConfig3=createBinaryKernelConfig(GreaterEqual,supportsFullBroadcast5,"bool"),wasmFunc3;function setupFunc2(backend22){wasmFunc3=backend22.wasm.cwrap(LeakyRelu,null,["number","number","number"])}function leakyRelu4(args){let{inputs:{x},attrs:{alpha},backend:backend22}=args,xId=backend22.dataIdMap.get(x.dataId).id,out=backend22.makeOutput(x.shape,x.dtype);if(util_exports.sizeFromShape(x.shape)!==0){let outId=backend22.dataIdMap.get(out.dataId).id;wasmFunc3(xId,alpha,outId)}return out}var leakyReluConfig3={kernelName:LeakyRelu,backendName:"wasm",setupFunc:setupFunc2,kernelFunc:leakyRelu4},supportsFullBroadcast6=!1,lessConfig3=createBinaryKernelConfig(Less,supportsFullBroadcast6,"bool"),supportsFullBroadcast7=!1,lessEqualConfig3=createBinaryKernelConfig(LessEqual,supportsFullBroadcast7,"bool"),logConfig3=createUnaryKernelConfig(Log),supportsFullBroadcast8=!1,logicalAndConfig3=createBinaryKernelConfig(LogicalAnd,supportsFullBroadcast8,"bool"),wasmMax;function setup19(backend22){wasmMax=backend22.wasm.cwrap(Max,null,["number, number, number"])}function max4(args){let{backend:backend22,inputs,attrs}=args,{reductionIndices:axis,keepDims}=attrs,{x}=inputs,inputId=backend22.dataIdMap.get(x.dataId).id,input2=x,{transposed,axes,originalAxes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend22);if(inputWasTransposed){let transposedId=backend22.dataIdMap.get(transposed.dataId).id;input2=transposed,inputId=transposedId}let inputRank=input2.shape.length;backend_util_exports.assertAxesAreInnerMostDims("max",axes,inputRank);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(input2.shape,axes),reduceSize=util_exports.sizeFromShape(reduceShape),out=backend22.makeOutput(outShape,x.dtype);if(util_exports.sizeFromShape(input2.shape)!==0){let outId=backend22.dataIdMap.get(out.dataId).id;wasmMax(inputId,reduceSize,outId)}if(inputWasTransposed&&backend22.disposeData(transposed.dataId),keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(out.shape,originalAxes);out.shape=newShape}return out}var maxConfig3={kernelName:Max,backendName:"wasm",setupFunc:setup19,kernelFunc:max4},supportsFullBroadcast9=!1,maximumConfig3=createBinaryKernelConfig(Maximum,supportsFullBroadcast9),wasmMaxPool;function setup20(backend22){wasmMaxPool=backend22.wasm.cwrap(MaxPool,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function maxPool4(args){let{inputs,attrs,backend:backend22}=args,x=inputs.x,xId=backend22.dataIdMap.get(x.dataId).id,{filterSize,strides,pad:pad3,dimRoundingMode}=attrs,convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,1,pad3,dimRoundingMode),filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,inputChannels=convInfo.inChannels,outputChannels=convInfo.outChannels;if(convInfo.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);let out=backend22.makeOutput(convInfo.outShape,"float32"),outId=backend22.dataIdMap.get(out.dataId).id;return wasmMaxPool(xId,x.shape[0],x.shape[1],x.shape[2],filterHeight,filterWidth,padTop,padRight,padBottom,padLeft,dilationHeight,dilationWidth,strideHeight,strideWidth,inputChannels,outputChannels,outId),out}var maxPoolConfig3={kernelName:MaxPool,backendName:"wasm",setupFunc:setup20,kernelFunc:maxPool4},wasmMean;function setup21(backend22){wasmMean=backend22.wasm.cwrap(Mean,null,["number, number, number"])}function mean3(args){let{backend:backend22,inputs,attrs}=args,{axis,keepDims}=attrs,{x}=inputs,xId=backend22.dataIdMap.get(x.dataId).id,inputId=xId,input2=x,{transposed,axes,originalAxes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend22),reductionAxes=axes;if(inputWasTransposed){let transposedId=backend22.dataIdMap.get(transposed.dataId).id;transposedId!==xId&&(input2=transposed,inputId=transposedId,reductionAxes=backend_util_exports.getInnerMostAxes(reductionAxes.length,input2.shape.length))}backend_util_exports.assertAxesAreInnerMostDims("mean",reductionAxes,input2.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(input2.shape,reductionAxes),reduceSize=util_exports.sizeFromShape(reduceShape),castedInput=input2;input2.dtype!=="float32"&&(castedInput=cast4({backend:backend22,inputs:{x:input2},attrs:{dtype:"float32"}}),inputId=backend22.dataIdMap.get(castedInput.dataId).id);let out=backend22.makeOutput(outShape,"float32");if(util_exports.sizeFromShape(input2.shape)!==0){let outId=backend22.dataIdMap.get(out.dataId).id;wasmMean(inputId,reduceSize,outId)}if(inputWasTransposed&&backend22.disposeData(transposed.dataId),keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(out.shape,originalAxes);out.shape=newShape}return input2.dtype!=="float32"&&backend22.disposeData(castedInput.dataId),out}var meanConfig3={kernelName:Mean,backendName:"wasm",setupFunc:setup21,kernelFunc:mean3},wasmMin;function setup22(backend22){wasmMin=backend22.wasm.cwrap(Min,null,["number, number, number"])}function min4(args){let{backend:backend22,inputs,attrs}=args,{axis,keepDims}=attrs,{x}=inputs,xId=backend22.dataIdMap.get(x.dataId).id,inputId=xId,input2=x,{transposed,axes,originalAxes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend22);if(inputWasTransposed){let transposedId=backend22.dataIdMap.get(transposed.dataId).id;transposedId!==xId&&(input2=transposed,inputId=transposedId)}let inputRank=input2.shape.length;backend_util_exports.assertAxesAreInnerMostDims("min",axes,inputRank);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(input2.shape,axes),reduceSize=util_exports.sizeFromShape(reduceShape),out=backend22.makeOutput(outShape,input2.dtype);if(util_exports.sizeFromShape(input2.shape)!==0){let outId=backend22.dataIdMap.get(out.dataId).id;wasmMin(inputId,reduceSize,outId)}if(inputWasTransposed&&backend22.disposeData(transposed.dataId),keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(out.shape,originalAxes);out.shape=newShape}return out}var minConfig3={kernelName:Min,backendName:"wasm",setupFunc:setup22,kernelFunc:min4},supportsFullBroadcast10=!1,minimumConfig3=createBinaryKernelConfig(Minimum,supportsFullBroadcast10),supportsFullBroadcast11=!0,multiplyConfig3=createBinaryKernelConfig(Multiply,supportsFullBroadcast11),negConfig3=createUnaryKernelConfig(Neg);function parseResultStruct(backend22,resOffset){let result=new Int32Array(backend22.wasm.HEAPU8.buffer,resOffset,4),pSelectedIndices=result[0],selectedSize=result[1],pSelectedScores=result[2],pValidOutputs=result[3];return backend22.wasm._free(resOffset),{pSelectedIndices,selectedSize,pSelectedScores,pValidOutputs}}var wasmFunc4;function setup23(backend22){wasmFunc4=backend22.wasm.cwrap(NonMaxSuppressionV3,"number",["number","number","number","number","number"])}function kernelFunc(args){let{backend:backend22,inputs,attrs}=args,{iouThreshold,maxOutputSize,scoreThreshold}=attrs,{boxes,scores}=inputs,boxesId=backend22.dataIdMap.get(boxes.dataId).id,scoresId=backend22.dataIdMap.get(scores.dataId).id,resOffset=wasmFunc4(boxesId,scoresId,maxOutputSize,iouThreshold,scoreThreshold),{pSelectedIndices,selectedSize,pSelectedScores,pValidOutputs}=parseResultStruct(backend22,resOffset);return backend22.wasm._free(pSelectedScores),backend22.wasm._free(pValidOutputs),backend22.makeOutput([selectedSize],"int32",pSelectedIndices)}var nonMaxSuppressionV3Config3={kernelName:NonMaxSuppressionV3,backendName:"wasm",setupFunc:setup23,kernelFunc},wasmFunc5;function setup24(backend22){wasmFunc5=backend22.wasm.cwrap(NonMaxSuppressionV4,"number",["number","number","number","number","number","bool"])}function nonMaxSuppressionV43(args){let{backend:backend22,inputs,attrs}=args,{iouThreshold,maxOutputSize,scoreThreshold,padToMaxOutputSize}=attrs,{boxes,scores}=inputs,boxesId=backend22.dataIdMap.get(boxes.dataId).id,scoresId=backend22.dataIdMap.get(scores.dataId).id,resOffset=wasmFunc5(boxesId,scoresId,maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize),{pSelectedIndices,selectedSize,pSelectedScores,pValidOutputs}=parseResultStruct(backend22,resOffset);backend22.wasm._free(pSelectedScores);let selectedIndicesTensor=backend22.makeOutput([selectedSize],"int32",pSelectedIndices),validOutputsTensor=backend22.makeOutput([],"int32",pValidOutputs);return[selectedIndicesTensor,validOutputsTensor]}var nonMaxSuppressionV4Config3={kernelName:NonMaxSuppressionV4,backendName:"wasm",setupFunc:setup24,kernelFunc:nonMaxSuppressionV43},wasmFunc6;function setup25(backend22){wasmFunc6=backend22.wasm.cwrap(NonMaxSuppressionV5,"number",["number","number","number","number","number","number"])}function kernelFunc2(args){let{backend:backend22,inputs,attrs}=args,{iouThreshold,maxOutputSize,scoreThreshold,softNmsSigma}=attrs,{boxes,scores}=inputs,boxesId=backend22.dataIdMap.get(boxes.dataId).id,scoresId=backend22.dataIdMap.get(scores.dataId).id,resOffset=wasmFunc6(boxesId,scoresId,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma),{pSelectedIndices,selectedSize,pSelectedScores,pValidOutputs}=parseResultStruct(backend22,resOffset);backend22.wasm._free(pValidOutputs);let selectedIndicesTensor=backend22.makeOutput([selectedSize],"int32",pSelectedIndices),selectedScoresTensor=backend22.makeOutput([selectedSize],"float32",pSelectedScores);return[selectedIndicesTensor,selectedScoresTensor]}var nonMaxSuppressionV5Config3={kernelName:NonMaxSuppressionV5,backendName:"wasm",setupFunc:setup25,kernelFunc:kernelFunc2},supportsFullBroadcast12=!1,notEqualConfig3=createBinaryKernelConfig(NotEqual,supportsFullBroadcast12,"bool"),wasmOneHot;function setup26(backend22){wasmOneHot=backend22.wasm.cwrap(OneHot,null,["number","number","number","number","number"])}function oneHot4(args){let{inputs,backend:backend22,attrs}=args,{indices}=inputs,{depth,onValue,offValue}=attrs,out=backend22.makeOutput([...indices.shape,depth],"int32"),outId=backend22.dataIdMap.get(out.dataId).id,indicesId=backend22.dataIdMap.get(indices.dataId).id;return wasmOneHot(indicesId,depth,onValue,offValue,outId),out}var oneHotConfig3={kernelName:OneHot,backendName:"wasm",setupFunc:setup26,kernelFunc:oneHot4};function onesLike4(args){let{inputs:{x},backend:backend22}=args,out=backend22.makeOutput(x.shape,x.dtype);return backend22.typedArrayFromHeap(out).fill(1),out}var onesLikeConfig3={kernelName:OnesLike,backendName:"wasm",kernelFunc:onesLike4};function pack3(args){let{inputs,backend:backend22,attrs}=args,{axis}=attrs;if(inputs.length===1)return expandDims4({inputs:{input:inputs[0]},backend:backend22,attrs:{dim:axis}});let shape=inputs[0].shape,dtype=inputs[0].dtype;inputs.forEach(t=>{util_exports.assertShapesMatch(shape,t.shape,"All tensors passed to stack must have matching shapes"),util_exports.assert(dtype===t.dtype,()=>"All tensors passed to stack must have matching dtypes")});let expandedTensors=inputs.map(t=>expandDims4({inputs:{input:t},backend:backend22,attrs:{dim:axis}}));return concat4({inputs:expandedTensors,backend:backend22,attrs:{axis}})}var packConfig3={kernelName:Pack,backendName:"wasm",kernelFunc:pack3},wasmPadV2;function setup27(backend22){wasmPadV2=backend22.wasm.cwrap(PadV2,null,["number","array","number","number","array","array","number","number"])}function pad2(args){let{inputs:{x},backend:backend22,attrs:{paddings,constantValue}}=args,outShape=paddings.map((p2,i)=>p2[0]+x.shape[i]+p2[1]),xId=backend22.dataIdMap.get(x.dataId).id,out=backend22.makeOutput(outShape,x.dtype),outId=backend22.dataIdMap.get(out.dataId).id,xShapeBytes=new Uint8Array(new Int32Array(x.shape).buffer),prePaddingsFlat=paddings.map(padTuple=>padTuple[0]),postPaddingsFlat=paddings.map(padTuple=>padTuple[1]),prePaddingsBytes=new Uint8Array(new Int32Array(prePaddingsFlat).buffer),postPaddingsBytes=new Uint8Array(new Int32Array(postPaddingsFlat).buffer);return wasmPadV2(xId,xShapeBytes,x.shape.length,CppDType[x.dtype],prePaddingsBytes,postPaddingsBytes,constantValue,outId),out}var padV2Config3={kernelName:PadV2,backendName:"wasm",kernelFunc:pad2,setupFunc:setup27},supportsFullBroadcast13=!1,powConfig3=createBinaryKernelConfig(Pow,supportsFullBroadcast13),wasmPrelu;function setup28(backend22){wasmPrelu=backend22.wasm.cwrap(Prelu,null,["number","number","number"])}function prelu4(args){let{inputs,backend:backend22}=args,{x,alpha}=inputs,xId=backend22.dataIdMap.get(x.dataId).id,weightsId=backend22.dataIdMap.get(alpha.dataId).id,out=backend22.makeOutput(x.shape,"float32"),outId=backend22.dataIdMap.get(out.dataId).id;return wasmPrelu(xId,weightsId,outId),out}var preluConfig3={kernelName:Prelu,backendName:"wasm",setupFunc:setup28,kernelFunc:prelu4},wasmProd;function setup29(backend22){wasmProd=backend22.wasm.cwrap(Prod,null,["number","number","number","number"])}function prod4(args){let{backend:backend22,inputs,attrs}=args,{axis,keepDims}=attrs,{x}=inputs,xId=backend22.dataIdMap.get(x.dataId).id,inputId=xId,input2=x,{transposed,axes,originalAxes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend22),reductionAxes=axes;if(inputWasTransposed){let transposedId=backend22.dataIdMap.get(transposed.dataId).id;transposedId!==xId&&(input2=transposed,inputId=transposedId,reductionAxes=backend_util_exports.getInnerMostAxes(reductionAxes.length,input2.shape.length))}backend_util_exports.assertAxesAreInnerMostDims("prod",reductionAxes,input2.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(input2.shape,reductionAxes),reduceSize=util_exports.sizeFromShape(reduceShape),out=backend22.makeOutput(outShape,input2.dtype);if(util_exports.sizeFromShape(input2.shape)!==0){let outId=backend22.dataIdMap.get(out.dataId).id;wasmProd(inputId,reduceSize,CppDType[out.dtype],outId)}if(inputWasTransposed&&backend22.disposeData(transposed.dataId),keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(out.shape,originalAxes);out.shape=newShape}return out}var prodConfig3={kernelName:Prod,backendName:"wasm",setupFunc:setup29,kernelFunc:prod4},range4=args=>{let{backend:backend22,attrs}=args,{start,stop,step:step4,dtype}=attrs,values=rangeImpl(start,stop,step4,dtype),out=backend22.makeOutput([values.length],dtype);return backend22.typedArrayFromHeap(out).set(values),out},rangeConfig3={kernelName:Range,backendName:"wasm",kernelFunc:range4},supportsFullBroadcast14=!0,realDivConfig3=createBinaryKernelConfig(RealDiv,supportsFullBroadcast14),reluConfig3=createUnaryKernelConfig(Relu),relu6Config3=createUnaryKernelConfig(Relu6),wasmResizeBilinear;function setup30(backend22){wasmResizeBilinear=backend22.wasm.cwrap(ResizeBilinear,null,["number","number","number","number","number","number","number","number","number","number"])}function resizeBilinear4(args){let{backend:backend22,inputs,attrs}=args,{images}=inputs,{alignCorners,halfPixelCenters,size}=attrs,[newHeight,newWidth]=size,[batch,oldHeight,oldWidth,numChannels]=images.shape,outShape=[batch,newHeight,newWidth,numChannels],xData=backend22.dataIdMap.get(images.dataId),castedData;xData.dtype!=="float32"&&(castedData=cast4({backend:backend22,inputs:{x:images},attrs:{dtype:"float32"}}),xData=backend22.dataIdMap.get(castedData.dataId));let xId=xData.id,out=backend22.makeOutput(outShape,"float32");if(util_exports.sizeFromShape(images.shape)===0)return out;let outId=backend22.dataIdMap.get(out.dataId).id;return wasmResizeBilinear(xId,batch,oldHeight,oldWidth,numChannels,newHeight,newWidth,alignCorners?1:0,halfPixelCenters?1:0,outId),castedData!=null&&backend22.disposeData(castedData.dataId),out}var resizeBilinearConfig3={kernelName:ResizeBilinear,backendName:"wasm",setupFunc:setup30,kernelFunc:resizeBilinear4},wasmReverse;function setup31(backend22){wasmReverse=backend22.wasm.cwrap(Reverse,null,["number","array","number","array","number","number"])}function reverse4(args){let{inputs,backend:backend22,attrs}=args,{x}=inputs,{dims}=attrs,axes=util_exports.parseAxisParam(dims,x.shape);if(x.shape.length===0)return identity3({inputs:{x},backend:backend22});let out=backend22.makeOutput(x.shape,x.dtype),xId=backend22.dataIdMap.get(x.dataId).id,outId=backend22.dataIdMap.get(out.dataId).id,axesBytes=new Uint8Array(new Int32Array(axes).buffer),outShapeBytes=new Uint8Array(new Int32Array(x.shape).buffer);return wasmReverse(xId,axesBytes,axes.length,outShapeBytes,x.shape.length,outId),reshape4({inputs:{x:out},attrs:{shape:x.shape},backend:backend22})}var reverseConfig3={kernelName:Reverse,backendName:"wasm",kernelFunc:reverse4,setupFunc:setup31},wasmRotate;function setup32(backend22){wasmRotate=backend22.wasm.cwrap(RotateWithOffset,null,["number","number","number","number","number","number","number","number","array","number","number"])}function rotateWithOffset2(args){let{inputs,backend:backend22,attrs}=args,{image:image3}=inputs,{radians,fillValue,center}=attrs,out=backend22.makeOutput(image3.shape,image3.dtype),imageId=backend22.dataIdMap.get(image3.dataId).id,outId=backend22.dataIdMap.get(out.dataId).id,[batch,imageHeight,imageWidth,numChannels]=image3.shape,[centerX,centerY]=backend_util_exports.getImageCenter(center,imageHeight,imageWidth),fillIsBlack=fillValue===0,fullOpacityValue=255,fillValues2=typeof fillValue=="number"?[fillValue,fillValue,fillValue,fillIsBlack?0:fullOpacityValue]:[...fillValue,fullOpacityValue],fillBytes=new Uint8Array(new Int32Array(fillValues2).buffer);return wasmRotate(imageId,batch,imageHeight,imageWidth,numChannels,radians,centerX,centerY,fillBytes,fillValues2.length,outId),out}var rotateWithOffsetConfig3={kernelName:RotateWithOffset,backendName:"wasm",kernelFunc:rotateWithOffset2,setupFunc:setup32},roundConfig3=createUnaryKernelConfig(Round),rsqrtConfig3=createUnaryKernelConfig(Rsqrt),wasmScatterNd;function setup33(backend22){wasmScatterNd=backend22.wasm.cwrap(ScatterNd,null,["number","number","number","number","number","number","array","number","number"])}function scatterNd3(args){let{backend:backend22,inputs,attrs}=args,{indices,updates}=inputs,{shape}=attrs,out=backend22.makeOutput(shape,updates.dtype);if(util_exports.sizeFromShape(shape)===0)return out;let{sliceRank,numUpdates,sliceSize,strides,outputSize}=scatter_nd_util_exports.calculateShapes(updates,indices,shape),indicesId=backend22.dataIdMap.get(indices.dataId).id,updatesId=backend22.dataIdMap.get(updates.dataId).id,stridesBytes=new Uint8Array(new Int32Array(strides).buffer),outId=backend22.dataIdMap.get(out.dataId).id;return wasmScatterNd(indicesId,updatesId,CppDType[updates.dtype],sliceRank,numUpdates,sliceSize,stridesBytes,outputSize,outId),out}var scatterNdConfig3={kernelName:ScatterNd,backendName:"wasm",setupFunc:setup33,kernelFunc:scatterNd3},wasmSelect;function setup34(backend22){wasmSelect=backend22.wasm.cwrap("SelectV2",null,["number","number","number","number","number"])}function select3(args){let{inputs,backend:backend22}=args,{condition,t,e}=inputs,conditionId=backend22.dataIdMap.get(condition.dataId).id,tId=backend22.dataIdMap.get(t.dataId).id,eId=backend22.dataIdMap.get(e.dataId).id,out=backend22.makeOutput(t.shape,t.dtype),outId=backend22.dataIdMap.get(out.dataId).id,cRank=condition.shape.length,tRank=t.shape.length,offset=cRank===0||cRank>1||tRank===1?1:util_exports.sizeFromShape(t.shape.slice(1));return wasmSelect(conditionId,tId,eId,offset,outId),out}var selectConfig3={kernelName:Select,backendName:"wasm",kernelFunc:select3,setupFunc:setup34},wasmFunc7;function setup35(backend22){wasmFunc7=backend22.wasm.cwrap(Sigmoid,null,["number","number"])}function sigmoid4(args){let{backend:backend22,inputs:{x}}=args,xId=backend22.dataIdMap.get(x.dataId).id,out=backend22.makeOutput(x.shape,x.dtype),outId=backend22.dataIdMap.get(out.dataId).id;return util_exports.sizeFromShape(out.shape)===0||wasmFunc7(xId,outId),out}var sigmoidConfig3={kernelName:"Sigmoid",backendName:"wasm",setupFunc:setup35,kernelFunc:sigmoid4},sinConfig3=createUnaryKernelConfig(Sin);function slice4(args){let{inputs:{x},attrs:{begin,size},backend:backend22}=args,[begin_,size_]=slice_util_exports.parseSliceParams(x,begin,size),isContinous=slice_util_exports.isSliceContinous(x.shape,begin_,size_),xVals=backend22.readSync(x.dataId),out=backend22.makeOutput(size_,x.dtype),xStrides=util_exports.computeStrides(x.shape),outData=backend22.dataIdMap.get(out.dataId);if(isContinous){let flatOffset=slice_util_exports.computeFlatOffset(begin_,xStrides);return x.dtype==="string"?outData.stringBytes=xVals.slice(flatOffset,flatOffset+util_exports.sizeFromShape(size_)):backend22.typedArrayFromHeap(out).set(xVals.subarray(flatOffset,flatOffset+util_exports.sizeFromShape(size_))),out}if(x.dtype==="string"){let res=sliceImpl(xVals,begin_,size_,x.shape,x.dtype);return outData.stringBytes=res,out}let outVals=backend22.typedArrayFromHeap(out),rank=x.shape.length;if(rank===2)slice2d2(xVals,xStrides[0],outVals,begin_,size_);else if(rank===3)slice3d2(xVals,xStrides[0],xStrides[1],outVals,begin_,size_);else if(rank===4)slice4d2(xVals,xStrides[0],xStrides[1],xStrides[2],outVals,begin_,size_);else{let res=sliceImpl(xVals,begin_,size_,x.shape,x.dtype);outVals.set(res)}return out}function slice2d2(xVals,xStride,outVals,begin,size){let outOffset=0,beginI=begin[0],beginJ=begin[1],endI=beginI+size[0];for(let i=beginI;i{let xSliceSize=[...size];xSliceSize[$axis]=s;let xSlice=slice4({inputs:{x},attrs:{begin,size:xSliceSize},backend:backend22});return begin[$axis]+=s,xSlice})}var splitVConfig3={kernelName:SplitV,backendName:"wasm",kernelFunc:splitV3},sqrtConfig3=createUnaryKernelConfig(Sqrt),squareConfig3=createUnaryKernelConfig(Square),supportsFullBroadcast15=!0,squaredDifferenceConfig3=createBinaryKernelConfig(SquaredDifference,supportsFullBroadcast15),wasmStridedSlice;function setup37(backend22){wasmStridedSlice=backend22.wasm.cwrap(StridedSlice,null,["number","array","number","array","array","array","array","array","number","number"])}function stridedSlice4(args){let{backend:backend22,inputs,attrs}=args,{x}=inputs,{begin,end,strides}=attrs;strides==null&&(strides=new Array(begin.length));let{beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask}=attrs,ellipsisAxes=backend_util_exports.slice_util.maskToAxes(ellipsisMask);if(ellipsisAxes.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(ellipsisMask!==0&&newAxisMask!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(ellipsisMask!==0&&shrinkAxisMask!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");let numInterpolatedAxes=x.shape.length-begin.length,expandAxes=backend_util_exports.slice_util.maskToAxes(newAxisMask),newShape=x.shape.slice();expandAxes.forEach(axis=>{begin[axis]=0,end[axis]=1,newShape.splice(axis,0,1)});let xReshaped=reshape4({inputs:{x},attrs:{shape:newShape},backend:backend22}),{begin:normalizedBegin,end:normalizedEnd,strides:normalizedStrides}=backend_util_exports.slice_util.getNormalizedAxes(xReshaped.shape,ellipsisAxes,numInterpolatedAxes,begin,end,strides,beginMask,endMask,ellipsisMask);begin=normalizedBegin,end=normalizedEnd,strides=normalizedStrides;let shrinkAxes=backend_util_exports.slice_util.maskToAxes(shrinkAxisMask);shrinkAxes.forEach(axis=>{end[axis]=begin[axis]+1,strides[axis]=1});let size=backend_util_exports.slice_util.computeOutShape(begin,end,strides),outShape=size.filter((_,axis)=>shrinkAxes.indexOf(axis)===-1);if(strides.every(v=>v===1)){let xSliced=slice4({inputs:{x},attrs:{begin,size},backend:backend22});return reshape4({inputs:{x:xSliced},attrs:{shape:outShape},backend:backend22})}let out=backend22.makeOutput(outShape,"float32");if(!outShape.some(axis=>axis===0)){let xId=backend22.dataIdMap.get(xReshaped.dataId).id,xStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(xReshaped.shape)).buffer),beginBytes=new Uint8Array(new Int32Array(begin).buffer),endBytes=new Uint8Array(new Int32Array(end).buffer),stridesBytes=new Uint8Array(new Int32Array(strides).buffer),outputShapeBytes=new Uint8Array(new Int32Array(outShape).buffer),outStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(outShape)).buffer),outId=backend22.dataIdMap.get(out.dataId).id;wasmStridedSlice(xId,xStridesBytes,xReshaped.shape.length,beginBytes,endBytes,stridesBytes,outputShapeBytes,outStridesBytes,outShape.length,outId)}return reshape4({inputs:{x:out},attrs:{shape:outShape},backend:backend22})}var stridedSliceConfig3={kernelName:StridedSlice,backendName:"wasm",setupFunc:setup37,kernelFunc:stridedSlice4},supportsFullBroadcast16=!0,subConfig3=createBinaryKernelConfig(Sub,supportsFullBroadcast16),wasmSum;function setup38(backend22){wasmSum=backend22.wasm.cwrap(Sum,null,["number, number, number"])}function sum5(args){let{backend:backend22,inputs,attrs}=args,{axis,keepDims}=attrs,{x}=inputs,xId=backend22.dataIdMap.get(x.dataId).id,inputId=xId,input2=x,{transposed,axes,originalAxes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend22),reductionAxes=axes;if(inputWasTransposed){let transposedId=backend22.dataIdMap.get(transposed.dataId).id;transposedId!==xId&&(input2=transposed,inputId=transposedId,reductionAxes=backend_util_exports.getInnerMostAxes(reductionAxes.length,input2.shape.length))}backend_util_exports.assertAxesAreInnerMostDims("sum",reductionAxes,input2.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(input2.shape,reductionAxes),reduceSize=util_exports.sizeFromShape(reduceShape),out=backend22.makeOutput(outShape,input2.dtype);if(util_exports.sizeFromShape(input2.shape)!==0){let outId=backend22.dataIdMap.get(out.dataId).id;wasmSum(inputId,reduceSize,outId)}if(inputWasTransposed&&backend22.disposeData(transposed.dataId),keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(out.shape,originalAxes);out.shape=newShape}return out}var sumConfig3={kernelName:Sum,backendName:"wasm",setupFunc:setup38,kernelFunc:sum5},tanhConfig3=createUnaryKernelConfig(Tanh),wasmTile;function setup39(backend22){wasmTile=backend22.wasm.cwrap(Tile,null,["number","array","number","array","number","number"])}function tile4(args){let{inputs,backend:backend22,attrs}=args,{x}=inputs,xId=backend22.dataIdMap.get(x.dataId).id,{reps}=attrs,newShape=new Array(x.shape.length);for(let i=0;i{let{x}=inputs,{k,sorted}=attrs,xId=backend22.dataIdMap.get(x.dataId).id,xShapeBytes=new Uint8Array(new Int32Array(x.shape).buffer),outputShape=x.shape.slice();outputShape[outputShape.length-1]=k;let outValues=backend22.makeOutput(outputShape,x.dtype),outValuesId=backend22.dataIdMap.get(outValues.dataId).id,outIndices=backend22.makeOutput(outputShape,"int32"),outIndicesId=backend22.dataIdMap.get(outIndices.dataId).id;return wasmTopK(xId,xShapeBytes,x.shape.length,CppDType[x.dtype],k,sorted,outValuesId,outIndicesId),[outValues,outIndices]},topKConfig3={kernelName:TopK,backendName:"wasm",setupFunc:setup40,kernelFunc:topk2};function unpack3(args){let{inputs,backend:backend22,attrs}=args,{value}=inputs,{axis}=attrs;axis<0&&(axis+=value.shape.length);let numOutputs=value.shape[axis],rank=value.shape.length,outShape=new Array(rank-1),outIndex=0;for(let i=0;i({dataId,dtype,shape:outShape}))}var unpackConfig3={kernelName:Unpack,backendName:"wasm",kernelFunc:unpack3};function zerosLike4(args){let{inputs:{x},backend:backend22}=args,out=backend22.makeOutput(x.shape,x.dtype);return backend22.typedArrayFromHeap(out).fill(0),out}var zerosLikeConfig3={kernelName:ZerosLike,backendName:"wasm",kernelFunc:zerosLike4},kernelConfigs3=[absConfig3,addConfig3,addNConfig3,argMaxConfig3,avgPoolConfig3,batchMatMulConfig3,castConfig3,clipByValueConfig2,concatConfig3,conv2DConfig3,conv2DBackpropInputConfig3,cosConfig3,cropAndResizeConfig3,cumsumConfig3,depthToSpaceConfig3,depthwiseConv2dNativeConfig3,equalConfig3,expConfig3,expandDimsConfig3,fillConfig3,flipLeftRightConfig3,floorConfig3,floorDivConfig3,fusedMatMulConfig,fusedBatchNormConfig,fusedConv2DConfig3,fusedDepthwiseConv2DConfig3,gatherNdConfig3,gatherV2Config3,greaterConfig3,greaterEqualConfig3,identityConfig3,leakyReluConfig3,lessConfig3,lessEqualConfig3,logConfig3,logicalAndConfig3,maxConfig3,maximumConfig3,maxPoolConfig3,meanConfig3,minConfig3,minimumConfig3,multiplyConfig3,negConfig3,nonMaxSuppressionV3Config3,nonMaxSuppressionV4Config3,nonMaxSuppressionV5Config3,notEqualConfig3,oneHotConfig3,onesLikeConfig3,packConfig3,padV2Config3,powConfig3,preluConfig3,prodConfig3,rangeConfig3,realDivConfig3,reluConfig3,relu6Config3,reshapeConfig3,resizeBilinearConfig3,reverseConfig3,rotateWithOffsetConfig3,rsqrtConfig3,roundConfig3,scatterNdConfig3,selectConfig3,sigmoidConfig3,sinConfig3,sliceConfig3,softmaxConfig3,splitVConfig3,sqrtConfig3,squareConfig3,squaredDifferenceConfig3,stridedSliceConfig3,subConfig3,sumConfig3,tanhConfig3,tileConfig3,topKConfig3,transposeConfig3,unpackConfig3,zerosLikeConfig3];for(let kernelConfig of kernelConfigs3)registerKernel(kernelConfig);var ENV4=env();ENV4.registerFlag("WASM_HAS_SIMD_SUPPORT",async()=>WebAssembly.validate(new Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,2,1,0,10,9,1,7,0,65,0,253,15,26,11])));ENV4.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT",async()=>{if(ENV4.get("IS_NODE"))return!1;try{return new MessageChannel().port1.postMessage(new SharedArrayBuffer(1)),WebAssembly.validate(new Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,2,1,0,5,4,1,3,1,1,10,11,1,9,0,65,0,254,16,2,0,26,11]))}catch(e){return!1}});var import_tfjs_backend_wasm_threaded_simd=__toModule2(require_tfjs_backend_wasm_threaded_simd()),wasmWorkerContents='var threadInfoStruct=0;var selfThreadId=0;var parentThreadId=0;var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(" ");console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(" ");postMessage({cmd:"alert",text:text,threadId:selfThreadId})}var err=threadPrintErr;this.alert=threadAlert;Module["instantiateWasm"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module["wasmModule"],info);Module["wasmModule"]=null;receiveInstance(instance);return instance.exports};this.onmessage=function(e){try{if(e.data.cmd==="load"){Module["DYNAMIC_BASE"]=e.data.DYNAMIC_BASE;Module["DYNAMICTOP_PTR"]=e.data.DYNAMICTOP_PTR;Module["wasmModule"]=e.data.wasmModule;Module["wasmMemory"]=e.data.wasmMemory;Module["buffer"]=Module["wasmMemory"].buffer;Module["ENVIRONMENT_IS_PTHREAD"]=true;if(typeof e.data.urlOrBlob==="string"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}Module=WasmBackendModuleThreadedSimd(Module);postMessage({"cmd":"loaded"})}else if(e.data.cmd==="objectTransfer"){Module["PThread"].receiveObjectTransfer(e.data)}else if(e.data.cmd==="run"){Module["__performance_now_clock_drift"]=performance.now()-e.data.time;threadInfoStruct=e.data.threadInfoStruct;Module["__register_pthread_ptr"](threadInfoStruct,0,0);selfThreadId=e.data.selfThreadId;parentThreadId=e.data.parentThreadId;var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module["establishStackSpace"](top,max);Module["_emscripten_tls_init"]();Module["PThread"].receiveObjectTransfer(e.data);Module["PThread"].setThreadStatus(Module["_pthread_self"](),1);try{var result=Module["dynCall_ii"](e.data.start_routine,e.data.arg);if(!Module["getNoExitRuntime"]())Module["PThread"].threadExit(result)}catch(ex){if(ex==="Canceled!"){Module["PThread"].threadCancel()}else if(ex!="unwind"){Atomics.store(Module["HEAPU32"],threadInfoStruct+4>>2,ex instanceof Module["ExitStatus"]?ex.status:-2);Atomics.store(Module["HEAPU32"],threadInfoStruct+0>>2,1);Module["_emscripten_futex_wake"](threadInfoStruct+0,2147483647);if(!(ex instanceof Module["ExitStatus"]))throw ex}}}else if(e.data.cmd==="cancel"){if(threadInfoStruct){Module["PThread"].threadCancel()}}else if(e.data.target==="setimmediate"){}else if(e.data.cmd==="processThreadQueue"){if(threadInfoStruct){Module["_emscripten_current_thread_process_queued_calls"]()}}else{err("worker.js received unknown command "+e.data.cmd);err(e.data)}}catch(ex){err("worker.js onmessage() captured an uncaught exception: "+ex);if(ex.stack)err(ex.stack);throw ex}};if(typeof process==="object"&&typeof process.versions==="object"&&typeof process.versions.node==="string"){self={location:{href:__filename}};var onmessage=this.onmessage;var nodeWorkerThreads=require("worker_threads");Worker=nodeWorkerThreads.Worker;var parentPort=nodeWorkerThreads.parentPort;parentPort.on("message",function(data){onmessage({data:data})});var nodeFS=require("fs");var nodeRead=function(filename){return nodeFS.readFileSync(filename,"utf8")};function globalEval(x){global.require=require;global.Module=Module;eval.call(null,x)}importScripts=function(f){globalEval(nodeRead(f))};postMessage=function(msg){parentPort.postMessage(msg)};if(typeof performance==="undefined"){performance={now:function(){return Date.now()}}}}',import_tfjs_backend_wasm=__toModule2(require_tfjs_backend_wasm()),BackendWasm=class extends KernelBackend{constructor(wasm){super();this.wasm=wasm,this.dataIdNextNumber=1,this.wasm.tfjs.init(),this.dataIdMap=new DataStorage(this,engine())}write(values,shape,dtype){let dataId={};return this.move(dataId,values,shape,dtype),dataId}numDataIds(){return this.dataIdMap.numDataIds()}async time(f){let start=util_exports.now();return f(),{kernelMs:util_exports.now()-start}}move(dataId,values,shape,dtype){let id=this.dataIdNextNumber++;if(dtype==="string"){let stringBytes=values;this.dataIdMap.set(dataId,{id,stringBytes,shape,dtype,memoryOffset:null});return}let size=util_exports.sizeFromShape(shape),numBytes=size*util_exports.bytesPerElement(dtype),memoryOffset=this.wasm._malloc(numBytes);this.dataIdMap.set(dataId,{id,memoryOffset,shape,dtype}),this.wasm.tfjs.registerTensor(id,size,memoryOffset),values!=null&&this.wasm.HEAPU8.set(new Uint8Array(values.buffer,values.byteOffset,numBytes),memoryOffset)}async read(dataId){return this.readSync(dataId)}readSync(dataId){let{memoryOffset,dtype,shape,stringBytes}=this.dataIdMap.get(dataId);if(dtype==="string")return stringBytes;let bytes=this.wasm.HEAPU8.slice(memoryOffset,memoryOffset+util_exports.sizeFromShape(shape)*util_exports.bytesPerElement(dtype));return typedArrayFromBuffer(bytes.buffer,dtype)}disposeData(dataId){let data2=this.dataIdMap.get(dataId);this.wasm._free(data2.memoryOffset),this.wasm.tfjs.disposeData(data2.id),this.dataIdMap.delete(dataId)}floatPrecision(){return 32}getMemoryOffset(dataId){return this.dataIdMap.get(dataId).memoryOffset}dispose(){this.wasm.tfjs.dispose(),this.wasm=null}memory(){return{unreliable:!1}}makeOutput(shape,dtype,memoryOffset){let dataId;if(memoryOffset==null)dataId=this.write(null,shape,dtype);else{dataId={};let id=this.dataIdNextNumber++;this.dataIdMap.set(dataId,{id,memoryOffset,shape,dtype});let size=util_exports.sizeFromShape(shape);this.wasm.tfjs.registerTensor(id,size,memoryOffset)}return{dataId,shape,dtype}}typedArrayFromHeap({shape,dtype,dataId}){let buffer2=this.wasm.HEAPU8.buffer,{memoryOffset}=this.dataIdMap.get(dataId),size=util_exports.sizeFromShape(shape);switch(dtype){case"float32":return new Float32Array(buffer2,memoryOffset,size);case"int32":return new Int32Array(buffer2,memoryOffset,size);case"bool":return new Uint8Array(buffer2,memoryOffset,size);default:throw new Error(`Unknown dtype ${dtype}`)}}};function createInstantiateWasmFunc(path){return(imports,callback)=>(util_exports.fetch(path,{credentials:"same-origin"}).then(response=>{response.ok||imports.env.a(`failed to load wasm binary file at '${path}'`),response.arrayBuffer().then(binary=>{WebAssembly.instantiate(binary,imports).then(output=>{callback(output.instance)})})}),{})}function getPathToWasmBinary(simdSupported,threadsSupported,wasmModuleFolder){if(wasmPath!=null)return wasmPath;let path="tfjs-backend-wasm.wasm";return simdSupported&&threadsSupported?path="tfjs-backend-wasm-threaded-simd.wasm":simdSupported&&(path="tfjs-backend-wasm-simd.wasm"),wasmFileMap!=null&&wasmFileMap[path]!=null?wasmFileMap[path]:wasmModuleFolder+path}async function init(){let[simdSupported,threadsSupported]=await Promise.all([env().getAsync("WASM_HAS_SIMD_SUPPORT"),env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]);return new Promise((resolve,reject)=>{let factoryConfig={};factoryConfig.locateFile=(path,prefix)=>{if(path.endsWith(".worker.js")){let response=wasmWorkerContents,blob=new Blob([response],{type:"application/javascript"});return URL.createObjectURL(blob)}return path.endsWith(".wasm")?getPathToWasmBinary(simdSupported,threadsSupported,wasmPathPrefix!=null?wasmPathPrefix:prefix):prefix+path},customFetch&&(factoryConfig.instantiateWasm=createInstantiateWasmFunc(getPathToWasmBinary(simdSupported,threadsSupported,wasmPathPrefix!=null?wasmPathPrefix:"")));let wasm;threadsSupported&&simdSupported&&wasmPath==null?(wasm=import_tfjs_backend_wasm_threaded_simd.default(factoryConfig),wasm.mainScriptUrlOrBlob=new Blob(["var WasmBackendModuleThreadedSimd = "+import_tfjs_backend_wasm_threaded_simd.default.toString()],{type:"text/javascript"})):wasm=import_tfjs_backend_wasm.default(factoryConfig);let voidReturnType=null;wasm.tfjs={init:wasm.cwrap("init",null,[]),registerTensor:wasm.cwrap("register_tensor",null,["number","number","number"]),disposeData:wasm.cwrap("dispose_data",voidReturnType,["number"]),dispose:wasm.cwrap("dispose",voidReturnType,[])};let initialized=!1;wasm.onRuntimeInitialized=()=>{initialized=!0,initAborted=!1,resolve({wasm})},wasm.onAbort=()=>{initialized||initAborted||(initAborted=!0,reject({message:"Make sure the server can serve the `.wasm` file relative to the bundled js file. For more details see https://github.com/tensorflow/tfjs/blob/master/tfjs-backend-wasm/README.md#using-bundlers"}))}})}function typedArrayFromBuffer(buffer2,dtype){switch(dtype){case"float32":return new Float32Array(buffer2);case"int32":return new Int32Array(buffer2);case"bool":return new Uint8Array(buffer2);default:throw new Error(`Unknown dtype ${dtype}`)}}var wasmBinaryNames=["tfjs-backend-wasm.wasm","tfjs-backend-wasm-simd.wasm","tfjs-backend-wasm-threaded-simd.wasm"],wasmPath=null,wasmPathPrefix=null,wasmFileMap={},initAborted=!1,customFetch=!1;function setWasmPath(path,usePlatformFetch=!1){if(deprecationWarn("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release."),initAborted)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`");wasmPath=path,customFetch=usePlatformFetch}function setWasmPaths(prefixOrFileMap,usePlatformFetch=!1){if(initAborted)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPaths()` before you call `tf.setBackend()` or `tf.ready()`");if(typeof prefixOrFileMap=="string")wasmPathPrefix=prefixOrFileMap;else{wasmFileMap=prefixOrFileMap;let missingPaths=wasmBinaryNames.filter(name=>wasmFileMap[name]==null);if(missingPaths.length>0)throw new Error(`There were no entries found for the following binaries: ${missingPaths.join(",")}. Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`)}customFetch=usePlatformFetch}var version9="2.8.3",WASM_PRIORITY=2;registerBackend("wasm",async()=>{let{wasm}=await init();return new BackendWasm(wasm)},WASM_PRIORITY);var exports_constraints_exports={};__export2(exports_constraints_exports,{maxNorm:()=>maxNorm,minMaxNorm:()=>minMaxNorm,nonNeg:()=>nonNeg,unitNorm:()=>unitNorm});var _epsilon;function epsilon2(){return _epsilon==null&&(_epsilon=backend().epsilon()),_epsilon}function imageDataFormat(){return"channelsLast"}var AttributeError=class extends Error{constructor(message){super(message);Object.setPrototypeOf(this,AttributeError.prototype)}},RuntimeError=class extends Error{constructor(message){super(message);Object.setPrototypeOf(this,RuntimeError.prototype)}},ValueError=class extends Error{constructor(message){super(message);Object.setPrototypeOf(this,ValueError.prototype)}},NotImplementedError=class extends Error{constructor(message){super(message);Object.setPrototypeOf(this,NotImplementedError.prototype)}},AssertionError=class extends Error{constructor(message){super(message);Object.setPrototypeOf(this,AssertionError.prototype)}},IndexError=class extends Error{constructor(message){super(message);Object.setPrototypeOf(this,IndexError.prototype)}};function pyListRepeat(value,numValues){if(Array.isArray(value)){let newArray=[];for(let i=0;ip1.toUpperCase())}var _GLOBAL_CUSTOM_OBJECTS={};function serializeKerasObject(instance2){if(instance2==null)return null;let dict={};return dict.className=instance2.getClassName(),dict.config=instance2.getConfig(),dict}function convertNDArrayScalarsInConfig(config2){if(!(config2==null||typeof config2!="object"))if(Array.isArray(config2))config2.forEach(configItem=>convertNDArrayScalarsInConfig(configItem));else{let fields=Object.keys(config2);for(let field of fields){let value=config2[field];value!=null&&typeof value=="object"&&(!Array.isArray(value)&&value.type==="ndarray"&&typeof value.value=="number"?config2[field]=value.value:convertNDArrayScalarsInConfig(value))}}}function deserializeKerasObject(identifier,moduleObjects={},customObjects={},printableModuleName="object",fastWeightInit=!1){if(typeof identifier=="string"){let functionName=identifier,fn;if(functionName in customObjects)fn=customObjects[functionName];else if(functionName in _GLOBAL_CUSTOM_OBJECTS)fn=_GLOBAL_CUSTOM_OBJECTS[functionName];else if(fn=moduleObjects[functionName],fn==null)throw new ValueError(`Unknown ${printableModuleName}: ${identifier}. This may be due to one of the following reasons: 1. The ${printableModuleName} is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code. -2. The custom ${printableModuleName} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);return fn}else{let config3=identifier;if(config3.className==null||config3.config==null)throw new ValueError(`${printableModuleName}: Improper config format: ${JSON.stringify(config3)}. -'className' and 'config' must set.`);let className=config3.className,cls,fromConfig;if(className in customObjects?[cls,fromConfig]=customObjects[className]:className in _GLOBAL_CUSTOM_OBJECTS?[cls,fromConfig]=_GLOBAL_CUSTOM_OBJECTS.className:className in moduleObjects&&([cls,fromConfig]=moduleObjects[className]),cls==null)throw new ValueError(`Unknown ${printableModuleName}: ${className}. This may be due to one of the following reasons: +2. The custom ${printableModuleName} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);return fn}else{let config2=identifier;if(config2.className==null||config2.config==null)throw new ValueError(`${printableModuleName}: Improper config format: ${JSON.stringify(config2)}. +'className' and 'config' must set.`);let className=config2.className,cls,fromConfig;if(className in customObjects?[cls,fromConfig]=customObjects[className]:className in _GLOBAL_CUSTOM_OBJECTS?[cls,fromConfig]=_GLOBAL_CUSTOM_OBJECTS.className:className in moduleObjects&&([cls,fromConfig]=moduleObjects[className]),cls==null)throw new ValueError(`Unknown ${printableModuleName}: ${className}. This may be due to one of the following reasons: 1. The ${printableModuleName} is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code. -2. The custom ${printableModuleName} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);if(fromConfig!=null){let customObjectsCombined={};for(let key of Object.keys(_GLOBAL_CUSTOM_OBJECTS))customObjectsCombined[key]=_GLOBAL_CUSTOM_OBJECTS[key];for(let key of Object.keys(customObjects))customObjectsCombined[key]=customObjects[key];let nestedConfig=config3.config;nestedConfig.customObjects=customObjectsCombined;let backupCustomObjects=Object.assign({},_GLOBAL_CUSTOM_OBJECTS);for(let key of Object.keys(customObjects))_GLOBAL_CUSTOM_OBJECTS[key]=customObjects[key];convertNDArrayScalarsInConfig(config3.config);let returnObj=fromConfig(cls,config3.config,customObjects,fastWeightInit);return _GLOBAL_CUSTOM_OBJECTS=Object.assign({},backupCustomObjects),returnObj}else{let backupCustomObjects=Object.assign({},_GLOBAL_CUSTOM_OBJECTS);for(let key of Object.keys(customObjects))_GLOBAL_CUSTOM_OBJECTS[key]=customObjects[key];let returnObj=new cls(config3.config);return _GLOBAL_CUSTOM_OBJECTS=Object.assign({},backupCustomObjects),returnObj}}}function numberCompare(a,b){return ab?1:0}function reverseNumberCompare(a,b){return-1*numberCompare(a,b)}function unique4(xs){if(xs==null)return xs;let out=[];for(let x of xs)out.indexOf(x)===-1&&out.push(x);return out}function isObjectEmpty(obj){if(obj==null)throw new ValueError(`Invalid value in obj: ${JSON.stringify(obj)}`);for(let key in obj)if(obj.hasOwnProperty(key))return!1;return!0}function checkStringTypeUnionValue(values,label,value){if(value!=null&&values.indexOf(value)<0)throw new ValueError(`${value} is not a valid ${label}. Valid values are ${values} or null/undefined.`)}function checkArrayTypeAndLength(x,expectedType,minLength=0,maxLength=Infinity){return assert2(minLength>=0),assert2(maxLength>=minLength),Array.isArray(x)&&x.length>=minLength&&x.length<=maxLength&&x.every(e=>typeof e===expectedType)}function assertPositiveInteger(value,name){Array.isArray(value)?(util_exports.assert(value.length>0,()=>`${name} is unexpectedly an empty array.`),value.forEach((v,i)=>assertPositiveInteger(v,`element ${i+1} of ${name}`))):util_exports.assert(Number.isInteger(value)&&value>0,()=>`Expected ${name} to be a positive integer, but got ${formatAsFriendlyString(value)}.`)}function formatAsFriendlyString(value){return value===null?"null":Array.isArray(value)?"["+value.map(v=>formatAsFriendlyString(v)).join(",")+"]":typeof value=="string"?`"${value}"`:`${value}`}function debounce(f,waitMs){let lastTime=util_exports.now(),lastResult;return(...args)=>{let now22=util_exports.now();return now22-lastTimesqrt(sum2(mul(w,w),axis,!0)))}var Constraint=class extends serialization_exports.Serializable{getConfig(){return{}}},MaxNorm=class extends Constraint{constructor(args){super();this.defaultMaxValue=2,this.defaultAxis=0,this.maxValue=args.maxValue!=null?args.maxValue:this.defaultMaxValue,this.axis=args.axis!=null?args.axis:this.defaultAxis}apply(w){return tidy(()=>{let norms=calcL2Norms(w,this.axis),desired=clipByValue(norms,0,this.maxValue);return mul(w,div(desired,add2(epsilon2(),norms)))})}getConfig(){return{maxValue:this.maxValue,axis:this.axis}}};MaxNorm.className="MaxNorm";serialization_exports.registerClass(MaxNorm);var UnitNorm=class extends Constraint{constructor(args){super();this.defaultAxis=0,this.axis=args.axis!=null?args.axis:this.defaultAxis}apply(w){return tidy(()=>div(w,add2(epsilon2(),calcL2Norms(w,this.axis))))}getConfig(){return{axis:this.axis}}};UnitNorm.className="UnitNorm";serialization_exports.registerClass(UnitNorm);var NonNeg=class extends Constraint{apply(w){return relu(w)}};NonNeg.className="NonNeg";serialization_exports.registerClass(NonNeg);var MinMaxNorm=class extends Constraint{constructor(args){super();this.defaultMinValue=0,this.defaultMaxValue=1,this.defaultRate=1,this.defaultAxis=0,this.minValue=args.minValue!=null?args.minValue:this.defaultMinValue,this.maxValue=args.maxValue!=null?args.maxValue:this.defaultMaxValue,this.rate=args.rate!=null?args.rate:this.defaultRate,this.axis=args.axis!=null?args.axis:this.defaultAxis}apply(w){return tidy(()=>{let norms=calcL2Norms(w,this.axis),desired=add2(mul(this.rate,clipByValue(norms,this.minValue,this.maxValue)),mul(1-this.rate,norms));return mul(w,div(desired,add2(epsilon2(),norms)))})}getConfig(){return{minValue:this.minValue,maxValue:this.maxValue,rate:this.rate,axis:this.axis}}};MinMaxNorm.className="MinMaxNorm";serialization_exports.registerClass(MinMaxNorm);var CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP={maxNorm:"MaxNorm",minMaxNorm:"MinMaxNorm",nonNeg:"NonNeg",unitNorm:"UnitNorm"};function serializeConstraint(constraint){return serializeKerasObject(constraint)}function deserializeConstraint(config3,customObjects={}){return deserializeKerasObject(config3,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"constraint")}function getConstraint(identifier){if(identifier==null)return null;if(typeof identifier=="string"){let config3={className:identifier in CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP?CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier]:identifier,config:{}};return deserializeConstraint(config3)}else return identifier instanceof Constraint?identifier:deserializeConstraint(identifier)}function maxNorm(args){return new MaxNorm(args)}function unitNorm(args){return new UnitNorm(args)}function nonNeg(){return new NonNeg}function minMaxNorm(config3){return new MinMaxNorm(config3)}var exports_initializers_exports={};__export2(exports_initializers_exports,{constant:()=>constant,glorotNormal:()=>glorotNormal,glorotUniform:()=>glorotUniform,heNormal:()=>heNormal,heUniform:()=>heUniform,identity:()=>identity4,leCunNormal:()=>leCunNormal,leCunUniform:()=>leCunUniform,ones:()=>ones3,orthogonal:()=>orthogonal,randomNormal:()=>randomNormal3,randomUniform:()=>randomUniform2,truncatedNormal:()=>truncatedNormal2,varianceScaling:()=>varianceScaling,zeros:()=>zeros3});var VALID_DATA_FORMAT_VALUES=["channelsFirst","channelsLast"],VALID_INTERPOLATION_FORMAT_VALUES=["nearest","bilinear"],VALID_PADDING_MODE_VALUES=["valid","same","causal"],VALID_POOL_MODE_VALUES=["max","avg"],VALID_BIDIRECTIONAL_MERGE_MODES=["sum","mul","concat","ave"],nameMap=new Map;function checkDataFormat(value){checkStringTypeUnionValue(VALID_DATA_FORMAT_VALUES,"DataFormat",value)}function checkInterpolationFormat(value){checkStringTypeUnionValue(VALID_INTERPOLATION_FORMAT_VALUES,"InterpolationFormat",value)}function checkPaddingMode(value){checkStringTypeUnionValue(VALID_PADDING_MODE_VALUES,"PaddingMode",value)}function checkPoolMode(value){checkStringTypeUnionValue(VALID_POOL_MODE_VALUES,"PoolMode",value)}var _nameScopeStack=[],_nameScopeDivider="/";function nameScope(name,fn){_nameScopeStack.push(name);try{let val=fn();return _nameScopeStack.pop(),val}catch(e){throw _nameScopeStack.pop(),e}}function currentNameScopePrefix(){return _nameScopeStack.length===0?"":_nameScopeStack.join(_nameScopeDivider)+_nameScopeDivider}function getScopedTensorName(tensorName){if(!isValidTensorName(tensorName))throw new Error("Not a valid tensor name: '"+tensorName+"'");return currentNameScopePrefix()+tensorName}function getUniqueTensorName(scopedName){if(!isValidTensorName(scopedName))throw new Error("Not a valid tensor name: '"+scopedName+"'");nameMap.has(scopedName)||nameMap.set(scopedName,0);let index=nameMap.get(scopedName);if(nameMap.set(scopedName,nameMap.get(scopedName)+1),index>0){let result=`${scopedName}_${index}`;return nameMap.set(result,1),result}else return scopedName}var tensorNameRegex=new RegExp(/^[A-Za-z0-9][-A-Za-z0-9\._\/]*$/);function isValidTensorName(name){return!!name.match(tensorNameRegex)}function isInteger(x){return x===parseInt(x.toString(),10)}function arrayProd(array2,begin,end){begin==null&&(begin=0),end==null&&(end=array2.length);let prod5=1;for(let i=begin;i{if(x.shape.length!==2)throw new ValueError(`repeat() expects a rank-2 tensor, but received a rank-${x.shape.length} tensor.`);let y=expandDims5(x,1);return tile5(y,[1,n,1])})}function flatten2(x){let newShape=[arrayProd(x.shape)];return x.reshape(newShape)}function batchFlatten(x){if(x.rank<=1)throw new ValueError(`batchFlatten requires a minimum rank of 2. Got rank: ${x.rank}.`);let newShape=[x.shape[0],arrayProd(x.shape,1)];return x.reshape(newShape)}function sliceAlongFirstAxis(array2,start,size){return tidy(()=>{switch(array2.rank){case 1:return slice1d(array2,start,size);case 2:return slice2d(array2,[start,0],[size,array2.shape[1]]);case 3:return slice3d(array2,[start,0,0],[size,array2.shape[1],array2.shape[2]]);case 4:return slice4d(array2,[start,0,0,0],[size,array2.shape[1],array2.shape[2],array2.shape[3]]);case 5:return slice(array2,[start,0,0,0,0],[size,array2.shape[1],array2.shape[2],array2.shape[3],array2.shape[4]]);case 6:return slice(array2,[start,0,0,0,0,0],[size,array2.shape[1],array2.shape[2],array2.shape[3],array2.shape[4],array2.shape[5]]);default:throw new ValueError(`sliceAlongFirstAxis() received an unsupported tensor rank: ${array2.rank}`)}})}function sliceAlongLastAxis(array2,start,size){return tidy(()=>{switch(array2.rank){case 1:return slice1d(array2,start,size);case 2:return slice2d(array2,[0,start],[array2.shape[0],size]);case 3:return slice3d(array2,[0,0,start],[array2.shape[0],array2.shape[1],size]);case 4:return slice4d(array2,[0,0,0,start],[array2.shape[0],array2.shape[1],array2.shape[2],size]);default:throw new ValueError(`sliceAlongLastAxis() received an unsupported tensor rank: ${array2.rank}`)}})}function sliceAlongAxis(array2,start,size,axis){return tidy(()=>{switch(array2.rank){case 1:return slice1d(array2,start,size);case 2:switch(axis){case 1:return sliceAlongFirstAxis(array2,start,size);case 2:return sliceAlongLastAxis(array2,start,size);default:throw new ValueError(`The axis is not within the rank of the tensor ${axis}`)}case 3:switch(axis){case 1:return sliceAlongFirstAxis(array2,start,size);case 2:return slice3d(array2,[0,start,0],[array2.shape[0],size,array2.shape[2]]);case 3:return sliceAlongLastAxis(array2,start,size);default:throw new ValueError(`The axis is not within the rank of the tensor ${axis}`)}case 4:switch(axis){case 1:return sliceAlongFirstAxis(array2,start,size);case 2:return slice4d(array2,[0,start,0,0],[array2.shape[0],size,array2.shape[2],array2.shape[3]]);case 3:return slice4d(array2,[0,0,start,0],[array2.shape[0],array2.shape[1],size,array2.shape[3]]);case 4:return sliceAlongLastAxis(array2,start,size);default:throw new ValueError(`The axis is not within the rank of the tensor ${axis}`)}default:throw new ValueError(`sliceAlongLastAxis() received an unsupported tensor rank: ${array2.rank}`)}})}function concatenate(tensors,axis=-1){let rank;return axis<0&&(rank=tensors[0].rank,rank!==0?axis=rank:axis=0),axis===tensors[0].rank&&(axis=-1),concat(tensors,axis)}function concatAlongFirstAxis(a,b){switch(a.rank){case 1:return concat1d([a,b]);case 2:return concat2d([a,b],0);case 3:return concat3d([a,b],0);case 4:return concat4d([a,b],0);default:throw new ValueError(`concatAlongFirstAxis() received an unsupported tensor rank: ${a.rank}`)}}function tile5(x,n){if(Array.isArray(n)||(n=[n]),x.rank!==n.length)throw new ValueError(`The length of input n (${n.length}) does not match the number of dimensions in input x (${x.rank})`);return tile(x,n)}function randomNormal2(shape,mean4=0,stddev=1,dtype,seed){return randomNormal(shape,mean4,stddev,dtype,seed)}function dot2(a,b,activation2,bias){if(a.rank<2||b.rank<2)throw new NotImplementedError(`dot requires both inputs to be rank >= 2 but got x shape = ${a.shape} and y shape = ${b.shape}`);if(b.rank>=3){let xLastDim=a.shape.slice(-1)[0],ySecondLastDim=b.shape.slice(-2)[0];if(xLastDim!==ySecondLastDim)throw new NotImplementedError(`If rank y >= 3, then the second last dim of y must equal the last dim of x but got x shape = ${a.shape} and y shape = ${b.shape}`)}if(a.rank===2&&b.rank===2){let transposeA=!1,transposeB=!1;return fused_ops_exports.matMul({a,b,transposeA,transposeB,bias:bias?reshapeBias(a.rank,bias,imageDataFormat()):null,activation:activation2})}else{let aFirstDims=a.shape.slice(),aLastDim=aFirstDims.pop();a=a.reshape([-1,aLastDim]);let bShape=b.shape.slice(),bLastDim=bShape.pop(),ySecondLastDim=bShape.pop(),yOtherDims=[...bShape,bLastDim],perm=Array.from({length:b.rank},(_,i)=>i===0?b.rank-2:i<=b.rank-2?i-1:i);b=b.transpose(perm).reshape([ySecondLastDim,-1]);let outputShape=[...aFirstDims,...yOtherDims],transposeA=!1,transposeB=!1;return fused_ops_exports.matMul({a,b,transposeA,transposeB,bias:bias?reshapeBias(a.rank,bias,imageDataFormat()):null,activation:activation2}).reshape(outputShape)}}function gather2(reference,indices,axis){return tidy(()=>(Array.isArray(indices)?indices=tensor1d(indices,"int32"):indices=indices.toInt(),gather(reference,indices,axis)))}function square3(x){return mul(x,x)}function reshapeBias(xRank,bias,dataFormat){let biasShape=bias.shape;if(bias.rank!==1&&bias.rank!==xRank)throw new ValueError(`Unexpected bias dimensions: ${bias.rank}; expected it to be 1 or ${xRank}`);if(xRank===5){if(dataFormat==="channelsFirst")return biasShape.length===1?bias.reshape([1,biasShape[0],1,1,1]):bias.reshape([1,biasShape[3],biasShape[0],biasShape[1],biasShape[2]]);if(dataFormat==="channelsLast")return biasShape.length===1?bias.reshape([1,1,1,1,biasShape[0]]):bias.reshape([1].concat(biasShape))}else if(xRank===4){if(dataFormat==="channelsFirst")return biasShape.length===1?bias.reshape([1,biasShape[0],1,1]):bias.reshape([1,biasShape[2],biasShape[0],biasShape[1]]);if(dataFormat==="channelsLast")return biasShape.length===1?bias.reshape([1,1,1,biasShape[0]]):bias.reshape([1].concat(biasShape))}else if(xRank===3){if(dataFormat==="channelsFirst")return biasShape.length===1?bias.reshape([1,biasShape[0],1]):bias.reshape([1,biasShape[1],biasShape[0]]);if(dataFormat==="channelsLast")return biasShape.length===1?bias.reshape([1,1,biasShape[0]]):bias.reshape([1].concat(biasShape))}else if(xRank<3)return bias;throw new ValueError(`Unsupported input rank by biasAdd: ${bias.rank}`)}function biasAdd(x,bias,dataFormat){return tidy(()=>(dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat),x.add(reshapeBias(x.rank,bias,dataFormat))))}function elu4(x,alpha=1){if(alpha!==1)throw new NotImplementedError(`Support for alpha values other than 1 (${alpha}) is not implemented yet.`);return elu(x)}function softsign(x){return tidy(()=>div(x,abs(x).add(1)))}function dropout2(x,level,noiseShape,seed){return tidy(()=>dropout(x,level,noiseShape,seed))}function hardSigmoid(x){return tidy(()=>{let y=add2(.5,mul(.2,x));return clipByValue(y,0,1)})}function inTrainPhase(x,alt,training=!1){return training?x():alt()}var VALID_FAN_MODE_VALUES=["fanIn","fanOut","fanAvg"],VALID_DISTRIBUTION_VALUES=["normal","uniform","truncatedNormal"];function checkFanMode(value){checkStringTypeUnionValue(VALID_FAN_MODE_VALUES,"FanMode",value)}function checkDistribution(value){checkStringTypeUnionValue(VALID_DISTRIBUTION_VALUES,"Distribution",value)}var Initializer=class extends serialization_exports.Serializable{fromConfigUsesCustomObjects(){return!1}getConfig(){return{}}},Zeros=class extends Initializer{apply(shape,dtype){return zeros(shape,dtype)}};Zeros.className="Zeros";serialization_exports.registerClass(Zeros);var Ones=class extends Initializer{apply(shape,dtype){return ones2(shape,dtype)}};Ones.className="Ones";serialization_exports.registerClass(Ones);var Constant=class extends Initializer{constructor(args){super();if(typeof args!="object")throw new ValueError(`Expected argument of type ConstantConfig but got ${args}`);if(args.value===void 0)throw new ValueError(`config must have value set but got ${args}`);this.value=args.value}apply(shape,dtype){return tidy(()=>mul(scalar(this.value),ones2(shape,dtype)))}getConfig(){return{value:this.value}}};Constant.className="Constant";serialization_exports.registerClass(Constant);var RandomUniform=class extends Initializer{constructor(args){super();this.DEFAULT_MINVAL=-.05,this.DEFAULT_MAXVAL=.05,this.minval=args.minval||this.DEFAULT_MINVAL,this.maxval=args.maxval||this.DEFAULT_MAXVAL,this.seed=args.seed}apply(shape,dtype){return randomUniform(shape,this.minval,this.maxval,dtype)}getConfig(){return{minval:this.minval,maxval:this.maxval,seed:this.seed}}};RandomUniform.className="RandomUniform";serialization_exports.registerClass(RandomUniform);var RandomNormal=class extends Initializer{constructor(args){super();this.DEFAULT_MEAN=0,this.DEFAULT_STDDEV=.05,this.mean=args.mean||this.DEFAULT_MEAN,this.stddev=args.stddev||this.DEFAULT_STDDEV,this.seed=args.seed}apply(shape,dtype){if(dtype=dtype||"float32",dtype!=="float32"&&dtype!=="int32")throw new NotImplementedError(`randomNormal does not support dType ${dtype}.`);return randomNormal2(shape,this.mean,this.stddev,dtype,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}};RandomNormal.className="RandomNormal";serialization_exports.registerClass(RandomNormal);var TruncatedNormal=class extends Initializer{constructor(args){super();this.DEFAULT_MEAN=0,this.DEFAULT_STDDEV=.05,this.mean=args.mean||this.DEFAULT_MEAN,this.stddev=args.stddev||this.DEFAULT_STDDEV,this.seed=args.seed}apply(shape,dtype){if(dtype=dtype||"float32",dtype!=="float32"&&dtype!=="int32")throw new NotImplementedError(`truncatedNormal does not support dType ${dtype}.`);return truncatedNormal(shape,this.mean,this.stddev,dtype,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}};TruncatedNormal.className="TruncatedNormal";serialization_exports.registerClass(TruncatedNormal);var Identity2=class extends Initializer{constructor(args){super();this.gain=args.gain!=null?args.gain:1}apply(shape,dtype){return tidy(()=>{if(shape.length!==2||shape[0]!==shape[1])throw new ValueError("Identity matrix initializer can only be used for 2D square matrices.");return mul(this.gain,eye(shape[0]))})}getConfig(){return{gain:this.gain}}};Identity2.className="Identity";serialization_exports.registerClass(Identity2);function computeFans(shape,dataFormat="channelsLast"){let fanIn,fanOut;if(checkDataFormat(dataFormat),shape.length===2)fanIn=shape[0],fanOut=shape[1];else if([3,4,5].indexOf(shape.length)!==-1){if(dataFormat==="channelsFirst"){let receptiveFieldSize=arrayProd(shape,2);fanIn=shape[1]*receptiveFieldSize,fanOut=shape[0]*receptiveFieldSize}else if(dataFormat==="channelsLast"){let receptiveFieldSize=arrayProd(shape,0,shape.length-2);fanIn=shape[shape.length-2]*receptiveFieldSize,fanOut=shape[shape.length-1]*receptiveFieldSize}}else{let shapeProd=arrayProd(shape);fanIn=Math.sqrt(shapeProd),fanOut=Math.sqrt(shapeProd)}return[fanIn,fanOut]}var VarianceScaling=class extends Initializer{constructor(args){super();if(args.scale<0)throw new ValueError(`scale must be a positive float. Got: ${args.scale}`);this.scale=args.scale==null?1:args.scale,this.mode=args.mode==null?"fanIn":args.mode,checkFanMode(this.mode),this.distribution=args.distribution==null?"normal":args.distribution,checkDistribution(this.distribution),this.seed=args.seed}apply(shape,dtype){let fans=computeFans(shape),fanIn=fans[0],fanOut=fans[1],scale2=this.scale;if(this.mode==="fanIn"?scale2/=Math.max(1,fanIn):this.mode==="fanOut"?scale2/=Math.max(1,fanOut):scale2/=Math.max(1,(fanIn+fanOut)/2),this.distribution==="normal"){let stddev=Math.sqrt(scale2);if(dtype=dtype||"float32",dtype!=="float32"&&dtype!=="int32")throw new NotImplementedError(`${this.getClassName()} does not support dType ${dtype}.`);return truncatedNormal(shape,0,stddev,dtype,this.seed)}else{let limit=Math.sqrt(3*scale2);return randomUniform(shape,-limit,limit,dtype)}}getConfig(){return{scale:this.scale,mode:this.mode,distribution:this.distribution,seed:this.seed}}};VarianceScaling.className="VarianceScaling";serialization_exports.registerClass(VarianceScaling);var GlorotUniform=class extends VarianceScaling{constructor(args){super({scale:1,mode:"fanAvg",distribution:"uniform",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};GlorotUniform.className="GlorotUniform";serialization_exports.registerClass(GlorotUniform);var GlorotNormal=class extends VarianceScaling{constructor(args){super({scale:1,mode:"fanAvg",distribution:"normal",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};GlorotNormal.className="GlorotNormal";serialization_exports.registerClass(GlorotNormal);var HeNormal=class extends VarianceScaling{constructor(args){super({scale:2,mode:"fanIn",distribution:"normal",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};HeNormal.className="HeNormal";serialization_exports.registerClass(HeNormal);var HeUniform=class extends VarianceScaling{constructor(args){super({scale:2,mode:"fanIn",distribution:"uniform",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};HeUniform.className="HeUniform";serialization_exports.registerClass(HeUniform);var LeCunNormal=class extends VarianceScaling{constructor(args){super({scale:1,mode:"fanIn",distribution:"normal",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};LeCunNormal.className="LeCunNormal";serialization_exports.registerClass(LeCunNormal);var LeCunUniform=class extends VarianceScaling{constructor(args){super({scale:1,mode:"fanIn",distribution:"uniform",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};LeCunUniform.className="LeCunNormal";serialization_exports.registerClass(LeCunUniform);var Orthogonal=class extends Initializer{constructor(args){super();if(this.DEFAULT_GAIN=1,this.gain=args.gain==null?this.DEFAULT_GAIN:args.gain,this.seed=args.seed,this.seed!=null)throw new NotImplementedError("Random seed is not implemented for Orthogonal Initializer yet.")}apply(shape,dtype){return tidy(()=>{if(shape.length<2)throw new NotImplementedError("Shape must be at least 2D.");shape[0]*shape[1]>2e3&&console.warn(`Orthogonal initializer is being called on a matrix with more than 2000 (${shape[0]*shape[1]}) elements: Slowness may result.`);let normalizedShape=shape[0]>shape[1]?[shape[1],shape[0]]:shape,a=randomNormal2(normalizedShape,0,1,"float32"),q=linalg.gramSchmidt(a);return shape[0]>shape[1]&&(q=q.transpose()),mul(this.gain,q)})}getConfig(){return{gain:this.gain,seed:this.seed}}};Orthogonal.className="Orthogonal";serialization_exports.registerClass(Orthogonal);var INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP={constant:"Constant",glorotNormal:"GlorotNormal",glorotUniform:"GlorotUniform",heNormal:"HeNormal",heUniform:"HeUniform",identity:"Identity",leCunNormal:"LeCunNormal",leCunUniform:"LeCunUniform",ones:"Ones",orthogonal:"Orthogonal",randomNormal:"RandomNormal",randomUniform:"RandomUniform",truncatedNormal:"TruncatedNormal",varianceScaling:"VarianceScaling",zeros:"Zeros"};function deserializeInitializer(config3,customObjects={}){return deserializeKerasObject(config3,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"initializer")}function serializeInitializer(initializer){return serializeKerasObject(initializer)}function getInitializer(identifier){if(typeof identifier=="string"){let className=identifier in INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP?INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier]:identifier;if(className==="GlorotNormal")return new GlorotNormal;if(className==="GlorotUniform")return new GlorotUniform;if(className==="HeNormal")return new HeNormal;if(className==="HeUniform")return new HeUniform;if(className==="LeCunNormal")return new LeCunNormal;if(className==="LeCunUniform")return new LeCunUniform;{let config3={};return config3.className=className,config3.config={},deserializeInitializer(config3)}}else return identifier instanceof Initializer?identifier:deserializeInitializer(identifier)}function zeros3(){return new Zeros}function ones3(){return new Ones}function constant(args){return new Constant(args)}function randomUniform2(args){return new RandomUniform(args)}function randomNormal3(args){return new RandomNormal(args)}function truncatedNormal2(args){return new TruncatedNormal(args)}function identity4(args){return new Identity2(args)}function varianceScaling(config3){return new VarianceScaling(config3)}function glorotUniform(args){return new GlorotUniform(args)}function glorotNormal(args){return new GlorotNormal(args)}function heNormal(args){return new HeNormal(args)}function heUniform(args){return new HeUniform(args)}function leCunNormal(args){return new LeCunNormal(args)}function leCunUniform(args){return new LeCunUniform(args)}function orthogonal(args){return new Orthogonal(args)}var exports_layers_exports={};__export2(exports_layers_exports,{Layer:()=>Layer,RNN:()=>RNN,RNNCell:()=>RNNCell,activation:()=>activation,add:()=>add4,alphaDropout:()=>alphaDropout,average:()=>average,averagePooling1d:()=>averagePooling1d,averagePooling2d:()=>averagePooling2d,averagePooling3d:()=>averagePooling3d,avgPool1d:()=>avgPool1d,avgPool2d:()=>avgPool2d,avgPool3d:()=>avgPool3d2,avgPooling1d:()=>avgPooling1d,avgPooling2d:()=>avgPooling2d,avgPooling3d:()=>avgPooling3d,batchNormalization:()=>batchNormalization2,bidirectional:()=>bidirectional,concatenate:()=>concatenate2,conv1d:()=>conv1d2,conv2d:()=>conv2d5,conv2dTranspose:()=>conv2dTranspose2,conv3d:()=>conv3d2,convLstm2d:()=>convLstm2d,convLstm2dCell:()=>convLstm2dCell,cropping2D:()=>cropping2D,dense:()=>dense,depthwiseConv2d:()=>depthwiseConv2d5,dot:()=>dot3,dropout:()=>dropout3,elu:()=>elu5,embedding:()=>embedding,flatten:()=>flatten3,gaussianDropout:()=>gaussianDropout,gaussianNoise:()=>gaussianNoise,globalAveragePooling1d:()=>globalAveragePooling1d,globalAveragePooling2d:()=>globalAveragePooling2d,globalMaxPool1d:()=>globalMaxPool1d,globalMaxPool2d:()=>globalMaxPool2d,globalMaxPooling1d:()=>globalMaxPooling1d,globalMaxPooling2d:()=>globalMaxPooling2d,gru:()=>gru,gruCell:()=>gruCell,input:()=>input,inputLayer:()=>inputLayer,layerNormalization:()=>layerNormalization,leakyReLU:()=>leakyReLU,lstm:()=>lstm,lstmCell:()=>lstmCell,masking:()=>masking,maxPool1d:()=>maxPool1d,maxPool2d:()=>maxPool2d,maxPooling1d:()=>maxPooling1d,maxPooling2d:()=>maxPooling2d,maxPooling3d:()=>maxPooling3d,maximum:()=>maximum4,minimum:()=>minimum4,multiply:()=>multiply3,permute:()=>permute,prelu:()=>prelu5,reLU:()=>reLU,repeatVector:()=>repeatVector,reshape:()=>reshape5,rnn:()=>rnn2,separableConv2d:()=>separableConv2d2,simpleRNN:()=>simpleRNN,simpleRNNCell:()=>simpleRNNCell,softmax:()=>softmax5,spatialDropout1d:()=>spatialDropout1d,stackedRNNCells:()=>stackedRNNCells,thresholdedReLU:()=>thresholdedReLU,timeDistributed:()=>timeDistributed,upSampling2d:()=>upSampling2d,zeroPadding2d:()=>zeroPadding2d});var _nextUniqueTensorId=0;function getNextUniqueTensorId(){return _nextUniqueTensorId++}var _uidPrefixes={};function getUid(prefix=""){return prefix in _uidPrefixes||(_uidPrefixes[prefix]=0),_uidPrefixes[prefix]+=1,prefix+_uidPrefixes[prefix].toString()}function isArrayOfShapes(x){return Array.isArray(x)&&Array.isArray(x[0])}function normalizeShapeList(x){return x.length===0?[]:Array.isArray(x[0])?x:[x]}function getExactlyOneTensor(xs){let x;if(Array.isArray(xs)){if(xs.length!==1)throw new ValueError(`Expected Tensor length to be 1; got ${xs.length}`);x=xs[0]}else x=xs;return x}function getExactlyOneShape(shapes){if(Array.isArray(shapes)&&Array.isArray(shapes[0])){if(shapes.length===1)return shapes=shapes,shapes[0];throw new ValueError(`Expected exactly 1 Shape; got ${shapes.length}`)}else return shapes}function countParamsInWeights(weights){let count2=0;for(let weight of weights)weight.shape.length===0?count2+=1:count2+=weight.shape.reduce((a,b)=>a*b);return count2}var DEFAULT_VARIABLE_NAME_PREFIX="Variable",LayerVariable=class{constructor(val,dtype="float32",name=DEFAULT_VARIABLE_NAME_PREFIX,trainable=!0,constraint=null){this.dtype=dtype==null?"float32":dtype,this.shape=val.shape,this.id=getNextUniqueTensorId(),name=name==null?DEFAULT_VARIABLE_NAME_PREFIX:name,this.originalName=getScopedTensorName(name),this.name=getUniqueTensorName(this.originalName),this.trainable_=trainable,this.constraint=constraint,this.val=variable(val,this.trainable_,this.name,this.dtype)}read(){return this.assertNotDisposed(),this.val}write(newVal){return this.assertNotDisposed(),checkShapesMatch(this.val,newVal),this.val.id!==newVal.id&&(this.val.assign(newVal),this.constraint!=null&&this.val.assign(this.constraint.apply(this.val))),this}dispose(){this.assertNotDisposed(),this.val.dispose()}assertNotDisposed(){if(this.val.isDisposed)throw new Error(`LayersVariable ${this.name} is already disposed.`)}get trainable(){return this.trainable_}set trainable(trainable){this.trainable_=trainable,this.val.trainable=trainable}};function checkShapesMatch(x,y){if(x.shape.toString()!==y.shape.toString())throw new Error("Shape mismatch: "+JSON.stringify(x.shape)+" vs. "+JSON.stringify(y.shape))}function batchGetValue(xs){return xs.map(x=>x.read())}function batchSetValue(variablesAndValues){variablesAndValues.forEach(variableAndValue=>{variableAndValue[0].write(variableAndValue[1])})}var InputSpec=class{constructor(args){this.dtype=args.dtype,this.shape=args.shape,args.shape!=null?this.ndim=args.shape.length:this.ndim=args.ndim,this.maxNDim=args.maxNDim,this.minNDim=args.minNDim,this.axes=args.axes||{}}},SymbolicTensor=class{constructor(dtype,shape,sourceLayer,inputs,callArgs,name,outputTensorIndex){this.dtype=dtype,this.shape=shape,this.sourceLayer=sourceLayer,this.inputs=inputs,this.callArgs=callArgs,this.outputTensorIndex=outputTensorIndex,this.id=getNextUniqueTensorId(),name!=null&&(this.originalName=getScopedTensorName(name),this.name=getUniqueTensorName(this.originalName)),this.rank=shape.length}},_nextNodeID=0,Node=class{constructor(args,callArgs){this.callArgs=callArgs,this.id=_nextNodeID++,this.outboundLayer=args.outboundLayer,this.inboundLayers=args.inboundLayers,this.nodeIndices=args.nodeIndices,this.tensorIndices=args.tensorIndices,this.inputTensors=args.inputTensors,this.outputTensors=args.outputTensors,this.inputMasks=args.inputMasks,this.outputMasks=args.outputMasks,this.inputShapes=args.inputShapes,this.outputShapes=args.outputShapes;for(let layer of args.inboundLayers)layer!=null&&layer.outboundNodes.push(this);args.outboundLayer.inboundNodes.push(this)}getConfig(){let inboundNames=[];for(let layer of this.inboundLayers)layer!=null?inboundNames.push(layer.name):inboundNames.push(null);return{outboundLayer:this.outboundLayer?this.outboundLayer.name:null,inboundLayers:inboundNames,nodeIndices:this.nodeIndices,tensorIndices:this.tensorIndices}}},_nextLayerID=0,Layer=class extends serialization_exports.Serializable{constructor(args={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=_nextLayerID++,this.activityRegularizer=null,this.inputSpec=null,this.supportsMasking=!1,this._trainableWeights=[],this._nonTrainableWeights=[],this._losses=[],this._updates=[],this._built=!1,this.inboundNodes=[],this.outboundNodes=[];let name=args.name;if(!name){let prefix=this.getClassName();name=toSnakeCase(prefix)+"_"+getUid(prefix)}if(this.name=name,this.trainable_=args.trainable==null?!0:args.trainable,args.inputShape!=null||args.batchInputShape!=null){let batchInputShape;if(args.batchInputShape!=null)batchInputShape=args.batchInputShape;else if(args.inputShape!=null){let batchSize=null;args.batchSize!=null&&(batchSize=args.batchSize),batchInputShape=[batchSize].concat(args.inputShape)}this.batchInputShape=batchInputShape;let dtype=args.dtype;dtype==null&&(dtype=args.inputDType),dtype==null&&(dtype="float32"),this.dtype=dtype}args.weights!=null?this.initialWeights=args.weights:this.initialWeights=null,this._refCount=null,this.fastWeightInitDuringBuild=!1}static nodeKey(layer,nodeIndex){return layer.name+"_ib-"+nodeIndex.toString()}getNodeAtIndex(nodeIndex,attrName){if(this.inboundNodes.length===0)throw new RuntimeError(`The layer has never been called and thus has no defined ${attrName}.`);if(this.inboundNodes.length<=nodeIndex)throw new ValueError(`Asked to get ${attrName} at node ${nodeIndex}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[nodeIndex]}getInputAt(nodeIndex){return singletonOrArray(this.getNodeAtIndex(nodeIndex,"input").inputTensors)}getOutputAt(nodeIndex){return singletonOrArray(this.getNodeAtIndex(nodeIndex,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new AttributeError(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer input" is ill-defined. Use \`getInputAt(nodeIndex)\` instead.`);if(this.inboundNodes.length===0)throw new AttributeError(`Layer ${this.name} is not connected, no input to return.`);return singletonOrArray(this.getNodeAtIndex(0,"input").inputTensors)}get output(){if(this.inboundNodes.length===0)throw new AttributeError(`Layer ${this.name} has no inbound nodes.`);if(this.inboundNodes.length>1)throw new AttributeError(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer output" is ill-defined. Use \`getOutputAt(nodeIndex)\` instead.`);return singletonOrArray(this.getNodeAtIndex(0,"output").outputTensors)}get losses(){return this._losses}calculateLosses(){return this.losses.map(lossFn=>lossFn())}get updates(){return this._updates}get built(){return this._built}set built(built){this._built=built}get trainable(){return this.trainable_}set trainable(trainable){this._trainableWeights.forEach(w=>w.trainable=trainable),this.trainable_=trainable}get trainableWeights(){return this.trainable_?this._trainableWeights.filter(w=>w.trainable):[]}set trainableWeights(weights){this._trainableWeights=weights}get nonTrainableWeights(){return this.trainable?this._trainableWeights.filter(w=>!w.trainable).concat(this._nonTrainableWeights):this._trainableWeights.concat(this._nonTrainableWeights)}set nonTrainableWeights(weights){this._nonTrainableWeights=weights}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}get stateful(){return this._stateful}resetStates(){if(!this.stateful)throw new Error("Cannot call the resetStates() method of a non-stateful Layer object.")}assertInputCompatibility(inputs){if(inputs=toList(inputs),this.inputSpec==null||this.inputSpec.length===0)return;let inputSpec=toList(this.inputSpec);if(inputs.length!==inputSpec.length)throw new ValueError(`Layer ${this.name} expects ${inputSpec.length} inputs, but it received ${inputs.length} input tensors. Input received: ${inputs}`);for(let inputIndex=0;inputIndexspec.maxNDim)throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected max_ndim=${spec.maxNDim}, found ndim=${ndim}`);if(spec.minNDim!=null&&ndim=0?xShape[axis]:xShape[xShape.length+axis];if(value!=null&&[value,null].indexOf(xShapeAtAxis)===-1)throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected axis ${axis} of input shape to have value ${value} but got shape ${xShape}.`)}}if(spec.shape!=null)for(let i=0;i{if(!this.built){this.assertInputCompatibility(inputs);let inputShapes=[];for(let xElem of toList(inputs))inputShapes.push(xElem.shape);this.build(singletonOrArray(inputShapes)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&noneAreSymbolic&&(this._refCount=1)}if(this.assertInputCompatibility(inputs),noneAreSymbolic){let output=this.call(inputs,kwargs),outputList=toList(output),outputListCopy=[];for(let x of outputList)inputsList.indexOf(x)!==-1&&(x=x.clone()),outputListCopy.push(x);if(output=singletonOrArray(outputListCopy),this.activityRegularizer!=null)throw new NotImplementedError("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return output}else{let inputShape=collectInputShape(inputs),outputShape=this.computeOutputShape(inputShape),output,outputDType=guessOutputDType(inputs);if(this.warnOnIncompatibleInputShape(Array.isArray(inputs)?inputShape[0]:inputShape),outputShape!=null&&outputShape.length>0&&Array.isArray(outputShape[0])?output=outputShape.map((shape,index)=>new SymbolicTensor(outputDType,shape,this,toList(inputs),kwargs,this.name,index)):output=new SymbolicTensor(outputDType,outputShape,this,toList(inputs),kwargs,this.name),this.addInboundNode(inputs,output,null,null,inputShape,outputShape,kwargs),this._refCount++,this.activityRegularizer!=null)throw new NotImplementedError("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return output}})}warnOnIncompatibleInputShape(inputShape){if(this.batchInputShape!=null)if(inputShape.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(inputShape)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let dimMismatch=!1;this.batchInputShape.forEach((dimension,i)=>{dimension!=null&&inputShape[i]!=null&&inputShape[i]!==dimension&&(dimMismatch=!0)}),dimMismatch&&console.warn(`The shape of the input tensor (${JSON.stringify(inputShape)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}get outputShape(){if(this.inboundNodes==null||this.inboundNodes.length===0)throw new AttributeError(`The layer ${this.name} has never been called and thus has no defined output shape.`);let allOutputShapes=[];for(let node of this.inboundNodes){let shapeString=JSON.stringify(node.outputShapes);allOutputShapes.indexOf(shapeString)===-1&&allOutputShapes.push(shapeString)}if(allOutputShapes.length===1){let outputShapes=this.inboundNodes[0].outputShapes;return Array.isArray(outputShapes)&&Array.isArray(outputShapes[0])&&outputShapes.length===1?outputShapes[0]:outputShapes}else throw new AttributeError(`The layer ${this.name} has multiple inbound nodes with different output shapes. Hence the notion of "output shape" is ill-defined for the layer.`)}countParams(){if(!this.built)throw new RuntimeError(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return countParamsInWeights(this.weights)}build(inputShape){this.built=!0}getWeights(trainableOnly=!1){return batchGetValue(trainableOnly?this.trainableWeights:this.weights)}setWeights(weights){tidy(()=>{let params=this.weights;if(params.length!==weights.length)throw new ValueError(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${weights.length}, but the layer was expecting ${params.length} weights. Provided weights: ${weights}...`);if(params.length===0)return;let weightValueTuples=[],paramValues=batchGetValue(params);for(let i=0;iregularizer.apply(weight.read())),trainable==null&&(trainable=!0),trainable?this._trainableWeights.push(weight):this._nonTrainableWeights.push(weight),weight}setFastWeightInitDuringBuild(value){this.fastWeightInitDuringBuild=value}addLoss(losses4){losses4==null||Array.isArray(losses4)&&losses4.length===0||(losses4=toList(losses4),this._losses!==void 0&&this._losses!==null&&this.losses.push(...losses4))}computeOutputShape(inputShape){return inputShape}computeMask(inputs,mask){if(!this.supportsMasking){if(mask!=null)if(Array.isArray(mask))mask.forEach(maskElement=>{if(maskElement!=null)throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`)});else throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);return null}return mask}addInboundNode(inputTensors,outputTensors,inputMasks,outputMasks,inputShapes,outputShapes,kwargs=null){let inputTensorList=toList(inputTensors);outputTensors=toList(outputTensors),inputMasks=toList(inputMasks),outputMasks=toList(outputMasks),inputShapes=normalizeShapeList(inputShapes),outputShapes=normalizeShapeList(outputShapes);let inboundLayers=[],nodeIndices=[],tensorIndices=[];for(let x of inputTensorList)inboundLayers.push(x.sourceLayer),nodeIndices.push(x.nodeIndex),tensorIndices.push(x.tensorIndex);new Node({outboundLayer:this,inboundLayers,nodeIndices,tensorIndices,inputTensors:inputTensorList,outputTensors,inputMasks,outputMasks,inputShapes,outputShapes},kwargs);for(let i=0;iweight.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${this.name}' is already disposed.`)}dispose(){if(!this.built)throw new Error(`Cannot dispose Layer ${this.name} because it has not been built yet.`);if(this._refCount===null)throw new Error(`Cannot dispose Layer ${this.name} because it has not been used yet.`);this.assertNotDisposed();let numDisposedVariables=0;return--this._refCount==0&&(numDisposedVariables=this.disposeWeights()),{refCountAfterDispose:this._refCount,numDisposedVariables}}};function collectInputShape(inputTensors){inputTensors=toList(inputTensors);let shapes=[];for(let x of inputTensors)shapes.push(x.shape);return singletonOrArray(shapes)}function guessOutputDType(inputTensors){return"float32"}function getSourceInputs(tensor2,layer,nodeIndex){if((layer==null||nodeIndex!=null&&nodeIndex>0)&&(layer=tensor2.sourceLayer,nodeIndex=tensor2.nodeIndex),layer.inboundNodes.length===0)return[tensor2];{let node=layer.inboundNodes[nodeIndex];if(node.inboundLayers.length===0)return node.inputTensors;{let sourceTensors=[];for(let i=0;i0){let values=await Promise.all(promises);for(let i=0;iadd2(this.totals[key],mul(value,batchSize)));this.totals[key]=total,oldTotalsToDispose!=null&&oldTotalsToDispose.dispose()}}}async onEpochEnd(epoch,logs){if(logs!=null)for(let key of this.params.metrics)this.totals[key]!=null&&(typeof this.totals[key]=="number"?logs[key]=this.totals[key]/this.seen:tidy(()=>{let log52=mul(div(1,this.seen),this.totals[key]);logs[key]=log52,this.totals[key].dispose(),keep(logs[key])}))}},History=class extends BaseCallback{async onTrainBegin(logs){this.epoch=[],this.history={}}async onEpochEnd(epoch,logs){logs==null&&(logs={}),this.epoch.push(epoch);for(let key in logs)this.history[key]==null&&(this.history[key]=[]),this.history[key].push(logs[key])}async syncData(){let promises=[],keys=[],indices=[];for(let key in this.history){let valueArray=this.history[key];for(let i=0;inew CustomCallback(callbackConfig,yieldEvery))}var CallbackConstructorRegistry=class{constructor(){}static registerCallbackConstructor(verbosityLevel,callbackConstructor){util_exports.assert(verbosityLevel>=0&&Number.isInteger(verbosityLevel),()=>`Verbosity level is expected to be an integer >= 0, but got ${verbosityLevel}`),CallbackConstructorRegistry.checkForDuplicate(callbackConstructor),CallbackConstructorRegistry.constructors[verbosityLevel]==null&&(CallbackConstructorRegistry.constructors[verbosityLevel]=[]),CallbackConstructorRegistry.constructors[verbosityLevel].push(callbackConstructor)}static checkForDuplicate(callbackConstructor){for(let levelName in CallbackConstructorRegistry.constructors)CallbackConstructorRegistry.constructors[+levelName].forEach(ctor=>{if(ctor===callbackConstructor)throw new ValueError("Duplicate callback constructor.")})}static clear(){CallbackConstructorRegistry.constructors={}}static createCallbacks(verbosityLevel){let constructors=[];for(let levelName in CallbackConstructorRegistry.constructors){let level=+levelName;verbosityLevel>=level&&constructors.push(...CallbackConstructorRegistry.constructors[level])}return constructors.map(ctor=>new ctor)}};CallbackConstructorRegistry.constructors={};function configureCallbacks(callbacks2,verbose,epochs,initialEpoch,numTrainSamples,stepsPerEpoch,batchSize,doValidation,callbackMetrics){let history=new History,actualCallbacks=[new BaseLogger,...CallbackConstructorRegistry.createCallbacks(verbose)];callbacks2!=null&&actualCallbacks.push(...callbacks2),actualCallbacks.push(history);let callbackList=new CallbackList(actualCallbacks);return callbackList.setParams({epochs,initialEpoch,samples:numTrainSamples,steps:stepsPerEpoch,batchSize,verbose,doValidation,metrics:callbackMetrics}),{callbackList,history}}function deserialize(config3,customObjects={},fastWeightInit=!1){return deserializeKerasObject(config3,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"layer",fastWeightInit)}function l2Normalize(x,axis){return tidy(()=>{x.dtype!=="float32"&&(x=x.asType("float32"));let squareSum=sum2(square3(x),axis,!0),epsilonTensor=fill(squareSum.shape,epsilon2()),norm2=sqrt(maximum(squareSum,epsilonTensor));return div(x,norm2)})}function meanSquaredError2(yTrue,yPred){return tidy(()=>mean(square3(sub(yPred,yTrue)),-1))}function meanAbsoluteError(yTrue,yPred){return tidy(()=>mean(abs(sub(yPred,yTrue)),-1))}function meanAbsolutePercentageError(yTrue,yPred){return tidy(()=>{let diff=sub(yTrue,yPred),clippedTrue=clipByValue(abs(yTrue),epsilon2(),Number.MAX_VALUE),absResult=abs(div(diff,clippedTrue));return mul(100,mean(absResult,-1))})}function meanSquaredLogarithmicError(yTrue,yPred){return tidy(()=>{let clippedPred=clipByValue(yPred,epsilon2(),Number.MAX_VALUE),firstLog=log2(add2(1,clippedPred)),clippedTrue=clipByValue(yTrue,epsilon2(),Number.MAX_VALUE),secondLog=log2(add2(1,clippedTrue));return mean(square3(sub(firstLog,secondLog)),-1)})}function squaredHinge(yTrue,yPred){return tidy(()=>{let maxResult=maximum(0,sub(1,mul(yTrue,yPred)));return mean(square3(maxResult),-1)})}function hinge(yTrue,yPred){return tidy(()=>{let maxResult=maximum(0,sub(1,mul(yTrue,yPred)));return mean(maxResult,-1)})}function categoricalHinge(yTrue,yPred){return tidy(()=>{let pos=sum2(mul(yTrue,yPred),-1),neg4=max(mul(sub(1,yTrue),yPred),-1);return maximum(0,add2(1,sub(neg4,pos)))})}function logcosh(yTrue,yPred){return tidy(()=>{let log222=Math.log(2),predictionDiff=sub(yPred,yTrue),logcoshResult=sub(add2(predictionDiff,softplus(mul(-2,predictionDiff))),log222);return mean(logcoshResult,-1)})}function categoricalCrossentropy(target,output,fromLogits=!1){return tidy(()=>{if(fromLogits)output=softmax(output);else{let outputSum=sum2(output,output.shape.length-1,!0);output=div(output,outputSum)}return output=clipByValue(output,epsilon2(),1-epsilon2()),neg(sum2(mul(target.toFloat(),log2(output)),output.shape.length-1))})}function sparseCategoricalCrossentropy(target,output,fromLogits=!1){return tidy(()=>{let flatTarget=floor(flatten2(target)).toInt();output=clipByValue(output,epsilon2(),1-epsilon2());let outputShape=output.shape,oneHotTarget=oneHot(flatTarget,outputShape[outputShape.length-1]).reshape(outputShape);return categoricalCrossentropy(oneHotTarget,output,fromLogits)})}function sigmoidCrossEntropyWithLogits(labels,logits){if(!util_exports.arraysEqual(labels.shape,logits.shape))throw new ValueError(`logits and labels must have the same shape, but got shapes ${JSON.stringify(labels.shape)} and ${JSON.stringify(logits.shape)}`);return tidy(()=>{let reluLogits=logits.relu(),negAbsLogits=logits.abs().neg();return reluLogits.sub(logits.mul(labels)).add(negAbsLogits.exp().log1p())})}function binaryCrossentropy(yTrue,yPred){return tidy(()=>{let y;return y=clipByValue(yPred,epsilon2(),1-epsilon2()),y=log2(div(y,sub(1,y))),mean(sigmoidCrossEntropyWithLogits(yTrue,y),-1)})}function kullbackLeiblerDivergence(yTrue,yPred){return tidy(()=>{let clippedTrue=clipByValue(yTrue,epsilon2(),1),clippedPred=clipByValue(yPred,epsilon2(),1);return sum2(mul(yTrue,log2(div(clippedTrue,clippedPred))),-1)})}function poisson(yTrue,yPred){return tidy(()=>{let logPred=log2(add2(epsilon2(),yPred));return mean(sub(yPred,mul(yTrue,logPred)),-1)})}function cosineProximity(yTrue,yPred){return tidy(()=>{let trueNormalized=l2Normalize(yTrue,-1),predNormalized=l2Normalize(yPred,-1),trueXPred=mul(trueNormalized,predNormalized);return neg(sum2(trueXPred,-1))})}var lossesMap={meanSquaredError:meanSquaredError2,meanAbsoluteError,meanAbsolutePercentageError,meanSquaredLogarithmicError,squaredHinge,hinge,categoricalHinge,logcosh,categoricalCrossentropy,sparseCategoricalCrossentropy,binaryCrossentropy,kullbackLeiblerDivergence,poisson,cosineProximity};function get(identifierOrFn){if(typeof identifierOrFn=="string"){if(identifierOrFn in lossesMap)return lossesMap[identifierOrFn];let errMsg=`Unknown loss ${identifierOrFn}`;throw identifierOrFn.toLowerCase().includes("softmaxcrossentropy")&&(errMsg=`Unknown loss ${identifierOrFn}. Use "categoricalCrossentropy" as the string name for tf.losses.softmaxCrossEntropy`),new ValueError(errMsg)}else return identifierOrFn}function binaryAccuracy(yTrue,yPred){return tidy(()=>{let threshold2=mul(.5,onesLike(yPred)),yPredThresholded=cast5(greater(yPred,threshold2),yTrue.dtype);return mean(equal(yTrue,yPredThresholded),-1)})}function categoricalAccuracy(yTrue,yPred){return tidy(()=>cast5(equal(argMax(yTrue,-1),argMax(yPred,-1)),"float32"))}function truePositives(yTrue,yPred){return tidy(()=>logicalAnd(yTrue.equal(1),yPred.equal(1)).sum().cast("float32"))}function falseNegatives(yTrue,yPred){return tidy(()=>logicalAnd(yTrue.equal(1),yPred.equal(0)).sum().cast("float32"))}function falsePositives(yTrue,yPred){return tidy(()=>logicalAnd(yTrue.equal(0),yPred.equal(1)).sum().cast("float32"))}function precision(yTrue,yPred){return tidy(()=>{let tp=truePositives(yTrue,yPred),fp=falsePositives(yTrue,yPred),denominator=tp.add(fp);return where(greater(denominator,0),tp.div(denominator),0).cast("float32")})}function recall(yTrue,yPred){return tidy(()=>{let tp=truePositives(yTrue,yPred),fn=falseNegatives(yTrue,yPred),denominator=tp.add(fn);return where(greater(denominator,0),tp.div(denominator),0).cast("float32")})}function binaryCrossentropy2(yTrue,yPred){return binaryCrossentropy(yTrue,yPred)}function sparseCategoricalAccuracy(yTrue,yPred){return yTrue.rank===yPred.rank&&(yTrue=yTrue.squeeze([yTrue.rank-1])),yPred=yPred.argMax(-1),yPred.dtype!==yTrue.dtype&&(yPred=yPred.asType(yTrue.dtype)),equal(yTrue,yPred).asType("float32")}var mse=meanSquaredError2,MSE=meanSquaredError2,mae=meanAbsoluteError,MAE=meanAbsoluteError,mape=meanAbsolutePercentageError,MAPE=meanAbsolutePercentageError,categoricalCrossentropy2=categoricalCrossentropy,cosine=cosineProximity,sparseCategoricalCrossentropy2=sparseCategoricalCrossentropy,metricsMap={binaryAccuracy,categoricalAccuracy,precision,categoricalCrossentropy:categoricalCrossentropy2,sparseCategoricalCrossentropy:sparseCategoricalCrossentropy2,mse,MSE,mae,MAE,mape,MAPE,cosine};function get2(identifier){if(typeof identifier=="string"&&identifier in metricsMap)return metricsMap[identifier];if(typeof identifier!="string"&&identifier!=null)return identifier;throw new ValueError(`Unknown metric ${identifier}`)}function getLossOrMetricName(fn){if(assert2(fn!==null,`Unknown LossOrMetricFn ${fn}`),typeof fn=="string")return fn;{let fnName;for(let key of Object.keys(lossesMap))if(lossesMap[key]===fn){fnName=key;break}if(fnName!==void 0)return fnName;for(let key of Object.keys(metricsMap))if(metricsMap[key]===fn){fnName=key;break}return fnName!==void 0?fnName:fn.name}}function getOptimizer(identifier){let optimizerMap={Adagrad:()=>train.adagrad(.01),Adadelta:()=>train.adadelta(1,.95,epsilon2()),Adam:()=>train.adam(.001,.9,.999,epsilon2()),Adamax:()=>train.adamax(.002,.9,.999,epsilon2(),0),RMSProp:()=>train.rmsprop(.001,.9,0,epsilon2()),SGD:()=>train.sgd(.01)};if(optimizerMap.adagrad=optimizerMap.Adagrad,optimizerMap.adadelta=optimizerMap.Adadelta,optimizerMap.adam=optimizerMap.Adam,optimizerMap.adamax=optimizerMap.Adamax,optimizerMap.rmsprop=optimizerMap.RMSProp,optimizerMap.sgd=optimizerMap.SGD,identifier in optimizerMap)return optimizerMap[identifier]();throw new ValueError(`Unknown Optimizer ${identifier}`)}var MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH=1*1024*1024;function checkUserDefinedMetadata(userDefinedMetadata,modelName,checkSize=!1){if(userDefinedMetadata==null||typeof userDefinedMetadata!="object"||Object.getPrototypeOf(userDefinedMetadata)!==Object.prototype||!plainObjectCheck(userDefinedMetadata))throw new Error("User-defined metadata is expected to be a JSON object, but is not.");if(checkSize){let out=JSON.stringify(userDefinedMetadata);out.length>MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH&&console.warn(`User-defined metadata of model "${modelName}" is too large in size (length=${out.length} when serialized). It is not recommended to store such large objects in user-defined metadata. Please make sure its serialized length is <= ${MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH}.`)}}function plainObjectCheck(x){if(x===null)return!0;if(typeof x=="object")if(Object.getPrototypeOf(x)===Object.prototype){let keys=Object.keys(x);for(let key of keys)if(typeof key!="string"||!plainObjectCheck(x[key]))return!1;return!0}else if(Array.isArray(x)){for(let item of x)if(!plainObjectCheck(item))return!1;return!0}else return!1;else{let xType=typeof x;return xType==="string"||xType==="number"||xType==="boolean"}}function printSummary(model2,lineLength,positions,printFn=console.log){let sequentialLike=isModelSequentialLike(model2),toDisplay=["Layer (type)","Output shape","Param #"];sequentialLike?(lineLength=lineLength||65,positions=positions||[.45,.85,1]):(lineLength=lineLength||98,positions=positions||[.33,.55,.67,1]),positions[positions.length-1]<=1&&(positions=positions.map(p2=>Math.floor(lineLength*p2)));let relevantNodes;if(!sequentialLike){toDisplay.push("Receives inputs"),relevantNodes=[];for(let depth in model2.nodesByDepth)relevantNodes.push(...model2.nodesByDepth[depth])}printFn("_".repeat(lineLength)),printRow(toDisplay,positions,printFn),printFn("=".repeat(lineLength));let layers=model2.layers;for(let i=0;i1||depthNodes.length===1&&depthNodes[0].inboundLayers.length>1){sequentialLike=!1;break}nodes.push(...depthNodes)}if(sequentialLike)for(let layer of model2.layers){let flag=!1;for(let node of layer.inboundNodes)if(nodes.indexOf(node)!==-1)if(flag){sequentialLike=!1;break}else flag=!0;if(!sequentialLike)break}return sequentialLike}function printRow(fields,positions,printFn=console.log){let line="";for(let i=0;i0&&(line=line.slice(0,line.length-1)+" "),line+=fields[i],line=line.slice(0,positions[i]),line+=" ".repeat(positions[i]-line.length);printFn(line)}function printLayerSummary(layer,positions,printFn){let outputShape;try{outputShape=JSON.stringify(layer.outputShape)}catch(err){outputShape="multiple"}let name=layer.name,className=layer.getClassName(),fields=[`${name} (${className})`,outputShape,layer.countParams().toString()];printRow(fields,positions,printFn)}function printLayerSummaryWithConnections(layer,positions,relevantNodes,printFn){let outputShape;try{outputShape=JSON.stringify(layer.outputShape)}catch(err){outputShape="multiple"}let connections=[];for(let node of layer.inboundNodes)if(!(relevantNodes!=null&&relevantNodes.length>0&&relevantNodes.indexOf(node)===-1))for(let i=0;it.name),finalOutputs=[],feedNames=feedDict.names();for(let outputName of outputNames)feedNames.indexOf(outputName)!==-1?finalOutputs.push(feedDict.getValue(outputName)):finalOutputs.push(null);probe!=null&&(probe.maxNumTensors=-Infinity,probe.minNumTensors=Infinity);let fetchAndFeedKey=outputNames.join(",")+"|"+feedDict.names().join(","),sorted,recipientCounts;if(cachedSorted[fetchAndFeedKey]==null){let out=getTopologicalSortAndRecipientCounts(fetchArray,feedDict);sorted=out.sorted,recipientCounts=out.recipientCounts,cachedSorted[fetchAndFeedKey]=sorted,cachedRecipientCounts[fetchAndFeedKey]=recipientCounts}sorted=cachedSorted[fetchAndFeedKey],recipientCounts={},training||Object.assign(recipientCounts,cachedRecipientCounts[fetchAndFeedKey]);let internalFeedDict=new FeedDict(feedDict);for(let i=0;iprobe.maxNumTensors&&(probe.maxNumTensors=numTensors),numTensors0,()=>"Expected at least one fetch, got none");let finalSorted=[],finalRecipientMap={};if(fetches.length===1){let out=getTopologicalSortAndRecipientCountsForOneFetch(fetches[0],feedDict);finalSorted=out.sorted,finalRecipientMap=out.recipientMap}else{let visited=new Set;for(let fetch3 of fetches){let{sorted,recipientMap}=getTopologicalSortAndRecipientCountsForOneFetch(fetch3,feedDict);for(let symbolicTensor of sorted)visited.has(symbolicTensor.name)||(finalSorted.push(symbolicTensor),visited.add(symbolicTensor.name));for(let name in recipientMap)finalRecipientMap[name]==null&&(finalRecipientMap[name]=new Set),recipientMap[name].forEach(recipient=>finalRecipientMap[name].add(recipient))}}return{sorted:finalSorted,recipientCounts:recipientMap2Counts(finalRecipientMap)}}function recipientMap2Counts(recipientMap){let recipientCounts={};for(let name in recipientMap)recipientCounts[name]=recipientMap[name].size;return recipientCounts}function getTopologicalSortAndRecipientCountsForOneFetch(fetch3,feedDict){let visited=new Set,sorted=[],recipientMap={};for(let key of feedDict.names())visited.add(key);let stack2=[],marks=[];for(stack2.push(fetch3);stack2.length>0;){let top=stack2[stack2.length-1];if(visited.has(top.name)){stack2.pop();continue}let topIsMarked=marks[marks.length-1]===stack2.length-1;if(top.inputs.length===0||topIsMarked)stack2.pop(),sorted.push(top),visited.add(top.name),topIsMarked&&marks.pop();else{marks.push(stack2.length-1);for(let input2 of top.inputs)recipientMap[input2.name]==null&&(recipientMap[input2.name]=new Set),recipientMap[input2.name].add(top.name),!visited.has(input2.name)&&stack2.push(input2)}}return{sorted,recipientMap}}function getNodeOutputs(fetch3){let layerOutputs;if(fetch3.sourceLayer.inboundNodes.length===1)layerOutputs=fetch3.sourceLayer.output;else{let nodeIndex=null;for(let i=0;ix.name)}`);unique4(this.outputs).length!==this.outputs.length&&console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map(x=>x.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(let x of this.outputs){let layer=x.sourceLayer,nodeIndex=x.nodeIndex,tensorIndex=x.tensorIndex;this.outputLayers.push(layer),this.outputLayersNodeIndices.push(nodeIndex),this.outputLayersTensorIndices.push(tensorIndex)}for(let x of this.inputs){let layer=x.sourceLayer,nodeIndex=x.nodeIndex,tensorIndex=x.tensorIndex;assert2(nodeIndex===0,"input layer has >1 nodes"),assert2(tensorIndex===0,"input layer has >1 tensors"),this.inputLayers.push(layer),this.inputLayersNodeIndices.push(nodeIndex),this.inputLayersTensorIndices.push(tensorIndex)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let i=0;ix.shape),this.internalOutputShapes=this.outputs.map(x=>x.shape);let nodesDepths={},nodeIDToNode={},layersDepths={},layerIDToLayer={},layerIndices={},nodesInDecreasingDepth=[],buildMapOfGraph=(tensor2,finishedNodes2,nodesInProgress2,layer,nodeIndex,tensorIndex)=>{(layer==null||nodeIndex==null||tensorIndex==null)&&(layer=tensor2.sourceLayer,nodeIndex=tensor2.nodeIndex,tensorIndex=tensor2.tensorIndex);let node=layer.inboundNodes[nodeIndex];if(nodesInProgress2.indexOf(node)!==-1)throw new RuntimeError(`The tensor ${tensor2.name} at layer "${layer.name}" is part of a cycle.`);if(finishedNodes2.indexOf(node)!==-1)return;this.containerNodes.add(Container.nodeKey(layer,nodeIndex)),layer.id in layerIndices||(layerIndices[layer.id]=Object.keys(layerIndices).length),nodesInProgress2.indexOf(node)===-1&&nodesInProgress2.push(node);let numInboundLayers=node.inboundLayers.length;for(let i=0;i=0;)nodesInProgress2.splice(nodesInProgress2.indexOf(node),1);nodesInDecreasingDepth.push(node)},finishedNodes=[],nodesInProgress=[];for(let x of this.outputs)buildMapOfGraph(x,finishedNodes,nodesInProgress);let reversedNodesInDecreasingDepth=nodesInDecreasingDepth.slice().reverse();for(let node of reversedNodesInDecreasingDepth){nodeIDToNode[node.id]=node,node.id in nodesDepths||(nodesDepths[node.id]=0);let depth=nodesDepths[node.id],previousDepth=layersDepths[node.outboundLayer.id]==null?0:layersDepths[node.outboundLayer.id];depth=Math.max(depth,previousDepth),layersDepths[node.outboundLayer.id]=depth,layerIDToLayer[node.outboundLayer.id]=node.outboundLayer,nodesDepths[node.id]=depth;for(let i=0;iparseInt(x,10)).sort(reverseNumberCompare);this.layers=[];for(let depth of depthKeys){let layersForDepth=layersByDepth[depth];layersForDepth.sort((a,b)=>{let aIndex=layerIndices[a.id],bIndex=layerIndices[b.id];return aIndexbIndex?1:0});for(let layer of layersForDepth)layer instanceof Container&&this.internalContainerRefs.push(layer),this.layers.push(layer)}this.layersByDepth=layersByDepth,depthKeys=Object.keys(nodesByDepth).map(x=>parseInt(x,10)).sort(reverseNumberCompare);let computableTensors=this.inputs.slice(),layersWithCompleteInput=[];for(let depth of depthKeys)for(let node of nodesByDepth[depth]){let layer=node.outboundLayer;if(layer!=null){for(let x of node.inputTensors)if(computableTensors.indexOf(x)===-1)throw new RuntimeError(`Graph disconnected: cannot obtain value for tensor ${x} at layer "${layer.name}". The following previous layers were accessed without issue: ${layersWithCompleteInput}`);for(let x of node.outputTensors)computableTensors.push(x);layersWithCompleteInput.push(layer.name)}}this.nodesByDepth=nodesByDepth;let allNames=this.layers.map(x=>x.name);for(let name of allNames){let numOccurrences=allNames.filter(x=>x===name).length;if(numOccurrences!==1)throw new RuntimeError(`The name "${name}" is used ${numOccurrences} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(allNames))}this.outboundNodes=[],this.inboundNodes=[],new Node({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(x=>null),outputMasks:this.outputs.map(x=>null),inputShapes:this.inputs.map(x=>x.shape),outputShapes:this.outputs.map(x=>x.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();let result={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount==0){for(let layer of this.layers)result.numDisposedVariables+=layer.dispose().numDisposedVariables;for(let container of this.internalContainerRefs)result.numDisposedVariables+=container.dispose().numDisposedVariables}return result.refCountAfterDispose=this._refCount,result}get trainable(){return this.trainable_}set trainable(trainable){this.layers.forEach(layer=>{layer._trainableWeights.forEach(w=>w.trainable=trainable)}),this.trainable_=trainable}get trainableWeights(){if(this._trainableWeights.length>0)throw new ValueError("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let weights=[];for(let layer of this.layers)weights=weights.concat(layer.trainableWeights);return weights}get nonTrainableWeights(){let weights=[];for(let layer of this.layers)weights.push(...layer.nonTrainableWeights);if(!this.trainable){let trainableWeights=[];for(let layer of this.layers)trainableWeights.push(...layer.trainableWeights);return trainableWeights.concat(weights)}return weights}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(weights,strict=!0){let nameToWeight={},totalWeightsCount=0;for(let layer of this.layers)for(let weight of layer.weights){if(nameToWeight[weight.originalName]!=null)throw new ValueError(`Duplicate weight name: ${weight.originalName}`);nameToWeight[weight.originalName]=weight,totalWeightsCount++}let weightValueTuples=[];for(let name in weights){let validatedName=name;if(nameToWeight[name]==null){let tokens=name.split("/");validatedName=tokens.slice(0,-2).concat([tokens[tokens.length-1]]).join("/")}if(nameToWeight[validatedName]!=null)weightValueTuples.push([nameToWeight[validatedName],weights[name]]);else if(strict)throw new ValueError(`Provided weight data has no target variable: ${name}`);delete nameToWeight[validatedName]}if(strict){let unsetNames=[];for(let name in nameToWeight)unsetNames.push(name);if(unsetNames.length>0)throw new ValueError(`${unsetNames.length} of ${totalWeightsCount} weights are not set: ${unsetNames}`)}batchSetValue(weightValueTuples)}updatedConfig(){let theConfig=this.getConfig(),modelConfig={};return modelConfig.className=this.getClassName(),modelConfig.config=theConfig,modelConfig.kerasVersion=`tfjs-layers ${version10}`,modelConfig.backend="TensorFlow.js",modelConfig}toJSON(unused,returnString=!0){let modelConfig=convertTsToPythonic(this.updatedConfig());return returnString?JSON.stringify(modelConfig):modelConfig}call(inputs,kwargs){return tidy(()=>{inputs=toList(inputs);let feedDict=new FeedDict;for(let i=0;i{inputs=toList(inputs);let masks;return mask==null?masks=pyListRepeat(null,inputs.length):masks=toList(mask),this.runInternalGraph(inputs,masks)[1]})}computeOutputShape(inputShape){let inputShapes=normalizeShapeList(inputShape);if(inputShapes.length!==this.inputLayers.length)throw new ValueError(`Invalid inputShape argument ${inputShape}: model has ${this.inputLayers.length} tensor inputs.`);let layersToOutputShapes={};for(let i=0;iparseInt(x,10)).sort(reverseNumberCompare);if(depthKeys.length>1)for(let depth of depthKeys){let nodes=this.nodesByDepth[depth];for(let node of nodes){let layer=node.outboundLayer;if(this.inputLayers.map(x=>x.id).indexOf(layer.id)!==-1)continue;let inputShapes2=[];for(let j=0;jparseInt(x,10)).sort(reverseNumberCompare);for(let depth of depthKeys){let nodes=this.nodesByDepth[depth];for(let node of nodes){let layer=node.outboundLayer,referenceInputTensors=node.inputTensors,referenceOutputTensors=node.outputTensors,computedData=new Array;for(let x of referenceInputTensors)x.id in tensorMap&&computedData.push(tensorMap[x.id]);if(computedData.length===referenceInputTensors.length){let kwargs={},computedTensors,computedMasks,outputTensors2,outputMasks2;if(node.callArgs!=null&&(kwargs=node.callArgs),computedData.length===1){let[computedTensor,computedMask]=computedData[0];kwargs.mask==null&&(kwargs.mask=computedMask),outputTensors2=toList(layer.call(computedTensor,kwargs)),outputMasks2=toList(layer.computeMask(computedTensor,computedMask)),computedTensors=[computedTensor],computedMasks=[computedMask]}else computedTensors=computedData.map(x=>x[0]),computedMasks=computedData.map(x=>x[1]),kwargs.mask==null&&(kwargs.mask=computedMasks),outputTensors2=toList(layer.call(computedTensors,kwargs)),outputMasks2=toList(layer.computeMask(computedTensors,computedMasks));if(layer.activityRegularizer)throw new NotImplementedError("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let i=0;i{let losses4=[];for(let layer of this.layers)for(let nodeIndex=0;nodeIndex0){let nodeData=[];for(let i=0;i0&&layer.apply(singletonOrArray(inputTensors2),kwargs)}function processLayer(layerData){let layerName=layerData.name,layer=deserialize(layerData,config3.customObjects!=null?config3.customObjects:{});layer.setFastWeightInitDuringBuild(fastWeightInit),createdLayers[layerName]=layer,layerData.inboundNodes.forEach(nodeData=>{if(!(nodeData instanceof Array))throw new ValueError(`Corrupted configuration, expected array for nodeData: ${nodeData}`);addUnprocessedNode(layer,nodeData)})}let name=config3.name,layersFromConfig=config3.layers;for(let layerData of layersFromConfig)processLayer(layerData);for(;!isObjectEmpty(unprocessedNodes);)for(let layerData of layersFromConfig){let layer=createdLayers[layerData.name];if(layer.name in unprocessedNodes){let currentUnprocessedNodesForLayer=unprocessedNodes[layer.name];delete unprocessedNodes[layer.name];for(let nodeData of currentUnprocessedNodesForLayer)processNode(layer,nodeData)}}let inputTensors=[],outputTensors=[],inputLayersFromConfig=config3.inputLayers;for(let layerData of inputLayersFromConfig){let layerName=layerData[0],nodeIndex=layerData[1],tensorIndex=layerData[2];assert2(layerName in createdLayers);let layerOutputTensors=createdLayers[layerName].inboundNodes[nodeIndex].outputTensors;inputTensors.push(layerOutputTensors[tensorIndex])}let outputLayersFromConfig=config3.outputLayers;for(let layerData of outputLayersFromConfig){let layerName=layerData[0],nodeIndex=layerData[1],tensorIndex=layerData[2];assert2(layerName in createdLayers);let layerOutputTensors=createdLayers[layerName].inboundNodes[nodeIndex].outputTensors;outputTensors.push(layerOutputTensors[tensorIndex])}return new cls({inputs:inputTensors,outputs:outputTensors,name})}get stateful(){if(this._stateful)throw new ValueError("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(let layer of this.layers)if(layer.stateful)return!0;return!1}resetStates(){tidy(()=>{this.layers.forEach(layer=>{layer.stateful&&layer.resetStates()})})}};function standardizeSampleOrClassWeights(xWeight,outputNames,weightType){let numOutputs=outputNames.length;if(xWeight==null||Array.isArray(xWeight)&&xWeight.length===0)return outputNames.map(name=>null);if(numOutputs===1)return Array.isArray(xWeight)&&xWeight.length===1?xWeight:typeof xWeight=="object"&&outputNames[0]in xWeight?[xWeight[outputNames[0]]]:[xWeight];if(Array.isArray(xWeight)){if(xWeight.length!==numOutputs)throw new Error(`Provided ${weightType} is an array of ${xWeight.length} element(s), but the model has ${numOutputs} outputs. Make sure a set of weights is provided for each model output.`);return xWeight}else if(typeof xWeight=="object"&&Object.keys(xWeight).length>0&&typeof xWeight[Object.keys(xWeight)[0]]=="object"){let output=[];return outputNames.forEach(outputName=>{outputName in xWeight?output.push(xWeight[outputName]):output.push(null)}),output}else throw new Error(`The model has multiple (${numOutputs}) outputs, so ${weightType} must be either an array with ${numOutputs} elements or an object with ${outputNames} keys. Provided ${weightType} not understood: ${JSON.stringify(xWeight)}`)}function standardizeClassWeights(classWeight,outputNames){return standardizeSampleOrClassWeights(classWeight,outputNames,"classWeight")}async function standardizeWeights(y,sampleWeight,classWeight,sampleWeightMode){if(sampleWeight!=null||sampleWeightMode!=null)throw new Error("Support sampleWeight is not implemented yet");if(classWeight!=null){let yClasses=tidy(()=>{if(y.shape.length===1)return y.clone();if(y.shape.length===2)if(y.shape[1]>1){let axis=1;return y.argMax(axis)}else{if(y.shape[1]===1)return y.reshape([y.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${y.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${y.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),yClassIndices=Array.from(await yClasses.data());dispose(yClasses);let classSampleWeight=[];return yClassIndices.forEach(classIndex=>{if(classWeight[classIndex]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${classIndex} exists in the data but not in classWeight`);classSampleWeight.push(classWeight[classIndex])}),tensor1d(classSampleWeight,"float32")}else return null}function computeWeightedLoss2(losses4,sampleWeights){return mul(losses4,sampleWeights)}var DEFAULT_VALIDATION_BATCH_SIZE=32;function standardizeDataIteratorOutput(model2,iteratorOut){let xs,ys,iteratorOutObj=iteratorOut;xs=iteratorOutObj.xs,ys=iteratorOutObj.ys,util_exports.assert(xs!=null&&ys!=null,()=>`A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor. The provided Dataset instead generates ${iteratorOut}`);let flattenedXs=flattenTensorOrArrayOrMap("input",model2.inputNames,xs),flattenedYs=flattenTensorOrArrayOrMap("output",model2.outputNames,ys),batchSize=flattenedXs[0].shape[0];util_exports.assert(flattenedXs.length===model2.inputs.length,()=>`LayersModel has ${model2.inputs.length} inputs, but the dataset provides ${flattenedXs.length} inputs. (Expected input keys: ${JSON.stringify(model2.inputNames)})`),util_exports.assert(flattenedYs.length===model2.outputs.length,()=>`LayersModel has ${model2.outputs.length} outputs, but the dataset provides ${flattenedYs.length} outputs. (Expected output keys: ${JSON.stringify(model2.outputNames)})`);for(let xIndex=0;xIndex`Batch size mismatch: input ${model2.inputNames[xIndex]} has ${flattenedXs[xIndex].shape[0]}; expected ${batchSize} based on input ${model2.inputNames[0]}.`);for(let yIndex=0;yIndex`Batch size mismatch: output ${model2.outputNames[yIndex]} has ${flattenedYs[yIndex].shape[0]}; expected ${batchSize} based on input ${model2.inputNames[0]}.`);return{xs:flattenedXs,ys:flattenedYs}}function flattenTensorOrArrayOrMap(inputOrOutput,names,values){if(values instanceof Tensor)return[values];if(Array.isArray(values))return util_exports.assert(values.length===names.length,()=>`Received an array of ${values.length} Tensors, but expected ${names.length} to match the ${inputOrOutput} keys ${names}.`),values;{let result=[];for(let name of names){if(values[name]==null)throw new ValueError(`The feature data generated by the dataset lacks the required ${inputOrOutput} key '${name}'.`);result.push(values[name])}return result}}function standardizeTensorValidationData(data2){if(data2.length===3)throw new NotImplementedError("Validation with sample weights is not implemented yet.");return{xs:data2[0],ys:data2[1]}}async function fitDataset(model2,dataset,args){let hasBatchesPerEpoch=args.batchesPerEpoch!=null;if(util_exports.assert(model2.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),util_exports.assert(args!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),util_exports.assert(args.epochs!=null&&args.epochs>0&&Number.isInteger(args.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${args.epochs}`),util_exports.assert(!hasBatchesPerEpoch||args.batchesPerEpoch>0&&Number.isInteger(args.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${args.batchesPerEpoch}`),util_exports.assert(args.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. Use validationData instead."),model2.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");model2.isTraining=!0;try{let doValidation=args.validationData!=null,valXs,valYs;if(doValidation)if(isDatasetObject(args.validationData))util_exports.assert(args.validationBatches==null||args.validationBatches>0&&Number.isInteger(args.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${args.validationBatches}`);else{let validationData=standardizeTensorValidationData(args.validationData);valXs=validationData.xs,valYs=validationData.ys}let trainFunction=model2.makeTrainFunction(),outLabels=model2.getDedupedMetricsNames(),callbackMetrics;doValidation?callbackMetrics=outLabels.slice().concat(outLabels.map(n=>"val_"+n)):callbackMetrics=outLabels.slice();let callbacks2=standardizeCallbacks(args.callbacks,args.yieldEvery),verbose=args.verbose==null?1:args.verbose,{callbackList,history}=configureCallbacks(callbacks2,verbose,args.epochs,null,null,getStepsPerEpoch(dataset,args),null,doValidation,callbackMetrics);callbackList.setModel(model2),model2.history=history,await callbackList.onTrainBegin(),model2.stopTraining_=!1;let epoch=args.initialEpoch==null?0:args.initialEpoch,dataIterator=await dataset.iterator();for(;epoch=args.batchesPerEpoch:iteratorOut.done){if(doValidation){let valOuts;isDatasetObject(args.validationData)?valOuts=toList(await model2.evaluateDataset(args.validationData,{batches:args.validationBatches})):valOuts=toList(model2.evaluate(valXs,valYs,{batchSize:args.validationBatchSize==null?DEFAULT_VALIDATION_BATCH_SIZE:args.validationBatchSize,verbose:0}));for(let i=0;i0)throw new NotImplementedError("Verbose mode is not implemented yet.");util_exports.assert(!hasBatches||args.batches>0&&Number.isInteger(args.batches),()=>`Test loop expects \`batches\` to be a positive integer, but received ${JSON.stringify(args.batches)}`);let dataIterator=isLazyIteratorObject(dataset)?dataset:await dataset.iterator(),numExamples=0,batch=0;for(;hasBatches?batch{if(iteratorOut.value){let{xs,ys}=standardizeDataIteratorOutput(model2,iteratorOut.value),xsAndYs=xs.concat(ys),batchOuts=tidy(()=>f(xsAndYs));if(dispose(xsAndYs),batch===0)for(let i=0;iadd2(outs[i],mul(batchSize,batchOut))),batch>0&&dispose(oldScalar)}dispose(batchOuts),numExamples+=batchSize,++batch}return outs}),iteratorOut.done){hasBatches&&console.warn(`Your dataset iterator ran out of data during evaluateDataset(). Interrupting evalution. Make sure that your dataset can generate at least \`batches\` batches (in this case, ${args.batches} batches). You may need to use the repeat() function when building your dataset.`);break}}for(let i=0;i0&&Number.isInteger(batchSize),()=>`batchSize is required to be a positive integer, but got ${batchSize}`)}function sliceArrays(arrays,start,stop){return arrays==null?[null]:Array.isArray(arrays)?arrays.map(array2=>sliceAlongFirstAxis(array2,start,stop-start)):sliceAlongFirstAxis(arrays,start,stop-start)}function sliceArraysByIndices(arrays,indices){return tidy(()=>arrays==null?null:Array.isArray(arrays)?arrays.map(array2=>sliceArraysByIndices(array2,indices)):gather2(arrays,indices.dtype==="int32"?indices:indices.toInt()))}function makeBatches(size,batchSize){let output=[],batchStart=0,batchEnd=null;for(;batchStart=size&&(batchEnd=size),output.push([batchStart,batchEnd]),batchStart=batchEnd;return output}async function fitLoop(model2,f,ins,outLabels,batchSize,epochs,verbose,callbacks2,valF,valIns,shuffle2,callbackMetrics,initialEpoch,stepsPerEpoch,validationSteps){batchSize==null&&(batchSize=32),epochs==null&&(epochs=1),shuffle2==null&&(shuffle2=!0),initialEpoch==null&&(initialEpoch=0);let doValidation=!1;if(valF!=null&&valIns!=null&&(doValidation=!0),validationSteps!=null&&(doValidation=!0,stepsPerEpoch==null))throw new ValueError("Can only use `validationSteps` when doing step-wise training, i.e., `stepsPerEpoch` must be set.");let numTrainSamples=model2.checkNumSamples(ins,batchSize,stepsPerEpoch,"steps_per_epoch"),indexArray;numTrainSamples!=null&&(indexArray=range5(0,numTrainSamples)),verbose==null&&(verbose=1);let{callbackList,history}=configureCallbacks(callbacks2,verbose,epochs,initialEpoch,numTrainSamples,stepsPerEpoch,batchSize,doValidation,callbackMetrics);callbackList.setModel(model2),model2.history=history,await callbackList.onTrainBegin(),model2.stopTraining_=!1;for(let epoch=initialEpoch;epoch{let batchStart=batches[batchIndex][0],batchEnd=batches[batchIndex][1],batchIds=sliceAlongFirstAxis(epochIndexArray1D,batchStart,batchEnd-batchStart);batchLogs.batch=batchIndex,batchLogs.size=batchEnd-batchStart;let insBatch=sliceArraysByIndices(ins,batchIds),outs=f(insBatch);for(let i=0;i0){if(doValidation=!0,args.validationData.length===2)inputValX=args.validationData[0],inputValY=args.validationData[1];else throw args.validationData.length===3?new NotImplementedError("validationData including sample weights is not supported yet."):new ValueError(`When passing validation data, it must contain 2 (valX, valY) or 3 (valX, valY, valSampleWeight) items; ${args.validationData} is invalid.`);let checkBatchAxis2=!0,valStandardized=await model2.standardizeUserData(inputValX,inputValY,null,null,checkBatchAxis2,batchSize);valX=valStandardized[0],valY=valStandardized[1],valIns=valX.concat(valY)}else if(args.validationSplit!=null&&args.validationSplit>0&&args.validationSplit<1){doValidation=!0;let splitAt=Math.floor(inputs[0].shape[0]*(1-args.validationSplit)),originalBatchSize=inputs[0].shape[0];valX=sliceArrays(inputs,splitAt,originalBatchSize),inputs=sliceArrays(inputs,0,splitAt),valY=sliceArrays(targets,splitAt,originalBatchSize),targets=sliceArrays(targets,0,splitAt),valIns=valX.concat(valY)}else args.validationSteps!=null&&(doValidation=!0);let ins=inputs.concat(targets).concat(sampleWeights);model2.checkTrainableWeightsConsistency();let trainFunction=model2.makeTrainFunction(),outLabels=model2.getDedupedMetricsNames(),valFunction,callbackMetrics;doValidation?(model2.makeTestFunction(),valFunction=model2.testFunction,callbackMetrics=outLabels.slice().concat(outLabels.map(n=>"val_"+n))):(valFunction=null,valIns=[],callbackMetrics=outLabels.slice());let callbacks2=standardizeCallbacks(args.callbacks,args.yieldEvery);return await fitLoop(model2,trainFunction,ins,outLabels,batchSize,args.epochs,args.verbose,callbacks2,valFunction,valIns,args.shuffle,callbackMetrics,args.initialEpoch,null,null)}finally{model2.isTraining=!1,disposeNewTensors(inputs,x),disposeNewTensors(targets,y),disposeNewTensors(valX,inputValX),disposeNewTensors(valY,inputValY),sampleWeights!=null&&dispose(sampleWeights)}}function ensureTensorsRank2OrHigher(tensors){let outs=[];tensors instanceof Tensor&&(tensors=[tensors]);for(let i=0;ioldTensorIds.push(t.id));else if(refTensors!=null)for(let name in refTensors){let oldTensor=refTensors[name];oldTensorIds.push(oldTensor.id)}let tensorsToDispose=[];if(tensors instanceof Tensor)oldTensorIds.indexOf(tensors.id)===-1&&tensorsToDispose.push(tensors);else if(Array.isArray(tensors))tensors.forEach(t=>{oldTensorIds.indexOf(t.id)===-1&&tensorsToDispose.push(t)});else if(tensors!=null)for(let name in tensors){let tensor2=tensors[name];oldTensorIds.indexOf(tensor2.id)===-1&&tensorsToDispose.push(tensor2)}tensorsToDispose.forEach(t=>{t.isDisposed||t.dispose()})}function isDataTensor(x){return x instanceof Tensor}function isDataArray(x){return Array.isArray(x)}function isDataDict(x){return!isDataTensor(x)&&!isDataArray(x)}function standardizeInputData(data2,names,shapes,checkBatchAxis=!0,exceptionPrefix=""){if(names==null||names.length===0){if(data2!=null){let gotUnexpectedData=!1;if(isDataArray(data2)&&data2.length>0)gotUnexpectedData=!0;else if(isDataDict(data2)){for(let key in data2)if(data2.hasOwnProperty(key)){gotUnexpectedData=!0;break}}else gotUnexpectedData=!0;if(gotUnexpectedData)throw new ValueError(`Error when checking model ${exceptionPrefix} expected no data, but got ${data2}`)}return[]}if(data2==null)return names.map(name=>null);let arrays;if(isDataDict(data2)){data2=data2,arrays=[];for(let name of names){if(data2[name]==null)throw new ValueError(`No data provided for "${name}". Need data for each key in: ${names}`);arrays.push(data2[name])}}else if(isDataArray(data2)){if(data2=data2,data2.length!==names.length)throw new ValueError(`Error when checking model ${exceptionPrefix}: the Array of Tensors that you are passing to your model is not the size the model expected. Expected to see ${names.length} Tensor(s), but instead got the following list of Tensor(s): ${data2}`);arrays=data2}else{if(data2=data2,names.length>1)throw new ValueError(`The model ${exceptionPrefix} expects ${names.length} Tensor(s), but only received one Tensor. Found: Tensor with shape ${data2.shape}`);arrays=[data2]}if(arrays=ensureTensorsRank2OrHigher(arrays),shapes!=null)for(let i=0;i=0&&dim!==refDim)throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} to have shape [${shapes[i]}], but got array with shape [${array2.shape}].`)}}return arrays}function checkArrayLengths(inputs,targets,weights){let setX=unique4(inputs.map(input2=>input2.shape[0]));setX.sort();let setY=unique4(targets.map(target=>target.shape[0]));if(setY.sort(),setX.length>1)throw new ValueError(`All input Tensors (x) should have the same number of samples. Got array shapes: ${JSON.stringify(inputs.map(input2=>input2.shape))}`);if(setY.length>1)throw new ValueError(`All target Tensors (y) should have the same number of samples. Got array shapes: ${JSON.stringify(targets.map(target=>target.shape))}`);if(setX.length>0&&setY.length>0&&!util_exports.arraysEqual(setX,setY))throw new ValueError(`Input Tensors should have the same number of samples as target Tensors. Found ${setX[0]} input sample(s) and ${setY[0]} target sample(s).`)}function checkLossAndTargetCompatibility(targets,lossFns,outputShapes){let keyLosses=[meanSquaredError2,binaryCrossentropy,categoricalCrossentropy];for(let i=0;i1)throw new ValueError(`The model expects ${names.length} ${exceptionPrefix} Tensors, but only received one Tensor. Found: array with shape ${JSON.stringify(data2.shape)}.`);arrays=[data2]}if(shapes!=null)for(let i=0;i[]);let wrappedMetrics;if(typeof metrics2=="string"||typeof metrics2=="function")wrappedMetrics=[metrics2];else if(Array.isArray(metrics2)||typeof metrics2=="object")wrappedMetrics=metrics2;else throw new TypeError(`Type of metrics argument not understood. Expected an string,function, Array, or Object, found: ${metrics2}`);if(Array.isArray(wrappedMetrics))return outputNames.map(name=>wrappedMetrics);{let nestedMetrics=[];for(let name of outputNames){let outputMetrics=wrappedMetrics.hasOwnProperty(name)?wrappedMetrics[name]:[];Array.isArray(outputMetrics)||(outputMetrics=[outputMetrics]),nestedMetrics.push(outputMetrics)}return nestedMetrics}}var LAYERS_MODEL_FORMAT_NAME="layers-model",LayersModel=class extends Container{constructor(args){super(args);this.isTraining=!1}summary(lineLength,positions,printFn=console.log){if(!this.built)throw new ValueError("This model has never been called, thus its weights have not been created yet. So no summary can be displayed. Build the model first (e.g., by calling it on some test data).");printSummary(this,lineLength,positions,printFn)}compile(args){if(args.loss==null&&(args.loss=[]),this.loss=args.loss,typeof args.optimizer=="string")this.optimizer_=getOptimizer(args.optimizer),this.isOptimizerOwned=!0;else{if(!(args.optimizer instanceof Optimizer))throw new ValueError("User-defined optimizer must be an instance of tf.Optimizer.");this.optimizer_=args.optimizer,this.isOptimizerOwned=!1}let lossFunctions=[];if(!Array.isArray(args.loss)&&typeof args.loss!="string"&&typeof args.loss!="function"){args.loss=args.loss;for(let name in args.loss)if(this.outputNames.indexOf(name)===-1)throw new ValueError(`Unknown entry in loss dictionary: "${name}". Only expected the following keys: ${this.outputNames}`);for(let name of this.outputNames)args.loss[name]==null&&console.warn(`Output "${name}" is missing from loss dictionary. We assume this was done on purpose, and we will not be expecting data to be passed to ${name} during training`),lossFunctions.push(get(args.loss[name]))}else if(Array.isArray(args.loss)){if(args.loss.length!==this.outputs.length)throw new ValueError(`When passing an Array as loss, it should have one entry per model output. The model has ${this.outputs.length} output(s), but you passed loss=${args.loss}.`);lossFunctions=args.loss.map(l=>get(l))}else{let lossFunction=get(args.loss);this.outputs.forEach(_=>{lossFunctions.push(lossFunction)})}this.lossFunctions=lossFunctions,this.feedOutputNames=[],this.feedOutputShapes=[],this.feedLossFns=[];for(let i=0;i{for(let i=0;i1&&(this.metricsTensors.push([weightedLoss,i]),this.metricsNames.push(this.outputNames[i]+"_loss"))}});let nestedMetrics=collectMetrics(args.metrics,this.outputNames),appendMetric=(outputIndex,metricName,metricTensor)=>{this.outputNames.length>1&&(metricName=this.outputNames[outputIndex]+"_"+metricName),this.metricsNames.push(metricName),this.metricsTensors.push([metricTensor,outputIndex])};nameScope("metric",()=>{for(let i=0;i{let metricNamePrefix="",metricName,accFn,weightedMetricFn;for(let metric of metrics2){if(typeof metric=="string"&&["accuracy","acc","crossentropy","ce"].indexOf(metric)!==-1){let outputShape=this.internalOutputShapes[i];outputShape[outputShape.length-1]===1||this.lossFunctions[i]===binaryCrossentropy?["accuracy","acc"].indexOf(metric)!==-1?accFn=binaryAccuracy:["crossentropy","ce"].indexOf(metric)!==-1&&(accFn=binaryCrossentropy2):this.lossFunctions[i]===sparseCategoricalCrossentropy?["accuracy","acc"].indexOf(metric)!==-1?accFn=sparseCategoricalAccuracy:["crossentropy","ce"].indexOf(metric)!==-1&&(accFn=sparseCategoricalCrossentropy2):["accuracy","acc"].indexOf(metric)!==-1?accFn=categoricalAccuracy:["crossentropy","ce"].indexOf(metric)!==-1&&(accFn=categoricalCrossentropy2);let suffix;["accuracy","acc"].indexOf(metric)!==-1?suffix="acc":["crossentropy","ce"].indexOf(metric)!==-1&&(suffix="ce"),weightedMetricFn=accFn,metricName=metricNamePrefix+suffix}else weightedMetricFn=get2(metric),metricName=metricNamePrefix+getLossOrMetricName(metric);let metricResult;nameScope(metricName,()=>{metricResult=weightedMetricFn}),appendMetric(i,metricName,metricResult)}})(outputMetrics)}}),this.collectedTrainableWeights=this.trainableWeights}checkTrainableWeightsConsistency(){this.collectedTrainableWeights!=null&&this.trainableWeights.length!==this.collectedTrainableWeights.length&&console.warn("Discrepancy between trainableweights and collected trainable weights. Did you set `model.trainable` without calling `model.compile()` afterwards?")}evaluate(x,y,args={}){let batchSize=args.batchSize==null?32:args.batchSize;checkBatchSize(batchSize);let checkBatchAxis=!0,standardizedOuts=this.standardizeUserDataXY(x,y,checkBatchAxis,batchSize);try{let ins=standardizedOuts[0].concat(standardizedOuts[1]);this.makeTestFunction();let f=this.testFunction,testOuts=this.testLoop(f,ins,batchSize,args.verbose,args.steps);return singletonOrArray(testOuts)}finally{disposeNewTensors(standardizedOuts[0],x),disposeNewTensors(standardizedOuts[1],y)}}async evaluateDataset(dataset,args){return this.makeTestFunction(),evaluateDataset(this,dataset,args)}checkNumSamples(ins,batchSize,steps,stepsName="steps"){let numSamples;if(steps!=null){if(numSamples=null,batchSize!=null)throw new ValueError(`If ${stepsName} is set, batchSize must be null or undefined.Got batchSize = ${batchSize}`)}else if(ins!=null)Array.isArray(ins)?numSamples=ins[0].shape[0]:numSamples=ins.shape[0];else throw new ValueError(`Either the input data should have a defined shape, or ${stepsName} shoud be specified.`);return numSamples}execute(inputs,outputs){if(Array.isArray(outputs)&&outputs.length===0)throw new ValueError("`outputs` is an empty Array, which is not allowed.");let outputsIsArray=Array.isArray(outputs),outputNames=outputsIsArray?outputs:[outputs],outputSymbolicTensors=this.retrieveSymbolicTensors(outputNames),feedDict=new FeedDict;if(inputs instanceof Tensor&&(inputs=[inputs]),Array.isArray(inputs)){if(inputs.length!==this.inputs.length)throw new ValueError(`The number of inputs provided (${inputs.length}) does not match the number of inputs of this model (${this.inputs.length}).`);for(let i=0;ioutput.name);for(let i=0;i0){let remainingNames=[];throw outputSymbolicTensors.forEach((tensor2,i)=>{tensor2==null&&remainingNames.push(symbolicTensorNames[i])}),new ValueError(`Cannot find SymbolicTensors for output name(s): ${JSON.stringify(remainingNames)}`)}return outputSymbolicTensors}predictLoop(ins,batchSize=32,verbose=!1){return tidy(()=>{let numSamples=this.checkNumSamples(ins);if(verbose)throw new NotImplementedError("Verbose predictLoop() is not implemented yet.");let batches=makeBatches(numSamples,batchSize),outsBatches=this.outputs.map(output=>[]);for(let batchIndex=0;batchIndex{let batchStart=batches[batchIndex][0],batchEnd=batches[batchIndex][1],insBatch=sliceArrays(ins,batchStart,batchEnd),feeds=[];if(Array.isArray(insBatch))for(let i=0;ioutsBatches[i].push(batchOut));return singletonOrArray(outsBatches.map(batches2=>concat(batches2,0)))})}predict(x,args={}){let xsRank2OrHigher=ensureTensorsRank2OrHigher(x);checkInputData(xsRank2OrHigher,this.inputNames,this.feedInputShapes,!1);try{let batchSize=args.batchSize==null?32:args.batchSize;return checkBatchSize(batchSize),this.predictLoop(xsRank2OrHigher,batchSize)}finally{disposeNewTensors(xsRank2OrHigher,x)}}predictOnBatch(x){checkInputData(x,this.inputNames,this.feedInputShapes,!0);let batchSize=(Array.isArray(x)?x[0]:x).shape[0];return this.predictLoop(x,batchSize)}standardizeUserDataXY(x,y,checkBatchAxis=!0,batchSize){if(this.optimizer_==null)throw new RuntimeError("You must compile a model before training/testing. Use LayersModel.compile(modelCompileArgs).");let outputShapes=[];for(let i=0;i0&&x[0].shape[0]%batchSize!=0)throw new ValueError(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${batchSize}. Found: ${x[0].shape[0]} sample(s).`);return[x,y]}async standardizeUserData(x,y,sampleWeight,classWeight,checkBatchAxis=!0,batchSize){let[standardXs,standardYs]=this.standardizeUserDataXY(x,y,checkBatchAxis,batchSize);if(sampleWeight!=null)throw new Error("sample weight is not supported yet.");let standardSampleWeights=null;if(classWeight!=null){let classWeights=standardizeClassWeights(classWeight,this.outputNames);standardSampleWeights=[];for(let i=0;i{let numSamples=this.checkNumSamples(ins,batchSize,steps,"steps"),outs=[];if(verbose>0)throw new NotImplementedError("Verbose mode is not implemented yet.");if(steps!=null)throw new NotImplementedError("steps mode in testLoop() is not implemented yet");{let batches=makeBatches(numSamples,batchSize),indexArray=tensor1d(range5(0,numSamples));for(let batchIndex=0;batchIndex1&&(newLabel+=`_${count(outLabels.slice(0,i),label)}`),dedupedOutLabels.push(newLabel)}return dedupedOutLabels}makeTrainFunction(){return data2=>{let lossValues=[],inputs=data2.slice(0,this.inputs.length),targets=data2.slice(this.inputs.length,this.inputs.length+this.outputs.length),sampleWeights=data2.slice(this.inputs.length+this.outputs.length,this.inputs.length+this.outputs.length*2),metricsValues=[],totalLossFunction=()=>{let feeds=[];for(let i=0;i1&&i{totalLoss=add2(totalLoss,regularizerLoss)}),totalLoss},variables=this.collectedTrainableWeights.map(param=>param.read()),returnCost=!0;return[this.optimizer_.minimize(totalLossFunction,returnCost,variables)].concat(metricsValues)}}makeTestFunction(){this.testFunction=data2=>tidy(()=>{let valOutputs=[],totalLoss,inputs=data2.slice(0,this.inputs.length),targets=data2.slice(this.inputs.length,this.inputs.length+this.outputs.length),feeds=[];for(let i=0;itoSnakeCase(name))}else{let outputNames=Object.keys(this.loss);lossNames={};let losses4=this.loss;for(let outputName of outputNames)if(typeof losses4[outputName]=="string")lossNames[outputName]=toSnakeCase(losses4[outputName]);else throw new Error("Serialization of non-string loss is not supported.")}return lossNames}getMetricIdentifiers(){if(typeof this.metrics=="string"||typeof this.metrics=="function")return[toSnakeCase(getLossOrMetricName(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(metric=>toSnakeCase(getLossOrMetricName(metric)));{let metricsIdentifiers={};for(let key in this.metrics)metricsIdentifiers[key]=toSnakeCase(getLossOrMetricName(this.metrics[key]));return metricsIdentifiers}}getTrainingConfig(){return{loss:this.getLossIdentifiers(),metrics:this.getMetricIdentifiers(),optimizer_config:{class_name:this.optimizer.getClassName(),config:this.optimizer.getConfig()}}}loadTrainingConfig(trainingConfig){if(trainingConfig.weighted_metrics!=null)throw new Error("Loading weight_metrics is not supported yet.");if(trainingConfig.loss_weights!=null)throw new Error("Loading loss_weights is not supported yet.");if(trainingConfig.sample_weight_mode!=null)throw new Error("Loading sample_weight_mode is not supported yet.");let tsConfig=convertPythonicToTs(trainingConfig.optimizer_config),optimizer=deserialize(tsConfig),loss;if(typeof trainingConfig.loss=="string")loss=toCamelCase(trainingConfig.loss);else if(Array.isArray(trainingConfig.loss))loss=trainingConfig.loss.map(lossEntry=>toCamelCase(lossEntry));else if(trainingConfig.loss!=null){loss={};for(let key in trainingConfig.loss)loss[key]=toCamelCase(trainingConfig.loss[key])}let metrics2;if(Array.isArray(trainingConfig.metrics))metrics2=trainingConfig.metrics.map(metric=>toCamelCase(metric));else if(trainingConfig.metrics!=null){metrics2={};for(let key in trainingConfig.metrics)metrics2[key]=toCamelCase(trainingConfig.metrics[key])}this.compile({loss,metrics:metrics2,optimizer})}async save(handlerOrURL,config3){if(typeof handlerOrURL=="string"){let handlers=io_exports.getSaveHandlers(handlerOrURL);if(handlers.length===0)throw new ValueError(`Cannot find any save handlers for URL '${handlerOrURL}'`);if(handlers.length>1)throw new ValueError(`Found more than one (${handlers.length}) save handlers for URL '${handlerOrURL}'`);handlerOrURL=handlers[0]}if(handlerOrURL.save==null)throw new ValueError("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let weightDataAndSpecs=await io_exports.encodeWeights(this.getNamedWeights(config3)),returnString=!1,unusedArg=null,modelArtifacts={modelTopology:this.toJSON(unusedArg,returnString),format:LAYERS_MODEL_FORMAT_NAME,generatedBy:`TensorFlow.js tfjs-layers v${version10}`,convertedBy:null};if((config3==null?!1:config3.includeOptimizer)&&this.optimizer!=null){modelArtifacts.trainingConfig=this.getTrainingConfig();let weightType="optimizer",{data:optimizerWeightData,specs:optimizerWeightSpecs}=await io_exports.encodeWeights(await this.optimizer.getWeights(),weightType);weightDataAndSpecs.specs.push(...optimizerWeightSpecs),weightDataAndSpecs.data=io_exports.concatenateArrayBuffers([weightDataAndSpecs.data,optimizerWeightData])}if(this.userDefinedMetadata!=null){let checkSize=!0;checkUserDefinedMetadata(this.userDefinedMetadata,this.name,checkSize),modelArtifacts.userDefinedMetadata=this.userDefinedMetadata}return modelArtifacts.weightData=weightDataAndSpecs.data,modelArtifacts.weightSpecs=weightDataAndSpecs.specs,handlerOrURL.save(modelArtifacts)}setUserDefinedMetadata(userDefinedMetadata){checkUserDefinedMetadata(userDefinedMetadata,this.name),this.userDefinedMetadata=userDefinedMetadata}getUserDefinedMetadata(){return this.userDefinedMetadata}};LayersModel.className="Model";serialization_exports.registerClass(LayersModel);var Functional=class extends LayersModel{};Functional.className="Functional";serialization_exports.registerClass(Functional);async function modelFromJSON(modelAndWeightsConfig,customObjects){"modelTopology"in modelAndWeightsConfig||(modelAndWeightsConfig={modelTopology:modelAndWeightsConfig}),modelAndWeightsConfig=modelAndWeightsConfig;let modelTopology=modelAndWeightsConfig.modelTopology;modelTopology.model_config!=null&&(modelTopology=modelTopology.model_config);let tsConfig=convertPythonicToTs(modelTopology),model2=deserialize(tsConfig,customObjects);if(modelAndWeightsConfig.weightsManifest!=null){let weightValues=await io_exports.loadWeights(modelAndWeightsConfig.weightsManifest,modelAndWeightsConfig.pathPrefix,model2.weights.map(weight=>weight.originalName)),uniqueWeightValues={};for(let weight of model2.weights)uniqueWeightValues[weight.originalName]=weightValues[weight.originalName];model2.loadWeights(uniqueWeightValues),dispose(weightValues)}return model2}async function loadLayersModelInternal(pathOrIOHandler,options){if(options==null&&(options={}),typeof pathOrIOHandler=="string"){let handlers=io_exports.getLoadHandlers(pathOrIOHandler,options);if(handlers.length===0)handlers.push(io_exports.browserHTTPRequest(pathOrIOHandler,options));else if(handlers.length>1)throw new ValueError(`Found more than one (${handlers.length}) load handlers for URL '${pathOrIOHandler}'`);pathOrIOHandler=handlers[0]}return loadLayersModelFromIOHandler(pathOrIOHandler,void 0,options)}async function loadLayersModelFromIOHandler(handler,customObjects,options){if(options==null&&(options={}),handler.load==null)throw new ValueError("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let artifacts=await handler.load(),modelTopology=artifacts.modelTopology;modelTopology.model_config!=null&&(modelTopology=modelTopology.model_config);let strict=options.strict==null?!0:options.strict,fastWeightInit=artifacts.weightData!=null&&artifacts.weightSpecs!=null&&strict,model2=deserialize(convertPythonicToTs(modelTopology),customObjects,fastWeightInit),trainingConfig=artifacts.trainingConfig;if(trainingConfig!=null&&model2.loadTrainingConfig(trainingConfig),artifacts.userDefinedMetadata!=null&&model2.setUserDefinedMetadata(artifacts.userDefinedMetadata),artifacts.weightData!=null){if(artifacts.weightSpecs==null)throw new ValueError("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");let{modelWeights,optimizerWeights}=decodeModelAndOptimizerWeights(artifacts.weightData,artifacts.weightSpecs);model2.loadWeights(modelWeights,strict),model2.optimizer!=null&&optimizerWeights.length>0&&await model2.optimizer.setWeights(optimizerWeights),dispose(modelWeights),dispose(optimizerWeights.map(w=>w.tensor))}return model2}function decodeModelAndOptimizerWeights(buffer2,specs){let name2Tensor=io_exports.decodeWeights(buffer2,specs),modelWeights={},optimizerWeights=[];return specs.forEach(spec=>{spec.group==="optimizer"?optimizerWeights.push({name:spec.name,tensor:name2Tensor[spec.name]}):modelWeights[spec.name]=name2Tensor[spec.name]}),{modelWeights,optimizerWeights}}var Sequential=class extends LayersModel{constructor(args){super({inputs:[],outputs:[]});if(args=args||{},this.trainable=!0,this.built=!1,this.name=args.name!=null?args.name:getUid("sequential_"),args.layers!=null)for(let layer of args.layers)this.add(layer)}checkShape(layer){if(layer.inboundNodes[0].outputTensors[0].shape.some(x=>x<0))throw new ValueError(`Negative dimension size caused by adding layer ${layer.name} with input shape [${layer.inboundNodes[0].inputTensors[0].shape}]`)}add(layer){let isLayerModelInstance=layer instanceof Sequential||layer instanceof LayersModel,modelLayer;if(isLayerModelInstance){if(modelLayer=layer,modelLayer.outputs.length!==1)throw new ValueError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");if(modelLayer.inputs.length!==1)throw new ValueError("All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.")}if(this.outputs.length===0){if(layer.inboundNodes.length===0){if(layer.batchInputShape==null)throw new ValueError("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");let x=Input({batchShape:layer.batchInputShape,dtype:layer.dtype,name:layer.name+"_input"});layer.apply(x)}if(isLayerModelInstance)this.outputs=modelLayer.outputs,this.inputs=modelLayer.inputs;else{if(layer.inboundNodes.length!==1)throw new ValueError(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${layer.name} which has ${layer.inboundNodes.length} pre-existing inbound connections.`);if(layer.inboundNodes[0].outputTensors.length!==1)throw new ValueError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(layer),this.outputs=[layer.inboundNodes[0].outputTensors[0]],this.inputs=getSourceInputs(this.outputs[0])}this.inboundNodes=[],new Node({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:pyListRepeat(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map(x=>x.shape),outputShapes:this.outputs[0].shape})}else{let outputTensor=layer.apply(this.outputs[0]);if(Array.isArray(outputTensor))throw new TypeError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(layer),this.outputs=[outputTensor],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}this.layers.push(layer),this.built=!1}pop(){if(this.layers.length===0)throw new TypeError("There are no layers in the model.");if(this.layers.pop(),this.layers.length===0)this.outputs=[],this.inboundNodes=[],this.outboundNodes=[];else{let lastLayerIndex=this.layers.length-1;this.layers[lastLayerIndex].outboundNodes=[],this.outputs=[this.layers[lastLayerIndex].output],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}}call(inputs,kwargs){return this.model==null&&this.build(),this.model.call(inputs,kwargs)}build(inputShape){if(getExactlyOneShape(inputShape),this.inputs.length===0||this.outputs.length===0)throw new TypeError("Sequential model cannot be built: model is empty. Add some layers first.");this.model=new LayersModel({inputs:this.inputs,outputs:this.outputs[0],name:this.name+"_model"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0}countParams(){return this.built||this.build(),super.countParams()}summary(lineLength,positions,printFn=console.log){this.built||this.build(),super.summary(lineLength,positions,printFn)}setWeights(weights){this.model==null&&this.build(),this.model.setWeights(weights)}evaluate(x,y,args={}){if(!this.built)throw new RuntimeError("The model needs to be compiled before being used.");return this.model.evaluate(x,y,args)}async evaluateDataset(dataset,args){if(!this.built)throw new RuntimeError("The model needs to be compiled before being used.");return this.model.evaluateDataset(dataset,args)}predict(x,args={}){return this.model==null&&this.build(),this.model.predict(x,args)}predictOnBatch(x){return this.model==null&&this.build(),this.model.predictOnBatch(x)}compile(args){this.build(),this.model.compile(args),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(optimizer){this.model.optimizer=optimizer}async fit(x,y,args={}){if(!this.built)throw new RuntimeError("The model needs to be compiled before being used.");return this.model.fit(x,y,args)}async fitDataset(dataset,args){if(!this.built)throw new RuntimeError("The model needs to be compiled before being used.");return this.model.fitDataset(dataset,args)}async trainOnBatch(x,y){return this.model.trainOnBatch(x,y)}static fromConfig(cls,config3,customObjects={},fastWeightInit=!1){let configArray,extraModelConfig={};if(config3 instanceof Array){if(config3[0].className==null||config3[0].className==="Merge")throw new ValueError("Legacy serialization format not supported yet.");configArray=config3}else util_exports.assert(config3.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),configArray=config3.layers,delete config3.layers,extraModelConfig=config3;let model2=new cls(extraModelConfig);if(!(model2 instanceof Sequential))throw new NotImplementedError(`Sequential.fromConfig called on non-Sequential input: ${model2}`);for(let conf of configArray){let layer=deserialize(conf,void 0,fastWeightInit);fastWeightInit&&layer.setFastWeightInitDuringBuild(!0),model2.add(layer)}return model2}set stopTraining(stop){if(this.model==null)throw new ValueError("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=stop}get stopTraining(){if(this.model==null)throw new ValueError("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let layers=[];for(let layer of this.layers){let dict={};dict.className=layer.getClassName(),dict.config=layer.getConfig(),layers.push(dict)}return{name:this.name,layers}}};Sequential.className="Sequential";serialization_exports.registerClass(Sequential);function model(args){return new LayersModel(args)}function sequential(config3){return new Sequential(config3)}function loadLayersModel(pathOrIOHandler,options){return options==null&&(options={}),loadLayersModelInternal(pathOrIOHandler,options)}function input(config3){return Input(config3)}function registerCallbackConstructor(verbosityLevel,callbackConstructor){CallbackConstructorRegistry.registerCallbackConstructor(verbosityLevel,callbackConstructor)}var Activation=class extends serialization_exports.Serializable{getConfig(){return{}}},Elu2=class extends Activation{apply(x,alpha=1){return elu4(x,alpha)}};Elu2.className="elu";serialization_exports.registerClass(Elu2);var Selu2=class extends Activation{apply(x){return selu(x)}};Selu2.className="selu";serialization_exports.registerClass(Selu2);var Relu2=class extends Activation{apply(x){return relu(x)}};Relu2.className="relu";serialization_exports.registerClass(Relu2);var Relu62=class extends Activation{apply(x){return tidy(()=>minimum(6,relu(x)))}};Relu62.className="relu6";serialization_exports.registerClass(Relu62);var Linear=class extends Activation{apply(x){return x}};Linear.className="linear";serialization_exports.registerClass(Linear);var Sigmoid2=class extends Activation{apply(x){return sigmoid(x)}};Sigmoid2.className="sigmoid";serialization_exports.registerClass(Sigmoid2);var HardSigmoid=class extends Activation{apply(x){return hardSigmoid(x)}};HardSigmoid.className="hardSigmoid";serialization_exports.registerClass(HardSigmoid);var Softplus2=class extends Activation{apply(x){return softplus(x)}};Softplus2.className="softplus";serialization_exports.registerClass(Softplus2);var Softsign=class extends Activation{apply(x){return softsign(x)}};Softsign.className="softsign";serialization_exports.registerClass(Softsign);var Tanh2=class extends Activation{apply(x){return tanh2(x)}};Tanh2.className="tanh";serialization_exports.registerClass(Tanh2);var Softmax2=class extends Activation{apply(x,axis=-1){return softmax(x,axis)}};Softmax2.className="softmax";serialization_exports.registerClass(Softmax2);var LogSoftmax2=class extends Activation{apply(x,axis=-1){return logSoftmax(x,axis)}};LogSoftmax2.className="logSoftmax";serialization_exports.registerClass(LogSoftmax2);var Swish=class extends Activation{apply(x,alpha=1){return tidy(()=>sigmoid(x.mul(alpha)).mul(x))}};Swish.className="swish";serialization_exports.registerClass(Swish);function serializeActivation(activation2){return activation2.getClassName()}function deserializeActivation(config3,customObjects={}){return deserializeKerasObject(config3,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"activation")}function getActivation(identifier){if(identifier==null){let config3={};return config3.className="linear",config3.config={},deserializeActivation(config3)}if(typeof identifier=="string"){let config3={};return config3.className=identifier,config3.config={},deserializeActivation(config3)}else return identifier instanceof Activation?identifier:deserializeActivation(identifier)}function assertObjectArgs(args){if(args!=null&&typeof args!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${args}`)}var Regularizer=class extends serialization_exports.Serializable{},L1L2=class extends Regularizer{constructor(args){super();assertObjectArgs(args),this.l1=args==null||args.l1==null?.01:args.l1,this.l2=args==null||args.l2==null?.01:args.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(x){return tidy(()=>{let regularization=zeros([1]);return this.hasL1&&(regularization=add2(regularization,sum2(mul(this.l1,abs(x))))),this.hasL2&&(regularization=add2(regularization,sum2(mul(this.l2,square3(x))))),regularization.asScalar()})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(cls,config3){return new cls({l1:config3.l1,l2:config3.l2})}};L1L2.className="L1L2";serialization_exports.registerClass(L1L2);function l1(args){return assertObjectArgs(args),new L1L2({l1:args!=null?args.l1:null,l2:0})}function l2(args){return assertObjectArgs(args),new L1L2({l2:args!=null?args.l2:null,l1:0})}var REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP={l1l2:"L1L2"};function serializeRegularizer(constraint){return serializeKerasObject(constraint)}function deserializeRegularizer(config3,customObjects={}){return deserializeKerasObject(config3,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"regularizer")}function getRegularizer(identifier){if(identifier==null)return null;if(typeof identifier=="string"){let config3={className:identifier in REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP?REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier]:identifier,config:{}};return deserializeRegularizer(config3)}else return identifier instanceof Regularizer?identifier:deserializeRegularizer(identifier)}var ReLU=class extends Layer{constructor(args){super(args==null?{}:args);this.supportsMasking=!0,args!=null&&(this.maxValue=args.maxValue)}call(inputs,kwargs){inputs=getExactlyOneTensor(inputs);let output=relu(inputs);return this.maxValue!=null&&(output=clipByValue(output,0,this.maxValue)),output}computeOutputShape(inputShape){return inputShape}getConfig(){let config3={maxValue:this.maxValue},baseConfig=super.getConfig();return Object.assign(config3,baseConfig),config3}};ReLU.className="ReLU";serialization_exports.registerClass(ReLU);var LeakyReLU=class extends Layer{constructor(args){super(args==null?{}:args);this.DEFAULT_ALPHA=.3,args==null&&(args={}),this.alpha=args.alpha==null?this.DEFAULT_ALPHA:args.alpha}call(inputs,kwargs){let x=getExactlyOneTensor(inputs);return leakyRelu(x,this.alpha)}computeOutputShape(inputShape){return inputShape}getConfig(){let config3={alpha:this.alpha},baseConfig=super.getConfig();return Object.assign(config3,baseConfig),config3}};LeakyReLU.className="LeakyReLU";serialization_exports.registerClass(LeakyReLU);var PReLU=class extends Layer{constructor(args){super(args==null?{}:args);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",args==null&&(args={}),this.supportsMasking=!0,this.alphaInitializer=getInitializer(args.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=getRegularizer(args.alphaRegularizer),this.alphaConstraint=getConstraint(args.alphaConstraint),args.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(args.sharedAxes))this.sharedAxes=args.sharedAxes;else if(typeof args.sharedAxes=="number")this.sharedAxes=[args.sharedAxes];else throw new ValueError(`Expected sharedAxes to be a number or an array of numbers, but got ${args.sharedAxes}`)}build(inputShape){inputShape=getExactlyOneShape(inputShape);let paramShape=inputShape.slice(1);if(this.sharedAxes!=null)for(let i of this.sharedAxes)paramShape[i-1]=1;this.alpha=this.addWeight("alpha",paramShape,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let axes={};if(this.sharedAxes!=null)for(let i=1;i(checkDataFormat(dataFormat),dataFormat==="channelsFirst"?transpose(x,[0,2,3,1]):x))}function preprocessConv3DInput(x,dataFormat){return tidy(()=>(checkDataFormat(dataFormat),dataFormat==="channelsFirst"?transpose(x,[0,2,3,4,1]):x))}function conv1dWithBias(x,kernel,bias,strides=1,padding="valid",dataFormat,dilationRate=1){return tidy(()=>{if(dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat),x.shape.length!==3)throw new ValueError(`The input of a conv1dWithBias operation should be 3, but is ${x.shape.length} instead.`);if(kernel.shape.length!==3)throw new ValueError(`The kernel for a conv1dWithBias operation should be 3, but is ${kernel.shape.length} instead`);if(bias!=null&&bias.shape.length!==1)throw new ValueError(`The bias for a conv1dWithBias operation should be 1, but is ${kernel.shape.length} instead`);if(dataFormat==="channelsFirst"&&(x=transpose(x,[0,2,1])),padding==="causal")throw new NotImplementedError("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let y=conv1d(x,kernel,strides,padding==="same"?"same":"valid","NWC",dilationRate);return bias!=null&&(y=biasAdd(y,bias)),y})}function conv2dWithBiasActivation(x,kernel,bias,strides=[1,1],padding="valid",dataFormat,dilationRate,activation2=null){return tidy(()=>{if(dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat),x.rank!==3&&x.rank!==4)throw new ValueError(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${x.rank}.`);if(kernel.rank!==3&&kernel.rank!==4)throw new ValueError(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${x.rank}.`);let y=preprocessConv2DInput(x,dataFormat);if(padding==="causal")throw new NotImplementedError("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return y=fused_ops_exports.conv2d({x:y,filter:kernel,strides,pad:padding==="same"?"same":"valid",dilations:dilationRate,dataFormat:"NHWC",bias,activation:activation2}),dataFormat==="channelsFirst"&&(y=transpose(y,[0,3,1,2])),y})}function conv3dWithBias(x,kernel,bias,strides=[1,1,1],padding="valid",dataFormat,dilationRate){return tidy(()=>{if(dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat),x.rank!==4&&x.rank!==5)throw new ValueError(`conv3dWithBias expects input to be of rank 4 or 5, but received ${x.rank}.`);if(kernel.rank!==4&&kernel.rank!==5)throw new ValueError(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${x.rank}.`);let y=preprocessConv3DInput(x,dataFormat);if(padding==="causal")throw new NotImplementedError("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return y=conv3d(y,kernel,strides,padding==="same"?"same":"valid","NDHWC",dilationRate),bias!=null&&(y=biasAdd(y,bias)),dataFormat==="channelsFirst"&&(y=transpose(y,[0,4,1,2,3])),y})}var BaseConv=class extends Layer{constructor(rank,args){super(args);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",BaseConv.verifyArgs(args),this.rank=rank,assertPositiveInteger(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new NotImplementedError(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=normalizeArray(args.kernelSize,rank,"kernelSize"),this.strides=normalizeArray(args.strides==null?1:args.strides,rank,"strides"),this.padding=args.padding==null?"valid":args.padding,checkPaddingMode(this.padding),this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),this.activation=getActivation(args.activation),this.useBias=args.useBias==null?!0:args.useBias,this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=getConstraint(args.biasConstraint),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.activityRegularizer=getRegularizer(args.activityRegularizer),this.dilationRate=normalizeArray(args.dilationRate==null?1:args.dilationRate,rank,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new ValueError(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new ValueError(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new ValueError(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(args){if(assert2("kernelSize"in args,"required key 'kernelSize' not in config"),typeof args.kernelSize!="number"&&!checkArrayTypeAndLength(args.kernelSize,"number",1,3))throw new ValueError(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(args.kernelSize)}.`)}getConfig(){let config3={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:serializeActivation(this.activation),useBias:this.useBias,biasInitializer:serializeInitializer(this.biasInitializer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),biasConstraint:serializeConstraint(this.biasConstraint)},baseConfig=super.getConfig();return Object.assign(config3,baseConfig),config3}},Conv=class extends BaseConv{constructor(rank,args){super(rank,args);this.kernel=null,Conv.verifyArgs(args),this.filters=args.filters,assertPositiveInteger(this.filters,"filters"),this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=getConstraint(args.kernelConstraint),this.kernelRegularizer=getRegularizer(args.kernelRegularizer)}build(inputShape){inputShape=getExactlyOneShape(inputShape);let channelAxis=this.dataFormat==="channelsFirst"?1:inputShape.length-1;if(inputShape[channelAxis]==null)throw new ValueError(`The channel dimension of the input should be defined. Found ${inputShape[channelAxis]}`);let inputDim=inputShape[channelAxis],kernelShape=this.kernelSize.concat([inputDim,this.filters]);this.kernel=this.addWeight("kernel",kernelShape,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[channelAxis]:inputDim}}],this.built=!0}call(inputs,kwargs){return tidy(()=>{inputs=getExactlyOneTensor(inputs);let outputs,biasValue=this.bias==null?null:this.bias.read(),fusedActivationName=mapActivationToFusedKernel(this.activation.getClassName());if(fusedActivationName!=null&&this.rank===2)outputs=conv2dWithBiasActivation(inputs,this.kernel.read(),biasValue,this.strides,this.padding,this.dataFormat,this.dilationRate,fusedActivationName);else{if(this.rank===1)outputs=conv1dWithBias(inputs,this.kernel.read(),biasValue,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)outputs=conv2dWithBiasActivation(inputs,this.kernel.read(),biasValue,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)outputs=conv3dWithBias(inputs,this.kernel.read(),biasValue,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new NotImplementedError("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(outputs=this.activation.apply(outputs))}return outputs})}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let newSpace=[],space=this.dataFormat==="channelsLast"?inputShape.slice(1,inputShape.length-1):inputShape.slice(2);for(let i=0;i 0 but got ${JSON.stringify(args.filters)}`)}},Conv2D2=class extends Conv{constructor(args){super(2,args);Conv2D2.verifyArgs(args)}getConfig(){let config3=super.getConfig();return delete config3.rank,config3}static verifyArgs(args){if(typeof args.kernelSize!="number"&&!checkArrayTypeAndLength(args.kernelSize,"number",1,2))throw new ValueError(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(args.kernelSize)}.`)}};Conv2D2.className="Conv2D";serialization_exports.registerClass(Conv2D2);var Conv3D2=class extends Conv{constructor(args){super(3,args);Conv3D2.verifyArgs(args)}getConfig(){let config3=super.getConfig();return delete config3.rank,config3}static verifyArgs(args){if(typeof args.kernelSize!="number"&&!(Array.isArray(args.kernelSize)&&(args.kernelSize.length===1||args.kernelSize.length===3)))throw new ValueError(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(args.kernelSize)}.`)}};Conv3D2.className="Conv3D";serialization_exports.registerClass(Conv3D2);var Conv2DTranspose=class extends Conv2D2{constructor(args){super(args);if(this.inputSpec=[new InputSpec({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new ValueError(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(inputShape){if(inputShape=getExactlyOneShape(inputShape),inputShape.length!==4)throw new ValueError("Input should have rank 4; Received input shape: "+JSON.stringify(inputShape));let channelAxis=this.dataFormat==="channelsFirst"?1:inputShape.length-1;if(inputShape[channelAxis]==null)throw new ValueError("The channel dimension of the inputs should be defined. Found `None`.");let inputDim=inputShape[channelAxis],kernelShape=this.kernelSize.concat([this.filters,inputDim]);this.kernel=this.addWeight("kernel",kernelShape,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new InputSpec({ndim:4,axes:{[channelAxis]:inputDim}})],this.built=!0}call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);if(input2.shape.length!==4)throw new ValueError(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${input2.shape.length}`);let inputShape=input2.shape,batchSize=inputShape[0],hAxis,wAxis;this.dataFormat==="channelsFirst"?(hAxis=2,wAxis=3):(hAxis=1,wAxis=2);let height=inputShape[hAxis],width=inputShape[wAxis],kernelH=this.kernelSize[0],kernelW=this.kernelSize[1],strideH=this.strides[0],strideW=this.strides[1],outHeight=deconvLength(height,strideH,kernelH,this.padding),outWidth=deconvLength(width,strideW,kernelW,this.padding),outputShape=[batchSize,outHeight,outWidth,this.filters];this.dataFormat!=="channelsLast"&&(input2=transpose(input2,[0,2,3,1]));let outputs=conv2dTranspose(input2,this.kernel.read(),outputShape,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(outputs=transpose(outputs,[0,3,1,2])),this.bias!=null&&(outputs=biasAdd(outputs,this.bias.read(),this.dataFormat)),this.activation!=null&&(outputs=this.activation.apply(outputs)),outputs})}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let outputShape=inputShape.slice(),channelAxis,heightAxis,widthAxis;this.dataFormat==="channelsFirst"?(channelAxis=1,heightAxis=2,widthAxis=3):(channelAxis=3,heightAxis=1,widthAxis=2);let kernelH=this.kernelSize[0],kernelW=this.kernelSize[1],strideH=this.strides[0],strideW=this.strides[1];return outputShape[channelAxis]=this.filters,outputShape[heightAxis]=deconvLength(outputShape[heightAxis],strideH,kernelH,this.padding),outputShape[widthAxis]=deconvLength(outputShape[widthAxis],strideW,kernelW,this.padding),outputShape}getConfig(){let config3=super.getConfig();return delete config3.dilationRate,config3}};Conv2DTranspose.className="Conv2DTranspose";serialization_exports.registerClass(Conv2DTranspose);var SeparableConv=class extends Conv{constructor(rank,config3){super(rank,config3);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,config3.filters==null)throw new ValueError("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(config3.kernelInitializer!=null||config3.kernelRegularizer!=null||config3.kernelConstraint!=null)throw new ValueError("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(config3.padding!=null&&config3.padding!=="same"&&config3.padding!=="valid")throw new ValueError(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(config3.padding)}`);this.depthMultiplier=config3.depthMultiplier==null?1:config3.depthMultiplier,this.depthwiseInitializer=getInitializer(config3.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=getRegularizer(config3.depthwiseRegularizer),this.depthwiseConstraint=getConstraint(config3.depthwiseConstraint),this.pointwiseInitializer=getInitializer(config3.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=getRegularizer(config3.pointwiseRegularizer),this.pointwiseConstraint=getConstraint(config3.pointwiseConstraint)}build(inputShape){if(inputShape=getExactlyOneShape(inputShape),inputShape.length{inputs=getExactlyOneTensor(inputs);let output;if(this.rank===1)throw new NotImplementedError("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(inputs=transpose(inputs,[0,2,3,1])),output=separableConv2d(inputs,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(output=biasAdd(output,this.bias.read(),this.dataFormat)),this.activation!=null&&(output=this.activation.apply(output)),this.dataFormat==="channelsFirst"&&(output=transpose(output,[0,3,1,2])),output})}getConfig(){let config3=super.getConfig();return delete config3.rank,delete config3.kernelInitializer,delete config3.kernelRegularizer,delete config3.kernelConstraint,config3.depthwiseInitializer=serializeInitializer(this.depthwiseInitializer),config3.pointwiseInitializer=serializeInitializer(this.pointwiseInitializer),config3.depthwiseRegularizer=serializeRegularizer(this.depthwiseRegularizer),config3.pointwiseRegularizer=serializeRegularizer(this.pointwiseRegularizer),config3.depthwiseConstraint=serializeConstraint(this.depthwiseConstraint),config3.pointwiseConstraint=serializeConstraint(this.pointwiseConstraint),config3}};SeparableConv.className="SeparableConv";var SeparableConv2D=class extends SeparableConv{constructor(args){super(2,args)}};SeparableConv2D.className="SeparableConv2D";serialization_exports.registerClass(SeparableConv2D);var Conv1D=class extends Conv{constructor(args){super(1,args);Conv1D.verifyArgs(args),this.inputSpec=[{ndim:3}]}getConfig(){let config3=super.getConfig();return delete config3.rank,delete config3.dataFormat,config3}static verifyArgs(args){if(typeof args.kernelSize!="number"&&!checkArrayTypeAndLength(args.kernelSize,"number",1,1))throw new ValueError(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(args.kernelSize)}.`)}};Conv1D.className="Conv1D";serialization_exports.registerClass(Conv1D);var Cropping2D=class extends Layer{constructor(args){super(args);typeof args.cropping=="number"?this.cropping=[[args.cropping,args.cropping],[args.cropping,args.cropping]]:typeof args.cropping[0]=="number"?this.cropping=[[args.cropping[0],args.cropping[0]],[args.cropping[1],args.cropping[1]]]:this.cropping=args.cropping,this.dataFormat=args.dataFormat===void 0?"channelsLast":args.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(inputShape){return this.dataFormat==="channelsFirst"?[inputShape[0],inputShape[1],inputShape[2]-this.cropping[0][0]-this.cropping[0][1],inputShape[3]-this.cropping[1][0]-this.cropping[1][1]]:[inputShape[0],inputShape[1]-this.cropping[0][0]-this.cropping[0][1],inputShape[2]-this.cropping[1][0]-this.cropping[1][1],inputShape[3]]}call(inputs,kwargs){return tidy(()=>{if(inputs=getExactlyOneTensor(inputs),this.dataFormat==="channelsLast"){let hSliced=sliceAlongAxis(inputs,this.cropping[0][0],inputs.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return sliceAlongAxis(hSliced,this.cropping[1][0],inputs.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let hSliced=sliceAlongAxis(inputs,this.cropping[0][0],inputs.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return sliceAlongAxis(hSliced,this.cropping[1][0],inputs.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let config3={cropping:this.cropping,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config3,baseConfig),config3}};Cropping2D.className="Cropping2D";serialization_exports.registerClass(Cropping2D);var UpSampling2D=class extends Layer{constructor(args){super(args);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=args.size==null?this.DEFAULT_SIZE:args.size,this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),this.interpolation=args.interpolation==null?"nearest":args.interpolation,checkInterpolationFormat(this.interpolation)}computeOutputShape(inputShape){if(this.dataFormat==="channelsFirst"){let height=inputShape[2]==null?null:this.size[0]*inputShape[2],width=inputShape[3]==null?null:this.size[1]*inputShape[3];return[inputShape[0],inputShape[1],height,width]}else{let height=inputShape[1]==null?null:this.size[0]*inputShape[1],width=inputShape[2]==null?null:this.size[1]*inputShape[2];return[inputShape[0],height,width,inputShape[3]]}}call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs),inputShape=input2.shape;if(this.dataFormat==="channelsFirst"){input2=transpose(input2,[0,2,3,1]);let height=this.size[0]*inputShape[2],width=this.size[1]*inputShape[3],resized=this.interpolation==="nearest"?input2.resizeNearestNeighbor([height,width]):input2.resizeBilinear([height,width]);return transpose(resized,[0,3,1,2])}else{let height=this.size[0]*inputShape[1],width=this.size[1]*inputShape[2];return this.interpolation==="nearest"?input2.resizeNearestNeighbor([height,width]):input2.resizeBilinear([height,width])}})}getConfig(){let config3={size:this.size,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config3,baseConfig),config3}};UpSampling2D.className="UpSampling2D";serialization_exports.registerClass(UpSampling2D);function depthwiseConv2d4(x,depthwiseKernel,strides=[1,1],padding="valid",dataFormat,dilationRate){return tidy(()=>{dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat);let y=preprocessConv2DInput(x,dataFormat);if(x.rank!==4)throw new ValueError(`Input for depthwiseConv2d is required to be 4-D, but is instead ${x.rank}-D`);if(depthwiseKernel.rank!==4)throw new ValueError(`depthwiseKernel is required to be 4-D, but is instead ${depthwiseKernel.rank}-D`);return y=depthwiseConv2d(y,depthwiseKernel,strides,padding==="same"?"same":"valid","NHWC",dilationRate),dataFormat==="channelsFirst"&&(y=transpose(y,[0,3,1,2])),y})}var DepthwiseConv2D=class extends BaseConv{constructor(args){super(2,args);this.depthwiseKernel=null,this.depthMultiplier=args.depthMultiplier==null?1:args.depthMultiplier,this.depthwiseInitializer=getInitializer(args.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=getConstraint(args.depthwiseConstraint),this.depthwiseRegularizer=getRegularizer(args.depthwiseRegularizer)}build(inputShape){if(inputShape=getExactlyOneShape(inputShape),inputShape.length<4)throw new ValueError(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(inputShape)}.`);let channelAxis=this.dataFormat==="channelsFirst"?1:3;if(inputShape[channelAxis]==null||inputShape[channelAxis]<0)throw new ValueError(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${inputShape[channelAxis]}).`);let inputDim=inputShape[channelAxis],depthwiseKernelShape=[this.kernelSize[0],this.kernelSize[1],inputDim,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",depthwiseKernelShape,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[inputDim*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(inputs,kwargs){return tidy(()=>{inputs=getExactlyOneTensor(inputs);let outputs=depthwiseConv2d4(inputs,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(outputs=biasAdd(outputs,this.bias.read(),this.dataFormat)),this.activation!=null&&(outputs=this.activation.apply(outputs)),outputs})}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let rows=this.dataFormat==="channelsFirst"?inputShape[2]:inputShape[1],cols=this.dataFormat==="channelsFirst"?inputShape[3]:inputShape[2],outFilters=this.dataFormat==="channelsFirst"?inputShape[1]*this.depthMultiplier:inputShape[3]*this.depthMultiplier,outRows=convOutputLength(rows,this.kernelSize[0],this.padding,this.strides[0]),outCols=convOutputLength(cols,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[inputShape[0],outFilters,outRows,outCols]:[inputShape[0],outRows,outCols,outFilters]}getConfig(){let config3=super.getConfig();return config3.depthMultiplier=this.depthMultiplier,config3.depthwiseInitializer=serializeInitializer(this.depthwiseInitializer),config3.depthwiseRegularizer=serializeRegularizer(this.depthwiseRegularizer),config3.depthwiseConstraint=serializeConstraint(this.depthwiseRegularizer),config3}};DepthwiseConv2D.className="DepthwiseConv2D";serialization_exports.registerClass(DepthwiseConv2D);function standardizeArgs(inputs,initialState,constants,numConstants){if(Array.isArray(inputs)){if(initialState!=null||constants!=null)throw new ValueError("When inputs is an array, neither initialState or constants should be provided");numConstants!=null&&(constants=inputs.slice(inputs.length-numConstants,inputs.length),inputs=inputs.slice(0,inputs.length-numConstants)),inputs.length>1&&(initialState=inputs.slice(1,inputs.length)),inputs=inputs[0]}function toListOrNull(x){return x==null||Array.isArray(x)?x:[x]}return initialState=toListOrNull(initialState),constants=toListOrNull(constants),{inputs,initialState,constants}}function rnn(stepFunction,inputs,initialStates,goBackwards=!1,mask,constants,unroll=!1,needPerStepOutputs=!1){return tidy(()=>{let ndim=inputs.shape.length;if(ndim<3)throw new ValueError(`Input should be at least 3D, but is ${ndim}D.`);let axes=[1,0].concat(range5(2,ndim));if(inputs=transpose(inputs,axes),constants!=null)throw new NotImplementedError("The rnn() functoin of the deeplearn.js backend does not support constants yet.");unroll&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),mask!=null&&(mask=mask.asType("bool").asType("float32"),mask.rank===ndim-1&&(mask=expandDims(mask,-1)),mask=transpose(mask,axes)),goBackwards&&(inputs=reverse(inputs,0),mask!=null&&(mask=reverse(mask,0)));let perStepOutputs=[],lastOutput,states=initialStates,timeSteps=inputs.shape[0],perStepInputs=unstack(inputs),perStepMasks;mask!=null&&(perStepMasks=unstack(mask));for(let t=0;tstepFunction(currentInput,states));if(mask==null)lastOutput=stepOutputs[0],states=stepOutputs[1];else{let maskedOutputs=tidy(()=>{let stepMask=perStepMasks[t],negStepMask=onesLike(stepMask).sub(stepMask),output=stepOutputs[0].mul(stepMask).add(states[0].mul(negStepMask)),newStates=states.map((state,i)=>stepOutputs[1][i].mul(stepMask).add(state.mul(negStepMask)));return{output,newStates}});lastOutput=maskedOutputs.output,states=maskedOutputs.newStates}needPerStepOutputs&&perStepOutputs.push(lastOutput)}let outputs;return needPerStepOutputs&&(outputs=stack(perStepOutputs,1)),[lastOutput,outputs,states]})}var RNN=class extends Layer{constructor(args){super(args);let cell;if(args.cell==null)throw new ValueError("cell property is missing for the constructor of RNN.");if(Array.isArray(args.cell)?cell=new StackedRNNCells({cells:args.cell}):cell=args.cell,cell.stateSize==null)throw new ValueError("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=cell,this.returnSequences=args.returnSequences==null?!1:args.returnSequences,this.returnState=args.returnState==null?!1:args.returnState,this.goBackwards=args.goBackwards==null?!1:args.goBackwards,this._stateful=args.stateful==null?!1:args.stateful,this.unroll=args.unroll==null?!1:args.unroll,this.supportsMasking=!0,this.inputSpec=[new InputSpec({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let numStates=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return range5(0,numStates).map(x=>null)}else return this.states_}setStates(states){this.states_=states}computeOutputShape(inputShape){isArrayOfShapes(inputShape)&&(inputShape=inputShape[0]),inputShape=inputShape;let stateSize=this.cell.stateSize;Array.isArray(stateSize)||(stateSize=[stateSize]);let outputDim=stateSize[0],outputShape;if(this.returnSequences?outputShape=[inputShape[0],inputShape[1],outputDim]:outputShape=[inputShape[0],outputDim],this.returnState){let stateShape=[];for(let dim of stateSize)stateShape.push([inputShape[0],dim]);return[outputShape].concat(stateShape)}else return outputShape}computeMask(inputs,mask){return tidy(()=>{Array.isArray(mask)&&(mask=mask[0]);let outputMask=this.returnSequences?mask:null;if(this.returnState){let stateMask=this.states.map(s=>null);return[outputMask].concat(stateMask)}else return outputMask})}get states(){if(this.states_==null){let numStates=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,output=[];for(let i=0;ispec.shape[spec.shape.length-1]),stateSize))throw new ValueError(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=stateSize.map(dim=>new InputSpec({shape:[null,dim]}));this.stateful&&this.resetStates()}resetStates(states,training=!1){tidy(()=>{if(!this.stateful)throw new AttributeError("Cannot call resetStates() on an RNN Layer that is not stateful.");let batchSize=this.inputSpec[0].shape[0];if(batchSize==null)throw new ValueError("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(dim=>zeros([batchSize,dim])):this.states_=[zeros([batchSize,this.cell.stateSize])];else if(states==null)dispose(this.states_),this.keptStates!=null&&(dispose(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(dim=>zeros([batchSize,dim])):this.states_[0]=zeros([batchSize,this.cell.stateSize]);else{if(Array.isArray(states)||(states=[states]),states.length!==this.states_.length)throw new ValueError(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${states.length} state value(s). Input received: ${states}`);training===!0?this.keptStates.push(this.states_.slice()):dispose(this.states_);for(let index=0;indexkeep(state.clone()))})}apply(inputs,kwargs){let initialState=kwargs==null?null:kwargs.initialState,constants=kwargs==null?null:kwargs.constants;kwargs==null&&(kwargs={});let standardized=standardizeArgs(inputs,initialState,constants,this.numConstants);inputs=standardized.inputs,initialState=standardized.initialState,constants=standardized.constants;let additionalInputs=[],additionalSpecs=[];if(initialState!=null){kwargs.initialState=initialState,additionalInputs=additionalInputs.concat(initialState),this.stateSpec=[];for(let state of initialState)this.stateSpec.push(new InputSpec({shape:state.shape}));additionalSpecs=additionalSpecs.concat(this.stateSpec)}if(constants!=null&&(kwargs.constants=constants,additionalInputs=additionalInputs.concat(constants),this.numConstants=constants.length),additionalInputs[0]instanceof SymbolicTensor){let fullInput=[inputs].concat(additionalInputs),fullInputSpec=this.inputSpec.concat(additionalSpecs),originalInputSpec=this.inputSpec;this.inputSpec=fullInputSpec;let output=super.apply(fullInput,kwargs);return this.inputSpec=originalInputSpec,output}else return super.apply(inputs,kwargs)}call(inputs,kwargs){return tidy(()=>{let mask=kwargs==null?null:kwargs.mask,training=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;inputs=getExactlyOneTensor(inputs),initialState==null&&(this.stateful?initialState=this.states_:initialState=this.getInitialState(inputs));let numStates=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(initialState.length!==numStates)throw new ValueError(`RNN Layer has ${numStates} state(s) but was passed ${initialState.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let cellCallKwargs={training},rnnOutputs=rnn((inputs2,states2)=>{let outputs2=this.cell.call([inputs2].concat(states2),cellCallKwargs);return[outputs2[0],outputs2.slice(1)]},inputs,initialState,this.goBackwards,mask,null,this.unroll,this.returnSequences),lastOutput=rnnOutputs[0],outputs=rnnOutputs[1],states=rnnOutputs[2];this.stateful&&this.resetStates(states,training);let output=this.returnSequences?outputs:lastOutput;return this.returnState?[output].concat(states):output})}getInitialState(inputs){return tidy(()=>{let initialState=zeros(inputs.shape);return initialState=sum2(initialState,[1,2]),initialState=expandDims5(initialState),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(dim=>dim>1?tile5(initialState,[1,dim]):initialState):this.cell.stateSize>1?[tile5(initialState,[1,this.cell.stateSize])]:[initialState]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(value){super.setFastWeightInitDuringBuild(value),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(value)}getConfig(){let baseConfig=super.getConfig(),config3={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(config3.numConstants=this.numConstants);let cellConfig=this.cell.getConfig();return this.getClassName()===RNN.className&&(config3.cell={className:this.cell.getClassName(),config:cellConfig}),Object.assign({},cellConfig,baseConfig,config3)}static fromConfig(cls,config3,customObjects={}){let cellConfig=config3.cell,cell=deserialize(cellConfig,customObjects);return new cls(Object.assign(config3,{cell}))}};RNN.className="RNN";serialization_exports.registerClass(RNN);var RNNCell=class extends Layer{},SimpleRNNCell=class extends RNNCell{constructor(args){super(args);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=args.units,assertPositiveInteger(this.units,"units"),this.activation=getActivation(args.activation==null?this.DEFAULT_ACTIVATION:args.activation),this.useBias=args.useBias==null?!0:args.useBias,this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=getInitializer(args.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=getRegularizer(args.kernelRegularizer),this.recurrentRegularizer=getRegularizer(args.recurrentRegularizer),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.kernelConstraint=getConstraint(args.kernelConstraint),this.recurrentConstraint=getConstraint(args.recurrentConstraint),this.biasConstraint=getConstraint(args.biasConstraint),this.dropout=min5([1,max5([0,args.dropout==null?0:args.dropout])]),this.recurrentDropout=min5([1,max5([0,args.recurrentDropout==null?0:args.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(inputShape){inputShape=getExactlyOneShape(inputShape),this.kernel=this.addWeight("kernel",[inputShape[inputShape.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(inputs,kwargs){return tidy(()=>{if(inputs=inputs,inputs.length!==2)throw new ValueError(`SimpleRNNCell expects 2 input Tensors, got ${inputs.length}.`);let prevOutput=inputs[1];inputs=inputs[0];let training=kwargs.training==null?!1:kwargs.training;0onesLike(inputs),rate:this.dropout,training})),0onesLike(prevOutput),rate:this.recurrentDropout,training}));let h,dpMask=this.dropoutMask,recDpMask=this.recurrentDropoutMask;dpMask!=null?h=dot2(mul(inputs,dpMask),this.kernel.read()):h=dot2(inputs,this.kernel.read()),this.bias!=null&&(h=biasAdd(h,this.bias.read())),recDpMask!=null&&(prevOutput=mul(prevOutput,recDpMask));let output=add2(h,dot2(prevOutput,this.recurrentKernel.read()));return this.activation!=null&&(output=this.activation.apply(output)),[output,output]})}getConfig(){let baseConfig=super.getConfig(),config3={units:this.units,activation:serializeActivation(this.activation),useBias:this.useBias,kernelInitializer:serializeInitializer(this.kernelInitializer),recurrentInitializer:serializeInitializer(this.recurrentInitializer),biasInitializer:serializeInitializer(this.biasInitializer),kernelRegularizer:serializeRegularizer(this.kernelRegularizer),recurrentRegularizer:serializeRegularizer(this.recurrentRegularizer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),kernelConstraint:serializeConstraint(this.kernelConstraint),recurrentConstraint:serializeConstraint(this.recurrentConstraint),biasConstraint:serializeConstraint(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},baseConfig,config3)}};SimpleRNNCell.className="SimpleRNNCell";serialization_exports.registerClass(SimpleRNNCell);var SimpleRNN=class extends RNN{constructor(args){args.cell=new SimpleRNNCell(args),super(args)}call(inputs,kwargs){return tidy(()=>{this.cell.dropoutMask!=null&&(dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let mask=kwargs==null?null:kwargs.mask,training=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;return super.call(inputs,{mask,training,initialState})})}static fromConfig(cls,config3){return new cls(config3)}};SimpleRNN.className="SimpleRNN";serialization_exports.registerClass(SimpleRNN);var GRUCell=class extends RNNCell{constructor(args){super(args);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",args.resetAfter)throw new ValueError("GRUCell does not support reset_after parameter set to true.");this.units=args.units,assertPositiveInteger(this.units,"units"),this.activation=getActivation(args.activation===void 0?this.DEFAULT_ACTIVATION:args.activation),this.recurrentActivation=getActivation(args.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:args.recurrentActivation),this.useBias=args.useBias==null?!0:args.useBias,this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=getInitializer(args.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=getRegularizer(args.kernelRegularizer),this.recurrentRegularizer=getRegularizer(args.recurrentRegularizer),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.kernelConstraint=getConstraint(args.kernelConstraint),this.recurrentConstraint=getConstraint(args.recurrentConstraint),this.biasConstraint=getConstraint(args.biasConstraint),this.dropout=min5([1,max5([0,args.dropout==null?0:args.dropout])]),this.recurrentDropout=min5([1,max5([0,args.recurrentDropout==null?0:args.recurrentDropout])]),this.implementation=args.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(inputShape){inputShape=getExactlyOneShape(inputShape);let inputDim=inputShape[inputShape.length-1];this.kernel=this.addWeight("kernel",[inputDim,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(inputs,kwargs){return tidy(()=>{if(inputs=inputs,inputs.length!==2)throw new ValueError(`GRUCell expects 2 input Tensors (inputs, h, c), got ${inputs.length}.`);let training=kwargs.training==null?!1:kwargs.training,hTMinus1=inputs[1];inputs=inputs[0],0onesLike(inputs),rate:this.dropout,training,count:3})),0onesLike(hTMinus1),rate:this.recurrentDropout,training,count:3}));let dpMask=this.dropoutMask,recDpMask=this.recurrentDropoutMask,z,r,hh;0{this.cell.dropoutMask!=null&&(dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let mask=kwargs==null?null:kwargs.mask,training=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;return super.call(inputs,{mask,training,initialState})})}static fromConfig(cls,config3){return config3.implmentation===0&&(config3.implementation=1),new cls(config3)}};GRU.className="GRU";serialization_exports.registerClass(GRU);var LSTMCell=class extends RNNCell{constructor(args){super(args);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=args.units,assertPositiveInteger(this.units,"units"),this.activation=getActivation(args.activation===void 0?this.DEFAULT_ACTIVATION:args.activation),this.recurrentActivation=getActivation(args.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:args.recurrentActivation),this.useBias=args.useBias==null?!0:args.useBias,this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=getInitializer(args.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=args.unitForgetBias,this.kernelRegularizer=getRegularizer(args.kernelRegularizer),this.recurrentRegularizer=getRegularizer(args.recurrentRegularizer),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.kernelConstraint=getConstraint(args.kernelConstraint),this.recurrentConstraint=getConstraint(args.recurrentConstraint),this.biasConstraint=getConstraint(args.biasConstraint),this.dropout=min5([1,max5([0,args.dropout==null?0:args.dropout])]),this.recurrentDropout=min5([1,max5([0,args.recurrentDropout==null?0:args.recurrentDropout])]),this.implementation=args.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(inputShape){var _a;inputShape=getExactlyOneShape(inputShape);let inputDim=inputShape[inputShape.length-1];this.kernel=this.addWeight("kernel",[inputDim,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let biasInitializer;if(this.useBias){if(this.unitForgetBias){let capturedBiasInit=this.biasInitializer,capturedUnits=this.units;biasInitializer=new(_a=class extends Initializer{apply(shape,dtype){let bI=capturedBiasInit.apply([capturedUnits]),bF=new Ones().apply([capturedUnits]),bCAndH=capturedBiasInit.apply([capturedUnits*2]);return concatAlongFirstAxis(concatAlongFirstAxis(bI,bF),bCAndH)}},_a.className="CustomInit",_a)}else biasInitializer=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,biasInitializer,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(inputs,kwargs){return tidy(()=>{let training=kwargs.training==null?!1:kwargs.training;if(inputs=inputs,inputs.length!==3)throw new ValueError(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${inputs.length}.`);let hTMinus1=inputs[1],cTMinus1=inputs[2];inputs=inputs[0],0onesLike(inputs),rate:this.dropout,training,count:4})),0onesLike(hTMinus1),rate:this.recurrentDropout,training,count:4}));let dpMask=this.dropoutMask,recDpMask=this.recurrentDropoutMask,i,f,c,o;0{this.cell.dropoutMask!=null&&(dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let mask=kwargs==null?null:kwargs.mask,training=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;return super.call(inputs,{mask,training,initialState})})}static fromConfig(cls,config3){return config3.implmentation===0&&(config3.implementation=1),new cls(config3)}};LSTM.className="LSTM";serialization_exports.registerClass(LSTM);var StackedRNNCells=class extends RNNCell{constructor(args){super(args);this.cells=args.cells}get stateSize(){let stateSize=[];for(let cell of this.cells.slice().reverse())Array.isArray(cell.stateSize)?stateSize.push(...cell.stateSize):stateSize.push(cell.stateSize);return stateSize}call(inputs,kwargs){return tidy(()=>{inputs=inputs;let states=inputs.slice(1),nestedStates=[];for(let cell of this.cells.slice().reverse())Array.isArray(cell.stateSize)?nestedStates.push(states.splice(0,cell.stateSize.length)):nestedStates.push(states.splice(0,1));nestedStates.reverse();let newNestedStates=[],callInputs;for(let i=0;i{nameScope(`RNNCell_${i}`,()=>{cell.build(inputShape),Array.isArray(cell.stateSize)?outputDim=cell.stateSize[0]:outputDim=cell.stateSize,inputShape=[inputShape[0],outputDim]})}),this.built=!0}getConfig(){let baseConfig=super.getConfig(),getCellConfig=cell=>({className:cell.getClassName(),config:cell.getConfig()}),config3={cells:this.cells.map(getCellConfig)};return Object.assign({},baseConfig,config3)}static fromConfig(cls,config3,customObjects={}){let cells=[];for(let cellConfig of config3.cells)cells.push(deserialize(cellConfig,customObjects));return new cls({cells})}get trainableWeights(){if(!this.trainable)return[];let weights=[];for(let cell of this.cells)weights.push(...cell.trainableWeights);return weights}get nonTrainableWeights(){let weights=[];for(let cell of this.cells)weights.push(...cell.nonTrainableWeights);if(!this.trainable){let trainableWeights=[];for(let cell of this.cells)trainableWeights.push(...cell.trainableWeights);return trainableWeights.concat(weights)}return weights}getWeights(){let weights=[];for(let cell of this.cells)weights.push(...cell.weights);return batchGetValue(weights)}setWeights(weights){let tuples=[];for(let cell of this.cells){let numParams=cell.weights.length,inputWeights=weights.splice(numParams);for(let i=0;idropout2(ones4(),rate),createMask=()=>inTrainPhase(droppedInputs,ones4,training);return!count2||count2<=1?keep(createMask().clone()):Array(count2).fill(void 0).map(createMask).map(m=>keep(m.clone()))}var __rest=function(s,e){var t={};for(var p2 in s)Object.prototype.hasOwnProperty.call(s,p2)&&e.indexOf(p2)<0&&(t[p2]=s[p2]);if(s!=null&&typeof Object.getOwnPropertySymbols=="function")for(var i=0,p2=Object.getOwnPropertySymbols(s);i{if(this.cell.dropoutMask!=null&&(dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),kwargs&&kwargs.constants)throw new ValueError("ConvRNN2D cell does not support constants");let mask=kwargs==null?null:kwargs.mask,training=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;return super.call(inputs,{mask,training,initialState})})}computeOutputShape(inputShape){let outShape=this.computeSingleOutputShape(inputShape);return this.returnSequences||(outShape=[outShape[0],...outShape.slice(2)]),this.returnState&&(outShape=[outShape,...Array(2).fill([inputShape[0],...outShape.slice(-3)])]),outShape}getInitialState(inputs){return tidy(()=>{let{stateSize}=this.cell,inputShape=inputs.shape,outputShape=this.computeSingleOutputShape(inputShape),stateShape=[outputShape[0],...outputShape.slice(2)],initialState=zeros(stateShape);return Array.isArray(stateSize)?Array(stateSize.length).fill(initialState):[initialState]})}resetStates(states,training=!1){tidy(()=>{if(!this.stateful)throw new AttributeError("Cannot call resetStates() on an RNN Layer that is not stateful.");let inputShape=this.inputSpec[0].shape,outputShape=this.computeSingleOutputShape(inputShape),stateShape=[outputShape[0],...outputShape.slice(2)];if(inputShape[0]==null)throw new ValueError("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>zeros(stateShape)):this.states_=[zeros(stateShape)];else if(states==null)dispose(this.states_),this.keptStates!=null&&(dispose(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>zeros(stateShape)):this.states_[0]=zeros(stateShape);else{if(Array.isArray(states)||(states=[states]),states.length!==this.states_.length)throw new ValueError(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${states.length} state value(s). Input received: ${states}`);training?this.keptStates.push(this.states_.slice()):dispose(this.states_);for(let index=0;indexkeep(state.clone()))})}computeSingleOutputShape(inputShape){let{dataFormat,filters,kernelSize,padding,strides,dilationRate}=this.cell,isChannelsFirst=dataFormat==="channelsFirst",h=inputShape[isChannelsFirst?3:2],w=inputShape[isChannelsFirst?4:3],hOut=convOutputLength(h,kernelSize[0],padding,strides[0],dilationRate[0]),wOut=convOutputLength(w,kernelSize[1],padding,strides[1],dilationRate[1]);return[...inputShape.slice(0,2),...isChannelsFirst?[filters,hOut,wOut]:[hOut,wOut,filters]]}};ConvRNN2D.className="ConvRNN2D";var ConvLSTM2DCell=class extends LSTMCell{constructor(args){let{filters,kernelSize,strides,padding,dataFormat,dilationRate}=args;super(Object.assign({},args,{units:filters}));this.filters=filters,assertPositiveInteger(this.filters,"filters"),this.kernelSize=normalizeArray(kernelSize,2,"kernelSize"),this.kernelSize.forEach(size=>assertPositiveInteger(size,"kernelSize")),this.strides=normalizeArray(strides||1,2,"strides"),this.strides.forEach(stride=>assertPositiveInteger(stride,"strides")),this.padding=padding||"valid",checkPaddingMode(this.padding),this.dataFormat=dataFormat||"channelsLast",checkDataFormat(this.dataFormat),this.dilationRate=normalizeArray(dilationRate||1,2,"dilationRate"),this.dilationRate.forEach(rate=>assertPositiveInteger(rate,"dilationRate"))}build(inputShape){var _a;inputShape=getExactlyOneShape(inputShape);let channelAxis=this.dataFormat==="channelsFirst"?1:inputShape.length-1;if(inputShape[channelAxis]==null)throw new ValueError(`The channel dimension of the input should be defined. Found ${inputShape[channelAxis]}`);let inputDim=inputShape[channelAxis],numOfKernels=4,kernelShape=this.kernelSize.concat([inputDim,this.filters*numOfKernels]);this.kernel=this.addWeight("kernel",kernelShape,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let recurrentKernelShape=this.kernelSize.concat([this.filters,this.filters*numOfKernels]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",recurrentKernelShape,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let biasInitializer;if(this.unitForgetBias){let init2=this.biasInitializer,filters=this.filters;biasInitializer=new(_a=class extends Initializer{apply(shape,dtype){let biasI=init2.apply([filters]),biasF=ones2([filters]),biasCAndO=init2.apply([filters*2]);return concatenate([biasI,biasF,biasCAndO])}},_a.className="CustomInit",_a)}else biasInitializer=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*numOfKernels],null,biasInitializer,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(inputs,kwargs){return tidy(()=>{if(inputs.length!==3)throw new ValueError(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${inputs.length}.`);let training=kwargs.training||!1,x=inputs[0],hTMinus1=inputs[1],cTMinus1=inputs[2],numOfKernels=4;0onesLike(x),rate:this.dropout,training,count:numOfKernels}));let dropoutMask=this.dropoutMask,applyDropout=(x2,mask,index)=>!mask||!mask[index]?x2:mul(mask[index],x2),xI=applyDropout(x,dropoutMask,0),xF=applyDropout(x,dropoutMask,1),xC=applyDropout(x,dropoutMask,2),xO=applyDropout(x,dropoutMask,3);0onesLike(hTMinus1),rate:this.recurrentDropout,training,count:numOfKernels}));let recDropoutMask=this.recurrentDropoutMask,hI=applyDropout(hTMinus1,recDropoutMask,0),hF=applyDropout(hTMinus1,recDropoutMask,1),hC=applyDropout(hTMinus1,recDropoutMask,2),hO=applyDropout(hTMinus1,recDropoutMask,3),kernelChannelAxis=3,[kernelI,kernelF,kernelC,kernelO]=split(this.kernel.read(),numOfKernels,kernelChannelAxis),[biasI,biasF,biasC,biasO]=this.useBias?split(this.bias.read(),numOfKernels):[null,null,null,null];xI=this.inputConv(xI,kernelI,biasI,this.padding),xF=this.inputConv(xF,kernelF,biasF,this.padding),xC=this.inputConv(xC,kernelC,biasC,this.padding),xO=this.inputConv(xO,kernelO,biasO,this.padding);let[recKernelI,recKernelF,recKernelC,recKernelO]=split(this.recurrentKernel.read(),numOfKernels,kernelChannelAxis);hI=this.recurrentConv(hI,recKernelI),hF=this.recurrentConv(hF,recKernelF),hC=this.recurrentConv(hC,recKernelC),hO=this.recurrentConv(hO,recKernelO);let i=this.recurrentActivation.apply(add2(xI,hI)),f=this.recurrentActivation.apply(add2(xF,hF)),c=add2(mul(f,cTMinus1),mul(i,this.activation.apply(add2(xC,hC)))),h=mul(this.recurrentActivation.apply(add2(xO,hO)),this.activation.apply(c));return[h,h,c]})}getConfig(){let _a=super.getConfig(),{units:_}=_a,baseConfig=__rest(_a,["units"]),config3={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},baseConfig,config3)}inputConv(x,w,b,padding){let out=conv2d(x,w,this.strides,padding||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return b?biasAdd(out,b,this.dataFormat):out}recurrentConv(x,w){return conv2d(x,w,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};ConvLSTM2DCell.className="ConvLSTM2DCell";serialization_exports.registerClass(ConvLSTM2DCell);var ConvLSTM2D=class extends ConvRNN2D{constructor(args){let cell=new ConvLSTM2DCell(args);super(Object.assign({},args,{cell}))}static fromConfig(cls,config3){return new cls(config3)}};ConvLSTM2D.className="ConvLSTM2D";serialization_exports.registerClass(ConvLSTM2D);var Dropout=class extends Layer{constructor(args){super(args);this.rate=Math.max(Math.min(args.rate,1),0),this.noiseShape=args.noiseShape,this.seed=args.seed,this.supportsMasking=!0}getNoiseShape(input2){if(this.noiseShape==null)return this.noiseShape;let inputShape=input2.shape,noiseShape=[];for(let i=0;i{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);if(0dropout2(input2,this.rate,noiseShape,this.seed),()=>input2,training)}return inputs})}getConfig(){let config3={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},baseConfig=super.getConfig();return Object.assign(config3,baseConfig),config3}dispose(){return super.dispose()}};Dropout.className="Dropout";serialization_exports.registerClass(Dropout);var SpatialDropout1D=class extends Dropout{constructor(args){super(args);this.inputSpec=[{ndim:3}]}getNoiseShape(input2){let inputShape=input2.shape;return[inputShape[0],1,inputShape[2]]}};SpatialDropout1D.className="SpatialDropout1D";serialization_exports.registerClass(SpatialDropout1D);var Dense=class extends Layer{constructor(args){super(args);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",args.batchInputShape==null&&args.inputShape==null&&args.inputDim!=null){let batchSize=null;args.batchSize!=null&&(batchSize=args.batchSize),this.batchInputShape=[batchSize,args.inputDim]}this.units=args.units,assertPositiveInteger(this.units,"units"),this.activation=getActivation(args.activation),args.useBias!=null&&(this.useBias=args.useBias),this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=getConstraint(args.kernelConstraint),this.biasConstraint=getConstraint(args.biasConstraint),this.kernelRegularizer=getRegularizer(args.kernelRegularizer),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.activityRegularizer=getRegularizer(args.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(inputShape){inputShape=getExactlyOneShape(inputShape);let inputLastDim=inputShape[inputShape.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[inputLastDim,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:inputLastDim}}],this.built=!0}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let outputShape=inputShape.slice();return outputShape[outputShape.length-1]=this.units,outputShape}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs),fusedActivationName=mapActivationToFusedKernel(this.activation.getClassName()),output;return fusedActivationName!=null?output=dot2(input2,this.kernel.read(),fusedActivationName,this.bias?this.bias.read():null):(output=dot2(input2,this.kernel.read()),this.bias!=null&&(output=biasAdd(output,this.bias.read())),this.activation!=null&&(output=this.activation.apply(output))),output})}getConfig(){let config3={units:this.units,activation:serializeActivation(this.activation),useBias:this.useBias,kernelInitializer:serializeInitializer(this.kernelInitializer),biasInitializer:serializeInitializer(this.biasInitializer),kernelRegularizer:serializeRegularizer(this.kernelRegularizer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),kernelConstraint:serializeConstraint(this.kernelConstraint),biasConstraint:serializeConstraint(this.biasConstraint)},baseConfig=super.getConfig();return Object.assign(config3,baseConfig),config3}};Dense.className="Dense";serialization_exports.registerClass(Dense);var Flatten=class extends Layer{constructor(args){args=args||{},super(args),this.inputSpec=[{minNDim:3}],this.dataFormat=args.dataFormat}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);for(let dim of inputShape.slice(1))if(dim==null)throw new ValueError(`The shape of the input to "Flatten" is not fully defined (got ${inputShape.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[inputShape[0],arrayProd(inputShape,1)]}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);if(this.dataFormat==="channelsFirst"&&input2.rank>1){let permutation=[0];for(let i=2;i{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);return this.activation.apply(input2)})}getConfig(){let config3={activation:serializeActivation(this.activation)},baseConfig=super.getConfig();return Object.assign(config3,baseConfig),config3}};Activation2.className="Activation";serialization_exports.registerClass(Activation2);var RepeatVector=class extends Layer{constructor(args){super(args);this.n=args.n,this.inputSpec=[{ndim:2}]}computeOutputShape(inputShape){return[inputShape[0],this.n,inputShape[1]]}call(inputs,kwargs){return tidy(()=>(inputs=getExactlyOneTensor(inputs),repeat(inputs,this.n)))}getConfig(){let config3={n:this.n},baseConfig=super.getConfig();return Object.assign(config3,baseConfig),config3}};RepeatVector.className="RepeatVector";serialization_exports.registerClass(RepeatVector);var Reshape2=class extends Layer{constructor(args){super(args);this.targetShape=args.targetShape;for(let i=0;i{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs),inputShape=input2.shape,outputShape=inputShape.slice(0,1).concat(this.fixUnknownDimension(inputShape.slice(1),this.targetShape));return input2.reshape(outputShape)})}getConfig(){let config3={targetShape:this.targetShape},baseConfig=super.getConfig();return Object.assign(config3,baseConfig),config3}};Reshape2.className="Reshape";serialization_exports.registerClass(Reshape2);var Permute=class extends Layer{constructor(args){super(args);if(args.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(args.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${args.dims} instead.`);let expectedSortedIndices=range5(1,args.dims.length+1);if(!util_exports.arraysEqual(args.dims.slice().sort(),expectedSortedIndices))throw new Error("Invalid permutation `dims`: "+JSON.stringify(args.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=args.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new InputSpec({ndim:this.dims.length+1})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let outputShape=inputShape.slice();return this.dims.forEach((dim,i)=>{outputShape[i+1]=inputShape[dim]}),outputShape}call(inputs,kwargs){return transpose(getExactlyOneTensor(inputs),this.dimsIncludingBatch)}getConfig(){let config3={dims:this.dims},baseConfig=super.getConfig();return Object.assign(config3,baseConfig),config3}};Permute.className="Permute";serialization_exports.registerClass(Permute);var Masking=class extends Layer{constructor(args){super(args==null?{}:args);this.supportsMasking=!0,args!=null?this.maskValue=args.maskValue==null?0:args.maskValue:this.maskValue=0}computeOutputShape(inputShape){return inputShape}getConfig(){let baseConfig=super.getConfig(),config3={maskValue:this.maskValue};return Object.assign(config3,baseConfig),config3}computeMask(inputs,mask){let input2=getExactlyOneTensor(inputs),axis=-1;return any(notEqual(input2,this.maskValue),axis)}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs),axis=-1,keepDims=!0,booleanMask=any(notEqual(input2,this.maskValue),axis,keepDims);return input2.mul(booleanMask.asType(input2.dtype))})}};Masking.className="Masking";serialization_exports.registerClass(Masking);var Embedding=class extends Layer{constructor(args){super(args);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",args.batchInputShape==null&&args.inputShape==null){let batchSize=null;args.batchSize!=null&&(batchSize=args.batchSize),args.inputLength==null?this.batchInputShape=[batchSize,null]:this.batchInputShape=[batchSize].concat(toList(args.inputLength))}this.inputDim=args.inputDim,assertPositiveInteger(this.inputDim,"inputDim"),this.outputDim=args.outputDim,assertPositiveInteger(this.outputDim,"outputDim"),this.embeddingsInitializer=getInitializer(args.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=getRegularizer(args.embeddingsRegularizer),this.activityRegularizer=getRegularizer(args.activityRegularizer),this.embeddingsConstraint=getConstraint(args.embeddingsConstraint),this.maskZero=args.maskZero,this.supportsMasking=args.maskZero,this.inputLength=args.inputLength}build(inputShape){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(inputShape){}computeMask(inputs,mask){return tidy(()=>this.maskZero?(inputs=getExactlyOneTensor(inputs),notEqual(inputs,zerosLike(inputs))):null)}computeOutputShape(inputShape){if(inputShape=getExactlyOneShape(inputShape),this.inputLength==null)return[...inputShape,this.outputDim];let inLens=toList(this.inputLength);if(inLens.length!==inputShape.length-1)throw new ValueError(`"inputLength" is ${this.inputLength}, but received input shape has shape ${inputShape}`);{let i=0;for(let k=0;k{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);return input2.dtype!=="int32"&&(input2=cast5(input2,"int32")),gather2(this.embeddings.read(),input2.as1D()).reshape(getExactlyOneShape(this.computeOutputShape(input2.shape)))})}getConfig(){let config3={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:serializeInitializer(this.embeddingsInitializer),embeddingsRegularizer:serializeRegularizer(this.embeddingsRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),embeddingsConstraint:serializeConstraint(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},baseConfig=super.getConfig();return Object.assign(config3,baseConfig),config3}};Embedding.className="Embedding";serialization_exports.registerClass(Embedding);var Merge=class extends Layer{constructor(args){super(args||{});this.supportsMasking=!0}mergeFunction(inputs){throw new NotImplementedError}computeElementwiseOpOutputShape(shape1,shape2){if(shape1==null||shape2==null)return null;if(shape1.length1)throw new ValueError(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(inputShape)}.`);let outputShape=inputShape[0]==null?null:inputShape[0].slice(1);for(let i=1;ishape.length);inputShape.indexOf(null)===-1&&unique4(allRanks).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(inputs,kwargs){return tidy(()=>{if(inputs=inputs,this.reshapeRequired){let reshapedInputs=[],inputDims=inputs.map(input2=>input2.rank);if(inputDims.indexOf(null)===-1){let maxNDim=max5(inputDims);for(let x of inputs){let xNDim=x.rank;for(let k=0;k1){let dims=range5(1,xNDim).concat([0]);reshapedInputs.push(transpose(x,dims)),transposed=!0}else reshapedInputs.push(x)}let y=this.mergeFunction(reshapedInputs),yNDim=y.rank;if(transposed){if(yNDim==null){let yShape=y.shape,yNDim2=yShape.length,batchSize=yShape[yNDim2-1],newShape=[batchSize].concat(yShape.slice(0,yShape.length-1));y=transpose(y.reshape([-1,batchSize]),[1,0]).reshape(newShape)}else if(yNDim>1){let dims=[yNDim-1].concat(range5(0,yNDim-1));y=transpose(y,dims)}}return y}}else return this.mergeFunction(inputs)})}computeOutputShape(inputShape){inputShape=inputShape;let outputShape;inputShape[0]==null?outputShape=null:outputShape=inputShape[0].slice(1);for(let i=1;i{if(mask==null)return null;if(!Array.isArray(mask))throw new ValueError("`mask` should be an Array");if(!Array.isArray(inputs))throw new ValueError("`inputs` should be an Array");if(mask.length!==inputs.length)throw new ValueError(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${inputs.length} vs ${mask.length})`);if(mask.every(m=>m==null))return null;mask=mask.map(m=>m==null?m:expandDims(m,0));let output=mask[0];for(let i=1;i{let output=inputs[0].clone();for(let i=1;i{let output=inputs[0].clone();for(let i=1;i{let output=inputs[0].clone();for(let i=1;i{let output=inputs[0];for(let i=1;i{let output=inputs[0];for(let i=1;i1)throw new ValueError("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(inputShape))}mergeFunction(inputs){return tidy(()=>concatenate(inputs,this.axis))}computeOutputShape(inputShape){if(!(Array.isArray(inputShape)&&Array.isArray(inputShape[0])))throw new ValueError("A `Concatenate` layer should be called on a list of inputs.");let inputShapes=inputShape,outputShape=inputShapes[0].slice(),axis=this.axis<0?outputShape.length+this.axis:this.axis;for(let shape of inputShapes.slice(1)){if(outputShape[axis]==null||shape[axis]==null){outputShape[axis]=null;break}outputShape[axis]+=shape[axis]}return outputShape}computeMask(inputs,mask){if(mask==null)return null;if(!Array.isArray(mask))throw new ValueError("`mask` should be an array for Concatenate");if(!Array.isArray(inputs))throw new ValueError("`inputs` should be an array for Concatenate");if(mask.length!==inputs.length)throw new ValueError(`Mismatch in the length of mask (${mask.length}) and the legnth of inputs (${inputs.length})`);return tidy(()=>{let allNullMasks=!0;if(mask.forEach(m=>{if(m!=null){allNullMasks=!1;return}}),allNullMasks)return null;let outputMasks=[];for(let i=0;i3||y.shape.length>3)throw new NotImplementedError("batchDot is not implemented for tensors of 4D or higher rank yet");if(util_exports.assert(x.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${x.shape.length}`),util_exports.assert(x.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${y.shape.length}`),typeof axes=="number"&&(axes=[axes,axes]),x.dtype==="complex64"||y.dtype==="complex64")throw new NotImplementedError("batchDot is not implemented for complex64-type Tensors yet.");let xNDim=x.shape.length,yNDim=y.shape.length;axes==null&&(axes=[xNDim-1,yNDim-2]);let axesArray=axes;return tidy(()=>{let diff;if(xNDim>yNDim){diff=xNDim-yNDim;let diffShape=[];for(let i=0;ixNDim){diff=yNDim-xNDim;let diffShape=[];for(let i=0;i0){let idx;xNDim>yNDim?idx=xNDim+yNDim-3:idx=xNDim-1;let squeezeAxes=[];for(let i=idx;i"A `Dot` layer should be called on a list of exactly 2 inputs.");let shape1=inputShape[0],shape2=inputShape[1];if(shape1.length>3||shape2.length>3)throw new NotImplementedError("Dot layer does not support tensors of 4D or higher rank yet.");let axes=this.interpretAxes(shape1,shape2);if(shape1[axes[0]]!==shape2[axes[1]])throw new ValueError(`Dimension incompatibility: ${shape1[axes[0]]} !== ${shape2[axes[1]]}`)}mergeFunction(inputs){if(inputs.length!==2)throw new ValueError(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${inputs.length} input(s).`);let x1=inputs[0],x2=inputs[1],axes;return Array.isArray(this.axes)?axes=this.axes.map((axis,i)=>interpretAxis(axis,inputs[i].shape.length)):axes=[interpretAxis(this.axes,x1.shape.length),interpretAxis(this.axes,x2.shape.length)],this.normalize&&(x1=l2Normalize(x1,axes[0]),x2=l2Normalize(x2,axes[1])),batchDot(x1,x2,axes)}interpretAxes(shape1,shape2){let axes;return Array.isArray(this.axes)?axes=this.axes:axes=[interpretAxis(this.axes,shape1.length),interpretAxis(this.axes,shape2.length)],axes}computeOutputShape(inputShape){util_exports.assert(Array.isArray(inputShape)&&inputShape.length===2&&Array.isArray(inputShape[0])&&Array.isArray(inputShape[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let shape1=inputShape[0].slice(),shape2=inputShape[1].slice();if(shape1.length>3||shape2.length>3)throw new NotImplementedError("Dot layer does not support tensors of 4D or higher rank yet.");let axes=this.interpretAxes(shape1,shape2);shape1.splice(axes[0],1),shape2.splice(axes[1],1),shape2.splice(0,1);let outputShape=shape1.concat(shape2);return outputShape.length===1&&outputShape.push(1),outputShape}computeMask(inputs,mask){return null}getConfig(){let config3={axes:this.axes,normalize:this.normalize},baseConfig=super.getConfig();return Object.assign(config3,baseConfig),config3}};Dot.className="Dot";serialization_exports.registerClass(Dot);var GaussianNoise=class extends Layer{constructor(args){super(args);this.supportsMasking=!0,this.stddev=args.stddev}computeOutputShape(inputShape){return inputShape}getConfig(){let baseConfig=super.getConfig(),config3={stddev:this.stddev};return Object.assign(config3,baseConfig),config3}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);return inTrainPhase(()=>randomNormal2(input2.shape,0,this.stddev).add(input2),()=>input2,kwargs.training||!1)})}};GaussianNoise.className="GaussianNoise";serialization_exports.registerClass(GaussianNoise);var GaussianDropout=class extends Layer{constructor(args){super(args);this.supportsMasking=!0,this.rate=args.rate}computeOutputShape(inputShape){return inputShape}getConfig(){let baseConfig=super.getConfig(),config3={rate:this.rate};return Object.assign(config3,baseConfig),config3}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);return this.rate>0&&this.rate<1?inTrainPhase(()=>{let stddev=Math.sqrt(this.rate/(1-this.rate));return input2.mul(randomNormal2(input2.shape,1,stddev))},()=>input2,kwargs.training||!1):input2})}};GaussianDropout.className="GaussianDropout";serialization_exports.registerClass(GaussianDropout);var AlphaDropout=class extends Layer{constructor(args){super(args);this.supportsMasking=!0,this.rate=args.rate,this.noiseShape=args.noiseShape}_getNoiseShape(inputs){return this.noiseShape||getExactlyOneTensor(inputs).shape}computeOutputShape(inputShape){return inputShape}getConfig(){let baseConfig=super.getConfig(),config3={rate:this.rate};return Object.assign(config3,baseConfig),config3}call(inputs,kwargs){return tidy(()=>{if(this.rate<1&&this.rate>0){let noiseShape=this._getNoiseShape(inputs);return inTrainPhase(()=>{let input2=getExactlyOneTensor(inputs),alpha=1.6732632423543772,scale2=1.0507009873554805,alphaP=-alpha*scale2,keptIdx=greaterEqual(randomUniform(noiseShape),this.rate);keptIdx=cast5(keptIdx,"float32");let a=((1-this.rate)*(1+this.rate*alphaP**2))**-.5,b=-a*alphaP*this.rate;return input2.mul(keptIdx).add(keptIdx.add(-1).mul(alphaP)).mul(a).add(b)},()=>getExactlyOneTensor(inputs),kwargs.training||!1)}return inputs})}};AlphaDropout.className="AlphaDropout";serialization_exports.registerClass(AlphaDropout);function batchNormalization(x,mean4,variance,beta,gamma,epsilon3=.001){let out;if(x.rank===2)out=batchNorm2d(x,mean4,variance,beta,gamma,epsilon3);else if(x.rank===3)out=batchNorm3d(x,mean4,variance,beta,gamma,epsilon3);else if(x.rank===4)out=batchNorm4d(x,mean4,variance,beta,gamma,epsilon3);else throw new NotImplementedError(`batchNormalization is not implemented for array of rank ${x.rank} yet`);return out}function regularNormalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon3=.001){return tidy(()=>{let meanAndVariance=moments(x,reductionAxes),mean4=meanAndVariance.mean,variance=meanAndVariance.variance;return[batchNormalization(x,mean4,variance,beta,gamma,epsilon3),mean4,variance]})}function broadcastNormalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon3=.001){return tidy(()=>{let meanAndVariance=moments(x,reductionAxes),mean4=meanAndVariance.mean,variance=meanAndVariance.variance,targetShape=[];for(let axis of range5(0,x.rank))reductionAxes.indexOf(axis)!==-1?targetShape.push(1):targetShape.push(x.shape[axis]);let broadcastMean=mean4.reshape(targetShape),broadcastVariance=variance.reshape(targetShape),broadcastGamma=gamma==null?null:gamma.reshape(targetShape),broadcastBeta=beta==null?null:beta.reshape(targetShape);return[batchNormalization(x,broadcastMean,broadcastVariance,broadcastBeta,broadcastGamma,epsilon3),mean4,variance]})}function normalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon3=.001){return util_exports.arraysEqual(reductionAxes.slice().sort(),range5(0,x.rank-1))?regularNormalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon3):broadcastNormalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon3)}var BatchNormalization=class extends Layer{constructor(args){args==null&&(args={}),super(args),this.supportsMasking=!0,this.axis=args.axis==null?-1:args.axis,this.momentum=args.momentum==null?.99:args.momentum,this.epsilon=args.epsilon==null?.001:args.epsilon,this.center=args.center==null?!0:args.center,this.scale=args.scale==null?!0:args.scale,this.betaInitializer=getInitializer(args.betaInitializer||"zeros"),this.gammaInitializer=getInitializer(args.gammaInitializer||"ones"),this.movingMeanInitializer=getInitializer(args.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=getInitializer(args.movingVarianceInitializer||"ones"),this.betaConstraint=getConstraint(args.betaConstraint),this.gammaConstraint=getConstraint(args.gammaConstraint),this.betaRegularizer=getRegularizer(args.betaRegularizer),this.gammaRegularizer=getRegularizer(args.gammaRegularizer)}build(inputShape){inputShape=getExactlyOneShape(inputShape);let axis=this.axis>=0?this.axis:this.axis+inputShape.length,dim=inputShape[axis];if(dim==null)throw new ValueError(`Axis ${axis} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(inputShape)}.`);this.inputSpec=[new InputSpec({ndim:inputShape.length,axes:{[axis]:dim}})];let shape=[dim];this.scale&&(this.gamma=this.addWeight("gamma",shape,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",shape,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",shape,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",shape,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(inputs,kwargs){return tidy(()=>{let training=kwargs.training==null?!1:kwargs.training,input2=getExactlyOneTensor(inputs),inputShape=input2.shape,ndim=inputShape.length,reductionAxes=range5(0,ndim),axis=this.axis>=0?this.axis:this.axis+ndim;reductionAxes.splice(axis,1);let broadcastShape=pyListRepeat(1,ndim);broadcastShape[axis]=inputShape[axis];let sortedReductionAxes=reductionAxes.slice();sortedReductionAxes.sort();let needsBroadcasting=!util_exports.arraysEqual(sortedReductionAxes,range5(0,ndim).slice(0,ndim-1)),normalizeInference=()=>{if(needsBroadcasting){let broadcastMovingMean=this.movingMean.read().reshape(broadcastShape),broadcastMovingVariance=this.movingVariance.read().reshape(broadcastShape),broadcastBeta=this.center?this.beta.read().reshape(broadcastShape):null,broadcastGamma=this.scale?this.gamma.read().reshape(broadcastShape):null;return batchNormalization(input2,broadcastMovingMean,broadcastMovingVariance,broadcastBeta,broadcastGamma,this.epsilon)}else return batchNormalization(input2,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!training)return normalizeInference();let[normedTraining,mean4,variance]=normalizeBatchInTraining(input2,this.gamma.read(),this.beta.read(),reductionAxes,this.epsilon),doMovingAverage=(variable2,value,momentum)=>{tidy(()=>{let decay=1-momentum,origValue=variable2.read(),updateDelta=origValue.sub(value).mul(decay);variable2.write(origValue.sub(updateDelta))})};return(()=>{doMovingAverage(this.movingMean,mean4,this.momentum),doMovingAverage(this.movingVariance,variance,this.momentum)})(),normedTraining})}getConfig(){let config3={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:serializeInitializer(this.betaInitializer),gammaInitializer:serializeInitializer(this.gammaInitializer),movingMeanInitializer:serializeInitializer(this.movingMeanInitializer),movingVarianceInitializer:serializeInitializer(this.movingVarianceInitializer),betaRegularizer:serializeRegularizer(this.betaRegularizer),gammaRegularizer:serializeRegularizer(this.gammaRegularizer),betaConstraint:serializeConstraint(this.betaConstraint),gammaConstraint:serializeConstraint(this.gammaConstraint)},baseConfig=super.getConfig();return Object.assign(config3,baseConfig),config3}};BatchNormalization.className="BatchNormalization";serialization_exports.registerClass(BatchNormalization);var LayerNormalization=class extends Layer{constructor(args){if(args==null&&(args={}),super(args),this.axis=args.axis==null?-1:args.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let axis of this.axis)if(!Number.isInteger(axis))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=args.epsilon==null?.001:args.epsilon,this.center=args.center==null?!0:args.center,this.scale=args.scale==null?!0:args.scale,this.betaInitializer=getInitializer(args.betaInitializer||"zeros"),this.gammaInitializer=getInitializer(args.gammaInitializer||"ones"),this.betaRegularizer=getRegularizer(args.betaRegularizer),this.gammaRegularizer=getRegularizer(args.gammaRegularizer),this.supportsMasking=!0}build(inputShape){inputShape=getExactlyOneShape(inputShape);let nDims=inputShape.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let i=0;i=nDims)throw new Error(`Invalid axis: ${axis}`);if(this.axis.length!==unique4(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let paramShape=this.axis.map(axis=>inputShape[axis]),trainable=!0;this.scale?this.gamma=this.addWeight("gamma",paramShape,"float32",this.gammaInitializer,this.gammaRegularizer,trainable):this.gamma=null,this.center?this.beta=this.addWeight("beta",paramShape,"float32",this.betaInitializer,this.betaRegularizer,trainable):this.beta=null,this.built=!0}call(inputs,kwargs){let input2=getExactlyOneTensor(inputs),inputShape=input2.shape,nDims=inputShape.length;return tidy(()=>{let keepDims=!0,{mean:mean4,variance}=moments(input2,this.axis,keepDims),broadcastShape=pyListRepeat(1,nDims);for(let dim of this.axis)broadcastShape[dim]=inputShape[dim];let broadcast=v=>v!=null&&v.shape.length!==nDims&&this.axis!==[nDims-1]?v.reshape(broadcastShape):v,scale2=broadcast(this.gamma.read()),offset=broadcast(this.beta.read()),momentsTiling=[],scaleOffsetTiling=[];for(let i=0;i{if(x.rank!==4)throw new ValueError(`temporalPadding expects input tensor to be 4-D, but received a ${x.rank}-D tensor.`);if(padding==null&&(padding=[[1,1],[1,1]]),padding.length!==2||padding[0].length!==2||padding[1].length!==2)throw new ValueError("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(dataFormat==null&&(dataFormat=imageDataFormat()),dataFormat!=="channelsLast"&&dataFormat!=="channelsFirst")throw new ValueError(`Unknown data format: ${dataFormat}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let pattern;return dataFormat==="channelsFirst"?pattern=[[0,0],[0,0],padding[0],padding[1]]:pattern=[[0,0],padding[0],padding[1],[0,0]],pad(x,pattern)})}var ZeroPadding2D=class extends Layer{constructor(args){if(args==null&&(args={}),super(args),this.dataFormat=args.dataFormat==null?imageDataFormat():args.dataFormat,args.padding==null)this.padding=[[1,1],[1,1]];else if(typeof args.padding=="number")this.padding=[[args.padding,args.padding],[args.padding,args.padding]];else{if(args.padding=args.padding,args.padding.length!==2)throw new ValueError(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${args.padding.length} array.`);let heightPadding,widthPadding;if(typeof args.padding[0]=="number")heightPadding=[args.padding[0],args.padding[0]],widthPadding=[args.padding[1],args.padding[1]];else{if(args.padding=args.padding,args.padding[0].length!==2)throw new ValueError(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${args.padding[0].length} array.`);if(heightPadding=args.padding[0],args.padding[1].length!==2)throw new ValueError(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${args.padding[1].length} array.`);widthPadding=args.padding[1]}this.padding=[heightPadding,widthPadding]}this.inputSpec=[new InputSpec({ndim:4})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let rows,cols;return this.dataFormat==="channelsFirst"?(inputShape[2]!=null&&inputShape[2]>=0?rows=inputShape[2]+this.padding[0][0]+this.padding[0][1]:rows=null,inputShape[3]!=null&&inputShape[3]>=0?cols=inputShape[3]+this.padding[1][0]+this.padding[1][1]:cols=null,[inputShape[0],inputShape[1],rows,cols]):(inputShape[1]!=null&&inputShape[1]>=0?rows=inputShape[1]+this.padding[0][0]+this.padding[0][1]:rows=null,inputShape[2]!=null&&inputShape[2]>=0?cols=inputShape[2]+this.padding[1][0]+this.padding[1][1]:cols=null,[inputShape[0],rows,cols,inputShape[3]])}call(inputs,kwargs){return tidy(()=>spatial2dPadding(getExactlyOneTensor(inputs),this.padding,this.dataFormat))}getConfig(){let config3={padding:this.padding,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config3,baseConfig),config3}};ZeroPadding2D.className="ZeroPadding2D";serialization_exports.registerClass(ZeroPadding2D);function pool2d(x,poolSize,strides,padding,dataFormat,poolMode){return tidy(()=>{checkDataFormat(dataFormat),checkPoolMode(poolMode),checkPaddingMode(padding),strides==null&&(strides=[1,1]),padding==null&&(padding="valid"),dataFormat==null&&(dataFormat=imageDataFormat()),poolMode==null&&(poolMode="max"),x=preprocessConv2DInput(x,dataFormat);let y,paddingString=padding==="same"?"same":"valid";return poolMode==="max"?y=maxPool(x,poolSize,strides,paddingString):y=avgPool(x,poolSize,strides,paddingString),dataFormat==="channelsFirst"&&(y=transpose(y,[0,3,1,2])),y})}function pool3d2(x,poolSize,strides,padding,dataFormat,poolMode){return tidy(()=>{checkDataFormat(dataFormat),checkPoolMode(poolMode),checkPaddingMode(padding),strides==null&&(strides=[1,1,1]),padding==null&&(padding="valid"),dataFormat==null&&(dataFormat=imageDataFormat()),poolMode==null&&(poolMode="max"),x=preprocessConv3DInput(x,dataFormat);let y,paddingString=padding==="same"?"same":"valid";return poolMode==="max"?y=maxPool3d(x,poolSize,strides,paddingString):y=avgPool3d(x,poolSize,strides,paddingString),dataFormat==="channelsFirst"&&(y=transpose(y,[0,4,1,2,3])),y})}var Pooling1D=class extends Layer{constructor(args){if(args.poolSize==null&&(args.poolSize=2),super(args),typeof args.poolSize=="number")this.poolSize=[args.poolSize];else if(Array.isArray(args.poolSize)&&args.poolSize.length===1&&typeof args.poolSize[0]=="number")this.poolSize=args.poolSize;else throw new ValueError(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(args.poolSize)}`);if(assertPositiveInteger(this.poolSize,"poolSize"),args.strides==null)this.strides=this.poolSize;else if(typeof args.strides=="number")this.strides=[args.strides];else if(Array.isArray(args.strides)&&args.strides.length===1&&typeof args.strides[0]=="number")this.strides=args.strides;else throw new ValueError(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(args.strides)}`);assertPositiveInteger(this.strides,"strides"),this.padding=args.padding==null?"valid":args.padding,checkPaddingMode(this.padding),this.inputSpec=[new InputSpec({ndim:3})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let length=convOutputLength(inputShape[1],this.poolSize[0],this.padding,this.strides[0]);return[inputShape[0],length,inputShape[2]]}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs),inputs=expandDims5(getExactlyOneTensor(inputs),2);let output=this.poolingFunction(getExactlyOneTensor(inputs),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return squeeze(output,[2])})}getConfig(){let config3={poolSize:this.poolSize,padding:this.padding,strides:this.strides},baseConfig=super.getConfig();return Object.assign(config3,baseConfig),config3}},MaxPooling1D=class extends Pooling1D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding),pool2d(inputs,poolSize,strides,padding,dataFormat,"max")}};MaxPooling1D.className="MaxPooling1D";serialization_exports.registerClass(MaxPooling1D);var AveragePooling1D=class extends Pooling1D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding),pool2d(inputs,poolSize,strides,padding,dataFormat,"avg")}};AveragePooling1D.className="AveragePooling1D";serialization_exports.registerClass(AveragePooling1D);var Pooling2D=class extends Layer{constructor(args){if(args.poolSize==null&&(args.poolSize=[2,2]),super(args),this.poolSize=Array.isArray(args.poolSize)?args.poolSize:[args.poolSize,args.poolSize],args.strides==null)this.strides=this.poolSize;else if(Array.isArray(args.strides)){if(args.strides.length!==2)throw new ValueError(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${args.strides.length}.`);this.strides=args.strides}else this.strides=[args.strides,args.strides];assertPositiveInteger(this.poolSize,"poolSize"),assertPositiveInteger(this.strides,"strides"),this.padding=args.padding==null?"valid":args.padding,this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),checkPaddingMode(this.padding),this.inputSpec=[new InputSpec({ndim:4})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let rows=this.dataFormat==="channelsFirst"?inputShape[2]:inputShape[1],cols=this.dataFormat==="channelsFirst"?inputShape[3]:inputShape[2];return rows=convOutputLength(rows,this.poolSize[0],this.padding,this.strides[0]),cols=convOutputLength(cols,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[inputShape[0],inputShape[1],rows,cols]:[inputShape[0],rows,cols,inputShape[3]]}call(inputs,kwargs){return tidy(()=>(this.invokeCallHook(inputs,kwargs),this.poolingFunction(getExactlyOneTensor(inputs),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let config3={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config3,baseConfig),config3}},MaxPooling2D=class extends Pooling2D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding),pool2d(inputs,poolSize,strides,padding,dataFormat,"max")}};MaxPooling2D.className="MaxPooling2D";serialization_exports.registerClass(MaxPooling2D);var AveragePooling2D=class extends Pooling2D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding),pool2d(inputs,poolSize,strides,padding,dataFormat,"avg")}};AveragePooling2D.className="AveragePooling2D";serialization_exports.registerClass(AveragePooling2D);var Pooling3D=class extends Layer{constructor(args){if(args.poolSize==null&&(args.poolSize=[2,2,2]),super(args),this.poolSize=Array.isArray(args.poolSize)?args.poolSize:[args.poolSize,args.poolSize,args.poolSize],args.strides==null)this.strides=this.poolSize;else if(Array.isArray(args.strides)){if(args.strides.length!==3)throw new ValueError(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${args.strides.length}.`);this.strides=args.strides}else this.strides=[args.strides,args.strides,args.strides];assertPositiveInteger(this.poolSize,"poolSize"),assertPositiveInteger(this.strides,"strides"),this.padding=args.padding==null?"valid":args.padding,this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),checkPaddingMode(this.padding),this.inputSpec=[new InputSpec({ndim:5})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let depths=this.dataFormat==="channelsFirst"?inputShape[2]:inputShape[1],rows=this.dataFormat==="channelsFirst"?inputShape[3]:inputShape[2],cols=this.dataFormat==="channelsFirst"?inputShape[4]:inputShape[3];return depths=convOutputLength(depths,this.poolSize[0],this.padding,this.strides[0]),rows=convOutputLength(rows,this.poolSize[1],this.padding,this.strides[1]),cols=convOutputLength(cols,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[inputShape[0],inputShape[1],depths,rows,cols]:[inputShape[0],depths,rows,cols,inputShape[4]]}call(inputs,kwargs){return tidy(()=>(this.invokeCallHook(inputs,kwargs),this.poolingFunction(getExactlyOneTensor(inputs),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let config3={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config3,baseConfig),config3}},MaxPooling3D=class extends Pooling3D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding),pool3d2(inputs,poolSize,strides,padding,dataFormat,"max")}};MaxPooling3D.className="MaxPooling3D";serialization_exports.registerClass(MaxPooling3D);var AveragePooling3D=class extends Pooling3D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding),pool3d2(inputs,poolSize,strides,padding,dataFormat,"avg")}};AveragePooling3D.className="AveragePooling3D";serialization_exports.registerClass(AveragePooling3D);var GlobalPooling1D=class extends Layer{constructor(args){super(args);this.inputSpec=[new InputSpec({ndim:3})]}computeOutputShape(inputShape){return[inputShape[0],inputShape[2]]}call(inputs,kwargs){throw new NotImplementedError}},GlobalAveragePooling1D=class extends GlobalPooling1D{constructor(args){super(args||{})}call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);return mean(input2,1)})}};GlobalAveragePooling1D.className="GlobalAveragePooling1D";serialization_exports.registerClass(GlobalAveragePooling1D);var GlobalMaxPooling1D=class extends GlobalPooling1D{constructor(args){super(args||{})}call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);return max(input2,1)})}};GlobalMaxPooling1D.className="GlobalMaxPooling1D";serialization_exports.registerClass(GlobalMaxPooling1D);var GlobalPooling2D=class extends Layer{constructor(args){super(args);this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),this.inputSpec=[new InputSpec({ndim:4})]}computeOutputShape(inputShape){return inputShape=inputShape,this.dataFormat==="channelsLast"?[inputShape[0],inputShape[3]]:[inputShape[0],inputShape[1]]}call(inputs,kwargs){throw new NotImplementedError}getConfig(){let config3={dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config3,baseConfig),config3}},GlobalAveragePooling2D=class extends GlobalPooling2D{call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);return this.dataFormat==="channelsLast"?mean(input2,[1,2]):mean(input2,[2,3])})}};GlobalAveragePooling2D.className="GlobalAveragePooling2D";serialization_exports.registerClass(GlobalAveragePooling2D);var GlobalMaxPooling2D=class extends GlobalPooling2D{call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);return this.dataFormat==="channelsLast"?max(input2,[1,2]):max(input2,[2,3])})}};GlobalMaxPooling2D.className="GlobalMaxPooling2D";serialization_exports.registerClass(GlobalMaxPooling2D);var Wrapper=class extends Layer{constructor(args){super(args);this.layer=args.layer}build(inputShape){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(value){this.layer!=null&&(this.layer.trainable=value)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(weights){this.layer.setWeights(weights)}getConfig(){let config3={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},baseConfig=super.getConfig();return Object.assign(config3,baseConfig),config3}setFastWeightInitDuringBuild(value){super.setFastWeightInitDuringBuild(value),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(value)}static fromConfig(cls,config3,customObjects={}){let layerConfig=config3.layer,layer=deserialize(layerConfig,customObjects);delete config3.layer;let newConfig={layer};return Object.assign(newConfig,config3),new cls(newConfig)}},TimeDistributed=class extends Wrapper{constructor(args){super(args);this.supportsMasking=!0}build(inputShape){if(inputShape=getExactlyOneShape(inputShape),inputShape.length<3)throw new ValueError(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(inputShape)}`);this.inputSpec=[{shape:inputShape}];let childInputShape=[inputShape[0]].concat(inputShape.slice(2));this.layer.built||(this.layer.build(childInputShape),this.layer.built=!0),super.build(inputShape)}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let childInputShape=[inputShape[0]].concat(inputShape.slice(2)),childOutputShape=this.layer.computeOutputShape(childInputShape),timesteps=inputShape[1];return[childOutputShape[0],timesteps].concat(childOutputShape.slice(1))}call(inputs,kwargs){return tidy(()=>(inputs=getExactlyOneTensor(inputs),rnn((inputs2,states)=>[getExactlyOneTensor(this.layer.call(inputs2,kwargs)),[]],inputs,[],!1,null,null,!1,!0)[1]))}};TimeDistributed.className="TimeDistributed";serialization_exports.registerClass(TimeDistributed);function checkBidirectionalMergeMode(value){checkStringTypeUnionValue(VALID_BIDIRECTIONAL_MERGE_MODES,"BidirectionalMergeMode",value)}var DEFAULT_BIDIRECTIONAL_MERGE_MODE="concat",Bidirectional=class extends Wrapper{constructor(args){super(args);let layerConfig=args.layer.getConfig(),forwDict={};forwDict.className=args.layer.getClassName(),forwDict.config=layerConfig,this.forwardLayer=deserialize(forwDict),layerConfig.goBackwards=layerConfig.goBackwards!==!0;let backDict={};if(backDict.className=args.layer.getClassName(),backDict.config=layerConfig,this.backwardLayer=deserialize(backDict),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=args.mergeMode===void 0?DEFAULT_BIDIRECTIONAL_MERGE_MODE:args.mergeMode,checkBidirectionalMergeMode(this.mergeMode),args.weights)throw new NotImplementedError("weights support is not implemented for Bidirectional layer yet.");this._stateful=args.layer.stateful,this.returnSequences=args.layer.returnSequences,this.returnState=args.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=args.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(value){this._trainable=value,this.forwardLayer!=null&&(this.forwardLayer.trainable=value),this.backwardLayer!=null&&(this.backwardLayer.trainable=value)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(weights){let numWeights=weights.length,numeightsOver2=Math.floor(numWeights/2);this.forwardLayer.setWeights(weights.slice(0,numeightsOver2)),this.backwardLayer.setWeights(weights.slice(numeightsOver2))}computeOutputShape(inputShape){let layerShapes=this.forwardLayer.computeOutputShape(inputShape);Array.isArray(layerShapes)&&Array.isArray(layerShapes[0])||(layerShapes=[layerShapes]),layerShapes=layerShapes;let outputShape,outputShapes,stateShape;return this.returnState&&(stateShape=layerShapes.slice(1)),outputShape=layerShapes[0],outputShape=outputShape,this.mergeMode==="concat"?(outputShape[outputShape.length-1]*=2,outputShapes=[outputShape]):this.mergeMode==null?outputShapes=[outputShape,outputShape.slice()]:outputShapes=[outputShape],this.returnState?this.mergeMode==null?outputShapes.concat(stateShape).concat(stateShape.slice()):[outputShape].concat(stateShape).concat(stateShape.slice()):singletonOrArray(outputShapes)}apply(inputs,kwargs){let initialState=kwargs==null?null:kwargs.initialState,constants=kwargs==null?null:kwargs.constants;kwargs==null&&(kwargs={});let standardized=standardizeArgs(inputs,initialState,constants,this.numConstants);if(inputs=standardized.inputs,initialState=standardized.initialState,constants=standardized.constants,Array.isArray(inputs)&&(initialState=inputs.slice(1),inputs=inputs[0]),(initialState==null||initialState.length===0)&&constants==null)return super.apply(inputs,kwargs);let additionalInputs=[],additionalSpecs=[];if(initialState!=null){let numStates=initialState.length;if(numStates%2>0)throw new ValueError("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");kwargs.initialState=initialState,additionalInputs.push(...initialState);let stateSpecs=initialState.map(state=>new InputSpec({shape:state.shape}));this.forwardLayer.stateSpec=stateSpecs.slice(0,numStates/2),this.backwardLayer.stateSpec=stateSpecs.slice(numStates/2),additionalSpecs.push(...stateSpecs)}if(constants!=null)throw new NotImplementedError("Support for constants in Bidirectional layers is not implemented yet.");let isSymbolicTensor=additionalInputs[0]instanceof SymbolicTensor;for(let tensor2 of additionalInputs)if(tensor2 instanceof SymbolicTensor!==isSymbolicTensor)throw new ValueError("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(isSymbolicTensor){let fullInput=[inputs].concat(additionalInputs),fullInputSpec=this.inputSpec.concat(additionalSpecs),originalInputSpec=this.inputSpec;this.inputSpec=fullInputSpec;let output=super.apply(fullInput,kwargs);return this.inputSpec=originalInputSpec,output}else return super.apply(inputs,kwargs)}call(inputs,kwargs){return tidy(()=>{let initialState=kwargs.initialState,y,yRev;if(initialState==null)y=this.forwardLayer.call(inputs,kwargs),yRev=this.backwardLayer.call(inputs,kwargs);else{let forwardState=initialState.slice(0,initialState.length/2),backwardState=initialState.slice(initialState.length/2);y=this.forwardLayer.call(inputs,Object.assign(kwargs,{initialState:forwardState})),yRev=this.backwardLayer.call(inputs,Object.assign(kwargs,{initialState:backwardState}))}let states;this.returnState&&(Array.isArray(y)&&(states=y.slice(1).concat(yRev.slice(1))),y=y[0],yRev=yRev[0]),this.returnSequences&&(yRev=reverse(yRev,1));let output;return this.mergeMode==="concat"?output=concatenate([y,yRev]):this.mergeMode==="sum"?output=add2(y,yRev):this.mergeMode==="ave"?output=mul(.5,add2(y,yRev)):this.mergeMode==="mul"?output=mul(y,yRev):this.mergeMode==null&&(output=[y,yRev]),this.returnState?this.mergeMode==null?output.concat(states):[output].concat(states):output})}resetStates(states){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(inputShape){nameScope(this.forwardLayer.name,()=>{this.forwardLayer.build(inputShape)}),nameScope(this.backwardLayer.name,()=>{this.backwardLayer.build(inputShape)}),this.built=!0}computeMask(inputs,mask){Array.isArray(mask)&&(mask=mask[0]);let outputMask;if(this.returnSequences?this.mergeMode==null?outputMask=[mask,mask]:outputMask=mask:this.mergeMode==null?outputMask=[null,null]:outputMask=null,this.returnState){let stateMask=this.forwardLayer.states.map(state=>null);return Array.isArray(outputMask)?outputMask.concat(stateMask).concat(stateMask):[outputMask].concat(stateMask).concat(stateMask)}else return outputMask}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)}setFastWeightInitDuringBuild(value){super.setFastWeightInitDuringBuild(value),this.forwardLayer!=null&&this.forwardLayer.setFastWeightInitDuringBuild(value),this.backwardLayer!=null&&this.backwardLayer.setFastWeightInitDuringBuild(value)}getConfig(){let config3={mergeMode:this.mergeMode},baseConfig=super.getConfig();return Object.assign(config3,baseConfig),config3}static fromConfig(cls,config3){let rnnLayer=deserialize(config3.layer);if(delete config3.layer,config3.numConstants!=null)throw new NotImplementedError("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");let newConfig=config3;return newConfig.layer=rnnLayer,new cls(newConfig)}};Bidirectional.className="Bidirectional";serialization_exports.registerClass(Bidirectional);function inputLayer(args){return new InputLayer(args)}function elu5(args){return new ELU4(args)}function reLU(args){return new ReLU(args)}function leakyReLU(args){return new LeakyReLU(args)}function prelu5(args){return new PReLU(args)}function softmax5(args){return new Softmax3(args)}function thresholdedReLU(args){return new ThresholdedReLU(args)}function conv1d2(args){return new Conv1D(args)}function conv2d5(args){return new Conv2D2(args)}function conv2dTranspose2(args){return new Conv2DTranspose(args)}function conv3d2(args){return new Conv3D2(args)}function separableConv2d2(args){return new SeparableConv2D(args)}function cropping2D(args){return new Cropping2D(args)}function upSampling2d(args){return new UpSampling2D(args)}function depthwiseConv2d5(args){return new DepthwiseConv2D(args)}function activation(args){return new Activation2(args)}function dense(args){return new Dense(args)}function dropout3(args){return new Dropout(args)}function spatialDropout1d(args){return new SpatialDropout1D(args)}function flatten3(args){return new Flatten(args)}function repeatVector(args){return new RepeatVector(args)}function reshape5(args){return new Reshape2(args)}function permute(args){return new Permute(args)}function embedding(args){return new Embedding(args)}function add4(args){return new Add2(args)}function average(args){return new Average(args)}function concatenate2(args){return new Concatenate(args)}function maximum4(args){return new Maximum2(args)}function minimum4(args){return new Minimum2(args)}function multiply3(args){return new Multiply2(args)}function dot3(args){return new Dot(args)}function batchNormalization2(args){return new BatchNormalization(args)}function layerNormalization(args){return new LayerNormalization(args)}function zeroPadding2d(args){return new ZeroPadding2D(args)}function averagePooling1d(args){return new AveragePooling1D(args)}function avgPool1d(args){return averagePooling1d(args)}function avgPooling1d(args){return averagePooling1d(args)}function averagePooling2d(args){return new AveragePooling2D(args)}function avgPool2d(args){return averagePooling2d(args)}function avgPooling2d(args){return averagePooling2d(args)}function averagePooling3d(args){return new AveragePooling3D(args)}function avgPool3d2(args){return averagePooling3d(args)}function avgPooling3d(args){return averagePooling3d(args)}function globalAveragePooling1d(args){return new GlobalAveragePooling1D(args)}function globalAveragePooling2d(args){return new GlobalAveragePooling2D(args)}function globalMaxPooling1d(args){return new GlobalMaxPooling1D(args)}function globalMaxPooling2d(args){return new GlobalMaxPooling2D(args)}function maxPooling1d(args){return new MaxPooling1D(args)}function maxPooling2d(args){return new MaxPooling2D(args)}function maxPooling3d(args){return new MaxPooling3D(args)}function gru(args){return new GRU(args)}function gruCell(args){return new GRUCell(args)}function lstm(args){return new LSTM(args)}function lstmCell(args){return new LSTMCell(args)}function simpleRNN(args){return new SimpleRNN(args)}function simpleRNNCell(args){return new SimpleRNNCell(args)}function convLstm2d(args){return new ConvLSTM2D(args)}function convLstm2dCell(args){return new ConvLSTM2DCell(args)}function rnn2(args){return new RNN(args)}function stackedRNNCells(args){return new StackedRNNCells(args)}function bidirectional(args){return new Bidirectional(args)}function timeDistributed(args){return new TimeDistributed(args)}var globalMaxPool1d=globalMaxPooling1d,globalMaxPool2d=globalMaxPooling2d,maxPool1d=maxPooling1d,maxPool2d=maxPooling2d;function gaussianNoise(args){return new GaussianNoise(args)}function gaussianDropout(args){return new GaussianDropout(args)}function alphaDropout(args){return new AlphaDropout(args)}function masking(args){return new Masking(args)}var exports_metrics_exports={};__export2(exports_metrics_exports,{MAPE:()=>MAPE2,MSE:()=>MSE2,binaryAccuracy:()=>binaryAccuracy2,binaryCrossentropy:()=>binaryCrossentropy3,categoricalAccuracy:()=>categoricalAccuracy2,categoricalCrossentropy:()=>categoricalCrossentropy3,cosineProximity:()=>cosineProximity2,mape:()=>mape2,meanAbsoluteError:()=>meanAbsoluteError2,meanAbsolutePercentageError:()=>meanAbsolutePercentageError2,meanSquaredError:()=>meanSquaredError3,mse:()=>mse2,precision:()=>precision2,recall:()=>recall2,sparseCategoricalAccuracy:()=>sparseCategoricalAccuracy2});function binaryAccuracy2(yTrue,yPred){return binaryAccuracy(yTrue,yPred)}function binaryCrossentropy3(yTrue,yPred){return binaryCrossentropy2(yTrue,yPred)}function sparseCategoricalAccuracy2(yTrue,yPred){return sparseCategoricalAccuracy(yTrue,yPred)}function categoricalAccuracy2(yTrue,yPred){return categoricalAccuracy(yTrue,yPred)}function categoricalCrossentropy3(yTrue,yPred){return categoricalCrossentropy2(yTrue,yPred)}function precision2(yTrue,yPred){return precision(yTrue,yPred)}function recall2(yTrue,yPred){return recall(yTrue,yPred)}function cosineProximity2(yTrue,yPred){return cosineProximity(yTrue,yPred)}function meanAbsoluteError2(yTrue,yPred){return meanAbsoluteError(yTrue,yPred)}function meanAbsolutePercentageError2(yTrue,yPred){return meanAbsolutePercentageError(yTrue,yPred)}function MAPE2(yTrue,yPred){return meanAbsolutePercentageError(yTrue,yPred)}function mape2(yTrue,yPred){return meanAbsolutePercentageError(yTrue,yPred)}function meanSquaredError3(yTrue,yPred){return meanSquaredError2(yTrue,yPred)}function MSE2(yTrue,yPred){return meanSquaredError2(yTrue,yPred)}function mse2(yTrue,yPred){return meanSquaredError2(yTrue,yPred)}var exports_models_exports={};__export2(exports_models_exports,{modelFromJSON:()=>modelFromJSON});var exports_regularizers_exports={};__export2(exports_regularizers_exports,{l1:()=>l12,l1l2:()=>l1l2,l2:()=>l22});function l1l2(config3){return new L1L2(config3)}function l12(config3){return l1(config3)}function l22(config3){return l2(config3)}var Callback=class extends BaseCallback{constructor(){super(...arguments);this.model=null}setModel(model2){if(!(model2 instanceof LayersModel))throw new Error("model must be a LayersModel, not some other Container");this.model=model2}};function less4(currVal,prevVal){return currValprevVal}var EarlyStopping=class extends Callback{constructor(args){super();if(args==null&&(args={}),args.restoreBestWeights)throw new NotImplementedError("restoreBestWeights = True is not implemented in EarlyStopping yet.");this.monitor=args.monitor||"val_loss",this.minDelta=Math.abs(args.minDelta||0),this.patience=args.patience||0,this.verbose=args.verbose||0,this.mode=args.mode||"auto",this.baseline=args.baseline,["auto","min","max"].indexOf(this.mode)===-1&&(console.warn(`EarlyStopping mode '${this.mode}' is invalid. Falling back to mode 'auto'.`),this.mode="auto"),this.mode==="min"?this.monitorFunc=less4:this.mode==="max"?this.monitorFunc=greater4:this.monitor.indexOf("acc")!==-1?this.monitorFunc=greater4:this.monitorFunc=less4,this.monitorFunc===less4&&(this.minDelta*=-1)}async onTrainBegin(logs){this.wait=0,this.stoppedEpoch=0,this.baseline!=null?this.best=this.baseline:this.best=this.monitorFunc===less4?Infinity:-Infinity}async onEpochEnd(epoch,logs){await resolveScalarsInLogs(logs);let current=this.getMonitorValue(logs);current!=null&&(this.monitorFunc(current-this.minDelta,this.best)?(this.best=current,this.wait=0):(this.wait++,this.wait>=this.patience&&(this.stoppedEpoch=epoch,this.model.stopTraining=!0)))}async onTrainEnd(logs){this.stoppedEpoch>0&&this.verbose&&console.log(`Epoch ${this.stoppedEpoch}: early stopping.`)}getMonitorValue(logs){logs==null&&(logs={});let monitorValue=logs[this.monitor];return monitorValue==null&&console.warn(`Metric for EarlyStopping ${this.monitor} is not available. Available metrics are: ${Object.keys(logs)}`),monitorValue}};function earlyStopping(args){return new EarlyStopping(args)}var callbacks={earlyStopping},DataType;(function(DataType2){DataType2[DataType2.DT_INVALID=0]="DT_INVALID",DataType2[DataType2.DT_FLOAT=1]="DT_FLOAT",DataType2[DataType2.DT_DOUBLE=2]="DT_DOUBLE",DataType2[DataType2.DT_INT32=3]="DT_INT32",DataType2[DataType2.DT_UINT8=4]="DT_UINT8",DataType2[DataType2.DT_INT16=5]="DT_INT16",DataType2[DataType2.DT_INT8=6]="DT_INT8",DataType2[DataType2.DT_STRING=7]="DT_STRING",DataType2[DataType2.DT_COMPLEX64=8]="DT_COMPLEX64",DataType2[DataType2.DT_INT64=9]="DT_INT64",DataType2[DataType2.DT_BOOL=10]="DT_BOOL",DataType2[DataType2.DT_QINT8=11]="DT_QINT8",DataType2[DataType2.DT_QUINT8=12]="DT_QUINT8",DataType2[DataType2.DT_QINT32=13]="DT_QINT32",DataType2[DataType2.DT_BFLOAT16=14]="DT_BFLOAT16",DataType2[DataType2.DT_FLOAT_REF=101]="DT_FLOAT_REF",DataType2[DataType2.DT_DOUBLE_REF=102]="DT_DOUBLE_REF",DataType2[DataType2.DT_INT32_REF=103]="DT_INT32_REF",DataType2[DataType2.DT_UINT8_REF=104]="DT_UINT8_REF",DataType2[DataType2.DT_INT16_REF=105]="DT_INT16_REF",DataType2[DataType2.DT_INT8_REF=106]="DT_INT8_REF",DataType2[DataType2.DT_STRING_REF=107]="DT_STRING_REF",DataType2[DataType2.DT_COMPLEX64_REF=108]="DT_COMPLEX64_REF",DataType2[DataType2.DT_INT64_REF=109]="DT_INT64_REF",DataType2[DataType2.DT_BOOL_REF=110]="DT_BOOL_REF",DataType2[DataType2.DT_QINT8_REF=111]="DT_QINT8_REF",DataType2[DataType2.DT_QUINT8_REF=112]="DT_QUINT8_REF",DataType2[DataType2.DT_QINT32_REF=113]="DT_QINT32_REF",DataType2[DataType2.DT_BFLOAT16_REF=114]="DT_BFLOAT16_REF"})(DataType||(DataType={}));var SaverDef;(function(SaverDef2){let CheckpointFormatVersion;(function(CheckpointFormatVersion2){CheckpointFormatVersion2[CheckpointFormatVersion2.LEGACY=0]="LEGACY",CheckpointFormatVersion2[CheckpointFormatVersion2.V1=1]="V1",CheckpointFormatVersion2[CheckpointFormatVersion2.V2=2]="V2"})(CheckpointFormatVersion=SaverDef2.CheckpointFormatVersion||(SaverDef2.CheckpointFormatVersion={}))})(SaverDef||(SaverDef={}));var CUSTOM_OPS={};function registerOp(name,opFunc){let opMapper={tfOpName:name,category:"custom",inputs:[],attrs:[],customExecutor:opFunc};CUSTOM_OPS[name]=opMapper}function getRegisteredOp(name){return CUSTOM_OPS[name]}function deregisterOp(name){delete CUSTOM_OPS[name]}function getParamValue(paramName,node,tensorMap,context,resourceManager){let inputParam=node.inputParams[paramName];if(inputParam&&inputParam.inputIndexStart!==void 0){let start=inputParam.inputIndexStart,end=inputParam.inputIndexEnd===0?void 0:inputParam.inputIndexEnd===void 0?start+1:inputParam.inputIndexEnd;if(inputParam.type==="tensor")return getTensor(node.inputNames[inputParam.inputIndexStart],tensorMap,context,resourceManager);if(inputParam.type==="tensors")return node.inputNames.slice(start,end).map(name=>getTensor(name,tensorMap,context,resourceManager));let tensor2=getTensor(node.inputNames.slice(start)[0],tensorMap,context,resourceManager),data2=tensor2.dataSync();return inputParam.type==="number"?data2[0]:util_exports.toNestedArray(tensor2.shape,data2)}let attrParam=node.attrParams[paramName];return attrParam&&attrParam.value}function getTensor(name,tensorsMap,context,resourceManager){let[nodeName,index]=parseNodeName(name);if(resourceManager!=null){let tensor2=resourceManager.getHashTableHandleByName(nodeName);if(tensor2!=null)return tensor2}let contextId=context.currentContextIds.find(contextId2=>!!tensorsMap[getNodeNameWithContextId(nodeName,contextId2)]);return contextId!==void 0?tensorsMap[getNodeNameWithContextId(nodeName,contextId)][index]:void 0}function getTensorsForCurrentContenxt(name,tensorsMap,context){return tensorsMap[getNodeNameWithContextId(name,context.currentContextId)]}function getNodeNameAndIndex(inputName,context){let[nodeName,index]=parseNodeName(inputName);return[getNodeNameWithContextId(nodeName,context&&context.currentContextId),index]}function getNodeNameWithContextId(name,contextId){return contextId?`${name}-${contextId}`:name}function parseNodeName(name){let parts=name.split(":");return parts.length===1?[name,0]:[parts[0],Number(parts[parts.length-1])]}function getPadding(node,tensorMap,context){let pad3=getParamValue("pad",node,tensorMap,context);if(pad3==="explicit"){pad3=getParamValue("explicitPaddings",node,tensorMap,context);let explicitPadding=[[0,0],[0,0],[0,0],[0,0]];for(let i=0;i<4;i++)explicitPadding[i][0]=pad3[i*2],explicitPadding[i][1]=pad3[i*2+1];return explicitPadding}return pad3}function cloneTensor(tensor2){return tensor2.kept?tensor2:clone(tensor2)}var arithmetic_exports={};__export2(arithmetic_exports,{json:()=>json});var json=[{tfOpName:"Add",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"AddV2",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"AddN",category:"arithmetic",inputs:[{start:0,end:0,name:"tensors",type:"tensors"}]},{tfOpName:"BiasAdd",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"Sub",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"RealDiv",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Div",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"DivNoNan",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"FloorDiv",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Mul",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Maximum",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Minimum",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Pow",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"SquaredDifference",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Mod",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"FloorMod",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]}],basic_math_exports={};__export2(basic_math_exports,{json:()=>json2});var json2=[{tfOpName:"Abs",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Acos",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Asin",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Atan",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Atan2",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"y",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Ceil",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"ClipByValue",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"clipValueMin",type:"number"},{start:2,name:"clipValueMax",type:"number"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Complex",category:"basic_math",inputs:[{start:0,name:"real",type:"tensor"},{start:1,name:"imag",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"ComplexAbs",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Cos",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Cosh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Elu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Exp",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Floor",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Log",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Imag",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"Tout",name:"outputType",type:"dtype",notSupported:!0}]},{tfOpName:"Neg",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Real",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"Tout",name:"outputType",type:"dtype",notSupported:!0}]},{tfOpName:"Prelu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"alpha",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Relu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Relu6",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Selu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sigmoid",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sin",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sinh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sqrt",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Rsqrt",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Square",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Tan",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Tanh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sign",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Round",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Expm1",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Log1p",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Reciprocal",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Softplus",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Asinh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Acosh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Atanh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Erf",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Prod",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axes",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool",notSupported:!0},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LeakyRelu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"alpha",name:"alpha",type:"number",defaultValue:.2},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]}],control_exports={};__export2(control_exports,{json:()=>json3});var 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json11=[{tfOpName:"Equal",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"NotEqual",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Greater",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"GreaterEqual",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Less",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LessEqual",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LogicalAnd",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LogicalNot",category:"logical",inputs:[{start:0,name:"a",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LogicalOr",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Select",category:"logical",inputs:[{start:0,name:"condition",type:"tensor"},{start:1,name:"a",type:"tensor"},{start:2,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"SelectV2",category:"logical",inputs:[{start:0,name:"condition",type:"tensor"},{start:1,name:"a",type:"tensor"},{start:2,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]}],matrices_exports={};__export2(matrices_exports,{json:()=>json12});var json12=[{tfOpName:"_FusedMatMul",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"},{start:2,end:0,name:"args",type:"tensors"}],attrs:[{tfName:"num_args",name:"numArgs",type:"number"},{tfName:"fused_ops",name:"fusedOps",type:"string[]",defaultValue:[]},{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:1e-4},{tfName:"transpose_a",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"transpose_b",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"MatMul",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"transpose_a",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"transpose_b",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"BatchMatMul",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"adj_x",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"adj_y",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"BatchMatMulV2",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"adj_x",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"adj_y",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Transpose",category:"matrices",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"perm",type:"number[]"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]}],normalization_exports={};__export2(normalization_exports,{json:()=>json13});var json13=[{tfOpName:"FusedBatchNorm",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"scale",type:"tensor"},{start:2,name:"offset",type:"tensor"},{start:3,name:"mean",type:"tensor"},{start:4,name:"variance",type:"tensor"}],attrs:[{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:.001},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"FusedBatchNormV2",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"scale",type:"tensor"},{start:2,name:"offset",type:"tensor"},{start:3,name:"mean",type:"tensor"},{start:4,name:"variance",type:"tensor"}],attrs:[{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:.001},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"FusedBatchNormV3",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"scale",type:"tensor"},{start:2,name:"offset",type:"tensor"},{start:3,name:"mean",type:"tensor"},{start:4,name:"variance",type:"tensor"}],attrs:[{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:.001},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"LRN",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"depth_radius",name:"radius",type:"number",defaultValue:5},{tfName:"bias",name:"bias",type:"number",defaultValue:1},{tfName:"alpha",name:"alpha",type:"number",defaultValue:1},{tfName:"beta",name:"beta",type:"number",defaultValue:.5}]},{tfOpName:"Softmax",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"LogSoftmax",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"SparseToDense",category:"normalization",inputs:[{start:0,name:"sparseIndices",type:"tensor"},{start:1,name:"outputShape",type:"number[]"},{start:2,name:"sparseValues",type:"tensor"},{start:3,name:"defaultValue",type:"tensor"}],attrs:[{tfName:"validate_indices",name:"validateIndices",type:"bool",defaultValue:!0,notSupported:!0}]}],reduction_exports={};__export2(reduction_exports,{json:()=>json14});var json14=[{tfOpName:"Bincount",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"size",type:"number"},{start:2,name:"weights",type:"tensor"}]},{tfOpName:"DenseBincount",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"size",type:"number"},{start:2,name:"weights",type:"tensor"}],attrs:[{tfName:"binary_output",name:"binaryOutput",type:"bool"}]},{tfOpName:"Max",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Mean",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Min",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Sum",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"All",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Any",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"ArgMax",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]},{tfOpName:"ArgMin",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]},{tfOpName:"Prod",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Cumsum",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}],attrs:[{tfName:"exclusive",name:"exclusive",type:"bool"},{tfName:"reverse",name:"reverse",type:"bool"}]}],slice_join_exports={};__export2(slice_join_exports,{json:()=>json15});var json15=[{tfOpName:"ConcatV2",category:"slice_join",inputs:[{start:0,end:-1,name:"tensors",type:"tensors"},{start:-1,name:"axis",type:"number"}],attrs:[{tfName:"N",name:"n",type:"number",defaultValue:2}]},{tfOpName:"Concat",category:"slice_join",inputs:[{start:1,end:0,name:"tensors",type:"tensors"},{start:0,name:"axis",type:"number"}],attrs:[{tfName:"N",name:"n",type:"number",defaultValue:2}]},{tfOpName:"GatherV2",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"indices",type:"tensor"},{start:2,name:"axis",type:"number",defaultValue:0}],attrs:[{tfName:"batch_dims",name:"batchDims",type:"number",defaultValue:0}]},{tfOpName:"Gather",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"indices",type:"tensor"}],attrs:[{tfName:"validate_indices",name:"validateIndices",type:"bool",notSupported:!0}]},{tfOpName:"Reverse",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"dims",type:"bool[]"}]},{tfOpName:"ReverseV2",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}]},{tfOpName:"Slice",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"begin",type:"number[]"},{start:2,name:"size",type:"number[]"}]},{tfOpName:"StridedSlice",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"begin",type:"number[]"},{start:2,name:"end",type:"number[]"},{start:3,name:"strides",type:"number[]"}],attrs:[{tfName:"begin_mask",name:"beginMask",type:"number",defaultValue:0},{tfName:"end_mask",name:"endMask",type:"number",defaultValue:0},{tfName:"new_axis_mask",name:"newAxisMask",type:"number",defaultValue:0},{tfName:"ellipsis_mask",name:"ellipsisMask",type:"number",defaultValue:0},{tfName:"shrink_axis_mask",name:"shrinkAxisMask",type:"number",defaultValue:0}]},{tfOpName:"Pack",category:"slice_join",inputs:[{start:0,end:0,name:"tensors",type:"tensors"}],attrs:[{tfName:"axis",name:"axis",type:"number",defaultValue:0}]},{tfOpName:"Unpack",category:"slice_join",inputs:[{start:0,name:"tensor",type:"tensor"}],attrs:[{tfName:"axis",name:"axis",type:"number",defaultValue:0},{tfName:"num",name:"num",type:"number",defaultValue:0,notSupported:!0}]},{tfOpName:"Tile",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"reps",type:"number[]"}]},{tfOpName:"Split",category:"slice_join",inputs:[{start:0,name:"axis",type:"number",defaultValue:0},{start:1,name:"x",type:"tensor"}],attrs:[{tfName:"num_split",name:"numOrSizeSplits",type:"number",defaultValue:1}]},{tfOpName:"SplitV",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"numOrSizeSplits",type:"number[]"},{start:2,name:"axis",type:"number",defaultValue:0}]},{tfOpName:"ScatterNd",category:"slice_join",inputs:[{start:0,name:"indices",type:"tensor"},{start:1,name:"values",type:"tensor"},{start:2,name:"shape",type:"number[]"}]},{tfOpName:"GatherNd",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"indices",type:"tensor"}]},{tfOpName:"SparseToDense",category:"slice_join",inputs:[{start:0,name:"sparseIndices",type:"tensor"},{start:1,name:"outputShape",type:"number[]"},{start:2,name:"sparseValues",type:"tensor"},{start:3,name:"defaultValue",type:"tensor"}],attrs:[{tfName:"validate_indices",name:"validateIndices",type:"bool",defaultValue:!1,notSupported:!0}]}],spectral_exports={};__export2(spectral_exports,{json:()=>json16});var json16=[{tfOpName:"FFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"IFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"RFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"fft_length",type:"number",notSupported:!0}]},{tfOpName:"IRFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"fft_length",type:"number",notSupported:!0}]}],transformation_exports={};__export2(transformation_exports,{json:()=>json17});var json17=[{tfOpName:"Cast",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"SrcT",name:"sdtype",type:"dtype",notSupported:!0},{tfName:"DstT",name:"dtype",type:"dtype"}]},{tfOpName:"ExpandDims",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]},{tfOpName:"MirrorPad",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"}],attrs:[{tfName:"mode",name:"mode",type:"string"}]},{tfOpName:"Pad",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"}],attrs:[{tfName:"constant_value",name:"constantValue",type:"number",defaultValue:0}]},{tfOpName:"PadV2",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"},{start:2,name:"constantValue",type:"number",defaultValue:0}]},{tfOpName:"Reshape",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"shape",type:"number[]"}]},{tfOpName:"Squeeze",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"axis",tfDeprecatedName:"squeeze_dims",name:"axis",type:"number[]"}]},{tfOpName:"SpaceToBatchND",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"blockShape",type:"number[]"},{start:2,name:"paddings",type:"number[]"}]},{tfOpName:"BatchToSpaceND",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"blockShape",type:"number[]"},{start:2,name:"crops",type:"number[]"}]},{tfOpName:"DepthToSpace",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"block_size",name:"blockSize",type:"number"},{tfName:"data_format",name:"dataFormat",type:"string"}]},{tfOpName:"BroadcastTo",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"shape",type:"number[]"}],attrs:[]}],OperationMapper=class{static get Instance(){return this._instance||(this._instance=new this)}constructor(){let ops=[arithmetic_exports,basic_math_exports,control_exports,convolution_exports,creation_exports,dynamic_exports,evaluation_exports,logical_exports,image_exports,graph_exports,matrices_exports,normalization_exports,reduction_exports,slice_join_exports,spectral_exports,transformation_exports,hash_table_exports],mappersJson=[].concat(...ops.map(op2=>op2.json));this.opMappers=mappersJson.reduce((map,mapper)=>(map[mapper.tfOpName]=mapper,map),{})}transformGraph(graph2,signature={}){let tfNodes=graph2.node,placeholders=[],weights=[],initNodes=[],nodes=tfNodes.reduce((map,node)=>(map[node.name]=this.mapNode(node),node.op.startsWith("Placeholder")?placeholders.push(map[node.name]):node.op==="Const"?weights.push(map[node.name]):(node.input==null||node.input.length===0)&&initNodes.push(map[node.name]),map),{}),inputs=[],outputs=[],inputNodeNameToKey={},outputNodeNameToKey={};signature!=null&&(inputNodeNameToKey=this.mapSignatureEntries(signature.inputs),outputNodeNameToKey=this.mapSignatureEntries(signature.outputs));let allNodes=Object.keys(nodes);allNodes.forEach(key=>{let node=nodes[key];node.inputNames.forEach(name=>{let[nodeName]=getNodeNameAndIndex(name);node.inputs.push(nodes[nodeName]),nodes[nodeName].children.push(node)})}),Object.keys(outputNodeNameToKey).length===0?allNodes.forEach(key=>{let node=nodes[key];node.children.length===0&&outputs.push(node)}):Object.keys(outputNodeNameToKey).forEach(name=>{let[nodeName]=getNodeNameAndIndex(name),node=nodes[nodeName];node!=null&&(node.signatureKey=outputNodeNameToKey[name],outputs.push(node))}),Object.keys(inputNodeNameToKey).length>0?Object.keys(inputNodeNameToKey).forEach(name=>{let[nodeName]=getNodeNameAndIndex(name),node=nodes[nodeName];node&&(node.signatureKey=inputNodeNameToKey[name],inputs.push(node))}):inputs=placeholders;let functions={};graph2.library!=null&&graph2.library.function!=null&&(functions=graph2.library.function.reduce((functions2,func2)=>(functions2[func2.signature.name]=this.mapFunction(func2),functions2),{}));let result={nodes,inputs,outputs,weights,placeholders,signature,functions};return initNodes.length>0&&(result.initNodes=initNodes),result}mapSignatureEntries(entries){return Object.keys(entries||{}).reduce((prev,curr)=>(prev[entries[curr].name]=curr,prev),{})}mapNode(node){let mapper=getRegisteredOp(node.op)||this.opMappers[node.op]||{};node.attr==null&&(node.attr={});let newNode={name:node.name,op:node.op,category:mapper.category,inputNames:(node.input||[]).map(input2=>input2.startsWith("^")?input2.substr(1):input2),inputs:[],children:[],inputParams:{},attrParams:{},rawAttrs:node.attr};return mapper.inputs!=null&&(newNode.inputParams=mapper.inputs.reduce((map,param)=>(map[param.name]={type:param.type,inputIndexStart:param.start,inputIndexEnd:param.end},map),{})),mapper.attrs!=null&&(newNode.attrParams=mapper.attrs.reduce((map,param)=>{let type=param.type,value;switch(param.type){case"string":value=getStringParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getStringParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"string[]":value=getStringArrayParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getStringArrayParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"number":value=getNumberParam(node.attr,param.tfName,param.defaultValue||0),value===void 0&&!!param.tfDeprecatedName&&(value=getNumberParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"number[]":value=getNumericArrayParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getNumericArrayParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"bool":value=getBoolParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getBoolParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"bool[]":value=getBoolArrayParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getBoolArrayParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"shape":value=getTensorShapeParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getTensorShapeParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"shape[]":value=getTensorShapeArrayParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getTensorShapeArrayParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"dtype":value=getDtypeParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getDtypeParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"dtype[]":value=getDtypeArrayParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getDtypeArrayParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"func":value=getFuncParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getFuncParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"tensor":case"tensors":break;default:throw new Error(`Unsupported param type: ${param.type} for op: ${node.op}`)}return map[param.name]={value,type},map},{})),newNode}mapFunction(functionDef){let tfNodes=functionDef.nodeDef,placeholders=[],weights=[],nodes={};tfNodes!=null&&(nodes=tfNodes.reduce((map,node)=>(map[node.name]=this.mapNode(node),node.op==="Const"&&weights.push(map[node.name]),map),{}));let inputs=[],outputs=[];functionDef.signature.inputArg.forEach(arg=>{let[nodeName]=getNodeNameAndIndex(arg.name),node={name:nodeName,op:"Placeholder",inputs:[],inputNames:[],category:"graph",inputParams:{},attrParams:{dtype:{value:parseDtypeParam(arg.type),type:"dtype"}},children:[]};node.signatureKey=arg.name,inputs.push(node),nodes[nodeName]=node}),Object.keys(nodes).forEach(key=>{let node=nodes[key];node.inputNames.forEach(name=>{let[nodeName]=getNodeNameAndIndex(name);node.inputs.push(nodes[nodeName]),nodes[nodeName].children.push(node)})});let returnNodeMap=functionDef.ret;functionDef.signature.outputArg.forEach(output=>{let[nodeName,index]=getNodeNameAndIndex(returnNodeMap[output.name]),node=nodes[nodeName];node!=null&&(node.defaultOutput=index,outputs.push(node))});let signature=this.mapArgsToSignature(functionDef);return{nodes,inputs,outputs,weights,placeholders,signature}}mapArgsToSignature(functionDef){return{methodName:functionDef.signature.name,inputs:functionDef.signature.inputArg.reduce((map,arg)=>(map[arg.name]=this.mapArgToTensorInfo(arg),map),{}),outputs:functionDef.signature.outputArg.reduce((map,arg)=>(map[arg.name]=this.mapArgToTensorInfo(arg,functionDef.ret),map),{})}}mapArgToTensorInfo(arg,nameMap2){let name=arg.name;return nameMap2!=null&&(name=nameMap2[name]),{name,dtype:arg.type}}};function decodeBase64(text){let global2=env().global;if(typeof global2.atob!="undefined")return global2.atob(text);if(typeof Buffer!="undefined")return new Buffer(text,"base64").toString();throw new Error("Unable to decode base64 in this environment. Missing built-in atob() or Buffer()")}function parseStringParam(s,keepCase){let value=Array.isArray(s)?String.fromCharCode.apply(null,s):decodeBase64(s);return keepCase?value:value.toLowerCase()}function getStringParam(attrs,name,def,keepCase=!1){let param=attrs[name];return param!=null?parseStringParam(param.s,keepCase):def}function getBoolParam(attrs,name,def){let param=attrs[name];return param?param.b:def}function getNumberParam(attrs,name,def){let param=attrs[name]||{},value=param.i!=null?param.i:param.f!=null?param.f:def;return typeof value=="number"?value:parseInt(value,10)}function parseDtypeParam(value){switch(typeof value=="string"&&(value=DataType[value]),value){case DataType.DT_FLOAT:return"float32";case DataType.DT_INT32:case DataType.DT_INT64:case DataType.DT_INT8:case DataType.DT_UINT8:return"int32";case DataType.DT_BOOL:return"bool";case DataType.DT_DOUBLE:return"float32";case DataType.DT_STRING:return"string";default:return null}}function getFuncParam(attrs,name,def){let param=attrs[name];return param&¶m.func?param.func.name:def}function getDtypeParam(attrs,name,def){let param=attrs[name];return param&¶m.type?parseDtypeParam(param.type):def}function getDtypeArrayParam(attrs,name,def){let param=attrs[name];return param&¶m.list&¶m.list.type?param.list.type.map(v=>parseDtypeParam(v)):def}function parseTensorShapeParam(shape){if(!shape.unknownRank)return shape.dim!=null?shape.dim.map(dim=>typeof dim.size=="number"?dim.size:parseInt(dim.size,10)):[]}function getTensorShapeParam(attrs,name,def){let param=attrs[name];return param&¶m.shape?parseTensorShapeParam(param.shape):def}function getNumericArrayParam(attrs,name,def){let param=attrs[name];return param?((param.list.f&¶m.list.f.length?param.list.f:param.list.i)||[]).map(v=>typeof v=="number"?v:parseInt(v,10)):def}function getStringArrayParam(attrs,name,def,keepCase=!1){let param=attrs[name];return param&¶m.list&¶m.list.s?param.list.s.map(v=>parseStringParam(v,keepCase)):def}function getTensorShapeArrayParam(attrs,name,def){let param=attrs[name];return param&¶m.list&¶m.list.shape?param.list.shape.map(v=>parseTensorShapeParam(v)):def}function getBoolArrayParam(attrs,name,def){let param=attrs[name];return param&¶m.list&¶m.list.b?param.list.b:def}var NodeValueImpl=class{constructor(node,tensorMap,context){this.node=node,this.tensorMap=tensorMap,this.context=context,this.inputs=[],this.attrs={},this.inputs=node.inputNames.map(name=>this.getInput(name)),node.rawAttrs!=null&&(this.attrs=Object.keys(node.rawAttrs).reduce((attrs,key)=>(attrs[key]=this.getAttr(key),attrs),{}))}getInput(name){return getTensor(name,this.tensorMap,this.context)}getAttr(name,defaultValue){let value=this.node.rawAttrs[name];if(value.tensor!=null)return getTensor(name,this.tensorMap,this.context);if(value.i!=null||value.f!=null)return getNumberParam(this.node.rawAttrs,name,defaultValue);if(value.s!=null)return getStringParam(this.node.rawAttrs,name,defaultValue);if(value.b!=null)return getBoolParam(this.node.rawAttrs,name,defaultValue);if(value.shape!=null)return getTensorShapeParam(this.node.rawAttrs,name,defaultValue);if(value.type!=null)return getDtypeParam(this.node.rawAttrs,name,defaultValue);if(value.list!=null){if(value.list.i!=null||value.list.f!=null)return getNumericArrayParam(this.node.rawAttrs,name,defaultValue);if(value.list.s!=null)return getStringArrayParam(this.node.rawAttrs,name,defaultValue);if(value.list.shape!=null)return getTensorShapeArrayParam(this.node.rawAttrs,name,defaultValue);if(value.list.b!=null)return getBoolArrayParam(this.node.rawAttrs,name,defaultValue);if(value.list.type!=null)return getDtypeArrayParam(this.node.rawAttrs,name,defaultValue)}return defaultValue}},executeOp=(node,tensorMap,context)=>{switch(node.op){case"BiasAdd":case"AddV2":case"Add":return[add2(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"AddN":return[addN(getParamValue("tensors",node,tensorMap,context))];case"FloorMod":case"Mod":return[mod(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Mul":return[mul(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"RealDiv":case"Div":return[div(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"DivNoNan":return[divNoNan(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"FloorDiv":return[floorDiv(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Sub":return[sub(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Minimum":return[minimum(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Maximum":return[maximum(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Pow":return[pow(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"SquaredDifference":return[squaredDifference(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp2=(node,tensorMap,context)=>{switch(node.op){case"Abs":case"ComplexAbs":return[abs(getParamValue("x",node,tensorMap,context))];case"Acos":return[acos(getParamValue("x",node,tensorMap,context))];case"Acosh":return[acosh(getParamValue("x",node,tensorMap,context))];case"Asin":return[asin(getParamValue("x",node,tensorMap,context))];case"Asinh":return[asinh(getParamValue("x",node,tensorMap,context))];case"Atan":return[atan(getParamValue("x",node,tensorMap,context))];case"Atan2":return[atan2(getParamValue("x",node,tensorMap,context),getParamValue("y",node,tensorMap,context))];case"Atanh":return[atanh(getParamValue("x",node,tensorMap,context))];case"Ceil":return[ceil(getParamValue("x",node,tensorMap,context))];case"Complex":return[complex(getParamValue("real",node,tensorMap,context),getParamValue("imag",node,tensorMap,context))];case"Cos":return[cos(getParamValue("x",node,tensorMap,context))];case"Cosh":return[cosh(getParamValue("x",node,tensorMap,context))];case"Elu":return[elu(getParamValue("x",node,tensorMap,context))];case"Erf":return[erf(getParamValue("x",node,tensorMap,context))];case"Exp":return[exp(getParamValue("x",node,tensorMap,context))];case"Expm1":return[expm1(getParamValue("x",node,tensorMap,context))];case"Floor":return[floor(getParamValue("x",node,tensorMap,context))];case"Log":return[log2(getParamValue("x",node,tensorMap,context))];case"Log1p":return[log1p(getParamValue("x",node,tensorMap,context))];case"Imag":return[imag(getParamValue("x",node,tensorMap,context))];case"Neg":return[neg(getParamValue("x",node,tensorMap,context))];case"Reciprocal":return[reciprocal(getParamValue("x",node,tensorMap,context))];case"Real":return[real(getParamValue("x",node,tensorMap,context))];case"Relu":return[relu(getParamValue("x",node,tensorMap,context))];case"Round":return[round2(getParamValue("x",node,tensorMap,context))];case"Selu":return[selu(getParamValue("x",node,tensorMap,context))];case"Sigmoid":return[sigmoid(getParamValue("x",node,tensorMap,context))];case"Sin":return[sin(getParamValue("x",node,tensorMap,context))];case"Sign":return[sign(getParamValue("x",node,tensorMap,context))];case"Sinh":return[sinh(getParamValue("x",node,tensorMap,context))];case"Softplus":return[softplus(getParamValue("x",node,tensorMap,context))];case"Sqrt":return[sqrt(getParamValue("x",node,tensorMap,context))];case"Square":return[square(getParamValue("x",node,tensorMap,context))];case"Tanh":return[tanh2(getParamValue("x",node,tensorMap,context))];case"Tan":return[tan(getParamValue("x",node,tensorMap,context))];case"ClipByValue":return[clipByValue(getParamValue("x",node,tensorMap,context),getParamValue("clipValueMin",node,tensorMap,context),getParamValue("clipValueMax",node,tensorMap,context))];case"Relu6":return[relu6(getParamValue("x",node,tensorMap,context))];case"Rsqrt":return[rsqrt(getTensor(node.inputNames[0],tensorMap,context))];case"Prod":return[prod(getParamValue("x",node,tensorMap,context),getParamValue("axes",node,tensorMap,context))];case"LeakyRelu":return[leakyRelu(getParamValue("x",node,tensorMap,context),getParamValue("alpha",node,tensorMap,context))];case"Prelu":return[prelu(getParamValue("x",node,tensorMap,context),getParamValue("alpha",node,tensorMap,context))];default:throw TypeError(`Node type ${node.op} is not implemented`)}};function assertShapesMatchAllowUndefinedSize(shapeA,shapeB,errorMessagePrefix=""){util_exports.assert(shapesEqualAllowUndefinedSize(shapeA,shapeB),()=>errorMessagePrefix+` Shapes ${shapeA} and ${shapeB} must match`)}function shapesEqualAllowUndefinedSize(n1,n2){if(n1.length!==n2.length)return!1;for(let i=0;i{(keepIds==null||!keepIds.has(tensor2.tensor.id))&&tensor2.tensor.dispose()}),this.tensors=[],this.closed_=!0,this.idTensor.dispose()}size(){return this.tensors.length}read(index){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(index<0||index>=this.size())throw new Error(`Tried to read from index ${index}, but array size is: ${this.size()}`);let tensorWithState=this.tensors[index];if(tensorWithState.cleared)throw new Error(`TensorArray ${this.name}: Could not read index ${index} twice because it was cleared after a previous read (perhaps try setting clear_after_read = false?).`);return this.clearAfterRead&&(tensorWithState.cleared=!0),tensorWithState.read=!0,tensorWithState.tensor}readMany(indices){return indices.map(index=>this.read(index))}write(index,tensor2){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(index<0||!this.dynamicSize&&index>=this.maxSize)throw new Error(`Tried to write to index ${index}, but array is not resizeable and size is: ${this.maxSize}`);let t=this.tensors[index]||{};if(tensor2.dtype!==this.dtype)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index}, +2. The custom ${printableModuleName} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);if(fromConfig!=null){let customObjectsCombined={};for(let key of Object.keys(_GLOBAL_CUSTOM_OBJECTS))customObjectsCombined[key]=_GLOBAL_CUSTOM_OBJECTS[key];for(let key of Object.keys(customObjects))customObjectsCombined[key]=customObjects[key];let nestedConfig=config2.config;nestedConfig.customObjects=customObjectsCombined;let backupCustomObjects=Object.assign({},_GLOBAL_CUSTOM_OBJECTS);for(let key of Object.keys(customObjects))_GLOBAL_CUSTOM_OBJECTS[key]=customObjects[key];convertNDArrayScalarsInConfig(config2.config);let returnObj=fromConfig(cls,config2.config,customObjects,fastWeightInit);return _GLOBAL_CUSTOM_OBJECTS=Object.assign({},backupCustomObjects),returnObj}else{let backupCustomObjects=Object.assign({},_GLOBAL_CUSTOM_OBJECTS);for(let key of Object.keys(customObjects))_GLOBAL_CUSTOM_OBJECTS[key]=customObjects[key];let returnObj=new cls(config2.config);return _GLOBAL_CUSTOM_OBJECTS=Object.assign({},backupCustomObjects),returnObj}}}function numberCompare(a,b){return ab?1:0}function reverseNumberCompare(a,b){return-1*numberCompare(a,b)}function unique4(xs){if(xs==null)return xs;let out=[];for(let x of xs)out.indexOf(x)===-1&&out.push(x);return out}function isObjectEmpty(obj){if(obj==null)throw new ValueError(`Invalid value in obj: ${JSON.stringify(obj)}`);for(let key in obj)if(obj.hasOwnProperty(key))return!1;return!0}function checkStringTypeUnionValue(values,label,value){if(value!=null&&values.indexOf(value)<0)throw new ValueError(`${value} is not a valid ${label}. Valid values are ${values} or null/undefined.`)}function checkArrayTypeAndLength(x,expectedType,minLength=0,maxLength=Infinity){return assert2(minLength>=0),assert2(maxLength>=minLength),Array.isArray(x)&&x.length>=minLength&&x.length<=maxLength&&x.every(e=>typeof e===expectedType)}function assertPositiveInteger(value,name){Array.isArray(value)?(util_exports.assert(value.length>0,()=>`${name} is unexpectedly an empty array.`),value.forEach((v,i)=>assertPositiveInteger(v,`element ${i+1} of ${name}`))):util_exports.assert(Number.isInteger(value)&&value>0,()=>`Expected ${name} to be a positive integer, but got ${formatAsFriendlyString(value)}.`)}function formatAsFriendlyString(value){return value===null?"null":Array.isArray(value)?"["+value.map(v=>formatAsFriendlyString(v)).join(",")+"]":typeof value=="string"?`"${value}"`:`${value}`}function debounce(f,waitMs){let lastTime=util_exports.now(),lastResult;return(...args)=>{let now22=util_exports.now();return now22-lastTimesqrt(sum2(mul(w,w),axis,!0)))}var Constraint=class extends serialization_exports.Serializable{getConfig(){return{}}},MaxNorm=class extends Constraint{constructor(args){super();this.defaultMaxValue=2,this.defaultAxis=0,this.maxValue=args.maxValue!=null?args.maxValue:this.defaultMaxValue,this.axis=args.axis!=null?args.axis:this.defaultAxis}apply(w){return tidy(()=>{let norms=calcL2Norms(w,this.axis),desired=clipByValue(norms,0,this.maxValue);return mul(w,div(desired,add2(epsilon2(),norms)))})}getConfig(){return{maxValue:this.maxValue,axis:this.axis}}};MaxNorm.className="MaxNorm";serialization_exports.registerClass(MaxNorm);var UnitNorm=class extends Constraint{constructor(args){super();this.defaultAxis=0,this.axis=args.axis!=null?args.axis:this.defaultAxis}apply(w){return tidy(()=>div(w,add2(epsilon2(),calcL2Norms(w,this.axis))))}getConfig(){return{axis:this.axis}}};UnitNorm.className="UnitNorm";serialization_exports.registerClass(UnitNorm);var NonNeg=class extends Constraint{apply(w){return relu(w)}};NonNeg.className="NonNeg";serialization_exports.registerClass(NonNeg);var MinMaxNorm=class extends Constraint{constructor(args){super();this.defaultMinValue=0,this.defaultMaxValue=1,this.defaultRate=1,this.defaultAxis=0,this.minValue=args.minValue!=null?args.minValue:this.defaultMinValue,this.maxValue=args.maxValue!=null?args.maxValue:this.defaultMaxValue,this.rate=args.rate!=null?args.rate:this.defaultRate,this.axis=args.axis!=null?args.axis:this.defaultAxis}apply(w){return tidy(()=>{let norms=calcL2Norms(w,this.axis),desired=add2(mul(this.rate,clipByValue(norms,this.minValue,this.maxValue)),mul(1-this.rate,norms));return mul(w,div(desired,add2(epsilon2(),norms)))})}getConfig(){return{minValue:this.minValue,maxValue:this.maxValue,rate:this.rate,axis:this.axis}}};MinMaxNorm.className="MinMaxNorm";serialization_exports.registerClass(MinMaxNorm);var CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP={maxNorm:"MaxNorm",minMaxNorm:"MinMaxNorm",nonNeg:"NonNeg",unitNorm:"UnitNorm"};function serializeConstraint(constraint){return serializeKerasObject(constraint)}function deserializeConstraint(config2,customObjects={}){return deserializeKerasObject(config2,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"constraint")}function getConstraint(identifier){if(identifier==null)return null;if(typeof identifier=="string"){let config2={className:identifier in CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP?CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier]:identifier,config:{}};return deserializeConstraint(config2)}else return identifier instanceof Constraint?identifier:deserializeConstraint(identifier)}function maxNorm(args){return new MaxNorm(args)}function unitNorm(args){return new UnitNorm(args)}function nonNeg(){return new NonNeg}function minMaxNorm(config2){return new MinMaxNorm(config2)}var exports_initializers_exports={};__export2(exports_initializers_exports,{constant:()=>constant,glorotNormal:()=>glorotNormal,glorotUniform:()=>glorotUniform,heNormal:()=>heNormal,heUniform:()=>heUniform,identity:()=>identity4,leCunNormal:()=>leCunNormal,leCunUniform:()=>leCunUniform,ones:()=>ones3,orthogonal:()=>orthogonal,randomNormal:()=>randomNormal3,randomUniform:()=>randomUniform2,truncatedNormal:()=>truncatedNormal2,varianceScaling:()=>varianceScaling,zeros:()=>zeros3});var VALID_DATA_FORMAT_VALUES=["channelsFirst","channelsLast"],VALID_INTERPOLATION_FORMAT_VALUES=["nearest","bilinear"],VALID_PADDING_MODE_VALUES=["valid","same","causal"],VALID_POOL_MODE_VALUES=["max","avg"],VALID_BIDIRECTIONAL_MERGE_MODES=["sum","mul","concat","ave"],nameMap=new Map;function checkDataFormat(value){checkStringTypeUnionValue(VALID_DATA_FORMAT_VALUES,"DataFormat",value)}function checkInterpolationFormat(value){checkStringTypeUnionValue(VALID_INTERPOLATION_FORMAT_VALUES,"InterpolationFormat",value)}function checkPaddingMode(value){checkStringTypeUnionValue(VALID_PADDING_MODE_VALUES,"PaddingMode",value)}function checkPoolMode(value){checkStringTypeUnionValue(VALID_POOL_MODE_VALUES,"PoolMode",value)}var _nameScopeStack=[],_nameScopeDivider="/";function nameScope(name,fn){_nameScopeStack.push(name);try{let val=fn();return _nameScopeStack.pop(),val}catch(e){throw _nameScopeStack.pop(),e}}function currentNameScopePrefix(){return _nameScopeStack.length===0?"":_nameScopeStack.join(_nameScopeDivider)+_nameScopeDivider}function getScopedTensorName(tensorName){if(!isValidTensorName(tensorName))throw new Error("Not a valid tensor name: '"+tensorName+"'");return currentNameScopePrefix()+tensorName}function getUniqueTensorName(scopedName){if(!isValidTensorName(scopedName))throw new Error("Not a valid tensor name: '"+scopedName+"'");nameMap.has(scopedName)||nameMap.set(scopedName,0);let index=nameMap.get(scopedName);if(nameMap.set(scopedName,nameMap.get(scopedName)+1),index>0){let result=`${scopedName}_${index}`;return nameMap.set(result,1),result}else return scopedName}var tensorNameRegex=new RegExp(/^[A-Za-z0-9][-A-Za-z0-9\._\/]*$/);function isValidTensorName(name){return!!name.match(tensorNameRegex)}function isInteger(x){return x===parseInt(x.toString(),10)}function arrayProd(array2,begin,end){begin==null&&(begin=0),end==null&&(end=array2.length);let prod5=1;for(let i=begin;i{if(x.shape.length!==2)throw new ValueError(`repeat() expects a rank-2 tensor, but received a rank-${x.shape.length} tensor.`);let y=expandDims5(x,1);return tile5(y,[1,n,1])})}function flatten2(x){let newShape=[arrayProd(x.shape)];return x.reshape(newShape)}function batchFlatten(x){if(x.rank<=1)throw new ValueError(`batchFlatten requires a minimum rank of 2. Got rank: ${x.rank}.`);let newShape=[x.shape[0],arrayProd(x.shape,1)];return x.reshape(newShape)}function sliceAlongFirstAxis(array2,start,size){return tidy(()=>{switch(array2.rank){case 1:return slice1d(array2,start,size);case 2:return slice2d(array2,[start,0],[size,array2.shape[1]]);case 3:return slice3d(array2,[start,0,0],[size,array2.shape[1],array2.shape[2]]);case 4:return slice4d(array2,[start,0,0,0],[size,array2.shape[1],array2.shape[2],array2.shape[3]]);case 5:return slice(array2,[start,0,0,0,0],[size,array2.shape[1],array2.shape[2],array2.shape[3],array2.shape[4]]);case 6:return slice(array2,[start,0,0,0,0,0],[size,array2.shape[1],array2.shape[2],array2.shape[3],array2.shape[4],array2.shape[5]]);default:throw new ValueError(`sliceAlongFirstAxis() received an unsupported tensor rank: ${array2.rank}`)}})}function sliceAlongLastAxis(array2,start,size){return tidy(()=>{switch(array2.rank){case 1:return slice1d(array2,start,size);case 2:return slice2d(array2,[0,start],[array2.shape[0],size]);case 3:return slice3d(array2,[0,0,start],[array2.shape[0],array2.shape[1],size]);case 4:return slice4d(array2,[0,0,0,start],[array2.shape[0],array2.shape[1],array2.shape[2],size]);default:throw new ValueError(`sliceAlongLastAxis() received an unsupported tensor rank: ${array2.rank}`)}})}function sliceAlongAxis(array2,start,size,axis){return tidy(()=>{switch(array2.rank){case 1:return slice1d(array2,start,size);case 2:switch(axis){case 1:return sliceAlongFirstAxis(array2,start,size);case 2:return sliceAlongLastAxis(array2,start,size);default:throw new ValueError(`The axis is not within the rank of the tensor ${axis}`)}case 3:switch(axis){case 1:return sliceAlongFirstAxis(array2,start,size);case 2:return slice3d(array2,[0,start,0],[array2.shape[0],size,array2.shape[2]]);case 3:return sliceAlongLastAxis(array2,start,size);default:throw new ValueError(`The axis is not within the rank of the tensor ${axis}`)}case 4:switch(axis){case 1:return sliceAlongFirstAxis(array2,start,size);case 2:return slice4d(array2,[0,start,0,0],[array2.shape[0],size,array2.shape[2],array2.shape[3]]);case 3:return slice4d(array2,[0,0,start,0],[array2.shape[0],array2.shape[1],size,array2.shape[3]]);case 4:return sliceAlongLastAxis(array2,start,size);default:throw new ValueError(`The axis is not within the rank of the tensor ${axis}`)}default:throw new ValueError(`sliceAlongLastAxis() received an unsupported tensor rank: ${array2.rank}`)}})}function concatenate(tensors,axis=-1){let rank;return axis<0&&(rank=tensors[0].rank,rank!==0?axis=rank:axis=0),axis===tensors[0].rank&&(axis=-1),concat(tensors,axis)}function concatAlongFirstAxis(a,b){switch(a.rank){case 1:return concat1d([a,b]);case 2:return concat2d([a,b],0);case 3:return concat3d([a,b],0);case 4:return concat4d([a,b],0);default:throw new ValueError(`concatAlongFirstAxis() received an unsupported tensor rank: ${a.rank}`)}}function tile5(x,n){if(Array.isArray(n)||(n=[n]),x.rank!==n.length)throw new ValueError(`The length of input n (${n.length}) does not match the number of dimensions in input x (${x.rank})`);return tile(x,n)}function randomNormal2(shape,mean4=0,stddev=1,dtype,seed){return randomNormal(shape,mean4,stddev,dtype,seed)}function dot2(a,b,activation2,bias){if(a.rank<2||b.rank<2)throw new NotImplementedError(`dot requires both inputs to be rank >= 2 but got x shape = ${a.shape} and y shape = ${b.shape}`);if(b.rank>=3){let xLastDim=a.shape.slice(-1)[0],ySecondLastDim=b.shape.slice(-2)[0];if(xLastDim!==ySecondLastDim)throw new NotImplementedError(`If rank y >= 3, then the second last dim of y must equal the last dim of x but got x shape = ${a.shape} and y shape = ${b.shape}`)}if(a.rank===2&&b.rank===2){let transposeA=!1,transposeB=!1;return fused_ops_exports.matMul({a,b,transposeA,transposeB,bias:bias?reshapeBias(a.rank,bias,imageDataFormat()):null,activation:activation2})}else{let aFirstDims=a.shape.slice(),aLastDim=aFirstDims.pop();a=a.reshape([-1,aLastDim]);let bShape=b.shape.slice(),bLastDim=bShape.pop(),ySecondLastDim=bShape.pop(),yOtherDims=[...bShape,bLastDim],perm=Array.from({length:b.rank},(_,i)=>i===0?b.rank-2:i<=b.rank-2?i-1:i);b=b.transpose(perm).reshape([ySecondLastDim,-1]);let outputShape=[...aFirstDims,...yOtherDims],transposeA=!1,transposeB=!1;return fused_ops_exports.matMul({a,b,transposeA,transposeB,bias:bias?reshapeBias(a.rank,bias,imageDataFormat()):null,activation:activation2}).reshape(outputShape)}}function gather2(reference,indices,axis){return tidy(()=>(Array.isArray(indices)?indices=tensor1d(indices,"int32"):indices=indices.toInt(),gather(reference,indices,axis)))}function square3(x){return mul(x,x)}function reshapeBias(xRank,bias,dataFormat){let biasShape=bias.shape;if(bias.rank!==1&&bias.rank!==xRank)throw new ValueError(`Unexpected bias dimensions: ${bias.rank}; expected it to be 1 or ${xRank}`);if(xRank===5){if(dataFormat==="channelsFirst")return biasShape.length===1?bias.reshape([1,biasShape[0],1,1,1]):bias.reshape([1,biasShape[3],biasShape[0],biasShape[1],biasShape[2]]);if(dataFormat==="channelsLast")return biasShape.length===1?bias.reshape([1,1,1,1,biasShape[0]]):bias.reshape([1].concat(biasShape))}else if(xRank===4){if(dataFormat==="channelsFirst")return biasShape.length===1?bias.reshape([1,biasShape[0],1,1]):bias.reshape([1,biasShape[2],biasShape[0],biasShape[1]]);if(dataFormat==="channelsLast")return biasShape.length===1?bias.reshape([1,1,1,biasShape[0]]):bias.reshape([1].concat(biasShape))}else if(xRank===3){if(dataFormat==="channelsFirst")return biasShape.length===1?bias.reshape([1,biasShape[0],1]):bias.reshape([1,biasShape[1],biasShape[0]]);if(dataFormat==="channelsLast")return biasShape.length===1?bias.reshape([1,1,biasShape[0]]):bias.reshape([1].concat(biasShape))}else if(xRank<3)return bias;throw new ValueError(`Unsupported input rank by biasAdd: ${bias.rank}`)}function biasAdd(x,bias,dataFormat){return tidy(()=>(dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat),x.add(reshapeBias(x.rank,bias,dataFormat))))}function elu4(x,alpha=1){if(alpha!==1)throw new NotImplementedError(`Support for alpha values other than 1 (${alpha}) is not implemented yet.`);return elu(x)}function softsign(x){return tidy(()=>div(x,abs(x).add(1)))}function dropout2(x,level,noiseShape,seed){return tidy(()=>dropout(x,level,noiseShape,seed))}function hardSigmoid(x){return tidy(()=>{let y=add2(.5,mul(.2,x));return clipByValue(y,0,1)})}function inTrainPhase(x,alt,training=!1){return training?x():alt()}var VALID_FAN_MODE_VALUES=["fanIn","fanOut","fanAvg"],VALID_DISTRIBUTION_VALUES=["normal","uniform","truncatedNormal"];function checkFanMode(value){checkStringTypeUnionValue(VALID_FAN_MODE_VALUES,"FanMode",value)}function checkDistribution(value){checkStringTypeUnionValue(VALID_DISTRIBUTION_VALUES,"Distribution",value)}var Initializer=class extends serialization_exports.Serializable{fromConfigUsesCustomObjects(){return!1}getConfig(){return{}}},Zeros=class extends Initializer{apply(shape,dtype){return zeros(shape,dtype)}};Zeros.className="Zeros";serialization_exports.registerClass(Zeros);var Ones=class extends Initializer{apply(shape,dtype){return ones2(shape,dtype)}};Ones.className="Ones";serialization_exports.registerClass(Ones);var Constant=class extends Initializer{constructor(args){super();if(typeof args!="object")throw new ValueError(`Expected argument of type ConstantConfig but got ${args}`);if(args.value===void 0)throw new ValueError(`config must have value set but got ${args}`);this.value=args.value}apply(shape,dtype){return tidy(()=>mul(scalar(this.value),ones2(shape,dtype)))}getConfig(){return{value:this.value}}};Constant.className="Constant";serialization_exports.registerClass(Constant);var RandomUniform=class extends Initializer{constructor(args){super();this.DEFAULT_MINVAL=-.05,this.DEFAULT_MAXVAL=.05,this.minval=args.minval||this.DEFAULT_MINVAL,this.maxval=args.maxval||this.DEFAULT_MAXVAL,this.seed=args.seed}apply(shape,dtype){return randomUniform(shape,this.minval,this.maxval,dtype)}getConfig(){return{minval:this.minval,maxval:this.maxval,seed:this.seed}}};RandomUniform.className="RandomUniform";serialization_exports.registerClass(RandomUniform);var RandomNormal=class extends Initializer{constructor(args){super();this.DEFAULT_MEAN=0,this.DEFAULT_STDDEV=.05,this.mean=args.mean||this.DEFAULT_MEAN,this.stddev=args.stddev||this.DEFAULT_STDDEV,this.seed=args.seed}apply(shape,dtype){if(dtype=dtype||"float32",dtype!=="float32"&&dtype!=="int32")throw new NotImplementedError(`randomNormal does not support dType ${dtype}.`);return randomNormal2(shape,this.mean,this.stddev,dtype,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}};RandomNormal.className="RandomNormal";serialization_exports.registerClass(RandomNormal);var TruncatedNormal=class extends Initializer{constructor(args){super();this.DEFAULT_MEAN=0,this.DEFAULT_STDDEV=.05,this.mean=args.mean||this.DEFAULT_MEAN,this.stddev=args.stddev||this.DEFAULT_STDDEV,this.seed=args.seed}apply(shape,dtype){if(dtype=dtype||"float32",dtype!=="float32"&&dtype!=="int32")throw new NotImplementedError(`truncatedNormal does not support dType ${dtype}.`);return truncatedNormal(shape,this.mean,this.stddev,dtype,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}};TruncatedNormal.className="TruncatedNormal";serialization_exports.registerClass(TruncatedNormal);var Identity2=class extends Initializer{constructor(args){super();this.gain=args.gain!=null?args.gain:1}apply(shape,dtype){return tidy(()=>{if(shape.length!==2||shape[0]!==shape[1])throw new ValueError("Identity matrix initializer can only be used for 2D square matrices.");return mul(this.gain,eye(shape[0]))})}getConfig(){return{gain:this.gain}}};Identity2.className="Identity";serialization_exports.registerClass(Identity2);function computeFans(shape,dataFormat="channelsLast"){let fanIn,fanOut;if(checkDataFormat(dataFormat),shape.length===2)fanIn=shape[0],fanOut=shape[1];else if([3,4,5].indexOf(shape.length)!==-1){if(dataFormat==="channelsFirst"){let receptiveFieldSize=arrayProd(shape,2);fanIn=shape[1]*receptiveFieldSize,fanOut=shape[0]*receptiveFieldSize}else if(dataFormat==="channelsLast"){let receptiveFieldSize=arrayProd(shape,0,shape.length-2);fanIn=shape[shape.length-2]*receptiveFieldSize,fanOut=shape[shape.length-1]*receptiveFieldSize}}else{let shapeProd=arrayProd(shape);fanIn=Math.sqrt(shapeProd),fanOut=Math.sqrt(shapeProd)}return[fanIn,fanOut]}var VarianceScaling=class extends Initializer{constructor(args){super();if(args.scale<0)throw new ValueError(`scale must be a positive float. Got: ${args.scale}`);this.scale=args.scale==null?1:args.scale,this.mode=args.mode==null?"fanIn":args.mode,checkFanMode(this.mode),this.distribution=args.distribution==null?"normal":args.distribution,checkDistribution(this.distribution),this.seed=args.seed}apply(shape,dtype){let fans=computeFans(shape),fanIn=fans[0],fanOut=fans[1],scale2=this.scale;if(this.mode==="fanIn"?scale2/=Math.max(1,fanIn):this.mode==="fanOut"?scale2/=Math.max(1,fanOut):scale2/=Math.max(1,(fanIn+fanOut)/2),this.distribution==="normal"){let stddev=Math.sqrt(scale2);if(dtype=dtype||"float32",dtype!=="float32"&&dtype!=="int32")throw new NotImplementedError(`${this.getClassName()} does not support dType ${dtype}.`);return truncatedNormal(shape,0,stddev,dtype,this.seed)}else{let limit=Math.sqrt(3*scale2);return randomUniform(shape,-limit,limit,dtype)}}getConfig(){return{scale:this.scale,mode:this.mode,distribution:this.distribution,seed:this.seed}}};VarianceScaling.className="VarianceScaling";serialization_exports.registerClass(VarianceScaling);var GlorotUniform=class extends VarianceScaling{constructor(args){super({scale:1,mode:"fanAvg",distribution:"uniform",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};GlorotUniform.className="GlorotUniform";serialization_exports.registerClass(GlorotUniform);var GlorotNormal=class extends VarianceScaling{constructor(args){super({scale:1,mode:"fanAvg",distribution:"normal",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};GlorotNormal.className="GlorotNormal";serialization_exports.registerClass(GlorotNormal);var HeNormal=class extends VarianceScaling{constructor(args){super({scale:2,mode:"fanIn",distribution:"normal",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};HeNormal.className="HeNormal";serialization_exports.registerClass(HeNormal);var HeUniform=class extends VarianceScaling{constructor(args){super({scale:2,mode:"fanIn",distribution:"uniform",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};HeUniform.className="HeUniform";serialization_exports.registerClass(HeUniform);var LeCunNormal=class extends VarianceScaling{constructor(args){super({scale:1,mode:"fanIn",distribution:"normal",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};LeCunNormal.className="LeCunNormal";serialization_exports.registerClass(LeCunNormal);var LeCunUniform=class extends VarianceScaling{constructor(args){super({scale:1,mode:"fanIn",distribution:"uniform",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};LeCunUniform.className="LeCunNormal";serialization_exports.registerClass(LeCunUniform);var Orthogonal=class extends Initializer{constructor(args){super();if(this.DEFAULT_GAIN=1,this.gain=args.gain==null?this.DEFAULT_GAIN:args.gain,this.seed=args.seed,this.seed!=null)throw new NotImplementedError("Random seed is not implemented for Orthogonal Initializer yet.")}apply(shape,dtype){return tidy(()=>{if(shape.length<2)throw new NotImplementedError("Shape must be at least 2D.");shape[0]*shape[1]>2e3&&console.warn(`Orthogonal initializer is being called on a matrix with more than 2000 (${shape[0]*shape[1]}) elements: Slowness may result.`);let normalizedShape=shape[0]>shape[1]?[shape[1],shape[0]]:shape,a=randomNormal2(normalizedShape,0,1,"float32"),q=linalg.gramSchmidt(a);return shape[0]>shape[1]&&(q=q.transpose()),mul(this.gain,q)})}getConfig(){return{gain:this.gain,seed:this.seed}}};Orthogonal.className="Orthogonal";serialization_exports.registerClass(Orthogonal);var INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP={constant:"Constant",glorotNormal:"GlorotNormal",glorotUniform:"GlorotUniform",heNormal:"HeNormal",heUniform:"HeUniform",identity:"Identity",leCunNormal:"LeCunNormal",leCunUniform:"LeCunUniform",ones:"Ones",orthogonal:"Orthogonal",randomNormal:"RandomNormal",randomUniform:"RandomUniform",truncatedNormal:"TruncatedNormal",varianceScaling:"VarianceScaling",zeros:"Zeros"};function deserializeInitializer(config2,customObjects={}){return deserializeKerasObject(config2,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"initializer")}function serializeInitializer(initializer){return serializeKerasObject(initializer)}function getInitializer(identifier){if(typeof identifier=="string"){let className=identifier in INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP?INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier]:identifier;if(className==="GlorotNormal")return new GlorotNormal;if(className==="GlorotUniform")return new GlorotUniform;if(className==="HeNormal")return new HeNormal;if(className==="HeUniform")return new HeUniform;if(className==="LeCunNormal")return new LeCunNormal;if(className==="LeCunUniform")return new LeCunUniform;{let config2={};return config2.className=className,config2.config={},deserializeInitializer(config2)}}else return identifier instanceof Initializer?identifier:deserializeInitializer(identifier)}function zeros3(){return new Zeros}function ones3(){return new Ones}function constant(args){return new Constant(args)}function randomUniform2(args){return new RandomUniform(args)}function randomNormal3(args){return new RandomNormal(args)}function truncatedNormal2(args){return new TruncatedNormal(args)}function identity4(args){return new Identity2(args)}function varianceScaling(config2){return new VarianceScaling(config2)}function glorotUniform(args){return new GlorotUniform(args)}function glorotNormal(args){return new GlorotNormal(args)}function heNormal(args){return new HeNormal(args)}function heUniform(args){return new HeUniform(args)}function leCunNormal(args){return new LeCunNormal(args)}function leCunUniform(args){return new LeCunUniform(args)}function orthogonal(args){return new Orthogonal(args)}var exports_layers_exports={};__export2(exports_layers_exports,{Layer:()=>Layer,RNN:()=>RNN,RNNCell:()=>RNNCell,activation:()=>activation,add:()=>add4,alphaDropout:()=>alphaDropout,average:()=>average,averagePooling1d:()=>averagePooling1d,averagePooling2d:()=>averagePooling2d,averagePooling3d:()=>averagePooling3d,avgPool1d:()=>avgPool1d,avgPool2d:()=>avgPool2d,avgPool3d:()=>avgPool3d2,avgPooling1d:()=>avgPooling1d,avgPooling2d:()=>avgPooling2d,avgPooling3d:()=>avgPooling3d,batchNormalization:()=>batchNormalization2,bidirectional:()=>bidirectional,concatenate:()=>concatenate2,conv1d:()=>conv1d2,conv2d:()=>conv2d5,conv2dTranspose:()=>conv2dTranspose2,conv3d:()=>conv3d2,convLstm2d:()=>convLstm2d,convLstm2dCell:()=>convLstm2dCell,cropping2D:()=>cropping2D,dense:()=>dense,depthwiseConv2d:()=>depthwiseConv2d5,dot:()=>dot3,dropout:()=>dropout3,elu:()=>elu5,embedding:()=>embedding,flatten:()=>flatten3,gaussianDropout:()=>gaussianDropout,gaussianNoise:()=>gaussianNoise,globalAveragePooling1d:()=>globalAveragePooling1d,globalAveragePooling2d:()=>globalAveragePooling2d,globalMaxPool1d:()=>globalMaxPool1d,globalMaxPool2d:()=>globalMaxPool2d,globalMaxPooling1d:()=>globalMaxPooling1d,globalMaxPooling2d:()=>globalMaxPooling2d,gru:()=>gru,gruCell:()=>gruCell,input:()=>input,inputLayer:()=>inputLayer,layerNormalization:()=>layerNormalization,leakyReLU:()=>leakyReLU,lstm:()=>lstm,lstmCell:()=>lstmCell,masking:()=>masking,maxPool1d:()=>maxPool1d,maxPool2d:()=>maxPool2d,maxPooling1d:()=>maxPooling1d,maxPooling2d:()=>maxPooling2d,maxPooling3d:()=>maxPooling3d,maximum:()=>maximum4,minimum:()=>minimum4,multiply:()=>multiply3,permute:()=>permute,prelu:()=>prelu5,reLU:()=>reLU,repeatVector:()=>repeatVector,reshape:()=>reshape5,rnn:()=>rnn2,separableConv2d:()=>separableConv2d2,simpleRNN:()=>simpleRNN,simpleRNNCell:()=>simpleRNNCell,softmax:()=>softmax5,spatialDropout1d:()=>spatialDropout1d,stackedRNNCells:()=>stackedRNNCells,thresholdedReLU:()=>thresholdedReLU,timeDistributed:()=>timeDistributed,upSampling2d:()=>upSampling2d,zeroPadding2d:()=>zeroPadding2d});var _nextUniqueTensorId=0;function getNextUniqueTensorId(){return _nextUniqueTensorId++}var _uidPrefixes={};function getUid(prefix=""){return prefix in _uidPrefixes||(_uidPrefixes[prefix]=0),_uidPrefixes[prefix]+=1,prefix+_uidPrefixes[prefix].toString()}function isArrayOfShapes(x){return Array.isArray(x)&&Array.isArray(x[0])}function normalizeShapeList(x){return x.length===0?[]:Array.isArray(x[0])?x:[x]}function getExactlyOneTensor(xs){let x;if(Array.isArray(xs)){if(xs.length!==1)throw new ValueError(`Expected Tensor length to be 1; got ${xs.length}`);x=xs[0]}else x=xs;return x}function getExactlyOneShape(shapes){if(Array.isArray(shapes)&&Array.isArray(shapes[0])){if(shapes.length===1)return shapes=shapes,shapes[0];throw new ValueError(`Expected exactly 1 Shape; got ${shapes.length}`)}else return shapes}function countParamsInWeights(weights){let count2=0;for(let weight of weights)weight.shape.length===0?count2+=1:count2+=weight.shape.reduce((a,b)=>a*b);return count2}var DEFAULT_VARIABLE_NAME_PREFIX="Variable",LayerVariable=class{constructor(val,dtype="float32",name=DEFAULT_VARIABLE_NAME_PREFIX,trainable=!0,constraint=null){this.dtype=dtype==null?"float32":dtype,this.shape=val.shape,this.id=getNextUniqueTensorId(),name=name==null?DEFAULT_VARIABLE_NAME_PREFIX:name,this.originalName=getScopedTensorName(name),this.name=getUniqueTensorName(this.originalName),this.trainable_=trainable,this.constraint=constraint,this.val=variable(val,this.trainable_,this.name,this.dtype)}read(){return this.assertNotDisposed(),this.val}write(newVal){return this.assertNotDisposed(),checkShapesMatch(this.val,newVal),this.val.id!==newVal.id&&(this.val.assign(newVal),this.constraint!=null&&this.val.assign(this.constraint.apply(this.val))),this}dispose(){this.assertNotDisposed(),this.val.dispose()}assertNotDisposed(){if(this.val.isDisposed)throw new Error(`LayersVariable ${this.name} is already disposed.`)}get trainable(){return this.trainable_}set trainable(trainable){this.trainable_=trainable,this.val.trainable=trainable}};function checkShapesMatch(x,y){if(x.shape.toString()!==y.shape.toString())throw new Error("Shape mismatch: "+JSON.stringify(x.shape)+" vs. "+JSON.stringify(y.shape))}function batchGetValue(xs){return xs.map(x=>x.read())}function batchSetValue(variablesAndValues){variablesAndValues.forEach(variableAndValue=>{variableAndValue[0].write(variableAndValue[1])})}var InputSpec=class{constructor(args){this.dtype=args.dtype,this.shape=args.shape,args.shape!=null?this.ndim=args.shape.length:this.ndim=args.ndim,this.maxNDim=args.maxNDim,this.minNDim=args.minNDim,this.axes=args.axes||{}}},SymbolicTensor=class{constructor(dtype,shape,sourceLayer,inputs,callArgs,name,outputTensorIndex){this.dtype=dtype,this.shape=shape,this.sourceLayer=sourceLayer,this.inputs=inputs,this.callArgs=callArgs,this.outputTensorIndex=outputTensorIndex,this.id=getNextUniqueTensorId(),name!=null&&(this.originalName=getScopedTensorName(name),this.name=getUniqueTensorName(this.originalName)),this.rank=shape.length}},_nextNodeID=0,Node=class{constructor(args,callArgs){this.callArgs=callArgs,this.id=_nextNodeID++,this.outboundLayer=args.outboundLayer,this.inboundLayers=args.inboundLayers,this.nodeIndices=args.nodeIndices,this.tensorIndices=args.tensorIndices,this.inputTensors=args.inputTensors,this.outputTensors=args.outputTensors,this.inputMasks=args.inputMasks,this.outputMasks=args.outputMasks,this.inputShapes=args.inputShapes,this.outputShapes=args.outputShapes;for(let layer of args.inboundLayers)layer!=null&&layer.outboundNodes.push(this);args.outboundLayer.inboundNodes.push(this)}getConfig(){let inboundNames=[];for(let layer of this.inboundLayers)layer!=null?inboundNames.push(layer.name):inboundNames.push(null);return{outboundLayer:this.outboundLayer?this.outboundLayer.name:null,inboundLayers:inboundNames,nodeIndices:this.nodeIndices,tensorIndices:this.tensorIndices}}},_nextLayerID=0,Layer=class extends serialization_exports.Serializable{constructor(args={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=_nextLayerID++,this.activityRegularizer=null,this.inputSpec=null,this.supportsMasking=!1,this._trainableWeights=[],this._nonTrainableWeights=[],this._losses=[],this._updates=[],this._built=!1,this.inboundNodes=[],this.outboundNodes=[];let name=args.name;if(!name){let prefix=this.getClassName();name=toSnakeCase(prefix)+"_"+getUid(prefix)}if(this.name=name,this.trainable_=args.trainable==null?!0:args.trainable,args.inputShape!=null||args.batchInputShape!=null){let batchInputShape;if(args.batchInputShape!=null)batchInputShape=args.batchInputShape;else if(args.inputShape!=null){let batchSize=null;args.batchSize!=null&&(batchSize=args.batchSize),batchInputShape=[batchSize].concat(args.inputShape)}this.batchInputShape=batchInputShape;let dtype=args.dtype;dtype==null&&(dtype=args.inputDType),dtype==null&&(dtype="float32"),this.dtype=dtype}args.weights!=null?this.initialWeights=args.weights:this.initialWeights=null,this._refCount=null,this.fastWeightInitDuringBuild=!1}static nodeKey(layer,nodeIndex){return layer.name+"_ib-"+nodeIndex.toString()}getNodeAtIndex(nodeIndex,attrName){if(this.inboundNodes.length===0)throw new RuntimeError(`The layer has never been called and thus has no defined ${attrName}.`);if(this.inboundNodes.length<=nodeIndex)throw new ValueError(`Asked to get ${attrName} at node ${nodeIndex}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[nodeIndex]}getInputAt(nodeIndex){return singletonOrArray(this.getNodeAtIndex(nodeIndex,"input").inputTensors)}getOutputAt(nodeIndex){return singletonOrArray(this.getNodeAtIndex(nodeIndex,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new AttributeError(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer input" is ill-defined. Use \`getInputAt(nodeIndex)\` instead.`);if(this.inboundNodes.length===0)throw new AttributeError(`Layer ${this.name} is not connected, no input to return.`);return singletonOrArray(this.getNodeAtIndex(0,"input").inputTensors)}get output(){if(this.inboundNodes.length===0)throw new AttributeError(`Layer ${this.name} has no inbound nodes.`);if(this.inboundNodes.length>1)throw new AttributeError(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer output" is ill-defined. Use \`getOutputAt(nodeIndex)\` instead.`);return singletonOrArray(this.getNodeAtIndex(0,"output").outputTensors)}get losses(){return this._losses}calculateLosses(){return this.losses.map(lossFn=>lossFn())}get updates(){return this._updates}get built(){return this._built}set built(built){this._built=built}get trainable(){return this.trainable_}set trainable(trainable){this._trainableWeights.forEach(w=>w.trainable=trainable),this.trainable_=trainable}get trainableWeights(){return this.trainable_?this._trainableWeights.filter(w=>w.trainable):[]}set trainableWeights(weights){this._trainableWeights=weights}get nonTrainableWeights(){return this.trainable?this._trainableWeights.filter(w=>!w.trainable).concat(this._nonTrainableWeights):this._trainableWeights.concat(this._nonTrainableWeights)}set nonTrainableWeights(weights){this._nonTrainableWeights=weights}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}get stateful(){return this._stateful}resetStates(){if(!this.stateful)throw new Error("Cannot call the resetStates() method of a non-stateful Layer object.")}assertInputCompatibility(inputs){if(inputs=toList(inputs),this.inputSpec==null||this.inputSpec.length===0)return;let inputSpec=toList(this.inputSpec);if(inputs.length!==inputSpec.length)throw new ValueError(`Layer ${this.name} expects ${inputSpec.length} inputs, but it received ${inputs.length} input tensors. Input received: ${inputs}`);for(let inputIndex=0;inputIndexspec.maxNDim)throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected max_ndim=${spec.maxNDim}, found ndim=${ndim}`);if(spec.minNDim!=null&&ndim=0?xShape[axis]:xShape[xShape.length+axis];if(value!=null&&[value,null].indexOf(xShapeAtAxis)===-1)throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected axis ${axis} of input shape to have value ${value} but got shape ${xShape}.`)}}if(spec.shape!=null)for(let i=0;i{if(!this.built){this.assertInputCompatibility(inputs);let inputShapes=[];for(let xElem of toList(inputs))inputShapes.push(xElem.shape);this.build(singletonOrArray(inputShapes)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&noneAreSymbolic&&(this._refCount=1)}if(this.assertInputCompatibility(inputs),noneAreSymbolic){let output=this.call(inputs,kwargs),outputList=toList(output),outputListCopy=[];for(let x of outputList)inputsList.indexOf(x)!==-1&&(x=x.clone()),outputListCopy.push(x);if(output=singletonOrArray(outputListCopy),this.activityRegularizer!=null)throw new NotImplementedError("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return output}else{let inputShape=collectInputShape(inputs),outputShape=this.computeOutputShape(inputShape),output,outputDType=guessOutputDType(inputs);if(this.warnOnIncompatibleInputShape(Array.isArray(inputs)?inputShape[0]:inputShape),outputShape!=null&&outputShape.length>0&&Array.isArray(outputShape[0])?output=outputShape.map((shape,index)=>new SymbolicTensor(outputDType,shape,this,toList(inputs),kwargs,this.name,index)):output=new SymbolicTensor(outputDType,outputShape,this,toList(inputs),kwargs,this.name),this.addInboundNode(inputs,output,null,null,inputShape,outputShape,kwargs),this._refCount++,this.activityRegularizer!=null)throw new NotImplementedError("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return output}})}warnOnIncompatibleInputShape(inputShape){if(this.batchInputShape!=null)if(inputShape.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(inputShape)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let dimMismatch=!1;this.batchInputShape.forEach((dimension,i)=>{dimension!=null&&inputShape[i]!=null&&inputShape[i]!==dimension&&(dimMismatch=!0)}),dimMismatch&&console.warn(`The shape of the input tensor (${JSON.stringify(inputShape)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}get outputShape(){if(this.inboundNodes==null||this.inboundNodes.length===0)throw new AttributeError(`The layer ${this.name} has never been called and thus has no defined output shape.`);let allOutputShapes=[];for(let node of this.inboundNodes){let shapeString=JSON.stringify(node.outputShapes);allOutputShapes.indexOf(shapeString)===-1&&allOutputShapes.push(shapeString)}if(allOutputShapes.length===1){let outputShapes=this.inboundNodes[0].outputShapes;return Array.isArray(outputShapes)&&Array.isArray(outputShapes[0])&&outputShapes.length===1?outputShapes[0]:outputShapes}else throw new AttributeError(`The layer ${this.name} has multiple inbound nodes with different output shapes. Hence the notion of "output shape" is ill-defined for the layer.`)}countParams(){if(!this.built)throw new RuntimeError(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return countParamsInWeights(this.weights)}build(inputShape){this.built=!0}getWeights(trainableOnly=!1){return batchGetValue(trainableOnly?this.trainableWeights:this.weights)}setWeights(weights){tidy(()=>{let params=this.weights;if(params.length!==weights.length)throw new ValueError(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${weights.length}, but the layer was expecting ${params.length} weights. Provided weights: ${weights}...`);if(params.length===0)return;let weightValueTuples=[],paramValues=batchGetValue(params);for(let i=0;iregularizer.apply(weight.read())),trainable==null&&(trainable=!0),trainable?this._trainableWeights.push(weight):this._nonTrainableWeights.push(weight),weight}setFastWeightInitDuringBuild(value){this.fastWeightInitDuringBuild=value}addLoss(losses4){losses4==null||Array.isArray(losses4)&&losses4.length===0||(losses4=toList(losses4),this._losses!==void 0&&this._losses!==null&&this.losses.push(...losses4))}computeOutputShape(inputShape){return inputShape}computeMask(inputs,mask){if(!this.supportsMasking){if(mask!=null)if(Array.isArray(mask))mask.forEach(maskElement=>{if(maskElement!=null)throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`)});else throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);return null}return mask}addInboundNode(inputTensors,outputTensors,inputMasks,outputMasks,inputShapes,outputShapes,kwargs=null){let inputTensorList=toList(inputTensors);outputTensors=toList(outputTensors),inputMasks=toList(inputMasks),outputMasks=toList(outputMasks),inputShapes=normalizeShapeList(inputShapes),outputShapes=normalizeShapeList(outputShapes);let inboundLayers=[],nodeIndices=[],tensorIndices=[];for(let x of inputTensorList)inboundLayers.push(x.sourceLayer),nodeIndices.push(x.nodeIndex),tensorIndices.push(x.tensorIndex);new Node({outboundLayer:this,inboundLayers,nodeIndices,tensorIndices,inputTensors:inputTensorList,outputTensors,inputMasks,outputMasks,inputShapes,outputShapes},kwargs);for(let i=0;iweight.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${this.name}' is already disposed.`)}dispose(){if(!this.built)throw new Error(`Cannot dispose Layer ${this.name} because it has not been built yet.`);if(this._refCount===null)throw new Error(`Cannot dispose Layer ${this.name} because it has not been used yet.`);this.assertNotDisposed();let numDisposedVariables=0;return--this._refCount==0&&(numDisposedVariables=this.disposeWeights()),{refCountAfterDispose:this._refCount,numDisposedVariables}}};function collectInputShape(inputTensors){inputTensors=toList(inputTensors);let shapes=[];for(let x of inputTensors)shapes.push(x.shape);return singletonOrArray(shapes)}function guessOutputDType(inputTensors){return"float32"}function getSourceInputs(tensor2,layer,nodeIndex){if((layer==null||nodeIndex!=null&&nodeIndex>0)&&(layer=tensor2.sourceLayer,nodeIndex=tensor2.nodeIndex),layer.inboundNodes.length===0)return[tensor2];{let node=layer.inboundNodes[nodeIndex];if(node.inboundLayers.length===0)return node.inputTensors;{let sourceTensors=[];for(let i=0;i0){let values=await Promise.all(promises);for(let i=0;iadd2(this.totals[key],mul(value,batchSize)));this.totals[key]=total,oldTotalsToDispose!=null&&oldTotalsToDispose.dispose()}}}async onEpochEnd(epoch,logs){if(logs!=null)for(let key of this.params.metrics)this.totals[key]!=null&&(typeof this.totals[key]=="number"?logs[key]=this.totals[key]/this.seen:tidy(()=>{let log52=mul(div(1,this.seen),this.totals[key]);logs[key]=log52,this.totals[key].dispose(),keep(logs[key])}))}},History=class extends BaseCallback{async onTrainBegin(logs){this.epoch=[],this.history={}}async onEpochEnd(epoch,logs){logs==null&&(logs={}),this.epoch.push(epoch);for(let key in logs)this.history[key]==null&&(this.history[key]=[]),this.history[key].push(logs[key])}async syncData(){let promises=[],keys=[],indices=[];for(let key in this.history){let valueArray=this.history[key];for(let i=0;inew CustomCallback(callbackConfig,yieldEvery))}var CallbackConstructorRegistry=class{constructor(){}static registerCallbackConstructor(verbosityLevel,callbackConstructor){util_exports.assert(verbosityLevel>=0&&Number.isInteger(verbosityLevel),()=>`Verbosity level is expected to be an integer >= 0, but got ${verbosityLevel}`),CallbackConstructorRegistry.checkForDuplicate(callbackConstructor),CallbackConstructorRegistry.constructors[verbosityLevel]==null&&(CallbackConstructorRegistry.constructors[verbosityLevel]=[]),CallbackConstructorRegistry.constructors[verbosityLevel].push(callbackConstructor)}static checkForDuplicate(callbackConstructor){for(let levelName in CallbackConstructorRegistry.constructors)CallbackConstructorRegistry.constructors[+levelName].forEach(ctor=>{if(ctor===callbackConstructor)throw new ValueError("Duplicate callback constructor.")})}static clear(){CallbackConstructorRegistry.constructors={}}static createCallbacks(verbosityLevel){let constructors=[];for(let levelName in CallbackConstructorRegistry.constructors){let level=+levelName;verbosityLevel>=level&&constructors.push(...CallbackConstructorRegistry.constructors[level])}return constructors.map(ctor=>new ctor)}};CallbackConstructorRegistry.constructors={};function configureCallbacks(callbacks2,verbose,epochs,initialEpoch,numTrainSamples,stepsPerEpoch,batchSize,doValidation,callbackMetrics){let history=new History,actualCallbacks=[new BaseLogger,...CallbackConstructorRegistry.createCallbacks(verbose)];callbacks2!=null&&actualCallbacks.push(...callbacks2),actualCallbacks.push(history);let callbackList=new CallbackList(actualCallbacks);return callbackList.setParams({epochs,initialEpoch,samples:numTrainSamples,steps:stepsPerEpoch,batchSize,verbose,doValidation,metrics:callbackMetrics}),{callbackList,history}}function deserialize(config2,customObjects={},fastWeightInit=!1){return deserializeKerasObject(config2,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"layer",fastWeightInit)}function l2Normalize(x,axis){return tidy(()=>{x.dtype!=="float32"&&(x=x.asType("float32"));let squareSum=sum2(square3(x),axis,!0),epsilonTensor=fill(squareSum.shape,epsilon2()),norm2=sqrt(maximum(squareSum,epsilonTensor));return div(x,norm2)})}function meanSquaredError2(yTrue,yPred){return tidy(()=>mean(square3(sub(yPred,yTrue)),-1))}function meanAbsoluteError(yTrue,yPred){return tidy(()=>mean(abs(sub(yPred,yTrue)),-1))}function meanAbsolutePercentageError(yTrue,yPred){return tidy(()=>{let diff=sub(yTrue,yPred),clippedTrue=clipByValue(abs(yTrue),epsilon2(),Number.MAX_VALUE),absResult=abs(div(diff,clippedTrue));return mul(100,mean(absResult,-1))})}function meanSquaredLogarithmicError(yTrue,yPred){return tidy(()=>{let clippedPred=clipByValue(yPred,epsilon2(),Number.MAX_VALUE),firstLog=log2(add2(1,clippedPred)),clippedTrue=clipByValue(yTrue,epsilon2(),Number.MAX_VALUE),secondLog=log2(add2(1,clippedTrue));return mean(square3(sub(firstLog,secondLog)),-1)})}function squaredHinge(yTrue,yPred){return tidy(()=>{let maxResult=maximum(0,sub(1,mul(yTrue,yPred)));return mean(square3(maxResult),-1)})}function hinge(yTrue,yPred){return tidy(()=>{let maxResult=maximum(0,sub(1,mul(yTrue,yPred)));return mean(maxResult,-1)})}function categoricalHinge(yTrue,yPred){return tidy(()=>{let pos=sum2(mul(yTrue,yPred),-1),neg4=max(mul(sub(1,yTrue),yPred),-1);return maximum(0,add2(1,sub(neg4,pos)))})}function logcosh(yTrue,yPred){return tidy(()=>{let log222=Math.log(2),predictionDiff=sub(yPred,yTrue),logcoshResult=sub(add2(predictionDiff,softplus(mul(-2,predictionDiff))),log222);return mean(logcoshResult,-1)})}function categoricalCrossentropy(target,output,fromLogits=!1){return tidy(()=>{if(fromLogits)output=softmax(output);else{let outputSum=sum2(output,output.shape.length-1,!0);output=div(output,outputSum)}return output=clipByValue(output,epsilon2(),1-epsilon2()),neg(sum2(mul(target.toFloat(),log2(output)),output.shape.length-1))})}function sparseCategoricalCrossentropy(target,output,fromLogits=!1){return tidy(()=>{let flatTarget=floor(flatten2(target)).toInt();output=clipByValue(output,epsilon2(),1-epsilon2());let outputShape=output.shape,oneHotTarget=oneHot(flatTarget,outputShape[outputShape.length-1]).reshape(outputShape);return categoricalCrossentropy(oneHotTarget,output,fromLogits)})}function sigmoidCrossEntropyWithLogits(labels,logits){if(!util_exports.arraysEqual(labels.shape,logits.shape))throw new ValueError(`logits and labels must have the same shape, but got shapes ${JSON.stringify(labels.shape)} and ${JSON.stringify(logits.shape)}`);return tidy(()=>{let reluLogits=logits.relu(),negAbsLogits=logits.abs().neg();return reluLogits.sub(logits.mul(labels)).add(negAbsLogits.exp().log1p())})}function binaryCrossentropy(yTrue,yPred){return tidy(()=>{let y;return y=clipByValue(yPred,epsilon2(),1-epsilon2()),y=log2(div(y,sub(1,y))),mean(sigmoidCrossEntropyWithLogits(yTrue,y),-1)})}function kullbackLeiblerDivergence(yTrue,yPred){return tidy(()=>{let clippedTrue=clipByValue(yTrue,epsilon2(),1),clippedPred=clipByValue(yPred,epsilon2(),1);return sum2(mul(yTrue,log2(div(clippedTrue,clippedPred))),-1)})}function poisson(yTrue,yPred){return tidy(()=>{let logPred=log2(add2(epsilon2(),yPred));return mean(sub(yPred,mul(yTrue,logPred)),-1)})}function cosineProximity(yTrue,yPred){return tidy(()=>{let trueNormalized=l2Normalize(yTrue,-1),predNormalized=l2Normalize(yPred,-1),trueXPred=mul(trueNormalized,predNormalized);return neg(sum2(trueXPred,-1))})}var lossesMap={meanSquaredError:meanSquaredError2,meanAbsoluteError,meanAbsolutePercentageError,meanSquaredLogarithmicError,squaredHinge,hinge,categoricalHinge,logcosh,categoricalCrossentropy,sparseCategoricalCrossentropy,binaryCrossentropy,kullbackLeiblerDivergence,poisson,cosineProximity};function get(identifierOrFn){if(typeof identifierOrFn=="string"){if(identifierOrFn in lossesMap)return lossesMap[identifierOrFn];let errMsg=`Unknown loss ${identifierOrFn}`;throw identifierOrFn.toLowerCase().includes("softmaxcrossentropy")&&(errMsg=`Unknown loss ${identifierOrFn}. Use "categoricalCrossentropy" as the string name for tf.losses.softmaxCrossEntropy`),new ValueError(errMsg)}else return identifierOrFn}function binaryAccuracy(yTrue,yPred){return tidy(()=>{let threshold2=mul(.5,onesLike(yPred)),yPredThresholded=cast5(greater(yPred,threshold2),yTrue.dtype);return mean(equal(yTrue,yPredThresholded),-1)})}function categoricalAccuracy(yTrue,yPred){return tidy(()=>cast5(equal(argMax(yTrue,-1),argMax(yPred,-1)),"float32"))}function truePositives(yTrue,yPred){return tidy(()=>logicalAnd(yTrue.equal(1),yPred.equal(1)).sum().cast("float32"))}function falseNegatives(yTrue,yPred){return tidy(()=>logicalAnd(yTrue.equal(1),yPred.equal(0)).sum().cast("float32"))}function falsePositives(yTrue,yPred){return tidy(()=>logicalAnd(yTrue.equal(0),yPred.equal(1)).sum().cast("float32"))}function precision(yTrue,yPred){return tidy(()=>{let tp=truePositives(yTrue,yPred),fp=falsePositives(yTrue,yPred),denominator=tp.add(fp);return where(greater(denominator,0),tp.div(denominator),0).cast("float32")})}function recall(yTrue,yPred){return tidy(()=>{let tp=truePositives(yTrue,yPred),fn=falseNegatives(yTrue,yPred),denominator=tp.add(fn);return where(greater(denominator,0),tp.div(denominator),0).cast("float32")})}function binaryCrossentropy2(yTrue,yPred){return binaryCrossentropy(yTrue,yPred)}function sparseCategoricalAccuracy(yTrue,yPred){return yTrue.rank===yPred.rank&&(yTrue=yTrue.squeeze([yTrue.rank-1])),yPred=yPred.argMax(-1),yPred.dtype!==yTrue.dtype&&(yPred=yPred.asType(yTrue.dtype)),equal(yTrue,yPred).asType("float32")}var mse=meanSquaredError2,MSE=meanSquaredError2,mae=meanAbsoluteError,MAE=meanAbsoluteError,mape=meanAbsolutePercentageError,MAPE=meanAbsolutePercentageError,categoricalCrossentropy2=categoricalCrossentropy,cosine=cosineProximity,sparseCategoricalCrossentropy2=sparseCategoricalCrossentropy,metricsMap={binaryAccuracy,categoricalAccuracy,precision,categoricalCrossentropy:categoricalCrossentropy2,sparseCategoricalCrossentropy:sparseCategoricalCrossentropy2,mse,MSE,mae,MAE,mape,MAPE,cosine};function get2(identifier){if(typeof identifier=="string"&&identifier in metricsMap)return metricsMap[identifier];if(typeof identifier!="string"&&identifier!=null)return identifier;throw new ValueError(`Unknown metric ${identifier}`)}function getLossOrMetricName(fn){if(assert2(fn!==null,`Unknown LossOrMetricFn ${fn}`),typeof fn=="string")return fn;{let fnName;for(let key of Object.keys(lossesMap))if(lossesMap[key]===fn){fnName=key;break}if(fnName!==void 0)return fnName;for(let key of Object.keys(metricsMap))if(metricsMap[key]===fn){fnName=key;break}return fnName!==void 0?fnName:fn.name}}function getOptimizer(identifier){let optimizerMap={Adagrad:()=>train.adagrad(.01),Adadelta:()=>train.adadelta(1,.95,epsilon2()),Adam:()=>train.adam(.001,.9,.999,epsilon2()),Adamax:()=>train.adamax(.002,.9,.999,epsilon2(),0),RMSProp:()=>train.rmsprop(.001,.9,0,epsilon2()),SGD:()=>train.sgd(.01)};if(optimizerMap.adagrad=optimizerMap.Adagrad,optimizerMap.adadelta=optimizerMap.Adadelta,optimizerMap.adam=optimizerMap.Adam,optimizerMap.adamax=optimizerMap.Adamax,optimizerMap.rmsprop=optimizerMap.RMSProp,optimizerMap.sgd=optimizerMap.SGD,identifier in optimizerMap)return optimizerMap[identifier]();throw new ValueError(`Unknown Optimizer ${identifier}`)}var MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH=1*1024*1024;function checkUserDefinedMetadata(userDefinedMetadata,modelName,checkSize=!1){if(userDefinedMetadata==null||typeof userDefinedMetadata!="object"||Object.getPrototypeOf(userDefinedMetadata)!==Object.prototype||!plainObjectCheck(userDefinedMetadata))throw new Error("User-defined metadata is expected to be a JSON object, but is not.");if(checkSize){let out=JSON.stringify(userDefinedMetadata);out.length>MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH&&console.warn(`User-defined metadata of model "${modelName}" is too large in size (length=${out.length} when serialized). It is not recommended to store such large objects in user-defined metadata. Please make sure its serialized length is <= ${MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH}.`)}}function plainObjectCheck(x){if(x===null)return!0;if(typeof x=="object")if(Object.getPrototypeOf(x)===Object.prototype){let keys=Object.keys(x);for(let key of keys)if(typeof key!="string"||!plainObjectCheck(x[key]))return!1;return!0}else if(Array.isArray(x)){for(let item of x)if(!plainObjectCheck(item))return!1;return!0}else return!1;else{let xType=typeof x;return xType==="string"||xType==="number"||xType==="boolean"}}function printSummary(model2,lineLength,positions,printFn=console.log){let sequentialLike=isModelSequentialLike(model2),toDisplay=["Layer (type)","Output shape","Param #"];sequentialLike?(lineLength=lineLength||65,positions=positions||[.45,.85,1]):(lineLength=lineLength||98,positions=positions||[.33,.55,.67,1]),positions[positions.length-1]<=1&&(positions=positions.map(p2=>Math.floor(lineLength*p2)));let relevantNodes;if(!sequentialLike){toDisplay.push("Receives inputs"),relevantNodes=[];for(let depth in model2.nodesByDepth)relevantNodes.push(...model2.nodesByDepth[depth])}printFn("_".repeat(lineLength)),printRow(toDisplay,positions,printFn),printFn("=".repeat(lineLength));let layers=model2.layers;for(let i=0;i1||depthNodes.length===1&&depthNodes[0].inboundLayers.length>1){sequentialLike=!1;break}nodes.push(...depthNodes)}if(sequentialLike)for(let layer of model2.layers){let flag=!1;for(let node of layer.inboundNodes)if(nodes.indexOf(node)!==-1)if(flag){sequentialLike=!1;break}else flag=!0;if(!sequentialLike)break}return sequentialLike}function printRow(fields,positions,printFn=console.log){let line="";for(let i=0;i0&&(line=line.slice(0,line.length-1)+" "),line+=fields[i],line=line.slice(0,positions[i]),line+=" ".repeat(positions[i]-line.length);printFn(line)}function printLayerSummary(layer,positions,printFn){let outputShape;try{outputShape=JSON.stringify(layer.outputShape)}catch(err){outputShape="multiple"}let name=layer.name,className=layer.getClassName(),fields=[`${name} (${className})`,outputShape,layer.countParams().toString()];printRow(fields,positions,printFn)}function printLayerSummaryWithConnections(layer,positions,relevantNodes,printFn){let outputShape;try{outputShape=JSON.stringify(layer.outputShape)}catch(err){outputShape="multiple"}let connections=[];for(let node of layer.inboundNodes)if(!(relevantNodes!=null&&relevantNodes.length>0&&relevantNodes.indexOf(node)===-1))for(let i=0;it.name),finalOutputs=[],feedNames=feedDict.names();for(let outputName of outputNames)feedNames.indexOf(outputName)!==-1?finalOutputs.push(feedDict.getValue(outputName)):finalOutputs.push(null);probe!=null&&(probe.maxNumTensors=-Infinity,probe.minNumTensors=Infinity);let fetchAndFeedKey=outputNames.join(",")+"|"+feedDict.names().join(","),sorted,recipientCounts;if(cachedSorted[fetchAndFeedKey]==null){let out=getTopologicalSortAndRecipientCounts(fetchArray,feedDict);sorted=out.sorted,recipientCounts=out.recipientCounts,cachedSorted[fetchAndFeedKey]=sorted,cachedRecipientCounts[fetchAndFeedKey]=recipientCounts}sorted=cachedSorted[fetchAndFeedKey],recipientCounts={},training||Object.assign(recipientCounts,cachedRecipientCounts[fetchAndFeedKey]);let internalFeedDict=new FeedDict(feedDict);for(let i=0;iprobe.maxNumTensors&&(probe.maxNumTensors=numTensors),numTensors0,()=>"Expected at least one fetch, got none");let finalSorted=[],finalRecipientMap={};if(fetches.length===1){let out=getTopologicalSortAndRecipientCountsForOneFetch(fetches[0],feedDict);finalSorted=out.sorted,finalRecipientMap=out.recipientMap}else{let visited=new Set;for(let fetch3 of fetches){let{sorted,recipientMap}=getTopologicalSortAndRecipientCountsForOneFetch(fetch3,feedDict);for(let symbolicTensor of sorted)visited.has(symbolicTensor.name)||(finalSorted.push(symbolicTensor),visited.add(symbolicTensor.name));for(let name in recipientMap)finalRecipientMap[name]==null&&(finalRecipientMap[name]=new Set),recipientMap[name].forEach(recipient=>finalRecipientMap[name].add(recipient))}}return{sorted:finalSorted,recipientCounts:recipientMap2Counts(finalRecipientMap)}}function recipientMap2Counts(recipientMap){let recipientCounts={};for(let name in recipientMap)recipientCounts[name]=recipientMap[name].size;return recipientCounts}function getTopologicalSortAndRecipientCountsForOneFetch(fetch3,feedDict){let visited=new Set,sorted=[],recipientMap={};for(let key of feedDict.names())visited.add(key);let stack2=[],marks=[];for(stack2.push(fetch3);stack2.length>0;){let top=stack2[stack2.length-1];if(visited.has(top.name)){stack2.pop();continue}let topIsMarked=marks[marks.length-1]===stack2.length-1;if(top.inputs.length===0||topIsMarked)stack2.pop(),sorted.push(top),visited.add(top.name),topIsMarked&&marks.pop();else{marks.push(stack2.length-1);for(let input2 of top.inputs)recipientMap[input2.name]==null&&(recipientMap[input2.name]=new Set),recipientMap[input2.name].add(top.name),!visited.has(input2.name)&&stack2.push(input2)}}return{sorted,recipientMap}}function getNodeOutputs(fetch3){let layerOutputs;if(fetch3.sourceLayer.inboundNodes.length===1)layerOutputs=fetch3.sourceLayer.output;else{let nodeIndex=null;for(let i=0;ix.name)}`);unique4(this.outputs).length!==this.outputs.length&&console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map(x=>x.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(let x of this.outputs){let layer=x.sourceLayer,nodeIndex=x.nodeIndex,tensorIndex=x.tensorIndex;this.outputLayers.push(layer),this.outputLayersNodeIndices.push(nodeIndex),this.outputLayersTensorIndices.push(tensorIndex)}for(let x of this.inputs){let layer=x.sourceLayer,nodeIndex=x.nodeIndex,tensorIndex=x.tensorIndex;assert2(nodeIndex===0,"input layer has >1 nodes"),assert2(tensorIndex===0,"input layer has >1 tensors"),this.inputLayers.push(layer),this.inputLayersNodeIndices.push(nodeIndex),this.inputLayersTensorIndices.push(tensorIndex)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let i=0;ix.shape),this.internalOutputShapes=this.outputs.map(x=>x.shape);let nodesDepths={},nodeIDToNode={},layersDepths={},layerIDToLayer={},layerIndices={},nodesInDecreasingDepth=[],buildMapOfGraph=(tensor2,finishedNodes2,nodesInProgress2,layer,nodeIndex,tensorIndex)=>{(layer==null||nodeIndex==null||tensorIndex==null)&&(layer=tensor2.sourceLayer,nodeIndex=tensor2.nodeIndex,tensorIndex=tensor2.tensorIndex);let node=layer.inboundNodes[nodeIndex];if(nodesInProgress2.indexOf(node)!==-1)throw new RuntimeError(`The tensor ${tensor2.name} at layer "${layer.name}" is part of a cycle.`);if(finishedNodes2.indexOf(node)!==-1)return;this.containerNodes.add(Container.nodeKey(layer,nodeIndex)),layer.id in layerIndices||(layerIndices[layer.id]=Object.keys(layerIndices).length),nodesInProgress2.indexOf(node)===-1&&nodesInProgress2.push(node);let numInboundLayers=node.inboundLayers.length;for(let i=0;i=0;)nodesInProgress2.splice(nodesInProgress2.indexOf(node),1);nodesInDecreasingDepth.push(node)},finishedNodes=[],nodesInProgress=[];for(let x of this.outputs)buildMapOfGraph(x,finishedNodes,nodesInProgress);let reversedNodesInDecreasingDepth=nodesInDecreasingDepth.slice().reverse();for(let node of reversedNodesInDecreasingDepth){nodeIDToNode[node.id]=node,node.id in nodesDepths||(nodesDepths[node.id]=0);let depth=nodesDepths[node.id],previousDepth=layersDepths[node.outboundLayer.id]==null?0:layersDepths[node.outboundLayer.id];depth=Math.max(depth,previousDepth),layersDepths[node.outboundLayer.id]=depth,layerIDToLayer[node.outboundLayer.id]=node.outboundLayer,nodesDepths[node.id]=depth;for(let i=0;iparseInt(x,10)).sort(reverseNumberCompare);this.layers=[];for(let depth of depthKeys){let layersForDepth=layersByDepth[depth];layersForDepth.sort((a,b)=>{let aIndex=layerIndices[a.id],bIndex=layerIndices[b.id];return aIndexbIndex?1:0});for(let layer of layersForDepth)layer instanceof Container&&this.internalContainerRefs.push(layer),this.layers.push(layer)}this.layersByDepth=layersByDepth,depthKeys=Object.keys(nodesByDepth).map(x=>parseInt(x,10)).sort(reverseNumberCompare);let computableTensors=this.inputs.slice(),layersWithCompleteInput=[];for(let depth of depthKeys)for(let node of nodesByDepth[depth]){let layer=node.outboundLayer;if(layer!=null){for(let x of node.inputTensors)if(computableTensors.indexOf(x)===-1)throw new RuntimeError(`Graph disconnected: cannot obtain value for tensor ${x} at layer "${layer.name}". The following previous layers were accessed without issue: ${layersWithCompleteInput}`);for(let x of node.outputTensors)computableTensors.push(x);layersWithCompleteInput.push(layer.name)}}this.nodesByDepth=nodesByDepth;let allNames=this.layers.map(x=>x.name);for(let name of allNames){let numOccurrences=allNames.filter(x=>x===name).length;if(numOccurrences!==1)throw new RuntimeError(`The name "${name}" is used ${numOccurrences} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(allNames))}this.outboundNodes=[],this.inboundNodes=[],new Node({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(x=>null),outputMasks:this.outputs.map(x=>null),inputShapes:this.inputs.map(x=>x.shape),outputShapes:this.outputs.map(x=>x.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();let result={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount==0){for(let layer of this.layers)result.numDisposedVariables+=layer.dispose().numDisposedVariables;for(let container of this.internalContainerRefs)result.numDisposedVariables+=container.dispose().numDisposedVariables}return result.refCountAfterDispose=this._refCount,result}get trainable(){return this.trainable_}set trainable(trainable){this.layers.forEach(layer=>{layer._trainableWeights.forEach(w=>w.trainable=trainable)}),this.trainable_=trainable}get trainableWeights(){if(this._trainableWeights.length>0)throw new ValueError("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let weights=[];for(let layer of this.layers)weights=weights.concat(layer.trainableWeights);return weights}get nonTrainableWeights(){let weights=[];for(let layer of this.layers)weights.push(...layer.nonTrainableWeights);if(!this.trainable){let trainableWeights=[];for(let layer of this.layers)trainableWeights.push(...layer.trainableWeights);return trainableWeights.concat(weights)}return weights}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(weights,strict=!0){let nameToWeight={},totalWeightsCount=0;for(let layer of this.layers)for(let weight of layer.weights){if(nameToWeight[weight.originalName]!=null)throw new ValueError(`Duplicate weight name: ${weight.originalName}`);nameToWeight[weight.originalName]=weight,totalWeightsCount++}let weightValueTuples=[];for(let name in weights){let validatedName=name;if(nameToWeight[name]==null){let tokens=name.split("/");validatedName=tokens.slice(0,-2).concat([tokens[tokens.length-1]]).join("/")}if(nameToWeight[validatedName]!=null)weightValueTuples.push([nameToWeight[validatedName],weights[name]]);else if(strict)throw new ValueError(`Provided weight data has no target variable: ${name}`);delete nameToWeight[validatedName]}if(strict){let unsetNames=[];for(let name in nameToWeight)unsetNames.push(name);if(unsetNames.length>0)throw new ValueError(`${unsetNames.length} of ${totalWeightsCount} weights are not set: ${unsetNames}`)}batchSetValue(weightValueTuples)}updatedConfig(){let theConfig=this.getConfig(),modelConfig={};return modelConfig.className=this.getClassName(),modelConfig.config=theConfig,modelConfig.kerasVersion=`tfjs-layers ${version10}`,modelConfig.backend="TensorFlow.js",modelConfig}toJSON(unused,returnString=!0){let modelConfig=convertTsToPythonic(this.updatedConfig());return returnString?JSON.stringify(modelConfig):modelConfig}call(inputs,kwargs){return tidy(()=>{inputs=toList(inputs);let feedDict=new FeedDict;for(let i=0;i{inputs=toList(inputs);let masks;return mask==null?masks=pyListRepeat(null,inputs.length):masks=toList(mask),this.runInternalGraph(inputs,masks)[1]})}computeOutputShape(inputShape){let inputShapes=normalizeShapeList(inputShape);if(inputShapes.length!==this.inputLayers.length)throw new ValueError(`Invalid inputShape argument ${inputShape}: model has ${this.inputLayers.length} tensor inputs.`);let layersToOutputShapes={};for(let i=0;iparseInt(x,10)).sort(reverseNumberCompare);if(depthKeys.length>1)for(let depth of depthKeys){let nodes=this.nodesByDepth[depth];for(let node of nodes){let layer=node.outboundLayer;if(this.inputLayers.map(x=>x.id).indexOf(layer.id)!==-1)continue;let inputShapes2=[];for(let j=0;jparseInt(x,10)).sort(reverseNumberCompare);for(let depth of depthKeys){let nodes=this.nodesByDepth[depth];for(let node of nodes){let layer=node.outboundLayer,referenceInputTensors=node.inputTensors,referenceOutputTensors=node.outputTensors,computedData=new Array;for(let x of referenceInputTensors)x.id in tensorMap&&computedData.push(tensorMap[x.id]);if(computedData.length===referenceInputTensors.length){let kwargs={},computedTensors,computedMasks,outputTensors2,outputMasks2;if(node.callArgs!=null&&(kwargs=node.callArgs),computedData.length===1){let[computedTensor,computedMask]=computedData[0];kwargs.mask==null&&(kwargs.mask=computedMask),outputTensors2=toList(layer.call(computedTensor,kwargs)),outputMasks2=toList(layer.computeMask(computedTensor,computedMask)),computedTensors=[computedTensor],computedMasks=[computedMask]}else computedTensors=computedData.map(x=>x[0]),computedMasks=computedData.map(x=>x[1]),kwargs.mask==null&&(kwargs.mask=computedMasks),outputTensors2=toList(layer.call(computedTensors,kwargs)),outputMasks2=toList(layer.computeMask(computedTensors,computedMasks));if(layer.activityRegularizer)throw new NotImplementedError("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let i=0;i{let losses4=[];for(let layer of this.layers)for(let nodeIndex=0;nodeIndex0){let nodeData=[];for(let i=0;i0&&layer.apply(singletonOrArray(inputTensors2),kwargs)}function processLayer(layerData){let layerName=layerData.name,layer=deserialize(layerData,config2.customObjects!=null?config2.customObjects:{});layer.setFastWeightInitDuringBuild(fastWeightInit),createdLayers[layerName]=layer,layerData.inboundNodes.forEach(nodeData=>{if(!(nodeData instanceof Array))throw new ValueError(`Corrupted configuration, expected array for nodeData: ${nodeData}`);addUnprocessedNode(layer,nodeData)})}let name=config2.name,layersFromConfig=config2.layers;for(let layerData of layersFromConfig)processLayer(layerData);for(;!isObjectEmpty(unprocessedNodes);)for(let layerData of layersFromConfig){let layer=createdLayers[layerData.name];if(layer.name in unprocessedNodes){let currentUnprocessedNodesForLayer=unprocessedNodes[layer.name];delete unprocessedNodes[layer.name];for(let nodeData of currentUnprocessedNodesForLayer)processNode(layer,nodeData)}}let inputTensors=[],outputTensors=[],inputLayersFromConfig=config2.inputLayers;for(let layerData of inputLayersFromConfig){let layerName=layerData[0],nodeIndex=layerData[1],tensorIndex=layerData[2];assert2(layerName in createdLayers);let layerOutputTensors=createdLayers[layerName].inboundNodes[nodeIndex].outputTensors;inputTensors.push(layerOutputTensors[tensorIndex])}let outputLayersFromConfig=config2.outputLayers;for(let layerData of outputLayersFromConfig){let layerName=layerData[0],nodeIndex=layerData[1],tensorIndex=layerData[2];assert2(layerName in createdLayers);let layerOutputTensors=createdLayers[layerName].inboundNodes[nodeIndex].outputTensors;outputTensors.push(layerOutputTensors[tensorIndex])}return new cls({inputs:inputTensors,outputs:outputTensors,name})}get stateful(){if(this._stateful)throw new ValueError("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(let layer of this.layers)if(layer.stateful)return!0;return!1}resetStates(){tidy(()=>{this.layers.forEach(layer=>{layer.stateful&&layer.resetStates()})})}};function standardizeSampleOrClassWeights(xWeight,outputNames,weightType){let numOutputs=outputNames.length;if(xWeight==null||Array.isArray(xWeight)&&xWeight.length===0)return outputNames.map(name=>null);if(numOutputs===1)return Array.isArray(xWeight)&&xWeight.length===1?xWeight:typeof xWeight=="object"&&outputNames[0]in xWeight?[xWeight[outputNames[0]]]:[xWeight];if(Array.isArray(xWeight)){if(xWeight.length!==numOutputs)throw new Error(`Provided ${weightType} is an array of ${xWeight.length} element(s), but the model has ${numOutputs} outputs. Make sure a set of weights is provided for each model output.`);return xWeight}else if(typeof xWeight=="object"&&Object.keys(xWeight).length>0&&typeof xWeight[Object.keys(xWeight)[0]]=="object"){let output=[];return outputNames.forEach(outputName=>{outputName in xWeight?output.push(xWeight[outputName]):output.push(null)}),output}else throw new Error(`The model has multiple (${numOutputs}) outputs, so ${weightType} must be either an array with ${numOutputs} elements or an object with ${outputNames} keys. Provided ${weightType} not understood: ${JSON.stringify(xWeight)}`)}function standardizeClassWeights(classWeight,outputNames){return standardizeSampleOrClassWeights(classWeight,outputNames,"classWeight")}async function standardizeWeights(y,sampleWeight,classWeight,sampleWeightMode){if(sampleWeight!=null||sampleWeightMode!=null)throw new Error("Support sampleWeight is not implemented yet");if(classWeight!=null){let yClasses=tidy(()=>{if(y.shape.length===1)return y.clone();if(y.shape.length===2)if(y.shape[1]>1){let axis=1;return y.argMax(axis)}else{if(y.shape[1]===1)return y.reshape([y.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${y.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${y.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),yClassIndices=Array.from(await yClasses.data());dispose(yClasses);let classSampleWeight=[];return yClassIndices.forEach(classIndex=>{if(classWeight[classIndex]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${classIndex} exists in the data but not in classWeight`);classSampleWeight.push(classWeight[classIndex])}),tensor1d(classSampleWeight,"float32")}else return null}function computeWeightedLoss2(losses4,sampleWeights){return mul(losses4,sampleWeights)}var DEFAULT_VALIDATION_BATCH_SIZE=32;function standardizeDataIteratorOutput(model2,iteratorOut){let xs,ys,iteratorOutObj=iteratorOut;xs=iteratorOutObj.xs,ys=iteratorOutObj.ys,util_exports.assert(xs!=null&&ys!=null,()=>`A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor. The provided Dataset instead generates ${iteratorOut}`);let flattenedXs=flattenTensorOrArrayOrMap("input",model2.inputNames,xs),flattenedYs=flattenTensorOrArrayOrMap("output",model2.outputNames,ys),batchSize=flattenedXs[0].shape[0];util_exports.assert(flattenedXs.length===model2.inputs.length,()=>`LayersModel has ${model2.inputs.length} inputs, but the dataset provides ${flattenedXs.length} inputs. (Expected input keys: ${JSON.stringify(model2.inputNames)})`),util_exports.assert(flattenedYs.length===model2.outputs.length,()=>`LayersModel has ${model2.outputs.length} outputs, but the dataset provides ${flattenedYs.length} outputs. (Expected output keys: ${JSON.stringify(model2.outputNames)})`);for(let xIndex=0;xIndex`Batch size mismatch: input ${model2.inputNames[xIndex]} has ${flattenedXs[xIndex].shape[0]}; expected ${batchSize} based on input ${model2.inputNames[0]}.`);for(let yIndex=0;yIndex`Batch size mismatch: output ${model2.outputNames[yIndex]} has ${flattenedYs[yIndex].shape[0]}; expected ${batchSize} based on input ${model2.inputNames[0]}.`);return{xs:flattenedXs,ys:flattenedYs}}function flattenTensorOrArrayOrMap(inputOrOutput,names,values){if(values instanceof Tensor)return[values];if(Array.isArray(values))return util_exports.assert(values.length===names.length,()=>`Received an array of ${values.length} Tensors, but expected ${names.length} to match the ${inputOrOutput} keys ${names}.`),values;{let result=[];for(let name of names){if(values[name]==null)throw new ValueError(`The feature data generated by the dataset lacks the required ${inputOrOutput} key '${name}'.`);result.push(values[name])}return result}}function standardizeTensorValidationData(data2){if(data2.length===3)throw new NotImplementedError("Validation with sample weights is not implemented yet.");return{xs:data2[0],ys:data2[1]}}async function fitDataset(model2,dataset,args){let hasBatchesPerEpoch=args.batchesPerEpoch!=null;if(util_exports.assert(model2.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),util_exports.assert(args!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),util_exports.assert(args.epochs!=null&&args.epochs>0&&Number.isInteger(args.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${args.epochs}`),util_exports.assert(!hasBatchesPerEpoch||args.batchesPerEpoch>0&&Number.isInteger(args.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${args.batchesPerEpoch}`),util_exports.assert(args.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. Use validationData instead."),model2.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");model2.isTraining=!0;try{let doValidation=args.validationData!=null,valXs,valYs;if(doValidation)if(isDatasetObject(args.validationData))util_exports.assert(args.validationBatches==null||args.validationBatches>0&&Number.isInteger(args.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${args.validationBatches}`);else{let validationData=standardizeTensorValidationData(args.validationData);valXs=validationData.xs,valYs=validationData.ys}let trainFunction=model2.makeTrainFunction(),outLabels=model2.getDedupedMetricsNames(),callbackMetrics;doValidation?callbackMetrics=outLabels.slice().concat(outLabels.map(n=>"val_"+n)):callbackMetrics=outLabels.slice();let callbacks2=standardizeCallbacks(args.callbacks,args.yieldEvery),verbose=args.verbose==null?1:args.verbose,{callbackList,history}=configureCallbacks(callbacks2,verbose,args.epochs,null,null,getStepsPerEpoch(dataset,args),null,doValidation,callbackMetrics);callbackList.setModel(model2),model2.history=history,await callbackList.onTrainBegin(),model2.stopTraining_=!1;let epoch=args.initialEpoch==null?0:args.initialEpoch,dataIterator=await dataset.iterator();for(;epoch=args.batchesPerEpoch:iteratorOut.done){if(doValidation){let valOuts;isDatasetObject(args.validationData)?valOuts=toList(await model2.evaluateDataset(args.validationData,{batches:args.validationBatches})):valOuts=toList(model2.evaluate(valXs,valYs,{batchSize:args.validationBatchSize==null?DEFAULT_VALIDATION_BATCH_SIZE:args.validationBatchSize,verbose:0}));for(let i=0;i0)throw new NotImplementedError("Verbose mode is not implemented yet.");util_exports.assert(!hasBatches||args.batches>0&&Number.isInteger(args.batches),()=>`Test loop expects \`batches\` to be a positive integer, but received ${JSON.stringify(args.batches)}`);let dataIterator=isLazyIteratorObject(dataset)?dataset:await dataset.iterator(),numExamples=0,batch=0;for(;hasBatches?batch{if(iteratorOut.value){let{xs,ys}=standardizeDataIteratorOutput(model2,iteratorOut.value),xsAndYs=xs.concat(ys),batchOuts=tidy(()=>f(xsAndYs));if(dispose(xsAndYs),batch===0)for(let i=0;iadd2(outs[i],mul(batchSize,batchOut))),batch>0&&dispose(oldScalar)}dispose(batchOuts),numExamples+=batchSize,++batch}return outs}),iteratorOut.done){hasBatches&&console.warn(`Your dataset iterator ran out of data during evaluateDataset(). Interrupting evalution. Make sure that your dataset can generate at least \`batches\` batches (in this case, ${args.batches} batches). You may need to use the repeat() function when building your dataset.`);break}}for(let i=0;i0&&Number.isInteger(batchSize),()=>`batchSize is required to be a positive integer, but got ${batchSize}`)}function sliceArrays(arrays,start,stop){return arrays==null?[null]:Array.isArray(arrays)?arrays.map(array2=>sliceAlongFirstAxis(array2,start,stop-start)):sliceAlongFirstAxis(arrays,start,stop-start)}function sliceArraysByIndices(arrays,indices){return tidy(()=>arrays==null?null:Array.isArray(arrays)?arrays.map(array2=>sliceArraysByIndices(array2,indices)):gather2(arrays,indices.dtype==="int32"?indices:indices.toInt()))}function makeBatches(size,batchSize){let output=[],batchStart=0,batchEnd=null;for(;batchStart=size&&(batchEnd=size),output.push([batchStart,batchEnd]),batchStart=batchEnd;return output}async function fitLoop(model2,f,ins,outLabels,batchSize,epochs,verbose,callbacks2,valF,valIns,shuffle2,callbackMetrics,initialEpoch,stepsPerEpoch,validationSteps){batchSize==null&&(batchSize=32),epochs==null&&(epochs=1),shuffle2==null&&(shuffle2=!0),initialEpoch==null&&(initialEpoch=0);let doValidation=!1;if(valF!=null&&valIns!=null&&(doValidation=!0),validationSteps!=null&&(doValidation=!0,stepsPerEpoch==null))throw new ValueError("Can only use `validationSteps` when doing step-wise training, i.e., `stepsPerEpoch` must be set.");let numTrainSamples=model2.checkNumSamples(ins,batchSize,stepsPerEpoch,"steps_per_epoch"),indexArray;numTrainSamples!=null&&(indexArray=range5(0,numTrainSamples)),verbose==null&&(verbose=1);let{callbackList,history}=configureCallbacks(callbacks2,verbose,epochs,initialEpoch,numTrainSamples,stepsPerEpoch,batchSize,doValidation,callbackMetrics);callbackList.setModel(model2),model2.history=history,await callbackList.onTrainBegin(),model2.stopTraining_=!1;for(let epoch=initialEpoch;epoch{let batchStart=batches[batchIndex][0],batchEnd=batches[batchIndex][1],batchIds=sliceAlongFirstAxis(epochIndexArray1D,batchStart,batchEnd-batchStart);batchLogs.batch=batchIndex,batchLogs.size=batchEnd-batchStart;let insBatch=sliceArraysByIndices(ins,batchIds),outs=f(insBatch);for(let i=0;i0){if(doValidation=!0,args.validationData.length===2)inputValX=args.validationData[0],inputValY=args.validationData[1];else throw args.validationData.length===3?new NotImplementedError("validationData including sample weights is not supported yet."):new ValueError(`When passing validation data, it must contain 2 (valX, valY) or 3 (valX, valY, valSampleWeight) items; ${args.validationData} is invalid.`);let checkBatchAxis2=!0,valStandardized=await model2.standardizeUserData(inputValX,inputValY,null,null,checkBatchAxis2,batchSize);valX=valStandardized[0],valY=valStandardized[1],valIns=valX.concat(valY)}else if(args.validationSplit!=null&&args.validationSplit>0&&args.validationSplit<1){doValidation=!0;let splitAt=Math.floor(inputs[0].shape[0]*(1-args.validationSplit)),originalBatchSize=inputs[0].shape[0];valX=sliceArrays(inputs,splitAt,originalBatchSize),inputs=sliceArrays(inputs,0,splitAt),valY=sliceArrays(targets,splitAt,originalBatchSize),targets=sliceArrays(targets,0,splitAt),valIns=valX.concat(valY)}else args.validationSteps!=null&&(doValidation=!0);let ins=inputs.concat(targets).concat(sampleWeights);model2.checkTrainableWeightsConsistency();let trainFunction=model2.makeTrainFunction(),outLabels=model2.getDedupedMetricsNames(),valFunction,callbackMetrics;doValidation?(model2.makeTestFunction(),valFunction=model2.testFunction,callbackMetrics=outLabels.slice().concat(outLabels.map(n=>"val_"+n))):(valFunction=null,valIns=[],callbackMetrics=outLabels.slice());let callbacks2=standardizeCallbacks(args.callbacks,args.yieldEvery);return await fitLoop(model2,trainFunction,ins,outLabels,batchSize,args.epochs,args.verbose,callbacks2,valFunction,valIns,args.shuffle,callbackMetrics,args.initialEpoch,null,null)}finally{model2.isTraining=!1,disposeNewTensors(inputs,x),disposeNewTensors(targets,y),disposeNewTensors(valX,inputValX),disposeNewTensors(valY,inputValY),sampleWeights!=null&&dispose(sampleWeights)}}function ensureTensorsRank2OrHigher(tensors){let outs=[];tensors instanceof Tensor&&(tensors=[tensors]);for(let i=0;ioldTensorIds.push(t.id));else if(refTensors!=null)for(let name in refTensors){let oldTensor=refTensors[name];oldTensorIds.push(oldTensor.id)}let tensorsToDispose=[];if(tensors instanceof Tensor)oldTensorIds.indexOf(tensors.id)===-1&&tensorsToDispose.push(tensors);else if(Array.isArray(tensors))tensors.forEach(t=>{oldTensorIds.indexOf(t.id)===-1&&tensorsToDispose.push(t)});else if(tensors!=null)for(let name in tensors){let tensor2=tensors[name];oldTensorIds.indexOf(tensor2.id)===-1&&tensorsToDispose.push(tensor2)}tensorsToDispose.forEach(t=>{t.isDisposed||t.dispose()})}function isDataTensor(x){return x instanceof Tensor}function isDataArray(x){return Array.isArray(x)}function isDataDict(x){return!isDataTensor(x)&&!isDataArray(x)}function standardizeInputData(data2,names,shapes,checkBatchAxis=!0,exceptionPrefix=""){if(names==null||names.length===0){if(data2!=null){let gotUnexpectedData=!1;if(isDataArray(data2)&&data2.length>0)gotUnexpectedData=!0;else if(isDataDict(data2)){for(let key in data2)if(data2.hasOwnProperty(key)){gotUnexpectedData=!0;break}}else gotUnexpectedData=!0;if(gotUnexpectedData)throw new ValueError(`Error when checking model ${exceptionPrefix} expected no data, but got ${data2}`)}return[]}if(data2==null)return names.map(name=>null);let arrays;if(isDataDict(data2)){data2=data2,arrays=[];for(let name of names){if(data2[name]==null)throw new ValueError(`No data provided for "${name}". Need data for each key in: ${names}`);arrays.push(data2[name])}}else if(isDataArray(data2)){if(data2=data2,data2.length!==names.length)throw new ValueError(`Error when checking model ${exceptionPrefix}: the Array of Tensors that you are passing to your model is not the size the model expected. Expected to see ${names.length} Tensor(s), but instead got the following list of Tensor(s): ${data2}`);arrays=data2}else{if(data2=data2,names.length>1)throw new ValueError(`The model ${exceptionPrefix} expects ${names.length} Tensor(s), but only received one Tensor. Found: Tensor with shape ${data2.shape}`);arrays=[data2]}if(arrays=ensureTensorsRank2OrHigher(arrays),shapes!=null)for(let i=0;i=0&&dim!==refDim)throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} to have shape [${shapes[i]}], but got array with shape [${array2.shape}].`)}}return arrays}function checkArrayLengths(inputs,targets,weights){let setX=unique4(inputs.map(input2=>input2.shape[0]));setX.sort();let setY=unique4(targets.map(target=>target.shape[0]));if(setY.sort(),setX.length>1)throw new ValueError(`All input Tensors (x) should have the same number of samples. Got array shapes: ${JSON.stringify(inputs.map(input2=>input2.shape))}`);if(setY.length>1)throw new ValueError(`All target Tensors (y) should have the same number of samples. Got array shapes: ${JSON.stringify(targets.map(target=>target.shape))}`);if(setX.length>0&&setY.length>0&&!util_exports.arraysEqual(setX,setY))throw new ValueError(`Input Tensors should have the same number of samples as target Tensors. Found ${setX[0]} input sample(s) and ${setY[0]} target sample(s).`)}function checkLossAndTargetCompatibility(targets,lossFns,outputShapes){let keyLosses=[meanSquaredError2,binaryCrossentropy,categoricalCrossentropy];for(let i=0;i1)throw new ValueError(`The model expects ${names.length} ${exceptionPrefix} Tensors, but only received one Tensor. Found: array with shape ${JSON.stringify(data2.shape)}.`);arrays=[data2]}if(shapes!=null)for(let i=0;i[]);let wrappedMetrics;if(typeof metrics2=="string"||typeof metrics2=="function")wrappedMetrics=[metrics2];else if(Array.isArray(metrics2)||typeof metrics2=="object")wrappedMetrics=metrics2;else throw new TypeError(`Type of metrics argument not understood. Expected an string,function, Array, or Object, found: ${metrics2}`);if(Array.isArray(wrappedMetrics))return outputNames.map(name=>wrappedMetrics);{let nestedMetrics=[];for(let name of outputNames){let outputMetrics=wrappedMetrics.hasOwnProperty(name)?wrappedMetrics[name]:[];Array.isArray(outputMetrics)||(outputMetrics=[outputMetrics]),nestedMetrics.push(outputMetrics)}return nestedMetrics}}var LAYERS_MODEL_FORMAT_NAME="layers-model",LayersModel=class extends Container{constructor(args){super(args);this.isTraining=!1}summary(lineLength,positions,printFn=console.log){if(!this.built)throw new ValueError("This model has never been called, thus its weights have not been created yet. So no summary can be displayed. Build the model first (e.g., by calling it on some test data).");printSummary(this,lineLength,positions,printFn)}compile(args){if(args.loss==null&&(args.loss=[]),this.loss=args.loss,typeof args.optimizer=="string")this.optimizer_=getOptimizer(args.optimizer),this.isOptimizerOwned=!0;else{if(!(args.optimizer instanceof Optimizer))throw new ValueError("User-defined optimizer must be an instance of tf.Optimizer.");this.optimizer_=args.optimizer,this.isOptimizerOwned=!1}let lossFunctions=[];if(!Array.isArray(args.loss)&&typeof args.loss!="string"&&typeof args.loss!="function"){args.loss=args.loss;for(let name in args.loss)if(this.outputNames.indexOf(name)===-1)throw new ValueError(`Unknown entry in loss dictionary: "${name}". Only expected the following keys: ${this.outputNames}`);for(let name of this.outputNames)args.loss[name]==null&&console.warn(`Output "${name}" is missing from loss dictionary. We assume this was done on purpose, and we will not be expecting data to be passed to ${name} during training`),lossFunctions.push(get(args.loss[name]))}else if(Array.isArray(args.loss)){if(args.loss.length!==this.outputs.length)throw new ValueError(`When passing an Array as loss, it should have one entry per model output. The model has ${this.outputs.length} output(s), but you passed loss=${args.loss}.`);lossFunctions=args.loss.map(l=>get(l))}else{let lossFunction=get(args.loss);this.outputs.forEach(_=>{lossFunctions.push(lossFunction)})}this.lossFunctions=lossFunctions,this.feedOutputNames=[],this.feedOutputShapes=[],this.feedLossFns=[];for(let i=0;i{for(let i=0;i1&&(this.metricsTensors.push([weightedLoss,i]),this.metricsNames.push(this.outputNames[i]+"_loss"))}});let nestedMetrics=collectMetrics(args.metrics,this.outputNames),appendMetric=(outputIndex,metricName,metricTensor)=>{this.outputNames.length>1&&(metricName=this.outputNames[outputIndex]+"_"+metricName),this.metricsNames.push(metricName),this.metricsTensors.push([metricTensor,outputIndex])};nameScope("metric",()=>{for(let i=0;i{let metricNamePrefix="",metricName,accFn,weightedMetricFn;for(let metric of metrics2){if(typeof metric=="string"&&["accuracy","acc","crossentropy","ce"].indexOf(metric)!==-1){let outputShape=this.internalOutputShapes[i];outputShape[outputShape.length-1]===1||this.lossFunctions[i]===binaryCrossentropy?["accuracy","acc"].indexOf(metric)!==-1?accFn=binaryAccuracy:["crossentropy","ce"].indexOf(metric)!==-1&&(accFn=binaryCrossentropy2):this.lossFunctions[i]===sparseCategoricalCrossentropy?["accuracy","acc"].indexOf(metric)!==-1?accFn=sparseCategoricalAccuracy:["crossentropy","ce"].indexOf(metric)!==-1&&(accFn=sparseCategoricalCrossentropy2):["accuracy","acc"].indexOf(metric)!==-1?accFn=categoricalAccuracy:["crossentropy","ce"].indexOf(metric)!==-1&&(accFn=categoricalCrossentropy2);let suffix;["accuracy","acc"].indexOf(metric)!==-1?suffix="acc":["crossentropy","ce"].indexOf(metric)!==-1&&(suffix="ce"),weightedMetricFn=accFn,metricName=metricNamePrefix+suffix}else weightedMetricFn=get2(metric),metricName=metricNamePrefix+getLossOrMetricName(metric);let metricResult;nameScope(metricName,()=>{metricResult=weightedMetricFn}),appendMetric(i,metricName,metricResult)}})(outputMetrics)}}),this.collectedTrainableWeights=this.trainableWeights}checkTrainableWeightsConsistency(){this.collectedTrainableWeights!=null&&this.trainableWeights.length!==this.collectedTrainableWeights.length&&console.warn("Discrepancy between trainableweights and collected trainable weights. Did you set `model.trainable` without calling `model.compile()` afterwards?")}evaluate(x,y,args={}){let batchSize=args.batchSize==null?32:args.batchSize;checkBatchSize(batchSize);let checkBatchAxis=!0,standardizedOuts=this.standardizeUserDataXY(x,y,checkBatchAxis,batchSize);try{let ins=standardizedOuts[0].concat(standardizedOuts[1]);this.makeTestFunction();let f=this.testFunction,testOuts=this.testLoop(f,ins,batchSize,args.verbose,args.steps);return singletonOrArray(testOuts)}finally{disposeNewTensors(standardizedOuts[0],x),disposeNewTensors(standardizedOuts[1],y)}}async evaluateDataset(dataset,args){return this.makeTestFunction(),evaluateDataset(this,dataset,args)}checkNumSamples(ins,batchSize,steps,stepsName="steps"){let numSamples;if(steps!=null){if(numSamples=null,batchSize!=null)throw new ValueError(`If ${stepsName} is set, batchSize must be null or undefined.Got batchSize = ${batchSize}`)}else if(ins!=null)Array.isArray(ins)?numSamples=ins[0].shape[0]:numSamples=ins.shape[0];else throw new ValueError(`Either the input data should have a defined shape, or ${stepsName} shoud be specified.`);return numSamples}execute(inputs,outputs){if(Array.isArray(outputs)&&outputs.length===0)throw new ValueError("`outputs` is an empty Array, which is not allowed.");let outputsIsArray=Array.isArray(outputs),outputNames=outputsIsArray?outputs:[outputs],outputSymbolicTensors=this.retrieveSymbolicTensors(outputNames),feedDict=new FeedDict;if(inputs instanceof Tensor&&(inputs=[inputs]),Array.isArray(inputs)){if(inputs.length!==this.inputs.length)throw new ValueError(`The number of inputs provided (${inputs.length}) does not match the number of inputs of this model (${this.inputs.length}).`);for(let i=0;ioutput.name);for(let i=0;i0){let remainingNames=[];throw outputSymbolicTensors.forEach((tensor2,i)=>{tensor2==null&&remainingNames.push(symbolicTensorNames[i])}),new ValueError(`Cannot find SymbolicTensors for output name(s): ${JSON.stringify(remainingNames)}`)}return outputSymbolicTensors}predictLoop(ins,batchSize=32,verbose=!1){return tidy(()=>{let numSamples=this.checkNumSamples(ins);if(verbose)throw new NotImplementedError("Verbose predictLoop() is not implemented yet.");let batches=makeBatches(numSamples,batchSize),outsBatches=this.outputs.map(output=>[]);for(let batchIndex=0;batchIndex{let batchStart=batches[batchIndex][0],batchEnd=batches[batchIndex][1],insBatch=sliceArrays(ins,batchStart,batchEnd),feeds=[];if(Array.isArray(insBatch))for(let i=0;ioutsBatches[i].push(batchOut));return singletonOrArray(outsBatches.map(batches2=>concat(batches2,0)))})}predict(x,args={}){let xsRank2OrHigher=ensureTensorsRank2OrHigher(x);checkInputData(xsRank2OrHigher,this.inputNames,this.feedInputShapes,!1);try{let batchSize=args.batchSize==null?32:args.batchSize;return checkBatchSize(batchSize),this.predictLoop(xsRank2OrHigher,batchSize)}finally{disposeNewTensors(xsRank2OrHigher,x)}}predictOnBatch(x){checkInputData(x,this.inputNames,this.feedInputShapes,!0);let batchSize=(Array.isArray(x)?x[0]:x).shape[0];return this.predictLoop(x,batchSize)}standardizeUserDataXY(x,y,checkBatchAxis=!0,batchSize){if(this.optimizer_==null)throw new RuntimeError("You must compile a model before training/testing. Use LayersModel.compile(modelCompileArgs).");let outputShapes=[];for(let i=0;i0&&x[0].shape[0]%batchSize!=0)throw new ValueError(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${batchSize}. Found: ${x[0].shape[0]} sample(s).`);return[x,y]}async standardizeUserData(x,y,sampleWeight,classWeight,checkBatchAxis=!0,batchSize){let[standardXs,standardYs]=this.standardizeUserDataXY(x,y,checkBatchAxis,batchSize);if(sampleWeight!=null)throw new Error("sample weight is not supported yet.");let standardSampleWeights=null;if(classWeight!=null){let classWeights=standardizeClassWeights(classWeight,this.outputNames);standardSampleWeights=[];for(let i=0;i{let numSamples=this.checkNumSamples(ins,batchSize,steps,"steps"),outs=[];if(verbose>0)throw new NotImplementedError("Verbose mode is not implemented yet.");if(steps!=null)throw new NotImplementedError("steps mode in testLoop() is not implemented yet");{let batches=makeBatches(numSamples,batchSize),indexArray=tensor1d(range5(0,numSamples));for(let batchIndex=0;batchIndex1&&(newLabel+=`_${count(outLabels.slice(0,i),label)}`),dedupedOutLabels.push(newLabel)}return dedupedOutLabels}makeTrainFunction(){return data2=>{let lossValues=[],inputs=data2.slice(0,this.inputs.length),targets=data2.slice(this.inputs.length,this.inputs.length+this.outputs.length),sampleWeights=data2.slice(this.inputs.length+this.outputs.length,this.inputs.length+this.outputs.length*2),metricsValues=[],totalLossFunction=()=>{let feeds=[];for(let i=0;i1&&i{totalLoss=add2(totalLoss,regularizerLoss)}),totalLoss},variables=this.collectedTrainableWeights.map(param=>param.read()),returnCost=!0;return[this.optimizer_.minimize(totalLossFunction,returnCost,variables)].concat(metricsValues)}}makeTestFunction(){this.testFunction=data2=>tidy(()=>{let valOutputs=[],totalLoss,inputs=data2.slice(0,this.inputs.length),targets=data2.slice(this.inputs.length,this.inputs.length+this.outputs.length),feeds=[];for(let i=0;itoSnakeCase(name))}else{let outputNames=Object.keys(this.loss);lossNames={};let losses4=this.loss;for(let outputName of outputNames)if(typeof losses4[outputName]=="string")lossNames[outputName]=toSnakeCase(losses4[outputName]);else throw new Error("Serialization of non-string loss is not supported.")}return lossNames}getMetricIdentifiers(){if(typeof this.metrics=="string"||typeof this.metrics=="function")return[toSnakeCase(getLossOrMetricName(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(metric=>toSnakeCase(getLossOrMetricName(metric)));{let metricsIdentifiers={};for(let key in this.metrics)metricsIdentifiers[key]=toSnakeCase(getLossOrMetricName(this.metrics[key]));return metricsIdentifiers}}getTrainingConfig(){return{loss:this.getLossIdentifiers(),metrics:this.getMetricIdentifiers(),optimizer_config:{class_name:this.optimizer.getClassName(),config:this.optimizer.getConfig()}}}loadTrainingConfig(trainingConfig){if(trainingConfig.weighted_metrics!=null)throw new Error("Loading weight_metrics is not supported yet.");if(trainingConfig.loss_weights!=null)throw new Error("Loading loss_weights is not supported yet.");if(trainingConfig.sample_weight_mode!=null)throw new Error("Loading sample_weight_mode is not supported yet.");let tsConfig=convertPythonicToTs(trainingConfig.optimizer_config),optimizer=deserialize(tsConfig),loss;if(typeof trainingConfig.loss=="string")loss=toCamelCase(trainingConfig.loss);else if(Array.isArray(trainingConfig.loss))loss=trainingConfig.loss.map(lossEntry=>toCamelCase(lossEntry));else if(trainingConfig.loss!=null){loss={};for(let key in trainingConfig.loss)loss[key]=toCamelCase(trainingConfig.loss[key])}let metrics2;if(Array.isArray(trainingConfig.metrics))metrics2=trainingConfig.metrics.map(metric=>toCamelCase(metric));else if(trainingConfig.metrics!=null){metrics2={};for(let key in trainingConfig.metrics)metrics2[key]=toCamelCase(trainingConfig.metrics[key])}this.compile({loss,metrics:metrics2,optimizer})}async save(handlerOrURL,config2){if(typeof handlerOrURL=="string"){let handlers=io_exports.getSaveHandlers(handlerOrURL);if(handlers.length===0)throw new ValueError(`Cannot find any save handlers for URL '${handlerOrURL}'`);if(handlers.length>1)throw new ValueError(`Found more than one (${handlers.length}) save handlers for URL '${handlerOrURL}'`);handlerOrURL=handlers[0]}if(handlerOrURL.save==null)throw new ValueError("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let weightDataAndSpecs=await io_exports.encodeWeights(this.getNamedWeights(config2)),returnString=!1,unusedArg=null,modelArtifacts={modelTopology:this.toJSON(unusedArg,returnString),format:LAYERS_MODEL_FORMAT_NAME,generatedBy:`TensorFlow.js tfjs-layers v${version10}`,convertedBy:null};if((config2==null?!1:config2.includeOptimizer)&&this.optimizer!=null){modelArtifacts.trainingConfig=this.getTrainingConfig();let weightType="optimizer",{data:optimizerWeightData,specs:optimizerWeightSpecs}=await io_exports.encodeWeights(await this.optimizer.getWeights(),weightType);weightDataAndSpecs.specs.push(...optimizerWeightSpecs),weightDataAndSpecs.data=io_exports.concatenateArrayBuffers([weightDataAndSpecs.data,optimizerWeightData])}if(this.userDefinedMetadata!=null){let checkSize=!0;checkUserDefinedMetadata(this.userDefinedMetadata,this.name,checkSize),modelArtifacts.userDefinedMetadata=this.userDefinedMetadata}return modelArtifacts.weightData=weightDataAndSpecs.data,modelArtifacts.weightSpecs=weightDataAndSpecs.specs,handlerOrURL.save(modelArtifacts)}setUserDefinedMetadata(userDefinedMetadata){checkUserDefinedMetadata(userDefinedMetadata,this.name),this.userDefinedMetadata=userDefinedMetadata}getUserDefinedMetadata(){return this.userDefinedMetadata}};LayersModel.className="Model";serialization_exports.registerClass(LayersModel);var Functional=class extends LayersModel{};Functional.className="Functional";serialization_exports.registerClass(Functional);async function modelFromJSON(modelAndWeightsConfig,customObjects){"modelTopology"in modelAndWeightsConfig||(modelAndWeightsConfig={modelTopology:modelAndWeightsConfig}),modelAndWeightsConfig=modelAndWeightsConfig;let modelTopology=modelAndWeightsConfig.modelTopology;modelTopology.model_config!=null&&(modelTopology=modelTopology.model_config);let tsConfig=convertPythonicToTs(modelTopology),model2=deserialize(tsConfig,customObjects);if(modelAndWeightsConfig.weightsManifest!=null){let weightValues=await io_exports.loadWeights(modelAndWeightsConfig.weightsManifest,modelAndWeightsConfig.pathPrefix,model2.weights.map(weight=>weight.originalName)),uniqueWeightValues={};for(let weight of model2.weights)uniqueWeightValues[weight.originalName]=weightValues[weight.originalName];model2.loadWeights(uniqueWeightValues),dispose(weightValues)}return model2}async function loadLayersModelInternal(pathOrIOHandler,options){if(options==null&&(options={}),typeof pathOrIOHandler=="string"){let handlers=io_exports.getLoadHandlers(pathOrIOHandler,options);if(handlers.length===0)handlers.push(io_exports.browserHTTPRequest(pathOrIOHandler,options));else if(handlers.length>1)throw new ValueError(`Found more than one (${handlers.length}) load handlers for URL '${pathOrIOHandler}'`);pathOrIOHandler=handlers[0]}return loadLayersModelFromIOHandler(pathOrIOHandler,void 0,options)}async function loadLayersModelFromIOHandler(handler,customObjects,options){if(options==null&&(options={}),handler.load==null)throw new ValueError("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let artifacts=await handler.load(),modelTopology=artifacts.modelTopology;modelTopology.model_config!=null&&(modelTopology=modelTopology.model_config);let strict=options.strict==null?!0:options.strict,fastWeightInit=artifacts.weightData!=null&&artifacts.weightSpecs!=null&&strict,model2=deserialize(convertPythonicToTs(modelTopology),customObjects,fastWeightInit),trainingConfig=artifacts.trainingConfig;if(trainingConfig!=null&&model2.loadTrainingConfig(trainingConfig),artifacts.userDefinedMetadata!=null&&model2.setUserDefinedMetadata(artifacts.userDefinedMetadata),artifacts.weightData!=null){if(artifacts.weightSpecs==null)throw new ValueError("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");let{modelWeights,optimizerWeights}=decodeModelAndOptimizerWeights(artifacts.weightData,artifacts.weightSpecs);model2.loadWeights(modelWeights,strict),model2.optimizer!=null&&optimizerWeights.length>0&&await model2.optimizer.setWeights(optimizerWeights),dispose(modelWeights),dispose(optimizerWeights.map(w=>w.tensor))}return model2}function decodeModelAndOptimizerWeights(buffer2,specs){let name2Tensor=io_exports.decodeWeights(buffer2,specs),modelWeights={},optimizerWeights=[];return specs.forEach(spec=>{spec.group==="optimizer"?optimizerWeights.push({name:spec.name,tensor:name2Tensor[spec.name]}):modelWeights[spec.name]=name2Tensor[spec.name]}),{modelWeights,optimizerWeights}}var Sequential=class extends LayersModel{constructor(args){super({inputs:[],outputs:[]});if(args=args||{},this.trainable=!0,this.built=!1,this.name=args.name!=null?args.name:getUid("sequential_"),args.layers!=null)for(let layer of args.layers)this.add(layer)}checkShape(layer){if(layer.inboundNodes[0].outputTensors[0].shape.some(x=>x<0))throw new ValueError(`Negative dimension size caused by adding layer ${layer.name} with input shape [${layer.inboundNodes[0].inputTensors[0].shape}]`)}add(layer){let isLayerModelInstance=layer instanceof Sequential||layer instanceof LayersModel,modelLayer;if(isLayerModelInstance){if(modelLayer=layer,modelLayer.outputs.length!==1)throw new ValueError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");if(modelLayer.inputs.length!==1)throw new ValueError("All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.")}if(this.outputs.length===0){if(layer.inboundNodes.length===0){if(layer.batchInputShape==null)throw new ValueError("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");let x=Input({batchShape:layer.batchInputShape,dtype:layer.dtype,name:layer.name+"_input"});layer.apply(x)}if(isLayerModelInstance)this.outputs=modelLayer.outputs,this.inputs=modelLayer.inputs;else{if(layer.inboundNodes.length!==1)throw new ValueError(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${layer.name} which has ${layer.inboundNodes.length} pre-existing inbound connections.`);if(layer.inboundNodes[0].outputTensors.length!==1)throw new ValueError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(layer),this.outputs=[layer.inboundNodes[0].outputTensors[0]],this.inputs=getSourceInputs(this.outputs[0])}this.inboundNodes=[],new Node({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:pyListRepeat(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map(x=>x.shape),outputShapes:this.outputs[0].shape})}else{let outputTensor=layer.apply(this.outputs[0]);if(Array.isArray(outputTensor))throw new TypeError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(layer),this.outputs=[outputTensor],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}this.layers.push(layer),this.built=!1}pop(){if(this.layers.length===0)throw new TypeError("There are no layers in the model.");if(this.layers.pop(),this.layers.length===0)this.outputs=[],this.inboundNodes=[],this.outboundNodes=[];else{let lastLayerIndex=this.layers.length-1;this.layers[lastLayerIndex].outboundNodes=[],this.outputs=[this.layers[lastLayerIndex].output],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}}call(inputs,kwargs){return this.model==null&&this.build(),this.model.call(inputs,kwargs)}build(inputShape){if(getExactlyOneShape(inputShape),this.inputs.length===0||this.outputs.length===0)throw new TypeError("Sequential model cannot be built: model is empty. Add some layers first.");this.model=new LayersModel({inputs:this.inputs,outputs:this.outputs[0],name:this.name+"_model"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0}countParams(){return this.built||this.build(),super.countParams()}summary(lineLength,positions,printFn=console.log){this.built||this.build(),super.summary(lineLength,positions,printFn)}setWeights(weights){this.model==null&&this.build(),this.model.setWeights(weights)}evaluate(x,y,args={}){if(!this.built)throw new RuntimeError("The model needs to be compiled before being used.");return this.model.evaluate(x,y,args)}async evaluateDataset(dataset,args){if(!this.built)throw new RuntimeError("The model needs to be compiled before being used.");return this.model.evaluateDataset(dataset,args)}predict(x,args={}){return this.model==null&&this.build(),this.model.predict(x,args)}predictOnBatch(x){return this.model==null&&this.build(),this.model.predictOnBatch(x)}compile(args){this.build(),this.model.compile(args),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(optimizer){this.model.optimizer=optimizer}async fit(x,y,args={}){if(!this.built)throw new RuntimeError("The model needs to be compiled before being used.");return this.model.fit(x,y,args)}async fitDataset(dataset,args){if(!this.built)throw new RuntimeError("The model needs to be compiled before being used.");return this.model.fitDataset(dataset,args)}async trainOnBatch(x,y){return this.model.trainOnBatch(x,y)}static fromConfig(cls,config2,customObjects={},fastWeightInit=!1){let configArray,extraModelConfig={};if(config2 instanceof Array){if(config2[0].className==null||config2[0].className==="Merge")throw new ValueError("Legacy serialization format not supported yet.");configArray=config2}else util_exports.assert(config2.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),configArray=config2.layers,delete config2.layers,extraModelConfig=config2;let model2=new cls(extraModelConfig);if(!(model2 instanceof Sequential))throw new NotImplementedError(`Sequential.fromConfig called on non-Sequential input: ${model2}`);for(let conf of configArray){let layer=deserialize(conf,void 0,fastWeightInit);fastWeightInit&&layer.setFastWeightInitDuringBuild(!0),model2.add(layer)}return model2}set stopTraining(stop){if(this.model==null)throw new ValueError("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=stop}get stopTraining(){if(this.model==null)throw new ValueError("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let layers=[];for(let layer of this.layers){let dict={};dict.className=layer.getClassName(),dict.config=layer.getConfig(),layers.push(dict)}return{name:this.name,layers}}};Sequential.className="Sequential";serialization_exports.registerClass(Sequential);function model(args){return new LayersModel(args)}function sequential(config2){return new Sequential(config2)}function loadLayersModel(pathOrIOHandler,options){return options==null&&(options={}),loadLayersModelInternal(pathOrIOHandler,options)}function input(config2){return Input(config2)}function registerCallbackConstructor(verbosityLevel,callbackConstructor){CallbackConstructorRegistry.registerCallbackConstructor(verbosityLevel,callbackConstructor)}var Activation=class extends serialization_exports.Serializable{getConfig(){return{}}},Elu2=class extends Activation{apply(x,alpha=1){return elu4(x,alpha)}};Elu2.className="elu";serialization_exports.registerClass(Elu2);var Selu2=class extends Activation{apply(x){return selu(x)}};Selu2.className="selu";serialization_exports.registerClass(Selu2);var Relu2=class extends Activation{apply(x){return relu(x)}};Relu2.className="relu";serialization_exports.registerClass(Relu2);var Relu62=class extends Activation{apply(x){return tidy(()=>minimum(6,relu(x)))}};Relu62.className="relu6";serialization_exports.registerClass(Relu62);var Linear=class extends Activation{apply(x){return x}};Linear.className="linear";serialization_exports.registerClass(Linear);var Sigmoid2=class extends Activation{apply(x){return sigmoid(x)}};Sigmoid2.className="sigmoid";serialization_exports.registerClass(Sigmoid2);var HardSigmoid=class extends Activation{apply(x){return hardSigmoid(x)}};HardSigmoid.className="hardSigmoid";serialization_exports.registerClass(HardSigmoid);var Softplus2=class extends Activation{apply(x){return softplus(x)}};Softplus2.className="softplus";serialization_exports.registerClass(Softplus2);var Softsign=class extends Activation{apply(x){return softsign(x)}};Softsign.className="softsign";serialization_exports.registerClass(Softsign);var Tanh2=class extends Activation{apply(x){return tanh2(x)}};Tanh2.className="tanh";serialization_exports.registerClass(Tanh2);var Softmax2=class extends Activation{apply(x,axis=-1){return softmax(x,axis)}};Softmax2.className="softmax";serialization_exports.registerClass(Softmax2);var LogSoftmax2=class extends Activation{apply(x,axis=-1){return logSoftmax(x,axis)}};LogSoftmax2.className="logSoftmax";serialization_exports.registerClass(LogSoftmax2);var Swish=class extends Activation{apply(x,alpha=1){return tidy(()=>sigmoid(x.mul(alpha)).mul(x))}};Swish.className="swish";serialization_exports.registerClass(Swish);function serializeActivation(activation2){return activation2.getClassName()}function deserializeActivation(config2,customObjects={}){return deserializeKerasObject(config2,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"activation")}function getActivation(identifier){if(identifier==null){let config2={};return config2.className="linear",config2.config={},deserializeActivation(config2)}if(typeof identifier=="string"){let config2={};return config2.className=identifier,config2.config={},deserializeActivation(config2)}else return identifier instanceof Activation?identifier:deserializeActivation(identifier)}function assertObjectArgs(args){if(args!=null&&typeof args!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${args}`)}var Regularizer=class extends serialization_exports.Serializable{},L1L2=class extends Regularizer{constructor(args){super();assertObjectArgs(args),this.l1=args==null||args.l1==null?.01:args.l1,this.l2=args==null||args.l2==null?.01:args.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(x){return tidy(()=>{let regularization=zeros([1]);return this.hasL1&&(regularization=add2(regularization,sum2(mul(this.l1,abs(x))))),this.hasL2&&(regularization=add2(regularization,sum2(mul(this.l2,square3(x))))),regularization.asScalar()})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(cls,config2){return new cls({l1:config2.l1,l2:config2.l2})}};L1L2.className="L1L2";serialization_exports.registerClass(L1L2);function l1(args){return assertObjectArgs(args),new L1L2({l1:args!=null?args.l1:null,l2:0})}function l2(args){return assertObjectArgs(args),new L1L2({l2:args!=null?args.l2:null,l1:0})}var REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP={l1l2:"L1L2"};function serializeRegularizer(constraint){return serializeKerasObject(constraint)}function deserializeRegularizer(config2,customObjects={}){return deserializeKerasObject(config2,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"regularizer")}function getRegularizer(identifier){if(identifier==null)return null;if(typeof identifier=="string"){let config2={className:identifier in REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP?REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier]:identifier,config:{}};return deserializeRegularizer(config2)}else return identifier instanceof Regularizer?identifier:deserializeRegularizer(identifier)}var ReLU=class extends Layer{constructor(args){super(args==null?{}:args);this.supportsMasking=!0,args!=null&&(this.maxValue=args.maxValue)}call(inputs,kwargs){inputs=getExactlyOneTensor(inputs);let output=relu(inputs);return this.maxValue!=null&&(output=clipByValue(output,0,this.maxValue)),output}computeOutputShape(inputShape){return inputShape}getConfig(){let config2={maxValue:this.maxValue},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};ReLU.className="ReLU";serialization_exports.registerClass(ReLU);var LeakyReLU=class extends Layer{constructor(args){super(args==null?{}:args);this.DEFAULT_ALPHA=.3,args==null&&(args={}),this.alpha=args.alpha==null?this.DEFAULT_ALPHA:args.alpha}call(inputs,kwargs){let x=getExactlyOneTensor(inputs);return leakyRelu(x,this.alpha)}computeOutputShape(inputShape){return inputShape}getConfig(){let config2={alpha:this.alpha},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};LeakyReLU.className="LeakyReLU";serialization_exports.registerClass(LeakyReLU);var PReLU=class extends Layer{constructor(args){super(args==null?{}:args);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",args==null&&(args={}),this.supportsMasking=!0,this.alphaInitializer=getInitializer(args.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=getRegularizer(args.alphaRegularizer),this.alphaConstraint=getConstraint(args.alphaConstraint),args.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(args.sharedAxes))this.sharedAxes=args.sharedAxes;else if(typeof args.sharedAxes=="number")this.sharedAxes=[args.sharedAxes];else throw new ValueError(`Expected sharedAxes to be a number or an array of numbers, but got ${args.sharedAxes}`)}build(inputShape){inputShape=getExactlyOneShape(inputShape);let paramShape=inputShape.slice(1);if(this.sharedAxes!=null)for(let i of this.sharedAxes)paramShape[i-1]=1;this.alpha=this.addWeight("alpha",paramShape,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let axes={};if(this.sharedAxes!=null)for(let i=1;i(checkDataFormat(dataFormat),dataFormat==="channelsFirst"?transpose(x,[0,2,3,1]):x))}function preprocessConv3DInput(x,dataFormat){return tidy(()=>(checkDataFormat(dataFormat),dataFormat==="channelsFirst"?transpose(x,[0,2,3,4,1]):x))}function conv1dWithBias(x,kernel,bias,strides=1,padding="valid",dataFormat,dilationRate=1){return tidy(()=>{if(dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat),x.shape.length!==3)throw new ValueError(`The input of a conv1dWithBias operation should be 3, but is ${x.shape.length} instead.`);if(kernel.shape.length!==3)throw new ValueError(`The kernel for a conv1dWithBias operation should be 3, but is ${kernel.shape.length} instead`);if(bias!=null&&bias.shape.length!==1)throw new ValueError(`The bias for a conv1dWithBias operation should be 1, but is ${kernel.shape.length} instead`);if(dataFormat==="channelsFirst"&&(x=transpose(x,[0,2,1])),padding==="causal")throw new NotImplementedError("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let y=conv1d(x,kernel,strides,padding==="same"?"same":"valid","NWC",dilationRate);return bias!=null&&(y=biasAdd(y,bias)),y})}function conv2dWithBiasActivation(x,kernel,bias,strides=[1,1],padding="valid",dataFormat,dilationRate,activation2=null){return tidy(()=>{if(dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat),x.rank!==3&&x.rank!==4)throw new ValueError(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${x.rank}.`);if(kernel.rank!==3&&kernel.rank!==4)throw new ValueError(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${x.rank}.`);let y=preprocessConv2DInput(x,dataFormat);if(padding==="causal")throw new NotImplementedError("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return y=fused_ops_exports.conv2d({x:y,filter:kernel,strides,pad:padding==="same"?"same":"valid",dilations:dilationRate,dataFormat:"NHWC",bias,activation:activation2}),dataFormat==="channelsFirst"&&(y=transpose(y,[0,3,1,2])),y})}function conv3dWithBias(x,kernel,bias,strides=[1,1,1],padding="valid",dataFormat,dilationRate){return tidy(()=>{if(dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat),x.rank!==4&&x.rank!==5)throw new ValueError(`conv3dWithBias expects input to be of rank 4 or 5, but received ${x.rank}.`);if(kernel.rank!==4&&kernel.rank!==5)throw new ValueError(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${x.rank}.`);let y=preprocessConv3DInput(x,dataFormat);if(padding==="causal")throw new NotImplementedError("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return y=conv3d(y,kernel,strides,padding==="same"?"same":"valid","NDHWC",dilationRate),bias!=null&&(y=biasAdd(y,bias)),dataFormat==="channelsFirst"&&(y=transpose(y,[0,4,1,2,3])),y})}var BaseConv=class extends Layer{constructor(rank,args){super(args);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",BaseConv.verifyArgs(args),this.rank=rank,assertPositiveInteger(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new NotImplementedError(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=normalizeArray(args.kernelSize,rank,"kernelSize"),this.strides=normalizeArray(args.strides==null?1:args.strides,rank,"strides"),this.padding=args.padding==null?"valid":args.padding,checkPaddingMode(this.padding),this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),this.activation=getActivation(args.activation),this.useBias=args.useBias==null?!0:args.useBias,this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=getConstraint(args.biasConstraint),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.activityRegularizer=getRegularizer(args.activityRegularizer),this.dilationRate=normalizeArray(args.dilationRate==null?1:args.dilationRate,rank,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new ValueError(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new ValueError(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new ValueError(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(args){if(assert2("kernelSize"in args,"required key 'kernelSize' not in config"),typeof args.kernelSize!="number"&&!checkArrayTypeAndLength(args.kernelSize,"number",1,3))throw new ValueError(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(args.kernelSize)}.`)}getConfig(){let config2={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:serializeActivation(this.activation),useBias:this.useBias,biasInitializer:serializeInitializer(this.biasInitializer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),biasConstraint:serializeConstraint(this.biasConstraint)},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}},Conv=class extends BaseConv{constructor(rank,args){super(rank,args);this.kernel=null,Conv.verifyArgs(args),this.filters=args.filters,assertPositiveInteger(this.filters,"filters"),this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=getConstraint(args.kernelConstraint),this.kernelRegularizer=getRegularizer(args.kernelRegularizer)}build(inputShape){inputShape=getExactlyOneShape(inputShape);let channelAxis=this.dataFormat==="channelsFirst"?1:inputShape.length-1;if(inputShape[channelAxis]==null)throw new ValueError(`The channel dimension of the input should be defined. Found ${inputShape[channelAxis]}`);let inputDim=inputShape[channelAxis],kernelShape=this.kernelSize.concat([inputDim,this.filters]);this.kernel=this.addWeight("kernel",kernelShape,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[channelAxis]:inputDim}}],this.built=!0}call(inputs,kwargs){return tidy(()=>{inputs=getExactlyOneTensor(inputs);let outputs,biasValue=this.bias==null?null:this.bias.read(),fusedActivationName=mapActivationToFusedKernel(this.activation.getClassName());if(fusedActivationName!=null&&this.rank===2)outputs=conv2dWithBiasActivation(inputs,this.kernel.read(),biasValue,this.strides,this.padding,this.dataFormat,this.dilationRate,fusedActivationName);else{if(this.rank===1)outputs=conv1dWithBias(inputs,this.kernel.read(),biasValue,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)outputs=conv2dWithBiasActivation(inputs,this.kernel.read(),biasValue,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)outputs=conv3dWithBias(inputs,this.kernel.read(),biasValue,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new NotImplementedError("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(outputs=this.activation.apply(outputs))}return outputs})}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let newSpace=[],space=this.dataFormat==="channelsLast"?inputShape.slice(1,inputShape.length-1):inputShape.slice(2);for(let i=0;i 0 but got ${JSON.stringify(args.filters)}`)}},Conv2D2=class extends Conv{constructor(args){super(2,args);Conv2D2.verifyArgs(args)}getConfig(){let config2=super.getConfig();return delete config2.rank,config2}static verifyArgs(args){if(typeof args.kernelSize!="number"&&!checkArrayTypeAndLength(args.kernelSize,"number",1,2))throw new ValueError(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(args.kernelSize)}.`)}};Conv2D2.className="Conv2D";serialization_exports.registerClass(Conv2D2);var Conv3D2=class extends Conv{constructor(args){super(3,args);Conv3D2.verifyArgs(args)}getConfig(){let config2=super.getConfig();return delete config2.rank,config2}static verifyArgs(args){if(typeof args.kernelSize!="number"&&!(Array.isArray(args.kernelSize)&&(args.kernelSize.length===1||args.kernelSize.length===3)))throw new ValueError(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(args.kernelSize)}.`)}};Conv3D2.className="Conv3D";serialization_exports.registerClass(Conv3D2);var Conv2DTranspose=class extends Conv2D2{constructor(args){super(args);if(this.inputSpec=[new InputSpec({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new ValueError(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(inputShape){if(inputShape=getExactlyOneShape(inputShape),inputShape.length!==4)throw new ValueError("Input should have rank 4; Received input shape: "+JSON.stringify(inputShape));let channelAxis=this.dataFormat==="channelsFirst"?1:inputShape.length-1;if(inputShape[channelAxis]==null)throw new ValueError("The channel dimension of the inputs should be defined. Found `None`.");let inputDim=inputShape[channelAxis],kernelShape=this.kernelSize.concat([this.filters,inputDim]);this.kernel=this.addWeight("kernel",kernelShape,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new InputSpec({ndim:4,axes:{[channelAxis]:inputDim}})],this.built=!0}call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);if(input2.shape.length!==4)throw new ValueError(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${input2.shape.length}`);let inputShape=input2.shape,batchSize=inputShape[0],hAxis,wAxis;this.dataFormat==="channelsFirst"?(hAxis=2,wAxis=3):(hAxis=1,wAxis=2);let height=inputShape[hAxis],width=inputShape[wAxis],kernelH=this.kernelSize[0],kernelW=this.kernelSize[1],strideH=this.strides[0],strideW=this.strides[1],outHeight=deconvLength(height,strideH,kernelH,this.padding),outWidth=deconvLength(width,strideW,kernelW,this.padding),outputShape=[batchSize,outHeight,outWidth,this.filters];this.dataFormat!=="channelsLast"&&(input2=transpose(input2,[0,2,3,1]));let outputs=conv2dTranspose(input2,this.kernel.read(),outputShape,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(outputs=transpose(outputs,[0,3,1,2])),this.bias!=null&&(outputs=biasAdd(outputs,this.bias.read(),this.dataFormat)),this.activation!=null&&(outputs=this.activation.apply(outputs)),outputs})}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let outputShape=inputShape.slice(),channelAxis,heightAxis,widthAxis;this.dataFormat==="channelsFirst"?(channelAxis=1,heightAxis=2,widthAxis=3):(channelAxis=3,heightAxis=1,widthAxis=2);let kernelH=this.kernelSize[0],kernelW=this.kernelSize[1],strideH=this.strides[0],strideW=this.strides[1];return outputShape[channelAxis]=this.filters,outputShape[heightAxis]=deconvLength(outputShape[heightAxis],strideH,kernelH,this.padding),outputShape[widthAxis]=deconvLength(outputShape[widthAxis],strideW,kernelW,this.padding),outputShape}getConfig(){let config2=super.getConfig();return delete config2.dilationRate,config2}};Conv2DTranspose.className="Conv2DTranspose";serialization_exports.registerClass(Conv2DTranspose);var SeparableConv=class extends Conv{constructor(rank,config2){super(rank,config2);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,config2.filters==null)throw new ValueError("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(config2.kernelInitializer!=null||config2.kernelRegularizer!=null||config2.kernelConstraint!=null)throw new ValueError("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(config2.padding!=null&&config2.padding!=="same"&&config2.padding!=="valid")throw new ValueError(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(config2.padding)}`);this.depthMultiplier=config2.depthMultiplier==null?1:config2.depthMultiplier,this.depthwiseInitializer=getInitializer(config2.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=getRegularizer(config2.depthwiseRegularizer),this.depthwiseConstraint=getConstraint(config2.depthwiseConstraint),this.pointwiseInitializer=getInitializer(config2.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=getRegularizer(config2.pointwiseRegularizer),this.pointwiseConstraint=getConstraint(config2.pointwiseConstraint)}build(inputShape){if(inputShape=getExactlyOneShape(inputShape),inputShape.length{inputs=getExactlyOneTensor(inputs);let output;if(this.rank===1)throw new NotImplementedError("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(inputs=transpose(inputs,[0,2,3,1])),output=separableConv2d(inputs,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(output=biasAdd(output,this.bias.read(),this.dataFormat)),this.activation!=null&&(output=this.activation.apply(output)),this.dataFormat==="channelsFirst"&&(output=transpose(output,[0,3,1,2])),output})}getConfig(){let config2=super.getConfig();return delete config2.rank,delete config2.kernelInitializer,delete config2.kernelRegularizer,delete config2.kernelConstraint,config2.depthwiseInitializer=serializeInitializer(this.depthwiseInitializer),config2.pointwiseInitializer=serializeInitializer(this.pointwiseInitializer),config2.depthwiseRegularizer=serializeRegularizer(this.depthwiseRegularizer),config2.pointwiseRegularizer=serializeRegularizer(this.pointwiseRegularizer),config2.depthwiseConstraint=serializeConstraint(this.depthwiseConstraint),config2.pointwiseConstraint=serializeConstraint(this.pointwiseConstraint),config2}};SeparableConv.className="SeparableConv";var SeparableConv2D=class extends SeparableConv{constructor(args){super(2,args)}};SeparableConv2D.className="SeparableConv2D";serialization_exports.registerClass(SeparableConv2D);var Conv1D=class extends Conv{constructor(args){super(1,args);Conv1D.verifyArgs(args),this.inputSpec=[{ndim:3}]}getConfig(){let config2=super.getConfig();return delete config2.rank,delete config2.dataFormat,config2}static verifyArgs(args){if(typeof args.kernelSize!="number"&&!checkArrayTypeAndLength(args.kernelSize,"number",1,1))throw new ValueError(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(args.kernelSize)}.`)}};Conv1D.className="Conv1D";serialization_exports.registerClass(Conv1D);var Cropping2D=class extends Layer{constructor(args){super(args);typeof args.cropping=="number"?this.cropping=[[args.cropping,args.cropping],[args.cropping,args.cropping]]:typeof args.cropping[0]=="number"?this.cropping=[[args.cropping[0],args.cropping[0]],[args.cropping[1],args.cropping[1]]]:this.cropping=args.cropping,this.dataFormat=args.dataFormat===void 0?"channelsLast":args.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(inputShape){return this.dataFormat==="channelsFirst"?[inputShape[0],inputShape[1],inputShape[2]-this.cropping[0][0]-this.cropping[0][1],inputShape[3]-this.cropping[1][0]-this.cropping[1][1]]:[inputShape[0],inputShape[1]-this.cropping[0][0]-this.cropping[0][1],inputShape[2]-this.cropping[1][0]-this.cropping[1][1],inputShape[3]]}call(inputs,kwargs){return tidy(()=>{if(inputs=getExactlyOneTensor(inputs),this.dataFormat==="channelsLast"){let hSliced=sliceAlongAxis(inputs,this.cropping[0][0],inputs.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return sliceAlongAxis(hSliced,this.cropping[1][0],inputs.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let hSliced=sliceAlongAxis(inputs,this.cropping[0][0],inputs.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return sliceAlongAxis(hSliced,this.cropping[1][0],inputs.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let config2={cropping:this.cropping,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};Cropping2D.className="Cropping2D";serialization_exports.registerClass(Cropping2D);var UpSampling2D=class extends Layer{constructor(args){super(args);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=args.size==null?this.DEFAULT_SIZE:args.size,this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),this.interpolation=args.interpolation==null?"nearest":args.interpolation,checkInterpolationFormat(this.interpolation)}computeOutputShape(inputShape){if(this.dataFormat==="channelsFirst"){let height=inputShape[2]==null?null:this.size[0]*inputShape[2],width=inputShape[3]==null?null:this.size[1]*inputShape[3];return[inputShape[0],inputShape[1],height,width]}else{let height=inputShape[1]==null?null:this.size[0]*inputShape[1],width=inputShape[2]==null?null:this.size[1]*inputShape[2];return[inputShape[0],height,width,inputShape[3]]}}call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs),inputShape=input2.shape;if(this.dataFormat==="channelsFirst"){input2=transpose(input2,[0,2,3,1]);let height=this.size[0]*inputShape[2],width=this.size[1]*inputShape[3],resized=this.interpolation==="nearest"?input2.resizeNearestNeighbor([height,width]):input2.resizeBilinear([height,width]);return transpose(resized,[0,3,1,2])}else{let height=this.size[0]*inputShape[1],width=this.size[1]*inputShape[2];return this.interpolation==="nearest"?input2.resizeNearestNeighbor([height,width]):input2.resizeBilinear([height,width])}})}getConfig(){let config2={size:this.size,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};UpSampling2D.className="UpSampling2D";serialization_exports.registerClass(UpSampling2D);function depthwiseConv2d4(x,depthwiseKernel,strides=[1,1],padding="valid",dataFormat,dilationRate){return tidy(()=>{dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat);let y=preprocessConv2DInput(x,dataFormat);if(x.rank!==4)throw new ValueError(`Input for depthwiseConv2d is required to be 4-D, but is instead ${x.rank}-D`);if(depthwiseKernel.rank!==4)throw new ValueError(`depthwiseKernel is required to be 4-D, but is instead ${depthwiseKernel.rank}-D`);return y=depthwiseConv2d(y,depthwiseKernel,strides,padding==="same"?"same":"valid","NHWC",dilationRate),dataFormat==="channelsFirst"&&(y=transpose(y,[0,3,1,2])),y})}var DepthwiseConv2D=class extends BaseConv{constructor(args){super(2,args);this.depthwiseKernel=null,this.depthMultiplier=args.depthMultiplier==null?1:args.depthMultiplier,this.depthwiseInitializer=getInitializer(args.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=getConstraint(args.depthwiseConstraint),this.depthwiseRegularizer=getRegularizer(args.depthwiseRegularizer)}build(inputShape){if(inputShape=getExactlyOneShape(inputShape),inputShape.length<4)throw new ValueError(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(inputShape)}.`);let channelAxis=this.dataFormat==="channelsFirst"?1:3;if(inputShape[channelAxis]==null||inputShape[channelAxis]<0)throw new ValueError(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${inputShape[channelAxis]}).`);let inputDim=inputShape[channelAxis],depthwiseKernelShape=[this.kernelSize[0],this.kernelSize[1],inputDim,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",depthwiseKernelShape,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[inputDim*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(inputs,kwargs){return tidy(()=>{inputs=getExactlyOneTensor(inputs);let outputs=depthwiseConv2d4(inputs,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(outputs=biasAdd(outputs,this.bias.read(),this.dataFormat)),this.activation!=null&&(outputs=this.activation.apply(outputs)),outputs})}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let rows=this.dataFormat==="channelsFirst"?inputShape[2]:inputShape[1],cols=this.dataFormat==="channelsFirst"?inputShape[3]:inputShape[2],outFilters=this.dataFormat==="channelsFirst"?inputShape[1]*this.depthMultiplier:inputShape[3]*this.depthMultiplier,outRows=convOutputLength(rows,this.kernelSize[0],this.padding,this.strides[0]),outCols=convOutputLength(cols,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[inputShape[0],outFilters,outRows,outCols]:[inputShape[0],outRows,outCols,outFilters]}getConfig(){let config2=super.getConfig();return config2.depthMultiplier=this.depthMultiplier,config2.depthwiseInitializer=serializeInitializer(this.depthwiseInitializer),config2.depthwiseRegularizer=serializeRegularizer(this.depthwiseRegularizer),config2.depthwiseConstraint=serializeConstraint(this.depthwiseRegularizer),config2}};DepthwiseConv2D.className="DepthwiseConv2D";serialization_exports.registerClass(DepthwiseConv2D);function standardizeArgs(inputs,initialState,constants,numConstants){if(Array.isArray(inputs)){if(initialState!=null||constants!=null)throw new ValueError("When inputs is an array, neither initialState or constants should be provided");numConstants!=null&&(constants=inputs.slice(inputs.length-numConstants,inputs.length),inputs=inputs.slice(0,inputs.length-numConstants)),inputs.length>1&&(initialState=inputs.slice(1,inputs.length)),inputs=inputs[0]}function toListOrNull(x){return x==null||Array.isArray(x)?x:[x]}return initialState=toListOrNull(initialState),constants=toListOrNull(constants),{inputs,initialState,constants}}function rnn(stepFunction,inputs,initialStates,goBackwards=!1,mask,constants,unroll=!1,needPerStepOutputs=!1){return tidy(()=>{let ndim=inputs.shape.length;if(ndim<3)throw new ValueError(`Input should be at least 3D, but is ${ndim}D.`);let axes=[1,0].concat(range5(2,ndim));if(inputs=transpose(inputs,axes),constants!=null)throw new NotImplementedError("The rnn() functoin of the deeplearn.js backend does not support constants yet.");unroll&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),mask!=null&&(mask=mask.asType("bool").asType("float32"),mask.rank===ndim-1&&(mask=expandDims(mask,-1)),mask=transpose(mask,axes)),goBackwards&&(inputs=reverse(inputs,0),mask!=null&&(mask=reverse(mask,0)));let perStepOutputs=[],lastOutput,states=initialStates,timeSteps=inputs.shape[0],perStepInputs=unstack(inputs),perStepMasks;mask!=null&&(perStepMasks=unstack(mask));for(let t=0;tstepFunction(currentInput,states));if(mask==null)lastOutput=stepOutputs[0],states=stepOutputs[1];else{let maskedOutputs=tidy(()=>{let stepMask=perStepMasks[t],negStepMask=onesLike(stepMask).sub(stepMask),output=stepOutputs[0].mul(stepMask).add(states[0].mul(negStepMask)),newStates=states.map((state,i)=>stepOutputs[1][i].mul(stepMask).add(state.mul(negStepMask)));return{output,newStates}});lastOutput=maskedOutputs.output,states=maskedOutputs.newStates}needPerStepOutputs&&perStepOutputs.push(lastOutput)}let outputs;return needPerStepOutputs&&(outputs=stack(perStepOutputs,1)),[lastOutput,outputs,states]})}var RNN=class extends Layer{constructor(args){super(args);let cell;if(args.cell==null)throw new ValueError("cell property is missing for the constructor of RNN.");if(Array.isArray(args.cell)?cell=new StackedRNNCells({cells:args.cell}):cell=args.cell,cell.stateSize==null)throw new ValueError("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=cell,this.returnSequences=args.returnSequences==null?!1:args.returnSequences,this.returnState=args.returnState==null?!1:args.returnState,this.goBackwards=args.goBackwards==null?!1:args.goBackwards,this._stateful=args.stateful==null?!1:args.stateful,this.unroll=args.unroll==null?!1:args.unroll,this.supportsMasking=!0,this.inputSpec=[new InputSpec({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let numStates=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return range5(0,numStates).map(x=>null)}else return this.states_}setStates(states){this.states_=states}computeOutputShape(inputShape){isArrayOfShapes(inputShape)&&(inputShape=inputShape[0]),inputShape=inputShape;let stateSize=this.cell.stateSize;Array.isArray(stateSize)||(stateSize=[stateSize]);let outputDim=stateSize[0],outputShape;if(this.returnSequences?outputShape=[inputShape[0],inputShape[1],outputDim]:outputShape=[inputShape[0],outputDim],this.returnState){let stateShape=[];for(let dim of stateSize)stateShape.push([inputShape[0],dim]);return[outputShape].concat(stateShape)}else return outputShape}computeMask(inputs,mask){return tidy(()=>{Array.isArray(mask)&&(mask=mask[0]);let outputMask=this.returnSequences?mask:null;if(this.returnState){let stateMask=this.states.map(s=>null);return[outputMask].concat(stateMask)}else return outputMask})}get states(){if(this.states_==null){let numStates=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,output=[];for(let i=0;ispec.shape[spec.shape.length-1]),stateSize))throw new ValueError(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=stateSize.map(dim=>new InputSpec({shape:[null,dim]}));this.stateful&&this.resetStates()}resetStates(states,training=!1){tidy(()=>{if(!this.stateful)throw new AttributeError("Cannot call resetStates() on an RNN Layer that is not stateful.");let batchSize=this.inputSpec[0].shape[0];if(batchSize==null)throw new ValueError("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(dim=>zeros([batchSize,dim])):this.states_=[zeros([batchSize,this.cell.stateSize])];else if(states==null)dispose(this.states_),this.keptStates!=null&&(dispose(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(dim=>zeros([batchSize,dim])):this.states_[0]=zeros([batchSize,this.cell.stateSize]);else{if(Array.isArray(states)||(states=[states]),states.length!==this.states_.length)throw new ValueError(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${states.length} state value(s). Input received: ${states}`);training===!0?this.keptStates.push(this.states_.slice()):dispose(this.states_);for(let index=0;indexkeep(state.clone()))})}apply(inputs,kwargs){let initialState=kwargs==null?null:kwargs.initialState,constants=kwargs==null?null:kwargs.constants;kwargs==null&&(kwargs={});let standardized=standardizeArgs(inputs,initialState,constants,this.numConstants);inputs=standardized.inputs,initialState=standardized.initialState,constants=standardized.constants;let additionalInputs=[],additionalSpecs=[];if(initialState!=null){kwargs.initialState=initialState,additionalInputs=additionalInputs.concat(initialState),this.stateSpec=[];for(let state of initialState)this.stateSpec.push(new InputSpec({shape:state.shape}));additionalSpecs=additionalSpecs.concat(this.stateSpec)}if(constants!=null&&(kwargs.constants=constants,additionalInputs=additionalInputs.concat(constants),this.numConstants=constants.length),additionalInputs[0]instanceof SymbolicTensor){let fullInput=[inputs].concat(additionalInputs),fullInputSpec=this.inputSpec.concat(additionalSpecs),originalInputSpec=this.inputSpec;this.inputSpec=fullInputSpec;let output=super.apply(fullInput,kwargs);return this.inputSpec=originalInputSpec,output}else return super.apply(inputs,kwargs)}call(inputs,kwargs){return tidy(()=>{let mask=kwargs==null?null:kwargs.mask,training=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;inputs=getExactlyOneTensor(inputs),initialState==null&&(this.stateful?initialState=this.states_:initialState=this.getInitialState(inputs));let numStates=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(initialState.length!==numStates)throw new ValueError(`RNN Layer has ${numStates} state(s) but was passed ${initialState.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let cellCallKwargs={training},rnnOutputs=rnn((inputs2,states2)=>{let outputs2=this.cell.call([inputs2].concat(states2),cellCallKwargs);return[outputs2[0],outputs2.slice(1)]},inputs,initialState,this.goBackwards,mask,null,this.unroll,this.returnSequences),lastOutput=rnnOutputs[0],outputs=rnnOutputs[1],states=rnnOutputs[2];this.stateful&&this.resetStates(states,training);let output=this.returnSequences?outputs:lastOutput;return this.returnState?[output].concat(states):output})}getInitialState(inputs){return tidy(()=>{let initialState=zeros(inputs.shape);return initialState=sum2(initialState,[1,2]),initialState=expandDims5(initialState),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(dim=>dim>1?tile5(initialState,[1,dim]):initialState):this.cell.stateSize>1?[tile5(initialState,[1,this.cell.stateSize])]:[initialState]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(value){super.setFastWeightInitDuringBuild(value),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(value)}getConfig(){let baseConfig=super.getConfig(),config2={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(config2.numConstants=this.numConstants);let cellConfig=this.cell.getConfig();return this.getClassName()===RNN.className&&(config2.cell={className:this.cell.getClassName(),config:cellConfig}),Object.assign({},cellConfig,baseConfig,config2)}static fromConfig(cls,config2,customObjects={}){let cellConfig=config2.cell,cell=deserialize(cellConfig,customObjects);return new cls(Object.assign(config2,{cell}))}};RNN.className="RNN";serialization_exports.registerClass(RNN);var RNNCell=class extends Layer{},SimpleRNNCell=class extends RNNCell{constructor(args){super(args);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=args.units,assertPositiveInteger(this.units,"units"),this.activation=getActivation(args.activation==null?this.DEFAULT_ACTIVATION:args.activation),this.useBias=args.useBias==null?!0:args.useBias,this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=getInitializer(args.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=getRegularizer(args.kernelRegularizer),this.recurrentRegularizer=getRegularizer(args.recurrentRegularizer),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.kernelConstraint=getConstraint(args.kernelConstraint),this.recurrentConstraint=getConstraint(args.recurrentConstraint),this.biasConstraint=getConstraint(args.biasConstraint),this.dropout=min5([1,max5([0,args.dropout==null?0:args.dropout])]),this.recurrentDropout=min5([1,max5([0,args.recurrentDropout==null?0:args.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(inputShape){inputShape=getExactlyOneShape(inputShape),this.kernel=this.addWeight("kernel",[inputShape[inputShape.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(inputs,kwargs){return tidy(()=>{if(inputs=inputs,inputs.length!==2)throw new ValueError(`SimpleRNNCell expects 2 input Tensors, got ${inputs.length}.`);let prevOutput=inputs[1];inputs=inputs[0];let training=kwargs.training==null?!1:kwargs.training;0onesLike(inputs),rate:this.dropout,training})),0onesLike(prevOutput),rate:this.recurrentDropout,training}));let h,dpMask=this.dropoutMask,recDpMask=this.recurrentDropoutMask;dpMask!=null?h=dot2(mul(inputs,dpMask),this.kernel.read()):h=dot2(inputs,this.kernel.read()),this.bias!=null&&(h=biasAdd(h,this.bias.read())),recDpMask!=null&&(prevOutput=mul(prevOutput,recDpMask));let output=add2(h,dot2(prevOutput,this.recurrentKernel.read()));return this.activation!=null&&(output=this.activation.apply(output)),[output,output]})}getConfig(){let baseConfig=super.getConfig(),config2={units:this.units,activation:serializeActivation(this.activation),useBias:this.useBias,kernelInitializer:serializeInitializer(this.kernelInitializer),recurrentInitializer:serializeInitializer(this.recurrentInitializer),biasInitializer:serializeInitializer(this.biasInitializer),kernelRegularizer:serializeRegularizer(this.kernelRegularizer),recurrentRegularizer:serializeRegularizer(this.recurrentRegularizer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),kernelConstraint:serializeConstraint(this.kernelConstraint),recurrentConstraint:serializeConstraint(this.recurrentConstraint),biasConstraint:serializeConstraint(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},baseConfig,config2)}};SimpleRNNCell.className="SimpleRNNCell";serialization_exports.registerClass(SimpleRNNCell);var SimpleRNN=class extends RNN{constructor(args){args.cell=new SimpleRNNCell(args),super(args)}call(inputs,kwargs){return tidy(()=>{this.cell.dropoutMask!=null&&(dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let mask=kwargs==null?null:kwargs.mask,training=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;return super.call(inputs,{mask,training,initialState})})}static fromConfig(cls,config2){return new cls(config2)}};SimpleRNN.className="SimpleRNN";serialization_exports.registerClass(SimpleRNN);var GRUCell=class extends RNNCell{constructor(args){super(args);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",args.resetAfter)throw new ValueError("GRUCell does not support reset_after parameter set to true.");this.units=args.units,assertPositiveInteger(this.units,"units"),this.activation=getActivation(args.activation===void 0?this.DEFAULT_ACTIVATION:args.activation),this.recurrentActivation=getActivation(args.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:args.recurrentActivation),this.useBias=args.useBias==null?!0:args.useBias,this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=getInitializer(args.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=getRegularizer(args.kernelRegularizer),this.recurrentRegularizer=getRegularizer(args.recurrentRegularizer),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.kernelConstraint=getConstraint(args.kernelConstraint),this.recurrentConstraint=getConstraint(args.recurrentConstraint),this.biasConstraint=getConstraint(args.biasConstraint),this.dropout=min5([1,max5([0,args.dropout==null?0:args.dropout])]),this.recurrentDropout=min5([1,max5([0,args.recurrentDropout==null?0:args.recurrentDropout])]),this.implementation=args.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(inputShape){inputShape=getExactlyOneShape(inputShape);let inputDim=inputShape[inputShape.length-1];this.kernel=this.addWeight("kernel",[inputDim,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(inputs,kwargs){return tidy(()=>{if(inputs=inputs,inputs.length!==2)throw new ValueError(`GRUCell expects 2 input Tensors (inputs, h, c), got ${inputs.length}.`);let training=kwargs.training==null?!1:kwargs.training,hTMinus1=inputs[1];inputs=inputs[0],0onesLike(inputs),rate:this.dropout,training,count:3})),0onesLike(hTMinus1),rate:this.recurrentDropout,training,count:3}));let dpMask=this.dropoutMask,recDpMask=this.recurrentDropoutMask,z,r,hh;0{this.cell.dropoutMask!=null&&(dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let mask=kwargs==null?null:kwargs.mask,training=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;return super.call(inputs,{mask,training,initialState})})}static fromConfig(cls,config2){return config2.implmentation===0&&(config2.implementation=1),new cls(config2)}};GRU.className="GRU";serialization_exports.registerClass(GRU);var LSTMCell=class extends RNNCell{constructor(args){super(args);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=args.units,assertPositiveInteger(this.units,"units"),this.activation=getActivation(args.activation===void 0?this.DEFAULT_ACTIVATION:args.activation),this.recurrentActivation=getActivation(args.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:args.recurrentActivation),this.useBias=args.useBias==null?!0:args.useBias,this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=getInitializer(args.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=args.unitForgetBias,this.kernelRegularizer=getRegularizer(args.kernelRegularizer),this.recurrentRegularizer=getRegularizer(args.recurrentRegularizer),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.kernelConstraint=getConstraint(args.kernelConstraint),this.recurrentConstraint=getConstraint(args.recurrentConstraint),this.biasConstraint=getConstraint(args.biasConstraint),this.dropout=min5([1,max5([0,args.dropout==null?0:args.dropout])]),this.recurrentDropout=min5([1,max5([0,args.recurrentDropout==null?0:args.recurrentDropout])]),this.implementation=args.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(inputShape){var _a;inputShape=getExactlyOneShape(inputShape);let inputDim=inputShape[inputShape.length-1];this.kernel=this.addWeight("kernel",[inputDim,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let biasInitializer;if(this.useBias){if(this.unitForgetBias){let capturedBiasInit=this.biasInitializer,capturedUnits=this.units;biasInitializer=new(_a=class extends Initializer{apply(shape,dtype){let bI=capturedBiasInit.apply([capturedUnits]),bF=new Ones().apply([capturedUnits]),bCAndH=capturedBiasInit.apply([capturedUnits*2]);return concatAlongFirstAxis(concatAlongFirstAxis(bI,bF),bCAndH)}},_a.className="CustomInit",_a)}else biasInitializer=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,biasInitializer,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(inputs,kwargs){return tidy(()=>{let training=kwargs.training==null?!1:kwargs.training;if(inputs=inputs,inputs.length!==3)throw new ValueError(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${inputs.length}.`);let hTMinus1=inputs[1],cTMinus1=inputs[2];inputs=inputs[0],0onesLike(inputs),rate:this.dropout,training,count:4})),0onesLike(hTMinus1),rate:this.recurrentDropout,training,count:4}));let dpMask=this.dropoutMask,recDpMask=this.recurrentDropoutMask,i,f,c,o;0{this.cell.dropoutMask!=null&&(dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let mask=kwargs==null?null:kwargs.mask,training=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;return super.call(inputs,{mask,training,initialState})})}static fromConfig(cls,config2){return config2.implmentation===0&&(config2.implementation=1),new cls(config2)}};LSTM.className="LSTM";serialization_exports.registerClass(LSTM);var StackedRNNCells=class extends RNNCell{constructor(args){super(args);this.cells=args.cells}get stateSize(){let stateSize=[];for(let cell of this.cells.slice().reverse())Array.isArray(cell.stateSize)?stateSize.push(...cell.stateSize):stateSize.push(cell.stateSize);return stateSize}call(inputs,kwargs){return tidy(()=>{inputs=inputs;let states=inputs.slice(1),nestedStates=[];for(let cell of this.cells.slice().reverse())Array.isArray(cell.stateSize)?nestedStates.push(states.splice(0,cell.stateSize.length)):nestedStates.push(states.splice(0,1));nestedStates.reverse();let newNestedStates=[],callInputs;for(let i=0;i{nameScope(`RNNCell_${i}`,()=>{cell.build(inputShape),Array.isArray(cell.stateSize)?outputDim=cell.stateSize[0]:outputDim=cell.stateSize,inputShape=[inputShape[0],outputDim]})}),this.built=!0}getConfig(){let baseConfig=super.getConfig(),getCellConfig=cell=>({className:cell.getClassName(),config:cell.getConfig()}),config2={cells:this.cells.map(getCellConfig)};return Object.assign({},baseConfig,config2)}static fromConfig(cls,config2,customObjects={}){let cells=[];for(let cellConfig of config2.cells)cells.push(deserialize(cellConfig,customObjects));return new cls({cells})}get trainableWeights(){if(!this.trainable)return[];let weights=[];for(let cell of this.cells)weights.push(...cell.trainableWeights);return weights}get nonTrainableWeights(){let weights=[];for(let cell of this.cells)weights.push(...cell.nonTrainableWeights);if(!this.trainable){let trainableWeights=[];for(let cell of this.cells)trainableWeights.push(...cell.trainableWeights);return trainableWeights.concat(weights)}return weights}getWeights(){let weights=[];for(let cell of this.cells)weights.push(...cell.weights);return batchGetValue(weights)}setWeights(weights){let tuples=[];for(let cell of this.cells){let numParams=cell.weights.length,inputWeights=weights.splice(numParams);for(let i=0;idropout2(ones4(),rate),createMask=()=>inTrainPhase(droppedInputs,ones4,training);return!count2||count2<=1?keep(createMask().clone()):Array(count2).fill(void 0).map(createMask).map(m=>keep(m.clone()))}var __rest=function(s,e){var t={};for(var p2 in s)Object.prototype.hasOwnProperty.call(s,p2)&&e.indexOf(p2)<0&&(t[p2]=s[p2]);if(s!=null&&typeof Object.getOwnPropertySymbols=="function")for(var i=0,p2=Object.getOwnPropertySymbols(s);i{if(this.cell.dropoutMask!=null&&(dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),kwargs&&kwargs.constants)throw new ValueError("ConvRNN2D cell does not support constants");let mask=kwargs==null?null:kwargs.mask,training=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;return super.call(inputs,{mask,training,initialState})})}computeOutputShape(inputShape){let outShape=this.computeSingleOutputShape(inputShape);return this.returnSequences||(outShape=[outShape[0],...outShape.slice(2)]),this.returnState&&(outShape=[outShape,...Array(2).fill([inputShape[0],...outShape.slice(-3)])]),outShape}getInitialState(inputs){return tidy(()=>{let{stateSize}=this.cell,inputShape=inputs.shape,outputShape=this.computeSingleOutputShape(inputShape),stateShape=[outputShape[0],...outputShape.slice(2)],initialState=zeros(stateShape);return Array.isArray(stateSize)?Array(stateSize.length).fill(initialState):[initialState]})}resetStates(states,training=!1){tidy(()=>{if(!this.stateful)throw new AttributeError("Cannot call resetStates() on an RNN Layer that is not stateful.");let inputShape=this.inputSpec[0].shape,outputShape=this.computeSingleOutputShape(inputShape),stateShape=[outputShape[0],...outputShape.slice(2)];if(inputShape[0]==null)throw new ValueError("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>zeros(stateShape)):this.states_=[zeros(stateShape)];else if(states==null)dispose(this.states_),this.keptStates!=null&&(dispose(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>zeros(stateShape)):this.states_[0]=zeros(stateShape);else{if(Array.isArray(states)||(states=[states]),states.length!==this.states_.length)throw new ValueError(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${states.length} state value(s). Input received: ${states}`);training?this.keptStates.push(this.states_.slice()):dispose(this.states_);for(let index=0;indexkeep(state.clone()))})}computeSingleOutputShape(inputShape){let{dataFormat,filters,kernelSize,padding,strides,dilationRate}=this.cell,isChannelsFirst=dataFormat==="channelsFirst",h=inputShape[isChannelsFirst?3:2],w=inputShape[isChannelsFirst?4:3],hOut=convOutputLength(h,kernelSize[0],padding,strides[0],dilationRate[0]),wOut=convOutputLength(w,kernelSize[1],padding,strides[1],dilationRate[1]);return[...inputShape.slice(0,2),...isChannelsFirst?[filters,hOut,wOut]:[hOut,wOut,filters]]}};ConvRNN2D.className="ConvRNN2D";var ConvLSTM2DCell=class extends LSTMCell{constructor(args){let{filters,kernelSize,strides,padding,dataFormat,dilationRate}=args;super(Object.assign({},args,{units:filters}));this.filters=filters,assertPositiveInteger(this.filters,"filters"),this.kernelSize=normalizeArray(kernelSize,2,"kernelSize"),this.kernelSize.forEach(size=>assertPositiveInteger(size,"kernelSize")),this.strides=normalizeArray(strides||1,2,"strides"),this.strides.forEach(stride=>assertPositiveInteger(stride,"strides")),this.padding=padding||"valid",checkPaddingMode(this.padding),this.dataFormat=dataFormat||"channelsLast",checkDataFormat(this.dataFormat),this.dilationRate=normalizeArray(dilationRate||1,2,"dilationRate"),this.dilationRate.forEach(rate=>assertPositiveInteger(rate,"dilationRate"))}build(inputShape){var _a;inputShape=getExactlyOneShape(inputShape);let channelAxis=this.dataFormat==="channelsFirst"?1:inputShape.length-1;if(inputShape[channelAxis]==null)throw new ValueError(`The channel dimension of the input should be defined. Found ${inputShape[channelAxis]}`);let inputDim=inputShape[channelAxis],numOfKernels=4,kernelShape=this.kernelSize.concat([inputDim,this.filters*numOfKernels]);this.kernel=this.addWeight("kernel",kernelShape,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let recurrentKernelShape=this.kernelSize.concat([this.filters,this.filters*numOfKernels]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",recurrentKernelShape,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let biasInitializer;if(this.unitForgetBias){let init2=this.biasInitializer,filters=this.filters;biasInitializer=new(_a=class extends Initializer{apply(shape,dtype){let biasI=init2.apply([filters]),biasF=ones2([filters]),biasCAndO=init2.apply([filters*2]);return concatenate([biasI,biasF,biasCAndO])}},_a.className="CustomInit",_a)}else biasInitializer=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*numOfKernels],null,biasInitializer,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(inputs,kwargs){return tidy(()=>{if(inputs.length!==3)throw new ValueError(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${inputs.length}.`);let training=kwargs.training||!1,x=inputs[0],hTMinus1=inputs[1],cTMinus1=inputs[2],numOfKernels=4;0onesLike(x),rate:this.dropout,training,count:numOfKernels}));let dropoutMask=this.dropoutMask,applyDropout=(x2,mask,index)=>!mask||!mask[index]?x2:mul(mask[index],x2),xI=applyDropout(x,dropoutMask,0),xF=applyDropout(x,dropoutMask,1),xC=applyDropout(x,dropoutMask,2),xO=applyDropout(x,dropoutMask,3);0onesLike(hTMinus1),rate:this.recurrentDropout,training,count:numOfKernels}));let recDropoutMask=this.recurrentDropoutMask,hI=applyDropout(hTMinus1,recDropoutMask,0),hF=applyDropout(hTMinus1,recDropoutMask,1),hC=applyDropout(hTMinus1,recDropoutMask,2),hO=applyDropout(hTMinus1,recDropoutMask,3),kernelChannelAxis=3,[kernelI,kernelF,kernelC,kernelO]=split(this.kernel.read(),numOfKernels,kernelChannelAxis),[biasI,biasF,biasC,biasO]=this.useBias?split(this.bias.read(),numOfKernels):[null,null,null,null];xI=this.inputConv(xI,kernelI,biasI,this.padding),xF=this.inputConv(xF,kernelF,biasF,this.padding),xC=this.inputConv(xC,kernelC,biasC,this.padding),xO=this.inputConv(xO,kernelO,biasO,this.padding);let[recKernelI,recKernelF,recKernelC,recKernelO]=split(this.recurrentKernel.read(),numOfKernels,kernelChannelAxis);hI=this.recurrentConv(hI,recKernelI),hF=this.recurrentConv(hF,recKernelF),hC=this.recurrentConv(hC,recKernelC),hO=this.recurrentConv(hO,recKernelO);let i=this.recurrentActivation.apply(add2(xI,hI)),f=this.recurrentActivation.apply(add2(xF,hF)),c=add2(mul(f,cTMinus1),mul(i,this.activation.apply(add2(xC,hC)))),h=mul(this.recurrentActivation.apply(add2(xO,hO)),this.activation.apply(c));return[h,h,c]})}getConfig(){let _a=super.getConfig(),{units:_}=_a,baseConfig=__rest(_a,["units"]),config2={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},baseConfig,config2)}inputConv(x,w,b,padding){let out=conv2d(x,w,this.strides,padding||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return b?biasAdd(out,b,this.dataFormat):out}recurrentConv(x,w){return conv2d(x,w,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};ConvLSTM2DCell.className="ConvLSTM2DCell";serialization_exports.registerClass(ConvLSTM2DCell);var ConvLSTM2D=class extends ConvRNN2D{constructor(args){let cell=new ConvLSTM2DCell(args);super(Object.assign({},args,{cell}))}static fromConfig(cls,config2){return new cls(config2)}};ConvLSTM2D.className="ConvLSTM2D";serialization_exports.registerClass(ConvLSTM2D);var Dropout=class extends Layer{constructor(args){super(args);this.rate=Math.max(Math.min(args.rate,1),0),this.noiseShape=args.noiseShape,this.seed=args.seed,this.supportsMasking=!0}getNoiseShape(input2){if(this.noiseShape==null)return this.noiseShape;let inputShape=input2.shape,noiseShape=[];for(let i=0;i{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);if(0dropout2(input2,this.rate,noiseShape,this.seed),()=>input2,training)}return inputs})}getConfig(){let config2={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}dispose(){return super.dispose()}};Dropout.className="Dropout";serialization_exports.registerClass(Dropout);var SpatialDropout1D=class extends Dropout{constructor(args){super(args);this.inputSpec=[{ndim:3}]}getNoiseShape(input2){let inputShape=input2.shape;return[inputShape[0],1,inputShape[2]]}};SpatialDropout1D.className="SpatialDropout1D";serialization_exports.registerClass(SpatialDropout1D);var Dense=class extends Layer{constructor(args){super(args);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",args.batchInputShape==null&&args.inputShape==null&&args.inputDim!=null){let batchSize=null;args.batchSize!=null&&(batchSize=args.batchSize),this.batchInputShape=[batchSize,args.inputDim]}this.units=args.units,assertPositiveInteger(this.units,"units"),this.activation=getActivation(args.activation),args.useBias!=null&&(this.useBias=args.useBias),this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=getConstraint(args.kernelConstraint),this.biasConstraint=getConstraint(args.biasConstraint),this.kernelRegularizer=getRegularizer(args.kernelRegularizer),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.activityRegularizer=getRegularizer(args.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(inputShape){inputShape=getExactlyOneShape(inputShape);let inputLastDim=inputShape[inputShape.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[inputLastDim,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:inputLastDim}}],this.built=!0}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let outputShape=inputShape.slice();return outputShape[outputShape.length-1]=this.units,outputShape}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs),fusedActivationName=mapActivationToFusedKernel(this.activation.getClassName()),output;return fusedActivationName!=null?output=dot2(input2,this.kernel.read(),fusedActivationName,this.bias?this.bias.read():null):(output=dot2(input2,this.kernel.read()),this.bias!=null&&(output=biasAdd(output,this.bias.read())),this.activation!=null&&(output=this.activation.apply(output))),output})}getConfig(){let config2={units:this.units,activation:serializeActivation(this.activation),useBias:this.useBias,kernelInitializer:serializeInitializer(this.kernelInitializer),biasInitializer:serializeInitializer(this.biasInitializer),kernelRegularizer:serializeRegularizer(this.kernelRegularizer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),kernelConstraint:serializeConstraint(this.kernelConstraint),biasConstraint:serializeConstraint(this.biasConstraint)},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};Dense.className="Dense";serialization_exports.registerClass(Dense);var Flatten=class extends Layer{constructor(args){args=args||{},super(args),this.inputSpec=[{minNDim:3}],this.dataFormat=args.dataFormat}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);for(let dim of inputShape.slice(1))if(dim==null)throw new ValueError(`The shape of the input to "Flatten" is not fully defined (got ${inputShape.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[inputShape[0],arrayProd(inputShape,1)]}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);if(this.dataFormat==="channelsFirst"&&input2.rank>1){let permutation=[0];for(let i=2;i{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);return this.activation.apply(input2)})}getConfig(){let config2={activation:serializeActivation(this.activation)},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};Activation2.className="Activation";serialization_exports.registerClass(Activation2);var RepeatVector=class extends Layer{constructor(args){super(args);this.n=args.n,this.inputSpec=[{ndim:2}]}computeOutputShape(inputShape){return[inputShape[0],this.n,inputShape[1]]}call(inputs,kwargs){return tidy(()=>(inputs=getExactlyOneTensor(inputs),repeat(inputs,this.n)))}getConfig(){let config2={n:this.n},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};RepeatVector.className="RepeatVector";serialization_exports.registerClass(RepeatVector);var Reshape2=class extends Layer{constructor(args){super(args);this.targetShape=args.targetShape;for(let i=0;i{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs),inputShape=input2.shape,outputShape=inputShape.slice(0,1).concat(this.fixUnknownDimension(inputShape.slice(1),this.targetShape));return input2.reshape(outputShape)})}getConfig(){let config2={targetShape:this.targetShape},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};Reshape2.className="Reshape";serialization_exports.registerClass(Reshape2);var Permute=class extends Layer{constructor(args){super(args);if(args.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(args.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${args.dims} instead.`);let expectedSortedIndices=range5(1,args.dims.length+1);if(!util_exports.arraysEqual(args.dims.slice().sort(),expectedSortedIndices))throw new Error("Invalid permutation `dims`: "+JSON.stringify(args.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=args.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new InputSpec({ndim:this.dims.length+1})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let outputShape=inputShape.slice();return this.dims.forEach((dim,i)=>{outputShape[i+1]=inputShape[dim]}),outputShape}call(inputs,kwargs){return transpose(getExactlyOneTensor(inputs),this.dimsIncludingBatch)}getConfig(){let config2={dims:this.dims},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};Permute.className="Permute";serialization_exports.registerClass(Permute);var Masking=class extends Layer{constructor(args){super(args==null?{}:args);this.supportsMasking=!0,args!=null?this.maskValue=args.maskValue==null?0:args.maskValue:this.maskValue=0}computeOutputShape(inputShape){return inputShape}getConfig(){let baseConfig=super.getConfig(),config2={maskValue:this.maskValue};return Object.assign(config2,baseConfig),config2}computeMask(inputs,mask){let input2=getExactlyOneTensor(inputs),axis=-1;return any(notEqual(input2,this.maskValue),axis)}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs),axis=-1,keepDims=!0,booleanMask=any(notEqual(input2,this.maskValue),axis,keepDims);return input2.mul(booleanMask.asType(input2.dtype))})}};Masking.className="Masking";serialization_exports.registerClass(Masking);var Embedding=class extends Layer{constructor(args){super(args);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",args.batchInputShape==null&&args.inputShape==null){let batchSize=null;args.batchSize!=null&&(batchSize=args.batchSize),args.inputLength==null?this.batchInputShape=[batchSize,null]:this.batchInputShape=[batchSize].concat(toList(args.inputLength))}this.inputDim=args.inputDim,assertPositiveInteger(this.inputDim,"inputDim"),this.outputDim=args.outputDim,assertPositiveInteger(this.outputDim,"outputDim"),this.embeddingsInitializer=getInitializer(args.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=getRegularizer(args.embeddingsRegularizer),this.activityRegularizer=getRegularizer(args.activityRegularizer),this.embeddingsConstraint=getConstraint(args.embeddingsConstraint),this.maskZero=args.maskZero,this.supportsMasking=args.maskZero,this.inputLength=args.inputLength}build(inputShape){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(inputShape){}computeMask(inputs,mask){return tidy(()=>this.maskZero?(inputs=getExactlyOneTensor(inputs),notEqual(inputs,zerosLike(inputs))):null)}computeOutputShape(inputShape){if(inputShape=getExactlyOneShape(inputShape),this.inputLength==null)return[...inputShape,this.outputDim];let inLens=toList(this.inputLength);if(inLens.length!==inputShape.length-1)throw new ValueError(`"inputLength" is ${this.inputLength}, but received input shape has shape ${inputShape}`);{let i=0;for(let k=0;k{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);return input2.dtype!=="int32"&&(input2=cast5(input2,"int32")),gather2(this.embeddings.read(),input2.as1D()).reshape(getExactlyOneShape(this.computeOutputShape(input2.shape)))})}getConfig(){let config2={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:serializeInitializer(this.embeddingsInitializer),embeddingsRegularizer:serializeRegularizer(this.embeddingsRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),embeddingsConstraint:serializeConstraint(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};Embedding.className="Embedding";serialization_exports.registerClass(Embedding);var Merge=class extends Layer{constructor(args){super(args||{});this.supportsMasking=!0}mergeFunction(inputs){throw new NotImplementedError}computeElementwiseOpOutputShape(shape1,shape2){if(shape1==null||shape2==null)return null;if(shape1.length1)throw new ValueError(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(inputShape)}.`);let outputShape=inputShape[0]==null?null:inputShape[0].slice(1);for(let i=1;ishape.length);inputShape.indexOf(null)===-1&&unique4(allRanks).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(inputs,kwargs){return tidy(()=>{if(inputs=inputs,this.reshapeRequired){let reshapedInputs=[],inputDims=inputs.map(input2=>input2.rank);if(inputDims.indexOf(null)===-1){let maxNDim=max5(inputDims);for(let x of inputs){let xNDim=x.rank;for(let k=0;k1){let dims=range5(1,xNDim).concat([0]);reshapedInputs.push(transpose(x,dims)),transposed=!0}else reshapedInputs.push(x)}let y=this.mergeFunction(reshapedInputs),yNDim=y.rank;if(transposed){if(yNDim==null){let yShape=y.shape,yNDim2=yShape.length,batchSize=yShape[yNDim2-1],newShape=[batchSize].concat(yShape.slice(0,yShape.length-1));y=transpose(y.reshape([-1,batchSize]),[1,0]).reshape(newShape)}else if(yNDim>1){let dims=[yNDim-1].concat(range5(0,yNDim-1));y=transpose(y,dims)}}return y}}else return this.mergeFunction(inputs)})}computeOutputShape(inputShape){inputShape=inputShape;let outputShape;inputShape[0]==null?outputShape=null:outputShape=inputShape[0].slice(1);for(let i=1;i{if(mask==null)return null;if(!Array.isArray(mask))throw new ValueError("`mask` should be an Array");if(!Array.isArray(inputs))throw new ValueError("`inputs` should be an Array");if(mask.length!==inputs.length)throw new ValueError(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${inputs.length} vs ${mask.length})`);if(mask.every(m=>m==null))return null;mask=mask.map(m=>m==null?m:expandDims(m,0));let output=mask[0];for(let i=1;i{let output=inputs[0].clone();for(let i=1;i{let output=inputs[0].clone();for(let i=1;i{let output=inputs[0].clone();for(let i=1;i{let output=inputs[0];for(let i=1;i{let output=inputs[0];for(let i=1;i1)throw new ValueError("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(inputShape))}mergeFunction(inputs){return tidy(()=>concatenate(inputs,this.axis))}computeOutputShape(inputShape){if(!(Array.isArray(inputShape)&&Array.isArray(inputShape[0])))throw new ValueError("A `Concatenate` layer should be called on a list of inputs.");let inputShapes=inputShape,outputShape=inputShapes[0].slice(),axis=this.axis<0?outputShape.length+this.axis:this.axis;for(let shape of inputShapes.slice(1)){if(outputShape[axis]==null||shape[axis]==null){outputShape[axis]=null;break}outputShape[axis]+=shape[axis]}return outputShape}computeMask(inputs,mask){if(mask==null)return null;if(!Array.isArray(mask))throw new ValueError("`mask` should be an array for Concatenate");if(!Array.isArray(inputs))throw new ValueError("`inputs` should be an array for Concatenate");if(mask.length!==inputs.length)throw new ValueError(`Mismatch in the length of mask (${mask.length}) and the legnth of inputs (${inputs.length})`);return tidy(()=>{let allNullMasks=!0;if(mask.forEach(m=>{if(m!=null){allNullMasks=!1;return}}),allNullMasks)return null;let outputMasks=[];for(let i=0;i3||y.shape.length>3)throw new NotImplementedError("batchDot is not implemented for tensors of 4D or higher rank yet");if(util_exports.assert(x.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${x.shape.length}`),util_exports.assert(x.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${y.shape.length}`),typeof axes=="number"&&(axes=[axes,axes]),x.dtype==="complex64"||y.dtype==="complex64")throw new NotImplementedError("batchDot is not implemented for complex64-type Tensors yet.");let xNDim=x.shape.length,yNDim=y.shape.length;axes==null&&(axes=[xNDim-1,yNDim-2]);let axesArray=axes;return tidy(()=>{let diff;if(xNDim>yNDim){diff=xNDim-yNDim;let diffShape=[];for(let i=0;ixNDim){diff=yNDim-xNDim;let diffShape=[];for(let i=0;i0){let idx;xNDim>yNDim?idx=xNDim+yNDim-3:idx=xNDim-1;let squeezeAxes=[];for(let i=idx;i"A `Dot` layer should be called on a list of exactly 2 inputs.");let shape1=inputShape[0],shape2=inputShape[1];if(shape1.length>3||shape2.length>3)throw new NotImplementedError("Dot layer does not support tensors of 4D or higher rank yet.");let axes=this.interpretAxes(shape1,shape2);if(shape1[axes[0]]!==shape2[axes[1]])throw new ValueError(`Dimension incompatibility: ${shape1[axes[0]]} !== ${shape2[axes[1]]}`)}mergeFunction(inputs){if(inputs.length!==2)throw new ValueError(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${inputs.length} input(s).`);let x1=inputs[0],x2=inputs[1],axes;return Array.isArray(this.axes)?axes=this.axes.map((axis,i)=>interpretAxis(axis,inputs[i].shape.length)):axes=[interpretAxis(this.axes,x1.shape.length),interpretAxis(this.axes,x2.shape.length)],this.normalize&&(x1=l2Normalize(x1,axes[0]),x2=l2Normalize(x2,axes[1])),batchDot(x1,x2,axes)}interpretAxes(shape1,shape2){let axes;return Array.isArray(this.axes)?axes=this.axes:axes=[interpretAxis(this.axes,shape1.length),interpretAxis(this.axes,shape2.length)],axes}computeOutputShape(inputShape){util_exports.assert(Array.isArray(inputShape)&&inputShape.length===2&&Array.isArray(inputShape[0])&&Array.isArray(inputShape[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let shape1=inputShape[0].slice(),shape2=inputShape[1].slice();if(shape1.length>3||shape2.length>3)throw new NotImplementedError("Dot layer does not support tensors of 4D or higher rank yet.");let axes=this.interpretAxes(shape1,shape2);shape1.splice(axes[0],1),shape2.splice(axes[1],1),shape2.splice(0,1);let outputShape=shape1.concat(shape2);return outputShape.length===1&&outputShape.push(1),outputShape}computeMask(inputs,mask){return null}getConfig(){let config2={axes:this.axes,normalize:this.normalize},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};Dot.className="Dot";serialization_exports.registerClass(Dot);var GaussianNoise=class extends Layer{constructor(args){super(args);this.supportsMasking=!0,this.stddev=args.stddev}computeOutputShape(inputShape){return inputShape}getConfig(){let baseConfig=super.getConfig(),config2={stddev:this.stddev};return Object.assign(config2,baseConfig),config2}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);return inTrainPhase(()=>randomNormal2(input2.shape,0,this.stddev).add(input2),()=>input2,kwargs.training||!1)})}};GaussianNoise.className="GaussianNoise";serialization_exports.registerClass(GaussianNoise);var GaussianDropout=class extends Layer{constructor(args){super(args);this.supportsMasking=!0,this.rate=args.rate}computeOutputShape(inputShape){return inputShape}getConfig(){let baseConfig=super.getConfig(),config2={rate:this.rate};return Object.assign(config2,baseConfig),config2}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);return this.rate>0&&this.rate<1?inTrainPhase(()=>{let stddev=Math.sqrt(this.rate/(1-this.rate));return input2.mul(randomNormal2(input2.shape,1,stddev))},()=>input2,kwargs.training||!1):input2})}};GaussianDropout.className="GaussianDropout";serialization_exports.registerClass(GaussianDropout);var AlphaDropout=class extends Layer{constructor(args){super(args);this.supportsMasking=!0,this.rate=args.rate,this.noiseShape=args.noiseShape}_getNoiseShape(inputs){return this.noiseShape||getExactlyOneTensor(inputs).shape}computeOutputShape(inputShape){return inputShape}getConfig(){let baseConfig=super.getConfig(),config2={rate:this.rate};return Object.assign(config2,baseConfig),config2}call(inputs,kwargs){return tidy(()=>{if(this.rate<1&&this.rate>0){let noiseShape=this._getNoiseShape(inputs);return inTrainPhase(()=>{let input2=getExactlyOneTensor(inputs),alpha=1.6732632423543772,scale2=1.0507009873554805,alphaP=-alpha*scale2,keptIdx=greaterEqual(randomUniform(noiseShape),this.rate);keptIdx=cast5(keptIdx,"float32");let a=((1-this.rate)*(1+this.rate*alphaP**2))**-.5,b=-a*alphaP*this.rate;return input2.mul(keptIdx).add(keptIdx.add(-1).mul(alphaP)).mul(a).add(b)},()=>getExactlyOneTensor(inputs),kwargs.training||!1)}return inputs})}};AlphaDropout.className="AlphaDropout";serialization_exports.registerClass(AlphaDropout);function batchNormalization(x,mean4,variance,beta,gamma,epsilon3=.001){let out;if(x.rank===2)out=batchNorm2d(x,mean4,variance,beta,gamma,epsilon3);else if(x.rank===3)out=batchNorm3d(x,mean4,variance,beta,gamma,epsilon3);else if(x.rank===4)out=batchNorm4d(x,mean4,variance,beta,gamma,epsilon3);else throw new NotImplementedError(`batchNormalization is not implemented for array of rank ${x.rank} yet`);return out}function regularNormalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon3=.001){return tidy(()=>{let meanAndVariance=moments(x,reductionAxes),mean4=meanAndVariance.mean,variance=meanAndVariance.variance;return[batchNormalization(x,mean4,variance,beta,gamma,epsilon3),mean4,variance]})}function broadcastNormalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon3=.001){return tidy(()=>{let meanAndVariance=moments(x,reductionAxes),mean4=meanAndVariance.mean,variance=meanAndVariance.variance,targetShape=[];for(let axis of range5(0,x.rank))reductionAxes.indexOf(axis)!==-1?targetShape.push(1):targetShape.push(x.shape[axis]);let broadcastMean=mean4.reshape(targetShape),broadcastVariance=variance.reshape(targetShape),broadcastGamma=gamma==null?null:gamma.reshape(targetShape),broadcastBeta=beta==null?null:beta.reshape(targetShape);return[batchNormalization(x,broadcastMean,broadcastVariance,broadcastBeta,broadcastGamma,epsilon3),mean4,variance]})}function normalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon3=.001){return util_exports.arraysEqual(reductionAxes.slice().sort(),range5(0,x.rank-1))?regularNormalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon3):broadcastNormalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon3)}var BatchNormalization=class extends Layer{constructor(args){args==null&&(args={}),super(args),this.supportsMasking=!0,this.axis=args.axis==null?-1:args.axis,this.momentum=args.momentum==null?.99:args.momentum,this.epsilon=args.epsilon==null?.001:args.epsilon,this.center=args.center==null?!0:args.center,this.scale=args.scale==null?!0:args.scale,this.betaInitializer=getInitializer(args.betaInitializer||"zeros"),this.gammaInitializer=getInitializer(args.gammaInitializer||"ones"),this.movingMeanInitializer=getInitializer(args.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=getInitializer(args.movingVarianceInitializer||"ones"),this.betaConstraint=getConstraint(args.betaConstraint),this.gammaConstraint=getConstraint(args.gammaConstraint),this.betaRegularizer=getRegularizer(args.betaRegularizer),this.gammaRegularizer=getRegularizer(args.gammaRegularizer)}build(inputShape){inputShape=getExactlyOneShape(inputShape);let axis=this.axis>=0?this.axis:this.axis+inputShape.length,dim=inputShape[axis];if(dim==null)throw new ValueError(`Axis ${axis} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(inputShape)}.`);this.inputSpec=[new InputSpec({ndim:inputShape.length,axes:{[axis]:dim}})];let shape=[dim];this.scale&&(this.gamma=this.addWeight("gamma",shape,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",shape,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",shape,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",shape,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(inputs,kwargs){return tidy(()=>{let training=kwargs.training==null?!1:kwargs.training,input2=getExactlyOneTensor(inputs),inputShape=input2.shape,ndim=inputShape.length,reductionAxes=range5(0,ndim),axis=this.axis>=0?this.axis:this.axis+ndim;reductionAxes.splice(axis,1);let broadcastShape=pyListRepeat(1,ndim);broadcastShape[axis]=inputShape[axis];let sortedReductionAxes=reductionAxes.slice();sortedReductionAxes.sort();let needsBroadcasting=!util_exports.arraysEqual(sortedReductionAxes,range5(0,ndim).slice(0,ndim-1)),normalizeInference=()=>{if(needsBroadcasting){let broadcastMovingMean=this.movingMean.read().reshape(broadcastShape),broadcastMovingVariance=this.movingVariance.read().reshape(broadcastShape),broadcastBeta=this.center?this.beta.read().reshape(broadcastShape):null,broadcastGamma=this.scale?this.gamma.read().reshape(broadcastShape):null;return batchNormalization(input2,broadcastMovingMean,broadcastMovingVariance,broadcastBeta,broadcastGamma,this.epsilon)}else return batchNormalization(input2,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!training)return normalizeInference();let[normedTraining,mean4,variance]=normalizeBatchInTraining(input2,this.gamma.read(),this.beta.read(),reductionAxes,this.epsilon),doMovingAverage=(variable2,value,momentum)=>{tidy(()=>{let decay=1-momentum,origValue=variable2.read(),updateDelta=origValue.sub(value).mul(decay);variable2.write(origValue.sub(updateDelta))})};return(()=>{doMovingAverage(this.movingMean,mean4,this.momentum),doMovingAverage(this.movingVariance,variance,this.momentum)})(),normedTraining})}getConfig(){let config2={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:serializeInitializer(this.betaInitializer),gammaInitializer:serializeInitializer(this.gammaInitializer),movingMeanInitializer:serializeInitializer(this.movingMeanInitializer),movingVarianceInitializer:serializeInitializer(this.movingVarianceInitializer),betaRegularizer:serializeRegularizer(this.betaRegularizer),gammaRegularizer:serializeRegularizer(this.gammaRegularizer),betaConstraint:serializeConstraint(this.betaConstraint),gammaConstraint:serializeConstraint(this.gammaConstraint)},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};BatchNormalization.className="BatchNormalization";serialization_exports.registerClass(BatchNormalization);var LayerNormalization=class extends Layer{constructor(args){if(args==null&&(args={}),super(args),this.axis=args.axis==null?-1:args.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let axis of this.axis)if(!Number.isInteger(axis))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=args.epsilon==null?.001:args.epsilon,this.center=args.center==null?!0:args.center,this.scale=args.scale==null?!0:args.scale,this.betaInitializer=getInitializer(args.betaInitializer||"zeros"),this.gammaInitializer=getInitializer(args.gammaInitializer||"ones"),this.betaRegularizer=getRegularizer(args.betaRegularizer),this.gammaRegularizer=getRegularizer(args.gammaRegularizer),this.supportsMasking=!0}build(inputShape){inputShape=getExactlyOneShape(inputShape);let nDims=inputShape.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let i=0;i=nDims)throw new Error(`Invalid axis: ${axis}`);if(this.axis.length!==unique4(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let paramShape=this.axis.map(axis=>inputShape[axis]),trainable=!0;this.scale?this.gamma=this.addWeight("gamma",paramShape,"float32",this.gammaInitializer,this.gammaRegularizer,trainable):this.gamma=null,this.center?this.beta=this.addWeight("beta",paramShape,"float32",this.betaInitializer,this.betaRegularizer,trainable):this.beta=null,this.built=!0}call(inputs,kwargs){let input2=getExactlyOneTensor(inputs),inputShape=input2.shape,nDims=inputShape.length;return tidy(()=>{let keepDims=!0,{mean:mean4,variance}=moments(input2,this.axis,keepDims),broadcastShape=pyListRepeat(1,nDims);for(let dim of this.axis)broadcastShape[dim]=inputShape[dim];let broadcast=v=>v!=null&&v.shape.length!==nDims&&this.axis!==[nDims-1]?v.reshape(broadcastShape):v,scale2=broadcast(this.gamma.read()),offset=broadcast(this.beta.read()),momentsTiling=[],scaleOffsetTiling=[];for(let i=0;i{if(x.rank!==4)throw new ValueError(`temporalPadding expects input tensor to be 4-D, but received a ${x.rank}-D tensor.`);if(padding==null&&(padding=[[1,1],[1,1]]),padding.length!==2||padding[0].length!==2||padding[1].length!==2)throw new ValueError("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(dataFormat==null&&(dataFormat=imageDataFormat()),dataFormat!=="channelsLast"&&dataFormat!=="channelsFirst")throw new ValueError(`Unknown data format: ${dataFormat}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let pattern;return dataFormat==="channelsFirst"?pattern=[[0,0],[0,0],padding[0],padding[1]]:pattern=[[0,0],padding[0],padding[1],[0,0]],pad(x,pattern)})}var ZeroPadding2D=class extends Layer{constructor(args){if(args==null&&(args={}),super(args),this.dataFormat=args.dataFormat==null?imageDataFormat():args.dataFormat,args.padding==null)this.padding=[[1,1],[1,1]];else if(typeof args.padding=="number")this.padding=[[args.padding,args.padding],[args.padding,args.padding]];else{if(args.padding=args.padding,args.padding.length!==2)throw new ValueError(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${args.padding.length} array.`);let heightPadding,widthPadding;if(typeof args.padding[0]=="number")heightPadding=[args.padding[0],args.padding[0]],widthPadding=[args.padding[1],args.padding[1]];else{if(args.padding=args.padding,args.padding[0].length!==2)throw new ValueError(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${args.padding[0].length} array.`);if(heightPadding=args.padding[0],args.padding[1].length!==2)throw new ValueError(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${args.padding[1].length} array.`);widthPadding=args.padding[1]}this.padding=[heightPadding,widthPadding]}this.inputSpec=[new InputSpec({ndim:4})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let rows,cols;return this.dataFormat==="channelsFirst"?(inputShape[2]!=null&&inputShape[2]>=0?rows=inputShape[2]+this.padding[0][0]+this.padding[0][1]:rows=null,inputShape[3]!=null&&inputShape[3]>=0?cols=inputShape[3]+this.padding[1][0]+this.padding[1][1]:cols=null,[inputShape[0],inputShape[1],rows,cols]):(inputShape[1]!=null&&inputShape[1]>=0?rows=inputShape[1]+this.padding[0][0]+this.padding[0][1]:rows=null,inputShape[2]!=null&&inputShape[2]>=0?cols=inputShape[2]+this.padding[1][0]+this.padding[1][1]:cols=null,[inputShape[0],rows,cols,inputShape[3]])}call(inputs,kwargs){return tidy(()=>spatial2dPadding(getExactlyOneTensor(inputs),this.padding,this.dataFormat))}getConfig(){let config2={padding:this.padding,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};ZeroPadding2D.className="ZeroPadding2D";serialization_exports.registerClass(ZeroPadding2D);function pool2d(x,poolSize,strides,padding,dataFormat,poolMode){return tidy(()=>{checkDataFormat(dataFormat),checkPoolMode(poolMode),checkPaddingMode(padding),strides==null&&(strides=[1,1]),padding==null&&(padding="valid"),dataFormat==null&&(dataFormat=imageDataFormat()),poolMode==null&&(poolMode="max"),x=preprocessConv2DInput(x,dataFormat);let y,paddingString=padding==="same"?"same":"valid";return poolMode==="max"?y=maxPool(x,poolSize,strides,paddingString):y=avgPool(x,poolSize,strides,paddingString),dataFormat==="channelsFirst"&&(y=transpose(y,[0,3,1,2])),y})}function pool3d2(x,poolSize,strides,padding,dataFormat,poolMode){return tidy(()=>{checkDataFormat(dataFormat),checkPoolMode(poolMode),checkPaddingMode(padding),strides==null&&(strides=[1,1,1]),padding==null&&(padding="valid"),dataFormat==null&&(dataFormat=imageDataFormat()),poolMode==null&&(poolMode="max"),x=preprocessConv3DInput(x,dataFormat);let y,paddingString=padding==="same"?"same":"valid";return poolMode==="max"?y=maxPool3d(x,poolSize,strides,paddingString):y=avgPool3d(x,poolSize,strides,paddingString),dataFormat==="channelsFirst"&&(y=transpose(y,[0,4,1,2,3])),y})}var Pooling1D=class extends Layer{constructor(args){if(args.poolSize==null&&(args.poolSize=2),super(args),typeof args.poolSize=="number")this.poolSize=[args.poolSize];else if(Array.isArray(args.poolSize)&&args.poolSize.length===1&&typeof args.poolSize[0]=="number")this.poolSize=args.poolSize;else throw new ValueError(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(args.poolSize)}`);if(assertPositiveInteger(this.poolSize,"poolSize"),args.strides==null)this.strides=this.poolSize;else if(typeof args.strides=="number")this.strides=[args.strides];else if(Array.isArray(args.strides)&&args.strides.length===1&&typeof args.strides[0]=="number")this.strides=args.strides;else throw new ValueError(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(args.strides)}`);assertPositiveInteger(this.strides,"strides"),this.padding=args.padding==null?"valid":args.padding,checkPaddingMode(this.padding),this.inputSpec=[new InputSpec({ndim:3})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let length=convOutputLength(inputShape[1],this.poolSize[0],this.padding,this.strides[0]);return[inputShape[0],length,inputShape[2]]}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs),inputs=expandDims5(getExactlyOneTensor(inputs),2);let output=this.poolingFunction(getExactlyOneTensor(inputs),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return squeeze(output,[2])})}getConfig(){let config2={poolSize:this.poolSize,padding:this.padding,strides:this.strides},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}},MaxPooling1D=class extends Pooling1D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding),pool2d(inputs,poolSize,strides,padding,dataFormat,"max")}};MaxPooling1D.className="MaxPooling1D";serialization_exports.registerClass(MaxPooling1D);var AveragePooling1D=class extends Pooling1D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding),pool2d(inputs,poolSize,strides,padding,dataFormat,"avg")}};AveragePooling1D.className="AveragePooling1D";serialization_exports.registerClass(AveragePooling1D);var Pooling2D=class extends Layer{constructor(args){if(args.poolSize==null&&(args.poolSize=[2,2]),super(args),this.poolSize=Array.isArray(args.poolSize)?args.poolSize:[args.poolSize,args.poolSize],args.strides==null)this.strides=this.poolSize;else if(Array.isArray(args.strides)){if(args.strides.length!==2)throw new ValueError(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${args.strides.length}.`);this.strides=args.strides}else this.strides=[args.strides,args.strides];assertPositiveInteger(this.poolSize,"poolSize"),assertPositiveInteger(this.strides,"strides"),this.padding=args.padding==null?"valid":args.padding,this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),checkPaddingMode(this.padding),this.inputSpec=[new InputSpec({ndim:4})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let rows=this.dataFormat==="channelsFirst"?inputShape[2]:inputShape[1],cols=this.dataFormat==="channelsFirst"?inputShape[3]:inputShape[2];return rows=convOutputLength(rows,this.poolSize[0],this.padding,this.strides[0]),cols=convOutputLength(cols,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[inputShape[0],inputShape[1],rows,cols]:[inputShape[0],rows,cols,inputShape[3]]}call(inputs,kwargs){return tidy(()=>(this.invokeCallHook(inputs,kwargs),this.poolingFunction(getExactlyOneTensor(inputs),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let config2={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}},MaxPooling2D=class extends Pooling2D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding),pool2d(inputs,poolSize,strides,padding,dataFormat,"max")}};MaxPooling2D.className="MaxPooling2D";serialization_exports.registerClass(MaxPooling2D);var AveragePooling2D=class extends Pooling2D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding),pool2d(inputs,poolSize,strides,padding,dataFormat,"avg")}};AveragePooling2D.className="AveragePooling2D";serialization_exports.registerClass(AveragePooling2D);var Pooling3D=class extends Layer{constructor(args){if(args.poolSize==null&&(args.poolSize=[2,2,2]),super(args),this.poolSize=Array.isArray(args.poolSize)?args.poolSize:[args.poolSize,args.poolSize,args.poolSize],args.strides==null)this.strides=this.poolSize;else if(Array.isArray(args.strides)){if(args.strides.length!==3)throw new ValueError(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${args.strides.length}.`);this.strides=args.strides}else this.strides=[args.strides,args.strides,args.strides];assertPositiveInteger(this.poolSize,"poolSize"),assertPositiveInteger(this.strides,"strides"),this.padding=args.padding==null?"valid":args.padding,this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),checkPaddingMode(this.padding),this.inputSpec=[new InputSpec({ndim:5})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let depths=this.dataFormat==="channelsFirst"?inputShape[2]:inputShape[1],rows=this.dataFormat==="channelsFirst"?inputShape[3]:inputShape[2],cols=this.dataFormat==="channelsFirst"?inputShape[4]:inputShape[3];return depths=convOutputLength(depths,this.poolSize[0],this.padding,this.strides[0]),rows=convOutputLength(rows,this.poolSize[1],this.padding,this.strides[1]),cols=convOutputLength(cols,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[inputShape[0],inputShape[1],depths,rows,cols]:[inputShape[0],depths,rows,cols,inputShape[4]]}call(inputs,kwargs){return tidy(()=>(this.invokeCallHook(inputs,kwargs),this.poolingFunction(getExactlyOneTensor(inputs),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let config2={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}},MaxPooling3D=class extends Pooling3D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding),pool3d2(inputs,poolSize,strides,padding,dataFormat,"max")}};MaxPooling3D.className="MaxPooling3D";serialization_exports.registerClass(MaxPooling3D);var AveragePooling3D=class extends Pooling3D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding),pool3d2(inputs,poolSize,strides,padding,dataFormat,"avg")}};AveragePooling3D.className="AveragePooling3D";serialization_exports.registerClass(AveragePooling3D);var GlobalPooling1D=class extends Layer{constructor(args){super(args);this.inputSpec=[new InputSpec({ndim:3})]}computeOutputShape(inputShape){return[inputShape[0],inputShape[2]]}call(inputs,kwargs){throw new NotImplementedError}},GlobalAveragePooling1D=class extends GlobalPooling1D{constructor(args){super(args||{})}call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);return mean(input2,1)})}};GlobalAveragePooling1D.className="GlobalAveragePooling1D";serialization_exports.registerClass(GlobalAveragePooling1D);var GlobalMaxPooling1D=class extends GlobalPooling1D{constructor(args){super(args||{})}call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);return max(input2,1)})}};GlobalMaxPooling1D.className="GlobalMaxPooling1D";serialization_exports.registerClass(GlobalMaxPooling1D);var GlobalPooling2D=class extends Layer{constructor(args){super(args);this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),this.inputSpec=[new InputSpec({ndim:4})]}computeOutputShape(inputShape){return inputShape=inputShape,this.dataFormat==="channelsLast"?[inputShape[0],inputShape[3]]:[inputShape[0],inputShape[1]]}call(inputs,kwargs){throw new NotImplementedError}getConfig(){let config2={dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}},GlobalAveragePooling2D=class extends GlobalPooling2D{call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);return this.dataFormat==="channelsLast"?mean(input2,[1,2]):mean(input2,[2,3])})}};GlobalAveragePooling2D.className="GlobalAveragePooling2D";serialization_exports.registerClass(GlobalAveragePooling2D);var GlobalMaxPooling2D=class extends GlobalPooling2D{call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);return this.dataFormat==="channelsLast"?max(input2,[1,2]):max(input2,[2,3])})}};GlobalMaxPooling2D.className="GlobalMaxPooling2D";serialization_exports.registerClass(GlobalMaxPooling2D);var Wrapper=class extends Layer{constructor(args){super(args);this.layer=args.layer}build(inputShape){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(value){this.layer!=null&&(this.layer.trainable=value)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(weights){this.layer.setWeights(weights)}getConfig(){let config2={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}setFastWeightInitDuringBuild(value){super.setFastWeightInitDuringBuild(value),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(value)}static fromConfig(cls,config2,customObjects={}){let layerConfig=config2.layer,layer=deserialize(layerConfig,customObjects);delete config2.layer;let newConfig={layer};return Object.assign(newConfig,config2),new cls(newConfig)}},TimeDistributed=class extends Wrapper{constructor(args){super(args);this.supportsMasking=!0}build(inputShape){if(inputShape=getExactlyOneShape(inputShape),inputShape.length<3)throw new ValueError(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(inputShape)}`);this.inputSpec=[{shape:inputShape}];let childInputShape=[inputShape[0]].concat(inputShape.slice(2));this.layer.built||(this.layer.build(childInputShape),this.layer.built=!0),super.build(inputShape)}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let childInputShape=[inputShape[0]].concat(inputShape.slice(2)),childOutputShape=this.layer.computeOutputShape(childInputShape),timesteps=inputShape[1];return[childOutputShape[0],timesteps].concat(childOutputShape.slice(1))}call(inputs,kwargs){return tidy(()=>(inputs=getExactlyOneTensor(inputs),rnn((inputs2,states)=>[getExactlyOneTensor(this.layer.call(inputs2,kwargs)),[]],inputs,[],!1,null,null,!1,!0)[1]))}};TimeDistributed.className="TimeDistributed";serialization_exports.registerClass(TimeDistributed);function checkBidirectionalMergeMode(value){checkStringTypeUnionValue(VALID_BIDIRECTIONAL_MERGE_MODES,"BidirectionalMergeMode",value)}var DEFAULT_BIDIRECTIONAL_MERGE_MODE="concat",Bidirectional=class extends Wrapper{constructor(args){super(args);let layerConfig=args.layer.getConfig(),forwDict={};forwDict.className=args.layer.getClassName(),forwDict.config=layerConfig,this.forwardLayer=deserialize(forwDict),layerConfig.goBackwards=layerConfig.goBackwards!==!0;let backDict={};if(backDict.className=args.layer.getClassName(),backDict.config=layerConfig,this.backwardLayer=deserialize(backDict),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=args.mergeMode===void 0?DEFAULT_BIDIRECTIONAL_MERGE_MODE:args.mergeMode,checkBidirectionalMergeMode(this.mergeMode),args.weights)throw new NotImplementedError("weights support is not implemented for Bidirectional layer yet.");this._stateful=args.layer.stateful,this.returnSequences=args.layer.returnSequences,this.returnState=args.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=args.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(value){this._trainable=value,this.forwardLayer!=null&&(this.forwardLayer.trainable=value),this.backwardLayer!=null&&(this.backwardLayer.trainable=value)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(weights){let numWeights=weights.length,numeightsOver2=Math.floor(numWeights/2);this.forwardLayer.setWeights(weights.slice(0,numeightsOver2)),this.backwardLayer.setWeights(weights.slice(numeightsOver2))}computeOutputShape(inputShape){let layerShapes=this.forwardLayer.computeOutputShape(inputShape);Array.isArray(layerShapes)&&Array.isArray(layerShapes[0])||(layerShapes=[layerShapes]),layerShapes=layerShapes;let outputShape,outputShapes,stateShape;return this.returnState&&(stateShape=layerShapes.slice(1)),outputShape=layerShapes[0],outputShape=outputShape,this.mergeMode==="concat"?(outputShape[outputShape.length-1]*=2,outputShapes=[outputShape]):this.mergeMode==null?outputShapes=[outputShape,outputShape.slice()]:outputShapes=[outputShape],this.returnState?this.mergeMode==null?outputShapes.concat(stateShape).concat(stateShape.slice()):[outputShape].concat(stateShape).concat(stateShape.slice()):singletonOrArray(outputShapes)}apply(inputs,kwargs){let initialState=kwargs==null?null:kwargs.initialState,constants=kwargs==null?null:kwargs.constants;kwargs==null&&(kwargs={});let standardized=standardizeArgs(inputs,initialState,constants,this.numConstants);if(inputs=standardized.inputs,initialState=standardized.initialState,constants=standardized.constants,Array.isArray(inputs)&&(initialState=inputs.slice(1),inputs=inputs[0]),(initialState==null||initialState.length===0)&&constants==null)return super.apply(inputs,kwargs);let additionalInputs=[],additionalSpecs=[];if(initialState!=null){let numStates=initialState.length;if(numStates%2>0)throw new ValueError("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");kwargs.initialState=initialState,additionalInputs.push(...initialState);let stateSpecs=initialState.map(state=>new InputSpec({shape:state.shape}));this.forwardLayer.stateSpec=stateSpecs.slice(0,numStates/2),this.backwardLayer.stateSpec=stateSpecs.slice(numStates/2),additionalSpecs.push(...stateSpecs)}if(constants!=null)throw new NotImplementedError("Support for constants in Bidirectional layers is not implemented yet.");let isSymbolicTensor=additionalInputs[0]instanceof SymbolicTensor;for(let tensor2 of additionalInputs)if(tensor2 instanceof SymbolicTensor!==isSymbolicTensor)throw new ValueError("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(isSymbolicTensor){let fullInput=[inputs].concat(additionalInputs),fullInputSpec=this.inputSpec.concat(additionalSpecs),originalInputSpec=this.inputSpec;this.inputSpec=fullInputSpec;let output=super.apply(fullInput,kwargs);return this.inputSpec=originalInputSpec,output}else return super.apply(inputs,kwargs)}call(inputs,kwargs){return tidy(()=>{let initialState=kwargs.initialState,y,yRev;if(initialState==null)y=this.forwardLayer.call(inputs,kwargs),yRev=this.backwardLayer.call(inputs,kwargs);else{let forwardState=initialState.slice(0,initialState.length/2),backwardState=initialState.slice(initialState.length/2);y=this.forwardLayer.call(inputs,Object.assign(kwargs,{initialState:forwardState})),yRev=this.backwardLayer.call(inputs,Object.assign(kwargs,{initialState:backwardState}))}let states;this.returnState&&(Array.isArray(y)&&(states=y.slice(1).concat(yRev.slice(1))),y=y[0],yRev=yRev[0]),this.returnSequences&&(yRev=reverse(yRev,1));let output;return this.mergeMode==="concat"?output=concatenate([y,yRev]):this.mergeMode==="sum"?output=add2(y,yRev):this.mergeMode==="ave"?output=mul(.5,add2(y,yRev)):this.mergeMode==="mul"?output=mul(y,yRev):this.mergeMode==null&&(output=[y,yRev]),this.returnState?this.mergeMode==null?output.concat(states):[output].concat(states):output})}resetStates(states){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(inputShape){nameScope(this.forwardLayer.name,()=>{this.forwardLayer.build(inputShape)}),nameScope(this.backwardLayer.name,()=>{this.backwardLayer.build(inputShape)}),this.built=!0}computeMask(inputs,mask){Array.isArray(mask)&&(mask=mask[0]);let outputMask;if(this.returnSequences?this.mergeMode==null?outputMask=[mask,mask]:outputMask=mask:this.mergeMode==null?outputMask=[null,null]:outputMask=null,this.returnState){let stateMask=this.forwardLayer.states.map(state=>null);return Array.isArray(outputMask)?outputMask.concat(stateMask).concat(stateMask):[outputMask].concat(stateMask).concat(stateMask)}else return outputMask}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)}setFastWeightInitDuringBuild(value){super.setFastWeightInitDuringBuild(value),this.forwardLayer!=null&&this.forwardLayer.setFastWeightInitDuringBuild(value),this.backwardLayer!=null&&this.backwardLayer.setFastWeightInitDuringBuild(value)}getConfig(){let config2={mergeMode:this.mergeMode},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}static fromConfig(cls,config2){let rnnLayer=deserialize(config2.layer);if(delete config2.layer,config2.numConstants!=null)throw new NotImplementedError("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");let newConfig=config2;return newConfig.layer=rnnLayer,new cls(newConfig)}};Bidirectional.className="Bidirectional";serialization_exports.registerClass(Bidirectional);function inputLayer(args){return new InputLayer(args)}function elu5(args){return new ELU4(args)}function reLU(args){return new ReLU(args)}function leakyReLU(args){return new LeakyReLU(args)}function prelu5(args){return new PReLU(args)}function softmax5(args){return new Softmax3(args)}function thresholdedReLU(args){return new ThresholdedReLU(args)}function conv1d2(args){return new Conv1D(args)}function conv2d5(args){return new Conv2D2(args)}function conv2dTranspose2(args){return new Conv2DTranspose(args)}function conv3d2(args){return new Conv3D2(args)}function separableConv2d2(args){return new SeparableConv2D(args)}function cropping2D(args){return new Cropping2D(args)}function upSampling2d(args){return new UpSampling2D(args)}function depthwiseConv2d5(args){return new DepthwiseConv2D(args)}function activation(args){return new Activation2(args)}function dense(args){return new Dense(args)}function dropout3(args){return new Dropout(args)}function spatialDropout1d(args){return new SpatialDropout1D(args)}function flatten3(args){return new Flatten(args)}function repeatVector(args){return new RepeatVector(args)}function reshape5(args){return new Reshape2(args)}function permute(args){return new Permute(args)}function embedding(args){return new Embedding(args)}function add4(args){return new Add2(args)}function average(args){return new Average(args)}function concatenate2(args){return new Concatenate(args)}function maximum4(args){return new Maximum2(args)}function minimum4(args){return new Minimum2(args)}function multiply3(args){return new Multiply2(args)}function dot3(args){return new Dot(args)}function batchNormalization2(args){return new BatchNormalization(args)}function layerNormalization(args){return new LayerNormalization(args)}function zeroPadding2d(args){return new ZeroPadding2D(args)}function averagePooling1d(args){return new AveragePooling1D(args)}function avgPool1d(args){return averagePooling1d(args)}function avgPooling1d(args){return averagePooling1d(args)}function averagePooling2d(args){return new AveragePooling2D(args)}function avgPool2d(args){return averagePooling2d(args)}function avgPooling2d(args){return averagePooling2d(args)}function averagePooling3d(args){return new AveragePooling3D(args)}function avgPool3d2(args){return averagePooling3d(args)}function avgPooling3d(args){return averagePooling3d(args)}function globalAveragePooling1d(args){return new GlobalAveragePooling1D(args)}function globalAveragePooling2d(args){return new GlobalAveragePooling2D(args)}function globalMaxPooling1d(args){return new GlobalMaxPooling1D(args)}function globalMaxPooling2d(args){return new GlobalMaxPooling2D(args)}function maxPooling1d(args){return new MaxPooling1D(args)}function maxPooling2d(args){return new MaxPooling2D(args)}function maxPooling3d(args){return new MaxPooling3D(args)}function gru(args){return new GRU(args)}function gruCell(args){return new GRUCell(args)}function lstm(args){return new LSTM(args)}function lstmCell(args){return new LSTMCell(args)}function simpleRNN(args){return new SimpleRNN(args)}function simpleRNNCell(args){return new SimpleRNNCell(args)}function convLstm2d(args){return new ConvLSTM2D(args)}function convLstm2dCell(args){return new ConvLSTM2DCell(args)}function rnn2(args){return new RNN(args)}function stackedRNNCells(args){return new StackedRNNCells(args)}function bidirectional(args){return new Bidirectional(args)}function timeDistributed(args){return new TimeDistributed(args)}var globalMaxPool1d=globalMaxPooling1d,globalMaxPool2d=globalMaxPooling2d,maxPool1d=maxPooling1d,maxPool2d=maxPooling2d;function gaussianNoise(args){return new GaussianNoise(args)}function gaussianDropout(args){return new GaussianDropout(args)}function alphaDropout(args){return new AlphaDropout(args)}function masking(args){return new Masking(args)}var exports_metrics_exports={};__export2(exports_metrics_exports,{MAPE:()=>MAPE2,MSE:()=>MSE2,binaryAccuracy:()=>binaryAccuracy2,binaryCrossentropy:()=>binaryCrossentropy3,categoricalAccuracy:()=>categoricalAccuracy2,categoricalCrossentropy:()=>categoricalCrossentropy3,cosineProximity:()=>cosineProximity2,mape:()=>mape2,meanAbsoluteError:()=>meanAbsoluteError2,meanAbsolutePercentageError:()=>meanAbsolutePercentageError2,meanSquaredError:()=>meanSquaredError3,mse:()=>mse2,precision:()=>precision2,recall:()=>recall2,sparseCategoricalAccuracy:()=>sparseCategoricalAccuracy2});function binaryAccuracy2(yTrue,yPred){return binaryAccuracy(yTrue,yPred)}function binaryCrossentropy3(yTrue,yPred){return binaryCrossentropy2(yTrue,yPred)}function sparseCategoricalAccuracy2(yTrue,yPred){return sparseCategoricalAccuracy(yTrue,yPred)}function categoricalAccuracy2(yTrue,yPred){return categoricalAccuracy(yTrue,yPred)}function categoricalCrossentropy3(yTrue,yPred){return categoricalCrossentropy2(yTrue,yPred)}function precision2(yTrue,yPred){return precision(yTrue,yPred)}function recall2(yTrue,yPred){return recall(yTrue,yPred)}function cosineProximity2(yTrue,yPred){return cosineProximity(yTrue,yPred)}function meanAbsoluteError2(yTrue,yPred){return meanAbsoluteError(yTrue,yPred)}function meanAbsolutePercentageError2(yTrue,yPred){return meanAbsolutePercentageError(yTrue,yPred)}function MAPE2(yTrue,yPred){return meanAbsolutePercentageError(yTrue,yPred)}function mape2(yTrue,yPred){return meanAbsolutePercentageError(yTrue,yPred)}function meanSquaredError3(yTrue,yPred){return meanSquaredError2(yTrue,yPred)}function MSE2(yTrue,yPred){return meanSquaredError2(yTrue,yPred)}function mse2(yTrue,yPred){return meanSquaredError2(yTrue,yPred)}var exports_models_exports={};__export2(exports_models_exports,{modelFromJSON:()=>modelFromJSON});var exports_regularizers_exports={};__export2(exports_regularizers_exports,{l1:()=>l12,l1l2:()=>l1l2,l2:()=>l22});function l1l2(config2){return new L1L2(config2)}function l12(config2){return l1(config2)}function l22(config2){return l2(config2)}var Callback=class extends BaseCallback{constructor(){super(...arguments);this.model=null}setModel(model2){if(!(model2 instanceof LayersModel))throw new Error("model must be a LayersModel, not some other Container");this.model=model2}};function less4(currVal,prevVal){return currValprevVal}var EarlyStopping=class extends Callback{constructor(args){super();if(args==null&&(args={}),args.restoreBestWeights)throw new NotImplementedError("restoreBestWeights = True is not implemented in EarlyStopping yet.");this.monitor=args.monitor||"val_loss",this.minDelta=Math.abs(args.minDelta||0),this.patience=args.patience||0,this.verbose=args.verbose||0,this.mode=args.mode||"auto",this.baseline=args.baseline,["auto","min","max"].indexOf(this.mode)===-1&&(console.warn(`EarlyStopping mode '${this.mode}' is invalid. Falling back to mode 'auto'.`),this.mode="auto"),this.mode==="min"?this.monitorFunc=less4:this.mode==="max"?this.monitorFunc=greater4:this.monitor.indexOf("acc")!==-1?this.monitorFunc=greater4:this.monitorFunc=less4,this.monitorFunc===less4&&(this.minDelta*=-1)}async onTrainBegin(logs){this.wait=0,this.stoppedEpoch=0,this.baseline!=null?this.best=this.baseline:this.best=this.monitorFunc===less4?Infinity:-Infinity}async onEpochEnd(epoch,logs){await resolveScalarsInLogs(logs);let current=this.getMonitorValue(logs);current!=null&&(this.monitorFunc(current-this.minDelta,this.best)?(this.best=current,this.wait=0):(this.wait++,this.wait>=this.patience&&(this.stoppedEpoch=epoch,this.model.stopTraining=!0)))}async onTrainEnd(logs){this.stoppedEpoch>0&&this.verbose&&console.log(`Epoch ${this.stoppedEpoch}: early stopping.`)}getMonitorValue(logs){logs==null&&(logs={});let monitorValue=logs[this.monitor];return monitorValue==null&&console.warn(`Metric for EarlyStopping ${this.monitor} is not available. Available metrics are: ${Object.keys(logs)}`),monitorValue}};function earlyStopping(args){return new EarlyStopping(args)}var callbacks={earlyStopping},DataType;(function(DataType2){DataType2[DataType2.DT_INVALID=0]="DT_INVALID",DataType2[DataType2.DT_FLOAT=1]="DT_FLOAT",DataType2[DataType2.DT_DOUBLE=2]="DT_DOUBLE",DataType2[DataType2.DT_INT32=3]="DT_INT32",DataType2[DataType2.DT_UINT8=4]="DT_UINT8",DataType2[DataType2.DT_INT16=5]="DT_INT16",DataType2[DataType2.DT_INT8=6]="DT_INT8",DataType2[DataType2.DT_STRING=7]="DT_STRING",DataType2[DataType2.DT_COMPLEX64=8]="DT_COMPLEX64",DataType2[DataType2.DT_INT64=9]="DT_INT64",DataType2[DataType2.DT_BOOL=10]="DT_BOOL",DataType2[DataType2.DT_QINT8=11]="DT_QINT8",DataType2[DataType2.DT_QUINT8=12]="DT_QUINT8",DataType2[DataType2.DT_QINT32=13]="DT_QINT32",DataType2[DataType2.DT_BFLOAT16=14]="DT_BFLOAT16",DataType2[DataType2.DT_FLOAT_REF=101]="DT_FLOAT_REF",DataType2[DataType2.DT_DOUBLE_REF=102]="DT_DOUBLE_REF",DataType2[DataType2.DT_INT32_REF=103]="DT_INT32_REF",DataType2[DataType2.DT_UINT8_REF=104]="DT_UINT8_REF",DataType2[DataType2.DT_INT16_REF=105]="DT_INT16_REF",DataType2[DataType2.DT_INT8_REF=106]="DT_INT8_REF",DataType2[DataType2.DT_STRING_REF=107]="DT_STRING_REF",DataType2[DataType2.DT_COMPLEX64_REF=108]="DT_COMPLEX64_REF",DataType2[DataType2.DT_INT64_REF=109]="DT_INT64_REF",DataType2[DataType2.DT_BOOL_REF=110]="DT_BOOL_REF",DataType2[DataType2.DT_QINT8_REF=111]="DT_QINT8_REF",DataType2[DataType2.DT_QUINT8_REF=112]="DT_QUINT8_REF",DataType2[DataType2.DT_QINT32_REF=113]="DT_QINT32_REF",DataType2[DataType2.DT_BFLOAT16_REF=114]="DT_BFLOAT16_REF"})(DataType||(DataType={}));var SaverDef;(function(SaverDef2){let CheckpointFormatVersion;(function(CheckpointFormatVersion2){CheckpointFormatVersion2[CheckpointFormatVersion2.LEGACY=0]="LEGACY",CheckpointFormatVersion2[CheckpointFormatVersion2.V1=1]="V1",CheckpointFormatVersion2[CheckpointFormatVersion2.V2=2]="V2"})(CheckpointFormatVersion=SaverDef2.CheckpointFormatVersion||(SaverDef2.CheckpointFormatVersion={}))})(SaverDef||(SaverDef={}));var CUSTOM_OPS={};function registerOp(name,opFunc){let opMapper={tfOpName:name,category:"custom",inputs:[],attrs:[],customExecutor:opFunc};CUSTOM_OPS[name]=opMapper}function getRegisteredOp(name){return CUSTOM_OPS[name]}function deregisterOp(name){delete CUSTOM_OPS[name]}function getParamValue(paramName,node,tensorMap,context,resourceManager){let inputParam=node.inputParams[paramName];if(inputParam&&inputParam.inputIndexStart!==void 0){let start=inputParam.inputIndexStart,end=inputParam.inputIndexEnd===0?void 0:inputParam.inputIndexEnd===void 0?start+1:inputParam.inputIndexEnd;if(inputParam.type==="tensor")return getTensor(node.inputNames[inputParam.inputIndexStart],tensorMap,context,resourceManager);if(inputParam.type==="tensors")return node.inputNames.slice(start,end).map(name=>getTensor(name,tensorMap,context,resourceManager));let tensor2=getTensor(node.inputNames.slice(start)[0],tensorMap,context,resourceManager),data2=tensor2.dataSync();return inputParam.type==="number"?data2[0]:util_exports.toNestedArray(tensor2.shape,data2)}let attrParam=node.attrParams[paramName];return attrParam&&attrParam.value}function getTensor(name,tensorsMap,context,resourceManager){let[nodeName,index]=parseNodeName(name);if(resourceManager!=null){let tensor2=resourceManager.getHashTableHandleByName(nodeName);if(tensor2!=null)return tensor2}let contextId=context.currentContextIds.find(contextId2=>!!tensorsMap[getNodeNameWithContextId(nodeName,contextId2)]);return contextId!==void 0?tensorsMap[getNodeNameWithContextId(nodeName,contextId)][index]:void 0}function getTensorsForCurrentContenxt(name,tensorsMap,context){return tensorsMap[getNodeNameWithContextId(name,context.currentContextId)]}function getNodeNameAndIndex(inputName,context){let[nodeName,index]=parseNodeName(inputName);return[getNodeNameWithContextId(nodeName,context&&context.currentContextId),index]}function getNodeNameWithContextId(name,contextId){return contextId?`${name}-${contextId}`:name}function parseNodeName(name){let parts=name.split(":");return parts.length===1?[name,0]:[parts[0],Number(parts[parts.length-1])]}function getPadding(node,tensorMap,context){let pad3=getParamValue("pad",node,tensorMap,context);if(pad3==="explicit"){pad3=getParamValue("explicitPaddings",node,tensorMap,context);let explicitPadding=[[0,0],[0,0],[0,0],[0,0]];for(let i=0;i<4;i++)explicitPadding[i][0]=pad3[i*2],explicitPadding[i][1]=pad3[i*2+1];return explicitPadding}return pad3}function cloneTensor(tensor2){return tensor2.kept?tensor2:clone(tensor2)}var arithmetic_exports={};__export2(arithmetic_exports,{json:()=>json});var json=[{tfOpName:"Add",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"AddV2",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"AddN",category:"arithmetic",inputs:[{start:0,end:0,name:"tensors",type:"tensors"}]},{tfOpName:"BiasAdd",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"Sub",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"RealDiv",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Div",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"DivNoNan",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"FloorDiv",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Mul",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Maximum",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Minimum",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Pow",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"SquaredDifference",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Mod",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"FloorMod",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]}],basic_math_exports={};__export2(basic_math_exports,{json:()=>json2});var json2=[{tfOpName:"Abs",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Acos",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Asin",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Atan",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Atan2",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"y",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Ceil",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"ClipByValue",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"clipValueMin",type:"number"},{start:2,name:"clipValueMax",type:"number"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Complex",category:"basic_math",inputs:[{start:0,name:"real",type:"tensor"},{start:1,name:"imag",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"ComplexAbs",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Cos",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Cosh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Elu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Exp",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Floor",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Log",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Imag",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"Tout",name:"outputType",type:"dtype",notSupported:!0}]},{tfOpName:"Neg",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Real",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"Tout",name:"outputType",type:"dtype",notSupported:!0}]},{tfOpName:"Prelu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"alpha",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Relu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Relu6",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Selu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sigmoid",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sin",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sinh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sqrt",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Rsqrt",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Square",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Tan",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Tanh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sign",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Round",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Expm1",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Log1p",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Reciprocal",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Softplus",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Asinh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Acosh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Atanh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Erf",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Prod",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axes",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool",notSupported:!0},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LeakyRelu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"alpha",name:"alpha",type:"number",defaultValue:.2},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]}],control_exports={};__export2(control_exports,{json:()=>json3});var 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json11=[{tfOpName:"Equal",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"NotEqual",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Greater",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"GreaterEqual",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Less",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LessEqual",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LogicalAnd",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LogicalNot",category:"logical",inputs:[{start:0,name:"a",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LogicalOr",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Select",category:"logical",inputs:[{start:0,name:"condition",type:"tensor"},{start:1,name:"a",type:"tensor"},{start:2,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"SelectV2",category:"logical",inputs:[{start:0,name:"condition",type:"tensor"},{start:1,name:"a",type:"tensor"},{start:2,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]}],matrices_exports={};__export2(matrices_exports,{json:()=>json12});var json12=[{tfOpName:"_FusedMatMul",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"},{start:2,end:0,name:"args",type:"tensors"}],attrs:[{tfName:"num_args",name:"numArgs",type:"number"},{tfName:"fused_ops",name:"fusedOps",type:"string[]",defaultValue:[]},{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:1e-4},{tfName:"transpose_a",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"transpose_b",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"MatMul",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"transpose_a",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"transpose_b",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"BatchMatMul",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"adj_x",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"adj_y",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"BatchMatMulV2",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"adj_x",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"adj_y",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Transpose",category:"matrices",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"perm",type:"number[]"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]}],normalization_exports={};__export2(normalization_exports,{json:()=>json13});var json13=[{tfOpName:"FusedBatchNorm",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"scale",type:"tensor"},{start:2,name:"offset",type:"tensor"},{start:3,name:"mean",type:"tensor"},{start:4,name:"variance",type:"tensor"}],attrs:[{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:.001},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"FusedBatchNormV2",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"scale",type:"tensor"},{start:2,name:"offset",type:"tensor"},{start:3,name:"mean",type:"tensor"},{start:4,name:"variance",type:"tensor"}],attrs:[{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:.001},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"FusedBatchNormV3",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"scale",type:"tensor"},{start:2,name:"offset",type:"tensor"},{start:3,name:"mean",type:"tensor"},{start:4,name:"variance",type:"tensor"}],attrs:[{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:.001},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"LRN",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"depth_radius",name:"radius",type:"number",defaultValue:5},{tfName:"bias",name:"bias",type:"number",defaultValue:1},{tfName:"alpha",name:"alpha",type:"number",defaultValue:1},{tfName:"beta",name:"beta",type:"number",defaultValue:.5}]},{tfOpName:"Softmax",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"LogSoftmax",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"SparseToDense",category:"normalization",inputs:[{start:0,name:"sparseIndices",type:"tensor"},{start:1,name:"outputShape",type:"number[]"},{start:2,name:"sparseValues",type:"tensor"},{start:3,name:"defaultValue",type:"tensor"}],attrs:[{tfName:"validate_indices",name:"validateIndices",type:"bool",defaultValue:!0,notSupported:!0}]}],reduction_exports={};__export2(reduction_exports,{json:()=>json14});var json14=[{tfOpName:"Bincount",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"size",type:"number"},{start:2,name:"weights",type:"tensor"}]},{tfOpName:"DenseBincount",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"size",type:"number"},{start:2,name:"weights",type:"tensor"}],attrs:[{tfName:"binary_output",name:"binaryOutput",type:"bool"}]},{tfOpName:"Max",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Mean",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Min",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Sum",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"All",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Any",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"ArgMax",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]},{tfOpName:"ArgMin",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]},{tfOpName:"Prod",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Cumsum",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}],attrs:[{tfName:"exclusive",name:"exclusive",type:"bool"},{tfName:"reverse",name:"reverse",type:"bool"}]}],slice_join_exports={};__export2(slice_join_exports,{json:()=>json15});var json15=[{tfOpName:"ConcatV2",category:"slice_join",inputs:[{start:0,end:-1,name:"tensors",type:"tensors"},{start:-1,name:"axis",type:"number"}],attrs:[{tfName:"N",name:"n",type:"number",defaultValue:2}]},{tfOpName:"Concat",category:"slice_join",inputs:[{start:1,end:0,name:"tensors",type:"tensors"},{start:0,name:"axis",type:"number"}],attrs:[{tfName:"N",name:"n",type:"number",defaultValue:2}]},{tfOpName:"GatherV2",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"indices",type:"tensor"},{start:2,name:"axis",type:"number",defaultValue:0}],attrs:[{tfName:"batch_dims",name:"batchDims",type:"number",defaultValue:0}]},{tfOpName:"Gather",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"indices",type:"tensor"}],attrs:[{tfName:"validate_indices",name:"validateIndices",type:"bool",notSupported:!0}]},{tfOpName:"Reverse",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"dims",type:"bool[]"}]},{tfOpName:"ReverseV2",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}]},{tfOpName:"Slice",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"begin",type:"number[]"},{start:2,name:"size",type:"number[]"}]},{tfOpName:"StridedSlice",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"begin",type:"number[]"},{start:2,name:"end",type:"number[]"},{start:3,name:"strides",type:"number[]"}],attrs:[{tfName:"begin_mask",name:"beginMask",type:"number",defaultValue:0},{tfName:"end_mask",name:"endMask",type:"number",defaultValue:0},{tfName:"new_axis_mask",name:"newAxisMask",type:"number",defaultValue:0},{tfName:"ellipsis_mask",name:"ellipsisMask",type:"number",defaultValue:0},{tfName:"shrink_axis_mask",name:"shrinkAxisMask",type:"number",defaultValue:0}]},{tfOpName:"Pack",category:"slice_join",inputs:[{start:0,end:0,name:"tensors",type:"tensors"}],attrs:[{tfName:"axis",name:"axis",type:"number",defaultValue:0}]},{tfOpName:"Unpack",category:"slice_join",inputs:[{start:0,name:"tensor",type:"tensor"}],attrs:[{tfName:"axis",name:"axis",type:"number",defaultValue:0},{tfName:"num",name:"num",type:"number",defaultValue:0,notSupported:!0}]},{tfOpName:"Tile",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"reps",type:"number[]"}]},{tfOpName:"Split",category:"slice_join",inputs:[{start:0,name:"axis",type:"number",defaultValue:0},{start:1,name:"x",type:"tensor"}],attrs:[{tfName:"num_split",name:"numOrSizeSplits",type:"number",defaultValue:1}]},{tfOpName:"SplitV",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"numOrSizeSplits",type:"number[]"},{start:2,name:"axis",type:"number",defaultValue:0}]},{tfOpName:"ScatterNd",category:"slice_join",inputs:[{start:0,name:"indices",type:"tensor"},{start:1,name:"values",type:"tensor"},{start:2,name:"shape",type:"number[]"}]},{tfOpName:"GatherNd",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"indices",type:"tensor"}]},{tfOpName:"SparseToDense",category:"slice_join",inputs:[{start:0,name:"sparseIndices",type:"tensor"},{start:1,name:"outputShape",type:"number[]"},{start:2,name:"sparseValues",type:"tensor"},{start:3,name:"defaultValue",type:"tensor"}],attrs:[{tfName:"validate_indices",name:"validateIndices",type:"bool",defaultValue:!1,notSupported:!0}]}],spectral_exports={};__export2(spectral_exports,{json:()=>json16});var json16=[{tfOpName:"FFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"IFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"RFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"fft_length",type:"number",notSupported:!0}]},{tfOpName:"IRFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"fft_length",type:"number",notSupported:!0}]}],transformation_exports={};__export2(transformation_exports,{json:()=>json17});var json17=[{tfOpName:"Cast",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"SrcT",name:"sdtype",type:"dtype",notSupported:!0},{tfName:"DstT",name:"dtype",type:"dtype"}]},{tfOpName:"ExpandDims",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]},{tfOpName:"MirrorPad",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"}],attrs:[{tfName:"mode",name:"mode",type:"string"}]},{tfOpName:"Pad",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"}],attrs:[{tfName:"constant_value",name:"constantValue",type:"number",defaultValue:0}]},{tfOpName:"PadV2",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"},{start:2,name:"constantValue",type:"number",defaultValue:0}]},{tfOpName:"Reshape",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"shape",type:"number[]"}]},{tfOpName:"Squeeze",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"axis",tfDeprecatedName:"squeeze_dims",name:"axis",type:"number[]"}]},{tfOpName:"SpaceToBatchND",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"blockShape",type:"number[]"},{start:2,name:"paddings",type:"number[]"}]},{tfOpName:"BatchToSpaceND",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"blockShape",type:"number[]"},{start:2,name:"crops",type:"number[]"}]},{tfOpName:"DepthToSpace",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"block_size",name:"blockSize",type:"number"},{tfName:"data_format",name:"dataFormat",type:"string"}]},{tfOpName:"BroadcastTo",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"shape",type:"number[]"}],attrs:[]}],OperationMapper=class{static get Instance(){return this._instance||(this._instance=new this)}constructor(){let ops=[arithmetic_exports,basic_math_exports,control_exports,convolution_exports,creation_exports,dynamic_exports,evaluation_exports,logical_exports,image_exports,graph_exports,matrices_exports,normalization_exports,reduction_exports,slice_join_exports,spectral_exports,transformation_exports,hash_table_exports],mappersJson=[].concat(...ops.map(op2=>op2.json));this.opMappers=mappersJson.reduce((map,mapper)=>(map[mapper.tfOpName]=mapper,map),{})}transformGraph(graph2,signature={}){let tfNodes=graph2.node,placeholders=[],weights=[],initNodes=[],nodes=tfNodes.reduce((map,node)=>(map[node.name]=this.mapNode(node),node.op.startsWith("Placeholder")?placeholders.push(map[node.name]):node.op==="Const"?weights.push(map[node.name]):(node.input==null||node.input.length===0)&&initNodes.push(map[node.name]),map),{}),inputs=[],outputs=[],inputNodeNameToKey={},outputNodeNameToKey={};signature!=null&&(inputNodeNameToKey=this.mapSignatureEntries(signature.inputs),outputNodeNameToKey=this.mapSignatureEntries(signature.outputs));let allNodes=Object.keys(nodes);allNodes.forEach(key=>{let node=nodes[key];node.inputNames.forEach(name=>{let[nodeName]=getNodeNameAndIndex(name);node.inputs.push(nodes[nodeName]),nodes[nodeName].children.push(node)})}),Object.keys(outputNodeNameToKey).length===0?allNodes.forEach(key=>{let node=nodes[key];node.children.length===0&&outputs.push(node)}):Object.keys(outputNodeNameToKey).forEach(name=>{let[nodeName]=getNodeNameAndIndex(name),node=nodes[nodeName];node!=null&&(node.signatureKey=outputNodeNameToKey[name],outputs.push(node))}),Object.keys(inputNodeNameToKey).length>0?Object.keys(inputNodeNameToKey).forEach(name=>{let[nodeName]=getNodeNameAndIndex(name),node=nodes[nodeName];node&&(node.signatureKey=inputNodeNameToKey[name],inputs.push(node))}):inputs=placeholders;let functions={};graph2.library!=null&&graph2.library.function!=null&&(functions=graph2.library.function.reduce((functions2,func2)=>(functions2[func2.signature.name]=this.mapFunction(func2),functions2),{}));let result={nodes,inputs,outputs,weights,placeholders,signature,functions};return initNodes.length>0&&(result.initNodes=initNodes),result}mapSignatureEntries(entries){return Object.keys(entries||{}).reduce((prev,curr)=>(prev[entries[curr].name]=curr,prev),{})}mapNode(node){let mapper=getRegisteredOp(node.op)||this.opMappers[node.op]||{};node.attr==null&&(node.attr={});let newNode={name:node.name,op:node.op,category:mapper.category,inputNames:(node.input||[]).map(input2=>input2.startsWith("^")?input2.substr(1):input2),inputs:[],children:[],inputParams:{},attrParams:{},rawAttrs:node.attr};return mapper.inputs!=null&&(newNode.inputParams=mapper.inputs.reduce((map,param)=>(map[param.name]={type:param.type,inputIndexStart:param.start,inputIndexEnd:param.end},map),{})),mapper.attrs!=null&&(newNode.attrParams=mapper.attrs.reduce((map,param)=>{let type=param.type,value;switch(param.type){case"string":value=getStringParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getStringParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"string[]":value=getStringArrayParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getStringArrayParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"number":value=getNumberParam(node.attr,param.tfName,param.defaultValue||0),value===void 0&&!!param.tfDeprecatedName&&(value=getNumberParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"number[]":value=getNumericArrayParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getNumericArrayParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"bool":value=getBoolParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getBoolParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"bool[]":value=getBoolArrayParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getBoolArrayParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"shape":value=getTensorShapeParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getTensorShapeParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"shape[]":value=getTensorShapeArrayParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getTensorShapeArrayParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"dtype":value=getDtypeParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getDtypeParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"dtype[]":value=getDtypeArrayParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getDtypeArrayParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"func":value=getFuncParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getFuncParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"tensor":case"tensors":break;default:throw new Error(`Unsupported param type: ${param.type} for op: ${node.op}`)}return map[param.name]={value,type},map},{})),newNode}mapFunction(functionDef){let tfNodes=functionDef.nodeDef,placeholders=[],weights=[],nodes={};tfNodes!=null&&(nodes=tfNodes.reduce((map,node)=>(map[node.name]=this.mapNode(node),node.op==="Const"&&weights.push(map[node.name]),map),{}));let inputs=[],outputs=[];functionDef.signature.inputArg.forEach(arg=>{let[nodeName]=getNodeNameAndIndex(arg.name),node={name:nodeName,op:"Placeholder",inputs:[],inputNames:[],category:"graph",inputParams:{},attrParams:{dtype:{value:parseDtypeParam(arg.type),type:"dtype"}},children:[]};node.signatureKey=arg.name,inputs.push(node),nodes[nodeName]=node}),Object.keys(nodes).forEach(key=>{let node=nodes[key];node.inputNames.forEach(name=>{let[nodeName]=getNodeNameAndIndex(name);node.inputs.push(nodes[nodeName]),nodes[nodeName].children.push(node)})});let returnNodeMap=functionDef.ret;functionDef.signature.outputArg.forEach(output=>{let[nodeName,index]=getNodeNameAndIndex(returnNodeMap[output.name]),node=nodes[nodeName];node!=null&&(node.defaultOutput=index,outputs.push(node))});let signature=this.mapArgsToSignature(functionDef);return{nodes,inputs,outputs,weights,placeholders,signature}}mapArgsToSignature(functionDef){return{methodName:functionDef.signature.name,inputs:functionDef.signature.inputArg.reduce((map,arg)=>(map[arg.name]=this.mapArgToTensorInfo(arg),map),{}),outputs:functionDef.signature.outputArg.reduce((map,arg)=>(map[arg.name]=this.mapArgToTensorInfo(arg,functionDef.ret),map),{})}}mapArgToTensorInfo(arg,nameMap2){let name=arg.name;return nameMap2!=null&&(name=nameMap2[name]),{name,dtype:arg.type}}};function decodeBase64(text){let global2=env().global;if(typeof global2.atob!="undefined")return global2.atob(text);if(typeof Buffer!="undefined")return new Buffer(text,"base64").toString();throw new Error("Unable to decode base64 in this environment. Missing built-in atob() or Buffer()")}function parseStringParam(s,keepCase){let value=Array.isArray(s)?String.fromCharCode.apply(null,s):decodeBase64(s);return keepCase?value:value.toLowerCase()}function getStringParam(attrs,name,def,keepCase=!1){let param=attrs[name];return param!=null?parseStringParam(param.s,keepCase):def}function getBoolParam(attrs,name,def){let param=attrs[name];return param?param.b:def}function getNumberParam(attrs,name,def){let param=attrs[name]||{},value=param.i!=null?param.i:param.f!=null?param.f:def;return typeof value=="number"?value:parseInt(value,10)}function parseDtypeParam(value){switch(typeof value=="string"&&(value=DataType[value]),value){case DataType.DT_FLOAT:return"float32";case DataType.DT_INT32:case DataType.DT_INT64:case DataType.DT_INT8:case DataType.DT_UINT8:return"int32";case DataType.DT_BOOL:return"bool";case DataType.DT_DOUBLE:return"float32";case DataType.DT_STRING:return"string";default:return null}}function getFuncParam(attrs,name,def){let param=attrs[name];return param&¶m.func?param.func.name:def}function getDtypeParam(attrs,name,def){let param=attrs[name];return param&¶m.type?parseDtypeParam(param.type):def}function getDtypeArrayParam(attrs,name,def){let param=attrs[name];return param&¶m.list&¶m.list.type?param.list.type.map(v=>parseDtypeParam(v)):def}function parseTensorShapeParam(shape){if(!shape.unknownRank)return shape.dim!=null?shape.dim.map(dim=>typeof dim.size=="number"?dim.size:parseInt(dim.size,10)):[]}function getTensorShapeParam(attrs,name,def){let param=attrs[name];return param&¶m.shape?parseTensorShapeParam(param.shape):def}function getNumericArrayParam(attrs,name,def){let param=attrs[name];return param?((param.list.f&¶m.list.f.length?param.list.f:param.list.i)||[]).map(v=>typeof v=="number"?v:parseInt(v,10)):def}function getStringArrayParam(attrs,name,def,keepCase=!1){let param=attrs[name];return param&¶m.list&¶m.list.s?param.list.s.map(v=>parseStringParam(v,keepCase)):def}function getTensorShapeArrayParam(attrs,name,def){let param=attrs[name];return param&¶m.list&¶m.list.shape?param.list.shape.map(v=>parseTensorShapeParam(v)):def}function getBoolArrayParam(attrs,name,def){let param=attrs[name];return param&¶m.list&¶m.list.b?param.list.b:def}var NodeValueImpl=class{constructor(node,tensorMap,context){this.node=node,this.tensorMap=tensorMap,this.context=context,this.inputs=[],this.attrs={},this.inputs=node.inputNames.map(name=>this.getInput(name)),node.rawAttrs!=null&&(this.attrs=Object.keys(node.rawAttrs).reduce((attrs,key)=>(attrs[key]=this.getAttr(key),attrs),{}))}getInput(name){return getTensor(name,this.tensorMap,this.context)}getAttr(name,defaultValue){let value=this.node.rawAttrs[name];if(value.tensor!=null)return getTensor(name,this.tensorMap,this.context);if(value.i!=null||value.f!=null)return getNumberParam(this.node.rawAttrs,name,defaultValue);if(value.s!=null)return getStringParam(this.node.rawAttrs,name,defaultValue);if(value.b!=null)return getBoolParam(this.node.rawAttrs,name,defaultValue);if(value.shape!=null)return getTensorShapeParam(this.node.rawAttrs,name,defaultValue);if(value.type!=null)return getDtypeParam(this.node.rawAttrs,name,defaultValue);if(value.list!=null){if(value.list.i!=null||value.list.f!=null)return getNumericArrayParam(this.node.rawAttrs,name,defaultValue);if(value.list.s!=null)return getStringArrayParam(this.node.rawAttrs,name,defaultValue);if(value.list.shape!=null)return getTensorShapeArrayParam(this.node.rawAttrs,name,defaultValue);if(value.list.b!=null)return getBoolArrayParam(this.node.rawAttrs,name,defaultValue);if(value.list.type!=null)return getDtypeArrayParam(this.node.rawAttrs,name,defaultValue)}return defaultValue}},executeOp=(node,tensorMap,context)=>{switch(node.op){case"BiasAdd":case"AddV2":case"Add":return[add2(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"AddN":return[addN(getParamValue("tensors",node,tensorMap,context))];case"FloorMod":case"Mod":return[mod(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Mul":return[mul(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"RealDiv":case"Div":return[div(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"DivNoNan":return[divNoNan(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"FloorDiv":return[floorDiv(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Sub":return[sub(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Minimum":return[minimum(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Maximum":return[maximum(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Pow":return[pow(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"SquaredDifference":return[squaredDifference(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp2=(node,tensorMap,context)=>{switch(node.op){case"Abs":case"ComplexAbs":return[abs(getParamValue("x",node,tensorMap,context))];case"Acos":return[acos(getParamValue("x",node,tensorMap,context))];case"Acosh":return[acosh(getParamValue("x",node,tensorMap,context))];case"Asin":return[asin(getParamValue("x",node,tensorMap,context))];case"Asinh":return[asinh(getParamValue("x",node,tensorMap,context))];case"Atan":return[atan(getParamValue("x",node,tensorMap,context))];case"Atan2":return[atan2(getParamValue("x",node,tensorMap,context),getParamValue("y",node,tensorMap,context))];case"Atanh":return[atanh(getParamValue("x",node,tensorMap,context))];case"Ceil":return[ceil(getParamValue("x",node,tensorMap,context))];case"Complex":return[complex(getParamValue("real",node,tensorMap,context),getParamValue("imag",node,tensorMap,context))];case"Cos":return[cos(getParamValue("x",node,tensorMap,context))];case"Cosh":return[cosh(getParamValue("x",node,tensorMap,context))];case"Elu":return[elu(getParamValue("x",node,tensorMap,context))];case"Erf":return[erf(getParamValue("x",node,tensorMap,context))];case"Exp":return[exp(getParamValue("x",node,tensorMap,context))];case"Expm1":return[expm1(getParamValue("x",node,tensorMap,context))];case"Floor":return[floor(getParamValue("x",node,tensorMap,context))];case"Log":return[log2(getParamValue("x",node,tensorMap,context))];case"Log1p":return[log1p(getParamValue("x",node,tensorMap,context))];case"Imag":return[imag(getParamValue("x",node,tensorMap,context))];case"Neg":return[neg(getParamValue("x",node,tensorMap,context))];case"Reciprocal":return[reciprocal(getParamValue("x",node,tensorMap,context))];case"Real":return[real(getParamValue("x",node,tensorMap,context))];case"Relu":return[relu(getParamValue("x",node,tensorMap,context))];case"Round":return[round2(getParamValue("x",node,tensorMap,context))];case"Selu":return[selu(getParamValue("x",node,tensorMap,context))];case"Sigmoid":return[sigmoid(getParamValue("x",node,tensorMap,context))];case"Sin":return[sin(getParamValue("x",node,tensorMap,context))];case"Sign":return[sign(getParamValue("x",node,tensorMap,context))];case"Sinh":return[sinh(getParamValue("x",node,tensorMap,context))];case"Softplus":return[softplus(getParamValue("x",node,tensorMap,context))];case"Sqrt":return[sqrt(getParamValue("x",node,tensorMap,context))];case"Square":return[square(getParamValue("x",node,tensorMap,context))];case"Tanh":return[tanh2(getParamValue("x",node,tensorMap,context))];case"Tan":return[tan(getParamValue("x",node,tensorMap,context))];case"ClipByValue":return[clipByValue(getParamValue("x",node,tensorMap,context),getParamValue("clipValueMin",node,tensorMap,context),getParamValue("clipValueMax",node,tensorMap,context))];case"Relu6":return[relu6(getParamValue("x",node,tensorMap,context))];case"Rsqrt":return[rsqrt(getTensor(node.inputNames[0],tensorMap,context))];case"Prod":return[prod(getParamValue("x",node,tensorMap,context),getParamValue("axes",node,tensorMap,context))];case"LeakyRelu":return[leakyRelu(getParamValue("x",node,tensorMap,context),getParamValue("alpha",node,tensorMap,context))];case"Prelu":return[prelu(getParamValue("x",node,tensorMap,context),getParamValue("alpha",node,tensorMap,context))];default:throw TypeError(`Node type ${node.op} is not implemented`)}};function assertShapesMatchAllowUndefinedSize(shapeA,shapeB,errorMessagePrefix=""){util_exports.assert(shapesEqualAllowUndefinedSize(shapeA,shapeB),()=>errorMessagePrefix+` Shapes ${shapeA} and ${shapeB} must match`)}function shapesEqualAllowUndefinedSize(n1,n2){if(n1.length!==n2.length)return!1;for(let i=0;i{(keepIds==null||!keepIds.has(tensor2.tensor.id))&&tensor2.tensor.dispose()}),this.tensors=[],this.closed_=!0,this.idTensor.dispose()}size(){return this.tensors.length}read(index){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(index<0||index>=this.size())throw new Error(`Tried to read from index ${index}, but array size is: ${this.size()}`);let tensorWithState=this.tensors[index];if(tensorWithState.cleared)throw new Error(`TensorArray ${this.name}: Could not read index ${index} twice because it was cleared after a previous read (perhaps try setting clear_after_read = false?).`);return this.clearAfterRead&&(tensorWithState.cleared=!0),tensorWithState.read=!0,tensorWithState.tensor}readMany(indices){return indices.map(index=>this.read(index))}write(index,tensor2){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(index<0||!this.dynamicSize&&index>=this.maxSize)throw new Error(`Tried to write to index ${index}, but array is not resizeable and size is: ${this.maxSize}`);let t=this.tensors[index]||{};if(tensor2.dtype!==this.dtype)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index}, because the value dtype is ${tensor2.dtype}, but TensorArray dtype is ${this.dtype}.`);if(this.size()===0&&(this.elementShape==null||this.elementShape.length===0)&&(this.elementShape=tensor2.shape),assertShapesMatchAllowUndefinedSize(this.elementShape,tensor2.shape,`TensorArray ${this.name}: Could not write to TensorArray index ${index}.`),t.read)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index}, because it has already been read.`);if(t.written)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index}, because it has already been written.`);t.tensor=tensor2,keep(tensor2),t.written=!0,this.tensors[index]=t}writeMany(indices,tensors){if(indices.length!==tensors.length)throw new Error(`TensorArray ${this.name}: could not write multiple tensors,because the index size: ${indices.length} is not the same as tensors size: ${tensors.length}.`);indices.forEach((i,index)=>this.write(i,tensors[index]))}gather(indices,dtype){if(!!dtype&&dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but gather requested dtype ${dtype}`);if(indices)indices=indices.slice(0,this.size());else{indices=[];for(let i=0;i=this.maxSize)throw new Error(`Max index must be < array size (${maxIndex} vs. ${this.maxSize})`);this.writeMany(indices,unstack(tensor2,0))}split(length,tensor2){if(tensor2.dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${tensor2.dtype}`);let totalLength=0,cumulativeLengths=length.map(len=>(totalLength+=len,totalLength));if(totalLength!==tensor2.shape[0])throw new Error(`Expected sum of lengths to be equal to tensor.shape[0], but sum of lengths is ${totalLength}, and tensor's shape is: ${tensor2.shape}`);if(!this.dynamicSize&&length.length!==this.maxSize)throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${length.length}), and the TensorArray is not marked as dynamically resizeable`);let elementPerRow=totalLength===0?0:tensor2.size/totalLength,tensors=[];tidy(()=>{tensor2=reshape(tensor2,[1,totalLength,elementPerRow]);for(let i=0;i{if(elementDtype!==tensor2.dtype)throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${tensor2.dtype}`);assertShapesMatchAllowUndefinedSize(elementShape,tensor2.shape,"TensorList shape mismatch: "),keep(tensor2)}),this.idTensor=scalar(0),this.maxNumElements=maxNumElements,keep(this.idTensor)}get id(){return this.idTensor.id}copy(){return new TensorList([...this.tensors],this.elementShape,this.elementDtype)}clearAndClose(keepIds){this.tensors.forEach(tensor2=>{(keepIds==null||!keepIds.has(tensor2.id))&&tensor2.dispose()}),this.tensors.length=0,this.idTensor.dispose()}size(){return this.tensors.length}stack(elementShape,elementDtype,numElements=-1){if(elementDtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);if(numElements!==-1&&this.tensors.length!==numElements)throw new Error(`Operation expected a list with ${numElements} elements but got a list with ${this.tensors.length} elements.`);return assertShapesMatchAllowUndefinedSize(elementShape,this.elementShape,"TensorList shape mismatch: "),tidy(()=>{let reshapedTensors=this.tensors.map(tensor2=>reshape(tensor2,elementShape));return stack(reshapedTensors,0)})}popBack(elementShape,elementDtype){if(elementDtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);if(this.size()===0)throw new Error("Trying to pop from an empty list.");let tensor2=this.tensors.pop();return assertShapesMatchAllowUndefinedSize(tensor2.shape,elementShape,"TensorList shape mismatch: "),reshape(tensor2,elementShape)}pushBack(tensor2){if(tensor2.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${tensor2.dtype}, but list elements ${this.elementDtype}`);if(assertShapesMatchAllowUndefinedSize(tensor2.shape,this.elementShape,"TensorList shape mismatch: "),this.maxNumElements===this.size())throw new Error("Trying to push element into a full list.");keep(tensor2),this.tensors.push(tensor2)}resize(size){if(size<0)throw new Error(`TensorListResize expects size to be non-negative. Got: ${size}`);if(this.maxNumElements!==-1&&size>this.maxNumElements)throw new Error(`TensorListResize input size ${size} is greater maxNumElement ${this.maxNumElements}.`);this.tensors.length=size}getItem(elementIndex,elementShape,elementDtype){if(elementDtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);if(elementIndex<0||elementIndex>this.tensors.length)throw new Error(`Trying to access element ${elementIndex} in a list with ${this.tensors.length} elements.`);if(this.tensors[elementIndex]==null)throw new Error(`element at index ${elementIndex} is null.`);return assertShapesMatchAllowUndefinedSize(this.tensors[elementIndex].shape,elementShape,"TensorList shape mismatch: "),this.tensors[elementIndex]}setItem(elementIndex,tensor2){if(tensor2.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${tensor2.dtype}, but list elements ${this.elementDtype}`);if(elementIndex<0||this.maxNumElements!==-1&&elementIndex>=this.maxNumElements)throw new Error(`Trying to set element ${elementIndex} in a list with max ${this.maxNumElements} elements.`);assertShapesMatchAllowUndefinedSize(this.elementShape,tensor2.shape,"TensorList shape mismatch: "),keep(tensor2),this.tensors[elementIndex]=tensor2}gather(indices,elementDtype,elementShape){if(elementDtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);return assertShapesMatchAllowUndefinedSize(this.elementShape,elementShape,"TensorList shape mismatch: "),indices=indices.slice(0,this.size()),indices.length===0?tensor([],[0].concat(this.elementShape)):tidy(()=>{let tensors=indices.map(i=>reshape(this.tensors[i],elementShape));return stack(tensors,0)})}concat(elementDtype,elementShape){if(!!elementDtype&&elementDtype!==this.elementDtype)throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${elementDtype}`);return assertShapesMatchAllowUndefinedSize(this.elementShape,elementShape,"TensorList shape mismatch: "),this.size()===0?tensor([],[0].concat(this.elementShape)):tidy(()=>{let tensors=this.tensors.map(t=>reshape(t,elementShape));return concat(tensors,0)})}};function fromTensor(tensor2,elementShape,elementDtype){let dtype=tensor2.dtype;if(tensor2.shape.length<1)throw new Error(`Tensor must be at least a vector, but saw shape: ${tensor2.shape}`);if(tensor2.dtype!==elementDtype)throw new Error(`Invalid data types; op elements ${tensor2.dtype}, but list elements ${elementDtype}`);let outputShape=tensor2.shape.slice(1);assertShapesMatchAllowUndefinedSize(outputShape,elementShape,"TensorList shape mismatch: ");let tensorList=unstack(tensor2);return new TensorList(tensorList,elementShape,dtype)}function reserve(elementShape,elementDtype,numElements){return new TensorList([],elementShape,elementDtype,numElements)}function scatter(tensor2,indices,elementShape,numElements){if(indices.length!==tensor2.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${indices.length} vs. ${tensor2.shape[0]}`);let maxIndex=Math.max(...indices);if(numElements!=null&&numElements!==-1&&maxIndex>=numElements)throw new Error(`Max index must be < array size (${maxIndex} vs. ${numElements})`);let list=new TensorList([],elementShape,tensor2.dtype,numElements),tensors=unstack(tensor2,0);return indices.forEach((value,index)=>{list.setItem(value,tensors[index])}),list}function split2(tensor2,length,elementShape){let totalLength=0,cumulativeLengths=length.map(len=>(totalLength+=len,totalLength));if(totalLength!==tensor2.shape[0])throw new Error(`Expected sum of lengths to be equal to tensor.shape[0], but sum of lengths is - ${totalLength}, and tensor's shape is: ${tensor2.shape}`);let elementPerRow=totalLength===0?0:tensor2.size/totalLength,tensors=tidy(()=>{let tensors2=[];tensor2=reshape(tensor2,[1,totalLength,elementPerRow]);for(let i=0;i{switch(node.op){case"If":case"StatelessIf":{let thenFunc=getParamValue("thenBranch",node,tensorMap,context),elseFunc=getParamValue("elseBranch",node,tensorMap,context),cond=getParamValue("cond",node,tensorMap,context),args=getParamValue("args",node,tensorMap,context);return(await cond.data())[0]?context.functionMap[thenFunc].executeFunctionAsync(args,context.tensorArrayMap,context.tensorListMap):context.functionMap[elseFunc].executeFunctionAsync(args,context.tensorArrayMap,context.tensorListMap)}case"While":case"StatelessWhile":{let bodyFunc=getParamValue("body",node,tensorMap,context),condFunc=getParamValue("cond",node,tensorMap,context),args=getParamValue("args",node,tensorMap,context),condResult=await context.functionMap[condFunc].executeFunctionAsync(args,context.tensorArrayMap,context.tensorListMap),argIds=args.map(tensor2=>tensor2.id),condValue=await condResult[0].data();condResult.forEach(tensor2=>{!tensor2.kept&&argIds.indexOf(tensor2.id)===-1&&tensor2.dispose()});let result=args;for(;condValue[0];){let origResult=result;result=await context.functionMap[bodyFunc].executeFunctionAsync(result,context.tensorArrayMap,context.tensorListMap);let resultIds=result.map(tensor2=>tensor2.id);origResult.forEach(tensor2=>{!tensor2.kept&&argIds.indexOf(tensor2.id)===-1&&resultIds.indexOf(tensor2.id)===-1&&tensor2.dispose()});let condResult2=await context.functionMap[condFunc].executeFunctionAsync(result,context.tensorArrayMap,context.tensorListMap);condValue=await condResult2[0].data(),condResult2.forEach(tensor2=>{!tensor2.kept&&argIds.indexOf(tensor2.id)===-1&&resultIds.indexOf(tensor2.id)===-1&&tensor2.dispose()})}return result}case"LoopCond":{let pred=getParamValue("pred",node,tensorMap,context);return[cloneTensor(pred)]}case"Switch":{let pred=getParamValue("pred",node,tensorMap,context),data2=getParamValue("data",node,tensorMap,context);return data2.kept||(data2=cloneTensor(data2)),(await pred.data())[0]?[void 0,data2]:[data2,void 0]}case"Merge":{let inputName=node.inputNames.find(name=>getTensor(name,tensorMap,context)!==void 0);if(inputName){let data2=getTensor(inputName,tensorMap,context);return[cloneTensor(data2)]}return}case"Enter":{let frameId=getParamValue("frameName",node,tensorMap,context),data2=getParamValue("tensor",node,tensorMap,context);return context.enterFrame(frameId),[cloneTensor(data2)]}case"Exit":{let data2=getParamValue("tensor",node,tensorMap,context);return context.exitFrame(),[cloneTensor(data2)]}case"NextIteration":{let data2=getParamValue("tensor",node,tensorMap,context);return context.nextIteration(),[cloneTensor(data2)]}case"TensorArrayV3":{let size=getParamValue("size",node,tensorMap,context),dtype=getParamValue("dtype",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),dynamicSize=getParamValue("dynamicSize",node,tensorMap,context),clearAfterRead=getParamValue("clearAfterRead",node,tensorMap,context),identicalElementShapes=getParamValue("identicalElementShapes",node,tensorMap,context),name=getParamValue("name",node,tensorMap,context),tensorArray=new TensorArray(name,dtype,size,elementShape,identicalElementShapes,dynamicSize,clearAfterRead);return context.addTensorArray(tensorArray),[tensorArray.idTensor,scalar(1)]}case"TensorArrayWriteV3":{let id=getParamValue("tensorArrayId",node,tensorMap,context),index=getParamValue("index",node,tensorMap,context),writeTensor=getParamValue("tensor",node,tensorMap,context),writeTensorArray=context.getTensorArray(id.id);return writeTensorArray.write(index,writeTensor),[writeTensorArray.idTensor]}case"TensorArrayReadV3":{let readId=getParamValue("tensorArrayId",node,tensorMap,context),readIndex=getParamValue("index",node,tensorMap,context);return[context.getTensorArray(readId.id).read(readIndex)]}case"TensorArrayGatherV3":{let gatherId=getParamValue("tensorArrayId",node,tensorMap,context),gatherIndices=getParamValue("indices",node,tensorMap,context),gatherDtype=getParamValue("dtype",node,tensorMap,context);return[context.getTensorArray(gatherId.id).gather(gatherIndices,gatherDtype)]}case"TensorArrayScatterV3":{let scatterId=getParamValue("tensorArrayId",node,tensorMap,context),scatterIndices=getParamValue("indices",node,tensorMap,context),scatterTensor=getParamValue("tensor",node,tensorMap,context),scatterTensorArray=context.getTensorArray(scatterId.id);return scatterTensorArray.scatter(scatterIndices,scatterTensor),[scatterTensorArray.idTensor]}case"TensorArrayConcatV3":{let concatId=getParamValue("tensorArrayId",node,tensorMap,context),concatTensorArray=context.getTensorArray(concatId.id),concatDtype=getParamValue("dtype",node,tensorMap,context);return[concatTensorArray.concat(concatDtype)]}case"TensorArraySplitV3":{let splitId=getParamValue("tensorArrayId",node,tensorMap,context),splitTensor=getParamValue("tensor",node,tensorMap,context),lengths=getParamValue("lengths",node,tensorMap,context),splitTensorArray=context.getTensorArray(splitId.id);return splitTensorArray.split(lengths,splitTensor),[splitTensorArray.idTensor]}case"TensorArraySizeV3":{let sizeId=getParamValue("tensorArrayId",node,tensorMap,context),sizeTensorArray=context.getTensorArray(sizeId.id);return[scalar(sizeTensorArray.size(),"int32")]}case"TensorArrayCloseV3":{let closeId=getParamValue("tensorArrayId",node,tensorMap,context),closeTensorArray=context.getTensorArray(closeId.id);return closeTensorArray.clearAndClose(),[closeTensorArray.idTensor]}case"TensorListSetItem":{let idTensor=getParamValue("tensorListId",node,tensorMap,context),index=getParamValue("index",node,tensorMap,context),writeTensor=getParamValue("tensor",node,tensorMap,context),tensorList=context.getTensorList(idTensor.id);return tensorList.setItem(index,writeTensor),[tensorList.idTensor]}case"TensorListGetItem":{let idTensor=getParamValue("tensorListId",node,tensorMap,context),readIndex=getParamValue("index",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),elementDType=getParamValue("elementDType",node,tensorMap,context);return[context.getTensorList(idTensor.id).getItem(readIndex,elementShape,elementDType)]}case"TensorListScatterV2":case"TensorListScatter":{let scatterIndices=getParamValue("indices",node,tensorMap,context),scatterTensor=getParamValue("tensor",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),numElements=getParamValue("numElements",node,tensorMap,context),tensorList=scatter(scatterTensor,scatterIndices,elementShape,numElements);return context.addTensorList(tensorList),[tensorList.idTensor]}case"TensorListReserve":case"EmptyTensorList":{let elementShape=getParamValue("elementShape",node,tensorMap,context),elementDtype=getParamValue("elementDType",node,tensorMap,context),numElementsParam;node.op==="TensorListReserve"?numElementsParam="numElements":numElementsParam="maxNumElements";let numElements=getParamValue(numElementsParam,node,tensorMap,context),tensorList=reserve(elementShape,elementDtype,numElements);return context.addTensorList(tensorList),[tensorList.idTensor]}case"TensorListGather":{let gatherId=getParamValue("tensorListId",node,tensorMap,context),gatherIndices=getParamValue("indices",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),elementDtype=getParamValue("elementDType",node,tensorMap,context);return[context.getTensorList(gatherId.id).gather(gatherIndices,elementDtype,elementShape)]}case"TensorListStack":{let idTensor=getParamValue("tensorListId",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),elementDtype=getParamValue("elementDType",node,tensorMap,context),numElements=getParamValue("numElements",node,tensorMap,context);return[context.getTensorList(idTensor.id).stack(elementShape,elementDtype,numElements)]}case"TensorListFromTensor":{let tensor2=getParamValue("tensor",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),elementDtype=getParamValue("elementDType",node,tensorMap,context),tensorList=fromTensor(tensor2,elementShape,elementDtype);return context.addTensorList(tensorList),[tensorList.idTensor]}case"TensorListConcat":{let concatId=getParamValue("tensorListId",node,tensorMap,context),tensorList=context.getTensorList(concatId.id),concatDtype=getParamValue("dtype",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context);return[tensorList.concat(concatDtype,elementShape)]}case"TensorListPushBack":{let idTensor=getParamValue("tensorListId",node,tensorMap,context),writeTensor=getParamValue("tensor",node,tensorMap,context),tensorList=context.getTensorList(idTensor.id);return tensorList.pushBack(writeTensor),[tensorList.idTensor]}case"TensorListPopBack":{let idTensor=getParamValue("tensorListId",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),elementDType=getParamValue("elementDType",node,tensorMap,context);return[context.getTensorList(idTensor.id).popBack(elementShape,elementDType)]}case"TensorListSplit":{let splitTensor=getParamValue("tensor",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),lengths=getParamValue("lengths",node,tensorMap,context),tensorList=split2(splitTensor,lengths,elementShape);return context.addTensorList(tensorList),[tensorList.idTensor]}default:throw TypeError(`Node type ${node.op} is not implemented`)}};function fusedConvAndDepthWiseParams(node,tensorMap,context){let[extraOp,activationFunc]=getParamValue("fusedOps",node,tensorMap,context),isBiasAdd=extraOp==="biasadd",isPrelu=activationFunc==="prelu",isBatchNorm=extraOp==="fusedbatchnorm",numArgs=getParamValue("numArgs",node,tensorMap,context);if(isBiasAdd){if(isPrelu&&numArgs!==2)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!isPrelu&&numArgs!==1)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(isBatchNorm)throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported.");let stride=getParamValue("strides",node,tensorMap,context),pad3=getPadding(node,tensorMap,context),dataFormat=getParamValue("dataFormat",node,tensorMap,context).toUpperCase(),dilations=getParamValue("dilations",node,tensorMap,context),[biasArg,preluArg]=getParamValue("args",node,tensorMap,context),leakyreluAlpha=getParamValue("leakyreluAlpha",node,tensorMap,context);return{stride,pad:pad3,dataFormat,dilations,biasArg,preluArg,activationFunc,leakyreluAlpha}}var executeOp4=(node,tensorMap,context)=>{switch(node.op){case"Conv1D":{let stride=getParamValue("stride",node,tensorMap,context),pad3=getParamValue("pad",node,tensorMap,context),dataFormat=getParamValue("dataFormat",node,tensorMap,context).toUpperCase(),dilation=getParamValue("dilation",node,tensorMap,context);return[conv1d(getParamValue("x",node,tensorMap,context),getParamValue("filter",node,tensorMap,context),stride,pad3,dataFormat,dilation)]}case"Conv2D":{let stride=getParamValue("strides",node,tensorMap,context),pad3=getPadding(node,tensorMap,context),dataFormat=getParamValue("dataFormat",node,tensorMap,context).toUpperCase(),dilations=getParamValue("dilations",node,tensorMap,context);return[conv2d(getParamValue("x",node,tensorMap,context),getParamValue("filter",node,tensorMap,context),[stride[1],stride[2]],pad3,dataFormat,[dilations[1],dilations[2]])]}case"_FusedConv2D":{let{stride,pad:pad3,dataFormat,dilations,biasArg,preluArg,activationFunc,leakyreluAlpha}=fusedConvAndDepthWiseParams(node,tensorMap,context);return[fused_ops_exports.conv2d({x:getParamValue("x",node,tensorMap,context),filter:getParamValue("filter",node,tensorMap,context),strides:[stride[1],stride[2]],pad:pad3,dataFormat,dilations:[dilations[1],dilations[2]],bias:biasArg,activation:activationFunc,preluActivationWeights:preluArg,leakyreluAlpha})]}case"FusedDepthwiseConv2dNative":{let{stride,pad:pad3,dataFormat,dilations,biasArg,preluArg,activationFunc,leakyreluAlpha}=fusedConvAndDepthWiseParams(node,tensorMap,context);return[fused_ops_exports.depthwiseConv2d({x:getParamValue("x",node,tensorMap,context),filter:getParamValue("filter",node,tensorMap,context),strides:[stride[1],stride[2]],pad:pad3,dataFormat,dilations:[dilations[1],dilations[2]],bias:biasArg,activation:activationFunc,preluActivationWeights:preluArg,leakyreluAlpha})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{let shape=getParamValue("outputShape",node,tensorMap,context),stride=getParamValue("strides",node,tensorMap,context),pad3=getPadding(node,tensorMap,context);return[conv2dTranspose(getParamValue("x",node,tensorMap,context),getParamValue("filter",node,tensorMap,context),shape,[stride[1],stride[2]],pad3)]}case"DepthwiseConv2dNative":case"DepthwiseConv2d":{let stride=getParamValue("strides",node,tensorMap,context),pad3=getPadding(node,tensorMap,context),dilations=getParamValue("dilations",node,tensorMap,context),dataFormat=getParamValue("dataFormat",node,tensorMap,context).toUpperCase();return[depthwiseConv2d(getParamValue("input",node,tensorMap,context),getParamValue("filter",node,tensorMap,context),[stride[1],stride[2]],pad3,dataFormat,[dilations[1],dilations[2]])]}case"Conv3D":{let stride=getParamValue("strides",node,tensorMap,context),pad3=getParamValue("pad",node,tensorMap,context),dataFormat=getParamValue("dataFormat",node,tensorMap,context).toUpperCase(),dilations=getParamValue("dilations",node,tensorMap,context);return[conv3d(getParamValue("x",node,tensorMap,context),getParamValue("filter",node,tensorMap,context),[stride[1],stride[2],stride[3]],pad3,dataFormat,[dilations[1],dilations[2],dilations[3]])]}case"AvgPool":{let stride=getParamValue("strides",node,tensorMap,context),pad3=getParamValue("pad",node,tensorMap,context),kernelSize=getParamValue("kernelSize",node,tensorMap,context);return[avgPool(getParamValue("x",node,tensorMap,context),[kernelSize[1],kernelSize[2]],[stride[1],stride[2]],pad3)]}case"MaxPool":{let stride=getParamValue("strides",node,tensorMap,context),pad3=getParamValue("pad",node,tensorMap,context),kernelSize=getParamValue("kernelSize",node,tensorMap,context);return[maxPool(getParamValue("x",node,tensorMap,context),[kernelSize[1],kernelSize[2]],[stride[1],stride[2]],pad3)]}case"MaxPoolWithArgmax":{let stride=getParamValue("strides",node,tensorMap,context),pad3=getParamValue("pad",node,tensorMap,context),kernelSize=getParamValue("kernelSize",node,tensorMap,context),includeBatchInIndex=getParamValue("includeBatchInIndex",node,tensorMap,context),{result,indexes}=maxPoolWithArgmax(getParamValue("x",node,tensorMap,context),[kernelSize[1],kernelSize[2]],[stride[1],stride[2]],pad3,includeBatchInIndex);return[result,indexes]}case"AvgPool3D":{let stride=getParamValue("strides",node,tensorMap,context),pad3=getParamValue("pad",node,tensorMap,context),kernelSize=getParamValue("kernelSize",node,tensorMap,context);return[avgPool3d(getParamValue("x",node,tensorMap,context),[kernelSize[1],kernelSize[2],kernelSize[3]],[stride[1],stride[2],stride[3]],pad3)]}case"MaxPool3D":{let stride=getParamValue("strides",node,tensorMap,context),pad3=getParamValue("pad",node,tensorMap,context),kernelSize=getParamValue("kernelSize",node,tensorMap,context);return[maxPool3d(getParamValue("x",node,tensorMap,context),[kernelSize[1],kernelSize[2],kernelSize[3]],[stride[1],stride[2],stride[3]],pad3)]}case"Dilation2D":{let strides=getParamValue("strides",node,tensorMap,context),pad3=getParamValue("pad",node,tensorMap,context),dilations=getParamValue("dilations",node,tensorMap,context),strideHeight=strides[1],strideWidth=strides[2],dilationHeight=dilations[1],dilationWidth=dilations[2];return[dilation2d(getParamValue("x",node,tensorMap,context),getParamValue("filter",node,tensorMap,context),[strideHeight,strideWidth],pad3,[dilationHeight,dilationWidth],"NHWC")]}default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp5=(node,tensorMap,context)=>{switch(node.op){case"Fill":{let shape=getParamValue("shape",node,tensorMap,context),dtype=getParamValue("dtype",node,tensorMap,context),value=getParamValue("value",node,tensorMap,context);return[fill(shape,value,dtype)]}case"LinSpace":{let start=getParamValue("start",node,tensorMap,context),stop=getParamValue("stop",node,tensorMap,context),num=getParamValue("num",node,tensorMap,context);return[linspace(start,stop,num)]}case"Multinomial":{let logits=getParamValue("logits",node,tensorMap,context),numSamples=getParamValue("numSamples",node,tensorMap,context),seed=getParamValue("seed",node,tensorMap,context);return[multinomial(logits,numSamples,seed)]}case"OneHot":{let indices=getParamValue("indices",node,tensorMap,context),depth=getParamValue("depth",node,tensorMap,context),onValue=getParamValue("onValue",node,tensorMap,context),offValue=getParamValue("offValue",node,tensorMap,context);return[oneHot(indices,depth,onValue,offValue)]}case"Ones":return[ones2(getParamValue("shape",node,tensorMap,context),getParamValue("dtype",node,tensorMap,context))];case"OnesLike":return[onesLike(getParamValue("x",node,tensorMap,context))];case"RandomUniform":return[randomUniform(getParamValue("shape",node,tensorMap,context),getParamValue("minval",node,tensorMap,context),getParamValue("maxval",node,tensorMap,context),getParamValue("dtype",node,tensorMap,context))];case"Range":{let start=getParamValue("start",node,tensorMap,context),stop=getParamValue("stop",node,tensorMap,context),step4=getParamValue("step",node,tensorMap,context);return[range(start,stop,step4,getParamValue("dtype",node,tensorMap,context))]}case"TruncatedNormal":{let shape=getParamValue("shape",node,tensorMap,context),mean4=getParamValue("mean",node,tensorMap,context),stdDev=getParamValue("stdDev",node,tensorMap,context),seed=getParamValue("seed",node,tensorMap,context);return[truncatedNormal(shape,mean4,stdDev,getParamValue("dtype",node,tensorMap,context),seed)]}case"Zeros":return[zeros(getParamValue("shape",node,tensorMap,context),getParamValue("dtype",node,tensorMap,context))];case"ZerosLike":return[zerosLike(getParamValue("x",node,tensorMap,context))];default:throw TypeError(`Node type ${node.op} is not implemented`)}};function nmsParams(node,tensorMap,context){let boxes=getParamValue("boxes",node,tensorMap,context),scores=getParamValue("scores",node,tensorMap,context),maxOutputSize=getParamValue("maxOutputSize",node,tensorMap,context),iouThreshold=getParamValue("iouThreshold",node,tensorMap,context),scoreThreshold=getParamValue("scoreThreshold",node,tensorMap,context),softNmsSigma=getParamValue("softNmsSigma",node,tensorMap,context);return{boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma}}var executeOp6=async(node,tensorMap,context)=>{switch(node.op){case"NonMaxSuppressionV5":{let{boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma}=nmsParams(node,tensorMap,context),result=await image.nonMaxSuppressionWithScoreAsync(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma);return[result.selectedIndices,result.selectedScores]}case"NonMaxSuppressionV4":{let{boxes,scores,maxOutputSize,iouThreshold,scoreThreshold}=nmsParams(node,tensorMap,context),padToMaxOutputSize=getParamValue("padToMaxOutputSize",node,tensorMap,context),result=await image.nonMaxSuppressionPaddedAsync(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize);return[result.selectedIndices,result.validOutputs]}case"NonMaxSuppressionV3":case"NonMaxSuppressionV2":{let{boxes,scores,maxOutputSize,iouThreshold,scoreThreshold}=nmsParams(node,tensorMap,context);return[await image.nonMaxSuppressionAsync(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold)]}case"Where":{let condition=cast(getParamValue("condition",node,tensorMap,context),"bool"),result=[await whereAsync(condition)];return condition.dispose(),result}case"ListDiff":return setdiff1dAsync(getParamValue("x",node,tensorMap,context),getParamValue("y",node,tensorMap,context));default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp7=(node,tensorMap,context)=>{switch(node.op){case"TopKV2":{let x=getParamValue("x",node,tensorMap,context),k=getParamValue("k",node,tensorMap,context),sorted=getParamValue("sorted",node,tensorMap,context),result=topk(x,k,sorted);return[result.values,result.indices]}case"Unique":{let x=getParamValue("x",node,tensorMap,context),result=unique(x);return[result.values,result.indices]}case"UniqueV2":{let x=getParamValue("x",node,tensorMap,context),axis=getParamValue("axis",node,tensorMap,context),result=unique(x,axis);return[result.values,result.indices]}default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp8=(node,tensorMap,context)=>{switch(node.op){case"Const":return tensorMap[node.name];case"PlaceholderWithDefault":let def=getParamValue("default",node,tensorMap,context);return[getTensor(node.name,tensorMap,context)||def];case"Placeholder":return[getTensor(node.name,tensorMap,context)];case"Identity":case"StopGradient":case"FakeQuantWithMinMaxVars":{let data22=getParamValue("x",node,tensorMap,context);return[cloneTensor(data22)]}case"IdentityN":return getParamValue("x",node,tensorMap,context).map(t=>cloneTensor(t));case"Snapshot":let snapshot=getParamValue("x",node,tensorMap,context);return[cloneTensor(snapshot)];case"Shape":return[tensor1d(getParamValue("x",node,tensorMap,context).shape,"int32")];case"ShapeN":return getParamValue("x",node,tensorMap,context).map(t=>tensor1d(t.shape));case"Size":return[scalar(getParamValue("x",node,tensorMap,context).size,"int32")];case"Rank":return[scalar(getParamValue("x",node,tensorMap,context).rank,"int32")];case"NoOp":return[scalar(1)];case"Print":let input2=getParamValue("x",node,tensorMap,context),data2=getParamValue("data",node,tensorMap,context),message=getParamValue("message",node,tensorMap,context),summarize=getParamValue("summarize",node,tensorMap,context);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."),console.log(message);for(let i=0;ivalue.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}async import(keys,values){this.checkKeyAndValueTensor(keys,values);let $keys=await keys.data();return this.tensorMap.forEach(value=>value.dispose()),this.tensorMap.clear(),tidy(()=>{let $values=unstack(values),keysLength=$keys.length,valuesLength=$values.length;util_exports.assert(keysLength===valuesLength,()=>`The number of elements doesn't match, keys has ${keysLength} elements, the values has ${valuesLength} elements.`);for(let i=0;i{let result=[];for(let i=0;i<$keys.length;i++){let key=$keys[i],value=this.findWithDefault(key,defaultValue);result.push(value)}return stack(result)})}findWithDefault(key,defaultValue){let result=this.tensorMap.get(key);return result!=null?result:defaultValue}checkKeyAndValueTensor(key,value){if(key.dtype!==this.keyDType)throw new Error(`Expect key dtype ${this.keyDType}, but got ${key.dtype}`);if(value.dtype!==this.valueDType)throw new Error(`Expect value dtype ${this.valueDType}, but got ${value.dtype}`)}},executeOp9=async(node,tensorMap,context,resourceManager)=>{switch(node.op){case"HashTable":case"HashTableV2":{let keyDType=getParamValue("keyDType",node,tensorMap,context),valueDType=getParamValue("valueDType",node,tensorMap,context),hashTable2=new HashTable(keyDType,valueDType);return resourceManager.addHashTable(node.name,hashTable2),[hashTable2.handle]}case"LookupTableImport":case"LookupTableImportV2":{let handle=getParamValue("tableHandle",node,tensorMap,context,resourceManager),keys=getParamValue("keys",node,tensorMap,context),values=getParamValue("values",node,tensorMap,context);return[await resourceManager.getHashTableById(handle.id).import(keys,values)]}case"LookupTableFind":case"LookupTableFindV2":{let handle=getParamValue("tableHandle",node,tensorMap,context,resourceManager),keys=getParamValue("keys",node,tensorMap,context),defaultValue=getParamValue("defaultValue",node,tensorMap,context);return[await resourceManager.getHashTableById(handle.id).find(keys,defaultValue)]}default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp10=(node,tensorMap,context)=>{switch(node.op){case"ResizeBilinear":{let images=getParamValue("images",node,tensorMap,context),size=getParamValue("size",node,tensorMap,context),alignCorners=getParamValue("alignCorners",node,tensorMap,context),halfPixelCenters=getParamValue("halfPixelCenters",node,tensorMap,context);return[image.resizeBilinear(images,[size[0],size[1]],alignCorners,halfPixelCenters)]}case"ResizeNearestNeighbor":{let images=getParamValue("images",node,tensorMap,context),size=getParamValue("size",node,tensorMap,context),alignCorners=getParamValue("alignCorners",node,tensorMap,context),halfPixelCenters=getParamValue("halfPixelCenters",node,tensorMap,context);return[image.resizeNearestNeighbor(images,[size[0],size[1]],alignCorners,halfPixelCenters)]}case"CropAndResize":{let image3=getParamValue("image",node,tensorMap,context),boxes=getParamValue("boxes",node,tensorMap,context),boxInd=getParamValue("boxInd",node,tensorMap,context),cropSize=getParamValue("cropSize",node,tensorMap,context),method=getParamValue("method",node,tensorMap,context),extrapolationValue=getParamValue("extrapolationValue",node,tensorMap,context);return[image.cropAndResize(image3,boxes,boxInd,cropSize,method,extrapolationValue)]}default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp11=(node,tensorMap,context)=>{switch(node.op){case"Equal":return[equal(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"NotEqual":return[notEqual(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Greater":return[greater(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"GreaterEqual":return[greaterEqual(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Less":return[less(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"LessEqual":return[lessEqual(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"LogicalAnd":return[logicalAnd(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"LogicalNot":return[logicalNot(getParamValue("a",node,tensorMap,context))];case"LogicalOr":return[logicalOr(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Select":case"SelectV2":return[where(getParamValue("condition",node,tensorMap,context),getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp12=(node,tensorMap,context)=>{switch(node.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[matMul(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context),getParamValue("transposeA",node,tensorMap,context),getParamValue("transposeB",node,tensorMap,context))];case"Transpose":return[transpose(getParamValue("x",node,tensorMap,context),getParamValue("perm",node,tensorMap,context))];case"_FusedMatMul":let[extraOp,activationFunc]=getParamValue("fusedOps",node,tensorMap,context),isBiasAdd=extraOp==="biasadd",isPrelu=activationFunc==="prelu",numArgs=getParamValue("numArgs",node,tensorMap,context),leakyreluAlpha=getParamValue("leakyreluAlpha",node,tensorMap,context);if(isBiasAdd){if(isPrelu&&numArgs!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!isPrelu&&numArgs!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[biasArg,preluArg]=getParamValue("args",node,tensorMap,context);return[fused_ops_exports.matMul({a:getParamValue("a",node,tensorMap,context),b:getParamValue("b",node,tensorMap,context),transposeA:getParamValue("transposeA",node,tensorMap,context),transposeB:getParamValue("transposeB",node,tensorMap,context),bias:biasArg,activation:activationFunc,preluActivationWeights:preluArg,leakyreluAlpha})];default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp13=(node,tensorMap,context)=>{switch(node.op){case"FusedBatchNorm":case"FusedBatchNormV2":return[batchNorm(getParamValue("x",node,tensorMap,context),getParamValue("mean",node,tensorMap,context),getParamValue("variance",node,tensorMap,context),getParamValue("offset",node,tensorMap,context),getParamValue("scale",node,tensorMap,context),getParamValue("epsilon",node,tensorMap,context))];case"FusedBatchNormV3":return[batchNorm(getParamValue("x",node,tensorMap,context),getParamValue("mean",node,tensorMap,context),getParamValue("variance",node,tensorMap,context),getParamValue("offset",node,tensorMap,context),getParamValue("scale",node,tensorMap,context),getParamValue("epsilon",node,tensorMap,context))];case"LRN":return[localResponseNormalization(getParamValue("x",node,tensorMap,context),getParamValue("radius",node,tensorMap,context),getParamValue("bias",node,tensorMap,context),getParamValue("alpha",node,tensorMap,context),getParamValue("beta",node,tensorMap,context))];case"Softmax":return[softmax(getParamValue("x",node,tensorMap,context))];case"LogSoftmax":return[logSoftmax(getParamValue("x",node,tensorMap,context))];case"SparseToDense":return[sparseToDense(getParamValue("sparseIndices",node,tensorMap,context),getParamValue("outputShape",node,tensorMap,context),getParamValue("sparseValues",node,tensorMap,context),getParamValue("defaultValue",node,tensorMap,context))];default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp14=(node,tensorMap,context)=>{switch(node.op){case"Max":{let axis=getParamValue("axis",node,tensorMap,context),keepDims=getParamValue("keepDims",node,tensorMap,context);return[max(getParamValue("x",node,tensorMap,context),axis,keepDims)]}case"Mean":{let axis=getParamValue("axis",node,tensorMap,context),keepDims=getParamValue("keepDims",node,tensorMap,context);return[mean(getParamValue("x",node,tensorMap,context),axis,keepDims)]}case"Min":{let axis=getParamValue("axis",node,tensorMap,context),keepDims=getParamValue("keepDims",node,tensorMap,context);return[min(getParamValue("x",node,tensorMap,context),axis,keepDims)]}case"Sum":{let axis=getParamValue("axis",node,tensorMap,context),keepDims=getParamValue("keepDims",node,tensorMap,context);return[sum2(getParamValue("x",node,tensorMap,context),axis,keepDims)]}case"All":{let axis=getParamValue("axis",node,tensorMap,context),keepDims=getParamValue("keepDims",node,tensorMap,context);return[all(getParamValue("x",node,tensorMap,context),axis,keepDims)]}case"Any":{let axis=getParamValue("axis",node,tensorMap,context),keepDims=getParamValue("keepDims",node,tensorMap,context);return[any(getParamValue("x",node,tensorMap,context),axis,keepDims)]}case"ArgMax":{let axis=getParamValue("axis",node,tensorMap,context);return[argMax(getParamValue("x",node,tensorMap,context),axis)]}case"ArgMin":{let axis=getParamValue("axis",node,tensorMap,context);return[argMin(getParamValue("x",node,tensorMap,context),axis)]}case"Prod":{let axis=getParamValue("axis",node,tensorMap,context),keepDims=getParamValue("keepDims",node,tensorMap,context);return[prod(getParamValue("x",node,tensorMap,context),axis,keepDims)]}case"Cumsum":{let axis=getParamValue("axis",node,tensorMap,context),exclusive=getParamValue("exclusive",node,tensorMap,context),reverse5=getParamValue("reverse",node,tensorMap,context);return[cumsum(getParamValue("x",node,tensorMap,context),axis,exclusive,reverse5)]}case"Bincount":let x=getParamValue("x",node,tensorMap,context),weights=getParamValue("weights",node,tensorMap,context),size=getParamValue("size",node,tensorMap,context);return[bincount(x,weights,size)];case"DenseBincount":{let x2=getParamValue("x",node,tensorMap,context),weights2=getParamValue("weights",node,tensorMap,context),size2=getParamValue("size",node,tensorMap,context),binaryOutput=getParamValue("binaryOutput",node,tensorMap,context);return[denseBincount(x2,weights2,size2,binaryOutput)]}default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp15=(node,tensorMap,context)=>{switch(node.op){case"ConcatV2":case"Concat":{let n=getParamValue("n",node,tensorMap,context),axis=getParamValue("axis",node,tensorMap,context),inputs=getParamValue("tensors",node,tensorMap,context);return inputs=inputs.slice(0,n),[concat(inputs,axis)]}case"Gather":{let input2=getParamValue("x",node,tensorMap,context),indices=getParamValue("indices",node,tensorMap,context);return[gather(input2,cast(indices,"int32"),0)]}case"GatherV2":{let axis=getParamValue("axis",node,tensorMap,context),batchDims=getParamValue("batchDims",node,tensorMap,context),input2=getParamValue("x",node,tensorMap,context),indices=getParamValue("indices",node,tensorMap,context);return[gather(input2,cast(indices,"int32"),axis,batchDims)]}case"Reverse":{let dims=getParamValue("dims",node,tensorMap,context),axis=[];for(let i=0;i{let axis=getParamValue("axis",node,tensorMap,context),tensors=getParamValue("tensors",node,tensorMap,context),shape=tensors[0].shape,squeezedShape=squeeze(tensors[0]).shape,mapped=tensors.map(tensor2=>{let sameShape=util_exports.arraysEqual(tensor2.shape,shape);if(!sameShape&&!util_exports.arraysEqual(squeeze(tensor2).shape,squeezedShape))throw new Error("the input tensors shape does not match");return sameShape?tensor2:reshape(tensor2,shape)});return[stack(mapped,axis)]});case"Unpack":{let axis=getParamValue("axis",node,tensorMap,context),tensor2=getParamValue("tensor",node,tensorMap,context);return unstack(tensor2,axis)}case"Tile":{let reps=getParamValue("reps",node,tensorMap,context);return[tile(getParamValue("x",node,tensorMap,context),reps)]}case"Split":case"SplitV":{let axis=getParamValue("axis",node,tensorMap,context),numOrSizeSplits=getParamValue("numOrSizeSplits",node,tensorMap,context),tensor2=getParamValue("x",node,tensorMap,context);return split(tensor2,numOrSizeSplits,axis)}case"ScatterNd":{let indices=getParamValue("indices",node,tensorMap,context),values=getParamValue("values",node,tensorMap,context),shape=getParamValue("shape",node,tensorMap,context);return[scatterND(indices,values,shape)]}case"GatherNd":{let x=getParamValue("x",node,tensorMap,context),indices=getParamValue("indices",node,tensorMap,context);return[gatherND(x,indices)]}case"SparseToDense":{let indices=getParamValue("sparseIndices",node,tensorMap,context),shape=getParamValue("outputShape",node,tensorMap,context),sparseValues=getParamValue("sparseValues",node,tensorMap,context),defaultValue=getParamValue("defaultValue",node,tensorMap,context);return[sparseToDense(indices,sparseValues,shape,sparseValues.dtype===defaultValue.dtype?defaultValue:cast(defaultValue,sparseValues.dtype))]}default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp16=(node,tensorMap,context)=>{switch(node.op){case"FFT":return[fft(getParamValue("x",node,tensorMap,context))];case"IFFT":return[ifft(getParamValue("x",node,tensorMap,context))];case"RFFT":return[rfft(getParamValue("x",node,tensorMap,context))];case"IRFFT":return[irfft(getParamValue("x",node,tensorMap,context))];default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp17=(node,tensorMap,context)=>{switch(node.op){case"Cast":return[cast(getParamValue("x",node,tensorMap,context),getParamValue("dtype",node,tensorMap,context))];case"ExpandDims":{let axis=getParamValue("axis",node,tensorMap,context);return[expandDims(getParamValue("x",node,tensorMap,context),axis)]}case"Squeeze":{let axis=getParamValue("axis",node,tensorMap,context);return[squeeze(getParamValue("x",node,tensorMap,context),axis)]}case"Reshape":return[reshape(getParamValue("x",node,tensorMap,context),getParamValue("shape",node,tensorMap,context))];case"MirrorPad":return[mirrorPad(getParamValue("x",node,tensorMap,context),getParamValue("padding",node,tensorMap,context),getParamValue("mode",node,tensorMap,context))];case"PadV2":case"Pad":return[pad(getParamValue("x",node,tensorMap,context),getParamValue("padding",node,tensorMap,context),getParamValue("constantValue",node,tensorMap,context))];case"SpaceToBatchND":{let blockShape=getParamValue("blockShape",node,tensorMap,context),paddings=getParamValue("paddings",node,tensorMap,context);return[spaceToBatchND(getParamValue("x",node,tensorMap,context),blockShape,paddings)]}case"BatchToSpaceND":{let blockShape=getParamValue("blockShape",node,tensorMap,context),crops=getParamValue("crops",node,tensorMap,context);return[batchToSpaceND(getParamValue("x",node,tensorMap,context),blockShape,crops)]}case"DepthToSpace":{let blockSize=getParamValue("blockSize",node,tensorMap,context),dataFormat=getParamValue("dataFormat",node,tensorMap,context).toUpperCase();return[depthToSpace(getParamValue("x",node,tensorMap,context),blockSize,dataFormat)]}case"BroadcastTo":return[broadcastTo(getParamValue("x",node,tensorMap,context),getParamValue("shape",node,tensorMap,context))];default:throw TypeError(`Node type ${node.op} is not implemented`)}};function executeOp18(node,tensorMap,context,resourceManager){let value=((node2,tensorMap2,context2)=>{switch(node2.category){case"arithmetic":return tidy(()=>executeOp(node2,tensorMap2,context2));case"basic_math":return tidy(()=>executeOp2(node2,tensorMap2,context2));case"control":return executeOp3(node2,tensorMap2,context2);case"convolution":return tidy(()=>executeOp4(node2,tensorMap2,context2));case"creation":return tidy(()=>executeOp5(node2,tensorMap2,context2));case"dynamic":return executeOp6(node2,tensorMap2,context2);case"evaluation":return tidy(()=>executeOp7(node2,tensorMap2,context2));case"image":return tidy(()=>executeOp10(node2,tensorMap2,context2));case"graph":return tidy(()=>executeOp8(node2,tensorMap2,context2));case"logical":return tidy(()=>executeOp11(node2,tensorMap2,context2));case"matrices":return tidy(()=>executeOp12(node2,tensorMap2,context2));case"normalization":return tidy(()=>executeOp13(node2,tensorMap2,context2));case"reduction":return tidy(()=>executeOp14(node2,tensorMap2,context2));case"slice_join":return tidy(()=>executeOp15(node2,tensorMap2,context2));case"spectral":return tidy(()=>executeOp16(node2,tensorMap2,context2));case"transformation":return tidy(()=>executeOp17(node2,tensorMap2,context2));case"hash_table":return executeOp9(node2,tensorMap2,context2,resourceManager);case"custom":let opMapper=getRegisteredOp(node2.op);if(opMapper&&opMapper.customExecutor)return opMapper.customExecutor(new NodeValueImpl(node2,tensorMap2,context2));throw TypeError(`Custom op ${node2.op} is not registered.`);default:throw TypeError(`Unknown op '${node2.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(node,tensorMap,context);return util_exports.isPromise(value)?value.then(data2=>[].concat(data2)):[].concat(value)}var ExecutionContext=class{constructor(weightMap={},tensorArrayMap={},tensorListMap={},functionMap={}){this.weightMap=weightMap,this.tensorArrayMap=tensorArrayMap,this.tensorListMap=tensorListMap,this.functionMap=functionMap,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(id,frameName){return{id,frameName,iterationId:0}}set currentContext(contexts2){this.contexts!==contexts2&&(this.contexts=contexts2,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let names=[];for(let i=0;icontext.id===0&&context.iterationId===0?"":`${context.frameName}-${context.iterationId}`).join("/"):""}enterFrame(frameId){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,frameId)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let context=Object.assign({},this.contexts[this.contexts.length-1]);context.iterationId+=1,context.id=this.lastId,this.contexts.splice(-1,1,context),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(name){return this.weightMap[name]}addTensorArray(tensorArray){this.tensorArrayMap[tensorArray.id]=tensorArray}getTensorArray(id){return this.tensorArrayMap[id]}addTensorList(tensorList){this.tensorListMap[tensorList.id]=tensorList}getTensorList(id){return this.tensorListMap[id]}dispose(keepIds){for(let key in this.tensorArrayMap)this.tensorArrayMap[key].clearAndClose(keepIds);for(let key in this.tensorListMap)this.tensorListMap[key].clearAndClose(keepIds)}};function getExecutionSubgraph(inputs,outputs,weightMap,initNodes){let usedNodes=new Set,missingInputs=[],dynamicNode=null,syncInputs=null,seen=new Set,inputNodeNames=Object.keys(inputs).map(name=>parseNodeName(name)[0]),initNodeNames=[];initNodes!=null&&(initNodeNames=initNodes.map(node=>parseNodeName(node.name)[0]));let frontier=[...outputs];for(;frontier.length>0;){let node=frontier.pop();if((isControlFlow(node)||isDynamicShape(node)||isHashTable(node))&&dynamicNode==null&&(dynamicNode=node,syncInputs=dynamicNode.children.map(child=>child.name).filter(name=>usedNodes.has(name))),usedNodes.add(node.name),weightMap[node.name]==null&&inputNodeNames.indexOf(node.name)===-1&&initNodeNames.indexOf(node.name)===-1){if(node.inputs.length===0){missingInputs.push(node.name);continue}node.inputs.forEach(input2=>{seen.has(input2.name)||(seen.add(input2.name),frontier.push(input2))})}}return{inputs,outputs,usedNodes,missingInputs,dynamicNode,syncInputs}}function getNodesInTopologicalOrder(graph2,weightMap,executionInfo){let{usedNodes,inputs}=executionInfo,frontier=[],inputNodes=Object.keys(inputs).map(name=>parseNodeName(name)[0]).map(name=>graph2.nodes[name]),initNodes=graph2.initNodes;inputNodes.forEach(input2=>{usedNodes.has(input2.name)&&frontier.push(input2)}),graph2.weights.forEach(weight=>{usedNodes.has(weight.name)&&frontier.push(weight)}),initNodes!=null&&initNodes.forEach(node=>{usedNodes.has(node.name)&&frontier.push(node)});let seen=new Set,orderedNodes=[];for(;frontier.length>0;){let node=frontier.pop();seen.add(node.name),weightMap[node.name]||orderedNodes.push(node),node.children.forEach(child=>{!seen.has(child.name)&&usedNodes.has(child.name)&&child.inputs.every(input2=>seen.has(input2.name))&&frontier.push(child)})}return orderedNodes}var CONTROL_FLOW_OPS=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],DYNAMIC_SHAPE_OPS=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],HASH_TABLE_OPS=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2"];function isControlFlow(node){return CONTROL_FLOW_OPS.indexOf(node.op)>=0}function isDynamicShape(node){return DYNAMIC_SHAPE_OPS.indexOf(node.op)>=0}function isHashTable(node){return HASH_TABLE_OPS.indexOf(node.op)>=0}var GraphExecutor=class{constructor(graph2,parent){this.graph=graph2,this.parent=parent,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this._outputs=graph2.outputs,this._inputs=graph2.inputs,this._initNodes=graph2.initNodes,this._signature=graph2.signature,this._functions=graph2.functions,graph2.functions!=null&&Object.keys(graph2.functions).forEach(name=>{this._functionExecutorMap[name]=new GraphExecutor(graph2.functions[name],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(weightMap){let weightIds=Object.keys(weightMap).map(key=>weightMap[key].map(tensor2=>tensor2.id));this._weightIds=[].concat(...weightIds),this._weightMap=weightMap}set resourceManager(resourceManager){this._resourceManager=resourceManager}get inputs(){return this._inputs.map(node=>({name:node.name,shape:node.attrParams.shape?node.attrParams.shape.value:void 0,dtype:node.attrParams.dtype?node.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(node=>({name:node.name,shape:node.attrParams.shape?node.attrParams.shape.value:void 0,dtype:node.attrParams.dtype?node.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(node=>node.signatureKey||node.name)}get outputNodes(){return this._outputs.map(node=>{let name=node.signatureKey||node.name;return node.defaultOutput?`${name}:${node.defaultOutput}`:name})}get functions(){return Object.keys(this._functions).reduce((map,key)=>(map[key]=this._functions[key].signature,map),{})}getCompilationKey(inputs,outputs){let sortedInputs=inputs.map(node=>node.name).sort(),sortedOutputs=outputs.map(node=>node.name).sort();return sortedInputs.join(this.SEPERATOR)+"--"+sortedOutputs.join(this.SEPERATOR)}compile(inputs,outputs){let executionInfo=getExecutionSubgraph(inputs,outputs,this.weightMap,this._initNodes),{missingInputs,dynamicNode,syncInputs}=executionInfo;if(dynamicNode!=null)throw new Error(`This execution contains the node '${dynamicNode.name}', which has the dynamic op '${dynamicNode.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${syncInputs}]`);if(missingInputs.length>0){let outNames=outputs.map(n=>n.name),inNames=Object.keys(inputs);throw new Error(`Cannot compute the outputs [${outNames}] from the provided inputs [${inNames}]. Missing the following inputs: [${missingInputs}]`)}return getNodesInTopologicalOrder(this.graph,this.weightMap,executionInfo)}execute(inputs,outputs){inputs=this.mapInputs(inputs);let names=Object.keys(inputs).sort();this.checkInputs(inputs),this.checkInputShapeAndType(inputs),outputs=this.mapOutputs(outputs),this.checkOutputs(outputs);let inputNodes=names.map(name=>this.graph.nodes[parseNodeName(name)[0]]),outputNodeNames=outputs.map(name=>parseNodeName(name)[0]),outputNodes=outputNodeNames.map(name=>this.graph.nodes[name]);outputNodes.length===0&&(outputNodes=this._outputs);let compilationKey=this.getCompilationKey(inputNodes,outputNodes),orderedNodes=this.compiledMap.get(compilationKey);orderedNodes==null&&(orderedNodes=this.compile(inputs,outputNodes),this.compiledMap.set(compilationKey,orderedNodes));let tensorArrayMap={},tensorListMap={};return tidy(()=>{let context=new ExecutionContext(this.weightMap,tensorArrayMap,tensorListMap,this.functionExecutorMap),tensorsMap=Object.assign({},this.weightMap);Object.keys(inputs).forEach(name=>{let[nodeName,index]=parseNodeName(name),tensors=[];tensors[index]=inputs[name],tensorsMap[nodeName]=tensors});let tensorsToKeep=this.getFrozenTensorIds(tensorsMap),intermediateTensorConsumerCount={};for(let i=0;igetTensor(name,tensorsMap,context))})}getFrozenTensorIds(tensorMap){let ids=[].concat.apply([],Object.keys(tensorMap).map(key=>tensorMap[key]).map(tensors=>tensors.map(tensor2=>tensor2.id)));return new Set(ids)}checkTensorForDisposal(nodeName,node,tensorMap,context,tensorsToKeep,outputNames,intermediateTensorConsumerCount){node.category==="control"||outputNames.indexOf(nodeName)!==-1||(tensorMap[nodeName].forEach(tensor2=>{tensor2!=null&&(intermediateTensorConsumerCount[tensor2.id]=(intermediateTensorConsumerCount[tensor2.id]||0)+node.children.length)}),node.inputs.forEach(input2=>{if(input2.category!=="control"){let tensors=getTensorsForCurrentContenxt(input2.name,tensorMap,context);tensors!=null&&tensors.forEach(tensor2=>{if(tensor2&&!tensorsToKeep.has(tensor2.id)){let count2=intermediateTensorConsumerCount[tensor2.id];count2===1?(tensor2.dispose(),delete intermediateTensorConsumerCount[tensor2.id]):count2!=null&&intermediateTensorConsumerCount[tensor2.id]--}})}}))}async executeAsync(inputs,outputs){return this._executeAsync(inputs,outputs)}async _executeAsync(inputs,outputs,isFunctionExecution=!1,tensorArrayMap={},tensorListMap={}){isFunctionExecution||(inputs=this.mapInputs(inputs),this.checkInputs(inputs),this.checkInputShapeAndType(inputs),outputs=this.mapOutputs(outputs),this.checkOutputs(outputs));let context=new ExecutionContext(this.weightMap,tensorArrayMap,tensorListMap,this.functionExecutorMap),tensorMap=await this.executeWithControlFlow(inputs,context,outputs,isFunctionExecution),results=outputs.map(name=>getTensor(name,tensorMap,context)),outputIds=results.map(t=>t.id),inputIds=Object.keys(inputs).map(name=>inputs[name].id),keepIds=new Set([...outputIds,...inputIds,...this.weightIds]);return Object.keys(tensorMap).forEach(key=>{tensorMap[key].forEach(tensor2=>{tensor2&&!tensor2.isDisposed&&!keepIds.has(tensor2.id)&&tensor2.dispose()})}),this.parent==null&&context.dispose(keepIds),results}async executeFunctionAsync(inputs,tensorArrayMap,tensorListMap){let mappedInputs=inputs.reduce((map,tensor2,index)=>(map[this.inputs[index].name]=tensor2,map),{});return this._executeAsync(mappedInputs,this.outputNodes,!0,tensorArrayMap,tensorListMap)}async executeWithControlFlow(inputs,context,outputNames,isFunctionExecution){let names=Object.keys(inputs),inputNodes=names.map(name=>this.graph.nodes[parseNodeName(name)[0]]),outputNodeNames=outputNames.map(name=>parseNodeName(name)[0]),outputNodes=outputNodeNames.map(name=>this.graph.nodes[name]);outputNodes.length===0&&(outputNodes=this._outputs);let{usedNodes,missingInputs,dynamicNode,syncInputs}=getExecutionSubgraph(inputs,outputNodes,this.weightMap,this._initNodes),stack2=[...inputNodes,...this.graph.weights,...this._initNodes||[]].map(node=>({node,contexts:context.currentContext})),tensorsMap=Object.assign({},this.weightMap);Object.keys(inputs).forEach(name=>{let[nodeName,index]=parseNodeName(name),tensors=[];tensors[index]=inputs[name],tensorsMap[nodeName]=tensors});let intermediateTensorConsumerCount={},tensorsToKeep=this.getFrozenTensorIds(tensorsMap),added={};for(;stack2.length>0;){let promises=this.processStack(inputNodes,stack2,context,tensorsMap,added,tensorsToKeep,outputNodeNames,intermediateTensorConsumerCount,usedNodes);await Promise.all(promises)}dynamicNode==null&&!isFunctionExecution&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let missingOutputs=outputNodes.filter(node=>!isControlFlow(node)&&!getTensor(node.name,tensorsMap,context)).map(node=>node.name);if(missingOutputs.length>0){let alternativeMsg="";throw dynamicNode!=null&&(alternativeMsg=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${syncInputs}]`),new Error(`Cannot compute the outputs [${missingOutputs}] from the provided inputs [${names}]. Consider providing the following inputs: [${missingInputs}]. ${alternativeMsg}`)}return tensorsMap}processStack(inputNodes,stack2,context,tensorMap,added,tensorsToKeep,outputNames,intermediateTensorConsumerCount,usedNodes){let promises=[];for(;stack2.length>0;){let item=stack2.pop();context.currentContext=item.contexts;let nodeName="";if(item.node.op==="Enter"&&getParamValue("isConstant",item.node,tensorMap,context)&&([nodeName]=getNodeNameAndIndex(item.node.name,context)),tensorMap[item.node.name]==null){let tensors=executeOp18(item.node,tensorMap,context,this._resourceManager);nodeName||([nodeName]=getNodeNameAndIndex(item.node.name,context));let currentContext=context.currentContext;util_exports.isPromise(tensors)?promises.push(tensors.then(t=>(tensorMap[nodeName]=t,context.currentContext=currentContext,this.checkTensorForDisposal(nodeName,item.node,tensorMap,context,tensorsToKeep,outputNames,intermediateTensorConsumerCount),this.processChildNodes(item.node,stack2,context,tensorMap,added,usedNodes),t))):(tensorMap[nodeName]=tensors,this.checkTensorForDisposal(nodeName,item.node,tensorMap,context,tensorsToKeep,outputNames,intermediateTensorConsumerCount),this.processChildNodes(item.node,stack2,context,tensorMap,added,usedNodes))}else this.processChildNodes(item.node,stack2,context,tensorMap,added,usedNodes)}return promises}processChildNodes(node,stack2,context,tensorMap,added,usedNodes){node.children.forEach(childNode=>{let[nodeName]=getNodeNameAndIndex(childNode.name,context);added[nodeName]||!usedNodes.has(childNode.name)||(childNode.op==="Merge"?childNode.inputNames.some(name=>!!getTensor(name,tensorMap,context))&&(added[nodeName]=!0,stack2.push({contexts:context.currentContext,node:childNode})):childNode.inputNames.every(name=>!!getTensor(name,tensorMap,context))&&(added[nodeName]=!0,stack2.push({contexts:context.currentContext,node:childNode})))})}dispose(){Object.keys(this.weightMap).forEach(key=>this.weightMap[key].forEach(tensor2=>tensor2.dispose()))}checkInputShapeAndType(inputs){Object.keys(inputs).forEach(name=>{let input2=inputs[name],[nodeName]=parseNodeName(name),node=this.graph.nodes[nodeName];if(node.attrParams.shape&&node.attrParams.shape.value){let shape=node.attrParams.shape.value,match=shape.length===input2.shape.length&&input2.shape.every((dim,index)=>shape[index]===-1||shape[index]===dim);util_exports.assert(match,()=>`The shape of dict['${node.name}'] provided in model.execute(dict) must be [${shape}], but was [${input2.shape}]`)}node.attrParams.dtype&&node.attrParams.dtype.value&&util_exports.assert(input2.dtype===node.attrParams.dtype.value,()=>`The dtype of dict['${node.name}'] provided in model.execute(dict) must be ${node.attrParams.dtype.value}, but was ${input2.dtype}`)})}mapInputs(inputs){let result={};for(let inputName in inputs)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[inputName]!=null){let tensor2=this._signature.inputs[inputName];result[tensor2.name]=inputs[inputName]}else result[inputName]=inputs[inputName];return result}checkInputs(inputs){let notInGraph=Object.keys(inputs).filter(name=>{let[nodeName]=parseNodeName(name);return this.graph.nodes[nodeName]==null});if(notInGraph.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${notInGraph}] that are not part of graph`)}mapOutputs(outputs){return outputs.map(name=>this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[name]!=null?this._signature.outputs[name].name:name,{})}checkOutputs(outputs){outputs.forEach(name=>{let[normalizedName]=parseNodeName(name);if(!this.graph.nodes[normalizedName])throw new Error(`The output '${name}' is not found in the graph`)})}},ResourceManager=class{constructor(hashTableNameToHandle={},hashTableMap={}){this.hashTableNameToHandle=hashTableNameToHandle,this.hashTableMap=hashTableMap}addHashTable(name,hashTable2){this.hashTableNameToHandle[name]=hashTable2.handle,this.hashTableMap[hashTable2.id]=hashTable2}getHashTableHandleByName(name){return this.hashTableNameToHandle[name]}getHashTableById(id){return this.hashTableMap[id]}dispose(){for(let key in this.hashTableMap)this.hashTableMap[key].clearAndClose(),delete this.hashTableMap[key];for(let name in this.hashTableNameToHandle)this.hashTableNameToHandle[name].dispose(),delete this.hashTableNameToHandle[name]}},TFHUB_SEARCH_PARAM="?tfjs-format=file",DEFAULT_MODEL_NAME="model.json",GraphModel=class{constructor(modelUrl,loadOptions={}){this.modelUrl=modelUrl,this.loadOptions=loadOptions,this.version="n/a",loadOptions==null&&(this.loadOptions={}),this.resourceManager=new ResourceManager}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let path=this.modelUrl;if(path.load!=null)this.handler=path;else if(this.loadOptions.requestInit!=null)this.handler=io_exports.browserHTTPRequest(path,this.loadOptions);else{let handlers=io_exports.getLoadHandlers(path,this.loadOptions);if(handlers.length===0)handlers.push(io_exports.browserHTTPRequest(path,this.loadOptions));else if(handlers.length>1)throw new Error(`Found more than one (${handlers.length}) load handlers for URL '${[path]}'`);this.handler=handlers[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let artifacts=await this.handler.load();return this.loadSync(artifacts)}loadSync(artifacts){this.artifacts=artifacts;let graph2=this.artifacts.modelTopology,signature;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?signature=this.artifacts.userDefinedMetadata.signature:signature=this.artifacts.signature,this.signature=signature,this.version=`${graph2.versions.producer}.${graph2.versions.minConsumer}`;let weightMap=io_exports.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new GraphExecutor(OperationMapper.Instance.transformGraph(graph2,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(weightMap),this.executor.resourceManager=this.resourceManager,artifacts.modelInitializer!=null&&artifacts.modelInitializer.node!=null){let initializer=OperationMapper.Instance.transformGraph(artifacts.modelInitializer);this.initializer=new GraphExecutor(initializer),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(handlerOrURL,config3){if(typeof handlerOrURL=="string"){let handlers=io_exports.getSaveHandlers(handlerOrURL);if(handlers.length===0)throw new Error(`Cannot find any save handlers for URL '${handlerOrURL}'`);if(handlers.length>1)throw new Error(`Found more than one (${handlers.length}) save handlers for URL '${handlerOrURL}'`);handlerOrURL=handlers[0]}if(handlerOrURL.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return handlerOrURL.save(this.artifacts)}predict(inputs,config3){return this.execute(inputs,this.outputNodes)}normalizeInputs(inputs){if(!(inputs instanceof Tensor)&&!Array.isArray(inputs))return inputs;if(inputs=Array.isArray(inputs)?inputs:[inputs],inputs.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${inputs.length} input tensors.`);return this.inputNodes.reduce((map,inputName,i)=>(map[inputName]=inputs[i],map),{})}normalizeOutputs(outputs){return outputs=outputs||this.outputNodes,Array.isArray(outputs)?outputs:[outputs]}execute(inputs,outputs){inputs=this.normalizeInputs(inputs),outputs=this.normalizeOutputs(outputs);let result=this.executor.execute(inputs,outputs);return result.length>1?result:result[0]}async executeAsync(inputs,outputs){inputs=this.normalizeInputs(inputs),outputs=this.normalizeOutputs(outputs);let result=await this.executor.executeAsync(inputs,outputs);return result.length>1?result:result[0]}convertTensorMapToTensorsMap(map){return Object.keys(map).reduce((newMap,key)=>(newMap[key]=[map[key]],newMap),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function loadGraphModel(modelUrl,options={}){if(modelUrl==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");options==null&&(options={}),options.fromTFHub&&modelUrl.load==null&&(modelUrl.endsWith("/")||(modelUrl=modelUrl+"/"),modelUrl=`${modelUrl}${DEFAULT_MODEL_NAME}${TFHUB_SEARCH_PARAM}`);let model2=new GraphModel(modelUrl,options);return await model2.load(),model2}var version11="2.8.3",dist_exports={};__export2(dist_exports,{CSVDataset:()=>CSVDataset,Dataset:()=>Dataset,FileDataSource:()=>FileDataSource,TextLineDataset:()=>TextLineDataset,URLDataSource:()=>URLDataSource,array:()=>array,csv:()=>csv,func:()=>func,generator:()=>generator,microphone:()=>microphone,version_data:()=>version12,webcam:()=>webcam,zip:()=>zip});var seedrandom4=__toModule2(require_seedrandom6()),seedrandom3=__toModule2(require_seedrandom6());function deepMap(input2,mapFn){return deepMapInternal(input2,mapFn)}function deepMapInternal(input2,mapFn,seen=new Map,containedIn=new Set){if(input2==null)return null;if(containedIn.has(input2))throw new Error("Circular references are not supported.");if(seen.has(input2))return seen.get(input2);let result=mapFn(input2);if(result.recurse&&result.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(result.recurse)if(isIterable2(input2)){let mappedIterable=Array.isArray(input2)?[]:{};containedIn.add(input2);for(let k in input2){let child=input2[k],childResult=deepMapInternal(child,mapFn,seen,containedIn);mappedIterable[k]=childResult}return containedIn.delete(input2),mappedIterable}else throw new Error(`Can't recurse into non-iterable type: ${input2}`);else return seen.set(input2,result.value),result.value}function deepZip(inputs,zipFn=zipToList){return deepZipInternal(inputs,zipFn)}function deepZipInternal(inputs,zipFn,containedIn=new Set){let input2=inputs[0];if(containedIn.has(input2))throw new Error("Circular references are not supported.");let result=zipFn(inputs);if(result.recurse&&result.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(result.recurse)if(isIterable2(input2)){let mappedIterable=Array.isArray(input2)?[]:{};containedIn.add(input2);for(let k in input2){let children=inputs.map(x=>x[k]),childResult=deepZipInternal(children,zipFn,containedIn);mappedIterable[k]=childResult}return containedIn.delete(input2),mappedIterable}else throw new Error(`Can't recurse into non-iterable type: ${input2}`);else return result.value}function zipToList(x){return x===null?null:isIterable2(x[0])?{value:null,recurse:!0}:{value:x,recurse:!1}}async function deepMapAndAwaitAll(input2,mapFn){let seen=new Map;deepMapInternal(input2,mapFn,seen);for(let key of Array.from(seen.keys())){let value=seen.get(key);if(util_exports.isPromise(value)){let mappedValue=await value;seen.set(key,mappedValue)}}return deepMapInternal(input2,mapFn,seen)}function isIterable2(obj){return obj!=null&&!ArrayBuffer.isView(obj)&&(Array.isArray(obj)||typeof obj=="object"&&!(obj instanceof Tensor))}function canTensorify(obj){return obj==null||isPrimitive(obj)||Array.isArray(obj)||typeof obj=="object"&&obj instanceof Tensor||util_exports.isTypedArray(obj)}function isPrimitive(value){return value===null||typeof value!="object"&&typeof value!="function"}function deepClone(container){return deepMap(container,cloneIfTensor)}function cloneIfTensor(item){return item instanceof Tensor?{value:item.clone(),recurse:!1}:isIterable2(item)?{value:null,recurse:!0}:{value:item,recurse:!1}}var RingBuffer=class{constructor(capacity){if(this.capacity=capacity,this.begin=0,this.end=0,capacity==null)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(capacity<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(capacity),this.doubledCapacity=2*capacity}wrap(index){for(;index<0;)index+=this.doubledCapacity;return index%this.doubledCapacity}get(index){if(index<0)throw new RangeError("Can't get item at a negative index.");return this.data[index%this.capacity]}set(index,value){if(index<0)throw new RangeError("Can't set item at a negative index.");this.data[index%this.capacity]=value}length(){let length=this.end-this.begin;return length<0&&(length=this.doubledCapacity+length),length}isFull(){return this.length()===this.capacity}isEmpty(){return this.length()===0}push(value){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,value),this.end=this.wrap(this.end+1)}pushAll(values){for(let value of values)this.push(value)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);let result=this.get(this.end);return this.set(this.end,void 0),result}unshift(value){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,value)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let result=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),result}shuffleExcise(relativeIndex){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let index=this.wrap(this.begin+relativeIndex),result=this.get(index);return this.set(index,this.pop()),result}},GrowingRingBuffer=class extends RingBuffer{constructor(){super(GrowingRingBuffer.INITIAL_CAPACITY)}isFull(){return!1}push(value){super.isFull()&&this.expand(),super.push(value)}unshift(value){super.isFull()&&this.expand(),super.unshift(value)}expand(){let newCapacity=this.capacity*2,newData=new Array(newCapacity),len=this.length();for(let i=0;ix===!0)}rowMajorBatch(batchSize,smallLastBatch=!0){return new RowMajorBatchIterator(this,batchSize,smallLastBatch)}columnMajorBatch(batchSize,smallLastBatch=!0,zipFn=zipToList){return this.rowMajorBatch(batchSize,smallLastBatch).map(x=>deepZip(x,zipFn))}concatenate(iterator,baseErrorHandler){return new ChainedIterator(iteratorFromItems([this,iterator]),baseErrorHandler)}take(count2){return count2<0||count2==null?this:new TakeIterator(this,count2)}skip(count2){return count2<0||count2==null?this:new SkipIterator(this,count2)}prefetch(bufferSize){return new PrefetchIterator(this,bufferSize)}shuffle(windowSize,seed){return new ShuffleIterator(this,windowSize,seed)}serial(){return new SerialIterator(this)}},ArrayIterator=class extends LazyIterator{constructor(items){super();this.items=items,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let item=this.items[this.trav];return this.trav++,{value:deepClone(item),done:!1}}},FunctionCallIterator=class extends LazyIterator{constructor(nextFn){super();this.nextFn=nextFn}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: ${e.message}`,e}}},SerialIterator=class extends LazyIterator{constructor(upstream){super();this.upstream=upstream,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},SkipIterator=class extends LazyIterator{constructor(upstream,maxCount){super();this.upstream=upstream,this.maxCount=maxCount,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++ Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},RowMajorBatchIterator=class extends LazyIterator{constructor(upstream,batchSize,enableSmallLastBatch=!0){super();this.upstream=upstream,this.batchSize=batchSize,this.enableSmallLastBatch=enableSmallLastBatch,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let batch=[];for(;batch.length0?{value:batch,done:!1}:{value:null,done:!0};batch.push(item.value)}return{value:batch,done:!1}}},FilterIterator=class extends LazyIterator{constructor(upstream,predicate){super();this.upstream=upstream,this.predicate=predicate,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let item=await this.upstream.next();if(item.done||this.predicate(item.value))return item;dispose(item.value)}}},MapIterator=class extends LazyIterator{constructor(upstream,transform){super();this.upstream=upstream,this.transform=transform}summary(){return`${this.upstream.summary()} -> Map`}async next(){let item=await this.upstream.next();if(item.done)return{value:null,done:!0};let inputTensors=tensor_util_exports.getTensorsInContainer(item.value),mapped=this.transform(item.value),outputTensors=tensor_util_exports.getTensorsInContainer(mapped);for(let t of inputTensors)tensor_util_exports.isTensorInList(t,outputTensors)||t.dispose();return{value:mapped,done:!1}}},ErrorHandlingLazyIterator=class extends LazyIterator{constructor(upstream,handler){super();this.upstream=upstream,this.handler=handler,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},AsyncMapIterator=class extends LazyIterator{constructor(upstream,transform){super();this.upstream=upstream,this.transform=transform}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let item=await this.upstream.next();if(item.done)return{value:null,done:!0};let inputTensors=tensor_util_exports.getTensorsInContainer(item.value),mapped=await this.transform(item.value),outputTensors=tensor_util_exports.getTensorsInContainer(mapped);for(let t of inputTensors)tensor_util_exports.isTensorInList(t,outputTensors)||t.dispose();return{value:mapped,done:!1}}},OneToManyIterator=class extends LazyIterator{constructor(){super();this.outputQueue=new GrowingRingBuffer,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},FlatmapIterator=class extends OneToManyIterator{constructor(upstream,transform){super();this.upstream=upstream,this.transform=transform}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let item=await this.upstream.next();if(item.done)return!1;let inputTensors=tensor_util_exports.getTensorsInContainer(item.value),mappedArray=this.transform(item.value),outputTensors=tensor_util_exports.getTensorsInContainer(mappedArray);this.outputQueue.pushAll(mappedArray);for(let t of inputTensors)tensor_util_exports.isTensorInList(t,outputTensors)||t.dispose();return!0}},ChainedIterator=class extends LazyIterator{constructor(iterators,baseErrorHandler){super();this.baseErrorHandler=baseErrorHandler,this.lastRead=null,this.iterator=null,this.moreIterators=iterators}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(lastRead){if(await lastRead,this.iterator==null){let iteratorResult=await this.moreIterators.next();if(iteratorResult.done)return{value:null,done:!0};this.iterator=iteratorResult.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let itemResult=await this.iterator.next();return itemResult.done?(this.iterator=null,this.readFromChain(lastRead)):itemResult}},ZipMismatchMode;(function(ZipMismatchMode2){ZipMismatchMode2[ZipMismatchMode2.FAIL=0]="FAIL",ZipMismatchMode2[ZipMismatchMode2.SHORTEST=1]="SHORTEST",ZipMismatchMode2[ZipMismatchMode2.LONGEST=2]="LONGEST"})(ZipMismatchMode||(ZipMismatchMode={}));var ZipIterator=class extends LazyIterator{constructor(iterators,mismatchMode=ZipMismatchMode.FAIL){super();this.iterators=iterators,this.mismatchMode=mismatchMode,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(afterState){await afterState;let numIterators=0,iteratorsDone=0;function getNext(container){return container instanceof LazyIterator?{value:container.next().then(x=>(numIterators++,x.done&&iteratorsDone++,x.value)),recurse:!1}:{value:null,recurse:!0}}let mapped=await deepMapAndAwaitAll(this.iterators,getNext);if(numIterators===iteratorsDone)return{value:null,done:!0};if(iteratorsDone>0)switch(this.mismatchMode){case ZipMismatchMode.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case ZipMismatchMode.SHORTEST:return{value:null,done:!0};case ZipMismatchMode.LONGEST:default:}return this.count++,{value:mapped,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},PrefetchIterator=class extends LazyIterator{constructor(upstream,bufferSize){super();this.upstream=upstream,this.bufferSize=bufferSize,this.buffer=new RingBuffer(bufferSize)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let v=this.upstream.next();this.buffer.push(v)}}next(){return this.refill(),this.buffer.shift()}},ShuffleIterator=class extends PrefetchIterator{constructor(upstream,windowSize,seed){super(upstream,windowSize);this.upstream=upstream,this.windowSize=windowSize,this.upstreamExhausted=!1,this.random=seedrandom3.alea(seed||util_exports.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(max6){return Math.floor(this.random()*max6)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let chosenIndex=this.chooseIndex(),result=await this.buffer.shuffleExcise(chosenIndex);if(result.done)this.upstreamExhausted=!0;else return this.refill(),result}return{value:null,done:!0}}},Dataset=class{constructor(){this.size=null}batch(batchSize,smallLastBatch=!0){let base2=this;util_exports.assert(batchSize>0,()=>`batchSize needs to be positive, but it is + ${totalLength}, and tensor's shape is: ${tensor2.shape}`);let elementPerRow=totalLength===0?0:tensor2.size/totalLength,tensors=tidy(()=>{let tensors2=[];tensor2=reshape(tensor2,[1,totalLength,elementPerRow]);for(let i=0;i{switch(node.op){case"If":case"StatelessIf":{let thenFunc=getParamValue("thenBranch",node,tensorMap,context),elseFunc=getParamValue("elseBranch",node,tensorMap,context),cond=getParamValue("cond",node,tensorMap,context),args=getParamValue("args",node,tensorMap,context);return(await cond.data())[0]?context.functionMap[thenFunc].executeFunctionAsync(args,context.tensorArrayMap,context.tensorListMap):context.functionMap[elseFunc].executeFunctionAsync(args,context.tensorArrayMap,context.tensorListMap)}case"While":case"StatelessWhile":{let bodyFunc=getParamValue("body",node,tensorMap,context),condFunc=getParamValue("cond",node,tensorMap,context),args=getParamValue("args",node,tensorMap,context),condResult=await context.functionMap[condFunc].executeFunctionAsync(args,context.tensorArrayMap,context.tensorListMap),argIds=args.map(tensor2=>tensor2.id),condValue=await condResult[0].data();condResult.forEach(tensor2=>{!tensor2.kept&&argIds.indexOf(tensor2.id)===-1&&tensor2.dispose()});let result=args;for(;condValue[0];){let origResult=result;result=await context.functionMap[bodyFunc].executeFunctionAsync(result,context.tensorArrayMap,context.tensorListMap);let resultIds=result.map(tensor2=>tensor2.id);origResult.forEach(tensor2=>{!tensor2.kept&&argIds.indexOf(tensor2.id)===-1&&resultIds.indexOf(tensor2.id)===-1&&tensor2.dispose()});let condResult2=await context.functionMap[condFunc].executeFunctionAsync(result,context.tensorArrayMap,context.tensorListMap);condValue=await condResult2[0].data(),condResult2.forEach(tensor2=>{!tensor2.kept&&argIds.indexOf(tensor2.id)===-1&&resultIds.indexOf(tensor2.id)===-1&&tensor2.dispose()})}return result}case"LoopCond":{let pred=getParamValue("pred",node,tensorMap,context);return[cloneTensor(pred)]}case"Switch":{let pred=getParamValue("pred",node,tensorMap,context),data2=getParamValue("data",node,tensorMap,context);return data2.kept||(data2=cloneTensor(data2)),(await pred.data())[0]?[void 0,data2]:[data2,void 0]}case"Merge":{let inputName=node.inputNames.find(name=>getTensor(name,tensorMap,context)!==void 0);if(inputName){let data2=getTensor(inputName,tensorMap,context);return[cloneTensor(data2)]}return}case"Enter":{let frameId=getParamValue("frameName",node,tensorMap,context),data2=getParamValue("tensor",node,tensorMap,context);return context.enterFrame(frameId),[cloneTensor(data2)]}case"Exit":{let data2=getParamValue("tensor",node,tensorMap,context);return context.exitFrame(),[cloneTensor(data2)]}case"NextIteration":{let data2=getParamValue("tensor",node,tensorMap,context);return context.nextIteration(),[cloneTensor(data2)]}case"TensorArrayV3":{let size=getParamValue("size",node,tensorMap,context),dtype=getParamValue("dtype",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),dynamicSize=getParamValue("dynamicSize",node,tensorMap,context),clearAfterRead=getParamValue("clearAfterRead",node,tensorMap,context),identicalElementShapes=getParamValue("identicalElementShapes",node,tensorMap,context),name=getParamValue("name",node,tensorMap,context),tensorArray=new TensorArray(name,dtype,size,elementShape,identicalElementShapes,dynamicSize,clearAfterRead);return context.addTensorArray(tensorArray),[tensorArray.idTensor,scalar(1)]}case"TensorArrayWriteV3":{let id=getParamValue("tensorArrayId",node,tensorMap,context),index=getParamValue("index",node,tensorMap,context),writeTensor=getParamValue("tensor",node,tensorMap,context),writeTensorArray=context.getTensorArray(id.id);return writeTensorArray.write(index,writeTensor),[writeTensorArray.idTensor]}case"TensorArrayReadV3":{let readId=getParamValue("tensorArrayId",node,tensorMap,context),readIndex=getParamValue("index",node,tensorMap,context);return[context.getTensorArray(readId.id).read(readIndex)]}case"TensorArrayGatherV3":{let gatherId=getParamValue("tensorArrayId",node,tensorMap,context),gatherIndices=getParamValue("indices",node,tensorMap,context),gatherDtype=getParamValue("dtype",node,tensorMap,context);return[context.getTensorArray(gatherId.id).gather(gatherIndices,gatherDtype)]}case"TensorArrayScatterV3":{let scatterId=getParamValue("tensorArrayId",node,tensorMap,context),scatterIndices=getParamValue("indices",node,tensorMap,context),scatterTensor=getParamValue("tensor",node,tensorMap,context),scatterTensorArray=context.getTensorArray(scatterId.id);return scatterTensorArray.scatter(scatterIndices,scatterTensor),[scatterTensorArray.idTensor]}case"TensorArrayConcatV3":{let concatId=getParamValue("tensorArrayId",node,tensorMap,context),concatTensorArray=context.getTensorArray(concatId.id),concatDtype=getParamValue("dtype",node,tensorMap,context);return[concatTensorArray.concat(concatDtype)]}case"TensorArraySplitV3":{let splitId=getParamValue("tensorArrayId",node,tensorMap,context),splitTensor=getParamValue("tensor",node,tensorMap,context),lengths=getParamValue("lengths",node,tensorMap,context),splitTensorArray=context.getTensorArray(splitId.id);return splitTensorArray.split(lengths,splitTensor),[splitTensorArray.idTensor]}case"TensorArraySizeV3":{let sizeId=getParamValue("tensorArrayId",node,tensorMap,context),sizeTensorArray=context.getTensorArray(sizeId.id);return[scalar(sizeTensorArray.size(),"int32")]}case"TensorArrayCloseV3":{let closeId=getParamValue("tensorArrayId",node,tensorMap,context),closeTensorArray=context.getTensorArray(closeId.id);return closeTensorArray.clearAndClose(),[closeTensorArray.idTensor]}case"TensorListSetItem":{let idTensor=getParamValue("tensorListId",node,tensorMap,context),index=getParamValue("index",node,tensorMap,context),writeTensor=getParamValue("tensor",node,tensorMap,context),tensorList=context.getTensorList(idTensor.id);return tensorList.setItem(index,writeTensor),[tensorList.idTensor]}case"TensorListGetItem":{let idTensor=getParamValue("tensorListId",node,tensorMap,context),readIndex=getParamValue("index",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),elementDType=getParamValue("elementDType",node,tensorMap,context);return[context.getTensorList(idTensor.id).getItem(readIndex,elementShape,elementDType)]}case"TensorListScatterV2":case"TensorListScatter":{let scatterIndices=getParamValue("indices",node,tensorMap,context),scatterTensor=getParamValue("tensor",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),numElements=getParamValue("numElements",node,tensorMap,context),tensorList=scatter(scatterTensor,scatterIndices,elementShape,numElements);return context.addTensorList(tensorList),[tensorList.idTensor]}case"TensorListReserve":case"EmptyTensorList":{let elementShape=getParamValue("elementShape",node,tensorMap,context),elementDtype=getParamValue("elementDType",node,tensorMap,context),numElementsParam;node.op==="TensorListReserve"?numElementsParam="numElements":numElementsParam="maxNumElements";let numElements=getParamValue(numElementsParam,node,tensorMap,context),tensorList=reserve(elementShape,elementDtype,numElements);return context.addTensorList(tensorList),[tensorList.idTensor]}case"TensorListGather":{let gatherId=getParamValue("tensorListId",node,tensorMap,context),gatherIndices=getParamValue("indices",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),elementDtype=getParamValue("elementDType",node,tensorMap,context);return[context.getTensorList(gatherId.id).gather(gatherIndices,elementDtype,elementShape)]}case"TensorListStack":{let idTensor=getParamValue("tensorListId",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),elementDtype=getParamValue("elementDType",node,tensorMap,context),numElements=getParamValue("numElements",node,tensorMap,context);return[context.getTensorList(idTensor.id).stack(elementShape,elementDtype,numElements)]}case"TensorListFromTensor":{let tensor2=getParamValue("tensor",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),elementDtype=getParamValue("elementDType",node,tensorMap,context),tensorList=fromTensor(tensor2,elementShape,elementDtype);return context.addTensorList(tensorList),[tensorList.idTensor]}case"TensorListConcat":{let concatId=getParamValue("tensorListId",node,tensorMap,context),tensorList=context.getTensorList(concatId.id),concatDtype=getParamValue("dtype",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context);return[tensorList.concat(concatDtype,elementShape)]}case"TensorListPushBack":{let idTensor=getParamValue("tensorListId",node,tensorMap,context),writeTensor=getParamValue("tensor",node,tensorMap,context),tensorList=context.getTensorList(idTensor.id);return tensorList.pushBack(writeTensor),[tensorList.idTensor]}case"TensorListPopBack":{let idTensor=getParamValue("tensorListId",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),elementDType=getParamValue("elementDType",node,tensorMap,context);return[context.getTensorList(idTensor.id).popBack(elementShape,elementDType)]}case"TensorListSplit":{let splitTensor=getParamValue("tensor",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),lengths=getParamValue("lengths",node,tensorMap,context),tensorList=split2(splitTensor,lengths,elementShape);return context.addTensorList(tensorList),[tensorList.idTensor]}default:throw TypeError(`Node type ${node.op} is not implemented`)}};function fusedConvAndDepthWiseParams(node,tensorMap,context){let[extraOp,activationFunc]=getParamValue("fusedOps",node,tensorMap,context),isBiasAdd=extraOp==="biasadd",isPrelu=activationFunc==="prelu",isBatchNorm=extraOp==="fusedbatchnorm",numArgs=getParamValue("numArgs",node,tensorMap,context);if(isBiasAdd){if(isPrelu&&numArgs!==2)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!isPrelu&&numArgs!==1)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(isBatchNorm)throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported.");let stride=getParamValue("strides",node,tensorMap,context),pad3=getPadding(node,tensorMap,context),dataFormat=getParamValue("dataFormat",node,tensorMap,context).toUpperCase(),dilations=getParamValue("dilations",node,tensorMap,context),[biasArg,preluArg]=getParamValue("args",node,tensorMap,context),leakyreluAlpha=getParamValue("leakyreluAlpha",node,tensorMap,context);return{stride,pad:pad3,dataFormat,dilations,biasArg,preluArg,activationFunc,leakyreluAlpha}}var executeOp4=(node,tensorMap,context)=>{switch(node.op){case"Conv1D":{let stride=getParamValue("stride",node,tensorMap,context),pad3=getParamValue("pad",node,tensorMap,context),dataFormat=getParamValue("dataFormat",node,tensorMap,context).toUpperCase(),dilation=getParamValue("dilation",node,tensorMap,context);return[conv1d(getParamValue("x",node,tensorMap,context),getParamValue("filter",node,tensorMap,context),stride,pad3,dataFormat,dilation)]}case"Conv2D":{let stride=getParamValue("strides",node,tensorMap,context),pad3=getPadding(node,tensorMap,context),dataFormat=getParamValue("dataFormat",node,tensorMap,context).toUpperCase(),dilations=getParamValue("dilations",node,tensorMap,context);return[conv2d(getParamValue("x",node,tensorMap,context),getParamValue("filter",node,tensorMap,context),[stride[1],stride[2]],pad3,dataFormat,[dilations[1],dilations[2]])]}case"_FusedConv2D":{let{stride,pad:pad3,dataFormat,dilations,biasArg,preluArg,activationFunc,leakyreluAlpha}=fusedConvAndDepthWiseParams(node,tensorMap,context);return[fused_ops_exports.conv2d({x:getParamValue("x",node,tensorMap,context),filter:getParamValue("filter",node,tensorMap,context),strides:[stride[1],stride[2]],pad:pad3,dataFormat,dilations:[dilations[1],dilations[2]],bias:biasArg,activation:activationFunc,preluActivationWeights:preluArg,leakyreluAlpha})]}case"FusedDepthwiseConv2dNative":{let{stride,pad:pad3,dataFormat,dilations,biasArg,preluArg,activationFunc,leakyreluAlpha}=fusedConvAndDepthWiseParams(node,tensorMap,context);return[fused_ops_exports.depthwiseConv2d({x:getParamValue("x",node,tensorMap,context),filter:getParamValue("filter",node,tensorMap,context),strides:[stride[1],stride[2]],pad:pad3,dataFormat,dilations:[dilations[1],dilations[2]],bias:biasArg,activation:activationFunc,preluActivationWeights:preluArg,leakyreluAlpha})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{let shape=getParamValue("outputShape",node,tensorMap,context),stride=getParamValue("strides",node,tensorMap,context),pad3=getPadding(node,tensorMap,context);return[conv2dTranspose(getParamValue("x",node,tensorMap,context),getParamValue("filter",node,tensorMap,context),shape,[stride[1],stride[2]],pad3)]}case"DepthwiseConv2dNative":case"DepthwiseConv2d":{let stride=getParamValue("strides",node,tensorMap,context),pad3=getPadding(node,tensorMap,context),dilations=getParamValue("dilations",node,tensorMap,context),dataFormat=getParamValue("dataFormat",node,tensorMap,context).toUpperCase();return[depthwiseConv2d(getParamValue("input",node,tensorMap,context),getParamValue("filter",node,tensorMap,context),[stride[1],stride[2]],pad3,dataFormat,[dilations[1],dilations[2]])]}case"Conv3D":{let stride=getParamValue("strides",node,tensorMap,context),pad3=getParamValue("pad",node,tensorMap,context),dataFormat=getParamValue("dataFormat",node,tensorMap,context).toUpperCase(),dilations=getParamValue("dilations",node,tensorMap,context);return[conv3d(getParamValue("x",node,tensorMap,context),getParamValue("filter",node,tensorMap,context),[stride[1],stride[2],stride[3]],pad3,dataFormat,[dilations[1],dilations[2],dilations[3]])]}case"AvgPool":{let stride=getParamValue("strides",node,tensorMap,context),pad3=getParamValue("pad",node,tensorMap,context),kernelSize=getParamValue("kernelSize",node,tensorMap,context);return[avgPool(getParamValue("x",node,tensorMap,context),[kernelSize[1],kernelSize[2]],[stride[1],stride[2]],pad3)]}case"MaxPool":{let stride=getParamValue("strides",node,tensorMap,context),pad3=getParamValue("pad",node,tensorMap,context),kernelSize=getParamValue("kernelSize",node,tensorMap,context);return[maxPool(getParamValue("x",node,tensorMap,context),[kernelSize[1],kernelSize[2]],[stride[1],stride[2]],pad3)]}case"MaxPoolWithArgmax":{let stride=getParamValue("strides",node,tensorMap,context),pad3=getParamValue("pad",node,tensorMap,context),kernelSize=getParamValue("kernelSize",node,tensorMap,context),includeBatchInIndex=getParamValue("includeBatchInIndex",node,tensorMap,context),{result,indexes}=maxPoolWithArgmax(getParamValue("x",node,tensorMap,context),[kernelSize[1],kernelSize[2]],[stride[1],stride[2]],pad3,includeBatchInIndex);return[result,indexes]}case"AvgPool3D":{let stride=getParamValue("strides",node,tensorMap,context),pad3=getParamValue("pad",node,tensorMap,context),kernelSize=getParamValue("kernelSize",node,tensorMap,context);return[avgPool3d(getParamValue("x",node,tensorMap,context),[kernelSize[1],kernelSize[2],kernelSize[3]],[stride[1],stride[2],stride[3]],pad3)]}case"MaxPool3D":{let stride=getParamValue("strides",node,tensorMap,context),pad3=getParamValue("pad",node,tensorMap,context),kernelSize=getParamValue("kernelSize",node,tensorMap,context);return[maxPool3d(getParamValue("x",node,tensorMap,context),[kernelSize[1],kernelSize[2],kernelSize[3]],[stride[1],stride[2],stride[3]],pad3)]}case"Dilation2D":{let strides=getParamValue("strides",node,tensorMap,context),pad3=getParamValue("pad",node,tensorMap,context),dilations=getParamValue("dilations",node,tensorMap,context),strideHeight=strides[1],strideWidth=strides[2],dilationHeight=dilations[1],dilationWidth=dilations[2];return[dilation2d(getParamValue("x",node,tensorMap,context),getParamValue("filter",node,tensorMap,context),[strideHeight,strideWidth],pad3,[dilationHeight,dilationWidth],"NHWC")]}default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp5=(node,tensorMap,context)=>{switch(node.op){case"Fill":{let shape=getParamValue("shape",node,tensorMap,context),dtype=getParamValue("dtype",node,tensorMap,context),value=getParamValue("value",node,tensorMap,context);return[fill(shape,value,dtype)]}case"LinSpace":{let start=getParamValue("start",node,tensorMap,context),stop=getParamValue("stop",node,tensorMap,context),num=getParamValue("num",node,tensorMap,context);return[linspace(start,stop,num)]}case"Multinomial":{let logits=getParamValue("logits",node,tensorMap,context),numSamples=getParamValue("numSamples",node,tensorMap,context),seed=getParamValue("seed",node,tensorMap,context);return[multinomial(logits,numSamples,seed)]}case"OneHot":{let indices=getParamValue("indices",node,tensorMap,context),depth=getParamValue("depth",node,tensorMap,context),onValue=getParamValue("onValue",node,tensorMap,context),offValue=getParamValue("offValue",node,tensorMap,context);return[oneHot(indices,depth,onValue,offValue)]}case"Ones":return[ones2(getParamValue("shape",node,tensorMap,context),getParamValue("dtype",node,tensorMap,context))];case"OnesLike":return[onesLike(getParamValue("x",node,tensorMap,context))];case"RandomUniform":return[randomUniform(getParamValue("shape",node,tensorMap,context),getParamValue("minval",node,tensorMap,context),getParamValue("maxval",node,tensorMap,context),getParamValue("dtype",node,tensorMap,context))];case"Range":{let start=getParamValue("start",node,tensorMap,context),stop=getParamValue("stop",node,tensorMap,context),step4=getParamValue("step",node,tensorMap,context);return[range(start,stop,step4,getParamValue("dtype",node,tensorMap,context))]}case"TruncatedNormal":{let shape=getParamValue("shape",node,tensorMap,context),mean4=getParamValue("mean",node,tensorMap,context),stdDev=getParamValue("stdDev",node,tensorMap,context),seed=getParamValue("seed",node,tensorMap,context);return[truncatedNormal(shape,mean4,stdDev,getParamValue("dtype",node,tensorMap,context),seed)]}case"Zeros":return[zeros(getParamValue("shape",node,tensorMap,context),getParamValue("dtype",node,tensorMap,context))];case"ZerosLike":return[zerosLike(getParamValue("x",node,tensorMap,context))];default:throw TypeError(`Node type ${node.op} is not implemented`)}};function nmsParams(node,tensorMap,context){let boxes=getParamValue("boxes",node,tensorMap,context),scores=getParamValue("scores",node,tensorMap,context),maxOutputSize=getParamValue("maxOutputSize",node,tensorMap,context),iouThreshold=getParamValue("iouThreshold",node,tensorMap,context),scoreThreshold=getParamValue("scoreThreshold",node,tensorMap,context),softNmsSigma=getParamValue("softNmsSigma",node,tensorMap,context);return{boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma}}var executeOp6=async(node,tensorMap,context)=>{switch(node.op){case"NonMaxSuppressionV5":{let{boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma}=nmsParams(node,tensorMap,context),result=await image.nonMaxSuppressionWithScoreAsync(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma);return[result.selectedIndices,result.selectedScores]}case"NonMaxSuppressionV4":{let{boxes,scores,maxOutputSize,iouThreshold,scoreThreshold}=nmsParams(node,tensorMap,context),padToMaxOutputSize=getParamValue("padToMaxOutputSize",node,tensorMap,context),result=await image.nonMaxSuppressionPaddedAsync(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize);return[result.selectedIndices,result.validOutputs]}case"NonMaxSuppressionV3":case"NonMaxSuppressionV2":{let{boxes,scores,maxOutputSize,iouThreshold,scoreThreshold}=nmsParams(node,tensorMap,context);return[await image.nonMaxSuppressionAsync(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold)]}case"Where":{let condition=cast(getParamValue("condition",node,tensorMap,context),"bool"),result=[await whereAsync(condition)];return condition.dispose(),result}case"ListDiff":return setdiff1dAsync(getParamValue("x",node,tensorMap,context),getParamValue("y",node,tensorMap,context));default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp7=(node,tensorMap,context)=>{switch(node.op){case"TopKV2":{let x=getParamValue("x",node,tensorMap,context),k=getParamValue("k",node,tensorMap,context),sorted=getParamValue("sorted",node,tensorMap,context),result=topk(x,k,sorted);return[result.values,result.indices]}case"Unique":{let x=getParamValue("x",node,tensorMap,context),result=unique(x);return[result.values,result.indices]}case"UniqueV2":{let x=getParamValue("x",node,tensorMap,context),axis=getParamValue("axis",node,tensorMap,context),result=unique(x,axis);return[result.values,result.indices]}default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp8=(node,tensorMap,context)=>{switch(node.op){case"Const":return tensorMap[node.name];case"PlaceholderWithDefault":let def=getParamValue("default",node,tensorMap,context);return[getTensor(node.name,tensorMap,context)||def];case"Placeholder":return[getTensor(node.name,tensorMap,context)];case"Identity":case"StopGradient":case"FakeQuantWithMinMaxVars":{let data22=getParamValue("x",node,tensorMap,context);return[cloneTensor(data22)]}case"IdentityN":return getParamValue("x",node,tensorMap,context).map(t=>cloneTensor(t));case"Snapshot":let snapshot=getParamValue("x",node,tensorMap,context);return[cloneTensor(snapshot)];case"Shape":return[tensor1d(getParamValue("x",node,tensorMap,context).shape,"int32")];case"ShapeN":return getParamValue("x",node,tensorMap,context).map(t=>tensor1d(t.shape));case"Size":return[scalar(getParamValue("x",node,tensorMap,context).size,"int32")];case"Rank":return[scalar(getParamValue("x",node,tensorMap,context).rank,"int32")];case"NoOp":return[scalar(1)];case"Print":let input2=getParamValue("x",node,tensorMap,context),data2=getParamValue("data",node,tensorMap,context),message=getParamValue("message",node,tensorMap,context),summarize=getParamValue("summarize",node,tensorMap,context);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."),console.log(message);for(let i=0;ivalue.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}async import(keys,values){this.checkKeyAndValueTensor(keys,values);let $keys=await keys.data();return this.tensorMap.forEach(value=>value.dispose()),this.tensorMap.clear(),tidy(()=>{let $values=unstack(values),keysLength=$keys.length,valuesLength=$values.length;util_exports.assert(keysLength===valuesLength,()=>`The number of elements doesn't match, keys has ${keysLength} elements, the values has ${valuesLength} elements.`);for(let i=0;i{let result=[];for(let i=0;i<$keys.length;i++){let key=$keys[i],value=this.findWithDefault(key,defaultValue);result.push(value)}return stack(result)})}findWithDefault(key,defaultValue){let result=this.tensorMap.get(key);return result!=null?result:defaultValue}checkKeyAndValueTensor(key,value){if(key.dtype!==this.keyDType)throw new Error(`Expect key dtype ${this.keyDType}, but got ${key.dtype}`);if(value.dtype!==this.valueDType)throw new Error(`Expect value dtype ${this.valueDType}, but got ${value.dtype}`)}},executeOp9=async(node,tensorMap,context,resourceManager)=>{switch(node.op){case"HashTable":case"HashTableV2":{let keyDType=getParamValue("keyDType",node,tensorMap,context),valueDType=getParamValue("valueDType",node,tensorMap,context),hashTable2=new HashTable(keyDType,valueDType);return resourceManager.addHashTable(node.name,hashTable2),[hashTable2.handle]}case"LookupTableImport":case"LookupTableImportV2":{let handle=getParamValue("tableHandle",node,tensorMap,context,resourceManager),keys=getParamValue("keys",node,tensorMap,context),values=getParamValue("values",node,tensorMap,context);return[await resourceManager.getHashTableById(handle.id).import(keys,values)]}case"LookupTableFind":case"LookupTableFindV2":{let handle=getParamValue("tableHandle",node,tensorMap,context,resourceManager),keys=getParamValue("keys",node,tensorMap,context),defaultValue=getParamValue("defaultValue",node,tensorMap,context);return[await resourceManager.getHashTableById(handle.id).find(keys,defaultValue)]}default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp10=(node,tensorMap,context)=>{switch(node.op){case"ResizeBilinear":{let images=getParamValue("images",node,tensorMap,context),size=getParamValue("size",node,tensorMap,context),alignCorners=getParamValue("alignCorners",node,tensorMap,context),halfPixelCenters=getParamValue("halfPixelCenters",node,tensorMap,context);return[image.resizeBilinear(images,[size[0],size[1]],alignCorners,halfPixelCenters)]}case"ResizeNearestNeighbor":{let images=getParamValue("images",node,tensorMap,context),size=getParamValue("size",node,tensorMap,context),alignCorners=getParamValue("alignCorners",node,tensorMap,context),halfPixelCenters=getParamValue("halfPixelCenters",node,tensorMap,context);return[image.resizeNearestNeighbor(images,[size[0],size[1]],alignCorners,halfPixelCenters)]}case"CropAndResize":{let image3=getParamValue("image",node,tensorMap,context),boxes=getParamValue("boxes",node,tensorMap,context),boxInd=getParamValue("boxInd",node,tensorMap,context),cropSize=getParamValue("cropSize",node,tensorMap,context),method=getParamValue("method",node,tensorMap,context),extrapolationValue=getParamValue("extrapolationValue",node,tensorMap,context);return[image.cropAndResize(image3,boxes,boxInd,cropSize,method,extrapolationValue)]}default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp11=(node,tensorMap,context)=>{switch(node.op){case"Equal":return[equal(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"NotEqual":return[notEqual(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Greater":return[greater(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"GreaterEqual":return[greaterEqual(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Less":return[less(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"LessEqual":return[lessEqual(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"LogicalAnd":return[logicalAnd(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"LogicalNot":return[logicalNot(getParamValue("a",node,tensorMap,context))];case"LogicalOr":return[logicalOr(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Select":case"SelectV2":return[where(getParamValue("condition",node,tensorMap,context),getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp12=(node,tensorMap,context)=>{switch(node.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[matMul(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context),getParamValue("transposeA",node,tensorMap,context),getParamValue("transposeB",node,tensorMap,context))];case"Transpose":return[transpose(getParamValue("x",node,tensorMap,context),getParamValue("perm",node,tensorMap,context))];case"_FusedMatMul":let[extraOp,activationFunc]=getParamValue("fusedOps",node,tensorMap,context),isBiasAdd=extraOp==="biasadd",isPrelu=activationFunc==="prelu",numArgs=getParamValue("numArgs",node,tensorMap,context),leakyreluAlpha=getParamValue("leakyreluAlpha",node,tensorMap,context);if(isBiasAdd){if(isPrelu&&numArgs!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!isPrelu&&numArgs!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[biasArg,preluArg]=getParamValue("args",node,tensorMap,context);return[fused_ops_exports.matMul({a:getParamValue("a",node,tensorMap,context),b:getParamValue("b",node,tensorMap,context),transposeA:getParamValue("transposeA",node,tensorMap,context),transposeB:getParamValue("transposeB",node,tensorMap,context),bias:biasArg,activation:activationFunc,preluActivationWeights:preluArg,leakyreluAlpha})];default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp13=(node,tensorMap,context)=>{switch(node.op){case"FusedBatchNorm":case"FusedBatchNormV2":return[batchNorm(getParamValue("x",node,tensorMap,context),getParamValue("mean",node,tensorMap,context),getParamValue("variance",node,tensorMap,context),getParamValue("offset",node,tensorMap,context),getParamValue("scale",node,tensorMap,context),getParamValue("epsilon",node,tensorMap,context))];case"FusedBatchNormV3":return[batchNorm(getParamValue("x",node,tensorMap,context),getParamValue("mean",node,tensorMap,context),getParamValue("variance",node,tensorMap,context),getParamValue("offset",node,tensorMap,context),getParamValue("scale",node,tensorMap,context),getParamValue("epsilon",node,tensorMap,context))];case"LRN":return[localResponseNormalization(getParamValue("x",node,tensorMap,context),getParamValue("radius",node,tensorMap,context),getParamValue("bias",node,tensorMap,context),getParamValue("alpha",node,tensorMap,context),getParamValue("beta",node,tensorMap,context))];case"Softmax":return[softmax(getParamValue("x",node,tensorMap,context))];case"LogSoftmax":return[logSoftmax(getParamValue("x",node,tensorMap,context))];case"SparseToDense":return[sparseToDense(getParamValue("sparseIndices",node,tensorMap,context),getParamValue("outputShape",node,tensorMap,context),getParamValue("sparseValues",node,tensorMap,context),getParamValue("defaultValue",node,tensorMap,context))];default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp14=(node,tensorMap,context)=>{switch(node.op){case"Max":{let axis=getParamValue("axis",node,tensorMap,context),keepDims=getParamValue("keepDims",node,tensorMap,context);return[max(getParamValue("x",node,tensorMap,context),axis,keepDims)]}case"Mean":{let axis=getParamValue("axis",node,tensorMap,context),keepDims=getParamValue("keepDims",node,tensorMap,context);return[mean(getParamValue("x",node,tensorMap,context),axis,keepDims)]}case"Min":{let axis=getParamValue("axis",node,tensorMap,context),keepDims=getParamValue("keepDims",node,tensorMap,context);return[min(getParamValue("x",node,tensorMap,context),axis,keepDims)]}case"Sum":{let axis=getParamValue("axis",node,tensorMap,context),keepDims=getParamValue("keepDims",node,tensorMap,context);return[sum2(getParamValue("x",node,tensorMap,context),axis,keepDims)]}case"All":{let axis=getParamValue("axis",node,tensorMap,context),keepDims=getParamValue("keepDims",node,tensorMap,context);return[all(getParamValue("x",node,tensorMap,context),axis,keepDims)]}case"Any":{let axis=getParamValue("axis",node,tensorMap,context),keepDims=getParamValue("keepDims",node,tensorMap,context);return[any(getParamValue("x",node,tensorMap,context),axis,keepDims)]}case"ArgMax":{let axis=getParamValue("axis",node,tensorMap,context);return[argMax(getParamValue("x",node,tensorMap,context),axis)]}case"ArgMin":{let axis=getParamValue("axis",node,tensorMap,context);return[argMin(getParamValue("x",node,tensorMap,context),axis)]}case"Prod":{let axis=getParamValue("axis",node,tensorMap,context),keepDims=getParamValue("keepDims",node,tensorMap,context);return[prod(getParamValue("x",node,tensorMap,context),axis,keepDims)]}case"Cumsum":{let axis=getParamValue("axis",node,tensorMap,context),exclusive=getParamValue("exclusive",node,tensorMap,context),reverse5=getParamValue("reverse",node,tensorMap,context);return[cumsum(getParamValue("x",node,tensorMap,context),axis,exclusive,reverse5)]}case"Bincount":let x=getParamValue("x",node,tensorMap,context),weights=getParamValue("weights",node,tensorMap,context),size=getParamValue("size",node,tensorMap,context);return[bincount(x,weights,size)];case"DenseBincount":{let x2=getParamValue("x",node,tensorMap,context),weights2=getParamValue("weights",node,tensorMap,context),size2=getParamValue("size",node,tensorMap,context),binaryOutput=getParamValue("binaryOutput",node,tensorMap,context);return[denseBincount(x2,weights2,size2,binaryOutput)]}default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp15=(node,tensorMap,context)=>{switch(node.op){case"ConcatV2":case"Concat":{let n=getParamValue("n",node,tensorMap,context),axis=getParamValue("axis",node,tensorMap,context),inputs=getParamValue("tensors",node,tensorMap,context);return inputs=inputs.slice(0,n),[concat(inputs,axis)]}case"Gather":{let input2=getParamValue("x",node,tensorMap,context),indices=getParamValue("indices",node,tensorMap,context);return[gather(input2,cast(indices,"int32"),0)]}case"GatherV2":{let axis=getParamValue("axis",node,tensorMap,context),batchDims=getParamValue("batchDims",node,tensorMap,context),input2=getParamValue("x",node,tensorMap,context),indices=getParamValue("indices",node,tensorMap,context);return[gather(input2,cast(indices,"int32"),axis,batchDims)]}case"Reverse":{let dims=getParamValue("dims",node,tensorMap,context),axis=[];for(let i=0;i{let axis=getParamValue("axis",node,tensorMap,context),tensors=getParamValue("tensors",node,tensorMap,context),shape=tensors[0].shape,squeezedShape=squeeze(tensors[0]).shape,mapped=tensors.map(tensor2=>{let sameShape=util_exports.arraysEqual(tensor2.shape,shape);if(!sameShape&&!util_exports.arraysEqual(squeeze(tensor2).shape,squeezedShape))throw new Error("the input tensors shape does not match");return sameShape?tensor2:reshape(tensor2,shape)});return[stack(mapped,axis)]});case"Unpack":{let axis=getParamValue("axis",node,tensorMap,context),tensor2=getParamValue("tensor",node,tensorMap,context);return unstack(tensor2,axis)}case"Tile":{let reps=getParamValue("reps",node,tensorMap,context);return[tile(getParamValue("x",node,tensorMap,context),reps)]}case"Split":case"SplitV":{let axis=getParamValue("axis",node,tensorMap,context),numOrSizeSplits=getParamValue("numOrSizeSplits",node,tensorMap,context),tensor2=getParamValue("x",node,tensorMap,context);return split(tensor2,numOrSizeSplits,axis)}case"ScatterNd":{let indices=getParamValue("indices",node,tensorMap,context),values=getParamValue("values",node,tensorMap,context),shape=getParamValue("shape",node,tensorMap,context);return[scatterND(indices,values,shape)]}case"GatherNd":{let x=getParamValue("x",node,tensorMap,context),indices=getParamValue("indices",node,tensorMap,context);return[gatherND(x,indices)]}case"SparseToDense":{let indices=getParamValue("sparseIndices",node,tensorMap,context),shape=getParamValue("outputShape",node,tensorMap,context),sparseValues=getParamValue("sparseValues",node,tensorMap,context),defaultValue=getParamValue("defaultValue",node,tensorMap,context);return[sparseToDense(indices,sparseValues,shape,sparseValues.dtype===defaultValue.dtype?defaultValue:cast(defaultValue,sparseValues.dtype))]}default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp16=(node,tensorMap,context)=>{switch(node.op){case"FFT":return[fft(getParamValue("x",node,tensorMap,context))];case"IFFT":return[ifft(getParamValue("x",node,tensorMap,context))];case"RFFT":return[rfft(getParamValue("x",node,tensorMap,context))];case"IRFFT":return[irfft(getParamValue("x",node,tensorMap,context))];default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp17=(node,tensorMap,context)=>{switch(node.op){case"Cast":return[cast(getParamValue("x",node,tensorMap,context),getParamValue("dtype",node,tensorMap,context))];case"ExpandDims":{let axis=getParamValue("axis",node,tensorMap,context);return[expandDims(getParamValue("x",node,tensorMap,context),axis)]}case"Squeeze":{let axis=getParamValue("axis",node,tensorMap,context);return[squeeze(getParamValue("x",node,tensorMap,context),axis)]}case"Reshape":return[reshape(getParamValue("x",node,tensorMap,context),getParamValue("shape",node,tensorMap,context))];case"MirrorPad":return[mirrorPad(getParamValue("x",node,tensorMap,context),getParamValue("padding",node,tensorMap,context),getParamValue("mode",node,tensorMap,context))];case"PadV2":case"Pad":return[pad(getParamValue("x",node,tensorMap,context),getParamValue("padding",node,tensorMap,context),getParamValue("constantValue",node,tensorMap,context))];case"SpaceToBatchND":{let blockShape=getParamValue("blockShape",node,tensorMap,context),paddings=getParamValue("paddings",node,tensorMap,context);return[spaceToBatchND(getParamValue("x",node,tensorMap,context),blockShape,paddings)]}case"BatchToSpaceND":{let blockShape=getParamValue("blockShape",node,tensorMap,context),crops=getParamValue("crops",node,tensorMap,context);return[batchToSpaceND(getParamValue("x",node,tensorMap,context),blockShape,crops)]}case"DepthToSpace":{let blockSize=getParamValue("blockSize",node,tensorMap,context),dataFormat=getParamValue("dataFormat",node,tensorMap,context).toUpperCase();return[depthToSpace(getParamValue("x",node,tensorMap,context),blockSize,dataFormat)]}case"BroadcastTo":return[broadcastTo(getParamValue("x",node,tensorMap,context),getParamValue("shape",node,tensorMap,context))];default:throw TypeError(`Node type ${node.op} is not implemented`)}};function executeOp18(node,tensorMap,context,resourceManager){let value=((node2,tensorMap2,context2)=>{switch(node2.category){case"arithmetic":return tidy(()=>executeOp(node2,tensorMap2,context2));case"basic_math":return tidy(()=>executeOp2(node2,tensorMap2,context2));case"control":return executeOp3(node2,tensorMap2,context2);case"convolution":return tidy(()=>executeOp4(node2,tensorMap2,context2));case"creation":return tidy(()=>executeOp5(node2,tensorMap2,context2));case"dynamic":return executeOp6(node2,tensorMap2,context2);case"evaluation":return tidy(()=>executeOp7(node2,tensorMap2,context2));case"image":return tidy(()=>executeOp10(node2,tensorMap2,context2));case"graph":return tidy(()=>executeOp8(node2,tensorMap2,context2));case"logical":return tidy(()=>executeOp11(node2,tensorMap2,context2));case"matrices":return tidy(()=>executeOp12(node2,tensorMap2,context2));case"normalization":return tidy(()=>executeOp13(node2,tensorMap2,context2));case"reduction":return tidy(()=>executeOp14(node2,tensorMap2,context2));case"slice_join":return tidy(()=>executeOp15(node2,tensorMap2,context2));case"spectral":return tidy(()=>executeOp16(node2,tensorMap2,context2));case"transformation":return tidy(()=>executeOp17(node2,tensorMap2,context2));case"hash_table":return executeOp9(node2,tensorMap2,context2,resourceManager);case"custom":let opMapper=getRegisteredOp(node2.op);if(opMapper&&opMapper.customExecutor)return opMapper.customExecutor(new NodeValueImpl(node2,tensorMap2,context2));throw TypeError(`Custom op ${node2.op} is not registered.`);default:throw TypeError(`Unknown op '${node2.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(node,tensorMap,context);return util_exports.isPromise(value)?value.then(data2=>[].concat(data2)):[].concat(value)}var ExecutionContext=class{constructor(weightMap={},tensorArrayMap={},tensorListMap={},functionMap={}){this.weightMap=weightMap,this.tensorArrayMap=tensorArrayMap,this.tensorListMap=tensorListMap,this.functionMap=functionMap,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(id,frameName){return{id,frameName,iterationId:0}}set currentContext(contexts2){this.contexts!==contexts2&&(this.contexts=contexts2,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let names=[];for(let i=0;icontext.id===0&&context.iterationId===0?"":`${context.frameName}-${context.iterationId}`).join("/"):""}enterFrame(frameId){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,frameId)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let context=Object.assign({},this.contexts[this.contexts.length-1]);context.iterationId+=1,context.id=this.lastId,this.contexts.splice(-1,1,context),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(name){return this.weightMap[name]}addTensorArray(tensorArray){this.tensorArrayMap[tensorArray.id]=tensorArray}getTensorArray(id){return this.tensorArrayMap[id]}addTensorList(tensorList){this.tensorListMap[tensorList.id]=tensorList}getTensorList(id){return this.tensorListMap[id]}dispose(keepIds){for(let key in this.tensorArrayMap)this.tensorArrayMap[key].clearAndClose(keepIds);for(let key in this.tensorListMap)this.tensorListMap[key].clearAndClose(keepIds)}};function getExecutionSubgraph(inputs,outputs,weightMap,initNodes){let usedNodes=new Set,missingInputs=[],dynamicNode=null,syncInputs=null,seen=new Set,inputNodeNames=Object.keys(inputs).map(name=>parseNodeName(name)[0]),initNodeNames=[];initNodes!=null&&(initNodeNames=initNodes.map(node=>parseNodeName(node.name)[0]));let frontier=[...outputs];for(;frontier.length>0;){let node=frontier.pop();if((isControlFlow(node)||isDynamicShape(node)||isHashTable(node))&&dynamicNode==null&&(dynamicNode=node,syncInputs=dynamicNode.children.map(child=>child.name).filter(name=>usedNodes.has(name))),usedNodes.add(node.name),weightMap[node.name]==null&&inputNodeNames.indexOf(node.name)===-1&&initNodeNames.indexOf(node.name)===-1){if(node.inputs.length===0){missingInputs.push(node.name);continue}node.inputs.forEach(input2=>{seen.has(input2.name)||(seen.add(input2.name),frontier.push(input2))})}}return{inputs,outputs,usedNodes,missingInputs,dynamicNode,syncInputs}}function getNodesInTopologicalOrder(graph2,weightMap,executionInfo){let{usedNodes,inputs}=executionInfo,frontier=[],inputNodes=Object.keys(inputs).map(name=>parseNodeName(name)[0]).map(name=>graph2.nodes[name]),initNodes=graph2.initNodes;inputNodes.forEach(input2=>{usedNodes.has(input2.name)&&frontier.push(input2)}),graph2.weights.forEach(weight=>{usedNodes.has(weight.name)&&frontier.push(weight)}),initNodes!=null&&initNodes.forEach(node=>{usedNodes.has(node.name)&&frontier.push(node)});let seen=new Set,orderedNodes=[];for(;frontier.length>0;){let node=frontier.pop();seen.add(node.name),weightMap[node.name]||orderedNodes.push(node),node.children.forEach(child=>{!seen.has(child.name)&&usedNodes.has(child.name)&&child.inputs.every(input2=>seen.has(input2.name))&&frontier.push(child)})}return orderedNodes}var CONTROL_FLOW_OPS=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],DYNAMIC_SHAPE_OPS=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],HASH_TABLE_OPS=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2"];function isControlFlow(node){return CONTROL_FLOW_OPS.indexOf(node.op)>=0}function isDynamicShape(node){return DYNAMIC_SHAPE_OPS.indexOf(node.op)>=0}function isHashTable(node){return HASH_TABLE_OPS.indexOf(node.op)>=0}var GraphExecutor=class{constructor(graph2,parent){this.graph=graph2,this.parent=parent,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this._outputs=graph2.outputs,this._inputs=graph2.inputs,this._initNodes=graph2.initNodes,this._signature=graph2.signature,this._functions=graph2.functions,graph2.functions!=null&&Object.keys(graph2.functions).forEach(name=>{this._functionExecutorMap[name]=new GraphExecutor(graph2.functions[name],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(weightMap){let weightIds=Object.keys(weightMap).map(key=>weightMap[key].map(tensor2=>tensor2.id));this._weightIds=[].concat(...weightIds),this._weightMap=weightMap}set resourceManager(resourceManager){this._resourceManager=resourceManager}get inputs(){return this._inputs.map(node=>({name:node.name,shape:node.attrParams.shape?node.attrParams.shape.value:void 0,dtype:node.attrParams.dtype?node.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(node=>({name:node.name,shape:node.attrParams.shape?node.attrParams.shape.value:void 0,dtype:node.attrParams.dtype?node.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(node=>node.signatureKey||node.name)}get outputNodes(){return this._outputs.map(node=>{let name=node.signatureKey||node.name;return node.defaultOutput?`${name}:${node.defaultOutput}`:name})}get functions(){return Object.keys(this._functions).reduce((map,key)=>(map[key]=this._functions[key].signature,map),{})}getCompilationKey(inputs,outputs){let sortedInputs=inputs.map(node=>node.name).sort(),sortedOutputs=outputs.map(node=>node.name).sort();return sortedInputs.join(this.SEPERATOR)+"--"+sortedOutputs.join(this.SEPERATOR)}compile(inputs,outputs){let executionInfo=getExecutionSubgraph(inputs,outputs,this.weightMap,this._initNodes),{missingInputs,dynamicNode,syncInputs}=executionInfo;if(dynamicNode!=null)throw new Error(`This execution contains the node '${dynamicNode.name}', which has the dynamic op '${dynamicNode.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${syncInputs}]`);if(missingInputs.length>0){let outNames=outputs.map(n=>n.name),inNames=Object.keys(inputs);throw new Error(`Cannot compute the outputs [${outNames}] from the provided inputs [${inNames}]. Missing the following inputs: [${missingInputs}]`)}return getNodesInTopologicalOrder(this.graph,this.weightMap,executionInfo)}execute(inputs,outputs){inputs=this.mapInputs(inputs);let names=Object.keys(inputs).sort();this.checkInputs(inputs),this.checkInputShapeAndType(inputs),outputs=this.mapOutputs(outputs),this.checkOutputs(outputs);let inputNodes=names.map(name=>this.graph.nodes[parseNodeName(name)[0]]),outputNodeNames=outputs.map(name=>parseNodeName(name)[0]),outputNodes=outputNodeNames.map(name=>this.graph.nodes[name]);outputNodes.length===0&&(outputNodes=this._outputs);let compilationKey=this.getCompilationKey(inputNodes,outputNodes),orderedNodes=this.compiledMap.get(compilationKey);orderedNodes==null&&(orderedNodes=this.compile(inputs,outputNodes),this.compiledMap.set(compilationKey,orderedNodes));let tensorArrayMap={},tensorListMap={};return tidy(()=>{let context=new ExecutionContext(this.weightMap,tensorArrayMap,tensorListMap,this.functionExecutorMap),tensorsMap=Object.assign({},this.weightMap);Object.keys(inputs).forEach(name=>{let[nodeName,index]=parseNodeName(name),tensors=[];tensors[index]=inputs[name],tensorsMap[nodeName]=tensors});let tensorsToKeep=this.getFrozenTensorIds(tensorsMap),intermediateTensorConsumerCount={};for(let i=0;igetTensor(name,tensorsMap,context))})}getFrozenTensorIds(tensorMap){let ids=[].concat.apply([],Object.keys(tensorMap).map(key=>tensorMap[key]).map(tensors=>tensors.map(tensor2=>tensor2.id)));return new Set(ids)}checkTensorForDisposal(nodeName,node,tensorMap,context,tensorsToKeep,outputNames,intermediateTensorConsumerCount){node.category==="control"||outputNames.indexOf(nodeName)!==-1||(tensorMap[nodeName].forEach(tensor2=>{tensor2!=null&&(intermediateTensorConsumerCount[tensor2.id]=(intermediateTensorConsumerCount[tensor2.id]||0)+node.children.length)}),node.inputs.forEach(input2=>{if(input2.category!=="control"){let tensors=getTensorsForCurrentContenxt(input2.name,tensorMap,context);tensors!=null&&tensors.forEach(tensor2=>{if(tensor2&&!tensorsToKeep.has(tensor2.id)){let count2=intermediateTensorConsumerCount[tensor2.id];count2===1?(tensor2.dispose(),delete intermediateTensorConsumerCount[tensor2.id]):count2!=null&&intermediateTensorConsumerCount[tensor2.id]--}})}}))}async executeAsync(inputs,outputs){return this._executeAsync(inputs,outputs)}async _executeAsync(inputs,outputs,isFunctionExecution=!1,tensorArrayMap={},tensorListMap={}){isFunctionExecution||(inputs=this.mapInputs(inputs),this.checkInputs(inputs),this.checkInputShapeAndType(inputs),outputs=this.mapOutputs(outputs),this.checkOutputs(outputs));let context=new ExecutionContext(this.weightMap,tensorArrayMap,tensorListMap,this.functionExecutorMap),tensorMap=await this.executeWithControlFlow(inputs,context,outputs,isFunctionExecution),results=outputs.map(name=>getTensor(name,tensorMap,context)),outputIds=results.map(t=>t.id),inputIds=Object.keys(inputs).map(name=>inputs[name].id),keepIds=new Set([...outputIds,...inputIds,...this.weightIds]);return Object.keys(tensorMap).forEach(key=>{tensorMap[key].forEach(tensor2=>{tensor2&&!tensor2.isDisposed&&!keepIds.has(tensor2.id)&&tensor2.dispose()})}),this.parent==null&&context.dispose(keepIds),results}async executeFunctionAsync(inputs,tensorArrayMap,tensorListMap){let mappedInputs=inputs.reduce((map,tensor2,index)=>(map[this.inputs[index].name]=tensor2,map),{});return this._executeAsync(mappedInputs,this.outputNodes,!0,tensorArrayMap,tensorListMap)}async executeWithControlFlow(inputs,context,outputNames,isFunctionExecution){let names=Object.keys(inputs),inputNodes=names.map(name=>this.graph.nodes[parseNodeName(name)[0]]),outputNodeNames=outputNames.map(name=>parseNodeName(name)[0]),outputNodes=outputNodeNames.map(name=>this.graph.nodes[name]);outputNodes.length===0&&(outputNodes=this._outputs);let{usedNodes,missingInputs,dynamicNode,syncInputs}=getExecutionSubgraph(inputs,outputNodes,this.weightMap,this._initNodes),stack2=[...inputNodes,...this.graph.weights,...this._initNodes||[]].map(node=>({node,contexts:context.currentContext})),tensorsMap=Object.assign({},this.weightMap);Object.keys(inputs).forEach(name=>{let[nodeName,index]=parseNodeName(name),tensors=[];tensors[index]=inputs[name],tensorsMap[nodeName]=tensors});let intermediateTensorConsumerCount={},tensorsToKeep=this.getFrozenTensorIds(tensorsMap),added={};for(;stack2.length>0;){let promises=this.processStack(inputNodes,stack2,context,tensorsMap,added,tensorsToKeep,outputNodeNames,intermediateTensorConsumerCount,usedNodes);await Promise.all(promises)}dynamicNode==null&&!isFunctionExecution&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let missingOutputs=outputNodes.filter(node=>!isControlFlow(node)&&!getTensor(node.name,tensorsMap,context)).map(node=>node.name);if(missingOutputs.length>0){let alternativeMsg="";throw dynamicNode!=null&&(alternativeMsg=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${syncInputs}]`),new Error(`Cannot compute the outputs [${missingOutputs}] from the provided inputs [${names}]. Consider providing the following inputs: [${missingInputs}]. ${alternativeMsg}`)}return tensorsMap}processStack(inputNodes,stack2,context,tensorMap,added,tensorsToKeep,outputNames,intermediateTensorConsumerCount,usedNodes){let promises=[];for(;stack2.length>0;){let item=stack2.pop();context.currentContext=item.contexts;let nodeName="";if(item.node.op==="Enter"&&getParamValue("isConstant",item.node,tensorMap,context)&&([nodeName]=getNodeNameAndIndex(item.node.name,context)),tensorMap[item.node.name]==null){let tensors=executeOp18(item.node,tensorMap,context,this._resourceManager);nodeName||([nodeName]=getNodeNameAndIndex(item.node.name,context));let currentContext=context.currentContext;util_exports.isPromise(tensors)?promises.push(tensors.then(t=>(tensorMap[nodeName]=t,context.currentContext=currentContext,this.checkTensorForDisposal(nodeName,item.node,tensorMap,context,tensorsToKeep,outputNames,intermediateTensorConsumerCount),this.processChildNodes(item.node,stack2,context,tensorMap,added,usedNodes),t))):(tensorMap[nodeName]=tensors,this.checkTensorForDisposal(nodeName,item.node,tensorMap,context,tensorsToKeep,outputNames,intermediateTensorConsumerCount),this.processChildNodes(item.node,stack2,context,tensorMap,added,usedNodes))}else this.processChildNodes(item.node,stack2,context,tensorMap,added,usedNodes)}return promises}processChildNodes(node,stack2,context,tensorMap,added,usedNodes){node.children.forEach(childNode=>{let[nodeName]=getNodeNameAndIndex(childNode.name,context);added[nodeName]||!usedNodes.has(childNode.name)||(childNode.op==="Merge"?childNode.inputNames.some(name=>!!getTensor(name,tensorMap,context))&&(added[nodeName]=!0,stack2.push({contexts:context.currentContext,node:childNode})):childNode.inputNames.every(name=>!!getTensor(name,tensorMap,context))&&(added[nodeName]=!0,stack2.push({contexts:context.currentContext,node:childNode})))})}dispose(){Object.keys(this.weightMap).forEach(key=>this.weightMap[key].forEach(tensor2=>tensor2.dispose()))}checkInputShapeAndType(inputs){Object.keys(inputs).forEach(name=>{let input2=inputs[name],[nodeName]=parseNodeName(name),node=this.graph.nodes[nodeName];if(node.attrParams.shape&&node.attrParams.shape.value){let shape=node.attrParams.shape.value,match=shape.length===input2.shape.length&&input2.shape.every((dim,index)=>shape[index]===-1||shape[index]===dim);util_exports.assert(match,()=>`The shape of dict['${node.name}'] provided in model.execute(dict) must be [${shape}], but was [${input2.shape}]`)}node.attrParams.dtype&&node.attrParams.dtype.value&&util_exports.assert(input2.dtype===node.attrParams.dtype.value,()=>`The dtype of dict['${node.name}'] provided in model.execute(dict) must be ${node.attrParams.dtype.value}, but was ${input2.dtype}`)})}mapInputs(inputs){let result={};for(let inputName in inputs)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[inputName]!=null){let tensor2=this._signature.inputs[inputName];result[tensor2.name]=inputs[inputName]}else result[inputName]=inputs[inputName];return result}checkInputs(inputs){let notInGraph=Object.keys(inputs).filter(name=>{let[nodeName]=parseNodeName(name);return this.graph.nodes[nodeName]==null});if(notInGraph.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${notInGraph}] that are not part of graph`)}mapOutputs(outputs){return outputs.map(name=>this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[name]!=null?this._signature.outputs[name].name:name,{})}checkOutputs(outputs){outputs.forEach(name=>{let[normalizedName]=parseNodeName(name);if(!this.graph.nodes[normalizedName])throw new Error(`The output '${name}' is not found in the graph`)})}},ResourceManager=class{constructor(hashTableNameToHandle={},hashTableMap={}){this.hashTableNameToHandle=hashTableNameToHandle,this.hashTableMap=hashTableMap}addHashTable(name,hashTable2){this.hashTableNameToHandle[name]=hashTable2.handle,this.hashTableMap[hashTable2.id]=hashTable2}getHashTableHandleByName(name){return this.hashTableNameToHandle[name]}getHashTableById(id){return this.hashTableMap[id]}dispose(){for(let key in this.hashTableMap)this.hashTableMap[key].clearAndClose(),delete this.hashTableMap[key];for(let name in this.hashTableNameToHandle)this.hashTableNameToHandle[name].dispose(),delete this.hashTableNameToHandle[name]}},TFHUB_SEARCH_PARAM="?tfjs-format=file",DEFAULT_MODEL_NAME="model.json",GraphModel=class{constructor(modelUrl,loadOptions={}){this.modelUrl=modelUrl,this.loadOptions=loadOptions,this.version="n/a",loadOptions==null&&(this.loadOptions={}),this.resourceManager=new ResourceManager}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let path=this.modelUrl;if(path.load!=null)this.handler=path;else if(this.loadOptions.requestInit!=null)this.handler=io_exports.browserHTTPRequest(path,this.loadOptions);else{let handlers=io_exports.getLoadHandlers(path,this.loadOptions);if(handlers.length===0)handlers.push(io_exports.browserHTTPRequest(path,this.loadOptions));else if(handlers.length>1)throw new Error(`Found more than one (${handlers.length}) load handlers for URL '${[path]}'`);this.handler=handlers[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let artifacts=await this.handler.load();return this.loadSync(artifacts)}loadSync(artifacts){this.artifacts=artifacts;let graph2=this.artifacts.modelTopology,signature;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?signature=this.artifacts.userDefinedMetadata.signature:signature=this.artifacts.signature,this.signature=signature,this.version=`${graph2.versions.producer}.${graph2.versions.minConsumer}`;let weightMap=io_exports.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new GraphExecutor(OperationMapper.Instance.transformGraph(graph2,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(weightMap),this.executor.resourceManager=this.resourceManager,artifacts.modelInitializer!=null&&artifacts.modelInitializer.node!=null){let initializer=OperationMapper.Instance.transformGraph(artifacts.modelInitializer);this.initializer=new GraphExecutor(initializer),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(handlerOrURL,config2){if(typeof handlerOrURL=="string"){let handlers=io_exports.getSaveHandlers(handlerOrURL);if(handlers.length===0)throw new Error(`Cannot find any save handlers for URL '${handlerOrURL}'`);if(handlers.length>1)throw new Error(`Found more than one (${handlers.length}) save handlers for URL '${handlerOrURL}'`);handlerOrURL=handlers[0]}if(handlerOrURL.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return handlerOrURL.save(this.artifacts)}predict(inputs,config2){return this.execute(inputs,this.outputNodes)}normalizeInputs(inputs){if(!(inputs instanceof Tensor)&&!Array.isArray(inputs))return inputs;if(inputs=Array.isArray(inputs)?inputs:[inputs],inputs.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${inputs.length} input tensors.`);return this.inputNodes.reduce((map,inputName,i)=>(map[inputName]=inputs[i],map),{})}normalizeOutputs(outputs){return outputs=outputs||this.outputNodes,Array.isArray(outputs)?outputs:[outputs]}execute(inputs,outputs){inputs=this.normalizeInputs(inputs),outputs=this.normalizeOutputs(outputs);let result=this.executor.execute(inputs,outputs);return result.length>1?result:result[0]}async executeAsync(inputs,outputs){inputs=this.normalizeInputs(inputs),outputs=this.normalizeOutputs(outputs);let result=await this.executor.executeAsync(inputs,outputs);return result.length>1?result:result[0]}convertTensorMapToTensorsMap(map){return Object.keys(map).reduce((newMap,key)=>(newMap[key]=[map[key]],newMap),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function loadGraphModel(modelUrl,options={}){if(modelUrl==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");options==null&&(options={}),options.fromTFHub&&modelUrl.load==null&&(modelUrl.endsWith("/")||(modelUrl=modelUrl+"/"),modelUrl=`${modelUrl}${DEFAULT_MODEL_NAME}${TFHUB_SEARCH_PARAM}`);let model2=new GraphModel(modelUrl,options);return await model2.load(),model2}var version11="2.8.3",dist_exports={};__export2(dist_exports,{CSVDataset:()=>CSVDataset,Dataset:()=>Dataset,FileDataSource:()=>FileDataSource,TextLineDataset:()=>TextLineDataset,URLDataSource:()=>URLDataSource,array:()=>array,csv:()=>csv,func:()=>func,generator:()=>generator,microphone:()=>microphone,version_data:()=>version12,webcam:()=>webcam,zip:()=>zip});var seedrandom4=__toModule2(require_seedrandom6()),seedrandom3=__toModule2(require_seedrandom6());function deepMap(input2,mapFn){return deepMapInternal(input2,mapFn)}function deepMapInternal(input2,mapFn,seen=new Map,containedIn=new Set){if(input2==null)return null;if(containedIn.has(input2))throw new Error("Circular references are not supported.");if(seen.has(input2))return seen.get(input2);let result=mapFn(input2);if(result.recurse&&result.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(result.recurse)if(isIterable2(input2)){let mappedIterable=Array.isArray(input2)?[]:{};containedIn.add(input2);for(let k in input2){let child=input2[k],childResult=deepMapInternal(child,mapFn,seen,containedIn);mappedIterable[k]=childResult}return containedIn.delete(input2),mappedIterable}else throw new Error(`Can't recurse into non-iterable type: ${input2}`);else return seen.set(input2,result.value),result.value}function deepZip(inputs,zipFn=zipToList){return deepZipInternal(inputs,zipFn)}function deepZipInternal(inputs,zipFn,containedIn=new Set){let input2=inputs[0];if(containedIn.has(input2))throw new Error("Circular references are not supported.");let result=zipFn(inputs);if(result.recurse&&result.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(result.recurse)if(isIterable2(input2)){let mappedIterable=Array.isArray(input2)?[]:{};containedIn.add(input2);for(let k in input2){let children=inputs.map(x=>x[k]),childResult=deepZipInternal(children,zipFn,containedIn);mappedIterable[k]=childResult}return containedIn.delete(input2),mappedIterable}else throw new Error(`Can't recurse into non-iterable type: ${input2}`);else return result.value}function zipToList(x){return x===null?null:isIterable2(x[0])?{value:null,recurse:!0}:{value:x,recurse:!1}}async function deepMapAndAwaitAll(input2,mapFn){let seen=new Map;deepMapInternal(input2,mapFn,seen);for(let key of Array.from(seen.keys())){let value=seen.get(key);if(util_exports.isPromise(value)){let mappedValue=await value;seen.set(key,mappedValue)}}return deepMapInternal(input2,mapFn,seen)}function isIterable2(obj){return obj!=null&&!ArrayBuffer.isView(obj)&&(Array.isArray(obj)||typeof obj=="object"&&!(obj instanceof Tensor))}function canTensorify(obj){return obj==null||isPrimitive(obj)||Array.isArray(obj)||typeof obj=="object"&&obj instanceof Tensor||util_exports.isTypedArray(obj)}function isPrimitive(value){return value===null||typeof value!="object"&&typeof value!="function"}function deepClone(container){return deepMap(container,cloneIfTensor)}function cloneIfTensor(item){return item instanceof Tensor?{value:item.clone(),recurse:!1}:isIterable2(item)?{value:null,recurse:!0}:{value:item,recurse:!1}}var RingBuffer=class{constructor(capacity){if(this.capacity=capacity,this.begin=0,this.end=0,capacity==null)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(capacity<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(capacity),this.doubledCapacity=2*capacity}wrap(index){for(;index<0;)index+=this.doubledCapacity;return index%this.doubledCapacity}get(index){if(index<0)throw new RangeError("Can't get item at a negative index.");return this.data[index%this.capacity]}set(index,value){if(index<0)throw new RangeError("Can't set item at a negative index.");this.data[index%this.capacity]=value}length(){let length=this.end-this.begin;return length<0&&(length=this.doubledCapacity+length),length}isFull(){return this.length()===this.capacity}isEmpty(){return this.length()===0}push(value){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,value),this.end=this.wrap(this.end+1)}pushAll(values){for(let value of values)this.push(value)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);let result=this.get(this.end);return this.set(this.end,void 0),result}unshift(value){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,value)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let result=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),result}shuffleExcise(relativeIndex){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let index=this.wrap(this.begin+relativeIndex),result=this.get(index);return this.set(index,this.pop()),result}},GrowingRingBuffer=class extends RingBuffer{constructor(){super(GrowingRingBuffer.INITIAL_CAPACITY)}isFull(){return!1}push(value){super.isFull()&&this.expand(),super.push(value)}unshift(value){super.isFull()&&this.expand(),super.unshift(value)}expand(){let newCapacity=this.capacity*2,newData=new Array(newCapacity),len=this.length();for(let i=0;ix===!0)}rowMajorBatch(batchSize,smallLastBatch=!0){return new RowMajorBatchIterator(this,batchSize,smallLastBatch)}columnMajorBatch(batchSize,smallLastBatch=!0,zipFn=zipToList){return this.rowMajorBatch(batchSize,smallLastBatch).map(x=>deepZip(x,zipFn))}concatenate(iterator,baseErrorHandler){return new ChainedIterator(iteratorFromItems([this,iterator]),baseErrorHandler)}take(count2){return count2<0||count2==null?this:new TakeIterator(this,count2)}skip(count2){return count2<0||count2==null?this:new SkipIterator(this,count2)}prefetch(bufferSize){return new PrefetchIterator(this,bufferSize)}shuffle(windowSize,seed){return new ShuffleIterator(this,windowSize,seed)}serial(){return new SerialIterator(this)}},ArrayIterator=class extends LazyIterator{constructor(items){super();this.items=items,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let item=this.items[this.trav];return this.trav++,{value:deepClone(item),done:!1}}},FunctionCallIterator=class extends LazyIterator{constructor(nextFn){super();this.nextFn=nextFn}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: ${e.message}`,e}}},SerialIterator=class extends LazyIterator{constructor(upstream){super();this.upstream=upstream,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},SkipIterator=class extends LazyIterator{constructor(upstream,maxCount){super();this.upstream=upstream,this.maxCount=maxCount,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++ Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},RowMajorBatchIterator=class extends LazyIterator{constructor(upstream,batchSize,enableSmallLastBatch=!0){super();this.upstream=upstream,this.batchSize=batchSize,this.enableSmallLastBatch=enableSmallLastBatch,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let batch=[];for(;batch.length0?{value:batch,done:!1}:{value:null,done:!0};batch.push(item.value)}return{value:batch,done:!1}}},FilterIterator=class extends LazyIterator{constructor(upstream,predicate){super();this.upstream=upstream,this.predicate=predicate,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let item=await this.upstream.next();if(item.done||this.predicate(item.value))return item;dispose(item.value)}}},MapIterator=class extends LazyIterator{constructor(upstream,transform){super();this.upstream=upstream,this.transform=transform}summary(){return`${this.upstream.summary()} -> Map`}async next(){let item=await this.upstream.next();if(item.done)return{value:null,done:!0};let inputTensors=tensor_util_exports.getTensorsInContainer(item.value),mapped=this.transform(item.value),outputTensors=tensor_util_exports.getTensorsInContainer(mapped);for(let t of inputTensors)tensor_util_exports.isTensorInList(t,outputTensors)||t.dispose();return{value:mapped,done:!1}}},ErrorHandlingLazyIterator=class extends LazyIterator{constructor(upstream,handler){super();this.upstream=upstream,this.handler=handler,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},AsyncMapIterator=class extends LazyIterator{constructor(upstream,transform){super();this.upstream=upstream,this.transform=transform}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let item=await this.upstream.next();if(item.done)return{value:null,done:!0};let inputTensors=tensor_util_exports.getTensorsInContainer(item.value),mapped=await this.transform(item.value),outputTensors=tensor_util_exports.getTensorsInContainer(mapped);for(let t of inputTensors)tensor_util_exports.isTensorInList(t,outputTensors)||t.dispose();return{value:mapped,done:!1}}},OneToManyIterator=class extends LazyIterator{constructor(){super();this.outputQueue=new GrowingRingBuffer,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},FlatmapIterator=class extends OneToManyIterator{constructor(upstream,transform){super();this.upstream=upstream,this.transform=transform}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let item=await this.upstream.next();if(item.done)return!1;let inputTensors=tensor_util_exports.getTensorsInContainer(item.value),mappedArray=this.transform(item.value),outputTensors=tensor_util_exports.getTensorsInContainer(mappedArray);this.outputQueue.pushAll(mappedArray);for(let t of inputTensors)tensor_util_exports.isTensorInList(t,outputTensors)||t.dispose();return!0}},ChainedIterator=class extends LazyIterator{constructor(iterators,baseErrorHandler){super();this.baseErrorHandler=baseErrorHandler,this.lastRead=null,this.iterator=null,this.moreIterators=iterators}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(lastRead){if(await lastRead,this.iterator==null){let iteratorResult=await this.moreIterators.next();if(iteratorResult.done)return{value:null,done:!0};this.iterator=iteratorResult.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let itemResult=await this.iterator.next();return itemResult.done?(this.iterator=null,this.readFromChain(lastRead)):itemResult}},ZipMismatchMode;(function(ZipMismatchMode2){ZipMismatchMode2[ZipMismatchMode2.FAIL=0]="FAIL",ZipMismatchMode2[ZipMismatchMode2.SHORTEST=1]="SHORTEST",ZipMismatchMode2[ZipMismatchMode2.LONGEST=2]="LONGEST"})(ZipMismatchMode||(ZipMismatchMode={}));var ZipIterator=class extends LazyIterator{constructor(iterators,mismatchMode=ZipMismatchMode.FAIL){super();this.iterators=iterators,this.mismatchMode=mismatchMode,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(afterState){await afterState;let numIterators=0,iteratorsDone=0;function getNext(container){return container instanceof LazyIterator?{value:container.next().then(x=>(numIterators++,x.done&&iteratorsDone++,x.value)),recurse:!1}:{value:null,recurse:!0}}let mapped=await deepMapAndAwaitAll(this.iterators,getNext);if(numIterators===iteratorsDone)return{value:null,done:!0};if(iteratorsDone>0)switch(this.mismatchMode){case ZipMismatchMode.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case ZipMismatchMode.SHORTEST:return{value:null,done:!0};case ZipMismatchMode.LONGEST:default:}return this.count++,{value:mapped,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},PrefetchIterator=class extends LazyIterator{constructor(upstream,bufferSize){super();this.upstream=upstream,this.bufferSize=bufferSize,this.buffer=new RingBuffer(bufferSize)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let v=this.upstream.next();this.buffer.push(v)}}next(){return this.refill(),this.buffer.shift()}},ShuffleIterator=class extends PrefetchIterator{constructor(upstream,windowSize,seed){super(upstream,windowSize);this.upstream=upstream,this.windowSize=windowSize,this.upstreamExhausted=!1,this.random=seedrandom3.alea(seed||util_exports.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(max6){return Math.floor(this.random()*max6)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let chosenIndex=this.chooseIndex(),result=await this.buffer.shuffleExcise(chosenIndex);if(result.done)this.upstreamExhausted=!0;else return this.refill(),result}return{value:null,done:!0}}},Dataset=class{constructor(){this.size=null}batch(batchSize,smallLastBatch=!0){let base2=this;util_exports.assert(batchSize>0,()=>`batchSize needs to be positive, but it is ${batchSize}`);let size;return this.size===Infinity||this.size==null?size=this.size:smallLastBatch?size=Math.ceil(this.size/batchSize):size=Math.floor(this.size/batchSize),datasetFromIteratorFn(async()=>(await base2.iterator()).columnMajorBatch(batchSize,smallLastBatch,deepBatchConcat),size)}concatenate(dataset){let base2=this,size;return this.size===Infinity||dataset.size===Infinity?size=Infinity:this.size!=null&&dataset.size!=null?size=this.size+dataset.size:size=null,datasetFromIteratorFn(async()=>(await base2.iterator()).concatenate(await dataset.iterator()),size)}filter(predicate){let base2=this,size;return this.size===Infinity?size=Infinity:size=null,datasetFromIteratorFn(async()=>(await base2.iterator()).filter(x=>tidy(()=>predicate(x))),size)}async forEachAsync(f){return(await this.iterator()).forEachAsync(f)}map(transform){let base2=this;return datasetFromIteratorFn(async()=>(await base2.iterator()).map(x=>tidy(()=>transform(x))),this.size)}mapAsync(transform){let base2=this;return datasetFromIteratorFn(async()=>(await base2.iterator()).mapAsync(transform),this.size)}prefetch(bufferSize){if(bufferSize==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let base2=this;return datasetFromIteratorFn(async()=>(await base2.iterator()).prefetch(bufferSize),this.size)}repeat(count2){let base2=this,size;return this.size!=null&&count2>0?size=this.size*count2:count2===0?size=0:this.size!=null&&(count2===void 0||count2<0)?size=Infinity:size=null,datasetFromIteratorFn(async()=>{let iteratorIterator=iteratorFromFunction(async()=>({value:await base2.iterator(),done:!1}));return iteratorFromConcatenated(iteratorIterator.take(count2))},size)}skip(count2){let base2=this,size;return this.size!=null&&count2>=0&&this.size>=count2?size=this.size-count2:this.size!=null&&(this.size(await base2.iterator()).skip(count2),size)}shuffle(bufferSize,seed,reshuffleEachIteration=!0){if(bufferSize==null||bufferSize<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let base2=this,random=seedrandom4.alea(seed||util_exports.now().toString());return datasetFromIteratorFn(async()=>{let seed2=random.int32();return reshuffleEachIteration&&(seed2+=random.int32()),(await base2.iterator()).shuffle(bufferSize,seed2.toString())},this.size)}take(count2){let base2=this,size;return this.size!=null&&this.size>count2?size=count2:this.size!=null&&this.size<=count2?size=this.size:size=null,datasetFromIteratorFn(async()=>(await base2.iterator()).take(count2),size)}async toArray(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};Dataset.MAX_BUFFER_SIZE=1e4;function datasetFromIteratorFn(iteratorFn,size=null){return new class extends Dataset{constructor(){super(...arguments);this.size=size}async iterator(){return iteratorFn()}}}function array(items){return datasetFromIteratorFn(async()=>iteratorFromItems(items),items.length)}function zip(datasets){if(!isIterable2(datasets))throw new Error("The argument to zip() must be an object or array.");let size;if(Array.isArray(datasets))for(let i=0;i{let streams=await deepMapAndAwaitAll(datasets,d=>{if(d instanceof Dataset)return{value:d.iterator(),recurse:!1};if(isIterable2(d))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return iteratorFromZipped(streams,ZipMismatchMode.SHORTEST)},size)}function deepBatchConcat(rows){if(rows===null)return null;let exampleRow=rows[0];return canTensorify(exampleRow)?{value:batchConcat(rows),recurse:!1}:{value:null,recurse:!0}}function batchConcat(arrays){if(arrays.length===0)throw new Error("Can't make a batch of zero elements.");return arrays[0]instanceof Tensor?stack(arrays):tensor(arrays)}var TextLineDataset=class extends Dataset{constructor(input2){super();this.input=input2}async iterator(){return(await this.input.iterator()).decodeUTF8().split(` -`).map(line=>(line.endsWith("\r")&&(line=line.slice(0,-1)),line))}},CODE_QUOTE='"',STATE_OUT=Symbol("out"),STATE_FIELD=Symbol("field"),STATE_QUOTE=Symbol("quote"),STATE_QUOTE_AFTER_QUOTE=Symbol("quoteafterquote"),STATE_WITHIN_QUOTE_IN_QUOTE=Symbol("quoteinquote"),CSVDataset=class extends Dataset{constructor(input2,csvConfig){super();this.input=input2,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new TextLineDataset(input2),csvConfig||(csvConfig={}),this.hasHeader=csvConfig.hasHeader!==!1,this.fullColumnNames=csvConfig.columnNames,this.columnConfigs=csvConfig.columnConfigs,this.configuredColumnsOnly=csvConfig.configuredColumnsOnly,csvConfig.delimWhitespace?(util_exports.assert(csvConfig.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=csvConfig.delimiter?csvConfig.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let columnNamesFromFile=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!columnNamesFromFile)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&columnNamesFromFile&&util_exports.assert(columnNamesFromFile.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+columnNamesFromFile.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=columnNamesFromFile);let counts=this.fullColumnNames.reduce((countAcc,name)=>(countAcc[name]=countAcc[name]+1||1,countAcc),{}),duplicateNames=Object.keys(counts).filter(name=>counts[name]>1);if(util_exports.assert(duplicateNames.length===0,()=>"Duplicate column names found: "+duplicateNames.toString()),this.columnConfigs){for(let key of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(key)===-1)throw new Error('The key "'+key+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let firstElement=await(await this.base.iterator()).next();if(firstElement.done)throw new Error("No data was found for CSV parsing.");let firstLine=firstElement.value;return this.parseRow(firstLine,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let lines=await this.base.iterator();return this.hasHeader&&(lines=lines.skip(1)),lines.map(x=>this.makeDataElement(x))}makeDataElement(line){let values=this.parseRow(line),features={},labels={};for(let i=0;i14||!Number.isInteger(fftSizeLog2))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=microphoneConfig.numFramesPerSpectrogram||43,this.sampleRateHz=microphoneConfig.sampleRateHz,this.columnTruncateLength=microphoneConfig.columnTruncateLength||this.fftSize,this.audioTrackConstraints=microphoneConfig.audioTrackConstraints,this.smoothingTimeConstant=microphoneConfig.smoothingTimeConstant||0,this.includeSpectrogram=microphoneConfig.includeSpectrogram!==!1,this.includeWaveform=microphoneConfig.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(microphoneConfig={}){if(env().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let microphoneIterator=new MicrophoneIterator(microphoneConfig);return await microphoneIterator.start(),microphoneIterator}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(e){throw new Error(`Error thrown while initializing video stream: ${e.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let ctxConstructor=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new ctxConstructor,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let streamSource=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,streamSource.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let spectrogramTensor,waveformTensor,audioDataQueue=await this.getAudioData();if(this.includeSpectrogram){let freqData=this.flattenQueue(audioDataQueue.freqDataQueue);spectrogramTensor=this.getTensorFromAudioDataArray(freqData,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let timeData=this.flattenQueue(audioDataQueue.timeDataQueue);waveformTensor=this.getTensorFromAudioDataArray(timeData,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:spectrogramTensor,waveform:waveformTensor},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let freqDataQueue=[],timeDataQueue=[],currentFrames=0;return new Promise(resolve=>{let intervalID=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&resolve({freqDataQueue,timeDataQueue}),freqDataQueue.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),timeDataQueue.push(this.timeData.slice())),++currentFrames===this.numFrames&&(clearInterval(intervalID),resolve({freqDataQueue,timeDataQueue}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(queue){let frameSize=queue[0].length,freqData=new Float32Array(queue.length*frameSize);return queue.forEach((data2,i)=>freqData.set(data2,i*frameSize)),freqData}getTensorFromAudioDataArray(freqData,shape){let vals=new Float32Array(util_exports.sizeFromShape(shape));return vals.set(freqData,vals.length-freqData.length),tensor(vals,shape)}},WebcamIterator=class extends LazyIterator{constructor(webcamVideoElement,webcamConfig){super();if(this.webcamVideoElement=webcamVideoElement,this.webcamConfig=webcamConfig,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=tensor1d([0],"int32"),this.webcamConfig.centerCrop){let widthCroppingRatio=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,heightCroppingRatio=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,widthCropStart=(1-widthCroppingRatio)/2,heightCropStart=(1-heightCroppingRatio)/2,widthCropEnd=widthCropStart+widthCroppingRatio,heightCropEnd=heightCroppingRatio+heightCropStart;this.cropBox=tensor2d([heightCropStart,widthCropStart,heightCropEnd,widthCropEnd],[1,4])}else this.cropBox=tensor2d([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(webcamVideoElement,webcamConfig={}){if(env().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!webcamVideoElement){if(webcamVideoElement=document.createElement("video"),!webcamConfig.resizeWidth||!webcamConfig.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");webcamVideoElement.width=webcamConfig.resizeWidth,webcamVideoElement.height=webcamConfig.resizeHeight}let webcamIterator=new WebcamIterator(webcamVideoElement,webcamConfig);return await webcamIterator.start(),webcamIterator}async start(){this.webcamConfig.facingMode&&util_exports.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(error){console.log(error),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(resolve=>{this.webcamVideoElement.onloadedmetadata=()=>{resolve()}})}async next(){if(this.isClosed)return{value:null,done:!0};let img;try{img=browser_exports.fromPixels(this.webcamVideoElement)}catch(e){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(e)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(img),done:!1}}catch(e){throw new Error(`Error thrown cropping the video: ${e.message}`)}finally{img.dispose()}else return{value:img,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(img){return tidy(()=>{let expandedImage=img.toFloat().expandDims(0),resizedImage;resizedImage=image.cropAndResize(expandedImage,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let shape=resizedImage.shape;return resizedImage.reshape(shape.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(track=>track.stop());try{this.webcamVideoElement.srcObject=null}catch(error){console.log(error),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},DataSource=class{},StringIterator=class extends LazyIterator{split(separator){return new SplitIterator(this,separator)}},SplitIterator=class extends StringIterator{constructor(upstream,separator){super();this.upstream=upstream,this.impl=new SplitIteratorImpl(upstream,separator)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},SplitIteratorImpl=class extends OneToManyIterator{constructor(upstream,separator){super();this.upstream=upstream,this.separator=separator,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let chunkResult=await this.upstream.next();if(chunkResult.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let lines=chunkResult.value.split(this.separator);lines[0]=this.carryover+lines[0];for(let line of lines.slice(0,-1))this.outputQueue.push(line);return this.carryover=lines[lines.length-1],!0}},ByteChunkIterator=class extends LazyIterator{decodeUTF8(){return new Utf8Iterator(this)}},Utf8Iterator=class extends StringIterator{constructor(upstream){super();this.upstream=upstream,this.impl=new Utf8IteratorImpl(upstream)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},Utf8IteratorImpl=class extends OneToManyIterator{constructor(upstream){super();if(this.upstream=upstream,env().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder}=require_string_decoder();this.decoder=new StringDecoder("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let chunkResult=await this.upstream.next(),chunk;if(chunkResult.done)return!1;chunk=chunkResult.value;let text;return env().get("IS_BROWSER")?text=this.decoder.decode(chunk,{stream:!0}):text=this.decoder.write(Buffer.from(chunk.buffer)),this.outputQueue.push(text),!0}},FileChunkIterator=class extends ByteChunkIterator{constructor(file,options={}){super();this.file=file,this.options=options,util_exports.assert(file instanceof Uint8Array||(env().get("IS_BROWSER")?file instanceof File||file instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=options.offset||0,this.chunkSize=options.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((resolve,reject)=>{let end=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)resolve(new Uint8Array(this.file.slice(this.offset,end)));else{let fileReader=new FileReader;fileReader.onload=event=>{let data2=fileReader.result;if(data2 instanceof ArrayBuffer&&(data2=new Uint8Array(data2)),!(data2 instanceof Uint8Array))return reject(new TypeError("FileReader returned unknown type."));resolve(data2)},fileReader.onabort=event=>reject(new Error("Aborted")),fileReader.onerror=event=>reject(new Error(event.type));let slice5=this.file.slice(this.offset,end);fileReader.readAsArrayBuffer(slice5)}this.offset=end}),done:!1}}};async function urlChunkIterator(url,options={}){let urlString,requestInit;typeof url=="string"?urlString=url:(urlString=url.url,requestInit=getRequestInitFromRequest(url));let response=await util_exports.fetch(urlString,requestInit);if(response.ok){let uint8Array=new Uint8Array(await response.arrayBuffer());return new FileChunkIterator(uint8Array,options)}else throw new Error(response.statusText)}var getRequestInitFromRequest=request=>({method:request.method,headers:request.headers,body:request.body,mode:request.mode,credentials:request.credentials,cache:request.cache,redirect:request.redirect,referrer:request.referrer,integrity:request.integrity});function isLocalPath(source){return typeof source=="string"&&source.substr(0,7)==="file://"}var FileDataSource=class extends DataSource{constructor(input2,options={}){super();this.input=input2,this.options=options}async iterator(){if(isLocalPath(this.input)&&env().get("IS_NODE")){let fs=require("fs");this.input=fs.readFileSync(this.input.substr(7))}return new FileChunkIterator(this.input,this.options)}},URLDataSource=class extends DataSource{constructor(url,fileOptions={}){super();this.url=url,this.fileOptions=fileOptions}async iterator(){return isLocalPath(this.url)?new FileDataSource(this.url,this.fileOptions).iterator():urlChunkIterator(this.url,this.fileOptions)}};function csv(source,csvConfig={}){return new CSVDataset(new URLDataSource(source),csvConfig)}function func(f){let iter=iteratorFromFunction(f);return datasetFromIteratorFn(async()=>iter)}function generator(generator2){return datasetFromIteratorFn(async()=>{let gen=await generator2();return iteratorFromFunction(()=>gen.next())})}async function webcam(webcamVideoElement,webcamConfig){return WebcamIterator.create(webcamVideoElement,webcamConfig)}async function microphone(microphoneConfig){return MicrophoneIterator.create(microphoneConfig)}var version12="2.8.3",version13={tfjs:version,"tfjs-core":version2,"tfjs-data":version3,"tfjs-layers":version4,"tfjs-converter":version5,"tfjs-backend-cpu":version7,"tfjs-backend-webgl":version8,"tfjs-backend-wasm":version9},config={name:"humangl",priority:99,canvas:null,width:1024,height:1024,webGLattr:{alpha:!1,antialias:!1,premultipliedAlpha:!1,preserveDrawingBuffer:!1,depth:!1,stencil:!1,failIfMajorPerformanceCaveat:!1,desynchronized:!0}};function register(){if(!findBackend(config.name)){config.canvas=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(config.width,config.height):document.createElement("canvas");let gl=config.canvas.getContext("webgl2",config.webGLattr);setWebGLContext(2,gl);let ctx=new GPGPUContext(gl);registerBackend(config.name,()=>new MathBackendWebGL(ctx),config.priority),getKernelsForBackend("webgl").forEach(kernelConfig=>{let newKernelConfig={...kernelConfig,backendName:config.name};registerKernel(newKernelConfig)}),ENV.set("WEBGL_VERSION",2),ENV.set("WEBGL_MAX_TEXTURE_SIZE",gl.getParameter(gl.MAX_TEXTURE_SIZE)),ENV.set("WEBGL_FORCE_F16_TEXTURES",!0),ENV.set("WEBGL_PACK_DEPTHWISECONV",!0)}}var facemesh=__toModule(require_facemesh()),age=__toModule(require_age()),gender=__toModule(require_gender()),emotion=__toModule(require_emotion()),embedding2=__toModule(require_embedding()),posenet=__toModule(require_posenet()),models={},config2={},anchors=[],kMidHipCenter=0,kPoseDetectKeyNum=2,kFullBodySizeRot=1;function calculateScale(min_scale,max_scale,stride_index,num_strides){return num_strides===1?(min_scale+max_scale)*.5:min_scale+(max_scale-min_scale)*1*stride_index/(num_strides-1)}function generateAnchors(){let options={};options.strides=[],options.aspect_ratios=[],options.feature_map_height=[],options.num_layers=4,options.min_scale=.1484375,options.max_scale=.75,options.input_size_height=128,options.input_size_width=128,options.anchor_offset_x=.5,options.anchor_offset_y=.5,options.strides.push(8),options.strides.push(16),options.strides.push(16),options.strides.push(16),options.aspect_ratios.push(1),options.reduce_boxes_in_lowest_layer=!1,options.interpolated_scale_aspect_ratio=1,options.fixed_anchor_size=!0;let layer_id=0;for(;layer_id0){let scale_next=last_same_stride_layer===options.strides.length-1?1:calculateScale(options.min_scale,options.max_scale,last_same_stride_layer+1,options.strides.length);scales.push(Math.sqrt(scale2*scale_next)),aspect_ratios.push(options.interpolated_scale_aspect_ratio)}}last_same_stride_layer++}for(let i=0;iconfig2.pose.scoreThreshold){let idx=(4+2*kPoseDetectKeyNum)*i,sx=boxes[idx+0],sy=boxes[idx+1],w=boxes[idx+2]/config2.pose.inputSize,h=boxes[idx+3]/config2.pose.inputSize,cx=(sx+anchors[i].x_center*config2.pose.inputSize)/config2.pose.inputSize,cy=(sy+anchors[i].y_center*config2.pose.inputSize)/config2.pose.inputSize;region.score=Math.round(1e3*score)/1e3,region.box=[cx-w*.5,cy-h*.5,w*.5,h*.5];let keys=new Array(kPoseDetectKeyNum);for(let j=0;j{config2=cfg;let resize=image.resizeBilinear(image3,[config2.pose.inputSize,config2.pose.inputSize],!1),enhance=div(resize,127.5).sub(1);dispose(resize);let logits=await models.blazepose.predict(enhance);dispose(enhance);let regions=await decode(logits);logits[0].dispose(),logits[1].dispose(),log("poses",regions),resolve(regions)}):null}function getBoxSize(box){return[Math.abs(box.endPoint[0]-box.startPoint[0]),Math.abs(box.endPoint[1]-box.startPoint[1])]}function getBoxCenter(box){return[box.startPoint[0]+(box.endPoint[0]-box.startPoint[0])/2,box.startPoint[1]+(box.endPoint[1]-box.startPoint[1])/2]}function cutBoxFromImageAndResize(box,image3,cropSize){let h=image3.shape[1],w=image3.shape[2],boxes=[[box.startPoint[1]/h,box.startPoint[0]/w,box.endPoint[1]/h,box.endPoint[0]/w]];return image.cropAndResize(image3,boxes,[0],cropSize)}function scaleBoxCoordinates(box,factor){let startPoint=[box.startPoint[0]*factor[0],box.startPoint[1]*factor[1]],endPoint=[box.endPoint[0]*factor[0],box.endPoint[1]*factor[1]],palmLandmarks=box.palmLandmarks.map(coord=>[coord[0]*factor[0],coord[1]*factor[1]]);return{startPoint,endPoint,palmLandmarks,confidence:box.confidence}}function enlargeBox(box,factor=1.5){let center=getBoxCenter(box),size=getBoxSize(box),newHalfSize=[factor*size[0]/2,factor*size[1]/2],startPoint=[center[0]-newHalfSize[0],center[1]-newHalfSize[1]],endPoint=[center[0]+newHalfSize[0],center[1]+newHalfSize[1]];return{startPoint,endPoint,palmLandmarks:box.palmLandmarks}}function squarifyBox(box){let centers=getBoxCenter(box),size=getBoxSize(box),halfSize=Math.max(...size)/2,startPoint=[centers[0]-halfSize,centers[1]-halfSize],endPoint=[centers[0]+halfSize,centers[1]+halfSize];return{startPoint,endPoint,palmLandmarks:box.palmLandmarks}}function normalizeRadians(angle){return angle-2*Math.PI*Math.floor((angle+Math.PI)/(2*Math.PI))}function computeRotation(point1,point2){let radians=Math.PI/2-Math.atan2(-(point2[1]-point1[1]),point2[0]-point1[0]);return normalizeRadians(radians)}var buildTranslationMatrix=(x,y)=>[[1,0,x],[0,1,y],[0,0,1]];function dot4(v1,v2){let product=0;for(let i=0;iconfig_default});var config_default={backend:"webgl",wasmPath:"../assets/",async:!0,profile:!1,deallocate:!1,scoped:!1,videoOptimized:!0,warmup:"face",filter:{enabled:!0,width:0,height:0,return:!0,brightness:0,contrast:0,sharpness:0,blur:0,saturation:0,hue:0,negative:!1,sepia:!1,vintage:!1,kodachrome:!1,technicolor:!1,polaroid:!1,pixelate:0},gesture:{enabled:!0},face:{enabled:!0,detector:{modelPath:"../models/blazeface-back.json",inputSize:256,rotation:!1,maxFaces:10,skipFrames:11,minConfidence:.5,iouThreshold:.2,scoreThreshold:.5},mesh:{enabled:!0,modelPath:"../models/facemesh.json",inputSize:192,returnRawData:!1},iris:{enabled:!0,modelPath:"../models/iris.json",inputSize:64},age:{enabled:!0,modelPath:"../models/age-ssrnet-imdb.json",inputSize:64,skipFrames:31},gender:{enabled:!0,minConfidence:.1,modelPath:"../models/gender-ssrnet-imdb.json",inputSize:64,skipFrames:41},emotion:{enabled:!0,inputSize:64,minConfidence:.2,skipFrames:21,modelPath:"../models/emotion-large.json"},embedding:{enabled:!1,inputSize:112,modelPath:"../models/mobilefacenet.json"}},body:{enabled:!0,modelPath:"../models/posenet.json",inputSize:257,maxDetections:10,scoreThreshold:.5,nmsRadius:20,outputStride:16,modelType:"MobileNet"},pose:{enabled:!1,scoreThreshold:.6,iouThreshold:.3,modelPath:"../models/blazepose.json",inputSize:128},hand:{enabled:!0,rotation:!1,inputSize:256,skipFrames:12,minConfidence:.1,iouThreshold:.1,scoreThreshold:.5,maxHands:1,landmarks:!0,detector:{modelPath:"../models/handdetect.json"},skeleton:{modelPath:"../models/handskeleton.json"}}},face=` +`).map(line=>(line.endsWith("\r")&&(line=line.slice(0,-1)),line))}},CODE_QUOTE='"',STATE_OUT=Symbol("out"),STATE_FIELD=Symbol("field"),STATE_QUOTE=Symbol("quote"),STATE_QUOTE_AFTER_QUOTE=Symbol("quoteafterquote"),STATE_WITHIN_QUOTE_IN_QUOTE=Symbol("quoteinquote"),CSVDataset=class extends Dataset{constructor(input2,csvConfig){super();this.input=input2,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new TextLineDataset(input2),csvConfig||(csvConfig={}),this.hasHeader=csvConfig.hasHeader!==!1,this.fullColumnNames=csvConfig.columnNames,this.columnConfigs=csvConfig.columnConfigs,this.configuredColumnsOnly=csvConfig.configuredColumnsOnly,csvConfig.delimWhitespace?(util_exports.assert(csvConfig.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=csvConfig.delimiter?csvConfig.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let columnNamesFromFile=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!columnNamesFromFile)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&columnNamesFromFile&&util_exports.assert(columnNamesFromFile.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+columnNamesFromFile.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=columnNamesFromFile);let counts=this.fullColumnNames.reduce((countAcc,name)=>(countAcc[name]=countAcc[name]+1||1,countAcc),{}),duplicateNames=Object.keys(counts).filter(name=>counts[name]>1);if(util_exports.assert(duplicateNames.length===0,()=>"Duplicate column names found: "+duplicateNames.toString()),this.columnConfigs){for(let key of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(key)===-1)throw new Error('The key "'+key+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let firstElement=await(await this.base.iterator()).next();if(firstElement.done)throw new Error("No data was found for CSV parsing.");let firstLine=firstElement.value;return this.parseRow(firstLine,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let lines=await this.base.iterator();return this.hasHeader&&(lines=lines.skip(1)),lines.map(x=>this.makeDataElement(x))}makeDataElement(line){let values=this.parseRow(line),features={},labels={};for(let i=0;i14||!Number.isInteger(fftSizeLog2))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=microphoneConfig.numFramesPerSpectrogram||43,this.sampleRateHz=microphoneConfig.sampleRateHz,this.columnTruncateLength=microphoneConfig.columnTruncateLength||this.fftSize,this.audioTrackConstraints=microphoneConfig.audioTrackConstraints,this.smoothingTimeConstant=microphoneConfig.smoothingTimeConstant||0,this.includeSpectrogram=microphoneConfig.includeSpectrogram!==!1,this.includeWaveform=microphoneConfig.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(microphoneConfig={}){if(env().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let microphoneIterator=new MicrophoneIterator(microphoneConfig);return await microphoneIterator.start(),microphoneIterator}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(e){throw new Error(`Error thrown while initializing video stream: ${e.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let ctxConstructor=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new ctxConstructor,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let streamSource=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,streamSource.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let spectrogramTensor,waveformTensor,audioDataQueue=await this.getAudioData();if(this.includeSpectrogram){let freqData=this.flattenQueue(audioDataQueue.freqDataQueue);spectrogramTensor=this.getTensorFromAudioDataArray(freqData,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let timeData=this.flattenQueue(audioDataQueue.timeDataQueue);waveformTensor=this.getTensorFromAudioDataArray(timeData,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:spectrogramTensor,waveform:waveformTensor},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let freqDataQueue=[],timeDataQueue=[],currentFrames=0;return new Promise(resolve=>{let intervalID=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&resolve({freqDataQueue,timeDataQueue}),freqDataQueue.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),timeDataQueue.push(this.timeData.slice())),++currentFrames===this.numFrames&&(clearInterval(intervalID),resolve({freqDataQueue,timeDataQueue}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(queue){let frameSize=queue[0].length,freqData=new Float32Array(queue.length*frameSize);return queue.forEach((data2,i)=>freqData.set(data2,i*frameSize)),freqData}getTensorFromAudioDataArray(freqData,shape){let vals=new Float32Array(util_exports.sizeFromShape(shape));return vals.set(freqData,vals.length-freqData.length),tensor(vals,shape)}},WebcamIterator=class extends LazyIterator{constructor(webcamVideoElement,webcamConfig){super();if(this.webcamVideoElement=webcamVideoElement,this.webcamConfig=webcamConfig,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=tensor1d([0],"int32"),this.webcamConfig.centerCrop){let widthCroppingRatio=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,heightCroppingRatio=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,widthCropStart=(1-widthCroppingRatio)/2,heightCropStart=(1-heightCroppingRatio)/2,widthCropEnd=widthCropStart+widthCroppingRatio,heightCropEnd=heightCroppingRatio+heightCropStart;this.cropBox=tensor2d([heightCropStart,widthCropStart,heightCropEnd,widthCropEnd],[1,4])}else this.cropBox=tensor2d([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(webcamVideoElement,webcamConfig={}){if(env().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!webcamVideoElement){if(webcamVideoElement=document.createElement("video"),!webcamConfig.resizeWidth||!webcamConfig.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");webcamVideoElement.width=webcamConfig.resizeWidth,webcamVideoElement.height=webcamConfig.resizeHeight}let webcamIterator=new WebcamIterator(webcamVideoElement,webcamConfig);return await webcamIterator.start(),webcamIterator}async start(){this.webcamConfig.facingMode&&util_exports.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(error){console.log(error),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(resolve=>{this.webcamVideoElement.onloadedmetadata=()=>{resolve()}})}async next(){if(this.isClosed)return{value:null,done:!0};let img;try{img=browser_exports.fromPixels(this.webcamVideoElement)}catch(e){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(e)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(img),done:!1}}catch(e){throw new Error(`Error thrown cropping the video: ${e.message}`)}finally{img.dispose()}else return{value:img,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(img){return tidy(()=>{let expandedImage=img.toFloat().expandDims(0),resizedImage;resizedImage=image.cropAndResize(expandedImage,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let shape=resizedImage.shape;return resizedImage.reshape(shape.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(track=>track.stop());try{this.webcamVideoElement.srcObject=null}catch(error){console.log(error),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},DataSource=class{},StringIterator=class extends LazyIterator{split(separator){return new SplitIterator(this,separator)}},SplitIterator=class extends StringIterator{constructor(upstream,separator){super();this.upstream=upstream,this.impl=new SplitIteratorImpl(upstream,separator)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},SplitIteratorImpl=class extends OneToManyIterator{constructor(upstream,separator){super();this.upstream=upstream,this.separator=separator,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let chunkResult=await this.upstream.next();if(chunkResult.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let lines=chunkResult.value.split(this.separator);lines[0]=this.carryover+lines[0];for(let line of lines.slice(0,-1))this.outputQueue.push(line);return this.carryover=lines[lines.length-1],!0}},ByteChunkIterator=class extends LazyIterator{decodeUTF8(){return new Utf8Iterator(this)}},Utf8Iterator=class extends StringIterator{constructor(upstream){super();this.upstream=upstream,this.impl=new Utf8IteratorImpl(upstream)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},Utf8IteratorImpl=class extends OneToManyIterator{constructor(upstream){super();if(this.upstream=upstream,env().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder}=require_string_decoder();this.decoder=new StringDecoder("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let chunkResult=await this.upstream.next(),chunk;if(chunkResult.done)return!1;chunk=chunkResult.value;let text;return env().get("IS_BROWSER")?text=this.decoder.decode(chunk,{stream:!0}):text=this.decoder.write(Buffer.from(chunk.buffer)),this.outputQueue.push(text),!0}},FileChunkIterator=class extends ByteChunkIterator{constructor(file,options={}){super();this.file=file,this.options=options,util_exports.assert(file instanceof Uint8Array||(env().get("IS_BROWSER")?file instanceof File||file instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=options.offset||0,this.chunkSize=options.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((resolve,reject)=>{let end=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)resolve(new Uint8Array(this.file.slice(this.offset,end)));else{let fileReader=new FileReader;fileReader.onload=event=>{let data2=fileReader.result;if(data2 instanceof ArrayBuffer&&(data2=new Uint8Array(data2)),!(data2 instanceof Uint8Array))return reject(new TypeError("FileReader returned unknown type."));resolve(data2)},fileReader.onabort=event=>reject(new Error("Aborted")),fileReader.onerror=event=>reject(new Error(event.type));let slice5=this.file.slice(this.offset,end);fileReader.readAsArrayBuffer(slice5)}this.offset=end}),done:!1}}};async function urlChunkIterator(url,options={}){let urlString,requestInit;typeof url=="string"?urlString=url:(urlString=url.url,requestInit=getRequestInitFromRequest(url));let response=await util_exports.fetch(urlString,requestInit);if(response.ok){let uint8Array=new Uint8Array(await response.arrayBuffer());return new FileChunkIterator(uint8Array,options)}else throw new Error(response.statusText)}var getRequestInitFromRequest=request=>({method:request.method,headers:request.headers,body:request.body,mode:request.mode,credentials:request.credentials,cache:request.cache,redirect:request.redirect,referrer:request.referrer,integrity:request.integrity});function isLocalPath(source){return typeof source=="string"&&source.substr(0,7)==="file://"}var FileDataSource=class extends DataSource{constructor(input2,options={}){super();this.input=input2,this.options=options}async iterator(){if(isLocalPath(this.input)&&env().get("IS_NODE")){let fs=require("fs");this.input=fs.readFileSync(this.input.substr(7))}return new FileChunkIterator(this.input,this.options)}},URLDataSource=class extends DataSource{constructor(url,fileOptions={}){super();this.url=url,this.fileOptions=fileOptions}async iterator(){return isLocalPath(this.url)?new FileDataSource(this.url,this.fileOptions).iterator():urlChunkIterator(this.url,this.fileOptions)}};function csv(source,csvConfig={}){return new CSVDataset(new URLDataSource(source),csvConfig)}function func(f){let iter=iteratorFromFunction(f);return datasetFromIteratorFn(async()=>iter)}function generator(generator2){return datasetFromIteratorFn(async()=>{let gen=await generator2();return iteratorFromFunction(()=>gen.next())})}async function webcam(webcamVideoElement,webcamConfig){return WebcamIterator.create(webcamVideoElement,webcamConfig)}async function microphone(microphoneConfig){return MicrophoneIterator.create(microphoneConfig)}var version12="2.8.3",version13={tfjs:version,"tfjs-core":version2,"tfjs-data":version3,"tfjs-layers":version4,"tfjs-converter":version5,"tfjs-backend-cpu":version7,"tfjs-backend-webgl":version8,"tfjs-backend-wasm":version9},config={name:"humangl",priority:99,canvas:null,width:1024,height:1024,webGLattr:{alpha:!1,antialias:!1,premultipliedAlpha:!1,preserveDrawingBuffer:!1,depth:!1,stencil:!1,failIfMajorPerformanceCaveat:!1,desynchronized:!0}};function register(){if(!findBackend(config.name)){config.canvas=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(config.width,config.height):document.createElement("canvas");let gl=config.canvas.getContext("webgl2",config.webGLattr);setWebGLContext(2,gl);let ctx=new GPGPUContext(gl);registerBackend(config.name,()=>new MathBackendWebGL(ctx),config.priority),getKernelsForBackend("webgl").forEach(kernelConfig=>{let newKernelConfig={...kernelConfig,backendName:config.name};registerKernel(newKernelConfig)}),ENV.set("WEBGL_VERSION",2),ENV.set("WEBGL_MAX_TEXTURE_SIZE",gl.getParameter(gl.MAX_TEXTURE_SIZE)),ENV.set("WEBGL_FORCE_F16_TEXTURES",!0),ENV.set("WEBGL_PACK_DEPTHWISECONV",!0)}}var facemesh=__toModule(require_facemesh()),age=__toModule(require_age()),gender=__toModule(require_gender()),emotion=__toModule(require_emotion()),embedding2=__toModule(require_embedding()),posenet=__toModule(require_posenet()),models={};async function load(config2){return models.blazepose||(models.blazepose=await loadGraphModel(config2.pose.modelPath),log(`load model: ${config2.pose.modelPath.match(/\/(.*)\./)[1]}`)),models.blazepose}async function predict(image3,config2){return models.blazepose?new Promise(async resolve=>{let resize=image.resizeBilinear(image3,[config2.pose.inputSize,config2.pose.inputSize],!1),enhance=div(resize,127.5).sub(1);dispose(resize);let logits=await models.blazepose.predict(enhance);dispose(enhance),logits.map(logit=>logit.dispose()),resolve(logits)}):null}function getBoxSize(box){return[Math.abs(box.endPoint[0]-box.startPoint[0]),Math.abs(box.endPoint[1]-box.startPoint[1])]}function getBoxCenter(box){return[box.startPoint[0]+(box.endPoint[0]-box.startPoint[0])/2,box.startPoint[1]+(box.endPoint[1]-box.startPoint[1])/2]}function cutBoxFromImageAndResize(box,image3,cropSize){let h=image3.shape[1],w=image3.shape[2],boxes=[[box.startPoint[1]/h,box.startPoint[0]/w,box.endPoint[1]/h,box.endPoint[0]/w]];return image.cropAndResize(image3,boxes,[0],cropSize)}function scaleBoxCoordinates(box,factor){let startPoint=[box.startPoint[0]*factor[0],box.startPoint[1]*factor[1]],endPoint=[box.endPoint[0]*factor[0],box.endPoint[1]*factor[1]],palmLandmarks=box.palmLandmarks.map(coord=>[coord[0]*factor[0],coord[1]*factor[1]]);return{startPoint,endPoint,palmLandmarks,confidence:box.confidence}}function enlargeBox(box,factor=1.5){let center=getBoxCenter(box),size=getBoxSize(box),newHalfSize=[factor*size[0]/2,factor*size[1]/2],startPoint=[center[0]-newHalfSize[0],center[1]-newHalfSize[1]],endPoint=[center[0]+newHalfSize[0],center[1]+newHalfSize[1]];return{startPoint,endPoint,palmLandmarks:box.palmLandmarks}}function squarifyBox(box){let centers=getBoxCenter(box),size=getBoxSize(box),halfSize=Math.max(...size)/2,startPoint=[centers[0]-halfSize,centers[1]-halfSize],endPoint=[centers[0]+halfSize,centers[1]+halfSize];return{startPoint,endPoint,palmLandmarks:box.palmLandmarks}}function normalizeRadians(angle){return angle-2*Math.PI*Math.floor((angle+Math.PI)/(2*Math.PI))}function computeRotation(point1,point2){let radians=Math.PI/2-Math.atan2(-(point2[1]-point1[1]),point2[0]-point1[0]);return normalizeRadians(radians)}var buildTranslationMatrix=(x,y)=>[[1,0,x],[0,1,y],[0,0,1]];function dot4(v1,v2){let product=0;for(let i=0;iconfig_default});var config_default={backend:"webgl",wasmPath:"../assets/",async:!0,profile:!1,deallocate:!1,scoped:!1,videoOptimized:!0,warmup:"face",filter:{enabled:!0,width:0,height:0,return:!0,brightness:0,contrast:0,sharpness:0,blur:0,saturation:0,hue:0,negative:!1,sepia:!1,vintage:!1,kodachrome:!1,technicolor:!1,polaroid:!1,pixelate:0},gesture:{enabled:!0},face:{enabled:!0,detector:{modelPath:"../models/blazeface-back.json",inputSize:256,rotation:!1,maxFaces:10,skipFrames:11,minConfidence:.5,iouThreshold:.2,scoreThreshold:.5},mesh:{enabled:!0,modelPath:"../models/facemesh.json",inputSize:192,returnRawData:!1},iris:{enabled:!0,modelPath:"../models/iris.json",inputSize:64},age:{enabled:!0,modelPath:"../models/age-ssrnet-imdb.json",inputSize:64,skipFrames:31},gender:{enabled:!0,minConfidence:.1,modelPath:"../models/gender-ssrnet-imdb.json",inputSize:64,skipFrames:41},emotion:{enabled:!0,inputSize:64,minConfidence:.2,skipFrames:21,modelPath:"../models/emotion-large.json"},embedding:{enabled:!1,inputSize:112,modelPath:"../models/mobilefacenet.json"}},body:{enabled:!0,modelPath:"../models/posenet.json",inputSize:257,maxDetections:10,scoreThreshold:.5,nmsRadius:20,outputStride:16,modelType:"MobileNet"},pose:{enabled:!1,scoreThreshold:.6,iouThreshold:.3,modelPath:"../models/blazepose.json",inputSize:256},hand:{enabled:!0,rotation:!1,inputSize:256,skipFrames:12,minConfidence:.1,iouThreshold:.1,scoreThreshold:.5,maxHands:1,landmarks:!0,detector:{modelPath:"../models/handdetect.json"},skeleton:{modelPath:"../models/handskeleton.json"}}},face=` /9j/4AAQSkZJRgABAQEAYABgAAD/4QBoRXhpZgAATU0AKgAAAAgABAEaAAUAAAABAAAAPgEbAAUA AAABAAAARgEoAAMAAAABAAIAAAExAAIAAAARAAAATgAAAAAAAABgAAAAAQAAAGAAAAABcGFpbnQu bmV0IDQuMi4xMwAA/9sAQwAGBAUGBQQGBgUGBwcGCAoQCgoJCQoUDg8MEBcUGBgXFBYWGh0lHxob @@ -4771,7 +4771,7 @@ AAAAAAJAAAAAAAAAAAAAABAJEAAAAAAAAAAAAAAAIEoBKAAAAAAAAAAAAAAABAlAAAAAAAIAAAAA BAkBAkBAkBAlACEgMZjdjbFW8bWrEx8YWANb6Fp+bfwab+vLDKMFK9qxH5L0bAr8OPRPKz2AY7J2 SbAjYZAI2E7AIEgIEgIEgMdkSy2NgY7MdlmyNoBXsxmFuyNgVTVjNV3KjlBRNTlXTVHKCrlIqt5T lBhEMohlFerLlBjEMohMVTEARDKCITsAk2AEgAAAkAAAAAAAAAAAAAAAAAAAAAAAASAAAAAAAAD/ -2Q==`,version14="0.9.23",now2=()=>typeof performance!="undefined"?performance.now():parseInt(Number(process.hrtime.bigint())/1e3/1e3);function mergeDeep(...objects){let isObject=obj=>obj&&typeof obj=="object";return objects.reduce((prev,obj)=>(Object.keys(obj||{}).forEach(key=>{let pVal=prev[key],oVal=obj[key];Array.isArray(pVal)&&Array.isArray(oVal)?prev[key]=pVal.concat(...oVal):isObject(pVal)&&isObject(oVal)?prev[key]=mergeDeep(pVal,oVal):prev[key]=oVal}),prev),{})}var Human=class{constructor(userConfig2={}){this.tf=tfjs_esm_exports,this.version=version14,this.config=mergeDeep(config_default,userConfig2),this.fx=null,this.state="idle",this.numTensors=0,this.analyzeMemoryLeaks=!1,this.checkSanity=!1,this.firstRun=!0,this.perf={},this.models={facemesh:null,posenet:null,handpose:null,iris:null,age:null,gender:null,emotion:null},this.facemesh=facemesh,this.age=age,this.gender=gender,this.emotion=emotion,this.body=posenet,this.hand=handpose}profile(){return this.config.profile?profile2.data:{}}analyze(...msg){if(!this.analyzeMemoryLeaks)return;let current=engine().state.numTensors,previous=this.numTensors;this.numTensors=current;let leaked=current-previous;leaked!==0&&log(...msg,leaked)}sanity(input2){if(!this.checkSanity)return null;if(!input2)return"input is not defined";if(ENV.flags.IS_NODE&&!(input2 instanceof Tensor))return"input must be a tensor";try{getBackend()}catch(e){return"backend not loaded"}return null}simmilarity(embedding1,embedding22){return this.config.face.embedding.enabled?embedding2.simmilarity(embedding1,embedding22):0}async load(userConfig2){this.state="load";let timeStamp=now2();userConfig2&&(this.config=mergeDeep(this.config,userConfig2)),this.firstRun&&(log(`version: ${this.version} TensorFlow/JS version: ${version6}`),await this.checkBackend(!0),ENV.flags.IS_BROWSER&&(log("configuration:",this.config),log("tf flags:",ENV.flags)),this.firstRun=!1),this.config.async?[this.models.facemesh,this.models.age,this.models.gender,this.models.emotion,this.models.embedding,this.models.posenet,this.models.handpose,this.models.blazepose]=await Promise.all([this.models.facemesh||(this.config.face.enabled?facemesh.load(this.config):null),this.models.age||(this.config.face.enabled&&this.config.face.age.enabled?age.load(this.config):null),this.models.gender||(this.config.face.enabled&&this.config.face.gender.enabled?gender.load(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?emotion.load(this.config):null),this.models.embedding||(this.config.face.enabled&&this.config.face.embedding.enabled?embedding2.load(this.config):null),this.models.posenet||(this.config.body.enabled?posenet.load(this.config):null),this.models.handpose||(this.config.hand.enabled?handpose.load(this.config):null),this.models.blazepose||(this.config.pose.enabled?load(this.config):null)]):(this.config.face.enabled&&!this.models.facemesh&&(this.models.facemesh=await facemesh.load(this.config)),this.config.face.enabled&&this.config.face.age.enabled&&!this.models.age&&(this.models.age=await age.load(this.config)),this.config.face.enabled&&this.config.face.gender.enabled&&!this.models.gender&&(this.models.gender=await gender.load(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await emotion.load(this.config)),this.config.face.enabled&&this.config.face.embedding.enabled&&!this.models.embedding&&(this.models.embedding=await embedding2.load(this.config)),this.config.body.enabled&&!this.models.posenet&&(this.models.posenet=await posenet.load(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await handpose.load(this.config)),this.config.pose.enabled&&!this.models.blazepose&&(this.models.blazepose=await load(this.config)));let current=Math.trunc(now2()-timeStamp);current>(this.perf.load||0)&&(this.perf.load=current)}async checkBackend(force){if(this.config.backend&&this.config.backend!==""&&force||getBackend()!==this.config.backend){let timeStamp=now2();if(this.state="backend",log("setting backend:",this.config.backend),this.config.backend==="wasm"&&(log("settings wasm path:",this.config.wasmPath),setWasmPaths(this.config.wasmPath),await env().getAsync("WASM_HAS_SIMD_SUPPORT")||log("warning: wasm simd support is not enabled")),this.config.backend==="humangl"&&(log("registering humangl backend"),register()),await setBackend(this.config.backend),enableProdMode(),getBackend()==="webgl"){this.config.deallocate&&(log("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",this.config.deallocate),ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",this.config.deallocate?0:-1)),ENV.set("WEBGL_FORCE_F16_TEXTURES",!0),ENV.set("WEBGL_PACK_DEPTHWISECONV",!0);let gl=await backend().getGPGPUContext().gl;log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`)}await ready(),this.perf.backend=Math.trunc(now2()-timeStamp)}}async detectFace(input2){var _a;let timeStamp,ageRes,genderRes,emotionRes,embeddingRes,faceRes=[];this.state="run:face",timeStamp=now2();let faces=await((_a=this.models.facemesh)==null?void 0:_a.estimateFaces(input2,this.config));this.perf.face=Math.trunc(now2()-timeStamp);for(let face3 of faces){if(this.analyze("Get Face"),!face3.image||face3.image.isDisposedInternal){log("Face object is disposed:",face3.image);continue}this.analyze("Start Age:"),this.config.async?ageRes=this.config.face.age.enabled?age.predict(face3.image,this.config):{}:(this.state="run:age",timeStamp=now2(),ageRes=this.config.face.age.enabled?await age.predict(face3.image,this.config):{},this.perf.age=Math.trunc(now2()-timeStamp)),this.analyze("Start Gender:"),this.config.async?genderRes=this.config.face.gender.enabled?gender.predict(face3.image,this.config):{}:(this.state="run:gender",timeStamp=now2(),genderRes=this.config.face.gender.enabled?await gender.predict(face3.image,this.config):{},this.perf.gender=Math.trunc(now2()-timeStamp)),this.analyze("Start Emotion:"),this.config.async?emotionRes=this.config.face.emotion.enabled?emotion.predict(face3.image,this.config):{}:(this.state="run:emotion",timeStamp=now2(),emotionRes=this.config.face.emotion.enabled?await emotion.predict(face3.image,this.config):{},this.perf.emotion=Math.trunc(now2()-timeStamp)),this.analyze("End Emotion:"),this.analyze("Start Embedding:"),this.config.async?embeddingRes=this.config.face.embedding.enabled?embedding2.predict(face3.image,this.config):{}:(this.state="run:embedding",timeStamp=now2(),embeddingRes=this.config.face.embedding.enabled?await embedding2.predict(face3.image,this.config):{},this.perf.embedding=Math.trunc(now2()-timeStamp)),this.analyze("End Emotion:"),this.config.async&&([ageRes,genderRes,emotionRes,embeddingRes]=await Promise.all([ageRes,genderRes,emotionRes,embeddingRes])),this.analyze("Finish Face:"),face3.image.dispose(),this.config.face.iris.enabled||(delete face3.annotations.leftEyeIris,delete face3.annotations.rightEyeIris);let irisSize=face3.annotations.leftEyeIris&&face3.annotations.rightEyeIris?11.7*Math.max(Math.abs(face3.annotations.leftEyeIris[3][0]-face3.annotations.leftEyeIris[1][0]),Math.abs(face3.annotations.rightEyeIris[4][1]-face3.annotations.rightEyeIris[2][1])):0;faceRes.push({confidence:face3.confidence,box:face3.box,mesh:face3.mesh,boxRaw:face3.boxRaw,meshRaw:face3.meshRaw,annotations:face3.annotations,age:ageRes.age,gender:genderRes.gender,genderConfidence:genderRes.confidence,emotion:emotionRes,embedding:embeddingRes,iris:irisSize!==0?Math.trunc(irisSize)/100:0}),this.analyze("End Face")}return this.analyze("End FaceMesh:"),this.config.async&&(this.perf.face&&delete this.perf.face,this.perf.age&&delete this.perf.age,this.perf.gender&&delete this.perf.gender,this.perf.emotion&&delete this.perf.emotion),faceRes}async image(input2,userConfig2={}){this.state="image",this.config=mergeDeep(this.config,userConfig2);let process3=image2.process(input2,this.config);return process3.tensor.dispose(),process3.canvas}async detect(input2,userConfig2={}){return new Promise(async resolve=>{var _a,_b,_c,_d;this.state="config";let timeStamp;this.config=mergeDeep(this.config,userConfig2),this.state="check";let error=this.sanity(input2);error&&(log(error,input2),resolve({error}));let poseRes,blazeposeRes,handRes,faceRes,timeStart=now2();await this.checkBackend(),await this.load(),this.config.scoped&&engine().startScope(),this.analyze("Start Scope:"),timeStamp=now2();let process3=image2.process(input2,this.config);if(!process3||!process3.tensor){log("could not convert input to tensor"),resolve({error:"could not convert input to tensor"});return}this.perf.image=Math.trunc(now2()-timeStamp),this.analyze("Get Image:"),this.config.async?(faceRes=this.config.face.enabled?this.detectFace(process3.tensor):[],this.perf.face&&delete this.perf.face):(this.state="run:face",timeStamp=now2(),faceRes=this.config.face.enabled?await this.detectFace(process3.tensor):[],this.perf.face=Math.trunc(now2()-timeStamp)),this.analyze("Start Body:"),this.config.async?(poseRes=this.config.body.enabled?(_a=this.models.posenet)==null?void 0:_a.estimatePoses(process3.tensor,this.config):[],this.perf.body&&delete this.perf.body):(this.state="run:body",timeStamp=now2(),poseRes=this.config.body.enabled?await((_b=this.models.posenet)==null?void 0:_b.estimatePoses(process3.tensor,this.config)):[],this.perf.body=Math.trunc(now2()-timeStamp)),this.analyze("End Body:"),this.analyze("Start Pose:"),this.config.async?(blazeposeRes=this.config.pose.enabled?predict(process3.tensor,this.config):[],this.perf.pose&&delete this.perf.pose):(this.state="run:pose",timeStamp=now2(),blazeposeRes=this.config.pose.enabled?await predict(process3.tensor,this.config):[],this.perf.pose=Math.trunc(now2()-timeStamp)),this.analyze("End Pose:"),this.analyze("Start Hand:"),this.config.async?(handRes=this.config.hand.enabled?(_c=this.models.handpose)==null?void 0:_c.estimateHands(process3.tensor,this.config):[],this.perf.hand&&delete this.perf.hand):(this.state="run:hand",timeStamp=now2(),handRes=this.config.hand.enabled?await((_d=this.models.handpose)==null?void 0:_d.estimateHands(process3.tensor,this.config)):[],this.perf.hand=Math.trunc(now2()-timeStamp)),this.analyze("End Hand:"),this.config.async&&([faceRes,poseRes,blazeposeRes,handRes]=await Promise.all([faceRes,poseRes,blazeposeRes,handRes])),process3.tensor.dispose(),this.config.scoped&&engine().endScope(),this.analyze("End Scope:");let gestureRes=[];this.config.gesture.enabled&&(timeStamp=now2(),gestureRes=[...gesture.face(faceRes),...gesture.body(poseRes),...gesture.hand(handRes),...gesture.iris(faceRes)],this.config.async?this.perf.gesture&&delete this.perf.gesture:this.perf.gesture=Math.trunc(now2()-timeStamp)),this.perf.total=Math.trunc(now2()-timeStart),this.state="idle",resolve({face:faceRes,body:poseRes,hand:handRes,pose:blazeposeRes,gesture:gestureRes,performance:this.perf,canvas:process3.canvas})})}async warmup(userConfig2){let b64toBlob=(base64,type="application/octet-stream")=>fetch(`data:${type};base64,${base64}`).then(res=>res.blob());userConfig2&&(this.config=mergeDeep(this.config,userConfig2));let video=this.config.videoOptimized;this.config.videoOptimized=!1;let blob;switch(this.config.warmup){case"face":blob=await b64toBlob(face);break;case"full":blob=await b64toBlob(body);break;default:blob=null}if(!blob)return null;let bitmap=await createImageBitmap(blob),t0=now2(),warmup=await this.detect(bitmap,config_exports),t1=now2();return bitmap.close(),log("Warmup",this.config.warmup,t1-t0,warmup),this.config.videoOptimized=video,warmup}};async function drawGesture(result,canvas,ui2){if(!result)return;let ctx=canvas.getContext("2d");ctx.font=ui2.baseFont,ctx.fillStyle=ui2.baseLabel;let i=1;for(let gesture2=0;gesture21&&what[1].length>0){let person=where2[1]>0?`#${where2[1]}`:"",label=`${where2[0]} ${person}: ${what[1]}`;ctx.fillStyle="black",ctx.fillText(label,8,2+i*ui2.baseLineHeight),ctx.fillStyle=ui2.baseLabel,ctx.fillText(label,6,0+i*ui2.baseLineHeight),i+=1}}}async function drawFace(result,canvas,ui2,triangulation){if(!result)return;let ctx=canvas.getContext("2d");for(let face2 of result){ctx.font=ui2.baseFont,ctx.strokeStyle=ui2.baseColor,ctx.fillStyle=ui2.baseColor,ctx.lineWidth=ui2.baseLineWidth,ctx.beginPath(),ui2.drawBoxes&&ctx.rect(face2.box[0],face2.box[1],face2.box[2],face2.box[3]);let labels=[];if(face2.genderConfidence&&labels.push(`${face2.gender||""} ${Math.trunc(100*face2.genderConfidence)}% confident`),face2.age&&labels.push(`age: ${face2.age||""}`),face2.iris&&labels.push(`iris distance: ${face2.iris}`),face2.emotion&&face2.emotion.length>0){let emotion2=face2.emotion.map(a=>`${Math.trunc(100*a.score)}% ${a.emotion}`);labels.push(emotion2.join(" "))}ctx.fillStyle=ui2.baseLabel;for(let i=0;iface2.mesh[index]),path=new Path2D;path.moveTo(points[0][0],points[0][1]);for(let point of points)path.lineTo(point[0],point[1]);path.closePath(),ctx.strokeStyle=ui2.useDepth?`rgba(${127.5+2*points[0][2]}, ${127.5-2*points[0][2]}, 255, 0.3)`:ui2.baseColor,ctx.stroke(path),ui2.fillPolygons&&(ctx.fillStyle=ui2.useDepth?`rgba(${127.5+2*points[0][2]}, ${127.5-2*points[0][2]}, 255, 0.3)`:ui2.baseColor,ctx.fill(path))}if(face2.annotations&&face2.annotations.leftEyeIris){ctx.strokeStyle=ui2.useDepth?"rgba(255, 200, 255, 0.3)":ui2.baseColor,ctx.beginPath();let sizeX=Math.abs(face2.annotations.leftEyeIris[3][0]-face2.annotations.leftEyeIris[1][0])/2,sizeY=Math.abs(face2.annotations.leftEyeIris[4][1]-face2.annotations.leftEyeIris[2][1])/2;ctx.ellipse(face2.annotations.leftEyeIris[0][0],face2.annotations.leftEyeIris[0][1],sizeX,sizeY,0,0,2*Math.PI),ctx.stroke(),ui2.fillPolygons&&(ctx.fillStyle=ui2.useDepth?"rgba(255, 255, 200, 0.3)":ui2.baseColor,ctx.fill())}if(face2.annotations&&face2.annotations.rightEyeIris){ctx.strokeStyle=ui2.useDepth?"rgba(255, 200, 255, 0.3)":ui2.baseColor,ctx.beginPath();let sizeX=Math.abs(face2.annotations.rightEyeIris[3][0]-face2.annotations.rightEyeIris[1][0])/2,sizeY=Math.abs(face2.annotations.rightEyeIris[4][1]-face2.annotations.rightEyeIris[2][1])/2;ctx.ellipse(face2.annotations.rightEyeIris[0][0],face2.annotations.rightEyeIris[0][1],sizeX,sizeY,0,0,2*Math.PI),ctx.stroke(),ui2.fillPolygons&&(ctx.fillStyle=ui2.useDepth?"rgba(255, 255, 200, 0.3)":ui2.baseColor,ctx.fill())}}}}}var lastDrawnPose=[];async function drawBody(result,canvas,ui2){if(!result)return;let ctx=canvas.getContext("2d");ctx.lineJoin="round";for(let i=0;ia.part==="leftShoulder"),root&&(path.moveTo(root.position.x,root.position.y),part=result[i].keypoints.find(a=>a.part==="rightShoulder"),part&&path.lineTo(part.position.x,part.position.y),part=result[i].keypoints.find(a=>a.part==="rightHip"),part&&path.lineTo(part.position.x,part.position.y),part=result[i].keypoints.find(a=>a.part==="leftHip"),part&&path.lineTo(part.position.x,part.position.y),part=result[i].keypoints.find(a=>a.part==="leftShoulder"),part&&path.lineTo(part.position.x,part.position.y)),root=result[i].keypoints.find(a=>a.part==="leftHip"),root&&(path.moveTo(root.position.x,root.position.y),part=result[i].keypoints.find(a=>a.part==="leftKnee"),part&&path.lineTo(part.position.x,part.position.y),part=result[i].keypoints.find(a=>a.part==="leftAnkle"),part&&path.lineTo(part.position.x,part.position.y)),root=result[i].keypoints.find(a=>a.part==="rightHip"),root&&(path.moveTo(root.position.x,root.position.y),part=result[i].keypoints.find(a=>a.part==="rightKnee"),part&&path.lineTo(part.position.x,part.position.y),part=result[i].keypoints.find(a=>a.part==="rightAnkle"),part&&path.lineTo(part.position.x,part.position.y)),root=result[i].keypoints.find(a=>a.part==="leftShoulder"),root&&(path.moveTo(root.position.x,root.position.y),part=result[i].keypoints.find(a=>a.part==="leftElbow"),part&&path.lineTo(part.position.x,part.position.y),part=result[i].keypoints.find(a=>a.part==="leftWrist"),part&&path.lineTo(part.position.x,part.position.y)),root=result[i].keypoints.find(a=>a.part==="rightShoulder"),root&&(path.moveTo(root.position.x,root.position.y),part=result[i].keypoints.find(a=>a.part==="rightElbow"),part&&path.lineTo(part.position.x,part.position.y),part=result[i].keypoints.find(a=>a.part==="rightWrist"),part&&path.lineTo(part.position.x,part.position.y)),ctx.stroke(path)}}}async function drawHand(result,canvas,ui2){if(!result)return;let ctx=canvas.getContext("2d");ctx.lineJoin="round";for(let hand of result){if(ctx.font=ui2.baseFont,ctx.lineWidth=ui2.baseLineWidth,ui2.drawBoxes&&(ctx.lineWidth=ui2.baseLineWidth,ctx.beginPath(),ctx.strokeStyle=ui2.baseColor,ctx.fillStyle=ui2.baseColor,ctx.rect(hand.box[0],hand.box[1],hand.box[2],hand.box[3]),ctx.fillStyle="black",ctx.fillText("hand",hand.box[0]+3,1+hand.box[1]+ui2.baseLineHeight,hand.box[2]),ctx.fillStyle=ui2.baseLabel,ctx.fillText("hand",hand.box[0]+2,0+hand.box[1]+ui2.baseLineHeight,hand.box[2]),ctx.stroke()),ui2.drawPoints&&hand.landmarks&&hand.landmarks.length>0)for(let point of hand.landmarks)ctx.fillStyle=ui2.useDepth?`rgba(${127.5+2*point[2]}, ${127.5-2*point[2]}, 255, 0.5)`:ui2.baseColor,ctx.beginPath(),ctx.arc(point[0],point[1],2,0,2*Math.PI),ctx.fill();if(ui2.drawPolygons){let addPart=part=>{if(!!part)for(let i=0;i0?i-1:0][0],part[i>0?i-1:0][1]),ctx.lineTo(part[i][0],part[i][1]),ctx.stroke()};addPart(hand.annotations.indexFinger),addPart(hand.annotations.middleFinger),addPart(hand.annotations.ringFinger),addPart(hand.annotations.pinky),addPart(hand.annotations.thumb)}}}var draw_default={face:drawFace,body:drawBody,hand:drawHand,gesture:drawGesture};var instance=0,CSScreated=!1,theme={background:"darkslategray",hover:"lightgray",itemBackground:"black",itemColor:"white",buttonBackground:"lightblue",buttonHover:"lightgreen",checkboxOn:"lightgreen",checkboxOff:"lightcoral",rangeBackground:"lightblue",rangeLabel:"white",chartColor:"lightblue"};function createCSS(){if(CSScreated)return;let css=` +2Q==`,version14="0.9.24",now2=()=>typeof performance!="undefined"?performance.now():parseInt(Number(process.hrtime.bigint())/1e3/1e3);function mergeDeep(...objects){let isObject=obj=>obj&&typeof obj=="object";return objects.reduce((prev,obj)=>(Object.keys(obj||{}).forEach(key=>{let pVal=prev[key],oVal=obj[key];Array.isArray(pVal)&&Array.isArray(oVal)?prev[key]=pVal.concat(...oVal):isObject(pVal)&&isObject(oVal)?prev[key]=mergeDeep(pVal,oVal):prev[key]=oVal}),prev),{})}var Human=class{constructor(userConfig2={}){this.tf=tfjs_esm_exports,this.version=version14,this.config=mergeDeep(config_default,userConfig2),this.fx=null,this.state="idle",this.numTensors=0,this.analyzeMemoryLeaks=!1,this.checkSanity=!1,this.firstRun=!0,this.perf={},this.models={facemesh:null,posenet:null,handpose:null,iris:null,age:null,gender:null,emotion:null},this.facemesh=facemesh,this.age=age,this.gender=gender,this.emotion=emotion,this.body=posenet,this.hand=handpose}profile(){return this.config.profile?profile2.data:{}}analyze(...msg){if(!this.analyzeMemoryLeaks)return;let current=engine().state.numTensors,previous=this.numTensors;this.numTensors=current;let leaked=current-previous;leaked!==0&&log(...msg,leaked)}sanity(input2){if(!this.checkSanity)return null;if(!input2)return"input is not defined";if(ENV.flags.IS_NODE&&!(input2 instanceof Tensor))return"input must be a tensor";try{getBackend()}catch(e){return"backend not loaded"}return null}simmilarity(embedding1,embedding22){return this.config.face.embedding.enabled?embedding2.simmilarity(embedding1,embedding22):0}async load(userConfig2){this.state="load";let timeStamp=now2();userConfig2&&(this.config=mergeDeep(this.config,userConfig2)),this.firstRun&&(log(`version: ${this.version} TensorFlow/JS version: ${version6}`),await this.checkBackend(!0),ENV.flags.IS_BROWSER&&(log("configuration:",this.config),log("tf flags:",ENV.flags)),this.firstRun=!1),this.config.async?[this.models.facemesh,this.models.age,this.models.gender,this.models.emotion,this.models.embedding,this.models.posenet,this.models.handpose,this.models.blazepose]=await Promise.all([this.models.facemesh||(this.config.face.enabled?facemesh.load(this.config):null),this.models.age||(this.config.face.enabled&&this.config.face.age.enabled?age.load(this.config):null),this.models.gender||(this.config.face.enabled&&this.config.face.gender.enabled?gender.load(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?emotion.load(this.config):null),this.models.embedding||(this.config.face.enabled&&this.config.face.embedding.enabled?embedding2.load(this.config):null),this.models.posenet||(this.config.body.enabled?posenet.load(this.config):null),this.models.handpose||(this.config.hand.enabled?handpose.load(this.config):null),this.models.blazepose||(this.config.pose.enabled?load(this.config):null)]):(this.config.face.enabled&&!this.models.facemesh&&(this.models.facemesh=await facemesh.load(this.config)),this.config.face.enabled&&this.config.face.age.enabled&&!this.models.age&&(this.models.age=await age.load(this.config)),this.config.face.enabled&&this.config.face.gender.enabled&&!this.models.gender&&(this.models.gender=await gender.load(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await emotion.load(this.config)),this.config.face.enabled&&this.config.face.embedding.enabled&&!this.models.embedding&&(this.models.embedding=await embedding2.load(this.config)),this.config.body.enabled&&!this.models.posenet&&(this.models.posenet=await posenet.load(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await handpose.load(this.config)),this.config.pose.enabled&&!this.models.blazepose&&(this.models.blazepose=await load(this.config)));let current=Math.trunc(now2()-timeStamp);current>(this.perf.load||0)&&(this.perf.load=current)}async checkBackend(force){if(this.config.backend&&this.config.backend!==""&&force||getBackend()!==this.config.backend){let timeStamp=now2();if(this.state="backend",log("setting backend:",this.config.backend),this.config.backend==="wasm"&&(log("settings wasm path:",this.config.wasmPath),setWasmPaths(this.config.wasmPath),await env().getAsync("WASM_HAS_SIMD_SUPPORT")||log("warning: wasm simd support is not enabled")),this.config.backend==="humangl"&&(log("registering humangl backend"),register()),await setBackend(this.config.backend),enableProdMode(),getBackend()==="webgl"){this.config.deallocate&&(log("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",this.config.deallocate),ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",this.config.deallocate?0:-1)),ENV.set("WEBGL_FORCE_F16_TEXTURES",!0),ENV.set("WEBGL_PACK_DEPTHWISECONV",!0);let gl=await backend().getGPGPUContext().gl;log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`)}await ready(),this.perf.backend=Math.trunc(now2()-timeStamp)}}async detectFace(input2){var _a;let timeStamp,ageRes,genderRes,emotionRes,embeddingRes,faceRes=[];this.state="run:face",timeStamp=now2();let faces=await((_a=this.models.facemesh)==null?void 0:_a.estimateFaces(input2,this.config));this.perf.face=Math.trunc(now2()-timeStamp);for(let face3 of faces){if(this.analyze("Get Face"),!face3.image||face3.image.isDisposedInternal){log("Face object is disposed:",face3.image);continue}this.analyze("Start Age:"),this.config.async?ageRes=this.config.face.age.enabled?age.predict(face3.image,this.config):{}:(this.state="run:age",timeStamp=now2(),ageRes=this.config.face.age.enabled?await age.predict(face3.image,this.config):{},this.perf.age=Math.trunc(now2()-timeStamp)),this.analyze("Start Gender:"),this.config.async?genderRes=this.config.face.gender.enabled?gender.predict(face3.image,this.config):{}:(this.state="run:gender",timeStamp=now2(),genderRes=this.config.face.gender.enabled?await gender.predict(face3.image,this.config):{},this.perf.gender=Math.trunc(now2()-timeStamp)),this.analyze("Start Emotion:"),this.config.async?emotionRes=this.config.face.emotion.enabled?emotion.predict(face3.image,this.config):{}:(this.state="run:emotion",timeStamp=now2(),emotionRes=this.config.face.emotion.enabled?await emotion.predict(face3.image,this.config):{},this.perf.emotion=Math.trunc(now2()-timeStamp)),this.analyze("End Emotion:"),this.analyze("Start Embedding:"),this.config.async?embeddingRes=this.config.face.embedding.enabled?embedding2.predict(face3.image,this.config):{}:(this.state="run:embedding",timeStamp=now2(),embeddingRes=this.config.face.embedding.enabled?await embedding2.predict(face3.image,this.config):{},this.perf.embedding=Math.trunc(now2()-timeStamp)),this.analyze("End Emotion:"),this.config.async&&([ageRes,genderRes,emotionRes,embeddingRes]=await Promise.all([ageRes,genderRes,emotionRes,embeddingRes])),this.analyze("Finish Face:"),face3.image.dispose(),this.config.face.iris.enabled||(delete face3.annotations.leftEyeIris,delete face3.annotations.rightEyeIris);let irisSize=face3.annotations.leftEyeIris&&face3.annotations.rightEyeIris?11.7*Math.max(Math.abs(face3.annotations.leftEyeIris[3][0]-face3.annotations.leftEyeIris[1][0]),Math.abs(face3.annotations.rightEyeIris[4][1]-face3.annotations.rightEyeIris[2][1])):0;faceRes.push({confidence:face3.confidence,box:face3.box,mesh:face3.mesh,boxRaw:face3.boxRaw,meshRaw:face3.meshRaw,annotations:face3.annotations,age:ageRes.age,gender:genderRes.gender,genderConfidence:genderRes.confidence,emotion:emotionRes,embedding:embeddingRes,iris:irisSize!==0?Math.trunc(irisSize)/100:0}),this.analyze("End Face")}return this.analyze("End FaceMesh:"),this.config.async&&(this.perf.face&&delete this.perf.face,this.perf.age&&delete this.perf.age,this.perf.gender&&delete this.perf.gender,this.perf.emotion&&delete this.perf.emotion),faceRes}async image(input2,userConfig2={}){this.state="image",this.config=mergeDeep(this.config,userConfig2);let process3=image2.process(input2,this.config);return process3.tensor.dispose(),process3.canvas}async detect(input2,userConfig2={}){return new Promise(async resolve=>{var _a,_b,_c,_d;this.state="config";let timeStamp;this.config=mergeDeep(this.config,userConfig2),this.state="check";let error=this.sanity(input2);error&&(log(error,input2),resolve({error}));let poseRes,blazeposeRes,handRes,faceRes,timeStart=now2();await this.checkBackend(),await this.load(),this.config.scoped&&engine().startScope(),this.analyze("Start Scope:"),timeStamp=now2();let process3=image2.process(input2,this.config);if(!process3||!process3.tensor){log("could not convert input to tensor"),resolve({error:"could not convert input to tensor"});return}this.perf.image=Math.trunc(now2()-timeStamp),this.analyze("Get Image:"),this.config.async?(faceRes=this.config.face.enabled?this.detectFace(process3.tensor):[],this.perf.face&&delete this.perf.face):(this.state="run:face",timeStamp=now2(),faceRes=this.config.face.enabled?await this.detectFace(process3.tensor):[],this.perf.face=Math.trunc(now2()-timeStamp)),this.analyze("Start Body:"),this.config.async?(poseRes=this.config.body.enabled?(_a=this.models.posenet)==null?void 0:_a.estimatePoses(process3.tensor,this.config):[],this.perf.body&&delete this.perf.body):(this.state="run:body",timeStamp=now2(),poseRes=this.config.body.enabled?await((_b=this.models.posenet)==null?void 0:_b.estimatePoses(process3.tensor,this.config)):[],this.perf.body=Math.trunc(now2()-timeStamp)),this.analyze("End Body:"),this.analyze("Start Pose:"),this.config.async?(blazeposeRes=this.config.pose.enabled?predict(process3.tensor,this.config):[],this.perf.pose&&delete this.perf.pose):(this.state="run:pose",timeStamp=now2(),blazeposeRes=this.config.pose.enabled?await predict(process3.tensor,this.config):[],this.perf.pose=Math.trunc(now2()-timeStamp)),this.analyze("End Pose:"),this.analyze("Start Hand:"),this.config.async?(handRes=this.config.hand.enabled?(_c=this.models.handpose)==null?void 0:_c.estimateHands(process3.tensor,this.config):[],this.perf.hand&&delete this.perf.hand):(this.state="run:hand",timeStamp=now2(),handRes=this.config.hand.enabled?await((_d=this.models.handpose)==null?void 0:_d.estimateHands(process3.tensor,this.config)):[],this.perf.hand=Math.trunc(now2()-timeStamp)),this.analyze("End Hand:"),this.config.async&&([faceRes,poseRes,blazeposeRes,handRes]=await Promise.all([faceRes,poseRes,blazeposeRes,handRes])),process3.tensor.dispose(),this.config.scoped&&engine().endScope(),this.analyze("End Scope:");let gestureRes=[];this.config.gesture.enabled&&(timeStamp=now2(),gestureRes=[...gesture.face(faceRes),...gesture.body(poseRes),...gesture.hand(handRes),...gesture.iris(faceRes)],this.config.async?this.perf.gesture&&delete this.perf.gesture:this.perf.gesture=Math.trunc(now2()-timeStamp)),this.perf.total=Math.trunc(now2()-timeStart),this.state="idle",resolve({face:faceRes,body:poseRes,hand:handRes,pose:blazeposeRes,gesture:gestureRes,performance:this.perf,canvas:process3.canvas})})}async warmupBitmap(){let b64toBlob=(base64,type="application/octet-stream")=>fetch(`data:${type};base64,${base64}`).then(res2=>res2.blob()),blob,res;switch(this.config.warmup){case"face":blob=await b64toBlob(face);break;case"full":blob=await b64toBlob(body);break;default:blob=null}if(blob){let bitmap=await createImageBitmap(blob);res=await this.detect(bitmap,config_exports),bitmap.close()}return res}async warmupCanvas(){return new Promise(resolve=>{let src,size=0;switch(this.config.warmup){case"face":size=256,src="data:image/jpeg;base64,"+face;break;case"full":size=1200,src="data:image/jpeg;base64,"+body;break;default:src=null}let img=new Image(size,size);img.onload=()=>{let canvas=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(size,size):document.createElement("canvas");canvas.width=size,canvas.height=size;let ctx=canvas.getContext("2d");ctx.drawImage(img,0,0);let data2=ctx.getImageData(0,0,size,size);this.detect(data2,config_exports).then(res=>resolve(res))},src?img.src=src:resolve(null)})}async warmup(userConfig2){let t0=now2();userConfig2&&(this.config=mergeDeep(this.config,userConfig2));let video=this.config.videoOptimized;this.config.videoOptimized=!1;let res;typeof createImageBitmap=="function"?res=await this.warmupBitmap():res=await this.warmupCanvas(),this.config.videoOptimized=video;let t1=now2();return log("Warmup",this.config.warmup,t1-t0,res),res}};async function drawGesture(result,canvas,ui2){if(!result)return;let ctx=canvas.getContext("2d");ctx.font=ui2.baseFont,ctx.fillStyle=ui2.baseLabel;let i=1;for(let gesture2=0;gesture21&&what[1].length>0){let person=where2[1]>0?`#${where2[1]}`:"",label=`${where2[0]} ${person}: ${what[1]}`;ctx.fillStyle="black",ctx.fillText(label,8,2+i*ui2.baseLineHeight),ctx.fillStyle=ui2.baseLabel,ctx.fillText(label,6,0+i*ui2.baseLineHeight),i+=1}}}async function drawFace(result,canvas,ui2,triangulation){if(!result)return;let ctx=canvas.getContext("2d");for(let face2 of result){ctx.font=ui2.baseFont,ctx.strokeStyle=ui2.baseColor,ctx.fillStyle=ui2.baseColor,ctx.lineWidth=ui2.baseLineWidth,ctx.beginPath(),ui2.drawBoxes&&ctx.rect(face2.box[0],face2.box[1],face2.box[2],face2.box[3]);let labels=[];if(face2.genderConfidence&&labels.push(`${face2.gender||""} ${Math.trunc(100*face2.genderConfidence)}% confident`),face2.age&&labels.push(`age: ${face2.age||""}`),face2.iris&&labels.push(`iris distance: ${face2.iris}`),face2.emotion&&face2.emotion.length>0){let emotion2=face2.emotion.map(a=>`${Math.trunc(100*a.score)}% ${a.emotion}`);labels.push(emotion2.join(" "))}ctx.fillStyle=ui2.baseLabel;for(let i=0;iface2.mesh[index]),path=new Path2D;path.moveTo(points[0][0],points[0][1]);for(let point of points)path.lineTo(point[0],point[1]);path.closePath(),ctx.strokeStyle=ui2.useDepth?`rgba(${127.5+2*points[0][2]}, ${127.5-2*points[0][2]}, 255, 0.3)`:ui2.baseColor,ctx.stroke(path),ui2.fillPolygons&&(ctx.fillStyle=ui2.useDepth?`rgba(${127.5+2*points[0][2]}, ${127.5-2*points[0][2]}, 255, 0.3)`:ui2.baseColor,ctx.fill(path))}if(face2.annotations&&face2.annotations.leftEyeIris){ctx.strokeStyle=ui2.useDepth?"rgba(255, 200, 255, 0.3)":ui2.baseColor,ctx.beginPath();let sizeX=Math.abs(face2.annotations.leftEyeIris[3][0]-face2.annotations.leftEyeIris[1][0])/2,sizeY=Math.abs(face2.annotations.leftEyeIris[4][1]-face2.annotations.leftEyeIris[2][1])/2;ctx.ellipse(face2.annotations.leftEyeIris[0][0],face2.annotations.leftEyeIris[0][1],sizeX,sizeY,0,0,2*Math.PI),ctx.stroke(),ui2.fillPolygons&&(ctx.fillStyle=ui2.useDepth?"rgba(255, 255, 200, 0.3)":ui2.baseColor,ctx.fill())}if(face2.annotations&&face2.annotations.rightEyeIris){ctx.strokeStyle=ui2.useDepth?"rgba(255, 200, 255, 0.3)":ui2.baseColor,ctx.beginPath();let sizeX=Math.abs(face2.annotations.rightEyeIris[3][0]-face2.annotations.rightEyeIris[1][0])/2,sizeY=Math.abs(face2.annotations.rightEyeIris[4][1]-face2.annotations.rightEyeIris[2][1])/2;ctx.ellipse(face2.annotations.rightEyeIris[0][0],face2.annotations.rightEyeIris[0][1],sizeX,sizeY,0,0,2*Math.PI),ctx.stroke(),ui2.fillPolygons&&(ctx.fillStyle=ui2.useDepth?"rgba(255, 255, 200, 0.3)":ui2.baseColor,ctx.fill())}}}}}var lastDrawnPose=[];async function drawBody(result,canvas,ui2){if(!result)return;let ctx=canvas.getContext("2d");ctx.lineJoin="round";for(let i=0;ia.part==="leftShoulder"),root&&(path.moveTo(root.position.x,root.position.y),part=result[i].keypoints.find(a=>a.part==="rightShoulder"),part&&path.lineTo(part.position.x,part.position.y),part=result[i].keypoints.find(a=>a.part==="rightHip"),part&&path.lineTo(part.position.x,part.position.y),part=result[i].keypoints.find(a=>a.part==="leftHip"),part&&path.lineTo(part.position.x,part.position.y),part=result[i].keypoints.find(a=>a.part==="leftShoulder"),part&&path.lineTo(part.position.x,part.position.y)),root=result[i].keypoints.find(a=>a.part==="leftHip"),root&&(path.moveTo(root.position.x,root.position.y),part=result[i].keypoints.find(a=>a.part==="leftKnee"),part&&path.lineTo(part.position.x,part.position.y),part=result[i].keypoints.find(a=>a.part==="leftAnkle"),part&&path.lineTo(part.position.x,part.position.y)),root=result[i].keypoints.find(a=>a.part==="rightHip"),root&&(path.moveTo(root.position.x,root.position.y),part=result[i].keypoints.find(a=>a.part==="rightKnee"),part&&path.lineTo(part.position.x,part.position.y),part=result[i].keypoints.find(a=>a.part==="rightAnkle"),part&&path.lineTo(part.position.x,part.position.y)),root=result[i].keypoints.find(a=>a.part==="leftShoulder"),root&&(path.moveTo(root.position.x,root.position.y),part=result[i].keypoints.find(a=>a.part==="leftElbow"),part&&path.lineTo(part.position.x,part.position.y),part=result[i].keypoints.find(a=>a.part==="leftWrist"),part&&path.lineTo(part.position.x,part.position.y)),root=result[i].keypoints.find(a=>a.part==="rightShoulder"),root&&(path.moveTo(root.position.x,root.position.y),part=result[i].keypoints.find(a=>a.part==="rightElbow"),part&&path.lineTo(part.position.x,part.position.y),part=result[i].keypoints.find(a=>a.part==="rightWrist"),part&&path.lineTo(part.position.x,part.position.y)),ctx.stroke(path)}}}async function drawHand(result,canvas,ui2){if(!result)return;let ctx=canvas.getContext("2d");ctx.lineJoin="round";for(let hand of result){if(ctx.font=ui2.baseFont,ctx.lineWidth=ui2.baseLineWidth,ui2.drawBoxes&&(ctx.lineWidth=ui2.baseLineWidth,ctx.beginPath(),ctx.strokeStyle=ui2.baseColor,ctx.fillStyle=ui2.baseColor,ctx.rect(hand.box[0],hand.box[1],hand.box[2],hand.box[3]),ctx.fillStyle="black",ctx.fillText("hand",hand.box[0]+3,1+hand.box[1]+ui2.baseLineHeight,hand.box[2]),ctx.fillStyle=ui2.baseLabel,ctx.fillText("hand",hand.box[0]+2,0+hand.box[1]+ui2.baseLineHeight,hand.box[2]),ctx.stroke()),ui2.drawPoints&&hand.landmarks&&hand.landmarks.length>0)for(let point of hand.landmarks)ctx.fillStyle=ui2.useDepth?`rgba(${127.5+2*point[2]}, ${127.5-2*point[2]}, 255, 0.5)`:ui2.baseColor,ctx.beginPath(),ctx.arc(point[0],point[1],2,0,2*Math.PI),ctx.fill();if(ui2.drawPolygons){let addPart=part=>{if(!!part)for(let i=0;i0?i-1:0][0],part[i>0?i-1:0][1]),ctx.lineTo(part[i][0],part[i][1]),ctx.stroke()};addPart(hand.annotations.indexFinger),addPart(hand.annotations.middleFinger),addPart(hand.annotations.ringFinger),addPart(hand.annotations.pinky),addPart(hand.annotations.thumb)}}}var draw_default={face:drawFace,body:drawBody,hand:drawHand,gesture:drawGesture};var instance=0,CSScreated=!1,theme={background:"darkslategray",hover:"lightgray",itemBackground:"black",itemColor:"white",buttonBackground:"lightblue",buttonHover:"lightgreen",checkboxOn:"lightgreen",checkboxOff:"lightcoral",rangeBackground:"lightblue",rangeLabel:"white",chartColor:"lightblue"};function createCSS(){if(CSScreated)return;let css=` :root { --rounded: 0.2rem; } .menu { position: absolute; top: 0rem; right: 0; width: max-content; padding: 0 0.2rem 0 0.2rem; line-height: 1.8rem; z-index: 10; box-shadow: 0 0 8px dimgrey; background: ${theme.background}; border-radius: var(--rounded); border-color: black; border-style: solid; 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"../node_modules/@tensorflow/tfjs-data/src/sources/file_data_source.ts", "../node_modules/@tensorflow/tfjs-data/src/sources/url_data_source.ts", "../node_modules/@tensorflow/tfjs-data/src/readers.ts", "../node_modules/@tensorflow/tfjs-data/src/version.ts", "../src/tfjs/tf-browser.js", "../src/tfjs/backend.js", "../src/human.js", "../src/blazepose/blazepose.js", "../src/handpose/box.js", "../src/handpose/util.js", "../config.js", "../src/sample.js", "../demo/draw.js", "../demo/menu.js", "../demo/gl-bench.js", "../demo/browser.js"], - "sourcesContent": ["import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nconst NUM_LANDMARKS = 6;\n\nfunction generateAnchors(inputSize) {\n const spec = { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] };\n const anchors = [];\n for (let i = 0; i < spec.strides.length; i++) {\n const stride = spec.strides[i];\n const gridRows = Math.floor((inputSize + stride - 1) / stride);\n const gridCols = Math.floor((inputSize + stride - 1) / stride);\n const anchorsNum = spec.anchors[i];\n for (let gridY = 0; gridY < gridRows; gridY++) {\n const anchorY = stride * (gridY + 0.5);\n for (let gridX = 0; gridX < gridCols; gridX++) {\n const anchorX = stride * (gridX + 0.5);\n for (let n = 0; n < anchorsNum; n++) {\n anchors.push([anchorX, anchorY]);\n }\n }\n }\n }\n return anchors;\n}\n\nconst disposeBox = (box) => {\n box.startEndTensor.dispose();\n box.startPoint.dispose();\n box.endPoint.dispose();\n};\n\nconst createBox = (startEndTensor) => ({\n startEndTensor,\n startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]),\n endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]),\n});\n\nconst scaleBox = (box, factors) => {\n const starts = tf.mul(box.startPoint, factors);\n const ends = tf.mul(box.endPoint, factors);\n const newCoordinates = tf.concat2d([starts, ends], 1);\n return createBox(newCoordinates);\n};\n\nfunction decodeBounds(boxOutputs, anchors, inputSize) {\n const boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n const centers = tf.add(boxStarts, anchors);\n const boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n const boxSizesNormalized = tf.div(boxSizes, inputSize);\n const centersNormalized = tf.div(centers, inputSize);\n const halfBoxSize = tf.div(boxSizesNormalized, 2);\n const starts = tf.sub(centersNormalized, halfBoxSize);\n const ends = tf.add(centersNormalized, halfBoxSize);\n const startNormalized = tf.mul(starts, inputSize);\n const endNormalized = tf.mul(ends, inputSize);\n const concatAxis = 1;\n return tf.concat2d([startNormalized, endNormalized], concatAxis);\n}\n\nfunction scaleBoxFromPrediction(face, scaleFactor) {\n return tf.tidy(() => {\n const box = face['box'] ? face['box'] : face;\n return scaleBox(box, scaleFactor).startEndTensor.squeeze();\n });\n}\n\nclass BlazeFaceModel {\n constructor(model, config) {\n this.blazeFaceModel = model;\n this.width = config.face.detector.inputSize;\n this.height = config.face.detector.inputSize;\n this.anchorsData = generateAnchors(config.face.detector.inputSize);\n this.anchors = tf.tensor2d(this.anchorsData);\n this.inputSize = tf.tensor1d([this.width, this.height]);\n this.config = config;\n this.scaleFaces = 0.8;\n }\n\n async getBoundingBoxes(inputImage) {\n // sanity check on input\n if ((!inputImage) || (inputImage.isDisposedInternal) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return null;\n const [detectedOutputs, boxes, scores] = tf.tidy(() => {\n const resizedImage = inputImage.resizeBilinear([this.width, this.height]);\n // const normalizedImage = tf.mul(tf.sub(resizedImage.div(255), 0.5), 2);\n const normalizedImage = tf.sub(resizedImage.div(127.5), 1);\n const batchedPrediction = this.blazeFaceModel.predict(normalizedImage);\n let prediction;\n // are we using tfhub or pinto converted model?\n if (Array.isArray(batchedPrediction)) {\n const sorted = batchedPrediction.sort((a, b) => a.size - b.size);\n const concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n const concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n const concat = tf.concat([concat512, concat384], 1);\n prediction = concat.squeeze(0);\n } else {\n prediction = batchedPrediction.squeeze(); // when using tfhub model\n }\n const decodedBounds = decodeBounds(prediction, this.anchors, this.inputSize);\n const logits = tf.slice(prediction, [0, 0], [-1, 1]);\n const scoresOut = tf.sigmoid(logits).squeeze();\n return [prediction, decodedBounds, scoresOut];\n });\n const boxIndicesTensor = await tf.image.nonMaxSuppressionAsync(boxes, scores, this.config.face.detector.maxFaces, this.config.face.detector.iouThreshold, this.config.face.detector.scoreThreshold);\n const boxIndices = boxIndicesTensor.arraySync();\n boxIndicesTensor.dispose();\n const boundingBoxesMap = boxIndices.map((boxIndex) => tf.slice(boxes, [boxIndex, 0], [1, -1]));\n const boundingBoxes = boundingBoxesMap.map((boundingBox) => {\n const vals = boundingBox.arraySync();\n boundingBox.dispose();\n return vals;\n });\n\n const scoresVal = scores.dataSync();\n const annotatedBoxes = [];\n for (let i = 0; i < boundingBoxes.length; i++) {\n const boxIndex = boxIndices[i];\n const confidence = scoresVal[boxIndex];\n if (confidence > this.config.face.detector.minConfidence) {\n const box = createBox(boundingBoxes[i]);\n const anchor = this.anchorsData[boxIndex];\n const landmarks = tf.tidy(() => tf.slice(detectedOutputs, [boxIndex, NUM_LANDMARKS - 1], [1, -1]).squeeze().reshape([NUM_LANDMARKS, -1]));\n annotatedBoxes.push({ box, landmarks, anchor, confidence });\n }\n }\n detectedOutputs.dispose();\n boxes.dispose();\n scores.dispose();\n detectedOutputs.dispose();\n return {\n boxes: annotatedBoxes,\n scaleFactor: [inputImage.shape[2] / this.width, inputImage.shape[1] / this.height],\n };\n }\n\n async estimateFaces(input) {\n // @ts-ignore\n const { boxes, scaleFactor } = await this.getBoundingBoxes(input);\n const faces = [];\n for (const face of boxes) {\n const landmarkData = face.landmarks.arraySync();\n const scaledBox = scaleBoxFromPrediction(face, scaleFactor);\n // @ts-ignore\n const boxData = scaleBox.arraySync();\n const probabilityData = face.probability.arraySync();\n const anchor = face.anchor;\n const [scaleFactorX, scaleFactorY] = scaleFactor;\n const scaledLandmarks = landmarkData\n .map((landmark) => ([\n (landmark[0] + anchor[0]) * scaleFactorX,\n (landmark[1] + anchor[1]) * scaleFactorY,\n ]));\n const normalizedFace = {\n topLeft: boxData.slice(0, 2),\n bottomRight: boxData.slice(2),\n landmarks: scaledLandmarks,\n probability: probabilityData,\n };\n disposeBox(face.box);\n face.landmarks.dispose();\n face.probability.dispose();\n scaledBox.dispose();\n faces.push(normalizedFace);\n }\n return faces;\n }\n}\n\nasync function load(config) {\n const blazeface = await tf.loadGraphModel(config.face.detector.modelPath, { fromTFHub: config.face.detector.modelPath.includes('tfhub.dev') });\n const model = new BlazeFaceModel(blazeface, config);\n log(`load model: ${config.face.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n return model;\n}\n\nexports.load = load;\nexports.BlazeFaceModel = BlazeFaceModel;\nexports.disposeBox = disposeBox;\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nfunction scaleBoxCoordinates(box, factor) {\n const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n return { startPoint, endPoint };\n}\nexports.scaleBoxCoordinates = scaleBoxCoordinates;\n\nfunction getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\nexports.getBoxSize = getBoxSize;\n\nfunction getBoxCenter(box) {\n return [\n box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n ];\n}\nexports.getBoxCenter = getBoxCenter;\n\nfunction cutBoxFromImageAndResize(box, image, cropSize) {\n const h = image.shape[1];\n const w = image.shape[2];\n const boxes = [[\n box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h,\n box.endPoint[0] / w,\n ]];\n return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\nexports.cutBoxFromImageAndResize = cutBoxFromImageAndResize;\n\nfunction enlargeBox(box, factor = 1.5) {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n return { startPoint, endPoint, landmarks: box.landmarks };\n}\nexports.enlargeBox = enlargeBox;\n\nfunction squarifyBox(box) {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const maxEdge = Math.max(...size);\n const halfSize = maxEdge / 2;\n const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n return { startPoint, endPoint, landmarks: box.landmarks };\n}\nexports.squarifyBox = squarifyBox;\n", "exports.IDENTITY_MATRIX = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n/**\n * Normalizes the provided angle to the range -pi to pi.\n * @param angle The angle in radians to be normalized.\n */\nfunction normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\nexports.normalizeRadians = normalizeRadians;\n\n/**\n * Computes the angle of rotation between two anchor points.\n * @param point1 First anchor point\n * @param point2 Second anchor point\n */\nfunction computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\nexports.computeRotation = computeRotation;\n\nfunction radToDegrees(rad) {\n return rad * 180 / Math.PI;\n}\nexports.radToDegrees = radToDegrees;\n\nfunction buildTranslationMatrix(x, y) {\n return [[1, 0, x], [0, 1, y], [0, 0, 1]];\n}\n\nfunction dot(v1, v2) {\n let product = 0;\n for (let i = 0; i < v1.length; i++) {\n product += v1[i] * v2[i];\n }\n return product;\n}\nexports.dot = dot;\n\nfunction getColumnFrom2DArr(arr, columnIndex) {\n const column = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\nexports.getColumnFrom2DArr = getColumnFrom2DArr;\n\nfunction multiplyTransformMatrices(mat1, mat2) {\n const product = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) {\n product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n }\n return product;\n}\nfunction buildRotationMatrix(rotation, center) {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\nexports.buildRotationMatrix = buildRotationMatrix;\n\nfunction invertTransformMatrix(matrix) {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [\n -dot(rotationComponent[0], translationComponent),\n -dot(rotationComponent[1], translationComponent),\n ];\n return [\n rotationComponent[0].concat(invertedTranslation[0]),\n rotationComponent[1].concat(invertedTranslation[1]),\n [0, 0, 1],\n ];\n}\nexports.invertTransformMatrix = invertTransformMatrix;\n\nfunction rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\nexports.rotatePoint = rotatePoint;\n\nfunction xyDistanceBetweenPoints(a, b) {\n return Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n}\nexports.xyDistanceBetweenPoints = xyDistanceBetweenPoints;\n", "const MESH_ANNOTATIONS = {\n silhouette: [\n 10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n 397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n 172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n ],\n lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291],\n lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291],\n lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308],\n lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],\n rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173],\n rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133],\n rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190],\n rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243],\n rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189],\n rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244],\n rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245],\n rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193],\n rightEyebrowLower: [35, 124, 46, 53, 52, 65],\n rightEyeIris: [473, 474, 475, 476, 477],\n leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n leftEyeIris: [468, 469, 470, 471, 472],\n midwayBetweenEyes: [168],\n noseTip: [1],\n noseBottom: [2],\n noseRightCorner: [98],\n noseLeftCorner: [327],\n rightCheek: [205],\n leftCheek: [425],\n};\n\nconst MESH_TO_IRIS_INDICES_MAP = [ // A mapping from facemesh model keypoints to iris model keypoints.\n { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] },\n { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] },\n { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] },\n { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] },\n { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] },\n { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] },\n { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] },\n // { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] },\n // { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] },\n];\n\nconst UV468 = [\n [0.499976992607117, 0.652534008026123],\n [0.500025987625122, 0.547487020492554],\n [0.499974012374878, 0.602371990680695],\n [0.482113003730774, 0.471979022026062],\n [0.500150978565216, 0.527155995368958],\n [0.499909996986389, 0.498252987861633],\n [0.499523013830185, 0.40106201171875],\n [0.289712011814117, 0.380764007568359],\n [0.499954998493195, 0.312398016452789],\n [0.499987006187439, 0.269918978214264],\n [0.500023007392883, 0.107050001621246],\n [0.500023007392883, 0.666234016418457],\n [0.5000159740448, 0.679224014282227],\n [0.500023007392883, 0.692348003387451],\n [0.499976992607117, 0.695277988910675],\n [0.499976992607117, 0.70593398809433],\n [0.499976992607117, 0.719385027885437],\n [0.499976992607117, 0.737019002437592],\n [0.499967992305756, 0.781370997428894],\n [0.499816000461578, 0.562981009483337],\n [0.473773002624512, 0.573909997940063],\n [0.104906998574734, 0.254140973091125],\n [0.365929991006851, 0.409575998783112],\n [0.338757991790771, 0.41302502155304],\n [0.311120003461838, 0.409460008144379],\n [0.274657994508743, 0.389131009578705],\n [0.393361985683441, 0.403706014156342],\n [0.345234006643295, 0.344011008739471],\n [0.370094001293182, 0.346076011657715],\n [0.319321990013123, 0.347265005111694],\n [0.297903001308441, 0.353591024875641],\n [0.24779200553894, 0.410809993743896],\n [0.396889001131058, 0.842755019664764],\n [0.280097991228104, 0.375599980354309],\n [0.106310002505779, 0.399955987930298],\n [0.2099249958992, 0.391353011131287],\n [0.355807989835739, 0.534406006336212],\n [0.471751004457474, 0.65040397644043],\n [0.474155008792877, 0.680191993713379],\n [0.439785003662109, 0.657229006290436],\n [0.414617002010345, 0.66654098033905],\n [0.450374007225037, 0.680860996246338],\n [0.428770989179611, 0.682690978050232],\n [0.374971002340317, 0.727805018424988],\n [0.486716985702515, 0.547628998756409],\n [0.485300987958908, 0.527395009994507],\n [0.257764995098114, 0.314490020275116],\n [0.401223003864288, 0.455172002315521],\n [0.429818987846375, 0.548614978790283],\n [0.421351999044418, 0.533740997314453],\n [0.276895999908447, 0.532056987285614],\n [0.483370006084442, 0.499586999416351],\n [0.33721199631691, 0.282882988452911],\n [0.296391993761063, 0.293242990970612],\n [0.169294998049736, 0.193813979625702],\n [0.447580009698868, 0.302609980106354],\n [0.392390012741089, 0.353887975215912],\n [0.354490011930466, 0.696784019470215],\n [0.067304998636246, 0.730105042457581],\n [0.442739009857178, 0.572826027870178],\n [0.457098007202148, 0.584792017936707],\n [0.381974011659622, 0.694710969924927],\n [0.392388999462128, 0.694203019142151],\n [0.277076005935669, 0.271932005882263],\n [0.422551989555359, 0.563233017921448],\n [0.385919004678726, 0.281364023685455],\n [0.383103013038635, 0.255840003490448],\n [0.331431001424789, 0.119714021682739],\n [0.229923993349075, 0.232002973556519],\n [0.364500999450684, 0.189113974571228],\n [0.229622006416321, 0.299540996551514],\n [0.173287004232407, 0.278747975826263],\n [0.472878992557526, 0.666198015213013],\n [0.446828007698059, 0.668527007102966],\n [0.422762006521225, 0.673889994621277],\n [0.445307999849319, 0.580065965652466],\n [0.388103008270264, 0.693961024284363],\n [0.403039008378983, 0.706539988517761],\n [0.403629004955292, 0.693953037261963],\n [0.460041999816895, 0.557139039039612],\n [0.431158006191254, 0.692366003990173],\n [0.452181994915009, 0.692366003990173],\n [0.475387006998062, 0.692366003990173],\n [0.465828001499176, 0.779190003871918],\n [0.472328990697861, 0.736225962638855],\n [0.473087012767792, 0.717857003211975],\n [0.473122000694275, 0.704625964164734],\n [0.473033010959625, 0.695277988910675],\n [0.427942007780075, 0.695277988910675],\n [0.426479011774063, 0.703539967536926],\n [0.423162013292313, 0.711845993995667],\n [0.4183090031147, 0.720062971115112],\n [0.390094995498657, 0.639572978019714],\n [0.013953999616206, 0.560034036636353],\n [0.499913990497589, 0.58014702796936],\n [0.413199990987778, 0.69539999961853],\n [0.409626007080078, 0.701822996139526],\n [0.468080013990402, 0.601534962654114],\n [0.422728985548019, 0.585985004901886],\n [0.463079988956451, 0.593783974647522],\n [0.37211999297142, 0.47341400384903],\n [0.334562003612518, 0.496073007583618],\n [0.411671012639999, 0.546965003013611],\n [0.242175996303558, 0.14767599105835],\n [0.290776997804642, 0.201445996761322],\n [0.327338010072708, 0.256527006626129],\n [0.399509996175766, 0.748921036720276],\n [0.441727995872498, 0.261676013469696],\n [0.429764986038208, 0.187834024429321],\n [0.412198007106781, 0.108901023864746],\n [0.288955003023148, 0.398952007293701],\n [0.218936994671822, 0.435410976409912],\n [0.41278201341629, 0.398970007896423],\n [0.257135003805161, 0.355440020561218],\n [0.427684992551804, 0.437960982322693],\n [0.448339998722076, 0.536936044692993],\n [0.178560003638268, 0.45755398273468],\n [0.247308000922203, 0.457193970680237],\n [0.286267012357712, 0.467674970626831],\n [0.332827985286713, 0.460712015628815],\n [0.368755996227264, 0.447206974029541],\n [0.398963987827301, 0.432654976844788],\n [0.476410001516342, 0.405806005001068],\n [0.189241006970406, 0.523923993110657],\n [0.228962004184723, 0.348950982093811],\n [0.490725994110107, 0.562400996685028],\n [0.404670000076294, 0.485132992267609],\n [0.019469000399113, 0.401564002037048],\n [0.426243007183075, 0.420431017875671],\n [0.396993011236191, 0.548797011375427],\n [0.266469985246658, 0.376977026462555],\n [0.439121007919312, 0.51895797252655],\n [0.032313998788595, 0.644356966018677],\n [0.419054001569748, 0.387154996395111],\n [0.462783008813858, 0.505746960639954],\n [0.238978996872902, 0.779744982719421],\n [0.198220998048782, 0.831938028335571],\n [0.107550002634525, 0.540755033493042],\n [0.183610007166862, 0.740257024765015],\n [0.134409993886948, 0.333683013916016],\n [0.385764002799988, 0.883153975009918],\n [0.490967005491257, 0.579378008842468],\n [0.382384985685349, 0.508572995662689],\n [0.174399003386497, 0.397670984268188],\n [0.318785011768341, 0.39623498916626],\n [0.343364000320435, 0.400596976280212],\n [0.396100014448166, 0.710216999053955],\n [0.187885001301765, 0.588537991046906],\n [0.430987000465393, 0.944064974784851],\n [0.318993002176285, 0.898285031318665],\n [0.266247987747192, 0.869701027870178],\n [0.500023007392883, 0.190576016902924],\n [0.499976992607117, 0.954452991485596],\n [0.366169989109039, 0.398822009563446],\n [0.393207013607025, 0.39553701877594],\n [0.410373002290726, 0.391080021858215],\n [0.194993004202843, 0.342101991176605],\n [0.388664990663528, 0.362284004688263],\n [0.365961998701096, 0.355970978736877],\n [0.343364000320435, 0.355356991291046],\n [0.318785011768341, 0.35834002494812],\n [0.301414996385574, 0.363156020641327],\n [0.058132998645306, 0.319076001644135],\n [0.301414996385574, 0.387449026107788],\n [0.499987989664078, 0.618434011936188],\n [0.415838003158569, 0.624195992946625],\n [0.445681989192963, 0.566076993942261],\n [0.465844005346298, 0.620640993118286],\n [0.49992299079895, 0.351523995399475],\n [0.288718998432159, 0.819945991039276],\n [0.335278987884521, 0.852819979190826],\n [0.440512001514435, 0.902418971061707],\n [0.128294005990028, 0.791940987110138],\n [0.408771991729736, 0.373893976211548],\n [0.455606997013092, 0.451801002025604],\n [0.499877005815506, 0.908990025520325],\n [0.375436991453171, 0.924192011356354],\n [0.11421000212431, 0.615022003650665],\n [0.448662012815475, 0.695277988910675],\n [0.4480200111866, 0.704632043838501],\n [0.447111994028091, 0.715808033943176],\n [0.444831997156143, 0.730794012546539],\n [0.430011987686157, 0.766808986663818],\n [0.406787008047104, 0.685672998428345],\n [0.400738000869751, 0.681069016456604],\n [0.392399996519089, 0.677703022956848],\n [0.367855995893478, 0.663918972015381],\n [0.247923001646996, 0.601333022117615],\n [0.452769994735718, 0.420849978923798],\n [0.43639200925827, 0.359887003898621],\n [0.416164010763168, 0.368713974952698],\n [0.413385987281799, 0.692366003990173],\n [0.228018000721931, 0.683571994304657],\n [0.468268007040024, 0.352671027183533],\n [0.411361992359161, 0.804327011108398],\n [0.499989002943039, 0.469825029373169],\n [0.479153990745544, 0.442654013633728],\n [0.499974012374878, 0.439637005329132],\n [0.432112008333206, 0.493588984012604],\n [0.499886006116867, 0.866917014122009],\n [0.49991300702095, 0.821729004383087],\n [0.456548988819122, 0.819200992584229],\n [0.344549000263214, 0.745438992977142],\n [0.37890899181366, 0.574010014533997],\n [0.374292999505997, 0.780184984207153],\n [0.319687992334366, 0.570737957954407],\n [0.357154995203018, 0.604269981384277],\n [0.295284003019333, 0.621580958366394],\n [0.447750002145767, 0.862477004528046],\n [0.410986006259918, 0.508723020553589],\n [0.31395098567009, 0.775308012962341],\n [0.354128003120422, 0.812552988529205],\n [0.324548006057739, 0.703992962837219],\n [0.189096003770828, 0.646299958229065],\n [0.279776990413666, 0.71465802192688],\n [0.1338230073452, 0.682700991630554],\n [0.336768001317978, 0.644733011722565],\n [0.429883986711502, 0.466521978378296],\n [0.455527991056442, 0.548622965812683],\n [0.437114000320435, 0.558896005153656],\n [0.467287987470627, 0.529924988746643],\n [0.414712011814117, 0.335219979286194],\n [0.37704598903656, 0.322777986526489],\n [0.344107985496521, 0.320150971412659],\n [0.312875986099243, 0.32233202457428],\n [0.283526003360748, 0.333190023899078],\n [0.241245999932289, 0.382785975933075],\n [0.102986000478268, 0.468762993812561],\n [0.267612010240555, 0.424560010433197],\n [0.297879010438919, 0.433175981044769],\n [0.333433985710144, 0.433878004550934],\n [0.366427004337311, 0.426115989685059],\n [0.396012008190155, 0.416696012020111],\n [0.420121014118195, 0.41022801399231],\n [0.007561000064015, 0.480777025222778],\n [0.432949006557465, 0.569517970085144],\n [0.458638995885849, 0.479089021682739],\n [0.473466008901596, 0.545744001865387],\n [0.476087987422943, 0.563830018043518],\n [0.468472003936768, 0.555056989192963],\n [0.433990985155106, 0.582361996173859],\n [0.483518004417419, 0.562983989715576],\n [0.482482999563217, 0.57784903049469],\n [0.42645001411438, 0.389798998832703],\n [0.438998997211456, 0.39649498462677],\n [0.450067013502121, 0.400434017181396],\n [0.289712011814117, 0.368252992630005],\n [0.276670008897781, 0.363372981548309],\n [0.517862021923065, 0.471948027610779],\n [0.710287988185883, 0.380764007568359],\n [0.526226997375488, 0.573909997940063],\n [0.895093023777008, 0.254140973091125],\n [0.634069979190826, 0.409575998783112],\n [0.661242008209229, 0.41302502155304],\n [0.688880026340485, 0.409460008144379],\n [0.725341975688934, 0.389131009578705],\n [0.606630027294159, 0.40370500087738],\n [0.654766023159027, 0.344011008739471],\n [0.629905998706818, 0.346076011657715],\n [0.680678009986877, 0.347265005111694],\n [0.702096998691559, 0.353591024875641],\n [0.75221198797226, 0.410804986953735],\n [0.602918028831482, 0.842862963676453],\n [0.719901978969574, 0.375599980354309],\n [0.893692970275879, 0.399959981441498],\n [0.790081977844238, 0.391354024410248],\n [0.643998026847839, 0.534487962722778],\n [0.528249025344849, 0.65040397644043],\n [0.525849997997284, 0.680191040039062],\n [0.560214996337891, 0.657229006290436],\n [0.585384011268616, 0.66654098033905],\n [0.549625992774963, 0.680860996246338],\n [0.57122802734375, 0.682691991329193],\n [0.624852001667023, 0.72809898853302],\n [0.513050019741058, 0.547281980514526],\n [0.51509702205658, 0.527251958847046],\n [0.742246985435486, 0.314507007598877],\n [0.598631024360657, 0.454979002475739],\n [0.570338010787964, 0.548575043678284],\n [0.578631997108459, 0.533622980117798],\n [0.723087012767792, 0.532054007053375],\n [0.516445994377136, 0.499638974666595],\n [0.662801027297974, 0.282917976379395],\n [0.70362401008606, 0.293271005153656],\n [0.830704987049103, 0.193813979625702],\n [0.552385985851288, 0.302568018436432],\n [0.607609987258911, 0.353887975215912],\n [0.645429015159607, 0.696707010269165],\n [0.932694971561432, 0.730105042457581],\n [0.557260990142822, 0.572826027870178],\n [0.542901992797852, 0.584792017936707],\n [0.6180260181427, 0.694710969924927],\n [0.607590973377228, 0.694203019142151],\n [0.722943007946014, 0.271963000297546],\n [0.577413976192474, 0.563166975975037],\n [0.614082992076874, 0.281386971473694],\n [0.616907000541687, 0.255886018276215],\n [0.668509006500244, 0.119913995265961],\n [0.770092010498047, 0.232020974159241],\n [0.635536015033722, 0.189248979091644],\n [0.77039098739624, 0.299556016921997],\n [0.826722025871277, 0.278755009174347],\n [0.527121007442474, 0.666198015213013],\n [0.553171992301941, 0.668527007102966],\n [0.577238023281097, 0.673889994621277],\n [0.554691970348358, 0.580065965652466],\n [0.611896991729736, 0.693961024284363],\n [0.59696102142334, 0.706539988517761],\n [0.596370995044708, 0.693953037261963],\n [0.539958000183105, 0.557139039039612],\n [0.568841993808746, 0.692366003990173],\n [0.547818005084991, 0.692366003990173],\n [0.52461302280426, 0.692366003990173],\n [0.534089982509613, 0.779141008853912],\n [0.527670979499817, 0.736225962638855],\n [0.526912987232208, 0.717857003211975],\n [0.526877999305725, 0.704625964164734],\n [0.526966989040375, 0.695277988910675],\n [0.572058022022247, 0.695277988910675],\n [0.573521018028259, 0.703539967536926],\n [0.57683801651001, 0.711845993995667],\n [0.581691026687622, 0.720062971115112],\n [0.609944999217987, 0.639909982681274],\n [0.986046016216278, 0.560034036636353],\n [0.5867999792099, 0.69539999961853],\n [0.590372025966644, 0.701822996139526],\n [0.531915009021759, 0.601536989212036],\n [0.577268004417419, 0.585934996604919],\n [0.536915004253387, 0.593786001205444],\n [0.627542972564697, 0.473352015018463],\n [0.665585994720459, 0.495950996875763],\n [0.588353991508484, 0.546862006187439],\n [0.757824003696442, 0.14767599105835],\n [0.709249973297119, 0.201507985591888],\n [0.672684013843536, 0.256581008434296],\n [0.600408971309662, 0.74900496006012],\n [0.55826598405838, 0.261672019958496],\n [0.570303976535797, 0.187870979309082],\n [0.588165998458862, 0.109044015407562],\n [0.711045026779175, 0.398952007293701],\n [0.781069993972778, 0.435405015945435],\n [0.587247014045715, 0.398931980133057],\n [0.742869973182678, 0.355445981025696],\n [0.572156012058258, 0.437651991844177],\n [0.55186802148819, 0.536570012569427],\n [0.821442008018494, 0.457556009292603],\n [0.752701997756958, 0.457181990146637],\n [0.71375697851181, 0.467626988887787],\n [0.66711300611496, 0.460672974586487],\n [0.631101012229919, 0.447153985500336],\n [0.6008620262146, 0.432473003864288],\n [0.523481011390686, 0.405627012252808],\n [0.810747981071472, 0.523926019668579],\n [0.771045982837677, 0.348959028720856],\n [0.509127020835876, 0.562718033790588],\n [0.595292985439301, 0.485023975372314],\n [0.980530977249146, 0.401564002037048],\n [0.573499977588654, 0.420000016689301],\n [0.602994978427887, 0.548687994480133],\n [0.733529984951019, 0.376977026462555],\n [0.560611009597778, 0.519016981124878],\n [0.967685997486115, 0.644356966018677],\n [0.580985009670258, 0.387160003185272],\n [0.537728011608124, 0.505385041236877],\n [0.760966002941132, 0.779752969741821],\n [0.801778972148895, 0.831938028335571],\n [0.892440974712372, 0.54076099395752],\n [0.816350996494293, 0.740260004997253],\n [0.865594983100891, 0.333687007427216],\n [0.614073991775513, 0.883246004581451],\n [0.508952975273132, 0.579437971115112],\n [0.617941975593567, 0.508316040039062],\n [0.825608015060425, 0.397674977779388],\n [0.681214988231659, 0.39623498916626],\n [0.656635999679565, 0.400596976280212],\n [0.603900015354156, 0.710216999053955],\n [0.81208598613739, 0.588539004325867],\n [0.56801301240921, 0.944564998149872],\n [0.681007981300354, 0.898285031318665],\n [0.733752012252808, 0.869701027870178],\n [0.633830010890961, 0.398822009563446],\n [0.606792986392975, 0.39553701877594],\n [0.589659988880157, 0.391062021255493],\n [0.805015981197357, 0.342108011245728],\n [0.611334979534149, 0.362284004688263],\n [0.634037971496582, 0.355970978736877],\n [0.656635999679565, 0.355356991291046],\n [0.681214988231659, 0.35834002494812],\n [0.698584973812103, 0.363156020641327],\n [0.941866993904114, 0.319076001644135],\n [0.698584973812103, 0.387449026107788],\n [0.584177017211914, 0.624107003211975],\n [0.554318010807037, 0.566076993942261],\n [0.534153997898102, 0.62064003944397],\n [0.711217999458313, 0.819975018501282],\n [0.664629995822906, 0.852871000766754],\n [0.559099972248077, 0.902631998062134],\n [0.871706008911133, 0.791940987110138],\n [0.591234028339386, 0.373893976211548],\n [0.544341027736664, 0.451583981513977],\n [0.624562978744507, 0.924192011356354],\n [0.88577002286911, 0.615028977394104],\n [0.551338016986847, 0.695277988910675],\n [0.551980018615723, 0.704632043838501],\n [0.552887976169586, 0.715808033943176],\n [0.555167973041534, 0.730794012546539],\n [0.569944024085999, 0.767035007476807],\n [0.593203008174896, 0.685675978660583],\n [0.599261999130249, 0.681069016456604],\n [0.607599973678589, 0.677703022956848],\n [0.631937980651855, 0.663500010967255],\n [0.752032995223999, 0.601315021514893],\n [0.547226011753082, 0.420395016670227],\n [0.563543975353241, 0.359827995300293],\n [0.583841025829315, 0.368713974952698],\n [0.586614012718201, 0.692366003990173],\n [0.771915018558502, 0.683578014373779],\n [0.531597018241882, 0.352482974529266],\n [0.588370978832245, 0.804440975189209],\n [0.52079701423645, 0.442565023899078],\n [0.567984998226166, 0.493479013442993],\n [0.543282985687256, 0.819254994392395],\n [0.655317008495331, 0.745514988899231],\n [0.621008992195129, 0.574018001556396],\n [0.625559985637665, 0.78031200170517],\n [0.680198013782501, 0.570719003677368],\n [0.64276397228241, 0.604337990283966],\n [0.704662978649139, 0.621529996395111],\n [0.552012026309967, 0.862591981887817],\n [0.589071989059448, 0.508637011051178],\n [0.685944974422455, 0.775357007980347],\n [0.645735025405884, 0.812640011310577],\n [0.675342977046967, 0.703978002071381],\n [0.810858011245728, 0.646304965019226],\n [0.72012197971344, 0.714666962623596],\n [0.866151988506317, 0.682704985141754],\n [0.663187026977539, 0.644596993923187],\n [0.570082008838654, 0.466325998306274],\n [0.544561982154846, 0.548375964164734],\n [0.562758982181549, 0.558784961700439],\n [0.531987011432648, 0.530140042304993],\n [0.585271000862122, 0.335177004337311],\n [0.622952997684479, 0.32277899980545],\n [0.655896008014679, 0.320163011550903],\n [0.687132000923157, 0.322345972061157],\n [0.716481983661652, 0.333200991153717],\n [0.758756995201111, 0.382786989212036],\n [0.897013008594513, 0.468769013881683],\n [0.732392013072968, 0.424547016620636],\n [0.70211398601532, 0.433162987232208],\n [0.66652500629425, 0.433866024017334],\n [0.633504986763, 0.426087975502014],\n [0.603875994682312, 0.416586995124817],\n [0.579657971858978, 0.409945011138916],\n [0.992439985275269, 0.480777025222778],\n [0.567192018032074, 0.569419980049133],\n [0.54136598110199, 0.478899002075195],\n [0.526564002037048, 0.546118021011353],\n [0.523913025856018, 0.563830018043518],\n [0.531529009342194, 0.555056989192963],\n [0.566035985946655, 0.582329034805298],\n [0.51631098985672, 0.563053965568542],\n [0.5174720287323, 0.577877044677734],\n [0.573594987392426, 0.389806985855103],\n [0.560697972774506, 0.395331978797913],\n [0.549755990505219, 0.399751007556915],\n [0.710287988185883, 0.368252992630005],\n [0.723330020904541, 0.363372981548309],\n];\n\nconst TRI468 = [\n 127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n 151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n 157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n 181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n 228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n 170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n 155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n 158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n 102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n 90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n 196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n 246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n 117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n 153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n 63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n 135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n 76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n 166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n 190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n 112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n 110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n 348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n 314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n 321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n 466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n 429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n 293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n 427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n 431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n 434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n 348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n 318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n 373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n 340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n 351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n 164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n 327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n 405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n 438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n 449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n 444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n 464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n 243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n 166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n 119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n 77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n 201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n 103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n 217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n 78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n 57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n 142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n 90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n 178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n 224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n 143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n 153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n 63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n 241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n 186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n 141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n 223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n 128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n 451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n 344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n 424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n 321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n 448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n 281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n 283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n 432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n 268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n 6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n 317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n 272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n 336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n 441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n 373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n 425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n 403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n 383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n 254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n 259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n 453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nconst TRI68 = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n 8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n 45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n 23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n 39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n 35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n 48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n 66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\nconst TRI33 = [\n /* eyes */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n /* brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n /* 4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n /* nose */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n /* chin */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n /* cont */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\nconst TRI7 = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nconst VTX68 = [\n /* cont */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n /* nose */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n /* eyes */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n /* lip */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\nconst VTX33 = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\nconst VTX7 = [33, 133, 362, 263, 1, 78, 308];\n\nexports.MESH_ANNOTATIONS = MESH_ANNOTATIONS;\nexports.MESH_TO_IRIS_INDICES_MAP = MESH_TO_IRIS_INDICES_MAP;\n\nexports.TRI468 = TRI468;\nexports.TRI68 = TRI68;\nexports.TRI33 = TRI33;\nexports.TRI7 = TRI7;\n\nexports.UV468 = UV468;\nexports.UV68 = VTX68.map((x) => UV468[x]);\nexports.UV33 = VTX33.map((x) => UV468[x]);\nexports.UV7 = VTX7.map((x) => UV468[x]);\n", "/* eslint-disable class-methods-use-this */\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as bounding from './box';\nimport * as util from './util';\nimport * as coords from './coords.js';\n// eslint-disable-next-line no-unused-vars\nimport { log } from '../log.js';\n\nconst LANDMARKS_COUNT = 468;\nconst MESH_MOUTH_INDEX = 13;\nconst MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES = [MESH_MOUTH_INDEX, coords.MESH_ANNOTATIONS['midwayBetweenEyes'][0]];\nconst BLAZEFACE_MOUTH_INDEX = 3;\nconst BLAZEFACE_NOSE_INDEX = 2;\nconst BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES = [BLAZEFACE_MOUTH_INDEX, BLAZEFACE_NOSE_INDEX];\nconst LEFT_EYE_OUTLINE = coords.MESH_ANNOTATIONS['leftEyeLower0'];\nconst LEFT_EYE_BOUNDS = [LEFT_EYE_OUTLINE[0], LEFT_EYE_OUTLINE[LEFT_EYE_OUTLINE.length - 1]];\nconst RIGHT_EYE_OUTLINE = coords.MESH_ANNOTATIONS['rightEyeLower0'];\nconst RIGHT_EYE_BOUNDS = [RIGHT_EYE_OUTLINE[0], RIGHT_EYE_OUTLINE[RIGHT_EYE_OUTLINE.length - 1]];\nconst IRIS_UPPER_CENTER_INDEX = 3;\nconst IRIS_LOWER_CENTER_INDEX = 4;\nconst IRIS_IRIS_INDEX = 71;\nconst IRIS_NUM_COORDINATES = 76;\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates. Update the z coordinate to be an average of the original and the new. This produces the best visual effect.\nfunction replaceRawCoordinates(rawCoords, newCoords, prefix, keys) {\n for (let i = 0; i < coords.MESH_TO_IRIS_INDICES_MAP.length; i++) {\n const { key, indices } = coords.MESH_TO_IRIS_INDICES_MAP[i];\n const originalIndices = coords.MESH_ANNOTATIONS[`${prefix}${key}`];\n const shouldReplaceAllKeys = keys == null;\n if (shouldReplaceAllKeys || keys.includes(key)) {\n for (let j = 0; j < indices.length; j++) {\n const index = indices[j];\n rawCoords[originalIndices[j]] = [\n newCoords[index][0], newCoords[index][1],\n (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n ];\n }\n }\n }\n}\n// The Pipeline coordinates between the bounding box and skeleton models.\nclass Pipeline {\n constructor(boundingBoxDetector, meshDetector, irisModel, config) {\n // An array of facial bounding boxes.\n this.storedBoxes = [];\n this.runsWithoutFaceDetector = 0;\n this.boundingBoxDetector = boundingBoxDetector;\n this.meshDetector = meshDetector;\n this.irisModel = irisModel;\n this.meshWidth = config.face.mesh.inputSize;\n this.meshHeight = config.face.mesh.inputSize;\n this.irisSize = config.face.iris.inputSize;\n this.irisEnlarge = 2.3;\n this.skipped = 0;\n this.detectedFaces = 0;\n }\n\n transformRawCoords(rawCoords, box, angle, rotationMatrix) {\n // @ts-ignore\n const boxSize = bounding.getBoxSize({ startPoint: box.startPoint, endPoint: box.endPoint });\n const scaleFactor = [boxSize[0] / this.meshWidth, boxSize[1] / this.meshHeight];\n const coordsScaled = rawCoords.map((coord) => ([\n scaleFactor[0] * (coord[0] - this.meshWidth / 2),\n scaleFactor[1] * (coord[1] - this.meshHeight / 2), coord[2],\n ]));\n const coordsRotationMatrix = (angle !== 0) ? util.buildRotationMatrix(angle, [0, 0]) : util.IDENTITY_MATRIX;\n const coordsRotated = (angle !== 0) ? coordsScaled.map((coord) => ([...util.rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n const inverseRotationMatrix = (angle !== 0) ? util.invertTransformMatrix(rotationMatrix) : util.IDENTITY_MATRIX;\n // @ts-ignore\n const boxCenter = [...bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint }), 1];\n return coordsRotated.map((coord) => ([\n coord[0] + util.dot(boxCenter, inverseRotationMatrix[0]),\n coord[1] + util.dot(boxCenter, inverseRotationMatrix[1]),\n coord[2],\n ]));\n }\n\n getLeftToRightEyeDepthDifference(rawCoords) {\n const leftEyeZ = rawCoords[LEFT_EYE_BOUNDS[0]][2];\n const rightEyeZ = rawCoords[RIGHT_EYE_BOUNDS[0]][2];\n return leftEyeZ - rightEyeZ;\n }\n\n // Returns a box describing a cropped region around the eye fit for passing to the iris model.\n getEyeBox(rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, flip = false) {\n // @ts-ignore\n const box = bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), this.irisEnlarge));\n // @ts-ignore\n const boxSize = bounding.getBoxSize(box);\n let crop = tf.image.cropAndResize(face, [[\n box.startPoint[1] / this.meshHeight,\n box.startPoint[0] / this.meshWidth, box.endPoint[1] / this.meshHeight,\n box.endPoint[0] / this.meshWidth,\n ]], [0], [this.irisSize, this.irisSize]);\n if (flip) {\n crop = tf.image.flipLeftRight(crop);\n }\n return { box, boxSize, crop };\n }\n\n // Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\n getEyeCoords(eyeData, eyeBox, eyeBoxSize, flip = false) {\n const eyeRawCoords = [];\n for (let i = 0; i < IRIS_NUM_COORDINATES; i++) {\n const x = eyeData[i * 3];\n const y = eyeData[i * 3 + 1];\n const z = eyeData[i * 3 + 2];\n eyeRawCoords.push([\n (flip\n ? (1 - (x / this.irisSize))\n : (x / this.irisSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n (y / this.irisSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n ]);\n }\n return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(IRIS_IRIS_INDEX) };\n }\n\n // The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\n getAdjustedIrisCoords(rawCoords, irisCoords, direction) {\n const upperCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeUpper0`][IRIS_UPPER_CENTER_INDEX]][2];\n const lowerCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeLower0`][IRIS_LOWER_CENTER_INDEX]][2];\n const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n return irisCoords.map((coord, i) => {\n let z = averageZ;\n if (i === 2) {\n z = upperCenterZ;\n } else if (i === 4) {\n z = lowerCenterZ;\n }\n return [coord[0], coord[1], z];\n });\n }\n\n async predict(input, config) {\n let useFreshBox = false;\n // run new detector every skipFrames unless we only want box to start with\n let detector;\n if ((this.skipped === 0) || (this.skipped > config.face.detector.skipFrames) || !config.face.mesh.enabled || !config.videoOptimized) {\n detector = await this.boundingBoxDetector.getBoundingBoxes(input);\n this.skipped = 0;\n }\n if (config.videoOptimized) this.skipped++;\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (detector && detector.boxes && (detector.boxes.length > 0) && (!config.face.mesh.enabled || (detector.boxes.length !== this.detectedFaces) && (this.detectedFaces !== config.face.detector.maxFaces))) {\n this.storedBoxes = [];\n this.detectedFaces = 0;\n for (const possible of detector.boxes) {\n this.storedBoxes.push({ startPoint: possible.box.startPoint.dataSync(), endPoint: possible.box.endPoint.dataSync(), landmarks: possible.landmarks, confidence: possible.confidence });\n }\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\n\n if (useFreshBox) {\n if (!detector || !detector.boxes || (detector.boxes.length === 0)) {\n this.storedBoxes = [];\n this.detectedFaces = 0;\n return null;\n }\n for (let i = 0; i < this.storedBoxes.length; i++) {\n // @ts-ignore\n const scaledBox = bounding.scaleBoxCoordinates({ startPoint: this.storedBoxes[i].startPoint, endPoint: this.storedBoxes[i].endPoint }, detector.scaleFactor);\n // @ts-ignore\n const enlargedBox = bounding.enlargeBox(scaledBox);\n // @ts-ignore\n const squarifiedBox = bounding.squarifyBox(enlargedBox);\n const landmarks = this.storedBoxes[i].landmarks.arraySync();\n const confidence = this.storedBoxes[i].confidence;\n this.storedBoxes[i] = { ...squarifiedBox, confidence, landmarks };\n }\n this.runsWithoutFaceDetector = 0;\n }\n if (detector && detector.boxes) {\n detector.boxes.forEach((prediction) => {\n prediction.box.startPoint.dispose();\n prediction.box.endPoint.dispose();\n prediction.landmarks.dispose();\n });\n }\n\n // log('face', `skipped: ${this.skipped} max: ${config.face.detector.maxFaces} detected: ${this.detectedFaces} stored: ${this.storedBoxes.length} new: ${detector?.boxes?.length}`);\n let results = tf.tidy(() => this.storedBoxes.map((box, i) => {\n // The facial bounding box landmarks could come either from blazeface (if we are using a fresh box), or from the mesh model (if we are reusing an old box).\n let face;\n let angle = 0;\n let rotationMatrix;\n if (config.face.detector.rotation) {\n const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= LANDMARKS_COUNT) ? MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES : BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES;\n angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n // @ts-ignore\n const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n const rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized);\n rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n // @ts-ignore\n face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshHeight, this.meshWidth]).div(255);\n } else {\n rotationMatrix = util.IDENTITY_MATRIX;\n const cloned = input.clone();\n // @ts-ignore\n face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, cloned, [this.meshHeight, this.meshWidth]).div(255);\n }\n\n // if we're not going to produce mesh, don't spend time with further processing\n if (!config.face.mesh.enabled) {\n const prediction = {\n coords: null,\n box,\n faceConfidence: null,\n confidence: box.confidence,\n image: face,\n };\n return prediction;\n }\n\n const [, confidence, contourCoords] = this.meshDetector.predict(face); // The first returned tensor represents facial contours, which are included in the coordinates.\n const confidenceVal = confidence.dataSync()[0];\n if (confidenceVal < config.face.detector.minConfidence) return null; // if below confidence just exit\n const coordsReshaped = tf.reshape(contourCoords, [-1, 3]);\n let rawCoords = coordsReshaped.arraySync();\n\n if (config.face.iris.enabled) {\n const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = this.getEyeBox(rawCoords, face, LEFT_EYE_BOUNDS[0], LEFT_EYE_BOUNDS[1], true);\n const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = this.getEyeBox(rawCoords, face, RIGHT_EYE_BOUNDS[0], RIGHT_EYE_BOUNDS[1]);\n const eyePredictions = this.irisModel.predict(tf.concat([leftEyeCrop, rightEyeCrop]));\n const eyePredictionsData = eyePredictions.dataSync();\n const leftEyeData = eyePredictionsData.slice(0, IRIS_NUM_COORDINATES * 3);\n const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = this.getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n const rightEyeData = eyePredictionsData.slice(IRIS_NUM_COORDINATES * 3);\n const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = this.getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize);\n const leftToRightEyeDepthDifference = this.getLeftToRightEyeDepthDifference(rawCoords);\n if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left');\n replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right');\n // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged. So we only update a single contour line above and below the eye.\n } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n } else { // User is looking towards the left.\n replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n }\n const adjustedLeftIrisCoords = this.getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n const adjustedRightIrisCoords = this.getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n rawCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n }\n\n const transformedCoordsData = this.transformRawCoords(rawCoords, box, angle, rotationMatrix);\n // @ts-ignore\n const landmarksBox = bounding.enlargeBox(this.calculateLandmarksBoundingBox(transformedCoordsData));\n // @ts-ignore\n const squarifiedLandmarksBox = bounding.squarifyBox(landmarksBox);\n const transformedCoords = tf.tensor2d(transformedCoordsData);\n const prediction = {\n coords: transformedCoords,\n box: landmarksBox,\n faceConfidence: confidenceVal,\n confidence: box.confidence,\n image: face,\n };\n if (config.face.mesh.returnRawData) prediction.rawCoords = rawCoords;\n this.storedBoxes[i] = { ...squarifiedLandmarksBox, landmarks: transformedCoords.arraySync(), confidence: box.confidence, faceConfidence: confidenceVal };\n\n return prediction;\n }));\n results = results.filter((a) => a !== null);\n this.detectedFaces = results.length;\n return results;\n }\n\n calculateLandmarksBoundingBox(landmarks) {\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint, landmarks };\n }\n}\nexports.Pipeline = Pipeline;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as blazeface from './blazeface.js';\nimport * as facepipeline from './facepipeline.js';\nimport * as coords from './coords.js';\n\nclass MediaPipeFaceMesh {\n constructor(blazeFace, blazeMeshModel, irisModel, config) {\n // @ts-ignore\n this.facePipeline = new facepipeline.Pipeline(blazeFace, blazeMeshModel, irisModel, config);\n this.config = config;\n }\n\n async estimateFaces(input, config) {\n const predictions = await this.facePipeline.predict(input, config);\n const results = [];\n for (const prediction of (predictions || [])) {\n if (prediction.isDisposedInternal) continue; // guard against disposed tensors on long running operations such as pause in middle of processing\n const mesh = prediction.coords ? prediction.coords.arraySync() : null;\n const meshRaw = prediction.rawCoords;\n const annotations = {};\n if (mesh && mesh.length > 0) {\n for (const key of Object.keys(coords.MESH_ANNOTATIONS)) {\n annotations[key] = coords.MESH_ANNOTATIONS[key].map((index) => mesh[index]);\n }\n }\n const boxRaw = (config.face.mesh.returnRawData && prediction.box) ? { topLeft: prediction.box.startPoint, bottomRight: prediction.box.endPoint } : null;\n const box = prediction.box ? [\n Math.max(0, prediction.box.startPoint[0]),\n Math.max(0, prediction.box.startPoint[1]),\n Math.min(input.shape[2], prediction.box.endPoint[0]) - prediction.box.startPoint[0],\n Math.min(input.shape[1], prediction.box.endPoint[1]) - prediction.box.startPoint[1],\n ] : 0;\n results.push({\n confidence: prediction.confidence || 0,\n box,\n mesh,\n boxRaw,\n meshRaw,\n annotations,\n image: prediction.image ? tf.clone(prediction.image) : null,\n });\n if (prediction.coords) prediction.coords.dispose();\n if (prediction.image) prediction.image.dispose();\n }\n return results;\n }\n}\n\nlet faceModels = [null, null, null];\nasync function load(config) {\n faceModels = await Promise.all([\n // @ts-ignore\n (!faceModels[0] && config.face.enabled) ? blazeface.load(config) : null,\n (!faceModels[1] && config.face.mesh.enabled) ? tf.loadGraphModel(config.face.mesh.modelPath, { fromTFHub: config.face.mesh.modelPath.includes('tfhub.dev') }) : null,\n (!faceModels[2] && config.face.iris.enabled) ? tf.loadGraphModel(config.face.iris.modelPath, { fromTFHub: config.face.iris.modelPath.includes('tfhub.dev') }) : null,\n ]);\n const faceMesh = new MediaPipeFaceMesh(faceModels[0], faceModels[1], faceModels[2], config);\n if (config.face.mesh.enabled) log(`load model: ${config.face.mesh.modelPath.match(/\\/(.*)\\./)[1]}`);\n if (config.face.iris.enabled) log(`load model: ${config.face.iris.modelPath.match(/\\/(.*)\\./)[1]}`);\n return faceMesh;\n}\n\nexports.load = load;\nexports.MediaPipeFaceMesh = MediaPipeFaceMesh;\nexports.triangulation = coords.TRI468;\n", "import { log } from './log.js';\n\nconst profileData = {};\n\nfunction profile(name, data) {\n if (!data || !data.kernels) return;\n const maxResults = 5;\n const time = data.kernels\n .filter((a) => a.kernelTimeMs > 0)\n .reduce((a, b) => a += b.kernelTimeMs, 0);\n const slowest = data.kernels\n .map((a, i) => { a.id = i; return a; })\n .filter((a) => a.kernelTimeMs > 0)\n .sort((a, b) => b.kernelTimeMs - a.kernelTimeMs);\n const largest = data.kernels\n .map((a, i) => { a.id = i; return a; })\n .filter((a) => a.totalBytesSnapshot > 0)\n .sort((a, b) => b.totalBytesSnapshot - a.totalBytesSnapshot);\n if (slowest.length > maxResults) slowest.length = maxResults;\n if (largest.length > maxResults) largest.length = maxResults;\n const res = { newBytes: data.newBytes, newTensors: data.newTensors, peakBytes: data.peakBytes, numKernelOps: data.kernels.length, timeKernelOps: time, slowestKernelOps: slowest, largestKernelOps: largest };\n profileData[name] = res;\n log('Human profiler', name, res);\n}\n\nexports.run = profile;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile.js';\n\nconst models = {};\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nasync function load(config) {\n if (!models.age) {\n models.age = await tf.loadGraphModel(config.face.age.modelPath);\n log(`load model: ${config.face.age.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.age;\n}\n\nasync function predict(image, config) {\n if (!models.age) return null;\n if ((skipped < config.face.age.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n /*\n const zoom = [0, 0]; // 0..1 meaning 0%..100%\n const box = [[\n (image.shape[1] * zoom[0]) / image.shape[1],\n (image.shape[2] * zoom[1]) / image.shape[2],\n (image.shape[1] - (image.shape[1] * zoom[0])) / image.shape[1],\n (image.shape[2] - (image.shape[2] * zoom[1])) / image.shape[2],\n ]];\n const resize = tf.image.cropAndResize(image, box, [0], [config.face.age.inputSize, config.face.age.inputSize]);\n */\n const resize = tf.image.resizeBilinear(image, [config.face.age.inputSize, config.face.age.inputSize], false);\n const enhance = tf.mul(resize, [255.0]);\n tf.dispose(resize);\n\n let ageT;\n const obj = {};\n\n if (!config.profile) {\n if (config.face.age.enabled) ageT = await models.age.predict(enhance);\n } else {\n const profileAge = config.face.age.enabled ? await tf.profile(() => models.age.predict(enhance)) : {};\n ageT = profileAge.result.clone();\n profileAge.result.dispose();\n // @ts-ignore\n profile.run('age', profileAge);\n }\n enhance.dispose();\n\n if (ageT) {\n const data = ageT.dataSync();\n obj.age = Math.trunc(10 * data[0]) / 10;\n }\n ageT.dispose();\n\n last = obj;\n resolve(obj);\n });\n}\n\nexports.predict = predict;\nexports.load = load;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile.js';\n\nconst models = {};\nlet last = { gender: '' };\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet alternative = false;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nasync function load(config) {\n if (!models.gender) {\n models.gender = await tf.loadGraphModel(config.face.gender.modelPath);\n alternative = models.gender.inputs[0].shape[3] === 1;\n log(`load model: ${config.face.gender.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.gender;\n}\n\nasync function predict(image, config) {\n if (!models.gender) return null;\n if ((skipped < config.face.gender.skipFrames) && config.videoOptimized && last.gender !== '') {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [config.face.gender.inputSize, config.face.gender.inputSize], false);\n let enhance;\n if (alternative) {\n enhance = tf.tidy(() => {\n const [red, green, blue] = tf.split(resize, 3, 3);\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n return grayscale.sub(0.5).mul(2);\n });\n } else {\n enhance = tf.mul(resize, [255.0]);\n }\n tf.dispose(resize);\n\n let genderT;\n const obj = {};\n\n if (!config.profile) {\n if (config.face.gender.enabled) genderT = await models.gender.predict(enhance);\n } else {\n const profileGender = config.face.gender.enabled ? await tf.profile(() => models.gender.predict(enhance)) : {};\n genderT = profileGender.result.clone();\n profileGender.result.dispose();\n // @ts-ignore\n profile.run('gender', profileGender);\n }\n enhance.dispose();\n\n if (genderT) {\n const data = genderT.dataSync();\n if (alternative) {\n // returns two values 0..1, bigger one is prediction\n const confidence = Math.trunc(100 * Math.abs(data[0] - data[1])) / 100;\n if (confidence > config.face.gender.minConfidence) {\n obj.gender = data[0] > data[1] ? 'female' : 'male';\n obj.confidence = confidence;\n }\n } else {\n // returns one value 0..1, .5 is prediction threshold\n const confidence = Math.trunc(200 * Math.abs((data[0] - 0.5))) / 100;\n if (confidence > config.face.gender.minConfidence) {\n obj.gender = data[0] <= 0.5 ? 'female' : 'male';\n obj.confidence = Math.min(0.99, confidence);\n }\n }\n }\n genderT.dispose();\n\n last = obj;\n resolve(obj);\n });\n}\n\nexports.predict = predict;\nexports.load = load;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile.js';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];\nconst models = {};\nlet last = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\nconst scale = 1; // score multiplication factor\n\nasync function load(config) {\n if (!models.emotion) {\n models.emotion = await tf.loadGraphModel(config.face.emotion.modelPath);\n log(`load model: ${config.face.emotion.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.emotion;\n}\n\nasync function predict(image, config) {\n if (!models.emotion) return null;\n if ((skipped < config.face.emotion.skipFrames) && config.videoOptimized && (last.length > 0)) {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n /*\n const zoom = [0, 0]; // 0..1 meaning 0%..100%\n const box = [[\n (image.shape[1] * zoom[0]) / image.shape[1],\n (image.shape[2] * zoom[1]) / image.shape[2],\n (image.shape[1] - (image.shape[1] * zoom[0])) / image.shape[1],\n (image.shape[2] - (image.shape[2] * zoom[1])) / image.shape[2],\n ]];\n const resize = tf.image.cropAndResize(image, box, [0], [config.face.emotion.inputSize, config.face.emotion.inputSize]);\n */\n const resize = tf.image.resizeBilinear(image, [config.face.emotion.inputSize, config.face.emotion.inputSize], false);\n const [red, green, blue] = tf.split(resize, 3, 3);\n resize.dispose();\n // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n red.dispose();\n green.dispose();\n blue.dispose();\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n redNorm.dispose();\n greenNorm.dispose();\n blueNorm.dispose();\n const normalize = tf.tidy(() => grayscale.sub(0.5).mul(2));\n grayscale.dispose();\n const obj = [];\n if (config.face.emotion.enabled) {\n let data;\n if (!config.profile) {\n const emotionT = await models.emotion.predict(normalize);\n data = emotionT.dataSync();\n tf.dispose(emotionT);\n } else {\n const profileData = await tf.profile(() => models.emotion.predict(normalize));\n data = profileData.result.dataSync();\n profileData.result.dispose();\n // @ts-ignore\n profile.run('emotion', profileData);\n }\n for (let i = 0; i < data.length; i++) {\n if (scale * data[i] > config.face.emotion.minConfidence) obj.push({ score: Math.min(0.99, Math.trunc(100 * scale * data[i]) / 100), emotion: annotations[i] });\n }\n obj.sort((a, b) => b.score - a.score);\n }\n normalize.dispose();\n last = obj;\n resolve(obj);\n });\n}\n\nexports.predict = predict;\nexports.load = load;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile.js';\n\n// based on https://github.com/sirius-ai/MobileFaceNet_TF\n// model converted from https://github.com/sirius-ai/MobileFaceNet_TF/files/3551493/FaceMobileNet192_train_false.zip\n\nconst models = {};\n\nasync function load(config) {\n if (!models.embedding) {\n models.embedding = await tf.loadGraphModel(config.face.embedding.modelPath);\n log(`load model: ${config.face.embedding.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.embedding;\n}\n\nfunction simmilarity(embedding1, embedding2) {\n if (embedding1?.length !== embedding2?.length) return 0;\n // general minkowski distance\n // euclidean distance is limited case where order is 2\n const order = 2;\n const distance = 10.0 * ((embedding1.map((val, i) => (val - embedding2[i])).reduce((dist, diff) => dist + (diff ** order), 0) ** (1 / order)));\n return (Math.trunc(1000 * (1 - distance)) / 1000);\n}\n\nasync function predict(image, config) {\n if (!models.embedding) return null;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [config.face.embedding.inputSize, config.face.embedding.inputSize], false);\n // const normalize = tf.tidy(() => resize.div(127.5).sub(0.5)); // this is -0.5...0.5 ???\n let data = [];\n if (config.face.embedding.enabled) {\n if (!config.profile) {\n const embeddingT = await models.embedding.predict({ img_inputs: resize });\n data = [...embeddingT.dataSync()]; // convert object array to standard array\n tf.dispose(embeddingT);\n } else {\n const profileData = await tf.profile(() => models.embedding.predict({ img_inputs: resize }));\n data = [...profileData.result.dataSync()];\n profileData.result.dispose();\n // @ts-ignore\n profile.run('emotion', profileData);\n }\n }\n resize.dispose();\n // normalize.dispose();\n resolve(data);\n });\n}\n\nexports.predict = predict;\nexports.simmilarity = simmilarity;\nexports.load = load;\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nconst imageNetMean = [-123.15, -115.90, -103.06];\n\nfunction nameOutputResultsMobileNet(results) {\n const [offsets, heatmap, displacementFwd, displacementBwd] = results;\n return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nfunction nameOutputResultsResNet(results) {\n const [displacementFwd, displacementBwd, offsets, heatmap] = results;\n return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nclass BaseModel {\n constructor(model) {\n this.model = model;\n }\n\n predict(input, config) {\n return tf.tidy(() => {\n const asFloat = (config.body.modelType === 'ResNet') ? input.toFloat().add(imageNetMean) : input.toFloat().div(127.5).sub(1.0);\n const asBatch = asFloat.expandDims(0);\n const results = this.model.predict(asBatch);\n const results3d = results.map((y) => y.squeeze([0]));\n const namedResults = (config.body.modelType === 'ResNet') ? nameOutputResultsResNet(results3d) : nameOutputResultsMobileNet(results3d);\n return {\n heatmapScores: namedResults.heatmap.sigmoid(),\n offsets: namedResults.offsets,\n displacementFwd: namedResults.displacementFwd,\n displacementBwd: namedResults.displacementBwd,\n };\n });\n }\n\n dispose() {\n this.model.dispose();\n }\n}\nexports.BaseModel = BaseModel;\n", "// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nfunction half(k) {\n return Math.floor(k / 2);\n}\nclass MaxHeap {\n constructor(maxSize, getElementValue) {\n this.priorityQueue = new Array(maxSize);\n this.numberOfElements = -1;\n this.getElementValue = getElementValue;\n }\n\n enqueue(x) {\n this.priorityQueue[++this.numberOfElements] = x;\n this.swim(this.numberOfElements);\n }\n\n dequeue() {\n const max = this.priorityQueue[0];\n this.exchange(0, this.numberOfElements--);\n this.sink(0);\n this.priorityQueue[this.numberOfElements + 1] = null;\n return max;\n }\n\n empty() {\n return this.numberOfElements === -1;\n }\n\n size() {\n return this.numberOfElements + 1;\n }\n\n all() {\n return this.priorityQueue.slice(0, this.numberOfElements + 1);\n }\n\n max() {\n return this.priorityQueue[0];\n }\n\n swim(k) {\n while (k > 0 && this.less(half(k), k)) {\n this.exchange(k, half(k));\n k = half(k);\n }\n }\n\n sink(k) {\n while (2 * k <= this.numberOfElements) {\n let j = 2 * k;\n if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n if (!this.less(k, j)) break;\n this.exchange(k, j);\n k = j;\n }\n }\n\n getValueAt(i) {\n return this.getElementValue(this.priorityQueue[i]);\n }\n\n less(i, j) {\n return this.getValueAt(i) < this.getValueAt(j);\n }\n\n exchange(i, j) {\n const t = this.priorityQueue[i];\n this.priorityQueue[i] = this.priorityQueue[j];\n this.priorityQueue[j] = t;\n }\n}\nexports.MaxHeap = MaxHeap;\n", "import * as heapSort from './heapSort';\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores) {\n const [height, width] = scores.shape;\n let localMaximum = true;\n const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n localMaximum = false;\n break;\n }\n }\n if (!localMaximum) break;\n }\n return localMaximum;\n}\n\nfunction buildPartWithScoreQueue(scoreThreshold, localMaximumRadius, scores) {\n const [height, width, numKeypoints] = scores.shape;\n const queue = new heapSort.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n const score = scores.get(heatmapY, heatmapX, keypointId);\n // Only consider parts with score greater or equal to threshold as root candidates.\n if (score < scoreThreshold) continue;\n // Only consider keypoints whose score is maximum in a local window.\n if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores)) {\n queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n }\n }\n }\n }\n return queue;\n}\nexports.buildPartWithScoreQueue = buildPartWithScoreQueue;\n", "exports.partNames = [\n 'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n 'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n 'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexports.NUM_KEYPOINTS = exports.partNames.length;\n\nexports.partIds = exports.partNames.reduce((result, jointName, i) => {\n result[jointName] = i;\n return result;\n}, {});\n\nconst connectedPartNames = [\n ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexports.connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([exports.partIds[jointNameA], exports.partIds[jointNameB]]));\n\nexports.poseChain = [\n ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n ['rightKnee', 'rightAnkle'],\n];\n\nexports.partChannels = [\n 'left_face',\n 'right_face',\n 'right_upper_leg_front',\n 'right_lower_leg_back',\n 'right_upper_leg_back',\n 'left_lower_leg_front',\n 'left_upper_leg_front',\n 'left_upper_leg_back',\n 'left_lower_leg_back',\n 'right_feet',\n 'right_lower_leg_front',\n 'left_feet',\n 'torso_front',\n 'torso_back',\n 'right_upper_arm_front',\n 'right_upper_arm_back',\n 'right_lower_arm_back',\n 'left_lower_arm_front',\n 'left_upper_arm_front',\n 'left_upper_arm_back',\n 'left_lower_arm_back',\n 'right_hand',\n 'right_lower_arm_front',\n 'left_hand',\n];\n", "import * as kpt from './keypoints';\n\nfunction getOffsetPoint(y, x, keypoint, offsets) {\n return {\n y: offsets.get(y, x, keypoint),\n x: offsets.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n };\n}\nexports.getOffsetPoint = getOffsetPoint;\n\nfunction getImageCoords(part, outputStride, offsets) {\n const { heatmapY, heatmapX, id: keypoint } = part;\n const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n return {\n x: part.heatmapX * outputStride + x,\n y: part.heatmapY * outputStride + y,\n };\n}\nexports.getImageCoords = getImageCoords;\n\nfunction fillArray(element, size) {\n const result = new Array(size);\n for (let i = 0; i < size; i++) {\n result[i] = element;\n }\n return result;\n}\nexports.fillArray = fillArray;\n\nfunction clamp(a, min, max) {\n if (a < min) return min;\n if (a > max) return max;\n return a;\n}\nexports.clamp = clamp;\n\nfunction squaredDistance(y1, x1, y2, x2) {\n const dy = y2 - y1;\n const dx = x2 - x1;\n return dy * dy + dx * dx;\n}\nexports.squaredDistance = squaredDistance;\n\nfunction addVectors(a, b) {\n return { x: a.x + b.x, y: a.y + b.y };\n}\nexports.addVectors = addVectors;\n\nfunction clampVector(a, min, max) {\n return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\nexports.clampVector = clampVector;\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as kpt from './keypoints';\n\nfunction getPointsConfidence(heatmapScores, heatMapCoords) {\n const numKeypoints = heatMapCoords.shape[0];\n const result = new Float32Array(numKeypoints);\n for (let keypoint = 0; keypoint < numKeypoints; keypoint++) {\n const y = heatMapCoords.get(keypoint, 0);\n const x = heatMapCoords.get(keypoint, 1);\n result[keypoint] = heatmapScores.get(y, x, keypoint);\n }\n return result;\n}\nexports.getPointsConfidence = getPointsConfidence;\n\nfunction getOffsetPoint(y, x, keypoint, offsetsBuffer) {\n return {\n y: offsetsBuffer.get(y, x, keypoint),\n x: offsetsBuffer.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n };\n}\n\nfunction getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer) {\n const result = [];\n for (let keypoint = 0; keypoint < kpt.NUM_KEYPOINTS; keypoint++) {\n const heatmapY = heatMapCoordsBuffer.get(keypoint, 0).valueOf();\n const heatmapX = heatMapCoordsBuffer.get(keypoint, 1).valueOf();\n const { x, y } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsetsBuffer);\n result.push(y);\n result.push(x);\n }\n return tf.tensor2d(result, [kpt.NUM_KEYPOINTS, 2]);\n}\nexports.getOffsetVectors = getOffsetVectors;\n\nfunction getOffsetPoints(heatMapCoordsBuffer, outputStride, offsetsBuffer) {\n return tf.tidy(() => heatMapCoordsBuffer.toTensor().mul(tf.scalar(outputStride, 'int32')).toFloat().add(getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer)));\n}\nexports.getOffsetPoints = getOffsetPoints;\n\nfunction mod(a, b) {\n return tf.tidy(() => {\n const floored = a.div(tf.scalar(b, 'int32'));\n return a.sub(floored.mul(tf.scalar(b, 'int32')));\n });\n}\n\nfunction argmax2d(inputs) {\n const [height, width, depth] = inputs.shape;\n return tf.tidy(() => {\n const reshaped = inputs.reshape([height * width, depth]);\n const coords = reshaped.argMax(0);\n const yCoords = coords.div(tf.scalar(width, 'int32')).expandDims(1);\n const xCoords = mod(coords, width).expandDims(1);\n return tf.concat([yCoords, xCoords], 1);\n });\n}\nexports.argmax2d = argmax2d;\n", "import * as keypoints from './keypoints';\nimport * as vectors from './vectors';\nimport * as decoders from './decoders';\n\nconst parentChildrenTuples = keypoints.poseChain.map(([parentJoinName, childJoinName]) => ([keypoints.partIds[parentJoinName], keypoints.partIds[childJoinName]]));\nconst parentToChildEdges = parentChildrenTuples.map(([, childJointId]) => childJointId);\nconst childToParentEdges = parentChildrenTuples.map(([parentJointId]) => parentJointId);\nfunction getDisplacement(edgeId, point, displacements) {\n const numEdges = displacements.shape[2] / 2;\n return {\n y: displacements.get(point.y, point.x, edgeId),\n x: displacements.get(point.y, point.x, numEdges + edgeId),\n };\n}\nfunction getStridedIndexNearPoint(point, outputStride, height, width) {\n return {\n // @ts-ignore\n y: vectors.clamp(Math.round(point.y / outputStride), 0, height - 1),\n // @ts-ignore\n x: vectors.clamp(Math.round(point.x / outputStride), 0, width - 1),\n };\n}\n\nfunction traverseToTargetKeypoint(edgeId, sourceKeypoint, targetKeypointId, scoresBuffer, offsets, outputStride, displacements, offsetRefineStep = 2) {\n const [height, width] = scoresBuffer.shape;\n // Nearest neighbor interpolation for the source->target displacements.\n const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, outputStride, height, width);\n const displacement = getDisplacement(edgeId, sourceKeypointIndices, displacements);\n // @ts-ignore\n const displacedPoint = vectors.addVectors(sourceKeypoint.position, displacement);\n let targetKeypoint = displacedPoint;\n for (let i = 0; i < offsetRefineStep; i++) {\n const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n // @ts-ignore\n const offsetPoint = vectors.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetKeypointId, offsets);\n // @ts-ignore\n targetKeypoint = vectors.addVectors({\n x: targetKeypointIndices.x * outputStride,\n y: targetKeypointIndices.y * outputStride,\n }, { x: offsetPoint.x, y: offsetPoint.y });\n }\n const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n const score = scoresBuffer.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetKeypointId);\n return { position: targetKeypoint, part: keypoints.partNames[targetKeypointId], score };\n}\n\nfunction decodePose(root, scores, offsets, outputStride, displacementsFwd, displacementsBwd) {\n const numParts = scores.shape[2];\n const numEdges = parentToChildEdges.length;\n const instanceKeypoints = new Array(numParts);\n // Start a new detection instance at the position of the root.\n const { part: rootPart, score: rootScore } = root;\n // @ts-ignore\n const rootPoint = vectors.getImageCoords(rootPart, outputStride, offsets);\n instanceKeypoints[rootPart.id] = {\n score: rootScore,\n part: keypoints.partNames[rootPart.id],\n position: rootPoint,\n };\n // Decode the part positions upwards in the tree, following the backward displacements.\n for (let edge = numEdges - 1; edge >= 0; --edge) {\n const sourceKeypointId = parentToChildEdges[edge];\n const targetKeypointId = childToParentEdges[edge];\n if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsBwd);\n }\n }\n // Decode the part positions downwards in the tree, following the forward displacements.\n for (let edge = 0; edge < numEdges; ++edge) {\n const sourceKeypointId = childToParentEdges[edge];\n const targetKeypointId = parentToChildEdges[edge];\n if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsFwd);\n }\n }\n return instanceKeypoints;\n}\nexports.decodePose = decodePose;\n\nasync function decodeSinglePose(heatmapScores, offsets, config) {\n let totalScore = 0.0;\n // @ts-ignore\n const heatmapValues = decoders.argmax2d(heatmapScores);\n const allTensorBuffers = await Promise.all([heatmapScores.buffer(), offsets.buffer(), heatmapValues.buffer()]);\n const scoresBuffer = allTensorBuffers[0];\n const offsetsBuffer = allTensorBuffers[1];\n const heatmapValuesBuffer = allTensorBuffers[2];\n // @ts-ignore\n const offsetPoints = decoders.getOffsetPoints(heatmapValuesBuffer, config.body.outputStride, offsetsBuffer);\n const offsetPointsBuffer = await offsetPoints.buffer();\n // @ts-ignore\n const keypointConfidence = Array.from(decoders.getPointsConfidence(scoresBuffer, heatmapValuesBuffer));\n const instanceKeypoints = keypointConfidence.map((score, i) => {\n totalScore += score;\n return {\n position: {\n y: offsetPointsBuffer.get(i, 0),\n x: offsetPointsBuffer.get(i, 1),\n },\n part: keypoints.partNames[i],\n score,\n };\n });\n const filteredKeypoints = instanceKeypoints.filter((kpt) => kpt.score > config.body.scoreThreshold);\n heatmapValues.dispose();\n offsetPoints.dispose();\n return { keypoints: filteredKeypoints, score: totalScore / instanceKeypoints.length };\n}\nexports.decodeSinglePose = decodeSinglePose;\n", "import * as buildParts from './buildParts';\nimport * as decodePose from './decodePose';\nimport * as vectors from './vectors';\n\nconst kLocalMaximumRadius = 1;\n\nfunction withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, { x, y }, keypointId) {\n return poses.some(({ keypoints }) => {\n const correspondingKeypoint = keypoints[keypointId].position;\n // @ts-ignore\n return vectors.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n });\n}\n\nfunction getInstanceScore(existingPoses, squaredNmsRadius, instanceKeypoints) {\n const notOverlappedKeypointScores = instanceKeypoints.reduce((result, { position, score }, keypointId) => {\n if (!withinNmsRadiusOfCorrespondingPoint(existingPoses, squaredNmsRadius, position, keypointId)) result += score;\n return result;\n }, 0.0);\n return notOverlappedKeypointScores / instanceKeypoints.length;\n}\n\nfunction decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config) {\n const poses = [];\n // @ts-ignore\n const queue = buildParts.buildPartWithScoreQueue(config.body.scoreThreshold, kLocalMaximumRadius, scoresBuffer);\n const squaredNmsRadius = config.body.nmsRadius ^ 2;\n // Generate at most maxDetections object instances per image in decreasing root part score order.\n while (poses.length < config.body.maxDetections && !queue.empty()) {\n // The top element in the queue is the next root candidate.\n const root = queue.dequeue();\n // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n // @ts-ignore\n const rootImageCoords = vectors.getImageCoords(root.part, config.body.outputStride, offsetsBuffer);\n if (withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, rootImageCoords, root.part.id)) continue;\n // Else start a new detection instance at the position of the root.\n // @ts-ignore\n const keypoints = decodePose.decodePose(root, scoresBuffer, offsetsBuffer, config.body.outputStride, displacementsFwdBuffer, displacementsBwdBuffer);\n const score = getInstanceScore(poses, squaredNmsRadius, keypoints);\n if (score > config.body.scoreThreshold) poses.push({ keypoints, score });\n }\n return poses;\n}\nexports.decodeMultiplePoses = decodeMultiplePoses;\n", "import * as kpt from './keypoints';\n\nfunction eitherPointDoesntMeetConfidence(a, b, minConfidence) {\n return (a < minConfidence || b < minConfidence);\n}\n\nfunction getAdjacentKeyPoints(keypoints, minConfidence) {\n return kpt.connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n return result;\n }\n result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n return result;\n }, []);\n}\nexports.getAdjacentKeyPoints = getAdjacentKeyPoints;\n\nconst { NEGATIVE_INFINITY, POSITIVE_INFINITY } = Number;\nfunction getBoundingBox(keypoints) {\n return keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n maxX: Math.max(maxX, x),\n maxY: Math.max(maxY, y),\n minX: Math.min(minX, x),\n minY: Math.min(minY, y),\n }), {\n maxX: NEGATIVE_INFINITY,\n maxY: NEGATIVE_INFINITY,\n minX: POSITIVE_INFINITY,\n minY: POSITIVE_INFINITY,\n });\n}\nexports.getBoundingBox = getBoundingBox;\n\nfunction getBoundingBoxPoints(keypoints) {\n const { minX, minY, maxX, maxY } = getBoundingBox(keypoints);\n return [{ x: minX, y: minY }, { x: maxX, y: minY }, { x: maxX, y: maxY }, { x: minX, y: maxY }];\n}\nexports.getBoundingBoxPoints = getBoundingBoxPoints;\n\nasync function toTensorBuffers3D(tensors) {\n return Promise.all(tensors.map((tensor) => tensor.buffer()));\n}\nexports.toTensorBuffers3D = toTensorBuffers3D;\n\nfunction scalePose(pose, scaleY, scaleX) {\n return {\n score: pose.score,\n keypoints: pose.keypoints.map(({ score, part, position }) => ({\n score,\n part,\n position: { x: position.x * scaleX, y: position.y * scaleY },\n })),\n };\n}\nexports.scalePose = scalePose;\n\nfunction resizeTo(image, [targetH, targetW]) {\n const input = image.squeeze(0);\n const resized = input.resizeBilinear([targetH, targetW]);\n input.dispose();\n return resized;\n}\nexports.resizeTo = resizeTo;\n\nfunction scaleAndFlipPoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) {\n const scaledPoses = poses.map((pose) => scalePose(pose, height / inputResolutionHeight, width / inputResolutionWidth));\n return scaledPoses;\n}\nexports.scaleAndFlipPoses = scaleAndFlipPoses;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as modelBase from './modelBase';\nimport * as decodeMultiple from './decodeMultiple';\nimport * as decodePose from './decodePose';\nimport * as util from './util';\n\nasync function estimateMultiple(input, res, config) {\n return new Promise(async (resolve) => {\n const height = input.shape[1];\n const width = input.shape[2];\n // @ts-ignore\n const allTensorBuffers = await util.toTensorBuffers3D([res.heatmapScores, res.offsets, res.displacementFwd, res.displacementBwd]);\n const scoresBuffer = allTensorBuffers[0];\n const offsetsBuffer = allTensorBuffers[1];\n const displacementsFwdBuffer = allTensorBuffers[2];\n const displacementsBwdBuffer = allTensorBuffers[3];\n // @ts-ignore\n const poses = await decodeMultiple.decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config);\n // @ts-ignore\n const scaled = util.scaleAndFlipPoses(poses, [height, width], [config.body.inputSize, config.body.inputSize]);\n resolve(scaled);\n });\n}\n\nasync function estimateSingle(input, res, config) {\n return new Promise(async (resolve) => {\n const height = input.shape[1];\n const width = input.shape[2];\n // @ts-ignore\n const pose = await decodePose.decodeSinglePose(res.heatmapScores, res.offsets, config);\n const poses = [pose];\n // @ts-ignore\n const scaled = util.scaleAndFlipPoses(poses, [height, width], [config.body.inputSize, config.body.inputSize]);\n resolve(scaled);\n });\n}\n\nclass PoseNet {\n constructor(model) {\n this.baseModel = model;\n }\n\n async estimatePoses(input, config) {\n // @ts-ignore\n const resized = util.resizeTo(input, [config.body.inputSize, config.body.inputSize]);\n const res = this.baseModel.predict(resized, config);\n\n const poses = (config.body.maxDetections < 2) ? await estimateSingle(input, res, config) : await estimateMultiple(input, res, config);\n\n res.heatmapScores.dispose();\n res.offsets.dispose();\n res.displacementFwd.dispose();\n res.displacementBwd.dispose();\n resized.dispose();\n\n return poses;\n }\n\n dispose() {\n this.baseModel.dispose();\n }\n}\nexports.PoseNet = PoseNet;\n\nasync function load(config) {\n const model = await tf.loadGraphModel(config.body.modelPath);\n // @ts-ignore\n const mobilenet = new modelBase.BaseModel(model);\n log(`load model: ${config.body.modelPath.match(/\\/(.*)\\./)[1]}`);\n return new PoseNet(mobilenet);\n}\nexports.load = load;\n", "import * as modelPoseNet from './modelPoseNet';\nimport * as keypoints from './keypoints';\nimport * as util from './util';\n\n// @ts-ignore\nexports.load = modelPoseNet.load;\n// @ts-ignore\nexports.PoseNet = modelPoseNet.PoseNet;\n\nexports.partChannels = keypoints.partChannels;\nexports.partIds = keypoints.partIds;\nexports.partNames = keypoints.partNames;\nexports.poseChain = keypoints.poseChain;\n// @ts-ignore\nexports.getAdjacentKeyPoints = util.getAdjacentKeyPoints;\n// @ts-ignore\nexports.getBoundingBox = util.getBoundingBox;\n// @ts-ignore\nexports.getBoundingBoxPoints = util.getBoundingBoxPoints;\n// @ts-ignore\nexports.scaleAndFlipPoses = util.scaleAndFlipPoses;\n// @ts-ignore\nexports.scalePose = util.scalePose;\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\n\nclass HandDetector {\n constructor(model, inputSize, anchorsAnnotated) {\n this.model = model;\n this.anchors = anchorsAnnotated.map((anchor) => [anchor.x_center, anchor.y_center]);\n this.anchorsTensor = tf.tensor2d(this.anchors);\n this.inputSizeTensor = tf.tensor1d([inputSize, inputSize]);\n this.doubleInputSizeTensor = tf.tensor1d([inputSize * 2, inputSize * 2]);\n }\n\n normalizeBoxes(boxes) {\n return tf.tidy(() => {\n const boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n const boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n const boxCenterPoints = tf.add(tf.div(boxOffsets, this.inputSizeTensor), this.anchorsTensor);\n const halfBoxSizes = tf.div(boxSizes, this.doubleInputSizeTensor);\n const startPoints = tf.mul(tf.sub(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n const endPoints = tf.mul(tf.add(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n return tf.concat2d([startPoints, endPoints], 1);\n });\n }\n\n normalizeLandmarks(rawPalmLandmarks, index) {\n return tf.tidy(() => {\n const landmarks = tf.add(tf.div(rawPalmLandmarks.reshape([-1, 7, 2]), this.inputSizeTensor), this.anchors[index]);\n return tf.mul(landmarks, this.inputSizeTensor);\n });\n }\n\n async getBoxes(input, config) {\n const batched = this.model.predict(input);\n const predictions = batched.squeeze();\n batched.dispose();\n const scoresT = tf.tidy(() => tf.sigmoid(tf.slice(predictions, [0, 0], [-1, 1])).squeeze());\n const scores = scoresT.dataSync();\n const rawBoxes = tf.slice(predictions, [0, 1], [-1, 4]);\n const boxes = this.normalizeBoxes(rawBoxes);\n rawBoxes.dispose();\n const filteredT = await tf.image.nonMaxSuppressionAsync(boxes, scores, config.hand.maxHands, config.hand.iouThreshold, config.hand.scoreThreshold);\n const filtered = filteredT.arraySync();\n\n scoresT.dispose();\n filteredT.dispose();\n const hands = [];\n for (const index of filtered) {\n if (scores[index] >= config.hand.minConfidence) {\n const matchingBox = tf.slice(boxes, [index, 0], [1, -1]);\n const rawPalmLandmarks = tf.slice(predictions, [index, 5], [1, 14]);\n const palmLandmarks = tf.tidy(() => this.normalizeLandmarks(rawPalmLandmarks, index).reshape([-1, 2]));\n rawPalmLandmarks.dispose();\n hands.push({ box: matchingBox, palmLandmarks, confidence: scores[index] });\n }\n }\n predictions.dispose();\n boxes.dispose();\n return hands;\n }\n\n async estimateHandBounds(input, config) {\n const inputHeight = input.shape[1];\n const inputWidth = input.shape[2];\n const image = tf.tidy(() => input.resizeBilinear([config.hand.inputSize, config.hand.inputSize]).div(127.5).sub(1));\n const predictions = await this.getBoxes(image, config);\n image.dispose();\n const hands = [];\n if (!predictions || predictions.length === 0) return hands;\n for (const prediction of predictions) {\n const boxes = prediction.box.dataSync();\n const startPoint = boxes.slice(0, 2);\n const endPoint = boxes.slice(2, 4);\n const palmLandmarks = prediction.palmLandmarks.arraySync();\n prediction.box.dispose();\n prediction.palmLandmarks.dispose();\n hands.push(box.scaleBoxCoordinates({ startPoint, endPoint, palmLandmarks, confidence: prediction.confidence }, [inputWidth / config.hand.inputSize, inputHeight / config.hand.inputSize]));\n }\n return hands;\n }\n}\nexports.HandDetector = HandDetector;\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\nimport * as util from './util';\n// eslint-disable-next-line no-unused-vars\nimport { log } from '../log.js';\n\n// const PALM_BOX_SHIFT_VECTOR = [0, -0.4];\nconst PALM_BOX_ENLARGE_FACTOR = 5; // default 3\n// const HAND_BOX_SHIFT_VECTOR = [0, -0.1]; // move detected hand box by x,y to ease landmark detection\nconst HAND_BOX_ENLARGE_FACTOR = 1.65; // default 1.65\nconst PALM_LANDMARK_IDS = [0, 5, 9, 13, 17, 1, 2];\nconst PALM_LANDMARKS_INDEX_OF_PALM_BASE = 0;\nconst PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE = 2;\n\nclass HandPipeline {\n constructor(handDetector, landmarkDetector, inputSize) {\n this.handDetector = handDetector;\n this.landmarkDetector = landmarkDetector;\n this.inputSize = inputSize;\n this.storedBoxes = [];\n this.skipped = 0;\n this.detectedHands = 0;\n }\n\n getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n // return box.enlargeBox(box.squarifyBox(box.shiftBox(boxAroundPalm, PALM_BOX_SHIFT_VECTOR)), PALM_BOX_ENLARGE_FACTOR);\n return box.enlargeBox(box.squarifyBox(boxAroundPalm), PALM_BOX_ENLARGE_FACTOR);\n }\n\n getBoxForHandLandmarks(landmarks) {\n const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n // const boxAroundHand = box.enlargeBox(box.squarifyBox(box.shiftBox(boundingBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n const boxAroundHand = box.enlargeBox(box.squarifyBox(boundingBox), HAND_BOX_ENLARGE_FACTOR);\n boxAroundHand.palmLandmarks = [];\n for (let i = 0; i < PALM_LANDMARK_IDS.length; i++) {\n boxAroundHand.palmLandmarks.push(landmarks[PALM_LANDMARK_IDS[i]].slice(0, 2));\n }\n return boxAroundHand;\n }\n\n transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n const boxSize = box.getBoxSize(box2);\n const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n const coordsScaled = rawCoords.map((coord) => [\n scaleFactor[0] * (coord[0] - this.inputSize / 2),\n scaleFactor[1] * (coord[1] - this.inputSize / 2),\n scaleFactor[2] * coord[2],\n ]);\n const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n const coordsRotated = coordsScaled.map((coord) => {\n const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n return [...rotated, coord[2]];\n });\n const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n const boxCenter = [...box.getBoxCenter(box2), 1];\n const originalBoxCenter = [\n util.dot(boxCenter, inverseRotationMatrix[0]),\n util.dot(boxCenter, inverseRotationMatrix[1]),\n ];\n return coordsRotated.map((coord) => [\n coord[0] + originalBoxCenter[0],\n coord[1] + originalBoxCenter[1],\n coord[2],\n ]);\n }\n\n async estimateHands(image, config) {\n let useFreshBox = false;\n\n // run new detector every skipFrames unless we only want box to start with\n let boxes;\n if ((this.skipped === 0) || (this.skipped > config.hand.skipFrames) || !config.hand.landmarks || !config.videoOptimized) {\n boxes = await this.handDetector.estimateHandBounds(image, config);\n this.skipped = 0;\n }\n if (config.videoOptimized) this.skipped++;\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxHands) || !config.hand.landmarks)) {\n this.detectedHands = 0;\n this.storedBoxes = [...boxes];\n // for (const possible of boxes) this.storedBoxes.push(possible);\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\n const hands = [];\n // log('hand', `skipped: ${this.skipped} max: ${config.hand.maxHands} detected: ${this.detectedHands} stored: ${this.storedBoxes.length} new: ${boxes?.length}`);\n\n // go through working set of boxes\n for (let i = 0; i < this.storedBoxes.length; i++) {\n const currentBox = this.storedBoxes[i];\n if (!currentBox) continue;\n if (config.hand.landmarks) {\n const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_PALM_BASE], currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE]) : 0;\n const palmCenter = box.getBoxCenter(currentBox);\n const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n const rotatedImage = config.hand.rotation ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n const croppedInput = box.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n const handImage = croppedInput.div(255);\n croppedInput.dispose();\n rotatedImage.dispose();\n const [confidenceT, keypoints] = await this.landmarkDetector.predict(handImage);\n handImage.dispose();\n const confidence = confidenceT.dataSync()[0];\n confidenceT.dispose();\n if (confidence >= config.hand.minConfidence) {\n const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n const rawCoords = keypointsReshaped.arraySync();\n keypoints.dispose();\n keypointsReshaped.dispose();\n const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n this.storedBoxes[i] = nextBoundingBox;\n const result = {\n landmarks: coords,\n confidence,\n box: {\n topLeft: nextBoundingBox.startPoint,\n bottomRight: nextBoundingBox.endPoint,\n },\n };\n hands.push(result);\n } else {\n this.storedBoxes[i] = null;\n }\n keypoints.dispose();\n } else {\n // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n const enlarged = box.enlargeBox(box.squarifyBox(currentBox), HAND_BOX_ENLARGE_FACTOR);\n const result = {\n confidence: currentBox.confidence,\n box: {\n topLeft: enlarged.startPoint,\n bottomRight: enlarged.endPoint,\n },\n };\n hands.push(result);\n }\n }\n this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n this.detectedHands = hands.length;\n return hands;\n }\n\n // eslint-disable-next-line class-methods-use-this\n calculateLandmarksBoundingBox(landmarks) {\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint };\n }\n}\n\nexports.HandPipeline = HandPipeline;\n", "exports.anchors = [\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.796875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.796875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.828125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.828125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.859375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.859375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.890625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.890625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.921875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.921875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.953125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.953125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.984375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.984375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n 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0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n];\n", "// https://storage.googleapis.com/tfjs-models/demos/handpose/index.html\n\nimport { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as handdetector from './handdetector';\nimport * as handpipeline from './handpipeline';\nimport * as anchors from './anchors';\n\nconst MESH_ANNOTATIONS = {\n thumb: [1, 2, 3, 4],\n indexFinger: [5, 6, 7, 8],\n middleFinger: [9, 10, 11, 12],\n ringFinger: [13, 14, 15, 16],\n pinky: [17, 18, 19, 20],\n palmBase: [0],\n};\n\nclass HandPose {\n constructor(handPipeline) {\n this.handPipeline = handPipeline;\n }\n\n static getAnnotations() {\n return MESH_ANNOTATIONS;\n }\n\n async estimateHands(input, config) {\n const predictions = await this.handPipeline.estimateHands(input, config);\n if (!predictions) return [];\n const hands = [];\n for (const prediction of predictions) {\n const annotations = {};\n if (prediction.landmarks) {\n for (const key of Object.keys(MESH_ANNOTATIONS)) {\n annotations[key] = MESH_ANNOTATIONS[key].map((index) => prediction.landmarks[index]);\n }\n }\n const box = prediction.box ? [\n Math.max(0, prediction.box.topLeft[0]),\n Math.max(0, prediction.box.topLeft[1]),\n Math.min(input.shape[2], prediction.box.bottomRight[0]) - prediction.box.topLeft[0],\n Math.min(input.shape[1], prediction.box.bottomRight[1]) - prediction.box.topLeft[1],\n ] : 0;\n hands.push({\n confidence: prediction.confidence,\n box,\n landmarks: prediction.landmarks,\n annotations,\n });\n }\n return hands;\n }\n}\nexports.HandPose = HandPose;\n\nasync function load(config) {\n const [handDetectorModel, handPoseModel] = await Promise.all([\n config.hand.enabled ? tf.loadGraphModel(config.hand.detector.modelPath, { fromTFHub: config.hand.detector.modelPath.includes('tfhub.dev') }) : null,\n config.hand.landmarks ? tf.loadGraphModel(config.hand.skeleton.modelPath, { fromTFHub: config.hand.skeleton.modelPath.includes('tfhub.dev') }) : null,\n ]);\n // @ts-ignore\n const handDetector = new handdetector.HandDetector(handDetectorModel, config.hand.inputSize, anchors.anchors);\n // @ts-ignore\n const handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel, config.hand.inputSize);\n const handPose = new HandPose(handPipeline);\n if (config.hand.enabled) log(`load model: ${config.hand.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n if (config.hand.landmarks) log(`load model: ${config.hand.skeleton.modelPath.match(/\\/(.*)\\./)[1]}`);\n return handPose;\n}\nexports.load = load;\n", "exports.body = (res) => {\n if (!res) return [];\n const gestures = [];\n for (let i = 0; i < res.length; i++) {\n // raising hands\n const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n if (nose && leftWrist && rightWrist && (leftWrist.position.y < nose.position.y) && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'i give up' });\n else if (nose && leftWrist && (leftWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise left hand' });\n else if (nose && rightWrist && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise right hand' });\n\n // leaning\n const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n if (leftShoulder && rightShoulder) gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position.y > rightShoulder.position.y) ? 'left' : 'right'}` });\n }\n return gestures;\n};\n\nexports.face = (res) => {\n if (!res) return [];\n const gestures = [];\n for (let i = 0; i < res.length; i++) {\n if (res[i].mesh && res[i].mesh.length > 0) {\n const eyeFacing = res[i].mesh[35][2] - res[i].mesh[263][2];\n if (Math.abs(eyeFacing) < 10) gestures.push({ face: i, gesture: 'facing camera' });\n else gestures.push({ face: i, gesture: `facing ${eyeFacing < 0 ? 'right' : 'left'}` });\n const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n const chinDepth = res[i].mesh[152][2];\n if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n }\n }\n return gestures;\n};\n\nexports.iris = (res) => {\n if (!res) return [];\n const gestures = [];\n for (let i = 0; i < res.length; i++) {\n if (!res[i].annotations || !res[i].annotations.leftEyeIris || !res[i].annotations.rightEyeIris) continue;\n const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n if (difference < 0.25) gestures.push({ iris: i, gesture: 'looking at camera' });\n }\n return gestures;\n};\n\nexports.hand = (res) => {\n if (!res) return [];\n const gestures = [];\n for (let i = 0; i < res.length; i++) {\n const fingers = [];\n for (const [finger, pos] of Object.entries(res[i]['annotations'])) {\n if (finger !== 'palmBase') fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n }\n if (fingers && fingers.length > 0) {\n const closest = fingers.reduce((best, a) => (best.position[2] < a.position[2] ? best : a));\n const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n gestures.push({ hand: i, gesture: `${closest.name} forward ${highest.name} up` });\n }\n }\n return gestures;\n};\n", "/* eslint-disable no-use-before-define */\n/*\nWebGLImageFilter - MIT Licensed\n2013, Dominic Szablewski - phoboslab.org\n\n*/\n\nconst WebGLProgram = function (gl, vertexSource, fragmentSource) {\n const _collect = function (source, prefix, collection) {\n const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n source.replace(r, (match, name) => {\n collection[name] = 0;\n return match;\n });\n };\n\n const _compile = function (source, type) {\n const shader = gl.createShader(type);\n gl.shaderSource(shader, source);\n gl.compileShader(shader);\n\n if (!gl.getShaderParameter(shader, gl.COMPILE_STATUS)) {\n // @ts-ignore\n throw new Error('Filter: GL compile failed', gl.getShaderInfoLog(shader));\n }\n return shader;\n };\n\n this.uniform = {};\n this.attribute = {};\n\n const _vsh = _compile(vertexSource, gl.VERTEX_SHADER);\n const _fsh = _compile(fragmentSource, gl.FRAGMENT_SHADER);\n\n this.id = gl.createProgram();\n gl.attachShader(this.id, _vsh);\n gl.attachShader(this.id, _fsh);\n gl.linkProgram(this.id);\n\n if (!gl.getProgramParameter(this.id, gl.LINK_STATUS)) {\n // @ts-ignore\n throw new Error('Filter: GL link failed', gl.getProgramInfoLog(this.id));\n }\n\n gl.useProgram(this.id);\n\n // Collect attributes\n _collect(vertexSource, 'attribute', this.attribute);\n for (const a in this.attribute) {\n this.attribute[a] = gl.getAttribLocation(this.id, a);\n }\n\n // Collect uniforms\n _collect(vertexSource, 'uniform', this.uniform);\n _collect(fragmentSource, 'uniform', this.uniform);\n for (const u in this.uniform) {\n this.uniform[u] = gl.getUniformLocation(this.id, u);\n }\n};\n\nconst WebGLImageFilter = function (params) {\n if (!params) params = { };\n let _drawCount = 0;\n let _sourceTexture = null;\n let _lastInChain = false;\n let _currentFramebufferIndex = -1;\n let _tempFramebuffers = [null, null];\n let _filterChain = [];\n let _width = -1;\n let _height = -1;\n let _vertexBuffer = null;\n let _currentProgram = null;\n const _canvas = params.canvas || document.createElement('canvas');\n\n // key is the shader program source, value is the compiled program\n const _shaderProgramCache = { };\n\n const gl = _canvas.getContext('webgl');\n if (!gl) throw new Error('Filter: getContext() failed');\n\n this.addFilter = function (name) {\n // eslint-disable-next-line prefer-rest-params\n const args = Array.prototype.slice.call(arguments, 1);\n const filter = _filter[name];\n\n _filterChain.push({ func: filter, args });\n };\n\n this.reset = function () {\n _filterChain = [];\n };\n\n this.apply = function (image) {\n _resize(image.width, image.height);\n _drawCount = 0;\n\n // Create the texture for the input image if we haven't yet\n if (!_sourceTexture) _sourceTexture = gl.createTexture();\n gl.bindTexture(gl.TEXTURE_2D, _sourceTexture);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n\n // No filters? Just draw\n if (_filterChain.length === 0) {\n // const program = _compileShader(SHADER.FRAGMENT_IDENTITY);\n _draw();\n return _canvas;\n }\n\n for (let i = 0; i < _filterChain.length; i++) {\n _lastInChain = (i === _filterChain.length - 1);\n const f = _filterChain[i];\n f.func.apply(this, f.args || []);\n }\n\n return _canvas;\n };\n\n const _resize = function (width, height) {\n // Same width/height? Nothing to do here\n if (width === _width && height === _height) { return; }\n\n _canvas.width = width;\n _width = width;\n _canvas.height = height;\n _height = height;\n\n // Create the context if we don't have it yet\n if (!_vertexBuffer) {\n // Create the vertex buffer for the two triangles [x, y, u, v] * 6\n const vertices = new Float32Array([\n -1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0,\n -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0,\n ]);\n // eslint-disable-next-line no-unused-expressions\n (_vertexBuffer = gl.createBuffer(), gl.bindBuffer(gl.ARRAY_BUFFER, _vertexBuffer));\n gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);\n\n // Note sure if this is a good idea; at least it makes texture loading\n // in Ejecta instant.\n gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n }\n\n gl.viewport(0, 0, _width, _height);\n\n // Delete old temp framebuffers\n _tempFramebuffers = [null, null];\n };\n\n const _getTempFramebuffer = function (index) {\n // @ts-ignore\n _tempFramebuffers[index] = _tempFramebuffers[index] || _createFramebufferTexture(_width, _height);\n return _tempFramebuffers[index];\n };\n\n const _createFramebufferTexture = function (width, height) {\n const fbo = gl.createFramebuffer();\n gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n\n const renderbuffer = gl.createRenderbuffer();\n gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n\n const texture = gl.createTexture();\n gl.bindTexture(gl.TEXTURE_2D, texture);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n\n gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n\n gl.bindTexture(gl.TEXTURE_2D, null);\n gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n\n return { fbo, texture };\n };\n\n const _draw = function (flags) {\n let source = null;\n let target = null;\n let flipY = false;\n\n // Set up the source\n if (_drawCount === 0) {\n // First draw call - use the source texture\n source = _sourceTexture;\n } else {\n // All following draw calls use the temp buffer last drawn to\n // @ts-ignore\n source = _getTempFramebuffer(_currentFramebufferIndex)?.texture;\n }\n _drawCount++;\n\n // Set up the target\n if (_lastInChain && !(flags & DRAW.INTERMEDIATE)) {\n // Last filter in our chain - draw directly to the WebGL Canvas. We may\n // also have to flip the image vertically now\n target = null;\n flipY = _drawCount % 2 === 0;\n } else {\n // Intermediate draw call - get a temp buffer to draw to\n _currentFramebufferIndex = (_currentFramebufferIndex + 1) % 2;\n // @ts-ignore\n target = _getTempFramebuffer(_currentFramebufferIndex)?.fbo;\n }\n\n // Bind the source and target and draw the two triangles\n gl.bindTexture(gl.TEXTURE_2D, source);\n gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n\n gl.uniform1f(_currentProgram.uniform.flipY, (flipY ? -1 : 1));\n gl.drawArrays(gl.TRIANGLES, 0, 6);\n };\n\n const _compileShader = function (fragmentSource) {\n if (_shaderProgramCache[fragmentSource]) {\n _currentProgram = _shaderProgramCache[fragmentSource];\n gl.useProgram(_currentProgram.id);\n return _currentProgram;\n }\n\n // Compile shaders\n _currentProgram = new WebGLProgram(gl, SHADER.VERTEX_IDENTITY, fragmentSource);\n\n const floatSize = Float32Array.BYTES_PER_ELEMENT;\n const vertSize = 4 * floatSize;\n gl.enableVertexAttribArray(_currentProgram.attribute.pos);\n gl.vertexAttribPointer(_currentProgram.attribute.pos, 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n gl.enableVertexAttribArray(_currentProgram.attribute.uv);\n gl.vertexAttribPointer(_currentProgram.attribute.uv, 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n\n _shaderProgramCache[fragmentSource] = _currentProgram;\n return _currentProgram;\n };\n\n let DRAW = { INTERMEDIATE: 1 };\n\n let SHADER = {};\n SHADER.VERTEX_IDENTITY = [\n 'precision highp float;',\n 'attribute vec2 pos;',\n 'attribute vec2 uv;',\n 'varying vec2 vUv;',\n 'uniform float flipY;',\n\n 'void main(void) {',\n 'vUv = uv;',\n 'gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);',\n '}',\n ].join('\\n');\n\n SHADER.FRAGMENT_IDENTITY = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n\n 'void main(void) {',\n 'gl_FragColor = texture2D(texture, vUv);',\n '}',\n ].join('\\n');\n\n let _filter = {};\n\n // -------------------------------------------------------------------------\n // Color Matrix Filter\n\n _filter.colorMatrix = function (matrix) {\n // Create a Float32 Array and normalize the offset component to 0-1\n const m = new Float32Array(matrix);\n m[4] /= 255;\n m[9] /= 255;\n m[14] /= 255;\n m[19] /= 255;\n\n // Can we ignore the alpha value? Makes things a bit faster.\n const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0)\n ? _filter.colorMatrix.SHADER.WITHOUT_ALPHA\n : _filter.colorMatrix.SHADER.WITH_ALPHA;\n\n const program = _compileShader(shader);\n gl.uniform1fv(program.uniform.m, m);\n _draw();\n };\n\n _filter.colorMatrix.SHADER = {};\n _filter.colorMatrix.SHADER.WITH_ALPHA = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform float m[20];',\n\n 'void main(void) {',\n 'vec4 c = texture2D(texture, vUv);',\n 'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];',\n 'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];',\n 'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];',\n 'gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];',\n '}',\n ].join('\\n');\n _filter.colorMatrix.SHADER.WITHOUT_ALPHA = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform float m[20];',\n\n 'void main(void) {',\n 'vec4 c = texture2D(texture, vUv);',\n 'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];',\n 'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];',\n 'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];',\n 'gl_FragColor.a = c.a;',\n '}',\n ].join('\\n');\n\n _filter.brightness = function (brightness) {\n const b = (brightness || 0) + 1;\n _filter.colorMatrix([\n b, 0, 0, 0, 0,\n 0, b, 0, 0, 0,\n 0, 0, b, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.saturation = function (amount) {\n const x = (amount || 0) * 2 / 3 + 1;\n const y = ((x - 1) * -0.5);\n _filter.colorMatrix([\n x, y, y, 0, 0,\n y, x, y, 0, 0,\n y, y, x, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.desaturate = function () {\n _filter.saturation(-1);\n };\n\n _filter.contrast = function (amount) {\n const v = (amount || 0) + 1;\n const o = -128 * (v - 1);\n\n _filter.colorMatrix([\n v, 0, 0, 0, o,\n 0, v, 0, 0, o,\n 0, 0, v, 0, o,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.negative = function () {\n _filter.contrast(-2);\n };\n\n _filter.hue = function (rotation) {\n rotation = (rotation || 0) / 180 * Math.PI;\n const cos = Math.cos(rotation);\n const sin = Math.sin(rotation);\n const lumR = 0.213;\n const lumG = 0.715;\n const lumB = 0.072;\n\n _filter.colorMatrix([\n lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.desaturateLuminance = function () {\n _filter.colorMatrix([\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.sepia = function () {\n _filter.colorMatrix([\n 0.393, 0.7689999, 0.18899999, 0, 0,\n 0.349, 0.6859999, 0.16799999, 0, 0,\n 0.272, 0.5339999, 0.13099999, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.brownie = function () {\n _filter.colorMatrix([\n 0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n 0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.vintagePinhole = function () {\n _filter.colorMatrix([\n 0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n 0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n 0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.kodachrome = function () {\n _filter.colorMatrix([\n 1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.technicolor = function () {\n _filter.colorMatrix([\n 1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.polaroid = function () {\n _filter.colorMatrix([\n 1.438, -0.062, -0.062, 0, 0,\n -0.122, 1.378, -0.122, 0, 0,\n -0.016, -0.016, 1.483, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.shiftToBGR = function () {\n _filter.colorMatrix([\n 0, 0, 1, 0, 0,\n 0, 1, 0, 0, 0,\n 1, 0, 0, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n // -------------------------------------------------------------------------\n // Convolution Filter\n\n _filter.convolution = function (matrix) {\n const m = new Float32Array(matrix);\n const pixelSizeX = 1 / _width;\n const pixelSizeY = 1 / _height;\n\n const program = _compileShader(_filter.convolution.SHADER);\n gl.uniform1fv(program.uniform.m, m);\n gl.uniform2f(program.uniform.px, pixelSizeX, pixelSizeY);\n _draw();\n };\n\n _filter.convolution.SHADER = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform vec2 px;',\n 'uniform float m[9];',\n\n 'void main(void) {',\n 'vec4 c11 = texture2D(texture, vUv - px);', // top left\n 'vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y));', // top center\n 'vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y));', // top right\n\n 'vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) );', // mid left\n 'vec4 c22 = texture2D(texture, vUv);', // mid center\n 'vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) );', // mid right\n\n 'vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) );', // bottom left\n 'vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) );', // bottom center\n 'vec4 c33 = texture2D(texture, vUv + px );', // bottom right\n\n 'gl_FragColor = ',\n 'c11 * m[0] + c12 * m[1] + c22 * m[2] +',\n 'c21 * m[3] + c22 * m[4] + c23 * m[5] +',\n 'c31 * m[6] + c32 * m[7] + c33 * m[8];',\n 'gl_FragColor.a = c22.a;',\n '}',\n ].join('\\n');\n\n _filter.detectEdges = function () {\n _filter.convolution.call(this, [\n 0, 1, 0,\n 1, -4, 1,\n 0, 1, 0,\n ]);\n };\n\n _filter.sobelX = function () {\n _filter.convolution.call(this, [\n -1, 0, 1,\n -2, 0, 2,\n -1, 0, 1,\n ]);\n };\n\n _filter.sobelY = function () {\n _filter.convolution.call(this, [\n -1, -2, -1,\n 0, 0, 0,\n 1, 2, 1,\n ]);\n };\n\n _filter.sharpen = function (amount) {\n const a = amount || 1;\n _filter.convolution.call(this, [\n 0, -1 * a, 0,\n -1 * a, 1 + 4 * a, -1 * a,\n 0, -1 * a, 0,\n ]);\n };\n\n _filter.emboss = function (size) {\n const s = size || 1;\n _filter.convolution.call(this, [\n -2 * s, -1 * s, 0,\n -1 * s, 1, 1 * s,\n 0, 1 * s, 2 * s,\n ]);\n };\n\n // -------------------------------------------------------------------------\n // Blur Filter\n\n _filter.blur = function (size) {\n const blurSizeX = (size / 7) / _width;\n const blurSizeY = (size / 7) / _height;\n\n const program = _compileShader(_filter.blur.SHADER);\n\n // Vertical\n gl.uniform2f(program.uniform.px, 0, blurSizeY);\n _draw(DRAW.INTERMEDIATE);\n\n // Horizontal\n gl.uniform2f(program.uniform.px, blurSizeX, 0);\n _draw();\n };\n\n _filter.blur.SHADER = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform vec2 px;',\n\n 'void main(void) {',\n 'gl_FragColor = vec4(0.0);',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;',\n 'gl_FragColor += texture2D(texture, vUv )*0.159576912161;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x, 1.0*px.y))*0.147308056121;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x, 2.0*px.y))*0.115876621105;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x, 3.0*px.y))*0.0776744219933;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x, 4.0*px.y))*0.0443683338718;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x, 5.0*px.y))*0.0215963866053;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265;',\n '}',\n ].join('\\n');\n\n // -------------------------------------------------------------------------\n // Pixelate Filter\n\n _filter.pixelate = function (size) {\n const blurSizeX = (size) / _width;\n const blurSizeY = (size) / _height;\n\n const program = _compileShader(_filter.pixelate.SHADER);\n\n // Horizontal\n gl.uniform2f(program.uniform.size, blurSizeX, blurSizeY);\n _draw();\n };\n\n _filter.pixelate.SHADER = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform vec2 size;',\n 'uniform sampler2D texture;',\n\n 'vec2 pixelate(vec2 coord, vec2 size) {',\n 'return floor( coord / size ) * size;',\n '}',\n\n 'void main(void) {',\n 'gl_FragColor = vec4(0.0);',\n 'vec2 coord = pixelate(vUv, size);',\n 'gl_FragColor += texture2D(texture, coord);',\n '}',\n ].join('\\n');\n};\n\nexports.Canvas = WebGLImageFilter;\n", "import { log } from './log.js';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as fxImage from './imagefx.js';\n\n// internal temp canvases\nlet inCanvas = null;\nlet outCanvas = null;\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nfunction process(input, config) {\n let tensor;\n if (input instanceof tf.Tensor) {\n tensor = tf.clone(input);\n } else {\n const originalWidth = input.naturalWidth || input.videoWidth || input.width || (input.shape && (input.shape[1] > 0));\n const originalHeight = input.naturalHeight || input.videoHeight || input.height || (input.shape && (input.shape[2] > 0));\n let targetWidth = originalWidth;\n let targetHeight = originalHeight;\n if (config.filter.width > 0) targetWidth = config.filter.width;\n else if (config.filter.height > 0) targetWidth = originalWidth * (config.filter.height / originalHeight);\n if (config.filter.height > 0) targetHeight = config.filter.height;\n else if (config.filter.width > 0) targetHeight = originalHeight * (config.filter.width / originalWidth);\n if (!targetWidth || !targetHeight) {\n log('Human: invalid input', input);\n return null;\n }\n if (!inCanvas || (inCanvas.width !== targetWidth) || (inCanvas.height !== targetHeight)) {\n inCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n if (inCanvas.width !== targetWidth) inCanvas.width = targetWidth;\n if (inCanvas.height !== targetHeight) inCanvas.height = targetHeight;\n }\n const ctx = inCanvas.getContext('2d');\n if (input instanceof ImageData) ctx.putImageData(input, 0, 0);\n else ctx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);\n if (config.filter.enabled) {\n if (!this.fx || !outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas.height !== outCanvas.height)) {\n outCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(inCanvas.width, inCanvas.height) : document.createElement('canvas');\n if (outCanvas.width !== inCanvas.width) outCanvas.width = inCanvas.width;\n if (outCanvas.height !== inCanvas.height) outCanvas.height = inCanvas.height;\n this.fx = tf.ENV.flags.IS_BROWSER ? new fxImage.Canvas({ canvas: outCanvas }) : null; // && (typeof document !== 'undefined')\n }\n if (!this.fx) return inCanvas;\n this.fx.reset();\n this.fx.addFilter('brightness', config.filter.brightness); // must have at least one filter enabled\n if (config.filter.contrast !== 0) this.fx.addFilter('contrast', config.filter.contrast);\n if (config.filter.sharpness !== 0) this.fx.addFilter('sharpen', config.filter.sharpness);\n if (config.filter.blur !== 0) this.fx.addFilter('blur', config.filter.blur);\n if (config.filter.saturation !== 0) this.fx.addFilter('saturation', config.filter.saturation);\n if (config.filter.hue !== 0) this.fx.addFilter('hue', config.filter.hue);\n if (config.filter.negative) this.fx.addFilter('negative');\n if (config.filter.sepia) this.fx.addFilter('sepia');\n if (config.filter.vintage) this.fx.addFilter('brownie');\n if (config.filter.sepia) this.fx.addFilter('sepia');\n if (config.filter.kodachrome) this.fx.addFilter('kodachrome');\n if (config.filter.technicolor) this.fx.addFilter('technicolor');\n if (config.filter.polaroid) this.fx.addFilter('polaroid');\n if (config.filter.pixelate !== 0) this.fx.addFilter('pixelate', config.filter.pixelate);\n this.fx.apply(inCanvas);\n // read pixel data\n /*\n const gl = outCanvas.getContext('webgl');\n if (gl) {\n const glBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 4);\n const pixBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 3);\n gl.readPixels(0, 0, outCanvas.width, outCanvas.height, gl.RGBA, gl.UNSIGNED_BYTE, glBuffer);\n // gl returns rbga while we only need rgb, so discarding alpha channel\n // gl returns starting point as lower left, so need to invert vertical\n let i = 0;\n for (let y = outCanvas.height - 1; y >= 0; y--) {\n for (let x = 0; x < outCanvas.width; x++) {\n const index = (x + y * outCanvas.width) * 4;\n pixBuffer[i++] = glBuffer[index + 0];\n pixBuffer[i++] = glBuffer[index + 1];\n pixBuffer[i++] = glBuffer[index + 2];\n }\n }\n outCanvas.data = pixBuffer;\n }\n */\n } else {\n outCanvas = inCanvas;\n }\n let pixels;\n if (outCanvas.data) {\n const shape = [outCanvas.height, outCanvas.width, 3];\n pixels = tf.tensor3d(outCanvas.data, shape, 'int32');\n } else if ((config.backend === 'webgl') || (outCanvas instanceof ImageData)) {\n // tf kernel-optimized method to get imagedata, also if input is imagedata, just use it\n pixels = tf.browser.fromPixels(outCanvas);\n } else {\n // cpu and wasm kernel does not implement efficient fromPixels method nor we can use canvas as-is, so we do a silly one more canvas\n const tempCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n tempCanvas.width = targetWidth;\n tempCanvas.height = targetHeight;\n const tempCtx = tempCanvas.getContext('2d');\n tempCtx?.drawImage(outCanvas, 0, 0);\n const data = tempCtx?.getImageData(0, 0, targetWidth, targetHeight);\n pixels = tf.browser.fromPixels(data);\n }\n const casted = pixels.toFloat();\n tensor = casted.expandDims(0);\n pixels.dispose();\n casted.dispose();\n }\n return { tensor, canvas: config.filter.return ? outCanvas : null };\n}\n\nexports.process = process;\n", "// helper function: wrapper around console output\nexport function log(...msg) {\n const dt = new Date();\n const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n // eslint-disable-next-line no-console\n if (msg) console.log(ts, 'Human:', ...msg);\n}\n", "", "// A port of an algorithm by Johannes Baag\u00F8e , 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e \n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n// \n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n// \n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n var me = this, mash = Mash();\n\n me.next = function() {\n var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n me.s0 = me.s1;\n me.s1 = me.s2;\n return me.s2 = t - (me.c = t | 0);\n };\n\n // Apply the seeding algorithm from Baagoe.\n me.c = 1;\n me.s0 = mash(' ');\n me.s1 = mash(' ');\n me.s2 = mash(' ');\n me.s0 -= mash(seed);\n if (me.s0 < 0) { me.s0 += 1; }\n me.s1 -= mash(seed);\n if (me.s1 < 0) { me.s1 += 1; }\n me.s2 -= mash(seed);\n if (me.s2 < 0) { me.s2 += 1; }\n mash = null;\n}\n\nfunction copy(f, t) {\n t.c = f.c;\n t.s0 = f.s0;\n t.s1 = f.s1;\n t.s2 = f.s2;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new Alea(seed),\n state = opts && opts.state,\n prng = xg.next;\n prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n prng.double = function() {\n return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n };\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nfunction Mash() {\n var n = 0xefc8249d;\n\n var mash = function(data) {\n data = data.toString();\n for (var i = 0; i < data.length; i++) {\n n += data.charCodeAt(i);\n var h = 0.02519603282416938 * n;\n n = h >>> 0;\n h -= n;\n h *= n;\n n = h >>> 0;\n h -= n;\n n += h * 0x100000000; // 2^32\n }\n return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n };\n\n return mash;\n}\n\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.alea = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n\n // Set up generator function.\n me.next = function() {\n var t = me.x ^ (me.x << 11);\n me.x = me.y;\n me.y = me.z;\n me.z = me.w;\n return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n };\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor128 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var t = (me.x ^ (me.x >>> 2));\n me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n return (me.d = (me.d + 362437 | 0)) +\n (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n };\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n me.v = 0;\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n if (k == strseed.length) {\n me.d = me.x << 10 ^ me.x >>> 4;\n }\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n t.v = f.v;\n t.d = f.d;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorwow = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n // Update xor generator.\n var X = me.x, i = me.i, t, v, w;\n t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n X[i] = v;\n me.i = (i + 1) & 7;\n return v;\n };\n\n function init(me, seed) {\n var j, w, X = [];\n\n if (seed === (seed | 0)) {\n // Seed state array using a 32-bit integer.\n w = X[0] = seed;\n } else {\n // Seed state using a string.\n seed = '' + seed;\n for (j = 0; j < seed.length; ++j) {\n X[j & 7] = (X[j & 7] << 15) ^\n (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n }\n }\n // Enforce an array length of 8, not all zeroes.\n while (X.length < 8) X.push(0);\n for (j = 0; j < 8 && X[j] === 0; ++j);\n if (j == 8) w = X[7] = -1; else w = X[j];\n\n me.x = X;\n me.i = 0;\n\n // Discard an initial 256 values.\n for (j = 256; j > 0; --j) {\n me.next();\n }\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.x = f.x.slice();\n t.i = f.i;\n return t;\n}\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.x) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorshift7 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated. For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1); // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897); // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C. This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n var w = me.w,\n X = me.X, i = me.i, t, v;\n // Update Weyl generator.\n me.w = w = (w + 0x61c88647) | 0;\n // Update xor generator.\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n // Update Xor generator array state.\n v = X[i] = v ^ t;\n me.i = i;\n // Result is the combination.\n return (v + (w ^ (w >>> 16))) | 0;\n };\n\n function init(me, seed) {\n var t, v, i, j, w, X = [], limit = 128;\n if (seed === (seed | 0)) {\n // Numeric seeds initialize v, which is used to generates X.\n v = seed;\n seed = null;\n } else {\n // String seeds are mixed into v and X one character at a time.\n seed = seed + '\\0';\n v = 0;\n limit = Math.max(limit, seed.length);\n }\n // Initialize circular array and weyl value.\n for (i = 0, j = -32; j < limit; ++j) {\n // Put the unicode characters into the array, and shuffle them.\n if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n // After 32 shuffles, take v as the starting w value.\n if (j === 0) w = v;\n v ^= v << 10;\n v ^= v >>> 15;\n v ^= v << 4;\n v ^= v >>> 13;\n if (j >= 0) {\n w = (w + 0x61c88647) | 0; // Weyl.\n t = (X[j & 127] ^= (v + w)); // Combine xor and weyl to init array.\n i = (0 == t) ? i + 1 : 0; // Count zeroes.\n }\n }\n // We have detected all zeroes; make the key nonzero.\n if (i >= 128) {\n X[(seed && seed.length || 0) & 127] = -1;\n }\n // Run the generator 512 times to further mix the state before using it.\n // Factoring this as a function slows the main generator, so it is just\n // unrolled here. The weyl generator is not advanced while warming up.\n i = 127;\n for (j = 4 * 128; j > 0; --j) {\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n X[i] = v ^ t;\n }\n // Storing state as object members is faster than using closure variables.\n me.w = w;\n me.X = X;\n me.i = i;\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.i = f.i;\n t.w = f.w;\n t.X = f.X.slice();\n return t;\n};\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.X) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor4096 = impl;\n}\n\n})(\n this, // window object or global\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var b = me.b, c = me.c, d = me.d, a = me.a;\n b = (b << 25) ^ (b >>> 7) ^ c;\n c = (c - d) | 0;\n d = (d << 24) ^ (d >>> 8) ^ a;\n a = (a - b) | 0;\n me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n me.c = c = (c - d) | 0;\n me.d = (d << 16) ^ (c >>> 16) ^ a;\n return me.a = (a - b) | 0;\n };\n\n /* The following is non-inverted tyche, which has better internal\n * bit diffusion, but which is about 25% slower than tyche-i in JS.\n me.next = function() {\n var a = me.a, b = me.b, c = me.c, d = me.d;\n a = (me.a + me.b | 0) >>> 0;\n d = me.d ^ a; d = d << 16 ^ d >>> 16;\n c = me.c + d | 0;\n b = me.b ^ c; b = b << 12 ^ d >>> 20;\n me.a = a = a + b | 0;\n d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n me.c = c = c + d | 0;\n b = b ^ c;\n return me.b = (b << 7 ^ b >>> 25);\n }\n */\n\n me.a = 0;\n me.b = 0;\n me.c = 2654435769 | 0;\n me.d = 1367130551;\n\n if (seed === Math.floor(seed)) {\n // Integer seed.\n me.a = (seed / 0x100000000) | 0;\n me.b = seed | 0;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 20; k++) {\n me.b ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.a = f.a;\n t.b = f.b;\n t.c = f.c;\n t.d = f.d;\n return t;\n};\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.tychei = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "", "/*\nCopyright 2014 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\nvar global = this,\n width = 256, // each RC4 output is 0 <= x < 256\n chunks = 6, // at least six RC4 outputs for each double\n digits = 52, // there are 52 significant digits in a double\n rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n startdenom = math.pow(width, chunks),\n significance = math.pow(2, digits),\n overflow = significance * 2,\n mask = width - 1,\n nodecrypto; // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n var key = [];\n options = (options == true) ? { entropy: true } : (options || {});\n\n // Flatten the seed string or build one from local entropy if needed.\n var shortseed = mixkey(flatten(\n options.entropy ? [seed, tostring(pool)] :\n (seed == null) ? autoseed() : seed, 3), key);\n\n // Use the seed to initialize an ARC4 generator.\n var arc4 = new ARC4(key);\n\n // This function returns a random double in [0, 1) that contains\n // randomness in every bit of the mantissa of the IEEE 754 value.\n var prng = function() {\n var n = arc4.g(chunks), // Start with a numerator n < 2 ^ 48\n d = startdenom, // and denominator d = 2 ^ 48.\n x = 0; // and no 'extra last byte'.\n while (n < significance) { // Fill up all significant digits by\n n = (n + x) * width; // shifting numerator and\n d *= width; // denominator and generating a\n x = arc4.g(1); // new least-significant-byte.\n }\n while (n >= overflow) { // To avoid rounding up, before adding\n n /= 2; // last byte, shift everything\n d /= 2; // right using integer math until\n x >>>= 1; // we have exactly the desired bits.\n }\n return (n + x) / d; // Form the number within [0, 1).\n };\n\n prng.int32 = function() { return arc4.g(4) | 0; }\n prng.quick = function() { return arc4.g(4) / 0x100000000; }\n prng.double = prng;\n\n // Mix the randomness into accumulated entropy.\n mixkey(tostring(arc4.S), pool);\n\n // Calling convention: what to return as a function of prng, seed, is_math.\n return (options.pass || callback ||\n function(prng, seed, is_math_call, state) {\n if (state) {\n // Load the arc4 state from the given state if it has an S array.\n if (state.S) { copy(state, arc4); }\n // Only provide the .state method if requested via options.state.\n prng.state = function() { return copy(arc4, {}); }\n }\n\n // If called as a method of Math (Math.seedrandom()), mutate\n // Math.random because that is how seedrandom.js has worked since v1.0.\n if (is_math_call) { math[rngname] = prng; return seed; }\n\n // Otherwise, it is a newer calling convention, so return the\n // prng directly.\n else return prng;\n })(\n prng,\n shortseed,\n 'global' in options ? options.global : (this == math),\n options.state);\n}\nmath['seed' + rngname] = seedrandom;\n\n//\n// ARC4\n//\n// An ARC4 implementation. The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4. Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n var t, keylen = key.length,\n me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n // The empty key [] is treated as [0].\n if (!keylen) { key = [keylen++]; }\n\n // Set up S using the standard key scheduling algorithm.\n while (i < width) {\n s[i] = i++;\n }\n for (i = 0; i < width; i++) {\n s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n s[j] = t;\n }\n\n // The \"g\" method returns the next (count) outputs as one number.\n (me.g = function(count) {\n // Using instance members instead of closure state nearly doubles speed.\n var t, r = 0,\n i = me.i, j = me.j, s = me.S;\n while (count--) {\n t = s[i = mask & (i + 1)];\n r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n }\n me.i = i; me.j = j;\n return r;\n // For robust unpredictability, the function call below automatically\n // discards an initial batch of values. This is called RC4-drop[256].\n // See http://google.com/search?q=rsa+fluhrer+response&btnI\n })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n t.i = f.i;\n t.j = f.j;\n t.S = f.S.slice();\n return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n var result = [], typ = (typeof obj), prop;\n if (depth && typ == 'object') {\n for (prop in obj) {\n try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n }\n }\n return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n var stringseed = seed + '', smear, j = 0;\n while (j < stringseed.length) {\n key[mask & j] =\n mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n }\n return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n try {\n var out;\n if (nodecrypto && (out = nodecrypto.randomBytes)) {\n // The use of 'out' to remember randomBytes makes tight minified code.\n out = out(width);\n } else {\n out = new Uint8Array(width);\n (global.crypto || global.msCrypto).getRandomValues(out);\n }\n return tostring(out);\n } catch (e) {\n var browser = global.navigator,\n plugins = browser && browser.plugins;\n return [+new Date, global, plugins, global.screen, tostring(pool)];\n }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool. Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n module.exports = seedrandom;\n // When in node.js, try using crypto package for autoseeding.\n try {\n nodecrypto = require('crypto');\n } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n define(function() { return seedrandom; });\n}\n\n// End anonymous scope, and pass initial values.\n})(\n [], // pool: entropy pool starts empty\n Math // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random(); // 0 <= x < 1. Every bit is random.\n// var x = random.quick(); // 0 <= x < 1. 32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three. It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures. Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "// A port of an algorithm by Johannes Baag\u00F8e , 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e \n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n// \n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n// \n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n var me = this, mash = Mash();\n\n me.next = function() {\n var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n me.s0 = me.s1;\n me.s1 = me.s2;\n return me.s2 = t - (me.c = t | 0);\n };\n\n // Apply the seeding algorithm from Baagoe.\n me.c = 1;\n me.s0 = mash(' ');\n me.s1 = mash(' ');\n me.s2 = mash(' ');\n me.s0 -= mash(seed);\n if (me.s0 < 0) { me.s0 += 1; }\n me.s1 -= mash(seed);\n if (me.s1 < 0) { me.s1 += 1; }\n me.s2 -= mash(seed);\n if (me.s2 < 0) { me.s2 += 1; }\n mash = null;\n}\n\nfunction copy(f, t) {\n t.c = f.c;\n t.s0 = f.s0;\n t.s1 = f.s1;\n t.s2 = f.s2;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new Alea(seed),\n state = opts && opts.state,\n prng = xg.next;\n prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n prng.double = function() {\n return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n };\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nfunction Mash() {\n var n = 0xefc8249d;\n\n var mash = function(data) {\n data = data.toString();\n for (var i = 0; i < data.length; i++) {\n n += data.charCodeAt(i);\n var h = 0.02519603282416938 * n;\n n = h >>> 0;\n h -= n;\n h *= n;\n n = h >>> 0;\n h -= n;\n n += h * 0x100000000; // 2^32\n }\n return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n };\n\n return mash;\n}\n\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.alea = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n\n // Set up generator function.\n me.next = function() {\n var t = me.x ^ (me.x << 11);\n me.x = me.y;\n me.y = me.z;\n me.z = me.w;\n return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n };\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor128 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var t = (me.x ^ (me.x >>> 2));\n me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n return (me.d = (me.d + 362437 | 0)) +\n (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n };\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n me.v = 0;\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n if (k == strseed.length) {\n me.d = me.x << 10 ^ me.x >>> 4;\n }\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n t.v = f.v;\n t.d = f.d;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorwow = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n // Update xor generator.\n var X = me.x, i = me.i, t, v, w;\n t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n X[i] = v;\n me.i = (i + 1) & 7;\n return v;\n };\n\n function init(me, seed) {\n var j, w, X = [];\n\n if (seed === (seed | 0)) {\n // Seed state array using a 32-bit integer.\n w = X[0] = seed;\n } else {\n // Seed state using a string.\n seed = '' + seed;\n for (j = 0; j < seed.length; ++j) {\n X[j & 7] = (X[j & 7] << 15) ^\n (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n }\n }\n // Enforce an array length of 8, not all zeroes.\n while (X.length < 8) X.push(0);\n for (j = 0; j < 8 && X[j] === 0; ++j);\n if (j == 8) w = X[7] = -1; else w = X[j];\n\n me.x = X;\n me.i = 0;\n\n // Discard an initial 256 values.\n for (j = 256; j > 0; --j) {\n me.next();\n }\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.x = f.x.slice();\n t.i = f.i;\n return t;\n}\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.x) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorshift7 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated. For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1); // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897); // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C. This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n var w = me.w,\n X = me.X, i = me.i, t, v;\n // Update Weyl generator.\n me.w = w = (w + 0x61c88647) | 0;\n // Update xor generator.\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n // Update Xor generator array state.\n v = X[i] = v ^ t;\n me.i = i;\n // Result is the combination.\n return (v + (w ^ (w >>> 16))) | 0;\n };\n\n function init(me, seed) {\n var t, v, i, j, w, X = [], limit = 128;\n if (seed === (seed | 0)) {\n // Numeric seeds initialize v, which is used to generates X.\n v = seed;\n seed = null;\n } else {\n // String seeds are mixed into v and X one character at a time.\n seed = seed + '\\0';\n v = 0;\n limit = Math.max(limit, seed.length);\n }\n // Initialize circular array and weyl value.\n for (i = 0, j = -32; j < limit; ++j) {\n // Put the unicode characters into the array, and shuffle them.\n if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n // After 32 shuffles, take v as the starting w value.\n if (j === 0) w = v;\n v ^= v << 10;\n v ^= v >>> 15;\n v ^= v << 4;\n v ^= v >>> 13;\n if (j >= 0) {\n w = (w + 0x61c88647) | 0; // Weyl.\n t = (X[j & 127] ^= (v + w)); // Combine xor and weyl to init array.\n i = (0 == t) ? i + 1 : 0; // Count zeroes.\n }\n }\n // We have detected all zeroes; make the key nonzero.\n if (i >= 128) {\n X[(seed && seed.length || 0) & 127] = -1;\n }\n // Run the generator 512 times to further mix the state before using it.\n // Factoring this as a function slows the main generator, so it is just\n // unrolled here. The weyl generator is not advanced while warming up.\n i = 127;\n for (j = 4 * 128; j > 0; --j) {\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n X[i] = v ^ t;\n }\n // Storing state as object members is faster than using closure variables.\n me.w = w;\n me.X = X;\n me.i = i;\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.i = f.i;\n t.w = f.w;\n t.X = f.X.slice();\n return t;\n};\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.X) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor4096 = impl;\n}\n\n})(\n this, // window object or global\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var b = me.b, c = me.c, d = me.d, a = me.a;\n b = (b << 25) ^ (b >>> 7) ^ c;\n c = (c - d) | 0;\n d = (d << 24) ^ (d >>> 8) ^ a;\n a = (a - b) | 0;\n me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n me.c = c = (c - d) | 0;\n me.d = (d << 16) ^ (c >>> 16) ^ a;\n return me.a = (a - b) | 0;\n };\n\n /* The following is non-inverted tyche, which has better internal\n * bit diffusion, but which is about 25% slower than tyche-i in JS.\n me.next = function() {\n var a = me.a, b = me.b, c = me.c, d = me.d;\n a = (me.a + me.b | 0) >>> 0;\n d = me.d ^ a; d = d << 16 ^ d >>> 16;\n c = me.c + d | 0;\n b = me.b ^ c; b = b << 12 ^ d >>> 20;\n me.a = a = a + b | 0;\n d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n me.c = c = c + d | 0;\n b = b ^ c;\n return me.b = (b << 7 ^ b >>> 25);\n }\n */\n\n me.a = 0;\n me.b = 0;\n me.c = 2654435769 | 0;\n me.d = 1367130551;\n\n if (seed === Math.floor(seed)) {\n // Integer seed.\n me.a = (seed / 0x100000000) | 0;\n me.b = seed | 0;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 20; k++) {\n me.b ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.a = f.a;\n t.b = f.b;\n t.c = f.c;\n t.d = f.d;\n return t;\n};\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.tychei = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "/*\nCopyright 2014 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\nvar global = this,\n width = 256, // each RC4 output is 0 <= x < 256\n chunks = 6, // at least six RC4 outputs for each double\n digits = 52, // there are 52 significant digits in a double\n rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n startdenom = math.pow(width, chunks),\n significance = math.pow(2, digits),\n overflow = significance * 2,\n mask = width - 1,\n nodecrypto; // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n var key = [];\n options = (options == true) ? { entropy: true } : (options || {});\n\n // Flatten the seed string or build one from local entropy if needed.\n var shortseed = mixkey(flatten(\n options.entropy ? [seed, tostring(pool)] :\n (seed == null) ? autoseed() : seed, 3), key);\n\n // Use the seed to initialize an ARC4 generator.\n var arc4 = new ARC4(key);\n\n // This function returns a random double in [0, 1) that contains\n // randomness in every bit of the mantissa of the IEEE 754 value.\n var prng = function() {\n var n = arc4.g(chunks), // Start with a numerator n < 2 ^ 48\n d = startdenom, // and denominator d = 2 ^ 48.\n x = 0; // and no 'extra last byte'.\n while (n < significance) { // Fill up all significant digits by\n n = (n + x) * width; // shifting numerator and\n d *= width; // denominator and generating a\n x = arc4.g(1); // new least-significant-byte.\n }\n while (n >= overflow) { // To avoid rounding up, before adding\n n /= 2; // last byte, shift everything\n d /= 2; // right using integer math until\n x >>>= 1; // we have exactly the desired bits.\n }\n return (n + x) / d; // Form the number within [0, 1).\n };\n\n prng.int32 = function() { return arc4.g(4) | 0; }\n prng.quick = function() { return arc4.g(4) / 0x100000000; }\n prng.double = prng;\n\n // Mix the randomness into accumulated entropy.\n mixkey(tostring(arc4.S), pool);\n\n // Calling convention: what to return as a function of prng, seed, is_math.\n return (options.pass || callback ||\n function(prng, seed, is_math_call, state) {\n if (state) {\n // Load the arc4 state from the given state if it has an S array.\n if (state.S) { copy(state, arc4); }\n // Only provide the .state method if requested via options.state.\n prng.state = function() { return copy(arc4, {}); }\n }\n\n // If called as a method of Math (Math.seedrandom()), mutate\n // Math.random because that is how seedrandom.js has worked since v1.0.\n if (is_math_call) { math[rngname] = prng; return seed; }\n\n // Otherwise, it is a newer calling convention, so return the\n // prng directly.\n else return prng;\n })(\n prng,\n shortseed,\n 'global' in options ? options.global : (this == math),\n options.state);\n}\nmath['seed' + rngname] = seedrandom;\n\n//\n// ARC4\n//\n// An ARC4 implementation. The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4. Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n var t, keylen = key.length,\n me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n // The empty key [] is treated as [0].\n if (!keylen) { key = [keylen++]; }\n\n // Set up S using the standard key scheduling algorithm.\n while (i < width) {\n s[i] = i++;\n }\n for (i = 0; i < width; i++) {\n s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n s[j] = t;\n }\n\n // The \"g\" method returns the next (count) outputs as one number.\n (me.g = function(count) {\n // Using instance members instead of closure state nearly doubles speed.\n var t, r = 0,\n i = me.i, j = me.j, s = me.S;\n while (count--) {\n t = s[i = mask & (i + 1)];\n r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n }\n me.i = i; me.j = j;\n return r;\n // For robust unpredictability, the function call below automatically\n // discards an initial batch of values. This is called RC4-drop[256].\n // See http://google.com/search?q=rsa+fluhrer+response&btnI\n })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n t.i = f.i;\n t.j = f.j;\n t.S = f.S.slice();\n return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n var result = [], typ = (typeof obj), prop;\n if (depth && typ == 'object') {\n for (prop in obj) {\n try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n }\n }\n return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n var stringseed = seed + '', smear, j = 0;\n while (j < stringseed.length) {\n key[mask & j] =\n mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n }\n return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n try {\n var out;\n if (nodecrypto && (out = nodecrypto.randomBytes)) {\n // The use of 'out' to remember randomBytes makes tight minified code.\n out = out(width);\n } else {\n out = new Uint8Array(width);\n (global.crypto || global.msCrypto).getRandomValues(out);\n }\n return tostring(out);\n } catch (e) {\n var browser = global.navigator,\n plugins = browser && browser.plugins;\n return [+new Date, global, plugins, global.screen, tostring(pool)];\n }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool. Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n module.exports = seedrandom;\n // When in node.js, try using crypto package for autoseeding.\n try {\n nodecrypto = require('crypto');\n } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n define(function() { return seedrandom; });\n}\n\n// End anonymous scope, and pass initial values.\n})(\n [], // pool: entropy pool starts empty\n Math // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random(); // 0 <= x < 1. Every bit is random.\n// var x = random.quick(); // 0 <= x < 1. 32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three. It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures. Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "", "", "", "\nvar WasmBackendModuleThreadedSimd = (function() {\n var _scriptDir = typeof document !== 'undefined' && document.currentScript ? document.currentScript.src : undefined;\n if (typeof __filename !== 'undefined') _scriptDir = _scriptDir || __filename;\n return (\nfunction(WasmBackendModuleThreadedSimd) {\n WasmBackendModuleThreadedSimd = WasmBackendModuleThreadedSimd || {};\n\nfunction GROWABLE_HEAP_I8(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAP8}function GROWABLE_HEAP_U8(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPU8}function GROWABLE_HEAP_I32(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAP32}function GROWABLE_HEAP_U32(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPU32}function GROWABLE_HEAP_F64(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPF64}var Module=typeof WasmBackendModuleThreadedSimd!==\"undefined\"?WasmBackendModuleThreadedSimd:{};var _scriptDir=undefined;var moduleOverrides={};var key;for(key in Module){if(Module.hasOwnProperty(key)){moduleOverrides[key]=Module[key]}}var arguments_=[];var thisProgram=\"./this.program\";var quit_=function(status,toThrow){throw toThrow};var ENVIRONMENT_IS_WEB=false;var ENVIRONMENT_IS_WORKER=false;var ENVIRONMENT_IS_NODE=false;var ENVIRONMENT_IS_SHELL=false;ENVIRONMENT_IS_WEB=typeof window===\"object\";ENVIRONMENT_IS_WORKER=typeof importScripts===\"function\";ENVIRONMENT_IS_NODE=typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\";ENVIRONMENT_IS_SHELL=!ENVIRONMENT_IS_WEB&&!ENVIRONMENT_IS_NODE&&!ENVIRONMENT_IS_WORKER;var ENVIRONMENT_IS_PTHREAD=Module[\"ENVIRONMENT_IS_PTHREAD\"]||false;if(ENVIRONMENT_IS_PTHREAD){buffer=Module[\"buffer\"];DYNAMIC_BASE=Module[\"DYNAMIC_BASE\"];DYNAMICTOP_PTR=Module[\"DYNAMICTOP_PTR\"]}var scriptDirectory=\"\";function locateFile(path){if(Module[\"locateFile\"]){return Module[\"locateFile\"](path,scriptDirectory)}return scriptDirectory+path}var read_,readAsync,readBinary,setWindowTitle;var nodeFS;var nodePath;if(ENVIRONMENT_IS_NODE){if(ENVIRONMENT_IS_WORKER){scriptDirectory=require(\"path\").dirname(scriptDirectory)+\"/\"}else{scriptDirectory=__dirname+\"/\"}read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret};if(process[\"argv\"].length>1){thisProgram=process[\"argv\"][1].replace(/\\\\/g,\"/\")}arguments_=process[\"argv\"].slice(2);process[\"on\"](\"uncaughtException\",function(ex){if(!(ex instanceof ExitStatus)){throw ex}});process[\"on\"](\"unhandledRejection\",abort);quit_=function(status){process[\"exit\"](status)};Module[\"inspect\"]=function(){return\"[Emscripten Module object]\"};var nodeWorkerThreads;try{nodeWorkerThreads=require(\"worker_threads\")}catch(e){console.error('The \"worker_threads\" module is not supported in this node.js build - perhaps a newer version is needed?');throw e}Worker=nodeWorkerThreads.Worker}else if(ENVIRONMENT_IS_SHELL){if(typeof read!=\"undefined\"){read_=function shell_read(f){return read(f)}}readBinary=function readBinary(f){var data;if(typeof readbuffer===\"function\"){return new Uint8Array(readbuffer(f))}data=read(f,\"binary\");assert(typeof data===\"object\");return data};if(typeof scriptArgs!=\"undefined\"){arguments_=scriptArgs}else if(typeof arguments!=\"undefined\"){arguments_=arguments}if(typeof quit===\"function\"){quit_=function(status){quit(status)}}if(typeof print!==\"undefined\"){if(typeof console===\"undefined\")console={};console.log=print;console.warn=console.error=typeof printErr!==\"undefined\"?printErr:print}}else if(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER){if(ENVIRONMENT_IS_WORKER){scriptDirectory=self.location.href}else if(document.currentScript){scriptDirectory=document.currentScript.src}if(_scriptDir){scriptDirectory=_scriptDir}if(scriptDirectory.indexOf(\"blob:\")!==0){scriptDirectory=scriptDirectory.substr(0,scriptDirectory.lastIndexOf(\"/\")+1)}else{scriptDirectory=\"\"}if(ENVIRONMENT_IS_NODE){read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret}}else{read_=function shell_read(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.send(null);return xhr.responseText};if(ENVIRONMENT_IS_WORKER){readBinary=function readBinary(url){var xhr=new 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Atomics_load=Atomics.load;var Atomics_store=Atomics.store;var Atomics_compareExchange=Atomics.compareExchange;var wasmBinary;if(Module[\"wasmBinary\"])wasmBinary=Module[\"wasmBinary\"];var noExitRuntime;if(Module[\"noExitRuntime\"])noExitRuntime=Module[\"noExitRuntime\"];if(typeof WebAssembly!==\"object\"){err(\"no native wasm support detected\")}var wasmMemory;var wasmTable=new WebAssembly.Table({\"initial\":169,\"maximum\":169+0,\"element\":\"anyfunc\"});var wasmModule;var threadInfoStruct=0;var selfThreadId=0;var ABORT=false;var EXITSTATUS=0;function assert(condition,text){if(!condition){abort(\"Assertion failed: \"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i=endIdx)){var u0=heap[idx++];if(!u0)return str;if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224){u0=(u0&15)<<12|u1<<6|u2}else{u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63}if(u0<65536){str+=String.fromCharCode(u0)}else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(GROWABLE_HEAP_U8(),ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var 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alignUp(x,multiple){if(x%multiple>0){x+=multiple-x%multiple}return x}var buffer,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer=buf;Module[\"HEAP8\"]=HEAP8=new Int8Array(buf);Module[\"HEAP16\"]=HEAP16=new Int16Array(buf);Module[\"HEAP32\"]=HEAP32=new Int32Array(buf);Module[\"HEAPU8\"]=HEAPU8=new Uint8Array(buf);Module[\"HEAPU16\"]=HEAPU16=new Uint16Array(buf);Module[\"HEAPU32\"]=HEAPU32=new Uint32Array(buf);Module[\"HEAPF32\"]=HEAPF32=new Float32Array(buf);Module[\"HEAPF64\"]=HEAPF64=new Float64Array(buf)}var STACK_BASE=5256464,STACKTOP=STACK_BASE,STACK_MAX=13584,DYNAMIC_BASE=5256464,DYNAMICTOP_PTR=12656;if(ENVIRONMENT_IS_PTHREAD){}var INITIAL_INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;if(ENVIRONMENT_IS_PTHREAD){wasmMemory=Module[\"wasmMemory\"];buffer=Module[\"buffer\"]}else{if(Module[\"wasmMemory\"]){wasmMemory=Module[\"wasmMemory\"]}else{wasmMemory=new WebAssembly.Memory({\"initial\":INITIAL_INITIAL_MEMORY/WASM_PAGE_SIZE,\"maximum\":2147483648/WASM_PAGE_SIZE,\"shared\":true});if(!(wasmMemory.buffer instanceof SharedArrayBuffer)){err(\"requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag\");if(ENVIRONMENT_IS_NODE){console.log(\"(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)\")}throw Error(\"bad memory\")}}}if(wasmMemory){buffer=wasmMemory.buffer}INITIAL_INITIAL_MEMORY=buffer.byteLength;updateGlobalBufferAndViews(buffer);if(!ENVIRONMENT_IS_PTHREAD){GROWABLE_HEAP_I32()[DYNAMICTOP_PTR>>2]=DYNAMIC_BASE}function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback==\"function\"){callback(Module);continue}var func=callback.func;if(typeof func===\"number\"){if(callback.arg===undefined){Module[\"dynCall_v\"](func)}else{Module[\"dynCall_vi\"](func,callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATEXIT__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;if(ENVIRONMENT_IS_PTHREAD)runtimeInitialized=true;function preRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"preRun\"]){if(typeof Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;callRuntimeCallbacks(__ATINIT__)}function preMain(){if(ENVIRONMENT_IS_PTHREAD)return;callRuntimeCallbacks(__ATMAIN__)}function postRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"postRun\"]){if(typeof Module[\"postRun\"]==\"function\")Module[\"postRun\"]=[Module[\"postRun\"]];while(Module[\"postRun\"].length){addOnPostRun(Module[\"postRun\"].shift())}}callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var Math_ceil=Math.ceil;var Math_floor=Math.floor;var runDependencies=0;var runDependencyWatcher=null;var dependenciesFulfilled=null;function addRunDependency(id){assert(!ENVIRONMENT_IS_PTHREAD,\"addRunDependency cannot be used in a pthread worker\");runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}if(ENVIRONMENT_IS_PTHREAD)console.error(\"Pthread aborting at \"+(new Error).stack);what+=\"\";out(what);err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";throw new WebAssembly.RuntimeError(what)}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm-threaded-simd.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(){try{if(wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(wasmBinaryFile)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary()})}return new Promise(function(resolve,reject){resolve(getBinary())})}function createWasm(){var info={\"a\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmModule=module;if(!ENVIRONMENT_IS_PTHREAD){var numWorkersToLoad=PThread.unusedWorkers.length;PThread.unusedWorkers.forEach(function(w){PThread.loadWasmModuleToWorker(w,function(){if(!--numWorkersToLoad)removeRunDependency(\"wasm-instantiate\")})})}}if(!ENVIRONMENT_IS_PTHREAD){addRunDependency(\"wasm-instantiate\")}function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"],output[\"module\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming===\"function\"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch===\"function\"){fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err(\"wasm streaming compile failed: \"+reason);err(\"falling back to ArrayBuffer instantiation\");instantiateArrayBuffer(receiveInstantiatedSource)})})}else{return instantiateArrayBuffer(receiveInstantiatedSource)}}if(Module[\"instantiateWasm\"]){try{var exports=Module[\"instantiateWasm\"](info,receiveInstance);return exports}catch(e){err(\"Module.instantiateWasm callback failed with error: \"+e);return false}}instantiateAsync();return{}}var ASM_CONSTS={};function initPthreadsJS(){PThread.initRuntime()}if(!ENVIRONMENT_IS_PTHREAD)__ATINIT__.push({func:function(){___wasm_call_ctors()}});var __pthread_ptr=0;var __pthread_is_main_runtime_thread=0;var __pthread_is_main_browser_thread=0;function __register_pthread_ptr(pthreadPtr,isMainBrowserThread,isMainRuntimeThread){pthreadPtr=pthreadPtr|0;isMainBrowserThread=isMainBrowserThread|0;isMainRuntimeThread=isMainRuntimeThread|0;__pthread_ptr=pthreadPtr;__pthread_is_main_browser_thread=isMainBrowserThread;__pthread_is_main_runtime_thread=isMainRuntimeThread}Module[\"__register_pthread_ptr\"]=__register_pthread_ptr;var ERRNO_CODES={EPERM:63,ENOENT:44,ESRCH:71,EINTR:27,EIO:29,ENXIO:60,E2BIG:1,ENOEXEC:45,EBADF:8,ECHILD:12,EAGAIN:6,EWOULDBLOCK:6,ENOMEM:48,EACCES:2,EFAULT:21,ENOTBLK:105,EBUSY:10,EEXIST:20,EXDEV:75,ENODEV:43,ENOTDIR:54,EISDIR:31,EINVAL:28,ENFILE:41,EMFILE:33,ENOTTY:59,ETXTBSY:74,EFBIG:22,ENOSPC:51,ESPIPE:70,EROFS:69,EMLINK:34,EPIPE:64,EDOM:18,ERANGE:68,ENOMSG:49,EIDRM:24,ECHRNG:106,EL2NSYNC:156,EL3HLT:107,EL3RST:108,ELNRNG:109,EUNATCH:110,ENOCSI:111,EL2HLT:112,EDEADLK:16,ENOLCK:46,EBADE:113,EBADR:114,EXFULL:115,ENOANO:104,EBADRQC:103,EBADSLT:102,EDEADLOCK:16,EBFONT:101,ENOSTR:100,ENODATA:116,ETIME:117,ENOSR:118,ENONET:119,ENOPKG:120,EREMOTE:121,ENOLINK:47,EADV:122,ESRMNT:123,ECOMM:124,EPROTO:65,EMULTIHOP:36,EDOTDOT:125,EBADMSG:9,ENOTUNIQ:126,EBADFD:127,EREMCHG:128,ELIBACC:129,ELIBBAD:130,ELIBSCN:131,ELIBMAX:132,ELIBEXEC:133,ENOSYS:52,ENOTEMPTY:55,ENAMETOOLONG:37,ELOOP:32,EOPNOTSUPP:138,EPFNOSUPPORT:139,ECONNRESET:15,ENOBUFS:42,EAFNOSUPPORT:5,EPROTOTYPE:67,ENOTSOCK:57,ENOPROTOOPT:50,ESHUTDOWN:140,ECONNREFUSED:14,EADDRINUSE:3,ECONNABORTED:13,ENETUNREACH:40,ENETDOWN:38,ETIMEDOUT:73,EHOSTDOWN:142,EHOSTUNREACH:23,EINPROGRESS:26,EALREADY:7,EDESTADDRREQ:17,EMSGSIZE:35,EPROTONOSUPPORT:66,ESOCKTNOSUPPORT:137,EADDRNOTAVAIL:4,ENETRESET:39,EISCONN:30,ENOTCONN:53,ETOOMANYREFS:141,EUSERS:136,EDQUOT:19,ESTALE:72,ENOTSUP:138,ENOMEDIUM:148,EILSEQ:25,EOVERFLOW:61,ECANCELED:11,ENOTRECOVERABLE:56,EOWNERDEAD:62,ESTRPIPE:135};var __main_thread_futex_wait_address=13568;function _emscripten_futex_wake(addr,count){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0||count<0)return-28;if(count==0)return 0;if(count>=2147483647)count=Infinity;var mainThreadWaitAddress=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2);var mainThreadWoken=0;if(mainThreadWaitAddress==addr){var loadedAddr=Atomics.compareExchange(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,mainThreadWaitAddress,0);if(loadedAddr==mainThreadWaitAddress){--count;mainThreadWoken=1;if(count<=0)return 1}}var ret=Atomics.notify(GROWABLE_HEAP_I32(),addr>>2,count);if(ret>=0)return ret+mainThreadWoken;throw\"Atomics.notify returned an unexpected value \"+ret}Module[\"_emscripten_futex_wake\"]=_emscripten_futex_wake;function __kill_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _kill_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _kill_thread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];pthread.worker.terminate();PThread.freeThreadData(pthread);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(pthread.worker),1);pthread.worker.pthread=undefined}function __cancel_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _cancel_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _cancel_thread!\";var pthread=PThread.pthreads[pthread_ptr];pthread.worker.postMessage({\"cmd\":\"cancel\"})}function __cleanup_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _cleanup_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _cleanup_thread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];if(pthread){var worker=pthread.worker;PThread.returnWorkerToPool(worker)}}var PThread={MAIN_THREAD_ID:1,mainThreadInfo:{schedPolicy:0,schedPrio:0},unusedWorkers:[],runningWorkers:[],initRuntime:function(){__register_pthread_ptr(PThread.mainThreadBlock,!ENVIRONMENT_IS_WORKER,1);_emscripten_register_main_browser_thread_id(PThread.mainThreadBlock)},initMainThreadBlock:function(){var pthreadPoolSize=8;for(var i=0;i>2]=PThread.mainThreadBlock;var headPtr=PThread.mainThreadBlock+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var tlsMemory=13056;for(var i=0;i<128;++i)GROWABLE_HEAP_U32()[tlsMemory/4+i]=0;Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+104>>2,tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+40>>2,PThread.mainThreadBlock);Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+44>>2,42)},initWorker:function(){},pthreads:{},exitHandlers:null,setThreadStatus:function(){},runExitHandlers:function(){if(PThread.exitHandlers!==null){while(PThread.exitHandlers.length>0){PThread.exitHandlers.pop()()}PThread.exitHandlers=null}if(ENVIRONMENT_IS_PTHREAD&&threadInfoStruct)___pthread_tsd_run_dtors()},threadExit:function(exitCode){var tb=_pthread_self();if(tb){Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,exitCode);Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+60>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+64>>2,0);PThread.runExitHandlers();_emscripten_futex_wake(tb+0,2147483647);__register_pthread_ptr(0,0,0);threadInfoStruct=0;if(ENVIRONMENT_IS_PTHREAD){postMessage({\"cmd\":\"exit\"})}}},threadCancel:function(){PThread.runExitHandlers();Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+4>>2,-1);Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+0>>2,1);_emscripten_futex_wake(threadInfoStruct+0,2147483647);threadInfoStruct=selfThreadId=0;__register_pthread_ptr(0,0,0);postMessage({\"cmd\":\"cancelDone\"})},terminateAllThreads:function(){for(var t in PThread.pthreads){var pthread=PThread.pthreads[t];if(pthread&&pthread.worker){PThread.returnWorkerToPool(pthread.worker)}}PThread.pthreads={};for(var i=0;i>2];GROWABLE_HEAP_I32()[pthread.threadInfoStruct+104>>2]=0;_free(tlsMemory);_free(pthread.threadInfoStruct)}pthread.threadInfoStruct=0;if(pthread.allocatedOwnStack&&pthread.stackBase)_free(pthread.stackBase);pthread.stackBase=0;if(pthread.worker)pthread.worker.pthread=null},returnWorkerToPool:function(worker){delete PThread.pthreads[worker.pthread.thread];PThread.unusedWorkers.push(worker);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker),1);PThread.freeThreadData(worker.pthread);worker.pthread=undefined},receiveObjectTransfer:function(data){},loadWasmModuleToWorker:function(worker,onFinishedLoading){worker.onmessage=function(e){var d=e[\"data\"];var cmd=d[\"cmd\"];if(worker.pthread)PThread.currentProxiedOperationCallerThread=worker.pthread.threadInfoStruct;if(d[\"targetThread\"]&&d[\"targetThread\"]!=_pthread_self()){var thread=PThread.pthreads[d.targetThread];if(thread){thread.worker.postMessage(e.data,d[\"transferList\"])}else{console.error('Internal error! Worker sent a message \"'+cmd+'\" to target pthread '+d[\"targetThread\"]+\", but that thread no longer exists!\")}PThread.currentProxiedOperationCallerThread=undefined;return}if(cmd===\"processQueuedMainThreadWork\"){_emscripten_main_thread_process_queued_calls()}else if(cmd===\"spawnThread\"){__spawn_thread(e.data)}else if(cmd===\"cleanupThread\"){__cleanup_thread(d[\"thread\"])}else if(cmd===\"killThread\"){__kill_thread(d[\"thread\"])}else if(cmd===\"cancelThread\"){__cancel_thread(d[\"thread\"])}else if(cmd===\"loaded\"){worker.loaded=true;if(onFinishedLoading)onFinishedLoading(worker);if(worker.runPthread){worker.runPthread();delete worker.runPthread}}else if(cmd===\"print\"){out(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"printErr\"){err(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"alert\"){alert(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"exit\"){var detached=worker.pthread&&Atomics.load(GROWABLE_HEAP_U32(),worker.pthread.thread+68>>2);if(detached){PThread.returnWorkerToPool(worker)}}else if(cmd===\"cancelDone\"){PThread.returnWorkerToPool(worker)}else if(cmd===\"objectTransfer\"){PThread.receiveObjectTransfer(e.data)}else if(e.data.target===\"setimmediate\"){worker.postMessage(e.data)}else{err(\"worker sent an unknown command \"+cmd)}PThread.currentProxiedOperationCallerThread=undefined};worker.onerror=function(e){err(\"pthread sent an error! \"+e.filename+\":\"+e.lineno+\": \"+e.message)};if(ENVIRONMENT_IS_NODE){worker.on(\"message\",function(data){worker.onmessage({data:data})});worker.on(\"error\",function(data){worker.onerror(data)});worker.on(\"exit\",function(data){console.log(\"worker exited - TODO: update the worker queue?\")})}worker.postMessage({\"cmd\":\"load\",\"urlOrBlob\":Module[\"mainScriptUrlOrBlob\"]||_scriptDir,\"wasmMemory\":wasmMemory,\"wasmModule\":wasmModule,\"DYNAMIC_BASE\":DYNAMIC_BASE,\"DYNAMICTOP_PTR\":DYNAMICTOP_PTR})},allocateUnusedWorker:function(){var pthreadMainJs=locateFile(\"tfjs-backend-wasm-threaded-simd.worker.js\");PThread.unusedWorkers.push(new Worker(pthreadMainJs))},getNewWorker:function(){if(PThread.unusedWorkers.length==0){PThread.allocateUnusedWorker();PThread.loadWasmModuleToWorker(PThread.unusedWorkers[0])}if(PThread.unusedWorkers.length>0)return PThread.unusedWorkers.pop();else return null},busySpinWait:function(msecs){var t=performance.now()+msecs;while(performance.now()>2]=value;return value}function _atexit(func,arg){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(1,1,func,arg);__ATEXIT__.unshift({func:func,arg:arg})}function __emscripten_notify_thread_queue(targetThreadId,mainThreadId){if(targetThreadId==mainThreadId){postMessage({\"cmd\":\"processQueuedMainThreadWork\"})}else if(ENVIRONMENT_IS_PTHREAD){postMessage({\"targetThread\":targetThreadId,\"cmd\":\"processThreadQueue\"})}else{var pthread=PThread.pthreads[targetThreadId];var worker=pthread&&pthread.worker;if(!worker){return}worker.postMessage({\"cmd\":\"processThreadQueue\"})}return 1}function _abort(){abort()}function _emscripten_conditional_set_current_thread_status(expectedStatus,newStatus){expectedStatus=expectedStatus|0;newStatus=newStatus|0}function _emscripten_futex_wait(addr,val,timeout){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0)return-28;if(ENVIRONMENT_IS_WORKER){var ret=Atomics.wait(GROWABLE_HEAP_I32(),addr>>2,val,timeout);if(ret===\"timed-out\")return-73;if(ret===\"not-equal\")return-6;if(ret===\"ok\")return 0;throw\"Atomics.wait returned an unexpected value \"+ret}else{var loadedVal=Atomics.load(GROWABLE_HEAP_I32(),addr>>2);if(val!=loadedVal)return-6;var tNow=performance.now();var tEnd=tNow+timeout;Atomics.store(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,addr);var ourWaitAddress=addr;while(addr==ourWaitAddress){tNow=performance.now();if(tNow>tEnd){return-73}_emscripten_main_thread_process_queued_calls();addr=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2)}return 0}}function _emscripten_is_main_browser_thread(){return __pthread_is_main_browser_thread|0}function _emscripten_is_main_runtime_thread(){return __pthread_is_main_runtime_thread|0}function _emscripten_memcpy_big(dest,src,num){GROWABLE_HEAP_U8().copyWithin(dest,src,src+num)}function _emscripten_num_logical_cores(){return navigator[\"hardwareConcurrency\"]}function _emscripten_proxy_to_main_thread_js(index,sync){var numCallArgs=arguments.length-2;var stack=stackSave();var args=stackAlloc(numCallArgs*8);var b=args>>3;for(var i=0;i>3]);buf+=8}else{buf=buf+3&~3;args.push(GROWABLE_HEAP_I32()[buf>>2]);buf+=4}}return args}function _emscripten_receive_on_main_thread_js(index,numCallArgs,args){_emscripten_receive_on_main_thread_js_callArgs.length=numCallArgs;var b=args>>3;for(var i=0;i>>16);updateGlobalBufferAndViews(wasmMemory.buffer);return 1}catch(e){}}function _emscripten_resize_heap(requestedSize){requestedSize=requestedSize>>>0;var oldSize=_emscripten_get_heap_size();if(requestedSize<=oldSize){return false}var PAGE_MULTIPLE=65536;var maxHeapSize=2147483648;if(requestedSize>maxHeapSize){return false}var minHeapSize=16777216;for(var cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(minHeapSize,requestedSize,overGrownHeapSize),PAGE_MULTIPLE));var replacement=emscripten_realloc_buffer(newSize);if(replacement){return true}}return false}var JSEvents={keyEvent:0,mouseEvent:0,wheelEvent:0,uiEvent:0,focusEvent:0,deviceOrientationEvent:0,deviceMotionEvent:0,fullscreenChangeEvent:0,pointerlockChangeEvent:0,visibilityChangeEvent:0,touchEvent:0,previousFullscreenElement:null,previousScreenX:null,previousScreenY:null,removeEventListenersRegistered:false,removeAllEventListeners:function(){for(var i=JSEvents.eventHandlers.length-1;i>=0;--i){JSEvents._removeHandler(i)}JSEvents.eventHandlers=[];JSEvents.deferredCalls=[]},registerRemoveEventListeners:function(){if(!JSEvents.removeEventListenersRegistered){__ATEXIT__.push(JSEvents.removeAllEventListeners);JSEvents.removeEventListenersRegistered=true}},deferredCalls:[],deferCall:function(targetFunction,precedence,argsList){function arraysHaveEqualContent(arrA,arrB){if(arrA.length!=arrB.length)return false;for(var i in arrA){if(arrA[i]!=arrB[i])return false}return true}for(var i in JSEvents.deferredCalls){var call=JSEvents.deferredCalls[i];if(call.targetFunction==targetFunction&&arraysHaveEqualContent(call.argsList,argsList)){return}}JSEvents.deferredCalls.push({targetFunction:targetFunction,precedence:precedence,argsList:argsList});JSEvents.deferredCalls.sort(function(x,y){return x.precedence>2]=eventTypeId;GROWABLE_HEAP_I32()[varargs+4>>2]=eventData;GROWABLE_HEAP_I32()[varargs+8>>2]=userData;_emscripten_async_queue_on_thread_(targetThread,637534208,eventHandlerFunc,eventData,varargs);stackRestore(stackTop)},getTargetThreadForEventCallback:function(targetThread){switch(targetThread){case 1:return 0;case 2:return PThread.currentProxiedOperationCallerThread;default:return targetThread}},getNodeNameForTarget:function(target){if(!target)return\"\";if(target==window)return\"#window\";if(target==screen)return\"#screen\";return target&&target.nodeName?target.nodeName:\"\"},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function stringToNewUTF8(jsString){var length=lengthBytesUTF8(jsString)+1;var cString=_malloc(length);stringToUTF8(jsString,cString,length);return cString}function _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height){var stackTop=stackSave();var varargs=stackAlloc(12);var targetCanvasPtr=0;if(targetCanvas){targetCanvasPtr=stringToNewUTF8(targetCanvas)}GROWABLE_HEAP_I32()[varargs>>2]=targetCanvasPtr;GROWABLE_HEAP_I32()[varargs+4>>2]=width;GROWABLE_HEAP_I32()[varargs+8>>2]=height;_emscripten_async_queue_on_thread_(targetThread,657457152,0,targetCanvasPtr,varargs);stackRestore(stackTop)}function _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,targetCanvas,width,height){targetCanvas=targetCanvas?UTF8ToString(targetCanvas):\"\";_emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height)}function __maybeCStringToJsString(cString){return cString>2?UTF8ToString(cString):cString}var specialHTMLTargets=[0,typeof document!==\"undefined\"?document:0,typeof window!==\"undefined\"?window:0];function __findEventTarget(target){target=__maybeCStringToJsString(target);var domElement=specialHTMLTargets[target]||(typeof document!==\"undefined\"?document.querySelector(target):undefined);return domElement}function __findCanvasEventTarget(target){return __findEventTarget(target)}function _emscripten_set_canvas_element_size_calling_thread(target,width,height){var canvas=__findCanvasEventTarget(target);if(!canvas)return-4;if(canvas.canvasSharedPtr){GROWABLE_HEAP_I32()[canvas.canvasSharedPtr>>2]=width;GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+4>>2]=height}if(canvas.offscreenCanvas||!canvas.controlTransferredOffscreen){if(canvas.offscreenCanvas)canvas=canvas.offscreenCanvas;var autoResizeViewport=false;if(canvas.GLctxObject&&canvas.GLctxObject.GLctx){var prevViewport=canvas.GLctxObject.GLctx.getParameter(2978);autoResizeViewport=prevViewport[0]===0&&prevViewport[1]===0&&prevViewport[2]===canvas.width&&prevViewport[3]===canvas.height}canvas.width=width;canvas.height=height;if(autoResizeViewport){canvas.GLctxObject.GLctx.viewport(0,0,width,height)}}else if(canvas.canvasSharedPtr){var targetThread=GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+8>>2];_emscripten_set_offscreencanvas_size_on_target_thread(targetThread,target,width,height);return 1}else{return-4}return 0}function _emscripten_set_canvas_element_size_main_thread(target,width,height){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(2,1,target,width,height);return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}function _emscripten_set_canvas_element_size(target,width,height){var canvas=__findCanvasEventTarget(target);if(canvas){return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}else{return _emscripten_set_canvas_element_size_main_thread(target,width,height)}}function _emscripten_set_current_thread_status(newStatus){newStatus=newStatus|0}function _emscripten_set_thread_name(threadId,name){threadId=threadId|0;name=name|0}function __webgl_enable_ANGLE_instanced_arrays(ctx){var ext=ctx.getExtension(\"ANGLE_instanced_arrays\");if(ext){ctx[\"vertexAttribDivisor\"]=function(index,divisor){ext[\"vertexAttribDivisorANGLE\"](index,divisor)};ctx[\"drawArraysInstanced\"]=function(mode,first,count,primcount){ext[\"drawArraysInstancedANGLE\"](mode,first,count,primcount)};ctx[\"drawElementsInstanced\"]=function(mode,count,type,indices,primcount){ext[\"drawElementsInstancedANGLE\"](mode,count,type,indices,primcount)};return 1}}function __webgl_enable_OES_vertex_array_object(ctx){var ext=ctx.getExtension(\"OES_vertex_array_object\");if(ext){ctx[\"createVertexArray\"]=function(){return ext[\"createVertexArrayOES\"]()};ctx[\"deleteVertexArray\"]=function(vao){ext[\"deleteVertexArrayOES\"](vao)};ctx[\"bindVertexArray\"]=function(vao){ext[\"bindVertexArrayOES\"](vao)};ctx[\"isVertexArray\"]=function(vao){return ext[\"isVertexArrayOES\"](vao)};return 1}}function __webgl_enable_WEBGL_draw_buffers(ctx){var ext=ctx.getExtension(\"WEBGL_draw_buffers\");if(ext){ctx[\"drawBuffers\"]=function(n,bufs){ext[\"drawBuffersWEBGL\"](n,bufs)};return 1}}var GL={counter:1,lastError:0,buffers:[],mappedBuffers:{},programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},currentContext:null,offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,init:function(){var miniTempFloatBuffer=new Float32Array(GL.MINI_TEMP_BUFFER_SIZE);for(var i=0;i>2]:-1;source+=UTF8ToString(GROWABLE_HEAP_I32()[string+i*4>>2],len<0?undefined:len)}return source},createContext:function(canvas,webGLContextAttributes){var ctx=canvas.getContext(\"webgl\",webGLContextAttributes);if(!ctx)return 0;var handle=GL.registerContext(ctx,webGLContextAttributes);return handle},registerContext:function(ctx,webGLContextAttributes){var handle=_malloc(8);GROWABLE_HEAP_I32()[handle+4>>2]=_pthread_self();var context={handle:handle,attributes:webGLContextAttributes,version:webGLContextAttributes.majorVersion,GLctx:ctx};if(ctx.canvas)ctx.canvas.GLctxObject=context;GL.contexts[handle]=context;if(typeof webGLContextAttributes.enableExtensionsByDefault===\"undefined\"||webGLContextAttributes.enableExtensionsByDefault){GL.initExtensions(context)}return handle},makeContextCurrent:function(contextHandle){GL.currentContext=GL.contexts[contextHandle];Module.ctx=GLctx=GL.currentContext&&GL.currentContext.GLctx;return!(contextHandle&&!GLctx)},getContext:function(contextHandle){return GL.contexts[contextHandle]},deleteContext:function(contextHandle){if(GL.currentContext===GL.contexts[contextHandle])GL.currentContext=null;if(typeof JSEvents===\"object\")JSEvents.removeAllHandlersOnTarget(GL.contexts[contextHandle].GLctx.canvas);if(GL.contexts[contextHandle]&&GL.contexts[contextHandle].GLctx.canvas)GL.contexts[contextHandle].GLctx.canvas.GLctxObject=undefined;_free(GL.contexts[contextHandle].handle);GL.contexts[contextHandle]=null},initExtensions:function(context){if(!context)context=GL.currentContext;if(context.initExtensionsDone)return;context.initExtensionsDone=true;var GLctx=context.GLctx;__webgl_enable_ANGLE_instanced_arrays(GLctx);__webgl_enable_OES_vertex_array_object(GLctx);__webgl_enable_WEBGL_draw_buffers(GLctx);GLctx.disjointTimerQueryExt=GLctx.getExtension(\"EXT_disjoint_timer_query\");var automaticallyEnabledExtensions=[\"OES_texture_float\",\"OES_texture_half_float\",\"OES_standard_derivatives\",\"OES_vertex_array_object\",\"WEBGL_compressed_texture_s3tc\",\"WEBGL_depth_texture\",\"OES_element_index_uint\",\"EXT_texture_filter_anisotropic\",\"EXT_frag_depth\",\"WEBGL_draw_buffers\",\"ANGLE_instanced_arrays\",\"OES_texture_float_linear\",\"OES_texture_half_float_linear\",\"EXT_blend_minmax\",\"EXT_shader_texture_lod\",\"EXT_texture_norm16\",\"WEBGL_compressed_texture_pvrtc\",\"EXT_color_buffer_half_float\",\"WEBGL_color_buffer_float\",\"EXT_sRGB\",\"WEBGL_compressed_texture_etc1\",\"EXT_disjoint_timer_query\",\"WEBGL_compressed_texture_etc\",\"WEBGL_compressed_texture_astc\",\"EXT_color_buffer_float\",\"WEBGL_compressed_texture_s3tc_srgb\",\"EXT_disjoint_timer_query_webgl2\",\"WEBKIT_WEBGL_compressed_texture_pvrtc\"];var exts=GLctx.getSupportedExtensions()||[];exts.forEach(function(ext){if(automaticallyEnabledExtensions.indexOf(ext)!=-1){GLctx.getExtension(ext)}})},populateUniformTable:function(program){var p=GL.programs[program];var ptable=GL.programInfos[program]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1};var utable=ptable.uniforms;var numUniforms=GLctx.getProgramParameter(p,35718);for(var i=0;i>2;contextAttributes[\"alpha\"]=!!GROWABLE_HEAP_I32()[a+(0>>2)];contextAttributes[\"depth\"]=!!GROWABLE_HEAP_I32()[a+(4>>2)];contextAttributes[\"stencil\"]=!!GROWABLE_HEAP_I32()[a+(8>>2)];contextAttributes[\"antialias\"]=!!GROWABLE_HEAP_I32()[a+(12>>2)];contextAttributes[\"premultipliedAlpha\"]=!!GROWABLE_HEAP_I32()[a+(16>>2)];contextAttributes[\"preserveDrawingBuffer\"]=!!GROWABLE_HEAP_I32()[a+(20>>2)];var powerPreference=GROWABLE_HEAP_I32()[a+(24>>2)];contextAttributes[\"powerPreference\"]=__emscripten_webgl_power_preferences[powerPreference];contextAttributes[\"failIfMajorPerformanceCaveat\"]=!!GROWABLE_HEAP_I32()[a+(28>>2)];contextAttributes.majorVersion=GROWABLE_HEAP_I32()[a+(32>>2)];contextAttributes.minorVersion=GROWABLE_HEAP_I32()[a+(36>>2)];contextAttributes.enableExtensionsByDefault=GROWABLE_HEAP_I32()[a+(40>>2)];contextAttributes.explicitSwapControl=GROWABLE_HEAP_I32()[a+(44>>2)];contextAttributes.proxyContextToMainThread=GROWABLE_HEAP_I32()[a+(48>>2)];contextAttributes.renderViaOffscreenBackBuffer=GROWABLE_HEAP_I32()[a+(52>>2)];var canvas=__findCanvasEventTarget(target);if(!canvas){return-4}if(contextAttributes.explicitSwapControl){return-1}var contextHandle=GL.createContext(canvas,contextAttributes);return contextHandle}function _emscripten_webgl_create_context(a0,a1){return _emscripten_webgl_do_create_context(a0,a1)}var PATH={splitPath:function(filename){var splitPathRe=/^(\\/?|)([\\s\\S]*?)((?:\\.{1,2}|[^\\/]+?|)(\\.[^.\\/]*|))(?:[\\/]*)$/;return splitPathRe.exec(filename).slice(1)},normalizeArray:function(parts,allowAboveRoot){var up=0;for(var i=parts.length-1;i>=0;i--){var last=parts[i];if(last===\".\"){parts.splice(i,1)}else if(last===\"..\"){parts.splice(i,1);up++}else if(up){parts.splice(i,1);up--}}if(allowAboveRoot){for(;up;up--){parts.unshift(\"..\")}}return parts},normalize:function(path){var isAbsolute=path.charAt(0)===\"/\",trailingSlash=path.substr(-1)===\"/\";path=PATH.normalizeArray(path.split(\"/\").filter(function(p){return!!p}),!isAbsolute).join(\"/\");if(!path&&!isAbsolute){path=\".\"}if(path&&trailingSlash){path+=\"/\"}return(isAbsolute?\"/\":\"\")+path},dirname:function(path){var result=PATH.splitPath(path),root=result[0],dir=result[1];if(!root&&!dir){return\".\"}if(dir){dir=dir.substr(0,dir.length-1)}return root+dir},basename:function(path){if(path===\"/\")return\"/\";var lastSlash=path.lastIndexOf(\"/\");if(lastSlash===-1)return path;return path.substr(lastSlash+1)},extname:function(path){return PATH.splitPath(path)[3]},join:function(){var paths=Array.prototype.slice.call(arguments,0);return PATH.normalize(paths.join(\"/\"))},join2:function(l,r){return PATH.normalize(l+\"/\"+r)}};var SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer=SYSCALLS.buffers[stream];if(curr===0||curr===10){(stream===1?out:err)(UTF8ArrayToString(buffer,0));buffer.length=0}else{buffer.push(curr)}},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=GROWABLE_HEAP_I32()[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(3,1,fd);return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(4,1,fd,offset_low,offset_high,whence,newOffset)}function _fd_write(fd,iov,iovcnt,pnum){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(5,1,fd,iov,iovcnt,pnum);var num=0;for(var i=0;i>2];var len=GROWABLE_HEAP_I32()[iov+(i*8+4)>>2];for(var j=0;j>2]=num;return 0}function _pthread_cleanup_pop(execute){var routine=PThread.exitHandlers.pop();if(execute)routine()}function _pthread_cleanup_push(routine,arg){if(PThread.exitHandlers===null){PThread.exitHandlers=[]}PThread.exitHandlers.push(function(){dynCall_vi(routine,arg)})}function __spawn_thread(threadParams){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _spawn_thread() can only ever be called from main application thread!\";var worker=PThread.getNewWorker();if(worker.pthread!==undefined)throw\"Internal error!\";if(!threadParams.pthread_ptr)throw\"Internal error, no pthread ptr!\";PThread.runningWorkers.push(worker);var tlsMemory=_malloc(128*4);for(var i=0;i<128;++i){GROWABLE_HEAP_I32()[tlsMemory+i*4>>2]=0}var stackHigh=threadParams.stackBase+threadParams.stackSize;var pthread=PThread.pthreads[threadParams.pthread_ptr]={worker:worker,stackBase:threadParams.stackBase,stackSize:threadParams.stackSize,allocatedOwnStack:threadParams.allocatedOwnStack,thread:threadParams.pthread_ptr,threadInfoStruct:threadParams.pthread_ptr};var tis=pthread.threadInfoStruct>>2;Atomics.store(GROWABLE_HEAP_U32(),tis+(0>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(4>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(8>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(68>>2),threadParams.detached);Atomics.store(GROWABLE_HEAP_U32(),tis+(104>>2),tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),tis+(48>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(40>>2),pthread.threadInfoStruct);Atomics.store(GROWABLE_HEAP_U32(),tis+(44>>2),42);Atomics.store(GROWABLE_HEAP_U32(),tis+(108>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(84>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(80>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+8>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+12>>2),threadParams.detached);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+20>>2),threadParams.schedPolicy);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+24>>2),threadParams.schedPrio);var global_libc=_emscripten_get_global_libc();var global_locale=global_libc+40;Atomics.store(GROWABLE_HEAP_U32(),tis+(176>>2),global_locale);worker.pthread=pthread;var msg={\"cmd\":\"run\",\"start_routine\":threadParams.startRoutine,\"arg\":threadParams.arg,\"threadInfoStruct\":threadParams.pthread_ptr,\"selfThreadId\":threadParams.pthread_ptr,\"parentThreadId\":threadParams.parent_pthread_ptr,\"stackBase\":threadParams.stackBase,\"stackSize\":threadParams.stackSize};worker.runPthread=function(){msg.time=performance.now();worker.postMessage(msg,threadParams.transferList)};if(worker.loaded){worker.runPthread();delete worker.runPthread}}function _pthread_getschedparam(thread,policy,schedparam){if(!policy&&!schedparam)return ERRNO_CODES.EINVAL;if(!thread){err(\"pthread_getschedparam called with a null thread pointer!\");return ERRNO_CODES.ESRCH}var self=GROWABLE_HEAP_I32()[thread+12>>2];if(self!==thread){err(\"pthread_getschedparam attempted on thread \"+thread+\", which does not point to a valid thread, or does not exist anymore!\");return ERRNO_CODES.ESRCH}var schedPolicy=Atomics.load(GROWABLE_HEAP_U32(),thread+108+20>>2);var schedPrio=Atomics.load(GROWABLE_HEAP_U32(),thread+108+24>>2);if(policy)GROWABLE_HEAP_I32()[policy>>2]=schedPolicy;if(schedparam)GROWABLE_HEAP_I32()[schedparam>>2]=schedPrio;return 0}function _pthread_self(){return __pthread_ptr|0}Module[\"_pthread_self\"]=_pthread_self;function _pthread_create(pthread_ptr,attr,start_routine,arg){if(typeof SharedArrayBuffer===\"undefined\"){err(\"Current environment does not support SharedArrayBuffer, pthreads are not available!\");return 6}if(!pthread_ptr){err(\"pthread_create called with a null thread pointer!\");return 28}var transferList=[];var error=0;if(ENVIRONMENT_IS_PTHREAD&&(transferList.length===0||error)){return _emscripten_sync_run_in_main_thread_4(687865856,pthread_ptr,attr,start_routine,arg)}if(error)return error;var stackSize=0;var stackBase=0;var detached=0;var schedPolicy=0;var schedPrio=0;if(attr){stackSize=GROWABLE_HEAP_I32()[attr>>2];stackSize+=81920;stackBase=GROWABLE_HEAP_I32()[attr+8>>2];detached=GROWABLE_HEAP_I32()[attr+12>>2]!==0;var inheritSched=GROWABLE_HEAP_I32()[attr+16>>2]===0;if(inheritSched){var prevSchedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];var prevSchedPrio=GROWABLE_HEAP_I32()[attr+24>>2];var parentThreadPtr=PThread.currentProxiedOperationCallerThread?PThread.currentProxiedOperationCallerThread:_pthread_self();_pthread_getschedparam(parentThreadPtr,attr+20,attr+24);schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];schedPrio=GROWABLE_HEAP_I32()[attr+24>>2];GROWABLE_HEAP_I32()[attr+20>>2]=prevSchedPolicy;GROWABLE_HEAP_I32()[attr+24>>2]=prevSchedPrio}else{schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];schedPrio=GROWABLE_HEAP_I32()[attr+24>>2]}}else{stackSize=2097152}var allocatedOwnStack=stackBase==0;if(allocatedOwnStack){stackBase=_memalign(16,stackSize)}else{stackBase-=stackSize;assert(stackBase>0)}var threadInfoStruct=_malloc(232);for(var i=0;i<232>>2;++i)GROWABLE_HEAP_U32()[(threadInfoStruct>>2)+i]=0;GROWABLE_HEAP_I32()[pthread_ptr>>2]=threadInfoStruct;GROWABLE_HEAP_I32()[threadInfoStruct+12>>2]=threadInfoStruct;var headPtr=threadInfoStruct+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var threadParams={stackBase:stackBase,stackSize:stackSize,allocatedOwnStack:allocatedOwnStack,schedPolicy:schedPolicy,schedPrio:schedPrio,detached:detached,startRoutine:start_routine,pthread_ptr:threadInfoStruct,parent_pthread_ptr:_pthread_self(),arg:arg,transferList:transferList};if(ENVIRONMENT_IS_PTHREAD){threadParams.cmd=\"spawnThread\";postMessage(threadParams,transferList)}else{__spawn_thread(threadParams)}return 0}function _roundf(d){d=+d;return d>=+0?+Math_floor(d+ +.5):+Math_ceil(d-+.5)}function _sysconf(name){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(6,1,name);switch(name){case 30:return 16384;case 85:var maxHeapSize=2147483648;return maxHeapSize/16384;case 132:case 133:case 12:case 137:case 138:case 15:case 235:case 16:case 17:case 18:case 19:case 20:case 149:case 13:case 10:case 236:case 153:case 9:case 21:case 22:case 159:case 154:case 14:case 77:case 78:case 139:case 80:case 81:case 82:case 68:case 67:case 164:case 11:case 29:case 47:case 48:case 95:case 52:case 51:case 46:case 79:return 200809;case 27:case 246:case 127:case 128:case 23:case 24:case 160:case 161:case 181:case 182:case 242:case 183:case 184:case 243:case 244:case 245:case 165:case 178:case 179:case 49:case 50:case 168:case 169:case 175:case 170:case 171:case 172:case 97:case 76:case 32:case 173:case 35:return-1;case 176:case 177:case 7:case 155:case 8:case 157:case 125:case 126:case 92:case 93:case 129:case 130:case 131:case 94:case 91:return 1;case 74:case 60:case 69:case 70:case 4:return 1024;case 31:case 42:case 72:return 32;case 87:case 26:case 33:return 2147483647;case 34:case 1:return 47839;case 38:case 36:return 99;case 43:case 37:return 2048;case 0:return 2097152;case 3:return 65536;case 28:return 32768;case 44:return 32767;case 75:return 16384;case 39:return 1e3;case 89:return 700;case 71:return 256;case 40:return 255;case 2:return 100;case 180:return 64;case 25:return 20;case 5:return 16;case 6:return 6;case 73:return 4;case 84:{if(typeof navigator===\"object\")return navigator[\"hardwareConcurrency\"]||1;return 1}}setErrNo(28);return-1}if(!ENVIRONMENT_IS_PTHREAD)PThread.initMainThreadBlock();else PThread.initWorker();var GLctx;GL.init();var proxiedFunctionTable=[null,_atexit,_emscripten_set_canvas_element_size_main_thread,_fd_close,_fd_seek,_fd_write,_sysconf];var asmLibraryArg={\"e\":___assert_fail,\"r\":___call_main,\"w\":__emscripten_notify_thread_queue,\"a\":_abort,\"l\":_emscripten_conditional_set_current_thread_status,\"d\":_emscripten_futex_wait,\"c\":_emscripten_futex_wake,\"h\":_emscripten_get_now,\"g\":_emscripten_is_main_browser_thread,\"x\":_emscripten_is_main_runtime_thread,\"q\":_emscripten_memcpy_big,\"B\":_emscripten_num_logical_cores,\"t\":_emscripten_receive_on_main_thread_js,\"A\":_emscripten_resize_heap,\"u\":_emscripten_set_canvas_element_size,\"k\":_emscripten_set_current_thread_status,\"s\":_emscripten_set_thread_name,\"v\":_emscripten_webgl_create_context,\"m\":_fd_close,\"o\":_fd_seek,\"i\":_fd_write,\"p\":initPthreadsJS,\"memory\":wasmMemory||Module[\"wasmMemory\"],\"y\":_pthread_cleanup_pop,\"z\":_pthread_cleanup_push,\"j\":_pthread_create,\"b\":_pthread_self,\"f\":_roundf,\"n\":_sysconf,\"table\":wasmTable};var asm=createWasm();Module[\"asm\"]=asm;var ___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=function(){return(___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=Module[\"asm\"][\"C\"]).apply(null,arguments)};var _init=Module[\"_init\"]=function(){return(_init=Module[\"_init\"]=Module[\"asm\"][\"D\"]).apply(null,arguments)};var _register_tensor=Module[\"_register_tensor\"]=function(){return(_register_tensor=Module[\"_register_tensor\"]=Module[\"asm\"][\"E\"]).apply(null,arguments)};var _dispose_data=Module[\"_dispose_data\"]=function(){return(_dispose_data=Module[\"_dispose_data\"]=Module[\"asm\"][\"F\"]).apply(null,arguments)};var _dispose=Module[\"_dispose\"]=function(){return(_dispose=Module[\"_dispose\"]=Module[\"asm\"][\"G\"]).apply(null,arguments)};var _Abs=Module[\"_Abs\"]=function(){return(_Abs=Module[\"_Abs\"]=Module[\"asm\"][\"H\"]).apply(null,arguments)};var _Add=Module[\"_Add\"]=function(){return(_Add=Module[\"_Add\"]=Module[\"asm\"][\"I\"]).apply(null,arguments)};var 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assert(condition,text){if(!condition){abort(\"Assertion failed: \"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i=endIdx))++endPtr;if(endPtr-idx>16&&heap.subarray&&UTF8Decoder){return UTF8Decoder.decode(heap.subarray(idx,endPtr))}else{var str=\"\";while(idx>10,56320|ch&1023)}}}return str}function UTF8ToString(ptr,maxBytesToRead){return 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addRunDependency(id){runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}what+=\"\";out(what);err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";throw new WebAssembly.RuntimeError(what)}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(){try{if(wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(wasmBinaryFile)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary()})}return new Promise(function(resolve,reject){resolve(getBinary())})}function createWasm(){var info={\"env\":asmLibraryArg,\"wasi_snapshot_preview1\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmMemory=exports[\"memory\"];updateGlobalBufferAndViews(wasmMemory.buffer);removeRunDependency(\"wasm-instantiate\")}addRunDependency(\"wasm-instantiate\");function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming===\"function\"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch===\"function\"){fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err(\"wasm streaming compile failed: \"+reason);err(\"falling back to ArrayBuffer instantiation\");instantiateArrayBuffer(receiveInstantiatedSource)})})}else{return instantiateArrayBuffer(receiveInstantiatedSource)}}if(Module[\"instantiateWasm\"]){try{var exports=Module[\"instantiateWasm\"](info,receiveInstance);return exports}catch(e){err(\"Module.instantiateWasm callback failed with error: \"+e);return false}}instantiateAsync();return{}}__ATINIT__.push();function _emscripten_notify_memory_growth(memoryIndex){updateGlobalBufferAndViews(wasmMemory.buffer)}var PATH={splitPath:function(filename){var splitPathRe=/^(\\/?|)([\\s\\S]*?)((?:\\.{1,2}|[^\\/]+?|)(\\.[^.\\/]*|))(?:[\\/]*)$/;return splitPathRe.exec(filename).slice(1)},normalizeArray:function(parts,allowAboveRoot){var up=0;for(var i=parts.length-1;i>=0;i--){var last=parts[i];if(last===\".\"){parts.splice(i,1)}else if(last===\"..\"){parts.splice(i,1);up++}else if(up){parts.splice(i,1);up--}}if(allowAboveRoot){for(;up;up--){parts.unshift(\"..\")}}return parts},normalize:function(path){var isAbsolute=path.charAt(0)===\"/\",trailingSlash=path.substr(-1)===\"/\";path=PATH.normalizeArray(path.split(\"/\").filter(function(p){return!!p}),!isAbsolute).join(\"/\");if(!path&&!isAbsolute){path=\".\"}if(path&&trailingSlash){path+=\"/\"}return(isAbsolute?\"/\":\"\")+path},dirname:function(path){var result=PATH.splitPath(path),root=result[0],dir=result[1];if(!root&&!dir){return\".\"}if(dir){dir=dir.substr(0,dir.length-1)}return root+dir},basename:function(path){if(path===\"/\")return\"/\";var lastSlash=path.lastIndexOf(\"/\");if(lastSlash===-1)return path;return path.substr(lastSlash+1)},extname:function(path){return PATH.splitPath(path)[3]},join:function(){var paths=Array.prototype.slice.call(arguments,0);return PATH.normalize(paths.join(\"/\"))},join2:function(l,r){return PATH.normalize(l+\"/\"+r)}};var SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer=SYSCALLS.buffers[stream];if(curr===0||curr===10){(stream===1?out:err)(UTF8ArrayToString(buffer,0));buffer.length=0}else{buffer.push(curr)}},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=HEAP32[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){}function _fd_write(fd,iov,iovcnt,pnum){var num=0;for(var i=0;i>2];var len=HEAP32[iov+(i*8+4)>>2];for(var j=0;j>2]=num;return 0}function _exit(status){exit(status)}function _proc_exit(code){_exit(code)}function _roundf(d){d=+d;return d>=+0?+Math_floor(d+ +.5):+Math_ceil(d-+.5)}var asmLibraryArg={\"emscripten_notify_memory_growth\":_emscripten_notify_memory_growth,\"fd_close\":_fd_close,\"fd_seek\":_fd_seek,\"fd_write\":_fd_write,\"proc_exit\":_proc_exit,\"roundf\":_roundf};var asm=createWasm();Module[\"asm\"]=asm;var _init=Module[\"_init\"]=function(){return(_init=Module[\"_init\"]=Module[\"asm\"][\"init\"]).apply(null,arguments)};var _register_tensor=Module[\"_register_tensor\"]=function(){return(_register_tensor=Module[\"_register_tensor\"]=Module[\"asm\"][\"register_tensor\"]).apply(null,arguments)};var _dispose_data=Module[\"_dispose_data\"]=function(){return(_dispose_data=Module[\"_dispose_data\"]=Module[\"asm\"][\"dispose_data\"]).apply(null,arguments)};var _dispose=Module[\"_dispose\"]=function(){return(_dispose=Module[\"_dispose\"]=Module[\"asm\"][\"dispose\"]).apply(null,arguments)};var 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ExitStatus(status){this.name=\"ExitStatus\";this.message=\"Program terminated with exit(\"+status+\")\";this.status=status}var calledMain=false;dependenciesFulfilled=function runCaller(){if(!calledRun)run();if(!calledRun)dependenciesFulfilled=runCaller};function callMain(args){var entryFunction=Module[\"__start\"];try{entryFunction();var ret=0;exit(ret,true)}catch(e){if(e instanceof ExitStatus){return}else if(e==\"unwind\"){noExitRuntime=true;return}else{var toLog=e;if(e&&typeof e===\"object\"&&e.stack){toLog=[e,e.stack]}err(\"exception thrown: \"+toLog);quit_(1,e)}}finally{calledMain=true}}function run(args){args=args||arguments_;if(runDependencies>0){return}preRun();if(runDependencies>0)return;function doRun(){if(calledRun)return;calledRun=true;Module[\"calledRun\"]=true;if(ABORT)return;initRuntime();preMain();if(Module[\"onRuntimeInitialized\"])Module[\"onRuntimeInitialized\"]();if(shouldRunNow)callMain(args);postRun()}if(Module[\"setStatus\"]){Module[\"setStatus\"](\"Running...\");setTimeout(function(){setTimeout(function(){Module[\"setStatus\"](\"\")},1);doRun()},1)}else{doRun()}}Module[\"run\"]=run;function exit(status,implicit){if(implicit&&noExitRuntime&&status===0){return}if(noExitRuntime){}else{ABORT=true;EXITSTATUS=status;exitRuntime();if(Module[\"onExit\"])Module[\"onExit\"](status)}quit_(status,new ExitStatus(status))}if(Module[\"preInit\"]){if(typeof Module[\"preInit\"]==\"function\")Module[\"preInit\"]=[Module[\"preInit\"]];while(Module[\"preInit\"].length>0){Module[\"preInit\"].pop()()}}var shouldRunNow=true;if(Module[\"noInitialRun\"])shouldRunNow=false;noExitRuntime=true;run();\n\n\n return WasmBackendModule\n}\n);\n})();\nif (typeof exports === 'object' && typeof module === 'object')\n module.exports = WasmBackendModule;\n else if (typeof define === 'function' && define['amd'])\n define([], function() { return WasmBackendModule; });\n else if (typeof exports === 'object')\n exports[\"WasmBackendModule\"] = WasmBackendModule;\n ", "// A port of an algorithm by Johannes Baag\u00F8e , 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e \n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n//\n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n//\n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n var me = this, mash = Mash();\n\n me.next = function() {\n var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n me.s0 = me.s1;\n me.s1 = me.s2;\n return me.s2 = t - (me.c = t | 0);\n };\n\n // Apply the seeding algorithm from Baagoe.\n me.c = 1;\n me.s0 = mash(' ');\n me.s1 = mash(' ');\n me.s2 = mash(' ');\n me.s0 -= mash(seed);\n if (me.s0 < 0) { me.s0 += 1; }\n me.s1 -= mash(seed);\n if (me.s1 < 0) { me.s1 += 1; }\n me.s2 -= mash(seed);\n if (me.s2 < 0) { me.s2 += 1; }\n mash = null;\n}\n\nfunction copy(f, t) {\n t.c = f.c;\n t.s0 = f.s0;\n t.s1 = f.s1;\n t.s2 = f.s2;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new Alea(seed),\n state = opts && opts.state,\n prng = xg.next;\n prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n prng.double = function() {\n return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n };\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nfunction Mash() {\n var n = 0xefc8249d;\n\n var mash = function(data) {\n data = String(data);\n for (var i = 0; i < data.length; i++) {\n n += data.charCodeAt(i);\n var h = 0.02519603282416938 * n;\n n = h >>> 0;\n h -= n;\n h *= n;\n n = h >>> 0;\n h -= n;\n n += h * 0x100000000; // 2^32\n }\n return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n };\n\n return mash;\n}\n\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.alea = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n\n // Set up generator function.\n me.next = function() {\n var t = me.x ^ (me.x << 11);\n me.x = me.y;\n me.y = me.z;\n me.z = me.w;\n return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n };\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor128 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var t = (me.x ^ (me.x >>> 2));\n me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n return (me.d = (me.d + 362437 | 0)) +\n (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n };\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n me.v = 0;\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n if (k == strseed.length) {\n me.d = me.x << 10 ^ me.x >>> 4;\n }\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n t.v = f.v;\n t.d = f.d;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorwow = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n // Update xor generator.\n var X = me.x, i = me.i, t, v, w;\n t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n X[i] = v;\n me.i = (i + 1) & 7;\n return v;\n };\n\n function init(me, seed) {\n var j, w, X = [];\n\n if (seed === (seed | 0)) {\n // Seed state array using a 32-bit integer.\n w = X[0] = seed;\n } else {\n // Seed state using a string.\n seed = '' + seed;\n for (j = 0; j < seed.length; ++j) {\n X[j & 7] = (X[j & 7] << 15) ^\n (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n }\n }\n // Enforce an array length of 8, not all zeroes.\n while (X.length < 8) X.push(0);\n for (j = 0; j < 8 && X[j] === 0; ++j);\n if (j == 8) w = X[7] = -1; else w = X[j];\n\n me.x = X;\n me.i = 0;\n\n // Discard an initial 256 values.\n for (j = 256; j > 0; --j) {\n me.next();\n }\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.x = f.x.slice();\n t.i = f.i;\n return t;\n}\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.x) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorshift7 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated. For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1); // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897); // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C. This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n var w = me.w,\n X = me.X, i = me.i, t, v;\n // Update Weyl generator.\n me.w = w = (w + 0x61c88647) | 0;\n // Update xor generator.\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n // Update Xor generator array state.\n v = X[i] = v ^ t;\n me.i = i;\n // Result is the combination.\n return (v + (w ^ (w >>> 16))) | 0;\n };\n\n function init(me, seed) {\n var t, v, i, j, w, X = [], limit = 128;\n if (seed === (seed | 0)) {\n // Numeric seeds initialize v, which is used to generates X.\n v = seed;\n seed = null;\n } else {\n // String seeds are mixed into v and X one character at a time.\n seed = seed + '\\0';\n v = 0;\n limit = Math.max(limit, seed.length);\n }\n // Initialize circular array and weyl value.\n for (i = 0, j = -32; j < limit; ++j) {\n // Put the unicode characters into the array, and shuffle them.\n if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n // After 32 shuffles, take v as the starting w value.\n if (j === 0) w = v;\n v ^= v << 10;\n v ^= v >>> 15;\n v ^= v << 4;\n v ^= v >>> 13;\n if (j >= 0) {\n w = (w + 0x61c88647) | 0; // Weyl.\n t = (X[j & 127] ^= (v + w)); // Combine xor and weyl to init array.\n i = (0 == t) ? i + 1 : 0; // Count zeroes.\n }\n }\n // We have detected all zeroes; make the key nonzero.\n if (i >= 128) {\n X[(seed && seed.length || 0) & 127] = -1;\n }\n // Run the generator 512 times to further mix the state before using it.\n // Factoring this as a function slows the main generator, so it is just\n // unrolled here. The weyl generator is not advanced while warming up.\n i = 127;\n for (j = 4 * 128; j > 0; --j) {\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n X[i] = v ^ t;\n }\n // Storing state as object members is faster than using closure variables.\n me.w = w;\n me.X = X;\n me.i = i;\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.i = f.i;\n t.w = f.w;\n t.X = f.X.slice();\n return t;\n};\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.X) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor4096 = impl;\n}\n\n})(\n this, // window object or global\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var b = me.b, c = me.c, d = me.d, a = me.a;\n b = (b << 25) ^ (b >>> 7) ^ c;\n c = (c - d) | 0;\n d = (d << 24) ^ (d >>> 8) ^ a;\n a = (a - b) | 0;\n me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n me.c = c = (c - d) | 0;\n me.d = (d << 16) ^ (c >>> 16) ^ a;\n return me.a = (a - b) | 0;\n };\n\n /* The following is non-inverted tyche, which has better internal\n * bit diffusion, but which is about 25% slower than tyche-i in JS.\n me.next = function() {\n var a = me.a, b = me.b, c = me.c, d = me.d;\n a = (me.a + me.b | 0) >>> 0;\n d = me.d ^ a; d = d << 16 ^ d >>> 16;\n c = me.c + d | 0;\n b = me.b ^ c; b = b << 12 ^ d >>> 20;\n me.a = a = a + b | 0;\n d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n me.c = c = c + d | 0;\n b = b ^ c;\n return me.b = (b << 7 ^ b >>> 25);\n }\n */\n\n me.a = 0;\n me.b = 0;\n me.c = 2654435769 | 0;\n me.d = 1367130551;\n\n if (seed === Math.floor(seed)) {\n // Integer seed.\n me.a = (seed / 0x100000000) | 0;\n me.b = seed | 0;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 20; k++) {\n me.b ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.a = f.a;\n t.b = f.b;\n t.c = f.c;\n t.d = f.d;\n return t;\n};\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.tychei = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "/*\nCopyright 2019 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (global, pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\n\nvar width = 256, // each RC4 output is 0 <= x < 256\n chunks = 6, // at least six RC4 outputs for each double\n digits = 52, // there are 52 significant digits in a double\n rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n startdenom = math.pow(width, chunks),\n significance = math.pow(2, digits),\n overflow = significance * 2,\n mask = width - 1,\n nodecrypto; // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n var key = [];\n options = (options == true) ? { entropy: true } : (options || {});\n\n // Flatten the seed string or build one from local entropy if needed.\n var shortseed = mixkey(flatten(\n options.entropy ? [seed, tostring(pool)] :\n (seed == null) ? autoseed() : seed, 3), key);\n\n // Use the seed to initialize an ARC4 generator.\n var arc4 = new ARC4(key);\n\n // This function returns a random double in [0, 1) that contains\n // randomness in every bit of the mantissa of the IEEE 754 value.\n var prng = function() {\n var n = arc4.g(chunks), // Start with a numerator n < 2 ^ 48\n d = startdenom, // and denominator d = 2 ^ 48.\n x = 0; // and no 'extra last byte'.\n while (n < significance) { // Fill up all significant digits by\n n = (n + x) * width; // shifting numerator and\n d *= width; // denominator and generating a\n x = arc4.g(1); // new least-significant-byte.\n }\n while (n >= overflow) { // To avoid rounding up, before adding\n n /= 2; // last byte, shift everything\n d /= 2; // right using integer math until\n x >>>= 1; // we have exactly the desired bits.\n }\n return (n + x) / d; // Form the number within [0, 1).\n };\n\n prng.int32 = function() { return arc4.g(4) | 0; }\n prng.quick = function() { return arc4.g(4) / 0x100000000; }\n prng.double = prng;\n\n // Mix the randomness into accumulated entropy.\n mixkey(tostring(arc4.S), pool);\n\n // Calling convention: what to return as a function of prng, seed, is_math.\n return (options.pass || callback ||\n function(prng, seed, is_math_call, state) {\n if (state) {\n // Load the arc4 state from the given state if it has an S array.\n if (state.S) { copy(state, arc4); }\n // Only provide the .state method if requested via options.state.\n prng.state = function() { return copy(arc4, {}); }\n }\n\n // If called as a method of Math (Math.seedrandom()), mutate\n // Math.random because that is how seedrandom.js has worked since v1.0.\n if (is_math_call) { math[rngname] = prng; return seed; }\n\n // Otherwise, it is a newer calling convention, so return the\n // prng directly.\n else return prng;\n })(\n prng,\n shortseed,\n 'global' in options ? options.global : (this == math),\n options.state);\n}\n\n//\n// ARC4\n//\n// An ARC4 implementation. The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4. Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n var t, keylen = key.length,\n me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n // The empty key [] is treated as [0].\n if (!keylen) { key = [keylen++]; }\n\n // Set up S using the standard key scheduling algorithm.\n while (i < width) {\n s[i] = i++;\n }\n for (i = 0; i < width; i++) {\n s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n s[j] = t;\n }\n\n // The \"g\" method returns the next (count) outputs as one number.\n (me.g = function(count) {\n // Using instance members instead of closure state nearly doubles speed.\n var t, r = 0,\n i = me.i, j = me.j, s = me.S;\n while (count--) {\n t = s[i = mask & (i + 1)];\n r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n }\n me.i = i; me.j = j;\n return r;\n // For robust unpredictability, the function call below automatically\n // discards an initial batch of values. This is called RC4-drop[256].\n // See http://google.com/search?q=rsa+fluhrer+response&btnI\n })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n t.i = f.i;\n t.j = f.j;\n t.S = f.S.slice();\n return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n var result = [], typ = (typeof obj), prop;\n if (depth && typ == 'object') {\n for (prop in obj) {\n try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n }\n }\n return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n var stringseed = seed + '', smear, j = 0;\n while (j < stringseed.length) {\n key[mask & j] =\n mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n }\n return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n try {\n var out;\n if (nodecrypto && (out = nodecrypto.randomBytes)) {\n // The use of 'out' to remember randomBytes makes tight minified code.\n out = out(width);\n } else {\n out = new Uint8Array(width);\n (global.crypto || global.msCrypto).getRandomValues(out);\n }\n return tostring(out);\n } catch (e) {\n var browser = global.navigator,\n plugins = browser && browser.plugins;\n return [+new Date, global, plugins, global.screen, tostring(pool)];\n }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool. Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n module.exports = seedrandom;\n // When in node.js, try using crypto package for autoseeding.\n try {\n nodecrypto = require('crypto');\n } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n define(function() { return seedrandom; });\n} else {\n // When included as a plain script, set up Math.seedrandom global.\n math['seed' + rngname] = seedrandom;\n}\n\n\n// End anonymous scope, and pass initial values.\n})(\n // global: `self` in browsers (including strict mode and web workers),\n // otherwise `this` in Node and other environments\n (typeof self !== 'undefined') ? self : this,\n [], // pool: entropy pool starts empty\n Math // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random(); // 0 <= x < 1. Every bit is random.\n// var x = random.quick(); // 0 <= x < 1. 32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three. It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures. Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "export const wasmWorkerContents = 'var threadInfoStruct=0;var selfThreadId=0;var parentThreadId=0;var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(\" \");console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(\" \");postMessage({cmd:\"alert\",text:text,threadId:selfThreadId})}var err=threadPrintErr;this.alert=threadAlert;Module[\"instantiateWasm\"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module[\"wasmModule\"],info);Module[\"wasmModule\"]=null;receiveInstance(instance);return instance.exports};this.onmessage=function(e){try{if(e.data.cmd===\"load\"){Module[\"DYNAMIC_BASE\"]=e.data.DYNAMIC_BASE;Module[\"DYNAMICTOP_PTR\"]=e.data.DYNAMICTOP_PTR;Module[\"wasmModule\"]=e.data.wasmModule;Module[\"wasmMemory\"]=e.data.wasmMemory;Module[\"buffer\"]=Module[\"wasmMemory\"].buffer;Module[\"ENVIRONMENT_IS_PTHREAD\"]=true;if(typeof e.data.urlOrBlob===\"string\"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}Module=WasmBackendModuleThreadedSimd(Module);postMessage({\"cmd\":\"loaded\"})}else if(e.data.cmd===\"objectTransfer\"){Module[\"PThread\"].receiveObjectTransfer(e.data)}else if(e.data.cmd===\"run\"){Module[\"__performance_now_clock_drift\"]=performance.now()-e.data.time;threadInfoStruct=e.data.threadInfoStruct;Module[\"__register_pthread_ptr\"](threadInfoStruct,0,0);selfThreadId=e.data.selfThreadId;parentThreadId=e.data.parentThreadId;var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module[\"establishStackSpace\"](top,max);Module[\"_emscripten_tls_init\"]();Module[\"PThread\"].receiveObjectTransfer(e.data);Module[\"PThread\"].setThreadStatus(Module[\"_pthread_self\"](),1);try{var result=Module[\"dynCall_ii\"](e.data.start_routine,e.data.arg);if(!Module[\"getNoExitRuntime\"]())Module[\"PThread\"].threadExit(result)}catch(ex){if(ex===\"Canceled!\"){Module[\"PThread\"].threadCancel()}else if(ex!=\"unwind\"){Atomics.store(Module[\"HEAPU32\"],threadInfoStruct+4>>2,ex instanceof Module[\"ExitStatus\"]?ex.status:-2);Atomics.store(Module[\"HEAPU32\"],threadInfoStruct+0>>2,1);Module[\"_emscripten_futex_wake\"](threadInfoStruct+0,2147483647);if(!(ex instanceof Module[\"ExitStatus\"]))throw ex}}}else if(e.data.cmd===\"cancel\"){if(threadInfoStruct){Module[\"PThread\"].threadCancel()}}else if(e.data.target===\"setimmediate\"){}else if(e.data.cmd===\"processThreadQueue\"){if(threadInfoStruct){Module[\"_emscripten_current_thread_process_queued_calls\"]()}}else{err(\"worker.js received unknown command \"+e.data.cmd);err(e.data)}}catch(ex){err(\"worker.js onmessage() captured an uncaught exception: \"+ex);if(ex.stack)err(ex.stack);throw ex}};if(typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\"){self={location:{href:__filename}};var onmessage=this.onmessage;var nodeWorkerThreads=require(\"worker_threads\");Worker=nodeWorkerThreads.Worker;var parentPort=nodeWorkerThreads.parentPort;parentPort.on(\"message\",function(data){onmessage({data:data})});var nodeFS=require(\"fs\");var nodeRead=function(filename){return nodeFS.readFileSync(filename,\"utf8\")};function globalEval(x){global.require=require;global.Module=Module;eval.call(null,x)}importScripts=function(f){globalEval(nodeRead(f))};postMessage=function(msg){parentPort.postMessage(msg)};if(typeof performance===\"undefined\"){performance={now:function(){return Date.now()}}}}';", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "// wrapper to load tfjs in a single place so version can be changed quickly\n\n// simplified\n// { modules: 1061, moduleBytes: 3772720, outputBytes: 1531035 }\n\n// export * from '@tensorflow/tfjs/dist/index.js';\n// export * from '@tensorflow/tfjs-backend-wasm';\n\n// modular\n// { modules: 1064, moduleBytes: 3793219, outputBytes: 1535600 }\n\n// get versions of all packages.\nimport { version as tfjs } from '@tensorflow/tfjs/package.json';\nimport { version as versionCore } from '@tensorflow/tfjs-core/package.json';\nimport { version as versionData } from '@tensorflow/tfjs-data/package.json';\nimport { version as versionLayers } from '@tensorflow/tfjs-layers/package.json';\nimport { version as versionConverter } from '@tensorflow/tfjs-converter/package.json';\n// for backends, get version from source so it can register backend during import\nimport { version_cpu } from '@tensorflow/tfjs-backend-cpu/dist/index.js';\nimport { version_webgl } from '@tensorflow/tfjs-backend-webgl/dist/index.js';\nimport { version_wasm } from '@tensorflow/tfjs-backend-wasm/dist/index.js';\n\n// export all\nexport * from '@tensorflow/tfjs-core/dist/index.js';\nexport * from '@tensorflow/tfjs-layers/dist/index.js';\nexport * from '@tensorflow/tfjs-converter/dist/index.js';\nexport * as data from '@tensorflow/tfjs-data/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-cpu/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-webgl/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-wasm/dist/index.js';\n\n// export versions\nexport const version = {\n tfjs,\n 'tfjs-core': versionCore,\n 'tfjs-data': versionData,\n 'tfjs-layers': versionLayers,\n 'tfjs-converter': versionConverter,\n 'tfjs-backend-cpu': version_cpu,\n 'tfjs-backend-webgl': version_webgl,\n 'tfjs-backend-wasm': version_wasm,\n};\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport const config = {\n name: 'humangl',\n priority: 99,\n canvas: null,\n width: 1024,\n height: 1024,\n webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n alpha: false,\n antialias: false,\n premultipliedAlpha: false,\n preserveDrawingBuffer: false,\n depth: false,\n stencil: false,\n failIfMajorPerformanceCaveat: false,\n desynchronized: true,\n },\n};\n\nexport function register() {\n if (!tf.findBackend(config.name)) {\n // @ts-ignore\n config.canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(config.width, config.height) : document.createElement('canvas');\n // @ts-ignore\n const gl = config.canvas.getContext('webgl2', config.webGLattr);\n tf.setWebGLContext(2, gl);\n const ctx = new tf.GPGPUContext(gl);\n tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n const kernels = tf.getKernelsForBackend('webgl');\n kernels.forEach((kernelConfig) => {\n const newKernelConfig = { ...kernelConfig, backendName: config.name };\n tf.registerKernel(newKernelConfig);\n });\n tf.ENV.set('WEBGL_VERSION', 2);\n tf.ENV.set('WEBGL_MAX_TEXTURE_SIZE', gl.getParameter(gl.MAX_TEXTURE_SIZE));\n tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n }\n}\n", "import { log } from './log.js';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as backend from './tfjs/backend.js';\nimport * as facemesh from './blazeface/facemesh.js';\nimport * as age from './age/age.js';\nimport * as gender from './gender/gender.js';\nimport * as emotion from './emotion/emotion.js';\nimport * as embedding from './embedding/embedding.js';\nimport * as posenet from './posenet/posenet.js';\nimport * as blazepose from './blazepose/blazepose.js';\nimport * as handpose from './handpose/handpose.js';\nimport * as gesture from './gesture/gesture.js';\nimport * as image from './image.js';\nimport * as profile from './profile.js';\nimport * as config from '../config.js';\nimport * as sample from './sample.js';\nimport * as app from '../package.json';\n\n// helper function: gets elapsed time on both browser and nodejs\nconst now = () => {\n if (typeof performance !== 'undefined') return performance.now();\n return parseInt(Number(process.hrtime.bigint()) / 1000 / 1000);\n};\n\n// helper function: perform deep merge of multiple objects so it allows full inheriance with overrides\nfunction mergeDeep(...objects) {\n const isObject = (obj) => obj && typeof obj === 'object';\n return objects.reduce((prev, obj) => {\n Object.keys(obj || {}).forEach((key) => {\n const pVal = prev[key];\n const oVal = obj[key];\n if (Array.isArray(pVal) && Array.isArray(oVal)) {\n prev[key] = pVal.concat(...oVal);\n } else if (isObject(pVal) && isObject(oVal)) {\n prev[key] = mergeDeep(pVal, oVal);\n } else {\n prev[key] = oVal;\n }\n });\n return prev;\n }, {});\n}\n\nclass Human {\n constructor(userConfig = {}) {\n this.tf = tf;\n this.version = app.version;\n this.config = mergeDeep(config.default, userConfig);\n this.fx = null;\n this.state = 'idle';\n this.numTensors = 0;\n this.analyzeMemoryLeaks = false;\n this.checkSanity = false;\n this.firstRun = true;\n this.perf = {};\n // object that contains all initialized models\n this.models = {\n facemesh: null,\n posenet: null,\n handpose: null,\n iris: null,\n age: null,\n gender: null,\n emotion: null,\n };\n // export raw access to underlying models\n this.facemesh = facemesh;\n this.age = age;\n this.gender = gender;\n this.emotion = emotion;\n this.body = posenet;\n this.hand = handpose;\n }\n\n profile() {\n if (this.config.profile) return profile.data;\n return {};\n }\n\n // helper function: measure tensor leak\n analyze(...msg) {\n if (!this.analyzeMemoryLeaks) return;\n const current = tf.engine().state.numTensors;\n const previous = this.numTensors;\n this.numTensors = current;\n const leaked = current - previous;\n if (leaked !== 0) log(...msg, leaked);\n }\n\n // quick sanity check on inputs\n sanity(input) {\n if (!this.checkSanity) return null;\n if (!input) return 'input is not defined';\n if (tf.ENV.flags.IS_NODE && !(input instanceof tf.Tensor)) {\n return 'input must be a tensor';\n }\n try {\n tf.getBackend();\n } catch {\n return 'backend not loaded';\n }\n return null;\n }\n\n simmilarity(embedding1, embedding2) {\n if (this.config.face.embedding.enabled) return embedding.simmilarity(embedding1, embedding2);\n return 0;\n }\n\n // preload models, not explicitly required as it's done automatically on first use\n async load(userConfig) {\n this.state = 'load';\n const timeStamp = now();\n if (userConfig) this.config = mergeDeep(this.config, userConfig);\n\n if (this.firstRun) {\n log(`version: ${this.version} TensorFlow/JS version: ${tf.version_core}`);\n await this.checkBackend(true);\n if (tf.ENV.flags.IS_BROWSER) {\n log('configuration:', this.config);\n log('tf flags:', tf.ENV.flags);\n }\n this.firstRun = false;\n }\n\n if (this.config.async) {\n [\n this.models.facemesh,\n this.models.age,\n this.models.gender,\n this.models.emotion,\n this.models.embedding,\n this.models.posenet,\n this.models.handpose,\n this.models.blazepose,\n ] = await Promise.all([\n this.models.facemesh || (this.config.face.enabled ? facemesh.load(this.config) : null),\n this.models.age || ((this.config.face.enabled && this.config.face.age.enabled) ? age.load(this.config) : null),\n this.models.gender || ((this.config.face.enabled && this.config.face.gender.enabled) ? gender.load(this.config) : null),\n this.models.emotion || ((this.config.face.enabled && this.config.face.emotion.enabled) ? emotion.load(this.config) : null),\n this.models.embedding || ((this.config.face.enabled && this.config.face.embedding.enabled) ? embedding.load(this.config) : null),\n this.models.posenet || (this.config.body.enabled ? posenet.load(this.config) : null),\n this.models.handpose || (this.config.hand.enabled ? handpose.load(this.config) : null),\n this.models.blazepose || (this.config.pose.enabled ? blazepose.load(this.config) : null),\n ]);\n } else {\n if (this.config.face.enabled && !this.models.facemesh) this.models.facemesh = await facemesh.load(this.config);\n if (this.config.face.enabled && this.config.face.age.enabled && !this.models.age) this.models.age = await age.load(this.config);\n if (this.config.face.enabled && this.config.face.gender.enabled && !this.models.gender) this.models.gender = await gender.load(this.config);\n if (this.config.face.enabled && this.config.face.emotion.enabled && !this.models.emotion) this.models.emotion = await emotion.load(this.config);\n if (this.config.face.enabled && this.config.face.embedding.enabled && !this.models.embedding) this.models.embedding = await embedding.load(this.config);\n if (this.config.body.enabled && !this.models.posenet) this.models.posenet = await posenet.load(this.config);\n if (this.config.hand.enabled && !this.models.handpose) this.models.handpose = await handpose.load(this.config);\n if (this.config.pose.enabled && !this.models.blazepose) this.models.blazepose = await blazepose.load(this.config);\n }\n const current = Math.trunc(now() - timeStamp);\n if (current > (this.perf.load || 0)) this.perf.load = current;\n }\n\n // check if backend needs initialization if it changed\n async checkBackend(force) {\n if (this.config.backend && (this.config.backend !== '') && force || (tf.getBackend() !== this.config.backend)) {\n const timeStamp = now();\n this.state = 'backend';\n /* force backend reload\n if (this.config.backend in tf.engine().registry) {\n const backendFactory = tf.findBackendFactory(this.config.backend);\n tf.removeBackend(this.config.backend);\n tf.registerBackend(this.config.backend, backendFactory);\n } else {\n log('Backend not registred:', this.config.backend);\n }\n */\n\n log('setting backend:', this.config.backend);\n\n if (this.config.backend === 'wasm') {\n log('settings wasm path:', this.config.wasmPath);\n tf.setWasmPaths(this.config.wasmPath);\n const simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n if (!simd) log('warning: wasm simd support is not enabled');\n }\n\n if (this.config.backend === 'humangl') {\n log('registering humangl backend');\n backend.register();\n }\n\n await tf.setBackend(this.config.backend);\n tf.enableProdMode();\n /* debug mode is really too mcuh\n tf.enableDebugMode();\n */\n if (tf.getBackend() === 'webgl') {\n if (this.config.deallocate) {\n log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', this.config.deallocate);\n tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', this.config.deallocate ? 0 : -1);\n }\n tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n const gl = await tf.backend().getGPGPUContext().gl;\n log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);\n }\n await tf.ready();\n this.perf.backend = Math.trunc(now() - timeStamp);\n }\n }\n\n async detectFace(input) {\n // run facemesh, includes blazeface and iris\n // eslint-disable-next-line no-async-promise-executor\n let timeStamp;\n let ageRes;\n let genderRes;\n let emotionRes;\n let embeddingRes;\n const faceRes = [];\n this.state = 'run:face';\n timeStamp = now();\n const faces = await this.models.facemesh?.estimateFaces(input, this.config);\n this.perf.face = Math.trunc(now() - timeStamp);\n for (const face of faces) {\n this.analyze('Get Face');\n\n // is something went wrong, skip the face\n if (!face.image || face.image.isDisposedInternal) {\n log('Face object is disposed:', face.image);\n continue;\n }\n\n // run age, inherits face from blazeface\n this.analyze('Start Age:');\n if (this.config.async) {\n ageRes = this.config.face.age.enabled ? age.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:age';\n timeStamp = now();\n ageRes = this.config.face.age.enabled ? await age.predict(face.image, this.config) : {};\n this.perf.age = Math.trunc(now() - timeStamp);\n }\n\n // run gender, inherits face from blazeface\n this.analyze('Start Gender:');\n if (this.config.async) {\n genderRes = this.config.face.gender.enabled ? gender.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:gender';\n timeStamp = now();\n genderRes = this.config.face.gender.enabled ? await gender.predict(face.image, this.config) : {};\n this.perf.gender = Math.trunc(now() - timeStamp);\n }\n\n // run emotion, inherits face from blazeface\n this.analyze('Start Emotion:');\n if (this.config.async) {\n emotionRes = this.config.face.emotion.enabled ? emotion.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:emotion';\n timeStamp = now();\n emotionRes = this.config.face.emotion.enabled ? await emotion.predict(face.image, this.config) : {};\n this.perf.emotion = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Emotion:');\n\n // run emotion, inherits face from blazeface\n this.analyze('Start Embedding:');\n if (this.config.async) {\n embeddingRes = this.config.face.embedding.enabled ? embedding.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:embedding';\n timeStamp = now();\n embeddingRes = this.config.face.embedding.enabled ? await embedding.predict(face.image, this.config) : {};\n this.perf.embedding = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Emotion:');\n\n // if async wait for results\n if (this.config.async) {\n [ageRes, genderRes, emotionRes, embeddingRes] = await Promise.all([ageRes, genderRes, emotionRes, embeddingRes]);\n }\n\n this.analyze('Finish Face:');\n // dont need face anymore\n face.image.dispose();\n\n // calculate iris distance\n // iris: array[ center, left, top, right, bottom]\n if (!this.config.face.iris.enabled) {\n delete face.annotations.leftEyeIris;\n delete face.annotations.rightEyeIris;\n }\n const irisSize = (face.annotations.leftEyeIris && face.annotations.rightEyeIris)\n /* average human iris size is 11.7mm */\n ? 11.7 * Math.max(Math.abs(face.annotations.leftEyeIris[3][0] - face.annotations.leftEyeIris[1][0]), Math.abs(face.annotations.rightEyeIris[4][1] - face.annotations.rightEyeIris[2][1]))\n : 0;\n\n // combine results\n faceRes.push({\n confidence: face.confidence,\n box: face.box,\n mesh: face.mesh,\n boxRaw: face.boxRaw,\n meshRaw: face.meshRaw,\n annotations: face.annotations,\n age: ageRes.age,\n gender: genderRes.gender,\n genderConfidence: genderRes.confidence,\n emotion: emotionRes,\n embedding: embeddingRes,\n iris: (irisSize !== 0) ? Math.trunc(irisSize) / 100 : 0,\n });\n this.analyze('End Face');\n }\n this.analyze('End FaceMesh:');\n if (this.config.async) {\n if (this.perf.face) delete this.perf.face;\n if (this.perf.age) delete this.perf.age;\n if (this.perf.gender) delete this.perf.gender;\n if (this.perf.emotion) delete this.perf.emotion;\n }\n return faceRes;\n }\n\n async image(input, userConfig = {}) {\n this.state = 'image';\n this.config = mergeDeep(this.config, userConfig);\n const process = image.process(input, this.config);\n process.tensor.dispose();\n return process.canvas;\n }\n\n // main detect function\n async detect(input, userConfig = {}) {\n // detection happens inside a promise\n return new Promise(async (resolve) => {\n this.state = 'config';\n let timeStamp;\n\n // update configuration\n this.config = mergeDeep(this.config, userConfig);\n\n // sanity checks\n this.state = 'check';\n const error = this.sanity(input);\n if (error) {\n log(error, input);\n resolve({ error });\n }\n\n let poseRes;\n let blazeposeRes;\n let handRes;\n let faceRes;\n\n const timeStart = now();\n\n // configure backend\n await this.checkBackend();\n\n // load models if enabled\n await this.load();\n\n if (this.config.scoped) tf.engine().startScope();\n this.analyze('Start Scope:');\n\n timeStamp = now();\n const process = image.process(input, this.config);\n if (!process || !process.tensor) {\n log('could not convert input to tensor');\n resolve({ error: 'could not convert input to tensor' });\n return;\n }\n this.perf.image = Math.trunc(now() - timeStamp);\n this.analyze('Get Image:');\n\n // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n if (this.config.async) {\n faceRes = this.config.face.enabled ? this.detectFace(process.tensor) : [];\n if (this.perf.face) delete this.perf.face;\n } else {\n this.state = 'run:face';\n timeStamp = now();\n faceRes = this.config.face.enabled ? await this.detectFace(process.tensor) : [];\n this.perf.face = Math.trunc(now() - timeStamp);\n }\n\n // run posenet\n this.analyze('Start Body:');\n if (this.config.async) {\n poseRes = this.config.body.enabled ? this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n if (this.perf.body) delete this.perf.body;\n } else {\n this.state = 'run:body';\n timeStamp = now();\n poseRes = this.config.body.enabled ? await this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n this.perf.body = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Body:');\n\n // run posenet\n this.analyze('Start Pose:');\n if (this.config.async) {\n blazeposeRes = this.config.pose.enabled ? blazepose.predict(process.tensor, this.config) : [];\n if (this.perf.pose) delete this.perf.pose;\n } else {\n this.state = 'run:pose';\n timeStamp = now();\n blazeposeRes = this.config.pose.enabled ? await blazepose.predict(process.tensor, this.config) : [];\n this.perf.pose = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Pose:');\n\n // run handpose\n this.analyze('Start Hand:');\n if (this.config.async) {\n handRes = this.config.hand.enabled ? this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n if (this.perf.hand) delete this.perf.hand;\n } else {\n this.state = 'run:hand';\n timeStamp = now();\n handRes = this.config.hand.enabled ? await this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n this.perf.hand = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Hand:');\n\n // if async wait for results\n if (this.config.async) {\n [faceRes, poseRes, blazeposeRes, handRes] = await Promise.all([faceRes, poseRes, blazeposeRes, handRes]);\n }\n process.tensor.dispose();\n\n if (this.config.scoped) tf.engine().endScope();\n this.analyze('End Scope:');\n\n let gestureRes = [];\n if (this.config.gesture.enabled) {\n timeStamp = now();\n gestureRes = [...gesture.face(faceRes), ...gesture.body(poseRes), ...gesture.hand(handRes), ...gesture.iris(faceRes)];\n if (!this.config.async) this.perf.gesture = Math.trunc(now() - timeStamp);\n else if (this.perf.gesture) delete this.perf.gesture;\n }\n\n this.perf.total = Math.trunc(now() - timeStart);\n this.state = 'idle';\n resolve({ face: faceRes, body: poseRes, hand: handRes, pose: blazeposeRes, gesture: gestureRes, performance: this.perf, canvas: process.canvas });\n });\n }\n\n async warmup(userConfig) {\n const b64toBlob = (base64, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n\n if (userConfig) this.config = mergeDeep(this.config, userConfig);\n const video = this.config.videoOptimized;\n this.config.videoOptimized = false;\n let blob;\n switch (this.config.warmup) {\n case 'face': blob = await b64toBlob(sample.face); break;\n case 'full': blob = await b64toBlob(sample.body); break;\n default: blob = null;\n }\n if (!blob) return null;\n const bitmap = await createImageBitmap(blob);\n const t0 = now();\n const warmup = await this.detect(bitmap, config);\n const t1 = now();\n bitmap.close();\n log('Warmup', this.config.warmup, (t1 - t0), warmup);\n this.config.videoOptimized = video;\n return warmup;\n }\n}\n\nexport { Human as default };\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\n// import * as helpers from './helpers.js';\n// import * as profile from '../profile.js';\n\nconst models = {};\nlet config = {};\nconst anchors = [];\nconst kMidHipCenter = 0;\nconst kPoseDetectKeyNum = 2;\nconst kFullBodySizeRot = 1;\n\nfunction calculateScale(min_scale, max_scale, stride_index, num_strides) {\n if (num_strides === 1) return (min_scale + max_scale) * 0.5;\n return min_scale + (max_scale - min_scale) * 1.0 * stride_index / (num_strides - 1.0);\n}\n\nexport function generateAnchors() {\n const options = {};\n options.strides = [];\n options.aspect_ratios = [];\n options.feature_map_height = [];\n options.num_layers = 4;\n options.min_scale = 0.1484375;\n options.max_scale = 0.75;\n options.input_size_height = 128;\n options.input_size_width = 128;\n options.anchor_offset_x = 0.5;\n options.anchor_offset_y = 0.5;\n options.strides.push(8);\n options.strides.push(16);\n options.strides.push(16);\n options.strides.push(16);\n options.aspect_ratios.push(1.0);\n options.reduce_boxes_in_lowest_layer = false;\n options.interpolated_scale_aspect_ratio = 1.0;\n options.fixed_anchor_size = true;\n let layer_id = 0;\n while (layer_id < options.strides.length) {\n const anchor_height = [];\n const anchor_width = [];\n const aspect_ratios = [];\n const scales = [];\n // For same strides, we merge the anchors in the same order.\n let last_same_stride_layer = layer_id;\n while (last_same_stride_layer < options.strides.length && options.strides[last_same_stride_layer] === options.strides[layer_id]) {\n const scale = calculateScale(options.min_scale, options.max_scale, last_same_stride_layer, options.strides.length);\n if (last_same_stride_layer === 0 && options.reduce_boxes_in_lowest_layer) {\n // For first layer, it can be specified to use predefined anchors.\n aspect_ratios.push(1.0);\n aspect_ratios.push(2.0);\n aspect_ratios.push(0.5);\n scales.push(0.1);\n scales.push(scale);\n scales.push(scale);\n } else {\n for (let aspect_ratio_id = 0; aspect_ratio_id < options.aspect_ratios.length; ++aspect_ratio_id) {\n aspect_ratios.push(options.aspect_ratios[aspect_ratio_id]);\n scales.push(scale);\n }\n if (options.interpolated_scale_aspect_ratio > 0.0) {\n const scale_next = last_same_stride_layer === options.strides.length - 1 ? 1.0 : calculateScale(options.min_scale, options.max_scale, last_same_stride_layer + 1, options.strides.length);\n scales.push(Math.sqrt(scale * scale_next));\n aspect_ratios.push(options.interpolated_scale_aspect_ratio);\n }\n }\n last_same_stride_layer++;\n }\n for (let i = 0; i < aspect_ratios.length; ++i) {\n const ratio_sqrts = Math.sqrt(aspect_ratios[i]);\n anchor_height.push(scales[i] / ratio_sqrts);\n anchor_width.push(scales[i] * ratio_sqrts);\n }\n let feature_map_height = 0;\n let feature_map_width = 0;\n if (options.feature_map_height.length) {\n feature_map_height = options.feature_map_height[layer_id];\n feature_map_width = options.feature_map_width[layer_id];\n } else {\n const stride = options.strides[layer_id];\n feature_map_height = Math.ceil(1.0 * options.input_size_height / stride);\n feature_map_width = Math.ceil(1.0 * options.input_size_width / stride);\n }\n for (let y = 0; y < feature_map_height; ++y) {\n for (let x = 0; x < feature_map_width; ++x) {\n for (let anchor_id = 0; anchor_id < anchor_height.length; ++anchor_id) {\n const x_center = (x + options.anchor_offset_x) * 1.0 / feature_map_width;\n const y_center = (y + options.anchor_offset_y) * 1.0 / feature_map_height;\n const new_anchor = {};\n new_anchor.x_center = x_center;\n new_anchor.y_center = y_center;\n if (options.fixed_anchor_size) {\n new_anchor.w = 1.0;\n new_anchor.h = 1.0;\n } else {\n new_anchor.w = anchor_width[anchor_id];\n new_anchor.h = anchor_height[anchor_id];\n }\n anchors.push(new_anchor);\n }\n }\n }\n layer_id = last_same_stride_layer;\n }\n}\n\nexport async function load(cfg) {\n config = cfg;\n if (!models.blazepose) {\n models.blazepose = await tf.loadGraphModel(config.pose.modelPath);\n log(`load model: ${config.pose.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n generateAnchors();\n return models.blazepose;\n}\n\nfunction rotateRegion(region) {\n const x0 = region.keys[kMidHipCenter].x;\n const y0 = region.keys[kMidHipCenter].y;\n const x1 = (region.box[0] + region.box[2]) * 0.5;\n const y1 = (region.box[1] + region.box[3]) * 0.5;\n const target_angle = Math.PI * 0.5;\n const angle = target_angle - Math.atan2(-(y1 - y0), x1 - x0);\n return Math.round(1000 * (angle - 2 * Math.PI * Math.floor((angle - (-Math.PI)) / (2 * Math.PI)))) / 1000;\n}\n\nfunction rotateVecor(vec, rotation) {\n const sx = vec.x;\n const sy = vec.y;\n vec.x = sx * Math.cos(rotation) - sy * Math.sin(rotation);\n vec.y = sx * Math.sin(rotation) + sy * Math.cos(rotation);\n}\n\nasync function decode(logits) {\n const scores = await logits[0].data();\n const boxes = await logits[1].data();\n // todo: add nms\n // todo scale output with image.shape\n const regions = [];\n for (let i = 0; i < anchors.length; i++) {\n const region = {};\n const score = 1.0 / (1.0 + Math.exp(-scores[i]));\n if (score > config.pose.scoreThreshold) {\n const idx = (4 + 2 * kPoseDetectKeyNum) * i;\n /* boundary box */\n const sx = boxes[idx + 0];\n const sy = boxes[idx + 1];\n const w = boxes[idx + 2] / config.pose.inputSize;\n const h = boxes[idx + 3] / config.pose.inputSize;\n const cx = (sx + anchors[i].x_center * config.pose.inputSize) / config.pose.inputSize;\n const cy = (sy + anchors[i].y_center * config.pose.inputSize) / config.pose.inputSize;\n region.score = Math.round(1000 * score) / 1000;\n region.box = [cx - w * 0.5, cy - h * 0.5, w * 0.5, h * 0.5];\n /* landmark positions (6 keys) */\n const keys = new Array(kPoseDetectKeyNum);\n for (let j = 0; j < kPoseDetectKeyNum; j++) {\n const lx = (boxes[idx + 4 + (2 * j) + 0] + anchors[i].x_center * config.pose.inputSize) / config.pose.inputSize;\n const ly = (boxes[idx + 4 + (2 * j) + 1] + anchors[i].y_center * config.pose.inputSize) / config.pose.inputSize;\n keys[j] = { x: lx, y: ly };\n }\n region.keys = keys;\n region.angle = rotateRegion(region);\n // add points\n const x_center = region.keys[kMidHipCenter].x * config.pose.inputSize;\n const y_center = region.keys[kMidHipCenter].y * config.pose.inputSize;\n const x_scale = region.keys[kFullBodySizeRot].x * config.pose.inputSize;\n const y_scale = region.keys[kFullBodySizeRot].y * config.pose.inputSize;\n // Bounding box size as double distance from center to scale point.\n const box_size = Math.sqrt((x_scale - x_center) * (x_scale - x_center) + (y_scale - y_center) * (y_scale - y_center)) * 2.0;\n /* RectTransformationCalculator::TransformNormalizedRect() */\n const roi_cx = region.angle === 0.0 ? x_center + box_size : x_center + box_size * Math.cos(region.angle) - box_size * Math.sin(region.angle);\n const roi_cy = region.angle === 0.0 ? y_center + box_size : y_center + box_size * Math.sin(region.angle) + box_size * Math.cos(region.angle);\n const long_side = Math.max(box_size, box_size);\n const roi_w = long_side * 1.5;\n const roi_h = long_side * 1.5;\n region.center = { x: roi_cx / config.pose.inputSize, y: roi_cy / config.pose.inputSize };\n region.size = { x: roi_w / config.pose.inputSize, y: roi_h / config.pose.inputSize };\n /* calculate ROI coordinates */\n const dx = roi_w * 0.5;\n const dy = roi_h * 0.5;\n region.coords = [];\n region.coords[0] = { x: -dx, y: -dy };\n region.coords[1] = { x: +dx, y: -dy };\n region.coords[2] = { x: +dx, y: +dy };\n region.coords[3] = { x: -dx, y: +dy };\n for (let j = 0; j < 4; j++) {\n rotateVecor(region.coords[i], region.angle);\n region.coords[j].x = (region.coords[j].x + roi_cx) / config.pose.inputSize;\n region.coords[j].y = (region.coords[j].y + roi_cy) / config.pose.inputSize;\n }\n\n regions.push(region);\n }\n }\n return regions;\n}\n\nexport async function predict(image, cfg) {\n if (!models.blazepose) return null;\n return new Promise(async (resolve) => {\n config = cfg;\n const resize = tf.image.resizeBilinear(image, [config.pose.inputSize, config.pose.inputSize], false);\n const enhance = tf.div(resize, 127.5).sub(1);\n tf.dispose(resize);\n const logits = await models.blazepose.predict(enhance);\n // todo: add landmarks model\n tf.dispose(enhance);\n const regions = await decode(logits);\n logits[0].dispose();\n logits[1].dispose();\n log('poses', regions);\n resolve(regions);\n });\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nfunction getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\nfunction getBoxCenter(box) {\n return [\n box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n ];\n}\nfunction cutBoxFromImageAndResize(box, image, cropSize) {\n const h = image.shape[1];\n const w = image.shape[2];\n const boxes = [[\n box.startPoint[1] / h,\n box.startPoint[0] / w,\n box.endPoint[1] / h,\n box.endPoint[0] / w,\n ]];\n return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\nfunction scaleBoxCoordinates(box, factor) {\n const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n const palmLandmarks = box.palmLandmarks.map((coord) => {\n const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n return scaledCoord;\n });\n return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\nfunction enlargeBox(box, factor = 1.5) {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\nfunction squarifyBox(box) {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const maxEdge = Math.max(...size);\n const halfSize = maxEdge / 2;\n const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\nfunction shiftBox(box, shiftFactor) {\n const boxSize = [\n box.endPoint[0] - box.startPoint[0],\n box.endPoint[1] - box.startPoint[1],\n ];\n const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]];\n const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\nexport {\n cutBoxFromImageAndResize,\n enlargeBox,\n getBoxCenter,\n getBoxSize,\n scaleBoxCoordinates,\n shiftBox,\n squarifyBox,\n};\n", "function normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\nfunction computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\nconst buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\nfunction dot(v1, v2) {\n let product = 0;\n for (let i = 0; i < v1.length; i++) {\n product += v1[i] * v2[i];\n }\n return product;\n}\nfunction getColumnFrom2DArr(arr, columnIndex) {\n const column = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\nfunction multiplyTransformMatrices(mat1, mat2) {\n const product = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) {\n // @ts-ignore\n product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n }\n return product;\n}\nfunction buildRotationMatrix(rotation, center) {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\nfunction invertTransformMatrix(matrix) {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [\n -dot(rotationComponent[0], translationComponent),\n -dot(rotationComponent[1], translationComponent),\n ];\n return [\n rotationComponent[0].concat(invertedTranslation[0]),\n rotationComponent[1].concat(invertedTranslation[1]),\n [0, 0, 1],\n ];\n}\nfunction rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\nexport {\n buildRotationMatrix,\n computeRotation,\n dot,\n getColumnFrom2DArr,\n invertTransformMatrix,\n normalizeRadians,\n rotatePoint,\n};\n", "/* eslint-disable indent */\n/* eslint-disable no-multi-spaces */\n\nexport default {\n backend: 'webgl', // select tfjs backend to use\n wasmPath: '../assets/', // path for wasm binaries\n // only used for backend: wasm\n async: true, // execute enabled models in parallel\n // this disables per-model performance data but\n // slightly increases performance\n // cannot be used if profiling is enabled\n profile: false, // enable tfjs profiling\n // this has significant performance impact\n // only enable for debugging purposes\n // currently only implemented for age,gender,emotion models\n deallocate: false, // aggresively deallocate gpu memory after each usage\n // only valid for webgl backend and only during first call\n // cannot be changed unless library is reloaded\n // this has significant performance impact\n // only enable on low-memory devices\n scoped: false, // enable scoped runs\n // some models *may* have memory leaks,\n // this wrapps everything in a local scope at a cost of performance\n // typically not needed\n videoOptimized: true, // perform additional optimizations when input is video,\n // must be disabled for images\n // basically this skips object box boundary detection for every n frames\n // while maintaining in-box detection since objects cannot move that fast\n warmup: 'face', // what to use for human.warmup(), can be 'none', 'face', 'full'\n // warmup pre-initializes all models for faster inference but can take\n // significant time on startup\n filter: {\n enabled: true, // enable image pre-processing filters\n width: 0, // resize input width\n height: 0, // resize input height\n // if both width and height are set to 0, there is no resizing\n // if just one is set, second one is scaled automatically\n // if both are set, values are used as-is\n return: true, // return processed canvas imagedata in result\n brightness: 0, // range: -1 (darken) to 1 (lighten)\n contrast: 0, // range: -1 (reduce contrast) to 1 (increase contrast)\n sharpness: 0, // range: 0 (no sharpening) to 1 (maximum sharpening)\n blur: 0, // range: 0 (no blur) to N (blur radius in pixels)\n saturation: 0, // range: -1 (reduce saturation) to 1 (increase saturation)\n hue: 0, // range: 0 (no change) to 360 (hue rotation in degrees)\n negative: false, // image negative\n sepia: false, // image sepia colors\n vintage: false, // image vintage colors\n kodachrome: false, // image kodachrome colors\n technicolor: false, // image technicolor colors\n polaroid: false, // image polaroid camera effect\n pixelate: 0, // range: 0 (no pixelate) to N (number of pixels to pixelate)\n },\n\n gesture: {\n enabled: true, // enable simple gesture recognition\n },\n\n face: {\n enabled: true, // controls if specified modul is enabled\n // face.enabled is required for all face models:\n // detector, mesh, iris, age, gender, emotion\n // (note: module is not loaded until it is required)\n detector: {\n modelPath: '../models/blazeface-back.json', // can be 'front' or 'back'.\n // 'front' is optimized for large faces\n // such as front-facing camera and\n // 'back' is optimized for distanct faces.\n inputSize: 256, // fixed value: 128 for front and 256 for 'back'\n rotation: false, // use best-guess rotated face image or just box with rotation as-is\n // false means higher performance, but incorrect mesh mapping if face angle is above 20 degrees\n maxFaces: 10, // maximum number of faces detected in the input\n // should be set to the minimum number for performance\n skipFrames: 11, // how many frames to go without re-running the face bounding box detector\n // only used for video inputs\n // e.g., if model is running st 25 FPS, we can re-use existing bounding\n // box for updated face analysis as the head probably hasn't moved much\n // in short time (10 * 1/25 = 0.25 sec)\n minConfidence: 0.5, // threshold for discarding a prediction\n iouThreshold: 0.2, // threshold for deciding whether boxes overlap too much in\n // non-maximum suppression (0.1 means drop if overlap 10%)\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on score\n // in non-maximum suppression,\n // this is applied on detection objects only and before minConfidence\n },\n\n mesh: {\n enabled: true,\n modelPath: '../models/facemesh.json',\n inputSize: 192, // fixed value\n returnRawData: false, // in addition to standard mesh and box values, return raw normalized values as well\n },\n\n iris: {\n enabled: true,\n modelPath: '../models/iris.json',\n inputSize: 64, // fixed value\n },\n\n age: {\n enabled: true,\n modelPath: '../models/age-ssrnet-imdb.json', // can be 'age-ssrnet-imdb' or 'age-ssrnet-wiki'\n // which determines training set for model\n inputSize: 64, // fixed value\n skipFrames: 31, // how many frames to go without re-running the detector\n // only used for video inputs\n },\n\n gender: {\n enabled: true,\n minConfidence: 0.1, // threshold for discarding a prediction\n modelPath: '../models/gender-ssrnet-imdb.json', // can be 'gender', 'gender-ssrnet-imdb' or 'gender-ssrnet-wiki'\n inputSize: 64, // fixed value\n skipFrames: 41, // how many frames to go without re-running the detector\n // only used for video inputs\n },\n\n emotion: {\n enabled: true,\n inputSize: 64, // fixed value\n minConfidence: 0.2, // threshold for discarding a prediction\n skipFrames: 21, // how many frames to go without re-running the detector\n modelPath: '../models/emotion-large.json', // can be 'mini', 'large'\n },\n\n embedding: {\n enabled: false,\n inputSize: 112, // fixed value\n modelPath: '../models/mobilefacenet.json',\n },\n },\n\n body: {\n enabled: true,\n modelPath: '../models/posenet.json',\n inputSize: 257, // fixed value\n maxDetections: 10, // maximum number of people detected in the input\n // should be set to the minimum number for performance\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on score\n // in non-maximum suppression\n nmsRadius: 20, // radius for deciding points are too close in non-maximum suppression\n outputStride: 16, // size of block in which to run point detectopn, smaller value means higher resolution\n // defined by model itself, can be 8, 16, or 32\n modelType: 'MobileNet', // Human includes MobileNet version, but you can switch to ResNet\n },\n\n pose: {\n enabled: false,\n scoreThreshold: 0.6, // threshold for deciding when to remove boxes based on score\n // in non-maximum suppression\n iouThreshold: 0.3, // threshold for deciding whether boxes overlap too much\n // in non-maximum suppression\n modelPath: '../models/blazepose.json',\n inputSize: 128, // fixed value\n },\n\n hand: {\n enabled: true,\n rotation: false, // use best-guess rotated hand image or just box with rotation as-is\n // false means higher performance, but incorrect finger mapping if hand is inverted\n inputSize: 256, // fixed value\n skipFrames: 12, // how many frames to go without re-running the hand bounding box detector\n // only used for video inputs\n // e.g., if model is running st 25 FPS, we can re-use existing bounding\n // box for updated hand skeleton analysis as the hand probably\n // hasn't moved much in short time (10 * 1/25 = 0.25 sec)\n minConfidence: 0.1, // threshold for discarding a prediction\n iouThreshold: 0.1, // threshold for deciding whether boxes overlap too much\n // in non-maximum suppression\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on\n // score in non-maximum suppression\n maxHands: 1, // maximum number of hands detected in the input\n // should be set to the minimum number for performance\n landmarks: true, // detect hand landmarks or just hand boundary box\n detector: {\n modelPath: '../models/handdetect.json',\n },\n skeleton: {\n modelPath: '../models/handskeleton.json',\n },\n },\n};\n", "// data:image/jpeg;base64,\nexport const face = 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"async function drawGesture(result, canvas, ui) {\n if (!result) return;\n const ctx = canvas.getContext('2d');\n ctx.font = ui.baseFont;\n ctx.fillStyle = ui.baseLabel;\n let i = 1;\n for (let gesture = 0; gesture < result.length; gesture++) {\n const [where, what] = Object.entries(result[gesture]);\n if ((what.length > 1) && (what[1].length > 0)) {\n const person = where[1] > 0 ? `#${where[1]}` : '';\n const label = `${where[0]} ${person}: ${what[1]}`;\n ctx.fillStyle = 'black';\n ctx.fillText(label, 8, 2 + (i * ui.baseLineHeight));\n ctx.fillStyle = ui.baseLabel;\n ctx.fillText(label, 6, 0 + (i * ui.baseLineHeight));\n i += 1;\n }\n }\n}\n\nasync function drawFace(result, canvas, ui, triangulation) {\n if (!result) return;\n const ctx = canvas.getContext('2d');\n for (const face of result) {\n ctx.font = ui.baseFont;\n ctx.strokeStyle = ui.baseColor;\n ctx.fillStyle = ui.baseColor;\n ctx.lineWidth = ui.baseLineWidth;\n ctx.beginPath();\n if (ui.drawBoxes) {\n ctx.rect(face.box[0], face.box[1], face.box[2], face.box[3]);\n }\n // silly hack since fillText does not suport new line\n const labels = [];\n // labels.push(`${Math.trunc(100 * face.confidence)}% face`);\n if (face.genderConfidence) labels.push(`${face.gender || ''} ${Math.trunc(100 * face.genderConfidence)}% confident`);\n // if (face.genderConfidence) labels.push(face.gender);\n if (face.age) labels.push(`age: ${face.age || ''}`);\n if (face.iris) labels.push(`iris distance: ${face.iris}`);\n if (face.emotion && face.emotion.length > 0) {\n const emotion = face.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`);\n labels.push(emotion.join(' '));\n }\n ctx.fillStyle = ui.baseLabel;\n for (let i = 0; i < labels.length; i++) {\n ctx.fillStyle = 'black';\n ctx.fillText(labels[i], face.box[0] + 1, face.box[1] - ((labels.length - i) * ui.baseLineHeight) + 6);\n ctx.fillStyle = ui.baseLabel;\n ctx.fillText(labels[i], face.box[0] + 0, face.box[1] - ((labels.length - i) * ui.baseLineHeight) + 5);\n }\n ctx.fillStyle = ui.baseColor;\n ctx.stroke();\n ctx.lineWidth = 1;\n if (face.mesh) {\n if (ui.drawPoints) {\n for (const point of face.mesh) {\n ctx.fillStyle = ui.useDepth ? `rgba(${127.5 + (2 * point[2])}, ${127.5 - (2 * point[2])}, 255, 0.5)` : ui.baseColor;\n ctx.beginPath();\n ctx.arc(point[0], point[1], 2, 0, 2 * Math.PI);\n ctx.fill();\n }\n }\n if (ui.drawPolygons) {\n for (let i = 0; i < triangulation.length / 3; i++) {\n const points = [\n triangulation[i * 3 + 0],\n triangulation[i * 3 + 1],\n triangulation[i * 3 + 2],\n ].map((index) => face.mesh[index]);\n const path = new Path2D();\n path.moveTo(points[0][0], points[0][1]);\n for (const point of points) {\n path.lineTo(point[0], point[1]);\n }\n path.closePath();\n ctx.strokeStyle = ui.useDepth ? `rgba(${127.5 + (2 * points[0][2])}, ${127.5 - (2 * points[0][2])}, 255, 0.3)` : ui.baseColor;\n ctx.stroke(path);\n if (ui.fillPolygons) {\n ctx.fillStyle = ui.useDepth ? `rgba(${127.5 + (2 * points[0][2])}, ${127.5 - (2 * points[0][2])}, 255, 0.3)` : ui.baseColor;\n ctx.fill(path);\n }\n }\n // iris: array[center, left, top, right, bottom]\n if (face.annotations && face.annotations.leftEyeIris) {\n ctx.strokeStyle = ui.useDepth ? 'rgba(255, 200, 255, 0.3)' : ui.baseColor;\n ctx.beginPath();\n const sizeX = Math.abs(face.annotations.leftEyeIris[3][0] - face.annotations.leftEyeIris[1][0]) / 2;\n const sizeY = Math.abs(face.annotations.leftEyeIris[4][1] - face.annotations.leftEyeIris[2][1]) / 2;\n ctx.ellipse(face.annotations.leftEyeIris[0][0], face.annotations.leftEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n ctx.stroke();\n if (ui.fillPolygons) {\n ctx.fillStyle = ui.useDepth ? 'rgba(255, 255, 200, 0.3)' : ui.baseColor;\n ctx.fill();\n }\n }\n if (face.annotations && face.annotations.rightEyeIris) {\n ctx.strokeStyle = ui.useDepth ? 'rgba(255, 200, 255, 0.3)' : ui.baseColor;\n ctx.beginPath();\n const sizeX = Math.abs(face.annotations.rightEyeIris[3][0] - face.annotations.rightEyeIris[1][0]) / 2;\n const sizeY = Math.abs(face.annotations.rightEyeIris[4][1] - face.annotations.rightEyeIris[2][1]) / 2;\n ctx.ellipse(face.annotations.rightEyeIris[0][0], face.annotations.rightEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n ctx.stroke();\n if (ui.fillPolygons) {\n ctx.fillStyle = ui.useDepth ? 'rgba(255, 255, 200, 0.3)' : ui.baseColor;\n ctx.fill();\n }\n }\n }\n }\n }\n}\n\nconst lastDrawnPose = [];\nasync function drawBody(result, canvas, ui) {\n if (!result) return;\n const ctx = canvas.getContext('2d');\n ctx.lineJoin = 'round';\n for (let i = 0; i < result.length; i++) {\n if (!lastDrawnPose[i] && ui.buffered) lastDrawnPose[i] = { ...result[i] };\n ctx.fillStyle = ui.baseColor;\n ctx.strokeStyle = ui.baseColor;\n ctx.font = ui.baseFont;\n ctx.lineWidth = ui.baseLineWidth;\n if (ui.drawPoints) {\n for (let pt = 0; pt < result[i].keypoints.length; pt++) {\n ctx.beginPath();\n if (ui.buffered) {\n lastDrawnPose[i].keypoints[pt].position.x = (lastDrawnPose[i].keypoints[pt].position.x + result[i].keypoints[pt].position.x) / 2;\n lastDrawnPose[i].keypoints[pt].position.y = (lastDrawnPose[i].keypoints[pt].position.y + result[i].keypoints[pt].position.y) / 2;\n ctx.arc(lastDrawnPose[i].keypoints[pt].position.x, lastDrawnPose[i].keypoints[pt].position.y, 2, 0, 2 * Math.PI);\n } else {\n ctx.arc(result[i].keypoints[pt].position.x, result[i].keypoints[pt].position.y, 2, 0, 2 * Math.PI);\n }\n ctx.fill();\n }\n }\n if (ui.drawPolygons) {\n const path = new Path2D();\n let root;\n let part;\n // torso\n root = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n if (root) {\n path.moveTo(root.position.x, root.position.y);\n part = result[i].keypoints.find((a) => a.part === 'rightShoulder');\n if (part) path.lineTo(part.position.x, part.position.y);\n part = result[i].keypoints.find((a) => a.part === 'rightHip');\n if (part) path.lineTo(part.position.x, part.position.y);\n part = result[i].keypoints.find((a) => a.part === 'leftHip');\n if (part) path.lineTo(part.position.x, part.position.y);\n part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n if (part) path.lineTo(part.position.x, part.position.y);\n }\n // leg left\n root = result[i].keypoints.find((a) => a.part === 'leftHip');\n if (root) {\n path.moveTo(root.position.x, root.position.y);\n part = result[i].keypoints.find((a) => a.part === 'leftKnee');\n if (part) path.lineTo(part.position.x, part.position.y);\n part = result[i].keypoints.find((a) => a.part === 'leftAnkle');\n if (part) path.lineTo(part.position.x, part.position.y);\n }\n // leg right\n root = result[i].keypoints.find((a) => a.part === 'rightHip');\n if (root) {\n path.moveTo(root.position.x, root.position.y);\n part = result[i].keypoints.find((a) => a.part === 'rightKnee');\n if (part) path.lineTo(part.position.x, part.position.y);\n part = result[i].keypoints.find((a) => a.part === 'rightAnkle');\n if (part) path.lineTo(part.position.x, part.position.y);\n }\n // arm left\n root = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n if (root) {\n path.moveTo(root.position.x, root.position.y);\n part = result[i].keypoints.find((a) => a.part === 'leftElbow');\n if (part) path.lineTo(part.position.x, part.position.y);\n part = result[i].keypoints.find((a) => a.part === 'leftWrist');\n if (part) path.lineTo(part.position.x, part.position.y);\n }\n // arm right\n root = result[i].keypoints.find((a) => a.part === 'rightShoulder');\n if (root) {\n path.moveTo(root.position.x, root.position.y);\n part = result[i].keypoints.find((a) => a.part === 'rightElbow');\n if (part) path.lineTo(part.position.x, part.position.y);\n part = result[i].keypoints.find((a) => a.part === 'rightWrist');\n if (part) path.lineTo(part.position.x, part.position.y);\n }\n // draw all\n ctx.stroke(path);\n }\n }\n}\n\nasync function drawHand(result, canvas, ui) {\n if (!result) return;\n const ctx = canvas.getContext('2d');\n ctx.lineJoin = 'round';\n for (const hand of result) {\n ctx.font = ui.baseFont;\n ctx.lineWidth = ui.baseLineWidth;\n if (ui.drawBoxes) {\n ctx.lineWidth = ui.baseLineWidth;\n ctx.beginPath();\n ctx.strokeStyle = ui.baseColor;\n ctx.fillStyle = ui.baseColor;\n ctx.rect(hand.box[0], hand.box[1], hand.box[2], hand.box[3]);\n ctx.fillStyle = 'black';\n ctx.fillText('hand', hand.box[0] + 3, 1 + hand.box[1] + ui.baseLineHeight, hand.box[2]);\n ctx.fillStyle = ui.baseLabel;\n ctx.fillText('hand', hand.box[0] + 2, 0 + hand.box[1] + ui.baseLineHeight, hand.box[2]);\n ctx.stroke();\n }\n if (ui.drawPoints) {\n if (hand.landmarks && hand.landmarks.length > 0) {\n for (const point of hand.landmarks) {\n ctx.fillStyle = ui.useDepth ? `rgba(${127.5 + (2 * point[2])}, ${127.5 - (2 * point[2])}, 255, 0.5)` : ui.baseColor;\n ctx.beginPath();\n ctx.arc(point[0], point[1], 2, 0, 2 * Math.PI);\n ctx.fill();\n }\n }\n }\n if (ui.drawPolygons) {\n const addPart = (part) => {\n if (!part) return;\n for (let i = 0; i < part.length; i++) {\n ctx.lineWidth = ui.baseLineWidth;\n ctx.beginPath();\n ctx.strokeStyle = ui.useDepth ? `rgba(${127.5 + (2 * part[i][2])}, ${127.5 - (2 * part[i][2])}, 255, 0.5)` : ui.baseColor;\n ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]);\n ctx.lineTo(part[i][0], part[i][1]);\n ctx.stroke();\n }\n };\n addPart(hand.annotations.indexFinger);\n addPart(hand.annotations.middleFinger);\n addPart(hand.annotations.ringFinger);\n addPart(hand.annotations.pinky);\n addPart(hand.annotations.thumb);\n // addPart(hand.annotations.palmBase);\n }\n }\n}\n\n// eslint-disable-next-line import/prefer-default-export\nexport default {\n face: drawFace,\n body: drawBody,\n hand: drawHand,\n gesture: drawGesture,\n};\n", "// @ts-nocheck\n\nlet instance = 0;\nlet CSScreated = false;\n\nlet theme = {\n background: 'darkslategray',\n hover: 'lightgray',\n itemBackground: 'black',\n itemColor: 'white',\n buttonBackground: 'lightblue',\n buttonHover: 'lightgreen',\n checkboxOn: 'lightgreen',\n checkboxOff: 'lightcoral',\n rangeBackground: 'lightblue',\n rangeLabel: 'white',\n chartColor: 'lightblue',\n};\n\nfunction createCSS() {\n if (CSScreated) return;\n const css = `\n :root { --rounded: 0.2rem; }\n .menu { position: absolute; top: 0rem; right: 0; width: max-content; padding: 0 0.2rem 0 0.2rem; line-height: 1.8rem; z-index: 10;\n box-shadow: 0 0 8px dimgrey; background: ${theme.background}; border-radius: var(--rounded); border-color: black; border-style: solid; border-width: thin; }\n\n .menu:hover { box-shadow: 0 0 8px ${theme.hover}; }\n .menu-container { display: block; max-height: 100vh; }\n .menu-container-fadeout { max-height: 0; overflow: hidden; transition: max-height, 0.5s ease; }\n .menu-container-fadein { max-height: 100vh; overflow: hidden; transition: max-height, 0.5s ease; }\n .menu-item { display: flex; white-space: nowrap; padding: 0.2rem; cursor: default; width: 100%; }\n .menu-title { cursor: pointer; }\n .menu-hr { margin: 0.2rem; border: 1px solid rgba(0, 0, 0, 0.5) }\n .menu-label { padding: 0; font-weight: 800; }\n\n .menu-list { margin-right: 0.8rem; }\n select:focus { outline: none; }\n .menu-list-item { background: ${theme.itemBackground}; color: ${theme.itemColor}; border: none; padding: 0.2rem; font-family: inherit;\n font-variant: inherit; border-radius: var(--rounded); font-weight: 800; }\n\n .menu-chart-title { padding: 0; font-size: 0.8rem; font-weight: 800; align-items: center}\n .menu-chart-canvas { background: transparent; margin: 0.2rem 0 0.2rem 0.6rem; }\n \n .menu-button { border: 0; background: ${theme.buttonBackground}; width: -webkit-fill-available; padding: 8px; margin: 8px; cursor: pointer; box-shadow: 4px 4px 4px 0 dimgrey;\n border-radius: var(--rounded); justify-content: center; font-family: inherit; font-variant: inherit; font-size: 1rem; font-weight: 800; }\n .menu-button:hover { background: ${theme.buttonHover}; box-shadow: 4px 4px 4px 0 black; }\n .menu-button:focus { outline: none; }\n\n .menu-checkbox { width: 2.8rem; height: 1rem; background: ${theme.itemBackground}; margin: 0.5rem 0.5rem 0 0; position: relative; border-radius: var(--rounded); }\n .menu-checkbox:after { content: 'OFF'; color: ${theme.checkboxOff}; position: absolute; right: 0.2rem; top: -0.4rem; font-weight: 800; font-size: 0.5rem; }\n .menu-checkbox:before { content: 'ON'; color: ${theme.checkboxOn}; position: absolute; left: 0.3rem; top: -0.4rem; font-weight: 800; font-size: 0.5rem; }\n .menu-checkbox-label { width: 1.3rem; height: 0.8rem; cursor: pointer; position: absolute; top: 0.1rem; left: 0.1rem; z-index: 1; background: ${theme.checkboxOff};\n border-radius: var(--rounded); transition: left 0.6s ease; }\n\n input[type=checkbox] { visibility: hidden; }\n input[type=checkbox]:checked + label { left: 1.4rem; background: ${theme.checkboxOn}; }\n\n .menu-range { margin: 0.2rem 0.5rem 0 0; width: 3.5rem; background: transparent; color: ${theme.rangeBackground}; }\n .menu-range:before { color: ${theme.rangeLabel}; margin: 0 0.4rem 0 0; font-weight: 800; font-size: 0.6rem; position: relative; top: 0.3rem; content: attr(value); }\n\n input[type=range] { -webkit-appearance: none; }\n input[type=range]::-webkit-slider-runnable-track { width: 100%; height: 1rem; cursor: pointer; background: ${theme.itemBackground}; border-radius: var(--rounded); border: 1px; }\n input[type=range]::-moz-range-track { width: 100%; height: 1rem; cursor: pointer; background: ${theme.itemBackground}; border-radius: var(--rounded); border: 1px; }\n input[type=range]::-webkit-slider-thumb { border: 1px solid #000000; margin-top: 0.05rem; height: 0.9rem; width: 1rem; border-radius: var(--rounded); background: ${theme.rangeBackground}; cursor: pointer; -webkit-appearance: none; }\n input[type=range]::-moz-range-thumb { border: 1px solid #000000; margin-top: 0.05rem; height: 0.9rem; width: 1rem; border-radius: var(--rounded); background: ${theme.rangeBackground}; cursor: pointer; -webkit-appearance: none; }\n\n .svg-background { fill:darkslategrey; cursor:pointer; opacity: 0.6; }\n .svg-foreground { fill:white; cursor:pointer; opacity: 0.8; }\n `;\n const el = document.createElement('style');\n el.innerHTML = css;\n document.getElementsByTagName('head')[0].appendChild(el);\n CSScreated = true;\n}\n\nclass Menu {\n constructor(parent, title, position, userTheme) {\n if (userTheme) theme = { ...theme, ...userTheme };\n createCSS();\n this.createMenu(parent, title, position);\n this.id = 0;\n this.instance = instance;\n instance++;\n this._maxFPS = 0;\n this.hidden = 0;\n }\n\n createMenu(parent, title = '', position = { top: null, left: null, bottom: null, right: null }) {\n this.menu = document.createElement('div');\n this.menu.id = `menu-${instance}`;\n this.menu.className = 'menu';\n if (position) {\n if (position.top) this.menu.style.top = position.top;\n if (position.bottom) this.menu.style.bottom = position.bottom;\n if (position.left) this.menu.style.left = position.left;\n if (position.right) this.menu.style.right = position.right;\n }\n\n this.container = document.createElement('div');\n this.container.id = `menu-container-${instance}`;\n this.container.className = 'menu-container menu-container-fadein';\n\n // set menu title with pulldown arrow\n const elTitle = document.createElement('div');\n elTitle.className = 'menu-title';\n elTitle.id = `menu-title-${instance}`;\n const svg = `\n \n \n `;\n if (title) elTitle.innerHTML = `${title}${svg}`;\n this.menu.appendChild(elTitle);\n elTitle.addEventListener('click', () => {\n this.container.classList.toggle('menu-container-fadeout');\n this.container.classList.toggle('menu-container-fadein');\n this.menu.style.borderStyle = this.container.classList.contains('menu-container-fadeout') ? 'none' : 'solid';\n });\n\n this.menu.appendChild(this.container);\n if (typeof parent === 'object') parent.appendChild(this.menu);\n else document.getElementById(parent).appendChild(this.menu);\n }\n\n get newID() {\n this.id++;\n return `menu-${this.instance}-${this.id}`;\n }\n\n get ID() {\n return `menu-${this.instance}-${this.id}`;\n }\n\n get width() {\n return this.menu.offsetWidth;\n }\n\n get height() {\n return this.menu.offsetHeight;\n }\n\n hide() {\n if (this.container.classList.contains('menu-container-fadein')) {\n this.container.classList.toggle('menu-container-fadeout');\n this.container.classList.toggle('menu-container-fadein');\n }\n }\n\n visible() {\n return (this.container.classList.contains('menu-container-fadein'));\n }\n\n toggle(evt) {\n this.container.classList.toggle('menu-container-fadeout');\n this.container.classList.toggle('menu-container-fadein');\n if (this.container.classList.contains('menu-container-fadein') && evt) {\n const x = evt.x || (evt.touches && evt.touches[0] ? evt.touches[0].pageX : null);\n // const y = evt.y || (evt.touches && evt.touches[0] ? evt.touches[0].pageY : null);\n if (x) this.menu.style.left = `${x - (this.menu.offsetWidth / 2)}px`;\n // if (y) this.menu.style.top = '5.5rem'; // `${evt.y + 55}px`;\n if (this.menu.offsetLeft < 0) this.menu.style.left = 0;\n if ((this.menu.offsetLeft + this.menu.offsetWidth) > window.innerWidth) {\n this.menu.style.left = null;\n this.menu.style.right = 0;\n }\n this.menu.style.borderStyle = 'solid';\n } else {\n this.menu.style.borderStyle = 'none';\n }\n }\n\n addTitle(title) {\n const el = document.createElement('div');\n el.className = 'menu-title';\n el.id = this.newID;\n el.innerHTML = title;\n this.menu.appendChild(el);\n el.addEventListener('click', () => {\n this.hidden = !this.hidden;\n const all = document.getElementsByClassName('menu');\n for (const item of all) {\n item.style.display = this.hidden ? 'none' : 'block';\n }\n });\n return el;\n }\n\n addLabel(title) {\n const el = document.createElement('div');\n el.className = 'menu-item menu-label';\n el.id = this.newID;\n el.innerHTML = title;\n this.container.appendChild(el);\n return el;\n }\n\n addBool(title, object, variable, callback) {\n const el = document.createElement('div');\n el.className = 'menu-item';\n el.innerHTML = `
${title}`;\n this.container.appendChild(el);\n el.addEventListener('change', (evt) => {\n object[variable] = evt.target.checked;\n if (callback) callback(evt.target.checked);\n });\n return el;\n }\n\n async addList(title, items, selected, callback) {\n const el = document.createElement('div');\n el.className = 'menu-item';\n let options = '';\n for (const item of items) {\n const def = item === selected ? 'selected' : '';\n options += ``;\n }\n el.innerHTML = `
${title}`;\n el.style.fontFamily = document.body.style.fontFamily;\n el.style.fontSize = document.body.style.fontSize;\n el.style.fontVariant = document.body.style.fontVariant;\n this.container.appendChild(el);\n el.addEventListener('change', (evt) => {\n if (callback) callback(items[evt.target.selectedIndex]);\n });\n return el;\n }\n\n addRange(title, object, variable, min, max, step, callback) {\n const el = document.createElement('div');\n el.className = 'menu-item';\n el.innerHTML = `${title}`;\n this.container.appendChild(el);\n el.addEventListener('change', (evt) => {\n object[variable] = parseInt(evt.target.value) === parseFloat(evt.target.value) ? parseInt(evt.target.value) : parseFloat(evt.target.value);\n evt.target.setAttribute('value', evt.target.value);\n if (callback) callback(evt.target.value);\n });\n el.input = el.children[0];\n return el;\n }\n\n addHTML(html) {\n const el = document.createElement('div');\n el.className = 'menu-item';\n el.id = this.newID;\n if (html) el.innerHTML = html;\n this.container.appendChild(el);\n return el;\n }\n\n addButton(titleOn, titleOff, callback) {\n const el = document.createElement('button');\n el.className = 'menu-item menu-button';\n el.style.fontFamily = document.body.style.fontFamily;\n el.style.fontSize = document.body.style.fontSize;\n el.style.fontVariant = document.body.style.fontVariant;\n el.type = 'button';\n el.id = this.newID;\n el.innerText = titleOn;\n this.container.appendChild(el);\n el.addEventListener('click', () => {\n if (el.innerText === titleOn) el.innerText = titleOff;\n else el.innerText = titleOn;\n if (callback) callback(el.innerText !== titleOn);\n });\n return el;\n }\n\n addValue(title, val, suffix = '') {\n const el = document.createElement('div');\n el.className = 'menu-item';\n el.id = `menu-val-${title}`;\n el.innerText = `${title}: ${val}${suffix}`;\n this.container.appendChild(el);\n return el;\n }\n\n // eslint-disable-next-line class-methods-use-this\n updateValue(title, val, suffix = '') {\n const el = document.getElementById(`menu-val-${title}`);\n if (el) el.innerText = `${title}: ${val}${suffix}`;\n else this.addValue(title, val);\n }\n\n addChart(title, id, width = 150, height = 40, color) {\n if (color) theme.chartColor = color;\n const el = document.createElement('div');\n el.className = 'menu-item menu-chart-title';\n el.id = this.newID;\n el.innerHTML = `${title}`;\n this.container.appendChild(el);\n return el;\n }\n\n // eslint-disable-next-line class-methods-use-this\n async updateChart(id, values) {\n if (!values || (values.length === 0)) return;\n const canvas = document.getElementById(`menu-canvas-${id}`);\n if (!canvas) return;\n const ctx = canvas.getContext('2d');\n ctx.fillStyle = theme.background;\n ctx.fillRect(0, 0, canvas.width, canvas.height);\n const width = canvas.width / values.length;\n const max = 1 + Math.max(...values);\n const height = canvas.height / max;\n for (let i = 0; i < values.length; i++) {\n const gradient = ctx.createLinearGradient(0, (max - values[i]) * height, 0, 0);\n gradient.addColorStop(0.1, theme.chartColor);\n gradient.addColorStop(0.4, theme.background);\n ctx.fillStyle = gradient;\n ctx.fillRect(i * width, 0, width - 4, canvas.height);\n ctx.fillStyle = theme.background;\n ctx.font = `${width / 1.5}px \"Segoe UI\"`;\n ctx.fillText(Math.round(values[i]), i * width + 1, canvas.height - 1, width - 1);\n }\n }\n}\n\nexport default Menu;\n", "// @ts-nocheck\n/* eslint-disable max-len */\n\n// based on: https://github.com/munrocket/gl-bench\n\nconst UICSS = `\n #gl-bench { position: absolute; right: 1rem; bottom: 1rem; z-index:1000; -webkit-user-select: none; -moz-user-select: none; user-select: none; }\n #gl-bench div { position: relative; display: block; margin: 4px; padding: 0 7px 0 10px; background: darkslategray; border-radius: 0.2rem; cursor: pointer; opacity: 0.9; }\n #gl-bench svg { height: 60px; margin: 0 0px 0px 4px; }\n #gl-bench text { font-size: 16px; font-family: 'Lato', 'Segoe UI'; dominant-baseline: middle; text-anchor: middle; }\n #gl-bench .gl-mem { font-size: 12px; fill: white; }\n #gl-bench .gl-fps { font-size: 13px; fill: white; }\n #gl-bench line { stroke-width: 5; stroke: white; stroke-linecap: round; }\n #gl-bench polyline { fill: none; stroke: white; stroke-linecap: round; stroke-linejoin: round; stroke-width: 3.5; }\n #gl-bench rect { fill: black; }\n #gl-bench .opacity { stroke: black; }\n `;\n\nconst UISVG = `\n
\n \n 00 FPS\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
\n `;\n\nclass GLBench {\n /** GLBench constructor\n * @param { WebGLRenderingContext | WebGL2RenderingContext } gl context\n * @param { Object | undefined } settings additional settings\n */\n constructor(gl, settings = {}) {\n this.css = UICSS;\n this.svg = UISVG;\n this.paramLogger = () => {};\n this.chartLogger = () => {};\n this.chartLen = 20;\n this.chartHz = 20;\n\n this.names = [];\n this.cpuAccums = [];\n this.gpuAccums = [];\n this.activeAccums = [];\n this.chart = new Array(this.chartLen);\n this.now = () => ((performance && performance.now) ? performance.now() : Date.now());\n this.updateUI = () => {\n [].forEach.call(this.nodes['gl-gpu-svg'], (node) => node.style.display = this.trackGPU ? 'inline' : 'none');\n };\n\n Object.assign(this, settings);\n this.detected = 0;\n this.finished = [];\n this.isFramebuffer = 0;\n this.frameId = 0;\n\n // 120hz device detection\n let rafId; let n = 0; let\n t0;\n const loop = (t) => {\n if (++n < 20) {\n rafId = requestAnimationFrame(loop);\n } else {\n this.detected = Math.ceil(1e3 * n / (t - t0) / 70);\n cancelAnimationFrame(rafId);\n }\n if (!t0) t0 = t;\n };\n requestAnimationFrame(loop);\n\n // attach gpu profilers\n if (gl) {\n const glFinish = async (t, activeAccums) => Promise.resolve(setTimeout(() => {\n gl.getError();\n const dt = this.now() - t;\n activeAccums.forEach((active, i) => {\n if (active) this.gpuAccums[i] += dt;\n });\n }, 0));\n\n const addProfiler = (fn, self, target) => {\n const t = self.now();\n // eslint-disable-next-line prefer-rest-params\n fn.apply(target, arguments);\n if (self.trackGPU) self.finished.push(glFinish(t, self.activeAccums.slice(0)));\n };\n\n /* ['drawArrays', 'drawElements', 'drawArraysInstanced', 'drawBuffers', 'drawElementsInstanced', 'drawRangeElements'].forEach((fn) => {\n if (gl[fn]) {\n gl[fn] = addProfiler(gl[fn], this, gl);\n }\n });\n */\n const fn = 'drawElements';\n if (gl[fn]) {\n gl[fn] = addProfiler(gl[fn], this, gl);\n } else {\n // eslint-disable-next-line no-console\n console.log('bench: cannot attach to webgl function');\n }\n\n /*\n gl.getExtension = ((fn, self) => {\n // eslint-disable-next-line prefer-rest-params\n const ext = fn.apply(gl, arguments);\n if (ext) {\n ['drawElementsInstancedANGLE', 'drawBuffersWEBGL'].forEach((fn2) => {\n if (ext[fn2]) {\n ext[fn2] = addProfiler(ext[fn2], self, ext);\n }\n });\n }\n return ext;\n })(gl.getExtension, this);\n */\n }\n\n // init ui and ui loggers\n if (!this.withoutUI) {\n if (!this.dom) this.dom = document.body;\n const elm = document.createElement('div');\n elm.id = 'gl-bench';\n this.dom.appendChild(elm);\n this.dom.insertAdjacentHTML('afterbegin', '');\n this.dom = elm;\n this.dom.addEventListener('click', () => {\n this.trackGPU = !this.trackGPU;\n this.updateUI();\n });\n\n this.paramLogger = ((logger, dom, names) => {\n const classes = ['gl-cpu', 'gl-gpu', 'gl-mem', 'gl-fps', 'gl-gpu-svg', 'gl-chart'];\n const nodes = { ...classes };\n classes.forEach((c) => nodes[c] = dom.getElementsByClassName(c));\n this.nodes = nodes;\n return (i, cpu, gpu, mem, fps, totalTime, frameId) => {\n nodes['gl-cpu'][i].style.strokeDasharray = (cpu * 0.27).toFixed(0) + ' 100';\n nodes['gl-gpu'][i].style.strokeDasharray = (gpu * 0.27).toFixed(0) + ' 100';\n // eslint-disable-next-line no-nested-ternary\n nodes['gl-mem'][i].innerHTML = names[i] ? names[i] : (mem ? 'mem: ' + mem.toFixed(0) + 'mb' : '');\n nodes['gl-fps'][i].innerHTML = 'FPS: ' + fps.toFixed(1);\n logger(names[i], cpu, gpu, mem, fps, totalTime, frameId);\n };\n })(this.paramLogger, this.dom, this.names);\n\n this.chartLogger = ((logger, dom) => {\n const nodes = { 'gl-chart': dom.getElementsByClassName('gl-chart') };\n return (i, chart, circularId) => {\n let points = '';\n const len = chart.length;\n for (let j = 0; j < len; j++) {\n const id = (circularId + j + 1) % len;\n if (chart[id] !== undefined) points = points + ' ' + (55 * j / (len - 1)).toFixed(1) + ',' + (45 - chart[id] * 22 / 60 / this.detected).toFixed(1);\n }\n nodes['gl-chart'][i].setAttribute('points', points);\n logger(this.names[i], chart, circularId);\n };\n })(this.chartLogger, this.dom);\n }\n }\n\n /**\n * Explicit UI add\n * @param { string | undefined } name\n */\n addUI(name) {\n if (this.names.indexOf(name) === -1) {\n this.names.push(name);\n if (this.dom) {\n this.dom.insertAdjacentHTML('beforeend', this.svg);\n this.updateUI();\n }\n this.cpuAccums.push(0);\n this.gpuAccums.push(0);\n this.activeAccums.push(false);\n }\n }\n\n /**\n * Increase frameID\n * @param { number | undefined } now\n */\n nextFrame(now) {\n this.frameId++;\n const t = now || this.now();\n\n // params\n if (this.frameId <= 1) {\n this.paramFrame = this.frameId;\n this.paramTime = t;\n } else {\n const duration = t - this.paramTime;\n if (duration >= 1e3) {\n const frameCount = this.frameId - this.paramFrame;\n const fps = frameCount / duration * 1e3;\n for (let i = 0; i < this.names.length; i++) {\n const cpu = this.cpuAccums[i] / duration * 100;\n const gpu = this.gpuAccums[i] / duration * 100;\n const mem = (performance && performance.memory) ? performance.memory.usedJSHeapSize / (1 << 20) : 0;\n this.paramLogger(i, cpu, gpu, mem, fps, duration, frameCount);\n this.cpuAccums[i] = 0;\n Promise.all(this.finished).then(() => {\n this.gpuAccums[i] = 0;\n this.finished = [];\n });\n }\n this.paramFrame = this.frameId;\n this.paramTime = t;\n }\n }\n\n // chart\n if (!this.detected || !this.chartFrame) {\n this.chartFrame = this.frameId;\n this.chartTime = t;\n this.circularId = 0;\n } else {\n const timespan = t - this.chartTime;\n let hz = this.chartHz * timespan / 1e3;\n while (--hz > 0 && this.detected) {\n const frameCount = this.frameId - this.chartFrame;\n const fps = frameCount / timespan * 1e3;\n this.chart[this.circularId % this.chartLen] = fps;\n for (let i = 0; i < this.names.length; i++) this.chartLogger(i, this.chart, this.circularId);\n this.circularId++;\n this.chartFrame = this.frameId;\n this.chartTime = t;\n }\n }\n }\n\n /**\n * Begin named measurement\n * @param { string | undefined } name\n */\n begin(name) {\n this.updateAccums(name);\n }\n\n /**\n * End named measure\n * @param { string | undefined } name\n */\n end(name) {\n this.updateAccums(name);\n }\n\n updateAccums(name) {\n let nameId = this.names.indexOf(name);\n if (nameId === -1) {\n nameId = this.names.length;\n this.addUI(name);\n }\n\n const t = this.now();\n const dt = t - this.t0;\n for (let i = 0; i < nameId + 1; i++) {\n if (this.activeAccums[i]) this.cpuAccums[i] += dt;\n }\n this.activeAccums[nameId] = !this.activeAccums[nameId];\n this.t0 = t;\n }\n}\n\nexport default GLBench;\n", "import Human from '../dist/human.esm.js';\nimport draw from './draw.js';\nimport Menu from './menu.js';\nimport GLBench from './gl-bench.js';\n\nconst userConfig = {}; // add any user configuration overrides\n\n/*\nconst userConfig = {\n face: { enabled: false, iris: { enabled: false } },\n body: { enabled: false },\n hand: { enabled: false },\n pose: { enabled: true },\n};\n*/\n\nconst human = new Human(userConfig);\n\n// ui options\nconst ui = {\n baseColor: 'rgba(173, 216, 230, 0.3)', // 'lightblue' with light alpha channel\n baseBackground: 'rgba(50, 50, 50, 1)', // 'grey'\n baseLabel: 'rgba(173, 216, 230, 1)', // 'lightblue' with dark alpha channel\n baseFontProto: 'small-caps {size} \"Segoe UI\"',\n baseLineWidth: 12,\n crop: true,\n columns: 2,\n busy: false,\n facing: true,\n useWorker: false,\n worker: 'worker.js',\n samples: ['../assets/sample6.jpg', '../assets/sample1.jpg', '../assets/sample4.jpg', '../assets/sample5.jpg', '../assets/sample3.jpg', '../assets/sample2.jpg'],\n compare: '../assets/sample-me.jpg',\n drawBoxes: true,\n drawPoints: false,\n drawPolygons: true,\n fillPolygons: false,\n useDepth: true,\n console: true,\n maxFPSframes: 10,\n modelsPreload: true,\n menuWidth: 0,\n menuHeight: 0,\n camera: {},\n detectFPS: [],\n drawFPS: [],\n buffered: false,\n drawThread: null,\n detectThread: null,\n framesDraw: 0,\n framesDetect: 0,\n bench: false,\n};\n\n// global variables\nconst menu = {};\nlet worker;\nlet bench;\nlet lastDetectedResult = {};\n\n// helper function: translates json to human readable string\nfunction str(...msg) {\n if (!Array.isArray(msg)) return msg;\n let line = '';\n for (const entry of msg) {\n if (typeof entry === 'object') line += JSON.stringify(entry).replace(/{|}|\"|\\[|\\]/g, '').replace(/,/g, ', ');\n else line += entry;\n }\n return line;\n}\n\n// helper function: wrapper around console output\nfunction log(...msg) {\n const dt = new Date();\n const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n // eslint-disable-next-line no-console\n if (ui.console) console.log(ts, ...msg);\n}\n\nfunction status(msg) {\n // eslint-disable-next-line no-console\n document.getElementById('status').innerText = msg;\n}\n\nlet original;\nasync function calcSimmilariry(result) {\n document.getElementById('compare-container').style.display = human.config.face.embedding.enabled ? 'block' : 'none';\n if (!human.config.face.embedding.enabled) return;\n if ((result?.face?.length > 0) && (result?.face[0].embedding?.length !== 192)) return;\n if (!original) {\n original = result;\n document.getElementById('compare-canvas').getContext('2d').drawImage(original.canvas, 0, 0, 200, 200);\n }\n const simmilarity = human.simmilarity(original?.face[0]?.embedding, result?.face[0]?.embedding);\n document.getElementById('simmilarity').innerText = `simmilarity: ${Math.trunc(1000 * simmilarity) / 10}%`;\n}\n\n// draws processed results and starts processing of a next frame\nlet lastDraw = performance.now();\nasync function drawResults(input) {\n const result = lastDetectedResult;\n const canvas = document.getElementById('canvas');\n\n // update draw fps data\n ui.drawFPS.push(1000 / (performance.now() - lastDraw));\n if (ui.drawFPS.length > ui.maxFPSframes) ui.drawFPS.shift();\n lastDraw = performance.now();\n\n // enable for continous performance monitoring\n // console.log(result.performance);\n\n // draw fps chart\n await menu.process.updateChart('FPS', ui.detectFPS);\n\n // get updated canvas\n if (ui.buffered || !result.canvas) result.canvas = await human.image(input, userConfig);\n\n // draw image from video\n const ctx = canvas.getContext('2d');\n ctx.fillStyle = ui.baseBackground;\n ctx.fillRect(0, 0, canvas.width, canvas.height);\n if (result.canvas) {\n if (result.canvas.width !== canvas.width) canvas.width = result.canvas.width;\n if (result.canvas.height !== canvas.height) canvas.height = result.canvas.height;\n ctx.drawImage(result.canvas, 0, 0, result.canvas.width, result.canvas.height, 0, 0, result.canvas.width, result.canvas.height);\n } else {\n ctx.drawImage(input, 0, 0, input.width, input.height, 0, 0, canvas.width, canvas.height);\n }\n\n // draw all results\n await draw.face(result.face, canvas, ui, human.facemesh.triangulation);\n await draw.body(result.body, canvas, ui);\n await draw.hand(result.hand, canvas, ui);\n await draw.gesture(result.gesture, canvas, ui);\n await calcSimmilariry(result);\n\n // update log\n const engine = human.tf.engine();\n const gpu = engine.backendInstance ? `gpu: ${(engine.backendInstance.numBytesInGPU ? engine.backendInstance.numBytesInGPU : 0).toLocaleString()} bytes` : '';\n const memory = `system: ${engine.state.numBytes.toLocaleString()} bytes ${gpu} | tensors: ${engine.state.numTensors.toLocaleString()}`;\n const processing = result.canvas ? `processing: ${result.canvas.width} x ${result.canvas.height}` : '';\n const avgDetect = Math.trunc(10 * ui.detectFPS.reduce((a, b) => a + b, 0) / ui.detectFPS.length) / 10;\n const avgDraw = Math.trunc(10 * ui.drawFPS.reduce((a, b) => a + b, 0) / ui.drawFPS.length) / 10;\n const warning = (ui.detectFPS.length > 5) && (avgDetect < 5) ? 'warning: your performance is low: try switching to higher performance backend, lowering resolution or disabling some models' : '';\n document.getElementById('log').innerHTML = `\n video: ${ui.camera.name} | facing: ${ui.camera.facing} | screen: ${window.innerWidth} x ${window.innerHeight} camera: ${ui.camera.width} x ${ui.camera.height} ${processing}
\n backend: ${human.tf.getBackend()} | ${memory}
\n performance: ${str(result.performance)}ms FPS process:${avgDetect} refresh:${avgDraw}
\n ${warning}
\n `;\n\n ui.framesDraw++;\n ui.lastFrame = performance.now();\n // if buffered, immediate loop but limit frame rate although it's going to run slower as JS is singlethreaded\n if (ui.buffered) {\n ui.drawThread = requestAnimationFrame(() => drawResults(input, canvas));\n } else if (!ui.buffered && ui.drawThread) {\n log('stopping buffered refresh');\n cancelAnimationFrame(ui.drawThread);\n ui.drawThread = null;\n }\n}\n\n// setup webcam\nasync function setupCamera() {\n if (ui.busy) return null;\n ui.busy = true;\n const video = document.getElementById('video');\n const canvas = document.getElementById('canvas');\n const output = document.getElementById('log');\n const live = video.srcObject ? ((video.srcObject.getVideoTracks()[0].readyState === 'live') && (video.readyState > 2) && (!video.paused)) : false;\n let msg = '';\n status('setting up camera');\n // setup webcam. note that navigator.mediaDevices requires that page is accessed via https\n if (!navigator.mediaDevices) {\n msg = 'camera access not supported';\n output.innerText += `\\n${msg}`;\n log(msg);\n status(msg);\n ui.busy = false;\n return msg;\n }\n let stream;\n const constraints = {\n audio: false,\n video: { facingMode: ui.facing ? 'user' : 'environment', resizeMode: ui.crop ? 'crop-and-scale' : 'none' },\n };\n if (window.innerWidth > window.innerHeight) constraints.video.width = { ideal: window.innerWidth };\n else constraints.video.height = { ideal: (window.innerHeight - document.getElementById('menubar').offsetHeight) };\n try {\n stream = await navigator.mediaDevices.getUserMedia(constraints);\n } catch (err) {\n if (err.name === 'PermissionDeniedError' || err.name === 'NotAllowedError') msg = 'camera permission denied';\n else if (err.name === 'SourceUnavailableError') msg = 'camera not available';\n else msg = `camera error: ${err.message || err}`;\n output.innerText += `\\n${msg}`;\n status(msg);\n log('camera error:', err);\n ui.busy = false;\n return msg;\n }\n if (stream) video.srcObject = stream;\n else {\n ui.busy = false;\n return 'camera stream empty';\n }\n const track = stream.getVideoTracks()[0];\n const settings = track.getSettings();\n // log('camera constraints:', constraints, 'window:', { width: window.innerWidth, height: window.innerHeight }, 'settings:', settings, 'track:', track);\n ui.camera = { name: track.label?.toLowerCase(), width: settings.width, height: settings.height, facing: settings.facingMode === 'user' ? 'front' : 'back' };\n return new Promise((resolve) => {\n video.onloadeddata = async () => {\n video.width = video.videoWidth;\n video.height = video.videoHeight;\n canvas.width = video.width;\n canvas.height = video.height;\n canvas.style.width = canvas.width > canvas.height ? '100vw' : '';\n canvas.style.height = canvas.width > canvas.height ? '' : '100vh';\n ui.menuWidth.input.setAttribute('value', video.width);\n ui.menuHeight.input.setAttribute('value', video.height);\n // silly font resizing for paint-on-canvas since viewport can be zoomed\n const size = Math.trunc(window.devicePixelRatio * (8 + (4 * canvas.width / window.innerWidth)));\n ui.baseFont = ui.baseFontProto.replace(/{size}/, `${size}px`);\n ui.baseLineHeight = size + 2;\n if (live) video.play();\n // eslint-disable-next-line no-use-before-define\n if (live && !ui.detectThread) runHumanDetect(video, canvas);\n ui.busy = false;\n // do once more because onresize events can be delayed or skipped\n // if (video.width > window.innerWidth) await setupCamera();\n status('');\n resolve();\n };\n });\n}\n\nfunction initPerfMonitor() {\n if (!bench) {\n const gl = null;\n // cosnt gl = human.tf.engine().backend.gpgpu.gl;\n // if (!gl) log('bench cannot get tensorflow webgl context');\n bench = new GLBench(gl, {\n trackGPU: false, // this is really slow\n chartHz: 20,\n chartLen: 20,\n });\n bench.begin();\n }\n}\n\n// wrapper for worker.postmessage that creates worker if one does not exist\nfunction webWorker(input, image, canvas, timestamp) {\n if (!worker) {\n // create new webworker and add event handler only once\n log('creating worker thread');\n worker = new Worker(ui.worker, { type: 'module' });\n // after receiving message from webworker, parse&draw results and send new frame for processing\n worker.addEventListener('message', (msg) => {\n if (msg.data.result.performance && msg.data.result.performance.total) ui.detectFPS.push(1000 / msg.data.result.performance.total);\n if (ui.detectFPS.length > ui.maxFPSframes) ui.detectFPS.shift();\n if (ui.bench) {\n if (!bench) initPerfMonitor();\n bench.nextFrame(timestamp);\n }\n if (document.getElementById('gl-bench')) document.getElementById('gl-bench').style.display = ui.bench ? 'block' : 'none';\n lastDetectedResult = msg.data.result;\n ui.framesDetect++;\n if (!ui.drawThread) drawResults(input);\n // eslint-disable-next-line no-use-before-define\n ui.detectThread = requestAnimationFrame((now) => runHumanDetect(input, canvas, now));\n });\n }\n // pass image data as arraybuffer to worker by reference to avoid copy\n worker.postMessage({ image: image.data.buffer, width: canvas.width, height: canvas.height, userConfig }, [image.data.buffer]);\n}\n\n// main processing function when input is webcam, can use direct invocation or web worker\nfunction runHumanDetect(input, canvas, timestamp) {\n // if live video\n const live = input.srcObject && (input.srcObject.getVideoTracks()[0].readyState === 'live') && (input.readyState > 2) && (!input.paused);\n if (!live && input.srcObject) {\n // stop ui refresh\n if (ui.drawThread) cancelAnimationFrame(ui.drawThread);\n if (ui.detectThread) cancelAnimationFrame(ui.detectThread);\n ui.drawThread = null;\n ui.detectThread = null;\n // if we want to continue and camera not ready, retry in 0.5sec, else just give up\n if (input.paused) log('camera paused');\n else if ((input.srcObject.getVideoTracks()[0].readyState === 'live') && (input.readyState <= 2)) setTimeout(() => runHumanDetect(input, canvas), 500);\n else log(`camera not ready: track state: ${input.srcObject?.getVideoTracks()[0].readyState} stream state: ${input.readyState}`);\n clearTimeout(ui.drawThread);\n ui.drawThread = null;\n log('frame statistics: process:', ui.framesDetect, 'refresh:', ui.framesDraw);\n log('memory', human.tf.engine().memory());\n return;\n }\n status('');\n if (ui.useWorker) {\n // get image data from video as we cannot send html objects to webworker\n const offscreen = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(canvas.width, canvas.height) : document.createElement('canvas');\n offscreen.width = canvas.width;\n offscreen.height = canvas.height;\n const ctx = offscreen.getContext('2d');\n ctx.drawImage(input, 0, 0, input.width, input.height, 0, 0, canvas.width, canvas.height);\n const data = ctx.getImageData(0, 0, canvas.width, canvas.height);\n // perform detection in worker\n webWorker(input, data, canvas, userConfig, timestamp);\n } else {\n human.detect(input, userConfig).then((result) => {\n if (result.performance && result.performance.total) ui.detectFPS.push(1000 / result.performance.total);\n if (ui.detectFPS.length > ui.maxFPSframes) ui.detectFPS.shift();\n if (ui.bench) {\n if (!bench) initPerfMonitor();\n bench.nextFrame(timestamp);\n }\n if (document.getElementById('gl-bench')) document.getElementById('gl-bench').style.display = ui.bench ? 'block' : 'none';\n if (result.error) {\n log(result.error);\n document.getElementById('log').innerText += `\\nHuman error: ${result.error}`;\n } else {\n lastDetectedResult = result;\n if (!ui.drawThread) drawResults(input);\n ui.framesDetect++;\n ui.detectThread = requestAnimationFrame((now) => runHumanDetect(input, canvas, now));\n }\n });\n }\n}\n\n// main processing function when input is image, can use direct invocation or web worker\nasync function processImage(input) {\n return new Promise((resolve) => {\n const image = new Image();\n image.onload = async () => {\n log('Processing image:', image.src);\n const canvas = document.getElementById('canvas');\n image.width = image.naturalWidth;\n image.height = image.naturalHeight;\n canvas.width = human.config.filter.width && human.config.filter.width > 0 ? human.config.filter.width : image.naturalWidth;\n canvas.height = human.config.filter.height && human.config.filter.height > 0 ? human.config.filter.height : image.naturalHeight;\n const result = await human.detect(image, userConfig);\n lastDetectedResult = result;\n await drawResults(image);\n const thumb = document.createElement('canvas');\n thumb.className = 'thumbnail';\n thumb.width = window.innerWidth / (ui.columns + 0.1);\n thumb.height = canvas.height / (window.innerWidth / thumb.width);\n const ctx = thumb.getContext('2d');\n ctx.drawImage(canvas, 0, 0, canvas.width, canvas.height, 0, 0, thumb.width, thumb.height);\n document.getElementById('samples-container').appendChild(thumb);\n image.src = '';\n resolve(true);\n };\n image.src = input;\n });\n}\n\n// just initialize everything and call main function\nasync function detectVideo() {\n userConfig.videoOptimized = true;\n document.getElementById('samples-container').style.display = 'none';\n document.getElementById('canvas').style.display = 'block';\n const video = document.getElementById('video');\n const canvas = document.getElementById('canvas');\n if ((video.srcObject !== null) && !video.paused) {\n document.getElementById('play').style.display = 'block';\n document.getElementById('btnStart').className = 'button button-start';\n document.getElementById('btnStart').innerHTML = 'start
video';\n status('paused');\n video.pause();\n } else {\n const cameraError = await setupCamera();\n if (!cameraError) {\n document.getElementById('play').style.display = 'none';\n for (const m of Object.values(menu)) m.hide();\n status('');\n document.getElementById('btnStart').className = 'button button-stop';\n document.getElementById('btnStart').innerHTML = 'pause
video';\n await video.play();\n if (!ui.detectThread) runHumanDetect(video, canvas);\n } else {\n status(cameraError);\n }\n }\n}\n\n// just initialize everything and call main function\nasync function detectSampleImages() {\n document.getElementById('play').style.display = 'none';\n userConfig.videoOptimized = false;\n const size = Math.trunc(window.devicePixelRatio * (8 + (4 * ui.columns)));\n ui.baseFont = ui.baseFontProto.replace(/{size}/, `${size}px`);\n ui.baseLineHeight = size + 2;\n document.getElementById('canvas').style.display = 'none';\n document.getElementById('samples-container').style.display = 'block';\n log('Running detection of sample images');\n status('processing images');\n document.getElementById('samples-container').innerHTML = '';\n for (const image of ui.samples) await processImage(image);\n status('');\n}\n\nfunction setupMenu() {\n let x = [];\n if (window.innerWidth > 800) {\n // initial position of menu items, later it's calculated based on mouse coordinates\n x = [`${document.getElementById('btnDisplay').offsetLeft - 50}px`, `${document.getElementById('btnImage').offsetLeft - 50}px`, `${document.getElementById('btnProcess').offsetLeft - 50}px`, `${document.getElementById('btnModel').offsetLeft - 50}px`];\n } else {\n // absolute minimum spacing for menus\n x = ['0rem', '11rem', '21.1rem', '33rem'];\n }\n\n menu.display = new Menu(document.body, '', { top: `${document.getElementById('menubar').offsetHeight}px`, left: x[0] });\n menu.display.addBool('perf monitor', ui, 'bench', (val) => ui.bench = val);\n menu.display.addBool('buffered output', ui, 'buffered', (val) => ui.buffered = val);\n menu.display.addBool('crop & scale', ui, 'crop', () => setupCamera());\n menu.display.addBool('camera facing', ui, 'facing', () => setupCamera());\n menu.display.addHTML('
');\n menu.display.addBool('use 3D depth', ui, 'useDepth');\n menu.display.addBool('draw boxes', ui, 'drawBoxes');\n menu.display.addBool('draw polygons', ui, 'drawPolygons');\n menu.display.addBool('Fill Polygons', ui, 'fillPolygons');\n menu.display.addBool('draw points', ui, 'drawPoints');\n\n menu.image = new Menu(document.body, '', { top: `${document.getElementById('menubar').offsetHeight}px`, left: x[1] });\n menu.image.addBool('enabled', human.config.filter, 'enabled');\n ui.menuWidth = menu.image.addRange('image width', human.config.filter, 'width', 0, 3840, 10, (val) => human.config.filter.width = parseInt(val));\n ui.menuHeight = menu.image.addRange('image height', human.config.filter, 'height', 0, 2160, 10, (val) => human.config.filter.height = parseInt(val));\n menu.image.addHTML('
');\n menu.image.addRange('brightness', human.config.filter, 'brightness', -1.0, 1.0, 0.05, (val) => human.config.filter.brightness = parseFloat(val));\n menu.image.addRange('contrast', human.config.filter, 'contrast', -1.0, 1.0, 0.05, (val) => human.config.filter.contrast = parseFloat(val));\n menu.image.addRange('sharpness', human.config.filter, 'sharpness', 0, 1.0, 0.05, (val) => human.config.filter.sharpness = parseFloat(val));\n menu.image.addRange('blur', human.config.filter, 'blur', 0, 20, 1, (val) => human.config.filter.blur = parseInt(val));\n menu.image.addRange('saturation', human.config.filter, 'saturation', -1.0, 1.0, 0.05, (val) => human.config.filter.saturation = parseFloat(val));\n menu.image.addRange('hue', human.config.filter, 'hue', 0, 360, 5, (val) => human.config.filter.hue = parseInt(val));\n menu.image.addRange('pixelate', human.config.filter, 'pixelate', 0, 32, 1, (val) => human.config.filter.pixelate = parseInt(val));\n menu.image.addHTML('
');\n menu.image.addBool('negative', human.config.filter, 'negative');\n menu.image.addBool('sepia', human.config.filter, 'sepia');\n menu.image.addBool('vintage', human.config.filter, 'vintage');\n menu.image.addBool('kodachrome', human.config.filter, 'kodachrome');\n menu.image.addBool('technicolor', human.config.filter, 'technicolor');\n menu.image.addBool('polaroid', human.config.filter, 'polaroid');\n\n menu.process = new Menu(document.body, '', { top: `${document.getElementById('menubar').offsetHeight}px`, left: x[2] });\n menu.process.addList('backend', ['cpu', 'webgl', 'wasm', 'humangl'], human.config.backend, (val) => human.config.backend = val);\n menu.process.addBool('async operations', human.config, 'async', (val) => human.config.async = val);\n menu.process.addBool('enable profiler', human.config, 'profile', (val) => human.config.profile = val);\n menu.process.addBool('memory shield', human.config, 'deallocate', (val) => human.config.deallocate = val);\n menu.process.addBool('use web worker', ui, 'useWorker');\n menu.process.addHTML('
');\n menu.process.addLabel('model parameters');\n menu.process.addRange('max objects', human.config.face.detector, 'maxFaces', 1, 50, 1, (val) => {\n human.config.face.detector.maxFaces = parseInt(val);\n human.config.body.maxDetections = parseInt(val);\n human.config.hand.maxHands = parseInt(val);\n });\n menu.process.addRange('skip frames', human.config.face.detector, 'skipFrames', 0, 50, 1, (val) => {\n human.config.face.detector.skipFrames = parseInt(val);\n human.config.face.emotion.skipFrames = parseInt(val);\n human.config.face.age.skipFrames = parseInt(val);\n human.config.hand.skipFrames = parseInt(val);\n });\n menu.process.addRange('min confidence', human.config.face.detector, 'minConfidence', 0.0, 1.0, 0.05, (val) => {\n human.config.face.detector.minConfidence = parseFloat(val);\n human.config.face.gender.minConfidence = parseFloat(val);\n human.config.face.emotion.minConfidence = parseFloat(val);\n human.config.hand.minConfidence = parseFloat(val);\n });\n menu.process.addRange('score threshold', human.config.face.detector, 'scoreThreshold', 0.1, 1.0, 0.05, (val) => {\n human.config.face.detector.scoreThreshold = parseFloat(val);\n human.config.hand.scoreThreshold = parseFloat(val);\n human.config.body.scoreThreshold = parseFloat(val);\n });\n menu.process.addRange('overlap', human.config.face.detector, 'iouThreshold', 0.1, 1.0, 0.05, (val) => {\n human.config.face.detector.iouThreshold = parseFloat(val);\n human.config.hand.iouThreshold = parseFloat(val);\n });\n menu.process.addBool('detection rotation', human.config.face.detector, 'rotation', (val) => {\n human.config.face.detector.rotation = val;\n human.config.hand.rotation = val;\n });\n menu.process.addHTML('
');\n menu.process.addButton('process sample images', 'process images', () => detectSampleImages());\n menu.process.addHTML('
');\n menu.process.addChart('FPS', 'FPS');\n\n menu.models = new Menu(document.body, '', { top: `${document.getElementById('menubar').offsetHeight}px`, left: x[3] });\n menu.models.addBool('face detect', human.config.face, 'enabled');\n menu.models.addBool('face mesh', human.config.face.mesh, 'enabled');\n menu.models.addBool('face iris', human.config.face.iris, 'enabled');\n menu.models.addBool('face age', human.config.face.age, 'enabled');\n menu.models.addBool('face gender', human.config.face.gender, 'enabled');\n menu.models.addBool('face emotion', human.config.face.emotion, 'enabled');\n menu.models.addHTML('
');\n menu.models.addBool('body pose', human.config.body, 'enabled');\n menu.models.addBool('hand pose', human.config.hand, 'enabled');\n menu.models.addHTML('
');\n menu.models.addBool('gestures', human.config.gesture, 'enabled');\n menu.models.addHTML('
');\n menu.models.addBool('face compare', human.config.face.embedding, 'enabled', (val) => {\n original = null;\n human.config.face.embedding.enabled = val;\n });\n\n document.getElementById('btnDisplay').addEventListener('click', (evt) => menu.display.toggle(evt));\n document.getElementById('btnImage').addEventListener('click', (evt) => menu.image.toggle(evt));\n document.getElementById('btnProcess').addEventListener('click', (evt) => menu.process.toggle(evt));\n document.getElementById('btnModel').addEventListener('click', (evt) => menu.models.toggle(evt));\n document.getElementById('btnStart').addEventListener('click', () => detectVideo());\n document.getElementById('play').addEventListener('click', () => detectVideo());\n}\n\nasync function main() {\n log('demo starting ...');\n setupMenu();\n document.getElementById('log').innerText = `Human: version ${human.version}`;\n if (ui.modelsPreload && !ui.useWorker) {\n status('loading');\n await human.load(userConfig); // this is not required, just pre-loads all models\n }\n if (!ui.useWorker) {\n status('initializing');\n await human.warmup(userConfig); // this is not required, just pre-warms all models for faster initial inference\n }\n status('human: ready');\n document.getElementById('loader').style.display = 'none';\n document.getElementById('play').style.display = 'block';\n log('ready');\n}\n\nwindow.onload = main;\nwindow.onresize = setupCamera;\n"], - "mappings": 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+ "sourcesContent": ["import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nconst NUM_LANDMARKS = 6;\n\nfunction generateAnchors(inputSize) {\n const spec = { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] };\n const anchors = [];\n for (let i = 0; i < spec.strides.length; i++) {\n const stride = spec.strides[i];\n const gridRows = Math.floor((inputSize + stride - 1) / stride);\n const gridCols = Math.floor((inputSize + stride - 1) / stride);\n const anchorsNum = spec.anchors[i];\n for (let gridY = 0; gridY < gridRows; gridY++) {\n const anchorY = stride * (gridY + 0.5);\n for (let gridX = 0; gridX < gridCols; gridX++) {\n const anchorX = stride * (gridX + 0.5);\n for (let n = 0; n < anchorsNum; n++) {\n anchors.push([anchorX, anchorY]);\n }\n }\n }\n }\n return anchors;\n}\n\nconst disposeBox = (box) => {\n box.startEndTensor.dispose();\n box.startPoint.dispose();\n box.endPoint.dispose();\n};\n\nconst createBox = (startEndTensor) => ({\n startEndTensor,\n startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]),\n endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]),\n});\n\nconst scaleBox = (box, factors) => {\n const starts = tf.mul(box.startPoint, factors);\n const ends = tf.mul(box.endPoint, factors);\n const newCoordinates = tf.concat2d([starts, ends], 1);\n return createBox(newCoordinates);\n};\n\nfunction decodeBounds(boxOutputs, anchors, inputSize) {\n const boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n const centers = tf.add(boxStarts, anchors);\n const boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n const boxSizesNormalized = tf.div(boxSizes, inputSize);\n const centersNormalized = tf.div(centers, inputSize);\n const halfBoxSize = tf.div(boxSizesNormalized, 2);\n const starts = tf.sub(centersNormalized, halfBoxSize);\n const ends = tf.add(centersNormalized, halfBoxSize);\n const startNormalized = tf.mul(starts, inputSize);\n const endNormalized = tf.mul(ends, inputSize);\n const concatAxis = 1;\n return tf.concat2d([startNormalized, endNormalized], concatAxis);\n}\n\nfunction scaleBoxFromPrediction(face, scaleFactor) {\n return tf.tidy(() => {\n const box = face['box'] ? face['box'] : face;\n return scaleBox(box, scaleFactor).startEndTensor.squeeze();\n });\n}\n\nclass BlazeFaceModel {\n constructor(model, config) {\n this.blazeFaceModel = model;\n this.width = config.face.detector.inputSize;\n this.height = config.face.detector.inputSize;\n this.anchorsData = generateAnchors(config.face.detector.inputSize);\n this.anchors = tf.tensor2d(this.anchorsData);\n this.inputSize = tf.tensor1d([this.width, this.height]);\n this.config = config;\n this.scaleFaces = 0.8;\n }\n\n async getBoundingBoxes(inputImage) {\n // sanity check on input\n if ((!inputImage) || (inputImage.isDisposedInternal) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return null;\n const [detectedOutputs, boxes, scores] = tf.tidy(() => {\n const resizedImage = inputImage.resizeBilinear([this.width, this.height]);\n // const normalizedImage = tf.mul(tf.sub(resizedImage.div(255), 0.5), 2);\n const normalizedImage = tf.sub(resizedImage.div(127.5), 1);\n const batchedPrediction = this.blazeFaceModel.predict(normalizedImage);\n let prediction;\n // are we using tfhub or pinto converted model?\n if (Array.isArray(batchedPrediction)) {\n const sorted = batchedPrediction.sort((a, b) => a.size - b.size);\n const concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n const concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n const concat = tf.concat([concat512, concat384], 1);\n prediction = concat.squeeze(0);\n } else {\n prediction = batchedPrediction.squeeze(); // when using tfhub model\n }\n const decodedBounds = decodeBounds(prediction, this.anchors, this.inputSize);\n const logits = tf.slice(prediction, [0, 0], [-1, 1]);\n const scoresOut = tf.sigmoid(logits).squeeze();\n return [prediction, decodedBounds, scoresOut];\n });\n const boxIndicesTensor = await tf.image.nonMaxSuppressionAsync(boxes, scores, this.config.face.detector.maxFaces, this.config.face.detector.iouThreshold, this.config.face.detector.scoreThreshold);\n const boxIndices = boxIndicesTensor.arraySync();\n boxIndicesTensor.dispose();\n const boundingBoxesMap = boxIndices.map((boxIndex) => tf.slice(boxes, [boxIndex, 0], [1, -1]));\n const boundingBoxes = boundingBoxesMap.map((boundingBox) => {\n const vals = boundingBox.arraySync();\n boundingBox.dispose();\n return vals;\n });\n\n const scoresVal = scores.dataSync();\n const annotatedBoxes = [];\n for (let i = 0; i < boundingBoxes.length; i++) {\n const boxIndex = boxIndices[i];\n const confidence = scoresVal[boxIndex];\n if (confidence > this.config.face.detector.minConfidence) {\n const box = createBox(boundingBoxes[i]);\n const anchor = this.anchorsData[boxIndex];\n const landmarks = tf.tidy(() => tf.slice(detectedOutputs, [boxIndex, NUM_LANDMARKS - 1], [1, -1]).squeeze().reshape([NUM_LANDMARKS, -1]));\n annotatedBoxes.push({ box, landmarks, anchor, confidence });\n }\n }\n detectedOutputs.dispose();\n boxes.dispose();\n scores.dispose();\n detectedOutputs.dispose();\n return {\n boxes: annotatedBoxes,\n scaleFactor: [inputImage.shape[2] / this.width, inputImage.shape[1] / this.height],\n };\n }\n\n async estimateFaces(input) {\n // @ts-ignore\n const { boxes, scaleFactor } = await this.getBoundingBoxes(input);\n const faces = [];\n for (const face of boxes) {\n const landmarkData = face.landmarks.arraySync();\n const scaledBox = scaleBoxFromPrediction(face, scaleFactor);\n // @ts-ignore\n const boxData = scaleBox.arraySync();\n const probabilityData = face.probability.arraySync();\n const anchor = face.anchor;\n const [scaleFactorX, scaleFactorY] = scaleFactor;\n const scaledLandmarks = landmarkData\n .map((landmark) => ([\n (landmark[0] + anchor[0]) * scaleFactorX,\n (landmark[1] + anchor[1]) * scaleFactorY,\n ]));\n const normalizedFace = {\n topLeft: boxData.slice(0, 2),\n bottomRight: boxData.slice(2),\n landmarks: scaledLandmarks,\n probability: probabilityData,\n };\n disposeBox(face.box);\n face.landmarks.dispose();\n face.probability.dispose();\n scaledBox.dispose();\n faces.push(normalizedFace);\n }\n return faces;\n }\n}\n\nasync function load(config) {\n const blazeface = await tf.loadGraphModel(config.face.detector.modelPath, { fromTFHub: config.face.detector.modelPath.includes('tfhub.dev') });\n const model = new BlazeFaceModel(blazeface, config);\n log(`load model: ${config.face.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n return model;\n}\n\nexports.load = load;\nexports.BlazeFaceModel = BlazeFaceModel;\nexports.disposeBox = disposeBox;\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nfunction scaleBoxCoordinates(box, factor) {\n const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n return { startPoint, endPoint };\n}\nexports.scaleBoxCoordinates = scaleBoxCoordinates;\n\nfunction getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\nexports.getBoxSize = getBoxSize;\n\nfunction getBoxCenter(box) {\n return [\n box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n ];\n}\nexports.getBoxCenter = getBoxCenter;\n\nfunction cutBoxFromImageAndResize(box, image, cropSize) {\n const h = image.shape[1];\n const w = image.shape[2];\n const boxes = [[\n box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h,\n box.endPoint[0] / w,\n ]];\n return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\nexports.cutBoxFromImageAndResize = cutBoxFromImageAndResize;\n\nfunction enlargeBox(box, factor = 1.5) {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n return { startPoint, endPoint, landmarks: box.landmarks };\n}\nexports.enlargeBox = enlargeBox;\n\nfunction squarifyBox(box) {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const maxEdge = Math.max(...size);\n const halfSize = maxEdge / 2;\n const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n return { startPoint, endPoint, landmarks: box.landmarks };\n}\nexports.squarifyBox = squarifyBox;\n", "exports.IDENTITY_MATRIX = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n/**\n * Normalizes the provided angle to the range -pi to pi.\n * @param angle The angle in radians to be normalized.\n */\nfunction normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\nexports.normalizeRadians = normalizeRadians;\n\n/**\n * Computes the angle of rotation between two anchor points.\n * @param point1 First anchor point\n * @param point2 Second anchor point\n */\nfunction computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\nexports.computeRotation = computeRotation;\n\nfunction radToDegrees(rad) {\n return rad * 180 / Math.PI;\n}\nexports.radToDegrees = radToDegrees;\n\nfunction buildTranslationMatrix(x, y) {\n return [[1, 0, x], [0, 1, y], [0, 0, 1]];\n}\n\nfunction dot(v1, v2) {\n let product = 0;\n for (let i = 0; i < v1.length; i++) {\n product += v1[i] * v2[i];\n }\n return product;\n}\nexports.dot = dot;\n\nfunction getColumnFrom2DArr(arr, columnIndex) {\n const column = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\nexports.getColumnFrom2DArr = getColumnFrom2DArr;\n\nfunction multiplyTransformMatrices(mat1, mat2) {\n const product = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) {\n product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n }\n return product;\n}\nfunction buildRotationMatrix(rotation, center) {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\nexports.buildRotationMatrix = buildRotationMatrix;\n\nfunction invertTransformMatrix(matrix) {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [\n -dot(rotationComponent[0], translationComponent),\n -dot(rotationComponent[1], translationComponent),\n ];\n return [\n rotationComponent[0].concat(invertedTranslation[0]),\n rotationComponent[1].concat(invertedTranslation[1]),\n [0, 0, 1],\n ];\n}\nexports.invertTransformMatrix = invertTransformMatrix;\n\nfunction rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\nexports.rotatePoint = rotatePoint;\n\nfunction xyDistanceBetweenPoints(a, b) {\n return Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n}\nexports.xyDistanceBetweenPoints = xyDistanceBetweenPoints;\n", "const MESH_ANNOTATIONS = {\n silhouette: [\n 10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n 397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n 172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n ],\n lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291],\n lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291],\n lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308],\n lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],\n rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173],\n rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133],\n rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190],\n rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243],\n rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189],\n rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244],\n rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245],\n rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193],\n rightEyebrowLower: [35, 124, 46, 53, 52, 65],\n rightEyeIris: [473, 474, 475, 476, 477],\n leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n leftEyeIris: [468, 469, 470, 471, 472],\n midwayBetweenEyes: [168],\n noseTip: [1],\n noseBottom: [2],\n noseRightCorner: [98],\n noseLeftCorner: [327],\n rightCheek: [205],\n leftCheek: [425],\n};\n\nconst MESH_TO_IRIS_INDICES_MAP = [ // A mapping from facemesh model keypoints to iris model keypoints.\n { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] },\n { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] },\n { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] },\n { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] },\n { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] },\n { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] },\n { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] },\n // { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] },\n // { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] },\n];\n\nconst UV468 = [\n [0.499976992607117, 0.652534008026123],\n [0.500025987625122, 0.547487020492554],\n [0.499974012374878, 0.602371990680695],\n [0.482113003730774, 0.471979022026062],\n [0.500150978565216, 0.527155995368958],\n [0.499909996986389, 0.498252987861633],\n [0.499523013830185, 0.40106201171875],\n [0.289712011814117, 0.380764007568359],\n [0.499954998493195, 0.312398016452789],\n [0.499987006187439, 0.269918978214264],\n [0.500023007392883, 0.107050001621246],\n [0.500023007392883, 0.666234016418457],\n [0.5000159740448, 0.679224014282227],\n [0.500023007392883, 0.692348003387451],\n [0.499976992607117, 0.695277988910675],\n [0.499976992607117, 0.70593398809433],\n [0.499976992607117, 0.719385027885437],\n [0.499976992607117, 0.737019002437592],\n [0.499967992305756, 0.781370997428894],\n [0.499816000461578, 0.562981009483337],\n [0.473773002624512, 0.573909997940063],\n [0.104906998574734, 0.254140973091125],\n [0.365929991006851, 0.409575998783112],\n [0.338757991790771, 0.41302502155304],\n [0.311120003461838, 0.409460008144379],\n [0.274657994508743, 0.389131009578705],\n [0.393361985683441, 0.403706014156342],\n [0.345234006643295, 0.344011008739471],\n [0.370094001293182, 0.346076011657715],\n [0.319321990013123, 0.347265005111694],\n [0.297903001308441, 0.353591024875641],\n [0.24779200553894, 0.410809993743896],\n [0.396889001131058, 0.842755019664764],\n [0.280097991228104, 0.375599980354309],\n [0.106310002505779, 0.399955987930298],\n [0.2099249958992, 0.391353011131287],\n [0.355807989835739, 0.534406006336212],\n [0.471751004457474, 0.65040397644043],\n [0.474155008792877, 0.680191993713379],\n [0.439785003662109, 0.657229006290436],\n [0.414617002010345, 0.66654098033905],\n [0.450374007225037, 0.680860996246338],\n [0.428770989179611, 0.682690978050232],\n [0.374971002340317, 0.727805018424988],\n [0.486716985702515, 0.547628998756409],\n [0.485300987958908, 0.527395009994507],\n [0.257764995098114, 0.314490020275116],\n [0.401223003864288, 0.455172002315521],\n [0.429818987846375, 0.548614978790283],\n [0.421351999044418, 0.533740997314453],\n [0.276895999908447, 0.532056987285614],\n [0.483370006084442, 0.499586999416351],\n [0.33721199631691, 0.282882988452911],\n [0.296391993761063, 0.293242990970612],\n [0.169294998049736, 0.193813979625702],\n [0.447580009698868, 0.302609980106354],\n [0.392390012741089, 0.353887975215912],\n [0.354490011930466, 0.696784019470215],\n [0.067304998636246, 0.730105042457581],\n [0.442739009857178, 0.572826027870178],\n [0.457098007202148, 0.584792017936707],\n [0.381974011659622, 0.694710969924927],\n [0.392388999462128, 0.694203019142151],\n [0.277076005935669, 0.271932005882263],\n [0.422551989555359, 0.563233017921448],\n [0.385919004678726, 0.281364023685455],\n [0.383103013038635, 0.255840003490448],\n [0.331431001424789, 0.119714021682739],\n [0.229923993349075, 0.232002973556519],\n [0.364500999450684, 0.189113974571228],\n [0.229622006416321, 0.299540996551514],\n [0.173287004232407, 0.278747975826263],\n [0.472878992557526, 0.666198015213013],\n [0.446828007698059, 0.668527007102966],\n [0.422762006521225, 0.673889994621277],\n [0.445307999849319, 0.580065965652466],\n [0.388103008270264, 0.693961024284363],\n [0.403039008378983, 0.706539988517761],\n [0.403629004955292, 0.693953037261963],\n [0.460041999816895, 0.557139039039612],\n [0.431158006191254, 0.692366003990173],\n [0.452181994915009, 0.692366003990173],\n [0.475387006998062, 0.692366003990173],\n [0.465828001499176, 0.779190003871918],\n [0.472328990697861, 0.736225962638855],\n [0.473087012767792, 0.717857003211975],\n [0.473122000694275, 0.704625964164734],\n [0.473033010959625, 0.695277988910675],\n [0.427942007780075, 0.695277988910675],\n [0.426479011774063, 0.703539967536926],\n [0.423162013292313, 0.711845993995667],\n [0.4183090031147, 0.720062971115112],\n [0.390094995498657, 0.639572978019714],\n [0.013953999616206, 0.560034036636353],\n [0.499913990497589, 0.58014702796936],\n [0.413199990987778, 0.69539999961853],\n [0.409626007080078, 0.701822996139526],\n [0.468080013990402, 0.601534962654114],\n [0.422728985548019, 0.585985004901886],\n [0.463079988956451, 0.593783974647522],\n [0.37211999297142, 0.47341400384903],\n [0.334562003612518, 0.496073007583618],\n [0.411671012639999, 0.546965003013611],\n [0.242175996303558, 0.14767599105835],\n [0.290776997804642, 0.201445996761322],\n [0.327338010072708, 0.256527006626129],\n [0.399509996175766, 0.748921036720276],\n [0.441727995872498, 0.261676013469696],\n [0.429764986038208, 0.187834024429321],\n [0.412198007106781, 0.108901023864746],\n [0.288955003023148, 0.398952007293701],\n [0.218936994671822, 0.435410976409912],\n [0.41278201341629, 0.398970007896423],\n [0.257135003805161, 0.355440020561218],\n [0.427684992551804, 0.437960982322693],\n [0.448339998722076, 0.536936044692993],\n [0.178560003638268, 0.45755398273468],\n [0.247308000922203, 0.457193970680237],\n [0.286267012357712, 0.467674970626831],\n [0.332827985286713, 0.460712015628815],\n [0.368755996227264, 0.447206974029541],\n [0.398963987827301, 0.432654976844788],\n [0.476410001516342, 0.405806005001068],\n [0.189241006970406, 0.523923993110657],\n [0.228962004184723, 0.348950982093811],\n [0.490725994110107, 0.562400996685028],\n [0.404670000076294, 0.485132992267609],\n [0.019469000399113, 0.401564002037048],\n [0.426243007183075, 0.420431017875671],\n [0.396993011236191, 0.548797011375427],\n [0.266469985246658, 0.376977026462555],\n [0.439121007919312, 0.51895797252655],\n [0.032313998788595, 0.644356966018677],\n [0.419054001569748, 0.387154996395111],\n [0.462783008813858, 0.505746960639954],\n [0.238978996872902, 0.779744982719421],\n [0.198220998048782, 0.831938028335571],\n [0.107550002634525, 0.540755033493042],\n [0.183610007166862, 0.740257024765015],\n [0.134409993886948, 0.333683013916016],\n [0.385764002799988, 0.883153975009918],\n [0.490967005491257, 0.579378008842468],\n [0.382384985685349, 0.508572995662689],\n [0.174399003386497, 0.397670984268188],\n [0.318785011768341, 0.39623498916626],\n [0.343364000320435, 0.400596976280212],\n [0.396100014448166, 0.710216999053955],\n [0.187885001301765, 0.588537991046906],\n [0.430987000465393, 0.944064974784851],\n [0.318993002176285, 0.898285031318665],\n [0.266247987747192, 0.869701027870178],\n [0.500023007392883, 0.190576016902924],\n [0.499976992607117, 0.954452991485596],\n [0.366169989109039, 0.398822009563446],\n [0.393207013607025, 0.39553701877594],\n [0.410373002290726, 0.391080021858215],\n [0.194993004202843, 0.342101991176605],\n [0.388664990663528, 0.362284004688263],\n [0.365961998701096, 0.355970978736877],\n [0.343364000320435, 0.355356991291046],\n [0.318785011768341, 0.35834002494812],\n [0.301414996385574, 0.363156020641327],\n [0.058132998645306, 0.319076001644135],\n [0.301414996385574, 0.387449026107788],\n [0.499987989664078, 0.618434011936188],\n [0.415838003158569, 0.624195992946625],\n [0.445681989192963, 0.566076993942261],\n [0.465844005346298, 0.620640993118286],\n [0.49992299079895, 0.351523995399475],\n [0.288718998432159, 0.819945991039276],\n [0.335278987884521, 0.852819979190826],\n [0.440512001514435, 0.902418971061707],\n [0.128294005990028, 0.791940987110138],\n [0.408771991729736, 0.373893976211548],\n [0.455606997013092, 0.451801002025604],\n [0.499877005815506, 0.908990025520325],\n [0.375436991453171, 0.924192011356354],\n [0.11421000212431, 0.615022003650665],\n [0.448662012815475, 0.695277988910675],\n [0.4480200111866, 0.704632043838501],\n [0.447111994028091, 0.715808033943176],\n [0.444831997156143, 0.730794012546539],\n [0.430011987686157, 0.766808986663818],\n [0.406787008047104, 0.685672998428345],\n [0.400738000869751, 0.681069016456604],\n [0.392399996519089, 0.677703022956848],\n [0.367855995893478, 0.663918972015381],\n [0.247923001646996, 0.601333022117615],\n [0.452769994735718, 0.420849978923798],\n [0.43639200925827, 0.359887003898621],\n [0.416164010763168, 0.368713974952698],\n [0.413385987281799, 0.692366003990173],\n [0.228018000721931, 0.683571994304657],\n [0.468268007040024, 0.352671027183533],\n [0.411361992359161, 0.804327011108398],\n [0.499989002943039, 0.469825029373169],\n [0.479153990745544, 0.442654013633728],\n [0.499974012374878, 0.439637005329132],\n [0.432112008333206, 0.493588984012604],\n [0.499886006116867, 0.866917014122009],\n [0.49991300702095, 0.821729004383087],\n [0.456548988819122, 0.819200992584229],\n [0.344549000263214, 0.745438992977142],\n [0.37890899181366, 0.574010014533997],\n [0.374292999505997, 0.780184984207153],\n [0.319687992334366, 0.570737957954407],\n [0.357154995203018, 0.604269981384277],\n [0.295284003019333, 0.621580958366394],\n [0.447750002145767, 0.862477004528046],\n [0.410986006259918, 0.508723020553589],\n [0.31395098567009, 0.775308012962341],\n [0.354128003120422, 0.812552988529205],\n [0.324548006057739, 0.703992962837219],\n [0.189096003770828, 0.646299958229065],\n [0.279776990413666, 0.71465802192688],\n [0.1338230073452, 0.682700991630554],\n [0.336768001317978, 0.644733011722565],\n [0.429883986711502, 0.466521978378296],\n [0.455527991056442, 0.548622965812683],\n [0.437114000320435, 0.558896005153656],\n [0.467287987470627, 0.529924988746643],\n [0.414712011814117, 0.335219979286194],\n [0.37704598903656, 0.322777986526489],\n [0.344107985496521, 0.320150971412659],\n [0.312875986099243, 0.32233202457428],\n [0.283526003360748, 0.333190023899078],\n [0.241245999932289, 0.382785975933075],\n [0.102986000478268, 0.468762993812561],\n [0.267612010240555, 0.424560010433197],\n [0.297879010438919, 0.433175981044769],\n [0.333433985710144, 0.433878004550934],\n [0.366427004337311, 0.426115989685059],\n [0.396012008190155, 0.416696012020111],\n [0.420121014118195, 0.41022801399231],\n [0.007561000064015, 0.480777025222778],\n [0.432949006557465, 0.569517970085144],\n [0.458638995885849, 0.479089021682739],\n [0.473466008901596, 0.545744001865387],\n [0.476087987422943, 0.563830018043518],\n [0.468472003936768, 0.555056989192963],\n [0.433990985155106, 0.582361996173859],\n [0.483518004417419, 0.562983989715576],\n [0.482482999563217, 0.57784903049469],\n [0.42645001411438, 0.389798998832703],\n [0.438998997211456, 0.39649498462677],\n [0.450067013502121, 0.400434017181396],\n [0.289712011814117, 0.368252992630005],\n [0.276670008897781, 0.363372981548309],\n [0.517862021923065, 0.471948027610779],\n [0.710287988185883, 0.380764007568359],\n [0.526226997375488, 0.573909997940063],\n [0.895093023777008, 0.254140973091125],\n [0.634069979190826, 0.409575998783112],\n [0.661242008209229, 0.41302502155304],\n [0.688880026340485, 0.409460008144379],\n [0.725341975688934, 0.389131009578705],\n [0.606630027294159, 0.40370500087738],\n [0.654766023159027, 0.344011008739471],\n [0.629905998706818, 0.346076011657715],\n [0.680678009986877, 0.347265005111694],\n [0.702096998691559, 0.353591024875641],\n [0.75221198797226, 0.410804986953735],\n [0.602918028831482, 0.842862963676453],\n [0.719901978969574, 0.375599980354309],\n [0.893692970275879, 0.399959981441498],\n [0.790081977844238, 0.391354024410248],\n [0.643998026847839, 0.534487962722778],\n [0.528249025344849, 0.65040397644043],\n [0.525849997997284, 0.680191040039062],\n [0.560214996337891, 0.657229006290436],\n [0.585384011268616, 0.66654098033905],\n [0.549625992774963, 0.680860996246338],\n [0.57122802734375, 0.682691991329193],\n [0.624852001667023, 0.72809898853302],\n [0.513050019741058, 0.547281980514526],\n [0.51509702205658, 0.527251958847046],\n [0.742246985435486, 0.314507007598877],\n [0.598631024360657, 0.454979002475739],\n [0.570338010787964, 0.548575043678284],\n [0.578631997108459, 0.533622980117798],\n [0.723087012767792, 0.532054007053375],\n [0.516445994377136, 0.499638974666595],\n [0.662801027297974, 0.282917976379395],\n [0.70362401008606, 0.293271005153656],\n [0.830704987049103, 0.193813979625702],\n [0.552385985851288, 0.302568018436432],\n [0.607609987258911, 0.353887975215912],\n [0.645429015159607, 0.696707010269165],\n [0.932694971561432, 0.730105042457581],\n [0.557260990142822, 0.572826027870178],\n [0.542901992797852, 0.584792017936707],\n [0.6180260181427, 0.694710969924927],\n [0.607590973377228, 0.694203019142151],\n [0.722943007946014, 0.271963000297546],\n [0.577413976192474, 0.563166975975037],\n [0.614082992076874, 0.281386971473694],\n [0.616907000541687, 0.255886018276215],\n [0.668509006500244, 0.119913995265961],\n [0.770092010498047, 0.232020974159241],\n [0.635536015033722, 0.189248979091644],\n [0.77039098739624, 0.299556016921997],\n [0.826722025871277, 0.278755009174347],\n [0.527121007442474, 0.666198015213013],\n [0.553171992301941, 0.668527007102966],\n [0.577238023281097, 0.673889994621277],\n [0.554691970348358, 0.580065965652466],\n [0.611896991729736, 0.693961024284363],\n [0.59696102142334, 0.706539988517761],\n [0.596370995044708, 0.693953037261963],\n [0.539958000183105, 0.557139039039612],\n [0.568841993808746, 0.692366003990173],\n [0.547818005084991, 0.692366003990173],\n [0.52461302280426, 0.692366003990173],\n [0.534089982509613, 0.779141008853912],\n [0.527670979499817, 0.736225962638855],\n [0.526912987232208, 0.717857003211975],\n [0.526877999305725, 0.704625964164734],\n [0.526966989040375, 0.695277988910675],\n [0.572058022022247, 0.695277988910675],\n [0.573521018028259, 0.703539967536926],\n [0.57683801651001, 0.711845993995667],\n [0.581691026687622, 0.720062971115112],\n [0.609944999217987, 0.639909982681274],\n [0.986046016216278, 0.560034036636353],\n [0.5867999792099, 0.69539999961853],\n [0.590372025966644, 0.701822996139526],\n [0.531915009021759, 0.601536989212036],\n [0.577268004417419, 0.585934996604919],\n [0.536915004253387, 0.593786001205444],\n [0.627542972564697, 0.473352015018463],\n [0.665585994720459, 0.495950996875763],\n [0.588353991508484, 0.546862006187439],\n [0.757824003696442, 0.14767599105835],\n [0.709249973297119, 0.201507985591888],\n [0.672684013843536, 0.256581008434296],\n [0.600408971309662, 0.74900496006012],\n [0.55826598405838, 0.261672019958496],\n [0.570303976535797, 0.187870979309082],\n [0.588165998458862, 0.109044015407562],\n [0.711045026779175, 0.398952007293701],\n [0.781069993972778, 0.435405015945435],\n [0.587247014045715, 0.398931980133057],\n [0.742869973182678, 0.355445981025696],\n [0.572156012058258, 0.437651991844177],\n [0.55186802148819, 0.536570012569427],\n [0.821442008018494, 0.457556009292603],\n [0.752701997756958, 0.457181990146637],\n [0.71375697851181, 0.467626988887787],\n [0.66711300611496, 0.460672974586487],\n [0.631101012229919, 0.447153985500336],\n [0.6008620262146, 0.432473003864288],\n [0.523481011390686, 0.405627012252808],\n [0.810747981071472, 0.523926019668579],\n [0.771045982837677, 0.348959028720856],\n [0.509127020835876, 0.562718033790588],\n [0.595292985439301, 0.485023975372314],\n [0.980530977249146, 0.401564002037048],\n [0.573499977588654, 0.420000016689301],\n [0.602994978427887, 0.548687994480133],\n [0.733529984951019, 0.376977026462555],\n [0.560611009597778, 0.519016981124878],\n [0.967685997486115, 0.644356966018677],\n [0.580985009670258, 0.387160003185272],\n [0.537728011608124, 0.505385041236877],\n [0.760966002941132, 0.779752969741821],\n [0.801778972148895, 0.831938028335571],\n [0.892440974712372, 0.54076099395752],\n [0.816350996494293, 0.740260004997253],\n [0.865594983100891, 0.333687007427216],\n [0.614073991775513, 0.883246004581451],\n [0.508952975273132, 0.579437971115112],\n [0.617941975593567, 0.508316040039062],\n [0.825608015060425, 0.397674977779388],\n [0.681214988231659, 0.39623498916626],\n [0.656635999679565, 0.400596976280212],\n [0.603900015354156, 0.710216999053955],\n [0.81208598613739, 0.588539004325867],\n [0.56801301240921, 0.944564998149872],\n [0.681007981300354, 0.898285031318665],\n [0.733752012252808, 0.869701027870178],\n [0.633830010890961, 0.398822009563446],\n [0.606792986392975, 0.39553701877594],\n [0.589659988880157, 0.391062021255493],\n [0.805015981197357, 0.342108011245728],\n [0.611334979534149, 0.362284004688263],\n [0.634037971496582, 0.355970978736877],\n [0.656635999679565, 0.355356991291046],\n [0.681214988231659, 0.35834002494812],\n [0.698584973812103, 0.363156020641327],\n [0.941866993904114, 0.319076001644135],\n [0.698584973812103, 0.387449026107788],\n [0.584177017211914, 0.624107003211975],\n [0.554318010807037, 0.566076993942261],\n [0.534153997898102, 0.62064003944397],\n [0.711217999458313, 0.819975018501282],\n [0.664629995822906, 0.852871000766754],\n [0.559099972248077, 0.902631998062134],\n [0.871706008911133, 0.791940987110138],\n [0.591234028339386, 0.373893976211548],\n [0.544341027736664, 0.451583981513977],\n [0.624562978744507, 0.924192011356354],\n [0.88577002286911, 0.615028977394104],\n [0.551338016986847, 0.695277988910675],\n [0.551980018615723, 0.704632043838501],\n [0.552887976169586, 0.715808033943176],\n [0.555167973041534, 0.730794012546539],\n [0.569944024085999, 0.767035007476807],\n [0.593203008174896, 0.685675978660583],\n [0.599261999130249, 0.681069016456604],\n [0.607599973678589, 0.677703022956848],\n [0.631937980651855, 0.663500010967255],\n [0.752032995223999, 0.601315021514893],\n [0.547226011753082, 0.420395016670227],\n [0.563543975353241, 0.359827995300293],\n [0.583841025829315, 0.368713974952698],\n [0.586614012718201, 0.692366003990173],\n [0.771915018558502, 0.683578014373779],\n [0.531597018241882, 0.352482974529266],\n [0.588370978832245, 0.804440975189209],\n [0.52079701423645, 0.442565023899078],\n [0.567984998226166, 0.493479013442993],\n [0.543282985687256, 0.819254994392395],\n [0.655317008495331, 0.745514988899231],\n [0.621008992195129, 0.574018001556396],\n [0.625559985637665, 0.78031200170517],\n [0.680198013782501, 0.570719003677368],\n [0.64276397228241, 0.604337990283966],\n [0.704662978649139, 0.621529996395111],\n [0.552012026309967, 0.862591981887817],\n [0.589071989059448, 0.508637011051178],\n [0.685944974422455, 0.775357007980347],\n [0.645735025405884, 0.812640011310577],\n [0.675342977046967, 0.703978002071381],\n [0.810858011245728, 0.646304965019226],\n [0.72012197971344, 0.714666962623596],\n [0.866151988506317, 0.682704985141754],\n [0.663187026977539, 0.644596993923187],\n [0.570082008838654, 0.466325998306274],\n [0.544561982154846, 0.548375964164734],\n [0.562758982181549, 0.558784961700439],\n [0.531987011432648, 0.530140042304993],\n [0.585271000862122, 0.335177004337311],\n [0.622952997684479, 0.32277899980545],\n [0.655896008014679, 0.320163011550903],\n [0.687132000923157, 0.322345972061157],\n [0.716481983661652, 0.333200991153717],\n [0.758756995201111, 0.382786989212036],\n [0.897013008594513, 0.468769013881683],\n [0.732392013072968, 0.424547016620636],\n [0.70211398601532, 0.433162987232208],\n [0.66652500629425, 0.433866024017334],\n [0.633504986763, 0.426087975502014],\n [0.603875994682312, 0.416586995124817],\n [0.579657971858978, 0.409945011138916],\n [0.992439985275269, 0.480777025222778],\n [0.567192018032074, 0.569419980049133],\n [0.54136598110199, 0.478899002075195],\n [0.526564002037048, 0.546118021011353],\n [0.523913025856018, 0.563830018043518],\n [0.531529009342194, 0.555056989192963],\n [0.566035985946655, 0.582329034805298],\n [0.51631098985672, 0.563053965568542],\n [0.5174720287323, 0.577877044677734],\n [0.573594987392426, 0.389806985855103],\n [0.560697972774506, 0.395331978797913],\n [0.549755990505219, 0.399751007556915],\n [0.710287988185883, 0.368252992630005],\n [0.723330020904541, 0.363372981548309],\n];\n\nconst TRI468 = [\n 127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n 151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n 157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n 181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n 228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n 170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n 155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n 158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n 102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n 90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n 196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n 246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n 117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n 153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n 63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n 135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n 76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n 166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n 190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n 112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n 110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n 348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n 314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n 321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n 466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n 429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n 293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n 427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n 431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n 434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n 348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n 318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n 373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n 340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n 351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n 164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n 327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n 405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n 438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n 449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n 444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n 464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n 243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n 166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n 119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n 77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n 201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n 103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n 217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n 78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n 57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n 142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n 90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n 178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n 224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n 143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n 153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n 63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n 241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n 186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n 141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n 223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n 128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n 451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n 344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n 424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n 321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n 448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n 281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n 283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n 432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n 268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n 6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n 317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n 272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n 336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n 441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n 373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n 425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n 403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n 383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n 254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n 259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n 453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nconst TRI68 = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n 8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n 45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n 23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n 39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n 35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n 48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n 66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\nconst TRI33 = [\n /* eyes */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n /* brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n /* 4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n /* nose */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n /* chin */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n /* cont */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\nconst TRI7 = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nconst VTX68 = [\n /* cont */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n /* nose */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n /* eyes */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n /* lip */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\nconst VTX33 = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\nconst VTX7 = [33, 133, 362, 263, 1, 78, 308];\n\nexports.MESH_ANNOTATIONS = MESH_ANNOTATIONS;\nexports.MESH_TO_IRIS_INDICES_MAP = MESH_TO_IRIS_INDICES_MAP;\n\nexports.TRI468 = TRI468;\nexports.TRI68 = TRI68;\nexports.TRI33 = TRI33;\nexports.TRI7 = TRI7;\n\nexports.UV468 = UV468;\nexports.UV68 = VTX68.map((x) => UV468[x]);\nexports.UV33 = VTX33.map((x) => UV468[x]);\nexports.UV7 = VTX7.map((x) => UV468[x]);\n", "/* eslint-disable class-methods-use-this */\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as bounding from './box';\nimport * as util from './util';\nimport * as coords from './coords.js';\n// eslint-disable-next-line no-unused-vars\nimport { log } from '../log.js';\n\nconst LANDMARKS_COUNT = 468;\nconst MESH_MOUTH_INDEX = 13;\nconst MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES = [MESH_MOUTH_INDEX, coords.MESH_ANNOTATIONS['midwayBetweenEyes'][0]];\nconst BLAZEFACE_MOUTH_INDEX = 3;\nconst BLAZEFACE_NOSE_INDEX = 2;\nconst BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES = [BLAZEFACE_MOUTH_INDEX, BLAZEFACE_NOSE_INDEX];\nconst LEFT_EYE_OUTLINE = coords.MESH_ANNOTATIONS['leftEyeLower0'];\nconst LEFT_EYE_BOUNDS = [LEFT_EYE_OUTLINE[0], LEFT_EYE_OUTLINE[LEFT_EYE_OUTLINE.length - 1]];\nconst RIGHT_EYE_OUTLINE = coords.MESH_ANNOTATIONS['rightEyeLower0'];\nconst RIGHT_EYE_BOUNDS = [RIGHT_EYE_OUTLINE[0], RIGHT_EYE_OUTLINE[RIGHT_EYE_OUTLINE.length - 1]];\nconst IRIS_UPPER_CENTER_INDEX = 3;\nconst IRIS_LOWER_CENTER_INDEX = 4;\nconst IRIS_IRIS_INDEX = 71;\nconst IRIS_NUM_COORDINATES = 76;\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates. Update the z coordinate to be an average of the original and the new. This produces the best visual effect.\nfunction replaceRawCoordinates(rawCoords, newCoords, prefix, keys) {\n for (let i = 0; i < coords.MESH_TO_IRIS_INDICES_MAP.length; i++) {\n const { key, indices } = coords.MESH_TO_IRIS_INDICES_MAP[i];\n const originalIndices = coords.MESH_ANNOTATIONS[`${prefix}${key}`];\n const shouldReplaceAllKeys = keys == null;\n if (shouldReplaceAllKeys || keys.includes(key)) {\n for (let j = 0; j < indices.length; j++) {\n const index = indices[j];\n rawCoords[originalIndices[j]] = [\n newCoords[index][0], newCoords[index][1],\n (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n ];\n }\n }\n }\n}\n// The Pipeline coordinates between the bounding box and skeleton models.\nclass Pipeline {\n constructor(boundingBoxDetector, meshDetector, irisModel, config) {\n // An array of facial bounding boxes.\n this.storedBoxes = [];\n this.runsWithoutFaceDetector = 0;\n this.boundingBoxDetector = boundingBoxDetector;\n this.meshDetector = meshDetector;\n this.irisModel = irisModel;\n this.meshWidth = config.face.mesh.inputSize;\n this.meshHeight = config.face.mesh.inputSize;\n this.irisSize = config.face.iris.inputSize;\n this.irisEnlarge = 2.3;\n this.skipped = 0;\n this.detectedFaces = 0;\n }\n\n transformRawCoords(rawCoords, box, angle, rotationMatrix) {\n // @ts-ignore\n const boxSize = bounding.getBoxSize({ startPoint: box.startPoint, endPoint: box.endPoint });\n const scaleFactor = [boxSize[0] / this.meshWidth, boxSize[1] / this.meshHeight];\n const coordsScaled = rawCoords.map((coord) => ([\n scaleFactor[0] * (coord[0] - this.meshWidth / 2),\n scaleFactor[1] * (coord[1] - this.meshHeight / 2), coord[2],\n ]));\n const coordsRotationMatrix = (angle !== 0) ? util.buildRotationMatrix(angle, [0, 0]) : util.IDENTITY_MATRIX;\n const coordsRotated = (angle !== 0) ? coordsScaled.map((coord) => ([...util.rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n const inverseRotationMatrix = (angle !== 0) ? util.invertTransformMatrix(rotationMatrix) : util.IDENTITY_MATRIX;\n // @ts-ignore\n const boxCenter = [...bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint }), 1];\n return coordsRotated.map((coord) => ([\n coord[0] + util.dot(boxCenter, inverseRotationMatrix[0]),\n coord[1] + util.dot(boxCenter, inverseRotationMatrix[1]),\n coord[2],\n ]));\n }\n\n getLeftToRightEyeDepthDifference(rawCoords) {\n const leftEyeZ = rawCoords[LEFT_EYE_BOUNDS[0]][2];\n const rightEyeZ = rawCoords[RIGHT_EYE_BOUNDS[0]][2];\n return leftEyeZ - rightEyeZ;\n }\n\n // Returns a box describing a cropped region around the eye fit for passing to the iris model.\n getEyeBox(rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, flip = false) {\n // @ts-ignore\n const box = bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), this.irisEnlarge));\n // @ts-ignore\n const boxSize = bounding.getBoxSize(box);\n let crop = tf.image.cropAndResize(face, [[\n box.startPoint[1] / this.meshHeight,\n box.startPoint[0] / this.meshWidth, box.endPoint[1] / this.meshHeight,\n box.endPoint[0] / this.meshWidth,\n ]], [0], [this.irisSize, this.irisSize]);\n if (flip) {\n crop = tf.image.flipLeftRight(crop);\n }\n return { box, boxSize, crop };\n }\n\n // Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\n getEyeCoords(eyeData, eyeBox, eyeBoxSize, flip = false) {\n const eyeRawCoords = [];\n for (let i = 0; i < IRIS_NUM_COORDINATES; i++) {\n const x = eyeData[i * 3];\n const y = eyeData[i * 3 + 1];\n const z = eyeData[i * 3 + 2];\n eyeRawCoords.push([\n (flip\n ? (1 - (x / this.irisSize))\n : (x / this.irisSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n (y / this.irisSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n ]);\n }\n return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(IRIS_IRIS_INDEX) };\n }\n\n // The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\n getAdjustedIrisCoords(rawCoords, irisCoords, direction) {\n const upperCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeUpper0`][IRIS_UPPER_CENTER_INDEX]][2];\n const lowerCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeLower0`][IRIS_LOWER_CENTER_INDEX]][2];\n const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n return irisCoords.map((coord, i) => {\n let z = averageZ;\n if (i === 2) {\n z = upperCenterZ;\n } else if (i === 4) {\n z = lowerCenterZ;\n }\n return [coord[0], coord[1], z];\n });\n }\n\n async predict(input, config) {\n let useFreshBox = false;\n // run new detector every skipFrames unless we only want box to start with\n let detector;\n if ((this.skipped === 0) || (this.skipped > config.face.detector.skipFrames) || !config.face.mesh.enabled || !config.videoOptimized) {\n detector = await this.boundingBoxDetector.getBoundingBoxes(input);\n this.skipped = 0;\n }\n if (config.videoOptimized) this.skipped++;\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (detector && detector.boxes && (detector.boxes.length > 0) && (!config.face.mesh.enabled || (detector.boxes.length !== this.detectedFaces) && (this.detectedFaces !== config.face.detector.maxFaces))) {\n this.storedBoxes = [];\n this.detectedFaces = 0;\n for (const possible of detector.boxes) {\n this.storedBoxes.push({ startPoint: possible.box.startPoint.dataSync(), endPoint: possible.box.endPoint.dataSync(), landmarks: possible.landmarks, confidence: possible.confidence });\n }\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\n\n if (useFreshBox) {\n if (!detector || !detector.boxes || (detector.boxes.length === 0)) {\n this.storedBoxes = [];\n this.detectedFaces = 0;\n return null;\n }\n for (let i = 0; i < this.storedBoxes.length; i++) {\n // @ts-ignore\n const scaledBox = bounding.scaleBoxCoordinates({ startPoint: this.storedBoxes[i].startPoint, endPoint: this.storedBoxes[i].endPoint }, detector.scaleFactor);\n // @ts-ignore\n const enlargedBox = bounding.enlargeBox(scaledBox);\n // @ts-ignore\n const squarifiedBox = bounding.squarifyBox(enlargedBox);\n const landmarks = this.storedBoxes[i].landmarks.arraySync();\n const confidence = this.storedBoxes[i].confidence;\n this.storedBoxes[i] = { ...squarifiedBox, confidence, landmarks };\n }\n this.runsWithoutFaceDetector = 0;\n }\n if (detector && detector.boxes) {\n detector.boxes.forEach((prediction) => {\n prediction.box.startPoint.dispose();\n prediction.box.endPoint.dispose();\n prediction.landmarks.dispose();\n });\n }\n\n // log('face', `skipped: ${this.skipped} max: ${config.face.detector.maxFaces} detected: ${this.detectedFaces} stored: ${this.storedBoxes.length} new: ${detector?.boxes?.length}`);\n let results = tf.tidy(() => this.storedBoxes.map((box, i) => {\n // The facial bounding box landmarks could come either from blazeface (if we are using a fresh box), or from the mesh model (if we are reusing an old box).\n let face;\n let angle = 0;\n let rotationMatrix;\n if (config.face.detector.rotation) {\n const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= LANDMARKS_COUNT) ? MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES : BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES;\n angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n // @ts-ignore\n const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n const rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized);\n rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n // @ts-ignore\n face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshHeight, this.meshWidth]).div(255);\n } else {\n rotationMatrix = util.IDENTITY_MATRIX;\n const cloned = input.clone();\n // @ts-ignore\n face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, cloned, [this.meshHeight, this.meshWidth]).div(255);\n }\n\n // if we're not going to produce mesh, don't spend time with further processing\n if (!config.face.mesh.enabled) {\n const prediction = {\n coords: null,\n box,\n faceConfidence: null,\n confidence: box.confidence,\n image: face,\n };\n return prediction;\n }\n\n const [, confidence, contourCoords] = this.meshDetector.predict(face); // The first returned tensor represents facial contours, which are included in the coordinates.\n const confidenceVal = confidence.dataSync()[0];\n if (confidenceVal < config.face.detector.minConfidence) return null; // if below confidence just exit\n const coordsReshaped = tf.reshape(contourCoords, [-1, 3]);\n let rawCoords = coordsReshaped.arraySync();\n\n if (config.face.iris.enabled) {\n const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = this.getEyeBox(rawCoords, face, LEFT_EYE_BOUNDS[0], LEFT_EYE_BOUNDS[1], true);\n const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = this.getEyeBox(rawCoords, face, RIGHT_EYE_BOUNDS[0], RIGHT_EYE_BOUNDS[1]);\n const eyePredictions = this.irisModel.predict(tf.concat([leftEyeCrop, rightEyeCrop]));\n const eyePredictionsData = eyePredictions.dataSync();\n const leftEyeData = eyePredictionsData.slice(0, IRIS_NUM_COORDINATES * 3);\n const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = this.getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n const rightEyeData = eyePredictionsData.slice(IRIS_NUM_COORDINATES * 3);\n const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = this.getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize);\n const leftToRightEyeDepthDifference = this.getLeftToRightEyeDepthDifference(rawCoords);\n if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left');\n replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right');\n // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged. So we only update a single contour line above and below the eye.\n } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n } else { // User is looking towards the left.\n replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n }\n const adjustedLeftIrisCoords = this.getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n const adjustedRightIrisCoords = this.getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n rawCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n }\n\n const transformedCoordsData = this.transformRawCoords(rawCoords, box, angle, rotationMatrix);\n // @ts-ignore\n const landmarksBox = bounding.enlargeBox(this.calculateLandmarksBoundingBox(transformedCoordsData));\n // @ts-ignore\n const squarifiedLandmarksBox = bounding.squarifyBox(landmarksBox);\n const transformedCoords = tf.tensor2d(transformedCoordsData);\n const prediction = {\n coords: transformedCoords,\n box: landmarksBox,\n faceConfidence: confidenceVal,\n confidence: box.confidence,\n image: face,\n };\n if (config.face.mesh.returnRawData) prediction.rawCoords = rawCoords;\n this.storedBoxes[i] = { ...squarifiedLandmarksBox, landmarks: transformedCoords.arraySync(), confidence: box.confidence, faceConfidence: confidenceVal };\n\n return prediction;\n }));\n results = results.filter((a) => a !== null);\n this.detectedFaces = results.length;\n return results;\n }\n\n calculateLandmarksBoundingBox(landmarks) {\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint, landmarks };\n }\n}\nexports.Pipeline = Pipeline;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as blazeface from './blazeface.js';\nimport * as facepipeline from './facepipeline.js';\nimport * as coords from './coords.js';\n\nclass MediaPipeFaceMesh {\n constructor(blazeFace, blazeMeshModel, irisModel, config) {\n // @ts-ignore\n this.facePipeline = new facepipeline.Pipeline(blazeFace, blazeMeshModel, irisModel, config);\n this.config = config;\n }\n\n async estimateFaces(input, config) {\n const predictions = await this.facePipeline.predict(input, config);\n const results = [];\n for (const prediction of (predictions || [])) {\n if (prediction.isDisposedInternal) continue; // guard against disposed tensors on long running operations such as pause in middle of processing\n const mesh = prediction.coords ? prediction.coords.arraySync() : null;\n const meshRaw = prediction.rawCoords;\n const annotations = {};\n if (mesh && mesh.length > 0) {\n for (const key of Object.keys(coords.MESH_ANNOTATIONS)) {\n annotations[key] = coords.MESH_ANNOTATIONS[key].map((index) => mesh[index]);\n }\n }\n const boxRaw = (config.face.mesh.returnRawData && prediction.box) ? { topLeft: prediction.box.startPoint, bottomRight: prediction.box.endPoint } : null;\n const box = prediction.box ? [\n Math.max(0, prediction.box.startPoint[0]),\n Math.max(0, prediction.box.startPoint[1]),\n Math.min(input.shape[2], prediction.box.endPoint[0]) - prediction.box.startPoint[0],\n Math.min(input.shape[1], prediction.box.endPoint[1]) - prediction.box.startPoint[1],\n ] : 0;\n results.push({\n confidence: prediction.confidence || 0,\n box,\n mesh,\n boxRaw,\n meshRaw,\n annotations,\n image: prediction.image ? tf.clone(prediction.image) : null,\n });\n if (prediction.coords) prediction.coords.dispose();\n if (prediction.image) prediction.image.dispose();\n }\n return results;\n }\n}\n\nlet faceModels = [null, null, null];\nasync function load(config) {\n faceModels = await Promise.all([\n // @ts-ignore\n (!faceModels[0] && config.face.enabled) ? blazeface.load(config) : null,\n (!faceModels[1] && config.face.mesh.enabled) ? tf.loadGraphModel(config.face.mesh.modelPath, { fromTFHub: config.face.mesh.modelPath.includes('tfhub.dev') }) : null,\n (!faceModels[2] && config.face.iris.enabled) ? tf.loadGraphModel(config.face.iris.modelPath, { fromTFHub: config.face.iris.modelPath.includes('tfhub.dev') }) : null,\n ]);\n const faceMesh = new MediaPipeFaceMesh(faceModels[0], faceModels[1], faceModels[2], config);\n if (config.face.mesh.enabled) log(`load model: ${config.face.mesh.modelPath.match(/\\/(.*)\\./)[1]}`);\n if (config.face.iris.enabled) log(`load model: ${config.face.iris.modelPath.match(/\\/(.*)\\./)[1]}`);\n return faceMesh;\n}\n\nexports.load = load;\nexports.MediaPipeFaceMesh = MediaPipeFaceMesh;\nexports.triangulation = coords.TRI468;\n", "import { log } from './log.js';\n\nconst profileData = {};\n\nfunction profile(name, data) {\n if (!data || !data.kernels) return;\n const maxResults = 5;\n const time = data.kernels\n .filter((a) => a.kernelTimeMs > 0)\n .reduce((a, b) => a += b.kernelTimeMs, 0);\n const slowest = data.kernels\n .map((a, i) => { a.id = i; return a; })\n .filter((a) => a.kernelTimeMs > 0)\n .sort((a, b) => b.kernelTimeMs - a.kernelTimeMs);\n const largest = data.kernels\n .map((a, i) => { a.id = i; return a; })\n .filter((a) => a.totalBytesSnapshot > 0)\n .sort((a, b) => b.totalBytesSnapshot - a.totalBytesSnapshot);\n if (slowest.length > maxResults) slowest.length = maxResults;\n if (largest.length > maxResults) largest.length = maxResults;\n const res = { newBytes: data.newBytes, newTensors: data.newTensors, peakBytes: data.peakBytes, numKernelOps: data.kernels.length, timeKernelOps: time, slowestKernelOps: slowest, largestKernelOps: largest };\n profileData[name] = res;\n log('Human profiler', name, res);\n}\n\nexports.run = profile;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile.js';\n\nconst models = {};\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nasync function load(config) {\n if (!models.age) {\n models.age = await tf.loadGraphModel(config.face.age.modelPath);\n log(`load model: ${config.face.age.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.age;\n}\n\nasync function predict(image, config) {\n if (!models.age) return null;\n if ((skipped < config.face.age.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n /*\n const zoom = [0, 0]; // 0..1 meaning 0%..100%\n const box = [[\n (image.shape[1] * zoom[0]) / image.shape[1],\n (image.shape[2] * zoom[1]) / image.shape[2],\n (image.shape[1] - (image.shape[1] * zoom[0])) / image.shape[1],\n (image.shape[2] - (image.shape[2] * zoom[1])) / image.shape[2],\n ]];\n const resize = tf.image.cropAndResize(image, box, [0], [config.face.age.inputSize, config.face.age.inputSize]);\n */\n const resize = tf.image.resizeBilinear(image, [config.face.age.inputSize, config.face.age.inputSize], false);\n const enhance = tf.mul(resize, [255.0]);\n tf.dispose(resize);\n\n let ageT;\n const obj = {};\n\n if (!config.profile) {\n if (config.face.age.enabled) ageT = await models.age.predict(enhance);\n } else {\n const profileAge = config.face.age.enabled ? await tf.profile(() => models.age.predict(enhance)) : {};\n ageT = profileAge.result.clone();\n profileAge.result.dispose();\n // @ts-ignore\n profile.run('age', profileAge);\n }\n enhance.dispose();\n\n if (ageT) {\n const data = ageT.dataSync();\n obj.age = Math.trunc(10 * data[0]) / 10;\n }\n ageT.dispose();\n\n last = obj;\n resolve(obj);\n });\n}\n\nexports.predict = predict;\nexports.load = load;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile.js';\n\nconst models = {};\nlet last = { gender: '' };\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet alternative = false;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nasync function load(config) {\n if (!models.gender) {\n models.gender = await tf.loadGraphModel(config.face.gender.modelPath);\n alternative = models.gender.inputs[0].shape[3] === 1;\n log(`load model: ${config.face.gender.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.gender;\n}\n\nasync function predict(image, config) {\n if (!models.gender) return null;\n if ((skipped < config.face.gender.skipFrames) && config.videoOptimized && last.gender !== '') {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [config.face.gender.inputSize, config.face.gender.inputSize], false);\n let enhance;\n if (alternative) {\n enhance = tf.tidy(() => {\n const [red, green, blue] = tf.split(resize, 3, 3);\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n return grayscale.sub(0.5).mul(2);\n });\n } else {\n enhance = tf.mul(resize, [255.0]);\n }\n tf.dispose(resize);\n\n let genderT;\n const obj = {};\n\n if (!config.profile) {\n if (config.face.gender.enabled) genderT = await models.gender.predict(enhance);\n } else {\n const profileGender = config.face.gender.enabled ? await tf.profile(() => models.gender.predict(enhance)) : {};\n genderT = profileGender.result.clone();\n profileGender.result.dispose();\n // @ts-ignore\n profile.run('gender', profileGender);\n }\n enhance.dispose();\n\n if (genderT) {\n const data = genderT.dataSync();\n if (alternative) {\n // returns two values 0..1, bigger one is prediction\n const confidence = Math.trunc(100 * Math.abs(data[0] - data[1])) / 100;\n if (confidence > config.face.gender.minConfidence) {\n obj.gender = data[0] > data[1] ? 'female' : 'male';\n obj.confidence = confidence;\n }\n } else {\n // returns one value 0..1, .5 is prediction threshold\n const confidence = Math.trunc(200 * Math.abs((data[0] - 0.5))) / 100;\n if (confidence > config.face.gender.minConfidence) {\n obj.gender = data[0] <= 0.5 ? 'female' : 'male';\n obj.confidence = Math.min(0.99, confidence);\n }\n }\n }\n genderT.dispose();\n\n last = obj;\n resolve(obj);\n });\n}\n\nexports.predict = predict;\nexports.load = load;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile.js';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];\nconst models = {};\nlet last = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\nconst scale = 1; // score multiplication factor\n\nasync function load(config) {\n if (!models.emotion) {\n models.emotion = await tf.loadGraphModel(config.face.emotion.modelPath);\n log(`load model: ${config.face.emotion.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.emotion;\n}\n\nasync function predict(image, config) {\n if (!models.emotion) return null;\n if ((skipped < config.face.emotion.skipFrames) && config.videoOptimized && (last.length > 0)) {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n /*\n const zoom = [0, 0]; // 0..1 meaning 0%..100%\n const box = [[\n (image.shape[1] * zoom[0]) / image.shape[1],\n (image.shape[2] * zoom[1]) / image.shape[2],\n (image.shape[1] - (image.shape[1] * zoom[0])) / image.shape[1],\n (image.shape[2] - (image.shape[2] * zoom[1])) / image.shape[2],\n ]];\n const resize = tf.image.cropAndResize(image, box, [0], [config.face.emotion.inputSize, config.face.emotion.inputSize]);\n */\n const resize = tf.image.resizeBilinear(image, [config.face.emotion.inputSize, config.face.emotion.inputSize], false);\n const [red, green, blue] = tf.split(resize, 3, 3);\n resize.dispose();\n // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n red.dispose();\n green.dispose();\n blue.dispose();\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n redNorm.dispose();\n greenNorm.dispose();\n blueNorm.dispose();\n const normalize = tf.tidy(() => grayscale.sub(0.5).mul(2));\n grayscale.dispose();\n const obj = [];\n if (config.face.emotion.enabled) {\n let data;\n if (!config.profile) {\n const emotionT = await models.emotion.predict(normalize);\n data = emotionT.dataSync();\n tf.dispose(emotionT);\n } else {\n const profileData = await tf.profile(() => models.emotion.predict(normalize));\n data = profileData.result.dataSync();\n profileData.result.dispose();\n // @ts-ignore\n profile.run('emotion', profileData);\n }\n for (let i = 0; i < data.length; i++) {\n if (scale * data[i] > config.face.emotion.minConfidence) obj.push({ score: Math.min(0.99, Math.trunc(100 * scale * data[i]) / 100), emotion: annotations[i] });\n }\n obj.sort((a, b) => b.score - a.score);\n }\n normalize.dispose();\n last = obj;\n resolve(obj);\n });\n}\n\nexports.predict = predict;\nexports.load = load;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile.js';\n\n// based on https://github.com/sirius-ai/MobileFaceNet_TF\n// model converted from https://github.com/sirius-ai/MobileFaceNet_TF/files/3551493/FaceMobileNet192_train_false.zip\n\nconst models = {};\n\nasync function load(config) {\n if (!models.embedding) {\n models.embedding = await tf.loadGraphModel(config.face.embedding.modelPath);\n log(`load model: ${config.face.embedding.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.embedding;\n}\n\nfunction simmilarity(embedding1, embedding2) {\n if (embedding1?.length !== embedding2?.length) return 0;\n // general minkowski distance\n // euclidean distance is limited case where order is 2\n const order = 2;\n const distance = 10.0 * ((embedding1.map((val, i) => (val - embedding2[i])).reduce((dist, diff) => dist + (diff ** order), 0) ** (1 / order)));\n return (Math.trunc(1000 * (1 - distance)) / 1000);\n}\n\nasync function predict(image, config) {\n if (!models.embedding) return null;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [config.face.embedding.inputSize, config.face.embedding.inputSize], false);\n // const normalize = tf.tidy(() => resize.div(127.5).sub(0.5)); // this is -0.5...0.5 ???\n let data = [];\n if (config.face.embedding.enabled) {\n if (!config.profile) {\n const embeddingT = await models.embedding.predict({ img_inputs: resize });\n data = [...embeddingT.dataSync()]; // convert object array to standard array\n tf.dispose(embeddingT);\n } else {\n const profileData = await tf.profile(() => models.embedding.predict({ img_inputs: resize }));\n data = [...profileData.result.dataSync()];\n profileData.result.dispose();\n // @ts-ignore\n profile.run('emotion', profileData);\n }\n }\n resize.dispose();\n // normalize.dispose();\n resolve(data);\n });\n}\n\nexports.predict = predict;\nexports.simmilarity = simmilarity;\nexports.load = load;\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nconst imageNetMean = [-123.15, -115.90, -103.06];\n\nfunction nameOutputResultsMobileNet(results) {\n const [offsets, heatmap, displacementFwd, displacementBwd] = results;\n return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nfunction nameOutputResultsResNet(results) {\n const [displacementFwd, displacementBwd, offsets, heatmap] = results;\n return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nclass BaseModel {\n constructor(model) {\n this.model = model;\n }\n\n predict(input, config) {\n return tf.tidy(() => {\n const asFloat = (config.body.modelType === 'ResNet') ? input.toFloat().add(imageNetMean) : input.toFloat().div(127.5).sub(1.0);\n const asBatch = asFloat.expandDims(0);\n const results = this.model.predict(asBatch);\n const results3d = results.map((y) => y.squeeze([0]));\n const namedResults = (config.body.modelType === 'ResNet') ? nameOutputResultsResNet(results3d) : nameOutputResultsMobileNet(results3d);\n return {\n heatmapScores: namedResults.heatmap.sigmoid(),\n offsets: namedResults.offsets,\n displacementFwd: namedResults.displacementFwd,\n displacementBwd: namedResults.displacementBwd,\n };\n });\n }\n\n dispose() {\n this.model.dispose();\n }\n}\nexports.BaseModel = BaseModel;\n", "// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nfunction half(k) {\n return Math.floor(k / 2);\n}\nclass MaxHeap {\n constructor(maxSize, getElementValue) {\n this.priorityQueue = new Array(maxSize);\n this.numberOfElements = -1;\n this.getElementValue = getElementValue;\n }\n\n enqueue(x) {\n this.priorityQueue[++this.numberOfElements] = x;\n this.swim(this.numberOfElements);\n }\n\n dequeue() {\n const max = this.priorityQueue[0];\n this.exchange(0, this.numberOfElements--);\n this.sink(0);\n this.priorityQueue[this.numberOfElements + 1] = null;\n return max;\n }\n\n empty() {\n return this.numberOfElements === -1;\n }\n\n size() {\n return this.numberOfElements + 1;\n }\n\n all() {\n return this.priorityQueue.slice(0, this.numberOfElements + 1);\n }\n\n max() {\n return this.priorityQueue[0];\n }\n\n swim(k) {\n while (k > 0 && this.less(half(k), k)) {\n this.exchange(k, half(k));\n k = half(k);\n }\n }\n\n sink(k) {\n while (2 * k <= this.numberOfElements) {\n let j = 2 * k;\n if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n if (!this.less(k, j)) break;\n this.exchange(k, j);\n k = j;\n }\n }\n\n getValueAt(i) {\n return this.getElementValue(this.priorityQueue[i]);\n }\n\n less(i, j) {\n return this.getValueAt(i) < this.getValueAt(j);\n }\n\n exchange(i, j) {\n const t = this.priorityQueue[i];\n this.priorityQueue[i] = this.priorityQueue[j];\n this.priorityQueue[j] = t;\n }\n}\nexports.MaxHeap = MaxHeap;\n", "import * as heapSort from './heapSort';\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores) {\n const [height, width] = scores.shape;\n let localMaximum = true;\n const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n localMaximum = false;\n break;\n }\n }\n if (!localMaximum) break;\n }\n return localMaximum;\n}\n\nfunction buildPartWithScoreQueue(scoreThreshold, localMaximumRadius, scores) {\n const [height, width, numKeypoints] = scores.shape;\n const queue = new heapSort.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n const score = scores.get(heatmapY, heatmapX, keypointId);\n // Only consider parts with score greater or equal to threshold as root candidates.\n if (score < scoreThreshold) continue;\n // Only consider keypoints whose score is maximum in a local window.\n if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores)) {\n queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n }\n }\n }\n }\n return queue;\n}\nexports.buildPartWithScoreQueue = buildPartWithScoreQueue;\n", "exports.partNames = [\n 'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n 'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n 'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexports.NUM_KEYPOINTS = exports.partNames.length;\n\nexports.partIds = exports.partNames.reduce((result, jointName, i) => {\n result[jointName] = i;\n return result;\n}, {});\n\nconst connectedPartNames = [\n ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexports.connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([exports.partIds[jointNameA], exports.partIds[jointNameB]]));\n\nexports.poseChain = [\n ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n ['rightKnee', 'rightAnkle'],\n];\n\nexports.partChannels = [\n 'left_face',\n 'right_face',\n 'right_upper_leg_front',\n 'right_lower_leg_back',\n 'right_upper_leg_back',\n 'left_lower_leg_front',\n 'left_upper_leg_front',\n 'left_upper_leg_back',\n 'left_lower_leg_back',\n 'right_feet',\n 'right_lower_leg_front',\n 'left_feet',\n 'torso_front',\n 'torso_back',\n 'right_upper_arm_front',\n 'right_upper_arm_back',\n 'right_lower_arm_back',\n 'left_lower_arm_front',\n 'left_upper_arm_front',\n 'left_upper_arm_back',\n 'left_lower_arm_back',\n 'right_hand',\n 'right_lower_arm_front',\n 'left_hand',\n];\n", "import * as kpt from './keypoints';\n\nfunction getOffsetPoint(y, x, keypoint, offsets) {\n return {\n y: offsets.get(y, x, keypoint),\n x: offsets.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n };\n}\nexports.getOffsetPoint = getOffsetPoint;\n\nfunction getImageCoords(part, outputStride, offsets) {\n const { heatmapY, heatmapX, id: keypoint } = part;\n const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n return {\n x: part.heatmapX * outputStride + x,\n y: part.heatmapY * outputStride + y,\n };\n}\nexports.getImageCoords = getImageCoords;\n\nfunction fillArray(element, size) {\n const result = new Array(size);\n for (let i = 0; i < size; i++) {\n result[i] = element;\n }\n return result;\n}\nexports.fillArray = fillArray;\n\nfunction clamp(a, min, max) {\n if (a < min) return min;\n if (a > max) return max;\n return a;\n}\nexports.clamp = clamp;\n\nfunction squaredDistance(y1, x1, y2, x2) {\n const dy = y2 - y1;\n const dx = x2 - x1;\n return dy * dy + dx * dx;\n}\nexports.squaredDistance = squaredDistance;\n\nfunction addVectors(a, b) {\n return { x: a.x + b.x, y: a.y + b.y };\n}\nexports.addVectors = addVectors;\n\nfunction clampVector(a, min, max) {\n return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\nexports.clampVector = clampVector;\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as kpt from './keypoints';\n\nfunction getPointsConfidence(heatmapScores, heatMapCoords) {\n const numKeypoints = heatMapCoords.shape[0];\n const result = new Float32Array(numKeypoints);\n for (let keypoint = 0; keypoint < numKeypoints; keypoint++) {\n const y = heatMapCoords.get(keypoint, 0);\n const x = heatMapCoords.get(keypoint, 1);\n result[keypoint] = heatmapScores.get(y, x, keypoint);\n }\n return result;\n}\nexports.getPointsConfidence = getPointsConfidence;\n\nfunction getOffsetPoint(y, x, keypoint, offsetsBuffer) {\n return {\n y: offsetsBuffer.get(y, x, keypoint),\n x: offsetsBuffer.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n };\n}\n\nfunction getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer) {\n const result = [];\n for (let keypoint = 0; keypoint < kpt.NUM_KEYPOINTS; keypoint++) {\n const heatmapY = heatMapCoordsBuffer.get(keypoint, 0).valueOf();\n const heatmapX = heatMapCoordsBuffer.get(keypoint, 1).valueOf();\n const { x, y } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsetsBuffer);\n result.push(y);\n result.push(x);\n }\n return tf.tensor2d(result, [kpt.NUM_KEYPOINTS, 2]);\n}\nexports.getOffsetVectors = getOffsetVectors;\n\nfunction getOffsetPoints(heatMapCoordsBuffer, outputStride, offsetsBuffer) {\n return tf.tidy(() => heatMapCoordsBuffer.toTensor().mul(tf.scalar(outputStride, 'int32')).toFloat().add(getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer)));\n}\nexports.getOffsetPoints = getOffsetPoints;\n\nfunction mod(a, b) {\n return tf.tidy(() => {\n const floored = a.div(tf.scalar(b, 'int32'));\n return a.sub(floored.mul(tf.scalar(b, 'int32')));\n });\n}\n\nfunction argmax2d(inputs) {\n const [height, width, depth] = inputs.shape;\n return tf.tidy(() => {\n const reshaped = inputs.reshape([height * width, depth]);\n const coords = reshaped.argMax(0);\n const yCoords = coords.div(tf.scalar(width, 'int32')).expandDims(1);\n const xCoords = mod(coords, width).expandDims(1);\n return tf.concat([yCoords, xCoords], 1);\n });\n}\nexports.argmax2d = argmax2d;\n", "import * as keypoints from './keypoints';\nimport * as vectors from './vectors';\nimport * as decoders from './decoders';\n\nconst parentChildrenTuples = keypoints.poseChain.map(([parentJoinName, childJoinName]) => ([keypoints.partIds[parentJoinName], keypoints.partIds[childJoinName]]));\nconst parentToChildEdges = parentChildrenTuples.map(([, childJointId]) => childJointId);\nconst childToParentEdges = parentChildrenTuples.map(([parentJointId]) => parentJointId);\nfunction getDisplacement(edgeId, point, displacements) {\n const numEdges = displacements.shape[2] / 2;\n return {\n y: displacements.get(point.y, point.x, edgeId),\n x: displacements.get(point.y, point.x, numEdges + edgeId),\n };\n}\nfunction getStridedIndexNearPoint(point, outputStride, height, width) {\n return {\n // @ts-ignore\n y: vectors.clamp(Math.round(point.y / outputStride), 0, height - 1),\n // @ts-ignore\n x: vectors.clamp(Math.round(point.x / outputStride), 0, width - 1),\n };\n}\n\nfunction traverseToTargetKeypoint(edgeId, sourceKeypoint, targetKeypointId, scoresBuffer, offsets, outputStride, displacements, offsetRefineStep = 2) {\n const [height, width] = scoresBuffer.shape;\n // Nearest neighbor interpolation for the source->target displacements.\n const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, outputStride, height, width);\n const displacement = getDisplacement(edgeId, sourceKeypointIndices, displacements);\n // @ts-ignore\n const displacedPoint = vectors.addVectors(sourceKeypoint.position, displacement);\n let targetKeypoint = displacedPoint;\n for (let i = 0; i < offsetRefineStep; i++) {\n const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n // @ts-ignore\n const offsetPoint = vectors.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetKeypointId, offsets);\n // @ts-ignore\n targetKeypoint = vectors.addVectors({\n x: targetKeypointIndices.x * outputStride,\n y: targetKeypointIndices.y * outputStride,\n }, { x: offsetPoint.x, y: offsetPoint.y });\n }\n const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n const score = scoresBuffer.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetKeypointId);\n return { position: targetKeypoint, part: keypoints.partNames[targetKeypointId], score };\n}\n\nfunction decodePose(root, scores, offsets, outputStride, displacementsFwd, displacementsBwd) {\n const numParts = scores.shape[2];\n const numEdges = parentToChildEdges.length;\n const instanceKeypoints = new Array(numParts);\n // Start a new detection instance at the position of the root.\n const { part: rootPart, score: rootScore } = root;\n // @ts-ignore\n const rootPoint = vectors.getImageCoords(rootPart, outputStride, offsets);\n instanceKeypoints[rootPart.id] = {\n score: rootScore,\n part: keypoints.partNames[rootPart.id],\n position: rootPoint,\n };\n // Decode the part positions upwards in the tree, following the backward displacements.\n for (let edge = numEdges - 1; edge >= 0; --edge) {\n const sourceKeypointId = parentToChildEdges[edge];\n const targetKeypointId = childToParentEdges[edge];\n if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsBwd);\n }\n }\n // Decode the part positions downwards in the tree, following the forward displacements.\n for (let edge = 0; edge < numEdges; ++edge) {\n const sourceKeypointId = childToParentEdges[edge];\n const targetKeypointId = parentToChildEdges[edge];\n if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsFwd);\n }\n }\n return instanceKeypoints;\n}\nexports.decodePose = decodePose;\n\nasync function decodeSinglePose(heatmapScores, offsets, config) {\n let totalScore = 0.0;\n // @ts-ignore\n const heatmapValues = decoders.argmax2d(heatmapScores);\n const allTensorBuffers = await Promise.all([heatmapScores.buffer(), offsets.buffer(), heatmapValues.buffer()]);\n const scoresBuffer = allTensorBuffers[0];\n const offsetsBuffer = allTensorBuffers[1];\n const heatmapValuesBuffer = allTensorBuffers[2];\n // @ts-ignore\n const offsetPoints = decoders.getOffsetPoints(heatmapValuesBuffer, config.body.outputStride, offsetsBuffer);\n const offsetPointsBuffer = await offsetPoints.buffer();\n // @ts-ignore\n const keypointConfidence = Array.from(decoders.getPointsConfidence(scoresBuffer, heatmapValuesBuffer));\n const instanceKeypoints = keypointConfidence.map((score, i) => {\n totalScore += score;\n return {\n position: {\n y: offsetPointsBuffer.get(i, 0),\n x: offsetPointsBuffer.get(i, 1),\n },\n part: keypoints.partNames[i],\n score,\n };\n });\n const filteredKeypoints = instanceKeypoints.filter((kpt) => kpt.score > config.body.scoreThreshold);\n heatmapValues.dispose();\n offsetPoints.dispose();\n return { keypoints: filteredKeypoints, score: totalScore / instanceKeypoints.length };\n}\nexports.decodeSinglePose = decodeSinglePose;\n", "import * as buildParts from './buildParts';\nimport * as decodePose from './decodePose';\nimport * as vectors from './vectors';\n\nconst kLocalMaximumRadius = 1;\n\nfunction withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, { x, y }, keypointId) {\n return poses.some(({ keypoints }) => {\n const correspondingKeypoint = keypoints[keypointId].position;\n // @ts-ignore\n return vectors.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n });\n}\n\nfunction getInstanceScore(existingPoses, squaredNmsRadius, instanceKeypoints) {\n const notOverlappedKeypointScores = instanceKeypoints.reduce((result, { position, score }, keypointId) => {\n if (!withinNmsRadiusOfCorrespondingPoint(existingPoses, squaredNmsRadius, position, keypointId)) result += score;\n return result;\n }, 0.0);\n return notOverlappedKeypointScores / instanceKeypoints.length;\n}\n\nfunction decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config) {\n const poses = [];\n // @ts-ignore\n const queue = buildParts.buildPartWithScoreQueue(config.body.scoreThreshold, kLocalMaximumRadius, scoresBuffer);\n const squaredNmsRadius = config.body.nmsRadius ^ 2;\n // Generate at most maxDetections object instances per image in decreasing root part score order.\n while (poses.length < config.body.maxDetections && !queue.empty()) {\n // The top element in the queue is the next root candidate.\n const root = queue.dequeue();\n // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n // @ts-ignore\n const rootImageCoords = vectors.getImageCoords(root.part, config.body.outputStride, offsetsBuffer);\n if (withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, rootImageCoords, root.part.id)) continue;\n // Else start a new detection instance at the position of the root.\n // @ts-ignore\n const keypoints = decodePose.decodePose(root, scoresBuffer, offsetsBuffer, config.body.outputStride, displacementsFwdBuffer, displacementsBwdBuffer);\n const score = getInstanceScore(poses, squaredNmsRadius, keypoints);\n if (score > config.body.scoreThreshold) poses.push({ keypoints, score });\n }\n return poses;\n}\nexports.decodeMultiplePoses = decodeMultiplePoses;\n", "import * as kpt from './keypoints';\n\nfunction eitherPointDoesntMeetConfidence(a, b, minConfidence) {\n return (a < minConfidence || b < minConfidence);\n}\n\nfunction getAdjacentKeyPoints(keypoints, minConfidence) {\n return kpt.connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n return result;\n }\n result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n return result;\n }, []);\n}\nexports.getAdjacentKeyPoints = getAdjacentKeyPoints;\n\nconst { NEGATIVE_INFINITY, POSITIVE_INFINITY } = Number;\nfunction getBoundingBox(keypoints) {\n return keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n maxX: Math.max(maxX, x),\n maxY: Math.max(maxY, y),\n minX: Math.min(minX, x),\n minY: Math.min(minY, y),\n }), {\n maxX: NEGATIVE_INFINITY,\n maxY: NEGATIVE_INFINITY,\n minX: POSITIVE_INFINITY,\n minY: POSITIVE_INFINITY,\n });\n}\nexports.getBoundingBox = getBoundingBox;\n\nfunction getBoundingBoxPoints(keypoints) {\n const { minX, minY, maxX, maxY } = getBoundingBox(keypoints);\n return [{ x: minX, y: minY }, { x: maxX, y: minY }, { x: maxX, y: maxY }, { x: minX, y: maxY }];\n}\nexports.getBoundingBoxPoints = getBoundingBoxPoints;\n\nasync function toTensorBuffers3D(tensors) {\n return Promise.all(tensors.map((tensor) => tensor.buffer()));\n}\nexports.toTensorBuffers3D = toTensorBuffers3D;\n\nfunction scalePose(pose, scaleY, scaleX) {\n return {\n score: pose.score,\n keypoints: pose.keypoints.map(({ score, part, position }) => ({\n score,\n part,\n position: { x: position.x * scaleX, y: position.y * scaleY },\n })),\n };\n}\nexports.scalePose = scalePose;\n\nfunction resizeTo(image, [targetH, targetW]) {\n const input = image.squeeze(0);\n const resized = input.resizeBilinear([targetH, targetW]);\n input.dispose();\n return resized;\n}\nexports.resizeTo = resizeTo;\n\nfunction scaleAndFlipPoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) {\n const scaledPoses = poses.map((pose) => scalePose(pose, height / inputResolutionHeight, width / inputResolutionWidth));\n return scaledPoses;\n}\nexports.scaleAndFlipPoses = scaleAndFlipPoses;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as modelBase from './modelBase';\nimport * as decodeMultiple from './decodeMultiple';\nimport * as decodePose from './decodePose';\nimport * as util from './util';\n\nasync function estimateMultiple(input, res, config) {\n return new Promise(async (resolve) => {\n const height = input.shape[1];\n const width = input.shape[2];\n // @ts-ignore\n const allTensorBuffers = await util.toTensorBuffers3D([res.heatmapScores, res.offsets, res.displacementFwd, res.displacementBwd]);\n const scoresBuffer = allTensorBuffers[0];\n const offsetsBuffer = allTensorBuffers[1];\n const displacementsFwdBuffer = allTensorBuffers[2];\n const displacementsBwdBuffer = allTensorBuffers[3];\n // @ts-ignore\n const poses = await decodeMultiple.decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config);\n // @ts-ignore\n const scaled = util.scaleAndFlipPoses(poses, [height, width], [config.body.inputSize, config.body.inputSize]);\n resolve(scaled);\n });\n}\n\nasync function estimateSingle(input, res, config) {\n return new Promise(async (resolve) => {\n const height = input.shape[1];\n const width = input.shape[2];\n // @ts-ignore\n const pose = await decodePose.decodeSinglePose(res.heatmapScores, res.offsets, config);\n const poses = [pose];\n // @ts-ignore\n const scaled = util.scaleAndFlipPoses(poses, [height, width], [config.body.inputSize, config.body.inputSize]);\n resolve(scaled);\n });\n}\n\nclass PoseNet {\n constructor(model) {\n this.baseModel = model;\n }\n\n async estimatePoses(input, config) {\n // @ts-ignore\n const resized = util.resizeTo(input, [config.body.inputSize, config.body.inputSize]);\n const res = this.baseModel.predict(resized, config);\n\n const poses = (config.body.maxDetections < 2) ? await estimateSingle(input, res, config) : await estimateMultiple(input, res, config);\n\n res.heatmapScores.dispose();\n res.offsets.dispose();\n res.displacementFwd.dispose();\n res.displacementBwd.dispose();\n resized.dispose();\n\n return poses;\n }\n\n dispose() {\n this.baseModel.dispose();\n }\n}\nexports.PoseNet = PoseNet;\n\nasync function load(config) {\n const model = await tf.loadGraphModel(config.body.modelPath);\n // @ts-ignore\n const mobilenet = new modelBase.BaseModel(model);\n log(`load model: ${config.body.modelPath.match(/\\/(.*)\\./)[1]}`);\n return new PoseNet(mobilenet);\n}\nexports.load = load;\n", "import * as modelPoseNet from './modelPoseNet';\nimport * as keypoints from './keypoints';\nimport * as util from './util';\n\n// @ts-ignore\nexports.load = modelPoseNet.load;\n// @ts-ignore\nexports.PoseNet = modelPoseNet.PoseNet;\n\nexports.partChannels = keypoints.partChannels;\nexports.partIds = keypoints.partIds;\nexports.partNames = keypoints.partNames;\nexports.poseChain = keypoints.poseChain;\n// @ts-ignore\nexports.getAdjacentKeyPoints = util.getAdjacentKeyPoints;\n// @ts-ignore\nexports.getBoundingBox = util.getBoundingBox;\n// @ts-ignore\nexports.getBoundingBoxPoints = util.getBoundingBoxPoints;\n// @ts-ignore\nexports.scaleAndFlipPoses = util.scaleAndFlipPoses;\n// @ts-ignore\nexports.scalePose = util.scalePose;\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\n\nclass HandDetector {\n constructor(model, inputSize, anchorsAnnotated) {\n this.model = model;\n this.anchors = anchorsAnnotated.map((anchor) => [anchor.x_center, anchor.y_center]);\n this.anchorsTensor = tf.tensor2d(this.anchors);\n this.inputSizeTensor = tf.tensor1d([inputSize, inputSize]);\n this.doubleInputSizeTensor = tf.tensor1d([inputSize * 2, inputSize * 2]);\n }\n\n normalizeBoxes(boxes) {\n return tf.tidy(() => {\n const boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n const boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n const boxCenterPoints = tf.add(tf.div(boxOffsets, this.inputSizeTensor), this.anchorsTensor);\n const halfBoxSizes = tf.div(boxSizes, this.doubleInputSizeTensor);\n const startPoints = tf.mul(tf.sub(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n const endPoints = tf.mul(tf.add(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n return tf.concat2d([startPoints, endPoints], 1);\n });\n }\n\n normalizeLandmarks(rawPalmLandmarks, index) {\n return tf.tidy(() => {\n const landmarks = tf.add(tf.div(rawPalmLandmarks.reshape([-1, 7, 2]), this.inputSizeTensor), this.anchors[index]);\n return tf.mul(landmarks, this.inputSizeTensor);\n });\n }\n\n async getBoxes(input, config) {\n const batched = this.model.predict(input);\n const predictions = batched.squeeze();\n batched.dispose();\n const scoresT = tf.tidy(() => tf.sigmoid(tf.slice(predictions, [0, 0], [-1, 1])).squeeze());\n const scores = scoresT.dataSync();\n const rawBoxes = tf.slice(predictions, [0, 1], [-1, 4]);\n const boxes = this.normalizeBoxes(rawBoxes);\n rawBoxes.dispose();\n const filteredT = await tf.image.nonMaxSuppressionAsync(boxes, scores, config.hand.maxHands, config.hand.iouThreshold, config.hand.scoreThreshold);\n const filtered = filteredT.arraySync();\n\n scoresT.dispose();\n filteredT.dispose();\n const hands = [];\n for (const index of filtered) {\n if (scores[index] >= config.hand.minConfidence) {\n const matchingBox = tf.slice(boxes, [index, 0], [1, -1]);\n const rawPalmLandmarks = tf.slice(predictions, [index, 5], [1, 14]);\n const palmLandmarks = tf.tidy(() => this.normalizeLandmarks(rawPalmLandmarks, index).reshape([-1, 2]));\n rawPalmLandmarks.dispose();\n hands.push({ box: matchingBox, palmLandmarks, confidence: scores[index] });\n }\n }\n predictions.dispose();\n boxes.dispose();\n return hands;\n }\n\n async estimateHandBounds(input, config) {\n const inputHeight = input.shape[1];\n const inputWidth = input.shape[2];\n const image = tf.tidy(() => input.resizeBilinear([config.hand.inputSize, config.hand.inputSize]).div(127.5).sub(1));\n const predictions = await this.getBoxes(image, config);\n image.dispose();\n const hands = [];\n if (!predictions || predictions.length === 0) return hands;\n for (const prediction of predictions) {\n const boxes = prediction.box.dataSync();\n const startPoint = boxes.slice(0, 2);\n const endPoint = boxes.slice(2, 4);\n const palmLandmarks = prediction.palmLandmarks.arraySync();\n prediction.box.dispose();\n prediction.palmLandmarks.dispose();\n hands.push(box.scaleBoxCoordinates({ startPoint, endPoint, palmLandmarks, confidence: prediction.confidence }, [inputWidth / config.hand.inputSize, inputHeight / config.hand.inputSize]));\n }\n return hands;\n }\n}\nexports.HandDetector = HandDetector;\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\nimport * as util from './util';\n// eslint-disable-next-line no-unused-vars\nimport { log } from '../log.js';\n\n// const PALM_BOX_SHIFT_VECTOR = [0, -0.4];\nconst PALM_BOX_ENLARGE_FACTOR = 5; // default 3\n// const HAND_BOX_SHIFT_VECTOR = [0, -0.1]; // move detected hand box by x,y to ease landmark detection\nconst HAND_BOX_ENLARGE_FACTOR = 1.65; // default 1.65\nconst PALM_LANDMARK_IDS = [0, 5, 9, 13, 17, 1, 2];\nconst PALM_LANDMARKS_INDEX_OF_PALM_BASE = 0;\nconst PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE = 2;\n\nclass HandPipeline {\n constructor(handDetector, landmarkDetector, inputSize) {\n this.handDetector = handDetector;\n this.landmarkDetector = landmarkDetector;\n this.inputSize = inputSize;\n this.storedBoxes = [];\n this.skipped = 0;\n this.detectedHands = 0;\n }\n\n getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n // return box.enlargeBox(box.squarifyBox(box.shiftBox(boxAroundPalm, PALM_BOX_SHIFT_VECTOR)), PALM_BOX_ENLARGE_FACTOR);\n return box.enlargeBox(box.squarifyBox(boxAroundPalm), PALM_BOX_ENLARGE_FACTOR);\n }\n\n getBoxForHandLandmarks(landmarks) {\n const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n // const boxAroundHand = box.enlargeBox(box.squarifyBox(box.shiftBox(boundingBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n const boxAroundHand = box.enlargeBox(box.squarifyBox(boundingBox), HAND_BOX_ENLARGE_FACTOR);\n boxAroundHand.palmLandmarks = [];\n for (let i = 0; i < PALM_LANDMARK_IDS.length; i++) {\n boxAroundHand.palmLandmarks.push(landmarks[PALM_LANDMARK_IDS[i]].slice(0, 2));\n }\n return boxAroundHand;\n }\n\n transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n const boxSize = box.getBoxSize(box2);\n const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n const coordsScaled = rawCoords.map((coord) => [\n scaleFactor[0] * (coord[0] - this.inputSize / 2),\n scaleFactor[1] * (coord[1] - this.inputSize / 2),\n scaleFactor[2] * coord[2],\n ]);\n const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n const coordsRotated = coordsScaled.map((coord) => {\n const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n return [...rotated, coord[2]];\n });\n const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n const boxCenter = [...box.getBoxCenter(box2), 1];\n const originalBoxCenter = [\n util.dot(boxCenter, inverseRotationMatrix[0]),\n util.dot(boxCenter, inverseRotationMatrix[1]),\n ];\n return coordsRotated.map((coord) => [\n coord[0] + originalBoxCenter[0],\n coord[1] + originalBoxCenter[1],\n coord[2],\n ]);\n }\n\n async estimateHands(image, config) {\n let useFreshBox = false;\n\n // run new detector every skipFrames unless we only want box to start with\n let boxes;\n if ((this.skipped === 0) || (this.skipped > config.hand.skipFrames) || !config.hand.landmarks || !config.videoOptimized) {\n boxes = await this.handDetector.estimateHandBounds(image, config);\n this.skipped = 0;\n }\n if (config.videoOptimized) this.skipped++;\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxHands) || !config.hand.landmarks)) {\n this.detectedHands = 0;\n this.storedBoxes = [...boxes];\n // for (const possible of boxes) this.storedBoxes.push(possible);\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\n const hands = [];\n // log('hand', `skipped: ${this.skipped} max: ${config.hand.maxHands} detected: ${this.detectedHands} stored: ${this.storedBoxes.length} new: ${boxes?.length}`);\n\n // go through working set of boxes\n for (let i = 0; i < this.storedBoxes.length; i++) {\n const currentBox = this.storedBoxes[i];\n if (!currentBox) continue;\n if (config.hand.landmarks) {\n const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_PALM_BASE], currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE]) : 0;\n const palmCenter = box.getBoxCenter(currentBox);\n const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n const rotatedImage = config.hand.rotation ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n const croppedInput = box.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n const handImage = croppedInput.div(255);\n croppedInput.dispose();\n rotatedImage.dispose();\n const [confidenceT, keypoints] = await this.landmarkDetector.predict(handImage);\n handImage.dispose();\n const confidence = confidenceT.dataSync()[0];\n confidenceT.dispose();\n if (confidence >= config.hand.minConfidence) {\n const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n const rawCoords = keypointsReshaped.arraySync();\n keypoints.dispose();\n keypointsReshaped.dispose();\n const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n this.storedBoxes[i] = nextBoundingBox;\n const result = {\n landmarks: coords,\n confidence,\n box: {\n topLeft: nextBoundingBox.startPoint,\n bottomRight: nextBoundingBox.endPoint,\n },\n };\n hands.push(result);\n } else {\n this.storedBoxes[i] = null;\n }\n keypoints.dispose();\n } else {\n // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n const enlarged = box.enlargeBox(box.squarifyBox(currentBox), HAND_BOX_ENLARGE_FACTOR);\n const result = {\n confidence: currentBox.confidence,\n box: {\n topLeft: enlarged.startPoint,\n bottomRight: enlarged.endPoint,\n },\n };\n hands.push(result);\n }\n }\n this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n this.detectedHands = hands.length;\n return hands;\n }\n\n // eslint-disable-next-line class-methods-use-this\n calculateLandmarksBoundingBox(landmarks) {\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint };\n }\n}\n\nexports.HandPipeline = HandPipeline;\n", "exports.anchors = [\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.796875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.796875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.828125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.828125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.859375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.859375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.890625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.890625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.921875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.921875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.953125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.953125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.984375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.984375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n 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0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n];\n", "// https://storage.googleapis.com/tfjs-models/demos/handpose/index.html\n\nimport { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as handdetector from './handdetector';\nimport * as handpipeline from './handpipeline';\nimport * as anchors from './anchors';\n\nconst MESH_ANNOTATIONS = {\n thumb: [1, 2, 3, 4],\n indexFinger: [5, 6, 7, 8],\n middleFinger: [9, 10, 11, 12],\n ringFinger: [13, 14, 15, 16],\n pinky: [17, 18, 19, 20],\n palmBase: [0],\n};\n\nclass HandPose {\n constructor(handPipeline) {\n this.handPipeline = handPipeline;\n }\n\n static getAnnotations() {\n return MESH_ANNOTATIONS;\n }\n\n async estimateHands(input, config) {\n const predictions = await this.handPipeline.estimateHands(input, config);\n if (!predictions) return [];\n const hands = [];\n for (const prediction of predictions) {\n const annotations = {};\n if (prediction.landmarks) {\n for (const key of Object.keys(MESH_ANNOTATIONS)) {\n annotations[key] = MESH_ANNOTATIONS[key].map((index) => prediction.landmarks[index]);\n }\n }\n const box = prediction.box ? [\n Math.max(0, prediction.box.topLeft[0]),\n Math.max(0, prediction.box.topLeft[1]),\n Math.min(input.shape[2], prediction.box.bottomRight[0]) - prediction.box.topLeft[0],\n Math.min(input.shape[1], prediction.box.bottomRight[1]) - prediction.box.topLeft[1],\n ] : 0;\n hands.push({\n confidence: prediction.confidence,\n box,\n landmarks: prediction.landmarks,\n annotations,\n });\n }\n return hands;\n }\n}\nexports.HandPose = HandPose;\n\nasync function load(config) {\n const [handDetectorModel, handPoseModel] = await Promise.all([\n config.hand.enabled ? tf.loadGraphModel(config.hand.detector.modelPath, { fromTFHub: config.hand.detector.modelPath.includes('tfhub.dev') }) : null,\n config.hand.landmarks ? tf.loadGraphModel(config.hand.skeleton.modelPath, { fromTFHub: config.hand.skeleton.modelPath.includes('tfhub.dev') }) : null,\n ]);\n // @ts-ignore\n const handDetector = new handdetector.HandDetector(handDetectorModel, config.hand.inputSize, anchors.anchors);\n // @ts-ignore\n const handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel, config.hand.inputSize);\n const handPose = new HandPose(handPipeline);\n if (config.hand.enabled) log(`load model: ${config.hand.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n if (config.hand.landmarks) log(`load model: ${config.hand.skeleton.modelPath.match(/\\/(.*)\\./)[1]}`);\n return handPose;\n}\nexports.load = load;\n", "exports.body = (res) => {\n if (!res) return [];\n const gestures = [];\n for (let i = 0; i < res.length; i++) {\n // raising hands\n const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n if (nose && leftWrist && rightWrist && (leftWrist.position.y < nose.position.y) && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'i give up' });\n else if (nose && leftWrist && (leftWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise left hand' });\n else if (nose && rightWrist && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise right hand' });\n\n // leaning\n const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n if (leftShoulder && rightShoulder) gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position.y > rightShoulder.position.y) ? 'left' : 'right'}` });\n }\n return gestures;\n};\n\nexports.face = (res) => {\n if (!res) return [];\n const gestures = [];\n for (let i = 0; i < res.length; i++) {\n if (res[i].mesh && res[i].mesh.length > 0) {\n const eyeFacing = res[i].mesh[35][2] - res[i].mesh[263][2];\n if (Math.abs(eyeFacing) < 10) gestures.push({ face: i, gesture: 'facing camera' });\n else gestures.push({ face: i, gesture: `facing ${eyeFacing < 0 ? 'right' : 'left'}` });\n const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n const chinDepth = res[i].mesh[152][2];\n if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n }\n }\n return gestures;\n};\n\nexports.iris = (res) => {\n if (!res) return [];\n const gestures = [];\n for (let i = 0; i < res.length; i++) {\n if (!res[i].annotations || !res[i].annotations.leftEyeIris || !res[i].annotations.rightEyeIris) continue;\n const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n if (difference < 0.25) gestures.push({ iris: i, gesture: 'looking at camera' });\n }\n return gestures;\n};\n\nexports.hand = (res) => {\n if (!res) return [];\n const gestures = [];\n for (let i = 0; i < res.length; i++) {\n const fingers = [];\n for (const [finger, pos] of Object.entries(res[i]['annotations'])) {\n if (finger !== 'palmBase') fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n }\n if (fingers && fingers.length > 0) {\n const closest = fingers.reduce((best, a) => (best.position[2] < a.position[2] ? best : a));\n const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n gestures.push({ hand: i, gesture: `${closest.name} forward ${highest.name} up` });\n }\n }\n return gestures;\n};\n", "/* eslint-disable no-use-before-define */\n/*\nWebGLImageFilter - MIT Licensed\n2013, Dominic Szablewski - phoboslab.org\n\n*/\n\nconst WebGLProgram = function (gl, vertexSource, fragmentSource) {\n const _collect = function (source, prefix, collection) {\n const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n source.replace(r, (match, name) => {\n collection[name] = 0;\n return match;\n });\n };\n\n const _compile = function (source, type) {\n const shader = gl.createShader(type);\n gl.shaderSource(shader, source);\n gl.compileShader(shader);\n\n if (!gl.getShaderParameter(shader, gl.COMPILE_STATUS)) {\n // @ts-ignore\n throw new Error('Filter: GL compile failed', gl.getShaderInfoLog(shader));\n }\n return shader;\n };\n\n this.uniform = {};\n this.attribute = {};\n\n const _vsh = _compile(vertexSource, gl.VERTEX_SHADER);\n const _fsh = _compile(fragmentSource, gl.FRAGMENT_SHADER);\n\n this.id = gl.createProgram();\n gl.attachShader(this.id, _vsh);\n gl.attachShader(this.id, _fsh);\n gl.linkProgram(this.id);\n\n if (!gl.getProgramParameter(this.id, gl.LINK_STATUS)) {\n // @ts-ignore\n throw new Error('Filter: GL link failed', gl.getProgramInfoLog(this.id));\n }\n\n gl.useProgram(this.id);\n\n // Collect attributes\n _collect(vertexSource, 'attribute', this.attribute);\n for (const a in this.attribute) {\n this.attribute[a] = gl.getAttribLocation(this.id, a);\n }\n\n // Collect uniforms\n _collect(vertexSource, 'uniform', this.uniform);\n _collect(fragmentSource, 'uniform', this.uniform);\n for (const u in this.uniform) {\n this.uniform[u] = gl.getUniformLocation(this.id, u);\n }\n};\n\nconst WebGLImageFilter = function (params) {\n if (!params) params = { };\n let _drawCount = 0;\n let _sourceTexture = null;\n let _lastInChain = false;\n let _currentFramebufferIndex = -1;\n let _tempFramebuffers = [null, null];\n let _filterChain = [];\n let _width = -1;\n let _height = -1;\n let _vertexBuffer = null;\n let _currentProgram = null;\n const _canvas = params.canvas || document.createElement('canvas');\n\n // key is the shader program source, value is the compiled program\n const _shaderProgramCache = { };\n\n const gl = _canvas.getContext('webgl');\n if (!gl) throw new Error('Filter: getContext() failed');\n\n this.addFilter = function (name) {\n // eslint-disable-next-line prefer-rest-params\n const args = Array.prototype.slice.call(arguments, 1);\n const filter = _filter[name];\n\n _filterChain.push({ func: filter, args });\n };\n\n this.reset = function () {\n _filterChain = [];\n };\n\n this.apply = function (image) {\n _resize(image.width, image.height);\n _drawCount = 0;\n\n // Create the texture for the input image if we haven't yet\n if (!_sourceTexture) _sourceTexture = gl.createTexture();\n gl.bindTexture(gl.TEXTURE_2D, _sourceTexture);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n\n // No filters? Just draw\n if (_filterChain.length === 0) {\n // const program = _compileShader(SHADER.FRAGMENT_IDENTITY);\n _draw();\n return _canvas;\n }\n\n for (let i = 0; i < _filterChain.length; i++) {\n _lastInChain = (i === _filterChain.length - 1);\n const f = _filterChain[i];\n f.func.apply(this, f.args || []);\n }\n\n return _canvas;\n };\n\n const _resize = function (width, height) {\n // Same width/height? Nothing to do here\n if (width === _width && height === _height) { return; }\n\n _canvas.width = width;\n _width = width;\n _canvas.height = height;\n _height = height;\n\n // Create the context if we don't have it yet\n if (!_vertexBuffer) {\n // Create the vertex buffer for the two triangles [x, y, u, v] * 6\n const vertices = new Float32Array([\n -1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0,\n -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0,\n ]);\n // eslint-disable-next-line no-unused-expressions\n (_vertexBuffer = gl.createBuffer(), gl.bindBuffer(gl.ARRAY_BUFFER, _vertexBuffer));\n gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);\n\n // Note sure if this is a good idea; at least it makes texture loading\n // in Ejecta instant.\n gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n }\n\n gl.viewport(0, 0, _width, _height);\n\n // Delete old temp framebuffers\n _tempFramebuffers = [null, null];\n };\n\n const _getTempFramebuffer = function (index) {\n // @ts-ignore\n _tempFramebuffers[index] = _tempFramebuffers[index] || _createFramebufferTexture(_width, _height);\n return _tempFramebuffers[index];\n };\n\n const _createFramebufferTexture = function (width, height) {\n const fbo = gl.createFramebuffer();\n gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n\n const renderbuffer = gl.createRenderbuffer();\n gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n\n const texture = gl.createTexture();\n gl.bindTexture(gl.TEXTURE_2D, texture);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n\n gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n\n gl.bindTexture(gl.TEXTURE_2D, null);\n gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n\n return { fbo, texture };\n };\n\n const _draw = function (flags) {\n let source = null;\n let target = null;\n let flipY = false;\n\n // Set up the source\n if (_drawCount === 0) {\n // First draw call - use the source texture\n source = _sourceTexture;\n } else {\n // All following draw calls use the temp buffer last drawn to\n // @ts-ignore\n source = _getTempFramebuffer(_currentFramebufferIndex)?.texture;\n }\n _drawCount++;\n\n // Set up the target\n if (_lastInChain && !(flags & DRAW.INTERMEDIATE)) {\n // Last filter in our chain - draw directly to the WebGL Canvas. We may\n // also have to flip the image vertically now\n target = null;\n flipY = _drawCount % 2 === 0;\n } else {\n // Intermediate draw call - get a temp buffer to draw to\n _currentFramebufferIndex = (_currentFramebufferIndex + 1) % 2;\n // @ts-ignore\n target = _getTempFramebuffer(_currentFramebufferIndex)?.fbo;\n }\n\n // Bind the source and target and draw the two triangles\n gl.bindTexture(gl.TEXTURE_2D, source);\n gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n\n gl.uniform1f(_currentProgram.uniform.flipY, (flipY ? -1 : 1));\n gl.drawArrays(gl.TRIANGLES, 0, 6);\n };\n\n const _compileShader = function (fragmentSource) {\n if (_shaderProgramCache[fragmentSource]) {\n _currentProgram = _shaderProgramCache[fragmentSource];\n gl.useProgram(_currentProgram.id);\n return _currentProgram;\n }\n\n // Compile shaders\n _currentProgram = new WebGLProgram(gl, SHADER.VERTEX_IDENTITY, fragmentSource);\n\n const floatSize = Float32Array.BYTES_PER_ELEMENT;\n const vertSize = 4 * floatSize;\n gl.enableVertexAttribArray(_currentProgram.attribute.pos);\n gl.vertexAttribPointer(_currentProgram.attribute.pos, 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n gl.enableVertexAttribArray(_currentProgram.attribute.uv);\n gl.vertexAttribPointer(_currentProgram.attribute.uv, 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n\n _shaderProgramCache[fragmentSource] = _currentProgram;\n return _currentProgram;\n };\n\n let DRAW = { INTERMEDIATE: 1 };\n\n let SHADER = {};\n SHADER.VERTEX_IDENTITY = [\n 'precision highp float;',\n 'attribute vec2 pos;',\n 'attribute vec2 uv;',\n 'varying vec2 vUv;',\n 'uniform float flipY;',\n\n 'void main(void) {',\n 'vUv = uv;',\n 'gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);',\n '}',\n ].join('\\n');\n\n SHADER.FRAGMENT_IDENTITY = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n\n 'void main(void) {',\n 'gl_FragColor = texture2D(texture, vUv);',\n '}',\n ].join('\\n');\n\n let _filter = {};\n\n // -------------------------------------------------------------------------\n // Color Matrix Filter\n\n _filter.colorMatrix = function (matrix) {\n // Create a Float32 Array and normalize the offset component to 0-1\n const m = new Float32Array(matrix);\n m[4] /= 255;\n m[9] /= 255;\n m[14] /= 255;\n m[19] /= 255;\n\n // Can we ignore the alpha value? Makes things a bit faster.\n const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0)\n ? _filter.colorMatrix.SHADER.WITHOUT_ALPHA\n : _filter.colorMatrix.SHADER.WITH_ALPHA;\n\n const program = _compileShader(shader);\n gl.uniform1fv(program.uniform.m, m);\n _draw();\n };\n\n _filter.colorMatrix.SHADER = {};\n _filter.colorMatrix.SHADER.WITH_ALPHA = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform float m[20];',\n\n 'void main(void) {',\n 'vec4 c = texture2D(texture, vUv);',\n 'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];',\n 'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];',\n 'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];',\n 'gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];',\n '}',\n ].join('\\n');\n _filter.colorMatrix.SHADER.WITHOUT_ALPHA = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform float m[20];',\n\n 'void main(void) {',\n 'vec4 c = texture2D(texture, vUv);',\n 'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];',\n 'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];',\n 'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];',\n 'gl_FragColor.a = c.a;',\n '}',\n ].join('\\n');\n\n _filter.brightness = function (brightness) {\n const b = (brightness || 0) + 1;\n _filter.colorMatrix([\n b, 0, 0, 0, 0,\n 0, b, 0, 0, 0,\n 0, 0, b, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.saturation = function (amount) {\n const x = (amount || 0) * 2 / 3 + 1;\n const y = ((x - 1) * -0.5);\n _filter.colorMatrix([\n x, y, y, 0, 0,\n y, x, y, 0, 0,\n y, y, x, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.desaturate = function () {\n _filter.saturation(-1);\n };\n\n _filter.contrast = function (amount) {\n const v = (amount || 0) + 1;\n const o = -128 * (v - 1);\n\n _filter.colorMatrix([\n v, 0, 0, 0, o,\n 0, v, 0, 0, o,\n 0, 0, v, 0, o,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.negative = function () {\n _filter.contrast(-2);\n };\n\n _filter.hue = function (rotation) {\n rotation = (rotation || 0) / 180 * Math.PI;\n const cos = Math.cos(rotation);\n const sin = Math.sin(rotation);\n const lumR = 0.213;\n const lumG = 0.715;\n const lumB = 0.072;\n\n _filter.colorMatrix([\n lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.desaturateLuminance = function () {\n _filter.colorMatrix([\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.sepia = function () {\n _filter.colorMatrix([\n 0.393, 0.7689999, 0.18899999, 0, 0,\n 0.349, 0.6859999, 0.16799999, 0, 0,\n 0.272, 0.5339999, 0.13099999, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.brownie = function () {\n _filter.colorMatrix([\n 0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n 0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.vintagePinhole = function () {\n _filter.colorMatrix([\n 0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n 0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n 0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.kodachrome = function () {\n _filter.colorMatrix([\n 1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.technicolor = function () {\n _filter.colorMatrix([\n 1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.polaroid = function () {\n _filter.colorMatrix([\n 1.438, -0.062, -0.062, 0, 0,\n -0.122, 1.378, -0.122, 0, 0,\n -0.016, -0.016, 1.483, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.shiftToBGR = function () {\n _filter.colorMatrix([\n 0, 0, 1, 0, 0,\n 0, 1, 0, 0, 0,\n 1, 0, 0, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n // -------------------------------------------------------------------------\n // Convolution Filter\n\n _filter.convolution = function (matrix) {\n const m = new Float32Array(matrix);\n const pixelSizeX = 1 / _width;\n const pixelSizeY = 1 / _height;\n\n const program = _compileShader(_filter.convolution.SHADER);\n gl.uniform1fv(program.uniform.m, m);\n gl.uniform2f(program.uniform.px, pixelSizeX, pixelSizeY);\n _draw();\n };\n\n _filter.convolution.SHADER = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform vec2 px;',\n 'uniform float m[9];',\n\n 'void main(void) {',\n 'vec4 c11 = texture2D(texture, vUv - px);', // top left\n 'vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y));', // top center\n 'vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y));', // top right\n\n 'vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) );', // mid left\n 'vec4 c22 = texture2D(texture, vUv);', // mid center\n 'vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) );', // mid right\n\n 'vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) );', // bottom left\n 'vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) );', // bottom center\n 'vec4 c33 = texture2D(texture, vUv + px );', // bottom right\n\n 'gl_FragColor = ',\n 'c11 * m[0] + c12 * m[1] + c22 * m[2] +',\n 'c21 * m[3] + c22 * m[4] + c23 * m[5] +',\n 'c31 * m[6] + c32 * m[7] + c33 * m[8];',\n 'gl_FragColor.a = c22.a;',\n '}',\n ].join('\\n');\n\n _filter.detectEdges = function () {\n _filter.convolution.call(this, [\n 0, 1, 0,\n 1, -4, 1,\n 0, 1, 0,\n ]);\n };\n\n _filter.sobelX = function () {\n _filter.convolution.call(this, [\n -1, 0, 1,\n -2, 0, 2,\n -1, 0, 1,\n ]);\n };\n\n _filter.sobelY = function () {\n _filter.convolution.call(this, [\n -1, -2, -1,\n 0, 0, 0,\n 1, 2, 1,\n ]);\n };\n\n _filter.sharpen = function (amount) {\n const a = amount || 1;\n _filter.convolution.call(this, [\n 0, -1 * a, 0,\n -1 * a, 1 + 4 * a, -1 * a,\n 0, -1 * a, 0,\n ]);\n };\n\n _filter.emboss = function (size) {\n const s = size || 1;\n _filter.convolution.call(this, [\n -2 * s, -1 * s, 0,\n -1 * s, 1, 1 * s,\n 0, 1 * s, 2 * s,\n ]);\n };\n\n // -------------------------------------------------------------------------\n // Blur Filter\n\n _filter.blur = function (size) {\n const blurSizeX = (size / 7) / _width;\n const blurSizeY = (size / 7) / _height;\n\n const program = _compileShader(_filter.blur.SHADER);\n\n // Vertical\n gl.uniform2f(program.uniform.px, 0, blurSizeY);\n _draw(DRAW.INTERMEDIATE);\n\n // Horizontal\n gl.uniform2f(program.uniform.px, blurSizeX, 0);\n _draw();\n };\n\n _filter.blur.SHADER = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform vec2 px;',\n\n 'void main(void) {',\n 'gl_FragColor = vec4(0.0);',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;',\n 'gl_FragColor += texture2D(texture, vUv )*0.159576912161;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x, 1.0*px.y))*0.147308056121;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x, 2.0*px.y))*0.115876621105;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x, 3.0*px.y))*0.0776744219933;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x, 4.0*px.y))*0.0443683338718;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x, 5.0*px.y))*0.0215963866053;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265;',\n '}',\n ].join('\\n');\n\n // -------------------------------------------------------------------------\n // Pixelate Filter\n\n _filter.pixelate = function (size) {\n const blurSizeX = (size) / _width;\n const blurSizeY = (size) / _height;\n\n const program = _compileShader(_filter.pixelate.SHADER);\n\n // Horizontal\n gl.uniform2f(program.uniform.size, blurSizeX, blurSizeY);\n _draw();\n };\n\n _filter.pixelate.SHADER = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform vec2 size;',\n 'uniform sampler2D texture;',\n\n 'vec2 pixelate(vec2 coord, vec2 size) {',\n 'return floor( coord / size ) * size;',\n '}',\n\n 'void main(void) {',\n 'gl_FragColor = vec4(0.0);',\n 'vec2 coord = pixelate(vUv, size);',\n 'gl_FragColor += texture2D(texture, coord);',\n '}',\n ].join('\\n');\n};\n\nexports.Canvas = WebGLImageFilter;\n", "import { log } from './log.js';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as fxImage from './imagefx.js';\n\n// internal temp canvases\nlet inCanvas = null;\nlet outCanvas = null;\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nfunction process(input, config) {\n let tensor;\n if (input instanceof tf.Tensor) {\n tensor = tf.clone(input);\n } else {\n const originalWidth = input.naturalWidth || input.videoWidth || input.width || (input.shape && (input.shape[1] > 0));\n const originalHeight = input.naturalHeight || input.videoHeight || input.height || (input.shape && (input.shape[2] > 0));\n let targetWidth = originalWidth;\n let targetHeight = originalHeight;\n if (config.filter.width > 0) targetWidth = config.filter.width;\n else if (config.filter.height > 0) targetWidth = originalWidth * (config.filter.height / originalHeight);\n if (config.filter.height > 0) targetHeight = config.filter.height;\n else if (config.filter.width > 0) targetHeight = originalHeight * (config.filter.width / originalWidth);\n if (!targetWidth || !targetHeight) {\n log('Human: invalid input', input);\n return null;\n }\n if (!inCanvas || (inCanvas.width !== targetWidth) || (inCanvas.height !== targetHeight)) {\n inCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n if (inCanvas.width !== targetWidth) inCanvas.width = targetWidth;\n if (inCanvas.height !== targetHeight) inCanvas.height = targetHeight;\n }\n const ctx = inCanvas.getContext('2d');\n if (input instanceof ImageData) ctx.putImageData(input, 0, 0);\n else ctx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);\n if (config.filter.enabled) {\n if (!this.fx || !outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas.height !== outCanvas.height)) {\n outCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(inCanvas.width, inCanvas.height) : document.createElement('canvas');\n if (outCanvas.width !== inCanvas.width) outCanvas.width = inCanvas.width;\n if (outCanvas.height !== inCanvas.height) outCanvas.height = inCanvas.height;\n this.fx = tf.ENV.flags.IS_BROWSER ? new fxImage.Canvas({ canvas: outCanvas }) : null; // && (typeof document !== 'undefined')\n }\n if (!this.fx) return inCanvas;\n this.fx.reset();\n this.fx.addFilter('brightness', config.filter.brightness); // must have at least one filter enabled\n if (config.filter.contrast !== 0) this.fx.addFilter('contrast', config.filter.contrast);\n if (config.filter.sharpness !== 0) this.fx.addFilter('sharpen', config.filter.sharpness);\n if (config.filter.blur !== 0) this.fx.addFilter('blur', config.filter.blur);\n if (config.filter.saturation !== 0) this.fx.addFilter('saturation', config.filter.saturation);\n if (config.filter.hue !== 0) this.fx.addFilter('hue', config.filter.hue);\n if (config.filter.negative) this.fx.addFilter('negative');\n if (config.filter.sepia) this.fx.addFilter('sepia');\n if (config.filter.vintage) this.fx.addFilter('brownie');\n if (config.filter.sepia) this.fx.addFilter('sepia');\n if (config.filter.kodachrome) this.fx.addFilter('kodachrome');\n if (config.filter.technicolor) this.fx.addFilter('technicolor');\n if (config.filter.polaroid) this.fx.addFilter('polaroid');\n if (config.filter.pixelate !== 0) this.fx.addFilter('pixelate', config.filter.pixelate);\n this.fx.apply(inCanvas);\n // read pixel data\n /*\n const gl = outCanvas.getContext('webgl');\n if (gl) {\n const glBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 4);\n const pixBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 3);\n gl.readPixels(0, 0, outCanvas.width, outCanvas.height, gl.RGBA, gl.UNSIGNED_BYTE, glBuffer);\n // gl returns rbga while we only need rgb, so discarding alpha channel\n // gl returns starting point as lower left, so need to invert vertical\n let i = 0;\n for (let y = outCanvas.height - 1; y >= 0; y--) {\n for (let x = 0; x < outCanvas.width; x++) {\n const index = (x + y * outCanvas.width) * 4;\n pixBuffer[i++] = glBuffer[index + 0];\n pixBuffer[i++] = glBuffer[index + 1];\n pixBuffer[i++] = glBuffer[index + 2];\n }\n }\n outCanvas.data = pixBuffer;\n }\n */\n } else {\n outCanvas = inCanvas;\n }\n let pixels;\n if (outCanvas.data) {\n const shape = [outCanvas.height, outCanvas.width, 3];\n pixels = tf.tensor3d(outCanvas.data, shape, 'int32');\n } else if ((config.backend === 'webgl') || (outCanvas instanceof ImageData)) {\n // tf kernel-optimized method to get imagedata, also if input is imagedata, just use it\n pixels = tf.browser.fromPixels(outCanvas);\n } else {\n // cpu and wasm kernel does not implement efficient fromPixels method nor we can use canvas as-is, so we do a silly one more canvas\n const tempCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n tempCanvas.width = targetWidth;\n tempCanvas.height = targetHeight;\n const tempCtx = tempCanvas.getContext('2d');\n tempCtx?.drawImage(outCanvas, 0, 0);\n const data = tempCtx?.getImageData(0, 0, targetWidth, targetHeight);\n pixels = tf.browser.fromPixels(data);\n }\n const casted = pixels.toFloat();\n tensor = casted.expandDims(0);\n pixels.dispose();\n casted.dispose();\n }\n return { tensor, canvas: config.filter.return ? outCanvas : null };\n}\n\nexports.process = process;\n", "// helper function: wrapper around console output\nexport function log(...msg) {\n const dt = new Date();\n const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n // eslint-disable-next-line no-console\n if (msg) console.log(ts, 'Human:', ...msg);\n}\n", "", "// A port of an algorithm by Johannes Baag\u00F8e , 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e \n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n// \n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n// \n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n var me = this, mash = Mash();\n\n me.next = function() {\n var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n me.s0 = me.s1;\n me.s1 = me.s2;\n return me.s2 = t - (me.c = t | 0);\n };\n\n // Apply the seeding algorithm from Baagoe.\n me.c = 1;\n me.s0 = mash(' ');\n me.s1 = mash(' ');\n me.s2 = mash(' ');\n me.s0 -= mash(seed);\n if (me.s0 < 0) { me.s0 += 1; }\n me.s1 -= mash(seed);\n if (me.s1 < 0) { me.s1 += 1; }\n me.s2 -= mash(seed);\n if (me.s2 < 0) { me.s2 += 1; }\n mash = null;\n}\n\nfunction copy(f, t) {\n t.c = f.c;\n t.s0 = f.s0;\n t.s1 = f.s1;\n t.s2 = f.s2;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new Alea(seed),\n state = opts && opts.state,\n prng = xg.next;\n prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n prng.double = function() {\n return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n };\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nfunction Mash() {\n var n = 0xefc8249d;\n\n var mash = function(data) {\n data = data.toString();\n for (var i = 0; i < data.length; i++) {\n n += data.charCodeAt(i);\n var h = 0.02519603282416938 * n;\n n = h >>> 0;\n h -= n;\n h *= n;\n n = h >>> 0;\n h -= n;\n n += h * 0x100000000; // 2^32\n }\n return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n };\n\n return mash;\n}\n\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.alea = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n\n // Set up generator function.\n me.next = function() {\n var t = me.x ^ (me.x << 11);\n me.x = me.y;\n me.y = me.z;\n me.z = me.w;\n return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n };\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor128 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var t = (me.x ^ (me.x >>> 2));\n me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n return (me.d = (me.d + 362437 | 0)) +\n (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n };\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n me.v = 0;\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n if (k == strseed.length) {\n me.d = me.x << 10 ^ me.x >>> 4;\n }\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n t.v = f.v;\n t.d = f.d;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorwow = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n // Update xor generator.\n var X = me.x, i = me.i, t, v, w;\n t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n X[i] = v;\n me.i = (i + 1) & 7;\n return v;\n };\n\n function init(me, seed) {\n var j, w, X = [];\n\n if (seed === (seed | 0)) {\n // Seed state array using a 32-bit integer.\n w = X[0] = seed;\n } else {\n // Seed state using a string.\n seed = '' + seed;\n for (j = 0; j < seed.length; ++j) {\n X[j & 7] = (X[j & 7] << 15) ^\n (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n }\n }\n // Enforce an array length of 8, not all zeroes.\n while (X.length < 8) X.push(0);\n for (j = 0; j < 8 && X[j] === 0; ++j);\n if (j == 8) w = X[7] = -1; else w = X[j];\n\n me.x = X;\n me.i = 0;\n\n // Discard an initial 256 values.\n for (j = 256; j > 0; --j) {\n me.next();\n }\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.x = f.x.slice();\n t.i = f.i;\n return t;\n}\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.x) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorshift7 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated. For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1); // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897); // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C. This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n var w = me.w,\n X = me.X, i = me.i, t, v;\n // Update Weyl generator.\n me.w = w = (w + 0x61c88647) | 0;\n // Update xor generator.\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n // Update Xor generator array state.\n v = X[i] = v ^ t;\n me.i = i;\n // Result is the combination.\n return (v + (w ^ (w >>> 16))) | 0;\n };\n\n function init(me, seed) {\n var t, v, i, j, w, X = [], limit = 128;\n if (seed === (seed | 0)) {\n // Numeric seeds initialize v, which is used to generates X.\n v = seed;\n seed = null;\n } else {\n // String seeds are mixed into v and X one character at a time.\n seed = seed + '\\0';\n v = 0;\n limit = Math.max(limit, seed.length);\n }\n // Initialize circular array and weyl value.\n for (i = 0, j = -32; j < limit; ++j) {\n // Put the unicode characters into the array, and shuffle them.\n if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n // After 32 shuffles, take v as the starting w value.\n if (j === 0) w = v;\n v ^= v << 10;\n v ^= v >>> 15;\n v ^= v << 4;\n v ^= v >>> 13;\n if (j >= 0) {\n w = (w + 0x61c88647) | 0; // Weyl.\n t = (X[j & 127] ^= (v + w)); // Combine xor and weyl to init array.\n i = (0 == t) ? i + 1 : 0; // Count zeroes.\n }\n }\n // We have detected all zeroes; make the key nonzero.\n if (i >= 128) {\n X[(seed && seed.length || 0) & 127] = -1;\n }\n // Run the generator 512 times to further mix the state before using it.\n // Factoring this as a function slows the main generator, so it is just\n // unrolled here. The weyl generator is not advanced while warming up.\n i = 127;\n for (j = 4 * 128; j > 0; --j) {\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n X[i] = v ^ t;\n }\n // Storing state as object members is faster than using closure variables.\n me.w = w;\n me.X = X;\n me.i = i;\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.i = f.i;\n t.w = f.w;\n t.X = f.X.slice();\n return t;\n};\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.X) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor4096 = impl;\n}\n\n})(\n this, // window object or global\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var b = me.b, c = me.c, d = me.d, a = me.a;\n b = (b << 25) ^ (b >>> 7) ^ c;\n c = (c - d) | 0;\n d = (d << 24) ^ (d >>> 8) ^ a;\n a = (a - b) | 0;\n me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n me.c = c = (c - d) | 0;\n me.d = (d << 16) ^ (c >>> 16) ^ a;\n return me.a = (a - b) | 0;\n };\n\n /* The following is non-inverted tyche, which has better internal\n * bit diffusion, but which is about 25% slower than tyche-i in JS.\n me.next = function() {\n var a = me.a, b = me.b, c = me.c, d = me.d;\n a = (me.a + me.b | 0) >>> 0;\n d = me.d ^ a; d = d << 16 ^ d >>> 16;\n c = me.c + d | 0;\n b = me.b ^ c; b = b << 12 ^ d >>> 20;\n me.a = a = a + b | 0;\n d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n me.c = c = c + d | 0;\n b = b ^ c;\n return me.b = (b << 7 ^ b >>> 25);\n }\n */\n\n me.a = 0;\n me.b = 0;\n me.c = 2654435769 | 0;\n me.d = 1367130551;\n\n if (seed === Math.floor(seed)) {\n // Integer seed.\n me.a = (seed / 0x100000000) | 0;\n me.b = seed | 0;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 20; k++) {\n me.b ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.a = f.a;\n t.b = f.b;\n t.c = f.c;\n t.d = f.d;\n return t;\n};\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.tychei = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "", "/*\nCopyright 2014 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\nvar global = this,\n width = 256, // each RC4 output is 0 <= x < 256\n chunks = 6, // at least six RC4 outputs for each double\n digits = 52, // there are 52 significant digits in a double\n rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n startdenom = math.pow(width, chunks),\n significance = math.pow(2, digits),\n overflow = significance * 2,\n mask = width - 1,\n nodecrypto; // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n var key = [];\n options = (options == true) ? { entropy: true } : (options || {});\n\n // Flatten the seed string or build one from local entropy if needed.\n var shortseed = mixkey(flatten(\n options.entropy ? [seed, tostring(pool)] :\n (seed == null) ? autoseed() : seed, 3), key);\n\n // Use the seed to initialize an ARC4 generator.\n var arc4 = new ARC4(key);\n\n // This function returns a random double in [0, 1) that contains\n // randomness in every bit of the mantissa of the IEEE 754 value.\n var prng = function() {\n var n = arc4.g(chunks), // Start with a numerator n < 2 ^ 48\n d = startdenom, // and denominator d = 2 ^ 48.\n x = 0; // and no 'extra last byte'.\n while (n < significance) { // Fill up all significant digits by\n n = (n + x) * width; // shifting numerator and\n d *= width; // denominator and generating a\n x = arc4.g(1); // new least-significant-byte.\n }\n while (n >= overflow) { // To avoid rounding up, before adding\n n /= 2; // last byte, shift everything\n d /= 2; // right using integer math until\n x >>>= 1; // we have exactly the desired bits.\n }\n return (n + x) / d; // Form the number within [0, 1).\n };\n\n prng.int32 = function() { return arc4.g(4) | 0; }\n prng.quick = function() { return arc4.g(4) / 0x100000000; }\n prng.double = prng;\n\n // Mix the randomness into accumulated entropy.\n mixkey(tostring(arc4.S), pool);\n\n // Calling convention: what to return as a function of prng, seed, is_math.\n return (options.pass || callback ||\n function(prng, seed, is_math_call, state) {\n if (state) {\n // Load the arc4 state from the given state if it has an S array.\n if (state.S) { copy(state, arc4); }\n // Only provide the .state method if requested via options.state.\n prng.state = function() { return copy(arc4, {}); }\n }\n\n // If called as a method of Math (Math.seedrandom()), mutate\n // Math.random because that is how seedrandom.js has worked since v1.0.\n if (is_math_call) { math[rngname] = prng; return seed; }\n\n // Otherwise, it is a newer calling convention, so return the\n // prng directly.\n else return prng;\n })(\n prng,\n shortseed,\n 'global' in options ? options.global : (this == math),\n options.state);\n}\nmath['seed' + rngname] = seedrandom;\n\n//\n// ARC4\n//\n// An ARC4 implementation. The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4. Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n var t, keylen = key.length,\n me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n // The empty key [] is treated as [0].\n if (!keylen) { key = [keylen++]; }\n\n // Set up S using the standard key scheduling algorithm.\n while (i < width) {\n s[i] = i++;\n }\n for (i = 0; i < width; i++) {\n s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n s[j] = t;\n }\n\n // The \"g\" method returns the next (count) outputs as one number.\n (me.g = function(count) {\n // Using instance members instead of closure state nearly doubles speed.\n var t, r = 0,\n i = me.i, j = me.j, s = me.S;\n while (count--) {\n t = s[i = mask & (i + 1)];\n r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n }\n me.i = i; me.j = j;\n return r;\n // For robust unpredictability, the function call below automatically\n // discards an initial batch of values. This is called RC4-drop[256].\n // See http://google.com/search?q=rsa+fluhrer+response&btnI\n })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n t.i = f.i;\n t.j = f.j;\n t.S = f.S.slice();\n return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n var result = [], typ = (typeof obj), prop;\n if (depth && typ == 'object') {\n for (prop in obj) {\n try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n }\n }\n return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n var stringseed = seed + '', smear, j = 0;\n while (j < stringseed.length) {\n key[mask & j] =\n mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n }\n return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n try {\n var out;\n if (nodecrypto && (out = nodecrypto.randomBytes)) {\n // The use of 'out' to remember randomBytes makes tight minified code.\n out = out(width);\n } else {\n out = new Uint8Array(width);\n (global.crypto || global.msCrypto).getRandomValues(out);\n }\n return tostring(out);\n } catch (e) {\n var browser = global.navigator,\n plugins = browser && browser.plugins;\n return [+new Date, global, plugins, global.screen, tostring(pool)];\n }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool. Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n module.exports = seedrandom;\n // When in node.js, try using crypto package for autoseeding.\n try {\n nodecrypto = require('crypto');\n } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n define(function() { return seedrandom; });\n}\n\n// End anonymous scope, and pass initial values.\n})(\n [], // pool: entropy pool starts empty\n Math // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random(); // 0 <= x < 1. Every bit is random.\n// var x = random.quick(); // 0 <= x < 1. 32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three. It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures. Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "// A port of an algorithm by Johannes Baag\u00F8e , 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e \n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n// \n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n// \n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n var me = this, mash = Mash();\n\n me.next = function() {\n var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n me.s0 = me.s1;\n me.s1 = me.s2;\n return me.s2 = t - (me.c = t | 0);\n };\n\n // Apply the seeding algorithm from Baagoe.\n me.c = 1;\n me.s0 = mash(' ');\n me.s1 = mash(' ');\n me.s2 = mash(' ');\n me.s0 -= mash(seed);\n if (me.s0 < 0) { me.s0 += 1; }\n me.s1 -= mash(seed);\n if (me.s1 < 0) { me.s1 += 1; }\n me.s2 -= mash(seed);\n if (me.s2 < 0) { me.s2 += 1; }\n mash = null;\n}\n\nfunction copy(f, t) {\n t.c = f.c;\n t.s0 = f.s0;\n t.s1 = f.s1;\n t.s2 = f.s2;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new Alea(seed),\n state = opts && opts.state,\n prng = xg.next;\n prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n prng.double = function() {\n return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n };\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nfunction Mash() {\n var n = 0xefc8249d;\n\n var mash = function(data) {\n data = data.toString();\n for (var i = 0; i < data.length; i++) {\n n += data.charCodeAt(i);\n var h = 0.02519603282416938 * n;\n n = h >>> 0;\n h -= n;\n h *= n;\n n = h >>> 0;\n h -= n;\n n += h * 0x100000000; // 2^32\n }\n return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n };\n\n return mash;\n}\n\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.alea = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n\n // Set up generator function.\n me.next = function() {\n var t = me.x ^ (me.x << 11);\n me.x = me.y;\n me.y = me.z;\n me.z = me.w;\n return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n };\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor128 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var t = (me.x ^ (me.x >>> 2));\n me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n return (me.d = (me.d + 362437 | 0)) +\n (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n };\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n me.v = 0;\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n if (k == strseed.length) {\n me.d = me.x << 10 ^ me.x >>> 4;\n }\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n t.v = f.v;\n t.d = f.d;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorwow = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n // Update xor generator.\n var X = me.x, i = me.i, t, v, w;\n t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n X[i] = v;\n me.i = (i + 1) & 7;\n return v;\n };\n\n function init(me, seed) {\n var j, w, X = [];\n\n if (seed === (seed | 0)) {\n // Seed state array using a 32-bit integer.\n w = X[0] = seed;\n } else {\n // Seed state using a string.\n seed = '' + seed;\n for (j = 0; j < seed.length; ++j) {\n X[j & 7] = (X[j & 7] << 15) ^\n (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n }\n }\n // Enforce an array length of 8, not all zeroes.\n while (X.length < 8) X.push(0);\n for (j = 0; j < 8 && X[j] === 0; ++j);\n if (j == 8) w = X[7] = -1; else w = X[j];\n\n me.x = X;\n me.i = 0;\n\n // Discard an initial 256 values.\n for (j = 256; j > 0; --j) {\n me.next();\n }\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.x = f.x.slice();\n t.i = f.i;\n return t;\n}\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.x) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorshift7 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated. For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1); // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897); // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C. This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n var w = me.w,\n X = me.X, i = me.i, t, v;\n // Update Weyl generator.\n me.w = w = (w + 0x61c88647) | 0;\n // Update xor generator.\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n // Update Xor generator array state.\n v = X[i] = v ^ t;\n me.i = i;\n // Result is the combination.\n return (v + (w ^ (w >>> 16))) | 0;\n };\n\n function init(me, seed) {\n var t, v, i, j, w, X = [], limit = 128;\n if (seed === (seed | 0)) {\n // Numeric seeds initialize v, which is used to generates X.\n v = seed;\n seed = null;\n } else {\n // String seeds are mixed into v and X one character at a time.\n seed = seed + '\\0';\n v = 0;\n limit = Math.max(limit, seed.length);\n }\n // Initialize circular array and weyl value.\n for (i = 0, j = -32; j < limit; ++j) {\n // Put the unicode characters into the array, and shuffle them.\n if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n // After 32 shuffles, take v as the starting w value.\n if (j === 0) w = v;\n v ^= v << 10;\n v ^= v >>> 15;\n v ^= v << 4;\n v ^= v >>> 13;\n if (j >= 0) {\n w = (w + 0x61c88647) | 0; // Weyl.\n t = (X[j & 127] ^= (v + w)); // Combine xor and weyl to init array.\n i = (0 == t) ? i + 1 : 0; // Count zeroes.\n }\n }\n // We have detected all zeroes; make the key nonzero.\n if (i >= 128) {\n X[(seed && seed.length || 0) & 127] = -1;\n }\n // Run the generator 512 times to further mix the state before using it.\n // Factoring this as a function slows the main generator, so it is just\n // unrolled here. The weyl generator is not advanced while warming up.\n i = 127;\n for (j = 4 * 128; j > 0; --j) {\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n X[i] = v ^ t;\n }\n // Storing state as object members is faster than using closure variables.\n me.w = w;\n me.X = X;\n me.i = i;\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.i = f.i;\n t.w = f.w;\n t.X = f.X.slice();\n return t;\n};\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.X) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor4096 = impl;\n}\n\n})(\n this, // window object or global\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var b = me.b, c = me.c, d = me.d, a = me.a;\n b = (b << 25) ^ (b >>> 7) ^ c;\n c = (c - d) | 0;\n d = (d << 24) ^ (d >>> 8) ^ a;\n a = (a - b) | 0;\n me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n me.c = c = (c - d) | 0;\n me.d = (d << 16) ^ (c >>> 16) ^ a;\n return me.a = (a - b) | 0;\n };\n\n /* The following is non-inverted tyche, which has better internal\n * bit diffusion, but which is about 25% slower than tyche-i in JS.\n me.next = function() {\n var a = me.a, b = me.b, c = me.c, d = me.d;\n a = (me.a + me.b | 0) >>> 0;\n d = me.d ^ a; d = d << 16 ^ d >>> 16;\n c = me.c + d | 0;\n b = me.b ^ c; b = b << 12 ^ d >>> 20;\n me.a = a = a + b | 0;\n d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n me.c = c = c + d | 0;\n b = b ^ c;\n return me.b = (b << 7 ^ b >>> 25);\n }\n */\n\n me.a = 0;\n me.b = 0;\n me.c = 2654435769 | 0;\n me.d = 1367130551;\n\n if (seed === Math.floor(seed)) {\n // Integer seed.\n me.a = (seed / 0x100000000) | 0;\n me.b = seed | 0;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 20; k++) {\n me.b ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.a = f.a;\n t.b = f.b;\n t.c = f.c;\n t.d = f.d;\n return t;\n};\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.tychei = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "/*\nCopyright 2014 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\nvar global = this,\n width = 256, // each RC4 output is 0 <= x < 256\n chunks = 6, // at least six RC4 outputs for each double\n digits = 52, // there are 52 significant digits in a double\n rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n startdenom = math.pow(width, chunks),\n significance = math.pow(2, digits),\n overflow = significance * 2,\n mask = width - 1,\n nodecrypto; // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n var key = [];\n options = (options == true) ? { entropy: true } : (options || {});\n\n // Flatten the seed string or build one from local entropy if needed.\n var shortseed = mixkey(flatten(\n options.entropy ? [seed, tostring(pool)] :\n (seed == null) ? autoseed() : seed, 3), key);\n\n // Use the seed to initialize an ARC4 generator.\n var arc4 = new ARC4(key);\n\n // This function returns a random double in [0, 1) that contains\n // randomness in every bit of the mantissa of the IEEE 754 value.\n var prng = function() {\n var n = arc4.g(chunks), // Start with a numerator n < 2 ^ 48\n d = startdenom, // and denominator d = 2 ^ 48.\n x = 0; // and no 'extra last byte'.\n while (n < significance) { // Fill up all significant digits by\n n = (n + x) * width; // shifting numerator and\n d *= width; // denominator and generating a\n x = arc4.g(1); // new least-significant-byte.\n }\n while (n >= overflow) { // To avoid rounding up, before adding\n n /= 2; // last byte, shift everything\n d /= 2; // right using integer math until\n x >>>= 1; // we have exactly the desired bits.\n }\n return (n + x) / d; // Form the number within [0, 1).\n };\n\n prng.int32 = function() { return arc4.g(4) | 0; }\n prng.quick = function() { return arc4.g(4) / 0x100000000; }\n prng.double = prng;\n\n // Mix the randomness into accumulated entropy.\n mixkey(tostring(arc4.S), pool);\n\n // Calling convention: what to return as a function of prng, seed, is_math.\n return (options.pass || callback ||\n function(prng, seed, is_math_call, state) {\n if (state) {\n // Load the arc4 state from the given state if it has an S array.\n if (state.S) { copy(state, arc4); }\n // Only provide the .state method if requested via options.state.\n prng.state = function() { return copy(arc4, {}); }\n }\n\n // If called as a method of Math (Math.seedrandom()), mutate\n // Math.random because that is how seedrandom.js has worked since v1.0.\n if (is_math_call) { math[rngname] = prng; return seed; }\n\n // Otherwise, it is a newer calling convention, so return the\n // prng directly.\n else return prng;\n })(\n prng,\n shortseed,\n 'global' in options ? options.global : (this == math),\n options.state);\n}\nmath['seed' + rngname] = seedrandom;\n\n//\n// ARC4\n//\n// An ARC4 implementation. The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4. Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n var t, keylen = key.length,\n me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n // The empty key [] is treated as [0].\n if (!keylen) { key = [keylen++]; }\n\n // Set up S using the standard key scheduling algorithm.\n while (i < width) {\n s[i] = i++;\n }\n for (i = 0; i < width; i++) {\n s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n s[j] = t;\n }\n\n // The \"g\" method returns the next (count) outputs as one number.\n (me.g = function(count) {\n // Using instance members instead of closure state nearly doubles speed.\n var t, r = 0,\n i = me.i, j = me.j, s = me.S;\n while (count--) {\n t = s[i = mask & (i + 1)];\n r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n }\n me.i = i; me.j = j;\n return r;\n // For robust unpredictability, the function call below automatically\n // discards an initial batch of values. This is called RC4-drop[256].\n // See http://google.com/search?q=rsa+fluhrer+response&btnI\n })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n t.i = f.i;\n t.j = f.j;\n t.S = f.S.slice();\n return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n var result = [], typ = (typeof obj), prop;\n if (depth && typ == 'object') {\n for (prop in obj) {\n try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n }\n }\n return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n var stringseed = seed + '', smear, j = 0;\n while (j < stringseed.length) {\n key[mask & j] =\n mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n }\n return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n try {\n var out;\n if (nodecrypto && (out = nodecrypto.randomBytes)) {\n // The use of 'out' to remember randomBytes makes tight minified code.\n out = out(width);\n } else {\n out = new Uint8Array(width);\n (global.crypto || global.msCrypto).getRandomValues(out);\n }\n return tostring(out);\n } catch (e) {\n var browser = global.navigator,\n plugins = browser && browser.plugins;\n return [+new Date, global, plugins, global.screen, tostring(pool)];\n }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool. Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n module.exports = seedrandom;\n // When in node.js, try using crypto package for autoseeding.\n try {\n nodecrypto = require('crypto');\n } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n define(function() { return seedrandom; });\n}\n\n// End anonymous scope, and pass initial values.\n})(\n [], // pool: entropy pool starts empty\n Math // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random(); // 0 <= x < 1. Every bit is random.\n// var x = random.quick(); // 0 <= x < 1. 32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three. It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures. Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "", "", "", "\nvar WasmBackendModuleThreadedSimd = (function() {\n var _scriptDir = typeof document !== 'undefined' && document.currentScript ? document.currentScript.src : undefined;\n if (typeof __filename !== 'undefined') _scriptDir = _scriptDir || __filename;\n return (\nfunction(WasmBackendModuleThreadedSimd) {\n WasmBackendModuleThreadedSimd = WasmBackendModuleThreadedSimd || {};\n\nfunction GROWABLE_HEAP_I8(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAP8}function GROWABLE_HEAP_U8(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPU8}function GROWABLE_HEAP_I32(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAP32}function GROWABLE_HEAP_U32(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPU32}function GROWABLE_HEAP_F64(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPF64}var Module=typeof WasmBackendModuleThreadedSimd!==\"undefined\"?WasmBackendModuleThreadedSimd:{};var _scriptDir=undefined;var moduleOverrides={};var key;for(key in Module){if(Module.hasOwnProperty(key)){moduleOverrides[key]=Module[key]}}var arguments_=[];var thisProgram=\"./this.program\";var quit_=function(status,toThrow){throw toThrow};var ENVIRONMENT_IS_WEB=false;var ENVIRONMENT_IS_WORKER=false;var ENVIRONMENT_IS_NODE=false;var ENVIRONMENT_IS_SHELL=false;ENVIRONMENT_IS_WEB=typeof window===\"object\";ENVIRONMENT_IS_WORKER=typeof importScripts===\"function\";ENVIRONMENT_IS_NODE=typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\";ENVIRONMENT_IS_SHELL=!ENVIRONMENT_IS_WEB&&!ENVIRONMENT_IS_NODE&&!ENVIRONMENT_IS_WORKER;var ENVIRONMENT_IS_PTHREAD=Module[\"ENVIRONMENT_IS_PTHREAD\"]||false;if(ENVIRONMENT_IS_PTHREAD){buffer=Module[\"buffer\"];DYNAMIC_BASE=Module[\"DYNAMIC_BASE\"];DYNAMICTOP_PTR=Module[\"DYNAMICTOP_PTR\"]}var scriptDirectory=\"\";function locateFile(path){if(Module[\"locateFile\"]){return Module[\"locateFile\"](path,scriptDirectory)}return scriptDirectory+path}var read_,readAsync,readBinary,setWindowTitle;var nodeFS;var nodePath;if(ENVIRONMENT_IS_NODE){if(ENVIRONMENT_IS_WORKER){scriptDirectory=require(\"path\").dirname(scriptDirectory)+\"/\"}else{scriptDirectory=__dirname+\"/\"}read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret};if(process[\"argv\"].length>1){thisProgram=process[\"argv\"][1].replace(/\\\\/g,\"/\")}arguments_=process[\"argv\"].slice(2);process[\"on\"](\"uncaughtException\",function(ex){if(!(ex instanceof ExitStatus)){throw ex}});process[\"on\"](\"unhandledRejection\",abort);quit_=function(status){process[\"exit\"](status)};Module[\"inspect\"]=function(){return\"[Emscripten Module object]\"};var nodeWorkerThreads;try{nodeWorkerThreads=require(\"worker_threads\")}catch(e){console.error('The \"worker_threads\" module is not supported in this node.js build - perhaps a newer version is needed?');throw e}Worker=nodeWorkerThreads.Worker}else if(ENVIRONMENT_IS_SHELL){if(typeof read!=\"undefined\"){read_=function shell_read(f){return read(f)}}readBinary=function readBinary(f){var data;if(typeof readbuffer===\"function\"){return new Uint8Array(readbuffer(f))}data=read(f,\"binary\");assert(typeof data===\"object\");return data};if(typeof scriptArgs!=\"undefined\"){arguments_=scriptArgs}else if(typeof arguments!=\"undefined\"){arguments_=arguments}if(typeof quit===\"function\"){quit_=function(status){quit(status)}}if(typeof print!==\"undefined\"){if(typeof console===\"undefined\")console={};console.log=print;console.warn=console.error=typeof printErr!==\"undefined\"?printErr:print}}else if(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER){if(ENVIRONMENT_IS_WORKER){scriptDirectory=self.location.href}else if(document.currentScript){scriptDirectory=document.currentScript.src}if(_scriptDir){scriptDirectory=_scriptDir}if(scriptDirectory.indexOf(\"blob:\")!==0){scriptDirectory=scriptDirectory.substr(0,scriptDirectory.lastIndexOf(\"/\")+1)}else{scriptDirectory=\"\"}if(ENVIRONMENT_IS_NODE){read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret}}else{read_=function shell_read(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.send(null);return xhr.responseText};if(ENVIRONMENT_IS_WORKER){readBinary=function readBinary(url){var xhr=new 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Atomics_load=Atomics.load;var Atomics_store=Atomics.store;var Atomics_compareExchange=Atomics.compareExchange;var wasmBinary;if(Module[\"wasmBinary\"])wasmBinary=Module[\"wasmBinary\"];var noExitRuntime;if(Module[\"noExitRuntime\"])noExitRuntime=Module[\"noExitRuntime\"];if(typeof WebAssembly!==\"object\"){err(\"no native wasm support detected\")}var wasmMemory;var wasmTable=new WebAssembly.Table({\"initial\":169,\"maximum\":169+0,\"element\":\"anyfunc\"});var wasmModule;var threadInfoStruct=0;var selfThreadId=0;var ABORT=false;var EXITSTATUS=0;function assert(condition,text){if(!condition){abort(\"Assertion failed: \"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i=endIdx)){var u0=heap[idx++];if(!u0)return str;if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224){u0=(u0&15)<<12|u1<<6|u2}else{u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63}if(u0<65536){str+=String.fromCharCode(u0)}else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(GROWABLE_HEAP_U8(),ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var 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alignUp(x,multiple){if(x%multiple>0){x+=multiple-x%multiple}return x}var buffer,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer=buf;Module[\"HEAP8\"]=HEAP8=new Int8Array(buf);Module[\"HEAP16\"]=HEAP16=new Int16Array(buf);Module[\"HEAP32\"]=HEAP32=new Int32Array(buf);Module[\"HEAPU8\"]=HEAPU8=new Uint8Array(buf);Module[\"HEAPU16\"]=HEAPU16=new Uint16Array(buf);Module[\"HEAPU32\"]=HEAPU32=new Uint32Array(buf);Module[\"HEAPF32\"]=HEAPF32=new Float32Array(buf);Module[\"HEAPF64\"]=HEAPF64=new Float64Array(buf)}var STACK_BASE=5256464,STACKTOP=STACK_BASE,STACK_MAX=13584,DYNAMIC_BASE=5256464,DYNAMICTOP_PTR=12656;if(ENVIRONMENT_IS_PTHREAD){}var INITIAL_INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;if(ENVIRONMENT_IS_PTHREAD){wasmMemory=Module[\"wasmMemory\"];buffer=Module[\"buffer\"]}else{if(Module[\"wasmMemory\"]){wasmMemory=Module[\"wasmMemory\"]}else{wasmMemory=new WebAssembly.Memory({\"initial\":INITIAL_INITIAL_MEMORY/WASM_PAGE_SIZE,\"maximum\":2147483648/WASM_PAGE_SIZE,\"shared\":true});if(!(wasmMemory.buffer instanceof SharedArrayBuffer)){err(\"requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag\");if(ENVIRONMENT_IS_NODE){console.log(\"(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)\")}throw Error(\"bad memory\")}}}if(wasmMemory){buffer=wasmMemory.buffer}INITIAL_INITIAL_MEMORY=buffer.byteLength;updateGlobalBufferAndViews(buffer);if(!ENVIRONMENT_IS_PTHREAD){GROWABLE_HEAP_I32()[DYNAMICTOP_PTR>>2]=DYNAMIC_BASE}function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback==\"function\"){callback(Module);continue}var func=callback.func;if(typeof func===\"number\"){if(callback.arg===undefined){Module[\"dynCall_v\"](func)}else{Module[\"dynCall_vi\"](func,callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATEXIT__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;if(ENVIRONMENT_IS_PTHREAD)runtimeInitialized=true;function preRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"preRun\"]){if(typeof Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;callRuntimeCallbacks(__ATINIT__)}function preMain(){if(ENVIRONMENT_IS_PTHREAD)return;callRuntimeCallbacks(__ATMAIN__)}function postRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"postRun\"]){if(typeof Module[\"postRun\"]==\"function\")Module[\"postRun\"]=[Module[\"postRun\"]];while(Module[\"postRun\"].length){addOnPostRun(Module[\"postRun\"].shift())}}callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var Math_ceil=Math.ceil;var Math_floor=Math.floor;var runDependencies=0;var runDependencyWatcher=null;var dependenciesFulfilled=null;function addRunDependency(id){assert(!ENVIRONMENT_IS_PTHREAD,\"addRunDependency cannot be used in a pthread worker\");runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}if(ENVIRONMENT_IS_PTHREAD)console.error(\"Pthread aborting at \"+(new Error).stack);what+=\"\";out(what);err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";throw new WebAssembly.RuntimeError(what)}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm-threaded-simd.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(){try{if(wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(wasmBinaryFile)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary()})}return new Promise(function(resolve,reject){resolve(getBinary())})}function createWasm(){var info={\"a\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmModule=module;if(!ENVIRONMENT_IS_PTHREAD){var numWorkersToLoad=PThread.unusedWorkers.length;PThread.unusedWorkers.forEach(function(w){PThread.loadWasmModuleToWorker(w,function(){if(!--numWorkersToLoad)removeRunDependency(\"wasm-instantiate\")})})}}if(!ENVIRONMENT_IS_PTHREAD){addRunDependency(\"wasm-instantiate\")}function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"],output[\"module\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming===\"function\"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch===\"function\"){fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err(\"wasm streaming compile failed: \"+reason);err(\"falling back to ArrayBuffer instantiation\");instantiateArrayBuffer(receiveInstantiatedSource)})})}else{return instantiateArrayBuffer(receiveInstantiatedSource)}}if(Module[\"instantiateWasm\"]){try{var exports=Module[\"instantiateWasm\"](info,receiveInstance);return exports}catch(e){err(\"Module.instantiateWasm callback failed with error: \"+e);return false}}instantiateAsync();return{}}var ASM_CONSTS={};function initPthreadsJS(){PThread.initRuntime()}if(!ENVIRONMENT_IS_PTHREAD)__ATINIT__.push({func:function(){___wasm_call_ctors()}});var __pthread_ptr=0;var __pthread_is_main_runtime_thread=0;var __pthread_is_main_browser_thread=0;function __register_pthread_ptr(pthreadPtr,isMainBrowserThread,isMainRuntimeThread){pthreadPtr=pthreadPtr|0;isMainBrowserThread=isMainBrowserThread|0;isMainRuntimeThread=isMainRuntimeThread|0;__pthread_ptr=pthreadPtr;__pthread_is_main_browser_thread=isMainBrowserThread;__pthread_is_main_runtime_thread=isMainRuntimeThread}Module[\"__register_pthread_ptr\"]=__register_pthread_ptr;var ERRNO_CODES={EPERM:63,ENOENT:44,ESRCH:71,EINTR:27,EIO:29,ENXIO:60,E2BIG:1,ENOEXEC:45,EBADF:8,ECHILD:12,EAGAIN:6,EWOULDBLOCK:6,ENOMEM:48,EACCES:2,EFAULT:21,ENOTBLK:105,EBUSY:10,EEXIST:20,EXDEV:75,ENODEV:43,ENOTDIR:54,EISDIR:31,EINVAL:28,ENFILE:41,EMFILE:33,ENOTTY:59,ETXTBSY:74,EFBIG:22,ENOSPC:51,ESPIPE:70,EROFS:69,EMLINK:34,EPIPE:64,EDOM:18,ERANGE:68,ENOMSG:49,EIDRM:24,ECHRNG:106,EL2NSYNC:156,EL3HLT:107,EL3RST:108,ELNRNG:109,EUNATCH:110,ENOCSI:111,EL2HLT:112,EDEADLK:16,ENOLCK:46,EBADE:113,EBADR:114,EXFULL:115,ENOANO:104,EBADRQC:103,EBADSLT:102,EDEADLOCK:16,EBFONT:101,ENOSTR:100,ENODATA:116,ETIME:117,ENOSR:118,ENONET:119,ENOPKG:120,EREMOTE:121,ENOLINK:47,EADV:122,ESRMNT:123,ECOMM:124,EPROTO:65,EMULTIHOP:36,EDOTDOT:125,EBADMSG:9,ENOTUNIQ:126,EBADFD:127,EREMCHG:128,ELIBACC:129,ELIBBAD:130,ELIBSCN:131,ELIBMAX:132,ELIBEXEC:133,ENOSYS:52,ENOTEMPTY:55,ENAMETOOLONG:37,ELOOP:32,EOPNOTSUPP:138,EPFNOSUPPORT:139,ECONNRESET:15,ENOBUFS:42,EAFNOSUPPORT:5,EPROTOTYPE:67,ENOTSOCK:57,ENOPROTOOPT:50,ESHUTDOWN:140,ECONNREFUSED:14,EADDRINUSE:3,ECONNABORTED:13,ENETUNREACH:40,ENETDOWN:38,ETIMEDOUT:73,EHOSTDOWN:142,EHOSTUNREACH:23,EINPROGRESS:26,EALREADY:7,EDESTADDRREQ:17,EMSGSIZE:35,EPROTONOSUPPORT:66,ESOCKTNOSUPPORT:137,EADDRNOTAVAIL:4,ENETRESET:39,EISCONN:30,ENOTCONN:53,ETOOMANYREFS:141,EUSERS:136,EDQUOT:19,ESTALE:72,ENOTSUP:138,ENOMEDIUM:148,EILSEQ:25,EOVERFLOW:61,ECANCELED:11,ENOTRECOVERABLE:56,EOWNERDEAD:62,ESTRPIPE:135};var __main_thread_futex_wait_address=13568;function _emscripten_futex_wake(addr,count){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0||count<0)return-28;if(count==0)return 0;if(count>=2147483647)count=Infinity;var mainThreadWaitAddress=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2);var mainThreadWoken=0;if(mainThreadWaitAddress==addr){var loadedAddr=Atomics.compareExchange(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,mainThreadWaitAddress,0);if(loadedAddr==mainThreadWaitAddress){--count;mainThreadWoken=1;if(count<=0)return 1}}var ret=Atomics.notify(GROWABLE_HEAP_I32(),addr>>2,count);if(ret>=0)return ret+mainThreadWoken;throw\"Atomics.notify returned an unexpected value \"+ret}Module[\"_emscripten_futex_wake\"]=_emscripten_futex_wake;function __kill_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _kill_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _kill_thread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];pthread.worker.terminate();PThread.freeThreadData(pthread);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(pthread.worker),1);pthread.worker.pthread=undefined}function __cancel_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _cancel_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _cancel_thread!\";var pthread=PThread.pthreads[pthread_ptr];pthread.worker.postMessage({\"cmd\":\"cancel\"})}function __cleanup_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _cleanup_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _cleanup_thread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];if(pthread){var worker=pthread.worker;PThread.returnWorkerToPool(worker)}}var PThread={MAIN_THREAD_ID:1,mainThreadInfo:{schedPolicy:0,schedPrio:0},unusedWorkers:[],runningWorkers:[],initRuntime:function(){__register_pthread_ptr(PThread.mainThreadBlock,!ENVIRONMENT_IS_WORKER,1);_emscripten_register_main_browser_thread_id(PThread.mainThreadBlock)},initMainThreadBlock:function(){var pthreadPoolSize=8;for(var i=0;i>2]=PThread.mainThreadBlock;var headPtr=PThread.mainThreadBlock+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var tlsMemory=13056;for(var i=0;i<128;++i)GROWABLE_HEAP_U32()[tlsMemory/4+i]=0;Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+104>>2,tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+40>>2,PThread.mainThreadBlock);Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+44>>2,42)},initWorker:function(){},pthreads:{},exitHandlers:null,setThreadStatus:function(){},runExitHandlers:function(){if(PThread.exitHandlers!==null){while(PThread.exitHandlers.length>0){PThread.exitHandlers.pop()()}PThread.exitHandlers=null}if(ENVIRONMENT_IS_PTHREAD&&threadInfoStruct)___pthread_tsd_run_dtors()},threadExit:function(exitCode){var tb=_pthread_self();if(tb){Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,exitCode);Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+60>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+64>>2,0);PThread.runExitHandlers();_emscripten_futex_wake(tb+0,2147483647);__register_pthread_ptr(0,0,0);threadInfoStruct=0;if(ENVIRONMENT_IS_PTHREAD){postMessage({\"cmd\":\"exit\"})}}},threadCancel:function(){PThread.runExitHandlers();Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+4>>2,-1);Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+0>>2,1);_emscripten_futex_wake(threadInfoStruct+0,2147483647);threadInfoStruct=selfThreadId=0;__register_pthread_ptr(0,0,0);postMessage({\"cmd\":\"cancelDone\"})},terminateAllThreads:function(){for(var t in PThread.pthreads){var pthread=PThread.pthreads[t];if(pthread&&pthread.worker){PThread.returnWorkerToPool(pthread.worker)}}PThread.pthreads={};for(var i=0;i>2];GROWABLE_HEAP_I32()[pthread.threadInfoStruct+104>>2]=0;_free(tlsMemory);_free(pthread.threadInfoStruct)}pthread.threadInfoStruct=0;if(pthread.allocatedOwnStack&&pthread.stackBase)_free(pthread.stackBase);pthread.stackBase=0;if(pthread.worker)pthread.worker.pthread=null},returnWorkerToPool:function(worker){delete PThread.pthreads[worker.pthread.thread];PThread.unusedWorkers.push(worker);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker),1);PThread.freeThreadData(worker.pthread);worker.pthread=undefined},receiveObjectTransfer:function(data){},loadWasmModuleToWorker:function(worker,onFinishedLoading){worker.onmessage=function(e){var d=e[\"data\"];var cmd=d[\"cmd\"];if(worker.pthread)PThread.currentProxiedOperationCallerThread=worker.pthread.threadInfoStruct;if(d[\"targetThread\"]&&d[\"targetThread\"]!=_pthread_self()){var thread=PThread.pthreads[d.targetThread];if(thread){thread.worker.postMessage(e.data,d[\"transferList\"])}else{console.error('Internal error! Worker sent a message \"'+cmd+'\" to target pthread '+d[\"targetThread\"]+\", but that thread no longer exists!\")}PThread.currentProxiedOperationCallerThread=undefined;return}if(cmd===\"processQueuedMainThreadWork\"){_emscripten_main_thread_process_queued_calls()}else if(cmd===\"spawnThread\"){__spawn_thread(e.data)}else if(cmd===\"cleanupThread\"){__cleanup_thread(d[\"thread\"])}else if(cmd===\"killThread\"){__kill_thread(d[\"thread\"])}else if(cmd===\"cancelThread\"){__cancel_thread(d[\"thread\"])}else if(cmd===\"loaded\"){worker.loaded=true;if(onFinishedLoading)onFinishedLoading(worker);if(worker.runPthread){worker.runPthread();delete worker.runPthread}}else if(cmd===\"print\"){out(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"printErr\"){err(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"alert\"){alert(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"exit\"){var detached=worker.pthread&&Atomics.load(GROWABLE_HEAP_U32(),worker.pthread.thread+68>>2);if(detached){PThread.returnWorkerToPool(worker)}}else if(cmd===\"cancelDone\"){PThread.returnWorkerToPool(worker)}else if(cmd===\"objectTransfer\"){PThread.receiveObjectTransfer(e.data)}else if(e.data.target===\"setimmediate\"){worker.postMessage(e.data)}else{err(\"worker sent an unknown command \"+cmd)}PThread.currentProxiedOperationCallerThread=undefined};worker.onerror=function(e){err(\"pthread sent an error! \"+e.filename+\":\"+e.lineno+\": \"+e.message)};if(ENVIRONMENT_IS_NODE){worker.on(\"message\",function(data){worker.onmessage({data:data})});worker.on(\"error\",function(data){worker.onerror(data)});worker.on(\"exit\",function(data){console.log(\"worker exited - TODO: update the worker queue?\")})}worker.postMessage({\"cmd\":\"load\",\"urlOrBlob\":Module[\"mainScriptUrlOrBlob\"]||_scriptDir,\"wasmMemory\":wasmMemory,\"wasmModule\":wasmModule,\"DYNAMIC_BASE\":DYNAMIC_BASE,\"DYNAMICTOP_PTR\":DYNAMICTOP_PTR})},allocateUnusedWorker:function(){var pthreadMainJs=locateFile(\"tfjs-backend-wasm-threaded-simd.worker.js\");PThread.unusedWorkers.push(new Worker(pthreadMainJs))},getNewWorker:function(){if(PThread.unusedWorkers.length==0){PThread.allocateUnusedWorker();PThread.loadWasmModuleToWorker(PThread.unusedWorkers[0])}if(PThread.unusedWorkers.length>0)return PThread.unusedWorkers.pop();else return null},busySpinWait:function(msecs){var t=performance.now()+msecs;while(performance.now()>2]=value;return value}function _atexit(func,arg){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(1,1,func,arg);__ATEXIT__.unshift({func:func,arg:arg})}function __emscripten_notify_thread_queue(targetThreadId,mainThreadId){if(targetThreadId==mainThreadId){postMessage({\"cmd\":\"processQueuedMainThreadWork\"})}else if(ENVIRONMENT_IS_PTHREAD){postMessage({\"targetThread\":targetThreadId,\"cmd\":\"processThreadQueue\"})}else{var pthread=PThread.pthreads[targetThreadId];var worker=pthread&&pthread.worker;if(!worker){return}worker.postMessage({\"cmd\":\"processThreadQueue\"})}return 1}function _abort(){abort()}function _emscripten_conditional_set_current_thread_status(expectedStatus,newStatus){expectedStatus=expectedStatus|0;newStatus=newStatus|0}function _emscripten_futex_wait(addr,val,timeout){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0)return-28;if(ENVIRONMENT_IS_WORKER){var ret=Atomics.wait(GROWABLE_HEAP_I32(),addr>>2,val,timeout);if(ret===\"timed-out\")return-73;if(ret===\"not-equal\")return-6;if(ret===\"ok\")return 0;throw\"Atomics.wait returned an unexpected value \"+ret}else{var loadedVal=Atomics.load(GROWABLE_HEAP_I32(),addr>>2);if(val!=loadedVal)return-6;var tNow=performance.now();var tEnd=tNow+timeout;Atomics.store(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,addr);var ourWaitAddress=addr;while(addr==ourWaitAddress){tNow=performance.now();if(tNow>tEnd){return-73}_emscripten_main_thread_process_queued_calls();addr=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2)}return 0}}function _emscripten_is_main_browser_thread(){return __pthread_is_main_browser_thread|0}function _emscripten_is_main_runtime_thread(){return __pthread_is_main_runtime_thread|0}function _emscripten_memcpy_big(dest,src,num){GROWABLE_HEAP_U8().copyWithin(dest,src,src+num)}function _emscripten_num_logical_cores(){return navigator[\"hardwareConcurrency\"]}function _emscripten_proxy_to_main_thread_js(index,sync){var numCallArgs=arguments.length-2;var stack=stackSave();var args=stackAlloc(numCallArgs*8);var b=args>>3;for(var i=0;i>3]);buf+=8}else{buf=buf+3&~3;args.push(GROWABLE_HEAP_I32()[buf>>2]);buf+=4}}return args}function _emscripten_receive_on_main_thread_js(index,numCallArgs,args){_emscripten_receive_on_main_thread_js_callArgs.length=numCallArgs;var b=args>>3;for(var i=0;i>>16);updateGlobalBufferAndViews(wasmMemory.buffer);return 1}catch(e){}}function _emscripten_resize_heap(requestedSize){requestedSize=requestedSize>>>0;var oldSize=_emscripten_get_heap_size();if(requestedSize<=oldSize){return false}var PAGE_MULTIPLE=65536;var maxHeapSize=2147483648;if(requestedSize>maxHeapSize){return false}var minHeapSize=16777216;for(var cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(minHeapSize,requestedSize,overGrownHeapSize),PAGE_MULTIPLE));var replacement=emscripten_realloc_buffer(newSize);if(replacement){return true}}return false}var JSEvents={keyEvent:0,mouseEvent:0,wheelEvent:0,uiEvent:0,focusEvent:0,deviceOrientationEvent:0,deviceMotionEvent:0,fullscreenChangeEvent:0,pointerlockChangeEvent:0,visibilityChangeEvent:0,touchEvent:0,previousFullscreenElement:null,previousScreenX:null,previousScreenY:null,removeEventListenersRegistered:false,removeAllEventListeners:function(){for(var i=JSEvents.eventHandlers.length-1;i>=0;--i){JSEvents._removeHandler(i)}JSEvents.eventHandlers=[];JSEvents.deferredCalls=[]},registerRemoveEventListeners:function(){if(!JSEvents.removeEventListenersRegistered){__ATEXIT__.push(JSEvents.removeAllEventListeners);JSEvents.removeEventListenersRegistered=true}},deferredCalls:[],deferCall:function(targetFunction,precedence,argsList){function arraysHaveEqualContent(arrA,arrB){if(arrA.length!=arrB.length)return false;for(var i in arrA){if(arrA[i]!=arrB[i])return false}return true}for(var i in JSEvents.deferredCalls){var call=JSEvents.deferredCalls[i];if(call.targetFunction==targetFunction&&arraysHaveEqualContent(call.argsList,argsList)){return}}JSEvents.deferredCalls.push({targetFunction:targetFunction,precedence:precedence,argsList:argsList});JSEvents.deferredCalls.sort(function(x,y){return x.precedence>2]=eventTypeId;GROWABLE_HEAP_I32()[varargs+4>>2]=eventData;GROWABLE_HEAP_I32()[varargs+8>>2]=userData;_emscripten_async_queue_on_thread_(targetThread,637534208,eventHandlerFunc,eventData,varargs);stackRestore(stackTop)},getTargetThreadForEventCallback:function(targetThread){switch(targetThread){case 1:return 0;case 2:return PThread.currentProxiedOperationCallerThread;default:return targetThread}},getNodeNameForTarget:function(target){if(!target)return\"\";if(target==window)return\"#window\";if(target==screen)return\"#screen\";return target&&target.nodeName?target.nodeName:\"\"},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function stringToNewUTF8(jsString){var length=lengthBytesUTF8(jsString)+1;var cString=_malloc(length);stringToUTF8(jsString,cString,length);return cString}function _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height){var stackTop=stackSave();var varargs=stackAlloc(12);var targetCanvasPtr=0;if(targetCanvas){targetCanvasPtr=stringToNewUTF8(targetCanvas)}GROWABLE_HEAP_I32()[varargs>>2]=targetCanvasPtr;GROWABLE_HEAP_I32()[varargs+4>>2]=width;GROWABLE_HEAP_I32()[varargs+8>>2]=height;_emscripten_async_queue_on_thread_(targetThread,657457152,0,targetCanvasPtr,varargs);stackRestore(stackTop)}function _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,targetCanvas,width,height){targetCanvas=targetCanvas?UTF8ToString(targetCanvas):\"\";_emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height)}function __maybeCStringToJsString(cString){return cString>2?UTF8ToString(cString):cString}var specialHTMLTargets=[0,typeof document!==\"undefined\"?document:0,typeof window!==\"undefined\"?window:0];function __findEventTarget(target){target=__maybeCStringToJsString(target);var domElement=specialHTMLTargets[target]||(typeof document!==\"undefined\"?document.querySelector(target):undefined);return domElement}function __findCanvasEventTarget(target){return __findEventTarget(target)}function _emscripten_set_canvas_element_size_calling_thread(target,width,height){var canvas=__findCanvasEventTarget(target);if(!canvas)return-4;if(canvas.canvasSharedPtr){GROWABLE_HEAP_I32()[canvas.canvasSharedPtr>>2]=width;GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+4>>2]=height}if(canvas.offscreenCanvas||!canvas.controlTransferredOffscreen){if(canvas.offscreenCanvas)canvas=canvas.offscreenCanvas;var autoResizeViewport=false;if(canvas.GLctxObject&&canvas.GLctxObject.GLctx){var prevViewport=canvas.GLctxObject.GLctx.getParameter(2978);autoResizeViewport=prevViewport[0]===0&&prevViewport[1]===0&&prevViewport[2]===canvas.width&&prevViewport[3]===canvas.height}canvas.width=width;canvas.height=height;if(autoResizeViewport){canvas.GLctxObject.GLctx.viewport(0,0,width,height)}}else if(canvas.canvasSharedPtr){var targetThread=GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+8>>2];_emscripten_set_offscreencanvas_size_on_target_thread(targetThread,target,width,height);return 1}else{return-4}return 0}function _emscripten_set_canvas_element_size_main_thread(target,width,height){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(2,1,target,width,height);return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}function _emscripten_set_canvas_element_size(target,width,height){var canvas=__findCanvasEventTarget(target);if(canvas){return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}else{return _emscripten_set_canvas_element_size_main_thread(target,width,height)}}function _emscripten_set_current_thread_status(newStatus){newStatus=newStatus|0}function _emscripten_set_thread_name(threadId,name){threadId=threadId|0;name=name|0}function __webgl_enable_ANGLE_instanced_arrays(ctx){var ext=ctx.getExtension(\"ANGLE_instanced_arrays\");if(ext){ctx[\"vertexAttribDivisor\"]=function(index,divisor){ext[\"vertexAttribDivisorANGLE\"](index,divisor)};ctx[\"drawArraysInstanced\"]=function(mode,first,count,primcount){ext[\"drawArraysInstancedANGLE\"](mode,first,count,primcount)};ctx[\"drawElementsInstanced\"]=function(mode,count,type,indices,primcount){ext[\"drawElementsInstancedANGLE\"](mode,count,type,indices,primcount)};return 1}}function __webgl_enable_OES_vertex_array_object(ctx){var ext=ctx.getExtension(\"OES_vertex_array_object\");if(ext){ctx[\"createVertexArray\"]=function(){return ext[\"createVertexArrayOES\"]()};ctx[\"deleteVertexArray\"]=function(vao){ext[\"deleteVertexArrayOES\"](vao)};ctx[\"bindVertexArray\"]=function(vao){ext[\"bindVertexArrayOES\"](vao)};ctx[\"isVertexArray\"]=function(vao){return ext[\"isVertexArrayOES\"](vao)};return 1}}function __webgl_enable_WEBGL_draw_buffers(ctx){var ext=ctx.getExtension(\"WEBGL_draw_buffers\");if(ext){ctx[\"drawBuffers\"]=function(n,bufs){ext[\"drawBuffersWEBGL\"](n,bufs)};return 1}}var GL={counter:1,lastError:0,buffers:[],mappedBuffers:{},programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},currentContext:null,offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,init:function(){var miniTempFloatBuffer=new Float32Array(GL.MINI_TEMP_BUFFER_SIZE);for(var i=0;i>2]:-1;source+=UTF8ToString(GROWABLE_HEAP_I32()[string+i*4>>2],len<0?undefined:len)}return source},createContext:function(canvas,webGLContextAttributes){var ctx=canvas.getContext(\"webgl\",webGLContextAttributes);if(!ctx)return 0;var handle=GL.registerContext(ctx,webGLContextAttributes);return handle},registerContext:function(ctx,webGLContextAttributes){var handle=_malloc(8);GROWABLE_HEAP_I32()[handle+4>>2]=_pthread_self();var context={handle:handle,attributes:webGLContextAttributes,version:webGLContextAttributes.majorVersion,GLctx:ctx};if(ctx.canvas)ctx.canvas.GLctxObject=context;GL.contexts[handle]=context;if(typeof webGLContextAttributes.enableExtensionsByDefault===\"undefined\"||webGLContextAttributes.enableExtensionsByDefault){GL.initExtensions(context)}return handle},makeContextCurrent:function(contextHandle){GL.currentContext=GL.contexts[contextHandle];Module.ctx=GLctx=GL.currentContext&&GL.currentContext.GLctx;return!(contextHandle&&!GLctx)},getContext:function(contextHandle){return GL.contexts[contextHandle]},deleteContext:function(contextHandle){if(GL.currentContext===GL.contexts[contextHandle])GL.currentContext=null;if(typeof JSEvents===\"object\")JSEvents.removeAllHandlersOnTarget(GL.contexts[contextHandle].GLctx.canvas);if(GL.contexts[contextHandle]&&GL.contexts[contextHandle].GLctx.canvas)GL.contexts[contextHandle].GLctx.canvas.GLctxObject=undefined;_free(GL.contexts[contextHandle].handle);GL.contexts[contextHandle]=null},initExtensions:function(context){if(!context)context=GL.currentContext;if(context.initExtensionsDone)return;context.initExtensionsDone=true;var GLctx=context.GLctx;__webgl_enable_ANGLE_instanced_arrays(GLctx);__webgl_enable_OES_vertex_array_object(GLctx);__webgl_enable_WEBGL_draw_buffers(GLctx);GLctx.disjointTimerQueryExt=GLctx.getExtension(\"EXT_disjoint_timer_query\");var automaticallyEnabledExtensions=[\"OES_texture_float\",\"OES_texture_half_float\",\"OES_standard_derivatives\",\"OES_vertex_array_object\",\"WEBGL_compressed_texture_s3tc\",\"WEBGL_depth_texture\",\"OES_element_index_uint\",\"EXT_texture_filter_anisotropic\",\"EXT_frag_depth\",\"WEBGL_draw_buffers\",\"ANGLE_instanced_arrays\",\"OES_texture_float_linear\",\"OES_texture_half_float_linear\",\"EXT_blend_minmax\",\"EXT_shader_texture_lod\",\"EXT_texture_norm16\",\"WEBGL_compressed_texture_pvrtc\",\"EXT_color_buffer_half_float\",\"WEBGL_color_buffer_float\",\"EXT_sRGB\",\"WEBGL_compressed_texture_etc1\",\"EXT_disjoint_timer_query\",\"WEBGL_compressed_texture_etc\",\"WEBGL_compressed_texture_astc\",\"EXT_color_buffer_float\",\"WEBGL_compressed_texture_s3tc_srgb\",\"EXT_disjoint_timer_query_webgl2\",\"WEBKIT_WEBGL_compressed_texture_pvrtc\"];var exts=GLctx.getSupportedExtensions()||[];exts.forEach(function(ext){if(automaticallyEnabledExtensions.indexOf(ext)!=-1){GLctx.getExtension(ext)}})},populateUniformTable:function(program){var p=GL.programs[program];var ptable=GL.programInfos[program]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1};var utable=ptable.uniforms;var numUniforms=GLctx.getProgramParameter(p,35718);for(var i=0;i>2;contextAttributes[\"alpha\"]=!!GROWABLE_HEAP_I32()[a+(0>>2)];contextAttributes[\"depth\"]=!!GROWABLE_HEAP_I32()[a+(4>>2)];contextAttributes[\"stencil\"]=!!GROWABLE_HEAP_I32()[a+(8>>2)];contextAttributes[\"antialias\"]=!!GROWABLE_HEAP_I32()[a+(12>>2)];contextAttributes[\"premultipliedAlpha\"]=!!GROWABLE_HEAP_I32()[a+(16>>2)];contextAttributes[\"preserveDrawingBuffer\"]=!!GROWABLE_HEAP_I32()[a+(20>>2)];var powerPreference=GROWABLE_HEAP_I32()[a+(24>>2)];contextAttributes[\"powerPreference\"]=__emscripten_webgl_power_preferences[powerPreference];contextAttributes[\"failIfMajorPerformanceCaveat\"]=!!GROWABLE_HEAP_I32()[a+(28>>2)];contextAttributes.majorVersion=GROWABLE_HEAP_I32()[a+(32>>2)];contextAttributes.minorVersion=GROWABLE_HEAP_I32()[a+(36>>2)];contextAttributes.enableExtensionsByDefault=GROWABLE_HEAP_I32()[a+(40>>2)];contextAttributes.explicitSwapControl=GROWABLE_HEAP_I32()[a+(44>>2)];contextAttributes.proxyContextToMainThread=GROWABLE_HEAP_I32()[a+(48>>2)];contextAttributes.renderViaOffscreenBackBuffer=GROWABLE_HEAP_I32()[a+(52>>2)];var canvas=__findCanvasEventTarget(target);if(!canvas){return-4}if(contextAttributes.explicitSwapControl){return-1}var contextHandle=GL.createContext(canvas,contextAttributes);return contextHandle}function _emscripten_webgl_create_context(a0,a1){return _emscripten_webgl_do_create_context(a0,a1)}var PATH={splitPath:function(filename){var splitPathRe=/^(\\/?|)([\\s\\S]*?)((?:\\.{1,2}|[^\\/]+?|)(\\.[^.\\/]*|))(?:[\\/]*)$/;return splitPathRe.exec(filename).slice(1)},normalizeArray:function(parts,allowAboveRoot){var up=0;for(var i=parts.length-1;i>=0;i--){var last=parts[i];if(last===\".\"){parts.splice(i,1)}else if(last===\"..\"){parts.splice(i,1);up++}else if(up){parts.splice(i,1);up--}}if(allowAboveRoot){for(;up;up--){parts.unshift(\"..\")}}return parts},normalize:function(path){var isAbsolute=path.charAt(0)===\"/\",trailingSlash=path.substr(-1)===\"/\";path=PATH.normalizeArray(path.split(\"/\").filter(function(p){return!!p}),!isAbsolute).join(\"/\");if(!path&&!isAbsolute){path=\".\"}if(path&&trailingSlash){path+=\"/\"}return(isAbsolute?\"/\":\"\")+path},dirname:function(path){var result=PATH.splitPath(path),root=result[0],dir=result[1];if(!root&&!dir){return\".\"}if(dir){dir=dir.substr(0,dir.length-1)}return root+dir},basename:function(path){if(path===\"/\")return\"/\";var lastSlash=path.lastIndexOf(\"/\");if(lastSlash===-1)return path;return path.substr(lastSlash+1)},extname:function(path){return PATH.splitPath(path)[3]},join:function(){var paths=Array.prototype.slice.call(arguments,0);return PATH.normalize(paths.join(\"/\"))},join2:function(l,r){return PATH.normalize(l+\"/\"+r)}};var SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer=SYSCALLS.buffers[stream];if(curr===0||curr===10){(stream===1?out:err)(UTF8ArrayToString(buffer,0));buffer.length=0}else{buffer.push(curr)}},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=GROWABLE_HEAP_I32()[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(3,1,fd);return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(4,1,fd,offset_low,offset_high,whence,newOffset)}function _fd_write(fd,iov,iovcnt,pnum){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(5,1,fd,iov,iovcnt,pnum);var num=0;for(var i=0;i>2];var len=GROWABLE_HEAP_I32()[iov+(i*8+4)>>2];for(var j=0;j>2]=num;return 0}function _pthread_cleanup_pop(execute){var routine=PThread.exitHandlers.pop();if(execute)routine()}function _pthread_cleanup_push(routine,arg){if(PThread.exitHandlers===null){PThread.exitHandlers=[]}PThread.exitHandlers.push(function(){dynCall_vi(routine,arg)})}function __spawn_thread(threadParams){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _spawn_thread() can only ever be called from main application thread!\";var worker=PThread.getNewWorker();if(worker.pthread!==undefined)throw\"Internal error!\";if(!threadParams.pthread_ptr)throw\"Internal error, no pthread ptr!\";PThread.runningWorkers.push(worker);var tlsMemory=_malloc(128*4);for(var i=0;i<128;++i){GROWABLE_HEAP_I32()[tlsMemory+i*4>>2]=0}var stackHigh=threadParams.stackBase+threadParams.stackSize;var pthread=PThread.pthreads[threadParams.pthread_ptr]={worker:worker,stackBase:threadParams.stackBase,stackSize:threadParams.stackSize,allocatedOwnStack:threadParams.allocatedOwnStack,thread:threadParams.pthread_ptr,threadInfoStruct:threadParams.pthread_ptr};var tis=pthread.threadInfoStruct>>2;Atomics.store(GROWABLE_HEAP_U32(),tis+(0>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(4>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(8>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(68>>2),threadParams.detached);Atomics.store(GROWABLE_HEAP_U32(),tis+(104>>2),tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),tis+(48>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(40>>2),pthread.threadInfoStruct);Atomics.store(GROWABLE_HEAP_U32(),tis+(44>>2),42);Atomics.store(GROWABLE_HEAP_U32(),tis+(108>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(84>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(80>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+8>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+12>>2),threadParams.detached);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+20>>2),threadParams.schedPolicy);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+24>>2),threadParams.schedPrio);var global_libc=_emscripten_get_global_libc();var global_locale=global_libc+40;Atomics.store(GROWABLE_HEAP_U32(),tis+(176>>2),global_locale);worker.pthread=pthread;var msg={\"cmd\":\"run\",\"start_routine\":threadParams.startRoutine,\"arg\":threadParams.arg,\"threadInfoStruct\":threadParams.pthread_ptr,\"selfThreadId\":threadParams.pthread_ptr,\"parentThreadId\":threadParams.parent_pthread_ptr,\"stackBase\":threadParams.stackBase,\"stackSize\":threadParams.stackSize};worker.runPthread=function(){msg.time=performance.now();worker.postMessage(msg,threadParams.transferList)};if(worker.loaded){worker.runPthread();delete worker.runPthread}}function _pthread_getschedparam(thread,policy,schedparam){if(!policy&&!schedparam)return ERRNO_CODES.EINVAL;if(!thread){err(\"pthread_getschedparam called with a null thread pointer!\");return ERRNO_CODES.ESRCH}var self=GROWABLE_HEAP_I32()[thread+12>>2];if(self!==thread){err(\"pthread_getschedparam attempted on thread \"+thread+\", which does not point to a valid thread, or does not exist anymore!\");return ERRNO_CODES.ESRCH}var schedPolicy=Atomics.load(GROWABLE_HEAP_U32(),thread+108+20>>2);var schedPrio=Atomics.load(GROWABLE_HEAP_U32(),thread+108+24>>2);if(policy)GROWABLE_HEAP_I32()[policy>>2]=schedPolicy;if(schedparam)GROWABLE_HEAP_I32()[schedparam>>2]=schedPrio;return 0}function _pthread_self(){return __pthread_ptr|0}Module[\"_pthread_self\"]=_pthread_self;function _pthread_create(pthread_ptr,attr,start_routine,arg){if(typeof SharedArrayBuffer===\"undefined\"){err(\"Current environment does not support SharedArrayBuffer, pthreads are not available!\");return 6}if(!pthread_ptr){err(\"pthread_create called with a null thread pointer!\");return 28}var transferList=[];var error=0;if(ENVIRONMENT_IS_PTHREAD&&(transferList.length===0||error)){return _emscripten_sync_run_in_main_thread_4(687865856,pthread_ptr,attr,start_routine,arg)}if(error)return error;var stackSize=0;var stackBase=0;var detached=0;var schedPolicy=0;var schedPrio=0;if(attr){stackSize=GROWABLE_HEAP_I32()[attr>>2];stackSize+=81920;stackBase=GROWABLE_HEAP_I32()[attr+8>>2];detached=GROWABLE_HEAP_I32()[attr+12>>2]!==0;var inheritSched=GROWABLE_HEAP_I32()[attr+16>>2]===0;if(inheritSched){var prevSchedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];var prevSchedPrio=GROWABLE_HEAP_I32()[attr+24>>2];var parentThreadPtr=PThread.currentProxiedOperationCallerThread?PThread.currentProxiedOperationCallerThread:_pthread_self();_pthread_getschedparam(parentThreadPtr,attr+20,attr+24);schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];schedPrio=GROWABLE_HEAP_I32()[attr+24>>2];GROWABLE_HEAP_I32()[attr+20>>2]=prevSchedPolicy;GROWABLE_HEAP_I32()[attr+24>>2]=prevSchedPrio}else{schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];schedPrio=GROWABLE_HEAP_I32()[attr+24>>2]}}else{stackSize=2097152}var allocatedOwnStack=stackBase==0;if(allocatedOwnStack){stackBase=_memalign(16,stackSize)}else{stackBase-=stackSize;assert(stackBase>0)}var threadInfoStruct=_malloc(232);for(var i=0;i<232>>2;++i)GROWABLE_HEAP_U32()[(threadInfoStruct>>2)+i]=0;GROWABLE_HEAP_I32()[pthread_ptr>>2]=threadInfoStruct;GROWABLE_HEAP_I32()[threadInfoStruct+12>>2]=threadInfoStruct;var headPtr=threadInfoStruct+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var threadParams={stackBase:stackBase,stackSize:stackSize,allocatedOwnStack:allocatedOwnStack,schedPolicy:schedPolicy,schedPrio:schedPrio,detached:detached,startRoutine:start_routine,pthread_ptr:threadInfoStruct,parent_pthread_ptr:_pthread_self(),arg:arg,transferList:transferList};if(ENVIRONMENT_IS_PTHREAD){threadParams.cmd=\"spawnThread\";postMessage(threadParams,transferList)}else{__spawn_thread(threadParams)}return 0}function _roundf(d){d=+d;return d>=+0?+Math_floor(d+ +.5):+Math_ceil(d-+.5)}function _sysconf(name){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(6,1,name);switch(name){case 30:return 16384;case 85:var maxHeapSize=2147483648;return maxHeapSize/16384;case 132:case 133:case 12:case 137:case 138:case 15:case 235:case 16:case 17:case 18:case 19:case 20:case 149:case 13:case 10:case 236:case 153:case 9:case 21:case 22:case 159:case 154:case 14:case 77:case 78:case 139:case 80:case 81:case 82:case 68:case 67:case 164:case 11:case 29:case 47:case 48:case 95:case 52:case 51:case 46:case 79:return 200809;case 27:case 246:case 127:case 128:case 23:case 24:case 160:case 161:case 181:case 182:case 242:case 183:case 184:case 243:case 244:case 245:case 165:case 178:case 179:case 49:case 50:case 168:case 169:case 175:case 170:case 171:case 172:case 97:case 76:case 32:case 173:case 35:return-1;case 176:case 177:case 7:case 155:case 8:case 157:case 125:case 126:case 92:case 93:case 129:case 130:case 131:case 94:case 91:return 1;case 74:case 60:case 69:case 70:case 4:return 1024;case 31:case 42:case 72:return 32;case 87:case 26:case 33:return 2147483647;case 34:case 1:return 47839;case 38:case 36:return 99;case 43:case 37:return 2048;case 0:return 2097152;case 3:return 65536;case 28:return 32768;case 44:return 32767;case 75:return 16384;case 39:return 1e3;case 89:return 700;case 71:return 256;case 40:return 255;case 2:return 100;case 180:return 64;case 25:return 20;case 5:return 16;case 6:return 6;case 73:return 4;case 84:{if(typeof navigator===\"object\")return navigator[\"hardwareConcurrency\"]||1;return 1}}setErrNo(28);return-1}if(!ENVIRONMENT_IS_PTHREAD)PThread.initMainThreadBlock();else PThread.initWorker();var GLctx;GL.init();var proxiedFunctionTable=[null,_atexit,_emscripten_set_canvas_element_size_main_thread,_fd_close,_fd_seek,_fd_write,_sysconf];var asmLibraryArg={\"e\":___assert_fail,\"r\":___call_main,\"w\":__emscripten_notify_thread_queue,\"a\":_abort,\"l\":_emscripten_conditional_set_current_thread_status,\"d\":_emscripten_futex_wait,\"c\":_emscripten_futex_wake,\"h\":_emscripten_get_now,\"g\":_emscripten_is_main_browser_thread,\"x\":_emscripten_is_main_runtime_thread,\"q\":_emscripten_memcpy_big,\"B\":_emscripten_num_logical_cores,\"t\":_emscripten_receive_on_main_thread_js,\"A\":_emscripten_resize_heap,\"u\":_emscripten_set_canvas_element_size,\"k\":_emscripten_set_current_thread_status,\"s\":_emscripten_set_thread_name,\"v\":_emscripten_webgl_create_context,\"m\":_fd_close,\"o\":_fd_seek,\"i\":_fd_write,\"p\":initPthreadsJS,\"memory\":wasmMemory||Module[\"wasmMemory\"],\"y\":_pthread_cleanup_pop,\"z\":_pthread_cleanup_push,\"j\":_pthread_create,\"b\":_pthread_self,\"f\":_roundf,\"n\":_sysconf,\"table\":wasmTable};var asm=createWasm();Module[\"asm\"]=asm;var ___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=function(){return(___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=Module[\"asm\"][\"C\"]).apply(null,arguments)};var _init=Module[\"_init\"]=function(){return(_init=Module[\"_init\"]=Module[\"asm\"][\"D\"]).apply(null,arguments)};var _register_tensor=Module[\"_register_tensor\"]=function(){return(_register_tensor=Module[\"_register_tensor\"]=Module[\"asm\"][\"E\"]).apply(null,arguments)};var _dispose_data=Module[\"_dispose_data\"]=function(){return(_dispose_data=Module[\"_dispose_data\"]=Module[\"asm\"][\"F\"]).apply(null,arguments)};var _dispose=Module[\"_dispose\"]=function(){return(_dispose=Module[\"_dispose\"]=Module[\"asm\"][\"G\"]).apply(null,arguments)};var _Abs=Module[\"_Abs\"]=function(){return(_Abs=Module[\"_Abs\"]=Module[\"asm\"][\"H\"]).apply(null,arguments)};var _Add=Module[\"_Add\"]=function(){return(_Add=Module[\"_Add\"]=Module[\"asm\"][\"I\"]).apply(null,arguments)};var 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assert(condition,text){if(!condition){abort(\"Assertion failed: \"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i=endIdx))++endPtr;if(endPtr-idx>16&&heap.subarray&&UTF8Decoder){return UTF8Decoder.decode(heap.subarray(idx,endPtr))}else{var str=\"\";while(idx>10,56320|ch&1023)}}}return str}function UTF8ToString(ptr,maxBytesToRead){return 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addRunDependency(id){runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}what+=\"\";out(what);err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";throw new WebAssembly.RuntimeError(what)}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(){try{if(wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(wasmBinaryFile)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary()})}return new Promise(function(resolve,reject){resolve(getBinary())})}function createWasm(){var info={\"env\":asmLibraryArg,\"wasi_snapshot_preview1\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmMemory=exports[\"memory\"];updateGlobalBufferAndViews(wasmMemory.buffer);removeRunDependency(\"wasm-instantiate\")}addRunDependency(\"wasm-instantiate\");function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming===\"function\"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch===\"function\"){fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err(\"wasm streaming compile failed: \"+reason);err(\"falling back to ArrayBuffer instantiation\");instantiateArrayBuffer(receiveInstantiatedSource)})})}else{return instantiateArrayBuffer(receiveInstantiatedSource)}}if(Module[\"instantiateWasm\"]){try{var exports=Module[\"instantiateWasm\"](info,receiveInstance);return exports}catch(e){err(\"Module.instantiateWasm callback failed with error: \"+e);return false}}instantiateAsync();return{}}__ATINIT__.push();function _emscripten_notify_memory_growth(memoryIndex){updateGlobalBufferAndViews(wasmMemory.buffer)}var PATH={splitPath:function(filename){var splitPathRe=/^(\\/?|)([\\s\\S]*?)((?:\\.{1,2}|[^\\/]+?|)(\\.[^.\\/]*|))(?:[\\/]*)$/;return splitPathRe.exec(filename).slice(1)},normalizeArray:function(parts,allowAboveRoot){var up=0;for(var i=parts.length-1;i>=0;i--){var last=parts[i];if(last===\".\"){parts.splice(i,1)}else if(last===\"..\"){parts.splice(i,1);up++}else if(up){parts.splice(i,1);up--}}if(allowAboveRoot){for(;up;up--){parts.unshift(\"..\")}}return parts},normalize:function(path){var isAbsolute=path.charAt(0)===\"/\",trailingSlash=path.substr(-1)===\"/\";path=PATH.normalizeArray(path.split(\"/\").filter(function(p){return!!p}),!isAbsolute).join(\"/\");if(!path&&!isAbsolute){path=\".\"}if(path&&trailingSlash){path+=\"/\"}return(isAbsolute?\"/\":\"\")+path},dirname:function(path){var result=PATH.splitPath(path),root=result[0],dir=result[1];if(!root&&!dir){return\".\"}if(dir){dir=dir.substr(0,dir.length-1)}return root+dir},basename:function(path){if(path===\"/\")return\"/\";var lastSlash=path.lastIndexOf(\"/\");if(lastSlash===-1)return path;return path.substr(lastSlash+1)},extname:function(path){return PATH.splitPath(path)[3]},join:function(){var paths=Array.prototype.slice.call(arguments,0);return PATH.normalize(paths.join(\"/\"))},join2:function(l,r){return PATH.normalize(l+\"/\"+r)}};var SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer=SYSCALLS.buffers[stream];if(curr===0||curr===10){(stream===1?out:err)(UTF8ArrayToString(buffer,0));buffer.length=0}else{buffer.push(curr)}},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=HEAP32[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){}function _fd_write(fd,iov,iovcnt,pnum){var num=0;for(var i=0;i>2];var len=HEAP32[iov+(i*8+4)>>2];for(var j=0;j>2]=num;return 0}function _exit(status){exit(status)}function _proc_exit(code){_exit(code)}function _roundf(d){d=+d;return d>=+0?+Math_floor(d+ +.5):+Math_ceil(d-+.5)}var asmLibraryArg={\"emscripten_notify_memory_growth\":_emscripten_notify_memory_growth,\"fd_close\":_fd_close,\"fd_seek\":_fd_seek,\"fd_write\":_fd_write,\"proc_exit\":_proc_exit,\"roundf\":_roundf};var asm=createWasm();Module[\"asm\"]=asm;var _init=Module[\"_init\"]=function(){return(_init=Module[\"_init\"]=Module[\"asm\"][\"init\"]).apply(null,arguments)};var _register_tensor=Module[\"_register_tensor\"]=function(){return(_register_tensor=Module[\"_register_tensor\"]=Module[\"asm\"][\"register_tensor\"]).apply(null,arguments)};var _dispose_data=Module[\"_dispose_data\"]=function(){return(_dispose_data=Module[\"_dispose_data\"]=Module[\"asm\"][\"dispose_data\"]).apply(null,arguments)};var _dispose=Module[\"_dispose\"]=function(){return(_dispose=Module[\"_dispose\"]=Module[\"asm\"][\"dispose\"]).apply(null,arguments)};var 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ExitStatus(status){this.name=\"ExitStatus\";this.message=\"Program terminated with exit(\"+status+\")\";this.status=status}var calledMain=false;dependenciesFulfilled=function runCaller(){if(!calledRun)run();if(!calledRun)dependenciesFulfilled=runCaller};function callMain(args){var entryFunction=Module[\"__start\"];try{entryFunction();var ret=0;exit(ret,true)}catch(e){if(e instanceof ExitStatus){return}else if(e==\"unwind\"){noExitRuntime=true;return}else{var toLog=e;if(e&&typeof e===\"object\"&&e.stack){toLog=[e,e.stack]}err(\"exception thrown: \"+toLog);quit_(1,e)}}finally{calledMain=true}}function run(args){args=args||arguments_;if(runDependencies>0){return}preRun();if(runDependencies>0)return;function doRun(){if(calledRun)return;calledRun=true;Module[\"calledRun\"]=true;if(ABORT)return;initRuntime();preMain();if(Module[\"onRuntimeInitialized\"])Module[\"onRuntimeInitialized\"]();if(shouldRunNow)callMain(args);postRun()}if(Module[\"setStatus\"]){Module[\"setStatus\"](\"Running...\");setTimeout(function(){setTimeout(function(){Module[\"setStatus\"](\"\")},1);doRun()},1)}else{doRun()}}Module[\"run\"]=run;function exit(status,implicit){if(implicit&&noExitRuntime&&status===0){return}if(noExitRuntime){}else{ABORT=true;EXITSTATUS=status;exitRuntime();if(Module[\"onExit\"])Module[\"onExit\"](status)}quit_(status,new ExitStatus(status))}if(Module[\"preInit\"]){if(typeof Module[\"preInit\"]==\"function\")Module[\"preInit\"]=[Module[\"preInit\"]];while(Module[\"preInit\"].length>0){Module[\"preInit\"].pop()()}}var shouldRunNow=true;if(Module[\"noInitialRun\"])shouldRunNow=false;noExitRuntime=true;run();\n\n\n return WasmBackendModule\n}\n);\n})();\nif (typeof exports === 'object' && typeof module === 'object')\n module.exports = WasmBackendModule;\n else if (typeof define === 'function' && define['amd'])\n define([], function() { return WasmBackendModule; });\n else if (typeof exports === 'object')\n exports[\"WasmBackendModule\"] = WasmBackendModule;\n ", "// A port of an algorithm by Johannes Baag\u00F8e , 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e \n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n//\n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n//\n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n var me = this, mash = Mash();\n\n me.next = function() {\n var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n me.s0 = me.s1;\n me.s1 = me.s2;\n return me.s2 = t - (me.c = t | 0);\n };\n\n // Apply the seeding algorithm from Baagoe.\n me.c = 1;\n me.s0 = mash(' ');\n me.s1 = mash(' ');\n me.s2 = mash(' ');\n me.s0 -= mash(seed);\n if (me.s0 < 0) { me.s0 += 1; }\n me.s1 -= mash(seed);\n if (me.s1 < 0) { me.s1 += 1; }\n me.s2 -= mash(seed);\n if (me.s2 < 0) { me.s2 += 1; }\n mash = null;\n}\n\nfunction copy(f, t) {\n t.c = f.c;\n t.s0 = f.s0;\n t.s1 = f.s1;\n t.s2 = f.s2;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new Alea(seed),\n state = opts && opts.state,\n prng = xg.next;\n prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n prng.double = function() {\n return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n };\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nfunction Mash() {\n var n = 0xefc8249d;\n\n var mash = function(data) {\n data = String(data);\n for (var i = 0; i < data.length; i++) {\n n += data.charCodeAt(i);\n var h = 0.02519603282416938 * n;\n n = h >>> 0;\n h -= n;\n h *= n;\n n = h >>> 0;\n h -= n;\n n += h * 0x100000000; // 2^32\n }\n return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n };\n\n return mash;\n}\n\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.alea = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n\n // Set up generator function.\n me.next = function() {\n var t = me.x ^ (me.x << 11);\n me.x = me.y;\n me.y = me.z;\n me.z = me.w;\n return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n };\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor128 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var t = (me.x ^ (me.x >>> 2));\n me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n return (me.d = (me.d + 362437 | 0)) +\n (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n };\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n me.v = 0;\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n if (k == strseed.length) {\n me.d = me.x << 10 ^ me.x >>> 4;\n }\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n t.v = f.v;\n t.d = f.d;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorwow = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n // Update xor generator.\n var X = me.x, i = me.i, t, v, w;\n t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n X[i] = v;\n me.i = (i + 1) & 7;\n return v;\n };\n\n function init(me, seed) {\n var j, w, X = [];\n\n if (seed === (seed | 0)) {\n // Seed state array using a 32-bit integer.\n w = X[0] = seed;\n } else {\n // Seed state using a string.\n seed = '' + seed;\n for (j = 0; j < seed.length; ++j) {\n X[j & 7] = (X[j & 7] << 15) ^\n (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n }\n }\n // Enforce an array length of 8, not all zeroes.\n while (X.length < 8) X.push(0);\n for (j = 0; j < 8 && X[j] === 0; ++j);\n if (j == 8) w = X[7] = -1; else w = X[j];\n\n me.x = X;\n me.i = 0;\n\n // Discard an initial 256 values.\n for (j = 256; j > 0; --j) {\n me.next();\n }\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.x = f.x.slice();\n t.i = f.i;\n return t;\n}\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.x) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorshift7 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated. For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1); // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897); // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C. This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n var w = me.w,\n X = me.X, i = me.i, t, v;\n // Update Weyl generator.\n me.w = w = (w + 0x61c88647) | 0;\n // Update xor generator.\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n // Update Xor generator array state.\n v = X[i] = v ^ t;\n me.i = i;\n // Result is the combination.\n return (v + (w ^ (w >>> 16))) | 0;\n };\n\n function init(me, seed) {\n var t, v, i, j, w, X = [], limit = 128;\n if (seed === (seed | 0)) {\n // Numeric seeds initialize v, which is used to generates X.\n v = seed;\n seed = null;\n } else {\n // String seeds are mixed into v and X one character at a time.\n seed = seed + '\\0';\n v = 0;\n limit = Math.max(limit, seed.length);\n }\n // Initialize circular array and weyl value.\n for (i = 0, j = -32; j < limit; ++j) {\n // Put the unicode characters into the array, and shuffle them.\n if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n // After 32 shuffles, take v as the starting w value.\n if (j === 0) w = v;\n v ^= v << 10;\n v ^= v >>> 15;\n v ^= v << 4;\n v ^= v >>> 13;\n if (j >= 0) {\n w = (w + 0x61c88647) | 0; // Weyl.\n t = (X[j & 127] ^= (v + w)); // Combine xor and weyl to init array.\n i = (0 == t) ? i + 1 : 0; // Count zeroes.\n }\n }\n // We have detected all zeroes; make the key nonzero.\n if (i >= 128) {\n X[(seed && seed.length || 0) & 127] = -1;\n }\n // Run the generator 512 times to further mix the state before using it.\n // Factoring this as a function slows the main generator, so it is just\n // unrolled here. The weyl generator is not advanced while warming up.\n i = 127;\n for (j = 4 * 128; j > 0; --j) {\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n X[i] = v ^ t;\n }\n // Storing state as object members is faster than using closure variables.\n me.w = w;\n me.X = X;\n me.i = i;\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.i = f.i;\n t.w = f.w;\n t.X = f.X.slice();\n return t;\n};\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.X) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor4096 = impl;\n}\n\n})(\n this, // window object or global\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var b = me.b, c = me.c, d = me.d, a = me.a;\n b = (b << 25) ^ (b >>> 7) ^ c;\n c = (c - d) | 0;\n d = (d << 24) ^ (d >>> 8) ^ a;\n a = (a - b) | 0;\n me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n me.c = c = (c - d) | 0;\n me.d = (d << 16) ^ (c >>> 16) ^ a;\n return me.a = (a - b) | 0;\n };\n\n /* The following is non-inverted tyche, which has better internal\n * bit diffusion, but which is about 25% slower than tyche-i in JS.\n me.next = function() {\n var a = me.a, b = me.b, c = me.c, d = me.d;\n a = (me.a + me.b | 0) >>> 0;\n d = me.d ^ a; d = d << 16 ^ d >>> 16;\n c = me.c + d | 0;\n b = me.b ^ c; b = b << 12 ^ d >>> 20;\n me.a = a = a + b | 0;\n d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n me.c = c = c + d | 0;\n b = b ^ c;\n return me.b = (b << 7 ^ b >>> 25);\n }\n */\n\n me.a = 0;\n me.b = 0;\n me.c = 2654435769 | 0;\n me.d = 1367130551;\n\n if (seed === Math.floor(seed)) {\n // Integer seed.\n me.a = (seed / 0x100000000) | 0;\n me.b = seed | 0;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 20; k++) {\n me.b ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.a = f.a;\n t.b = f.b;\n t.c = f.c;\n t.d = f.d;\n return t;\n};\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.tychei = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "/*\nCopyright 2019 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (global, pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\n\nvar width = 256, // each RC4 output is 0 <= x < 256\n chunks = 6, // at least six RC4 outputs for each double\n digits = 52, // there are 52 significant digits in a double\n rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n startdenom = math.pow(width, chunks),\n significance = math.pow(2, digits),\n overflow = significance * 2,\n mask = width - 1,\n nodecrypto; // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n var key = [];\n options = (options == true) ? { entropy: true } : (options || {});\n\n // Flatten the seed string or build one from local entropy if needed.\n var shortseed = mixkey(flatten(\n options.entropy ? [seed, tostring(pool)] :\n (seed == null) ? autoseed() : seed, 3), key);\n\n // Use the seed to initialize an ARC4 generator.\n var arc4 = new ARC4(key);\n\n // This function returns a random double in [0, 1) that contains\n // randomness in every bit of the mantissa of the IEEE 754 value.\n var prng = function() {\n var n = arc4.g(chunks), // Start with a numerator n < 2 ^ 48\n d = startdenom, // and denominator d = 2 ^ 48.\n x = 0; // and no 'extra last byte'.\n while (n < significance) { // Fill up all significant digits by\n n = (n + x) * width; // shifting numerator and\n d *= width; // denominator and generating a\n x = arc4.g(1); // new least-significant-byte.\n }\n while (n >= overflow) { // To avoid rounding up, before adding\n n /= 2; // last byte, shift everything\n d /= 2; // right using integer math until\n x >>>= 1; // we have exactly the desired bits.\n }\n return (n + x) / d; // Form the number within [0, 1).\n };\n\n prng.int32 = function() { return arc4.g(4) | 0; }\n prng.quick = function() { return arc4.g(4) / 0x100000000; }\n prng.double = prng;\n\n // Mix the randomness into accumulated entropy.\n mixkey(tostring(arc4.S), pool);\n\n // Calling convention: what to return as a function of prng, seed, is_math.\n return (options.pass || callback ||\n function(prng, seed, is_math_call, state) {\n if (state) {\n // Load the arc4 state from the given state if it has an S array.\n if (state.S) { copy(state, arc4); }\n // Only provide the .state method if requested via options.state.\n prng.state = function() { return copy(arc4, {}); }\n }\n\n // If called as a method of Math (Math.seedrandom()), mutate\n // Math.random because that is how seedrandom.js has worked since v1.0.\n if (is_math_call) { math[rngname] = prng; return seed; }\n\n // Otherwise, it is a newer calling convention, so return the\n // prng directly.\n else return prng;\n })(\n prng,\n shortseed,\n 'global' in options ? options.global : (this == math),\n options.state);\n}\n\n//\n// ARC4\n//\n// An ARC4 implementation. The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4. Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n var t, keylen = key.length,\n me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n // The empty key [] is treated as [0].\n if (!keylen) { key = [keylen++]; }\n\n // Set up S using the standard key scheduling algorithm.\n while (i < width) {\n s[i] = i++;\n }\n for (i = 0; i < width; i++) {\n s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n s[j] = t;\n }\n\n // The \"g\" method returns the next (count) outputs as one number.\n (me.g = function(count) {\n // Using instance members instead of closure state nearly doubles speed.\n var t, r = 0,\n i = me.i, j = me.j, s = me.S;\n while (count--) {\n t = s[i = mask & (i + 1)];\n r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n }\n me.i = i; me.j = j;\n return r;\n // For robust unpredictability, the function call below automatically\n // discards an initial batch of values. This is called RC4-drop[256].\n // See http://google.com/search?q=rsa+fluhrer+response&btnI\n })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n t.i = f.i;\n t.j = f.j;\n t.S = f.S.slice();\n return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n var result = [], typ = (typeof obj), prop;\n if (depth && typ == 'object') {\n for (prop in obj) {\n try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n }\n }\n return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n var stringseed = seed + '', smear, j = 0;\n while (j < stringseed.length) {\n key[mask & j] =\n mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n }\n return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n try {\n var out;\n if (nodecrypto && (out = nodecrypto.randomBytes)) {\n // The use of 'out' to remember randomBytes makes tight minified code.\n out = out(width);\n } else {\n out = new Uint8Array(width);\n (global.crypto || global.msCrypto).getRandomValues(out);\n }\n return tostring(out);\n } catch (e) {\n var browser = global.navigator,\n plugins = browser && browser.plugins;\n return [+new Date, global, plugins, global.screen, tostring(pool)];\n }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool. Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n module.exports = seedrandom;\n // When in node.js, try using crypto package for autoseeding.\n try {\n nodecrypto = require('crypto');\n } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n define(function() { return seedrandom; });\n} else {\n // When included as a plain script, set up Math.seedrandom global.\n math['seed' + rngname] = seedrandom;\n}\n\n\n// End anonymous scope, and pass initial values.\n})(\n // global: `self` in browsers (including strict mode and web workers),\n // otherwise `this` in Node and other environments\n (typeof self !== 'undefined') ? self : this,\n [], // pool: entropy pool starts empty\n Math // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random(); // 0 <= x < 1. Every bit is random.\n// var x = random.quick(); // 0 <= x < 1. 32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three. It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures. Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "export const wasmWorkerContents = 'var threadInfoStruct=0;var selfThreadId=0;var parentThreadId=0;var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(\" \");console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(\" \");postMessage({cmd:\"alert\",text:text,threadId:selfThreadId})}var err=threadPrintErr;this.alert=threadAlert;Module[\"instantiateWasm\"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module[\"wasmModule\"],info);Module[\"wasmModule\"]=null;receiveInstance(instance);return instance.exports};this.onmessage=function(e){try{if(e.data.cmd===\"load\"){Module[\"DYNAMIC_BASE\"]=e.data.DYNAMIC_BASE;Module[\"DYNAMICTOP_PTR\"]=e.data.DYNAMICTOP_PTR;Module[\"wasmModule\"]=e.data.wasmModule;Module[\"wasmMemory\"]=e.data.wasmMemory;Module[\"buffer\"]=Module[\"wasmMemory\"].buffer;Module[\"ENVIRONMENT_IS_PTHREAD\"]=true;if(typeof e.data.urlOrBlob===\"string\"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}Module=WasmBackendModuleThreadedSimd(Module);postMessage({\"cmd\":\"loaded\"})}else if(e.data.cmd===\"objectTransfer\"){Module[\"PThread\"].receiveObjectTransfer(e.data)}else if(e.data.cmd===\"run\"){Module[\"__performance_now_clock_drift\"]=performance.now()-e.data.time;threadInfoStruct=e.data.threadInfoStruct;Module[\"__register_pthread_ptr\"](threadInfoStruct,0,0);selfThreadId=e.data.selfThreadId;parentThreadId=e.data.parentThreadId;var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module[\"establishStackSpace\"](top,max);Module[\"_emscripten_tls_init\"]();Module[\"PThread\"].receiveObjectTransfer(e.data);Module[\"PThread\"].setThreadStatus(Module[\"_pthread_self\"](),1);try{var result=Module[\"dynCall_ii\"](e.data.start_routine,e.data.arg);if(!Module[\"getNoExitRuntime\"]())Module[\"PThread\"].threadExit(result)}catch(ex){if(ex===\"Canceled!\"){Module[\"PThread\"].threadCancel()}else if(ex!=\"unwind\"){Atomics.store(Module[\"HEAPU32\"],threadInfoStruct+4>>2,ex instanceof Module[\"ExitStatus\"]?ex.status:-2);Atomics.store(Module[\"HEAPU32\"],threadInfoStruct+0>>2,1);Module[\"_emscripten_futex_wake\"](threadInfoStruct+0,2147483647);if(!(ex instanceof Module[\"ExitStatus\"]))throw ex}}}else if(e.data.cmd===\"cancel\"){if(threadInfoStruct){Module[\"PThread\"].threadCancel()}}else if(e.data.target===\"setimmediate\"){}else if(e.data.cmd===\"processThreadQueue\"){if(threadInfoStruct){Module[\"_emscripten_current_thread_process_queued_calls\"]()}}else{err(\"worker.js received unknown command \"+e.data.cmd);err(e.data)}}catch(ex){err(\"worker.js onmessage() captured an uncaught exception: \"+ex);if(ex.stack)err(ex.stack);throw ex}};if(typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\"){self={location:{href:__filename}};var onmessage=this.onmessage;var nodeWorkerThreads=require(\"worker_threads\");Worker=nodeWorkerThreads.Worker;var parentPort=nodeWorkerThreads.parentPort;parentPort.on(\"message\",function(data){onmessage({data:data})});var nodeFS=require(\"fs\");var nodeRead=function(filename){return nodeFS.readFileSync(filename,\"utf8\")};function globalEval(x){global.require=require;global.Module=Module;eval.call(null,x)}importScripts=function(f){globalEval(nodeRead(f))};postMessage=function(msg){parentPort.postMessage(msg)};if(typeof performance===\"undefined\"){performance={now:function(){return Date.now()}}}}';", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "// wrapper to load tfjs in a single place so version can be changed quickly\n\n// simplified\n// { modules: 1061, moduleBytes: 3772720, outputBytes: 1531035 }\n\n// export * from '@tensorflow/tfjs/dist/index.js';\n// export * from '@tensorflow/tfjs-backend-wasm';\n\n// modular\n// { modules: 1064, moduleBytes: 3793219, outputBytes: 1535600 }\n\n// get versions of all packages.\nimport { version as tfjs } from '@tensorflow/tfjs/package.json';\nimport { version as versionCore } from '@tensorflow/tfjs-core/package.json';\nimport { version as versionData } from '@tensorflow/tfjs-data/package.json';\nimport { version as versionLayers } from '@tensorflow/tfjs-layers/package.json';\nimport { version as versionConverter } from '@tensorflow/tfjs-converter/package.json';\n// for backends, get version from source so it can register backend during import\nimport { version_cpu } from '@tensorflow/tfjs-backend-cpu/dist/index.js';\nimport { version_webgl } from '@tensorflow/tfjs-backend-webgl/dist/index.js';\nimport { version_wasm } from '@tensorflow/tfjs-backend-wasm/dist/index.js';\n\n// export all\nexport * from '@tensorflow/tfjs-core/dist/index.js';\nexport * from '@tensorflow/tfjs-layers/dist/index.js';\nexport * from '@tensorflow/tfjs-converter/dist/index.js';\nexport * as data from '@tensorflow/tfjs-data/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-cpu/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-webgl/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-wasm/dist/index.js';\n\n// export versions\nexport const version = {\n tfjs,\n 'tfjs-core': versionCore,\n 'tfjs-data': versionData,\n 'tfjs-layers': versionLayers,\n 'tfjs-converter': versionConverter,\n 'tfjs-backend-cpu': version_cpu,\n 'tfjs-backend-webgl': version_webgl,\n 'tfjs-backend-wasm': version_wasm,\n};\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport const config = {\n name: 'humangl',\n priority: 99,\n canvas: null,\n width: 1024,\n height: 1024,\n webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n alpha: false,\n antialias: false,\n premultipliedAlpha: false,\n preserveDrawingBuffer: false,\n depth: false,\n stencil: false,\n failIfMajorPerformanceCaveat: false,\n desynchronized: true,\n },\n};\n\nexport function register() {\n if (!tf.findBackend(config.name)) {\n // @ts-ignore\n config.canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(config.width, config.height) : document.createElement('canvas');\n // @ts-ignore\n const gl = config.canvas.getContext('webgl2', config.webGLattr);\n tf.setWebGLContext(2, gl);\n const ctx = new tf.GPGPUContext(gl);\n tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n const kernels = tf.getKernelsForBackend('webgl');\n kernels.forEach((kernelConfig) => {\n const newKernelConfig = { ...kernelConfig, backendName: config.name };\n tf.registerKernel(newKernelConfig);\n });\n tf.ENV.set('WEBGL_VERSION', 2);\n tf.ENV.set('WEBGL_MAX_TEXTURE_SIZE', gl.getParameter(gl.MAX_TEXTURE_SIZE));\n tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n }\n}\n", "import { log } from './log.js';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as backend from './tfjs/backend.js';\nimport * as facemesh from './blazeface/facemesh.js';\nimport * as age from './age/age.js';\nimport * as gender from './gender/gender.js';\nimport * as emotion from './emotion/emotion.js';\nimport * as embedding from './embedding/embedding.js';\nimport * as posenet from './posenet/posenet.js';\nimport * as blazepose from './blazepose/blazepose.js';\nimport * as handpose from './handpose/handpose.js';\nimport * as gesture from './gesture/gesture.js';\nimport * as image from './image.js';\nimport * as profile from './profile.js';\nimport * as config from '../config.js';\nimport * as sample from './sample.js';\nimport * as app from '../package.json';\n\n// helper function: gets elapsed time on both browser and nodejs\nconst now = () => {\n if (typeof performance !== 'undefined') return performance.now();\n return parseInt(Number(process.hrtime.bigint()) / 1000 / 1000);\n};\n\n// helper function: perform deep merge of multiple objects so it allows full inheriance with overrides\nfunction mergeDeep(...objects) {\n const isObject = (obj) => obj && typeof obj === 'object';\n return objects.reduce((prev, obj) => {\n Object.keys(obj || {}).forEach((key) => {\n const pVal = prev[key];\n const oVal = obj[key];\n if (Array.isArray(pVal) && Array.isArray(oVal)) {\n prev[key] = pVal.concat(...oVal);\n } else if (isObject(pVal) && isObject(oVal)) {\n prev[key] = mergeDeep(pVal, oVal);\n } else {\n prev[key] = oVal;\n }\n });\n return prev;\n }, {});\n}\n\nclass Human {\n constructor(userConfig = {}) {\n this.tf = tf;\n this.version = app.version;\n this.config = mergeDeep(config.default, userConfig);\n this.fx = null;\n this.state = 'idle';\n this.numTensors = 0;\n this.analyzeMemoryLeaks = false;\n this.checkSanity = false;\n this.firstRun = true;\n this.perf = {};\n // object that contains all initialized models\n this.models = {\n facemesh: null,\n posenet: null,\n handpose: null,\n iris: null,\n age: null,\n gender: null,\n emotion: null,\n };\n // export raw access to underlying models\n this.facemesh = facemesh;\n this.age = age;\n this.gender = gender;\n this.emotion = emotion;\n this.body = posenet;\n this.hand = handpose;\n }\n\n profile() {\n if (this.config.profile) return profile.data;\n return {};\n }\n\n // helper function: measure tensor leak\n analyze(...msg) {\n if (!this.analyzeMemoryLeaks) return;\n const current = tf.engine().state.numTensors;\n const previous = this.numTensors;\n this.numTensors = current;\n const leaked = current - previous;\n if (leaked !== 0) log(...msg, leaked);\n }\n\n // quick sanity check on inputs\n sanity(input) {\n if (!this.checkSanity) return null;\n if (!input) return 'input is not defined';\n if (tf.ENV.flags.IS_NODE && !(input instanceof tf.Tensor)) {\n return 'input must be a tensor';\n }\n try {\n tf.getBackend();\n } catch {\n return 'backend not loaded';\n }\n return null;\n }\n\n simmilarity(embedding1, embedding2) {\n if (this.config.face.embedding.enabled) return embedding.simmilarity(embedding1, embedding2);\n return 0;\n }\n\n // preload models, not explicitly required as it's done automatically on first use\n async load(userConfig) {\n this.state = 'load';\n const timeStamp = now();\n if (userConfig) this.config = mergeDeep(this.config, userConfig);\n\n if (this.firstRun) {\n log(`version: ${this.version} TensorFlow/JS version: ${tf.version_core}`);\n await this.checkBackend(true);\n if (tf.ENV.flags.IS_BROWSER) {\n log('configuration:', this.config);\n log('tf flags:', tf.ENV.flags);\n }\n this.firstRun = false;\n }\n\n if (this.config.async) {\n [\n this.models.facemesh,\n this.models.age,\n this.models.gender,\n this.models.emotion,\n this.models.embedding,\n this.models.posenet,\n this.models.handpose,\n this.models.blazepose,\n ] = await Promise.all([\n this.models.facemesh || (this.config.face.enabled ? facemesh.load(this.config) : null),\n this.models.age || ((this.config.face.enabled && this.config.face.age.enabled) ? age.load(this.config) : null),\n this.models.gender || ((this.config.face.enabled && this.config.face.gender.enabled) ? gender.load(this.config) : null),\n this.models.emotion || ((this.config.face.enabled && this.config.face.emotion.enabled) ? emotion.load(this.config) : null),\n this.models.embedding || ((this.config.face.enabled && this.config.face.embedding.enabled) ? embedding.load(this.config) : null),\n this.models.posenet || (this.config.body.enabled ? posenet.load(this.config) : null),\n this.models.handpose || (this.config.hand.enabled ? handpose.load(this.config) : null),\n this.models.blazepose || (this.config.pose.enabled ? blazepose.load(this.config) : null),\n ]);\n } else {\n if (this.config.face.enabled && !this.models.facemesh) this.models.facemesh = await facemesh.load(this.config);\n if (this.config.face.enabled && this.config.face.age.enabled && !this.models.age) this.models.age = await age.load(this.config);\n if (this.config.face.enabled && this.config.face.gender.enabled && !this.models.gender) this.models.gender = await gender.load(this.config);\n if (this.config.face.enabled && this.config.face.emotion.enabled && !this.models.emotion) this.models.emotion = await emotion.load(this.config);\n if (this.config.face.enabled && this.config.face.embedding.enabled && !this.models.embedding) this.models.embedding = await embedding.load(this.config);\n if (this.config.body.enabled && !this.models.posenet) this.models.posenet = await posenet.load(this.config);\n if (this.config.hand.enabled && !this.models.handpose) this.models.handpose = await handpose.load(this.config);\n if (this.config.pose.enabled && !this.models.blazepose) this.models.blazepose = await blazepose.load(this.config);\n }\n const current = Math.trunc(now() - timeStamp);\n if (current > (this.perf.load || 0)) this.perf.load = current;\n }\n\n // check if backend needs initialization if it changed\n async checkBackend(force) {\n if (this.config.backend && (this.config.backend !== '') && force || (tf.getBackend() !== this.config.backend)) {\n const timeStamp = now();\n this.state = 'backend';\n /* force backend reload\n if (this.config.backend in tf.engine().registry) {\n const backendFactory = tf.findBackendFactory(this.config.backend);\n tf.removeBackend(this.config.backend);\n tf.registerBackend(this.config.backend, backendFactory);\n } else {\n log('Backend not registred:', this.config.backend);\n }\n */\n\n log('setting backend:', this.config.backend);\n\n if (this.config.backend === 'wasm') {\n log('settings wasm path:', this.config.wasmPath);\n tf.setWasmPaths(this.config.wasmPath);\n const simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n if (!simd) log('warning: wasm simd support is not enabled');\n }\n\n if (this.config.backend === 'humangl') {\n log('registering humangl backend');\n backend.register();\n }\n\n await tf.setBackend(this.config.backend);\n tf.enableProdMode();\n /* debug mode is really too mcuh\n tf.enableDebugMode();\n */\n if (tf.getBackend() === 'webgl') {\n if (this.config.deallocate) {\n log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', this.config.deallocate);\n tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', this.config.deallocate ? 0 : -1);\n }\n tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n const gl = await tf.backend().getGPGPUContext().gl;\n log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);\n }\n await tf.ready();\n this.perf.backend = Math.trunc(now() - timeStamp);\n }\n }\n\n async detectFace(input) {\n // run facemesh, includes blazeface and iris\n // eslint-disable-next-line no-async-promise-executor\n let timeStamp;\n let ageRes;\n let genderRes;\n let emotionRes;\n let embeddingRes;\n const faceRes = [];\n this.state = 'run:face';\n timeStamp = now();\n const faces = await this.models.facemesh?.estimateFaces(input, this.config);\n this.perf.face = Math.trunc(now() - timeStamp);\n for (const face of faces) {\n this.analyze('Get Face');\n\n // is something went wrong, skip the face\n if (!face.image || face.image.isDisposedInternal) {\n log('Face object is disposed:', face.image);\n continue;\n }\n\n // run age, inherits face from blazeface\n this.analyze('Start Age:');\n if (this.config.async) {\n ageRes = this.config.face.age.enabled ? age.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:age';\n timeStamp = now();\n ageRes = this.config.face.age.enabled ? await age.predict(face.image, this.config) : {};\n this.perf.age = Math.trunc(now() - timeStamp);\n }\n\n // run gender, inherits face from blazeface\n this.analyze('Start Gender:');\n if (this.config.async) {\n genderRes = this.config.face.gender.enabled ? gender.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:gender';\n timeStamp = now();\n genderRes = this.config.face.gender.enabled ? await gender.predict(face.image, this.config) : {};\n this.perf.gender = Math.trunc(now() - timeStamp);\n }\n\n // run emotion, inherits face from blazeface\n this.analyze('Start Emotion:');\n if (this.config.async) {\n emotionRes = this.config.face.emotion.enabled ? emotion.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:emotion';\n timeStamp = now();\n emotionRes = this.config.face.emotion.enabled ? await emotion.predict(face.image, this.config) : {};\n this.perf.emotion = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Emotion:');\n\n // run emotion, inherits face from blazeface\n this.analyze('Start Embedding:');\n if (this.config.async) {\n embeddingRes = this.config.face.embedding.enabled ? embedding.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:embedding';\n timeStamp = now();\n embeddingRes = this.config.face.embedding.enabled ? await embedding.predict(face.image, this.config) : {};\n this.perf.embedding = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Emotion:');\n\n // if async wait for results\n if (this.config.async) {\n [ageRes, genderRes, emotionRes, embeddingRes] = await Promise.all([ageRes, genderRes, emotionRes, embeddingRes]);\n }\n\n this.analyze('Finish Face:');\n // dont need face anymore\n face.image.dispose();\n\n // calculate iris distance\n // iris: array[ center, left, top, right, bottom]\n if (!this.config.face.iris.enabled) {\n delete face.annotations.leftEyeIris;\n delete face.annotations.rightEyeIris;\n }\n const irisSize = (face.annotations.leftEyeIris && face.annotations.rightEyeIris)\n /* average human iris size is 11.7mm */\n ? 11.7 * Math.max(Math.abs(face.annotations.leftEyeIris[3][0] - face.annotations.leftEyeIris[1][0]), Math.abs(face.annotations.rightEyeIris[4][1] - face.annotations.rightEyeIris[2][1]))\n : 0;\n\n // combine results\n faceRes.push({\n confidence: face.confidence,\n box: face.box,\n mesh: face.mesh,\n boxRaw: face.boxRaw,\n meshRaw: face.meshRaw,\n annotations: face.annotations,\n age: ageRes.age,\n gender: genderRes.gender,\n genderConfidence: genderRes.confidence,\n emotion: emotionRes,\n embedding: embeddingRes,\n iris: (irisSize !== 0) ? Math.trunc(irisSize) / 100 : 0,\n });\n this.analyze('End Face');\n }\n this.analyze('End FaceMesh:');\n if (this.config.async) {\n if (this.perf.face) delete this.perf.face;\n if (this.perf.age) delete this.perf.age;\n if (this.perf.gender) delete this.perf.gender;\n if (this.perf.emotion) delete this.perf.emotion;\n }\n return faceRes;\n }\n\n async image(input, userConfig = {}) {\n this.state = 'image';\n this.config = mergeDeep(this.config, userConfig);\n const process = image.process(input, this.config);\n process.tensor.dispose();\n return process.canvas;\n }\n\n // main detect function\n async detect(input, userConfig = {}) {\n // detection happens inside a promise\n return new Promise(async (resolve) => {\n this.state = 'config';\n let timeStamp;\n\n // update configuration\n this.config = mergeDeep(this.config, userConfig);\n\n // sanity checks\n this.state = 'check';\n const error = this.sanity(input);\n if (error) {\n log(error, input);\n resolve({ error });\n }\n\n let poseRes;\n let blazeposeRes;\n let handRes;\n let faceRes;\n\n const timeStart = now();\n\n // configure backend\n await this.checkBackend();\n\n // load models if enabled\n await this.load();\n\n if (this.config.scoped) tf.engine().startScope();\n this.analyze('Start Scope:');\n\n timeStamp = now();\n const process = image.process(input, this.config);\n if (!process || !process.tensor) {\n log('could not convert input to tensor');\n resolve({ error: 'could not convert input to tensor' });\n return;\n }\n this.perf.image = Math.trunc(now() - timeStamp);\n this.analyze('Get Image:');\n\n // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n if (this.config.async) {\n faceRes = this.config.face.enabled ? this.detectFace(process.tensor) : [];\n if (this.perf.face) delete this.perf.face;\n } else {\n this.state = 'run:face';\n timeStamp = now();\n faceRes = this.config.face.enabled ? await this.detectFace(process.tensor) : [];\n this.perf.face = Math.trunc(now() - timeStamp);\n }\n\n // run posenet\n this.analyze('Start Body:');\n if (this.config.async) {\n poseRes = this.config.body.enabled ? this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n if (this.perf.body) delete this.perf.body;\n } else {\n this.state = 'run:body';\n timeStamp = now();\n poseRes = this.config.body.enabled ? await this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n this.perf.body = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Body:');\n\n // run posenet\n this.analyze('Start Pose:');\n if (this.config.async) {\n blazeposeRes = this.config.pose.enabled ? blazepose.predict(process.tensor, this.config) : [];\n if (this.perf.pose) delete this.perf.pose;\n } else {\n this.state = 'run:pose';\n timeStamp = now();\n blazeposeRes = this.config.pose.enabled ? await blazepose.predict(process.tensor, this.config) : [];\n this.perf.pose = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Pose:');\n\n // run handpose\n this.analyze('Start Hand:');\n if (this.config.async) {\n handRes = this.config.hand.enabled ? this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n if (this.perf.hand) delete this.perf.hand;\n } else {\n this.state = 'run:hand';\n timeStamp = now();\n handRes = this.config.hand.enabled ? await this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n this.perf.hand = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Hand:');\n\n // if async wait for results\n if (this.config.async) {\n [faceRes, poseRes, blazeposeRes, handRes] = await Promise.all([faceRes, poseRes, blazeposeRes, handRes]);\n }\n process.tensor.dispose();\n\n if (this.config.scoped) tf.engine().endScope();\n this.analyze('End Scope:');\n\n let gestureRes = [];\n if (this.config.gesture.enabled) {\n timeStamp = now();\n gestureRes = [...gesture.face(faceRes), ...gesture.body(poseRes), ...gesture.hand(handRes), ...gesture.iris(faceRes)];\n if (!this.config.async) this.perf.gesture = Math.trunc(now() - timeStamp);\n else if (this.perf.gesture) delete this.perf.gesture;\n }\n\n this.perf.total = Math.trunc(now() - timeStart);\n this.state = 'idle';\n resolve({ face: faceRes, body: poseRes, hand: handRes, pose: blazeposeRes, gesture: gestureRes, performance: this.perf, canvas: process.canvas });\n });\n }\n\n async warmupBitmap() {\n const b64toBlob = (base64, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n let blob;\n let res;\n switch (this.config.warmup) {\n case 'face': blob = await b64toBlob(sample.face); break;\n case 'full': blob = await b64toBlob(sample.body); break;\n default: blob = null;\n }\n if (blob) {\n const bitmap = await createImageBitmap(blob);\n res = await this.detect(bitmap, config);\n bitmap.close();\n }\n return res;\n }\n\n async warmupCanvas() {\n return new Promise((resolve) => {\n let src;\n let size = 0;\n switch (this.config.warmup) {\n case 'face':\n size = 256;\n src = 'data:image/jpeg;base64,' + sample.face;\n break;\n case 'full':\n size = 1200;\n src = 'data:image/jpeg;base64,' + sample.body;\n break;\n default:\n src = null;\n }\n const img = new Image(size, size);\n img.onload = () => {\n const canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(size, size) : document.createElement('canvas');\n canvas.width = size;\n canvas.height = size;\n const ctx = canvas.getContext('2d');\n ctx.drawImage(img, 0, 0);\n const data = ctx.getImageData(0, 0, size, size);\n this.detect(data, config).then((res) => resolve(res));\n };\n if (src) img.src = src;\n else resolve(null);\n });\n }\n\n async warmup(userConfig) {\n const t0 = now();\n if (userConfig) this.config = mergeDeep(this.config, userConfig);\n const video = this.config.videoOptimized;\n this.config.videoOptimized = false;\n let res;\n if (typeof createImageBitmap === 'function') res = await this.warmupBitmap();\n else res = await this.warmupCanvas();\n this.config.videoOptimized = video;\n const t1 = now();\n log('Warmup', this.config.warmup, (t1 - t0), res);\n return res;\n }\n}\n\nexport { Human as default };\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\n// import * as profile from '../profile.js';\n\nconst models = {};\n\nexport async function load(config) {\n if (!models.blazepose) {\n models.blazepose = await tf.loadGraphModel(config.pose.modelPath);\n log(`load model: ${config.pose.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.blazepose;\n}\n\nexport async function predict(image, config) {\n if (!models.blazepose) return null;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [config.pose.inputSize, config.pose.inputSize], false);\n const enhance = tf.div(resize, 127.5).sub(1);\n tf.dispose(resize);\n const logits = await models.blazepose.predict(enhance);\n //\n tf.dispose(enhance);\n logits.map((logit) => logit.dispose());\n resolve(logits);\n });\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nfunction getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\nfunction getBoxCenter(box) {\n return [\n box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n ];\n}\nfunction cutBoxFromImageAndResize(box, image, cropSize) {\n const h = image.shape[1];\n const w = image.shape[2];\n const boxes = [[\n box.startPoint[1] / h,\n box.startPoint[0] / w,\n box.endPoint[1] / h,\n box.endPoint[0] / w,\n ]];\n return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\nfunction scaleBoxCoordinates(box, factor) {\n const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n const palmLandmarks = box.palmLandmarks.map((coord) => {\n const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n return scaledCoord;\n });\n return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\nfunction enlargeBox(box, factor = 1.5) {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\nfunction squarifyBox(box) {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const maxEdge = Math.max(...size);\n const halfSize = maxEdge / 2;\n const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\nfunction shiftBox(box, shiftFactor) {\n const boxSize = [\n box.endPoint[0] - box.startPoint[0],\n box.endPoint[1] - box.startPoint[1],\n ];\n const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]];\n const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\nexport {\n cutBoxFromImageAndResize,\n enlargeBox,\n getBoxCenter,\n getBoxSize,\n scaleBoxCoordinates,\n shiftBox,\n squarifyBox,\n};\n", "function normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\nfunction computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\nconst buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\nfunction dot(v1, v2) {\n let product = 0;\n for (let i = 0; i < v1.length; i++) {\n product += v1[i] * v2[i];\n }\n return product;\n}\nfunction getColumnFrom2DArr(arr, columnIndex) {\n const column = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\nfunction multiplyTransformMatrices(mat1, mat2) {\n const product = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) {\n // @ts-ignore\n product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n }\n return product;\n}\nfunction buildRotationMatrix(rotation, center) {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\nfunction invertTransformMatrix(matrix) {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [\n -dot(rotationComponent[0], translationComponent),\n -dot(rotationComponent[1], translationComponent),\n ];\n return [\n rotationComponent[0].concat(invertedTranslation[0]),\n rotationComponent[1].concat(invertedTranslation[1]),\n [0, 0, 1],\n ];\n}\nfunction rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\nexport {\n buildRotationMatrix,\n computeRotation,\n dot,\n getColumnFrom2DArr,\n invertTransformMatrix,\n normalizeRadians,\n rotatePoint,\n};\n", "/* eslint-disable indent */\n/* eslint-disable no-multi-spaces */\n\nexport default {\n backend: 'webgl', // select tfjs backend to use\n wasmPath: '../assets/', // path for wasm binaries\n // only used for backend: wasm\n async: true, // execute enabled models in parallel\n // this disables per-model performance data but\n // slightly increases performance\n // cannot be used if profiling is enabled\n profile: false, // enable tfjs profiling\n // this has significant performance impact\n // only enable for debugging purposes\n // currently only implemented for age,gender,emotion models\n deallocate: false, // aggresively deallocate gpu memory after each usage\n // only valid for webgl backend and only during first call\n // cannot be changed unless library is reloaded\n // this has significant performance impact\n // only enable on low-memory devices\n scoped: false, // enable scoped runs\n // some models *may* have memory leaks,\n // this wrapps everything in a local scope at a cost of performance\n // typically not needed\n videoOptimized: true, // perform additional optimizations when input is video,\n // must be disabled for images\n // basically this skips object box boundary detection for every n frames\n // while maintaining in-box detection since objects cannot move that fast\n warmup: 'face', // what to use for human.warmup(), can be 'none', 'face', 'full'\n // warmup pre-initializes all models for faster inference but can take\n // significant time on startup\n filter: {\n enabled: true, // enable image pre-processing filters\n width: 0, // resize input width\n height: 0, // resize input height\n // if both width and height are set to 0, there is no resizing\n // if just one is set, second one is scaled automatically\n // if both are set, values are used as-is\n return: true, // return processed canvas imagedata in result\n brightness: 0, // range: -1 (darken) to 1 (lighten)\n contrast: 0, // range: -1 (reduce contrast) to 1 (increase contrast)\n sharpness: 0, // range: 0 (no sharpening) to 1 (maximum sharpening)\n blur: 0, // range: 0 (no blur) to N (blur radius in pixels)\n saturation: 0, // range: -1 (reduce saturation) to 1 (increase saturation)\n hue: 0, // range: 0 (no change) to 360 (hue rotation in degrees)\n negative: false, // image negative\n sepia: false, // image sepia colors\n vintage: false, // image vintage colors\n kodachrome: false, // image kodachrome colors\n technicolor: false, // image technicolor colors\n polaroid: false, // image polaroid camera effect\n pixelate: 0, // range: 0 (no pixelate) to N (number of pixels to pixelate)\n },\n\n gesture: {\n enabled: true, // enable simple gesture recognition\n },\n\n face: {\n enabled: true, // controls if specified modul is enabled\n // face.enabled is required for all face models:\n // detector, mesh, iris, age, gender, emotion\n // (note: module is not loaded until it is required)\n detector: {\n modelPath: '../models/blazeface-back.json', // can be 'front' or 'back'.\n // 'front' is optimized for large faces\n // such as front-facing camera and\n // 'back' is optimized for distanct faces.\n inputSize: 256, // fixed value: 128 for front and 256 for 'back'\n rotation: false, // use best-guess rotated face image or just box with rotation as-is\n // false means higher performance, but incorrect mesh mapping if face angle is above 20 degrees\n maxFaces: 10, // maximum number of faces detected in the input\n // should be set to the minimum number for performance\n skipFrames: 11, // how many frames to go without re-running the face bounding box detector\n // only used for video inputs\n // e.g., if model is running st 25 FPS, we can re-use existing bounding\n // box for updated face analysis as the head probably hasn't moved much\n // in short time (10 * 1/25 = 0.25 sec)\n minConfidence: 0.5, // threshold for discarding a prediction\n iouThreshold: 0.2, // threshold for deciding whether boxes overlap too much in\n // non-maximum suppression (0.1 means drop if overlap 10%)\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on score\n // in non-maximum suppression,\n // this is applied on detection objects only and before minConfidence\n },\n\n mesh: {\n enabled: true,\n modelPath: '../models/facemesh.json',\n inputSize: 192, // fixed value\n returnRawData: false, // in addition to standard mesh and box values, return raw normalized values as well\n },\n\n iris: {\n enabled: true,\n modelPath: '../models/iris.json',\n inputSize: 64, // fixed value\n },\n\n age: {\n enabled: true,\n modelPath: '../models/age-ssrnet-imdb.json', // can be 'age-ssrnet-imdb' or 'age-ssrnet-wiki'\n // which determines training set for model\n inputSize: 64, // fixed value\n skipFrames: 31, // how many frames to go without re-running the detector\n // only used for video inputs\n },\n\n gender: {\n enabled: true,\n minConfidence: 0.1, // threshold for discarding a prediction\n modelPath: '../models/gender-ssrnet-imdb.json', // can be 'gender', 'gender-ssrnet-imdb' or 'gender-ssrnet-wiki'\n inputSize: 64, // fixed value\n skipFrames: 41, // how many frames to go without re-running the detector\n // only used for video inputs\n },\n\n emotion: {\n enabled: true,\n inputSize: 64, // fixed value\n minConfidence: 0.2, // threshold for discarding a prediction\n skipFrames: 21, // how many frames to go without re-running the detector\n modelPath: '../models/emotion-large.json', // can be 'mini', 'large'\n },\n\n embedding: {\n enabled: false,\n inputSize: 112, // fixed value\n modelPath: '../models/mobilefacenet.json',\n },\n },\n\n body: {\n enabled: true,\n modelPath: '../models/posenet.json',\n inputSize: 257, // fixed value\n maxDetections: 10, // maximum number of people detected in the input\n // should be set to the minimum number for performance\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on score\n // in non-maximum suppression\n nmsRadius: 20, // radius for deciding points are too close in non-maximum suppression\n outputStride: 16, // size of block in which to run point detectopn, smaller value means higher resolution\n // defined by model itself, can be 8, 16, or 32\n modelType: 'MobileNet', // Human includes MobileNet version, but you can switch to ResNet\n },\n\n pose: { // TBD: not currently in use\n enabled: false,\n scoreThreshold: 0.6, // threshold for deciding when to remove boxes based on score\n // in non-maximum suppression\n iouThreshold: 0.3, // threshold for deciding whether boxes overlap too much\n // in non-maximum suppression\n modelPath: '../models/blazepose.json',\n inputSize: 256, // fixed value\n },\n\n hand: {\n enabled: true,\n rotation: false, // use best-guess rotated hand image or just box with rotation as-is\n // false means higher performance, but incorrect finger mapping if hand is inverted\n inputSize: 256, // fixed value\n skipFrames: 12, // how many frames to go without re-running the hand bounding box detector\n // only used for video inputs\n // e.g., if model is running st 25 FPS, we can re-use existing bounding\n // box for updated hand skeleton analysis as the hand probably\n // hasn't moved much in short time (10 * 1/25 = 0.25 sec)\n minConfidence: 0.1, // threshold for discarding a prediction\n iouThreshold: 0.1, // threshold for deciding whether boxes overlap too much\n // in non-maximum suppression\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on\n // score in non-maximum suppression\n maxHands: 1, // maximum number of hands detected in the input\n // should be set to the minimum number for performance\n landmarks: true, // detect hand landmarks or just hand boundary box\n detector: {\n modelPath: '../models/handdetect.json',\n },\n skeleton: {\n modelPath: '../models/handskeleton.json',\n },\n },\n};\n", "// data:image/jpeg;base64,\nexport const face = 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"async function drawGesture(result, canvas, ui) {\n if (!result) return;\n const ctx = canvas.getContext('2d');\n ctx.font = ui.baseFont;\n ctx.fillStyle = ui.baseLabel;\n let i = 1;\n for (let gesture = 0; gesture < result.length; gesture++) {\n const [where, what] = Object.entries(result[gesture]);\n if ((what.length > 1) && (what[1].length > 0)) {\n const person = where[1] > 0 ? `#${where[1]}` : '';\n const label = `${where[0]} ${person}: ${what[1]}`;\n ctx.fillStyle = 'black';\n ctx.fillText(label, 8, 2 + (i * ui.baseLineHeight));\n ctx.fillStyle = ui.baseLabel;\n ctx.fillText(label, 6, 0 + (i * ui.baseLineHeight));\n i += 1;\n }\n }\n}\n\nasync function drawFace(result, canvas, ui, triangulation) {\n if (!result) return;\n const ctx = canvas.getContext('2d');\n for (const face of result) {\n ctx.font = ui.baseFont;\n ctx.strokeStyle = ui.baseColor;\n ctx.fillStyle = ui.baseColor;\n ctx.lineWidth = ui.baseLineWidth;\n ctx.beginPath();\n if (ui.drawBoxes) {\n ctx.rect(face.box[0], face.box[1], face.box[2], face.box[3]);\n }\n // silly hack since fillText does not suport new line\n const labels = [];\n // labels.push(`${Math.trunc(100 * face.confidence)}% face`);\n if (face.genderConfidence) labels.push(`${face.gender || ''} ${Math.trunc(100 * face.genderConfidence)}% confident`);\n // if (face.genderConfidence) labels.push(face.gender);\n if (face.age) labels.push(`age: ${face.age || ''}`);\n if (face.iris) labels.push(`iris distance: ${face.iris}`);\n if (face.emotion && face.emotion.length > 0) {\n const emotion = face.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`);\n labels.push(emotion.join(' '));\n }\n ctx.fillStyle = ui.baseLabel;\n for (let i = 0; i < labels.length; i++) {\n ctx.fillStyle = 'black';\n ctx.fillText(labels[i], face.box[0] + 1, face.box[1] - ((labels.length - i) * ui.baseLineHeight) + 6);\n ctx.fillStyle = ui.baseLabel;\n ctx.fillText(labels[i], face.box[0] + 0, face.box[1] - ((labels.length - i) * ui.baseLineHeight) + 5);\n }\n ctx.fillStyle = ui.baseColor;\n ctx.stroke();\n ctx.lineWidth = 1;\n if (face.mesh) {\n if (ui.drawPoints) {\n for (const point of face.mesh) {\n ctx.fillStyle = ui.useDepth ? `rgba(${127.5 + (2 * point[2])}, ${127.5 - (2 * point[2])}, 255, 0.5)` : ui.baseColor;\n ctx.beginPath();\n ctx.arc(point[0], point[1], 2, 0, 2 * Math.PI);\n ctx.fill();\n }\n }\n if (ui.drawPolygons) {\n for (let i = 0; i < triangulation.length / 3; i++) {\n const points = [\n triangulation[i * 3 + 0],\n triangulation[i * 3 + 1],\n triangulation[i * 3 + 2],\n ].map((index) => face.mesh[index]);\n const path = new Path2D();\n path.moveTo(points[0][0], points[0][1]);\n for (const point of points) {\n path.lineTo(point[0], point[1]);\n }\n path.closePath();\n ctx.strokeStyle = ui.useDepth ? `rgba(${127.5 + (2 * points[0][2])}, ${127.5 - (2 * points[0][2])}, 255, 0.3)` : ui.baseColor;\n ctx.stroke(path);\n if (ui.fillPolygons) {\n ctx.fillStyle = ui.useDepth ? `rgba(${127.5 + (2 * points[0][2])}, ${127.5 - (2 * points[0][2])}, 255, 0.3)` : ui.baseColor;\n ctx.fill(path);\n }\n }\n // iris: array[center, left, top, right, bottom]\n if (face.annotations && face.annotations.leftEyeIris) {\n ctx.strokeStyle = ui.useDepth ? 'rgba(255, 200, 255, 0.3)' : ui.baseColor;\n ctx.beginPath();\n const sizeX = Math.abs(face.annotations.leftEyeIris[3][0] - face.annotations.leftEyeIris[1][0]) / 2;\n const sizeY = Math.abs(face.annotations.leftEyeIris[4][1] - face.annotations.leftEyeIris[2][1]) / 2;\n ctx.ellipse(face.annotations.leftEyeIris[0][0], face.annotations.leftEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n ctx.stroke();\n if (ui.fillPolygons) {\n ctx.fillStyle = ui.useDepth ? 'rgba(255, 255, 200, 0.3)' : ui.baseColor;\n ctx.fill();\n }\n }\n if (face.annotations && face.annotations.rightEyeIris) {\n ctx.strokeStyle = ui.useDepth ? 'rgba(255, 200, 255, 0.3)' : ui.baseColor;\n ctx.beginPath();\n const sizeX = Math.abs(face.annotations.rightEyeIris[3][0] - face.annotations.rightEyeIris[1][0]) / 2;\n const sizeY = Math.abs(face.annotations.rightEyeIris[4][1] - face.annotations.rightEyeIris[2][1]) / 2;\n ctx.ellipse(face.annotations.rightEyeIris[0][0], face.annotations.rightEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n ctx.stroke();\n if (ui.fillPolygons) {\n ctx.fillStyle = ui.useDepth ? 'rgba(255, 255, 200, 0.3)' : ui.baseColor;\n ctx.fill();\n }\n }\n }\n }\n }\n}\n\nconst lastDrawnPose = [];\nasync function drawBody(result, canvas, ui) {\n if (!result) return;\n const ctx = canvas.getContext('2d');\n ctx.lineJoin = 'round';\n for (let i = 0; i < result.length; i++) {\n if (!lastDrawnPose[i] && ui.buffered) lastDrawnPose[i] = { ...result[i] };\n ctx.fillStyle = ui.baseColor;\n ctx.strokeStyle = ui.baseColor;\n ctx.font = ui.baseFont;\n ctx.lineWidth = ui.baseLineWidth;\n if (ui.drawPoints) {\n for (let pt = 0; pt < result[i].keypoints.length; pt++) {\n ctx.beginPath();\n if (ui.buffered) {\n lastDrawnPose[i].keypoints[pt].position.x = (lastDrawnPose[i].keypoints[pt].position.x + result[i].keypoints[pt].position.x) / 2;\n lastDrawnPose[i].keypoints[pt].position.y = (lastDrawnPose[i].keypoints[pt].position.y + result[i].keypoints[pt].position.y) / 2;\n ctx.arc(lastDrawnPose[i].keypoints[pt].position.x, lastDrawnPose[i].keypoints[pt].position.y, 2, 0, 2 * Math.PI);\n } else {\n ctx.arc(result[i].keypoints[pt].position.x, result[i].keypoints[pt].position.y, 2, 0, 2 * Math.PI);\n }\n ctx.fill();\n }\n }\n if (ui.drawPolygons) {\n const path = new Path2D();\n let root;\n let part;\n // torso\n root = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n if (root) {\n path.moveTo(root.position.x, root.position.y);\n part = result[i].keypoints.find((a) => a.part === 'rightShoulder');\n if (part) path.lineTo(part.position.x, part.position.y);\n part = result[i].keypoints.find((a) => a.part === 'rightHip');\n if (part) path.lineTo(part.position.x, part.position.y);\n part = result[i].keypoints.find((a) => a.part === 'leftHip');\n if (part) path.lineTo(part.position.x, part.position.y);\n part = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n if (part) path.lineTo(part.position.x, part.position.y);\n }\n // leg left\n root = result[i].keypoints.find((a) => a.part === 'leftHip');\n if (root) {\n path.moveTo(root.position.x, root.position.y);\n part = result[i].keypoints.find((a) => a.part === 'leftKnee');\n if (part) path.lineTo(part.position.x, part.position.y);\n part = result[i].keypoints.find((a) => a.part === 'leftAnkle');\n if (part) path.lineTo(part.position.x, part.position.y);\n }\n // leg right\n root = result[i].keypoints.find((a) => a.part === 'rightHip');\n if (root) {\n path.moveTo(root.position.x, root.position.y);\n part = result[i].keypoints.find((a) => a.part === 'rightKnee');\n if (part) path.lineTo(part.position.x, part.position.y);\n part = result[i].keypoints.find((a) => a.part === 'rightAnkle');\n if (part) path.lineTo(part.position.x, part.position.y);\n }\n // arm left\n root = result[i].keypoints.find((a) => a.part === 'leftShoulder');\n if (root) {\n path.moveTo(root.position.x, root.position.y);\n part = result[i].keypoints.find((a) => a.part === 'leftElbow');\n if (part) path.lineTo(part.position.x, part.position.y);\n part = result[i].keypoints.find((a) => a.part === 'leftWrist');\n if (part) path.lineTo(part.position.x, part.position.y);\n }\n // arm right\n root = result[i].keypoints.find((a) => a.part === 'rightShoulder');\n if (root) {\n path.moveTo(root.position.x, root.position.y);\n part = result[i].keypoints.find((a) => a.part === 'rightElbow');\n if (part) path.lineTo(part.position.x, part.position.y);\n part = result[i].keypoints.find((a) => a.part === 'rightWrist');\n if (part) path.lineTo(part.position.x, part.position.y);\n }\n // draw all\n ctx.stroke(path);\n }\n }\n}\n\nasync function drawHand(result, canvas, ui) {\n if (!result) return;\n const ctx = canvas.getContext('2d');\n ctx.lineJoin = 'round';\n for (const hand of result) {\n ctx.font = ui.baseFont;\n ctx.lineWidth = ui.baseLineWidth;\n if (ui.drawBoxes) {\n ctx.lineWidth = ui.baseLineWidth;\n ctx.beginPath();\n ctx.strokeStyle = ui.baseColor;\n ctx.fillStyle = ui.baseColor;\n ctx.rect(hand.box[0], hand.box[1], hand.box[2], hand.box[3]);\n ctx.fillStyle = 'black';\n ctx.fillText('hand', hand.box[0] + 3, 1 + hand.box[1] + ui.baseLineHeight, hand.box[2]);\n ctx.fillStyle = ui.baseLabel;\n ctx.fillText('hand', hand.box[0] + 2, 0 + hand.box[1] + ui.baseLineHeight, hand.box[2]);\n ctx.stroke();\n }\n if (ui.drawPoints) {\n if (hand.landmarks && hand.landmarks.length > 0) {\n for (const point of hand.landmarks) {\n ctx.fillStyle = ui.useDepth ? `rgba(${127.5 + (2 * point[2])}, ${127.5 - (2 * point[2])}, 255, 0.5)` : ui.baseColor;\n ctx.beginPath();\n ctx.arc(point[0], point[1], 2, 0, 2 * Math.PI);\n ctx.fill();\n }\n }\n }\n if (ui.drawPolygons) {\n const addPart = (part) => {\n if (!part) return;\n for (let i = 0; i < part.length; i++) {\n ctx.lineWidth = ui.baseLineWidth;\n ctx.beginPath();\n ctx.strokeStyle = ui.useDepth ? `rgba(${127.5 + (2 * part[i][2])}, ${127.5 - (2 * part[i][2])}, 255, 0.5)` : ui.baseColor;\n ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]);\n ctx.lineTo(part[i][0], part[i][1]);\n ctx.stroke();\n }\n };\n addPart(hand.annotations.indexFinger);\n addPart(hand.annotations.middleFinger);\n addPart(hand.annotations.ringFinger);\n addPart(hand.annotations.pinky);\n addPart(hand.annotations.thumb);\n // addPart(hand.annotations.palmBase);\n }\n }\n}\n\n// eslint-disable-next-line import/prefer-default-export\nexport default {\n face: drawFace,\n body: drawBody,\n hand: drawHand,\n gesture: drawGesture,\n};\n", "// @ts-nocheck\n\nlet instance = 0;\nlet CSScreated = false;\n\nlet theme = {\n background: 'darkslategray',\n hover: 'lightgray',\n itemBackground: 'black',\n itemColor: 'white',\n buttonBackground: 'lightblue',\n buttonHover: 'lightgreen',\n checkboxOn: 'lightgreen',\n checkboxOff: 'lightcoral',\n rangeBackground: 'lightblue',\n rangeLabel: 'white',\n chartColor: 'lightblue',\n};\n\nfunction createCSS() {\n if (CSScreated) return;\n const css = `\n :root { --rounded: 0.2rem; }\n .menu { position: absolute; top: 0rem; right: 0; width: max-content; padding: 0 0.2rem 0 0.2rem; line-height: 1.8rem; z-index: 10;\n box-shadow: 0 0 8px dimgrey; background: ${theme.background}; border-radius: var(--rounded); border-color: black; border-style: solid; border-width: thin; }\n\n .menu:hover { box-shadow: 0 0 8px ${theme.hover}; }\n .menu-container { display: block; max-height: 100vh; }\n .menu-container-fadeout { max-height: 0; overflow: hidden; transition: max-height, 0.5s ease; }\n .menu-container-fadein { max-height: 100vh; overflow: hidden; transition: max-height, 0.5s ease; }\n .menu-item { display: flex; white-space: nowrap; padding: 0.2rem; cursor: default; width: 100%; }\n .menu-title { cursor: pointer; }\n .menu-hr { margin: 0.2rem; border: 1px solid rgba(0, 0, 0, 0.5) }\n .menu-label { padding: 0; font-weight: 800; }\n\n .menu-list { margin-right: 0.8rem; }\n select:focus { outline: none; }\n .menu-list-item { background: ${theme.itemBackground}; color: ${theme.itemColor}; border: none; padding: 0.2rem; font-family: inherit;\n font-variant: inherit; border-radius: var(--rounded); font-weight: 800; }\n\n .menu-chart-title { padding: 0; font-size: 0.8rem; font-weight: 800; align-items: center}\n .menu-chart-canvas { background: transparent; margin: 0.2rem 0 0.2rem 0.6rem; }\n \n .menu-button { border: 0; background: ${theme.buttonBackground}; width: -webkit-fill-available; padding: 8px; margin: 8px; cursor: pointer; box-shadow: 4px 4px 4px 0 dimgrey;\n border-radius: var(--rounded); justify-content: center; font-family: inherit; font-variant: inherit; font-size: 1rem; font-weight: 800; }\n .menu-button:hover { background: ${theme.buttonHover}; box-shadow: 4px 4px 4px 0 black; }\n .menu-button:focus { outline: none; }\n\n .menu-checkbox { width: 2.8rem; height: 1rem; background: ${theme.itemBackground}; margin: 0.5rem 0.5rem 0 0; position: relative; border-radius: var(--rounded); }\n .menu-checkbox:after { content: 'OFF'; color: ${theme.checkboxOff}; position: absolute; right: 0.2rem; top: -0.4rem; font-weight: 800; font-size: 0.5rem; }\n .menu-checkbox:before { content: 'ON'; color: ${theme.checkboxOn}; position: absolute; left: 0.3rem; top: -0.4rem; font-weight: 800; font-size: 0.5rem; }\n .menu-checkbox-label { width: 1.3rem; height: 0.8rem; cursor: pointer; position: absolute; top: 0.1rem; left: 0.1rem; z-index: 1; background: ${theme.checkboxOff};\n border-radius: var(--rounded); transition: left 0.6s ease; }\n\n input[type=checkbox] { visibility: hidden; }\n input[type=checkbox]:checked + label { left: 1.4rem; background: ${theme.checkboxOn}; }\n\n .menu-range { margin: 0.2rem 0.5rem 0 0; width: 3.5rem; background: transparent; color: ${theme.rangeBackground}; }\n .menu-range:before { color: ${theme.rangeLabel}; margin: 0 0.4rem 0 0; font-weight: 800; font-size: 0.6rem; position: relative; top: 0.3rem; content: attr(value); }\n\n input[type=range] { -webkit-appearance: none; }\n input[type=range]::-webkit-slider-runnable-track { width: 100%; height: 1rem; cursor: pointer; background: ${theme.itemBackground}; border-radius: var(--rounded); border: 1px; }\n input[type=range]::-moz-range-track { width: 100%; height: 1rem; cursor: pointer; background: ${theme.itemBackground}; border-radius: var(--rounded); border: 1px; }\n input[type=range]::-webkit-slider-thumb { border: 1px solid #000000; margin-top: 0.05rem; height: 0.9rem; width: 1rem; border-radius: var(--rounded); background: ${theme.rangeBackground}; cursor: pointer; -webkit-appearance: none; }\n input[type=range]::-moz-range-thumb { border: 1px solid #000000; margin-top: 0.05rem; height: 0.9rem; width: 1rem; border-radius: var(--rounded); background: ${theme.rangeBackground}; cursor: pointer; -webkit-appearance: none; }\n\n .svg-background { fill:darkslategrey; cursor:pointer; opacity: 0.6; }\n .svg-foreground { fill:white; cursor:pointer; opacity: 0.8; }\n `;\n const el = document.createElement('style');\n el.innerHTML = css;\n document.getElementsByTagName('head')[0].appendChild(el);\n CSScreated = true;\n}\n\nclass Menu {\n constructor(parent, title, position, userTheme) {\n if (userTheme) theme = { ...theme, ...userTheme };\n createCSS();\n this.createMenu(parent, title, position);\n this.id = 0;\n this.instance = instance;\n instance++;\n this._maxFPS = 0;\n this.hidden = 0;\n }\n\n createMenu(parent, title = '', position = { top: null, left: null, bottom: null, right: null }) {\n this.menu = document.createElement('div');\n this.menu.id = `menu-${instance}`;\n this.menu.className = 'menu';\n if (position) {\n if (position.top) this.menu.style.top = position.top;\n if (position.bottom) this.menu.style.bottom = position.bottom;\n if (position.left) this.menu.style.left = position.left;\n if (position.right) this.menu.style.right = position.right;\n }\n\n this.container = document.createElement('div');\n this.container.id = `menu-container-${instance}`;\n this.container.className = 'menu-container menu-container-fadein';\n\n // set menu title with pulldown arrow\n const elTitle = document.createElement('div');\n elTitle.className = 'menu-title';\n elTitle.id = `menu-title-${instance}`;\n const svg = `\n \n \n `;\n if (title) elTitle.innerHTML = `${title}${svg}`;\n this.menu.appendChild(elTitle);\n elTitle.addEventListener('click', () => {\n this.container.classList.toggle('menu-container-fadeout');\n this.container.classList.toggle('menu-container-fadein');\n this.menu.style.borderStyle = this.container.classList.contains('menu-container-fadeout') ? 'none' : 'solid';\n });\n\n this.menu.appendChild(this.container);\n if (typeof parent === 'object') parent.appendChild(this.menu);\n else document.getElementById(parent).appendChild(this.menu);\n }\n\n get newID() {\n this.id++;\n return `menu-${this.instance}-${this.id}`;\n }\n\n get ID() {\n return `menu-${this.instance}-${this.id}`;\n }\n\n get width() {\n return this.menu.offsetWidth;\n }\n\n get height() {\n return this.menu.offsetHeight;\n }\n\n hide() {\n if (this.container.classList.contains('menu-container-fadein')) {\n this.container.classList.toggle('menu-container-fadeout');\n this.container.classList.toggle('menu-container-fadein');\n }\n }\n\n visible() {\n return (this.container.classList.contains('menu-container-fadein'));\n }\n\n toggle(evt) {\n this.container.classList.toggle('menu-container-fadeout');\n this.container.classList.toggle('menu-container-fadein');\n if (this.container.classList.contains('menu-container-fadein') && evt) {\n const x = evt.x || (evt.touches && evt.touches[0] ? evt.touches[0].pageX : null);\n // const y = evt.y || (evt.touches && evt.touches[0] ? evt.touches[0].pageY : null);\n if (x) this.menu.style.left = `${x - (this.menu.offsetWidth / 2)}px`;\n // if (y) this.menu.style.top = '5.5rem'; // `${evt.y + 55}px`;\n if (this.menu.offsetLeft < 0) this.menu.style.left = 0;\n if ((this.menu.offsetLeft + this.menu.offsetWidth) > window.innerWidth) {\n this.menu.style.left = null;\n this.menu.style.right = 0;\n }\n this.menu.style.borderStyle = 'solid';\n } else {\n this.menu.style.borderStyle = 'none';\n }\n }\n\n addTitle(title) {\n const el = document.createElement('div');\n el.className = 'menu-title';\n el.id = this.newID;\n el.innerHTML = title;\n this.menu.appendChild(el);\n el.addEventListener('click', () => {\n this.hidden = !this.hidden;\n const all = document.getElementsByClassName('menu');\n for (const item of all) {\n item.style.display = this.hidden ? 'none' : 'block';\n }\n });\n return el;\n }\n\n addLabel(title) {\n const el = document.createElement('div');\n el.className = 'menu-item menu-label';\n el.id = this.newID;\n el.innerHTML = title;\n this.container.appendChild(el);\n return el;\n }\n\n addBool(title, object, variable, callback) {\n const el = document.createElement('div');\n el.className = 'menu-item';\n el.innerHTML = `
${title}`;\n this.container.appendChild(el);\n el.addEventListener('change', (evt) => {\n object[variable] = evt.target.checked;\n if (callback) callback(evt.target.checked);\n });\n return el;\n }\n\n async addList(title, items, selected, callback) {\n const el = document.createElement('div');\n el.className = 'menu-item';\n let options = '';\n for (const item of items) {\n const def = item === selected ? 'selected' : '';\n options += ``;\n }\n el.innerHTML = `
${title}`;\n el.style.fontFamily = document.body.style.fontFamily;\n el.style.fontSize = document.body.style.fontSize;\n el.style.fontVariant = document.body.style.fontVariant;\n this.container.appendChild(el);\n el.addEventListener('change', (evt) => {\n if (callback) callback(items[evt.target.selectedIndex]);\n });\n return el;\n }\n\n addRange(title, object, variable, min, max, step, callback) {\n const el = document.createElement('div');\n el.className = 'menu-item';\n el.innerHTML = `${title}`;\n this.container.appendChild(el);\n el.addEventListener('change', (evt) => {\n object[variable] = parseInt(evt.target.value) === parseFloat(evt.target.value) ? parseInt(evt.target.value) : parseFloat(evt.target.value);\n evt.target.setAttribute('value', evt.target.value);\n if (callback) callback(evt.target.value);\n });\n el.input = el.children[0];\n return el;\n }\n\n addHTML(html) {\n const el = document.createElement('div');\n el.className = 'menu-item';\n el.id = this.newID;\n if (html) el.innerHTML = html;\n this.container.appendChild(el);\n return el;\n }\n\n addButton(titleOn, titleOff, callback) {\n const el = document.createElement('button');\n el.className = 'menu-item menu-button';\n el.style.fontFamily = document.body.style.fontFamily;\n el.style.fontSize = document.body.style.fontSize;\n el.style.fontVariant = document.body.style.fontVariant;\n el.type = 'button';\n el.id = this.newID;\n el.innerText = titleOn;\n this.container.appendChild(el);\n el.addEventListener('click', () => {\n if (el.innerText === titleOn) el.innerText = titleOff;\n else el.innerText = titleOn;\n if (callback) callback(el.innerText !== titleOn);\n });\n return el;\n }\n\n addValue(title, val, suffix = '') {\n const el = document.createElement('div');\n el.className = 'menu-item';\n el.id = `menu-val-${title}`;\n el.innerText = `${title}: ${val}${suffix}`;\n this.container.appendChild(el);\n return el;\n }\n\n // eslint-disable-next-line class-methods-use-this\n updateValue(title, val, suffix = '') {\n const el = document.getElementById(`menu-val-${title}`);\n if (el) el.innerText = `${title}: ${val}${suffix}`;\n else this.addValue(title, val);\n }\n\n addChart(title, id, width = 150, height = 40, color) {\n if (color) theme.chartColor = color;\n const el = document.createElement('div');\n el.className = 'menu-item menu-chart-title';\n el.id = this.newID;\n el.innerHTML = `${title}`;\n this.container.appendChild(el);\n return el;\n }\n\n // eslint-disable-next-line class-methods-use-this\n async updateChart(id, values) {\n if (!values || (values.length === 0)) return;\n const canvas = document.getElementById(`menu-canvas-${id}`);\n if (!canvas) return;\n const ctx = canvas.getContext('2d');\n ctx.fillStyle = theme.background;\n ctx.fillRect(0, 0, canvas.width, canvas.height);\n const width = canvas.width / values.length;\n const max = 1 + Math.max(...values);\n const height = canvas.height / max;\n for (let i = 0; i < values.length; i++) {\n const gradient = ctx.createLinearGradient(0, (max - values[i]) * height, 0, 0);\n gradient.addColorStop(0.1, theme.chartColor);\n gradient.addColorStop(0.4, theme.background);\n ctx.fillStyle = gradient;\n ctx.fillRect(i * width, 0, width - 4, canvas.height);\n ctx.fillStyle = theme.background;\n ctx.font = `${width / 1.5}px \"Segoe UI\"`;\n ctx.fillText(Math.round(values[i]), i * width + 1, canvas.height - 1, width - 1);\n }\n }\n}\n\nexport default Menu;\n", "// @ts-nocheck\n/* eslint-disable max-len */\n\n// based on: https://github.com/munrocket/gl-bench\n\nconst UICSS = `\n #gl-bench { position: absolute; right: 1rem; bottom: 1rem; z-index:1000; -webkit-user-select: none; -moz-user-select: none; user-select: none; }\n #gl-bench div { position: relative; display: block; margin: 4px; padding: 0 7px 0 10px; background: darkslategray; border-radius: 0.2rem; cursor: pointer; opacity: 0.9; }\n #gl-bench svg { height: 60px; margin: 0 0px 0px 4px; }\n #gl-bench text { font-size: 16px; font-family: 'Lato', 'Segoe UI'; dominant-baseline: middle; text-anchor: middle; }\n #gl-bench .gl-mem { font-size: 12px; fill: white; }\n #gl-bench .gl-fps { font-size: 13px; fill: white; }\n #gl-bench line { stroke-width: 5; stroke: white; stroke-linecap: round; }\n #gl-bench polyline { fill: none; stroke: white; stroke-linecap: round; stroke-linejoin: round; stroke-width: 3.5; }\n #gl-bench rect { fill: black; }\n #gl-bench .opacity { stroke: black; }\n `;\n\nconst UISVG = `\n
\n \n 00 FPS\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
\n `;\n\nclass GLBench {\n /** GLBench constructor\n * @param { WebGLRenderingContext | WebGL2RenderingContext } gl context\n * @param { Object | undefined } settings additional settings\n */\n constructor(gl, settings = {}) {\n this.css = UICSS;\n this.svg = UISVG;\n this.paramLogger = () => {};\n this.chartLogger = () => {};\n this.chartLen = 20;\n this.chartHz = 20;\n\n this.names = [];\n this.cpuAccums = [];\n this.gpuAccums = [];\n this.activeAccums = [];\n this.chart = new Array(this.chartLen);\n this.now = () => ((performance && performance.now) ? performance.now() : Date.now());\n this.updateUI = () => {\n [].forEach.call(this.nodes['gl-gpu-svg'], (node) => node.style.display = this.trackGPU ? 'inline' : 'none');\n };\n\n Object.assign(this, settings);\n this.detected = 0;\n this.finished = [];\n this.isFramebuffer = 0;\n this.frameId = 0;\n\n // 120hz device detection\n let rafId; let n = 0; let\n t0;\n const loop = (t) => {\n if (++n < 20) {\n rafId = requestAnimationFrame(loop);\n } else {\n this.detected = Math.ceil(1e3 * n / (t - t0) / 70);\n cancelAnimationFrame(rafId);\n }\n if (!t0) t0 = t;\n };\n requestAnimationFrame(loop);\n\n // attach gpu profilers\n if (gl) {\n const glFinish = async (t, activeAccums) => Promise.resolve(setTimeout(() => {\n gl.getError();\n const dt = this.now() - t;\n activeAccums.forEach((active, i) => {\n if (active) this.gpuAccums[i] += dt;\n });\n }, 0));\n\n const addProfiler = (fn, self, target) => {\n const t = self.now();\n // eslint-disable-next-line prefer-rest-params\n fn.apply(target, arguments);\n if (self.trackGPU) self.finished.push(glFinish(t, self.activeAccums.slice(0)));\n };\n\n /* ['drawArrays', 'drawElements', 'drawArraysInstanced', 'drawBuffers', 'drawElementsInstanced', 'drawRangeElements'].forEach((fn) => {\n if (gl[fn]) {\n gl[fn] = addProfiler(gl[fn], this, gl);\n }\n });\n */\n const fn = 'drawElements';\n if (gl[fn]) {\n gl[fn] = addProfiler(gl[fn], this, gl);\n } else {\n // eslint-disable-next-line no-console\n console.log('bench: cannot attach to webgl function');\n }\n\n /*\n gl.getExtension = ((fn, self) => {\n // eslint-disable-next-line prefer-rest-params\n const ext = fn.apply(gl, arguments);\n if (ext) {\n ['drawElementsInstancedANGLE', 'drawBuffersWEBGL'].forEach((fn2) => {\n if (ext[fn2]) {\n ext[fn2] = addProfiler(ext[fn2], self, ext);\n }\n });\n }\n return ext;\n })(gl.getExtension, this);\n */\n }\n\n // init ui and ui loggers\n if (!this.withoutUI) {\n if (!this.dom) this.dom = document.body;\n const elm = document.createElement('div');\n elm.id = 'gl-bench';\n this.dom.appendChild(elm);\n this.dom.insertAdjacentHTML('afterbegin', '');\n this.dom = elm;\n this.dom.addEventListener('click', () => {\n this.trackGPU = !this.trackGPU;\n this.updateUI();\n });\n\n this.paramLogger = ((logger, dom, names) => {\n const classes = ['gl-cpu', 'gl-gpu', 'gl-mem', 'gl-fps', 'gl-gpu-svg', 'gl-chart'];\n const nodes = { ...classes };\n classes.forEach((c) => nodes[c] = dom.getElementsByClassName(c));\n this.nodes = nodes;\n return (i, cpu, gpu, mem, fps, totalTime, frameId) => {\n nodes['gl-cpu'][i].style.strokeDasharray = (cpu * 0.27).toFixed(0) + ' 100';\n nodes['gl-gpu'][i].style.strokeDasharray = (gpu * 0.27).toFixed(0) + ' 100';\n // eslint-disable-next-line no-nested-ternary\n nodes['gl-mem'][i].innerHTML = names[i] ? names[i] : (mem ? 'mem: ' + mem.toFixed(0) + 'mb' : '');\n nodes['gl-fps'][i].innerHTML = 'FPS: ' + fps.toFixed(1);\n logger(names[i], cpu, gpu, mem, fps, totalTime, frameId);\n };\n })(this.paramLogger, this.dom, this.names);\n\n this.chartLogger = ((logger, dom) => {\n const nodes = { 'gl-chart': dom.getElementsByClassName('gl-chart') };\n return (i, chart, circularId) => {\n let points = '';\n const len = chart.length;\n for (let j = 0; j < len; j++) {\n const id = (circularId + j + 1) % len;\n if (chart[id] !== undefined) points = points + ' ' + (55 * j / (len - 1)).toFixed(1) + ',' + (45 - chart[id] * 22 / 60 / this.detected).toFixed(1);\n }\n nodes['gl-chart'][i].setAttribute('points', points);\n logger(this.names[i], chart, circularId);\n };\n })(this.chartLogger, this.dom);\n }\n }\n\n /**\n * Explicit UI add\n * @param { string | undefined } name\n */\n addUI(name) {\n if (this.names.indexOf(name) === -1) {\n this.names.push(name);\n if (this.dom) {\n this.dom.insertAdjacentHTML('beforeend', this.svg);\n this.updateUI();\n }\n this.cpuAccums.push(0);\n this.gpuAccums.push(0);\n this.activeAccums.push(false);\n }\n }\n\n /**\n * Increase frameID\n * @param { number | undefined } now\n */\n nextFrame(now) {\n this.frameId++;\n const t = now || this.now();\n\n // params\n if (this.frameId <= 1) {\n this.paramFrame = this.frameId;\n this.paramTime = t;\n } else {\n const duration = t - this.paramTime;\n if (duration >= 1e3) {\n const frameCount = this.frameId - this.paramFrame;\n const fps = frameCount / duration * 1e3;\n for (let i = 0; i < this.names.length; i++) {\n const cpu = this.cpuAccums[i] / duration * 100;\n const gpu = this.gpuAccums[i] / duration * 100;\n const mem = (performance && performance.memory) ? performance.memory.usedJSHeapSize / (1 << 20) : 0;\n this.paramLogger(i, cpu, gpu, mem, fps, duration, frameCount);\n this.cpuAccums[i] = 0;\n Promise.all(this.finished).then(() => {\n this.gpuAccums[i] = 0;\n this.finished = [];\n });\n }\n this.paramFrame = this.frameId;\n this.paramTime = t;\n }\n }\n\n // chart\n if (!this.detected || !this.chartFrame) {\n this.chartFrame = this.frameId;\n this.chartTime = t;\n this.circularId = 0;\n } else {\n const timespan = t - this.chartTime;\n let hz = this.chartHz * timespan / 1e3;\n while (--hz > 0 && this.detected) {\n const frameCount = this.frameId - this.chartFrame;\n const fps = frameCount / timespan * 1e3;\n this.chart[this.circularId % this.chartLen] = fps;\n for (let i = 0; i < this.names.length; i++) this.chartLogger(i, this.chart, this.circularId);\n this.circularId++;\n this.chartFrame = this.frameId;\n this.chartTime = t;\n }\n }\n }\n\n /**\n * Begin named measurement\n * @param { string | undefined } name\n */\n begin(name) {\n this.updateAccums(name);\n }\n\n /**\n * End named measure\n * @param { string | undefined } name\n */\n end(name) {\n this.updateAccums(name);\n }\n\n updateAccums(name) {\n let nameId = this.names.indexOf(name);\n if (nameId === -1) {\n nameId = this.names.length;\n this.addUI(name);\n }\n\n const t = this.now();\n const dt = t - this.t0;\n for (let i = 0; i < nameId + 1; i++) {\n if (this.activeAccums[i]) this.cpuAccums[i] += dt;\n }\n this.activeAccums[nameId] = !this.activeAccums[nameId];\n this.t0 = t;\n }\n}\n\nexport default GLBench;\n", "import Human from '../dist/human.esm.js';\nimport draw from './draw.js';\nimport Menu from './menu.js';\nimport GLBench from './gl-bench.js';\n\nconst userConfig = {}; // add any user configuration overrides\n\n/*\nconst userConfig = {\n face: { enabled: false, iris: { enabled: false } },\n body: { enabled: false },\n hand: { enabled: false },\n pose: { enabled: true },\n};\n*/\n\nconst human = new Human(userConfig);\n\n// ui options\nconst ui = {\n baseColor: 'rgba(173, 216, 230, 0.3)', // 'lightblue' with light alpha channel\n baseBackground: 'rgba(50, 50, 50, 1)', // 'grey'\n baseLabel: 'rgba(173, 216, 230, 1)', // 'lightblue' with dark alpha channel\n baseFontProto: 'small-caps {size} \"Segoe UI\"',\n baseLineWidth: 12,\n crop: true,\n columns: 2,\n busy: false,\n facing: true,\n useWorker: false,\n worker: 'worker.js',\n samples: ['../assets/sample6.jpg', '../assets/sample1.jpg', '../assets/sample4.jpg', '../assets/sample5.jpg', '../assets/sample3.jpg', '../assets/sample2.jpg'],\n compare: '../assets/sample-me.jpg',\n drawBoxes: true,\n drawPoints: false,\n drawPolygons: true,\n fillPolygons: false,\n useDepth: true,\n console: true,\n maxFPSframes: 10,\n modelsPreload: true,\n menuWidth: 0,\n menuHeight: 0,\n camera: {},\n detectFPS: [],\n drawFPS: [],\n buffered: false,\n drawThread: null,\n detectThread: null,\n framesDraw: 0,\n framesDetect: 0,\n bench: false,\n};\n\n// global variables\nconst menu = {};\nlet worker;\nlet bench;\nlet lastDetectedResult = {};\n\n// helper function: translates json to human readable string\nfunction str(...msg) {\n if (!Array.isArray(msg)) return msg;\n let line = '';\n for (const entry of msg) {\n if (typeof entry === 'object') line += JSON.stringify(entry).replace(/{|}|\"|\\[|\\]/g, '').replace(/,/g, ', ');\n else line += entry;\n }\n return line;\n}\n\n// helper function: wrapper around console output\nfunction log(...msg) {\n const dt = new Date();\n const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n // eslint-disable-next-line no-console\n if (ui.console) console.log(ts, ...msg);\n}\n\nfunction status(msg) {\n // eslint-disable-next-line no-console\n document.getElementById('status').innerText = msg;\n}\n\nlet original;\nasync function calcSimmilariry(result) {\n document.getElementById('compare-container').style.display = human.config.face.embedding.enabled ? 'block' : 'none';\n if (!human.config.face.embedding.enabled) return;\n if ((result?.face?.length > 0) && (result?.face[0].embedding?.length !== 192)) return;\n if (!original) {\n original = result;\n document.getElementById('compare-canvas').getContext('2d').drawImage(original.canvas, 0, 0, 200, 200);\n }\n const simmilarity = human.simmilarity(original?.face[0]?.embedding, result?.face[0]?.embedding);\n document.getElementById('simmilarity').innerText = `simmilarity: ${Math.trunc(1000 * simmilarity) / 10}%`;\n}\n\n// draws processed results and starts processing of a next frame\nlet lastDraw = performance.now();\nasync function drawResults(input) {\n const result = lastDetectedResult;\n const canvas = document.getElementById('canvas');\n\n // update draw fps data\n ui.drawFPS.push(1000 / (performance.now() - lastDraw));\n if (ui.drawFPS.length > ui.maxFPSframes) ui.drawFPS.shift();\n lastDraw = performance.now();\n\n // enable for continous performance monitoring\n // console.log(result.performance);\n\n // draw fps chart\n await menu.process.updateChart('FPS', ui.detectFPS);\n\n // get updated canvas\n if (ui.buffered || !result.canvas) result.canvas = await human.image(input, userConfig);\n\n // draw image from video\n const ctx = canvas.getContext('2d');\n ctx.fillStyle = ui.baseBackground;\n ctx.fillRect(0, 0, canvas.width, canvas.height);\n if (result.canvas) {\n if (result.canvas.width !== canvas.width) canvas.width = result.canvas.width;\n if (result.canvas.height !== canvas.height) canvas.height = result.canvas.height;\n ctx.drawImage(result.canvas, 0, 0, result.canvas.width, result.canvas.height, 0, 0, result.canvas.width, result.canvas.height);\n } else {\n ctx.drawImage(input, 0, 0, input.width, input.height, 0, 0, canvas.width, canvas.height);\n }\n\n // draw all results\n await draw.face(result.face, canvas, ui, human.facemesh.triangulation);\n await draw.body(result.body, canvas, ui);\n await draw.hand(result.hand, canvas, ui);\n await draw.gesture(result.gesture, canvas, ui);\n await calcSimmilariry(result);\n\n // update log\n const engine = human.tf.engine();\n const gpu = engine.backendInstance ? `gpu: ${(engine.backendInstance.numBytesInGPU ? engine.backendInstance.numBytesInGPU : 0).toLocaleString()} bytes` : '';\n const memory = `system: ${engine.state.numBytes.toLocaleString()} bytes ${gpu} | tensors: ${engine.state.numTensors.toLocaleString()}`;\n const processing = result.canvas ? `processing: ${result.canvas.width} x ${result.canvas.height}` : '';\n const avgDetect = Math.trunc(10 * ui.detectFPS.reduce((a, b) => a + b, 0) / ui.detectFPS.length) / 10;\n const avgDraw = Math.trunc(10 * ui.drawFPS.reduce((a, b) => a + b, 0) / ui.drawFPS.length) / 10;\n const warning = (ui.detectFPS.length > 5) && (avgDetect < 5) ? 'warning: your performance is low: try switching to higher performance backend, lowering resolution or disabling some models' : '';\n document.getElementById('log').innerHTML = `\n video: ${ui.camera.name} | facing: ${ui.camera.facing} | screen: ${window.innerWidth} x ${window.innerHeight} camera: ${ui.camera.width} x ${ui.camera.height} ${processing}
\n backend: ${human.tf.getBackend()} | ${memory}
\n performance: ${str(result.performance)}ms FPS process:${avgDetect} refresh:${avgDraw}
\n ${warning}
\n `;\n\n ui.framesDraw++;\n ui.lastFrame = performance.now();\n // if buffered, immediate loop but limit frame rate although it's going to run slower as JS is singlethreaded\n if (ui.buffered) {\n ui.drawThread = requestAnimationFrame(() => drawResults(input, canvas));\n } else if (!ui.buffered && ui.drawThread) {\n log('stopping buffered refresh');\n cancelAnimationFrame(ui.drawThread);\n ui.drawThread = null;\n }\n}\n\n// setup webcam\nasync function setupCamera() {\n if (ui.busy) return null;\n ui.busy = true;\n const video = document.getElementById('video');\n const canvas = document.getElementById('canvas');\n const output = document.getElementById('log');\n const live = video.srcObject ? ((video.srcObject.getVideoTracks()[0].readyState === 'live') && (video.readyState > 2) && (!video.paused)) : false;\n let msg = '';\n status('setting up camera');\n // setup webcam. note that navigator.mediaDevices requires that page is accessed via https\n if (!navigator.mediaDevices) {\n msg = 'camera access not supported';\n output.innerText += `\\n${msg}`;\n log(msg);\n status(msg);\n ui.busy = false;\n return msg;\n }\n let stream;\n const constraints = {\n audio: false,\n video: { facingMode: ui.facing ? 'user' : 'environment', resizeMode: ui.crop ? 'crop-and-scale' : 'none' },\n };\n if (window.innerWidth > window.innerHeight) constraints.video.width = { ideal: window.innerWidth };\n else constraints.video.height = { ideal: (window.innerHeight - document.getElementById('menubar').offsetHeight) };\n try {\n stream = await navigator.mediaDevices.getUserMedia(constraints);\n } catch (err) {\n if (err.name === 'PermissionDeniedError' || err.name === 'NotAllowedError') msg = 'camera permission denied';\n else if (err.name === 'SourceUnavailableError') msg = 'camera not available';\n else msg = `camera error: ${err.message || err}`;\n output.innerText += `\\n${msg}`;\n status(msg);\n log('camera error:', err);\n ui.busy = false;\n return msg;\n }\n if (stream) video.srcObject = stream;\n else {\n ui.busy = false;\n return 'camera stream empty';\n }\n const track = stream.getVideoTracks()[0];\n const settings = track.getSettings();\n // log('camera constraints:', constraints, 'window:', { width: window.innerWidth, height: window.innerHeight }, 'settings:', settings, 'track:', track);\n ui.camera = { name: track.label?.toLowerCase(), width: settings.width, height: settings.height, facing: settings.facingMode === 'user' ? 'front' : 'back' };\n return new Promise((resolve) => {\n video.onloadeddata = async () => {\n video.width = video.videoWidth;\n video.height = video.videoHeight;\n canvas.width = video.width;\n canvas.height = video.height;\n canvas.style.width = canvas.width > canvas.height ? '100vw' : '';\n canvas.style.height = canvas.width > canvas.height ? '' : '100vh';\n ui.menuWidth.input.setAttribute('value', video.width);\n ui.menuHeight.input.setAttribute('value', video.height);\n // silly font resizing for paint-on-canvas since viewport can be zoomed\n const size = Math.trunc(window.devicePixelRatio * (8 + (4 * canvas.width / window.innerWidth)));\n ui.baseFont = ui.baseFontProto.replace(/{size}/, `${size}px`);\n ui.baseLineHeight = size + 2;\n if (live) video.play();\n // eslint-disable-next-line no-use-before-define\n if (live && !ui.detectThread) runHumanDetect(video, canvas);\n ui.busy = false;\n // do once more because onresize events can be delayed or skipped\n // if (video.width > window.innerWidth) await setupCamera();\n status('');\n resolve();\n };\n });\n}\n\nfunction initPerfMonitor() {\n if (!bench) {\n const gl = null;\n // cosnt gl = human.tf.engine().backend.gpgpu.gl;\n // if (!gl) log('bench cannot get tensorflow webgl context');\n bench = new GLBench(gl, {\n trackGPU: false, // this is really slow\n chartHz: 20,\n chartLen: 20,\n });\n bench.begin();\n }\n}\n\n// wrapper for worker.postmessage that creates worker if one does not exist\nfunction webWorker(input, image, canvas, timestamp) {\n if (!worker) {\n // create new webworker and add event handler only once\n log('creating worker thread');\n worker = new Worker(ui.worker, { type: 'module' });\n // after receiving message from webworker, parse&draw results and send new frame for processing\n worker.addEventListener('message', (msg) => {\n if (msg.data.result.performance && msg.data.result.performance.total) ui.detectFPS.push(1000 / msg.data.result.performance.total);\n if (ui.detectFPS.length > ui.maxFPSframes) ui.detectFPS.shift();\n if (ui.bench) {\n if (!bench) initPerfMonitor();\n bench.nextFrame(timestamp);\n }\n if (document.getElementById('gl-bench')) document.getElementById('gl-bench').style.display = ui.bench ? 'block' : 'none';\n lastDetectedResult = msg.data.result;\n ui.framesDetect++;\n if (!ui.drawThread) drawResults(input);\n // eslint-disable-next-line no-use-before-define\n ui.detectThread = requestAnimationFrame((now) => runHumanDetect(input, canvas, now));\n });\n }\n // pass image data as arraybuffer to worker by reference to avoid copy\n worker.postMessage({ image: image.data.buffer, width: canvas.width, height: canvas.height, userConfig }, [image.data.buffer]);\n}\n\n// main processing function when input is webcam, can use direct invocation or web worker\nfunction runHumanDetect(input, canvas, timestamp) {\n // if live video\n const live = input.srcObject && (input.srcObject.getVideoTracks()[0].readyState === 'live') && (input.readyState > 2) && (!input.paused);\n if (!live && input.srcObject) {\n // stop ui refresh\n if (ui.drawThread) cancelAnimationFrame(ui.drawThread);\n if (ui.detectThread) cancelAnimationFrame(ui.detectThread);\n ui.drawThread = null;\n ui.detectThread = null;\n // if we want to continue and camera not ready, retry in 0.5sec, else just give up\n if (input.paused) log('camera paused');\n else if ((input.srcObject.getVideoTracks()[0].readyState === 'live') && (input.readyState <= 2)) setTimeout(() => runHumanDetect(input, canvas), 500);\n else log(`camera not ready: track state: ${input.srcObject?.getVideoTracks()[0].readyState} stream state: ${input.readyState}`);\n clearTimeout(ui.drawThread);\n ui.drawThread = null;\n log('frame statistics: process:', ui.framesDetect, 'refresh:', ui.framesDraw);\n log('memory', human.tf.engine().memory());\n return;\n }\n status('');\n if (ui.useWorker) {\n // get image data from video as we cannot send html objects to webworker\n const offscreen = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(canvas.width, canvas.height) : document.createElement('canvas');\n offscreen.width = canvas.width;\n offscreen.height = canvas.height;\n const ctx = offscreen.getContext('2d');\n ctx.drawImage(input, 0, 0, input.width, input.height, 0, 0, canvas.width, canvas.height);\n const data = ctx.getImageData(0, 0, canvas.width, canvas.height);\n // perform detection in worker\n webWorker(input, data, canvas, userConfig, timestamp);\n } else {\n human.detect(input, userConfig).then((result) => {\n if (result.performance && result.performance.total) ui.detectFPS.push(1000 / result.performance.total);\n if (ui.detectFPS.length > ui.maxFPSframes) ui.detectFPS.shift();\n if (ui.bench) {\n if (!bench) initPerfMonitor();\n bench.nextFrame(timestamp);\n }\n if (document.getElementById('gl-bench')) document.getElementById('gl-bench').style.display = ui.bench ? 'block' : 'none';\n if (result.error) {\n log(result.error);\n document.getElementById('log').innerText += `\\nHuman error: ${result.error}`;\n } else {\n lastDetectedResult = result;\n if (!ui.drawThread) drawResults(input);\n ui.framesDetect++;\n ui.detectThread = requestAnimationFrame((now) => runHumanDetect(input, canvas, now));\n }\n });\n }\n}\n\n// main processing function when input is image, can use direct invocation or web worker\nasync function processImage(input) {\n return new Promise((resolve) => {\n const image = new Image();\n image.onload = async () => {\n log('Processing image:', image.src);\n const canvas = document.getElementById('canvas');\n image.width = image.naturalWidth;\n image.height = image.naturalHeight;\n canvas.width = human.config.filter.width && human.config.filter.width > 0 ? human.config.filter.width : image.naturalWidth;\n canvas.height = human.config.filter.height && human.config.filter.height > 0 ? human.config.filter.height : image.naturalHeight;\n const result = await human.detect(image, userConfig);\n lastDetectedResult = result;\n await drawResults(image);\n const thumb = document.createElement('canvas');\n thumb.className = 'thumbnail';\n thumb.width = window.innerWidth / (ui.columns + 0.1);\n thumb.height = canvas.height / (window.innerWidth / thumb.width);\n const ctx = thumb.getContext('2d');\n ctx.drawImage(canvas, 0, 0, canvas.width, canvas.height, 0, 0, thumb.width, thumb.height);\n document.getElementById('samples-container').appendChild(thumb);\n image.src = '';\n resolve(true);\n };\n image.src = input;\n });\n}\n\n// just initialize everything and call main function\nasync function detectVideo() {\n userConfig.videoOptimized = true;\n document.getElementById('samples-container').style.display = 'none';\n document.getElementById('canvas').style.display = 'block';\n const video = document.getElementById('video');\n const canvas = document.getElementById('canvas');\n if ((video.srcObject !== null) && !video.paused) {\n document.getElementById('play').style.display = 'block';\n document.getElementById('btnStart').className = 'button button-start';\n document.getElementById('btnStart').innerHTML = 'start
video';\n status('paused');\n video.pause();\n } else {\n const cameraError = await setupCamera();\n if (!cameraError) {\n document.getElementById('play').style.display = 'none';\n for (const m of Object.values(menu)) m.hide();\n status('');\n document.getElementById('btnStart').className = 'button button-stop';\n document.getElementById('btnStart').innerHTML = 'pause
video';\n await video.play();\n if (!ui.detectThread) runHumanDetect(video, canvas);\n } else {\n status(cameraError);\n }\n }\n}\n\n// just initialize everything and call main function\nasync function detectSampleImages() {\n document.getElementById('play').style.display = 'none';\n userConfig.videoOptimized = false;\n const size = Math.trunc(window.devicePixelRatio * (8 + (4 * ui.columns)));\n ui.baseFont = ui.baseFontProto.replace(/{size}/, `${size}px`);\n ui.baseLineHeight = size + 2;\n document.getElementById('canvas').style.display = 'none';\n document.getElementById('samples-container').style.display = 'block';\n log('Running detection of sample images');\n status('processing images');\n document.getElementById('samples-container').innerHTML = '';\n for (const image of ui.samples) await processImage(image);\n status('');\n}\n\nfunction setupMenu() {\n let x = [];\n if (window.innerWidth > 800) {\n // initial position of menu items, later it's calculated based on mouse coordinates\n x = [`${document.getElementById('btnDisplay').offsetLeft - 50}px`, `${document.getElementById('btnImage').offsetLeft - 50}px`, `${document.getElementById('btnProcess').offsetLeft - 50}px`, `${document.getElementById('btnModel').offsetLeft - 50}px`];\n } else {\n // absolute minimum spacing for menus\n x = ['0rem', '11rem', '21.1rem', '33rem'];\n }\n\n menu.display = new Menu(document.body, '', { top: `${document.getElementById('menubar').offsetHeight}px`, left: x[0] });\n menu.display.addBool('perf monitor', ui, 'bench', (val) => ui.bench = val);\n menu.display.addBool('buffered output', ui, 'buffered', (val) => ui.buffered = val);\n menu.display.addBool('crop & scale', ui, 'crop', () => setupCamera());\n menu.display.addBool('camera facing', ui, 'facing', () => setupCamera());\n menu.display.addHTML('
');\n menu.display.addBool('use 3D depth', ui, 'useDepth');\n menu.display.addBool('draw boxes', ui, 'drawBoxes');\n menu.display.addBool('draw polygons', ui, 'drawPolygons');\n menu.display.addBool('Fill Polygons', ui, 'fillPolygons');\n menu.display.addBool('draw points', ui, 'drawPoints');\n\n menu.image = new Menu(document.body, '', { top: `${document.getElementById('menubar').offsetHeight}px`, left: x[1] });\n menu.image.addBool('enabled', human.config.filter, 'enabled');\n ui.menuWidth = menu.image.addRange('image width', human.config.filter, 'width', 0, 3840, 10, (val) => human.config.filter.width = parseInt(val));\n ui.menuHeight = menu.image.addRange('image height', human.config.filter, 'height', 0, 2160, 10, (val) => human.config.filter.height = parseInt(val));\n menu.image.addHTML('
');\n menu.image.addRange('brightness', human.config.filter, 'brightness', -1.0, 1.0, 0.05, (val) => human.config.filter.brightness = parseFloat(val));\n menu.image.addRange('contrast', human.config.filter, 'contrast', -1.0, 1.0, 0.05, (val) => human.config.filter.contrast = parseFloat(val));\n menu.image.addRange('sharpness', human.config.filter, 'sharpness', 0, 1.0, 0.05, (val) => human.config.filter.sharpness = parseFloat(val));\n menu.image.addRange('blur', human.config.filter, 'blur', 0, 20, 1, (val) => human.config.filter.blur = parseInt(val));\n menu.image.addRange('saturation', human.config.filter, 'saturation', -1.0, 1.0, 0.05, (val) => human.config.filter.saturation = parseFloat(val));\n menu.image.addRange('hue', human.config.filter, 'hue', 0, 360, 5, (val) => human.config.filter.hue = parseInt(val));\n menu.image.addRange('pixelate', human.config.filter, 'pixelate', 0, 32, 1, (val) => human.config.filter.pixelate = parseInt(val));\n menu.image.addHTML('
');\n menu.image.addBool('negative', human.config.filter, 'negative');\n menu.image.addBool('sepia', human.config.filter, 'sepia');\n menu.image.addBool('vintage', human.config.filter, 'vintage');\n menu.image.addBool('kodachrome', human.config.filter, 'kodachrome');\n menu.image.addBool('technicolor', human.config.filter, 'technicolor');\n menu.image.addBool('polaroid', human.config.filter, 'polaroid');\n\n menu.process = new Menu(document.body, '', { top: `${document.getElementById('menubar').offsetHeight}px`, left: x[2] });\n menu.process.addList('backend', ['cpu', 'webgl', 'wasm', 'humangl'], human.config.backend, (val) => human.config.backend = val);\n menu.process.addBool('async operations', human.config, 'async', (val) => human.config.async = val);\n menu.process.addBool('enable profiler', human.config, 'profile', (val) => human.config.profile = val);\n menu.process.addBool('memory shield', human.config, 'deallocate', (val) => human.config.deallocate = val);\n menu.process.addBool('use web worker', ui, 'useWorker');\n menu.process.addHTML('
');\n menu.process.addLabel('model parameters');\n menu.process.addRange('max objects', human.config.face.detector, 'maxFaces', 1, 50, 1, (val) => {\n human.config.face.detector.maxFaces = parseInt(val);\n human.config.body.maxDetections = parseInt(val);\n human.config.hand.maxHands = parseInt(val);\n });\n menu.process.addRange('skip frames', human.config.face.detector, 'skipFrames', 0, 50, 1, (val) => {\n human.config.face.detector.skipFrames = parseInt(val);\n human.config.face.emotion.skipFrames = parseInt(val);\n human.config.face.age.skipFrames = parseInt(val);\n human.config.hand.skipFrames = parseInt(val);\n });\n menu.process.addRange('min confidence', human.config.face.detector, 'minConfidence', 0.0, 1.0, 0.05, (val) => {\n human.config.face.detector.minConfidence = parseFloat(val);\n human.config.face.gender.minConfidence = parseFloat(val);\n human.config.face.emotion.minConfidence = parseFloat(val);\n human.config.hand.minConfidence = parseFloat(val);\n });\n menu.process.addRange('score threshold', human.config.face.detector, 'scoreThreshold', 0.1, 1.0, 0.05, (val) => {\n human.config.face.detector.scoreThreshold = parseFloat(val);\n human.config.hand.scoreThreshold = parseFloat(val);\n human.config.body.scoreThreshold = parseFloat(val);\n });\n menu.process.addRange('overlap', human.config.face.detector, 'iouThreshold', 0.1, 1.0, 0.05, (val) => {\n human.config.face.detector.iouThreshold = parseFloat(val);\n human.config.hand.iouThreshold = parseFloat(val);\n });\n menu.process.addBool('detection rotation', human.config.face.detector, 'rotation', (val) => {\n human.config.face.detector.rotation = val;\n human.config.hand.rotation = val;\n });\n menu.process.addHTML('
');\n menu.process.addButton('process sample images', 'process images', () => detectSampleImages());\n menu.process.addHTML('
');\n menu.process.addChart('FPS', 'FPS');\n\n menu.models = new Menu(document.body, '', { top: `${document.getElementById('menubar').offsetHeight}px`, left: x[3] });\n menu.models.addBool('face detect', human.config.face, 'enabled');\n menu.models.addBool('face mesh', human.config.face.mesh, 'enabled');\n menu.models.addBool('face iris', human.config.face.iris, 'enabled');\n menu.models.addBool('face age', human.config.face.age, 'enabled');\n menu.models.addBool('face gender', human.config.face.gender, 'enabled');\n menu.models.addBool('face emotion', human.config.face.emotion, 'enabled');\n menu.models.addHTML('
');\n menu.models.addBool('body pose', human.config.body, 'enabled');\n menu.models.addBool('hand pose', human.config.hand, 'enabled');\n menu.models.addHTML('
');\n menu.models.addBool('gestures', human.config.gesture, 'enabled');\n menu.models.addHTML('
');\n menu.models.addBool('face compare', human.config.face.embedding, 'enabled', (val) => {\n original = null;\n human.config.face.embedding.enabled = val;\n });\n\n document.getElementById('btnDisplay').addEventListener('click', (evt) => menu.display.toggle(evt));\n document.getElementById('btnImage').addEventListener('click', (evt) => menu.image.toggle(evt));\n document.getElementById('btnProcess').addEventListener('click', (evt) => menu.process.toggle(evt));\n document.getElementById('btnModel').addEventListener('click', (evt) => menu.models.toggle(evt));\n document.getElementById('btnStart').addEventListener('click', () => detectVideo());\n document.getElementById('play').addEventListener('click', () => detectVideo());\n}\n\nasync function main() {\n log('demo starting ...');\n setupMenu();\n document.getElementById('log').innerText = `Human: version ${human.version}`;\n if (ui.modelsPreload && !ui.useWorker) {\n status('loading');\n await human.load(userConfig); // this is not required, just pre-loads all models\n }\n if (!ui.useWorker) {\n status('initializing');\n await human.warmup(userConfig); // this is not required, just pre-warms all models for faster initial inference\n }\n status('human: ready');\n document.getElementById('loader').style.display = 'none';\n document.getElementById('play').style.display = 'block';\n log('ready');\n}\n\nwindow.onload = main;\nwindow.onresize = setupCamera;\n"], + "mappings": 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"names": [] } diff --git a/dist/demo-browser-index.json b/dist/demo-browser-index.json index 05b3035a..237b09c6 100644 --- a/dist/demo-browser-index.json +++ b/dist/demo-browser-index.json @@ -1,7 +1,7 @@ { "inputs": { "dist/human.esm.js": { - "bytes": 1912580, + "bytes": 1907681, "imports": [] }, "demo/draw.js": { @@ -38,14 +38,14 @@ "dist/demo-browser-index.js.map": { "imports": [], "inputs": {}, - "bytes": 2040931 + "bytes": 2030116 }, "dist/demo-browser-index.js": { "imports": [], "exports": [], "inputs": { "dist/human.esm.js": { - "bytesInOutput": 1905205 + "bytesInOutput": 1900306 }, "demo/draw.js": { "bytesInOutput": 7736 @@ -60,7 +60,7 @@ "bytesInOutput": 19424 } }, - "bytes": 1958909 + "bytes": 1954010 } } } diff --git a/dist/human.esm-nobundle.js b/dist/human.esm-nobundle.js index bf69b70d..fd2b2d60 100644 --- a/dist/human.esm-nobundle.js +++ b/dist/human.esm-nobundle.js @@ -5,14 +5,14 @@ author: ' */ -var __create=Object.create,__defProp=Object.defineProperty,__getProtoOf=Object.getPrototypeOf,__hasOwnProp=Object.prototype.hasOwnProperty,__getOwnPropNames=Object.getOwnPropertyNames,__getOwnPropDesc=Object.getOwnPropertyDescriptor;var __markAsModule=target=>__defProp(target,"__esModule",{value:!0});var __commonJS=(callback,module)=>()=>(module||(module={exports:{}},callback(module.exports,module)),module.exports),__export=(target,all)=>{__markAsModule(target);for(var name in all)__defProp(target,name,{get:all[name],enumerable:!0})},__exportStar=(target,module,desc)=>{if(__markAsModule(target),module&&typeof module=="object"||typeof module=="function")for(let key of __getOwnPropNames(module))!__hasOwnProp.call(target,key)&&key!=="default"&&__defProp(target,key,{get:()=>module[key],enumerable:!(desc=__getOwnPropDesc(module,key))||desc.enumerable});return target},__toModule=module=>module&&module.__esModule?module:__exportStar(__defProp(module!=null?__create(__getProtoOf(module)):{},"default",{value:module,enumerable:!0}),module);import{version as tfjs}from"@tensorflow/tfjs/package.json";import{version as versionCore}from"@tensorflow/tfjs-core/package.json";import{version as versionData}from"@tensorflow/tfjs-data/package.json";import{version as versionLayers}from"@tensorflow/tfjs-layers/package.json";import{version as versionConverter}from"@tensorflow/tfjs-converter/package.json";import{version_cpu}from"@tensorflow/tfjs-backend-cpu/dist/index.js";import{version_webgl}from"@tensorflow/tfjs-backend-webgl/dist/index.js";import{version_wasm}from"@tensorflow/tfjs-backend-wasm/dist/index.js";import*as dist_star from"@tensorflow/tfjs-core/dist/index.js";import*as dist_star2 from"@tensorflow/tfjs-layers/dist/index.js";import*as dist_star3 from"@tensorflow/tfjs-converter/dist/index.js";import*as data from"@tensorflow/tfjs-data/dist/index.js";import*as dist_star4 from"@tensorflow/tfjs-backend-cpu/dist/index.js";import*as dist_star5 from"@tensorflow/tfjs-backend-webgl/dist/index.js";import*as dist_star6 from"@tensorflow/tfjs-backend-wasm/dist/index.js";var require_tfjs_esm=__commonJS(exports=>{__export(exports,{data:()=>data,version:()=>version2});__exportStar(exports,dist_star);__exportStar(exports,dist_star2);__exportStar(exports,dist_star3);__exportStar(exports,dist_star4);__exportStar(exports,dist_star5);__exportStar(exports,dist_star6);var version2={tfjs,"tfjs-core":versionCore,"tfjs-data":versionData,"tfjs-layers":versionLayers,"tfjs-converter":versionConverter,"tfjs-backend-cpu":version_cpu,"tfjs-backend-webgl":version_webgl,"tfjs-backend-wasm":version_wasm}});var require_blazeface=__commonJS(exports=>{var tf4=__toModule(require_tfjs_esm()),NUM_LANDMARKS=6;function generateAnchors(inputSize){let spec={strides:[inputSize/16,inputSize/8],anchors:[2,6]},anchors=[];for(let i=0;i{box.startEndTensor.dispose(),box.startPoint.dispose(),box.endPoint.dispose()},createBox=startEndTensor=>({startEndTensor,startPoint:tf4.slice(startEndTensor,[0,0],[-1,2]),endPoint:tf4.slice(startEndTensor,[0,2],[-1,2])}),scaleBox=(box,factors)=>{let starts=tf4.mul(box.startPoint,factors),ends=tf4.mul(box.endPoint,factors),newCoordinates=tf4.concat2d([starts,ends],1);return createBox(newCoordinates)};function decodeBounds(boxOutputs,anchors,inputSize){let boxStarts=tf4.slice(boxOutputs,[0,1],[-1,2]),centers=tf4.add(boxStarts,anchors),boxSizes=tf4.slice(boxOutputs,[0,3],[-1,2]),boxSizesNormalized=tf4.div(boxSizes,inputSize),centersNormalized=tf4.div(centers,inputSize),halfBoxSize=tf4.div(boxSizesNormalized,2),starts=tf4.sub(centersNormalized,halfBoxSize),ends=tf4.add(centersNormalized,halfBoxSize),startNormalized=tf4.mul(starts,inputSize),endNormalized=tf4.mul(ends,inputSize);return tf4.concat2d([startNormalized,endNormalized],1)}function scaleBoxFromPrediction(face3,scaleFactor){return tf4.tidy(()=>{let box=face3.box?face3.box:face3;return scaleBox(box,scaleFactor).startEndTensor.squeeze()})}var BlazeFaceModel=class{constructor(model,config2){this.blazeFaceModel=model,this.width=config2.face.detector.inputSize,this.height=config2.face.detector.inputSize,this.anchorsData=generateAnchors(config2.face.detector.inputSize),this.anchors=tf4.tensor2d(this.anchorsData),this.inputSize=tf4.tensor1d([this.width,this.height]),this.config=config2,this.scaleFaces=.8}async getBoundingBoxes(inputImage){if(!inputImage||inputImage.isDisposedInternal||inputImage.shape.length!==4||inputImage.shape[1]<1||inputImage.shape[2]<1)return null;let[detectedOutputs,boxes,scores]=tf4.tidy(()=>{let resizedImage=inputImage.resizeBilinear([this.width,this.height]),normalizedImage=tf4.sub(resizedImage.div(127.5),1),batchedPrediction=this.blazeFaceModel.predict(normalizedImage),prediction;if(Array.isArray(batchedPrediction)){let sorted=batchedPrediction.sort((a,b)=>a.size-b.size),concat384=tf4.concat([sorted[0],sorted[2]],2),concat512=tf4.concat([sorted[1],sorted[3]],2);prediction=tf4.concat([concat512,concat384],1).squeeze(0)}else prediction=batchedPrediction.squeeze();let decodedBounds=decodeBounds(prediction,this.anchors,this.inputSize),logits=tf4.slice(prediction,[0,0],[-1,1]),scoresOut=tf4.sigmoid(logits).squeeze();return[prediction,decodedBounds,scoresOut]}),boxIndicesTensor=await tf4.image.nonMaxSuppressionAsync(boxes,scores,this.config.face.detector.maxFaces,this.config.face.detector.iouThreshold,this.config.face.detector.scoreThreshold),boxIndices=boxIndicesTensor.arraySync();boxIndicesTensor.dispose();let boundingBoxes=boxIndices.map(boxIndex=>tf4.slice(boxes,[boxIndex,0],[1,-1])).map(boundingBox=>{let vals=boundingBox.arraySync();return boundingBox.dispose(),vals}),scoresVal=scores.dataSync(),annotatedBoxes=[];for(let i=0;ithis.config.face.detector.minConfidence){let box=createBox(boundingBoxes[i]),anchor=this.anchorsData[boxIndex],landmarks=tf4.tidy(()=>tf4.slice(detectedOutputs,[boxIndex,NUM_LANDMARKS-1],[1,-1]).squeeze().reshape([NUM_LANDMARKS,-1]));annotatedBoxes.push({box,landmarks,anchor,confidence})}}return detectedOutputs.dispose(),boxes.dispose(),scores.dispose(),detectedOutputs.dispose(),{boxes:annotatedBoxes,scaleFactor:[inputImage.shape[2]/this.width,inputImage.shape[1]/this.height]}}async estimateFaces(input){let{boxes,scaleFactor}=await this.getBoundingBoxes(input),faces=[];for(let face3 of boxes){let landmarkData=face3.landmarks.arraySync(),scaledBox=scaleBoxFromPrediction(face3,scaleFactor),boxData=scaleBox.arraySync(),probabilityData=face3.probability.arraySync(),anchor=face3.anchor,[scaleFactorX,scaleFactorY]=scaleFactor,scaledLandmarks=landmarkData.map(landmark=>[(landmark[0]+anchor[0])*scaleFactorX,(landmark[1]+anchor[1])*scaleFactorY]),normalizedFace={topLeft:boxData.slice(0,2),bottomRight:boxData.slice(2),landmarks:scaledLandmarks,probability:probabilityData};disposeBox(face3.box),face3.landmarks.dispose(),face3.probability.dispose(),scaledBox.dispose(),faces.push(normalizedFace)}return faces}};async function load2(config2){let blazeface=await tf4.loadGraphModel(config2.face.detector.modelPath,{fromTFHub:config2.face.detector.modelPath.includes("tfhub.dev")}),model=new BlazeFaceModel(blazeface,config2);return log(`load model: ${config2.face.detector.modelPath.match(/\/(.*)\./)[1]}`),model}exports.load=load2;exports.BlazeFaceModel=BlazeFaceModel;exports.disposeBox=disposeBox});var require_box=__commonJS(exports=>{var tf4=__toModule(require_tfjs_esm());function scaleBoxCoordinates2(box,factor){let startPoint=[box.startPoint[0]*factor[0],box.startPoint[1]*factor[1]],endPoint=[box.endPoint[0]*factor[0],box.endPoint[1]*factor[1]];return{startPoint,endPoint}}exports.scaleBoxCoordinates=scaleBoxCoordinates2;function getBoxSize2(box){return[Math.abs(box.endPoint[0]-box.startPoint[0]),Math.abs(box.endPoint[1]-box.startPoint[1])]}exports.getBoxSize=getBoxSize2;function getBoxCenter2(box){return[box.startPoint[0]+(box.endPoint[0]-box.startPoint[0])/2,box.startPoint[1]+(box.endPoint[1]-box.startPoint[1])/2]}exports.getBoxCenter=getBoxCenter2;function cutBoxFromImageAndResize2(box,image4,cropSize){let h=image4.shape[1],w=image4.shape[2],boxes=[[box.startPoint[1]/h,box.startPoint[0]/w,box.endPoint[1]/h,box.endPoint[0]/w]];return tf4.image.cropAndResize(image4,boxes,[0],cropSize)}exports.cutBoxFromImageAndResize=cutBoxFromImageAndResize2;function enlargeBox2(box,factor=1.5){let center=getBoxCenter2(box),size=getBoxSize2(box),newHalfSize=[factor*size[0]/2,factor*size[1]/2],startPoint=[center[0]-newHalfSize[0],center[1]-newHalfSize[1]],endPoint=[center[0]+newHalfSize[0],center[1]+newHalfSize[1]];return{startPoint,endPoint,landmarks:box.landmarks}}exports.enlargeBox=enlargeBox2;function squarifyBox2(box){let centers=getBoxCenter2(box),size=getBoxSize2(box),halfSize=Math.max(...size)/2,startPoint=[centers[0]-halfSize,centers[1]-halfSize],endPoint=[centers[0]+halfSize,centers[1]+halfSize];return{startPoint,endPoint,landmarks:box.landmarks}}exports.squarifyBox=squarifyBox2});var require_util=__commonJS(exports=>{exports.IDENTITY_MATRIX=[[1,0,0],[0,1,0],[0,0,1]];function normalizeRadians2(angle){return angle-2*Math.PI*Math.floor((angle+Math.PI)/(2*Math.PI))}exports.normalizeRadians=normalizeRadians2;function computeRotation2(point1,point2){let radians=Math.PI/2-Math.atan2(-(point2[1]-point1[1]),point2[0]-point1[0]);return normalizeRadians2(radians)}exports.computeRotation=computeRotation2;function radToDegrees(rad){return rad*180/Math.PI}exports.radToDegrees=radToDegrees;function buildTranslationMatrix2(x,y){return[[1,0,x],[0,1,y],[0,0,1]]}function dot2(v1,v2){let product=0;for(let i=0;i{var 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,378,379,397,288,361,454,356,70,63,105,66,107,336,296,334,293,300,168,6,195,4,98,97,2,326,327,33,160,158,133,153,144,362,385,387,263,373,380,57,40,37,0,267,270,287,321,314,17,84,91,78,81,13,311,308,402,14,178],VTX33=[33,133,362,263,1,62,308,159,145,386,374,6,102,331,2,13,14,70,105,107,336,334,300,54,10,284,50,280,234,454,58,288,152],VTX7=[33,133,362,263,1,78,308];exports.MESH_ANNOTATIONS=MESH_ANNOTATIONS;exports.MESH_TO_IRIS_INDICES_MAP=MESH_TO_IRIS_INDICES_MAP;exports.TRI468=TRI468;exports.TRI68=TRI68;exports.TRI33=TRI33;exports.TRI7=TRI7;exports.UV468=UV468;exports.UV68=VTX68.map(x=>UV468[x]);exports.UV33=VTX33.map(x=>UV468[x]);exports.UV7=VTX7.map(x=>UV468[x])});var require_facepipeline=__commonJS(exports=>{var tf4=__toModule(require_tfjs_esm()),bounding=__toModule(require_box()),util=__toModule(require_util()),coords=__toModule(require_coords()),LANDMARKS_COUNT=468,MESH_MOUTH_INDEX=13,MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES=[MESH_MOUTH_INDEX,coords.MESH_ANNOTATIONS.midwayBetweenEyes[0]],BLAZEFACE_MOUTH_INDEX=3,BLAZEFACE_NOSE_INDEX=2,BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES=[BLAZEFACE_MOUTH_INDEX,BLAZEFACE_NOSE_INDEX],LEFT_EYE_OUTLINE=coords.MESH_ANNOTATIONS.leftEyeLower0,LEFT_EYE_BOUNDS=[LEFT_EYE_OUTLINE[0],LEFT_EYE_OUTLINE[LEFT_EYE_OUTLINE.length-1]],RIGHT_EYE_OUTLINE=coords.MESH_ANNOTATIONS.rightEyeLower0,RIGHT_EYE_BOUNDS=[RIGHT_EYE_OUTLINE[0],RIGHT_EYE_OUTLINE[RIGHT_EYE_OUTLINE.length-1]],IRIS_UPPER_CENTER_INDEX=3,IRIS_LOWER_CENTER_INDEX=4,IRIS_IRIS_INDEX=71,IRIS_NUM_COORDINATES=76;function replaceRawCoordinates(rawCoords,newCoords,prefix,keys){for(let i=0;i[scaleFactor[0]*(coord[0]-this.meshWidth/2),scaleFactor[1]*(coord[1]-this.meshHeight/2),coord[2]]),coordsRotationMatrix=angle!==0?util.buildRotationMatrix(angle,[0,0]):util.IDENTITY_MATRIX,coordsRotated=angle!==0?coordsScaled.map(coord=>[...util.rotatePoint(coord,coordsRotationMatrix),coord[2]]):coordsScaled,inverseRotationMatrix=angle!==0?util.invertTransformMatrix(rotationMatrix):util.IDENTITY_MATRIX,boxCenter=[...bounding.getBoxCenter({startPoint:box.startPoint,endPoint:box.endPoint}),1];return coordsRotated.map(coord=>[coord[0]+util.dot(boxCenter,inverseRotationMatrix[0]),coord[1]+util.dot(boxCenter,inverseRotationMatrix[1]),coord[2]])}getLeftToRightEyeDepthDifference(rawCoords){let leftEyeZ=rawCoords[LEFT_EYE_BOUNDS[0]][2],rightEyeZ=rawCoords[RIGHT_EYE_BOUNDS[0]][2];return leftEyeZ-rightEyeZ}getEyeBox(rawCoords,face3,eyeInnerCornerIndex,eyeOuterCornerIndex,flip=!1){let box=bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex],rawCoords[eyeOuterCornerIndex]]),this.irisEnlarge)),boxSize=bounding.getBoxSize(box),crop=tf4.image.cropAndResize(face3,[[box.startPoint[1]/this.meshHeight,box.startPoint[0]/this.meshWidth,box.endPoint[1]/this.meshHeight,box.endPoint[0]/this.meshWidth]],[0],[this.irisSize,this.irisSize]);return flip&&(crop=tf4.image.flipLeftRight(crop)),{box,boxSize,crop}}getEyeCoords(eyeData,eyeBox,eyeBoxSize,flip=!1){let eyeRawCoords=[];for(let i=0;i{let z=averageZ;return i===2?z=upperCenterZ:i===4&&(z=lowerCenterZ),[coord[0],coord[1],z]})}async predict(input,config2){let useFreshBox=!1,detector;if((this.skipped===0||this.skipped>config2.face.detector.skipFrames||!config2.face.mesh.enabled||!config2.videoOptimized)&&(detector=await this.boundingBoxDetector.getBoundingBoxes(input),this.skipped=0),config2.videoOptimized&&this.skipped++,detector&&detector.boxes&&detector.boxes.length>0&&(!config2.face.mesh.enabled||detector.boxes.length!==this.detectedFaces&&this.detectedFaces!==config2.face.detector.maxFaces)){this.storedBoxes=[],this.detectedFaces=0;for(let possible of detector.boxes)this.storedBoxes.push({startPoint:possible.box.startPoint.dataSync(),endPoint:possible.box.endPoint.dataSync(),landmarks:possible.landmarks,confidence:possible.confidence});this.storedBoxes.length>0&&(useFreshBox=!0)}if(useFreshBox){if(!detector||!detector.boxes||detector.boxes.length===0)return this.storedBoxes=[],this.detectedFaces=0,null;for(let i=0;i{prediction.box.startPoint.dispose(),prediction.box.endPoint.dispose(),prediction.landmarks.dispose()});let results=tf4.tidy(()=>this.storedBoxes.map((box,i)=>{let face3,angle=0,rotationMatrix;if(config2.face.detector.rotation){let[indexOfMouth,indexOfForehead]=box.landmarks.length>=LANDMARKS_COUNT?MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES:BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES;angle=util.computeRotation(box.landmarks[indexOfMouth],box.landmarks[indexOfForehead]);let faceCenter=bounding.getBoxCenter({startPoint:box.startPoint,endPoint:box.endPoint}),faceCenterNormalized=[faceCenter[0]/input.shape[2],faceCenter[1]/input.shape[1]],rotatedImage=tf4.image.rotateWithOffset(input,angle,0,faceCenterNormalized);rotationMatrix=util.buildRotationMatrix(-angle,faceCenter),face3=bounding.cutBoxFromImageAndResize({startPoint:box.startPoint,endPoint:box.endPoint},rotatedImage,[this.meshHeight,this.meshWidth]).div(255)}else{rotationMatrix=util.IDENTITY_MATRIX;let cloned=input.clone();face3=bounding.cutBoxFromImageAndResize({startPoint:box.startPoint,endPoint:box.endPoint},cloned,[this.meshHeight,this.meshWidth]).div(255)}if(!config2.face.mesh.enabled)return{coords:null,box,faceConfidence:null,confidence:box.confidence,image:face3};let[,confidence,contourCoords]=this.meshDetector.predict(face3),confidenceVal=confidence.dataSync()[0];if(confidenceVala!==null),this.detectedFaces=results.length,results}calculateLandmarksBoundingBox(landmarks){let xs=landmarks.map(d=>d[0]),ys=landmarks.map(d=>d[1]),startPoint=[Math.min(...xs),Math.min(...ys)],endPoint=[Math.max(...xs),Math.max(...ys)];return{startPoint,endPoint,landmarks}}};exports.Pipeline=Pipeline});var require_facemesh=__commonJS(exports=>{var tf4=__toModule(require_tfjs_esm()),blazeface=__toModule(require_blazeface()),facepipeline=__toModule(require_facepipeline()),coords=__toModule(require_coords()),MediaPipeFaceMesh=class{constructor(blazeFace,blazeMeshModel,irisModel,config2){this.facePipeline=new facepipeline.Pipeline(blazeFace,blazeMeshModel,irisModel,config2),this.config=config2}async estimateFaces(input,config2){let predictions=await this.facePipeline.predict(input,config2),results=[];for(let prediction of predictions||[]){if(prediction.isDisposedInternal)continue;let mesh=prediction.coords?prediction.coords.arraySync():null,meshRaw=prediction.rawCoords,annotations={};if(mesh&&mesh.length>0)for(let key of Object.keys(coords.MESH_ANNOTATIONS))annotations[key]=coords.MESH_ANNOTATIONS[key].map(index=>mesh[index]);let boxRaw=config2.face.mesh.returnRawData&&prediction.box?{topLeft:prediction.box.startPoint,bottomRight:prediction.box.endPoint}:null,box=prediction.box?[Math.max(0,prediction.box.startPoint[0]),Math.max(0,prediction.box.startPoint[1]),Math.min(input.shape[2],prediction.box.endPoint[0])-prediction.box.startPoint[0],Math.min(input.shape[1],prediction.box.endPoint[1])-prediction.box.startPoint[1]]:0;results.push({confidence:prediction.confidence||0,box,mesh,boxRaw,meshRaw,annotations,image:prediction.image?tf4.clone(prediction.image):null}),prediction.coords&&prediction.coords.dispose(),prediction.image&&prediction.image.dispose()}return results}},faceModels=[null,null,null];async function load2(config2){faceModels=await Promise.all([!faceModels[0]&&config2.face.enabled?blazeface.load(config2):null,!faceModels[1]&&config2.face.mesh.enabled?tf4.loadGraphModel(config2.face.mesh.modelPath,{fromTFHub:config2.face.mesh.modelPath.includes("tfhub.dev")}):null,!faceModels[2]&&config2.face.iris.enabled?tf4.loadGraphModel(config2.face.iris.modelPath,{fromTFHub:config2.face.iris.modelPath.includes("tfhub.dev")}):null]);let faceMesh=new MediaPipeFaceMesh(faceModels[0],faceModels[1],faceModels[2],config2);return config2.face.mesh.enabled&&log(`load model: ${config2.face.mesh.modelPath.match(/\/(.*)\./)[1]}`),config2.face.iris.enabled&&log(`load model: ${config2.face.iris.modelPath.match(/\/(.*)\./)[1]}`),faceMesh}exports.load=load2;exports.MediaPipeFaceMesh=MediaPipeFaceMesh;exports.triangulation=coords.TRI468});var require_profile=__commonJS(exports=>{var profileData={};function profile2(name,data3){if(!data3||!data3.kernels)return;let maxResults=5,time=data3.kernels.filter(a=>a.kernelTimeMs>0).reduce((a,b)=>a+=b.kernelTimeMs,0),slowest=data3.kernels.map((a,i)=>(a.id=i,a)).filter(a=>a.kernelTimeMs>0).sort((a,b)=>b.kernelTimeMs-a.kernelTimeMs),largest=data3.kernels.map((a,i)=>(a.id=i,a)).filter(a=>a.totalBytesSnapshot>0).sort((a,b)=>b.totalBytesSnapshot-a.totalBytesSnapshot);slowest.length>maxResults&&(slowest.length=maxResults),largest.length>maxResults&&(largest.length=maxResults);let res={newBytes:data3.newBytes,newTensors:data3.newTensors,peakBytes:data3.peakBytes,numKernelOps:data3.kernels.length,timeKernelOps:time,slowestKernelOps:slowest,largestKernelOps:largest};profileData[name]=res,log("Human profiler",name,res)}exports.run=profile2});var require_age=__commonJS(exports=>{var tf4=__toModule(require_tfjs_esm()),profile2=__toModule(require_profile()),models={},last={age:0},skipped=Number.MAX_SAFE_INTEGER;async function load2(config2){return models.age||(models.age=await tf4.loadGraphModel(config2.face.age.modelPath),log(`load model: ${config2.face.age.modelPath.match(/\/(.*)\./)[1]}`)),models.age}async function predict2(image4,config2){return models.age?skipped0?(skipped++,last):(config2.videoOptimized?skipped=0:skipped=Number.MAX_SAFE_INTEGER,new Promise(async resolve=>{let resize=tf4.image.resizeBilinear(image4,[config2.face.age.inputSize,config2.face.age.inputSize],!1),enhance=tf4.mul(resize,[255]);tf4.dispose(resize);let ageT,obj={};if(!config2.profile)config2.face.age.enabled&&(ageT=await models.age.predict(enhance));else{let profileAge=config2.face.age.enabled?await tf4.profile(()=>models.age.predict(enhance)):{};ageT=profileAge.result.clone(),profileAge.result.dispose(),profile2.run("age",profileAge)}if(enhance.dispose(),ageT){let data3=ageT.dataSync();obj.age=Math.trunc(10*data3[0])/10}ageT.dispose(),last=obj,resolve(obj)})):null}exports.predict=predict2;exports.load=load2});var require_gender=__commonJS(exports=>{var tf4=__toModule(require_tfjs_esm()),profile2=__toModule(require_profile()),models={},last={gender:""},skipped=Number.MAX_SAFE_INTEGER,alternative=!1,rgb=[.2989,.587,.114];async function load2(config2){return models.gender||(models.gender=await tf4.loadGraphModel(config2.face.gender.modelPath),alternative=models.gender.inputs[0].shape[3]===1,log(`load model: ${config2.face.gender.modelPath.match(/\/(.*)\./)[1]}`)),models.gender}async function predict2(image4,config2){return models.gender?skipped{let resize=tf4.image.resizeBilinear(image4,[config2.face.gender.inputSize,config2.face.gender.inputSize],!1),enhance;alternative?enhance=tf4.tidy(()=>{let[red,green,blue]=tf4.split(resize,3,3),redNorm=tf4.mul(red,rgb[0]),greenNorm=tf4.mul(green,rgb[1]),blueNorm=tf4.mul(blue,rgb[2]);return tf4.addN([redNorm,greenNorm,blueNorm]).sub(.5).mul(2)}):enhance=tf4.mul(resize,[255]),tf4.dispose(resize);let genderT,obj={};if(!config2.profile)config2.face.gender.enabled&&(genderT=await models.gender.predict(enhance));else{let profileGender=config2.face.gender.enabled?await tf4.profile(()=>models.gender.predict(enhance)):{};genderT=profileGender.result.clone(),profileGender.result.dispose(),profile2.run("gender",profileGender)}if(enhance.dispose(),genderT){let data3=genderT.dataSync();if(alternative){let confidence=Math.trunc(100*Math.abs(data3[0]-data3[1]))/100;confidence>config2.face.gender.minConfidence&&(obj.gender=data3[0]>data3[1]?"female":"male",obj.confidence=confidence)}else{let confidence=Math.trunc(200*Math.abs(data3[0]-.5))/100;confidence>config2.face.gender.minConfidence&&(obj.gender=data3[0]<=.5?"female":"male",obj.confidence=Math.min(.99,confidence))}}genderT.dispose(),last=obj,resolve(obj)})):null}exports.predict=predict2;exports.load=load2});var require_emotion=__commonJS(exports=>{var tf4=__toModule(require_tfjs_esm()),profile2=__toModule(require_profile()),annotations=["angry","disgust","fear","happy","sad","surprise","neutral"],models={},last=[],skipped=Number.MAX_SAFE_INTEGER,rgb=[.2989,.587,.114],scale=1;async function load2(config2){return models.emotion||(models.emotion=await tf4.loadGraphModel(config2.face.emotion.modelPath),log(`load model: ${config2.face.emotion.modelPath.match(/\/(.*)\./)[1]}`)),models.emotion}async function predict2(image4,config2){return models.emotion?skipped0?(skipped++,last):(config2.videoOptimized?skipped=0:skipped=Number.MAX_SAFE_INTEGER,new Promise(async resolve=>{let resize=tf4.image.resizeBilinear(image4,[config2.face.emotion.inputSize,config2.face.emotion.inputSize],!1),[red,green,blue]=tf4.split(resize,3,3);resize.dispose();let redNorm=tf4.mul(red,rgb[0]),greenNorm=tf4.mul(green,rgb[1]),blueNorm=tf4.mul(blue,rgb[2]);red.dispose(),green.dispose(),blue.dispose();let grayscale=tf4.addN([redNorm,greenNorm,blueNorm]);redNorm.dispose(),greenNorm.dispose(),blueNorm.dispose();let normalize=tf4.tidy(()=>grayscale.sub(.5).mul(2));grayscale.dispose();let obj=[];if(config2.face.emotion.enabled){let data3;if(config2.profile){let profileData=await tf4.profile(()=>models.emotion.predict(normalize));data3=profileData.result.dataSync(),profileData.result.dispose(),profile2.run("emotion",profileData)}else{let emotionT=await models.emotion.predict(normalize);data3=emotionT.dataSync(),tf4.dispose(emotionT)}for(let i=0;iconfig2.face.emotion.minConfidence&&obj.push({score:Math.min(.99,Math.trunc(100*scale*data3[i])/100),emotion:annotations[i]});obj.sort((a,b)=>b.score-a.score)}normalize.dispose(),last=obj,resolve(obj)})):null}exports.predict=predict2;exports.load=load2});var require_embedding=__commonJS(exports=>{var tf4=__toModule(require_tfjs_esm()),profile2=__toModule(require_profile()),models={};async function load2(config2){return models.embedding||(models.embedding=await tf4.loadGraphModel(config2.face.embedding.modelPath),log(`load model: ${config2.face.embedding.modelPath.match(/\/(.*)\./)[1]}`)),models.embedding}function simmilarity2(embedding1,embedding2){if((embedding1==null?void 0:embedding1.length)!==(embedding2==null?void 0:embedding2.length))return 0;let order=2,distance=10*embedding1.map((val,i)=>val-embedding2[i]).reduce((dist,diff)=>dist+diff**order,0)**(1/order);return Math.trunc(1e3*(1-distance))/1e3}async function predict2(image4,config2){return models.embedding?new Promise(async resolve=>{let resize=tf4.image.resizeBilinear(image4,[config2.face.embedding.inputSize,config2.face.embedding.inputSize],!1),data3=[];if(config2.face.embedding.enabled)if(config2.profile){let profileData=await tf4.profile(()=>models.embedding.predict({img_inputs:resize}));data3=[...profileData.result.dataSync()],profileData.result.dispose(),profile2.run("emotion",profileData)}else{let embeddingT=await models.embedding.predict({img_inputs:resize});data3=[...embeddingT.dataSync()],tf4.dispose(embeddingT)}resize.dispose(),resolve(data3)}):null}exports.predict=predict2;exports.simmilarity=simmilarity2;exports.load=load2});var require_modelBase=__commonJS(exports=>{var tf4=__toModule(require_tfjs_esm()),imageNetMean=[-123.15,-115.9,-103.06];function nameOutputResultsMobileNet(results){let[offsets,heatmap,displacementFwd,displacementBwd]=results;return{offsets,heatmap,displacementFwd,displacementBwd}}function nameOutputResultsResNet(results){let[displacementFwd,displacementBwd,offsets,heatmap]=results;return{offsets,heatmap,displacementFwd,displacementBwd}}var BaseModel=class{constructor(model){this.model=model}predict(input,config2){return tf4.tidy(()=>{let asBatch=(config2.body.modelType==="ResNet"?input.toFloat().add(imageNetMean):input.toFloat().div(127.5).sub(1)).expandDims(0),results3d=this.model.predict(asBatch).map(y=>y.squeeze([0])),namedResults=config2.body.modelType==="ResNet"?nameOutputResultsResNet(results3d):nameOutputResultsMobileNet(results3d);return{heatmapScores:namedResults.heatmap.sigmoid(),offsets:namedResults.offsets,displacementFwd:namedResults.displacementFwd,displacementBwd:namedResults.displacementBwd}})}dispose(){this.model.dispose()}};exports.BaseModel=BaseModel});var require_heapSort=__commonJS(exports=>{function half(k){return Math.floor(k/2)}var MaxHeap=class{constructor(maxSize,getElementValue){this.priorityQueue=new Array(maxSize),this.numberOfElements=-1,this.getElementValue=getElementValue}enqueue(x){this.priorityQueue[++this.numberOfElements]=x,this.swim(this.numberOfElements)}dequeue(){let max=this.priorityQueue[0];return this.exchange(0,this.numberOfElements--),this.sink(0),this.priorityQueue[this.numberOfElements+1]=null,max}empty(){return this.numberOfElements===-1}size(){return this.numberOfElements+1}all(){return this.priorityQueue.slice(0,this.numberOfElements+1)}max(){return this.priorityQueue[0]}swim(k){for(;k>0&&this.less(half(k),k);)this.exchange(k,half(k)),k=half(k)}sink(k){for(;2*k<=this.numberOfElements;){let j=2*k;if(j{var heapSort=__toModule(require_heapSort());function scoreIsMaximumInLocalWindow(keypointId,score,heatmapY,heatmapX,localMaximumRadius,scores){let[height,width]=scores.shape,localMaximum=!0,yStart=Math.max(heatmapY-localMaximumRadius,0),yEnd=Math.min(heatmapY+localMaximumRadius+1,height);for(let yCurrent=yStart;yCurrentscore){localMaximum=!1;break}if(!localMaximum)break}return localMaximum}function buildPartWithScoreQueue(scoreThreshold,localMaximumRadius,scores){let[height,width,numKeypoints]=scores.shape,queue=new heapSort.MaxHeap(height*width*numKeypoints,({score})=>score);for(let heatmapY=0;heatmapY{exports.partNames=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"];exports.NUM_KEYPOINTS=exports.partNames.length;exports.partIds=exports.partNames.reduce((result,jointName,i)=>(result[jointName]=i,result),{});var connectedPartNames=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]];exports.connectedPartIndices=connectedPartNames.map(([jointNameA,jointNameB])=>[exports.partIds[jointNameA],exports.partIds[jointNameB]]);exports.poseChain=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];exports.partChannels=["left_face","right_face","right_upper_leg_front","right_lower_leg_back","right_upper_leg_back","left_lower_leg_front","left_upper_leg_front","left_upper_leg_back","left_lower_leg_back","right_feet","right_lower_leg_front","left_feet","torso_front","torso_back","right_upper_arm_front","right_upper_arm_back","right_lower_arm_back","left_lower_arm_front","left_upper_arm_front","left_upper_arm_back","left_lower_arm_back","right_hand","right_lower_arm_front","left_hand"]});var require_vectors=__commonJS(exports=>{var kpt=__toModule(require_keypoints());function getOffsetPoint(y,x,keypoint,offsets){return{y:offsets.get(y,x,keypoint),x:offsets.get(y,x,keypoint+kpt.NUM_KEYPOINTS)}}exports.getOffsetPoint=getOffsetPoint;function getImageCoords(part,outputStride,offsets){let{heatmapY,heatmapX,id:keypoint}=part,{y,x}=getOffsetPoint(heatmapY,heatmapX,keypoint,offsets);return{x:part.heatmapX*outputStride+x,y:part.heatmapY*outputStride+y}}exports.getImageCoords=getImageCoords;function fillArray(element,size){let result=new Array(size);for(let i=0;imax?max:a}exports.clamp=clamp;function squaredDistance(y1,x1,y2,x2){let dy=y2-y1,dx=x2-x1;return dy*dy+dx*dx}exports.squaredDistance=squaredDistance;function addVectors(a,b){return{x:a.x+b.x,y:a.y+b.y}}exports.addVectors=addVectors;function clampVector(a,min,max){return{y:clamp(a.y,min,max),x:clamp(a.x,min,max)}}exports.clampVector=clampVector});var require_decoders=__commonJS(exports=>{var tf4=__toModule(require_tfjs_esm()),kpt=__toModule(require_keypoints());function getPointsConfidence(heatmapScores,heatMapCoords){let numKeypoints=heatMapCoords.shape[0],result=new Float32Array(numKeypoints);for(let keypoint=0;keypointheatMapCoordsBuffer.toTensor().mul(tf4.scalar(outputStride,"int32")).toFloat().add(getOffsetVectors(heatMapCoordsBuffer,offsetsBuffer)))}exports.getOffsetPoints=getOffsetPoints;function mod(a,b){return tf4.tidy(()=>{let floored=a.div(tf4.scalar(b,"int32"));return a.sub(floored.mul(tf4.scalar(b,"int32")))})}function argmax2d(inputs){let[height,width,depth]=inputs.shape;return tf4.tidy(()=>{let coords=inputs.reshape([height*width,depth]).argMax(0),yCoords=coords.div(tf4.scalar(width,"int32")).expandDims(1),xCoords=mod(coords,width).expandDims(1);return tf4.concat([yCoords,xCoords],1)})}exports.argmax2d=argmax2d});var require_decodePose=__commonJS(exports=>{var keypoints=__toModule(require_keypoints()),vectors=__toModule(require_vectors()),decoders=__toModule(require_decoders()),parentChildrenTuples=keypoints.poseChain.map(([parentJoinName,childJoinName])=>[keypoints.partIds[parentJoinName],keypoints.partIds[childJoinName]]),parentToChildEdges=parentChildrenTuples.map(([,childJointId])=>childJointId),childToParentEdges=parentChildrenTuples.map(([parentJointId])=>parentJointId);function getDisplacement(edgeId,point,displacements){let numEdges=displacements.shape[2]/2;return{y:displacements.get(point.y,point.x,edgeId),x:displacements.get(point.y,point.x,numEdges+edgeId)}}function getStridedIndexNearPoint(point,outputStride,height,width){return{y:vectors.clamp(Math.round(point.y/outputStride),0,height-1),x:vectors.clamp(Math.round(point.x/outputStride),0,width-1)}}function traverseToTargetKeypoint(edgeId,sourceKeypoint,targetKeypointId,scoresBuffer,offsets,outputStride,displacements,offsetRefineStep=2){let[height,width]=scoresBuffer.shape,sourceKeypointIndices=getStridedIndexNearPoint(sourceKeypoint.position,outputStride,height,width),displacement=getDisplacement(edgeId,sourceKeypointIndices,displacements),targetKeypoint=vectors.addVectors(sourceKeypoint.position,displacement);for(let i=0;i=0;--edge){let sourceKeypointId=parentToChildEdges[edge],targetKeypointId=childToParentEdges[edge];instanceKeypoints[sourceKeypointId]&&!instanceKeypoints[targetKeypointId]&&(instanceKeypoints[targetKeypointId]=traverseToTargetKeypoint(edge,instanceKeypoints[sourceKeypointId],targetKeypointId,scores,offsets,outputStride,displacementsBwd))}for(let edge=0;edge(totalScore+=score,{position:{y:offsetPointsBuffer.get(i,0),x:offsetPointsBuffer.get(i,1)},part:keypoints.partNames[i],score})),filteredKeypoints=instanceKeypoints.filter(kpt=>kpt.score>config2.body.scoreThreshold);return heatmapValues.dispose(),offsetPoints.dispose(),{keypoints:filteredKeypoints,score:totalScore/instanceKeypoints.length}}exports.decodeSinglePose=decodeSinglePose});var require_decodeMultiple=__commonJS(exports=>{var buildParts=__toModule(require_buildParts()),decodePose=__toModule(require_decodePose()),vectors=__toModule(require_vectors()),kLocalMaximumRadius=1;function withinNmsRadiusOfCorrespondingPoint(poses,squaredNmsRadius,{x,y},keypointId){return poses.some(({keypoints})=>{let correspondingKeypoint=keypoints[keypointId].position;return vectors.squaredDistance(y,x,correspondingKeypoint.y,correspondingKeypoint.x)<=squaredNmsRadius})}function getInstanceScore(existingPoses,squaredNmsRadius,instanceKeypoints){return instanceKeypoints.reduce((result,{position,score},keypointId)=>(withinNmsRadiusOfCorrespondingPoint(existingPoses,squaredNmsRadius,position,keypointId)||(result+=score),result),0)/instanceKeypoints.length}function decodeMultiplePoses(scoresBuffer,offsetsBuffer,displacementsFwdBuffer,displacementsBwdBuffer,config2){let poses=[],queue=buildParts.buildPartWithScoreQueue(config2.body.scoreThreshold,kLocalMaximumRadius,scoresBuffer),squaredNmsRadius=config2.body.nmsRadius^2;for(;poses.lengthconfig2.body.scoreThreshold&&poses.push({keypoints,score})}return poses}exports.decodeMultiplePoses=decodeMultiplePoses});var require_util2=__commonJS(exports=>{var kpt=__toModule(require_keypoints());function eitherPointDoesntMeetConfidence(a,b,minConfidence){return a(eitherPointDoesntMeetConfidence(keypoints[leftJoint].score,keypoints[rightJoint].score,minConfidence)||result.push([keypoints[leftJoint],keypoints[rightJoint]]),result),[])}exports.getAdjacentKeyPoints=getAdjacentKeyPoints;var{NEGATIVE_INFINITY,POSITIVE_INFINITY}=Number;function getBoundingBox(keypoints){return keypoints.reduce(({maxX,maxY,minX,minY},{position:{x,y}})=>({maxX:Math.max(maxX,x),maxY:Math.max(maxY,y),minX:Math.min(minX,x),minY:Math.min(minY,y)}),{maxX:NEGATIVE_INFINITY,maxY:NEGATIVE_INFINITY,minX:POSITIVE_INFINITY,minY:POSITIVE_INFINITY})}exports.getBoundingBox=getBoundingBox;function getBoundingBoxPoints(keypoints){let{minX,minY,maxX,maxY}=getBoundingBox(keypoints);return[{x:minX,y:minY},{x:maxX,y:minY},{x:maxX,y:maxY},{x:minX,y:maxY}]}exports.getBoundingBoxPoints=getBoundingBoxPoints;async function toTensorBuffers3D(tensors){return Promise.all(tensors.map(tensor=>tensor.buffer()))}exports.toTensorBuffers3D=toTensorBuffers3D;function scalePose(pose,scaleY,scaleX){return{score:pose.score,keypoints:pose.keypoints.map(({score,part,position})=>({score,part,position:{x:position.x*scaleX,y:position.y*scaleY}}))}}exports.scalePose=scalePose;function resizeTo(image3,[targetH,targetW]){let input=image3.squeeze(0),resized=input.resizeBilinear([targetH,targetW]);return input.dispose(),resized}exports.resizeTo=resizeTo;function scaleAndFlipPoses(poses,[height,width],[inputResolutionHeight,inputResolutionWidth]){return poses.map(pose=>scalePose(pose,height/inputResolutionHeight,width/inputResolutionWidth))}exports.scaleAndFlipPoses=scaleAndFlipPoses});var require_modelPoseNet=__commonJS(exports=>{var tf4=__toModule(require_tfjs_esm()),modelBase=__toModule(require_modelBase()),decodeMultiple=__toModule(require_decodeMultiple()),decodePose=__toModule(require_decodePose()),util=__toModule(require_util2());async function estimateMultiple(input,res,config2){return new Promise(async resolve=>{let height=input.shape[1],width=input.shape[2],allTensorBuffers=await util.toTensorBuffers3D([res.heatmapScores,res.offsets,res.displacementFwd,res.displacementBwd]),scoresBuffer=allTensorBuffers[0],offsetsBuffer=allTensorBuffers[1],displacementsFwdBuffer=allTensorBuffers[2],displacementsBwdBuffer=allTensorBuffers[3],poses=await decodeMultiple.decodeMultiplePoses(scoresBuffer,offsetsBuffer,displacementsFwdBuffer,displacementsBwdBuffer,config2),scaled=util.scaleAndFlipPoses(poses,[height,width],[config2.body.inputSize,config2.body.inputSize]);resolve(scaled)})}async function estimateSingle(input,res,config2){return new Promise(async resolve=>{let height=input.shape[1],width=input.shape[2],poses=[await decodePose.decodeSinglePose(res.heatmapScores,res.offsets,config2)],scaled=util.scaleAndFlipPoses(poses,[height,width],[config2.body.inputSize,config2.body.inputSize]);resolve(scaled)})}var PoseNet=class{constructor(model){this.baseModel=model}async estimatePoses(input,config2){let resized=util.resizeTo(input,[config2.body.inputSize,config2.body.inputSize]),res=this.baseModel.predict(resized,config2),poses=config2.body.maxDetections<2?await estimateSingle(input,res,config2):await estimateMultiple(input,res,config2);return res.heatmapScores.dispose(),res.offsets.dispose(),res.displacementFwd.dispose(),res.displacementBwd.dispose(),resized.dispose(),poses}dispose(){this.baseModel.dispose()}};exports.PoseNet=PoseNet;async function load2(config2){let model=await tf4.loadGraphModel(config2.body.modelPath),mobilenet=new modelBase.BaseModel(model);return log(`load model: ${config2.body.modelPath.match(/\/(.*)\./)[1]}`),new PoseNet(mobilenet)}exports.load=load2});var require_posenet=__commonJS(exports=>{var modelPoseNet=__toModule(require_modelPoseNet()),keypoints=__toModule(require_keypoints()),util=__toModule(require_util2());exports.load=modelPoseNet.load;exports.PoseNet=modelPoseNet.PoseNet;exports.partChannels=keypoints.partChannels;exports.partIds=keypoints.partIds;exports.partNames=keypoints.partNames;exports.poseChain=keypoints.poseChain;exports.getAdjacentKeyPoints=util.getAdjacentKeyPoints;exports.getBoundingBox=util.getBoundingBox;exports.getBoundingBoxPoints=util.getBoundingBoxPoints;exports.scaleAndFlipPoses=util.scaleAndFlipPoses;exports.scalePose=util.scalePose});var require_handdetector=__commonJS(exports=>{var tf4=__toModule(require_tfjs_esm()),HandDetector=class{constructor(model,inputSize,anchorsAnnotated){this.model=model,this.anchors=anchorsAnnotated.map(anchor=>[anchor.x_center,anchor.y_center]),this.anchorsTensor=tf4.tensor2d(this.anchors),this.inputSizeTensor=tf4.tensor1d([inputSize,inputSize]),this.doubleInputSizeTensor=tf4.tensor1d([inputSize*2,inputSize*2])}normalizeBoxes(boxes){return tf4.tidy(()=>{let boxOffsets=tf4.slice(boxes,[0,0],[-1,2]),boxSizes=tf4.slice(boxes,[0,2],[-1,2]),boxCenterPoints=tf4.add(tf4.div(boxOffsets,this.inputSizeTensor),this.anchorsTensor),halfBoxSizes=tf4.div(boxSizes,this.doubleInputSizeTensor),startPoints=tf4.mul(tf4.sub(boxCenterPoints,halfBoxSizes),this.inputSizeTensor),endPoints=tf4.mul(tf4.add(boxCenterPoints,halfBoxSizes),this.inputSizeTensor);return tf4.concat2d([startPoints,endPoints],1)})}normalizeLandmarks(rawPalmLandmarks,index){return tf4.tidy(()=>{let landmarks=tf4.add(tf4.div(rawPalmLandmarks.reshape([-1,7,2]),this.inputSizeTensor),this.anchors[index]);return tf4.mul(landmarks,this.inputSizeTensor)})}async getBoxes(input,config2){let batched=this.model.predict(input),predictions=batched.squeeze();batched.dispose();let scoresT=tf4.tidy(()=>tf4.sigmoid(tf4.slice(predictions,[0,0],[-1,1])).squeeze()),scores=scoresT.dataSync(),rawBoxes=tf4.slice(predictions,[0,1],[-1,4]),boxes=this.normalizeBoxes(rawBoxes);rawBoxes.dispose();let filteredT=await tf4.image.nonMaxSuppressionAsync(boxes,scores,config2.hand.maxHands,config2.hand.iouThreshold,config2.hand.scoreThreshold),filtered=filteredT.arraySync();scoresT.dispose(),filteredT.dispose();let hands=[];for(let index of filtered)if(scores[index]>=config2.hand.minConfidence){let matchingBox=tf4.slice(boxes,[index,0],[1,-1]),rawPalmLandmarks=tf4.slice(predictions,[index,5],[1,14]),palmLandmarks=tf4.tidy(()=>this.normalizeLandmarks(rawPalmLandmarks,index).reshape([-1,2]));rawPalmLandmarks.dispose(),hands.push({box:matchingBox,palmLandmarks,confidence:scores[index]})}return predictions.dispose(),boxes.dispose(),hands}async estimateHandBounds(input,config2){let inputHeight=input.shape[1],inputWidth=input.shape[2],image4=tf4.tidy(()=>input.resizeBilinear([config2.hand.inputSize,config2.hand.inputSize]).div(127.5).sub(1)),predictions=await this.getBoxes(image4,config2);image4.dispose();let hands=[];if(!predictions||predictions.length===0)return hands;for(let prediction of predictions){let boxes=prediction.box.dataSync(),startPoint=boxes.slice(0,2),endPoint=boxes.slice(2,4),palmLandmarks=prediction.palmLandmarks.arraySync();prediction.box.dispose(),prediction.palmLandmarks.dispose(),hands.push(scaleBoxCoordinates({startPoint,endPoint,palmLandmarks,confidence:prediction.confidence},[inputWidth/config2.hand.inputSize,inputHeight/config2.hand.inputSize]))}return hands}};exports.HandDetector=HandDetector});var require_handpipeline=__commonJS(exports=>{var tf4=__toModule(require_tfjs_esm()),PALM_BOX_ENLARGE_FACTOR=5,HAND_BOX_ENLARGE_FACTOR=1.65,PALM_LANDMARK_IDS=[0,5,9,13,17,1,2],PALM_LANDMARKS_INDEX_OF_PALM_BASE=0,PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE=2,HandPipeline=class{constructor(handDetector,landmarkDetector,inputSize){this.handDetector=handDetector,this.landmarkDetector=landmarkDetector,this.inputSize=inputSize,this.storedBoxes=[],this.skipped=0,this.detectedHands=0}getBoxForPalmLandmarks(palmLandmarks,rotationMatrix){let rotatedPalmLandmarks=palmLandmarks.map(coord=>rotatePoint([...coord,1],rotationMatrix)),boxAroundPalm=this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);return enlargeBox(squarifyBox(boxAroundPalm),PALM_BOX_ENLARGE_FACTOR)}getBoxForHandLandmarks(landmarks){let boundingBox=this.calculateLandmarksBoundingBox(landmarks),boxAroundHand=enlargeBox(squarifyBox(boundingBox),HAND_BOX_ENLARGE_FACTOR);boxAroundHand.palmLandmarks=[];for(let i=0;i[scaleFactor[0]*(coord[0]-this.inputSize/2),scaleFactor[1]*(coord[1]-this.inputSize/2),scaleFactor[2]*coord[2]]),coordsRotationMatrix=buildRotationMatrix(angle,[0,0]),coordsRotated=coordsScaled.map(coord=>[...rotatePoint(coord,coordsRotationMatrix),coord[2]]),inverseRotationMatrix=invertTransformMatrix(rotationMatrix),boxCenter=[...getBoxCenter(box2),1],originalBoxCenter=[dot(boxCenter,inverseRotationMatrix[0]),dot(boxCenter,inverseRotationMatrix[1])];return coordsRotated.map(coord=>[coord[0]+originalBoxCenter[0],coord[1]+originalBoxCenter[1],coord[2]])}async estimateHands(image4,config2){let useFreshBox=!1,boxes;(this.skipped===0||this.skipped>config2.hand.skipFrames||!config2.hand.landmarks||!config2.videoOptimized)&&(boxes=await this.handDetector.estimateHandBounds(image4,config2),this.skipped=0),config2.videoOptimized&&this.skipped++,boxes&&boxes.length>0&&(boxes.length!==this.detectedHands&&this.detectedHands!==config2.hand.maxHands||!config2.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...boxes],this.storedBoxes.length>0&&(useFreshBox=!0));let hands=[];for(let i=0;i=config2.hand.minConfidence){let keypointsReshaped=tf4.reshape(keypoints,[-1,3]),rawCoords=keypointsReshaped.arraySync();keypoints.dispose(),keypointsReshaped.dispose();let coords=this.transformRawCoords(rawCoords,newBox,angle,rotationMatrix),nextBoundingBox=this.getBoxForHandLandmarks(coords);this.storedBoxes[i]=nextBoundingBox;let result={landmarks:coords,confidence,box:{topLeft:nextBoundingBox.startPoint,bottomRight:nextBoundingBox.endPoint}};hands.push(result)}else this.storedBoxes[i]=null;keypoints.dispose()}else{let enlarged=enlargeBox(squarifyBox(currentBox),HAND_BOX_ENLARGE_FACTOR),result={confidence:currentBox.confidence,box:{topLeft:enlarged.startPoint,bottomRight:enlarged.endPoint}};hands.push(result)}}return this.storedBoxes=this.storedBoxes.filter(a=>a!==null),this.detectedHands=hands.length,hands}calculateLandmarksBoundingBox(landmarks){let xs=landmarks.map(d=>d[0]),ys=landmarks.map(d=>d[1]),startPoint=[Math.min(...xs),Math.min(...ys)],endPoint=[Math.max(...xs),Math.max(...ys)];return{startPoint,endPoint}}};exports.HandPipeline=HandPipeline});var 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HandPose(handPipeline);return config2.hand.enabled&&log(`load model: ${config2.hand.detector.modelPath.match(/\/(.*)\./)[1]}`),config2.hand.landmarks&&log(`load model: ${config2.hand.skeleton.modelPath.match(/\/(.*)\./)[1]}`),handPose}exports.load=load2});var require_gesture=__commonJS(exports=>{exports.body=res=>{if(!res)return[];let gestures=[];for(let i=0;ia.part==="leftWrist"),rightWrist=res[i].keypoints.find(a=>a.part==="rightWrist"),nose=res[i].keypoints.find(a=>a.part==="nose");nose&&leftWrist&&rightWrist&&leftWrist.position.ya.part==="leftShoulder"),rightShoulder=res[i].keypoints.find(a=>a.part==="rightShoulder");leftShoulder&&rightShoulder&&gestures.push({body:i,gesture:`leaning ${leftShoulder.position.y>rightShoulder.position.y?"left":"right"}`})}return gestures};exports.face=res=>{if(!res)return[];let gestures=[];for(let i=0;i0){let eyeFacing=res[i].mesh[35][2]-res[i].mesh[263][2];Math.abs(eyeFacing)<10?gestures.push({face:i,gesture:"facing camera"}):gestures.push({face:i,gesture:`facing ${eyeFacing<0?"right":"left"}`}),Math.abs(res[i].mesh[374][1]-res[i].mesh[386][1])/Math.abs(res[i].mesh[443][1]-res[i].mesh[450][1])<.2&&gestures.push({face:i,gesture:"blink left eye"}),Math.abs(res[i].mesh[145][1]-res[i].mesh[159][1])/Math.abs(res[i].mesh[223][1]-res[i].mesh[230][1])<.2&&gestures.push({face:i,gesture:"blink right eye"});let mouthOpen=Math.min(100,500*Math.abs(res[i].mesh[13][1]-res[i].mesh[14][1])/Math.abs(res[i].mesh[10][1]-res[i].mesh[152][1]));mouthOpen>10&&gestures.push({face:i,gesture:`mouth ${Math.trunc(mouthOpen)}% open`});let chinDepth=res[i].mesh[152][2];Math.abs(chinDepth)>10&&gestures.push({face:i,gesture:`head ${chinDepth<0?"up":"down"}`})}return gestures};exports.iris=res=>{if(!res)return[];let gestures=[];for(let i=0;i{if(!res)return[];let gestures=[];for(let i=0;i0){let closest=fingers.reduce((best,a)=>best.position[2]best.position[1]{var WebGLProgram=function(gl,vertexSource,fragmentSource){let _collect=function(source,prefix,collection){let r=new RegExp("\\b"+prefix+" \\w+ (\\w+)","ig");source.replace(r,(match,name)=>(collection[name]=0,match))},_compile=function(source,type){let shader=gl.createShader(type);if(gl.shaderSource(shader,source),gl.compileShader(shader),!gl.getShaderParameter(shader,gl.COMPILE_STATUS))throw new Error("Filter: GL compile failed",gl.getShaderInfoLog(shader));return shader};this.uniform={},this.attribute={};let _vsh=_compile(vertexSource,gl.VERTEX_SHADER),_fsh=_compile(fragmentSource,gl.FRAGMENT_SHADER);if(this.id=gl.createProgram(),gl.attachShader(this.id,_vsh),gl.attachShader(this.id,_fsh),gl.linkProgram(this.id),!gl.getProgramParameter(this.id,gl.LINK_STATUS))throw new Error("Filter: GL link failed",gl.getProgramInfoLog(this.id));gl.useProgram(this.id),_collect(vertexSource,"attribute",this.attribute);for(let a in this.attribute)this.attribute[a]=gl.getAttribLocation(this.id,a);_collect(vertexSource,"uniform",this.uniform),_collect(fragmentSource,"uniform",this.uniform);for(let u in this.uniform)this.uniform[u]=gl.getUniformLocation(this.id,u)},WebGLImageFilter=function(params){params||(params={});let _drawCount=0,_sourceTexture=null,_lastInChain=!1,_currentFramebufferIndex=-1,_tempFramebuffers=[null,null],_filterChain=[],_width=-1,_height=-1,_vertexBuffer=null,_currentProgram=null,_canvas=params.canvas||document.createElement("canvas"),_shaderProgramCache={},gl=_canvas.getContext("webgl");if(!gl)throw new Error("Filter: getContext() failed");this.addFilter=function(name){let args=Array.prototype.slice.call(arguments,1),filter=_filter[name];_filterChain.push({func:filter,args})},this.reset=function(){_filterChain=[]},this.apply=function(image3){if(_resize(image3.width,image3.height),_drawCount=0,_sourceTexture||(_sourceTexture=gl.createTexture()),gl.bindTexture(gl.TEXTURE_2D,_sourceTexture),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_WRAP_S,gl.CLAMP_TO_EDGE),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_WRAP_T,gl.CLAMP_TO_EDGE),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_MIN_FILTER,gl.NEAREST),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_MAG_FILTER,gl.NEAREST),gl.texImage2D(gl.TEXTURE_2D,0,gl.RGBA,gl.RGBA,gl.UNSIGNED_BYTE,image3),_filterChain.length===0)return _draw(),_canvas;for(let i=0;i<_filterChain.length;i++){_lastInChain=i===_filterChain.length-1;let f=_filterChain[i];f.func.apply(this,f.args||[])}return _canvas};let _resize=function(width,height){if(!(width===_width&&height===_height)){if(_canvas.width=width,_width=width,_canvas.height=height,_height=height,!_vertexBuffer){let vertices=new Float32Array([-1,-1,0,1,1,-1,1,1,-1,1,0,0,-1,1,0,0,1,-1,1,1,1,1,1,0]);_vertexBuffer=gl.createBuffer(),gl.bindBuffer(gl.ARRAY_BUFFER,_vertexBuffer),gl.bufferData(gl.ARRAY_BUFFER,vertices,gl.STATIC_DRAW),gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL,!0)}gl.viewport(0,0,_width,_height),_tempFramebuffers=[null,null]}},_getTempFramebuffer=function(index){return _tempFramebuffers[index]=_tempFramebuffers[index]||_createFramebufferTexture(_width,_height),_tempFramebuffers[index]},_createFramebufferTexture=function(width,height){let fbo=gl.createFramebuffer();gl.bindFramebuffer(gl.FRAMEBUFFER,fbo);let renderbuffer=gl.createRenderbuffer();gl.bindRenderbuffer(gl.RENDERBUFFER,renderbuffer);let texture=gl.createTexture();return gl.bindTexture(gl.TEXTURE_2D,texture),gl.texImage2D(gl.TEXTURE_2D,0,gl.RGBA,width,height,0,gl.RGBA,gl.UNSIGNED_BYTE,null),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_MAG_FILTER,gl.LINEAR),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_MIN_FILTER,gl.LINEAR),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_WRAP_S,gl.CLAMP_TO_EDGE),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_WRAP_T,gl.CLAMP_TO_EDGE),gl.framebufferTexture2D(gl.FRAMEBUFFER,gl.COLOR_ATTACHMENT0,gl.TEXTURE_2D,texture,0),gl.bindTexture(gl.TEXTURE_2D,null),gl.bindFramebuffer(gl.FRAMEBUFFER,null),{fbo,texture}},_draw=function(flags){var _a,_b;let source=null,target=null,flipY=!1;_drawCount===0?source=_sourceTexture:source=(_a=_getTempFramebuffer(_currentFramebufferIndex))==null?void 0:_a.texture,_drawCount++,_lastInChain&&!(flags&DRAW.INTERMEDIATE)?(target=null,flipY=_drawCount%2==0):(_currentFramebufferIndex=(_currentFramebufferIndex+1)%2,target=(_b=_getTempFramebuffer(_currentFramebufferIndex))==null?void 0:_b.fbo),gl.bindTexture(gl.TEXTURE_2D,source),gl.bindFramebuffer(gl.FRAMEBUFFER,target),gl.uniform1f(_currentProgram.uniform.flipY,flipY?-1:1),gl.drawArrays(gl.TRIANGLES,0,6)},_compileShader=function(fragmentSource){if(_shaderProgramCache[fragmentSource])return _currentProgram=_shaderProgramCache[fragmentSource],gl.useProgram(_currentProgram.id),_currentProgram;_currentProgram=new WebGLProgram(gl,SHADER.VERTEX_IDENTITY,fragmentSource);let floatSize=Float32Array.BYTES_PER_ELEMENT,vertSize=4*floatSize;return gl.enableVertexAttribArray(_currentProgram.attribute.pos),gl.vertexAttribPointer(_currentProgram.attribute.pos,2,gl.FLOAT,!1,vertSize,0*floatSize),gl.enableVertexAttribArray(_currentProgram.attribute.uv),gl.vertexAttribPointer(_currentProgram.attribute.uv,2,gl.FLOAT,!1,vertSize,2*floatSize),_shaderProgramCache[fragmentSource]=_currentProgram,_currentProgram},DRAW={INTERMEDIATE:1},SHADER={};SHADER.VERTEX_IDENTITY=["precision highp float;","attribute vec2 pos;","attribute vec2 uv;","varying vec2 vUv;","uniform float flipY;","void main(void) {","vUv = uv;","gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);","}"].join(` +var __create=Object.create,__defProp=Object.defineProperty,__getProtoOf=Object.getPrototypeOf,__hasOwnProp=Object.prototype.hasOwnProperty,__getOwnPropNames=Object.getOwnPropertyNames,__getOwnPropDesc=Object.getOwnPropertyDescriptor;var __markAsModule=target=>__defProp(target,"__esModule",{value:!0});var __commonJS=(callback,module)=>()=>(module||(module={exports:{}},callback(module.exports,module)),module.exports),__export=(target,all)=>{__markAsModule(target);for(var name in all)__defProp(target,name,{get:all[name],enumerable:!0})},__exportStar=(target,module,desc)=>{if(__markAsModule(target),module&&typeof module=="object"||typeof module=="function")for(let key of __getOwnPropNames(module))!__hasOwnProp.call(target,key)&&key!=="default"&&__defProp(target,key,{get:()=>module[key],enumerable:!(desc=__getOwnPropDesc(module,key))||desc.enumerable});return target},__toModule=module=>module&&module.__esModule?module:__exportStar(__defProp(module!=null?__create(__getProtoOf(module)):{},"default",{value:module,enumerable:!0}),module);import{version as tfjs}from"@tensorflow/tfjs/package.json";import{version as versionCore}from"@tensorflow/tfjs-core/package.json";import{version as versionData}from"@tensorflow/tfjs-data/package.json";import{version as versionLayers}from"@tensorflow/tfjs-layers/package.json";import{version as versionConverter}from"@tensorflow/tfjs-converter/package.json";import{version_cpu}from"@tensorflow/tfjs-backend-cpu/dist/index.js";import{version_webgl}from"@tensorflow/tfjs-backend-webgl/dist/index.js";import{version_wasm}from"@tensorflow/tfjs-backend-wasm/dist/index.js";import*as dist_star from"@tensorflow/tfjs-core/dist/index.js";import*as dist_star2 from"@tensorflow/tfjs-layers/dist/index.js";import*as dist_star3 from"@tensorflow/tfjs-converter/dist/index.js";import*as data from"@tensorflow/tfjs-data/dist/index.js";import*as dist_star4 from"@tensorflow/tfjs-backend-cpu/dist/index.js";import*as dist_star5 from"@tensorflow/tfjs-backend-webgl/dist/index.js";import*as dist_star6 from"@tensorflow/tfjs-backend-wasm/dist/index.js";var require_tfjs_esm=__commonJS(exports=>{__export(exports,{data:()=>data,version:()=>version2});__exportStar(exports,dist_star);__exportStar(exports,dist_star2);__exportStar(exports,dist_star3);__exportStar(exports,dist_star4);__exportStar(exports,dist_star5);__exportStar(exports,dist_star6);var version2={tfjs,"tfjs-core":versionCore,"tfjs-data":versionData,"tfjs-layers":versionLayers,"tfjs-converter":versionConverter,"tfjs-backend-cpu":version_cpu,"tfjs-backend-webgl":version_webgl,"tfjs-backend-wasm":version_wasm}});var require_blazeface=__commonJS(exports=>{var tf5=__toModule(require_tfjs_esm()),NUM_LANDMARKS=6;function generateAnchors(inputSize){let spec={strides:[inputSize/16,inputSize/8],anchors:[2,6]},anchors=[];for(let i=0;i{box.startEndTensor.dispose(),box.startPoint.dispose(),box.endPoint.dispose()},createBox=startEndTensor=>({startEndTensor,startPoint:tf5.slice(startEndTensor,[0,0],[-1,2]),endPoint:tf5.slice(startEndTensor,[0,2],[-1,2])}),scaleBox=(box,factors)=>{let starts=tf5.mul(box.startPoint,factors),ends=tf5.mul(box.endPoint,factors),newCoordinates=tf5.concat2d([starts,ends],1);return createBox(newCoordinates)};function decodeBounds(boxOutputs,anchors,inputSize){let boxStarts=tf5.slice(boxOutputs,[0,1],[-1,2]),centers=tf5.add(boxStarts,anchors),boxSizes=tf5.slice(boxOutputs,[0,3],[-1,2]),boxSizesNormalized=tf5.div(boxSizes,inputSize),centersNormalized=tf5.div(centers,inputSize),halfBoxSize=tf5.div(boxSizesNormalized,2),starts=tf5.sub(centersNormalized,halfBoxSize),ends=tf5.add(centersNormalized,halfBoxSize),startNormalized=tf5.mul(starts,inputSize),endNormalized=tf5.mul(ends,inputSize);return tf5.concat2d([startNormalized,endNormalized],1)}function scaleBoxFromPrediction(face3,scaleFactor){return tf5.tidy(()=>{let box=face3.box?face3.box:face3;return scaleBox(box,scaleFactor).startEndTensor.squeeze()})}var BlazeFaceModel=class{constructor(model,config2){this.blazeFaceModel=model,this.width=config2.face.detector.inputSize,this.height=config2.face.detector.inputSize,this.anchorsData=generateAnchors(config2.face.detector.inputSize),this.anchors=tf5.tensor2d(this.anchorsData),this.inputSize=tf5.tensor1d([this.width,this.height]),this.config=config2,this.scaleFaces=.8}async getBoundingBoxes(inputImage){if(!inputImage||inputImage.isDisposedInternal||inputImage.shape.length!==4||inputImage.shape[1]<1||inputImage.shape[2]<1)return null;let[detectedOutputs,boxes,scores]=tf5.tidy(()=>{let resizedImage=inputImage.resizeBilinear([this.width,this.height]),normalizedImage=tf5.sub(resizedImage.div(127.5),1),batchedPrediction=this.blazeFaceModel.predict(normalizedImage),prediction;if(Array.isArray(batchedPrediction)){let sorted=batchedPrediction.sort((a,b)=>a.size-b.size),concat384=tf5.concat([sorted[0],sorted[2]],2),concat512=tf5.concat([sorted[1],sorted[3]],2);prediction=tf5.concat([concat512,concat384],1).squeeze(0)}else prediction=batchedPrediction.squeeze();let decodedBounds=decodeBounds(prediction,this.anchors,this.inputSize),logits=tf5.slice(prediction,[0,0],[-1,1]),scoresOut=tf5.sigmoid(logits).squeeze();return[prediction,decodedBounds,scoresOut]}),boxIndicesTensor=await tf5.image.nonMaxSuppressionAsync(boxes,scores,this.config.face.detector.maxFaces,this.config.face.detector.iouThreshold,this.config.face.detector.scoreThreshold),boxIndices=boxIndicesTensor.arraySync();boxIndicesTensor.dispose();let boundingBoxes=boxIndices.map(boxIndex=>tf5.slice(boxes,[boxIndex,0],[1,-1])).map(boundingBox=>{let vals=boundingBox.arraySync();return boundingBox.dispose(),vals}),scoresVal=scores.dataSync(),annotatedBoxes=[];for(let i=0;ithis.config.face.detector.minConfidence){let box=createBox(boundingBoxes[i]),anchor=this.anchorsData[boxIndex],landmarks=tf5.tidy(()=>tf5.slice(detectedOutputs,[boxIndex,NUM_LANDMARKS-1],[1,-1]).squeeze().reshape([NUM_LANDMARKS,-1]));annotatedBoxes.push({box,landmarks,anchor,confidence})}}return detectedOutputs.dispose(),boxes.dispose(),scores.dispose(),detectedOutputs.dispose(),{boxes:annotatedBoxes,scaleFactor:[inputImage.shape[2]/this.width,inputImage.shape[1]/this.height]}}async estimateFaces(input){let{boxes,scaleFactor}=await this.getBoundingBoxes(input),faces=[];for(let face3 of boxes){let landmarkData=face3.landmarks.arraySync(),scaledBox=scaleBoxFromPrediction(face3,scaleFactor),boxData=scaleBox.arraySync(),probabilityData=face3.probability.arraySync(),anchor=face3.anchor,[scaleFactorX,scaleFactorY]=scaleFactor,scaledLandmarks=landmarkData.map(landmark=>[(landmark[0]+anchor[0])*scaleFactorX,(landmark[1]+anchor[1])*scaleFactorY]),normalizedFace={topLeft:boxData.slice(0,2),bottomRight:boxData.slice(2),landmarks:scaledLandmarks,probability:probabilityData};disposeBox(face3.box),face3.landmarks.dispose(),face3.probability.dispose(),scaledBox.dispose(),faces.push(normalizedFace)}return faces}};async function load3(config2){let blazeface=await tf5.loadGraphModel(config2.face.detector.modelPath,{fromTFHub:config2.face.detector.modelPath.includes("tfhub.dev")}),model=new BlazeFaceModel(blazeface,config2);return log(`load model: ${config2.face.detector.modelPath.match(/\/(.*)\./)[1]}`),model}exports.load=load3;exports.BlazeFaceModel=BlazeFaceModel;exports.disposeBox=disposeBox});var require_box=__commonJS(exports=>{var tf5=__toModule(require_tfjs_esm());function scaleBoxCoordinates2(box,factor){let startPoint=[box.startPoint[0]*factor[0],box.startPoint[1]*factor[1]],endPoint=[box.endPoint[0]*factor[0],box.endPoint[1]*factor[1]];return{startPoint,endPoint}}exports.scaleBoxCoordinates=scaleBoxCoordinates2;function getBoxSize2(box){return[Math.abs(box.endPoint[0]-box.startPoint[0]),Math.abs(box.endPoint[1]-box.startPoint[1])]}exports.getBoxSize=getBoxSize2;function getBoxCenter2(box){return[box.startPoint[0]+(box.endPoint[0]-box.startPoint[0])/2,box.startPoint[1]+(box.endPoint[1]-box.startPoint[1])/2]}exports.getBoxCenter=getBoxCenter2;function cutBoxFromImageAndResize2(box,image5,cropSize){let h=image5.shape[1],w=image5.shape[2],boxes=[[box.startPoint[1]/h,box.startPoint[0]/w,box.endPoint[1]/h,box.endPoint[0]/w]];return tf5.image.cropAndResize(image5,boxes,[0],cropSize)}exports.cutBoxFromImageAndResize=cutBoxFromImageAndResize2;function enlargeBox2(box,factor=1.5){let center=getBoxCenter2(box),size=getBoxSize2(box),newHalfSize=[factor*size[0]/2,factor*size[1]/2],startPoint=[center[0]-newHalfSize[0],center[1]-newHalfSize[1]],endPoint=[center[0]+newHalfSize[0],center[1]+newHalfSize[1]];return{startPoint,endPoint,landmarks:box.landmarks}}exports.enlargeBox=enlargeBox2;function squarifyBox2(box){let centers=getBoxCenter2(box),size=getBoxSize2(box),halfSize=Math.max(...size)/2,startPoint=[centers[0]-halfSize,centers[1]-halfSize],endPoint=[centers[0]+halfSize,centers[1]+halfSize];return{startPoint,endPoint,landmarks:box.landmarks}}exports.squarifyBox=squarifyBox2});var require_util=__commonJS(exports=>{exports.IDENTITY_MATRIX=[[1,0,0],[0,1,0],[0,0,1]];function normalizeRadians2(angle){return angle-2*Math.PI*Math.floor((angle+Math.PI)/(2*Math.PI))}exports.normalizeRadians=normalizeRadians2;function computeRotation2(point1,point2){let radians=Math.PI/2-Math.atan2(-(point2[1]-point1[1]),point2[0]-point1[0]);return normalizeRadians2(radians)}exports.computeRotation=computeRotation2;function radToDegrees(rad){return rad*180/Math.PI}exports.radToDegrees=radToDegrees;function buildTranslationMatrix2(x,y){return[[1,0,x],[0,1,y],[0,0,1]]}function dot2(v1,v2){let product=0;for(let i=0;i{var 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,378,379,397,288,361,454,356,70,63,105,66,107,336,296,334,293,300,168,6,195,4,98,97,2,326,327,33,160,158,133,153,144,362,385,387,263,373,380,57,40,37,0,267,270,287,321,314,17,84,91,78,81,13,311,308,402,14,178],VTX33=[33,133,362,263,1,62,308,159,145,386,374,6,102,331,2,13,14,70,105,107,336,334,300,54,10,284,50,280,234,454,58,288,152],VTX7=[33,133,362,263,1,78,308];exports.MESH_ANNOTATIONS=MESH_ANNOTATIONS;exports.MESH_TO_IRIS_INDICES_MAP=MESH_TO_IRIS_INDICES_MAP;exports.TRI468=TRI468;exports.TRI68=TRI68;exports.TRI33=TRI33;exports.TRI7=TRI7;exports.UV468=UV468;exports.UV68=VTX68.map(x=>UV468[x]);exports.UV33=VTX33.map(x=>UV468[x]);exports.UV7=VTX7.map(x=>UV468[x])});var require_facepipeline=__commonJS(exports=>{var tf5=__toModule(require_tfjs_esm()),bounding=__toModule(require_box()),util=__toModule(require_util()),coords=__toModule(require_coords()),LANDMARKS_COUNT=468,MESH_MOUTH_INDEX=13,MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES=[MESH_MOUTH_INDEX,coords.MESH_ANNOTATIONS.midwayBetweenEyes[0]],BLAZEFACE_MOUTH_INDEX=3,BLAZEFACE_NOSE_INDEX=2,BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES=[BLAZEFACE_MOUTH_INDEX,BLAZEFACE_NOSE_INDEX],LEFT_EYE_OUTLINE=coords.MESH_ANNOTATIONS.leftEyeLower0,LEFT_EYE_BOUNDS=[LEFT_EYE_OUTLINE[0],LEFT_EYE_OUTLINE[LEFT_EYE_OUTLINE.length-1]],RIGHT_EYE_OUTLINE=coords.MESH_ANNOTATIONS.rightEyeLower0,RIGHT_EYE_BOUNDS=[RIGHT_EYE_OUTLINE[0],RIGHT_EYE_OUTLINE[RIGHT_EYE_OUTLINE.length-1]],IRIS_UPPER_CENTER_INDEX=3,IRIS_LOWER_CENTER_INDEX=4,IRIS_IRIS_INDEX=71,IRIS_NUM_COORDINATES=76;function replaceRawCoordinates(rawCoords,newCoords,prefix,keys){for(let i=0;i[scaleFactor[0]*(coord[0]-this.meshWidth/2),scaleFactor[1]*(coord[1]-this.meshHeight/2),coord[2]]),coordsRotationMatrix=angle!==0?util.buildRotationMatrix(angle,[0,0]):util.IDENTITY_MATRIX,coordsRotated=angle!==0?coordsScaled.map(coord=>[...util.rotatePoint(coord,coordsRotationMatrix),coord[2]]):coordsScaled,inverseRotationMatrix=angle!==0?util.invertTransformMatrix(rotationMatrix):util.IDENTITY_MATRIX,boxCenter=[...bounding.getBoxCenter({startPoint:box.startPoint,endPoint:box.endPoint}),1];return coordsRotated.map(coord=>[coord[0]+util.dot(boxCenter,inverseRotationMatrix[0]),coord[1]+util.dot(boxCenter,inverseRotationMatrix[1]),coord[2]])}getLeftToRightEyeDepthDifference(rawCoords){let leftEyeZ=rawCoords[LEFT_EYE_BOUNDS[0]][2],rightEyeZ=rawCoords[RIGHT_EYE_BOUNDS[0]][2];return leftEyeZ-rightEyeZ}getEyeBox(rawCoords,face3,eyeInnerCornerIndex,eyeOuterCornerIndex,flip=!1){let box=bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex],rawCoords[eyeOuterCornerIndex]]),this.irisEnlarge)),boxSize=bounding.getBoxSize(box),crop=tf5.image.cropAndResize(face3,[[box.startPoint[1]/this.meshHeight,box.startPoint[0]/this.meshWidth,box.endPoint[1]/this.meshHeight,box.endPoint[0]/this.meshWidth]],[0],[this.irisSize,this.irisSize]);return flip&&(crop=tf5.image.flipLeftRight(crop)),{box,boxSize,crop}}getEyeCoords(eyeData,eyeBox,eyeBoxSize,flip=!1){let eyeRawCoords=[];for(let i=0;i{let z=averageZ;return i===2?z=upperCenterZ:i===4&&(z=lowerCenterZ),[coord[0],coord[1],z]})}async predict(input,config2){let useFreshBox=!1,detector;if((this.skipped===0||this.skipped>config2.face.detector.skipFrames||!config2.face.mesh.enabled||!config2.videoOptimized)&&(detector=await this.boundingBoxDetector.getBoundingBoxes(input),this.skipped=0),config2.videoOptimized&&this.skipped++,detector&&detector.boxes&&detector.boxes.length>0&&(!config2.face.mesh.enabled||detector.boxes.length!==this.detectedFaces&&this.detectedFaces!==config2.face.detector.maxFaces)){this.storedBoxes=[],this.detectedFaces=0;for(let possible of detector.boxes)this.storedBoxes.push({startPoint:possible.box.startPoint.dataSync(),endPoint:possible.box.endPoint.dataSync(),landmarks:possible.landmarks,confidence:possible.confidence});this.storedBoxes.length>0&&(useFreshBox=!0)}if(useFreshBox){if(!detector||!detector.boxes||detector.boxes.length===0)return this.storedBoxes=[],this.detectedFaces=0,null;for(let i=0;i{prediction.box.startPoint.dispose(),prediction.box.endPoint.dispose(),prediction.landmarks.dispose()});let results=tf5.tidy(()=>this.storedBoxes.map((box,i)=>{let face3,angle=0,rotationMatrix;if(config2.face.detector.rotation){let[indexOfMouth,indexOfForehead]=box.landmarks.length>=LANDMARKS_COUNT?MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES:BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES;angle=util.computeRotation(box.landmarks[indexOfMouth],box.landmarks[indexOfForehead]);let faceCenter=bounding.getBoxCenter({startPoint:box.startPoint,endPoint:box.endPoint}),faceCenterNormalized=[faceCenter[0]/input.shape[2],faceCenter[1]/input.shape[1]],rotatedImage=tf5.image.rotateWithOffset(input,angle,0,faceCenterNormalized);rotationMatrix=util.buildRotationMatrix(-angle,faceCenter),face3=bounding.cutBoxFromImageAndResize({startPoint:box.startPoint,endPoint:box.endPoint},rotatedImage,[this.meshHeight,this.meshWidth]).div(255)}else{rotationMatrix=util.IDENTITY_MATRIX;let cloned=input.clone();face3=bounding.cutBoxFromImageAndResize({startPoint:box.startPoint,endPoint:box.endPoint},cloned,[this.meshHeight,this.meshWidth]).div(255)}if(!config2.face.mesh.enabled)return{coords:null,box,faceConfidence:null,confidence:box.confidence,image:face3};let[,confidence,contourCoords]=this.meshDetector.predict(face3),confidenceVal=confidence.dataSync()[0];if(confidenceVala!==null),this.detectedFaces=results.length,results}calculateLandmarksBoundingBox(landmarks){let xs=landmarks.map(d=>d[0]),ys=landmarks.map(d=>d[1]),startPoint=[Math.min(...xs),Math.min(...ys)],endPoint=[Math.max(...xs),Math.max(...ys)];return{startPoint,endPoint,landmarks}}};exports.Pipeline=Pipeline});var require_facemesh=__commonJS(exports=>{var tf5=__toModule(require_tfjs_esm()),blazeface=__toModule(require_blazeface()),facepipeline=__toModule(require_facepipeline()),coords=__toModule(require_coords()),MediaPipeFaceMesh=class{constructor(blazeFace,blazeMeshModel,irisModel,config2){this.facePipeline=new facepipeline.Pipeline(blazeFace,blazeMeshModel,irisModel,config2),this.config=config2}async estimateFaces(input,config2){let predictions=await this.facePipeline.predict(input,config2),results=[];for(let prediction of predictions||[]){if(prediction.isDisposedInternal)continue;let mesh=prediction.coords?prediction.coords.arraySync():null,meshRaw=prediction.rawCoords,annotations={};if(mesh&&mesh.length>0)for(let key of Object.keys(coords.MESH_ANNOTATIONS))annotations[key]=coords.MESH_ANNOTATIONS[key].map(index=>mesh[index]);let boxRaw=config2.face.mesh.returnRawData&&prediction.box?{topLeft:prediction.box.startPoint,bottomRight:prediction.box.endPoint}:null,box=prediction.box?[Math.max(0,prediction.box.startPoint[0]),Math.max(0,prediction.box.startPoint[1]),Math.min(input.shape[2],prediction.box.endPoint[0])-prediction.box.startPoint[0],Math.min(input.shape[1],prediction.box.endPoint[1])-prediction.box.startPoint[1]]:0;results.push({confidence:prediction.confidence||0,box,mesh,boxRaw,meshRaw,annotations,image:prediction.image?tf5.clone(prediction.image):null}),prediction.coords&&prediction.coords.dispose(),prediction.image&&prediction.image.dispose()}return results}},faceModels=[null,null,null];async function load3(config2){faceModels=await Promise.all([!faceModels[0]&&config2.face.enabled?blazeface.load(config2):null,!faceModels[1]&&config2.face.mesh.enabled?tf5.loadGraphModel(config2.face.mesh.modelPath,{fromTFHub:config2.face.mesh.modelPath.includes("tfhub.dev")}):null,!faceModels[2]&&config2.face.iris.enabled?tf5.loadGraphModel(config2.face.iris.modelPath,{fromTFHub:config2.face.iris.modelPath.includes("tfhub.dev")}):null]);let faceMesh=new MediaPipeFaceMesh(faceModels[0],faceModels[1],faceModels[2],config2);return config2.face.mesh.enabled&&log(`load model: ${config2.face.mesh.modelPath.match(/\/(.*)\./)[1]}`),config2.face.iris.enabled&&log(`load model: ${config2.face.iris.modelPath.match(/\/(.*)\./)[1]}`),faceMesh}exports.load=load3;exports.MediaPipeFaceMesh=MediaPipeFaceMesh;exports.triangulation=coords.TRI468});var require_profile=__commonJS(exports=>{var profileData={};function profile2(name,data3){if(!data3||!data3.kernels)return;let maxResults=5,time=data3.kernels.filter(a=>a.kernelTimeMs>0).reduce((a,b)=>a+=b.kernelTimeMs,0),slowest=data3.kernels.map((a,i)=>(a.id=i,a)).filter(a=>a.kernelTimeMs>0).sort((a,b)=>b.kernelTimeMs-a.kernelTimeMs),largest=data3.kernels.map((a,i)=>(a.id=i,a)).filter(a=>a.totalBytesSnapshot>0).sort((a,b)=>b.totalBytesSnapshot-a.totalBytesSnapshot);slowest.length>maxResults&&(slowest.length=maxResults),largest.length>maxResults&&(largest.length=maxResults);let res={newBytes:data3.newBytes,newTensors:data3.newTensors,peakBytes:data3.peakBytes,numKernelOps:data3.kernels.length,timeKernelOps:time,slowestKernelOps:slowest,largestKernelOps:largest};profileData[name]=res,log("Human profiler",name,res)}exports.run=profile2});var require_age=__commonJS(exports=>{var tf5=__toModule(require_tfjs_esm()),profile2=__toModule(require_profile()),models2={},last={age:0},skipped=Number.MAX_SAFE_INTEGER;async function load3(config2){return models2.age||(models2.age=await tf5.loadGraphModel(config2.face.age.modelPath),log(`load model: ${config2.face.age.modelPath.match(/\/(.*)\./)[1]}`)),models2.age}async function predict3(image5,config2){return models2.age?skipped0?(skipped++,last):(config2.videoOptimized?skipped=0:skipped=Number.MAX_SAFE_INTEGER,new Promise(async resolve=>{let resize=tf5.image.resizeBilinear(image5,[config2.face.age.inputSize,config2.face.age.inputSize],!1),enhance=tf5.mul(resize,[255]);tf5.dispose(resize);let ageT,obj={};if(!config2.profile)config2.face.age.enabled&&(ageT=await models2.age.predict(enhance));else{let profileAge=config2.face.age.enabled?await tf5.profile(()=>models2.age.predict(enhance)):{};ageT=profileAge.result.clone(),profileAge.result.dispose(),profile2.run("age",profileAge)}if(enhance.dispose(),ageT){let data3=ageT.dataSync();obj.age=Math.trunc(10*data3[0])/10}ageT.dispose(),last=obj,resolve(obj)})):null}exports.predict=predict3;exports.load=load3});var require_gender=__commonJS(exports=>{var tf5=__toModule(require_tfjs_esm()),profile2=__toModule(require_profile()),models2={},last={gender:""},skipped=Number.MAX_SAFE_INTEGER,alternative=!1,rgb=[.2989,.587,.114];async function load3(config2){return models2.gender||(models2.gender=await tf5.loadGraphModel(config2.face.gender.modelPath),alternative=models2.gender.inputs[0].shape[3]===1,log(`load model: ${config2.face.gender.modelPath.match(/\/(.*)\./)[1]}`)),models2.gender}async function predict3(image5,config2){return models2.gender?skipped{let resize=tf5.image.resizeBilinear(image5,[config2.face.gender.inputSize,config2.face.gender.inputSize],!1),enhance;alternative?enhance=tf5.tidy(()=>{let[red,green,blue]=tf5.split(resize,3,3),redNorm=tf5.mul(red,rgb[0]),greenNorm=tf5.mul(green,rgb[1]),blueNorm=tf5.mul(blue,rgb[2]);return tf5.addN([redNorm,greenNorm,blueNorm]).sub(.5).mul(2)}):enhance=tf5.mul(resize,[255]),tf5.dispose(resize);let genderT,obj={};if(!config2.profile)config2.face.gender.enabled&&(genderT=await models2.gender.predict(enhance));else{let profileGender=config2.face.gender.enabled?await tf5.profile(()=>models2.gender.predict(enhance)):{};genderT=profileGender.result.clone(),profileGender.result.dispose(),profile2.run("gender",profileGender)}if(enhance.dispose(),genderT){let data3=genderT.dataSync();if(alternative){let confidence=Math.trunc(100*Math.abs(data3[0]-data3[1]))/100;confidence>config2.face.gender.minConfidence&&(obj.gender=data3[0]>data3[1]?"female":"male",obj.confidence=confidence)}else{let confidence=Math.trunc(200*Math.abs(data3[0]-.5))/100;confidence>config2.face.gender.minConfidence&&(obj.gender=data3[0]<=.5?"female":"male",obj.confidence=Math.min(.99,confidence))}}genderT.dispose(),last=obj,resolve(obj)})):null}exports.predict=predict3;exports.load=load3});var require_emotion=__commonJS(exports=>{var tf5=__toModule(require_tfjs_esm()),profile2=__toModule(require_profile()),annotations=["angry","disgust","fear","happy","sad","surprise","neutral"],models2={},last=[],skipped=Number.MAX_SAFE_INTEGER,rgb=[.2989,.587,.114],scale=1;async function load3(config2){return models2.emotion||(models2.emotion=await tf5.loadGraphModel(config2.face.emotion.modelPath),log(`load model: ${config2.face.emotion.modelPath.match(/\/(.*)\./)[1]}`)),models2.emotion}async function predict3(image5,config2){return models2.emotion?skipped0?(skipped++,last):(config2.videoOptimized?skipped=0:skipped=Number.MAX_SAFE_INTEGER,new Promise(async resolve=>{let resize=tf5.image.resizeBilinear(image5,[config2.face.emotion.inputSize,config2.face.emotion.inputSize],!1),[red,green,blue]=tf5.split(resize,3,3);resize.dispose();let redNorm=tf5.mul(red,rgb[0]),greenNorm=tf5.mul(green,rgb[1]),blueNorm=tf5.mul(blue,rgb[2]);red.dispose(),green.dispose(),blue.dispose();let grayscale=tf5.addN([redNorm,greenNorm,blueNorm]);redNorm.dispose(),greenNorm.dispose(),blueNorm.dispose();let normalize=tf5.tidy(()=>grayscale.sub(.5).mul(2));grayscale.dispose();let obj=[];if(config2.face.emotion.enabled){let data3;if(config2.profile){let profileData=await tf5.profile(()=>models2.emotion.predict(normalize));data3=profileData.result.dataSync(),profileData.result.dispose(),profile2.run("emotion",profileData)}else{let emotionT=await models2.emotion.predict(normalize);data3=emotionT.dataSync(),tf5.dispose(emotionT)}for(let i=0;iconfig2.face.emotion.minConfidence&&obj.push({score:Math.min(.99,Math.trunc(100*scale*data3[i])/100),emotion:annotations[i]});obj.sort((a,b)=>b.score-a.score)}normalize.dispose(),last=obj,resolve(obj)})):null}exports.predict=predict3;exports.load=load3});var require_embedding=__commonJS(exports=>{var tf5=__toModule(require_tfjs_esm()),profile2=__toModule(require_profile()),models2={};async function load3(config2){return models2.embedding||(models2.embedding=await tf5.loadGraphModel(config2.face.embedding.modelPath),log(`load model: ${config2.face.embedding.modelPath.match(/\/(.*)\./)[1]}`)),models2.embedding}function simmilarity2(embedding1,embedding2){if((embedding1==null?void 0:embedding1.length)!==(embedding2==null?void 0:embedding2.length))return 0;let order=2,distance=10*embedding1.map((val,i)=>val-embedding2[i]).reduce((dist,diff)=>dist+diff**order,0)**(1/order);return Math.trunc(1e3*(1-distance))/1e3}async function predict3(image5,config2){return models2.embedding?new Promise(async resolve=>{let resize=tf5.image.resizeBilinear(image5,[config2.face.embedding.inputSize,config2.face.embedding.inputSize],!1),data3=[];if(config2.face.embedding.enabled)if(config2.profile){let profileData=await tf5.profile(()=>models2.embedding.predict({img_inputs:resize}));data3=[...profileData.result.dataSync()],profileData.result.dispose(),profile2.run("emotion",profileData)}else{let embeddingT=await models2.embedding.predict({img_inputs:resize});data3=[...embeddingT.dataSync()],tf5.dispose(embeddingT)}resize.dispose(),resolve(data3)}):null}exports.predict=predict3;exports.simmilarity=simmilarity2;exports.load=load3});var require_modelBase=__commonJS(exports=>{var tf5=__toModule(require_tfjs_esm()),imageNetMean=[-123.15,-115.9,-103.06];function nameOutputResultsMobileNet(results){let[offsets,heatmap,displacementFwd,displacementBwd]=results;return{offsets,heatmap,displacementFwd,displacementBwd}}function nameOutputResultsResNet(results){let[displacementFwd,displacementBwd,offsets,heatmap]=results;return{offsets,heatmap,displacementFwd,displacementBwd}}var BaseModel=class{constructor(model){this.model=model}predict(input,config2){return tf5.tidy(()=>{let asBatch=(config2.body.modelType==="ResNet"?input.toFloat().add(imageNetMean):input.toFloat().div(127.5).sub(1)).expandDims(0),results3d=this.model.predict(asBatch).map(y=>y.squeeze([0])),namedResults=config2.body.modelType==="ResNet"?nameOutputResultsResNet(results3d):nameOutputResultsMobileNet(results3d);return{heatmapScores:namedResults.heatmap.sigmoid(),offsets:namedResults.offsets,displacementFwd:namedResults.displacementFwd,displacementBwd:namedResults.displacementBwd}})}dispose(){this.model.dispose()}};exports.BaseModel=BaseModel});var require_heapSort=__commonJS(exports=>{function half(k){return Math.floor(k/2)}var MaxHeap=class{constructor(maxSize,getElementValue){this.priorityQueue=new Array(maxSize),this.numberOfElements=-1,this.getElementValue=getElementValue}enqueue(x){this.priorityQueue[++this.numberOfElements]=x,this.swim(this.numberOfElements)}dequeue(){let max=this.priorityQueue[0];return this.exchange(0,this.numberOfElements--),this.sink(0),this.priorityQueue[this.numberOfElements+1]=null,max}empty(){return this.numberOfElements===-1}size(){return this.numberOfElements+1}all(){return this.priorityQueue.slice(0,this.numberOfElements+1)}max(){return this.priorityQueue[0]}swim(k){for(;k>0&&this.less(half(k),k);)this.exchange(k,half(k)),k=half(k)}sink(k){for(;2*k<=this.numberOfElements;){let j=2*k;if(j{var heapSort=__toModule(require_heapSort());function scoreIsMaximumInLocalWindow(keypointId,score,heatmapY,heatmapX,localMaximumRadius,scores){let[height,width]=scores.shape,localMaximum=!0,yStart=Math.max(heatmapY-localMaximumRadius,0),yEnd=Math.min(heatmapY+localMaximumRadius+1,height);for(let yCurrent=yStart;yCurrentscore){localMaximum=!1;break}if(!localMaximum)break}return localMaximum}function buildPartWithScoreQueue(scoreThreshold,localMaximumRadius,scores){let[height,width,numKeypoints]=scores.shape,queue=new heapSort.MaxHeap(height*width*numKeypoints,({score})=>score);for(let heatmapY=0;heatmapY{exports.partNames=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"];exports.NUM_KEYPOINTS=exports.partNames.length;exports.partIds=exports.partNames.reduce((result,jointName,i)=>(result[jointName]=i,result),{});var connectedPartNames=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]];exports.connectedPartIndices=connectedPartNames.map(([jointNameA,jointNameB])=>[exports.partIds[jointNameA],exports.partIds[jointNameB]]);exports.poseChain=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];exports.partChannels=["left_face","right_face","right_upper_leg_front","right_lower_leg_back","right_upper_leg_back","left_lower_leg_front","left_upper_leg_front","left_upper_leg_back","left_lower_leg_back","right_feet","right_lower_leg_front","left_feet","torso_front","torso_back","right_upper_arm_front","right_upper_arm_back","right_lower_arm_back","left_lower_arm_front","left_upper_arm_front","left_upper_arm_back","left_lower_arm_back","right_hand","right_lower_arm_front","left_hand"]});var require_vectors=__commonJS(exports=>{var kpt=__toModule(require_keypoints());function getOffsetPoint(y,x,keypoint,offsets){return{y:offsets.get(y,x,keypoint),x:offsets.get(y,x,keypoint+kpt.NUM_KEYPOINTS)}}exports.getOffsetPoint=getOffsetPoint;function getImageCoords(part,outputStride,offsets){let{heatmapY,heatmapX,id:keypoint}=part,{y,x}=getOffsetPoint(heatmapY,heatmapX,keypoint,offsets);return{x:part.heatmapX*outputStride+x,y:part.heatmapY*outputStride+y}}exports.getImageCoords=getImageCoords;function fillArray(element,size){let result=new Array(size);for(let i=0;imax?max:a}exports.clamp=clamp;function squaredDistance(y1,x1,y2,x2){let dy=y2-y1,dx=x2-x1;return dy*dy+dx*dx}exports.squaredDistance=squaredDistance;function addVectors(a,b){return{x:a.x+b.x,y:a.y+b.y}}exports.addVectors=addVectors;function clampVector(a,min,max){return{y:clamp(a.y,min,max),x:clamp(a.x,min,max)}}exports.clampVector=clampVector});var require_decoders=__commonJS(exports=>{var tf5=__toModule(require_tfjs_esm()),kpt=__toModule(require_keypoints());function getPointsConfidence(heatmapScores,heatMapCoords){let numKeypoints=heatMapCoords.shape[0],result=new Float32Array(numKeypoints);for(let keypoint=0;keypointheatMapCoordsBuffer.toTensor().mul(tf5.scalar(outputStride,"int32")).toFloat().add(getOffsetVectors(heatMapCoordsBuffer,offsetsBuffer)))}exports.getOffsetPoints=getOffsetPoints;function mod(a,b){return tf5.tidy(()=>{let floored=a.div(tf5.scalar(b,"int32"));return a.sub(floored.mul(tf5.scalar(b,"int32")))})}function argmax2d(inputs){let[height,width,depth]=inputs.shape;return tf5.tidy(()=>{let coords=inputs.reshape([height*width,depth]).argMax(0),yCoords=coords.div(tf5.scalar(width,"int32")).expandDims(1),xCoords=mod(coords,width).expandDims(1);return tf5.concat([yCoords,xCoords],1)})}exports.argmax2d=argmax2d});var require_decodePose=__commonJS(exports=>{var keypoints=__toModule(require_keypoints()),vectors=__toModule(require_vectors()),decoders=__toModule(require_decoders()),parentChildrenTuples=keypoints.poseChain.map(([parentJoinName,childJoinName])=>[keypoints.partIds[parentJoinName],keypoints.partIds[childJoinName]]),parentToChildEdges=parentChildrenTuples.map(([,childJointId])=>childJointId),childToParentEdges=parentChildrenTuples.map(([parentJointId])=>parentJointId);function getDisplacement(edgeId,point,displacements){let numEdges=displacements.shape[2]/2;return{y:displacements.get(point.y,point.x,edgeId),x:displacements.get(point.y,point.x,numEdges+edgeId)}}function getStridedIndexNearPoint(point,outputStride,height,width){return{y:vectors.clamp(Math.round(point.y/outputStride),0,height-1),x:vectors.clamp(Math.round(point.x/outputStride),0,width-1)}}function traverseToTargetKeypoint(edgeId,sourceKeypoint,targetKeypointId,scoresBuffer,offsets,outputStride,displacements,offsetRefineStep=2){let[height,width]=scoresBuffer.shape,sourceKeypointIndices=getStridedIndexNearPoint(sourceKeypoint.position,outputStride,height,width),displacement=getDisplacement(edgeId,sourceKeypointIndices,displacements),targetKeypoint=vectors.addVectors(sourceKeypoint.position,displacement);for(let i=0;i=0;--edge){let sourceKeypointId=parentToChildEdges[edge],targetKeypointId=childToParentEdges[edge];instanceKeypoints[sourceKeypointId]&&!instanceKeypoints[targetKeypointId]&&(instanceKeypoints[targetKeypointId]=traverseToTargetKeypoint(edge,instanceKeypoints[sourceKeypointId],targetKeypointId,scores,offsets,outputStride,displacementsBwd))}for(let edge=0;edge(totalScore+=score,{position:{y:offsetPointsBuffer.get(i,0),x:offsetPointsBuffer.get(i,1)},part:keypoints.partNames[i],score})),filteredKeypoints=instanceKeypoints.filter(kpt=>kpt.score>config2.body.scoreThreshold);return heatmapValues.dispose(),offsetPoints.dispose(),{keypoints:filteredKeypoints,score:totalScore/instanceKeypoints.length}}exports.decodeSinglePose=decodeSinglePose});var require_decodeMultiple=__commonJS(exports=>{var buildParts=__toModule(require_buildParts()),decodePose=__toModule(require_decodePose()),vectors=__toModule(require_vectors()),kLocalMaximumRadius=1;function withinNmsRadiusOfCorrespondingPoint(poses,squaredNmsRadius,{x,y},keypointId){return poses.some(({keypoints})=>{let correspondingKeypoint=keypoints[keypointId].position;return vectors.squaredDistance(y,x,correspondingKeypoint.y,correspondingKeypoint.x)<=squaredNmsRadius})}function getInstanceScore(existingPoses,squaredNmsRadius,instanceKeypoints){return instanceKeypoints.reduce((result,{position,score},keypointId)=>(withinNmsRadiusOfCorrespondingPoint(existingPoses,squaredNmsRadius,position,keypointId)||(result+=score),result),0)/instanceKeypoints.length}function decodeMultiplePoses(scoresBuffer,offsetsBuffer,displacementsFwdBuffer,displacementsBwdBuffer,config2){let poses=[],queue=buildParts.buildPartWithScoreQueue(config2.body.scoreThreshold,kLocalMaximumRadius,scoresBuffer),squaredNmsRadius=config2.body.nmsRadius^2;for(;poses.lengthconfig2.body.scoreThreshold&&poses.push({keypoints,score})}return poses}exports.decodeMultiplePoses=decodeMultiplePoses});var require_util2=__commonJS(exports=>{var kpt=__toModule(require_keypoints());function eitherPointDoesntMeetConfidence(a,b,minConfidence){return a(eitherPointDoesntMeetConfidence(keypoints[leftJoint].score,keypoints[rightJoint].score,minConfidence)||result.push([keypoints[leftJoint],keypoints[rightJoint]]),result),[])}exports.getAdjacentKeyPoints=getAdjacentKeyPoints;var{NEGATIVE_INFINITY,POSITIVE_INFINITY}=Number;function getBoundingBox(keypoints){return keypoints.reduce(({maxX,maxY,minX,minY},{position:{x,y}})=>({maxX:Math.max(maxX,x),maxY:Math.max(maxY,y),minX:Math.min(minX,x),minY:Math.min(minY,y)}),{maxX:NEGATIVE_INFINITY,maxY:NEGATIVE_INFINITY,minX:POSITIVE_INFINITY,minY:POSITIVE_INFINITY})}exports.getBoundingBox=getBoundingBox;function getBoundingBoxPoints(keypoints){let{minX,minY,maxX,maxY}=getBoundingBox(keypoints);return[{x:minX,y:minY},{x:maxX,y:minY},{x:maxX,y:maxY},{x:minX,y:maxY}]}exports.getBoundingBoxPoints=getBoundingBoxPoints;async function toTensorBuffers3D(tensors){return Promise.all(tensors.map(tensor=>tensor.buffer()))}exports.toTensorBuffers3D=toTensorBuffers3D;function scalePose(pose,scaleY,scaleX){return{score:pose.score,keypoints:pose.keypoints.map(({score,part,position})=>({score,part,position:{x:position.x*scaleX,y:position.y*scaleY}}))}}exports.scalePose=scalePose;function resizeTo(image4,[targetH,targetW]){let input=image4.squeeze(0),resized=input.resizeBilinear([targetH,targetW]);return input.dispose(),resized}exports.resizeTo=resizeTo;function scaleAndFlipPoses(poses,[height,width],[inputResolutionHeight,inputResolutionWidth]){return poses.map(pose=>scalePose(pose,height/inputResolutionHeight,width/inputResolutionWidth))}exports.scaleAndFlipPoses=scaleAndFlipPoses});var require_modelPoseNet=__commonJS(exports=>{var tf5=__toModule(require_tfjs_esm()),modelBase=__toModule(require_modelBase()),decodeMultiple=__toModule(require_decodeMultiple()),decodePose=__toModule(require_decodePose()),util=__toModule(require_util2());async function estimateMultiple(input,res,config2){return new Promise(async resolve=>{let height=input.shape[1],width=input.shape[2],allTensorBuffers=await util.toTensorBuffers3D([res.heatmapScores,res.offsets,res.displacementFwd,res.displacementBwd]),scoresBuffer=allTensorBuffers[0],offsetsBuffer=allTensorBuffers[1],displacementsFwdBuffer=allTensorBuffers[2],displacementsBwdBuffer=allTensorBuffers[3],poses=await decodeMultiple.decodeMultiplePoses(scoresBuffer,offsetsBuffer,displacementsFwdBuffer,displacementsBwdBuffer,config2),scaled=util.scaleAndFlipPoses(poses,[height,width],[config2.body.inputSize,config2.body.inputSize]);resolve(scaled)})}async function estimateSingle(input,res,config2){return new Promise(async resolve=>{let height=input.shape[1],width=input.shape[2],poses=[await decodePose.decodeSinglePose(res.heatmapScores,res.offsets,config2)],scaled=util.scaleAndFlipPoses(poses,[height,width],[config2.body.inputSize,config2.body.inputSize]);resolve(scaled)})}var PoseNet=class{constructor(model){this.baseModel=model}async estimatePoses(input,config2){let resized=util.resizeTo(input,[config2.body.inputSize,config2.body.inputSize]),res=this.baseModel.predict(resized,config2),poses=config2.body.maxDetections<2?await estimateSingle(input,res,config2):await estimateMultiple(input,res,config2);return res.heatmapScores.dispose(),res.offsets.dispose(),res.displacementFwd.dispose(),res.displacementBwd.dispose(),resized.dispose(),poses}dispose(){this.baseModel.dispose()}};exports.PoseNet=PoseNet;async function load3(config2){let model=await tf5.loadGraphModel(config2.body.modelPath),mobilenet=new modelBase.BaseModel(model);return log(`load model: ${config2.body.modelPath.match(/\/(.*)\./)[1]}`),new PoseNet(mobilenet)}exports.load=load3});var require_posenet=__commonJS(exports=>{var modelPoseNet=__toModule(require_modelPoseNet()),keypoints=__toModule(require_keypoints()),util=__toModule(require_util2());exports.load=modelPoseNet.load;exports.PoseNet=modelPoseNet.PoseNet;exports.partChannels=keypoints.partChannels;exports.partIds=keypoints.partIds;exports.partNames=keypoints.partNames;exports.poseChain=keypoints.poseChain;exports.getAdjacentKeyPoints=util.getAdjacentKeyPoints;exports.getBoundingBox=util.getBoundingBox;exports.getBoundingBoxPoints=util.getBoundingBoxPoints;exports.scaleAndFlipPoses=util.scaleAndFlipPoses;exports.scalePose=util.scalePose});var require_handdetector=__commonJS(exports=>{var tf5=__toModule(require_tfjs_esm()),HandDetector=class{constructor(model,inputSize,anchorsAnnotated){this.model=model,this.anchors=anchorsAnnotated.map(anchor=>[anchor.x_center,anchor.y_center]),this.anchorsTensor=tf5.tensor2d(this.anchors),this.inputSizeTensor=tf5.tensor1d([inputSize,inputSize]),this.doubleInputSizeTensor=tf5.tensor1d([inputSize*2,inputSize*2])}normalizeBoxes(boxes){return tf5.tidy(()=>{let boxOffsets=tf5.slice(boxes,[0,0],[-1,2]),boxSizes=tf5.slice(boxes,[0,2],[-1,2]),boxCenterPoints=tf5.add(tf5.div(boxOffsets,this.inputSizeTensor),this.anchorsTensor),halfBoxSizes=tf5.div(boxSizes,this.doubleInputSizeTensor),startPoints=tf5.mul(tf5.sub(boxCenterPoints,halfBoxSizes),this.inputSizeTensor),endPoints=tf5.mul(tf5.add(boxCenterPoints,halfBoxSizes),this.inputSizeTensor);return tf5.concat2d([startPoints,endPoints],1)})}normalizeLandmarks(rawPalmLandmarks,index){return tf5.tidy(()=>{let landmarks=tf5.add(tf5.div(rawPalmLandmarks.reshape([-1,7,2]),this.inputSizeTensor),this.anchors[index]);return tf5.mul(landmarks,this.inputSizeTensor)})}async getBoxes(input,config2){let batched=this.model.predict(input),predictions=batched.squeeze();batched.dispose();let scoresT=tf5.tidy(()=>tf5.sigmoid(tf5.slice(predictions,[0,0],[-1,1])).squeeze()),scores=scoresT.dataSync(),rawBoxes=tf5.slice(predictions,[0,1],[-1,4]),boxes=this.normalizeBoxes(rawBoxes);rawBoxes.dispose();let filteredT=await tf5.image.nonMaxSuppressionAsync(boxes,scores,config2.hand.maxHands,config2.hand.iouThreshold,config2.hand.scoreThreshold),filtered=filteredT.arraySync();scoresT.dispose(),filteredT.dispose();let hands=[];for(let index of filtered)if(scores[index]>=config2.hand.minConfidence){let matchingBox=tf5.slice(boxes,[index,0],[1,-1]),rawPalmLandmarks=tf5.slice(predictions,[index,5],[1,14]),palmLandmarks=tf5.tidy(()=>this.normalizeLandmarks(rawPalmLandmarks,index).reshape([-1,2]));rawPalmLandmarks.dispose(),hands.push({box:matchingBox,palmLandmarks,confidence:scores[index]})}return predictions.dispose(),boxes.dispose(),hands}async estimateHandBounds(input,config2){let inputHeight=input.shape[1],inputWidth=input.shape[2],image5=tf5.tidy(()=>input.resizeBilinear([config2.hand.inputSize,config2.hand.inputSize]).div(127.5).sub(1)),predictions=await this.getBoxes(image5,config2);image5.dispose();let hands=[];if(!predictions||predictions.length===0)return hands;for(let prediction of predictions){let boxes=prediction.box.dataSync(),startPoint=boxes.slice(0,2),endPoint=boxes.slice(2,4),palmLandmarks=prediction.palmLandmarks.arraySync();prediction.box.dispose(),prediction.palmLandmarks.dispose(),hands.push(scaleBoxCoordinates({startPoint,endPoint,palmLandmarks,confidence:prediction.confidence},[inputWidth/config2.hand.inputSize,inputHeight/config2.hand.inputSize]))}return hands}};exports.HandDetector=HandDetector});var require_handpipeline=__commonJS(exports=>{var tf5=__toModule(require_tfjs_esm()),PALM_BOX_ENLARGE_FACTOR=5,HAND_BOX_ENLARGE_FACTOR=1.65,PALM_LANDMARK_IDS=[0,5,9,13,17,1,2],PALM_LANDMARKS_INDEX_OF_PALM_BASE=0,PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE=2,HandPipeline=class{constructor(handDetector,landmarkDetector,inputSize){this.handDetector=handDetector,this.landmarkDetector=landmarkDetector,this.inputSize=inputSize,this.storedBoxes=[],this.skipped=0,this.detectedHands=0}getBoxForPalmLandmarks(palmLandmarks,rotationMatrix){let rotatedPalmLandmarks=palmLandmarks.map(coord=>rotatePoint([...coord,1],rotationMatrix)),boxAroundPalm=this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);return enlargeBox(squarifyBox(boxAroundPalm),PALM_BOX_ENLARGE_FACTOR)}getBoxForHandLandmarks(landmarks){let boundingBox=this.calculateLandmarksBoundingBox(landmarks),boxAroundHand=enlargeBox(squarifyBox(boundingBox),HAND_BOX_ENLARGE_FACTOR);boxAroundHand.palmLandmarks=[];for(let i=0;i[scaleFactor[0]*(coord[0]-this.inputSize/2),scaleFactor[1]*(coord[1]-this.inputSize/2),scaleFactor[2]*coord[2]]),coordsRotationMatrix=buildRotationMatrix(angle,[0,0]),coordsRotated=coordsScaled.map(coord=>[...rotatePoint(coord,coordsRotationMatrix),coord[2]]),inverseRotationMatrix=invertTransformMatrix(rotationMatrix),boxCenter=[...getBoxCenter(box2),1],originalBoxCenter=[dot(boxCenter,inverseRotationMatrix[0]),dot(boxCenter,inverseRotationMatrix[1])];return coordsRotated.map(coord=>[coord[0]+originalBoxCenter[0],coord[1]+originalBoxCenter[1],coord[2]])}async estimateHands(image5,config2){let useFreshBox=!1,boxes;(this.skipped===0||this.skipped>config2.hand.skipFrames||!config2.hand.landmarks||!config2.videoOptimized)&&(boxes=await this.handDetector.estimateHandBounds(image5,config2),this.skipped=0),config2.videoOptimized&&this.skipped++,boxes&&boxes.length>0&&(boxes.length!==this.detectedHands&&this.detectedHands!==config2.hand.maxHands||!config2.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...boxes],this.storedBoxes.length>0&&(useFreshBox=!0));let hands=[];for(let i=0;i=config2.hand.minConfidence){let keypointsReshaped=tf5.reshape(keypoints,[-1,3]),rawCoords=keypointsReshaped.arraySync();keypoints.dispose(),keypointsReshaped.dispose();let coords=this.transformRawCoords(rawCoords,newBox,angle,rotationMatrix),nextBoundingBox=this.getBoxForHandLandmarks(coords);this.storedBoxes[i]=nextBoundingBox;let result={landmarks:coords,confidence,box:{topLeft:nextBoundingBox.startPoint,bottomRight:nextBoundingBox.endPoint}};hands.push(result)}else this.storedBoxes[i]=null;keypoints.dispose()}else{let enlarged=enlargeBox(squarifyBox(currentBox),HAND_BOX_ENLARGE_FACTOR),result={confidence:currentBox.confidence,box:{topLeft:enlarged.startPoint,bottomRight:enlarged.endPoint}};hands.push(result)}}return this.storedBoxes=this.storedBoxes.filter(a=>a!==null),this.detectedHands=hands.length,hands}calculateLandmarksBoundingBox(landmarks){let xs=landmarks.map(d=>d[0]),ys=landmarks.map(d=>d[1]),startPoint=[Math.min(...xs),Math.min(...ys)],endPoint=[Math.max(...xs),Math.max(...ys)];return{startPoint,endPoint}}};exports.HandPipeline=HandPipeline});var 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HandPose(handPipeline);return config2.hand.enabled&&log(`load model: ${config2.hand.detector.modelPath.match(/\/(.*)\./)[1]}`),config2.hand.landmarks&&log(`load model: ${config2.hand.skeleton.modelPath.match(/\/(.*)\./)[1]}`),handPose}exports.load=load3});var require_gesture=__commonJS(exports=>{exports.body=res=>{if(!res)return[];let gestures=[];for(let i=0;ia.part==="leftWrist"),rightWrist=res[i].keypoints.find(a=>a.part==="rightWrist"),nose=res[i].keypoints.find(a=>a.part==="nose");nose&&leftWrist&&rightWrist&&leftWrist.position.ya.part==="leftShoulder"),rightShoulder=res[i].keypoints.find(a=>a.part==="rightShoulder");leftShoulder&&rightShoulder&&gestures.push({body:i,gesture:`leaning ${leftShoulder.position.y>rightShoulder.position.y?"left":"right"}`})}return gestures};exports.face=res=>{if(!res)return[];let gestures=[];for(let i=0;i0){let eyeFacing=res[i].mesh[35][2]-res[i].mesh[263][2];Math.abs(eyeFacing)<10?gestures.push({face:i,gesture:"facing camera"}):gestures.push({face:i,gesture:`facing ${eyeFacing<0?"right":"left"}`}),Math.abs(res[i].mesh[374][1]-res[i].mesh[386][1])/Math.abs(res[i].mesh[443][1]-res[i].mesh[450][1])<.2&&gestures.push({face:i,gesture:"blink left eye"}),Math.abs(res[i].mesh[145][1]-res[i].mesh[159][1])/Math.abs(res[i].mesh[223][1]-res[i].mesh[230][1])<.2&&gestures.push({face:i,gesture:"blink right eye"});let mouthOpen=Math.min(100,500*Math.abs(res[i].mesh[13][1]-res[i].mesh[14][1])/Math.abs(res[i].mesh[10][1]-res[i].mesh[152][1]));mouthOpen>10&&gestures.push({face:i,gesture:`mouth ${Math.trunc(mouthOpen)}% open`});let chinDepth=res[i].mesh[152][2];Math.abs(chinDepth)>10&&gestures.push({face:i,gesture:`head ${chinDepth<0?"up":"down"}`})}return gestures};exports.iris=res=>{if(!res)return[];let gestures=[];for(let i=0;i{if(!res)return[];let gestures=[];for(let i=0;i0){let closest=fingers.reduce((best,a)=>best.position[2]best.position[1]{var WebGLProgram=function(gl,vertexSource,fragmentSource){let _collect=function(source,prefix,collection){let r=new RegExp("\\b"+prefix+" \\w+ (\\w+)","ig");source.replace(r,(match,name)=>(collection[name]=0,match))},_compile=function(source,type){let shader=gl.createShader(type);if(gl.shaderSource(shader,source),gl.compileShader(shader),!gl.getShaderParameter(shader,gl.COMPILE_STATUS))throw new Error("Filter: GL compile failed",gl.getShaderInfoLog(shader));return shader};this.uniform={},this.attribute={};let _vsh=_compile(vertexSource,gl.VERTEX_SHADER),_fsh=_compile(fragmentSource,gl.FRAGMENT_SHADER);if(this.id=gl.createProgram(),gl.attachShader(this.id,_vsh),gl.attachShader(this.id,_fsh),gl.linkProgram(this.id),!gl.getProgramParameter(this.id,gl.LINK_STATUS))throw new Error("Filter: GL link failed",gl.getProgramInfoLog(this.id));gl.useProgram(this.id),_collect(vertexSource,"attribute",this.attribute);for(let a in this.attribute)this.attribute[a]=gl.getAttribLocation(this.id,a);_collect(vertexSource,"uniform",this.uniform),_collect(fragmentSource,"uniform",this.uniform);for(let u in this.uniform)this.uniform[u]=gl.getUniformLocation(this.id,u)},WebGLImageFilter=function(params){params||(params={});let _drawCount=0,_sourceTexture=null,_lastInChain=!1,_currentFramebufferIndex=-1,_tempFramebuffers=[null,null],_filterChain=[],_width=-1,_height=-1,_vertexBuffer=null,_currentProgram=null,_canvas=params.canvas||document.createElement("canvas"),_shaderProgramCache={},gl=_canvas.getContext("webgl");if(!gl)throw new Error("Filter: getContext() failed");this.addFilter=function(name){let args=Array.prototype.slice.call(arguments,1),filter=_filter[name];_filterChain.push({func:filter,args})},this.reset=function(){_filterChain=[]},this.apply=function(image4){if(_resize(image4.width,image4.height),_drawCount=0,_sourceTexture||(_sourceTexture=gl.createTexture()),gl.bindTexture(gl.TEXTURE_2D,_sourceTexture),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_WRAP_S,gl.CLAMP_TO_EDGE),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_WRAP_T,gl.CLAMP_TO_EDGE),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_MIN_FILTER,gl.NEAREST),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_MAG_FILTER,gl.NEAREST),gl.texImage2D(gl.TEXTURE_2D,0,gl.RGBA,gl.RGBA,gl.UNSIGNED_BYTE,image4),_filterChain.length===0)return _draw(),_canvas;for(let i=0;i<_filterChain.length;i++){_lastInChain=i===_filterChain.length-1;let f=_filterChain[i];f.func.apply(this,f.args||[])}return _canvas};let _resize=function(width,height){if(!(width===_width&&height===_height)){if(_canvas.width=width,_width=width,_canvas.height=height,_height=height,!_vertexBuffer){let vertices=new Float32Array([-1,-1,0,1,1,-1,1,1,-1,1,0,0,-1,1,0,0,1,-1,1,1,1,1,1,0]);_vertexBuffer=gl.createBuffer(),gl.bindBuffer(gl.ARRAY_BUFFER,_vertexBuffer),gl.bufferData(gl.ARRAY_BUFFER,vertices,gl.STATIC_DRAW),gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL,!0)}gl.viewport(0,0,_width,_height),_tempFramebuffers=[null,null]}},_getTempFramebuffer=function(index){return _tempFramebuffers[index]=_tempFramebuffers[index]||_createFramebufferTexture(_width,_height),_tempFramebuffers[index]},_createFramebufferTexture=function(width,height){let fbo=gl.createFramebuffer();gl.bindFramebuffer(gl.FRAMEBUFFER,fbo);let renderbuffer=gl.createRenderbuffer();gl.bindRenderbuffer(gl.RENDERBUFFER,renderbuffer);let texture=gl.createTexture();return gl.bindTexture(gl.TEXTURE_2D,texture),gl.texImage2D(gl.TEXTURE_2D,0,gl.RGBA,width,height,0,gl.RGBA,gl.UNSIGNED_BYTE,null),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_MAG_FILTER,gl.LINEAR),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_MIN_FILTER,gl.LINEAR),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_WRAP_S,gl.CLAMP_TO_EDGE),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_WRAP_T,gl.CLAMP_TO_EDGE),gl.framebufferTexture2D(gl.FRAMEBUFFER,gl.COLOR_ATTACHMENT0,gl.TEXTURE_2D,texture,0),gl.bindTexture(gl.TEXTURE_2D,null),gl.bindFramebuffer(gl.FRAMEBUFFER,null),{fbo,texture}},_draw=function(flags){var _a,_b;let source=null,target=null,flipY=!1;_drawCount===0?source=_sourceTexture:source=(_a=_getTempFramebuffer(_currentFramebufferIndex))==null?void 0:_a.texture,_drawCount++,_lastInChain&&!(flags&DRAW.INTERMEDIATE)?(target=null,flipY=_drawCount%2==0):(_currentFramebufferIndex=(_currentFramebufferIndex+1)%2,target=(_b=_getTempFramebuffer(_currentFramebufferIndex))==null?void 0:_b.fbo),gl.bindTexture(gl.TEXTURE_2D,source),gl.bindFramebuffer(gl.FRAMEBUFFER,target),gl.uniform1f(_currentProgram.uniform.flipY,flipY?-1:1),gl.drawArrays(gl.TRIANGLES,0,6)},_compileShader=function(fragmentSource){if(_shaderProgramCache[fragmentSource])return _currentProgram=_shaderProgramCache[fragmentSource],gl.useProgram(_currentProgram.id),_currentProgram;_currentProgram=new WebGLProgram(gl,SHADER.VERTEX_IDENTITY,fragmentSource);let floatSize=Float32Array.BYTES_PER_ELEMENT,vertSize=4*floatSize;return gl.enableVertexAttribArray(_currentProgram.attribute.pos),gl.vertexAttribPointer(_currentProgram.attribute.pos,2,gl.FLOAT,!1,vertSize,0*floatSize),gl.enableVertexAttribArray(_currentProgram.attribute.uv),gl.vertexAttribPointer(_currentProgram.attribute.uv,2,gl.FLOAT,!1,vertSize,2*floatSize),_shaderProgramCache[fragmentSource]=_currentProgram,_currentProgram},DRAW={INTERMEDIATE:1},SHADER={};SHADER.VERTEX_IDENTITY=["precision highp float;","attribute vec2 pos;","attribute vec2 uv;","varying vec2 vUv;","uniform float flipY;","void main(void) {","vUv = uv;","gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);","}"].join(` `),SHADER.FRAGMENT_IDENTITY=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","void main(void) {","gl_FragColor = texture2D(texture, vUv);","}"].join(` `);let _filter={};_filter.colorMatrix=function(matrix){let m=new Float32Array(matrix);m[4]/=255,m[9]/=255,m[14]/=255,m[19]/=255;let shader=m[18]===1&&m[3]===0&&m[8]===0&&m[13]===0&&m[15]===0&&m[16]===0&&m[17]===0&&m[19]===0?_filter.colorMatrix.SHADER.WITHOUT_ALPHA:_filter.colorMatrix.SHADER.WITH_ALPHA,program=_compileShader(shader);gl.uniform1fv(program.uniform.m,m),_draw()},_filter.colorMatrix.SHADER={},_filter.colorMatrix.SHADER.WITH_ALPHA=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","uniform float m[20];","void main(void) {","vec4 c = texture2D(texture, vUv);","gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];","gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];","gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];","gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];","}"].join(` `),_filter.colorMatrix.SHADER.WITHOUT_ALPHA=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","uniform float m[20];","void main(void) {","vec4 c = texture2D(texture, vUv);","gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];","gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];","gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];","gl_FragColor.a = c.a;","}"].join(` `),_filter.brightness=function(brightness){let b=(brightness||0)+1;_filter.colorMatrix([b,0,0,0,0,0,b,0,0,0,0,0,b,0,0,0,0,0,1,0])},_filter.saturation=function(amount){let x=(amount||0)*2/3+1,y=(x-1)*-.5;_filter.colorMatrix([x,y,y,0,0,y,x,y,0,0,y,y,x,0,0,0,0,0,1,0])},_filter.desaturate=function(){_filter.saturation(-1)},_filter.contrast=function(amount){let v=(amount||0)+1,o=-128*(v-1);_filter.colorMatrix([v,0,0,0,o,0,v,0,0,o,0,0,v,0,o,0,0,0,1,0])},_filter.negative=function(){_filter.contrast(-2)},_filter.hue=function(rotation){rotation=(rotation||0)/180*Math.PI;let cos=Math.cos(rotation),sin=Math.sin(rotation),lumR=.213,lumG=.715,lumB=.072;_filter.colorMatrix([lumR+cos*(1-lumR)+sin*-lumR,lumG+cos*-lumG+sin*-lumG,lumB+cos*-lumB+sin*(1-lumB),0,0,lumR+cos*-lumR+sin*.143,lumG+cos*(1-lumG)+sin*.14,lumB+cos*-lumB+sin*-.283,0,0,lumR+cos*-lumR+sin*-(1-lumR),lumG+cos*-lumG+sin*lumG,lumB+cos*(1-lumB)+sin*lumB,0,0,0,0,0,1,0])},_filter.desaturateLuminance=function(){_filter.colorMatrix([.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,0,0,0,1,0])},_filter.sepia=function(){_filter.colorMatrix([.393,.7689999,.18899999,0,0,.349,.6859999,.16799999,0,0,.272,.5339999,.13099999,0,0,0,0,0,1,0])},_filter.brownie=function(){_filter.colorMatrix([.5997023498159715,.34553243048391263,-.2708298674538042,0,47.43192855600873,-.037703249837783157,.8609577587992641,.15059552388459913,0,-36.96841498319127,.24113635128153335,-.07441037908422492,.44972182064877153,0,-7.562075277591283,0,0,0,1,0])},_filter.vintagePinhole=function(){_filter.colorMatrix([.6279345635605994,.3202183420819367,-.03965408211312453,0,9.651285835294123,.02578397704808868,.6441188644374771,.03259127616149294,0,7.462829176470591,.0466055556782719,-.0851232987247891,.5241648018700465,0,5.159190588235296,0,0,0,1,0])},_filter.kodachrome=function(){_filter.colorMatrix([1.1285582396593525,-.3967382283601348,-.03992559172921793,0,63.72958762196502,-.16404339962244616,1.0835251566291304,-.05498805115633132,0,24.732407896706203,-.16786010706155763,-.5603416277695248,1.6014850761964943,0,35.62982807460946,0,0,0,1,0])},_filter.technicolor=function(){_filter.colorMatrix([1.9125277891456083,-.8545344976951645,-.09155508482755585,0,11.793603434377337,-.3087833385928097,1.7658908555458428,-.10601743074722245,0,-70.35205161461398,-.231103377548616,-.7501899197440212,1.847597816108189,0,30.950940869491138,0,0,0,1,0])},_filter.polaroid=function(){_filter.colorMatrix([1.438,-.062,-.062,0,0,-.122,1.378,-.122,0,0,-.016,-.016,1.483,0,0,0,0,0,1,0])},_filter.shiftToBGR=function(){_filter.colorMatrix([0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0])},_filter.convolution=function(matrix){let m=new Float32Array(matrix),pixelSizeX=1/_width,pixelSizeY=1/_height,program=_compileShader(_filter.convolution.SHADER);gl.uniform1fv(program.uniform.m,m),gl.uniform2f(program.uniform.px,pixelSizeX,pixelSizeY),_draw()},_filter.convolution.SHADER=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","uniform vec2 px;","uniform float m[9];","void main(void) {","vec4 c11 = texture2D(texture, vUv - px);","vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y));","vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y));","vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) );","vec4 c22 = texture2D(texture, vUv);","vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) );","vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) );","vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) );","vec4 c33 = texture2D(texture, vUv + px );","gl_FragColor = ","c11 * m[0] + c12 * m[1] + c22 * m[2] +","c21 * m[3] + c22 * m[4] + c23 * m[5] +","c31 * m[6] + c32 * m[7] + c33 * m[8];","gl_FragColor.a = c22.a;","}"].join(` `),_filter.detectEdges=function(){_filter.convolution.call(this,[0,1,0,1,-4,1,0,1,0])},_filter.sobelX=function(){_filter.convolution.call(this,[-1,0,1,-2,0,2,-1,0,1])},_filter.sobelY=function(){_filter.convolution.call(this,[-1,-2,-1,0,0,0,1,2,1])},_filter.sharpen=function(amount){let a=amount||1;_filter.convolution.call(this,[0,-1*a,0,-1*a,1+4*a,-1*a,0,-1*a,0])},_filter.emboss=function(size){let s=size||1;_filter.convolution.call(this,[-2*s,-1*s,0,-1*s,1,1*s,0,1*s,2*s])},_filter.blur=function(size){let blurSizeX=size/7/_width,blurSizeY=size/7/_height,program=_compileShader(_filter.blur.SHADER);gl.uniform2f(program.uniform.px,0,blurSizeY),_draw(DRAW.INTERMEDIATE),gl.uniform2f(program.uniform.px,blurSizeX,0),_draw()},_filter.blur.SHADER=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","uniform vec2 px;","void main(void) {","gl_FragColor = vec4(0.0);","gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;","gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;","gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;","gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;","gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;","gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;","gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;","gl_FragColor += texture2D(texture, vUv )*0.159576912161;","gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x, 1.0*px.y))*0.147308056121;","gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x, 2.0*px.y))*0.115876621105;","gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x, 3.0*px.y))*0.0776744219933;","gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x, 4.0*px.y))*0.0443683338718;","gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x, 5.0*px.y))*0.0215963866053;","gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794;","gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265;","}"].join(` `),_filter.pixelate=function(size){let blurSizeX=size/_width,blurSizeY=size/_height,program=_compileShader(_filter.pixelate.SHADER);gl.uniform2f(program.uniform.size,blurSizeX,blurSizeY),_draw()},_filter.pixelate.SHADER=["precision highp float;","varying vec2 vUv;","uniform vec2 size;","uniform sampler2D texture;","vec2 pixelate(vec2 coord, vec2 size) {","return floor( coord / size ) * size;","}","void main(void) {","gl_FragColor = vec4(0.0);","vec2 coord = pixelate(vUv, size);","gl_FragColor += texture2D(texture, coord);","}"].join(` -`)};exports.Canvas=WebGLImageFilter});var require_image=__commonJS(exports=>{var tf4=__toModule(require_tfjs_esm()),fxImage=__toModule(require_imagefx()),inCanvas=null,outCanvas=null;function process3(input,config2){let tensor;if(input instanceof tf4.Tensor)tensor=tf4.clone(input);else{let originalWidth=input.naturalWidth||input.videoWidth||input.width||input.shape&&input.shape[1]>0,originalHeight=input.naturalHeight||input.videoHeight||input.height||input.shape&&input.shape[2]>0,targetWidth=originalWidth,targetHeight=originalHeight;if(config2.filter.width>0?targetWidth=config2.filter.width:config2.filter.height>0&&(targetWidth=originalWidth*(config2.filter.height/originalHeight)),config2.filter.height>0?targetHeight=config2.filter.height:config2.filter.width>0&&(targetHeight=originalHeight*(config2.filter.width/originalWidth)),!targetWidth||!targetHeight)return log("Human: invalid input",input),null;(!inCanvas||inCanvas.width!==targetWidth||inCanvas.height!==targetHeight)&&(inCanvas=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(targetWidth,targetHeight):document.createElement("canvas"),inCanvas.width!==targetWidth&&(inCanvas.width=targetWidth),inCanvas.height!==targetHeight&&(inCanvas.height=targetHeight));let ctx=inCanvas.getContext("2d");if(input instanceof ImageData?ctx.putImageData(input,0,0):ctx.drawImage(input,0,0,originalWidth,originalHeight,0,0,inCanvas.width,inCanvas.height),config2.filter.enabled){if((!this.fx||!outCanvas||inCanvas.width!==outCanvas.width||inCanvas.height!==outCanvas.height)&&(outCanvas=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(inCanvas.width,inCanvas.height):document.createElement("canvas"),outCanvas.width!==inCanvas.width&&(outCanvas.width=inCanvas.width),outCanvas.height!==inCanvas.height&&(outCanvas.height=inCanvas.height),this.fx=tf4.ENV.flags.IS_BROWSER?new fxImage.Canvas({canvas:outCanvas}):null),!this.fx)return inCanvas;this.fx.reset(),this.fx.addFilter("brightness",config2.filter.brightness),config2.filter.contrast!==0&&this.fx.addFilter("contrast",config2.filter.contrast),config2.filter.sharpness!==0&&this.fx.addFilter("sharpen",config2.filter.sharpness),config2.filter.blur!==0&&this.fx.addFilter("blur",config2.filter.blur),config2.filter.saturation!==0&&this.fx.addFilter("saturation",config2.filter.saturation),config2.filter.hue!==0&&this.fx.addFilter("hue",config2.filter.hue),config2.filter.negative&&this.fx.addFilter("negative"),config2.filter.sepia&&this.fx.addFilter("sepia"),config2.filter.vintage&&this.fx.addFilter("brownie"),config2.filter.sepia&&this.fx.addFilter("sepia"),config2.filter.kodachrome&&this.fx.addFilter("kodachrome"),config2.filter.technicolor&&this.fx.addFilter("technicolor"),config2.filter.polaroid&&this.fx.addFilter("polaroid"),config2.filter.pixelate!==0&&this.fx.addFilter("pixelate",config2.filter.pixelate),this.fx.apply(inCanvas)}else outCanvas=inCanvas;let pixels;if(outCanvas.data){let shape=[outCanvas.height,outCanvas.width,3];pixels=tf4.tensor3d(outCanvas.data,shape,"int32")}else if(config2.backend==="webgl"||outCanvas instanceof ImageData)pixels=tf4.browser.fromPixels(outCanvas);else{let tempCanvas=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(targetWidth,targetHeight):document.createElement("canvas");tempCanvas.width=targetWidth,tempCanvas.height=targetHeight;let tempCtx=tempCanvas.getContext("2d");tempCtx==null||tempCtx.drawImage(outCanvas,0,0);let data3=tempCtx==null?void 0:tempCtx.getImageData(0,0,targetWidth,targetHeight);pixels=tf4.browser.fromPixels(data3)}let casted=pixels.toFloat();tensor=casted.expandDims(0),pixels.dispose(),casted.dispose()}return{tensor,canvas:config2.filter.return?outCanvas:null}}exports.process=process3});function log(...msg){let dt=new Date,ts=`${dt.getHours().toString().padStart(2,"0")}:${dt.getMinutes().toString().padStart(2,"0")}:${dt.getSeconds().toString().padStart(2,"0")}.${dt.getMilliseconds().toString().padStart(3,"0")}`;msg&&console.log(ts,"Human:",...msg)}var tf3=__toModule(require_tfjs_esm());var tf=__toModule(require_tfjs_esm()),config={name:"humangl",priority:99,canvas:null,width:1024,height:1024,webGLattr:{alpha:!1,antialias:!1,premultipliedAlpha:!1,preserveDrawingBuffer:!1,depth:!1,stencil:!1,failIfMajorPerformanceCaveat:!1,desynchronized:!0}};function register(){if(!tf.findBackend(config.name)){config.canvas=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(config.width,config.height):document.createElement("canvas");let gl=config.canvas.getContext("webgl2",config.webGLattr);tf.setWebGLContext(2,gl);let ctx=new tf.GPGPUContext(gl);tf.registerBackend(config.name,()=>new tf.MathBackendWebGL(ctx),config.priority),tf.getKernelsForBackend("webgl").forEach(kernelConfig=>{let newKernelConfig={...kernelConfig,backendName:config.name};tf.registerKernel(newKernelConfig)}),tf.ENV.set("WEBGL_VERSION",2),tf.ENV.set("WEBGL_MAX_TEXTURE_SIZE",gl.getParameter(gl.MAX_TEXTURE_SIZE)),tf.ENV.set("WEBGL_FORCE_F16_TEXTURES",!0),tf.ENV.set("WEBGL_PACK_DEPTHWISECONV",!0)}}var facemesh=__toModule(require_facemesh()),age=__toModule(require_age()),gender=__toModule(require_gender()),emotion=__toModule(require_emotion()),embedding=__toModule(require_embedding()),posenet=__toModule(require_posenet());var tf2=__toModule(require_tfjs_esm());function getBoxSize(box){return[Math.abs(box.endPoint[0]-box.startPoint[0]),Math.abs(box.endPoint[1]-box.startPoint[1])]}function getBoxCenter(box){return[box.startPoint[0]+(box.endPoint[0]-box.startPoint[0])/2,box.startPoint[1]+(box.endPoint[1]-box.startPoint[1])/2]}function cutBoxFromImageAndResize(box,image3,cropSize){let h=image3.shape[1],w=image3.shape[2],boxes=[[box.startPoint[1]/h,box.startPoint[0]/w,box.endPoint[1]/h,box.endPoint[0]/w]];return tf2.image.cropAndResize(image3,boxes,[0],cropSize)}function scaleBoxCoordinates(box,factor){let startPoint=[box.startPoint[0]*factor[0],box.startPoint[1]*factor[1]],endPoint=[box.endPoint[0]*factor[0],box.endPoint[1]*factor[1]],palmLandmarks=box.palmLandmarks.map(coord=>[coord[0]*factor[0],coord[1]*factor[1]]);return{startPoint,endPoint,palmLandmarks,confidence:box.confidence}}function enlargeBox(box,factor=1.5){let center=getBoxCenter(box),size=getBoxSize(box),newHalfSize=[factor*size[0]/2,factor*size[1]/2],startPoint=[center[0]-newHalfSize[0],center[1]-newHalfSize[1]],endPoint=[center[0]+newHalfSize[0],center[1]+newHalfSize[1]];return{startPoint,endPoint,palmLandmarks:box.palmLandmarks}}function squarifyBox(box){let centers=getBoxCenter(box),size=getBoxSize(box),halfSize=Math.max(...size)/2,startPoint=[centers[0]-halfSize,centers[1]-halfSize],endPoint=[centers[0]+halfSize,centers[1]+halfSize];return{startPoint,endPoint,palmLandmarks:box.palmLandmarks}}function normalizeRadians(angle){return angle-2*Math.PI*Math.floor((angle+Math.PI)/(2*Math.PI))}function computeRotation(point1,point2){let radians=Math.PI/2-Math.atan2(-(point2[1]-point1[1]),point2[0]-point1[0]);return normalizeRadians(radians)}var buildTranslationMatrix=(x,y)=>[[1,0,x],[0,1,y],[0,0,1]];function dot(v1,v2){let product=0;for(let i=0;iconfig_default});var config_default={backend:"webgl",wasmPath:"../assets/",async:!0,profile:!1,deallocate:!1,scoped:!1,videoOptimized:!0,warmup:"face",filter:{enabled:!0,width:0,height:0,return:!0,brightness:0,contrast:0,sharpness:0,blur:0,saturation:0,hue:0,negative:!1,sepia:!1,vintage:!1,kodachrome:!1,technicolor:!1,polaroid:!1,pixelate:0},gesture:{enabled:!0},face:{enabled:!0,detector:{modelPath:"../models/blazeface-back.json",inputSize:256,rotation:!1,maxFaces:10,skipFrames:11,minConfidence:.5,iouThreshold:.2,scoreThreshold:.5},mesh:{enabled:!0,modelPath:"../models/facemesh.json",inputSize:192,returnRawData:!1},iris:{enabled:!0,modelPath:"../models/iris.json",inputSize:64},age:{enabled:!0,modelPath:"../models/age-ssrnet-imdb.json",inputSize:64,skipFrames:31},gender:{enabled:!0,minConfidence:.1,modelPath:"../models/gender-ssrnet-imdb.json",inputSize:64,skipFrames:41},emotion:{enabled:!0,inputSize:64,minConfidence:.2,skipFrames:21,modelPath:"../models/emotion-large.json"},embedding:{enabled:!1,inputSize:112,modelPath:"../models/mobilefacenet.json"}},body:{enabled:!0,modelPath:"../models/posenet.json",inputSize:257,maxDetections:10,scoreThreshold:.5,nmsRadius:20,outputStride:16,modelType:"MobileNet"},hand:{enabled:!0,rotation:!1,inputSize:256,skipFrames:12,minConfidence:.1,iouThreshold:.1,scoreThreshold:.5,maxHands:1,landmarks:!0,detector:{modelPath:"../models/handdetect.json"},skeleton:{modelPath:"../models/handskeleton.json"}}};var face=` +`)};exports.Canvas=WebGLImageFilter});var require_image=__commonJS(exports=>{var tf5=__toModule(require_tfjs_esm()),fxImage=__toModule(require_imagefx()),inCanvas=null,outCanvas=null;function process3(input,config2){let tensor;if(input instanceof tf5.Tensor)tensor=tf5.clone(input);else{let originalWidth=input.naturalWidth||input.videoWidth||input.width||input.shape&&input.shape[1]>0,originalHeight=input.naturalHeight||input.videoHeight||input.height||input.shape&&input.shape[2]>0,targetWidth=originalWidth,targetHeight=originalHeight;if(config2.filter.width>0?targetWidth=config2.filter.width:config2.filter.height>0&&(targetWidth=originalWidth*(config2.filter.height/originalHeight)),config2.filter.height>0?targetHeight=config2.filter.height:config2.filter.width>0&&(targetHeight=originalHeight*(config2.filter.width/originalWidth)),!targetWidth||!targetHeight)return log("Human: invalid input",input),null;(!inCanvas||inCanvas.width!==targetWidth||inCanvas.height!==targetHeight)&&(inCanvas=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(targetWidth,targetHeight):document.createElement("canvas"),inCanvas.width!==targetWidth&&(inCanvas.width=targetWidth),inCanvas.height!==targetHeight&&(inCanvas.height=targetHeight));let ctx=inCanvas.getContext("2d");if(input instanceof ImageData?ctx.putImageData(input,0,0):ctx.drawImage(input,0,0,originalWidth,originalHeight,0,0,inCanvas.width,inCanvas.height),config2.filter.enabled){if((!this.fx||!outCanvas||inCanvas.width!==outCanvas.width||inCanvas.height!==outCanvas.height)&&(outCanvas=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(inCanvas.width,inCanvas.height):document.createElement("canvas"),outCanvas.width!==inCanvas.width&&(outCanvas.width=inCanvas.width),outCanvas.height!==inCanvas.height&&(outCanvas.height=inCanvas.height),this.fx=tf5.ENV.flags.IS_BROWSER?new fxImage.Canvas({canvas:outCanvas}):null),!this.fx)return inCanvas;this.fx.reset(),this.fx.addFilter("brightness",config2.filter.brightness),config2.filter.contrast!==0&&this.fx.addFilter("contrast",config2.filter.contrast),config2.filter.sharpness!==0&&this.fx.addFilter("sharpen",config2.filter.sharpness),config2.filter.blur!==0&&this.fx.addFilter("blur",config2.filter.blur),config2.filter.saturation!==0&&this.fx.addFilter("saturation",config2.filter.saturation),config2.filter.hue!==0&&this.fx.addFilter("hue",config2.filter.hue),config2.filter.negative&&this.fx.addFilter("negative"),config2.filter.sepia&&this.fx.addFilter("sepia"),config2.filter.vintage&&this.fx.addFilter("brownie"),config2.filter.sepia&&this.fx.addFilter("sepia"),config2.filter.kodachrome&&this.fx.addFilter("kodachrome"),config2.filter.technicolor&&this.fx.addFilter("technicolor"),config2.filter.polaroid&&this.fx.addFilter("polaroid"),config2.filter.pixelate!==0&&this.fx.addFilter("pixelate",config2.filter.pixelate),this.fx.apply(inCanvas)}else outCanvas=inCanvas;let pixels;if(outCanvas.data){let shape=[outCanvas.height,outCanvas.width,3];pixels=tf5.tensor3d(outCanvas.data,shape,"int32")}else if(config2.backend==="webgl"||outCanvas instanceof ImageData)pixels=tf5.browser.fromPixels(outCanvas);else{let tempCanvas=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(targetWidth,targetHeight):document.createElement("canvas");tempCanvas.width=targetWidth,tempCanvas.height=targetHeight;let tempCtx=tempCanvas.getContext("2d");tempCtx==null||tempCtx.drawImage(outCanvas,0,0);let data3=tempCtx==null?void 0:tempCtx.getImageData(0,0,targetWidth,targetHeight);pixels=tf5.browser.fromPixels(data3)}let casted=pixels.toFloat();tensor=casted.expandDims(0),pixels.dispose(),casted.dispose()}return{tensor,canvas:config2.filter.return?outCanvas:null}}exports.process=process3});function log(...msg){let dt=new Date,ts=`${dt.getHours().toString().padStart(2,"0")}:${dt.getMinutes().toString().padStart(2,"0")}:${dt.getSeconds().toString().padStart(2,"0")}.${dt.getMilliseconds().toString().padStart(3,"0")}`;msg&&console.log(ts,"Human:",...msg)}var tf4=__toModule(require_tfjs_esm());var tf=__toModule(require_tfjs_esm()),config={name:"humangl",priority:99,canvas:null,width:1024,height:1024,webGLattr:{alpha:!1,antialias:!1,premultipliedAlpha:!1,preserveDrawingBuffer:!1,depth:!1,stencil:!1,failIfMajorPerformanceCaveat:!1,desynchronized:!0}};function register(){if(!tf.findBackend(config.name)){config.canvas=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(config.width,config.height):document.createElement("canvas");let gl=config.canvas.getContext("webgl2",config.webGLattr);tf.setWebGLContext(2,gl);let ctx=new tf.GPGPUContext(gl);tf.registerBackend(config.name,()=>new tf.MathBackendWebGL(ctx),config.priority),tf.getKernelsForBackend("webgl").forEach(kernelConfig=>{let newKernelConfig={...kernelConfig,backendName:config.name};tf.registerKernel(newKernelConfig)}),tf.ENV.set("WEBGL_VERSION",2),tf.ENV.set("WEBGL_MAX_TEXTURE_SIZE",gl.getParameter(gl.MAX_TEXTURE_SIZE)),tf.ENV.set("WEBGL_FORCE_F16_TEXTURES",!0),tf.ENV.set("WEBGL_PACK_DEPTHWISECONV",!0)}}var facemesh=__toModule(require_facemesh()),age=__toModule(require_age()),gender=__toModule(require_gender()),emotion=__toModule(require_emotion()),embedding=__toModule(require_embedding()),posenet=__toModule(require_posenet());var tf2=__toModule(require_tfjs_esm()),models={};async function load(config2){return models.blazepose||(models.blazepose=await tf2.loadGraphModel(config2.pose.modelPath),log(`load model: ${config2.pose.modelPath.match(/\/(.*)\./)[1]}`)),models.blazepose}async function predict(image4,config2){return models.blazepose?new Promise(async resolve=>{let resize=tf2.image.resizeBilinear(image4,[config2.pose.inputSize,config2.pose.inputSize],!1),enhance=tf2.div(resize,127.5).sub(1);tf2.dispose(resize);let logits=await models.blazepose.predict(enhance);tf2.dispose(enhance),logits.map(logit=>logit.dispose()),resolve(logits)}):null}var tf3=__toModule(require_tfjs_esm());function getBoxSize(box){return[Math.abs(box.endPoint[0]-box.startPoint[0]),Math.abs(box.endPoint[1]-box.startPoint[1])]}function getBoxCenter(box){return[box.startPoint[0]+(box.endPoint[0]-box.startPoint[0])/2,box.startPoint[1]+(box.endPoint[1]-box.startPoint[1])/2]}function cutBoxFromImageAndResize(box,image4,cropSize){let h=image4.shape[1],w=image4.shape[2],boxes=[[box.startPoint[1]/h,box.startPoint[0]/w,box.endPoint[1]/h,box.endPoint[0]/w]];return tf3.image.cropAndResize(image4,boxes,[0],cropSize)}function scaleBoxCoordinates(box,factor){let startPoint=[box.startPoint[0]*factor[0],box.startPoint[1]*factor[1]],endPoint=[box.endPoint[0]*factor[0],box.endPoint[1]*factor[1]],palmLandmarks=box.palmLandmarks.map(coord=>[coord[0]*factor[0],coord[1]*factor[1]]);return{startPoint,endPoint,palmLandmarks,confidence:box.confidence}}function enlargeBox(box,factor=1.5){let center=getBoxCenter(box),size=getBoxSize(box),newHalfSize=[factor*size[0]/2,factor*size[1]/2],startPoint=[center[0]-newHalfSize[0],center[1]-newHalfSize[1]],endPoint=[center[0]+newHalfSize[0],center[1]+newHalfSize[1]];return{startPoint,endPoint,palmLandmarks:box.palmLandmarks}}function squarifyBox(box){let centers=getBoxCenter(box),size=getBoxSize(box),halfSize=Math.max(...size)/2,startPoint=[centers[0]-halfSize,centers[1]-halfSize],endPoint=[centers[0]+halfSize,centers[1]+halfSize];return{startPoint,endPoint,palmLandmarks:box.palmLandmarks}}function normalizeRadians(angle){return angle-2*Math.PI*Math.floor((angle+Math.PI)/(2*Math.PI))}function computeRotation(point1,point2){let radians=Math.PI/2-Math.atan2(-(point2[1]-point1[1]),point2[0]-point1[0]);return normalizeRadians(radians)}var buildTranslationMatrix=(x,y)=>[[1,0,x],[0,1,y],[0,0,1]];function dot(v1,v2){let product=0;for(let i=0;iconfig_default});var config_default={backend:"webgl",wasmPath:"../assets/",async:!0,profile:!1,deallocate:!1,scoped:!1,videoOptimized:!0,warmup:"face",filter:{enabled:!0,width:0,height:0,return:!0,brightness:0,contrast:0,sharpness:0,blur:0,saturation:0,hue:0,negative:!1,sepia:!1,vintage:!1,kodachrome:!1,technicolor:!1,polaroid:!1,pixelate:0},gesture:{enabled:!0},face:{enabled:!0,detector:{modelPath:"../models/blazeface-back.json",inputSize:256,rotation:!1,maxFaces:10,skipFrames:11,minConfidence:.5,iouThreshold:.2,scoreThreshold:.5},mesh:{enabled:!0,modelPath:"../models/facemesh.json",inputSize:192,returnRawData:!1},iris:{enabled:!0,modelPath:"../models/iris.json",inputSize:64},age:{enabled:!0,modelPath:"../models/age-ssrnet-imdb.json",inputSize:64,skipFrames:31},gender:{enabled:!0,minConfidence:.1,modelPath:"../models/gender-ssrnet-imdb.json",inputSize:64,skipFrames:41},emotion:{enabled:!0,inputSize:64,minConfidence:.2,skipFrames:21,modelPath:"../models/emotion-large.json"},embedding:{enabled:!1,inputSize:112,modelPath:"../models/mobilefacenet.json"}},body:{enabled:!0,modelPath:"../models/posenet.json",inputSize:257,maxDetections:10,scoreThreshold:.5,nmsRadius:20,outputStride:16,modelType:"MobileNet"},pose:{enabled:!1,scoreThreshold:.6,iouThreshold:.3,modelPath:"../models/blazepose.json",inputSize:256},hand:{enabled:!0,rotation:!1,inputSize:256,skipFrames:12,minConfidence:.1,iouThreshold:.1,scoreThreshold:.5,maxHands:1,landmarks:!0,detector:{modelPath:"../models/handdetect.json"},skeleton:{modelPath:"../models/handskeleton.json"}}};var face=` /9j/4AAQSkZJRgABAQEAYABgAAD/4QBoRXhpZgAATU0AKgAAAAgABAEaAAUAAAABAAAAPgEbAAUA AAABAAAARgEoAAMAAAABAAIAAAExAAIAAAARAAAATgAAAAAAAABgAAAAAQAAAGAAAAABcGFpbnQu bmV0IDQuMi4xMwAA/9sAQwAGBAUGBQQGBgUGBwcGCAoQCgoJCQoUDg8MEBcUGBgXFBYWGh0lHxob @@ -731,5 +731,5 @@ AAAAAAJAAAAAAAAAAAAAABAJEAAAAAAAAAAAAAAAIEoBKAAAAAAAAAAAAAAABAlAAAAAAAIAAAAA BAkBAkBAkBAlACEgMZjdjbFW8bWrEx8YWANb6Fp+bfwab+vLDKMFK9qxH5L0bAr8OPRPKz2AY7J2 SbAjYZAI2E7AIEgIEgIEgMdkSy2NgY7MdlmyNoBXsxmFuyNgVTVjNV3KjlBRNTlXTVHKCrlIqt5T lBhEMohlFerLlBjEMohMVTEARDKCITsAk2AEgAAAkAAAAAAAAAAAAAAAAAAAAAAAASAAAAAAAAD/ -2Q==`;var version="0.9.23";var now=()=>typeof performance!="undefined"?performance.now():parseInt(Number(process.hrtime.bigint())/1e3/1e3);function mergeDeep(...objects){let isObject=obj=>obj&&typeof obj=="object";return objects.reduce((prev,obj)=>(Object.keys(obj||{}).forEach(key=>{let pVal=prev[key],oVal=obj[key];Array.isArray(pVal)&&Array.isArray(oVal)?prev[key]=pVal.concat(...oVal):isObject(pVal)&&isObject(oVal)?prev[key]=mergeDeep(pVal,oVal):prev[key]=oVal}),prev),{})}var Human=class{constructor(userConfig={}){this.tf=tf3,this.version=version,this.config=mergeDeep(config_default,userConfig),this.fx=null,this.state="idle",this.numTensors=0,this.analyzeMemoryLeaks=!1,this.checkSanity=!1,this.firstRun=!0,this.perf={},this.models={facemesh:null,posenet:null,handpose:null,iris:null,age:null,gender:null,emotion:null},this.facemesh=facemesh,this.age=age,this.gender=gender,this.emotion=emotion,this.body=posenet,this.hand=handpose}profile(){return this.config.profile?profile.data:{}}analyze(...msg){if(!this.analyzeMemoryLeaks)return;let current=tf3.engine().state.numTensors,previous=this.numTensors;this.numTensors=current;let leaked=current-previous;leaked!==0&&log(...msg,leaked)}sanity(input){if(!this.checkSanity)return null;if(!input)return"input is not defined";if(tf3.ENV.flags.IS_NODE&&!(input instanceof tf3.Tensor))return"input must be a tensor";try{tf3.getBackend()}catch(e){return"backend not loaded"}return null}simmilarity(embedding1,embedding2){return this.config.face.embedding.enabled?embedding.simmilarity(embedding1,embedding2):0}async load(userConfig){this.state="load";let timeStamp=now();userConfig&&(this.config=mergeDeep(this.config,userConfig)),this.firstRun&&(log(`version: ${this.version} TensorFlow/JS version: ${tf3.version_core}`),await this.checkBackend(!0),tf3.ENV.flags.IS_BROWSER&&(log("configuration:",this.config),log("tf flags:",tf3.ENV.flags)),this.firstRun=!1),this.config.async?[this.models.facemesh,this.models.age,this.models.gender,this.models.emotion,this.models.embedding,this.models.posenet,this.models.handpose]=await Promise.all([this.models.facemesh||(this.config.face.enabled?facemesh.load(this.config):null),this.models.age||(this.config.face.enabled&&this.config.face.age.enabled?age.load(this.config):null),this.models.gender||(this.config.face.enabled&&this.config.face.gender.enabled?gender.load(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?emotion.load(this.config):null),this.models.embedding||(this.config.face.enabled&&this.config.face.embedding.enabled?embedding.load(this.config):null),this.models.posenet||(this.config.body.enabled?posenet.load(this.config):null),this.models.handpose||(this.config.hand.enabled?handpose.load(this.config):null)]):(this.config.face.enabled&&!this.models.facemesh&&(this.models.facemesh=await facemesh.load(this.config)),this.config.face.enabled&&this.config.face.age.enabled&&!this.models.age&&(this.models.age=await age.load(this.config)),this.config.face.enabled&&this.config.face.gender.enabled&&!this.models.gender&&(this.models.gender=await gender.load(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await emotion.load(this.config)),this.config.face.enabled&&this.config.face.embedding.enabled&&!this.models.embedding&&(this.models.embedding=await embedding.load(this.config)),this.config.body.enabled&&!this.models.posenet&&(this.models.posenet=await posenet.load(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await handpose.load(this.config)));let current=Math.trunc(now()-timeStamp);current>(this.perf.load||0)&&(this.perf.load=current)}async checkBackend(force){if(this.config.backend&&this.config.backend!==""&&force||tf3.getBackend()!==this.config.backend){let timeStamp=now();if(this.state="backend",log("setting backend:",this.config.backend),this.config.backend==="wasm"&&(log("settings wasm path:",this.config.wasmPath),tf3.setWasmPaths(this.config.wasmPath),await tf3.env().getAsync("WASM_HAS_SIMD_SUPPORT")||log("warning: wasm simd support is not enabled")),this.config.backend==="humangl"&&(log("registering humangl backend"),register()),await tf3.setBackend(this.config.backend),tf3.enableProdMode(),tf3.getBackend()==="webgl"){this.config.deallocate&&(log("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",this.config.deallocate),tf3.ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",this.config.deallocate?0:-1)),tf3.ENV.set("WEBGL_FORCE_F16_TEXTURES",!0),tf3.ENV.set("WEBGL_PACK_DEPTHWISECONV",!0);let gl=await tf3.backend().getGPGPUContext().gl;log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`)}await tf3.ready(),this.perf.backend=Math.trunc(now()-timeStamp)}}async detectFace(input){var _a;let timeStamp,ageRes,genderRes,emotionRes,embeddingRes,faceRes=[];this.state="run:face",timeStamp=now();let faces=await((_a=this.models.facemesh)==null?void 0:_a.estimateFaces(input,this.config));this.perf.face=Math.trunc(now()-timeStamp);for(let face3 of faces){if(this.analyze("Get Face"),!face3.image||face3.image.isDisposedInternal){log("Face object is disposed:",face3.image);continue}this.analyze("Start Age:"),this.config.async?ageRes=this.config.face.age.enabled?age.predict(face3.image,this.config):{}:(this.state="run:age",timeStamp=now(),ageRes=this.config.face.age.enabled?await age.predict(face3.image,this.config):{},this.perf.age=Math.trunc(now()-timeStamp)),this.analyze("Start Gender:"),this.config.async?genderRes=this.config.face.gender.enabled?gender.predict(face3.image,this.config):{}:(this.state="run:gender",timeStamp=now(),genderRes=this.config.face.gender.enabled?await gender.predict(face3.image,this.config):{},this.perf.gender=Math.trunc(now()-timeStamp)),this.analyze("Start Emotion:"),this.config.async?emotionRes=this.config.face.emotion.enabled?emotion.predict(face3.image,this.config):{}:(this.state="run:emotion",timeStamp=now(),emotionRes=this.config.face.emotion.enabled?await emotion.predict(face3.image,this.config):{},this.perf.emotion=Math.trunc(now()-timeStamp)),this.analyze("End Emotion:"),this.analyze("Start Embedding:"),this.config.async?embeddingRes=this.config.face.embedding.enabled?embedding.predict(face3.image,this.config):{}:(this.state="run:embedding",timeStamp=now(),embeddingRes=this.config.face.embedding.enabled?await embedding.predict(face3.image,this.config):{},this.perf.embedding=Math.trunc(now()-timeStamp)),this.analyze("End Emotion:"),this.config.async&&([ageRes,genderRes,emotionRes,embeddingRes]=await Promise.all([ageRes,genderRes,emotionRes,embeddingRes])),this.analyze("Finish Face:"),face3.image.dispose(),this.config.face.iris.enabled||(delete face3.annotations.leftEyeIris,delete face3.annotations.rightEyeIris);let irisSize=face3.annotations.leftEyeIris&&face3.annotations.rightEyeIris?11.7*Math.max(Math.abs(face3.annotations.leftEyeIris[3][0]-face3.annotations.leftEyeIris[1][0]),Math.abs(face3.annotations.rightEyeIris[4][1]-face3.annotations.rightEyeIris[2][1])):0;faceRes.push({confidence:face3.confidence,box:face3.box,mesh:face3.mesh,boxRaw:face3.boxRaw,meshRaw:face3.meshRaw,annotations:face3.annotations,age:ageRes.age,gender:genderRes.gender,genderConfidence:genderRes.confidence,emotion:emotionRes,embedding:embeddingRes,iris:irisSize!==0?Math.trunc(irisSize)/100:0}),this.analyze("End Face")}return this.analyze("End FaceMesh:"),this.config.async&&(this.perf.face&&delete this.perf.face,this.perf.age&&delete this.perf.age,this.perf.gender&&delete this.perf.gender,this.perf.emotion&&delete this.perf.emotion),faceRes}async image(input,userConfig={}){this.state="image",this.config=mergeDeep(this.config,userConfig);let process3=image2.process(input,this.config);return process3.tensor.dispose(),process3.canvas}async detect(input,userConfig={}){return new Promise(async resolve=>{var _a,_b,_c,_d;this.state="config";let timeStamp;this.config=mergeDeep(this.config,userConfig),this.state="check";let error=this.sanity(input);error&&(log(error,input),resolve({error}));let poseRes,handRes,faceRes,timeStart=now();await this.checkBackend(),await this.load(),this.config.scoped&&tf3.engine().startScope(),this.analyze("Start Scope:"),timeStamp=now();let process3=image2.process(input,this.config);if(!process3||!process3.tensor){log("could not convert input to tensor"),resolve({error:"could not convert input to tensor"});return}this.perf.image=Math.trunc(now()-timeStamp),this.analyze("Get Image:"),this.config.async?(faceRes=this.config.face.enabled?this.detectFace(process3.tensor):[],this.perf.face&&delete this.perf.face):(this.state="run:face",timeStamp=now(),faceRes=this.config.face.enabled?await this.detectFace(process3.tensor):[],this.perf.face=Math.trunc(now()-timeStamp)),this.analyze("Start Body:"),this.config.async?(poseRes=this.config.body.enabled?(_a=this.models.posenet)==null?void 0:_a.estimatePoses(process3.tensor,this.config):[],this.perf.body&&delete this.perf.body):(this.state="run:body",timeStamp=now(),poseRes=this.config.body.enabled?await((_b=this.models.posenet)==null?void 0:_b.estimatePoses(process3.tensor,this.config)):[],this.perf.body=Math.trunc(now()-timeStamp)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(handRes=this.config.hand.enabled?(_c=this.models.handpose)==null?void 0:_c.estimateHands(process3.tensor,this.config):[],this.perf.hand&&delete this.perf.hand):(this.state="run:hand",timeStamp=now(),handRes=this.config.hand.enabled?await((_d=this.models.handpose)==null?void 0:_d.estimateHands(process3.tensor,this.config)):[],this.perf.hand=Math.trunc(now()-timeStamp)),this.config.async&&([faceRes,poseRes,handRes]=await Promise.all([faceRes,poseRes,handRes])),process3.tensor.dispose(),this.config.scoped&&tf3.engine().endScope(),this.analyze("End Scope:");let gestureRes=[];this.config.gesture.enabled&&(timeStamp=now(),gestureRes=[...gesture.face(faceRes),...gesture.body(poseRes),...gesture.hand(handRes),...gesture.iris(faceRes)],this.config.async?this.perf.gesture&&delete this.perf.gesture:this.perf.gesture=Math.trunc(now()-timeStamp)),this.perf.total=Math.trunc(now()-timeStart),this.state="idle",resolve({face:faceRes,body:poseRes,hand:handRes,gesture:gestureRes,performance:this.perf,canvas:process3.canvas})})}async warmup(userConfig){let b64toBlob=(base64,type="application/octet-stream")=>fetch(`data:${type};base64,${base64}`).then(res=>res.blob());userConfig&&(this.config=mergeDeep(this.config,userConfig));let video=this.config.videoOptimized;this.config.videoOptimized=!1;let blob;switch(this.config.warmup){case"face":blob=await b64toBlob(face);break;case"full":blob=await b64toBlob(body);break;default:blob=null}if(!blob)return null;let bitmap=await createImageBitmap(blob),t0=now(),warmup=await this.detect(bitmap,config_exports),t1=now();return bitmap.close(),log("Warmup",this.config.warmup,t1-t0,warmup),this.config.videoOptimized=video,warmup}};export{Human as default}; +2Q==`;var version="0.9.24";var now=()=>typeof performance!="undefined"?performance.now():parseInt(Number(process.hrtime.bigint())/1e3/1e3);function mergeDeep(...objects){let isObject=obj=>obj&&typeof obj=="object";return objects.reduce((prev,obj)=>(Object.keys(obj||{}).forEach(key=>{let pVal=prev[key],oVal=obj[key];Array.isArray(pVal)&&Array.isArray(oVal)?prev[key]=pVal.concat(...oVal):isObject(pVal)&&isObject(oVal)?prev[key]=mergeDeep(pVal,oVal):prev[key]=oVal}),prev),{})}var Human=class{constructor(userConfig={}){this.tf=tf4,this.version=version,this.config=mergeDeep(config_default,userConfig),this.fx=null,this.state="idle",this.numTensors=0,this.analyzeMemoryLeaks=!1,this.checkSanity=!1,this.firstRun=!0,this.perf={},this.models={facemesh:null,posenet:null,handpose:null,iris:null,age:null,gender:null,emotion:null},this.facemesh=facemesh,this.age=age,this.gender=gender,this.emotion=emotion,this.body=posenet,this.hand=handpose}profile(){return this.config.profile?profile.data:{}}analyze(...msg){if(!this.analyzeMemoryLeaks)return;let current=tf4.engine().state.numTensors,previous=this.numTensors;this.numTensors=current;let leaked=current-previous;leaked!==0&&log(...msg,leaked)}sanity(input){if(!this.checkSanity)return null;if(!input)return"input is not defined";if(tf4.ENV.flags.IS_NODE&&!(input instanceof tf4.Tensor))return"input must be a tensor";try{tf4.getBackend()}catch(e){return"backend not loaded"}return null}simmilarity(embedding1,embedding2){return this.config.face.embedding.enabled?embedding.simmilarity(embedding1,embedding2):0}async load(userConfig){this.state="load";let timeStamp=now();userConfig&&(this.config=mergeDeep(this.config,userConfig)),this.firstRun&&(log(`version: ${this.version} TensorFlow/JS version: ${tf4.version_core}`),await this.checkBackend(!0),tf4.ENV.flags.IS_BROWSER&&(log("configuration:",this.config),log("tf flags:",tf4.ENV.flags)),this.firstRun=!1),this.config.async?[this.models.facemesh,this.models.age,this.models.gender,this.models.emotion,this.models.embedding,this.models.posenet,this.models.handpose,this.models.blazepose]=await Promise.all([this.models.facemesh||(this.config.face.enabled?facemesh.load(this.config):null),this.models.age||(this.config.face.enabled&&this.config.face.age.enabled?age.load(this.config):null),this.models.gender||(this.config.face.enabled&&this.config.face.gender.enabled?gender.load(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?emotion.load(this.config):null),this.models.embedding||(this.config.face.enabled&&this.config.face.embedding.enabled?embedding.load(this.config):null),this.models.posenet||(this.config.body.enabled?posenet.load(this.config):null),this.models.handpose||(this.config.hand.enabled?handpose.load(this.config):null),this.models.blazepose||(this.config.pose.enabled?load(this.config):null)]):(this.config.face.enabled&&!this.models.facemesh&&(this.models.facemesh=await facemesh.load(this.config)),this.config.face.enabled&&this.config.face.age.enabled&&!this.models.age&&(this.models.age=await age.load(this.config)),this.config.face.enabled&&this.config.face.gender.enabled&&!this.models.gender&&(this.models.gender=await gender.load(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await emotion.load(this.config)),this.config.face.enabled&&this.config.face.embedding.enabled&&!this.models.embedding&&(this.models.embedding=await embedding.load(this.config)),this.config.body.enabled&&!this.models.posenet&&(this.models.posenet=await posenet.load(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await handpose.load(this.config)),this.config.pose.enabled&&!this.models.blazepose&&(this.models.blazepose=await load(this.config)));let current=Math.trunc(now()-timeStamp);current>(this.perf.load||0)&&(this.perf.load=current)}async checkBackend(force){if(this.config.backend&&this.config.backend!==""&&force||tf4.getBackend()!==this.config.backend){let timeStamp=now();if(this.state="backend",log("setting backend:",this.config.backend),this.config.backend==="wasm"&&(log("settings wasm path:",this.config.wasmPath),tf4.setWasmPaths(this.config.wasmPath),await tf4.env().getAsync("WASM_HAS_SIMD_SUPPORT")||log("warning: wasm simd support is not enabled")),this.config.backend==="humangl"&&(log("registering humangl backend"),register()),await tf4.setBackend(this.config.backend),tf4.enableProdMode(),tf4.getBackend()==="webgl"){this.config.deallocate&&(log("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",this.config.deallocate),tf4.ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",this.config.deallocate?0:-1)),tf4.ENV.set("WEBGL_FORCE_F16_TEXTURES",!0),tf4.ENV.set("WEBGL_PACK_DEPTHWISECONV",!0);let gl=await tf4.backend().getGPGPUContext().gl;log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`)}await tf4.ready(),this.perf.backend=Math.trunc(now()-timeStamp)}}async detectFace(input){var _a;let timeStamp,ageRes,genderRes,emotionRes,embeddingRes,faceRes=[];this.state="run:face",timeStamp=now();let faces=await((_a=this.models.facemesh)==null?void 0:_a.estimateFaces(input,this.config));this.perf.face=Math.trunc(now()-timeStamp);for(let face3 of faces){if(this.analyze("Get Face"),!face3.image||face3.image.isDisposedInternal){log("Face object is disposed:",face3.image);continue}this.analyze("Start Age:"),this.config.async?ageRes=this.config.face.age.enabled?age.predict(face3.image,this.config):{}:(this.state="run:age",timeStamp=now(),ageRes=this.config.face.age.enabled?await age.predict(face3.image,this.config):{},this.perf.age=Math.trunc(now()-timeStamp)),this.analyze("Start Gender:"),this.config.async?genderRes=this.config.face.gender.enabled?gender.predict(face3.image,this.config):{}:(this.state="run:gender",timeStamp=now(),genderRes=this.config.face.gender.enabled?await gender.predict(face3.image,this.config):{},this.perf.gender=Math.trunc(now()-timeStamp)),this.analyze("Start Emotion:"),this.config.async?emotionRes=this.config.face.emotion.enabled?emotion.predict(face3.image,this.config):{}:(this.state="run:emotion",timeStamp=now(),emotionRes=this.config.face.emotion.enabled?await emotion.predict(face3.image,this.config):{},this.perf.emotion=Math.trunc(now()-timeStamp)),this.analyze("End Emotion:"),this.analyze("Start Embedding:"),this.config.async?embeddingRes=this.config.face.embedding.enabled?embedding.predict(face3.image,this.config):{}:(this.state="run:embedding",timeStamp=now(),embeddingRes=this.config.face.embedding.enabled?await embedding.predict(face3.image,this.config):{},this.perf.embedding=Math.trunc(now()-timeStamp)),this.analyze("End Emotion:"),this.config.async&&([ageRes,genderRes,emotionRes,embeddingRes]=await Promise.all([ageRes,genderRes,emotionRes,embeddingRes])),this.analyze("Finish Face:"),face3.image.dispose(),this.config.face.iris.enabled||(delete face3.annotations.leftEyeIris,delete face3.annotations.rightEyeIris);let irisSize=face3.annotations.leftEyeIris&&face3.annotations.rightEyeIris?11.7*Math.max(Math.abs(face3.annotations.leftEyeIris[3][0]-face3.annotations.leftEyeIris[1][0]),Math.abs(face3.annotations.rightEyeIris[4][1]-face3.annotations.rightEyeIris[2][1])):0;faceRes.push({confidence:face3.confidence,box:face3.box,mesh:face3.mesh,boxRaw:face3.boxRaw,meshRaw:face3.meshRaw,annotations:face3.annotations,age:ageRes.age,gender:genderRes.gender,genderConfidence:genderRes.confidence,emotion:emotionRes,embedding:embeddingRes,iris:irisSize!==0?Math.trunc(irisSize)/100:0}),this.analyze("End Face")}return this.analyze("End FaceMesh:"),this.config.async&&(this.perf.face&&delete this.perf.face,this.perf.age&&delete this.perf.age,this.perf.gender&&delete this.perf.gender,this.perf.emotion&&delete this.perf.emotion),faceRes}async image(input,userConfig={}){this.state="image",this.config=mergeDeep(this.config,userConfig);let process3=image3.process(input,this.config);return process3.tensor.dispose(),process3.canvas}async detect(input,userConfig={}){return new Promise(async resolve=>{var _a,_b,_c,_d;this.state="config";let timeStamp;this.config=mergeDeep(this.config,userConfig),this.state="check";let error=this.sanity(input);error&&(log(error,input),resolve({error}));let poseRes,blazeposeRes,handRes,faceRes,timeStart=now();await this.checkBackend(),await this.load(),this.config.scoped&&tf4.engine().startScope(),this.analyze("Start Scope:"),timeStamp=now();let process3=image3.process(input,this.config);if(!process3||!process3.tensor){log("could not convert input to tensor"),resolve({error:"could not convert input to tensor"});return}this.perf.image=Math.trunc(now()-timeStamp),this.analyze("Get Image:"),this.config.async?(faceRes=this.config.face.enabled?this.detectFace(process3.tensor):[],this.perf.face&&delete this.perf.face):(this.state="run:face",timeStamp=now(),faceRes=this.config.face.enabled?await this.detectFace(process3.tensor):[],this.perf.face=Math.trunc(now()-timeStamp)),this.analyze("Start Body:"),this.config.async?(poseRes=this.config.body.enabled?(_a=this.models.posenet)==null?void 0:_a.estimatePoses(process3.tensor,this.config):[],this.perf.body&&delete this.perf.body):(this.state="run:body",timeStamp=now(),poseRes=this.config.body.enabled?await((_b=this.models.posenet)==null?void 0:_b.estimatePoses(process3.tensor,this.config)):[],this.perf.body=Math.trunc(now()-timeStamp)),this.analyze("End Body:"),this.analyze("Start Pose:"),this.config.async?(blazeposeRes=this.config.pose.enabled?predict(process3.tensor,this.config):[],this.perf.pose&&delete this.perf.pose):(this.state="run:pose",timeStamp=now(),blazeposeRes=this.config.pose.enabled?await predict(process3.tensor,this.config):[],this.perf.pose=Math.trunc(now()-timeStamp)),this.analyze("End Pose:"),this.analyze("Start Hand:"),this.config.async?(handRes=this.config.hand.enabled?(_c=this.models.handpose)==null?void 0:_c.estimateHands(process3.tensor,this.config):[],this.perf.hand&&delete this.perf.hand):(this.state="run:hand",timeStamp=now(),handRes=this.config.hand.enabled?await((_d=this.models.handpose)==null?void 0:_d.estimateHands(process3.tensor,this.config)):[],this.perf.hand=Math.trunc(now()-timeStamp)),this.analyze("End Hand:"),this.config.async&&([faceRes,poseRes,blazeposeRes,handRes]=await Promise.all([faceRes,poseRes,blazeposeRes,handRes])),process3.tensor.dispose(),this.config.scoped&&tf4.engine().endScope(),this.analyze("End Scope:");let gestureRes=[];this.config.gesture.enabled&&(timeStamp=now(),gestureRes=[...gesture.face(faceRes),...gesture.body(poseRes),...gesture.hand(handRes),...gesture.iris(faceRes)],this.config.async?this.perf.gesture&&delete this.perf.gesture:this.perf.gesture=Math.trunc(now()-timeStamp)),this.perf.total=Math.trunc(now()-timeStart),this.state="idle",resolve({face:faceRes,body:poseRes,hand:handRes,pose:blazeposeRes,gesture:gestureRes,performance:this.perf,canvas:process3.canvas})})}async warmupBitmap(){let b64toBlob=(base64,type="application/octet-stream")=>fetch(`data:${type};base64,${base64}`).then(res2=>res2.blob()),blob,res;switch(this.config.warmup){case"face":blob=await b64toBlob(face);break;case"full":blob=await b64toBlob(body);break;default:blob=null}if(blob){let bitmap=await createImageBitmap(blob);res=await this.detect(bitmap,config_exports),bitmap.close()}return res}async warmupCanvas(){return new Promise(resolve=>{let src,size=0;switch(this.config.warmup){case"face":size=256,src="data:image/jpeg;base64,"+face;break;case"full":size=1200,src="data:image/jpeg;base64,"+body;break;default:src=null}let img=new Image(size,size);img.onload=()=>{let canvas=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(size,size):document.createElement("canvas");canvas.width=size,canvas.height=size;let ctx=canvas.getContext("2d");ctx.drawImage(img,0,0);let data3=ctx.getImageData(0,0,size,size);this.detect(data3,config_exports).then(res=>resolve(res))},src?img.src=src:resolve(null)})}async warmup(userConfig){let t0=now();userConfig&&(this.config=mergeDeep(this.config,userConfig));let video=this.config.videoOptimized;this.config.videoOptimized=!1;let res;typeof createImageBitmap=="function"?res=await this.warmupBitmap():res=await this.warmupCanvas(),this.config.videoOptimized=video;let t1=now();return log("Warmup",this.config.warmup,t1-t0,res),res}};export{Human as default}; //# sourceMappingURL=human.esm-nobundle.js.map diff --git a/dist/human.esm-nobundle.js.map b/dist/human.esm-nobundle.js.map index ab7b72b7..52797d9f 100644 --- a/dist/human.esm-nobundle.js.map +++ b/dist/human.esm-nobundle.js.map @@ -1,7 +1,7 @@ { "version": 3, - "sources": ["../src/tfjs/tf-browser.js", "../src/face/blazeface.js", "../src/face/box.js", "../src/face/util.js", "../src/face/coords.js", "../src/face/facepipeline.js", "../src/face/facemesh.js", "../src/profile.js", "../src/age/age.js", "../src/gender/gender.js", "../src/emotion/emotion.js", "../src/embedding/embedding.js", "../src/body/modelBase.js", "../src/body/heapSort.js", "../src/body/buildParts.js", "../src/body/keypoints.js", "../src/body/vectors.js", "../src/body/decoders.js", "../src/body/decodePose.js", "../src/body/decodeMultiple.js", "../src/body/util.js", "../src/body/modelPoseNet.js", "../src/body/posenet.js", "../src/hand/handdetector.js", "../src/hand/handpipeline.js", "../src/hand/anchors.js", "../src/hand/handpose.js", "../src/gesture/gesture.js", "../src/imagefx.js", "../src/image.js", "../src/log.js", "../src/human.js", "../src/tfjs/backend.js", "../src/hand/box.js", "../src/hand/util.js", "../config.js", "../src/sample.js"], - "sourcesContent": ["// wrapper to load tfjs in a single place so version can be changed quickly\n\n// simplified\n// { modules: 1061, moduleBytes: 3772720, outputBytes: 1531035 }\n\n// export * from '@tensorflow/tfjs/dist/index.js';\n// export * from '@tensorflow/tfjs-backend-wasm';\n\n// modular\n// { modules: 1064, moduleBytes: 3793219, outputBytes: 1535600 }\n\n// get versions of all packages.\nimport { version as tfjs } from '@tensorflow/tfjs/package.json';\nimport { version as versionCore } from '@tensorflow/tfjs-core/package.json';\nimport { version as versionData } from '@tensorflow/tfjs-data/package.json';\nimport { version as versionLayers } from '@tensorflow/tfjs-layers/package.json';\nimport { version as versionConverter } from '@tensorflow/tfjs-converter/package.json';\n// for backends, get version from source so it can register backend during import\nimport { version_cpu } from '@tensorflow/tfjs-backend-cpu/dist/index.js';\nimport { version_webgl } from '@tensorflow/tfjs-backend-webgl/dist/index.js';\nimport { version_wasm } from '@tensorflow/tfjs-backend-wasm/dist/index.js';\n\n// export all\nexport * from '@tensorflow/tfjs-core/dist/index.js';\nexport * from '@tensorflow/tfjs-layers/dist/index.js';\nexport * from '@tensorflow/tfjs-converter/dist/index.js';\nexport * as data from '@tensorflow/tfjs-data/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-cpu/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-webgl/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-wasm/dist/index.js';\n\n// export versions\nexport const version = {\n tfjs,\n 'tfjs-core': versionCore,\n 'tfjs-data': versionData,\n 'tfjs-layers': versionLayers,\n 'tfjs-converter': versionConverter,\n 'tfjs-backend-cpu': version_cpu,\n 'tfjs-backend-webgl': version_webgl,\n 'tfjs-backend-wasm': version_wasm,\n};\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nconst NUM_LANDMARKS = 6;\n\nfunction generateAnchors(inputSize) {\n const spec = { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] };\n const anchors = [];\n for (let i = 0; i < spec.strides.length; i++) {\n const stride = spec.strides[i];\n const gridRows = Math.floor((inputSize + stride - 1) / stride);\n const gridCols = Math.floor((inputSize + stride - 1) / stride);\n const anchorsNum = spec.anchors[i];\n for (let gridY = 0; gridY < gridRows; gridY++) {\n const anchorY = stride * (gridY + 0.5);\n for (let gridX = 0; gridX < gridCols; gridX++) {\n const anchorX = stride * (gridX + 0.5);\n for (let n = 0; n < anchorsNum; n++) {\n anchors.push([anchorX, anchorY]);\n }\n }\n }\n }\n return anchors;\n}\n\nconst disposeBox = (box) => {\n box.startEndTensor.dispose();\n box.startPoint.dispose();\n box.endPoint.dispose();\n};\n\nconst createBox = (startEndTensor) => ({\n startEndTensor,\n startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]),\n endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]),\n});\n\nconst scaleBox = (box, factors) => {\n const starts = tf.mul(box.startPoint, factors);\n const ends = tf.mul(box.endPoint, factors);\n const newCoordinates = tf.concat2d([starts, ends], 1);\n return createBox(newCoordinates);\n};\n\nfunction decodeBounds(boxOutputs, anchors, inputSize) {\n const boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n const centers = tf.add(boxStarts, anchors);\n const boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n const boxSizesNormalized = tf.div(boxSizes, inputSize);\n const centersNormalized = tf.div(centers, inputSize);\n const halfBoxSize = tf.div(boxSizesNormalized, 2);\n const starts = tf.sub(centersNormalized, halfBoxSize);\n const ends = tf.add(centersNormalized, halfBoxSize);\n const startNormalized = tf.mul(starts, inputSize);\n const endNormalized = tf.mul(ends, inputSize);\n const concatAxis = 1;\n return tf.concat2d([startNormalized, endNormalized], concatAxis);\n}\n\nfunction scaleBoxFromPrediction(face, scaleFactor) {\n return tf.tidy(() => {\n const box = face['box'] ? face['box'] : face;\n return scaleBox(box, scaleFactor).startEndTensor.squeeze();\n });\n}\n\nclass BlazeFaceModel {\n constructor(model, config) {\n this.blazeFaceModel = model;\n this.width = config.face.detector.inputSize;\n this.height = config.face.detector.inputSize;\n this.anchorsData = generateAnchors(config.face.detector.inputSize);\n this.anchors = tf.tensor2d(this.anchorsData);\n this.inputSize = tf.tensor1d([this.width, this.height]);\n this.config = config;\n this.scaleFaces = 0.8;\n }\n\n async getBoundingBoxes(inputImage) {\n // sanity check on input\n if ((!inputImage) || (inputImage.isDisposedInternal) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return null;\n const [detectedOutputs, boxes, scores] = tf.tidy(() => {\n const resizedImage = inputImage.resizeBilinear([this.width, this.height]);\n // const normalizedImage = tf.mul(tf.sub(resizedImage.div(255), 0.5), 2);\n const normalizedImage = tf.sub(resizedImage.div(127.5), 1);\n const batchedPrediction = this.blazeFaceModel.predict(normalizedImage);\n let prediction;\n // are we using tfhub or pinto converted model?\n if (Array.isArray(batchedPrediction)) {\n const sorted = batchedPrediction.sort((a, b) => a.size - b.size);\n const concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n const concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n const concat = tf.concat([concat512, concat384], 1);\n prediction = concat.squeeze(0);\n } else {\n prediction = batchedPrediction.squeeze(); // when using tfhub model\n }\n const decodedBounds = decodeBounds(prediction, this.anchors, this.inputSize);\n const logits = tf.slice(prediction, [0, 0], [-1, 1]);\n const scoresOut = tf.sigmoid(logits).squeeze();\n return [prediction, decodedBounds, scoresOut];\n });\n const boxIndicesTensor = await tf.image.nonMaxSuppressionAsync(boxes, scores, this.config.face.detector.maxFaces, this.config.face.detector.iouThreshold, this.config.face.detector.scoreThreshold);\n const boxIndices = boxIndicesTensor.arraySync();\n boxIndicesTensor.dispose();\n const boundingBoxesMap = boxIndices.map((boxIndex) => tf.slice(boxes, [boxIndex, 0], [1, -1]));\n const boundingBoxes = boundingBoxesMap.map((boundingBox) => {\n const vals = boundingBox.arraySync();\n boundingBox.dispose();\n return vals;\n });\n\n const scoresVal = scores.dataSync();\n const annotatedBoxes = [];\n for (let i = 0; i < boundingBoxes.length; i++) {\n const boxIndex = boxIndices[i];\n const confidence = scoresVal[boxIndex];\n if (confidence > this.config.face.detector.minConfidence) {\n const box = createBox(boundingBoxes[i]);\n const anchor = this.anchorsData[boxIndex];\n const landmarks = tf.tidy(() => tf.slice(detectedOutputs, [boxIndex, NUM_LANDMARKS - 1], [1, -1]).squeeze().reshape([NUM_LANDMARKS, -1]));\n annotatedBoxes.push({ box, landmarks, anchor, confidence });\n }\n }\n detectedOutputs.dispose();\n boxes.dispose();\n scores.dispose();\n detectedOutputs.dispose();\n return {\n boxes: annotatedBoxes,\n scaleFactor: [inputImage.shape[2] / this.width, inputImage.shape[1] / this.height],\n };\n }\n\n async estimateFaces(input) {\n // @ts-ignore\n const { boxes, scaleFactor } = await this.getBoundingBoxes(input);\n const faces = [];\n for (const face of boxes) {\n const landmarkData = face.landmarks.arraySync();\n const scaledBox = scaleBoxFromPrediction(face, scaleFactor);\n // @ts-ignore\n const boxData = scaleBox.arraySync();\n const probabilityData = face.probability.arraySync();\n const anchor = face.anchor;\n const [scaleFactorX, scaleFactorY] = scaleFactor;\n const scaledLandmarks = landmarkData\n .map((landmark) => ([\n (landmark[0] + anchor[0]) * scaleFactorX,\n (landmark[1] + anchor[1]) * scaleFactorY,\n ]));\n const normalizedFace = {\n topLeft: boxData.slice(0, 2),\n bottomRight: boxData.slice(2),\n landmarks: scaledLandmarks,\n probability: probabilityData,\n };\n disposeBox(face.box);\n face.landmarks.dispose();\n face.probability.dispose();\n scaledBox.dispose();\n faces.push(normalizedFace);\n }\n return faces;\n }\n}\n\nasync function load(config) {\n const blazeface = await tf.loadGraphModel(config.face.detector.modelPath, { fromTFHub: config.face.detector.modelPath.includes('tfhub.dev') });\n const model = new BlazeFaceModel(blazeface, config);\n log(`load model: ${config.face.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n return model;\n}\n\nexports.load = load;\nexports.BlazeFaceModel = BlazeFaceModel;\nexports.disposeBox = disposeBox;\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nfunction scaleBoxCoordinates(box, factor) {\n const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n return { startPoint, endPoint };\n}\nexports.scaleBoxCoordinates = scaleBoxCoordinates;\n\nfunction getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\nexports.getBoxSize = getBoxSize;\n\nfunction getBoxCenter(box) {\n return [\n box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n ];\n}\nexports.getBoxCenter = getBoxCenter;\n\nfunction cutBoxFromImageAndResize(box, image, cropSize) {\n const h = image.shape[1];\n const w = image.shape[2];\n const boxes = [[\n box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h,\n box.endPoint[0] / w,\n ]];\n return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\nexports.cutBoxFromImageAndResize = cutBoxFromImageAndResize;\n\nfunction enlargeBox(box, factor = 1.5) {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n return { startPoint, endPoint, landmarks: box.landmarks };\n}\nexports.enlargeBox = enlargeBox;\n\nfunction squarifyBox(box) {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const maxEdge = Math.max(...size);\n const halfSize = maxEdge / 2;\n const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n return { startPoint, endPoint, landmarks: box.landmarks };\n}\nexports.squarifyBox = squarifyBox;\n", "exports.IDENTITY_MATRIX = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n/**\n * Normalizes the provided angle to the range -pi to pi.\n * @param angle The angle in radians to be normalized.\n */\nfunction normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\nexports.normalizeRadians = normalizeRadians;\n\n/**\n * Computes the angle of rotation between two anchor points.\n * @param point1 First anchor point\n * @param point2 Second anchor point\n */\nfunction computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\nexports.computeRotation = computeRotation;\n\nfunction radToDegrees(rad) {\n return rad * 180 / Math.PI;\n}\nexports.radToDegrees = radToDegrees;\n\nfunction buildTranslationMatrix(x, y) {\n return [[1, 0, x], [0, 1, y], [0, 0, 1]];\n}\n\nfunction dot(v1, v2) {\n let product = 0;\n for (let i = 0; i < v1.length; i++) {\n product += v1[i] * v2[i];\n }\n return product;\n}\nexports.dot = dot;\n\nfunction getColumnFrom2DArr(arr, columnIndex) {\n const column = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\nexports.getColumnFrom2DArr = getColumnFrom2DArr;\n\nfunction multiplyTransformMatrices(mat1, mat2) {\n const product = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) {\n product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n }\n return product;\n}\nfunction buildRotationMatrix(rotation, center) {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\nexports.buildRotationMatrix = buildRotationMatrix;\n\nfunction invertTransformMatrix(matrix) {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [\n -dot(rotationComponent[0], translationComponent),\n -dot(rotationComponent[1], translationComponent),\n ];\n return [\n rotationComponent[0].concat(invertedTranslation[0]),\n rotationComponent[1].concat(invertedTranslation[1]),\n [0, 0, 1],\n ];\n}\nexports.invertTransformMatrix = invertTransformMatrix;\n\nfunction rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\nexports.rotatePoint = rotatePoint;\n\nfunction xyDistanceBetweenPoints(a, b) {\n return Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n}\nexports.xyDistanceBetweenPoints = xyDistanceBetweenPoints;\n", "const MESH_ANNOTATIONS = {\n silhouette: [\n 10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n 397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n 172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n ],\n lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291],\n lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291],\n lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308],\n lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],\n rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173],\n rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133],\n rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190],\n rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243],\n rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189],\n rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244],\n rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245],\n rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193],\n rightEyebrowLower: [35, 124, 46, 53, 52, 65],\n rightEyeIris: [473, 474, 475, 476, 477],\n leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n leftEyeIris: [468, 469, 470, 471, 472],\n midwayBetweenEyes: [168],\n noseTip: [1],\n noseBottom: [2],\n noseRightCorner: [98],\n noseLeftCorner: [327],\n rightCheek: [205],\n leftCheek: [425],\n};\n\nconst MESH_TO_IRIS_INDICES_MAP = [ // A mapping from facemesh model keypoints to iris model keypoints.\n { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] },\n { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] },\n { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] },\n { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] },\n { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] },\n { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] },\n { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] },\n // { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] },\n // { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] },\n];\n\nconst UV468 = [\n [0.499976992607117, 0.652534008026123],\n [0.500025987625122, 0.547487020492554],\n [0.499974012374878, 0.602371990680695],\n [0.482113003730774, 0.471979022026062],\n [0.500150978565216, 0.527155995368958],\n [0.499909996986389, 0.498252987861633],\n [0.499523013830185, 0.40106201171875],\n [0.289712011814117, 0.380764007568359],\n [0.499954998493195, 0.312398016452789],\n [0.499987006187439, 0.269918978214264],\n [0.500023007392883, 0.107050001621246],\n [0.500023007392883, 0.666234016418457],\n [0.5000159740448, 0.679224014282227],\n [0.500023007392883, 0.692348003387451],\n [0.499976992607117, 0.695277988910675],\n [0.499976992607117, 0.70593398809433],\n [0.499976992607117, 0.719385027885437],\n [0.499976992607117, 0.737019002437592],\n [0.499967992305756, 0.781370997428894],\n [0.499816000461578, 0.562981009483337],\n [0.473773002624512, 0.573909997940063],\n [0.104906998574734, 0.254140973091125],\n [0.365929991006851, 0.409575998783112],\n [0.338757991790771, 0.41302502155304],\n [0.311120003461838, 0.409460008144379],\n [0.274657994508743, 0.389131009578705],\n [0.393361985683441, 0.403706014156342],\n [0.345234006643295, 0.344011008739471],\n [0.370094001293182, 0.346076011657715],\n [0.319321990013123, 0.347265005111694],\n [0.297903001308441, 0.353591024875641],\n [0.24779200553894, 0.410809993743896],\n [0.396889001131058, 0.842755019664764],\n [0.280097991228104, 0.375599980354309],\n [0.106310002505779, 0.399955987930298],\n [0.2099249958992, 0.391353011131287],\n [0.355807989835739, 0.534406006336212],\n [0.471751004457474, 0.65040397644043],\n [0.474155008792877, 0.680191993713379],\n [0.439785003662109, 0.657229006290436],\n [0.414617002010345, 0.66654098033905],\n [0.450374007225037, 0.680860996246338],\n [0.428770989179611, 0.682690978050232],\n [0.374971002340317, 0.727805018424988],\n [0.486716985702515, 0.547628998756409],\n [0.485300987958908, 0.527395009994507],\n [0.257764995098114, 0.314490020275116],\n [0.401223003864288, 0.455172002315521],\n [0.429818987846375, 0.548614978790283],\n [0.421351999044418, 0.533740997314453],\n [0.276895999908447, 0.532056987285614],\n [0.483370006084442, 0.499586999416351],\n [0.33721199631691, 0.282882988452911],\n [0.296391993761063, 0.293242990970612],\n [0.169294998049736, 0.193813979625702],\n [0.447580009698868, 0.302609980106354],\n [0.392390012741089, 0.353887975215912],\n [0.354490011930466, 0.696784019470215],\n [0.067304998636246, 0.730105042457581],\n [0.442739009857178, 0.572826027870178],\n [0.457098007202148, 0.584792017936707],\n [0.381974011659622, 0.694710969924927],\n [0.392388999462128, 0.694203019142151],\n [0.277076005935669, 0.271932005882263],\n [0.422551989555359, 0.563233017921448],\n [0.385919004678726, 0.281364023685455],\n [0.383103013038635, 0.255840003490448],\n [0.331431001424789, 0.119714021682739],\n [0.229923993349075, 0.232002973556519],\n [0.364500999450684, 0.189113974571228],\n [0.229622006416321, 0.299540996551514],\n [0.173287004232407, 0.278747975826263],\n [0.472878992557526, 0.666198015213013],\n [0.446828007698059, 0.668527007102966],\n [0.422762006521225, 0.673889994621277],\n [0.445307999849319, 0.580065965652466],\n [0.388103008270264, 0.693961024284363],\n [0.403039008378983, 0.706539988517761],\n [0.403629004955292, 0.693953037261963],\n [0.460041999816895, 0.557139039039612],\n [0.431158006191254, 0.692366003990173],\n [0.452181994915009, 0.692366003990173],\n [0.475387006998062, 0.692366003990173],\n [0.465828001499176, 0.779190003871918],\n [0.472328990697861, 0.736225962638855],\n [0.473087012767792, 0.717857003211975],\n [0.473122000694275, 0.704625964164734],\n [0.473033010959625, 0.695277988910675],\n [0.427942007780075, 0.695277988910675],\n [0.426479011774063, 0.703539967536926],\n 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[0.567984998226166, 0.493479013442993],\n [0.543282985687256, 0.819254994392395],\n [0.655317008495331, 0.745514988899231],\n [0.621008992195129, 0.574018001556396],\n [0.625559985637665, 0.78031200170517],\n [0.680198013782501, 0.570719003677368],\n [0.64276397228241, 0.604337990283966],\n [0.704662978649139, 0.621529996395111],\n [0.552012026309967, 0.862591981887817],\n [0.589071989059448, 0.508637011051178],\n [0.685944974422455, 0.775357007980347],\n [0.645735025405884, 0.812640011310577],\n [0.675342977046967, 0.703978002071381],\n [0.810858011245728, 0.646304965019226],\n [0.72012197971344, 0.714666962623596],\n [0.866151988506317, 0.682704985141754],\n [0.663187026977539, 0.644596993923187],\n [0.570082008838654, 0.466325998306274],\n [0.544561982154846, 0.548375964164734],\n [0.562758982181549, 0.558784961700439],\n [0.531987011432648, 0.530140042304993],\n [0.585271000862122, 0.335177004337311],\n [0.622952997684479, 0.32277899980545],\n [0.655896008014679, 0.320163011550903],\n [0.687132000923157, 0.322345972061157],\n [0.716481983661652, 0.333200991153717],\n [0.758756995201111, 0.382786989212036],\n [0.897013008594513, 0.468769013881683],\n [0.732392013072968, 0.424547016620636],\n [0.70211398601532, 0.433162987232208],\n [0.66652500629425, 0.433866024017334],\n [0.633504986763, 0.426087975502014],\n [0.603875994682312, 0.416586995124817],\n [0.579657971858978, 0.409945011138916],\n [0.992439985275269, 0.480777025222778],\n [0.567192018032074, 0.569419980049133],\n [0.54136598110199, 0.478899002075195],\n [0.526564002037048, 0.546118021011353],\n [0.523913025856018, 0.563830018043518],\n [0.531529009342194, 0.555056989192963],\n [0.566035985946655, 0.582329034805298],\n [0.51631098985672, 0.563053965568542],\n [0.5174720287323, 0.577877044677734],\n [0.573594987392426, 0.389806985855103],\n [0.560697972774506, 0.395331978797913],\n [0.549755990505219, 0.399751007556915],\n [0.710287988185883, 0.368252992630005],\n [0.723330020904541, 0.363372981548309],\n];\n\nconst TRI468 = [\n 127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n 151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n 157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n 181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n 228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n 170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n 155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n 158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n 102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n 90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n 196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n 246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n 117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n 153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n 63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n 135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n 76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n 166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n 190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n 112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n 110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n 348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n 314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n 321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n 466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n 429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n 293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n 427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n 431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n 434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n 348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n 318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n 373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n 340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n 351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n 164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n 327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n 405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n 438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n 449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n 444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n 464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n 243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n 166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n 119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n 77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n 201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n 103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n 217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n 78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n 57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n 142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n 90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n 178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n 224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n 143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n 153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n 63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n 241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n 186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n 141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n 223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n 128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n 451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n 344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n 424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n 321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n 448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n 281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n 283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n 432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n 268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n 6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n 317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n 272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n 336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n 441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n 373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n 425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n 403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n 383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n 254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n 259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n 453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nconst TRI68 = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n 8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n 45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n 23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n 39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n 35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n 48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n 66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\nconst TRI33 = [\n /* eyes */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n /* brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n /* 4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n /* nose */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n /* chin */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n /* cont */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\nconst TRI7 = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nconst VTX68 = [\n /* cont */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n /* nose */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n /* eyes */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n /* lip */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\nconst VTX33 = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\nconst VTX7 = [33, 133, 362, 263, 1, 78, 308];\n\nexports.MESH_ANNOTATIONS = MESH_ANNOTATIONS;\nexports.MESH_TO_IRIS_INDICES_MAP = MESH_TO_IRIS_INDICES_MAP;\n\nexports.TRI468 = TRI468;\nexports.TRI68 = TRI68;\nexports.TRI33 = TRI33;\nexports.TRI7 = TRI7;\n\nexports.UV468 = UV468;\nexports.UV68 = VTX68.map((x) => UV468[x]);\nexports.UV33 = VTX33.map((x) => UV468[x]);\nexports.UV7 = VTX7.map((x) => UV468[x]);\n", "/* eslint-disable class-methods-use-this */\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as bounding from './box';\nimport * as util from './util';\nimport * as coords from './coords.js';\n// eslint-disable-next-line no-unused-vars\nimport { log } from '../log.js';\n\nconst LANDMARKS_COUNT = 468;\nconst MESH_MOUTH_INDEX = 13;\nconst MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES = [MESH_MOUTH_INDEX, coords.MESH_ANNOTATIONS['midwayBetweenEyes'][0]];\nconst BLAZEFACE_MOUTH_INDEX = 3;\nconst BLAZEFACE_NOSE_INDEX = 2;\nconst BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES = [BLAZEFACE_MOUTH_INDEX, BLAZEFACE_NOSE_INDEX];\nconst LEFT_EYE_OUTLINE = coords.MESH_ANNOTATIONS['leftEyeLower0'];\nconst LEFT_EYE_BOUNDS = [LEFT_EYE_OUTLINE[0], LEFT_EYE_OUTLINE[LEFT_EYE_OUTLINE.length - 1]];\nconst RIGHT_EYE_OUTLINE = coords.MESH_ANNOTATIONS['rightEyeLower0'];\nconst RIGHT_EYE_BOUNDS = [RIGHT_EYE_OUTLINE[0], RIGHT_EYE_OUTLINE[RIGHT_EYE_OUTLINE.length - 1]];\nconst IRIS_UPPER_CENTER_INDEX = 3;\nconst IRIS_LOWER_CENTER_INDEX = 4;\nconst IRIS_IRIS_INDEX = 71;\nconst IRIS_NUM_COORDINATES = 76;\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates. Update the z coordinate to be an average of the original and the new. This produces the best visual effect.\nfunction replaceRawCoordinates(rawCoords, newCoords, prefix, keys) {\n for (let i = 0; i < coords.MESH_TO_IRIS_INDICES_MAP.length; i++) {\n const { key, indices } = coords.MESH_TO_IRIS_INDICES_MAP[i];\n const originalIndices = coords.MESH_ANNOTATIONS[`${prefix}${key}`];\n const shouldReplaceAllKeys = keys == null;\n if (shouldReplaceAllKeys || keys.includes(key)) {\n for (let j = 0; j < indices.length; j++) {\n const index = indices[j];\n rawCoords[originalIndices[j]] = [\n newCoords[index][0], newCoords[index][1],\n (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n ];\n }\n }\n }\n}\n// The Pipeline coordinates between the bounding box and skeleton models.\nclass Pipeline {\n constructor(boundingBoxDetector, meshDetector, irisModel, config) {\n // An array of facial bounding boxes.\n this.storedBoxes = [];\n this.runsWithoutFaceDetector = 0;\n this.boundingBoxDetector = boundingBoxDetector;\n this.meshDetector = meshDetector;\n this.irisModel = irisModel;\n this.meshWidth = config.face.mesh.inputSize;\n this.meshHeight = config.face.mesh.inputSize;\n this.irisSize = config.face.iris.inputSize;\n this.irisEnlarge = 2.3;\n this.skipped = 0;\n this.detectedFaces = 0;\n }\n\n transformRawCoords(rawCoords, box, angle, rotationMatrix) {\n // @ts-ignore\n const boxSize = bounding.getBoxSize({ startPoint: box.startPoint, endPoint: box.endPoint });\n const scaleFactor = [boxSize[0] / this.meshWidth, boxSize[1] / this.meshHeight];\n const coordsScaled = rawCoords.map((coord) => ([\n scaleFactor[0] * (coord[0] - this.meshWidth / 2),\n scaleFactor[1] * (coord[1] - this.meshHeight / 2), coord[2],\n ]));\n const coordsRotationMatrix = (angle !== 0) ? util.buildRotationMatrix(angle, [0, 0]) : util.IDENTITY_MATRIX;\n const coordsRotated = (angle !== 0) ? coordsScaled.map((coord) => ([...util.rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n const inverseRotationMatrix = (angle !== 0) ? util.invertTransformMatrix(rotationMatrix) : util.IDENTITY_MATRIX;\n // @ts-ignore\n const boxCenter = [...bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint }), 1];\n return coordsRotated.map((coord) => ([\n coord[0] + util.dot(boxCenter, inverseRotationMatrix[0]),\n coord[1] + util.dot(boxCenter, inverseRotationMatrix[1]),\n coord[2],\n ]));\n }\n\n getLeftToRightEyeDepthDifference(rawCoords) {\n const leftEyeZ = rawCoords[LEFT_EYE_BOUNDS[0]][2];\n const rightEyeZ = rawCoords[RIGHT_EYE_BOUNDS[0]][2];\n return leftEyeZ - rightEyeZ;\n }\n\n // Returns a box describing a cropped region around the eye fit for passing to the iris model.\n getEyeBox(rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, flip = false) {\n // @ts-ignore\n const box = bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), this.irisEnlarge));\n // @ts-ignore\n const boxSize = bounding.getBoxSize(box);\n let crop = tf.image.cropAndResize(face, [[\n box.startPoint[1] / this.meshHeight,\n box.startPoint[0] / this.meshWidth, box.endPoint[1] / this.meshHeight,\n box.endPoint[0] / this.meshWidth,\n ]], [0], [this.irisSize, this.irisSize]);\n if (flip) {\n crop = tf.image.flipLeftRight(crop);\n }\n return { box, boxSize, crop };\n }\n\n // Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\n getEyeCoords(eyeData, eyeBox, eyeBoxSize, flip = false) {\n const eyeRawCoords = [];\n for (let i = 0; i < IRIS_NUM_COORDINATES; i++) {\n const x = eyeData[i * 3];\n const y = eyeData[i * 3 + 1];\n const z = eyeData[i * 3 + 2];\n eyeRawCoords.push([\n (flip\n ? (1 - (x / this.irisSize))\n : (x / this.irisSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n (y / this.irisSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n ]);\n }\n return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(IRIS_IRIS_INDEX) };\n }\n\n // The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\n getAdjustedIrisCoords(rawCoords, irisCoords, direction) {\n const upperCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeUpper0`][IRIS_UPPER_CENTER_INDEX]][2];\n const lowerCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeLower0`][IRIS_LOWER_CENTER_INDEX]][2];\n const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n return irisCoords.map((coord, i) => {\n let z = averageZ;\n if (i === 2) {\n z = upperCenterZ;\n } else if (i === 4) {\n z = lowerCenterZ;\n }\n return [coord[0], coord[1], z];\n });\n }\n\n async predict(input, config) {\n let useFreshBox = false;\n // run new detector every skipFrames unless we only want box to start with\n let detector;\n if ((this.skipped === 0) || (this.skipped > config.face.detector.skipFrames) || !config.face.mesh.enabled || !config.videoOptimized) {\n detector = await this.boundingBoxDetector.getBoundingBoxes(input);\n this.skipped = 0;\n }\n if (config.videoOptimized) this.skipped++;\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (detector && detector.boxes && (detector.boxes.length > 0) && (!config.face.mesh.enabled || (detector.boxes.length !== this.detectedFaces) && (this.detectedFaces !== config.face.detector.maxFaces))) {\n this.storedBoxes = [];\n this.detectedFaces = 0;\n for (const possible of detector.boxes) {\n this.storedBoxes.push({ startPoint: possible.box.startPoint.dataSync(), endPoint: possible.box.endPoint.dataSync(), landmarks: possible.landmarks, confidence: possible.confidence });\n }\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\n\n if (useFreshBox) {\n if (!detector || !detector.boxes || (detector.boxes.length === 0)) {\n this.storedBoxes = [];\n this.detectedFaces = 0;\n return null;\n }\n for (let i = 0; i < this.storedBoxes.length; i++) {\n // @ts-ignore\n const scaledBox = bounding.scaleBoxCoordinates({ startPoint: this.storedBoxes[i].startPoint, endPoint: this.storedBoxes[i].endPoint }, detector.scaleFactor);\n // @ts-ignore\n const enlargedBox = bounding.enlargeBox(scaledBox);\n // @ts-ignore\n const squarifiedBox = bounding.squarifyBox(enlargedBox);\n const landmarks = this.storedBoxes[i].landmarks.arraySync();\n const confidence = this.storedBoxes[i].confidence;\n this.storedBoxes[i] = { ...squarifiedBox, confidence, landmarks };\n }\n this.runsWithoutFaceDetector = 0;\n }\n if (detector && detector.boxes) {\n detector.boxes.forEach((prediction) => {\n prediction.box.startPoint.dispose();\n prediction.box.endPoint.dispose();\n prediction.landmarks.dispose();\n });\n }\n\n // log('face', `skipped: ${this.skipped} max: ${config.face.detector.maxFaces} detected: ${this.detectedFaces} stored: ${this.storedBoxes.length} new: ${detector?.boxes?.length}`);\n let results = tf.tidy(() => this.storedBoxes.map((box, i) => {\n // The facial bounding box landmarks could come either from blazeface (if we are using a fresh box), or from the mesh model (if we are reusing an old box).\n let face;\n let angle = 0;\n let rotationMatrix;\n if (config.face.detector.rotation) {\n const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= LANDMARKS_COUNT) ? MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES : BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES;\n angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n // @ts-ignore\n const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n const rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized);\n rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n // @ts-ignore\n face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshHeight, this.meshWidth]).div(255);\n } else {\n rotationMatrix = util.IDENTITY_MATRIX;\n const cloned = input.clone();\n // @ts-ignore\n face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, cloned, [this.meshHeight, this.meshWidth]).div(255);\n }\n\n // if we're not going to produce mesh, don't spend time with further processing\n if (!config.face.mesh.enabled) {\n const prediction = {\n coords: null,\n box,\n faceConfidence: null,\n confidence: box.confidence,\n image: face,\n };\n return prediction;\n }\n\n const [, confidence, contourCoords] = this.meshDetector.predict(face); // The first returned tensor represents facial contours, which are included in the coordinates.\n const confidenceVal = confidence.dataSync()[0];\n if (confidenceVal < config.face.detector.minConfidence) return null; // if below confidence just exit\n const coordsReshaped = tf.reshape(contourCoords, [-1, 3]);\n let rawCoords = coordsReshaped.arraySync();\n\n if (config.face.iris.enabled) {\n const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = this.getEyeBox(rawCoords, face, LEFT_EYE_BOUNDS[0], LEFT_EYE_BOUNDS[1], true);\n const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = this.getEyeBox(rawCoords, face, RIGHT_EYE_BOUNDS[0], RIGHT_EYE_BOUNDS[1]);\n const eyePredictions = this.irisModel.predict(tf.concat([leftEyeCrop, rightEyeCrop]));\n const eyePredictionsData = eyePredictions.dataSync();\n const leftEyeData = eyePredictionsData.slice(0, IRIS_NUM_COORDINATES * 3);\n const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = this.getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n const rightEyeData = eyePredictionsData.slice(IRIS_NUM_COORDINATES * 3);\n const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = this.getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize);\n const leftToRightEyeDepthDifference = this.getLeftToRightEyeDepthDifference(rawCoords);\n if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left');\n replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right');\n // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged. So we only update a single contour line above and below the eye.\n } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n } else { // User is looking towards the left.\n replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n }\n const adjustedLeftIrisCoords = this.getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n const adjustedRightIrisCoords = this.getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n rawCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n }\n\n const transformedCoordsData = this.transformRawCoords(rawCoords, box, angle, rotationMatrix);\n // @ts-ignore\n const landmarksBox = bounding.enlargeBox(this.calculateLandmarksBoundingBox(transformedCoordsData));\n // @ts-ignore\n const squarifiedLandmarksBox = bounding.squarifyBox(landmarksBox);\n const transformedCoords = tf.tensor2d(transformedCoordsData);\n const prediction = {\n coords: transformedCoords,\n box: landmarksBox,\n faceConfidence: confidenceVal,\n confidence: box.confidence,\n image: face,\n };\n if (config.face.mesh.returnRawData) prediction.rawCoords = rawCoords;\n this.storedBoxes[i] = { ...squarifiedLandmarksBox, landmarks: transformedCoords.arraySync(), confidence: box.confidence, faceConfidence: confidenceVal };\n\n return prediction;\n }));\n results = results.filter((a) => a !== null);\n this.detectedFaces = results.length;\n return results;\n }\n\n calculateLandmarksBoundingBox(landmarks) {\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint, landmarks };\n }\n}\nexports.Pipeline = Pipeline;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as blazeface from './blazeface.js';\nimport * as facepipeline from './facepipeline.js';\nimport * as coords from './coords.js';\n\nclass MediaPipeFaceMesh {\n constructor(blazeFace, blazeMeshModel, irisModel, config) {\n // @ts-ignore\n this.facePipeline = new facepipeline.Pipeline(blazeFace, blazeMeshModel, irisModel, config);\n this.config = config;\n }\n\n async estimateFaces(input, config) {\n const predictions = await this.facePipeline.predict(input, config);\n const results = [];\n for (const prediction of (predictions || [])) {\n if (prediction.isDisposedInternal) continue; // guard against disposed tensors on long running operations such as pause in middle of processing\n const mesh = prediction.coords ? prediction.coords.arraySync() : null;\n const meshRaw = prediction.rawCoords;\n const annotations = {};\n if (mesh && mesh.length > 0) {\n for (const key of Object.keys(coords.MESH_ANNOTATIONS)) {\n annotations[key] = coords.MESH_ANNOTATIONS[key].map((index) => mesh[index]);\n }\n }\n const boxRaw = (config.face.mesh.returnRawData && prediction.box) ? { topLeft: prediction.box.startPoint, bottomRight: prediction.box.endPoint } : null;\n const box = prediction.box ? [\n Math.max(0, prediction.box.startPoint[0]),\n Math.max(0, prediction.box.startPoint[1]),\n Math.min(input.shape[2], prediction.box.endPoint[0]) - prediction.box.startPoint[0],\n Math.min(input.shape[1], prediction.box.endPoint[1]) - prediction.box.startPoint[1],\n ] : 0;\n results.push({\n confidence: prediction.confidence || 0,\n box,\n mesh,\n boxRaw,\n meshRaw,\n annotations,\n image: prediction.image ? tf.clone(prediction.image) : null,\n });\n if (prediction.coords) prediction.coords.dispose();\n if (prediction.image) prediction.image.dispose();\n }\n return results;\n }\n}\n\nlet faceModels = [null, null, null];\nasync function load(config) {\n faceModels = await Promise.all([\n // @ts-ignore\n (!faceModels[0] && config.face.enabled) ? blazeface.load(config) : null,\n (!faceModels[1] && config.face.mesh.enabled) ? tf.loadGraphModel(config.face.mesh.modelPath, { fromTFHub: config.face.mesh.modelPath.includes('tfhub.dev') }) : null,\n (!faceModels[2] && config.face.iris.enabled) ? tf.loadGraphModel(config.face.iris.modelPath, { fromTFHub: config.face.iris.modelPath.includes('tfhub.dev') }) : null,\n ]);\n const faceMesh = new MediaPipeFaceMesh(faceModels[0], faceModels[1], faceModels[2], config);\n if (config.face.mesh.enabled) log(`load model: ${config.face.mesh.modelPath.match(/\\/(.*)\\./)[1]}`);\n if (config.face.iris.enabled) log(`load model: ${config.face.iris.modelPath.match(/\\/(.*)\\./)[1]}`);\n return faceMesh;\n}\n\nexports.load = load;\nexports.MediaPipeFaceMesh = MediaPipeFaceMesh;\nexports.triangulation = coords.TRI468;\n", "import { log } from './log.js';\n\nconst profileData = {};\n\nfunction profile(name, data) {\n if (!data || !data.kernels) return;\n const maxResults = 5;\n const time = data.kernels\n .filter((a) => a.kernelTimeMs > 0)\n .reduce((a, b) => a += b.kernelTimeMs, 0);\n const slowest = data.kernels\n .map((a, i) => { a.id = i; return a; })\n .filter((a) => a.kernelTimeMs > 0)\n .sort((a, b) => b.kernelTimeMs - a.kernelTimeMs);\n const largest = data.kernels\n .map((a, i) => { a.id = i; return a; })\n .filter((a) => a.totalBytesSnapshot > 0)\n .sort((a, b) => b.totalBytesSnapshot - a.totalBytesSnapshot);\n if (slowest.length > maxResults) slowest.length = maxResults;\n if (largest.length > maxResults) largest.length = maxResults;\n const res = { newBytes: data.newBytes, newTensors: data.newTensors, peakBytes: data.peakBytes, numKernelOps: data.kernels.length, timeKernelOps: time, slowestKernelOps: slowest, largestKernelOps: largest };\n profileData[name] = res;\n log('Human profiler', name, res);\n}\n\nexports.run = profile;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile.js';\n\nconst models = {};\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nasync function load(config) {\n if (!models.age) {\n models.age = await tf.loadGraphModel(config.face.age.modelPath);\n log(`load model: ${config.face.age.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.age;\n}\n\nasync function predict(image, config) {\n if (!models.age) return null;\n if ((skipped < config.face.age.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n /*\n const zoom = [0, 0]; // 0..1 meaning 0%..100%\n const box = [[\n (image.shape[1] * zoom[0]) / image.shape[1],\n (image.shape[2] * zoom[1]) / image.shape[2],\n (image.shape[1] - (image.shape[1] * zoom[0])) / image.shape[1],\n (image.shape[2] - (image.shape[2] * zoom[1])) / image.shape[2],\n ]];\n const resize = tf.image.cropAndResize(image, box, [0], [config.face.age.inputSize, config.face.age.inputSize]);\n */\n const resize = tf.image.resizeBilinear(image, [config.face.age.inputSize, config.face.age.inputSize], false);\n const enhance = tf.mul(resize, [255.0]);\n tf.dispose(resize);\n\n let ageT;\n const obj = {};\n\n if (!config.profile) {\n if (config.face.age.enabled) ageT = await models.age.predict(enhance);\n } else {\n const profileAge = config.face.age.enabled ? await tf.profile(() => models.age.predict(enhance)) : {};\n ageT = profileAge.result.clone();\n profileAge.result.dispose();\n // @ts-ignore\n profile.run('age', profileAge);\n }\n enhance.dispose();\n\n if (ageT) {\n const data = ageT.dataSync();\n obj.age = Math.trunc(10 * data[0]) / 10;\n }\n ageT.dispose();\n\n last = obj;\n resolve(obj);\n });\n}\n\nexports.predict = predict;\nexports.load = load;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile.js';\n\nconst models = {};\nlet last = { gender: '' };\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet alternative = false;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nasync function load(config) {\n if (!models.gender) {\n models.gender = await tf.loadGraphModel(config.face.gender.modelPath);\n alternative = models.gender.inputs[0].shape[3] === 1;\n log(`load model: ${config.face.gender.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.gender;\n}\n\nasync function predict(image, config) {\n if (!models.gender) return null;\n if ((skipped < config.face.gender.skipFrames) && config.videoOptimized && last.gender !== '') {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [config.face.gender.inputSize, config.face.gender.inputSize], false);\n let enhance;\n if (alternative) {\n enhance = tf.tidy(() => {\n const [red, green, blue] = tf.split(resize, 3, 3);\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n return grayscale.sub(0.5).mul(2);\n });\n } else {\n enhance = tf.mul(resize, [255.0]);\n }\n tf.dispose(resize);\n\n let genderT;\n const obj = {};\n\n if (!config.profile) {\n if (config.face.gender.enabled) genderT = await models.gender.predict(enhance);\n } else {\n const profileGender = config.face.gender.enabled ? await tf.profile(() => models.gender.predict(enhance)) : {};\n genderT = profileGender.result.clone();\n profileGender.result.dispose();\n // @ts-ignore\n profile.run('gender', profileGender);\n }\n enhance.dispose();\n\n if (genderT) {\n const data = genderT.dataSync();\n if (alternative) {\n // returns two values 0..1, bigger one is prediction\n const confidence = Math.trunc(100 * Math.abs(data[0] - data[1])) / 100;\n if (confidence > config.face.gender.minConfidence) {\n obj.gender = data[0] > data[1] ? 'female' : 'male';\n obj.confidence = confidence;\n }\n } else {\n // returns one value 0..1, .5 is prediction threshold\n const confidence = Math.trunc(200 * Math.abs((data[0] - 0.5))) / 100;\n if (confidence > config.face.gender.minConfidence) {\n obj.gender = data[0] <= 0.5 ? 'female' : 'male';\n obj.confidence = Math.min(0.99, confidence);\n }\n }\n }\n genderT.dispose();\n\n last = obj;\n resolve(obj);\n });\n}\n\nexports.predict = predict;\nexports.load = load;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile.js';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];\nconst models = {};\nlet last = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\nconst scale = 1; // score multiplication factor\n\nasync function load(config) {\n if (!models.emotion) {\n models.emotion = await tf.loadGraphModel(config.face.emotion.modelPath);\n log(`load model: ${config.face.emotion.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.emotion;\n}\n\nasync function predict(image, config) {\n if (!models.emotion) return null;\n if ((skipped < config.face.emotion.skipFrames) && config.videoOptimized && (last.length > 0)) {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n /*\n const zoom = [0, 0]; // 0..1 meaning 0%..100%\n const box = [[\n (image.shape[1] * zoom[0]) / image.shape[1],\n (image.shape[2] * zoom[1]) / image.shape[2],\n (image.shape[1] - (image.shape[1] * zoom[0])) / image.shape[1],\n (image.shape[2] - (image.shape[2] * zoom[1])) / image.shape[2],\n ]];\n const resize = tf.image.cropAndResize(image, box, [0], [config.face.emotion.inputSize, config.face.emotion.inputSize]);\n */\n const resize = tf.image.resizeBilinear(image, [config.face.emotion.inputSize, config.face.emotion.inputSize], false);\n const [red, green, blue] = tf.split(resize, 3, 3);\n resize.dispose();\n // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n red.dispose();\n green.dispose();\n blue.dispose();\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n redNorm.dispose();\n greenNorm.dispose();\n blueNorm.dispose();\n const normalize = tf.tidy(() => grayscale.sub(0.5).mul(2));\n grayscale.dispose();\n const obj = [];\n if (config.face.emotion.enabled) {\n let data;\n if (!config.profile) {\n const emotionT = await models.emotion.predict(normalize);\n data = emotionT.dataSync();\n tf.dispose(emotionT);\n } else {\n const profileData = await tf.profile(() => models.emotion.predict(normalize));\n data = profileData.result.dataSync();\n profileData.result.dispose();\n // @ts-ignore\n profile.run('emotion', profileData);\n }\n for (let i = 0; i < data.length; i++) {\n if (scale * data[i] > config.face.emotion.minConfidence) obj.push({ score: Math.min(0.99, Math.trunc(100 * scale * data[i]) / 100), emotion: annotations[i] });\n }\n obj.sort((a, b) => b.score - a.score);\n }\n normalize.dispose();\n last = obj;\n resolve(obj);\n });\n}\n\nexports.predict = predict;\nexports.load = load;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile.js';\n\n// based on https://github.com/sirius-ai/MobileFaceNet_TF\n// model converted from https://github.com/sirius-ai/MobileFaceNet_TF/files/3551493/FaceMobileNet192_train_false.zip\n\nconst models = {};\n\nasync function load(config) {\n if (!models.embedding) {\n models.embedding = await tf.loadGraphModel(config.face.embedding.modelPath);\n log(`load model: ${config.face.embedding.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.embedding;\n}\n\nfunction simmilarity(embedding1, embedding2) {\n if (embedding1?.length !== embedding2?.length) return 0;\n // general minkowski distance\n // euclidean distance is limited case where order is 2\n const order = 2;\n const distance = 10.0 * ((embedding1.map((val, i) => (val - embedding2[i])).reduce((dist, diff) => dist + (diff ** order), 0) ** (1 / order)));\n return (Math.trunc(1000 * (1 - distance)) / 1000);\n}\n\nasync function predict(image, config) {\n if (!models.embedding) return null;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [config.face.embedding.inputSize, config.face.embedding.inputSize], false);\n // const normalize = tf.tidy(() => resize.div(127.5).sub(0.5)); // this is -0.5...0.5 ???\n let data = [];\n if (config.face.embedding.enabled) {\n if (!config.profile) {\n const embeddingT = await models.embedding.predict({ img_inputs: resize });\n data = [...embeddingT.dataSync()]; // convert object array to standard array\n tf.dispose(embeddingT);\n } else {\n const profileData = await tf.profile(() => models.embedding.predict({ img_inputs: resize }));\n data = [...profileData.result.dataSync()];\n profileData.result.dispose();\n // @ts-ignore\n profile.run('emotion', profileData);\n }\n }\n resize.dispose();\n // normalize.dispose();\n resolve(data);\n });\n}\n\nexports.predict = predict;\nexports.simmilarity = simmilarity;\nexports.load = load;\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nconst imageNetMean = [-123.15, -115.90, -103.06];\n\nfunction nameOutputResultsMobileNet(results) {\n const [offsets, heatmap, displacementFwd, displacementBwd] = results;\n return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nfunction nameOutputResultsResNet(results) {\n const [displacementFwd, displacementBwd, offsets, heatmap] = results;\n return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nclass BaseModel {\n constructor(model) {\n this.model = model;\n }\n\n predict(input, config) {\n return tf.tidy(() => {\n const asFloat = (config.body.modelType === 'ResNet') ? input.toFloat().add(imageNetMean) : input.toFloat().div(127.5).sub(1.0);\n const asBatch = asFloat.expandDims(0);\n const results = this.model.predict(asBatch);\n const results3d = results.map((y) => y.squeeze([0]));\n const namedResults = (config.body.modelType === 'ResNet') ? nameOutputResultsResNet(results3d) : nameOutputResultsMobileNet(results3d);\n return {\n heatmapScores: namedResults.heatmap.sigmoid(),\n offsets: namedResults.offsets,\n displacementFwd: namedResults.displacementFwd,\n displacementBwd: namedResults.displacementBwd,\n };\n });\n }\n\n dispose() {\n this.model.dispose();\n }\n}\nexports.BaseModel = BaseModel;\n", "// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nfunction half(k) {\n return Math.floor(k / 2);\n}\nclass MaxHeap {\n constructor(maxSize, getElementValue) {\n this.priorityQueue = new Array(maxSize);\n this.numberOfElements = -1;\n this.getElementValue = getElementValue;\n }\n\n enqueue(x) {\n this.priorityQueue[++this.numberOfElements] = x;\n this.swim(this.numberOfElements);\n }\n\n dequeue() {\n const max = this.priorityQueue[0];\n this.exchange(0, this.numberOfElements--);\n this.sink(0);\n this.priorityQueue[this.numberOfElements + 1] = null;\n return max;\n }\n\n empty() {\n return this.numberOfElements === -1;\n }\n\n size() {\n return this.numberOfElements + 1;\n }\n\n all() {\n return this.priorityQueue.slice(0, this.numberOfElements + 1);\n }\n\n max() {\n return this.priorityQueue[0];\n }\n\n swim(k) {\n while (k > 0 && this.less(half(k), k)) {\n this.exchange(k, half(k));\n k = half(k);\n }\n }\n\n sink(k) {\n while (2 * k <= this.numberOfElements) {\n let j = 2 * k;\n if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n if (!this.less(k, j)) break;\n this.exchange(k, j);\n k = j;\n }\n }\n\n getValueAt(i) {\n return this.getElementValue(this.priorityQueue[i]);\n }\n\n less(i, j) {\n return this.getValueAt(i) < this.getValueAt(j);\n }\n\n exchange(i, j) {\n const t = this.priorityQueue[i];\n this.priorityQueue[i] = this.priorityQueue[j];\n this.priorityQueue[j] = t;\n }\n}\nexports.MaxHeap = MaxHeap;\n", "import * as heapSort from './heapSort';\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores) {\n const [height, width] = scores.shape;\n let localMaximum = true;\n const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n localMaximum = false;\n break;\n }\n }\n if (!localMaximum) break;\n }\n return localMaximum;\n}\n\nfunction buildPartWithScoreQueue(scoreThreshold, localMaximumRadius, scores) {\n const [height, width, numKeypoints] = scores.shape;\n const queue = new heapSort.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n const score = scores.get(heatmapY, heatmapX, keypointId);\n // Only consider parts with score greater or equal to threshold as root candidates.\n if (score < scoreThreshold) continue;\n // Only consider keypoints whose score is maximum in a local window.\n if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores)) {\n queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n }\n }\n }\n }\n return queue;\n}\nexports.buildPartWithScoreQueue = buildPartWithScoreQueue;\n", "exports.partNames = [\n 'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n 'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n 'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexports.NUM_KEYPOINTS = exports.partNames.length;\n\nexports.partIds = exports.partNames.reduce((result, jointName, i) => {\n result[jointName] = i;\n return result;\n}, {});\n\nconst connectedPartNames = [\n ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexports.connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([exports.partIds[jointNameA], exports.partIds[jointNameB]]));\n\nexports.poseChain = [\n ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n ['rightKnee', 'rightAnkle'],\n];\n\nexports.partChannels = [\n 'left_face',\n 'right_face',\n 'right_upper_leg_front',\n 'right_lower_leg_back',\n 'right_upper_leg_back',\n 'left_lower_leg_front',\n 'left_upper_leg_front',\n 'left_upper_leg_back',\n 'left_lower_leg_back',\n 'right_feet',\n 'right_lower_leg_front',\n 'left_feet',\n 'torso_front',\n 'torso_back',\n 'right_upper_arm_front',\n 'right_upper_arm_back',\n 'right_lower_arm_back',\n 'left_lower_arm_front',\n 'left_upper_arm_front',\n 'left_upper_arm_back',\n 'left_lower_arm_back',\n 'right_hand',\n 'right_lower_arm_front',\n 'left_hand',\n];\n", "import * as kpt from './keypoints';\n\nfunction getOffsetPoint(y, x, keypoint, offsets) {\n return {\n y: offsets.get(y, x, keypoint),\n x: offsets.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n };\n}\nexports.getOffsetPoint = getOffsetPoint;\n\nfunction getImageCoords(part, outputStride, offsets) {\n const { heatmapY, heatmapX, id: keypoint } = part;\n const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n return {\n x: part.heatmapX * outputStride + x,\n y: part.heatmapY * outputStride + y,\n };\n}\nexports.getImageCoords = getImageCoords;\n\nfunction fillArray(element, size) {\n const result = new Array(size);\n for (let i = 0; i < size; i++) {\n result[i] = element;\n }\n return result;\n}\nexports.fillArray = fillArray;\n\nfunction clamp(a, min, max) {\n if (a < min) return min;\n if (a > max) return max;\n return a;\n}\nexports.clamp = clamp;\n\nfunction squaredDistance(y1, x1, y2, x2) {\n const dy = y2 - y1;\n const dx = x2 - x1;\n return dy * dy + dx * dx;\n}\nexports.squaredDistance = squaredDistance;\n\nfunction addVectors(a, b) {\n return { x: a.x + b.x, y: a.y + b.y };\n}\nexports.addVectors = addVectors;\n\nfunction clampVector(a, min, max) {\n return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\nexports.clampVector = clampVector;\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as kpt from './keypoints';\n\nfunction getPointsConfidence(heatmapScores, heatMapCoords) {\n const numKeypoints = heatMapCoords.shape[0];\n const result = new Float32Array(numKeypoints);\n for (let keypoint = 0; keypoint < numKeypoints; keypoint++) {\n const y = heatMapCoords.get(keypoint, 0);\n const x = heatMapCoords.get(keypoint, 1);\n result[keypoint] = heatmapScores.get(y, x, keypoint);\n }\n return result;\n}\nexports.getPointsConfidence = getPointsConfidence;\n\nfunction getOffsetPoint(y, x, keypoint, offsetsBuffer) {\n return {\n y: offsetsBuffer.get(y, x, keypoint),\n x: offsetsBuffer.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n };\n}\n\nfunction getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer) {\n const result = [];\n for (let keypoint = 0; keypoint < kpt.NUM_KEYPOINTS; keypoint++) {\n const heatmapY = heatMapCoordsBuffer.get(keypoint, 0).valueOf();\n const heatmapX = heatMapCoordsBuffer.get(keypoint, 1).valueOf();\n const { x, y } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsetsBuffer);\n result.push(y);\n result.push(x);\n }\n return tf.tensor2d(result, [kpt.NUM_KEYPOINTS, 2]);\n}\nexports.getOffsetVectors = getOffsetVectors;\n\nfunction getOffsetPoints(heatMapCoordsBuffer, outputStride, offsetsBuffer) {\n return tf.tidy(() => heatMapCoordsBuffer.toTensor().mul(tf.scalar(outputStride, 'int32')).toFloat().add(getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer)));\n}\nexports.getOffsetPoints = getOffsetPoints;\n\nfunction mod(a, b) {\n return tf.tidy(() => {\n const floored = a.div(tf.scalar(b, 'int32'));\n return a.sub(floored.mul(tf.scalar(b, 'int32')));\n });\n}\n\nfunction argmax2d(inputs) {\n const [height, width, depth] = inputs.shape;\n return tf.tidy(() => {\n const reshaped = inputs.reshape([height * width, depth]);\n const coords = reshaped.argMax(0);\n const yCoords = coords.div(tf.scalar(width, 'int32')).expandDims(1);\n const xCoords = mod(coords, width).expandDims(1);\n return tf.concat([yCoords, xCoords], 1);\n });\n}\nexports.argmax2d = argmax2d;\n", "import * as keypoints from './keypoints';\nimport * as vectors from './vectors';\nimport * as decoders from './decoders';\n\nconst parentChildrenTuples = keypoints.poseChain.map(([parentJoinName, childJoinName]) => ([keypoints.partIds[parentJoinName], keypoints.partIds[childJoinName]]));\nconst parentToChildEdges = parentChildrenTuples.map(([, childJointId]) => childJointId);\nconst childToParentEdges = parentChildrenTuples.map(([parentJointId]) => parentJointId);\nfunction getDisplacement(edgeId, point, displacements) {\n const numEdges = displacements.shape[2] / 2;\n return {\n y: displacements.get(point.y, point.x, edgeId),\n x: displacements.get(point.y, point.x, numEdges + edgeId),\n };\n}\nfunction getStridedIndexNearPoint(point, outputStride, height, width) {\n return {\n // @ts-ignore\n y: vectors.clamp(Math.round(point.y / outputStride), 0, height - 1),\n // @ts-ignore\n x: vectors.clamp(Math.round(point.x / outputStride), 0, width - 1),\n };\n}\n\nfunction traverseToTargetKeypoint(edgeId, sourceKeypoint, targetKeypointId, scoresBuffer, offsets, outputStride, displacements, offsetRefineStep = 2) {\n const [height, width] = scoresBuffer.shape;\n // Nearest neighbor interpolation for the source->target displacements.\n const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, outputStride, height, width);\n const displacement = getDisplacement(edgeId, sourceKeypointIndices, displacements);\n // @ts-ignore\n const displacedPoint = vectors.addVectors(sourceKeypoint.position, displacement);\n let targetKeypoint = displacedPoint;\n for (let i = 0; i < offsetRefineStep; i++) {\n const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n // @ts-ignore\n const offsetPoint = vectors.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetKeypointId, offsets);\n // @ts-ignore\n targetKeypoint = vectors.addVectors({\n x: targetKeypointIndices.x * outputStride,\n y: targetKeypointIndices.y * outputStride,\n }, { x: offsetPoint.x, y: offsetPoint.y });\n }\n const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n const score = scoresBuffer.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetKeypointId);\n return { position: targetKeypoint, part: keypoints.partNames[targetKeypointId], score };\n}\n\nfunction decodePose(root, scores, offsets, outputStride, displacementsFwd, displacementsBwd) {\n const numParts = scores.shape[2];\n const numEdges = parentToChildEdges.length;\n const instanceKeypoints = new Array(numParts);\n // Start a new detection instance at the position of the root.\n const { part: rootPart, score: rootScore } = root;\n // @ts-ignore\n const rootPoint = vectors.getImageCoords(rootPart, outputStride, offsets);\n instanceKeypoints[rootPart.id] = {\n score: rootScore,\n part: keypoints.partNames[rootPart.id],\n position: rootPoint,\n };\n // Decode the part positions upwards in the tree, following the backward displacements.\n for (let edge = numEdges - 1; edge >= 0; --edge) {\n const sourceKeypointId = parentToChildEdges[edge];\n const targetKeypointId = childToParentEdges[edge];\n if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsBwd);\n }\n }\n // Decode the part positions downwards in the tree, following the forward displacements.\n for (let edge = 0; edge < numEdges; ++edge) {\n const sourceKeypointId = childToParentEdges[edge];\n const targetKeypointId = parentToChildEdges[edge];\n if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsFwd);\n }\n }\n return instanceKeypoints;\n}\nexports.decodePose = decodePose;\n\nasync function decodeSinglePose(heatmapScores, offsets, config) {\n let totalScore = 0.0;\n // @ts-ignore\n const heatmapValues = decoders.argmax2d(heatmapScores);\n const allTensorBuffers = await Promise.all([heatmapScores.buffer(), offsets.buffer(), heatmapValues.buffer()]);\n const scoresBuffer = allTensorBuffers[0];\n const offsetsBuffer = allTensorBuffers[1];\n const heatmapValuesBuffer = allTensorBuffers[2];\n // @ts-ignore\n const offsetPoints = decoders.getOffsetPoints(heatmapValuesBuffer, config.body.outputStride, offsetsBuffer);\n const offsetPointsBuffer = await offsetPoints.buffer();\n // @ts-ignore\n const keypointConfidence = Array.from(decoders.getPointsConfidence(scoresBuffer, heatmapValuesBuffer));\n const instanceKeypoints = keypointConfidence.map((score, i) => {\n totalScore += score;\n return {\n position: {\n y: offsetPointsBuffer.get(i, 0),\n x: offsetPointsBuffer.get(i, 1),\n },\n part: keypoints.partNames[i],\n score,\n };\n });\n const filteredKeypoints = instanceKeypoints.filter((kpt) => kpt.score > config.body.scoreThreshold);\n heatmapValues.dispose();\n offsetPoints.dispose();\n return { keypoints: filteredKeypoints, score: totalScore / instanceKeypoints.length };\n}\nexports.decodeSinglePose = decodeSinglePose;\n", "import * as buildParts from './buildParts';\nimport * as decodePose from './decodePose';\nimport * as vectors from './vectors';\n\nconst kLocalMaximumRadius = 1;\n\nfunction withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, { x, y }, keypointId) {\n return poses.some(({ keypoints }) => {\n const correspondingKeypoint = keypoints[keypointId].position;\n // @ts-ignore\n return vectors.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n });\n}\n\nfunction getInstanceScore(existingPoses, squaredNmsRadius, instanceKeypoints) {\n const notOverlappedKeypointScores = instanceKeypoints.reduce((result, { position, score }, keypointId) => {\n if (!withinNmsRadiusOfCorrespondingPoint(existingPoses, squaredNmsRadius, position, keypointId)) result += score;\n return result;\n }, 0.0);\n return notOverlappedKeypointScores / instanceKeypoints.length;\n}\n\nfunction decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config) {\n const poses = [];\n // @ts-ignore\n const queue = buildParts.buildPartWithScoreQueue(config.body.scoreThreshold, kLocalMaximumRadius, scoresBuffer);\n const squaredNmsRadius = config.body.nmsRadius ^ 2;\n // Generate at most maxDetections object instances per image in decreasing root part score order.\n while (poses.length < config.body.maxDetections && !queue.empty()) {\n // The top element in the queue is the next root candidate.\n const root = queue.dequeue();\n // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n // @ts-ignore\n const rootImageCoords = vectors.getImageCoords(root.part, config.body.outputStride, offsetsBuffer);\n if (withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, rootImageCoords, root.part.id)) continue;\n // Else start a new detection instance at the position of the root.\n // @ts-ignore\n const keypoints = decodePose.decodePose(root, scoresBuffer, offsetsBuffer, config.body.outputStride, displacementsFwdBuffer, displacementsBwdBuffer);\n const score = getInstanceScore(poses, squaredNmsRadius, keypoints);\n if (score > config.body.scoreThreshold) poses.push({ keypoints, score });\n }\n return poses;\n}\nexports.decodeMultiplePoses = decodeMultiplePoses;\n", "import * as kpt from './keypoints';\n\nfunction eitherPointDoesntMeetConfidence(a, b, minConfidence) {\n return (a < minConfidence || b < minConfidence);\n}\n\nfunction getAdjacentKeyPoints(keypoints, minConfidence) {\n return kpt.connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n return result;\n }\n result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n return result;\n }, []);\n}\nexports.getAdjacentKeyPoints = getAdjacentKeyPoints;\n\nconst { NEGATIVE_INFINITY, POSITIVE_INFINITY } = Number;\nfunction getBoundingBox(keypoints) {\n return keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n maxX: Math.max(maxX, x),\n maxY: Math.max(maxY, y),\n minX: Math.min(minX, x),\n minY: Math.min(minY, y),\n }), {\n maxX: NEGATIVE_INFINITY,\n maxY: NEGATIVE_INFINITY,\n minX: POSITIVE_INFINITY,\n minY: POSITIVE_INFINITY,\n });\n}\nexports.getBoundingBox = getBoundingBox;\n\nfunction getBoundingBoxPoints(keypoints) {\n const { minX, minY, maxX, maxY } = getBoundingBox(keypoints);\n return [{ x: minX, y: minY }, { x: maxX, y: minY }, { x: maxX, y: maxY }, { x: minX, y: maxY }];\n}\nexports.getBoundingBoxPoints = getBoundingBoxPoints;\n\nasync function toTensorBuffers3D(tensors) {\n return Promise.all(tensors.map((tensor) => tensor.buffer()));\n}\nexports.toTensorBuffers3D = toTensorBuffers3D;\n\nfunction scalePose(pose, scaleY, scaleX) {\n return {\n score: pose.score,\n keypoints: pose.keypoints.map(({ score, part, position }) => ({\n score,\n part,\n position: { x: position.x * scaleX, y: position.y * scaleY },\n })),\n };\n}\nexports.scalePose = scalePose;\n\nfunction resizeTo(image, [targetH, targetW]) {\n const input = image.squeeze(0);\n const resized = input.resizeBilinear([targetH, targetW]);\n input.dispose();\n return resized;\n}\nexports.resizeTo = resizeTo;\n\nfunction scaleAndFlipPoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) {\n const scaledPoses = poses.map((pose) => scalePose(pose, height / inputResolutionHeight, width / inputResolutionWidth));\n return scaledPoses;\n}\nexports.scaleAndFlipPoses = scaleAndFlipPoses;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as modelBase from './modelBase';\nimport * as decodeMultiple from './decodeMultiple';\nimport * as decodePose from './decodePose';\nimport * as util from './util';\n\nasync function estimateMultiple(input, res, config) {\n return new Promise(async (resolve) => {\n const height = input.shape[1];\n const width = input.shape[2];\n // @ts-ignore\n const allTensorBuffers = await util.toTensorBuffers3D([res.heatmapScores, res.offsets, res.displacementFwd, res.displacementBwd]);\n const scoresBuffer = allTensorBuffers[0];\n const offsetsBuffer = allTensorBuffers[1];\n const displacementsFwdBuffer = allTensorBuffers[2];\n const displacementsBwdBuffer = allTensorBuffers[3];\n // @ts-ignore\n const poses = await decodeMultiple.decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config);\n // @ts-ignore\n const scaled = util.scaleAndFlipPoses(poses, [height, width], [config.body.inputSize, config.body.inputSize]);\n resolve(scaled);\n });\n}\n\nasync function estimateSingle(input, res, config) {\n return new Promise(async (resolve) => {\n const height = input.shape[1];\n const width = input.shape[2];\n // @ts-ignore\n const pose = await decodePose.decodeSinglePose(res.heatmapScores, res.offsets, config);\n const poses = [pose];\n // @ts-ignore\n const scaled = util.scaleAndFlipPoses(poses, [height, width], [config.body.inputSize, config.body.inputSize]);\n resolve(scaled);\n });\n}\n\nclass PoseNet {\n constructor(model) {\n this.baseModel = model;\n }\n\n async estimatePoses(input, config) {\n // @ts-ignore\n const resized = util.resizeTo(input, [config.body.inputSize, config.body.inputSize]);\n const res = this.baseModel.predict(resized, config);\n\n const poses = (config.body.maxDetections < 2) ? await estimateSingle(input, res, config) : await estimateMultiple(input, res, config);\n\n res.heatmapScores.dispose();\n res.offsets.dispose();\n res.displacementFwd.dispose();\n res.displacementBwd.dispose();\n resized.dispose();\n\n return poses;\n }\n\n dispose() {\n this.baseModel.dispose();\n }\n}\nexports.PoseNet = PoseNet;\n\nasync function load(config) {\n const model = await tf.loadGraphModel(config.body.modelPath);\n // @ts-ignore\n const mobilenet = new modelBase.BaseModel(model);\n log(`load model: ${config.body.modelPath.match(/\\/(.*)\\./)[1]}`);\n return new PoseNet(mobilenet);\n}\nexports.load = load;\n", "import * as modelPoseNet from './modelPoseNet';\nimport * as keypoints from './keypoints';\nimport * as util from './util';\n\n// @ts-ignore\nexports.load = modelPoseNet.load;\n// @ts-ignore\nexports.PoseNet = modelPoseNet.PoseNet;\n\nexports.partChannels = keypoints.partChannels;\nexports.partIds = keypoints.partIds;\nexports.partNames = keypoints.partNames;\nexports.poseChain = keypoints.poseChain;\n// @ts-ignore\nexports.getAdjacentKeyPoints = util.getAdjacentKeyPoints;\n// @ts-ignore\nexports.getBoundingBox = util.getBoundingBox;\n// @ts-ignore\nexports.getBoundingBoxPoints = util.getBoundingBoxPoints;\n// @ts-ignore\nexports.scaleAndFlipPoses = util.scaleAndFlipPoses;\n// @ts-ignore\nexports.scalePose = util.scalePose;\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\n\nclass HandDetector {\n constructor(model, inputSize, anchorsAnnotated) {\n this.model = model;\n this.anchors = anchorsAnnotated.map((anchor) => [anchor.x_center, anchor.y_center]);\n this.anchorsTensor = tf.tensor2d(this.anchors);\n this.inputSizeTensor = tf.tensor1d([inputSize, inputSize]);\n this.doubleInputSizeTensor = tf.tensor1d([inputSize * 2, inputSize * 2]);\n }\n\n normalizeBoxes(boxes) {\n return tf.tidy(() => {\n const boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n const boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n const boxCenterPoints = tf.add(tf.div(boxOffsets, this.inputSizeTensor), this.anchorsTensor);\n const halfBoxSizes = tf.div(boxSizes, this.doubleInputSizeTensor);\n const startPoints = tf.mul(tf.sub(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n const endPoints = tf.mul(tf.add(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n return tf.concat2d([startPoints, endPoints], 1);\n });\n }\n\n normalizeLandmarks(rawPalmLandmarks, index) {\n return tf.tidy(() => {\n const landmarks = tf.add(tf.div(rawPalmLandmarks.reshape([-1, 7, 2]), this.inputSizeTensor), this.anchors[index]);\n return tf.mul(landmarks, this.inputSizeTensor);\n });\n }\n\n async getBoxes(input, config) {\n const batched = this.model.predict(input);\n const predictions = batched.squeeze();\n batched.dispose();\n const scoresT = tf.tidy(() => tf.sigmoid(tf.slice(predictions, [0, 0], [-1, 1])).squeeze());\n const scores = scoresT.dataSync();\n const rawBoxes = tf.slice(predictions, [0, 1], [-1, 4]);\n const boxes = this.normalizeBoxes(rawBoxes);\n rawBoxes.dispose();\n const filteredT = await tf.image.nonMaxSuppressionAsync(boxes, scores, config.hand.maxHands, config.hand.iouThreshold, config.hand.scoreThreshold);\n const filtered = filteredT.arraySync();\n\n scoresT.dispose();\n filteredT.dispose();\n const hands = [];\n for (const index of filtered) {\n if (scores[index] >= config.hand.minConfidence) {\n const matchingBox = tf.slice(boxes, [index, 0], [1, -1]);\n const rawPalmLandmarks = tf.slice(predictions, [index, 5], [1, 14]);\n const palmLandmarks = tf.tidy(() => this.normalizeLandmarks(rawPalmLandmarks, index).reshape([-1, 2]));\n rawPalmLandmarks.dispose();\n hands.push({ box: matchingBox, palmLandmarks, confidence: scores[index] });\n }\n }\n predictions.dispose();\n boxes.dispose();\n return hands;\n }\n\n async estimateHandBounds(input, config) {\n const inputHeight = input.shape[1];\n const inputWidth = input.shape[2];\n const image = tf.tidy(() => input.resizeBilinear([config.hand.inputSize, config.hand.inputSize]).div(127.5).sub(1));\n const predictions = await this.getBoxes(image, config);\n image.dispose();\n const hands = [];\n if (!predictions || predictions.length === 0) return hands;\n for (const prediction of predictions) {\n const boxes = prediction.box.dataSync();\n const startPoint = boxes.slice(0, 2);\n const endPoint = boxes.slice(2, 4);\n const palmLandmarks = prediction.palmLandmarks.arraySync();\n prediction.box.dispose();\n prediction.palmLandmarks.dispose();\n hands.push(box.scaleBoxCoordinates({ startPoint, endPoint, palmLandmarks, confidence: prediction.confidence }, [inputWidth / config.hand.inputSize, inputHeight / config.hand.inputSize]));\n }\n return hands;\n }\n}\nexports.HandDetector = HandDetector;\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\nimport * as util from './util';\n// eslint-disable-next-line no-unused-vars\nimport { log } from '../log.js';\n\n// const PALM_BOX_SHIFT_VECTOR = [0, -0.4];\nconst PALM_BOX_ENLARGE_FACTOR = 5; // default 3\n// const HAND_BOX_SHIFT_VECTOR = [0, -0.1]; // move detected hand box by x,y to ease landmark detection\nconst HAND_BOX_ENLARGE_FACTOR = 1.65; // default 1.65\nconst PALM_LANDMARK_IDS = [0, 5, 9, 13, 17, 1, 2];\nconst PALM_LANDMARKS_INDEX_OF_PALM_BASE = 0;\nconst PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE = 2;\n\nclass HandPipeline {\n constructor(handDetector, landmarkDetector, inputSize) {\n this.handDetector = handDetector;\n this.landmarkDetector = landmarkDetector;\n this.inputSize = inputSize;\n this.storedBoxes = [];\n this.skipped = 0;\n this.detectedHands = 0;\n }\n\n getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n // return box.enlargeBox(box.squarifyBox(box.shiftBox(boxAroundPalm, PALM_BOX_SHIFT_VECTOR)), PALM_BOX_ENLARGE_FACTOR);\n return box.enlargeBox(box.squarifyBox(boxAroundPalm), PALM_BOX_ENLARGE_FACTOR);\n }\n\n getBoxForHandLandmarks(landmarks) {\n const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n // const boxAroundHand = box.enlargeBox(box.squarifyBox(box.shiftBox(boundingBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n const boxAroundHand = box.enlargeBox(box.squarifyBox(boundingBox), HAND_BOX_ENLARGE_FACTOR);\n boxAroundHand.palmLandmarks = [];\n for (let i = 0; i < PALM_LANDMARK_IDS.length; i++) {\n boxAroundHand.palmLandmarks.push(landmarks[PALM_LANDMARK_IDS[i]].slice(0, 2));\n }\n return boxAroundHand;\n }\n\n transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n const boxSize = box.getBoxSize(box2);\n const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n const coordsScaled = rawCoords.map((coord) => [\n scaleFactor[0] * (coord[0] - this.inputSize / 2),\n scaleFactor[1] * (coord[1] - this.inputSize / 2),\n scaleFactor[2] * coord[2],\n ]);\n const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n const coordsRotated = coordsScaled.map((coord) => {\n const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n return [...rotated, coord[2]];\n });\n const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n const boxCenter = [...box.getBoxCenter(box2), 1];\n const originalBoxCenter = [\n util.dot(boxCenter, inverseRotationMatrix[0]),\n util.dot(boxCenter, inverseRotationMatrix[1]),\n ];\n return coordsRotated.map((coord) => [\n coord[0] + originalBoxCenter[0],\n coord[1] + originalBoxCenter[1],\n coord[2],\n ]);\n }\n\n async estimateHands(image, config) {\n let useFreshBox = false;\n\n // run new detector every skipFrames unless we only want box to start with\n let boxes;\n if ((this.skipped === 0) || (this.skipped > config.hand.skipFrames) || !config.hand.landmarks || !config.videoOptimized) {\n boxes = await this.handDetector.estimateHandBounds(image, config);\n this.skipped = 0;\n }\n if (config.videoOptimized) this.skipped++;\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxHands) || !config.hand.landmarks)) {\n this.detectedHands = 0;\n this.storedBoxes = [...boxes];\n // for (const possible of boxes) this.storedBoxes.push(possible);\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\n const hands = [];\n // log('hand', `skipped: ${this.skipped} max: ${config.hand.maxHands} detected: ${this.detectedHands} stored: ${this.storedBoxes.length} new: ${boxes?.length}`);\n\n // go through working set of boxes\n for (let i = 0; i < this.storedBoxes.length; i++) {\n const currentBox = this.storedBoxes[i];\n if (!currentBox) continue;\n if (config.hand.landmarks) {\n const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_PALM_BASE], currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE]) : 0;\n const palmCenter = box.getBoxCenter(currentBox);\n const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n const rotatedImage = config.hand.rotation ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n const croppedInput = box.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n const handImage = croppedInput.div(255);\n croppedInput.dispose();\n rotatedImage.dispose();\n const [confidenceT, keypoints] = await this.landmarkDetector.predict(handImage);\n handImage.dispose();\n const confidence = confidenceT.dataSync()[0];\n confidenceT.dispose();\n if (confidence >= config.hand.minConfidence) {\n const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n const rawCoords = keypointsReshaped.arraySync();\n keypoints.dispose();\n keypointsReshaped.dispose();\n const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n this.storedBoxes[i] = nextBoundingBox;\n const result = {\n landmarks: coords,\n confidence,\n box: {\n topLeft: nextBoundingBox.startPoint,\n bottomRight: nextBoundingBox.endPoint,\n },\n };\n hands.push(result);\n } else {\n this.storedBoxes[i] = null;\n }\n keypoints.dispose();\n } else {\n // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n const enlarged = box.enlargeBox(box.squarifyBox(currentBox), HAND_BOX_ENLARGE_FACTOR);\n const result = {\n confidence: currentBox.confidence,\n box: {\n topLeft: enlarged.startPoint,\n bottomRight: enlarged.endPoint,\n },\n };\n hands.push(result);\n }\n }\n this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n this.detectedHands = hands.length;\n return hands;\n }\n\n // eslint-disable-next-line class-methods-use-this\n calculateLandmarksBoundingBox(landmarks) {\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint };\n }\n}\n\nexports.HandPipeline = HandPipeline;\n", "exports.anchors = [\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n 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1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n];\n", "// https://storage.googleapis.com/tfjs-models/demos/handpose/index.html\n\nimport { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as handdetector from './handdetector';\nimport * as handpipeline from './handpipeline';\nimport * as anchors from './anchors';\n\nconst MESH_ANNOTATIONS = {\n thumb: [1, 2, 3, 4],\n indexFinger: [5, 6, 7, 8],\n middleFinger: [9, 10, 11, 12],\n ringFinger: [13, 14, 15, 16],\n pinky: [17, 18, 19, 20],\n palmBase: [0],\n};\n\nclass HandPose {\n constructor(handPipeline) {\n this.handPipeline = handPipeline;\n }\n\n static getAnnotations() {\n return MESH_ANNOTATIONS;\n }\n\n async estimateHands(input, config) {\n const predictions = await this.handPipeline.estimateHands(input, config);\n if (!predictions) return [];\n const hands = [];\n for (const prediction of predictions) {\n const annotations = {};\n if (prediction.landmarks) {\n for (const key of Object.keys(MESH_ANNOTATIONS)) {\n annotations[key] = MESH_ANNOTATIONS[key].map((index) => prediction.landmarks[index]);\n }\n }\n const box = prediction.box ? [\n Math.max(0, prediction.box.topLeft[0]),\n Math.max(0, prediction.box.topLeft[1]),\n Math.min(input.shape[2], prediction.box.bottomRight[0]) - prediction.box.topLeft[0],\n Math.min(input.shape[1], prediction.box.bottomRight[1]) - prediction.box.topLeft[1],\n ] : 0;\n hands.push({\n confidence: prediction.confidence,\n box,\n landmarks: prediction.landmarks,\n annotations,\n });\n }\n return hands;\n }\n}\nexports.HandPose = HandPose;\n\nasync function load(config) {\n const [handDetectorModel, handPoseModel] = await Promise.all([\n config.hand.enabled ? tf.loadGraphModel(config.hand.detector.modelPath, { fromTFHub: config.hand.detector.modelPath.includes('tfhub.dev') }) : null,\n config.hand.landmarks ? tf.loadGraphModel(config.hand.skeleton.modelPath, { fromTFHub: config.hand.skeleton.modelPath.includes('tfhub.dev') }) : null,\n ]);\n // @ts-ignore\n const handDetector = new handdetector.HandDetector(handDetectorModel, config.hand.inputSize, anchors.anchors);\n // @ts-ignore\n const handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel, config.hand.inputSize);\n const handPose = new HandPose(handPipeline);\n if (config.hand.enabled) log(`load model: ${config.hand.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n if (config.hand.landmarks) log(`load model: ${config.hand.skeleton.modelPath.match(/\\/(.*)\\./)[1]}`);\n return handPose;\n}\nexports.load = load;\n", "exports.body = (res) => {\n if (!res) return [];\n const gestures = [];\n for (let i = 0; i < res.length; i++) {\n // raising hands\n const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n if (nose && leftWrist && rightWrist && (leftWrist.position.y < nose.position.y) && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'i give up' });\n else if (nose && leftWrist && (leftWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise left hand' });\n else if (nose && rightWrist && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise right hand' });\n\n // leaning\n const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n if (leftShoulder && rightShoulder) gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position.y > rightShoulder.position.y) ? 'left' : 'right'}` });\n }\n return gestures;\n};\n\nexports.face = (res) => {\n if (!res) return [];\n const gestures = [];\n for (let i = 0; i < res.length; i++) {\n if (res[i].mesh && res[i].mesh.length > 0) {\n const eyeFacing = res[i].mesh[35][2] - res[i].mesh[263][2];\n if (Math.abs(eyeFacing) < 10) gestures.push({ face: i, gesture: 'facing camera' });\n else gestures.push({ face: i, gesture: `facing ${eyeFacing < 0 ? 'right' : 'left'}` });\n const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n const chinDepth = res[i].mesh[152][2];\n if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n }\n }\n return gestures;\n};\n\nexports.iris = (res) => {\n if (!res) return [];\n const gestures = [];\n for (let i = 0; i < res.length; i++) {\n if (!res[i].annotations || !res[i].annotations.leftEyeIris || !res[i].annotations.rightEyeIris) continue;\n const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n if (difference < 0.25) gestures.push({ iris: i, gesture: 'looking at camera' });\n }\n return gestures;\n};\n\nexports.hand = (res) => {\n if (!res) return [];\n const gestures = [];\n for (let i = 0; i < res.length; i++) {\n const fingers = [];\n for (const [finger, pos] of Object.entries(res[i]['annotations'])) {\n if (finger !== 'palmBase') fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n }\n if (fingers && fingers.length > 0) {\n const closest = fingers.reduce((best, a) => (best.position[2] < a.position[2] ? best : a));\n const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n gestures.push({ hand: i, gesture: `${closest.name} forward ${highest.name} up` });\n }\n }\n return gestures;\n};\n", "/* eslint-disable no-use-before-define */\n/*\nWebGLImageFilter - MIT Licensed\n2013, Dominic Szablewski - phoboslab.org\n\n*/\n\nconst WebGLProgram = function (gl, vertexSource, fragmentSource) {\n const _collect = function (source, prefix, collection) {\n const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n source.replace(r, (match, name) => {\n collection[name] = 0;\n return match;\n });\n };\n\n const _compile = function (source, type) {\n const shader = gl.createShader(type);\n gl.shaderSource(shader, source);\n gl.compileShader(shader);\n\n if (!gl.getShaderParameter(shader, gl.COMPILE_STATUS)) {\n // @ts-ignore\n throw new Error('Filter: GL compile failed', gl.getShaderInfoLog(shader));\n }\n return shader;\n };\n\n this.uniform = {};\n this.attribute = {};\n\n const _vsh = _compile(vertexSource, gl.VERTEX_SHADER);\n const _fsh = _compile(fragmentSource, gl.FRAGMENT_SHADER);\n\n this.id = gl.createProgram();\n gl.attachShader(this.id, _vsh);\n gl.attachShader(this.id, _fsh);\n gl.linkProgram(this.id);\n\n if (!gl.getProgramParameter(this.id, gl.LINK_STATUS)) {\n // @ts-ignore\n throw new Error('Filter: GL link failed', gl.getProgramInfoLog(this.id));\n }\n\n gl.useProgram(this.id);\n\n // Collect attributes\n _collect(vertexSource, 'attribute', this.attribute);\n for (const a in this.attribute) {\n this.attribute[a] = gl.getAttribLocation(this.id, a);\n }\n\n // Collect uniforms\n _collect(vertexSource, 'uniform', this.uniform);\n _collect(fragmentSource, 'uniform', this.uniform);\n for (const u in this.uniform) {\n this.uniform[u] = gl.getUniformLocation(this.id, u);\n }\n};\n\nconst WebGLImageFilter = function (params) {\n if (!params) params = { };\n let _drawCount = 0;\n let _sourceTexture = null;\n let _lastInChain = false;\n let _currentFramebufferIndex = -1;\n let _tempFramebuffers = [null, null];\n let _filterChain = [];\n let _width = -1;\n let _height = -1;\n let _vertexBuffer = null;\n let _currentProgram = null;\n const _canvas = params.canvas || document.createElement('canvas');\n\n // key is the shader program source, value is the compiled program\n const _shaderProgramCache = { };\n\n const gl = _canvas.getContext('webgl');\n if (!gl) throw new Error('Filter: getContext() failed');\n\n this.addFilter = function (name) {\n // eslint-disable-next-line prefer-rest-params\n const args = Array.prototype.slice.call(arguments, 1);\n const filter = _filter[name];\n\n _filterChain.push({ func: filter, args });\n };\n\n this.reset = function () {\n _filterChain = [];\n };\n\n this.apply = function (image) {\n _resize(image.width, image.height);\n _drawCount = 0;\n\n // Create the texture for the input image if we haven't yet\n if (!_sourceTexture) _sourceTexture = gl.createTexture();\n gl.bindTexture(gl.TEXTURE_2D, _sourceTexture);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n\n // No filters? Just draw\n if (_filterChain.length === 0) {\n // const program = _compileShader(SHADER.FRAGMENT_IDENTITY);\n _draw();\n return _canvas;\n }\n\n for (let i = 0; i < _filterChain.length; i++) {\n _lastInChain = (i === _filterChain.length - 1);\n const f = _filterChain[i];\n f.func.apply(this, f.args || []);\n }\n\n return _canvas;\n };\n\n const _resize = function (width, height) {\n // Same width/height? Nothing to do here\n if (width === _width && height === _height) { return; }\n\n _canvas.width = width;\n _width = width;\n _canvas.height = height;\n _height = height;\n\n // Create the context if we don't have it yet\n if (!_vertexBuffer) {\n // Create the vertex buffer for the two triangles [x, y, u, v] * 6\n const vertices = new Float32Array([\n -1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0,\n -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0,\n ]);\n // eslint-disable-next-line no-unused-expressions\n (_vertexBuffer = gl.createBuffer(), gl.bindBuffer(gl.ARRAY_BUFFER, _vertexBuffer));\n gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);\n\n // Note sure if this is a good idea; at least it makes texture loading\n // in Ejecta instant.\n gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n }\n\n gl.viewport(0, 0, _width, _height);\n\n // Delete old temp framebuffers\n _tempFramebuffers = [null, null];\n };\n\n const _getTempFramebuffer = function (index) {\n // @ts-ignore\n _tempFramebuffers[index] = _tempFramebuffers[index] || _createFramebufferTexture(_width, _height);\n return _tempFramebuffers[index];\n };\n\n const _createFramebufferTexture = function (width, height) {\n const fbo = gl.createFramebuffer();\n gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n\n const renderbuffer = gl.createRenderbuffer();\n gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n\n const texture = gl.createTexture();\n gl.bindTexture(gl.TEXTURE_2D, texture);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n\n gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n\n gl.bindTexture(gl.TEXTURE_2D, null);\n gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n\n return { fbo, texture };\n };\n\n const _draw = function (flags) {\n let source = null;\n let target = null;\n let flipY = false;\n\n // Set up the source\n if (_drawCount === 0) {\n // First draw call - use the source texture\n source = _sourceTexture;\n } else {\n // All following draw calls use the temp buffer last drawn to\n // @ts-ignore\n source = _getTempFramebuffer(_currentFramebufferIndex)?.texture;\n }\n _drawCount++;\n\n // Set up the target\n if (_lastInChain && !(flags & DRAW.INTERMEDIATE)) {\n // Last filter in our chain - draw directly to the WebGL Canvas. We may\n // also have to flip the image vertically now\n target = null;\n flipY = _drawCount % 2 === 0;\n } else {\n // Intermediate draw call - get a temp buffer to draw to\n _currentFramebufferIndex = (_currentFramebufferIndex + 1) % 2;\n // @ts-ignore\n target = _getTempFramebuffer(_currentFramebufferIndex)?.fbo;\n }\n\n // Bind the source and target and draw the two triangles\n gl.bindTexture(gl.TEXTURE_2D, source);\n gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n\n gl.uniform1f(_currentProgram.uniform.flipY, (flipY ? -1 : 1));\n gl.drawArrays(gl.TRIANGLES, 0, 6);\n };\n\n const _compileShader = function (fragmentSource) {\n if (_shaderProgramCache[fragmentSource]) {\n _currentProgram = _shaderProgramCache[fragmentSource];\n gl.useProgram(_currentProgram.id);\n return _currentProgram;\n }\n\n // Compile shaders\n _currentProgram = new WebGLProgram(gl, SHADER.VERTEX_IDENTITY, fragmentSource);\n\n const floatSize = Float32Array.BYTES_PER_ELEMENT;\n const vertSize = 4 * floatSize;\n gl.enableVertexAttribArray(_currentProgram.attribute.pos);\n gl.vertexAttribPointer(_currentProgram.attribute.pos, 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n gl.enableVertexAttribArray(_currentProgram.attribute.uv);\n gl.vertexAttribPointer(_currentProgram.attribute.uv, 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n\n _shaderProgramCache[fragmentSource] = _currentProgram;\n return _currentProgram;\n };\n\n let DRAW = { INTERMEDIATE: 1 };\n\n let SHADER = {};\n SHADER.VERTEX_IDENTITY = [\n 'precision highp float;',\n 'attribute vec2 pos;',\n 'attribute vec2 uv;',\n 'varying vec2 vUv;',\n 'uniform float flipY;',\n\n 'void main(void) {',\n 'vUv = uv;',\n 'gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);',\n '}',\n ].join('\\n');\n\n SHADER.FRAGMENT_IDENTITY = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n\n 'void main(void) {',\n 'gl_FragColor = texture2D(texture, vUv);',\n '}',\n ].join('\\n');\n\n let _filter = {};\n\n // -------------------------------------------------------------------------\n // Color Matrix Filter\n\n _filter.colorMatrix = function (matrix) {\n // Create a Float32 Array and normalize the offset component to 0-1\n const m = new Float32Array(matrix);\n m[4] /= 255;\n m[9] /= 255;\n m[14] /= 255;\n m[19] /= 255;\n\n // Can we ignore the alpha value? Makes things a bit faster.\n const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0)\n ? _filter.colorMatrix.SHADER.WITHOUT_ALPHA\n : _filter.colorMatrix.SHADER.WITH_ALPHA;\n\n const program = _compileShader(shader);\n gl.uniform1fv(program.uniform.m, m);\n _draw();\n };\n\n _filter.colorMatrix.SHADER = {};\n _filter.colorMatrix.SHADER.WITH_ALPHA = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform float m[20];',\n\n 'void main(void) {',\n 'vec4 c = texture2D(texture, vUv);',\n 'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];',\n 'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];',\n 'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];',\n 'gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];',\n '}',\n ].join('\\n');\n _filter.colorMatrix.SHADER.WITHOUT_ALPHA = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform float m[20];',\n\n 'void main(void) {',\n 'vec4 c = texture2D(texture, vUv);',\n 'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];',\n 'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];',\n 'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];',\n 'gl_FragColor.a = c.a;',\n '}',\n ].join('\\n');\n\n _filter.brightness = function (brightness) {\n const b = (brightness || 0) + 1;\n _filter.colorMatrix([\n b, 0, 0, 0, 0,\n 0, b, 0, 0, 0,\n 0, 0, b, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.saturation = function (amount) {\n const x = (amount || 0) * 2 / 3 + 1;\n const y = ((x - 1) * -0.5);\n _filter.colorMatrix([\n x, y, y, 0, 0,\n y, x, y, 0, 0,\n y, y, x, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.desaturate = function () {\n _filter.saturation(-1);\n };\n\n _filter.contrast = function (amount) {\n const v = (amount || 0) + 1;\n const o = -128 * (v - 1);\n\n _filter.colorMatrix([\n v, 0, 0, 0, o,\n 0, v, 0, 0, o,\n 0, 0, v, 0, o,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.negative = function () {\n _filter.contrast(-2);\n };\n\n _filter.hue = function (rotation) {\n rotation = (rotation || 0) / 180 * Math.PI;\n const cos = Math.cos(rotation);\n const sin = Math.sin(rotation);\n const lumR = 0.213;\n const lumG = 0.715;\n const lumB = 0.072;\n\n _filter.colorMatrix([\n lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.desaturateLuminance = function () {\n _filter.colorMatrix([\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.sepia = function () {\n _filter.colorMatrix([\n 0.393, 0.7689999, 0.18899999, 0, 0,\n 0.349, 0.6859999, 0.16799999, 0, 0,\n 0.272, 0.5339999, 0.13099999, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.brownie = function () {\n _filter.colorMatrix([\n 0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n 0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.vintagePinhole = function () {\n _filter.colorMatrix([\n 0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n 0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n 0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.kodachrome = function () {\n _filter.colorMatrix([\n 1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.technicolor = function () {\n _filter.colorMatrix([\n 1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.polaroid = function () {\n _filter.colorMatrix([\n 1.438, -0.062, -0.062, 0, 0,\n -0.122, 1.378, -0.122, 0, 0,\n -0.016, -0.016, 1.483, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.shiftToBGR = function () {\n _filter.colorMatrix([\n 0, 0, 1, 0, 0,\n 0, 1, 0, 0, 0,\n 1, 0, 0, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n // -------------------------------------------------------------------------\n // Convolution Filter\n\n _filter.convolution = function (matrix) {\n const m = new Float32Array(matrix);\n const pixelSizeX = 1 / _width;\n const pixelSizeY = 1 / _height;\n\n const program = _compileShader(_filter.convolution.SHADER);\n gl.uniform1fv(program.uniform.m, m);\n gl.uniform2f(program.uniform.px, pixelSizeX, pixelSizeY);\n _draw();\n };\n\n _filter.convolution.SHADER = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform vec2 px;',\n 'uniform float m[9];',\n\n 'void main(void) {',\n 'vec4 c11 = texture2D(texture, vUv - px);', // top left\n 'vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y));', // top center\n 'vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y));', // top right\n\n 'vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) );', // mid left\n 'vec4 c22 = texture2D(texture, vUv);', // mid center\n 'vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) );', // mid right\n\n 'vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) );', // bottom left\n 'vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) );', // bottom center\n 'vec4 c33 = texture2D(texture, vUv + px );', // bottom right\n\n 'gl_FragColor = ',\n 'c11 * m[0] + c12 * m[1] + c22 * m[2] +',\n 'c21 * m[3] + c22 * m[4] + c23 * m[5] +',\n 'c31 * m[6] + c32 * m[7] + c33 * m[8];',\n 'gl_FragColor.a = c22.a;',\n '}',\n ].join('\\n');\n\n _filter.detectEdges = function () {\n _filter.convolution.call(this, [\n 0, 1, 0,\n 1, -4, 1,\n 0, 1, 0,\n ]);\n };\n\n _filter.sobelX = function () {\n _filter.convolution.call(this, [\n -1, 0, 1,\n -2, 0, 2,\n -1, 0, 1,\n ]);\n };\n\n _filter.sobelY = function () {\n _filter.convolution.call(this, [\n -1, -2, -1,\n 0, 0, 0,\n 1, 2, 1,\n ]);\n };\n\n _filter.sharpen = function (amount) {\n const a = amount || 1;\n _filter.convolution.call(this, [\n 0, -1 * a, 0,\n -1 * a, 1 + 4 * a, -1 * a,\n 0, -1 * a, 0,\n ]);\n };\n\n _filter.emboss = function (size) {\n const s = size || 1;\n _filter.convolution.call(this, [\n -2 * s, -1 * s, 0,\n -1 * s, 1, 1 * s,\n 0, 1 * s, 2 * s,\n ]);\n };\n\n // -------------------------------------------------------------------------\n // Blur Filter\n\n _filter.blur = function (size) {\n const blurSizeX = (size / 7) / _width;\n const blurSizeY = (size / 7) / _height;\n\n const program = _compileShader(_filter.blur.SHADER);\n\n // Vertical\n gl.uniform2f(program.uniform.px, 0, blurSizeY);\n _draw(DRAW.INTERMEDIATE);\n\n // Horizontal\n gl.uniform2f(program.uniform.px, blurSizeX, 0);\n _draw();\n };\n\n _filter.blur.SHADER = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform vec2 px;',\n\n 'void main(void) {',\n 'gl_FragColor = vec4(0.0);',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;',\n 'gl_FragColor += texture2D(texture, vUv )*0.159576912161;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x, 1.0*px.y))*0.147308056121;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x, 2.0*px.y))*0.115876621105;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x, 3.0*px.y))*0.0776744219933;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x, 4.0*px.y))*0.0443683338718;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x, 5.0*px.y))*0.0215963866053;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265;',\n '}',\n ].join('\\n');\n\n // -------------------------------------------------------------------------\n // Pixelate Filter\n\n _filter.pixelate = function (size) {\n const blurSizeX = (size) / _width;\n const blurSizeY = (size) / _height;\n\n const program = _compileShader(_filter.pixelate.SHADER);\n\n // Horizontal\n gl.uniform2f(program.uniform.size, blurSizeX, blurSizeY);\n _draw();\n };\n\n _filter.pixelate.SHADER = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform vec2 size;',\n 'uniform sampler2D texture;',\n\n 'vec2 pixelate(vec2 coord, vec2 size) {',\n 'return floor( coord / size ) * size;',\n '}',\n\n 'void main(void) {',\n 'gl_FragColor = vec4(0.0);',\n 'vec2 coord = pixelate(vUv, size);',\n 'gl_FragColor += texture2D(texture, coord);',\n '}',\n ].join('\\n');\n};\n\nexports.Canvas = WebGLImageFilter;\n", "import { log } from './log.js';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as fxImage from './imagefx.js';\n\n// internal temp canvases\nlet inCanvas = null;\nlet outCanvas = null;\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nfunction process(input, config) {\n let tensor;\n if (input instanceof tf.Tensor) {\n tensor = tf.clone(input);\n } else {\n const originalWidth = input.naturalWidth || input.videoWidth || input.width || (input.shape && (input.shape[1] > 0));\n const originalHeight = input.naturalHeight || input.videoHeight || input.height || (input.shape && (input.shape[2] > 0));\n let targetWidth = originalWidth;\n let targetHeight = originalHeight;\n if (config.filter.width > 0) targetWidth = config.filter.width;\n else if (config.filter.height > 0) targetWidth = originalWidth * (config.filter.height / originalHeight);\n if (config.filter.height > 0) targetHeight = config.filter.height;\n else if (config.filter.width > 0) targetHeight = originalHeight * (config.filter.width / originalWidth);\n if (!targetWidth || !targetHeight) {\n log('Human: invalid input', input);\n return null;\n }\n if (!inCanvas || (inCanvas.width !== targetWidth) || (inCanvas.height !== targetHeight)) {\n inCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n if (inCanvas.width !== targetWidth) inCanvas.width = targetWidth;\n if (inCanvas.height !== targetHeight) inCanvas.height = targetHeight;\n }\n const ctx = inCanvas.getContext('2d');\n if (input instanceof ImageData) ctx.putImageData(input, 0, 0);\n else ctx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);\n if (config.filter.enabled) {\n if (!this.fx || !outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas.height !== outCanvas.height)) {\n outCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(inCanvas.width, inCanvas.height) : document.createElement('canvas');\n if (outCanvas.width !== inCanvas.width) outCanvas.width = inCanvas.width;\n if (outCanvas.height !== inCanvas.height) outCanvas.height = inCanvas.height;\n this.fx = tf.ENV.flags.IS_BROWSER ? new fxImage.Canvas({ canvas: outCanvas }) : null; // && (typeof document !== 'undefined')\n }\n if (!this.fx) return inCanvas;\n this.fx.reset();\n this.fx.addFilter('brightness', config.filter.brightness); // must have at least one filter enabled\n if (config.filter.contrast !== 0) this.fx.addFilter('contrast', config.filter.contrast);\n if (config.filter.sharpness !== 0) this.fx.addFilter('sharpen', config.filter.sharpness);\n if (config.filter.blur !== 0) this.fx.addFilter('blur', config.filter.blur);\n if (config.filter.saturation !== 0) this.fx.addFilter('saturation', config.filter.saturation);\n if (config.filter.hue !== 0) this.fx.addFilter('hue', config.filter.hue);\n if (config.filter.negative) this.fx.addFilter('negative');\n if (config.filter.sepia) this.fx.addFilter('sepia');\n if (config.filter.vintage) this.fx.addFilter('brownie');\n if (config.filter.sepia) this.fx.addFilter('sepia');\n if (config.filter.kodachrome) this.fx.addFilter('kodachrome');\n if (config.filter.technicolor) this.fx.addFilter('technicolor');\n if (config.filter.polaroid) this.fx.addFilter('polaroid');\n if (config.filter.pixelate !== 0) this.fx.addFilter('pixelate', config.filter.pixelate);\n this.fx.apply(inCanvas);\n // read pixel data\n /*\n const gl = outCanvas.getContext('webgl');\n if (gl) {\n const glBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 4);\n const pixBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 3);\n gl.readPixels(0, 0, outCanvas.width, outCanvas.height, gl.RGBA, gl.UNSIGNED_BYTE, glBuffer);\n // gl returns rbga while we only need rgb, so discarding alpha channel\n // gl returns starting point as lower left, so need to invert vertical\n let i = 0;\n for (let y = outCanvas.height - 1; y >= 0; y--) {\n for (let x = 0; x < outCanvas.width; x++) {\n const index = (x + y * outCanvas.width) * 4;\n pixBuffer[i++] = glBuffer[index + 0];\n pixBuffer[i++] = glBuffer[index + 1];\n pixBuffer[i++] = glBuffer[index + 2];\n }\n }\n outCanvas.data = pixBuffer;\n }\n */\n } else {\n outCanvas = inCanvas;\n }\n let pixels;\n if (outCanvas.data) {\n const shape = [outCanvas.height, outCanvas.width, 3];\n pixels = tf.tensor3d(outCanvas.data, shape, 'int32');\n } else if ((config.backend === 'webgl') || (outCanvas instanceof ImageData)) {\n // tf kernel-optimized method to get imagedata, also if input is imagedata, just use it\n pixels = tf.browser.fromPixels(outCanvas);\n } else {\n // cpu and wasm kernel does not implement efficient fromPixels method nor we can use canvas as-is, so we do a silly one more canvas\n const tempCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n tempCanvas.width = targetWidth;\n tempCanvas.height = targetHeight;\n const tempCtx = tempCanvas.getContext('2d');\n tempCtx?.drawImage(outCanvas, 0, 0);\n const data = tempCtx?.getImageData(0, 0, targetWidth, targetHeight);\n pixels = tf.browser.fromPixels(data);\n }\n const casted = pixels.toFloat();\n tensor = casted.expandDims(0);\n pixels.dispose();\n casted.dispose();\n }\n return { tensor, canvas: config.filter.return ? outCanvas : null };\n}\n\nexports.process = process;\n", "// helper function: wrapper around console output\nexport function log(...msg) {\n const dt = new Date();\n const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n // eslint-disable-next-line no-console\n if (msg) console.log(ts, 'Human:', ...msg);\n}\n", "import { log } from './log.js';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as backend from './tfjs/backend.js';\nimport * as facemesh from './face/facemesh.js';\nimport * as age from './age/age.js';\nimport * as gender from './gender/gender.js';\nimport * as emotion from './emotion/emotion.js';\nimport * as embedding from './embedding/embedding.js';\nimport * as posenet from './body/posenet.js';\nimport * as handpose from './hand/handpose.js';\nimport * as gesture from './gesture/gesture.js';\nimport * as image from './image.js';\nimport * as profile from './profile.js';\nimport * as config from '../config.js';\nimport * as sample from './sample.js';\nimport * as app from '../package.json';\n\n// helper function: gets elapsed time on both browser and nodejs\nconst now = () => {\n if (typeof performance !== 'undefined') return performance.now();\n // @ts-ignore\n return parseInt(Number(process.hrtime.bigint()) / 1000 / 1000);\n};\n\n// helper function: perform deep merge of multiple objects so it allows full inheriance with overrides\nfunction mergeDeep(...objects) {\n const isObject = (obj) => obj && typeof obj === 'object';\n return objects.reduce((prev, obj) => {\n Object.keys(obj || {}).forEach((key) => {\n const pVal = prev[key];\n const oVal = obj[key];\n if (Array.isArray(pVal) && Array.isArray(oVal)) {\n prev[key] = pVal.concat(...oVal);\n } else if (isObject(pVal) && isObject(oVal)) {\n prev[key] = mergeDeep(pVal, oVal);\n } else {\n prev[key] = oVal;\n }\n });\n return prev;\n }, {});\n}\n\nclass Human {\n constructor(userConfig = {}) {\n this.tf = tf;\n this.version = app.version;\n this.config = mergeDeep(config.default, userConfig);\n this.fx = null;\n this.state = 'idle';\n this.numTensors = 0;\n this.analyzeMemoryLeaks = false;\n this.checkSanity = false;\n this.firstRun = true;\n this.perf = {};\n // object that contains all initialized models\n this.models = {\n facemesh: null,\n posenet: null,\n handpose: null,\n iris: null,\n age: null,\n gender: null,\n emotion: null,\n };\n // export raw access to underlying models\n this.facemesh = facemesh;\n this.age = age;\n this.gender = gender;\n this.emotion = emotion;\n this.body = posenet;\n this.hand = handpose;\n }\n\n profile() {\n // @ts-ignore\n if (this.config.profile) return profile.data;\n return {};\n }\n\n // helper function: measure tensor leak\n analyze(...msg) {\n if (!this.analyzeMemoryLeaks) return;\n const current = tf.engine().state.numTensors;\n const previous = this.numTensors;\n this.numTensors = current;\n const leaked = current - previous;\n if (leaked !== 0) log(...msg, leaked);\n }\n\n // quick sanity check on inputs\n sanity(input) {\n if (!this.checkSanity) return null;\n if (!input) return 'input is not defined';\n if (tf.ENV.flags.IS_NODE && !(input instanceof tf.Tensor)) {\n return 'input must be a tensor';\n }\n try {\n tf.getBackend();\n } catch {\n return 'backend not loaded';\n }\n return null;\n }\n\n simmilarity(embedding1, embedding2) {\n // @ts-ignore\n if (this.config.face.embedding.enabled) return embedding.simmilarity(embedding1, embedding2);\n return 0;\n }\n\n // preload models, not explicitly required as it's done automatically on first use\n async load(userConfig) {\n this.state = 'load';\n const timeStamp = now();\n if (userConfig) this.config = mergeDeep(this.config, userConfig);\n\n if (this.firstRun) {\n log(`version: ${this.version} TensorFlow/JS version: ${tf.version_core}`);\n await this.checkBackend(true);\n if (tf.ENV.flags.IS_BROWSER) {\n log('configuration:', this.config);\n log('tf flags:', tf.ENV.flags);\n }\n this.firstRun = false;\n }\n\n if (this.config.async) {\n [\n this.models.facemesh,\n this.models.age,\n this.models.gender,\n this.models.emotion,\n this.models.embedding,\n this.models.posenet,\n this.models.handpose,\n ] = await Promise.all([\n // @ts-ignore\n this.models.facemesh || (this.config.face.enabled ? facemesh.load(this.config) : null),\n // @ts-ignore\n this.models.age || ((this.config.face.enabled && this.config.face.age.enabled) ? age.load(this.config) : null),\n // @ts-ignore\n this.models.gender || ((this.config.face.enabled && this.config.face.gender.enabled) ? gender.load(this.config) : null),\n // @ts-ignore\n this.models.emotion || ((this.config.face.enabled && this.config.face.emotion.enabled) ? emotion.load(this.config) : null),\n // @ts-ignore\n this.models.embedding || ((this.config.face.enabled && this.config.face.embedding.enabled) ? embedding.load(this.config) : null),\n // @ts-ignore\n this.models.posenet || (this.config.body.enabled ? posenet.load(this.config) : null),\n // @ts-ignore\n this.models.handpose || (this.config.hand.enabled ? handpose.load(this.config) : null),\n ]);\n } else {\n // @ts-ignore\n if (this.config.face.enabled && !this.models.facemesh) this.models.facemesh = await facemesh.load(this.config);\n // @ts-ignore\n if (this.config.face.enabled && this.config.face.age.enabled && !this.models.age) this.models.age = await age.load(this.config);\n // @ts-ignore\n if (this.config.face.enabled && this.config.face.gender.enabled && !this.models.gender) this.models.gender = await gender.load(this.config);\n // @ts-ignore\n if (this.config.face.enabled && this.config.face.emotion.enabled && !this.models.emotion) this.models.emotion = await emotion.load(this.config);\n // @ts-ignore\n if (this.config.face.enabled && this.config.face.embedding.enabled && !this.models.embedding) this.models.embedding = await embedding.load(this.config);\n // @ts-ignore\n if (this.config.body.enabled && !this.models.posenet) this.models.posenet = await posenet.load(this.config);\n // @ts-ignore\n if (this.config.hand.enabled && !this.models.handpose) this.models.handpose = await handpose.load(this.config);\n }\n const current = Math.trunc(now() - timeStamp);\n if (current > (this.perf.load || 0)) this.perf.load = current;\n }\n\n // check if backend needs initialization if it changed\n async checkBackend(force) {\n if (this.config.backend && (this.config.backend !== '') && force || (tf.getBackend() !== this.config.backend)) {\n const timeStamp = now();\n this.state = 'backend';\n /* force backend reload\n if (this.config.backend in tf.engine().registry) {\n const backendFactory = tf.findBackendFactory(this.config.backend);\n tf.removeBackend(this.config.backend);\n tf.registerBackend(this.config.backend, backendFactory);\n } else {\n log('Backend not registred:', this.config.backend);\n }\n */\n\n log('setting backend:', this.config.backend);\n\n if (this.config.backend === 'wasm') {\n log('settings wasm path:', this.config.wasmPath);\n tf.setWasmPaths(this.config.wasmPath);\n const simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n if (!simd) log('warning: wasm simd support is not enabled');\n }\n\n if (this.config.backend === 'humangl') {\n log('registering humangl backend');\n backend.register();\n }\n\n await tf.setBackend(this.config.backend);\n tf.enableProdMode();\n /* debug mode is really too mcuh\n tf.enableDebugMode();\n */\n if (tf.getBackend() === 'webgl') {\n if (this.config.deallocate) {\n log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', this.config.deallocate);\n tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', this.config.deallocate ? 0 : -1);\n }\n tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n const gl = await tf.backend().getGPGPUContext().gl;\n log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);\n }\n await tf.ready();\n this.perf.backend = Math.trunc(now() - timeStamp);\n }\n }\n\n async detectFace(input) {\n // run facemesh, includes blazeface and iris\n // eslint-disable-next-line no-async-promise-executor\n let timeStamp;\n let ageRes;\n let genderRes;\n let emotionRes;\n let embeddingRes;\n const faceRes = [];\n this.state = 'run:face';\n timeStamp = now();\n // @ts-ignore\n const faces = await this.models.facemesh?.estimateFaces(input, this.config);\n this.perf.face = Math.trunc(now() - timeStamp);\n for (const face of faces) {\n this.analyze('Get Face');\n\n // is something went wrong, skip the face\n if (!face.image || face.image.isDisposedInternal) {\n log('Face object is disposed:', face.image);\n continue;\n }\n\n // run age, inherits face from blazeface\n this.analyze('Start Age:');\n if (this.config.async) {\n // @ts-ignore\n ageRes = this.config.face.age.enabled ? age.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:age';\n timeStamp = now();\n // @ts-ignore\n ageRes = this.config.face.age.enabled ? await age.predict(face.image, this.config) : {};\n this.perf.age = Math.trunc(now() - timeStamp);\n }\n\n // run gender, inherits face from blazeface\n this.analyze('Start Gender:');\n if (this.config.async) {\n // @ts-ignore\n genderRes = this.config.face.gender.enabled ? gender.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:gender';\n timeStamp = now();\n // @ts-ignore\n genderRes = this.config.face.gender.enabled ? await gender.predict(face.image, this.config) : {};\n this.perf.gender = Math.trunc(now() - timeStamp);\n }\n\n // run emotion, inherits face from blazeface\n this.analyze('Start Emotion:');\n if (this.config.async) {\n // @ts-ignore\n emotionRes = this.config.face.emotion.enabled ? emotion.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:emotion';\n timeStamp = now();\n // @ts-ignore\n emotionRes = this.config.face.emotion.enabled ? await emotion.predict(face.image, this.config) : {};\n this.perf.emotion = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Emotion:');\n\n // run emotion, inherits face from blazeface\n this.analyze('Start Embedding:');\n if (this.config.async) {\n // @ts-ignore\n embeddingRes = this.config.face.embedding.enabled ? embedding.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:embedding';\n timeStamp = now();\n // @ts-ignore\n embeddingRes = this.config.face.embedding.enabled ? await embedding.predict(face.image, this.config) : {};\n this.perf.embedding = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Emotion:');\n\n // if async wait for results\n if (this.config.async) {\n [ageRes, genderRes, emotionRes, embeddingRes] = await Promise.all([ageRes, genderRes, emotionRes, embeddingRes]);\n }\n\n this.analyze('Finish Face:');\n // dont need face anymore\n face.image.dispose();\n\n // calculate iris distance\n // iris: array[ center, left, top, right, bottom]\n if (!this.config.face.iris.enabled) {\n delete face.annotations.leftEyeIris;\n delete face.annotations.rightEyeIris;\n }\n const irisSize = (face.annotations.leftEyeIris && face.annotations.rightEyeIris)\n /* average human iris size is 11.7mm */\n ? 11.7 * Math.max(Math.abs(face.annotations.leftEyeIris[3][0] - face.annotations.leftEyeIris[1][0]), Math.abs(face.annotations.rightEyeIris[4][1] - face.annotations.rightEyeIris[2][1]))\n : 0;\n\n // combine results\n faceRes.push({\n confidence: face.confidence,\n box: face.box,\n mesh: face.mesh,\n boxRaw: face.boxRaw,\n meshRaw: face.meshRaw,\n annotations: face.annotations,\n age: ageRes.age,\n gender: genderRes.gender,\n genderConfidence: genderRes.confidence,\n emotion: emotionRes,\n embedding: embeddingRes,\n iris: (irisSize !== 0) ? Math.trunc(irisSize) / 100 : 0,\n });\n this.analyze('End Face');\n }\n this.analyze('End FaceMesh:');\n if (this.config.async) {\n if (this.perf.face) delete this.perf.face;\n if (this.perf.age) delete this.perf.age;\n if (this.perf.gender) delete this.perf.gender;\n if (this.perf.emotion) delete this.perf.emotion;\n }\n return faceRes;\n }\n\n async image(input, userConfig = {}) {\n this.state = 'image';\n this.config = mergeDeep(this.config, userConfig);\n // @ts-ignore\n const process = image.process(input, this.config);\n process.tensor.dispose();\n return process.canvas;\n }\n\n // main detect function\n async detect(input, userConfig = {}) {\n // detection happens inside a promise\n return new Promise(async (resolve) => {\n this.state = 'config';\n let timeStamp;\n\n // update configuration\n this.config = mergeDeep(this.config, userConfig);\n\n // sanity checks\n this.state = 'check';\n const error = this.sanity(input);\n if (error) {\n log(error, input);\n resolve({ error });\n }\n\n let poseRes;\n let handRes;\n let faceRes;\n\n const timeStart = now();\n\n // configure backend\n await this.checkBackend();\n\n // load models if enabled\n await this.load();\n\n if (this.config.scoped) tf.engine().startScope();\n this.analyze('Start Scope:');\n\n timeStamp = now();\n // @ts-ignore\n const process = image.process(input, this.config);\n if (!process || !process.tensor) {\n log('could not convert input to tensor');\n resolve({ error: 'could not convert input to tensor' });\n return;\n }\n this.perf.image = Math.trunc(now() - timeStamp);\n this.analyze('Get Image:');\n\n // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n if (this.config.async) {\n faceRes = this.config.face.enabled ? this.detectFace(process.tensor) : [];\n if (this.perf.face) delete this.perf.face;\n } else {\n this.state = 'run:face';\n timeStamp = now();\n faceRes = this.config.face.enabled ? await this.detectFace(process.tensor) : [];\n this.perf.face = Math.trunc(now() - timeStamp);\n }\n\n // run posenet\n this.analyze('Start Body:');\n if (this.config.async) {\n // @ts-ignore\n poseRes = this.config.body.enabled ? this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n if (this.perf.body) delete this.perf.body;\n } else {\n this.state = 'run:body';\n timeStamp = now();\n // @ts-ignore\n poseRes = this.config.body.enabled ? await this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n this.perf.body = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Body:');\n\n // run handpose\n this.analyze('Start Hand:');\n if (this.config.async) {\n // @ts-ignore\n handRes = this.config.hand.enabled ? this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n if (this.perf.hand) delete this.perf.hand;\n } else {\n this.state = 'run:hand';\n timeStamp = now();\n // @ts-ignore\n handRes = this.config.hand.enabled ? await this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n this.perf.hand = Math.trunc(now() - timeStamp);\n }\n // this.analyze('End Hand:');\n\n // if async wait for results\n if (this.config.async) {\n [faceRes, poseRes, handRes] = await Promise.all([faceRes, poseRes, handRes]);\n }\n process.tensor.dispose();\n\n if (this.config.scoped) tf.engine().endScope();\n this.analyze('End Scope:');\n\n let gestureRes = [];\n if (this.config.gesture.enabled) {\n timeStamp = now();\n gestureRes = [...gesture.face(faceRes), ...gesture.body(poseRes), ...gesture.hand(handRes), ...gesture.iris(faceRes)];\n if (!this.config.async) this.perf.gesture = Math.trunc(now() - timeStamp);\n else if (this.perf.gesture) delete this.perf.gesture;\n }\n\n this.perf.total = Math.trunc(now() - timeStart);\n this.state = 'idle';\n resolve({ face: faceRes, body: poseRes, hand: handRes, gesture: gestureRes, performance: this.perf, canvas: process.canvas });\n });\n }\n\n async warmup(userConfig) {\n const b64toBlob = (base64, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n\n if (userConfig) this.config = mergeDeep(this.config, userConfig);\n const video = this.config.videoOptimized;\n this.config.videoOptimized = false;\n let blob;\n switch (this.config.warmup) {\n case 'face': blob = await b64toBlob(sample.face); break;\n case 'full': blob = await b64toBlob(sample.body); break;\n default: blob = null;\n }\n if (!blob) return null;\n const bitmap = await createImageBitmap(blob);\n const t0 = now();\n const warmup = await this.detect(bitmap, config);\n const t1 = now();\n bitmap.close();\n log('Warmup', this.config.warmup, (t1 - t0), warmup);\n this.config.videoOptimized = video;\n return warmup;\n }\n}\n\nexport { Human as default };\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport const config = {\n name: 'humangl',\n priority: 99,\n canvas: null,\n width: 1024,\n height: 1024,\n webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n alpha: false,\n antialias: false,\n premultipliedAlpha: false,\n preserveDrawingBuffer: false,\n depth: false,\n stencil: false,\n failIfMajorPerformanceCaveat: false,\n desynchronized: true,\n },\n};\n\nexport function register() {\n if (!tf.findBackend(config.name)) {\n // @ts-ignore\n config.canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(config.width, config.height) : document.createElement('canvas');\n // @ts-ignore\n const gl = config.canvas.getContext('webgl2', config.webGLattr);\n tf.setWebGLContext(2, gl);\n const ctx = new tf.GPGPUContext(gl);\n tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n const kernels = tf.getKernelsForBackend('webgl');\n kernels.forEach((kernelConfig) => {\n const newKernelConfig = { ...kernelConfig, backendName: config.name };\n tf.registerKernel(newKernelConfig);\n });\n tf.ENV.set('WEBGL_VERSION', 2);\n tf.ENV.set('WEBGL_MAX_TEXTURE_SIZE', gl.getParameter(gl.MAX_TEXTURE_SIZE));\n tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n }\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nfunction getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\nfunction getBoxCenter(box) {\n return [\n box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n ];\n}\nfunction cutBoxFromImageAndResize(box, image, cropSize) {\n const h = image.shape[1];\n const w = image.shape[2];\n const boxes = [[\n box.startPoint[1] / h,\n box.startPoint[0] / w,\n box.endPoint[1] / h,\n box.endPoint[0] / w,\n ]];\n return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\nfunction scaleBoxCoordinates(box, factor) {\n const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n const palmLandmarks = box.palmLandmarks.map((coord) => {\n const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n return scaledCoord;\n });\n return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\nfunction enlargeBox(box, factor = 1.5) {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\nfunction squarifyBox(box) {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const maxEdge = Math.max(...size);\n const halfSize = maxEdge / 2;\n const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\nfunction shiftBox(box, shiftFactor) {\n const boxSize = [\n box.endPoint[0] - box.startPoint[0],\n box.endPoint[1] - box.startPoint[1],\n ];\n const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]];\n const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\nexport {\n cutBoxFromImageAndResize,\n enlargeBox,\n getBoxCenter,\n getBoxSize,\n scaleBoxCoordinates,\n shiftBox,\n squarifyBox,\n};\n", "function normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\nfunction computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\nconst buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\nfunction dot(v1, v2) {\n let product = 0;\n for (let i = 0; i < v1.length; i++) {\n product += v1[i] * v2[i];\n }\n return product;\n}\nfunction getColumnFrom2DArr(arr, columnIndex) {\n const column = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\nfunction multiplyTransformMatrices(mat1, mat2) {\n const product = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) {\n // @ts-ignore\n product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n }\n return product;\n}\nfunction buildRotationMatrix(rotation, center) {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\nfunction invertTransformMatrix(matrix) {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [\n -dot(rotationComponent[0], translationComponent),\n -dot(rotationComponent[1], translationComponent),\n ];\n return [\n rotationComponent[0].concat(invertedTranslation[0]),\n rotationComponent[1].concat(invertedTranslation[1]),\n [0, 0, 1],\n ];\n}\nfunction rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\nexport {\n buildRotationMatrix,\n computeRotation,\n dot,\n getColumnFrom2DArr,\n invertTransformMatrix,\n normalizeRadians,\n rotatePoint,\n};\n", "/* eslint-disable indent */\n/* eslint-disable no-multi-spaces */\n\nexport default {\n backend: 'webgl', // select tfjs backend to use\n wasmPath: '../assets/', // path for wasm binaries\n // only used for backend: wasm\n async: true, // execute enabled models in parallel\n // this disables per-model performance data but\n // slightly increases performance\n // cannot be used if profiling is enabled\n profile: false, // enable tfjs profiling\n // this has significant performance impact\n // only enable for debugging purposes\n // currently only implemented for age,gender,emotion models\n deallocate: false, // aggresively deallocate gpu memory after each usage\n // only valid for webgl backend and only during first call\n // cannot be changed unless library is reloaded\n // this has significant performance impact\n // only enable on low-memory devices\n scoped: false, // enable scoped runs\n // some models *may* have memory leaks,\n // this wrapps everything in a local scope at a cost of performance\n // typically not needed\n videoOptimized: true, // perform additional optimizations when input is video,\n // must be disabled for images\n // basically this skips object box boundary detection for every n frames\n // while maintaining in-box detection since objects cannot move that fast\n warmup: 'face', // what to use for human.warmup(), can be 'none', 'face', 'full'\n // warmup pre-initializes all models for faster inference but can take\n // significant time on startup\n filter: {\n enabled: true, // enable image pre-processing filters\n width: 0, // resize input width\n height: 0, // resize input height\n // if both width and height are set to 0, there is no resizing\n // if just one is set, second one is scaled automatically\n // if both are set, values are used as-is\n return: true, // return processed canvas imagedata in result\n brightness: 0, // range: -1 (darken) to 1 (lighten)\n contrast: 0, // range: -1 (reduce contrast) to 1 (increase contrast)\n sharpness: 0, // range: 0 (no sharpening) to 1 (maximum sharpening)\n blur: 0, // range: 0 (no blur) to N (blur radius in pixels)\n saturation: 0, // range: -1 (reduce saturation) to 1 (increase saturation)\n hue: 0, // range: 0 (no change) to 360 (hue rotation in degrees)\n negative: false, // image negative\n sepia: false, // image sepia colors\n vintage: false, // image vintage colors\n kodachrome: false, // image kodachrome colors\n technicolor: false, // image technicolor colors\n polaroid: false, // image polaroid camera effect\n pixelate: 0, // range: 0 (no pixelate) to N (number of pixels to pixelate)\n },\n\n gesture: {\n enabled: true, // enable simple gesture recognition\n },\n\n face: {\n enabled: true, // controls if specified modul is enabled\n // face.enabled is required for all face models:\n // detector, mesh, iris, age, gender, emotion\n // (note: module is not loaded until it is required)\n detector: {\n modelPath: '../models/blazeface-back.json', // can be 'front' or 'back'.\n // 'front' is optimized for large faces\n // such as front-facing camera and\n // 'back' is optimized for distanct faces.\n inputSize: 256, // fixed value: 128 for front and 256 for 'back'\n rotation: false, // use best-guess rotated face image or just box with rotation as-is\n // false means higher performance, but incorrect mesh mapping if face angle is above 20 degrees\n maxFaces: 10, // maximum number of faces detected in the input\n // should be set to the minimum number for performance\n skipFrames: 11, // how many frames to go without re-running the face bounding box detector\n // only used for video inputs\n // e.g., if model is running st 25 FPS, we can re-use existing bounding\n // box for updated face analysis as the head probably hasn't moved much\n // in short time (10 * 1/25 = 0.25 sec)\n minConfidence: 0.5, // threshold for discarding a prediction\n iouThreshold: 0.2, // threshold for deciding whether boxes overlap too much in\n // non-maximum suppression (0.1 means drop if overlap 10%)\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on score\n // in non-maximum suppression,\n // this is applied on detection objects only and before minConfidence\n },\n\n mesh: {\n enabled: true,\n modelPath: '../models/facemesh.json',\n inputSize: 192, // fixed value\n returnRawData: false, // in addition to standard mesh and box values, return raw normalized values as well\n },\n\n iris: {\n enabled: true,\n modelPath: '../models/iris.json',\n inputSize: 64, // fixed value\n },\n\n age: {\n enabled: true,\n modelPath: '../models/age-ssrnet-imdb.json', // can be 'age-ssrnet-imdb' or 'age-ssrnet-wiki'\n // which determines training set for model\n inputSize: 64, // fixed value\n skipFrames: 31, // how many frames to go without re-running the detector\n // only used for video inputs\n },\n\n gender: {\n enabled: true,\n minConfidence: 0.1, // threshold for discarding a prediction\n modelPath: '../models/gender-ssrnet-imdb.json', // can be 'gender', 'gender-ssrnet-imdb' or 'gender-ssrnet-wiki'\n inputSize: 64, // fixed value\n skipFrames: 41, // how many frames to go without re-running the detector\n // only used for video inputs\n },\n\n emotion: {\n enabled: true,\n inputSize: 64, // fixed value\n minConfidence: 0.2, // threshold for discarding a prediction\n skipFrames: 21, // how many frames to go without re-running the detector\n modelPath: '../models/emotion-large.json', // can be 'mini', 'large'\n },\n\n embedding: {\n enabled: false,\n inputSize: 112, // fixed value\n modelPath: '../models/mobilefacenet.json',\n },\n },\n\n body: {\n enabled: true,\n modelPath: '../models/posenet.json',\n inputSize: 257, // fixed value\n maxDetections: 10, // maximum number of people detected in the input\n // should be set to the minimum number for performance\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on score\n // in non-maximum suppression\n nmsRadius: 20, // radius for deciding points are too close in non-maximum suppression\n outputStride: 16, // size of block in which to run point detectopn, smaller value means higher resolution\n // defined by model itself, can be 8, 16, or 32\n modelType: 'MobileNet', // Human includes MobileNet version, but you can switch to ResNet\n },\n\n hand: {\n enabled: true,\n rotation: false, // use best-guess rotated hand image or just box with rotation as-is\n // false means higher performance, but incorrect finger mapping if hand is inverted\n inputSize: 256, // fixed value\n skipFrames: 12, // how many frames to go without re-running the hand bounding box detector\n // only used for video inputs\n // e.g., if model is running st 25 FPS, we can re-use existing bounding\n // box for updated hand skeleton analysis as the hand probably\n // hasn't moved much in short time (10 * 1/25 = 0.25 sec)\n minConfidence: 0.1, // threshold for discarding a prediction\n iouThreshold: 0.1, // threshold for deciding whether boxes overlap too much\n // in non-maximum suppression\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on\n // score in non-maximum suppression\n maxHands: 1, // maximum number of hands detected in the input\n // should be set to the minimum number for performance\n landmarks: true, // detect hand landmarks or just hand boundary box\n detector: {\n modelPath: '../models/handdetect.json',\n },\n skeleton: {\n modelPath: '../models/handskeleton.json',\n },\n },\n};\n", "// data:image/jpeg;base64,\nexport const face = 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+ "sources": ["../src/tfjs/tf-browser.js", "../src/blazeface/blazeface.js", "../src/blazeface/box.js", "../src/blazeface/util.js", "../src/blazeface/coords.js", "../src/blazeface/facepipeline.js", "../src/blazeface/facemesh.js", "../src/profile.js", "../src/age/age.js", "../src/gender/gender.js", "../src/emotion/emotion.js", "../src/embedding/embedding.js", "../src/posenet/modelBase.js", "../src/posenet/heapSort.js", "../src/posenet/buildParts.js", "../src/posenet/keypoints.js", "../src/posenet/vectors.js", "../src/posenet/decoders.js", "../src/posenet/decodePose.js", "../src/posenet/decodeMultiple.js", "../src/posenet/util.js", "../src/posenet/modelPoseNet.js", "../src/posenet/posenet.js", "../src/handpose/handdetector.js", "../src/handpose/handpipeline.js", "../src/handpose/anchors.js", "../src/handpose/handpose.js", "../src/gesture/gesture.js", "../src/imagefx.js", "../src/image.js", "../src/log.js", "../src/human.js", "../src/tfjs/backend.js", "../src/blazepose/blazepose.js", "../src/handpose/box.js", "../src/handpose/util.js", "../config.js", "../src/sample.js"], + "sourcesContent": ["// wrapper to load tfjs in a single place so version can be changed quickly\n\n// simplified\n// { modules: 1061, moduleBytes: 3772720, outputBytes: 1531035 }\n\n// export * from '@tensorflow/tfjs/dist/index.js';\n// export * from '@tensorflow/tfjs-backend-wasm';\n\n// modular\n// { modules: 1064, moduleBytes: 3793219, outputBytes: 1535600 }\n\n// get versions of all packages.\nimport { version as tfjs } from '@tensorflow/tfjs/package.json';\nimport { version as versionCore } from '@tensorflow/tfjs-core/package.json';\nimport { version as versionData } from '@tensorflow/tfjs-data/package.json';\nimport { version as versionLayers } from '@tensorflow/tfjs-layers/package.json';\nimport { version as versionConverter } from '@tensorflow/tfjs-converter/package.json';\n// for backends, get version from source so it can register backend during import\nimport { version_cpu } from '@tensorflow/tfjs-backend-cpu/dist/index.js';\nimport { version_webgl } from '@tensorflow/tfjs-backend-webgl/dist/index.js';\nimport { version_wasm } from '@tensorflow/tfjs-backend-wasm/dist/index.js';\n\n// export all\nexport * from '@tensorflow/tfjs-core/dist/index.js';\nexport * from '@tensorflow/tfjs-layers/dist/index.js';\nexport * from '@tensorflow/tfjs-converter/dist/index.js';\nexport * as data from '@tensorflow/tfjs-data/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-cpu/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-webgl/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-wasm/dist/index.js';\n\n// export versions\nexport const version = {\n tfjs,\n 'tfjs-core': versionCore,\n 'tfjs-data': versionData,\n 'tfjs-layers': versionLayers,\n 'tfjs-converter': versionConverter,\n 'tfjs-backend-cpu': version_cpu,\n 'tfjs-backend-webgl': version_webgl,\n 'tfjs-backend-wasm': version_wasm,\n};\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nconst NUM_LANDMARKS = 6;\n\nfunction generateAnchors(inputSize) {\n const spec = { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] };\n const anchors = [];\n for (let i = 0; i < spec.strides.length; i++) {\n const stride = spec.strides[i];\n const gridRows = Math.floor((inputSize + stride - 1) / stride);\n const gridCols = Math.floor((inputSize + stride - 1) / stride);\n const anchorsNum = spec.anchors[i];\n for (let gridY = 0; gridY < gridRows; gridY++) {\n const anchorY = stride * (gridY + 0.5);\n for (let gridX = 0; gridX < gridCols; gridX++) {\n const anchorX = stride * (gridX + 0.5);\n for (let n = 0; n < anchorsNum; n++) {\n anchors.push([anchorX, anchorY]);\n }\n }\n }\n }\n return anchors;\n}\n\nconst disposeBox = (box) => {\n box.startEndTensor.dispose();\n box.startPoint.dispose();\n box.endPoint.dispose();\n};\n\nconst createBox = (startEndTensor) => ({\n startEndTensor,\n startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]),\n endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]),\n});\n\nconst scaleBox = (box, factors) => {\n const starts = tf.mul(box.startPoint, factors);\n const ends = tf.mul(box.endPoint, factors);\n const newCoordinates = tf.concat2d([starts, ends], 1);\n return createBox(newCoordinates);\n};\n\nfunction decodeBounds(boxOutputs, anchors, inputSize) {\n const boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n const centers = tf.add(boxStarts, anchors);\n const boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n const boxSizesNormalized = tf.div(boxSizes, inputSize);\n const centersNormalized = tf.div(centers, inputSize);\n const halfBoxSize = tf.div(boxSizesNormalized, 2);\n const starts = tf.sub(centersNormalized, halfBoxSize);\n const ends = tf.add(centersNormalized, halfBoxSize);\n const startNormalized = tf.mul(starts, inputSize);\n const endNormalized = tf.mul(ends, inputSize);\n const concatAxis = 1;\n return tf.concat2d([startNormalized, endNormalized], concatAxis);\n}\n\nfunction scaleBoxFromPrediction(face, scaleFactor) {\n return tf.tidy(() => {\n const box = face['box'] ? face['box'] : face;\n return scaleBox(box, scaleFactor).startEndTensor.squeeze();\n });\n}\n\nclass BlazeFaceModel {\n constructor(model, config) {\n this.blazeFaceModel = model;\n this.width = config.face.detector.inputSize;\n this.height = config.face.detector.inputSize;\n this.anchorsData = generateAnchors(config.face.detector.inputSize);\n this.anchors = tf.tensor2d(this.anchorsData);\n this.inputSize = tf.tensor1d([this.width, this.height]);\n this.config = config;\n this.scaleFaces = 0.8;\n }\n\n async getBoundingBoxes(inputImage) {\n // sanity check on input\n if ((!inputImage) || (inputImage.isDisposedInternal) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return null;\n const [detectedOutputs, boxes, scores] = tf.tidy(() => {\n const resizedImage = inputImage.resizeBilinear([this.width, this.height]);\n // const normalizedImage = tf.mul(tf.sub(resizedImage.div(255), 0.5), 2);\n const normalizedImage = tf.sub(resizedImage.div(127.5), 1);\n const batchedPrediction = this.blazeFaceModel.predict(normalizedImage);\n let prediction;\n // are we using tfhub or pinto converted model?\n if (Array.isArray(batchedPrediction)) {\n const sorted = batchedPrediction.sort((a, b) => a.size - b.size);\n const concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n const concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n const concat = tf.concat([concat512, concat384], 1);\n prediction = concat.squeeze(0);\n } else {\n prediction = batchedPrediction.squeeze(); // when using tfhub model\n }\n const decodedBounds = decodeBounds(prediction, this.anchors, this.inputSize);\n const logits = tf.slice(prediction, [0, 0], [-1, 1]);\n const scoresOut = tf.sigmoid(logits).squeeze();\n return [prediction, decodedBounds, scoresOut];\n });\n const boxIndicesTensor = await tf.image.nonMaxSuppressionAsync(boxes, scores, this.config.face.detector.maxFaces, this.config.face.detector.iouThreshold, this.config.face.detector.scoreThreshold);\n const boxIndices = boxIndicesTensor.arraySync();\n boxIndicesTensor.dispose();\n const boundingBoxesMap = boxIndices.map((boxIndex) => tf.slice(boxes, [boxIndex, 0], [1, -1]));\n const boundingBoxes = boundingBoxesMap.map((boundingBox) => {\n const vals = boundingBox.arraySync();\n boundingBox.dispose();\n return vals;\n });\n\n const scoresVal = scores.dataSync();\n const annotatedBoxes = [];\n for (let i = 0; i < boundingBoxes.length; i++) {\n const boxIndex = boxIndices[i];\n const confidence = scoresVal[boxIndex];\n if (confidence > this.config.face.detector.minConfidence) {\n const box = createBox(boundingBoxes[i]);\n const anchor = this.anchorsData[boxIndex];\n const landmarks = tf.tidy(() => tf.slice(detectedOutputs, [boxIndex, NUM_LANDMARKS - 1], [1, -1]).squeeze().reshape([NUM_LANDMARKS, -1]));\n annotatedBoxes.push({ box, landmarks, anchor, confidence });\n }\n }\n detectedOutputs.dispose();\n boxes.dispose();\n scores.dispose();\n detectedOutputs.dispose();\n return {\n boxes: annotatedBoxes,\n scaleFactor: [inputImage.shape[2] / this.width, inputImage.shape[1] / this.height],\n };\n }\n\n async estimateFaces(input) {\n // @ts-ignore\n const { boxes, scaleFactor } = await this.getBoundingBoxes(input);\n const faces = [];\n for (const face of boxes) {\n const landmarkData = face.landmarks.arraySync();\n const scaledBox = scaleBoxFromPrediction(face, scaleFactor);\n // @ts-ignore\n const boxData = scaleBox.arraySync();\n const probabilityData = face.probability.arraySync();\n const anchor = face.anchor;\n const [scaleFactorX, scaleFactorY] = scaleFactor;\n const scaledLandmarks = landmarkData\n .map((landmark) => ([\n (landmark[0] + anchor[0]) * scaleFactorX,\n (landmark[1] + anchor[1]) * scaleFactorY,\n ]));\n const normalizedFace = {\n topLeft: boxData.slice(0, 2),\n bottomRight: boxData.slice(2),\n landmarks: scaledLandmarks,\n probability: probabilityData,\n };\n disposeBox(face.box);\n face.landmarks.dispose();\n face.probability.dispose();\n scaledBox.dispose();\n faces.push(normalizedFace);\n }\n return faces;\n }\n}\n\nasync function load(config) {\n const blazeface = await tf.loadGraphModel(config.face.detector.modelPath, { fromTFHub: config.face.detector.modelPath.includes('tfhub.dev') });\n const model = new BlazeFaceModel(blazeface, config);\n log(`load model: ${config.face.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n return model;\n}\n\nexports.load = load;\nexports.BlazeFaceModel = BlazeFaceModel;\nexports.disposeBox = disposeBox;\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nfunction scaleBoxCoordinates(box, factor) {\n const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n return { startPoint, endPoint };\n}\nexports.scaleBoxCoordinates = scaleBoxCoordinates;\n\nfunction getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\nexports.getBoxSize = getBoxSize;\n\nfunction getBoxCenter(box) {\n return [\n box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n ];\n}\nexports.getBoxCenter = getBoxCenter;\n\nfunction cutBoxFromImageAndResize(box, image, cropSize) {\n const h = image.shape[1];\n const w = image.shape[2];\n const boxes = [[\n box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h,\n box.endPoint[0] / w,\n ]];\n return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\nexports.cutBoxFromImageAndResize = cutBoxFromImageAndResize;\n\nfunction enlargeBox(box, factor = 1.5) {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n return { startPoint, endPoint, landmarks: box.landmarks };\n}\nexports.enlargeBox = enlargeBox;\n\nfunction squarifyBox(box) {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const maxEdge = Math.max(...size);\n const halfSize = maxEdge / 2;\n const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n return { startPoint, endPoint, landmarks: box.landmarks };\n}\nexports.squarifyBox = squarifyBox;\n", "exports.IDENTITY_MATRIX = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n/**\n * Normalizes the provided angle to the range -pi to pi.\n * @param angle The angle in radians to be normalized.\n */\nfunction normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\nexports.normalizeRadians = normalizeRadians;\n\n/**\n * Computes the angle of rotation between two anchor points.\n * @param point1 First anchor point\n * @param point2 Second anchor point\n */\nfunction computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\nexports.computeRotation = computeRotation;\n\nfunction radToDegrees(rad) {\n return rad * 180 / Math.PI;\n}\nexports.radToDegrees = radToDegrees;\n\nfunction buildTranslationMatrix(x, y) {\n return [[1, 0, x], [0, 1, y], [0, 0, 1]];\n}\n\nfunction dot(v1, v2) {\n let product = 0;\n for (let i = 0; i < v1.length; i++) {\n product += v1[i] * v2[i];\n }\n return product;\n}\nexports.dot = dot;\n\nfunction getColumnFrom2DArr(arr, columnIndex) {\n const column = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\nexports.getColumnFrom2DArr = getColumnFrom2DArr;\n\nfunction multiplyTransformMatrices(mat1, mat2) {\n const product = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) {\n product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n }\n return product;\n}\nfunction buildRotationMatrix(rotation, center) {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\nexports.buildRotationMatrix = buildRotationMatrix;\n\nfunction invertTransformMatrix(matrix) {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [\n -dot(rotationComponent[0], translationComponent),\n -dot(rotationComponent[1], translationComponent),\n ];\n return [\n rotationComponent[0].concat(invertedTranslation[0]),\n rotationComponent[1].concat(invertedTranslation[1]),\n [0, 0, 1],\n ];\n}\nexports.invertTransformMatrix = invertTransformMatrix;\n\nfunction rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\nexports.rotatePoint = rotatePoint;\n\nfunction xyDistanceBetweenPoints(a, b) {\n return Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n}\nexports.xyDistanceBetweenPoints = xyDistanceBetweenPoints;\n", "const MESH_ANNOTATIONS = {\n silhouette: [\n 10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n 397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n 172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n ],\n lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291],\n lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291],\n lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308],\n lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],\n rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173],\n rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133],\n rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190],\n rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243],\n rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189],\n rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244],\n rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245],\n rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193],\n rightEyebrowLower: [35, 124, 46, 53, 52, 65],\n rightEyeIris: [473, 474, 475, 476, 477],\n leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n leftEyeIris: [468, 469, 470, 471, 472],\n midwayBetweenEyes: [168],\n noseTip: [1],\n noseBottom: [2],\n noseRightCorner: [98],\n noseLeftCorner: [327],\n rightCheek: [205],\n leftCheek: [425],\n};\n\nconst MESH_TO_IRIS_INDICES_MAP = [ // A mapping from facemesh model keypoints to iris model keypoints.\n { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] },\n { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] },\n { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] },\n { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] },\n { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] },\n { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] },\n { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] },\n // { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] },\n // { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] },\n];\n\nconst UV468 = [\n [0.499976992607117, 0.652534008026123],\n [0.500025987625122, 0.547487020492554],\n [0.499974012374878, 0.602371990680695],\n [0.482113003730774, 0.471979022026062],\n [0.500150978565216, 0.527155995368958],\n [0.499909996986389, 0.498252987861633],\n [0.499523013830185, 0.40106201171875],\n [0.289712011814117, 0.380764007568359],\n [0.499954998493195, 0.312398016452789],\n [0.499987006187439, 0.269918978214264],\n [0.500023007392883, 0.107050001621246],\n [0.500023007392883, 0.666234016418457],\n [0.5000159740448, 0.679224014282227],\n [0.500023007392883, 0.692348003387451],\n [0.499976992607117, 0.695277988910675],\n [0.499976992607117, 0.70593398809433],\n [0.499976992607117, 0.719385027885437],\n [0.499976992607117, 0.737019002437592],\n [0.499967992305756, 0.781370997428894],\n [0.499816000461578, 0.562981009483337],\n [0.473773002624512, 0.573909997940063],\n [0.104906998574734, 0.254140973091125],\n [0.365929991006851, 0.409575998783112],\n [0.338757991790771, 0.41302502155304],\n [0.311120003461838, 0.409460008144379],\n [0.274657994508743, 0.389131009578705],\n [0.393361985683441, 0.403706014156342],\n [0.345234006643295, 0.344011008739471],\n [0.370094001293182, 0.346076011657715],\n [0.319321990013123, 0.347265005111694],\n [0.297903001308441, 0.353591024875641],\n [0.24779200553894, 0.410809993743896],\n [0.396889001131058, 0.842755019664764],\n [0.280097991228104, 0.375599980354309],\n [0.106310002505779, 0.399955987930298],\n [0.2099249958992, 0.391353011131287],\n [0.355807989835739, 0.534406006336212],\n [0.471751004457474, 0.65040397644043],\n [0.474155008792877, 0.680191993713379],\n 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[0.534153997898102, 0.62064003944397],\n [0.711217999458313, 0.819975018501282],\n [0.664629995822906, 0.852871000766754],\n [0.559099972248077, 0.902631998062134],\n [0.871706008911133, 0.791940987110138],\n [0.591234028339386, 0.373893976211548],\n [0.544341027736664, 0.451583981513977],\n [0.624562978744507, 0.924192011356354],\n [0.88577002286911, 0.615028977394104],\n [0.551338016986847, 0.695277988910675],\n [0.551980018615723, 0.704632043838501],\n [0.552887976169586, 0.715808033943176],\n [0.555167973041534, 0.730794012546539],\n [0.569944024085999, 0.767035007476807],\n [0.593203008174896, 0.685675978660583],\n [0.599261999130249, 0.681069016456604],\n [0.607599973678589, 0.677703022956848],\n [0.631937980651855, 0.663500010967255],\n [0.752032995223999, 0.601315021514893],\n [0.547226011753082, 0.420395016670227],\n [0.563543975353241, 0.359827995300293],\n [0.583841025829315, 0.368713974952698],\n [0.586614012718201, 0.692366003990173],\n [0.771915018558502, 0.683578014373779],\n [0.531597018241882, 0.352482974529266],\n [0.588370978832245, 0.804440975189209],\n [0.52079701423645, 0.442565023899078],\n [0.567984998226166, 0.493479013442993],\n [0.543282985687256, 0.819254994392395],\n [0.655317008495331, 0.745514988899231],\n [0.621008992195129, 0.574018001556396],\n [0.625559985637665, 0.78031200170517],\n [0.680198013782501, 0.570719003677368],\n [0.64276397228241, 0.604337990283966],\n [0.704662978649139, 0.621529996395111],\n [0.552012026309967, 0.862591981887817],\n [0.589071989059448, 0.508637011051178],\n [0.685944974422455, 0.775357007980347],\n [0.645735025405884, 0.812640011310577],\n [0.675342977046967, 0.703978002071381],\n [0.810858011245728, 0.646304965019226],\n [0.72012197971344, 0.714666962623596],\n [0.866151988506317, 0.682704985141754],\n [0.663187026977539, 0.644596993923187],\n [0.570082008838654, 0.466325998306274],\n [0.544561982154846, 0.548375964164734],\n [0.562758982181549, 0.558784961700439],\n [0.531987011432648, 0.530140042304993],\n [0.585271000862122, 0.335177004337311],\n [0.622952997684479, 0.32277899980545],\n [0.655896008014679, 0.320163011550903],\n [0.687132000923157, 0.322345972061157],\n [0.716481983661652, 0.333200991153717],\n [0.758756995201111, 0.382786989212036],\n [0.897013008594513, 0.468769013881683],\n [0.732392013072968, 0.424547016620636],\n [0.70211398601532, 0.433162987232208],\n [0.66652500629425, 0.433866024017334],\n [0.633504986763, 0.426087975502014],\n [0.603875994682312, 0.416586995124817],\n [0.579657971858978, 0.409945011138916],\n [0.992439985275269, 0.480777025222778],\n [0.567192018032074, 0.569419980049133],\n [0.54136598110199, 0.478899002075195],\n [0.526564002037048, 0.546118021011353],\n [0.523913025856018, 0.563830018043518],\n [0.531529009342194, 0.555056989192963],\n [0.566035985946655, 0.582329034805298],\n [0.51631098985672, 0.563053965568542],\n [0.5174720287323, 0.577877044677734],\n [0.573594987392426, 0.389806985855103],\n [0.560697972774506, 0.395331978797913],\n [0.549755990505219, 0.399751007556915],\n [0.710287988185883, 0.368252992630005],\n [0.723330020904541, 0.363372981548309],\n];\n\nconst TRI468 = [\n 127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n 151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n 157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n 181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n 228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n 170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n 155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n 158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n 102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n 90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n 196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n 246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n 117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n 153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n 63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n 135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n 76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n 166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n 190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n 112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n 110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n 348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n 314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n 321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n 466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n 429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n 293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n 427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n 431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n 434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n 348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n 318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n 373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n 340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n 351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n 164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n 327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n 405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n 438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n 449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n 444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n 464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n 243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n 166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n 119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n 77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n 201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n 103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n 217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n 78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n 57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n 142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n 90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n 178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n 224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n 143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n 153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n 63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n 241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n 186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n 141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n 223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n 128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n 451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n 344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n 424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n 321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n 448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n 281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n 283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n 432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n 268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n 6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n 317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n 272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n 336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n 441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n 373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n 425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n 403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n 383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n 254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n 259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n 453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nconst TRI68 = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n 8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n 45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n 23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n 39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n 35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n 48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n 66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\nconst TRI33 = [\n /* eyes */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n /* brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n /* 4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n /* nose */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n /* chin */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n /* cont */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\nconst TRI7 = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nconst VTX68 = [\n /* cont */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n /* nose */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n /* eyes */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n /* lip */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\nconst VTX33 = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\nconst VTX7 = [33, 133, 362, 263, 1, 78, 308];\n\nexports.MESH_ANNOTATIONS = MESH_ANNOTATIONS;\nexports.MESH_TO_IRIS_INDICES_MAP = MESH_TO_IRIS_INDICES_MAP;\n\nexports.TRI468 = TRI468;\nexports.TRI68 = TRI68;\nexports.TRI33 = TRI33;\nexports.TRI7 = TRI7;\n\nexports.UV468 = UV468;\nexports.UV68 = VTX68.map((x) => UV468[x]);\nexports.UV33 = VTX33.map((x) => UV468[x]);\nexports.UV7 = VTX7.map((x) => UV468[x]);\n", "/* eslint-disable class-methods-use-this */\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as bounding from './box';\nimport * as util from './util';\nimport * as coords from './coords.js';\n// eslint-disable-next-line no-unused-vars\nimport { log } from '../log.js';\n\nconst LANDMARKS_COUNT = 468;\nconst MESH_MOUTH_INDEX = 13;\nconst MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES = [MESH_MOUTH_INDEX, coords.MESH_ANNOTATIONS['midwayBetweenEyes'][0]];\nconst BLAZEFACE_MOUTH_INDEX = 3;\nconst BLAZEFACE_NOSE_INDEX = 2;\nconst BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES = [BLAZEFACE_MOUTH_INDEX, BLAZEFACE_NOSE_INDEX];\nconst LEFT_EYE_OUTLINE = coords.MESH_ANNOTATIONS['leftEyeLower0'];\nconst LEFT_EYE_BOUNDS = [LEFT_EYE_OUTLINE[0], LEFT_EYE_OUTLINE[LEFT_EYE_OUTLINE.length - 1]];\nconst RIGHT_EYE_OUTLINE = coords.MESH_ANNOTATIONS['rightEyeLower0'];\nconst RIGHT_EYE_BOUNDS = [RIGHT_EYE_OUTLINE[0], RIGHT_EYE_OUTLINE[RIGHT_EYE_OUTLINE.length - 1]];\nconst IRIS_UPPER_CENTER_INDEX = 3;\nconst IRIS_LOWER_CENTER_INDEX = 4;\nconst IRIS_IRIS_INDEX = 71;\nconst IRIS_NUM_COORDINATES = 76;\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates. Update the z coordinate to be an average of the original and the new. This produces the best visual effect.\nfunction replaceRawCoordinates(rawCoords, newCoords, prefix, keys) {\n for (let i = 0; i < coords.MESH_TO_IRIS_INDICES_MAP.length; i++) {\n const { key, indices } = coords.MESH_TO_IRIS_INDICES_MAP[i];\n const originalIndices = coords.MESH_ANNOTATIONS[`${prefix}${key}`];\n const shouldReplaceAllKeys = keys == null;\n if (shouldReplaceAllKeys || keys.includes(key)) {\n for (let j = 0; j < indices.length; j++) {\n const index = indices[j];\n rawCoords[originalIndices[j]] = [\n newCoords[index][0], newCoords[index][1],\n (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n ];\n }\n }\n }\n}\n// The Pipeline coordinates between the bounding box and skeleton models.\nclass Pipeline {\n constructor(boundingBoxDetector, meshDetector, irisModel, config) {\n // An array of facial bounding boxes.\n this.storedBoxes = [];\n this.runsWithoutFaceDetector = 0;\n this.boundingBoxDetector = boundingBoxDetector;\n this.meshDetector = meshDetector;\n this.irisModel = irisModel;\n this.meshWidth = config.face.mesh.inputSize;\n this.meshHeight = config.face.mesh.inputSize;\n this.irisSize = config.face.iris.inputSize;\n this.irisEnlarge = 2.3;\n this.skipped = 0;\n this.detectedFaces = 0;\n }\n\n transformRawCoords(rawCoords, box, angle, rotationMatrix) {\n // @ts-ignore\n const boxSize = bounding.getBoxSize({ startPoint: box.startPoint, endPoint: box.endPoint });\n const scaleFactor = [boxSize[0] / this.meshWidth, boxSize[1] / this.meshHeight];\n const coordsScaled = rawCoords.map((coord) => ([\n scaleFactor[0] * (coord[0] - this.meshWidth / 2),\n scaleFactor[1] * (coord[1] - this.meshHeight / 2), coord[2],\n ]));\n const coordsRotationMatrix = (angle !== 0) ? util.buildRotationMatrix(angle, [0, 0]) : util.IDENTITY_MATRIX;\n const coordsRotated = (angle !== 0) ? coordsScaled.map((coord) => ([...util.rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n const inverseRotationMatrix = (angle !== 0) ? util.invertTransformMatrix(rotationMatrix) : util.IDENTITY_MATRIX;\n // @ts-ignore\n const boxCenter = [...bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint }), 1];\n return coordsRotated.map((coord) => ([\n coord[0] + util.dot(boxCenter, inverseRotationMatrix[0]),\n coord[1] + util.dot(boxCenter, inverseRotationMatrix[1]),\n coord[2],\n ]));\n }\n\n getLeftToRightEyeDepthDifference(rawCoords) {\n const leftEyeZ = rawCoords[LEFT_EYE_BOUNDS[0]][2];\n const rightEyeZ = rawCoords[RIGHT_EYE_BOUNDS[0]][2];\n return leftEyeZ - rightEyeZ;\n }\n\n // Returns a box describing a cropped region around the eye fit for passing to the iris model.\n getEyeBox(rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, flip = false) {\n // @ts-ignore\n const box = bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), this.irisEnlarge));\n // @ts-ignore\n const boxSize = bounding.getBoxSize(box);\n let crop = tf.image.cropAndResize(face, [[\n box.startPoint[1] / this.meshHeight,\n box.startPoint[0] / this.meshWidth, box.endPoint[1] / this.meshHeight,\n box.endPoint[0] / this.meshWidth,\n ]], [0], [this.irisSize, this.irisSize]);\n if (flip) {\n crop = tf.image.flipLeftRight(crop);\n }\n return { box, boxSize, crop };\n }\n\n // Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\n getEyeCoords(eyeData, eyeBox, eyeBoxSize, flip = false) {\n const eyeRawCoords = [];\n for (let i = 0; i < IRIS_NUM_COORDINATES; i++) {\n const x = eyeData[i * 3];\n const y = eyeData[i * 3 + 1];\n const z = eyeData[i * 3 + 2];\n eyeRawCoords.push([\n (flip\n ? (1 - (x / this.irisSize))\n : (x / this.irisSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n (y / this.irisSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n ]);\n }\n return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(IRIS_IRIS_INDEX) };\n }\n\n // The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\n getAdjustedIrisCoords(rawCoords, irisCoords, direction) {\n const upperCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeUpper0`][IRIS_UPPER_CENTER_INDEX]][2];\n const lowerCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeLower0`][IRIS_LOWER_CENTER_INDEX]][2];\n const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n return irisCoords.map((coord, i) => {\n let z = averageZ;\n if (i === 2) {\n z = upperCenterZ;\n } else if (i === 4) {\n z = lowerCenterZ;\n }\n return [coord[0], coord[1], z];\n });\n }\n\n async predict(input, config) {\n let useFreshBox = false;\n // run new detector every skipFrames unless we only want box to start with\n let detector;\n if ((this.skipped === 0) || (this.skipped > config.face.detector.skipFrames) || !config.face.mesh.enabled || !config.videoOptimized) {\n detector = await this.boundingBoxDetector.getBoundingBoxes(input);\n this.skipped = 0;\n }\n if (config.videoOptimized) this.skipped++;\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (detector && detector.boxes && (detector.boxes.length > 0) && (!config.face.mesh.enabled || (detector.boxes.length !== this.detectedFaces) && (this.detectedFaces !== config.face.detector.maxFaces))) {\n this.storedBoxes = [];\n this.detectedFaces = 0;\n for (const possible of detector.boxes) {\n this.storedBoxes.push({ startPoint: possible.box.startPoint.dataSync(), endPoint: possible.box.endPoint.dataSync(), landmarks: possible.landmarks, confidence: possible.confidence });\n }\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\n\n if (useFreshBox) {\n if (!detector || !detector.boxes || (detector.boxes.length === 0)) {\n this.storedBoxes = [];\n this.detectedFaces = 0;\n return null;\n }\n for (let i = 0; i < this.storedBoxes.length; i++) {\n // @ts-ignore\n const scaledBox = bounding.scaleBoxCoordinates({ startPoint: this.storedBoxes[i].startPoint, endPoint: this.storedBoxes[i].endPoint }, detector.scaleFactor);\n // @ts-ignore\n const enlargedBox = bounding.enlargeBox(scaledBox);\n // @ts-ignore\n const squarifiedBox = bounding.squarifyBox(enlargedBox);\n const landmarks = this.storedBoxes[i].landmarks.arraySync();\n const confidence = this.storedBoxes[i].confidence;\n this.storedBoxes[i] = { ...squarifiedBox, confidence, landmarks };\n }\n this.runsWithoutFaceDetector = 0;\n }\n if (detector && detector.boxes) {\n detector.boxes.forEach((prediction) => {\n prediction.box.startPoint.dispose();\n prediction.box.endPoint.dispose();\n prediction.landmarks.dispose();\n });\n }\n\n // log('face', `skipped: ${this.skipped} max: ${config.face.detector.maxFaces} detected: ${this.detectedFaces} stored: ${this.storedBoxes.length} new: ${detector?.boxes?.length}`);\n let results = tf.tidy(() => this.storedBoxes.map((box, i) => {\n // The facial bounding box landmarks could come either from blazeface (if we are using a fresh box), or from the mesh model (if we are reusing an old box).\n let face;\n let angle = 0;\n let rotationMatrix;\n if (config.face.detector.rotation) {\n const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= LANDMARKS_COUNT) ? MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES : BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES;\n angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n // @ts-ignore\n const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n const rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized);\n rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n // @ts-ignore\n face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshHeight, this.meshWidth]).div(255);\n } else {\n rotationMatrix = util.IDENTITY_MATRIX;\n const cloned = input.clone();\n // @ts-ignore\n face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, cloned, [this.meshHeight, this.meshWidth]).div(255);\n }\n\n // if we're not going to produce mesh, don't spend time with further processing\n if (!config.face.mesh.enabled) {\n const prediction = {\n coords: null,\n box,\n faceConfidence: null,\n confidence: box.confidence,\n image: face,\n };\n return prediction;\n }\n\n const [, confidence, contourCoords] = this.meshDetector.predict(face); // The first returned tensor represents facial contours, which are included in the coordinates.\n const confidenceVal = confidence.dataSync()[0];\n if (confidenceVal < config.face.detector.minConfidence) return null; // if below confidence just exit\n const coordsReshaped = tf.reshape(contourCoords, [-1, 3]);\n let rawCoords = coordsReshaped.arraySync();\n\n if (config.face.iris.enabled) {\n const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = this.getEyeBox(rawCoords, face, LEFT_EYE_BOUNDS[0], LEFT_EYE_BOUNDS[1], true);\n const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = this.getEyeBox(rawCoords, face, RIGHT_EYE_BOUNDS[0], RIGHT_EYE_BOUNDS[1]);\n const eyePredictions = this.irisModel.predict(tf.concat([leftEyeCrop, rightEyeCrop]));\n const eyePredictionsData = eyePredictions.dataSync();\n const leftEyeData = eyePredictionsData.slice(0, IRIS_NUM_COORDINATES * 3);\n const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = this.getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n const rightEyeData = eyePredictionsData.slice(IRIS_NUM_COORDINATES * 3);\n const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = this.getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize);\n const leftToRightEyeDepthDifference = this.getLeftToRightEyeDepthDifference(rawCoords);\n if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left');\n replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right');\n // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged. So we only update a single contour line above and below the eye.\n } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n } else { // User is looking towards the left.\n replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n }\n const adjustedLeftIrisCoords = this.getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n const adjustedRightIrisCoords = this.getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n rawCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n }\n\n const transformedCoordsData = this.transformRawCoords(rawCoords, box, angle, rotationMatrix);\n // @ts-ignore\n const landmarksBox = bounding.enlargeBox(this.calculateLandmarksBoundingBox(transformedCoordsData));\n // @ts-ignore\n const squarifiedLandmarksBox = bounding.squarifyBox(landmarksBox);\n const transformedCoords = tf.tensor2d(transformedCoordsData);\n const prediction = {\n coords: transformedCoords,\n box: landmarksBox,\n faceConfidence: confidenceVal,\n confidence: box.confidence,\n image: face,\n };\n if (config.face.mesh.returnRawData) prediction.rawCoords = rawCoords;\n this.storedBoxes[i] = { ...squarifiedLandmarksBox, landmarks: transformedCoords.arraySync(), confidence: box.confidence, faceConfidence: confidenceVal };\n\n return prediction;\n }));\n results = results.filter((a) => a !== null);\n this.detectedFaces = results.length;\n return results;\n }\n\n calculateLandmarksBoundingBox(landmarks) {\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint, landmarks };\n }\n}\nexports.Pipeline = Pipeline;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as blazeface from './blazeface.js';\nimport * as facepipeline from './facepipeline.js';\nimport * as coords from './coords.js';\n\nclass MediaPipeFaceMesh {\n constructor(blazeFace, blazeMeshModel, irisModel, config) {\n // @ts-ignore\n this.facePipeline = new facepipeline.Pipeline(blazeFace, blazeMeshModel, irisModel, config);\n this.config = config;\n }\n\n async estimateFaces(input, config) {\n const predictions = await this.facePipeline.predict(input, config);\n const results = [];\n for (const prediction of (predictions || [])) {\n if (prediction.isDisposedInternal) continue; // guard against disposed tensors on long running operations such as pause in middle of processing\n const mesh = prediction.coords ? prediction.coords.arraySync() : null;\n const meshRaw = prediction.rawCoords;\n const annotations = {};\n if (mesh && mesh.length > 0) {\n for (const key of Object.keys(coords.MESH_ANNOTATIONS)) {\n annotations[key] = coords.MESH_ANNOTATIONS[key].map((index) => mesh[index]);\n }\n }\n const boxRaw = (config.face.mesh.returnRawData && prediction.box) ? { topLeft: prediction.box.startPoint, bottomRight: prediction.box.endPoint } : null;\n const box = prediction.box ? [\n Math.max(0, prediction.box.startPoint[0]),\n Math.max(0, prediction.box.startPoint[1]),\n Math.min(input.shape[2], prediction.box.endPoint[0]) - prediction.box.startPoint[0],\n Math.min(input.shape[1], prediction.box.endPoint[1]) - prediction.box.startPoint[1],\n ] : 0;\n results.push({\n confidence: prediction.confidence || 0,\n box,\n mesh,\n boxRaw,\n meshRaw,\n annotations,\n image: prediction.image ? tf.clone(prediction.image) : null,\n });\n if (prediction.coords) prediction.coords.dispose();\n if (prediction.image) prediction.image.dispose();\n }\n return results;\n }\n}\n\nlet faceModels = [null, null, null];\nasync function load(config) {\n faceModels = await Promise.all([\n // @ts-ignore\n (!faceModels[0] && config.face.enabled) ? blazeface.load(config) : null,\n (!faceModels[1] && config.face.mesh.enabled) ? tf.loadGraphModel(config.face.mesh.modelPath, { fromTFHub: config.face.mesh.modelPath.includes('tfhub.dev') }) : null,\n (!faceModels[2] && config.face.iris.enabled) ? tf.loadGraphModel(config.face.iris.modelPath, { fromTFHub: config.face.iris.modelPath.includes('tfhub.dev') }) : null,\n ]);\n const faceMesh = new MediaPipeFaceMesh(faceModels[0], faceModels[1], faceModels[2], config);\n if (config.face.mesh.enabled) log(`load model: ${config.face.mesh.modelPath.match(/\\/(.*)\\./)[1]}`);\n if (config.face.iris.enabled) log(`load model: ${config.face.iris.modelPath.match(/\\/(.*)\\./)[1]}`);\n return faceMesh;\n}\n\nexports.load = load;\nexports.MediaPipeFaceMesh = MediaPipeFaceMesh;\nexports.triangulation = coords.TRI468;\n", "import { log } from './log.js';\n\nconst profileData = {};\n\nfunction profile(name, data) {\n if (!data || !data.kernels) return;\n const maxResults = 5;\n const time = data.kernels\n .filter((a) => a.kernelTimeMs > 0)\n .reduce((a, b) => a += b.kernelTimeMs, 0);\n const slowest = data.kernels\n .map((a, i) => { a.id = i; return a; })\n .filter((a) => a.kernelTimeMs > 0)\n .sort((a, b) => b.kernelTimeMs - a.kernelTimeMs);\n const largest = data.kernels\n .map((a, i) => { a.id = i; return a; })\n .filter((a) => a.totalBytesSnapshot > 0)\n .sort((a, b) => b.totalBytesSnapshot - a.totalBytesSnapshot);\n if (slowest.length > maxResults) slowest.length = maxResults;\n if (largest.length > maxResults) largest.length = maxResults;\n const res = { newBytes: data.newBytes, newTensors: data.newTensors, peakBytes: data.peakBytes, numKernelOps: data.kernels.length, timeKernelOps: time, slowestKernelOps: slowest, largestKernelOps: largest };\n profileData[name] = res;\n log('Human profiler', name, res);\n}\n\nexports.run = profile;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile.js';\n\nconst models = {};\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nasync function load(config) {\n if (!models.age) {\n models.age = await tf.loadGraphModel(config.face.age.modelPath);\n log(`load model: ${config.face.age.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.age;\n}\n\nasync function predict(image, config) {\n if (!models.age) return null;\n if ((skipped < config.face.age.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n /*\n const zoom = [0, 0]; // 0..1 meaning 0%..100%\n const box = [[\n (image.shape[1] * zoom[0]) / image.shape[1],\n (image.shape[2] * zoom[1]) / image.shape[2],\n (image.shape[1] - (image.shape[1] * zoom[0])) / image.shape[1],\n (image.shape[2] - (image.shape[2] * zoom[1])) / image.shape[2],\n ]];\n const resize = tf.image.cropAndResize(image, box, [0], [config.face.age.inputSize, config.face.age.inputSize]);\n */\n const resize = tf.image.resizeBilinear(image, [config.face.age.inputSize, config.face.age.inputSize], false);\n const enhance = tf.mul(resize, [255.0]);\n tf.dispose(resize);\n\n let ageT;\n const obj = {};\n\n if (!config.profile) {\n if (config.face.age.enabled) ageT = await models.age.predict(enhance);\n } else {\n const profileAge = config.face.age.enabled ? await tf.profile(() => models.age.predict(enhance)) : {};\n ageT = profileAge.result.clone();\n profileAge.result.dispose();\n // @ts-ignore\n profile.run('age', profileAge);\n }\n enhance.dispose();\n\n if (ageT) {\n const data = ageT.dataSync();\n obj.age = Math.trunc(10 * data[0]) / 10;\n }\n ageT.dispose();\n\n last = obj;\n resolve(obj);\n });\n}\n\nexports.predict = predict;\nexports.load = load;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile.js';\n\nconst models = {};\nlet last = { gender: '' };\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet alternative = false;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nasync function load(config) {\n if (!models.gender) {\n models.gender = await tf.loadGraphModel(config.face.gender.modelPath);\n alternative = models.gender.inputs[0].shape[3] === 1;\n log(`load model: ${config.face.gender.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.gender;\n}\n\nasync function predict(image, config) {\n if (!models.gender) return null;\n if ((skipped < config.face.gender.skipFrames) && config.videoOptimized && last.gender !== '') {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [config.face.gender.inputSize, config.face.gender.inputSize], false);\n let enhance;\n if (alternative) {\n enhance = tf.tidy(() => {\n const [red, green, blue] = tf.split(resize, 3, 3);\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n return grayscale.sub(0.5).mul(2);\n });\n } else {\n enhance = tf.mul(resize, [255.0]);\n }\n tf.dispose(resize);\n\n let genderT;\n const obj = {};\n\n if (!config.profile) {\n if (config.face.gender.enabled) genderT = await models.gender.predict(enhance);\n } else {\n const profileGender = config.face.gender.enabled ? await tf.profile(() => models.gender.predict(enhance)) : {};\n genderT = profileGender.result.clone();\n profileGender.result.dispose();\n // @ts-ignore\n profile.run('gender', profileGender);\n }\n enhance.dispose();\n\n if (genderT) {\n const data = genderT.dataSync();\n if (alternative) {\n // returns two values 0..1, bigger one is prediction\n const confidence = Math.trunc(100 * Math.abs(data[0] - data[1])) / 100;\n if (confidence > config.face.gender.minConfidence) {\n obj.gender = data[0] > data[1] ? 'female' : 'male';\n obj.confidence = confidence;\n }\n } else {\n // returns one value 0..1, .5 is prediction threshold\n const confidence = Math.trunc(200 * Math.abs((data[0] - 0.5))) / 100;\n if (confidence > config.face.gender.minConfidence) {\n obj.gender = data[0] <= 0.5 ? 'female' : 'male';\n obj.confidence = Math.min(0.99, confidence);\n }\n }\n }\n genderT.dispose();\n\n last = obj;\n resolve(obj);\n });\n}\n\nexports.predict = predict;\nexports.load = load;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile.js';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];\nconst models = {};\nlet last = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\nconst scale = 1; // score multiplication factor\n\nasync function load(config) {\n if (!models.emotion) {\n models.emotion = await tf.loadGraphModel(config.face.emotion.modelPath);\n log(`load model: ${config.face.emotion.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.emotion;\n}\n\nasync function predict(image, config) {\n if (!models.emotion) return null;\n if ((skipped < config.face.emotion.skipFrames) && config.videoOptimized && (last.length > 0)) {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n /*\n const zoom = [0, 0]; // 0..1 meaning 0%..100%\n const box = [[\n (image.shape[1] * zoom[0]) / image.shape[1],\n (image.shape[2] * zoom[1]) / image.shape[2],\n (image.shape[1] - (image.shape[1] * zoom[0])) / image.shape[1],\n (image.shape[2] - (image.shape[2] * zoom[1])) / image.shape[2],\n ]];\n const resize = tf.image.cropAndResize(image, box, [0], [config.face.emotion.inputSize, config.face.emotion.inputSize]);\n */\n const resize = tf.image.resizeBilinear(image, [config.face.emotion.inputSize, config.face.emotion.inputSize], false);\n const [red, green, blue] = tf.split(resize, 3, 3);\n resize.dispose();\n // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n red.dispose();\n green.dispose();\n blue.dispose();\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n redNorm.dispose();\n greenNorm.dispose();\n blueNorm.dispose();\n const normalize = tf.tidy(() => grayscale.sub(0.5).mul(2));\n grayscale.dispose();\n const obj = [];\n if (config.face.emotion.enabled) {\n let data;\n if (!config.profile) {\n const emotionT = await models.emotion.predict(normalize);\n data = emotionT.dataSync();\n tf.dispose(emotionT);\n } else {\n const profileData = await tf.profile(() => models.emotion.predict(normalize));\n data = profileData.result.dataSync();\n profileData.result.dispose();\n // @ts-ignore\n profile.run('emotion', profileData);\n }\n for (let i = 0; i < data.length; i++) {\n if (scale * data[i] > config.face.emotion.minConfidence) obj.push({ score: Math.min(0.99, Math.trunc(100 * scale * data[i]) / 100), emotion: annotations[i] });\n }\n obj.sort((a, b) => b.score - a.score);\n }\n normalize.dispose();\n last = obj;\n resolve(obj);\n });\n}\n\nexports.predict = predict;\nexports.load = load;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile.js';\n\n// based on https://github.com/sirius-ai/MobileFaceNet_TF\n// model converted from https://github.com/sirius-ai/MobileFaceNet_TF/files/3551493/FaceMobileNet192_train_false.zip\n\nconst models = {};\n\nasync function load(config) {\n if (!models.embedding) {\n models.embedding = await tf.loadGraphModel(config.face.embedding.modelPath);\n log(`load model: ${config.face.embedding.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.embedding;\n}\n\nfunction simmilarity(embedding1, embedding2) {\n if (embedding1?.length !== embedding2?.length) return 0;\n // general minkowski distance\n // euclidean distance is limited case where order is 2\n const order = 2;\n const distance = 10.0 * ((embedding1.map((val, i) => (val - embedding2[i])).reduce((dist, diff) => dist + (diff ** order), 0) ** (1 / order)));\n return (Math.trunc(1000 * (1 - distance)) / 1000);\n}\n\nasync function predict(image, config) {\n if (!models.embedding) return null;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [config.face.embedding.inputSize, config.face.embedding.inputSize], false);\n // const normalize = tf.tidy(() => resize.div(127.5).sub(0.5)); // this is -0.5...0.5 ???\n let data = [];\n if (config.face.embedding.enabled) {\n if (!config.profile) {\n const embeddingT = await models.embedding.predict({ img_inputs: resize });\n data = [...embeddingT.dataSync()]; // convert object array to standard array\n tf.dispose(embeddingT);\n } else {\n const profileData = await tf.profile(() => models.embedding.predict({ img_inputs: resize }));\n data = [...profileData.result.dataSync()];\n profileData.result.dispose();\n // @ts-ignore\n profile.run('emotion', profileData);\n }\n }\n resize.dispose();\n // normalize.dispose();\n resolve(data);\n });\n}\n\nexports.predict = predict;\nexports.simmilarity = simmilarity;\nexports.load = load;\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nconst imageNetMean = [-123.15, -115.90, -103.06];\n\nfunction nameOutputResultsMobileNet(results) {\n const [offsets, heatmap, displacementFwd, displacementBwd] = results;\n return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nfunction nameOutputResultsResNet(results) {\n const [displacementFwd, displacementBwd, offsets, heatmap] = results;\n return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nclass BaseModel {\n constructor(model) {\n this.model = model;\n }\n\n predict(input, config) {\n return tf.tidy(() => {\n const asFloat = (config.body.modelType === 'ResNet') ? input.toFloat().add(imageNetMean) : input.toFloat().div(127.5).sub(1.0);\n const asBatch = asFloat.expandDims(0);\n const results = this.model.predict(asBatch);\n const results3d = results.map((y) => y.squeeze([0]));\n const namedResults = (config.body.modelType === 'ResNet') ? nameOutputResultsResNet(results3d) : nameOutputResultsMobileNet(results3d);\n return {\n heatmapScores: namedResults.heatmap.sigmoid(),\n offsets: namedResults.offsets,\n displacementFwd: namedResults.displacementFwd,\n displacementBwd: namedResults.displacementBwd,\n };\n });\n }\n\n dispose() {\n this.model.dispose();\n }\n}\nexports.BaseModel = BaseModel;\n", "// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nfunction half(k) {\n return Math.floor(k / 2);\n}\nclass MaxHeap {\n constructor(maxSize, getElementValue) {\n this.priorityQueue = new Array(maxSize);\n this.numberOfElements = -1;\n this.getElementValue = getElementValue;\n }\n\n enqueue(x) {\n this.priorityQueue[++this.numberOfElements] = x;\n this.swim(this.numberOfElements);\n }\n\n dequeue() {\n const max = this.priorityQueue[0];\n this.exchange(0, this.numberOfElements--);\n this.sink(0);\n this.priorityQueue[this.numberOfElements + 1] = null;\n return max;\n }\n\n empty() {\n return this.numberOfElements === -1;\n }\n\n size() {\n return this.numberOfElements + 1;\n }\n\n all() {\n return this.priorityQueue.slice(0, this.numberOfElements + 1);\n }\n\n max() {\n return this.priorityQueue[0];\n }\n\n swim(k) {\n while (k > 0 && this.less(half(k), k)) {\n this.exchange(k, half(k));\n k = half(k);\n }\n }\n\n sink(k) {\n while (2 * k <= this.numberOfElements) {\n let j = 2 * k;\n if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n if (!this.less(k, j)) break;\n this.exchange(k, j);\n k = j;\n }\n }\n\n getValueAt(i) {\n return this.getElementValue(this.priorityQueue[i]);\n }\n\n less(i, j) {\n return this.getValueAt(i) < this.getValueAt(j);\n }\n\n exchange(i, j) {\n const t = this.priorityQueue[i];\n this.priorityQueue[i] = this.priorityQueue[j];\n this.priorityQueue[j] = t;\n }\n}\nexports.MaxHeap = MaxHeap;\n", "import * as heapSort from './heapSort';\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores) {\n const [height, width] = scores.shape;\n let localMaximum = true;\n const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n localMaximum = false;\n break;\n }\n }\n if (!localMaximum) break;\n }\n return localMaximum;\n}\n\nfunction buildPartWithScoreQueue(scoreThreshold, localMaximumRadius, scores) {\n const [height, width, numKeypoints] = scores.shape;\n const queue = new heapSort.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n const score = scores.get(heatmapY, heatmapX, keypointId);\n // Only consider parts with score greater or equal to threshold as root candidates.\n if (score < scoreThreshold) continue;\n // Only consider keypoints whose score is maximum in a local window.\n if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores)) {\n queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n }\n }\n }\n }\n return queue;\n}\nexports.buildPartWithScoreQueue = buildPartWithScoreQueue;\n", "exports.partNames = [\n 'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n 'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n 'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexports.NUM_KEYPOINTS = exports.partNames.length;\n\nexports.partIds = exports.partNames.reduce((result, jointName, i) => {\n result[jointName] = i;\n return result;\n}, {});\n\nconst connectedPartNames = [\n ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexports.connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([exports.partIds[jointNameA], exports.partIds[jointNameB]]));\n\nexports.poseChain = [\n ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n ['rightKnee', 'rightAnkle'],\n];\n\nexports.partChannels = [\n 'left_face',\n 'right_face',\n 'right_upper_leg_front',\n 'right_lower_leg_back',\n 'right_upper_leg_back',\n 'left_lower_leg_front',\n 'left_upper_leg_front',\n 'left_upper_leg_back',\n 'left_lower_leg_back',\n 'right_feet',\n 'right_lower_leg_front',\n 'left_feet',\n 'torso_front',\n 'torso_back',\n 'right_upper_arm_front',\n 'right_upper_arm_back',\n 'right_lower_arm_back',\n 'left_lower_arm_front',\n 'left_upper_arm_front',\n 'left_upper_arm_back',\n 'left_lower_arm_back',\n 'right_hand',\n 'right_lower_arm_front',\n 'left_hand',\n];\n", "import * as kpt from './keypoints';\n\nfunction getOffsetPoint(y, x, keypoint, offsets) {\n return {\n y: offsets.get(y, x, keypoint),\n x: offsets.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n };\n}\nexports.getOffsetPoint = getOffsetPoint;\n\nfunction getImageCoords(part, outputStride, offsets) {\n const { heatmapY, heatmapX, id: keypoint } = part;\n const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n return {\n x: part.heatmapX * outputStride + x,\n y: part.heatmapY * outputStride + y,\n };\n}\nexports.getImageCoords = getImageCoords;\n\nfunction fillArray(element, size) {\n const result = new Array(size);\n for (let i = 0; i < size; i++) {\n result[i] = element;\n }\n return result;\n}\nexports.fillArray = fillArray;\n\nfunction clamp(a, min, max) {\n if (a < min) return min;\n if (a > max) return max;\n return a;\n}\nexports.clamp = clamp;\n\nfunction squaredDistance(y1, x1, y2, x2) {\n const dy = y2 - y1;\n const dx = x2 - x1;\n return dy * dy + dx * dx;\n}\nexports.squaredDistance = squaredDistance;\n\nfunction addVectors(a, b) {\n return { x: a.x + b.x, y: a.y + b.y };\n}\nexports.addVectors = addVectors;\n\nfunction clampVector(a, min, max) {\n return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\nexports.clampVector = clampVector;\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as kpt from './keypoints';\n\nfunction getPointsConfidence(heatmapScores, heatMapCoords) {\n const numKeypoints = heatMapCoords.shape[0];\n const result = new Float32Array(numKeypoints);\n for (let keypoint = 0; keypoint < numKeypoints; keypoint++) {\n const y = heatMapCoords.get(keypoint, 0);\n const x = heatMapCoords.get(keypoint, 1);\n result[keypoint] = heatmapScores.get(y, x, keypoint);\n }\n return result;\n}\nexports.getPointsConfidence = getPointsConfidence;\n\nfunction getOffsetPoint(y, x, keypoint, offsetsBuffer) {\n return {\n y: offsetsBuffer.get(y, x, keypoint),\n x: offsetsBuffer.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n };\n}\n\nfunction getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer) {\n const result = [];\n for (let keypoint = 0; keypoint < kpt.NUM_KEYPOINTS; keypoint++) {\n const heatmapY = heatMapCoordsBuffer.get(keypoint, 0).valueOf();\n const heatmapX = heatMapCoordsBuffer.get(keypoint, 1).valueOf();\n const { x, y } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsetsBuffer);\n result.push(y);\n result.push(x);\n }\n return tf.tensor2d(result, [kpt.NUM_KEYPOINTS, 2]);\n}\nexports.getOffsetVectors = getOffsetVectors;\n\nfunction getOffsetPoints(heatMapCoordsBuffer, outputStride, offsetsBuffer) {\n return tf.tidy(() => heatMapCoordsBuffer.toTensor().mul(tf.scalar(outputStride, 'int32')).toFloat().add(getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer)));\n}\nexports.getOffsetPoints = getOffsetPoints;\n\nfunction mod(a, b) {\n return tf.tidy(() => {\n const floored = a.div(tf.scalar(b, 'int32'));\n return a.sub(floored.mul(tf.scalar(b, 'int32')));\n });\n}\n\nfunction argmax2d(inputs) {\n const [height, width, depth] = inputs.shape;\n return tf.tidy(() => {\n const reshaped = inputs.reshape([height * width, depth]);\n const coords = reshaped.argMax(0);\n const yCoords = coords.div(tf.scalar(width, 'int32')).expandDims(1);\n const xCoords = mod(coords, width).expandDims(1);\n return tf.concat([yCoords, xCoords], 1);\n });\n}\nexports.argmax2d = argmax2d;\n", "import * as keypoints from './keypoints';\nimport * as vectors from './vectors';\nimport * as decoders from './decoders';\n\nconst parentChildrenTuples = keypoints.poseChain.map(([parentJoinName, childJoinName]) => ([keypoints.partIds[parentJoinName], keypoints.partIds[childJoinName]]));\nconst parentToChildEdges = parentChildrenTuples.map(([, childJointId]) => childJointId);\nconst childToParentEdges = parentChildrenTuples.map(([parentJointId]) => parentJointId);\nfunction getDisplacement(edgeId, point, displacements) {\n const numEdges = displacements.shape[2] / 2;\n return {\n y: displacements.get(point.y, point.x, edgeId),\n x: displacements.get(point.y, point.x, numEdges + edgeId),\n };\n}\nfunction getStridedIndexNearPoint(point, outputStride, height, width) {\n return {\n // @ts-ignore\n y: vectors.clamp(Math.round(point.y / outputStride), 0, height - 1),\n // @ts-ignore\n x: vectors.clamp(Math.round(point.x / outputStride), 0, width - 1),\n };\n}\n\nfunction traverseToTargetKeypoint(edgeId, sourceKeypoint, targetKeypointId, scoresBuffer, offsets, outputStride, displacements, offsetRefineStep = 2) {\n const [height, width] = scoresBuffer.shape;\n // Nearest neighbor interpolation for the source->target displacements.\n const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, outputStride, height, width);\n const displacement = getDisplacement(edgeId, sourceKeypointIndices, displacements);\n // @ts-ignore\n const displacedPoint = vectors.addVectors(sourceKeypoint.position, displacement);\n let targetKeypoint = displacedPoint;\n for (let i = 0; i < offsetRefineStep; i++) {\n const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n // @ts-ignore\n const offsetPoint = vectors.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetKeypointId, offsets);\n // @ts-ignore\n targetKeypoint = vectors.addVectors({\n x: targetKeypointIndices.x * outputStride,\n y: targetKeypointIndices.y * outputStride,\n }, { x: offsetPoint.x, y: offsetPoint.y });\n }\n const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n const score = scoresBuffer.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetKeypointId);\n return { position: targetKeypoint, part: keypoints.partNames[targetKeypointId], score };\n}\n\nfunction decodePose(root, scores, offsets, outputStride, displacementsFwd, displacementsBwd) {\n const numParts = scores.shape[2];\n const numEdges = parentToChildEdges.length;\n const instanceKeypoints = new Array(numParts);\n // Start a new detection instance at the position of the root.\n const { part: rootPart, score: rootScore } = root;\n // @ts-ignore\n const rootPoint = vectors.getImageCoords(rootPart, outputStride, offsets);\n instanceKeypoints[rootPart.id] = {\n score: rootScore,\n part: keypoints.partNames[rootPart.id],\n position: rootPoint,\n };\n // Decode the part positions upwards in the tree, following the backward displacements.\n for (let edge = numEdges - 1; edge >= 0; --edge) {\n const sourceKeypointId = parentToChildEdges[edge];\n const targetKeypointId = childToParentEdges[edge];\n if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsBwd);\n }\n }\n // Decode the part positions downwards in the tree, following the forward displacements.\n for (let edge = 0; edge < numEdges; ++edge) {\n const sourceKeypointId = childToParentEdges[edge];\n const targetKeypointId = parentToChildEdges[edge];\n if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsFwd);\n }\n }\n return instanceKeypoints;\n}\nexports.decodePose = decodePose;\n\nasync function decodeSinglePose(heatmapScores, offsets, config) {\n let totalScore = 0.0;\n // @ts-ignore\n const heatmapValues = decoders.argmax2d(heatmapScores);\n const allTensorBuffers = await Promise.all([heatmapScores.buffer(), offsets.buffer(), heatmapValues.buffer()]);\n const scoresBuffer = allTensorBuffers[0];\n const offsetsBuffer = allTensorBuffers[1];\n const heatmapValuesBuffer = allTensorBuffers[2];\n // @ts-ignore\n const offsetPoints = decoders.getOffsetPoints(heatmapValuesBuffer, config.body.outputStride, offsetsBuffer);\n const offsetPointsBuffer = await offsetPoints.buffer();\n // @ts-ignore\n const keypointConfidence = Array.from(decoders.getPointsConfidence(scoresBuffer, heatmapValuesBuffer));\n const instanceKeypoints = keypointConfidence.map((score, i) => {\n totalScore += score;\n return {\n position: {\n y: offsetPointsBuffer.get(i, 0),\n x: offsetPointsBuffer.get(i, 1),\n },\n part: keypoints.partNames[i],\n score,\n };\n });\n const filteredKeypoints = instanceKeypoints.filter((kpt) => kpt.score > config.body.scoreThreshold);\n heatmapValues.dispose();\n offsetPoints.dispose();\n return { keypoints: filteredKeypoints, score: totalScore / instanceKeypoints.length };\n}\nexports.decodeSinglePose = decodeSinglePose;\n", "import * as buildParts from './buildParts';\nimport * as decodePose from './decodePose';\nimport * as vectors from './vectors';\n\nconst kLocalMaximumRadius = 1;\n\nfunction withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, { x, y }, keypointId) {\n return poses.some(({ keypoints }) => {\n const correspondingKeypoint = keypoints[keypointId].position;\n // @ts-ignore\n return vectors.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n });\n}\n\nfunction getInstanceScore(existingPoses, squaredNmsRadius, instanceKeypoints) {\n const notOverlappedKeypointScores = instanceKeypoints.reduce((result, { position, score }, keypointId) => {\n if (!withinNmsRadiusOfCorrespondingPoint(existingPoses, squaredNmsRadius, position, keypointId)) result += score;\n return result;\n }, 0.0);\n return notOverlappedKeypointScores / instanceKeypoints.length;\n}\n\nfunction decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config) {\n const poses = [];\n // @ts-ignore\n const queue = buildParts.buildPartWithScoreQueue(config.body.scoreThreshold, kLocalMaximumRadius, scoresBuffer);\n const squaredNmsRadius = config.body.nmsRadius ^ 2;\n // Generate at most maxDetections object instances per image in decreasing root part score order.\n while (poses.length < config.body.maxDetections && !queue.empty()) {\n // The top element in the queue is the next root candidate.\n const root = queue.dequeue();\n // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n // @ts-ignore\n const rootImageCoords = vectors.getImageCoords(root.part, config.body.outputStride, offsetsBuffer);\n if (withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, rootImageCoords, root.part.id)) continue;\n // Else start a new detection instance at the position of the root.\n // @ts-ignore\n const keypoints = decodePose.decodePose(root, scoresBuffer, offsetsBuffer, config.body.outputStride, displacementsFwdBuffer, displacementsBwdBuffer);\n const score = getInstanceScore(poses, squaredNmsRadius, keypoints);\n if (score > config.body.scoreThreshold) poses.push({ keypoints, score });\n }\n return poses;\n}\nexports.decodeMultiplePoses = decodeMultiplePoses;\n", "import * as kpt from './keypoints';\n\nfunction eitherPointDoesntMeetConfidence(a, b, minConfidence) {\n return (a < minConfidence || b < minConfidence);\n}\n\nfunction getAdjacentKeyPoints(keypoints, minConfidence) {\n return kpt.connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n return result;\n }\n result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n return result;\n }, []);\n}\nexports.getAdjacentKeyPoints = getAdjacentKeyPoints;\n\nconst { NEGATIVE_INFINITY, POSITIVE_INFINITY } = Number;\nfunction getBoundingBox(keypoints) {\n return keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n maxX: Math.max(maxX, x),\n maxY: Math.max(maxY, y),\n minX: Math.min(minX, x),\n minY: Math.min(minY, y),\n }), {\n maxX: NEGATIVE_INFINITY,\n maxY: NEGATIVE_INFINITY,\n minX: POSITIVE_INFINITY,\n minY: POSITIVE_INFINITY,\n });\n}\nexports.getBoundingBox = getBoundingBox;\n\nfunction getBoundingBoxPoints(keypoints) {\n const { minX, minY, maxX, maxY } = getBoundingBox(keypoints);\n return [{ x: minX, y: minY }, { x: maxX, y: minY }, { x: maxX, y: maxY }, { x: minX, y: maxY }];\n}\nexports.getBoundingBoxPoints = getBoundingBoxPoints;\n\nasync function toTensorBuffers3D(tensors) {\n return Promise.all(tensors.map((tensor) => tensor.buffer()));\n}\nexports.toTensorBuffers3D = toTensorBuffers3D;\n\nfunction scalePose(pose, scaleY, scaleX) {\n return {\n score: pose.score,\n keypoints: pose.keypoints.map(({ score, part, position }) => ({\n score,\n part,\n position: { x: position.x * scaleX, y: position.y * scaleY },\n })),\n };\n}\nexports.scalePose = scalePose;\n\nfunction resizeTo(image, [targetH, targetW]) {\n const input = image.squeeze(0);\n const resized = input.resizeBilinear([targetH, targetW]);\n input.dispose();\n return resized;\n}\nexports.resizeTo = resizeTo;\n\nfunction scaleAndFlipPoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) {\n const scaledPoses = poses.map((pose) => scalePose(pose, height / inputResolutionHeight, width / inputResolutionWidth));\n return scaledPoses;\n}\nexports.scaleAndFlipPoses = scaleAndFlipPoses;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as modelBase from './modelBase';\nimport * as decodeMultiple from './decodeMultiple';\nimport * as decodePose from './decodePose';\nimport * as util from './util';\n\nasync function estimateMultiple(input, res, config) {\n return new Promise(async (resolve) => {\n const height = input.shape[1];\n const width = input.shape[2];\n // @ts-ignore\n const allTensorBuffers = await util.toTensorBuffers3D([res.heatmapScores, res.offsets, res.displacementFwd, res.displacementBwd]);\n const scoresBuffer = allTensorBuffers[0];\n const offsetsBuffer = allTensorBuffers[1];\n const displacementsFwdBuffer = allTensorBuffers[2];\n const displacementsBwdBuffer = allTensorBuffers[3];\n // @ts-ignore\n const poses = await decodeMultiple.decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config);\n // @ts-ignore\n const scaled = util.scaleAndFlipPoses(poses, [height, width], [config.body.inputSize, config.body.inputSize]);\n resolve(scaled);\n });\n}\n\nasync function estimateSingle(input, res, config) {\n return new Promise(async (resolve) => {\n const height = input.shape[1];\n const width = input.shape[2];\n // @ts-ignore\n const pose = await decodePose.decodeSinglePose(res.heatmapScores, res.offsets, config);\n const poses = [pose];\n // @ts-ignore\n const scaled = util.scaleAndFlipPoses(poses, [height, width], [config.body.inputSize, config.body.inputSize]);\n resolve(scaled);\n });\n}\n\nclass PoseNet {\n constructor(model) {\n this.baseModel = model;\n }\n\n async estimatePoses(input, config) {\n // @ts-ignore\n const resized = util.resizeTo(input, [config.body.inputSize, config.body.inputSize]);\n const res = this.baseModel.predict(resized, config);\n\n const poses = (config.body.maxDetections < 2) ? await estimateSingle(input, res, config) : await estimateMultiple(input, res, config);\n\n res.heatmapScores.dispose();\n res.offsets.dispose();\n res.displacementFwd.dispose();\n res.displacementBwd.dispose();\n resized.dispose();\n\n return poses;\n }\n\n dispose() {\n this.baseModel.dispose();\n }\n}\nexports.PoseNet = PoseNet;\n\nasync function load(config) {\n const model = await tf.loadGraphModel(config.body.modelPath);\n // @ts-ignore\n const mobilenet = new modelBase.BaseModel(model);\n log(`load model: ${config.body.modelPath.match(/\\/(.*)\\./)[1]}`);\n return new PoseNet(mobilenet);\n}\nexports.load = load;\n", "import * as modelPoseNet from './modelPoseNet';\nimport * as keypoints from './keypoints';\nimport * as util from './util';\n\n// @ts-ignore\nexports.load = modelPoseNet.load;\n// @ts-ignore\nexports.PoseNet = modelPoseNet.PoseNet;\n\nexports.partChannels = keypoints.partChannels;\nexports.partIds = keypoints.partIds;\nexports.partNames = keypoints.partNames;\nexports.poseChain = keypoints.poseChain;\n// @ts-ignore\nexports.getAdjacentKeyPoints = util.getAdjacentKeyPoints;\n// @ts-ignore\nexports.getBoundingBox = util.getBoundingBox;\n// @ts-ignore\nexports.getBoundingBoxPoints = util.getBoundingBoxPoints;\n// @ts-ignore\nexports.scaleAndFlipPoses = util.scaleAndFlipPoses;\n// @ts-ignore\nexports.scalePose = util.scalePose;\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\n\nclass HandDetector {\n constructor(model, inputSize, anchorsAnnotated) {\n this.model = model;\n this.anchors = anchorsAnnotated.map((anchor) => [anchor.x_center, anchor.y_center]);\n this.anchorsTensor = tf.tensor2d(this.anchors);\n this.inputSizeTensor = tf.tensor1d([inputSize, inputSize]);\n this.doubleInputSizeTensor = tf.tensor1d([inputSize * 2, inputSize * 2]);\n }\n\n normalizeBoxes(boxes) {\n return tf.tidy(() => {\n const boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n const boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n const boxCenterPoints = tf.add(tf.div(boxOffsets, this.inputSizeTensor), this.anchorsTensor);\n const halfBoxSizes = tf.div(boxSizes, this.doubleInputSizeTensor);\n const startPoints = tf.mul(tf.sub(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n const endPoints = tf.mul(tf.add(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n return tf.concat2d([startPoints, endPoints], 1);\n });\n }\n\n normalizeLandmarks(rawPalmLandmarks, index) {\n return tf.tidy(() => {\n const landmarks = tf.add(tf.div(rawPalmLandmarks.reshape([-1, 7, 2]), this.inputSizeTensor), this.anchors[index]);\n return tf.mul(landmarks, this.inputSizeTensor);\n });\n }\n\n async getBoxes(input, config) {\n const batched = this.model.predict(input);\n const predictions = batched.squeeze();\n batched.dispose();\n const scoresT = tf.tidy(() => tf.sigmoid(tf.slice(predictions, [0, 0], [-1, 1])).squeeze());\n const scores = scoresT.dataSync();\n const rawBoxes = tf.slice(predictions, [0, 1], [-1, 4]);\n const boxes = this.normalizeBoxes(rawBoxes);\n rawBoxes.dispose();\n const filteredT = await tf.image.nonMaxSuppressionAsync(boxes, scores, config.hand.maxHands, config.hand.iouThreshold, config.hand.scoreThreshold);\n const filtered = filteredT.arraySync();\n\n scoresT.dispose();\n filteredT.dispose();\n const hands = [];\n for (const index of filtered) {\n if (scores[index] >= config.hand.minConfidence) {\n const matchingBox = tf.slice(boxes, [index, 0], [1, -1]);\n const rawPalmLandmarks = tf.slice(predictions, [index, 5], [1, 14]);\n const palmLandmarks = tf.tidy(() => this.normalizeLandmarks(rawPalmLandmarks, index).reshape([-1, 2]));\n rawPalmLandmarks.dispose();\n hands.push({ box: matchingBox, palmLandmarks, confidence: scores[index] });\n }\n }\n predictions.dispose();\n boxes.dispose();\n return hands;\n }\n\n async estimateHandBounds(input, config) {\n const inputHeight = input.shape[1];\n const inputWidth = input.shape[2];\n const image = tf.tidy(() => input.resizeBilinear([config.hand.inputSize, config.hand.inputSize]).div(127.5).sub(1));\n const predictions = await this.getBoxes(image, config);\n image.dispose();\n const hands = [];\n if (!predictions || predictions.length === 0) return hands;\n for (const prediction of predictions) {\n const boxes = prediction.box.dataSync();\n const startPoint = boxes.slice(0, 2);\n const endPoint = boxes.slice(2, 4);\n const palmLandmarks = prediction.palmLandmarks.arraySync();\n prediction.box.dispose();\n prediction.palmLandmarks.dispose();\n hands.push(box.scaleBoxCoordinates({ startPoint, endPoint, palmLandmarks, confidence: prediction.confidence }, [inputWidth / config.hand.inputSize, inputHeight / config.hand.inputSize]));\n }\n return hands;\n }\n}\nexports.HandDetector = HandDetector;\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\nimport * as util from './util';\n// eslint-disable-next-line no-unused-vars\nimport { log } from '../log.js';\n\n// const PALM_BOX_SHIFT_VECTOR = [0, -0.4];\nconst PALM_BOX_ENLARGE_FACTOR = 5; // default 3\n// const HAND_BOX_SHIFT_VECTOR = [0, -0.1]; // move detected hand box by x,y to ease landmark detection\nconst HAND_BOX_ENLARGE_FACTOR = 1.65; // default 1.65\nconst PALM_LANDMARK_IDS = [0, 5, 9, 13, 17, 1, 2];\nconst PALM_LANDMARKS_INDEX_OF_PALM_BASE = 0;\nconst PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE = 2;\n\nclass HandPipeline {\n constructor(handDetector, landmarkDetector, inputSize) {\n this.handDetector = handDetector;\n this.landmarkDetector = landmarkDetector;\n this.inputSize = inputSize;\n this.storedBoxes = [];\n this.skipped = 0;\n this.detectedHands = 0;\n }\n\n getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n // return box.enlargeBox(box.squarifyBox(box.shiftBox(boxAroundPalm, PALM_BOX_SHIFT_VECTOR)), PALM_BOX_ENLARGE_FACTOR);\n return box.enlargeBox(box.squarifyBox(boxAroundPalm), PALM_BOX_ENLARGE_FACTOR);\n }\n\n getBoxForHandLandmarks(landmarks) {\n const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n // const boxAroundHand = box.enlargeBox(box.squarifyBox(box.shiftBox(boundingBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n const boxAroundHand = box.enlargeBox(box.squarifyBox(boundingBox), HAND_BOX_ENLARGE_FACTOR);\n boxAroundHand.palmLandmarks = [];\n for (let i = 0; i < PALM_LANDMARK_IDS.length; i++) {\n boxAroundHand.palmLandmarks.push(landmarks[PALM_LANDMARK_IDS[i]].slice(0, 2));\n }\n return boxAroundHand;\n }\n\n transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n const boxSize = box.getBoxSize(box2);\n const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n const coordsScaled = rawCoords.map((coord) => [\n scaleFactor[0] * (coord[0] - this.inputSize / 2),\n scaleFactor[1] * (coord[1] - this.inputSize / 2),\n scaleFactor[2] * coord[2],\n ]);\n const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n const coordsRotated = coordsScaled.map((coord) => {\n const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n return [...rotated, coord[2]];\n });\n const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n const boxCenter = [...box.getBoxCenter(box2), 1];\n const originalBoxCenter = [\n util.dot(boxCenter, inverseRotationMatrix[0]),\n util.dot(boxCenter, inverseRotationMatrix[1]),\n ];\n return coordsRotated.map((coord) => [\n coord[0] + originalBoxCenter[0],\n coord[1] + originalBoxCenter[1],\n coord[2],\n ]);\n }\n\n async estimateHands(image, config) {\n let useFreshBox = false;\n\n // run new detector every skipFrames unless we only want box to start with\n let boxes;\n if ((this.skipped === 0) || (this.skipped > config.hand.skipFrames) || !config.hand.landmarks || !config.videoOptimized) {\n boxes = await this.handDetector.estimateHandBounds(image, config);\n this.skipped = 0;\n }\n if (config.videoOptimized) this.skipped++;\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxHands) || !config.hand.landmarks)) {\n this.detectedHands = 0;\n this.storedBoxes = [...boxes];\n // for (const possible of boxes) this.storedBoxes.push(possible);\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\n const hands = [];\n // log('hand', `skipped: ${this.skipped} max: ${config.hand.maxHands} detected: ${this.detectedHands} stored: ${this.storedBoxes.length} new: ${boxes?.length}`);\n\n // go through working set of boxes\n for (let i = 0; i < this.storedBoxes.length; i++) {\n const currentBox = this.storedBoxes[i];\n if (!currentBox) continue;\n if (config.hand.landmarks) {\n const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_PALM_BASE], currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE]) : 0;\n const palmCenter = box.getBoxCenter(currentBox);\n const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n const rotatedImage = config.hand.rotation ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n const croppedInput = box.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n const handImage = croppedInput.div(255);\n croppedInput.dispose();\n rotatedImage.dispose();\n const [confidenceT, keypoints] = await this.landmarkDetector.predict(handImage);\n handImage.dispose();\n const confidence = confidenceT.dataSync()[0];\n confidenceT.dispose();\n if (confidence >= config.hand.minConfidence) {\n const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n const rawCoords = keypointsReshaped.arraySync();\n keypoints.dispose();\n keypointsReshaped.dispose();\n const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n this.storedBoxes[i] = nextBoundingBox;\n const result = {\n landmarks: coords,\n confidence,\n box: {\n topLeft: nextBoundingBox.startPoint,\n bottomRight: nextBoundingBox.endPoint,\n },\n };\n hands.push(result);\n } else {\n this.storedBoxes[i] = null;\n }\n keypoints.dispose();\n } else {\n // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n const enlarged = box.enlargeBox(box.squarifyBox(currentBox), HAND_BOX_ENLARGE_FACTOR);\n const result = {\n confidence: currentBox.confidence,\n box: {\n topLeft: enlarged.startPoint,\n bottomRight: enlarged.endPoint,\n },\n };\n hands.push(result);\n }\n }\n this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n this.detectedHands = hands.length;\n return hands;\n }\n\n // eslint-disable-next-line class-methods-use-this\n calculateLandmarksBoundingBox(landmarks) {\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint };\n }\n}\n\nexports.HandPipeline = HandPipeline;\n", "exports.anchors = [\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.796875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.796875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.828125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.828125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.859375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.859375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.890625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.890625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.921875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.921875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.953125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.953125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.984375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.984375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.796875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.796875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.828125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.828125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.859375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.859375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.890625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.890625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.921875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.921875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.953125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.953125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.984375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.984375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.078125,\n },\n {\n w: 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0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.6875,\n },\n {\n w: 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0.9375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n];\n", "// https://storage.googleapis.com/tfjs-models/demos/handpose/index.html\n\nimport { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as handdetector from './handdetector';\nimport * as handpipeline from './handpipeline';\nimport * as anchors from './anchors';\n\nconst MESH_ANNOTATIONS = {\n thumb: [1, 2, 3, 4],\n indexFinger: [5, 6, 7, 8],\n middleFinger: [9, 10, 11, 12],\n ringFinger: [13, 14, 15, 16],\n pinky: [17, 18, 19, 20],\n palmBase: [0],\n};\n\nclass HandPose {\n constructor(handPipeline) {\n this.handPipeline = handPipeline;\n }\n\n static getAnnotations() {\n return MESH_ANNOTATIONS;\n }\n\n async estimateHands(input, config) {\n const predictions = await this.handPipeline.estimateHands(input, config);\n if (!predictions) return [];\n const hands = [];\n for (const prediction of predictions) {\n const annotations = {};\n if (prediction.landmarks) {\n for (const key of Object.keys(MESH_ANNOTATIONS)) {\n annotations[key] = MESH_ANNOTATIONS[key].map((index) => prediction.landmarks[index]);\n }\n }\n const box = prediction.box ? [\n Math.max(0, prediction.box.topLeft[0]),\n Math.max(0, prediction.box.topLeft[1]),\n Math.min(input.shape[2], prediction.box.bottomRight[0]) - prediction.box.topLeft[0],\n Math.min(input.shape[1], prediction.box.bottomRight[1]) - prediction.box.topLeft[1],\n ] : 0;\n hands.push({\n confidence: prediction.confidence,\n box,\n landmarks: prediction.landmarks,\n annotations,\n });\n }\n return hands;\n }\n}\nexports.HandPose = HandPose;\n\nasync function load(config) {\n const [handDetectorModel, handPoseModel] = await Promise.all([\n config.hand.enabled ? tf.loadGraphModel(config.hand.detector.modelPath, { fromTFHub: config.hand.detector.modelPath.includes('tfhub.dev') }) : null,\n config.hand.landmarks ? tf.loadGraphModel(config.hand.skeleton.modelPath, { fromTFHub: config.hand.skeleton.modelPath.includes('tfhub.dev') }) : null,\n ]);\n // @ts-ignore\n const handDetector = new handdetector.HandDetector(handDetectorModel, config.hand.inputSize, anchors.anchors);\n // @ts-ignore\n const handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel, config.hand.inputSize);\n const handPose = new HandPose(handPipeline);\n if (config.hand.enabled) log(`load model: ${config.hand.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n if (config.hand.landmarks) log(`load model: ${config.hand.skeleton.modelPath.match(/\\/(.*)\\./)[1]}`);\n return handPose;\n}\nexports.load = load;\n", "exports.body = (res) => {\n if (!res) return [];\n const gestures = [];\n for (let i = 0; i < res.length; i++) {\n // raising hands\n const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n if (nose && leftWrist && rightWrist && (leftWrist.position.y < nose.position.y) && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'i give up' });\n else if (nose && leftWrist && (leftWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise left hand' });\n else if (nose && rightWrist && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise right hand' });\n\n // leaning\n const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n if (leftShoulder && rightShoulder) gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position.y > rightShoulder.position.y) ? 'left' : 'right'}` });\n }\n return gestures;\n};\n\nexports.face = (res) => {\n if (!res) return [];\n const gestures = [];\n for (let i = 0; i < res.length; i++) {\n if (res[i].mesh && res[i].mesh.length > 0) {\n const eyeFacing = res[i].mesh[35][2] - res[i].mesh[263][2];\n if (Math.abs(eyeFacing) < 10) gestures.push({ face: i, gesture: 'facing camera' });\n else gestures.push({ face: i, gesture: `facing ${eyeFacing < 0 ? 'right' : 'left'}` });\n const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n const chinDepth = res[i].mesh[152][2];\n if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n }\n }\n return gestures;\n};\n\nexports.iris = (res) => {\n if (!res) return [];\n const gestures = [];\n for (let i = 0; i < res.length; i++) {\n if (!res[i].annotations || !res[i].annotations.leftEyeIris || !res[i].annotations.rightEyeIris) continue;\n const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n if (difference < 0.25) gestures.push({ iris: i, gesture: 'looking at camera' });\n }\n return gestures;\n};\n\nexports.hand = (res) => {\n if (!res) return [];\n const gestures = [];\n for (let i = 0; i < res.length; i++) {\n const fingers = [];\n for (const [finger, pos] of Object.entries(res[i]['annotations'])) {\n if (finger !== 'palmBase') fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n }\n if (fingers && fingers.length > 0) {\n const closest = fingers.reduce((best, a) => (best.position[2] < a.position[2] ? best : a));\n const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n gestures.push({ hand: i, gesture: `${closest.name} forward ${highest.name} up` });\n }\n }\n return gestures;\n};\n", "/* eslint-disable no-use-before-define */\n/*\nWebGLImageFilter - MIT Licensed\n2013, Dominic Szablewski - phoboslab.org\n\n*/\n\nconst WebGLProgram = function (gl, vertexSource, fragmentSource) {\n const _collect = function (source, prefix, collection) {\n const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n source.replace(r, (match, name) => {\n collection[name] = 0;\n return match;\n });\n };\n\n const _compile = function (source, type) {\n const shader = gl.createShader(type);\n gl.shaderSource(shader, source);\n gl.compileShader(shader);\n\n if (!gl.getShaderParameter(shader, gl.COMPILE_STATUS)) {\n // @ts-ignore\n throw new Error('Filter: GL compile failed', gl.getShaderInfoLog(shader));\n }\n return shader;\n };\n\n this.uniform = {};\n this.attribute = {};\n\n const _vsh = _compile(vertexSource, gl.VERTEX_SHADER);\n const _fsh = _compile(fragmentSource, gl.FRAGMENT_SHADER);\n\n this.id = gl.createProgram();\n gl.attachShader(this.id, _vsh);\n gl.attachShader(this.id, _fsh);\n gl.linkProgram(this.id);\n\n if (!gl.getProgramParameter(this.id, gl.LINK_STATUS)) {\n // @ts-ignore\n throw new Error('Filter: GL link failed', gl.getProgramInfoLog(this.id));\n }\n\n gl.useProgram(this.id);\n\n // Collect attributes\n _collect(vertexSource, 'attribute', this.attribute);\n for (const a in this.attribute) {\n this.attribute[a] = gl.getAttribLocation(this.id, a);\n }\n\n // Collect uniforms\n _collect(vertexSource, 'uniform', this.uniform);\n _collect(fragmentSource, 'uniform', this.uniform);\n for (const u in this.uniform) {\n this.uniform[u] = gl.getUniformLocation(this.id, u);\n }\n};\n\nconst WebGLImageFilter = function (params) {\n if (!params) params = { };\n let _drawCount = 0;\n let _sourceTexture = null;\n let _lastInChain = false;\n let _currentFramebufferIndex = -1;\n let _tempFramebuffers = [null, null];\n let _filterChain = [];\n let _width = -1;\n let _height = -1;\n let _vertexBuffer = null;\n let _currentProgram = null;\n const _canvas = params.canvas || document.createElement('canvas');\n\n // key is the shader program source, value is the compiled program\n const _shaderProgramCache = { };\n\n const gl = _canvas.getContext('webgl');\n if (!gl) throw new Error('Filter: getContext() failed');\n\n this.addFilter = function (name) {\n // eslint-disable-next-line prefer-rest-params\n const args = Array.prototype.slice.call(arguments, 1);\n const filter = _filter[name];\n\n _filterChain.push({ func: filter, args });\n };\n\n this.reset = function () {\n _filterChain = [];\n };\n\n this.apply = function (image) {\n _resize(image.width, image.height);\n _drawCount = 0;\n\n // Create the texture for the input image if we haven't yet\n if (!_sourceTexture) _sourceTexture = gl.createTexture();\n gl.bindTexture(gl.TEXTURE_2D, _sourceTexture);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n\n // No filters? Just draw\n if (_filterChain.length === 0) {\n // const program = _compileShader(SHADER.FRAGMENT_IDENTITY);\n _draw();\n return _canvas;\n }\n\n for (let i = 0; i < _filterChain.length; i++) {\n _lastInChain = (i === _filterChain.length - 1);\n const f = _filterChain[i];\n f.func.apply(this, f.args || []);\n }\n\n return _canvas;\n };\n\n const _resize = function (width, height) {\n // Same width/height? Nothing to do here\n if (width === _width && height === _height) { return; }\n\n _canvas.width = width;\n _width = width;\n _canvas.height = height;\n _height = height;\n\n // Create the context if we don't have it yet\n if (!_vertexBuffer) {\n // Create the vertex buffer for the two triangles [x, y, u, v] * 6\n const vertices = new Float32Array([\n -1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0,\n -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0,\n ]);\n // eslint-disable-next-line no-unused-expressions\n (_vertexBuffer = gl.createBuffer(), gl.bindBuffer(gl.ARRAY_BUFFER, _vertexBuffer));\n gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);\n\n // Note sure if this is a good idea; at least it makes texture loading\n // in Ejecta instant.\n gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n }\n\n gl.viewport(0, 0, _width, _height);\n\n // Delete old temp framebuffers\n _tempFramebuffers = [null, null];\n };\n\n const _getTempFramebuffer = function (index) {\n // @ts-ignore\n _tempFramebuffers[index] = _tempFramebuffers[index] || _createFramebufferTexture(_width, _height);\n return _tempFramebuffers[index];\n };\n\n const _createFramebufferTexture = function (width, height) {\n const fbo = gl.createFramebuffer();\n gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n\n const renderbuffer = gl.createRenderbuffer();\n gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n\n const texture = gl.createTexture();\n gl.bindTexture(gl.TEXTURE_2D, texture);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n\n gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n\n gl.bindTexture(gl.TEXTURE_2D, null);\n gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n\n return { fbo, texture };\n };\n\n const _draw = function (flags) {\n let source = null;\n let target = null;\n let flipY = false;\n\n // Set up the source\n if (_drawCount === 0) {\n // First draw call - use the source texture\n source = _sourceTexture;\n } else {\n // All following draw calls use the temp buffer last drawn to\n // @ts-ignore\n source = _getTempFramebuffer(_currentFramebufferIndex)?.texture;\n }\n _drawCount++;\n\n // Set up the target\n if (_lastInChain && !(flags & DRAW.INTERMEDIATE)) {\n // Last filter in our chain - draw directly to the WebGL Canvas. We may\n // also have to flip the image vertically now\n target = null;\n flipY = _drawCount % 2 === 0;\n } else {\n // Intermediate draw call - get a temp buffer to draw to\n _currentFramebufferIndex = (_currentFramebufferIndex + 1) % 2;\n // @ts-ignore\n target = _getTempFramebuffer(_currentFramebufferIndex)?.fbo;\n }\n\n // Bind the source and target and draw the two triangles\n gl.bindTexture(gl.TEXTURE_2D, source);\n gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n\n gl.uniform1f(_currentProgram.uniform.flipY, (flipY ? -1 : 1));\n gl.drawArrays(gl.TRIANGLES, 0, 6);\n };\n\n const _compileShader = function (fragmentSource) {\n if (_shaderProgramCache[fragmentSource]) {\n _currentProgram = _shaderProgramCache[fragmentSource];\n gl.useProgram(_currentProgram.id);\n return _currentProgram;\n }\n\n // Compile shaders\n _currentProgram = new WebGLProgram(gl, SHADER.VERTEX_IDENTITY, fragmentSource);\n\n const floatSize = Float32Array.BYTES_PER_ELEMENT;\n const vertSize = 4 * floatSize;\n gl.enableVertexAttribArray(_currentProgram.attribute.pos);\n gl.vertexAttribPointer(_currentProgram.attribute.pos, 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n gl.enableVertexAttribArray(_currentProgram.attribute.uv);\n gl.vertexAttribPointer(_currentProgram.attribute.uv, 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n\n _shaderProgramCache[fragmentSource] = _currentProgram;\n return _currentProgram;\n };\n\n let DRAW = { INTERMEDIATE: 1 };\n\n let SHADER = {};\n SHADER.VERTEX_IDENTITY = [\n 'precision highp float;',\n 'attribute vec2 pos;',\n 'attribute vec2 uv;',\n 'varying vec2 vUv;',\n 'uniform float flipY;',\n\n 'void main(void) {',\n 'vUv = uv;',\n 'gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);',\n '}',\n ].join('\\n');\n\n SHADER.FRAGMENT_IDENTITY = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n\n 'void main(void) {',\n 'gl_FragColor = texture2D(texture, vUv);',\n '}',\n ].join('\\n');\n\n let _filter = {};\n\n // -------------------------------------------------------------------------\n // Color Matrix Filter\n\n _filter.colorMatrix = function (matrix) {\n // Create a Float32 Array and normalize the offset component to 0-1\n const m = new Float32Array(matrix);\n m[4] /= 255;\n m[9] /= 255;\n m[14] /= 255;\n m[19] /= 255;\n\n // Can we ignore the alpha value? Makes things a bit faster.\n const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0)\n ? _filter.colorMatrix.SHADER.WITHOUT_ALPHA\n : _filter.colorMatrix.SHADER.WITH_ALPHA;\n\n const program = _compileShader(shader);\n gl.uniform1fv(program.uniform.m, m);\n _draw();\n };\n\n _filter.colorMatrix.SHADER = {};\n _filter.colorMatrix.SHADER.WITH_ALPHA = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform float m[20];',\n\n 'void main(void) {',\n 'vec4 c = texture2D(texture, vUv);',\n 'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];',\n 'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];',\n 'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];',\n 'gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];',\n '}',\n ].join('\\n');\n _filter.colorMatrix.SHADER.WITHOUT_ALPHA = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform float m[20];',\n\n 'void main(void) {',\n 'vec4 c = texture2D(texture, vUv);',\n 'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];',\n 'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];',\n 'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];',\n 'gl_FragColor.a = c.a;',\n '}',\n ].join('\\n');\n\n _filter.brightness = function (brightness) {\n const b = (brightness || 0) + 1;\n _filter.colorMatrix([\n b, 0, 0, 0, 0,\n 0, b, 0, 0, 0,\n 0, 0, b, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.saturation = function (amount) {\n const x = (amount || 0) * 2 / 3 + 1;\n const y = ((x - 1) * -0.5);\n _filter.colorMatrix([\n x, y, y, 0, 0,\n y, x, y, 0, 0,\n y, y, x, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.desaturate = function () {\n _filter.saturation(-1);\n };\n\n _filter.contrast = function (amount) {\n const v = (amount || 0) + 1;\n const o = -128 * (v - 1);\n\n _filter.colorMatrix([\n v, 0, 0, 0, o,\n 0, v, 0, 0, o,\n 0, 0, v, 0, o,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.negative = function () {\n _filter.contrast(-2);\n };\n\n _filter.hue = function (rotation) {\n rotation = (rotation || 0) / 180 * Math.PI;\n const cos = Math.cos(rotation);\n const sin = Math.sin(rotation);\n const lumR = 0.213;\n const lumG = 0.715;\n const lumB = 0.072;\n\n _filter.colorMatrix([\n lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.desaturateLuminance = function () {\n _filter.colorMatrix([\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.sepia = function () {\n _filter.colorMatrix([\n 0.393, 0.7689999, 0.18899999, 0, 0,\n 0.349, 0.6859999, 0.16799999, 0, 0,\n 0.272, 0.5339999, 0.13099999, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.brownie = function () {\n _filter.colorMatrix([\n 0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n 0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.vintagePinhole = function () {\n _filter.colorMatrix([\n 0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n 0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n 0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.kodachrome = function () {\n _filter.colorMatrix([\n 1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.technicolor = function () {\n _filter.colorMatrix([\n 1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.polaroid = function () {\n _filter.colorMatrix([\n 1.438, -0.062, -0.062, 0, 0,\n -0.122, 1.378, -0.122, 0, 0,\n -0.016, -0.016, 1.483, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.shiftToBGR = function () {\n _filter.colorMatrix([\n 0, 0, 1, 0, 0,\n 0, 1, 0, 0, 0,\n 1, 0, 0, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n // -------------------------------------------------------------------------\n // Convolution Filter\n\n _filter.convolution = function (matrix) {\n const m = new Float32Array(matrix);\n const pixelSizeX = 1 / _width;\n const pixelSizeY = 1 / _height;\n\n const program = _compileShader(_filter.convolution.SHADER);\n gl.uniform1fv(program.uniform.m, m);\n gl.uniform2f(program.uniform.px, pixelSizeX, pixelSizeY);\n _draw();\n };\n\n _filter.convolution.SHADER = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform vec2 px;',\n 'uniform float m[9];',\n\n 'void main(void) {',\n 'vec4 c11 = texture2D(texture, vUv - px);', // top left\n 'vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y));', // top center\n 'vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y));', // top right\n\n 'vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) );', // mid left\n 'vec4 c22 = texture2D(texture, vUv);', // mid center\n 'vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) );', // mid right\n\n 'vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) );', // bottom left\n 'vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) );', // bottom center\n 'vec4 c33 = texture2D(texture, vUv + px );', // bottom right\n\n 'gl_FragColor = ',\n 'c11 * m[0] + c12 * m[1] + c22 * m[2] +',\n 'c21 * m[3] + c22 * m[4] + c23 * m[5] +',\n 'c31 * m[6] + c32 * m[7] + c33 * m[8];',\n 'gl_FragColor.a = c22.a;',\n '}',\n ].join('\\n');\n\n _filter.detectEdges = function () {\n _filter.convolution.call(this, [\n 0, 1, 0,\n 1, -4, 1,\n 0, 1, 0,\n ]);\n };\n\n _filter.sobelX = function () {\n _filter.convolution.call(this, [\n -1, 0, 1,\n -2, 0, 2,\n -1, 0, 1,\n ]);\n };\n\n _filter.sobelY = function () {\n _filter.convolution.call(this, [\n -1, -2, -1,\n 0, 0, 0,\n 1, 2, 1,\n ]);\n };\n\n _filter.sharpen = function (amount) {\n const a = amount || 1;\n _filter.convolution.call(this, [\n 0, -1 * a, 0,\n -1 * a, 1 + 4 * a, -1 * a,\n 0, -1 * a, 0,\n ]);\n };\n\n _filter.emboss = function (size) {\n const s = size || 1;\n _filter.convolution.call(this, [\n -2 * s, -1 * s, 0,\n -1 * s, 1, 1 * s,\n 0, 1 * s, 2 * s,\n ]);\n };\n\n // -------------------------------------------------------------------------\n // Blur Filter\n\n _filter.blur = function (size) {\n const blurSizeX = (size / 7) / _width;\n const blurSizeY = (size / 7) / _height;\n\n const program = _compileShader(_filter.blur.SHADER);\n\n // Vertical\n gl.uniform2f(program.uniform.px, 0, blurSizeY);\n _draw(DRAW.INTERMEDIATE);\n\n // Horizontal\n gl.uniform2f(program.uniform.px, blurSizeX, 0);\n _draw();\n };\n\n _filter.blur.SHADER = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform vec2 px;',\n\n 'void main(void) {',\n 'gl_FragColor = vec4(0.0);',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;',\n 'gl_FragColor += texture2D(texture, vUv )*0.159576912161;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x, 1.0*px.y))*0.147308056121;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x, 2.0*px.y))*0.115876621105;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x, 3.0*px.y))*0.0776744219933;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x, 4.0*px.y))*0.0443683338718;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x, 5.0*px.y))*0.0215963866053;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265;',\n '}',\n ].join('\\n');\n\n // -------------------------------------------------------------------------\n // Pixelate Filter\n\n _filter.pixelate = function (size) {\n const blurSizeX = (size) / _width;\n const blurSizeY = (size) / _height;\n\n const program = _compileShader(_filter.pixelate.SHADER);\n\n // Horizontal\n gl.uniform2f(program.uniform.size, blurSizeX, blurSizeY);\n _draw();\n };\n\n _filter.pixelate.SHADER = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform vec2 size;',\n 'uniform sampler2D texture;',\n\n 'vec2 pixelate(vec2 coord, vec2 size) {',\n 'return floor( coord / size ) * size;',\n '}',\n\n 'void main(void) {',\n 'gl_FragColor = vec4(0.0);',\n 'vec2 coord = pixelate(vUv, size);',\n 'gl_FragColor += texture2D(texture, coord);',\n '}',\n ].join('\\n');\n};\n\nexports.Canvas = WebGLImageFilter;\n", "import { log } from './log.js';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as fxImage from './imagefx.js';\n\n// internal temp canvases\nlet inCanvas = null;\nlet outCanvas = null;\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nfunction process(input, config) {\n let tensor;\n if (input instanceof tf.Tensor) {\n tensor = tf.clone(input);\n } else {\n const originalWidth = input.naturalWidth || input.videoWidth || input.width || (input.shape && (input.shape[1] > 0));\n const originalHeight = input.naturalHeight || input.videoHeight || input.height || (input.shape && (input.shape[2] > 0));\n let targetWidth = originalWidth;\n let targetHeight = originalHeight;\n if (config.filter.width > 0) targetWidth = config.filter.width;\n else if (config.filter.height > 0) targetWidth = originalWidth * (config.filter.height / originalHeight);\n if (config.filter.height > 0) targetHeight = config.filter.height;\n else if (config.filter.width > 0) targetHeight = originalHeight * (config.filter.width / originalWidth);\n if (!targetWidth || !targetHeight) {\n log('Human: invalid input', input);\n return null;\n }\n if (!inCanvas || (inCanvas.width !== targetWidth) || (inCanvas.height !== targetHeight)) {\n inCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n if (inCanvas.width !== targetWidth) inCanvas.width = targetWidth;\n if (inCanvas.height !== targetHeight) inCanvas.height = targetHeight;\n }\n const ctx = inCanvas.getContext('2d');\n if (input instanceof ImageData) ctx.putImageData(input, 0, 0);\n else ctx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);\n if (config.filter.enabled) {\n if (!this.fx || !outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas.height !== outCanvas.height)) {\n outCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(inCanvas.width, inCanvas.height) : document.createElement('canvas');\n if (outCanvas.width !== inCanvas.width) outCanvas.width = inCanvas.width;\n if (outCanvas.height !== inCanvas.height) outCanvas.height = inCanvas.height;\n this.fx = tf.ENV.flags.IS_BROWSER ? new fxImage.Canvas({ canvas: outCanvas }) : null; // && (typeof document !== 'undefined')\n }\n if (!this.fx) return inCanvas;\n this.fx.reset();\n this.fx.addFilter('brightness', config.filter.brightness); // must have at least one filter enabled\n if (config.filter.contrast !== 0) this.fx.addFilter('contrast', config.filter.contrast);\n if (config.filter.sharpness !== 0) this.fx.addFilter('sharpen', config.filter.sharpness);\n if (config.filter.blur !== 0) this.fx.addFilter('blur', config.filter.blur);\n if (config.filter.saturation !== 0) this.fx.addFilter('saturation', config.filter.saturation);\n if (config.filter.hue !== 0) this.fx.addFilter('hue', config.filter.hue);\n if (config.filter.negative) this.fx.addFilter('negative');\n if (config.filter.sepia) this.fx.addFilter('sepia');\n if (config.filter.vintage) this.fx.addFilter('brownie');\n if (config.filter.sepia) this.fx.addFilter('sepia');\n if (config.filter.kodachrome) this.fx.addFilter('kodachrome');\n if (config.filter.technicolor) this.fx.addFilter('technicolor');\n if (config.filter.polaroid) this.fx.addFilter('polaroid');\n if (config.filter.pixelate !== 0) this.fx.addFilter('pixelate', config.filter.pixelate);\n this.fx.apply(inCanvas);\n // read pixel data\n /*\n const gl = outCanvas.getContext('webgl');\n if (gl) {\n const glBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 4);\n const pixBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 3);\n gl.readPixels(0, 0, outCanvas.width, outCanvas.height, gl.RGBA, gl.UNSIGNED_BYTE, glBuffer);\n // gl returns rbga while we only need rgb, so discarding alpha channel\n // gl returns starting point as lower left, so need to invert vertical\n let i = 0;\n for (let y = outCanvas.height - 1; y >= 0; y--) {\n for (let x = 0; x < outCanvas.width; x++) {\n const index = (x + y * outCanvas.width) * 4;\n pixBuffer[i++] = glBuffer[index + 0];\n pixBuffer[i++] = glBuffer[index + 1];\n pixBuffer[i++] = glBuffer[index + 2];\n }\n }\n outCanvas.data = pixBuffer;\n }\n */\n } else {\n outCanvas = inCanvas;\n }\n let pixels;\n if (outCanvas.data) {\n const shape = [outCanvas.height, outCanvas.width, 3];\n pixels = tf.tensor3d(outCanvas.data, shape, 'int32');\n } else if ((config.backend === 'webgl') || (outCanvas instanceof ImageData)) {\n // tf kernel-optimized method to get imagedata, also if input is imagedata, just use it\n pixels = tf.browser.fromPixels(outCanvas);\n } else {\n // cpu and wasm kernel does not implement efficient fromPixels method nor we can use canvas as-is, so we do a silly one more canvas\n const tempCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n tempCanvas.width = targetWidth;\n tempCanvas.height = targetHeight;\n const tempCtx = tempCanvas.getContext('2d');\n tempCtx?.drawImage(outCanvas, 0, 0);\n const data = tempCtx?.getImageData(0, 0, targetWidth, targetHeight);\n pixels = tf.browser.fromPixels(data);\n }\n const casted = pixels.toFloat();\n tensor = casted.expandDims(0);\n pixels.dispose();\n casted.dispose();\n }\n return { tensor, canvas: config.filter.return ? outCanvas : null };\n}\n\nexports.process = process;\n", "// helper function: wrapper around console output\nexport function log(...msg) {\n const dt = new Date();\n const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n // eslint-disable-next-line no-console\n if (msg) console.log(ts, 'Human:', ...msg);\n}\n", "import { log } from './log.js';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as backend from './tfjs/backend.js';\nimport * as facemesh from './blazeface/facemesh.js';\nimport * as age from './age/age.js';\nimport * as gender from './gender/gender.js';\nimport * as emotion from './emotion/emotion.js';\nimport * as embedding from './embedding/embedding.js';\nimport * as posenet from './posenet/posenet.js';\nimport * as blazepose from './blazepose/blazepose.js';\nimport * as handpose from './handpose/handpose.js';\nimport * as gesture from './gesture/gesture.js';\nimport * as image from './image.js';\nimport * as profile from './profile.js';\nimport * as config from '../config.js';\nimport * as sample from './sample.js';\nimport * as app from '../package.json';\n\n// helper function: gets elapsed time on both browser and nodejs\nconst now = () => {\n if (typeof performance !== 'undefined') return performance.now();\n return parseInt(Number(process.hrtime.bigint()) / 1000 / 1000);\n};\n\n// helper function: perform deep merge of multiple objects so it allows full inheriance with overrides\nfunction mergeDeep(...objects) {\n const isObject = (obj) => obj && typeof obj === 'object';\n return objects.reduce((prev, obj) => {\n Object.keys(obj || {}).forEach((key) => {\n const pVal = prev[key];\n const oVal = obj[key];\n if (Array.isArray(pVal) && Array.isArray(oVal)) {\n prev[key] = pVal.concat(...oVal);\n } else if (isObject(pVal) && isObject(oVal)) {\n prev[key] = mergeDeep(pVal, oVal);\n } else {\n prev[key] = oVal;\n }\n });\n return prev;\n }, {});\n}\n\nclass Human {\n constructor(userConfig = {}) {\n this.tf = tf;\n this.version = app.version;\n this.config = mergeDeep(config.default, userConfig);\n this.fx = null;\n this.state = 'idle';\n this.numTensors = 0;\n this.analyzeMemoryLeaks = false;\n this.checkSanity = false;\n this.firstRun = true;\n this.perf = {};\n // object that contains all initialized models\n this.models = {\n facemesh: null,\n posenet: null,\n handpose: null,\n iris: null,\n age: null,\n gender: null,\n emotion: null,\n };\n // export raw access to underlying models\n this.facemesh = facemesh;\n this.age = age;\n this.gender = gender;\n this.emotion = emotion;\n this.body = posenet;\n this.hand = handpose;\n }\n\n profile() {\n if (this.config.profile) return profile.data;\n return {};\n }\n\n // helper function: measure tensor leak\n analyze(...msg) {\n if (!this.analyzeMemoryLeaks) return;\n const current = tf.engine().state.numTensors;\n const previous = this.numTensors;\n this.numTensors = current;\n const leaked = current - previous;\n if (leaked !== 0) log(...msg, leaked);\n }\n\n // quick sanity check on inputs\n sanity(input) {\n if (!this.checkSanity) return null;\n if (!input) return 'input is not defined';\n if (tf.ENV.flags.IS_NODE && !(input instanceof tf.Tensor)) {\n return 'input must be a tensor';\n }\n try {\n tf.getBackend();\n } catch {\n return 'backend not loaded';\n }\n return null;\n }\n\n simmilarity(embedding1, embedding2) {\n if (this.config.face.embedding.enabled) return embedding.simmilarity(embedding1, embedding2);\n return 0;\n }\n\n // preload models, not explicitly required as it's done automatically on first use\n async load(userConfig) {\n this.state = 'load';\n const timeStamp = now();\n if (userConfig) this.config = mergeDeep(this.config, userConfig);\n\n if (this.firstRun) {\n log(`version: ${this.version} TensorFlow/JS version: ${tf.version_core}`);\n await this.checkBackend(true);\n if (tf.ENV.flags.IS_BROWSER) {\n log('configuration:', this.config);\n log('tf flags:', tf.ENV.flags);\n }\n this.firstRun = false;\n }\n\n if (this.config.async) {\n [\n this.models.facemesh,\n this.models.age,\n this.models.gender,\n this.models.emotion,\n this.models.embedding,\n this.models.posenet,\n this.models.handpose,\n this.models.blazepose,\n ] = await Promise.all([\n this.models.facemesh || (this.config.face.enabled ? facemesh.load(this.config) : null),\n this.models.age || ((this.config.face.enabled && this.config.face.age.enabled) ? age.load(this.config) : null),\n this.models.gender || ((this.config.face.enabled && this.config.face.gender.enabled) ? gender.load(this.config) : null),\n this.models.emotion || ((this.config.face.enabled && this.config.face.emotion.enabled) ? emotion.load(this.config) : null),\n this.models.embedding || ((this.config.face.enabled && this.config.face.embedding.enabled) ? embedding.load(this.config) : null),\n this.models.posenet || (this.config.body.enabled ? posenet.load(this.config) : null),\n this.models.handpose || (this.config.hand.enabled ? handpose.load(this.config) : null),\n this.models.blazepose || (this.config.pose.enabled ? blazepose.load(this.config) : null),\n ]);\n } else {\n if (this.config.face.enabled && !this.models.facemesh) this.models.facemesh = await facemesh.load(this.config);\n if (this.config.face.enabled && this.config.face.age.enabled && !this.models.age) this.models.age = await age.load(this.config);\n if (this.config.face.enabled && this.config.face.gender.enabled && !this.models.gender) this.models.gender = await gender.load(this.config);\n if (this.config.face.enabled && this.config.face.emotion.enabled && !this.models.emotion) this.models.emotion = await emotion.load(this.config);\n if (this.config.face.enabled && this.config.face.embedding.enabled && !this.models.embedding) this.models.embedding = await embedding.load(this.config);\n if (this.config.body.enabled && !this.models.posenet) this.models.posenet = await posenet.load(this.config);\n if (this.config.hand.enabled && !this.models.handpose) this.models.handpose = await handpose.load(this.config);\n if (this.config.pose.enabled && !this.models.blazepose) this.models.blazepose = await blazepose.load(this.config);\n }\n const current = Math.trunc(now() - timeStamp);\n if (current > (this.perf.load || 0)) this.perf.load = current;\n }\n\n // check if backend needs initialization if it changed\n async checkBackend(force) {\n if (this.config.backend && (this.config.backend !== '') && force || (tf.getBackend() !== this.config.backend)) {\n const timeStamp = now();\n this.state = 'backend';\n /* force backend reload\n if (this.config.backend in tf.engine().registry) {\n const backendFactory = tf.findBackendFactory(this.config.backend);\n tf.removeBackend(this.config.backend);\n tf.registerBackend(this.config.backend, backendFactory);\n } else {\n log('Backend not registred:', this.config.backend);\n }\n */\n\n log('setting backend:', this.config.backend);\n\n if (this.config.backend === 'wasm') {\n log('settings wasm path:', this.config.wasmPath);\n tf.setWasmPaths(this.config.wasmPath);\n const simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n if (!simd) log('warning: wasm simd support is not enabled');\n }\n\n if (this.config.backend === 'humangl') {\n log('registering humangl backend');\n backend.register();\n }\n\n await tf.setBackend(this.config.backend);\n tf.enableProdMode();\n /* debug mode is really too mcuh\n tf.enableDebugMode();\n */\n if (tf.getBackend() === 'webgl') {\n if (this.config.deallocate) {\n log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', this.config.deallocate);\n tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', this.config.deallocate ? 0 : -1);\n }\n tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n const gl = await tf.backend().getGPGPUContext().gl;\n log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);\n }\n await tf.ready();\n this.perf.backend = Math.trunc(now() - timeStamp);\n }\n }\n\n async detectFace(input) {\n // run facemesh, includes blazeface and iris\n // eslint-disable-next-line no-async-promise-executor\n let timeStamp;\n let ageRes;\n let genderRes;\n let emotionRes;\n let embeddingRes;\n const faceRes = [];\n this.state = 'run:face';\n timeStamp = now();\n const faces = await this.models.facemesh?.estimateFaces(input, this.config);\n this.perf.face = Math.trunc(now() - timeStamp);\n for (const face of faces) {\n this.analyze('Get Face');\n\n // is something went wrong, skip the face\n if (!face.image || face.image.isDisposedInternal) {\n log('Face object is disposed:', face.image);\n continue;\n }\n\n // run age, inherits face from blazeface\n this.analyze('Start Age:');\n if (this.config.async) {\n ageRes = this.config.face.age.enabled ? age.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:age';\n timeStamp = now();\n ageRes = this.config.face.age.enabled ? await age.predict(face.image, this.config) : {};\n this.perf.age = Math.trunc(now() - timeStamp);\n }\n\n // run gender, inherits face from blazeface\n this.analyze('Start Gender:');\n if (this.config.async) {\n genderRes = this.config.face.gender.enabled ? gender.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:gender';\n timeStamp = now();\n genderRes = this.config.face.gender.enabled ? await gender.predict(face.image, this.config) : {};\n this.perf.gender = Math.trunc(now() - timeStamp);\n }\n\n // run emotion, inherits face from blazeface\n this.analyze('Start Emotion:');\n if (this.config.async) {\n emotionRes = this.config.face.emotion.enabled ? emotion.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:emotion';\n timeStamp = now();\n emotionRes = this.config.face.emotion.enabled ? await emotion.predict(face.image, this.config) : {};\n this.perf.emotion = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Emotion:');\n\n // run emotion, inherits face from blazeface\n this.analyze('Start Embedding:');\n if (this.config.async) {\n embeddingRes = this.config.face.embedding.enabled ? embedding.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:embedding';\n timeStamp = now();\n embeddingRes = this.config.face.embedding.enabled ? await embedding.predict(face.image, this.config) : {};\n this.perf.embedding = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Emotion:');\n\n // if async wait for results\n if (this.config.async) {\n [ageRes, genderRes, emotionRes, embeddingRes] = await Promise.all([ageRes, genderRes, emotionRes, embeddingRes]);\n }\n\n this.analyze('Finish Face:');\n // dont need face anymore\n face.image.dispose();\n\n // calculate iris distance\n // iris: array[ center, left, top, right, bottom]\n if (!this.config.face.iris.enabled) {\n delete face.annotations.leftEyeIris;\n delete face.annotations.rightEyeIris;\n }\n const irisSize = (face.annotations.leftEyeIris && face.annotations.rightEyeIris)\n /* average human iris size is 11.7mm */\n ? 11.7 * Math.max(Math.abs(face.annotations.leftEyeIris[3][0] - face.annotations.leftEyeIris[1][0]), Math.abs(face.annotations.rightEyeIris[4][1] - face.annotations.rightEyeIris[2][1]))\n : 0;\n\n // combine results\n faceRes.push({\n confidence: face.confidence,\n box: face.box,\n mesh: face.mesh,\n boxRaw: face.boxRaw,\n meshRaw: face.meshRaw,\n annotations: face.annotations,\n age: ageRes.age,\n gender: genderRes.gender,\n genderConfidence: genderRes.confidence,\n emotion: emotionRes,\n embedding: embeddingRes,\n iris: (irisSize !== 0) ? Math.trunc(irisSize) / 100 : 0,\n });\n this.analyze('End Face');\n }\n this.analyze('End FaceMesh:');\n if (this.config.async) {\n if (this.perf.face) delete this.perf.face;\n if (this.perf.age) delete this.perf.age;\n if (this.perf.gender) delete this.perf.gender;\n if (this.perf.emotion) delete this.perf.emotion;\n }\n return faceRes;\n }\n\n async image(input, userConfig = {}) {\n this.state = 'image';\n this.config = mergeDeep(this.config, userConfig);\n const process = image.process(input, this.config);\n process.tensor.dispose();\n return process.canvas;\n }\n\n // main detect function\n async detect(input, userConfig = {}) {\n // detection happens inside a promise\n return new Promise(async (resolve) => {\n this.state = 'config';\n let timeStamp;\n\n // update configuration\n this.config = mergeDeep(this.config, userConfig);\n\n // sanity checks\n this.state = 'check';\n const error = this.sanity(input);\n if (error) {\n log(error, input);\n resolve({ error });\n }\n\n let poseRes;\n let blazeposeRes;\n let handRes;\n let faceRes;\n\n const timeStart = now();\n\n // configure backend\n await this.checkBackend();\n\n // load models if enabled\n await this.load();\n\n if (this.config.scoped) tf.engine().startScope();\n this.analyze('Start Scope:');\n\n timeStamp = now();\n const process = image.process(input, this.config);\n if (!process || !process.tensor) {\n log('could not convert input to tensor');\n resolve({ error: 'could not convert input to tensor' });\n return;\n }\n this.perf.image = Math.trunc(now() - timeStamp);\n this.analyze('Get Image:');\n\n // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n if (this.config.async) {\n faceRes = this.config.face.enabled ? this.detectFace(process.tensor) : [];\n if (this.perf.face) delete this.perf.face;\n } else {\n this.state = 'run:face';\n timeStamp = now();\n faceRes = this.config.face.enabled ? await this.detectFace(process.tensor) : [];\n this.perf.face = Math.trunc(now() - timeStamp);\n }\n\n // run posenet\n this.analyze('Start Body:');\n if (this.config.async) {\n poseRes = this.config.body.enabled ? this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n if (this.perf.body) delete this.perf.body;\n } else {\n this.state = 'run:body';\n timeStamp = now();\n poseRes = this.config.body.enabled ? await this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n this.perf.body = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Body:');\n\n // run posenet\n this.analyze('Start Pose:');\n if (this.config.async) {\n blazeposeRes = this.config.pose.enabled ? blazepose.predict(process.tensor, this.config) : [];\n if (this.perf.pose) delete this.perf.pose;\n } else {\n this.state = 'run:pose';\n timeStamp = now();\n blazeposeRes = this.config.pose.enabled ? await blazepose.predict(process.tensor, this.config) : [];\n this.perf.pose = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Pose:');\n\n // run handpose\n this.analyze('Start Hand:');\n if (this.config.async) {\n handRes = this.config.hand.enabled ? this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n if (this.perf.hand) delete this.perf.hand;\n } else {\n this.state = 'run:hand';\n timeStamp = now();\n handRes = this.config.hand.enabled ? await this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n this.perf.hand = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Hand:');\n\n // if async wait for results\n if (this.config.async) {\n [faceRes, poseRes, blazeposeRes, handRes] = await Promise.all([faceRes, poseRes, blazeposeRes, handRes]);\n }\n process.tensor.dispose();\n\n if (this.config.scoped) tf.engine().endScope();\n this.analyze('End Scope:');\n\n let gestureRes = [];\n if (this.config.gesture.enabled) {\n timeStamp = now();\n gestureRes = [...gesture.face(faceRes), ...gesture.body(poseRes), ...gesture.hand(handRes), ...gesture.iris(faceRes)];\n if (!this.config.async) this.perf.gesture = Math.trunc(now() - timeStamp);\n else if (this.perf.gesture) delete this.perf.gesture;\n }\n\n this.perf.total = Math.trunc(now() - timeStart);\n this.state = 'idle';\n resolve({ face: faceRes, body: poseRes, hand: handRes, pose: blazeposeRes, gesture: gestureRes, performance: this.perf, canvas: process.canvas });\n });\n }\n\n async warmupBitmap() {\n const b64toBlob = (base64, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n let blob;\n let res;\n switch (this.config.warmup) {\n case 'face': blob = await b64toBlob(sample.face); break;\n case 'full': blob = await b64toBlob(sample.body); break;\n default: blob = null;\n }\n if (blob) {\n const bitmap = await createImageBitmap(blob);\n res = await this.detect(bitmap, config);\n bitmap.close();\n }\n return res;\n }\n\n async warmupCanvas() {\n return new Promise((resolve) => {\n let src;\n let size = 0;\n switch (this.config.warmup) {\n case 'face':\n size = 256;\n src = 'data:image/jpeg;base64,' + sample.face;\n break;\n case 'full':\n size = 1200;\n src = 'data:image/jpeg;base64,' + sample.body;\n break;\n default:\n src = null;\n }\n const img = new Image(size, size);\n img.onload = () => {\n const canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(size, size) : document.createElement('canvas');\n canvas.width = size;\n canvas.height = size;\n const ctx = canvas.getContext('2d');\n ctx.drawImage(img, 0, 0);\n const data = ctx.getImageData(0, 0, size, size);\n this.detect(data, config).then((res) => resolve(res));\n };\n if (src) img.src = src;\n else resolve(null);\n });\n }\n\n async warmup(userConfig) {\n const t0 = now();\n if (userConfig) this.config = mergeDeep(this.config, userConfig);\n const video = this.config.videoOptimized;\n this.config.videoOptimized = false;\n let res;\n if (typeof createImageBitmap === 'function') res = await this.warmupBitmap();\n else res = await this.warmupCanvas();\n this.config.videoOptimized = video;\n const t1 = now();\n log('Warmup', this.config.warmup, (t1 - t0), res);\n return res;\n }\n}\n\nexport { Human as default };\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport const config = {\n name: 'humangl',\n priority: 99,\n canvas: null,\n width: 1024,\n height: 1024,\n webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n alpha: false,\n antialias: false,\n premultipliedAlpha: false,\n preserveDrawingBuffer: false,\n depth: false,\n stencil: false,\n failIfMajorPerformanceCaveat: false,\n desynchronized: true,\n },\n};\n\nexport function register() {\n if (!tf.findBackend(config.name)) {\n // @ts-ignore\n config.canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(config.width, config.height) : document.createElement('canvas');\n // @ts-ignore\n const gl = config.canvas.getContext('webgl2', config.webGLattr);\n tf.setWebGLContext(2, gl);\n const ctx = new tf.GPGPUContext(gl);\n tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n const kernels = tf.getKernelsForBackend('webgl');\n kernels.forEach((kernelConfig) => {\n const newKernelConfig = { ...kernelConfig, backendName: config.name };\n tf.registerKernel(newKernelConfig);\n });\n tf.ENV.set('WEBGL_VERSION', 2);\n tf.ENV.set('WEBGL_MAX_TEXTURE_SIZE', gl.getParameter(gl.MAX_TEXTURE_SIZE));\n tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n }\n}\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\n// import * as profile from '../profile.js';\n\nconst models = {};\n\nexport async function load(config) {\n if (!models.blazepose) {\n models.blazepose = await tf.loadGraphModel(config.pose.modelPath);\n log(`load model: ${config.pose.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.blazepose;\n}\n\nexport async function predict(image, config) {\n if (!models.blazepose) return null;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [config.pose.inputSize, config.pose.inputSize], false);\n const enhance = tf.div(resize, 127.5).sub(1);\n tf.dispose(resize);\n const logits = await models.blazepose.predict(enhance);\n //\n tf.dispose(enhance);\n logits.map((logit) => logit.dispose());\n resolve(logits);\n });\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nfunction getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\nfunction getBoxCenter(box) {\n return [\n box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n ];\n}\nfunction cutBoxFromImageAndResize(box, image, cropSize) {\n const h = image.shape[1];\n const w = image.shape[2];\n const boxes = [[\n box.startPoint[1] / h,\n box.startPoint[0] / w,\n box.endPoint[1] / h,\n box.endPoint[0] / w,\n ]];\n return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\nfunction scaleBoxCoordinates(box, factor) {\n const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n const palmLandmarks = box.palmLandmarks.map((coord) => {\n const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n return scaledCoord;\n });\n return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\nfunction enlargeBox(box, factor = 1.5) {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\nfunction squarifyBox(box) {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const maxEdge = Math.max(...size);\n const halfSize = maxEdge / 2;\n const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\nfunction shiftBox(box, shiftFactor) {\n const boxSize = [\n box.endPoint[0] - box.startPoint[0],\n box.endPoint[1] - box.startPoint[1],\n ];\n const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]];\n const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\nexport {\n cutBoxFromImageAndResize,\n enlargeBox,\n getBoxCenter,\n getBoxSize,\n scaleBoxCoordinates,\n shiftBox,\n squarifyBox,\n};\n", "function normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\nfunction computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\nconst buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\nfunction dot(v1, v2) {\n let product = 0;\n for (let i = 0; i < v1.length; i++) {\n product += v1[i] * v2[i];\n }\n return product;\n}\nfunction getColumnFrom2DArr(arr, columnIndex) {\n const column = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\nfunction multiplyTransformMatrices(mat1, mat2) {\n const product = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) {\n // @ts-ignore\n product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n }\n return product;\n}\nfunction buildRotationMatrix(rotation, center) {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\nfunction invertTransformMatrix(matrix) {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [\n -dot(rotationComponent[0], translationComponent),\n -dot(rotationComponent[1], translationComponent),\n ];\n return [\n rotationComponent[0].concat(invertedTranslation[0]),\n rotationComponent[1].concat(invertedTranslation[1]),\n [0, 0, 1],\n ];\n}\nfunction rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\nexport {\n buildRotationMatrix,\n computeRotation,\n dot,\n getColumnFrom2DArr,\n invertTransformMatrix,\n normalizeRadians,\n rotatePoint,\n};\n", "/* eslint-disable indent */\n/* eslint-disable no-multi-spaces */\n\nexport default {\n backend: 'webgl', // select tfjs backend to use\n wasmPath: '../assets/', // path for wasm binaries\n // only used for backend: wasm\n async: true, // execute enabled models in parallel\n // this disables per-model performance data but\n // slightly increases performance\n // cannot be used if profiling is enabled\n profile: false, // enable tfjs profiling\n // this has significant performance impact\n // only enable for debugging purposes\n // currently only implemented for age,gender,emotion models\n deallocate: false, // aggresively deallocate gpu memory after each usage\n // only valid for webgl backend and only during first call\n // cannot be changed unless library is reloaded\n // this has significant performance impact\n // only enable on low-memory devices\n scoped: false, // enable scoped runs\n // some models *may* have memory leaks,\n // this wrapps everything in a local scope at a cost of performance\n // typically not needed\n videoOptimized: true, // perform additional optimizations when input is video,\n // must be disabled for images\n // basically this skips object box boundary detection for every n frames\n // while maintaining in-box detection since objects cannot move that fast\n warmup: 'face', // what to use for human.warmup(), can be 'none', 'face', 'full'\n // warmup pre-initializes all models for faster inference but can take\n // significant time on startup\n filter: {\n enabled: true, // enable image pre-processing filters\n width: 0, // resize input width\n height: 0, // resize input height\n // if both width and height are set to 0, there is no resizing\n // if just one is set, second one is scaled automatically\n // if both are set, values are used as-is\n return: true, // return processed canvas imagedata in result\n brightness: 0, // range: -1 (darken) to 1 (lighten)\n contrast: 0, // range: -1 (reduce contrast) to 1 (increase contrast)\n sharpness: 0, // range: 0 (no sharpening) to 1 (maximum sharpening)\n blur: 0, // range: 0 (no blur) to N (blur radius in pixels)\n saturation: 0, // range: -1 (reduce saturation) to 1 (increase saturation)\n hue: 0, // range: 0 (no change) to 360 (hue rotation in degrees)\n negative: false, // image negative\n sepia: false, // image sepia colors\n vintage: false, // image vintage colors\n kodachrome: false, // image kodachrome colors\n technicolor: false, // image technicolor colors\n polaroid: false, // image polaroid camera effect\n pixelate: 0, // range: 0 (no pixelate) to N (number of pixels to pixelate)\n },\n\n gesture: {\n enabled: true, // enable simple gesture recognition\n },\n\n face: {\n enabled: true, // controls if specified modul is enabled\n // face.enabled is required for all face models:\n // detector, mesh, iris, age, gender, emotion\n // (note: module is not loaded until it is required)\n detector: {\n modelPath: '../models/blazeface-back.json', // can be 'front' or 'back'.\n // 'front' is optimized for large faces\n // such as front-facing camera and\n // 'back' is optimized for distanct faces.\n inputSize: 256, // fixed value: 128 for front and 256 for 'back'\n rotation: false, // use best-guess rotated face image or just box with rotation as-is\n // false means higher performance, but incorrect mesh mapping if face angle is above 20 degrees\n maxFaces: 10, // maximum number of faces detected in the input\n // should be set to the minimum number for performance\n skipFrames: 11, // how many frames to go without re-running the face bounding box detector\n // only used for video inputs\n // e.g., if model is running st 25 FPS, we can re-use existing bounding\n // box for updated face analysis as the head probably hasn't moved much\n // in short time (10 * 1/25 = 0.25 sec)\n minConfidence: 0.5, // threshold for discarding a prediction\n iouThreshold: 0.2, // threshold for deciding whether boxes overlap too much in\n // non-maximum suppression (0.1 means drop if overlap 10%)\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on score\n // in non-maximum suppression,\n // this is applied on detection objects only and before minConfidence\n },\n\n mesh: {\n enabled: true,\n modelPath: '../models/facemesh.json',\n inputSize: 192, // fixed value\n returnRawData: false, // in addition to standard mesh and box values, return raw normalized values as well\n },\n\n iris: {\n enabled: true,\n modelPath: '../models/iris.json',\n inputSize: 64, // fixed value\n },\n\n age: {\n enabled: true,\n modelPath: '../models/age-ssrnet-imdb.json', // can be 'age-ssrnet-imdb' or 'age-ssrnet-wiki'\n // which determines training set for model\n inputSize: 64, // fixed value\n skipFrames: 31, // how many frames to go without re-running the detector\n // only used for video inputs\n },\n\n gender: {\n enabled: true,\n minConfidence: 0.1, // threshold for discarding a prediction\n modelPath: '../models/gender-ssrnet-imdb.json', // can be 'gender', 'gender-ssrnet-imdb' or 'gender-ssrnet-wiki'\n inputSize: 64, // fixed value\n skipFrames: 41, // how many frames to go without re-running the detector\n // only used for video inputs\n },\n\n emotion: {\n enabled: true,\n inputSize: 64, // fixed value\n minConfidence: 0.2, // threshold for discarding a prediction\n skipFrames: 21, // how many frames to go without re-running the detector\n modelPath: '../models/emotion-large.json', // can be 'mini', 'large'\n },\n\n embedding: {\n enabled: false,\n inputSize: 112, // fixed value\n modelPath: '../models/mobilefacenet.json',\n },\n },\n\n body: {\n enabled: true,\n modelPath: '../models/posenet.json',\n inputSize: 257, // fixed value\n maxDetections: 10, // maximum number of people detected in the input\n // should be set to the minimum number for performance\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on score\n // in non-maximum suppression\n nmsRadius: 20, // radius for deciding points are too close in non-maximum suppression\n outputStride: 16, // size of block in which to run point detectopn, smaller value means higher resolution\n // defined by model itself, can be 8, 16, or 32\n modelType: 'MobileNet', // Human includes MobileNet version, but you can switch to ResNet\n },\n\n pose: { // TBD: not currently in use\n enabled: false,\n scoreThreshold: 0.6, // threshold for deciding when to remove boxes based on score\n // in non-maximum suppression\n iouThreshold: 0.3, // threshold for deciding whether boxes overlap too much\n // in non-maximum suppression\n modelPath: '../models/blazepose.json',\n inputSize: 256, // fixed value\n },\n\n hand: {\n enabled: true,\n rotation: false, // use best-guess rotated hand image or just box with rotation as-is\n // false means higher performance, but incorrect finger mapping if hand is inverted\n inputSize: 256, // fixed value\n skipFrames: 12, // how many frames to go without re-running the hand bounding box detector\n // only used for video inputs\n // e.g., if model is running st 25 FPS, we can re-use existing bounding\n // box for updated hand skeleton analysis as the hand probably\n // hasn't moved much in short time (10 * 1/25 = 0.25 sec)\n minConfidence: 0.1, // threshold for discarding a prediction\n iouThreshold: 0.1, // threshold for deciding whether boxes overlap too much\n // in non-maximum suppression\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on\n // score in non-maximum suppression\n maxHands: 1, // maximum number of hands detected in the input\n // should be set to the minimum number for performance\n landmarks: true, // detect hand landmarks or just hand boundary box\n detector: {\n modelPath: '../models/handdetect.json',\n },\n skeleton: {\n modelPath: '../models/handskeleton.json',\n },\n },\n};\n", "// data:image/jpeg;base64,\nexport const face = 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"names": [] } diff --git a/dist/human.esm.js b/dist/human.esm.js index e22051c8..dc1b9812 100644 --- a/dist/human.esm.js +++ b/dist/human.esm.js @@ -5,19 +5,19 @@ author: ' */ -var __create=Object.create,__defProp=Object.defineProperty,__getProtoOf=Object.getPrototypeOf,__hasOwnProp=Object.prototype.hasOwnProperty,__getOwnPropNames=Object.getOwnPropertyNames,__getOwnPropDesc=Object.getOwnPropertyDescriptor;var __markAsModule=target=>__defProp(target,"__esModule",{value:!0});var __commonJS=(callback,module)=>()=>(module||(module={exports:{}},callback(module.exports,module)),module.exports),__export=(target,all4)=>{__markAsModule(target);for(var name in all4)__defProp(target,name,{get:all4[name],enumerable:!0})},__exportStar=(target,module,desc)=>{if(__markAsModule(target),module&&typeof module=="object"||typeof module=="function")for(let key of __getOwnPropNames(module))!__hasOwnProp.call(target,key)&&key!=="default"&&__defProp(target,key,{get:()=>module[key],enumerable:!(desc=__getOwnPropDesc(module,key))||desc.enumerable});return target},__toModule=module=>module&&module.__esModule?module:__exportStar(__defProp(module!=null?__create(__getProtoOf(module)):{},"default",{value:module,enumerable:!0}),module);var require_blazeface=__commonJS(exports=>{var NUM_LANDMARKS=6;function generateAnchors2(inputSize){let spec={strides:[inputSize/16,inputSize/8],anchors:[2,6]},anchors2=[];for(let i=0;i{box.startEndTensor.dispose(),box.startPoint.dispose(),box.endPoint.dispose()},createBox=startEndTensor=>({startEndTensor,startPoint:slice(startEndTensor,[0,0],[-1,2]),endPoint:slice(startEndTensor,[0,2],[-1,2])}),scaleBox=(box,factors)=>{let starts=mul(box.startPoint,factors),ends=mul(box.endPoint,factors),newCoordinates=concat2d([starts,ends],1);return createBox(newCoordinates)};function decodeBounds(boxOutputs,anchors2,inputSize){let boxStarts=slice(boxOutputs,[0,1],[-1,2]),centers=add2(boxStarts,anchors2),boxSizes=slice(boxOutputs,[0,3],[-1,2]),boxSizesNormalized=div(boxSizes,inputSize),centersNormalized=div(centers,inputSize),halfBoxSize=div(boxSizesNormalized,2),starts=sub(centersNormalized,halfBoxSize),ends=add2(centersNormalized,halfBoxSize),startNormalized=mul(starts,inputSize),endNormalized=mul(ends,inputSize);return concat2d([startNormalized,endNormalized],1)}function scaleBoxFromPrediction(face3,scaleFactor){return tidy(()=>{let box=face3.box?face3.box:face3;return scaleBox(box,scaleFactor).startEndTensor.squeeze()})}var BlazeFaceModel=class{constructor(model2,config3){this.blazeFaceModel=model2,this.width=config3.face.detector.inputSize,this.height=config3.face.detector.inputSize,this.anchorsData=generateAnchors2(config3.face.detector.inputSize),this.anchors=tensor2d(this.anchorsData),this.inputSize=tensor1d([this.width,this.height]),this.config=config3,this.scaleFaces=.8}async getBoundingBoxes(inputImage){if(!inputImage||inputImage.isDisposedInternal||inputImage.shape.length!==4||inputImage.shape[1]<1||inputImage.shape[2]<1)return null;let[detectedOutputs,boxes,scores]=tidy(()=>{let resizedImage=inputImage.resizeBilinear([this.width,this.height]),normalizedImage=sub(resizedImage.div(127.5),1),batchedPrediction=this.blazeFaceModel.predict(normalizedImage),prediction;if(Array.isArray(batchedPrediction)){let sorted=batchedPrediction.sort((a,b)=>a.size-b.size),concat384=concat([sorted[0],sorted[2]],2),concat512=concat([sorted[1],sorted[3]],2);prediction=concat([concat512,concat384],1).squeeze(0)}else prediction=batchedPrediction.squeeze();let decodedBounds=decodeBounds(prediction,this.anchors,this.inputSize),logits=slice(prediction,[0,0],[-1,1]),scoresOut=sigmoid(logits).squeeze();return[prediction,decodedBounds,scoresOut]}),boxIndicesTensor=await image.nonMaxSuppressionAsync(boxes,scores,this.config.face.detector.maxFaces,this.config.face.detector.iouThreshold,this.config.face.detector.scoreThreshold),boxIndices=boxIndicesTensor.arraySync();boxIndicesTensor.dispose();let boundingBoxes=boxIndices.map(boxIndex=>slice(boxes,[boxIndex,0],[1,-1])).map(boundingBox=>{let vals=boundingBox.arraySync();return boundingBox.dispose(),vals}),scoresVal=scores.dataSync(),annotatedBoxes=[];for(let i=0;ithis.config.face.detector.minConfidence){let box=createBox(boundingBoxes[i]),anchor=this.anchorsData[boxIndex],landmarks=tidy(()=>slice(detectedOutputs,[boxIndex,NUM_LANDMARKS-1],[1,-1]).squeeze().reshape([NUM_LANDMARKS,-1]));annotatedBoxes.push({box,landmarks,anchor,confidence})}}return detectedOutputs.dispose(),boxes.dispose(),scores.dispose(),detectedOutputs.dispose(),{boxes:annotatedBoxes,scaleFactor:[inputImage.shape[2]/this.width,inputImage.shape[1]/this.height]}}async estimateFaces(input2){let{boxes,scaleFactor}=await this.getBoundingBoxes(input2),faces=[];for(let face3 of boxes){let landmarkData=face3.landmarks.arraySync(),scaledBox=scaleBoxFromPrediction(face3,scaleFactor),boxData=scaleBox.arraySync(),probabilityData=face3.probability.arraySync(),anchor=face3.anchor,[scaleFactorX,scaleFactorY]=scaleFactor,scaledLandmarks=landmarkData.map(landmark=>[(landmark[0]+anchor[0])*scaleFactorX,(landmark[1]+anchor[1])*scaleFactorY]),normalizedFace={topLeft:boxData.slice(0,2),bottomRight:boxData.slice(2),landmarks:scaledLandmarks,probability:probabilityData};disposeBox(face3.box),face3.landmarks.dispose(),face3.probability.dispose(),scaledBox.dispose(),faces.push(normalizedFace)}return faces}};async function load3(config3){let blazeface=await loadGraphModel(config3.face.detector.modelPath,{fromTFHub:config3.face.detector.modelPath.includes("tfhub.dev")}),model2=new BlazeFaceModel(blazeface,config3);return log(`load model: ${config3.face.detector.modelPath.match(/\/(.*)\./)[1]}`),model2}exports.load=load3;exports.BlazeFaceModel=BlazeFaceModel;exports.disposeBox=disposeBox});var require_box=__commonJS(exports=>{function scaleBoxCoordinates2(box,factor){let startPoint=[box.startPoint[0]*factor[0],box.startPoint[1]*factor[1]],endPoint=[box.endPoint[0]*factor[0],box.endPoint[1]*factor[1]];return{startPoint,endPoint}}exports.scaleBoxCoordinates=scaleBoxCoordinates2;function getBoxSize2(box){return[Math.abs(box.endPoint[0]-box.startPoint[0]),Math.abs(box.endPoint[1]-box.startPoint[1])]}exports.getBoxSize=getBoxSize2;function getBoxCenter2(box){return[box.startPoint[0]+(box.endPoint[0]-box.startPoint[0])/2,box.startPoint[1]+(box.endPoint[1]-box.startPoint[1])/2]}exports.getBoxCenter=getBoxCenter2;function cutBoxFromImageAndResize2(box,image3,cropSize){let h=image3.shape[1],w=image3.shape[2],boxes=[[box.startPoint[1]/h,box.startPoint[0]/w,box.endPoint[1]/h,box.endPoint[0]/w]];return image.cropAndResize(image3,boxes,[0],cropSize)}exports.cutBoxFromImageAndResize=cutBoxFromImageAndResize2;function enlargeBox2(box,factor=1.5){let center=getBoxCenter2(box),size=getBoxSize2(box),newHalfSize=[factor*size[0]/2,factor*size[1]/2],startPoint=[center[0]-newHalfSize[0],center[1]-newHalfSize[1]],endPoint=[center[0]+newHalfSize[0],center[1]+newHalfSize[1]];return{startPoint,endPoint,landmarks:box.landmarks}}exports.enlargeBox=enlargeBox2;function squarifyBox2(box){let centers=getBoxCenter2(box),size=getBoxSize2(box),halfSize=Math.max(...size)/2,startPoint=[centers[0]-halfSize,centers[1]-halfSize],endPoint=[centers[0]+halfSize,centers[1]+halfSize];return{startPoint,endPoint,landmarks:box.landmarks}}exports.squarifyBox=squarifyBox2});var require_util=__commonJS(exports=>{exports.IDENTITY_MATRIX=[[1,0,0],[0,1,0],[0,0,1]];function normalizeRadians2(angle){return angle-2*Math.PI*Math.floor((angle+Math.PI)/(2*Math.PI))}exports.normalizeRadians=normalizeRadians2;function computeRotation2(point1,point2){let radians=Math.PI/2-Math.atan2(-(point2[1]-point1[1]),point2[0]-point1[0]);return normalizeRadians2(radians)}exports.computeRotation=computeRotation2;function radToDegrees(rad){return rad*180/Math.PI}exports.radToDegrees=radToDegrees;function buildTranslationMatrix2(x,y){return[[1,0,x],[0,1,y],[0,0,1]]}function dot5(v1,v2){let product=0;for(let i=0;i{var 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,378,379,397,288,361,454,356,70,63,105,66,107,336,296,334,293,300,168,6,195,4,98,97,2,326,327,33,160,158,133,153,144,362,385,387,263,373,380,57,40,37,0,267,270,287,321,314,17,84,91,78,81,13,311,308,402,14,178],VTX33=[33,133,362,263,1,62,308,159,145,386,374,6,102,331,2,13,14,70,105,107,336,334,300,54,10,284,50,280,234,454,58,288,152],VTX7=[33,133,362,263,1,78,308];exports.MESH_ANNOTATIONS=MESH_ANNOTATIONS;exports.MESH_TO_IRIS_INDICES_MAP=MESH_TO_IRIS_INDICES_MAP;exports.TRI468=TRI468;exports.TRI68=TRI68;exports.TRI33=TRI33;exports.TRI7=TRI7;exports.UV468=UV468;exports.UV68=VTX68.map(x=>UV468[x]);exports.UV33=VTX33.map(x=>UV468[x]);exports.UV7=VTX7.map(x=>UV468[x])});var require_facepipeline=__commonJS(exports=>{var bounding=__toModule(require_box()),util=__toModule(require_util()),coords2=__toModule(require_coords()),LANDMARKS_COUNT=468,MESH_MOUTH_INDEX=13,MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES=[MESH_MOUTH_INDEX,coords2.MESH_ANNOTATIONS.midwayBetweenEyes[0]],BLAZEFACE_MOUTH_INDEX=3,BLAZEFACE_NOSE_INDEX=2,BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES=[BLAZEFACE_MOUTH_INDEX,BLAZEFACE_NOSE_INDEX],LEFT_EYE_OUTLINE=coords2.MESH_ANNOTATIONS.leftEyeLower0,LEFT_EYE_BOUNDS=[LEFT_EYE_OUTLINE[0],LEFT_EYE_OUTLINE[LEFT_EYE_OUTLINE.length-1]],RIGHT_EYE_OUTLINE=coords2.MESH_ANNOTATIONS.rightEyeLower0,RIGHT_EYE_BOUNDS=[RIGHT_EYE_OUTLINE[0],RIGHT_EYE_OUTLINE[RIGHT_EYE_OUTLINE.length-1]],IRIS_UPPER_CENTER_INDEX=3,IRIS_LOWER_CENTER_INDEX=4,IRIS_IRIS_INDEX=71,IRIS_NUM_COORDINATES=76;function replaceRawCoordinates(rawCoords,newCoords,prefix,keys){for(let i=0;i[scaleFactor[0]*(coord[0]-this.meshWidth/2),scaleFactor[1]*(coord[1]-this.meshHeight/2),coord[2]]),coordsRotationMatrix=angle!==0?util.buildRotationMatrix(angle,[0,0]):util.IDENTITY_MATRIX,coordsRotated=angle!==0?coordsScaled.map(coord=>[...util.rotatePoint(coord,coordsRotationMatrix),coord[2]]):coordsScaled,inverseRotationMatrix=angle!==0?util.invertTransformMatrix(rotationMatrix):util.IDENTITY_MATRIX,boxCenter=[...bounding.getBoxCenter({startPoint:box.startPoint,endPoint:box.endPoint}),1];return coordsRotated.map(coord=>[coord[0]+util.dot(boxCenter,inverseRotationMatrix[0]),coord[1]+util.dot(boxCenter,inverseRotationMatrix[1]),coord[2]])}getLeftToRightEyeDepthDifference(rawCoords){let leftEyeZ=rawCoords[LEFT_EYE_BOUNDS[0]][2],rightEyeZ=rawCoords[RIGHT_EYE_BOUNDS[0]][2];return leftEyeZ-rightEyeZ}getEyeBox(rawCoords,face3,eyeInnerCornerIndex,eyeOuterCornerIndex,flip=!1){let box=bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex],rawCoords[eyeOuterCornerIndex]]),this.irisEnlarge)),boxSize=bounding.getBoxSize(box),crop=image.cropAndResize(face3,[[box.startPoint[1]/this.meshHeight,box.startPoint[0]/this.meshWidth,box.endPoint[1]/this.meshHeight,box.endPoint[0]/this.meshWidth]],[0],[this.irisSize,this.irisSize]);return flip&&(crop=image.flipLeftRight(crop)),{box,boxSize,crop}}getEyeCoords(eyeData,eyeBox,eyeBoxSize,flip=!1){let eyeRawCoords=[];for(let i=0;i{let z=averageZ;return i===2?z=upperCenterZ:i===4&&(z=lowerCenterZ),[coord[0],coord[1],z]})}async predict(input2,config3){let useFreshBox=!1,detector;if((this.skipped===0||this.skipped>config3.face.detector.skipFrames||!config3.face.mesh.enabled||!config3.videoOptimized)&&(detector=await this.boundingBoxDetector.getBoundingBoxes(input2),this.skipped=0),config3.videoOptimized&&this.skipped++,detector&&detector.boxes&&detector.boxes.length>0&&(!config3.face.mesh.enabled||detector.boxes.length!==this.detectedFaces&&this.detectedFaces!==config3.face.detector.maxFaces)){this.storedBoxes=[],this.detectedFaces=0;for(let possible of detector.boxes)this.storedBoxes.push({startPoint:possible.box.startPoint.dataSync(),endPoint:possible.box.endPoint.dataSync(),landmarks:possible.landmarks,confidence:possible.confidence});this.storedBoxes.length>0&&(useFreshBox=!0)}if(useFreshBox){if(!detector||!detector.boxes||detector.boxes.length===0)return this.storedBoxes=[],this.detectedFaces=0,null;for(let i=0;i{prediction.box.startPoint.dispose(),prediction.box.endPoint.dispose(),prediction.landmarks.dispose()});let results=tidy(()=>this.storedBoxes.map((box,i)=>{let face3,angle=0,rotationMatrix;if(config3.face.detector.rotation){let[indexOfMouth,indexOfForehead]=box.landmarks.length>=LANDMARKS_COUNT?MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES:BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES;angle=util.computeRotation(box.landmarks[indexOfMouth],box.landmarks[indexOfForehead]);let faceCenter=bounding.getBoxCenter({startPoint:box.startPoint,endPoint:box.endPoint}),faceCenterNormalized=[faceCenter[0]/input2.shape[2],faceCenter[1]/input2.shape[1]],rotatedImage=image.rotateWithOffset(input2,angle,0,faceCenterNormalized);rotationMatrix=util.buildRotationMatrix(-angle,faceCenter),face3=bounding.cutBoxFromImageAndResize({startPoint:box.startPoint,endPoint:box.endPoint},rotatedImage,[this.meshHeight,this.meshWidth]).div(255)}else{rotationMatrix=util.IDENTITY_MATRIX;let cloned=input2.clone();face3=bounding.cutBoxFromImageAndResize({startPoint:box.startPoint,endPoint:box.endPoint},cloned,[this.meshHeight,this.meshWidth]).div(255)}if(!config3.face.mesh.enabled)return{coords:null,box,faceConfidence:null,confidence:box.confidence,image:face3};let[,confidence,contourCoords]=this.meshDetector.predict(face3),confidenceVal=confidence.dataSync()[0];if(confidenceVala!==null),this.detectedFaces=results.length,results}calculateLandmarksBoundingBox(landmarks){let xs=landmarks.map(d=>d[0]),ys=landmarks.map(d=>d[1]),startPoint=[Math.min(...xs),Math.min(...ys)],endPoint=[Math.max(...xs),Math.max(...ys)];return{startPoint,endPoint,landmarks}}};exports.Pipeline=Pipeline});var require_facemesh=__commonJS(exports=>{var blazeface=__toModule(require_blazeface()),facepipeline=__toModule(require_facepipeline()),coords2=__toModule(require_coords()),MediaPipeFaceMesh=class{constructor(blazeFace,blazeMeshModel,irisModel,config3){this.facePipeline=new facepipeline.Pipeline(blazeFace,blazeMeshModel,irisModel,config3),this.config=config3}async estimateFaces(input2,config3){let predictions=await this.facePipeline.predict(input2,config3),results=[];for(let prediction of predictions||[]){if(prediction.isDisposedInternal)continue;let mesh=prediction.coords?prediction.coords.arraySync():null,meshRaw=prediction.rawCoords,annotations={};if(mesh&&mesh.length>0)for(let key of Object.keys(coords2.MESH_ANNOTATIONS))annotations[key]=coords2.MESH_ANNOTATIONS[key].map(index=>mesh[index]);let boxRaw=config3.face.mesh.returnRawData&&prediction.box?{topLeft:prediction.box.startPoint,bottomRight:prediction.box.endPoint}:null,box=prediction.box?[Math.max(0,prediction.box.startPoint[0]),Math.max(0,prediction.box.startPoint[1]),Math.min(input2.shape[2],prediction.box.endPoint[0])-prediction.box.startPoint[0],Math.min(input2.shape[1],prediction.box.endPoint[1])-prediction.box.startPoint[1]]:0;results.push({confidence:prediction.confidence||0,box,mesh,boxRaw,meshRaw,annotations,image:prediction.image?clone(prediction.image):null}),prediction.coords&&prediction.coords.dispose(),prediction.image&&prediction.image.dispose()}return results}},faceModels=[null,null,null];async function load3(config3){faceModels=await Promise.all([!faceModels[0]&&config3.face.enabled?blazeface.load(config3):null,!faceModels[1]&&config3.face.mesh.enabled?loadGraphModel(config3.face.mesh.modelPath,{fromTFHub:config3.face.mesh.modelPath.includes("tfhub.dev")}):null,!faceModels[2]&&config3.face.iris.enabled?loadGraphModel(config3.face.iris.modelPath,{fromTFHub:config3.face.iris.modelPath.includes("tfhub.dev")}):null]);let faceMesh=new MediaPipeFaceMesh(faceModels[0],faceModels[1],faceModels[2],config3);return config3.face.mesh.enabled&&log(`load model: ${config3.face.mesh.modelPath.match(/\/(.*)\./)[1]}`),config3.face.iris.enabled&&log(`load model: ${config3.face.iris.modelPath.match(/\/(.*)\./)[1]}`),faceMesh}exports.load=load3;exports.MediaPipeFaceMesh=MediaPipeFaceMesh;exports.triangulation=coords2.TRI468});var require_profile=__commonJS(exports=>{var profileData={};function profile3(name,data2){if(!data2||!data2.kernels)return;let maxResults=5,time2=data2.kernels.filter(a=>a.kernelTimeMs>0).reduce((a,b)=>a+=b.kernelTimeMs,0),slowest=data2.kernels.map((a,i)=>(a.id=i,a)).filter(a=>a.kernelTimeMs>0).sort((a,b)=>b.kernelTimeMs-a.kernelTimeMs),largest=data2.kernels.map((a,i)=>(a.id=i,a)).filter(a=>a.totalBytesSnapshot>0).sort((a,b)=>b.totalBytesSnapshot-a.totalBytesSnapshot);slowest.length>maxResults&&(slowest.length=maxResults),largest.length>maxResults&&(largest.length=maxResults);let res={newBytes:data2.newBytes,newTensors:data2.newTensors,peakBytes:data2.peakBytes,numKernelOps:data2.kernels.length,timeKernelOps:time2,slowestKernelOps:slowest,largestKernelOps:largest};profileData[name]=res,log("Human profiler",name,res)}exports.run=profile3});var require_age=__commonJS(exports=>{var profile3=__toModule(require_profile()),models2={},last={age:0},skipped=Number.MAX_SAFE_INTEGER;async function load3(config3){return models2.age||(models2.age=await loadGraphModel(config3.face.age.modelPath),log(`load model: ${config3.face.age.modelPath.match(/\/(.*)\./)[1]}`)),models2.age}async function predict3(image3,config3){return models2.age?skipped0?(skipped++,last):(config3.videoOptimized?skipped=0:skipped=Number.MAX_SAFE_INTEGER,new Promise(async resolve=>{let resize=image.resizeBilinear(image3,[config3.face.age.inputSize,config3.face.age.inputSize],!1),enhance=mul(resize,[255]);dispose(resize);let ageT,obj={};if(!config3.profile)config3.face.age.enabled&&(ageT=await models2.age.predict(enhance));else{let profileAge=config3.face.age.enabled?await profile(()=>models2.age.predict(enhance)):{};ageT=profileAge.result.clone(),profileAge.result.dispose(),profile3.run("age",profileAge)}if(enhance.dispose(),ageT){let data2=ageT.dataSync();obj.age=Math.trunc(10*data2[0])/10}ageT.dispose(),last=obj,resolve(obj)})):null}exports.predict=predict3;exports.load=load3});var require_gender=__commonJS(exports=>{var profile3=__toModule(require_profile()),models2={},last={gender:""},skipped=Number.MAX_SAFE_INTEGER,alternative=!1,rgb=[.2989,.587,.114];async function load3(config3){return models2.gender||(models2.gender=await loadGraphModel(config3.face.gender.modelPath),alternative=models2.gender.inputs[0].shape[3]===1,log(`load model: ${config3.face.gender.modelPath.match(/\/(.*)\./)[1]}`)),models2.gender}async function predict3(image3,config3){return models2.gender?skipped{let resize=image.resizeBilinear(image3,[config3.face.gender.inputSize,config3.face.gender.inputSize],!1),enhance;alternative?enhance=tidy(()=>{let[red,green,blue]=split(resize,3,3),redNorm=mul(red,rgb[0]),greenNorm=mul(green,rgb[1]),blueNorm=mul(blue,rgb[2]);return addN([redNorm,greenNorm,blueNorm]).sub(.5).mul(2)}):enhance=mul(resize,[255]),dispose(resize);let genderT,obj={};if(!config3.profile)config3.face.gender.enabled&&(genderT=await models2.gender.predict(enhance));else{let profileGender=config3.face.gender.enabled?await profile(()=>models2.gender.predict(enhance)):{};genderT=profileGender.result.clone(),profileGender.result.dispose(),profile3.run("gender",profileGender)}if(enhance.dispose(),genderT){let data2=genderT.dataSync();if(alternative){let confidence=Math.trunc(100*Math.abs(data2[0]-data2[1]))/100;confidence>config3.face.gender.minConfidence&&(obj.gender=data2[0]>data2[1]?"female":"male",obj.confidence=confidence)}else{let confidence=Math.trunc(200*Math.abs(data2[0]-.5))/100;confidence>config3.face.gender.minConfidence&&(obj.gender=data2[0]<=.5?"female":"male",obj.confidence=Math.min(.99,confidence))}}genderT.dispose(),last=obj,resolve(obj)})):null}exports.predict=predict3;exports.load=load3});var require_emotion=__commonJS(exports=>{var profile3=__toModule(require_profile()),annotations=["angry","disgust","fear","happy","sad","surprise","neutral"],models2={},last=[],skipped=Number.MAX_SAFE_INTEGER,rgb=[.2989,.587,.114],scale2=1;async function load3(config3){return models2.emotion||(models2.emotion=await loadGraphModel(config3.face.emotion.modelPath),log(`load model: ${config3.face.emotion.modelPath.match(/\/(.*)\./)[1]}`)),models2.emotion}async function predict3(image3,config3){return models2.emotion?skipped0?(skipped++,last):(config3.videoOptimized?skipped=0:skipped=Number.MAX_SAFE_INTEGER,new Promise(async resolve=>{let resize=image.resizeBilinear(image3,[config3.face.emotion.inputSize,config3.face.emotion.inputSize],!1),[red,green,blue]=split(resize,3,3);resize.dispose();let redNorm=mul(red,rgb[0]),greenNorm=mul(green,rgb[1]),blueNorm=mul(blue,rgb[2]);red.dispose(),green.dispose(),blue.dispose();let grayscale=addN([redNorm,greenNorm,blueNorm]);redNorm.dispose(),greenNorm.dispose(),blueNorm.dispose();let normalize=tidy(()=>grayscale.sub(.5).mul(2));grayscale.dispose();let obj=[];if(config3.face.emotion.enabled){let data2;if(config3.profile){let profileData=await profile(()=>models2.emotion.predict(normalize));data2=profileData.result.dataSync(),profileData.result.dispose(),profile3.run("emotion",profileData)}else{let emotionT=await models2.emotion.predict(normalize);data2=emotionT.dataSync(),dispose(emotionT)}for(let i=0;iconfig3.face.emotion.minConfidence&&obj.push({score:Math.min(.99,Math.trunc(100*scale2*data2[i])/100),emotion:annotations[i]});obj.sort((a,b)=>b.score-a.score)}normalize.dispose(),last=obj,resolve(obj)})):null}exports.predict=predict3;exports.load=load3});var require_embedding=__commonJS(exports=>{var profile3=__toModule(require_profile()),models2={};async function load3(config3){return models2.embedding||(models2.embedding=await loadGraphModel(config3.face.embedding.modelPath),log(`load model: ${config3.face.embedding.modelPath.match(/\/(.*)\./)[1]}`)),models2.embedding}function simmilarity2(embedding1,embedding22){if((embedding1==null?void 0:embedding1.length)!==(embedding22==null?void 0:embedding22.length))return 0;let order=2,distance=10*embedding1.map((val,i)=>val-embedding22[i]).reduce((dist,diff)=>dist+diff**order,0)**(1/order);return Math.trunc(1e3*(1-distance))/1e3}async function predict3(image3,config3){return models2.embedding?new Promise(async resolve=>{let resize=image.resizeBilinear(image3,[config3.face.embedding.inputSize,config3.face.embedding.inputSize],!1),data2=[];if(config3.face.embedding.enabled)if(config3.profile){let profileData=await profile(()=>models2.embedding.predict({img_inputs:resize}));data2=[...profileData.result.dataSync()],profileData.result.dispose(),profile3.run("emotion",profileData)}else{let embeddingT=await models2.embedding.predict({img_inputs:resize});data2=[...embeddingT.dataSync()],dispose(embeddingT)}resize.dispose(),resolve(data2)}):null}exports.predict=predict3;exports.simmilarity=simmilarity2;exports.load=load3});var require_modelBase=__commonJS(exports=>{var imageNetMean=[-123.15,-115.9,-103.06];function nameOutputResultsMobileNet(results){let[offsets,heatmap,displacementFwd,displacementBwd]=results;return{offsets,heatmap,displacementFwd,displacementBwd}}function nameOutputResultsResNet(results){let[displacementFwd,displacementBwd,offsets,heatmap]=results;return{offsets,heatmap,displacementFwd,displacementBwd}}var BaseModel=class{constructor(model2){this.model=model2}predict(input2,config3){return tidy(()=>{let asBatch=(config3.body.modelType==="ResNet"?input2.toFloat().add(imageNetMean):input2.toFloat().div(127.5).sub(1)).expandDims(0),results3d=this.model.predict(asBatch).map(y=>y.squeeze([0])),namedResults=config3.body.modelType==="ResNet"?nameOutputResultsResNet(results3d):nameOutputResultsMobileNet(results3d);return{heatmapScores:namedResults.heatmap.sigmoid(),offsets:namedResults.offsets,displacementFwd:namedResults.displacementFwd,displacementBwd:namedResults.displacementBwd}})}dispose(){this.model.dispose()}};exports.BaseModel=BaseModel});var require_heapSort=__commonJS(exports=>{function half(k){return Math.floor(k/2)}var MaxHeap=class{constructor(maxSize,getElementValue){this.priorityQueue=new Array(maxSize),this.numberOfElements=-1,this.getElementValue=getElementValue}enqueue(x){this.priorityQueue[++this.numberOfElements]=x,this.swim(this.numberOfElements)}dequeue(){let max6=this.priorityQueue[0];return this.exchange(0,this.numberOfElements--),this.sink(0),this.priorityQueue[this.numberOfElements+1]=null,max6}empty(){return this.numberOfElements===-1}size(){return this.numberOfElements+1}all(){return this.priorityQueue.slice(0,this.numberOfElements+1)}max(){return this.priorityQueue[0]}swim(k){for(;k>0&&this.less(half(k),k);)this.exchange(k,half(k)),k=half(k)}sink(k){for(;2*k<=this.numberOfElements;){let j=2*k;if(j{var heapSort=__toModule(require_heapSort());function scoreIsMaximumInLocalWindow(keypointId,score,heatmapY,heatmapX,localMaximumRadius,scores){let[height,width]=scores.shape,localMaximum=!0,yStart=Math.max(heatmapY-localMaximumRadius,0),yEnd=Math.min(heatmapY+localMaximumRadius+1,height);for(let yCurrent=yStart;yCurrentscore){localMaximum=!1;break}if(!localMaximum)break}return localMaximum}function buildPartWithScoreQueue(scoreThreshold,localMaximumRadius,scores){let[height,width,numKeypoints]=scores.shape,queue=new heapSort.MaxHeap(height*width*numKeypoints,({score})=>score);for(let heatmapY=0;heatmapY{exports.partNames=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"];exports.NUM_KEYPOINTS=exports.partNames.length;exports.partIds=exports.partNames.reduce((result,jointName,i)=>(result[jointName]=i,result),{});var connectedPartNames=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]];exports.connectedPartIndices=connectedPartNames.map(([jointNameA,jointNameB])=>[exports.partIds[jointNameA],exports.partIds[jointNameB]]);exports.poseChain=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];exports.partChannels=["left_face","right_face","right_upper_leg_front","right_lower_leg_back","right_upper_leg_back","left_lower_leg_front","left_upper_leg_front","left_upper_leg_back","left_lower_leg_back","right_feet","right_lower_leg_front","left_feet","torso_front","torso_back","right_upper_arm_front","right_upper_arm_back","right_lower_arm_back","left_lower_arm_front","left_upper_arm_front","left_upper_arm_back","left_lower_arm_back","right_hand","right_lower_arm_front","left_hand"]});var require_vectors=__commonJS(exports=>{var kpt=__toModule(require_keypoints());function getOffsetPoint(y,x,keypoint,offsets){return{y:offsets.get(y,x,keypoint),x:offsets.get(y,x,keypoint+kpt.NUM_KEYPOINTS)}}exports.getOffsetPoint=getOffsetPoint;function getImageCoords(part,outputStride,offsets){let{heatmapY,heatmapX,id:keypoint}=part,{y,x}=getOffsetPoint(heatmapY,heatmapX,keypoint,offsets);return{x:part.heatmapX*outputStride+x,y:part.heatmapY*outputStride+y}}exports.getImageCoords=getImageCoords;function fillArray(element,size){let result=new Array(size);for(let i=0;imax6?max6:a}exports.clamp=clamp2;function squaredDistance(y1,x1,y2,x2){let dy=y2-y1,dx=x2-x1;return dy*dy+dx*dx}exports.squaredDistance=squaredDistance;function addVectors(a,b){return{x:a.x+b.x,y:a.y+b.y}}exports.addVectors=addVectors;function clampVector(a,min6,max6){return{y:clamp2(a.y,min6,max6),x:clamp2(a.x,min6,max6)}}exports.clampVector=clampVector});var require_decoders=__commonJS(exports=>{var kpt=__toModule(require_keypoints());function getPointsConfidence(heatmapScores,heatMapCoords){let numKeypoints=heatMapCoords.shape[0],result=new Float32Array(numKeypoints);for(let keypoint=0;keypointheatMapCoordsBuffer.toTensor().mul(scalar(outputStride,"int32")).toFloat().add(getOffsetVectors(heatMapCoordsBuffer,offsetsBuffer)))}exports.getOffsetPoints=getOffsetPoints;function mod4(a,b){return tidy(()=>{let floored=a.div(scalar(b,"int32"));return a.sub(floored.mul(scalar(b,"int32")))})}function argmax2d(inputs){let[height,width,depth]=inputs.shape;return tidy(()=>{let coords2=inputs.reshape([height*width,depth]).argMax(0),yCoords=coords2.div(scalar(width,"int32")).expandDims(1),xCoords=mod4(coords2,width).expandDims(1);return concat([yCoords,xCoords],1)})}exports.argmax2d=argmax2d});var require_decodePose=__commonJS(exports=>{var keypoints=__toModule(require_keypoints()),vectors=__toModule(require_vectors()),decoders=__toModule(require_decoders()),parentChildrenTuples=keypoints.poseChain.map(([parentJoinName,childJoinName])=>[keypoints.partIds[parentJoinName],keypoints.partIds[childJoinName]]),parentToChildEdges=parentChildrenTuples.map(([,childJointId])=>childJointId),childToParentEdges=parentChildrenTuples.map(([parentJointId])=>parentJointId);function getDisplacement(edgeId,point,displacements){let numEdges=displacements.shape[2]/2;return{y:displacements.get(point.y,point.x,edgeId),x:displacements.get(point.y,point.x,numEdges+edgeId)}}function getStridedIndexNearPoint(point,outputStride,height,width){return{y:vectors.clamp(Math.round(point.y/outputStride),0,height-1),x:vectors.clamp(Math.round(point.x/outputStride),0,width-1)}}function traverseToTargetKeypoint(edgeId,sourceKeypoint,targetKeypointId,scoresBuffer,offsets,outputStride,displacements,offsetRefineStep=2){let[height,width]=scoresBuffer.shape,sourceKeypointIndices=getStridedIndexNearPoint(sourceKeypoint.position,outputStride,height,width),displacement=getDisplacement(edgeId,sourceKeypointIndices,displacements),targetKeypoint=vectors.addVectors(sourceKeypoint.position,displacement);for(let i=0;i=0;--edge){let sourceKeypointId=parentToChildEdges[edge],targetKeypointId=childToParentEdges[edge];instanceKeypoints[sourceKeypointId]&&!instanceKeypoints[targetKeypointId]&&(instanceKeypoints[targetKeypointId]=traverseToTargetKeypoint(edge,instanceKeypoints[sourceKeypointId],targetKeypointId,scores,offsets,outputStride,displacementsBwd))}for(let edge=0;edge(totalScore+=score,{position:{y:offsetPointsBuffer.get(i,0),x:offsetPointsBuffer.get(i,1)},part:keypoints.partNames[i],score})),filteredKeypoints=instanceKeypoints.filter(kpt=>kpt.score>config3.body.scoreThreshold);return heatmapValues.dispose(),offsetPoints.dispose(),{keypoints:filteredKeypoints,score:totalScore/instanceKeypoints.length}}exports.decodeSinglePose=decodeSinglePose});var require_decodeMultiple=__commonJS(exports=>{var buildParts=__toModule(require_buildParts()),decodePose=__toModule(require_decodePose()),vectors=__toModule(require_vectors()),kLocalMaximumRadius=1;function withinNmsRadiusOfCorrespondingPoint(poses,squaredNmsRadius,{x,y},keypointId){return poses.some(({keypoints})=>{let correspondingKeypoint=keypoints[keypointId].position;return vectors.squaredDistance(y,x,correspondingKeypoint.y,correspondingKeypoint.x)<=squaredNmsRadius})}function getInstanceScore(existingPoses,squaredNmsRadius,instanceKeypoints){return instanceKeypoints.reduce((result,{position,score},keypointId)=>(withinNmsRadiusOfCorrespondingPoint(existingPoses,squaredNmsRadius,position,keypointId)||(result+=score),result),0)/instanceKeypoints.length}function decodeMultiplePoses(scoresBuffer,offsetsBuffer,displacementsFwdBuffer,displacementsBwdBuffer,config3){let poses=[],queue=buildParts.buildPartWithScoreQueue(config3.body.scoreThreshold,kLocalMaximumRadius,scoresBuffer),squaredNmsRadius=config3.body.nmsRadius^2;for(;poses.lengthconfig3.body.scoreThreshold&&poses.push({keypoints,score})}return poses}exports.decodeMultiplePoses=decodeMultiplePoses});var require_util2=__commonJS(exports=>{var kpt=__toModule(require_keypoints());function eitherPointDoesntMeetConfidence(a,b,minConfidence){return a(eitherPointDoesntMeetConfidence(keypoints[leftJoint].score,keypoints[rightJoint].score,minConfidence)||result.push([keypoints[leftJoint],keypoints[rightJoint]]),result),[])}exports.getAdjacentKeyPoints=getAdjacentKeyPoints;var{NEGATIVE_INFINITY,POSITIVE_INFINITY}=Number;function getBoundingBox(keypoints){return keypoints.reduce(({maxX,maxY,minX,minY},{position:{x,y}})=>({maxX:Math.max(maxX,x),maxY:Math.max(maxY,y),minX:Math.min(minX,x),minY:Math.min(minY,y)}),{maxX:NEGATIVE_INFINITY,maxY:NEGATIVE_INFINITY,minX:POSITIVE_INFINITY,minY:POSITIVE_INFINITY})}exports.getBoundingBox=getBoundingBox;function getBoundingBoxPoints(keypoints){let{minX,minY,maxX,maxY}=getBoundingBox(keypoints);return[{x:minX,y:minY},{x:maxX,y:minY},{x:maxX,y:maxY},{x:minX,y:maxY}]}exports.getBoundingBoxPoints=getBoundingBoxPoints;async function toTensorBuffers3D(tensors){return Promise.all(tensors.map(tensor2=>tensor2.buffer()))}exports.toTensorBuffers3D=toTensorBuffers3D;function scalePose(pose,scaleY,scaleX){return{score:pose.score,keypoints:pose.keypoints.map(({score,part,position})=>({score,part,position:{x:position.x*scaleX,y:position.y*scaleY}}))}}exports.scalePose=scalePose;function resizeTo(image3,[targetH,targetW]){let input2=image3.squeeze(0),resized=input2.resizeBilinear([targetH,targetW]);return input2.dispose(),resized}exports.resizeTo=resizeTo;function scaleAndFlipPoses(poses,[height,width],[inputResolutionHeight,inputResolutionWidth]){return poses.map(pose=>scalePose(pose,height/inputResolutionHeight,width/inputResolutionWidth))}exports.scaleAndFlipPoses=scaleAndFlipPoses});var require_modelPoseNet=__commonJS(exports=>{var modelBase=__toModule(require_modelBase()),decodeMultiple=__toModule(require_decodeMultiple()),decodePose=__toModule(require_decodePose()),util=__toModule(require_util2());async function estimateMultiple(input2,res,config3){return new Promise(async resolve=>{let height=input2.shape[1],width=input2.shape[2],allTensorBuffers=await util.toTensorBuffers3D([res.heatmapScores,res.offsets,res.displacementFwd,res.displacementBwd]),scoresBuffer=allTensorBuffers[0],offsetsBuffer=allTensorBuffers[1],displacementsFwdBuffer=allTensorBuffers[2],displacementsBwdBuffer=allTensorBuffers[3],poses=await decodeMultiple.decodeMultiplePoses(scoresBuffer,offsetsBuffer,displacementsFwdBuffer,displacementsBwdBuffer,config3),scaled=util.scaleAndFlipPoses(poses,[height,width],[config3.body.inputSize,config3.body.inputSize]);resolve(scaled)})}async function estimateSingle(input2,res,config3){return new Promise(async resolve=>{let height=input2.shape[1],width=input2.shape[2],poses=[await decodePose.decodeSinglePose(res.heatmapScores,res.offsets,config3)],scaled=util.scaleAndFlipPoses(poses,[height,width],[config3.body.inputSize,config3.body.inputSize]);resolve(scaled)})}var PoseNet=class{constructor(model2){this.baseModel=model2}async estimatePoses(input2,config3){let resized=util.resizeTo(input2,[config3.body.inputSize,config3.body.inputSize]),res=this.baseModel.predict(resized,config3),poses=config3.body.maxDetections<2?await estimateSingle(input2,res,config3):await estimateMultiple(input2,res,config3);return res.heatmapScores.dispose(),res.offsets.dispose(),res.displacementFwd.dispose(),res.displacementBwd.dispose(),resized.dispose(),poses}dispose(){this.baseModel.dispose()}};exports.PoseNet=PoseNet;async function load3(config3){let model2=await loadGraphModel(config3.body.modelPath),mobilenet=new modelBase.BaseModel(model2);return log(`load model: ${config3.body.modelPath.match(/\/(.*)\./)[1]}`),new PoseNet(mobilenet)}exports.load=load3});var require_posenet=__commonJS(exports=>{var modelPoseNet=__toModule(require_modelPoseNet()),keypoints=__toModule(require_keypoints()),util=__toModule(require_util2());exports.load=modelPoseNet.load;exports.PoseNet=modelPoseNet.PoseNet;exports.partChannels=keypoints.partChannels;exports.partIds=keypoints.partIds;exports.partNames=keypoints.partNames;exports.poseChain=keypoints.poseChain;exports.getAdjacentKeyPoints=util.getAdjacentKeyPoints;exports.getBoundingBox=util.getBoundingBox;exports.getBoundingBoxPoints=util.getBoundingBoxPoints;exports.scaleAndFlipPoses=util.scaleAndFlipPoses;exports.scalePose=util.scalePose});var require_handdetector=__commonJS(exports=>{var HandDetector=class{constructor(model2,inputSize,anchorsAnnotated){this.model=model2,this.anchors=anchorsAnnotated.map(anchor=>[anchor.x_center,anchor.y_center]),this.anchorsTensor=tensor2d(this.anchors),this.inputSizeTensor=tensor1d([inputSize,inputSize]),this.doubleInputSizeTensor=tensor1d([inputSize*2,inputSize*2])}normalizeBoxes(boxes){return tidy(()=>{let boxOffsets=slice(boxes,[0,0],[-1,2]),boxSizes=slice(boxes,[0,2],[-1,2]),boxCenterPoints=add2(div(boxOffsets,this.inputSizeTensor),this.anchorsTensor),halfBoxSizes=div(boxSizes,this.doubleInputSizeTensor),startPoints=mul(sub(boxCenterPoints,halfBoxSizes),this.inputSizeTensor),endPoints=mul(add2(boxCenterPoints,halfBoxSizes),this.inputSizeTensor);return concat2d([startPoints,endPoints],1)})}normalizeLandmarks(rawPalmLandmarks,index){return tidy(()=>{let landmarks=add2(div(rawPalmLandmarks.reshape([-1,7,2]),this.inputSizeTensor),this.anchors[index]);return mul(landmarks,this.inputSizeTensor)})}async getBoxes(input2,config3){let batched=this.model.predict(input2),predictions=batched.squeeze();batched.dispose();let scoresT=tidy(()=>sigmoid(slice(predictions,[0,0],[-1,1])).squeeze()),scores=scoresT.dataSync(),rawBoxes=slice(predictions,[0,1],[-1,4]),boxes=this.normalizeBoxes(rawBoxes);rawBoxes.dispose();let filteredT=await image.nonMaxSuppressionAsync(boxes,scores,config3.hand.maxHands,config3.hand.iouThreshold,config3.hand.scoreThreshold),filtered=filteredT.arraySync();scoresT.dispose(),filteredT.dispose();let hands=[];for(let index of filtered)if(scores[index]>=config3.hand.minConfidence){let matchingBox=slice(boxes,[index,0],[1,-1]),rawPalmLandmarks=slice(predictions,[index,5],[1,14]),palmLandmarks=tidy(()=>this.normalizeLandmarks(rawPalmLandmarks,index).reshape([-1,2]));rawPalmLandmarks.dispose(),hands.push({box:matchingBox,palmLandmarks,confidence:scores[index]})}return predictions.dispose(),boxes.dispose(),hands}async estimateHandBounds(input2,config3){let inputHeight=input2.shape[1],inputWidth=input2.shape[2],image3=tidy(()=>input2.resizeBilinear([config3.hand.inputSize,config3.hand.inputSize]).div(127.5).sub(1)),predictions=await this.getBoxes(image3,config3);image3.dispose();let hands=[];if(!predictions||predictions.length===0)return hands;for(let prediction of predictions){let boxes=prediction.box.dataSync(),startPoint=boxes.slice(0,2),endPoint=boxes.slice(2,4),palmLandmarks=prediction.palmLandmarks.arraySync();prediction.box.dispose(),prediction.palmLandmarks.dispose(),hands.push(scaleBoxCoordinates({startPoint,endPoint,palmLandmarks,confidence:prediction.confidence},[inputWidth/config3.hand.inputSize,inputHeight/config3.hand.inputSize]))}return hands}};exports.HandDetector=HandDetector});var require_handpipeline=__commonJS(exports=>{var PALM_BOX_ENLARGE_FACTOR=5,HAND_BOX_ENLARGE_FACTOR=1.65,PALM_LANDMARK_IDS=[0,5,9,13,17,1,2],PALM_LANDMARKS_INDEX_OF_PALM_BASE=0,PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE=2,HandPipeline=class{constructor(handDetector,landmarkDetector,inputSize){this.handDetector=handDetector,this.landmarkDetector=landmarkDetector,this.inputSize=inputSize,this.storedBoxes=[],this.skipped=0,this.detectedHands=0}getBoxForPalmLandmarks(palmLandmarks,rotationMatrix){let rotatedPalmLandmarks=palmLandmarks.map(coord=>rotatePoint([...coord,1],rotationMatrix)),boxAroundPalm=this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);return enlargeBox(squarifyBox(boxAroundPalm),PALM_BOX_ENLARGE_FACTOR)}getBoxForHandLandmarks(landmarks){let boundingBox=this.calculateLandmarksBoundingBox(landmarks),boxAroundHand=enlargeBox(squarifyBox(boundingBox),HAND_BOX_ENLARGE_FACTOR);boxAroundHand.palmLandmarks=[];for(let i=0;i[scaleFactor[0]*(coord[0]-this.inputSize/2),scaleFactor[1]*(coord[1]-this.inputSize/2),scaleFactor[2]*coord[2]]),coordsRotationMatrix=buildRotationMatrix(angle,[0,0]),coordsRotated=coordsScaled.map(coord=>[...rotatePoint(coord,coordsRotationMatrix),coord[2]]),inverseRotationMatrix=invertTransformMatrix(rotationMatrix),boxCenter=[...getBoxCenter(box2),1],originalBoxCenter=[dot4(boxCenter,inverseRotationMatrix[0]),dot4(boxCenter,inverseRotationMatrix[1])];return coordsRotated.map(coord=>[coord[0]+originalBoxCenter[0],coord[1]+originalBoxCenter[1],coord[2]])}async estimateHands(image3,config3){let useFreshBox=!1,boxes;(this.skipped===0||this.skipped>config3.hand.skipFrames||!config3.hand.landmarks||!config3.videoOptimized)&&(boxes=await this.handDetector.estimateHandBounds(image3,config3),this.skipped=0),config3.videoOptimized&&this.skipped++,boxes&&boxes.length>0&&(boxes.length!==this.detectedHands&&this.detectedHands!==config3.hand.maxHands||!config3.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...boxes],this.storedBoxes.length>0&&(useFreshBox=!0));let hands=[];for(let i=0;i=config3.hand.minConfidence){let keypointsReshaped=reshape(keypoints,[-1,3]),rawCoords=keypointsReshaped.arraySync();keypoints.dispose(),keypointsReshaped.dispose();let coords2=this.transformRawCoords(rawCoords,newBox,angle,rotationMatrix),nextBoundingBox=this.getBoxForHandLandmarks(coords2);this.storedBoxes[i]=nextBoundingBox;let result={landmarks:coords2,confidence,box:{topLeft:nextBoundingBox.startPoint,bottomRight:nextBoundingBox.endPoint}};hands.push(result)}else this.storedBoxes[i]=null;keypoints.dispose()}else{let enlarged=enlargeBox(squarifyBox(currentBox),HAND_BOX_ENLARGE_FACTOR),result={confidence:currentBox.confidence,box:{topLeft:enlarged.startPoint,bottomRight:enlarged.endPoint}};hands.push(result)}}return this.storedBoxes=this.storedBoxes.filter(a=>a!==null),this.detectedHands=hands.length,hands}calculateLandmarksBoundingBox(landmarks){let xs=landmarks.map(d=>d[0]),ys=landmarks.map(d=>d[1]),startPoint=[Math.min(...xs),Math.min(...ys)],endPoint=[Math.max(...xs),Math.max(...ys)];return{startPoint,endPoint}}};exports.HandPipeline=HandPipeline});var 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HandPose(handPipeline);return config3.hand.enabled&&log(`load model: ${config3.hand.detector.modelPath.match(/\/(.*)\./)[1]}`),config3.hand.landmarks&&log(`load model: ${config3.hand.skeleton.modelPath.match(/\/(.*)\./)[1]}`),handPose}exports.load=load3});var require_gesture=__commonJS(exports=>{exports.body=res=>{if(!res)return[];let gestures=[];for(let i=0;ia.part==="leftWrist"),rightWrist=res[i].keypoints.find(a=>a.part==="rightWrist"),nose=res[i].keypoints.find(a=>a.part==="nose");nose&&leftWrist&&rightWrist&&leftWrist.position.ya.part==="leftShoulder"),rightShoulder=res[i].keypoints.find(a=>a.part==="rightShoulder");leftShoulder&&rightShoulder&&gestures.push({body:i,gesture:`leaning ${leftShoulder.position.y>rightShoulder.position.y?"left":"right"}`})}return gestures};exports.face=res=>{if(!res)return[];let gestures=[];for(let i=0;i0){let eyeFacing=res[i].mesh[35][2]-res[i].mesh[263][2];Math.abs(eyeFacing)<10?gestures.push({face:i,gesture:"facing camera"}):gestures.push({face:i,gesture:`facing ${eyeFacing<0?"right":"left"}`}),Math.abs(res[i].mesh[374][1]-res[i].mesh[386][1])/Math.abs(res[i].mesh[443][1]-res[i].mesh[450][1])<.2&&gestures.push({face:i,gesture:"blink left eye"}),Math.abs(res[i].mesh[145][1]-res[i].mesh[159][1])/Math.abs(res[i].mesh[223][1]-res[i].mesh[230][1])<.2&&gestures.push({face:i,gesture:"blink right eye"});let mouthOpen=Math.min(100,500*Math.abs(res[i].mesh[13][1]-res[i].mesh[14][1])/Math.abs(res[i].mesh[10][1]-res[i].mesh[152][1]));mouthOpen>10&&gestures.push({face:i,gesture:`mouth ${Math.trunc(mouthOpen)}% open`});let chinDepth=res[i].mesh[152][2];Math.abs(chinDepth)>10&&gestures.push({face:i,gesture:`head ${chinDepth<0?"up":"down"}`})}return gestures};exports.iris=res=>{if(!res)return[];let gestures=[];for(let i=0;i{if(!res)return[];let gestures=[];for(let i=0;i0){let closest=fingers.reduce((best,a)=>best.position[2]best.position[1]{var WebGLProgram=function(gl,vertexSource,fragmentSource){let _collect=function(source,prefix,collection){let r=new RegExp("\\b"+prefix+" \\w+ (\\w+)","ig");source.replace(r,(match,name)=>(collection[name]=0,match))},_compile=function(source,type){let shader=gl.createShader(type);if(gl.shaderSource(shader,source),gl.compileShader(shader),!gl.getShaderParameter(shader,gl.COMPILE_STATUS))throw new Error("Filter: GL compile failed",gl.getShaderInfoLog(shader));return shader};this.uniform={},this.attribute={};let _vsh=_compile(vertexSource,gl.VERTEX_SHADER),_fsh=_compile(fragmentSource,gl.FRAGMENT_SHADER);if(this.id=gl.createProgram(),gl.attachShader(this.id,_vsh),gl.attachShader(this.id,_fsh),gl.linkProgram(this.id),!gl.getProgramParameter(this.id,gl.LINK_STATUS))throw new Error("Filter: GL link failed",gl.getProgramInfoLog(this.id));gl.useProgram(this.id),_collect(vertexSource,"attribute",this.attribute);for(let a in this.attribute)this.attribute[a]=gl.getAttribLocation(this.id,a);_collect(vertexSource,"uniform",this.uniform),_collect(fragmentSource,"uniform",this.uniform);for(let u in this.uniform)this.uniform[u]=gl.getUniformLocation(this.id,u)},WebGLImageFilter=function(params){params||(params={});let _drawCount=0,_sourceTexture=null,_lastInChain=!1,_currentFramebufferIndex=-1,_tempFramebuffers=[null,null],_filterChain=[],_width=-1,_height=-1,_vertexBuffer=null,_currentProgram=null,_canvas=params.canvas||document.createElement("canvas"),_shaderProgramCache={},gl=_canvas.getContext("webgl");if(!gl)throw new Error("Filter: getContext() failed");this.addFilter=function(name){let args=Array.prototype.slice.call(arguments,1),filter=_filter[name];_filterChain.push({func:filter,args})},this.reset=function(){_filterChain=[]},this.apply=function(image3){if(_resize(image3.width,image3.height),_drawCount=0,_sourceTexture||(_sourceTexture=gl.createTexture()),gl.bindTexture(gl.TEXTURE_2D,_sourceTexture),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_WRAP_S,gl.CLAMP_TO_EDGE),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_WRAP_T,gl.CLAMP_TO_EDGE),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_MIN_FILTER,gl.NEAREST),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_MAG_FILTER,gl.NEAREST),gl.texImage2D(gl.TEXTURE_2D,0,gl.RGBA,gl.RGBA,gl.UNSIGNED_BYTE,image3),_filterChain.length===0)return _draw(),_canvas;for(let i=0;i<_filterChain.length;i++){_lastInChain=i===_filterChain.length-1;let f=_filterChain[i];f.func.apply(this,f.args||[])}return _canvas};let _resize=function(width,height){if(!(width===_width&&height===_height)){if(_canvas.width=width,_width=width,_canvas.height=height,_height=height,!_vertexBuffer){let vertices=new Float32Array([-1,-1,0,1,1,-1,1,1,-1,1,0,0,-1,1,0,0,1,-1,1,1,1,1,1,0]);_vertexBuffer=gl.createBuffer(),gl.bindBuffer(gl.ARRAY_BUFFER,_vertexBuffer),gl.bufferData(gl.ARRAY_BUFFER,vertices,gl.STATIC_DRAW),gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL,!0)}gl.viewport(0,0,_width,_height),_tempFramebuffers=[null,null]}},_getTempFramebuffer=function(index){return _tempFramebuffers[index]=_tempFramebuffers[index]||_createFramebufferTexture(_width,_height),_tempFramebuffers[index]},_createFramebufferTexture=function(width,height){let fbo=gl.createFramebuffer();gl.bindFramebuffer(gl.FRAMEBUFFER,fbo);let renderbuffer=gl.createRenderbuffer();gl.bindRenderbuffer(gl.RENDERBUFFER,renderbuffer);let texture=gl.createTexture();return gl.bindTexture(gl.TEXTURE_2D,texture),gl.texImage2D(gl.TEXTURE_2D,0,gl.RGBA,width,height,0,gl.RGBA,gl.UNSIGNED_BYTE,null),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_MAG_FILTER,gl.LINEAR),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_MIN_FILTER,gl.LINEAR),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_WRAP_S,gl.CLAMP_TO_EDGE),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_WRAP_T,gl.CLAMP_TO_EDGE),gl.framebufferTexture2D(gl.FRAMEBUFFER,gl.COLOR_ATTACHMENT0,gl.TEXTURE_2D,texture,0),gl.bindTexture(gl.TEXTURE_2D,null),gl.bindFramebuffer(gl.FRAMEBUFFER,null),{fbo,texture}},_draw=function(flags){var _a,_b;let source=null,target=null,flipY=!1;_drawCount===0?source=_sourceTexture:source=(_a=_getTempFramebuffer(_currentFramebufferIndex))==null?void 0:_a.texture,_drawCount++,_lastInChain&&!(flags&DRAW.INTERMEDIATE)?(target=null,flipY=_drawCount%2==0):(_currentFramebufferIndex=(_currentFramebufferIndex+1)%2,target=(_b=_getTempFramebuffer(_currentFramebufferIndex))==null?void 0:_b.fbo),gl.bindTexture(gl.TEXTURE_2D,source),gl.bindFramebuffer(gl.FRAMEBUFFER,target),gl.uniform1f(_currentProgram.uniform.flipY,flipY?-1:1),gl.drawArrays(gl.TRIANGLES,0,6)},_compileShader=function(fragmentSource){if(_shaderProgramCache[fragmentSource])return _currentProgram=_shaderProgramCache[fragmentSource],gl.useProgram(_currentProgram.id),_currentProgram;_currentProgram=new WebGLProgram(gl,SHADER.VERTEX_IDENTITY,fragmentSource);let floatSize=Float32Array.BYTES_PER_ELEMENT,vertSize=4*floatSize;return gl.enableVertexAttribArray(_currentProgram.attribute.pos),gl.vertexAttribPointer(_currentProgram.attribute.pos,2,gl.FLOAT,!1,vertSize,0*floatSize),gl.enableVertexAttribArray(_currentProgram.attribute.uv),gl.vertexAttribPointer(_currentProgram.attribute.uv,2,gl.FLOAT,!1,vertSize,2*floatSize),_shaderProgramCache[fragmentSource]=_currentProgram,_currentProgram},DRAW={INTERMEDIATE:1},SHADER={};SHADER.VERTEX_IDENTITY=["precision highp float;","attribute vec2 pos;","attribute vec2 uv;","varying vec2 vUv;","uniform float flipY;","void main(void) {","vUv = uv;","gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);","}"].join(` +var __create=Object.create,__defProp=Object.defineProperty,__getProtoOf=Object.getPrototypeOf,__hasOwnProp=Object.prototype.hasOwnProperty,__getOwnPropNames=Object.getOwnPropertyNames,__getOwnPropDesc=Object.getOwnPropertyDescriptor;var __markAsModule=target=>__defProp(target,"__esModule",{value:!0});var __commonJS=(callback,module)=>()=>(module||(module={exports:{}},callback(module.exports,module)),module.exports),__export=(target,all4)=>{__markAsModule(target);for(var name in all4)__defProp(target,name,{get:all4[name],enumerable:!0})},__exportStar=(target,module,desc)=>{if(__markAsModule(target),module&&typeof module=="object"||typeof module=="function")for(let key of __getOwnPropNames(module))!__hasOwnProp.call(target,key)&&key!=="default"&&__defProp(target,key,{get:()=>module[key],enumerable:!(desc=__getOwnPropDesc(module,key))||desc.enumerable});return target},__toModule=module=>module&&module.__esModule?module:__exportStar(__defProp(module!=null?__create(__getProtoOf(module)):{},"default",{value:module,enumerable:!0}),module);var require_blazeface=__commonJS(exports=>{var NUM_LANDMARKS=6;function generateAnchors(inputSize){let spec={strides:[inputSize/16,inputSize/8],anchors:[2,6]},anchors=[];for(let i=0;i{box.startEndTensor.dispose(),box.startPoint.dispose(),box.endPoint.dispose()},createBox=startEndTensor=>({startEndTensor,startPoint:slice(startEndTensor,[0,0],[-1,2]),endPoint:slice(startEndTensor,[0,2],[-1,2])}),scaleBox=(box,factors)=>{let starts=mul(box.startPoint,factors),ends=mul(box.endPoint,factors),newCoordinates=concat2d([starts,ends],1);return createBox(newCoordinates)};function decodeBounds(boxOutputs,anchors,inputSize){let boxStarts=slice(boxOutputs,[0,1],[-1,2]),centers=add2(boxStarts,anchors),boxSizes=slice(boxOutputs,[0,3],[-1,2]),boxSizesNormalized=div(boxSizes,inputSize),centersNormalized=div(centers,inputSize),halfBoxSize=div(boxSizesNormalized,2),starts=sub(centersNormalized,halfBoxSize),ends=add2(centersNormalized,halfBoxSize),startNormalized=mul(starts,inputSize),endNormalized=mul(ends,inputSize);return concat2d([startNormalized,endNormalized],1)}function scaleBoxFromPrediction(face3,scaleFactor){return tidy(()=>{let box=face3.box?face3.box:face3;return scaleBox(box,scaleFactor).startEndTensor.squeeze()})}var BlazeFaceModel=class{constructor(model2,config2){this.blazeFaceModel=model2,this.width=config2.face.detector.inputSize,this.height=config2.face.detector.inputSize,this.anchorsData=generateAnchors(config2.face.detector.inputSize),this.anchors=tensor2d(this.anchorsData),this.inputSize=tensor1d([this.width,this.height]),this.config=config2,this.scaleFaces=.8}async getBoundingBoxes(inputImage){if(!inputImage||inputImage.isDisposedInternal||inputImage.shape.length!==4||inputImage.shape[1]<1||inputImage.shape[2]<1)return null;let[detectedOutputs,boxes,scores]=tidy(()=>{let resizedImage=inputImage.resizeBilinear([this.width,this.height]),normalizedImage=sub(resizedImage.div(127.5),1),batchedPrediction=this.blazeFaceModel.predict(normalizedImage),prediction;if(Array.isArray(batchedPrediction)){let sorted=batchedPrediction.sort((a,b)=>a.size-b.size),concat384=concat([sorted[0],sorted[2]],2),concat512=concat([sorted[1],sorted[3]],2);prediction=concat([concat512,concat384],1).squeeze(0)}else prediction=batchedPrediction.squeeze();let decodedBounds=decodeBounds(prediction,this.anchors,this.inputSize),logits=slice(prediction,[0,0],[-1,1]),scoresOut=sigmoid(logits).squeeze();return[prediction,decodedBounds,scoresOut]}),boxIndicesTensor=await image.nonMaxSuppressionAsync(boxes,scores,this.config.face.detector.maxFaces,this.config.face.detector.iouThreshold,this.config.face.detector.scoreThreshold),boxIndices=boxIndicesTensor.arraySync();boxIndicesTensor.dispose();let boundingBoxes=boxIndices.map(boxIndex=>slice(boxes,[boxIndex,0],[1,-1])).map(boundingBox=>{let vals=boundingBox.arraySync();return boundingBox.dispose(),vals}),scoresVal=scores.dataSync(),annotatedBoxes=[];for(let i=0;ithis.config.face.detector.minConfidence){let box=createBox(boundingBoxes[i]),anchor=this.anchorsData[boxIndex],landmarks=tidy(()=>slice(detectedOutputs,[boxIndex,NUM_LANDMARKS-1],[1,-1]).squeeze().reshape([NUM_LANDMARKS,-1]));annotatedBoxes.push({box,landmarks,anchor,confidence})}}return detectedOutputs.dispose(),boxes.dispose(),scores.dispose(),detectedOutputs.dispose(),{boxes:annotatedBoxes,scaleFactor:[inputImage.shape[2]/this.width,inputImage.shape[1]/this.height]}}async estimateFaces(input2){let{boxes,scaleFactor}=await this.getBoundingBoxes(input2),faces=[];for(let face3 of boxes){let landmarkData=face3.landmarks.arraySync(),scaledBox=scaleBoxFromPrediction(face3,scaleFactor),boxData=scaleBox.arraySync(),probabilityData=face3.probability.arraySync(),anchor=face3.anchor,[scaleFactorX,scaleFactorY]=scaleFactor,scaledLandmarks=landmarkData.map(landmark=>[(landmark[0]+anchor[0])*scaleFactorX,(landmark[1]+anchor[1])*scaleFactorY]),normalizedFace={topLeft:boxData.slice(0,2),bottomRight:boxData.slice(2),landmarks:scaledLandmarks,probability:probabilityData};disposeBox(face3.box),face3.landmarks.dispose(),face3.probability.dispose(),scaledBox.dispose(),faces.push(normalizedFace)}return faces}};async function load3(config2){let blazeface=await loadGraphModel(config2.face.detector.modelPath,{fromTFHub:config2.face.detector.modelPath.includes("tfhub.dev")}),model2=new BlazeFaceModel(blazeface,config2);return log(`load model: ${config2.face.detector.modelPath.match(/\/(.*)\./)[1]}`),model2}exports.load=load3;exports.BlazeFaceModel=BlazeFaceModel;exports.disposeBox=disposeBox});var require_box=__commonJS(exports=>{function scaleBoxCoordinates2(box,factor){let startPoint=[box.startPoint[0]*factor[0],box.startPoint[1]*factor[1]],endPoint=[box.endPoint[0]*factor[0],box.endPoint[1]*factor[1]];return{startPoint,endPoint}}exports.scaleBoxCoordinates=scaleBoxCoordinates2;function getBoxSize2(box){return[Math.abs(box.endPoint[0]-box.startPoint[0]),Math.abs(box.endPoint[1]-box.startPoint[1])]}exports.getBoxSize=getBoxSize2;function getBoxCenter2(box){return[box.startPoint[0]+(box.endPoint[0]-box.startPoint[0])/2,box.startPoint[1]+(box.endPoint[1]-box.startPoint[1])/2]}exports.getBoxCenter=getBoxCenter2;function cutBoxFromImageAndResize2(box,image3,cropSize){let h=image3.shape[1],w=image3.shape[2],boxes=[[box.startPoint[1]/h,box.startPoint[0]/w,box.endPoint[1]/h,box.endPoint[0]/w]];return image.cropAndResize(image3,boxes,[0],cropSize)}exports.cutBoxFromImageAndResize=cutBoxFromImageAndResize2;function enlargeBox2(box,factor=1.5){let center=getBoxCenter2(box),size=getBoxSize2(box),newHalfSize=[factor*size[0]/2,factor*size[1]/2],startPoint=[center[0]-newHalfSize[0],center[1]-newHalfSize[1]],endPoint=[center[0]+newHalfSize[0],center[1]+newHalfSize[1]];return{startPoint,endPoint,landmarks:box.landmarks}}exports.enlargeBox=enlargeBox2;function squarifyBox2(box){let centers=getBoxCenter2(box),size=getBoxSize2(box),halfSize=Math.max(...size)/2,startPoint=[centers[0]-halfSize,centers[1]-halfSize],endPoint=[centers[0]+halfSize,centers[1]+halfSize];return{startPoint,endPoint,landmarks:box.landmarks}}exports.squarifyBox=squarifyBox2});var require_util=__commonJS(exports=>{exports.IDENTITY_MATRIX=[[1,0,0],[0,1,0],[0,0,1]];function normalizeRadians2(angle){return angle-2*Math.PI*Math.floor((angle+Math.PI)/(2*Math.PI))}exports.normalizeRadians=normalizeRadians2;function computeRotation2(point1,point2){let radians=Math.PI/2-Math.atan2(-(point2[1]-point1[1]),point2[0]-point1[0]);return normalizeRadians2(radians)}exports.computeRotation=computeRotation2;function radToDegrees(rad){return rad*180/Math.PI}exports.radToDegrees=radToDegrees;function buildTranslationMatrix2(x,y){return[[1,0,x],[0,1,y],[0,0,1]]}function dot5(v1,v2){let product=0;for(let i=0;i{var 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,378,379,397,288,361,454,356,70,63,105,66,107,336,296,334,293,300,168,6,195,4,98,97,2,326,327,33,160,158,133,153,144,362,385,387,263,373,380,57,40,37,0,267,270,287,321,314,17,84,91,78,81,13,311,308,402,14,178],VTX33=[33,133,362,263,1,62,308,159,145,386,374,6,102,331,2,13,14,70,105,107,336,334,300,54,10,284,50,280,234,454,58,288,152],VTX7=[33,133,362,263,1,78,308];exports.MESH_ANNOTATIONS=MESH_ANNOTATIONS;exports.MESH_TO_IRIS_INDICES_MAP=MESH_TO_IRIS_INDICES_MAP;exports.TRI468=TRI468;exports.TRI68=TRI68;exports.TRI33=TRI33;exports.TRI7=TRI7;exports.UV468=UV468;exports.UV68=VTX68.map(x=>UV468[x]);exports.UV33=VTX33.map(x=>UV468[x]);exports.UV7=VTX7.map(x=>UV468[x])});var require_facepipeline=__commonJS(exports=>{var bounding=__toModule(require_box()),util=__toModule(require_util()),coords2=__toModule(require_coords()),LANDMARKS_COUNT=468,MESH_MOUTH_INDEX=13,MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES=[MESH_MOUTH_INDEX,coords2.MESH_ANNOTATIONS.midwayBetweenEyes[0]],BLAZEFACE_MOUTH_INDEX=3,BLAZEFACE_NOSE_INDEX=2,BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES=[BLAZEFACE_MOUTH_INDEX,BLAZEFACE_NOSE_INDEX],LEFT_EYE_OUTLINE=coords2.MESH_ANNOTATIONS.leftEyeLower0,LEFT_EYE_BOUNDS=[LEFT_EYE_OUTLINE[0],LEFT_EYE_OUTLINE[LEFT_EYE_OUTLINE.length-1]],RIGHT_EYE_OUTLINE=coords2.MESH_ANNOTATIONS.rightEyeLower0,RIGHT_EYE_BOUNDS=[RIGHT_EYE_OUTLINE[0],RIGHT_EYE_OUTLINE[RIGHT_EYE_OUTLINE.length-1]],IRIS_UPPER_CENTER_INDEX=3,IRIS_LOWER_CENTER_INDEX=4,IRIS_IRIS_INDEX=71,IRIS_NUM_COORDINATES=76;function replaceRawCoordinates(rawCoords,newCoords,prefix,keys){for(let i=0;i[scaleFactor[0]*(coord[0]-this.meshWidth/2),scaleFactor[1]*(coord[1]-this.meshHeight/2),coord[2]]),coordsRotationMatrix=angle!==0?util.buildRotationMatrix(angle,[0,0]):util.IDENTITY_MATRIX,coordsRotated=angle!==0?coordsScaled.map(coord=>[...util.rotatePoint(coord,coordsRotationMatrix),coord[2]]):coordsScaled,inverseRotationMatrix=angle!==0?util.invertTransformMatrix(rotationMatrix):util.IDENTITY_MATRIX,boxCenter=[...bounding.getBoxCenter({startPoint:box.startPoint,endPoint:box.endPoint}),1];return coordsRotated.map(coord=>[coord[0]+util.dot(boxCenter,inverseRotationMatrix[0]),coord[1]+util.dot(boxCenter,inverseRotationMatrix[1]),coord[2]])}getLeftToRightEyeDepthDifference(rawCoords){let leftEyeZ=rawCoords[LEFT_EYE_BOUNDS[0]][2],rightEyeZ=rawCoords[RIGHT_EYE_BOUNDS[0]][2];return leftEyeZ-rightEyeZ}getEyeBox(rawCoords,face3,eyeInnerCornerIndex,eyeOuterCornerIndex,flip=!1){let box=bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex],rawCoords[eyeOuterCornerIndex]]),this.irisEnlarge)),boxSize=bounding.getBoxSize(box),crop=image.cropAndResize(face3,[[box.startPoint[1]/this.meshHeight,box.startPoint[0]/this.meshWidth,box.endPoint[1]/this.meshHeight,box.endPoint[0]/this.meshWidth]],[0],[this.irisSize,this.irisSize]);return flip&&(crop=image.flipLeftRight(crop)),{box,boxSize,crop}}getEyeCoords(eyeData,eyeBox,eyeBoxSize,flip=!1){let eyeRawCoords=[];for(let i=0;i{let z=averageZ;return i===2?z=upperCenterZ:i===4&&(z=lowerCenterZ),[coord[0],coord[1],z]})}async predict(input2,config2){let useFreshBox=!1,detector;if((this.skipped===0||this.skipped>config2.face.detector.skipFrames||!config2.face.mesh.enabled||!config2.videoOptimized)&&(detector=await this.boundingBoxDetector.getBoundingBoxes(input2),this.skipped=0),config2.videoOptimized&&this.skipped++,detector&&detector.boxes&&detector.boxes.length>0&&(!config2.face.mesh.enabled||detector.boxes.length!==this.detectedFaces&&this.detectedFaces!==config2.face.detector.maxFaces)){this.storedBoxes=[],this.detectedFaces=0;for(let possible of detector.boxes)this.storedBoxes.push({startPoint:possible.box.startPoint.dataSync(),endPoint:possible.box.endPoint.dataSync(),landmarks:possible.landmarks,confidence:possible.confidence});this.storedBoxes.length>0&&(useFreshBox=!0)}if(useFreshBox){if(!detector||!detector.boxes||detector.boxes.length===0)return this.storedBoxes=[],this.detectedFaces=0,null;for(let i=0;i{prediction.box.startPoint.dispose(),prediction.box.endPoint.dispose(),prediction.landmarks.dispose()});let results=tidy(()=>this.storedBoxes.map((box,i)=>{let face3,angle=0,rotationMatrix;if(config2.face.detector.rotation){let[indexOfMouth,indexOfForehead]=box.landmarks.length>=LANDMARKS_COUNT?MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES:BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES;angle=util.computeRotation(box.landmarks[indexOfMouth],box.landmarks[indexOfForehead]);let faceCenter=bounding.getBoxCenter({startPoint:box.startPoint,endPoint:box.endPoint}),faceCenterNormalized=[faceCenter[0]/input2.shape[2],faceCenter[1]/input2.shape[1]],rotatedImage=image.rotateWithOffset(input2,angle,0,faceCenterNormalized);rotationMatrix=util.buildRotationMatrix(-angle,faceCenter),face3=bounding.cutBoxFromImageAndResize({startPoint:box.startPoint,endPoint:box.endPoint},rotatedImage,[this.meshHeight,this.meshWidth]).div(255)}else{rotationMatrix=util.IDENTITY_MATRIX;let cloned=input2.clone();face3=bounding.cutBoxFromImageAndResize({startPoint:box.startPoint,endPoint:box.endPoint},cloned,[this.meshHeight,this.meshWidth]).div(255)}if(!config2.face.mesh.enabled)return{coords:null,box,faceConfidence:null,confidence:box.confidence,image:face3};let[,confidence,contourCoords]=this.meshDetector.predict(face3),confidenceVal=confidence.dataSync()[0];if(confidenceVala!==null),this.detectedFaces=results.length,results}calculateLandmarksBoundingBox(landmarks){let xs=landmarks.map(d=>d[0]),ys=landmarks.map(d=>d[1]),startPoint=[Math.min(...xs),Math.min(...ys)],endPoint=[Math.max(...xs),Math.max(...ys)];return{startPoint,endPoint,landmarks}}};exports.Pipeline=Pipeline});var require_facemesh=__commonJS(exports=>{var blazeface=__toModule(require_blazeface()),facepipeline=__toModule(require_facepipeline()),coords2=__toModule(require_coords()),MediaPipeFaceMesh=class{constructor(blazeFace,blazeMeshModel,irisModel,config2){this.facePipeline=new facepipeline.Pipeline(blazeFace,blazeMeshModel,irisModel,config2),this.config=config2}async estimateFaces(input2,config2){let predictions=await this.facePipeline.predict(input2,config2),results=[];for(let prediction of predictions||[]){if(prediction.isDisposedInternal)continue;let mesh=prediction.coords?prediction.coords.arraySync():null,meshRaw=prediction.rawCoords,annotations={};if(mesh&&mesh.length>0)for(let key of Object.keys(coords2.MESH_ANNOTATIONS))annotations[key]=coords2.MESH_ANNOTATIONS[key].map(index=>mesh[index]);let boxRaw=config2.face.mesh.returnRawData&&prediction.box?{topLeft:prediction.box.startPoint,bottomRight:prediction.box.endPoint}:null,box=prediction.box?[Math.max(0,prediction.box.startPoint[0]),Math.max(0,prediction.box.startPoint[1]),Math.min(input2.shape[2],prediction.box.endPoint[0])-prediction.box.startPoint[0],Math.min(input2.shape[1],prediction.box.endPoint[1])-prediction.box.startPoint[1]]:0;results.push({confidence:prediction.confidence||0,box,mesh,boxRaw,meshRaw,annotations,image:prediction.image?clone(prediction.image):null}),prediction.coords&&prediction.coords.dispose(),prediction.image&&prediction.image.dispose()}return results}},faceModels=[null,null,null];async function load3(config2){faceModels=await Promise.all([!faceModels[0]&&config2.face.enabled?blazeface.load(config2):null,!faceModels[1]&&config2.face.mesh.enabled?loadGraphModel(config2.face.mesh.modelPath,{fromTFHub:config2.face.mesh.modelPath.includes("tfhub.dev")}):null,!faceModels[2]&&config2.face.iris.enabled?loadGraphModel(config2.face.iris.modelPath,{fromTFHub:config2.face.iris.modelPath.includes("tfhub.dev")}):null]);let faceMesh=new MediaPipeFaceMesh(faceModels[0],faceModels[1],faceModels[2],config2);return config2.face.mesh.enabled&&log(`load model: ${config2.face.mesh.modelPath.match(/\/(.*)\./)[1]}`),config2.face.iris.enabled&&log(`load model: ${config2.face.iris.modelPath.match(/\/(.*)\./)[1]}`),faceMesh}exports.load=load3;exports.MediaPipeFaceMesh=MediaPipeFaceMesh;exports.triangulation=coords2.TRI468});var require_profile=__commonJS(exports=>{var profileData={};function profile3(name,data2){if(!data2||!data2.kernels)return;let maxResults=5,time2=data2.kernels.filter(a=>a.kernelTimeMs>0).reduce((a,b)=>a+=b.kernelTimeMs,0),slowest=data2.kernels.map((a,i)=>(a.id=i,a)).filter(a=>a.kernelTimeMs>0).sort((a,b)=>b.kernelTimeMs-a.kernelTimeMs),largest=data2.kernels.map((a,i)=>(a.id=i,a)).filter(a=>a.totalBytesSnapshot>0).sort((a,b)=>b.totalBytesSnapshot-a.totalBytesSnapshot);slowest.length>maxResults&&(slowest.length=maxResults),largest.length>maxResults&&(largest.length=maxResults);let res={newBytes:data2.newBytes,newTensors:data2.newTensors,peakBytes:data2.peakBytes,numKernelOps:data2.kernels.length,timeKernelOps:time2,slowestKernelOps:slowest,largestKernelOps:largest};profileData[name]=res,log("Human profiler",name,res)}exports.run=profile3});var require_age=__commonJS(exports=>{var profile3=__toModule(require_profile()),models2={},last={age:0},skipped=Number.MAX_SAFE_INTEGER;async function load3(config2){return models2.age||(models2.age=await loadGraphModel(config2.face.age.modelPath),log(`load model: ${config2.face.age.modelPath.match(/\/(.*)\./)[1]}`)),models2.age}async function predict3(image3,config2){return models2.age?skipped0?(skipped++,last):(config2.videoOptimized?skipped=0:skipped=Number.MAX_SAFE_INTEGER,new Promise(async resolve=>{let resize=image.resizeBilinear(image3,[config2.face.age.inputSize,config2.face.age.inputSize],!1),enhance=mul(resize,[255]);dispose(resize);let ageT,obj={};if(!config2.profile)config2.face.age.enabled&&(ageT=await models2.age.predict(enhance));else{let profileAge=config2.face.age.enabled?await profile(()=>models2.age.predict(enhance)):{};ageT=profileAge.result.clone(),profileAge.result.dispose(),profile3.run("age",profileAge)}if(enhance.dispose(),ageT){let data2=ageT.dataSync();obj.age=Math.trunc(10*data2[0])/10}ageT.dispose(),last=obj,resolve(obj)})):null}exports.predict=predict3;exports.load=load3});var require_gender=__commonJS(exports=>{var profile3=__toModule(require_profile()),models2={},last={gender:""},skipped=Number.MAX_SAFE_INTEGER,alternative=!1,rgb=[.2989,.587,.114];async function load3(config2){return models2.gender||(models2.gender=await loadGraphModel(config2.face.gender.modelPath),alternative=models2.gender.inputs[0].shape[3]===1,log(`load model: ${config2.face.gender.modelPath.match(/\/(.*)\./)[1]}`)),models2.gender}async function predict3(image3,config2){return models2.gender?skipped{let resize=image.resizeBilinear(image3,[config2.face.gender.inputSize,config2.face.gender.inputSize],!1),enhance;alternative?enhance=tidy(()=>{let[red,green,blue]=split(resize,3,3),redNorm=mul(red,rgb[0]),greenNorm=mul(green,rgb[1]),blueNorm=mul(blue,rgb[2]);return addN([redNorm,greenNorm,blueNorm]).sub(.5).mul(2)}):enhance=mul(resize,[255]),dispose(resize);let genderT,obj={};if(!config2.profile)config2.face.gender.enabled&&(genderT=await models2.gender.predict(enhance));else{let profileGender=config2.face.gender.enabled?await profile(()=>models2.gender.predict(enhance)):{};genderT=profileGender.result.clone(),profileGender.result.dispose(),profile3.run("gender",profileGender)}if(enhance.dispose(),genderT){let data2=genderT.dataSync();if(alternative){let confidence=Math.trunc(100*Math.abs(data2[0]-data2[1]))/100;confidence>config2.face.gender.minConfidence&&(obj.gender=data2[0]>data2[1]?"female":"male",obj.confidence=confidence)}else{let confidence=Math.trunc(200*Math.abs(data2[0]-.5))/100;confidence>config2.face.gender.minConfidence&&(obj.gender=data2[0]<=.5?"female":"male",obj.confidence=Math.min(.99,confidence))}}genderT.dispose(),last=obj,resolve(obj)})):null}exports.predict=predict3;exports.load=load3});var require_emotion=__commonJS(exports=>{var profile3=__toModule(require_profile()),annotations=["angry","disgust","fear","happy","sad","surprise","neutral"],models2={},last=[],skipped=Number.MAX_SAFE_INTEGER,rgb=[.2989,.587,.114],scale2=1;async function load3(config2){return models2.emotion||(models2.emotion=await loadGraphModel(config2.face.emotion.modelPath),log(`load model: ${config2.face.emotion.modelPath.match(/\/(.*)\./)[1]}`)),models2.emotion}async function predict3(image3,config2){return models2.emotion?skipped0?(skipped++,last):(config2.videoOptimized?skipped=0:skipped=Number.MAX_SAFE_INTEGER,new Promise(async resolve=>{let resize=image.resizeBilinear(image3,[config2.face.emotion.inputSize,config2.face.emotion.inputSize],!1),[red,green,blue]=split(resize,3,3);resize.dispose();let redNorm=mul(red,rgb[0]),greenNorm=mul(green,rgb[1]),blueNorm=mul(blue,rgb[2]);red.dispose(),green.dispose(),blue.dispose();let grayscale=addN([redNorm,greenNorm,blueNorm]);redNorm.dispose(),greenNorm.dispose(),blueNorm.dispose();let normalize=tidy(()=>grayscale.sub(.5).mul(2));grayscale.dispose();let obj=[];if(config2.face.emotion.enabled){let data2;if(config2.profile){let profileData=await profile(()=>models2.emotion.predict(normalize));data2=profileData.result.dataSync(),profileData.result.dispose(),profile3.run("emotion",profileData)}else{let emotionT=await models2.emotion.predict(normalize);data2=emotionT.dataSync(),dispose(emotionT)}for(let i=0;iconfig2.face.emotion.minConfidence&&obj.push({score:Math.min(.99,Math.trunc(100*scale2*data2[i])/100),emotion:annotations[i]});obj.sort((a,b)=>b.score-a.score)}normalize.dispose(),last=obj,resolve(obj)})):null}exports.predict=predict3;exports.load=load3});var require_embedding=__commonJS(exports=>{var profile3=__toModule(require_profile()),models2={};async function load3(config2){return models2.embedding||(models2.embedding=await loadGraphModel(config2.face.embedding.modelPath),log(`load model: ${config2.face.embedding.modelPath.match(/\/(.*)\./)[1]}`)),models2.embedding}function simmilarity2(embedding1,embedding22){if((embedding1==null?void 0:embedding1.length)!==(embedding22==null?void 0:embedding22.length))return 0;let order=2,distance=10*embedding1.map((val,i)=>val-embedding22[i]).reduce((dist,diff)=>dist+diff**order,0)**(1/order);return Math.trunc(1e3*(1-distance))/1e3}async function predict3(image3,config2){return models2.embedding?new Promise(async resolve=>{let resize=image.resizeBilinear(image3,[config2.face.embedding.inputSize,config2.face.embedding.inputSize],!1),data2=[];if(config2.face.embedding.enabled)if(config2.profile){let profileData=await profile(()=>models2.embedding.predict({img_inputs:resize}));data2=[...profileData.result.dataSync()],profileData.result.dispose(),profile3.run("emotion",profileData)}else{let embeddingT=await models2.embedding.predict({img_inputs:resize});data2=[...embeddingT.dataSync()],dispose(embeddingT)}resize.dispose(),resolve(data2)}):null}exports.predict=predict3;exports.simmilarity=simmilarity2;exports.load=load3});var require_modelBase=__commonJS(exports=>{var imageNetMean=[-123.15,-115.9,-103.06];function nameOutputResultsMobileNet(results){let[offsets,heatmap,displacementFwd,displacementBwd]=results;return{offsets,heatmap,displacementFwd,displacementBwd}}function nameOutputResultsResNet(results){let[displacementFwd,displacementBwd,offsets,heatmap]=results;return{offsets,heatmap,displacementFwd,displacementBwd}}var BaseModel=class{constructor(model2){this.model=model2}predict(input2,config2){return tidy(()=>{let asBatch=(config2.body.modelType==="ResNet"?input2.toFloat().add(imageNetMean):input2.toFloat().div(127.5).sub(1)).expandDims(0),results3d=this.model.predict(asBatch).map(y=>y.squeeze([0])),namedResults=config2.body.modelType==="ResNet"?nameOutputResultsResNet(results3d):nameOutputResultsMobileNet(results3d);return{heatmapScores:namedResults.heatmap.sigmoid(),offsets:namedResults.offsets,displacementFwd:namedResults.displacementFwd,displacementBwd:namedResults.displacementBwd}})}dispose(){this.model.dispose()}};exports.BaseModel=BaseModel});var require_heapSort=__commonJS(exports=>{function half(k){return Math.floor(k/2)}var MaxHeap=class{constructor(maxSize,getElementValue){this.priorityQueue=new Array(maxSize),this.numberOfElements=-1,this.getElementValue=getElementValue}enqueue(x){this.priorityQueue[++this.numberOfElements]=x,this.swim(this.numberOfElements)}dequeue(){let max6=this.priorityQueue[0];return this.exchange(0,this.numberOfElements--),this.sink(0),this.priorityQueue[this.numberOfElements+1]=null,max6}empty(){return this.numberOfElements===-1}size(){return this.numberOfElements+1}all(){return this.priorityQueue.slice(0,this.numberOfElements+1)}max(){return this.priorityQueue[0]}swim(k){for(;k>0&&this.less(half(k),k);)this.exchange(k,half(k)),k=half(k)}sink(k){for(;2*k<=this.numberOfElements;){let j=2*k;if(j{var heapSort=__toModule(require_heapSort());function scoreIsMaximumInLocalWindow(keypointId,score,heatmapY,heatmapX,localMaximumRadius,scores){let[height,width]=scores.shape,localMaximum=!0,yStart=Math.max(heatmapY-localMaximumRadius,0),yEnd=Math.min(heatmapY+localMaximumRadius+1,height);for(let yCurrent=yStart;yCurrentscore){localMaximum=!1;break}if(!localMaximum)break}return localMaximum}function buildPartWithScoreQueue(scoreThreshold,localMaximumRadius,scores){let[height,width,numKeypoints]=scores.shape,queue=new heapSort.MaxHeap(height*width*numKeypoints,({score})=>score);for(let heatmapY=0;heatmapY{exports.partNames=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"];exports.NUM_KEYPOINTS=exports.partNames.length;exports.partIds=exports.partNames.reduce((result,jointName,i)=>(result[jointName]=i,result),{});var connectedPartNames=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]];exports.connectedPartIndices=connectedPartNames.map(([jointNameA,jointNameB])=>[exports.partIds[jointNameA],exports.partIds[jointNameB]]);exports.poseChain=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];exports.partChannels=["left_face","right_face","right_upper_leg_front","right_lower_leg_back","right_upper_leg_back","left_lower_leg_front","left_upper_leg_front","left_upper_leg_back","left_lower_leg_back","right_feet","right_lower_leg_front","left_feet","torso_front","torso_back","right_upper_arm_front","right_upper_arm_back","right_lower_arm_back","left_lower_arm_front","left_upper_arm_front","left_upper_arm_back","left_lower_arm_back","right_hand","right_lower_arm_front","left_hand"]});var require_vectors=__commonJS(exports=>{var kpt=__toModule(require_keypoints());function getOffsetPoint(y,x,keypoint,offsets){return{y:offsets.get(y,x,keypoint),x:offsets.get(y,x,keypoint+kpt.NUM_KEYPOINTS)}}exports.getOffsetPoint=getOffsetPoint;function getImageCoords(part,outputStride,offsets){let{heatmapY,heatmapX,id:keypoint}=part,{y,x}=getOffsetPoint(heatmapY,heatmapX,keypoint,offsets);return{x:part.heatmapX*outputStride+x,y:part.heatmapY*outputStride+y}}exports.getImageCoords=getImageCoords;function fillArray(element,size){let result=new Array(size);for(let i=0;imax6?max6:a}exports.clamp=clamp2;function squaredDistance(y1,x1,y2,x2){let dy=y2-y1,dx=x2-x1;return dy*dy+dx*dx}exports.squaredDistance=squaredDistance;function addVectors(a,b){return{x:a.x+b.x,y:a.y+b.y}}exports.addVectors=addVectors;function clampVector(a,min6,max6){return{y:clamp2(a.y,min6,max6),x:clamp2(a.x,min6,max6)}}exports.clampVector=clampVector});var require_decoders=__commonJS(exports=>{var kpt=__toModule(require_keypoints());function getPointsConfidence(heatmapScores,heatMapCoords){let numKeypoints=heatMapCoords.shape[0],result=new Float32Array(numKeypoints);for(let keypoint=0;keypointheatMapCoordsBuffer.toTensor().mul(scalar(outputStride,"int32")).toFloat().add(getOffsetVectors(heatMapCoordsBuffer,offsetsBuffer)))}exports.getOffsetPoints=getOffsetPoints;function mod4(a,b){return tidy(()=>{let floored=a.div(scalar(b,"int32"));return a.sub(floored.mul(scalar(b,"int32")))})}function argmax2d(inputs){let[height,width,depth]=inputs.shape;return tidy(()=>{let coords2=inputs.reshape([height*width,depth]).argMax(0),yCoords=coords2.div(scalar(width,"int32")).expandDims(1),xCoords=mod4(coords2,width).expandDims(1);return concat([yCoords,xCoords],1)})}exports.argmax2d=argmax2d});var require_decodePose=__commonJS(exports=>{var keypoints=__toModule(require_keypoints()),vectors=__toModule(require_vectors()),decoders=__toModule(require_decoders()),parentChildrenTuples=keypoints.poseChain.map(([parentJoinName,childJoinName])=>[keypoints.partIds[parentJoinName],keypoints.partIds[childJoinName]]),parentToChildEdges=parentChildrenTuples.map(([,childJointId])=>childJointId),childToParentEdges=parentChildrenTuples.map(([parentJointId])=>parentJointId);function getDisplacement(edgeId,point,displacements){let numEdges=displacements.shape[2]/2;return{y:displacements.get(point.y,point.x,edgeId),x:displacements.get(point.y,point.x,numEdges+edgeId)}}function getStridedIndexNearPoint(point,outputStride,height,width){return{y:vectors.clamp(Math.round(point.y/outputStride),0,height-1),x:vectors.clamp(Math.round(point.x/outputStride),0,width-1)}}function traverseToTargetKeypoint(edgeId,sourceKeypoint,targetKeypointId,scoresBuffer,offsets,outputStride,displacements,offsetRefineStep=2){let[height,width]=scoresBuffer.shape,sourceKeypointIndices=getStridedIndexNearPoint(sourceKeypoint.position,outputStride,height,width),displacement=getDisplacement(edgeId,sourceKeypointIndices,displacements),targetKeypoint=vectors.addVectors(sourceKeypoint.position,displacement);for(let i=0;i=0;--edge){let sourceKeypointId=parentToChildEdges[edge],targetKeypointId=childToParentEdges[edge];instanceKeypoints[sourceKeypointId]&&!instanceKeypoints[targetKeypointId]&&(instanceKeypoints[targetKeypointId]=traverseToTargetKeypoint(edge,instanceKeypoints[sourceKeypointId],targetKeypointId,scores,offsets,outputStride,displacementsBwd))}for(let edge=0;edge(totalScore+=score,{position:{y:offsetPointsBuffer.get(i,0),x:offsetPointsBuffer.get(i,1)},part:keypoints.partNames[i],score})),filteredKeypoints=instanceKeypoints.filter(kpt=>kpt.score>config2.body.scoreThreshold);return heatmapValues.dispose(),offsetPoints.dispose(),{keypoints:filteredKeypoints,score:totalScore/instanceKeypoints.length}}exports.decodeSinglePose=decodeSinglePose});var require_decodeMultiple=__commonJS(exports=>{var buildParts=__toModule(require_buildParts()),decodePose=__toModule(require_decodePose()),vectors=__toModule(require_vectors()),kLocalMaximumRadius=1;function withinNmsRadiusOfCorrespondingPoint(poses,squaredNmsRadius,{x,y},keypointId){return poses.some(({keypoints})=>{let correspondingKeypoint=keypoints[keypointId].position;return vectors.squaredDistance(y,x,correspondingKeypoint.y,correspondingKeypoint.x)<=squaredNmsRadius})}function getInstanceScore(existingPoses,squaredNmsRadius,instanceKeypoints){return instanceKeypoints.reduce((result,{position,score},keypointId)=>(withinNmsRadiusOfCorrespondingPoint(existingPoses,squaredNmsRadius,position,keypointId)||(result+=score),result),0)/instanceKeypoints.length}function decodeMultiplePoses(scoresBuffer,offsetsBuffer,displacementsFwdBuffer,displacementsBwdBuffer,config2){let poses=[],queue=buildParts.buildPartWithScoreQueue(config2.body.scoreThreshold,kLocalMaximumRadius,scoresBuffer),squaredNmsRadius=config2.body.nmsRadius^2;for(;poses.lengthconfig2.body.scoreThreshold&&poses.push({keypoints,score})}return poses}exports.decodeMultiplePoses=decodeMultiplePoses});var require_util2=__commonJS(exports=>{var kpt=__toModule(require_keypoints());function eitherPointDoesntMeetConfidence(a,b,minConfidence){return a(eitherPointDoesntMeetConfidence(keypoints[leftJoint].score,keypoints[rightJoint].score,minConfidence)||result.push([keypoints[leftJoint],keypoints[rightJoint]]),result),[])}exports.getAdjacentKeyPoints=getAdjacentKeyPoints;var{NEGATIVE_INFINITY,POSITIVE_INFINITY}=Number;function getBoundingBox(keypoints){return keypoints.reduce(({maxX,maxY,minX,minY},{position:{x,y}})=>({maxX:Math.max(maxX,x),maxY:Math.max(maxY,y),minX:Math.min(minX,x),minY:Math.min(minY,y)}),{maxX:NEGATIVE_INFINITY,maxY:NEGATIVE_INFINITY,minX:POSITIVE_INFINITY,minY:POSITIVE_INFINITY})}exports.getBoundingBox=getBoundingBox;function getBoundingBoxPoints(keypoints){let{minX,minY,maxX,maxY}=getBoundingBox(keypoints);return[{x:minX,y:minY},{x:maxX,y:minY},{x:maxX,y:maxY},{x:minX,y:maxY}]}exports.getBoundingBoxPoints=getBoundingBoxPoints;async function toTensorBuffers3D(tensors){return Promise.all(tensors.map(tensor2=>tensor2.buffer()))}exports.toTensorBuffers3D=toTensorBuffers3D;function scalePose(pose,scaleY,scaleX){return{score:pose.score,keypoints:pose.keypoints.map(({score,part,position})=>({score,part,position:{x:position.x*scaleX,y:position.y*scaleY}}))}}exports.scalePose=scalePose;function resizeTo(image3,[targetH,targetW]){let input2=image3.squeeze(0),resized=input2.resizeBilinear([targetH,targetW]);return input2.dispose(),resized}exports.resizeTo=resizeTo;function scaleAndFlipPoses(poses,[height,width],[inputResolutionHeight,inputResolutionWidth]){return poses.map(pose=>scalePose(pose,height/inputResolutionHeight,width/inputResolutionWidth))}exports.scaleAndFlipPoses=scaleAndFlipPoses});var require_modelPoseNet=__commonJS(exports=>{var modelBase=__toModule(require_modelBase()),decodeMultiple=__toModule(require_decodeMultiple()),decodePose=__toModule(require_decodePose()),util=__toModule(require_util2());async function estimateMultiple(input2,res,config2){return new Promise(async resolve=>{let height=input2.shape[1],width=input2.shape[2],allTensorBuffers=await util.toTensorBuffers3D([res.heatmapScores,res.offsets,res.displacementFwd,res.displacementBwd]),scoresBuffer=allTensorBuffers[0],offsetsBuffer=allTensorBuffers[1],displacementsFwdBuffer=allTensorBuffers[2],displacementsBwdBuffer=allTensorBuffers[3],poses=await decodeMultiple.decodeMultiplePoses(scoresBuffer,offsetsBuffer,displacementsFwdBuffer,displacementsBwdBuffer,config2),scaled=util.scaleAndFlipPoses(poses,[height,width],[config2.body.inputSize,config2.body.inputSize]);resolve(scaled)})}async function estimateSingle(input2,res,config2){return new Promise(async resolve=>{let height=input2.shape[1],width=input2.shape[2],poses=[await decodePose.decodeSinglePose(res.heatmapScores,res.offsets,config2)],scaled=util.scaleAndFlipPoses(poses,[height,width],[config2.body.inputSize,config2.body.inputSize]);resolve(scaled)})}var PoseNet=class{constructor(model2){this.baseModel=model2}async estimatePoses(input2,config2){let resized=util.resizeTo(input2,[config2.body.inputSize,config2.body.inputSize]),res=this.baseModel.predict(resized,config2),poses=config2.body.maxDetections<2?await estimateSingle(input2,res,config2):await estimateMultiple(input2,res,config2);return res.heatmapScores.dispose(),res.offsets.dispose(),res.displacementFwd.dispose(),res.displacementBwd.dispose(),resized.dispose(),poses}dispose(){this.baseModel.dispose()}};exports.PoseNet=PoseNet;async function load3(config2){let model2=await loadGraphModel(config2.body.modelPath),mobilenet=new modelBase.BaseModel(model2);return log(`load model: ${config2.body.modelPath.match(/\/(.*)\./)[1]}`),new PoseNet(mobilenet)}exports.load=load3});var require_posenet=__commonJS(exports=>{var modelPoseNet=__toModule(require_modelPoseNet()),keypoints=__toModule(require_keypoints()),util=__toModule(require_util2());exports.load=modelPoseNet.load;exports.PoseNet=modelPoseNet.PoseNet;exports.partChannels=keypoints.partChannels;exports.partIds=keypoints.partIds;exports.partNames=keypoints.partNames;exports.poseChain=keypoints.poseChain;exports.getAdjacentKeyPoints=util.getAdjacentKeyPoints;exports.getBoundingBox=util.getBoundingBox;exports.getBoundingBoxPoints=util.getBoundingBoxPoints;exports.scaleAndFlipPoses=util.scaleAndFlipPoses;exports.scalePose=util.scalePose});var require_handdetector=__commonJS(exports=>{var HandDetector=class{constructor(model2,inputSize,anchorsAnnotated){this.model=model2,this.anchors=anchorsAnnotated.map(anchor=>[anchor.x_center,anchor.y_center]),this.anchorsTensor=tensor2d(this.anchors),this.inputSizeTensor=tensor1d([inputSize,inputSize]),this.doubleInputSizeTensor=tensor1d([inputSize*2,inputSize*2])}normalizeBoxes(boxes){return tidy(()=>{let boxOffsets=slice(boxes,[0,0],[-1,2]),boxSizes=slice(boxes,[0,2],[-1,2]),boxCenterPoints=add2(div(boxOffsets,this.inputSizeTensor),this.anchorsTensor),halfBoxSizes=div(boxSizes,this.doubleInputSizeTensor),startPoints=mul(sub(boxCenterPoints,halfBoxSizes),this.inputSizeTensor),endPoints=mul(add2(boxCenterPoints,halfBoxSizes),this.inputSizeTensor);return concat2d([startPoints,endPoints],1)})}normalizeLandmarks(rawPalmLandmarks,index){return tidy(()=>{let landmarks=add2(div(rawPalmLandmarks.reshape([-1,7,2]),this.inputSizeTensor),this.anchors[index]);return mul(landmarks,this.inputSizeTensor)})}async getBoxes(input2,config2){let batched=this.model.predict(input2),predictions=batched.squeeze();batched.dispose();let scoresT=tidy(()=>sigmoid(slice(predictions,[0,0],[-1,1])).squeeze()),scores=scoresT.dataSync(),rawBoxes=slice(predictions,[0,1],[-1,4]),boxes=this.normalizeBoxes(rawBoxes);rawBoxes.dispose();let filteredT=await image.nonMaxSuppressionAsync(boxes,scores,config2.hand.maxHands,config2.hand.iouThreshold,config2.hand.scoreThreshold),filtered=filteredT.arraySync();scoresT.dispose(),filteredT.dispose();let hands=[];for(let index of filtered)if(scores[index]>=config2.hand.minConfidence){let matchingBox=slice(boxes,[index,0],[1,-1]),rawPalmLandmarks=slice(predictions,[index,5],[1,14]),palmLandmarks=tidy(()=>this.normalizeLandmarks(rawPalmLandmarks,index).reshape([-1,2]));rawPalmLandmarks.dispose(),hands.push({box:matchingBox,palmLandmarks,confidence:scores[index]})}return predictions.dispose(),boxes.dispose(),hands}async estimateHandBounds(input2,config2){let inputHeight=input2.shape[1],inputWidth=input2.shape[2],image3=tidy(()=>input2.resizeBilinear([config2.hand.inputSize,config2.hand.inputSize]).div(127.5).sub(1)),predictions=await this.getBoxes(image3,config2);image3.dispose();let hands=[];if(!predictions||predictions.length===0)return hands;for(let prediction of predictions){let boxes=prediction.box.dataSync(),startPoint=boxes.slice(0,2),endPoint=boxes.slice(2,4),palmLandmarks=prediction.palmLandmarks.arraySync();prediction.box.dispose(),prediction.palmLandmarks.dispose(),hands.push(scaleBoxCoordinates({startPoint,endPoint,palmLandmarks,confidence:prediction.confidence},[inputWidth/config2.hand.inputSize,inputHeight/config2.hand.inputSize]))}return hands}};exports.HandDetector=HandDetector});var require_handpipeline=__commonJS(exports=>{var PALM_BOX_ENLARGE_FACTOR=5,HAND_BOX_ENLARGE_FACTOR=1.65,PALM_LANDMARK_IDS=[0,5,9,13,17,1,2],PALM_LANDMARKS_INDEX_OF_PALM_BASE=0,PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE=2,HandPipeline=class{constructor(handDetector,landmarkDetector,inputSize){this.handDetector=handDetector,this.landmarkDetector=landmarkDetector,this.inputSize=inputSize,this.storedBoxes=[],this.skipped=0,this.detectedHands=0}getBoxForPalmLandmarks(palmLandmarks,rotationMatrix){let rotatedPalmLandmarks=palmLandmarks.map(coord=>rotatePoint([...coord,1],rotationMatrix)),boxAroundPalm=this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);return enlargeBox(squarifyBox(boxAroundPalm),PALM_BOX_ENLARGE_FACTOR)}getBoxForHandLandmarks(landmarks){let boundingBox=this.calculateLandmarksBoundingBox(landmarks),boxAroundHand=enlargeBox(squarifyBox(boundingBox),HAND_BOX_ENLARGE_FACTOR);boxAroundHand.palmLandmarks=[];for(let i=0;i[scaleFactor[0]*(coord[0]-this.inputSize/2),scaleFactor[1]*(coord[1]-this.inputSize/2),scaleFactor[2]*coord[2]]),coordsRotationMatrix=buildRotationMatrix(angle,[0,0]),coordsRotated=coordsScaled.map(coord=>[...rotatePoint(coord,coordsRotationMatrix),coord[2]]),inverseRotationMatrix=invertTransformMatrix(rotationMatrix),boxCenter=[...getBoxCenter(box2),1],originalBoxCenter=[dot4(boxCenter,inverseRotationMatrix[0]),dot4(boxCenter,inverseRotationMatrix[1])];return coordsRotated.map(coord=>[coord[0]+originalBoxCenter[0],coord[1]+originalBoxCenter[1],coord[2]])}async estimateHands(image3,config2){let useFreshBox=!1,boxes;(this.skipped===0||this.skipped>config2.hand.skipFrames||!config2.hand.landmarks||!config2.videoOptimized)&&(boxes=await this.handDetector.estimateHandBounds(image3,config2),this.skipped=0),config2.videoOptimized&&this.skipped++,boxes&&boxes.length>0&&(boxes.length!==this.detectedHands&&this.detectedHands!==config2.hand.maxHands||!config2.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...boxes],this.storedBoxes.length>0&&(useFreshBox=!0));let hands=[];for(let i=0;i=config2.hand.minConfidence){let keypointsReshaped=reshape(keypoints,[-1,3]),rawCoords=keypointsReshaped.arraySync();keypoints.dispose(),keypointsReshaped.dispose();let coords2=this.transformRawCoords(rawCoords,newBox,angle,rotationMatrix),nextBoundingBox=this.getBoxForHandLandmarks(coords2);this.storedBoxes[i]=nextBoundingBox;let result={landmarks:coords2,confidence,box:{topLeft:nextBoundingBox.startPoint,bottomRight:nextBoundingBox.endPoint}};hands.push(result)}else this.storedBoxes[i]=null;keypoints.dispose()}else{let enlarged=enlargeBox(squarifyBox(currentBox),HAND_BOX_ENLARGE_FACTOR),result={confidence:currentBox.confidence,box:{topLeft:enlarged.startPoint,bottomRight:enlarged.endPoint}};hands.push(result)}}return this.storedBoxes=this.storedBoxes.filter(a=>a!==null),this.detectedHands=hands.length,hands}calculateLandmarksBoundingBox(landmarks){let xs=landmarks.map(d=>d[0]),ys=landmarks.map(d=>d[1]),startPoint=[Math.min(...xs),Math.min(...ys)],endPoint=[Math.max(...xs),Math.max(...ys)];return{startPoint,endPoint}}};exports.HandPipeline=HandPipeline});var 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HandPose(handPipeline);return config2.hand.enabled&&log(`load model: ${config2.hand.detector.modelPath.match(/\/(.*)\./)[1]}`),config2.hand.landmarks&&log(`load model: ${config2.hand.skeleton.modelPath.match(/\/(.*)\./)[1]}`),handPose}exports.load=load3});var require_gesture=__commonJS(exports=>{exports.body=res=>{if(!res)return[];let gestures=[];for(let i=0;ia.part==="leftWrist"),rightWrist=res[i].keypoints.find(a=>a.part==="rightWrist"),nose=res[i].keypoints.find(a=>a.part==="nose");nose&&leftWrist&&rightWrist&&leftWrist.position.ya.part==="leftShoulder"),rightShoulder=res[i].keypoints.find(a=>a.part==="rightShoulder");leftShoulder&&rightShoulder&&gestures.push({body:i,gesture:`leaning ${leftShoulder.position.y>rightShoulder.position.y?"left":"right"}`})}return gestures};exports.face=res=>{if(!res)return[];let gestures=[];for(let i=0;i0){let eyeFacing=res[i].mesh[35][2]-res[i].mesh[263][2];Math.abs(eyeFacing)<10?gestures.push({face:i,gesture:"facing camera"}):gestures.push({face:i,gesture:`facing ${eyeFacing<0?"right":"left"}`}),Math.abs(res[i].mesh[374][1]-res[i].mesh[386][1])/Math.abs(res[i].mesh[443][1]-res[i].mesh[450][1])<.2&&gestures.push({face:i,gesture:"blink left eye"}),Math.abs(res[i].mesh[145][1]-res[i].mesh[159][1])/Math.abs(res[i].mesh[223][1]-res[i].mesh[230][1])<.2&&gestures.push({face:i,gesture:"blink right eye"});let mouthOpen=Math.min(100,500*Math.abs(res[i].mesh[13][1]-res[i].mesh[14][1])/Math.abs(res[i].mesh[10][1]-res[i].mesh[152][1]));mouthOpen>10&&gestures.push({face:i,gesture:`mouth ${Math.trunc(mouthOpen)}% open`});let chinDepth=res[i].mesh[152][2];Math.abs(chinDepth)>10&&gestures.push({face:i,gesture:`head ${chinDepth<0?"up":"down"}`})}return gestures};exports.iris=res=>{if(!res)return[];let gestures=[];for(let i=0;i{if(!res)return[];let gestures=[];for(let i=0;i0){let closest=fingers.reduce((best,a)=>best.position[2]best.position[1]{var WebGLProgram=function(gl,vertexSource,fragmentSource){let _collect=function(source,prefix,collection){let r=new RegExp("\\b"+prefix+" \\w+ (\\w+)","ig");source.replace(r,(match,name)=>(collection[name]=0,match))},_compile=function(source,type){let shader=gl.createShader(type);if(gl.shaderSource(shader,source),gl.compileShader(shader),!gl.getShaderParameter(shader,gl.COMPILE_STATUS))throw new Error("Filter: GL compile failed",gl.getShaderInfoLog(shader));return shader};this.uniform={},this.attribute={};let _vsh=_compile(vertexSource,gl.VERTEX_SHADER),_fsh=_compile(fragmentSource,gl.FRAGMENT_SHADER);if(this.id=gl.createProgram(),gl.attachShader(this.id,_vsh),gl.attachShader(this.id,_fsh),gl.linkProgram(this.id),!gl.getProgramParameter(this.id,gl.LINK_STATUS))throw new Error("Filter: GL link failed",gl.getProgramInfoLog(this.id));gl.useProgram(this.id),_collect(vertexSource,"attribute",this.attribute);for(let a in this.attribute)this.attribute[a]=gl.getAttribLocation(this.id,a);_collect(vertexSource,"uniform",this.uniform),_collect(fragmentSource,"uniform",this.uniform);for(let u in this.uniform)this.uniform[u]=gl.getUniformLocation(this.id,u)},WebGLImageFilter=function(params){params||(params={});let _drawCount=0,_sourceTexture=null,_lastInChain=!1,_currentFramebufferIndex=-1,_tempFramebuffers=[null,null],_filterChain=[],_width=-1,_height=-1,_vertexBuffer=null,_currentProgram=null,_canvas=params.canvas||document.createElement("canvas"),_shaderProgramCache={},gl=_canvas.getContext("webgl");if(!gl)throw new Error("Filter: getContext() failed");this.addFilter=function(name){let args=Array.prototype.slice.call(arguments,1),filter=_filter[name];_filterChain.push({func:filter,args})},this.reset=function(){_filterChain=[]},this.apply=function(image3){if(_resize(image3.width,image3.height),_drawCount=0,_sourceTexture||(_sourceTexture=gl.createTexture()),gl.bindTexture(gl.TEXTURE_2D,_sourceTexture),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_WRAP_S,gl.CLAMP_TO_EDGE),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_WRAP_T,gl.CLAMP_TO_EDGE),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_MIN_FILTER,gl.NEAREST),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_MAG_FILTER,gl.NEAREST),gl.texImage2D(gl.TEXTURE_2D,0,gl.RGBA,gl.RGBA,gl.UNSIGNED_BYTE,image3),_filterChain.length===0)return _draw(),_canvas;for(let i=0;i<_filterChain.length;i++){_lastInChain=i===_filterChain.length-1;let f=_filterChain[i];f.func.apply(this,f.args||[])}return _canvas};let _resize=function(width,height){if(!(width===_width&&height===_height)){if(_canvas.width=width,_width=width,_canvas.height=height,_height=height,!_vertexBuffer){let vertices=new Float32Array([-1,-1,0,1,1,-1,1,1,-1,1,0,0,-1,1,0,0,1,-1,1,1,1,1,1,0]);_vertexBuffer=gl.createBuffer(),gl.bindBuffer(gl.ARRAY_BUFFER,_vertexBuffer),gl.bufferData(gl.ARRAY_BUFFER,vertices,gl.STATIC_DRAW),gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL,!0)}gl.viewport(0,0,_width,_height),_tempFramebuffers=[null,null]}},_getTempFramebuffer=function(index){return _tempFramebuffers[index]=_tempFramebuffers[index]||_createFramebufferTexture(_width,_height),_tempFramebuffers[index]},_createFramebufferTexture=function(width,height){let fbo=gl.createFramebuffer();gl.bindFramebuffer(gl.FRAMEBUFFER,fbo);let renderbuffer=gl.createRenderbuffer();gl.bindRenderbuffer(gl.RENDERBUFFER,renderbuffer);let texture=gl.createTexture();return gl.bindTexture(gl.TEXTURE_2D,texture),gl.texImage2D(gl.TEXTURE_2D,0,gl.RGBA,width,height,0,gl.RGBA,gl.UNSIGNED_BYTE,null),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_MAG_FILTER,gl.LINEAR),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_MIN_FILTER,gl.LINEAR),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_WRAP_S,gl.CLAMP_TO_EDGE),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_WRAP_T,gl.CLAMP_TO_EDGE),gl.framebufferTexture2D(gl.FRAMEBUFFER,gl.COLOR_ATTACHMENT0,gl.TEXTURE_2D,texture,0),gl.bindTexture(gl.TEXTURE_2D,null),gl.bindFramebuffer(gl.FRAMEBUFFER,null),{fbo,texture}},_draw=function(flags){var _a,_b;let source=null,target=null,flipY=!1;_drawCount===0?source=_sourceTexture:source=(_a=_getTempFramebuffer(_currentFramebufferIndex))==null?void 0:_a.texture,_drawCount++,_lastInChain&&!(flags&DRAW.INTERMEDIATE)?(target=null,flipY=_drawCount%2==0):(_currentFramebufferIndex=(_currentFramebufferIndex+1)%2,target=(_b=_getTempFramebuffer(_currentFramebufferIndex))==null?void 0:_b.fbo),gl.bindTexture(gl.TEXTURE_2D,source),gl.bindFramebuffer(gl.FRAMEBUFFER,target),gl.uniform1f(_currentProgram.uniform.flipY,flipY?-1:1),gl.drawArrays(gl.TRIANGLES,0,6)},_compileShader=function(fragmentSource){if(_shaderProgramCache[fragmentSource])return _currentProgram=_shaderProgramCache[fragmentSource],gl.useProgram(_currentProgram.id),_currentProgram;_currentProgram=new WebGLProgram(gl,SHADER.VERTEX_IDENTITY,fragmentSource);let floatSize=Float32Array.BYTES_PER_ELEMENT,vertSize=4*floatSize;return gl.enableVertexAttribArray(_currentProgram.attribute.pos),gl.vertexAttribPointer(_currentProgram.attribute.pos,2,gl.FLOAT,!1,vertSize,0*floatSize),gl.enableVertexAttribArray(_currentProgram.attribute.uv),gl.vertexAttribPointer(_currentProgram.attribute.uv,2,gl.FLOAT,!1,vertSize,2*floatSize),_shaderProgramCache[fragmentSource]=_currentProgram,_currentProgram},DRAW={INTERMEDIATE:1},SHADER={};SHADER.VERTEX_IDENTITY=["precision highp float;","attribute vec2 pos;","attribute vec2 uv;","varying vec2 vUv;","uniform float flipY;","void main(void) {","vUv = uv;","gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);","}"].join(` `),SHADER.FRAGMENT_IDENTITY=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","void main(void) {","gl_FragColor = texture2D(texture, vUv);","}"].join(` `);let _filter={};_filter.colorMatrix=function(matrix){let m=new Float32Array(matrix);m[4]/=255,m[9]/=255,m[14]/=255,m[19]/=255;let shader=m[18]===1&&m[3]===0&&m[8]===0&&m[13]===0&&m[15]===0&&m[16]===0&&m[17]===0&&m[19]===0?_filter.colorMatrix.SHADER.WITHOUT_ALPHA:_filter.colorMatrix.SHADER.WITH_ALPHA,program=_compileShader(shader);gl.uniform1fv(program.uniform.m,m),_draw()},_filter.colorMatrix.SHADER={},_filter.colorMatrix.SHADER.WITH_ALPHA=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","uniform float m[20];","void main(void) {","vec4 c = texture2D(texture, vUv);","gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];","gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];","gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];","gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];","}"].join(` `),_filter.colorMatrix.SHADER.WITHOUT_ALPHA=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","uniform float m[20];","void main(void) {","vec4 c = texture2D(texture, vUv);","gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];","gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];","gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];","gl_FragColor.a = c.a;","}"].join(` `),_filter.brightness=function(brightness){let b=(brightness||0)+1;_filter.colorMatrix([b,0,0,0,0,0,b,0,0,0,0,0,b,0,0,0,0,0,1,0])},_filter.saturation=function(amount){let x=(amount||0)*2/3+1,y=(x-1)*-.5;_filter.colorMatrix([x,y,y,0,0,y,x,y,0,0,y,y,x,0,0,0,0,0,1,0])},_filter.desaturate=function(){_filter.saturation(-1)},_filter.contrast=function(amount){let v=(amount||0)+1,o=-128*(v-1);_filter.colorMatrix([v,0,0,0,o,0,v,0,0,o,0,0,v,0,o,0,0,0,1,0])},_filter.negative=function(){_filter.contrast(-2)},_filter.hue=function(rotation){rotation=(rotation||0)/180*Math.PI;let cos4=Math.cos(rotation),sin4=Math.sin(rotation),lumR=.213,lumG=.715,lumB=.072;_filter.colorMatrix([lumR+cos4*(1-lumR)+sin4*-lumR,lumG+cos4*-lumG+sin4*-lumG,lumB+cos4*-lumB+sin4*(1-lumB),0,0,lumR+cos4*-lumR+sin4*.143,lumG+cos4*(1-lumG)+sin4*.14,lumB+cos4*-lumB+sin4*-.283,0,0,lumR+cos4*-lumR+sin4*-(1-lumR),lumG+cos4*-lumG+sin4*lumG,lumB+cos4*(1-lumB)+sin4*lumB,0,0,0,0,0,1,0])},_filter.desaturateLuminance=function(){_filter.colorMatrix([.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,0,0,0,1,0])},_filter.sepia=function(){_filter.colorMatrix([.393,.7689999,.18899999,0,0,.349,.6859999,.16799999,0,0,.272,.5339999,.13099999,0,0,0,0,0,1,0])},_filter.brownie=function(){_filter.colorMatrix([.5997023498159715,.34553243048391263,-.2708298674538042,0,47.43192855600873,-.037703249837783157,.8609577587992641,.15059552388459913,0,-36.96841498319127,.24113635128153335,-.07441037908422492,.44972182064877153,0,-7.562075277591283,0,0,0,1,0])},_filter.vintagePinhole=function(){_filter.colorMatrix([.6279345635605994,.3202183420819367,-.03965408211312453,0,9.651285835294123,.02578397704808868,.6441188644374771,.03259127616149294,0,7.462829176470591,.0466055556782719,-.0851232987247891,.5241648018700465,0,5.159190588235296,0,0,0,1,0])},_filter.kodachrome=function(){_filter.colorMatrix([1.1285582396593525,-.3967382283601348,-.03992559172921793,0,63.72958762196502,-.16404339962244616,1.0835251566291304,-.05498805115633132,0,24.732407896706203,-.16786010706155763,-.5603416277695248,1.6014850761964943,0,35.62982807460946,0,0,0,1,0])},_filter.technicolor=function(){_filter.colorMatrix([1.9125277891456083,-.8545344976951645,-.09155508482755585,0,11.793603434377337,-.3087833385928097,1.7658908555458428,-.10601743074722245,0,-70.35205161461398,-.231103377548616,-.7501899197440212,1.847597816108189,0,30.950940869491138,0,0,0,1,0])},_filter.polaroid=function(){_filter.colorMatrix([1.438,-.062,-.062,0,0,-.122,1.378,-.122,0,0,-.016,-.016,1.483,0,0,0,0,0,1,0])},_filter.shiftToBGR=function(){_filter.colorMatrix([0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0])},_filter.convolution=function(matrix){let m=new Float32Array(matrix),pixelSizeX=1/_width,pixelSizeY=1/_height,program=_compileShader(_filter.convolution.SHADER);gl.uniform1fv(program.uniform.m,m),gl.uniform2f(program.uniform.px,pixelSizeX,pixelSizeY),_draw()},_filter.convolution.SHADER=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","uniform vec2 px;","uniform float m[9];","void main(void) {","vec4 c11 = texture2D(texture, vUv - px);","vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y));","vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y));","vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) );","vec4 c22 = texture2D(texture, vUv);","vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) );","vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) );","vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) );","vec4 c33 = texture2D(texture, vUv + px );","gl_FragColor = ","c11 * m[0] + c12 * m[1] + c22 * m[2] +","c21 * m[3] + c22 * m[4] + c23 * m[5] +","c31 * m[6] + c32 * m[7] + c33 * m[8];","gl_FragColor.a = c22.a;","}"].join(` `),_filter.detectEdges=function(){_filter.convolution.call(this,[0,1,0,1,-4,1,0,1,0])},_filter.sobelX=function(){_filter.convolution.call(this,[-1,0,1,-2,0,2,-1,0,1])},_filter.sobelY=function(){_filter.convolution.call(this,[-1,-2,-1,0,0,0,1,2,1])},_filter.sharpen=function(amount){let a=amount||1;_filter.convolution.call(this,[0,-1*a,0,-1*a,1+4*a,-1*a,0,-1*a,0])},_filter.emboss=function(size){let s=size||1;_filter.convolution.call(this,[-2*s,-1*s,0,-1*s,1,1*s,0,1*s,2*s])},_filter.blur=function(size){let blurSizeX=size/7/_width,blurSizeY=size/7/_height,program=_compileShader(_filter.blur.SHADER);gl.uniform2f(program.uniform.px,0,blurSizeY),_draw(DRAW.INTERMEDIATE),gl.uniform2f(program.uniform.px,blurSizeX,0),_draw()},_filter.blur.SHADER=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","uniform vec2 px;","void main(void) {","gl_FragColor = vec4(0.0);","gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;","gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;","gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;","gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;","gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;","gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;","gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;","gl_FragColor += texture2D(texture, vUv )*0.159576912161;","gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x, 1.0*px.y))*0.147308056121;","gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x, 2.0*px.y))*0.115876621105;","gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x, 3.0*px.y))*0.0776744219933;","gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x, 4.0*px.y))*0.0443683338718;","gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x, 5.0*px.y))*0.0215963866053;","gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794;","gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265;","}"].join(` `),_filter.pixelate=function(size){let blurSizeX=size/_width,blurSizeY=size/_height,program=_compileShader(_filter.pixelate.SHADER);gl.uniform2f(program.uniform.size,blurSizeX,blurSizeY),_draw()},_filter.pixelate.SHADER=["precision highp float;","varying vec2 vUv;","uniform vec2 size;","uniform sampler2D texture;","vec2 pixelate(vec2 coord, vec2 size) {","return floor( coord / size ) * size;","}","void main(void) {","gl_FragColor = vec4(0.0);","vec2 coord = pixelate(vUv, size);","gl_FragColor += texture2D(texture, coord);","}"].join(` -`)};exports.Canvas=WebGLImageFilter});var require_image=__commonJS(exports=>{var fxImage=__toModule(require_imagefx()),inCanvas=null,outCanvas=null;function process3(input2,config3){let tensor2;if(input2 instanceof Tensor)tensor2=clone(input2);else{let originalWidth=input2.naturalWidth||input2.videoWidth||input2.width||input2.shape&&input2.shape[1]>0,originalHeight=input2.naturalHeight||input2.videoHeight||input2.height||input2.shape&&input2.shape[2]>0,targetWidth=originalWidth,targetHeight=originalHeight;if(config3.filter.width>0?targetWidth=config3.filter.width:config3.filter.height>0&&(targetWidth=originalWidth*(config3.filter.height/originalHeight)),config3.filter.height>0?targetHeight=config3.filter.height:config3.filter.width>0&&(targetHeight=originalHeight*(config3.filter.width/originalWidth)),!targetWidth||!targetHeight)return log("Human: invalid input",input2),null;(!inCanvas||inCanvas.width!==targetWidth||inCanvas.height!==targetHeight)&&(inCanvas=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(targetWidth,targetHeight):document.createElement("canvas"),inCanvas.width!==targetWidth&&(inCanvas.width=targetWidth),inCanvas.height!==targetHeight&&(inCanvas.height=targetHeight));let ctx=inCanvas.getContext("2d");if(input2 instanceof ImageData?ctx.putImageData(input2,0,0):ctx.drawImage(input2,0,0,originalWidth,originalHeight,0,0,inCanvas.width,inCanvas.height),config3.filter.enabled){if((!this.fx||!outCanvas||inCanvas.width!==outCanvas.width||inCanvas.height!==outCanvas.height)&&(outCanvas=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(inCanvas.width,inCanvas.height):document.createElement("canvas"),outCanvas.width!==inCanvas.width&&(outCanvas.width=inCanvas.width),outCanvas.height!==inCanvas.height&&(outCanvas.height=inCanvas.height),this.fx=ENV.flags.IS_BROWSER?new fxImage.Canvas({canvas:outCanvas}):null),!this.fx)return inCanvas;this.fx.reset(),this.fx.addFilter("brightness",config3.filter.brightness),config3.filter.contrast!==0&&this.fx.addFilter("contrast",config3.filter.contrast),config3.filter.sharpness!==0&&this.fx.addFilter("sharpen",config3.filter.sharpness),config3.filter.blur!==0&&this.fx.addFilter("blur",config3.filter.blur),config3.filter.saturation!==0&&this.fx.addFilter("saturation",config3.filter.saturation),config3.filter.hue!==0&&this.fx.addFilter("hue",config3.filter.hue),config3.filter.negative&&this.fx.addFilter("negative"),config3.filter.sepia&&this.fx.addFilter("sepia"),config3.filter.vintage&&this.fx.addFilter("brownie"),config3.filter.sepia&&this.fx.addFilter("sepia"),config3.filter.kodachrome&&this.fx.addFilter("kodachrome"),config3.filter.technicolor&&this.fx.addFilter("technicolor"),config3.filter.polaroid&&this.fx.addFilter("polaroid"),config3.filter.pixelate!==0&&this.fx.addFilter("pixelate",config3.filter.pixelate),this.fx.apply(inCanvas)}else outCanvas=inCanvas;let pixels;if(outCanvas.data){let shape=[outCanvas.height,outCanvas.width,3];pixels=tensor3d(outCanvas.data,shape,"int32")}else if(config3.backend==="webgl"||outCanvas instanceof ImageData)pixels=browser_exports.fromPixels(outCanvas);else{let tempCanvas=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(targetWidth,targetHeight):document.createElement("canvas");tempCanvas.width=targetWidth,tempCanvas.height=targetHeight;let tempCtx=tempCanvas.getContext("2d");tempCtx==null||tempCtx.drawImage(outCanvas,0,0);let data2=tempCtx==null?void 0:tempCtx.getImageData(0,0,targetWidth,targetHeight);pixels=browser_exports.fromPixels(data2)}let casted=pixels.toFloat();tensor2=casted.expandDims(0),pixels.dispose(),casted.dispose()}return{tensor:tensor2,canvas:config3.filter.return?outCanvas:null}}exports.process=process3});function log(...msg){let dt=new Date,ts=`${dt.getHours().toString().padStart(2,"0")}:${dt.getMinutes().toString().padStart(2,"0")}:${dt.getSeconds().toString().padStart(2,"0")}.${dt.getMilliseconds().toString().padStart(3,"0")}`;msg&&console.log(ts,"Human:",...msg)}var 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impl}):this.xor4096=impl})(exports,typeof module=="object"&&module,typeof define=="function"&&define)}),require_tychei=__commonJS2((exports,module)=>{(function(global2,module2,define2){function XorGen(seed){var me=this,strseed="";me.next=function(){var b=me.b,c=me.c,d=me.d,a=me.a;return b=b<<25^b>>>7^c,c=c-d|0,d=d<<24^d>>>8^a,a=a-b|0,me.b=b=b<<20^b>>>12^c,me.c=c=c-d|0,me.d=d<<16^c>>>16^a,me.a=a-b|0},me.a=0,me.b=0,me.c=2654435769|0,me.d=1367130551,seed===Math.floor(seed)?(me.a=seed/4294967296|0,me.b=seed|0):strseed+=seed;for(var k=0;k>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state&&(typeof state=="object"&©(state,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.tychei=impl})(exports,typeof module=="object"&&module,typeof define=="function"&&define)}),require_crypto=__commonJS2(()=>{}),require_seedrandom=__commonJS2((exports,module)=>{(function(pool3,math){var global2=this,width=256,chunks=6,digits=52,rngname="random",startdenom=math.pow(width,chunks),significance=math.pow(2,digits),overflow=significance*2,mask=width-1,nodecrypto;function seedrandom5(seed,options,callback){var key=[];options=options==!0?{entropy:!0}:options||{};var shortseed=mixkey(flatten4(options.entropy?[seed,tostring(pool3)]:seed==null?autoseed():seed,3),key),arc4=new ARC4(key),prng=function(){for(var n=arc4.g(chunks),d=startdenom,x=0;n=overflow;)n/=2,d/=2,x>>>=1;return(n+x)/d};return prng.int32=function(){return arc4.g(4)|0},prng.quick=function(){return arc4.g(4)/4294967296},prng.double=prng,mixkey(tostring(arc4.S),pool3),(options.pass||callback||function(prng2,seed2,is_math_call,state){return state&&(state.S&©(state,arc4),prng2.state=function(){return copy(arc4,{})}),is_math_call?(math[rngname]=prng2,seed2):prng2})(prng,shortseed,"global"in options?options.global:this==math,options.state)}math["seed"+rngname]=seedrandom5;function ARC4(key){var t,keylen=key.length,me=this,i=0,j=me.i=me.j=0,s=me.S=[];for(keylen||(key=[keylen++]);i{var alea5=require_alea(),xor128=require_xor128(),xorwow=require_xorwow(),xorshift7=require_xorshift7(),xor4096=require_xor4096(),tychei=require_tychei(),sr=require_seedrandom();sr.alea=alea5,sr.xor128=xor128,sr.xorwow=xorwow,sr.xorshift7=xorshift7,sr.xor4096=xor4096,sr.tychei=tychei,module.exports=sr}),require_alea2=__commonJS2((exports,module)=>{(function(global2,module2,define2){function Alea(seed){var me=this,mash=Mash();me.next=function(){var t=2091639*me.s0+me.c*23283064365386963e-26;return me.s0=me.s1,me.s1=me.s2,me.s2=t-(me.c=t|0)},me.c=1,me.s0=mash(" "),me.s1=mash(" "),me.s2=mash(" "),me.s0-=mash(seed),me.s0<0&&(me.s0+=1),me.s1-=mash(seed),me.s1<0&&(me.s1+=1),me.s2-=mash(seed),me.s2<0&&(me.s2+=1),mash=null}function copy(f,t){return t.c=f.c,t.s0=f.s0,t.s1=f.s1,t.s2=f.s2,t}function impl(seed,opts){var xg=new Alea(seed),state=opts&&opts.state,prng=xg.next;return prng.int32=function(){return xg.next()*4294967296|0},prng.double=function(){return prng()+(prng()*2097152|0)*11102230246251565e-32},prng.quick=prng,state&&(typeof state=="object"&©(state,xg),prng.state=function(){return copy(xg,{})}),prng}function Mash(){var n=4022871197,mash=function(data2){data2=data2.toString();for(var i=0;i>>0,h-=n,h*=n,n=h>>>0,h-=n,n+=h*4294967296}return(n>>>0)*23283064365386963e-26};return mash}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.alea=impl})(exports,typeof module=="object"&&module,typeof define=="function"&&define)}),require_xor1282=__commonJS2((exports,module)=>{(function(global2,module2,define2){function XorGen(seed){var me=this,strseed="";me.x=0,me.y=0,me.z=0,me.w=0,me.next=function(){var t=me.x^me.x<<11;return me.x=me.y,me.y=me.z,me.z=me.w,me.w^=me.w>>>19^t^t>>>8},seed===(seed|0)?me.x=seed:strseed+=seed;for(var k=0;k>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state&&(typeof state=="object"&©(state,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.xor128=impl})(exports,typeof module=="object"&&module,typeof define=="function"&&define)}),require_xorwow2=__commonJS2((exports,module)=>{(function(global2,module2,define2){function XorGen(seed){var me=this,strseed="";me.next=function(){var t=me.x^me.x>>>2;return me.x=me.y,me.y=me.z,me.z=me.w,me.w=me.v,(me.d=me.d+362437|0)+(me.v=me.v^me.v<<4^(t^t<<1))|0},me.x=0,me.y=0,me.z=0,me.w=0,me.v=0,seed===(seed|0)?me.x=seed:strseed+=seed;for(var k=0;k>>4),me.next()}function copy(f,t){return t.x=f.x,t.y=f.y,t.z=f.z,t.w=f.w,t.v=f.v,t.d=f.d,t}function impl(seed,opts){var xg=new XorGen(seed),state=opts&&opts.state,prng=function(){return(xg.next()>>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state&&(typeof state=="object"&©(state,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.xorwow=impl})(exports,typeof module=="object"&&module,typeof define=="function"&&define)}),require_xorshift72=__commonJS2((exports,module)=>{(function(global2,module2,define2){function XorGen(seed){var me=this;me.next=function(){var X=me.x,i=me.i,t,v,w;return t=X[i],t^=t>>>7,v=t^t<<24,t=X[i+1&7],v^=t^t>>>10,t=X[i+3&7],v^=t^t>>>3,t=X[i+4&7],v^=t^t<<7,t=X[i+7&7],t=t^t<<13,v^=t^t<<9,X[i]=v,me.i=i+1&7,v};function init2(me2,seed2){var j,w,X=[];if(seed2===(seed2|0))w=X[0]=seed2;else for(seed2=""+seed2,j=0;j0;--j)me2.next()}init2(me,seed)}function copy(f,t){return t.x=f.x.slice(),t.i=f.i,t}function impl(seed,opts){seed==null&&(seed=+new Date);var xg=new XorGen(seed),state=opts&&opts.state,prng=function(){return(xg.next()>>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state&&(state.x&©(state,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.xorshift7=impl})(exports,typeof module=="object"&&module,typeof define=="function"&&define)}),require_xor40962=__commonJS2((exports,module)=>{(function(global2,module2,define2){function XorGen(seed){var me=this;me.next=function(){var w=me.w,X=me.X,i=me.i,t,v;return me.w=w=w+1640531527|0,v=X[i+34&127],t=X[i=i+1&127],v^=v<<13,t^=t<<17,v^=v>>>15,t^=t>>>12,v=X[i]=v^t,me.i=i,v+(w^w>>>16)|0};function init2(me2,seed2){var t,v,i,j,w,X=[],limit=128;for(seed2===(seed2|0)?(v=seed2,seed2=null):(seed2=seed2+"\0",v=0,limit=Math.max(limit,seed2.length)),i=0,j=-32;j>>15,v^=v<<4,v^=v>>>13,j>=0&&(w=w+1640531527|0,t=X[j&127]^=v+w,i=t==0?i+1:0);for(i>=128&&(X[(seed2&&seed2.length||0)&127]=-1),i=127,j=4*128;j>0;--j)v=X[i+34&127],t=X[i=i+1&127],v^=v<<13,t^=t<<17,v^=v>>>15,t^=t>>>12,X[i]=v^t;me2.w=w,me2.X=X,me2.i=i}init2(me,seed)}function copy(f,t){return t.i=f.i,t.w=f.w,t.X=f.X.slice(),t}function impl(seed,opts){seed==null&&(seed=+new Date);var xg=new XorGen(seed),state=opts&&opts.state,prng=function(){return(xg.next()>>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state&&(state.X&©(state,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.xor4096=impl})(exports,typeof module=="object"&&module,typeof define=="function"&&define)}),require_tychei2=__commonJS2((exports,module)=>{(function(global2,module2,define2){function XorGen(seed){var me=this,strseed="";me.next=function(){var b=me.b,c=me.c,d=me.d,a=me.a;return b=b<<25^b>>>7^c,c=c-d|0,d=d<<24^d>>>8^a,a=a-b|0,me.b=b=b<<20^b>>>12^c,me.c=c=c-d|0,me.d=d<<16^c>>>16^a,me.a=a-b|0},me.a=0,me.b=0,me.c=2654435769|0,me.d=1367130551,seed===Math.floor(seed)?(me.a=seed/4294967296|0,me.b=seed|0):strseed+=seed;for(var k=0;k>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state&&(typeof state=="object"&©(state,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.tychei=impl})(exports,typeof module=="object"&&module,typeof define=="function"&&define)}),require_seedrandom3=__commonJS2((exports,module)=>{(function(pool3,math){var global2=this,width=256,chunks=6,digits=52,rngname="random",startdenom=math.pow(width,chunks),significance=math.pow(2,digits),overflow=significance*2,mask=width-1,nodecrypto;function seedrandom5(seed,options,callback){var key=[];options=options==!0?{entropy:!0}:options||{};var shortseed=mixkey(flatten4(options.entropy?[seed,tostring(pool3)]:seed==null?autoseed():seed,3),key),arc4=new ARC4(key),prng=function(){for(var n=arc4.g(chunks),d=startdenom,x=0;n=overflow;)n/=2,d/=2,x>>>=1;return(n+x)/d};return prng.int32=function(){return arc4.g(4)|0},prng.quick=function(){return arc4.g(4)/4294967296},prng.double=prng,mixkey(tostring(arc4.S),pool3),(options.pass||callback||function(prng2,seed2,is_math_call,state){return state&&(state.S&©(state,arc4),prng2.state=function(){return copy(arc4,{})}),is_math_call?(math[rngname]=prng2,seed2):prng2})(prng,shortseed,"global"in options?options.global:this==math,options.state)}math["seed"+rngname]=seedrandom5;function ARC4(key){var t,keylen=key.length,me=this,i=0,j=me.i=me.j=0,s=me.S=[];for(keylen||(key=[keylen++]);i{var alea5=require_alea2(),xor128=require_xor1282(),xorwow=require_xorwow2(),xorshift7=require_xorshift72(),xor4096=require_xor40962(),tychei=require_tychei2(),sr=require_seedrandom3();sr.alea=alea5,sr.xor128=xor128,sr.xorwow=xorwow,sr.xorshift7=xorshift7,sr.xor4096=xor4096,sr.tychei=tychei,module.exports=sr}),require_path=__commonJS2(()=>{}),require_worker_threads=__commonJS2(()=>{}),require_perf_hooks=__commonJS2(()=>{}),require_tfjs_backend_wasm_threaded_simd=__commonJS2((exports,module)=>{var WasmBackendModuleThreadedSimd=function(){var _scriptDir=typeof document!="undefined"&&document.currentScript?document.currentScript.src:void 0;return typeof __filename!="undefined"&&(_scriptDir=_scriptDir||__filename),function(WasmBackendModuleThreadedSimd2){WasmBackendModuleThreadedSimd2=WasmBackendModuleThreadedSimd2||{};function GROWABLE_HEAP_I8(){return wasmMemory.buffer!=buffer2&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAP8}function GROWABLE_HEAP_U8(){return wasmMemory.buffer!=buffer2&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAPU8}function GROWABLE_HEAP_I32(){return wasmMemory.buffer!=buffer2&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAP32}function GROWABLE_HEAP_U32(){return wasmMemory.buffer!=buffer2&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAPU32}function GROWABLE_HEAP_F64(){return wasmMemory.buffer!=buffer2&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAPF64}var Module=typeof WasmBackendModuleThreadedSimd2!="undefined"?WasmBackendModuleThreadedSimd2:{},_scriptDir2=void 0,moduleOverrides={},key;for(key in Module)Module.hasOwnProperty(key)&&(moduleOverrides[key]=Module[key]);var arguments_=[],thisProgram="./this.program",quit_=function(status,toThrow){throw toThrow},ENVIRONMENT_IS_WEB=!1,ENVIRONMENT_IS_WORKER=!1,ENVIRONMENT_IS_NODE=!1,ENVIRONMENT_IS_SHELL=!1;ENVIRONMENT_IS_WEB=typeof window=="object",ENVIRONMENT_IS_WORKER=typeof importScripts=="function",ENVIRONMENT_IS_NODE=typeof process=="object"&&typeof process.versions=="object"&&typeof process.versions.node=="string",ENVIRONMENT_IS_SHELL=!ENVIRONMENT_IS_WEB&&!ENVIRONMENT_IS_NODE&&!ENVIRONMENT_IS_WORKER;var ENVIRONMENT_IS_PTHREAD=Module.ENVIRONMENT_IS_PTHREAD||!1;ENVIRONMENT_IS_PTHREAD&&(buffer2=Module.buffer,DYNAMIC_BASE=Module.DYNAMIC_BASE,DYNAMICTOP_PTR=Module.DYNAMICTOP_PTR);var scriptDirectory="";function locateFile(path){return Module.locateFile?Module.locateFile(path,scriptDirectory):scriptDirectory+path}var read_,readAsync,readBinary,setWindowTitle,nodeFS,nodePath;if(ENVIRONMENT_IS_NODE){ENVIRONMENT_IS_WORKER?scriptDirectory=require_path().dirname(scriptDirectory)+"/":scriptDirectory=__dirname+"/",read_=function(filename,binary){return nodeFS||(nodeFS=require("fs")),nodePath||(nodePath=require_path()),filename=nodePath.normalize(filename),nodeFS.readFileSync(filename,binary?null:"utf8")},readBinary=function(filename){var ret=read_(filename,!0);return ret.buffer||(ret=new Uint8Array(ret)),assert3(ret.buffer),ret},process.argv.length>1&&(thisProgram=process.argv[1].replace(/\\/g,"/")),arguments_=process.argv.slice(2),process.on("uncaughtException",function(ex){if(!(ex instanceof ExitStatus))throw ex}),process.on("unhandledRejection",abort),quit_=function(status){process.exit(status)},Module.inspect=function(){return"[Emscripten Module object]"};var nodeWorkerThreads;try{nodeWorkerThreads=require_worker_threads()}catch(e){throw console.error('The "worker_threads" module is not supported in this node.js build - perhaps a newer version is needed?'),e}Worker=nodeWorkerThreads.Worker}else ENVIRONMENT_IS_SHELL?(typeof read!="undefined"&&(read_=function(f){return read(f)}),readBinary=function(f){var data2;return typeof readbuffer=="function"?new Uint8Array(readbuffer(f)):(data2=read(f,"binary"),assert3(typeof data2=="object"),data2)},typeof scriptArgs!="undefined"?arguments_=scriptArgs:typeof arguments!="undefined"&&(arguments_=arguments),typeof quit=="function"&&(quit_=function(status){quit(status)}),typeof print!="undefined"&&(typeof console=="undefined"&&(console={}),console.log=print,console.warn=console.error=typeof printErr!="undefined"?printErr:print)):(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&(ENVIRONMENT_IS_WORKER?scriptDirectory=self.location.href:document.currentScript&&(scriptDirectory=document.currentScript.src),_scriptDir2&&(scriptDirectory=_scriptDir2),scriptDirectory.indexOf("blob:")!==0?scriptDirectory=scriptDirectory.substr(0,scriptDirectory.lastIndexOf("/")+1):scriptDirectory="",ENVIRONMENT_IS_NODE?(read_=function(filename,binary){return nodeFS||(nodeFS=require("fs")),nodePath||(nodePath=require_path()),filename=nodePath.normalize(filename),nodeFS.readFileSync(filename,binary?null:"utf8")},readBinary=function(filename){var ret=read_(filename,!0);return ret.buffer||(ret=new Uint8Array(ret)),assert3(ret.buffer),ret}):(read_=function(url){var xhr=new XMLHttpRequest;return xhr.open("GET",url,!1),xhr.send(null),xhr.responseText},ENVIRONMENT_IS_WORKER&&(readBinary=function(url){var xhr=new XMLHttpRequest;return xhr.open("GET",url,!1),xhr.responseType="arraybuffer",xhr.send(null),new Uint8Array(xhr.response)}),readAsync=function(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open("GET",url,!0),xhr.responseType="arraybuffer",xhr.onload=function(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()},xhr.onerror=onerror,xhr.send(null)}),setWindowTitle=function(title){document.title=title});ENVIRONMENT_IS_NODE&&typeof performance=="undefined"&&(performance=require_perf_hooks().performance);var out=Module.print||console.log.bind(console),err=Module.printErr||console.warn.bind(console);for(key in moduleOverrides)moduleOverrides.hasOwnProperty(key)&&(Module[key]=moduleOverrides[key]);moduleOverrides=null,Module.arguments&&(arguments_=Module.arguments),Module.thisProgram&&(thisProgram=Module.thisProgram),Module.quit&&(quit_=Module.quit);var Atomics_load=Atomics.load,Atomics_store=Atomics.store,Atomics_compareExchange=Atomics.compareExchange,wasmBinary;Module.wasmBinary&&(wasmBinary=Module.wasmBinary);var noExitRuntime;Module.noExitRuntime&&(noExitRuntime=Module.noExitRuntime),typeof WebAssembly!="object"&&err("no native wasm support detected");var wasmMemory,wasmTable=new WebAssembly.Table({initial:169,maximum:169+0,element:"anyfunc"}),wasmModule,threadInfoStruct=0,selfThreadId=0,ABORT=!1,EXITSTATUS=0;function assert3(condition,text){condition||abort("Assertion failed: "+text)}function getCFunc(ident){var func2=Module["_"+ident];return assert3(func2,"Cannot call unknown function "+ident+", make sure it is exported"),func2}function ccall(ident,returnType,argTypes,args,opts){var toC={string:function(str){var ret2=0;if(str!=null&&str!==0){var len=(str.length<<2)+1;ret2=stackAlloc(len),stringToUTF8(str,ret2,len)}return ret2},array:function(arr){var ret2=stackAlloc(arr.length);return writeArrayToMemory(arr,ret2),ret2}};function convertReturnValue(ret2){return returnType==="string"?UTF8ToString(ret2):returnType==="boolean"?Boolean(ret2):ret2}var func2=getCFunc(ident),cArgs=[],stack2=0;if(args)for(var i=0;i=endIdx);){var u0=heap[idx++];if(!u0)return str;if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224?u0=(u0&15)<<12|u1<<6|u2:u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63,u0<65536)str+=String.fromCharCode(u0);else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(GROWABLE_HEAP_U8(),ptr,maxBytesToRead):""}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;for(var startIdx=outIdx,endIdx=outIdx+maxBytesToWrite-1,i=0;i=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6,heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12,heap[outIdx++]=128|u>>6&63,heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18,heap[outIdx++]=128|u>>12&63,heap[outIdx++]=128|u>>6&63,heap[outIdx++]=128|u&63}}return heap[outIdx]=0,outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,GROWABLE_HEAP_U8(),outPtr,maxBytesToWrite)}function lengthBytesUTF8(str){for(var len=0,i=0;i=55296&&u<=57343&&(u=65536+((u&1023)<<10)|str.charCodeAt(++i)&1023),u<=127?++len:u<=2047?len+=2:u<=65535?len+=3:len+=4}return len}function writeArrayToMemory(array2,buffer3){GROWABLE_HEAP_I8().set(array2,buffer3)}var WASM_PAGE_SIZE=65536;function alignUp(x,multiple){return x%multiple>0&&(x+=multiple-x%multiple),x}var buffer2,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer2=buf,Module.HEAP8=HEAP8=new Int8Array(buf),Module.HEAP16=HEAP16=new Int16Array(buf),Module.HEAP32=HEAP32=new Int32Array(buf),Module.HEAPU8=HEAPU8=new Uint8Array(buf),Module.HEAPU16=HEAPU16=new Uint16Array(buf),Module.HEAPU32=HEAPU32=new Uint32Array(buf),Module.HEAPF32=HEAPF32=new Float32Array(buf),Module.HEAPF64=HEAPF64=new Float64Array(buf)}var STACK_BASE=5256464,STACKTOP=STACK_BASE,STACK_MAX=13584,DYNAMIC_BASE=5256464,DYNAMICTOP_PTR=12656,INITIAL_INITIAL_MEMORY=Module.INITIAL_MEMORY||16777216;if(ENVIRONMENT_IS_PTHREAD)wasmMemory=Module.wasmMemory,buffer2=Module.buffer;else if(Module.wasmMemory)wasmMemory=Module.wasmMemory;else if(wasmMemory=new WebAssembly.Memory({initial:INITIAL_INITIAL_MEMORY/WASM_PAGE_SIZE,maximum:2147483648/WASM_PAGE_SIZE,shared:!0}),!(wasmMemory.buffer instanceof SharedArrayBuffer))throw err("requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag"),ENVIRONMENT_IS_NODE&&console.log("(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)"),Error("bad memory");wasmMemory&&(buffer2=wasmMemory.buffer),INITIAL_INITIAL_MEMORY=buffer2.byteLength,updateGlobalBufferAndViews(buffer2),ENVIRONMENT_IS_PTHREAD||(GROWABLE_HEAP_I32()[DYNAMICTOP_PTR>>2]=DYNAMIC_BASE);function callRuntimeCallbacks(callbacks2){for(;callbacks2.length>0;){var callback=callbacks2.shift();if(typeof callback=="function"){callback(Module);continue}var func2=callback.func;typeof func2=="number"?callback.arg===void 0?Module.dynCall_v(func2):Module.dynCall_vi(func2,callback.arg):func2(callback.arg===void 0?null:callback.arg)}}var __ATPRERUN__=[],__ATINIT__=[],__ATMAIN__=[],__ATEXIT__=[],__ATPOSTRUN__=[],runtimeInitialized=!1;ENVIRONMENT_IS_PTHREAD&&(runtimeInitialized=!0);function preRun(){if(!ENVIRONMENT_IS_PTHREAD){if(Module.preRun)for(typeof Module.preRun=="function"&&(Module.preRun=[Module.preRun]);Module.preRun.length;)addOnPreRun(Module.preRun.shift());callRuntimeCallbacks(__ATPRERUN__)}}function initRuntime(){runtimeInitialized=!0,callRuntimeCallbacks(__ATINIT__)}function preMain(){ENVIRONMENT_IS_PTHREAD||callRuntimeCallbacks(__ATMAIN__)}function postRun(){if(!ENVIRONMENT_IS_PTHREAD){if(Module.postRun)for(typeof Module.postRun=="function"&&(Module.postRun=[Module.postRun]);Module.postRun.length;)addOnPostRun(Module.postRun.shift());callRuntimeCallbacks(__ATPOSTRUN__)}}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var Math_ceil=Math.ceil,Math_floor=Math.floor,runDependencies=0,runDependencyWatcher=null,dependenciesFulfilled=null;function addRunDependency(id){assert3(!ENVIRONMENT_IS_PTHREAD,"addRunDependency cannot be used in a pthread worker"),runDependencies++,Module.monitorRunDependencies&&Module.monitorRunDependencies(runDependencies)}function removeRunDependency(id){if(runDependencies--,Module.monitorRunDependencies&&Module.monitorRunDependencies(runDependencies),runDependencies==0&&(runDependencyWatcher!==null&&(clearInterval(runDependencyWatcher),runDependencyWatcher=null),dependenciesFulfilled)){var callback=dependenciesFulfilled;dependenciesFulfilled=null,callback()}}Module.preloadedImages={},Module.preloadedAudios={};function abort(what){throw Module.onAbort&&Module.onAbort(what),ENVIRONMENT_IS_PTHREAD&&console.error("Pthread aborting at "+new Error().stack),what+="",out(what),err(what),ABORT=!0,EXITSTATUS=1,what="abort("+what+"). Build with -s ASSERTIONS=1 for more info.",new WebAssembly.RuntimeError(what)}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix="data:application/octet-stream;base64,";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix="file://";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile="tfjs-backend-wasm-threaded-simd.wasm";isDataURI(wasmBinaryFile)||(wasmBinaryFile=locateFile(wasmBinaryFile));function getBinary(){try{if(wasmBinary)return new Uint8Array(wasmBinary);if(readBinary)return readBinary(wasmBinaryFile);throw"both async and sync fetching of the wasm failed"}catch(err2){abort(err2)}}function getBinaryPromise(){return!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&typeof fetch=="function"&&!isFileURI(wasmBinaryFile)?fetch(wasmBinaryFile,{credentials:"same-origin"}).then(function(response){if(!response.ok)throw"failed to load wasm binary file at '"+wasmBinaryFile+"'";return response.arrayBuffer()}).catch(function(){return getBinary()}):new Promise(function(resolve,reject){resolve(getBinary())})}function createWasm(){var info={a:asmLibraryArg};function receiveInstance(instance,module2){var exports3=instance.exports;if(Module.asm=exports3,wasmModule=module2,!ENVIRONMENT_IS_PTHREAD){var numWorkersToLoad=PThread.unusedWorkers.length;PThread.unusedWorkers.forEach(function(w){PThread.loadWasmModuleToWorker(w,function(){--numWorkersToLoad||removeRunDependency("wasm-instantiate")})})}}ENVIRONMENT_IS_PTHREAD||addRunDependency("wasm-instantiate");function receiveInstantiatedSource(output){receiveInstance(output.instance,output.module)}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err("failed to asynchronously prepare wasm: "+reason),abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming=="function"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch=="function")fetch(wasmBinaryFile,{credentials:"same-origin"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err("wasm streaming compile failed: "+reason),err("falling back to ArrayBuffer instantiation"),instantiateArrayBuffer(receiveInstantiatedSource)})});else return instantiateArrayBuffer(receiveInstantiatedSource)}if(Module.instantiateWasm)try{var exports2=Module.instantiateWasm(info,receiveInstance);return exports2}catch(e){return err("Module.instantiateWasm callback failed with error: "+e),!1}return instantiateAsync(),{}}var ASM_CONSTS={};function initPthreadsJS(){PThread.initRuntime()}ENVIRONMENT_IS_PTHREAD||__ATINIT__.push({func:function(){___wasm_call_ctors()}});var __pthread_ptr=0,__pthread_is_main_runtime_thread=0,__pthread_is_main_browser_thread=0;function __register_pthread_ptr(pthreadPtr,isMainBrowserThread,isMainRuntimeThread){pthreadPtr=pthreadPtr|0,isMainBrowserThread=isMainBrowserThread|0,isMainRuntimeThread=isMainRuntimeThread|0,__pthread_ptr=pthreadPtr,__pthread_is_main_browser_thread=isMainBrowserThread,__pthread_is_main_runtime_thread=isMainRuntimeThread}Module.__register_pthread_ptr=__register_pthread_ptr;var ERRNO_CODES={EPERM:63,ENOENT:44,ESRCH:71,EINTR:27,EIO:29,ENXIO:60,E2BIG:1,ENOEXEC:45,EBADF:8,ECHILD:12,EAGAIN:6,EWOULDBLOCK:6,ENOMEM:48,EACCES:2,EFAULT:21,ENOTBLK:105,EBUSY:10,EEXIST:20,EXDEV:75,ENODEV:43,ENOTDIR:54,EISDIR:31,EINVAL:28,ENFILE:41,EMFILE:33,ENOTTY:59,ETXTBSY:74,EFBIG:22,ENOSPC:51,ESPIPE:70,EROFS:69,EMLINK:34,EPIPE:64,EDOM:18,ERANGE:68,ENOMSG:49,EIDRM:24,ECHRNG:106,EL2NSYNC:156,EL3HLT:107,EL3RST:108,ELNRNG:109,EUNATCH:110,ENOCSI:111,EL2HLT:112,EDEADLK:16,ENOLCK:46,EBADE:113,EBADR:114,EXFULL:115,ENOANO:104,EBADRQC:103,EBADSLT:102,EDEADLOCK:16,EBFONT:101,ENOSTR:100,ENODATA:116,ETIME:117,ENOSR:118,ENONET:119,ENOPKG:120,EREMOTE:121,ENOLINK:47,EADV:122,ESRMNT:123,ECOMM:124,EPROTO:65,EMULTIHOP:36,EDOTDOT:125,EBADMSG:9,ENOTUNIQ:126,EBADFD:127,EREMCHG:128,ELIBACC:129,ELIBBAD:130,ELIBSCN:131,ELIBMAX:132,ELIBEXEC:133,ENOSYS:52,ENOTEMPTY:55,ENAMETOOLONG:37,ELOOP:32,EOPNOTSUPP:138,EPFNOSUPPORT:139,ECONNRESET:15,ENOBUFS:42,EAFNOSUPPORT:5,EPROTOTYPE:67,ENOTSOCK:57,ENOPROTOOPT:50,ESHUTDOWN:140,ECONNREFUSED:14,EADDRINUSE:3,ECONNABORTED:13,ENETUNREACH:40,ENETDOWN:38,ETIMEDOUT:73,EHOSTDOWN:142,EHOSTUNREACH:23,EINPROGRESS:26,EALREADY:7,EDESTADDRREQ:17,EMSGSIZE:35,EPROTONOSUPPORT:66,ESOCKTNOSUPPORT:137,EADDRNOTAVAIL:4,ENETRESET:39,EISCONN:30,ENOTCONN:53,ETOOMANYREFS:141,EUSERS:136,EDQUOT:19,ESTALE:72,ENOTSUP:138,ENOMEDIUM:148,EILSEQ:25,EOVERFLOW:61,ECANCELED:11,ENOTRECOVERABLE:56,EOWNERDEAD:62,ESTRPIPE:135},__main_thread_futex_wait_address=13568;function _emscripten_futex_wake(addr,count2){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&!0||count2<0)return-28;if(count2==0)return 0;count2>=2147483647&&(count2=Infinity);var mainThreadWaitAddress=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2),mainThreadWoken=0;if(mainThreadWaitAddress==addr){var loadedAddr=Atomics.compareExchange(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,mainThreadWaitAddress,0);if(loadedAddr==mainThreadWaitAddress&&(--count2,mainThreadWoken=1,count2<=0))return 1}var ret=Atomics.notify(GROWABLE_HEAP_I32(),addr>>2,count2);if(ret>=0)return ret+mainThreadWoken;throw"Atomics.notify returned an unexpected value "+ret}Module._emscripten_futex_wake=_emscripten_futex_wake;function __kill_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw"Internal Error! _kill_thread() can only ever be called from main application thread!";if(!pthread_ptr)throw"Internal Error! Null pthread_ptr in _kill_thread!";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];pthread.worker.terminate(),PThread.freeThreadData(pthread),PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(pthread.worker),1),pthread.worker.pthread=void 0}function __cancel_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw"Internal Error! _cancel_thread() can only ever be called from main application thread!";if(!pthread_ptr)throw"Internal Error! Null pthread_ptr in _cancel_thread!";var pthread=PThread.pthreads[pthread_ptr];pthread.worker.postMessage({cmd:"cancel"})}function __cleanup_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw"Internal Error! _cleanup_thread() can only ever be called from main application thread!";if(!pthread_ptr)throw"Internal Error! Null pthread_ptr in _cleanup_thread!";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];if(pthread){var worker=pthread.worker;PThread.returnWorkerToPool(worker)}}var PThread={MAIN_THREAD_ID:1,mainThreadInfo:{schedPolicy:0,schedPrio:0},unusedWorkers:[],runningWorkers:[],initRuntime:function(){__register_pthread_ptr(PThread.mainThreadBlock,!ENVIRONMENT_IS_WORKER,1),_emscripten_register_main_browser_thread_id(PThread.mainThreadBlock)},initMainThreadBlock:function(){for(var pthreadPoolSize=8,i=0;i>2]=PThread.mainThreadBlock;var headPtr=PThread.mainThreadBlock+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;for(var tlsMemory=13056,i=0;i<128;++i)GROWABLE_HEAP_U32()[tlsMemory/4+i]=0;Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+104>>2,tlsMemory),Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+40>>2,PThread.mainThreadBlock),Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+44>>2,42)},initWorker:function(){},pthreads:{},exitHandlers:null,setThreadStatus:function(){},runExitHandlers:function(){if(PThread.exitHandlers!==null){for(;PThread.exitHandlers.length>0;)PThread.exitHandlers.pop()();PThread.exitHandlers=null}ENVIRONMENT_IS_PTHREAD&&threadInfoStruct&&___pthread_tsd_run_dtors()},threadExit:function(exitCode){var tb=_pthread_self();tb&&(Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,exitCode),Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1),Atomics.store(GROWABLE_HEAP_U32(),tb+60>>2,1),Atomics.store(GROWABLE_HEAP_U32(),tb+64>>2,0),PThread.runExitHandlers(),_emscripten_futex_wake(tb+0,2147483647),__register_pthread_ptr(0,0,0),threadInfoStruct=0,ENVIRONMENT_IS_PTHREAD&&postMessage({cmd:"exit"}))},threadCancel:function(){PThread.runExitHandlers(),Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+4>>2,-1),Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+0>>2,1),_emscripten_futex_wake(threadInfoStruct+0,2147483647),threadInfoStruct=selfThreadId=0,__register_pthread_ptr(0,0,0),postMessage({cmd:"cancelDone"})},terminateAllThreads:function(){for(var t in PThread.pthreads){var pthread=PThread.pthreads[t];pthread&&pthread.worker&&PThread.returnWorkerToPool(pthread.worker)}PThread.pthreads={};for(var i=0;i>2];GROWABLE_HEAP_I32()[pthread.threadInfoStruct+104>>2]=0,_free(tlsMemory),_free(pthread.threadInfoStruct)}pthread.threadInfoStruct=0,pthread.allocatedOwnStack&&pthread.stackBase&&_free(pthread.stackBase),pthread.stackBase=0,pthread.worker&&(pthread.worker.pthread=null)}},returnWorkerToPool:function(worker){delete PThread.pthreads[worker.pthread.thread],PThread.unusedWorkers.push(worker),PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker),1),PThread.freeThreadData(worker.pthread),worker.pthread=void 0},receiveObjectTransfer:function(data2){},loadWasmModuleToWorker:function(worker,onFinishedLoading){worker.onmessage=function(e){var d=e.data,cmd=d.cmd;if(worker.pthread&&(PThread.currentProxiedOperationCallerThread=worker.pthread.threadInfoStruct),d.targetThread&&d.targetThread!=_pthread_self()){var thread=PThread.pthreads[d.targetThread];thread?thread.worker.postMessage(e.data,d.transferList):console.error('Internal error! Worker sent a message "'+cmd+'" to target pthread '+d.targetThread+", but that thread no longer exists!"),PThread.currentProxiedOperationCallerThread=void 0;return}if(cmd==="processQueuedMainThreadWork")_emscripten_main_thread_process_queued_calls();else if(cmd==="spawnThread")__spawn_thread(e.data);else if(cmd==="cleanupThread")__cleanup_thread(d.thread);else if(cmd==="killThread")__kill_thread(d.thread);else if(cmd==="cancelThread")__cancel_thread(d.thread);else if(cmd==="loaded")worker.loaded=!0,onFinishedLoading&&onFinishedLoading(worker),worker.runPthread&&(worker.runPthread(),delete worker.runPthread);else if(cmd==="print")out("Thread "+d.threadId+": "+d.text);else if(cmd==="printErr")err("Thread "+d.threadId+": "+d.text);else if(cmd==="alert")alert("Thread "+d.threadId+": "+d.text);else if(cmd==="exit"){var detached=worker.pthread&&Atomics.load(GROWABLE_HEAP_U32(),worker.pthread.thread+68>>2);detached&&PThread.returnWorkerToPool(worker)}else cmd==="cancelDone"?PThread.returnWorkerToPool(worker):cmd==="objectTransfer"?PThread.receiveObjectTransfer(e.data):e.data.target==="setimmediate"?worker.postMessage(e.data):err("worker sent an unknown command "+cmd);PThread.currentProxiedOperationCallerThread=void 0},worker.onerror=function(e){err("pthread sent an error! "+e.filename+":"+e.lineno+": "+e.message)},ENVIRONMENT_IS_NODE&&(worker.on("message",function(data2){worker.onmessage({data:data2})}),worker.on("error",function(data2){worker.onerror(data2)}),worker.on("exit",function(data2){console.log("worker exited - TODO: update the worker queue?")})),worker.postMessage({cmd:"load",urlOrBlob:Module.mainScriptUrlOrBlob||_scriptDir2,wasmMemory,wasmModule,DYNAMIC_BASE,DYNAMICTOP_PTR})},allocateUnusedWorker:function(){var pthreadMainJs=locateFile("tfjs-backend-wasm-threaded-simd.worker.js");PThread.unusedWorkers.push(new Worker(pthreadMainJs))},getNewWorker:function(){return PThread.unusedWorkers.length==0&&(PThread.allocateUnusedWorker(),PThread.loadWasmModuleToWorker(PThread.unusedWorkers[0])),PThread.unusedWorkers.length>0?PThread.unusedWorkers.pop():null},busySpinWait:function(msecs){for(var t=performance.now()+msecs;performance.now()>2]=value,value}function _atexit(func2,arg){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(1,1,func2,arg);__ATEXIT__.unshift({func:func2,arg})}function __emscripten_notify_thread_queue(targetThreadId,mainThreadId){if(targetThreadId==mainThreadId)postMessage({cmd:"processQueuedMainThreadWork"});else if(ENVIRONMENT_IS_PTHREAD)postMessage({targetThread:targetThreadId,cmd:"processThreadQueue"});else{var pthread=PThread.pthreads[targetThreadId],worker=pthread&&pthread.worker;if(!worker)return;worker.postMessage({cmd:"processThreadQueue"})}return 1}function _abort(){abort()}function _emscripten_conditional_set_current_thread_status(expectedStatus,newStatus){expectedStatus=expectedStatus|0,newStatus=newStatus|0}function _emscripten_futex_wait(addr,val,timeout){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&!0)return-28;if(ENVIRONMENT_IS_WORKER){var ret=Atomics.wait(GROWABLE_HEAP_I32(),addr>>2,val,timeout);if(ret==="timed-out")return-73;if(ret==="not-equal")return-6;if(ret==="ok")return 0;throw"Atomics.wait returned an unexpected value "+ret}else{var loadedVal=Atomics.load(GROWABLE_HEAP_I32(),addr>>2);if(val!=loadedVal)return-6;var tNow=performance.now(),tEnd=tNow+timeout;Atomics.store(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,addr);for(var ourWaitAddress=addr;addr==ourWaitAddress;){if(tNow=performance.now(),tNow>tEnd)return-73;_emscripten_main_thread_process_queued_calls(),addr=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2)}return 0}}function _emscripten_is_main_browser_thread(){return __pthread_is_main_browser_thread|0}function _emscripten_is_main_runtime_thread(){return __pthread_is_main_runtime_thread|0}function _emscripten_memcpy_big(dest,src,num){GROWABLE_HEAP_U8().copyWithin(dest,src,src+num)}function _emscripten_num_logical_cores(){return navigator.hardwareConcurrency}function _emscripten_proxy_to_main_thread_js(index,sync){for(var numCallArgs=arguments.length-2,stack2=stackSave(),args=stackAlloc(numCallArgs*8),b=args>>3,i=0;i>3]),buf+=8):(buf=buf+3&~3,args.push(GROWABLE_HEAP_I32()[buf>>2]),buf+=4);return args}function _emscripten_receive_on_main_thread_js(index,numCallArgs,args){_emscripten_receive_on_main_thread_js_callArgs.length=numCallArgs;for(var b=args>>3,i=0;i>>16),updateGlobalBufferAndViews(wasmMemory.buffer),1}catch(e){}}function _emscripten_resize_heap(requestedSize){requestedSize=requestedSize>>>0;var oldSize=_emscripten_get_heap_size();if(requestedSize<=oldSize)return!1;var PAGE_MULTIPLE=65536,maxHeapSize=2147483648;if(requestedSize>maxHeapSize)return!1;for(var minHeapSize=16777216,cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(minHeapSize,requestedSize,overGrownHeapSize),PAGE_MULTIPLE)),replacement=emscripten_realloc_buffer(newSize);if(replacement)return!0}return!1}var JSEvents={keyEvent:0,mouseEvent:0,wheelEvent:0,uiEvent:0,focusEvent:0,deviceOrientationEvent:0,deviceMotionEvent:0,fullscreenChangeEvent:0,pointerlockChangeEvent:0,visibilityChangeEvent:0,touchEvent:0,previousFullscreenElement:null,previousScreenX:null,previousScreenY:null,removeEventListenersRegistered:!1,removeAllEventListeners:function(){for(var i=JSEvents.eventHandlers.length-1;i>=0;--i)JSEvents._removeHandler(i);JSEvents.eventHandlers=[],JSEvents.deferredCalls=[]},registerRemoveEventListeners:function(){JSEvents.removeEventListenersRegistered||(__ATEXIT__.push(JSEvents.removeAllEventListeners),JSEvents.removeEventListenersRegistered=!0)},deferredCalls:[],deferCall:function(targetFunction,precedence,argsList){function arraysHaveEqualContent(arrA,arrB){if(arrA.length!=arrB.length)return!1;for(var i2 in arrA)if(arrA[i2]!=arrB[i2])return!1;return!0}for(var i in JSEvents.deferredCalls){var call=JSEvents.deferredCalls[i];if(call.targetFunction==targetFunction&&arraysHaveEqualContent(call.argsList,argsList))return}JSEvents.deferredCalls.push({targetFunction,precedence,argsList}),JSEvents.deferredCalls.sort(function(x,y){return x.precedence>2]=eventTypeId,GROWABLE_HEAP_I32()[varargs+4>>2]=eventData,GROWABLE_HEAP_I32()[varargs+8>>2]=userData,_emscripten_async_queue_on_thread_(targetThread,637534208,eventHandlerFunc,eventData,varargs),stackRestore(stackTop)},getTargetThreadForEventCallback:function(targetThread){switch(targetThread){case 1:return 0;case 2:return PThread.currentProxiedOperationCallerThread;default:return targetThread}},getNodeNameForTarget:function(target){return target?target==window?"#window":target==screen?"#screen":target&&target.nodeName?target.nodeName:"":""},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function stringToNewUTF8(jsString){var length=lengthBytesUTF8(jsString)+1,cString=_malloc(length);return stringToUTF8(jsString,cString,length),cString}function _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height){var stackTop=stackSave(),varargs=stackAlloc(12),targetCanvasPtr=0;targetCanvas&&(targetCanvasPtr=stringToNewUTF8(targetCanvas)),GROWABLE_HEAP_I32()[varargs>>2]=targetCanvasPtr,GROWABLE_HEAP_I32()[varargs+4>>2]=width,GROWABLE_HEAP_I32()[varargs+8>>2]=height,_emscripten_async_queue_on_thread_(targetThread,657457152,0,targetCanvasPtr,varargs),stackRestore(stackTop)}function _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,targetCanvas,width,height){targetCanvas=targetCanvas?UTF8ToString(targetCanvas):"",_emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height)}function __maybeCStringToJsString(cString){return cString>2?UTF8ToString(cString):cString}var specialHTMLTargets=[0,typeof document!="undefined"?document:0,typeof window!="undefined"?window:0];function __findEventTarget(target){target=__maybeCStringToJsString(target);var domElement=specialHTMLTargets[target]||(typeof document!="undefined"?document.querySelector(target):void 0);return domElement}function __findCanvasEventTarget(target){return __findEventTarget(target)}function _emscripten_set_canvas_element_size_calling_thread(target,width,height){var canvas=__findCanvasEventTarget(target);if(!canvas)return-4;if(canvas.canvasSharedPtr&&(GROWABLE_HEAP_I32()[canvas.canvasSharedPtr>>2]=width,GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+4>>2]=height),canvas.offscreenCanvas||!canvas.controlTransferredOffscreen){canvas.offscreenCanvas&&(canvas=canvas.offscreenCanvas);var autoResizeViewport=!1;if(canvas.GLctxObject&&canvas.GLctxObject.GLctx){var prevViewport=canvas.GLctxObject.GLctx.getParameter(2978);autoResizeViewport=prevViewport[0]===0&&prevViewport[1]===0&&prevViewport[2]===canvas.width&&prevViewport[3]===canvas.height}canvas.width=width,canvas.height=height,autoResizeViewport&&canvas.GLctxObject.GLctx.viewport(0,0,width,height)}else if(canvas.canvasSharedPtr){var targetThread=GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+8>>2];return _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,target,width,height),1}else return-4;return 0}function _emscripten_set_canvas_element_size_main_thread(target,width,height){return ENVIRONMENT_IS_PTHREAD?_emscripten_proxy_to_main_thread_js(2,1,target,width,height):_emscripten_set_canvas_element_size_calling_thread(target,width,height)}function _emscripten_set_canvas_element_size(target,width,height){var canvas=__findCanvasEventTarget(target);return canvas?_emscripten_set_canvas_element_size_calling_thread(target,width,height):_emscripten_set_canvas_element_size_main_thread(target,width,height)}function _emscripten_set_current_thread_status(newStatus){newStatus=newStatus|0}function _emscripten_set_thread_name(threadId,name){threadId=threadId|0,name=name|0}function __webgl_enable_ANGLE_instanced_arrays(ctx){var ext=ctx.getExtension("ANGLE_instanced_arrays");if(ext)return ctx.vertexAttribDivisor=function(index,divisor){ext.vertexAttribDivisorANGLE(index,divisor)},ctx.drawArraysInstanced=function(mode,first,count2,primcount){ext.drawArraysInstancedANGLE(mode,first,count2,primcount)},ctx.drawElementsInstanced=function(mode,count2,type,indices,primcount){ext.drawElementsInstancedANGLE(mode,count2,type,indices,primcount)},1}function __webgl_enable_OES_vertex_array_object(ctx){var ext=ctx.getExtension("OES_vertex_array_object");if(ext)return ctx.createVertexArray=function(){return ext.createVertexArrayOES()},ctx.deleteVertexArray=function(vao){ext.deleteVertexArrayOES(vao)},ctx.bindVertexArray=function(vao){ext.bindVertexArrayOES(vao)},ctx.isVertexArray=function(vao){return ext.isVertexArrayOES(vao)},1}function __webgl_enable_WEBGL_draw_buffers(ctx){var ext=ctx.getExtension("WEBGL_draw_buffers");if(ext)return ctx.drawBuffers=function(n,bufs){ext.drawBuffersWEBGL(n,bufs)},1}var GL={counter:1,lastError:0,buffers:[],mappedBuffers:{},programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},currentContext:null,offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,init:function(){for(var miniTempFloatBuffer=new Float32Array(GL.MINI_TEMP_BUFFER_SIZE),i=0;i>2]:-1;source+=UTF8ToString(GROWABLE_HEAP_I32()[string+i*4>>2],len<0?void 0:len)}return source},createContext:function(canvas,webGLContextAttributes){var ctx=canvas.getContext("webgl",webGLContextAttributes);if(!ctx)return 0;var handle=GL.registerContext(ctx,webGLContextAttributes);return handle},registerContext:function(ctx,webGLContextAttributes){var handle=_malloc(8);GROWABLE_HEAP_I32()[handle+4>>2]=_pthread_self();var context={handle,attributes:webGLContextAttributes,version:webGLContextAttributes.majorVersion,GLctx:ctx};return ctx.canvas&&(ctx.canvas.GLctxObject=context),GL.contexts[handle]=context,(typeof webGLContextAttributes.enableExtensionsByDefault=="undefined"||webGLContextAttributes.enableExtensionsByDefault)&&GL.initExtensions(context),handle},makeContextCurrent:function(contextHandle){return GL.currentContext=GL.contexts[contextHandle],Module.ctx=GLctx=GL.currentContext&&GL.currentContext.GLctx,!(contextHandle&&!GLctx)},getContext:function(contextHandle){return GL.contexts[contextHandle]},deleteContext:function(contextHandle){GL.currentContext===GL.contexts[contextHandle]&&(GL.currentContext=null),typeof JSEvents=="object"&&JSEvents.removeAllHandlersOnTarget(GL.contexts[contextHandle].GLctx.canvas),GL.contexts[contextHandle]&&GL.contexts[contextHandle].GLctx.canvas&&(GL.contexts[contextHandle].GLctx.canvas.GLctxObject=void 0),_free(GL.contexts[contextHandle].handle),GL.contexts[contextHandle]=null},initExtensions:function(context){if(context||(context=GL.currentContext),!context.initExtensionsDone){context.initExtensionsDone=!0;var GLctx2=context.GLctx;__webgl_enable_ANGLE_instanced_arrays(GLctx2),__webgl_enable_OES_vertex_array_object(GLctx2),__webgl_enable_WEBGL_draw_buffers(GLctx2),GLctx2.disjointTimerQueryExt=GLctx2.getExtension("EXT_disjoint_timer_query");var automaticallyEnabledExtensions=["OES_texture_float","OES_texture_half_float","OES_standard_derivatives","OES_vertex_array_object","WEBGL_compressed_texture_s3tc","WEBGL_depth_texture","OES_element_index_uint","EXT_texture_filter_anisotropic","EXT_frag_depth","WEBGL_draw_buffers","ANGLE_instanced_arrays","OES_texture_float_linear","OES_texture_half_float_linear","EXT_blend_minmax","EXT_shader_texture_lod","EXT_texture_norm16","WEBGL_compressed_texture_pvrtc","EXT_color_buffer_half_float","WEBGL_color_buffer_float","EXT_sRGB","WEBGL_compressed_texture_etc1","EXT_disjoint_timer_query","WEBGL_compressed_texture_etc","WEBGL_compressed_texture_astc","EXT_color_buffer_float","WEBGL_compressed_texture_s3tc_srgb","EXT_disjoint_timer_query_webgl2","WEBKIT_WEBGL_compressed_texture_pvrtc"],exts=GLctx2.getSupportedExtensions()||[];exts.forEach(function(ext){automaticallyEnabledExtensions.indexOf(ext)!=-1&&GLctx2.getExtension(ext)})}},populateUniformTable:function(program){for(var p2=GL.programs[program],ptable=GL.programInfos[program]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1},utable=ptable.uniforms,numUniforms=GLctx.getProgramParameter(p2,35718),i=0;i>2;contextAttributes.alpha=!!GROWABLE_HEAP_I32()[a+(0>>2)],contextAttributes.depth=!!GROWABLE_HEAP_I32()[a+(4>>2)],contextAttributes.stencil=!!GROWABLE_HEAP_I32()[a+(8>>2)],contextAttributes.antialias=!!GROWABLE_HEAP_I32()[a+(12>>2)],contextAttributes.premultipliedAlpha=!!GROWABLE_HEAP_I32()[a+(16>>2)],contextAttributes.preserveDrawingBuffer=!!GROWABLE_HEAP_I32()[a+(20>>2)];var powerPreference=GROWABLE_HEAP_I32()[a+(24>>2)];contextAttributes.powerPreference=__emscripten_webgl_power_preferences[powerPreference],contextAttributes.failIfMajorPerformanceCaveat=!!GROWABLE_HEAP_I32()[a+(28>>2)],contextAttributes.majorVersion=GROWABLE_HEAP_I32()[a+(32>>2)],contextAttributes.minorVersion=GROWABLE_HEAP_I32()[a+(36>>2)],contextAttributes.enableExtensionsByDefault=GROWABLE_HEAP_I32()[a+(40>>2)],contextAttributes.explicitSwapControl=GROWABLE_HEAP_I32()[a+(44>>2)],contextAttributes.proxyContextToMainThread=GROWABLE_HEAP_I32()[a+(48>>2)],contextAttributes.renderViaOffscreenBackBuffer=GROWABLE_HEAP_I32()[a+(52>>2)];var canvas=__findCanvasEventTarget(target);if(!canvas)return-4;if(contextAttributes.explicitSwapControl)return-1;var contextHandle=GL.createContext(canvas,contextAttributes);return contextHandle}function _emscripten_webgl_create_context(a0,a12){return _emscripten_webgl_do_create_context(a0,a12)}var PATH={splitPath:function(filename){var splitPathRe=/^(\/?|)([\s\S]*?)((?:\.{1,2}|[^\/]+?|)(\.[^.\/]*|))(?:[\/]*)$/;return splitPathRe.exec(filename).slice(1)},normalizeArray:function(parts,allowAboveRoot){for(var up=0,i=parts.length-1;i>=0;i--){var last=parts[i];last==="."?parts.splice(i,1):last===".."?(parts.splice(i,1),up++):up&&(parts.splice(i,1),up--)}if(allowAboveRoot)for(;up;up--)parts.unshift("..");return parts},normalize:function(path){var isAbsolute=path.charAt(0)==="/",trailingSlash=path.substr(-1)==="/";return path=PATH.normalizeArray(path.split("/").filter(function(p2){return!!p2}),!isAbsolute).join("/"),!path&&!isAbsolute&&(path="."),path&&trailingSlash&&(path+="/"),(isAbsolute?"/":"")+path},dirname:function(path){var result=PATH.splitPath(path),root=result[0],dir=result[1];return!root&&!dir?".":(dir&&(dir=dir.substr(0,dir.length-1)),root+dir)},basename:function(path){if(path==="/")return"/";var lastSlash=path.lastIndexOf("/");return lastSlash===-1?path:path.substr(lastSlash+1)},extname:function(path){return PATH.splitPath(path)[3]},join:function(){var paths=Array.prototype.slice.call(arguments,0);return PATH.normalize(paths.join("/"))},join2:function(l,r){return PATH.normalize(l+"/"+r)}},SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer3=SYSCALLS.buffers[stream];curr===0||curr===10?((stream===1?out:err)(UTF8ArrayToString(buffer3,0)),buffer3.length=0):buffer3.push(curr)},varargs:void 0,get:function(){SYSCALLS.varargs+=4;var ret=GROWABLE_HEAP_I32()[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){return ENVIRONMENT_IS_PTHREAD?_emscripten_proxy_to_main_thread_js(3,1,fd):0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(4,1,fd,offset_low,offset_high,whence,newOffset)}function _fd_write(fd,iov,iovcnt,pnum){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(5,1,fd,iov,iovcnt,pnum);for(var num=0,i=0;i>2],len=GROWABLE_HEAP_I32()[iov+(i*8+4)>>2],j=0;j>2]=num,0}function _pthread_cleanup_pop(execute2){var routine=PThread.exitHandlers.pop();execute2&&routine()}function _pthread_cleanup_push(routine,arg){PThread.exitHandlers===null&&(PThread.exitHandlers=[]),PThread.exitHandlers.push(function(){dynCall_vi(routine,arg)})}function __spawn_thread(threadParams){if(ENVIRONMENT_IS_PTHREAD)throw"Internal Error! _spawn_thread() can only ever be called from main application thread!";var worker=PThread.getNewWorker();if(worker.pthread!==void 0)throw"Internal error!";if(!threadParams.pthread_ptr)throw"Internal error, no pthread ptr!";PThread.runningWorkers.push(worker);for(var tlsMemory=_malloc(128*4),i=0;i<128;++i)GROWABLE_HEAP_I32()[tlsMemory+i*4>>2]=0;var stackHigh=threadParams.stackBase+threadParams.stackSize,pthread=PThread.pthreads[threadParams.pthread_ptr]={worker,stackBase:threadParams.stackBase,stackSize:threadParams.stackSize,allocatedOwnStack:threadParams.allocatedOwnStack,thread:threadParams.pthread_ptr,threadInfoStruct:threadParams.pthread_ptr},tis=pthread.threadInfoStruct>>2;Atomics.store(GROWABLE_HEAP_U32(),tis+(0>>2),0),Atomics.store(GROWABLE_HEAP_U32(),tis+(4>>2),0),Atomics.store(GROWABLE_HEAP_U32(),tis+(8>>2),0),Atomics.store(GROWABLE_HEAP_U32(),tis+(68>>2),threadParams.detached),Atomics.store(GROWABLE_HEAP_U32(),tis+(104>>2),tlsMemory),Atomics.store(GROWABLE_HEAP_U32(),tis+(48>>2),0),Atomics.store(GROWABLE_HEAP_U32(),tis+(40>>2),pthread.threadInfoStruct),Atomics.store(GROWABLE_HEAP_U32(),tis+(44>>2),42),Atomics.store(GROWABLE_HEAP_U32(),tis+(108>>2),threadParams.stackSize),Atomics.store(GROWABLE_HEAP_U32(),tis+(84>>2),threadParams.stackSize),Atomics.store(GROWABLE_HEAP_U32(),tis+(80>>2),stackHigh),Atomics.store(GROWABLE_HEAP_U32(),tis+(108+8>>2),stackHigh),Atomics.store(GROWABLE_HEAP_U32(),tis+(108+12>>2),threadParams.detached),Atomics.store(GROWABLE_HEAP_U32(),tis+(108+20>>2),threadParams.schedPolicy),Atomics.store(GROWABLE_HEAP_U32(),tis+(108+24>>2),threadParams.schedPrio);var global_libc=_emscripten_get_global_libc(),global_locale=global_libc+40;Atomics.store(GROWABLE_HEAP_U32(),tis+(176>>2),global_locale),worker.pthread=pthread;var msg={cmd:"run",start_routine:threadParams.startRoutine,arg:threadParams.arg,threadInfoStruct:threadParams.pthread_ptr,selfThreadId:threadParams.pthread_ptr,parentThreadId:threadParams.parent_pthread_ptr,stackBase:threadParams.stackBase,stackSize:threadParams.stackSize};worker.runPthread=function(){msg.time=performance.now(),worker.postMessage(msg,threadParams.transferList)},worker.loaded&&(worker.runPthread(),delete worker.runPthread)}function _pthread_getschedparam(thread,policy,schedparam){if(!policy&&!schedparam)return ERRNO_CODES.EINVAL;if(!thread)return err("pthread_getschedparam called with a null thread pointer!"),ERRNO_CODES.ESRCH;var self2=GROWABLE_HEAP_I32()[thread+12>>2];if(self2!==thread)return err("pthread_getschedparam attempted on thread "+thread+", which does not point to a valid thread, or does not exist anymore!"),ERRNO_CODES.ESRCH;var schedPolicy=Atomics.load(GROWABLE_HEAP_U32(),thread+108+20>>2),schedPrio=Atomics.load(GROWABLE_HEAP_U32(),thread+108+24>>2);return policy&&(GROWABLE_HEAP_I32()[policy>>2]=schedPolicy),schedparam&&(GROWABLE_HEAP_I32()[schedparam>>2]=schedPrio),0}function _pthread_self(){return __pthread_ptr|0}Module._pthread_self=_pthread_self;function _pthread_create(pthread_ptr,attr,start_routine,arg){if(typeof SharedArrayBuffer=="undefined")return err("Current environment does not support SharedArrayBuffer, pthreads are not available!"),6;if(!pthread_ptr)return err("pthread_create called with a null thread pointer!"),28;var transferList=[],error=0;if(ENVIRONMENT_IS_PTHREAD&&(transferList.length===0||error))return _emscripten_sync_run_in_main_thread_4(687865856,pthread_ptr,attr,start_routine,arg);if(error)return error;var stackSize=0,stackBase=0,detached=0,schedPolicy=0,schedPrio=0;if(attr){stackSize=GROWABLE_HEAP_I32()[attr>>2],stackSize+=81920,stackBase=GROWABLE_HEAP_I32()[attr+8>>2],detached=GROWABLE_HEAP_I32()[attr+12>>2]!==0;var inheritSched=GROWABLE_HEAP_I32()[attr+16>>2]===0;if(inheritSched){var prevSchedPolicy=GROWABLE_HEAP_I32()[attr+20>>2],prevSchedPrio=GROWABLE_HEAP_I32()[attr+24>>2],parentThreadPtr=PThread.currentProxiedOperationCallerThread?PThread.currentProxiedOperationCallerThread:_pthread_self();_pthread_getschedparam(parentThreadPtr,attr+20,attr+24),schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2],schedPrio=GROWABLE_HEAP_I32()[attr+24>>2],GROWABLE_HEAP_I32()[attr+20>>2]=prevSchedPolicy,GROWABLE_HEAP_I32()[attr+24>>2]=prevSchedPrio}else schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2],schedPrio=GROWABLE_HEAP_I32()[attr+24>>2]}else stackSize=2097152;var allocatedOwnStack=stackBase==0;allocatedOwnStack?stackBase=_memalign(16,stackSize):(stackBase-=stackSize,assert3(stackBase>0));for(var threadInfoStruct2=_malloc(232),i=0;i<232>>2;++i)GROWABLE_HEAP_U32()[(threadInfoStruct2>>2)+i]=0;GROWABLE_HEAP_I32()[pthread_ptr>>2]=threadInfoStruct2,GROWABLE_HEAP_I32()[threadInfoStruct2+12>>2]=threadInfoStruct2;var headPtr=threadInfoStruct2+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var threadParams={stackBase,stackSize,allocatedOwnStack,schedPolicy,schedPrio,detached,startRoutine:start_routine,pthread_ptr:threadInfoStruct2,parent_pthread_ptr:_pthread_self(),arg,transferList};return ENVIRONMENT_IS_PTHREAD?(threadParams.cmd="spawnThread",postMessage(threadParams,transferList)):__spawn_thread(threadParams),0}function _roundf(d){return d=+d,d>=0?+Math_floor(d+.5):+Math_ceil(d-.5)}function _sysconf(name){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(6,1,name);switch(name){case 30:return 16384;case 85:var maxHeapSize=2147483648;return maxHeapSize/16384;case 132:case 133:case 12:case 137:case 138:case 15:case 235:case 16:case 17:case 18:case 19:case 20:case 149:case 13:case 10:case 236:case 153:case 9:case 21:case 22:case 159:case 154:case 14:case 77:case 78:case 139:case 80:case 81:case 82:case 68:case 67:case 164:case 11:case 29:case 47:case 48:case 95:case 52:case 51:case 46:case 79:return 200809;case 27:case 246:case 127:case 128:case 23:case 24:case 160:case 161:case 181:case 182:case 242:case 183:case 184:case 243:case 244:case 245:case 165:case 178:case 179:case 49:case 50:case 168:case 169:case 175:case 170:case 171:case 172:case 97:case 76:case 32:case 173:case 35:return-1;case 176:case 177:case 7:case 155:case 8:case 157:case 125:case 126:case 92:case 93:case 129:case 130:case 131:case 94:case 91:return 1;case 74:case 60:case 69:case 70:case 4:return 1024;case 31:case 42:case 72:return 32;case 87:case 26:case 33:return 2147483647;case 34:case 1:return 47839;case 38:case 36:return 99;case 43:case 37:return 2048;case 0:return 2097152;case 3:return 65536;case 28:return 32768;case 44:return 32767;case 75:return 16384;case 39:return 1e3;case 89:return 700;case 71:return 256;case 40:return 255;case 2:return 100;case 180:return 64;case 25:return 20;case 5:return 16;case 6:return 6;case 73:return 4;case 84:return typeof navigator=="object"&&navigator.hardwareConcurrency||1}return setErrNo(28),-1}ENVIRONMENT_IS_PTHREAD?PThread.initWorker():PThread.initMainThreadBlock();var GLctx;GL.init();var proxiedFunctionTable=[null,_atexit,_emscripten_set_canvas_element_size_main_thread,_fd_close,_fd_seek,_fd_write,_sysconf],asmLibraryArg={e:___assert_fail,r:___call_main,w:__emscripten_notify_thread_queue,a:_abort,l:_emscripten_conditional_set_current_thread_status,d:_emscripten_futex_wait,c:_emscripten_futex_wake,h:_emscripten_get_now,g:_emscripten_is_main_browser_thread,x:_emscripten_is_main_runtime_thread,q:_emscripten_memcpy_big,B:_emscripten_num_logical_cores,t:_emscripten_receive_on_main_thread_js,A:_emscripten_resize_heap,u:_emscripten_set_canvas_element_size,k:_emscripten_set_current_thread_status,s:_emscripten_set_thread_name,v:_emscripten_webgl_create_context,m:_fd_close,o:_fd_seek,i:_fd_write,p:initPthreadsJS,memory:wasmMemory||Module.wasmMemory,y:_pthread_cleanup_pop,z:_pthread_cleanup_push,j:_pthread_create,b:_pthread_self,f:_roundf,n:_sysconf,table:wasmTable},asm=createWasm();Module.asm=asm;var 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Uint8Array?"int32":isNumber(values)?"float32":isString(values)?"string":isBoolean(values)?"bool":"float32"}function isFunction(f){return!!(f&&f.constructor&&f.call&&f.apply)}function nearestDivisor(size,start){for(let i=start;i=0;--i)strides[i]=strides[i+1]*shape[i+1];return strides}function createNestedArray(offset,shape,a){let ret=new Array;if(shape.length===1){let d=shape[0];for(let i=0;iacc*c);for(let i=0;iacc*c);if(size===0)return[];if(size!==a.length)throw new Error(`[${shape}] does not match the input size ${a.length}.`);return createNestedArray(0,shape,a)}function makeOnesTypedArray(size,dtype){let array2=makeZerosTypedArray(size,dtype);for(let i=0;iprev*curr,1);if(dtype==null||dtype==="float32")return toNestedArray(shape,new Float32Array(size));if(dtype==="int32")return toNestedArray(shape,new Int32Array(size));if(dtype==="bool")return toNestedArray(shape,new Uint8Array(size));throw new Error(`Unknown data type ${dtype}`)}function assertNonNegativeIntegerDimensions(shape){shape.forEach(dimSize=>{assert(Number.isInteger(dimSize)&&dimSize>=0,()=>`Tensor must have a shape comprised of positive integers but got shape [${shape}].`)})}function locToIndex(locs,rank,strides){if(rank===0)return 0;if(rank===1)return locs[0];let index=locs[locs.length-1];for(let i=0;i{let[key,value]=keyValue.split(":");this.urlFlags[key]=parseValue(key,value)})}};function getQueryParams(queryString){let params={};return queryString.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g,(s,...t)=>(decodeParam(params,t[0],t[1]),t.join("="))),params}function decodeParam(params,name,value){params[decodeURIComponent(name)]=decodeURIComponent(value||"")}function parseValue(flagName,value){if(value=value.toLowerCase(),value==="true"||value==="false")return value==="true";if(`${+value}`===value)return+value;throw new Error(`Could not parse value flag value ${value} for flag ${flagName}.`)}function env(){return ENV}var ENV=null;function setEnvironmentGlobal(environment){ENV=environment}var globalNameSpace;function getGlobalNamespace(){if(globalNameSpace==null){let ns;if(typeof window!="undefined")ns=window;else if(typeof global!="undefined")ns=global;else if(typeof process!="undefined")ns=process;else if(typeof self!="undefined")ns=self;else throw new Error("Could not find a global object");globalNameSpace=ns}return globalNameSpace}function getGlobalMap(){let ns=getGlobalNamespace();return ns._tfGlobals==null&&(ns._tfGlobals=new Map),ns._tfGlobals}function getGlobal(key,init2){let globalMap=getGlobalMap();if(globalMap.has(key))return globalMap.get(key);{let singleton=init2();return globalMap.set(key,singleton),globalMap.get(key)}}var Abs="Abs",Acos="Acos",Acosh="Acosh",Add="Add",AddN="AddN",All="All",Any="Any",ArgMax="ArgMax",ArgMin="ArgMin",Asin="Asin",Asinh="Asinh",Atan="Atan",Atanh="Atanh",Atan2="Atan2",AvgPool="AvgPool",AvgPoolGrad="AvgPoolGrad",AvgPool3D="AvgPool3D",AvgPool3DGrad="AvgPool3DGrad",BatchMatMul="BatchMatMul",BatchToSpaceND="BatchToSpaceND",Bincount="Bincount",BroadcastTo="BroadcastTo",Cast="Cast",Ceil="Ceil",ClipByValue="ClipByValue",Complex="Complex",ComplexAbs="ComplexAbs",Concat="Concat",Conv2D="Conv2D",Conv2DBackpropFilter="Conv2DBackpropFilter",Conv2DBackpropInput="Conv2DBackpropInput",Conv3D="Conv3D",Conv3DBackpropFilterV2="Conv3DBackpropFilterV2",Conv3DBackpropInputV2="Conv3DBackpropInputV2",Cos="Cos",Cosh="Cosh",Cumsum="Cumsum",CropAndResize="CropAndResize",DenseBincount="DenseBincount",DepthToSpace="DepthToSpace",DepthwiseConv2dNative="DepthwiseConv2dNative",DepthwiseConv2dNativeBackpropFilter="DepthwiseConv2dNativeBackpropFilter",DepthwiseConv2dNativeBackpropInput="DepthwiseConv2dNativeBackpropInput",Diag="Diag",Dilation2D="Dilation2D",Dilation2DBackpropInput="Dilation2DBackpropInput",Dilation2DBackpropFilter="Dilation2DBackpropFilter",RealDiv="RealDiv",Elu="Elu",EluGrad="EluGrad",Erf="Erf",Equal="Equal",Exp="Exp",ExpandDims="ExpandDims",Expm1="Expm1",FFT="FFT",Fill="Fill",FlipLeftRight="FlipLeftRight",Floor="Floor",FloorDiv="FloorDiv",FusedBatchNorm="FusedBatchNorm",GatherV2="GatherV2",GatherNd="GatherNd",Greater="Greater",GreaterEqual="GreaterEqual",Identity="Identity",IFFT="IFFT",Imag="Imag",IsFinite="IsFinite",IsInf="IsInf",IsNan="IsNan",LeakyRelu="LeakyRelu",Less="Less",LessEqual="LessEqual",LinSpace="LinSpace",Log="Log",Log1p="Log1p",LogicalAnd="LogicalAnd",LogicalNot="LogicalNot",LogicalOr="LogicalOr",LogSoftmax="LogSoftmax",LRN="LRN",LRNGrad="LRNGrad",Max="Max",Maximum="Maximum",MaxPool="MaxPool",MaxPoolGrad="MaxPoolGrad",MaxPool3D="MaxPool3D",MaxPool3DGrad="MaxPool3DGrad",MaxPoolWithArgmax="MaxPoolWithArgmax",Mean="Mean",Min="Min",Minimum="Minimum",MirrorPad="MirrorPad",Mod="Mod",Multinomial="Multinomial",Multiply="Multiply",Neg="Neg",NotEqual="NotEqual",NonMaxSuppressionV3="NonMaxSuppressionV3",NonMaxSuppressionV4="NonMaxSuppressionV4",NonMaxSuppressionV5="NonMaxSuppressionV5",OnesLike="OnesLike",OneHot="OneHot",Pack="Pack",PadV2="PadV2",Pool="Pool",Pow="Pow",Prelu="Prelu",Prod="Prod",Range="Range",Real="Real",Reciprocal="Reciprocal",Relu="Relu",Reshape="Reshape",ResizeNearestNeighbor="ResizeNearestNeighbor",ResizeNearestNeighborGrad="ResizeNearestNeighborGrad",ResizeBilinear="ResizeBilinear",ResizeBilinearGrad="ResizeBilinearGrad",Relu6="Relu6",Reverse="Reverse",Round="Round",Rsqrt="Rsqrt",ScatterNd="ScatterNd",Select="Select",Selu="Selu",Slice="Slice",Sin="Sin",Sinh="Sinh",Sign="Sign",Sigmoid="Sigmoid",Softplus="Softplus",Sqrt="Sqrt",Sum="Sum",SpaceToBatchND="SpaceToBatchND",SplitV="SplitV",Softmax="Softmax",SquaredDifference="SquaredDifference",Square="Square",Sub="Sub",SparseToDense="SparseToDense",StridedSlice="StridedSlice",Tan="Tan",Tanh="Tanh",Tile="Tile",TopK="TopK",Transpose="Transpose",Unique="Unique",Unpack="Unpack",UnsortedSegmentSum="UnsortedSegmentSum",ZerosLike="ZerosLike",Step="Step",FromPixels="FromPixels",RotateWithOffset="RotateWithOffset",_FusedMatMul="_FusedMatMul",FusedConv2D="FusedConv2D",FusedDepthwiseConv2D="FusedDepthwiseConv2D",kernelRegistry=getGlobal("kernelRegistry",()=>new Map),gradRegistry=getGlobal("gradRegistry",()=>new Map);function getKernel(kernelName,backendName){let key=makeKey(kernelName,backendName);return kernelRegistry.get(key)}function getGradient(kernelName){return gradRegistry.get(kernelName)}function getKernelsForBackend(backendName){let it=kernelRegistry.entries(),result=[];for(;;){let{done,value}=it.next();if(done)break;let[key,config3]=value,[backend22]=key.split("_");backend22===backendName&&result.push(config3)}return result}function registerKernel(config3){let{kernelName,backendName}=config3,key=makeKey(kernelName,backendName);kernelRegistry.has(key)&&console.warn(`The kernel '${kernelName}' for backend '${backendName}' is already registered`),kernelRegistry.set(key,config3)}function registerGradient(config3){let{kernelName}=config3;gradRegistry.has(kernelName)&&env().getBool("DEBUG")&&console.warn(`Overriding the gradient for '${kernelName}'`),gradRegistry.set(kernelName,config3)}function unregisterKernel(kernelName,backendName){let key=makeKey(kernelName,backendName);if(!kernelRegistry.has(key))throw new Error(`The kernel '${kernelName}' for backend '${backendName}' is not registered`);kernelRegistry.delete(key)}function unregisterGradient(kernelName){if(!gradRegistry.has(kernelName))throw new Error(`The gradient '${kernelName}' for backend is not registered`);gradRegistry.delete(kernelName)}function copyRegisteredKernels(registeredBackendName,newBackendName){getKernelsForBackend(registeredBackendName).forEach(kernelConfig=>{let newKernelConfig=Object.assign({},kernelConfig,{backendName:newBackendName});registerKernel(newKernelConfig)})}function makeKey(kernelName,backendName){return`${backendName}_${kernelName}`}var util_exports={};__export2(util_exports,{arraysEqual:()=>arraysEqual,assert:()=>assert,assertNonNegativeIntegerDimensions:()=>assertNonNegativeIntegerDimensions,assertNonNull:()=>assertNonNull,assertShapesMatch:()=>assertShapesMatch,bytesFromStringArray:()=>bytesFromStringArray,bytesPerElement:()=>bytesPerElement,checkConversionForErrors:()=>checkConversionForErrors,clamp:()=>clamp,computeStrides:()=>computeStrides,createScalarValue:()=>createScalarValue,createShuffledIndices:()=>createShuffledIndices,decodeString:()=>decodeString,distSquared:()=>distSquared,encodeString:()=>encodeString,fetch:()=>fetch2,flatten:()=>flatten,getArrayFromDType:()=>getArrayFromDType,getTypedArrayFromDType:()=>getTypedArrayFromDType,hasEncodingLoss:()=>hasEncodingLoss,indexToLoc:()=>indexToLoc,inferDtype:()=>inferDtype,inferFromImplicitShape:()=>inferFromImplicitShape,isBoolean:()=>isBoolean,isFunction:()=>isFunction,isInt:()=>isInt,isNumber:()=>isNumber,isPromise:()=>isPromise,isScalarShape:()=>isScalarShape,isString:()=>isString,isTypedArray:()=>isTypedArray,isValidDtype:()=>isValidDtype,locToIndex:()=>locToIndex,makeOnesTypedArray:()=>makeOnesTypedArray,makeZerosNestedTypedArray:()=>makeZerosNestedTypedArray,makeZerosTypedArray:()=>makeZerosTypedArray,nearestDivisor:()=>nearestDivisor,nearestLargerEven:()=>nearestLargerEven,now:()=>now,parseAxisParam:()=>parseAxisParam,randUniform:()=>randUniform,repeatedTry:()=>repeatedTry,rightPad:()=>rightPad,shuffle:()=>shuffle,sizeFromShape:()=>sizeFromShape,sizeToSquarishShape:()=>sizeToSquarishShape,squeezeShape:()=>squeezeShape,sum:()=>sum,tanh:()=>tanh,toNestedArray:()=>toNestedArray,toTypedArray:()=>toTypedArray});function createScalarValue(value,dtype){return dtype==="string"?encodeString(value):toTypedArray([value],dtype)}function noConversionNeeded(a,dtype){return a instanceof Float32Array&&dtype==="float32"||a instanceof Int32Array&&dtype==="int32"||a instanceof Uint8Array&&dtype==="bool"}function toTypedArray(a,dtype){if(dtype==="string")throw new Error("Cannot convert a string[] to a TypedArray");if(Array.isArray(a)&&(a=flatten(a)),env().getBool("DEBUG")&&checkConversionForErrors(a,dtype),noConversionNeeded(a,dtype))return a;if(dtype==null||dtype==="float32"||dtype==="complex64")return new Float32Array(a);if(dtype==="int32")return new Int32Array(a);if(dtype==="bool"){let bool=new Uint8Array(a.length);for(let i=0;i{outputs=f()},timer=this.backendTimer.time(holdResultWrapperFn);if(env().getBool("CHECK_COMPUTATION_FOR_ERRORS"))for(let i=0;i{checkComputationForErrors(tensorVals,output.dtype,kernelName)})}return{kernelName,outputs,inputs,timeMs:timer.then(timing=>timing.kernelMs),extraInfo:timer.then(timing=>timing.getExtraProfileInfo!=null?timing.getExtraProfileInfo():"")}}logKernelProfile(kernelProfile){let{kernelName,outputs,timeMs,inputs,extraInfo}=kernelProfile;outputs.forEach(result=>{Promise.all([result.data(),timeMs,extraInfo]).then(valueContainer=>{this.logger.logKernelProfile(kernelName,result,valueContainer[0],valueContainer[1],inputs,valueContainer[2])})})}};function checkComputationForErrors(vals,dtype,kernelName){if(dtype!=="float32")return!1;for(let i=0;i0?inputShape:""} `}}console.log(`%c${paddedName} %c${time2} %c${rank}D ${shape} %c${size} %c${inputShapesDescription} %c${extraInfo}`,"font-weight:bold","color:red","color:blue","color: orange","color: green","color: steelblue")}};function getFilteredNodesXToY(tape,xs,y){let tensorsFromX={},nodesFromX={};for(let i=0;itensorsFromX[output.id]=!0),anyInputFromX=!0,nodesFromX[node.id]=!0;break}if(anyInputFromX)break}}let tensorsLeadToY={};tensorsLeadToY[y.id]=!0;let nodesToY={};for(let i=tape.length-1;i>=0;i--){let node=tape[i],nodeInputs=node.inputs;for(let j=0;j=0;i--){let node=filteredTape[i],dys=[];if(node.outputs.forEach(o=>{let gradTensor=tensorAccumulatedGradientMap[o.id];gradTensor!=null?dys.push(gradTensor):dys.push(null)}),node.gradient==null)throw new Error(`Cannot compute gradient: gradient function not found for ${node.kernelName}.`);let inputGradients=node.gradient(dys);for(let inputName in node.inputs){if(!(inputName in inputGradients))throw new Error(`Cannot backprop through input ${inputName}. Available gradients found: ${Object.keys(inputGradients)}.`);let dx=tidy2(()=>inputGradients[inputName]());if(dx.dtype!=="float32")throw new Error(`Error in gradient for op ${node.kernelName}. The gradient of input ${inputName} must have 'float32' dtype, but has '${dx.dtype}'`);let x=node.inputs[inputName];if(!arraysEqual(dx.shape,x.shape))throw new Error(`Error in gradient for op ${node.kernelName}. The gradient of input '${inputName}' has shape '${dx.shape}', which does not match the shape of the input '${x.shape}'`);if(tensorAccumulatedGradientMap[x.id]==null)tensorAccumulatedGradientMap[x.id]=dx;else{let curGradient=tensorAccumulatedGradientMap[x.id];tensorAccumulatedGradientMap[x.id]=add5(curGradient,dx),curGradient.dispose()}}}}var FORMAT_LIMIT_NUM_VALS=20,FORMAT_NUM_FIRST_LAST_VALS=3,FORMAT_NUM_SIG_DIGITS=7;function tensorToString(vals,shape,dtype,verbose){let strides=computeStrides(shape),padPerCol=computeMaxSizePerColumn(vals,shape,dtype,strides),rank=shape.length,valsLines=subTensorToString(vals,shape,dtype,strides,padPerCol),lines=["Tensor"];return verbose&&(lines.push(` dtype: ${dtype}`),lines.push(` rank: ${rank}`),lines.push(` shape: [${shape}]`),lines.push(" values:")),lines.push(valsLines.map(l=>" "+l).join(` +`)};exports.Canvas=WebGLImageFilter});var require_image=__commonJS(exports=>{var fxImage=__toModule(require_imagefx()),inCanvas=null,outCanvas=null;function process3(input2,config2){let tensor2;if(input2 instanceof Tensor)tensor2=clone(input2);else{let originalWidth=input2.naturalWidth||input2.videoWidth||input2.width||input2.shape&&input2.shape[1]>0,originalHeight=input2.naturalHeight||input2.videoHeight||input2.height||input2.shape&&input2.shape[2]>0,targetWidth=originalWidth,targetHeight=originalHeight;if(config2.filter.width>0?targetWidth=config2.filter.width:config2.filter.height>0&&(targetWidth=originalWidth*(config2.filter.height/originalHeight)),config2.filter.height>0?targetHeight=config2.filter.height:config2.filter.width>0&&(targetHeight=originalHeight*(config2.filter.width/originalWidth)),!targetWidth||!targetHeight)return log("Human: invalid input",input2),null;(!inCanvas||inCanvas.width!==targetWidth||inCanvas.height!==targetHeight)&&(inCanvas=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(targetWidth,targetHeight):document.createElement("canvas"),inCanvas.width!==targetWidth&&(inCanvas.width=targetWidth),inCanvas.height!==targetHeight&&(inCanvas.height=targetHeight));let ctx=inCanvas.getContext("2d");if(input2 instanceof ImageData?ctx.putImageData(input2,0,0):ctx.drawImage(input2,0,0,originalWidth,originalHeight,0,0,inCanvas.width,inCanvas.height),config2.filter.enabled){if((!this.fx||!outCanvas||inCanvas.width!==outCanvas.width||inCanvas.height!==outCanvas.height)&&(outCanvas=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(inCanvas.width,inCanvas.height):document.createElement("canvas"),outCanvas.width!==inCanvas.width&&(outCanvas.width=inCanvas.width),outCanvas.height!==inCanvas.height&&(outCanvas.height=inCanvas.height),this.fx=ENV.flags.IS_BROWSER?new fxImage.Canvas({canvas:outCanvas}):null),!this.fx)return inCanvas;this.fx.reset(),this.fx.addFilter("brightness",config2.filter.brightness),config2.filter.contrast!==0&&this.fx.addFilter("contrast",config2.filter.contrast),config2.filter.sharpness!==0&&this.fx.addFilter("sharpen",config2.filter.sharpness),config2.filter.blur!==0&&this.fx.addFilter("blur",config2.filter.blur),config2.filter.saturation!==0&&this.fx.addFilter("saturation",config2.filter.saturation),config2.filter.hue!==0&&this.fx.addFilter("hue",config2.filter.hue),config2.filter.negative&&this.fx.addFilter("negative"),config2.filter.sepia&&this.fx.addFilter("sepia"),config2.filter.vintage&&this.fx.addFilter("brownie"),config2.filter.sepia&&this.fx.addFilter("sepia"),config2.filter.kodachrome&&this.fx.addFilter("kodachrome"),config2.filter.technicolor&&this.fx.addFilter("technicolor"),config2.filter.polaroid&&this.fx.addFilter("polaroid"),config2.filter.pixelate!==0&&this.fx.addFilter("pixelate",config2.filter.pixelate),this.fx.apply(inCanvas)}else outCanvas=inCanvas;let pixels;if(outCanvas.data){let shape=[outCanvas.height,outCanvas.width,3];pixels=tensor3d(outCanvas.data,shape,"int32")}else if(config2.backend==="webgl"||outCanvas instanceof ImageData)pixels=browser_exports.fromPixels(outCanvas);else{let tempCanvas=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(targetWidth,targetHeight):document.createElement("canvas");tempCanvas.width=targetWidth,tempCanvas.height=targetHeight;let tempCtx=tempCanvas.getContext("2d");tempCtx==null||tempCtx.drawImage(outCanvas,0,0);let data2=tempCtx==null?void 0:tempCtx.getImageData(0,0,targetWidth,targetHeight);pixels=browser_exports.fromPixels(data2)}let casted=pixels.toFloat();tensor2=casted.expandDims(0),pixels.dispose(),casted.dispose()}return{tensor:tensor2,canvas:config2.filter.return?outCanvas:null}}exports.process=process3});function log(...msg){let dt=new Date,ts=`${dt.getHours().toString().padStart(2,"0")}:${dt.getMinutes().toString().padStart(2,"0")}:${dt.getSeconds().toString().padStart(2,"0")}.${dt.getMilliseconds().toString().padStart(3,"0")}`;msg&&console.log(ts,"Human:",...msg)}var tfjs_esm_exports={};__export(tfjs_esm_exports,{Abs:()=>Abs,Acos:()=>Acos,Acosh:()=>Acosh,AdadeltaOptimizer:()=>AdadeltaOptimizer,AdagradOptimizer:()=>AdagradOptimizer,AdamOptimizer:()=>AdamOptimizer,AdamaxOptimizer:()=>AdamaxOptimizer,Add:()=>Add,AddN:()=>AddN,All:()=>All,Any:()=>Any,ArgMax:()=>ArgMax,ArgMin:()=>ArgMin,Asin:()=>Asin,Asinh:()=>Asinh,Atan:()=>Atan,Atan2:()=>Atan2,Atanh:()=>Atanh,AvgPool:()=>AvgPool,AvgPool3D:()=>AvgPool3D,AvgPool3DGrad:()=>AvgPool3DGrad,AvgPoolGrad:()=>AvgPoolGrad,BackendWasm:()=>BackendWasm,BatchMatMul:()=>BatchMatMul,BatchToSpaceND:()=>BatchToSpaceND,Bincount:()=>Bincount,BroadcastTo:()=>BroadcastTo,Callback:()=>Callback,CallbackList:()=>CallbackList,Cast:()=>Cast,Ceil:()=>Ceil,ClipByValue:()=>ClipByValue,Complex:()=>Complex,ComplexAbs:()=>ComplexAbs,Concat:()=>Concat,Conv2D:()=>Conv2D,Conv2DBackpropFilter:()=>Conv2DBackpropFilter,Conv2DBackpropInput:()=>Conv2DBackpropInput,Conv3D:()=>Conv3D,Conv3DBackpropFilterV2:()=>Conv3DBackpropFilterV2,Conv3DBackpropInputV2:()=>Conv3DBackpropInputV2,Cos:()=>Cos,Cosh:()=>Cosh,CropAndResize:()=>CropAndResize,Cumsum:()=>Cumsum,CustomCallback:()=>CustomCallback,DataStorage:()=>DataStorage,DenseBincount:()=>DenseBincount,DepthToSpace:()=>DepthToSpace,DepthwiseConv2dNative:()=>DepthwiseConv2dNative,DepthwiseConv2dNativeBackpropFilter:()=>DepthwiseConv2dNativeBackpropFilter,DepthwiseConv2dNativeBackpropInput:()=>DepthwiseConv2dNativeBackpropInput,Diag:()=>Diag,Dilation2D:()=>Dilation2D,Dilation2DBackpropFilter:()=>Dilation2DBackpropFilter,Dilation2DBackpropInput:()=>Dilation2DBackpropInput,ENV:()=>ENV,EarlyStopping:()=>EarlyStopping,Elu:()=>Elu,EluGrad:()=>EluGrad,Environment:()=>Environment,Equal:()=>Equal,Erf:()=>Erf,Exp:()=>Exp,ExpandDims:()=>ExpandDims,Expm1:()=>Expm1,FFT:()=>FFT,Fill:()=>Fill,FlipLeftRight:()=>FlipLeftRight,Floor:()=>Floor,FloorDiv:()=>FloorDiv,FromPixels:()=>FromPixels,FusedBatchNorm:()=>FusedBatchNorm,FusedConv2D:()=>FusedConv2D,FusedDepthwiseConv2D:()=>FusedDepthwiseConv2D,GPGPUContext:()=>GPGPUContext,GatherNd:()=>GatherNd,GatherV2:()=>GatherV2,GraphModel:()=>GraphModel,Greater:()=>Greater,GreaterEqual:()=>GreaterEqual,History:()=>History,IFFT:()=>IFFT,Identity:()=>Identity,Imag:()=>Imag,InputSpec:()=>InputSpec,IsFinite:()=>IsFinite,IsInf:()=>IsInf,IsNan:()=>IsNan,KernelBackend:()=>KernelBackend,LRN:()=>LRN,LRNGrad:()=>LRNGrad,LayerVariable:()=>LayerVariable,LayersModel:()=>LayersModel,LeakyRelu:()=>LeakyRelu,Less:()=>Less,LessEqual:()=>LessEqual,LinSpace:()=>LinSpace,Log:()=>Log,Log1p:()=>Log1p,LogSoftmax:()=>LogSoftmax,LogicalAnd:()=>LogicalAnd,LogicalNot:()=>LogicalNot,LogicalOr:()=>LogicalOr,MathBackendCPU:()=>MathBackendCPU,MathBackendWebGL:()=>MathBackendWebGL,Max:()=>Max,MaxPool:()=>MaxPool,MaxPool3D:()=>MaxPool3D,MaxPool3DGrad:()=>MaxPool3DGrad,MaxPoolGrad:()=>MaxPoolGrad,MaxPoolWithArgmax:()=>MaxPoolWithArgmax,Maximum:()=>Maximum,Mean:()=>Mean,Min:()=>Min,Minimum:()=>Minimum,MirrorPad:()=>MirrorPad,Mod:()=>Mod,MomentumOptimizer:()=>MomentumOptimizer,Multinomial:()=>Multinomial,Multiply:()=>Multiply,Neg:()=>Neg,NonMaxSuppressionV3:()=>NonMaxSuppressionV3,NonMaxSuppressionV4:()=>NonMaxSuppressionV4,NonMaxSuppressionV5:()=>NonMaxSuppressionV5,NotEqual:()=>NotEqual,OP_SCOPE_SUFFIX:()=>OP_SCOPE_SUFFIX,OneHot:()=>OneHot,OnesLike:()=>OnesLike,Optimizer:()=>Optimizer,Pack:()=>Pack,PadV2:()=>PadV2,Pool:()=>Pool,Pow:()=>Pow,Prelu:()=>Prelu,Prod:()=>Prod,RMSPropOptimizer:()=>RMSPropOptimizer,RNN:()=>RNN,Range:()=>Range,Rank:()=>Rank,Real:()=>Real,RealDiv:()=>RealDiv,Reciprocal:()=>Reciprocal,Reduction:()=>Reduction,Relu:()=>Relu,Relu6:()=>Relu6,Reshape:()=>Reshape,ResizeBilinear:()=>ResizeBilinear,ResizeBilinearGrad:()=>ResizeBilinearGrad,ResizeNearestNeighbor:()=>ResizeNearestNeighbor,ResizeNearestNeighborGrad:()=>ResizeNearestNeighborGrad,Reverse:()=>Reverse,RotateWithOffset:()=>RotateWithOffset,Round:()=>Round,Rsqrt:()=>Rsqrt,SGDOptimizer:()=>SGDOptimizer,ScatterNd:()=>ScatterNd,Select:()=>Select,Selu:()=>Selu,Sequential:()=>Sequential,Sigmoid:()=>Sigmoid,Sign:()=>Sign,Sin:()=>Sin,Sinh:()=>Sinh,Slice:()=>Slice,Softmax:()=>Softmax,Softplus:()=>Softplus,SpaceToBatchND:()=>SpaceToBatchND,SparseToDense:()=>SparseToDense,SplitV:()=>SplitV,Sqrt:()=>Sqrt,Square:()=>Square,SquaredDifference:()=>SquaredDifference,Step:()=>Step,StridedSlice:()=>StridedSlice,Sub:()=>Sub,Sum:()=>Sum,SymbolicTensor:()=>SymbolicTensor,Tan:()=>Tan,Tanh:()=>Tanh,Tensor:()=>Tensor,TensorBuffer:()=>TensorBuffer,Tile:()=>Tile,TopK:()=>TopK,Transpose:()=>Transpose,Unique:()=>Unique,Unpack:()=>Unpack,UnsortedSegmentSum:()=>UnsortedSegmentSum,Variable:()=>Variable,ZerosLike:()=>ZerosLike,_FusedMatMul:()=>_FusedMatMul,abs:()=>abs,acos:()=>acos,acosh:()=>acosh,add:()=>add2,addN:()=>addN,addStrict:()=>addStrict,all:()=>all,any:()=>any,argMax:()=>argMax,argMin:()=>argMin,asin:()=>asin,asinh:()=>asinh,atan:()=>atan,atan2:()=>atan2,atanh:()=>atanh,avgPool:()=>avgPool,avgPool3d:()=>avgPool3d,backend:()=>backend,backend_util:()=>backend_util_exports,basicLSTMCel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define=="function"&&define)}),require_crypto=__commonJS2(()=>{}),require_seedrandom=__commonJS2((exports,module)=>{(function(pool3,math){var global2=this,width=256,chunks=6,digits=52,rngname="random",startdenom=math.pow(width,chunks),significance=math.pow(2,digits),overflow=significance*2,mask=width-1,nodecrypto;function seedrandom5(seed,options,callback){var key=[];options=options==!0?{entropy:!0}:options||{};var shortseed=mixkey(flatten4(options.entropy?[seed,tostring(pool3)]:seed==null?autoseed():seed,3),key),arc4=new ARC4(key),prng=function(){for(var n=arc4.g(chunks),d=startdenom,x=0;n=overflow;)n/=2,d/=2,x>>>=1;return(n+x)/d};return prng.int32=function(){return arc4.g(4)|0},prng.quick=function(){return arc4.g(4)/4294967296},prng.double=prng,mixkey(tostring(arc4.S),pool3),(options.pass||callback||function(prng2,seed2,is_math_call,state){return state&&(state.S&©(state,arc4),prng2.state=function(){return copy(arc4,{})}),is_math_call?(math[rngname]=prng2,seed2):prng2})(prng,shortseed,"global"in options?options.global:this==math,options.state)}math["seed"+rngname]=seedrandom5;function ARC4(key){var t,keylen=key.length,me=this,i=0,j=me.i=me.j=0,s=me.S=[];for(keylen||(key=[keylen++]);i{var alea5=require_alea(),xor128=require_xor128(),xorwow=require_xorwow(),xorshift7=require_xorshift7(),xor4096=require_xor4096(),tychei=require_tychei(),sr=require_seedrandom();sr.alea=alea5,sr.xor128=xor128,sr.xorwow=xorwow,sr.xorshift7=xorshift7,sr.xor4096=xor4096,sr.tychei=tychei,module.exports=sr}),require_alea2=__commonJS2((exports,module)=>{(function(global2,module2,define2){function Alea(seed){var me=this,mash=Mash();me.next=function(){var t=2091639*me.s0+me.c*23283064365386963e-26;return me.s0=me.s1,me.s1=me.s2,me.s2=t-(me.c=t|0)},me.c=1,me.s0=mash(" "),me.s1=mash(" "),me.s2=mash(" "),me.s0-=mash(seed),me.s0<0&&(me.s0+=1),me.s1-=mash(seed),me.s1<0&&(me.s1+=1),me.s2-=mash(seed),me.s2<0&&(me.s2+=1),mash=null}function copy(f,t){return t.c=f.c,t.s0=f.s0,t.s1=f.s1,t.s2=f.s2,t}function impl(seed,opts){var xg=new Alea(seed),state=opts&&opts.state,prng=xg.next;return prng.int32=function(){return xg.next()*4294967296|0},prng.double=function(){return prng()+(prng()*2097152|0)*11102230246251565e-32},prng.quick=prng,state&&(typeof state=="object"&©(state,xg),prng.state=function(){return copy(xg,{})}),prng}function Mash(){var n=4022871197,mash=function(data2){data2=data2.toString();for(var i=0;i>>0,h-=n,h*=n,n=h>>>0,h-=n,n+=h*4294967296}return(n>>>0)*23283064365386963e-26};return mash}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.alea=impl})(exports,typeof module=="object"&&module,typeof define=="function"&&define)}),require_xor1282=__commonJS2((exports,module)=>{(function(global2,module2,define2){function XorGen(seed){var me=this,strseed="";me.x=0,me.y=0,me.z=0,me.w=0,me.next=function(){var t=me.x^me.x<<11;return me.x=me.y,me.y=me.z,me.z=me.w,me.w^=me.w>>>19^t^t>>>8},seed===(seed|0)?me.x=seed:strseed+=seed;for(var k=0;k>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state&&(typeof state=="object"&©(state,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.xor128=impl})(exports,typeof module=="object"&&module,typeof define=="function"&&define)}),require_xorwow2=__commonJS2((exports,module)=>{(function(global2,module2,define2){function XorGen(seed){var me=this,strseed="";me.next=function(){var t=me.x^me.x>>>2;return me.x=me.y,me.y=me.z,me.z=me.w,me.w=me.v,(me.d=me.d+362437|0)+(me.v=me.v^me.v<<4^(t^t<<1))|0},me.x=0,me.y=0,me.z=0,me.w=0,me.v=0,seed===(seed|0)?me.x=seed:strseed+=seed;for(var k=0;k>>4),me.next()}function copy(f,t){return t.x=f.x,t.y=f.y,t.z=f.z,t.w=f.w,t.v=f.v,t.d=f.d,t}function impl(seed,opts){var xg=new XorGen(seed),state=opts&&opts.state,prng=function(){return(xg.next()>>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state&&(typeof state=="object"&©(state,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.xorwow=impl})(exports,typeof module=="object"&&module,typeof define=="function"&&define)}),require_xorshift72=__commonJS2((exports,module)=>{(function(global2,module2,define2){function XorGen(seed){var me=this;me.next=function(){var X=me.x,i=me.i,t,v,w;return t=X[i],t^=t>>>7,v=t^t<<24,t=X[i+1&7],v^=t^t>>>10,t=X[i+3&7],v^=t^t>>>3,t=X[i+4&7],v^=t^t<<7,t=X[i+7&7],t=t^t<<13,v^=t^t<<9,X[i]=v,me.i=i+1&7,v};function init2(me2,seed2){var j,w,X=[];if(seed2===(seed2|0))w=X[0]=seed2;else for(seed2=""+seed2,j=0;j0;--j)me2.next()}init2(me,seed)}function copy(f,t){return t.x=f.x.slice(),t.i=f.i,t}function impl(seed,opts){seed==null&&(seed=+new Date);var xg=new XorGen(seed),state=opts&&opts.state,prng=function(){return(xg.next()>>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state&&(state.x&©(state,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.xorshift7=impl})(exports,typeof module=="object"&&module,typeof define=="function"&&define)}),require_xor40962=__commonJS2((exports,module)=>{(function(global2,module2,define2){function XorGen(seed){var me=this;me.next=function(){var w=me.w,X=me.X,i=me.i,t,v;return me.w=w=w+1640531527|0,v=X[i+34&127],t=X[i=i+1&127],v^=v<<13,t^=t<<17,v^=v>>>15,t^=t>>>12,v=X[i]=v^t,me.i=i,v+(w^w>>>16)|0};function init2(me2,seed2){var t,v,i,j,w,X=[],limit=128;for(seed2===(seed2|0)?(v=seed2,seed2=null):(seed2=seed2+"\0",v=0,limit=Math.max(limit,seed2.length)),i=0,j=-32;j>>15,v^=v<<4,v^=v>>>13,j>=0&&(w=w+1640531527|0,t=X[j&127]^=v+w,i=t==0?i+1:0);for(i>=128&&(X[(seed2&&seed2.length||0)&127]=-1),i=127,j=4*128;j>0;--j)v=X[i+34&127],t=X[i=i+1&127],v^=v<<13,t^=t<<17,v^=v>>>15,t^=t>>>12,X[i]=v^t;me2.w=w,me2.X=X,me2.i=i}init2(me,seed)}function copy(f,t){return t.i=f.i,t.w=f.w,t.X=f.X.slice(),t}function impl(seed,opts){seed==null&&(seed=+new Date);var xg=new XorGen(seed),state=opts&&opts.state,prng=function(){return(xg.next()>>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state&&(state.X&©(state,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.xor4096=impl})(exports,typeof module=="object"&&module,typeof define=="function"&&define)}),require_tychei2=__commonJS2((exports,module)=>{(function(global2,module2,define2){function XorGen(seed){var me=this,strseed="";me.next=function(){var b=me.b,c=me.c,d=me.d,a=me.a;return b=b<<25^b>>>7^c,c=c-d|0,d=d<<24^d>>>8^a,a=a-b|0,me.b=b=b<<20^b>>>12^c,me.c=c=c-d|0,me.d=d<<16^c>>>16^a,me.a=a-b|0},me.a=0,me.b=0,me.c=2654435769|0,me.d=1367130551,seed===Math.floor(seed)?(me.a=seed/4294967296|0,me.b=seed|0):strseed+=seed;for(var k=0;k>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state&&(typeof state=="object"&©(state,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.tychei=impl})(exports,typeof module=="object"&&module,typeof define=="function"&&define)}),require_seedrandom3=__commonJS2((exports,module)=>{(function(pool3,math){var global2=this,width=256,chunks=6,digits=52,rngname="random",startdenom=math.pow(width,chunks),significance=math.pow(2,digits),overflow=significance*2,mask=width-1,nodecrypto;function seedrandom5(seed,options,callback){var key=[];options=options==!0?{entropy:!0}:options||{};var shortseed=mixkey(flatten4(options.entropy?[seed,tostring(pool3)]:seed==null?autoseed():seed,3),key),arc4=new ARC4(key),prng=function(){for(var n=arc4.g(chunks),d=startdenom,x=0;n=overflow;)n/=2,d/=2,x>>>=1;return(n+x)/d};return prng.int32=function(){return arc4.g(4)|0},prng.quick=function(){return arc4.g(4)/4294967296},prng.double=prng,mixkey(tostring(arc4.S),pool3),(options.pass||callback||function(prng2,seed2,is_math_call,state){return state&&(state.S&©(state,arc4),prng2.state=function(){return copy(arc4,{})}),is_math_call?(math[rngname]=prng2,seed2):prng2})(prng,shortseed,"global"in options?options.global:this==math,options.state)}math["seed"+rngname]=seedrandom5;function ARC4(key){var t,keylen=key.length,me=this,i=0,j=me.i=me.j=0,s=me.S=[];for(keylen||(key=[keylen++]);i{var alea5=require_alea2(),xor128=require_xor1282(),xorwow=require_xorwow2(),xorshift7=require_xorshift72(),xor4096=require_xor40962(),tychei=require_tychei2(),sr=require_seedrandom3();sr.alea=alea5,sr.xor128=xor128,sr.xorwow=xorwow,sr.xorshift7=xorshift7,sr.xor4096=xor4096,sr.tychei=tychei,module.exports=sr}),require_path=__commonJS2(()=>{}),require_worker_threads=__commonJS2(()=>{}),require_perf_hooks=__commonJS2(()=>{}),require_tfjs_backend_wasm_threaded_simd=__commonJS2((exports,module)=>{var WasmBackendModuleThreadedSimd=function(){var _scriptDir=typeof document!="undefined"&&document.currentScript?document.currentScript.src:void 0;return typeof __filename!="undefined"&&(_scriptDir=_scriptDir||__filename),function(WasmBackendModuleThreadedSimd2){WasmBackendModuleThreadedSimd2=WasmBackendModuleThreadedSimd2||{};function GROWABLE_HEAP_I8(){return wasmMemory.buffer!=buffer2&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAP8}function GROWABLE_HEAP_U8(){return wasmMemory.buffer!=buffer2&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAPU8}function GROWABLE_HEAP_I32(){return wasmMemory.buffer!=buffer2&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAP32}function GROWABLE_HEAP_U32(){return wasmMemory.buffer!=buffer2&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAPU32}function GROWABLE_HEAP_F64(){return wasmMemory.buffer!=buffer2&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAPF64}var Module=typeof WasmBackendModuleThreadedSimd2!="undefined"?WasmBackendModuleThreadedSimd2:{},_scriptDir2=void 0,moduleOverrides={},key;for(key in Module)Module.hasOwnProperty(key)&&(moduleOverrides[key]=Module[key]);var arguments_=[],thisProgram="./this.program",quit_=function(status,toThrow){throw toThrow},ENVIRONMENT_IS_WEB=!1,ENVIRONMENT_IS_WORKER=!1,ENVIRONMENT_IS_NODE=!1,ENVIRONMENT_IS_SHELL=!1;ENVIRONMENT_IS_WEB=typeof window=="object",ENVIRONMENT_IS_WORKER=typeof importScripts=="function",ENVIRONMENT_IS_NODE=typeof process=="object"&&typeof process.versions=="object"&&typeof process.versions.node=="string",ENVIRONMENT_IS_SHELL=!ENVIRONMENT_IS_WEB&&!ENVIRONMENT_IS_NODE&&!ENVIRONMENT_IS_WORKER;var ENVIRONMENT_IS_PTHREAD=Module.ENVIRONMENT_IS_PTHREAD||!1;ENVIRONMENT_IS_PTHREAD&&(buffer2=Module.buffer,DYNAMIC_BASE=Module.DYNAMIC_BASE,DYNAMICTOP_PTR=Module.DYNAMICTOP_PTR);var scriptDirectory="";function locateFile(path){return Module.locateFile?Module.locateFile(path,scriptDirectory):scriptDirectory+path}var read_,readAsync,readBinary,setWindowTitle,nodeFS,nodePath;if(ENVIRONMENT_IS_NODE){ENVIRONMENT_IS_WORKER?scriptDirectory=require_path().dirname(scriptDirectory)+"/":scriptDirectory=__dirname+"/",read_=function(filename,binary){return nodeFS||(nodeFS=require("fs")),nodePath||(nodePath=require_path()),filename=nodePath.normalize(filename),nodeFS.readFileSync(filename,binary?null:"utf8")},readBinary=function(filename){var ret=read_(filename,!0);return ret.buffer||(ret=new Uint8Array(ret)),assert3(ret.buffer),ret},process.argv.length>1&&(thisProgram=process.argv[1].replace(/\\/g,"/")),arguments_=process.argv.slice(2),process.on("uncaughtException",function(ex){if(!(ex instanceof ExitStatus))throw ex}),process.on("unhandledRejection",abort),quit_=function(status){process.exit(status)},Module.inspect=function(){return"[Emscripten Module object]"};var nodeWorkerThreads;try{nodeWorkerThreads=require_worker_threads()}catch(e){throw console.error('The "worker_threads" module is not supported in this node.js build - perhaps a newer version is needed?'),e}Worker=nodeWorkerThreads.Worker}else ENVIRONMENT_IS_SHELL?(typeof read!="undefined"&&(read_=function(f){return read(f)}),readBinary=function(f){var data2;return typeof readbuffer=="function"?new Uint8Array(readbuffer(f)):(data2=read(f,"binary"),assert3(typeof data2=="object"),data2)},typeof scriptArgs!="undefined"?arguments_=scriptArgs:typeof arguments!="undefined"&&(arguments_=arguments),typeof quit=="function"&&(quit_=function(status){quit(status)}),typeof print!="undefined"&&(typeof console=="undefined"&&(console={}),console.log=print,console.warn=console.error=typeof printErr!="undefined"?printErr:print)):(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&(ENVIRONMENT_IS_WORKER?scriptDirectory=self.location.href:document.currentScript&&(scriptDirectory=document.currentScript.src),_scriptDir2&&(scriptDirectory=_scriptDir2),scriptDirectory.indexOf("blob:")!==0?scriptDirectory=scriptDirectory.substr(0,scriptDirectory.lastIndexOf("/")+1):scriptDirectory="",ENVIRONMENT_IS_NODE?(read_=function(filename,binary){return nodeFS||(nodeFS=require("fs")),nodePath||(nodePath=require_path()),filename=nodePath.normalize(filename),nodeFS.readFileSync(filename,binary?null:"utf8")},readBinary=function(filename){var ret=read_(filename,!0);return ret.buffer||(ret=new Uint8Array(ret)),assert3(ret.buffer),ret}):(read_=function(url){var xhr=new XMLHttpRequest;return xhr.open("GET",url,!1),xhr.send(null),xhr.responseText},ENVIRONMENT_IS_WORKER&&(readBinary=function(url){var xhr=new XMLHttpRequest;return xhr.open("GET",url,!1),xhr.responseType="arraybuffer",xhr.send(null),new Uint8Array(xhr.response)}),readAsync=function(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open("GET",url,!0),xhr.responseType="arraybuffer",xhr.onload=function(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()},xhr.onerror=onerror,xhr.send(null)}),setWindowTitle=function(title){document.title=title});ENVIRONMENT_IS_NODE&&typeof performance=="undefined"&&(performance=require_perf_hooks().performance);var out=Module.print||console.log.bind(console),err=Module.printErr||console.warn.bind(console);for(key in moduleOverrides)moduleOverrides.hasOwnProperty(key)&&(Module[key]=moduleOverrides[key]);moduleOverrides=null,Module.arguments&&(arguments_=Module.arguments),Module.thisProgram&&(thisProgram=Module.thisProgram),Module.quit&&(quit_=Module.quit);var Atomics_load=Atomics.load,Atomics_store=Atomics.store,Atomics_compareExchange=Atomics.compareExchange,wasmBinary;Module.wasmBinary&&(wasmBinary=Module.wasmBinary);var noExitRuntime;Module.noExitRuntime&&(noExitRuntime=Module.noExitRuntime),typeof WebAssembly!="object"&&err("no native wasm support detected");var wasmMemory,wasmTable=new WebAssembly.Table({initial:169,maximum:169+0,element:"anyfunc"}),wasmModule,threadInfoStruct=0,selfThreadId=0,ABORT=!1,EXITSTATUS=0;function assert3(condition,text){condition||abort("Assertion failed: "+text)}function getCFunc(ident){var func2=Module["_"+ident];return assert3(func2,"Cannot call unknown function "+ident+", make sure it is exported"),func2}function ccall(ident,returnType,argTypes,args,opts){var toC={string:function(str){var ret2=0;if(str!=null&&str!==0){var len=(str.length<<2)+1;ret2=stackAlloc(len),stringToUTF8(str,ret2,len)}return ret2},array:function(arr){var ret2=stackAlloc(arr.length);return writeArrayToMemory(arr,ret2),ret2}};function convertReturnValue(ret2){return returnType==="string"?UTF8ToString(ret2):returnType==="boolean"?Boolean(ret2):ret2}var func2=getCFunc(ident),cArgs=[],stack2=0;if(args)for(var i=0;i=endIdx);){var u0=heap[idx++];if(!u0)return str;if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224?u0=(u0&15)<<12|u1<<6|u2:u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63,u0<65536)str+=String.fromCharCode(u0);else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(GROWABLE_HEAP_U8(),ptr,maxBytesToRead):""}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;for(var startIdx=outIdx,endIdx=outIdx+maxBytesToWrite-1,i=0;i=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6,heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12,heap[outIdx++]=128|u>>6&63,heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18,heap[outIdx++]=128|u>>12&63,heap[outIdx++]=128|u>>6&63,heap[outIdx++]=128|u&63}}return heap[outIdx]=0,outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,GROWABLE_HEAP_U8(),outPtr,maxBytesToWrite)}function lengthBytesUTF8(str){for(var len=0,i=0;i=55296&&u<=57343&&(u=65536+((u&1023)<<10)|str.charCodeAt(++i)&1023),u<=127?++len:u<=2047?len+=2:u<=65535?len+=3:len+=4}return len}function writeArrayToMemory(array2,buffer3){GROWABLE_HEAP_I8().set(array2,buffer3)}var WASM_PAGE_SIZE=65536;function alignUp(x,multiple){return x%multiple>0&&(x+=multiple-x%multiple),x}var buffer2,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer2=buf,Module.HEAP8=HEAP8=new Int8Array(buf),Module.HEAP16=HEAP16=new Int16Array(buf),Module.HEAP32=HEAP32=new Int32Array(buf),Module.HEAPU8=HEAPU8=new Uint8Array(buf),Module.HEAPU16=HEAPU16=new Uint16Array(buf),Module.HEAPU32=HEAPU32=new Uint32Array(buf),Module.HEAPF32=HEAPF32=new Float32Array(buf),Module.HEAPF64=HEAPF64=new Float64Array(buf)}var STACK_BASE=5256464,STACKTOP=STACK_BASE,STACK_MAX=13584,DYNAMIC_BASE=5256464,DYNAMICTOP_PTR=12656,INITIAL_INITIAL_MEMORY=Module.INITIAL_MEMORY||16777216;if(ENVIRONMENT_IS_PTHREAD)wasmMemory=Module.wasmMemory,buffer2=Module.buffer;else if(Module.wasmMemory)wasmMemory=Module.wasmMemory;else if(wasmMemory=new WebAssembly.Memory({initial:INITIAL_INITIAL_MEMORY/WASM_PAGE_SIZE,maximum:2147483648/WASM_PAGE_SIZE,shared:!0}),!(wasmMemory.buffer instanceof SharedArrayBuffer))throw err("requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag"),ENVIRONMENT_IS_NODE&&console.log("(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)"),Error("bad memory");wasmMemory&&(buffer2=wasmMemory.buffer),INITIAL_INITIAL_MEMORY=buffer2.byteLength,updateGlobalBufferAndViews(buffer2),ENVIRONMENT_IS_PTHREAD||(GROWABLE_HEAP_I32()[DYNAMICTOP_PTR>>2]=DYNAMIC_BASE);function callRuntimeCallbacks(callbacks2){for(;callbacks2.length>0;){var callback=callbacks2.shift();if(typeof callback=="function"){callback(Module);continue}var func2=callback.func;typeof func2=="number"?callback.arg===void 0?Module.dynCall_v(func2):Module.dynCall_vi(func2,callback.arg):func2(callback.arg===void 0?null:callback.arg)}}var __ATPRERUN__=[],__ATINIT__=[],__ATMAIN__=[],__ATEXIT__=[],__ATPOSTRUN__=[],runtimeInitialized=!1;ENVIRONMENT_IS_PTHREAD&&(runtimeInitialized=!0);function preRun(){if(!ENVIRONMENT_IS_PTHREAD){if(Module.preRun)for(typeof Module.preRun=="function"&&(Module.preRun=[Module.preRun]);Module.preRun.length;)addOnPreRun(Module.preRun.shift());callRuntimeCallbacks(__ATPRERUN__)}}function initRuntime(){runtimeInitialized=!0,callRuntimeCallbacks(__ATINIT__)}function preMain(){ENVIRONMENT_IS_PTHREAD||callRuntimeCallbacks(__ATMAIN__)}function postRun(){if(!ENVIRONMENT_IS_PTHREAD){if(Module.postRun)for(typeof Module.postRun=="function"&&(Module.postRun=[Module.postRun]);Module.postRun.length;)addOnPostRun(Module.postRun.shift());callRuntimeCallbacks(__ATPOSTRUN__)}}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var Math_ceil=Math.ceil,Math_floor=Math.floor,runDependencies=0,runDependencyWatcher=null,dependenciesFulfilled=null;function addRunDependency(id){assert3(!ENVIRONMENT_IS_PTHREAD,"addRunDependency cannot be used in a pthread worker"),runDependencies++,Module.monitorRunDependencies&&Module.monitorRunDependencies(runDependencies)}function removeRunDependency(id){if(runDependencies--,Module.monitorRunDependencies&&Module.monitorRunDependencies(runDependencies),runDependencies==0&&(runDependencyWatcher!==null&&(clearInterval(runDependencyWatcher),runDependencyWatcher=null),dependenciesFulfilled)){var callback=dependenciesFulfilled;dependenciesFulfilled=null,callback()}}Module.preloadedImages={},Module.preloadedAudios={};function abort(what){throw Module.onAbort&&Module.onAbort(what),ENVIRONMENT_IS_PTHREAD&&console.error("Pthread aborting at "+new Error().stack),what+="",out(what),err(what),ABORT=!0,EXITSTATUS=1,what="abort("+what+"). Build with -s ASSERTIONS=1 for more info.",new WebAssembly.RuntimeError(what)}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix="data:application/octet-stream;base64,";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix="file://";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile="tfjs-backend-wasm-threaded-simd.wasm";isDataURI(wasmBinaryFile)||(wasmBinaryFile=locateFile(wasmBinaryFile));function getBinary(){try{if(wasmBinary)return new Uint8Array(wasmBinary);if(readBinary)return readBinary(wasmBinaryFile);throw"both async and sync fetching of the wasm failed"}catch(err2){abort(err2)}}function getBinaryPromise(){return!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&typeof fetch=="function"&&!isFileURI(wasmBinaryFile)?fetch(wasmBinaryFile,{credentials:"same-origin"}).then(function(response){if(!response.ok)throw"failed to load wasm binary file at '"+wasmBinaryFile+"'";return response.arrayBuffer()}).catch(function(){return getBinary()}):new Promise(function(resolve,reject){resolve(getBinary())})}function createWasm(){var info={a:asmLibraryArg};function receiveInstance(instance,module2){var exports3=instance.exports;if(Module.asm=exports3,wasmModule=module2,!ENVIRONMENT_IS_PTHREAD){var numWorkersToLoad=PThread.unusedWorkers.length;PThread.unusedWorkers.forEach(function(w){PThread.loadWasmModuleToWorker(w,function(){--numWorkersToLoad||removeRunDependency("wasm-instantiate")})})}}ENVIRONMENT_IS_PTHREAD||addRunDependency("wasm-instantiate");function receiveInstantiatedSource(output){receiveInstance(output.instance,output.module)}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err("failed to asynchronously prepare wasm: "+reason),abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming=="function"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch=="function")fetch(wasmBinaryFile,{credentials:"same-origin"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err("wasm streaming compile failed: "+reason),err("falling back to ArrayBuffer instantiation"),instantiateArrayBuffer(receiveInstantiatedSource)})});else return instantiateArrayBuffer(receiveInstantiatedSource)}if(Module.instantiateWasm)try{var exports2=Module.instantiateWasm(info,receiveInstance);return exports2}catch(e){return err("Module.instantiateWasm callback failed with error: "+e),!1}return instantiateAsync(),{}}var ASM_CONSTS={};function initPthreadsJS(){PThread.initRuntime()}ENVIRONMENT_IS_PTHREAD||__ATINIT__.push({func:function(){___wasm_call_ctors()}});var __pthread_ptr=0,__pthread_is_main_runtime_thread=0,__pthread_is_main_browser_thread=0;function __register_pthread_ptr(pthreadPtr,isMainBrowserThread,isMainRuntimeThread){pthreadPtr=pthreadPtr|0,isMainBrowserThread=isMainBrowserThread|0,isMainRuntimeThread=isMainRuntimeThread|0,__pthread_ptr=pthreadPtr,__pthread_is_main_browser_thread=isMainBrowserThread,__pthread_is_main_runtime_thread=isMainRuntimeThread}Module.__register_pthread_ptr=__register_pthread_ptr;var ERRNO_CODES={EPERM:63,ENOENT:44,ESRCH:71,EINTR:27,EIO:29,ENXIO:60,E2BIG:1,ENOEXEC:45,EBADF:8,ECHILD:12,EAGAIN:6,EWOULDBLOCK:6,ENOMEM:48,EACCES:2,EFAULT:21,ENOTBLK:105,EBUSY:10,EEXIST:20,EXDEV:75,ENODEV:43,ENOTDIR:54,EISDIR:31,EINVAL:28,ENFILE:41,EMFILE:33,ENOTTY:59,ETXTBSY:74,EFBIG:22,ENOSPC:51,ESPIPE:70,EROFS:69,EMLINK:34,EPIPE:64,EDOM:18,ERANGE:68,ENOMSG:49,EIDRM:24,ECHRNG:106,EL2NSYNC:156,EL3HLT:107,EL3RST:108,ELNRNG:109,EUNATCH:110,ENOCSI:111,EL2HLT:112,EDEADLK:16,ENOLCK:46,EBADE:113,EBADR:114,EXFULL:115,ENOANO:104,EBADRQC:103,EBADSLT:102,EDEADLOCK:16,EBFONT:101,ENOSTR:100,ENODATA:116,ETIME:117,ENOSR:118,ENONET:119,ENOPKG:120,EREMOTE:121,ENOLINK:47,EADV:122,ESRMNT:123,ECOMM:124,EPROTO:65,EMULTIHOP:36,EDOTDOT:125,EBADMSG:9,ENOTUNIQ:126,EBADFD:127,EREMCHG:128,ELIBACC:129,ELIBBAD:130,ELIBSCN:131,ELIBMAX:132,ELIBEXEC:133,ENOSYS:52,ENOTEMPTY:55,ENAMETOOLONG:37,ELOOP:32,EOPNOTSUPP:138,EPFNOSUPPORT:139,ECONNRESET:15,ENOBUFS:42,EAFNOSUPPORT:5,EPROTOTYPE:67,ENOTSOCK:57,ENOPROTOOPT:50,ESHUTDOWN:140,ECONNREFUSED:14,EADDRINUSE:3,ECONNABORTED:13,ENETUNREACH:40,ENETDOWN:38,ETIMEDOUT:73,EHOSTDOWN:142,EHOSTUNREACH:23,EINPROGRESS:26,EALREADY:7,EDESTADDRREQ:17,EMSGSIZE:35,EPROTONOSUPPORT:66,ESOCKTNOSUPPORT:137,EADDRNOTAVAIL:4,ENETRESET:39,EISCONN:30,ENOTCONN:53,ETOOMANYREFS:141,EUSERS:136,EDQUOT:19,ESTALE:72,ENOTSUP:138,ENOMEDIUM:148,EILSEQ:25,EOVERFLOW:61,ECANCELED:11,ENOTRECOVERABLE:56,EOWNERDEAD:62,ESTRPIPE:135},__main_thread_futex_wait_address=13568;function _emscripten_futex_wake(addr,count2){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&!0||count2<0)return-28;if(count2==0)return 0;count2>=2147483647&&(count2=Infinity);var mainThreadWaitAddress=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2),mainThreadWoken=0;if(mainThreadWaitAddress==addr){var loadedAddr=Atomics.compareExchange(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,mainThreadWaitAddress,0);if(loadedAddr==mainThreadWaitAddress&&(--count2,mainThreadWoken=1,count2<=0))return 1}var ret=Atomics.notify(GROWABLE_HEAP_I32(),addr>>2,count2);if(ret>=0)return ret+mainThreadWoken;throw"Atomics.notify returned an unexpected value "+ret}Module._emscripten_futex_wake=_emscripten_futex_wake;function __kill_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw"Internal Error! _kill_thread() can only ever be called from main application thread!";if(!pthread_ptr)throw"Internal Error! Null pthread_ptr in _kill_thread!";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];pthread.worker.terminate(),PThread.freeThreadData(pthread),PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(pthread.worker),1),pthread.worker.pthread=void 0}function __cancel_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw"Internal Error! _cancel_thread() can only ever be called from main application thread!";if(!pthread_ptr)throw"Internal Error! Null pthread_ptr in _cancel_thread!";var pthread=PThread.pthreads[pthread_ptr];pthread.worker.postMessage({cmd:"cancel"})}function __cleanup_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw"Internal Error! _cleanup_thread() can only ever be called from main application thread!";if(!pthread_ptr)throw"Internal Error! Null pthread_ptr in _cleanup_thread!";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];if(pthread){var worker=pthread.worker;PThread.returnWorkerToPool(worker)}}var PThread={MAIN_THREAD_ID:1,mainThreadInfo:{schedPolicy:0,schedPrio:0},unusedWorkers:[],runningWorkers:[],initRuntime:function(){__register_pthread_ptr(PThread.mainThreadBlock,!ENVIRONMENT_IS_WORKER,1),_emscripten_register_main_browser_thread_id(PThread.mainThreadBlock)},initMainThreadBlock:function(){for(var pthreadPoolSize=8,i=0;i>2]=PThread.mainThreadBlock;var headPtr=PThread.mainThreadBlock+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;for(var tlsMemory=13056,i=0;i<128;++i)GROWABLE_HEAP_U32()[tlsMemory/4+i]=0;Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+104>>2,tlsMemory),Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+40>>2,PThread.mainThreadBlock),Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+44>>2,42)},initWorker:function(){},pthreads:{},exitHandlers:null,setThreadStatus:function(){},runExitHandlers:function(){if(PThread.exitHandlers!==null){for(;PThread.exitHandlers.length>0;)PThread.exitHandlers.pop()();PThread.exitHandlers=null}ENVIRONMENT_IS_PTHREAD&&threadInfoStruct&&___pthread_tsd_run_dtors()},threadExit:function(exitCode){var tb=_pthread_self();tb&&(Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,exitCode),Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1),Atomics.store(GROWABLE_HEAP_U32(),tb+60>>2,1),Atomics.store(GROWABLE_HEAP_U32(),tb+64>>2,0),PThread.runExitHandlers(),_emscripten_futex_wake(tb+0,2147483647),__register_pthread_ptr(0,0,0),threadInfoStruct=0,ENVIRONMENT_IS_PTHREAD&&postMessage({cmd:"exit"}))},threadCancel:function(){PThread.runExitHandlers(),Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+4>>2,-1),Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+0>>2,1),_emscripten_futex_wake(threadInfoStruct+0,2147483647),threadInfoStruct=selfThreadId=0,__register_pthread_ptr(0,0,0),postMessage({cmd:"cancelDone"})},terminateAllThreads:function(){for(var t in PThread.pthreads){var pthread=PThread.pthreads[t];pthread&&pthread.worker&&PThread.returnWorkerToPool(pthread.worker)}PThread.pthreads={};for(var i=0;i>2];GROWABLE_HEAP_I32()[pthread.threadInfoStruct+104>>2]=0,_free(tlsMemory),_free(pthread.threadInfoStruct)}pthread.threadInfoStruct=0,pthread.allocatedOwnStack&&pthread.stackBase&&_free(pthread.stackBase),pthread.stackBase=0,pthread.worker&&(pthread.worker.pthread=null)}},returnWorkerToPool:function(worker){delete PThread.pthreads[worker.pthread.thread],PThread.unusedWorkers.push(worker),PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker),1),PThread.freeThreadData(worker.pthread),worker.pthread=void 0},receiveObjectTransfer:function(data2){},loadWasmModuleToWorker:function(worker,onFinishedLoading){worker.onmessage=function(e){var d=e.data,cmd=d.cmd;if(worker.pthread&&(PThread.currentProxiedOperationCallerThread=worker.pthread.threadInfoStruct),d.targetThread&&d.targetThread!=_pthread_self()){var thread=PThread.pthreads[d.targetThread];thread?thread.worker.postMessage(e.data,d.transferList):console.error('Internal error! Worker sent a message "'+cmd+'" to target pthread '+d.targetThread+", but that thread no longer exists!"),PThread.currentProxiedOperationCallerThread=void 0;return}if(cmd==="processQueuedMainThreadWork")_emscripten_main_thread_process_queued_calls();else if(cmd==="spawnThread")__spawn_thread(e.data);else if(cmd==="cleanupThread")__cleanup_thread(d.thread);else if(cmd==="killThread")__kill_thread(d.thread);else if(cmd==="cancelThread")__cancel_thread(d.thread);else if(cmd==="loaded")worker.loaded=!0,onFinishedLoading&&onFinishedLoading(worker),worker.runPthread&&(worker.runPthread(),delete worker.runPthread);else if(cmd==="print")out("Thread "+d.threadId+": "+d.text);else if(cmd==="printErr")err("Thread "+d.threadId+": "+d.text);else if(cmd==="alert")alert("Thread "+d.threadId+": "+d.text);else if(cmd==="exit"){var detached=worker.pthread&&Atomics.load(GROWABLE_HEAP_U32(),worker.pthread.thread+68>>2);detached&&PThread.returnWorkerToPool(worker)}else cmd==="cancelDone"?PThread.returnWorkerToPool(worker):cmd==="objectTransfer"?PThread.receiveObjectTransfer(e.data):e.data.target==="setimmediate"?worker.postMessage(e.data):err("worker sent an unknown command "+cmd);PThread.currentProxiedOperationCallerThread=void 0},worker.onerror=function(e){err("pthread sent an error! "+e.filename+":"+e.lineno+": "+e.message)},ENVIRONMENT_IS_NODE&&(worker.on("message",function(data2){worker.onmessage({data:data2})}),worker.on("error",function(data2){worker.onerror(data2)}),worker.on("exit",function(data2){console.log("worker exited - TODO: update the worker queue?")})),worker.postMessage({cmd:"load",urlOrBlob:Module.mainScriptUrlOrBlob||_scriptDir2,wasmMemory,wasmModule,DYNAMIC_BASE,DYNAMICTOP_PTR})},allocateUnusedWorker:function(){var pthreadMainJs=locateFile("tfjs-backend-wasm-threaded-simd.worker.js");PThread.unusedWorkers.push(new Worker(pthreadMainJs))},getNewWorker:function(){return PThread.unusedWorkers.length==0&&(PThread.allocateUnusedWorker(),PThread.loadWasmModuleToWorker(PThread.unusedWorkers[0])),PThread.unusedWorkers.length>0?PThread.unusedWorkers.pop():null},busySpinWait:function(msecs){for(var t=performance.now()+msecs;performance.now()>2]=value,value}function _atexit(func2,arg){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(1,1,func2,arg);__ATEXIT__.unshift({func:func2,arg})}function __emscripten_notify_thread_queue(targetThreadId,mainThreadId){if(targetThreadId==mainThreadId)postMessage({cmd:"processQueuedMainThreadWork"});else if(ENVIRONMENT_IS_PTHREAD)postMessage({targetThread:targetThreadId,cmd:"processThreadQueue"});else{var pthread=PThread.pthreads[targetThreadId],worker=pthread&&pthread.worker;if(!worker)return;worker.postMessage({cmd:"processThreadQueue"})}return 1}function _abort(){abort()}function _emscripten_conditional_set_current_thread_status(expectedStatus,newStatus){expectedStatus=expectedStatus|0,newStatus=newStatus|0}function _emscripten_futex_wait(addr,val,timeout){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&!0)return-28;if(ENVIRONMENT_IS_WORKER){var ret=Atomics.wait(GROWABLE_HEAP_I32(),addr>>2,val,timeout);if(ret==="timed-out")return-73;if(ret==="not-equal")return-6;if(ret==="ok")return 0;throw"Atomics.wait returned an unexpected value "+ret}else{var loadedVal=Atomics.load(GROWABLE_HEAP_I32(),addr>>2);if(val!=loadedVal)return-6;var tNow=performance.now(),tEnd=tNow+timeout;Atomics.store(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,addr);for(var ourWaitAddress=addr;addr==ourWaitAddress;){if(tNow=performance.now(),tNow>tEnd)return-73;_emscripten_main_thread_process_queued_calls(),addr=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2)}return 0}}function _emscripten_is_main_browser_thread(){return __pthread_is_main_browser_thread|0}function _emscripten_is_main_runtime_thread(){return __pthread_is_main_runtime_thread|0}function _emscripten_memcpy_big(dest,src,num){GROWABLE_HEAP_U8().copyWithin(dest,src,src+num)}function _emscripten_num_logical_cores(){return navigator.hardwareConcurrency}function _emscripten_proxy_to_main_thread_js(index,sync){for(var numCallArgs=arguments.length-2,stack2=stackSave(),args=stackAlloc(numCallArgs*8),b=args>>3,i=0;i>3]),buf+=8):(buf=buf+3&~3,args.push(GROWABLE_HEAP_I32()[buf>>2]),buf+=4);return args}function _emscripten_receive_on_main_thread_js(index,numCallArgs,args){_emscripten_receive_on_main_thread_js_callArgs.length=numCallArgs;for(var b=args>>3,i=0;i>>16),updateGlobalBufferAndViews(wasmMemory.buffer),1}catch(e){}}function _emscripten_resize_heap(requestedSize){requestedSize=requestedSize>>>0;var oldSize=_emscripten_get_heap_size();if(requestedSize<=oldSize)return!1;var PAGE_MULTIPLE=65536,maxHeapSize=2147483648;if(requestedSize>maxHeapSize)return!1;for(var minHeapSize=16777216,cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(minHeapSize,requestedSize,overGrownHeapSize),PAGE_MULTIPLE)),replacement=emscripten_realloc_buffer(newSize);if(replacement)return!0}return!1}var JSEvents={keyEvent:0,mouseEvent:0,wheelEvent:0,uiEvent:0,focusEvent:0,deviceOrientationEvent:0,deviceMotionEvent:0,fullscreenChangeEvent:0,pointerlockChangeEvent:0,visibilityChangeEvent:0,touchEvent:0,previousFullscreenElement:null,previousScreenX:null,previousScreenY:null,removeEventListenersRegistered:!1,removeAllEventListeners:function(){for(var i=JSEvents.eventHandlers.length-1;i>=0;--i)JSEvents._removeHandler(i);JSEvents.eventHandlers=[],JSEvents.deferredCalls=[]},registerRemoveEventListeners:function(){JSEvents.removeEventListenersRegistered||(__ATEXIT__.push(JSEvents.removeAllEventListeners),JSEvents.removeEventListenersRegistered=!0)},deferredCalls:[],deferCall:function(targetFunction,precedence,argsList){function arraysHaveEqualContent(arrA,arrB){if(arrA.length!=arrB.length)return!1;for(var i2 in arrA)if(arrA[i2]!=arrB[i2])return!1;return!0}for(var i in JSEvents.deferredCalls){var call=JSEvents.deferredCalls[i];if(call.targetFunction==targetFunction&&arraysHaveEqualContent(call.argsList,argsList))return}JSEvents.deferredCalls.push({targetFunction,precedence,argsList}),JSEvents.deferredCalls.sort(function(x,y){return x.precedence>2]=eventTypeId,GROWABLE_HEAP_I32()[varargs+4>>2]=eventData,GROWABLE_HEAP_I32()[varargs+8>>2]=userData,_emscripten_async_queue_on_thread_(targetThread,637534208,eventHandlerFunc,eventData,varargs),stackRestore(stackTop)},getTargetThreadForEventCallback:function(targetThread){switch(targetThread){case 1:return 0;case 2:return PThread.currentProxiedOperationCallerThread;default:return targetThread}},getNodeNameForTarget:function(target){return target?target==window?"#window":target==screen?"#screen":target&&target.nodeName?target.nodeName:"":""},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function stringToNewUTF8(jsString){var length=lengthBytesUTF8(jsString)+1,cString=_malloc(length);return stringToUTF8(jsString,cString,length),cString}function _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height){var stackTop=stackSave(),varargs=stackAlloc(12),targetCanvasPtr=0;targetCanvas&&(targetCanvasPtr=stringToNewUTF8(targetCanvas)),GROWABLE_HEAP_I32()[varargs>>2]=targetCanvasPtr,GROWABLE_HEAP_I32()[varargs+4>>2]=width,GROWABLE_HEAP_I32()[varargs+8>>2]=height,_emscripten_async_queue_on_thread_(targetThread,657457152,0,targetCanvasPtr,varargs),stackRestore(stackTop)}function _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,targetCanvas,width,height){targetCanvas=targetCanvas?UTF8ToString(targetCanvas):"",_emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height)}function __maybeCStringToJsString(cString){return cString>2?UTF8ToString(cString):cString}var specialHTMLTargets=[0,typeof document!="undefined"?document:0,typeof window!="undefined"?window:0];function __findEventTarget(target){target=__maybeCStringToJsString(target);var domElement=specialHTMLTargets[target]||(typeof document!="undefined"?document.querySelector(target):void 0);return domElement}function __findCanvasEventTarget(target){return __findEventTarget(target)}function _emscripten_set_canvas_element_size_calling_thread(target,width,height){var canvas=__findCanvasEventTarget(target);if(!canvas)return-4;if(canvas.canvasSharedPtr&&(GROWABLE_HEAP_I32()[canvas.canvasSharedPtr>>2]=width,GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+4>>2]=height),canvas.offscreenCanvas||!canvas.controlTransferredOffscreen){canvas.offscreenCanvas&&(canvas=canvas.offscreenCanvas);var autoResizeViewport=!1;if(canvas.GLctxObject&&canvas.GLctxObject.GLctx){var prevViewport=canvas.GLctxObject.GLctx.getParameter(2978);autoResizeViewport=prevViewport[0]===0&&prevViewport[1]===0&&prevViewport[2]===canvas.width&&prevViewport[3]===canvas.height}canvas.width=width,canvas.height=height,autoResizeViewport&&canvas.GLctxObject.GLctx.viewport(0,0,width,height)}else if(canvas.canvasSharedPtr){var targetThread=GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+8>>2];return _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,target,width,height),1}else return-4;return 0}function _emscripten_set_canvas_element_size_main_thread(target,width,height){return ENVIRONMENT_IS_PTHREAD?_emscripten_proxy_to_main_thread_js(2,1,target,width,height):_emscripten_set_canvas_element_size_calling_thread(target,width,height)}function _emscripten_set_canvas_element_size(target,width,height){var canvas=__findCanvasEventTarget(target);return canvas?_emscripten_set_canvas_element_size_calling_thread(target,width,height):_emscripten_set_canvas_element_size_main_thread(target,width,height)}function _emscripten_set_current_thread_status(newStatus){newStatus=newStatus|0}function _emscripten_set_thread_name(threadId,name){threadId=threadId|0,name=name|0}function __webgl_enable_ANGLE_instanced_arrays(ctx){var ext=ctx.getExtension("ANGLE_instanced_arrays");if(ext)return ctx.vertexAttribDivisor=function(index,divisor){ext.vertexAttribDivisorANGLE(index,divisor)},ctx.drawArraysInstanced=function(mode,first,count2,primcount){ext.drawArraysInstancedANGLE(mode,first,count2,primcount)},ctx.drawElementsInstanced=function(mode,count2,type,indices,primcount){ext.drawElementsInstancedANGLE(mode,count2,type,indices,primcount)},1}function __webgl_enable_OES_vertex_array_object(ctx){var ext=ctx.getExtension("OES_vertex_array_object");if(ext)return ctx.createVertexArray=function(){return ext.createVertexArrayOES()},ctx.deleteVertexArray=function(vao){ext.deleteVertexArrayOES(vao)},ctx.bindVertexArray=function(vao){ext.bindVertexArrayOES(vao)},ctx.isVertexArray=function(vao){return ext.isVertexArrayOES(vao)},1}function __webgl_enable_WEBGL_draw_buffers(ctx){var ext=ctx.getExtension("WEBGL_draw_buffers");if(ext)return ctx.drawBuffers=function(n,bufs){ext.drawBuffersWEBGL(n,bufs)},1}var GL={counter:1,lastError:0,buffers:[],mappedBuffers:{},programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},currentContext:null,offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,init:function(){for(var miniTempFloatBuffer=new Float32Array(GL.MINI_TEMP_BUFFER_SIZE),i=0;i>2]:-1;source+=UTF8ToString(GROWABLE_HEAP_I32()[string+i*4>>2],len<0?void 0:len)}return source},createContext:function(canvas,webGLContextAttributes){var ctx=canvas.getContext("webgl",webGLContextAttributes);if(!ctx)return 0;var handle=GL.registerContext(ctx,webGLContextAttributes);return handle},registerContext:function(ctx,webGLContextAttributes){var handle=_malloc(8);GROWABLE_HEAP_I32()[handle+4>>2]=_pthread_self();var context={handle,attributes:webGLContextAttributes,version:webGLContextAttributes.majorVersion,GLctx:ctx};return ctx.canvas&&(ctx.canvas.GLctxObject=context),GL.contexts[handle]=context,(typeof webGLContextAttributes.enableExtensionsByDefault=="undefined"||webGLContextAttributes.enableExtensionsByDefault)&&GL.initExtensions(context),handle},makeContextCurrent:function(contextHandle){return GL.currentContext=GL.contexts[contextHandle],Module.ctx=GLctx=GL.currentContext&&GL.currentContext.GLctx,!(contextHandle&&!GLctx)},getContext:function(contextHandle){return GL.contexts[contextHandle]},deleteContext:function(contextHandle){GL.currentContext===GL.contexts[contextHandle]&&(GL.currentContext=null),typeof JSEvents=="object"&&JSEvents.removeAllHandlersOnTarget(GL.contexts[contextHandle].GLctx.canvas),GL.contexts[contextHandle]&&GL.contexts[contextHandle].GLctx.canvas&&(GL.contexts[contextHandle].GLctx.canvas.GLctxObject=void 0),_free(GL.contexts[contextHandle].handle),GL.contexts[contextHandle]=null},initExtensions:function(context){if(context||(context=GL.currentContext),!context.initExtensionsDone){context.initExtensionsDone=!0;var GLctx2=context.GLctx;__webgl_enable_ANGLE_instanced_arrays(GLctx2),__webgl_enable_OES_vertex_array_object(GLctx2),__webgl_enable_WEBGL_draw_buffers(GLctx2),GLctx2.disjointTimerQueryExt=GLctx2.getExtension("EXT_disjoint_timer_query");var automaticallyEnabledExtensions=["OES_texture_float","OES_texture_half_float","OES_standard_derivatives","OES_vertex_array_object","WEBGL_compressed_texture_s3tc","WEBGL_depth_texture","OES_element_index_uint","EXT_texture_filter_anisotropic","EXT_frag_depth","WEBGL_draw_buffers","ANGLE_instanced_arrays","OES_texture_float_linear","OES_texture_half_float_linear","EXT_blend_minmax","EXT_shader_texture_lod","EXT_texture_norm16","WEBGL_compressed_texture_pvrtc","EXT_color_buffer_half_float","WEBGL_color_buffer_float","EXT_sRGB","WEBGL_compressed_texture_etc1","EXT_disjoint_timer_query","WEBGL_compressed_texture_etc","WEBGL_compressed_texture_astc","EXT_color_buffer_float","WEBGL_compressed_texture_s3tc_srgb","EXT_disjoint_timer_query_webgl2","WEBKIT_WEBGL_compressed_texture_pvrtc"],exts=GLctx2.getSupportedExtensions()||[];exts.forEach(function(ext){automaticallyEnabledExtensions.indexOf(ext)!=-1&&GLctx2.getExtension(ext)})}},populateUniformTable:function(program){for(var p2=GL.programs[program],ptable=GL.programInfos[program]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1},utable=ptable.uniforms,numUniforms=GLctx.getProgramParameter(p2,35718),i=0;i>2;contextAttributes.alpha=!!GROWABLE_HEAP_I32()[a+(0>>2)],contextAttributes.depth=!!GROWABLE_HEAP_I32()[a+(4>>2)],contextAttributes.stencil=!!GROWABLE_HEAP_I32()[a+(8>>2)],contextAttributes.antialias=!!GROWABLE_HEAP_I32()[a+(12>>2)],contextAttributes.premultipliedAlpha=!!GROWABLE_HEAP_I32()[a+(16>>2)],contextAttributes.preserveDrawingBuffer=!!GROWABLE_HEAP_I32()[a+(20>>2)];var powerPreference=GROWABLE_HEAP_I32()[a+(24>>2)];contextAttributes.powerPreference=__emscripten_webgl_power_preferences[powerPreference],contextAttributes.failIfMajorPerformanceCaveat=!!GROWABLE_HEAP_I32()[a+(28>>2)],contextAttributes.majorVersion=GROWABLE_HEAP_I32()[a+(32>>2)],contextAttributes.minorVersion=GROWABLE_HEAP_I32()[a+(36>>2)],contextAttributes.enableExtensionsByDefault=GROWABLE_HEAP_I32()[a+(40>>2)],contextAttributes.explicitSwapControl=GROWABLE_HEAP_I32()[a+(44>>2)],contextAttributes.proxyContextToMainThread=GROWABLE_HEAP_I32()[a+(48>>2)],contextAttributes.renderViaOffscreenBackBuffer=GROWABLE_HEAP_I32()[a+(52>>2)];var canvas=__findCanvasEventTarget(target);if(!canvas)return-4;if(contextAttributes.explicitSwapControl)return-1;var contextHandle=GL.createContext(canvas,contextAttributes);return contextHandle}function _emscripten_webgl_create_context(a0,a12){return _emscripten_webgl_do_create_context(a0,a12)}var PATH={splitPath:function(filename){var splitPathRe=/^(\/?|)([\s\S]*?)((?:\.{1,2}|[^\/]+?|)(\.[^.\/]*|))(?:[\/]*)$/;return splitPathRe.exec(filename).slice(1)},normalizeArray:function(parts,allowAboveRoot){for(var up=0,i=parts.length-1;i>=0;i--){var last=parts[i];last==="."?parts.splice(i,1):last===".."?(parts.splice(i,1),up++):up&&(parts.splice(i,1),up--)}if(allowAboveRoot)for(;up;up--)parts.unshift("..");return parts},normalize:function(path){var isAbsolute=path.charAt(0)==="/",trailingSlash=path.substr(-1)==="/";return path=PATH.normalizeArray(path.split("/").filter(function(p2){return!!p2}),!isAbsolute).join("/"),!path&&!isAbsolute&&(path="."),path&&trailingSlash&&(path+="/"),(isAbsolute?"/":"")+path},dirname:function(path){var result=PATH.splitPath(path),root=result[0],dir=result[1];return!root&&!dir?".":(dir&&(dir=dir.substr(0,dir.length-1)),root+dir)},basename:function(path){if(path==="/")return"/";var lastSlash=path.lastIndexOf("/");return lastSlash===-1?path:path.substr(lastSlash+1)},extname:function(path){return PATH.splitPath(path)[3]},join:function(){var paths=Array.prototype.slice.call(arguments,0);return PATH.normalize(paths.join("/"))},join2:function(l,r){return PATH.normalize(l+"/"+r)}},SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer3=SYSCALLS.buffers[stream];curr===0||curr===10?((stream===1?out:err)(UTF8ArrayToString(buffer3,0)),buffer3.length=0):buffer3.push(curr)},varargs:void 0,get:function(){SYSCALLS.varargs+=4;var ret=GROWABLE_HEAP_I32()[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){return ENVIRONMENT_IS_PTHREAD?_emscripten_proxy_to_main_thread_js(3,1,fd):0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(4,1,fd,offset_low,offset_high,whence,newOffset)}function _fd_write(fd,iov,iovcnt,pnum){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(5,1,fd,iov,iovcnt,pnum);for(var num=0,i=0;i>2],len=GROWABLE_HEAP_I32()[iov+(i*8+4)>>2],j=0;j>2]=num,0}function _pthread_cleanup_pop(execute2){var routine=PThread.exitHandlers.pop();execute2&&routine()}function _pthread_cleanup_push(routine,arg){PThread.exitHandlers===null&&(PThread.exitHandlers=[]),PThread.exitHandlers.push(function(){dynCall_vi(routine,arg)})}function __spawn_thread(threadParams){if(ENVIRONMENT_IS_PTHREAD)throw"Internal Error! _spawn_thread() can only ever be called from main application thread!";var worker=PThread.getNewWorker();if(worker.pthread!==void 0)throw"Internal error!";if(!threadParams.pthread_ptr)throw"Internal error, no pthread ptr!";PThread.runningWorkers.push(worker);for(var tlsMemory=_malloc(128*4),i=0;i<128;++i)GROWABLE_HEAP_I32()[tlsMemory+i*4>>2]=0;var stackHigh=threadParams.stackBase+threadParams.stackSize,pthread=PThread.pthreads[threadParams.pthread_ptr]={worker,stackBase:threadParams.stackBase,stackSize:threadParams.stackSize,allocatedOwnStack:threadParams.allocatedOwnStack,thread:threadParams.pthread_ptr,threadInfoStruct:threadParams.pthread_ptr},tis=pthread.threadInfoStruct>>2;Atomics.store(GROWABLE_HEAP_U32(),tis+(0>>2),0),Atomics.store(GROWABLE_HEAP_U32(),tis+(4>>2),0),Atomics.store(GROWABLE_HEAP_U32(),tis+(8>>2),0),Atomics.store(GROWABLE_HEAP_U32(),tis+(68>>2),threadParams.detached),Atomics.store(GROWABLE_HEAP_U32(),tis+(104>>2),tlsMemory),Atomics.store(GROWABLE_HEAP_U32(),tis+(48>>2),0),Atomics.store(GROWABLE_HEAP_U32(),tis+(40>>2),pthread.threadInfoStruct),Atomics.store(GROWABLE_HEAP_U32(),tis+(44>>2),42),Atomics.store(GROWABLE_HEAP_U32(),tis+(108>>2),threadParams.stackSize),Atomics.store(GROWABLE_HEAP_U32(),tis+(84>>2),threadParams.stackSize),Atomics.store(GROWABLE_HEAP_U32(),tis+(80>>2),stackHigh),Atomics.store(GROWABLE_HEAP_U32(),tis+(108+8>>2),stackHigh),Atomics.store(GROWABLE_HEAP_U32(),tis+(108+12>>2),threadParams.detached),Atomics.store(GROWABLE_HEAP_U32(),tis+(108+20>>2),threadParams.schedPolicy),Atomics.store(GROWABLE_HEAP_U32(),tis+(108+24>>2),threadParams.schedPrio);var global_libc=_emscripten_get_global_libc(),global_locale=global_libc+40;Atomics.store(GROWABLE_HEAP_U32(),tis+(176>>2),global_locale),worker.pthread=pthread;var msg={cmd:"run",start_routine:threadParams.startRoutine,arg:threadParams.arg,threadInfoStruct:threadParams.pthread_ptr,selfThreadId:threadParams.pthread_ptr,parentThreadId:threadParams.parent_pthread_ptr,stackBase:threadParams.stackBase,stackSize:threadParams.stackSize};worker.runPthread=function(){msg.time=performance.now(),worker.postMessage(msg,threadParams.transferList)},worker.loaded&&(worker.runPthread(),delete worker.runPthread)}function _pthread_getschedparam(thread,policy,schedparam){if(!policy&&!schedparam)return ERRNO_CODES.EINVAL;if(!thread)return err("pthread_getschedparam called with a null thread pointer!"),ERRNO_CODES.ESRCH;var self2=GROWABLE_HEAP_I32()[thread+12>>2];if(self2!==thread)return err("pthread_getschedparam attempted on thread "+thread+", which does not point to a valid thread, or does not exist anymore!"),ERRNO_CODES.ESRCH;var schedPolicy=Atomics.load(GROWABLE_HEAP_U32(),thread+108+20>>2),schedPrio=Atomics.load(GROWABLE_HEAP_U32(),thread+108+24>>2);return policy&&(GROWABLE_HEAP_I32()[policy>>2]=schedPolicy),schedparam&&(GROWABLE_HEAP_I32()[schedparam>>2]=schedPrio),0}function _pthread_self(){return __pthread_ptr|0}Module._pthread_self=_pthread_self;function _pthread_create(pthread_ptr,attr,start_routine,arg){if(typeof SharedArrayBuffer=="undefined")return err("Current environment does not support SharedArrayBuffer, pthreads are not available!"),6;if(!pthread_ptr)return err("pthread_create called with a null thread pointer!"),28;var transferList=[],error=0;if(ENVIRONMENT_IS_PTHREAD&&(transferList.length===0||error))return _emscripten_sync_run_in_main_thread_4(687865856,pthread_ptr,attr,start_routine,arg);if(error)return error;var stackSize=0,stackBase=0,detached=0,schedPolicy=0,schedPrio=0;if(attr){stackSize=GROWABLE_HEAP_I32()[attr>>2],stackSize+=81920,stackBase=GROWABLE_HEAP_I32()[attr+8>>2],detached=GROWABLE_HEAP_I32()[attr+12>>2]!==0;var inheritSched=GROWABLE_HEAP_I32()[attr+16>>2]===0;if(inheritSched){var prevSchedPolicy=GROWABLE_HEAP_I32()[attr+20>>2],prevSchedPrio=GROWABLE_HEAP_I32()[attr+24>>2],parentThreadPtr=PThread.currentProxiedOperationCallerThread?PThread.currentProxiedOperationCallerThread:_pthread_self();_pthread_getschedparam(parentThreadPtr,attr+20,attr+24),schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2],schedPrio=GROWABLE_HEAP_I32()[attr+24>>2],GROWABLE_HEAP_I32()[attr+20>>2]=prevSchedPolicy,GROWABLE_HEAP_I32()[attr+24>>2]=prevSchedPrio}else schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2],schedPrio=GROWABLE_HEAP_I32()[attr+24>>2]}else stackSize=2097152;var allocatedOwnStack=stackBase==0;allocatedOwnStack?stackBase=_memalign(16,stackSize):(stackBase-=stackSize,assert3(stackBase>0));for(var threadInfoStruct2=_malloc(232),i=0;i<232>>2;++i)GROWABLE_HEAP_U32()[(threadInfoStruct2>>2)+i]=0;GROWABLE_HEAP_I32()[pthread_ptr>>2]=threadInfoStruct2,GROWABLE_HEAP_I32()[threadInfoStruct2+12>>2]=threadInfoStruct2;var headPtr=threadInfoStruct2+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var threadParams={stackBase,stackSize,allocatedOwnStack,schedPolicy,schedPrio,detached,startRoutine:start_routine,pthread_ptr:threadInfoStruct2,parent_pthread_ptr:_pthread_self(),arg,transferList};return ENVIRONMENT_IS_PTHREAD?(threadParams.cmd="spawnThread",postMessage(threadParams,transferList)):__spawn_thread(threadParams),0}function _roundf(d){return d=+d,d>=0?+Math_floor(d+.5):+Math_ceil(d-.5)}function _sysconf(name){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(6,1,name);switch(name){case 30:return 16384;case 85:var maxHeapSize=2147483648;return maxHeapSize/16384;case 132:case 133:case 12:case 137:case 138:case 15:case 235:case 16:case 17:case 18:case 19:case 20:case 149:case 13:case 10:case 236:case 153:case 9:case 21:case 22:case 159:case 154:case 14:case 77:case 78:case 139:case 80:case 81:case 82:case 68:case 67:case 164:case 11:case 29:case 47:case 48:case 95:case 52:case 51:case 46:case 79:return 200809;case 27:case 246:case 127:case 128:case 23:case 24:case 160:case 161:case 181:case 182:case 242:case 183:case 184:case 243:case 244:case 245:case 165:case 178:case 179:case 49:case 50:case 168:case 169:case 175:case 170:case 171:case 172:case 97:case 76:case 32:case 173:case 35:return-1;case 176:case 177:case 7:case 155:case 8:case 157:case 125:case 126:case 92:case 93:case 129:case 130:case 131:case 94:case 91:return 1;case 74:case 60:case 69:case 70:case 4:return 1024;case 31:case 42:case 72:return 32;case 87:case 26:case 33:return 2147483647;case 34:case 1:return 47839;case 38:case 36:return 99;case 43:case 37:return 2048;case 0:return 2097152;case 3:return 65536;case 28:return 32768;case 44:return 32767;case 75:return 16384;case 39:return 1e3;case 89:return 700;case 71:return 256;case 40:return 255;case 2:return 100;case 180:return 64;case 25:return 20;case 5:return 16;case 6:return 6;case 73:return 4;case 84:return typeof navigator=="object"&&navigator.hardwareConcurrency||1}return setErrNo(28),-1}ENVIRONMENT_IS_PTHREAD?PThread.initWorker():PThread.initMainThreadBlock();var GLctx;GL.init();var proxiedFunctionTable=[null,_atexit,_emscripten_set_canvas_element_size_main_thread,_fd_close,_fd_seek,_fd_write,_sysconf],asmLibraryArg={e:___assert_fail,r:___call_main,w:__emscripten_notify_thread_queue,a:_abort,l:_emscripten_conditional_set_current_thread_status,d:_emscripten_futex_wait,c:_emscripten_futex_wake,h:_emscripten_get_now,g:_emscripten_is_main_browser_thread,x:_emscripten_is_main_runtime_thread,q:_emscripten_memcpy_big,B:_emscripten_num_logical_cores,t:_emscripten_receive_on_main_thread_js,A:_emscripten_resize_heap,u:_emscripten_set_canvas_element_size,k:_emscripten_set_current_thread_status,s:_emscripten_set_thread_name,v:_emscripten_webgl_create_context,m:_fd_close,o:_fd_seek,i:_fd_write,p:initPthreadsJS,memory:wasmMemory||Module.wasmMemory,y:_pthread_cleanup_pop,z:_pthread_cleanup_push,j:_pthread_create,b:_pthread_self,f:_roundf,n:_sysconf,table:wasmTable},asm=createWasm();Module.asm=asm;var 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Uint8Array?"int32":isNumber(values)?"float32":isString(values)?"string":isBoolean(values)?"bool":"float32"}function isFunction(f){return!!(f&&f.constructor&&f.call&&f.apply)}function nearestDivisor(size,start){for(let i=start;i=0;--i)strides[i]=strides[i+1]*shape[i+1];return strides}function createNestedArray(offset,shape,a){let ret=new Array;if(shape.length===1){let d=shape[0];for(let i=0;iacc*c);for(let i=0;iacc*c);if(size===0)return[];if(size!==a.length)throw new Error(`[${shape}] does not match the input size ${a.length}.`);return createNestedArray(0,shape,a)}function makeOnesTypedArray(size,dtype){let array2=makeZerosTypedArray(size,dtype);for(let i=0;iprev*curr,1);if(dtype==null||dtype==="float32")return toNestedArray(shape,new Float32Array(size));if(dtype==="int32")return toNestedArray(shape,new Int32Array(size));if(dtype==="bool")return toNestedArray(shape,new Uint8Array(size));throw new Error(`Unknown data type ${dtype}`)}function assertNonNegativeIntegerDimensions(shape){shape.forEach(dimSize=>{assert(Number.isInteger(dimSize)&&dimSize>=0,()=>`Tensor must have a shape comprised of positive integers but got shape [${shape}].`)})}function locToIndex(locs,rank,strides){if(rank===0)return 0;if(rank===1)return locs[0];let index=locs[locs.length-1];for(let i=0;i{let[key,value]=keyValue.split(":");this.urlFlags[key]=parseValue(key,value)})}};function getQueryParams(queryString){let params={};return queryString.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g,(s,...t)=>(decodeParam(params,t[0],t[1]),t.join("="))),params}function decodeParam(params,name,value){params[decodeURIComponent(name)]=decodeURIComponent(value||"")}function parseValue(flagName,value){if(value=value.toLowerCase(),value==="true"||value==="false")return value==="true";if(`${+value}`===value)return+value;throw new Error(`Could not parse value flag value ${value} for flag ${flagName}.`)}function env(){return ENV}var ENV=null;function setEnvironmentGlobal(environment){ENV=environment}var globalNameSpace;function getGlobalNamespace(){if(globalNameSpace==null){let ns;if(typeof window!="undefined")ns=window;else if(typeof global!="undefined")ns=global;else if(typeof process!="undefined")ns=process;else if(typeof self!="undefined")ns=self;else throw new Error("Could not find a global object");globalNameSpace=ns}return globalNameSpace}function getGlobalMap(){let ns=getGlobalNamespace();return ns._tfGlobals==null&&(ns._tfGlobals=new Map),ns._tfGlobals}function getGlobal(key,init2){let globalMap=getGlobalMap();if(globalMap.has(key))return globalMap.get(key);{let singleton=init2();return globalMap.set(key,singleton),globalMap.get(key)}}var Abs="Abs",Acos="Acos",Acosh="Acosh",Add="Add",AddN="AddN",All="All",Any="Any",ArgMax="ArgMax",ArgMin="ArgMin",Asin="Asin",Asinh="Asinh",Atan="Atan",Atanh="Atanh",Atan2="Atan2",AvgPool="AvgPool",AvgPoolGrad="AvgPoolGrad",AvgPool3D="AvgPool3D",AvgPool3DGrad="AvgPool3DGrad",BatchMatMul="BatchMatMul",BatchToSpaceND="BatchToSpaceND",Bincount="Bincount",BroadcastTo="BroadcastTo",Cast="Cast",Ceil="Ceil",ClipByValue="ClipByValue",Complex="Complex",ComplexAbs="ComplexAbs",Concat="Concat",Conv2D="Conv2D",Conv2DBackpropFilter="Conv2DBackpropFilter",Conv2DBackpropInput="Conv2DBackpropInput",Conv3D="Conv3D",Conv3DBackpropFilterV2="Conv3DBackpropFilterV2",Conv3DBackpropInputV2="Conv3DBackpropInputV2",Cos="Cos",Cosh="Cosh",Cumsum="Cumsum",CropAndResize="CropAndResize",DenseBincount="DenseBincount",DepthToSpace="DepthToSpace",DepthwiseConv2dNative="DepthwiseConv2dNative",DepthwiseConv2dNativeBackpropFilter="DepthwiseConv2dNativeBackpropFilter",DepthwiseConv2dNativeBackpropInput="DepthwiseConv2dNativeBackpropInput",Diag="Diag",Dilation2D="Dilation2D",Dilation2DBackpropInput="Dilation2DBackpropInput",Dilation2DBackpropFilter="Dilation2DBackpropFilter",RealDiv="RealDiv",Elu="Elu",EluGrad="EluGrad",Erf="Erf",Equal="Equal",Exp="Exp",ExpandDims="ExpandDims",Expm1="Expm1",FFT="FFT",Fill="Fill",FlipLeftRight="FlipLeftRight",Floor="Floor",FloorDiv="FloorDiv",FusedBatchNorm="FusedBatchNorm",GatherV2="GatherV2",GatherNd="GatherNd",Greater="Greater",GreaterEqual="GreaterEqual",Identity="Identity",IFFT="IFFT",Imag="Imag",IsFinite="IsFinite",IsInf="IsInf",IsNan="IsNan",LeakyRelu="LeakyRelu",Less="Less",LessEqual="LessEqual",LinSpace="LinSpace",Log="Log",Log1p="Log1p",LogicalAnd="LogicalAnd",LogicalNot="LogicalNot",LogicalOr="LogicalOr",LogSoftmax="LogSoftmax",LRN="LRN",LRNGrad="LRNGrad",Max="Max",Maximum="Maximum",MaxPool="MaxPool",MaxPoolGrad="MaxPoolGrad",MaxPool3D="MaxPool3D",MaxPool3DGrad="MaxPool3DGrad",MaxPoolWithArgmax="MaxPoolWithArgmax",Mean="Mean",Min="Min",Minimum="Minimum",MirrorPad="MirrorPad",Mod="Mod",Multinomial="Multinomial",Multiply="Multiply",Neg="Neg",NotEqual="NotEqual",NonMaxSuppressionV3="NonMaxSuppressionV3",NonMaxSuppressionV4="NonMaxSuppressionV4",NonMaxSuppressionV5="NonMaxSuppressionV5",OnesLike="OnesLike",OneHot="OneHot",Pack="Pack",PadV2="PadV2",Pool="Pool",Pow="Pow",Prelu="Prelu",Prod="Prod",Range="Range",Real="Real",Reciprocal="Reciprocal",Relu="Relu",Reshape="Reshape",ResizeNearestNeighbor="ResizeNearestNeighbor",ResizeNearestNeighborGrad="ResizeNearestNeighborGrad",ResizeBilinear="ResizeBilinear",ResizeBilinearGrad="ResizeBilinearGrad",Relu6="Relu6",Reverse="Reverse",Round="Round",Rsqrt="Rsqrt",ScatterNd="ScatterNd",Select="Select",Selu="Selu",Slice="Slice",Sin="Sin",Sinh="Sinh",Sign="Sign",Sigmoid="Sigmoid",Softplus="Softplus",Sqrt="Sqrt",Sum="Sum",SpaceToBatchND="SpaceToBatchND",SplitV="SplitV",Softmax="Softmax",SquaredDifference="SquaredDifference",Square="Square",Sub="Sub",SparseToDense="SparseToDense",StridedSlice="StridedSlice",Tan="Tan",Tanh="Tanh",Tile="Tile",TopK="TopK",Transpose="Transpose",Unique="Unique",Unpack="Unpack",UnsortedSegmentSum="UnsortedSegmentSum",ZerosLike="ZerosLike",Step="Step",FromPixels="FromPixels",RotateWithOffset="RotateWithOffset",_FusedMatMul="_FusedMatMul",FusedConv2D="FusedConv2D",FusedDepthwiseConv2D="FusedDepthwiseConv2D",kernelRegistry=getGlobal("kernelRegistry",()=>new Map),gradRegistry=getGlobal("gradRegistry",()=>new Map);function getKernel(kernelName,backendName){let key=makeKey(kernelName,backendName);return kernelRegistry.get(key)}function getGradient(kernelName){return gradRegistry.get(kernelName)}function getKernelsForBackend(backendName){let it=kernelRegistry.entries(),result=[];for(;;){let{done,value}=it.next();if(done)break;let[key,config2]=value,[backend22]=key.split("_");backend22===backendName&&result.push(config2)}return result}function registerKernel(config2){let{kernelName,backendName}=config2,key=makeKey(kernelName,backendName);kernelRegistry.has(key)&&console.warn(`The kernel '${kernelName}' for backend '${backendName}' is already registered`),kernelRegistry.set(key,config2)}function registerGradient(config2){let{kernelName}=config2;gradRegistry.has(kernelName)&&env().getBool("DEBUG")&&console.warn(`Overriding the gradient for '${kernelName}'`),gradRegistry.set(kernelName,config2)}function unregisterKernel(kernelName,backendName){let key=makeKey(kernelName,backendName);if(!kernelRegistry.has(key))throw new Error(`The kernel '${kernelName}' for backend '${backendName}' is not registered`);kernelRegistry.delete(key)}function unregisterGradient(kernelName){if(!gradRegistry.has(kernelName))throw new Error(`The gradient '${kernelName}' for backend is not registered`);gradRegistry.delete(kernelName)}function copyRegisteredKernels(registeredBackendName,newBackendName){getKernelsForBackend(registeredBackendName).forEach(kernelConfig=>{let newKernelConfig=Object.assign({},kernelConfig,{backendName:newBackendName});registerKernel(newKernelConfig)})}function makeKey(kernelName,backendName){return`${backendName}_${kernelName}`}var util_exports={};__export2(util_exports,{arraysEqual:()=>arraysEqual,assert:()=>assert,assertNonNegativeIntegerDimensions:()=>assertNonNegativeIntegerDimensions,assertNonNull:()=>assertNonNull,assertShapesMatch:()=>assertShapesMatch,bytesFromStringArray:()=>bytesFromStringArray,bytesPerElement:()=>bytesPerElement,checkConversionForErrors:()=>checkConversionForErrors,clamp:()=>clamp,computeStrides:()=>computeStrides,createScalarValue:()=>createScalarValue,createShuffledIndices:()=>createShuffledIndices,decodeString:()=>decodeString,distSquared:()=>distSquared,encodeString:()=>encodeString,fetch:()=>fetch2,flatten:()=>flatten,getArrayFromDType:()=>getArrayFromDType,getTypedArrayFromDType:()=>getTypedArrayFromDType,hasEncodingLoss:()=>hasEncodingLoss,indexToLoc:()=>indexToLoc,inferDtype:()=>inferDtype,inferFromImplicitShape:()=>inferFromImplicitShape,isBoolean:()=>isBoolean,isFunction:()=>isFunction,isInt:()=>isInt,isNumber:()=>isNumber,isPromise:()=>isPromise,isScalarShape:()=>isScalarShape,isString:()=>isString,isTypedArray:()=>isTypedArray,isValidDtype:()=>isValidDtype,locToIndex:()=>locToIndex,makeOnesTypedArray:()=>makeOnesTypedArray,makeZerosNestedTypedArray:()=>makeZerosNestedTypedArray,makeZerosTypedArray:()=>makeZerosTypedArray,nearestDivisor:()=>nearestDivisor,nearestLargerEven:()=>nearestLargerEven,now:()=>now,parseAxisParam:()=>parseAxisParam,randUniform:()=>randUniform,repeatedTry:()=>repeatedTry,rightPad:()=>rightPad,shuffle:()=>shuffle,sizeFromShape:()=>sizeFromShape,sizeToSquarishShape:()=>sizeToSquarishShape,squeezeShape:()=>squeezeShape,sum:()=>sum,tanh:()=>tanh,toNestedArray:()=>toNestedArray,toTypedArray:()=>toTypedArray});function createScalarValue(value,dtype){return dtype==="string"?encodeString(value):toTypedArray([value],dtype)}function noConversionNeeded(a,dtype){return a instanceof Float32Array&&dtype==="float32"||a instanceof Int32Array&&dtype==="int32"||a instanceof Uint8Array&&dtype==="bool"}function toTypedArray(a,dtype){if(dtype==="string")throw new Error("Cannot convert a string[] to a TypedArray");if(Array.isArray(a)&&(a=flatten(a)),env().getBool("DEBUG")&&checkConversionForErrors(a,dtype),noConversionNeeded(a,dtype))return a;if(dtype==null||dtype==="float32"||dtype==="complex64")return new Float32Array(a);if(dtype==="int32")return new Int32Array(a);if(dtype==="bool"){let bool=new Uint8Array(a.length);for(let i=0;i{outputs=f()},timer=this.backendTimer.time(holdResultWrapperFn);if(env().getBool("CHECK_COMPUTATION_FOR_ERRORS"))for(let i=0;i{checkComputationForErrors(tensorVals,output.dtype,kernelName)})}return{kernelName,outputs,inputs,timeMs:timer.then(timing=>timing.kernelMs),extraInfo:timer.then(timing=>timing.getExtraProfileInfo!=null?timing.getExtraProfileInfo():"")}}logKernelProfile(kernelProfile){let{kernelName,outputs,timeMs,inputs,extraInfo}=kernelProfile;outputs.forEach(result=>{Promise.all([result.data(),timeMs,extraInfo]).then(valueContainer=>{this.logger.logKernelProfile(kernelName,result,valueContainer[0],valueContainer[1],inputs,valueContainer[2])})})}};function checkComputationForErrors(vals,dtype,kernelName){if(dtype!=="float32")return!1;for(let i=0;i0?inputShape:""} `}}console.log(`%c${paddedName} %c${time2} %c${rank}D ${shape} %c${size} %c${inputShapesDescription} %c${extraInfo}`,"font-weight:bold","color:red","color:blue","color: orange","color: green","color: steelblue")}};function getFilteredNodesXToY(tape,xs,y){let tensorsFromX={},nodesFromX={};for(let i=0;itensorsFromX[output.id]=!0),anyInputFromX=!0,nodesFromX[node.id]=!0;break}if(anyInputFromX)break}}let tensorsLeadToY={};tensorsLeadToY[y.id]=!0;let nodesToY={};for(let i=tape.length-1;i>=0;i--){let node=tape[i],nodeInputs=node.inputs;for(let j=0;j=0;i--){let node=filteredTape[i],dys=[];if(node.outputs.forEach(o=>{let gradTensor=tensorAccumulatedGradientMap[o.id];gradTensor!=null?dys.push(gradTensor):dys.push(null)}),node.gradient==null)throw new Error(`Cannot compute gradient: gradient function not found for ${node.kernelName}.`);let inputGradients=node.gradient(dys);for(let inputName in node.inputs){if(!(inputName in inputGradients))throw new Error(`Cannot backprop through input ${inputName}. Available gradients found: ${Object.keys(inputGradients)}.`);let dx=tidy2(()=>inputGradients[inputName]());if(dx.dtype!=="float32")throw new Error(`Error in gradient for op ${node.kernelName}. The gradient of input ${inputName} must have 'float32' dtype, but has '${dx.dtype}'`);let x=node.inputs[inputName];if(!arraysEqual(dx.shape,x.shape))throw new Error(`Error in gradient for op ${node.kernelName}. The gradient of input '${inputName}' has shape '${dx.shape}', which does not match the shape of the input '${x.shape}'`);if(tensorAccumulatedGradientMap[x.id]==null)tensorAccumulatedGradientMap[x.id]=dx;else{let curGradient=tensorAccumulatedGradientMap[x.id];tensorAccumulatedGradientMap[x.id]=add5(curGradient,dx),curGradient.dispose()}}}}var FORMAT_LIMIT_NUM_VALS=20,FORMAT_NUM_FIRST_LAST_VALS=3,FORMAT_NUM_SIG_DIGITS=7;function tensorToString(vals,shape,dtype,verbose){let strides=computeStrides(shape),padPerCol=computeMaxSizePerColumn(vals,shape,dtype,strides),rank=shape.length,valsLines=subTensorToString(vals,shape,dtype,strides,padPerCol),lines=["Tensor"];return verbose&&(lines.push(` dtype: ${dtype}`),lines.push(` rank: ${rank}`),lines.push(` shape: [${shape}]`),lines.push(" values:")),lines.push(valsLines.map(l=>" "+l).join(` `)),lines.join(` `)}function computeMaxSizePerColumn(vals,shape,dtype,strides){let n=sizeFromShape(shape),numCols=strides[strides.length-1],padPerCol=new Array(numCols).fill(0),rank=shape.length,valuesOrTuples=dtype==="complex64"?createComplexTuples(vals):vals;if(rank>1)for(let row=0;rowFORMAT_LIMIT_NUM_VALS){let firstValsSize=FORMAT_NUM_FIRST_LAST_VALS*storagePerElement,firstVals=Array.from(vals.slice(0,firstValsSize)),lastVals=Array.from(vals.slice((size-FORMAT_NUM_FIRST_LAST_VALS)*storagePerElement,size*storagePerElement));return dtype==="complex64"&&(firstVals=createComplexTuples(firstVals),lastVals=createComplexTuples(lastVals)),["["+firstVals.map((x,i)=>valToString(x,padPerCol[i],dtype)).join(", ")+", ..., "+lastVals.map((x,i)=>valToString(x,padPerCol[size-FORMAT_NUM_FIRST_LAST_VALS+i],dtype)).join(", ")+"]"]}let displayVals=dtype==="complex64"?createComplexTuples(vals):Array.from(vals);return["["+displayVals.map((x,i)=>valToString(x,padPerCol[i],dtype)).join(", ")+"]"]}let subshape=shape.slice(1),substrides=strides.slice(1),stride=strides[0]*storagePerElement,lines=[];if(size>FORMAT_LIMIT_NUM_VALS){for(let i=0;i`Length of values '${n}' does not match the size inferred by the shape '${this.size}'.`)}if(dtype==="complex64")throw new Error("complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).");this.values=values||getArrayFromDType(dtype,this.size),this.strides=computeStrides(shape)}set(value,...locs){locs.length===0&&(locs=[0]),assert(locs.length===this.rank,()=>`The number of provided coordinates (${locs.length}) must match the rank (${this.rank})`);let index=this.locToIndex(locs);this.values[index]=value}get(...locs){locs.length===0&&(locs=[0]);let i=0;for(let loc of locs){if(loc<0||loc>=this.shape[i]){let msg=`Requested out of range element at ${locs}. Buffer shape=${this.shape}`;throw new Error(msg)}i++}let index=locs[locs.length-1];for(let i2=0;i2decodeString(b))}catch(_a){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}}return data2}dataSync(){this.throwIfDisposed();let data2=trackerFn().readSync(this.dataId);if(this.dtype==="string")try{return data2.map(b=>decodeString(b))}catch(_a){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}return data2}async bytes(){this.throwIfDisposed();let data2=await trackerFn().read(this.dataId);return this.dtype==="string"?data2:new Uint8Array(data2.buffer)}dispose(){this.isDisposed||(trackerFn().disposeTensor(this),this.isDisposedInternal=!0)}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(verbose=!1){return opHandler.print(this,verbose)}clone(){return this.throwIfDisposed(),opHandler.clone(this)}toString(verbose=!1){let vals=this.dataSync();return tensorToString(vals,this.shape,this.dtype,verbose)}cast(dtype){return this.throwIfDisposed(),opHandler.cast(this,dtype)}variable(trainable=!0,name,dtype){return this.throwIfDisposed(),trackerFn().makeVariable(this,trainable,name,dtype)}};Object.defineProperty(Tensor,Symbol.hasInstance,{value:instance=>!!instance&&instance.data!=null&&instance.dataSync!=null&&instance.throwIfDisposed!=null});var Variable=class extends Tensor{constructor(initialValue,trainable,name,tensorId){super(initialValue.shape,initialValue.dtype,initialValue.dataId,tensorId);this.trainable=trainable,this.name=name}assign(newValue){if(newValue.dtype!==this.dtype)throw new Error(`dtype of the new value (${newValue.dtype}) and previous value (${this.dtype}) must match`);if(!arraysEqual(newValue.shape,this.shape))throw new Error(`shape of the new value (${newValue.shape}) and previous value (${this.shape}) must match`);trackerFn().disposeTensor(this),this.dataId=newValue.dataId,trackerFn().incRef(this,null)}dispose(){trackerFn().disposeVariable(this),this.isDisposedInternal=!0}};Object.defineProperty(Variable,Symbol.hasInstance,{value:instance=>instance instanceof Tensor&&instance.assign!=null&&instance.assign instanceof Function});var tensor_util_exports={};__export2(tensor_util_exports,{assertTypesMatch:()=>assertTypesMatch,getTensorsInContainer:()=>getTensorsInContainer,isTensorInList:()=>isTensorInList,makeTypesMatch:()=>makeTypesMatch});var Rank;(function(Rank2){Rank2.R0="R0",Rank2.R1="R1",Rank2.R2="R2",Rank2.R3="R3",Rank2.R4="R4",Rank2.R5="R5",Rank2.R6="R6"})(Rank||(Rank={}));var UpcastInt32AndMap;(function(UpcastInt32AndMap2){UpcastInt32AndMap2.float32="float32",UpcastInt32AndMap2.int32="int32",UpcastInt32AndMap2.bool="int32",UpcastInt32AndMap2.complex64="complex64"})(UpcastInt32AndMap||(UpcastInt32AndMap={}));var UpcastBoolAndMap;(function(UpcastBoolAndMap2){UpcastBoolAndMap2.float32="float32",UpcastBoolAndMap2.int32="int32",UpcastBoolAndMap2.bool="bool",UpcastBoolAndMap2.complex64="complex64"})(UpcastBoolAndMap||(UpcastBoolAndMap={}));var UpcastFloat32AndMap;(function(UpcastFloat32AndMap2){UpcastFloat32AndMap2.float32="float32",UpcastFloat32AndMap2.int32="float32",UpcastFloat32AndMap2.bool="float32",UpcastFloat32AndMap2.complex64="complex64"})(UpcastFloat32AndMap||(UpcastFloat32AndMap={}));var UpcastComplex64AndMap;(function(UpcastComplex64AndMap2){UpcastComplex64AndMap2.float32="complex64",UpcastComplex64AndMap2.int32="complex64",UpcastComplex64AndMap2.bool="complex64",UpcastComplex64AndMap2.complex64="complex64"})(UpcastComplex64AndMap||(UpcastComplex64AndMap={}));var upcastTypeMap={float32:UpcastFloat32AndMap,int32:UpcastInt32AndMap,bool:UpcastBoolAndMap,complex64:UpcastComplex64AndMap};function upcastType(typeA,typeB){if(typeA==="string"||typeB==="string"){if(typeA==="string"&&typeB==="string")return"string";throw new Error(`Can not upcast ${typeA} with ${typeB}`)}return upcastTypeMap[typeA][typeB]}function sumOutType(type){return upcastType(type,"int32")}function makeTypesMatch(a,b){if(a.dtype===b.dtype)return[a,b];let dtype=upcastType(a.dtype,b.dtype);return[a.cast(dtype),b.cast(dtype)]}function assertTypesMatch(a,b){assert(a.dtype===b.dtype,()=>`The dtypes of the first(${a.dtype}) and second(${b.dtype}) input must match`)}function isTensorInList(tensor2,tensorList){return tensorList.some(x=>x.id===tensor2.id)}function getTensorsInContainer(result){let list=[],seen=new Set;return walkTensorContainer(result,list,seen),list}function walkTensorContainer(container,list,seen){if(container==null)return;if(container instanceof Tensor){list.push(container);return}if(!isIterable(container))return;let iterable=container;for(let k in iterable){let val=iterable[k];seen.has(val)||(seen.add(val),walkTensorContainer(val,list,seen))}}function isIterable(obj){return Array.isArray(obj)||typeof obj=="object"}var EngineState=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(k=>k.name)))}}}dispose(){for(let variableName in this.registeredVariables)this.registeredVariables[variableName].dispose()}},Engine=class{constructor(ENV5){this.ENV=ENV5,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new EngineState}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let sortedBackends=this.getSortedBackends();for(let i=0;i{kernel.setupFunc!=null&&kernel.setupFunc(this.backendInstance)})}disposeRegisteredKernels(backendName){getKernelsForBackend(backendName).forEach(kernel=>{kernel.disposeFunc!=null&&kernel.disposeFunc(this.registry[backendName])})}initializeBackend(backendName){let registryFactoryEntry=this.registryFactory[backendName];if(registryFactoryEntry==null)throw new Error(`Cannot initialize backend ${backendName}, no registration found.`);try{let backend22=registryFactoryEntry.factory();if(backend22&&!(backend22 instanceof KernelBackend)&&typeof backend22.then=="function"){let promiseId=++this.pendingBackendInitId,success=backend22.then(backendInstance=>promiseId(promiseIdthis.registryFactory[b].priority-this.registryFactory[a].priority)}initializeBackendsAndReturnBest(){let sortedBackends=this.getSortedBackends();for(let i=0;ithis.startScope(name),()=>this.endScope(result),()=>(result=fn(),result instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),result))}scopedRun(start,end,f){start();try{let res=f();return end(),res}catch(ex){throw end(),ex}}nextTensorId(){return Engine.nextTensorId++}nextVariableId(){return Engine.nextVariableId++}clone(x){let y=this.makeTensorFromDataId(x.dataId,x.shape,x.dtype),inputs={x},grad2=dy=>({x:()=>{let dtype="float32",gradInputs={x:dy},attrs={dtype};return ENGINE.runKernelFunc(backend22=>backend22.cast(dy,dtype),gradInputs,null,Cast,attrs)}}),saved=[];return this.addTapeNode(this.state.activeScope.name,inputs,[y],grad2,saved,{}),y}runKernel(kernelName,inputs,attrs,inputsToSave,outputsToSave){let forwardFunc=null,backwardsFunc=null;return this.runKernelFunc(forwardFunc,inputs,backwardsFunc,kernelName,attrs,inputsToSave,outputsToSave)}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(kernelName,numDataIdsBefore,outInfos){let numDataIdsAfter=this.backend.numDataIds(),numOutputDataIds=0;outInfos.forEach(info=>{numOutputDataIds+=info.dtype==="complex64"?3:1});let numMoves=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],dataIdsLeaked=numDataIdsAfter-numDataIdsBefore-numOutputDataIds-numMoves;if(dataIdsLeaked>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${dataIdsLeaked} data ids) after running '${kernelName}'`)}runKernelFunc(forwardFunc,inputs,backwardsFunc,kernelName,attrs,inputsToSave,outputsToSave){let outputs,saved=[],isTapeOn=this.isTapeOn();kernelName==null&&(kernelName=this.state.activeScope!=null?this.state.activeScope.name:"");let startingBytecount=this.state.numBytes,startingNumTensors=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let kernelFunc3;this.backendName==null&&this.backend;let kernel=getKernel(kernelName,this.backendName),out;if(kernel!=null)kernelFunc3=()=>{let numDataIdsBefore=this.backend.numDataIds();out=kernel.kernelFunc({inputs,attrs,backend:this.backend});let outInfos=Array.isArray(out)?out:[out];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(kernelName,numDataIdsBefore,outInfos);let outTensors=outInfos.map(outInfo=>{if(outInfo.rank!=null)return outInfo;let{dataId,shape,dtype}=outInfo;return this.makeTensorFromDataId(dataId,shape,dtype)});if(isTapeOn){let tensorsToSave=this.getTensorsForGradient(kernelName,inputs,outTensors);if(tensorsToSave==null){outputsToSave==null&&(outputsToSave=[]);let outsToSave=outTensors.filter((_,i)=>outputsToSave[i]);tensorsToSave=(inputsToSave||[]).slice().concat(outsToSave)}saved=this.saveTensorsForBackwardMode(tensorsToSave)}return outTensors};else{if(forwardFunc==null)throw new Error(`Error running ${kernelName}: Neither modular kernel nor forward func passed`);let saveFunc=tensors=>{!isTapeOn||(saved=tensors.map(tensor2=>this.keep(this.clone(tensor2))))};kernelFunc3=()=>{let numDataIdsBefore=this.backend.numDataIds();out=this.tidy(()=>forwardFunc(this.backend,saveFunc));let outs=Array.isArray(out)?out:[out];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(kernelName,numDataIdsBefore,outs),outs}}let kernelProfile;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?outputs=kernelFunc3():(kernelProfile=this.profiler.profileKernel(kernelName,inputs,()=>kernelFunc3()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(kernelProfile),outputs=kernelProfile.outputs)}),isTapeOn&&this.addTapeNode(kernelName,inputs,outputs,backwardsFunc,saved,attrs),this.state.profiling&&this.state.activeProfile.kernels.push({name:kernelName,bytesAdded:this.state.numBytes-startingBytecount,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-startingNumTensors,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(inputs).map(key=>inputs[key]!=null?inputs[key].shape:null),outputShapes:outputs.map(item=>item.shape),kernelTimeMs:kernelProfile.timeMs,extraInfo:kernelProfile.extraInfo}),Array.isArray(out)?outputs:outputs[0]}saveTensorsForBackwardMode(tensors){return tensors.map(tensor2=>this.keep(this.clone(tensor2)))}getTensorsForGradient(kernelName,inputs,outputs){let gradConfig=getGradient(kernelName);if(gradConfig!=null){let inputsToSave=gradConfig.inputsToSave||[],outputsToSave=gradConfig.outputsToSave||[],inputTensorsToSave;gradConfig.saveAllInputs?(assert(Array.isArray(inputs),()=>"saveAllInputs is true, expected inputs to be an array."),inputTensorsToSave=Object.keys(inputs).map(key=>inputs[key])):inputTensorsToSave=inputsToSave.map(inputName=>inputs[inputName]);let outputTensorsToSave=outputs.filter((_,i)=>outputsToSave[i]);return inputTensorsToSave.concat(outputTensorsToSave)}return null}makeTensor(values,shape,dtype,backend22){if(values==null)throw new Error("Values passed to engine.makeTensor() are null");dtype=dtype||"float32",backend22=backend22||this.backend;let backendVals=values;dtype==="string"&&isString(values[0])&&(backendVals=values.map(d=>encodeString(d)));let dataId=backend22.write(backendVals,shape,dtype),t=new Tensor(shape,dtype,dataId,this.nextTensorId());if(this.incRef(t,backend22),dtype==="string"){let info=this.state.tensorInfo.get(dataId),newBytes=bytesFromStringArray(backendVals);this.state.numBytes+=newBytes-info.bytes,info.bytes=newBytes}return t}makeTensorFromDataId(dataId,shape,dtype,backend22){dtype=dtype||"float32";let t=new Tensor(shape,dtype,dataId,this.nextTensorId());return this.incRef(t,backend22),t}makeVariable(initialValue,trainable=!0,name,dtype){name=name||this.nextVariableId().toString(),dtype!=null&&dtype!==initialValue.dtype&&(initialValue=initialValue.cast(dtype));let v=new Variable(initialValue,trainable,name,this.nextTensorId());if(this.state.registeredVariables[v.name]!=null)throw new Error(`Variable with name ${v.name} was already registered`);return this.state.registeredVariables[v.name]=v,this.incRef(v,this.backend),v}incRef(a,backend22){let refCount=this.state.tensorInfo.has(a.dataId)?this.state.tensorInfo.get(a.dataId).refCount:0;if(this.state.numTensors++,a.dtype==="string"&&this.state.numStringTensors++,refCount===0){this.state.numDataBuffers++;let bytes=0;a.dtype!=="complex64"&&a.dtype!=="string"&&(bytes=a.size*bytesPerElement(a.dtype)),this.state.tensorInfo.set(a.dataId,{backend:backend22||this.backend,dtype:a.dtype,shape:a.shape,bytes,refCount:0}),this.state.numBytes+=bytes}this.state.tensorInfo.get(a.dataId).refCount++,a instanceof Variable||this.track(a)}disposeTensor(a){if(!this.state.tensorInfo.has(a.dataId))return;this.state.numTensors--,a.dtype==="string"&&this.state.numStringTensors--;let info=this.state.tensorInfo.get(a.dataId);info.refCount<=1?(a.dtype!=="complex64"&&(this.state.numBytes-=info.bytes),this.state.numDataBuffers--,info.backend.disposeData(a.dataId),this.state.tensorInfo.delete(a.dataId)):this.state.tensorInfo.get(a.dataId).refCount--}disposeVariables(){for(let varName in this.state.registeredVariables){let v=this.state.registeredVariables[varName];this.disposeVariable(v)}}disposeVariable(v){this.disposeTensor(v),this.state.registeredVariables[v.name]!=null&&delete this.state.registeredVariables[v.name]}memory(){let info=this.backend.memory();return info.numTensors=this.state.numTensors,info.numDataBuffers=this.state.numDataBuffers,info.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(info.unreliable=!0,info.reasons==null&&(info.reasons=[]),info.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),info}async profile(query){this.state.profiling=!0;let startBytes=this.state.numBytes,startNumTensors=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await query(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(d=>d.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-startBytes,this.state.activeProfile.newTensors=this.state.numTensors-startNumTensors;for(let kernel of this.state.activeProfile.kernels)kernel.kernelTimeMs=await kernel.kernelTimeMs,kernel.extraInfo=await kernel.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(kernelName,inputs,outputs,gradientsFunc,saved,attrs){let tapeNode={id:this.state.nextTapeNodeId++,kernelName,inputs,outputs,saved},gradConfig=getGradient(kernelName);gradConfig!=null&&(gradientsFunc=gradConfig.gradFunc),gradientsFunc!=null&&(tapeNode.gradient=dys=>(dys=dys.map((dy,i)=>{if(dy==null){let output=outputs[i],vals=makeZerosTypedArray(output.size,output.dtype);return this.makeTensor(vals,output.shape,output.dtype)}return dy}),gradientsFunc(dys.length>1?dys:dys[0],saved,attrs))),this.state.activeTape.push(tapeNode)}keep(result){return result.kept=!0,result}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(name){let scopeInfo={track:[],name:"unnamed scope",id:this.state.nextScopeId++};name&&(scopeInfo.name=name),this.state.scopeStack.push(scopeInfo),this.state.activeScope=scopeInfo}endScope(result){let tensorsToTrackInParent=getTensorsInContainer(result),tensorsToTrackInParentSet=new Set(tensorsToTrackInParent.map(t=>t.id));for(let i=0;i{!tensor2.kept&&tensor2.scopeId===oldScope.id&&this.track(tensor2)})}gradients(f,xs,dy,allowNoGradients=!1){if(assert(xs.length>0,()=>"gradients() received an empty list of xs."),dy!=null&&dy.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${dy.dtype}'`);let y=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",f));assert(y instanceof Tensor,()=>"The result y returned by f() must be a tensor.");let filteredTape=getFilteredNodesXToY(this.state.activeTape,xs,y);if(!allowNoGradients&&filteredTape.length===0&&xs.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.");return this.tidy("backward",()=>{let accumulatedGradientMap={};accumulatedGradientMap[y.id]=dy==null?ones(y.shape):dy,backpropagateGradients(accumulatedGradientMap,filteredTape,f2=>this.tidy(f2),add);let grads2=xs.map(x=>accumulatedGradientMap[x.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(node=>{for(let tensor2 of node.saved)tensor2.dispose()}),this.state.activeTape=null),{value:y,grads:grads2}})}customGrad(f){return assert(isFunction(f),()=>"The f passed in customGrad(f) must be a function."),(...inputs)=>{assert(inputs.every(t=>t instanceof Tensor),()=>"The args passed in customGrad(f)(x1, x2,...) must all be tensors");let res,inputMap={};return inputs.forEach((input2,i)=>{inputMap[i]=input2}),this.runKernelFunc((_,save)=>(res=f(...inputs,save),assert(res.value instanceof Tensor,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),assert(isFunction(res.gradFunc),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."),res.value),inputMap,(dy,saved)=>{let gradRes=res.gradFunc(dy,saved),grads2=Array.isArray(gradRes)?gradRes:[gradRes];assert(grads2.length===inputs.length,()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...)."),assert(grads2.every(t=>t instanceof Tensor),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.");let gradMap={};return grads2.forEach((grad2,i)=>{gradMap[i]=()=>grad2}),gradMap})}}readSync(dataId){return this.state.tensorInfo.get(dataId).backend.readSync(dataId)}read(dataId){return this.state.tensorInfo.get(dataId).backend.read(dataId)}async time(query){let start=now(),timingInfo=await this.backend.time(query);return timingInfo.wallMs=now()-start,timingInfo}track(result){return this.state.activeScope!=null&&(result.scopeId=this.state.activeScope.id,this.state.activeScope.track.push(result)),result}get registeredVariables(){return this.state.registeredVariables}reset(){this.pendingBackendInitId++,this.state.dispose(),this.ENV.reset(),this.state=new EngineState;for(let backendName in this.registry)this.disposeRegisteredKernels(backendName),this.registry[backendName].dispose(),delete this.registry[backendName];this.backendName=null,this.backendInstance=null,this.pendingBackendInit=null}};Engine.nextTensorId=0;Engine.nextVariableId=0;function ones(shape){let values=makeOnesTypedArray(sizeFromShape(shape),"float32");return ENGINE.makeTensor(values,shape,"float32")}function getOrMakeEngine(){let ns=getGlobalNamespace();if(ns._tfengine==null){let environment=new Environment(ns);ns._tfengine=new Engine(environment)}return setEnvironmentGlobal(ns._tfengine.ENV),setTensorTracker(()=>ns._tfengine),ns._tfengine}var ENGINE=getOrMakeEngine();function add(a,b){let inputs={a,b};return ENGINE.runKernel(Add,inputs)}var device_util_exports={};__export2(device_util_exports,{isBrowser:()=>isBrowser,isMobile:()=>isMobile});function _isNavigatorDefined(){return typeof navigator!="undefined"&&navigator!=null}function isMobile(){if(_isNavigatorDefined()){let a=navigator.userAgent||navigator.vendor||window.opera;return/(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i.test(a)||/1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\-m|r |s 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is ON. The output of every math call will be downloaded to CPU and checked for NaNs. This significantly impacts performance.")});ENV2.registerFlag("IS_BROWSER",()=>isBrowser());ENV2.registerFlag("IS_NODE",()=>typeof process!="undefined"&&typeof process.versions!="undefined"&&typeof process.versions.node!="undefined");ENV2.registerFlag("IS_CHROME",()=>typeof navigator!="undefined"&&navigator!=null&&navigator.userAgent!=null&&/Chrome/.test(navigator.userAgent)&&/Google Inc/.test(navigator.vendor));ENV2.registerFlag("PROD",()=>!1);ENV2.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY",()=>ENV2.getBool("DEBUG"));ENV2.registerFlag("DEPRECATION_WARNINGS_ENABLED",()=>!0);ENV2.registerFlag("IS_TEST",()=>!1);ENV2.registerFlag("CHECK_COMPUTATION_FOR_ERRORS",()=>!0);function inferShape(val,dtype){let firstElem=val;if(isTypedArray(val))return dtype==="string"?[]:[val.length];if(!Array.isArray(val))return[];let shape=[];for(;Array.isArray(firstElem)||isTypedArray(firstElem)&&dtype!=="string";)shape.push(firstElem.length),firstElem=firstElem[0];return Array.isArray(val)&&env().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY")&&deepAssertShapeConsistency(val,shape,[]),shape}function deepAssertShapeConsistency(val,shape,indices){if(indices=indices||[],!Array.isArray(val)&&!isTypedArray(val)){assert(shape.length===0,()=>`Element arr[${indices.join("][")}] is a primitive, but should be an array/TypedArray of ${shape[0]} elements`);return}assert(shape.length>0,()=>`Element arr[${indices.join("][")}] should be a primitive, but is an array of ${val.length} elements`),assert(val.length===shape[0],()=>`Element arr[${indices.join("][")}] should have ${shape[0]} elements, but has ${val.length} elements`);let subShape=shape.slice(1);for(let i=0;i=0&&(inferredDtype=parseAsDtype),assertDtype(parseAsDtype,inferredDtype,argName,functionName),x==null||!isTypedArray(x)&&!Array.isArray(x)&&typeof x!="number"&&typeof x!="boolean"&&typeof x!="string"){let type=x==null?"null":x.constructor.name;throw new Error(`Argument '${argName}' passed to '${functionName}' must be a Tensor or TensorLike, but got '${type}'`)}let inferredShape=inferShape(x,inferredDtype);!isTypedArray(x)&&!Array.isArray(x)&&(x=[x]);let values=inferredDtype!=="string"?toTypedArray(x,inferredDtype):flatten(x,[],!0);return ENGINE.makeTensor(values,inferredShape,inferredDtype)}function convertToTensorArray(arg,argName,functionName,parseAsDtype="numeric"){if(!Array.isArray(arg))throw new Error(`Argument ${argName} passed to ${functionName} must be a \`Tensor[]\` or \`TensorLike[]\``);return arg.map((t,i)=>convertToTensor(t,`${argName}[${i}]`,functionName,parseAsDtype))}var OP_SCOPE_SUFFIX="__op";function op(f){let keys=Object.keys(f);if(keys.length!==1)throw new Error(`Please provide an object with a single key (operation name) mapping to a function. Got an object with ${keys.length} keys.`);let opName=keys[0],fn=f[opName];opName.endsWith("_")&&(opName=opName.substring(0,opName.length-1)),opName=opName+OP_SCOPE_SUFFIX;let f2=(...args)=>{ENGINE.startScope(opName);try{let result=fn(...args);return isPromise(result)&&console.error("Cannot return a Promise inside of tidy."),ENGINE.endScope(result),result}catch(ex){throw ENGINE.endScope(null),ex}};return Object.defineProperty(f2,"name",{value:opName,configurable:!0}),f2}function complex_(real4,imag4){let $real=convertToTensor(real4,"real","complex"),$imag=convertToTensor(imag4,"imag","complex");assertShapesMatch($real.shape,$imag.shape,`real and imag shapes, ${$real.shape} and ${$imag.shape}, must match in call to tf.complex().`);let inputs={real:$real,imag:$imag};return ENGINE.runKernel(Complex,inputs)}var complex=op({complex_});function makeTensor(values,shape,inferredShape,dtype){if(dtype==null&&(dtype=inferDtype(values)),dtype==="complex64")throw new Error("Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).");if(!isTypedArray(values)&&!Array.isArray(values)&&typeof values!="number"&&typeof values!="boolean"&&typeof values!="string")throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray");if(shape!=null){assertNonNegativeIntegerDimensions(shape);let providedSize=sizeFromShape(shape),inferredSize=sizeFromShape(inferredShape);assert(providedSize===inferredSize,()=>`Based on the provided shape, [${shape}], the tensor should have ${providedSize} values but has ${inferredSize}`);for(let i=0;i`Error creating a new Tensor. Inferred shape (${inferredShape}) does not match the provided shape (${shape}). `)}}return!isTypedArray(values)&&!Array.isArray(values)&&(values=[values]),shape=shape||inferredShape,values=dtype!=="string"?toTypedArray(values,dtype):flatten(values,[],!0),ENGINE.makeTensor(values,shape,dtype)}function tensor(values,shape,dtype){let inferredShape=inferShape(values,dtype);return makeTensor(values,shape,inferredShape,dtype)}var DTYPE_VALUE_SIZE_MAP={float32:4,float16:2,int32:4,uint16:2,uint8:1,bool:1,complex64:8},NUM_BYTES_STRING_LENGTH=4;async function encodeWeights(tensors,group){let specs=[],dataPromises=[],names=Array.isArray(tensors)?tensors.map(tensor2=>tensor2.name):Object.keys(tensors);for(let i=0;i{let vals=await t.bytes(),totalNumBytes=vals.reduce((p2,c)=>p2+c.length,0)+NUM_BYTES_STRING_LENGTH*vals.length,bytes=new Uint8Array(totalNumBytes),offset=0;for(let i2=0;i2{if(totalByteLength+=x.byteLength,normalizedXs.push(x.byteLength===x.buffer.byteLength?x:new x.constructor(x)),!(x instanceof Float32Array||x instanceof Int32Array||x instanceof Uint8Array))throw new Error(`Unsupported TypedArray subtype: ${x.constructor.name}`)});let y=new Uint8Array(totalByteLength),offset=0;return normalizedXs.forEach(x=>{y.set(new Uint8Array(x.buffer),offset),offset+=x.byteLength}),y.buffer}var useNodeBuffer=typeof Buffer!="undefined"&&(typeof Blob=="undefined"||typeof atob=="undefined"||typeof btoa=="undefined");function stringByteLength(str){return useNodeBuffer?Buffer.byteLength(str):new Blob([str]).size}function arrayBufferToBase64String(buffer2){if(useNodeBuffer)return Buffer.from(buffer2).toString("base64");let buf=new Uint8Array(buffer2),s="";for(let i=0,l=buf.length;i{totalByteLength+=buffer2.byteLength});let temp=new Uint8Array(totalByteLength),offset=0;return buffers.forEach(buffer2=>{temp.set(new Uint8Array(buffer2),offset),offset+=buffer2.byteLength}),temp.buffer}function basename(path){let SEPARATOR="/";for(path=path.trim();path.endsWith(SEPARATOR);)path=path.slice(0,path.length-1);let items=path.split(SEPARATOR);return items[items.length-1]}function getModelArtifactsInfoForJSON(modelArtifacts){if(modelArtifacts.modelTopology instanceof ArrayBuffer)throw new Error("Expected JSON model topology, received ArrayBuffer.");return{dateSaved:new Date,modelTopologyType:"JSON",modelTopologyBytes:modelArtifacts.modelTopology==null?0:stringByteLength(JSON.stringify(modelArtifacts.modelTopology)),weightSpecsBytes:modelArtifacts.weightSpecs==null?0:stringByteLength(JSON.stringify(modelArtifacts.weightSpecs)),weightDataBytes:modelArtifacts.weightData==null?0:modelArtifacts.weightData.byteLength}}function computeFloat16MantisaTable(){let convertMantissa=i=>{let m=i<<13,e=0;for(;(m&8388608)==0;)e-=8388608,m<<=1;return m&=~8388608,e+=947912704,m|e},mantisaTable=new Uint32Array(2048);mantisaTable[0]=0;for(let i=1;i<1024;i++)mantisaTable[i]=convertMantissa(i);for(let i=1024;i<2048;i++)mantisaTable[i]=939524096+(i-1024<<13);return mantisaTable}function computeFloat16ExponentTable(){let exponentTable=new Uint32Array(64);exponentTable[0]=0,exponentTable[31]=1199570944,exponentTable[32]=2147483648,exponentTable[63]=3347054592;for(let i=1;i<31;i++)exponentTable[i]=i<<23;for(let i=33;i<63;i++)exponentTable[i]=2147483648+(i-32<<23);return exponentTable}function computeFloat16OffsetTable(){let offsetTable=new Uint32Array(64);for(let i=0;i<64;i++)offsetTable[i]=1024;return offsetTable[0]=offsetTable[32]=0,offsetTable}function getFloat16Decoder(){let mantisaTable=computeFloat16MantisaTable(),exponentTable=computeFloat16ExponentTable(),offsetTable=computeFloat16OffsetTable();return quantizedArray=>{let buffer2=new ArrayBuffer(4*quantizedArray.length),bufferUint32View=new Uint32Array(buffer2);for(let index=0;index>10]+(float16Bits&1023)]+exponentTable[float16Bits>>10];bufferUint32View[index]=float32Bits}return new Float32Array(buffer2)}}var IORouterRegistry=class{constructor(){this.saveRouters=[],this.loadRouters=[]}static getInstance(){return IORouterRegistry.instance==null&&(IORouterRegistry.instance=new IORouterRegistry),IORouterRegistry.instance}static registerSaveRouter(saveRouter){IORouterRegistry.getInstance().saveRouters.push(saveRouter)}static registerLoadRouter(loadRouter){IORouterRegistry.getInstance().loadRouters.push(loadRouter)}static getSaveHandlers(url){return IORouterRegistry.getHandlers(url,"save")}static getLoadHandlers(url,loadOptions){return IORouterRegistry.getHandlers(url,"load",loadOptions)}static getHandlers(url,handlerType,loadOptions){let validHandlers=[];return(handlerType==="load"?IORouterRegistry.getInstance().loadRouters:IORouterRegistry.getInstance().saveRouters).forEach(router=>{let handler=router(url,loadOptions);handler!==null&&validHandlers.push(handler)}),validHandlers}},registerSaveRouter=loudRouter=>IORouterRegistry.registerSaveRouter(loudRouter),registerLoadRouter=loudRouter=>IORouterRegistry.registerLoadRouter(loudRouter),getSaveHandlers=url=>IORouterRegistry.getSaveHandlers(url),getLoadHandlers=(url,loadOptions)=>IORouterRegistry.getLoadHandlers(url,loadOptions),DATABASE_NAME="tensorflowjs",DATABASE_VERSION=1,MODEL_STORE_NAME="models_store",INFO_STORE_NAME="model_info_store";function getIndexedDBFactory(){if(!env().getBool("IS_BROWSER"))throw new Error("Failed to obtain IndexedDB factory because the current environmentis not a web browser.");let theWindow=typeof window=="undefined"?self:window,factory=theWindow.indexedDB||theWindow.mozIndexedDB||theWindow.webkitIndexedDB||theWindow.msIndexedDB||theWindow.shimIndexedDB;if(factory==null)throw new Error("The current browser does not appear to support IndexedDB.");return factory}function setUpDatabase(openRequest){let db=openRequest.result;db.createObjectStore(MODEL_STORE_NAME,{keyPath:"modelPath"}),db.createObjectStore(INFO_STORE_NAME,{keyPath:"modelPath"})}var BrowserIndexedDB=class{constructor(modelPath){if(this.indexedDB=getIndexedDBFactory(),modelPath==null||!modelPath)throw new Error("For IndexedDB, modelPath must not be null, undefined or empty.");this.modelPath=modelPath}async save(modelArtifacts){if(modelArtifacts.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");return this.databaseAction(this.modelPath,modelArtifacts)}async load(){return this.databaseAction(this.modelPath)}databaseAction(modelPath,modelArtifacts){return new Promise((resolve,reject)=>{let openRequest=this.indexedDB.open(DATABASE_NAME,DATABASE_VERSION);openRequest.onupgradeneeded=()=>setUpDatabase(openRequest),openRequest.onsuccess=()=>{let db=openRequest.result;if(modelArtifacts==null){let modelTx=db.transaction(MODEL_STORE_NAME,"readonly"),getRequest=modelTx.objectStore(MODEL_STORE_NAME).get(this.modelPath);getRequest.onsuccess=()=>{if(getRequest.result==null)return db.close(),reject(new Error(`Cannot find model with path '${this.modelPath}' in IndexedDB.`));resolve(getRequest.result.modelArtifacts)},getRequest.onerror=error=>(db.close(),reject(getRequest.error)),modelTx.oncomplete=()=>db.close()}else{let modelArtifactsInfo=getModelArtifactsInfoForJSON(modelArtifacts),infoTx=db.transaction(INFO_STORE_NAME,"readwrite"),infoStore=infoTx.objectStore(INFO_STORE_NAME),putInfoRequest=infoStore.put({modelPath:this.modelPath,modelArtifactsInfo}),modelTx;putInfoRequest.onsuccess=()=>{modelTx=db.transaction(MODEL_STORE_NAME,"readwrite");let putModelRequest=modelTx.objectStore(MODEL_STORE_NAME).put({modelPath:this.modelPath,modelArtifacts,modelArtifactsInfo});putModelRequest.onsuccess=()=>resolve({modelArtifactsInfo}),putModelRequest.onerror=error=>{infoStore=infoTx.objectStore(INFO_STORE_NAME);let deleteInfoRequest=infoStore.delete(this.modelPath);deleteInfoRequest.onsuccess=()=>(db.close(),reject(putModelRequest.error)),deleteInfoRequest.onerror=error2=>(db.close(),reject(putModelRequest.error))}},putInfoRequest.onerror=error=>(db.close(),reject(putInfoRequest.error)),infoTx.oncomplete=()=>{modelTx==null?db.close():modelTx.oncomplete=()=>db.close()}}},openRequest.onerror=error=>reject(openRequest.error)})}};BrowserIndexedDB.URL_SCHEME="indexeddb://";var indexedDBRouter=url=>env().getBool("IS_BROWSER")&&!Array.isArray(url)&&url.startsWith(BrowserIndexedDB.URL_SCHEME)?browserIndexedDB(url.slice(BrowserIndexedDB.URL_SCHEME.length)):null;IORouterRegistry.registerSaveRouter(indexedDBRouter);IORouterRegistry.registerLoadRouter(indexedDBRouter);function browserIndexedDB(modelPath){return new BrowserIndexedDB(modelPath)}function maybeStripScheme(key){return key.startsWith(BrowserIndexedDB.URL_SCHEME)?key.slice(BrowserIndexedDB.URL_SCHEME.length):key}var BrowserIndexedDBManager=class{constructor(){this.indexedDB=getIndexedDBFactory()}async listModels(){return new Promise((resolve,reject)=>{let openRequest=this.indexedDB.open(DATABASE_NAME,DATABASE_VERSION);openRequest.onupgradeneeded=()=>setUpDatabase(openRequest),openRequest.onsuccess=()=>{let db=openRequest.result,tx=db.transaction(INFO_STORE_NAME,"readonly"),getAllInfoRequest=tx.objectStore(INFO_STORE_NAME).getAll();getAllInfoRequest.onsuccess=()=>{let out={};for(let item of getAllInfoRequest.result)out[item.modelPath]=item.modelArtifactsInfo;resolve(out)},getAllInfoRequest.onerror=error=>(db.close(),reject(getAllInfoRequest.error)),tx.oncomplete=()=>db.close()},openRequest.onerror=error=>reject(openRequest.error)})}async removeModel(path){return path=maybeStripScheme(path),new Promise((resolve,reject)=>{let openRequest=this.indexedDB.open(DATABASE_NAME,DATABASE_VERSION);openRequest.onupgradeneeded=()=>setUpDatabase(openRequest),openRequest.onsuccess=()=>{let db=openRequest.result,infoTx=db.transaction(INFO_STORE_NAME,"readwrite"),infoStore=infoTx.objectStore(INFO_STORE_NAME),getInfoRequest=infoStore.get(path),modelTx;getInfoRequest.onsuccess=()=>{if(getInfoRequest.result==null)return db.close(),reject(new Error(`Cannot find model with path '${path}' in IndexedDB.`));{let deleteInfoRequest=infoStore.delete(path),deleteModelData=()=>{modelTx=db.transaction(MODEL_STORE_NAME,"readwrite");let deleteModelRequest=modelTx.objectStore(MODEL_STORE_NAME).delete(path);deleteModelRequest.onsuccess=()=>resolve(getInfoRequest.result.modelArtifactsInfo),deleteModelRequest.onerror=error=>reject(getInfoRequest.error)};deleteInfoRequest.onsuccess=deleteModelData,deleteInfoRequest.onerror=error=>(deleteModelData(),db.close(),reject(getInfoRequest.error))}},getInfoRequest.onerror=error=>(db.close(),reject(getInfoRequest.error)),infoTx.oncomplete=()=>{modelTx==null?db.close():modelTx.oncomplete=()=>db.close()}},openRequest.onerror=error=>reject(openRequest.error)})}},PATH_SEPARATOR="/",PATH_PREFIX="tensorflowjs_models",INFO_SUFFIX="info",MODEL_TOPOLOGY_SUFFIX="model_topology",WEIGHT_SPECS_SUFFIX="weight_specs",WEIGHT_DATA_SUFFIX="weight_data",MODEL_METADATA_SUFFIX="model_metadata";function getModelKeys(path){return{info:[PATH_PREFIX,path,INFO_SUFFIX].join(PATH_SEPARATOR),topology:[PATH_PREFIX,path,MODEL_TOPOLOGY_SUFFIX].join(PATH_SEPARATOR),weightSpecs:[PATH_PREFIX,path,WEIGHT_SPECS_SUFFIX].join(PATH_SEPARATOR),weightData:[PATH_PREFIX,path,WEIGHT_DATA_SUFFIX].join(PATH_SEPARATOR),modelMetadata:[PATH_PREFIX,path,MODEL_METADATA_SUFFIX].join(PATH_SEPARATOR)}}function getModelPathFromKey(key){let items=key.split(PATH_SEPARATOR);if(items.length<3)throw new Error(`Invalid key format: ${key}`);return items.slice(1,items.length-1).join(PATH_SEPARATOR)}function maybeStripScheme2(key){return key.startsWith(BrowserLocalStorage.URL_SCHEME)?key.slice(BrowserLocalStorage.URL_SCHEME.length):key}var BrowserLocalStorage=class{constructor(modelPath){if(!env().getBool("IS_BROWSER")||typeof window=="undefined"||typeof window.localStorage=="undefined")throw new Error("The current environment does not support local storage.");if(this.LS=window.localStorage,modelPath==null||!modelPath)throw new Error("For local storage, modelPath must not be null, undefined or empty.");this.modelPath=modelPath,this.keys=getModelKeys(this.modelPath)}async save(modelArtifacts){if(modelArtifacts.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");{let topology=JSON.stringify(modelArtifacts.modelTopology),weightSpecs=JSON.stringify(modelArtifacts.weightSpecs),modelArtifactsInfo=getModelArtifactsInfoForJSON(modelArtifacts);try{this.LS.setItem(this.keys.info,JSON.stringify(modelArtifactsInfo)),this.LS.setItem(this.keys.topology,topology),this.LS.setItem(this.keys.weightSpecs,weightSpecs),this.LS.setItem(this.keys.weightData,arrayBufferToBase64String(modelArtifacts.weightData));let result={format:modelArtifacts.format,generatedBy:modelArtifacts.generatedBy,convertedBy:modelArtifacts.convertedBy};return modelArtifacts.signature!=null&&(result.signature=modelArtifacts.signature),modelArtifacts.userDefinedMetadata!=null&&(result.userDefinedMetadata=modelArtifacts.userDefinedMetadata),modelArtifacts.modelInitializer!=null&&(result.modelInitializer=modelArtifacts.modelInitializer),this.LS.setItem(this.keys.modelMetadata,JSON.stringify(result)),{modelArtifactsInfo}}catch(err){throw this.LS.removeItem(this.keys.info),this.LS.removeItem(this.keys.topology),this.LS.removeItem(this.keys.weightSpecs),this.LS.removeItem(this.keys.weightData),this.LS.removeItem(this.keys.modelMetadata),new Error(`Failed to save model '${this.modelPath}' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes=${modelArtifactsInfo.modelTopologyBytes}, weightSpecsBytes=${modelArtifactsInfo.weightSpecsBytes}, weightDataBytes=${modelArtifactsInfo.weightDataBytes}.`)}}}async load(){let info=JSON.parse(this.LS.getItem(this.keys.info));if(info==null)throw new Error(`In local storage, there is no model with name '${this.modelPath}'`);if(info.modelTopologyType!=="JSON")throw new Error("BrowserLocalStorage does not support loading non-JSON model topology yet.");let out={},topology=JSON.parse(this.LS.getItem(this.keys.topology));if(topology==null)throw new Error(`In local storage, the topology of model '${this.modelPath}' is missing.`);out.modelTopology=topology;let weightSpecs=JSON.parse(this.LS.getItem(this.keys.weightSpecs));if(weightSpecs==null)throw new Error(`In local storage, the weight specs of model '${this.modelPath}' are missing.`);out.weightSpecs=weightSpecs;let metadataString=this.LS.getItem(this.keys.modelMetadata);if(metadataString!=null){let metadata=JSON.parse(metadataString);out.format=metadata.format,out.generatedBy=metadata.generatedBy,out.convertedBy=metadata.convertedBy,metadata.signature!=null&&(out.signature=metadata.signature),metadata.userDefinedMetadata!=null&&(out.userDefinedMetadata=metadata.userDefinedMetadata),metadata.modelInitializer!=null&&(out.modelInitializer=metadata.modelInitializer)}let weightDataBase64=this.LS.getItem(this.keys.weightData);if(weightDataBase64==null)throw new Error(`In local storage, the binary weight values of model '${this.modelPath}' are missing.`);return out.weightData=base64StringToArrayBuffer(weightDataBase64),out}};BrowserLocalStorage.URL_SCHEME="localstorage://";var localStorageRouter=url=>env().getBool("IS_BROWSER")&&!Array.isArray(url)&&url.startsWith(BrowserLocalStorage.URL_SCHEME)?browserLocalStorage(url.slice(BrowserLocalStorage.URL_SCHEME.length)):null;IORouterRegistry.registerSaveRouter(localStorageRouter);IORouterRegistry.registerLoadRouter(localStorageRouter);function browserLocalStorage(modelPath){return new BrowserLocalStorage(modelPath)}var BrowserLocalStorageManager=class{constructor(){assert(env().getBool("IS_BROWSER"),()=>"Current environment is not a web browser"),assert(typeof window=="undefined"||typeof window.localStorage!="undefined",()=>"Current browser does not appear to support localStorage"),this.LS=window.localStorage}async listModels(){let out={},prefix=PATH_PREFIX+PATH_SEPARATOR,suffix=PATH_SEPARATOR+INFO_SUFFIX;for(let i=0;i"scheme must not be undefined or null."),scheme.endsWith(URL_SCHEME_SUFFIX)&&(scheme=scheme.slice(0,scheme.indexOf(URL_SCHEME_SUFFIX))),assert(scheme.length>0,()=>"scheme must not be an empty string.");let registry=ModelStoreManagerRegistry.getInstance();assert(registry.managers[scheme]==null,()=>`A model store manager is already registered for scheme '${scheme}'.`),registry.managers[scheme]=manager}static getManager(scheme){let manager=this.getInstance().managers[scheme];if(manager==null)throw new Error(`Cannot find model manager for scheme '${scheme}'`);return manager}static getSchemes(){return Object.keys(this.getInstance().managers)}};function parseURL(url){if(url.indexOf(URL_SCHEME_SUFFIX)===-1)throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${ModelStoreManagerRegistry.getSchemes().join(",")}`);return{scheme:url.split(URL_SCHEME_SUFFIX)[0],path:url.split(URL_SCHEME_SUFFIX)[1]}}async function cloneModelInternal(sourceURL,destURL,deleteSource=!1){assert(sourceURL!==destURL,()=>`Old path and new path are the same: '${sourceURL}'`);let loadHandlers=IORouterRegistry.getLoadHandlers(sourceURL);assert(loadHandlers.length>0,()=>`Copying failed because no load handler is found for source URL ${sourceURL}.`),assert(loadHandlers.length<2,()=>`Copying failed because more than one (${loadHandlers.length}) load handlers for source URL ${sourceURL}.`);let loadHandler=loadHandlers[0],saveHandlers=IORouterRegistry.getSaveHandlers(destURL);assert(saveHandlers.length>0,()=>`Copying failed because no save handler is found for destination URL ${destURL}.`),assert(saveHandlers.length<2,()=>`Copying failed because more than one (${loadHandlers.length}) save handlers for destination URL ${destURL}.`);let saveHandler=saveHandlers[0],sourceScheme=parseURL(sourceURL).scheme,sourcePath=parseURL(sourceURL).path,sameMedium=sourceScheme===parseURL(sourceURL).scheme,modelArtifacts=await loadHandler.load();deleteSource&&sameMedium&&await ModelStoreManagerRegistry.getManager(sourceScheme).removeModel(sourcePath);let saveResult=await saveHandler.save(modelArtifacts);return deleteSource&&!sameMedium&&await ModelStoreManagerRegistry.getManager(sourceScheme).removeModel(sourcePath),saveResult.modelArtifactsInfo}async function listModels(){let schemes=ModelStoreManagerRegistry.getSchemes(),out={};for(let scheme of schemes){let schemeOut=await ModelStoreManagerRegistry.getManager(scheme).listModels();for(let path in schemeOut){let url=scheme+URL_SCHEME_SUFFIX+path;out[url]=schemeOut[path]}}return out}async function removeModel(url){let schemeAndPath=parseURL(url);return ModelStoreManagerRegistry.getManager(schemeAndPath.scheme).removeModel(schemeAndPath.path)}async function copyModel(sourceURL,destURL){return cloneModelInternal(sourceURL,destURL,!1)}async function moveModel(sourceURL,destURL){return cloneModelInternal(sourceURL,destURL,!0)}var PlatformBrowser=class{fetch(path,init2){return fetch(path,init2)}now(){return performance.now()}encode(text,encoding){if(encoding!=="utf-8"&&encoding!=="utf8")throw new Error(`Browser's encoder only supports utf-8, but got ${encoding}`);return this.textEncoder==null&&(this.textEncoder=new TextEncoder),this.textEncoder.encode(text)}decode(bytes,encoding){return new TextDecoder(encoding).decode(bytes)}};if(env().get("IS_BROWSER")){env().setPlatform("browser",new PlatformBrowser);try{ModelStoreManagerRegistry.registerManager(BrowserLocalStorage.URL_SCHEME,new BrowserLocalStorageManager)}catch(err){}try{ModelStoreManagerRegistry.registerManager(BrowserIndexedDB.URL_SCHEME,new BrowserIndexedDBManager)}catch(err){}}var getNodeFetch={importFetch:()=>require_browser()},systemFetch,PlatformNode=class{constructor(){this.util=require("util"),this.textEncoder=new this.util.TextEncoder}fetch(path,requestInits){return env().global.fetch!=null?env().global.fetch(path,requestInits):(systemFetch==null&&(systemFetch=getNodeFetch.importFetch()),systemFetch(path,requestInits))}now(){let time2=process.hrtime();return time2[0]*1e3+time2[1]/1e6}encode(text,encoding){if(encoding!=="utf-8"&&encoding!=="utf8")throw new Error(`Node built-in encoder only supports utf-8, but got ${encoding}`);return this.textEncoder.encode(text)}decode(bytes,encoding){return bytes.length===0?"":new this.util.TextDecoder(encoding).decode(bytes)}};env().get("IS_NODE")&&env().setPlatform("node",new PlatformNode);function buffer(shape,dtype="float32",values){return dtype=dtype||"float32",assertNonNegativeIntegerDimensions(shape),new TensorBuffer(shape,dtype,values)}function cast_(x,dtype){let $x=convertToTensor(x,"x","cast");if(!isValidDtype(dtype))throw new Error(`Failed to cast to unknown dtype ${dtype}`);if(dtype==="string"&&$x.dtype!=="string"||dtype!=="string"&&$x.dtype==="string")throw new Error("Only strings can be casted to strings");let inputs={x:$x},attrs={dtype};return ENGINE.runKernel(Cast,inputs,attrs)}var cast=op({cast_});function clone_(x){let inputs={x:convertToTensor(x,"x","clone","string_or_numeric")};return ENGINE.runKernel(Identity,inputs)}var clone=op({clone_});function print2(x,verbose=!1){console.log(x.toString(verbose))}getOrMakeEngine();var opHandler2={buffer,cast,clone,print:print2};setOpHandler(opHandler2);var io_exports={};__export2(io_exports,{browserFiles:()=>browserFiles,browserHTTPRequest:()=>browserHTTPRequest,concatenateArrayBuffers:()=>concatenateArrayBuffers,copyModel:()=>copyModel,decodeWeights:()=>decodeWeights,encodeWeights:()=>encodeWeights,fromMemory:()=>fromMemory,getLoadHandlers:()=>getLoadHandlers,getModelArtifactsInfoForJSON:()=>getModelArtifactsInfoForJSON,getSaveHandlers:()=>getSaveHandlers,http:()=>http,isHTTPScheme:()=>isHTTPScheme,listModels:()=>listModels,loadWeights:()=>loadWeights,moveModel:()=>moveModel,registerLoadRouter:()=>registerLoadRouter,registerSaveRouter:()=>registerSaveRouter,removeModel:()=>removeModel,weightsLoaderFactory:()=>weightsLoaderFactory,withSaveHandler:()=>withSaveHandler});var DEFAULT_FILE_NAME_PREFIX="model",DEFAULT_JSON_EXTENSION_NAME=".json",DEFAULT_WEIGHT_DATA_EXTENSION_NAME=".weights.bin";function defer(f){return new Promise(resolve=>setTimeout(resolve)).then(f)}var BrowserDownloads=class{constructor(fileNamePrefix){if(!env().getBool("IS_BROWSER"))throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");fileNamePrefix.startsWith(BrowserDownloads.URL_SCHEME)&&(fileNamePrefix=fileNamePrefix.slice(BrowserDownloads.URL_SCHEME.length)),(fileNamePrefix==null||fileNamePrefix.length===0)&&(fileNamePrefix=DEFAULT_FILE_NAME_PREFIX),this.modelTopologyFileName=fileNamePrefix+DEFAULT_JSON_EXTENSION_NAME,this.weightDataFileName=fileNamePrefix+DEFAULT_WEIGHT_DATA_EXTENSION_NAME}async save(modelArtifacts){if(typeof document=="undefined")throw new Error("Browser downloads are not supported in this environment since `document` is not present");let weightsURL=window.URL.createObjectURL(new Blob([modelArtifacts.weightData],{type:"application/octet-stream"}));if(modelArtifacts.modelTopology instanceof ArrayBuffer)throw new Error("BrowserDownloads.save() does not support saving model topology in binary formats yet.");{let weightsManifest=[{paths:["./"+this.weightDataFileName],weights:modelArtifacts.weightSpecs}],modelTopologyAndWeightManifest={modelTopology:modelArtifacts.modelTopology,format:modelArtifacts.format,generatedBy:modelArtifacts.generatedBy,convertedBy:modelArtifacts.convertedBy,weightsManifest};modelArtifacts.signature!=null&&(modelTopologyAndWeightManifest.signature=modelArtifacts.signature),modelArtifacts.userDefinedMetadata!=null&&(modelTopologyAndWeightManifest.userDefinedMetadata=modelArtifacts.userDefinedMetadata),modelArtifacts.modelInitializer!=null&&(modelTopologyAndWeightManifest.modelInitializer=modelArtifacts.modelInitializer);let modelTopologyAndWeightManifestURL=window.URL.createObjectURL(new Blob([JSON.stringify(modelTopologyAndWeightManifest)],{type:"application/json"})),jsonAnchor=this.jsonAnchor==null?document.createElement("a"):this.jsonAnchor;if(jsonAnchor.download=this.modelTopologyFileName,jsonAnchor.href=modelTopologyAndWeightManifestURL,await defer(()=>jsonAnchor.dispatchEvent(new MouseEvent("click"))),modelArtifacts.weightData!=null){let weightDataAnchor=this.weightDataAnchor==null?document.createElement("a"):this.weightDataAnchor;weightDataAnchor.download=this.weightDataFileName,weightDataAnchor.href=weightsURL,await defer(()=>weightDataAnchor.dispatchEvent(new MouseEvent("click")))}return{modelArtifactsInfo:getModelArtifactsInfoForJSON(modelArtifacts)}}}};BrowserDownloads.URL_SCHEME="downloads://";var BrowserFiles=class{constructor(files){if(files==null||files.length<1)throw new Error(`When calling browserFiles, at least 1 file is required, but received ${files}`);this.files=files}async load(){let jsonFile=this.files[0],weightFiles=this.files.slice(1);return new Promise((resolve,reject)=>{let jsonReader=new FileReader;jsonReader.onload=event=>{let modelJSON=JSON.parse(event.target.result),modelTopology=modelJSON.modelTopology;if(modelTopology==null){reject(new Error(`modelTopology field is missing from file ${jsonFile.name}`));return}weightFiles.length===0&&resolve({modelTopology});let weightsManifest=modelJSON.weightsManifest;if(weightsManifest==null){reject(new Error(`weightManifest field is missing from file ${jsonFile.name}`));return}let pathToFile;try{pathToFile=this.checkManifestAndWeightFiles(weightsManifest,weightFiles)}catch(err){reject(err);return}let weightSpecs=[],paths=[],perFileBuffers=[];weightsManifest.forEach(weightsGroup=>{weightsGroup.paths.forEach(path=>{paths.push(path),perFileBuffers.push(null)}),weightSpecs.push(...weightsGroup.weights)}),weightsManifest.forEach(weightsGroup=>{weightsGroup.paths.forEach(path=>{let weightFileReader=new FileReader;weightFileReader.onload=event2=>{let weightData=event2.target.result,index=paths.indexOf(path);if(perFileBuffers[index]=weightData,perFileBuffers.indexOf(null)===-1){let result={modelTopology,weightSpecs,weightData:concatenateArrayBuffers(perFileBuffers),format:modelJSON.format,generatedBy:modelJSON.generatedBy,convertedBy:modelJSON.convertedBy};modelJSON.signature!=null&&(result.signature=modelJSON.signature),modelJSON.userDefinedMetadata!=null&&(result.userDefinedMetadata=modelJSON.userDefinedMetadata),modelJSON.modelInitializer!=null&&(result.modelInitializer=modelJSON.modelInitializer),resolve(result)}},weightFileReader.onerror=error=>reject(`Failed to weights data from file of path '${path}'.`),weightFileReader.readAsArrayBuffer(pathToFile[path])})})},jsonReader.onerror=error=>reject(`Failed to read model topology and weights manifest JSON from file '${jsonFile.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`),jsonReader.readAsText(jsonFile)})}checkManifestAndWeightFiles(manifest,files){let basenames=[],fileNames=files.map(file=>basename(file.name)),pathToFile={};for(let group of manifest)group.paths.forEach(path=>{let pathBasename=basename(path);if(basenames.indexOf(pathBasename)!==-1)throw new Error(`Duplicate file basename found in weights manifest: '${pathBasename}'`);if(basenames.push(pathBasename),fileNames.indexOf(pathBasename)===-1)throw new Error(`Weight file with basename '${pathBasename}' is not provided.`);pathToFile[path]=files[fileNames.indexOf(pathBasename)]});if(basenames.length!==files.length)throw new Error(`Mismatch in the number of files in weights manifest (${basenames.length}) and the number of weight files provided (${files.length}).`);return pathToFile}},browserDownloadsRouter=url=>env().getBool("IS_BROWSER")&&!Array.isArray(url)&&url.startsWith(BrowserDownloads.URL_SCHEME)?browserDownloads(url.slice(BrowserDownloads.URL_SCHEME.length)):null;IORouterRegistry.registerSaveRouter(browserDownloadsRouter);function browserDownloads(fileNamePrefix="model"){return new BrowserDownloads(fileNamePrefix)}function browserFiles(files){return new BrowserFiles(files)}function monitorPromisesProgress(promises,onProgress,startFraction,endFraction){checkPromises(promises),startFraction=startFraction==null?0:startFraction,endFraction=endFraction==null?1:endFraction,checkFraction(startFraction,endFraction);let resolvedPromise=0,registerMonitor=promise=>(promise.then(value=>{let fraction=startFraction+ ++resolvedPromise/promises.length*(endFraction-startFraction);return onProgress(fraction),value}),promise);function checkPromises(promises2){assert(promises2!=null&&Array.isArray(promises2)&&promises2.length>0,()=>"promises must be a none empty array")}function checkFraction(startFraction2,endFraction2){assert(startFraction2>=0&&startFraction2<=1,()=>`Progress fraction must be in range [0, 1], but got startFraction ${startFraction2}`),assert(endFraction2>=0&&endFraction2<=1,()=>`Progress fraction must be in range [0, 1], but got endFraction ${endFraction2}`),assert(endFraction2>=startFraction2,()=>`startFraction must be no more than endFraction, but got startFraction ${startFraction2} and endFraction ${endFraction2}`)}return Promise.all(promises.map(registerMonitor))}async function loadWeightsAsArrayBuffer(fetchURLs,loadOptions){loadOptions==null&&(loadOptions={});let fetchFunc=loadOptions.fetchFunc==null?env().platform.fetch:loadOptions.fetchFunc,requests=fetchURLs.map(fetchURL=>fetchFunc(fetchURL,loadOptions.requestInit,{isBinary:!0})),fetchStartFraction=0,fetchEndFraction=.5,bufferPromises=(loadOptions.onProgress==null?await Promise.all(requests):await monitorPromisesProgress(requests,loadOptions.onProgress,fetchStartFraction,fetchEndFraction)).map(response=>response.arrayBuffer()),bufferStartFraction=.5,bufferEndFraction=1;return loadOptions.onProgress==null?await Promise.all(bufferPromises):await monitorPromisesProgress(bufferPromises,loadOptions.onProgress,bufferStartFraction,bufferEndFraction)}async function loadWeights(manifest,filePathPrefix="",weightNames,requestInit){return weightsLoaderFactory(fetchUrls=>loadWeightsAsArrayBuffer(fetchUrls,{requestInit}))(manifest,filePathPrefix,weightNames)}function weightsLoaderFactory(fetchWeightsFunction){return async(manifest,filePathPrefix="",weightNames)=>{let groupIndicesToFetchMap=manifest.map(()=>!1),groupWeightsToFetch={},weightsFound=weightNames!=null?weightNames.map(()=>!1):[],allManifestWeightNames=[];if(manifest.forEach((manifestGroupConfig,groupIndex)=>{let groupOffset=0;manifestGroupConfig.weights.forEach(weightsEntry=>{let rawDtype="quantization"in weightsEntry?weightsEntry.quantization.dtype:weightsEntry.dtype,weightsBytes=DTYPE_VALUE_SIZE_MAP[rawDtype]*sizeFromShape(weightsEntry.shape),enqueueWeightsForFetchingFn=()=>{groupIndicesToFetchMap[groupIndex]=!0,groupWeightsToFetch[groupIndex]==null&&(groupWeightsToFetch[groupIndex]=[]),groupWeightsToFetch[groupIndex].push({manifestEntry:weightsEntry,groupOffset,sizeBytes:weightsBytes})};weightNames!=null?weightNames.forEach((weightName,weightIndex)=>{weightName===weightsEntry.name&&(enqueueWeightsForFetchingFn(),weightsFound[weightIndex]=!0)}):enqueueWeightsForFetchingFn(),allManifestWeightNames.push(weightsEntry.name),groupOffset+=weightsBytes})}),!weightsFound.every(found=>found)){let weightsNotFound=weightNames.filter((_,i)=>!weightsFound[i]);throw new Error(`Could not find weights in manifest with names: ${weightsNotFound.join(", ")}. -Manifest JSON has weights with names: ${allManifestWeightNames.join(", ")}.`)}let groupIndicesToFetch=groupIndicesToFetchMap.reduce((accumulator,shouldFetch,i)=>(shouldFetch&&accumulator.push(i),accumulator),[]),fetchUrls=[];groupIndicesToFetch.forEach(i=>{manifest[i].paths.forEach(filepath=>{let fetchUrl=filePathPrefix+(filePathPrefix.endsWith("/")?"":"/")+filepath;fetchUrls.push(fetchUrl)})});let buffers=await fetchWeightsFunction(fetchUrls),weightsTensorMap={},bufferIndexOffset=0;return groupIndicesToFetch.forEach(i=>{let numBuffers=manifest[i].paths.length,groupBytes=0;for(let i2=0;i2{let byteBuffer=groupBuffer.slice(weightsEntry.groupOffset,weightsEntry.groupOffset+weightsEntry.sizeBytes),nameToTensorMap=decodeWeights(byteBuffer,[weightsEntry.manifestEntry]);for(let name in nameToTensorMap)weightsTensorMap[name]=nameToTensorMap[name]}),bufferIndexOffset+=numBuffers}),weightsTensorMap}}var OCTET_STREAM_MIME_TYPE="application/octet-stream",JSON_TYPE="application/json",HTTPRequest=class{constructor(path,loadOptions){if(this.DEFAULT_METHOD="POST",loadOptions==null&&(loadOptions={}),this.weightPathPrefix=loadOptions.weightPathPrefix,this.onProgress=loadOptions.onProgress,this.weightUrlConverter=loadOptions.weightUrlConverter,loadOptions.fetchFunc!=null?(assert(typeof loadOptions.fetchFunc=="function",()=>"Must pass a function that matches the signature of `fetch` (see https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)"),this.fetch=loadOptions.fetchFunc):this.fetch=env().platform.fetch,assert(path!=null&&path.length>0,()=>"URL path for http must not be null, undefined or empty."),Array.isArray(path)&&assert(path.length===2,()=>`URL paths for http must have a length of 2, (actual length is ${path.length}).`),this.path=path,loadOptions.requestInit!=null&&loadOptions.requestInit.body!=null)throw new Error("requestInit is expected to have no pre-existing body, but has one.");this.requestInit=loadOptions.requestInit||{}}async save(modelArtifacts){if(modelArtifacts.modelTopology instanceof ArrayBuffer)throw new Error("BrowserHTTPRequest.save() does not support saving model topology in binary formats yet.");let init2=Object.assign({method:this.DEFAULT_METHOD},this.requestInit);init2.body=new FormData;let weightsManifest=[{paths:["./model.weights.bin"],weights:modelArtifacts.weightSpecs}],modelTopologyAndWeightManifest={modelTopology:modelArtifacts.modelTopology,format:modelArtifacts.format,generatedBy:modelArtifacts.generatedBy,convertedBy:modelArtifacts.convertedBy,weightsManifest};modelArtifacts.signature!=null&&(modelTopologyAndWeightManifest.signature=modelArtifacts.signature),modelArtifacts.userDefinedMetadata!=null&&(modelTopologyAndWeightManifest.userDefinedMetadata=modelArtifacts.userDefinedMetadata),modelArtifacts.modelInitializer!=null&&(modelTopologyAndWeightManifest.modelInitializer=modelArtifacts.modelInitializer),init2.body.append("model.json",new Blob([JSON.stringify(modelTopologyAndWeightManifest)],{type:JSON_TYPE}),"model.json"),modelArtifacts.weightData!=null&&init2.body.append("model.weights.bin",new Blob([modelArtifacts.weightData],{type:OCTET_STREAM_MIME_TYPE}),"model.weights.bin");let response=await this.fetch(this.path,init2);if(response.ok)return{modelArtifactsInfo:getModelArtifactsInfoForJSON(modelArtifacts),responses:[response]};throw new Error(`BrowserHTTPRequest.save() failed due to HTTP response status ${response.status}.`)}async load(){let modelConfigRequest=await this.fetch(this.path,this.requestInit);if(!modelConfigRequest.ok)throw new Error(`Request to ${this.path} failed with status code ${modelConfigRequest.status}. Please verify this URL points to the model JSON of the model to load.`);let modelConfig;try{modelConfig=await modelConfigRequest.json()}catch(e){let message=`Failed to parse model JSON of response from ${this.path}.`;throw this.path.endsWith(".pb")?message+=" Your path contains a .pb file extension. Support for .pb models have been removed in TensorFlow.js 1.0 in favor of .json models. You can re-convert your Python TensorFlow model using the TensorFlow.js 1.0 conversion scripts or you can convert your.pb models with the 'pb2json'NPM script in the tensorflow/tfjs-converter repository.":message+=" Please make sure the server is serving valid JSON for this request.",new Error(message)}let modelTopology=modelConfig.modelTopology,weightsManifest=modelConfig.weightsManifest,generatedBy=modelConfig.generatedBy,convertedBy=modelConfig.convertedBy,format=modelConfig.format,signature=modelConfig.signature,userDefinedMetadata=modelConfig.userDefinedMetadata;if(modelTopology==null&&weightsManifest==null)throw new Error(`The JSON from HTTP path ${this.path} contains neither model topology or manifest for weights.`);let weightSpecs,weightData;weightsManifest!=null&&([weightSpecs,weightData]=await this.loadWeights(weightsManifest));let artifacts={modelTopology,weightSpecs,weightData,generatedBy,convertedBy,format};signature!=null&&(artifacts.signature=signature),userDefinedMetadata!=null&&(artifacts.userDefinedMetadata=userDefinedMetadata);let initializer=modelConfig.modelInitializer;return initializer&&(artifacts.modelInitializer=initializer),artifacts}async loadWeights(weightsManifest){let weightPath=Array.isArray(this.path)?this.path[1]:this.path,[prefix,suffix]=parseUrl(weightPath),pathPrefix=this.weightPathPrefix||prefix,weightSpecs=[];for(let entry of weightsManifest)weightSpecs.push(...entry.weights);let fetchURLs=[],urlPromises=[];for(let weightsGroup of weightsManifest)for(let path of weightsGroup.paths)this.weightUrlConverter!=null?urlPromises.push(this.weightUrlConverter(path)):fetchURLs.push(pathPrefix+path+suffix);this.weightUrlConverter&&fetchURLs.push(...await Promise.all(urlPromises));let buffers=await loadWeightsAsArrayBuffer(fetchURLs,{requestInit:this.requestInit,fetchFunc:this.fetch,onProgress:this.onProgress});return[weightSpecs,concatenateArrayBuffers(buffers)]}};HTTPRequest.URL_SCHEME_REGEX=/^https?:\/\//;function parseUrl(url){let lastSlash=url.lastIndexOf("/"),lastSearchParam=url.lastIndexOf("?"),prefix=url.substring(0,lastSlash),suffix=lastSearchParam>lastSlash?url.substring(lastSearchParam):"";return[prefix+"/",suffix]}function isHTTPScheme(url){return url.match(HTTPRequest.URL_SCHEME_REGEX)!=null}var httpRouter=(url,loadOptions)=>{if(typeof fetch=="undefined"&&(loadOptions==null||loadOptions.fetchFunc==null))return null;{let isHTTP=!0;if(Array.isArray(url)?isHTTP=url.every(urlItem=>isHTTPScheme(urlItem)):isHTTP=isHTTPScheme(url),isHTTP)return http(url,loadOptions)}return null};IORouterRegistry.registerSaveRouter(httpRouter);IORouterRegistry.registerLoadRouter(httpRouter);function http(path,loadOptions){return new HTTPRequest(path,loadOptions)}function browserHTTPRequest(path,loadOptions){return http(path,loadOptions)}var PassthroughLoader=class{constructor(modelArtifacts){this.modelArtifacts=modelArtifacts}async load(){return this.modelArtifacts}},PassthroughSaver=class{constructor(saveHandler){this.saveHandler=saveHandler}async save(modelArtifacts){return this.saveHandler(modelArtifacts)}};function fromMemory(modelArtifacts,weightSpecs,weightData,trainingConfig){return arguments.length===1?modelArtifacts.modelTopology!=null||modelArtifacts.weightSpecs!=null?new PassthroughLoader(modelArtifacts):(console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."),new PassthroughLoader({modelTopology:modelArtifacts})):(console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."),new PassthroughLoader({modelTopology:modelArtifacts,weightSpecs,weightData,trainingConfig}))}function withSaveHandler(saveHandler){return new PassthroughSaver(saveHandler)}var math_exports={};__export2(math_exports,{confusionMatrix:()=>confusionMatrix});function matMul_(a,b,transposeA=!1,transposeB=!1){let $a=convertToTensor(a,"a","matMul"),$b=convertToTensor(b,"b","matMul");[$a,$b]=makeTypesMatch($a,$b);let inputs={a:$a,b:$b},attrs={transposeA,transposeB};return ENGINE.runKernel(BatchMatMul,inputs,attrs)}var matMul=op({matMul_});function oneHot_(indices,depth,onValue=1,offValue=0){if(depth<2)throw new Error(`Error in oneHot: depth must be >=2, but it is ${depth}`);let inputs={indices:convertToTensor(indices,"indices","oneHot","int32")},attrs={depth,onValue,offValue};return ENGINE.runKernel(OneHot,inputs,attrs)}var oneHot=op({oneHot_});function transpose_(x,perm){let $x=convertToTensor(x,"x","transpose");if(perm==null&&(perm=$x.shape.map((s,i)=>i).reverse()),assert($x.rank===perm.length,()=>`Error in transpose: rank of input ${$x.rank} must match length of perm ${perm}.`),perm.forEach(axis=>{assert(axis>=0&&axis<$x.rank,()=>`All entries in 'perm' must be between 0 and ${$x.rank-1} but got ${perm}`)}),$x.rank<=1)return $x.clone();let inputs={x:$x},attrs={perm};return ENGINE.runKernel(Transpose,inputs,attrs)}var transpose=op({transpose_});function confusionMatrix_(labels,predictions,numClasses){let $labels=convertToTensor(labels,"labels","confusionMatrix"),$predictions=convertToTensor(predictions,"predictions","confusionMatrix");assert(numClasses==null||numClasses>0&&Number.isInteger(numClasses),()=>`If provided, numClasses must be a positive integer, but got ${numClasses}`),assert($labels.rank===1,()=>`Expected the rank of labels to be 1, but got ${$labels.rank}`),assert($predictions.rank===1,()=>`Expected the rank of predictions to be 1, but got ${$predictions.rank}`),assert($labels.shape[0]===$predictions.shape[0],()=>`Mismatch in the number of examples: ${$labels.shape[0]} vs. ${$predictions.shape[0]}. Labels and predictions should have the same number of elements.`),assert(numClasses>0&&Number.isInteger(numClasses),()=>`numClasses is required to be a positive integer, but got ${numClasses}`);let oneHotLabels=oneHot(cast($labels,"int32"),numClasses),oneHotPredictions=oneHot(cast($predictions,"int32"),numClasses),oneHotLabelsT=transpose(oneHotLabels),product=matMul(oneHotLabelsT,oneHotPredictions);return cast(product,"int32")}var confusionMatrix=op({confusionMatrix_}),browser_exports={};__export2(browser_exports,{fromPixels:()=>fromPixels,toPixels:()=>toPixels});function tensor3d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==3)throw new Error("tensor3d() requires shape to have three numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==3&&inferredShape.length!==1)throw new Error("tensor3d() requires values to be number[][][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor3d() requires shape to be provided when `values` are a flat array");return makeTensor(values,shape,inferredShape,dtype)}var fromPixels2DContext;function fromPixels_(pixels,numChannels=3){if(numChannels>4)throw new Error("Cannot construct Tensor with more than 4 channels from pixels.");if(pixels==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let isPixelData=!1,isImageData=!1,isVideo=!1,isImage=!1,isCanvasLike=!1,isImageBitmap=!1;if(pixels.data instanceof Uint8Array)isPixelData=!0;else if(typeof ImageData!="undefined"&&pixels instanceof ImageData)isImageData=!0;else if(typeof HTMLVideoElement!="undefined"&&pixels instanceof HTMLVideoElement)isVideo=!0;else if(typeof HTMLImageElement!="undefined"&&pixels instanceof HTMLImageElement)isImage=!0;else if(pixels.getContext!=null)isCanvasLike=!0;else if(typeof ImageBitmap!="undefined"&&pixels instanceof ImageBitmap)isImageBitmap=!0;else throw new Error(`pixels passed to tf.browser.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData in browser, or OffscreenCanvas, ImageData in webworker or {data: Uint32Array, width: number, height: number}, but was ${pixels.constructor.name}`);if(isVideo){let HAVE_CURRENT_DATA_READY_STATE=2;if(isVideo&&pixels.readyState element.")}if(getKernel(FromPixels,ENGINE.backendName)!=null){let inputs={pixels},attrs={numChannels};return ENGINE.runKernel(FromPixels,inputs,attrs)}let[width,height]=isVideo?[pixels.videoWidth,pixels.videoHeight]:[pixels.width,pixels.height],vals;isCanvasLike?vals=pixels.getContext("2d").getImageData(0,0,width,height).data:isImageData||isPixelData?vals=pixels.data:(isImage||isVideo||isImageBitmap)&&(fromPixels2DContext==null&&(fromPixels2DContext=document.createElement("canvas").getContext("2d")),fromPixels2DContext.canvas.width=width,fromPixels2DContext.canvas.height=height,fromPixels2DContext.drawImage(pixels,0,0,width,height),vals=fromPixels2DContext.getImageData(0,0,width,height).data);let values;if(numChannels===4)values=new Int32Array(vals);else{let numPixels=width*height;values=new Int32Array(numPixels*numChannels);for(let i=0;i4||depth===2)throw new Error(`toPixels only supports depth of size 1, 3 or 4 but got ${depth}`);if($img.dtype!=="float32"&&$img.dtype!=="int32")throw new Error(`Unsupported type for toPixels: ${$img.dtype}. Please use float32 or int32 tensors.`);let data2=await $img.data(),multiplier=$img.dtype==="float32"?255:1,bytes=new Uint8ClampedArray(width*height*4);for(let i=0;i1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${value}.`)}else if($img.dtype==="int32"&&(value<0||value>255))throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${value}.`);depth===1?(rgba[0]=value*multiplier,rgba[1]=value*multiplier,rgba[2]=value*multiplier):rgba[d]=value*multiplier}let j=i*4;bytes[j+0]=Math.round(rgba[0]),bytes[j+1]=Math.round(rgba[1]),bytes[j+2]=Math.round(rgba[2]),bytes[j+3]=Math.round(rgba[3])}if(canvas!=null){canvas.width=width,canvas.height=height;let ctx=canvas.getContext("2d"),imageData=new ImageData(bytes,width,height);ctx.putImageData(imageData,0,0)}return $img!==img&&$img.dispose(),bytes}var fromPixels=op({fromPixels_}),gather_nd_util_exports={};__export2(gather_nd_util_exports,{prepareAndValidate:()=>prepareAndValidate});function prepareAndValidate(tensor2,indices){let tensorRank=tensor2.shape.length,indicesRank=indices.shape.length;if(tensorRank<1)throw new Error(`tf.gatherND() expects the input to be rank 1 or higher, but the rank was ${tensorRank}.`);if(indicesRank<1)throw new Error(`tf.gatherND() expects the indices to be rank 1 or higher, but the rank was ${indicesRank}.`);if(indices.dtype!=="int32")throw new Error(`tf.gatherND() expects the indices to be int32 type, but the dtype was ${indices.dtype}.`);if(indices.shape[indicesRank-1]>tensorRank)throw new Error(`index innermost dimension length must be <= tensor rank; saw: ${indices.shape[indicesRank-1]} vs. ${tensorRank}`);if(sizeFromShape(tensor2.shape)===0)throw new Error(`Requested more than 0 entries, but input is empty. Input shape: ${tensor2.shape}.`);let indicesShape=indices.shape,sliceRank=indicesShape[indicesShape.length-1],nResult=1;for(let i=0;istride/sliceSize),1].slice(0,sliceRank);return[resultShape,nResult,sliceSize,strides]}var scatter_nd_util_exports={};__export2(scatter_nd_util_exports,{calculateShapes:()=>calculateShapes,validateInput:()=>validateInput,validateUpdateShape:()=>validateUpdateShape});function validateUpdateShape(shape,indices,updates){let sliceDim=indices.rank>1?indices.shape[indices.rank-1]:1,batchDim=indices.rank>1?indices.rank-1:1,shapeError=`Must have updates.shape = indices.shape[:batchDim] + shape[sliceDim:], got updates.shape: ${updates.shape}, indices.shape: ${indices.shape}, shape: ${shape}, sliceDim: ${sliceDim}, and batchDim: ${batchDim}.`;if(updates.rank1?indices.shape[indicesRank-1]:1,totalNd=shape.length,sliceSize=1;for(let i=sliceRank;iassertParamsValid,computeFlatOffset:()=>computeFlatOffset,computeOutShape:()=>computeOutShape,getNormalizedAxes:()=>getNormalizedAxes,isSliceContinous:()=>isSliceContinous,maskToAxes:()=>maskToAxes,parseSliceParams:()=>parseSliceParams,sliceInfo:()=>sliceInfo,startForAxis:()=>startForAxis,startIndicesWithElidedDims:()=>startIndicesWithElidedDims,stopForAxis:()=>stopForAxis,stopIndicesWithElidedDims:()=>stopIndicesWithElidedDims,stridesForAxis:()=>stridesForAxis,stridesWithElidedDims:()=>stridesWithElidedDims});function assertParamsValid(input2,begin,size){let inputRank=input2.shape.length;assert(inputRank===begin.length,()=>`Error in slice${inputRank}D: Length of begin ${begin} must match the rank of the array (${inputRank}).`),assert(inputRank===size.length,()=>`Error in slice${inputRank}D: Length of size ${size} must match the rank of the array (${inputRank}).`);for(let i=0;i`Error in slice${inputRank}D: begin[${i}] + size[${i}] (${begin[i]+size[i]}) would overflow input.shape[${i}] (${input2.shape[i]})`)}function maskToAxes(mask){let axes=[],axis=0;for(;mask>0;)mask&1&&axes.push(axis),mask/=2,axis++;return axes}function computeOutShape(begin,end,strides){let size=[];for(let axis=0;axis0){let fullIndex=ellipsisAxes[0],numElidedAxes=numInterpolatedAxes+1;normalizedBegin=startIndicesWithElidedDims(beginMask,fullIndex,numElidedAxes,begin,inputShape),normalizedEnd=stopIndicesWithElidedDims(endMask,fullIndex,numElidedAxes,end,inputShape),normalizedStrides=stridesWithElidedDims(strides,fullIndex,numElidedAxes,inputShape)}else for(let axis=0;axis-1)newIndices[axis]=0;else{let originalAxis=unnormalizeAxis(ellipsisInsertionIndex,numElidedAxes,axis),originalValue=originalBegin[originalAxis];beginMask&1<-1)newIndices[axis]=Number.MAX_SAFE_INTEGER;else{let originalAxis=unnormalizeAxis(ellipsisInsertionIndex,numElidedAxes,axis),originalValue=originalEnd[originalAxis];endMask&1<0?start=Number.MIN_SAFE_INTEGER:start=Number.MAX_SAFE_INTEGER);let axisSize=inputShape[axis];return start<0&&(start+=axisSize),start=clamp(0,start,axisSize-1),start}function stopForAxis(endMask,stopIndices,strides,inputShape,axis,ellipsisMask){let stop=stopIndices[axis],stride=strides[axis]||1;(endMask&1<0?stop=Number.MAX_SAFE_INTEGER:stop=Number.MIN_SAFE_INTEGER);let axisSize=inputShape[axis];return stop<0&&(stop+=axisSize),stride>0?stop=clamp(0,stop,axisSize):stop=clamp(-1,stop,axisSize-1),stop}function isSliceContinous(shape,begin,size){let firstNonOneAxis=size.length;for(let i=0;i1){firstNonOneAxis=i;break}for(let i=firstNonOneAxis+1;i0||size[i]!==shape[i])return!1;return!0}function computeFlatOffset(begin,strides){let flatOffset=begin.length>0?begin[begin.length-1]:1;for(let i=0;i{assert(d!==-1,()=>"slice() does not support negative begin indexing.")});let size_;return size==null?size_=new Array(xRank).fill(-1):typeof size=="number"?size_=[size,...new Array(xRank-1).fill(-1)]:size.lengthd>=0?d:(assert(d===-1,()=>`Negative size values should be exactly -1 but got ${d} for the slice() size at index ${i}.`),x.shape[i]-begin_[i])),[begin_,size_]}function sliceInfo(xShape,begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask){let $begin=begin.slice(),$end=end.slice(),$strides=strides;strides==null&&($strides=new Array($begin.length));let ellipsisAxes=maskToAxes(ellipsisMask);if(ellipsisAxes.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(ellipsisMask!==0&&newAxisMask!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(ellipsisMask!==0&&shrinkAxisMask!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");let numInterpolatedAxes=xShape.length-$begin.length,expandAxes=maskToAxes(newAxisMask),newShape=xShape.slice();expandAxes.forEach(axis=>{$begin[axis]=0,$end[axis]=1,newShape.splice(axis,0,1)});let{begin:normalizedBegin,end:normalizedEnd,strides:normalizedStrides}=getNormalizedAxes(newShape,ellipsisAxes,numInterpolatedAxes,$begin,$end,$strides,beginMask,endMask,ellipsisMask);$begin=normalizedBegin,$end=normalizedEnd,$strides=normalizedStrides;let shrinkAxes=maskToAxes(shrinkAxisMask);shrinkAxes.forEach(axis=>{$end[axis]=$begin[axis]+1,$strides[axis]=1});let size=computeOutShape($begin,$end,$strides),outShape=size.filter((_,axis)=>shrinkAxes.indexOf(axis)===-1);return{nonStrided:$strides.every(v=>v===1),$begin,$end,$strides,size,newShape,outShape}}var serialization_exports={};__export2(serialization_exports,{Serializable:()=>Serializable,SerializationMap:()=>SerializationMap,registerClass:()=>registerClass});var Serializable=class{getClassName(){return this.constructor.className}static fromConfig(cls,config3){return new cls(config3)}},SerializationMap=class{constructor(){this.classNameMap={}}static getMap(){return SerializationMap.instance==null&&(SerializationMap.instance=new SerializationMap),SerializationMap.instance}static register(cls){SerializationMap.getMap().classNameMap[cls.className]=[cls,cls.fromConfig]}};function registerClass(cls){assert(cls.className!=null,()=>"Class being registered does not have the static className property defined."),assert(typeof cls.className=="string",()=>"className is required to be a string, but got type "+typeof cls.className),assert(cls.className.length>0,()=>"Class being registered has an empty-string as its className, which is disallowed."),SerializationMap.register(cls)}var test_util_exports={};__export2(test_util_exports,{TEST_EPSILON_FLOAT16:()=>TEST_EPSILON_FLOAT16,encodeStrings:()=>encodeStrings,expectArrayBuffersEqual:()=>expectArrayBuffersEqual,expectArraysClose:()=>expectArraysClose,expectArraysEqual:()=>expectArraysEqual,expectNumbersClose:()=>expectNumbersClose,expectPromiseToFail:()=>expectPromiseToFail,expectValuesInRange:()=>expectValuesInRange,testEpsilon:()=>testEpsilon});var TEST_EPSILON_FLOAT32=.001,TEST_EPSILON_FLOAT16=.1;function expectArraysClose(actual,expected,epsilon3){return epsilon3==null&&(epsilon3=testEpsilon()),expectArraysPredicate(actual,expected,(a,b)=>areClose(a,b,epsilon3))}function testEpsilon(){return ENGINE.backend.floatPrecision()===32?TEST_EPSILON_FLOAT32:TEST_EPSILON_FLOAT16}function expectArraysPredicate(actual,expected,predicate){let checkClassType=!0;if((isTypedArray(actual)||isTypedArray(expected))&&(checkClassType=!1),isTypedArray(actual)&&isTypedArray(expected)&&(checkClassType=!0),checkClassType){let aType=actual.constructor.name,bType=expected.constructor.name;if(aType!==bType)throw new Error(`Arrays are of different type. Actual: ${aType}. Expected: ${bType}`)}if(Array.isArray(actual)&&Array.isArray(expected)){let actualShape=inferShape(actual),expectedShape=inferShape(expected);if(!arraysEqual(actualShape,expectedShape))throw new Error(`Arrays have different shapes. Actual: [${actualShape}]. Expected: [${expectedShape}]`)}let actualFlat=isTypedArray(actual)?actual:flatten(actual),expectedFlat=isTypedArray(expected)?expected:flatten(expected);if(actualFlat.length!==expectedFlat.length)throw new Error(`Arrays have different lengths actual: ${actualFlat.length} vs expected: ${expectedFlat.length}. +Manifest JSON has weights with names: ${allManifestWeightNames.join(", ")}.`)}let groupIndicesToFetch=groupIndicesToFetchMap.reduce((accumulator,shouldFetch,i)=>(shouldFetch&&accumulator.push(i),accumulator),[]),fetchUrls=[];groupIndicesToFetch.forEach(i=>{manifest[i].paths.forEach(filepath=>{let fetchUrl=filePathPrefix+(filePathPrefix.endsWith("/")?"":"/")+filepath;fetchUrls.push(fetchUrl)})});let buffers=await fetchWeightsFunction(fetchUrls),weightsTensorMap={},bufferIndexOffset=0;return groupIndicesToFetch.forEach(i=>{let numBuffers=manifest[i].paths.length,groupBytes=0;for(let i2=0;i2{let byteBuffer=groupBuffer.slice(weightsEntry.groupOffset,weightsEntry.groupOffset+weightsEntry.sizeBytes),nameToTensorMap=decodeWeights(byteBuffer,[weightsEntry.manifestEntry]);for(let name in nameToTensorMap)weightsTensorMap[name]=nameToTensorMap[name]}),bufferIndexOffset+=numBuffers}),weightsTensorMap}}var OCTET_STREAM_MIME_TYPE="application/octet-stream",JSON_TYPE="application/json",HTTPRequest=class{constructor(path,loadOptions){if(this.DEFAULT_METHOD="POST",loadOptions==null&&(loadOptions={}),this.weightPathPrefix=loadOptions.weightPathPrefix,this.onProgress=loadOptions.onProgress,this.weightUrlConverter=loadOptions.weightUrlConverter,loadOptions.fetchFunc!=null?(assert(typeof loadOptions.fetchFunc=="function",()=>"Must pass a function that matches the signature of `fetch` (see https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)"),this.fetch=loadOptions.fetchFunc):this.fetch=env().platform.fetch,assert(path!=null&&path.length>0,()=>"URL path for http must not be null, undefined or empty."),Array.isArray(path)&&assert(path.length===2,()=>`URL paths for http must have a length of 2, (actual length is ${path.length}).`),this.path=path,loadOptions.requestInit!=null&&loadOptions.requestInit.body!=null)throw new Error("requestInit is expected to have no pre-existing body, but has one.");this.requestInit=loadOptions.requestInit||{}}async save(modelArtifacts){if(modelArtifacts.modelTopology instanceof ArrayBuffer)throw new Error("BrowserHTTPRequest.save() does not support saving model topology in binary formats yet.");let init2=Object.assign({method:this.DEFAULT_METHOD},this.requestInit);init2.body=new FormData;let weightsManifest=[{paths:["./model.weights.bin"],weights:modelArtifacts.weightSpecs}],modelTopologyAndWeightManifest={modelTopology:modelArtifacts.modelTopology,format:modelArtifacts.format,generatedBy:modelArtifacts.generatedBy,convertedBy:modelArtifacts.convertedBy,weightsManifest};modelArtifacts.signature!=null&&(modelTopologyAndWeightManifest.signature=modelArtifacts.signature),modelArtifacts.userDefinedMetadata!=null&&(modelTopologyAndWeightManifest.userDefinedMetadata=modelArtifacts.userDefinedMetadata),modelArtifacts.modelInitializer!=null&&(modelTopologyAndWeightManifest.modelInitializer=modelArtifacts.modelInitializer),init2.body.append("model.json",new Blob([JSON.stringify(modelTopologyAndWeightManifest)],{type:JSON_TYPE}),"model.json"),modelArtifacts.weightData!=null&&init2.body.append("model.weights.bin",new Blob([modelArtifacts.weightData],{type:OCTET_STREAM_MIME_TYPE}),"model.weights.bin");let response=await this.fetch(this.path,init2);if(response.ok)return{modelArtifactsInfo:getModelArtifactsInfoForJSON(modelArtifacts),responses:[response]};throw new Error(`BrowserHTTPRequest.save() failed due to HTTP response status ${response.status}.`)}async load(){let modelConfigRequest=await this.fetch(this.path,this.requestInit);if(!modelConfigRequest.ok)throw new Error(`Request to ${this.path} failed with status code ${modelConfigRequest.status}. Please verify this URL points to the model JSON of the model to load.`);let modelConfig;try{modelConfig=await modelConfigRequest.json()}catch(e){let message=`Failed to parse model JSON of response from ${this.path}.`;throw this.path.endsWith(".pb")?message+=" Your path contains a .pb file extension. Support for .pb models have been removed in TensorFlow.js 1.0 in favor of .json models. You can re-convert your Python TensorFlow model using the TensorFlow.js 1.0 conversion scripts or you can convert your.pb models with the 'pb2json'NPM script in the tensorflow/tfjs-converter repository.":message+=" Please make sure the server is serving valid JSON for this request.",new Error(message)}let modelTopology=modelConfig.modelTopology,weightsManifest=modelConfig.weightsManifest,generatedBy=modelConfig.generatedBy,convertedBy=modelConfig.convertedBy,format=modelConfig.format,signature=modelConfig.signature,userDefinedMetadata=modelConfig.userDefinedMetadata;if(modelTopology==null&&weightsManifest==null)throw new Error(`The JSON from HTTP path ${this.path} contains neither model topology or manifest for weights.`);let weightSpecs,weightData;weightsManifest!=null&&([weightSpecs,weightData]=await this.loadWeights(weightsManifest));let artifacts={modelTopology,weightSpecs,weightData,generatedBy,convertedBy,format};signature!=null&&(artifacts.signature=signature),userDefinedMetadata!=null&&(artifacts.userDefinedMetadata=userDefinedMetadata);let initializer=modelConfig.modelInitializer;return initializer&&(artifacts.modelInitializer=initializer),artifacts}async loadWeights(weightsManifest){let weightPath=Array.isArray(this.path)?this.path[1]:this.path,[prefix,suffix]=parseUrl(weightPath),pathPrefix=this.weightPathPrefix||prefix,weightSpecs=[];for(let entry of weightsManifest)weightSpecs.push(...entry.weights);let fetchURLs=[],urlPromises=[];for(let weightsGroup of weightsManifest)for(let path of weightsGroup.paths)this.weightUrlConverter!=null?urlPromises.push(this.weightUrlConverter(path)):fetchURLs.push(pathPrefix+path+suffix);this.weightUrlConverter&&fetchURLs.push(...await Promise.all(urlPromises));let buffers=await loadWeightsAsArrayBuffer(fetchURLs,{requestInit:this.requestInit,fetchFunc:this.fetch,onProgress:this.onProgress});return[weightSpecs,concatenateArrayBuffers(buffers)]}};HTTPRequest.URL_SCHEME_REGEX=/^https?:\/\//;function parseUrl(url){let lastSlash=url.lastIndexOf("/"),lastSearchParam=url.lastIndexOf("?"),prefix=url.substring(0,lastSlash),suffix=lastSearchParam>lastSlash?url.substring(lastSearchParam):"";return[prefix+"/",suffix]}function isHTTPScheme(url){return url.match(HTTPRequest.URL_SCHEME_REGEX)!=null}var httpRouter=(url,loadOptions)=>{if(typeof fetch=="undefined"&&(loadOptions==null||loadOptions.fetchFunc==null))return null;{let isHTTP=!0;if(Array.isArray(url)?isHTTP=url.every(urlItem=>isHTTPScheme(urlItem)):isHTTP=isHTTPScheme(url),isHTTP)return http(url,loadOptions)}return null};IORouterRegistry.registerSaveRouter(httpRouter);IORouterRegistry.registerLoadRouter(httpRouter);function http(path,loadOptions){return new HTTPRequest(path,loadOptions)}function browserHTTPRequest(path,loadOptions){return http(path,loadOptions)}var PassthroughLoader=class{constructor(modelArtifacts){this.modelArtifacts=modelArtifacts}async load(){return this.modelArtifacts}},PassthroughSaver=class{constructor(saveHandler){this.saveHandler=saveHandler}async save(modelArtifacts){return this.saveHandler(modelArtifacts)}};function fromMemory(modelArtifacts,weightSpecs,weightData,trainingConfig){return arguments.length===1?modelArtifacts.modelTopology!=null||modelArtifacts.weightSpecs!=null?new PassthroughLoader(modelArtifacts):(console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."),new PassthroughLoader({modelTopology:modelArtifacts})):(console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."),new PassthroughLoader({modelTopology:modelArtifacts,weightSpecs,weightData,trainingConfig}))}function withSaveHandler(saveHandler){return new PassthroughSaver(saveHandler)}var math_exports={};__export2(math_exports,{confusionMatrix:()=>confusionMatrix});function matMul_(a,b,transposeA=!1,transposeB=!1){let $a=convertToTensor(a,"a","matMul"),$b=convertToTensor(b,"b","matMul");[$a,$b]=makeTypesMatch($a,$b);let inputs={a:$a,b:$b},attrs={transposeA,transposeB};return ENGINE.runKernel(BatchMatMul,inputs,attrs)}var matMul=op({matMul_});function oneHot_(indices,depth,onValue=1,offValue=0){if(depth<2)throw new Error(`Error in oneHot: depth must be >=2, but it is ${depth}`);let inputs={indices:convertToTensor(indices,"indices","oneHot","int32")},attrs={depth,onValue,offValue};return ENGINE.runKernel(OneHot,inputs,attrs)}var oneHot=op({oneHot_});function transpose_(x,perm){let $x=convertToTensor(x,"x","transpose");if(perm==null&&(perm=$x.shape.map((s,i)=>i).reverse()),assert($x.rank===perm.length,()=>`Error in transpose: rank of input ${$x.rank} must match length of perm ${perm}.`),perm.forEach(axis=>{assert(axis>=0&&axis<$x.rank,()=>`All entries in 'perm' must be between 0 and ${$x.rank-1} but got ${perm}`)}),$x.rank<=1)return $x.clone();let inputs={x:$x},attrs={perm};return ENGINE.runKernel(Transpose,inputs,attrs)}var transpose=op({transpose_});function confusionMatrix_(labels,predictions,numClasses){let $labels=convertToTensor(labels,"labels","confusionMatrix"),$predictions=convertToTensor(predictions,"predictions","confusionMatrix");assert(numClasses==null||numClasses>0&&Number.isInteger(numClasses),()=>`If provided, numClasses must be a positive integer, but got ${numClasses}`),assert($labels.rank===1,()=>`Expected the rank of labels to be 1, but got ${$labels.rank}`),assert($predictions.rank===1,()=>`Expected the rank of predictions to be 1, but got ${$predictions.rank}`),assert($labels.shape[0]===$predictions.shape[0],()=>`Mismatch in the number of examples: ${$labels.shape[0]} vs. ${$predictions.shape[0]}. Labels and predictions should have the same number of elements.`),assert(numClasses>0&&Number.isInteger(numClasses),()=>`numClasses is required to be a positive integer, but got ${numClasses}`);let oneHotLabels=oneHot(cast($labels,"int32"),numClasses),oneHotPredictions=oneHot(cast($predictions,"int32"),numClasses),oneHotLabelsT=transpose(oneHotLabels),product=matMul(oneHotLabelsT,oneHotPredictions);return cast(product,"int32")}var confusionMatrix=op({confusionMatrix_}),browser_exports={};__export2(browser_exports,{fromPixels:()=>fromPixels,toPixels:()=>toPixels});function tensor3d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==3)throw new Error("tensor3d() requires shape to have three numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==3&&inferredShape.length!==1)throw new Error("tensor3d() requires values to be number[][][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor3d() requires shape to be provided when `values` are a flat array");return makeTensor(values,shape,inferredShape,dtype)}var fromPixels2DContext;function fromPixels_(pixels,numChannels=3){if(numChannels>4)throw new Error("Cannot construct Tensor with more than 4 channels from pixels.");if(pixels==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let isPixelData=!1,isImageData=!1,isVideo=!1,isImage=!1,isCanvasLike=!1,isImageBitmap=!1;if(pixels.data instanceof Uint8Array)isPixelData=!0;else if(typeof ImageData!="undefined"&&pixels instanceof ImageData)isImageData=!0;else if(typeof HTMLVideoElement!="undefined"&&pixels instanceof HTMLVideoElement)isVideo=!0;else if(typeof HTMLImageElement!="undefined"&&pixels instanceof HTMLImageElement)isImage=!0;else if(pixels.getContext!=null)isCanvasLike=!0;else if(typeof ImageBitmap!="undefined"&&pixels instanceof ImageBitmap)isImageBitmap=!0;else throw new Error(`pixels passed to tf.browser.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData in browser, or OffscreenCanvas, ImageData in webworker or {data: Uint32Array, width: number, height: number}, but was ${pixels.constructor.name}`);if(isVideo){let HAVE_CURRENT_DATA_READY_STATE=2;if(isVideo&&pixels.readyState element.")}if(getKernel(FromPixels,ENGINE.backendName)!=null){let inputs={pixels},attrs={numChannels};return ENGINE.runKernel(FromPixels,inputs,attrs)}let[width,height]=isVideo?[pixels.videoWidth,pixels.videoHeight]:[pixels.width,pixels.height],vals;isCanvasLike?vals=pixels.getContext("2d").getImageData(0,0,width,height).data:isImageData||isPixelData?vals=pixels.data:(isImage||isVideo||isImageBitmap)&&(fromPixels2DContext==null&&(fromPixels2DContext=document.createElement("canvas").getContext("2d")),fromPixels2DContext.canvas.width=width,fromPixels2DContext.canvas.height=height,fromPixels2DContext.drawImage(pixels,0,0,width,height),vals=fromPixels2DContext.getImageData(0,0,width,height).data);let values;if(numChannels===4)values=new Int32Array(vals);else{let numPixels=width*height;values=new Int32Array(numPixels*numChannels);for(let i=0;i4||depth===2)throw new Error(`toPixels only supports depth of size 1, 3 or 4 but got ${depth}`);if($img.dtype!=="float32"&&$img.dtype!=="int32")throw new Error(`Unsupported type for toPixels: ${$img.dtype}. Please use float32 or int32 tensors.`);let data2=await $img.data(),multiplier=$img.dtype==="float32"?255:1,bytes=new Uint8ClampedArray(width*height*4);for(let i=0;i1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${value}.`)}else if($img.dtype==="int32"&&(value<0||value>255))throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${value}.`);depth===1?(rgba[0]=value*multiplier,rgba[1]=value*multiplier,rgba[2]=value*multiplier):rgba[d]=value*multiplier}let j=i*4;bytes[j+0]=Math.round(rgba[0]),bytes[j+1]=Math.round(rgba[1]),bytes[j+2]=Math.round(rgba[2]),bytes[j+3]=Math.round(rgba[3])}if(canvas!=null){canvas.width=width,canvas.height=height;let ctx=canvas.getContext("2d"),imageData=new ImageData(bytes,width,height);ctx.putImageData(imageData,0,0)}return $img!==img&&$img.dispose(),bytes}var fromPixels=op({fromPixels_}),gather_nd_util_exports={};__export2(gather_nd_util_exports,{prepareAndValidate:()=>prepareAndValidate});function prepareAndValidate(tensor2,indices){let tensorRank=tensor2.shape.length,indicesRank=indices.shape.length;if(tensorRank<1)throw new Error(`tf.gatherND() expects the input to be rank 1 or higher, but the rank was ${tensorRank}.`);if(indicesRank<1)throw new Error(`tf.gatherND() expects the indices to be rank 1 or higher, but the rank was ${indicesRank}.`);if(indices.dtype!=="int32")throw new Error(`tf.gatherND() expects the indices to be int32 type, but the dtype was ${indices.dtype}.`);if(indices.shape[indicesRank-1]>tensorRank)throw new Error(`index innermost dimension length must be <= tensor rank; saw: ${indices.shape[indicesRank-1]} vs. ${tensorRank}`);if(sizeFromShape(tensor2.shape)===0)throw new Error(`Requested more than 0 entries, but input is empty. Input shape: ${tensor2.shape}.`);let indicesShape=indices.shape,sliceRank=indicesShape[indicesShape.length-1],nResult=1;for(let i=0;istride/sliceSize),1].slice(0,sliceRank);return[resultShape,nResult,sliceSize,strides]}var scatter_nd_util_exports={};__export2(scatter_nd_util_exports,{calculateShapes:()=>calculateShapes,validateInput:()=>validateInput,validateUpdateShape:()=>validateUpdateShape});function validateUpdateShape(shape,indices,updates){let sliceDim=indices.rank>1?indices.shape[indices.rank-1]:1,batchDim=indices.rank>1?indices.rank-1:1,shapeError=`Must have updates.shape = indices.shape[:batchDim] + shape[sliceDim:], got updates.shape: ${updates.shape}, indices.shape: ${indices.shape}, shape: ${shape}, sliceDim: ${sliceDim}, and batchDim: ${batchDim}.`;if(updates.rank1?indices.shape[indicesRank-1]:1,totalNd=shape.length,sliceSize=1;for(let i=sliceRank;iassertParamsValid,computeFlatOffset:()=>computeFlatOffset,computeOutShape:()=>computeOutShape,getNormalizedAxes:()=>getNormalizedAxes,isSliceContinous:()=>isSliceContinous,maskToAxes:()=>maskToAxes,parseSliceParams:()=>parseSliceParams,sliceInfo:()=>sliceInfo,startForAxis:()=>startForAxis,startIndicesWithElidedDims:()=>startIndicesWithElidedDims,stopForAxis:()=>stopForAxis,stopIndicesWithElidedDims:()=>stopIndicesWithElidedDims,stridesForAxis:()=>stridesForAxis,stridesWithElidedDims:()=>stridesWithElidedDims});function assertParamsValid(input2,begin,size){let inputRank=input2.shape.length;assert(inputRank===begin.length,()=>`Error in slice${inputRank}D: Length of begin ${begin} must match the rank of the array (${inputRank}).`),assert(inputRank===size.length,()=>`Error in slice${inputRank}D: Length of size ${size} must match the rank of the array (${inputRank}).`);for(let i=0;i`Error in slice${inputRank}D: begin[${i}] + size[${i}] (${begin[i]+size[i]}) would overflow input.shape[${i}] (${input2.shape[i]})`)}function maskToAxes(mask){let axes=[],axis=0;for(;mask>0;)mask&1&&axes.push(axis),mask/=2,axis++;return axes}function computeOutShape(begin,end,strides){let size=[];for(let axis=0;axis0){let fullIndex=ellipsisAxes[0],numElidedAxes=numInterpolatedAxes+1;normalizedBegin=startIndicesWithElidedDims(beginMask,fullIndex,numElidedAxes,begin,inputShape),normalizedEnd=stopIndicesWithElidedDims(endMask,fullIndex,numElidedAxes,end,inputShape),normalizedStrides=stridesWithElidedDims(strides,fullIndex,numElidedAxes,inputShape)}else for(let axis=0;axis-1)newIndices[axis]=0;else{let originalAxis=unnormalizeAxis(ellipsisInsertionIndex,numElidedAxes,axis),originalValue=originalBegin[originalAxis];beginMask&1<-1)newIndices[axis]=Number.MAX_SAFE_INTEGER;else{let originalAxis=unnormalizeAxis(ellipsisInsertionIndex,numElidedAxes,axis),originalValue=originalEnd[originalAxis];endMask&1<0?start=Number.MIN_SAFE_INTEGER:start=Number.MAX_SAFE_INTEGER);let axisSize=inputShape[axis];return start<0&&(start+=axisSize),start=clamp(0,start,axisSize-1),start}function stopForAxis(endMask,stopIndices,strides,inputShape,axis,ellipsisMask){let stop=stopIndices[axis],stride=strides[axis]||1;(endMask&1<0?stop=Number.MAX_SAFE_INTEGER:stop=Number.MIN_SAFE_INTEGER);let axisSize=inputShape[axis];return stop<0&&(stop+=axisSize),stride>0?stop=clamp(0,stop,axisSize):stop=clamp(-1,stop,axisSize-1),stop}function isSliceContinous(shape,begin,size){let firstNonOneAxis=size.length;for(let i=0;i1){firstNonOneAxis=i;break}for(let i=firstNonOneAxis+1;i0||size[i]!==shape[i])return!1;return!0}function computeFlatOffset(begin,strides){let flatOffset=begin.length>0?begin[begin.length-1]:1;for(let i=0;i{assert(d!==-1,()=>"slice() does not support negative begin indexing.")});let size_;return size==null?size_=new Array(xRank).fill(-1):typeof size=="number"?size_=[size,...new Array(xRank-1).fill(-1)]:size.lengthd>=0?d:(assert(d===-1,()=>`Negative size values should be exactly -1 but got ${d} for the slice() size at index ${i}.`),x.shape[i]-begin_[i])),[begin_,size_]}function sliceInfo(xShape,begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask){let $begin=begin.slice(),$end=end.slice(),$strides=strides;strides==null&&($strides=new Array($begin.length));let ellipsisAxes=maskToAxes(ellipsisMask);if(ellipsisAxes.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(ellipsisMask!==0&&newAxisMask!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(ellipsisMask!==0&&shrinkAxisMask!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");let numInterpolatedAxes=xShape.length-$begin.length,expandAxes=maskToAxes(newAxisMask),newShape=xShape.slice();expandAxes.forEach(axis=>{$begin[axis]=0,$end[axis]=1,newShape.splice(axis,0,1)});let{begin:normalizedBegin,end:normalizedEnd,strides:normalizedStrides}=getNormalizedAxes(newShape,ellipsisAxes,numInterpolatedAxes,$begin,$end,$strides,beginMask,endMask,ellipsisMask);$begin=normalizedBegin,$end=normalizedEnd,$strides=normalizedStrides;let shrinkAxes=maskToAxes(shrinkAxisMask);shrinkAxes.forEach(axis=>{$end[axis]=$begin[axis]+1,$strides[axis]=1});let size=computeOutShape($begin,$end,$strides),outShape=size.filter((_,axis)=>shrinkAxes.indexOf(axis)===-1);return{nonStrided:$strides.every(v=>v===1),$begin,$end,$strides,size,newShape,outShape}}var serialization_exports={};__export2(serialization_exports,{Serializable:()=>Serializable,SerializationMap:()=>SerializationMap,registerClass:()=>registerClass});var Serializable=class{getClassName(){return this.constructor.className}static fromConfig(cls,config2){return new cls(config2)}},SerializationMap=class{constructor(){this.classNameMap={}}static getMap(){return SerializationMap.instance==null&&(SerializationMap.instance=new SerializationMap),SerializationMap.instance}static register(cls){SerializationMap.getMap().classNameMap[cls.className]=[cls,cls.fromConfig]}};function registerClass(cls){assert(cls.className!=null,()=>"Class being registered does not have the static className property defined."),assert(typeof cls.className=="string",()=>"className is required to be a string, but got type "+typeof cls.className),assert(cls.className.length>0,()=>"Class being registered has an empty-string as its className, which is disallowed."),SerializationMap.register(cls)}var test_util_exports={};__export2(test_util_exports,{TEST_EPSILON_FLOAT16:()=>TEST_EPSILON_FLOAT16,encodeStrings:()=>encodeStrings,expectArrayBuffersEqual:()=>expectArrayBuffersEqual,expectArraysClose:()=>expectArraysClose,expectArraysEqual:()=>expectArraysEqual,expectNumbersClose:()=>expectNumbersClose,expectPromiseToFail:()=>expectPromiseToFail,expectValuesInRange:()=>expectValuesInRange,testEpsilon:()=>testEpsilon});var TEST_EPSILON_FLOAT32=.001,TEST_EPSILON_FLOAT16=.1;function expectArraysClose(actual,expected,epsilon3){return epsilon3==null&&(epsilon3=testEpsilon()),expectArraysPredicate(actual,expected,(a,b)=>areClose(a,b,epsilon3))}function testEpsilon(){return ENGINE.backend.floatPrecision()===32?TEST_EPSILON_FLOAT32:TEST_EPSILON_FLOAT16}function expectArraysPredicate(actual,expected,predicate){let checkClassType=!0;if((isTypedArray(actual)||isTypedArray(expected))&&(checkClassType=!1),isTypedArray(actual)&&isTypedArray(expected)&&(checkClassType=!0),checkClassType){let aType=actual.constructor.name,bType=expected.constructor.name;if(aType!==bType)throw new Error(`Arrays are of different type. Actual: ${aType}. Expected: ${bType}`)}if(Array.isArray(actual)&&Array.isArray(expected)){let actualShape=inferShape(actual),expectedShape=inferShape(expected);if(!arraysEqual(actualShape,expectedShape))throw new Error(`Arrays have different shapes. Actual: [${actualShape}]. Expected: [${expectedShape}]`)}let actualFlat=isTypedArray(actual)?actual:flatten(actual),expectedFlat=isTypedArray(expected)?expected:flatten(expected);if(actualFlat.length!==expectedFlat.length)throw new Error(`Arrays have different lengths actual: ${actualFlat.length} vs expected: ${expectedFlat.length}. Actual: ${actualFlat}. Expected: ${expectedFlat}.`);for(let i=0;i`Dimension size must be evenly divisible by ${blockSize*blockSize} but is ${inputDepth} for depthToSpace with input shape ${$x.shape}`);let inputs={x:$x},attrs={blockSize,dataFormat};return ENGINE.runKernel(DepthToSpace,inputs,attrs)}var depthToSpace=op({depthToSpace_});function depthwiseConv2d_(x,filter,strides,pad3,dataFormat="NHWC",dilations=[1,1],dimRoundingMode){let $x=convertToTensor(x,"x","depthwiseConv2d"),$filter=convertToTensor(filter,"filter","depthwiseConv2d"),x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in depthwiseConv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert($filter.rank===4,()=>`Error in depthwiseConv2d: filter must be rank 4, but got rank ${$filter.rank}.`),assert(x4D.shape[3]===$filter.shape[2],()=>`Error in depthwiseConv2d: number of input channels (${x4D.shape[3]}) must match the inChannels dimension in filter ${$filter.shape[2]}.`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={x:x4D,filter:$filter},attrs={strides,pad:pad3,dataFormat,dilations,dimRoundingMode},res=ENGINE.runKernel(DepthwiseConv2dNative,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var depthwiseConv2d=op({depthwiseConv2d_});function diag_(x){let inputs={x:convertToTensor(x,"x","diag")};return ENGINE.runKernel(Diag,inputs)}var diag=op({diag_});function dilation2d_(x,filter,strides,pad3,dilations=[1,1],dataFormat="NHWC"){let $x=convertToTensor(x,"x","dilation2d"),$filter=convertToTensor(filter,"filter","dilation2d");assert($x.rank===3||$x.rank===4,()=>`Error in dilation2d: input must be rank 3 or 4, but got rank ${$x.rank}.`),assert($filter.rank===3,()=>`Error in dilation2d: filter must be rank 3, but got rank ${$filter.rank}.`),assert(dataFormat==="NHWC",()=>`Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${dataFormat}`);let x4D=$x,reshapedTo4D=!1;$x.rank===3&&(x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]]),reshapedTo4D=!0);let inputs={x:x4D,filter:$filter},attrs={strides,pad:pad3,dilations},res=ENGINE.runKernel(Dilation2D,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var dilation2d=op({dilation2d_});function getBroadcastDims(inShape,outShape){let inRank=inShape.length,dims=[];for(let i=0;i1&&a===1&&dims.unshift(dim)}return dims}function getReductionAxes(inShape,outShape){let result=[];for(let i=0;i1)&&result.unshift(outAxis)}return result}function assertAndGetBroadcastShape(shapeA,shapeB){let result=[],l=Math.max(shapeA.length,shapeB.length);for(let i=0;i"The first dimension of `a` must match the size of `condition`."),$condition.rank!==1&&assertShapesMatch($condition.shape,$broadcastedB.shape,"Error in where: ");let inputs={condition:$condition,t:$broadcastedA,e:$broadcastedB};return ENGINE.runKernel(Select,inputs)}var where=op({where_});function zerosLike_(x){let inputs={x:convertToTensor(x,"x","zerosLike")};return ENGINE.runKernel(ZerosLike,inputs)}var zerosLike=op({zerosLike_});function divNoNan_(a,b){let $a=convertToTensor(a,"a","div"),$b=convertToTensor(b,"b","div");[$a,$b]=makeTypesMatch($a,$b);let divResult=div($a,$b),zeros4=zerosLike(divResult),bEqualsZero=equal($b,zeros4);return where(bEqualsZero,zeros4,divResult)}var divNoNan=op({divNoNan_});function dot_(t1,t2){let $t1=convertToTensor(t1,"t1","dot"),$t2=convertToTensor(t2,"t2","dot");assert(($t1.rank===1||$t1.rank===2)&&($t2.rank===1||$t2.rank===2),()=>`Error in dot: inputs must all be rank 1 or 2, but got ranks ${$t1.rank} and ${$t2.rank}.`);let t1Inner=$t1.rank===1?$t1.size:$t1.shape[1],t2Inner=$t2.rank===1?$t2.size:$t2.shape[0];if(assert(t1Inner===t2Inner,()=>`Error in dot: inner dimensions of inputs must match, but got ${t1Inner} and ${t2Inner}.`),$t1.rank===1&&$t2.rank===1){let t12D=reshape($t1,[1,-1]),t22D=reshape($t2,[-1,1]),t1t2=matMul(t12D,t22D);return reshape(t1t2,[])}else if($t1.rank===1&&$t2.rank===2){let t12D=reshape($t1,[1,-1]),t22D=reshape($t2,[$t2.shape[0],$t2.shape[1]]),t1t2=matMul(t12D,t22D);return reshape(t1t2,[t1t2.size])}else if($t1.rank===2&&$t2.rank===1){let t22D=reshape($t2,[-1,1]),t1t2=matMul($t1,t22D);return reshape(t1t2,[t1t2.size])}else{let t22D=reshape($t2,[$t2.shape[0],$t2.shape[1]]);return matMul($t1,t22D)}}var dot=op({dot_});function elu_(x){let inputs={x:convertToTensor(x,"x","elu")};return ENGINE.runKernel(Elu,inputs)}var elu=op({elu_});function erf_(x){let $x=convertToTensor(x,"x","erf");assert($x.dtype==="int32"||$x.dtype==="float32",()=>"Input dtype must be `int32` or `float32`."),$x.dtype==="int32"&&($x=cast($x,"float32"));let inputs={x:$x};return ENGINE.runKernel(Erf,inputs)}var erf=op({erf_});function exp_(x){let inputs={x:convertToTensor(x,"x","exp")};return ENGINE.runKernel(Exp,inputs)}var exp=op({exp_});function expandDims_(x,axis=0){let $x=convertToTensor(x,"x","expandDims","string_or_numeric");assert(axis<=$x.rank,()=>"Axis must be <= rank of the tensor");let inputs={input:$x},attrs={dim:axis};return ENGINE.runKernel(ExpandDims,inputs,attrs)}var expandDims=op({expandDims_});function expm1_(x){let inputs={x:convertToTensor(x,"x","expm1")};return ENGINE.runKernel(Expm1,inputs)}var expm1=op({expm1_});function tile_(x,reps){let $x=convertToTensor(x,"x","tile","string_or_numeric");assert($x.rank===reps.length,()=>`Error in transpose: rank of input ${$x.rank} must match length of reps ${reps}.`);let inputs={x:$x},attrs={reps};return ENGINE.runKernel(Tile,inputs,attrs)}var tile=op({tile_});function eye_(numRows,numColumns,batchShape,dtype="float32"){numColumns==null&&(numColumns=numRows);let buff=buffer([numRows,numColumns],dtype),n=numRows<=numColumns?numRows:numColumns;for(let i=0;i`Error in localResponseNormalization: x must be rank 3 or 4 but got rank ${$x.rank}.`),assert(isInt(depthRadius),()=>`Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${depthRadius}.`);let x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]]));let inputs={x:x4D},attrs={depthRadius,bias,alpha,beta},res=ENGINE.runKernel(LRN,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var localResponseNormalization=op({localResponseNormalization_});function log_(x){let inputs={x:convertToTensor(x,"x","log")};return ENGINE.runKernel(Log,inputs)}var log2=op({log_});function log1p_(x){let inputs={x:convertToTensor(x,"x","log1p")};return ENGINE.runKernel(Log1p,inputs)}var log1p=op({log1p_});function grad(f){return assert(isFunction(f),()=>"The f passed in grad(f) must be a function"),(x,dy)=>{let $x=convertToTensor(x,"x","tf.grad","string_or_numeric"),$dy=dy!=null?convertToTensor(dy,"dy","tf.grad"):null;return ENGINE.tidy(()=>{let{value,grads:grads2}=ENGINE.gradients(()=>f($x),[$x],$dy);return $dy!=null&&assertShapesMatch(value.shape,$dy.shape,"The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)"),checkGrads(grads2),grads2[0]})}}function grads(f){return assert(isFunction(f),()=>"The f passed in grads(f) must be a function"),(args,dy)=>{assert(Array.isArray(args),()=>"The args passed in grads(f)(args) must be an array of `Tensor`s or `TensorLike`s");let $args=convertToTensorArray(args,"args","tf.grads","string_or_numeric"),$dy=dy!=null?convertToTensor(dy,"dy","tf.grads"):null;return ENGINE.tidy(()=>{let{value,grads:grads2}=ENGINE.gradients(()=>f(...$args),$args,$dy);return $dy!=null&&assertShapesMatch(value.shape,$dy.shape,"The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),checkGrads(grads2),grads2})}}function valueAndGrad(f){return assert(isFunction(f),()=>"The f passed in valueAndGrad(f) must be a function"),(x,dy)=>{assert(x instanceof Tensor,()=>"The x passed in valueAndGrad(f)(x) must be a tensor"),assert(dy==null||dy instanceof Tensor,()=>"The dy passed in valueAndGrad(f)(x, dy) must be a tensor");let{grads:grads2,value}=ENGINE.gradients(()=>f(x),[x],dy);return checkGrads(grads2),{grad:grads2[0],value}}}function valueAndGrads(f){return assert(isFunction(f),()=>"The f passed in valueAndGrads(f) must be a function"),(args,dy)=>{assert(Array.isArray(args)&&args.every(arg=>arg instanceof Tensor),()=>"The args passed in valueAndGrads(f)(args) must be array of tensors"),assert(dy==null||dy instanceof Tensor,()=>"The dy passed in valueAndGrads(f)(args, dy) must be a tensor");let res=ENGINE.gradients(()=>f(...args),args,dy);return dy!=null&&assertShapesMatch(res.value.shape,dy.shape,"The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),checkGrads(res.grads),res}}function variableGrads(f,varList){assert(isFunction(f),()=>"The f passed in variableGrads(f) must be a function"),assert(varList==null||Array.isArray(varList)&&varList.every(v=>v instanceof Variable),()=>"The varList passed in variableGrads(f, varList) must be an array of variables");let specifiedVarList=varList!=null;if(!specifiedVarList){varList=[];for(let varName in ENGINE.registeredVariables)varList.push(ENGINE.registeredVariables[varName])}let specifiedNonTrainable=specifiedVarList?varList.filter(variable2=>!variable2.trainable):null,originalVarCount=varList.length;varList=varList.filter(variable2=>variable2.trainable),assert(varList.length>0,()=>`variableGrads() expects at least one of the input variables to be trainable, but none of the ${originalVarCount} variables is trainable.`);let allowNoGradients=!0,{value,grads:grads2}=ENGINE.gradients(f,varList,null,allowNoGradients);assert(grads2.some(g=>g!=null),()=>"Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize()."),assert(value.rank===0,()=>`The f passed in variableGrads(f) must return a scalar, but it returned a rank-${value.rank} tensor`);let namedGrads={};return varList.forEach((v,i)=>{grads2[i]!=null&&(namedGrads[v.name]=grads2[i])}),specifiedNonTrainable!=null&&specifiedNonTrainable.forEach(v=>namedGrads[v.name]=null),{value,grads:namedGrads}}function customGrad(f){return ENGINE.customGrad(f)}function checkGrads(grads2){if(grads2.filter(g=>g==null).length>0)throw new Error(`Cannot compute gradient of y=f(x) with respect to x. Make sure that - the f you passed encloses all operations that lead from x to y.`)}function neg_(x){let inputs={x:convertToTensor(x,"x","neg")};return ENGINE.runKernel(Neg,inputs)}var neg=op({neg_});function softplus_(x){let inputs={x:convertToTensor(x,"x","softplus")};return ENGINE.runKernel(Softplus,inputs)}var softplus=op({softplus_});function logSigmoid_(x){let $x=convertToTensor(x,"x","logSigmoid");return customGrad(x2=>({value:neg(softplus(neg(x2))),gradFunc:dy=>mul(dy,sigmoid(neg(x2)))}))($x)}var logSigmoid=op({logSigmoid_});function max_(x,axis=null,keepDims=!1){let inputs={x:convertToTensor(x,"x","max")},attrs={reductionIndices:axis,keepDims};return ENGINE.runKernel(Max,inputs,attrs)}var max=op({max_});function sub_(a,b){let $a=convertToTensor(a,"a","sub"),$b=convertToTensor(b,"b","sub");[$a,$b]=makeTypesMatch($a,$b);let inputs={a:$a,b:$b};return ENGINE.runKernel(Sub,inputs)}var sub=op({sub_});function sum_(x,axis=null,keepDims=!1){let $x=convertToTensor(x,"x","sum");$x.dtype==="bool"&&($x=cast($x,"int32"));let inputs={x:$x},attrs={axis,keepDims};return ENGINE.runKernel(Sum,inputs,attrs)}var sum2=op({sum_});function logSoftmax_(logits,axis=-1){let $logits=convertToTensor(logits,"logits","logSoftmax");if(axis===-1&&(axis=$logits.rank-1),axis!==$logits.rank-1)throw Error(`Log Softmax along a non-last dimension is not yet supported. Logits was rank ${$logits.rank} and axis was ${axis}`);return customGrad((logits2,save)=>{let keepDims=!0,xMax=max(logits2,axis,!0),shifted=sub(logits2,xMax),value=sub(cast(shifted,"float32"),log2(sum2(exp(shifted),axis,keepDims)));return save([value]),{value,gradFunc:(dy,saved)=>{let[value2]=saved,keepDims2=!0,softmax6=exp(value2);return sub(dy,mul(sum2(dy,axis,keepDims2),softmax6))}}})($logits)}var logSoftmax=op({logSoftmax_});function axesAreInnerMostDims(axes,rank){for(let i=0;iaShape[dim]);return[outShape,reduceShape]}function expandShapeToKeepDim(shape,axes){let reduceSubShape=axes.map(x=>1);return combineLocations(shape,reduceSubShape,axes)}function assertAxesAreInnerMostDims(msg,axes,rank){assert(axesAreInnerMostDims(axes,rank),()=>`${msg} supports only inner-most axes for now. Got axes ${axes} and rank-${rank} input.`)}function getAxesPermutation(axes,rank){if(axesAreInnerMostDims(axes,rank))return null;let result=[];for(let i=0;iresult.push(axis)),result}function getUndoAxesPermutation(axes){return axes.map((axis,i)=>[i,axis]).sort((a,b)=>a[1]-b[1]).map(x=>x[0])}function getInnerMostAxes(numAxes,rank){let res=[];for(let i=rank-numAxes;i`Error in maxPool: input must be rank 4 but got rank ${x4D.rank}.`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in maxPool: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={x:x4D},attrs={filterSize,strides,pad:pad3,dimRoundingMode},res=ENGINE.runKernel(MaxPool,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var maxPool=op({maxPool_});function maxPool3d_(x,filterSize=[1,1,1],strides,pad3,dimRoundingMode,dataFormat="NDHWC",dilations){dilations==null?dilations=[1,1,1]:deprecationWarn("dilations is deprecated, this field will be gone in v3.0.0.");let $x=convertToTensor(x,"x","maxPool3d"),x5D=$x,reshapedTo5D=!1;$x.rank===4&&(reshapedTo5D=!0,x5D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2],$x.shape[3]])),assert(x5D.rank===5,()=>`Error in maxPool3d: x must be rank 5 but got rank ${x5D.rank}.`),assert(dataFormat==="NDHWC",()=>`Error in maxPool3d: Only NDHWC is currently supported, but got dataFormat of ${dataFormat}`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in maxPool3d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in maxPool3d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={x:x5D},attrs={filterSize,strides,pad:pad3,dimRoundingMode,dataFormat,dilations},res=ENGINE.runKernel(MaxPool3D,inputs,attrs);return reshapedTo5D?reshape(res,[res.shape[1],res.shape[2],res.shape[3],res.shape[4]]):res}var maxPool3d=op({maxPool3d_});function maxPoolWithArgmax_(x,filterSize,strides,pad3,includeBatchInIndex=!1){let inputs={x:convertToTensor(x,"x","maxPoolWithArgmax")},attrs={filterSize,strides,pad:pad3,includeBatchInIndex},result=ENGINE.runKernel(MaxPoolWithArgmax,inputs,attrs);return{result:result[0],indexes:result[1]}}var maxPoolWithArgmax=op({maxPoolWithArgmax_});function maximum_(a,b){let $a=convertToTensor(a,"a","maximum"),$b=convertToTensor(b,"b","maximum");[$a,$b]=makeTypesMatch($a,$b),$a.dtype==="bool"&&($a=cast($a,"int32"),$b=cast($b,"int32")),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(Maximum,inputs)}var maximum=op({maximum_});function mean_(x,axis=null,keepDims=!1){let inputs={x:convertToTensor(x,"x","mean")},attrs={axis,keepDims};return ENGINE.runKernel(Mean,inputs,attrs)}var mean=op({mean_});function min_(x,axis=null,keepDims=!1){let inputs={x:convertToTensor(x,"x","min")},attrs={axis,keepDims};return ENGINE.runKernel(Min,inputs,attrs)}var min=op({min_});function minimum_(a,b){let $a=convertToTensor(a,"a","minimum"),$b=convertToTensor(b,"b","minimum");[$a,$b]=makeTypesMatch($a,$b),$a.dtype==="bool"&&($a=cast($a,"int32"),$b=cast($b,"int32")),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(Minimum,inputs)}var minimum=op({minimum_});function mirrorPad_(x,paddings,mode){assert(mode==="reflect"||mode==="symmetric",()=>`Invalid mode. Mode must be either reflect or symmetric. Got ${mode}.`);let $x=convertToTensor(x,"x","mirrorPad");if($x.rank===0)throw new Error("mirrorPad(scalar) is not defined. Pass non-scalar to mirrorPad");assert(paddings.length===$x.rank,()=>`Padding doesn't match input. Must be ${$x.rank}. Got ${paddings.length}.`);let shapeOffset=mode==="reflect"?1:0;for(let i=0;i<$x.rank;i++)assert(paddings[i].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),assert(paddings[i][0]>=0&&paddings[i][0]<=$x.shape[i]-shapeOffset&&paddings[i][1]>=0&&paddings[i][1]<=$x.shape[i]-shapeOffset,()=>`Padding in dimension ${i} cannot be greater than or equal to ${$x.shape[i]-shapeOffset} or less than 0 for input of shape ${$x.shape}`);let attrs={paddings,mode},inputs={x:$x};return ENGINE.runKernel(MirrorPad,inputs,attrs)}var mirrorPad=op({mirrorPad_});function mod_(a,b){let $a=convertToTensor(a,"a","mod"),$b=convertToTensor(b,"b","mod");[$a,$b]=makeTypesMatch($a,$b);let inputs={a:$a,b:$b};return ENGINE.runKernel(Mod,inputs)}var mod=op({mod_});function square_(x){let $x=convertToTensor(x,"x","square"),attrs={};return ENGINE.runKernel("Square",{x:$x},attrs)}var square=op({square_});function moments_(x,axis=null,keepDims=!1){x=convertToTensor(x,"x","moments");let axes=parseAxisParam(axis,x.shape),xMean=mean(x,axes,keepDims),keepDimsShape=xMean.shape;keepDims||(keepDimsShape=expandShapeToKeepDim(xMean.shape,axes));let devSquared=square(sub(cast(x,"float32"),reshape(xMean,keepDimsShape))),variance=mean(devSquared,axes,keepDims);return{mean:xMean,variance}}var moments=op({moments_});function multiRNNCell_(lstmCells,data2,c,h){let $data=convertToTensor(data2,"data","multiRNNCell"),$c=convertToTensorArray(c,"c","multiRNNCell"),$h=convertToTensorArray(h,"h","multiRNNCell"),input2=$data,newStates=[];for(let i=0;i2)throw new Error(`Rank of probabilities must be 1 or 2, but is ${origRank}`);seed=seed||Math.random();let inputs={logits:origRank===1?reshape($logits,[1,-1]):$logits},attrs={numSamples,seed,normalized},res=ENGINE.runKernel(Multinomial,inputs,attrs);return origRank===1?reshape(res,[res.size]):res}var multinomial=op({multinomial_});function notEqual_(a,b){let $a=convertToTensor(a,"a","notEqual"),$b=convertToTensor(b,"b","notEqual");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(NotEqual,inputs)}var notEqual=op({notEqual_});function zeros(shape,dtype="float32"){if(dtype==="complex64"){let real4=zeros(shape,"float32"),imag4=zeros(shape,"float32");return complex(real4,imag4)}let values=makeZerosTypedArray(sizeFromShape(shape),dtype);return ENGINE.makeTensor(values,shape,dtype)}function ones2(shape,dtype="float32"){if(dtype==="complex64"){let real4=ones2(shape,"float32"),imag4=zeros(shape,"float32");return complex(real4,imag4)}let values=makeOnesTypedArray(sizeFromShape(shape),dtype);return ENGINE.makeTensor(values,shape,dtype)}function onesLike_(x){let inputs={x:convertToTensor(x,"x","onesLike")};return ENGINE.runKernel(OnesLike,inputs)}var onesLike=op({onesLike_});function outerProduct_(v1,v2){let $v1=convertToTensor(v1,"v1","outerProduct"),$v2=convertToTensor(v2,"v2","outerProduct");assert($v1.rank===1&&$v2.rank===1,()=>`Error in outerProduct: inputs must be rank 1, but got ranks ${$v1.rank} and ${$v2.rank}.`);let v12D=reshape($v1,[-1,1]),v22D=reshape($v2,[1,-1]);return matMul(v12D,v22D)}var outerProduct=op({outerProduct_});function pad_(x,paddings,constantValue=0){let $x=convertToTensor(x,"x","pad");if($x.rank===0)throw new Error("pad(scalar) is not defined. Pass non-scalar to pad");let attrs={paddings,constantValue},inputs={x:$x};return ENGINE.runKernel(PadV2,inputs,attrs)}var pad=op({pad_});function pad1d_(x,paddings,constantValue=0){return assert(paddings.length===2,()=>"Invalid number of paddings. Must be length of 2."),pad(x,[paddings],constantValue)}var pad1d=op({pad1d_});function pad2d_(x,paddings,constantValue=0){return assert(paddings.length===2&&paddings[0].length===2&&paddings[1].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),pad(x,paddings,constantValue)}var pad2d=op({pad2d_});function pad3d_(x,paddings,constantValue=0){return assert(paddings.length===3&&paddings[0].length===2&&paddings[1].length===2&&paddings[2].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),pad(x,paddings,constantValue)}var pad3d=op({pad3d_});function pad4d_(x,paddings,constantValue=0){return assert(paddings.length===4&&paddings[0].length===2&&paddings[1].length===2&&paddings[2].length===2&&paddings[3].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),pad(x,paddings,constantValue)}var pad4d=op({pad4d_});function spaceToBatchND_(x,blockShape,paddings){let $x=convertToTensor(x,"x","spaceToBatchND");assert($x.rank>=1+blockShape.length,()=>`input rank ${$x.rank} should be > than [blockShape] ${blockShape.length}`),assert(paddings.length===blockShape.length,()=>`paddings.shape[0] ${paddings.length} must be equal to [blockShape] ${blockShape.length}`),assert($x.shape.reduce((a,b,i)=>i>0&&i<=blockShape.length?a&&(b+paddings[i-1][0]+paddings[i-1][1])%blockShape[i-1]==0:a,!0),()=>`input spatial dimensions ${$x.shape.slice(1)} with paddings ${paddings.toString()} must be divisible by blockShapes ${blockShape.toString()}`);let inputs={x:$x},attrs={blockShape,paddings};return ENGINE.runKernel(SpaceToBatchND,inputs,attrs)}var spaceToBatchND=op({spaceToBatchND_});function pool_(input2,windowShape,poolingType,pad3,dilations,strides){dilations==null&&(dilations=[1,1]),strides==null&&(strides=1),pad3===0&&(pad3="valid");let $x=convertToTensor(input2,"x","maxPool"),x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in pool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);let convInfo=computePool2DInfo(x4D.shape,windowShape,strides,dilations,pad3),dilation=[convInfo.dilationHeight,convInfo.dilationWidth],basePadding;pad3==="same"?basePadding=withSpaceToBatchBasePaddings([convInfo.filterHeight,convInfo.filterWidth],dilation):basePadding=[[0,0],[0,0]];let isDilationOne=dilation[0]===1&&dilation[1]===1,[adjustedPadding,adjustedCrops]=requiredSpaceToBatchPaddings([convInfo.inHeight,convInfo.inWidth],dilation,basePadding),convertedPad=isDilationOne?pad3:"valid",convertedX=isDilationOne?x4D:spaceToBatchND(x4D,dilation,adjustedPadding),y=(poolingType==="avg"?()=>avgPool(convertedX,windowShape,strides,convertedPad):()=>maxPool(convertedX,windowShape,strides,convertedPad))(),res=isDilationOne?y:batchToSpaceND(y,dilation,adjustedCrops);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}function requiredSpaceToBatchPaddings(inputShape,blockShape,basePadding){let padStart=basePadding.map(b=>b[0]),origPadEnd=basePadding.map(b=>b[1]),fullInputShape=inputShape.concat(padStart,origPadEnd),padEndExtra=blockShape.map((b,i)=>(b-fullInputShape[i]%b)%b),padEnd=origPadEnd.map((s,i)=>s+padEndExtra[i]),paddings=blockShape.map((_,i)=>[padStart[i],padEnd[i]]),crops=blockShape.map((_,i)=>[0,padEndExtra[i]]);return[paddings,crops]}function withSpaceToBatchBasePaddings(filterShape,dilation){let padExtraShape=filterShape.map((s,i)=>s+(s-1)*(dilation[i]-1)).map(s=>s-1),padExtraStart=padExtraShape.map(s=>Math.floor(s/2)),padExtraEnd=padExtraShape.map((s,i)=>s-padExtraStart[i]);return padExtraShape.map((_,i)=>[padExtraStart[i],padExtraEnd[i]])}var pool=op({pool_});function pow_(base2,exp4){let $base=convertToTensor(base2,"base","pow"),$exp=convertToTensor(exp4,"exp","pow");[$base,$exp]=makeTypesMatch($base,$exp);let inputs={a:$base,b:$exp};return ENGINE.runKernel(Pow,inputs)}var pow=op({pow_});function prelu_(x,alpha){let $x=convertToTensor(x,"x","prelu"),$alpha=convertToTensor(alpha,"alpha","prelu"),inputs={x:$x,alpha:$alpha};return ENGINE.runKernel(Prelu,inputs)}var prelu=op({prelu_});function prod_(x,axis=null,keepDims=!1){let $x=convertToTensor(x,"x","prod");$x.dtype==="bool"&&($x=cast($x,"int32"));let inputs={x:$x},attrs={axis,keepDims};return ENGINE.runKernel(Prod,inputs,attrs)}var prod=op({prod_});function rand_(shape,randFunction,dtype){let size=sizeFromShape(shape),values=null;if(dtype==null||dtype==="float32")values=new Float32Array(size);else if(dtype==="int32")values=new Int32Array(size);else if(dtype==="bool")values=new Uint8Array(size);else throw new Error(`Unknown data type ${dtype}`);for(let i=0;i=1||s===0);let mul2=Math.sqrt(-2*Math.log(s)/s);resultX=this.mean+this.stdDev*v1*mul2,resultY=this.mean+this.stdDev*v2*mul2,(!this.truncated||this.isValidTruncated(resultX))&&(isValid=!0)}return(!this.truncated||this.isValidTruncated(resultY))&&(this.nextVal=this.convertValue(resultY)),this.convertValue(resultX)}convertValue(value){return this.dtype==null||this.dtype==="float32"?value:Math.round(value)}isValidTruncated(value){return value<=this.upper&&value>=this.lower}},RandGamma=class{constructor(alpha,beta,dtype,seed){this.alpha=alpha,this.beta=1/beta,this.dtype=dtype;let seedValue=seed||Math.random();this.randu=seedrandom.alea(seedValue.toString()),this.randn=new MPRandGauss(0,1,dtype,!1,this.randu()),alpha<1?this.d=alpha+2/3:this.d=alpha-1/3,this.c=1/Math.sqrt(9*this.d)}nextValue(){let x2,v0,v1,x,u,v;for(;;){do x=this.randn.nextValue(),v=1+this.c*x;while(v<=0);if(v*=v*v,x2=x*x,v0=1-.331*x2*x2,v1=.5*x2+this.d*(1-v+Math.log(v)),u=this.randu(),uthis.dtype==null||this.dtype==="float32",this.min=min6,this.range=max6-min6,this.dtype=dtype,seed==null&&(seed=Math.random()),typeof seed=="number"&&(seed=seed.toString()),!this.canReturnFloat()&&this.range<=1)throw new Error(`The difference between ${min6} - ${max6} <= 1 and dtype is not float`);this.random=seedrandom.alea(seed)}convertValue(value){return this.canReturnFloat()?value:Math.round(value)}nextValue(){return this.convertValue(this.min+this.range*this.random())}};function randomGamma_(shape,alpha,beta=1,dtype="float32",seed){if(beta==null&&(beta=1),dtype==null&&(dtype="float32"),dtype!=="float32"&&dtype!=="int32")throw new Error(`Unsupported data type ${dtype}`);let rgamma=new RandGamma(alpha,beta,dtype,seed),res=buffer(shape,dtype);for(let i=0;i`Error in reverse1D: x must be rank 1 but got rank ${$x.rank}.`),reverse($x,0)}var reverse1d=op({reverse1d_});function reverse2d_(x,axis){let $x=convertToTensor(x,"x","reverse");return assert($x.rank===2,()=>`Error in reverse2D: x must be rank 2 but got rank ${$x.rank}.`),reverse($x,axis)}var reverse2d=op({reverse2d_});function reverse3d_(x,axis){let $x=convertToTensor(x,"x","reverse");return assert($x.rank===3,()=>`Error in reverse3D: x must be rank 3 but got rank ${$x.rank}.`),reverse($x,axis)}var reverse3d=op({reverse3d_});function reverse4d_(x,axis){let $x=convertToTensor(x,"x","reverse");return assert($x.rank===4,()=>`Error in reverse4D: x must be rank 4 but got rank ${$x.rank}.`),reverse($x,axis)}var reverse4d=op({reverse4d_});function round_(x){let inputs={x:convertToTensor(x,"x","round")};return ENGINE.runKernel(Round,inputs)}var round2=op({round_});function rsqrt_(x){let inputs={x:convertToTensor(x,"x","rsqrt")};return ENGINE.runKernel(Rsqrt,inputs)}var rsqrt=op({rsqrt_});function scalar(value,dtype){if((isTypedArray(value)&&dtype!=="string"||Array.isArray(value))&&dtype!=="complex64")throw new Error("Error creating a new Scalar: value must be a primitive (number|boolean|string)");if(dtype==="string"&&isTypedArray(value)&&!(value instanceof Uint8Array))throw new Error("When making a scalar from encoded string, the value must be `Uint8Array`.");return makeTensor(value,[],[],dtype)}function selu_(x){let inputs={x:convertToTensor(x,"x","selu")};return ENGINE.runKernel(Selu,inputs)}var selu=op({selu_});function separableConv2d_(x,depthwiseFilter,pointwiseFilter,strides,pad3,dilation=[1,1],dataFormat="NHWC"){let $x=convertToTensor(x,"x","separableConv2d"),$depthwiseFilter=convertToTensor(depthwiseFilter,"depthwiseFilter","separableConv2d"),$pointwiseFilter=convertToTensor(pointwiseFilter,"pointwiseFilter","separableConv2d"),x4D=$x,reshapedTo4D=!1;if($x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),dataFormat==="NCHW")throw new Error("separableConv2d currently does not support dataFormat NCHW; only NHWC is supported");assert(x4D.rank===4,()=>`Error in separableConv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert($depthwiseFilter.rank===4,()=>`Error in separableConv2d: depthwise filter must be rank 4, but got rank ${$depthwiseFilter.rank}.`),assert($pointwiseFilter.rank===4,()=>`Error in separableConv2d: pointwise filter must be rank 4, but got rank ${$depthwiseFilter.rank}.`),assert($pointwiseFilter.shape[0]===1,()=>`Error in separableConv2d: the first dimension of pointwise filter must be 1, but got ${$pointwiseFilter.shape[0]}.`),assert($pointwiseFilter.shape[1]===1,()=>`Error in separableConv2d: the second dimension of pointwise filter must be 1, but got ${$pointwiseFilter.shape[1]}.`);let inChannels=$depthwiseFilter.shape[2],channelMultiplier=$depthwiseFilter.shape[3];assert($pointwiseFilter.shape[2]===inChannels*channelMultiplier,()=>`Error in separableConv2d: the third dimension of pointwise filter must be ${inChannels*channelMultiplier}, but got ${$pointwiseFilter.shape[2]}.`);let depthwise=depthwiseConv2d(x4D,$depthwiseFilter,strides,pad3,dataFormat,dilation),res=conv2d(depthwise,$pointwiseFilter,1,"valid",dataFormat);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var separableConv2d=op({separableConv2d_});async function setdiff1dAsync_(x,y){let $x=convertToTensor(x,"x","setdiff1d"),$y=convertToTensor(y,"y","setdiff1d");assert($x.dtype===$y.dtype,()=>`x and y should have the same dtype, but got x (${$x.dtype}) and y (${$y.dtype}).`),assert($x.rank===1,()=>`x should be 1D tensor, but got x (${$x.shape}).`),assert($y.rank===1,()=>`y should be 1D tensor, but got y (${$y.shape}).`);let xVals=await $x.data(),yVals=await $y.data(),ySet=new Set(yVals),outputSize=0;for(let i=0;i`slice1d expects a rank-1 tensor, but got a rank-${$x.rank} tensor`),slice($x,[begin],[size])}var slice1d=op({slice1d_});function slice2d_(x,begin,size){let $x=convertToTensor(x,"x","slice2d");return assert($x.rank===2,()=>`slice2d expects a rank-2 tensor, but got a rank-${$x.rank} tensor`),slice($x,begin,size)}var slice2d=op({slice2d_});function slice3d_(x,begin,size){let $x=convertToTensor(x,"x","slice3d");return assert($x.rank===3,()=>`slice3d expects a rank-3 tensor, but got a rank-${$x.rank} tensor`),slice($x,begin,size)}var slice3d=op({slice3d_});function slice4d_(x,begin,size){let $x=convertToTensor(x,"x","slice4d");return assert($x.rank===4,()=>`slice4d expects a rank-4 tensor, but got a rank-${$x.rank} tensor`),slice($x,begin,size)}var slice4d=op({slice4d_});function softmax_(logits,dim=-1){let $logits=convertToTensor(logits,"logits","softmax","float32");if(dim===-1&&(dim=$logits.rank-1),dim!==$logits.rank-1)throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${$logits.rank} and dim was ${dim}`);let inputs={logits:$logits},attrs={dim};return ENGINE.runKernel(Softmax,inputs,attrs)}var softmax=op({softmax_});function fft_(input2){assert(input2.dtype==="complex64",()=>`The dtype for tf.spectral.fft() must be complex64 but got ${input2.dtype}.`);let inputs={input:input2};return ENGINE.runKernel(FFT,inputs)}var fft=op({fft_});function ifft_(input2){assert(input2.dtype==="complex64",()=>`The dtype for tf.spectral.ifft() must be complex64 but got ${input2.dtype}.`);let inputs={input:input2};return ENGINE.runKernel(IFFT,inputs)}var ifft=op({ifft_});function irfft_(input2){let innerDimensionSize=input2.shape[input2.shape.length-1],batch=input2.size/innerDimensionSize,ret;if(innerDimensionSize<=2){let complexInput=reshape(input2,[batch,innerDimensionSize]);ret=ifft(complexInput)}else{let outputShape=[batch,2*(innerDimensionSize-1)],realInput=reshape(real(input2),[batch,innerDimensionSize]),imagInput=reshape(imag(input2),[batch,innerDimensionSize]),realConjugate=reverse(slice(realInput,[0,1],[batch,innerDimensionSize-2]),1),imagConjugate=mul(reverse(slice(imagInput,[0,1],[batch,innerDimensionSize-2]),1),scalar(-1)),r=concat([realInput,realConjugate],1),i=concat([imagInput,imagConjugate],1),complexInput=reshape(complex(r,i),[outputShape[0],outputShape[1]]);ret=ifft(complexInput)}if(ret=real(ret),input2.rank===3&&input2.shape[0]!==0){let temp=ret,batch2=input2.shape[0];ret=reshape(ret,[batch2,ret.shape[0]/batch2,ret.shape[1]]),temp.dispose()}return ret}var irfft=op({irfft_});function split_(x,numOrSizeSplits,axis=0){let inputs={x:convertToTensor(x,"x","split")},attr={numOrSizeSplits,axis};return ENGINE.runKernel(SplitV,inputs,attr)}var split=op({split_});function rfft_(input2,fftLength){assert(input2.dtype==="float32",()=>`The dtype for rfft() must be real value but got ${input2.dtype}`);let innerDimensionSize=input2.shape[input2.shape.length-1],batch=input2.size/innerDimensionSize,adjustedInput;if(fftLength!=null&&fftLength0),size=input2.shape.map(v=>v);size[input2.shape.length-1]=fftLength,adjustedInput=slice(input2,begin,size),innerDimensionSize=fftLength}else if(fftLength!=null&&fftLength>innerDimensionSize){let zerosShape=input2.shape.map(v=>v);zerosShape[input2.shape.length-1]=fftLength-innerDimensionSize,adjustedInput=concat([input2,zeros(zerosShape)],input2.shape.length-1),innerDimensionSize=fftLength}else adjustedInput=input2;let zerosInput=zerosLike(adjustedInput),complexInput=reshape(complex(adjustedInput,zerosInput),[batch,innerDimensionSize]),ret=fft(complexInput),half=Math.floor(innerDimensionSize/2)+1,realValues=real(ret),imagValues=imag(ret),realComplexConjugate=split(realValues,[half,innerDimensionSize-half],realValues.shape.length-1),imagComplexConjugate=split(imagValues,[half,innerDimensionSize-half],imagValues.shape.length-1),outputShape=adjustedInput.shape.slice();return outputShape[adjustedInput.shape.length-1]=half,reshape(complex(realComplexConjugate[0],imagComplexConjugate[0]),outputShape)}var rfft=op({rfft_});function sqrt_(x){let inputs={x:convertToTensor(x,"x","sqrt")};return ENGINE.runKernel(Sqrt,inputs)}var sqrt=op({sqrt_});function squaredDifference_(a,b){let $a=convertToTensor(a,"a","squaredDifference"),$b=convertToTensor(b,"b","squaredDifference");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b},attrs={};return ENGINE.runKernel(SquaredDifference,inputs,attrs)}var squaredDifference=op({squaredDifference_});function squeeze_(x,axis){let $x=convertToTensor(x,"x","squeeze");return reshape($x,squeezeShape($x.shape,axis).newShape)}var squeeze=op({squeeze_});function stack_(tensors,axis=0){let $tensors=convertToTensorArray(tensors,"tensors","stack","string_or_numeric");assert($tensors.length>=1,()=>"Pass at least one tensor to tf.stack"),$tensors.length>0&&assert(axis<=$tensors[0].rank,()=>"Axis must be <= rank of the tensor");let inputs=$tensors,attrs={axis};return ENGINE.runKernel(Pack,inputs,attrs)}var stack=op({stack_});function step_(x,alpha=0){let inputs={x:convertToTensor(x,"x","step")},attrs={alpha};return ENGINE.runKernel(Step,inputs,attrs)}var step=op({step_});function stridedSlice_(x,begin,end,strides,beginMask=0,endMask=0,ellipsisMask=0,newAxisMask=0,shrinkAxisMask=0){let inputs={x:convertToTensor(x,"x","stridedSlice")},attrs={begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask};return ENGINE.runKernel(StridedSlice,inputs,attrs)}var stridedSlice=op({stridedSlice_});function tan_(x){let inputs={x:convertToTensor(x,"x","tan")};return ENGINE.runKernel(Tan,inputs)}var tan=op({tan_});function tensor1d(values,dtype){assertNonNull(values);let inferredShape=inferShape(values,dtype);if(inferredShape.length!==1)throw new Error("tensor1d() requires values to be a flat/TypedArray");return makeTensor(values,null,inferredShape,dtype)}function tensor2d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==2)throw new Error("tensor2d() requires shape to have two numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==2&&inferredShape.length!==1)throw new Error("tensor2d() requires values to be number[][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor2d() requires shape to be provided when `values` are a flat/TypedArray");return makeTensor(values,shape,inferredShape,dtype)}function tensor4d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==4)throw new Error("tensor4d() requires shape to have four numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==4&&inferredShape.length!==1)throw new Error("tensor4d() requires values to be number[][][][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor4d() requires shape to be provided when `values` are a flat array");return makeTensor(values,shape,inferredShape,dtype)}function tensor5d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==5)throw new Error("tensor5d() requires shape to have five numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==5&&inferredShape.length!==1)throw new Error("tensor5d() requires values to be number[][][][][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor5d() requires shape to be provided when `values` are a flat array");return makeTensor(values,shape,inferredShape,dtype)}function tensor6d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==6)throw new Error("tensor6d() requires shape to have six numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==6&&inferredShape.length!==1)throw new Error("tensor6d() requires values to be number[][][][][][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor6d() requires shape to be provided when `values` are a flat array");return shape=shape||inferredShape,makeTensor(values,shape,inferredShape,dtype)}function topk_(x,k=1,sorted=!0){let $x=convertToTensor(x,"x","topk");if($x.rank===0)throw new Error("topk() expects the input to be of rank 1 or higher");let lastDim=$x.shape[$x.shape.length-1];if(k>lastDim)throw new Error(`'k' passed to topk() must be <= the last dimension (${lastDim}) but got ${k}`);let inputs={x:$x},attrs={k,sorted},[values,indices]=ENGINE.runKernel(TopK,inputs,attrs);return{values,indices}}var topk=op({topk_});function truncatedNormal_(shape,mean4=0,stdDev=1,dtype,seed){if(dtype!=null&&dtype==="bool")throw new Error("Unsupported data type $ { dtype }");let randGauss=new MPRandGauss(mean4,stdDev,dtype,!0,seed),res=buffer(shape,dtype);for(let i=0;i0,()=>"The input tensor must be at least 1D");let inputs={x:$x},attrs={axis},[values,indices]=ENGINE.runKernel(Unique,inputs,attrs);return{values,indices}}var unique=op({unique_});function unsortedSegmentSum_(x,segmentIds,numSegments){let $x=convertToTensor(x,"x","unsortedSegmentSum"),$segmentIds=convertToTensor(segmentIds,"segmentIds","unsortedSegmentSum","int32");assert(isInt(numSegments),()=>"numSegments must be of dtype int");let inputs={x:$x,segmentIds:$segmentIds},attrs={numSegments};return ENGINE.runKernel(UnsortedSegmentSum,inputs,attrs)}var unsortedSegmentSum=op({unsortedSegmentSum_});function unstack_(x,axis=0){let $x=convertToTensor(x,"x","unstack","string_or_numeric");assert(axis>=-$x.shape.length&&axis<$x.shape.length,()=>`Axis = ${axis} is not in [-${$x.shape.length}, ${$x.shape.length})`);let inputs={value:$x},attrs={axis};return ENGINE.runKernel(Unpack,inputs,attrs)}var unstack=op({unstack_});function variable(initialValue,trainable=!0,name,dtype){return ENGINE.makeVariable(initialValue,trainable,name,dtype)}function whereImpl(condShape,condVals){let indices=[];for(let i=0;i0,()=>"mask cannot be scalar"),assertShapesMatch(tensorShape.slice(axisFrom,axisFrom+maskDim),$mask.shape,"mask's shape must match the first K dimensions of tensor's shape,");let leadingSize=1;for(let i=axisFrom;i"Shape mismatch in v and x");let one=scalar(1),oneMinusDecay=sub(one,$decay),update=mul(sub($x,$v),oneMinusDecay);if(zeroDebias){assert(step4!=null,()=>"When using zeroDebias: true, step is required.");let $step=convertToTensor(step4,"step","movingAverage");update=div(update,sub(one,pow($decay,$step)))}return add2($v,update)}var movingAverage=op({movingAverage_});function scatterND_(indices,updates,shape){let $indices=convertToTensor(indices,"indices","scatterND","int32"),$updates=convertToTensor(updates,"updates","scatterND");validateInput($updates,$indices,shape);let inputs={indices:$indices,updates:$updates},attrs={shape};return ENGINE.runKernel(ScatterNd,inputs,attrs)}var scatterND=op({scatterND_});function validateInput2(sparseIndices,sparseValues,outputShape,defaultValues){if(sparseIndices.dtype!=="int32")throw new Error(`tf.sparseToDense() expects the indices to be int32 type, but the dtype was ${sparseIndices.dtype}.`);if(sparseIndices.rank>2)throw new Error(`sparseIndices should be a scalar, vector, or matrix, but got shape ${sparseIndices.shape}.`);let numElems=sparseIndices.rank>0?sparseIndices.shape[0]:1,numDims=sparseIndices.rank>1?sparseIndices.shape[1]:1;if(outputShape.length!==numDims)throw new Error(`outputShape has incorrect number of elements:, ${outputShape.length}, should be: ${numDims}.`);let numValues=sparseValues.size;if(!(sparseValues.rank===0||sparseValues.rank===1&&numValues===numElems))throw new Error(`sparseValues has incorrect shape ${sparseValues.shape}, should be [] or [${numElems}]`);if(sparseValues.dtype!==defaultValues.dtype)throw new Error("sparseValues.dtype must match defaultValues.dtype")}function sparseToDense_(sparseIndices,sparseValues,outputShape,defaultValue=0){let $sparseIndices=convertToTensor(sparseIndices,"sparseIndices","sparseToDense","int32"),$sparseValues=convertToTensor(sparseValues,"sparseValues","sparseToDense"),$defaultValue=convertToTensor(defaultValue,"defaultValue","sparseToDense",$sparseValues.dtype);validateInput2($sparseIndices,$sparseValues,outputShape,$defaultValue);let inputs={sparseIndices:$sparseIndices,sparseValues:$sparseValues,defaultValue:$defaultValue},attrs={outputShape};return ENGINE.runKernel(SparseToDense,inputs,attrs)}var sparseToDense=op({sparseToDense_});function gatherND_(x,indices){let $indices=convertToTensor(indices,"indices","gatherND","int32"),inputs={params:convertToTensor(x,"x","gatherND"),indices:$indices};return ENGINE.runKernel(GatherNd,inputs)}var gatherND=op({gatherND_});function getNoiseShape(x,noiseShape){if(noiseShape==null)return x.shape.slice();if(arraysEqual(x.shape,noiseShape))return noiseShape;if(x.shape.length===noiseShape.length){let newDimension=[];for(let i=0;i`x has to be a floating point tensor since it's going to be scaled, but got a ${$x.dtype} tensor instead.`),assert(rate>=0&&rate<1,()=>`rate must be a float in the range [0, 1), but got ${rate}.`),rate===0)return x instanceof Tensor?$x.clone():$x;let $noiseShape=getNoiseShape($x,noiseShape),keepProb=1-rate,multiplier=div(floor(add2(randomUniform($noiseShape,0,1,"float32",seed),keepProb)),keepProb);return mul($x,multiplier)}var dropout=op({dropout_});function enclosingPowerOfTwo(value){return Math.floor(Math.pow(2,Math.ceil(Math.log(value)/Math.log(2))))}function cosineWindow(windowLength,a,b){let even=1-windowLength%2,newValues=new Float32Array(windowLength);for(let i=0;i1,()=>`inTopK() expects the predictions to be of rank 2 or higher, but got ${$predictions.rank}`),assert($predictions.rank-1===$targets.rank,()=>`predictions rank should be 1 larger than targets rank, but got predictions rank ${$predictions.rank} and targets rank ${$targets.rank}`),assertShapesMatch($predictions.shape.slice(0,$predictions.shape.length-1),$targets.shape,"predictions's shape should be align with the targets' shape, except the last dimension.");let lastDim=$predictions.shape[$predictions.shape.length-1];assert(k>0&&k<=lastDim,()=>`'k' passed to inTopK() must be > 0 && <= the predictions last dimension (${lastDim}), but got ${k}`);let predictionsVals=await $predictions.data(),targetsVals=await $targets.data(),[batch,size]=[predictionsVals.length/lastDim,lastDim],precision3=getTypedArrayFromDType("bool",batch);for(let b=0;bb2.value-a.value),precision3[b]=0;for(let i=0;iconv2d2,depthwiseConv2d:()=>depthwiseConv2d2,matMul:()=>matMul2});function conv2DBackpropFilter_(x,dy,filterShape,strides,pad3,dataFormat="NHWC",dimRoundingMode){let x4D=x;x.rank===3&&(x4D=reshape(x,[1,x.shape[0],x.shape[1],x.shape[2]]));let dy4D=dy;dy4D.rank===3&&(dy4D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2]])),assert(x4D.rank===4,()=>`Error in conv2dDerFilter: input must be rank 4, but got shape ${x4D.shape}.`),assert(dy4D.rank===4,()=>`Error in conv2dDerFilter: dy must be rank 4, but got shape ${dy4D.shape}.`),assert(filterShape.length===4,()=>`Error in conv2dDerFilter: filterShape must be length 4, but got ${filterShape}.`);let inDepth=dataFormat==="NHWC"?x4D.shape[3]:x4D.shape[1],outDepth=dataFormat==="NHWC"?dy4D.shape[3]:dy4D.shape[1];assert(inDepth===filterShape[2],()=>`Error in conv2dDerFilter: depth of input ${inDepth}) must match input depth in filter (${filterShape[2]}.`),assert(outDepth===filterShape[3],()=>`Error in conv2dDerFilter: depth of dy (${outDepth}) must match output depth for filter (${filterShape[3]}).`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in conv2dDerFilter: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={x:x4D,dy:dy4D},attrs={strides,pad:pad3,dataFormat,dimRoundingMode,filterShape};return ENGINE.runKernel(Conv2DBackpropFilter,inputs,attrs)}var conv2DBackpropFilter=op({conv2DBackpropFilter_});function getFusedDyActivation(dy,y,activation2){if(activation2==null||activation2==="linear")return dy;if(activation2==="relu")return mul(dy,step(y));throw new Error(`Cannot compute gradient for fused activation ${activation2}.`)}function getFusedBiasGradient(bias,dyActivation){let res=dyActivation,reduceAxes=getReductionAxes(bias.shape,dyActivation.shape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,bias.shape)}function applyActivation(x,activation2,preluActivationWeights,leakyreluAlpha){if(activation2==="linear")return x;if(activation2==="relu")return relu(x);if(activation2==="elu")return elu(x);if(activation2==="relu6")return relu6(x);if(activation2==="prelu")return prelu(x,preluActivationWeights);if(activation2==="leakyrelu")return leakyRelu(x,leakyreluAlpha);throw new Error(`Unknown fused activation ${activation2}.`)}var shouldFuse=(gradientDepth,activation2)=>!(gradientDepth>0)||activation2==="linear";function fusedConv2d_({x,filter,strides,pad:pad3,dataFormat="NHWC",dilations=[1,1],dimRoundingMode,bias,activation:activation2="linear",preluActivationWeights,leakyreluAlpha}){if(activation2=activation2||"linear",shouldFuse(ENGINE.state.gradientDepth,activation2)===!1){let result=conv2d(x,filter,strides,pad3,dataFormat,dilations,dimRoundingMode);return bias!=null&&(result=add2(result,bias)),applyActivation(result,activation2,preluActivationWeights,leakyreluAlpha)}let $x=convertToTensor(x,"x","conv2d"),$filter=convertToTensor(filter,"filter","conv2d"),x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in fused conv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert($filter.rank===4,()=>`Error in fused conv2d: filter must be rank 4, but got rank ${$filter.rank}.`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in fused conv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`),assert(x4D.shape[3]===$filter.shape[2],()=>`Error in conv2d: depth of input (${x4D.shape[3]}) must match input depth for filter ${$filter.shape[2]}.`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),assert(dataFormat==="NHWC",()=>`Error in conv2d: got dataFormat of ${dataFormat} but only NHWC is currently supported.`);let convInfo=computeConv2DInfo(x4D.shape,$filter.shape,strides,dilations,pad3,dimRoundingMode),$bias;bias!=null&&($bias=convertToTensor(bias,"bias","fused conv2d"),[$bias]=makeTypesMatch($bias,$x),assertAndGetBroadcastShape(convInfo.outShape,$bias.shape));let $preluActivationWeights;preluActivationWeights!=null&&($preluActivationWeights=convertToTensor(preluActivationWeights,"prelu weights","fused conv2d"));let grad2=(dy,saved)=>{let[$filter2,x4D2,y,$bias2]=saved,dyActivation=getFusedDyActivation(dy,y,activation2);assert(tupleValuesAreOne(dilations),()=>`Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`);let xDer=conv2DBackpropInput(x4D2.shape,dyActivation,$filter2,strides,pad3),filterDer=conv2DBackpropFilter(x4D2,dyActivation,$filter2.shape,strides,pad3),der=[xDer,filterDer];if($bias2!=null){let biasDer=getFusedBiasGradient($bias2,dyActivation);der.push(biasDer)}return der},inputs={x:x4D,filter:$filter,bias:$bias,preluActivationWeights:$preluActivationWeights},attrs={strides,pad:pad3,dataFormat,dilations,dimRoundingMode,activation:activation2,leakyreluAlpha};return bias==null?customGrad((x4D2,filter2,save)=>{let res=ENGINE.runKernel(FusedConv2D,inputs,attrs);return save([filter2,x4D2,res]),reshapedTo4D&&(res=reshape(res,[res.shape[1],res.shape[2],res.shape[3]])),{value:res,gradFunc:grad2}})(x4D,$filter):customGrad((x4D2,filter2,bias2,save)=>{let res=ENGINE.runKernel(FusedConv2D,inputs,attrs);return save([filter2,x4D2,res,bias2]),reshapedTo4D&&(res=reshape(res,[res.shape[1],res.shape[2],res.shape[3]])),{value:res,gradFunc:grad2}})(x4D,$filter,$bias)}var conv2d2=op({fusedConv2d_});function depthwiseConv2dNativeBackpropFilter_(x,dy,filterShape,strides,pad3,dilations=[1,1],dimRoundingMode){let x4D=x;x.rank===3&&(x4D=reshape(x,[1,x.shape[0],x.shape[1],x.shape[2]]));let dy4D=dy;dy4D.rank===3&&(dy4D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2]]));let inputs={x:x4D,dy:dy4D},attrs={strides,pad:pad3,dimRoundingMode,dilations,filterShape};return ENGINE.runKernel(DepthwiseConv2dNativeBackpropFilter,inputs,attrs)}var depthwiseConv2dNativeBackpropFilter=op({depthwiseConv2dNativeBackpropFilter_});function depthwiseConv2dNativeBackpropInput_(xShape,dy,filter,strides,pad3,dilations=[1,1],dimRoundingMode){let dy4D=dy,reshapedTo4D=!1;dy.rank===3&&(reshapedTo4D=!0,dy4D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2]]));let inputs={dy:dy4D,filter},attrs={strides,pad:pad3,dimRoundingMode,dilations,inputShape:xShape},res=ENGINE.runKernel(DepthwiseConv2dNativeBackpropInput,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var depthwiseConv2dNativeBackpropInput=op({depthwiseConv2dNativeBackpropInput_});function fusedDepthwiseConv2d_({x,filter,strides,pad:pad3,dataFormat="NHWC",dilations=[1,1],dimRoundingMode,bias,activation:activation2="linear",preluActivationWeights,leakyreluAlpha}){if(shouldFuse(ENGINE.state.gradientDepth,activation2)===!1){let result=depthwiseConv2d(x,filter,strides,pad3,dataFormat,dilations,dimRoundingMode);return bias!=null&&(result=add2(result,bias)),applyActivation(result,activation2,preluActivationWeights,leakyreluAlpha)}let $x=convertToTensor(x,"x","depthwiseConv2d"),$filter=convertToTensor(filter,"filter","depthwiseConv2d"),x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in fused depthwiseConv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert($filter.rank===4,()=>`Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${$filter.rank}.`),assert(x4D.shape[3]===$filter.shape[2],()=>`Error in fused depthwiseConv2d: number of input channels (${x4D.shape[3]}) must match the inChannels dimension in filter ${$filter.shape[2]}.`),dilations==null&&(dilations=[1,1]),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in fused depthwiseConv2d: pad must be an integer when using dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let convInfo=computeConv2DInfo(x4D.shape,$filter.shape,strides,dilations,pad3,dimRoundingMode,!0),$bias;bias!=null&&($bias=convertToTensor(bias,"bias","fused conv2d"),[$bias]=makeTypesMatch($bias,$x),assertAndGetBroadcastShape(convInfo.outShape,$bias.shape));let $preluActivationWeights;preluActivationWeights!=null&&($preluActivationWeights=convertToTensor(preluActivationWeights,"prelu weights","fused depthwiseConv2d"));let grad2=(dy,saved)=>{assert(tupleValuesAreOne(dilations),()=>`Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${dilations}'`);let[$filter2,x4D2,y,bias2]=saved,dyActivation=getFusedDyActivation(dy,y,activation2),xDer=depthwiseConv2dNativeBackpropInput(x4D2.shape,dyActivation,$filter2,strides,pad3,dilations,dimRoundingMode),filterDer=depthwiseConv2dNativeBackpropFilter(x4D2,dyActivation,$filter2.shape,strides,pad3,dilations,dimRoundingMode);if(bias2!=null){let biasDer=getFusedBiasGradient($bias,dyActivation);return[xDer,filterDer,biasDer]}return[xDer,filterDer]},inputs={x:x4D,filter:$filter,bias:$bias,preluActivationWeights:$preluActivationWeights},attrs={strides,pad:pad3,dataFormat,dilations,dimRoundingMode,activation:activation2,leakyreluAlpha};return bias==null?customGrad((x4D2,filter2,save)=>{let res=ENGINE.runKernel(FusedDepthwiseConv2D,inputs,attrs);return save([filter2,x4D2,res]),reshapedTo4D&&(res=reshape(res,[res.shape[1],res.shape[2],res.shape[3]])),{value:res,gradFunc:grad2}})(x4D,$filter):customGrad((x4D2,filter2,bias2,save)=>{let res=ENGINE.runKernel(FusedDepthwiseConv2D,inputs,attrs);return save([filter2,x4D2,res,bias2]),reshapedTo4D&&(res=reshape(res,[res.shape[1],res.shape[2],res.shape[3]])),{value:res,gradFunc:grad2}})(x4D,$filter,$bias)}var depthwiseConv2d2=op({fusedDepthwiseConv2d_});function fusedMatMul_({a,b,transposeA=!1,transposeB=!1,bias,activation:activation2="linear",preluActivationWeights,leakyreluAlpha}){if(shouldFuse(ENGINE.state.gradientDepth,activation2)===!1){let result=matMul(a,b,transposeA,transposeB);return bias!=null&&(result=add2(result,bias)),applyActivation(result,activation2,preluActivationWeights,leakyreluAlpha)}let $a=convertToTensor(a,"a","fused matMul"),$b=convertToTensor(b,"b","fused matMul");[$a,$b]=makeTypesMatch($a,$b);let innerShapeA=transposeA?$a.shape[$a.rank-2]:$a.shape[$a.rank-1],innerShapeB=transposeB?$b.shape[$b.rank-1]:$b.shape[$b.rank-2],outerShapeA=transposeA?$a.shape[$a.rank-1]:$a.shape[$a.rank-2],outerShapeB=transposeB?$b.shape[$b.rank-2]:$b.shape[$b.rank-1],outerDimsA=$a.shape.slice(0,-2),outerDimsB=$b.shape.slice(0,-2),batchDimA=sizeFromShape(outerDimsA),batchDimB=sizeFromShape(outerDimsB);assert($a.rank>=2&&$b.rank>=2&&$a.rank===$b.rank,()=>`Error in fused matMul: inputs must have the same rank of at least 2, got ranks ${$a.rank} and ${$b.rank}.`),assert(arraysEqual(outerDimsA,outerDimsB),()=>`Error in fused matMul: outer dimensions (${outerDimsA}) and (${outerDimsB}) of Tensors with shapes ${$a.shape} and ${$b.shape} must match.`),assert(innerShapeA===innerShapeB,()=>`Error in fused matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${$a.shape} and ${$b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);let outShape=$a.shape.slice(0,-2).concat([outerShapeA,outerShapeB]),a3D=transposeA?reshape($a,[batchDimA,innerShapeA,outerShapeA]):reshape($a,[batchDimA,outerShapeA,innerShapeA]),b3D=transposeB?reshape($b,[batchDimB,outerShapeB,innerShapeB]):reshape($b,[batchDimB,innerShapeB,outerShapeB]),$bias;bias!=null&&($bias=convertToTensor(bias,"bias","fused matMul"),[$bias]=makeTypesMatch($bias,$a),assertAndGetBroadcastShape(outShape,$bias.shape));let $preluActivationWeights;preluActivationWeights!=null&&($preluActivationWeights=convertToTensor(preluActivationWeights,"prelu weights","fused matMul"));let grad2=(dy,saved)=>{let[a3D2,b3D2,y,$bias2]=saved,dyActivation=getFusedDyActivation(reshape(dy,y.shape),y,activation2),aDer,bDer;if(!transposeA&&!transposeB?(aDer=matMul(dyActivation,b3D2,!1,!0),bDer=matMul(a3D2,dyActivation,!0,!1)):!transposeA&&transposeB?(aDer=matMul(dyActivation,b3D2,!1,!1),bDer=matMul(dyActivation,a3D2,!0,!1)):transposeA&&!transposeB?(aDer=matMul(b3D2,dyActivation,!1,!0),bDer=matMul(a3D2,dyActivation,!1,!1)):(aDer=matMul(b3D2,dyActivation,!0,!0),bDer=matMul(dyActivation,a3D2,!0,!0)),bias!=null){let biasDer=getFusedBiasGradient($bias2,dyActivation);return[aDer,bDer,biasDer]}else return[aDer,bDer]},inputs={a:a3D,b:b3D,bias:$bias,preluActivationWeights:$preluActivationWeights},attrs={transposeA,transposeB,activation:activation2,leakyreluAlpha};return bias==null?customGrad((a3D2,b3D2,save)=>{let res=ENGINE.runKernel(_FusedMatMul,inputs,attrs);return save([a3D2,b3D2,res]),{value:reshape(res,outShape),gradFunc:grad2}})(a3D,b3D):customGrad((a3D2,b3D2,$bias2,save)=>{let res=ENGINE.runKernel(_FusedMatMul,inputs,attrs);return save([a3D2,b3D2,res,$bias2]),{value:reshape(res,outShape),gradFunc:grad2}})(a3D,b3D,$bias)}var matMul2=op({fusedMatMul_});function hammingWindow_(windowLength){return cosineWindow(windowLength,.54,.46)}var hammingWindow=op({hammingWindow_});function hannWindow_(windowLength){return cosineWindow(windowLength,.5,.5)}var hannWindow=op({hannWindow_});function frame_(signal2,frameLength,frameStep,padEnd=!1,padValue=0){let start=0,output=[];for(;start+frameLength<=signal2.size;)output.push(slice(signal2,start,frameLength)),start+=frameStep;if(padEnd)for(;start`Error in cropAndResize: image must be rank 4,but got rank ${$image.rank}.`),assert($boxes.rank===2&&$boxes.shape[1]===4,()=>`Error in cropAndResize: boxes must be have size [${numBoxes},4] but had shape ${$boxes.shape}.`),assert($boxInd.rank===1&&$boxInd.shape[0]===numBoxes,()=>`Error in cropAndResize: boxInd must be have size [${numBoxes}] but had shape ${$boxes.shape}.`),assert(cropSize.length===2,()=>`Error in cropAndResize: cropSize must be of length 2, but got length ${cropSize.length}.`),assert(cropSize[0]>=1&&cropSize[1]>=1,()=>`cropSize must be atleast [1,1], but was ${cropSize}`),assert(method==="bilinear"||method==="nearest",()=>`method must be bilinear or nearest, but was ${method}`);let inputs={image:$image,boxes:$boxes,boxInd:$boxInd},attrs={method,extrapolationValue,cropSize};return ENGINE.runKernel(CropAndResize,inputs,attrs)}var cropAndResize=op({cropAndResize_});function flipLeftRight_(image3){let $image=convertToTensor(image3,"image","flipLeftRight","float32");assert($image.rank===4,()=>`Error in flipLeftRight: image must be rank 4,but got rank ${$image.rank}.`);let inputs={image:$image};return ENGINE.runKernel(FlipLeftRight,inputs,{})}var flipLeftRight=op({flipLeftRight_});function rotateWithOffset_(image3,radians,fillValue=0,center=.5){let $image=convertToTensor(image3,"image","rotateWithOffset","float32");assert($image.rank===4,()=>`Error in rotateWithOffset: image must be rank 4,but got rank ${$image.rank}.`);let inputs={image:$image},attrs={radians,fillValue,center};return ENGINE.runKernel(RotateWithOffset,inputs,attrs)}var rotateWithOffset=op({rotateWithOffset_});function nonMaxSuppSanityCheck(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma){iouThreshold==null&&(iouThreshold=.5),scoreThreshold==null&&(scoreThreshold=Number.NEGATIVE_INFINITY),softNmsSigma==null&&(softNmsSigma=0);let numBoxes=boxes.shape[0];return maxOutputSize=Math.min(maxOutputSize,numBoxes),assert(0<=iouThreshold&&iouThreshold<=1,()=>`iouThreshold must be in [0, 1], but was '${iouThreshold}'`),assert(boxes.rank===2,()=>`boxes must be a 2D tensor, but was of rank '${boxes.rank}'`),assert(boxes.shape[1]===4,()=>`boxes must have 4 columns, but 2nd dimension was ${boxes.shape[1]}`),assert(scores.rank===1,()=>"scores must be a 1D tensor"),assert(scores.shape[0]===numBoxes,()=>`scores has incompatible shape with boxes. Expected ${numBoxes}, but was ${scores.shape[0]}`),assert(0<=softNmsSigma&&softNmsSigma<=1,()=>`softNmsSigma must be in [0, 1], but was '${softNmsSigma}'`),{maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma}}function nonMaxSuppression_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppression"),$scores=convertToTensor(scores,"scores","nonMaxSuppression"),inputs=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold);maxOutputSize=inputs.maxOutputSize,iouThreshold=inputs.iouThreshold,scoreThreshold=inputs.scoreThreshold;let attrs={maxOutputSize,iouThreshold,scoreThreshold};return ENGINE.runKernel(NonMaxSuppressionV3,{boxes:$boxes,scores:$scores},attrs)}var nonMaxSuppression=op({nonMaxSuppression_});function binaryInsert(arr,element,comparator){let index=binarySearch(arr,element,comparator),insertionPoint=index<0?-(index+1):index;arr.splice(insertionPoint,0,element)}function binarySearch(arr,target,comparator){return binarySearch_(arr,target,comparator||defaultComparator)}function defaultComparator(a,b){return a>b?1:a>>1);let compareResult=comparator(target,arr[middle]);compareResult>0?left=middle+1:(right=middle,found=!compareResult)}return found?left:-left-1}function nonMaxSuppressionV3Impl(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold){return nonMaxSuppressionImpl_(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,0)}function nonMaxSuppressionV4Impl(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize){return nonMaxSuppressionImpl_(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,0,!1,padToMaxOutputSize,!0)}function nonMaxSuppressionV5Impl(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma){return nonMaxSuppressionImpl_(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma,!0)}function nonMaxSuppressionImpl_(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma,returnScoresTensor=!1,padToMaxOutputSize=!1,returnValidOutputs=!1){let candidates=[];for(let i=0;iscoreThreshold&&candidates.push({score:scores[i],boxIndex:i,suppressBeginIndex:0});candidates.sort(ascendingComparator);let scale2=softNmsSigma>0?-.5/softNmsSigma:0,selectedIndices=[],selectedScores=[];for(;selectedIndices.length0;){let candidate=candidates.pop(),{score:originalScore,boxIndex,suppressBeginIndex}=candidate;if(originalScore=suppressBeginIndex;--j){let iou=intersectionOverUnion(boxes,boxIndex,selectedIndices[j]);if(iou>=iouThreshold){ignoreCandidate=!0;break}if(candidate.score=candidate.score*suppressWeight(iouThreshold,scale2,iou),candidate.score<=scoreThreshold)break}candidate.suppressBeginIndex=selectedIndices.length,ignoreCandidate||(candidate.score===originalScore?(selectedIndices.push(boxIndex),selectedScores.push(candidate.score)):candidate.score>scoreThreshold&&binaryInsert(candidates,candidate,ascendingComparator))}let validOutputs=selectedIndices.length,elemsToPad=maxOutputSize-validOutputs;padToMaxOutputSize&&elemsToPad>0&&(selectedIndices.push(...new Array(elemsToPad).fill(0)),selectedScores.push(...new Array(elemsToPad).fill(0)));let result={selectedIndices};return returnScoresTensor&&(result.selectedScores=selectedScores),returnValidOutputs&&(result.validOutputs=validOutputs),result}function intersectionOverUnion(boxes,i,j){let iCoord=boxes.subarray(i*4,i*4+4),jCoord=boxes.subarray(j*4,j*4+4),yminI=Math.min(iCoord[0],iCoord[2]),xminI=Math.min(iCoord[1],iCoord[3]),ymaxI=Math.max(iCoord[0],iCoord[2]),xmaxI=Math.max(iCoord[1],iCoord[3]),yminJ=Math.min(jCoord[0],jCoord[2]),xminJ=Math.min(jCoord[1],jCoord[3]),ymaxJ=Math.max(jCoord[0],jCoord[2]),xmaxJ=Math.max(jCoord[1],jCoord[3]),areaI=(ymaxI-yminI)*(xmaxI-xminI),areaJ=(ymaxJ-yminJ)*(xmaxJ-xminJ);if(areaI<=0||areaJ<=0)return 0;let intersectionYmin=Math.max(yminI,yminJ),intersectionXmin=Math.max(xminI,xminJ),intersectionYmax=Math.min(ymaxI,ymaxJ),intersectionXmax=Math.min(xmaxI,xmaxJ),intersectionArea=Math.max(intersectionYmax-intersectionYmin,0)*Math.max(intersectionXmax-intersectionXmin,0);return intersectionArea/(areaI+areaJ-intersectionArea)}function suppressWeight(iouThreshold,scale2,iou){let weight=Math.exp(scale2*iou*iou);return iou<=iouThreshold?weight:0}function ascendingComparator(c1,c2){return c1.score-c2.score||c1.score===c2.score&&c2.boxIndex-c1.boxIndex}async function nonMaxSuppressionAsync_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppressionAsync"),$scores=convertToTensor(scores,"scores","nonMaxSuppressionAsync"),inputs=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold);maxOutputSize=inputs.maxOutputSize,iouThreshold=inputs.iouThreshold,scoreThreshold=inputs.scoreThreshold;let boxesAndScores=await Promise.all([$boxes.data(),$scores.data()]),boxesVals=boxesAndScores[0],scoresVals=boxesAndScores[1],{selectedIndices}=nonMaxSuppressionV3Impl(boxesVals,scoresVals,maxOutputSize,iouThreshold,scoreThreshold);return $boxes!==boxes&&$boxes.dispose(),$scores!==scores&&$scores.dispose(),tensor1d(selectedIndices,"int32")}var nonMaxSuppressionAsync=nonMaxSuppressionAsync_;function nonMaxSuppressionWithScore_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY,softNmsSigma=0){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppression"),$scores=convertToTensor(scores,"scores","nonMaxSuppression"),params=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma);maxOutputSize=params.maxOutputSize,iouThreshold=params.iouThreshold,scoreThreshold=params.scoreThreshold,softNmsSigma=params.softNmsSigma;let inputs={boxes:$boxes,scores:$scores},attrs={maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma},result=ENGINE.runKernel(NonMaxSuppressionV5,inputs,attrs);return{selectedIndices:result[0],selectedScores:result[1]}}var nonMaxSuppressionWithScore=op({nonMaxSuppressionWithScore_});async function nonMaxSuppressionWithScoreAsync_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY,softNmsSigma=0){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppressionAsync"),$scores=convertToTensor(scores,"scores","nonMaxSuppressionAsync"),params=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma);maxOutputSize=params.maxOutputSize,iouThreshold=params.iouThreshold,scoreThreshold=params.scoreThreshold,softNmsSigma=params.softNmsSigma;let boxesAndScores=await Promise.all([$boxes.data(),$scores.data()]),boxesVals=boxesAndScores[0],scoresVals=boxesAndScores[1],{selectedIndices,selectedScores}=nonMaxSuppressionV5Impl(boxesVals,scoresVals,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma);return $boxes!==boxes&&$boxes.dispose(),$scores!==scores&&$scores.dispose(),{selectedIndices:tensor1d(selectedIndices,"int32"),selectedScores:tensor1d(selectedScores)}}var nonMaxSuppressionWithScoreAsync=nonMaxSuppressionWithScoreAsync_;function nonMaxSuppressionPadded_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY,padToMaxOutputSize=!1){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppression"),$scores=convertToTensor(scores,"scores","nonMaxSuppression"),params=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold,null),$maxOutputSize=params.maxOutputSize,$iouThreshold=params.iouThreshold,$scoreThreshold=params.scoreThreshold,inputs={boxes:$boxes,scores:$scores},attrs={maxOutputSize:$maxOutputSize,iouThreshold:$iouThreshold,scoreThreshold:$scoreThreshold,padToMaxOutputSize},result=ENGINE.runKernel(NonMaxSuppressionV4,inputs,attrs);return{selectedIndices:result[0],validOutputs:result[1]}}var nonMaxSuppressionPadded=op({nonMaxSuppressionPadded_});async function nonMaxSuppressionPaddedAsync_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY,padToMaxOutputSize=!1){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppressionAsync"),$scores=convertToTensor(scores,"scores","nonMaxSuppressionAsync"),params=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold,null),$maxOutputSize=params.maxOutputSize,$iouThreshold=params.iouThreshold,$scoreThreshold=params.scoreThreshold,[boxesVals,scoresVals]=await Promise.all([$boxes.data(),$scores.data()]),{selectedIndices,validOutputs}=nonMaxSuppressionV4Impl(boxesVals,scoresVals,$maxOutputSize,$iouThreshold,$scoreThreshold,padToMaxOutputSize);return $boxes!==boxes&&$boxes.dispose(),$scores!==scores&&$scores.dispose(),{selectedIndices:tensor1d(selectedIndices,"int32"),validOutputs:scalar(validOutputs,"int32")}}var nonMaxSuppressionPaddedAsync=nonMaxSuppressionPaddedAsync_;function resizeBilinear_(images,size,alignCorners=!1,halfPixelCenters=!1){let $images=convertToTensor(images,"images","resizeBilinear");assert($images.rank===3||$images.rank===4,()=>`Error in resizeBilinear: x must be rank 3 or 4, but got rank ${$images.rank}.`),assert(size.length===2,()=>`Error in resizeBilinear: new shape must 2D, but got shape ${size}.`),assert(halfPixelCenters===!1||alignCorners===!1,()=>"Error in resizeBilinear: If halfPixelCenters is true, alignCorners must be false.");let batchImages=$images,reshapedTo4D=!1;$images.rank===3&&(reshapedTo4D=!0,batchImages=reshape($images,[1,$images.shape[0],$images.shape[1],$images.shape[2]]));let[]=size,inputs={images:batchImages},attrs={alignCorners,halfPixelCenters,size},res=ENGINE.runKernel(ResizeBilinear,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var resizeBilinear=op({resizeBilinear_});function resizeNearestNeighbor_(images,size,alignCorners=!1,halfPixelCenters=!1){let $images=convertToTensor(images,"images","resizeNearestNeighbor");assert($images.rank===3||$images.rank===4,()=>`Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${$images.rank}.`),assert(size.length===2,()=>`Error in resizeNearestNeighbor: new shape must 2D, but got shape ${size}.`),assert($images.dtype==="float32"||$images.dtype==="int32",()=>"`images` must have `int32` or `float32` as dtype"),assert(halfPixelCenters===!1||alignCorners===!1,()=>"Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false.");let batchImages=$images,reshapedTo4D=!1;$images.rank===3&&(reshapedTo4D=!0,batchImages=reshape($images,[1,$images.shape[0],$images.shape[1],$images.shape[2]]));let[]=size,inputs={images:batchImages},attrs={alignCorners,halfPixelCenters,size},res=ENGINE.runKernel(ResizeNearestNeighbor,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var resizeNearestNeighbor=op({resizeNearestNeighbor_});function bandPart_(a,numLower,numUpper){assert(numLower%1==0,()=>`bandPart(): numLower must be an integer, got ${numLower}.`),assert(numUpper%1==0,()=>`bandPart(): numUpper must be an integer, got ${numUpper}.`);let $a=convertToTensor(a,"a","bandPart");assert($a.rank>=2,()=>`bandPart(): Rank must be at least 2, got ${$a.rank}.`);let shape=$a.shape,[M,N]=$a.shape.slice(-2);if(!(numLower<=M))throw new Error(`bandPart(): numLower (${numLower}) must not be greater than the number of rows (${M}).`);if(!(numUpper<=N))throw new Error(`bandPart(): numUpper (${numUpper}) must not be greater than the number of columns (${N}).`);numLower<0&&(numLower=M),numUpper<0&&(numUpper=N);let i=reshape(range(0,M,1,"int32"),[-1,1]),j=range(0,N,1,"int32"),ij=sub(i,j),inBand=logicalAnd(lessEqual(ij,scalar(+numLower,"int32")),greaterEqual(ij,scalar(-numUpper,"int32"))),zero=zeros([M,N],$a.dtype);return reshape(stack(unstack(reshape($a,[-1,M,N])).map(mat=>where(inBand,mat,zero))),shape)}var bandPart=op({bandPart_});function gramSchmidt_(xs){let inputIsTensor2D;if(Array.isArray(xs)){inputIsTensor2D=!1,assert(xs!=null&&xs.length>0,()=>"Gram-Schmidt process: input must not be null, undefined, or empty");let dim=xs[0].shape[0];for(let i=1;i`Gram-Schmidt: Non-unique lengths found in the input vectors: (${xs[i].shape[0]} vs. ${dim})`)}else inputIsTensor2D=!0,xs=split(xs,xs.shape[0],0).map(x=>squeeze(x,[0]));assert(xs.length<=xs[0].shape[0],()=>`Gram-Schmidt: Number of vectors (${xs.length}) exceeds number of dimensions (${xs[0].shape[0]}).`);let ys=[],xs1d=xs;for(let i=0;i{let x=xs1d[i];if(i>0)for(let j=0;j=2,()=>`qr() requires input tensor to have a rank >= 2, but got rank ${x.rank}`),x.rank===2)return qr2d(x,fullMatrices);{let outerDimsProd=x.shape.slice(0,x.shape.length-2).reduce((value,prev)=>value*prev),x2ds=unstack(reshape(x,[outerDimsProd,x.shape[x.shape.length-2],x.shape[x.shape.length-1]]),0),q2ds=[],r2ds=[];x2ds.forEach(x2d=>{let[q2d,r2d]=qr2d(x2d,fullMatrices);q2ds.push(q2d),r2ds.push(r2d)});let q=reshape(stack(q2ds,0),x.shape),r=reshape(stack(r2ds,0),x.shape);return[q,r]}}function qr2d(x,fullMatrices=!1){return ENGINE.tidy(()=>{assert(x.shape.length===2,()=>`qr2d() requires a 2D Tensor, but got a ${x.shape.length}D Tensor.`);let m=x.shape[0],n=x.shape[1],q=eye(m),r=clone(x),one2D=tensor2d([[1]],[1,1]),w=clone(one2D),iters=m>=n?n:m;for(let j=0;j{let rjEnd1=slice(r,[j,j],[m-j,1]),normX=norm(rjEnd1),rjj=slice(r,[j,j],[1,1]),s=where(greater(rjj,0),tensor2d([[-1]]),tensor2d([[1]])),u1=sub(rjj,mul(s,normX)),wPre=div(rjEnd1,u1);wPre.shape[0]===1?w=clone(one2D):w=concat([one2D,slice(wPre,[1,0],[wPre.shape[0]-1,wPre.shape[1]])],0);let tau=neg(div(matMul(s,u1),normX)),rjEndAll=slice(r,[j,0],[m-j,n]),tauTimesW=mul(tau,w),wT=transpose(w);if(j===0)r=sub(rjEndAll,matMul(tauTimesW,matMul(wT,rjEndAll)));else{let rTimesTau=sub(rjEndAll,matMul(tauTimesW,matMul(wT,rjEndAll)));r=concat([slice(r,[0,0],[j,n]),rTimesTau],0)}let tawTimesWT=transpose(tauTimesW),qAllJEnd=slice(q,[0,j],[m,q.shape[1]-j]);if(j===0)q=sub(qAllJEnd,matMul(matMul(qAllJEnd,w),tawTimesWT));else{let qTimesTau=sub(qAllJEnd,matMul(matMul(qAllJEnd,w),tawTimesWT));q=concat([slice(q,[0,0],[m,j]),qTimesTau],1)}return[w,r,q]}),dispose([rTemp,wTemp,qTemp])}return!fullMatrices&&m>n&&(q=slice(q,[0,0],[m,n]),r=slice(r,[0,0],[n,n])),[q,r]})}var qr=op({qr_}),Reduction;(function(Reduction2){Reduction2[Reduction2.NONE=0]="NONE",Reduction2[Reduction2.MEAN=1]="MEAN",Reduction2[Reduction2.SUM=2]="SUM",Reduction2[Reduction2.SUM_BY_NONZERO_WEIGHTS=3]="SUM_BY_NONZERO_WEIGHTS"})(Reduction||(Reduction={}));function computeWeightedLoss_(losses4,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $losses=convertToTensor(losses4,"losses","computeWeightedLoss"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","computeWeightedLoss"));let weightedLoss=$weights==null?$losses:mul($losses,$weights);if(reduction2===Reduction.NONE)return weightedLoss;if(reduction2===Reduction.SUM)return sum2(weightedLoss);if(reduction2===Reduction.MEAN){if($weights==null)return mean(weightedLoss);{let broadcastFactor=$losses.size/$weights.size,result=div(sum2(weightedLoss),sum2($weights));return broadcastFactor>1?div(result,scalar(broadcastFactor)):result}}if(reduction2===Reduction.SUM_BY_NONZERO_WEIGHTS){if($weights==null)return div(sum2(weightedLoss),scalar($losses.size));{let broadcastedWeights=mul($weights,ones2($losses.shape)),numNonZeros=cast(sum2(notEqual(broadcastedWeights,scalar(0))),"float32");return div(sum2(weightedLoss),numNonZeros)}}throw Error(`Unknown reduction: ${reduction2}`)}var computeWeightedLoss=op({computeWeightedLoss_});function absoluteDifference_(labels,predictions,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","absoluteDifference"),$predictions=convertToTensor(predictions,"predictions","absoluteDifference"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","absoluteDifference")),assertShapesMatch($labels.shape,$predictions.shape,"Error in absoluteDifference: ");let losses4=abs(sub($labels,$predictions));return computeWeightedLoss(losses4,$weights,reduction2)}var absoluteDifference=op({absoluteDifference_});function cosineDistance_(labels,predictions,axis,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","cosineDistance"),$predictions=convertToTensor(predictions,"predictions","cosineDistance"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","cosineDistance")),assertShapesMatch($labels.shape,$predictions.shape,"Error in cosineDistance: ");let one=scalar(1),losses4=sub(one,sum2(mul($labels,$predictions),axis,!0));return computeWeightedLoss(losses4,$weights,reduction2)}var cosineDistance=op({cosineDistance_});function hingeLoss_(labels,predictions,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","hingeLoss"),$predictions=convertToTensor(predictions,"predictions","hingeLoss"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","hingeLoss")),assertShapesMatch($labels.shape,$predictions.shape,"Error in hingeLoss: ");let one=scalar(1);$labels=sub(mul(scalar(2),$labels),one);let losses4=relu(sub(one,mul($labels,$predictions)));return computeWeightedLoss(losses4,$weights,reduction2)}var hingeLoss=op({hingeLoss_});function huberLoss_(labels,predictions,weights,delta=1,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","huberLoss"),$predictions=convertToTensor(predictions,"predictions","huberLoss"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","huberLoss")),assertShapesMatch($labels.shape,$predictions.shape,"Error in huberLoss: ");let deltaScalar=scalar(delta),error=abs(sub($predictions,$labels)),quadratic=minimum(error,deltaScalar),linear=sub(error,quadratic),losses4=add2(mul(scalar(.5),square(quadratic)),mul(deltaScalar,linear));return computeWeightedLoss(losses4,$weights,reduction2)}var huberLoss=op({huberLoss_});function logLoss_(labels,predictions,weights,epsilon3=1e-7,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","logLoss"),$predictions=convertToTensor(predictions,"predictions","logLoss"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","logLoss")),assertShapesMatch($labels.shape,$predictions.shape,"Error in logLoss: ");let one=scalar(1),epsilonScalar=scalar(epsilon3),l13=neg(mul($labels,log2(add2($predictions,epsilonScalar)))),l23=mul(sub(one,$labels),log2(add2(sub(one,$predictions),epsilonScalar))),losses4=sub(l13,l23);return computeWeightedLoss(losses4,$weights,reduction2)}var logLoss=op({logLoss_});function meanSquaredError_(labels,predictions,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","meanSquaredError"),$predictions=convertToTensor(predictions,"predictions","meanSquaredError"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","meanSquaredError")),assertShapesMatch($labels.shape,$predictions.shape,"Error in meanSquaredError: ");let losses4=squaredDifference($labels,$predictions);return computeWeightedLoss(losses4,$weights,reduction2)}var meanSquaredError=op({meanSquaredError_});function sigmoidCrossEntropyWithLogits_(labels,logits){let $labels=convertToTensor(labels,"labels","sigmoidCrossEntropyWithLogits"),$logits=convertToTensor(logits,"logits","sigmoidCrossEntropyWithLogits");assertShapesMatch($labels.shape,$logits.shape,"Error in sigmoidCrossEntropyWithLogits: ");let maxOutput=relu($logits),outputXTarget=mul($logits,$labels),sigmoidOutput=log1p(exp(neg(abs($logits))));return add2(sub(maxOutput,outputXTarget),sigmoidOutput)}function sigmoidCrossEntropy_(multiClassLabels,logits,weights,labelSmoothing=0,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $multiClassLabels=convertToTensor(multiClassLabels,"multiClassLabels","sigmoidCrossEntropy"),$logits=convertToTensor(logits,"logits","sigmoidCrossEntropy"),$weights=null;if(weights!=null&&($weights=convertToTensor(weights,"weights","sigmoidCrossEntropy")),assertShapesMatch($multiClassLabels.shape,$logits.shape,"Error in sigmoidCrossEntropy: "),labelSmoothing>0){let labelSmoothingScalar=scalar(labelSmoothing),one=scalar(1),half=scalar(.5);$multiClassLabels=add2(mul($multiClassLabels,sub(one,labelSmoothingScalar)),mul(half,labelSmoothingScalar))}let losses4=sigmoidCrossEntropyWithLogits_($multiClassLabels,$logits);return computeWeightedLoss(losses4,$weights,reduction2)}var sigmoidCrossEntropy=op({sigmoidCrossEntropy_});function softmaxCrossEntropyWithLogits_(labels,logits,dim=-1){if(dim===-1&&(dim=logits.rank-1),dim!==logits.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${logits.rank} and dim was ${dim}`);return customGrad((labels2,logits2,save)=>{let lse=logSumExp(logits2,[dim],!0),logResult=sub(cast(logits2,"float32"),lse);save([labels2,logResult]);let costVector=neg(mul(logResult,labels2));return{value:sum2(costVector,[dim]),gradFunc:(dy,saved)=>{let[labels3,logResult2]=saved,dyShape=expandShapeToKeepDim(dy.shape,[dim]);return[mul(reshape(dy,dyShape),sub(cast(labels3,"float32"),exp(logResult2))),mul(reshape(dy,dyShape),sub(exp(logResult2),cast(labels3,"float32")))]}}})(labels,logits)}function softmaxCrossEntropy_(onehotLabels,logits,weights,labelSmoothing=0,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $onehotLabels=convertToTensor(onehotLabels,"onehotLabels","softmaxCrossEntropy"),$logits=convertToTensor(logits,"logits","softmaxCrossEntropy"),$weights=null;if(weights!=null&&($weights=convertToTensor(weights,"weights","softmaxCrossEntropy")),assertShapesMatch($onehotLabels.shape,$logits.shape,"Error in softmaxCrossEntropy: "),labelSmoothing>0){let labelSmoothingScalar=scalar(labelSmoothing),one=scalar(1),numClasses=scalar($onehotLabels.shape[1]);$onehotLabels=add2(mul($onehotLabels,sub(one,labelSmoothingScalar)),div(labelSmoothingScalar,numClasses))}let losses4=softmaxCrossEntropyWithLogits_($onehotLabels,$logits);return computeWeightedLoss(losses4,$weights,reduction2)}var softmaxCrossEntropy=op({softmaxCrossEntropy_}),spectral={fft,ifft,rfft,irfft},signal={hammingWindow,hannWindow,frame,stft},image={flipLeftRight,resizeNearestNeighbor,resizeBilinear,rotateWithOffset,cropAndResize,nonMaxSuppression,nonMaxSuppressionAsync,nonMaxSuppressionWithScore,nonMaxSuppressionWithScoreAsync,nonMaxSuppressionPadded,nonMaxSuppressionPaddedAsync},linalg={bandPart,gramSchmidt,qr},losses={absoluteDifference,computeWeightedLoss,cosineDistance,hingeLoss,huberLoss,logLoss,meanSquaredError,sigmoidCrossEntropy,softmaxCrossEntropy},Optimizer=class extends Serializable{minimize(f,returnCost=!1,varList){let{value,grads:grads2}=this.computeGradients(f,varList);if(varList!=null){let gradArray=varList.map(v=>({name:v.name,tensor:grads2[v.name]}));this.applyGradients(gradArray)}else this.applyGradients(grads2);return dispose(grads2),returnCost?value:(value.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(f,varList){return variableGrads(f,varList)}dispose(){this.iterations_!=null&&dispose(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:scalar(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(weightValues){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(weightValues){return this.iterations_=(await weightValues[0].tensor.data())[0],weightValues.slice(1)}};Object.defineProperty(Optimizer,Symbol.hasInstance,{value:instance=>instance.minimize!=null&&instance.computeGradients!=null&&instance.applyGradients!=null});var AdadeltaOptimizer=class extends Optimizer{constructor(learningRate,rho,epsilon3=null){super();this.learningRate=learningRate,this.rho=rho,this.epsilon=epsilon3,this.accumulatedGrads=[],this.accumulatedUpdates=[],epsilon3==null&&(this.epsilon=ENGINE.backend.epsilon())}applyGradients(variableGradients){(Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients)).forEach((name,i)=>{let value=ENGINE.registeredVariables[name],trainable=!1;this.accumulatedGrads[i]==null&&(this.accumulatedGrads[i]={originalName:`${name}/accum_grad`,variable:tidy(()=>zerosLike(value).variable(trainable))}),this.accumulatedUpdates[i]==null&&(this.accumulatedUpdates[i]={originalName:`${name}/accum_var`,variable:tidy(()=>zerosLike(value).variable(trainable))});let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;let accumulatedGrad=this.accumulatedGrads[i].variable,accumulatedUpdate=this.accumulatedUpdates[i].variable;tidy(()=>{let newAccumulatedGrad=add2(mul(accumulatedGrad,this.rho),mul(square(gradient),1-this.rho)),updates=mul(div(sqrt(add2(accumulatedUpdate,this.epsilon)),sqrt(add2(accumulatedGrad,this.epsilon))),gradient),newAccumulatedUpdate=add2(mul(accumulatedUpdate,this.rho),mul(square(updates),1-this.rho));accumulatedGrad.assign(newAccumulatedGrad),accumulatedUpdate.assign(newAccumulatedUpdate);let newValue=add2(mul(updates,-this.learningRate),value);value.assign(newValue)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(dispose(this.accumulatedGrads.map(v=>v.variable)),dispose(this.accumulatedUpdates.map(v=>v.variable)))}async getWeights(){let variables=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(variables.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues);let variableCount=weightValues.length/2,trainable=!1;this.accumulatedGrads=weightValues.slice(0,variableCount).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})),this.accumulatedUpdates=weightValues.slice(variableCount,variableCount*2).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(cls,config3){return new cls(config3.learningRate,config3.rho,config3.epsilon)}};AdadeltaOptimizer.className="Adadelta";registerClass(AdadeltaOptimizer);var AdagradOptimizer=class extends Optimizer{constructor(learningRate,initialAccumulatorValue=.1){super();this.learningRate=learningRate,this.initialAccumulatorValue=initialAccumulatorValue,this.accumulatedGrads=[]}applyGradients(variableGradients){(Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients)).forEach((name,i)=>{let value=ENGINE.registeredVariables[name];if(this.accumulatedGrads[i]==null){let trainable=!1;this.accumulatedGrads[i]={originalName:`${name}/accumulator`,variable:tidy(()=>fill(value.shape,this.initialAccumulatorValue).variable(trainable))}}let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;let accumulatedGrad=this.accumulatedGrads[i].variable;tidy(()=>{let newAccumulatedGrad=add2(accumulatedGrad,square(gradient));accumulatedGrad.assign(newAccumulatedGrad);let newValue=add2(mul(div(gradient,sqrt(add2(newAccumulatedGrad,ENGINE.backend.epsilon()))),-this.learningRate),value);value.assign(newValue)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&dispose(this.accumulatedGrads.map(v=>v.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues);let trainable=!1;this.accumulatedGrads=weightValues.map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(cls,config3){return new cls(config3.learningRate,config3.initialAccumulatorValue)}};AdagradOptimizer.className="Adagrad";registerClass(AdagradOptimizer);var AdamOptimizer=class extends Optimizer{constructor(learningRate,beta1,beta2,epsilon3=null){super();this.learningRate=learningRate,this.beta1=beta1,this.beta2=beta2,this.epsilon=epsilon3,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],tidy(()=>{this.accBeta1=scalar(beta1).variable(),this.accBeta2=scalar(beta2).variable()}),epsilon3==null&&(this.epsilon=ENGINE.backend.epsilon())}applyGradients(variableGradients){let varNames=Array.isArray(variableGradients)?variableGradients.map(v=>v.name):Object.keys(variableGradients);tidy(()=>{let oneMinusAccBeta1=sub(1,this.accBeta1),oneMinusAccBeta2=sub(1,this.accBeta2);varNames.forEach((name,i)=>{let value=ENGINE.registeredVariables[name],trainable=!1;this.accumulatedFirstMoment[i]==null&&(this.accumulatedFirstMoment[i]={originalName:`${name}/m`,variable:tidy(()=>zerosLike(value).variable(trainable))}),this.accumulatedSecondMoment[i]==null&&(this.accumulatedSecondMoment[i]={originalName:`${name}/v`,variable:tidy(()=>zerosLike(value).variable(trainable))});let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;let firstMoment=this.accumulatedFirstMoment[i].variable,secondMoment=this.accumulatedSecondMoment[i].variable,newFirstMoment=add2(mul(firstMoment,this.beta1),mul(gradient,1-this.beta1)),newSecondMoment=add2(mul(secondMoment,this.beta2),mul(square(gradient),1-this.beta2)),biasCorrectedFirstMoment=div(newFirstMoment,oneMinusAccBeta1),biasCorrectedSecondMoment=div(newSecondMoment,oneMinusAccBeta2);firstMoment.assign(newFirstMoment),secondMoment.assign(newSecondMoment);let newValue=add2(mul(div(biasCorrectedFirstMoment,add2(sqrt(biasCorrectedSecondMoment),this.epsilon)),-this.learningRate),value);value.assign(newValue)}),this.accBeta1.assign(mul(this.accBeta1,this.beta1)),this.accBeta2.assign(mul(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&dispose(this.accumulatedFirstMoment.map(v=>v.variable)),this.accumulatedSecondMoment!=null&&dispose(this.accumulatedSecondMoment.map(v=>v.variable))}async getWeights(){let variables=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(variables.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues),tidy(()=>{this.accBeta1.assign(pow(this.beta1,this.iterations_+1)),this.accBeta2.assign(pow(this.beta2,this.iterations_+1))});let variableCount=weightValues.length/2,trainable=!1;this.accumulatedFirstMoment=weightValues.slice(0,variableCount).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})),this.accumulatedSecondMoment=weightValues.slice(variableCount,variableCount*2).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(cls,config3){return new cls(config3.learningRate,config3.beta1,config3.beta2,config3.epsilon)}};AdamOptimizer.className="Adam";registerClass(AdamOptimizer);var AdamaxOptimizer=class extends Optimizer{constructor(learningRate,beta1,beta2,epsilon3=null,decay=0){super();this.learningRate=learningRate,this.beta1=beta1,this.beta2=beta2,this.epsilon=epsilon3,this.decay=decay,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],tidy(()=>{this.iteration=scalar(0).variable(),this.accBeta1=scalar(beta1).variable()}),epsilon3==null&&(this.epsilon=ENGINE.backend.epsilon())}applyGradients(variableGradients){let variableNames=Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients);tidy(()=>{let oneMinusAccBeta1=sub(1,this.accBeta1),lr=div(-this.learningRate,add2(mul(this.iteration,this.decay),1));variableNames.forEach((name,i)=>{let value=ENGINE.registeredVariables[name],trainable=!1;this.accumulatedFirstMoment[i]==null&&(this.accumulatedFirstMoment[i]={originalName:`${name}/m`,variable:zerosLike(value).variable(trainable)}),this.accumulatedWeightedInfNorm[i]==null&&(this.accumulatedWeightedInfNorm[i]={originalName:`${name}/v`,variable:zerosLike(value).variable(trainable)});let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;let firstMoment=this.accumulatedFirstMoment[i].variable,weightedInfNorm=this.accumulatedWeightedInfNorm[i].variable,newFirstMoment=add2(mul(firstMoment,this.beta1),mul(gradient,1-this.beta1)),ut0=mul(weightedInfNorm,this.beta2),ut1=abs(gradient),newWeightedInfNorm=maximum(ut0,ut1);firstMoment.assign(newFirstMoment),weightedInfNorm.assign(newWeightedInfNorm);let newValue=add2(mul(div(lr,oneMinusAccBeta1),div(newFirstMoment,add2(newWeightedInfNorm,this.epsilon))),value);value.assign(newValue)}),this.iteration.assign(add2(this.iteration,1)),this.accBeta1.assign(mul(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&dispose(this.accumulatedFirstMoment.map(v=>v.variable)),this.accumulatedWeightedInfNorm!=null&&dispose(this.accumulatedWeightedInfNorm.map(v=>v.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(weightValues){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(cls,config3){return new cls(config3.learningRate,config3.beta1,config3.beta2,config3.epsilon,config3.decay)}};AdamaxOptimizer.className="Adamax";registerClass(AdamaxOptimizer);var SGDOptimizer=class extends Optimizer{constructor(learningRate){super();this.learningRate=learningRate,this.setLearningRate(learningRate)}applyGradients(variableGradients){(Array.isArray(variableGradients)?variableGradients.map(v=>v.name):Object.keys(variableGradients)).forEach((name,i)=>{let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;let value=ENGINE.registeredVariables[name];tidy(()=>{let newValue=add2(mul(this.c,gradient),value);value.assign(newValue)})}),this.incrementIterations()}setLearningRate(learningRate){this.learningRate=learningRate,this.c!=null&&this.c.dispose(),this.c=keep(scalar(-learningRate))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(weightValues){if(weightValues=await this.extractIterations(weightValues),weightValues.length!==0)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(cls,config3){return new cls(config3.learningRate)}};SGDOptimizer.className="SGD";registerClass(SGDOptimizer);var MomentumOptimizer=class extends SGDOptimizer{constructor(learningRate,momentum,useNesterov=!1){super(learningRate);this.learningRate=learningRate,this.momentum=momentum,this.useNesterov=useNesterov,this.accumulations=[],this.m=scalar(this.momentum)}applyGradients(variableGradients){(Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients)).forEach((name,i)=>{let value=ENGINE.registeredVariables[name];if(this.accumulations[i]==null){let trainable=!1;this.accumulations[i]={originalName:`${name}/momentum`,variable:tidy(()=>zerosLike(value).variable(trainable))}}let accumulation=this.accumulations[i].variable,gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];gradient!=null&&tidy(()=>{let newValue,newAccumulation=add2(mul(this.m,accumulation),gradient);this.useNesterov?newValue=add2(mul(this.c,add2(gradient,mul(newAccumulation,this.m))),value):newValue=add2(mul(this.c,newAccumulation),value),accumulation.assign(newAccumulation),value.assign(newValue)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&dispose(this.accumulations.map(v=>v.variable))}setMomentum(momentum){this.momentum=momentum}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues);let trainable=!1;this.accumulations=weightValues.map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(cls,config3){return new cls(config3.learningRate,config3.momentum,config3.useNesterov)}};MomentumOptimizer.className="Momentum";registerClass(MomentumOptimizer);var RMSPropOptimizer=class extends Optimizer{constructor(learningRate,decay=.9,momentum=0,epsilon3=null,centered=!1){super();if(this.learningRate=learningRate,this.decay=decay,this.momentum=momentum,this.epsilon=epsilon3,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=centered,epsilon3==null&&(this.epsilon=ENGINE.backend.epsilon()),learningRate==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(variableGradients){(Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients)).forEach((name,i)=>{let value=ENGINE.registeredVariables[name],trainable=!1;this.accumulatedMeanSquares[i]==null&&(this.accumulatedMeanSquares[i]={originalName:`${name}/rms`,variable:tidy(()=>zerosLike(value).variable(trainable))}),this.accumulatedMoments[i]==null&&(this.accumulatedMoments[i]={originalName:`${name}/momentum`,variable:tidy(()=>zerosLike(value).variable(trainable))}),this.accumulatedMeanGrads[i]==null&&this.centered&&(this.accumulatedMeanGrads[i]={originalName:`${name}/mg`,variable:tidy(()=>zerosLike(value).variable(trainable))});let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;let accumulatedMeanSquare=this.accumulatedMeanSquares[i].variable,accumulatedMoments=this.accumulatedMoments[i].variable;tidy(()=>{let newAccumulatedMeanSquare=add2(mul(accumulatedMeanSquare,this.decay),mul(square(gradient),1-this.decay));if(this.centered){let accumulatedMeanGrad=this.accumulatedMeanGrads[i].variable,newAccumulatedMeanGrad=add2(mul(accumulatedMeanGrad,this.decay),mul(gradient,1-this.decay)),gradContribution=div(mul(gradient,this.learningRate),sqrt(sub(newAccumulatedMeanSquare,add2(square(newAccumulatedMeanGrad),this.epsilon)))),newAccumulatedMoments=add2(mul(accumulatedMoments,this.momentum),gradContribution);accumulatedMeanSquare.assign(newAccumulatedMeanSquare),accumulatedMeanGrad.assign(newAccumulatedMeanGrad),accumulatedMoments.assign(newAccumulatedMoments);let newValue=sub(value,newAccumulatedMoments);value.assign(newValue)}else{let newAccumulatedMeanSquare2=add2(mul(accumulatedMeanSquare,this.decay),mul(square(gradient),1-this.decay)),newAccumulatedMoments=add2(mul(accumulatedMoments,this.momentum),div(mul(gradient,this.learningRate),sqrt(add2(newAccumulatedMeanSquare2,this.epsilon))));accumulatedMeanSquare.assign(newAccumulatedMeanSquare2),accumulatedMoments.assign(newAccumulatedMoments);let newValue=sub(value,newAccumulatedMoments);value.assign(newValue)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&dispose(this.accumulatedMeanSquares.map(v=>v.variable)),this.accumulatedMeanGrads!=null&&this.centered&&dispose(this.accumulatedMeanGrads.map(v=>v.variable)),this.accumulatedMoments!=null&&dispose(this.accumulatedMoments.map(v=>v.variable))}async getWeights(){let variables=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&variables.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(variables.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues);let variableCount=this.centered?weightValues.length/3:weightValues.length/2,trainable=!1;this.accumulatedMeanSquares=weightValues.slice(0,variableCount).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})),this.accumulatedMoments=weightValues.slice(variableCount,variableCount*2).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})),this.centered&&(this.accumulatedMeanGrads=weightValues.slice(variableCount*2,variableCount*3).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(cls,config3){return new cls(config3.learningRate,config3.decay,config3.momentum,config3.epsilon,config3.centered)}};RMSPropOptimizer.className="RMSProp";registerClass(RMSPropOptimizer);var OptimizerConstructors=class{static sgd(learningRate){return new SGDOptimizer(learningRate)}static momentum(learningRate,momentum,useNesterov=!1){return new MomentumOptimizer(learningRate,momentum,useNesterov)}static rmsprop(learningRate,decay=.9,momentum=0,epsilon3=null,centered=!1){return new RMSPropOptimizer(learningRate,decay,momentum,epsilon3,centered)}static adam(learningRate=.001,beta1=.9,beta2=.999,epsilon3=null){return new AdamOptimizer(learningRate,beta1,beta2,epsilon3)}static adadelta(learningRate=.001,rho=.95,epsilon3=null){return new AdadeltaOptimizer(learningRate,rho,epsilon3)}static adamax(learningRate=.002,beta1=.9,beta2=.999,epsilon3=null,decay=0){return new AdamaxOptimizer(learningRate,beta1,beta2,epsilon3,decay)}static adagrad(learningRate,initialAccumulatorValue=.1){return new AdagradOptimizer(learningRate,initialAccumulatorValue)}},train={sgd:OptimizerConstructors.sgd,momentum:OptimizerConstructors.momentum,adadelta:OptimizerConstructors.adadelta,adagrad:OptimizerConstructors.adagrad,rmsprop:OptimizerConstructors.rmsprop,adamax:OptimizerConstructors.adamax,adam:OptimizerConstructors.adam},delayCallback=(()=>typeof requestAnimationFrame!="undefined"?requestAnimationFrame:typeof setImmediate!="undefined"?setImmediate:f=>f())();function nextFrame(){return new Promise(resolve=>delayCallback(()=>resolve()))}var backend_util_exports={};__export2(backend_util_exports,{ERF_A1:()=>ERF_A1,ERF_A2:()=>ERF_A2,ERF_A3:()=>ERF_A3,ERF_A4:()=>ERF_A4,ERF_A5:()=>ERF_A5,ERF_P:()=>ERF_P,PARALLELIZE_THRESHOLD:()=>PARALLELIZE_THRESHOLD,SELU_SCALE:()=>SELU_SCALE,SELU_SCALEALPHA:()=>SELU_SCALEALPHA,applyActivation:()=>applyActivation,assertAndGetBroadcastShape:()=>assertAndGetBroadcastShape,assertAxesAreInnerMostDims:()=>assertAxesAreInnerMostDims,assertParamsConsistent:()=>assertParamsConsistent,assignToTypedArray:()=>assignToTypedArray,axesAreInnerMostDims:()=>axesAreInnerMostDims,calculateShapes:()=>calculateShapes,castTensor:()=>castTensor,combineLocations:()=>combineLocations,complexWithEvenIndex:()=>complexWithEvenIndex,complexWithOddIndex:()=>complexWithOddIndex,computeConv2DInfo:()=>computeConv2DInfo,computeConv3DInfo:()=>computeConv3DInfo,computeDefaultPad:()=>computeDefaultPad,computeDilation2DInfo:()=>computeDilation2DInfo,computeOptimalWindowSize:()=>computeOptimalWindowSize,computeOutAndReduceShapes:()=>computeOutAndReduceShapes,computeOutShape:()=>computeOutShape2,computePool2DInfo:()=>computePool2DInfo,computePool3DInfo:()=>computePool3DInfo,convertConv2DDataFormat:()=>convertConv2DDataFormat,eitherStridesOrDilationsAreOne:()=>eitherStridesOrDilationsAreOne,expandShapeToKeepDim:()=>expandShapeToKeepDim,exponent:()=>exponent,exponents:()=>exponents,fromStringArrayToUint8:()=>fromStringArrayToUint8,fromUint8ToStringArray:()=>fromUint8ToStringArray,getAxesPermutation:()=>getAxesPermutation,getBroadcastDims:()=>getBroadcastDims,getComplexWithIndex:()=>getComplexWithIndex,getFusedBiasGradient:()=>getFusedBiasGradient,getFusedDyActivation:()=>getFusedDyActivation,getImageCenter:()=>getImageCenter,getInnerMostAxes:()=>getInnerMostAxes,getPermuted:()=>getPermuted,getReductionAxes:()=>getReductionAxes,getReshaped:()=>getReshaped,getReshapedPermuted:()=>getReshapedPermuted,getSliceBeginCoords:()=>getSliceBeginCoords,getSliceSize:()=>getSliceSize,getUndoAxesPermutation:()=>getUndoAxesPermutation,log:()=>log22,mergeRealAndImagArrays:()=>mergeRealAndImagArrays,prepareAndValidate:()=>prepareAndValidate,prepareSplitSize:()=>prepareSplitSize,reshapeTensor:()=>reshapeTensor,segment_util:()=>segment_util_exports,shouldFuse:()=>shouldFuse,slice_util:()=>slice_util_exports,splitRealAndImagArrays:()=>splitRealAndImagArrays,tupleValuesAreOne:()=>tupleValuesAreOne,upcastType:()=>upcastType,validateInput:()=>validateInput,validateUpdateShape:()=>validateUpdateShape,warn:()=>warn});function assertParamsConsistent(shapes,axis){let rank=shapes[0].length;shapes.forEach((shape,i)=>{assert(shape.length===rank,()=>`Error in concat${rank}D: rank of tensors[${i}] must be the same as the rank of the rest (${rank})`)}),assert(axis>=0&&axis`Error in concat${rank}D: axis must be between 0 and ${rank-1}.`);let firstShape=shapes[0];shapes.forEach((shape,i)=>{for(let r=0;r`Error in concat${rank}D: Shape of tensors[${i}] (${shape}) does not match the shape of the rest (${firstShape}) along the non-concatenated axis ${i}.`)})}function computeOutShape2(shapes,axis){let outputShape=shapes[0].slice();for(let i=1;i=blockShapeRank*2+1||i%2==1?permutedAfterBatch.push(i):permutedBeforeBatch.push(i);permuted.push(...permutedBeforeBatch),permuted.push(0),permuted.push(...permutedAfterBatch)}return permuted}function getReshapedPermuted(inputShape,blockShape,prod5,batchToSpace=!0){let reshapedPermuted=[];batchToSpace?reshapedPermuted.push(inputShape[0]/prod5):reshapedPermuted.push(inputShape[0]*prod5);for(let i=1;i"Number of splits must evenly divide the axis."),splitSizes=new Array(numOrSizeSplits).fill(x.shape[axis]/numOrSizeSplits);else{let numOfNegs=numOrSizeSplits.reduce((count2,value)=>(value===-1&&(count2+=1),count2),0);assert(numOfNegs<=1,()=>"There should be only one negative value in split array.");let negIndex=numOrSizeSplits.indexOf(-1);if(negIndex!==-1){let total=numOrSizeSplits.reduce((a,b)=>b>0?a+b:a);numOrSizeSplits[negIndex]=x.shape[axis]-total}assert(x.shape[axis]===numOrSizeSplits.reduce((a,b)=>a+b),()=>"The sum of sizes must match the size of the axis dimension."),splitSizes=numOrSizeSplits}return splitSizes}var segment_util_exports={};__export2(segment_util_exports,{collectGatherOpShapeInfo:()=>collectGatherOpShapeInfo,computeOutShape:()=>computeOutShape3,segOpComputeOptimalWindowSize:()=>segOpComputeOptimalWindowSize});function segOpComputeOptimalWindowSize(inSize,numSegments){let done=!1,res;for(inSize<=PARALLELIZE_THRESHOLD?(res=inSize,done=!0):res=nearestDivisor(inSize,Math.floor(Math.sqrt(inSize)));!done;)res>numSegments||res===inSize?done=!0:res=nearestDivisor(inSize,res+1);return res}function computeOutShape3(aShape,axis,numSegments){let outShape=[],rank=aShape.length;for(let dim=0;dimindicesRank))throw new Error(`Expect batchDims in the range of [-${indicesRank}, ${indicesRank}], but got ${batchDims}`);if(batchDims<0&&(batchDims+=indicesRank),batchDims>xRank)throw new Error(`batchDims (${batchDims}) must be less than rank(x) ( + the f you passed encloses all operations that lead from x to y.`)}function neg_(x){let inputs={x:convertToTensor(x,"x","neg")};return ENGINE.runKernel(Neg,inputs)}var neg=op({neg_});function softplus_(x){let inputs={x:convertToTensor(x,"x","softplus")};return ENGINE.runKernel(Softplus,inputs)}var softplus=op({softplus_});function logSigmoid_(x){let $x=convertToTensor(x,"x","logSigmoid");return customGrad(x2=>({value:neg(softplus(neg(x2))),gradFunc:dy=>mul(dy,sigmoid(neg(x2)))}))($x)}var logSigmoid=op({logSigmoid_});function max_(x,axis=null,keepDims=!1){let inputs={x:convertToTensor(x,"x","max")},attrs={reductionIndices:axis,keepDims};return ENGINE.runKernel(Max,inputs,attrs)}var max=op({max_});function sub_(a,b){let $a=convertToTensor(a,"a","sub"),$b=convertToTensor(b,"b","sub");[$a,$b]=makeTypesMatch($a,$b);let inputs={a:$a,b:$b};return ENGINE.runKernel(Sub,inputs)}var sub=op({sub_});function sum_(x,axis=null,keepDims=!1){let $x=convertToTensor(x,"x","sum");$x.dtype==="bool"&&($x=cast($x,"int32"));let inputs={x:$x},attrs={axis,keepDims};return ENGINE.runKernel(Sum,inputs,attrs)}var sum2=op({sum_});function logSoftmax_(logits,axis=-1){let $logits=convertToTensor(logits,"logits","logSoftmax");if(axis===-1&&(axis=$logits.rank-1),axis!==$logits.rank-1)throw Error(`Log Softmax along a non-last dimension is not yet supported. Logits was rank ${$logits.rank} and axis was ${axis}`);return customGrad((logits2,save)=>{let keepDims=!0,xMax=max(logits2,axis,!0),shifted=sub(logits2,xMax),value=sub(cast(shifted,"float32"),log2(sum2(exp(shifted),axis,keepDims)));return save([value]),{value,gradFunc:(dy,saved)=>{let[value2]=saved,keepDims2=!0,softmax6=exp(value2);return sub(dy,mul(sum2(dy,axis,keepDims2),softmax6))}}})($logits)}var logSoftmax=op({logSoftmax_});function axesAreInnerMostDims(axes,rank){for(let i=0;iaShape[dim]);return[outShape,reduceShape]}function expandShapeToKeepDim(shape,axes){let reduceSubShape=axes.map(x=>1);return combineLocations(shape,reduceSubShape,axes)}function assertAxesAreInnerMostDims(msg,axes,rank){assert(axesAreInnerMostDims(axes,rank),()=>`${msg} supports only inner-most axes for now. Got axes ${axes} and rank-${rank} input.`)}function getAxesPermutation(axes,rank){if(axesAreInnerMostDims(axes,rank))return null;let result=[];for(let i=0;iresult.push(axis)),result}function getUndoAxesPermutation(axes){return axes.map((axis,i)=>[i,axis]).sort((a,b)=>a[1]-b[1]).map(x=>x[0])}function getInnerMostAxes(numAxes,rank){let res=[];for(let i=rank-numAxes;i`Error in maxPool: input must be rank 4 but got rank ${x4D.rank}.`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in maxPool: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={x:x4D},attrs={filterSize,strides,pad:pad3,dimRoundingMode},res=ENGINE.runKernel(MaxPool,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var maxPool=op({maxPool_});function maxPool3d_(x,filterSize=[1,1,1],strides,pad3,dimRoundingMode,dataFormat="NDHWC",dilations){dilations==null?dilations=[1,1,1]:deprecationWarn("dilations is deprecated, this field will be gone in v3.0.0.");let $x=convertToTensor(x,"x","maxPool3d"),x5D=$x,reshapedTo5D=!1;$x.rank===4&&(reshapedTo5D=!0,x5D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2],$x.shape[3]])),assert(x5D.rank===5,()=>`Error in maxPool3d: x must be rank 5 but got rank ${x5D.rank}.`),assert(dataFormat==="NDHWC",()=>`Error in maxPool3d: Only NDHWC is currently supported, but got dataFormat of ${dataFormat}`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in maxPool3d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in maxPool3d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={x:x5D},attrs={filterSize,strides,pad:pad3,dimRoundingMode,dataFormat,dilations},res=ENGINE.runKernel(MaxPool3D,inputs,attrs);return reshapedTo5D?reshape(res,[res.shape[1],res.shape[2],res.shape[3],res.shape[4]]):res}var maxPool3d=op({maxPool3d_});function maxPoolWithArgmax_(x,filterSize,strides,pad3,includeBatchInIndex=!1){let inputs={x:convertToTensor(x,"x","maxPoolWithArgmax")},attrs={filterSize,strides,pad:pad3,includeBatchInIndex},result=ENGINE.runKernel(MaxPoolWithArgmax,inputs,attrs);return{result:result[0],indexes:result[1]}}var maxPoolWithArgmax=op({maxPoolWithArgmax_});function maximum_(a,b){let $a=convertToTensor(a,"a","maximum"),$b=convertToTensor(b,"b","maximum");[$a,$b]=makeTypesMatch($a,$b),$a.dtype==="bool"&&($a=cast($a,"int32"),$b=cast($b,"int32")),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(Maximum,inputs)}var maximum=op({maximum_});function mean_(x,axis=null,keepDims=!1){let inputs={x:convertToTensor(x,"x","mean")},attrs={axis,keepDims};return ENGINE.runKernel(Mean,inputs,attrs)}var mean=op({mean_});function min_(x,axis=null,keepDims=!1){let inputs={x:convertToTensor(x,"x","min")},attrs={axis,keepDims};return ENGINE.runKernel(Min,inputs,attrs)}var min=op({min_});function minimum_(a,b){let $a=convertToTensor(a,"a","minimum"),$b=convertToTensor(b,"b","minimum");[$a,$b]=makeTypesMatch($a,$b),$a.dtype==="bool"&&($a=cast($a,"int32"),$b=cast($b,"int32")),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(Minimum,inputs)}var minimum=op({minimum_});function mirrorPad_(x,paddings,mode){assert(mode==="reflect"||mode==="symmetric",()=>`Invalid mode. Mode must be either reflect or symmetric. Got ${mode}.`);let $x=convertToTensor(x,"x","mirrorPad");if($x.rank===0)throw new Error("mirrorPad(scalar) is not defined. Pass non-scalar to mirrorPad");assert(paddings.length===$x.rank,()=>`Padding doesn't match input. Must be ${$x.rank}. Got ${paddings.length}.`);let shapeOffset=mode==="reflect"?1:0;for(let i=0;i<$x.rank;i++)assert(paddings[i].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),assert(paddings[i][0]>=0&&paddings[i][0]<=$x.shape[i]-shapeOffset&&paddings[i][1]>=0&&paddings[i][1]<=$x.shape[i]-shapeOffset,()=>`Padding in dimension ${i} cannot be greater than or equal to ${$x.shape[i]-shapeOffset} or less than 0 for input of shape ${$x.shape}`);let attrs={paddings,mode},inputs={x:$x};return ENGINE.runKernel(MirrorPad,inputs,attrs)}var mirrorPad=op({mirrorPad_});function mod_(a,b){let $a=convertToTensor(a,"a","mod"),$b=convertToTensor(b,"b","mod");[$a,$b]=makeTypesMatch($a,$b);let inputs={a:$a,b:$b};return ENGINE.runKernel(Mod,inputs)}var mod=op({mod_});function square_(x){let $x=convertToTensor(x,"x","square"),attrs={};return ENGINE.runKernel("Square",{x:$x},attrs)}var square=op({square_});function moments_(x,axis=null,keepDims=!1){x=convertToTensor(x,"x","moments");let axes=parseAxisParam(axis,x.shape),xMean=mean(x,axes,keepDims),keepDimsShape=xMean.shape;keepDims||(keepDimsShape=expandShapeToKeepDim(xMean.shape,axes));let devSquared=square(sub(cast(x,"float32"),reshape(xMean,keepDimsShape))),variance=mean(devSquared,axes,keepDims);return{mean:xMean,variance}}var moments=op({moments_});function multiRNNCell_(lstmCells,data2,c,h){let $data=convertToTensor(data2,"data","multiRNNCell"),$c=convertToTensorArray(c,"c","multiRNNCell"),$h=convertToTensorArray(h,"h","multiRNNCell"),input2=$data,newStates=[];for(let i=0;i2)throw new Error(`Rank of probabilities must be 1 or 2, but is ${origRank}`);seed=seed||Math.random();let inputs={logits:origRank===1?reshape($logits,[1,-1]):$logits},attrs={numSamples,seed,normalized},res=ENGINE.runKernel(Multinomial,inputs,attrs);return origRank===1?reshape(res,[res.size]):res}var multinomial=op({multinomial_});function notEqual_(a,b){let $a=convertToTensor(a,"a","notEqual"),$b=convertToTensor(b,"b","notEqual");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(NotEqual,inputs)}var notEqual=op({notEqual_});function zeros(shape,dtype="float32"){if(dtype==="complex64"){let real4=zeros(shape,"float32"),imag4=zeros(shape,"float32");return complex(real4,imag4)}let values=makeZerosTypedArray(sizeFromShape(shape),dtype);return ENGINE.makeTensor(values,shape,dtype)}function ones2(shape,dtype="float32"){if(dtype==="complex64"){let real4=ones2(shape,"float32"),imag4=zeros(shape,"float32");return complex(real4,imag4)}let values=makeOnesTypedArray(sizeFromShape(shape),dtype);return ENGINE.makeTensor(values,shape,dtype)}function onesLike_(x){let inputs={x:convertToTensor(x,"x","onesLike")};return ENGINE.runKernel(OnesLike,inputs)}var onesLike=op({onesLike_});function outerProduct_(v1,v2){let $v1=convertToTensor(v1,"v1","outerProduct"),$v2=convertToTensor(v2,"v2","outerProduct");assert($v1.rank===1&&$v2.rank===1,()=>`Error in outerProduct: inputs must be rank 1, but got ranks ${$v1.rank} and ${$v2.rank}.`);let v12D=reshape($v1,[-1,1]),v22D=reshape($v2,[1,-1]);return matMul(v12D,v22D)}var outerProduct=op({outerProduct_});function pad_(x,paddings,constantValue=0){let $x=convertToTensor(x,"x","pad");if($x.rank===0)throw new Error("pad(scalar) is not defined. Pass non-scalar to pad");let attrs={paddings,constantValue},inputs={x:$x};return ENGINE.runKernel(PadV2,inputs,attrs)}var pad=op({pad_});function pad1d_(x,paddings,constantValue=0){return assert(paddings.length===2,()=>"Invalid number of paddings. Must be length of 2."),pad(x,[paddings],constantValue)}var pad1d=op({pad1d_});function pad2d_(x,paddings,constantValue=0){return assert(paddings.length===2&&paddings[0].length===2&&paddings[1].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),pad(x,paddings,constantValue)}var pad2d=op({pad2d_});function pad3d_(x,paddings,constantValue=0){return assert(paddings.length===3&&paddings[0].length===2&&paddings[1].length===2&&paddings[2].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),pad(x,paddings,constantValue)}var pad3d=op({pad3d_});function pad4d_(x,paddings,constantValue=0){return assert(paddings.length===4&&paddings[0].length===2&&paddings[1].length===2&&paddings[2].length===2&&paddings[3].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),pad(x,paddings,constantValue)}var pad4d=op({pad4d_});function spaceToBatchND_(x,blockShape,paddings){let $x=convertToTensor(x,"x","spaceToBatchND");assert($x.rank>=1+blockShape.length,()=>`input rank ${$x.rank} should be > than [blockShape] ${blockShape.length}`),assert(paddings.length===blockShape.length,()=>`paddings.shape[0] ${paddings.length} must be equal to [blockShape] ${blockShape.length}`),assert($x.shape.reduce((a,b,i)=>i>0&&i<=blockShape.length?a&&(b+paddings[i-1][0]+paddings[i-1][1])%blockShape[i-1]==0:a,!0),()=>`input spatial dimensions ${$x.shape.slice(1)} with paddings ${paddings.toString()} must be divisible by blockShapes ${blockShape.toString()}`);let inputs={x:$x},attrs={blockShape,paddings};return ENGINE.runKernel(SpaceToBatchND,inputs,attrs)}var spaceToBatchND=op({spaceToBatchND_});function pool_(input2,windowShape,poolingType,pad3,dilations,strides){dilations==null&&(dilations=[1,1]),strides==null&&(strides=1),pad3===0&&(pad3="valid");let $x=convertToTensor(input2,"x","maxPool"),x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in pool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);let convInfo=computePool2DInfo(x4D.shape,windowShape,strides,dilations,pad3),dilation=[convInfo.dilationHeight,convInfo.dilationWidth],basePadding;pad3==="same"?basePadding=withSpaceToBatchBasePaddings([convInfo.filterHeight,convInfo.filterWidth],dilation):basePadding=[[0,0],[0,0]];let isDilationOne=dilation[0]===1&&dilation[1]===1,[adjustedPadding,adjustedCrops]=requiredSpaceToBatchPaddings([convInfo.inHeight,convInfo.inWidth],dilation,basePadding),convertedPad=isDilationOne?pad3:"valid",convertedX=isDilationOne?x4D:spaceToBatchND(x4D,dilation,adjustedPadding),y=(poolingType==="avg"?()=>avgPool(convertedX,windowShape,strides,convertedPad):()=>maxPool(convertedX,windowShape,strides,convertedPad))(),res=isDilationOne?y:batchToSpaceND(y,dilation,adjustedCrops);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}function requiredSpaceToBatchPaddings(inputShape,blockShape,basePadding){let padStart=basePadding.map(b=>b[0]),origPadEnd=basePadding.map(b=>b[1]),fullInputShape=inputShape.concat(padStart,origPadEnd),padEndExtra=blockShape.map((b,i)=>(b-fullInputShape[i]%b)%b),padEnd=origPadEnd.map((s,i)=>s+padEndExtra[i]),paddings=blockShape.map((_,i)=>[padStart[i],padEnd[i]]),crops=blockShape.map((_,i)=>[0,padEndExtra[i]]);return[paddings,crops]}function withSpaceToBatchBasePaddings(filterShape,dilation){let padExtraShape=filterShape.map((s,i)=>s+(s-1)*(dilation[i]-1)).map(s=>s-1),padExtraStart=padExtraShape.map(s=>Math.floor(s/2)),padExtraEnd=padExtraShape.map((s,i)=>s-padExtraStart[i]);return padExtraShape.map((_,i)=>[padExtraStart[i],padExtraEnd[i]])}var pool=op({pool_});function pow_(base2,exp4){let $base=convertToTensor(base2,"base","pow"),$exp=convertToTensor(exp4,"exp","pow");[$base,$exp]=makeTypesMatch($base,$exp);let inputs={a:$base,b:$exp};return ENGINE.runKernel(Pow,inputs)}var pow=op({pow_});function prelu_(x,alpha){let $x=convertToTensor(x,"x","prelu"),$alpha=convertToTensor(alpha,"alpha","prelu"),inputs={x:$x,alpha:$alpha};return ENGINE.runKernel(Prelu,inputs)}var prelu=op({prelu_});function prod_(x,axis=null,keepDims=!1){let $x=convertToTensor(x,"x","prod");$x.dtype==="bool"&&($x=cast($x,"int32"));let inputs={x:$x},attrs={axis,keepDims};return ENGINE.runKernel(Prod,inputs,attrs)}var prod=op({prod_});function rand_(shape,randFunction,dtype){let size=sizeFromShape(shape),values=null;if(dtype==null||dtype==="float32")values=new Float32Array(size);else if(dtype==="int32")values=new Int32Array(size);else if(dtype==="bool")values=new Uint8Array(size);else throw new Error(`Unknown data type ${dtype}`);for(let i=0;i=1||s===0);let mul2=Math.sqrt(-2*Math.log(s)/s);resultX=this.mean+this.stdDev*v1*mul2,resultY=this.mean+this.stdDev*v2*mul2,(!this.truncated||this.isValidTruncated(resultX))&&(isValid=!0)}return(!this.truncated||this.isValidTruncated(resultY))&&(this.nextVal=this.convertValue(resultY)),this.convertValue(resultX)}convertValue(value){return this.dtype==null||this.dtype==="float32"?value:Math.round(value)}isValidTruncated(value){return value<=this.upper&&value>=this.lower}},RandGamma=class{constructor(alpha,beta,dtype,seed){this.alpha=alpha,this.beta=1/beta,this.dtype=dtype;let seedValue=seed||Math.random();this.randu=seedrandom.alea(seedValue.toString()),this.randn=new MPRandGauss(0,1,dtype,!1,this.randu()),alpha<1?this.d=alpha+2/3:this.d=alpha-1/3,this.c=1/Math.sqrt(9*this.d)}nextValue(){let x2,v0,v1,x,u,v;for(;;){do x=this.randn.nextValue(),v=1+this.c*x;while(v<=0);if(v*=v*v,x2=x*x,v0=1-.331*x2*x2,v1=.5*x2+this.d*(1-v+Math.log(v)),u=this.randu(),uthis.dtype==null||this.dtype==="float32",this.min=min6,this.range=max6-min6,this.dtype=dtype,seed==null&&(seed=Math.random()),typeof seed=="number"&&(seed=seed.toString()),!this.canReturnFloat()&&this.range<=1)throw new Error(`The difference between ${min6} - ${max6} <= 1 and dtype is not float`);this.random=seedrandom.alea(seed)}convertValue(value){return this.canReturnFloat()?value:Math.round(value)}nextValue(){return this.convertValue(this.min+this.range*this.random())}};function randomGamma_(shape,alpha,beta=1,dtype="float32",seed){if(beta==null&&(beta=1),dtype==null&&(dtype="float32"),dtype!=="float32"&&dtype!=="int32")throw new Error(`Unsupported data type ${dtype}`);let rgamma=new RandGamma(alpha,beta,dtype,seed),res=buffer(shape,dtype);for(let i=0;i`Error in reverse1D: x must be rank 1 but got rank ${$x.rank}.`),reverse($x,0)}var reverse1d=op({reverse1d_});function reverse2d_(x,axis){let $x=convertToTensor(x,"x","reverse");return assert($x.rank===2,()=>`Error in reverse2D: x must be rank 2 but got rank ${$x.rank}.`),reverse($x,axis)}var reverse2d=op({reverse2d_});function reverse3d_(x,axis){let $x=convertToTensor(x,"x","reverse");return assert($x.rank===3,()=>`Error in reverse3D: x must be rank 3 but got rank ${$x.rank}.`),reverse($x,axis)}var reverse3d=op({reverse3d_});function reverse4d_(x,axis){let $x=convertToTensor(x,"x","reverse");return assert($x.rank===4,()=>`Error in reverse4D: x must be rank 4 but got rank ${$x.rank}.`),reverse($x,axis)}var reverse4d=op({reverse4d_});function round_(x){let inputs={x:convertToTensor(x,"x","round")};return ENGINE.runKernel(Round,inputs)}var round2=op({round_});function rsqrt_(x){let inputs={x:convertToTensor(x,"x","rsqrt")};return ENGINE.runKernel(Rsqrt,inputs)}var rsqrt=op({rsqrt_});function scalar(value,dtype){if((isTypedArray(value)&&dtype!=="string"||Array.isArray(value))&&dtype!=="complex64")throw new Error("Error creating a new Scalar: value must be a primitive (number|boolean|string)");if(dtype==="string"&&isTypedArray(value)&&!(value instanceof Uint8Array))throw new Error("When making a scalar from encoded string, the value must be `Uint8Array`.");return makeTensor(value,[],[],dtype)}function selu_(x){let inputs={x:convertToTensor(x,"x","selu")};return ENGINE.runKernel(Selu,inputs)}var selu=op({selu_});function separableConv2d_(x,depthwiseFilter,pointwiseFilter,strides,pad3,dilation=[1,1],dataFormat="NHWC"){let $x=convertToTensor(x,"x","separableConv2d"),$depthwiseFilter=convertToTensor(depthwiseFilter,"depthwiseFilter","separableConv2d"),$pointwiseFilter=convertToTensor(pointwiseFilter,"pointwiseFilter","separableConv2d"),x4D=$x,reshapedTo4D=!1;if($x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),dataFormat==="NCHW")throw new Error("separableConv2d currently does not support dataFormat NCHW; only NHWC is supported");assert(x4D.rank===4,()=>`Error in separableConv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert($depthwiseFilter.rank===4,()=>`Error in separableConv2d: depthwise filter must be rank 4, but got rank ${$depthwiseFilter.rank}.`),assert($pointwiseFilter.rank===4,()=>`Error in separableConv2d: pointwise filter must be rank 4, but got rank ${$depthwiseFilter.rank}.`),assert($pointwiseFilter.shape[0]===1,()=>`Error in separableConv2d: the first dimension of pointwise filter must be 1, but got ${$pointwiseFilter.shape[0]}.`),assert($pointwiseFilter.shape[1]===1,()=>`Error in separableConv2d: the second dimension of pointwise filter must be 1, but got ${$pointwiseFilter.shape[1]}.`);let inChannels=$depthwiseFilter.shape[2],channelMultiplier=$depthwiseFilter.shape[3];assert($pointwiseFilter.shape[2]===inChannels*channelMultiplier,()=>`Error in separableConv2d: the third dimension of pointwise filter must be ${inChannels*channelMultiplier}, but got ${$pointwiseFilter.shape[2]}.`);let depthwise=depthwiseConv2d(x4D,$depthwiseFilter,strides,pad3,dataFormat,dilation),res=conv2d(depthwise,$pointwiseFilter,1,"valid",dataFormat);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var separableConv2d=op({separableConv2d_});async function setdiff1dAsync_(x,y){let $x=convertToTensor(x,"x","setdiff1d"),$y=convertToTensor(y,"y","setdiff1d");assert($x.dtype===$y.dtype,()=>`x and y should have the same dtype, but got x (${$x.dtype}) and y (${$y.dtype}).`),assert($x.rank===1,()=>`x should be 1D tensor, but got x (${$x.shape}).`),assert($y.rank===1,()=>`y should be 1D tensor, but got y (${$y.shape}).`);let xVals=await $x.data(),yVals=await $y.data(),ySet=new Set(yVals),outputSize=0;for(let i=0;i`slice1d expects a rank-1 tensor, but got a rank-${$x.rank} tensor`),slice($x,[begin],[size])}var slice1d=op({slice1d_});function slice2d_(x,begin,size){let $x=convertToTensor(x,"x","slice2d");return assert($x.rank===2,()=>`slice2d expects a rank-2 tensor, but got a rank-${$x.rank} tensor`),slice($x,begin,size)}var slice2d=op({slice2d_});function slice3d_(x,begin,size){let $x=convertToTensor(x,"x","slice3d");return assert($x.rank===3,()=>`slice3d expects a rank-3 tensor, but got a rank-${$x.rank} tensor`),slice($x,begin,size)}var slice3d=op({slice3d_});function slice4d_(x,begin,size){let $x=convertToTensor(x,"x","slice4d");return assert($x.rank===4,()=>`slice4d expects a rank-4 tensor, but got a rank-${$x.rank} tensor`),slice($x,begin,size)}var slice4d=op({slice4d_});function softmax_(logits,dim=-1){let $logits=convertToTensor(logits,"logits","softmax","float32");if(dim===-1&&(dim=$logits.rank-1),dim!==$logits.rank-1)throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${$logits.rank} and dim was ${dim}`);let inputs={logits:$logits},attrs={dim};return ENGINE.runKernel(Softmax,inputs,attrs)}var softmax=op({softmax_});function fft_(input2){assert(input2.dtype==="complex64",()=>`The dtype for tf.spectral.fft() must be complex64 but got ${input2.dtype}.`);let inputs={input:input2};return ENGINE.runKernel(FFT,inputs)}var fft=op({fft_});function ifft_(input2){assert(input2.dtype==="complex64",()=>`The dtype for tf.spectral.ifft() must be complex64 but got ${input2.dtype}.`);let inputs={input:input2};return ENGINE.runKernel(IFFT,inputs)}var ifft=op({ifft_});function irfft_(input2){let innerDimensionSize=input2.shape[input2.shape.length-1],batch=input2.size/innerDimensionSize,ret;if(innerDimensionSize<=2){let complexInput=reshape(input2,[batch,innerDimensionSize]);ret=ifft(complexInput)}else{let outputShape=[batch,2*(innerDimensionSize-1)],realInput=reshape(real(input2),[batch,innerDimensionSize]),imagInput=reshape(imag(input2),[batch,innerDimensionSize]),realConjugate=reverse(slice(realInput,[0,1],[batch,innerDimensionSize-2]),1),imagConjugate=mul(reverse(slice(imagInput,[0,1],[batch,innerDimensionSize-2]),1),scalar(-1)),r=concat([realInput,realConjugate],1),i=concat([imagInput,imagConjugate],1),complexInput=reshape(complex(r,i),[outputShape[0],outputShape[1]]);ret=ifft(complexInput)}if(ret=real(ret),input2.rank===3&&input2.shape[0]!==0){let temp=ret,batch2=input2.shape[0];ret=reshape(ret,[batch2,ret.shape[0]/batch2,ret.shape[1]]),temp.dispose()}return ret}var irfft=op({irfft_});function split_(x,numOrSizeSplits,axis=0){let inputs={x:convertToTensor(x,"x","split")},attr={numOrSizeSplits,axis};return ENGINE.runKernel(SplitV,inputs,attr)}var split=op({split_});function rfft_(input2,fftLength){assert(input2.dtype==="float32",()=>`The dtype for rfft() must be real value but got ${input2.dtype}`);let innerDimensionSize=input2.shape[input2.shape.length-1],batch=input2.size/innerDimensionSize,adjustedInput;if(fftLength!=null&&fftLength0),size=input2.shape.map(v=>v);size[input2.shape.length-1]=fftLength,adjustedInput=slice(input2,begin,size),innerDimensionSize=fftLength}else if(fftLength!=null&&fftLength>innerDimensionSize){let zerosShape=input2.shape.map(v=>v);zerosShape[input2.shape.length-1]=fftLength-innerDimensionSize,adjustedInput=concat([input2,zeros(zerosShape)],input2.shape.length-1),innerDimensionSize=fftLength}else adjustedInput=input2;let zerosInput=zerosLike(adjustedInput),complexInput=reshape(complex(adjustedInput,zerosInput),[batch,innerDimensionSize]),ret=fft(complexInput),half=Math.floor(innerDimensionSize/2)+1,realValues=real(ret),imagValues=imag(ret),realComplexConjugate=split(realValues,[half,innerDimensionSize-half],realValues.shape.length-1),imagComplexConjugate=split(imagValues,[half,innerDimensionSize-half],imagValues.shape.length-1),outputShape=adjustedInput.shape.slice();return outputShape[adjustedInput.shape.length-1]=half,reshape(complex(realComplexConjugate[0],imagComplexConjugate[0]),outputShape)}var rfft=op({rfft_});function sqrt_(x){let inputs={x:convertToTensor(x,"x","sqrt")};return ENGINE.runKernel(Sqrt,inputs)}var sqrt=op({sqrt_});function squaredDifference_(a,b){let $a=convertToTensor(a,"a","squaredDifference"),$b=convertToTensor(b,"b","squaredDifference");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b},attrs={};return ENGINE.runKernel(SquaredDifference,inputs,attrs)}var squaredDifference=op({squaredDifference_});function squeeze_(x,axis){let $x=convertToTensor(x,"x","squeeze");return reshape($x,squeezeShape($x.shape,axis).newShape)}var squeeze=op({squeeze_});function stack_(tensors,axis=0){let $tensors=convertToTensorArray(tensors,"tensors","stack","string_or_numeric");assert($tensors.length>=1,()=>"Pass at least one tensor to tf.stack"),$tensors.length>0&&assert(axis<=$tensors[0].rank,()=>"Axis must be <= rank of the tensor");let inputs=$tensors,attrs={axis};return ENGINE.runKernel(Pack,inputs,attrs)}var stack=op({stack_});function step_(x,alpha=0){let inputs={x:convertToTensor(x,"x","step")},attrs={alpha};return ENGINE.runKernel(Step,inputs,attrs)}var step=op({step_});function stridedSlice_(x,begin,end,strides,beginMask=0,endMask=0,ellipsisMask=0,newAxisMask=0,shrinkAxisMask=0){let inputs={x:convertToTensor(x,"x","stridedSlice")},attrs={begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask};return ENGINE.runKernel(StridedSlice,inputs,attrs)}var stridedSlice=op({stridedSlice_});function tan_(x){let inputs={x:convertToTensor(x,"x","tan")};return ENGINE.runKernel(Tan,inputs)}var tan=op({tan_});function tensor1d(values,dtype){assertNonNull(values);let inferredShape=inferShape(values,dtype);if(inferredShape.length!==1)throw new Error("tensor1d() requires values to be a flat/TypedArray");return makeTensor(values,null,inferredShape,dtype)}function tensor2d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==2)throw new Error("tensor2d() requires shape to have two numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==2&&inferredShape.length!==1)throw new Error("tensor2d() requires values to be number[][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor2d() requires shape to be provided when `values` are a flat/TypedArray");return makeTensor(values,shape,inferredShape,dtype)}function tensor4d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==4)throw new Error("tensor4d() requires shape to have four numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==4&&inferredShape.length!==1)throw new Error("tensor4d() requires values to be number[][][][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor4d() requires shape to be provided when `values` are a flat array");return makeTensor(values,shape,inferredShape,dtype)}function tensor5d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==5)throw new Error("tensor5d() requires shape to have five numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==5&&inferredShape.length!==1)throw new Error("tensor5d() requires values to be number[][][][][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor5d() requires shape to be provided when `values` are a flat array");return makeTensor(values,shape,inferredShape,dtype)}function tensor6d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==6)throw new Error("tensor6d() requires shape to have six numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==6&&inferredShape.length!==1)throw new Error("tensor6d() requires values to be number[][][][][][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor6d() requires shape to be provided when `values` are a flat array");return shape=shape||inferredShape,makeTensor(values,shape,inferredShape,dtype)}function topk_(x,k=1,sorted=!0){let $x=convertToTensor(x,"x","topk");if($x.rank===0)throw new Error("topk() expects the input to be of rank 1 or higher");let lastDim=$x.shape[$x.shape.length-1];if(k>lastDim)throw new Error(`'k' passed to topk() must be <= the last dimension (${lastDim}) but got ${k}`);let inputs={x:$x},attrs={k,sorted},[values,indices]=ENGINE.runKernel(TopK,inputs,attrs);return{values,indices}}var topk=op({topk_});function truncatedNormal_(shape,mean4=0,stdDev=1,dtype,seed){if(dtype!=null&&dtype==="bool")throw new Error("Unsupported data type $ { dtype }");let randGauss=new MPRandGauss(mean4,stdDev,dtype,!0,seed),res=buffer(shape,dtype);for(let i=0;i0,()=>"The input tensor must be at least 1D");let inputs={x:$x},attrs={axis},[values,indices]=ENGINE.runKernel(Unique,inputs,attrs);return{values,indices}}var unique=op({unique_});function unsortedSegmentSum_(x,segmentIds,numSegments){let $x=convertToTensor(x,"x","unsortedSegmentSum"),$segmentIds=convertToTensor(segmentIds,"segmentIds","unsortedSegmentSum","int32");assert(isInt(numSegments),()=>"numSegments must be of dtype int");let inputs={x:$x,segmentIds:$segmentIds},attrs={numSegments};return ENGINE.runKernel(UnsortedSegmentSum,inputs,attrs)}var unsortedSegmentSum=op({unsortedSegmentSum_});function unstack_(x,axis=0){let $x=convertToTensor(x,"x","unstack","string_or_numeric");assert(axis>=-$x.shape.length&&axis<$x.shape.length,()=>`Axis = ${axis} is not in [-${$x.shape.length}, ${$x.shape.length})`);let inputs={value:$x},attrs={axis};return ENGINE.runKernel(Unpack,inputs,attrs)}var unstack=op({unstack_});function variable(initialValue,trainable=!0,name,dtype){return ENGINE.makeVariable(initialValue,trainable,name,dtype)}function whereImpl(condShape,condVals){let indices=[];for(let i=0;i0,()=>"mask cannot be scalar"),assertShapesMatch(tensorShape.slice(axisFrom,axisFrom+maskDim),$mask.shape,"mask's shape must match the first K dimensions of tensor's shape,");let leadingSize=1;for(let i=axisFrom;i"Shape mismatch in v and x");let one=scalar(1),oneMinusDecay=sub(one,$decay),update=mul(sub($x,$v),oneMinusDecay);if(zeroDebias){assert(step4!=null,()=>"When using zeroDebias: true, step is required.");let $step=convertToTensor(step4,"step","movingAverage");update=div(update,sub(one,pow($decay,$step)))}return add2($v,update)}var movingAverage=op({movingAverage_});function scatterND_(indices,updates,shape){let $indices=convertToTensor(indices,"indices","scatterND","int32"),$updates=convertToTensor(updates,"updates","scatterND");validateInput($updates,$indices,shape);let inputs={indices:$indices,updates:$updates},attrs={shape};return ENGINE.runKernel(ScatterNd,inputs,attrs)}var scatterND=op({scatterND_});function validateInput2(sparseIndices,sparseValues,outputShape,defaultValues){if(sparseIndices.dtype!=="int32")throw new Error(`tf.sparseToDense() expects the indices to be int32 type, but the dtype was ${sparseIndices.dtype}.`);if(sparseIndices.rank>2)throw new Error(`sparseIndices should be a scalar, vector, or matrix, but got shape ${sparseIndices.shape}.`);let numElems=sparseIndices.rank>0?sparseIndices.shape[0]:1,numDims=sparseIndices.rank>1?sparseIndices.shape[1]:1;if(outputShape.length!==numDims)throw new Error(`outputShape has incorrect number of elements:, ${outputShape.length}, should be: ${numDims}.`);let numValues=sparseValues.size;if(!(sparseValues.rank===0||sparseValues.rank===1&&numValues===numElems))throw new Error(`sparseValues has incorrect shape ${sparseValues.shape}, should be [] or [${numElems}]`);if(sparseValues.dtype!==defaultValues.dtype)throw new Error("sparseValues.dtype must match defaultValues.dtype")}function sparseToDense_(sparseIndices,sparseValues,outputShape,defaultValue=0){let $sparseIndices=convertToTensor(sparseIndices,"sparseIndices","sparseToDense","int32"),$sparseValues=convertToTensor(sparseValues,"sparseValues","sparseToDense"),$defaultValue=convertToTensor(defaultValue,"defaultValue","sparseToDense",$sparseValues.dtype);validateInput2($sparseIndices,$sparseValues,outputShape,$defaultValue);let inputs={sparseIndices:$sparseIndices,sparseValues:$sparseValues,defaultValue:$defaultValue},attrs={outputShape};return ENGINE.runKernel(SparseToDense,inputs,attrs)}var sparseToDense=op({sparseToDense_});function gatherND_(x,indices){let $indices=convertToTensor(indices,"indices","gatherND","int32"),inputs={params:convertToTensor(x,"x","gatherND"),indices:$indices};return ENGINE.runKernel(GatherNd,inputs)}var gatherND=op({gatherND_});function getNoiseShape(x,noiseShape){if(noiseShape==null)return x.shape.slice();if(arraysEqual(x.shape,noiseShape))return noiseShape;if(x.shape.length===noiseShape.length){let newDimension=[];for(let i=0;i`x has to be a floating point tensor since it's going to be scaled, but got a ${$x.dtype} tensor instead.`),assert(rate>=0&&rate<1,()=>`rate must be a float in the range [0, 1), but got ${rate}.`),rate===0)return x instanceof Tensor?$x.clone():$x;let $noiseShape=getNoiseShape($x,noiseShape),keepProb=1-rate,multiplier=div(floor(add2(randomUniform($noiseShape,0,1,"float32",seed),keepProb)),keepProb);return mul($x,multiplier)}var dropout=op({dropout_});function enclosingPowerOfTwo(value){return Math.floor(Math.pow(2,Math.ceil(Math.log(value)/Math.log(2))))}function cosineWindow(windowLength,a,b){let even=1-windowLength%2,newValues=new Float32Array(windowLength);for(let i=0;i1,()=>`inTopK() expects the predictions to be of rank 2 or higher, but got ${$predictions.rank}`),assert($predictions.rank-1===$targets.rank,()=>`predictions rank should be 1 larger than targets rank, but got predictions rank ${$predictions.rank} and targets rank ${$targets.rank}`),assertShapesMatch($predictions.shape.slice(0,$predictions.shape.length-1),$targets.shape,"predictions's shape should be align with the targets' shape, except the last dimension.");let lastDim=$predictions.shape[$predictions.shape.length-1];assert(k>0&&k<=lastDim,()=>`'k' passed to inTopK() must be > 0 && <= the predictions last dimension (${lastDim}), but got ${k}`);let predictionsVals=await $predictions.data(),targetsVals=await $targets.data(),[batch,size]=[predictionsVals.length/lastDim,lastDim],precision3=getTypedArrayFromDType("bool",batch);for(let b=0;bb2.value-a.value),precision3[b]=0;for(let i=0;iconv2d2,depthwiseConv2d:()=>depthwiseConv2d2,matMul:()=>matMul2});function conv2DBackpropFilter_(x,dy,filterShape,strides,pad3,dataFormat="NHWC",dimRoundingMode){let x4D=x;x.rank===3&&(x4D=reshape(x,[1,x.shape[0],x.shape[1],x.shape[2]]));let dy4D=dy;dy4D.rank===3&&(dy4D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2]])),assert(x4D.rank===4,()=>`Error in conv2dDerFilter: input must be rank 4, but got shape ${x4D.shape}.`),assert(dy4D.rank===4,()=>`Error in conv2dDerFilter: dy must be rank 4, but got shape ${dy4D.shape}.`),assert(filterShape.length===4,()=>`Error in conv2dDerFilter: filterShape must be length 4, but got ${filterShape}.`);let inDepth=dataFormat==="NHWC"?x4D.shape[3]:x4D.shape[1],outDepth=dataFormat==="NHWC"?dy4D.shape[3]:dy4D.shape[1];assert(inDepth===filterShape[2],()=>`Error in conv2dDerFilter: depth of input ${inDepth}) must match input depth in filter (${filterShape[2]}.`),assert(outDepth===filterShape[3],()=>`Error in conv2dDerFilter: depth of dy (${outDepth}) must match output depth for filter (${filterShape[3]}).`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in conv2dDerFilter: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={x:x4D,dy:dy4D},attrs={strides,pad:pad3,dataFormat,dimRoundingMode,filterShape};return ENGINE.runKernel(Conv2DBackpropFilter,inputs,attrs)}var conv2DBackpropFilter=op({conv2DBackpropFilter_});function getFusedDyActivation(dy,y,activation2){if(activation2==null||activation2==="linear")return dy;if(activation2==="relu")return mul(dy,step(y));throw new Error(`Cannot compute gradient for fused activation ${activation2}.`)}function getFusedBiasGradient(bias,dyActivation){let res=dyActivation,reduceAxes=getReductionAxes(bias.shape,dyActivation.shape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,bias.shape)}function applyActivation(x,activation2,preluActivationWeights,leakyreluAlpha){if(activation2==="linear")return x;if(activation2==="relu")return relu(x);if(activation2==="elu")return elu(x);if(activation2==="relu6")return relu6(x);if(activation2==="prelu")return prelu(x,preluActivationWeights);if(activation2==="leakyrelu")return leakyRelu(x,leakyreluAlpha);throw new Error(`Unknown fused activation ${activation2}.`)}var shouldFuse=(gradientDepth,activation2)=>!(gradientDepth>0)||activation2==="linear";function fusedConv2d_({x,filter,strides,pad:pad3,dataFormat="NHWC",dilations=[1,1],dimRoundingMode,bias,activation:activation2="linear",preluActivationWeights,leakyreluAlpha}){if(activation2=activation2||"linear",shouldFuse(ENGINE.state.gradientDepth,activation2)===!1){let result=conv2d(x,filter,strides,pad3,dataFormat,dilations,dimRoundingMode);return bias!=null&&(result=add2(result,bias)),applyActivation(result,activation2,preluActivationWeights,leakyreluAlpha)}let $x=convertToTensor(x,"x","conv2d"),$filter=convertToTensor(filter,"filter","conv2d"),x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in fused conv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert($filter.rank===4,()=>`Error in fused conv2d: filter must be rank 4, but got rank ${$filter.rank}.`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in fused conv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`),assert(x4D.shape[3]===$filter.shape[2],()=>`Error in conv2d: depth of input (${x4D.shape[3]}) must match input depth for filter ${$filter.shape[2]}.`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),assert(dataFormat==="NHWC",()=>`Error in conv2d: got dataFormat of ${dataFormat} but only NHWC is currently supported.`);let convInfo=computeConv2DInfo(x4D.shape,$filter.shape,strides,dilations,pad3,dimRoundingMode),$bias;bias!=null&&($bias=convertToTensor(bias,"bias","fused conv2d"),[$bias]=makeTypesMatch($bias,$x),assertAndGetBroadcastShape(convInfo.outShape,$bias.shape));let $preluActivationWeights;preluActivationWeights!=null&&($preluActivationWeights=convertToTensor(preluActivationWeights,"prelu weights","fused conv2d"));let grad2=(dy,saved)=>{let[$filter2,x4D2,y,$bias2]=saved,dyActivation=getFusedDyActivation(dy,y,activation2);assert(tupleValuesAreOne(dilations),()=>`Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`);let xDer=conv2DBackpropInput(x4D2.shape,dyActivation,$filter2,strides,pad3),filterDer=conv2DBackpropFilter(x4D2,dyActivation,$filter2.shape,strides,pad3),der=[xDer,filterDer];if($bias2!=null){let biasDer=getFusedBiasGradient($bias2,dyActivation);der.push(biasDer)}return der},inputs={x:x4D,filter:$filter,bias:$bias,preluActivationWeights:$preluActivationWeights},attrs={strides,pad:pad3,dataFormat,dilations,dimRoundingMode,activation:activation2,leakyreluAlpha};return bias==null?customGrad((x4D2,filter2,save)=>{let res=ENGINE.runKernel(FusedConv2D,inputs,attrs);return save([filter2,x4D2,res]),reshapedTo4D&&(res=reshape(res,[res.shape[1],res.shape[2],res.shape[3]])),{value:res,gradFunc:grad2}})(x4D,$filter):customGrad((x4D2,filter2,bias2,save)=>{let res=ENGINE.runKernel(FusedConv2D,inputs,attrs);return save([filter2,x4D2,res,bias2]),reshapedTo4D&&(res=reshape(res,[res.shape[1],res.shape[2],res.shape[3]])),{value:res,gradFunc:grad2}})(x4D,$filter,$bias)}var conv2d2=op({fusedConv2d_});function depthwiseConv2dNativeBackpropFilter_(x,dy,filterShape,strides,pad3,dilations=[1,1],dimRoundingMode){let x4D=x;x.rank===3&&(x4D=reshape(x,[1,x.shape[0],x.shape[1],x.shape[2]]));let dy4D=dy;dy4D.rank===3&&(dy4D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2]]));let inputs={x:x4D,dy:dy4D},attrs={strides,pad:pad3,dimRoundingMode,dilations,filterShape};return ENGINE.runKernel(DepthwiseConv2dNativeBackpropFilter,inputs,attrs)}var depthwiseConv2dNativeBackpropFilter=op({depthwiseConv2dNativeBackpropFilter_});function depthwiseConv2dNativeBackpropInput_(xShape,dy,filter,strides,pad3,dilations=[1,1],dimRoundingMode){let dy4D=dy,reshapedTo4D=!1;dy.rank===3&&(reshapedTo4D=!0,dy4D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2]]));let inputs={dy:dy4D,filter},attrs={strides,pad:pad3,dimRoundingMode,dilations,inputShape:xShape},res=ENGINE.runKernel(DepthwiseConv2dNativeBackpropInput,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var depthwiseConv2dNativeBackpropInput=op({depthwiseConv2dNativeBackpropInput_});function fusedDepthwiseConv2d_({x,filter,strides,pad:pad3,dataFormat="NHWC",dilations=[1,1],dimRoundingMode,bias,activation:activation2="linear",preluActivationWeights,leakyreluAlpha}){if(shouldFuse(ENGINE.state.gradientDepth,activation2)===!1){let result=depthwiseConv2d(x,filter,strides,pad3,dataFormat,dilations,dimRoundingMode);return bias!=null&&(result=add2(result,bias)),applyActivation(result,activation2,preluActivationWeights,leakyreluAlpha)}let $x=convertToTensor(x,"x","depthwiseConv2d"),$filter=convertToTensor(filter,"filter","depthwiseConv2d"),x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in fused depthwiseConv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert($filter.rank===4,()=>`Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${$filter.rank}.`),assert(x4D.shape[3]===$filter.shape[2],()=>`Error in fused depthwiseConv2d: number of input channels (${x4D.shape[3]}) must match the inChannels dimension in filter ${$filter.shape[2]}.`),dilations==null&&(dilations=[1,1]),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in fused depthwiseConv2d: pad must be an integer when using dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let convInfo=computeConv2DInfo(x4D.shape,$filter.shape,strides,dilations,pad3,dimRoundingMode,!0),$bias;bias!=null&&($bias=convertToTensor(bias,"bias","fused conv2d"),[$bias]=makeTypesMatch($bias,$x),assertAndGetBroadcastShape(convInfo.outShape,$bias.shape));let $preluActivationWeights;preluActivationWeights!=null&&($preluActivationWeights=convertToTensor(preluActivationWeights,"prelu weights","fused depthwiseConv2d"));let grad2=(dy,saved)=>{assert(tupleValuesAreOne(dilations),()=>`Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${dilations}'`);let[$filter2,x4D2,y,bias2]=saved,dyActivation=getFusedDyActivation(dy,y,activation2),xDer=depthwiseConv2dNativeBackpropInput(x4D2.shape,dyActivation,$filter2,strides,pad3,dilations,dimRoundingMode),filterDer=depthwiseConv2dNativeBackpropFilter(x4D2,dyActivation,$filter2.shape,strides,pad3,dilations,dimRoundingMode);if(bias2!=null){let biasDer=getFusedBiasGradient($bias,dyActivation);return[xDer,filterDer,biasDer]}return[xDer,filterDer]},inputs={x:x4D,filter:$filter,bias:$bias,preluActivationWeights:$preluActivationWeights},attrs={strides,pad:pad3,dataFormat,dilations,dimRoundingMode,activation:activation2,leakyreluAlpha};return bias==null?customGrad((x4D2,filter2,save)=>{let res=ENGINE.runKernel(FusedDepthwiseConv2D,inputs,attrs);return save([filter2,x4D2,res]),reshapedTo4D&&(res=reshape(res,[res.shape[1],res.shape[2],res.shape[3]])),{value:res,gradFunc:grad2}})(x4D,$filter):customGrad((x4D2,filter2,bias2,save)=>{let res=ENGINE.runKernel(FusedDepthwiseConv2D,inputs,attrs);return save([filter2,x4D2,res,bias2]),reshapedTo4D&&(res=reshape(res,[res.shape[1],res.shape[2],res.shape[3]])),{value:res,gradFunc:grad2}})(x4D,$filter,$bias)}var depthwiseConv2d2=op({fusedDepthwiseConv2d_});function fusedMatMul_({a,b,transposeA=!1,transposeB=!1,bias,activation:activation2="linear",preluActivationWeights,leakyreluAlpha}){if(shouldFuse(ENGINE.state.gradientDepth,activation2)===!1){let result=matMul(a,b,transposeA,transposeB);return bias!=null&&(result=add2(result,bias)),applyActivation(result,activation2,preluActivationWeights,leakyreluAlpha)}let $a=convertToTensor(a,"a","fused matMul"),$b=convertToTensor(b,"b","fused matMul");[$a,$b]=makeTypesMatch($a,$b);let innerShapeA=transposeA?$a.shape[$a.rank-2]:$a.shape[$a.rank-1],innerShapeB=transposeB?$b.shape[$b.rank-1]:$b.shape[$b.rank-2],outerShapeA=transposeA?$a.shape[$a.rank-1]:$a.shape[$a.rank-2],outerShapeB=transposeB?$b.shape[$b.rank-2]:$b.shape[$b.rank-1],outerDimsA=$a.shape.slice(0,-2),outerDimsB=$b.shape.slice(0,-2),batchDimA=sizeFromShape(outerDimsA),batchDimB=sizeFromShape(outerDimsB);assert($a.rank>=2&&$b.rank>=2&&$a.rank===$b.rank,()=>`Error in fused matMul: inputs must have the same rank of at least 2, got ranks ${$a.rank} and ${$b.rank}.`),assert(arraysEqual(outerDimsA,outerDimsB),()=>`Error in fused matMul: outer dimensions (${outerDimsA}) and (${outerDimsB}) of Tensors with shapes ${$a.shape} and ${$b.shape} must match.`),assert(innerShapeA===innerShapeB,()=>`Error in fused matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${$a.shape} and ${$b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);let outShape=$a.shape.slice(0,-2).concat([outerShapeA,outerShapeB]),a3D=transposeA?reshape($a,[batchDimA,innerShapeA,outerShapeA]):reshape($a,[batchDimA,outerShapeA,innerShapeA]),b3D=transposeB?reshape($b,[batchDimB,outerShapeB,innerShapeB]):reshape($b,[batchDimB,innerShapeB,outerShapeB]),$bias;bias!=null&&($bias=convertToTensor(bias,"bias","fused matMul"),[$bias]=makeTypesMatch($bias,$a),assertAndGetBroadcastShape(outShape,$bias.shape));let $preluActivationWeights;preluActivationWeights!=null&&($preluActivationWeights=convertToTensor(preluActivationWeights,"prelu weights","fused matMul"));let grad2=(dy,saved)=>{let[a3D2,b3D2,y,$bias2]=saved,dyActivation=getFusedDyActivation(reshape(dy,y.shape),y,activation2),aDer,bDer;if(!transposeA&&!transposeB?(aDer=matMul(dyActivation,b3D2,!1,!0),bDer=matMul(a3D2,dyActivation,!0,!1)):!transposeA&&transposeB?(aDer=matMul(dyActivation,b3D2,!1,!1),bDer=matMul(dyActivation,a3D2,!0,!1)):transposeA&&!transposeB?(aDer=matMul(b3D2,dyActivation,!1,!0),bDer=matMul(a3D2,dyActivation,!1,!1)):(aDer=matMul(b3D2,dyActivation,!0,!0),bDer=matMul(dyActivation,a3D2,!0,!0)),bias!=null){let biasDer=getFusedBiasGradient($bias2,dyActivation);return[aDer,bDer,biasDer]}else return[aDer,bDer]},inputs={a:a3D,b:b3D,bias:$bias,preluActivationWeights:$preluActivationWeights},attrs={transposeA,transposeB,activation:activation2,leakyreluAlpha};return bias==null?customGrad((a3D2,b3D2,save)=>{let res=ENGINE.runKernel(_FusedMatMul,inputs,attrs);return save([a3D2,b3D2,res]),{value:reshape(res,outShape),gradFunc:grad2}})(a3D,b3D):customGrad((a3D2,b3D2,$bias2,save)=>{let res=ENGINE.runKernel(_FusedMatMul,inputs,attrs);return save([a3D2,b3D2,res,$bias2]),{value:reshape(res,outShape),gradFunc:grad2}})(a3D,b3D,$bias)}var matMul2=op({fusedMatMul_});function hammingWindow_(windowLength){return cosineWindow(windowLength,.54,.46)}var hammingWindow=op({hammingWindow_});function hannWindow_(windowLength){return cosineWindow(windowLength,.5,.5)}var hannWindow=op({hannWindow_});function frame_(signal2,frameLength,frameStep,padEnd=!1,padValue=0){let start=0,output=[];for(;start+frameLength<=signal2.size;)output.push(slice(signal2,start,frameLength)),start+=frameStep;if(padEnd)for(;start`Error in cropAndResize: image must be rank 4,but got rank ${$image.rank}.`),assert($boxes.rank===2&&$boxes.shape[1]===4,()=>`Error in cropAndResize: boxes must be have size [${numBoxes},4] but had shape ${$boxes.shape}.`),assert($boxInd.rank===1&&$boxInd.shape[0]===numBoxes,()=>`Error in cropAndResize: boxInd must be have size [${numBoxes}] but had shape ${$boxes.shape}.`),assert(cropSize.length===2,()=>`Error in cropAndResize: cropSize must be of length 2, but got length ${cropSize.length}.`),assert(cropSize[0]>=1&&cropSize[1]>=1,()=>`cropSize must be atleast [1,1], but was ${cropSize}`),assert(method==="bilinear"||method==="nearest",()=>`method must be bilinear or nearest, but was ${method}`);let inputs={image:$image,boxes:$boxes,boxInd:$boxInd},attrs={method,extrapolationValue,cropSize};return ENGINE.runKernel(CropAndResize,inputs,attrs)}var cropAndResize=op({cropAndResize_});function flipLeftRight_(image3){let $image=convertToTensor(image3,"image","flipLeftRight","float32");assert($image.rank===4,()=>`Error in flipLeftRight: image must be rank 4,but got rank ${$image.rank}.`);let inputs={image:$image};return ENGINE.runKernel(FlipLeftRight,inputs,{})}var flipLeftRight=op({flipLeftRight_});function rotateWithOffset_(image3,radians,fillValue=0,center=.5){let $image=convertToTensor(image3,"image","rotateWithOffset","float32");assert($image.rank===4,()=>`Error in rotateWithOffset: image must be rank 4,but got rank ${$image.rank}.`);let inputs={image:$image},attrs={radians,fillValue,center};return ENGINE.runKernel(RotateWithOffset,inputs,attrs)}var rotateWithOffset=op({rotateWithOffset_});function nonMaxSuppSanityCheck(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma){iouThreshold==null&&(iouThreshold=.5),scoreThreshold==null&&(scoreThreshold=Number.NEGATIVE_INFINITY),softNmsSigma==null&&(softNmsSigma=0);let numBoxes=boxes.shape[0];return maxOutputSize=Math.min(maxOutputSize,numBoxes),assert(0<=iouThreshold&&iouThreshold<=1,()=>`iouThreshold must be in [0, 1], but was '${iouThreshold}'`),assert(boxes.rank===2,()=>`boxes must be a 2D tensor, but was of rank '${boxes.rank}'`),assert(boxes.shape[1]===4,()=>`boxes must have 4 columns, but 2nd dimension was ${boxes.shape[1]}`),assert(scores.rank===1,()=>"scores must be a 1D tensor"),assert(scores.shape[0]===numBoxes,()=>`scores has incompatible shape with boxes. Expected ${numBoxes}, but was ${scores.shape[0]}`),assert(0<=softNmsSigma&&softNmsSigma<=1,()=>`softNmsSigma must be in [0, 1], but was '${softNmsSigma}'`),{maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma}}function nonMaxSuppression_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppression"),$scores=convertToTensor(scores,"scores","nonMaxSuppression"),inputs=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold);maxOutputSize=inputs.maxOutputSize,iouThreshold=inputs.iouThreshold,scoreThreshold=inputs.scoreThreshold;let attrs={maxOutputSize,iouThreshold,scoreThreshold};return ENGINE.runKernel(NonMaxSuppressionV3,{boxes:$boxes,scores:$scores},attrs)}var nonMaxSuppression=op({nonMaxSuppression_});function binaryInsert(arr,element,comparator){let index=binarySearch(arr,element,comparator),insertionPoint=index<0?-(index+1):index;arr.splice(insertionPoint,0,element)}function binarySearch(arr,target,comparator){return binarySearch_(arr,target,comparator||defaultComparator)}function defaultComparator(a,b){return a>b?1:a>>1);let compareResult=comparator(target,arr[middle]);compareResult>0?left=middle+1:(right=middle,found=!compareResult)}return found?left:-left-1}function nonMaxSuppressionV3Impl(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold){return nonMaxSuppressionImpl_(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,0)}function nonMaxSuppressionV4Impl(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize){return nonMaxSuppressionImpl_(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,0,!1,padToMaxOutputSize,!0)}function nonMaxSuppressionV5Impl(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma){return nonMaxSuppressionImpl_(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma,!0)}function nonMaxSuppressionImpl_(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma,returnScoresTensor=!1,padToMaxOutputSize=!1,returnValidOutputs=!1){let candidates=[];for(let i=0;iscoreThreshold&&candidates.push({score:scores[i],boxIndex:i,suppressBeginIndex:0});candidates.sort(ascendingComparator);let scale2=softNmsSigma>0?-.5/softNmsSigma:0,selectedIndices=[],selectedScores=[];for(;selectedIndices.length0;){let candidate=candidates.pop(),{score:originalScore,boxIndex,suppressBeginIndex}=candidate;if(originalScore=suppressBeginIndex;--j){let iou=intersectionOverUnion(boxes,boxIndex,selectedIndices[j]);if(iou>=iouThreshold){ignoreCandidate=!0;break}if(candidate.score=candidate.score*suppressWeight(iouThreshold,scale2,iou),candidate.score<=scoreThreshold)break}candidate.suppressBeginIndex=selectedIndices.length,ignoreCandidate||(candidate.score===originalScore?(selectedIndices.push(boxIndex),selectedScores.push(candidate.score)):candidate.score>scoreThreshold&&binaryInsert(candidates,candidate,ascendingComparator))}let validOutputs=selectedIndices.length,elemsToPad=maxOutputSize-validOutputs;padToMaxOutputSize&&elemsToPad>0&&(selectedIndices.push(...new Array(elemsToPad).fill(0)),selectedScores.push(...new Array(elemsToPad).fill(0)));let result={selectedIndices};return returnScoresTensor&&(result.selectedScores=selectedScores),returnValidOutputs&&(result.validOutputs=validOutputs),result}function intersectionOverUnion(boxes,i,j){let iCoord=boxes.subarray(i*4,i*4+4),jCoord=boxes.subarray(j*4,j*4+4),yminI=Math.min(iCoord[0],iCoord[2]),xminI=Math.min(iCoord[1],iCoord[3]),ymaxI=Math.max(iCoord[0],iCoord[2]),xmaxI=Math.max(iCoord[1],iCoord[3]),yminJ=Math.min(jCoord[0],jCoord[2]),xminJ=Math.min(jCoord[1],jCoord[3]),ymaxJ=Math.max(jCoord[0],jCoord[2]),xmaxJ=Math.max(jCoord[1],jCoord[3]),areaI=(ymaxI-yminI)*(xmaxI-xminI),areaJ=(ymaxJ-yminJ)*(xmaxJ-xminJ);if(areaI<=0||areaJ<=0)return 0;let intersectionYmin=Math.max(yminI,yminJ),intersectionXmin=Math.max(xminI,xminJ),intersectionYmax=Math.min(ymaxI,ymaxJ),intersectionXmax=Math.min(xmaxI,xmaxJ),intersectionArea=Math.max(intersectionYmax-intersectionYmin,0)*Math.max(intersectionXmax-intersectionXmin,0);return intersectionArea/(areaI+areaJ-intersectionArea)}function suppressWeight(iouThreshold,scale2,iou){let weight=Math.exp(scale2*iou*iou);return iou<=iouThreshold?weight:0}function ascendingComparator(c1,c2){return c1.score-c2.score||c1.score===c2.score&&c2.boxIndex-c1.boxIndex}async function nonMaxSuppressionAsync_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppressionAsync"),$scores=convertToTensor(scores,"scores","nonMaxSuppressionAsync"),inputs=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold);maxOutputSize=inputs.maxOutputSize,iouThreshold=inputs.iouThreshold,scoreThreshold=inputs.scoreThreshold;let boxesAndScores=await Promise.all([$boxes.data(),$scores.data()]),boxesVals=boxesAndScores[0],scoresVals=boxesAndScores[1],{selectedIndices}=nonMaxSuppressionV3Impl(boxesVals,scoresVals,maxOutputSize,iouThreshold,scoreThreshold);return $boxes!==boxes&&$boxes.dispose(),$scores!==scores&&$scores.dispose(),tensor1d(selectedIndices,"int32")}var nonMaxSuppressionAsync=nonMaxSuppressionAsync_;function nonMaxSuppressionWithScore_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY,softNmsSigma=0){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppression"),$scores=convertToTensor(scores,"scores","nonMaxSuppression"),params=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma);maxOutputSize=params.maxOutputSize,iouThreshold=params.iouThreshold,scoreThreshold=params.scoreThreshold,softNmsSigma=params.softNmsSigma;let inputs={boxes:$boxes,scores:$scores},attrs={maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma},result=ENGINE.runKernel(NonMaxSuppressionV5,inputs,attrs);return{selectedIndices:result[0],selectedScores:result[1]}}var nonMaxSuppressionWithScore=op({nonMaxSuppressionWithScore_});async function nonMaxSuppressionWithScoreAsync_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY,softNmsSigma=0){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppressionAsync"),$scores=convertToTensor(scores,"scores","nonMaxSuppressionAsync"),params=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma);maxOutputSize=params.maxOutputSize,iouThreshold=params.iouThreshold,scoreThreshold=params.scoreThreshold,softNmsSigma=params.softNmsSigma;let boxesAndScores=await Promise.all([$boxes.data(),$scores.data()]),boxesVals=boxesAndScores[0],scoresVals=boxesAndScores[1],{selectedIndices,selectedScores}=nonMaxSuppressionV5Impl(boxesVals,scoresVals,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma);return $boxes!==boxes&&$boxes.dispose(),$scores!==scores&&$scores.dispose(),{selectedIndices:tensor1d(selectedIndices,"int32"),selectedScores:tensor1d(selectedScores)}}var nonMaxSuppressionWithScoreAsync=nonMaxSuppressionWithScoreAsync_;function nonMaxSuppressionPadded_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY,padToMaxOutputSize=!1){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppression"),$scores=convertToTensor(scores,"scores","nonMaxSuppression"),params=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold,null),$maxOutputSize=params.maxOutputSize,$iouThreshold=params.iouThreshold,$scoreThreshold=params.scoreThreshold,inputs={boxes:$boxes,scores:$scores},attrs={maxOutputSize:$maxOutputSize,iouThreshold:$iouThreshold,scoreThreshold:$scoreThreshold,padToMaxOutputSize},result=ENGINE.runKernel(NonMaxSuppressionV4,inputs,attrs);return{selectedIndices:result[0],validOutputs:result[1]}}var nonMaxSuppressionPadded=op({nonMaxSuppressionPadded_});async function nonMaxSuppressionPaddedAsync_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY,padToMaxOutputSize=!1){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppressionAsync"),$scores=convertToTensor(scores,"scores","nonMaxSuppressionAsync"),params=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold,null),$maxOutputSize=params.maxOutputSize,$iouThreshold=params.iouThreshold,$scoreThreshold=params.scoreThreshold,[boxesVals,scoresVals]=await Promise.all([$boxes.data(),$scores.data()]),{selectedIndices,validOutputs}=nonMaxSuppressionV4Impl(boxesVals,scoresVals,$maxOutputSize,$iouThreshold,$scoreThreshold,padToMaxOutputSize);return $boxes!==boxes&&$boxes.dispose(),$scores!==scores&&$scores.dispose(),{selectedIndices:tensor1d(selectedIndices,"int32"),validOutputs:scalar(validOutputs,"int32")}}var nonMaxSuppressionPaddedAsync=nonMaxSuppressionPaddedAsync_;function resizeBilinear_(images,size,alignCorners=!1,halfPixelCenters=!1){let $images=convertToTensor(images,"images","resizeBilinear");assert($images.rank===3||$images.rank===4,()=>`Error in resizeBilinear: x must be rank 3 or 4, but got rank ${$images.rank}.`),assert(size.length===2,()=>`Error in resizeBilinear: new shape must 2D, but got shape ${size}.`),assert(halfPixelCenters===!1||alignCorners===!1,()=>"Error in resizeBilinear: If halfPixelCenters is true, alignCorners must be false.");let batchImages=$images,reshapedTo4D=!1;$images.rank===3&&(reshapedTo4D=!0,batchImages=reshape($images,[1,$images.shape[0],$images.shape[1],$images.shape[2]]));let[]=size,inputs={images:batchImages},attrs={alignCorners,halfPixelCenters,size},res=ENGINE.runKernel(ResizeBilinear,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var resizeBilinear=op({resizeBilinear_});function resizeNearestNeighbor_(images,size,alignCorners=!1,halfPixelCenters=!1){let $images=convertToTensor(images,"images","resizeNearestNeighbor");assert($images.rank===3||$images.rank===4,()=>`Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${$images.rank}.`),assert(size.length===2,()=>`Error in resizeNearestNeighbor: new shape must 2D, but got shape ${size}.`),assert($images.dtype==="float32"||$images.dtype==="int32",()=>"`images` must have `int32` or `float32` as dtype"),assert(halfPixelCenters===!1||alignCorners===!1,()=>"Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false.");let batchImages=$images,reshapedTo4D=!1;$images.rank===3&&(reshapedTo4D=!0,batchImages=reshape($images,[1,$images.shape[0],$images.shape[1],$images.shape[2]]));let[]=size,inputs={images:batchImages},attrs={alignCorners,halfPixelCenters,size},res=ENGINE.runKernel(ResizeNearestNeighbor,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var resizeNearestNeighbor=op({resizeNearestNeighbor_});function bandPart_(a,numLower,numUpper){assert(numLower%1==0,()=>`bandPart(): numLower must be an integer, got ${numLower}.`),assert(numUpper%1==0,()=>`bandPart(): numUpper must be an integer, got ${numUpper}.`);let $a=convertToTensor(a,"a","bandPart");assert($a.rank>=2,()=>`bandPart(): Rank must be at least 2, got ${$a.rank}.`);let shape=$a.shape,[M,N]=$a.shape.slice(-2);if(!(numLower<=M))throw new Error(`bandPart(): numLower (${numLower}) must not be greater than the number of rows (${M}).`);if(!(numUpper<=N))throw new Error(`bandPart(): numUpper (${numUpper}) must not be greater than the number of columns (${N}).`);numLower<0&&(numLower=M),numUpper<0&&(numUpper=N);let i=reshape(range(0,M,1,"int32"),[-1,1]),j=range(0,N,1,"int32"),ij=sub(i,j),inBand=logicalAnd(lessEqual(ij,scalar(+numLower,"int32")),greaterEqual(ij,scalar(-numUpper,"int32"))),zero=zeros([M,N],$a.dtype);return reshape(stack(unstack(reshape($a,[-1,M,N])).map(mat=>where(inBand,mat,zero))),shape)}var bandPart=op({bandPart_});function gramSchmidt_(xs){let inputIsTensor2D;if(Array.isArray(xs)){inputIsTensor2D=!1,assert(xs!=null&&xs.length>0,()=>"Gram-Schmidt process: input must not be null, undefined, or empty");let dim=xs[0].shape[0];for(let i=1;i`Gram-Schmidt: Non-unique lengths found in the input vectors: (${xs[i].shape[0]} vs. ${dim})`)}else inputIsTensor2D=!0,xs=split(xs,xs.shape[0],0).map(x=>squeeze(x,[0]));assert(xs.length<=xs[0].shape[0],()=>`Gram-Schmidt: Number of vectors (${xs.length}) exceeds number of dimensions (${xs[0].shape[0]}).`);let ys=[],xs1d=xs;for(let i=0;i{let x=xs1d[i];if(i>0)for(let j=0;j=2,()=>`qr() requires input tensor to have a rank >= 2, but got rank ${x.rank}`),x.rank===2)return qr2d(x,fullMatrices);{let outerDimsProd=x.shape.slice(0,x.shape.length-2).reduce((value,prev)=>value*prev),x2ds=unstack(reshape(x,[outerDimsProd,x.shape[x.shape.length-2],x.shape[x.shape.length-1]]),0),q2ds=[],r2ds=[];x2ds.forEach(x2d=>{let[q2d,r2d]=qr2d(x2d,fullMatrices);q2ds.push(q2d),r2ds.push(r2d)});let q=reshape(stack(q2ds,0),x.shape),r=reshape(stack(r2ds,0),x.shape);return[q,r]}}function qr2d(x,fullMatrices=!1){return ENGINE.tidy(()=>{assert(x.shape.length===2,()=>`qr2d() requires a 2D Tensor, but got a ${x.shape.length}D Tensor.`);let m=x.shape[0],n=x.shape[1],q=eye(m),r=clone(x),one2D=tensor2d([[1]],[1,1]),w=clone(one2D),iters=m>=n?n:m;for(let j=0;j{let rjEnd1=slice(r,[j,j],[m-j,1]),normX=norm(rjEnd1),rjj=slice(r,[j,j],[1,1]),s=where(greater(rjj,0),tensor2d([[-1]]),tensor2d([[1]])),u1=sub(rjj,mul(s,normX)),wPre=div(rjEnd1,u1);wPre.shape[0]===1?w=clone(one2D):w=concat([one2D,slice(wPre,[1,0],[wPre.shape[0]-1,wPre.shape[1]])],0);let tau=neg(div(matMul(s,u1),normX)),rjEndAll=slice(r,[j,0],[m-j,n]),tauTimesW=mul(tau,w),wT=transpose(w);if(j===0)r=sub(rjEndAll,matMul(tauTimesW,matMul(wT,rjEndAll)));else{let rTimesTau=sub(rjEndAll,matMul(tauTimesW,matMul(wT,rjEndAll)));r=concat([slice(r,[0,0],[j,n]),rTimesTau],0)}let tawTimesWT=transpose(tauTimesW),qAllJEnd=slice(q,[0,j],[m,q.shape[1]-j]);if(j===0)q=sub(qAllJEnd,matMul(matMul(qAllJEnd,w),tawTimesWT));else{let qTimesTau=sub(qAllJEnd,matMul(matMul(qAllJEnd,w),tawTimesWT));q=concat([slice(q,[0,0],[m,j]),qTimesTau],1)}return[w,r,q]}),dispose([rTemp,wTemp,qTemp])}return!fullMatrices&&m>n&&(q=slice(q,[0,0],[m,n]),r=slice(r,[0,0],[n,n])),[q,r]})}var qr=op({qr_}),Reduction;(function(Reduction2){Reduction2[Reduction2.NONE=0]="NONE",Reduction2[Reduction2.MEAN=1]="MEAN",Reduction2[Reduction2.SUM=2]="SUM",Reduction2[Reduction2.SUM_BY_NONZERO_WEIGHTS=3]="SUM_BY_NONZERO_WEIGHTS"})(Reduction||(Reduction={}));function computeWeightedLoss_(losses4,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $losses=convertToTensor(losses4,"losses","computeWeightedLoss"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","computeWeightedLoss"));let weightedLoss=$weights==null?$losses:mul($losses,$weights);if(reduction2===Reduction.NONE)return weightedLoss;if(reduction2===Reduction.SUM)return sum2(weightedLoss);if(reduction2===Reduction.MEAN){if($weights==null)return mean(weightedLoss);{let broadcastFactor=$losses.size/$weights.size,result=div(sum2(weightedLoss),sum2($weights));return broadcastFactor>1?div(result,scalar(broadcastFactor)):result}}if(reduction2===Reduction.SUM_BY_NONZERO_WEIGHTS){if($weights==null)return div(sum2(weightedLoss),scalar($losses.size));{let broadcastedWeights=mul($weights,ones2($losses.shape)),numNonZeros=cast(sum2(notEqual(broadcastedWeights,scalar(0))),"float32");return div(sum2(weightedLoss),numNonZeros)}}throw Error(`Unknown reduction: ${reduction2}`)}var computeWeightedLoss=op({computeWeightedLoss_});function absoluteDifference_(labels,predictions,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","absoluteDifference"),$predictions=convertToTensor(predictions,"predictions","absoluteDifference"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","absoluteDifference")),assertShapesMatch($labels.shape,$predictions.shape,"Error in absoluteDifference: ");let losses4=abs(sub($labels,$predictions));return computeWeightedLoss(losses4,$weights,reduction2)}var absoluteDifference=op({absoluteDifference_});function cosineDistance_(labels,predictions,axis,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","cosineDistance"),$predictions=convertToTensor(predictions,"predictions","cosineDistance"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","cosineDistance")),assertShapesMatch($labels.shape,$predictions.shape,"Error in cosineDistance: ");let one=scalar(1),losses4=sub(one,sum2(mul($labels,$predictions),axis,!0));return computeWeightedLoss(losses4,$weights,reduction2)}var cosineDistance=op({cosineDistance_});function hingeLoss_(labels,predictions,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","hingeLoss"),$predictions=convertToTensor(predictions,"predictions","hingeLoss"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","hingeLoss")),assertShapesMatch($labels.shape,$predictions.shape,"Error in hingeLoss: ");let one=scalar(1);$labels=sub(mul(scalar(2),$labels),one);let losses4=relu(sub(one,mul($labels,$predictions)));return computeWeightedLoss(losses4,$weights,reduction2)}var hingeLoss=op({hingeLoss_});function huberLoss_(labels,predictions,weights,delta=1,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","huberLoss"),$predictions=convertToTensor(predictions,"predictions","huberLoss"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","huberLoss")),assertShapesMatch($labels.shape,$predictions.shape,"Error in huberLoss: ");let deltaScalar=scalar(delta),error=abs(sub($predictions,$labels)),quadratic=minimum(error,deltaScalar),linear=sub(error,quadratic),losses4=add2(mul(scalar(.5),square(quadratic)),mul(deltaScalar,linear));return computeWeightedLoss(losses4,$weights,reduction2)}var huberLoss=op({huberLoss_});function logLoss_(labels,predictions,weights,epsilon3=1e-7,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","logLoss"),$predictions=convertToTensor(predictions,"predictions","logLoss"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","logLoss")),assertShapesMatch($labels.shape,$predictions.shape,"Error in logLoss: ");let one=scalar(1),epsilonScalar=scalar(epsilon3),l13=neg(mul($labels,log2(add2($predictions,epsilonScalar)))),l23=mul(sub(one,$labels),log2(add2(sub(one,$predictions),epsilonScalar))),losses4=sub(l13,l23);return computeWeightedLoss(losses4,$weights,reduction2)}var logLoss=op({logLoss_});function meanSquaredError_(labels,predictions,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","meanSquaredError"),$predictions=convertToTensor(predictions,"predictions","meanSquaredError"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","meanSquaredError")),assertShapesMatch($labels.shape,$predictions.shape,"Error in meanSquaredError: ");let losses4=squaredDifference($labels,$predictions);return computeWeightedLoss(losses4,$weights,reduction2)}var meanSquaredError=op({meanSquaredError_});function sigmoidCrossEntropyWithLogits_(labels,logits){let $labels=convertToTensor(labels,"labels","sigmoidCrossEntropyWithLogits"),$logits=convertToTensor(logits,"logits","sigmoidCrossEntropyWithLogits");assertShapesMatch($labels.shape,$logits.shape,"Error in sigmoidCrossEntropyWithLogits: ");let maxOutput=relu($logits),outputXTarget=mul($logits,$labels),sigmoidOutput=log1p(exp(neg(abs($logits))));return add2(sub(maxOutput,outputXTarget),sigmoidOutput)}function sigmoidCrossEntropy_(multiClassLabels,logits,weights,labelSmoothing=0,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $multiClassLabels=convertToTensor(multiClassLabels,"multiClassLabels","sigmoidCrossEntropy"),$logits=convertToTensor(logits,"logits","sigmoidCrossEntropy"),$weights=null;if(weights!=null&&($weights=convertToTensor(weights,"weights","sigmoidCrossEntropy")),assertShapesMatch($multiClassLabels.shape,$logits.shape,"Error in sigmoidCrossEntropy: "),labelSmoothing>0){let labelSmoothingScalar=scalar(labelSmoothing),one=scalar(1),half=scalar(.5);$multiClassLabels=add2(mul($multiClassLabels,sub(one,labelSmoothingScalar)),mul(half,labelSmoothingScalar))}let losses4=sigmoidCrossEntropyWithLogits_($multiClassLabels,$logits);return computeWeightedLoss(losses4,$weights,reduction2)}var sigmoidCrossEntropy=op({sigmoidCrossEntropy_});function softmaxCrossEntropyWithLogits_(labels,logits,dim=-1){if(dim===-1&&(dim=logits.rank-1),dim!==logits.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${logits.rank} and dim was ${dim}`);return customGrad((labels2,logits2,save)=>{let lse=logSumExp(logits2,[dim],!0),logResult=sub(cast(logits2,"float32"),lse);save([labels2,logResult]);let costVector=neg(mul(logResult,labels2));return{value:sum2(costVector,[dim]),gradFunc:(dy,saved)=>{let[labels3,logResult2]=saved,dyShape=expandShapeToKeepDim(dy.shape,[dim]);return[mul(reshape(dy,dyShape),sub(cast(labels3,"float32"),exp(logResult2))),mul(reshape(dy,dyShape),sub(exp(logResult2),cast(labels3,"float32")))]}}})(labels,logits)}function softmaxCrossEntropy_(onehotLabels,logits,weights,labelSmoothing=0,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $onehotLabels=convertToTensor(onehotLabels,"onehotLabels","softmaxCrossEntropy"),$logits=convertToTensor(logits,"logits","softmaxCrossEntropy"),$weights=null;if(weights!=null&&($weights=convertToTensor(weights,"weights","softmaxCrossEntropy")),assertShapesMatch($onehotLabels.shape,$logits.shape,"Error in softmaxCrossEntropy: "),labelSmoothing>0){let labelSmoothingScalar=scalar(labelSmoothing),one=scalar(1),numClasses=scalar($onehotLabels.shape[1]);$onehotLabels=add2(mul($onehotLabels,sub(one,labelSmoothingScalar)),div(labelSmoothingScalar,numClasses))}let losses4=softmaxCrossEntropyWithLogits_($onehotLabels,$logits);return computeWeightedLoss(losses4,$weights,reduction2)}var softmaxCrossEntropy=op({softmaxCrossEntropy_}),spectral={fft,ifft,rfft,irfft},signal={hammingWindow,hannWindow,frame,stft},image={flipLeftRight,resizeNearestNeighbor,resizeBilinear,rotateWithOffset,cropAndResize,nonMaxSuppression,nonMaxSuppressionAsync,nonMaxSuppressionWithScore,nonMaxSuppressionWithScoreAsync,nonMaxSuppressionPadded,nonMaxSuppressionPaddedAsync},linalg={bandPart,gramSchmidt,qr},losses={absoluteDifference,computeWeightedLoss,cosineDistance,hingeLoss,huberLoss,logLoss,meanSquaredError,sigmoidCrossEntropy,softmaxCrossEntropy},Optimizer=class extends Serializable{minimize(f,returnCost=!1,varList){let{value,grads:grads2}=this.computeGradients(f,varList);if(varList!=null){let gradArray=varList.map(v=>({name:v.name,tensor:grads2[v.name]}));this.applyGradients(gradArray)}else this.applyGradients(grads2);return dispose(grads2),returnCost?value:(value.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(f,varList){return variableGrads(f,varList)}dispose(){this.iterations_!=null&&dispose(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:scalar(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(weightValues){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(weightValues){return this.iterations_=(await weightValues[0].tensor.data())[0],weightValues.slice(1)}};Object.defineProperty(Optimizer,Symbol.hasInstance,{value:instance=>instance.minimize!=null&&instance.computeGradients!=null&&instance.applyGradients!=null});var AdadeltaOptimizer=class extends Optimizer{constructor(learningRate,rho,epsilon3=null){super();this.learningRate=learningRate,this.rho=rho,this.epsilon=epsilon3,this.accumulatedGrads=[],this.accumulatedUpdates=[],epsilon3==null&&(this.epsilon=ENGINE.backend.epsilon())}applyGradients(variableGradients){(Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients)).forEach((name,i)=>{let value=ENGINE.registeredVariables[name],trainable=!1;this.accumulatedGrads[i]==null&&(this.accumulatedGrads[i]={originalName:`${name}/accum_grad`,variable:tidy(()=>zerosLike(value).variable(trainable))}),this.accumulatedUpdates[i]==null&&(this.accumulatedUpdates[i]={originalName:`${name}/accum_var`,variable:tidy(()=>zerosLike(value).variable(trainable))});let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;let accumulatedGrad=this.accumulatedGrads[i].variable,accumulatedUpdate=this.accumulatedUpdates[i].variable;tidy(()=>{let newAccumulatedGrad=add2(mul(accumulatedGrad,this.rho),mul(square(gradient),1-this.rho)),updates=mul(div(sqrt(add2(accumulatedUpdate,this.epsilon)),sqrt(add2(accumulatedGrad,this.epsilon))),gradient),newAccumulatedUpdate=add2(mul(accumulatedUpdate,this.rho),mul(square(updates),1-this.rho));accumulatedGrad.assign(newAccumulatedGrad),accumulatedUpdate.assign(newAccumulatedUpdate);let newValue=add2(mul(updates,-this.learningRate),value);value.assign(newValue)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(dispose(this.accumulatedGrads.map(v=>v.variable)),dispose(this.accumulatedUpdates.map(v=>v.variable)))}async getWeights(){let variables=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(variables.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues);let variableCount=weightValues.length/2,trainable=!1;this.accumulatedGrads=weightValues.slice(0,variableCount).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})),this.accumulatedUpdates=weightValues.slice(variableCount,variableCount*2).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(cls,config2){return new cls(config2.learningRate,config2.rho,config2.epsilon)}};AdadeltaOptimizer.className="Adadelta";registerClass(AdadeltaOptimizer);var AdagradOptimizer=class extends Optimizer{constructor(learningRate,initialAccumulatorValue=.1){super();this.learningRate=learningRate,this.initialAccumulatorValue=initialAccumulatorValue,this.accumulatedGrads=[]}applyGradients(variableGradients){(Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients)).forEach((name,i)=>{let value=ENGINE.registeredVariables[name];if(this.accumulatedGrads[i]==null){let trainable=!1;this.accumulatedGrads[i]={originalName:`${name}/accumulator`,variable:tidy(()=>fill(value.shape,this.initialAccumulatorValue).variable(trainable))}}let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;let accumulatedGrad=this.accumulatedGrads[i].variable;tidy(()=>{let newAccumulatedGrad=add2(accumulatedGrad,square(gradient));accumulatedGrad.assign(newAccumulatedGrad);let newValue=add2(mul(div(gradient,sqrt(add2(newAccumulatedGrad,ENGINE.backend.epsilon()))),-this.learningRate),value);value.assign(newValue)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&dispose(this.accumulatedGrads.map(v=>v.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues);let trainable=!1;this.accumulatedGrads=weightValues.map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(cls,config2){return new cls(config2.learningRate,config2.initialAccumulatorValue)}};AdagradOptimizer.className="Adagrad";registerClass(AdagradOptimizer);var AdamOptimizer=class extends Optimizer{constructor(learningRate,beta1,beta2,epsilon3=null){super();this.learningRate=learningRate,this.beta1=beta1,this.beta2=beta2,this.epsilon=epsilon3,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],tidy(()=>{this.accBeta1=scalar(beta1).variable(),this.accBeta2=scalar(beta2).variable()}),epsilon3==null&&(this.epsilon=ENGINE.backend.epsilon())}applyGradients(variableGradients){let varNames=Array.isArray(variableGradients)?variableGradients.map(v=>v.name):Object.keys(variableGradients);tidy(()=>{let oneMinusAccBeta1=sub(1,this.accBeta1),oneMinusAccBeta2=sub(1,this.accBeta2);varNames.forEach((name,i)=>{let value=ENGINE.registeredVariables[name],trainable=!1;this.accumulatedFirstMoment[i]==null&&(this.accumulatedFirstMoment[i]={originalName:`${name}/m`,variable:tidy(()=>zerosLike(value).variable(trainable))}),this.accumulatedSecondMoment[i]==null&&(this.accumulatedSecondMoment[i]={originalName:`${name}/v`,variable:tidy(()=>zerosLike(value).variable(trainable))});let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;let firstMoment=this.accumulatedFirstMoment[i].variable,secondMoment=this.accumulatedSecondMoment[i].variable,newFirstMoment=add2(mul(firstMoment,this.beta1),mul(gradient,1-this.beta1)),newSecondMoment=add2(mul(secondMoment,this.beta2),mul(square(gradient),1-this.beta2)),biasCorrectedFirstMoment=div(newFirstMoment,oneMinusAccBeta1),biasCorrectedSecondMoment=div(newSecondMoment,oneMinusAccBeta2);firstMoment.assign(newFirstMoment),secondMoment.assign(newSecondMoment);let newValue=add2(mul(div(biasCorrectedFirstMoment,add2(sqrt(biasCorrectedSecondMoment),this.epsilon)),-this.learningRate),value);value.assign(newValue)}),this.accBeta1.assign(mul(this.accBeta1,this.beta1)),this.accBeta2.assign(mul(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&dispose(this.accumulatedFirstMoment.map(v=>v.variable)),this.accumulatedSecondMoment!=null&&dispose(this.accumulatedSecondMoment.map(v=>v.variable))}async getWeights(){let variables=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(variables.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues),tidy(()=>{this.accBeta1.assign(pow(this.beta1,this.iterations_+1)),this.accBeta2.assign(pow(this.beta2,this.iterations_+1))});let variableCount=weightValues.length/2,trainable=!1;this.accumulatedFirstMoment=weightValues.slice(0,variableCount).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})),this.accumulatedSecondMoment=weightValues.slice(variableCount,variableCount*2).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(cls,config2){return new cls(config2.learningRate,config2.beta1,config2.beta2,config2.epsilon)}};AdamOptimizer.className="Adam";registerClass(AdamOptimizer);var AdamaxOptimizer=class extends Optimizer{constructor(learningRate,beta1,beta2,epsilon3=null,decay=0){super();this.learningRate=learningRate,this.beta1=beta1,this.beta2=beta2,this.epsilon=epsilon3,this.decay=decay,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],tidy(()=>{this.iteration=scalar(0).variable(),this.accBeta1=scalar(beta1).variable()}),epsilon3==null&&(this.epsilon=ENGINE.backend.epsilon())}applyGradients(variableGradients){let variableNames=Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients);tidy(()=>{let oneMinusAccBeta1=sub(1,this.accBeta1),lr=div(-this.learningRate,add2(mul(this.iteration,this.decay),1));variableNames.forEach((name,i)=>{let value=ENGINE.registeredVariables[name],trainable=!1;this.accumulatedFirstMoment[i]==null&&(this.accumulatedFirstMoment[i]={originalName:`${name}/m`,variable:zerosLike(value).variable(trainable)}),this.accumulatedWeightedInfNorm[i]==null&&(this.accumulatedWeightedInfNorm[i]={originalName:`${name}/v`,variable:zerosLike(value).variable(trainable)});let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;let firstMoment=this.accumulatedFirstMoment[i].variable,weightedInfNorm=this.accumulatedWeightedInfNorm[i].variable,newFirstMoment=add2(mul(firstMoment,this.beta1),mul(gradient,1-this.beta1)),ut0=mul(weightedInfNorm,this.beta2),ut1=abs(gradient),newWeightedInfNorm=maximum(ut0,ut1);firstMoment.assign(newFirstMoment),weightedInfNorm.assign(newWeightedInfNorm);let newValue=add2(mul(div(lr,oneMinusAccBeta1),div(newFirstMoment,add2(newWeightedInfNorm,this.epsilon))),value);value.assign(newValue)}),this.iteration.assign(add2(this.iteration,1)),this.accBeta1.assign(mul(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&dispose(this.accumulatedFirstMoment.map(v=>v.variable)),this.accumulatedWeightedInfNorm!=null&&dispose(this.accumulatedWeightedInfNorm.map(v=>v.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(weightValues){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(cls,config2){return new cls(config2.learningRate,config2.beta1,config2.beta2,config2.epsilon,config2.decay)}};AdamaxOptimizer.className="Adamax";registerClass(AdamaxOptimizer);var SGDOptimizer=class extends Optimizer{constructor(learningRate){super();this.learningRate=learningRate,this.setLearningRate(learningRate)}applyGradients(variableGradients){(Array.isArray(variableGradients)?variableGradients.map(v=>v.name):Object.keys(variableGradients)).forEach((name,i)=>{let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;let value=ENGINE.registeredVariables[name];tidy(()=>{let newValue=add2(mul(this.c,gradient),value);value.assign(newValue)})}),this.incrementIterations()}setLearningRate(learningRate){this.learningRate=learningRate,this.c!=null&&this.c.dispose(),this.c=keep(scalar(-learningRate))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(weightValues){if(weightValues=await this.extractIterations(weightValues),weightValues.length!==0)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(cls,config2){return new cls(config2.learningRate)}};SGDOptimizer.className="SGD";registerClass(SGDOptimizer);var MomentumOptimizer=class extends SGDOptimizer{constructor(learningRate,momentum,useNesterov=!1){super(learningRate);this.learningRate=learningRate,this.momentum=momentum,this.useNesterov=useNesterov,this.accumulations=[],this.m=scalar(this.momentum)}applyGradients(variableGradients){(Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients)).forEach((name,i)=>{let value=ENGINE.registeredVariables[name];if(this.accumulations[i]==null){let trainable=!1;this.accumulations[i]={originalName:`${name}/momentum`,variable:tidy(()=>zerosLike(value).variable(trainable))}}let accumulation=this.accumulations[i].variable,gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];gradient!=null&&tidy(()=>{let newValue,newAccumulation=add2(mul(this.m,accumulation),gradient);this.useNesterov?newValue=add2(mul(this.c,add2(gradient,mul(newAccumulation,this.m))),value):newValue=add2(mul(this.c,newAccumulation),value),accumulation.assign(newAccumulation),value.assign(newValue)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&dispose(this.accumulations.map(v=>v.variable))}setMomentum(momentum){this.momentum=momentum}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues);let trainable=!1;this.accumulations=weightValues.map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(cls,config2){return new cls(config2.learningRate,config2.momentum,config2.useNesterov)}};MomentumOptimizer.className="Momentum";registerClass(MomentumOptimizer);var RMSPropOptimizer=class extends Optimizer{constructor(learningRate,decay=.9,momentum=0,epsilon3=null,centered=!1){super();if(this.learningRate=learningRate,this.decay=decay,this.momentum=momentum,this.epsilon=epsilon3,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=centered,epsilon3==null&&(this.epsilon=ENGINE.backend.epsilon()),learningRate==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(variableGradients){(Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients)).forEach((name,i)=>{let value=ENGINE.registeredVariables[name],trainable=!1;this.accumulatedMeanSquares[i]==null&&(this.accumulatedMeanSquares[i]={originalName:`${name}/rms`,variable:tidy(()=>zerosLike(value).variable(trainable))}),this.accumulatedMoments[i]==null&&(this.accumulatedMoments[i]={originalName:`${name}/momentum`,variable:tidy(()=>zerosLike(value).variable(trainable))}),this.accumulatedMeanGrads[i]==null&&this.centered&&(this.accumulatedMeanGrads[i]={originalName:`${name}/mg`,variable:tidy(()=>zerosLike(value).variable(trainable))});let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;let accumulatedMeanSquare=this.accumulatedMeanSquares[i].variable,accumulatedMoments=this.accumulatedMoments[i].variable;tidy(()=>{let newAccumulatedMeanSquare=add2(mul(accumulatedMeanSquare,this.decay),mul(square(gradient),1-this.decay));if(this.centered){let accumulatedMeanGrad=this.accumulatedMeanGrads[i].variable,newAccumulatedMeanGrad=add2(mul(accumulatedMeanGrad,this.decay),mul(gradient,1-this.decay)),gradContribution=div(mul(gradient,this.learningRate),sqrt(sub(newAccumulatedMeanSquare,add2(square(newAccumulatedMeanGrad),this.epsilon)))),newAccumulatedMoments=add2(mul(accumulatedMoments,this.momentum),gradContribution);accumulatedMeanSquare.assign(newAccumulatedMeanSquare),accumulatedMeanGrad.assign(newAccumulatedMeanGrad),accumulatedMoments.assign(newAccumulatedMoments);let newValue=sub(value,newAccumulatedMoments);value.assign(newValue)}else{let newAccumulatedMeanSquare2=add2(mul(accumulatedMeanSquare,this.decay),mul(square(gradient),1-this.decay)),newAccumulatedMoments=add2(mul(accumulatedMoments,this.momentum),div(mul(gradient,this.learningRate),sqrt(add2(newAccumulatedMeanSquare2,this.epsilon))));accumulatedMeanSquare.assign(newAccumulatedMeanSquare2),accumulatedMoments.assign(newAccumulatedMoments);let newValue=sub(value,newAccumulatedMoments);value.assign(newValue)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&dispose(this.accumulatedMeanSquares.map(v=>v.variable)),this.accumulatedMeanGrads!=null&&this.centered&&dispose(this.accumulatedMeanGrads.map(v=>v.variable)),this.accumulatedMoments!=null&&dispose(this.accumulatedMoments.map(v=>v.variable))}async getWeights(){let variables=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&variables.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(variables.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues);let variableCount=this.centered?weightValues.length/3:weightValues.length/2,trainable=!1;this.accumulatedMeanSquares=weightValues.slice(0,variableCount).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})),this.accumulatedMoments=weightValues.slice(variableCount,variableCount*2).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})),this.centered&&(this.accumulatedMeanGrads=weightValues.slice(variableCount*2,variableCount*3).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(cls,config2){return new cls(config2.learningRate,config2.decay,config2.momentum,config2.epsilon,config2.centered)}};RMSPropOptimizer.className="RMSProp";registerClass(RMSPropOptimizer);var OptimizerConstructors=class{static sgd(learningRate){return new SGDOptimizer(learningRate)}static momentum(learningRate,momentum,useNesterov=!1){return new MomentumOptimizer(learningRate,momentum,useNesterov)}static rmsprop(learningRate,decay=.9,momentum=0,epsilon3=null,centered=!1){return new RMSPropOptimizer(learningRate,decay,momentum,epsilon3,centered)}static adam(learningRate=.001,beta1=.9,beta2=.999,epsilon3=null){return new AdamOptimizer(learningRate,beta1,beta2,epsilon3)}static adadelta(learningRate=.001,rho=.95,epsilon3=null){return new AdadeltaOptimizer(learningRate,rho,epsilon3)}static adamax(learningRate=.002,beta1=.9,beta2=.999,epsilon3=null,decay=0){return new AdamaxOptimizer(learningRate,beta1,beta2,epsilon3,decay)}static adagrad(learningRate,initialAccumulatorValue=.1){return new AdagradOptimizer(learningRate,initialAccumulatorValue)}},train={sgd:OptimizerConstructors.sgd,momentum:OptimizerConstructors.momentum,adadelta:OptimizerConstructors.adadelta,adagrad:OptimizerConstructors.adagrad,rmsprop:OptimizerConstructors.rmsprop,adamax:OptimizerConstructors.adamax,adam:OptimizerConstructors.adam},delayCallback=(()=>typeof requestAnimationFrame!="undefined"?requestAnimationFrame:typeof setImmediate!="undefined"?setImmediate:f=>f())();function nextFrame(){return new Promise(resolve=>delayCallback(()=>resolve()))}var backend_util_exports={};__export2(backend_util_exports,{ERF_A1:()=>ERF_A1,ERF_A2:()=>ERF_A2,ERF_A3:()=>ERF_A3,ERF_A4:()=>ERF_A4,ERF_A5:()=>ERF_A5,ERF_P:()=>ERF_P,PARALLELIZE_THRESHOLD:()=>PARALLELIZE_THRESHOLD,SELU_SCALE:()=>SELU_SCALE,SELU_SCALEALPHA:()=>SELU_SCALEALPHA,applyActivation:()=>applyActivation,assertAndGetBroadcastShape:()=>assertAndGetBroadcastShape,assertAxesAreInnerMostDims:()=>assertAxesAreInnerMostDims,assertParamsConsistent:()=>assertParamsConsistent,assignToTypedArray:()=>assignToTypedArray,axesAreInnerMostDims:()=>axesAreInnerMostDims,calculateShapes:()=>calculateShapes,castTensor:()=>castTensor,combineLocations:()=>combineLocations,complexWithEvenIndex:()=>complexWithEvenIndex,complexWithOddIndex:()=>complexWithOddIndex,computeConv2DInfo:()=>computeConv2DInfo,computeConv3DInfo:()=>computeConv3DInfo,computeDefaultPad:()=>computeDefaultPad,computeDilation2DInfo:()=>computeDilation2DInfo,computeOptimalWindowSize:()=>computeOptimalWindowSize,computeOutAndReduceShapes:()=>computeOutAndReduceShapes,computeOutShape:()=>computeOutShape2,computePool2DInfo:()=>computePool2DInfo,computePool3DInfo:()=>computePool3DInfo,convertConv2DDataFormat:()=>convertConv2DDataFormat,eitherStridesOrDilationsAreOne:()=>eitherStridesOrDilationsAreOne,expandShapeToKeepDim:()=>expandShapeToKeepDim,exponent:()=>exponent,exponents:()=>exponents,fromStringArrayToUint8:()=>fromStringArrayToUint8,fromUint8ToStringArray:()=>fromUint8ToStringArray,getAxesPermutation:()=>getAxesPermutation,getBroadcastDims:()=>getBroadcastDims,getComplexWithIndex:()=>getComplexWithIndex,getFusedBiasGradient:()=>getFusedBiasGradient,getFusedDyActivation:()=>getFusedDyActivation,getImageCenter:()=>getImageCenter,getInnerMostAxes:()=>getInnerMostAxes,getPermuted:()=>getPermuted,getReductionAxes:()=>getReductionAxes,getReshaped:()=>getReshaped,getReshapedPermuted:()=>getReshapedPermuted,getSliceBeginCoords:()=>getSliceBeginCoords,getSliceSize:()=>getSliceSize,getUndoAxesPermutation:()=>getUndoAxesPermutation,log:()=>log22,mergeRealAndImagArrays:()=>mergeRealAndImagArrays,prepareAndValidate:()=>prepareAndValidate,prepareSplitSize:()=>prepareSplitSize,reshapeTensor:()=>reshapeTensor,segment_util:()=>segment_util_exports,shouldFuse:()=>shouldFuse,slice_util:()=>slice_util_exports,splitRealAndImagArrays:()=>splitRealAndImagArrays,tupleValuesAreOne:()=>tupleValuesAreOne,upcastType:()=>upcastType,validateInput:()=>validateInput,validateUpdateShape:()=>validateUpdateShape,warn:()=>warn});function assertParamsConsistent(shapes,axis){let rank=shapes[0].length;shapes.forEach((shape,i)=>{assert(shape.length===rank,()=>`Error in concat${rank}D: rank of tensors[${i}] must be the same as the rank of the rest (${rank})`)}),assert(axis>=0&&axis`Error in concat${rank}D: axis must be between 0 and ${rank-1}.`);let firstShape=shapes[0];shapes.forEach((shape,i)=>{for(let r=0;r`Error in concat${rank}D: Shape of tensors[${i}] (${shape}) does not match the shape of the rest (${firstShape}) along the non-concatenated axis ${i}.`)})}function computeOutShape2(shapes,axis){let outputShape=shapes[0].slice();for(let i=1;i=blockShapeRank*2+1||i%2==1?permutedAfterBatch.push(i):permutedBeforeBatch.push(i);permuted.push(...permutedBeforeBatch),permuted.push(0),permuted.push(...permutedAfterBatch)}return permuted}function getReshapedPermuted(inputShape,blockShape,prod5,batchToSpace=!0){let reshapedPermuted=[];batchToSpace?reshapedPermuted.push(inputShape[0]/prod5):reshapedPermuted.push(inputShape[0]*prod5);for(let i=1;i"Number of splits must evenly divide the axis."),splitSizes=new Array(numOrSizeSplits).fill(x.shape[axis]/numOrSizeSplits);else{let numOfNegs=numOrSizeSplits.reduce((count2,value)=>(value===-1&&(count2+=1),count2),0);assert(numOfNegs<=1,()=>"There should be only one negative value in split array.");let negIndex=numOrSizeSplits.indexOf(-1);if(negIndex!==-1){let total=numOrSizeSplits.reduce((a,b)=>b>0?a+b:a);numOrSizeSplits[negIndex]=x.shape[axis]-total}assert(x.shape[axis]===numOrSizeSplits.reduce((a,b)=>a+b),()=>"The sum of sizes must match the size of the axis dimension."),splitSizes=numOrSizeSplits}return splitSizes}var segment_util_exports={};__export2(segment_util_exports,{collectGatherOpShapeInfo:()=>collectGatherOpShapeInfo,computeOutShape:()=>computeOutShape3,segOpComputeOptimalWindowSize:()=>segOpComputeOptimalWindowSize});function segOpComputeOptimalWindowSize(inSize,numSegments){let done=!1,res;for(inSize<=PARALLELIZE_THRESHOLD?(res=inSize,done=!0):res=nearestDivisor(inSize,Math.floor(Math.sqrt(inSize)));!done;)res>numSegments||res===inSize?done=!0:res=nearestDivisor(inSize,res+1);return res}function computeOutShape3(aShape,axis,numSegments){let outShape=[],rank=aShape.length;for(let dim=0;dimindicesRank))throw new Error(`Expect batchDims in the range of [-${indicesRank}, ${indicesRank}], but got ${batchDims}`);if(batchDims<0&&(batchDims+=indicesRank),batchDims>xRank)throw new Error(`batchDims (${batchDims}) must be less than rank(x) ( ${xRank}).`);if(axisdecodeString(val))}catch(err){throw new Error(`Failed to decode encoded string bytes into utf-8, error: ${err}`)}}function fromStringArrayToUint8(strings){return strings.map(s=>encodeString(s))}var kernel_impls_exports={};__export2(kernel_impls_exports,{nonMaxSuppressionV3Impl:()=>nonMaxSuppressionV3Impl,nonMaxSuppressionV4Impl:()=>nonMaxSuppressionV4Impl,nonMaxSuppressionV5Impl:()=>nonMaxSuppressionV5Impl,whereImpl:()=>whereImpl});var absGradConfig={kernelName:Abs,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(dy,step(cast(x,"float32"),-1))}}},acosGradConfig={kernelName:Acos,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>{let a=square(cast(x,"float32")),b=sqrt(sub(scalar(1),a));return neg(div(dy,b))}}}},acoshGradConfig={kernelName:Acosh,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>{let a=sqrt(sub(square(cast(x,"float32")),1));return div(dy,a)}}}},addGradConfig={kernelName:Add,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,outShape=assertAndGetBroadcastShape(a.shape,b.shape);return{a:()=>{let res=dy,reduceAxes=getReductionAxes(a.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,a.shape)},b:()=>{let res=dy,reduceAxes=getReductionAxes(b.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,b.shape)}}}},addNGradConfig={kernelName:AddN,saveAllInputs:!0,gradFunc:(dy,saved)=>{let ders={};return saved.forEach((_,i)=>{ders[i]=()=>dy.clone()}),ders}},argMaxGradConfig={kernelName:ArgMax,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>zerosLike(x)}}},argMinGradConfig={kernelName:ArgMin,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>zerosLike(x)}}},asinGradConfig={kernelName:Asin,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div(dy,sqrt(sub(scalar(1),square(cast(x,"float32")))))}}},asinhGradConfig={kernelName:Asinh,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>{let a=sqrt(add2(scalar(1),square(cast(x,"float32"))));return div(dy,a)}}}},atan2GradConfig={kernelName:Atan2,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,outShape=assertAndGetBroadcastShape(a.shape,b.shape);return{a:()=>{let d=add2(square(a),square(b)),res=mul(dy,div(b,d)),reduceAxes=getReductionAxes(a.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,a.shape)},b:()=>{let d=add2(square(a),square(b)),res=neg(mul(dy,div(a,d))),reduceAxes=getReductionAxes(b.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,b.shape)}}}},atanGradConfig={kernelName:Atan,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div(dy,add2(square(cast(x,"float32")),1))}}},atanhGradConfig={kernelName:Atanh,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div(dy,sub(scalar(1),square(cast(x,"float32"))))}}};function avgPool3dGrad_(dy,input2,filterSize,strides,dilations=[1,1,1],pad3,dimRoundingMode){let $dy=convertToTensor(dy,"dy","avgPool3dGrad"),$input=convertToTensor(input2,"input","avgPool3dGrad"),dy5D=$dy,input5D=$input,reshapedTo5D=!1;$input.rank===4&&(reshapedTo5D=!0,dy5D=reshape($dy,[1,$dy.shape[0],$dy.shape[1],$dy.shape[2],$dy.shape[3]]),input5D=reshape($input,[1,$input.shape[0],$input.shape[1],$input.shape[2],$input.shape[3]])),assert(dy5D.rank===5,()=>`Error in avgPool3dGrad: dy must be rank 5 but got rank ${dy5D.rank}.`),assert(input5D.rank===5,()=>`Error in avgPool3dGrad: input must be rank 5 but got rank ${input5D.rank}.`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in avgPool3dGrad: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in avgPool3dGrad: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={dy:dy5D,input:input5D},attrs={filterSize,strides,dilations,pad:pad3,dimRoundingMode},res=ENGINE.runKernel(AvgPool3DGrad,inputs,attrs);return reshapedTo5D?reshape(res,[res.shape[1],res.shape[2],res.shape[3],res.shape[4]]):res}var avgPool3dGrad=op({avgPool3dGrad_}),avgPool3DGradConfig={kernelName:AvgPool3D,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{filterSize,strides,dilations,pad:pad3,dimRoundingMode}=attrs,$dilations=dilations==null?[1,1,1]:dilations;return{x:()=>avgPool3dGrad(dy,x,filterSize,strides,$dilations,pad3,dimRoundingMode)}}};function avgPoolGrad_(dy,input2,filterSize,strides,pad3){let $dy=convertToTensor(dy,"dy","avgPoolGrad"),$input=convertToTensor(input2,"input","avgPoolGrad");assert($input.rank===$dy.rank,()=>`Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`);let input4D=$input,dy4D=$dy,reshapedTo4D=!1;$input.rank===3&&(reshapedTo4D=!0,input4D=reshape($input,[1,$input.shape[0],$input.shape[1],$input.shape[2]]),dy4D=reshape($dy,[1,$dy.shape[0],$dy.shape[1],$dy.shape[2]])),assert(dy4D.rank===4,()=>`Error in avgPoolGrad: dy must be rank 4 but got rank ${dy4D.rank}.`),assert(input4D.rank===4,()=>`Error in avgPoolGrad: input must be rank 4 but got rank ${input4D.rank}.`);let inputs={dy:dy4D,input:input4D},attrs={filterSize,strides,pad:pad3},res=ENGINE.runKernel(AvgPoolGrad,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var avgPoolGrad=op({avgPoolGrad_}),avgPoolGradConfig={kernelName:AvgPool,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{filterSize,strides,pad:pad3}=attrs;return{x:()=>avgPoolGrad(dy,x,filterSize,strides,pad3)}}},batchMatMulGradConfig={kernelName:BatchMatMul,inputsToSave:["a","b"],gradFunc:(dy,saved,attrs)=>{let[a,b]=saved,{transposeA,transposeB}=attrs;return!transposeA&&!transposeB?{a:()=>matMul(dy,b,!1,!0),b:()=>matMul(a,dy,!0,!1)}:!transposeA&&transposeB?{a:()=>matMul(dy,b,!1,!1),b:()=>matMul(dy,a,!0,!1)}:transposeA&&!transposeB?{a:()=>matMul(b,dy,!1,!0),b:()=>matMul(a,dy,!1,!1)}:{a:()=>matMul(b,dy,!0,!0),b:()=>matMul(dy,a,!0,!0)}}},batchToSpaceNDGradConfig={kernelName:BatchToSpaceND,gradFunc:(dy,saved,attrs)=>{let{blockShape,crops}=attrs;return{x:()=>spaceToBatchND(dy,blockShape,crops)}}},broadcastToGradConfig={kernelName:BroadcastTo,gradFunc:(dy,saved,attrs)=>{let broadCastToAttrs=attrs,inputShape=broadCastToAttrs.inputShape,outputShape=broadCastToAttrs.shape,reps=Array.from(outputShape);for(let i=inputShape.length-1;i>=0;i--)if(inputShape[i]===outputShape[i])reps[i]=1;else if(inputShape[i]!==1)throw new Error(`broadcastTo(): [${inputShape}] cannot be broadcast to [${outputShape}].`);let axes=[];for(let i=0;i1&&axes.push(i);return{x:()=>sum2(dy,axes,!0)}}},castGradConfig={kernelName:Cast,gradFunc:dy=>({x:()=>dy.clone()})},ceilGradConfig={kernelName:Ceil,gradFunc:dy=>({x:()=>zerosLike(dy)})},clipByValueGradConfig={kernelName:ClipByValue,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{clipValueMin,clipValueMax}=attrs;return{x:()=>where(logicalAnd(greaterEqual(x,clipValueMin),lessEqual(x,clipValueMax)),dy,zerosLike(dy))}}},complexAbsGradConfig={kernelName:ComplexAbs,inputsToSave:["x"],gradFunc:absGradConfig.gradFunc},concatGradConfig={kernelName:Concat,saveAllInputs:!0,gradFunc:(dy,saved,attrs)=>{let shapes=saved.map(t=>t.shape),{axis}=attrs,$axis=parseAxisParam(axis,saved[0].shape)[0],sizeSplits=shapes.map(s=>s[$axis]);return split(dy,sizeSplits,$axis).map(t=>()=>t)}},conv2DGradConfig={kernelName:Conv2D,inputsToSave:["x","filter"],gradFunc:(dy,saved,attrs)=>{let[x4D,$filter]=saved,{dilations,strides,pad:pad3,dataFormat}=attrs;return assert(tupleValuesAreOne(dilations),()=>`Error in gradient of conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`),{x:()=>conv2DBackpropInput(x4D.shape,dy,$filter,strides,pad3,dataFormat),filter:()=>conv2DBackpropFilter(x4D,dy,$filter.shape,strides,pad3,dataFormat)}}},conv2DBackpropInputGradConfig={kernelName:Conv2DBackpropInput,inputsToSave:["dy","filter"],gradFunc:(ddx,saved,attrs)=>{let[dy,filter]=saved,{strides,pad:pad3,dataFormat,dimRoundingMode}=attrs;return{dy:()=>conv2d(ddx,filter,strides,pad3,dataFormat,1,dimRoundingMode),filter:()=>conv2DBackpropFilter(ddx,dy,filter.shape,strides,pad3,dataFormat,dimRoundingMode)}}};function conv3DBackpropFilter_(x,dy,filterShape,strides,pad3){let x5D=x;x.rank===4&&(x5D=reshape(x,[1,x.shape[0],x.shape[1],x.shape[2],x.shape[3]]));let dy5D=dy;dy5D.rank===4&&(dy5D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2],dy.shape[3]])),assert(x5D.rank===5,()=>`Error in conv3dDerFilter: input must be rank 5, but got shape ${x5D.shape}.`),assert(dy5D.rank===5,()=>`Error in conv3dDerFilter: dy must be rank 5, but got shape ${dy5D.shape}.`),assert(filterShape.length===5,()=>`Error in conv3dDerFilter: filterShape must be length 5, but got ${filterShape}.`),assert(x5D.shape[4]===filterShape[3],()=>`Error in conv3dDerFilter: depth of input ${x5D.shape[4]}) must match input depth in filter (${filterShape[3]}.`),assert(dy5D.shape[4]===filterShape[4],()=>`Error in conv3dDerFilter: depth of dy (${dy5D.shape[4]}) must match output depth for filter (${filterShape[4]}).`);let inputs={x:x5D,dy:dy5D},attrs={strides,pad:pad3,filterShape};return ENGINE.runKernel(Conv3DBackpropFilterV2,inputs,attrs)}var conv3DBackpropFilter=op({conv3DBackpropFilter_}),conv3DGradConfig={kernelName:Conv3D,inputsToSave:["x","filter"],gradFunc:(dy,saved,attrs)=>{let{dilations,strides,pad:pad3}=attrs;assert(tupleValuesAreOne(dilations),()=>`Error in gradient of conv3D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`);let[x5D,$filter]=saved;return{x:()=>conv3DBackpropInput(x5D.shape,dy,$filter,strides,pad3),filter:()=>conv3DBackpropFilter(x5D,dy,$filter.shape,strides,pad3)}}},cosGradConfig={kernelName:Cos,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(neg(sin(cast(x,"float32"))),dy)}}},coshGradConfig={kernelName:Cosh,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(sinh(cast(x,"float32")),dy)}}},cumsumGradConfig={kernelName:Cumsum,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{axis,exclusive,reverse:reverse5}=attrs;return{x:()=>{let permutation=getAxesPermutation([axis],x.rank),out=cumsum(dy,axis,exclusive,!reverse5);return permutation!=null&&(out=transpose(out,permutation)),out}}}},depthwiseConv2dNativeGradConfig={kernelName:DepthwiseConv2dNative,inputsToSave:["x","filter"],gradFunc:(dy,saved,attrs)=>{let{dilations,strides,pad:pad3,dimRoundingMode}=attrs,$dilations=dilations==null?[1,1]:dilations;assert(tupleValuesAreOne($dilations),()=>`Error in gradient of depthwiseConv2dNative: dilation rates greater than 1 are not yet supported. Got dilations '${$dilations}'`);let[x,filter]=saved;return assert(x.rank===4,()=>`Error in gradient of depthwiseConv2dNative: input must be rank 4, but got rank ${x.rank}.`),assert(filter.rank===4,()=>`Error in gradient of depthwiseConv2dNative: filter must be rank 4, but got rank ${filter.rank}.`),assert(x.shape[3]===filter.shape[2],()=>`Error in gradient of depthwiseConv2d: number of input channels (${x.shape[3]}) must match the inChannels dimension in filter ${filter.shape[2]}.`),assert(eitherStridesOrDilationsAreOne(strides,$dilations),()=>`Error in gradient of depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${$dilations}'.`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`),{x:()=>depthwiseConv2dNativeBackpropInput(x.shape,dy,filter,strides,pad3,dilations,dimRoundingMode),filter:()=>depthwiseConv2dNativeBackpropFilter(x,dy,filter.shape,strides,pad3,dilations,dimRoundingMode)}}},dilation2dGradConfig={kernelName:Dilation2D,inputsToSave:["x","filter"],gradFunc:(dy,saved,attrs)=>{let[x,filter]=saved,inputInputs={x,filter,dy},filterInputs={x,filter,dy};return{x:()=>ENGINE.runKernel(Dilation2DBackpropInput,inputInputs,attrs),filter:()=>ENGINE.runKernel(Dilation2DBackpropFilter,filterInputs,attrs)}}},eluGradConfig={kernelName:Elu,outputsToSave:[!0],gradFunc:(dy,saved)=>{let[y]=saved,inputs={dy,y};return{x:()=>ENGINE.runKernel(EluGrad,inputs)}}},erfGradConfig={kernelName:Erf,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved,a=mul(exp(neg(square(x))),2/Math.sqrt(Math.PI));return{x:()=>mul(dy,a)}}},expGradConfig={kernelName:Exp,outputsToSave:[!0],gradFunc:(dy,saved)=>{let[y]=saved;return{x:()=>mul(dy,y)}}},expandDimsGradConfig={kernelName:ExpandDims,inputsToSave:["input"],gradFunc:(dy,saved)=>{let[input2]=saved;return{input:()=>reshape(dy,input2.shape)}}},expm1GradConfig={kernelName:Expm1,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(dy,exp(x))}}},floorGradConfig={kernelName:Floor,gradFunc:dy=>({x:()=>zerosLike(dy)})},floorDivGradConfig={kernelName:FloorDiv,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,outShape=assertAndGetBroadcastShape(a.shape,b.shape);return{a:()=>{let res=div(dy,cast(b,"float32")),reduceAxes=getReductionAxes(a.shape,outShape);return reduceAxes.length>0?reshape(sum2(res,reduceAxes),a.shape):res},b:()=>{let res=mul(dy,cast(a,"float32")),reduceAxes=getReductionAxes(b.shape,outShape);reduceAxes.length>0&&(res=reshape(sum2(res,reduceAxes),b.shape));let tmp=square(b);return neg(div(res,cast(tmp,"float32")))}}}},fusedBatchNormGradConfig={kernelName:FusedBatchNorm,inputsToSave:["x","mean","variance","scale"],gradFunc:(dy,saved,attrs)=>{let{varianceEpsilon}=attrs,[x,mean4,variance,scale2]=saved,scaleValue=scale2==null?scalar(1):scale2,reductionAxes=getReductionAxes(mean4.shape,x.shape),tileShape=[];if(mean4.rank===1){for(let i=0;imean4.rank===1?reshape(mul(mul(dy,tile(reshape(oneOverSqrtVariance,[1,1,1,mean4.shape[0]]),tileShape)),scaleValue),x.shape):reshape(mul(mul(dy,oneOverSqrtVariance),scaleValue),x.shape),mean:()=>{let meanDer=mul(mul(oneOverSqrtVariance,scalar(-1)),dyTimesScaleValue);return mean4.rank===1&&(meanDer=sum2(meanDer,reductionAxes)),reshape(meanDer,mean4.shape)},variance:()=>{let varianceDer=mul(mul(minusHalfRCube,xMinusMean),dyTimesScaleValue);return mean4.rank===1&&(varianceDer=sum2(varianceDer,reductionAxes)),reshape(varianceDer,mean4.shape)},scale:()=>{let xMinusMean2TimesRsqrt=mul(xMinusMean,oneOverSqrtVariance),scaleDer=mul(dy,xMinusMean2TimesRsqrt);return mean4.rank===1&&(scaleDer=sum2(scaleDer,reductionAxes)),reshape(scaleDer,mean4.shape)},offset:()=>{let offsetDer=dy;return mean4.rank===1&&(offsetDer=sum2(offsetDer,reductionAxes)),reshape(offsetDer,mean4.shape)}}}},gatherGradConfig={kernelName:GatherV2,inputsToSave:["x","indices"],gradFunc:(dy,saved,attrs)=>{let[x,indices]=saved,{axis}=attrs,parsedAxis=parseAxisParam(axis,x.shape)[0];return{x:()=>{let paramsShape=x.shape,indicesSize=indices.size,outerShape=paramsShape.slice(0,parsedAxis),outerDims=outerShape.length,innerShape=paramsShape.slice(axis,paramsShape.length).slice(1),innerDims=innerShape.length,outerAxesIndices=arrayRange(0,outerDims),innerAxesIndices=arrayRange(outerDims+1,outerDims+1+innerDims),valuesShape=arrayConcat([outerShape,[indicesSize],innerShape]),values=reshape(dy,valuesShape),reshapedIndices=reshape(indices,[indicesSize]),transposeDims=arrayConcat([[outerDims],outerAxesIndices,innerAxesIndices]),valuesTranspose=transpose(values,transposeDims),paramsGrad=unsortedSegmentSum(valuesTranspose,reshapedIndices,x.shape[parsedAxis]),invertTransposeDims=getUndoAxesPermutation(transposeDims);return paramsGrad=transpose(paramsGrad,invertTransposeDims),paramsGrad},indices:()=>indices}}};function arrayRange(start,stop){let result=[];for(let i=start;i{let[a,b]=saved;return{a:()=>zerosLike(a),b:()=>zerosLike(b)}}},identityGradConfig={kernelName:Identity,gradFunc:dy=>({x:()=>cast(dy,"float32")})},isFiniteGradConfig={kernelName:IsFinite,gradFunc:dy=>({x:()=>zerosLike(dy)})},isInfGradConfig={kernelName:IsInf,gradFunc:dy=>({x:()=>zerosLike(dy)})},isNanGradConfig={kernelName:IsNan,gradFunc:dy=>({x:()=>zerosLike(dy)})},leakyReluGradConfig={kernelName:LeakyRelu,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{alpha}=attrs,mask=greater(x,0);return{x:()=>where(mask,dy,mul(dy,alpha))}}},log1pGradConfig={kernelName:Log1p,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div(dy,add2(x,1))}}},logGradConfig={kernelName:Log,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div(dy,cast(x,"float32"))}}},logSoftmaxGradConfig={kernelName:LogSoftmax,inputsToSave:[],outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let[value]=saved,{axis}=attrs;return{logits:()=>{let keepDims=!0,softmax6=exp(value);return sub(dy,mul(sum2(dy,axis,keepDims),softmax6))}}}};function localResponseNormalizationBackprop_(x,y,dy,depthRadius=5,bias=1,alpha=1,beta=.5){let inputs={x,y,dy},attrs={depthRadius,bias,alpha,beta};return ENGINE.runKernel(LRNGrad,inputs,attrs)}var localResponseNormalizationBackprop=op({localResponseNormalizationBackprop_}),lrnGradConfig={kernelName:LRN,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let[x,y]=saved,{depthRadius,bias,alpha,beta}=attrs;return{x:()=>localResponseNormalizationBackprop(x,y,dy,depthRadius,bias,alpha,beta)}}};function gradForMinAndMax(dy,y,xOrig,origAxes){return y.rankmul(dy,cast(equal(xOrig,y),dy.dtype))}}var maxGradConfig={kernelName:Max,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let maxAttrs=attrs,{reductionIndices}=maxAttrs,x=saved[0],y=saved[1],origAxes=parseAxisParam(reductionIndices,x.shape),maxGrad=gradForMinAndMax(dy,y,x,origAxes);return{x:()=>maxGrad.x()}}},maximumGradConfig={kernelName:Maximum,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved;return{a:()=>mul(dy,cast(greaterEqual(a,b),"float32")),b:()=>mul(dy,cast(less(a,b),"float32"))}}};function maxPool3dGrad_(dy,input2,output,filterSize,strides,dilations=[1,1,1],pad3,dimRoundingMode){let $dy=convertToTensor(dy,"dy","maxPool3dGrad"),$input=convertToTensor(input2,"input","maxPool3dGrad"),$output=convertToTensor(output,"output","maxPool3dGrad"),dy5D=$dy,input5D=$input,output5D=$output,reshapedTo5D=!1;$input.rank===4&&(reshapedTo5D=!0,dy5D=reshape($dy,[1,$dy.shape[0],$dy.shape[1],$dy.shape[2],$dy.shape[3]]),input5D=reshape($input,[1,$input.shape[0],$input.shape[1],$input.shape[2],$input.shape[3]]),output5D=reshape($output,[1,$output.shape[0],$output.shape[1],$output.shape[2],$output.shape[3]])),assert(dy5D.rank===5,()=>`Error in maxPool3dGrad: dy must be rank 5 but got rank ${dy5D.rank}.`),assert(input5D.rank===5,()=>`Error in maxPool3dGrad: input must be rank 5 but got rank ${input5D.rank}.`),assert(output5D.rank===5,()=>`Error in maxPool3dGrad: output must be rank 5 but got rank ${output5D.rank}.`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in maxPool3dGrad: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in maxPool3dGrad: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={dy:dy5D,input:input5D,output:output5D},attrs={filterSize,strides,dilations,pad:pad3,dimRoundingMode},res=ENGINE.runKernel(MaxPool3DGrad,inputs,attrs);return reshapedTo5D?reshape(res,[res.shape[1],res.shape[2],res.shape[3],res.shape[4]]):res}var maxPool3dGrad=op({maxPool3dGrad_}),maxPool3DGradConfig={kernelName:MaxPool3D,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let[x,y]=saved,{filterSize,strides,dilations,pad:pad3,dimRoundingMode}=attrs,$dilations=dilations==null?[1,1,1]:dilations;return{x:()=>maxPool3dGrad(dy,x,y,filterSize,strides,$dilations,pad3,dimRoundingMode)}}};function maxPoolGrad_(dy,input2,output,filterSize,strides,pad3,dimRoundingMode){let $dy=convertToTensor(dy,"dy","maxPoolGrad"),$input=convertToTensor(input2,"input","maxPoolGrad"),$output=convertToTensor(output,"output","maxPoolGrad");assert($input.rank===$dy.rank,()=>`Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`),assert($dy.rank===4,()=>`Error in maxPoolGrad: dy must be rank 4 but got rank ${$dy.rank}.`),assert($input.rank===4,()=>`Error in maxPoolGrad: input must be rank 4 but got rank ${$input.rank}.`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in maxPoolGrad: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={dy:$dy,input:$input,output:$output},attrs={filterSize,strides,pad:pad3,dimRoundingMode};return ENGINE.runKernel(MaxPoolGrad,inputs,attrs)}var maxPoolGrad=op({maxPoolGrad_}),maxPoolGradConfig={kernelName:MaxPool,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let[x,y]=saved,{filterSize,strides,pad:pad3}=attrs;return{x:()=>maxPoolGrad(dy,x,y,filterSize,strides,pad3)}}},meanGradConfig={kernelName:Mean,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{axis}=attrs,axes=parseAxisParam(axis,x.shape),reduceShape=computeOutAndReduceShapes(x.shape,axes)[1],reduceSize=sizeFromShape(reduceShape);return{x:()=>{let expandedDyShape=x.shape.slice();axes.forEach(axis2=>{expandedDyShape[axis2]=1});let expandedDy=reshape(dy,expandedDyShape);return div(mul(expandedDy,ones2(x.shape,"float32")),reduceSize)}}}},minGradConfig={kernelName:Min,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let minAttrs=attrs,{axis}=minAttrs,[x,y]=saved,origAxes=parseAxisParam(axis,x.shape),minGrad=gradForMinAndMax(dy,y,x,origAxes);return{x:()=>minGrad.x()}}},minimumGradConfig={kernelName:Minimum,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved;return{a:()=>mul(dy,cast(lessEqual(a,b),"float32")),b:()=>mul(dy,cast(greater(a,b),"float32"))}}},mirrorPadGradConfig={kernelName:MirrorPad,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let x=saved[0],{paddings}=attrs,begin=paddings.map(p2=>p2[0]);return{x:()=>slice(dy,begin,x.shape)}}},modGradConfig={kernelName:Mod,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,outShape=assertAndGetBroadcastShape(a.shape,b.shape);return{a:()=>{let reduceAxes=getReductionAxes(a.shape,outShape);return reduceAxes.length>0?reshape(sum2(dy,reduceAxes),a.shape):dy},b:()=>{let res=mul(dy,neg(floor(div(a,b)))),reduceAxes=getReductionAxes(b.shape,outShape);return reduceAxes.length>0?reshape(sum2(res,reduceAxes),b.shape):res}}}},multiplyGradConfig={kernelName:Multiply,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,outShape=assertAndGetBroadcastShape(a.shape,b.shape);return{a:()=>{let res=mul(dy,cast(b,"float32")),reduceAxes=getReductionAxes(a.shape,outShape);return reduceAxes.length>0?reshape(sum2(res,reduceAxes),a.shape):res},b:()=>{let res=mul(dy,cast(a,"float32")),reduceAxes=getReductionAxes(b.shape,outShape);return reduceAxes.length>0?reshape(sum2(res,reduceAxes),b.shape):res}}}},negGradConfig={kernelName:Neg,gradFunc:dy=>({x:()=>neg(dy)})},oneHotGradConfig={kernelName:OneHot,inputsToSave:["indices"],gradFunc:(dy,saved)=>{let indices=saved[0];return{indices:()=>zeros(indices.shape,"float32")}}},onesLikeGradConfig={kernelName:OnesLike,gradFunc:dy=>({x:()=>zerosLike(dy)})},packGradConfig={kernelName:Pack,saveAllInputs:!0,gradFunc:(dy,saved,attrs)=>{let{axis}=attrs;return unstack(dy,axis).map(t=>()=>t)}},padV2GradConfig={kernelName:PadV2,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let x=saved[0],{paddings}=attrs,begin=paddings.map(p2=>p2[0]);return{x:()=>slice(dy,begin,x.shape)}}},powGradConfig={kernelName:Pow,inputsToSave:["a","b"],outputsToSave:[!0],gradFunc:(dy,saved)=>{let[a,b,y]=saved,base2=a,exp4=b,outShape=assertAndGetBroadcastShape(base2.shape,exp4.shape);return{a:()=>{let expFloat=cast(exp4,"float32"),res=mul(dy,mul(expFloat,pow(base2,sub(expFloat,scalar(1))))),reduceAxes=getReductionAxes(base2.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,base2.shape)},b:()=>{let condition=greater(base2,0),logBase=where(condition,log2(base2),zerosLike(base2)),res=mul(dy,mul(y,logBase)),reduceAxes=getReductionAxes(exp4.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,exp4.shape)}}}},preluGradConfig={kernelName:Prelu,inputsToSave:["x","alpha"],gradFunc:(dy,saved)=>{let[x,alpha]=saved,mask=greater(x,0);return{x:()=>where(mask,dy,mul(dy,alpha)),alpha:()=>{let res=where(mask,zerosLike(dy),mul(dy,x)),reduceAxes=getReductionAxes(alpha.shape,dy.shape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,alpha.shape)}}}},divGradConfig={kernelName:RealDiv,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,outShape=assertAndGetBroadcastShape(a.shape,b.shape);return{a:()=>{let res=div(dy,cast(b,"float32")),reduceAxes=getReductionAxes(a.shape,outShape);return reduceAxes.length>0?reshape(sum2(res,reduceAxes),a.shape):res},b:()=>{let res=mul(dy,cast(a,"float32")),reduceAxes=getReductionAxes(b.shape,outShape);reduceAxes.length>0&&(res=reshape(sum2(res,reduceAxes),b.shape));let tmp=square(b);return neg(div(res,cast(tmp,"float32")))}}}},reciprocalGradConfig={kernelName:Reciprocal,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div(dy,neg(square(x)))}}},relu6GradConfig={kernelName:Relu6,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved,mask=mul(lessEqual(x,6),step(x));return{x:()=>mul(dy,cast(mask,"float32"))}}},reluGradConfig={kernelName:Relu,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(dy,cast(step(x),"float32"))}}},reshapeGradConfig={kernelName:Reshape,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>reshape(dy,x.shape)}}},resizeBilinearGradConfig={kernelName:ResizeBilinear,inputsToSave:["images"],gradFunc:(dy,saved,attrs)=>{let[images]=saved,inputs={dy,images};return{images:()=>ENGINE.runKernel(ResizeBilinearGrad,inputs,attrs)}}},resizeNearestNeighborGradConfig={kernelName:ResizeNearestNeighbor,inputsToSave:["images"],gradFunc:(dy,saved,attrs)=>{let[images]=saved,inputs={dy,images};return{images:()=>ENGINE.runKernel(ResizeNearestNeighborGrad,inputs,attrs)}}},reverseGradConfig={kernelName:Reverse,gradFunc:(dy,saved,attrs)=>{let{dims}=attrs,axes=parseAxisParam(dims,dy.shape);return{x:()=>reverse(dy,axes)}}},roundGradConfig={kernelName:Round,gradFunc:dy=>({x:()=>zerosLike(dy)})},rsqrtGradConfig={kernelName:Rsqrt,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>neg(div(dy,mul(pow(x,1.5),2)))}}},selectGradConfig={kernelName:Select,inputsToSave:["condition"],gradFunc:(dy,saved)=>{let[condition]=saved;return{condition:()=>cast(zerosLike(condition),"float32"),t:()=>mul(dy,cast(condition,dy.dtype)),e:()=>mul(dy,cast(logicalNot(condition),dy.dtype))}}},seluGradConfig={kernelName:Selu,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>{let mask=greater(x,scalar(0)),scaleAlpha2=scalar(SELU_SCALEALPHA),scale2=scalar(SELU_SCALE),greaterThanZeroDer=mul(dy,scale2),lessEqualZeroDer=mul(mul(dy,scaleAlpha2),exp(cast(x,"float32")));return where(mask,greaterThanZeroDer,lessEqualZeroDer)}}}},sigmoidGradConfig={kernelName:Sigmoid,outputsToSave:[!0],gradFunc:(dy,saved)=>{let[y]=saved;return{x:()=>mul(dy,mul(y,sub(scalar(1),y)))}}},signGradConfig={kernelName:Sign,gradFunc:dy=>({x:()=>zerosLike(dy)})},sinGradConfig={kernelName:Sin,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(cos(cast(x,"float32")),dy)}}},sinhGradConfig={kernelName:Sinh,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(cosh(cast(x,"float32")),dy)}}},sliceGradConfig={kernelName:Slice,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{begin,size}=attrs,inputShape=x.shape,[begin_,size_]=parseSliceParams(x,begin,size),paddings=[];for(let i=0;ipad(dy,paddings)}}},softmaxGradConfig={kernelName:Softmax,outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let[y]=saved,{dim}=attrs,keepDims=!0,dyTimesY=mul(dy,y);return{logits:()=>sub(dyTimesY,mul(sum2(dyTimesY,[dim],keepDims),y))}}},softplusGradConfig={kernelName:Softplus,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(dy,sigmoid(x))}}},spaceToBatchNDGradConfig={kernelName:SpaceToBatchND,gradFunc:(dy,saved,attrs)=>{let{blockShape,paddings}=attrs;return{x:()=>batchToSpaceND(dy,blockShape,paddings)}}},splitVGradConfig={kernelName:SplitV,gradFunc:(dy,saved,attrs)=>{let{axis}=attrs;return{x:()=>concat(dy,axis)}}},sqrtGradConfig={kernelName:Sqrt,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div(dy,mul(sqrt(cast(x,"float32")),2))}}},squareGradConfig={kernelName:Square,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(dy,mul(cast(x,"float32"),2))}}},squaredDifferenceGradConfig={kernelName:SquaredDifference,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,two=scalar(2);return{a:()=>mul(dy,mul(two,sub(a,b))),b:()=>mul(dy,mul(two,sub(b,a)))}}},stepGradConfig={kernelName:Step,gradFunc:dy=>({x:()=>zerosLike(dy)})},subGradConfig={kernelName:Sub,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,outShape=assertAndGetBroadcastShape(a.shape,b.shape);return{a:()=>{let res=dy,reduceAxes=getReductionAxes(a.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,a.shape)},b:()=>{let res=dy,reduceAxes=getReductionAxes(b.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(neg(res),b.shape)}}}},sumGradConfig={kernelName:Sum,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,expandedDyShape=x.shape.slice(),{axis}=attrs;parseAxisParam(axis,x.shape).forEach(axis2=>{expandedDyShape[axis2]=1});let expandedDy=reshape(dy,expandedDyShape),derX=mul(expandedDy,ones2(x.shape,"float32"));return{x:()=>derX}}},tanGradConfig={kernelName:Tan,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div(dy,square(cos(x)))}}},tanhGradConfig={kernelName:Tanh,outputsToSave:[!0],gradFunc:(dy,saved)=>{let[y]=saved;return{x:()=>mul(sub(scalar(1),square(y)),dy)}}},tileGradConfig={kernelName:Tile,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{reps}=attrs;return{x:()=>{let xGrad=zerosLike(x);if(x.rank===1)for(let i=0;i{let transposeAttrs=attrs,{perm}=transposeAttrs,undoPerm=getUndoAxesPermutation(perm);return{x:()=>transpose(dy,undoPerm)}}},unpackGradConfig={kernelName:Unpack,gradFunc:(dy,saved,attrs)=>{let unpackAttrs=attrs,{axis}=unpackAttrs;return{value:()=>stack(dy,axis)}}},unsortedSegmentSumGradConfig={kernelName:UnsortedSegmentSum,inputsToSave:["segmentIds"],gradFunc:(dy,saved)=>{let[segmentIds]=saved;return{x:()=>gatherDropNegatives(dy,segmentIds)}}};function gatherDropNegatives(x,indices){let zeroClippedIndices=maximum(indices,zerosLike(indices)),gathered=gather(x,zeroClippedIndices),isPositive=greaterEqual(indices,scalar(0,"int32")),numIters=gathered.rank-isPositive.rank;for(let i=0;i({x:()=>zerosLike(dy)})},gradConfigs=[absGradConfig,acosGradConfig,acoshGradConfig,addGradConfig,addNGradConfig,argMaxGradConfig,argMinGradConfig,asinGradConfig,asinhGradConfig,atan2GradConfig,atanGradConfig,atanhGradConfig,avgPool3DGradConfig,avgPoolGradConfig,batchMatMulGradConfig,batchToSpaceNDGradConfig,broadcastToGradConfig,castGradConfig,ceilGradConfig,clipByValueGradConfig,complexAbsGradConfig,concatGradConfig,conv2DBackpropInputGradConfig,conv2DGradConfig,conv3DGradConfig,cosGradConfig,coshGradConfig,cumsumGradConfig,depthwiseConv2dNativeGradConfig,dilation2dGradConfig,divGradConfig,eluGradConfig,erfGradConfig,expGradConfig,expandDimsGradConfig,expm1GradConfig,floorDivGradConfig,floorGradConfig,fusedBatchNormGradConfig,gatherGradConfig,greaterEqualGradConfig,identityGradConfig,isFiniteGradConfig,isInfGradConfig,isNanGradConfig,leakyReluGradConfig,log1pGradConfig,logGradConfig,logSoftmaxGradConfig,lrnGradConfig,maxGradConfig,maxGradConfig,maximumGradConfig,maxPool3DGradConfig,maxPoolGradConfig,meanGradConfig,minGradConfig,minimumGradConfig,mirrorPadGradConfig,modGradConfig,multiplyGradConfig,negGradConfig,oneHotGradConfig,onesLikeGradConfig,packGradConfig,padV2GradConfig,padV2GradConfig,powGradConfig,preluGradConfig,reciprocalGradConfig,relu6GradConfig,reluGradConfig,reshapeGradConfig,resizeBilinearGradConfig,resizeNearestNeighborGradConfig,reverseGradConfig,roundGradConfig,rsqrtGradConfig,selectGradConfig,seluGradConfig,sigmoidGradConfig,signGradConfig,sinGradConfig,sinhGradConfig,sliceGradConfig,softmaxGradConfig,softplusGradConfig,spaceToBatchNDGradConfig,spaceToBatchNDGradConfig,splitVGradConfig,splitVGradConfig,sqrtGradConfig,squaredDifferenceGradConfig,squareGradConfig,stepGradConfig,subGradConfig,sumGradConfig,tanGradConfig,tanhGradConfig,tileGradConfig,transposeGradConfig,unpackGradConfig,unsortedSegmentSumGradConfig,zerosLikeGradConfig];for(let gradientConfig of gradConfigs)registerGradient(gradientConfig);Tensor.prototype.abs=function(){return this.throwIfDisposed(),abs(this)};Tensor.prototype.acos=function(){return this.throwIfDisposed(),acos(this)};Tensor.prototype.acosh=function(){return this.throwIfDisposed(),acosh(this)};Tensor.prototype.addStrict=function(x){return this.throwIfDisposed(),addStrict(this,x)};Tensor.prototype.add=function(b){return this.throwIfDisposed(),add2(this,b)};Tensor.prototype.all=function(axis,keepDims){return this.throwIfDisposed(),all(this,axis,keepDims)};Tensor.prototype.any=function(axis,keepDims){return this.throwIfDisposed(),any(this,axis,keepDims)};Tensor.prototype.argMax=function(axis){return this.throwIfDisposed(),argMax(this,axis)};Tensor.prototype.argMin=function(axis){return this.throwIfDisposed(),argMin(this,axis)};Tensor.prototype.asScalar=function(){return this.throwIfDisposed(),assert(this.size===1,()=>"The array must have only 1 element."),reshape(this,[])};Tensor.prototype.asType=function(dtype){return this.throwIfDisposed(),cast(this,dtype)};Tensor.prototype.as1D=function(){return this.throwIfDisposed(),reshape(this,[this.size])};Tensor.prototype.as2D=function(rows,columns){return this.throwIfDisposed(),reshape(this,[rows,columns])};Tensor.prototype.as3D=function(rows,columns,depth){return this.throwIfDisposed(),reshape(this,[rows,columns,depth])};Tensor.prototype.as4D=function(rows,columns,depth,depth2){return this.throwIfDisposed(),reshape(this,[rows,columns,depth,depth2])};Tensor.prototype.as5D=function(rows,columns,depth,depth2,depth3){return this.throwIfDisposed(),reshape(this,[rows,columns,depth,depth2,depth3])};Tensor.prototype.asin=function(){return this.throwIfDisposed(),asin(this)};Tensor.prototype.asinh=function(){return this.throwIfDisposed(),asinh(this)};Tensor.prototype.atan=function(){return this.throwIfDisposed(),atan(this)};Tensor.prototype.atan2=function(b){return this.throwIfDisposed(),atan2(this,b)};Tensor.prototype.atanh=function(){return this.throwIfDisposed(),atanh(this)};Tensor.prototype.avgPool=function(filterSize,strides,pad3,dimRoundingMode){return this.throwIfDisposed(),avgPool(this,filterSize,strides,pad3,dimRoundingMode)};Tensor.prototype.batchToSpaceND=function(blockShape,crops){return this.throwIfDisposed(),batchToSpaceND(this,blockShape,crops)};Tensor.prototype.batchNorm=function(mean4,variance,offset,scale2,varianceEpsilon){return this.throwIfDisposed(),batchNorm(this,mean4,variance,offset,scale2,varianceEpsilon)};Tensor.prototype.broadcastTo=function(shape){return this.throwIfDisposed(),broadcastTo(this,shape)};Tensor.prototype.cast=function(dtype){return this.throwIfDisposed(),cast(this,dtype)};Tensor.prototype.ceil=function(){return this.throwIfDisposed(),ceil(this)};Tensor.prototype.clipByValue=function(min6,max6){return this.throwIfDisposed(),clipByValue(this,min6,max6)};Tensor.prototype.concat=function(x,axis){return this.throwIfDisposed(),x instanceof Tensor&&(x=[x]),concat([this,...x],axis)};Tensor.prototype.conv1d=function(filter,stride,pad3,dataFormat,dilation,dimRoundingMode){return this.throwIfDisposed(),conv1d(this,filter,stride,pad3,dataFormat,dilation,dimRoundingMode)};Tensor.prototype.conv2dTranspose=function(filter,outputShape,strides,pad3,dimRoundingMode){return this.throwIfDisposed(),conv2dTranspose(this,filter,outputShape,strides,pad3,dimRoundingMode)};Tensor.prototype.conv2d=function(filter,strides,pad3,dataFormat,dilations,dimRoundingMode){return this.throwIfDisposed(),conv2d(this,filter,strides,pad3,dataFormat,dilations,dimRoundingMode)};Tensor.prototype.cos=function(){return this.throwIfDisposed(),cos(this)};Tensor.prototype.cosh=function(){return this.throwIfDisposed(),cosh(this)};Tensor.prototype.cumsum=function(axis,exclusive,reverse5){return this.throwIfDisposed(),cumsum(this,axis,exclusive,reverse5)};Tensor.prototype.depthToSpace=function(blockSize,dataFormat){return this.throwIfDisposed(),depthToSpace(this,blockSize,dataFormat)};Tensor.prototype.depthwiseConv2D=function(filter,strides,pad3,dataFormat,dilations,dimRoundingMode){return deprecationWarn("depthwiseConv2D is deprecated, use depthwiseConv2d instead"),this.throwIfDisposed(),depthwiseConv2d(this,filter,strides,pad3,dataFormat,dilations,dimRoundingMode)};Tensor.prototype.depthwiseConv2d=function(filter,strides,pad3,dataFormat,dilations,dimRoundingMode){return this.throwIfDisposed(),depthwiseConv2d(this,filter,strides,pad3,dataFormat,dilations,dimRoundingMode)};Tensor.prototype.dilation2d=function(filter,strides,pad3,dilations,dataFormat){return this.throwIfDisposed(),dilation2d(this,filter,strides,pad3,dilations,dataFormat)};Tensor.prototype.divNoNan=function(b){return this.throwIfDisposed(),divNoNan(this,b)};Tensor.prototype.divStrict=function(x){return this.throwIfDisposed(),divStrict(this,x)};Tensor.prototype.div=function(b){return this.throwIfDisposed(),div(this,b)};Tensor.prototype.dot=function(b){return this.throwIfDisposed(),dot(this,b)};Tensor.prototype.elu=function(){return this.throwIfDisposed(),elu(this)};Tensor.prototype.equalStrict=function(x){return this.throwIfDisposed(),equalStrict(this,x)};Tensor.prototype.equal=function(b){return this.throwIfDisposed(),equal(this,b)};Tensor.prototype.erf=function(){return this.throwIfDisposed(),erf(this)};Tensor.prototype.exp=function(){return this.throwIfDisposed(),exp(this)};Tensor.prototype.expandDims=function(axis){return this.throwIfDisposed(),expandDims(this,axis)};Tensor.prototype.expm1=function(){return this.throwIfDisposed(),expm1(this)};Tensor.prototype.fft=function(){return this.throwIfDisposed(),fft(this)};Tensor.prototype.flatten=function(){return this.throwIfDisposed(),reshape(this,[this.size])};Tensor.prototype.floor=function(){return this.throwIfDisposed(),floor(this)};Tensor.prototype.floorDiv=function(b){return this.throwIfDisposed(),floorDiv(this,b)};Tensor.prototype.gather=function(indices,axis){return this.throwIfDisposed(),gather(this,indices,axis)};Tensor.prototype.greaterEqualStrict=function(x){return this.throwIfDisposed(),greaterEqualStrict(this,x)};Tensor.prototype.greaterEqual=function(b){return this.throwIfDisposed(),greaterEqual(this,b)};Tensor.prototype.greaterStrict=function(x){return this.throwIfDisposed(),greaterStrict(this,x)};Tensor.prototype.greater=function(b){return this.throwIfDisposed(),greater(this,b)};Tensor.prototype.ifft=function(){return this.throwIfDisposed(),ifft(this)};Tensor.prototype.irfft=function(){return this.throwIfDisposed(),irfft(this)};Tensor.prototype.isFinite=function(){return this.throwIfDisposed(),isFinite2(this)};Tensor.prototype.isInf=function(){return this.throwIfDisposed(),isInf(this)};Tensor.prototype.isNaN=function(){return this.throwIfDisposed(),isNaN2(this)};Tensor.prototype.leakyRelu=function(alpha){return this.throwIfDisposed(),leakyRelu(this,alpha)};Tensor.prototype.lessEqualStrict=function(x){return this.throwIfDisposed(),lessEqualStrict(this,x)};Tensor.prototype.lessEqual=function(b){return this.throwIfDisposed(),lessEqual(this,b)};Tensor.prototype.lessStrict=function(x){return this.throwIfDisposed(),lessStrict(this,x)};Tensor.prototype.less=function(b){return this.throwIfDisposed(),less(this,b)};Tensor.prototype.localResponseNormalization=function(depthRadius,bias,alpha,beta){return this.throwIfDisposed(),localResponseNormalization(this,depthRadius,bias,alpha,beta)};Tensor.prototype.logSigmoid=function(){return this.throwIfDisposed(),logSigmoid(this)};Tensor.prototype.logSoftmax=function(axis){return this.throwIfDisposed(),logSoftmax(this,axis)};Tensor.prototype.logSumExp=function(axis,keepDims){return this.throwIfDisposed(),logSumExp(this,axis,keepDims)};Tensor.prototype.log=function(){return this.throwIfDisposed(),log2(this)};Tensor.prototype.log1p=function(){return this.throwIfDisposed(),log1p(this)};Tensor.prototype.logicalAnd=function(b){return this.throwIfDisposed(),logicalAnd(this,b)};Tensor.prototype.logicalNot=function(){return this.throwIfDisposed(),logicalNot(this)};Tensor.prototype.logicalOr=function(b){return this.throwIfDisposed(),logicalOr(this,b)};Tensor.prototype.logicalXor=function(b){return this.throwIfDisposed(),logicalXor(this,b)};Tensor.prototype.matMul=function(b,transposeA,transposeB){return this.throwIfDisposed(),matMul(this,b,transposeA,transposeB)};Tensor.prototype.maxPool=function(filterSize,strides,pad3,dimRoundingMode){return this.throwIfDisposed(),maxPool(this,filterSize,strides,pad3,dimRoundingMode)};Tensor.prototype.max=function(axis,keepDims){return this.throwIfDisposed(),max(this,axis,keepDims)};Tensor.prototype.maximumStrict=function(x){return this.throwIfDisposed(),maximumStrict(this,x)};Tensor.prototype.maximum=function(b){return this.throwIfDisposed(),maximum(this,b)};Tensor.prototype.mean=function(axis,keepDims){return this.throwIfDisposed(),mean(this,axis,keepDims)};Tensor.prototype.min=function(axis,keepDims){return this.throwIfDisposed(),min(this,axis,keepDims)};Tensor.prototype.minimumStrict=function(x){return this.throwIfDisposed(),minimumStrict(this,x)};Tensor.prototype.minimum=function(b){return this.throwIfDisposed(),minimum(this,b)};Tensor.prototype.mirrorPad=function(paddings,mode){return this.throwIfDisposed(),mirrorPad(this,paddings,mode)};Tensor.prototype.modStrict=function(x){return this.throwIfDisposed(),modStrict(this,x)};Tensor.prototype.mod=function(b){return this.throwIfDisposed(),mod(this,b)};Tensor.prototype.mulStrict=function(x){return this.throwIfDisposed(),mulStrict(this,x)};Tensor.prototype.mul=function(b){return this.throwIfDisposed(),mul(this,b)};Tensor.prototype.neg=function(){return this.throwIfDisposed(),neg(this)};Tensor.prototype.norm=function(ord,axis,keepDims){return this.throwIfDisposed(),norm(this,ord,axis,keepDims)};Tensor.prototype.notEqualStrict=function(x){return this.throwIfDisposed(),notEqualStrict(this,x)};Tensor.prototype.notEqual=function(b){return this.throwIfDisposed(),notEqual(this,b)};Tensor.prototype.oneHot=function(depth,onValue=1,offValue=0){return this.throwIfDisposed(),oneHot(this,depth,onValue,offValue)};Tensor.prototype.onesLike=function(){return this.throwIfDisposed(),onesLike(this)};Tensor.prototype.pad=function(paddings,constantValue){return this.throwIfDisposed(),pad(this,paddings,constantValue)};Tensor.prototype.pool=function(windowShape,poolingType,padding,dilationRate,strides){return this.throwIfDisposed(),pool(this,windowShape,poolingType,padding,dilationRate,strides)};Tensor.prototype.powStrict=function(exp4){return this.throwIfDisposed(),powStrict(this,exp4)};Tensor.prototype.pow=function(exp4){return this.throwIfDisposed(),pow(this,exp4)};Tensor.prototype.prelu=function(alpha){return this.throwIfDisposed(),prelu(this,alpha)};Tensor.prototype.prod=function(axis,keepDims){return this.throwIfDisposed(),prod(this,axis,keepDims)};Tensor.prototype.reciprocal=function(){return this.throwIfDisposed(),reciprocal(this)};Tensor.prototype.relu=function(){return this.throwIfDisposed(),relu(this)};Tensor.prototype.relu6=function(){return this.throwIfDisposed(),relu6(this)};Tensor.prototype.reshapeAs=function(x){return this.throwIfDisposed(),reshape(this,x.shape)};Tensor.prototype.reshape=function(shape){return this.throwIfDisposed(),reshape(this,shape)};Tensor.prototype.resizeBilinear=function(newShape2D,alignCorners,halfPixelCenters){return this.throwIfDisposed(),resizeBilinear(this,newShape2D,alignCorners,halfPixelCenters)};Tensor.prototype.resizeNearestNeighbor=function(newShape2D,alignCorners,halfFloatCenters){return this.throwIfDisposed(),resizeNearestNeighbor(this,newShape2D,alignCorners,halfFloatCenters)};Tensor.prototype.reverse=function(axis){return this.throwIfDisposed(),reverse(this,axis)};Tensor.prototype.rfft=function(){return this.throwIfDisposed(),rfft(this)};Tensor.prototype.round=function(){return this.throwIfDisposed(),round2(this)};Tensor.prototype.rsqrt=function(){return this.throwIfDisposed(),rsqrt(this)};Tensor.prototype.selu=function(){return this.throwIfDisposed(),selu(this)};Tensor.prototype.separableConv2d=function(depthwiseFilter,pointwiseFilter,strides,pad3,dilation,dataFormat){return this.throwIfDisposed(),separableConv2d(this,depthwiseFilter,pointwiseFilter,strides,pad3,dilation,dataFormat)};Tensor.prototype.sigmoid=function(){return this.throwIfDisposed(),sigmoid(this)};Tensor.prototype.sign=function(){return this.throwIfDisposed(),sign(this)};Tensor.prototype.sin=function(){return this.throwIfDisposed(),sin(this)};Tensor.prototype.sinh=function(){return this.throwIfDisposed(),sinh(this)};Tensor.prototype.slice=function(begin,size){return this.throwIfDisposed(),slice(this,begin,size)};Tensor.prototype.softmax=function(dim){return this.throwIfDisposed(),softmax(this,dim)};Tensor.prototype.softplus=function(){return this.throwIfDisposed(),softplus(this)};Tensor.prototype.spaceToBatchND=function(blockShape,paddings){return this.throwIfDisposed(),spaceToBatchND(this,blockShape,paddings)};Tensor.prototype.split=function(numOrSizeSplits,axis){return this.throwIfDisposed(),split(this,numOrSizeSplits,axis)};Tensor.prototype.sqrt=function(){return this.throwIfDisposed(),sqrt(this)};Tensor.prototype.square=function(){return this.throwIfDisposed(),square(this)};Tensor.prototype.squaredDifference=function(b){return this.throwIfDisposed(),squaredDifference(this,b)};Tensor.prototype.squaredDifferenceStrict=function(x){return this.throwIfDisposed(),squaredDifferenceStrict(this,x)};Tensor.prototype.squeeze=function(axis){return this.throwIfDisposed(),squeeze(this,axis)};Tensor.prototype.stack=function(x,axis){this.throwIfDisposed();let tensorsToBeStacked=x instanceof Tensor?[this,x]:[this,...x];return stack(tensorsToBeStacked,axis)};Tensor.prototype.step=function(alpha){return this.throwIfDisposed(),step(this,alpha)};Tensor.prototype.stridedSlice=function(begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask){return this.throwIfDisposed(),stridedSlice(this,begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask)};Tensor.prototype.subStrict=function(x){return this.throwIfDisposed(),subStrict(this,x)};Tensor.prototype.sub=function(b){return this.throwIfDisposed(),sub(this,b)};Tensor.prototype.sum=function(axis,keepDims){return this.throwIfDisposed(),sum2(this,axis,keepDims)};Tensor.prototype.tan=function(){return this.throwIfDisposed(),tan(this)};Tensor.prototype.tanh=function(){return this.throwIfDisposed(),tanh2(this)};Tensor.prototype.tile=function(reps){return this.throwIfDisposed(),tile(this,reps)};Tensor.prototype.toBool=function(){return this.throwIfDisposed(),cast(this,"bool")};Tensor.prototype.toFloat=function(){return this.throwIfDisposed(),cast(this,"float32")};Tensor.prototype.toInt=function(){return this.throwIfDisposed(),cast(this,"int32")};Tensor.prototype.topk=function(k,sorted){return this.throwIfDisposed(),topk(this,k,sorted)};Tensor.prototype.transpose=function(perm){return this.throwIfDisposed(),transpose(this,perm)};Tensor.prototype.unique=function(axis){return this.throwIfDisposed(),unique(this,axis)};Tensor.prototype.unsortedSegmentSum=function(segmentIds,numSegments){return this.throwIfDisposed(),unsortedSegmentSum(this,segmentIds,numSegments)};Tensor.prototype.unstack=function(axis){return this.throwIfDisposed(),unstack(this,axis)};Tensor.prototype.where=function(condition,x){return this.throwIfDisposed(),where(condition,this,x)};Tensor.prototype.zerosLike=function(){return this.throwIfDisposed(),zerosLike(this)};function assertNotComplex(tensor2,opName){Array.isArray(tensor2)||(tensor2=[tensor2]),tensor2.forEach(t=>{t!=null&&util_exports.assert(t.dtype!=="complex64",()=>`${opName} does not support complex64 tensors in the CPU backend.`)})}var whereImpl2=kernel_impls_exports.whereImpl,MathBackendCPU=class extends KernelBackend{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new DataStorage(this,engine())}write(values,shape,dtype){this.firstUse&&(this.firstUse=!1,env().get("IS_NODE")&&backend_util_exports.warn(` ============================ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, which binds to TensorFlow C++, by running npm i @tensorflow/tfjs-node, or npm i @tensorflow/tfjs-node-gpu if you have CUDA. Then call require('@tensorflow/tfjs-node'); (-gpu suffix for CUDA) at the start of your program. Visit https://github.com/tensorflow/tfjs-node for more details. @@ -4040,19 +4040,19 @@ return a / b;`,DIV_PACKED=` } setOutput(${returnValue}); } - `}};function unsortedSegmentSum3(args){let{inputs,backend:backend22,attrs}=args,{x,segmentIds}=inputs,{numSegments}=attrs,xRank=x.shape.length,toDispose=[],axis=0,permutation=backend_util_exports.getAxesPermutation([axis],xRank),permutedX=x;permutation!=null&&(permutedX=transpose3({inputs:{x},backend:backend22,attrs:{perm:permutation}}),toDispose.push(permutedX),axis=backend_util_exports.getInnerMostAxes(1,xRank)[0]);let outShape=backend_util_exports.segment_util.computeOutShape(permutedX.shape,axis,numSegments),inSize=util_exports.sizeFromShape([permutedX.shape[axis]]),a2D=reshape3({inputs:{x:permutedX},backend:backend22,attrs:{shape:[-1,inSize]}});toDispose.push(a2D);let outputDType=sumOutType(x.dtype),segOpCompute=(x2,segOpType,segmentIds2,dtype,numSegments2)=>{let batchSize=x2.shape[0],inSize2=x2.shape[1],windowSize=backend_util_exports.segment_util.segOpComputeOptimalWindowSize(inSize2,numSegments2),segOpInfo={windowSize,inSize:inSize2,batchSize,numSegments:numSegments2},program=new SegmentOpProgram(segOpInfo,segOpType),output=backend22.compileAndRun(program,[x2,segmentIds2],dtype);if(toDispose.push(output),output.shape[1]===numSegments2)return output;let rangeInfo=range3({backend:backend22,attrs:{start:0,stop:numSegments2,step:1,dtype:"float32"}}),tileInfo=tile3({inputs:{x:rangeInfo},backend:backend22,attrs:{reps:[inSize2/windowSize]}});return toDispose.push(rangeInfo),toDispose.push(tileInfo),segOpCompute(output,segOpType,tileInfo,dtype,numSegments2)},segOpResult=segOpCompute(a2D,"unsortedSegmentSum",segmentIds,outputDType,numSegments),reshaped=reshape3({inputs:{x:segOpResult},backend:backend22,attrs:{shape:outShape}}),result=reshaped;if(permutation!=null){toDispose.push(reshaped);let perm=backend_util_exports.getUndoAxesPermutation(permutation);result=transpose3({inputs:{x:result},backend:backend22,attrs:{perm}})}return toDispose.forEach(t=>backend22.disposeIntermediateTensorInfo(t)),result}var unsortedSegmentSumConfig2={kernelName:UnsortedSegmentSum,backendName:"webgl",kernelFunc:unsortedSegmentSum3},kernelConfigs2=[LRNConfig,LRNGradConfig,_fusedMatMulConfig2,absConfig2,acosConfig2,acoshConfig2,addConfig2,addNConfig2,allConfig2,anyConfig2,argMaxConfig2,argMinConfig2,asinConfig2,asinhConfig2,atan2Config2,atanConfig2,atanhConfig2,avgPool3DConfig2,avgPoolConfig2,avgPoolGrad3DConfig,avgPoolGradConfig3,batchMatMulConfig2,batchNormConfig2,batchToSpaceNDConfig2,bincountConfig2,castConfig2,ceilConfig2,clipByValueConfig,complexAbsConfig2,complexConfig2,concatConfig2,conv2DBackpropFilterConfig2,conv2DBackpropInputConfig2,conv2DConfig2,conv3DBackpropFilterV2Config2,conv3DBackpropInputConfig,conv3DConfig2,cosConfig2,coshConfig2,cropAndResizeConfig2,cumsumConfig2,denseBincountConfig2,depthToSpaceConfig2,depthwiseConv2dNativeBackpropFilterConfig2,depthwiseConv2dNativeBackpropInputConfig2,depthwiseConv2dNativeConfig2,diagConfig2,dilation2DConfig,eluConfig2,eluGradConfig3,equalConfig2,erfConfig2,expConfig2,expandDimsConfig2,expm1Config2,fftConfig2,fillConfig2,flipLeftRightConfig2,floorConfig2,floorDivConfig2,fromPixelsConfig,fusedConv2DConfig2,fusedDepthwiseConv2DConfig2,gatherNdConfig2,gatherV2Config2,greaterConfig2,greaterEqualConfig2,identityConfig2,ifftConfig2,imagConfig2,isFiniteConfig2,isInfConfig2,isNaNConfig2,leakyReluConfig2,lessConfig2,lessEqualConfig2,linSpaceConfig2,log1pConfig2,logConfig2,logicalAndConfig2,logicalNotConfig2,logicalOrConfig2,maxConfig2,maxPool3DConfig2,maxPoolConfig2,maxPoolGrad3DConfig,maxPoolGradConfig3,maxPoolWithArgmaxConfig2,maximumConfig2,meanConfig2,minConfig2,minimumConfig2,mirrorPadConfig2,modConfig2,multinomialConfig2,multiplyConfig2,negConfig2,nonMaxSuppressionV3Config2,nonMaxSuppressionV4Config2,nonMaxSuppressionV5Config2,notEqualConfig2,oneHotConfig2,onesLikeConfig2,packConfig2,padV2Config2,powConfig2,preluConfig2,prodConfig2,rangeConfig2,realConfig2,realDivConfig2,reciprocalConfig2,relu6Config2,reluConfig2,reshapeConfig2,resizeBilinearConfig2,resizeBilinearGradConfig3,resizeNearestNeighborConfig2,resizeNearestNeighborGradConfig3,reverseConfig2,rotateWithOffsetConfig2,roundConfig2,rsqrtConfig2,scatterNdConfig2,selectConfig2,seluConfig2,sigmoidConfig2,signConfig2,sinConfig2,sinhConfig2,sliceConfig2,softmaxConfig2,softplusConfig2,spaceToBatchNDConfig2,sparseToDenseConfig2,splitVConfig2,sqrtConfig2,squareConfig2,squaredDifferenceConfig2,stepConfig2,stridedSliceConfig2,subConfig2,sumConfig2,tanConfig2,tanhConfig2,tileConfig2,topKConfig2,transposeConfig2,uniqueConfig2,unpackConfig2,unsortedSegmentSumConfig2,zerosLikeConfig2];for(let kernelConfig of kernelConfigs2)registerKernel(kernelConfig);var CppDType;(function(CppDType2){CppDType2[CppDType2.float32=0]="float32",CppDType2[CppDType2.int32=1]="int32",CppDType2[CppDType2.bool=2]="bool",CppDType2[CppDType2.string=3]="string",CppDType2[CppDType2.complex64=4]="complex64"})(CppDType||(CppDType={}));var FusableActivation;(function(FusableActivation2){FusableActivation2[FusableActivation2.linear=0]="linear",FusableActivation2[FusableActivation2.relu=1]="relu",FusableActivation2[FusableActivation2.relu6=2]="relu6",FusableActivation2[FusableActivation2.prelu=3]="prelu",FusableActivation2[FusableActivation2.leakyrelu=4]="leakyrelu"})(FusableActivation||(FusableActivation={}));var wasmFusedMatMul;function setup(backend22){wasmFusedMatMul=backend22.wasm.cwrap(_FusedMatMul,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function fusedBatchMatMul(args){let{inputs,backend:backend22,attrs}=args,{a,b,bias,preluActivationWeights}=inputs;if(a.dtype!=="float32"||b.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA,transposeB,activation:activation2,leakyreluAlpha}=attrs,aId=backend22.dataIdMap.get(a.dataId).id,bId=backend22.dataIdMap.get(b.dataId).id,biasId=0;if(bias!=null){let biasData=backend22.dataIdMap.get(bias.dataId);if(biasData.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${biasData.shape.length}.`);biasId=biasData.id}let preluActivationWeightsId=preluActivationWeights==null?0:backend22.dataIdMap.get(preluActivationWeights.dataId).id,fusedActivation=FusableActivation[activation2];if(fusedActivation==null)throw new Error(`${activation2} activation not yet supported for FusedConv2D in the wasm backend.`);let leftDim=transposeA?a.shape[2]:a.shape[1],rightDim=transposeB?b.shape[1]:b.shape[2],batchDim=a.shape[0],out=backend22.makeOutput([batchDim,leftDim,rightDim],a.dtype),outId=backend22.dataIdMap.get(out.dataId).id,aShapeBytes=new Uint8Array(new Int32Array(a.shape).buffer),bShapeBytes=new Uint8Array(new Int32Array(b.shape).buffer);return wasmFusedMatMul(aId,aShapeBytes,a.shape.length,bId,bShapeBytes,b.shape.length,transposeA,transposeB,fusedActivation,biasId,preluActivationWeightsId,leakyreluAlpha||0,outId),out}var fusedMatMulConfig={kernelName:_FusedMatMul,backendName:"wasm",setupFunc:setup,kernelFunc:fusedBatchMatMul};function createUnaryKernelConfig(kernelName){let wasmFunc9;function setupFunc3(backend22){wasmFunc9=backend22.wasm.cwrap(kernelName,null,["number","number"])}function kernelFunc3(args){let{backend:backend22,inputs:{x}}=args,xId=backend22.dataIdMap.get(x.dataId).id,out=backend22.makeOutput(x.shape,x.dtype),outId=backend22.dataIdMap.get(out.dataId).id;return util_exports.sizeFromShape(out.shape)===0||wasmFunc9(xId,outId),out}return{kernelName,backendName:"wasm",setupFunc:setupFunc3,kernelFunc:kernelFunc3}}var absConfig3=createUnaryKernelConfig(Abs);function createBinaryKernelConfig(kernelName,supportsFullBroadcast17,dtype){let wasmFunc9;function setupFunc3(backend22){wasmFunc9=backend22.wasm.cwrap(kernelName,null,["number","array","number","number","array","number","number","number"])}function kernelFunc3(args){let{backend:backend22,inputs}=args,{a,b}=inputs,aId=backend22.dataIdMap.get(a.dataId).id,bId=backend22.dataIdMap.get(b.dataId).id,outputType=dtype!=null?dtype:a.dtype,newShape=backend_util_exports.assertAndGetBroadcastShape(a.shape,b.shape),out=backend22.makeOutput(newShape,outputType);if(util_exports.sizeFromShape(newShape)===0)return out;let aShapeBytes=new Uint8Array(new Int32Array(a.shape).buffer),bShapeBytes=new Uint8Array(new Int32Array(b.shape).buffer),outId=backend22.dataIdMap.get(out.dataId).id,kernelFunc4=()=>wasmFunc9(aId,aShapeBytes,a.shape.length,bId,bShapeBytes,b.shape.length,CppDType[a.dtype],outId);if(supportsFullBroadcast17&&a.dtype==="float32")return kernelFunc4(),out;let aBroadcastDims=backend_util_exports.getBroadcastDims(a.shape,newShape),bBroadcastDims=backend_util_exports.getBroadcastDims(b.shape,newShape),loopsOverAllOfA=aBroadcastDims.every((v,i)=>v===i),loopsOverAllOfB=bBroadcastDims.every((v,i)=>v===i);if(loopsOverAllOfA&&loopsOverAllOfB)return kernelFunc4(),out;throw new Error(`Broadcasting along outer dims is not yet supported for ${a.dtype} ${kernelName}.`)}return{kernelName,backendName:"wasm",setupFunc:setupFunc3,kernelFunc:kernelFunc3}}var supportsFullBroadcast=!0,addConfig3=createBinaryKernelConfig(Add,supportsFullBroadcast),wasmFunc;function setupFunc(backend22){wasmFunc=backend22.wasm.cwrap(AddN,null,["array","number","number","number"])}function addn(args){let{inputs,backend:backend22}=args,out=backend22.makeOutput(inputs[0].shape,inputs[0].dtype);if(util_exports.sizeFromShape(out.shape)===0)return out;let inputIds=inputs.map(x=>backend22.dataIdMap.get(x.dataId).id),inputIdsBytes=new Uint8Array(new Int32Array(inputIds).buffer),outId=backend22.dataIdMap.get(out.dataId).id;return wasmFunc(inputIdsBytes,inputIds.length,CppDType[out.dtype],outId),out}var addNConfig3={kernelName:AddN,backendName:"wasm",setupFunc,kernelFunc:addn};function identity3(args){let{inputs:{x},backend:backend22}=args,out=backend22.makeOutput(x.shape,x.dtype),inVals=backend22.typedArrayFromHeap(x);return backend22.typedArrayFromHeap(out).set(inVals),out}var identityConfig3={kernelName:Identity,backendName:"wasm",kernelFunc:identity3},wasmTranspose;function setup2(backend22){wasmTranspose=backend22.wasm.cwrap(Transpose,null,["number","array","number","number","number","array","number"])}function transpose4(args){let{inputs,backend:backend22,attrs}=args,[reducedShape,perm]=removeOneSizeDims(inputs.x.shape,attrs.perm),permIsNoOp=!0;for(let i=0;i=i&&(minValIdx===-1||newPerm[minValIdx]>newPerm[j])&&(minValIdx=j);newPerm[minValIdx]=i}return[newShape,newPerm]}var transposeConfig3={kernelName:Transpose,backendName:"wasm",kernelFunc:transpose4,setupFunc:setup2};function permuteAxesAndTranspose(x,axis,backend22){let xShape=x.shape,xRank=x.shape.length,originalAxes=util_exports.parseAxisParam(axis,xShape),axes=originalAxes,permutedAxes=backend_util_exports.getAxesPermutation(axes,xRank),xTransposed=null,inputWasTransposed=!1;if(permutedAxes!=null){let newShape=new Array(xRank);for(let i=0;i`new shape: ${$shape}, old shape: ${x.shape}. New shape and old shape must have the same number of elements.`),{dataId:x.dataId,shape:$shape,dtype:x.dtype}}var reshapeConfig3={kernelName:Reshape,backendName:"wasm",kernelFunc:reshape4},wasmBatchMatMul;function setup5(backend22){wasmBatchMatMul=backend22.wasm.cwrap(BatchMatMul,null,["number","array","number","number","array","number","number","number","number"])}function batchMatMul3(args){let{inputs,backend:backend22,attrs}=args,{a,b}=inputs,{transposeA,transposeB}=attrs;if(a.dtype!=="float32"||b.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let aRank=a.shape.length,bRank=b.shape.length,innerShapeA=transposeA?a.shape[aRank-2]:a.shape[aRank-1],innerShapeB=transposeB?b.shape[bRank-1]:b.shape[bRank-2],outerShapeA=transposeA?a.shape[aRank-1]:a.shape[aRank-2],outerShapeB=transposeB?b.shape[bRank-2]:b.shape[bRank-1],outerDimsA=a.shape.slice(0,-2),outerDimsB=b.shape.slice(0,-2),batchDimA=util_exports.sizeFromShape(outerDimsA),batchDimB=util_exports.sizeFromShape(outerDimsB),batchDimsCompatible=batchDimA===batchDimB||batchDimA===1||batchDimB===1;util_exports.assert(aRank>=2&&bRank>=2&&batchDimsCompatible,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${outerDimsA}) and (${outerDimsB}).`);let outShape=(batchDimA>batchDimB?a.shape.slice(0,-2):b.shape.slice(0,-2)).concat([outerShapeA,outerShapeB]);util_exports.assert(innerShapeA===innerShapeB,()=>`Error in matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${a.shape} and ${b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);let a3dShape=transposeA?[batchDimA,innerShapeA,outerShapeA]:[batchDimA,outerShapeA,innerShapeA],b3dShape=transposeB?[batchDimB,outerShapeB,innerShapeB]:[batchDimB,innerShapeB,outerShapeB],a3d=reshape4({inputs:{x:a},backend:backend22,attrs:{shape:a3dShape}}),b3d=reshape4({inputs:{x:b},backend:backend22,attrs:{shape:b3dShape}}),a3dId=backend22.dataIdMap.get(a3d.dataId).id,b3dId=backend22.dataIdMap.get(b3d.dataId).id,leftDim=transposeA?a3d.shape[2]:a3d.shape[1],rightDim=transposeB?b3d.shape[1]:b3d.shape[2],batchDim=Math.max(batchDimA,batchDimB),out=backend22.makeOutput([batchDim,leftDim,rightDim],a3d.dtype),outId=backend22.dataIdMap.get(out.dataId).id,aShapeBytes=new Uint8Array(new Int32Array(a3d.shape).buffer),bShapeBytes=new Uint8Array(new Int32Array(b3d.shape).buffer);return wasmBatchMatMul(a3dId,aShapeBytes,a3d.shape.length,b3dId,bShapeBytes,b3d.shape.length,transposeA,transposeB,outId),out.shape=outShape,out}var batchMatMulConfig3={kernelName:BatchMatMul,backendName:"wasm",setupFunc:setup5,kernelFunc:batchMatMul3};function cast4(args){let{inputs:{x},attrs:{dtype},backend:backend22}=args,out=backend22.makeOutput(x.shape,dtype),inVals=backend22.typedArrayFromHeap(x);return backend22.typedArrayFromHeap(out).set(inVals),out}var castConfig3={kernelName:Cast,backendName:"wasm",kernelFunc:cast4},wasmClip;function setup6(backend22){wasmClip=backend22.wasm.cwrap(ClipByValue,null,["number","number","number","number"])}function clip2(args){let{inputs,backend:backend22,attrs}=args,{x}=inputs,{clipValueMin,clipValueMax}=attrs,xId=backend22.dataIdMap.get(x.dataId).id,out=backend22.makeOutput(x.shape,x.dtype),outId=backend22.dataIdMap.get(out.dataId).id;return wasmClip(xId,clipValueMin,clipValueMax,outId),out}var clipByValueConfig2={kernelName:ClipByValue,backendName:"wasm",setupFunc:setup6,kernelFunc:clip2};function concat4(args){let{inputs,backend:backend22}=args,axis=util_exports.parseAxisParam(args.attrs.axis,inputs[0].shape)[0],outShape=backend_util_exports.computeOutShape(inputs.map(t=>t.shape),axis),$inputs=inputs.filter(t=>util_exports.sizeFromShape(t.shape)>0);if($inputs.length===1)return identity3({inputs:{x:$inputs[0]},backend:backend22});let out=backend22.makeOutput(outShape,inputs[0].dtype);if(util_exports.sizeFromShape(outShape)===0)return out;let shapes=$inputs.map(t=>t.shape);if(backend_util_exports.assertParamsConsistent(shapes,axis),$inputs[0].dtype==="string"){let inputs2D=$inputs.map(t=>{let innerSize=util_exports.sizeFromShape(t.shape.slice(axis));return reshape4({inputs:{x:t},backend:backend22,attrs:{shape:[-1,innerSize]}})}),inputsValShapes=inputs2D.map(t=>({vals:backend22.readSync(t.dataId),shape:t.shape}));outShape=backend_util_exports.computeOutShape(inputs2D.map(t=>t.shape),1);let simplyConcat=inputs2D[0].shape[0]===1,outVals2=concatImpl(inputsValShapes,outShape,inputs[0].dtype,simplyConcat),finalOutShape=backend_util_exports.computeOutShape($inputs.map(t=>t.shape),axis);out.shape=finalOutShape;let outData=backend22.dataIdMap.get(out.dataId);return outData.stringBytes=backend_util_exports.fromStringArrayToUint8(outVals2),out}let batchDim=util_exports.sizeFromShape($inputs[0].shape.slice(0,axis)),sumInnerDims=0,innerDims=$inputs.map(input2=>{let innerDim=util_exports.sizeFromShape(input2.shape.slice(axis));return sumInnerDims+=innerDim,innerDim}),inVals=$inputs.map(input2=>backend22.typedArrayFromHeap(input2)),outVals=backend22.typedArrayFromHeap(out);for(let b=0;b`cumsum does not support ${x.dtype} tensors in the WASM backend`);let permutation=backend_util_exports.getAxesPermutation([axis],xRank),permutedX=x;permutation!==null&&(permutedX=transpose4({inputs:{x},attrs:{perm:permutation},backend:backend22}));let permutedAxis=backend_util_exports.getInnerMostAxes(1,xRank)[0];backend_util_exports.assertAxesAreInnerMostDims("cumsum",[permutedAxis],xRank);let permutedOut=backend22.makeOutput(permutedX.shape,permutedX.dtype),finalDim=permutedX.shape[permutedAxis],permutedXId=backend22.dataIdMap.get(permutedX.dataId).id,permutedOutId=backend22.dataIdMap.get(permutedOut.dataId).id;wasmCumsum(permutedXId,exclusive?1:0,reverse5?1:0,finalDim,permutedOutId,CppDType[x.dtype]);let out=permutedOut;if(permutation!==null){let undoPermutation=backend_util_exports.getUndoAxesPermutation(permutation);out=transpose4({inputs:{x:permutedOut},attrs:{perm:undoPermutation},backend:backend22}),backend22.disposeData(permutedX.dataId),backend22.disposeData(permutedOut.dataId)}return out}var cumsumConfig3={kernelName:Cumsum,backendName:"wasm",setupFunc:setup10,kernelFunc:cumsum4},wasmDepthToSpace;function setup11(backend22){wasmDepthToSpace=backend22.wasm.cwrap(DepthToSpace,null,["number","number","number","array","number","array","array","number","number"])}function depthToSpace4(args){let{backend:backend22,inputs,attrs}=args,{x}=inputs,{blockSize,dataFormat}=attrs;util_exports.assert(blockSize>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);let batchSize=x.shape[0],inputHeight=dataFormat==="NHWC"?x.shape[1]:x.shape[2],inputWidth=dataFormat==="NHWC"?x.shape[2]:x.shape[3],inputDepth=dataFormat==="NHWC"?x.shape[3]:x.shape[1],outputHeight=inputHeight*blockSize,outputWidth=inputWidth*blockSize,outputDepth=inputDepth/(blockSize*blockSize),outputShape=dataFormat==="NHWC"?[batchSize,outputHeight,outputWidth,outputDepth]:[batchSize,outputDepth,outputHeight,outputWidth],out=backend22.makeOutput(outputShape,"float32"),xId=backend22.dataIdMap.get(x.dataId).id,xStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(x.shape)).buffer),outputShapeBytes=new Uint8Array(new Int32Array(outputShape).buffer),outStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(outputShape)).buffer),outId=backend22.dataIdMap.get(out.dataId).id;return wasmDepthToSpace(xId,blockSize,dataFormat==="NHWC"?1:0,xStridesBytes,x.shape.length-1,outputShapeBytes,outStridesBytes,outputShape.length,outId),out}var depthToSpaceConfig3={kernelName:DepthToSpace,backendName:"wasm",setupFunc:setup11,kernelFunc:depthToSpace4},wasmDepthwiseConv2d;function setup12(backend22){wasmDepthwiseConv2d=backend22.wasm.cwrap(DepthwiseConv2dNative,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function depthwiseConv2d3(args){let{inputs,attrs,backend:backend22}=args,{x,filter}=inputs,xId=backend22.dataIdMap.get(x.dataId).id,filterId=backend22.dataIdMap.get(filter.dataId).id,{strides,dilations,pad:pad3,dimRoundingMode}=attrs,$dilations=dilations==null?[1,1]:dilations,convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,$dilations,pad3,dimRoundingMode,!0),filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,inputChannels=convInfo.inChannels,outputChannels=convInfo.outChannels,isSamePad=convInfo.padInfo.type==="SAME"?1:0;if(convInfo.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);let out=backend22.makeOutput(convInfo.outShape,"float32"),outId=backend22.dataIdMap.get(out.dataId).id;return wasmDepthwiseConv2d(xId,x.shape[0],x.shape[1],x.shape[2],filterId,filterHeight,filterWidth,padTop,padRight,padBottom,padLeft,isSamePad,dilationHeight,dilationWidth,strideHeight,strideWidth,inputChannels,outputChannels,outId),out}var depthwiseConv2dNativeConfig3={kernelName:DepthwiseConv2dNative,backendName:"wasm",setupFunc:setup12,kernelFunc:depthwiseConv2d3},supportsFullBroadcast2=!1,equalConfig3=createBinaryKernelConfig(Equal,supportsFullBroadcast2,"bool"),expConfig3=createUnaryKernelConfig(Exp);function expandDims4(args){let{inputs,attrs,backend:backend22}=args,{input:input2}=inputs,{dim}=attrs,inputRank=input2.shape.length,newShape=input2.shape.slice(),$dim=dim;return dim<0&&(util_exports.assert(-(inputRank+1)<=dim,()=>`Axis must be in the interval [${-(inputRank+1)}, ${inputRank}]`),$dim=inputRank+dim+1),newShape.splice($dim,0,1),reshape4({inputs:{x:input2},backend:backend22,attrs:{shape:newShape}})}var expandDimsConfig3={kernelName:ExpandDims,backendName:"wasm",kernelFunc:expandDims4};function fill4(args){let{attrs:{shape,value,dtype},backend:backend22}=args,out=backend22.makeOutput(shape,dtype);return backend22.typedArrayFromHeap(out).fill(value),out}var fillConfig3={kernelName:Fill,backendName:"wasm",kernelFunc:fill4},wasmFlipLeftRight;function setup13(backend22){wasmFlipLeftRight=backend22.wasm.cwrap(FlipLeftRight,null,["number","number","number","number","number","number"])}function flipLeftRight2(args){let{inputs,backend:backend22}=args,{image:image3}=inputs,out=backend22.makeOutput(image3.shape,image3.dtype),imageId=backend22.dataIdMap.get(image3.dataId).id,outId=backend22.dataIdMap.get(out.dataId).id,[batch,imageHeight,imageWidth,numChannels]=image3.shape;return wasmFlipLeftRight(imageId,batch,imageHeight,imageWidth,numChannels,outId),out}var flipLeftRightConfig3={kernelName:FlipLeftRight,backendName:"wasm",kernelFunc:flipLeftRight2,setupFunc:setup13},floorConfig3=createUnaryKernelConfig(Floor),supportsFullBroadcast3=!1,floorDivConfig3=createBinaryKernelConfig(FloorDiv,supportsFullBroadcast3),wasmBatchNorm;function setup14(backend22){wasmBatchNorm=backend22.wasm.cwrap(FusedBatchNorm,null,["number","number","number","number","number","number","number"])}function fusedBatchNorm(args){let{backend:backend22,inputs,attrs}=args,{varianceEpsilon}=attrs,{x,mean:mean4,variance,offset,scale:scale2}=inputs,xId=backend22.dataIdMap.get(x.dataId).id,meanId=backend22.dataIdMap.get(mean4.dataId).id,varianceId=backend22.dataIdMap.get(variance.dataId).id,offsetId=offset!=null?backend22.dataIdMap.get(offset.dataId).id:0,scaleId=scale2!=null?backend22.dataIdMap.get(scale2.dataId).id:0,out=backend22.makeOutput(x.shape,x.dtype);if(util_exports.sizeFromShape(x.shape)===0)return out;let outId=backend22.dataIdMap.get(out.dataId).id;return wasmBatchNorm(xId,meanId,varianceId,offsetId,scaleId,varianceEpsilon,outId),out}var fusedBatchNormConfig={kernelName:FusedBatchNorm,backendName:"wasm",setupFunc:setup14,kernelFunc:fusedBatchNorm},wasmFusedConv2d;function setup15(backend22){wasmFusedConv2d=backend22.wasm.cwrap(FusedConv2D,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function fusedConv2d2(args){let{inputs,attrs,backend:backend22}=args,{x,filter,bias,preluActivationWeights}=inputs,{strides,pad:pad3,dilations,dataFormat,dimRoundingMode,activation:activation2,leakyreluAlpha}=attrs,convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,dilations,pad3,dimRoundingMode),fusedActivation=FusableActivation[activation2];if(fusedActivation==null)throw new Error(`${activation2} activation not yet supported for FusedConv2D in the wasm backend.`);let xId=backend22.dataIdMap.get(x.dataId).id,filterId=backend22.dataIdMap.get(filter.dataId).id,outputChannels=convInfo.outChannels,biasId=0;if(bias!=null){let biasData=backend22.dataIdMap.get(bias.dataId);if(biasData.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${biasData.shape.length}.`);if(biasData.shape[0]!==outputChannels)throw new Error(`FusedConv2D bias shape (${biasData.shape}) does not match the number of output channels (${outputChannels})`);biasId=biasData.id}let filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,inputChannels=convInfo.inChannels,isSamePad=convInfo.padInfo.type==="SAME"?1:0,batchSize=convInfo.batchSize,inHeight=convInfo.inHeight,inWidth=convInfo.inWidth;if(dataFormat!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${dataFormat}'. Please use 'NHWC'.`);let out=backend22.makeOutput(convInfo.outShape,"float32"),outId=backend22.dataIdMap.get(out.dataId).id,preluActivationWeightsId=preluActivationWeights==null?0:backend22.dataIdMap.get(preluActivationWeights.dataId).id;return wasmFusedConv2d(xId,batchSize,inHeight,inWidth,filterId,filterHeight,filterWidth,biasId,padTop,padRight,padBottom,padLeft,isSamePad,dilationHeight,dilationWidth,strideHeight,strideWidth,inputChannels,outputChannels,fusedActivation,preluActivationWeightsId,leakyreluAlpha||0,outId),out}var fusedConv2DConfig3={kernelName:FusedConv2D,backendName:"wasm",setupFunc:setup15,kernelFunc:fusedConv2d2},wasmFusedDepthwiseConv2d;function setup16(backend22){wasmFusedDepthwiseConv2d=backend22.wasm.cwrap(FusedDepthwiseConv2D,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function fusedDepthwiseConv2d(args){let{inputs,attrs,backend:backend22}=args,{x,filter,bias,preluActivationWeights}=inputs,{strides,pad:pad3,dilations,dataFormat,dimRoundingMode,activation:activation2,leakyreluAlpha}=attrs,convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,dilations,pad3,dimRoundingMode,!0),fusedActivation=FusableActivation[activation2];if(fusedActivation==null)throw new Error(`${activation2} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let xId=backend22.dataIdMap.get(x.dataId).id,filterId=backend22.dataIdMap.get(filter.dataId).id,outputChannels=convInfo.outChannels,biasId=0;if(bias!=null){let biasData=backend22.dataIdMap.get(bias.dataId);if(biasData.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${biasData.shape.length}.`);if(biasData.shape[0]!==outputChannels)throw new Error(`FusedDepthwiseConv2D bias shape (${biasData.shape}) does not match the number of output channels (${outputChannels})`);biasId=biasData.id}let filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,inputChannels=convInfo.inChannels,isSamePad=convInfo.padInfo.type==="SAME"?1:0,batchSize=convInfo.batchSize,inHeight=convInfo.inHeight,inWidth=convInfo.inWidth;if(dataFormat!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${dataFormat}'. Please use 'NHWC'.`);let out=backend22.makeOutput(convInfo.outShape,"float32"),outId=backend22.dataIdMap.get(out.dataId).id,preluActivationWeightsId=preluActivationWeights==null?0:backend22.dataIdMap.get(preluActivationWeights.dataId).id;return wasmFusedDepthwiseConv2d(xId,batchSize,inHeight,inWidth,filterId,filterHeight,filterWidth,biasId,padTop,padRight,padBottom,padLeft,isSamePad,dilationHeight,dilationWidth,strideHeight,strideWidth,inputChannels,outputChannels,fusedActivation,preluActivationWeightsId,leakyreluAlpha||0,outId),out}var fusedDepthwiseConv2DConfig3={kernelName:FusedDepthwiseConv2D,backendName:"wasm",setupFunc:setup16,kernelFunc:fusedDepthwiseConv2d},wasmGatherNd;function setup17(backend22){wasmGatherNd=backend22.wasm.cwrap(GatherNd,null,["number","number","number","number","number","number","array","number"])}function gatherNd3(args){let{backend:backend22,inputs}=args,{params,indices}=inputs,[resultShape,numSlices,sliceSize,strides]=gather_nd_util_exports.prepareAndValidate(params,indices),out=backend22.makeOutput(resultShape,params.dtype);if(numSlices===0)return out;let indicesShape=indices.shape,sliceRank=indicesShape[indicesShape.length-1],xId=backend22.dataIdMap.get(params.dataId).id,indicesId=backend22.dataIdMap.get(indices.dataId).id,stridesBytes=new Uint8Array(new Int32Array(strides).buffer),outId=backend22.dataIdMap.get(out.dataId).id;return wasmGatherNd(xId,CppDType[params.dtype],indicesId,numSlices,sliceRank,sliceSize,stridesBytes,outId),out}var gatherNdConfig3={kernelName:GatherNd,backendName:"wasm",setupFunc:setup17,kernelFunc:gatherNd3},wasmGather;function setup18(backend22){wasmGather=backend22.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function gatherV23(args){let{backend:backend22,inputs,attrs}=args,{x,indices}=inputs,{axis,batchDims}=attrs,parsedAxis=util_exports.parseAxisParam(axis,x.shape)[0],shapeInfo=backend_util_exports.segment_util.collectGatherOpShapeInfo(x,indices,parsedAxis,batchDims),flattenX=reshape4({inputs:{x},attrs:{shape:[shapeInfo.batchSize,shapeInfo.outerSize,shapeInfo.dimSize,shapeInfo.sliceSize]},backend:backend22}),indicesSize=util_exports.sizeFromShape(indices.shape),flattenIndex=reshape4({inputs:{x:indices},attrs:{shape:[shapeInfo.batchSize,indicesSize/shapeInfo.batchSize]},backend:backend22}),flattenOutputShape=[shapeInfo.batchSize,shapeInfo.outerSize,indicesSize/shapeInfo.batchSize,shapeInfo.sliceSize],out=backend22.makeOutput(flattenOutputShape,x.dtype);if(util_exports.sizeFromShape(x.shape)===0)return out;let stridesSize=flattenX.shape.length-1,xId=backend22.dataIdMap.get(flattenX.dataId).id,indicesId=backend22.dataIdMap.get(flattenIndex.dataId).id,outId=backend22.dataIdMap.get(out.dataId).id,xStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(flattenX.shape)).buffer),outStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(flattenOutputShape)).buffer);return wasmGather(xId,CppDType[x.dtype],xStridesBytes,stridesSize,indicesId,shapeInfo.batchSize,outStridesBytes,outId),out.shape=shapeInfo.outputShape,out}var gatherV2Config3={kernelName:GatherV2,backendName:"wasm",setupFunc:setup18,kernelFunc:gatherV23},supportsFullBroadcast4=!1,greaterConfig3=createBinaryKernelConfig(Greater,supportsFullBroadcast4,"bool"),supportsFullBroadcast5=!1,greaterEqualConfig3=createBinaryKernelConfig(GreaterEqual,supportsFullBroadcast5,"bool"),wasmFunc3;function setupFunc2(backend22){wasmFunc3=backend22.wasm.cwrap(LeakyRelu,null,["number","number","number"])}function leakyRelu4(args){let{inputs:{x},attrs:{alpha},backend:backend22}=args,xId=backend22.dataIdMap.get(x.dataId).id,out=backend22.makeOutput(x.shape,x.dtype);if(util_exports.sizeFromShape(x.shape)!==0){let outId=backend22.dataIdMap.get(out.dataId).id;wasmFunc3(xId,alpha,outId)}return out}var leakyReluConfig3={kernelName:LeakyRelu,backendName:"wasm",setupFunc:setupFunc2,kernelFunc:leakyRelu4},supportsFullBroadcast6=!1,lessConfig3=createBinaryKernelConfig(Less,supportsFullBroadcast6,"bool"),supportsFullBroadcast7=!1,lessEqualConfig3=createBinaryKernelConfig(LessEqual,supportsFullBroadcast7,"bool"),logConfig3=createUnaryKernelConfig(Log),supportsFullBroadcast8=!1,logicalAndConfig3=createBinaryKernelConfig(LogicalAnd,supportsFullBroadcast8,"bool"),wasmMax;function setup19(backend22){wasmMax=backend22.wasm.cwrap(Max,null,["number, number, number"])}function max4(args){let{backend:backend22,inputs,attrs}=args,{reductionIndices:axis,keepDims}=attrs,{x}=inputs,inputId=backend22.dataIdMap.get(x.dataId).id,input2=x,{transposed,axes,originalAxes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend22);if(inputWasTransposed){let transposedId=backend22.dataIdMap.get(transposed.dataId).id;input2=transposed,inputId=transposedId}let inputRank=input2.shape.length;backend_util_exports.assertAxesAreInnerMostDims("max",axes,inputRank);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(input2.shape,axes),reduceSize=util_exports.sizeFromShape(reduceShape),out=backend22.makeOutput(outShape,x.dtype);if(util_exports.sizeFromShape(input2.shape)!==0){let outId=backend22.dataIdMap.get(out.dataId).id;wasmMax(inputId,reduceSize,outId)}if(inputWasTransposed&&backend22.disposeData(transposed.dataId),keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(out.shape,originalAxes);out.shape=newShape}return out}var maxConfig3={kernelName:Max,backendName:"wasm",setupFunc:setup19,kernelFunc:max4},supportsFullBroadcast9=!1,maximumConfig3=createBinaryKernelConfig(Maximum,supportsFullBroadcast9),wasmMaxPool;function setup20(backend22){wasmMaxPool=backend22.wasm.cwrap(MaxPool,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function maxPool4(args){let{inputs,attrs,backend:backend22}=args,x=inputs.x,xId=backend22.dataIdMap.get(x.dataId).id,{filterSize,strides,pad:pad3,dimRoundingMode}=attrs,convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,1,pad3,dimRoundingMode),filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,inputChannels=convInfo.inChannels,outputChannels=convInfo.outChannels;if(convInfo.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);let out=backend22.makeOutput(convInfo.outShape,"float32"),outId=backend22.dataIdMap.get(out.dataId).id;return wasmMaxPool(xId,x.shape[0],x.shape[1],x.shape[2],filterHeight,filterWidth,padTop,padRight,padBottom,padLeft,dilationHeight,dilationWidth,strideHeight,strideWidth,inputChannels,outputChannels,outId),out}var maxPoolConfig3={kernelName:MaxPool,backendName:"wasm",setupFunc:setup20,kernelFunc:maxPool4},wasmMean;function setup21(backend22){wasmMean=backend22.wasm.cwrap(Mean,null,["number, number, number"])}function mean3(args){let{backend:backend22,inputs,attrs}=args,{axis,keepDims}=attrs,{x}=inputs,xId=backend22.dataIdMap.get(x.dataId).id,inputId=xId,input2=x,{transposed,axes,originalAxes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend22),reductionAxes=axes;if(inputWasTransposed){let transposedId=backend22.dataIdMap.get(transposed.dataId).id;transposedId!==xId&&(input2=transposed,inputId=transposedId,reductionAxes=backend_util_exports.getInnerMostAxes(reductionAxes.length,input2.shape.length))}backend_util_exports.assertAxesAreInnerMostDims("mean",reductionAxes,input2.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(input2.shape,reductionAxes),reduceSize=util_exports.sizeFromShape(reduceShape),castedInput=input2;input2.dtype!=="float32"&&(castedInput=cast4({backend:backend22,inputs:{x:input2},attrs:{dtype:"float32"}}),inputId=backend22.dataIdMap.get(castedInput.dataId).id);let out=backend22.makeOutput(outShape,"float32");if(util_exports.sizeFromShape(input2.shape)!==0){let outId=backend22.dataIdMap.get(out.dataId).id;wasmMean(inputId,reduceSize,outId)}if(inputWasTransposed&&backend22.disposeData(transposed.dataId),keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(out.shape,originalAxes);out.shape=newShape}return input2.dtype!=="float32"&&backend22.disposeData(castedInput.dataId),out}var meanConfig3={kernelName:Mean,backendName:"wasm",setupFunc:setup21,kernelFunc:mean3},wasmMin;function setup22(backend22){wasmMin=backend22.wasm.cwrap(Min,null,["number, number, number"])}function min4(args){let{backend:backend22,inputs,attrs}=args,{axis,keepDims}=attrs,{x}=inputs,xId=backend22.dataIdMap.get(x.dataId).id,inputId=xId,input2=x,{transposed,axes,originalAxes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend22);if(inputWasTransposed){let transposedId=backend22.dataIdMap.get(transposed.dataId).id;transposedId!==xId&&(input2=transposed,inputId=transposedId)}let inputRank=input2.shape.length;backend_util_exports.assertAxesAreInnerMostDims("min",axes,inputRank);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(input2.shape,axes),reduceSize=util_exports.sizeFromShape(reduceShape),out=backend22.makeOutput(outShape,input2.dtype);if(util_exports.sizeFromShape(input2.shape)!==0){let outId=backend22.dataIdMap.get(out.dataId).id;wasmMin(inputId,reduceSize,outId)}if(inputWasTransposed&&backend22.disposeData(transposed.dataId),keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(out.shape,originalAxes);out.shape=newShape}return out}var minConfig3={kernelName:Min,backendName:"wasm",setupFunc:setup22,kernelFunc:min4},supportsFullBroadcast10=!1,minimumConfig3=createBinaryKernelConfig(Minimum,supportsFullBroadcast10),supportsFullBroadcast11=!0,multiplyConfig3=createBinaryKernelConfig(Multiply,supportsFullBroadcast11),negConfig3=createUnaryKernelConfig(Neg);function parseResultStruct(backend22,resOffset){let result=new Int32Array(backend22.wasm.HEAPU8.buffer,resOffset,4),pSelectedIndices=result[0],selectedSize=result[1],pSelectedScores=result[2],pValidOutputs=result[3];return backend22.wasm._free(resOffset),{pSelectedIndices,selectedSize,pSelectedScores,pValidOutputs}}var wasmFunc4;function setup23(backend22){wasmFunc4=backend22.wasm.cwrap(NonMaxSuppressionV3,"number",["number","number","number","number","number"])}function kernelFunc(args){let{backend:backend22,inputs,attrs}=args,{iouThreshold,maxOutputSize,scoreThreshold}=attrs,{boxes,scores}=inputs,boxesId=backend22.dataIdMap.get(boxes.dataId).id,scoresId=backend22.dataIdMap.get(scores.dataId).id,resOffset=wasmFunc4(boxesId,scoresId,maxOutputSize,iouThreshold,scoreThreshold),{pSelectedIndices,selectedSize,pSelectedScores,pValidOutputs}=parseResultStruct(backend22,resOffset);return backend22.wasm._free(pSelectedScores),backend22.wasm._free(pValidOutputs),backend22.makeOutput([selectedSize],"int32",pSelectedIndices)}var nonMaxSuppressionV3Config3={kernelName:NonMaxSuppressionV3,backendName:"wasm",setupFunc:setup23,kernelFunc},wasmFunc5;function setup24(backend22){wasmFunc5=backend22.wasm.cwrap(NonMaxSuppressionV4,"number",["number","number","number","number","number","bool"])}function nonMaxSuppressionV43(args){let{backend:backend22,inputs,attrs}=args,{iouThreshold,maxOutputSize,scoreThreshold,padToMaxOutputSize}=attrs,{boxes,scores}=inputs,boxesId=backend22.dataIdMap.get(boxes.dataId).id,scoresId=backend22.dataIdMap.get(scores.dataId).id,resOffset=wasmFunc5(boxesId,scoresId,maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize),{pSelectedIndices,selectedSize,pSelectedScores,pValidOutputs}=parseResultStruct(backend22,resOffset);backend22.wasm._free(pSelectedScores);let selectedIndicesTensor=backend22.makeOutput([selectedSize],"int32",pSelectedIndices),validOutputsTensor=backend22.makeOutput([],"int32",pValidOutputs);return[selectedIndicesTensor,validOutputsTensor]}var nonMaxSuppressionV4Config3={kernelName:NonMaxSuppressionV4,backendName:"wasm",setupFunc:setup24,kernelFunc:nonMaxSuppressionV43},wasmFunc6;function setup25(backend22){wasmFunc6=backend22.wasm.cwrap(NonMaxSuppressionV5,"number",["number","number","number","number","number","number"])}function kernelFunc2(args){let{backend:backend22,inputs,attrs}=args,{iouThreshold,maxOutputSize,scoreThreshold,softNmsSigma}=attrs,{boxes,scores}=inputs,boxesId=backend22.dataIdMap.get(boxes.dataId).id,scoresId=backend22.dataIdMap.get(scores.dataId).id,resOffset=wasmFunc6(boxesId,scoresId,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma),{pSelectedIndices,selectedSize,pSelectedScores,pValidOutputs}=parseResultStruct(backend22,resOffset);backend22.wasm._free(pValidOutputs);let selectedIndicesTensor=backend22.makeOutput([selectedSize],"int32",pSelectedIndices),selectedScoresTensor=backend22.makeOutput([selectedSize],"float32",pSelectedScores);return[selectedIndicesTensor,selectedScoresTensor]}var nonMaxSuppressionV5Config3={kernelName:NonMaxSuppressionV5,backendName:"wasm",setupFunc:setup25,kernelFunc:kernelFunc2},supportsFullBroadcast12=!1,notEqualConfig3=createBinaryKernelConfig(NotEqual,supportsFullBroadcast12,"bool"),wasmOneHot;function setup26(backend22){wasmOneHot=backend22.wasm.cwrap(OneHot,null,["number","number","number","number","number"])}function oneHot4(args){let{inputs,backend:backend22,attrs}=args,{indices}=inputs,{depth,onValue,offValue}=attrs,out=backend22.makeOutput([...indices.shape,depth],"int32"),outId=backend22.dataIdMap.get(out.dataId).id,indicesId=backend22.dataIdMap.get(indices.dataId).id;return wasmOneHot(indicesId,depth,onValue,offValue,outId),out}var oneHotConfig3={kernelName:OneHot,backendName:"wasm",setupFunc:setup26,kernelFunc:oneHot4};function onesLike4(args){let{inputs:{x},backend:backend22}=args,out=backend22.makeOutput(x.shape,x.dtype);return backend22.typedArrayFromHeap(out).fill(1),out}var onesLikeConfig3={kernelName:OnesLike,backendName:"wasm",kernelFunc:onesLike4};function pack3(args){let{inputs,backend:backend22,attrs}=args,{axis}=attrs;if(inputs.length===1)return expandDims4({inputs:{input:inputs[0]},backend:backend22,attrs:{dim:axis}});let shape=inputs[0].shape,dtype=inputs[0].dtype;inputs.forEach(t=>{util_exports.assertShapesMatch(shape,t.shape,"All tensors passed to stack must have matching shapes"),util_exports.assert(dtype===t.dtype,()=>"All tensors passed to stack must have matching dtypes")});let expandedTensors=inputs.map(t=>expandDims4({inputs:{input:t},backend:backend22,attrs:{dim:axis}}));return concat4({inputs:expandedTensors,backend:backend22,attrs:{axis}})}var packConfig3={kernelName:Pack,backendName:"wasm",kernelFunc:pack3},wasmPadV2;function setup27(backend22){wasmPadV2=backend22.wasm.cwrap(PadV2,null,["number","array","number","number","array","array","number","number"])}function pad2(args){let{inputs:{x},backend:backend22,attrs:{paddings,constantValue}}=args,outShape=paddings.map((p2,i)=>p2[0]+x.shape[i]+p2[1]),xId=backend22.dataIdMap.get(x.dataId).id,out=backend22.makeOutput(outShape,x.dtype),outId=backend22.dataIdMap.get(out.dataId).id,xShapeBytes=new Uint8Array(new Int32Array(x.shape).buffer),prePaddingsFlat=paddings.map(padTuple=>padTuple[0]),postPaddingsFlat=paddings.map(padTuple=>padTuple[1]),prePaddingsBytes=new Uint8Array(new Int32Array(prePaddingsFlat).buffer),postPaddingsBytes=new Uint8Array(new Int32Array(postPaddingsFlat).buffer);return wasmPadV2(xId,xShapeBytes,x.shape.length,CppDType[x.dtype],prePaddingsBytes,postPaddingsBytes,constantValue,outId),out}var padV2Config3={kernelName:PadV2,backendName:"wasm",kernelFunc:pad2,setupFunc:setup27},supportsFullBroadcast13=!1,powConfig3=createBinaryKernelConfig(Pow,supportsFullBroadcast13),wasmPrelu;function setup28(backend22){wasmPrelu=backend22.wasm.cwrap(Prelu,null,["number","number","number"])}function prelu4(args){let{inputs,backend:backend22}=args,{x,alpha}=inputs,xId=backend22.dataIdMap.get(x.dataId).id,weightsId=backend22.dataIdMap.get(alpha.dataId).id,out=backend22.makeOutput(x.shape,"float32"),outId=backend22.dataIdMap.get(out.dataId).id;return wasmPrelu(xId,weightsId,outId),out}var preluConfig3={kernelName:Prelu,backendName:"wasm",setupFunc:setup28,kernelFunc:prelu4},wasmProd;function setup29(backend22){wasmProd=backend22.wasm.cwrap(Prod,null,["number","number","number","number"])}function prod4(args){let{backend:backend22,inputs,attrs}=args,{axis,keepDims}=attrs,{x}=inputs,xId=backend22.dataIdMap.get(x.dataId).id,inputId=xId,input2=x,{transposed,axes,originalAxes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend22),reductionAxes=axes;if(inputWasTransposed){let transposedId=backend22.dataIdMap.get(transposed.dataId).id;transposedId!==xId&&(input2=transposed,inputId=transposedId,reductionAxes=backend_util_exports.getInnerMostAxes(reductionAxes.length,input2.shape.length))}backend_util_exports.assertAxesAreInnerMostDims("prod",reductionAxes,input2.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(input2.shape,reductionAxes),reduceSize=util_exports.sizeFromShape(reduceShape),out=backend22.makeOutput(outShape,input2.dtype);if(util_exports.sizeFromShape(input2.shape)!==0){let outId=backend22.dataIdMap.get(out.dataId).id;wasmProd(inputId,reduceSize,CppDType[out.dtype],outId)}if(inputWasTransposed&&backend22.disposeData(transposed.dataId),keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(out.shape,originalAxes);out.shape=newShape}return out}var prodConfig3={kernelName:Prod,backendName:"wasm",setupFunc:setup29,kernelFunc:prod4},range4=args=>{let{backend:backend22,attrs}=args,{start,stop,step:step4,dtype}=attrs,values=rangeImpl(start,stop,step4,dtype),out=backend22.makeOutput([values.length],dtype);return backend22.typedArrayFromHeap(out).set(values),out},rangeConfig3={kernelName:Range,backendName:"wasm",kernelFunc:range4},supportsFullBroadcast14=!0,realDivConfig3=createBinaryKernelConfig(RealDiv,supportsFullBroadcast14),reluConfig3=createUnaryKernelConfig(Relu),relu6Config3=createUnaryKernelConfig(Relu6),wasmResizeBilinear;function setup30(backend22){wasmResizeBilinear=backend22.wasm.cwrap(ResizeBilinear,null,["number","number","number","number","number","number","number","number","number","number"])}function resizeBilinear4(args){let{backend:backend22,inputs,attrs}=args,{images}=inputs,{alignCorners,halfPixelCenters,size}=attrs,[newHeight,newWidth]=size,[batch,oldHeight,oldWidth,numChannels]=images.shape,outShape=[batch,newHeight,newWidth,numChannels],xData=backend22.dataIdMap.get(images.dataId),castedData;xData.dtype!=="float32"&&(castedData=cast4({backend:backend22,inputs:{x:images},attrs:{dtype:"float32"}}),xData=backend22.dataIdMap.get(castedData.dataId));let xId=xData.id,out=backend22.makeOutput(outShape,"float32");if(util_exports.sizeFromShape(images.shape)===0)return out;let outId=backend22.dataIdMap.get(out.dataId).id;return wasmResizeBilinear(xId,batch,oldHeight,oldWidth,numChannels,newHeight,newWidth,alignCorners?1:0,halfPixelCenters?1:0,outId),castedData!=null&&backend22.disposeData(castedData.dataId),out}var resizeBilinearConfig3={kernelName:ResizeBilinear,backendName:"wasm",setupFunc:setup30,kernelFunc:resizeBilinear4},wasmReverse;function setup31(backend22){wasmReverse=backend22.wasm.cwrap(Reverse,null,["number","array","number","array","number","number"])}function reverse4(args){let{inputs,backend:backend22,attrs}=args,{x}=inputs,{dims}=attrs,axes=util_exports.parseAxisParam(dims,x.shape);if(x.shape.length===0)return identity3({inputs:{x},backend:backend22});let out=backend22.makeOutput(x.shape,x.dtype),xId=backend22.dataIdMap.get(x.dataId).id,outId=backend22.dataIdMap.get(out.dataId).id,axesBytes=new Uint8Array(new Int32Array(axes).buffer),outShapeBytes=new Uint8Array(new Int32Array(x.shape).buffer);return wasmReverse(xId,axesBytes,axes.length,outShapeBytes,x.shape.length,outId),reshape4({inputs:{x:out},attrs:{shape:x.shape},backend:backend22})}var reverseConfig3={kernelName:Reverse,backendName:"wasm",kernelFunc:reverse4,setupFunc:setup31},wasmRotate;function setup32(backend22){wasmRotate=backend22.wasm.cwrap(RotateWithOffset,null,["number","number","number","number","number","number","number","number","array","number","number"])}function rotateWithOffset2(args){let{inputs,backend:backend22,attrs}=args,{image:image3}=inputs,{radians,fillValue,center}=attrs,out=backend22.makeOutput(image3.shape,image3.dtype),imageId=backend22.dataIdMap.get(image3.dataId).id,outId=backend22.dataIdMap.get(out.dataId).id,[batch,imageHeight,imageWidth,numChannels]=image3.shape,[centerX,centerY]=backend_util_exports.getImageCenter(center,imageHeight,imageWidth),fillIsBlack=fillValue===0,fullOpacityValue=255,fillValues2=typeof fillValue=="number"?[fillValue,fillValue,fillValue,fillIsBlack?0:fullOpacityValue]:[...fillValue,fullOpacityValue],fillBytes=new Uint8Array(new Int32Array(fillValues2).buffer);return wasmRotate(imageId,batch,imageHeight,imageWidth,numChannels,radians,centerX,centerY,fillBytes,fillValues2.length,outId),out}var rotateWithOffsetConfig3={kernelName:RotateWithOffset,backendName:"wasm",kernelFunc:rotateWithOffset2,setupFunc:setup32},roundConfig3=createUnaryKernelConfig(Round),rsqrtConfig3=createUnaryKernelConfig(Rsqrt),wasmScatterNd;function setup33(backend22){wasmScatterNd=backend22.wasm.cwrap(ScatterNd,null,["number","number","number","number","number","number","array","number","number"])}function scatterNd3(args){let{backend:backend22,inputs,attrs}=args,{indices,updates}=inputs,{shape}=attrs,out=backend22.makeOutput(shape,updates.dtype);if(util_exports.sizeFromShape(shape)===0)return out;let{sliceRank,numUpdates,sliceSize,strides,outputSize}=scatter_nd_util_exports.calculateShapes(updates,indices,shape),indicesId=backend22.dataIdMap.get(indices.dataId).id,updatesId=backend22.dataIdMap.get(updates.dataId).id,stridesBytes=new Uint8Array(new Int32Array(strides).buffer),outId=backend22.dataIdMap.get(out.dataId).id;return wasmScatterNd(indicesId,updatesId,CppDType[updates.dtype],sliceRank,numUpdates,sliceSize,stridesBytes,outputSize,outId),out}var scatterNdConfig3={kernelName:ScatterNd,backendName:"wasm",setupFunc:setup33,kernelFunc:scatterNd3},wasmSelect;function setup34(backend22){wasmSelect=backend22.wasm.cwrap("SelectV2",null,["number","number","number","number","number"])}function select3(args){let{inputs,backend:backend22}=args,{condition,t,e}=inputs,conditionId=backend22.dataIdMap.get(condition.dataId).id,tId=backend22.dataIdMap.get(t.dataId).id,eId=backend22.dataIdMap.get(e.dataId).id,out=backend22.makeOutput(t.shape,t.dtype),outId=backend22.dataIdMap.get(out.dataId).id,cRank=condition.shape.length,tRank=t.shape.length,offset=cRank===0||cRank>1||tRank===1?1:util_exports.sizeFromShape(t.shape.slice(1));return wasmSelect(conditionId,tId,eId,offset,outId),out}var selectConfig3={kernelName:Select,backendName:"wasm",kernelFunc:select3,setupFunc:setup34},wasmFunc7;function setup35(backend22){wasmFunc7=backend22.wasm.cwrap(Sigmoid,null,["number","number"])}function sigmoid4(args){let{backend:backend22,inputs:{x}}=args,xId=backend22.dataIdMap.get(x.dataId).id,out=backend22.makeOutput(x.shape,x.dtype),outId=backend22.dataIdMap.get(out.dataId).id;return util_exports.sizeFromShape(out.shape)===0||wasmFunc7(xId,outId),out}var sigmoidConfig3={kernelName:"Sigmoid",backendName:"wasm",setupFunc:setup35,kernelFunc:sigmoid4},sinConfig3=createUnaryKernelConfig(Sin);function slice4(args){let{inputs:{x},attrs:{begin,size},backend:backend22}=args,[begin_,size_]=slice_util_exports.parseSliceParams(x,begin,size),isContinous=slice_util_exports.isSliceContinous(x.shape,begin_,size_),xVals=backend22.readSync(x.dataId),out=backend22.makeOutput(size_,x.dtype),xStrides=util_exports.computeStrides(x.shape),outData=backend22.dataIdMap.get(out.dataId);if(isContinous){let flatOffset=slice_util_exports.computeFlatOffset(begin_,xStrides);return x.dtype==="string"?outData.stringBytes=xVals.slice(flatOffset,flatOffset+util_exports.sizeFromShape(size_)):backend22.typedArrayFromHeap(out).set(xVals.subarray(flatOffset,flatOffset+util_exports.sizeFromShape(size_))),out}if(x.dtype==="string"){let res=sliceImpl(xVals,begin_,size_,x.shape,x.dtype);return outData.stringBytes=res,out}let outVals=backend22.typedArrayFromHeap(out),rank=x.shape.length;if(rank===2)slice2d2(xVals,xStrides[0],outVals,begin_,size_);else if(rank===3)slice3d2(xVals,xStrides[0],xStrides[1],outVals,begin_,size_);else if(rank===4)slice4d2(xVals,xStrides[0],xStrides[1],xStrides[2],outVals,begin_,size_);else{let res=sliceImpl(xVals,begin_,size_,x.shape,x.dtype);outVals.set(res)}return out}function slice2d2(xVals,xStride,outVals,begin,size){let outOffset=0,beginI=begin[0],beginJ=begin[1],endI=beginI+size[0];for(let i=beginI;i{let xSliceSize=[...size];xSliceSize[$axis]=s;let xSlice=slice4({inputs:{x},attrs:{begin,size:xSliceSize},backend:backend22});return begin[$axis]+=s,xSlice})}var splitVConfig3={kernelName:SplitV,backendName:"wasm",kernelFunc:splitV3},sqrtConfig3=createUnaryKernelConfig(Sqrt),squareConfig3=createUnaryKernelConfig(Square),supportsFullBroadcast15=!0,squaredDifferenceConfig3=createBinaryKernelConfig(SquaredDifference,supportsFullBroadcast15),wasmStridedSlice;function setup37(backend22){wasmStridedSlice=backend22.wasm.cwrap(StridedSlice,null,["number","array","number","array","array","array","array","array","number","number"])}function stridedSlice4(args){let{backend:backend22,inputs,attrs}=args,{x}=inputs,{begin,end,strides}=attrs;strides==null&&(strides=new Array(begin.length));let{beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask}=attrs,ellipsisAxes=backend_util_exports.slice_util.maskToAxes(ellipsisMask);if(ellipsisAxes.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(ellipsisMask!==0&&newAxisMask!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(ellipsisMask!==0&&shrinkAxisMask!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");let numInterpolatedAxes=x.shape.length-begin.length,expandAxes=backend_util_exports.slice_util.maskToAxes(newAxisMask),newShape=x.shape.slice();expandAxes.forEach(axis=>{begin[axis]=0,end[axis]=1,newShape.splice(axis,0,1)});let xReshaped=reshape4({inputs:{x},attrs:{shape:newShape},backend:backend22}),{begin:normalizedBegin,end:normalizedEnd,strides:normalizedStrides}=backend_util_exports.slice_util.getNormalizedAxes(xReshaped.shape,ellipsisAxes,numInterpolatedAxes,begin,end,strides,beginMask,endMask,ellipsisMask);begin=normalizedBegin,end=normalizedEnd,strides=normalizedStrides;let shrinkAxes=backend_util_exports.slice_util.maskToAxes(shrinkAxisMask);shrinkAxes.forEach(axis=>{end[axis]=begin[axis]+1,strides[axis]=1});let size=backend_util_exports.slice_util.computeOutShape(begin,end,strides),outShape=size.filter((_,axis)=>shrinkAxes.indexOf(axis)===-1);if(strides.every(v=>v===1)){let xSliced=slice4({inputs:{x},attrs:{begin,size},backend:backend22});return reshape4({inputs:{x:xSliced},attrs:{shape:outShape},backend:backend22})}let out=backend22.makeOutput(outShape,"float32");if(!outShape.some(axis=>axis===0)){let xId=backend22.dataIdMap.get(xReshaped.dataId).id,xStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(xReshaped.shape)).buffer),beginBytes=new Uint8Array(new Int32Array(begin).buffer),endBytes=new Uint8Array(new Int32Array(end).buffer),stridesBytes=new Uint8Array(new Int32Array(strides).buffer),outputShapeBytes=new Uint8Array(new Int32Array(outShape).buffer),outStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(outShape)).buffer),outId=backend22.dataIdMap.get(out.dataId).id;wasmStridedSlice(xId,xStridesBytes,xReshaped.shape.length,beginBytes,endBytes,stridesBytes,outputShapeBytes,outStridesBytes,outShape.length,outId)}return reshape4({inputs:{x:out},attrs:{shape:outShape},backend:backend22})}var stridedSliceConfig3={kernelName:StridedSlice,backendName:"wasm",setupFunc:setup37,kernelFunc:stridedSlice4},supportsFullBroadcast16=!0,subConfig3=createBinaryKernelConfig(Sub,supportsFullBroadcast16),wasmSum;function setup38(backend22){wasmSum=backend22.wasm.cwrap(Sum,null,["number, number, number"])}function sum5(args){let{backend:backend22,inputs,attrs}=args,{axis,keepDims}=attrs,{x}=inputs,xId=backend22.dataIdMap.get(x.dataId).id,inputId=xId,input2=x,{transposed,axes,originalAxes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend22),reductionAxes=axes;if(inputWasTransposed){let transposedId=backend22.dataIdMap.get(transposed.dataId).id;transposedId!==xId&&(input2=transposed,inputId=transposedId,reductionAxes=backend_util_exports.getInnerMostAxes(reductionAxes.length,input2.shape.length))}backend_util_exports.assertAxesAreInnerMostDims("sum",reductionAxes,input2.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(input2.shape,reductionAxes),reduceSize=util_exports.sizeFromShape(reduceShape),out=backend22.makeOutput(outShape,input2.dtype);if(util_exports.sizeFromShape(input2.shape)!==0){let outId=backend22.dataIdMap.get(out.dataId).id;wasmSum(inputId,reduceSize,outId)}if(inputWasTransposed&&backend22.disposeData(transposed.dataId),keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(out.shape,originalAxes);out.shape=newShape}return out}var sumConfig3={kernelName:Sum,backendName:"wasm",setupFunc:setup38,kernelFunc:sum5},tanhConfig3=createUnaryKernelConfig(Tanh),wasmTile;function setup39(backend22){wasmTile=backend22.wasm.cwrap(Tile,null,["number","array","number","array","number","number"])}function tile4(args){let{inputs,backend:backend22,attrs}=args,{x}=inputs,xId=backend22.dataIdMap.get(x.dataId).id,{reps}=attrs,newShape=new Array(x.shape.length);for(let i=0;i{let{x}=inputs,{k,sorted}=attrs,xId=backend22.dataIdMap.get(x.dataId).id,xShapeBytes=new Uint8Array(new Int32Array(x.shape).buffer),outputShape=x.shape.slice();outputShape[outputShape.length-1]=k;let outValues=backend22.makeOutput(outputShape,x.dtype),outValuesId=backend22.dataIdMap.get(outValues.dataId).id,outIndices=backend22.makeOutput(outputShape,"int32"),outIndicesId=backend22.dataIdMap.get(outIndices.dataId).id;return wasmTopK(xId,xShapeBytes,x.shape.length,CppDType[x.dtype],k,sorted,outValuesId,outIndicesId),[outValues,outIndices]},topKConfig3={kernelName:TopK,backendName:"wasm",setupFunc:setup40,kernelFunc:topk2};function unpack3(args){let{inputs,backend:backend22,attrs}=args,{value}=inputs,{axis}=attrs;axis<0&&(axis+=value.shape.length);let numOutputs=value.shape[axis],rank=value.shape.length,outShape=new Array(rank-1),outIndex=0;for(let i=0;i({dataId,dtype,shape:outShape}))}var unpackConfig3={kernelName:Unpack,backendName:"wasm",kernelFunc:unpack3};function zerosLike4(args){let{inputs:{x},backend:backend22}=args,out=backend22.makeOutput(x.shape,x.dtype);return backend22.typedArrayFromHeap(out).fill(0),out}var zerosLikeConfig3={kernelName:ZerosLike,backendName:"wasm",kernelFunc:zerosLike4},kernelConfigs3=[absConfig3,addConfig3,addNConfig3,argMaxConfig3,avgPoolConfig3,batchMatMulConfig3,castConfig3,clipByValueConfig2,concatConfig3,conv2DConfig3,conv2DBackpropInputConfig3,cosConfig3,cropAndResizeConfig3,cumsumConfig3,depthToSpaceConfig3,depthwiseConv2dNativeConfig3,equalConfig3,expConfig3,expandDimsConfig3,fillConfig3,flipLeftRightConfig3,floorConfig3,floorDivConfig3,fusedMatMulConfig,fusedBatchNormConfig,fusedConv2DConfig3,fusedDepthwiseConv2DConfig3,gatherNdConfig3,gatherV2Config3,greaterConfig3,greaterEqualConfig3,identityConfig3,leakyReluConfig3,lessConfig3,lessEqualConfig3,logConfig3,logicalAndConfig3,maxConfig3,maximumConfig3,maxPoolConfig3,meanConfig3,minConfig3,minimumConfig3,multiplyConfig3,negConfig3,nonMaxSuppressionV3Config3,nonMaxSuppressionV4Config3,nonMaxSuppressionV5Config3,notEqualConfig3,oneHotConfig3,onesLikeConfig3,packConfig3,padV2Config3,powConfig3,preluConfig3,prodConfig3,rangeConfig3,realDivConfig3,reluConfig3,relu6Config3,reshapeConfig3,resizeBilinearConfig3,reverseConfig3,rotateWithOffsetConfig3,rsqrtConfig3,roundConfig3,scatterNdConfig3,selectConfig3,sigmoidConfig3,sinConfig3,sliceConfig3,softmaxConfig3,splitVConfig3,sqrtConfig3,squareConfig3,squaredDifferenceConfig3,stridedSliceConfig3,subConfig3,sumConfig3,tanhConfig3,tileConfig3,topKConfig3,transposeConfig3,unpackConfig3,zerosLikeConfig3];for(let kernelConfig of kernelConfigs3)registerKernel(kernelConfig);var ENV4=env();ENV4.registerFlag("WASM_HAS_SIMD_SUPPORT",async()=>WebAssembly.validate(new Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,2,1,0,10,9,1,7,0,65,0,253,15,26,11])));ENV4.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT",async()=>{if(ENV4.get("IS_NODE"))return!1;try{return new MessageChannel().port1.postMessage(new SharedArrayBuffer(1)),WebAssembly.validate(new Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,2,1,0,5,4,1,3,1,1,10,11,1,9,0,65,0,254,16,2,0,26,11]))}catch(e){return!1}});var import_tfjs_backend_wasm_threaded_simd=__toModule2(require_tfjs_backend_wasm_threaded_simd()),wasmWorkerContents='var threadInfoStruct=0;var selfThreadId=0;var parentThreadId=0;var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(" ");console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(" ");postMessage({cmd:"alert",text:text,threadId:selfThreadId})}var err=threadPrintErr;this.alert=threadAlert;Module["instantiateWasm"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module["wasmModule"],info);Module["wasmModule"]=null;receiveInstance(instance);return instance.exports};this.onmessage=function(e){try{if(e.data.cmd==="load"){Module["DYNAMIC_BASE"]=e.data.DYNAMIC_BASE;Module["DYNAMICTOP_PTR"]=e.data.DYNAMICTOP_PTR;Module["wasmModule"]=e.data.wasmModule;Module["wasmMemory"]=e.data.wasmMemory;Module["buffer"]=Module["wasmMemory"].buffer;Module["ENVIRONMENT_IS_PTHREAD"]=true;if(typeof e.data.urlOrBlob==="string"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}Module=WasmBackendModuleThreadedSimd(Module);postMessage({"cmd":"loaded"})}else if(e.data.cmd==="objectTransfer"){Module["PThread"].receiveObjectTransfer(e.data)}else if(e.data.cmd==="run"){Module["__performance_now_clock_drift"]=performance.now()-e.data.time;threadInfoStruct=e.data.threadInfoStruct;Module["__register_pthread_ptr"](threadInfoStruct,0,0);selfThreadId=e.data.selfThreadId;parentThreadId=e.data.parentThreadId;var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module["establishStackSpace"](top,max);Module["_emscripten_tls_init"]();Module["PThread"].receiveObjectTransfer(e.data);Module["PThread"].setThreadStatus(Module["_pthread_self"](),1);try{var result=Module["dynCall_ii"](e.data.start_routine,e.data.arg);if(!Module["getNoExitRuntime"]())Module["PThread"].threadExit(result)}catch(ex){if(ex==="Canceled!"){Module["PThread"].threadCancel()}else if(ex!="unwind"){Atomics.store(Module["HEAPU32"],threadInfoStruct+4>>2,ex instanceof Module["ExitStatus"]?ex.status:-2);Atomics.store(Module["HEAPU32"],threadInfoStruct+0>>2,1);Module["_emscripten_futex_wake"](threadInfoStruct+0,2147483647);if(!(ex instanceof Module["ExitStatus"]))throw ex}}}else if(e.data.cmd==="cancel"){if(threadInfoStruct){Module["PThread"].threadCancel()}}else if(e.data.target==="setimmediate"){}else if(e.data.cmd==="processThreadQueue"){if(threadInfoStruct){Module["_emscripten_current_thread_process_queued_calls"]()}}else{err("worker.js received unknown command "+e.data.cmd);err(e.data)}}catch(ex){err("worker.js onmessage() captured an uncaught exception: "+ex);if(ex.stack)err(ex.stack);throw ex}};if(typeof process==="object"&&typeof process.versions==="object"&&typeof process.versions.node==="string"){self={location:{href:__filename}};var onmessage=this.onmessage;var nodeWorkerThreads=require("worker_threads");Worker=nodeWorkerThreads.Worker;var parentPort=nodeWorkerThreads.parentPort;parentPort.on("message",function(data){onmessage({data:data})});var nodeFS=require("fs");var nodeRead=function(filename){return nodeFS.readFileSync(filename,"utf8")};function globalEval(x){global.require=require;global.Module=Module;eval.call(null,x)}importScripts=function(f){globalEval(nodeRead(f))};postMessage=function(msg){parentPort.postMessage(msg)};if(typeof performance==="undefined"){performance={now:function(){return Date.now()}}}}',import_tfjs_backend_wasm=__toModule2(require_tfjs_backend_wasm()),BackendWasm=class extends KernelBackend{constructor(wasm){super();this.wasm=wasm,this.dataIdNextNumber=1,this.wasm.tfjs.init(),this.dataIdMap=new DataStorage(this,engine())}write(values,shape,dtype){let dataId={};return this.move(dataId,values,shape,dtype),dataId}numDataIds(){return this.dataIdMap.numDataIds()}async time(f){let start=util_exports.now();return f(),{kernelMs:util_exports.now()-start}}move(dataId,values,shape,dtype){let id=this.dataIdNextNumber++;if(dtype==="string"){let stringBytes=values;this.dataIdMap.set(dataId,{id,stringBytes,shape,dtype,memoryOffset:null});return}let size=util_exports.sizeFromShape(shape),numBytes=size*util_exports.bytesPerElement(dtype),memoryOffset=this.wasm._malloc(numBytes);this.dataIdMap.set(dataId,{id,memoryOffset,shape,dtype}),this.wasm.tfjs.registerTensor(id,size,memoryOffset),values!=null&&this.wasm.HEAPU8.set(new Uint8Array(values.buffer,values.byteOffset,numBytes),memoryOffset)}async read(dataId){return this.readSync(dataId)}readSync(dataId){let{memoryOffset,dtype,shape,stringBytes}=this.dataIdMap.get(dataId);if(dtype==="string")return stringBytes;let bytes=this.wasm.HEAPU8.slice(memoryOffset,memoryOffset+util_exports.sizeFromShape(shape)*util_exports.bytesPerElement(dtype));return typedArrayFromBuffer(bytes.buffer,dtype)}disposeData(dataId){let data2=this.dataIdMap.get(dataId);this.wasm._free(data2.memoryOffset),this.wasm.tfjs.disposeData(data2.id),this.dataIdMap.delete(dataId)}floatPrecision(){return 32}getMemoryOffset(dataId){return this.dataIdMap.get(dataId).memoryOffset}dispose(){this.wasm.tfjs.dispose(),this.wasm=null}memory(){return{unreliable:!1}}makeOutput(shape,dtype,memoryOffset){let dataId;if(memoryOffset==null)dataId=this.write(null,shape,dtype);else{dataId={};let id=this.dataIdNextNumber++;this.dataIdMap.set(dataId,{id,memoryOffset,shape,dtype});let size=util_exports.sizeFromShape(shape);this.wasm.tfjs.registerTensor(id,size,memoryOffset)}return{dataId,shape,dtype}}typedArrayFromHeap({shape,dtype,dataId}){let buffer2=this.wasm.HEAPU8.buffer,{memoryOffset}=this.dataIdMap.get(dataId),size=util_exports.sizeFromShape(shape);switch(dtype){case"float32":return new Float32Array(buffer2,memoryOffset,size);case"int32":return new Int32Array(buffer2,memoryOffset,size);case"bool":return new Uint8Array(buffer2,memoryOffset,size);default:throw new Error(`Unknown dtype ${dtype}`)}}};function createInstantiateWasmFunc(path){return(imports,callback)=>(util_exports.fetch(path,{credentials:"same-origin"}).then(response=>{response.ok||imports.env.a(`failed to load wasm binary file at '${path}'`),response.arrayBuffer().then(binary=>{WebAssembly.instantiate(binary,imports).then(output=>{callback(output.instance)})})}),{})}function getPathToWasmBinary(simdSupported,threadsSupported,wasmModuleFolder){if(wasmPath!=null)return wasmPath;let path="tfjs-backend-wasm.wasm";return simdSupported&&threadsSupported?path="tfjs-backend-wasm-threaded-simd.wasm":simdSupported&&(path="tfjs-backend-wasm-simd.wasm"),wasmFileMap!=null&&wasmFileMap[path]!=null?wasmFileMap[path]:wasmModuleFolder+path}async function init(){let[simdSupported,threadsSupported]=await Promise.all([env().getAsync("WASM_HAS_SIMD_SUPPORT"),env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]);return new Promise((resolve,reject)=>{let factoryConfig={};factoryConfig.locateFile=(path,prefix)=>{if(path.endsWith(".worker.js")){let response=wasmWorkerContents,blob=new Blob([response],{type:"application/javascript"});return URL.createObjectURL(blob)}return path.endsWith(".wasm")?getPathToWasmBinary(simdSupported,threadsSupported,wasmPathPrefix!=null?wasmPathPrefix:prefix):prefix+path},customFetch&&(factoryConfig.instantiateWasm=createInstantiateWasmFunc(getPathToWasmBinary(simdSupported,threadsSupported,wasmPathPrefix!=null?wasmPathPrefix:"")));let wasm;threadsSupported&&simdSupported&&wasmPath==null?(wasm=import_tfjs_backend_wasm_threaded_simd.default(factoryConfig),wasm.mainScriptUrlOrBlob=new Blob(["var WasmBackendModuleThreadedSimd = "+import_tfjs_backend_wasm_threaded_simd.default.toString()],{type:"text/javascript"})):wasm=import_tfjs_backend_wasm.default(factoryConfig);let voidReturnType=null;wasm.tfjs={init:wasm.cwrap("init",null,[]),registerTensor:wasm.cwrap("register_tensor",null,["number","number","number"]),disposeData:wasm.cwrap("dispose_data",voidReturnType,["number"]),dispose:wasm.cwrap("dispose",voidReturnType,[])};let initialized=!1;wasm.onRuntimeInitialized=()=>{initialized=!0,initAborted=!1,resolve({wasm})},wasm.onAbort=()=>{initialized||initAborted||(initAborted=!0,reject({message:"Make sure the server can serve the `.wasm` file relative to the bundled js file. For more details see https://github.com/tensorflow/tfjs/blob/master/tfjs-backend-wasm/README.md#using-bundlers"}))}})}function typedArrayFromBuffer(buffer2,dtype){switch(dtype){case"float32":return new Float32Array(buffer2);case"int32":return new Int32Array(buffer2);case"bool":return new Uint8Array(buffer2);default:throw new Error(`Unknown dtype ${dtype}`)}}var wasmBinaryNames=["tfjs-backend-wasm.wasm","tfjs-backend-wasm-simd.wasm","tfjs-backend-wasm-threaded-simd.wasm"],wasmPath=null,wasmPathPrefix=null,wasmFileMap={},initAborted=!1,customFetch=!1;function setWasmPath(path,usePlatformFetch=!1){if(deprecationWarn("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release."),initAborted)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`");wasmPath=path,customFetch=usePlatformFetch}function setWasmPaths(prefixOrFileMap,usePlatformFetch=!1){if(initAborted)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPaths()` before you call `tf.setBackend()` or `tf.ready()`");if(typeof prefixOrFileMap=="string")wasmPathPrefix=prefixOrFileMap;else{wasmFileMap=prefixOrFileMap;let missingPaths=wasmBinaryNames.filter(name=>wasmFileMap[name]==null);if(missingPaths.length>0)throw new Error(`There were no entries found for the following binaries: ${missingPaths.join(",")}. Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`)}customFetch=usePlatformFetch}var version9="2.8.3",WASM_PRIORITY=2;registerBackend("wasm",async()=>{let{wasm}=await init();return new BackendWasm(wasm)},WASM_PRIORITY);var exports_constraints_exports={};__export2(exports_constraints_exports,{maxNorm:()=>maxNorm,minMaxNorm:()=>minMaxNorm,nonNeg:()=>nonNeg,unitNorm:()=>unitNorm});var _epsilon;function epsilon2(){return _epsilon==null&&(_epsilon=backend().epsilon()),_epsilon}function imageDataFormat(){return"channelsLast"}var AttributeError=class extends Error{constructor(message){super(message);Object.setPrototypeOf(this,AttributeError.prototype)}},RuntimeError=class extends Error{constructor(message){super(message);Object.setPrototypeOf(this,RuntimeError.prototype)}},ValueError=class extends Error{constructor(message){super(message);Object.setPrototypeOf(this,ValueError.prototype)}},NotImplementedError=class extends Error{constructor(message){super(message);Object.setPrototypeOf(this,NotImplementedError.prototype)}},AssertionError=class extends Error{constructor(message){super(message);Object.setPrototypeOf(this,AssertionError.prototype)}},IndexError=class extends Error{constructor(message){super(message);Object.setPrototypeOf(this,IndexError.prototype)}};function pyListRepeat(value,numValues){if(Array.isArray(value)){let newArray=[];for(let i=0;ip1.toUpperCase())}var _GLOBAL_CUSTOM_OBJECTS={};function serializeKerasObject(instance){if(instance==null)return null;let dict={};return dict.className=instance.getClassName(),dict.config=instance.getConfig(),dict}function convertNDArrayScalarsInConfig(config3){if(!(config3==null||typeof config3!="object"))if(Array.isArray(config3))config3.forEach(configItem=>convertNDArrayScalarsInConfig(configItem));else{let fields=Object.keys(config3);for(let field of fields){let value=config3[field];value!=null&&typeof value=="object"&&(!Array.isArray(value)&&value.type==="ndarray"&&typeof value.value=="number"?config3[field]=value.value:convertNDArrayScalarsInConfig(value))}}}function deserializeKerasObject(identifier,moduleObjects={},customObjects={},printableModuleName="object",fastWeightInit=!1){if(typeof identifier=="string"){let functionName=identifier,fn;if(functionName in customObjects)fn=customObjects[functionName];else if(functionName in _GLOBAL_CUSTOM_OBJECTS)fn=_GLOBAL_CUSTOM_OBJECTS[functionName];else if(fn=moduleObjects[functionName],fn==null)throw new ValueError(`Unknown ${printableModuleName}: ${identifier}. This may be due to one of the following reasons: + `}};function unsortedSegmentSum3(args){let{inputs,backend:backend22,attrs}=args,{x,segmentIds}=inputs,{numSegments}=attrs,xRank=x.shape.length,toDispose=[],axis=0,permutation=backend_util_exports.getAxesPermutation([axis],xRank),permutedX=x;permutation!=null&&(permutedX=transpose3({inputs:{x},backend:backend22,attrs:{perm:permutation}}),toDispose.push(permutedX),axis=backend_util_exports.getInnerMostAxes(1,xRank)[0]);let outShape=backend_util_exports.segment_util.computeOutShape(permutedX.shape,axis,numSegments),inSize=util_exports.sizeFromShape([permutedX.shape[axis]]),a2D=reshape3({inputs:{x:permutedX},backend:backend22,attrs:{shape:[-1,inSize]}});toDispose.push(a2D);let outputDType=sumOutType(x.dtype),segOpCompute=(x2,segOpType,segmentIds2,dtype,numSegments2)=>{let batchSize=x2.shape[0],inSize2=x2.shape[1],windowSize=backend_util_exports.segment_util.segOpComputeOptimalWindowSize(inSize2,numSegments2),segOpInfo={windowSize,inSize:inSize2,batchSize,numSegments:numSegments2},program=new SegmentOpProgram(segOpInfo,segOpType),output=backend22.compileAndRun(program,[x2,segmentIds2],dtype);if(toDispose.push(output),output.shape[1]===numSegments2)return output;let rangeInfo=range3({backend:backend22,attrs:{start:0,stop:numSegments2,step:1,dtype:"float32"}}),tileInfo=tile3({inputs:{x:rangeInfo},backend:backend22,attrs:{reps:[inSize2/windowSize]}});return toDispose.push(rangeInfo),toDispose.push(tileInfo),segOpCompute(output,segOpType,tileInfo,dtype,numSegments2)},segOpResult=segOpCompute(a2D,"unsortedSegmentSum",segmentIds,outputDType,numSegments),reshaped=reshape3({inputs:{x:segOpResult},backend:backend22,attrs:{shape:outShape}}),result=reshaped;if(permutation!=null){toDispose.push(reshaped);let perm=backend_util_exports.getUndoAxesPermutation(permutation);result=transpose3({inputs:{x:result},backend:backend22,attrs:{perm}})}return toDispose.forEach(t=>backend22.disposeIntermediateTensorInfo(t)),result}var unsortedSegmentSumConfig2={kernelName:UnsortedSegmentSum,backendName:"webgl",kernelFunc:unsortedSegmentSum3},kernelConfigs2=[LRNConfig,LRNGradConfig,_fusedMatMulConfig2,absConfig2,acosConfig2,acoshConfig2,addConfig2,addNConfig2,allConfig2,anyConfig2,argMaxConfig2,argMinConfig2,asinConfig2,asinhConfig2,atan2Config2,atanConfig2,atanhConfig2,avgPool3DConfig2,avgPoolConfig2,avgPoolGrad3DConfig,avgPoolGradConfig3,batchMatMulConfig2,batchNormConfig2,batchToSpaceNDConfig2,bincountConfig2,castConfig2,ceilConfig2,clipByValueConfig,complexAbsConfig2,complexConfig2,concatConfig2,conv2DBackpropFilterConfig2,conv2DBackpropInputConfig2,conv2DConfig2,conv3DBackpropFilterV2Config2,conv3DBackpropInputConfig,conv3DConfig2,cosConfig2,coshConfig2,cropAndResizeConfig2,cumsumConfig2,denseBincountConfig2,depthToSpaceConfig2,depthwiseConv2dNativeBackpropFilterConfig2,depthwiseConv2dNativeBackpropInputConfig2,depthwiseConv2dNativeConfig2,diagConfig2,dilation2DConfig,eluConfig2,eluGradConfig3,equalConfig2,erfConfig2,expConfig2,expandDimsConfig2,expm1Config2,fftConfig2,fillConfig2,flipLeftRightConfig2,floorConfig2,floorDivConfig2,fromPixelsConfig,fusedConv2DConfig2,fusedDepthwiseConv2DConfig2,gatherNdConfig2,gatherV2Config2,greaterConfig2,greaterEqualConfig2,identityConfig2,ifftConfig2,imagConfig2,isFiniteConfig2,isInfConfig2,isNaNConfig2,leakyReluConfig2,lessConfig2,lessEqualConfig2,linSpaceConfig2,log1pConfig2,logConfig2,logicalAndConfig2,logicalNotConfig2,logicalOrConfig2,maxConfig2,maxPool3DConfig2,maxPoolConfig2,maxPoolGrad3DConfig,maxPoolGradConfig3,maxPoolWithArgmaxConfig2,maximumConfig2,meanConfig2,minConfig2,minimumConfig2,mirrorPadConfig2,modConfig2,multinomialConfig2,multiplyConfig2,negConfig2,nonMaxSuppressionV3Config2,nonMaxSuppressionV4Config2,nonMaxSuppressionV5Config2,notEqualConfig2,oneHotConfig2,onesLikeConfig2,packConfig2,padV2Config2,powConfig2,preluConfig2,prodConfig2,rangeConfig2,realConfig2,realDivConfig2,reciprocalConfig2,relu6Config2,reluConfig2,reshapeConfig2,resizeBilinearConfig2,resizeBilinearGradConfig3,resizeNearestNeighborConfig2,resizeNearestNeighborGradConfig3,reverseConfig2,rotateWithOffsetConfig2,roundConfig2,rsqrtConfig2,scatterNdConfig2,selectConfig2,seluConfig2,sigmoidConfig2,signConfig2,sinConfig2,sinhConfig2,sliceConfig2,softmaxConfig2,softplusConfig2,spaceToBatchNDConfig2,sparseToDenseConfig2,splitVConfig2,sqrtConfig2,squareConfig2,squaredDifferenceConfig2,stepConfig2,stridedSliceConfig2,subConfig2,sumConfig2,tanConfig2,tanhConfig2,tileConfig2,topKConfig2,transposeConfig2,uniqueConfig2,unpackConfig2,unsortedSegmentSumConfig2,zerosLikeConfig2];for(let kernelConfig of kernelConfigs2)registerKernel(kernelConfig);var CppDType;(function(CppDType2){CppDType2[CppDType2.float32=0]="float32",CppDType2[CppDType2.int32=1]="int32",CppDType2[CppDType2.bool=2]="bool",CppDType2[CppDType2.string=3]="string",CppDType2[CppDType2.complex64=4]="complex64"})(CppDType||(CppDType={}));var FusableActivation;(function(FusableActivation2){FusableActivation2[FusableActivation2.linear=0]="linear",FusableActivation2[FusableActivation2.relu=1]="relu",FusableActivation2[FusableActivation2.relu6=2]="relu6",FusableActivation2[FusableActivation2.prelu=3]="prelu",FusableActivation2[FusableActivation2.leakyrelu=4]="leakyrelu"})(FusableActivation||(FusableActivation={}));var wasmFusedMatMul;function setup(backend22){wasmFusedMatMul=backend22.wasm.cwrap(_FusedMatMul,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function fusedBatchMatMul(args){let{inputs,backend:backend22,attrs}=args,{a,b,bias,preluActivationWeights}=inputs;if(a.dtype!=="float32"||b.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA,transposeB,activation:activation2,leakyreluAlpha}=attrs,aId=backend22.dataIdMap.get(a.dataId).id,bId=backend22.dataIdMap.get(b.dataId).id,biasId=0;if(bias!=null){let biasData=backend22.dataIdMap.get(bias.dataId);if(biasData.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${biasData.shape.length}.`);biasId=biasData.id}let preluActivationWeightsId=preluActivationWeights==null?0:backend22.dataIdMap.get(preluActivationWeights.dataId).id,fusedActivation=FusableActivation[activation2];if(fusedActivation==null)throw new Error(`${activation2} activation not yet supported for FusedConv2D in the wasm backend.`);let leftDim=transposeA?a.shape[2]:a.shape[1],rightDim=transposeB?b.shape[1]:b.shape[2],batchDim=a.shape[0],out=backend22.makeOutput([batchDim,leftDim,rightDim],a.dtype),outId=backend22.dataIdMap.get(out.dataId).id,aShapeBytes=new Uint8Array(new Int32Array(a.shape).buffer),bShapeBytes=new Uint8Array(new Int32Array(b.shape).buffer);return wasmFusedMatMul(aId,aShapeBytes,a.shape.length,bId,bShapeBytes,b.shape.length,transposeA,transposeB,fusedActivation,biasId,preluActivationWeightsId,leakyreluAlpha||0,outId),out}var fusedMatMulConfig={kernelName:_FusedMatMul,backendName:"wasm",setupFunc:setup,kernelFunc:fusedBatchMatMul};function createUnaryKernelConfig(kernelName){let wasmFunc9;function setupFunc3(backend22){wasmFunc9=backend22.wasm.cwrap(kernelName,null,["number","number"])}function kernelFunc3(args){let{backend:backend22,inputs:{x}}=args,xId=backend22.dataIdMap.get(x.dataId).id,out=backend22.makeOutput(x.shape,x.dtype),outId=backend22.dataIdMap.get(out.dataId).id;return util_exports.sizeFromShape(out.shape)===0||wasmFunc9(xId,outId),out}return{kernelName,backendName:"wasm",setupFunc:setupFunc3,kernelFunc:kernelFunc3}}var absConfig3=createUnaryKernelConfig(Abs);function createBinaryKernelConfig(kernelName,supportsFullBroadcast17,dtype){let wasmFunc9;function setupFunc3(backend22){wasmFunc9=backend22.wasm.cwrap(kernelName,null,["number","array","number","number","array","number","number","number"])}function kernelFunc3(args){let{backend:backend22,inputs}=args,{a,b}=inputs,aId=backend22.dataIdMap.get(a.dataId).id,bId=backend22.dataIdMap.get(b.dataId).id,outputType=dtype!=null?dtype:a.dtype,newShape=backend_util_exports.assertAndGetBroadcastShape(a.shape,b.shape),out=backend22.makeOutput(newShape,outputType);if(util_exports.sizeFromShape(newShape)===0)return out;let aShapeBytes=new Uint8Array(new Int32Array(a.shape).buffer),bShapeBytes=new Uint8Array(new Int32Array(b.shape).buffer),outId=backend22.dataIdMap.get(out.dataId).id,kernelFunc4=()=>wasmFunc9(aId,aShapeBytes,a.shape.length,bId,bShapeBytes,b.shape.length,CppDType[a.dtype],outId);if(supportsFullBroadcast17&&a.dtype==="float32")return kernelFunc4(),out;let aBroadcastDims=backend_util_exports.getBroadcastDims(a.shape,newShape),bBroadcastDims=backend_util_exports.getBroadcastDims(b.shape,newShape),loopsOverAllOfA=aBroadcastDims.every((v,i)=>v===i),loopsOverAllOfB=bBroadcastDims.every((v,i)=>v===i);if(loopsOverAllOfA&&loopsOverAllOfB)return kernelFunc4(),out;throw new Error(`Broadcasting along outer dims is not yet supported for ${a.dtype} ${kernelName}.`)}return{kernelName,backendName:"wasm",setupFunc:setupFunc3,kernelFunc:kernelFunc3}}var supportsFullBroadcast=!0,addConfig3=createBinaryKernelConfig(Add,supportsFullBroadcast),wasmFunc;function setupFunc(backend22){wasmFunc=backend22.wasm.cwrap(AddN,null,["array","number","number","number"])}function addn(args){let{inputs,backend:backend22}=args,out=backend22.makeOutput(inputs[0].shape,inputs[0].dtype);if(util_exports.sizeFromShape(out.shape)===0)return out;let inputIds=inputs.map(x=>backend22.dataIdMap.get(x.dataId).id),inputIdsBytes=new Uint8Array(new Int32Array(inputIds).buffer),outId=backend22.dataIdMap.get(out.dataId).id;return wasmFunc(inputIdsBytes,inputIds.length,CppDType[out.dtype],outId),out}var addNConfig3={kernelName:AddN,backendName:"wasm",setupFunc,kernelFunc:addn};function identity3(args){let{inputs:{x},backend:backend22}=args,out=backend22.makeOutput(x.shape,x.dtype),inVals=backend22.typedArrayFromHeap(x);return backend22.typedArrayFromHeap(out).set(inVals),out}var identityConfig3={kernelName:Identity,backendName:"wasm",kernelFunc:identity3},wasmTranspose;function setup2(backend22){wasmTranspose=backend22.wasm.cwrap(Transpose,null,["number","array","number","number","number","array","number"])}function transpose4(args){let{inputs,backend:backend22,attrs}=args,[reducedShape,perm]=removeOneSizeDims(inputs.x.shape,attrs.perm),permIsNoOp=!0;for(let i=0;i=i&&(minValIdx===-1||newPerm[minValIdx]>newPerm[j])&&(minValIdx=j);newPerm[minValIdx]=i}return[newShape,newPerm]}var transposeConfig3={kernelName:Transpose,backendName:"wasm",kernelFunc:transpose4,setupFunc:setup2};function permuteAxesAndTranspose(x,axis,backend22){let xShape=x.shape,xRank=x.shape.length,originalAxes=util_exports.parseAxisParam(axis,xShape),axes=originalAxes,permutedAxes=backend_util_exports.getAxesPermutation(axes,xRank),xTransposed=null,inputWasTransposed=!1;if(permutedAxes!=null){let newShape=new Array(xRank);for(let i=0;i`new shape: ${$shape}, old shape: ${x.shape}. New shape and old shape must have the same number of elements.`),{dataId:x.dataId,shape:$shape,dtype:x.dtype}}var reshapeConfig3={kernelName:Reshape,backendName:"wasm",kernelFunc:reshape4},wasmBatchMatMul;function setup5(backend22){wasmBatchMatMul=backend22.wasm.cwrap(BatchMatMul,null,["number","array","number","number","array","number","number","number","number"])}function batchMatMul3(args){let{inputs,backend:backend22,attrs}=args,{a,b}=inputs,{transposeA,transposeB}=attrs;if(a.dtype!=="float32"||b.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let aRank=a.shape.length,bRank=b.shape.length,innerShapeA=transposeA?a.shape[aRank-2]:a.shape[aRank-1],innerShapeB=transposeB?b.shape[bRank-1]:b.shape[bRank-2],outerShapeA=transposeA?a.shape[aRank-1]:a.shape[aRank-2],outerShapeB=transposeB?b.shape[bRank-2]:b.shape[bRank-1],outerDimsA=a.shape.slice(0,-2),outerDimsB=b.shape.slice(0,-2),batchDimA=util_exports.sizeFromShape(outerDimsA),batchDimB=util_exports.sizeFromShape(outerDimsB),batchDimsCompatible=batchDimA===batchDimB||batchDimA===1||batchDimB===1;util_exports.assert(aRank>=2&&bRank>=2&&batchDimsCompatible,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${outerDimsA}) and (${outerDimsB}).`);let outShape=(batchDimA>batchDimB?a.shape.slice(0,-2):b.shape.slice(0,-2)).concat([outerShapeA,outerShapeB]);util_exports.assert(innerShapeA===innerShapeB,()=>`Error in matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${a.shape} and ${b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);let a3dShape=transposeA?[batchDimA,innerShapeA,outerShapeA]:[batchDimA,outerShapeA,innerShapeA],b3dShape=transposeB?[batchDimB,outerShapeB,innerShapeB]:[batchDimB,innerShapeB,outerShapeB],a3d=reshape4({inputs:{x:a},backend:backend22,attrs:{shape:a3dShape}}),b3d=reshape4({inputs:{x:b},backend:backend22,attrs:{shape:b3dShape}}),a3dId=backend22.dataIdMap.get(a3d.dataId).id,b3dId=backend22.dataIdMap.get(b3d.dataId).id,leftDim=transposeA?a3d.shape[2]:a3d.shape[1],rightDim=transposeB?b3d.shape[1]:b3d.shape[2],batchDim=Math.max(batchDimA,batchDimB),out=backend22.makeOutput([batchDim,leftDim,rightDim],a3d.dtype),outId=backend22.dataIdMap.get(out.dataId).id,aShapeBytes=new Uint8Array(new Int32Array(a3d.shape).buffer),bShapeBytes=new Uint8Array(new Int32Array(b3d.shape).buffer);return wasmBatchMatMul(a3dId,aShapeBytes,a3d.shape.length,b3dId,bShapeBytes,b3d.shape.length,transposeA,transposeB,outId),out.shape=outShape,out}var batchMatMulConfig3={kernelName:BatchMatMul,backendName:"wasm",setupFunc:setup5,kernelFunc:batchMatMul3};function cast4(args){let{inputs:{x},attrs:{dtype},backend:backend22}=args,out=backend22.makeOutput(x.shape,dtype),inVals=backend22.typedArrayFromHeap(x);return backend22.typedArrayFromHeap(out).set(inVals),out}var castConfig3={kernelName:Cast,backendName:"wasm",kernelFunc:cast4},wasmClip;function setup6(backend22){wasmClip=backend22.wasm.cwrap(ClipByValue,null,["number","number","number","number"])}function clip2(args){let{inputs,backend:backend22,attrs}=args,{x}=inputs,{clipValueMin,clipValueMax}=attrs,xId=backend22.dataIdMap.get(x.dataId).id,out=backend22.makeOutput(x.shape,x.dtype),outId=backend22.dataIdMap.get(out.dataId).id;return wasmClip(xId,clipValueMin,clipValueMax,outId),out}var clipByValueConfig2={kernelName:ClipByValue,backendName:"wasm",setupFunc:setup6,kernelFunc:clip2};function concat4(args){let{inputs,backend:backend22}=args,axis=util_exports.parseAxisParam(args.attrs.axis,inputs[0].shape)[0],outShape=backend_util_exports.computeOutShape(inputs.map(t=>t.shape),axis),$inputs=inputs.filter(t=>util_exports.sizeFromShape(t.shape)>0);if($inputs.length===1)return identity3({inputs:{x:$inputs[0]},backend:backend22});let out=backend22.makeOutput(outShape,inputs[0].dtype);if(util_exports.sizeFromShape(outShape)===0)return out;let shapes=$inputs.map(t=>t.shape);if(backend_util_exports.assertParamsConsistent(shapes,axis),$inputs[0].dtype==="string"){let inputs2D=$inputs.map(t=>{let innerSize=util_exports.sizeFromShape(t.shape.slice(axis));return reshape4({inputs:{x:t},backend:backend22,attrs:{shape:[-1,innerSize]}})}),inputsValShapes=inputs2D.map(t=>({vals:backend22.readSync(t.dataId),shape:t.shape}));outShape=backend_util_exports.computeOutShape(inputs2D.map(t=>t.shape),1);let simplyConcat=inputs2D[0].shape[0]===1,outVals2=concatImpl(inputsValShapes,outShape,inputs[0].dtype,simplyConcat),finalOutShape=backend_util_exports.computeOutShape($inputs.map(t=>t.shape),axis);out.shape=finalOutShape;let outData=backend22.dataIdMap.get(out.dataId);return outData.stringBytes=backend_util_exports.fromStringArrayToUint8(outVals2),out}let batchDim=util_exports.sizeFromShape($inputs[0].shape.slice(0,axis)),sumInnerDims=0,innerDims=$inputs.map(input2=>{let innerDim=util_exports.sizeFromShape(input2.shape.slice(axis));return sumInnerDims+=innerDim,innerDim}),inVals=$inputs.map(input2=>backend22.typedArrayFromHeap(input2)),outVals=backend22.typedArrayFromHeap(out);for(let b=0;b`cumsum does not support ${x.dtype} tensors in the WASM backend`);let permutation=backend_util_exports.getAxesPermutation([axis],xRank),permutedX=x;permutation!==null&&(permutedX=transpose4({inputs:{x},attrs:{perm:permutation},backend:backend22}));let permutedAxis=backend_util_exports.getInnerMostAxes(1,xRank)[0];backend_util_exports.assertAxesAreInnerMostDims("cumsum",[permutedAxis],xRank);let permutedOut=backend22.makeOutput(permutedX.shape,permutedX.dtype),finalDim=permutedX.shape[permutedAxis],permutedXId=backend22.dataIdMap.get(permutedX.dataId).id,permutedOutId=backend22.dataIdMap.get(permutedOut.dataId).id;wasmCumsum(permutedXId,exclusive?1:0,reverse5?1:0,finalDim,permutedOutId,CppDType[x.dtype]);let out=permutedOut;if(permutation!==null){let undoPermutation=backend_util_exports.getUndoAxesPermutation(permutation);out=transpose4({inputs:{x:permutedOut},attrs:{perm:undoPermutation},backend:backend22}),backend22.disposeData(permutedX.dataId),backend22.disposeData(permutedOut.dataId)}return out}var cumsumConfig3={kernelName:Cumsum,backendName:"wasm",setupFunc:setup10,kernelFunc:cumsum4},wasmDepthToSpace;function setup11(backend22){wasmDepthToSpace=backend22.wasm.cwrap(DepthToSpace,null,["number","number","number","array","number","array","array","number","number"])}function depthToSpace4(args){let{backend:backend22,inputs,attrs}=args,{x}=inputs,{blockSize,dataFormat}=attrs;util_exports.assert(blockSize>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);let batchSize=x.shape[0],inputHeight=dataFormat==="NHWC"?x.shape[1]:x.shape[2],inputWidth=dataFormat==="NHWC"?x.shape[2]:x.shape[3],inputDepth=dataFormat==="NHWC"?x.shape[3]:x.shape[1],outputHeight=inputHeight*blockSize,outputWidth=inputWidth*blockSize,outputDepth=inputDepth/(blockSize*blockSize),outputShape=dataFormat==="NHWC"?[batchSize,outputHeight,outputWidth,outputDepth]:[batchSize,outputDepth,outputHeight,outputWidth],out=backend22.makeOutput(outputShape,"float32"),xId=backend22.dataIdMap.get(x.dataId).id,xStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(x.shape)).buffer),outputShapeBytes=new Uint8Array(new Int32Array(outputShape).buffer),outStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(outputShape)).buffer),outId=backend22.dataIdMap.get(out.dataId).id;return wasmDepthToSpace(xId,blockSize,dataFormat==="NHWC"?1:0,xStridesBytes,x.shape.length-1,outputShapeBytes,outStridesBytes,outputShape.length,outId),out}var depthToSpaceConfig3={kernelName:DepthToSpace,backendName:"wasm",setupFunc:setup11,kernelFunc:depthToSpace4},wasmDepthwiseConv2d;function setup12(backend22){wasmDepthwiseConv2d=backend22.wasm.cwrap(DepthwiseConv2dNative,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function depthwiseConv2d3(args){let{inputs,attrs,backend:backend22}=args,{x,filter}=inputs,xId=backend22.dataIdMap.get(x.dataId).id,filterId=backend22.dataIdMap.get(filter.dataId).id,{strides,dilations,pad:pad3,dimRoundingMode}=attrs,$dilations=dilations==null?[1,1]:dilations,convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,$dilations,pad3,dimRoundingMode,!0),filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,inputChannels=convInfo.inChannels,outputChannels=convInfo.outChannels,isSamePad=convInfo.padInfo.type==="SAME"?1:0;if(convInfo.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);let out=backend22.makeOutput(convInfo.outShape,"float32"),outId=backend22.dataIdMap.get(out.dataId).id;return wasmDepthwiseConv2d(xId,x.shape[0],x.shape[1],x.shape[2],filterId,filterHeight,filterWidth,padTop,padRight,padBottom,padLeft,isSamePad,dilationHeight,dilationWidth,strideHeight,strideWidth,inputChannels,outputChannels,outId),out}var depthwiseConv2dNativeConfig3={kernelName:DepthwiseConv2dNative,backendName:"wasm",setupFunc:setup12,kernelFunc:depthwiseConv2d3},supportsFullBroadcast2=!1,equalConfig3=createBinaryKernelConfig(Equal,supportsFullBroadcast2,"bool"),expConfig3=createUnaryKernelConfig(Exp);function expandDims4(args){let{inputs,attrs,backend:backend22}=args,{input:input2}=inputs,{dim}=attrs,inputRank=input2.shape.length,newShape=input2.shape.slice(),$dim=dim;return dim<0&&(util_exports.assert(-(inputRank+1)<=dim,()=>`Axis must be in the interval [${-(inputRank+1)}, ${inputRank}]`),$dim=inputRank+dim+1),newShape.splice($dim,0,1),reshape4({inputs:{x:input2},backend:backend22,attrs:{shape:newShape}})}var expandDimsConfig3={kernelName:ExpandDims,backendName:"wasm",kernelFunc:expandDims4};function fill4(args){let{attrs:{shape,value,dtype},backend:backend22}=args,out=backend22.makeOutput(shape,dtype);return backend22.typedArrayFromHeap(out).fill(value),out}var fillConfig3={kernelName:Fill,backendName:"wasm",kernelFunc:fill4},wasmFlipLeftRight;function setup13(backend22){wasmFlipLeftRight=backend22.wasm.cwrap(FlipLeftRight,null,["number","number","number","number","number","number"])}function flipLeftRight2(args){let{inputs,backend:backend22}=args,{image:image3}=inputs,out=backend22.makeOutput(image3.shape,image3.dtype),imageId=backend22.dataIdMap.get(image3.dataId).id,outId=backend22.dataIdMap.get(out.dataId).id,[batch,imageHeight,imageWidth,numChannels]=image3.shape;return wasmFlipLeftRight(imageId,batch,imageHeight,imageWidth,numChannels,outId),out}var flipLeftRightConfig3={kernelName:FlipLeftRight,backendName:"wasm",kernelFunc:flipLeftRight2,setupFunc:setup13},floorConfig3=createUnaryKernelConfig(Floor),supportsFullBroadcast3=!1,floorDivConfig3=createBinaryKernelConfig(FloorDiv,supportsFullBroadcast3),wasmBatchNorm;function setup14(backend22){wasmBatchNorm=backend22.wasm.cwrap(FusedBatchNorm,null,["number","number","number","number","number","number","number"])}function fusedBatchNorm(args){let{backend:backend22,inputs,attrs}=args,{varianceEpsilon}=attrs,{x,mean:mean4,variance,offset,scale:scale2}=inputs,xId=backend22.dataIdMap.get(x.dataId).id,meanId=backend22.dataIdMap.get(mean4.dataId).id,varianceId=backend22.dataIdMap.get(variance.dataId).id,offsetId=offset!=null?backend22.dataIdMap.get(offset.dataId).id:0,scaleId=scale2!=null?backend22.dataIdMap.get(scale2.dataId).id:0,out=backend22.makeOutput(x.shape,x.dtype);if(util_exports.sizeFromShape(x.shape)===0)return out;let outId=backend22.dataIdMap.get(out.dataId).id;return wasmBatchNorm(xId,meanId,varianceId,offsetId,scaleId,varianceEpsilon,outId),out}var fusedBatchNormConfig={kernelName:FusedBatchNorm,backendName:"wasm",setupFunc:setup14,kernelFunc:fusedBatchNorm},wasmFusedConv2d;function setup15(backend22){wasmFusedConv2d=backend22.wasm.cwrap(FusedConv2D,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function fusedConv2d2(args){let{inputs,attrs,backend:backend22}=args,{x,filter,bias,preluActivationWeights}=inputs,{strides,pad:pad3,dilations,dataFormat,dimRoundingMode,activation:activation2,leakyreluAlpha}=attrs,convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,dilations,pad3,dimRoundingMode),fusedActivation=FusableActivation[activation2];if(fusedActivation==null)throw new Error(`${activation2} activation not yet supported for FusedConv2D in the wasm backend.`);let xId=backend22.dataIdMap.get(x.dataId).id,filterId=backend22.dataIdMap.get(filter.dataId).id,outputChannels=convInfo.outChannels,biasId=0;if(bias!=null){let biasData=backend22.dataIdMap.get(bias.dataId);if(biasData.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${biasData.shape.length}.`);if(biasData.shape[0]!==outputChannels)throw new Error(`FusedConv2D bias shape (${biasData.shape}) does not match the number of output channels (${outputChannels})`);biasId=biasData.id}let filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,inputChannels=convInfo.inChannels,isSamePad=convInfo.padInfo.type==="SAME"?1:0,batchSize=convInfo.batchSize,inHeight=convInfo.inHeight,inWidth=convInfo.inWidth;if(dataFormat!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${dataFormat}'. Please use 'NHWC'.`);let out=backend22.makeOutput(convInfo.outShape,"float32"),outId=backend22.dataIdMap.get(out.dataId).id,preluActivationWeightsId=preluActivationWeights==null?0:backend22.dataIdMap.get(preluActivationWeights.dataId).id;return wasmFusedConv2d(xId,batchSize,inHeight,inWidth,filterId,filterHeight,filterWidth,biasId,padTop,padRight,padBottom,padLeft,isSamePad,dilationHeight,dilationWidth,strideHeight,strideWidth,inputChannels,outputChannels,fusedActivation,preluActivationWeightsId,leakyreluAlpha||0,outId),out}var fusedConv2DConfig3={kernelName:FusedConv2D,backendName:"wasm",setupFunc:setup15,kernelFunc:fusedConv2d2},wasmFusedDepthwiseConv2d;function setup16(backend22){wasmFusedDepthwiseConv2d=backend22.wasm.cwrap(FusedDepthwiseConv2D,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function fusedDepthwiseConv2d(args){let{inputs,attrs,backend:backend22}=args,{x,filter,bias,preluActivationWeights}=inputs,{strides,pad:pad3,dilations,dataFormat,dimRoundingMode,activation:activation2,leakyreluAlpha}=attrs,convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,dilations,pad3,dimRoundingMode,!0),fusedActivation=FusableActivation[activation2];if(fusedActivation==null)throw new Error(`${activation2} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let xId=backend22.dataIdMap.get(x.dataId).id,filterId=backend22.dataIdMap.get(filter.dataId).id,outputChannels=convInfo.outChannels,biasId=0;if(bias!=null){let biasData=backend22.dataIdMap.get(bias.dataId);if(biasData.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${biasData.shape.length}.`);if(biasData.shape[0]!==outputChannels)throw new Error(`FusedDepthwiseConv2D bias shape (${biasData.shape}) does not match the number of output channels (${outputChannels})`);biasId=biasData.id}let filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,inputChannels=convInfo.inChannels,isSamePad=convInfo.padInfo.type==="SAME"?1:0,batchSize=convInfo.batchSize,inHeight=convInfo.inHeight,inWidth=convInfo.inWidth;if(dataFormat!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${dataFormat}'. Please use 'NHWC'.`);let out=backend22.makeOutput(convInfo.outShape,"float32"),outId=backend22.dataIdMap.get(out.dataId).id,preluActivationWeightsId=preluActivationWeights==null?0:backend22.dataIdMap.get(preluActivationWeights.dataId).id;return wasmFusedDepthwiseConv2d(xId,batchSize,inHeight,inWidth,filterId,filterHeight,filterWidth,biasId,padTop,padRight,padBottom,padLeft,isSamePad,dilationHeight,dilationWidth,strideHeight,strideWidth,inputChannels,outputChannels,fusedActivation,preluActivationWeightsId,leakyreluAlpha||0,outId),out}var fusedDepthwiseConv2DConfig3={kernelName:FusedDepthwiseConv2D,backendName:"wasm",setupFunc:setup16,kernelFunc:fusedDepthwiseConv2d},wasmGatherNd;function setup17(backend22){wasmGatherNd=backend22.wasm.cwrap(GatherNd,null,["number","number","number","number","number","number","array","number"])}function gatherNd3(args){let{backend:backend22,inputs}=args,{params,indices}=inputs,[resultShape,numSlices,sliceSize,strides]=gather_nd_util_exports.prepareAndValidate(params,indices),out=backend22.makeOutput(resultShape,params.dtype);if(numSlices===0)return out;let indicesShape=indices.shape,sliceRank=indicesShape[indicesShape.length-1],xId=backend22.dataIdMap.get(params.dataId).id,indicesId=backend22.dataIdMap.get(indices.dataId).id,stridesBytes=new Uint8Array(new Int32Array(strides).buffer),outId=backend22.dataIdMap.get(out.dataId).id;return wasmGatherNd(xId,CppDType[params.dtype],indicesId,numSlices,sliceRank,sliceSize,stridesBytes,outId),out}var gatherNdConfig3={kernelName:GatherNd,backendName:"wasm",setupFunc:setup17,kernelFunc:gatherNd3},wasmGather;function setup18(backend22){wasmGather=backend22.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function gatherV23(args){let{backend:backend22,inputs,attrs}=args,{x,indices}=inputs,{axis,batchDims}=attrs,parsedAxis=util_exports.parseAxisParam(axis,x.shape)[0],shapeInfo=backend_util_exports.segment_util.collectGatherOpShapeInfo(x,indices,parsedAxis,batchDims),flattenX=reshape4({inputs:{x},attrs:{shape:[shapeInfo.batchSize,shapeInfo.outerSize,shapeInfo.dimSize,shapeInfo.sliceSize]},backend:backend22}),indicesSize=util_exports.sizeFromShape(indices.shape),flattenIndex=reshape4({inputs:{x:indices},attrs:{shape:[shapeInfo.batchSize,indicesSize/shapeInfo.batchSize]},backend:backend22}),flattenOutputShape=[shapeInfo.batchSize,shapeInfo.outerSize,indicesSize/shapeInfo.batchSize,shapeInfo.sliceSize],out=backend22.makeOutput(flattenOutputShape,x.dtype);if(util_exports.sizeFromShape(x.shape)===0)return out;let stridesSize=flattenX.shape.length-1,xId=backend22.dataIdMap.get(flattenX.dataId).id,indicesId=backend22.dataIdMap.get(flattenIndex.dataId).id,outId=backend22.dataIdMap.get(out.dataId).id,xStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(flattenX.shape)).buffer),outStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(flattenOutputShape)).buffer);return wasmGather(xId,CppDType[x.dtype],xStridesBytes,stridesSize,indicesId,shapeInfo.batchSize,outStridesBytes,outId),out.shape=shapeInfo.outputShape,out}var gatherV2Config3={kernelName:GatherV2,backendName:"wasm",setupFunc:setup18,kernelFunc:gatherV23},supportsFullBroadcast4=!1,greaterConfig3=createBinaryKernelConfig(Greater,supportsFullBroadcast4,"bool"),supportsFullBroadcast5=!1,greaterEqualConfig3=createBinaryKernelConfig(GreaterEqual,supportsFullBroadcast5,"bool"),wasmFunc3;function setupFunc2(backend22){wasmFunc3=backend22.wasm.cwrap(LeakyRelu,null,["number","number","number"])}function leakyRelu4(args){let{inputs:{x},attrs:{alpha},backend:backend22}=args,xId=backend22.dataIdMap.get(x.dataId).id,out=backend22.makeOutput(x.shape,x.dtype);if(util_exports.sizeFromShape(x.shape)!==0){let outId=backend22.dataIdMap.get(out.dataId).id;wasmFunc3(xId,alpha,outId)}return out}var leakyReluConfig3={kernelName:LeakyRelu,backendName:"wasm",setupFunc:setupFunc2,kernelFunc:leakyRelu4},supportsFullBroadcast6=!1,lessConfig3=createBinaryKernelConfig(Less,supportsFullBroadcast6,"bool"),supportsFullBroadcast7=!1,lessEqualConfig3=createBinaryKernelConfig(LessEqual,supportsFullBroadcast7,"bool"),logConfig3=createUnaryKernelConfig(Log),supportsFullBroadcast8=!1,logicalAndConfig3=createBinaryKernelConfig(LogicalAnd,supportsFullBroadcast8,"bool"),wasmMax;function setup19(backend22){wasmMax=backend22.wasm.cwrap(Max,null,["number, number, number"])}function max4(args){let{backend:backend22,inputs,attrs}=args,{reductionIndices:axis,keepDims}=attrs,{x}=inputs,inputId=backend22.dataIdMap.get(x.dataId).id,input2=x,{transposed,axes,originalAxes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend22);if(inputWasTransposed){let transposedId=backend22.dataIdMap.get(transposed.dataId).id;input2=transposed,inputId=transposedId}let inputRank=input2.shape.length;backend_util_exports.assertAxesAreInnerMostDims("max",axes,inputRank);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(input2.shape,axes),reduceSize=util_exports.sizeFromShape(reduceShape),out=backend22.makeOutput(outShape,x.dtype);if(util_exports.sizeFromShape(input2.shape)!==0){let outId=backend22.dataIdMap.get(out.dataId).id;wasmMax(inputId,reduceSize,outId)}if(inputWasTransposed&&backend22.disposeData(transposed.dataId),keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(out.shape,originalAxes);out.shape=newShape}return out}var maxConfig3={kernelName:Max,backendName:"wasm",setupFunc:setup19,kernelFunc:max4},supportsFullBroadcast9=!1,maximumConfig3=createBinaryKernelConfig(Maximum,supportsFullBroadcast9),wasmMaxPool;function setup20(backend22){wasmMaxPool=backend22.wasm.cwrap(MaxPool,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function maxPool4(args){let{inputs,attrs,backend:backend22}=args,x=inputs.x,xId=backend22.dataIdMap.get(x.dataId).id,{filterSize,strides,pad:pad3,dimRoundingMode}=attrs,convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,1,pad3,dimRoundingMode),filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,inputChannels=convInfo.inChannels,outputChannels=convInfo.outChannels;if(convInfo.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);let out=backend22.makeOutput(convInfo.outShape,"float32"),outId=backend22.dataIdMap.get(out.dataId).id;return wasmMaxPool(xId,x.shape[0],x.shape[1],x.shape[2],filterHeight,filterWidth,padTop,padRight,padBottom,padLeft,dilationHeight,dilationWidth,strideHeight,strideWidth,inputChannels,outputChannels,outId),out}var maxPoolConfig3={kernelName:MaxPool,backendName:"wasm",setupFunc:setup20,kernelFunc:maxPool4},wasmMean;function setup21(backend22){wasmMean=backend22.wasm.cwrap(Mean,null,["number, number, number"])}function mean3(args){let{backend:backend22,inputs,attrs}=args,{axis,keepDims}=attrs,{x}=inputs,xId=backend22.dataIdMap.get(x.dataId).id,inputId=xId,input2=x,{transposed,axes,originalAxes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend22),reductionAxes=axes;if(inputWasTransposed){let transposedId=backend22.dataIdMap.get(transposed.dataId).id;transposedId!==xId&&(input2=transposed,inputId=transposedId,reductionAxes=backend_util_exports.getInnerMostAxes(reductionAxes.length,input2.shape.length))}backend_util_exports.assertAxesAreInnerMostDims("mean",reductionAxes,input2.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(input2.shape,reductionAxes),reduceSize=util_exports.sizeFromShape(reduceShape),castedInput=input2;input2.dtype!=="float32"&&(castedInput=cast4({backend:backend22,inputs:{x:input2},attrs:{dtype:"float32"}}),inputId=backend22.dataIdMap.get(castedInput.dataId).id);let out=backend22.makeOutput(outShape,"float32");if(util_exports.sizeFromShape(input2.shape)!==0){let outId=backend22.dataIdMap.get(out.dataId).id;wasmMean(inputId,reduceSize,outId)}if(inputWasTransposed&&backend22.disposeData(transposed.dataId),keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(out.shape,originalAxes);out.shape=newShape}return input2.dtype!=="float32"&&backend22.disposeData(castedInput.dataId),out}var meanConfig3={kernelName:Mean,backendName:"wasm",setupFunc:setup21,kernelFunc:mean3},wasmMin;function setup22(backend22){wasmMin=backend22.wasm.cwrap(Min,null,["number, number, number"])}function min4(args){let{backend:backend22,inputs,attrs}=args,{axis,keepDims}=attrs,{x}=inputs,xId=backend22.dataIdMap.get(x.dataId).id,inputId=xId,input2=x,{transposed,axes,originalAxes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend22);if(inputWasTransposed){let transposedId=backend22.dataIdMap.get(transposed.dataId).id;transposedId!==xId&&(input2=transposed,inputId=transposedId)}let inputRank=input2.shape.length;backend_util_exports.assertAxesAreInnerMostDims("min",axes,inputRank);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(input2.shape,axes),reduceSize=util_exports.sizeFromShape(reduceShape),out=backend22.makeOutput(outShape,input2.dtype);if(util_exports.sizeFromShape(input2.shape)!==0){let outId=backend22.dataIdMap.get(out.dataId).id;wasmMin(inputId,reduceSize,outId)}if(inputWasTransposed&&backend22.disposeData(transposed.dataId),keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(out.shape,originalAxes);out.shape=newShape}return out}var minConfig3={kernelName:Min,backendName:"wasm",setupFunc:setup22,kernelFunc:min4},supportsFullBroadcast10=!1,minimumConfig3=createBinaryKernelConfig(Minimum,supportsFullBroadcast10),supportsFullBroadcast11=!0,multiplyConfig3=createBinaryKernelConfig(Multiply,supportsFullBroadcast11),negConfig3=createUnaryKernelConfig(Neg);function parseResultStruct(backend22,resOffset){let result=new Int32Array(backend22.wasm.HEAPU8.buffer,resOffset,4),pSelectedIndices=result[0],selectedSize=result[1],pSelectedScores=result[2],pValidOutputs=result[3];return backend22.wasm._free(resOffset),{pSelectedIndices,selectedSize,pSelectedScores,pValidOutputs}}var wasmFunc4;function setup23(backend22){wasmFunc4=backend22.wasm.cwrap(NonMaxSuppressionV3,"number",["number","number","number","number","number"])}function kernelFunc(args){let{backend:backend22,inputs,attrs}=args,{iouThreshold,maxOutputSize,scoreThreshold}=attrs,{boxes,scores}=inputs,boxesId=backend22.dataIdMap.get(boxes.dataId).id,scoresId=backend22.dataIdMap.get(scores.dataId).id,resOffset=wasmFunc4(boxesId,scoresId,maxOutputSize,iouThreshold,scoreThreshold),{pSelectedIndices,selectedSize,pSelectedScores,pValidOutputs}=parseResultStruct(backend22,resOffset);return backend22.wasm._free(pSelectedScores),backend22.wasm._free(pValidOutputs),backend22.makeOutput([selectedSize],"int32",pSelectedIndices)}var nonMaxSuppressionV3Config3={kernelName:NonMaxSuppressionV3,backendName:"wasm",setupFunc:setup23,kernelFunc},wasmFunc5;function setup24(backend22){wasmFunc5=backend22.wasm.cwrap(NonMaxSuppressionV4,"number",["number","number","number","number","number","bool"])}function nonMaxSuppressionV43(args){let{backend:backend22,inputs,attrs}=args,{iouThreshold,maxOutputSize,scoreThreshold,padToMaxOutputSize}=attrs,{boxes,scores}=inputs,boxesId=backend22.dataIdMap.get(boxes.dataId).id,scoresId=backend22.dataIdMap.get(scores.dataId).id,resOffset=wasmFunc5(boxesId,scoresId,maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize),{pSelectedIndices,selectedSize,pSelectedScores,pValidOutputs}=parseResultStruct(backend22,resOffset);backend22.wasm._free(pSelectedScores);let selectedIndicesTensor=backend22.makeOutput([selectedSize],"int32",pSelectedIndices),validOutputsTensor=backend22.makeOutput([],"int32",pValidOutputs);return[selectedIndicesTensor,validOutputsTensor]}var nonMaxSuppressionV4Config3={kernelName:NonMaxSuppressionV4,backendName:"wasm",setupFunc:setup24,kernelFunc:nonMaxSuppressionV43},wasmFunc6;function setup25(backend22){wasmFunc6=backend22.wasm.cwrap(NonMaxSuppressionV5,"number",["number","number","number","number","number","number"])}function kernelFunc2(args){let{backend:backend22,inputs,attrs}=args,{iouThreshold,maxOutputSize,scoreThreshold,softNmsSigma}=attrs,{boxes,scores}=inputs,boxesId=backend22.dataIdMap.get(boxes.dataId).id,scoresId=backend22.dataIdMap.get(scores.dataId).id,resOffset=wasmFunc6(boxesId,scoresId,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma),{pSelectedIndices,selectedSize,pSelectedScores,pValidOutputs}=parseResultStruct(backend22,resOffset);backend22.wasm._free(pValidOutputs);let selectedIndicesTensor=backend22.makeOutput([selectedSize],"int32",pSelectedIndices),selectedScoresTensor=backend22.makeOutput([selectedSize],"float32",pSelectedScores);return[selectedIndicesTensor,selectedScoresTensor]}var nonMaxSuppressionV5Config3={kernelName:NonMaxSuppressionV5,backendName:"wasm",setupFunc:setup25,kernelFunc:kernelFunc2},supportsFullBroadcast12=!1,notEqualConfig3=createBinaryKernelConfig(NotEqual,supportsFullBroadcast12,"bool"),wasmOneHot;function setup26(backend22){wasmOneHot=backend22.wasm.cwrap(OneHot,null,["number","number","number","number","number"])}function oneHot4(args){let{inputs,backend:backend22,attrs}=args,{indices}=inputs,{depth,onValue,offValue}=attrs,out=backend22.makeOutput([...indices.shape,depth],"int32"),outId=backend22.dataIdMap.get(out.dataId).id,indicesId=backend22.dataIdMap.get(indices.dataId).id;return wasmOneHot(indicesId,depth,onValue,offValue,outId),out}var oneHotConfig3={kernelName:OneHot,backendName:"wasm",setupFunc:setup26,kernelFunc:oneHot4};function onesLike4(args){let{inputs:{x},backend:backend22}=args,out=backend22.makeOutput(x.shape,x.dtype);return backend22.typedArrayFromHeap(out).fill(1),out}var onesLikeConfig3={kernelName:OnesLike,backendName:"wasm",kernelFunc:onesLike4};function pack3(args){let{inputs,backend:backend22,attrs}=args,{axis}=attrs;if(inputs.length===1)return expandDims4({inputs:{input:inputs[0]},backend:backend22,attrs:{dim:axis}});let shape=inputs[0].shape,dtype=inputs[0].dtype;inputs.forEach(t=>{util_exports.assertShapesMatch(shape,t.shape,"All tensors passed to stack must have matching shapes"),util_exports.assert(dtype===t.dtype,()=>"All tensors passed to stack must have matching dtypes")});let expandedTensors=inputs.map(t=>expandDims4({inputs:{input:t},backend:backend22,attrs:{dim:axis}}));return concat4({inputs:expandedTensors,backend:backend22,attrs:{axis}})}var packConfig3={kernelName:Pack,backendName:"wasm",kernelFunc:pack3},wasmPadV2;function setup27(backend22){wasmPadV2=backend22.wasm.cwrap(PadV2,null,["number","array","number","number","array","array","number","number"])}function pad2(args){let{inputs:{x},backend:backend22,attrs:{paddings,constantValue}}=args,outShape=paddings.map((p2,i)=>p2[0]+x.shape[i]+p2[1]),xId=backend22.dataIdMap.get(x.dataId).id,out=backend22.makeOutput(outShape,x.dtype),outId=backend22.dataIdMap.get(out.dataId).id,xShapeBytes=new Uint8Array(new Int32Array(x.shape).buffer),prePaddingsFlat=paddings.map(padTuple=>padTuple[0]),postPaddingsFlat=paddings.map(padTuple=>padTuple[1]),prePaddingsBytes=new Uint8Array(new Int32Array(prePaddingsFlat).buffer),postPaddingsBytes=new Uint8Array(new Int32Array(postPaddingsFlat).buffer);return wasmPadV2(xId,xShapeBytes,x.shape.length,CppDType[x.dtype],prePaddingsBytes,postPaddingsBytes,constantValue,outId),out}var padV2Config3={kernelName:PadV2,backendName:"wasm",kernelFunc:pad2,setupFunc:setup27},supportsFullBroadcast13=!1,powConfig3=createBinaryKernelConfig(Pow,supportsFullBroadcast13),wasmPrelu;function setup28(backend22){wasmPrelu=backend22.wasm.cwrap(Prelu,null,["number","number","number"])}function prelu4(args){let{inputs,backend:backend22}=args,{x,alpha}=inputs,xId=backend22.dataIdMap.get(x.dataId).id,weightsId=backend22.dataIdMap.get(alpha.dataId).id,out=backend22.makeOutput(x.shape,"float32"),outId=backend22.dataIdMap.get(out.dataId).id;return wasmPrelu(xId,weightsId,outId),out}var preluConfig3={kernelName:Prelu,backendName:"wasm",setupFunc:setup28,kernelFunc:prelu4},wasmProd;function setup29(backend22){wasmProd=backend22.wasm.cwrap(Prod,null,["number","number","number","number"])}function prod4(args){let{backend:backend22,inputs,attrs}=args,{axis,keepDims}=attrs,{x}=inputs,xId=backend22.dataIdMap.get(x.dataId).id,inputId=xId,input2=x,{transposed,axes,originalAxes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend22),reductionAxes=axes;if(inputWasTransposed){let transposedId=backend22.dataIdMap.get(transposed.dataId).id;transposedId!==xId&&(input2=transposed,inputId=transposedId,reductionAxes=backend_util_exports.getInnerMostAxes(reductionAxes.length,input2.shape.length))}backend_util_exports.assertAxesAreInnerMostDims("prod",reductionAxes,input2.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(input2.shape,reductionAxes),reduceSize=util_exports.sizeFromShape(reduceShape),out=backend22.makeOutput(outShape,input2.dtype);if(util_exports.sizeFromShape(input2.shape)!==0){let outId=backend22.dataIdMap.get(out.dataId).id;wasmProd(inputId,reduceSize,CppDType[out.dtype],outId)}if(inputWasTransposed&&backend22.disposeData(transposed.dataId),keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(out.shape,originalAxes);out.shape=newShape}return out}var prodConfig3={kernelName:Prod,backendName:"wasm",setupFunc:setup29,kernelFunc:prod4},range4=args=>{let{backend:backend22,attrs}=args,{start,stop,step:step4,dtype}=attrs,values=rangeImpl(start,stop,step4,dtype),out=backend22.makeOutput([values.length],dtype);return backend22.typedArrayFromHeap(out).set(values),out},rangeConfig3={kernelName:Range,backendName:"wasm",kernelFunc:range4},supportsFullBroadcast14=!0,realDivConfig3=createBinaryKernelConfig(RealDiv,supportsFullBroadcast14),reluConfig3=createUnaryKernelConfig(Relu),relu6Config3=createUnaryKernelConfig(Relu6),wasmResizeBilinear;function setup30(backend22){wasmResizeBilinear=backend22.wasm.cwrap(ResizeBilinear,null,["number","number","number","number","number","number","number","number","number","number"])}function resizeBilinear4(args){let{backend:backend22,inputs,attrs}=args,{images}=inputs,{alignCorners,halfPixelCenters,size}=attrs,[newHeight,newWidth]=size,[batch,oldHeight,oldWidth,numChannels]=images.shape,outShape=[batch,newHeight,newWidth,numChannels],xData=backend22.dataIdMap.get(images.dataId),castedData;xData.dtype!=="float32"&&(castedData=cast4({backend:backend22,inputs:{x:images},attrs:{dtype:"float32"}}),xData=backend22.dataIdMap.get(castedData.dataId));let xId=xData.id,out=backend22.makeOutput(outShape,"float32");if(util_exports.sizeFromShape(images.shape)===0)return out;let outId=backend22.dataIdMap.get(out.dataId).id;return wasmResizeBilinear(xId,batch,oldHeight,oldWidth,numChannels,newHeight,newWidth,alignCorners?1:0,halfPixelCenters?1:0,outId),castedData!=null&&backend22.disposeData(castedData.dataId),out}var resizeBilinearConfig3={kernelName:ResizeBilinear,backendName:"wasm",setupFunc:setup30,kernelFunc:resizeBilinear4},wasmReverse;function setup31(backend22){wasmReverse=backend22.wasm.cwrap(Reverse,null,["number","array","number","array","number","number"])}function reverse4(args){let{inputs,backend:backend22,attrs}=args,{x}=inputs,{dims}=attrs,axes=util_exports.parseAxisParam(dims,x.shape);if(x.shape.length===0)return identity3({inputs:{x},backend:backend22});let out=backend22.makeOutput(x.shape,x.dtype),xId=backend22.dataIdMap.get(x.dataId).id,outId=backend22.dataIdMap.get(out.dataId).id,axesBytes=new Uint8Array(new Int32Array(axes).buffer),outShapeBytes=new Uint8Array(new Int32Array(x.shape).buffer);return wasmReverse(xId,axesBytes,axes.length,outShapeBytes,x.shape.length,outId),reshape4({inputs:{x:out},attrs:{shape:x.shape},backend:backend22})}var reverseConfig3={kernelName:Reverse,backendName:"wasm",kernelFunc:reverse4,setupFunc:setup31},wasmRotate;function setup32(backend22){wasmRotate=backend22.wasm.cwrap(RotateWithOffset,null,["number","number","number","number","number","number","number","number","array","number","number"])}function rotateWithOffset2(args){let{inputs,backend:backend22,attrs}=args,{image:image3}=inputs,{radians,fillValue,center}=attrs,out=backend22.makeOutput(image3.shape,image3.dtype),imageId=backend22.dataIdMap.get(image3.dataId).id,outId=backend22.dataIdMap.get(out.dataId).id,[batch,imageHeight,imageWidth,numChannels]=image3.shape,[centerX,centerY]=backend_util_exports.getImageCenter(center,imageHeight,imageWidth),fillIsBlack=fillValue===0,fullOpacityValue=255,fillValues2=typeof fillValue=="number"?[fillValue,fillValue,fillValue,fillIsBlack?0:fullOpacityValue]:[...fillValue,fullOpacityValue],fillBytes=new Uint8Array(new Int32Array(fillValues2).buffer);return wasmRotate(imageId,batch,imageHeight,imageWidth,numChannels,radians,centerX,centerY,fillBytes,fillValues2.length,outId),out}var rotateWithOffsetConfig3={kernelName:RotateWithOffset,backendName:"wasm",kernelFunc:rotateWithOffset2,setupFunc:setup32},roundConfig3=createUnaryKernelConfig(Round),rsqrtConfig3=createUnaryKernelConfig(Rsqrt),wasmScatterNd;function setup33(backend22){wasmScatterNd=backend22.wasm.cwrap(ScatterNd,null,["number","number","number","number","number","number","array","number","number"])}function scatterNd3(args){let{backend:backend22,inputs,attrs}=args,{indices,updates}=inputs,{shape}=attrs,out=backend22.makeOutput(shape,updates.dtype);if(util_exports.sizeFromShape(shape)===0)return out;let{sliceRank,numUpdates,sliceSize,strides,outputSize}=scatter_nd_util_exports.calculateShapes(updates,indices,shape),indicesId=backend22.dataIdMap.get(indices.dataId).id,updatesId=backend22.dataIdMap.get(updates.dataId).id,stridesBytes=new Uint8Array(new Int32Array(strides).buffer),outId=backend22.dataIdMap.get(out.dataId).id;return wasmScatterNd(indicesId,updatesId,CppDType[updates.dtype],sliceRank,numUpdates,sliceSize,stridesBytes,outputSize,outId),out}var scatterNdConfig3={kernelName:ScatterNd,backendName:"wasm",setupFunc:setup33,kernelFunc:scatterNd3},wasmSelect;function setup34(backend22){wasmSelect=backend22.wasm.cwrap("SelectV2",null,["number","number","number","number","number"])}function select3(args){let{inputs,backend:backend22}=args,{condition,t,e}=inputs,conditionId=backend22.dataIdMap.get(condition.dataId).id,tId=backend22.dataIdMap.get(t.dataId).id,eId=backend22.dataIdMap.get(e.dataId).id,out=backend22.makeOutput(t.shape,t.dtype),outId=backend22.dataIdMap.get(out.dataId).id,cRank=condition.shape.length,tRank=t.shape.length,offset=cRank===0||cRank>1||tRank===1?1:util_exports.sizeFromShape(t.shape.slice(1));return wasmSelect(conditionId,tId,eId,offset,outId),out}var selectConfig3={kernelName:Select,backendName:"wasm",kernelFunc:select3,setupFunc:setup34},wasmFunc7;function setup35(backend22){wasmFunc7=backend22.wasm.cwrap(Sigmoid,null,["number","number"])}function sigmoid4(args){let{backend:backend22,inputs:{x}}=args,xId=backend22.dataIdMap.get(x.dataId).id,out=backend22.makeOutput(x.shape,x.dtype),outId=backend22.dataIdMap.get(out.dataId).id;return util_exports.sizeFromShape(out.shape)===0||wasmFunc7(xId,outId),out}var sigmoidConfig3={kernelName:"Sigmoid",backendName:"wasm",setupFunc:setup35,kernelFunc:sigmoid4},sinConfig3=createUnaryKernelConfig(Sin);function slice4(args){let{inputs:{x},attrs:{begin,size},backend:backend22}=args,[begin_,size_]=slice_util_exports.parseSliceParams(x,begin,size),isContinous=slice_util_exports.isSliceContinous(x.shape,begin_,size_),xVals=backend22.readSync(x.dataId),out=backend22.makeOutput(size_,x.dtype),xStrides=util_exports.computeStrides(x.shape),outData=backend22.dataIdMap.get(out.dataId);if(isContinous){let flatOffset=slice_util_exports.computeFlatOffset(begin_,xStrides);return x.dtype==="string"?outData.stringBytes=xVals.slice(flatOffset,flatOffset+util_exports.sizeFromShape(size_)):backend22.typedArrayFromHeap(out).set(xVals.subarray(flatOffset,flatOffset+util_exports.sizeFromShape(size_))),out}if(x.dtype==="string"){let res=sliceImpl(xVals,begin_,size_,x.shape,x.dtype);return outData.stringBytes=res,out}let outVals=backend22.typedArrayFromHeap(out),rank=x.shape.length;if(rank===2)slice2d2(xVals,xStrides[0],outVals,begin_,size_);else if(rank===3)slice3d2(xVals,xStrides[0],xStrides[1],outVals,begin_,size_);else if(rank===4)slice4d2(xVals,xStrides[0],xStrides[1],xStrides[2],outVals,begin_,size_);else{let res=sliceImpl(xVals,begin_,size_,x.shape,x.dtype);outVals.set(res)}return out}function slice2d2(xVals,xStride,outVals,begin,size){let outOffset=0,beginI=begin[0],beginJ=begin[1],endI=beginI+size[0];for(let i=beginI;i{let xSliceSize=[...size];xSliceSize[$axis]=s;let xSlice=slice4({inputs:{x},attrs:{begin,size:xSliceSize},backend:backend22});return begin[$axis]+=s,xSlice})}var splitVConfig3={kernelName:SplitV,backendName:"wasm",kernelFunc:splitV3},sqrtConfig3=createUnaryKernelConfig(Sqrt),squareConfig3=createUnaryKernelConfig(Square),supportsFullBroadcast15=!0,squaredDifferenceConfig3=createBinaryKernelConfig(SquaredDifference,supportsFullBroadcast15),wasmStridedSlice;function setup37(backend22){wasmStridedSlice=backend22.wasm.cwrap(StridedSlice,null,["number","array","number","array","array","array","array","array","number","number"])}function stridedSlice4(args){let{backend:backend22,inputs,attrs}=args,{x}=inputs,{begin,end,strides}=attrs;strides==null&&(strides=new Array(begin.length));let{beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask}=attrs,ellipsisAxes=backend_util_exports.slice_util.maskToAxes(ellipsisMask);if(ellipsisAxes.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(ellipsisMask!==0&&newAxisMask!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(ellipsisMask!==0&&shrinkAxisMask!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");let numInterpolatedAxes=x.shape.length-begin.length,expandAxes=backend_util_exports.slice_util.maskToAxes(newAxisMask),newShape=x.shape.slice();expandAxes.forEach(axis=>{begin[axis]=0,end[axis]=1,newShape.splice(axis,0,1)});let xReshaped=reshape4({inputs:{x},attrs:{shape:newShape},backend:backend22}),{begin:normalizedBegin,end:normalizedEnd,strides:normalizedStrides}=backend_util_exports.slice_util.getNormalizedAxes(xReshaped.shape,ellipsisAxes,numInterpolatedAxes,begin,end,strides,beginMask,endMask,ellipsisMask);begin=normalizedBegin,end=normalizedEnd,strides=normalizedStrides;let shrinkAxes=backend_util_exports.slice_util.maskToAxes(shrinkAxisMask);shrinkAxes.forEach(axis=>{end[axis]=begin[axis]+1,strides[axis]=1});let size=backend_util_exports.slice_util.computeOutShape(begin,end,strides),outShape=size.filter((_,axis)=>shrinkAxes.indexOf(axis)===-1);if(strides.every(v=>v===1)){let xSliced=slice4({inputs:{x},attrs:{begin,size},backend:backend22});return reshape4({inputs:{x:xSliced},attrs:{shape:outShape},backend:backend22})}let out=backend22.makeOutput(outShape,"float32");if(!outShape.some(axis=>axis===0)){let xId=backend22.dataIdMap.get(xReshaped.dataId).id,xStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(xReshaped.shape)).buffer),beginBytes=new Uint8Array(new Int32Array(begin).buffer),endBytes=new Uint8Array(new Int32Array(end).buffer),stridesBytes=new Uint8Array(new Int32Array(strides).buffer),outputShapeBytes=new Uint8Array(new Int32Array(outShape).buffer),outStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(outShape)).buffer),outId=backend22.dataIdMap.get(out.dataId).id;wasmStridedSlice(xId,xStridesBytes,xReshaped.shape.length,beginBytes,endBytes,stridesBytes,outputShapeBytes,outStridesBytes,outShape.length,outId)}return reshape4({inputs:{x:out},attrs:{shape:outShape},backend:backend22})}var stridedSliceConfig3={kernelName:StridedSlice,backendName:"wasm",setupFunc:setup37,kernelFunc:stridedSlice4},supportsFullBroadcast16=!0,subConfig3=createBinaryKernelConfig(Sub,supportsFullBroadcast16),wasmSum;function setup38(backend22){wasmSum=backend22.wasm.cwrap(Sum,null,["number, number, number"])}function sum5(args){let{backend:backend22,inputs,attrs}=args,{axis,keepDims}=attrs,{x}=inputs,xId=backend22.dataIdMap.get(x.dataId).id,inputId=xId,input2=x,{transposed,axes,originalAxes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend22),reductionAxes=axes;if(inputWasTransposed){let transposedId=backend22.dataIdMap.get(transposed.dataId).id;transposedId!==xId&&(input2=transposed,inputId=transposedId,reductionAxes=backend_util_exports.getInnerMostAxes(reductionAxes.length,input2.shape.length))}backend_util_exports.assertAxesAreInnerMostDims("sum",reductionAxes,input2.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(input2.shape,reductionAxes),reduceSize=util_exports.sizeFromShape(reduceShape),out=backend22.makeOutput(outShape,input2.dtype);if(util_exports.sizeFromShape(input2.shape)!==0){let outId=backend22.dataIdMap.get(out.dataId).id;wasmSum(inputId,reduceSize,outId)}if(inputWasTransposed&&backend22.disposeData(transposed.dataId),keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(out.shape,originalAxes);out.shape=newShape}return out}var sumConfig3={kernelName:Sum,backendName:"wasm",setupFunc:setup38,kernelFunc:sum5},tanhConfig3=createUnaryKernelConfig(Tanh),wasmTile;function setup39(backend22){wasmTile=backend22.wasm.cwrap(Tile,null,["number","array","number","array","number","number"])}function tile4(args){let{inputs,backend:backend22,attrs}=args,{x}=inputs,xId=backend22.dataIdMap.get(x.dataId).id,{reps}=attrs,newShape=new Array(x.shape.length);for(let i=0;i{let{x}=inputs,{k,sorted}=attrs,xId=backend22.dataIdMap.get(x.dataId).id,xShapeBytes=new Uint8Array(new Int32Array(x.shape).buffer),outputShape=x.shape.slice();outputShape[outputShape.length-1]=k;let outValues=backend22.makeOutput(outputShape,x.dtype),outValuesId=backend22.dataIdMap.get(outValues.dataId).id,outIndices=backend22.makeOutput(outputShape,"int32"),outIndicesId=backend22.dataIdMap.get(outIndices.dataId).id;return wasmTopK(xId,xShapeBytes,x.shape.length,CppDType[x.dtype],k,sorted,outValuesId,outIndicesId),[outValues,outIndices]},topKConfig3={kernelName:TopK,backendName:"wasm",setupFunc:setup40,kernelFunc:topk2};function unpack3(args){let{inputs,backend:backend22,attrs}=args,{value}=inputs,{axis}=attrs;axis<0&&(axis+=value.shape.length);let numOutputs=value.shape[axis],rank=value.shape.length,outShape=new Array(rank-1),outIndex=0;for(let i=0;i({dataId,dtype,shape:outShape}))}var unpackConfig3={kernelName:Unpack,backendName:"wasm",kernelFunc:unpack3};function zerosLike4(args){let{inputs:{x},backend:backend22}=args,out=backend22.makeOutput(x.shape,x.dtype);return backend22.typedArrayFromHeap(out).fill(0),out}var zerosLikeConfig3={kernelName:ZerosLike,backendName:"wasm",kernelFunc:zerosLike4},kernelConfigs3=[absConfig3,addConfig3,addNConfig3,argMaxConfig3,avgPoolConfig3,batchMatMulConfig3,castConfig3,clipByValueConfig2,concatConfig3,conv2DConfig3,conv2DBackpropInputConfig3,cosConfig3,cropAndResizeConfig3,cumsumConfig3,depthToSpaceConfig3,depthwiseConv2dNativeConfig3,equalConfig3,expConfig3,expandDimsConfig3,fillConfig3,flipLeftRightConfig3,floorConfig3,floorDivConfig3,fusedMatMulConfig,fusedBatchNormConfig,fusedConv2DConfig3,fusedDepthwiseConv2DConfig3,gatherNdConfig3,gatherV2Config3,greaterConfig3,greaterEqualConfig3,identityConfig3,leakyReluConfig3,lessConfig3,lessEqualConfig3,logConfig3,logicalAndConfig3,maxConfig3,maximumConfig3,maxPoolConfig3,meanConfig3,minConfig3,minimumConfig3,multiplyConfig3,negConfig3,nonMaxSuppressionV3Config3,nonMaxSuppressionV4Config3,nonMaxSuppressionV5Config3,notEqualConfig3,oneHotConfig3,onesLikeConfig3,packConfig3,padV2Config3,powConfig3,preluConfig3,prodConfig3,rangeConfig3,realDivConfig3,reluConfig3,relu6Config3,reshapeConfig3,resizeBilinearConfig3,reverseConfig3,rotateWithOffsetConfig3,rsqrtConfig3,roundConfig3,scatterNdConfig3,selectConfig3,sigmoidConfig3,sinConfig3,sliceConfig3,softmaxConfig3,splitVConfig3,sqrtConfig3,squareConfig3,squaredDifferenceConfig3,stridedSliceConfig3,subConfig3,sumConfig3,tanhConfig3,tileConfig3,topKConfig3,transposeConfig3,unpackConfig3,zerosLikeConfig3];for(let kernelConfig of kernelConfigs3)registerKernel(kernelConfig);var ENV4=env();ENV4.registerFlag("WASM_HAS_SIMD_SUPPORT",async()=>WebAssembly.validate(new Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,2,1,0,10,9,1,7,0,65,0,253,15,26,11])));ENV4.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT",async()=>{if(ENV4.get("IS_NODE"))return!1;try{return new MessageChannel().port1.postMessage(new SharedArrayBuffer(1)),WebAssembly.validate(new Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,2,1,0,5,4,1,3,1,1,10,11,1,9,0,65,0,254,16,2,0,26,11]))}catch(e){return!1}});var import_tfjs_backend_wasm_threaded_simd=__toModule2(require_tfjs_backend_wasm_threaded_simd()),wasmWorkerContents='var threadInfoStruct=0;var selfThreadId=0;var parentThreadId=0;var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(" ");console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(" ");postMessage({cmd:"alert",text:text,threadId:selfThreadId})}var err=threadPrintErr;this.alert=threadAlert;Module["instantiateWasm"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module["wasmModule"],info);Module["wasmModule"]=null;receiveInstance(instance);return instance.exports};this.onmessage=function(e){try{if(e.data.cmd==="load"){Module["DYNAMIC_BASE"]=e.data.DYNAMIC_BASE;Module["DYNAMICTOP_PTR"]=e.data.DYNAMICTOP_PTR;Module["wasmModule"]=e.data.wasmModule;Module["wasmMemory"]=e.data.wasmMemory;Module["buffer"]=Module["wasmMemory"].buffer;Module["ENVIRONMENT_IS_PTHREAD"]=true;if(typeof e.data.urlOrBlob==="string"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}Module=WasmBackendModuleThreadedSimd(Module);postMessage({"cmd":"loaded"})}else if(e.data.cmd==="objectTransfer"){Module["PThread"].receiveObjectTransfer(e.data)}else if(e.data.cmd==="run"){Module["__performance_now_clock_drift"]=performance.now()-e.data.time;threadInfoStruct=e.data.threadInfoStruct;Module["__register_pthread_ptr"](threadInfoStruct,0,0);selfThreadId=e.data.selfThreadId;parentThreadId=e.data.parentThreadId;var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module["establishStackSpace"](top,max);Module["_emscripten_tls_init"]();Module["PThread"].receiveObjectTransfer(e.data);Module["PThread"].setThreadStatus(Module["_pthread_self"](),1);try{var result=Module["dynCall_ii"](e.data.start_routine,e.data.arg);if(!Module["getNoExitRuntime"]())Module["PThread"].threadExit(result)}catch(ex){if(ex==="Canceled!"){Module["PThread"].threadCancel()}else if(ex!="unwind"){Atomics.store(Module["HEAPU32"],threadInfoStruct+4>>2,ex instanceof Module["ExitStatus"]?ex.status:-2);Atomics.store(Module["HEAPU32"],threadInfoStruct+0>>2,1);Module["_emscripten_futex_wake"](threadInfoStruct+0,2147483647);if(!(ex instanceof Module["ExitStatus"]))throw ex}}}else if(e.data.cmd==="cancel"){if(threadInfoStruct){Module["PThread"].threadCancel()}}else if(e.data.target==="setimmediate"){}else if(e.data.cmd==="processThreadQueue"){if(threadInfoStruct){Module["_emscripten_current_thread_process_queued_calls"]()}}else{err("worker.js received unknown command "+e.data.cmd);err(e.data)}}catch(ex){err("worker.js onmessage() captured an uncaught exception: "+ex);if(ex.stack)err(ex.stack);throw ex}};if(typeof process==="object"&&typeof process.versions==="object"&&typeof process.versions.node==="string"){self={location:{href:__filename}};var onmessage=this.onmessage;var nodeWorkerThreads=require("worker_threads");Worker=nodeWorkerThreads.Worker;var parentPort=nodeWorkerThreads.parentPort;parentPort.on("message",function(data){onmessage({data:data})});var nodeFS=require("fs");var nodeRead=function(filename){return nodeFS.readFileSync(filename,"utf8")};function globalEval(x){global.require=require;global.Module=Module;eval.call(null,x)}importScripts=function(f){globalEval(nodeRead(f))};postMessage=function(msg){parentPort.postMessage(msg)};if(typeof performance==="undefined"){performance={now:function(){return Date.now()}}}}',import_tfjs_backend_wasm=__toModule2(require_tfjs_backend_wasm()),BackendWasm=class extends KernelBackend{constructor(wasm){super();this.wasm=wasm,this.dataIdNextNumber=1,this.wasm.tfjs.init(),this.dataIdMap=new DataStorage(this,engine())}write(values,shape,dtype){let dataId={};return this.move(dataId,values,shape,dtype),dataId}numDataIds(){return this.dataIdMap.numDataIds()}async time(f){let start=util_exports.now();return f(),{kernelMs:util_exports.now()-start}}move(dataId,values,shape,dtype){let id=this.dataIdNextNumber++;if(dtype==="string"){let stringBytes=values;this.dataIdMap.set(dataId,{id,stringBytes,shape,dtype,memoryOffset:null});return}let size=util_exports.sizeFromShape(shape),numBytes=size*util_exports.bytesPerElement(dtype),memoryOffset=this.wasm._malloc(numBytes);this.dataIdMap.set(dataId,{id,memoryOffset,shape,dtype}),this.wasm.tfjs.registerTensor(id,size,memoryOffset),values!=null&&this.wasm.HEAPU8.set(new Uint8Array(values.buffer,values.byteOffset,numBytes),memoryOffset)}async read(dataId){return this.readSync(dataId)}readSync(dataId){let{memoryOffset,dtype,shape,stringBytes}=this.dataIdMap.get(dataId);if(dtype==="string")return stringBytes;let bytes=this.wasm.HEAPU8.slice(memoryOffset,memoryOffset+util_exports.sizeFromShape(shape)*util_exports.bytesPerElement(dtype));return typedArrayFromBuffer(bytes.buffer,dtype)}disposeData(dataId){let data2=this.dataIdMap.get(dataId);this.wasm._free(data2.memoryOffset),this.wasm.tfjs.disposeData(data2.id),this.dataIdMap.delete(dataId)}floatPrecision(){return 32}getMemoryOffset(dataId){return this.dataIdMap.get(dataId).memoryOffset}dispose(){this.wasm.tfjs.dispose(),this.wasm=null}memory(){return{unreliable:!1}}makeOutput(shape,dtype,memoryOffset){let dataId;if(memoryOffset==null)dataId=this.write(null,shape,dtype);else{dataId={};let id=this.dataIdNextNumber++;this.dataIdMap.set(dataId,{id,memoryOffset,shape,dtype});let size=util_exports.sizeFromShape(shape);this.wasm.tfjs.registerTensor(id,size,memoryOffset)}return{dataId,shape,dtype}}typedArrayFromHeap({shape,dtype,dataId}){let buffer2=this.wasm.HEAPU8.buffer,{memoryOffset}=this.dataIdMap.get(dataId),size=util_exports.sizeFromShape(shape);switch(dtype){case"float32":return new Float32Array(buffer2,memoryOffset,size);case"int32":return new Int32Array(buffer2,memoryOffset,size);case"bool":return new Uint8Array(buffer2,memoryOffset,size);default:throw new Error(`Unknown dtype ${dtype}`)}}};function createInstantiateWasmFunc(path){return(imports,callback)=>(util_exports.fetch(path,{credentials:"same-origin"}).then(response=>{response.ok||imports.env.a(`failed to load wasm binary file at '${path}'`),response.arrayBuffer().then(binary=>{WebAssembly.instantiate(binary,imports).then(output=>{callback(output.instance)})})}),{})}function getPathToWasmBinary(simdSupported,threadsSupported,wasmModuleFolder){if(wasmPath!=null)return wasmPath;let path="tfjs-backend-wasm.wasm";return simdSupported&&threadsSupported?path="tfjs-backend-wasm-threaded-simd.wasm":simdSupported&&(path="tfjs-backend-wasm-simd.wasm"),wasmFileMap!=null&&wasmFileMap[path]!=null?wasmFileMap[path]:wasmModuleFolder+path}async function init(){let[simdSupported,threadsSupported]=await Promise.all([env().getAsync("WASM_HAS_SIMD_SUPPORT"),env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]);return new Promise((resolve,reject)=>{let factoryConfig={};factoryConfig.locateFile=(path,prefix)=>{if(path.endsWith(".worker.js")){let response=wasmWorkerContents,blob=new Blob([response],{type:"application/javascript"});return URL.createObjectURL(blob)}return path.endsWith(".wasm")?getPathToWasmBinary(simdSupported,threadsSupported,wasmPathPrefix!=null?wasmPathPrefix:prefix):prefix+path},customFetch&&(factoryConfig.instantiateWasm=createInstantiateWasmFunc(getPathToWasmBinary(simdSupported,threadsSupported,wasmPathPrefix!=null?wasmPathPrefix:"")));let wasm;threadsSupported&&simdSupported&&wasmPath==null?(wasm=import_tfjs_backend_wasm_threaded_simd.default(factoryConfig),wasm.mainScriptUrlOrBlob=new Blob(["var WasmBackendModuleThreadedSimd = "+import_tfjs_backend_wasm_threaded_simd.default.toString()],{type:"text/javascript"})):wasm=import_tfjs_backend_wasm.default(factoryConfig);let voidReturnType=null;wasm.tfjs={init:wasm.cwrap("init",null,[]),registerTensor:wasm.cwrap("register_tensor",null,["number","number","number"]),disposeData:wasm.cwrap("dispose_data",voidReturnType,["number"]),dispose:wasm.cwrap("dispose",voidReturnType,[])};let initialized=!1;wasm.onRuntimeInitialized=()=>{initialized=!0,initAborted=!1,resolve({wasm})},wasm.onAbort=()=>{initialized||initAborted||(initAborted=!0,reject({message:"Make sure the server can serve the `.wasm` file relative to the bundled js file. For more details see https://github.com/tensorflow/tfjs/blob/master/tfjs-backend-wasm/README.md#using-bundlers"}))}})}function typedArrayFromBuffer(buffer2,dtype){switch(dtype){case"float32":return new Float32Array(buffer2);case"int32":return new Int32Array(buffer2);case"bool":return new Uint8Array(buffer2);default:throw new Error(`Unknown dtype ${dtype}`)}}var wasmBinaryNames=["tfjs-backend-wasm.wasm","tfjs-backend-wasm-simd.wasm","tfjs-backend-wasm-threaded-simd.wasm"],wasmPath=null,wasmPathPrefix=null,wasmFileMap={},initAborted=!1,customFetch=!1;function setWasmPath(path,usePlatformFetch=!1){if(deprecationWarn("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release."),initAborted)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`");wasmPath=path,customFetch=usePlatformFetch}function setWasmPaths(prefixOrFileMap,usePlatformFetch=!1){if(initAborted)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPaths()` before you call `tf.setBackend()` or `tf.ready()`");if(typeof prefixOrFileMap=="string")wasmPathPrefix=prefixOrFileMap;else{wasmFileMap=prefixOrFileMap;let missingPaths=wasmBinaryNames.filter(name=>wasmFileMap[name]==null);if(missingPaths.length>0)throw new Error(`There were no entries found for the following binaries: ${missingPaths.join(",")}. Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`)}customFetch=usePlatformFetch}var version9="2.8.3",WASM_PRIORITY=2;registerBackend("wasm",async()=>{let{wasm}=await init();return new BackendWasm(wasm)},WASM_PRIORITY);var exports_constraints_exports={};__export2(exports_constraints_exports,{maxNorm:()=>maxNorm,minMaxNorm:()=>minMaxNorm,nonNeg:()=>nonNeg,unitNorm:()=>unitNorm});var _epsilon;function epsilon2(){return _epsilon==null&&(_epsilon=backend().epsilon()),_epsilon}function imageDataFormat(){return"channelsLast"}var AttributeError=class extends Error{constructor(message){super(message);Object.setPrototypeOf(this,AttributeError.prototype)}},RuntimeError=class extends Error{constructor(message){super(message);Object.setPrototypeOf(this,RuntimeError.prototype)}},ValueError=class extends Error{constructor(message){super(message);Object.setPrototypeOf(this,ValueError.prototype)}},NotImplementedError=class extends Error{constructor(message){super(message);Object.setPrototypeOf(this,NotImplementedError.prototype)}},AssertionError=class extends Error{constructor(message){super(message);Object.setPrototypeOf(this,AssertionError.prototype)}},IndexError=class extends Error{constructor(message){super(message);Object.setPrototypeOf(this,IndexError.prototype)}};function pyListRepeat(value,numValues){if(Array.isArray(value)){let newArray=[];for(let i=0;ip1.toUpperCase())}var _GLOBAL_CUSTOM_OBJECTS={};function serializeKerasObject(instance){if(instance==null)return null;let dict={};return dict.className=instance.getClassName(),dict.config=instance.getConfig(),dict}function convertNDArrayScalarsInConfig(config2){if(!(config2==null||typeof config2!="object"))if(Array.isArray(config2))config2.forEach(configItem=>convertNDArrayScalarsInConfig(configItem));else{let fields=Object.keys(config2);for(let field of fields){let value=config2[field];value!=null&&typeof value=="object"&&(!Array.isArray(value)&&value.type==="ndarray"&&typeof value.value=="number"?config2[field]=value.value:convertNDArrayScalarsInConfig(value))}}}function deserializeKerasObject(identifier,moduleObjects={},customObjects={},printableModuleName="object",fastWeightInit=!1){if(typeof identifier=="string"){let functionName=identifier,fn;if(functionName in customObjects)fn=customObjects[functionName];else if(functionName in _GLOBAL_CUSTOM_OBJECTS)fn=_GLOBAL_CUSTOM_OBJECTS[functionName];else if(fn=moduleObjects[functionName],fn==null)throw new ValueError(`Unknown ${printableModuleName}: ${identifier}. This may be due to one of the following reasons: 1. The ${printableModuleName} is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code. -2. The custom ${printableModuleName} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);return fn}else{let config3=identifier;if(config3.className==null||config3.config==null)throw new ValueError(`${printableModuleName}: Improper config format: ${JSON.stringify(config3)}. -'className' and 'config' must set.`);let className=config3.className,cls,fromConfig;if(className in customObjects?[cls,fromConfig]=customObjects[className]:className in _GLOBAL_CUSTOM_OBJECTS?[cls,fromConfig]=_GLOBAL_CUSTOM_OBJECTS.className:className in moduleObjects&&([cls,fromConfig]=moduleObjects[className]),cls==null)throw new ValueError(`Unknown ${printableModuleName}: ${className}. This may be due to one of the following reasons: +2. The custom ${printableModuleName} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);return fn}else{let config2=identifier;if(config2.className==null||config2.config==null)throw new ValueError(`${printableModuleName}: Improper config format: ${JSON.stringify(config2)}. +'className' and 'config' must set.`);let className=config2.className,cls,fromConfig;if(className in customObjects?[cls,fromConfig]=customObjects[className]:className in _GLOBAL_CUSTOM_OBJECTS?[cls,fromConfig]=_GLOBAL_CUSTOM_OBJECTS.className:className in moduleObjects&&([cls,fromConfig]=moduleObjects[className]),cls==null)throw new ValueError(`Unknown ${printableModuleName}: ${className}. This may be due to one of the following reasons: 1. The ${printableModuleName} is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code. -2. The custom ${printableModuleName} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);if(fromConfig!=null){let customObjectsCombined={};for(let key of Object.keys(_GLOBAL_CUSTOM_OBJECTS))customObjectsCombined[key]=_GLOBAL_CUSTOM_OBJECTS[key];for(let key of Object.keys(customObjects))customObjectsCombined[key]=customObjects[key];let nestedConfig=config3.config;nestedConfig.customObjects=customObjectsCombined;let backupCustomObjects=Object.assign({},_GLOBAL_CUSTOM_OBJECTS);for(let key of Object.keys(customObjects))_GLOBAL_CUSTOM_OBJECTS[key]=customObjects[key];convertNDArrayScalarsInConfig(config3.config);let returnObj=fromConfig(cls,config3.config,customObjects,fastWeightInit);return _GLOBAL_CUSTOM_OBJECTS=Object.assign({},backupCustomObjects),returnObj}else{let backupCustomObjects=Object.assign({},_GLOBAL_CUSTOM_OBJECTS);for(let key of Object.keys(customObjects))_GLOBAL_CUSTOM_OBJECTS[key]=customObjects[key];let returnObj=new cls(config3.config);return _GLOBAL_CUSTOM_OBJECTS=Object.assign({},backupCustomObjects),returnObj}}}function numberCompare(a,b){return ab?1:0}function reverseNumberCompare(a,b){return-1*numberCompare(a,b)}function unique4(xs){if(xs==null)return xs;let out=[];for(let x of xs)out.indexOf(x)===-1&&out.push(x);return out}function isObjectEmpty(obj){if(obj==null)throw new ValueError(`Invalid value in obj: ${JSON.stringify(obj)}`);for(let key in obj)if(obj.hasOwnProperty(key))return!1;return!0}function checkStringTypeUnionValue(values,label,value){if(value!=null&&values.indexOf(value)<0)throw new ValueError(`${value} is not a valid ${label}. Valid values are ${values} or null/undefined.`)}function checkArrayTypeAndLength(x,expectedType,minLength=0,maxLength=Infinity){return assert2(minLength>=0),assert2(maxLength>=minLength),Array.isArray(x)&&x.length>=minLength&&x.length<=maxLength&&x.every(e=>typeof e===expectedType)}function assertPositiveInteger(value,name){Array.isArray(value)?(util_exports.assert(value.length>0,()=>`${name} is unexpectedly an empty array.`),value.forEach((v,i)=>assertPositiveInteger(v,`element ${i+1} of ${name}`))):util_exports.assert(Number.isInteger(value)&&value>0,()=>`Expected ${name} to be a positive integer, but got ${formatAsFriendlyString(value)}.`)}function formatAsFriendlyString(value){return value===null?"null":Array.isArray(value)?"["+value.map(v=>formatAsFriendlyString(v)).join(",")+"]":typeof value=="string"?`"${value}"`:`${value}`}function debounce(f,waitMs){let lastTime=util_exports.now(),lastResult;return(...args)=>{let now22=util_exports.now();return now22-lastTimesqrt(sum2(mul(w,w),axis,!0)))}var Constraint=class extends serialization_exports.Serializable{getConfig(){return{}}},MaxNorm=class extends Constraint{constructor(args){super();this.defaultMaxValue=2,this.defaultAxis=0,this.maxValue=args.maxValue!=null?args.maxValue:this.defaultMaxValue,this.axis=args.axis!=null?args.axis:this.defaultAxis}apply(w){return tidy(()=>{let norms=calcL2Norms(w,this.axis),desired=clipByValue(norms,0,this.maxValue);return mul(w,div(desired,add2(epsilon2(),norms)))})}getConfig(){return{maxValue:this.maxValue,axis:this.axis}}};MaxNorm.className="MaxNorm";serialization_exports.registerClass(MaxNorm);var UnitNorm=class extends Constraint{constructor(args){super();this.defaultAxis=0,this.axis=args.axis!=null?args.axis:this.defaultAxis}apply(w){return tidy(()=>div(w,add2(epsilon2(),calcL2Norms(w,this.axis))))}getConfig(){return{axis:this.axis}}};UnitNorm.className="UnitNorm";serialization_exports.registerClass(UnitNorm);var NonNeg=class extends Constraint{apply(w){return relu(w)}};NonNeg.className="NonNeg";serialization_exports.registerClass(NonNeg);var MinMaxNorm=class extends Constraint{constructor(args){super();this.defaultMinValue=0,this.defaultMaxValue=1,this.defaultRate=1,this.defaultAxis=0,this.minValue=args.minValue!=null?args.minValue:this.defaultMinValue,this.maxValue=args.maxValue!=null?args.maxValue:this.defaultMaxValue,this.rate=args.rate!=null?args.rate:this.defaultRate,this.axis=args.axis!=null?args.axis:this.defaultAxis}apply(w){return tidy(()=>{let norms=calcL2Norms(w,this.axis),desired=add2(mul(this.rate,clipByValue(norms,this.minValue,this.maxValue)),mul(1-this.rate,norms));return mul(w,div(desired,add2(epsilon2(),norms)))})}getConfig(){return{minValue:this.minValue,maxValue:this.maxValue,rate:this.rate,axis:this.axis}}};MinMaxNorm.className="MinMaxNorm";serialization_exports.registerClass(MinMaxNorm);var CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP={maxNorm:"MaxNorm",minMaxNorm:"MinMaxNorm",nonNeg:"NonNeg",unitNorm:"UnitNorm"};function serializeConstraint(constraint){return serializeKerasObject(constraint)}function deserializeConstraint(config3,customObjects={}){return deserializeKerasObject(config3,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"constraint")}function getConstraint(identifier){if(identifier==null)return null;if(typeof identifier=="string"){let config3={className:identifier in CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP?CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier]:identifier,config:{}};return deserializeConstraint(config3)}else return identifier instanceof Constraint?identifier:deserializeConstraint(identifier)}function maxNorm(args){return new MaxNorm(args)}function unitNorm(args){return new UnitNorm(args)}function nonNeg(){return new NonNeg}function minMaxNorm(config3){return new MinMaxNorm(config3)}var exports_initializers_exports={};__export2(exports_initializers_exports,{constant:()=>constant,glorotNormal:()=>glorotNormal,glorotUniform:()=>glorotUniform,heNormal:()=>heNormal,heUniform:()=>heUniform,identity:()=>identity4,leCunNormal:()=>leCunNormal,leCunUniform:()=>leCunUniform,ones:()=>ones3,orthogonal:()=>orthogonal,randomNormal:()=>randomNormal3,randomUniform:()=>randomUniform2,truncatedNormal:()=>truncatedNormal2,varianceScaling:()=>varianceScaling,zeros:()=>zeros3});var VALID_DATA_FORMAT_VALUES=["channelsFirst","channelsLast"],VALID_INTERPOLATION_FORMAT_VALUES=["nearest","bilinear"],VALID_PADDING_MODE_VALUES=["valid","same","causal"],VALID_POOL_MODE_VALUES=["max","avg"],VALID_BIDIRECTIONAL_MERGE_MODES=["sum","mul","concat","ave"],nameMap=new Map;function checkDataFormat(value){checkStringTypeUnionValue(VALID_DATA_FORMAT_VALUES,"DataFormat",value)}function checkInterpolationFormat(value){checkStringTypeUnionValue(VALID_INTERPOLATION_FORMAT_VALUES,"InterpolationFormat",value)}function checkPaddingMode(value){checkStringTypeUnionValue(VALID_PADDING_MODE_VALUES,"PaddingMode",value)}function checkPoolMode(value){checkStringTypeUnionValue(VALID_POOL_MODE_VALUES,"PoolMode",value)}var _nameScopeStack=[],_nameScopeDivider="/";function nameScope(name,fn){_nameScopeStack.push(name);try{let val=fn();return _nameScopeStack.pop(),val}catch(e){throw _nameScopeStack.pop(),e}}function currentNameScopePrefix(){return _nameScopeStack.length===0?"":_nameScopeStack.join(_nameScopeDivider)+_nameScopeDivider}function getScopedTensorName(tensorName){if(!isValidTensorName(tensorName))throw new Error("Not a valid tensor name: '"+tensorName+"'");return currentNameScopePrefix()+tensorName}function getUniqueTensorName(scopedName){if(!isValidTensorName(scopedName))throw new Error("Not a valid tensor name: '"+scopedName+"'");nameMap.has(scopedName)||nameMap.set(scopedName,0);let index=nameMap.get(scopedName);if(nameMap.set(scopedName,nameMap.get(scopedName)+1),index>0){let result=`${scopedName}_${index}`;return nameMap.set(result,1),result}else return scopedName}var tensorNameRegex=new RegExp(/^[A-Za-z0-9][-A-Za-z0-9\._\/]*$/);function isValidTensorName(name){return!!name.match(tensorNameRegex)}function isInteger(x){return x===parseInt(x.toString(),10)}function arrayProd(array2,begin,end){begin==null&&(begin=0),end==null&&(end=array2.length);let prod5=1;for(let i=begin;i{if(x.shape.length!==2)throw new ValueError(`repeat() expects a rank-2 tensor, but received a rank-${x.shape.length} tensor.`);let y=expandDims5(x,1);return tile5(y,[1,n,1])})}function flatten2(x){let newShape=[arrayProd(x.shape)];return x.reshape(newShape)}function batchFlatten(x){if(x.rank<=1)throw new ValueError(`batchFlatten requires a minimum rank of 2. Got rank: ${x.rank}.`);let newShape=[x.shape[0],arrayProd(x.shape,1)];return x.reshape(newShape)}function sliceAlongFirstAxis(array2,start,size){return tidy(()=>{switch(array2.rank){case 1:return slice1d(array2,start,size);case 2:return slice2d(array2,[start,0],[size,array2.shape[1]]);case 3:return slice3d(array2,[start,0,0],[size,array2.shape[1],array2.shape[2]]);case 4:return slice4d(array2,[start,0,0,0],[size,array2.shape[1],array2.shape[2],array2.shape[3]]);case 5:return slice(array2,[start,0,0,0,0],[size,array2.shape[1],array2.shape[2],array2.shape[3],array2.shape[4]]);case 6:return slice(array2,[start,0,0,0,0,0],[size,array2.shape[1],array2.shape[2],array2.shape[3],array2.shape[4],array2.shape[5]]);default:throw new ValueError(`sliceAlongFirstAxis() received an unsupported tensor rank: ${array2.rank}`)}})}function sliceAlongLastAxis(array2,start,size){return tidy(()=>{switch(array2.rank){case 1:return slice1d(array2,start,size);case 2:return slice2d(array2,[0,start],[array2.shape[0],size]);case 3:return slice3d(array2,[0,0,start],[array2.shape[0],array2.shape[1],size]);case 4:return slice4d(array2,[0,0,0,start],[array2.shape[0],array2.shape[1],array2.shape[2],size]);default:throw new ValueError(`sliceAlongLastAxis() received an unsupported tensor rank: ${array2.rank}`)}})}function sliceAlongAxis(array2,start,size,axis){return tidy(()=>{switch(array2.rank){case 1:return slice1d(array2,start,size);case 2:switch(axis){case 1:return sliceAlongFirstAxis(array2,start,size);case 2:return sliceAlongLastAxis(array2,start,size);default:throw new ValueError(`The axis is not within the rank of the tensor ${axis}`)}case 3:switch(axis){case 1:return sliceAlongFirstAxis(array2,start,size);case 2:return slice3d(array2,[0,start,0],[array2.shape[0],size,array2.shape[2]]);case 3:return sliceAlongLastAxis(array2,start,size);default:throw new ValueError(`The axis is not within the rank of the tensor ${axis}`)}case 4:switch(axis){case 1:return sliceAlongFirstAxis(array2,start,size);case 2:return slice4d(array2,[0,start,0,0],[array2.shape[0],size,array2.shape[2],array2.shape[3]]);case 3:return slice4d(array2,[0,0,start,0],[array2.shape[0],array2.shape[1],size,array2.shape[3]]);case 4:return sliceAlongLastAxis(array2,start,size);default:throw new ValueError(`The axis is not within the rank of the tensor ${axis}`)}default:throw new ValueError(`sliceAlongLastAxis() received an unsupported tensor rank: ${array2.rank}`)}})}function concatenate(tensors,axis=-1){let rank;return axis<0&&(rank=tensors[0].rank,rank!==0?axis=rank:axis=0),axis===tensors[0].rank&&(axis=-1),concat(tensors,axis)}function concatAlongFirstAxis(a,b){switch(a.rank){case 1:return concat1d([a,b]);case 2:return concat2d([a,b],0);case 3:return concat3d([a,b],0);case 4:return concat4d([a,b],0);default:throw new ValueError(`concatAlongFirstAxis() received an unsupported tensor rank: ${a.rank}`)}}function tile5(x,n){if(Array.isArray(n)||(n=[n]),x.rank!==n.length)throw new ValueError(`The length of input n (${n.length}) does not match the number of dimensions in input x (${x.rank})`);return tile(x,n)}function randomNormal2(shape,mean4=0,stddev=1,dtype,seed){return randomNormal(shape,mean4,stddev,dtype,seed)}function dot2(a,b,activation2,bias){if(a.rank<2||b.rank<2)throw new NotImplementedError(`dot requires both inputs to be rank >= 2 but got x shape = ${a.shape} and y shape = ${b.shape}`);if(b.rank>=3){let xLastDim=a.shape.slice(-1)[0],ySecondLastDim=b.shape.slice(-2)[0];if(xLastDim!==ySecondLastDim)throw new NotImplementedError(`If rank y >= 3, then the second last dim of y must equal the last dim of x but got x shape = ${a.shape} and y shape = ${b.shape}`)}if(a.rank===2&&b.rank===2){let transposeA=!1,transposeB=!1;return fused_ops_exports.matMul({a,b,transposeA,transposeB,bias:bias?reshapeBias(a.rank,bias,imageDataFormat()):null,activation:activation2})}else{let aFirstDims=a.shape.slice(),aLastDim=aFirstDims.pop();a=a.reshape([-1,aLastDim]);let bShape=b.shape.slice(),bLastDim=bShape.pop(),ySecondLastDim=bShape.pop(),yOtherDims=[...bShape,bLastDim],perm=Array.from({length:b.rank},(_,i)=>i===0?b.rank-2:i<=b.rank-2?i-1:i);b=b.transpose(perm).reshape([ySecondLastDim,-1]);let outputShape=[...aFirstDims,...yOtherDims],transposeA=!1,transposeB=!1;return fused_ops_exports.matMul({a,b,transposeA,transposeB,bias:bias?reshapeBias(a.rank,bias,imageDataFormat()):null,activation:activation2}).reshape(outputShape)}}function gather2(reference,indices,axis){return tidy(()=>(Array.isArray(indices)?indices=tensor1d(indices,"int32"):indices=indices.toInt(),gather(reference,indices,axis)))}function square3(x){return mul(x,x)}function reshapeBias(xRank,bias,dataFormat){let biasShape=bias.shape;if(bias.rank!==1&&bias.rank!==xRank)throw new ValueError(`Unexpected bias dimensions: ${bias.rank}; expected it to be 1 or ${xRank}`);if(xRank===5){if(dataFormat==="channelsFirst")return biasShape.length===1?bias.reshape([1,biasShape[0],1,1,1]):bias.reshape([1,biasShape[3],biasShape[0],biasShape[1],biasShape[2]]);if(dataFormat==="channelsLast")return biasShape.length===1?bias.reshape([1,1,1,1,biasShape[0]]):bias.reshape([1].concat(biasShape))}else if(xRank===4){if(dataFormat==="channelsFirst")return biasShape.length===1?bias.reshape([1,biasShape[0],1,1]):bias.reshape([1,biasShape[2],biasShape[0],biasShape[1]]);if(dataFormat==="channelsLast")return biasShape.length===1?bias.reshape([1,1,1,biasShape[0]]):bias.reshape([1].concat(biasShape))}else if(xRank===3){if(dataFormat==="channelsFirst")return biasShape.length===1?bias.reshape([1,biasShape[0],1]):bias.reshape([1,biasShape[1],biasShape[0]]);if(dataFormat==="channelsLast")return biasShape.length===1?bias.reshape([1,1,biasShape[0]]):bias.reshape([1].concat(biasShape))}else if(xRank<3)return bias;throw new ValueError(`Unsupported input rank by biasAdd: ${bias.rank}`)}function biasAdd(x,bias,dataFormat){return tidy(()=>(dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat),x.add(reshapeBias(x.rank,bias,dataFormat))))}function elu4(x,alpha=1){if(alpha!==1)throw new NotImplementedError(`Support for alpha values other than 1 (${alpha}) is not implemented yet.`);return elu(x)}function softsign(x){return tidy(()=>div(x,abs(x).add(1)))}function dropout2(x,level,noiseShape,seed){return tidy(()=>dropout(x,level,noiseShape,seed))}function hardSigmoid(x){return tidy(()=>{let y=add2(.5,mul(.2,x));return clipByValue(y,0,1)})}function inTrainPhase(x,alt,training=!1){return training?x():alt()}var VALID_FAN_MODE_VALUES=["fanIn","fanOut","fanAvg"],VALID_DISTRIBUTION_VALUES=["normal","uniform","truncatedNormal"];function checkFanMode(value){checkStringTypeUnionValue(VALID_FAN_MODE_VALUES,"FanMode",value)}function checkDistribution(value){checkStringTypeUnionValue(VALID_DISTRIBUTION_VALUES,"Distribution",value)}var Initializer=class extends serialization_exports.Serializable{fromConfigUsesCustomObjects(){return!1}getConfig(){return{}}},Zeros=class extends Initializer{apply(shape,dtype){return zeros(shape,dtype)}};Zeros.className="Zeros";serialization_exports.registerClass(Zeros);var Ones=class extends Initializer{apply(shape,dtype){return ones2(shape,dtype)}};Ones.className="Ones";serialization_exports.registerClass(Ones);var Constant=class extends Initializer{constructor(args){super();if(typeof args!="object")throw new ValueError(`Expected argument of type ConstantConfig but got ${args}`);if(args.value===void 0)throw new ValueError(`config must have value set but got ${args}`);this.value=args.value}apply(shape,dtype){return tidy(()=>mul(scalar(this.value),ones2(shape,dtype)))}getConfig(){return{value:this.value}}};Constant.className="Constant";serialization_exports.registerClass(Constant);var RandomUniform=class extends Initializer{constructor(args){super();this.DEFAULT_MINVAL=-.05,this.DEFAULT_MAXVAL=.05,this.minval=args.minval||this.DEFAULT_MINVAL,this.maxval=args.maxval||this.DEFAULT_MAXVAL,this.seed=args.seed}apply(shape,dtype){return randomUniform(shape,this.minval,this.maxval,dtype)}getConfig(){return{minval:this.minval,maxval:this.maxval,seed:this.seed}}};RandomUniform.className="RandomUniform";serialization_exports.registerClass(RandomUniform);var RandomNormal=class extends Initializer{constructor(args){super();this.DEFAULT_MEAN=0,this.DEFAULT_STDDEV=.05,this.mean=args.mean||this.DEFAULT_MEAN,this.stddev=args.stddev||this.DEFAULT_STDDEV,this.seed=args.seed}apply(shape,dtype){if(dtype=dtype||"float32",dtype!=="float32"&&dtype!=="int32")throw new NotImplementedError(`randomNormal does not support dType ${dtype}.`);return randomNormal2(shape,this.mean,this.stddev,dtype,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}};RandomNormal.className="RandomNormal";serialization_exports.registerClass(RandomNormal);var TruncatedNormal=class extends Initializer{constructor(args){super();this.DEFAULT_MEAN=0,this.DEFAULT_STDDEV=.05,this.mean=args.mean||this.DEFAULT_MEAN,this.stddev=args.stddev||this.DEFAULT_STDDEV,this.seed=args.seed}apply(shape,dtype){if(dtype=dtype||"float32",dtype!=="float32"&&dtype!=="int32")throw new NotImplementedError(`truncatedNormal does not support dType ${dtype}.`);return truncatedNormal(shape,this.mean,this.stddev,dtype,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}};TruncatedNormal.className="TruncatedNormal";serialization_exports.registerClass(TruncatedNormal);var Identity2=class extends Initializer{constructor(args){super();this.gain=args.gain!=null?args.gain:1}apply(shape,dtype){return tidy(()=>{if(shape.length!==2||shape[0]!==shape[1])throw new ValueError("Identity matrix initializer can only be used for 2D square matrices.");return mul(this.gain,eye(shape[0]))})}getConfig(){return{gain:this.gain}}};Identity2.className="Identity";serialization_exports.registerClass(Identity2);function computeFans(shape,dataFormat="channelsLast"){let fanIn,fanOut;if(checkDataFormat(dataFormat),shape.length===2)fanIn=shape[0],fanOut=shape[1];else if([3,4,5].indexOf(shape.length)!==-1){if(dataFormat==="channelsFirst"){let receptiveFieldSize=arrayProd(shape,2);fanIn=shape[1]*receptiveFieldSize,fanOut=shape[0]*receptiveFieldSize}else if(dataFormat==="channelsLast"){let receptiveFieldSize=arrayProd(shape,0,shape.length-2);fanIn=shape[shape.length-2]*receptiveFieldSize,fanOut=shape[shape.length-1]*receptiveFieldSize}}else{let shapeProd=arrayProd(shape);fanIn=Math.sqrt(shapeProd),fanOut=Math.sqrt(shapeProd)}return[fanIn,fanOut]}var VarianceScaling=class extends Initializer{constructor(args){super();if(args.scale<0)throw new ValueError(`scale must be a positive float. Got: ${args.scale}`);this.scale=args.scale==null?1:args.scale,this.mode=args.mode==null?"fanIn":args.mode,checkFanMode(this.mode),this.distribution=args.distribution==null?"normal":args.distribution,checkDistribution(this.distribution),this.seed=args.seed}apply(shape,dtype){let fans=computeFans(shape),fanIn=fans[0],fanOut=fans[1],scale2=this.scale;if(this.mode==="fanIn"?scale2/=Math.max(1,fanIn):this.mode==="fanOut"?scale2/=Math.max(1,fanOut):scale2/=Math.max(1,(fanIn+fanOut)/2),this.distribution==="normal"){let stddev=Math.sqrt(scale2);if(dtype=dtype||"float32",dtype!=="float32"&&dtype!=="int32")throw new NotImplementedError(`${this.getClassName()} does not support dType ${dtype}.`);return truncatedNormal(shape,0,stddev,dtype,this.seed)}else{let limit=Math.sqrt(3*scale2);return randomUniform(shape,-limit,limit,dtype)}}getConfig(){return{scale:this.scale,mode:this.mode,distribution:this.distribution,seed:this.seed}}};VarianceScaling.className="VarianceScaling";serialization_exports.registerClass(VarianceScaling);var GlorotUniform=class extends VarianceScaling{constructor(args){super({scale:1,mode:"fanAvg",distribution:"uniform",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};GlorotUniform.className="GlorotUniform";serialization_exports.registerClass(GlorotUniform);var GlorotNormal=class extends VarianceScaling{constructor(args){super({scale:1,mode:"fanAvg",distribution:"normal",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};GlorotNormal.className="GlorotNormal";serialization_exports.registerClass(GlorotNormal);var HeNormal=class extends VarianceScaling{constructor(args){super({scale:2,mode:"fanIn",distribution:"normal",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};HeNormal.className="HeNormal";serialization_exports.registerClass(HeNormal);var HeUniform=class extends VarianceScaling{constructor(args){super({scale:2,mode:"fanIn",distribution:"uniform",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};HeUniform.className="HeUniform";serialization_exports.registerClass(HeUniform);var LeCunNormal=class extends VarianceScaling{constructor(args){super({scale:1,mode:"fanIn",distribution:"normal",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};LeCunNormal.className="LeCunNormal";serialization_exports.registerClass(LeCunNormal);var LeCunUniform=class extends VarianceScaling{constructor(args){super({scale:1,mode:"fanIn",distribution:"uniform",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};LeCunUniform.className="LeCunNormal";serialization_exports.registerClass(LeCunUniform);var Orthogonal=class extends Initializer{constructor(args){super();if(this.DEFAULT_GAIN=1,this.gain=args.gain==null?this.DEFAULT_GAIN:args.gain,this.seed=args.seed,this.seed!=null)throw new NotImplementedError("Random seed is not implemented for Orthogonal Initializer yet.")}apply(shape,dtype){return tidy(()=>{if(shape.length<2)throw new NotImplementedError("Shape must be at least 2D.");shape[0]*shape[1]>2e3&&console.warn(`Orthogonal initializer is being called on a matrix with more than 2000 (${shape[0]*shape[1]}) elements: Slowness may result.`);let normalizedShape=shape[0]>shape[1]?[shape[1],shape[0]]:shape,a=randomNormal2(normalizedShape,0,1,"float32"),q=linalg.gramSchmidt(a);return shape[0]>shape[1]&&(q=q.transpose()),mul(this.gain,q)})}getConfig(){return{gain:this.gain,seed:this.seed}}};Orthogonal.className="Orthogonal";serialization_exports.registerClass(Orthogonal);var INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP={constant:"Constant",glorotNormal:"GlorotNormal",glorotUniform:"GlorotUniform",heNormal:"HeNormal",heUniform:"HeUniform",identity:"Identity",leCunNormal:"LeCunNormal",leCunUniform:"LeCunUniform",ones:"Ones",orthogonal:"Orthogonal",randomNormal:"RandomNormal",randomUniform:"RandomUniform",truncatedNormal:"TruncatedNormal",varianceScaling:"VarianceScaling",zeros:"Zeros"};function deserializeInitializer(config3,customObjects={}){return deserializeKerasObject(config3,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"initializer")}function serializeInitializer(initializer){return serializeKerasObject(initializer)}function getInitializer(identifier){if(typeof identifier=="string"){let className=identifier in INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP?INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier]:identifier;if(className==="GlorotNormal")return new GlorotNormal;if(className==="GlorotUniform")return new GlorotUniform;if(className==="HeNormal")return new HeNormal;if(className==="HeUniform")return new HeUniform;if(className==="LeCunNormal")return new LeCunNormal;if(className==="LeCunUniform")return new LeCunUniform;{let config3={};return config3.className=className,config3.config={},deserializeInitializer(config3)}}else return identifier instanceof Initializer?identifier:deserializeInitializer(identifier)}function zeros3(){return new Zeros}function ones3(){return new Ones}function constant(args){return new Constant(args)}function randomUniform2(args){return new RandomUniform(args)}function randomNormal3(args){return new RandomNormal(args)}function truncatedNormal2(args){return new TruncatedNormal(args)}function identity4(args){return new Identity2(args)}function varianceScaling(config3){return new VarianceScaling(config3)}function glorotUniform(args){return new GlorotUniform(args)}function glorotNormal(args){return new GlorotNormal(args)}function heNormal(args){return new HeNormal(args)}function heUniform(args){return new HeUniform(args)}function leCunNormal(args){return new LeCunNormal(args)}function leCunUniform(args){return new LeCunUniform(args)}function orthogonal(args){return new Orthogonal(args)}var exports_layers_exports={};__export2(exports_layers_exports,{Layer:()=>Layer,RNN:()=>RNN,RNNCell:()=>RNNCell,activation:()=>activation,add:()=>add4,alphaDropout:()=>alphaDropout,average:()=>average,averagePooling1d:()=>averagePooling1d,averagePooling2d:()=>averagePooling2d,averagePooling3d:()=>averagePooling3d,avgPool1d:()=>avgPool1d,avgPool2d:()=>avgPool2d,avgPool3d:()=>avgPool3d2,avgPooling1d:()=>avgPooling1d,avgPooling2d:()=>avgPooling2d,avgPooling3d:()=>avgPooling3d,batchNormalization:()=>batchNormalization2,bidirectional:()=>bidirectional,concatenate:()=>concatenate2,conv1d:()=>conv1d2,conv2d:()=>conv2d5,conv2dTranspose:()=>conv2dTranspose2,conv3d:()=>conv3d2,convLstm2d:()=>convLstm2d,convLstm2dCell:()=>convLstm2dCell,cropping2D:()=>cropping2D,dense:()=>dense,depthwiseConv2d:()=>depthwiseConv2d5,dot:()=>dot3,dropout:()=>dropout3,elu:()=>elu5,embedding:()=>embedding,flatten:()=>flatten3,gaussianDropout:()=>gaussianDropout,gaussianNoise:()=>gaussianNoise,globalAveragePooling1d:()=>globalAveragePooling1d,globalAveragePooling2d:()=>globalAveragePooling2d,globalMaxPool1d:()=>globalMaxPool1d,globalMaxPool2d:()=>globalMaxPool2d,globalMaxPooling1d:()=>globalMaxPooling1d,globalMaxPooling2d:()=>globalMaxPooling2d,gru:()=>gru,gruCell:()=>gruCell,input:()=>input,inputLayer:()=>inputLayer,layerNormalization:()=>layerNormalization,leakyReLU:()=>leakyReLU,lstm:()=>lstm,lstmCell:()=>lstmCell,masking:()=>masking,maxPool1d:()=>maxPool1d,maxPool2d:()=>maxPool2d,maxPooling1d:()=>maxPooling1d,maxPooling2d:()=>maxPooling2d,maxPooling3d:()=>maxPooling3d,maximum:()=>maximum4,minimum:()=>minimum4,multiply:()=>multiply3,permute:()=>permute,prelu:()=>prelu5,reLU:()=>reLU,repeatVector:()=>repeatVector,reshape:()=>reshape5,rnn:()=>rnn2,separableConv2d:()=>separableConv2d2,simpleRNN:()=>simpleRNN,simpleRNNCell:()=>simpleRNNCell,softmax:()=>softmax5,spatialDropout1d:()=>spatialDropout1d,stackedRNNCells:()=>stackedRNNCells,thresholdedReLU:()=>thresholdedReLU,timeDistributed:()=>timeDistributed,upSampling2d:()=>upSampling2d,zeroPadding2d:()=>zeroPadding2d});var _nextUniqueTensorId=0;function getNextUniqueTensorId(){return _nextUniqueTensorId++}var _uidPrefixes={};function getUid(prefix=""){return prefix in _uidPrefixes||(_uidPrefixes[prefix]=0),_uidPrefixes[prefix]+=1,prefix+_uidPrefixes[prefix].toString()}function isArrayOfShapes(x){return Array.isArray(x)&&Array.isArray(x[0])}function normalizeShapeList(x){return x.length===0?[]:Array.isArray(x[0])?x:[x]}function getExactlyOneTensor(xs){let x;if(Array.isArray(xs)){if(xs.length!==1)throw new ValueError(`Expected Tensor length to be 1; got ${xs.length}`);x=xs[0]}else x=xs;return x}function getExactlyOneShape(shapes){if(Array.isArray(shapes)&&Array.isArray(shapes[0])){if(shapes.length===1)return shapes=shapes,shapes[0];throw new ValueError(`Expected exactly 1 Shape; got ${shapes.length}`)}else return shapes}function countParamsInWeights(weights){let count2=0;for(let weight of weights)weight.shape.length===0?count2+=1:count2+=weight.shape.reduce((a,b)=>a*b);return count2}var DEFAULT_VARIABLE_NAME_PREFIX="Variable",LayerVariable=class{constructor(val,dtype="float32",name=DEFAULT_VARIABLE_NAME_PREFIX,trainable=!0,constraint=null){this.dtype=dtype==null?"float32":dtype,this.shape=val.shape,this.id=getNextUniqueTensorId(),name=name==null?DEFAULT_VARIABLE_NAME_PREFIX:name,this.originalName=getScopedTensorName(name),this.name=getUniqueTensorName(this.originalName),this.trainable_=trainable,this.constraint=constraint,this.val=variable(val,this.trainable_,this.name,this.dtype)}read(){return this.assertNotDisposed(),this.val}write(newVal){return this.assertNotDisposed(),checkShapesMatch(this.val,newVal),this.val.id!==newVal.id&&(this.val.assign(newVal),this.constraint!=null&&this.val.assign(this.constraint.apply(this.val))),this}dispose(){this.assertNotDisposed(),this.val.dispose()}assertNotDisposed(){if(this.val.isDisposed)throw new Error(`LayersVariable ${this.name} is already disposed.`)}get trainable(){return this.trainable_}set trainable(trainable){this.trainable_=trainable,this.val.trainable=trainable}};function checkShapesMatch(x,y){if(x.shape.toString()!==y.shape.toString())throw new Error("Shape mismatch: "+JSON.stringify(x.shape)+" vs. "+JSON.stringify(y.shape))}function batchGetValue(xs){return xs.map(x=>x.read())}function batchSetValue(variablesAndValues){variablesAndValues.forEach(variableAndValue=>{variableAndValue[0].write(variableAndValue[1])})}var InputSpec=class{constructor(args){this.dtype=args.dtype,this.shape=args.shape,args.shape!=null?this.ndim=args.shape.length:this.ndim=args.ndim,this.maxNDim=args.maxNDim,this.minNDim=args.minNDim,this.axes=args.axes||{}}},SymbolicTensor=class{constructor(dtype,shape,sourceLayer,inputs,callArgs,name,outputTensorIndex){this.dtype=dtype,this.shape=shape,this.sourceLayer=sourceLayer,this.inputs=inputs,this.callArgs=callArgs,this.outputTensorIndex=outputTensorIndex,this.id=getNextUniqueTensorId(),name!=null&&(this.originalName=getScopedTensorName(name),this.name=getUniqueTensorName(this.originalName)),this.rank=shape.length}},_nextNodeID=0,Node=class{constructor(args,callArgs){this.callArgs=callArgs,this.id=_nextNodeID++,this.outboundLayer=args.outboundLayer,this.inboundLayers=args.inboundLayers,this.nodeIndices=args.nodeIndices,this.tensorIndices=args.tensorIndices,this.inputTensors=args.inputTensors,this.outputTensors=args.outputTensors,this.inputMasks=args.inputMasks,this.outputMasks=args.outputMasks,this.inputShapes=args.inputShapes,this.outputShapes=args.outputShapes;for(let layer of args.inboundLayers)layer!=null&&layer.outboundNodes.push(this);args.outboundLayer.inboundNodes.push(this)}getConfig(){let inboundNames=[];for(let layer of this.inboundLayers)layer!=null?inboundNames.push(layer.name):inboundNames.push(null);return{outboundLayer:this.outboundLayer?this.outboundLayer.name:null,inboundLayers:inboundNames,nodeIndices:this.nodeIndices,tensorIndices:this.tensorIndices}}},_nextLayerID=0,Layer=class extends serialization_exports.Serializable{constructor(args={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=_nextLayerID++,this.activityRegularizer=null,this.inputSpec=null,this.supportsMasking=!1,this._trainableWeights=[],this._nonTrainableWeights=[],this._losses=[],this._updates=[],this._built=!1,this.inboundNodes=[],this.outboundNodes=[];let name=args.name;if(!name){let prefix=this.getClassName();name=toSnakeCase(prefix)+"_"+getUid(prefix)}if(this.name=name,this.trainable_=args.trainable==null?!0:args.trainable,args.inputShape!=null||args.batchInputShape!=null){let batchInputShape;if(args.batchInputShape!=null)batchInputShape=args.batchInputShape;else if(args.inputShape!=null){let batchSize=null;args.batchSize!=null&&(batchSize=args.batchSize),batchInputShape=[batchSize].concat(args.inputShape)}this.batchInputShape=batchInputShape;let dtype=args.dtype;dtype==null&&(dtype=args.inputDType),dtype==null&&(dtype="float32"),this.dtype=dtype}args.weights!=null?this.initialWeights=args.weights:this.initialWeights=null,this._refCount=null,this.fastWeightInitDuringBuild=!1}static nodeKey(layer,nodeIndex){return layer.name+"_ib-"+nodeIndex.toString()}getNodeAtIndex(nodeIndex,attrName){if(this.inboundNodes.length===0)throw new RuntimeError(`The layer has never been called and thus has no defined ${attrName}.`);if(this.inboundNodes.length<=nodeIndex)throw new ValueError(`Asked to get ${attrName} at node ${nodeIndex}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[nodeIndex]}getInputAt(nodeIndex){return singletonOrArray(this.getNodeAtIndex(nodeIndex,"input").inputTensors)}getOutputAt(nodeIndex){return singletonOrArray(this.getNodeAtIndex(nodeIndex,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new AttributeError(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer input" is ill-defined. Use \`getInputAt(nodeIndex)\` instead.`);if(this.inboundNodes.length===0)throw new AttributeError(`Layer ${this.name} is not connected, no input to return.`);return singletonOrArray(this.getNodeAtIndex(0,"input").inputTensors)}get output(){if(this.inboundNodes.length===0)throw new AttributeError(`Layer ${this.name} has no inbound nodes.`);if(this.inboundNodes.length>1)throw new AttributeError(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer output" is ill-defined. Use \`getOutputAt(nodeIndex)\` instead.`);return singletonOrArray(this.getNodeAtIndex(0,"output").outputTensors)}get losses(){return this._losses}calculateLosses(){return this.losses.map(lossFn=>lossFn())}get updates(){return this._updates}get built(){return this._built}set built(built){this._built=built}get trainable(){return this.trainable_}set trainable(trainable){this._trainableWeights.forEach(w=>w.trainable=trainable),this.trainable_=trainable}get trainableWeights(){return this.trainable_?this._trainableWeights.filter(w=>w.trainable):[]}set trainableWeights(weights){this._trainableWeights=weights}get nonTrainableWeights(){return this.trainable?this._trainableWeights.filter(w=>!w.trainable).concat(this._nonTrainableWeights):this._trainableWeights.concat(this._nonTrainableWeights)}set nonTrainableWeights(weights){this._nonTrainableWeights=weights}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}get stateful(){return this._stateful}resetStates(){if(!this.stateful)throw new Error("Cannot call the resetStates() method of a non-stateful Layer object.")}assertInputCompatibility(inputs){if(inputs=toList(inputs),this.inputSpec==null||this.inputSpec.length===0)return;let inputSpec=toList(this.inputSpec);if(inputs.length!==inputSpec.length)throw new ValueError(`Layer ${this.name} expects ${inputSpec.length} inputs, but it received ${inputs.length} input tensors. Input received: ${inputs}`);for(let inputIndex=0;inputIndexspec.maxNDim)throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected max_ndim=${spec.maxNDim}, found ndim=${ndim}`);if(spec.minNDim!=null&&ndim=0?xShape[axis]:xShape[xShape.length+axis];if(value!=null&&[value,null].indexOf(xShapeAtAxis)===-1)throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected axis ${axis} of input shape to have value ${value} but got shape ${xShape}.`)}}if(spec.shape!=null)for(let i=0;i{if(!this.built){this.assertInputCompatibility(inputs);let inputShapes=[];for(let xElem of toList(inputs))inputShapes.push(xElem.shape);this.build(singletonOrArray(inputShapes)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&noneAreSymbolic&&(this._refCount=1)}if(this.assertInputCompatibility(inputs),noneAreSymbolic){let output=this.call(inputs,kwargs),outputList=toList(output),outputListCopy=[];for(let x of outputList)inputsList.indexOf(x)!==-1&&(x=x.clone()),outputListCopy.push(x);if(output=singletonOrArray(outputListCopy),this.activityRegularizer!=null)throw new NotImplementedError("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return output}else{let inputShape=collectInputShape(inputs),outputShape=this.computeOutputShape(inputShape),output,outputDType=guessOutputDType(inputs);if(this.warnOnIncompatibleInputShape(Array.isArray(inputs)?inputShape[0]:inputShape),outputShape!=null&&outputShape.length>0&&Array.isArray(outputShape[0])?output=outputShape.map((shape,index)=>new SymbolicTensor(outputDType,shape,this,toList(inputs),kwargs,this.name,index)):output=new SymbolicTensor(outputDType,outputShape,this,toList(inputs),kwargs,this.name),this.addInboundNode(inputs,output,null,null,inputShape,outputShape,kwargs),this._refCount++,this.activityRegularizer!=null)throw new NotImplementedError("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return output}})}warnOnIncompatibleInputShape(inputShape){if(this.batchInputShape!=null)if(inputShape.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(inputShape)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let dimMismatch=!1;this.batchInputShape.forEach((dimension,i)=>{dimension!=null&&inputShape[i]!=null&&inputShape[i]!==dimension&&(dimMismatch=!0)}),dimMismatch&&console.warn(`The shape of the input tensor (${JSON.stringify(inputShape)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}get outputShape(){if(this.inboundNodes==null||this.inboundNodes.length===0)throw new AttributeError(`The layer ${this.name} has never been called and thus has no defined output shape.`);let allOutputShapes=[];for(let node of this.inboundNodes){let shapeString=JSON.stringify(node.outputShapes);allOutputShapes.indexOf(shapeString)===-1&&allOutputShapes.push(shapeString)}if(allOutputShapes.length===1){let outputShapes=this.inboundNodes[0].outputShapes;return Array.isArray(outputShapes)&&Array.isArray(outputShapes[0])&&outputShapes.length===1?outputShapes[0]:outputShapes}else throw new AttributeError(`The layer ${this.name} has multiple inbound nodes with different output shapes. Hence the notion of "output shape" is ill-defined for the layer.`)}countParams(){if(!this.built)throw new RuntimeError(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return countParamsInWeights(this.weights)}build(inputShape){this.built=!0}getWeights(trainableOnly=!1){return batchGetValue(trainableOnly?this.trainableWeights:this.weights)}setWeights(weights){tidy(()=>{let params=this.weights;if(params.length!==weights.length)throw new ValueError(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${weights.length}, but the layer was expecting ${params.length} weights. Provided weights: ${weights}...`);if(params.length===0)return;let weightValueTuples=[],paramValues=batchGetValue(params);for(let i=0;iregularizer.apply(weight.read())),trainable==null&&(trainable=!0),trainable?this._trainableWeights.push(weight):this._nonTrainableWeights.push(weight),weight}setFastWeightInitDuringBuild(value){this.fastWeightInitDuringBuild=value}addLoss(losses4){losses4==null||Array.isArray(losses4)&&losses4.length===0||(losses4=toList(losses4),this._losses!==void 0&&this._losses!==null&&this.losses.push(...losses4))}computeOutputShape(inputShape){return inputShape}computeMask(inputs,mask){if(!this.supportsMasking){if(mask!=null)if(Array.isArray(mask))mask.forEach(maskElement=>{if(maskElement!=null)throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`)});else throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);return null}return mask}addInboundNode(inputTensors,outputTensors,inputMasks,outputMasks,inputShapes,outputShapes,kwargs=null){let inputTensorList=toList(inputTensors);outputTensors=toList(outputTensors),inputMasks=toList(inputMasks),outputMasks=toList(outputMasks),inputShapes=normalizeShapeList(inputShapes),outputShapes=normalizeShapeList(outputShapes);let inboundLayers=[],nodeIndices=[],tensorIndices=[];for(let x of inputTensorList)inboundLayers.push(x.sourceLayer),nodeIndices.push(x.nodeIndex),tensorIndices.push(x.tensorIndex);new Node({outboundLayer:this,inboundLayers,nodeIndices,tensorIndices,inputTensors:inputTensorList,outputTensors,inputMasks,outputMasks,inputShapes,outputShapes},kwargs);for(let i=0;iweight.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${this.name}' is already disposed.`)}dispose(){if(!this.built)throw new Error(`Cannot dispose Layer ${this.name} because it has not been built yet.`);if(this._refCount===null)throw new Error(`Cannot dispose Layer ${this.name} because it has not been used yet.`);this.assertNotDisposed();let numDisposedVariables=0;return--this._refCount==0&&(numDisposedVariables=this.disposeWeights()),{refCountAfterDispose:this._refCount,numDisposedVariables}}};function collectInputShape(inputTensors){inputTensors=toList(inputTensors);let shapes=[];for(let x of inputTensors)shapes.push(x.shape);return singletonOrArray(shapes)}function guessOutputDType(inputTensors){return"float32"}function getSourceInputs(tensor2,layer,nodeIndex){if((layer==null||nodeIndex!=null&&nodeIndex>0)&&(layer=tensor2.sourceLayer,nodeIndex=tensor2.nodeIndex),layer.inboundNodes.length===0)return[tensor2];{let node=layer.inboundNodes[nodeIndex];if(node.inboundLayers.length===0)return node.inputTensors;{let sourceTensors=[];for(let i=0;i0){let values=await Promise.all(promises);for(let i=0;iadd2(this.totals[key],mul(value,batchSize)));this.totals[key]=total,oldTotalsToDispose!=null&&oldTotalsToDispose.dispose()}}}async onEpochEnd(epoch,logs){if(logs!=null)for(let key of this.params.metrics)this.totals[key]!=null&&(typeof this.totals[key]=="number"?logs[key]=this.totals[key]/this.seen:tidy(()=>{let log5=mul(div(1,this.seen),this.totals[key]);logs[key]=log5,this.totals[key].dispose(),keep(logs[key])}))}},History=class extends BaseCallback{async onTrainBegin(logs){this.epoch=[],this.history={}}async onEpochEnd(epoch,logs){logs==null&&(logs={}),this.epoch.push(epoch);for(let key in logs)this.history[key]==null&&(this.history[key]=[]),this.history[key].push(logs[key])}async syncData(){let promises=[],keys=[],indices=[];for(let key in this.history){let valueArray=this.history[key];for(let i=0;inew CustomCallback(callbackConfig,yieldEvery))}var CallbackConstructorRegistry=class{constructor(){}static registerCallbackConstructor(verbosityLevel,callbackConstructor){util_exports.assert(verbosityLevel>=0&&Number.isInteger(verbosityLevel),()=>`Verbosity level is expected to be an integer >= 0, but got ${verbosityLevel}`),CallbackConstructorRegistry.checkForDuplicate(callbackConstructor),CallbackConstructorRegistry.constructors[verbosityLevel]==null&&(CallbackConstructorRegistry.constructors[verbosityLevel]=[]),CallbackConstructorRegistry.constructors[verbosityLevel].push(callbackConstructor)}static checkForDuplicate(callbackConstructor){for(let levelName in CallbackConstructorRegistry.constructors)CallbackConstructorRegistry.constructors[+levelName].forEach(ctor=>{if(ctor===callbackConstructor)throw new ValueError("Duplicate callback constructor.")})}static clear(){CallbackConstructorRegistry.constructors={}}static createCallbacks(verbosityLevel){let constructors=[];for(let levelName in CallbackConstructorRegistry.constructors){let level=+levelName;verbosityLevel>=level&&constructors.push(...CallbackConstructorRegistry.constructors[level])}return constructors.map(ctor=>new ctor)}};CallbackConstructorRegistry.constructors={};function configureCallbacks(callbacks2,verbose,epochs,initialEpoch,numTrainSamples,stepsPerEpoch,batchSize,doValidation,callbackMetrics){let history=new History,actualCallbacks=[new BaseLogger,...CallbackConstructorRegistry.createCallbacks(verbose)];callbacks2!=null&&actualCallbacks.push(...callbacks2),actualCallbacks.push(history);let callbackList=new CallbackList(actualCallbacks);return callbackList.setParams({epochs,initialEpoch,samples:numTrainSamples,steps:stepsPerEpoch,batchSize,verbose,doValidation,metrics:callbackMetrics}),{callbackList,history}}function deserialize(config3,customObjects={},fastWeightInit=!1){return deserializeKerasObject(config3,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"layer",fastWeightInit)}function l2Normalize(x,axis){return tidy(()=>{x.dtype!=="float32"&&(x=x.asType("float32"));let squareSum=sum2(square3(x),axis,!0),epsilonTensor=fill(squareSum.shape,epsilon2()),norm2=sqrt(maximum(squareSum,epsilonTensor));return div(x,norm2)})}function meanSquaredError2(yTrue,yPred){return tidy(()=>mean(square3(sub(yPred,yTrue)),-1))}function meanAbsoluteError(yTrue,yPred){return tidy(()=>mean(abs(sub(yPred,yTrue)),-1))}function meanAbsolutePercentageError(yTrue,yPred){return tidy(()=>{let diff=sub(yTrue,yPred),clippedTrue=clipByValue(abs(yTrue),epsilon2(),Number.MAX_VALUE),absResult=abs(div(diff,clippedTrue));return mul(100,mean(absResult,-1))})}function meanSquaredLogarithmicError(yTrue,yPred){return tidy(()=>{let clippedPred=clipByValue(yPred,epsilon2(),Number.MAX_VALUE),firstLog=log2(add2(1,clippedPred)),clippedTrue=clipByValue(yTrue,epsilon2(),Number.MAX_VALUE),secondLog=log2(add2(1,clippedTrue));return mean(square3(sub(firstLog,secondLog)),-1)})}function squaredHinge(yTrue,yPred){return tidy(()=>{let maxResult=maximum(0,sub(1,mul(yTrue,yPred)));return mean(square3(maxResult),-1)})}function hinge(yTrue,yPred){return tidy(()=>{let maxResult=maximum(0,sub(1,mul(yTrue,yPred)));return mean(maxResult,-1)})}function categoricalHinge(yTrue,yPred){return tidy(()=>{let pos=sum2(mul(yTrue,yPred),-1),neg4=max(mul(sub(1,yTrue),yPred),-1);return maximum(0,add2(1,sub(neg4,pos)))})}function logcosh(yTrue,yPred){return tidy(()=>{let log222=Math.log(2),predictionDiff=sub(yPred,yTrue),logcoshResult=sub(add2(predictionDiff,softplus(mul(-2,predictionDiff))),log222);return mean(logcoshResult,-1)})}function categoricalCrossentropy(target,output,fromLogits=!1){return tidy(()=>{if(fromLogits)output=softmax(output);else{let outputSum=sum2(output,output.shape.length-1,!0);output=div(output,outputSum)}return output=clipByValue(output,epsilon2(),1-epsilon2()),neg(sum2(mul(target.toFloat(),log2(output)),output.shape.length-1))})}function sparseCategoricalCrossentropy(target,output,fromLogits=!1){return tidy(()=>{let flatTarget=floor(flatten2(target)).toInt();output=clipByValue(output,epsilon2(),1-epsilon2());let outputShape=output.shape,oneHotTarget=oneHot(flatTarget,outputShape[outputShape.length-1]).reshape(outputShape);return categoricalCrossentropy(oneHotTarget,output,fromLogits)})}function sigmoidCrossEntropyWithLogits(labels,logits){if(!util_exports.arraysEqual(labels.shape,logits.shape))throw new ValueError(`logits and labels must have the same shape, but got shapes ${JSON.stringify(labels.shape)} and ${JSON.stringify(logits.shape)}`);return tidy(()=>{let reluLogits=logits.relu(),negAbsLogits=logits.abs().neg();return reluLogits.sub(logits.mul(labels)).add(negAbsLogits.exp().log1p())})}function binaryCrossentropy(yTrue,yPred){return tidy(()=>{let y;return y=clipByValue(yPred,epsilon2(),1-epsilon2()),y=log2(div(y,sub(1,y))),mean(sigmoidCrossEntropyWithLogits(yTrue,y),-1)})}function kullbackLeiblerDivergence(yTrue,yPred){return tidy(()=>{let clippedTrue=clipByValue(yTrue,epsilon2(),1),clippedPred=clipByValue(yPred,epsilon2(),1);return sum2(mul(yTrue,log2(div(clippedTrue,clippedPred))),-1)})}function poisson(yTrue,yPred){return tidy(()=>{let logPred=log2(add2(epsilon2(),yPred));return mean(sub(yPred,mul(yTrue,logPred)),-1)})}function cosineProximity(yTrue,yPred){return tidy(()=>{let trueNormalized=l2Normalize(yTrue,-1),predNormalized=l2Normalize(yPred,-1),trueXPred=mul(trueNormalized,predNormalized);return neg(sum2(trueXPred,-1))})}var lossesMap={meanSquaredError:meanSquaredError2,meanAbsoluteError,meanAbsolutePercentageError,meanSquaredLogarithmicError,squaredHinge,hinge,categoricalHinge,logcosh,categoricalCrossentropy,sparseCategoricalCrossentropy,binaryCrossentropy,kullbackLeiblerDivergence,poisson,cosineProximity};function get(identifierOrFn){if(typeof identifierOrFn=="string"){if(identifierOrFn in lossesMap)return lossesMap[identifierOrFn];let errMsg=`Unknown loss ${identifierOrFn}`;throw identifierOrFn.toLowerCase().includes("softmaxcrossentropy")&&(errMsg=`Unknown loss ${identifierOrFn}. Use "categoricalCrossentropy" as the string name for tf.losses.softmaxCrossEntropy`),new ValueError(errMsg)}else return identifierOrFn}function binaryAccuracy(yTrue,yPred){return tidy(()=>{let threshold2=mul(.5,onesLike(yPred)),yPredThresholded=cast5(greater(yPred,threshold2),yTrue.dtype);return mean(equal(yTrue,yPredThresholded),-1)})}function categoricalAccuracy(yTrue,yPred){return tidy(()=>cast5(equal(argMax(yTrue,-1),argMax(yPred,-1)),"float32"))}function truePositives(yTrue,yPred){return tidy(()=>logicalAnd(yTrue.equal(1),yPred.equal(1)).sum().cast("float32"))}function falseNegatives(yTrue,yPred){return tidy(()=>logicalAnd(yTrue.equal(1),yPred.equal(0)).sum().cast("float32"))}function falsePositives(yTrue,yPred){return tidy(()=>logicalAnd(yTrue.equal(0),yPred.equal(1)).sum().cast("float32"))}function precision(yTrue,yPred){return tidy(()=>{let tp=truePositives(yTrue,yPred),fp=falsePositives(yTrue,yPred),denominator=tp.add(fp);return where(greater(denominator,0),tp.div(denominator),0).cast("float32")})}function recall(yTrue,yPred){return tidy(()=>{let tp=truePositives(yTrue,yPred),fn=falseNegatives(yTrue,yPred),denominator=tp.add(fn);return where(greater(denominator,0),tp.div(denominator),0).cast("float32")})}function binaryCrossentropy2(yTrue,yPred){return binaryCrossentropy(yTrue,yPred)}function sparseCategoricalAccuracy(yTrue,yPred){return yTrue.rank===yPred.rank&&(yTrue=yTrue.squeeze([yTrue.rank-1])),yPred=yPred.argMax(-1),yPred.dtype!==yTrue.dtype&&(yPred=yPred.asType(yTrue.dtype)),equal(yTrue,yPred).asType("float32")}var mse=meanSquaredError2,MSE=meanSquaredError2,mae=meanAbsoluteError,MAE=meanAbsoluteError,mape=meanAbsolutePercentageError,MAPE=meanAbsolutePercentageError,categoricalCrossentropy2=categoricalCrossentropy,cosine=cosineProximity,sparseCategoricalCrossentropy2=sparseCategoricalCrossentropy,metricsMap={binaryAccuracy,categoricalAccuracy,precision,categoricalCrossentropy:categoricalCrossentropy2,sparseCategoricalCrossentropy:sparseCategoricalCrossentropy2,mse,MSE,mae,MAE,mape,MAPE,cosine};function get2(identifier){if(typeof identifier=="string"&&identifier in metricsMap)return metricsMap[identifier];if(typeof identifier!="string"&&identifier!=null)return identifier;throw new ValueError(`Unknown metric ${identifier}`)}function getLossOrMetricName(fn){if(assert2(fn!==null,`Unknown LossOrMetricFn ${fn}`),typeof fn=="string")return fn;{let fnName;for(let key of Object.keys(lossesMap))if(lossesMap[key]===fn){fnName=key;break}if(fnName!==void 0)return fnName;for(let key of Object.keys(metricsMap))if(metricsMap[key]===fn){fnName=key;break}return fnName!==void 0?fnName:fn.name}}function getOptimizer(identifier){let optimizerMap={Adagrad:()=>train.adagrad(.01),Adadelta:()=>train.adadelta(1,.95,epsilon2()),Adam:()=>train.adam(.001,.9,.999,epsilon2()),Adamax:()=>train.adamax(.002,.9,.999,epsilon2(),0),RMSProp:()=>train.rmsprop(.001,.9,0,epsilon2()),SGD:()=>train.sgd(.01)};if(optimizerMap.adagrad=optimizerMap.Adagrad,optimizerMap.adadelta=optimizerMap.Adadelta,optimizerMap.adam=optimizerMap.Adam,optimizerMap.adamax=optimizerMap.Adamax,optimizerMap.rmsprop=optimizerMap.RMSProp,optimizerMap.sgd=optimizerMap.SGD,identifier in optimizerMap)return optimizerMap[identifier]();throw new ValueError(`Unknown Optimizer ${identifier}`)}var MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH=1*1024*1024;function checkUserDefinedMetadata(userDefinedMetadata,modelName,checkSize=!1){if(userDefinedMetadata==null||typeof userDefinedMetadata!="object"||Object.getPrototypeOf(userDefinedMetadata)!==Object.prototype||!plainObjectCheck(userDefinedMetadata))throw new Error("User-defined metadata is expected to be a JSON object, but is not.");if(checkSize){let out=JSON.stringify(userDefinedMetadata);out.length>MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH&&console.warn(`User-defined metadata of model "${modelName}" is too large in size (length=${out.length} when serialized). It is not recommended to store such large objects in user-defined metadata. Please make sure its serialized length is <= ${MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH}.`)}}function plainObjectCheck(x){if(x===null)return!0;if(typeof x=="object")if(Object.getPrototypeOf(x)===Object.prototype){let keys=Object.keys(x);for(let key of keys)if(typeof key!="string"||!plainObjectCheck(x[key]))return!1;return!0}else if(Array.isArray(x)){for(let item of x)if(!plainObjectCheck(item))return!1;return!0}else return!1;else{let xType=typeof x;return xType==="string"||xType==="number"||xType==="boolean"}}function printSummary(model2,lineLength,positions,printFn=console.log){let sequentialLike=isModelSequentialLike(model2),toDisplay=["Layer (type)","Output shape","Param #"];sequentialLike?(lineLength=lineLength||65,positions=positions||[.45,.85,1]):(lineLength=lineLength||98,positions=positions||[.33,.55,.67,1]),positions[positions.length-1]<=1&&(positions=positions.map(p2=>Math.floor(lineLength*p2)));let relevantNodes;if(!sequentialLike){toDisplay.push("Receives inputs"),relevantNodes=[];for(let depth in model2.nodesByDepth)relevantNodes.push(...model2.nodesByDepth[depth])}printFn("_".repeat(lineLength)),printRow(toDisplay,positions,printFn),printFn("=".repeat(lineLength));let layers=model2.layers;for(let i=0;i1||depthNodes.length===1&&depthNodes[0].inboundLayers.length>1){sequentialLike=!1;break}nodes.push(...depthNodes)}if(sequentialLike)for(let layer of model2.layers){let flag=!1;for(let node of layer.inboundNodes)if(nodes.indexOf(node)!==-1)if(flag){sequentialLike=!1;break}else flag=!0;if(!sequentialLike)break}return sequentialLike}function printRow(fields,positions,printFn=console.log){let line="";for(let i=0;i0&&(line=line.slice(0,line.length-1)+" "),line+=fields[i],line=line.slice(0,positions[i]),line+=" ".repeat(positions[i]-line.length);printFn(line)}function printLayerSummary(layer,positions,printFn){let outputShape;try{outputShape=JSON.stringify(layer.outputShape)}catch(err){outputShape="multiple"}let name=layer.name,className=layer.getClassName(),fields=[`${name} (${className})`,outputShape,layer.countParams().toString()];printRow(fields,positions,printFn)}function printLayerSummaryWithConnections(layer,positions,relevantNodes,printFn){let outputShape;try{outputShape=JSON.stringify(layer.outputShape)}catch(err){outputShape="multiple"}let connections=[];for(let node of layer.inboundNodes)if(!(relevantNodes!=null&&relevantNodes.length>0&&relevantNodes.indexOf(node)===-1))for(let i=0;it.name),finalOutputs=[],feedNames=feedDict.names();for(let outputName of outputNames)feedNames.indexOf(outputName)!==-1?finalOutputs.push(feedDict.getValue(outputName)):finalOutputs.push(null);probe!=null&&(probe.maxNumTensors=-Infinity,probe.minNumTensors=Infinity);let fetchAndFeedKey=outputNames.join(",")+"|"+feedDict.names().join(","),sorted,recipientCounts;if(cachedSorted[fetchAndFeedKey]==null){let out=getTopologicalSortAndRecipientCounts(fetchArray,feedDict);sorted=out.sorted,recipientCounts=out.recipientCounts,cachedSorted[fetchAndFeedKey]=sorted,cachedRecipientCounts[fetchAndFeedKey]=recipientCounts}sorted=cachedSorted[fetchAndFeedKey],recipientCounts={},training||Object.assign(recipientCounts,cachedRecipientCounts[fetchAndFeedKey]);let internalFeedDict=new FeedDict(feedDict);for(let i=0;iprobe.maxNumTensors&&(probe.maxNumTensors=numTensors),numTensors0,()=>"Expected at least one fetch, got none");let finalSorted=[],finalRecipientMap={};if(fetches.length===1){let out=getTopologicalSortAndRecipientCountsForOneFetch(fetches[0],feedDict);finalSorted=out.sorted,finalRecipientMap=out.recipientMap}else{let visited=new Set;for(let fetch3 of fetches){let{sorted,recipientMap}=getTopologicalSortAndRecipientCountsForOneFetch(fetch3,feedDict);for(let symbolicTensor of sorted)visited.has(symbolicTensor.name)||(finalSorted.push(symbolicTensor),visited.add(symbolicTensor.name));for(let name in recipientMap)finalRecipientMap[name]==null&&(finalRecipientMap[name]=new Set),recipientMap[name].forEach(recipient=>finalRecipientMap[name].add(recipient))}}return{sorted:finalSorted,recipientCounts:recipientMap2Counts(finalRecipientMap)}}function recipientMap2Counts(recipientMap){let recipientCounts={};for(let name in recipientMap)recipientCounts[name]=recipientMap[name].size;return recipientCounts}function getTopologicalSortAndRecipientCountsForOneFetch(fetch3,feedDict){let visited=new Set,sorted=[],recipientMap={};for(let key of feedDict.names())visited.add(key);let stack2=[],marks=[];for(stack2.push(fetch3);stack2.length>0;){let top=stack2[stack2.length-1];if(visited.has(top.name)){stack2.pop();continue}let topIsMarked=marks[marks.length-1]===stack2.length-1;if(top.inputs.length===0||topIsMarked)stack2.pop(),sorted.push(top),visited.add(top.name),topIsMarked&&marks.pop();else{marks.push(stack2.length-1);for(let input2 of top.inputs)recipientMap[input2.name]==null&&(recipientMap[input2.name]=new Set),recipientMap[input2.name].add(top.name),!visited.has(input2.name)&&stack2.push(input2)}}return{sorted,recipientMap}}function getNodeOutputs(fetch3){let layerOutputs;if(fetch3.sourceLayer.inboundNodes.length===1)layerOutputs=fetch3.sourceLayer.output;else{let nodeIndex=null;for(let i=0;ix.name)}`);unique4(this.outputs).length!==this.outputs.length&&console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map(x=>x.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(let x of this.outputs){let layer=x.sourceLayer,nodeIndex=x.nodeIndex,tensorIndex=x.tensorIndex;this.outputLayers.push(layer),this.outputLayersNodeIndices.push(nodeIndex),this.outputLayersTensorIndices.push(tensorIndex)}for(let x of this.inputs){let layer=x.sourceLayer,nodeIndex=x.nodeIndex,tensorIndex=x.tensorIndex;assert2(nodeIndex===0,"input layer has >1 nodes"),assert2(tensorIndex===0,"input layer has >1 tensors"),this.inputLayers.push(layer),this.inputLayersNodeIndices.push(nodeIndex),this.inputLayersTensorIndices.push(tensorIndex)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let i=0;ix.shape),this.internalOutputShapes=this.outputs.map(x=>x.shape);let nodesDepths={},nodeIDToNode={},layersDepths={},layerIDToLayer={},layerIndices={},nodesInDecreasingDepth=[],buildMapOfGraph=(tensor2,finishedNodes2,nodesInProgress2,layer,nodeIndex,tensorIndex)=>{(layer==null||nodeIndex==null||tensorIndex==null)&&(layer=tensor2.sourceLayer,nodeIndex=tensor2.nodeIndex,tensorIndex=tensor2.tensorIndex);let node=layer.inboundNodes[nodeIndex];if(nodesInProgress2.indexOf(node)!==-1)throw new RuntimeError(`The tensor ${tensor2.name} at layer "${layer.name}" is part of a cycle.`);if(finishedNodes2.indexOf(node)!==-1)return;this.containerNodes.add(Container.nodeKey(layer,nodeIndex)),layer.id in layerIndices||(layerIndices[layer.id]=Object.keys(layerIndices).length),nodesInProgress2.indexOf(node)===-1&&nodesInProgress2.push(node);let numInboundLayers=node.inboundLayers.length;for(let i=0;i=0;)nodesInProgress2.splice(nodesInProgress2.indexOf(node),1);nodesInDecreasingDepth.push(node)},finishedNodes=[],nodesInProgress=[];for(let x of this.outputs)buildMapOfGraph(x,finishedNodes,nodesInProgress);let reversedNodesInDecreasingDepth=nodesInDecreasingDepth.slice().reverse();for(let node of reversedNodesInDecreasingDepth){nodeIDToNode[node.id]=node,node.id in nodesDepths||(nodesDepths[node.id]=0);let depth=nodesDepths[node.id],previousDepth=layersDepths[node.outboundLayer.id]==null?0:layersDepths[node.outboundLayer.id];depth=Math.max(depth,previousDepth),layersDepths[node.outboundLayer.id]=depth,layerIDToLayer[node.outboundLayer.id]=node.outboundLayer,nodesDepths[node.id]=depth;for(let i=0;iparseInt(x,10)).sort(reverseNumberCompare);this.layers=[];for(let depth of depthKeys){let layersForDepth=layersByDepth[depth];layersForDepth.sort((a,b)=>{let aIndex=layerIndices[a.id],bIndex=layerIndices[b.id];return aIndexbIndex?1:0});for(let layer of layersForDepth)layer instanceof Container&&this.internalContainerRefs.push(layer),this.layers.push(layer)}this.layersByDepth=layersByDepth,depthKeys=Object.keys(nodesByDepth).map(x=>parseInt(x,10)).sort(reverseNumberCompare);let computableTensors=this.inputs.slice(),layersWithCompleteInput=[];for(let depth of depthKeys)for(let node of nodesByDepth[depth]){let layer=node.outboundLayer;if(layer!=null){for(let x of node.inputTensors)if(computableTensors.indexOf(x)===-1)throw new RuntimeError(`Graph disconnected: cannot obtain value for tensor ${x} at layer "${layer.name}". The following previous layers were accessed without issue: ${layersWithCompleteInput}`);for(let x of node.outputTensors)computableTensors.push(x);layersWithCompleteInput.push(layer.name)}}this.nodesByDepth=nodesByDepth;let allNames=this.layers.map(x=>x.name);for(let name of allNames){let numOccurrences=allNames.filter(x=>x===name).length;if(numOccurrences!==1)throw new RuntimeError(`The name "${name}" is used ${numOccurrences} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(allNames))}this.outboundNodes=[],this.inboundNodes=[],new Node({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(x=>null),outputMasks:this.outputs.map(x=>null),inputShapes:this.inputs.map(x=>x.shape),outputShapes:this.outputs.map(x=>x.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();let result={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount==0){for(let layer of this.layers)result.numDisposedVariables+=layer.dispose().numDisposedVariables;for(let container of this.internalContainerRefs)result.numDisposedVariables+=container.dispose().numDisposedVariables}return result.refCountAfterDispose=this._refCount,result}get trainable(){return this.trainable_}set trainable(trainable){this.layers.forEach(layer=>{layer._trainableWeights.forEach(w=>w.trainable=trainable)}),this.trainable_=trainable}get trainableWeights(){if(this._trainableWeights.length>0)throw new ValueError("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let weights=[];for(let layer of this.layers)weights=weights.concat(layer.trainableWeights);return weights}get nonTrainableWeights(){let weights=[];for(let layer of this.layers)weights.push(...layer.nonTrainableWeights);if(!this.trainable){let trainableWeights=[];for(let layer of this.layers)trainableWeights.push(...layer.trainableWeights);return trainableWeights.concat(weights)}return weights}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(weights,strict=!0){let nameToWeight={},totalWeightsCount=0;for(let layer of this.layers)for(let weight of layer.weights){if(nameToWeight[weight.originalName]!=null)throw new ValueError(`Duplicate weight name: ${weight.originalName}`);nameToWeight[weight.originalName]=weight,totalWeightsCount++}let weightValueTuples=[];for(let name in weights){let validatedName=name;if(nameToWeight[name]==null){let tokens=name.split("/");validatedName=tokens.slice(0,-2).concat([tokens[tokens.length-1]]).join("/")}if(nameToWeight[validatedName]!=null)weightValueTuples.push([nameToWeight[validatedName],weights[name]]);else if(strict)throw new ValueError(`Provided weight data has no target variable: ${name}`);delete nameToWeight[validatedName]}if(strict){let unsetNames=[];for(let name in nameToWeight)unsetNames.push(name);if(unsetNames.length>0)throw new ValueError(`${unsetNames.length} of ${totalWeightsCount} weights are not set: ${unsetNames}`)}batchSetValue(weightValueTuples)}updatedConfig(){let theConfig=this.getConfig(),modelConfig={};return modelConfig.className=this.getClassName(),modelConfig.config=theConfig,modelConfig.kerasVersion=`tfjs-layers ${version10}`,modelConfig.backend="TensorFlow.js",modelConfig}toJSON(unused,returnString=!0){let modelConfig=convertTsToPythonic(this.updatedConfig());return returnString?JSON.stringify(modelConfig):modelConfig}call(inputs,kwargs){return tidy(()=>{inputs=toList(inputs);let feedDict=new FeedDict;for(let i=0;i{inputs=toList(inputs);let masks;return mask==null?masks=pyListRepeat(null,inputs.length):masks=toList(mask),this.runInternalGraph(inputs,masks)[1]})}computeOutputShape(inputShape){let inputShapes=normalizeShapeList(inputShape);if(inputShapes.length!==this.inputLayers.length)throw new ValueError(`Invalid inputShape argument ${inputShape}: model has ${this.inputLayers.length} tensor inputs.`);let layersToOutputShapes={};for(let i=0;iparseInt(x,10)).sort(reverseNumberCompare);if(depthKeys.length>1)for(let depth of depthKeys){let nodes=this.nodesByDepth[depth];for(let node of nodes){let layer=node.outboundLayer;if(this.inputLayers.map(x=>x.id).indexOf(layer.id)!==-1)continue;let inputShapes2=[];for(let j=0;jparseInt(x,10)).sort(reverseNumberCompare);for(let depth of depthKeys){let nodes=this.nodesByDepth[depth];for(let node of nodes){let layer=node.outboundLayer,referenceInputTensors=node.inputTensors,referenceOutputTensors=node.outputTensors,computedData=new Array;for(let x of referenceInputTensors)x.id in tensorMap&&computedData.push(tensorMap[x.id]);if(computedData.length===referenceInputTensors.length){let kwargs={},computedTensors,computedMasks,outputTensors2,outputMasks2;if(node.callArgs!=null&&(kwargs=node.callArgs),computedData.length===1){let[computedTensor,computedMask]=computedData[0];kwargs.mask==null&&(kwargs.mask=computedMask),outputTensors2=toList(layer.call(computedTensor,kwargs)),outputMasks2=toList(layer.computeMask(computedTensor,computedMask)),computedTensors=[computedTensor],computedMasks=[computedMask]}else computedTensors=computedData.map(x=>x[0]),computedMasks=computedData.map(x=>x[1]),kwargs.mask==null&&(kwargs.mask=computedMasks),outputTensors2=toList(layer.call(computedTensors,kwargs)),outputMasks2=toList(layer.computeMask(computedTensors,computedMasks));if(layer.activityRegularizer)throw new NotImplementedError("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let i=0;i{let losses4=[];for(let layer of this.layers)for(let nodeIndex=0;nodeIndex0){let nodeData=[];for(let i=0;i0&&layer.apply(singletonOrArray(inputTensors2),kwargs)}function processLayer(layerData){let layerName=layerData.name,layer=deserialize(layerData,config3.customObjects!=null?config3.customObjects:{});layer.setFastWeightInitDuringBuild(fastWeightInit),createdLayers[layerName]=layer,layerData.inboundNodes.forEach(nodeData=>{if(!(nodeData instanceof Array))throw new ValueError(`Corrupted configuration, expected array for nodeData: ${nodeData}`);addUnprocessedNode(layer,nodeData)})}let name=config3.name,layersFromConfig=config3.layers;for(let layerData of layersFromConfig)processLayer(layerData);for(;!isObjectEmpty(unprocessedNodes);)for(let layerData of layersFromConfig){let layer=createdLayers[layerData.name];if(layer.name in unprocessedNodes){let currentUnprocessedNodesForLayer=unprocessedNodes[layer.name];delete unprocessedNodes[layer.name];for(let nodeData of currentUnprocessedNodesForLayer)processNode(layer,nodeData)}}let inputTensors=[],outputTensors=[],inputLayersFromConfig=config3.inputLayers;for(let layerData of inputLayersFromConfig){let layerName=layerData[0],nodeIndex=layerData[1],tensorIndex=layerData[2];assert2(layerName in createdLayers);let layerOutputTensors=createdLayers[layerName].inboundNodes[nodeIndex].outputTensors;inputTensors.push(layerOutputTensors[tensorIndex])}let outputLayersFromConfig=config3.outputLayers;for(let layerData of outputLayersFromConfig){let layerName=layerData[0],nodeIndex=layerData[1],tensorIndex=layerData[2];assert2(layerName in createdLayers);let layerOutputTensors=createdLayers[layerName].inboundNodes[nodeIndex].outputTensors;outputTensors.push(layerOutputTensors[tensorIndex])}return new cls({inputs:inputTensors,outputs:outputTensors,name})}get stateful(){if(this._stateful)throw new ValueError("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(let layer of this.layers)if(layer.stateful)return!0;return!1}resetStates(){tidy(()=>{this.layers.forEach(layer=>{layer.stateful&&layer.resetStates()})})}};function standardizeSampleOrClassWeights(xWeight,outputNames,weightType){let numOutputs=outputNames.length;if(xWeight==null||Array.isArray(xWeight)&&xWeight.length===0)return outputNames.map(name=>null);if(numOutputs===1)return Array.isArray(xWeight)&&xWeight.length===1?xWeight:typeof xWeight=="object"&&outputNames[0]in xWeight?[xWeight[outputNames[0]]]:[xWeight];if(Array.isArray(xWeight)){if(xWeight.length!==numOutputs)throw new Error(`Provided ${weightType} is an array of ${xWeight.length} element(s), but the model has ${numOutputs} outputs. Make sure a set of weights is provided for each model output.`);return xWeight}else if(typeof xWeight=="object"&&Object.keys(xWeight).length>0&&typeof xWeight[Object.keys(xWeight)[0]]=="object"){let output=[];return outputNames.forEach(outputName=>{outputName in xWeight?output.push(xWeight[outputName]):output.push(null)}),output}else throw new Error(`The model has multiple (${numOutputs}) outputs, so ${weightType} must be either an array with ${numOutputs} elements or an object with ${outputNames} keys. Provided ${weightType} not understood: ${JSON.stringify(xWeight)}`)}function standardizeClassWeights(classWeight,outputNames){return standardizeSampleOrClassWeights(classWeight,outputNames,"classWeight")}async function standardizeWeights(y,sampleWeight,classWeight,sampleWeightMode){if(sampleWeight!=null||sampleWeightMode!=null)throw new Error("Support sampleWeight is not implemented yet");if(classWeight!=null){let yClasses=tidy(()=>{if(y.shape.length===1)return y.clone();if(y.shape.length===2)if(y.shape[1]>1){let axis=1;return y.argMax(axis)}else{if(y.shape[1]===1)return y.reshape([y.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${y.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${y.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),yClassIndices=Array.from(await yClasses.data());dispose(yClasses);let classSampleWeight=[];return yClassIndices.forEach(classIndex=>{if(classWeight[classIndex]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${classIndex} exists in the data but not in classWeight`);classSampleWeight.push(classWeight[classIndex])}),tensor1d(classSampleWeight,"float32")}else return null}function computeWeightedLoss2(losses4,sampleWeights){return mul(losses4,sampleWeights)}var DEFAULT_VALIDATION_BATCH_SIZE=32;function standardizeDataIteratorOutput(model2,iteratorOut){let xs,ys,iteratorOutObj=iteratorOut;xs=iteratorOutObj.xs,ys=iteratorOutObj.ys,util_exports.assert(xs!=null&&ys!=null,()=>`A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor. The provided Dataset instead generates ${iteratorOut}`);let flattenedXs=flattenTensorOrArrayOrMap("input",model2.inputNames,xs),flattenedYs=flattenTensorOrArrayOrMap("output",model2.outputNames,ys),batchSize=flattenedXs[0].shape[0];util_exports.assert(flattenedXs.length===model2.inputs.length,()=>`LayersModel has ${model2.inputs.length} inputs, but the dataset provides ${flattenedXs.length} inputs. (Expected input keys: ${JSON.stringify(model2.inputNames)})`),util_exports.assert(flattenedYs.length===model2.outputs.length,()=>`LayersModel has ${model2.outputs.length} outputs, but the dataset provides ${flattenedYs.length} outputs. (Expected output keys: ${JSON.stringify(model2.outputNames)})`);for(let xIndex=0;xIndex`Batch size mismatch: input ${model2.inputNames[xIndex]} has ${flattenedXs[xIndex].shape[0]}; expected ${batchSize} based on input ${model2.inputNames[0]}.`);for(let yIndex=0;yIndex`Batch size mismatch: output ${model2.outputNames[yIndex]} has ${flattenedYs[yIndex].shape[0]}; expected ${batchSize} based on input ${model2.inputNames[0]}.`);return{xs:flattenedXs,ys:flattenedYs}}function flattenTensorOrArrayOrMap(inputOrOutput,names,values){if(values instanceof Tensor)return[values];if(Array.isArray(values))return util_exports.assert(values.length===names.length,()=>`Received an array of ${values.length} Tensors, but expected ${names.length} to match the ${inputOrOutput} keys ${names}.`),values;{let result=[];for(let name of names){if(values[name]==null)throw new ValueError(`The feature data generated by the dataset lacks the required ${inputOrOutput} key '${name}'.`);result.push(values[name])}return result}}function standardizeTensorValidationData(data2){if(data2.length===3)throw new NotImplementedError("Validation with sample weights is not implemented yet.");return{xs:data2[0],ys:data2[1]}}async function fitDataset(model2,dataset,args){let hasBatchesPerEpoch=args.batchesPerEpoch!=null;if(util_exports.assert(model2.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),util_exports.assert(args!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),util_exports.assert(args.epochs!=null&&args.epochs>0&&Number.isInteger(args.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${args.epochs}`),util_exports.assert(!hasBatchesPerEpoch||args.batchesPerEpoch>0&&Number.isInteger(args.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${args.batchesPerEpoch}`),util_exports.assert(args.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. Use validationData instead."),model2.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");model2.isTraining=!0;try{let doValidation=args.validationData!=null,valXs,valYs;if(doValidation)if(isDatasetObject(args.validationData))util_exports.assert(args.validationBatches==null||args.validationBatches>0&&Number.isInteger(args.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${args.validationBatches}`);else{let validationData=standardizeTensorValidationData(args.validationData);valXs=validationData.xs,valYs=validationData.ys}let trainFunction=model2.makeTrainFunction(),outLabels=model2.getDedupedMetricsNames(),callbackMetrics;doValidation?callbackMetrics=outLabels.slice().concat(outLabels.map(n=>"val_"+n)):callbackMetrics=outLabels.slice();let callbacks2=standardizeCallbacks(args.callbacks,args.yieldEvery),verbose=args.verbose==null?1:args.verbose,{callbackList,history}=configureCallbacks(callbacks2,verbose,args.epochs,null,null,getStepsPerEpoch(dataset,args),null,doValidation,callbackMetrics);callbackList.setModel(model2),model2.history=history,await callbackList.onTrainBegin(),model2.stopTraining_=!1;let epoch=args.initialEpoch==null?0:args.initialEpoch,dataIterator=await dataset.iterator();for(;epoch=args.batchesPerEpoch:iteratorOut.done){if(doValidation){let valOuts;isDatasetObject(args.validationData)?valOuts=toList(await model2.evaluateDataset(args.validationData,{batches:args.validationBatches})):valOuts=toList(model2.evaluate(valXs,valYs,{batchSize:args.validationBatchSize==null?DEFAULT_VALIDATION_BATCH_SIZE:args.validationBatchSize,verbose:0}));for(let i=0;i0)throw new NotImplementedError("Verbose mode is not implemented yet.");util_exports.assert(!hasBatches||args.batches>0&&Number.isInteger(args.batches),()=>`Test loop expects \`batches\` to be a positive integer, but received ${JSON.stringify(args.batches)}`);let dataIterator=isLazyIteratorObject(dataset)?dataset:await dataset.iterator(),numExamples=0,batch=0;for(;hasBatches?batch{if(iteratorOut.value){let{xs,ys}=standardizeDataIteratorOutput(model2,iteratorOut.value),xsAndYs=xs.concat(ys),batchOuts=tidy(()=>f(xsAndYs));if(dispose(xsAndYs),batch===0)for(let i=0;iadd2(outs[i],mul(batchSize,batchOut))),batch>0&&dispose(oldScalar)}dispose(batchOuts),numExamples+=batchSize,++batch}return outs}),iteratorOut.done){hasBatches&&console.warn(`Your dataset iterator ran out of data during evaluateDataset(). Interrupting evalution. Make sure that your dataset can generate at least \`batches\` batches (in this case, ${args.batches} batches). You may need to use the repeat() function when building your dataset.`);break}}for(let i=0;i0&&Number.isInteger(batchSize),()=>`batchSize is required to be a positive integer, but got ${batchSize}`)}function sliceArrays(arrays,start,stop){return arrays==null?[null]:Array.isArray(arrays)?arrays.map(array2=>sliceAlongFirstAxis(array2,start,stop-start)):sliceAlongFirstAxis(arrays,start,stop-start)}function sliceArraysByIndices(arrays,indices){return tidy(()=>arrays==null?null:Array.isArray(arrays)?arrays.map(array2=>sliceArraysByIndices(array2,indices)):gather2(arrays,indices.dtype==="int32"?indices:indices.toInt()))}function makeBatches(size,batchSize){let output=[],batchStart=0,batchEnd=null;for(;batchStart=size&&(batchEnd=size),output.push([batchStart,batchEnd]),batchStart=batchEnd;return output}async function fitLoop(model2,f,ins,outLabels,batchSize,epochs,verbose,callbacks2,valF,valIns,shuffle2,callbackMetrics,initialEpoch,stepsPerEpoch,validationSteps){batchSize==null&&(batchSize=32),epochs==null&&(epochs=1),shuffle2==null&&(shuffle2=!0),initialEpoch==null&&(initialEpoch=0);let doValidation=!1;if(valF!=null&&valIns!=null&&(doValidation=!0),validationSteps!=null&&(doValidation=!0,stepsPerEpoch==null))throw new ValueError("Can only use `validationSteps` when doing step-wise training, i.e., `stepsPerEpoch` must be set.");let numTrainSamples=model2.checkNumSamples(ins,batchSize,stepsPerEpoch,"steps_per_epoch"),indexArray;numTrainSamples!=null&&(indexArray=range5(0,numTrainSamples)),verbose==null&&(verbose=1);let{callbackList,history}=configureCallbacks(callbacks2,verbose,epochs,initialEpoch,numTrainSamples,stepsPerEpoch,batchSize,doValidation,callbackMetrics);callbackList.setModel(model2),model2.history=history,await callbackList.onTrainBegin(),model2.stopTraining_=!1;for(let epoch=initialEpoch;epoch{let batchStart=batches[batchIndex][0],batchEnd=batches[batchIndex][1],batchIds=sliceAlongFirstAxis(epochIndexArray1D,batchStart,batchEnd-batchStart);batchLogs.batch=batchIndex,batchLogs.size=batchEnd-batchStart;let insBatch=sliceArraysByIndices(ins,batchIds),outs=f(insBatch);for(let i=0;i0){if(doValidation=!0,args.validationData.length===2)inputValX=args.validationData[0],inputValY=args.validationData[1];else throw args.validationData.length===3?new NotImplementedError("validationData including sample weights is not supported yet."):new ValueError(`When passing validation data, it must contain 2 (valX, valY) or 3 (valX, valY, valSampleWeight) items; ${args.validationData} is invalid.`);let checkBatchAxis2=!0,valStandardized=await model2.standardizeUserData(inputValX,inputValY,null,null,checkBatchAxis2,batchSize);valX=valStandardized[0],valY=valStandardized[1],valIns=valX.concat(valY)}else if(args.validationSplit!=null&&args.validationSplit>0&&args.validationSplit<1){doValidation=!0;let splitAt=Math.floor(inputs[0].shape[0]*(1-args.validationSplit)),originalBatchSize=inputs[0].shape[0];valX=sliceArrays(inputs,splitAt,originalBatchSize),inputs=sliceArrays(inputs,0,splitAt),valY=sliceArrays(targets,splitAt,originalBatchSize),targets=sliceArrays(targets,0,splitAt),valIns=valX.concat(valY)}else args.validationSteps!=null&&(doValidation=!0);let ins=inputs.concat(targets).concat(sampleWeights);model2.checkTrainableWeightsConsistency();let trainFunction=model2.makeTrainFunction(),outLabels=model2.getDedupedMetricsNames(),valFunction,callbackMetrics;doValidation?(model2.makeTestFunction(),valFunction=model2.testFunction,callbackMetrics=outLabels.slice().concat(outLabels.map(n=>"val_"+n))):(valFunction=null,valIns=[],callbackMetrics=outLabels.slice());let callbacks2=standardizeCallbacks(args.callbacks,args.yieldEvery);return await fitLoop(model2,trainFunction,ins,outLabels,batchSize,args.epochs,args.verbose,callbacks2,valFunction,valIns,args.shuffle,callbackMetrics,args.initialEpoch,null,null)}finally{model2.isTraining=!1,disposeNewTensors(inputs,x),disposeNewTensors(targets,y),disposeNewTensors(valX,inputValX),disposeNewTensors(valY,inputValY),sampleWeights!=null&&dispose(sampleWeights)}}function ensureTensorsRank2OrHigher(tensors){let outs=[];tensors instanceof Tensor&&(tensors=[tensors]);for(let i=0;ioldTensorIds.push(t.id));else if(refTensors!=null)for(let name in refTensors){let oldTensor=refTensors[name];oldTensorIds.push(oldTensor.id)}let tensorsToDispose=[];if(tensors instanceof Tensor)oldTensorIds.indexOf(tensors.id)===-1&&tensorsToDispose.push(tensors);else if(Array.isArray(tensors))tensors.forEach(t=>{oldTensorIds.indexOf(t.id)===-1&&tensorsToDispose.push(t)});else if(tensors!=null)for(let name in tensors){let tensor2=tensors[name];oldTensorIds.indexOf(tensor2.id)===-1&&tensorsToDispose.push(tensor2)}tensorsToDispose.forEach(t=>{t.isDisposed||t.dispose()})}function isDataTensor(x){return x instanceof Tensor}function isDataArray(x){return Array.isArray(x)}function isDataDict(x){return!isDataTensor(x)&&!isDataArray(x)}function standardizeInputData(data2,names,shapes,checkBatchAxis=!0,exceptionPrefix=""){if(names==null||names.length===0){if(data2!=null){let gotUnexpectedData=!1;if(isDataArray(data2)&&data2.length>0)gotUnexpectedData=!0;else if(isDataDict(data2)){for(let key in data2)if(data2.hasOwnProperty(key)){gotUnexpectedData=!0;break}}else gotUnexpectedData=!0;if(gotUnexpectedData)throw new ValueError(`Error when checking model ${exceptionPrefix} expected no data, but got ${data2}`)}return[]}if(data2==null)return names.map(name=>null);let arrays;if(isDataDict(data2)){data2=data2,arrays=[];for(let name of names){if(data2[name]==null)throw new ValueError(`No data provided for "${name}". Need data for each key in: ${names}`);arrays.push(data2[name])}}else if(isDataArray(data2)){if(data2=data2,data2.length!==names.length)throw new ValueError(`Error when checking model ${exceptionPrefix}: the Array of Tensors that you are passing to your model is not the size the model expected. Expected to see ${names.length} Tensor(s), but instead got the following list of Tensor(s): ${data2}`);arrays=data2}else{if(data2=data2,names.length>1)throw new ValueError(`The model ${exceptionPrefix} expects ${names.length} Tensor(s), but only received one Tensor. Found: Tensor with shape ${data2.shape}`);arrays=[data2]}if(arrays=ensureTensorsRank2OrHigher(arrays),shapes!=null)for(let i=0;i=0&&dim!==refDim)throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} to have shape [${shapes[i]}], but got array with shape [${array2.shape}].`)}}return arrays}function checkArrayLengths(inputs,targets,weights){let setX=unique4(inputs.map(input2=>input2.shape[0]));setX.sort();let setY=unique4(targets.map(target=>target.shape[0]));if(setY.sort(),setX.length>1)throw new ValueError(`All input Tensors (x) should have the same number of samples. Got array shapes: ${JSON.stringify(inputs.map(input2=>input2.shape))}`);if(setY.length>1)throw new ValueError(`All target Tensors (y) should have the same number of samples. Got array shapes: ${JSON.stringify(targets.map(target=>target.shape))}`);if(setX.length>0&&setY.length>0&&!util_exports.arraysEqual(setX,setY))throw new ValueError(`Input Tensors should have the same number of samples as target Tensors. Found ${setX[0]} input sample(s) and ${setY[0]} target sample(s).`)}function checkLossAndTargetCompatibility(targets,lossFns,outputShapes){let keyLosses=[meanSquaredError2,binaryCrossentropy,categoricalCrossentropy];for(let i=0;i1)throw new ValueError(`The model expects ${names.length} ${exceptionPrefix} Tensors, but only received one Tensor. Found: array with shape ${JSON.stringify(data2.shape)}.`);arrays=[data2]}if(shapes!=null)for(let i=0;i[]);let wrappedMetrics;if(typeof metrics2=="string"||typeof metrics2=="function")wrappedMetrics=[metrics2];else if(Array.isArray(metrics2)||typeof metrics2=="object")wrappedMetrics=metrics2;else throw new TypeError(`Type of metrics argument not understood. Expected an string,function, Array, or Object, found: ${metrics2}`);if(Array.isArray(wrappedMetrics))return outputNames.map(name=>wrappedMetrics);{let nestedMetrics=[];for(let name of outputNames){let outputMetrics=wrappedMetrics.hasOwnProperty(name)?wrappedMetrics[name]:[];Array.isArray(outputMetrics)||(outputMetrics=[outputMetrics]),nestedMetrics.push(outputMetrics)}return nestedMetrics}}var LAYERS_MODEL_FORMAT_NAME="layers-model",LayersModel=class extends Container{constructor(args){super(args);this.isTraining=!1}summary(lineLength,positions,printFn=console.log){if(!this.built)throw new ValueError("This model has never been called, thus its weights have not been created yet. So no summary can be displayed. Build the model first (e.g., by calling it on some test data).");printSummary(this,lineLength,positions,printFn)}compile(args){if(args.loss==null&&(args.loss=[]),this.loss=args.loss,typeof args.optimizer=="string")this.optimizer_=getOptimizer(args.optimizer),this.isOptimizerOwned=!0;else{if(!(args.optimizer instanceof Optimizer))throw new ValueError("User-defined optimizer must be an instance of tf.Optimizer.");this.optimizer_=args.optimizer,this.isOptimizerOwned=!1}let lossFunctions=[];if(!Array.isArray(args.loss)&&typeof args.loss!="string"&&typeof args.loss!="function"){args.loss=args.loss;for(let name in args.loss)if(this.outputNames.indexOf(name)===-1)throw new ValueError(`Unknown entry in loss dictionary: "${name}". Only expected the following keys: ${this.outputNames}`);for(let name of this.outputNames)args.loss[name]==null&&console.warn(`Output "${name}" is missing from loss dictionary. We assume this was done on purpose, and we will not be expecting data to be passed to ${name} during training`),lossFunctions.push(get(args.loss[name]))}else if(Array.isArray(args.loss)){if(args.loss.length!==this.outputs.length)throw new ValueError(`When passing an Array as loss, it should have one entry per model output. The model has ${this.outputs.length} output(s), but you passed loss=${args.loss}.`);lossFunctions=args.loss.map(l=>get(l))}else{let lossFunction=get(args.loss);this.outputs.forEach(_=>{lossFunctions.push(lossFunction)})}this.lossFunctions=lossFunctions,this.feedOutputNames=[],this.feedOutputShapes=[],this.feedLossFns=[];for(let i=0;i{for(let i=0;i1&&(this.metricsTensors.push([weightedLoss,i]),this.metricsNames.push(this.outputNames[i]+"_loss"))}});let nestedMetrics=collectMetrics(args.metrics,this.outputNames),appendMetric=(outputIndex,metricName,metricTensor)=>{this.outputNames.length>1&&(metricName=this.outputNames[outputIndex]+"_"+metricName),this.metricsNames.push(metricName),this.metricsTensors.push([metricTensor,outputIndex])};nameScope("metric",()=>{for(let i=0;i{let metricNamePrefix="",metricName,accFn,weightedMetricFn;for(let metric of metrics2){if(typeof metric=="string"&&["accuracy","acc","crossentropy","ce"].indexOf(metric)!==-1){let outputShape=this.internalOutputShapes[i];outputShape[outputShape.length-1]===1||this.lossFunctions[i]===binaryCrossentropy?["accuracy","acc"].indexOf(metric)!==-1?accFn=binaryAccuracy:["crossentropy","ce"].indexOf(metric)!==-1&&(accFn=binaryCrossentropy2):this.lossFunctions[i]===sparseCategoricalCrossentropy?["accuracy","acc"].indexOf(metric)!==-1?accFn=sparseCategoricalAccuracy:["crossentropy","ce"].indexOf(metric)!==-1&&(accFn=sparseCategoricalCrossentropy2):["accuracy","acc"].indexOf(metric)!==-1?accFn=categoricalAccuracy:["crossentropy","ce"].indexOf(metric)!==-1&&(accFn=categoricalCrossentropy2);let suffix;["accuracy","acc"].indexOf(metric)!==-1?suffix="acc":["crossentropy","ce"].indexOf(metric)!==-1&&(suffix="ce"),weightedMetricFn=accFn,metricName=metricNamePrefix+suffix}else weightedMetricFn=get2(metric),metricName=metricNamePrefix+getLossOrMetricName(metric);let metricResult;nameScope(metricName,()=>{metricResult=weightedMetricFn}),appendMetric(i,metricName,metricResult)}})(outputMetrics)}}),this.collectedTrainableWeights=this.trainableWeights}checkTrainableWeightsConsistency(){this.collectedTrainableWeights!=null&&this.trainableWeights.length!==this.collectedTrainableWeights.length&&console.warn("Discrepancy between trainableweights and collected trainable weights. Did you set `model.trainable` without calling `model.compile()` afterwards?")}evaluate(x,y,args={}){let batchSize=args.batchSize==null?32:args.batchSize;checkBatchSize(batchSize);let checkBatchAxis=!0,standardizedOuts=this.standardizeUserDataXY(x,y,checkBatchAxis,batchSize);try{let ins=standardizedOuts[0].concat(standardizedOuts[1]);this.makeTestFunction();let f=this.testFunction,testOuts=this.testLoop(f,ins,batchSize,args.verbose,args.steps);return singletonOrArray(testOuts)}finally{disposeNewTensors(standardizedOuts[0],x),disposeNewTensors(standardizedOuts[1],y)}}async evaluateDataset(dataset,args){return this.makeTestFunction(),evaluateDataset(this,dataset,args)}checkNumSamples(ins,batchSize,steps,stepsName="steps"){let numSamples;if(steps!=null){if(numSamples=null,batchSize!=null)throw new ValueError(`If ${stepsName} is set, batchSize must be null or undefined.Got batchSize = ${batchSize}`)}else if(ins!=null)Array.isArray(ins)?numSamples=ins[0].shape[0]:numSamples=ins.shape[0];else throw new ValueError(`Either the input data should have a defined shape, or ${stepsName} shoud be specified.`);return numSamples}execute(inputs,outputs){if(Array.isArray(outputs)&&outputs.length===0)throw new ValueError("`outputs` is an empty Array, which is not allowed.");let outputsIsArray=Array.isArray(outputs),outputNames=outputsIsArray?outputs:[outputs],outputSymbolicTensors=this.retrieveSymbolicTensors(outputNames),feedDict=new FeedDict;if(inputs instanceof Tensor&&(inputs=[inputs]),Array.isArray(inputs)){if(inputs.length!==this.inputs.length)throw new ValueError(`The number of inputs provided (${inputs.length}) does not match the number of inputs of this model (${this.inputs.length}).`);for(let i=0;ioutput.name);for(let i=0;i0){let remainingNames=[];throw outputSymbolicTensors.forEach((tensor2,i)=>{tensor2==null&&remainingNames.push(symbolicTensorNames[i])}),new ValueError(`Cannot find SymbolicTensors for output name(s): ${JSON.stringify(remainingNames)}`)}return outputSymbolicTensors}predictLoop(ins,batchSize=32,verbose=!1){return tidy(()=>{let numSamples=this.checkNumSamples(ins);if(verbose)throw new NotImplementedError("Verbose predictLoop() is not implemented yet.");let batches=makeBatches(numSamples,batchSize),outsBatches=this.outputs.map(output=>[]);for(let batchIndex=0;batchIndex{let batchStart=batches[batchIndex][0],batchEnd=batches[batchIndex][1],insBatch=sliceArrays(ins,batchStart,batchEnd),feeds=[];if(Array.isArray(insBatch))for(let i=0;ioutsBatches[i].push(batchOut));return singletonOrArray(outsBatches.map(batches2=>concat(batches2,0)))})}predict(x,args={}){let xsRank2OrHigher=ensureTensorsRank2OrHigher(x);checkInputData(xsRank2OrHigher,this.inputNames,this.feedInputShapes,!1);try{let batchSize=args.batchSize==null?32:args.batchSize;return checkBatchSize(batchSize),this.predictLoop(xsRank2OrHigher,batchSize)}finally{disposeNewTensors(xsRank2OrHigher,x)}}predictOnBatch(x){checkInputData(x,this.inputNames,this.feedInputShapes,!0);let batchSize=(Array.isArray(x)?x[0]:x).shape[0];return this.predictLoop(x,batchSize)}standardizeUserDataXY(x,y,checkBatchAxis=!0,batchSize){if(this.optimizer_==null)throw new RuntimeError("You must compile a model before training/testing. Use LayersModel.compile(modelCompileArgs).");let outputShapes=[];for(let i=0;i0&&x[0].shape[0]%batchSize!=0)throw new ValueError(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${batchSize}. Found: ${x[0].shape[0]} sample(s).`);return[x,y]}async standardizeUserData(x,y,sampleWeight,classWeight,checkBatchAxis=!0,batchSize){let[standardXs,standardYs]=this.standardizeUserDataXY(x,y,checkBatchAxis,batchSize);if(sampleWeight!=null)throw new Error("sample weight is not supported yet.");let standardSampleWeights=null;if(classWeight!=null){let classWeights=standardizeClassWeights(classWeight,this.outputNames);standardSampleWeights=[];for(let i=0;i{let numSamples=this.checkNumSamples(ins,batchSize,steps,"steps"),outs=[];if(verbose>0)throw new NotImplementedError("Verbose mode is not implemented yet.");if(steps!=null)throw new NotImplementedError("steps mode in testLoop() is not implemented yet");{let batches=makeBatches(numSamples,batchSize),indexArray=tensor1d(range5(0,numSamples));for(let batchIndex=0;batchIndex1&&(newLabel+=`_${count(outLabels.slice(0,i),label)}`),dedupedOutLabels.push(newLabel)}return dedupedOutLabels}makeTrainFunction(){return data2=>{let lossValues=[],inputs=data2.slice(0,this.inputs.length),targets=data2.slice(this.inputs.length,this.inputs.length+this.outputs.length),sampleWeights=data2.slice(this.inputs.length+this.outputs.length,this.inputs.length+this.outputs.length*2),metricsValues=[],totalLossFunction=()=>{let feeds=[];for(let i=0;i1&&i{totalLoss=add2(totalLoss,regularizerLoss)}),totalLoss},variables=this.collectedTrainableWeights.map(param=>param.read()),returnCost=!0;return[this.optimizer_.minimize(totalLossFunction,returnCost,variables)].concat(metricsValues)}}makeTestFunction(){this.testFunction=data2=>tidy(()=>{let valOutputs=[],totalLoss,inputs=data2.slice(0,this.inputs.length),targets=data2.slice(this.inputs.length,this.inputs.length+this.outputs.length),feeds=[];for(let i=0;itoSnakeCase(name))}else{let outputNames=Object.keys(this.loss);lossNames={};let losses4=this.loss;for(let outputName of outputNames)if(typeof losses4[outputName]=="string")lossNames[outputName]=toSnakeCase(losses4[outputName]);else throw new Error("Serialization of non-string loss is not supported.")}return lossNames}getMetricIdentifiers(){if(typeof this.metrics=="string"||typeof this.metrics=="function")return[toSnakeCase(getLossOrMetricName(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(metric=>toSnakeCase(getLossOrMetricName(metric)));{let metricsIdentifiers={};for(let key in this.metrics)metricsIdentifiers[key]=toSnakeCase(getLossOrMetricName(this.metrics[key]));return metricsIdentifiers}}getTrainingConfig(){return{loss:this.getLossIdentifiers(),metrics:this.getMetricIdentifiers(),optimizer_config:{class_name:this.optimizer.getClassName(),config:this.optimizer.getConfig()}}}loadTrainingConfig(trainingConfig){if(trainingConfig.weighted_metrics!=null)throw new Error("Loading weight_metrics is not supported yet.");if(trainingConfig.loss_weights!=null)throw new Error("Loading loss_weights is not supported yet.");if(trainingConfig.sample_weight_mode!=null)throw new Error("Loading sample_weight_mode is not supported yet.");let tsConfig=convertPythonicToTs(trainingConfig.optimizer_config),optimizer=deserialize(tsConfig),loss;if(typeof trainingConfig.loss=="string")loss=toCamelCase(trainingConfig.loss);else if(Array.isArray(trainingConfig.loss))loss=trainingConfig.loss.map(lossEntry=>toCamelCase(lossEntry));else if(trainingConfig.loss!=null){loss={};for(let key in trainingConfig.loss)loss[key]=toCamelCase(trainingConfig.loss[key])}let metrics2;if(Array.isArray(trainingConfig.metrics))metrics2=trainingConfig.metrics.map(metric=>toCamelCase(metric));else if(trainingConfig.metrics!=null){metrics2={};for(let key in trainingConfig.metrics)metrics2[key]=toCamelCase(trainingConfig.metrics[key])}this.compile({loss,metrics:metrics2,optimizer})}async save(handlerOrURL,config3){if(typeof handlerOrURL=="string"){let handlers=io_exports.getSaveHandlers(handlerOrURL);if(handlers.length===0)throw new ValueError(`Cannot find any save handlers for URL '${handlerOrURL}'`);if(handlers.length>1)throw new ValueError(`Found more than one (${handlers.length}) save handlers for URL '${handlerOrURL}'`);handlerOrURL=handlers[0]}if(handlerOrURL.save==null)throw new ValueError("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let weightDataAndSpecs=await io_exports.encodeWeights(this.getNamedWeights(config3)),returnString=!1,unusedArg=null,modelArtifacts={modelTopology:this.toJSON(unusedArg,returnString),format:LAYERS_MODEL_FORMAT_NAME,generatedBy:`TensorFlow.js tfjs-layers v${version10}`,convertedBy:null};if((config3==null?!1:config3.includeOptimizer)&&this.optimizer!=null){modelArtifacts.trainingConfig=this.getTrainingConfig();let weightType="optimizer",{data:optimizerWeightData,specs:optimizerWeightSpecs}=await io_exports.encodeWeights(await this.optimizer.getWeights(),weightType);weightDataAndSpecs.specs.push(...optimizerWeightSpecs),weightDataAndSpecs.data=io_exports.concatenateArrayBuffers([weightDataAndSpecs.data,optimizerWeightData])}if(this.userDefinedMetadata!=null){let checkSize=!0;checkUserDefinedMetadata(this.userDefinedMetadata,this.name,checkSize),modelArtifacts.userDefinedMetadata=this.userDefinedMetadata}return modelArtifacts.weightData=weightDataAndSpecs.data,modelArtifacts.weightSpecs=weightDataAndSpecs.specs,handlerOrURL.save(modelArtifacts)}setUserDefinedMetadata(userDefinedMetadata){checkUserDefinedMetadata(userDefinedMetadata,this.name),this.userDefinedMetadata=userDefinedMetadata}getUserDefinedMetadata(){return this.userDefinedMetadata}};LayersModel.className="Model";serialization_exports.registerClass(LayersModel);var Functional=class extends LayersModel{};Functional.className="Functional";serialization_exports.registerClass(Functional);async function modelFromJSON(modelAndWeightsConfig,customObjects){"modelTopology"in modelAndWeightsConfig||(modelAndWeightsConfig={modelTopology:modelAndWeightsConfig}),modelAndWeightsConfig=modelAndWeightsConfig;let modelTopology=modelAndWeightsConfig.modelTopology;modelTopology.model_config!=null&&(modelTopology=modelTopology.model_config);let tsConfig=convertPythonicToTs(modelTopology),model2=deserialize(tsConfig,customObjects);if(modelAndWeightsConfig.weightsManifest!=null){let weightValues=await io_exports.loadWeights(modelAndWeightsConfig.weightsManifest,modelAndWeightsConfig.pathPrefix,model2.weights.map(weight=>weight.originalName)),uniqueWeightValues={};for(let weight of model2.weights)uniqueWeightValues[weight.originalName]=weightValues[weight.originalName];model2.loadWeights(uniqueWeightValues),dispose(weightValues)}return model2}async function loadLayersModelInternal(pathOrIOHandler,options){if(options==null&&(options={}),typeof pathOrIOHandler=="string"){let handlers=io_exports.getLoadHandlers(pathOrIOHandler,options);if(handlers.length===0)handlers.push(io_exports.browserHTTPRequest(pathOrIOHandler,options));else if(handlers.length>1)throw new ValueError(`Found more than one (${handlers.length}) load handlers for URL '${pathOrIOHandler}'`);pathOrIOHandler=handlers[0]}return loadLayersModelFromIOHandler(pathOrIOHandler,void 0,options)}async function loadLayersModelFromIOHandler(handler,customObjects,options){if(options==null&&(options={}),handler.load==null)throw new ValueError("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let artifacts=await handler.load(),modelTopology=artifacts.modelTopology;modelTopology.model_config!=null&&(modelTopology=modelTopology.model_config);let strict=options.strict==null?!0:options.strict,fastWeightInit=artifacts.weightData!=null&&artifacts.weightSpecs!=null&&strict,model2=deserialize(convertPythonicToTs(modelTopology),customObjects,fastWeightInit),trainingConfig=artifacts.trainingConfig;if(trainingConfig!=null&&model2.loadTrainingConfig(trainingConfig),artifacts.userDefinedMetadata!=null&&model2.setUserDefinedMetadata(artifacts.userDefinedMetadata),artifacts.weightData!=null){if(artifacts.weightSpecs==null)throw new ValueError("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");let{modelWeights,optimizerWeights}=decodeModelAndOptimizerWeights(artifacts.weightData,artifacts.weightSpecs);model2.loadWeights(modelWeights,strict),model2.optimizer!=null&&optimizerWeights.length>0&&await model2.optimizer.setWeights(optimizerWeights),dispose(modelWeights),dispose(optimizerWeights.map(w=>w.tensor))}return model2}function decodeModelAndOptimizerWeights(buffer2,specs){let name2Tensor=io_exports.decodeWeights(buffer2,specs),modelWeights={},optimizerWeights=[];return specs.forEach(spec=>{spec.group==="optimizer"?optimizerWeights.push({name:spec.name,tensor:name2Tensor[spec.name]}):modelWeights[spec.name]=name2Tensor[spec.name]}),{modelWeights,optimizerWeights}}var Sequential=class extends LayersModel{constructor(args){super({inputs:[],outputs:[]});if(args=args||{},this.trainable=!0,this.built=!1,this.name=args.name!=null?args.name:getUid("sequential_"),args.layers!=null)for(let layer of args.layers)this.add(layer)}checkShape(layer){if(layer.inboundNodes[0].outputTensors[0].shape.some(x=>x<0))throw new ValueError(`Negative dimension size caused by adding layer ${layer.name} with input shape [${layer.inboundNodes[0].inputTensors[0].shape}]`)}add(layer){let isLayerModelInstance=layer instanceof Sequential||layer instanceof LayersModel,modelLayer;if(isLayerModelInstance){if(modelLayer=layer,modelLayer.outputs.length!==1)throw new ValueError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");if(modelLayer.inputs.length!==1)throw new ValueError("All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.")}if(this.outputs.length===0){if(layer.inboundNodes.length===0){if(layer.batchInputShape==null)throw new ValueError("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");let x=Input({batchShape:layer.batchInputShape,dtype:layer.dtype,name:layer.name+"_input"});layer.apply(x)}if(isLayerModelInstance)this.outputs=modelLayer.outputs,this.inputs=modelLayer.inputs;else{if(layer.inboundNodes.length!==1)throw new ValueError(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${layer.name} which has ${layer.inboundNodes.length} pre-existing inbound connections.`);if(layer.inboundNodes[0].outputTensors.length!==1)throw new ValueError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(layer),this.outputs=[layer.inboundNodes[0].outputTensors[0]],this.inputs=getSourceInputs(this.outputs[0])}this.inboundNodes=[],new Node({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:pyListRepeat(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map(x=>x.shape),outputShapes:this.outputs[0].shape})}else{let outputTensor=layer.apply(this.outputs[0]);if(Array.isArray(outputTensor))throw new TypeError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(layer),this.outputs=[outputTensor],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}this.layers.push(layer),this.built=!1}pop(){if(this.layers.length===0)throw new TypeError("There are no layers in the model.");if(this.layers.pop(),this.layers.length===0)this.outputs=[],this.inboundNodes=[],this.outboundNodes=[];else{let lastLayerIndex=this.layers.length-1;this.layers[lastLayerIndex].outboundNodes=[],this.outputs=[this.layers[lastLayerIndex].output],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}}call(inputs,kwargs){return this.model==null&&this.build(),this.model.call(inputs,kwargs)}build(inputShape){if(getExactlyOneShape(inputShape),this.inputs.length===0||this.outputs.length===0)throw new TypeError("Sequential model cannot be built: model is empty. Add some layers first.");this.model=new LayersModel({inputs:this.inputs,outputs:this.outputs[0],name:this.name+"_model"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0}countParams(){return this.built||this.build(),super.countParams()}summary(lineLength,positions,printFn=console.log){this.built||this.build(),super.summary(lineLength,positions,printFn)}setWeights(weights){this.model==null&&this.build(),this.model.setWeights(weights)}evaluate(x,y,args={}){if(!this.built)throw new RuntimeError("The model needs to be compiled before being used.");return this.model.evaluate(x,y,args)}async evaluateDataset(dataset,args){if(!this.built)throw new RuntimeError("The model needs to be compiled before being used.");return this.model.evaluateDataset(dataset,args)}predict(x,args={}){return this.model==null&&this.build(),this.model.predict(x,args)}predictOnBatch(x){return this.model==null&&this.build(),this.model.predictOnBatch(x)}compile(args){this.build(),this.model.compile(args),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(optimizer){this.model.optimizer=optimizer}async fit(x,y,args={}){if(!this.built)throw new RuntimeError("The model needs to be compiled before being used.");return this.model.fit(x,y,args)}async fitDataset(dataset,args){if(!this.built)throw new RuntimeError("The model needs to be compiled before being used.");return this.model.fitDataset(dataset,args)}async trainOnBatch(x,y){return this.model.trainOnBatch(x,y)}static fromConfig(cls,config3,customObjects={},fastWeightInit=!1){let configArray,extraModelConfig={};if(config3 instanceof Array){if(config3[0].className==null||config3[0].className==="Merge")throw new ValueError("Legacy serialization format not supported yet.");configArray=config3}else util_exports.assert(config3.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),configArray=config3.layers,delete config3.layers,extraModelConfig=config3;let model2=new cls(extraModelConfig);if(!(model2 instanceof Sequential))throw new NotImplementedError(`Sequential.fromConfig called on non-Sequential input: ${model2}`);for(let conf of configArray){let layer=deserialize(conf,void 0,fastWeightInit);fastWeightInit&&layer.setFastWeightInitDuringBuild(!0),model2.add(layer)}return model2}set stopTraining(stop){if(this.model==null)throw new ValueError("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=stop}get stopTraining(){if(this.model==null)throw new ValueError("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let layers=[];for(let layer of this.layers){let dict={};dict.className=layer.getClassName(),dict.config=layer.getConfig(),layers.push(dict)}return{name:this.name,layers}}};Sequential.className="Sequential";serialization_exports.registerClass(Sequential);function model(args){return new LayersModel(args)}function sequential(config3){return new Sequential(config3)}function loadLayersModel(pathOrIOHandler,options){return options==null&&(options={}),loadLayersModelInternal(pathOrIOHandler,options)}function input(config3){return Input(config3)}function registerCallbackConstructor(verbosityLevel,callbackConstructor){CallbackConstructorRegistry.registerCallbackConstructor(verbosityLevel,callbackConstructor)}var Activation=class extends serialization_exports.Serializable{getConfig(){return{}}},Elu2=class extends Activation{apply(x,alpha=1){return elu4(x,alpha)}};Elu2.className="elu";serialization_exports.registerClass(Elu2);var Selu2=class extends Activation{apply(x){return selu(x)}};Selu2.className="selu";serialization_exports.registerClass(Selu2);var Relu2=class extends Activation{apply(x){return relu(x)}};Relu2.className="relu";serialization_exports.registerClass(Relu2);var Relu62=class extends Activation{apply(x){return tidy(()=>minimum(6,relu(x)))}};Relu62.className="relu6";serialization_exports.registerClass(Relu62);var Linear=class extends Activation{apply(x){return x}};Linear.className="linear";serialization_exports.registerClass(Linear);var Sigmoid2=class extends Activation{apply(x){return sigmoid(x)}};Sigmoid2.className="sigmoid";serialization_exports.registerClass(Sigmoid2);var HardSigmoid=class extends Activation{apply(x){return hardSigmoid(x)}};HardSigmoid.className="hardSigmoid";serialization_exports.registerClass(HardSigmoid);var Softplus2=class extends Activation{apply(x){return softplus(x)}};Softplus2.className="softplus";serialization_exports.registerClass(Softplus2);var Softsign=class extends Activation{apply(x){return softsign(x)}};Softsign.className="softsign";serialization_exports.registerClass(Softsign);var Tanh2=class extends Activation{apply(x){return tanh2(x)}};Tanh2.className="tanh";serialization_exports.registerClass(Tanh2);var Softmax2=class extends Activation{apply(x,axis=-1){return softmax(x,axis)}};Softmax2.className="softmax";serialization_exports.registerClass(Softmax2);var LogSoftmax2=class extends Activation{apply(x,axis=-1){return logSoftmax(x,axis)}};LogSoftmax2.className="logSoftmax";serialization_exports.registerClass(LogSoftmax2);var Swish=class extends Activation{apply(x,alpha=1){return tidy(()=>sigmoid(x.mul(alpha)).mul(x))}};Swish.className="swish";serialization_exports.registerClass(Swish);function serializeActivation(activation2){return activation2.getClassName()}function deserializeActivation(config3,customObjects={}){return deserializeKerasObject(config3,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"activation")}function getActivation(identifier){if(identifier==null){let config3={};return config3.className="linear",config3.config={},deserializeActivation(config3)}if(typeof identifier=="string"){let config3={};return config3.className=identifier,config3.config={},deserializeActivation(config3)}else return identifier instanceof Activation?identifier:deserializeActivation(identifier)}function assertObjectArgs(args){if(args!=null&&typeof args!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${args}`)}var Regularizer=class extends serialization_exports.Serializable{},L1L2=class extends Regularizer{constructor(args){super();assertObjectArgs(args),this.l1=args==null||args.l1==null?.01:args.l1,this.l2=args==null||args.l2==null?.01:args.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(x){return tidy(()=>{let regularization=zeros([1]);return this.hasL1&&(regularization=add2(regularization,sum2(mul(this.l1,abs(x))))),this.hasL2&&(regularization=add2(regularization,sum2(mul(this.l2,square3(x))))),regularization.asScalar()})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(cls,config3){return new cls({l1:config3.l1,l2:config3.l2})}};L1L2.className="L1L2";serialization_exports.registerClass(L1L2);function l1(args){return assertObjectArgs(args),new L1L2({l1:args!=null?args.l1:null,l2:0})}function l2(args){return assertObjectArgs(args),new L1L2({l2:args!=null?args.l2:null,l1:0})}var REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP={l1l2:"L1L2"};function serializeRegularizer(constraint){return serializeKerasObject(constraint)}function deserializeRegularizer(config3,customObjects={}){return deserializeKerasObject(config3,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"regularizer")}function getRegularizer(identifier){if(identifier==null)return null;if(typeof identifier=="string"){let config3={className:identifier in REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP?REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier]:identifier,config:{}};return deserializeRegularizer(config3)}else return identifier instanceof Regularizer?identifier:deserializeRegularizer(identifier)}var ReLU=class extends Layer{constructor(args){super(args==null?{}:args);this.supportsMasking=!0,args!=null&&(this.maxValue=args.maxValue)}call(inputs,kwargs){inputs=getExactlyOneTensor(inputs);let output=relu(inputs);return this.maxValue!=null&&(output=clipByValue(output,0,this.maxValue)),output}computeOutputShape(inputShape){return inputShape}getConfig(){let config3={maxValue:this.maxValue},baseConfig=super.getConfig();return Object.assign(config3,baseConfig),config3}};ReLU.className="ReLU";serialization_exports.registerClass(ReLU);var LeakyReLU=class extends Layer{constructor(args){super(args==null?{}:args);this.DEFAULT_ALPHA=.3,args==null&&(args={}),this.alpha=args.alpha==null?this.DEFAULT_ALPHA:args.alpha}call(inputs,kwargs){let x=getExactlyOneTensor(inputs);return leakyRelu(x,this.alpha)}computeOutputShape(inputShape){return inputShape}getConfig(){let config3={alpha:this.alpha},baseConfig=super.getConfig();return Object.assign(config3,baseConfig),config3}};LeakyReLU.className="LeakyReLU";serialization_exports.registerClass(LeakyReLU);var PReLU=class extends Layer{constructor(args){super(args==null?{}:args);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",args==null&&(args={}),this.supportsMasking=!0,this.alphaInitializer=getInitializer(args.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=getRegularizer(args.alphaRegularizer),this.alphaConstraint=getConstraint(args.alphaConstraint),args.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(args.sharedAxes))this.sharedAxes=args.sharedAxes;else if(typeof args.sharedAxes=="number")this.sharedAxes=[args.sharedAxes];else throw new ValueError(`Expected sharedAxes to be a number or an array of numbers, but got ${args.sharedAxes}`)}build(inputShape){inputShape=getExactlyOneShape(inputShape);let paramShape=inputShape.slice(1);if(this.sharedAxes!=null)for(let i of this.sharedAxes)paramShape[i-1]=1;this.alpha=this.addWeight("alpha",paramShape,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let axes={};if(this.sharedAxes!=null)for(let i=1;i(checkDataFormat(dataFormat),dataFormat==="channelsFirst"?transpose(x,[0,2,3,1]):x))}function preprocessConv3DInput(x,dataFormat){return tidy(()=>(checkDataFormat(dataFormat),dataFormat==="channelsFirst"?transpose(x,[0,2,3,4,1]):x))}function conv1dWithBias(x,kernel,bias,strides=1,padding="valid",dataFormat,dilationRate=1){return tidy(()=>{if(dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat),x.shape.length!==3)throw new ValueError(`The input of a conv1dWithBias operation should be 3, but is ${x.shape.length} instead.`);if(kernel.shape.length!==3)throw new ValueError(`The kernel for a conv1dWithBias operation should be 3, but is ${kernel.shape.length} instead`);if(bias!=null&&bias.shape.length!==1)throw new ValueError(`The bias for a conv1dWithBias operation should be 1, but is ${kernel.shape.length} instead`);if(dataFormat==="channelsFirst"&&(x=transpose(x,[0,2,1])),padding==="causal")throw new NotImplementedError("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let y=conv1d(x,kernel,strides,padding==="same"?"same":"valid","NWC",dilationRate);return bias!=null&&(y=biasAdd(y,bias)),y})}function conv2dWithBiasActivation(x,kernel,bias,strides=[1,1],padding="valid",dataFormat,dilationRate,activation2=null){return tidy(()=>{if(dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat),x.rank!==3&&x.rank!==4)throw new ValueError(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${x.rank}.`);if(kernel.rank!==3&&kernel.rank!==4)throw new ValueError(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${x.rank}.`);let y=preprocessConv2DInput(x,dataFormat);if(padding==="causal")throw new NotImplementedError("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return y=fused_ops_exports.conv2d({x:y,filter:kernel,strides,pad:padding==="same"?"same":"valid",dilations:dilationRate,dataFormat:"NHWC",bias,activation:activation2}),dataFormat==="channelsFirst"&&(y=transpose(y,[0,3,1,2])),y})}function conv3dWithBias(x,kernel,bias,strides=[1,1,1],padding="valid",dataFormat,dilationRate){return tidy(()=>{if(dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat),x.rank!==4&&x.rank!==5)throw new ValueError(`conv3dWithBias expects input to be of rank 4 or 5, but received ${x.rank}.`);if(kernel.rank!==4&&kernel.rank!==5)throw new ValueError(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${x.rank}.`);let y=preprocessConv3DInput(x,dataFormat);if(padding==="causal")throw new NotImplementedError("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return y=conv3d(y,kernel,strides,padding==="same"?"same":"valid","NDHWC",dilationRate),bias!=null&&(y=biasAdd(y,bias)),dataFormat==="channelsFirst"&&(y=transpose(y,[0,4,1,2,3])),y})}var BaseConv=class extends Layer{constructor(rank,args){super(args);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",BaseConv.verifyArgs(args),this.rank=rank,assertPositiveInteger(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new NotImplementedError(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=normalizeArray(args.kernelSize,rank,"kernelSize"),this.strides=normalizeArray(args.strides==null?1:args.strides,rank,"strides"),this.padding=args.padding==null?"valid":args.padding,checkPaddingMode(this.padding),this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),this.activation=getActivation(args.activation),this.useBias=args.useBias==null?!0:args.useBias,this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=getConstraint(args.biasConstraint),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.activityRegularizer=getRegularizer(args.activityRegularizer),this.dilationRate=normalizeArray(args.dilationRate==null?1:args.dilationRate,rank,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new ValueError(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new ValueError(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new ValueError(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(args){if(assert2("kernelSize"in args,"required key 'kernelSize' not in config"),typeof args.kernelSize!="number"&&!checkArrayTypeAndLength(args.kernelSize,"number",1,3))throw new ValueError(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(args.kernelSize)}.`)}getConfig(){let config3={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:serializeActivation(this.activation),useBias:this.useBias,biasInitializer:serializeInitializer(this.biasInitializer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),biasConstraint:serializeConstraint(this.biasConstraint)},baseConfig=super.getConfig();return Object.assign(config3,baseConfig),config3}},Conv=class extends BaseConv{constructor(rank,args){super(rank,args);this.kernel=null,Conv.verifyArgs(args),this.filters=args.filters,assertPositiveInteger(this.filters,"filters"),this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=getConstraint(args.kernelConstraint),this.kernelRegularizer=getRegularizer(args.kernelRegularizer)}build(inputShape){inputShape=getExactlyOneShape(inputShape);let channelAxis=this.dataFormat==="channelsFirst"?1:inputShape.length-1;if(inputShape[channelAxis]==null)throw new ValueError(`The channel dimension of the input should be defined. Found ${inputShape[channelAxis]}`);let inputDim=inputShape[channelAxis],kernelShape=this.kernelSize.concat([inputDim,this.filters]);this.kernel=this.addWeight("kernel",kernelShape,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[channelAxis]:inputDim}}],this.built=!0}call(inputs,kwargs){return tidy(()=>{inputs=getExactlyOneTensor(inputs);let outputs,biasValue=this.bias==null?null:this.bias.read(),fusedActivationName=mapActivationToFusedKernel(this.activation.getClassName());if(fusedActivationName!=null&&this.rank===2)outputs=conv2dWithBiasActivation(inputs,this.kernel.read(),biasValue,this.strides,this.padding,this.dataFormat,this.dilationRate,fusedActivationName);else{if(this.rank===1)outputs=conv1dWithBias(inputs,this.kernel.read(),biasValue,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)outputs=conv2dWithBiasActivation(inputs,this.kernel.read(),biasValue,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)outputs=conv3dWithBias(inputs,this.kernel.read(),biasValue,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new NotImplementedError("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(outputs=this.activation.apply(outputs))}return outputs})}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let newSpace=[],space=this.dataFormat==="channelsLast"?inputShape.slice(1,inputShape.length-1):inputShape.slice(2);for(let i=0;i 0 but got ${JSON.stringify(args.filters)}`)}},Conv2D2=class extends Conv{constructor(args){super(2,args);Conv2D2.verifyArgs(args)}getConfig(){let config3=super.getConfig();return delete config3.rank,config3}static verifyArgs(args){if(typeof args.kernelSize!="number"&&!checkArrayTypeAndLength(args.kernelSize,"number",1,2))throw new ValueError(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(args.kernelSize)}.`)}};Conv2D2.className="Conv2D";serialization_exports.registerClass(Conv2D2);var Conv3D2=class extends Conv{constructor(args){super(3,args);Conv3D2.verifyArgs(args)}getConfig(){let config3=super.getConfig();return delete config3.rank,config3}static verifyArgs(args){if(typeof args.kernelSize!="number"&&!(Array.isArray(args.kernelSize)&&(args.kernelSize.length===1||args.kernelSize.length===3)))throw new ValueError(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(args.kernelSize)}.`)}};Conv3D2.className="Conv3D";serialization_exports.registerClass(Conv3D2);var Conv2DTranspose=class extends Conv2D2{constructor(args){super(args);if(this.inputSpec=[new InputSpec({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new ValueError(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(inputShape){if(inputShape=getExactlyOneShape(inputShape),inputShape.length!==4)throw new ValueError("Input should have rank 4; Received input shape: "+JSON.stringify(inputShape));let channelAxis=this.dataFormat==="channelsFirst"?1:inputShape.length-1;if(inputShape[channelAxis]==null)throw new ValueError("The channel dimension of the inputs should be defined. Found `None`.");let inputDim=inputShape[channelAxis],kernelShape=this.kernelSize.concat([this.filters,inputDim]);this.kernel=this.addWeight("kernel",kernelShape,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new InputSpec({ndim:4,axes:{[channelAxis]:inputDim}})],this.built=!0}call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);if(input2.shape.length!==4)throw new ValueError(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${input2.shape.length}`);let inputShape=input2.shape,batchSize=inputShape[0],hAxis,wAxis;this.dataFormat==="channelsFirst"?(hAxis=2,wAxis=3):(hAxis=1,wAxis=2);let height=inputShape[hAxis],width=inputShape[wAxis],kernelH=this.kernelSize[0],kernelW=this.kernelSize[1],strideH=this.strides[0],strideW=this.strides[1],outHeight=deconvLength(height,strideH,kernelH,this.padding),outWidth=deconvLength(width,strideW,kernelW,this.padding),outputShape=[batchSize,outHeight,outWidth,this.filters];this.dataFormat!=="channelsLast"&&(input2=transpose(input2,[0,2,3,1]));let outputs=conv2dTranspose(input2,this.kernel.read(),outputShape,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(outputs=transpose(outputs,[0,3,1,2])),this.bias!=null&&(outputs=biasAdd(outputs,this.bias.read(),this.dataFormat)),this.activation!=null&&(outputs=this.activation.apply(outputs)),outputs})}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let outputShape=inputShape.slice(),channelAxis,heightAxis,widthAxis;this.dataFormat==="channelsFirst"?(channelAxis=1,heightAxis=2,widthAxis=3):(channelAxis=3,heightAxis=1,widthAxis=2);let kernelH=this.kernelSize[0],kernelW=this.kernelSize[1],strideH=this.strides[0],strideW=this.strides[1];return outputShape[channelAxis]=this.filters,outputShape[heightAxis]=deconvLength(outputShape[heightAxis],strideH,kernelH,this.padding),outputShape[widthAxis]=deconvLength(outputShape[widthAxis],strideW,kernelW,this.padding),outputShape}getConfig(){let config3=super.getConfig();return delete config3.dilationRate,config3}};Conv2DTranspose.className="Conv2DTranspose";serialization_exports.registerClass(Conv2DTranspose);var SeparableConv=class extends Conv{constructor(rank,config3){super(rank,config3);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,config3.filters==null)throw new ValueError("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(config3.kernelInitializer!=null||config3.kernelRegularizer!=null||config3.kernelConstraint!=null)throw new ValueError("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(config3.padding!=null&&config3.padding!=="same"&&config3.padding!=="valid")throw new ValueError(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(config3.padding)}`);this.depthMultiplier=config3.depthMultiplier==null?1:config3.depthMultiplier,this.depthwiseInitializer=getInitializer(config3.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=getRegularizer(config3.depthwiseRegularizer),this.depthwiseConstraint=getConstraint(config3.depthwiseConstraint),this.pointwiseInitializer=getInitializer(config3.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=getRegularizer(config3.pointwiseRegularizer),this.pointwiseConstraint=getConstraint(config3.pointwiseConstraint)}build(inputShape){if(inputShape=getExactlyOneShape(inputShape),inputShape.length{inputs=getExactlyOneTensor(inputs);let output;if(this.rank===1)throw new NotImplementedError("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(inputs=transpose(inputs,[0,2,3,1])),output=separableConv2d(inputs,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(output=biasAdd(output,this.bias.read(),this.dataFormat)),this.activation!=null&&(output=this.activation.apply(output)),this.dataFormat==="channelsFirst"&&(output=transpose(output,[0,3,1,2])),output})}getConfig(){let config3=super.getConfig();return delete config3.rank,delete config3.kernelInitializer,delete config3.kernelRegularizer,delete config3.kernelConstraint,config3.depthwiseInitializer=serializeInitializer(this.depthwiseInitializer),config3.pointwiseInitializer=serializeInitializer(this.pointwiseInitializer),config3.depthwiseRegularizer=serializeRegularizer(this.depthwiseRegularizer),config3.pointwiseRegularizer=serializeRegularizer(this.pointwiseRegularizer),config3.depthwiseConstraint=serializeConstraint(this.depthwiseConstraint),config3.pointwiseConstraint=serializeConstraint(this.pointwiseConstraint),config3}};SeparableConv.className="SeparableConv";var SeparableConv2D=class extends SeparableConv{constructor(args){super(2,args)}};SeparableConv2D.className="SeparableConv2D";serialization_exports.registerClass(SeparableConv2D);var Conv1D=class extends Conv{constructor(args){super(1,args);Conv1D.verifyArgs(args),this.inputSpec=[{ndim:3}]}getConfig(){let config3=super.getConfig();return delete config3.rank,delete config3.dataFormat,config3}static verifyArgs(args){if(typeof args.kernelSize!="number"&&!checkArrayTypeAndLength(args.kernelSize,"number",1,1))throw new ValueError(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(args.kernelSize)}.`)}};Conv1D.className="Conv1D";serialization_exports.registerClass(Conv1D);var Cropping2D=class extends Layer{constructor(args){super(args);typeof args.cropping=="number"?this.cropping=[[args.cropping,args.cropping],[args.cropping,args.cropping]]:typeof args.cropping[0]=="number"?this.cropping=[[args.cropping[0],args.cropping[0]],[args.cropping[1],args.cropping[1]]]:this.cropping=args.cropping,this.dataFormat=args.dataFormat===void 0?"channelsLast":args.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(inputShape){return this.dataFormat==="channelsFirst"?[inputShape[0],inputShape[1],inputShape[2]-this.cropping[0][0]-this.cropping[0][1],inputShape[3]-this.cropping[1][0]-this.cropping[1][1]]:[inputShape[0],inputShape[1]-this.cropping[0][0]-this.cropping[0][1],inputShape[2]-this.cropping[1][0]-this.cropping[1][1],inputShape[3]]}call(inputs,kwargs){return tidy(()=>{if(inputs=getExactlyOneTensor(inputs),this.dataFormat==="channelsLast"){let hSliced=sliceAlongAxis(inputs,this.cropping[0][0],inputs.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return sliceAlongAxis(hSliced,this.cropping[1][0],inputs.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let hSliced=sliceAlongAxis(inputs,this.cropping[0][0],inputs.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return sliceAlongAxis(hSliced,this.cropping[1][0],inputs.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let config3={cropping:this.cropping,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config3,baseConfig),config3}};Cropping2D.className="Cropping2D";serialization_exports.registerClass(Cropping2D);var UpSampling2D=class extends Layer{constructor(args){super(args);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=args.size==null?this.DEFAULT_SIZE:args.size,this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),this.interpolation=args.interpolation==null?"nearest":args.interpolation,checkInterpolationFormat(this.interpolation)}computeOutputShape(inputShape){if(this.dataFormat==="channelsFirst"){let height=inputShape[2]==null?null:this.size[0]*inputShape[2],width=inputShape[3]==null?null:this.size[1]*inputShape[3];return[inputShape[0],inputShape[1],height,width]}else{let height=inputShape[1]==null?null:this.size[0]*inputShape[1],width=inputShape[2]==null?null:this.size[1]*inputShape[2];return[inputShape[0],height,width,inputShape[3]]}}call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs),inputShape=input2.shape;if(this.dataFormat==="channelsFirst"){input2=transpose(input2,[0,2,3,1]);let height=this.size[0]*inputShape[2],width=this.size[1]*inputShape[3],resized=this.interpolation==="nearest"?input2.resizeNearestNeighbor([height,width]):input2.resizeBilinear([height,width]);return transpose(resized,[0,3,1,2])}else{let height=this.size[0]*inputShape[1],width=this.size[1]*inputShape[2];return this.interpolation==="nearest"?input2.resizeNearestNeighbor([height,width]):input2.resizeBilinear([height,width])}})}getConfig(){let config3={size:this.size,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config3,baseConfig),config3}};UpSampling2D.className="UpSampling2D";serialization_exports.registerClass(UpSampling2D);function depthwiseConv2d4(x,depthwiseKernel,strides=[1,1],padding="valid",dataFormat,dilationRate){return tidy(()=>{dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat);let y=preprocessConv2DInput(x,dataFormat);if(x.rank!==4)throw new ValueError(`Input for depthwiseConv2d is required to be 4-D, but is instead ${x.rank}-D`);if(depthwiseKernel.rank!==4)throw new ValueError(`depthwiseKernel is required to be 4-D, but is instead ${depthwiseKernel.rank}-D`);return y=depthwiseConv2d(y,depthwiseKernel,strides,padding==="same"?"same":"valid","NHWC",dilationRate),dataFormat==="channelsFirst"&&(y=transpose(y,[0,3,1,2])),y})}var DepthwiseConv2D=class extends BaseConv{constructor(args){super(2,args);this.depthwiseKernel=null,this.depthMultiplier=args.depthMultiplier==null?1:args.depthMultiplier,this.depthwiseInitializer=getInitializer(args.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=getConstraint(args.depthwiseConstraint),this.depthwiseRegularizer=getRegularizer(args.depthwiseRegularizer)}build(inputShape){if(inputShape=getExactlyOneShape(inputShape),inputShape.length<4)throw new ValueError(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(inputShape)}.`);let channelAxis=this.dataFormat==="channelsFirst"?1:3;if(inputShape[channelAxis]==null||inputShape[channelAxis]<0)throw new ValueError(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${inputShape[channelAxis]}).`);let inputDim=inputShape[channelAxis],depthwiseKernelShape=[this.kernelSize[0],this.kernelSize[1],inputDim,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",depthwiseKernelShape,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[inputDim*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(inputs,kwargs){return tidy(()=>{inputs=getExactlyOneTensor(inputs);let outputs=depthwiseConv2d4(inputs,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(outputs=biasAdd(outputs,this.bias.read(),this.dataFormat)),this.activation!=null&&(outputs=this.activation.apply(outputs)),outputs})}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let rows=this.dataFormat==="channelsFirst"?inputShape[2]:inputShape[1],cols=this.dataFormat==="channelsFirst"?inputShape[3]:inputShape[2],outFilters=this.dataFormat==="channelsFirst"?inputShape[1]*this.depthMultiplier:inputShape[3]*this.depthMultiplier,outRows=convOutputLength(rows,this.kernelSize[0],this.padding,this.strides[0]),outCols=convOutputLength(cols,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[inputShape[0],outFilters,outRows,outCols]:[inputShape[0],outRows,outCols,outFilters]}getConfig(){let config3=super.getConfig();return config3.depthMultiplier=this.depthMultiplier,config3.depthwiseInitializer=serializeInitializer(this.depthwiseInitializer),config3.depthwiseRegularizer=serializeRegularizer(this.depthwiseRegularizer),config3.depthwiseConstraint=serializeConstraint(this.depthwiseRegularizer),config3}};DepthwiseConv2D.className="DepthwiseConv2D";serialization_exports.registerClass(DepthwiseConv2D);function standardizeArgs(inputs,initialState,constants,numConstants){if(Array.isArray(inputs)){if(initialState!=null||constants!=null)throw new ValueError("When inputs is an array, neither initialState or constants should be provided");numConstants!=null&&(constants=inputs.slice(inputs.length-numConstants,inputs.length),inputs=inputs.slice(0,inputs.length-numConstants)),inputs.length>1&&(initialState=inputs.slice(1,inputs.length)),inputs=inputs[0]}function toListOrNull(x){return x==null||Array.isArray(x)?x:[x]}return initialState=toListOrNull(initialState),constants=toListOrNull(constants),{inputs,initialState,constants}}function rnn(stepFunction,inputs,initialStates,goBackwards=!1,mask,constants,unroll=!1,needPerStepOutputs=!1){return tidy(()=>{let ndim=inputs.shape.length;if(ndim<3)throw new ValueError(`Input should be at least 3D, but is ${ndim}D.`);let axes=[1,0].concat(range5(2,ndim));if(inputs=transpose(inputs,axes),constants!=null)throw new NotImplementedError("The rnn() functoin of the deeplearn.js backend does not support constants yet.");unroll&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),mask!=null&&(mask=mask.asType("bool").asType("float32"),mask.rank===ndim-1&&(mask=expandDims(mask,-1)),mask=transpose(mask,axes)),goBackwards&&(inputs=reverse(inputs,0),mask!=null&&(mask=reverse(mask,0)));let perStepOutputs=[],lastOutput,states=initialStates,timeSteps=inputs.shape[0],perStepInputs=unstack(inputs),perStepMasks;mask!=null&&(perStepMasks=unstack(mask));for(let t=0;tstepFunction(currentInput,states));if(mask==null)lastOutput=stepOutputs[0],states=stepOutputs[1];else{let maskedOutputs=tidy(()=>{let stepMask=perStepMasks[t],negStepMask=onesLike(stepMask).sub(stepMask),output=stepOutputs[0].mul(stepMask).add(states[0].mul(negStepMask)),newStates=states.map((state,i)=>stepOutputs[1][i].mul(stepMask).add(state.mul(negStepMask)));return{output,newStates}});lastOutput=maskedOutputs.output,states=maskedOutputs.newStates}needPerStepOutputs&&perStepOutputs.push(lastOutput)}let outputs;return needPerStepOutputs&&(outputs=stack(perStepOutputs,1)),[lastOutput,outputs,states]})}var RNN=class extends Layer{constructor(args){super(args);let cell;if(args.cell==null)throw new ValueError("cell property is missing for the constructor of RNN.");if(Array.isArray(args.cell)?cell=new StackedRNNCells({cells:args.cell}):cell=args.cell,cell.stateSize==null)throw new ValueError("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=cell,this.returnSequences=args.returnSequences==null?!1:args.returnSequences,this.returnState=args.returnState==null?!1:args.returnState,this.goBackwards=args.goBackwards==null?!1:args.goBackwards,this._stateful=args.stateful==null?!1:args.stateful,this.unroll=args.unroll==null?!1:args.unroll,this.supportsMasking=!0,this.inputSpec=[new InputSpec({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let numStates=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return range5(0,numStates).map(x=>null)}else return this.states_}setStates(states){this.states_=states}computeOutputShape(inputShape){isArrayOfShapes(inputShape)&&(inputShape=inputShape[0]),inputShape=inputShape;let stateSize=this.cell.stateSize;Array.isArray(stateSize)||(stateSize=[stateSize]);let outputDim=stateSize[0],outputShape;if(this.returnSequences?outputShape=[inputShape[0],inputShape[1],outputDim]:outputShape=[inputShape[0],outputDim],this.returnState){let stateShape=[];for(let dim of stateSize)stateShape.push([inputShape[0],dim]);return[outputShape].concat(stateShape)}else return outputShape}computeMask(inputs,mask){return tidy(()=>{Array.isArray(mask)&&(mask=mask[0]);let outputMask=this.returnSequences?mask:null;if(this.returnState){let stateMask=this.states.map(s=>null);return[outputMask].concat(stateMask)}else return outputMask})}get states(){if(this.states_==null){let numStates=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,output=[];for(let i=0;ispec.shape[spec.shape.length-1]),stateSize))throw new ValueError(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=stateSize.map(dim=>new InputSpec({shape:[null,dim]}));this.stateful&&this.resetStates()}resetStates(states,training=!1){tidy(()=>{if(!this.stateful)throw new AttributeError("Cannot call resetStates() on an RNN Layer that is not stateful.");let batchSize=this.inputSpec[0].shape[0];if(batchSize==null)throw new ValueError("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(dim=>zeros([batchSize,dim])):this.states_=[zeros([batchSize,this.cell.stateSize])];else if(states==null)dispose(this.states_),this.keptStates!=null&&(dispose(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(dim=>zeros([batchSize,dim])):this.states_[0]=zeros([batchSize,this.cell.stateSize]);else{if(Array.isArray(states)||(states=[states]),states.length!==this.states_.length)throw new ValueError(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${states.length} state value(s). Input received: ${states}`);training===!0?this.keptStates.push(this.states_.slice()):dispose(this.states_);for(let index=0;indexkeep(state.clone()))})}apply(inputs,kwargs){let initialState=kwargs==null?null:kwargs.initialState,constants=kwargs==null?null:kwargs.constants;kwargs==null&&(kwargs={});let standardized=standardizeArgs(inputs,initialState,constants,this.numConstants);inputs=standardized.inputs,initialState=standardized.initialState,constants=standardized.constants;let additionalInputs=[],additionalSpecs=[];if(initialState!=null){kwargs.initialState=initialState,additionalInputs=additionalInputs.concat(initialState),this.stateSpec=[];for(let state of initialState)this.stateSpec.push(new InputSpec({shape:state.shape}));additionalSpecs=additionalSpecs.concat(this.stateSpec)}if(constants!=null&&(kwargs.constants=constants,additionalInputs=additionalInputs.concat(constants),this.numConstants=constants.length),additionalInputs[0]instanceof SymbolicTensor){let fullInput=[inputs].concat(additionalInputs),fullInputSpec=this.inputSpec.concat(additionalSpecs),originalInputSpec=this.inputSpec;this.inputSpec=fullInputSpec;let output=super.apply(fullInput,kwargs);return this.inputSpec=originalInputSpec,output}else return super.apply(inputs,kwargs)}call(inputs,kwargs){return tidy(()=>{let mask=kwargs==null?null:kwargs.mask,training=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;inputs=getExactlyOneTensor(inputs),initialState==null&&(this.stateful?initialState=this.states_:initialState=this.getInitialState(inputs));let numStates=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(initialState.length!==numStates)throw new ValueError(`RNN Layer has ${numStates} state(s) but was passed ${initialState.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let cellCallKwargs={training},rnnOutputs=rnn((inputs2,states2)=>{let outputs2=this.cell.call([inputs2].concat(states2),cellCallKwargs);return[outputs2[0],outputs2.slice(1)]},inputs,initialState,this.goBackwards,mask,null,this.unroll,this.returnSequences),lastOutput=rnnOutputs[0],outputs=rnnOutputs[1],states=rnnOutputs[2];this.stateful&&this.resetStates(states,training);let output=this.returnSequences?outputs:lastOutput;return this.returnState?[output].concat(states):output})}getInitialState(inputs){return tidy(()=>{let initialState=zeros(inputs.shape);return initialState=sum2(initialState,[1,2]),initialState=expandDims5(initialState),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(dim=>dim>1?tile5(initialState,[1,dim]):initialState):this.cell.stateSize>1?[tile5(initialState,[1,this.cell.stateSize])]:[initialState]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(value){super.setFastWeightInitDuringBuild(value),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(value)}getConfig(){let baseConfig=super.getConfig(),config3={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(config3.numConstants=this.numConstants);let cellConfig=this.cell.getConfig();return this.getClassName()===RNN.className&&(config3.cell={className:this.cell.getClassName(),config:cellConfig}),Object.assign({},cellConfig,baseConfig,config3)}static fromConfig(cls,config3,customObjects={}){let cellConfig=config3.cell,cell=deserialize(cellConfig,customObjects);return new cls(Object.assign(config3,{cell}))}};RNN.className="RNN";serialization_exports.registerClass(RNN);var RNNCell=class extends Layer{},SimpleRNNCell=class extends RNNCell{constructor(args){super(args);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=args.units,assertPositiveInteger(this.units,"units"),this.activation=getActivation(args.activation==null?this.DEFAULT_ACTIVATION:args.activation),this.useBias=args.useBias==null?!0:args.useBias,this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=getInitializer(args.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=getRegularizer(args.kernelRegularizer),this.recurrentRegularizer=getRegularizer(args.recurrentRegularizer),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.kernelConstraint=getConstraint(args.kernelConstraint),this.recurrentConstraint=getConstraint(args.recurrentConstraint),this.biasConstraint=getConstraint(args.biasConstraint),this.dropout=min5([1,max5([0,args.dropout==null?0:args.dropout])]),this.recurrentDropout=min5([1,max5([0,args.recurrentDropout==null?0:args.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(inputShape){inputShape=getExactlyOneShape(inputShape),this.kernel=this.addWeight("kernel",[inputShape[inputShape.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(inputs,kwargs){return tidy(()=>{if(inputs=inputs,inputs.length!==2)throw new ValueError(`SimpleRNNCell expects 2 input Tensors, got ${inputs.length}.`);let prevOutput=inputs[1];inputs=inputs[0];let training=kwargs.training==null?!1:kwargs.training;0onesLike(inputs),rate:this.dropout,training})),0onesLike(prevOutput),rate:this.recurrentDropout,training}));let h,dpMask=this.dropoutMask,recDpMask=this.recurrentDropoutMask;dpMask!=null?h=dot2(mul(inputs,dpMask),this.kernel.read()):h=dot2(inputs,this.kernel.read()),this.bias!=null&&(h=biasAdd(h,this.bias.read())),recDpMask!=null&&(prevOutput=mul(prevOutput,recDpMask));let output=add2(h,dot2(prevOutput,this.recurrentKernel.read()));return this.activation!=null&&(output=this.activation.apply(output)),[output,output]})}getConfig(){let baseConfig=super.getConfig(),config3={units:this.units,activation:serializeActivation(this.activation),useBias:this.useBias,kernelInitializer:serializeInitializer(this.kernelInitializer),recurrentInitializer:serializeInitializer(this.recurrentInitializer),biasInitializer:serializeInitializer(this.biasInitializer),kernelRegularizer:serializeRegularizer(this.kernelRegularizer),recurrentRegularizer:serializeRegularizer(this.recurrentRegularizer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),kernelConstraint:serializeConstraint(this.kernelConstraint),recurrentConstraint:serializeConstraint(this.recurrentConstraint),biasConstraint:serializeConstraint(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},baseConfig,config3)}};SimpleRNNCell.className="SimpleRNNCell";serialization_exports.registerClass(SimpleRNNCell);var SimpleRNN=class extends RNN{constructor(args){args.cell=new SimpleRNNCell(args),super(args)}call(inputs,kwargs){return tidy(()=>{this.cell.dropoutMask!=null&&(dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let mask=kwargs==null?null:kwargs.mask,training=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;return super.call(inputs,{mask,training,initialState})})}static fromConfig(cls,config3){return new cls(config3)}};SimpleRNN.className="SimpleRNN";serialization_exports.registerClass(SimpleRNN);var GRUCell=class extends RNNCell{constructor(args){super(args);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",args.resetAfter)throw new ValueError("GRUCell does not support reset_after parameter set to true.");this.units=args.units,assertPositiveInteger(this.units,"units"),this.activation=getActivation(args.activation===void 0?this.DEFAULT_ACTIVATION:args.activation),this.recurrentActivation=getActivation(args.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:args.recurrentActivation),this.useBias=args.useBias==null?!0:args.useBias,this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=getInitializer(args.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=getRegularizer(args.kernelRegularizer),this.recurrentRegularizer=getRegularizer(args.recurrentRegularizer),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.kernelConstraint=getConstraint(args.kernelConstraint),this.recurrentConstraint=getConstraint(args.recurrentConstraint),this.biasConstraint=getConstraint(args.biasConstraint),this.dropout=min5([1,max5([0,args.dropout==null?0:args.dropout])]),this.recurrentDropout=min5([1,max5([0,args.recurrentDropout==null?0:args.recurrentDropout])]),this.implementation=args.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(inputShape){inputShape=getExactlyOneShape(inputShape);let inputDim=inputShape[inputShape.length-1];this.kernel=this.addWeight("kernel",[inputDim,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(inputs,kwargs){return tidy(()=>{if(inputs=inputs,inputs.length!==2)throw new ValueError(`GRUCell expects 2 input Tensors (inputs, h, c), got ${inputs.length}.`);let training=kwargs.training==null?!1:kwargs.training,hTMinus1=inputs[1];inputs=inputs[0],0onesLike(inputs),rate:this.dropout,training,count:3})),0onesLike(hTMinus1),rate:this.recurrentDropout,training,count:3}));let dpMask=this.dropoutMask,recDpMask=this.recurrentDropoutMask,z,r,hh;0{this.cell.dropoutMask!=null&&(dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let mask=kwargs==null?null:kwargs.mask,training=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;return super.call(inputs,{mask,training,initialState})})}static fromConfig(cls,config3){return config3.implmentation===0&&(config3.implementation=1),new cls(config3)}};GRU.className="GRU";serialization_exports.registerClass(GRU);var LSTMCell=class extends RNNCell{constructor(args){super(args);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=args.units,assertPositiveInteger(this.units,"units"),this.activation=getActivation(args.activation===void 0?this.DEFAULT_ACTIVATION:args.activation),this.recurrentActivation=getActivation(args.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:args.recurrentActivation),this.useBias=args.useBias==null?!0:args.useBias,this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=getInitializer(args.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=args.unitForgetBias,this.kernelRegularizer=getRegularizer(args.kernelRegularizer),this.recurrentRegularizer=getRegularizer(args.recurrentRegularizer),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.kernelConstraint=getConstraint(args.kernelConstraint),this.recurrentConstraint=getConstraint(args.recurrentConstraint),this.biasConstraint=getConstraint(args.biasConstraint),this.dropout=min5([1,max5([0,args.dropout==null?0:args.dropout])]),this.recurrentDropout=min5([1,max5([0,args.recurrentDropout==null?0:args.recurrentDropout])]),this.implementation=args.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(inputShape){var _a;inputShape=getExactlyOneShape(inputShape);let inputDim=inputShape[inputShape.length-1];this.kernel=this.addWeight("kernel",[inputDim,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let biasInitializer;if(this.useBias){if(this.unitForgetBias){let capturedBiasInit=this.biasInitializer,capturedUnits=this.units;biasInitializer=new(_a=class extends Initializer{apply(shape,dtype){let bI=capturedBiasInit.apply([capturedUnits]),bF=new Ones().apply([capturedUnits]),bCAndH=capturedBiasInit.apply([capturedUnits*2]);return concatAlongFirstAxis(concatAlongFirstAxis(bI,bF),bCAndH)}},_a.className="CustomInit",_a)}else biasInitializer=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,biasInitializer,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(inputs,kwargs){return tidy(()=>{let training=kwargs.training==null?!1:kwargs.training;if(inputs=inputs,inputs.length!==3)throw new ValueError(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${inputs.length}.`);let hTMinus1=inputs[1],cTMinus1=inputs[2];inputs=inputs[0],0onesLike(inputs),rate:this.dropout,training,count:4})),0onesLike(hTMinus1),rate:this.recurrentDropout,training,count:4}));let dpMask=this.dropoutMask,recDpMask=this.recurrentDropoutMask,i,f,c,o;0{this.cell.dropoutMask!=null&&(dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let mask=kwargs==null?null:kwargs.mask,training=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;return super.call(inputs,{mask,training,initialState})})}static fromConfig(cls,config3){return config3.implmentation===0&&(config3.implementation=1),new cls(config3)}};LSTM.className="LSTM";serialization_exports.registerClass(LSTM);var StackedRNNCells=class extends RNNCell{constructor(args){super(args);this.cells=args.cells}get stateSize(){let stateSize=[];for(let cell of this.cells.slice().reverse())Array.isArray(cell.stateSize)?stateSize.push(...cell.stateSize):stateSize.push(cell.stateSize);return stateSize}call(inputs,kwargs){return tidy(()=>{inputs=inputs;let states=inputs.slice(1),nestedStates=[];for(let cell of this.cells.slice().reverse())Array.isArray(cell.stateSize)?nestedStates.push(states.splice(0,cell.stateSize.length)):nestedStates.push(states.splice(0,1));nestedStates.reverse();let newNestedStates=[],callInputs;for(let i=0;i{nameScope(`RNNCell_${i}`,()=>{cell.build(inputShape),Array.isArray(cell.stateSize)?outputDim=cell.stateSize[0]:outputDim=cell.stateSize,inputShape=[inputShape[0],outputDim]})}),this.built=!0}getConfig(){let baseConfig=super.getConfig(),getCellConfig=cell=>({className:cell.getClassName(),config:cell.getConfig()}),config3={cells:this.cells.map(getCellConfig)};return Object.assign({},baseConfig,config3)}static fromConfig(cls,config3,customObjects={}){let cells=[];for(let cellConfig of config3.cells)cells.push(deserialize(cellConfig,customObjects));return new cls({cells})}get trainableWeights(){if(!this.trainable)return[];let weights=[];for(let cell of this.cells)weights.push(...cell.trainableWeights);return weights}get nonTrainableWeights(){let weights=[];for(let cell of this.cells)weights.push(...cell.nonTrainableWeights);if(!this.trainable){let trainableWeights=[];for(let cell of this.cells)trainableWeights.push(...cell.trainableWeights);return trainableWeights.concat(weights)}return weights}getWeights(){let weights=[];for(let cell of this.cells)weights.push(...cell.weights);return batchGetValue(weights)}setWeights(weights){let tuples=[];for(let cell of this.cells){let numParams=cell.weights.length,inputWeights=weights.splice(numParams);for(let i=0;idropout2(ones4(),rate),createMask=()=>inTrainPhase(droppedInputs,ones4,training);return!count2||count2<=1?keep(createMask().clone()):Array(count2).fill(void 0).map(createMask).map(m=>keep(m.clone()))}var __rest=function(s,e){var t={};for(var p2 in s)Object.prototype.hasOwnProperty.call(s,p2)&&e.indexOf(p2)<0&&(t[p2]=s[p2]);if(s!=null&&typeof Object.getOwnPropertySymbols=="function")for(var i=0,p2=Object.getOwnPropertySymbols(s);i{if(this.cell.dropoutMask!=null&&(dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),kwargs&&kwargs.constants)throw new ValueError("ConvRNN2D cell does not support constants");let mask=kwargs==null?null:kwargs.mask,training=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;return super.call(inputs,{mask,training,initialState})})}computeOutputShape(inputShape){let outShape=this.computeSingleOutputShape(inputShape);return this.returnSequences||(outShape=[outShape[0],...outShape.slice(2)]),this.returnState&&(outShape=[outShape,...Array(2).fill([inputShape[0],...outShape.slice(-3)])]),outShape}getInitialState(inputs){return tidy(()=>{let{stateSize}=this.cell,inputShape=inputs.shape,outputShape=this.computeSingleOutputShape(inputShape),stateShape=[outputShape[0],...outputShape.slice(2)],initialState=zeros(stateShape);return Array.isArray(stateSize)?Array(stateSize.length).fill(initialState):[initialState]})}resetStates(states,training=!1){tidy(()=>{if(!this.stateful)throw new AttributeError("Cannot call resetStates() on an RNN Layer that is not stateful.");let inputShape=this.inputSpec[0].shape,outputShape=this.computeSingleOutputShape(inputShape),stateShape=[outputShape[0],...outputShape.slice(2)];if(inputShape[0]==null)throw new ValueError("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>zeros(stateShape)):this.states_=[zeros(stateShape)];else if(states==null)dispose(this.states_),this.keptStates!=null&&(dispose(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>zeros(stateShape)):this.states_[0]=zeros(stateShape);else{if(Array.isArray(states)||(states=[states]),states.length!==this.states_.length)throw new ValueError(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${states.length} state value(s). Input received: ${states}`);training?this.keptStates.push(this.states_.slice()):dispose(this.states_);for(let index=0;indexkeep(state.clone()))})}computeSingleOutputShape(inputShape){let{dataFormat,filters,kernelSize,padding,strides,dilationRate}=this.cell,isChannelsFirst=dataFormat==="channelsFirst",h=inputShape[isChannelsFirst?3:2],w=inputShape[isChannelsFirst?4:3],hOut=convOutputLength(h,kernelSize[0],padding,strides[0],dilationRate[0]),wOut=convOutputLength(w,kernelSize[1],padding,strides[1],dilationRate[1]);return[...inputShape.slice(0,2),...isChannelsFirst?[filters,hOut,wOut]:[hOut,wOut,filters]]}};ConvRNN2D.className="ConvRNN2D";var ConvLSTM2DCell=class extends LSTMCell{constructor(args){let{filters,kernelSize,strides,padding,dataFormat,dilationRate}=args;super(Object.assign({},args,{units:filters}));this.filters=filters,assertPositiveInteger(this.filters,"filters"),this.kernelSize=normalizeArray(kernelSize,2,"kernelSize"),this.kernelSize.forEach(size=>assertPositiveInteger(size,"kernelSize")),this.strides=normalizeArray(strides||1,2,"strides"),this.strides.forEach(stride=>assertPositiveInteger(stride,"strides")),this.padding=padding||"valid",checkPaddingMode(this.padding),this.dataFormat=dataFormat||"channelsLast",checkDataFormat(this.dataFormat),this.dilationRate=normalizeArray(dilationRate||1,2,"dilationRate"),this.dilationRate.forEach(rate=>assertPositiveInteger(rate,"dilationRate"))}build(inputShape){var _a;inputShape=getExactlyOneShape(inputShape);let channelAxis=this.dataFormat==="channelsFirst"?1:inputShape.length-1;if(inputShape[channelAxis]==null)throw new ValueError(`The channel dimension of the input should be defined. Found ${inputShape[channelAxis]}`);let inputDim=inputShape[channelAxis],numOfKernels=4,kernelShape=this.kernelSize.concat([inputDim,this.filters*numOfKernels]);this.kernel=this.addWeight("kernel",kernelShape,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let recurrentKernelShape=this.kernelSize.concat([this.filters,this.filters*numOfKernels]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",recurrentKernelShape,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let biasInitializer;if(this.unitForgetBias){let init2=this.biasInitializer,filters=this.filters;biasInitializer=new(_a=class extends Initializer{apply(shape,dtype){let biasI=init2.apply([filters]),biasF=ones2([filters]),biasCAndO=init2.apply([filters*2]);return concatenate([biasI,biasF,biasCAndO])}},_a.className="CustomInit",_a)}else biasInitializer=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*numOfKernels],null,biasInitializer,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(inputs,kwargs){return tidy(()=>{if(inputs.length!==3)throw new ValueError(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${inputs.length}.`);let training=kwargs.training||!1,x=inputs[0],hTMinus1=inputs[1],cTMinus1=inputs[2],numOfKernels=4;0onesLike(x),rate:this.dropout,training,count:numOfKernels}));let dropoutMask=this.dropoutMask,applyDropout=(x2,mask,index)=>!mask||!mask[index]?x2:mul(mask[index],x2),xI=applyDropout(x,dropoutMask,0),xF=applyDropout(x,dropoutMask,1),xC=applyDropout(x,dropoutMask,2),xO=applyDropout(x,dropoutMask,3);0onesLike(hTMinus1),rate:this.recurrentDropout,training,count:numOfKernels}));let recDropoutMask=this.recurrentDropoutMask,hI=applyDropout(hTMinus1,recDropoutMask,0),hF=applyDropout(hTMinus1,recDropoutMask,1),hC=applyDropout(hTMinus1,recDropoutMask,2),hO=applyDropout(hTMinus1,recDropoutMask,3),kernelChannelAxis=3,[kernelI,kernelF,kernelC,kernelO]=split(this.kernel.read(),numOfKernels,kernelChannelAxis),[biasI,biasF,biasC,biasO]=this.useBias?split(this.bias.read(),numOfKernels):[null,null,null,null];xI=this.inputConv(xI,kernelI,biasI,this.padding),xF=this.inputConv(xF,kernelF,biasF,this.padding),xC=this.inputConv(xC,kernelC,biasC,this.padding),xO=this.inputConv(xO,kernelO,biasO,this.padding);let[recKernelI,recKernelF,recKernelC,recKernelO]=split(this.recurrentKernel.read(),numOfKernels,kernelChannelAxis);hI=this.recurrentConv(hI,recKernelI),hF=this.recurrentConv(hF,recKernelF),hC=this.recurrentConv(hC,recKernelC),hO=this.recurrentConv(hO,recKernelO);let i=this.recurrentActivation.apply(add2(xI,hI)),f=this.recurrentActivation.apply(add2(xF,hF)),c=add2(mul(f,cTMinus1),mul(i,this.activation.apply(add2(xC,hC)))),h=mul(this.recurrentActivation.apply(add2(xO,hO)),this.activation.apply(c));return[h,h,c]})}getConfig(){let _a=super.getConfig(),{units:_}=_a,baseConfig=__rest(_a,["units"]),config3={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},baseConfig,config3)}inputConv(x,w,b,padding){let out=conv2d(x,w,this.strides,padding||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return b?biasAdd(out,b,this.dataFormat):out}recurrentConv(x,w){return conv2d(x,w,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};ConvLSTM2DCell.className="ConvLSTM2DCell";serialization_exports.registerClass(ConvLSTM2DCell);var ConvLSTM2D=class extends ConvRNN2D{constructor(args){let cell=new ConvLSTM2DCell(args);super(Object.assign({},args,{cell}))}static fromConfig(cls,config3){return new cls(config3)}};ConvLSTM2D.className="ConvLSTM2D";serialization_exports.registerClass(ConvLSTM2D);var Dropout=class extends Layer{constructor(args){super(args);this.rate=Math.max(Math.min(args.rate,1),0),this.noiseShape=args.noiseShape,this.seed=args.seed,this.supportsMasking=!0}getNoiseShape(input2){if(this.noiseShape==null)return this.noiseShape;let inputShape=input2.shape,noiseShape=[];for(let i=0;i{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);if(0dropout2(input2,this.rate,noiseShape,this.seed),()=>input2,training)}return inputs})}getConfig(){let config3={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},baseConfig=super.getConfig();return Object.assign(config3,baseConfig),config3}dispose(){return super.dispose()}};Dropout.className="Dropout";serialization_exports.registerClass(Dropout);var SpatialDropout1D=class extends Dropout{constructor(args){super(args);this.inputSpec=[{ndim:3}]}getNoiseShape(input2){let inputShape=input2.shape;return[inputShape[0],1,inputShape[2]]}};SpatialDropout1D.className="SpatialDropout1D";serialization_exports.registerClass(SpatialDropout1D);var Dense=class extends Layer{constructor(args){super(args);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",args.batchInputShape==null&&args.inputShape==null&&args.inputDim!=null){let batchSize=null;args.batchSize!=null&&(batchSize=args.batchSize),this.batchInputShape=[batchSize,args.inputDim]}this.units=args.units,assertPositiveInteger(this.units,"units"),this.activation=getActivation(args.activation),args.useBias!=null&&(this.useBias=args.useBias),this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=getConstraint(args.kernelConstraint),this.biasConstraint=getConstraint(args.biasConstraint),this.kernelRegularizer=getRegularizer(args.kernelRegularizer),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.activityRegularizer=getRegularizer(args.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(inputShape){inputShape=getExactlyOneShape(inputShape);let inputLastDim=inputShape[inputShape.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[inputLastDim,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:inputLastDim}}],this.built=!0}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let outputShape=inputShape.slice();return outputShape[outputShape.length-1]=this.units,outputShape}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs),fusedActivationName=mapActivationToFusedKernel(this.activation.getClassName()),output;return fusedActivationName!=null?output=dot2(input2,this.kernel.read(),fusedActivationName,this.bias?this.bias.read():null):(output=dot2(input2,this.kernel.read()),this.bias!=null&&(output=biasAdd(output,this.bias.read())),this.activation!=null&&(output=this.activation.apply(output))),output})}getConfig(){let config3={units:this.units,activation:serializeActivation(this.activation),useBias:this.useBias,kernelInitializer:serializeInitializer(this.kernelInitializer),biasInitializer:serializeInitializer(this.biasInitializer),kernelRegularizer:serializeRegularizer(this.kernelRegularizer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),kernelConstraint:serializeConstraint(this.kernelConstraint),biasConstraint:serializeConstraint(this.biasConstraint)},baseConfig=super.getConfig();return Object.assign(config3,baseConfig),config3}};Dense.className="Dense";serialization_exports.registerClass(Dense);var Flatten=class extends Layer{constructor(args){args=args||{},super(args),this.inputSpec=[{minNDim:3}],this.dataFormat=args.dataFormat}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);for(let dim of inputShape.slice(1))if(dim==null)throw new ValueError(`The shape of the input to "Flatten" is not fully defined (got ${inputShape.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[inputShape[0],arrayProd(inputShape,1)]}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);if(this.dataFormat==="channelsFirst"&&input2.rank>1){let permutation=[0];for(let i=2;i{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);return this.activation.apply(input2)})}getConfig(){let config3={activation:serializeActivation(this.activation)},baseConfig=super.getConfig();return Object.assign(config3,baseConfig),config3}};Activation2.className="Activation";serialization_exports.registerClass(Activation2);var RepeatVector=class extends Layer{constructor(args){super(args);this.n=args.n,this.inputSpec=[{ndim:2}]}computeOutputShape(inputShape){return[inputShape[0],this.n,inputShape[1]]}call(inputs,kwargs){return tidy(()=>(inputs=getExactlyOneTensor(inputs),repeat(inputs,this.n)))}getConfig(){let config3={n:this.n},baseConfig=super.getConfig();return Object.assign(config3,baseConfig),config3}};RepeatVector.className="RepeatVector";serialization_exports.registerClass(RepeatVector);var Reshape2=class extends Layer{constructor(args){super(args);this.targetShape=args.targetShape;for(let i=0;i{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs),inputShape=input2.shape,outputShape=inputShape.slice(0,1).concat(this.fixUnknownDimension(inputShape.slice(1),this.targetShape));return input2.reshape(outputShape)})}getConfig(){let config3={targetShape:this.targetShape},baseConfig=super.getConfig();return Object.assign(config3,baseConfig),config3}};Reshape2.className="Reshape";serialization_exports.registerClass(Reshape2);var Permute=class extends Layer{constructor(args){super(args);if(args.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(args.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${args.dims} instead.`);let expectedSortedIndices=range5(1,args.dims.length+1);if(!util_exports.arraysEqual(args.dims.slice().sort(),expectedSortedIndices))throw new Error("Invalid permutation `dims`: "+JSON.stringify(args.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=args.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new InputSpec({ndim:this.dims.length+1})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let outputShape=inputShape.slice();return this.dims.forEach((dim,i)=>{outputShape[i+1]=inputShape[dim]}),outputShape}call(inputs,kwargs){return transpose(getExactlyOneTensor(inputs),this.dimsIncludingBatch)}getConfig(){let config3={dims:this.dims},baseConfig=super.getConfig();return Object.assign(config3,baseConfig),config3}};Permute.className="Permute";serialization_exports.registerClass(Permute);var Masking=class extends Layer{constructor(args){super(args==null?{}:args);this.supportsMasking=!0,args!=null?this.maskValue=args.maskValue==null?0:args.maskValue:this.maskValue=0}computeOutputShape(inputShape){return inputShape}getConfig(){let baseConfig=super.getConfig(),config3={maskValue:this.maskValue};return Object.assign(config3,baseConfig),config3}computeMask(inputs,mask){let input2=getExactlyOneTensor(inputs),axis=-1;return any(notEqual(input2,this.maskValue),axis)}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs),axis=-1,keepDims=!0,booleanMask=any(notEqual(input2,this.maskValue),axis,keepDims);return input2.mul(booleanMask.asType(input2.dtype))})}};Masking.className="Masking";serialization_exports.registerClass(Masking);var Embedding=class extends Layer{constructor(args){super(args);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",args.batchInputShape==null&&args.inputShape==null){let batchSize=null;args.batchSize!=null&&(batchSize=args.batchSize),args.inputLength==null?this.batchInputShape=[batchSize,null]:this.batchInputShape=[batchSize].concat(toList(args.inputLength))}this.inputDim=args.inputDim,assertPositiveInteger(this.inputDim,"inputDim"),this.outputDim=args.outputDim,assertPositiveInteger(this.outputDim,"outputDim"),this.embeddingsInitializer=getInitializer(args.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=getRegularizer(args.embeddingsRegularizer),this.activityRegularizer=getRegularizer(args.activityRegularizer),this.embeddingsConstraint=getConstraint(args.embeddingsConstraint),this.maskZero=args.maskZero,this.supportsMasking=args.maskZero,this.inputLength=args.inputLength}build(inputShape){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(inputShape){}computeMask(inputs,mask){return tidy(()=>this.maskZero?(inputs=getExactlyOneTensor(inputs),notEqual(inputs,zerosLike(inputs))):null)}computeOutputShape(inputShape){if(inputShape=getExactlyOneShape(inputShape),this.inputLength==null)return[...inputShape,this.outputDim];let inLens=toList(this.inputLength);if(inLens.length!==inputShape.length-1)throw new ValueError(`"inputLength" is ${this.inputLength}, but received input shape has shape ${inputShape}`);{let i=0;for(let k=0;k{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);return input2.dtype!=="int32"&&(input2=cast5(input2,"int32")),gather2(this.embeddings.read(),input2.as1D()).reshape(getExactlyOneShape(this.computeOutputShape(input2.shape)))})}getConfig(){let config3={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:serializeInitializer(this.embeddingsInitializer),embeddingsRegularizer:serializeRegularizer(this.embeddingsRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),embeddingsConstraint:serializeConstraint(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},baseConfig=super.getConfig();return Object.assign(config3,baseConfig),config3}};Embedding.className="Embedding";serialization_exports.registerClass(Embedding);var Merge=class extends Layer{constructor(args){super(args||{});this.supportsMasking=!0}mergeFunction(inputs){throw new NotImplementedError}computeElementwiseOpOutputShape(shape1,shape2){if(shape1==null||shape2==null)return null;if(shape1.length1)throw new ValueError(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(inputShape)}.`);let outputShape=inputShape[0]==null?null:inputShape[0].slice(1);for(let i=1;ishape.length);inputShape.indexOf(null)===-1&&unique4(allRanks).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(inputs,kwargs){return tidy(()=>{if(inputs=inputs,this.reshapeRequired){let reshapedInputs=[],inputDims=inputs.map(input2=>input2.rank);if(inputDims.indexOf(null)===-1){let maxNDim=max5(inputDims);for(let x of inputs){let xNDim=x.rank;for(let k=0;k1){let dims=range5(1,xNDim).concat([0]);reshapedInputs.push(transpose(x,dims)),transposed=!0}else reshapedInputs.push(x)}let y=this.mergeFunction(reshapedInputs),yNDim=y.rank;if(transposed){if(yNDim==null){let yShape=y.shape,yNDim2=yShape.length,batchSize=yShape[yNDim2-1],newShape=[batchSize].concat(yShape.slice(0,yShape.length-1));y=transpose(y.reshape([-1,batchSize]),[1,0]).reshape(newShape)}else if(yNDim>1){let dims=[yNDim-1].concat(range5(0,yNDim-1));y=transpose(y,dims)}}return y}}else return this.mergeFunction(inputs)})}computeOutputShape(inputShape){inputShape=inputShape;let outputShape;inputShape[0]==null?outputShape=null:outputShape=inputShape[0].slice(1);for(let i=1;i{if(mask==null)return null;if(!Array.isArray(mask))throw new ValueError("`mask` should be an Array");if(!Array.isArray(inputs))throw new ValueError("`inputs` should be an Array");if(mask.length!==inputs.length)throw new ValueError(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${inputs.length} vs ${mask.length})`);if(mask.every(m=>m==null))return null;mask=mask.map(m=>m==null?m:expandDims(m,0));let output=mask[0];for(let i=1;i{let output=inputs[0].clone();for(let i=1;i{let output=inputs[0].clone();for(let i=1;i{let output=inputs[0].clone();for(let i=1;i{let output=inputs[0];for(let i=1;i{let output=inputs[0];for(let i=1;i1)throw new ValueError("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(inputShape))}mergeFunction(inputs){return tidy(()=>concatenate(inputs,this.axis))}computeOutputShape(inputShape){if(!(Array.isArray(inputShape)&&Array.isArray(inputShape[0])))throw new ValueError("A `Concatenate` layer should be called on a list of inputs.");let inputShapes=inputShape,outputShape=inputShapes[0].slice(),axis=this.axis<0?outputShape.length+this.axis:this.axis;for(let shape of inputShapes.slice(1)){if(outputShape[axis]==null||shape[axis]==null){outputShape[axis]=null;break}outputShape[axis]+=shape[axis]}return outputShape}computeMask(inputs,mask){if(mask==null)return null;if(!Array.isArray(mask))throw new ValueError("`mask` should be an array for Concatenate");if(!Array.isArray(inputs))throw new ValueError("`inputs` should be an array for Concatenate");if(mask.length!==inputs.length)throw new ValueError(`Mismatch in the length of mask (${mask.length}) and the legnth of inputs (${inputs.length})`);return tidy(()=>{let allNullMasks=!0;if(mask.forEach(m=>{if(m!=null){allNullMasks=!1;return}}),allNullMasks)return null;let outputMasks=[];for(let i=0;i3||y.shape.length>3)throw new NotImplementedError("batchDot is not implemented for tensors of 4D or higher rank yet");if(util_exports.assert(x.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${x.shape.length}`),util_exports.assert(x.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${y.shape.length}`),typeof axes=="number"&&(axes=[axes,axes]),x.dtype==="complex64"||y.dtype==="complex64")throw new NotImplementedError("batchDot is not implemented for complex64-type Tensors yet.");let xNDim=x.shape.length,yNDim=y.shape.length;axes==null&&(axes=[xNDim-1,yNDim-2]);let axesArray=axes;return tidy(()=>{let diff;if(xNDim>yNDim){diff=xNDim-yNDim;let diffShape=[];for(let i=0;ixNDim){diff=yNDim-xNDim;let diffShape=[];for(let i=0;i0){let idx;xNDim>yNDim?idx=xNDim+yNDim-3:idx=xNDim-1;let squeezeAxes=[];for(let i=idx;i"A `Dot` layer should be called on a list of exactly 2 inputs.");let shape1=inputShape[0],shape2=inputShape[1];if(shape1.length>3||shape2.length>3)throw new NotImplementedError("Dot layer does not support tensors of 4D or higher rank yet.");let axes=this.interpretAxes(shape1,shape2);if(shape1[axes[0]]!==shape2[axes[1]])throw new ValueError(`Dimension incompatibility: ${shape1[axes[0]]} !== ${shape2[axes[1]]}`)}mergeFunction(inputs){if(inputs.length!==2)throw new ValueError(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${inputs.length} input(s).`);let x1=inputs[0],x2=inputs[1],axes;return Array.isArray(this.axes)?axes=this.axes.map((axis,i)=>interpretAxis(axis,inputs[i].shape.length)):axes=[interpretAxis(this.axes,x1.shape.length),interpretAxis(this.axes,x2.shape.length)],this.normalize&&(x1=l2Normalize(x1,axes[0]),x2=l2Normalize(x2,axes[1])),batchDot(x1,x2,axes)}interpretAxes(shape1,shape2){let axes;return Array.isArray(this.axes)?axes=this.axes:axes=[interpretAxis(this.axes,shape1.length),interpretAxis(this.axes,shape2.length)],axes}computeOutputShape(inputShape){util_exports.assert(Array.isArray(inputShape)&&inputShape.length===2&&Array.isArray(inputShape[0])&&Array.isArray(inputShape[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let shape1=inputShape[0].slice(),shape2=inputShape[1].slice();if(shape1.length>3||shape2.length>3)throw new NotImplementedError("Dot layer does not support tensors of 4D or higher rank yet.");let axes=this.interpretAxes(shape1,shape2);shape1.splice(axes[0],1),shape2.splice(axes[1],1),shape2.splice(0,1);let outputShape=shape1.concat(shape2);return outputShape.length===1&&outputShape.push(1),outputShape}computeMask(inputs,mask){return null}getConfig(){let config3={axes:this.axes,normalize:this.normalize},baseConfig=super.getConfig();return Object.assign(config3,baseConfig),config3}};Dot.className="Dot";serialization_exports.registerClass(Dot);var GaussianNoise=class extends Layer{constructor(args){super(args);this.supportsMasking=!0,this.stddev=args.stddev}computeOutputShape(inputShape){return inputShape}getConfig(){let baseConfig=super.getConfig(),config3={stddev:this.stddev};return Object.assign(config3,baseConfig),config3}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);return inTrainPhase(()=>randomNormal2(input2.shape,0,this.stddev).add(input2),()=>input2,kwargs.training||!1)})}};GaussianNoise.className="GaussianNoise";serialization_exports.registerClass(GaussianNoise);var GaussianDropout=class extends Layer{constructor(args){super(args);this.supportsMasking=!0,this.rate=args.rate}computeOutputShape(inputShape){return inputShape}getConfig(){let baseConfig=super.getConfig(),config3={rate:this.rate};return Object.assign(config3,baseConfig),config3}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);return this.rate>0&&this.rate<1?inTrainPhase(()=>{let stddev=Math.sqrt(this.rate/(1-this.rate));return input2.mul(randomNormal2(input2.shape,1,stddev))},()=>input2,kwargs.training||!1):input2})}};GaussianDropout.className="GaussianDropout";serialization_exports.registerClass(GaussianDropout);var AlphaDropout=class extends Layer{constructor(args){super(args);this.supportsMasking=!0,this.rate=args.rate,this.noiseShape=args.noiseShape}_getNoiseShape(inputs){return this.noiseShape||getExactlyOneTensor(inputs).shape}computeOutputShape(inputShape){return inputShape}getConfig(){let baseConfig=super.getConfig(),config3={rate:this.rate};return Object.assign(config3,baseConfig),config3}call(inputs,kwargs){return tidy(()=>{if(this.rate<1&&this.rate>0){let noiseShape=this._getNoiseShape(inputs);return inTrainPhase(()=>{let input2=getExactlyOneTensor(inputs),alpha=1.6732632423543772,scale2=1.0507009873554805,alphaP=-alpha*scale2,keptIdx=greaterEqual(randomUniform(noiseShape),this.rate);keptIdx=cast5(keptIdx,"float32");let a=((1-this.rate)*(1+this.rate*alphaP**2))**-.5,b=-a*alphaP*this.rate;return input2.mul(keptIdx).add(keptIdx.add(-1).mul(alphaP)).mul(a).add(b)},()=>getExactlyOneTensor(inputs),kwargs.training||!1)}return inputs})}};AlphaDropout.className="AlphaDropout";serialization_exports.registerClass(AlphaDropout);function batchNormalization(x,mean4,variance,beta,gamma,epsilon3=.001){let out;if(x.rank===2)out=batchNorm2d(x,mean4,variance,beta,gamma,epsilon3);else if(x.rank===3)out=batchNorm3d(x,mean4,variance,beta,gamma,epsilon3);else if(x.rank===4)out=batchNorm4d(x,mean4,variance,beta,gamma,epsilon3);else throw new NotImplementedError(`batchNormalization is not implemented for array of rank ${x.rank} yet`);return out}function regularNormalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon3=.001){return tidy(()=>{let meanAndVariance=moments(x,reductionAxes),mean4=meanAndVariance.mean,variance=meanAndVariance.variance;return[batchNormalization(x,mean4,variance,beta,gamma,epsilon3),mean4,variance]})}function broadcastNormalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon3=.001){return tidy(()=>{let meanAndVariance=moments(x,reductionAxes),mean4=meanAndVariance.mean,variance=meanAndVariance.variance,targetShape=[];for(let axis of range5(0,x.rank))reductionAxes.indexOf(axis)!==-1?targetShape.push(1):targetShape.push(x.shape[axis]);let broadcastMean=mean4.reshape(targetShape),broadcastVariance=variance.reshape(targetShape),broadcastGamma=gamma==null?null:gamma.reshape(targetShape),broadcastBeta=beta==null?null:beta.reshape(targetShape);return[batchNormalization(x,broadcastMean,broadcastVariance,broadcastBeta,broadcastGamma,epsilon3),mean4,variance]})}function normalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon3=.001){return util_exports.arraysEqual(reductionAxes.slice().sort(),range5(0,x.rank-1))?regularNormalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon3):broadcastNormalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon3)}var BatchNormalization=class extends Layer{constructor(args){args==null&&(args={}),super(args),this.supportsMasking=!0,this.axis=args.axis==null?-1:args.axis,this.momentum=args.momentum==null?.99:args.momentum,this.epsilon=args.epsilon==null?.001:args.epsilon,this.center=args.center==null?!0:args.center,this.scale=args.scale==null?!0:args.scale,this.betaInitializer=getInitializer(args.betaInitializer||"zeros"),this.gammaInitializer=getInitializer(args.gammaInitializer||"ones"),this.movingMeanInitializer=getInitializer(args.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=getInitializer(args.movingVarianceInitializer||"ones"),this.betaConstraint=getConstraint(args.betaConstraint),this.gammaConstraint=getConstraint(args.gammaConstraint),this.betaRegularizer=getRegularizer(args.betaRegularizer),this.gammaRegularizer=getRegularizer(args.gammaRegularizer)}build(inputShape){inputShape=getExactlyOneShape(inputShape);let axis=this.axis>=0?this.axis:this.axis+inputShape.length,dim=inputShape[axis];if(dim==null)throw new ValueError(`Axis ${axis} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(inputShape)}.`);this.inputSpec=[new InputSpec({ndim:inputShape.length,axes:{[axis]:dim}})];let shape=[dim];this.scale&&(this.gamma=this.addWeight("gamma",shape,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",shape,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",shape,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",shape,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(inputs,kwargs){return tidy(()=>{let training=kwargs.training==null?!1:kwargs.training,input2=getExactlyOneTensor(inputs),inputShape=input2.shape,ndim=inputShape.length,reductionAxes=range5(0,ndim),axis=this.axis>=0?this.axis:this.axis+ndim;reductionAxes.splice(axis,1);let broadcastShape=pyListRepeat(1,ndim);broadcastShape[axis]=inputShape[axis];let sortedReductionAxes=reductionAxes.slice();sortedReductionAxes.sort();let needsBroadcasting=!util_exports.arraysEqual(sortedReductionAxes,range5(0,ndim).slice(0,ndim-1)),normalizeInference=()=>{if(needsBroadcasting){let broadcastMovingMean=this.movingMean.read().reshape(broadcastShape),broadcastMovingVariance=this.movingVariance.read().reshape(broadcastShape),broadcastBeta=this.center?this.beta.read().reshape(broadcastShape):null,broadcastGamma=this.scale?this.gamma.read().reshape(broadcastShape):null;return batchNormalization(input2,broadcastMovingMean,broadcastMovingVariance,broadcastBeta,broadcastGamma,this.epsilon)}else return batchNormalization(input2,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!training)return normalizeInference();let[normedTraining,mean4,variance]=normalizeBatchInTraining(input2,this.gamma.read(),this.beta.read(),reductionAxes,this.epsilon),doMovingAverage=(variable2,value,momentum)=>{tidy(()=>{let decay=1-momentum,origValue=variable2.read(),updateDelta=origValue.sub(value).mul(decay);variable2.write(origValue.sub(updateDelta))})};return(()=>{doMovingAverage(this.movingMean,mean4,this.momentum),doMovingAverage(this.movingVariance,variance,this.momentum)})(),normedTraining})}getConfig(){let config3={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:serializeInitializer(this.betaInitializer),gammaInitializer:serializeInitializer(this.gammaInitializer),movingMeanInitializer:serializeInitializer(this.movingMeanInitializer),movingVarianceInitializer:serializeInitializer(this.movingVarianceInitializer),betaRegularizer:serializeRegularizer(this.betaRegularizer),gammaRegularizer:serializeRegularizer(this.gammaRegularizer),betaConstraint:serializeConstraint(this.betaConstraint),gammaConstraint:serializeConstraint(this.gammaConstraint)},baseConfig=super.getConfig();return Object.assign(config3,baseConfig),config3}};BatchNormalization.className="BatchNormalization";serialization_exports.registerClass(BatchNormalization);var LayerNormalization=class extends Layer{constructor(args){if(args==null&&(args={}),super(args),this.axis=args.axis==null?-1:args.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let axis of this.axis)if(!Number.isInteger(axis))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=args.epsilon==null?.001:args.epsilon,this.center=args.center==null?!0:args.center,this.scale=args.scale==null?!0:args.scale,this.betaInitializer=getInitializer(args.betaInitializer||"zeros"),this.gammaInitializer=getInitializer(args.gammaInitializer||"ones"),this.betaRegularizer=getRegularizer(args.betaRegularizer),this.gammaRegularizer=getRegularizer(args.gammaRegularizer),this.supportsMasking=!0}build(inputShape){inputShape=getExactlyOneShape(inputShape);let nDims=inputShape.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let i=0;i=nDims)throw new Error(`Invalid axis: ${axis}`);if(this.axis.length!==unique4(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let paramShape=this.axis.map(axis=>inputShape[axis]),trainable=!0;this.scale?this.gamma=this.addWeight("gamma",paramShape,"float32",this.gammaInitializer,this.gammaRegularizer,trainable):this.gamma=null,this.center?this.beta=this.addWeight("beta",paramShape,"float32",this.betaInitializer,this.betaRegularizer,trainable):this.beta=null,this.built=!0}call(inputs,kwargs){let input2=getExactlyOneTensor(inputs),inputShape=input2.shape,nDims=inputShape.length;return tidy(()=>{let keepDims=!0,{mean:mean4,variance}=moments(input2,this.axis,keepDims),broadcastShape=pyListRepeat(1,nDims);for(let dim of this.axis)broadcastShape[dim]=inputShape[dim];let broadcast=v=>v!=null&&v.shape.length!==nDims&&this.axis!==[nDims-1]?v.reshape(broadcastShape):v,scale2=broadcast(this.gamma.read()),offset=broadcast(this.beta.read()),momentsTiling=[],scaleOffsetTiling=[];for(let i=0;i{if(x.rank!==4)throw new ValueError(`temporalPadding expects input tensor to be 4-D, but received a ${x.rank}-D tensor.`);if(padding==null&&(padding=[[1,1],[1,1]]),padding.length!==2||padding[0].length!==2||padding[1].length!==2)throw new ValueError("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(dataFormat==null&&(dataFormat=imageDataFormat()),dataFormat!=="channelsLast"&&dataFormat!=="channelsFirst")throw new ValueError(`Unknown data format: ${dataFormat}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let pattern;return dataFormat==="channelsFirst"?pattern=[[0,0],[0,0],padding[0],padding[1]]:pattern=[[0,0],padding[0],padding[1],[0,0]],pad(x,pattern)})}var ZeroPadding2D=class extends Layer{constructor(args){if(args==null&&(args={}),super(args),this.dataFormat=args.dataFormat==null?imageDataFormat():args.dataFormat,args.padding==null)this.padding=[[1,1],[1,1]];else if(typeof args.padding=="number")this.padding=[[args.padding,args.padding],[args.padding,args.padding]];else{if(args.padding=args.padding,args.padding.length!==2)throw new ValueError(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${args.padding.length} array.`);let heightPadding,widthPadding;if(typeof args.padding[0]=="number")heightPadding=[args.padding[0],args.padding[0]],widthPadding=[args.padding[1],args.padding[1]];else{if(args.padding=args.padding,args.padding[0].length!==2)throw new ValueError(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${args.padding[0].length} array.`);if(heightPadding=args.padding[0],args.padding[1].length!==2)throw new ValueError(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${args.padding[1].length} array.`);widthPadding=args.padding[1]}this.padding=[heightPadding,widthPadding]}this.inputSpec=[new InputSpec({ndim:4})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let rows,cols;return this.dataFormat==="channelsFirst"?(inputShape[2]!=null&&inputShape[2]>=0?rows=inputShape[2]+this.padding[0][0]+this.padding[0][1]:rows=null,inputShape[3]!=null&&inputShape[3]>=0?cols=inputShape[3]+this.padding[1][0]+this.padding[1][1]:cols=null,[inputShape[0],inputShape[1],rows,cols]):(inputShape[1]!=null&&inputShape[1]>=0?rows=inputShape[1]+this.padding[0][0]+this.padding[0][1]:rows=null,inputShape[2]!=null&&inputShape[2]>=0?cols=inputShape[2]+this.padding[1][0]+this.padding[1][1]:cols=null,[inputShape[0],rows,cols,inputShape[3]])}call(inputs,kwargs){return tidy(()=>spatial2dPadding(getExactlyOneTensor(inputs),this.padding,this.dataFormat))}getConfig(){let config3={padding:this.padding,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config3,baseConfig),config3}};ZeroPadding2D.className="ZeroPadding2D";serialization_exports.registerClass(ZeroPadding2D);function pool2d(x,poolSize,strides,padding,dataFormat,poolMode){return tidy(()=>{checkDataFormat(dataFormat),checkPoolMode(poolMode),checkPaddingMode(padding),strides==null&&(strides=[1,1]),padding==null&&(padding="valid"),dataFormat==null&&(dataFormat=imageDataFormat()),poolMode==null&&(poolMode="max"),x=preprocessConv2DInput(x,dataFormat);let y,paddingString=padding==="same"?"same":"valid";return poolMode==="max"?y=maxPool(x,poolSize,strides,paddingString):y=avgPool(x,poolSize,strides,paddingString),dataFormat==="channelsFirst"&&(y=transpose(y,[0,3,1,2])),y})}function pool3d2(x,poolSize,strides,padding,dataFormat,poolMode){return tidy(()=>{checkDataFormat(dataFormat),checkPoolMode(poolMode),checkPaddingMode(padding),strides==null&&(strides=[1,1,1]),padding==null&&(padding="valid"),dataFormat==null&&(dataFormat=imageDataFormat()),poolMode==null&&(poolMode="max"),x=preprocessConv3DInput(x,dataFormat);let y,paddingString=padding==="same"?"same":"valid";return poolMode==="max"?y=maxPool3d(x,poolSize,strides,paddingString):y=avgPool3d(x,poolSize,strides,paddingString),dataFormat==="channelsFirst"&&(y=transpose(y,[0,4,1,2,3])),y})}var Pooling1D=class extends Layer{constructor(args){if(args.poolSize==null&&(args.poolSize=2),super(args),typeof args.poolSize=="number")this.poolSize=[args.poolSize];else if(Array.isArray(args.poolSize)&&args.poolSize.length===1&&typeof args.poolSize[0]=="number")this.poolSize=args.poolSize;else throw new ValueError(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(args.poolSize)}`);if(assertPositiveInteger(this.poolSize,"poolSize"),args.strides==null)this.strides=this.poolSize;else if(typeof args.strides=="number")this.strides=[args.strides];else if(Array.isArray(args.strides)&&args.strides.length===1&&typeof args.strides[0]=="number")this.strides=args.strides;else throw new ValueError(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(args.strides)}`);assertPositiveInteger(this.strides,"strides"),this.padding=args.padding==null?"valid":args.padding,checkPaddingMode(this.padding),this.inputSpec=[new InputSpec({ndim:3})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let length=convOutputLength(inputShape[1],this.poolSize[0],this.padding,this.strides[0]);return[inputShape[0],length,inputShape[2]]}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs),inputs=expandDims5(getExactlyOneTensor(inputs),2);let output=this.poolingFunction(getExactlyOneTensor(inputs),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return squeeze(output,[2])})}getConfig(){let config3={poolSize:this.poolSize,padding:this.padding,strides:this.strides},baseConfig=super.getConfig();return Object.assign(config3,baseConfig),config3}},MaxPooling1D=class extends Pooling1D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding),pool2d(inputs,poolSize,strides,padding,dataFormat,"max")}};MaxPooling1D.className="MaxPooling1D";serialization_exports.registerClass(MaxPooling1D);var AveragePooling1D=class extends Pooling1D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding),pool2d(inputs,poolSize,strides,padding,dataFormat,"avg")}};AveragePooling1D.className="AveragePooling1D";serialization_exports.registerClass(AveragePooling1D);var Pooling2D=class extends Layer{constructor(args){if(args.poolSize==null&&(args.poolSize=[2,2]),super(args),this.poolSize=Array.isArray(args.poolSize)?args.poolSize:[args.poolSize,args.poolSize],args.strides==null)this.strides=this.poolSize;else if(Array.isArray(args.strides)){if(args.strides.length!==2)throw new ValueError(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${args.strides.length}.`);this.strides=args.strides}else this.strides=[args.strides,args.strides];assertPositiveInteger(this.poolSize,"poolSize"),assertPositiveInteger(this.strides,"strides"),this.padding=args.padding==null?"valid":args.padding,this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),checkPaddingMode(this.padding),this.inputSpec=[new InputSpec({ndim:4})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let rows=this.dataFormat==="channelsFirst"?inputShape[2]:inputShape[1],cols=this.dataFormat==="channelsFirst"?inputShape[3]:inputShape[2];return rows=convOutputLength(rows,this.poolSize[0],this.padding,this.strides[0]),cols=convOutputLength(cols,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[inputShape[0],inputShape[1],rows,cols]:[inputShape[0],rows,cols,inputShape[3]]}call(inputs,kwargs){return tidy(()=>(this.invokeCallHook(inputs,kwargs),this.poolingFunction(getExactlyOneTensor(inputs),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let config3={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config3,baseConfig),config3}},MaxPooling2D=class extends Pooling2D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding),pool2d(inputs,poolSize,strides,padding,dataFormat,"max")}};MaxPooling2D.className="MaxPooling2D";serialization_exports.registerClass(MaxPooling2D);var AveragePooling2D=class extends Pooling2D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding),pool2d(inputs,poolSize,strides,padding,dataFormat,"avg")}};AveragePooling2D.className="AveragePooling2D";serialization_exports.registerClass(AveragePooling2D);var Pooling3D=class extends Layer{constructor(args){if(args.poolSize==null&&(args.poolSize=[2,2,2]),super(args),this.poolSize=Array.isArray(args.poolSize)?args.poolSize:[args.poolSize,args.poolSize,args.poolSize],args.strides==null)this.strides=this.poolSize;else if(Array.isArray(args.strides)){if(args.strides.length!==3)throw new ValueError(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${args.strides.length}.`);this.strides=args.strides}else this.strides=[args.strides,args.strides,args.strides];assertPositiveInteger(this.poolSize,"poolSize"),assertPositiveInteger(this.strides,"strides"),this.padding=args.padding==null?"valid":args.padding,this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),checkPaddingMode(this.padding),this.inputSpec=[new InputSpec({ndim:5})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let depths=this.dataFormat==="channelsFirst"?inputShape[2]:inputShape[1],rows=this.dataFormat==="channelsFirst"?inputShape[3]:inputShape[2],cols=this.dataFormat==="channelsFirst"?inputShape[4]:inputShape[3];return depths=convOutputLength(depths,this.poolSize[0],this.padding,this.strides[0]),rows=convOutputLength(rows,this.poolSize[1],this.padding,this.strides[1]),cols=convOutputLength(cols,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[inputShape[0],inputShape[1],depths,rows,cols]:[inputShape[0],depths,rows,cols,inputShape[4]]}call(inputs,kwargs){return tidy(()=>(this.invokeCallHook(inputs,kwargs),this.poolingFunction(getExactlyOneTensor(inputs),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let config3={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config3,baseConfig),config3}},MaxPooling3D=class extends Pooling3D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding),pool3d2(inputs,poolSize,strides,padding,dataFormat,"max")}};MaxPooling3D.className="MaxPooling3D";serialization_exports.registerClass(MaxPooling3D);var AveragePooling3D=class extends Pooling3D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding),pool3d2(inputs,poolSize,strides,padding,dataFormat,"avg")}};AveragePooling3D.className="AveragePooling3D";serialization_exports.registerClass(AveragePooling3D);var GlobalPooling1D=class extends Layer{constructor(args){super(args);this.inputSpec=[new InputSpec({ndim:3})]}computeOutputShape(inputShape){return[inputShape[0],inputShape[2]]}call(inputs,kwargs){throw new NotImplementedError}},GlobalAveragePooling1D=class extends GlobalPooling1D{constructor(args){super(args||{})}call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);return mean(input2,1)})}};GlobalAveragePooling1D.className="GlobalAveragePooling1D";serialization_exports.registerClass(GlobalAveragePooling1D);var GlobalMaxPooling1D=class extends GlobalPooling1D{constructor(args){super(args||{})}call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);return max(input2,1)})}};GlobalMaxPooling1D.className="GlobalMaxPooling1D";serialization_exports.registerClass(GlobalMaxPooling1D);var GlobalPooling2D=class extends Layer{constructor(args){super(args);this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),this.inputSpec=[new InputSpec({ndim:4})]}computeOutputShape(inputShape){return inputShape=inputShape,this.dataFormat==="channelsLast"?[inputShape[0],inputShape[3]]:[inputShape[0],inputShape[1]]}call(inputs,kwargs){throw new NotImplementedError}getConfig(){let config3={dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config3,baseConfig),config3}},GlobalAveragePooling2D=class extends GlobalPooling2D{call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);return this.dataFormat==="channelsLast"?mean(input2,[1,2]):mean(input2,[2,3])})}};GlobalAveragePooling2D.className="GlobalAveragePooling2D";serialization_exports.registerClass(GlobalAveragePooling2D);var GlobalMaxPooling2D=class extends GlobalPooling2D{call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);return this.dataFormat==="channelsLast"?max(input2,[1,2]):max(input2,[2,3])})}};GlobalMaxPooling2D.className="GlobalMaxPooling2D";serialization_exports.registerClass(GlobalMaxPooling2D);var Wrapper=class extends Layer{constructor(args){super(args);this.layer=args.layer}build(inputShape){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(value){this.layer!=null&&(this.layer.trainable=value)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(weights){this.layer.setWeights(weights)}getConfig(){let config3={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},baseConfig=super.getConfig();return Object.assign(config3,baseConfig),config3}setFastWeightInitDuringBuild(value){super.setFastWeightInitDuringBuild(value),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(value)}static fromConfig(cls,config3,customObjects={}){let layerConfig=config3.layer,layer=deserialize(layerConfig,customObjects);delete config3.layer;let newConfig={layer};return Object.assign(newConfig,config3),new cls(newConfig)}},TimeDistributed=class extends Wrapper{constructor(args){super(args);this.supportsMasking=!0}build(inputShape){if(inputShape=getExactlyOneShape(inputShape),inputShape.length<3)throw new ValueError(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(inputShape)}`);this.inputSpec=[{shape:inputShape}];let childInputShape=[inputShape[0]].concat(inputShape.slice(2));this.layer.built||(this.layer.build(childInputShape),this.layer.built=!0),super.build(inputShape)}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let childInputShape=[inputShape[0]].concat(inputShape.slice(2)),childOutputShape=this.layer.computeOutputShape(childInputShape),timesteps=inputShape[1];return[childOutputShape[0],timesteps].concat(childOutputShape.slice(1))}call(inputs,kwargs){return tidy(()=>(inputs=getExactlyOneTensor(inputs),rnn((inputs2,states)=>[getExactlyOneTensor(this.layer.call(inputs2,kwargs)),[]],inputs,[],!1,null,null,!1,!0)[1]))}};TimeDistributed.className="TimeDistributed";serialization_exports.registerClass(TimeDistributed);function checkBidirectionalMergeMode(value){checkStringTypeUnionValue(VALID_BIDIRECTIONAL_MERGE_MODES,"BidirectionalMergeMode",value)}var DEFAULT_BIDIRECTIONAL_MERGE_MODE="concat",Bidirectional=class extends Wrapper{constructor(args){super(args);let layerConfig=args.layer.getConfig(),forwDict={};forwDict.className=args.layer.getClassName(),forwDict.config=layerConfig,this.forwardLayer=deserialize(forwDict),layerConfig.goBackwards=layerConfig.goBackwards!==!0;let backDict={};if(backDict.className=args.layer.getClassName(),backDict.config=layerConfig,this.backwardLayer=deserialize(backDict),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=args.mergeMode===void 0?DEFAULT_BIDIRECTIONAL_MERGE_MODE:args.mergeMode,checkBidirectionalMergeMode(this.mergeMode),args.weights)throw new NotImplementedError("weights support is not implemented for Bidirectional layer yet.");this._stateful=args.layer.stateful,this.returnSequences=args.layer.returnSequences,this.returnState=args.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=args.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(value){this._trainable=value,this.forwardLayer!=null&&(this.forwardLayer.trainable=value),this.backwardLayer!=null&&(this.backwardLayer.trainable=value)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(weights){let numWeights=weights.length,numeightsOver2=Math.floor(numWeights/2);this.forwardLayer.setWeights(weights.slice(0,numeightsOver2)),this.backwardLayer.setWeights(weights.slice(numeightsOver2))}computeOutputShape(inputShape){let layerShapes=this.forwardLayer.computeOutputShape(inputShape);Array.isArray(layerShapes)&&Array.isArray(layerShapes[0])||(layerShapes=[layerShapes]),layerShapes=layerShapes;let outputShape,outputShapes,stateShape;return this.returnState&&(stateShape=layerShapes.slice(1)),outputShape=layerShapes[0],outputShape=outputShape,this.mergeMode==="concat"?(outputShape[outputShape.length-1]*=2,outputShapes=[outputShape]):this.mergeMode==null?outputShapes=[outputShape,outputShape.slice()]:outputShapes=[outputShape],this.returnState?this.mergeMode==null?outputShapes.concat(stateShape).concat(stateShape.slice()):[outputShape].concat(stateShape).concat(stateShape.slice()):singletonOrArray(outputShapes)}apply(inputs,kwargs){let initialState=kwargs==null?null:kwargs.initialState,constants=kwargs==null?null:kwargs.constants;kwargs==null&&(kwargs={});let standardized=standardizeArgs(inputs,initialState,constants,this.numConstants);if(inputs=standardized.inputs,initialState=standardized.initialState,constants=standardized.constants,Array.isArray(inputs)&&(initialState=inputs.slice(1),inputs=inputs[0]),(initialState==null||initialState.length===0)&&constants==null)return super.apply(inputs,kwargs);let additionalInputs=[],additionalSpecs=[];if(initialState!=null){let numStates=initialState.length;if(numStates%2>0)throw new ValueError("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");kwargs.initialState=initialState,additionalInputs.push(...initialState);let stateSpecs=initialState.map(state=>new InputSpec({shape:state.shape}));this.forwardLayer.stateSpec=stateSpecs.slice(0,numStates/2),this.backwardLayer.stateSpec=stateSpecs.slice(numStates/2),additionalSpecs.push(...stateSpecs)}if(constants!=null)throw new NotImplementedError("Support for constants in Bidirectional layers is not implemented yet.");let isSymbolicTensor=additionalInputs[0]instanceof SymbolicTensor;for(let tensor2 of additionalInputs)if(tensor2 instanceof SymbolicTensor!==isSymbolicTensor)throw new ValueError("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(isSymbolicTensor){let fullInput=[inputs].concat(additionalInputs),fullInputSpec=this.inputSpec.concat(additionalSpecs),originalInputSpec=this.inputSpec;this.inputSpec=fullInputSpec;let output=super.apply(fullInput,kwargs);return this.inputSpec=originalInputSpec,output}else return super.apply(inputs,kwargs)}call(inputs,kwargs){return tidy(()=>{let initialState=kwargs.initialState,y,yRev;if(initialState==null)y=this.forwardLayer.call(inputs,kwargs),yRev=this.backwardLayer.call(inputs,kwargs);else{let forwardState=initialState.slice(0,initialState.length/2),backwardState=initialState.slice(initialState.length/2);y=this.forwardLayer.call(inputs,Object.assign(kwargs,{initialState:forwardState})),yRev=this.backwardLayer.call(inputs,Object.assign(kwargs,{initialState:backwardState}))}let states;this.returnState&&(Array.isArray(y)&&(states=y.slice(1).concat(yRev.slice(1))),y=y[0],yRev=yRev[0]),this.returnSequences&&(yRev=reverse(yRev,1));let output;return this.mergeMode==="concat"?output=concatenate([y,yRev]):this.mergeMode==="sum"?output=add2(y,yRev):this.mergeMode==="ave"?output=mul(.5,add2(y,yRev)):this.mergeMode==="mul"?output=mul(y,yRev):this.mergeMode==null&&(output=[y,yRev]),this.returnState?this.mergeMode==null?output.concat(states):[output].concat(states):output})}resetStates(states){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(inputShape){nameScope(this.forwardLayer.name,()=>{this.forwardLayer.build(inputShape)}),nameScope(this.backwardLayer.name,()=>{this.backwardLayer.build(inputShape)}),this.built=!0}computeMask(inputs,mask){Array.isArray(mask)&&(mask=mask[0]);let outputMask;if(this.returnSequences?this.mergeMode==null?outputMask=[mask,mask]:outputMask=mask:this.mergeMode==null?outputMask=[null,null]:outputMask=null,this.returnState){let stateMask=this.forwardLayer.states.map(state=>null);return Array.isArray(outputMask)?outputMask.concat(stateMask).concat(stateMask):[outputMask].concat(stateMask).concat(stateMask)}else return outputMask}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)}setFastWeightInitDuringBuild(value){super.setFastWeightInitDuringBuild(value),this.forwardLayer!=null&&this.forwardLayer.setFastWeightInitDuringBuild(value),this.backwardLayer!=null&&this.backwardLayer.setFastWeightInitDuringBuild(value)}getConfig(){let config3={mergeMode:this.mergeMode},baseConfig=super.getConfig();return Object.assign(config3,baseConfig),config3}static fromConfig(cls,config3){let rnnLayer=deserialize(config3.layer);if(delete config3.layer,config3.numConstants!=null)throw new NotImplementedError("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");let newConfig=config3;return newConfig.layer=rnnLayer,new cls(newConfig)}};Bidirectional.className="Bidirectional";serialization_exports.registerClass(Bidirectional);function inputLayer(args){return new InputLayer(args)}function elu5(args){return new ELU4(args)}function reLU(args){return new ReLU(args)}function leakyReLU(args){return new LeakyReLU(args)}function prelu5(args){return new PReLU(args)}function softmax5(args){return new Softmax3(args)}function thresholdedReLU(args){return new ThresholdedReLU(args)}function conv1d2(args){return new Conv1D(args)}function conv2d5(args){return new Conv2D2(args)}function conv2dTranspose2(args){return new Conv2DTranspose(args)}function conv3d2(args){return new Conv3D2(args)}function separableConv2d2(args){return new SeparableConv2D(args)}function cropping2D(args){return new Cropping2D(args)}function upSampling2d(args){return new UpSampling2D(args)}function depthwiseConv2d5(args){return new DepthwiseConv2D(args)}function activation(args){return new Activation2(args)}function dense(args){return new Dense(args)}function dropout3(args){return new Dropout(args)}function spatialDropout1d(args){return new SpatialDropout1D(args)}function flatten3(args){return new Flatten(args)}function repeatVector(args){return new RepeatVector(args)}function reshape5(args){return new Reshape2(args)}function permute(args){return new Permute(args)}function embedding(args){return new Embedding(args)}function add4(args){return new Add2(args)}function average(args){return new Average(args)}function concatenate2(args){return new Concatenate(args)}function maximum4(args){return new Maximum2(args)}function minimum4(args){return new Minimum2(args)}function multiply3(args){return new Multiply2(args)}function dot3(args){return new Dot(args)}function batchNormalization2(args){return new BatchNormalization(args)}function layerNormalization(args){return new LayerNormalization(args)}function zeroPadding2d(args){return new ZeroPadding2D(args)}function averagePooling1d(args){return new AveragePooling1D(args)}function avgPool1d(args){return averagePooling1d(args)}function avgPooling1d(args){return averagePooling1d(args)}function averagePooling2d(args){return new AveragePooling2D(args)}function avgPool2d(args){return averagePooling2d(args)}function avgPooling2d(args){return averagePooling2d(args)}function averagePooling3d(args){return new AveragePooling3D(args)}function avgPool3d2(args){return averagePooling3d(args)}function avgPooling3d(args){return averagePooling3d(args)}function globalAveragePooling1d(args){return new GlobalAveragePooling1D(args)}function globalAveragePooling2d(args){return new GlobalAveragePooling2D(args)}function globalMaxPooling1d(args){return new GlobalMaxPooling1D(args)}function globalMaxPooling2d(args){return new GlobalMaxPooling2D(args)}function maxPooling1d(args){return new MaxPooling1D(args)}function maxPooling2d(args){return new MaxPooling2D(args)}function maxPooling3d(args){return new MaxPooling3D(args)}function gru(args){return new GRU(args)}function gruCell(args){return new GRUCell(args)}function lstm(args){return new LSTM(args)}function lstmCell(args){return new LSTMCell(args)}function simpleRNN(args){return new SimpleRNN(args)}function simpleRNNCell(args){return new SimpleRNNCell(args)}function convLstm2d(args){return new ConvLSTM2D(args)}function convLstm2dCell(args){return new ConvLSTM2DCell(args)}function rnn2(args){return new RNN(args)}function stackedRNNCells(args){return new StackedRNNCells(args)}function bidirectional(args){return new Bidirectional(args)}function timeDistributed(args){return new TimeDistributed(args)}var globalMaxPool1d=globalMaxPooling1d,globalMaxPool2d=globalMaxPooling2d,maxPool1d=maxPooling1d,maxPool2d=maxPooling2d;function gaussianNoise(args){return new GaussianNoise(args)}function gaussianDropout(args){return new GaussianDropout(args)}function alphaDropout(args){return new AlphaDropout(args)}function masking(args){return new Masking(args)}var exports_metrics_exports={};__export2(exports_metrics_exports,{MAPE:()=>MAPE2,MSE:()=>MSE2,binaryAccuracy:()=>binaryAccuracy2,binaryCrossentropy:()=>binaryCrossentropy3,categoricalAccuracy:()=>categoricalAccuracy2,categoricalCrossentropy:()=>categoricalCrossentropy3,cosineProximity:()=>cosineProximity2,mape:()=>mape2,meanAbsoluteError:()=>meanAbsoluteError2,meanAbsolutePercentageError:()=>meanAbsolutePercentageError2,meanSquaredError:()=>meanSquaredError3,mse:()=>mse2,precision:()=>precision2,recall:()=>recall2,sparseCategoricalAccuracy:()=>sparseCategoricalAccuracy2});function binaryAccuracy2(yTrue,yPred){return binaryAccuracy(yTrue,yPred)}function binaryCrossentropy3(yTrue,yPred){return binaryCrossentropy2(yTrue,yPred)}function sparseCategoricalAccuracy2(yTrue,yPred){return sparseCategoricalAccuracy(yTrue,yPred)}function categoricalAccuracy2(yTrue,yPred){return categoricalAccuracy(yTrue,yPred)}function categoricalCrossentropy3(yTrue,yPred){return categoricalCrossentropy2(yTrue,yPred)}function precision2(yTrue,yPred){return precision(yTrue,yPred)}function recall2(yTrue,yPred){return recall(yTrue,yPred)}function cosineProximity2(yTrue,yPred){return cosineProximity(yTrue,yPred)}function meanAbsoluteError2(yTrue,yPred){return meanAbsoluteError(yTrue,yPred)}function meanAbsolutePercentageError2(yTrue,yPred){return meanAbsolutePercentageError(yTrue,yPred)}function MAPE2(yTrue,yPred){return meanAbsolutePercentageError(yTrue,yPred)}function mape2(yTrue,yPred){return meanAbsolutePercentageError(yTrue,yPred)}function meanSquaredError3(yTrue,yPred){return meanSquaredError2(yTrue,yPred)}function MSE2(yTrue,yPred){return meanSquaredError2(yTrue,yPred)}function mse2(yTrue,yPred){return meanSquaredError2(yTrue,yPred)}var exports_models_exports={};__export2(exports_models_exports,{modelFromJSON:()=>modelFromJSON});var exports_regularizers_exports={};__export2(exports_regularizers_exports,{l1:()=>l12,l1l2:()=>l1l2,l2:()=>l22});function l1l2(config3){return new L1L2(config3)}function l12(config3){return l1(config3)}function l22(config3){return l2(config3)}var Callback=class extends BaseCallback{constructor(){super(...arguments);this.model=null}setModel(model2){if(!(model2 instanceof LayersModel))throw new Error("model must be a LayersModel, not some other Container");this.model=model2}};function less4(currVal,prevVal){return currValprevVal}var EarlyStopping=class extends Callback{constructor(args){super();if(args==null&&(args={}),args.restoreBestWeights)throw new NotImplementedError("restoreBestWeights = True is not implemented in EarlyStopping yet.");this.monitor=args.monitor||"val_loss",this.minDelta=Math.abs(args.minDelta||0),this.patience=args.patience||0,this.verbose=args.verbose||0,this.mode=args.mode||"auto",this.baseline=args.baseline,["auto","min","max"].indexOf(this.mode)===-1&&(console.warn(`EarlyStopping mode '${this.mode}' is invalid. Falling back to mode 'auto'.`),this.mode="auto"),this.mode==="min"?this.monitorFunc=less4:this.mode==="max"?this.monitorFunc=greater4:this.monitor.indexOf("acc")!==-1?this.monitorFunc=greater4:this.monitorFunc=less4,this.monitorFunc===less4&&(this.minDelta*=-1)}async onTrainBegin(logs){this.wait=0,this.stoppedEpoch=0,this.baseline!=null?this.best=this.baseline:this.best=this.monitorFunc===less4?Infinity:-Infinity}async onEpochEnd(epoch,logs){await resolveScalarsInLogs(logs);let current=this.getMonitorValue(logs);current!=null&&(this.monitorFunc(current-this.minDelta,this.best)?(this.best=current,this.wait=0):(this.wait++,this.wait>=this.patience&&(this.stoppedEpoch=epoch,this.model.stopTraining=!0)))}async onTrainEnd(logs){this.stoppedEpoch>0&&this.verbose&&console.log(`Epoch ${this.stoppedEpoch}: early stopping.`)}getMonitorValue(logs){logs==null&&(logs={});let monitorValue=logs[this.monitor];return monitorValue==null&&console.warn(`Metric for EarlyStopping ${this.monitor} is not available. Available metrics are: ${Object.keys(logs)}`),monitorValue}};function earlyStopping(args){return new EarlyStopping(args)}var callbacks={earlyStopping},DataType;(function(DataType2){DataType2[DataType2.DT_INVALID=0]="DT_INVALID",DataType2[DataType2.DT_FLOAT=1]="DT_FLOAT",DataType2[DataType2.DT_DOUBLE=2]="DT_DOUBLE",DataType2[DataType2.DT_INT32=3]="DT_INT32",DataType2[DataType2.DT_UINT8=4]="DT_UINT8",DataType2[DataType2.DT_INT16=5]="DT_INT16",DataType2[DataType2.DT_INT8=6]="DT_INT8",DataType2[DataType2.DT_STRING=7]="DT_STRING",DataType2[DataType2.DT_COMPLEX64=8]="DT_COMPLEX64",DataType2[DataType2.DT_INT64=9]="DT_INT64",DataType2[DataType2.DT_BOOL=10]="DT_BOOL",DataType2[DataType2.DT_QINT8=11]="DT_QINT8",DataType2[DataType2.DT_QUINT8=12]="DT_QUINT8",DataType2[DataType2.DT_QINT32=13]="DT_QINT32",DataType2[DataType2.DT_BFLOAT16=14]="DT_BFLOAT16",DataType2[DataType2.DT_FLOAT_REF=101]="DT_FLOAT_REF",DataType2[DataType2.DT_DOUBLE_REF=102]="DT_DOUBLE_REF",DataType2[DataType2.DT_INT32_REF=103]="DT_INT32_REF",DataType2[DataType2.DT_UINT8_REF=104]="DT_UINT8_REF",DataType2[DataType2.DT_INT16_REF=105]="DT_INT16_REF",DataType2[DataType2.DT_INT8_REF=106]="DT_INT8_REF",DataType2[DataType2.DT_STRING_REF=107]="DT_STRING_REF",DataType2[DataType2.DT_COMPLEX64_REF=108]="DT_COMPLEX64_REF",DataType2[DataType2.DT_INT64_REF=109]="DT_INT64_REF",DataType2[DataType2.DT_BOOL_REF=110]="DT_BOOL_REF",DataType2[DataType2.DT_QINT8_REF=111]="DT_QINT8_REF",DataType2[DataType2.DT_QUINT8_REF=112]="DT_QUINT8_REF",DataType2[DataType2.DT_QINT32_REF=113]="DT_QINT32_REF",DataType2[DataType2.DT_BFLOAT16_REF=114]="DT_BFLOAT16_REF"})(DataType||(DataType={}));var SaverDef;(function(SaverDef2){let CheckpointFormatVersion;(function(CheckpointFormatVersion2){CheckpointFormatVersion2[CheckpointFormatVersion2.LEGACY=0]="LEGACY",CheckpointFormatVersion2[CheckpointFormatVersion2.V1=1]="V1",CheckpointFormatVersion2[CheckpointFormatVersion2.V2=2]="V2"})(CheckpointFormatVersion=SaverDef2.CheckpointFormatVersion||(SaverDef2.CheckpointFormatVersion={}))})(SaverDef||(SaverDef={}));var CUSTOM_OPS={};function registerOp(name,opFunc){let opMapper={tfOpName:name,category:"custom",inputs:[],attrs:[],customExecutor:opFunc};CUSTOM_OPS[name]=opMapper}function getRegisteredOp(name){return CUSTOM_OPS[name]}function deregisterOp(name){delete CUSTOM_OPS[name]}function getParamValue(paramName,node,tensorMap,context,resourceManager){let inputParam=node.inputParams[paramName];if(inputParam&&inputParam.inputIndexStart!==void 0){let start=inputParam.inputIndexStart,end=inputParam.inputIndexEnd===0?void 0:inputParam.inputIndexEnd===void 0?start+1:inputParam.inputIndexEnd;if(inputParam.type==="tensor")return getTensor(node.inputNames[inputParam.inputIndexStart],tensorMap,context,resourceManager);if(inputParam.type==="tensors")return node.inputNames.slice(start,end).map(name=>getTensor(name,tensorMap,context,resourceManager));let tensor2=getTensor(node.inputNames.slice(start)[0],tensorMap,context,resourceManager),data2=tensor2.dataSync();return inputParam.type==="number"?data2[0]:util_exports.toNestedArray(tensor2.shape,data2)}let attrParam=node.attrParams[paramName];return attrParam&&attrParam.value}function getTensor(name,tensorsMap,context,resourceManager){let[nodeName,index]=parseNodeName(name);if(resourceManager!=null){let tensor2=resourceManager.getHashTableHandleByName(nodeName);if(tensor2!=null)return tensor2}let contextId=context.currentContextIds.find(contextId2=>!!tensorsMap[getNodeNameWithContextId(nodeName,contextId2)]);return contextId!==void 0?tensorsMap[getNodeNameWithContextId(nodeName,contextId)][index]:void 0}function getTensorsForCurrentContenxt(name,tensorsMap,context){return tensorsMap[getNodeNameWithContextId(name,context.currentContextId)]}function getNodeNameAndIndex(inputName,context){let[nodeName,index]=parseNodeName(inputName);return[getNodeNameWithContextId(nodeName,context&&context.currentContextId),index]}function getNodeNameWithContextId(name,contextId){return contextId?`${name}-${contextId}`:name}function parseNodeName(name){let parts=name.split(":");return parts.length===1?[name,0]:[parts[0],Number(parts[parts.length-1])]}function getPadding(node,tensorMap,context){let pad3=getParamValue("pad",node,tensorMap,context);if(pad3==="explicit"){pad3=getParamValue("explicitPaddings",node,tensorMap,context);let explicitPadding=[[0,0],[0,0],[0,0],[0,0]];for(let i=0;i<4;i++)explicitPadding[i][0]=pad3[i*2],explicitPadding[i][1]=pad3[i*2+1];return explicitPadding}return pad3}function cloneTensor(tensor2){return tensor2.kept?tensor2:clone(tensor2)}var arithmetic_exports={};__export2(arithmetic_exports,{json:()=>json});var json=[{tfOpName:"Add",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"AddV2",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"AddN",category:"arithmetic",inputs:[{start:0,end:0,name:"tensors",type:"tensors"}]},{tfOpName:"BiasAdd",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"Sub",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"RealDiv",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Div",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"DivNoNan",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"FloorDiv",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Mul",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Maximum",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Minimum",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Pow",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"SquaredDifference",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Mod",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"FloorMod",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]}],basic_math_exports={};__export2(basic_math_exports,{json:()=>json2});var json2=[{tfOpName:"Abs",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Acos",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Asin",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Atan",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Atan2",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"y",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Ceil",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"ClipByValue",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"clipValueMin",type:"number"},{start:2,name:"clipValueMax",type:"number"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Complex",category:"basic_math",inputs:[{start:0,name:"real",type:"tensor"},{start:1,name:"imag",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"ComplexAbs",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Cos",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Cosh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Elu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Exp",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Floor",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Log",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Imag",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"Tout",name:"outputType",type:"dtype",notSupported:!0}]},{tfOpName:"Neg",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Real",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"Tout",name:"outputType",type:"dtype",notSupported:!0}]},{tfOpName:"Prelu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"alpha",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Relu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Relu6",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Selu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sigmoid",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sin",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sinh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sqrt",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Rsqrt",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Square",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Tan",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Tanh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sign",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Round",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Expm1",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Log1p",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Reciprocal",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Softplus",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Asinh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Acosh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Atanh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Erf",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Prod",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axes",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool",notSupported:!0},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LeakyRelu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"alpha",name:"alpha",type:"number",defaultValue:.2},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]}],control_exports={};__export2(control_exports,{json:()=>json3});var 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json11=[{tfOpName:"Equal",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"NotEqual",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Greater",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"GreaterEqual",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Less",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LessEqual",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LogicalAnd",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LogicalNot",category:"logical",inputs:[{start:0,name:"a",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LogicalOr",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Select",category:"logical",inputs:[{start:0,name:"condition",type:"tensor"},{start:1,name:"a",type:"tensor"},{start:2,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"SelectV2",category:"logical",inputs:[{start:0,name:"condition",type:"tensor"},{start:1,name:"a",type:"tensor"},{start:2,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]}],matrices_exports={};__export2(matrices_exports,{json:()=>json12});var json12=[{tfOpName:"_FusedMatMul",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"},{start:2,end:0,name:"args",type:"tensors"}],attrs:[{tfName:"num_args",name:"numArgs",type:"number"},{tfName:"fused_ops",name:"fusedOps",type:"string[]",defaultValue:[]},{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:1e-4},{tfName:"transpose_a",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"transpose_b",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"MatMul",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"transpose_a",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"transpose_b",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"BatchMatMul",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"adj_x",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"adj_y",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"BatchMatMulV2",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"adj_x",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"adj_y",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Transpose",category:"matrices",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"perm",type:"number[]"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]}],normalization_exports={};__export2(normalization_exports,{json:()=>json13});var json13=[{tfOpName:"FusedBatchNorm",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"scale",type:"tensor"},{start:2,name:"offset",type:"tensor"},{start:3,name:"mean",type:"tensor"},{start:4,name:"variance",type:"tensor"}],attrs:[{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:.001},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"FusedBatchNormV2",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"scale",type:"tensor"},{start:2,name:"offset",type:"tensor"},{start:3,name:"mean",type:"tensor"},{start:4,name:"variance",type:"tensor"}],attrs:[{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:.001},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"FusedBatchNormV3",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"scale",type:"tensor"},{start:2,name:"offset",type:"tensor"},{start:3,name:"mean",type:"tensor"},{start:4,name:"variance",type:"tensor"}],attrs:[{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:.001},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"LRN",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"depth_radius",name:"radius",type:"number",defaultValue:5},{tfName:"bias",name:"bias",type:"number",defaultValue:1},{tfName:"alpha",name:"alpha",type:"number",defaultValue:1},{tfName:"beta",name:"beta",type:"number",defaultValue:.5}]},{tfOpName:"Softmax",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"LogSoftmax",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"SparseToDense",category:"normalization",inputs:[{start:0,name:"sparseIndices",type:"tensor"},{start:1,name:"outputShape",type:"number[]"},{start:2,name:"sparseValues",type:"tensor"},{start:3,name:"defaultValue",type:"tensor"}],attrs:[{tfName:"validate_indices",name:"validateIndices",type:"bool",defaultValue:!0,notSupported:!0}]}],reduction_exports={};__export2(reduction_exports,{json:()=>json14});var json14=[{tfOpName:"Bincount",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"size",type:"number"},{start:2,name:"weights",type:"tensor"}]},{tfOpName:"DenseBincount",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"size",type:"number"},{start:2,name:"weights",type:"tensor"}],attrs:[{tfName:"binary_output",name:"binaryOutput",type:"bool"}]},{tfOpName:"Max",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Mean",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Min",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Sum",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"All",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Any",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"ArgMax",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]},{tfOpName:"ArgMin",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]},{tfOpName:"Prod",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Cumsum",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}],attrs:[{tfName:"exclusive",name:"exclusive",type:"bool"},{tfName:"reverse",name:"reverse",type:"bool"}]}],slice_join_exports={};__export2(slice_join_exports,{json:()=>json15});var json15=[{tfOpName:"ConcatV2",category:"slice_join",inputs:[{start:0,end:-1,name:"tensors",type:"tensors"},{start:-1,name:"axis",type:"number"}],attrs:[{tfName:"N",name:"n",type:"number",defaultValue:2}]},{tfOpName:"Concat",category:"slice_join",inputs:[{start:1,end:0,name:"tensors",type:"tensors"},{start:0,name:"axis",type:"number"}],attrs:[{tfName:"N",name:"n",type:"number",defaultValue:2}]},{tfOpName:"GatherV2",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"indices",type:"tensor"},{start:2,name:"axis",type:"number",defaultValue:0}],attrs:[{tfName:"batch_dims",name:"batchDims",type:"number",defaultValue:0}]},{tfOpName:"Gather",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"indices",type:"tensor"}],attrs:[{tfName:"validate_indices",name:"validateIndices",type:"bool",notSupported:!0}]},{tfOpName:"Reverse",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"dims",type:"bool[]"}]},{tfOpName:"ReverseV2",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}]},{tfOpName:"Slice",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"begin",type:"number[]"},{start:2,name:"size",type:"number[]"}]},{tfOpName:"StridedSlice",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"begin",type:"number[]"},{start:2,name:"end",type:"number[]"},{start:3,name:"strides",type:"number[]"}],attrs:[{tfName:"begin_mask",name:"beginMask",type:"number",defaultValue:0},{tfName:"end_mask",name:"endMask",type:"number",defaultValue:0},{tfName:"new_axis_mask",name:"newAxisMask",type:"number",defaultValue:0},{tfName:"ellipsis_mask",name:"ellipsisMask",type:"number",defaultValue:0},{tfName:"shrink_axis_mask",name:"shrinkAxisMask",type:"number",defaultValue:0}]},{tfOpName:"Pack",category:"slice_join",inputs:[{start:0,end:0,name:"tensors",type:"tensors"}],attrs:[{tfName:"axis",name:"axis",type:"number",defaultValue:0}]},{tfOpName:"Unpack",category:"slice_join",inputs:[{start:0,name:"tensor",type:"tensor"}],attrs:[{tfName:"axis",name:"axis",type:"number",defaultValue:0},{tfName:"num",name:"num",type:"number",defaultValue:0,notSupported:!0}]},{tfOpName:"Tile",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"reps",type:"number[]"}]},{tfOpName:"Split",category:"slice_join",inputs:[{start:0,name:"axis",type:"number",defaultValue:0},{start:1,name:"x",type:"tensor"}],attrs:[{tfName:"num_split",name:"numOrSizeSplits",type:"number",defaultValue:1}]},{tfOpName:"SplitV",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"numOrSizeSplits",type:"number[]"},{start:2,name:"axis",type:"number",defaultValue:0}]},{tfOpName:"ScatterNd",category:"slice_join",inputs:[{start:0,name:"indices",type:"tensor"},{start:1,name:"values",type:"tensor"},{start:2,name:"shape",type:"number[]"}]},{tfOpName:"GatherNd",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"indices",type:"tensor"}]},{tfOpName:"SparseToDense",category:"slice_join",inputs:[{start:0,name:"sparseIndices",type:"tensor"},{start:1,name:"outputShape",type:"number[]"},{start:2,name:"sparseValues",type:"tensor"},{start:3,name:"defaultValue",type:"tensor"}],attrs:[{tfName:"validate_indices",name:"validateIndices",type:"bool",defaultValue:!1,notSupported:!0}]}],spectral_exports={};__export2(spectral_exports,{json:()=>json16});var json16=[{tfOpName:"FFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"IFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"RFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"fft_length",type:"number",notSupported:!0}]},{tfOpName:"IRFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"fft_length",type:"number",notSupported:!0}]}],transformation_exports={};__export2(transformation_exports,{json:()=>json17});var json17=[{tfOpName:"Cast",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"SrcT",name:"sdtype",type:"dtype",notSupported:!0},{tfName:"DstT",name:"dtype",type:"dtype"}]},{tfOpName:"ExpandDims",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]},{tfOpName:"MirrorPad",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"}],attrs:[{tfName:"mode",name:"mode",type:"string"}]},{tfOpName:"Pad",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"}],attrs:[{tfName:"constant_value",name:"constantValue",type:"number",defaultValue:0}]},{tfOpName:"PadV2",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"},{start:2,name:"constantValue",type:"number",defaultValue:0}]},{tfOpName:"Reshape",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"shape",type:"number[]"}]},{tfOpName:"Squeeze",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"axis",tfDeprecatedName:"squeeze_dims",name:"axis",type:"number[]"}]},{tfOpName:"SpaceToBatchND",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"blockShape",type:"number[]"},{start:2,name:"paddings",type:"number[]"}]},{tfOpName:"BatchToSpaceND",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"blockShape",type:"number[]"},{start:2,name:"crops",type:"number[]"}]},{tfOpName:"DepthToSpace",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"block_size",name:"blockSize",type:"number"},{tfName:"data_format",name:"dataFormat",type:"string"}]},{tfOpName:"BroadcastTo",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"shape",type:"number[]"}],attrs:[]}],OperationMapper=class{static get Instance(){return this._instance||(this._instance=new this)}constructor(){let ops=[arithmetic_exports,basic_math_exports,control_exports,convolution_exports,creation_exports,dynamic_exports,evaluation_exports,logical_exports,image_exports,graph_exports,matrices_exports,normalization_exports,reduction_exports,slice_join_exports,spectral_exports,transformation_exports,hash_table_exports],mappersJson=[].concat(...ops.map(op2=>op2.json));this.opMappers=mappersJson.reduce((map,mapper)=>(map[mapper.tfOpName]=mapper,map),{})}transformGraph(graph2,signature={}){let tfNodes=graph2.node,placeholders=[],weights=[],initNodes=[],nodes=tfNodes.reduce((map,node)=>(map[node.name]=this.mapNode(node),node.op.startsWith("Placeholder")?placeholders.push(map[node.name]):node.op==="Const"?weights.push(map[node.name]):(node.input==null||node.input.length===0)&&initNodes.push(map[node.name]),map),{}),inputs=[],outputs=[],inputNodeNameToKey={},outputNodeNameToKey={};signature!=null&&(inputNodeNameToKey=this.mapSignatureEntries(signature.inputs),outputNodeNameToKey=this.mapSignatureEntries(signature.outputs));let allNodes=Object.keys(nodes);allNodes.forEach(key=>{let node=nodes[key];node.inputNames.forEach(name=>{let[nodeName]=getNodeNameAndIndex(name);node.inputs.push(nodes[nodeName]),nodes[nodeName].children.push(node)})}),Object.keys(outputNodeNameToKey).length===0?allNodes.forEach(key=>{let node=nodes[key];node.children.length===0&&outputs.push(node)}):Object.keys(outputNodeNameToKey).forEach(name=>{let[nodeName]=getNodeNameAndIndex(name),node=nodes[nodeName];node!=null&&(node.signatureKey=outputNodeNameToKey[name],outputs.push(node))}),Object.keys(inputNodeNameToKey).length>0?Object.keys(inputNodeNameToKey).forEach(name=>{let[nodeName]=getNodeNameAndIndex(name),node=nodes[nodeName];node&&(node.signatureKey=inputNodeNameToKey[name],inputs.push(node))}):inputs=placeholders;let functions={};graph2.library!=null&&graph2.library.function!=null&&(functions=graph2.library.function.reduce((functions2,func2)=>(functions2[func2.signature.name]=this.mapFunction(func2),functions2),{}));let result={nodes,inputs,outputs,weights,placeholders,signature,functions};return initNodes.length>0&&(result.initNodes=initNodes),result}mapSignatureEntries(entries){return Object.keys(entries||{}).reduce((prev,curr)=>(prev[entries[curr].name]=curr,prev),{})}mapNode(node){let mapper=getRegisteredOp(node.op)||this.opMappers[node.op]||{};node.attr==null&&(node.attr={});let newNode={name:node.name,op:node.op,category:mapper.category,inputNames:(node.input||[]).map(input2=>input2.startsWith("^")?input2.substr(1):input2),inputs:[],children:[],inputParams:{},attrParams:{},rawAttrs:node.attr};return mapper.inputs!=null&&(newNode.inputParams=mapper.inputs.reduce((map,param)=>(map[param.name]={type:param.type,inputIndexStart:param.start,inputIndexEnd:param.end},map),{})),mapper.attrs!=null&&(newNode.attrParams=mapper.attrs.reduce((map,param)=>{let type=param.type,value;switch(param.type){case"string":value=getStringParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getStringParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"string[]":value=getStringArrayParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getStringArrayParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"number":value=getNumberParam(node.attr,param.tfName,param.defaultValue||0),value===void 0&&!!param.tfDeprecatedName&&(value=getNumberParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"number[]":value=getNumericArrayParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getNumericArrayParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"bool":value=getBoolParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getBoolParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"bool[]":value=getBoolArrayParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getBoolArrayParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"shape":value=getTensorShapeParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getTensorShapeParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"shape[]":value=getTensorShapeArrayParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getTensorShapeArrayParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"dtype":value=getDtypeParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getDtypeParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"dtype[]":value=getDtypeArrayParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getDtypeArrayParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"func":value=getFuncParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getFuncParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"tensor":case"tensors":break;default:throw new Error(`Unsupported param type: ${param.type} for op: ${node.op}`)}return map[param.name]={value,type},map},{})),newNode}mapFunction(functionDef){let tfNodes=functionDef.nodeDef,placeholders=[],weights=[],nodes={};tfNodes!=null&&(nodes=tfNodes.reduce((map,node)=>(map[node.name]=this.mapNode(node),node.op==="Const"&&weights.push(map[node.name]),map),{}));let inputs=[],outputs=[];functionDef.signature.inputArg.forEach(arg=>{let[nodeName]=getNodeNameAndIndex(arg.name),node={name:nodeName,op:"Placeholder",inputs:[],inputNames:[],category:"graph",inputParams:{},attrParams:{dtype:{value:parseDtypeParam(arg.type),type:"dtype"}},children:[]};node.signatureKey=arg.name,inputs.push(node),nodes[nodeName]=node}),Object.keys(nodes).forEach(key=>{let node=nodes[key];node.inputNames.forEach(name=>{let[nodeName]=getNodeNameAndIndex(name);node.inputs.push(nodes[nodeName]),nodes[nodeName].children.push(node)})});let returnNodeMap=functionDef.ret;functionDef.signature.outputArg.forEach(output=>{let[nodeName,index]=getNodeNameAndIndex(returnNodeMap[output.name]),node=nodes[nodeName];node!=null&&(node.defaultOutput=index,outputs.push(node))});let signature=this.mapArgsToSignature(functionDef);return{nodes,inputs,outputs,weights,placeholders,signature}}mapArgsToSignature(functionDef){return{methodName:functionDef.signature.name,inputs:functionDef.signature.inputArg.reduce((map,arg)=>(map[arg.name]=this.mapArgToTensorInfo(arg),map),{}),outputs:functionDef.signature.outputArg.reduce((map,arg)=>(map[arg.name]=this.mapArgToTensorInfo(arg,functionDef.ret),map),{})}}mapArgToTensorInfo(arg,nameMap2){let name=arg.name;return nameMap2!=null&&(name=nameMap2[name]),{name,dtype:arg.type}}};function decodeBase64(text){let global2=env().global;if(typeof global2.atob!="undefined")return global2.atob(text);if(typeof Buffer!="undefined")return new Buffer(text,"base64").toString();throw new Error("Unable to decode base64 in this environment. Missing built-in atob() or Buffer()")}function parseStringParam(s,keepCase){let value=Array.isArray(s)?String.fromCharCode.apply(null,s):decodeBase64(s);return keepCase?value:value.toLowerCase()}function getStringParam(attrs,name,def,keepCase=!1){let param=attrs[name];return param!=null?parseStringParam(param.s,keepCase):def}function getBoolParam(attrs,name,def){let param=attrs[name];return param?param.b:def}function getNumberParam(attrs,name,def){let param=attrs[name]||{},value=param.i!=null?param.i:param.f!=null?param.f:def;return typeof value=="number"?value:parseInt(value,10)}function parseDtypeParam(value){switch(typeof value=="string"&&(value=DataType[value]),value){case DataType.DT_FLOAT:return"float32";case DataType.DT_INT32:case DataType.DT_INT64:case DataType.DT_INT8:case DataType.DT_UINT8:return"int32";case DataType.DT_BOOL:return"bool";case DataType.DT_DOUBLE:return"float32";case DataType.DT_STRING:return"string";default:return null}}function getFuncParam(attrs,name,def){let param=attrs[name];return param&¶m.func?param.func.name:def}function getDtypeParam(attrs,name,def){let param=attrs[name];return param&¶m.type?parseDtypeParam(param.type):def}function getDtypeArrayParam(attrs,name,def){let param=attrs[name];return param&¶m.list&¶m.list.type?param.list.type.map(v=>parseDtypeParam(v)):def}function parseTensorShapeParam(shape){if(!shape.unknownRank)return shape.dim!=null?shape.dim.map(dim=>typeof dim.size=="number"?dim.size:parseInt(dim.size,10)):[]}function getTensorShapeParam(attrs,name,def){let param=attrs[name];return param&¶m.shape?parseTensorShapeParam(param.shape):def}function getNumericArrayParam(attrs,name,def){let param=attrs[name];return param?((param.list.f&¶m.list.f.length?param.list.f:param.list.i)||[]).map(v=>typeof v=="number"?v:parseInt(v,10)):def}function getStringArrayParam(attrs,name,def,keepCase=!1){let param=attrs[name];return param&¶m.list&¶m.list.s?param.list.s.map(v=>parseStringParam(v,keepCase)):def}function getTensorShapeArrayParam(attrs,name,def){let param=attrs[name];return param&¶m.list&¶m.list.shape?param.list.shape.map(v=>parseTensorShapeParam(v)):def}function getBoolArrayParam(attrs,name,def){let param=attrs[name];return param&¶m.list&¶m.list.b?param.list.b:def}var NodeValueImpl=class{constructor(node,tensorMap,context){this.node=node,this.tensorMap=tensorMap,this.context=context,this.inputs=[],this.attrs={},this.inputs=node.inputNames.map(name=>this.getInput(name)),node.rawAttrs!=null&&(this.attrs=Object.keys(node.rawAttrs).reduce((attrs,key)=>(attrs[key]=this.getAttr(key),attrs),{}))}getInput(name){return getTensor(name,this.tensorMap,this.context)}getAttr(name,defaultValue){let value=this.node.rawAttrs[name];if(value.tensor!=null)return getTensor(name,this.tensorMap,this.context);if(value.i!=null||value.f!=null)return getNumberParam(this.node.rawAttrs,name,defaultValue);if(value.s!=null)return getStringParam(this.node.rawAttrs,name,defaultValue);if(value.b!=null)return getBoolParam(this.node.rawAttrs,name,defaultValue);if(value.shape!=null)return getTensorShapeParam(this.node.rawAttrs,name,defaultValue);if(value.type!=null)return getDtypeParam(this.node.rawAttrs,name,defaultValue);if(value.list!=null){if(value.list.i!=null||value.list.f!=null)return getNumericArrayParam(this.node.rawAttrs,name,defaultValue);if(value.list.s!=null)return getStringArrayParam(this.node.rawAttrs,name,defaultValue);if(value.list.shape!=null)return getTensorShapeArrayParam(this.node.rawAttrs,name,defaultValue);if(value.list.b!=null)return getBoolArrayParam(this.node.rawAttrs,name,defaultValue);if(value.list.type!=null)return getDtypeArrayParam(this.node.rawAttrs,name,defaultValue)}return defaultValue}},executeOp=(node,tensorMap,context)=>{switch(node.op){case"BiasAdd":case"AddV2":case"Add":return[add2(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"AddN":return[addN(getParamValue("tensors",node,tensorMap,context))];case"FloorMod":case"Mod":return[mod(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Mul":return[mul(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"RealDiv":case"Div":return[div(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"DivNoNan":return[divNoNan(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"FloorDiv":return[floorDiv(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Sub":return[sub(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Minimum":return[minimum(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Maximum":return[maximum(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Pow":return[pow(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"SquaredDifference":return[squaredDifference(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp2=(node,tensorMap,context)=>{switch(node.op){case"Abs":case"ComplexAbs":return[abs(getParamValue("x",node,tensorMap,context))];case"Acos":return[acos(getParamValue("x",node,tensorMap,context))];case"Acosh":return[acosh(getParamValue("x",node,tensorMap,context))];case"Asin":return[asin(getParamValue("x",node,tensorMap,context))];case"Asinh":return[asinh(getParamValue("x",node,tensorMap,context))];case"Atan":return[atan(getParamValue("x",node,tensorMap,context))];case"Atan2":return[atan2(getParamValue("x",node,tensorMap,context),getParamValue("y",node,tensorMap,context))];case"Atanh":return[atanh(getParamValue("x",node,tensorMap,context))];case"Ceil":return[ceil(getParamValue("x",node,tensorMap,context))];case"Complex":return[complex(getParamValue("real",node,tensorMap,context),getParamValue("imag",node,tensorMap,context))];case"Cos":return[cos(getParamValue("x",node,tensorMap,context))];case"Cosh":return[cosh(getParamValue("x",node,tensorMap,context))];case"Elu":return[elu(getParamValue("x",node,tensorMap,context))];case"Erf":return[erf(getParamValue("x",node,tensorMap,context))];case"Exp":return[exp(getParamValue("x",node,tensorMap,context))];case"Expm1":return[expm1(getParamValue("x",node,tensorMap,context))];case"Floor":return[floor(getParamValue("x",node,tensorMap,context))];case"Log":return[log2(getParamValue("x",node,tensorMap,context))];case"Log1p":return[log1p(getParamValue("x",node,tensorMap,context))];case"Imag":return[imag(getParamValue("x",node,tensorMap,context))];case"Neg":return[neg(getParamValue("x",node,tensorMap,context))];case"Reciprocal":return[reciprocal(getParamValue("x",node,tensorMap,context))];case"Real":return[real(getParamValue("x",node,tensorMap,context))];case"Relu":return[relu(getParamValue("x",node,tensorMap,context))];case"Round":return[round2(getParamValue("x",node,tensorMap,context))];case"Selu":return[selu(getParamValue("x",node,tensorMap,context))];case"Sigmoid":return[sigmoid(getParamValue("x",node,tensorMap,context))];case"Sin":return[sin(getParamValue("x",node,tensorMap,context))];case"Sign":return[sign(getParamValue("x",node,tensorMap,context))];case"Sinh":return[sinh(getParamValue("x",node,tensorMap,context))];case"Softplus":return[softplus(getParamValue("x",node,tensorMap,context))];case"Sqrt":return[sqrt(getParamValue("x",node,tensorMap,context))];case"Square":return[square(getParamValue("x",node,tensorMap,context))];case"Tanh":return[tanh2(getParamValue("x",node,tensorMap,context))];case"Tan":return[tan(getParamValue("x",node,tensorMap,context))];case"ClipByValue":return[clipByValue(getParamValue("x",node,tensorMap,context),getParamValue("clipValueMin",node,tensorMap,context),getParamValue("clipValueMax",node,tensorMap,context))];case"Relu6":return[relu6(getParamValue("x",node,tensorMap,context))];case"Rsqrt":return[rsqrt(getTensor(node.inputNames[0],tensorMap,context))];case"Prod":return[prod(getParamValue("x",node,tensorMap,context),getParamValue("axes",node,tensorMap,context))];case"LeakyRelu":return[leakyRelu(getParamValue("x",node,tensorMap,context),getParamValue("alpha",node,tensorMap,context))];case"Prelu":return[prelu(getParamValue("x",node,tensorMap,context),getParamValue("alpha",node,tensorMap,context))];default:throw TypeError(`Node type ${node.op} is not implemented`)}};function assertShapesMatchAllowUndefinedSize(shapeA,shapeB,errorMessagePrefix=""){util_exports.assert(shapesEqualAllowUndefinedSize(shapeA,shapeB),()=>errorMessagePrefix+` Shapes ${shapeA} and ${shapeB} must match`)}function shapesEqualAllowUndefinedSize(n1,n2){if(n1.length!==n2.length)return!1;for(let i=0;i{(keepIds==null||!keepIds.has(tensor2.tensor.id))&&tensor2.tensor.dispose()}),this.tensors=[],this.closed_=!0,this.idTensor.dispose()}size(){return this.tensors.length}read(index){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(index<0||index>=this.size())throw new Error(`Tried to read from index ${index}, but array size is: ${this.size()}`);let tensorWithState=this.tensors[index];if(tensorWithState.cleared)throw new Error(`TensorArray ${this.name}: Could not read index ${index} twice because it was cleared after a previous read (perhaps try setting clear_after_read = false?).`);return this.clearAfterRead&&(tensorWithState.cleared=!0),tensorWithState.read=!0,tensorWithState.tensor}readMany(indices){return indices.map(index=>this.read(index))}write(index,tensor2){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(index<0||!this.dynamicSize&&index>=this.maxSize)throw new Error(`Tried to write to index ${index}, but array is not resizeable and size is: ${this.maxSize}`);let t=this.tensors[index]||{};if(tensor2.dtype!==this.dtype)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index}, +2. The custom ${printableModuleName} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);if(fromConfig!=null){let customObjectsCombined={};for(let key of Object.keys(_GLOBAL_CUSTOM_OBJECTS))customObjectsCombined[key]=_GLOBAL_CUSTOM_OBJECTS[key];for(let key of Object.keys(customObjects))customObjectsCombined[key]=customObjects[key];let nestedConfig=config2.config;nestedConfig.customObjects=customObjectsCombined;let backupCustomObjects=Object.assign({},_GLOBAL_CUSTOM_OBJECTS);for(let key of Object.keys(customObjects))_GLOBAL_CUSTOM_OBJECTS[key]=customObjects[key];convertNDArrayScalarsInConfig(config2.config);let returnObj=fromConfig(cls,config2.config,customObjects,fastWeightInit);return _GLOBAL_CUSTOM_OBJECTS=Object.assign({},backupCustomObjects),returnObj}else{let backupCustomObjects=Object.assign({},_GLOBAL_CUSTOM_OBJECTS);for(let key of Object.keys(customObjects))_GLOBAL_CUSTOM_OBJECTS[key]=customObjects[key];let returnObj=new cls(config2.config);return _GLOBAL_CUSTOM_OBJECTS=Object.assign({},backupCustomObjects),returnObj}}}function numberCompare(a,b){return ab?1:0}function reverseNumberCompare(a,b){return-1*numberCompare(a,b)}function unique4(xs){if(xs==null)return xs;let out=[];for(let x of xs)out.indexOf(x)===-1&&out.push(x);return out}function isObjectEmpty(obj){if(obj==null)throw new ValueError(`Invalid value in obj: ${JSON.stringify(obj)}`);for(let key in obj)if(obj.hasOwnProperty(key))return!1;return!0}function checkStringTypeUnionValue(values,label,value){if(value!=null&&values.indexOf(value)<0)throw new ValueError(`${value} is not a valid ${label}. Valid values are ${values} or null/undefined.`)}function checkArrayTypeAndLength(x,expectedType,minLength=0,maxLength=Infinity){return assert2(minLength>=0),assert2(maxLength>=minLength),Array.isArray(x)&&x.length>=minLength&&x.length<=maxLength&&x.every(e=>typeof e===expectedType)}function assertPositiveInteger(value,name){Array.isArray(value)?(util_exports.assert(value.length>0,()=>`${name} is unexpectedly an empty array.`),value.forEach((v,i)=>assertPositiveInteger(v,`element ${i+1} of ${name}`))):util_exports.assert(Number.isInteger(value)&&value>0,()=>`Expected ${name} to be a positive integer, but got ${formatAsFriendlyString(value)}.`)}function formatAsFriendlyString(value){return value===null?"null":Array.isArray(value)?"["+value.map(v=>formatAsFriendlyString(v)).join(",")+"]":typeof value=="string"?`"${value}"`:`${value}`}function debounce(f,waitMs){let lastTime=util_exports.now(),lastResult;return(...args)=>{let now22=util_exports.now();return now22-lastTimesqrt(sum2(mul(w,w),axis,!0)))}var Constraint=class extends serialization_exports.Serializable{getConfig(){return{}}},MaxNorm=class extends Constraint{constructor(args){super();this.defaultMaxValue=2,this.defaultAxis=0,this.maxValue=args.maxValue!=null?args.maxValue:this.defaultMaxValue,this.axis=args.axis!=null?args.axis:this.defaultAxis}apply(w){return tidy(()=>{let norms=calcL2Norms(w,this.axis),desired=clipByValue(norms,0,this.maxValue);return mul(w,div(desired,add2(epsilon2(),norms)))})}getConfig(){return{maxValue:this.maxValue,axis:this.axis}}};MaxNorm.className="MaxNorm";serialization_exports.registerClass(MaxNorm);var UnitNorm=class extends Constraint{constructor(args){super();this.defaultAxis=0,this.axis=args.axis!=null?args.axis:this.defaultAxis}apply(w){return tidy(()=>div(w,add2(epsilon2(),calcL2Norms(w,this.axis))))}getConfig(){return{axis:this.axis}}};UnitNorm.className="UnitNorm";serialization_exports.registerClass(UnitNorm);var NonNeg=class extends Constraint{apply(w){return relu(w)}};NonNeg.className="NonNeg";serialization_exports.registerClass(NonNeg);var MinMaxNorm=class extends Constraint{constructor(args){super();this.defaultMinValue=0,this.defaultMaxValue=1,this.defaultRate=1,this.defaultAxis=0,this.minValue=args.minValue!=null?args.minValue:this.defaultMinValue,this.maxValue=args.maxValue!=null?args.maxValue:this.defaultMaxValue,this.rate=args.rate!=null?args.rate:this.defaultRate,this.axis=args.axis!=null?args.axis:this.defaultAxis}apply(w){return tidy(()=>{let norms=calcL2Norms(w,this.axis),desired=add2(mul(this.rate,clipByValue(norms,this.minValue,this.maxValue)),mul(1-this.rate,norms));return mul(w,div(desired,add2(epsilon2(),norms)))})}getConfig(){return{minValue:this.minValue,maxValue:this.maxValue,rate:this.rate,axis:this.axis}}};MinMaxNorm.className="MinMaxNorm";serialization_exports.registerClass(MinMaxNorm);var CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP={maxNorm:"MaxNorm",minMaxNorm:"MinMaxNorm",nonNeg:"NonNeg",unitNorm:"UnitNorm"};function serializeConstraint(constraint){return serializeKerasObject(constraint)}function deserializeConstraint(config2,customObjects={}){return deserializeKerasObject(config2,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"constraint")}function getConstraint(identifier){if(identifier==null)return null;if(typeof identifier=="string"){let config2={className:identifier in CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP?CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier]:identifier,config:{}};return deserializeConstraint(config2)}else return identifier instanceof Constraint?identifier:deserializeConstraint(identifier)}function maxNorm(args){return new MaxNorm(args)}function unitNorm(args){return new UnitNorm(args)}function nonNeg(){return new NonNeg}function minMaxNorm(config2){return new MinMaxNorm(config2)}var exports_initializers_exports={};__export2(exports_initializers_exports,{constant:()=>constant,glorotNormal:()=>glorotNormal,glorotUniform:()=>glorotUniform,heNormal:()=>heNormal,heUniform:()=>heUniform,identity:()=>identity4,leCunNormal:()=>leCunNormal,leCunUniform:()=>leCunUniform,ones:()=>ones3,orthogonal:()=>orthogonal,randomNormal:()=>randomNormal3,randomUniform:()=>randomUniform2,truncatedNormal:()=>truncatedNormal2,varianceScaling:()=>varianceScaling,zeros:()=>zeros3});var VALID_DATA_FORMAT_VALUES=["channelsFirst","channelsLast"],VALID_INTERPOLATION_FORMAT_VALUES=["nearest","bilinear"],VALID_PADDING_MODE_VALUES=["valid","same","causal"],VALID_POOL_MODE_VALUES=["max","avg"],VALID_BIDIRECTIONAL_MERGE_MODES=["sum","mul","concat","ave"],nameMap=new Map;function checkDataFormat(value){checkStringTypeUnionValue(VALID_DATA_FORMAT_VALUES,"DataFormat",value)}function checkInterpolationFormat(value){checkStringTypeUnionValue(VALID_INTERPOLATION_FORMAT_VALUES,"InterpolationFormat",value)}function checkPaddingMode(value){checkStringTypeUnionValue(VALID_PADDING_MODE_VALUES,"PaddingMode",value)}function checkPoolMode(value){checkStringTypeUnionValue(VALID_POOL_MODE_VALUES,"PoolMode",value)}var _nameScopeStack=[],_nameScopeDivider="/";function nameScope(name,fn){_nameScopeStack.push(name);try{let val=fn();return _nameScopeStack.pop(),val}catch(e){throw _nameScopeStack.pop(),e}}function currentNameScopePrefix(){return _nameScopeStack.length===0?"":_nameScopeStack.join(_nameScopeDivider)+_nameScopeDivider}function getScopedTensorName(tensorName){if(!isValidTensorName(tensorName))throw new Error("Not a valid tensor name: '"+tensorName+"'");return currentNameScopePrefix()+tensorName}function getUniqueTensorName(scopedName){if(!isValidTensorName(scopedName))throw new Error("Not a valid tensor name: '"+scopedName+"'");nameMap.has(scopedName)||nameMap.set(scopedName,0);let index=nameMap.get(scopedName);if(nameMap.set(scopedName,nameMap.get(scopedName)+1),index>0){let result=`${scopedName}_${index}`;return nameMap.set(result,1),result}else return scopedName}var tensorNameRegex=new RegExp(/^[A-Za-z0-9][-A-Za-z0-9\._\/]*$/);function isValidTensorName(name){return!!name.match(tensorNameRegex)}function isInteger(x){return x===parseInt(x.toString(),10)}function arrayProd(array2,begin,end){begin==null&&(begin=0),end==null&&(end=array2.length);let prod5=1;for(let i=begin;i{if(x.shape.length!==2)throw new ValueError(`repeat() expects a rank-2 tensor, but received a rank-${x.shape.length} tensor.`);let y=expandDims5(x,1);return tile5(y,[1,n,1])})}function flatten2(x){let newShape=[arrayProd(x.shape)];return x.reshape(newShape)}function batchFlatten(x){if(x.rank<=1)throw new ValueError(`batchFlatten requires a minimum rank of 2. Got rank: ${x.rank}.`);let newShape=[x.shape[0],arrayProd(x.shape,1)];return x.reshape(newShape)}function sliceAlongFirstAxis(array2,start,size){return tidy(()=>{switch(array2.rank){case 1:return slice1d(array2,start,size);case 2:return slice2d(array2,[start,0],[size,array2.shape[1]]);case 3:return slice3d(array2,[start,0,0],[size,array2.shape[1],array2.shape[2]]);case 4:return slice4d(array2,[start,0,0,0],[size,array2.shape[1],array2.shape[2],array2.shape[3]]);case 5:return slice(array2,[start,0,0,0,0],[size,array2.shape[1],array2.shape[2],array2.shape[3],array2.shape[4]]);case 6:return slice(array2,[start,0,0,0,0,0],[size,array2.shape[1],array2.shape[2],array2.shape[3],array2.shape[4],array2.shape[5]]);default:throw new ValueError(`sliceAlongFirstAxis() received an unsupported tensor rank: ${array2.rank}`)}})}function sliceAlongLastAxis(array2,start,size){return tidy(()=>{switch(array2.rank){case 1:return slice1d(array2,start,size);case 2:return slice2d(array2,[0,start],[array2.shape[0],size]);case 3:return slice3d(array2,[0,0,start],[array2.shape[0],array2.shape[1],size]);case 4:return slice4d(array2,[0,0,0,start],[array2.shape[0],array2.shape[1],array2.shape[2],size]);default:throw new ValueError(`sliceAlongLastAxis() received an unsupported tensor rank: ${array2.rank}`)}})}function sliceAlongAxis(array2,start,size,axis){return tidy(()=>{switch(array2.rank){case 1:return slice1d(array2,start,size);case 2:switch(axis){case 1:return sliceAlongFirstAxis(array2,start,size);case 2:return sliceAlongLastAxis(array2,start,size);default:throw new ValueError(`The axis is not within the rank of the tensor ${axis}`)}case 3:switch(axis){case 1:return sliceAlongFirstAxis(array2,start,size);case 2:return slice3d(array2,[0,start,0],[array2.shape[0],size,array2.shape[2]]);case 3:return sliceAlongLastAxis(array2,start,size);default:throw new ValueError(`The axis is not within the rank of the tensor ${axis}`)}case 4:switch(axis){case 1:return sliceAlongFirstAxis(array2,start,size);case 2:return slice4d(array2,[0,start,0,0],[array2.shape[0],size,array2.shape[2],array2.shape[3]]);case 3:return slice4d(array2,[0,0,start,0],[array2.shape[0],array2.shape[1],size,array2.shape[3]]);case 4:return sliceAlongLastAxis(array2,start,size);default:throw new ValueError(`The axis is not within the rank of the tensor ${axis}`)}default:throw new ValueError(`sliceAlongLastAxis() received an unsupported tensor rank: ${array2.rank}`)}})}function concatenate(tensors,axis=-1){let rank;return axis<0&&(rank=tensors[0].rank,rank!==0?axis=rank:axis=0),axis===tensors[0].rank&&(axis=-1),concat(tensors,axis)}function concatAlongFirstAxis(a,b){switch(a.rank){case 1:return concat1d([a,b]);case 2:return concat2d([a,b],0);case 3:return concat3d([a,b],0);case 4:return concat4d([a,b],0);default:throw new ValueError(`concatAlongFirstAxis() received an unsupported tensor rank: ${a.rank}`)}}function tile5(x,n){if(Array.isArray(n)||(n=[n]),x.rank!==n.length)throw new ValueError(`The length of input n (${n.length}) does not match the number of dimensions in input x (${x.rank})`);return tile(x,n)}function randomNormal2(shape,mean4=0,stddev=1,dtype,seed){return randomNormal(shape,mean4,stddev,dtype,seed)}function dot2(a,b,activation2,bias){if(a.rank<2||b.rank<2)throw new NotImplementedError(`dot requires both inputs to be rank >= 2 but got x shape = ${a.shape} and y shape = ${b.shape}`);if(b.rank>=3){let xLastDim=a.shape.slice(-1)[0],ySecondLastDim=b.shape.slice(-2)[0];if(xLastDim!==ySecondLastDim)throw new NotImplementedError(`If rank y >= 3, then the second last dim of y must equal the last dim of x but got x shape = ${a.shape} and y shape = ${b.shape}`)}if(a.rank===2&&b.rank===2){let transposeA=!1,transposeB=!1;return fused_ops_exports.matMul({a,b,transposeA,transposeB,bias:bias?reshapeBias(a.rank,bias,imageDataFormat()):null,activation:activation2})}else{let aFirstDims=a.shape.slice(),aLastDim=aFirstDims.pop();a=a.reshape([-1,aLastDim]);let bShape=b.shape.slice(),bLastDim=bShape.pop(),ySecondLastDim=bShape.pop(),yOtherDims=[...bShape,bLastDim],perm=Array.from({length:b.rank},(_,i)=>i===0?b.rank-2:i<=b.rank-2?i-1:i);b=b.transpose(perm).reshape([ySecondLastDim,-1]);let outputShape=[...aFirstDims,...yOtherDims],transposeA=!1,transposeB=!1;return fused_ops_exports.matMul({a,b,transposeA,transposeB,bias:bias?reshapeBias(a.rank,bias,imageDataFormat()):null,activation:activation2}).reshape(outputShape)}}function gather2(reference,indices,axis){return tidy(()=>(Array.isArray(indices)?indices=tensor1d(indices,"int32"):indices=indices.toInt(),gather(reference,indices,axis)))}function square3(x){return mul(x,x)}function reshapeBias(xRank,bias,dataFormat){let biasShape=bias.shape;if(bias.rank!==1&&bias.rank!==xRank)throw new ValueError(`Unexpected bias dimensions: ${bias.rank}; expected it to be 1 or ${xRank}`);if(xRank===5){if(dataFormat==="channelsFirst")return biasShape.length===1?bias.reshape([1,biasShape[0],1,1,1]):bias.reshape([1,biasShape[3],biasShape[0],biasShape[1],biasShape[2]]);if(dataFormat==="channelsLast")return biasShape.length===1?bias.reshape([1,1,1,1,biasShape[0]]):bias.reshape([1].concat(biasShape))}else if(xRank===4){if(dataFormat==="channelsFirst")return biasShape.length===1?bias.reshape([1,biasShape[0],1,1]):bias.reshape([1,biasShape[2],biasShape[0],biasShape[1]]);if(dataFormat==="channelsLast")return biasShape.length===1?bias.reshape([1,1,1,biasShape[0]]):bias.reshape([1].concat(biasShape))}else if(xRank===3){if(dataFormat==="channelsFirst")return biasShape.length===1?bias.reshape([1,biasShape[0],1]):bias.reshape([1,biasShape[1],biasShape[0]]);if(dataFormat==="channelsLast")return biasShape.length===1?bias.reshape([1,1,biasShape[0]]):bias.reshape([1].concat(biasShape))}else if(xRank<3)return bias;throw new ValueError(`Unsupported input rank by biasAdd: ${bias.rank}`)}function biasAdd(x,bias,dataFormat){return tidy(()=>(dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat),x.add(reshapeBias(x.rank,bias,dataFormat))))}function elu4(x,alpha=1){if(alpha!==1)throw new NotImplementedError(`Support for alpha values other than 1 (${alpha}) is not implemented yet.`);return elu(x)}function softsign(x){return tidy(()=>div(x,abs(x).add(1)))}function dropout2(x,level,noiseShape,seed){return tidy(()=>dropout(x,level,noiseShape,seed))}function hardSigmoid(x){return tidy(()=>{let y=add2(.5,mul(.2,x));return clipByValue(y,0,1)})}function inTrainPhase(x,alt,training=!1){return training?x():alt()}var VALID_FAN_MODE_VALUES=["fanIn","fanOut","fanAvg"],VALID_DISTRIBUTION_VALUES=["normal","uniform","truncatedNormal"];function checkFanMode(value){checkStringTypeUnionValue(VALID_FAN_MODE_VALUES,"FanMode",value)}function checkDistribution(value){checkStringTypeUnionValue(VALID_DISTRIBUTION_VALUES,"Distribution",value)}var Initializer=class extends serialization_exports.Serializable{fromConfigUsesCustomObjects(){return!1}getConfig(){return{}}},Zeros=class extends Initializer{apply(shape,dtype){return zeros(shape,dtype)}};Zeros.className="Zeros";serialization_exports.registerClass(Zeros);var Ones=class extends Initializer{apply(shape,dtype){return ones2(shape,dtype)}};Ones.className="Ones";serialization_exports.registerClass(Ones);var Constant=class extends Initializer{constructor(args){super();if(typeof args!="object")throw new ValueError(`Expected argument of type ConstantConfig but got ${args}`);if(args.value===void 0)throw new ValueError(`config must have value set but got ${args}`);this.value=args.value}apply(shape,dtype){return tidy(()=>mul(scalar(this.value),ones2(shape,dtype)))}getConfig(){return{value:this.value}}};Constant.className="Constant";serialization_exports.registerClass(Constant);var RandomUniform=class extends Initializer{constructor(args){super();this.DEFAULT_MINVAL=-.05,this.DEFAULT_MAXVAL=.05,this.minval=args.minval||this.DEFAULT_MINVAL,this.maxval=args.maxval||this.DEFAULT_MAXVAL,this.seed=args.seed}apply(shape,dtype){return randomUniform(shape,this.minval,this.maxval,dtype)}getConfig(){return{minval:this.minval,maxval:this.maxval,seed:this.seed}}};RandomUniform.className="RandomUniform";serialization_exports.registerClass(RandomUniform);var RandomNormal=class extends Initializer{constructor(args){super();this.DEFAULT_MEAN=0,this.DEFAULT_STDDEV=.05,this.mean=args.mean||this.DEFAULT_MEAN,this.stddev=args.stddev||this.DEFAULT_STDDEV,this.seed=args.seed}apply(shape,dtype){if(dtype=dtype||"float32",dtype!=="float32"&&dtype!=="int32")throw new NotImplementedError(`randomNormal does not support dType ${dtype}.`);return randomNormal2(shape,this.mean,this.stddev,dtype,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}};RandomNormal.className="RandomNormal";serialization_exports.registerClass(RandomNormal);var TruncatedNormal=class extends Initializer{constructor(args){super();this.DEFAULT_MEAN=0,this.DEFAULT_STDDEV=.05,this.mean=args.mean||this.DEFAULT_MEAN,this.stddev=args.stddev||this.DEFAULT_STDDEV,this.seed=args.seed}apply(shape,dtype){if(dtype=dtype||"float32",dtype!=="float32"&&dtype!=="int32")throw new NotImplementedError(`truncatedNormal does not support dType ${dtype}.`);return truncatedNormal(shape,this.mean,this.stddev,dtype,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}};TruncatedNormal.className="TruncatedNormal";serialization_exports.registerClass(TruncatedNormal);var Identity2=class extends Initializer{constructor(args){super();this.gain=args.gain!=null?args.gain:1}apply(shape,dtype){return tidy(()=>{if(shape.length!==2||shape[0]!==shape[1])throw new ValueError("Identity matrix initializer can only be used for 2D square matrices.");return mul(this.gain,eye(shape[0]))})}getConfig(){return{gain:this.gain}}};Identity2.className="Identity";serialization_exports.registerClass(Identity2);function computeFans(shape,dataFormat="channelsLast"){let fanIn,fanOut;if(checkDataFormat(dataFormat),shape.length===2)fanIn=shape[0],fanOut=shape[1];else if([3,4,5].indexOf(shape.length)!==-1){if(dataFormat==="channelsFirst"){let receptiveFieldSize=arrayProd(shape,2);fanIn=shape[1]*receptiveFieldSize,fanOut=shape[0]*receptiveFieldSize}else if(dataFormat==="channelsLast"){let receptiveFieldSize=arrayProd(shape,0,shape.length-2);fanIn=shape[shape.length-2]*receptiveFieldSize,fanOut=shape[shape.length-1]*receptiveFieldSize}}else{let shapeProd=arrayProd(shape);fanIn=Math.sqrt(shapeProd),fanOut=Math.sqrt(shapeProd)}return[fanIn,fanOut]}var VarianceScaling=class extends Initializer{constructor(args){super();if(args.scale<0)throw new ValueError(`scale must be a positive float. Got: ${args.scale}`);this.scale=args.scale==null?1:args.scale,this.mode=args.mode==null?"fanIn":args.mode,checkFanMode(this.mode),this.distribution=args.distribution==null?"normal":args.distribution,checkDistribution(this.distribution),this.seed=args.seed}apply(shape,dtype){let fans=computeFans(shape),fanIn=fans[0],fanOut=fans[1],scale2=this.scale;if(this.mode==="fanIn"?scale2/=Math.max(1,fanIn):this.mode==="fanOut"?scale2/=Math.max(1,fanOut):scale2/=Math.max(1,(fanIn+fanOut)/2),this.distribution==="normal"){let stddev=Math.sqrt(scale2);if(dtype=dtype||"float32",dtype!=="float32"&&dtype!=="int32")throw new NotImplementedError(`${this.getClassName()} does not support dType ${dtype}.`);return truncatedNormal(shape,0,stddev,dtype,this.seed)}else{let limit=Math.sqrt(3*scale2);return randomUniform(shape,-limit,limit,dtype)}}getConfig(){return{scale:this.scale,mode:this.mode,distribution:this.distribution,seed:this.seed}}};VarianceScaling.className="VarianceScaling";serialization_exports.registerClass(VarianceScaling);var GlorotUniform=class extends VarianceScaling{constructor(args){super({scale:1,mode:"fanAvg",distribution:"uniform",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};GlorotUniform.className="GlorotUniform";serialization_exports.registerClass(GlorotUniform);var GlorotNormal=class extends VarianceScaling{constructor(args){super({scale:1,mode:"fanAvg",distribution:"normal",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};GlorotNormal.className="GlorotNormal";serialization_exports.registerClass(GlorotNormal);var HeNormal=class extends VarianceScaling{constructor(args){super({scale:2,mode:"fanIn",distribution:"normal",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};HeNormal.className="HeNormal";serialization_exports.registerClass(HeNormal);var HeUniform=class extends VarianceScaling{constructor(args){super({scale:2,mode:"fanIn",distribution:"uniform",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};HeUniform.className="HeUniform";serialization_exports.registerClass(HeUniform);var LeCunNormal=class extends VarianceScaling{constructor(args){super({scale:1,mode:"fanIn",distribution:"normal",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};LeCunNormal.className="LeCunNormal";serialization_exports.registerClass(LeCunNormal);var LeCunUniform=class extends VarianceScaling{constructor(args){super({scale:1,mode:"fanIn",distribution:"uniform",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};LeCunUniform.className="LeCunNormal";serialization_exports.registerClass(LeCunUniform);var Orthogonal=class extends Initializer{constructor(args){super();if(this.DEFAULT_GAIN=1,this.gain=args.gain==null?this.DEFAULT_GAIN:args.gain,this.seed=args.seed,this.seed!=null)throw new NotImplementedError("Random seed is not implemented for Orthogonal Initializer yet.")}apply(shape,dtype){return tidy(()=>{if(shape.length<2)throw new NotImplementedError("Shape must be at least 2D.");shape[0]*shape[1]>2e3&&console.warn(`Orthogonal initializer is being called on a matrix with more than 2000 (${shape[0]*shape[1]}) elements: Slowness may result.`);let normalizedShape=shape[0]>shape[1]?[shape[1],shape[0]]:shape,a=randomNormal2(normalizedShape,0,1,"float32"),q=linalg.gramSchmidt(a);return shape[0]>shape[1]&&(q=q.transpose()),mul(this.gain,q)})}getConfig(){return{gain:this.gain,seed:this.seed}}};Orthogonal.className="Orthogonal";serialization_exports.registerClass(Orthogonal);var INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP={constant:"Constant",glorotNormal:"GlorotNormal",glorotUniform:"GlorotUniform",heNormal:"HeNormal",heUniform:"HeUniform",identity:"Identity",leCunNormal:"LeCunNormal",leCunUniform:"LeCunUniform",ones:"Ones",orthogonal:"Orthogonal",randomNormal:"RandomNormal",randomUniform:"RandomUniform",truncatedNormal:"TruncatedNormal",varianceScaling:"VarianceScaling",zeros:"Zeros"};function deserializeInitializer(config2,customObjects={}){return deserializeKerasObject(config2,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"initializer")}function serializeInitializer(initializer){return serializeKerasObject(initializer)}function getInitializer(identifier){if(typeof identifier=="string"){let className=identifier in INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP?INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier]:identifier;if(className==="GlorotNormal")return new GlorotNormal;if(className==="GlorotUniform")return new GlorotUniform;if(className==="HeNormal")return new HeNormal;if(className==="HeUniform")return new HeUniform;if(className==="LeCunNormal")return new LeCunNormal;if(className==="LeCunUniform")return new LeCunUniform;{let config2={};return config2.className=className,config2.config={},deserializeInitializer(config2)}}else return identifier instanceof Initializer?identifier:deserializeInitializer(identifier)}function zeros3(){return new Zeros}function ones3(){return new Ones}function constant(args){return new Constant(args)}function randomUniform2(args){return new RandomUniform(args)}function randomNormal3(args){return new RandomNormal(args)}function truncatedNormal2(args){return new TruncatedNormal(args)}function identity4(args){return new Identity2(args)}function varianceScaling(config2){return new VarianceScaling(config2)}function glorotUniform(args){return new GlorotUniform(args)}function glorotNormal(args){return new GlorotNormal(args)}function heNormal(args){return new HeNormal(args)}function heUniform(args){return new HeUniform(args)}function leCunNormal(args){return new LeCunNormal(args)}function leCunUniform(args){return new LeCunUniform(args)}function orthogonal(args){return new Orthogonal(args)}var exports_layers_exports={};__export2(exports_layers_exports,{Layer:()=>Layer,RNN:()=>RNN,RNNCell:()=>RNNCell,activation:()=>activation,add:()=>add4,alphaDropout:()=>alphaDropout,average:()=>average,averagePooling1d:()=>averagePooling1d,averagePooling2d:()=>averagePooling2d,averagePooling3d:()=>averagePooling3d,avgPool1d:()=>avgPool1d,avgPool2d:()=>avgPool2d,avgPool3d:()=>avgPool3d2,avgPooling1d:()=>avgPooling1d,avgPooling2d:()=>avgPooling2d,avgPooling3d:()=>avgPooling3d,batchNormalization:()=>batchNormalization2,bidirectional:()=>bidirectional,concatenate:()=>concatenate2,conv1d:()=>conv1d2,conv2d:()=>conv2d5,conv2dTranspose:()=>conv2dTranspose2,conv3d:()=>conv3d2,convLstm2d:()=>convLstm2d,convLstm2dCell:()=>convLstm2dCell,cropping2D:()=>cropping2D,dense:()=>dense,depthwiseConv2d:()=>depthwiseConv2d5,dot:()=>dot3,dropout:()=>dropout3,elu:()=>elu5,embedding:()=>embedding,flatten:()=>flatten3,gaussianDropout:()=>gaussianDropout,gaussianNoise:()=>gaussianNoise,globalAveragePooling1d:()=>globalAveragePooling1d,globalAveragePooling2d:()=>globalAveragePooling2d,globalMaxPool1d:()=>globalMaxPool1d,globalMaxPool2d:()=>globalMaxPool2d,globalMaxPooling1d:()=>globalMaxPooling1d,globalMaxPooling2d:()=>globalMaxPooling2d,gru:()=>gru,gruCell:()=>gruCell,input:()=>input,inputLayer:()=>inputLayer,layerNormalization:()=>layerNormalization,leakyReLU:()=>leakyReLU,lstm:()=>lstm,lstmCell:()=>lstmCell,masking:()=>masking,maxPool1d:()=>maxPool1d,maxPool2d:()=>maxPool2d,maxPooling1d:()=>maxPooling1d,maxPooling2d:()=>maxPooling2d,maxPooling3d:()=>maxPooling3d,maximum:()=>maximum4,minimum:()=>minimum4,multiply:()=>multiply3,permute:()=>permute,prelu:()=>prelu5,reLU:()=>reLU,repeatVector:()=>repeatVector,reshape:()=>reshape5,rnn:()=>rnn2,separableConv2d:()=>separableConv2d2,simpleRNN:()=>simpleRNN,simpleRNNCell:()=>simpleRNNCell,softmax:()=>softmax5,spatialDropout1d:()=>spatialDropout1d,stackedRNNCells:()=>stackedRNNCells,thresholdedReLU:()=>thresholdedReLU,timeDistributed:()=>timeDistributed,upSampling2d:()=>upSampling2d,zeroPadding2d:()=>zeroPadding2d});var _nextUniqueTensorId=0;function getNextUniqueTensorId(){return _nextUniqueTensorId++}var _uidPrefixes={};function getUid(prefix=""){return prefix in _uidPrefixes||(_uidPrefixes[prefix]=0),_uidPrefixes[prefix]+=1,prefix+_uidPrefixes[prefix].toString()}function isArrayOfShapes(x){return Array.isArray(x)&&Array.isArray(x[0])}function normalizeShapeList(x){return x.length===0?[]:Array.isArray(x[0])?x:[x]}function getExactlyOneTensor(xs){let x;if(Array.isArray(xs)){if(xs.length!==1)throw new ValueError(`Expected Tensor length to be 1; got ${xs.length}`);x=xs[0]}else x=xs;return x}function getExactlyOneShape(shapes){if(Array.isArray(shapes)&&Array.isArray(shapes[0])){if(shapes.length===1)return shapes=shapes,shapes[0];throw new ValueError(`Expected exactly 1 Shape; got ${shapes.length}`)}else return shapes}function countParamsInWeights(weights){let count2=0;for(let weight of weights)weight.shape.length===0?count2+=1:count2+=weight.shape.reduce((a,b)=>a*b);return count2}var DEFAULT_VARIABLE_NAME_PREFIX="Variable",LayerVariable=class{constructor(val,dtype="float32",name=DEFAULT_VARIABLE_NAME_PREFIX,trainable=!0,constraint=null){this.dtype=dtype==null?"float32":dtype,this.shape=val.shape,this.id=getNextUniqueTensorId(),name=name==null?DEFAULT_VARIABLE_NAME_PREFIX:name,this.originalName=getScopedTensorName(name),this.name=getUniqueTensorName(this.originalName),this.trainable_=trainable,this.constraint=constraint,this.val=variable(val,this.trainable_,this.name,this.dtype)}read(){return this.assertNotDisposed(),this.val}write(newVal){return this.assertNotDisposed(),checkShapesMatch(this.val,newVal),this.val.id!==newVal.id&&(this.val.assign(newVal),this.constraint!=null&&this.val.assign(this.constraint.apply(this.val))),this}dispose(){this.assertNotDisposed(),this.val.dispose()}assertNotDisposed(){if(this.val.isDisposed)throw new Error(`LayersVariable ${this.name} is already disposed.`)}get trainable(){return this.trainable_}set trainable(trainable){this.trainable_=trainable,this.val.trainable=trainable}};function checkShapesMatch(x,y){if(x.shape.toString()!==y.shape.toString())throw new Error("Shape mismatch: "+JSON.stringify(x.shape)+" vs. "+JSON.stringify(y.shape))}function batchGetValue(xs){return xs.map(x=>x.read())}function batchSetValue(variablesAndValues){variablesAndValues.forEach(variableAndValue=>{variableAndValue[0].write(variableAndValue[1])})}var InputSpec=class{constructor(args){this.dtype=args.dtype,this.shape=args.shape,args.shape!=null?this.ndim=args.shape.length:this.ndim=args.ndim,this.maxNDim=args.maxNDim,this.minNDim=args.minNDim,this.axes=args.axes||{}}},SymbolicTensor=class{constructor(dtype,shape,sourceLayer,inputs,callArgs,name,outputTensorIndex){this.dtype=dtype,this.shape=shape,this.sourceLayer=sourceLayer,this.inputs=inputs,this.callArgs=callArgs,this.outputTensorIndex=outputTensorIndex,this.id=getNextUniqueTensorId(),name!=null&&(this.originalName=getScopedTensorName(name),this.name=getUniqueTensorName(this.originalName)),this.rank=shape.length}},_nextNodeID=0,Node=class{constructor(args,callArgs){this.callArgs=callArgs,this.id=_nextNodeID++,this.outboundLayer=args.outboundLayer,this.inboundLayers=args.inboundLayers,this.nodeIndices=args.nodeIndices,this.tensorIndices=args.tensorIndices,this.inputTensors=args.inputTensors,this.outputTensors=args.outputTensors,this.inputMasks=args.inputMasks,this.outputMasks=args.outputMasks,this.inputShapes=args.inputShapes,this.outputShapes=args.outputShapes;for(let layer of args.inboundLayers)layer!=null&&layer.outboundNodes.push(this);args.outboundLayer.inboundNodes.push(this)}getConfig(){let inboundNames=[];for(let layer of this.inboundLayers)layer!=null?inboundNames.push(layer.name):inboundNames.push(null);return{outboundLayer:this.outboundLayer?this.outboundLayer.name:null,inboundLayers:inboundNames,nodeIndices:this.nodeIndices,tensorIndices:this.tensorIndices}}},_nextLayerID=0,Layer=class extends serialization_exports.Serializable{constructor(args={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=_nextLayerID++,this.activityRegularizer=null,this.inputSpec=null,this.supportsMasking=!1,this._trainableWeights=[],this._nonTrainableWeights=[],this._losses=[],this._updates=[],this._built=!1,this.inboundNodes=[],this.outboundNodes=[];let name=args.name;if(!name){let prefix=this.getClassName();name=toSnakeCase(prefix)+"_"+getUid(prefix)}if(this.name=name,this.trainable_=args.trainable==null?!0:args.trainable,args.inputShape!=null||args.batchInputShape!=null){let batchInputShape;if(args.batchInputShape!=null)batchInputShape=args.batchInputShape;else if(args.inputShape!=null){let batchSize=null;args.batchSize!=null&&(batchSize=args.batchSize),batchInputShape=[batchSize].concat(args.inputShape)}this.batchInputShape=batchInputShape;let dtype=args.dtype;dtype==null&&(dtype=args.inputDType),dtype==null&&(dtype="float32"),this.dtype=dtype}args.weights!=null?this.initialWeights=args.weights:this.initialWeights=null,this._refCount=null,this.fastWeightInitDuringBuild=!1}static nodeKey(layer,nodeIndex){return layer.name+"_ib-"+nodeIndex.toString()}getNodeAtIndex(nodeIndex,attrName){if(this.inboundNodes.length===0)throw new RuntimeError(`The layer has never been called and thus has no defined ${attrName}.`);if(this.inboundNodes.length<=nodeIndex)throw new ValueError(`Asked to get ${attrName} at node ${nodeIndex}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[nodeIndex]}getInputAt(nodeIndex){return singletonOrArray(this.getNodeAtIndex(nodeIndex,"input").inputTensors)}getOutputAt(nodeIndex){return singletonOrArray(this.getNodeAtIndex(nodeIndex,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new AttributeError(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer input" is ill-defined. Use \`getInputAt(nodeIndex)\` instead.`);if(this.inboundNodes.length===0)throw new AttributeError(`Layer ${this.name} is not connected, no input to return.`);return singletonOrArray(this.getNodeAtIndex(0,"input").inputTensors)}get output(){if(this.inboundNodes.length===0)throw new AttributeError(`Layer ${this.name} has no inbound nodes.`);if(this.inboundNodes.length>1)throw new AttributeError(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer output" is ill-defined. Use \`getOutputAt(nodeIndex)\` instead.`);return singletonOrArray(this.getNodeAtIndex(0,"output").outputTensors)}get losses(){return this._losses}calculateLosses(){return this.losses.map(lossFn=>lossFn())}get updates(){return this._updates}get built(){return this._built}set built(built){this._built=built}get trainable(){return this.trainable_}set trainable(trainable){this._trainableWeights.forEach(w=>w.trainable=trainable),this.trainable_=trainable}get trainableWeights(){return this.trainable_?this._trainableWeights.filter(w=>w.trainable):[]}set trainableWeights(weights){this._trainableWeights=weights}get nonTrainableWeights(){return this.trainable?this._trainableWeights.filter(w=>!w.trainable).concat(this._nonTrainableWeights):this._trainableWeights.concat(this._nonTrainableWeights)}set nonTrainableWeights(weights){this._nonTrainableWeights=weights}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}get stateful(){return this._stateful}resetStates(){if(!this.stateful)throw new Error("Cannot call the resetStates() method of a non-stateful Layer object.")}assertInputCompatibility(inputs){if(inputs=toList(inputs),this.inputSpec==null||this.inputSpec.length===0)return;let inputSpec=toList(this.inputSpec);if(inputs.length!==inputSpec.length)throw new ValueError(`Layer ${this.name} expects ${inputSpec.length} inputs, but it received ${inputs.length} input tensors. Input received: ${inputs}`);for(let inputIndex=0;inputIndexspec.maxNDim)throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected max_ndim=${spec.maxNDim}, found ndim=${ndim}`);if(spec.minNDim!=null&&ndim=0?xShape[axis]:xShape[xShape.length+axis];if(value!=null&&[value,null].indexOf(xShapeAtAxis)===-1)throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected axis ${axis} of input shape to have value ${value} but got shape ${xShape}.`)}}if(spec.shape!=null)for(let i=0;i{if(!this.built){this.assertInputCompatibility(inputs);let inputShapes=[];for(let xElem of toList(inputs))inputShapes.push(xElem.shape);this.build(singletonOrArray(inputShapes)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&noneAreSymbolic&&(this._refCount=1)}if(this.assertInputCompatibility(inputs),noneAreSymbolic){let output=this.call(inputs,kwargs),outputList=toList(output),outputListCopy=[];for(let x of outputList)inputsList.indexOf(x)!==-1&&(x=x.clone()),outputListCopy.push(x);if(output=singletonOrArray(outputListCopy),this.activityRegularizer!=null)throw new NotImplementedError("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return output}else{let inputShape=collectInputShape(inputs),outputShape=this.computeOutputShape(inputShape),output,outputDType=guessOutputDType(inputs);if(this.warnOnIncompatibleInputShape(Array.isArray(inputs)?inputShape[0]:inputShape),outputShape!=null&&outputShape.length>0&&Array.isArray(outputShape[0])?output=outputShape.map((shape,index)=>new SymbolicTensor(outputDType,shape,this,toList(inputs),kwargs,this.name,index)):output=new SymbolicTensor(outputDType,outputShape,this,toList(inputs),kwargs,this.name),this.addInboundNode(inputs,output,null,null,inputShape,outputShape,kwargs),this._refCount++,this.activityRegularizer!=null)throw new NotImplementedError("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return output}})}warnOnIncompatibleInputShape(inputShape){if(this.batchInputShape!=null)if(inputShape.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(inputShape)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let dimMismatch=!1;this.batchInputShape.forEach((dimension,i)=>{dimension!=null&&inputShape[i]!=null&&inputShape[i]!==dimension&&(dimMismatch=!0)}),dimMismatch&&console.warn(`The shape of the input tensor (${JSON.stringify(inputShape)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}get outputShape(){if(this.inboundNodes==null||this.inboundNodes.length===0)throw new AttributeError(`The layer ${this.name} has never been called and thus has no defined output shape.`);let allOutputShapes=[];for(let node of this.inboundNodes){let shapeString=JSON.stringify(node.outputShapes);allOutputShapes.indexOf(shapeString)===-1&&allOutputShapes.push(shapeString)}if(allOutputShapes.length===1){let outputShapes=this.inboundNodes[0].outputShapes;return Array.isArray(outputShapes)&&Array.isArray(outputShapes[0])&&outputShapes.length===1?outputShapes[0]:outputShapes}else throw new AttributeError(`The layer ${this.name} has multiple inbound nodes with different output shapes. Hence the notion of "output shape" is ill-defined for the layer.`)}countParams(){if(!this.built)throw new RuntimeError(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return countParamsInWeights(this.weights)}build(inputShape){this.built=!0}getWeights(trainableOnly=!1){return batchGetValue(trainableOnly?this.trainableWeights:this.weights)}setWeights(weights){tidy(()=>{let params=this.weights;if(params.length!==weights.length)throw new ValueError(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${weights.length}, but the layer was expecting ${params.length} weights. Provided weights: ${weights}...`);if(params.length===0)return;let weightValueTuples=[],paramValues=batchGetValue(params);for(let i=0;iregularizer.apply(weight.read())),trainable==null&&(trainable=!0),trainable?this._trainableWeights.push(weight):this._nonTrainableWeights.push(weight),weight}setFastWeightInitDuringBuild(value){this.fastWeightInitDuringBuild=value}addLoss(losses4){losses4==null||Array.isArray(losses4)&&losses4.length===0||(losses4=toList(losses4),this._losses!==void 0&&this._losses!==null&&this.losses.push(...losses4))}computeOutputShape(inputShape){return inputShape}computeMask(inputs,mask){if(!this.supportsMasking){if(mask!=null)if(Array.isArray(mask))mask.forEach(maskElement=>{if(maskElement!=null)throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`)});else throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);return null}return mask}addInboundNode(inputTensors,outputTensors,inputMasks,outputMasks,inputShapes,outputShapes,kwargs=null){let inputTensorList=toList(inputTensors);outputTensors=toList(outputTensors),inputMasks=toList(inputMasks),outputMasks=toList(outputMasks),inputShapes=normalizeShapeList(inputShapes),outputShapes=normalizeShapeList(outputShapes);let inboundLayers=[],nodeIndices=[],tensorIndices=[];for(let x of inputTensorList)inboundLayers.push(x.sourceLayer),nodeIndices.push(x.nodeIndex),tensorIndices.push(x.tensorIndex);new Node({outboundLayer:this,inboundLayers,nodeIndices,tensorIndices,inputTensors:inputTensorList,outputTensors,inputMasks,outputMasks,inputShapes,outputShapes},kwargs);for(let i=0;iweight.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${this.name}' is already disposed.`)}dispose(){if(!this.built)throw new Error(`Cannot dispose Layer ${this.name} because it has not been built yet.`);if(this._refCount===null)throw new Error(`Cannot dispose Layer ${this.name} because it has not been used yet.`);this.assertNotDisposed();let numDisposedVariables=0;return--this._refCount==0&&(numDisposedVariables=this.disposeWeights()),{refCountAfterDispose:this._refCount,numDisposedVariables}}};function collectInputShape(inputTensors){inputTensors=toList(inputTensors);let shapes=[];for(let x of inputTensors)shapes.push(x.shape);return singletonOrArray(shapes)}function guessOutputDType(inputTensors){return"float32"}function getSourceInputs(tensor2,layer,nodeIndex){if((layer==null||nodeIndex!=null&&nodeIndex>0)&&(layer=tensor2.sourceLayer,nodeIndex=tensor2.nodeIndex),layer.inboundNodes.length===0)return[tensor2];{let node=layer.inboundNodes[nodeIndex];if(node.inboundLayers.length===0)return node.inputTensors;{let sourceTensors=[];for(let i=0;i0){let values=await Promise.all(promises);for(let i=0;iadd2(this.totals[key],mul(value,batchSize)));this.totals[key]=total,oldTotalsToDispose!=null&&oldTotalsToDispose.dispose()}}}async onEpochEnd(epoch,logs){if(logs!=null)for(let key of this.params.metrics)this.totals[key]!=null&&(typeof this.totals[key]=="number"?logs[key]=this.totals[key]/this.seen:tidy(()=>{let log5=mul(div(1,this.seen),this.totals[key]);logs[key]=log5,this.totals[key].dispose(),keep(logs[key])}))}},History=class extends BaseCallback{async onTrainBegin(logs){this.epoch=[],this.history={}}async onEpochEnd(epoch,logs){logs==null&&(logs={}),this.epoch.push(epoch);for(let key in logs)this.history[key]==null&&(this.history[key]=[]),this.history[key].push(logs[key])}async syncData(){let promises=[],keys=[],indices=[];for(let key in this.history){let valueArray=this.history[key];for(let i=0;inew CustomCallback(callbackConfig,yieldEvery))}var CallbackConstructorRegistry=class{constructor(){}static registerCallbackConstructor(verbosityLevel,callbackConstructor){util_exports.assert(verbosityLevel>=0&&Number.isInteger(verbosityLevel),()=>`Verbosity level is expected to be an integer >= 0, but got ${verbosityLevel}`),CallbackConstructorRegistry.checkForDuplicate(callbackConstructor),CallbackConstructorRegistry.constructors[verbosityLevel]==null&&(CallbackConstructorRegistry.constructors[verbosityLevel]=[]),CallbackConstructorRegistry.constructors[verbosityLevel].push(callbackConstructor)}static checkForDuplicate(callbackConstructor){for(let levelName in CallbackConstructorRegistry.constructors)CallbackConstructorRegistry.constructors[+levelName].forEach(ctor=>{if(ctor===callbackConstructor)throw new ValueError("Duplicate callback constructor.")})}static clear(){CallbackConstructorRegistry.constructors={}}static createCallbacks(verbosityLevel){let constructors=[];for(let levelName in CallbackConstructorRegistry.constructors){let level=+levelName;verbosityLevel>=level&&constructors.push(...CallbackConstructorRegistry.constructors[level])}return constructors.map(ctor=>new ctor)}};CallbackConstructorRegistry.constructors={};function configureCallbacks(callbacks2,verbose,epochs,initialEpoch,numTrainSamples,stepsPerEpoch,batchSize,doValidation,callbackMetrics){let history=new History,actualCallbacks=[new BaseLogger,...CallbackConstructorRegistry.createCallbacks(verbose)];callbacks2!=null&&actualCallbacks.push(...callbacks2),actualCallbacks.push(history);let callbackList=new CallbackList(actualCallbacks);return callbackList.setParams({epochs,initialEpoch,samples:numTrainSamples,steps:stepsPerEpoch,batchSize,verbose,doValidation,metrics:callbackMetrics}),{callbackList,history}}function deserialize(config2,customObjects={},fastWeightInit=!1){return deserializeKerasObject(config2,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"layer",fastWeightInit)}function l2Normalize(x,axis){return tidy(()=>{x.dtype!=="float32"&&(x=x.asType("float32"));let squareSum=sum2(square3(x),axis,!0),epsilonTensor=fill(squareSum.shape,epsilon2()),norm2=sqrt(maximum(squareSum,epsilonTensor));return div(x,norm2)})}function meanSquaredError2(yTrue,yPred){return tidy(()=>mean(square3(sub(yPred,yTrue)),-1))}function meanAbsoluteError(yTrue,yPred){return tidy(()=>mean(abs(sub(yPred,yTrue)),-1))}function meanAbsolutePercentageError(yTrue,yPred){return tidy(()=>{let diff=sub(yTrue,yPred),clippedTrue=clipByValue(abs(yTrue),epsilon2(),Number.MAX_VALUE),absResult=abs(div(diff,clippedTrue));return mul(100,mean(absResult,-1))})}function meanSquaredLogarithmicError(yTrue,yPred){return tidy(()=>{let clippedPred=clipByValue(yPred,epsilon2(),Number.MAX_VALUE),firstLog=log2(add2(1,clippedPred)),clippedTrue=clipByValue(yTrue,epsilon2(),Number.MAX_VALUE),secondLog=log2(add2(1,clippedTrue));return mean(square3(sub(firstLog,secondLog)),-1)})}function squaredHinge(yTrue,yPred){return tidy(()=>{let maxResult=maximum(0,sub(1,mul(yTrue,yPred)));return mean(square3(maxResult),-1)})}function hinge(yTrue,yPred){return tidy(()=>{let maxResult=maximum(0,sub(1,mul(yTrue,yPred)));return mean(maxResult,-1)})}function categoricalHinge(yTrue,yPred){return tidy(()=>{let pos=sum2(mul(yTrue,yPred),-1),neg4=max(mul(sub(1,yTrue),yPred),-1);return maximum(0,add2(1,sub(neg4,pos)))})}function logcosh(yTrue,yPred){return tidy(()=>{let log222=Math.log(2),predictionDiff=sub(yPred,yTrue),logcoshResult=sub(add2(predictionDiff,softplus(mul(-2,predictionDiff))),log222);return mean(logcoshResult,-1)})}function categoricalCrossentropy(target,output,fromLogits=!1){return tidy(()=>{if(fromLogits)output=softmax(output);else{let outputSum=sum2(output,output.shape.length-1,!0);output=div(output,outputSum)}return output=clipByValue(output,epsilon2(),1-epsilon2()),neg(sum2(mul(target.toFloat(),log2(output)),output.shape.length-1))})}function sparseCategoricalCrossentropy(target,output,fromLogits=!1){return tidy(()=>{let flatTarget=floor(flatten2(target)).toInt();output=clipByValue(output,epsilon2(),1-epsilon2());let outputShape=output.shape,oneHotTarget=oneHot(flatTarget,outputShape[outputShape.length-1]).reshape(outputShape);return categoricalCrossentropy(oneHotTarget,output,fromLogits)})}function sigmoidCrossEntropyWithLogits(labels,logits){if(!util_exports.arraysEqual(labels.shape,logits.shape))throw new ValueError(`logits and labels must have the same shape, but got shapes ${JSON.stringify(labels.shape)} and ${JSON.stringify(logits.shape)}`);return tidy(()=>{let reluLogits=logits.relu(),negAbsLogits=logits.abs().neg();return reluLogits.sub(logits.mul(labels)).add(negAbsLogits.exp().log1p())})}function binaryCrossentropy(yTrue,yPred){return tidy(()=>{let y;return y=clipByValue(yPred,epsilon2(),1-epsilon2()),y=log2(div(y,sub(1,y))),mean(sigmoidCrossEntropyWithLogits(yTrue,y),-1)})}function kullbackLeiblerDivergence(yTrue,yPred){return tidy(()=>{let clippedTrue=clipByValue(yTrue,epsilon2(),1),clippedPred=clipByValue(yPred,epsilon2(),1);return sum2(mul(yTrue,log2(div(clippedTrue,clippedPred))),-1)})}function poisson(yTrue,yPred){return tidy(()=>{let logPred=log2(add2(epsilon2(),yPred));return mean(sub(yPred,mul(yTrue,logPred)),-1)})}function cosineProximity(yTrue,yPred){return tidy(()=>{let trueNormalized=l2Normalize(yTrue,-1),predNormalized=l2Normalize(yPred,-1),trueXPred=mul(trueNormalized,predNormalized);return neg(sum2(trueXPred,-1))})}var lossesMap={meanSquaredError:meanSquaredError2,meanAbsoluteError,meanAbsolutePercentageError,meanSquaredLogarithmicError,squaredHinge,hinge,categoricalHinge,logcosh,categoricalCrossentropy,sparseCategoricalCrossentropy,binaryCrossentropy,kullbackLeiblerDivergence,poisson,cosineProximity};function get(identifierOrFn){if(typeof identifierOrFn=="string"){if(identifierOrFn in lossesMap)return lossesMap[identifierOrFn];let errMsg=`Unknown loss ${identifierOrFn}`;throw identifierOrFn.toLowerCase().includes("softmaxcrossentropy")&&(errMsg=`Unknown loss ${identifierOrFn}. Use "categoricalCrossentropy" as the string name for tf.losses.softmaxCrossEntropy`),new ValueError(errMsg)}else return identifierOrFn}function binaryAccuracy(yTrue,yPred){return tidy(()=>{let threshold2=mul(.5,onesLike(yPred)),yPredThresholded=cast5(greater(yPred,threshold2),yTrue.dtype);return mean(equal(yTrue,yPredThresholded),-1)})}function categoricalAccuracy(yTrue,yPred){return tidy(()=>cast5(equal(argMax(yTrue,-1),argMax(yPred,-1)),"float32"))}function truePositives(yTrue,yPred){return tidy(()=>logicalAnd(yTrue.equal(1),yPred.equal(1)).sum().cast("float32"))}function falseNegatives(yTrue,yPred){return tidy(()=>logicalAnd(yTrue.equal(1),yPred.equal(0)).sum().cast("float32"))}function falsePositives(yTrue,yPred){return tidy(()=>logicalAnd(yTrue.equal(0),yPred.equal(1)).sum().cast("float32"))}function precision(yTrue,yPred){return tidy(()=>{let tp=truePositives(yTrue,yPred),fp=falsePositives(yTrue,yPred),denominator=tp.add(fp);return where(greater(denominator,0),tp.div(denominator),0).cast("float32")})}function recall(yTrue,yPred){return tidy(()=>{let tp=truePositives(yTrue,yPred),fn=falseNegatives(yTrue,yPred),denominator=tp.add(fn);return where(greater(denominator,0),tp.div(denominator),0).cast("float32")})}function binaryCrossentropy2(yTrue,yPred){return binaryCrossentropy(yTrue,yPred)}function sparseCategoricalAccuracy(yTrue,yPred){return yTrue.rank===yPred.rank&&(yTrue=yTrue.squeeze([yTrue.rank-1])),yPred=yPred.argMax(-1),yPred.dtype!==yTrue.dtype&&(yPred=yPred.asType(yTrue.dtype)),equal(yTrue,yPred).asType("float32")}var mse=meanSquaredError2,MSE=meanSquaredError2,mae=meanAbsoluteError,MAE=meanAbsoluteError,mape=meanAbsolutePercentageError,MAPE=meanAbsolutePercentageError,categoricalCrossentropy2=categoricalCrossentropy,cosine=cosineProximity,sparseCategoricalCrossentropy2=sparseCategoricalCrossentropy,metricsMap={binaryAccuracy,categoricalAccuracy,precision,categoricalCrossentropy:categoricalCrossentropy2,sparseCategoricalCrossentropy:sparseCategoricalCrossentropy2,mse,MSE,mae,MAE,mape,MAPE,cosine};function get2(identifier){if(typeof identifier=="string"&&identifier in metricsMap)return metricsMap[identifier];if(typeof identifier!="string"&&identifier!=null)return identifier;throw new ValueError(`Unknown metric ${identifier}`)}function getLossOrMetricName(fn){if(assert2(fn!==null,`Unknown LossOrMetricFn ${fn}`),typeof fn=="string")return fn;{let fnName;for(let key of Object.keys(lossesMap))if(lossesMap[key]===fn){fnName=key;break}if(fnName!==void 0)return fnName;for(let key of Object.keys(metricsMap))if(metricsMap[key]===fn){fnName=key;break}return fnName!==void 0?fnName:fn.name}}function getOptimizer(identifier){let optimizerMap={Adagrad:()=>train.adagrad(.01),Adadelta:()=>train.adadelta(1,.95,epsilon2()),Adam:()=>train.adam(.001,.9,.999,epsilon2()),Adamax:()=>train.adamax(.002,.9,.999,epsilon2(),0),RMSProp:()=>train.rmsprop(.001,.9,0,epsilon2()),SGD:()=>train.sgd(.01)};if(optimizerMap.adagrad=optimizerMap.Adagrad,optimizerMap.adadelta=optimizerMap.Adadelta,optimizerMap.adam=optimizerMap.Adam,optimizerMap.adamax=optimizerMap.Adamax,optimizerMap.rmsprop=optimizerMap.RMSProp,optimizerMap.sgd=optimizerMap.SGD,identifier in optimizerMap)return optimizerMap[identifier]();throw new ValueError(`Unknown Optimizer ${identifier}`)}var MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH=1*1024*1024;function checkUserDefinedMetadata(userDefinedMetadata,modelName,checkSize=!1){if(userDefinedMetadata==null||typeof userDefinedMetadata!="object"||Object.getPrototypeOf(userDefinedMetadata)!==Object.prototype||!plainObjectCheck(userDefinedMetadata))throw new Error("User-defined metadata is expected to be a JSON object, but is not.");if(checkSize){let out=JSON.stringify(userDefinedMetadata);out.length>MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH&&console.warn(`User-defined metadata of model "${modelName}" is too large in size (length=${out.length} when serialized). It is not recommended to store such large objects in user-defined metadata. Please make sure its serialized length is <= ${MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH}.`)}}function plainObjectCheck(x){if(x===null)return!0;if(typeof x=="object")if(Object.getPrototypeOf(x)===Object.prototype){let keys=Object.keys(x);for(let key of keys)if(typeof key!="string"||!plainObjectCheck(x[key]))return!1;return!0}else if(Array.isArray(x)){for(let item of x)if(!plainObjectCheck(item))return!1;return!0}else return!1;else{let xType=typeof x;return xType==="string"||xType==="number"||xType==="boolean"}}function printSummary(model2,lineLength,positions,printFn=console.log){let sequentialLike=isModelSequentialLike(model2),toDisplay=["Layer (type)","Output shape","Param #"];sequentialLike?(lineLength=lineLength||65,positions=positions||[.45,.85,1]):(lineLength=lineLength||98,positions=positions||[.33,.55,.67,1]),positions[positions.length-1]<=1&&(positions=positions.map(p2=>Math.floor(lineLength*p2)));let relevantNodes;if(!sequentialLike){toDisplay.push("Receives inputs"),relevantNodes=[];for(let depth in model2.nodesByDepth)relevantNodes.push(...model2.nodesByDepth[depth])}printFn("_".repeat(lineLength)),printRow(toDisplay,positions,printFn),printFn("=".repeat(lineLength));let layers=model2.layers;for(let i=0;i1||depthNodes.length===1&&depthNodes[0].inboundLayers.length>1){sequentialLike=!1;break}nodes.push(...depthNodes)}if(sequentialLike)for(let layer of model2.layers){let flag=!1;for(let node of layer.inboundNodes)if(nodes.indexOf(node)!==-1)if(flag){sequentialLike=!1;break}else flag=!0;if(!sequentialLike)break}return sequentialLike}function printRow(fields,positions,printFn=console.log){let line="";for(let i=0;i0&&(line=line.slice(0,line.length-1)+" "),line+=fields[i],line=line.slice(0,positions[i]),line+=" ".repeat(positions[i]-line.length);printFn(line)}function printLayerSummary(layer,positions,printFn){let outputShape;try{outputShape=JSON.stringify(layer.outputShape)}catch(err){outputShape="multiple"}let name=layer.name,className=layer.getClassName(),fields=[`${name} (${className})`,outputShape,layer.countParams().toString()];printRow(fields,positions,printFn)}function printLayerSummaryWithConnections(layer,positions,relevantNodes,printFn){let outputShape;try{outputShape=JSON.stringify(layer.outputShape)}catch(err){outputShape="multiple"}let connections=[];for(let node of layer.inboundNodes)if(!(relevantNodes!=null&&relevantNodes.length>0&&relevantNodes.indexOf(node)===-1))for(let i=0;it.name),finalOutputs=[],feedNames=feedDict.names();for(let outputName of outputNames)feedNames.indexOf(outputName)!==-1?finalOutputs.push(feedDict.getValue(outputName)):finalOutputs.push(null);probe!=null&&(probe.maxNumTensors=-Infinity,probe.minNumTensors=Infinity);let fetchAndFeedKey=outputNames.join(",")+"|"+feedDict.names().join(","),sorted,recipientCounts;if(cachedSorted[fetchAndFeedKey]==null){let out=getTopologicalSortAndRecipientCounts(fetchArray,feedDict);sorted=out.sorted,recipientCounts=out.recipientCounts,cachedSorted[fetchAndFeedKey]=sorted,cachedRecipientCounts[fetchAndFeedKey]=recipientCounts}sorted=cachedSorted[fetchAndFeedKey],recipientCounts={},training||Object.assign(recipientCounts,cachedRecipientCounts[fetchAndFeedKey]);let internalFeedDict=new FeedDict(feedDict);for(let i=0;iprobe.maxNumTensors&&(probe.maxNumTensors=numTensors),numTensors0,()=>"Expected at least one fetch, got none");let finalSorted=[],finalRecipientMap={};if(fetches.length===1){let out=getTopologicalSortAndRecipientCountsForOneFetch(fetches[0],feedDict);finalSorted=out.sorted,finalRecipientMap=out.recipientMap}else{let visited=new Set;for(let fetch3 of fetches){let{sorted,recipientMap}=getTopologicalSortAndRecipientCountsForOneFetch(fetch3,feedDict);for(let symbolicTensor of sorted)visited.has(symbolicTensor.name)||(finalSorted.push(symbolicTensor),visited.add(symbolicTensor.name));for(let name in recipientMap)finalRecipientMap[name]==null&&(finalRecipientMap[name]=new Set),recipientMap[name].forEach(recipient=>finalRecipientMap[name].add(recipient))}}return{sorted:finalSorted,recipientCounts:recipientMap2Counts(finalRecipientMap)}}function recipientMap2Counts(recipientMap){let recipientCounts={};for(let name in recipientMap)recipientCounts[name]=recipientMap[name].size;return recipientCounts}function getTopologicalSortAndRecipientCountsForOneFetch(fetch3,feedDict){let visited=new Set,sorted=[],recipientMap={};for(let key of feedDict.names())visited.add(key);let stack2=[],marks=[];for(stack2.push(fetch3);stack2.length>0;){let top=stack2[stack2.length-1];if(visited.has(top.name)){stack2.pop();continue}let topIsMarked=marks[marks.length-1]===stack2.length-1;if(top.inputs.length===0||topIsMarked)stack2.pop(),sorted.push(top),visited.add(top.name),topIsMarked&&marks.pop();else{marks.push(stack2.length-1);for(let input2 of top.inputs)recipientMap[input2.name]==null&&(recipientMap[input2.name]=new Set),recipientMap[input2.name].add(top.name),!visited.has(input2.name)&&stack2.push(input2)}}return{sorted,recipientMap}}function getNodeOutputs(fetch3){let layerOutputs;if(fetch3.sourceLayer.inboundNodes.length===1)layerOutputs=fetch3.sourceLayer.output;else{let nodeIndex=null;for(let i=0;ix.name)}`);unique4(this.outputs).length!==this.outputs.length&&console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map(x=>x.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(let x of this.outputs){let layer=x.sourceLayer,nodeIndex=x.nodeIndex,tensorIndex=x.tensorIndex;this.outputLayers.push(layer),this.outputLayersNodeIndices.push(nodeIndex),this.outputLayersTensorIndices.push(tensorIndex)}for(let x of this.inputs){let layer=x.sourceLayer,nodeIndex=x.nodeIndex,tensorIndex=x.tensorIndex;assert2(nodeIndex===0,"input layer has >1 nodes"),assert2(tensorIndex===0,"input layer has >1 tensors"),this.inputLayers.push(layer),this.inputLayersNodeIndices.push(nodeIndex),this.inputLayersTensorIndices.push(tensorIndex)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let i=0;ix.shape),this.internalOutputShapes=this.outputs.map(x=>x.shape);let nodesDepths={},nodeIDToNode={},layersDepths={},layerIDToLayer={},layerIndices={},nodesInDecreasingDepth=[],buildMapOfGraph=(tensor2,finishedNodes2,nodesInProgress2,layer,nodeIndex,tensorIndex)=>{(layer==null||nodeIndex==null||tensorIndex==null)&&(layer=tensor2.sourceLayer,nodeIndex=tensor2.nodeIndex,tensorIndex=tensor2.tensorIndex);let node=layer.inboundNodes[nodeIndex];if(nodesInProgress2.indexOf(node)!==-1)throw new RuntimeError(`The tensor ${tensor2.name} at layer "${layer.name}" is part of a cycle.`);if(finishedNodes2.indexOf(node)!==-1)return;this.containerNodes.add(Container.nodeKey(layer,nodeIndex)),layer.id in layerIndices||(layerIndices[layer.id]=Object.keys(layerIndices).length),nodesInProgress2.indexOf(node)===-1&&nodesInProgress2.push(node);let numInboundLayers=node.inboundLayers.length;for(let i=0;i=0;)nodesInProgress2.splice(nodesInProgress2.indexOf(node),1);nodesInDecreasingDepth.push(node)},finishedNodes=[],nodesInProgress=[];for(let x of this.outputs)buildMapOfGraph(x,finishedNodes,nodesInProgress);let reversedNodesInDecreasingDepth=nodesInDecreasingDepth.slice().reverse();for(let node of reversedNodesInDecreasingDepth){nodeIDToNode[node.id]=node,node.id in nodesDepths||(nodesDepths[node.id]=0);let depth=nodesDepths[node.id],previousDepth=layersDepths[node.outboundLayer.id]==null?0:layersDepths[node.outboundLayer.id];depth=Math.max(depth,previousDepth),layersDepths[node.outboundLayer.id]=depth,layerIDToLayer[node.outboundLayer.id]=node.outboundLayer,nodesDepths[node.id]=depth;for(let i=0;iparseInt(x,10)).sort(reverseNumberCompare);this.layers=[];for(let depth of depthKeys){let layersForDepth=layersByDepth[depth];layersForDepth.sort((a,b)=>{let aIndex=layerIndices[a.id],bIndex=layerIndices[b.id];return aIndexbIndex?1:0});for(let layer of layersForDepth)layer instanceof Container&&this.internalContainerRefs.push(layer),this.layers.push(layer)}this.layersByDepth=layersByDepth,depthKeys=Object.keys(nodesByDepth).map(x=>parseInt(x,10)).sort(reverseNumberCompare);let computableTensors=this.inputs.slice(),layersWithCompleteInput=[];for(let depth of depthKeys)for(let node of nodesByDepth[depth]){let layer=node.outboundLayer;if(layer!=null){for(let x of node.inputTensors)if(computableTensors.indexOf(x)===-1)throw new RuntimeError(`Graph disconnected: cannot obtain value for tensor ${x} at layer "${layer.name}". The following previous layers were accessed without issue: ${layersWithCompleteInput}`);for(let x of node.outputTensors)computableTensors.push(x);layersWithCompleteInput.push(layer.name)}}this.nodesByDepth=nodesByDepth;let allNames=this.layers.map(x=>x.name);for(let name of allNames){let numOccurrences=allNames.filter(x=>x===name).length;if(numOccurrences!==1)throw new RuntimeError(`The name "${name}" is used ${numOccurrences} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(allNames))}this.outboundNodes=[],this.inboundNodes=[],new Node({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(x=>null),outputMasks:this.outputs.map(x=>null),inputShapes:this.inputs.map(x=>x.shape),outputShapes:this.outputs.map(x=>x.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();let result={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount==0){for(let layer of this.layers)result.numDisposedVariables+=layer.dispose().numDisposedVariables;for(let container of this.internalContainerRefs)result.numDisposedVariables+=container.dispose().numDisposedVariables}return result.refCountAfterDispose=this._refCount,result}get trainable(){return this.trainable_}set trainable(trainable){this.layers.forEach(layer=>{layer._trainableWeights.forEach(w=>w.trainable=trainable)}),this.trainable_=trainable}get trainableWeights(){if(this._trainableWeights.length>0)throw new ValueError("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let weights=[];for(let layer of this.layers)weights=weights.concat(layer.trainableWeights);return weights}get nonTrainableWeights(){let weights=[];for(let layer of this.layers)weights.push(...layer.nonTrainableWeights);if(!this.trainable){let trainableWeights=[];for(let layer of this.layers)trainableWeights.push(...layer.trainableWeights);return trainableWeights.concat(weights)}return weights}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(weights,strict=!0){let nameToWeight={},totalWeightsCount=0;for(let layer of this.layers)for(let weight of layer.weights){if(nameToWeight[weight.originalName]!=null)throw new ValueError(`Duplicate weight name: ${weight.originalName}`);nameToWeight[weight.originalName]=weight,totalWeightsCount++}let weightValueTuples=[];for(let name in weights){let validatedName=name;if(nameToWeight[name]==null){let tokens=name.split("/");validatedName=tokens.slice(0,-2).concat([tokens[tokens.length-1]]).join("/")}if(nameToWeight[validatedName]!=null)weightValueTuples.push([nameToWeight[validatedName],weights[name]]);else if(strict)throw new ValueError(`Provided weight data has no target variable: ${name}`);delete nameToWeight[validatedName]}if(strict){let unsetNames=[];for(let name in nameToWeight)unsetNames.push(name);if(unsetNames.length>0)throw new ValueError(`${unsetNames.length} of ${totalWeightsCount} weights are not set: ${unsetNames}`)}batchSetValue(weightValueTuples)}updatedConfig(){let theConfig=this.getConfig(),modelConfig={};return modelConfig.className=this.getClassName(),modelConfig.config=theConfig,modelConfig.kerasVersion=`tfjs-layers ${version10}`,modelConfig.backend="TensorFlow.js",modelConfig}toJSON(unused,returnString=!0){let modelConfig=convertTsToPythonic(this.updatedConfig());return returnString?JSON.stringify(modelConfig):modelConfig}call(inputs,kwargs){return tidy(()=>{inputs=toList(inputs);let feedDict=new FeedDict;for(let i=0;i{inputs=toList(inputs);let masks;return mask==null?masks=pyListRepeat(null,inputs.length):masks=toList(mask),this.runInternalGraph(inputs,masks)[1]})}computeOutputShape(inputShape){let inputShapes=normalizeShapeList(inputShape);if(inputShapes.length!==this.inputLayers.length)throw new ValueError(`Invalid inputShape argument ${inputShape}: model has ${this.inputLayers.length} tensor inputs.`);let layersToOutputShapes={};for(let i=0;iparseInt(x,10)).sort(reverseNumberCompare);if(depthKeys.length>1)for(let depth of depthKeys){let nodes=this.nodesByDepth[depth];for(let node of nodes){let layer=node.outboundLayer;if(this.inputLayers.map(x=>x.id).indexOf(layer.id)!==-1)continue;let inputShapes2=[];for(let j=0;jparseInt(x,10)).sort(reverseNumberCompare);for(let depth of depthKeys){let nodes=this.nodesByDepth[depth];for(let node of nodes){let layer=node.outboundLayer,referenceInputTensors=node.inputTensors,referenceOutputTensors=node.outputTensors,computedData=new Array;for(let x of referenceInputTensors)x.id in tensorMap&&computedData.push(tensorMap[x.id]);if(computedData.length===referenceInputTensors.length){let kwargs={},computedTensors,computedMasks,outputTensors2,outputMasks2;if(node.callArgs!=null&&(kwargs=node.callArgs),computedData.length===1){let[computedTensor,computedMask]=computedData[0];kwargs.mask==null&&(kwargs.mask=computedMask),outputTensors2=toList(layer.call(computedTensor,kwargs)),outputMasks2=toList(layer.computeMask(computedTensor,computedMask)),computedTensors=[computedTensor],computedMasks=[computedMask]}else computedTensors=computedData.map(x=>x[0]),computedMasks=computedData.map(x=>x[1]),kwargs.mask==null&&(kwargs.mask=computedMasks),outputTensors2=toList(layer.call(computedTensors,kwargs)),outputMasks2=toList(layer.computeMask(computedTensors,computedMasks));if(layer.activityRegularizer)throw new NotImplementedError("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let i=0;i{let losses4=[];for(let layer of this.layers)for(let nodeIndex=0;nodeIndex0){let nodeData=[];for(let i=0;i0&&layer.apply(singletonOrArray(inputTensors2),kwargs)}function processLayer(layerData){let layerName=layerData.name,layer=deserialize(layerData,config2.customObjects!=null?config2.customObjects:{});layer.setFastWeightInitDuringBuild(fastWeightInit),createdLayers[layerName]=layer,layerData.inboundNodes.forEach(nodeData=>{if(!(nodeData instanceof Array))throw new ValueError(`Corrupted configuration, expected array for nodeData: ${nodeData}`);addUnprocessedNode(layer,nodeData)})}let name=config2.name,layersFromConfig=config2.layers;for(let layerData of layersFromConfig)processLayer(layerData);for(;!isObjectEmpty(unprocessedNodes);)for(let layerData of layersFromConfig){let layer=createdLayers[layerData.name];if(layer.name in unprocessedNodes){let currentUnprocessedNodesForLayer=unprocessedNodes[layer.name];delete unprocessedNodes[layer.name];for(let nodeData of currentUnprocessedNodesForLayer)processNode(layer,nodeData)}}let inputTensors=[],outputTensors=[],inputLayersFromConfig=config2.inputLayers;for(let layerData of inputLayersFromConfig){let layerName=layerData[0],nodeIndex=layerData[1],tensorIndex=layerData[2];assert2(layerName in createdLayers);let layerOutputTensors=createdLayers[layerName].inboundNodes[nodeIndex].outputTensors;inputTensors.push(layerOutputTensors[tensorIndex])}let outputLayersFromConfig=config2.outputLayers;for(let layerData of outputLayersFromConfig){let layerName=layerData[0],nodeIndex=layerData[1],tensorIndex=layerData[2];assert2(layerName in createdLayers);let layerOutputTensors=createdLayers[layerName].inboundNodes[nodeIndex].outputTensors;outputTensors.push(layerOutputTensors[tensorIndex])}return new cls({inputs:inputTensors,outputs:outputTensors,name})}get stateful(){if(this._stateful)throw new ValueError("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(let layer of this.layers)if(layer.stateful)return!0;return!1}resetStates(){tidy(()=>{this.layers.forEach(layer=>{layer.stateful&&layer.resetStates()})})}};function standardizeSampleOrClassWeights(xWeight,outputNames,weightType){let numOutputs=outputNames.length;if(xWeight==null||Array.isArray(xWeight)&&xWeight.length===0)return outputNames.map(name=>null);if(numOutputs===1)return Array.isArray(xWeight)&&xWeight.length===1?xWeight:typeof xWeight=="object"&&outputNames[0]in xWeight?[xWeight[outputNames[0]]]:[xWeight];if(Array.isArray(xWeight)){if(xWeight.length!==numOutputs)throw new Error(`Provided ${weightType} is an array of ${xWeight.length} element(s), but the model has ${numOutputs} outputs. Make sure a set of weights is provided for each model output.`);return xWeight}else if(typeof xWeight=="object"&&Object.keys(xWeight).length>0&&typeof xWeight[Object.keys(xWeight)[0]]=="object"){let output=[];return outputNames.forEach(outputName=>{outputName in xWeight?output.push(xWeight[outputName]):output.push(null)}),output}else throw new Error(`The model has multiple (${numOutputs}) outputs, so ${weightType} must be either an array with ${numOutputs} elements or an object with ${outputNames} keys. Provided ${weightType} not understood: ${JSON.stringify(xWeight)}`)}function standardizeClassWeights(classWeight,outputNames){return standardizeSampleOrClassWeights(classWeight,outputNames,"classWeight")}async function standardizeWeights(y,sampleWeight,classWeight,sampleWeightMode){if(sampleWeight!=null||sampleWeightMode!=null)throw new Error("Support sampleWeight is not implemented yet");if(classWeight!=null){let yClasses=tidy(()=>{if(y.shape.length===1)return y.clone();if(y.shape.length===2)if(y.shape[1]>1){let axis=1;return y.argMax(axis)}else{if(y.shape[1]===1)return y.reshape([y.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${y.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${y.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),yClassIndices=Array.from(await yClasses.data());dispose(yClasses);let classSampleWeight=[];return yClassIndices.forEach(classIndex=>{if(classWeight[classIndex]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${classIndex} exists in the data but not in classWeight`);classSampleWeight.push(classWeight[classIndex])}),tensor1d(classSampleWeight,"float32")}else return null}function computeWeightedLoss2(losses4,sampleWeights){return mul(losses4,sampleWeights)}var DEFAULT_VALIDATION_BATCH_SIZE=32;function standardizeDataIteratorOutput(model2,iteratorOut){let xs,ys,iteratorOutObj=iteratorOut;xs=iteratorOutObj.xs,ys=iteratorOutObj.ys,util_exports.assert(xs!=null&&ys!=null,()=>`A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor. The provided Dataset instead generates ${iteratorOut}`);let flattenedXs=flattenTensorOrArrayOrMap("input",model2.inputNames,xs),flattenedYs=flattenTensorOrArrayOrMap("output",model2.outputNames,ys),batchSize=flattenedXs[0].shape[0];util_exports.assert(flattenedXs.length===model2.inputs.length,()=>`LayersModel has ${model2.inputs.length} inputs, but the dataset provides ${flattenedXs.length} inputs. (Expected input keys: ${JSON.stringify(model2.inputNames)})`),util_exports.assert(flattenedYs.length===model2.outputs.length,()=>`LayersModel has ${model2.outputs.length} outputs, but the dataset provides ${flattenedYs.length} outputs. (Expected output keys: ${JSON.stringify(model2.outputNames)})`);for(let xIndex=0;xIndex`Batch size mismatch: input ${model2.inputNames[xIndex]} has ${flattenedXs[xIndex].shape[0]}; expected ${batchSize} based on input ${model2.inputNames[0]}.`);for(let yIndex=0;yIndex`Batch size mismatch: output ${model2.outputNames[yIndex]} has ${flattenedYs[yIndex].shape[0]}; expected ${batchSize} based on input ${model2.inputNames[0]}.`);return{xs:flattenedXs,ys:flattenedYs}}function flattenTensorOrArrayOrMap(inputOrOutput,names,values){if(values instanceof Tensor)return[values];if(Array.isArray(values))return util_exports.assert(values.length===names.length,()=>`Received an array of ${values.length} Tensors, but expected ${names.length} to match the ${inputOrOutput} keys ${names}.`),values;{let result=[];for(let name of names){if(values[name]==null)throw new ValueError(`The feature data generated by the dataset lacks the required ${inputOrOutput} key '${name}'.`);result.push(values[name])}return result}}function standardizeTensorValidationData(data2){if(data2.length===3)throw new NotImplementedError("Validation with sample weights is not implemented yet.");return{xs:data2[0],ys:data2[1]}}async function fitDataset(model2,dataset,args){let hasBatchesPerEpoch=args.batchesPerEpoch!=null;if(util_exports.assert(model2.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),util_exports.assert(args!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),util_exports.assert(args.epochs!=null&&args.epochs>0&&Number.isInteger(args.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${args.epochs}`),util_exports.assert(!hasBatchesPerEpoch||args.batchesPerEpoch>0&&Number.isInteger(args.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${args.batchesPerEpoch}`),util_exports.assert(args.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. Use validationData instead."),model2.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");model2.isTraining=!0;try{let doValidation=args.validationData!=null,valXs,valYs;if(doValidation)if(isDatasetObject(args.validationData))util_exports.assert(args.validationBatches==null||args.validationBatches>0&&Number.isInteger(args.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${args.validationBatches}`);else{let validationData=standardizeTensorValidationData(args.validationData);valXs=validationData.xs,valYs=validationData.ys}let trainFunction=model2.makeTrainFunction(),outLabels=model2.getDedupedMetricsNames(),callbackMetrics;doValidation?callbackMetrics=outLabels.slice().concat(outLabels.map(n=>"val_"+n)):callbackMetrics=outLabels.slice();let callbacks2=standardizeCallbacks(args.callbacks,args.yieldEvery),verbose=args.verbose==null?1:args.verbose,{callbackList,history}=configureCallbacks(callbacks2,verbose,args.epochs,null,null,getStepsPerEpoch(dataset,args),null,doValidation,callbackMetrics);callbackList.setModel(model2),model2.history=history,await callbackList.onTrainBegin(),model2.stopTraining_=!1;let epoch=args.initialEpoch==null?0:args.initialEpoch,dataIterator=await dataset.iterator();for(;epoch=args.batchesPerEpoch:iteratorOut.done){if(doValidation){let valOuts;isDatasetObject(args.validationData)?valOuts=toList(await model2.evaluateDataset(args.validationData,{batches:args.validationBatches})):valOuts=toList(model2.evaluate(valXs,valYs,{batchSize:args.validationBatchSize==null?DEFAULT_VALIDATION_BATCH_SIZE:args.validationBatchSize,verbose:0}));for(let i=0;i0)throw new NotImplementedError("Verbose mode is not implemented yet.");util_exports.assert(!hasBatches||args.batches>0&&Number.isInteger(args.batches),()=>`Test loop expects \`batches\` to be a positive integer, but received ${JSON.stringify(args.batches)}`);let dataIterator=isLazyIteratorObject(dataset)?dataset:await dataset.iterator(),numExamples=0,batch=0;for(;hasBatches?batch{if(iteratorOut.value){let{xs,ys}=standardizeDataIteratorOutput(model2,iteratorOut.value),xsAndYs=xs.concat(ys),batchOuts=tidy(()=>f(xsAndYs));if(dispose(xsAndYs),batch===0)for(let i=0;iadd2(outs[i],mul(batchSize,batchOut))),batch>0&&dispose(oldScalar)}dispose(batchOuts),numExamples+=batchSize,++batch}return outs}),iteratorOut.done){hasBatches&&console.warn(`Your dataset iterator ran out of data during evaluateDataset(). Interrupting evalution. Make sure that your dataset can generate at least \`batches\` batches (in this case, ${args.batches} batches). You may need to use the repeat() function when building your dataset.`);break}}for(let i=0;i0&&Number.isInteger(batchSize),()=>`batchSize is required to be a positive integer, but got ${batchSize}`)}function sliceArrays(arrays,start,stop){return arrays==null?[null]:Array.isArray(arrays)?arrays.map(array2=>sliceAlongFirstAxis(array2,start,stop-start)):sliceAlongFirstAxis(arrays,start,stop-start)}function sliceArraysByIndices(arrays,indices){return tidy(()=>arrays==null?null:Array.isArray(arrays)?arrays.map(array2=>sliceArraysByIndices(array2,indices)):gather2(arrays,indices.dtype==="int32"?indices:indices.toInt()))}function makeBatches(size,batchSize){let output=[],batchStart=0,batchEnd=null;for(;batchStart=size&&(batchEnd=size),output.push([batchStart,batchEnd]),batchStart=batchEnd;return output}async function fitLoop(model2,f,ins,outLabels,batchSize,epochs,verbose,callbacks2,valF,valIns,shuffle2,callbackMetrics,initialEpoch,stepsPerEpoch,validationSteps){batchSize==null&&(batchSize=32),epochs==null&&(epochs=1),shuffle2==null&&(shuffle2=!0),initialEpoch==null&&(initialEpoch=0);let doValidation=!1;if(valF!=null&&valIns!=null&&(doValidation=!0),validationSteps!=null&&(doValidation=!0,stepsPerEpoch==null))throw new ValueError("Can only use `validationSteps` when doing step-wise training, i.e., `stepsPerEpoch` must be set.");let numTrainSamples=model2.checkNumSamples(ins,batchSize,stepsPerEpoch,"steps_per_epoch"),indexArray;numTrainSamples!=null&&(indexArray=range5(0,numTrainSamples)),verbose==null&&(verbose=1);let{callbackList,history}=configureCallbacks(callbacks2,verbose,epochs,initialEpoch,numTrainSamples,stepsPerEpoch,batchSize,doValidation,callbackMetrics);callbackList.setModel(model2),model2.history=history,await callbackList.onTrainBegin(),model2.stopTraining_=!1;for(let epoch=initialEpoch;epoch{let batchStart=batches[batchIndex][0],batchEnd=batches[batchIndex][1],batchIds=sliceAlongFirstAxis(epochIndexArray1D,batchStart,batchEnd-batchStart);batchLogs.batch=batchIndex,batchLogs.size=batchEnd-batchStart;let insBatch=sliceArraysByIndices(ins,batchIds),outs=f(insBatch);for(let i=0;i0){if(doValidation=!0,args.validationData.length===2)inputValX=args.validationData[0],inputValY=args.validationData[1];else throw args.validationData.length===3?new NotImplementedError("validationData including sample weights is not supported yet."):new ValueError(`When passing validation data, it must contain 2 (valX, valY) or 3 (valX, valY, valSampleWeight) items; ${args.validationData} is invalid.`);let checkBatchAxis2=!0,valStandardized=await model2.standardizeUserData(inputValX,inputValY,null,null,checkBatchAxis2,batchSize);valX=valStandardized[0],valY=valStandardized[1],valIns=valX.concat(valY)}else if(args.validationSplit!=null&&args.validationSplit>0&&args.validationSplit<1){doValidation=!0;let splitAt=Math.floor(inputs[0].shape[0]*(1-args.validationSplit)),originalBatchSize=inputs[0].shape[0];valX=sliceArrays(inputs,splitAt,originalBatchSize),inputs=sliceArrays(inputs,0,splitAt),valY=sliceArrays(targets,splitAt,originalBatchSize),targets=sliceArrays(targets,0,splitAt),valIns=valX.concat(valY)}else args.validationSteps!=null&&(doValidation=!0);let ins=inputs.concat(targets).concat(sampleWeights);model2.checkTrainableWeightsConsistency();let trainFunction=model2.makeTrainFunction(),outLabels=model2.getDedupedMetricsNames(),valFunction,callbackMetrics;doValidation?(model2.makeTestFunction(),valFunction=model2.testFunction,callbackMetrics=outLabels.slice().concat(outLabels.map(n=>"val_"+n))):(valFunction=null,valIns=[],callbackMetrics=outLabels.slice());let callbacks2=standardizeCallbacks(args.callbacks,args.yieldEvery);return await fitLoop(model2,trainFunction,ins,outLabels,batchSize,args.epochs,args.verbose,callbacks2,valFunction,valIns,args.shuffle,callbackMetrics,args.initialEpoch,null,null)}finally{model2.isTraining=!1,disposeNewTensors(inputs,x),disposeNewTensors(targets,y),disposeNewTensors(valX,inputValX),disposeNewTensors(valY,inputValY),sampleWeights!=null&&dispose(sampleWeights)}}function ensureTensorsRank2OrHigher(tensors){let outs=[];tensors instanceof Tensor&&(tensors=[tensors]);for(let i=0;ioldTensorIds.push(t.id));else if(refTensors!=null)for(let name in refTensors){let oldTensor=refTensors[name];oldTensorIds.push(oldTensor.id)}let tensorsToDispose=[];if(tensors instanceof Tensor)oldTensorIds.indexOf(tensors.id)===-1&&tensorsToDispose.push(tensors);else if(Array.isArray(tensors))tensors.forEach(t=>{oldTensorIds.indexOf(t.id)===-1&&tensorsToDispose.push(t)});else if(tensors!=null)for(let name in tensors){let tensor2=tensors[name];oldTensorIds.indexOf(tensor2.id)===-1&&tensorsToDispose.push(tensor2)}tensorsToDispose.forEach(t=>{t.isDisposed||t.dispose()})}function isDataTensor(x){return x instanceof Tensor}function isDataArray(x){return Array.isArray(x)}function isDataDict(x){return!isDataTensor(x)&&!isDataArray(x)}function standardizeInputData(data2,names,shapes,checkBatchAxis=!0,exceptionPrefix=""){if(names==null||names.length===0){if(data2!=null){let gotUnexpectedData=!1;if(isDataArray(data2)&&data2.length>0)gotUnexpectedData=!0;else if(isDataDict(data2)){for(let key in data2)if(data2.hasOwnProperty(key)){gotUnexpectedData=!0;break}}else gotUnexpectedData=!0;if(gotUnexpectedData)throw new ValueError(`Error when checking model ${exceptionPrefix} expected no data, but got ${data2}`)}return[]}if(data2==null)return names.map(name=>null);let arrays;if(isDataDict(data2)){data2=data2,arrays=[];for(let name of names){if(data2[name]==null)throw new ValueError(`No data provided for "${name}". Need data for each key in: ${names}`);arrays.push(data2[name])}}else if(isDataArray(data2)){if(data2=data2,data2.length!==names.length)throw new ValueError(`Error when checking model ${exceptionPrefix}: the Array of Tensors that you are passing to your model is not the size the model expected. Expected to see ${names.length} Tensor(s), but instead got the following list of Tensor(s): ${data2}`);arrays=data2}else{if(data2=data2,names.length>1)throw new ValueError(`The model ${exceptionPrefix} expects ${names.length} Tensor(s), but only received one Tensor. Found: Tensor with shape ${data2.shape}`);arrays=[data2]}if(arrays=ensureTensorsRank2OrHigher(arrays),shapes!=null)for(let i=0;i=0&&dim!==refDim)throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} to have shape [${shapes[i]}], but got array with shape [${array2.shape}].`)}}return arrays}function checkArrayLengths(inputs,targets,weights){let setX=unique4(inputs.map(input2=>input2.shape[0]));setX.sort();let setY=unique4(targets.map(target=>target.shape[0]));if(setY.sort(),setX.length>1)throw new ValueError(`All input Tensors (x) should have the same number of samples. Got array shapes: ${JSON.stringify(inputs.map(input2=>input2.shape))}`);if(setY.length>1)throw new ValueError(`All target Tensors (y) should have the same number of samples. Got array shapes: ${JSON.stringify(targets.map(target=>target.shape))}`);if(setX.length>0&&setY.length>0&&!util_exports.arraysEqual(setX,setY))throw new ValueError(`Input Tensors should have the same number of samples as target Tensors. Found ${setX[0]} input sample(s) and ${setY[0]} target sample(s).`)}function checkLossAndTargetCompatibility(targets,lossFns,outputShapes){let keyLosses=[meanSquaredError2,binaryCrossentropy,categoricalCrossentropy];for(let i=0;i1)throw new ValueError(`The model expects ${names.length} ${exceptionPrefix} Tensors, but only received one Tensor. Found: array with shape ${JSON.stringify(data2.shape)}.`);arrays=[data2]}if(shapes!=null)for(let i=0;i[]);let wrappedMetrics;if(typeof metrics2=="string"||typeof metrics2=="function")wrappedMetrics=[metrics2];else if(Array.isArray(metrics2)||typeof metrics2=="object")wrappedMetrics=metrics2;else throw new TypeError(`Type of metrics argument not understood. Expected an string,function, Array, or Object, found: ${metrics2}`);if(Array.isArray(wrappedMetrics))return outputNames.map(name=>wrappedMetrics);{let nestedMetrics=[];for(let name of outputNames){let outputMetrics=wrappedMetrics.hasOwnProperty(name)?wrappedMetrics[name]:[];Array.isArray(outputMetrics)||(outputMetrics=[outputMetrics]),nestedMetrics.push(outputMetrics)}return nestedMetrics}}var LAYERS_MODEL_FORMAT_NAME="layers-model",LayersModel=class extends Container{constructor(args){super(args);this.isTraining=!1}summary(lineLength,positions,printFn=console.log){if(!this.built)throw new ValueError("This model has never been called, thus its weights have not been created yet. So no summary can be displayed. Build the model first (e.g., by calling it on some test data).");printSummary(this,lineLength,positions,printFn)}compile(args){if(args.loss==null&&(args.loss=[]),this.loss=args.loss,typeof args.optimizer=="string")this.optimizer_=getOptimizer(args.optimizer),this.isOptimizerOwned=!0;else{if(!(args.optimizer instanceof Optimizer))throw new ValueError("User-defined optimizer must be an instance of tf.Optimizer.");this.optimizer_=args.optimizer,this.isOptimizerOwned=!1}let lossFunctions=[];if(!Array.isArray(args.loss)&&typeof args.loss!="string"&&typeof args.loss!="function"){args.loss=args.loss;for(let name in args.loss)if(this.outputNames.indexOf(name)===-1)throw new ValueError(`Unknown entry in loss dictionary: "${name}". Only expected the following keys: ${this.outputNames}`);for(let name of this.outputNames)args.loss[name]==null&&console.warn(`Output "${name}" is missing from loss dictionary. We assume this was done on purpose, and we will not be expecting data to be passed to ${name} during training`),lossFunctions.push(get(args.loss[name]))}else if(Array.isArray(args.loss)){if(args.loss.length!==this.outputs.length)throw new ValueError(`When passing an Array as loss, it should have one entry per model output. The model has ${this.outputs.length} output(s), but you passed loss=${args.loss}.`);lossFunctions=args.loss.map(l=>get(l))}else{let lossFunction=get(args.loss);this.outputs.forEach(_=>{lossFunctions.push(lossFunction)})}this.lossFunctions=lossFunctions,this.feedOutputNames=[],this.feedOutputShapes=[],this.feedLossFns=[];for(let i=0;i{for(let i=0;i1&&(this.metricsTensors.push([weightedLoss,i]),this.metricsNames.push(this.outputNames[i]+"_loss"))}});let nestedMetrics=collectMetrics(args.metrics,this.outputNames),appendMetric=(outputIndex,metricName,metricTensor)=>{this.outputNames.length>1&&(metricName=this.outputNames[outputIndex]+"_"+metricName),this.metricsNames.push(metricName),this.metricsTensors.push([metricTensor,outputIndex])};nameScope("metric",()=>{for(let i=0;i{let metricNamePrefix="",metricName,accFn,weightedMetricFn;for(let metric of metrics2){if(typeof metric=="string"&&["accuracy","acc","crossentropy","ce"].indexOf(metric)!==-1){let outputShape=this.internalOutputShapes[i];outputShape[outputShape.length-1]===1||this.lossFunctions[i]===binaryCrossentropy?["accuracy","acc"].indexOf(metric)!==-1?accFn=binaryAccuracy:["crossentropy","ce"].indexOf(metric)!==-1&&(accFn=binaryCrossentropy2):this.lossFunctions[i]===sparseCategoricalCrossentropy?["accuracy","acc"].indexOf(metric)!==-1?accFn=sparseCategoricalAccuracy:["crossentropy","ce"].indexOf(metric)!==-1&&(accFn=sparseCategoricalCrossentropy2):["accuracy","acc"].indexOf(metric)!==-1?accFn=categoricalAccuracy:["crossentropy","ce"].indexOf(metric)!==-1&&(accFn=categoricalCrossentropy2);let suffix;["accuracy","acc"].indexOf(metric)!==-1?suffix="acc":["crossentropy","ce"].indexOf(metric)!==-1&&(suffix="ce"),weightedMetricFn=accFn,metricName=metricNamePrefix+suffix}else weightedMetricFn=get2(metric),metricName=metricNamePrefix+getLossOrMetricName(metric);let metricResult;nameScope(metricName,()=>{metricResult=weightedMetricFn}),appendMetric(i,metricName,metricResult)}})(outputMetrics)}}),this.collectedTrainableWeights=this.trainableWeights}checkTrainableWeightsConsistency(){this.collectedTrainableWeights!=null&&this.trainableWeights.length!==this.collectedTrainableWeights.length&&console.warn("Discrepancy between trainableweights and collected trainable weights. Did you set `model.trainable` without calling `model.compile()` afterwards?")}evaluate(x,y,args={}){let batchSize=args.batchSize==null?32:args.batchSize;checkBatchSize(batchSize);let checkBatchAxis=!0,standardizedOuts=this.standardizeUserDataXY(x,y,checkBatchAxis,batchSize);try{let ins=standardizedOuts[0].concat(standardizedOuts[1]);this.makeTestFunction();let f=this.testFunction,testOuts=this.testLoop(f,ins,batchSize,args.verbose,args.steps);return singletonOrArray(testOuts)}finally{disposeNewTensors(standardizedOuts[0],x),disposeNewTensors(standardizedOuts[1],y)}}async evaluateDataset(dataset,args){return this.makeTestFunction(),evaluateDataset(this,dataset,args)}checkNumSamples(ins,batchSize,steps,stepsName="steps"){let numSamples;if(steps!=null){if(numSamples=null,batchSize!=null)throw new ValueError(`If ${stepsName} is set, batchSize must be null or undefined.Got batchSize = ${batchSize}`)}else if(ins!=null)Array.isArray(ins)?numSamples=ins[0].shape[0]:numSamples=ins.shape[0];else throw new ValueError(`Either the input data should have a defined shape, or ${stepsName} shoud be specified.`);return numSamples}execute(inputs,outputs){if(Array.isArray(outputs)&&outputs.length===0)throw new ValueError("`outputs` is an empty Array, which is not allowed.");let outputsIsArray=Array.isArray(outputs),outputNames=outputsIsArray?outputs:[outputs],outputSymbolicTensors=this.retrieveSymbolicTensors(outputNames),feedDict=new FeedDict;if(inputs instanceof Tensor&&(inputs=[inputs]),Array.isArray(inputs)){if(inputs.length!==this.inputs.length)throw new ValueError(`The number of inputs provided (${inputs.length}) does not match the number of inputs of this model (${this.inputs.length}).`);for(let i=0;ioutput.name);for(let i=0;i0){let remainingNames=[];throw outputSymbolicTensors.forEach((tensor2,i)=>{tensor2==null&&remainingNames.push(symbolicTensorNames[i])}),new ValueError(`Cannot find SymbolicTensors for output name(s): ${JSON.stringify(remainingNames)}`)}return outputSymbolicTensors}predictLoop(ins,batchSize=32,verbose=!1){return tidy(()=>{let numSamples=this.checkNumSamples(ins);if(verbose)throw new NotImplementedError("Verbose predictLoop() is not implemented yet.");let batches=makeBatches(numSamples,batchSize),outsBatches=this.outputs.map(output=>[]);for(let batchIndex=0;batchIndex{let batchStart=batches[batchIndex][0],batchEnd=batches[batchIndex][1],insBatch=sliceArrays(ins,batchStart,batchEnd),feeds=[];if(Array.isArray(insBatch))for(let i=0;ioutsBatches[i].push(batchOut));return singletonOrArray(outsBatches.map(batches2=>concat(batches2,0)))})}predict(x,args={}){let xsRank2OrHigher=ensureTensorsRank2OrHigher(x);checkInputData(xsRank2OrHigher,this.inputNames,this.feedInputShapes,!1);try{let batchSize=args.batchSize==null?32:args.batchSize;return checkBatchSize(batchSize),this.predictLoop(xsRank2OrHigher,batchSize)}finally{disposeNewTensors(xsRank2OrHigher,x)}}predictOnBatch(x){checkInputData(x,this.inputNames,this.feedInputShapes,!0);let batchSize=(Array.isArray(x)?x[0]:x).shape[0];return this.predictLoop(x,batchSize)}standardizeUserDataXY(x,y,checkBatchAxis=!0,batchSize){if(this.optimizer_==null)throw new RuntimeError("You must compile a model before training/testing. Use LayersModel.compile(modelCompileArgs).");let outputShapes=[];for(let i=0;i0&&x[0].shape[0]%batchSize!=0)throw new ValueError(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${batchSize}. Found: ${x[0].shape[0]} sample(s).`);return[x,y]}async standardizeUserData(x,y,sampleWeight,classWeight,checkBatchAxis=!0,batchSize){let[standardXs,standardYs]=this.standardizeUserDataXY(x,y,checkBatchAxis,batchSize);if(sampleWeight!=null)throw new Error("sample weight is not supported yet.");let standardSampleWeights=null;if(classWeight!=null){let classWeights=standardizeClassWeights(classWeight,this.outputNames);standardSampleWeights=[];for(let i=0;i{let numSamples=this.checkNumSamples(ins,batchSize,steps,"steps"),outs=[];if(verbose>0)throw new NotImplementedError("Verbose mode is not implemented yet.");if(steps!=null)throw new NotImplementedError("steps mode in testLoop() is not implemented yet");{let batches=makeBatches(numSamples,batchSize),indexArray=tensor1d(range5(0,numSamples));for(let batchIndex=0;batchIndex1&&(newLabel+=`_${count(outLabels.slice(0,i),label)}`),dedupedOutLabels.push(newLabel)}return dedupedOutLabels}makeTrainFunction(){return data2=>{let lossValues=[],inputs=data2.slice(0,this.inputs.length),targets=data2.slice(this.inputs.length,this.inputs.length+this.outputs.length),sampleWeights=data2.slice(this.inputs.length+this.outputs.length,this.inputs.length+this.outputs.length*2),metricsValues=[],totalLossFunction=()=>{let feeds=[];for(let i=0;i1&&i{totalLoss=add2(totalLoss,regularizerLoss)}),totalLoss},variables=this.collectedTrainableWeights.map(param=>param.read()),returnCost=!0;return[this.optimizer_.minimize(totalLossFunction,returnCost,variables)].concat(metricsValues)}}makeTestFunction(){this.testFunction=data2=>tidy(()=>{let valOutputs=[],totalLoss,inputs=data2.slice(0,this.inputs.length),targets=data2.slice(this.inputs.length,this.inputs.length+this.outputs.length),feeds=[];for(let i=0;itoSnakeCase(name))}else{let outputNames=Object.keys(this.loss);lossNames={};let losses4=this.loss;for(let outputName of outputNames)if(typeof losses4[outputName]=="string")lossNames[outputName]=toSnakeCase(losses4[outputName]);else throw new Error("Serialization of non-string loss is not supported.")}return lossNames}getMetricIdentifiers(){if(typeof this.metrics=="string"||typeof this.metrics=="function")return[toSnakeCase(getLossOrMetricName(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(metric=>toSnakeCase(getLossOrMetricName(metric)));{let metricsIdentifiers={};for(let key in this.metrics)metricsIdentifiers[key]=toSnakeCase(getLossOrMetricName(this.metrics[key]));return metricsIdentifiers}}getTrainingConfig(){return{loss:this.getLossIdentifiers(),metrics:this.getMetricIdentifiers(),optimizer_config:{class_name:this.optimizer.getClassName(),config:this.optimizer.getConfig()}}}loadTrainingConfig(trainingConfig){if(trainingConfig.weighted_metrics!=null)throw new Error("Loading weight_metrics is not supported yet.");if(trainingConfig.loss_weights!=null)throw new Error("Loading loss_weights is not supported yet.");if(trainingConfig.sample_weight_mode!=null)throw new Error("Loading sample_weight_mode is not supported yet.");let tsConfig=convertPythonicToTs(trainingConfig.optimizer_config),optimizer=deserialize(tsConfig),loss;if(typeof trainingConfig.loss=="string")loss=toCamelCase(trainingConfig.loss);else if(Array.isArray(trainingConfig.loss))loss=trainingConfig.loss.map(lossEntry=>toCamelCase(lossEntry));else if(trainingConfig.loss!=null){loss={};for(let key in trainingConfig.loss)loss[key]=toCamelCase(trainingConfig.loss[key])}let metrics2;if(Array.isArray(trainingConfig.metrics))metrics2=trainingConfig.metrics.map(metric=>toCamelCase(metric));else if(trainingConfig.metrics!=null){metrics2={};for(let key in trainingConfig.metrics)metrics2[key]=toCamelCase(trainingConfig.metrics[key])}this.compile({loss,metrics:metrics2,optimizer})}async save(handlerOrURL,config2){if(typeof handlerOrURL=="string"){let handlers=io_exports.getSaveHandlers(handlerOrURL);if(handlers.length===0)throw new ValueError(`Cannot find any save handlers for URL '${handlerOrURL}'`);if(handlers.length>1)throw new ValueError(`Found more than one (${handlers.length}) save handlers for URL '${handlerOrURL}'`);handlerOrURL=handlers[0]}if(handlerOrURL.save==null)throw new ValueError("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let weightDataAndSpecs=await io_exports.encodeWeights(this.getNamedWeights(config2)),returnString=!1,unusedArg=null,modelArtifacts={modelTopology:this.toJSON(unusedArg,returnString),format:LAYERS_MODEL_FORMAT_NAME,generatedBy:`TensorFlow.js tfjs-layers v${version10}`,convertedBy:null};if((config2==null?!1:config2.includeOptimizer)&&this.optimizer!=null){modelArtifacts.trainingConfig=this.getTrainingConfig();let weightType="optimizer",{data:optimizerWeightData,specs:optimizerWeightSpecs}=await io_exports.encodeWeights(await this.optimizer.getWeights(),weightType);weightDataAndSpecs.specs.push(...optimizerWeightSpecs),weightDataAndSpecs.data=io_exports.concatenateArrayBuffers([weightDataAndSpecs.data,optimizerWeightData])}if(this.userDefinedMetadata!=null){let checkSize=!0;checkUserDefinedMetadata(this.userDefinedMetadata,this.name,checkSize),modelArtifacts.userDefinedMetadata=this.userDefinedMetadata}return modelArtifacts.weightData=weightDataAndSpecs.data,modelArtifacts.weightSpecs=weightDataAndSpecs.specs,handlerOrURL.save(modelArtifacts)}setUserDefinedMetadata(userDefinedMetadata){checkUserDefinedMetadata(userDefinedMetadata,this.name),this.userDefinedMetadata=userDefinedMetadata}getUserDefinedMetadata(){return this.userDefinedMetadata}};LayersModel.className="Model";serialization_exports.registerClass(LayersModel);var Functional=class extends LayersModel{};Functional.className="Functional";serialization_exports.registerClass(Functional);async function modelFromJSON(modelAndWeightsConfig,customObjects){"modelTopology"in modelAndWeightsConfig||(modelAndWeightsConfig={modelTopology:modelAndWeightsConfig}),modelAndWeightsConfig=modelAndWeightsConfig;let modelTopology=modelAndWeightsConfig.modelTopology;modelTopology.model_config!=null&&(modelTopology=modelTopology.model_config);let tsConfig=convertPythonicToTs(modelTopology),model2=deserialize(tsConfig,customObjects);if(modelAndWeightsConfig.weightsManifest!=null){let weightValues=await io_exports.loadWeights(modelAndWeightsConfig.weightsManifest,modelAndWeightsConfig.pathPrefix,model2.weights.map(weight=>weight.originalName)),uniqueWeightValues={};for(let weight of model2.weights)uniqueWeightValues[weight.originalName]=weightValues[weight.originalName];model2.loadWeights(uniqueWeightValues),dispose(weightValues)}return model2}async function loadLayersModelInternal(pathOrIOHandler,options){if(options==null&&(options={}),typeof pathOrIOHandler=="string"){let handlers=io_exports.getLoadHandlers(pathOrIOHandler,options);if(handlers.length===0)handlers.push(io_exports.browserHTTPRequest(pathOrIOHandler,options));else if(handlers.length>1)throw new ValueError(`Found more than one (${handlers.length}) load handlers for URL '${pathOrIOHandler}'`);pathOrIOHandler=handlers[0]}return loadLayersModelFromIOHandler(pathOrIOHandler,void 0,options)}async function loadLayersModelFromIOHandler(handler,customObjects,options){if(options==null&&(options={}),handler.load==null)throw new ValueError("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let artifacts=await handler.load(),modelTopology=artifacts.modelTopology;modelTopology.model_config!=null&&(modelTopology=modelTopology.model_config);let strict=options.strict==null?!0:options.strict,fastWeightInit=artifacts.weightData!=null&&artifacts.weightSpecs!=null&&strict,model2=deserialize(convertPythonicToTs(modelTopology),customObjects,fastWeightInit),trainingConfig=artifacts.trainingConfig;if(trainingConfig!=null&&model2.loadTrainingConfig(trainingConfig),artifacts.userDefinedMetadata!=null&&model2.setUserDefinedMetadata(artifacts.userDefinedMetadata),artifacts.weightData!=null){if(artifacts.weightSpecs==null)throw new ValueError("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");let{modelWeights,optimizerWeights}=decodeModelAndOptimizerWeights(artifacts.weightData,artifacts.weightSpecs);model2.loadWeights(modelWeights,strict),model2.optimizer!=null&&optimizerWeights.length>0&&await model2.optimizer.setWeights(optimizerWeights),dispose(modelWeights),dispose(optimizerWeights.map(w=>w.tensor))}return model2}function decodeModelAndOptimizerWeights(buffer2,specs){let name2Tensor=io_exports.decodeWeights(buffer2,specs),modelWeights={},optimizerWeights=[];return specs.forEach(spec=>{spec.group==="optimizer"?optimizerWeights.push({name:spec.name,tensor:name2Tensor[spec.name]}):modelWeights[spec.name]=name2Tensor[spec.name]}),{modelWeights,optimizerWeights}}var Sequential=class extends LayersModel{constructor(args){super({inputs:[],outputs:[]});if(args=args||{},this.trainable=!0,this.built=!1,this.name=args.name!=null?args.name:getUid("sequential_"),args.layers!=null)for(let layer of args.layers)this.add(layer)}checkShape(layer){if(layer.inboundNodes[0].outputTensors[0].shape.some(x=>x<0))throw new ValueError(`Negative dimension size caused by adding layer ${layer.name} with input shape [${layer.inboundNodes[0].inputTensors[0].shape}]`)}add(layer){let isLayerModelInstance=layer instanceof Sequential||layer instanceof LayersModel,modelLayer;if(isLayerModelInstance){if(modelLayer=layer,modelLayer.outputs.length!==1)throw new ValueError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");if(modelLayer.inputs.length!==1)throw new ValueError("All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.")}if(this.outputs.length===0){if(layer.inboundNodes.length===0){if(layer.batchInputShape==null)throw new ValueError("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");let x=Input({batchShape:layer.batchInputShape,dtype:layer.dtype,name:layer.name+"_input"});layer.apply(x)}if(isLayerModelInstance)this.outputs=modelLayer.outputs,this.inputs=modelLayer.inputs;else{if(layer.inboundNodes.length!==1)throw new ValueError(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${layer.name} which has ${layer.inboundNodes.length} pre-existing inbound connections.`);if(layer.inboundNodes[0].outputTensors.length!==1)throw new ValueError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(layer),this.outputs=[layer.inboundNodes[0].outputTensors[0]],this.inputs=getSourceInputs(this.outputs[0])}this.inboundNodes=[],new Node({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:pyListRepeat(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map(x=>x.shape),outputShapes:this.outputs[0].shape})}else{let outputTensor=layer.apply(this.outputs[0]);if(Array.isArray(outputTensor))throw new TypeError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(layer),this.outputs=[outputTensor],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}this.layers.push(layer),this.built=!1}pop(){if(this.layers.length===0)throw new TypeError("There are no layers in the model.");if(this.layers.pop(),this.layers.length===0)this.outputs=[],this.inboundNodes=[],this.outboundNodes=[];else{let lastLayerIndex=this.layers.length-1;this.layers[lastLayerIndex].outboundNodes=[],this.outputs=[this.layers[lastLayerIndex].output],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}}call(inputs,kwargs){return this.model==null&&this.build(),this.model.call(inputs,kwargs)}build(inputShape){if(getExactlyOneShape(inputShape),this.inputs.length===0||this.outputs.length===0)throw new TypeError("Sequential model cannot be built: model is empty. Add some layers first.");this.model=new LayersModel({inputs:this.inputs,outputs:this.outputs[0],name:this.name+"_model"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0}countParams(){return this.built||this.build(),super.countParams()}summary(lineLength,positions,printFn=console.log){this.built||this.build(),super.summary(lineLength,positions,printFn)}setWeights(weights){this.model==null&&this.build(),this.model.setWeights(weights)}evaluate(x,y,args={}){if(!this.built)throw new RuntimeError("The model needs to be compiled before being used.");return this.model.evaluate(x,y,args)}async evaluateDataset(dataset,args){if(!this.built)throw new RuntimeError("The model needs to be compiled before being used.");return this.model.evaluateDataset(dataset,args)}predict(x,args={}){return this.model==null&&this.build(),this.model.predict(x,args)}predictOnBatch(x){return this.model==null&&this.build(),this.model.predictOnBatch(x)}compile(args){this.build(),this.model.compile(args),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(optimizer){this.model.optimizer=optimizer}async fit(x,y,args={}){if(!this.built)throw new RuntimeError("The model needs to be compiled before being used.");return this.model.fit(x,y,args)}async fitDataset(dataset,args){if(!this.built)throw new RuntimeError("The model needs to be compiled before being used.");return this.model.fitDataset(dataset,args)}async trainOnBatch(x,y){return this.model.trainOnBatch(x,y)}static fromConfig(cls,config2,customObjects={},fastWeightInit=!1){let configArray,extraModelConfig={};if(config2 instanceof Array){if(config2[0].className==null||config2[0].className==="Merge")throw new ValueError("Legacy serialization format not supported yet.");configArray=config2}else util_exports.assert(config2.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),configArray=config2.layers,delete config2.layers,extraModelConfig=config2;let model2=new cls(extraModelConfig);if(!(model2 instanceof Sequential))throw new NotImplementedError(`Sequential.fromConfig called on non-Sequential input: ${model2}`);for(let conf of configArray){let layer=deserialize(conf,void 0,fastWeightInit);fastWeightInit&&layer.setFastWeightInitDuringBuild(!0),model2.add(layer)}return model2}set stopTraining(stop){if(this.model==null)throw new ValueError("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=stop}get stopTraining(){if(this.model==null)throw new ValueError("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let layers=[];for(let layer of this.layers){let dict={};dict.className=layer.getClassName(),dict.config=layer.getConfig(),layers.push(dict)}return{name:this.name,layers}}};Sequential.className="Sequential";serialization_exports.registerClass(Sequential);function model(args){return new LayersModel(args)}function sequential(config2){return new Sequential(config2)}function loadLayersModel(pathOrIOHandler,options){return options==null&&(options={}),loadLayersModelInternal(pathOrIOHandler,options)}function input(config2){return Input(config2)}function registerCallbackConstructor(verbosityLevel,callbackConstructor){CallbackConstructorRegistry.registerCallbackConstructor(verbosityLevel,callbackConstructor)}var Activation=class extends serialization_exports.Serializable{getConfig(){return{}}},Elu2=class extends Activation{apply(x,alpha=1){return elu4(x,alpha)}};Elu2.className="elu";serialization_exports.registerClass(Elu2);var Selu2=class extends Activation{apply(x){return selu(x)}};Selu2.className="selu";serialization_exports.registerClass(Selu2);var Relu2=class extends Activation{apply(x){return relu(x)}};Relu2.className="relu";serialization_exports.registerClass(Relu2);var Relu62=class extends Activation{apply(x){return tidy(()=>minimum(6,relu(x)))}};Relu62.className="relu6";serialization_exports.registerClass(Relu62);var Linear=class extends Activation{apply(x){return x}};Linear.className="linear";serialization_exports.registerClass(Linear);var Sigmoid2=class extends Activation{apply(x){return sigmoid(x)}};Sigmoid2.className="sigmoid";serialization_exports.registerClass(Sigmoid2);var HardSigmoid=class extends Activation{apply(x){return hardSigmoid(x)}};HardSigmoid.className="hardSigmoid";serialization_exports.registerClass(HardSigmoid);var Softplus2=class extends Activation{apply(x){return softplus(x)}};Softplus2.className="softplus";serialization_exports.registerClass(Softplus2);var Softsign=class extends Activation{apply(x){return softsign(x)}};Softsign.className="softsign";serialization_exports.registerClass(Softsign);var Tanh2=class extends Activation{apply(x){return tanh2(x)}};Tanh2.className="tanh";serialization_exports.registerClass(Tanh2);var Softmax2=class extends Activation{apply(x,axis=-1){return softmax(x,axis)}};Softmax2.className="softmax";serialization_exports.registerClass(Softmax2);var LogSoftmax2=class extends Activation{apply(x,axis=-1){return logSoftmax(x,axis)}};LogSoftmax2.className="logSoftmax";serialization_exports.registerClass(LogSoftmax2);var Swish=class extends Activation{apply(x,alpha=1){return tidy(()=>sigmoid(x.mul(alpha)).mul(x))}};Swish.className="swish";serialization_exports.registerClass(Swish);function serializeActivation(activation2){return activation2.getClassName()}function deserializeActivation(config2,customObjects={}){return deserializeKerasObject(config2,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"activation")}function getActivation(identifier){if(identifier==null){let config2={};return config2.className="linear",config2.config={},deserializeActivation(config2)}if(typeof identifier=="string"){let config2={};return config2.className=identifier,config2.config={},deserializeActivation(config2)}else return identifier instanceof Activation?identifier:deserializeActivation(identifier)}function assertObjectArgs(args){if(args!=null&&typeof args!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${args}`)}var Regularizer=class extends serialization_exports.Serializable{},L1L2=class extends Regularizer{constructor(args){super();assertObjectArgs(args),this.l1=args==null||args.l1==null?.01:args.l1,this.l2=args==null||args.l2==null?.01:args.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(x){return tidy(()=>{let regularization=zeros([1]);return this.hasL1&&(regularization=add2(regularization,sum2(mul(this.l1,abs(x))))),this.hasL2&&(regularization=add2(regularization,sum2(mul(this.l2,square3(x))))),regularization.asScalar()})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(cls,config2){return new cls({l1:config2.l1,l2:config2.l2})}};L1L2.className="L1L2";serialization_exports.registerClass(L1L2);function l1(args){return assertObjectArgs(args),new L1L2({l1:args!=null?args.l1:null,l2:0})}function l2(args){return assertObjectArgs(args),new L1L2({l2:args!=null?args.l2:null,l1:0})}var REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP={l1l2:"L1L2"};function serializeRegularizer(constraint){return serializeKerasObject(constraint)}function deserializeRegularizer(config2,customObjects={}){return deserializeKerasObject(config2,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"regularizer")}function getRegularizer(identifier){if(identifier==null)return null;if(typeof identifier=="string"){let config2={className:identifier in REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP?REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier]:identifier,config:{}};return deserializeRegularizer(config2)}else return identifier instanceof Regularizer?identifier:deserializeRegularizer(identifier)}var ReLU=class extends Layer{constructor(args){super(args==null?{}:args);this.supportsMasking=!0,args!=null&&(this.maxValue=args.maxValue)}call(inputs,kwargs){inputs=getExactlyOneTensor(inputs);let output=relu(inputs);return this.maxValue!=null&&(output=clipByValue(output,0,this.maxValue)),output}computeOutputShape(inputShape){return inputShape}getConfig(){let config2={maxValue:this.maxValue},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};ReLU.className="ReLU";serialization_exports.registerClass(ReLU);var LeakyReLU=class extends Layer{constructor(args){super(args==null?{}:args);this.DEFAULT_ALPHA=.3,args==null&&(args={}),this.alpha=args.alpha==null?this.DEFAULT_ALPHA:args.alpha}call(inputs,kwargs){let x=getExactlyOneTensor(inputs);return leakyRelu(x,this.alpha)}computeOutputShape(inputShape){return inputShape}getConfig(){let config2={alpha:this.alpha},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};LeakyReLU.className="LeakyReLU";serialization_exports.registerClass(LeakyReLU);var PReLU=class extends Layer{constructor(args){super(args==null?{}:args);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",args==null&&(args={}),this.supportsMasking=!0,this.alphaInitializer=getInitializer(args.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=getRegularizer(args.alphaRegularizer),this.alphaConstraint=getConstraint(args.alphaConstraint),args.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(args.sharedAxes))this.sharedAxes=args.sharedAxes;else if(typeof args.sharedAxes=="number")this.sharedAxes=[args.sharedAxes];else throw new ValueError(`Expected sharedAxes to be a number or an array of numbers, but got ${args.sharedAxes}`)}build(inputShape){inputShape=getExactlyOneShape(inputShape);let paramShape=inputShape.slice(1);if(this.sharedAxes!=null)for(let i of this.sharedAxes)paramShape[i-1]=1;this.alpha=this.addWeight("alpha",paramShape,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let axes={};if(this.sharedAxes!=null)for(let i=1;i(checkDataFormat(dataFormat),dataFormat==="channelsFirst"?transpose(x,[0,2,3,1]):x))}function preprocessConv3DInput(x,dataFormat){return tidy(()=>(checkDataFormat(dataFormat),dataFormat==="channelsFirst"?transpose(x,[0,2,3,4,1]):x))}function conv1dWithBias(x,kernel,bias,strides=1,padding="valid",dataFormat,dilationRate=1){return tidy(()=>{if(dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat),x.shape.length!==3)throw new ValueError(`The input of a conv1dWithBias operation should be 3, but is ${x.shape.length} instead.`);if(kernel.shape.length!==3)throw new ValueError(`The kernel for a conv1dWithBias operation should be 3, but is ${kernel.shape.length} instead`);if(bias!=null&&bias.shape.length!==1)throw new ValueError(`The bias for a conv1dWithBias operation should be 1, but is ${kernel.shape.length} instead`);if(dataFormat==="channelsFirst"&&(x=transpose(x,[0,2,1])),padding==="causal")throw new NotImplementedError("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let y=conv1d(x,kernel,strides,padding==="same"?"same":"valid","NWC",dilationRate);return bias!=null&&(y=biasAdd(y,bias)),y})}function conv2dWithBiasActivation(x,kernel,bias,strides=[1,1],padding="valid",dataFormat,dilationRate,activation2=null){return tidy(()=>{if(dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat),x.rank!==3&&x.rank!==4)throw new ValueError(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${x.rank}.`);if(kernel.rank!==3&&kernel.rank!==4)throw new ValueError(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${x.rank}.`);let y=preprocessConv2DInput(x,dataFormat);if(padding==="causal")throw new NotImplementedError("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return y=fused_ops_exports.conv2d({x:y,filter:kernel,strides,pad:padding==="same"?"same":"valid",dilations:dilationRate,dataFormat:"NHWC",bias,activation:activation2}),dataFormat==="channelsFirst"&&(y=transpose(y,[0,3,1,2])),y})}function conv3dWithBias(x,kernel,bias,strides=[1,1,1],padding="valid",dataFormat,dilationRate){return tidy(()=>{if(dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat),x.rank!==4&&x.rank!==5)throw new ValueError(`conv3dWithBias expects input to be of rank 4 or 5, but received ${x.rank}.`);if(kernel.rank!==4&&kernel.rank!==5)throw new ValueError(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${x.rank}.`);let y=preprocessConv3DInput(x,dataFormat);if(padding==="causal")throw new NotImplementedError("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return y=conv3d(y,kernel,strides,padding==="same"?"same":"valid","NDHWC",dilationRate),bias!=null&&(y=biasAdd(y,bias)),dataFormat==="channelsFirst"&&(y=transpose(y,[0,4,1,2,3])),y})}var BaseConv=class extends Layer{constructor(rank,args){super(args);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",BaseConv.verifyArgs(args),this.rank=rank,assertPositiveInteger(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new NotImplementedError(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=normalizeArray(args.kernelSize,rank,"kernelSize"),this.strides=normalizeArray(args.strides==null?1:args.strides,rank,"strides"),this.padding=args.padding==null?"valid":args.padding,checkPaddingMode(this.padding),this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),this.activation=getActivation(args.activation),this.useBias=args.useBias==null?!0:args.useBias,this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=getConstraint(args.biasConstraint),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.activityRegularizer=getRegularizer(args.activityRegularizer),this.dilationRate=normalizeArray(args.dilationRate==null?1:args.dilationRate,rank,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new ValueError(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new ValueError(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new ValueError(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(args){if(assert2("kernelSize"in args,"required key 'kernelSize' not in config"),typeof args.kernelSize!="number"&&!checkArrayTypeAndLength(args.kernelSize,"number",1,3))throw new ValueError(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(args.kernelSize)}.`)}getConfig(){let config2={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:serializeActivation(this.activation),useBias:this.useBias,biasInitializer:serializeInitializer(this.biasInitializer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),biasConstraint:serializeConstraint(this.biasConstraint)},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}},Conv=class extends BaseConv{constructor(rank,args){super(rank,args);this.kernel=null,Conv.verifyArgs(args),this.filters=args.filters,assertPositiveInteger(this.filters,"filters"),this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=getConstraint(args.kernelConstraint),this.kernelRegularizer=getRegularizer(args.kernelRegularizer)}build(inputShape){inputShape=getExactlyOneShape(inputShape);let channelAxis=this.dataFormat==="channelsFirst"?1:inputShape.length-1;if(inputShape[channelAxis]==null)throw new ValueError(`The channel dimension of the input should be defined. Found ${inputShape[channelAxis]}`);let inputDim=inputShape[channelAxis],kernelShape=this.kernelSize.concat([inputDim,this.filters]);this.kernel=this.addWeight("kernel",kernelShape,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[channelAxis]:inputDim}}],this.built=!0}call(inputs,kwargs){return tidy(()=>{inputs=getExactlyOneTensor(inputs);let outputs,biasValue=this.bias==null?null:this.bias.read(),fusedActivationName=mapActivationToFusedKernel(this.activation.getClassName());if(fusedActivationName!=null&&this.rank===2)outputs=conv2dWithBiasActivation(inputs,this.kernel.read(),biasValue,this.strides,this.padding,this.dataFormat,this.dilationRate,fusedActivationName);else{if(this.rank===1)outputs=conv1dWithBias(inputs,this.kernel.read(),biasValue,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)outputs=conv2dWithBiasActivation(inputs,this.kernel.read(),biasValue,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)outputs=conv3dWithBias(inputs,this.kernel.read(),biasValue,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new NotImplementedError("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(outputs=this.activation.apply(outputs))}return outputs})}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let newSpace=[],space=this.dataFormat==="channelsLast"?inputShape.slice(1,inputShape.length-1):inputShape.slice(2);for(let i=0;i 0 but got ${JSON.stringify(args.filters)}`)}},Conv2D2=class extends Conv{constructor(args){super(2,args);Conv2D2.verifyArgs(args)}getConfig(){let config2=super.getConfig();return delete config2.rank,config2}static verifyArgs(args){if(typeof args.kernelSize!="number"&&!checkArrayTypeAndLength(args.kernelSize,"number",1,2))throw new ValueError(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(args.kernelSize)}.`)}};Conv2D2.className="Conv2D";serialization_exports.registerClass(Conv2D2);var Conv3D2=class extends Conv{constructor(args){super(3,args);Conv3D2.verifyArgs(args)}getConfig(){let config2=super.getConfig();return delete config2.rank,config2}static verifyArgs(args){if(typeof args.kernelSize!="number"&&!(Array.isArray(args.kernelSize)&&(args.kernelSize.length===1||args.kernelSize.length===3)))throw new ValueError(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(args.kernelSize)}.`)}};Conv3D2.className="Conv3D";serialization_exports.registerClass(Conv3D2);var Conv2DTranspose=class extends Conv2D2{constructor(args){super(args);if(this.inputSpec=[new InputSpec({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new ValueError(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(inputShape){if(inputShape=getExactlyOneShape(inputShape),inputShape.length!==4)throw new ValueError("Input should have rank 4; Received input shape: "+JSON.stringify(inputShape));let channelAxis=this.dataFormat==="channelsFirst"?1:inputShape.length-1;if(inputShape[channelAxis]==null)throw new ValueError("The channel dimension of the inputs should be defined. Found `None`.");let inputDim=inputShape[channelAxis],kernelShape=this.kernelSize.concat([this.filters,inputDim]);this.kernel=this.addWeight("kernel",kernelShape,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new InputSpec({ndim:4,axes:{[channelAxis]:inputDim}})],this.built=!0}call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);if(input2.shape.length!==4)throw new ValueError(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${input2.shape.length}`);let inputShape=input2.shape,batchSize=inputShape[0],hAxis,wAxis;this.dataFormat==="channelsFirst"?(hAxis=2,wAxis=3):(hAxis=1,wAxis=2);let height=inputShape[hAxis],width=inputShape[wAxis],kernelH=this.kernelSize[0],kernelW=this.kernelSize[1],strideH=this.strides[0],strideW=this.strides[1],outHeight=deconvLength(height,strideH,kernelH,this.padding),outWidth=deconvLength(width,strideW,kernelW,this.padding),outputShape=[batchSize,outHeight,outWidth,this.filters];this.dataFormat!=="channelsLast"&&(input2=transpose(input2,[0,2,3,1]));let outputs=conv2dTranspose(input2,this.kernel.read(),outputShape,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(outputs=transpose(outputs,[0,3,1,2])),this.bias!=null&&(outputs=biasAdd(outputs,this.bias.read(),this.dataFormat)),this.activation!=null&&(outputs=this.activation.apply(outputs)),outputs})}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let outputShape=inputShape.slice(),channelAxis,heightAxis,widthAxis;this.dataFormat==="channelsFirst"?(channelAxis=1,heightAxis=2,widthAxis=3):(channelAxis=3,heightAxis=1,widthAxis=2);let kernelH=this.kernelSize[0],kernelW=this.kernelSize[1],strideH=this.strides[0],strideW=this.strides[1];return outputShape[channelAxis]=this.filters,outputShape[heightAxis]=deconvLength(outputShape[heightAxis],strideH,kernelH,this.padding),outputShape[widthAxis]=deconvLength(outputShape[widthAxis],strideW,kernelW,this.padding),outputShape}getConfig(){let config2=super.getConfig();return delete config2.dilationRate,config2}};Conv2DTranspose.className="Conv2DTranspose";serialization_exports.registerClass(Conv2DTranspose);var SeparableConv=class extends Conv{constructor(rank,config2){super(rank,config2);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,config2.filters==null)throw new ValueError("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(config2.kernelInitializer!=null||config2.kernelRegularizer!=null||config2.kernelConstraint!=null)throw new ValueError("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(config2.padding!=null&&config2.padding!=="same"&&config2.padding!=="valid")throw new ValueError(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(config2.padding)}`);this.depthMultiplier=config2.depthMultiplier==null?1:config2.depthMultiplier,this.depthwiseInitializer=getInitializer(config2.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=getRegularizer(config2.depthwiseRegularizer),this.depthwiseConstraint=getConstraint(config2.depthwiseConstraint),this.pointwiseInitializer=getInitializer(config2.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=getRegularizer(config2.pointwiseRegularizer),this.pointwiseConstraint=getConstraint(config2.pointwiseConstraint)}build(inputShape){if(inputShape=getExactlyOneShape(inputShape),inputShape.length{inputs=getExactlyOneTensor(inputs);let output;if(this.rank===1)throw new NotImplementedError("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(inputs=transpose(inputs,[0,2,3,1])),output=separableConv2d(inputs,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(output=biasAdd(output,this.bias.read(),this.dataFormat)),this.activation!=null&&(output=this.activation.apply(output)),this.dataFormat==="channelsFirst"&&(output=transpose(output,[0,3,1,2])),output})}getConfig(){let config2=super.getConfig();return delete config2.rank,delete config2.kernelInitializer,delete config2.kernelRegularizer,delete config2.kernelConstraint,config2.depthwiseInitializer=serializeInitializer(this.depthwiseInitializer),config2.pointwiseInitializer=serializeInitializer(this.pointwiseInitializer),config2.depthwiseRegularizer=serializeRegularizer(this.depthwiseRegularizer),config2.pointwiseRegularizer=serializeRegularizer(this.pointwiseRegularizer),config2.depthwiseConstraint=serializeConstraint(this.depthwiseConstraint),config2.pointwiseConstraint=serializeConstraint(this.pointwiseConstraint),config2}};SeparableConv.className="SeparableConv";var SeparableConv2D=class extends SeparableConv{constructor(args){super(2,args)}};SeparableConv2D.className="SeparableConv2D";serialization_exports.registerClass(SeparableConv2D);var Conv1D=class extends Conv{constructor(args){super(1,args);Conv1D.verifyArgs(args),this.inputSpec=[{ndim:3}]}getConfig(){let config2=super.getConfig();return delete config2.rank,delete config2.dataFormat,config2}static verifyArgs(args){if(typeof args.kernelSize!="number"&&!checkArrayTypeAndLength(args.kernelSize,"number",1,1))throw new ValueError(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(args.kernelSize)}.`)}};Conv1D.className="Conv1D";serialization_exports.registerClass(Conv1D);var Cropping2D=class extends Layer{constructor(args){super(args);typeof args.cropping=="number"?this.cropping=[[args.cropping,args.cropping],[args.cropping,args.cropping]]:typeof args.cropping[0]=="number"?this.cropping=[[args.cropping[0],args.cropping[0]],[args.cropping[1],args.cropping[1]]]:this.cropping=args.cropping,this.dataFormat=args.dataFormat===void 0?"channelsLast":args.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(inputShape){return this.dataFormat==="channelsFirst"?[inputShape[0],inputShape[1],inputShape[2]-this.cropping[0][0]-this.cropping[0][1],inputShape[3]-this.cropping[1][0]-this.cropping[1][1]]:[inputShape[0],inputShape[1]-this.cropping[0][0]-this.cropping[0][1],inputShape[2]-this.cropping[1][0]-this.cropping[1][1],inputShape[3]]}call(inputs,kwargs){return tidy(()=>{if(inputs=getExactlyOneTensor(inputs),this.dataFormat==="channelsLast"){let hSliced=sliceAlongAxis(inputs,this.cropping[0][0],inputs.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return sliceAlongAxis(hSliced,this.cropping[1][0],inputs.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let hSliced=sliceAlongAxis(inputs,this.cropping[0][0],inputs.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return sliceAlongAxis(hSliced,this.cropping[1][0],inputs.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let config2={cropping:this.cropping,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};Cropping2D.className="Cropping2D";serialization_exports.registerClass(Cropping2D);var UpSampling2D=class extends Layer{constructor(args){super(args);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=args.size==null?this.DEFAULT_SIZE:args.size,this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),this.interpolation=args.interpolation==null?"nearest":args.interpolation,checkInterpolationFormat(this.interpolation)}computeOutputShape(inputShape){if(this.dataFormat==="channelsFirst"){let height=inputShape[2]==null?null:this.size[0]*inputShape[2],width=inputShape[3]==null?null:this.size[1]*inputShape[3];return[inputShape[0],inputShape[1],height,width]}else{let height=inputShape[1]==null?null:this.size[0]*inputShape[1],width=inputShape[2]==null?null:this.size[1]*inputShape[2];return[inputShape[0],height,width,inputShape[3]]}}call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs),inputShape=input2.shape;if(this.dataFormat==="channelsFirst"){input2=transpose(input2,[0,2,3,1]);let height=this.size[0]*inputShape[2],width=this.size[1]*inputShape[3],resized=this.interpolation==="nearest"?input2.resizeNearestNeighbor([height,width]):input2.resizeBilinear([height,width]);return transpose(resized,[0,3,1,2])}else{let height=this.size[0]*inputShape[1],width=this.size[1]*inputShape[2];return this.interpolation==="nearest"?input2.resizeNearestNeighbor([height,width]):input2.resizeBilinear([height,width])}})}getConfig(){let config2={size:this.size,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};UpSampling2D.className="UpSampling2D";serialization_exports.registerClass(UpSampling2D);function depthwiseConv2d4(x,depthwiseKernel,strides=[1,1],padding="valid",dataFormat,dilationRate){return tidy(()=>{dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat);let y=preprocessConv2DInput(x,dataFormat);if(x.rank!==4)throw new ValueError(`Input for depthwiseConv2d is required to be 4-D, but is instead ${x.rank}-D`);if(depthwiseKernel.rank!==4)throw new ValueError(`depthwiseKernel is required to be 4-D, but is instead ${depthwiseKernel.rank}-D`);return y=depthwiseConv2d(y,depthwiseKernel,strides,padding==="same"?"same":"valid","NHWC",dilationRate),dataFormat==="channelsFirst"&&(y=transpose(y,[0,3,1,2])),y})}var DepthwiseConv2D=class extends BaseConv{constructor(args){super(2,args);this.depthwiseKernel=null,this.depthMultiplier=args.depthMultiplier==null?1:args.depthMultiplier,this.depthwiseInitializer=getInitializer(args.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=getConstraint(args.depthwiseConstraint),this.depthwiseRegularizer=getRegularizer(args.depthwiseRegularizer)}build(inputShape){if(inputShape=getExactlyOneShape(inputShape),inputShape.length<4)throw new ValueError(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(inputShape)}.`);let channelAxis=this.dataFormat==="channelsFirst"?1:3;if(inputShape[channelAxis]==null||inputShape[channelAxis]<0)throw new ValueError(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${inputShape[channelAxis]}).`);let inputDim=inputShape[channelAxis],depthwiseKernelShape=[this.kernelSize[0],this.kernelSize[1],inputDim,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",depthwiseKernelShape,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[inputDim*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(inputs,kwargs){return tidy(()=>{inputs=getExactlyOneTensor(inputs);let outputs=depthwiseConv2d4(inputs,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(outputs=biasAdd(outputs,this.bias.read(),this.dataFormat)),this.activation!=null&&(outputs=this.activation.apply(outputs)),outputs})}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let rows=this.dataFormat==="channelsFirst"?inputShape[2]:inputShape[1],cols=this.dataFormat==="channelsFirst"?inputShape[3]:inputShape[2],outFilters=this.dataFormat==="channelsFirst"?inputShape[1]*this.depthMultiplier:inputShape[3]*this.depthMultiplier,outRows=convOutputLength(rows,this.kernelSize[0],this.padding,this.strides[0]),outCols=convOutputLength(cols,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[inputShape[0],outFilters,outRows,outCols]:[inputShape[0],outRows,outCols,outFilters]}getConfig(){let config2=super.getConfig();return config2.depthMultiplier=this.depthMultiplier,config2.depthwiseInitializer=serializeInitializer(this.depthwiseInitializer),config2.depthwiseRegularizer=serializeRegularizer(this.depthwiseRegularizer),config2.depthwiseConstraint=serializeConstraint(this.depthwiseRegularizer),config2}};DepthwiseConv2D.className="DepthwiseConv2D";serialization_exports.registerClass(DepthwiseConv2D);function standardizeArgs(inputs,initialState,constants,numConstants){if(Array.isArray(inputs)){if(initialState!=null||constants!=null)throw new ValueError("When inputs is an array, neither initialState or constants should be provided");numConstants!=null&&(constants=inputs.slice(inputs.length-numConstants,inputs.length),inputs=inputs.slice(0,inputs.length-numConstants)),inputs.length>1&&(initialState=inputs.slice(1,inputs.length)),inputs=inputs[0]}function toListOrNull(x){return x==null||Array.isArray(x)?x:[x]}return initialState=toListOrNull(initialState),constants=toListOrNull(constants),{inputs,initialState,constants}}function rnn(stepFunction,inputs,initialStates,goBackwards=!1,mask,constants,unroll=!1,needPerStepOutputs=!1){return tidy(()=>{let ndim=inputs.shape.length;if(ndim<3)throw new ValueError(`Input should be at least 3D, but is ${ndim}D.`);let axes=[1,0].concat(range5(2,ndim));if(inputs=transpose(inputs,axes),constants!=null)throw new NotImplementedError("The rnn() functoin of the deeplearn.js backend does not support constants yet.");unroll&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),mask!=null&&(mask=mask.asType("bool").asType("float32"),mask.rank===ndim-1&&(mask=expandDims(mask,-1)),mask=transpose(mask,axes)),goBackwards&&(inputs=reverse(inputs,0),mask!=null&&(mask=reverse(mask,0)));let perStepOutputs=[],lastOutput,states=initialStates,timeSteps=inputs.shape[0],perStepInputs=unstack(inputs),perStepMasks;mask!=null&&(perStepMasks=unstack(mask));for(let t=0;tstepFunction(currentInput,states));if(mask==null)lastOutput=stepOutputs[0],states=stepOutputs[1];else{let maskedOutputs=tidy(()=>{let stepMask=perStepMasks[t],negStepMask=onesLike(stepMask).sub(stepMask),output=stepOutputs[0].mul(stepMask).add(states[0].mul(negStepMask)),newStates=states.map((state,i)=>stepOutputs[1][i].mul(stepMask).add(state.mul(negStepMask)));return{output,newStates}});lastOutput=maskedOutputs.output,states=maskedOutputs.newStates}needPerStepOutputs&&perStepOutputs.push(lastOutput)}let outputs;return needPerStepOutputs&&(outputs=stack(perStepOutputs,1)),[lastOutput,outputs,states]})}var RNN=class extends Layer{constructor(args){super(args);let cell;if(args.cell==null)throw new ValueError("cell property is missing for the constructor of RNN.");if(Array.isArray(args.cell)?cell=new StackedRNNCells({cells:args.cell}):cell=args.cell,cell.stateSize==null)throw new ValueError("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=cell,this.returnSequences=args.returnSequences==null?!1:args.returnSequences,this.returnState=args.returnState==null?!1:args.returnState,this.goBackwards=args.goBackwards==null?!1:args.goBackwards,this._stateful=args.stateful==null?!1:args.stateful,this.unroll=args.unroll==null?!1:args.unroll,this.supportsMasking=!0,this.inputSpec=[new InputSpec({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let numStates=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return range5(0,numStates).map(x=>null)}else return this.states_}setStates(states){this.states_=states}computeOutputShape(inputShape){isArrayOfShapes(inputShape)&&(inputShape=inputShape[0]),inputShape=inputShape;let stateSize=this.cell.stateSize;Array.isArray(stateSize)||(stateSize=[stateSize]);let outputDim=stateSize[0],outputShape;if(this.returnSequences?outputShape=[inputShape[0],inputShape[1],outputDim]:outputShape=[inputShape[0],outputDim],this.returnState){let stateShape=[];for(let dim of stateSize)stateShape.push([inputShape[0],dim]);return[outputShape].concat(stateShape)}else return outputShape}computeMask(inputs,mask){return tidy(()=>{Array.isArray(mask)&&(mask=mask[0]);let outputMask=this.returnSequences?mask:null;if(this.returnState){let stateMask=this.states.map(s=>null);return[outputMask].concat(stateMask)}else return outputMask})}get states(){if(this.states_==null){let numStates=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,output=[];for(let i=0;ispec.shape[spec.shape.length-1]),stateSize))throw new ValueError(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=stateSize.map(dim=>new InputSpec({shape:[null,dim]}));this.stateful&&this.resetStates()}resetStates(states,training=!1){tidy(()=>{if(!this.stateful)throw new AttributeError("Cannot call resetStates() on an RNN Layer that is not stateful.");let batchSize=this.inputSpec[0].shape[0];if(batchSize==null)throw new ValueError("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(dim=>zeros([batchSize,dim])):this.states_=[zeros([batchSize,this.cell.stateSize])];else if(states==null)dispose(this.states_),this.keptStates!=null&&(dispose(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(dim=>zeros([batchSize,dim])):this.states_[0]=zeros([batchSize,this.cell.stateSize]);else{if(Array.isArray(states)||(states=[states]),states.length!==this.states_.length)throw new ValueError(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${states.length} state value(s). Input received: ${states}`);training===!0?this.keptStates.push(this.states_.slice()):dispose(this.states_);for(let index=0;indexkeep(state.clone()))})}apply(inputs,kwargs){let initialState=kwargs==null?null:kwargs.initialState,constants=kwargs==null?null:kwargs.constants;kwargs==null&&(kwargs={});let standardized=standardizeArgs(inputs,initialState,constants,this.numConstants);inputs=standardized.inputs,initialState=standardized.initialState,constants=standardized.constants;let additionalInputs=[],additionalSpecs=[];if(initialState!=null){kwargs.initialState=initialState,additionalInputs=additionalInputs.concat(initialState),this.stateSpec=[];for(let state of initialState)this.stateSpec.push(new InputSpec({shape:state.shape}));additionalSpecs=additionalSpecs.concat(this.stateSpec)}if(constants!=null&&(kwargs.constants=constants,additionalInputs=additionalInputs.concat(constants),this.numConstants=constants.length),additionalInputs[0]instanceof SymbolicTensor){let fullInput=[inputs].concat(additionalInputs),fullInputSpec=this.inputSpec.concat(additionalSpecs),originalInputSpec=this.inputSpec;this.inputSpec=fullInputSpec;let output=super.apply(fullInput,kwargs);return this.inputSpec=originalInputSpec,output}else return super.apply(inputs,kwargs)}call(inputs,kwargs){return tidy(()=>{let mask=kwargs==null?null:kwargs.mask,training=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;inputs=getExactlyOneTensor(inputs),initialState==null&&(this.stateful?initialState=this.states_:initialState=this.getInitialState(inputs));let numStates=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(initialState.length!==numStates)throw new ValueError(`RNN Layer has ${numStates} state(s) but was passed ${initialState.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let cellCallKwargs={training},rnnOutputs=rnn((inputs2,states2)=>{let outputs2=this.cell.call([inputs2].concat(states2),cellCallKwargs);return[outputs2[0],outputs2.slice(1)]},inputs,initialState,this.goBackwards,mask,null,this.unroll,this.returnSequences),lastOutput=rnnOutputs[0],outputs=rnnOutputs[1],states=rnnOutputs[2];this.stateful&&this.resetStates(states,training);let output=this.returnSequences?outputs:lastOutput;return this.returnState?[output].concat(states):output})}getInitialState(inputs){return tidy(()=>{let initialState=zeros(inputs.shape);return initialState=sum2(initialState,[1,2]),initialState=expandDims5(initialState),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(dim=>dim>1?tile5(initialState,[1,dim]):initialState):this.cell.stateSize>1?[tile5(initialState,[1,this.cell.stateSize])]:[initialState]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(value){super.setFastWeightInitDuringBuild(value),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(value)}getConfig(){let baseConfig=super.getConfig(),config2={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(config2.numConstants=this.numConstants);let cellConfig=this.cell.getConfig();return this.getClassName()===RNN.className&&(config2.cell={className:this.cell.getClassName(),config:cellConfig}),Object.assign({},cellConfig,baseConfig,config2)}static fromConfig(cls,config2,customObjects={}){let cellConfig=config2.cell,cell=deserialize(cellConfig,customObjects);return new cls(Object.assign(config2,{cell}))}};RNN.className="RNN";serialization_exports.registerClass(RNN);var RNNCell=class extends Layer{},SimpleRNNCell=class extends RNNCell{constructor(args){super(args);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=args.units,assertPositiveInteger(this.units,"units"),this.activation=getActivation(args.activation==null?this.DEFAULT_ACTIVATION:args.activation),this.useBias=args.useBias==null?!0:args.useBias,this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=getInitializer(args.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=getRegularizer(args.kernelRegularizer),this.recurrentRegularizer=getRegularizer(args.recurrentRegularizer),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.kernelConstraint=getConstraint(args.kernelConstraint),this.recurrentConstraint=getConstraint(args.recurrentConstraint),this.biasConstraint=getConstraint(args.biasConstraint),this.dropout=min5([1,max5([0,args.dropout==null?0:args.dropout])]),this.recurrentDropout=min5([1,max5([0,args.recurrentDropout==null?0:args.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(inputShape){inputShape=getExactlyOneShape(inputShape),this.kernel=this.addWeight("kernel",[inputShape[inputShape.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(inputs,kwargs){return tidy(()=>{if(inputs=inputs,inputs.length!==2)throw new ValueError(`SimpleRNNCell expects 2 input Tensors, got ${inputs.length}.`);let prevOutput=inputs[1];inputs=inputs[0];let training=kwargs.training==null?!1:kwargs.training;0onesLike(inputs),rate:this.dropout,training})),0onesLike(prevOutput),rate:this.recurrentDropout,training}));let h,dpMask=this.dropoutMask,recDpMask=this.recurrentDropoutMask;dpMask!=null?h=dot2(mul(inputs,dpMask),this.kernel.read()):h=dot2(inputs,this.kernel.read()),this.bias!=null&&(h=biasAdd(h,this.bias.read())),recDpMask!=null&&(prevOutput=mul(prevOutput,recDpMask));let output=add2(h,dot2(prevOutput,this.recurrentKernel.read()));return this.activation!=null&&(output=this.activation.apply(output)),[output,output]})}getConfig(){let baseConfig=super.getConfig(),config2={units:this.units,activation:serializeActivation(this.activation),useBias:this.useBias,kernelInitializer:serializeInitializer(this.kernelInitializer),recurrentInitializer:serializeInitializer(this.recurrentInitializer),biasInitializer:serializeInitializer(this.biasInitializer),kernelRegularizer:serializeRegularizer(this.kernelRegularizer),recurrentRegularizer:serializeRegularizer(this.recurrentRegularizer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),kernelConstraint:serializeConstraint(this.kernelConstraint),recurrentConstraint:serializeConstraint(this.recurrentConstraint),biasConstraint:serializeConstraint(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},baseConfig,config2)}};SimpleRNNCell.className="SimpleRNNCell";serialization_exports.registerClass(SimpleRNNCell);var SimpleRNN=class extends RNN{constructor(args){args.cell=new SimpleRNNCell(args),super(args)}call(inputs,kwargs){return tidy(()=>{this.cell.dropoutMask!=null&&(dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let mask=kwargs==null?null:kwargs.mask,training=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;return super.call(inputs,{mask,training,initialState})})}static fromConfig(cls,config2){return new cls(config2)}};SimpleRNN.className="SimpleRNN";serialization_exports.registerClass(SimpleRNN);var GRUCell=class extends RNNCell{constructor(args){super(args);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",args.resetAfter)throw new ValueError("GRUCell does not support reset_after parameter set to true.");this.units=args.units,assertPositiveInteger(this.units,"units"),this.activation=getActivation(args.activation===void 0?this.DEFAULT_ACTIVATION:args.activation),this.recurrentActivation=getActivation(args.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:args.recurrentActivation),this.useBias=args.useBias==null?!0:args.useBias,this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=getInitializer(args.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=getRegularizer(args.kernelRegularizer),this.recurrentRegularizer=getRegularizer(args.recurrentRegularizer),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.kernelConstraint=getConstraint(args.kernelConstraint),this.recurrentConstraint=getConstraint(args.recurrentConstraint),this.biasConstraint=getConstraint(args.biasConstraint),this.dropout=min5([1,max5([0,args.dropout==null?0:args.dropout])]),this.recurrentDropout=min5([1,max5([0,args.recurrentDropout==null?0:args.recurrentDropout])]),this.implementation=args.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(inputShape){inputShape=getExactlyOneShape(inputShape);let inputDim=inputShape[inputShape.length-1];this.kernel=this.addWeight("kernel",[inputDim,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(inputs,kwargs){return tidy(()=>{if(inputs=inputs,inputs.length!==2)throw new ValueError(`GRUCell expects 2 input Tensors (inputs, h, c), got ${inputs.length}.`);let training=kwargs.training==null?!1:kwargs.training,hTMinus1=inputs[1];inputs=inputs[0],0onesLike(inputs),rate:this.dropout,training,count:3})),0onesLike(hTMinus1),rate:this.recurrentDropout,training,count:3}));let dpMask=this.dropoutMask,recDpMask=this.recurrentDropoutMask,z,r,hh;0{this.cell.dropoutMask!=null&&(dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let mask=kwargs==null?null:kwargs.mask,training=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;return super.call(inputs,{mask,training,initialState})})}static fromConfig(cls,config2){return config2.implmentation===0&&(config2.implementation=1),new cls(config2)}};GRU.className="GRU";serialization_exports.registerClass(GRU);var LSTMCell=class extends RNNCell{constructor(args){super(args);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=args.units,assertPositiveInteger(this.units,"units"),this.activation=getActivation(args.activation===void 0?this.DEFAULT_ACTIVATION:args.activation),this.recurrentActivation=getActivation(args.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:args.recurrentActivation),this.useBias=args.useBias==null?!0:args.useBias,this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=getInitializer(args.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=args.unitForgetBias,this.kernelRegularizer=getRegularizer(args.kernelRegularizer),this.recurrentRegularizer=getRegularizer(args.recurrentRegularizer),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.kernelConstraint=getConstraint(args.kernelConstraint),this.recurrentConstraint=getConstraint(args.recurrentConstraint),this.biasConstraint=getConstraint(args.biasConstraint),this.dropout=min5([1,max5([0,args.dropout==null?0:args.dropout])]),this.recurrentDropout=min5([1,max5([0,args.recurrentDropout==null?0:args.recurrentDropout])]),this.implementation=args.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(inputShape){var _a;inputShape=getExactlyOneShape(inputShape);let inputDim=inputShape[inputShape.length-1];this.kernel=this.addWeight("kernel",[inputDim,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let biasInitializer;if(this.useBias){if(this.unitForgetBias){let capturedBiasInit=this.biasInitializer,capturedUnits=this.units;biasInitializer=new(_a=class extends Initializer{apply(shape,dtype){let bI=capturedBiasInit.apply([capturedUnits]),bF=new Ones().apply([capturedUnits]),bCAndH=capturedBiasInit.apply([capturedUnits*2]);return concatAlongFirstAxis(concatAlongFirstAxis(bI,bF),bCAndH)}},_a.className="CustomInit",_a)}else biasInitializer=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,biasInitializer,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(inputs,kwargs){return tidy(()=>{let training=kwargs.training==null?!1:kwargs.training;if(inputs=inputs,inputs.length!==3)throw new ValueError(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${inputs.length}.`);let hTMinus1=inputs[1],cTMinus1=inputs[2];inputs=inputs[0],0onesLike(inputs),rate:this.dropout,training,count:4})),0onesLike(hTMinus1),rate:this.recurrentDropout,training,count:4}));let dpMask=this.dropoutMask,recDpMask=this.recurrentDropoutMask,i,f,c,o;0{this.cell.dropoutMask!=null&&(dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let mask=kwargs==null?null:kwargs.mask,training=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;return super.call(inputs,{mask,training,initialState})})}static fromConfig(cls,config2){return config2.implmentation===0&&(config2.implementation=1),new cls(config2)}};LSTM.className="LSTM";serialization_exports.registerClass(LSTM);var StackedRNNCells=class extends RNNCell{constructor(args){super(args);this.cells=args.cells}get stateSize(){let stateSize=[];for(let cell of this.cells.slice().reverse())Array.isArray(cell.stateSize)?stateSize.push(...cell.stateSize):stateSize.push(cell.stateSize);return stateSize}call(inputs,kwargs){return tidy(()=>{inputs=inputs;let states=inputs.slice(1),nestedStates=[];for(let cell of this.cells.slice().reverse())Array.isArray(cell.stateSize)?nestedStates.push(states.splice(0,cell.stateSize.length)):nestedStates.push(states.splice(0,1));nestedStates.reverse();let newNestedStates=[],callInputs;for(let i=0;i{nameScope(`RNNCell_${i}`,()=>{cell.build(inputShape),Array.isArray(cell.stateSize)?outputDim=cell.stateSize[0]:outputDim=cell.stateSize,inputShape=[inputShape[0],outputDim]})}),this.built=!0}getConfig(){let baseConfig=super.getConfig(),getCellConfig=cell=>({className:cell.getClassName(),config:cell.getConfig()}),config2={cells:this.cells.map(getCellConfig)};return Object.assign({},baseConfig,config2)}static fromConfig(cls,config2,customObjects={}){let cells=[];for(let cellConfig of config2.cells)cells.push(deserialize(cellConfig,customObjects));return new cls({cells})}get trainableWeights(){if(!this.trainable)return[];let weights=[];for(let cell of this.cells)weights.push(...cell.trainableWeights);return weights}get nonTrainableWeights(){let weights=[];for(let cell of this.cells)weights.push(...cell.nonTrainableWeights);if(!this.trainable){let trainableWeights=[];for(let cell of this.cells)trainableWeights.push(...cell.trainableWeights);return trainableWeights.concat(weights)}return weights}getWeights(){let weights=[];for(let cell of this.cells)weights.push(...cell.weights);return batchGetValue(weights)}setWeights(weights){let tuples=[];for(let cell of this.cells){let numParams=cell.weights.length,inputWeights=weights.splice(numParams);for(let i=0;idropout2(ones4(),rate),createMask=()=>inTrainPhase(droppedInputs,ones4,training);return!count2||count2<=1?keep(createMask().clone()):Array(count2).fill(void 0).map(createMask).map(m=>keep(m.clone()))}var __rest=function(s,e){var t={};for(var p2 in s)Object.prototype.hasOwnProperty.call(s,p2)&&e.indexOf(p2)<0&&(t[p2]=s[p2]);if(s!=null&&typeof Object.getOwnPropertySymbols=="function")for(var i=0,p2=Object.getOwnPropertySymbols(s);i{if(this.cell.dropoutMask!=null&&(dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),kwargs&&kwargs.constants)throw new ValueError("ConvRNN2D cell does not support constants");let mask=kwargs==null?null:kwargs.mask,training=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;return super.call(inputs,{mask,training,initialState})})}computeOutputShape(inputShape){let outShape=this.computeSingleOutputShape(inputShape);return this.returnSequences||(outShape=[outShape[0],...outShape.slice(2)]),this.returnState&&(outShape=[outShape,...Array(2).fill([inputShape[0],...outShape.slice(-3)])]),outShape}getInitialState(inputs){return tidy(()=>{let{stateSize}=this.cell,inputShape=inputs.shape,outputShape=this.computeSingleOutputShape(inputShape),stateShape=[outputShape[0],...outputShape.slice(2)],initialState=zeros(stateShape);return Array.isArray(stateSize)?Array(stateSize.length).fill(initialState):[initialState]})}resetStates(states,training=!1){tidy(()=>{if(!this.stateful)throw new AttributeError("Cannot call resetStates() on an RNN Layer that is not stateful.");let inputShape=this.inputSpec[0].shape,outputShape=this.computeSingleOutputShape(inputShape),stateShape=[outputShape[0],...outputShape.slice(2)];if(inputShape[0]==null)throw new ValueError("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>zeros(stateShape)):this.states_=[zeros(stateShape)];else if(states==null)dispose(this.states_),this.keptStates!=null&&(dispose(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>zeros(stateShape)):this.states_[0]=zeros(stateShape);else{if(Array.isArray(states)||(states=[states]),states.length!==this.states_.length)throw new ValueError(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${states.length} state value(s). Input received: ${states}`);training?this.keptStates.push(this.states_.slice()):dispose(this.states_);for(let index=0;indexkeep(state.clone()))})}computeSingleOutputShape(inputShape){let{dataFormat,filters,kernelSize,padding,strides,dilationRate}=this.cell,isChannelsFirst=dataFormat==="channelsFirst",h=inputShape[isChannelsFirst?3:2],w=inputShape[isChannelsFirst?4:3],hOut=convOutputLength(h,kernelSize[0],padding,strides[0],dilationRate[0]),wOut=convOutputLength(w,kernelSize[1],padding,strides[1],dilationRate[1]);return[...inputShape.slice(0,2),...isChannelsFirst?[filters,hOut,wOut]:[hOut,wOut,filters]]}};ConvRNN2D.className="ConvRNN2D";var ConvLSTM2DCell=class extends LSTMCell{constructor(args){let{filters,kernelSize,strides,padding,dataFormat,dilationRate}=args;super(Object.assign({},args,{units:filters}));this.filters=filters,assertPositiveInteger(this.filters,"filters"),this.kernelSize=normalizeArray(kernelSize,2,"kernelSize"),this.kernelSize.forEach(size=>assertPositiveInteger(size,"kernelSize")),this.strides=normalizeArray(strides||1,2,"strides"),this.strides.forEach(stride=>assertPositiveInteger(stride,"strides")),this.padding=padding||"valid",checkPaddingMode(this.padding),this.dataFormat=dataFormat||"channelsLast",checkDataFormat(this.dataFormat),this.dilationRate=normalizeArray(dilationRate||1,2,"dilationRate"),this.dilationRate.forEach(rate=>assertPositiveInteger(rate,"dilationRate"))}build(inputShape){var _a;inputShape=getExactlyOneShape(inputShape);let channelAxis=this.dataFormat==="channelsFirst"?1:inputShape.length-1;if(inputShape[channelAxis]==null)throw new ValueError(`The channel dimension of the input should be defined. Found ${inputShape[channelAxis]}`);let inputDim=inputShape[channelAxis],numOfKernels=4,kernelShape=this.kernelSize.concat([inputDim,this.filters*numOfKernels]);this.kernel=this.addWeight("kernel",kernelShape,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let recurrentKernelShape=this.kernelSize.concat([this.filters,this.filters*numOfKernels]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",recurrentKernelShape,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let biasInitializer;if(this.unitForgetBias){let init2=this.biasInitializer,filters=this.filters;biasInitializer=new(_a=class extends Initializer{apply(shape,dtype){let biasI=init2.apply([filters]),biasF=ones2([filters]),biasCAndO=init2.apply([filters*2]);return concatenate([biasI,biasF,biasCAndO])}},_a.className="CustomInit",_a)}else biasInitializer=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*numOfKernels],null,biasInitializer,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(inputs,kwargs){return tidy(()=>{if(inputs.length!==3)throw new ValueError(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${inputs.length}.`);let training=kwargs.training||!1,x=inputs[0],hTMinus1=inputs[1],cTMinus1=inputs[2],numOfKernels=4;0onesLike(x),rate:this.dropout,training,count:numOfKernels}));let dropoutMask=this.dropoutMask,applyDropout=(x2,mask,index)=>!mask||!mask[index]?x2:mul(mask[index],x2),xI=applyDropout(x,dropoutMask,0),xF=applyDropout(x,dropoutMask,1),xC=applyDropout(x,dropoutMask,2),xO=applyDropout(x,dropoutMask,3);0onesLike(hTMinus1),rate:this.recurrentDropout,training,count:numOfKernels}));let recDropoutMask=this.recurrentDropoutMask,hI=applyDropout(hTMinus1,recDropoutMask,0),hF=applyDropout(hTMinus1,recDropoutMask,1),hC=applyDropout(hTMinus1,recDropoutMask,2),hO=applyDropout(hTMinus1,recDropoutMask,3),kernelChannelAxis=3,[kernelI,kernelF,kernelC,kernelO]=split(this.kernel.read(),numOfKernels,kernelChannelAxis),[biasI,biasF,biasC,biasO]=this.useBias?split(this.bias.read(),numOfKernels):[null,null,null,null];xI=this.inputConv(xI,kernelI,biasI,this.padding),xF=this.inputConv(xF,kernelF,biasF,this.padding),xC=this.inputConv(xC,kernelC,biasC,this.padding),xO=this.inputConv(xO,kernelO,biasO,this.padding);let[recKernelI,recKernelF,recKernelC,recKernelO]=split(this.recurrentKernel.read(),numOfKernels,kernelChannelAxis);hI=this.recurrentConv(hI,recKernelI),hF=this.recurrentConv(hF,recKernelF),hC=this.recurrentConv(hC,recKernelC),hO=this.recurrentConv(hO,recKernelO);let i=this.recurrentActivation.apply(add2(xI,hI)),f=this.recurrentActivation.apply(add2(xF,hF)),c=add2(mul(f,cTMinus1),mul(i,this.activation.apply(add2(xC,hC)))),h=mul(this.recurrentActivation.apply(add2(xO,hO)),this.activation.apply(c));return[h,h,c]})}getConfig(){let _a=super.getConfig(),{units:_}=_a,baseConfig=__rest(_a,["units"]),config2={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},baseConfig,config2)}inputConv(x,w,b,padding){let out=conv2d(x,w,this.strides,padding||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return b?biasAdd(out,b,this.dataFormat):out}recurrentConv(x,w){return conv2d(x,w,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};ConvLSTM2DCell.className="ConvLSTM2DCell";serialization_exports.registerClass(ConvLSTM2DCell);var ConvLSTM2D=class extends ConvRNN2D{constructor(args){let cell=new ConvLSTM2DCell(args);super(Object.assign({},args,{cell}))}static fromConfig(cls,config2){return new cls(config2)}};ConvLSTM2D.className="ConvLSTM2D";serialization_exports.registerClass(ConvLSTM2D);var Dropout=class extends Layer{constructor(args){super(args);this.rate=Math.max(Math.min(args.rate,1),0),this.noiseShape=args.noiseShape,this.seed=args.seed,this.supportsMasking=!0}getNoiseShape(input2){if(this.noiseShape==null)return this.noiseShape;let inputShape=input2.shape,noiseShape=[];for(let i=0;i{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);if(0dropout2(input2,this.rate,noiseShape,this.seed),()=>input2,training)}return inputs})}getConfig(){let config2={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}dispose(){return super.dispose()}};Dropout.className="Dropout";serialization_exports.registerClass(Dropout);var SpatialDropout1D=class extends Dropout{constructor(args){super(args);this.inputSpec=[{ndim:3}]}getNoiseShape(input2){let inputShape=input2.shape;return[inputShape[0],1,inputShape[2]]}};SpatialDropout1D.className="SpatialDropout1D";serialization_exports.registerClass(SpatialDropout1D);var Dense=class extends Layer{constructor(args){super(args);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",args.batchInputShape==null&&args.inputShape==null&&args.inputDim!=null){let batchSize=null;args.batchSize!=null&&(batchSize=args.batchSize),this.batchInputShape=[batchSize,args.inputDim]}this.units=args.units,assertPositiveInteger(this.units,"units"),this.activation=getActivation(args.activation),args.useBias!=null&&(this.useBias=args.useBias),this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=getConstraint(args.kernelConstraint),this.biasConstraint=getConstraint(args.biasConstraint),this.kernelRegularizer=getRegularizer(args.kernelRegularizer),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.activityRegularizer=getRegularizer(args.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(inputShape){inputShape=getExactlyOneShape(inputShape);let inputLastDim=inputShape[inputShape.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[inputLastDim,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:inputLastDim}}],this.built=!0}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let outputShape=inputShape.slice();return outputShape[outputShape.length-1]=this.units,outputShape}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs),fusedActivationName=mapActivationToFusedKernel(this.activation.getClassName()),output;return fusedActivationName!=null?output=dot2(input2,this.kernel.read(),fusedActivationName,this.bias?this.bias.read():null):(output=dot2(input2,this.kernel.read()),this.bias!=null&&(output=biasAdd(output,this.bias.read())),this.activation!=null&&(output=this.activation.apply(output))),output})}getConfig(){let config2={units:this.units,activation:serializeActivation(this.activation),useBias:this.useBias,kernelInitializer:serializeInitializer(this.kernelInitializer),biasInitializer:serializeInitializer(this.biasInitializer),kernelRegularizer:serializeRegularizer(this.kernelRegularizer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),kernelConstraint:serializeConstraint(this.kernelConstraint),biasConstraint:serializeConstraint(this.biasConstraint)},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};Dense.className="Dense";serialization_exports.registerClass(Dense);var Flatten=class extends Layer{constructor(args){args=args||{},super(args),this.inputSpec=[{minNDim:3}],this.dataFormat=args.dataFormat}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);for(let dim of inputShape.slice(1))if(dim==null)throw new ValueError(`The shape of the input to "Flatten" is not fully defined (got ${inputShape.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[inputShape[0],arrayProd(inputShape,1)]}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);if(this.dataFormat==="channelsFirst"&&input2.rank>1){let permutation=[0];for(let i=2;i{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);return this.activation.apply(input2)})}getConfig(){let config2={activation:serializeActivation(this.activation)},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};Activation2.className="Activation";serialization_exports.registerClass(Activation2);var RepeatVector=class extends Layer{constructor(args){super(args);this.n=args.n,this.inputSpec=[{ndim:2}]}computeOutputShape(inputShape){return[inputShape[0],this.n,inputShape[1]]}call(inputs,kwargs){return tidy(()=>(inputs=getExactlyOneTensor(inputs),repeat(inputs,this.n)))}getConfig(){let config2={n:this.n},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};RepeatVector.className="RepeatVector";serialization_exports.registerClass(RepeatVector);var Reshape2=class extends Layer{constructor(args){super(args);this.targetShape=args.targetShape;for(let i=0;i{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs),inputShape=input2.shape,outputShape=inputShape.slice(0,1).concat(this.fixUnknownDimension(inputShape.slice(1),this.targetShape));return input2.reshape(outputShape)})}getConfig(){let config2={targetShape:this.targetShape},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};Reshape2.className="Reshape";serialization_exports.registerClass(Reshape2);var Permute=class extends Layer{constructor(args){super(args);if(args.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(args.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${args.dims} instead.`);let expectedSortedIndices=range5(1,args.dims.length+1);if(!util_exports.arraysEqual(args.dims.slice().sort(),expectedSortedIndices))throw new Error("Invalid permutation `dims`: "+JSON.stringify(args.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=args.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new InputSpec({ndim:this.dims.length+1})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let outputShape=inputShape.slice();return this.dims.forEach((dim,i)=>{outputShape[i+1]=inputShape[dim]}),outputShape}call(inputs,kwargs){return transpose(getExactlyOneTensor(inputs),this.dimsIncludingBatch)}getConfig(){let config2={dims:this.dims},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};Permute.className="Permute";serialization_exports.registerClass(Permute);var Masking=class extends Layer{constructor(args){super(args==null?{}:args);this.supportsMasking=!0,args!=null?this.maskValue=args.maskValue==null?0:args.maskValue:this.maskValue=0}computeOutputShape(inputShape){return inputShape}getConfig(){let baseConfig=super.getConfig(),config2={maskValue:this.maskValue};return Object.assign(config2,baseConfig),config2}computeMask(inputs,mask){let input2=getExactlyOneTensor(inputs),axis=-1;return any(notEqual(input2,this.maskValue),axis)}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs),axis=-1,keepDims=!0,booleanMask=any(notEqual(input2,this.maskValue),axis,keepDims);return input2.mul(booleanMask.asType(input2.dtype))})}};Masking.className="Masking";serialization_exports.registerClass(Masking);var Embedding=class extends Layer{constructor(args){super(args);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",args.batchInputShape==null&&args.inputShape==null){let batchSize=null;args.batchSize!=null&&(batchSize=args.batchSize),args.inputLength==null?this.batchInputShape=[batchSize,null]:this.batchInputShape=[batchSize].concat(toList(args.inputLength))}this.inputDim=args.inputDim,assertPositiveInteger(this.inputDim,"inputDim"),this.outputDim=args.outputDim,assertPositiveInteger(this.outputDim,"outputDim"),this.embeddingsInitializer=getInitializer(args.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=getRegularizer(args.embeddingsRegularizer),this.activityRegularizer=getRegularizer(args.activityRegularizer),this.embeddingsConstraint=getConstraint(args.embeddingsConstraint),this.maskZero=args.maskZero,this.supportsMasking=args.maskZero,this.inputLength=args.inputLength}build(inputShape){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(inputShape){}computeMask(inputs,mask){return tidy(()=>this.maskZero?(inputs=getExactlyOneTensor(inputs),notEqual(inputs,zerosLike(inputs))):null)}computeOutputShape(inputShape){if(inputShape=getExactlyOneShape(inputShape),this.inputLength==null)return[...inputShape,this.outputDim];let inLens=toList(this.inputLength);if(inLens.length!==inputShape.length-1)throw new ValueError(`"inputLength" is ${this.inputLength}, but received input shape has shape ${inputShape}`);{let i=0;for(let k=0;k{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);return input2.dtype!=="int32"&&(input2=cast5(input2,"int32")),gather2(this.embeddings.read(),input2.as1D()).reshape(getExactlyOneShape(this.computeOutputShape(input2.shape)))})}getConfig(){let config2={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:serializeInitializer(this.embeddingsInitializer),embeddingsRegularizer:serializeRegularizer(this.embeddingsRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),embeddingsConstraint:serializeConstraint(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};Embedding.className="Embedding";serialization_exports.registerClass(Embedding);var Merge=class extends Layer{constructor(args){super(args||{});this.supportsMasking=!0}mergeFunction(inputs){throw new NotImplementedError}computeElementwiseOpOutputShape(shape1,shape2){if(shape1==null||shape2==null)return null;if(shape1.length1)throw new ValueError(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(inputShape)}.`);let outputShape=inputShape[0]==null?null:inputShape[0].slice(1);for(let i=1;ishape.length);inputShape.indexOf(null)===-1&&unique4(allRanks).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(inputs,kwargs){return tidy(()=>{if(inputs=inputs,this.reshapeRequired){let reshapedInputs=[],inputDims=inputs.map(input2=>input2.rank);if(inputDims.indexOf(null)===-1){let maxNDim=max5(inputDims);for(let x of inputs){let xNDim=x.rank;for(let k=0;k1){let dims=range5(1,xNDim).concat([0]);reshapedInputs.push(transpose(x,dims)),transposed=!0}else reshapedInputs.push(x)}let y=this.mergeFunction(reshapedInputs),yNDim=y.rank;if(transposed){if(yNDim==null){let yShape=y.shape,yNDim2=yShape.length,batchSize=yShape[yNDim2-1],newShape=[batchSize].concat(yShape.slice(0,yShape.length-1));y=transpose(y.reshape([-1,batchSize]),[1,0]).reshape(newShape)}else if(yNDim>1){let dims=[yNDim-1].concat(range5(0,yNDim-1));y=transpose(y,dims)}}return y}}else return this.mergeFunction(inputs)})}computeOutputShape(inputShape){inputShape=inputShape;let outputShape;inputShape[0]==null?outputShape=null:outputShape=inputShape[0].slice(1);for(let i=1;i{if(mask==null)return null;if(!Array.isArray(mask))throw new ValueError("`mask` should be an Array");if(!Array.isArray(inputs))throw new ValueError("`inputs` should be an Array");if(mask.length!==inputs.length)throw new ValueError(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${inputs.length} vs ${mask.length})`);if(mask.every(m=>m==null))return null;mask=mask.map(m=>m==null?m:expandDims(m,0));let output=mask[0];for(let i=1;i{let output=inputs[0].clone();for(let i=1;i{let output=inputs[0].clone();for(let i=1;i{let output=inputs[0].clone();for(let i=1;i{let output=inputs[0];for(let i=1;i{let output=inputs[0];for(let i=1;i1)throw new ValueError("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(inputShape))}mergeFunction(inputs){return tidy(()=>concatenate(inputs,this.axis))}computeOutputShape(inputShape){if(!(Array.isArray(inputShape)&&Array.isArray(inputShape[0])))throw new ValueError("A `Concatenate` layer should be called on a list of inputs.");let inputShapes=inputShape,outputShape=inputShapes[0].slice(),axis=this.axis<0?outputShape.length+this.axis:this.axis;for(let shape of inputShapes.slice(1)){if(outputShape[axis]==null||shape[axis]==null){outputShape[axis]=null;break}outputShape[axis]+=shape[axis]}return outputShape}computeMask(inputs,mask){if(mask==null)return null;if(!Array.isArray(mask))throw new ValueError("`mask` should be an array for Concatenate");if(!Array.isArray(inputs))throw new ValueError("`inputs` should be an array for Concatenate");if(mask.length!==inputs.length)throw new ValueError(`Mismatch in the length of mask (${mask.length}) and the legnth of inputs (${inputs.length})`);return tidy(()=>{let allNullMasks=!0;if(mask.forEach(m=>{if(m!=null){allNullMasks=!1;return}}),allNullMasks)return null;let outputMasks=[];for(let i=0;i3||y.shape.length>3)throw new NotImplementedError("batchDot is not implemented for tensors of 4D or higher rank yet");if(util_exports.assert(x.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${x.shape.length}`),util_exports.assert(x.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${y.shape.length}`),typeof axes=="number"&&(axes=[axes,axes]),x.dtype==="complex64"||y.dtype==="complex64")throw new NotImplementedError("batchDot is not implemented for complex64-type Tensors yet.");let xNDim=x.shape.length,yNDim=y.shape.length;axes==null&&(axes=[xNDim-1,yNDim-2]);let axesArray=axes;return tidy(()=>{let diff;if(xNDim>yNDim){diff=xNDim-yNDim;let diffShape=[];for(let i=0;ixNDim){diff=yNDim-xNDim;let diffShape=[];for(let i=0;i0){let idx;xNDim>yNDim?idx=xNDim+yNDim-3:idx=xNDim-1;let squeezeAxes=[];for(let i=idx;i"A `Dot` layer should be called on a list of exactly 2 inputs.");let shape1=inputShape[0],shape2=inputShape[1];if(shape1.length>3||shape2.length>3)throw new NotImplementedError("Dot layer does not support tensors of 4D or higher rank yet.");let axes=this.interpretAxes(shape1,shape2);if(shape1[axes[0]]!==shape2[axes[1]])throw new ValueError(`Dimension incompatibility: ${shape1[axes[0]]} !== ${shape2[axes[1]]}`)}mergeFunction(inputs){if(inputs.length!==2)throw new ValueError(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${inputs.length} input(s).`);let x1=inputs[0],x2=inputs[1],axes;return Array.isArray(this.axes)?axes=this.axes.map((axis,i)=>interpretAxis(axis,inputs[i].shape.length)):axes=[interpretAxis(this.axes,x1.shape.length),interpretAxis(this.axes,x2.shape.length)],this.normalize&&(x1=l2Normalize(x1,axes[0]),x2=l2Normalize(x2,axes[1])),batchDot(x1,x2,axes)}interpretAxes(shape1,shape2){let axes;return Array.isArray(this.axes)?axes=this.axes:axes=[interpretAxis(this.axes,shape1.length),interpretAxis(this.axes,shape2.length)],axes}computeOutputShape(inputShape){util_exports.assert(Array.isArray(inputShape)&&inputShape.length===2&&Array.isArray(inputShape[0])&&Array.isArray(inputShape[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let shape1=inputShape[0].slice(),shape2=inputShape[1].slice();if(shape1.length>3||shape2.length>3)throw new NotImplementedError("Dot layer does not support tensors of 4D or higher rank yet.");let axes=this.interpretAxes(shape1,shape2);shape1.splice(axes[0],1),shape2.splice(axes[1],1),shape2.splice(0,1);let outputShape=shape1.concat(shape2);return outputShape.length===1&&outputShape.push(1),outputShape}computeMask(inputs,mask){return null}getConfig(){let config2={axes:this.axes,normalize:this.normalize},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};Dot.className="Dot";serialization_exports.registerClass(Dot);var GaussianNoise=class extends Layer{constructor(args){super(args);this.supportsMasking=!0,this.stddev=args.stddev}computeOutputShape(inputShape){return inputShape}getConfig(){let baseConfig=super.getConfig(),config2={stddev:this.stddev};return Object.assign(config2,baseConfig),config2}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);return inTrainPhase(()=>randomNormal2(input2.shape,0,this.stddev).add(input2),()=>input2,kwargs.training||!1)})}};GaussianNoise.className="GaussianNoise";serialization_exports.registerClass(GaussianNoise);var GaussianDropout=class extends Layer{constructor(args){super(args);this.supportsMasking=!0,this.rate=args.rate}computeOutputShape(inputShape){return inputShape}getConfig(){let baseConfig=super.getConfig(),config2={rate:this.rate};return Object.assign(config2,baseConfig),config2}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);return this.rate>0&&this.rate<1?inTrainPhase(()=>{let stddev=Math.sqrt(this.rate/(1-this.rate));return input2.mul(randomNormal2(input2.shape,1,stddev))},()=>input2,kwargs.training||!1):input2})}};GaussianDropout.className="GaussianDropout";serialization_exports.registerClass(GaussianDropout);var AlphaDropout=class extends Layer{constructor(args){super(args);this.supportsMasking=!0,this.rate=args.rate,this.noiseShape=args.noiseShape}_getNoiseShape(inputs){return this.noiseShape||getExactlyOneTensor(inputs).shape}computeOutputShape(inputShape){return inputShape}getConfig(){let baseConfig=super.getConfig(),config2={rate:this.rate};return Object.assign(config2,baseConfig),config2}call(inputs,kwargs){return tidy(()=>{if(this.rate<1&&this.rate>0){let noiseShape=this._getNoiseShape(inputs);return inTrainPhase(()=>{let input2=getExactlyOneTensor(inputs),alpha=1.6732632423543772,scale2=1.0507009873554805,alphaP=-alpha*scale2,keptIdx=greaterEqual(randomUniform(noiseShape),this.rate);keptIdx=cast5(keptIdx,"float32");let a=((1-this.rate)*(1+this.rate*alphaP**2))**-.5,b=-a*alphaP*this.rate;return input2.mul(keptIdx).add(keptIdx.add(-1).mul(alphaP)).mul(a).add(b)},()=>getExactlyOneTensor(inputs),kwargs.training||!1)}return inputs})}};AlphaDropout.className="AlphaDropout";serialization_exports.registerClass(AlphaDropout);function batchNormalization(x,mean4,variance,beta,gamma,epsilon3=.001){let out;if(x.rank===2)out=batchNorm2d(x,mean4,variance,beta,gamma,epsilon3);else if(x.rank===3)out=batchNorm3d(x,mean4,variance,beta,gamma,epsilon3);else if(x.rank===4)out=batchNorm4d(x,mean4,variance,beta,gamma,epsilon3);else throw new NotImplementedError(`batchNormalization is not implemented for array of rank ${x.rank} yet`);return out}function regularNormalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon3=.001){return tidy(()=>{let meanAndVariance=moments(x,reductionAxes),mean4=meanAndVariance.mean,variance=meanAndVariance.variance;return[batchNormalization(x,mean4,variance,beta,gamma,epsilon3),mean4,variance]})}function broadcastNormalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon3=.001){return tidy(()=>{let meanAndVariance=moments(x,reductionAxes),mean4=meanAndVariance.mean,variance=meanAndVariance.variance,targetShape=[];for(let axis of range5(0,x.rank))reductionAxes.indexOf(axis)!==-1?targetShape.push(1):targetShape.push(x.shape[axis]);let broadcastMean=mean4.reshape(targetShape),broadcastVariance=variance.reshape(targetShape),broadcastGamma=gamma==null?null:gamma.reshape(targetShape),broadcastBeta=beta==null?null:beta.reshape(targetShape);return[batchNormalization(x,broadcastMean,broadcastVariance,broadcastBeta,broadcastGamma,epsilon3),mean4,variance]})}function normalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon3=.001){return util_exports.arraysEqual(reductionAxes.slice().sort(),range5(0,x.rank-1))?regularNormalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon3):broadcastNormalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon3)}var BatchNormalization=class extends Layer{constructor(args){args==null&&(args={}),super(args),this.supportsMasking=!0,this.axis=args.axis==null?-1:args.axis,this.momentum=args.momentum==null?.99:args.momentum,this.epsilon=args.epsilon==null?.001:args.epsilon,this.center=args.center==null?!0:args.center,this.scale=args.scale==null?!0:args.scale,this.betaInitializer=getInitializer(args.betaInitializer||"zeros"),this.gammaInitializer=getInitializer(args.gammaInitializer||"ones"),this.movingMeanInitializer=getInitializer(args.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=getInitializer(args.movingVarianceInitializer||"ones"),this.betaConstraint=getConstraint(args.betaConstraint),this.gammaConstraint=getConstraint(args.gammaConstraint),this.betaRegularizer=getRegularizer(args.betaRegularizer),this.gammaRegularizer=getRegularizer(args.gammaRegularizer)}build(inputShape){inputShape=getExactlyOneShape(inputShape);let axis=this.axis>=0?this.axis:this.axis+inputShape.length,dim=inputShape[axis];if(dim==null)throw new ValueError(`Axis ${axis} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(inputShape)}.`);this.inputSpec=[new InputSpec({ndim:inputShape.length,axes:{[axis]:dim}})];let shape=[dim];this.scale&&(this.gamma=this.addWeight("gamma",shape,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",shape,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",shape,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",shape,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(inputs,kwargs){return tidy(()=>{let training=kwargs.training==null?!1:kwargs.training,input2=getExactlyOneTensor(inputs),inputShape=input2.shape,ndim=inputShape.length,reductionAxes=range5(0,ndim),axis=this.axis>=0?this.axis:this.axis+ndim;reductionAxes.splice(axis,1);let broadcastShape=pyListRepeat(1,ndim);broadcastShape[axis]=inputShape[axis];let sortedReductionAxes=reductionAxes.slice();sortedReductionAxes.sort();let needsBroadcasting=!util_exports.arraysEqual(sortedReductionAxes,range5(0,ndim).slice(0,ndim-1)),normalizeInference=()=>{if(needsBroadcasting){let broadcastMovingMean=this.movingMean.read().reshape(broadcastShape),broadcastMovingVariance=this.movingVariance.read().reshape(broadcastShape),broadcastBeta=this.center?this.beta.read().reshape(broadcastShape):null,broadcastGamma=this.scale?this.gamma.read().reshape(broadcastShape):null;return batchNormalization(input2,broadcastMovingMean,broadcastMovingVariance,broadcastBeta,broadcastGamma,this.epsilon)}else return batchNormalization(input2,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!training)return normalizeInference();let[normedTraining,mean4,variance]=normalizeBatchInTraining(input2,this.gamma.read(),this.beta.read(),reductionAxes,this.epsilon),doMovingAverage=(variable2,value,momentum)=>{tidy(()=>{let decay=1-momentum,origValue=variable2.read(),updateDelta=origValue.sub(value).mul(decay);variable2.write(origValue.sub(updateDelta))})};return(()=>{doMovingAverage(this.movingMean,mean4,this.momentum),doMovingAverage(this.movingVariance,variance,this.momentum)})(),normedTraining})}getConfig(){let config2={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:serializeInitializer(this.betaInitializer),gammaInitializer:serializeInitializer(this.gammaInitializer),movingMeanInitializer:serializeInitializer(this.movingMeanInitializer),movingVarianceInitializer:serializeInitializer(this.movingVarianceInitializer),betaRegularizer:serializeRegularizer(this.betaRegularizer),gammaRegularizer:serializeRegularizer(this.gammaRegularizer),betaConstraint:serializeConstraint(this.betaConstraint),gammaConstraint:serializeConstraint(this.gammaConstraint)},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};BatchNormalization.className="BatchNormalization";serialization_exports.registerClass(BatchNormalization);var LayerNormalization=class extends Layer{constructor(args){if(args==null&&(args={}),super(args),this.axis=args.axis==null?-1:args.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let axis of this.axis)if(!Number.isInteger(axis))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=args.epsilon==null?.001:args.epsilon,this.center=args.center==null?!0:args.center,this.scale=args.scale==null?!0:args.scale,this.betaInitializer=getInitializer(args.betaInitializer||"zeros"),this.gammaInitializer=getInitializer(args.gammaInitializer||"ones"),this.betaRegularizer=getRegularizer(args.betaRegularizer),this.gammaRegularizer=getRegularizer(args.gammaRegularizer),this.supportsMasking=!0}build(inputShape){inputShape=getExactlyOneShape(inputShape);let nDims=inputShape.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let i=0;i=nDims)throw new Error(`Invalid axis: ${axis}`);if(this.axis.length!==unique4(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let paramShape=this.axis.map(axis=>inputShape[axis]),trainable=!0;this.scale?this.gamma=this.addWeight("gamma",paramShape,"float32",this.gammaInitializer,this.gammaRegularizer,trainable):this.gamma=null,this.center?this.beta=this.addWeight("beta",paramShape,"float32",this.betaInitializer,this.betaRegularizer,trainable):this.beta=null,this.built=!0}call(inputs,kwargs){let input2=getExactlyOneTensor(inputs),inputShape=input2.shape,nDims=inputShape.length;return tidy(()=>{let keepDims=!0,{mean:mean4,variance}=moments(input2,this.axis,keepDims),broadcastShape=pyListRepeat(1,nDims);for(let dim of this.axis)broadcastShape[dim]=inputShape[dim];let broadcast=v=>v!=null&&v.shape.length!==nDims&&this.axis!==[nDims-1]?v.reshape(broadcastShape):v,scale2=broadcast(this.gamma.read()),offset=broadcast(this.beta.read()),momentsTiling=[],scaleOffsetTiling=[];for(let i=0;i{if(x.rank!==4)throw new ValueError(`temporalPadding expects input tensor to be 4-D, but received a ${x.rank}-D tensor.`);if(padding==null&&(padding=[[1,1],[1,1]]),padding.length!==2||padding[0].length!==2||padding[1].length!==2)throw new ValueError("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(dataFormat==null&&(dataFormat=imageDataFormat()),dataFormat!=="channelsLast"&&dataFormat!=="channelsFirst")throw new ValueError(`Unknown data format: ${dataFormat}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let pattern;return dataFormat==="channelsFirst"?pattern=[[0,0],[0,0],padding[0],padding[1]]:pattern=[[0,0],padding[0],padding[1],[0,0]],pad(x,pattern)})}var ZeroPadding2D=class extends Layer{constructor(args){if(args==null&&(args={}),super(args),this.dataFormat=args.dataFormat==null?imageDataFormat():args.dataFormat,args.padding==null)this.padding=[[1,1],[1,1]];else if(typeof args.padding=="number")this.padding=[[args.padding,args.padding],[args.padding,args.padding]];else{if(args.padding=args.padding,args.padding.length!==2)throw new ValueError(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${args.padding.length} array.`);let heightPadding,widthPadding;if(typeof args.padding[0]=="number")heightPadding=[args.padding[0],args.padding[0]],widthPadding=[args.padding[1],args.padding[1]];else{if(args.padding=args.padding,args.padding[0].length!==2)throw new ValueError(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${args.padding[0].length} array.`);if(heightPadding=args.padding[0],args.padding[1].length!==2)throw new ValueError(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${args.padding[1].length} array.`);widthPadding=args.padding[1]}this.padding=[heightPadding,widthPadding]}this.inputSpec=[new InputSpec({ndim:4})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let rows,cols;return this.dataFormat==="channelsFirst"?(inputShape[2]!=null&&inputShape[2]>=0?rows=inputShape[2]+this.padding[0][0]+this.padding[0][1]:rows=null,inputShape[3]!=null&&inputShape[3]>=0?cols=inputShape[3]+this.padding[1][0]+this.padding[1][1]:cols=null,[inputShape[0],inputShape[1],rows,cols]):(inputShape[1]!=null&&inputShape[1]>=0?rows=inputShape[1]+this.padding[0][0]+this.padding[0][1]:rows=null,inputShape[2]!=null&&inputShape[2]>=0?cols=inputShape[2]+this.padding[1][0]+this.padding[1][1]:cols=null,[inputShape[0],rows,cols,inputShape[3]])}call(inputs,kwargs){return tidy(()=>spatial2dPadding(getExactlyOneTensor(inputs),this.padding,this.dataFormat))}getConfig(){let config2={padding:this.padding,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};ZeroPadding2D.className="ZeroPadding2D";serialization_exports.registerClass(ZeroPadding2D);function pool2d(x,poolSize,strides,padding,dataFormat,poolMode){return tidy(()=>{checkDataFormat(dataFormat),checkPoolMode(poolMode),checkPaddingMode(padding),strides==null&&(strides=[1,1]),padding==null&&(padding="valid"),dataFormat==null&&(dataFormat=imageDataFormat()),poolMode==null&&(poolMode="max"),x=preprocessConv2DInput(x,dataFormat);let y,paddingString=padding==="same"?"same":"valid";return poolMode==="max"?y=maxPool(x,poolSize,strides,paddingString):y=avgPool(x,poolSize,strides,paddingString),dataFormat==="channelsFirst"&&(y=transpose(y,[0,3,1,2])),y})}function pool3d2(x,poolSize,strides,padding,dataFormat,poolMode){return tidy(()=>{checkDataFormat(dataFormat),checkPoolMode(poolMode),checkPaddingMode(padding),strides==null&&(strides=[1,1,1]),padding==null&&(padding="valid"),dataFormat==null&&(dataFormat=imageDataFormat()),poolMode==null&&(poolMode="max"),x=preprocessConv3DInput(x,dataFormat);let y,paddingString=padding==="same"?"same":"valid";return poolMode==="max"?y=maxPool3d(x,poolSize,strides,paddingString):y=avgPool3d(x,poolSize,strides,paddingString),dataFormat==="channelsFirst"&&(y=transpose(y,[0,4,1,2,3])),y})}var Pooling1D=class extends Layer{constructor(args){if(args.poolSize==null&&(args.poolSize=2),super(args),typeof args.poolSize=="number")this.poolSize=[args.poolSize];else if(Array.isArray(args.poolSize)&&args.poolSize.length===1&&typeof args.poolSize[0]=="number")this.poolSize=args.poolSize;else throw new ValueError(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(args.poolSize)}`);if(assertPositiveInteger(this.poolSize,"poolSize"),args.strides==null)this.strides=this.poolSize;else if(typeof args.strides=="number")this.strides=[args.strides];else if(Array.isArray(args.strides)&&args.strides.length===1&&typeof args.strides[0]=="number")this.strides=args.strides;else throw new ValueError(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(args.strides)}`);assertPositiveInteger(this.strides,"strides"),this.padding=args.padding==null?"valid":args.padding,checkPaddingMode(this.padding),this.inputSpec=[new InputSpec({ndim:3})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let length=convOutputLength(inputShape[1],this.poolSize[0],this.padding,this.strides[0]);return[inputShape[0],length,inputShape[2]]}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs),inputs=expandDims5(getExactlyOneTensor(inputs),2);let output=this.poolingFunction(getExactlyOneTensor(inputs),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return squeeze(output,[2])})}getConfig(){let config2={poolSize:this.poolSize,padding:this.padding,strides:this.strides},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}},MaxPooling1D=class extends Pooling1D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding),pool2d(inputs,poolSize,strides,padding,dataFormat,"max")}};MaxPooling1D.className="MaxPooling1D";serialization_exports.registerClass(MaxPooling1D);var AveragePooling1D=class extends Pooling1D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding),pool2d(inputs,poolSize,strides,padding,dataFormat,"avg")}};AveragePooling1D.className="AveragePooling1D";serialization_exports.registerClass(AveragePooling1D);var Pooling2D=class extends Layer{constructor(args){if(args.poolSize==null&&(args.poolSize=[2,2]),super(args),this.poolSize=Array.isArray(args.poolSize)?args.poolSize:[args.poolSize,args.poolSize],args.strides==null)this.strides=this.poolSize;else if(Array.isArray(args.strides)){if(args.strides.length!==2)throw new ValueError(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${args.strides.length}.`);this.strides=args.strides}else this.strides=[args.strides,args.strides];assertPositiveInteger(this.poolSize,"poolSize"),assertPositiveInteger(this.strides,"strides"),this.padding=args.padding==null?"valid":args.padding,this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),checkPaddingMode(this.padding),this.inputSpec=[new InputSpec({ndim:4})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let rows=this.dataFormat==="channelsFirst"?inputShape[2]:inputShape[1],cols=this.dataFormat==="channelsFirst"?inputShape[3]:inputShape[2];return rows=convOutputLength(rows,this.poolSize[0],this.padding,this.strides[0]),cols=convOutputLength(cols,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[inputShape[0],inputShape[1],rows,cols]:[inputShape[0],rows,cols,inputShape[3]]}call(inputs,kwargs){return tidy(()=>(this.invokeCallHook(inputs,kwargs),this.poolingFunction(getExactlyOneTensor(inputs),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let config2={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}},MaxPooling2D=class extends Pooling2D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding),pool2d(inputs,poolSize,strides,padding,dataFormat,"max")}};MaxPooling2D.className="MaxPooling2D";serialization_exports.registerClass(MaxPooling2D);var AveragePooling2D=class extends Pooling2D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding),pool2d(inputs,poolSize,strides,padding,dataFormat,"avg")}};AveragePooling2D.className="AveragePooling2D";serialization_exports.registerClass(AveragePooling2D);var Pooling3D=class extends Layer{constructor(args){if(args.poolSize==null&&(args.poolSize=[2,2,2]),super(args),this.poolSize=Array.isArray(args.poolSize)?args.poolSize:[args.poolSize,args.poolSize,args.poolSize],args.strides==null)this.strides=this.poolSize;else if(Array.isArray(args.strides)){if(args.strides.length!==3)throw new ValueError(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${args.strides.length}.`);this.strides=args.strides}else this.strides=[args.strides,args.strides,args.strides];assertPositiveInteger(this.poolSize,"poolSize"),assertPositiveInteger(this.strides,"strides"),this.padding=args.padding==null?"valid":args.padding,this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),checkPaddingMode(this.padding),this.inputSpec=[new InputSpec({ndim:5})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let depths=this.dataFormat==="channelsFirst"?inputShape[2]:inputShape[1],rows=this.dataFormat==="channelsFirst"?inputShape[3]:inputShape[2],cols=this.dataFormat==="channelsFirst"?inputShape[4]:inputShape[3];return depths=convOutputLength(depths,this.poolSize[0],this.padding,this.strides[0]),rows=convOutputLength(rows,this.poolSize[1],this.padding,this.strides[1]),cols=convOutputLength(cols,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[inputShape[0],inputShape[1],depths,rows,cols]:[inputShape[0],depths,rows,cols,inputShape[4]]}call(inputs,kwargs){return tidy(()=>(this.invokeCallHook(inputs,kwargs),this.poolingFunction(getExactlyOneTensor(inputs),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let config2={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}},MaxPooling3D=class extends Pooling3D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding),pool3d2(inputs,poolSize,strides,padding,dataFormat,"max")}};MaxPooling3D.className="MaxPooling3D";serialization_exports.registerClass(MaxPooling3D);var AveragePooling3D=class extends Pooling3D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding),pool3d2(inputs,poolSize,strides,padding,dataFormat,"avg")}};AveragePooling3D.className="AveragePooling3D";serialization_exports.registerClass(AveragePooling3D);var GlobalPooling1D=class extends Layer{constructor(args){super(args);this.inputSpec=[new InputSpec({ndim:3})]}computeOutputShape(inputShape){return[inputShape[0],inputShape[2]]}call(inputs,kwargs){throw new NotImplementedError}},GlobalAveragePooling1D=class extends GlobalPooling1D{constructor(args){super(args||{})}call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);return mean(input2,1)})}};GlobalAveragePooling1D.className="GlobalAveragePooling1D";serialization_exports.registerClass(GlobalAveragePooling1D);var GlobalMaxPooling1D=class extends GlobalPooling1D{constructor(args){super(args||{})}call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);return max(input2,1)})}};GlobalMaxPooling1D.className="GlobalMaxPooling1D";serialization_exports.registerClass(GlobalMaxPooling1D);var GlobalPooling2D=class extends Layer{constructor(args){super(args);this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),this.inputSpec=[new InputSpec({ndim:4})]}computeOutputShape(inputShape){return inputShape=inputShape,this.dataFormat==="channelsLast"?[inputShape[0],inputShape[3]]:[inputShape[0],inputShape[1]]}call(inputs,kwargs){throw new NotImplementedError}getConfig(){let config2={dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}},GlobalAveragePooling2D=class extends GlobalPooling2D{call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);return this.dataFormat==="channelsLast"?mean(input2,[1,2]):mean(input2,[2,3])})}};GlobalAveragePooling2D.className="GlobalAveragePooling2D";serialization_exports.registerClass(GlobalAveragePooling2D);var GlobalMaxPooling2D=class extends GlobalPooling2D{call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);return this.dataFormat==="channelsLast"?max(input2,[1,2]):max(input2,[2,3])})}};GlobalMaxPooling2D.className="GlobalMaxPooling2D";serialization_exports.registerClass(GlobalMaxPooling2D);var Wrapper=class extends Layer{constructor(args){super(args);this.layer=args.layer}build(inputShape){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(value){this.layer!=null&&(this.layer.trainable=value)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(weights){this.layer.setWeights(weights)}getConfig(){let config2={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}setFastWeightInitDuringBuild(value){super.setFastWeightInitDuringBuild(value),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(value)}static fromConfig(cls,config2,customObjects={}){let layerConfig=config2.layer,layer=deserialize(layerConfig,customObjects);delete config2.layer;let newConfig={layer};return Object.assign(newConfig,config2),new cls(newConfig)}},TimeDistributed=class extends Wrapper{constructor(args){super(args);this.supportsMasking=!0}build(inputShape){if(inputShape=getExactlyOneShape(inputShape),inputShape.length<3)throw new ValueError(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(inputShape)}`);this.inputSpec=[{shape:inputShape}];let childInputShape=[inputShape[0]].concat(inputShape.slice(2));this.layer.built||(this.layer.build(childInputShape),this.layer.built=!0),super.build(inputShape)}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let childInputShape=[inputShape[0]].concat(inputShape.slice(2)),childOutputShape=this.layer.computeOutputShape(childInputShape),timesteps=inputShape[1];return[childOutputShape[0],timesteps].concat(childOutputShape.slice(1))}call(inputs,kwargs){return tidy(()=>(inputs=getExactlyOneTensor(inputs),rnn((inputs2,states)=>[getExactlyOneTensor(this.layer.call(inputs2,kwargs)),[]],inputs,[],!1,null,null,!1,!0)[1]))}};TimeDistributed.className="TimeDistributed";serialization_exports.registerClass(TimeDistributed);function checkBidirectionalMergeMode(value){checkStringTypeUnionValue(VALID_BIDIRECTIONAL_MERGE_MODES,"BidirectionalMergeMode",value)}var DEFAULT_BIDIRECTIONAL_MERGE_MODE="concat",Bidirectional=class extends Wrapper{constructor(args){super(args);let layerConfig=args.layer.getConfig(),forwDict={};forwDict.className=args.layer.getClassName(),forwDict.config=layerConfig,this.forwardLayer=deserialize(forwDict),layerConfig.goBackwards=layerConfig.goBackwards!==!0;let backDict={};if(backDict.className=args.layer.getClassName(),backDict.config=layerConfig,this.backwardLayer=deserialize(backDict),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=args.mergeMode===void 0?DEFAULT_BIDIRECTIONAL_MERGE_MODE:args.mergeMode,checkBidirectionalMergeMode(this.mergeMode),args.weights)throw new NotImplementedError("weights support is not implemented for Bidirectional layer yet.");this._stateful=args.layer.stateful,this.returnSequences=args.layer.returnSequences,this.returnState=args.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=args.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(value){this._trainable=value,this.forwardLayer!=null&&(this.forwardLayer.trainable=value),this.backwardLayer!=null&&(this.backwardLayer.trainable=value)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(weights){let numWeights=weights.length,numeightsOver2=Math.floor(numWeights/2);this.forwardLayer.setWeights(weights.slice(0,numeightsOver2)),this.backwardLayer.setWeights(weights.slice(numeightsOver2))}computeOutputShape(inputShape){let layerShapes=this.forwardLayer.computeOutputShape(inputShape);Array.isArray(layerShapes)&&Array.isArray(layerShapes[0])||(layerShapes=[layerShapes]),layerShapes=layerShapes;let outputShape,outputShapes,stateShape;return this.returnState&&(stateShape=layerShapes.slice(1)),outputShape=layerShapes[0],outputShape=outputShape,this.mergeMode==="concat"?(outputShape[outputShape.length-1]*=2,outputShapes=[outputShape]):this.mergeMode==null?outputShapes=[outputShape,outputShape.slice()]:outputShapes=[outputShape],this.returnState?this.mergeMode==null?outputShapes.concat(stateShape).concat(stateShape.slice()):[outputShape].concat(stateShape).concat(stateShape.slice()):singletonOrArray(outputShapes)}apply(inputs,kwargs){let initialState=kwargs==null?null:kwargs.initialState,constants=kwargs==null?null:kwargs.constants;kwargs==null&&(kwargs={});let standardized=standardizeArgs(inputs,initialState,constants,this.numConstants);if(inputs=standardized.inputs,initialState=standardized.initialState,constants=standardized.constants,Array.isArray(inputs)&&(initialState=inputs.slice(1),inputs=inputs[0]),(initialState==null||initialState.length===0)&&constants==null)return super.apply(inputs,kwargs);let additionalInputs=[],additionalSpecs=[];if(initialState!=null){let numStates=initialState.length;if(numStates%2>0)throw new ValueError("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");kwargs.initialState=initialState,additionalInputs.push(...initialState);let stateSpecs=initialState.map(state=>new InputSpec({shape:state.shape}));this.forwardLayer.stateSpec=stateSpecs.slice(0,numStates/2),this.backwardLayer.stateSpec=stateSpecs.slice(numStates/2),additionalSpecs.push(...stateSpecs)}if(constants!=null)throw new NotImplementedError("Support for constants in Bidirectional layers is not implemented yet.");let isSymbolicTensor=additionalInputs[0]instanceof SymbolicTensor;for(let tensor2 of additionalInputs)if(tensor2 instanceof SymbolicTensor!==isSymbolicTensor)throw new ValueError("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(isSymbolicTensor){let fullInput=[inputs].concat(additionalInputs),fullInputSpec=this.inputSpec.concat(additionalSpecs),originalInputSpec=this.inputSpec;this.inputSpec=fullInputSpec;let output=super.apply(fullInput,kwargs);return this.inputSpec=originalInputSpec,output}else return super.apply(inputs,kwargs)}call(inputs,kwargs){return tidy(()=>{let initialState=kwargs.initialState,y,yRev;if(initialState==null)y=this.forwardLayer.call(inputs,kwargs),yRev=this.backwardLayer.call(inputs,kwargs);else{let forwardState=initialState.slice(0,initialState.length/2),backwardState=initialState.slice(initialState.length/2);y=this.forwardLayer.call(inputs,Object.assign(kwargs,{initialState:forwardState})),yRev=this.backwardLayer.call(inputs,Object.assign(kwargs,{initialState:backwardState}))}let states;this.returnState&&(Array.isArray(y)&&(states=y.slice(1).concat(yRev.slice(1))),y=y[0],yRev=yRev[0]),this.returnSequences&&(yRev=reverse(yRev,1));let output;return this.mergeMode==="concat"?output=concatenate([y,yRev]):this.mergeMode==="sum"?output=add2(y,yRev):this.mergeMode==="ave"?output=mul(.5,add2(y,yRev)):this.mergeMode==="mul"?output=mul(y,yRev):this.mergeMode==null&&(output=[y,yRev]),this.returnState?this.mergeMode==null?output.concat(states):[output].concat(states):output})}resetStates(states){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(inputShape){nameScope(this.forwardLayer.name,()=>{this.forwardLayer.build(inputShape)}),nameScope(this.backwardLayer.name,()=>{this.backwardLayer.build(inputShape)}),this.built=!0}computeMask(inputs,mask){Array.isArray(mask)&&(mask=mask[0]);let outputMask;if(this.returnSequences?this.mergeMode==null?outputMask=[mask,mask]:outputMask=mask:this.mergeMode==null?outputMask=[null,null]:outputMask=null,this.returnState){let stateMask=this.forwardLayer.states.map(state=>null);return Array.isArray(outputMask)?outputMask.concat(stateMask).concat(stateMask):[outputMask].concat(stateMask).concat(stateMask)}else return outputMask}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)}setFastWeightInitDuringBuild(value){super.setFastWeightInitDuringBuild(value),this.forwardLayer!=null&&this.forwardLayer.setFastWeightInitDuringBuild(value),this.backwardLayer!=null&&this.backwardLayer.setFastWeightInitDuringBuild(value)}getConfig(){let config2={mergeMode:this.mergeMode},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}static fromConfig(cls,config2){let rnnLayer=deserialize(config2.layer);if(delete config2.layer,config2.numConstants!=null)throw new NotImplementedError("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");let newConfig=config2;return newConfig.layer=rnnLayer,new cls(newConfig)}};Bidirectional.className="Bidirectional";serialization_exports.registerClass(Bidirectional);function inputLayer(args){return new InputLayer(args)}function elu5(args){return new ELU4(args)}function reLU(args){return new ReLU(args)}function leakyReLU(args){return new LeakyReLU(args)}function prelu5(args){return new PReLU(args)}function softmax5(args){return new Softmax3(args)}function thresholdedReLU(args){return new ThresholdedReLU(args)}function conv1d2(args){return new Conv1D(args)}function conv2d5(args){return new Conv2D2(args)}function conv2dTranspose2(args){return new Conv2DTranspose(args)}function conv3d2(args){return new Conv3D2(args)}function separableConv2d2(args){return new SeparableConv2D(args)}function cropping2D(args){return new Cropping2D(args)}function upSampling2d(args){return new UpSampling2D(args)}function depthwiseConv2d5(args){return new DepthwiseConv2D(args)}function activation(args){return new Activation2(args)}function dense(args){return new Dense(args)}function dropout3(args){return new Dropout(args)}function spatialDropout1d(args){return new SpatialDropout1D(args)}function flatten3(args){return new Flatten(args)}function repeatVector(args){return new RepeatVector(args)}function reshape5(args){return new Reshape2(args)}function permute(args){return new Permute(args)}function embedding(args){return new Embedding(args)}function add4(args){return new Add2(args)}function average(args){return new Average(args)}function concatenate2(args){return new Concatenate(args)}function maximum4(args){return new Maximum2(args)}function minimum4(args){return new Minimum2(args)}function multiply3(args){return new Multiply2(args)}function dot3(args){return new Dot(args)}function batchNormalization2(args){return new BatchNormalization(args)}function layerNormalization(args){return new LayerNormalization(args)}function zeroPadding2d(args){return new ZeroPadding2D(args)}function averagePooling1d(args){return new AveragePooling1D(args)}function avgPool1d(args){return averagePooling1d(args)}function avgPooling1d(args){return averagePooling1d(args)}function averagePooling2d(args){return new AveragePooling2D(args)}function avgPool2d(args){return averagePooling2d(args)}function avgPooling2d(args){return averagePooling2d(args)}function averagePooling3d(args){return new AveragePooling3D(args)}function avgPool3d2(args){return averagePooling3d(args)}function avgPooling3d(args){return averagePooling3d(args)}function globalAveragePooling1d(args){return new GlobalAveragePooling1D(args)}function globalAveragePooling2d(args){return new GlobalAveragePooling2D(args)}function globalMaxPooling1d(args){return new GlobalMaxPooling1D(args)}function globalMaxPooling2d(args){return new GlobalMaxPooling2D(args)}function maxPooling1d(args){return new MaxPooling1D(args)}function maxPooling2d(args){return new MaxPooling2D(args)}function maxPooling3d(args){return new MaxPooling3D(args)}function gru(args){return new GRU(args)}function gruCell(args){return new GRUCell(args)}function lstm(args){return new LSTM(args)}function lstmCell(args){return new LSTMCell(args)}function simpleRNN(args){return new SimpleRNN(args)}function simpleRNNCell(args){return new SimpleRNNCell(args)}function convLstm2d(args){return new ConvLSTM2D(args)}function convLstm2dCell(args){return new ConvLSTM2DCell(args)}function rnn2(args){return new RNN(args)}function stackedRNNCells(args){return new StackedRNNCells(args)}function bidirectional(args){return new Bidirectional(args)}function timeDistributed(args){return new TimeDistributed(args)}var globalMaxPool1d=globalMaxPooling1d,globalMaxPool2d=globalMaxPooling2d,maxPool1d=maxPooling1d,maxPool2d=maxPooling2d;function gaussianNoise(args){return new GaussianNoise(args)}function gaussianDropout(args){return new GaussianDropout(args)}function alphaDropout(args){return new AlphaDropout(args)}function masking(args){return new Masking(args)}var exports_metrics_exports={};__export2(exports_metrics_exports,{MAPE:()=>MAPE2,MSE:()=>MSE2,binaryAccuracy:()=>binaryAccuracy2,binaryCrossentropy:()=>binaryCrossentropy3,categoricalAccuracy:()=>categoricalAccuracy2,categoricalCrossentropy:()=>categoricalCrossentropy3,cosineProximity:()=>cosineProximity2,mape:()=>mape2,meanAbsoluteError:()=>meanAbsoluteError2,meanAbsolutePercentageError:()=>meanAbsolutePercentageError2,meanSquaredError:()=>meanSquaredError3,mse:()=>mse2,precision:()=>precision2,recall:()=>recall2,sparseCategoricalAccuracy:()=>sparseCategoricalAccuracy2});function binaryAccuracy2(yTrue,yPred){return binaryAccuracy(yTrue,yPred)}function binaryCrossentropy3(yTrue,yPred){return binaryCrossentropy2(yTrue,yPred)}function sparseCategoricalAccuracy2(yTrue,yPred){return sparseCategoricalAccuracy(yTrue,yPred)}function categoricalAccuracy2(yTrue,yPred){return categoricalAccuracy(yTrue,yPred)}function categoricalCrossentropy3(yTrue,yPred){return categoricalCrossentropy2(yTrue,yPred)}function precision2(yTrue,yPred){return precision(yTrue,yPred)}function recall2(yTrue,yPred){return recall(yTrue,yPred)}function cosineProximity2(yTrue,yPred){return cosineProximity(yTrue,yPred)}function meanAbsoluteError2(yTrue,yPred){return meanAbsoluteError(yTrue,yPred)}function meanAbsolutePercentageError2(yTrue,yPred){return meanAbsolutePercentageError(yTrue,yPred)}function MAPE2(yTrue,yPred){return meanAbsolutePercentageError(yTrue,yPred)}function mape2(yTrue,yPred){return meanAbsolutePercentageError(yTrue,yPred)}function meanSquaredError3(yTrue,yPred){return meanSquaredError2(yTrue,yPred)}function MSE2(yTrue,yPred){return meanSquaredError2(yTrue,yPred)}function mse2(yTrue,yPred){return meanSquaredError2(yTrue,yPred)}var exports_models_exports={};__export2(exports_models_exports,{modelFromJSON:()=>modelFromJSON});var exports_regularizers_exports={};__export2(exports_regularizers_exports,{l1:()=>l12,l1l2:()=>l1l2,l2:()=>l22});function l1l2(config2){return new L1L2(config2)}function l12(config2){return l1(config2)}function l22(config2){return l2(config2)}var Callback=class extends BaseCallback{constructor(){super(...arguments);this.model=null}setModel(model2){if(!(model2 instanceof LayersModel))throw new Error("model must be a LayersModel, not some other Container");this.model=model2}};function less4(currVal,prevVal){return currValprevVal}var EarlyStopping=class extends Callback{constructor(args){super();if(args==null&&(args={}),args.restoreBestWeights)throw new NotImplementedError("restoreBestWeights = True is not implemented in EarlyStopping yet.");this.monitor=args.monitor||"val_loss",this.minDelta=Math.abs(args.minDelta||0),this.patience=args.patience||0,this.verbose=args.verbose||0,this.mode=args.mode||"auto",this.baseline=args.baseline,["auto","min","max"].indexOf(this.mode)===-1&&(console.warn(`EarlyStopping mode '${this.mode}' is invalid. Falling back to mode 'auto'.`),this.mode="auto"),this.mode==="min"?this.monitorFunc=less4:this.mode==="max"?this.monitorFunc=greater4:this.monitor.indexOf("acc")!==-1?this.monitorFunc=greater4:this.monitorFunc=less4,this.monitorFunc===less4&&(this.minDelta*=-1)}async onTrainBegin(logs){this.wait=0,this.stoppedEpoch=0,this.baseline!=null?this.best=this.baseline:this.best=this.monitorFunc===less4?Infinity:-Infinity}async onEpochEnd(epoch,logs){await resolveScalarsInLogs(logs);let current=this.getMonitorValue(logs);current!=null&&(this.monitorFunc(current-this.minDelta,this.best)?(this.best=current,this.wait=0):(this.wait++,this.wait>=this.patience&&(this.stoppedEpoch=epoch,this.model.stopTraining=!0)))}async onTrainEnd(logs){this.stoppedEpoch>0&&this.verbose&&console.log(`Epoch ${this.stoppedEpoch}: early stopping.`)}getMonitorValue(logs){logs==null&&(logs={});let monitorValue=logs[this.monitor];return monitorValue==null&&console.warn(`Metric for EarlyStopping ${this.monitor} is not available. Available metrics are: ${Object.keys(logs)}`),monitorValue}};function earlyStopping(args){return new EarlyStopping(args)}var callbacks={earlyStopping},DataType;(function(DataType2){DataType2[DataType2.DT_INVALID=0]="DT_INVALID",DataType2[DataType2.DT_FLOAT=1]="DT_FLOAT",DataType2[DataType2.DT_DOUBLE=2]="DT_DOUBLE",DataType2[DataType2.DT_INT32=3]="DT_INT32",DataType2[DataType2.DT_UINT8=4]="DT_UINT8",DataType2[DataType2.DT_INT16=5]="DT_INT16",DataType2[DataType2.DT_INT8=6]="DT_INT8",DataType2[DataType2.DT_STRING=7]="DT_STRING",DataType2[DataType2.DT_COMPLEX64=8]="DT_COMPLEX64",DataType2[DataType2.DT_INT64=9]="DT_INT64",DataType2[DataType2.DT_BOOL=10]="DT_BOOL",DataType2[DataType2.DT_QINT8=11]="DT_QINT8",DataType2[DataType2.DT_QUINT8=12]="DT_QUINT8",DataType2[DataType2.DT_QINT32=13]="DT_QINT32",DataType2[DataType2.DT_BFLOAT16=14]="DT_BFLOAT16",DataType2[DataType2.DT_FLOAT_REF=101]="DT_FLOAT_REF",DataType2[DataType2.DT_DOUBLE_REF=102]="DT_DOUBLE_REF",DataType2[DataType2.DT_INT32_REF=103]="DT_INT32_REF",DataType2[DataType2.DT_UINT8_REF=104]="DT_UINT8_REF",DataType2[DataType2.DT_INT16_REF=105]="DT_INT16_REF",DataType2[DataType2.DT_INT8_REF=106]="DT_INT8_REF",DataType2[DataType2.DT_STRING_REF=107]="DT_STRING_REF",DataType2[DataType2.DT_COMPLEX64_REF=108]="DT_COMPLEX64_REF",DataType2[DataType2.DT_INT64_REF=109]="DT_INT64_REF",DataType2[DataType2.DT_BOOL_REF=110]="DT_BOOL_REF",DataType2[DataType2.DT_QINT8_REF=111]="DT_QINT8_REF",DataType2[DataType2.DT_QUINT8_REF=112]="DT_QUINT8_REF",DataType2[DataType2.DT_QINT32_REF=113]="DT_QINT32_REF",DataType2[DataType2.DT_BFLOAT16_REF=114]="DT_BFLOAT16_REF"})(DataType||(DataType={}));var SaverDef;(function(SaverDef2){let CheckpointFormatVersion;(function(CheckpointFormatVersion2){CheckpointFormatVersion2[CheckpointFormatVersion2.LEGACY=0]="LEGACY",CheckpointFormatVersion2[CheckpointFormatVersion2.V1=1]="V1",CheckpointFormatVersion2[CheckpointFormatVersion2.V2=2]="V2"})(CheckpointFormatVersion=SaverDef2.CheckpointFormatVersion||(SaverDef2.CheckpointFormatVersion={}))})(SaverDef||(SaverDef={}));var CUSTOM_OPS={};function registerOp(name,opFunc){let opMapper={tfOpName:name,category:"custom",inputs:[],attrs:[],customExecutor:opFunc};CUSTOM_OPS[name]=opMapper}function getRegisteredOp(name){return CUSTOM_OPS[name]}function deregisterOp(name){delete CUSTOM_OPS[name]}function getParamValue(paramName,node,tensorMap,context,resourceManager){let inputParam=node.inputParams[paramName];if(inputParam&&inputParam.inputIndexStart!==void 0){let start=inputParam.inputIndexStart,end=inputParam.inputIndexEnd===0?void 0:inputParam.inputIndexEnd===void 0?start+1:inputParam.inputIndexEnd;if(inputParam.type==="tensor")return getTensor(node.inputNames[inputParam.inputIndexStart],tensorMap,context,resourceManager);if(inputParam.type==="tensors")return node.inputNames.slice(start,end).map(name=>getTensor(name,tensorMap,context,resourceManager));let tensor2=getTensor(node.inputNames.slice(start)[0],tensorMap,context,resourceManager),data2=tensor2.dataSync();return inputParam.type==="number"?data2[0]:util_exports.toNestedArray(tensor2.shape,data2)}let attrParam=node.attrParams[paramName];return attrParam&&attrParam.value}function getTensor(name,tensorsMap,context,resourceManager){let[nodeName,index]=parseNodeName(name);if(resourceManager!=null){let tensor2=resourceManager.getHashTableHandleByName(nodeName);if(tensor2!=null)return tensor2}let contextId=context.currentContextIds.find(contextId2=>!!tensorsMap[getNodeNameWithContextId(nodeName,contextId2)]);return contextId!==void 0?tensorsMap[getNodeNameWithContextId(nodeName,contextId)][index]:void 0}function getTensorsForCurrentContenxt(name,tensorsMap,context){return tensorsMap[getNodeNameWithContextId(name,context.currentContextId)]}function getNodeNameAndIndex(inputName,context){let[nodeName,index]=parseNodeName(inputName);return[getNodeNameWithContextId(nodeName,context&&context.currentContextId),index]}function getNodeNameWithContextId(name,contextId){return contextId?`${name}-${contextId}`:name}function parseNodeName(name){let parts=name.split(":");return parts.length===1?[name,0]:[parts[0],Number(parts[parts.length-1])]}function getPadding(node,tensorMap,context){let pad3=getParamValue("pad",node,tensorMap,context);if(pad3==="explicit"){pad3=getParamValue("explicitPaddings",node,tensorMap,context);let explicitPadding=[[0,0],[0,0],[0,0],[0,0]];for(let i=0;i<4;i++)explicitPadding[i][0]=pad3[i*2],explicitPadding[i][1]=pad3[i*2+1];return explicitPadding}return pad3}function cloneTensor(tensor2){return tensor2.kept?tensor2:clone(tensor2)}var arithmetic_exports={};__export2(arithmetic_exports,{json:()=>json});var json=[{tfOpName:"Add",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"AddV2",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"AddN",category:"arithmetic",inputs:[{start:0,end:0,name:"tensors",type:"tensors"}]},{tfOpName:"BiasAdd",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"Sub",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"RealDiv",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Div",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"DivNoNan",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"FloorDiv",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Mul",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Maximum",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Minimum",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Pow",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"SquaredDifference",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Mod",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"FloorMod",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]}],basic_math_exports={};__export2(basic_math_exports,{json:()=>json2});var json2=[{tfOpName:"Abs",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Acos",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Asin",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Atan",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Atan2",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"y",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Ceil",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"ClipByValue",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"clipValueMin",type:"number"},{start:2,name:"clipValueMax",type:"number"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Complex",category:"basic_math",inputs:[{start:0,name:"real",type:"tensor"},{start:1,name:"imag",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"ComplexAbs",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Cos",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Cosh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Elu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Exp",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Floor",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Log",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Imag",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"Tout",name:"outputType",type:"dtype",notSupported:!0}]},{tfOpName:"Neg",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Real",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"Tout",name:"outputType",type:"dtype",notSupported:!0}]},{tfOpName:"Prelu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"alpha",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Relu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Relu6",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Selu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sigmoid",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sin",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sinh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sqrt",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Rsqrt",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Square",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Tan",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Tanh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sign",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Round",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Expm1",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Log1p",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Reciprocal",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Softplus",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Asinh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Acosh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Atanh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Erf",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Prod",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axes",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool",notSupported:!0},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LeakyRelu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"alpha",name:"alpha",type:"number",defaultValue:.2},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]}],control_exports={};__export2(control_exports,{json:()=>json3});var 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json11=[{tfOpName:"Equal",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"NotEqual",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Greater",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"GreaterEqual",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Less",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LessEqual",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LogicalAnd",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LogicalNot",category:"logical",inputs:[{start:0,name:"a",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LogicalOr",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Select",category:"logical",inputs:[{start:0,name:"condition",type:"tensor"},{start:1,name:"a",type:"tensor"},{start:2,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"SelectV2",category:"logical",inputs:[{start:0,name:"condition",type:"tensor"},{start:1,name:"a",type:"tensor"},{start:2,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]}],matrices_exports={};__export2(matrices_exports,{json:()=>json12});var json12=[{tfOpName:"_FusedMatMul",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"},{start:2,end:0,name:"args",type:"tensors"}],attrs:[{tfName:"num_args",name:"numArgs",type:"number"},{tfName:"fused_ops",name:"fusedOps",type:"string[]",defaultValue:[]},{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:1e-4},{tfName:"transpose_a",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"transpose_b",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"MatMul",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"transpose_a",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"transpose_b",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"BatchMatMul",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"adj_x",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"adj_y",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"BatchMatMulV2",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"adj_x",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"adj_y",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Transpose",category:"matrices",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"perm",type:"number[]"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]}],normalization_exports={};__export2(normalization_exports,{json:()=>json13});var json13=[{tfOpName:"FusedBatchNorm",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"scale",type:"tensor"},{start:2,name:"offset",type:"tensor"},{start:3,name:"mean",type:"tensor"},{start:4,name:"variance",type:"tensor"}],attrs:[{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:.001},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"FusedBatchNormV2",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"scale",type:"tensor"},{start:2,name:"offset",type:"tensor"},{start:3,name:"mean",type:"tensor"},{start:4,name:"variance",type:"tensor"}],attrs:[{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:.001},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"FusedBatchNormV3",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"scale",type:"tensor"},{start:2,name:"offset",type:"tensor"},{start:3,name:"mean",type:"tensor"},{start:4,name:"variance",type:"tensor"}],attrs:[{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:.001},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"LRN",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"depth_radius",name:"radius",type:"number",defaultValue:5},{tfName:"bias",name:"bias",type:"number",defaultValue:1},{tfName:"alpha",name:"alpha",type:"number",defaultValue:1},{tfName:"beta",name:"beta",type:"number",defaultValue:.5}]},{tfOpName:"Softmax",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"LogSoftmax",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"SparseToDense",category:"normalization",inputs:[{start:0,name:"sparseIndices",type:"tensor"},{start:1,name:"outputShape",type:"number[]"},{start:2,name:"sparseValues",type:"tensor"},{start:3,name:"defaultValue",type:"tensor"}],attrs:[{tfName:"validate_indices",name:"validateIndices",type:"bool",defaultValue:!0,notSupported:!0}]}],reduction_exports={};__export2(reduction_exports,{json:()=>json14});var json14=[{tfOpName:"Bincount",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"size",type:"number"},{start:2,name:"weights",type:"tensor"}]},{tfOpName:"DenseBincount",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"size",type:"number"},{start:2,name:"weights",type:"tensor"}],attrs:[{tfName:"binary_output",name:"binaryOutput",type:"bool"}]},{tfOpName:"Max",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Mean",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Min",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Sum",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"All",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Any",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"ArgMax",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]},{tfOpName:"ArgMin",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]},{tfOpName:"Prod",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Cumsum",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}],attrs:[{tfName:"exclusive",name:"exclusive",type:"bool"},{tfName:"reverse",name:"reverse",type:"bool"}]}],slice_join_exports={};__export2(slice_join_exports,{json:()=>json15});var json15=[{tfOpName:"ConcatV2",category:"slice_join",inputs:[{start:0,end:-1,name:"tensors",type:"tensors"},{start:-1,name:"axis",type:"number"}],attrs:[{tfName:"N",name:"n",type:"number",defaultValue:2}]},{tfOpName:"Concat",category:"slice_join",inputs:[{start:1,end:0,name:"tensors",type:"tensors"},{start:0,name:"axis",type:"number"}],attrs:[{tfName:"N",name:"n",type:"number",defaultValue:2}]},{tfOpName:"GatherV2",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"indices",type:"tensor"},{start:2,name:"axis",type:"number",defaultValue:0}],attrs:[{tfName:"batch_dims",name:"batchDims",type:"number",defaultValue:0}]},{tfOpName:"Gather",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"indices",type:"tensor"}],attrs:[{tfName:"validate_indices",name:"validateIndices",type:"bool",notSupported:!0}]},{tfOpName:"Reverse",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"dims",type:"bool[]"}]},{tfOpName:"ReverseV2",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}]},{tfOpName:"Slice",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"begin",type:"number[]"},{start:2,name:"size",type:"number[]"}]},{tfOpName:"StridedSlice",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"begin",type:"number[]"},{start:2,name:"end",type:"number[]"},{start:3,name:"strides",type:"number[]"}],attrs:[{tfName:"begin_mask",name:"beginMask",type:"number",defaultValue:0},{tfName:"end_mask",name:"endMask",type:"number",defaultValue:0},{tfName:"new_axis_mask",name:"newAxisMask",type:"number",defaultValue:0},{tfName:"ellipsis_mask",name:"ellipsisMask",type:"number",defaultValue:0},{tfName:"shrink_axis_mask",name:"shrinkAxisMask",type:"number",defaultValue:0}]},{tfOpName:"Pack",category:"slice_join",inputs:[{start:0,end:0,name:"tensors",type:"tensors"}],attrs:[{tfName:"axis",name:"axis",type:"number",defaultValue:0}]},{tfOpName:"Unpack",category:"slice_join",inputs:[{start:0,name:"tensor",type:"tensor"}],attrs:[{tfName:"axis",name:"axis",type:"number",defaultValue:0},{tfName:"num",name:"num",type:"number",defaultValue:0,notSupported:!0}]},{tfOpName:"Tile",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"reps",type:"number[]"}]},{tfOpName:"Split",category:"slice_join",inputs:[{start:0,name:"axis",type:"number",defaultValue:0},{start:1,name:"x",type:"tensor"}],attrs:[{tfName:"num_split",name:"numOrSizeSplits",type:"number",defaultValue:1}]},{tfOpName:"SplitV",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"numOrSizeSplits",type:"number[]"},{start:2,name:"axis",type:"number",defaultValue:0}]},{tfOpName:"ScatterNd",category:"slice_join",inputs:[{start:0,name:"indices",type:"tensor"},{start:1,name:"values",type:"tensor"},{start:2,name:"shape",type:"number[]"}]},{tfOpName:"GatherNd",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"indices",type:"tensor"}]},{tfOpName:"SparseToDense",category:"slice_join",inputs:[{start:0,name:"sparseIndices",type:"tensor"},{start:1,name:"outputShape",type:"number[]"},{start:2,name:"sparseValues",type:"tensor"},{start:3,name:"defaultValue",type:"tensor"}],attrs:[{tfName:"validate_indices",name:"validateIndices",type:"bool",defaultValue:!1,notSupported:!0}]}],spectral_exports={};__export2(spectral_exports,{json:()=>json16});var json16=[{tfOpName:"FFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"IFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"RFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"fft_length",type:"number",notSupported:!0}]},{tfOpName:"IRFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"fft_length",type:"number",notSupported:!0}]}],transformation_exports={};__export2(transformation_exports,{json:()=>json17});var json17=[{tfOpName:"Cast",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"SrcT",name:"sdtype",type:"dtype",notSupported:!0},{tfName:"DstT",name:"dtype",type:"dtype"}]},{tfOpName:"ExpandDims",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]},{tfOpName:"MirrorPad",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"}],attrs:[{tfName:"mode",name:"mode",type:"string"}]},{tfOpName:"Pad",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"}],attrs:[{tfName:"constant_value",name:"constantValue",type:"number",defaultValue:0}]},{tfOpName:"PadV2",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"},{start:2,name:"constantValue",type:"number",defaultValue:0}]},{tfOpName:"Reshape",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"shape",type:"number[]"}]},{tfOpName:"Squeeze",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"axis",tfDeprecatedName:"squeeze_dims",name:"axis",type:"number[]"}]},{tfOpName:"SpaceToBatchND",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"blockShape",type:"number[]"},{start:2,name:"paddings",type:"number[]"}]},{tfOpName:"BatchToSpaceND",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"blockShape",type:"number[]"},{start:2,name:"crops",type:"number[]"}]},{tfOpName:"DepthToSpace",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"block_size",name:"blockSize",type:"number"},{tfName:"data_format",name:"dataFormat",type:"string"}]},{tfOpName:"BroadcastTo",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"shape",type:"number[]"}],attrs:[]}],OperationMapper=class{static get Instance(){return this._instance||(this._instance=new this)}constructor(){let ops=[arithmetic_exports,basic_math_exports,control_exports,convolution_exports,creation_exports,dynamic_exports,evaluation_exports,logical_exports,image_exports,graph_exports,matrices_exports,normalization_exports,reduction_exports,slice_join_exports,spectral_exports,transformation_exports,hash_table_exports],mappersJson=[].concat(...ops.map(op2=>op2.json));this.opMappers=mappersJson.reduce((map,mapper)=>(map[mapper.tfOpName]=mapper,map),{})}transformGraph(graph2,signature={}){let tfNodes=graph2.node,placeholders=[],weights=[],initNodes=[],nodes=tfNodes.reduce((map,node)=>(map[node.name]=this.mapNode(node),node.op.startsWith("Placeholder")?placeholders.push(map[node.name]):node.op==="Const"?weights.push(map[node.name]):(node.input==null||node.input.length===0)&&initNodes.push(map[node.name]),map),{}),inputs=[],outputs=[],inputNodeNameToKey={},outputNodeNameToKey={};signature!=null&&(inputNodeNameToKey=this.mapSignatureEntries(signature.inputs),outputNodeNameToKey=this.mapSignatureEntries(signature.outputs));let allNodes=Object.keys(nodes);allNodes.forEach(key=>{let node=nodes[key];node.inputNames.forEach(name=>{let[nodeName]=getNodeNameAndIndex(name);node.inputs.push(nodes[nodeName]),nodes[nodeName].children.push(node)})}),Object.keys(outputNodeNameToKey).length===0?allNodes.forEach(key=>{let node=nodes[key];node.children.length===0&&outputs.push(node)}):Object.keys(outputNodeNameToKey).forEach(name=>{let[nodeName]=getNodeNameAndIndex(name),node=nodes[nodeName];node!=null&&(node.signatureKey=outputNodeNameToKey[name],outputs.push(node))}),Object.keys(inputNodeNameToKey).length>0?Object.keys(inputNodeNameToKey).forEach(name=>{let[nodeName]=getNodeNameAndIndex(name),node=nodes[nodeName];node&&(node.signatureKey=inputNodeNameToKey[name],inputs.push(node))}):inputs=placeholders;let functions={};graph2.library!=null&&graph2.library.function!=null&&(functions=graph2.library.function.reduce((functions2,func2)=>(functions2[func2.signature.name]=this.mapFunction(func2),functions2),{}));let result={nodes,inputs,outputs,weights,placeholders,signature,functions};return initNodes.length>0&&(result.initNodes=initNodes),result}mapSignatureEntries(entries){return Object.keys(entries||{}).reduce((prev,curr)=>(prev[entries[curr].name]=curr,prev),{})}mapNode(node){let mapper=getRegisteredOp(node.op)||this.opMappers[node.op]||{};node.attr==null&&(node.attr={});let newNode={name:node.name,op:node.op,category:mapper.category,inputNames:(node.input||[]).map(input2=>input2.startsWith("^")?input2.substr(1):input2),inputs:[],children:[],inputParams:{},attrParams:{},rawAttrs:node.attr};return mapper.inputs!=null&&(newNode.inputParams=mapper.inputs.reduce((map,param)=>(map[param.name]={type:param.type,inputIndexStart:param.start,inputIndexEnd:param.end},map),{})),mapper.attrs!=null&&(newNode.attrParams=mapper.attrs.reduce((map,param)=>{let type=param.type,value;switch(param.type){case"string":value=getStringParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getStringParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"string[]":value=getStringArrayParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getStringArrayParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"number":value=getNumberParam(node.attr,param.tfName,param.defaultValue||0),value===void 0&&!!param.tfDeprecatedName&&(value=getNumberParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"number[]":value=getNumericArrayParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getNumericArrayParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"bool":value=getBoolParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getBoolParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"bool[]":value=getBoolArrayParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getBoolArrayParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"shape":value=getTensorShapeParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getTensorShapeParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"shape[]":value=getTensorShapeArrayParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getTensorShapeArrayParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"dtype":value=getDtypeParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getDtypeParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"dtype[]":value=getDtypeArrayParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getDtypeArrayParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"func":value=getFuncParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getFuncParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"tensor":case"tensors":break;default:throw new Error(`Unsupported param type: ${param.type} for op: ${node.op}`)}return map[param.name]={value,type},map},{})),newNode}mapFunction(functionDef){let tfNodes=functionDef.nodeDef,placeholders=[],weights=[],nodes={};tfNodes!=null&&(nodes=tfNodes.reduce((map,node)=>(map[node.name]=this.mapNode(node),node.op==="Const"&&weights.push(map[node.name]),map),{}));let inputs=[],outputs=[];functionDef.signature.inputArg.forEach(arg=>{let[nodeName]=getNodeNameAndIndex(arg.name),node={name:nodeName,op:"Placeholder",inputs:[],inputNames:[],category:"graph",inputParams:{},attrParams:{dtype:{value:parseDtypeParam(arg.type),type:"dtype"}},children:[]};node.signatureKey=arg.name,inputs.push(node),nodes[nodeName]=node}),Object.keys(nodes).forEach(key=>{let node=nodes[key];node.inputNames.forEach(name=>{let[nodeName]=getNodeNameAndIndex(name);node.inputs.push(nodes[nodeName]),nodes[nodeName].children.push(node)})});let returnNodeMap=functionDef.ret;functionDef.signature.outputArg.forEach(output=>{let[nodeName,index]=getNodeNameAndIndex(returnNodeMap[output.name]),node=nodes[nodeName];node!=null&&(node.defaultOutput=index,outputs.push(node))});let signature=this.mapArgsToSignature(functionDef);return{nodes,inputs,outputs,weights,placeholders,signature}}mapArgsToSignature(functionDef){return{methodName:functionDef.signature.name,inputs:functionDef.signature.inputArg.reduce((map,arg)=>(map[arg.name]=this.mapArgToTensorInfo(arg),map),{}),outputs:functionDef.signature.outputArg.reduce((map,arg)=>(map[arg.name]=this.mapArgToTensorInfo(arg,functionDef.ret),map),{})}}mapArgToTensorInfo(arg,nameMap2){let name=arg.name;return nameMap2!=null&&(name=nameMap2[name]),{name,dtype:arg.type}}};function decodeBase64(text){let global2=env().global;if(typeof global2.atob!="undefined")return global2.atob(text);if(typeof Buffer!="undefined")return new Buffer(text,"base64").toString();throw new Error("Unable to decode base64 in this environment. Missing built-in atob() or Buffer()")}function parseStringParam(s,keepCase){let value=Array.isArray(s)?String.fromCharCode.apply(null,s):decodeBase64(s);return keepCase?value:value.toLowerCase()}function getStringParam(attrs,name,def,keepCase=!1){let param=attrs[name];return param!=null?parseStringParam(param.s,keepCase):def}function getBoolParam(attrs,name,def){let param=attrs[name];return param?param.b:def}function getNumberParam(attrs,name,def){let param=attrs[name]||{},value=param.i!=null?param.i:param.f!=null?param.f:def;return typeof value=="number"?value:parseInt(value,10)}function parseDtypeParam(value){switch(typeof value=="string"&&(value=DataType[value]),value){case DataType.DT_FLOAT:return"float32";case DataType.DT_INT32:case DataType.DT_INT64:case DataType.DT_INT8:case DataType.DT_UINT8:return"int32";case DataType.DT_BOOL:return"bool";case DataType.DT_DOUBLE:return"float32";case DataType.DT_STRING:return"string";default:return null}}function getFuncParam(attrs,name,def){let param=attrs[name];return param&¶m.func?param.func.name:def}function getDtypeParam(attrs,name,def){let param=attrs[name];return param&¶m.type?parseDtypeParam(param.type):def}function getDtypeArrayParam(attrs,name,def){let param=attrs[name];return param&¶m.list&¶m.list.type?param.list.type.map(v=>parseDtypeParam(v)):def}function parseTensorShapeParam(shape){if(!shape.unknownRank)return shape.dim!=null?shape.dim.map(dim=>typeof dim.size=="number"?dim.size:parseInt(dim.size,10)):[]}function getTensorShapeParam(attrs,name,def){let param=attrs[name];return param&¶m.shape?parseTensorShapeParam(param.shape):def}function getNumericArrayParam(attrs,name,def){let param=attrs[name];return param?((param.list.f&¶m.list.f.length?param.list.f:param.list.i)||[]).map(v=>typeof v=="number"?v:parseInt(v,10)):def}function getStringArrayParam(attrs,name,def,keepCase=!1){let param=attrs[name];return param&¶m.list&¶m.list.s?param.list.s.map(v=>parseStringParam(v,keepCase)):def}function getTensorShapeArrayParam(attrs,name,def){let param=attrs[name];return param&¶m.list&¶m.list.shape?param.list.shape.map(v=>parseTensorShapeParam(v)):def}function getBoolArrayParam(attrs,name,def){let param=attrs[name];return param&¶m.list&¶m.list.b?param.list.b:def}var NodeValueImpl=class{constructor(node,tensorMap,context){this.node=node,this.tensorMap=tensorMap,this.context=context,this.inputs=[],this.attrs={},this.inputs=node.inputNames.map(name=>this.getInput(name)),node.rawAttrs!=null&&(this.attrs=Object.keys(node.rawAttrs).reduce((attrs,key)=>(attrs[key]=this.getAttr(key),attrs),{}))}getInput(name){return getTensor(name,this.tensorMap,this.context)}getAttr(name,defaultValue){let value=this.node.rawAttrs[name];if(value.tensor!=null)return getTensor(name,this.tensorMap,this.context);if(value.i!=null||value.f!=null)return getNumberParam(this.node.rawAttrs,name,defaultValue);if(value.s!=null)return getStringParam(this.node.rawAttrs,name,defaultValue);if(value.b!=null)return getBoolParam(this.node.rawAttrs,name,defaultValue);if(value.shape!=null)return getTensorShapeParam(this.node.rawAttrs,name,defaultValue);if(value.type!=null)return getDtypeParam(this.node.rawAttrs,name,defaultValue);if(value.list!=null){if(value.list.i!=null||value.list.f!=null)return getNumericArrayParam(this.node.rawAttrs,name,defaultValue);if(value.list.s!=null)return getStringArrayParam(this.node.rawAttrs,name,defaultValue);if(value.list.shape!=null)return getTensorShapeArrayParam(this.node.rawAttrs,name,defaultValue);if(value.list.b!=null)return getBoolArrayParam(this.node.rawAttrs,name,defaultValue);if(value.list.type!=null)return getDtypeArrayParam(this.node.rawAttrs,name,defaultValue)}return defaultValue}},executeOp=(node,tensorMap,context)=>{switch(node.op){case"BiasAdd":case"AddV2":case"Add":return[add2(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"AddN":return[addN(getParamValue("tensors",node,tensorMap,context))];case"FloorMod":case"Mod":return[mod(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Mul":return[mul(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"RealDiv":case"Div":return[div(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"DivNoNan":return[divNoNan(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"FloorDiv":return[floorDiv(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Sub":return[sub(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Minimum":return[minimum(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Maximum":return[maximum(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Pow":return[pow(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"SquaredDifference":return[squaredDifference(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp2=(node,tensorMap,context)=>{switch(node.op){case"Abs":case"ComplexAbs":return[abs(getParamValue("x",node,tensorMap,context))];case"Acos":return[acos(getParamValue("x",node,tensorMap,context))];case"Acosh":return[acosh(getParamValue("x",node,tensorMap,context))];case"Asin":return[asin(getParamValue("x",node,tensorMap,context))];case"Asinh":return[asinh(getParamValue("x",node,tensorMap,context))];case"Atan":return[atan(getParamValue("x",node,tensorMap,context))];case"Atan2":return[atan2(getParamValue("x",node,tensorMap,context),getParamValue("y",node,tensorMap,context))];case"Atanh":return[atanh(getParamValue("x",node,tensorMap,context))];case"Ceil":return[ceil(getParamValue("x",node,tensorMap,context))];case"Complex":return[complex(getParamValue("real",node,tensorMap,context),getParamValue("imag",node,tensorMap,context))];case"Cos":return[cos(getParamValue("x",node,tensorMap,context))];case"Cosh":return[cosh(getParamValue("x",node,tensorMap,context))];case"Elu":return[elu(getParamValue("x",node,tensorMap,context))];case"Erf":return[erf(getParamValue("x",node,tensorMap,context))];case"Exp":return[exp(getParamValue("x",node,tensorMap,context))];case"Expm1":return[expm1(getParamValue("x",node,tensorMap,context))];case"Floor":return[floor(getParamValue("x",node,tensorMap,context))];case"Log":return[log2(getParamValue("x",node,tensorMap,context))];case"Log1p":return[log1p(getParamValue("x",node,tensorMap,context))];case"Imag":return[imag(getParamValue("x",node,tensorMap,context))];case"Neg":return[neg(getParamValue("x",node,tensorMap,context))];case"Reciprocal":return[reciprocal(getParamValue("x",node,tensorMap,context))];case"Real":return[real(getParamValue("x",node,tensorMap,context))];case"Relu":return[relu(getParamValue("x",node,tensorMap,context))];case"Round":return[round2(getParamValue("x",node,tensorMap,context))];case"Selu":return[selu(getParamValue("x",node,tensorMap,context))];case"Sigmoid":return[sigmoid(getParamValue("x",node,tensorMap,context))];case"Sin":return[sin(getParamValue("x",node,tensorMap,context))];case"Sign":return[sign(getParamValue("x",node,tensorMap,context))];case"Sinh":return[sinh(getParamValue("x",node,tensorMap,context))];case"Softplus":return[softplus(getParamValue("x",node,tensorMap,context))];case"Sqrt":return[sqrt(getParamValue("x",node,tensorMap,context))];case"Square":return[square(getParamValue("x",node,tensorMap,context))];case"Tanh":return[tanh2(getParamValue("x",node,tensorMap,context))];case"Tan":return[tan(getParamValue("x",node,tensorMap,context))];case"ClipByValue":return[clipByValue(getParamValue("x",node,tensorMap,context),getParamValue("clipValueMin",node,tensorMap,context),getParamValue("clipValueMax",node,tensorMap,context))];case"Relu6":return[relu6(getParamValue("x",node,tensorMap,context))];case"Rsqrt":return[rsqrt(getTensor(node.inputNames[0],tensorMap,context))];case"Prod":return[prod(getParamValue("x",node,tensorMap,context),getParamValue("axes",node,tensorMap,context))];case"LeakyRelu":return[leakyRelu(getParamValue("x",node,tensorMap,context),getParamValue("alpha",node,tensorMap,context))];case"Prelu":return[prelu(getParamValue("x",node,tensorMap,context),getParamValue("alpha",node,tensorMap,context))];default:throw TypeError(`Node type ${node.op} is not implemented`)}};function assertShapesMatchAllowUndefinedSize(shapeA,shapeB,errorMessagePrefix=""){util_exports.assert(shapesEqualAllowUndefinedSize(shapeA,shapeB),()=>errorMessagePrefix+` Shapes ${shapeA} and ${shapeB} must match`)}function shapesEqualAllowUndefinedSize(n1,n2){if(n1.length!==n2.length)return!1;for(let i=0;i{(keepIds==null||!keepIds.has(tensor2.tensor.id))&&tensor2.tensor.dispose()}),this.tensors=[],this.closed_=!0,this.idTensor.dispose()}size(){return this.tensors.length}read(index){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(index<0||index>=this.size())throw new Error(`Tried to read from index ${index}, but array size is: ${this.size()}`);let tensorWithState=this.tensors[index];if(tensorWithState.cleared)throw new Error(`TensorArray ${this.name}: Could not read index ${index} twice because it was cleared after a previous read (perhaps try setting clear_after_read = false?).`);return this.clearAfterRead&&(tensorWithState.cleared=!0),tensorWithState.read=!0,tensorWithState.tensor}readMany(indices){return indices.map(index=>this.read(index))}write(index,tensor2){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(index<0||!this.dynamicSize&&index>=this.maxSize)throw new Error(`Tried to write to index ${index}, but array is not resizeable and size is: ${this.maxSize}`);let t=this.tensors[index]||{};if(tensor2.dtype!==this.dtype)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index}, because the value dtype is ${tensor2.dtype}, but TensorArray dtype is ${this.dtype}.`);if(this.size()===0&&(this.elementShape==null||this.elementShape.length===0)&&(this.elementShape=tensor2.shape),assertShapesMatchAllowUndefinedSize(this.elementShape,tensor2.shape,`TensorArray ${this.name}: Could not write to TensorArray index ${index}.`),t.read)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index}, because it has already been read.`);if(t.written)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index}, because it has already been written.`);t.tensor=tensor2,keep(tensor2),t.written=!0,this.tensors[index]=t}writeMany(indices,tensors){if(indices.length!==tensors.length)throw new Error(`TensorArray ${this.name}: could not write multiple tensors,because the index size: ${indices.length} is not the same as tensors size: ${tensors.length}.`);indices.forEach((i,index)=>this.write(i,tensors[index]))}gather(indices,dtype){if(!!dtype&&dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but gather requested dtype ${dtype}`);if(indices)indices=indices.slice(0,this.size());else{indices=[];for(let i=0;i=this.maxSize)throw new Error(`Max index must be < array size (${maxIndex} vs. ${this.maxSize})`);this.writeMany(indices,unstack(tensor2,0))}split(length,tensor2){if(tensor2.dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${tensor2.dtype}`);let totalLength=0,cumulativeLengths=length.map(len=>(totalLength+=len,totalLength));if(totalLength!==tensor2.shape[0])throw new Error(`Expected sum of lengths to be equal to tensor.shape[0], but sum of lengths is ${totalLength}, and tensor's shape is: ${tensor2.shape}`);if(!this.dynamicSize&&length.length!==this.maxSize)throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${length.length}), and the TensorArray is not marked as dynamically resizeable`);let elementPerRow=totalLength===0?0:tensor2.size/totalLength,tensors=[];tidy(()=>{tensor2=reshape(tensor2,[1,totalLength,elementPerRow]);for(let i=0;i{if(elementDtype!==tensor2.dtype)throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${tensor2.dtype}`);assertShapesMatchAllowUndefinedSize(elementShape,tensor2.shape,"TensorList shape mismatch: "),keep(tensor2)}),this.idTensor=scalar(0),this.maxNumElements=maxNumElements,keep(this.idTensor)}get id(){return this.idTensor.id}copy(){return new TensorList([...this.tensors],this.elementShape,this.elementDtype)}clearAndClose(keepIds){this.tensors.forEach(tensor2=>{(keepIds==null||!keepIds.has(tensor2.id))&&tensor2.dispose()}),this.tensors.length=0,this.idTensor.dispose()}size(){return this.tensors.length}stack(elementShape,elementDtype,numElements=-1){if(elementDtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);if(numElements!==-1&&this.tensors.length!==numElements)throw new Error(`Operation expected a list with ${numElements} elements but got a list with ${this.tensors.length} elements.`);return assertShapesMatchAllowUndefinedSize(elementShape,this.elementShape,"TensorList shape mismatch: "),tidy(()=>{let reshapedTensors=this.tensors.map(tensor2=>reshape(tensor2,elementShape));return stack(reshapedTensors,0)})}popBack(elementShape,elementDtype){if(elementDtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);if(this.size()===0)throw new Error("Trying to pop from an empty list.");let tensor2=this.tensors.pop();return assertShapesMatchAllowUndefinedSize(tensor2.shape,elementShape,"TensorList shape mismatch: "),reshape(tensor2,elementShape)}pushBack(tensor2){if(tensor2.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${tensor2.dtype}, but list elements ${this.elementDtype}`);if(assertShapesMatchAllowUndefinedSize(tensor2.shape,this.elementShape,"TensorList shape mismatch: "),this.maxNumElements===this.size())throw new Error("Trying to push element into a full list.");keep(tensor2),this.tensors.push(tensor2)}resize(size){if(size<0)throw new Error(`TensorListResize expects size to be non-negative. Got: ${size}`);if(this.maxNumElements!==-1&&size>this.maxNumElements)throw new Error(`TensorListResize input size ${size} is greater maxNumElement ${this.maxNumElements}.`);this.tensors.length=size}getItem(elementIndex,elementShape,elementDtype){if(elementDtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);if(elementIndex<0||elementIndex>this.tensors.length)throw new Error(`Trying to access element ${elementIndex} in a list with ${this.tensors.length} elements.`);if(this.tensors[elementIndex]==null)throw new Error(`element at index ${elementIndex} is null.`);return assertShapesMatchAllowUndefinedSize(this.tensors[elementIndex].shape,elementShape,"TensorList shape mismatch: "),this.tensors[elementIndex]}setItem(elementIndex,tensor2){if(tensor2.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${tensor2.dtype}, but list elements ${this.elementDtype}`);if(elementIndex<0||this.maxNumElements!==-1&&elementIndex>=this.maxNumElements)throw new Error(`Trying to set element ${elementIndex} in a list with max ${this.maxNumElements} elements.`);assertShapesMatchAllowUndefinedSize(this.elementShape,tensor2.shape,"TensorList shape mismatch: "),keep(tensor2),this.tensors[elementIndex]=tensor2}gather(indices,elementDtype,elementShape){if(elementDtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);return assertShapesMatchAllowUndefinedSize(this.elementShape,elementShape,"TensorList shape mismatch: "),indices=indices.slice(0,this.size()),indices.length===0?tensor([],[0].concat(this.elementShape)):tidy(()=>{let tensors=indices.map(i=>reshape(this.tensors[i],elementShape));return stack(tensors,0)})}concat(elementDtype,elementShape){if(!!elementDtype&&elementDtype!==this.elementDtype)throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${elementDtype}`);return assertShapesMatchAllowUndefinedSize(this.elementShape,elementShape,"TensorList shape mismatch: "),this.size()===0?tensor([],[0].concat(this.elementShape)):tidy(()=>{let tensors=this.tensors.map(t=>reshape(t,elementShape));return concat(tensors,0)})}};function fromTensor(tensor2,elementShape,elementDtype){let dtype=tensor2.dtype;if(tensor2.shape.length<1)throw new Error(`Tensor must be at least a vector, but saw shape: ${tensor2.shape}`);if(tensor2.dtype!==elementDtype)throw new Error(`Invalid data types; op elements ${tensor2.dtype}, but list elements ${elementDtype}`);let outputShape=tensor2.shape.slice(1);assertShapesMatchAllowUndefinedSize(outputShape,elementShape,"TensorList shape mismatch: ");let tensorList=unstack(tensor2);return new TensorList(tensorList,elementShape,dtype)}function reserve(elementShape,elementDtype,numElements){return new TensorList([],elementShape,elementDtype,numElements)}function scatter(tensor2,indices,elementShape,numElements){if(indices.length!==tensor2.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${indices.length} vs. ${tensor2.shape[0]}`);let maxIndex=Math.max(...indices);if(numElements!=null&&numElements!==-1&&maxIndex>=numElements)throw new Error(`Max index must be < array size (${maxIndex} vs. ${numElements})`);let list=new TensorList([],elementShape,tensor2.dtype,numElements),tensors=unstack(tensor2,0);return indices.forEach((value,index)=>{list.setItem(value,tensors[index])}),list}function split2(tensor2,length,elementShape){let totalLength=0,cumulativeLengths=length.map(len=>(totalLength+=len,totalLength));if(totalLength!==tensor2.shape[0])throw new Error(`Expected sum of lengths to be equal to tensor.shape[0], but sum of lengths is - ${totalLength}, and tensor's shape is: ${tensor2.shape}`);let elementPerRow=totalLength===0?0:tensor2.size/totalLength,tensors=tidy(()=>{let tensors2=[];tensor2=reshape(tensor2,[1,totalLength,elementPerRow]);for(let i=0;i{switch(node.op){case"If":case"StatelessIf":{let thenFunc=getParamValue("thenBranch",node,tensorMap,context),elseFunc=getParamValue("elseBranch",node,tensorMap,context),cond=getParamValue("cond",node,tensorMap,context),args=getParamValue("args",node,tensorMap,context);return(await cond.data())[0]?context.functionMap[thenFunc].executeFunctionAsync(args,context.tensorArrayMap,context.tensorListMap):context.functionMap[elseFunc].executeFunctionAsync(args,context.tensorArrayMap,context.tensorListMap)}case"While":case"StatelessWhile":{let bodyFunc=getParamValue("body",node,tensorMap,context),condFunc=getParamValue("cond",node,tensorMap,context),args=getParamValue("args",node,tensorMap,context),condResult=await context.functionMap[condFunc].executeFunctionAsync(args,context.tensorArrayMap,context.tensorListMap),argIds=args.map(tensor2=>tensor2.id),condValue=await condResult[0].data();condResult.forEach(tensor2=>{!tensor2.kept&&argIds.indexOf(tensor2.id)===-1&&tensor2.dispose()});let result=args;for(;condValue[0];){let origResult=result;result=await context.functionMap[bodyFunc].executeFunctionAsync(result,context.tensorArrayMap,context.tensorListMap);let resultIds=result.map(tensor2=>tensor2.id);origResult.forEach(tensor2=>{!tensor2.kept&&argIds.indexOf(tensor2.id)===-1&&resultIds.indexOf(tensor2.id)===-1&&tensor2.dispose()});let condResult2=await context.functionMap[condFunc].executeFunctionAsync(result,context.tensorArrayMap,context.tensorListMap);condValue=await condResult2[0].data(),condResult2.forEach(tensor2=>{!tensor2.kept&&argIds.indexOf(tensor2.id)===-1&&resultIds.indexOf(tensor2.id)===-1&&tensor2.dispose()})}return result}case"LoopCond":{let pred=getParamValue("pred",node,tensorMap,context);return[cloneTensor(pred)]}case"Switch":{let pred=getParamValue("pred",node,tensorMap,context),data2=getParamValue("data",node,tensorMap,context);return data2.kept||(data2=cloneTensor(data2)),(await pred.data())[0]?[void 0,data2]:[data2,void 0]}case"Merge":{let inputName=node.inputNames.find(name=>getTensor(name,tensorMap,context)!==void 0);if(inputName){let data2=getTensor(inputName,tensorMap,context);return[cloneTensor(data2)]}return}case"Enter":{let frameId=getParamValue("frameName",node,tensorMap,context),data2=getParamValue("tensor",node,tensorMap,context);return context.enterFrame(frameId),[cloneTensor(data2)]}case"Exit":{let data2=getParamValue("tensor",node,tensorMap,context);return context.exitFrame(),[cloneTensor(data2)]}case"NextIteration":{let data2=getParamValue("tensor",node,tensorMap,context);return context.nextIteration(),[cloneTensor(data2)]}case"TensorArrayV3":{let size=getParamValue("size",node,tensorMap,context),dtype=getParamValue("dtype",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),dynamicSize=getParamValue("dynamicSize",node,tensorMap,context),clearAfterRead=getParamValue("clearAfterRead",node,tensorMap,context),identicalElementShapes=getParamValue("identicalElementShapes",node,tensorMap,context),name=getParamValue("name",node,tensorMap,context),tensorArray=new TensorArray(name,dtype,size,elementShape,identicalElementShapes,dynamicSize,clearAfterRead);return context.addTensorArray(tensorArray),[tensorArray.idTensor,scalar(1)]}case"TensorArrayWriteV3":{let id=getParamValue("tensorArrayId",node,tensorMap,context),index=getParamValue("index",node,tensorMap,context),writeTensor=getParamValue("tensor",node,tensorMap,context),writeTensorArray=context.getTensorArray(id.id);return writeTensorArray.write(index,writeTensor),[writeTensorArray.idTensor]}case"TensorArrayReadV3":{let readId=getParamValue("tensorArrayId",node,tensorMap,context),readIndex=getParamValue("index",node,tensorMap,context);return[context.getTensorArray(readId.id).read(readIndex)]}case"TensorArrayGatherV3":{let gatherId=getParamValue("tensorArrayId",node,tensorMap,context),gatherIndices=getParamValue("indices",node,tensorMap,context),gatherDtype=getParamValue("dtype",node,tensorMap,context);return[context.getTensorArray(gatherId.id).gather(gatherIndices,gatherDtype)]}case"TensorArrayScatterV3":{let scatterId=getParamValue("tensorArrayId",node,tensorMap,context),scatterIndices=getParamValue("indices",node,tensorMap,context),scatterTensor=getParamValue("tensor",node,tensorMap,context),scatterTensorArray=context.getTensorArray(scatterId.id);return scatterTensorArray.scatter(scatterIndices,scatterTensor),[scatterTensorArray.idTensor]}case"TensorArrayConcatV3":{let concatId=getParamValue("tensorArrayId",node,tensorMap,context),concatTensorArray=context.getTensorArray(concatId.id),concatDtype=getParamValue("dtype",node,tensorMap,context);return[concatTensorArray.concat(concatDtype)]}case"TensorArraySplitV3":{let splitId=getParamValue("tensorArrayId",node,tensorMap,context),splitTensor=getParamValue("tensor",node,tensorMap,context),lengths=getParamValue("lengths",node,tensorMap,context),splitTensorArray=context.getTensorArray(splitId.id);return splitTensorArray.split(lengths,splitTensor),[splitTensorArray.idTensor]}case"TensorArraySizeV3":{let sizeId=getParamValue("tensorArrayId",node,tensorMap,context),sizeTensorArray=context.getTensorArray(sizeId.id);return[scalar(sizeTensorArray.size(),"int32")]}case"TensorArrayCloseV3":{let closeId=getParamValue("tensorArrayId",node,tensorMap,context),closeTensorArray=context.getTensorArray(closeId.id);return closeTensorArray.clearAndClose(),[closeTensorArray.idTensor]}case"TensorListSetItem":{let idTensor=getParamValue("tensorListId",node,tensorMap,context),index=getParamValue("index",node,tensorMap,context),writeTensor=getParamValue("tensor",node,tensorMap,context),tensorList=context.getTensorList(idTensor.id);return tensorList.setItem(index,writeTensor),[tensorList.idTensor]}case"TensorListGetItem":{let idTensor=getParamValue("tensorListId",node,tensorMap,context),readIndex=getParamValue("index",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),elementDType=getParamValue("elementDType",node,tensorMap,context);return[context.getTensorList(idTensor.id).getItem(readIndex,elementShape,elementDType)]}case"TensorListScatterV2":case"TensorListScatter":{let scatterIndices=getParamValue("indices",node,tensorMap,context),scatterTensor=getParamValue("tensor",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),numElements=getParamValue("numElements",node,tensorMap,context),tensorList=scatter(scatterTensor,scatterIndices,elementShape,numElements);return context.addTensorList(tensorList),[tensorList.idTensor]}case"TensorListReserve":case"EmptyTensorList":{let elementShape=getParamValue("elementShape",node,tensorMap,context),elementDtype=getParamValue("elementDType",node,tensorMap,context),numElementsParam;node.op==="TensorListReserve"?numElementsParam="numElements":numElementsParam="maxNumElements";let numElements=getParamValue(numElementsParam,node,tensorMap,context),tensorList=reserve(elementShape,elementDtype,numElements);return context.addTensorList(tensorList),[tensorList.idTensor]}case"TensorListGather":{let gatherId=getParamValue("tensorListId",node,tensorMap,context),gatherIndices=getParamValue("indices",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),elementDtype=getParamValue("elementDType",node,tensorMap,context);return[context.getTensorList(gatherId.id).gather(gatherIndices,elementDtype,elementShape)]}case"TensorListStack":{let idTensor=getParamValue("tensorListId",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),elementDtype=getParamValue("elementDType",node,tensorMap,context),numElements=getParamValue("numElements",node,tensorMap,context);return[context.getTensorList(idTensor.id).stack(elementShape,elementDtype,numElements)]}case"TensorListFromTensor":{let tensor2=getParamValue("tensor",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),elementDtype=getParamValue("elementDType",node,tensorMap,context),tensorList=fromTensor(tensor2,elementShape,elementDtype);return context.addTensorList(tensorList),[tensorList.idTensor]}case"TensorListConcat":{let concatId=getParamValue("tensorListId",node,tensorMap,context),tensorList=context.getTensorList(concatId.id),concatDtype=getParamValue("dtype",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context);return[tensorList.concat(concatDtype,elementShape)]}case"TensorListPushBack":{let idTensor=getParamValue("tensorListId",node,tensorMap,context),writeTensor=getParamValue("tensor",node,tensorMap,context),tensorList=context.getTensorList(idTensor.id);return tensorList.pushBack(writeTensor),[tensorList.idTensor]}case"TensorListPopBack":{let idTensor=getParamValue("tensorListId",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),elementDType=getParamValue("elementDType",node,tensorMap,context);return[context.getTensorList(idTensor.id).popBack(elementShape,elementDType)]}case"TensorListSplit":{let splitTensor=getParamValue("tensor",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),lengths=getParamValue("lengths",node,tensorMap,context),tensorList=split2(splitTensor,lengths,elementShape);return context.addTensorList(tensorList),[tensorList.idTensor]}default:throw TypeError(`Node type ${node.op} is not implemented`)}};function fusedConvAndDepthWiseParams(node,tensorMap,context){let[extraOp,activationFunc]=getParamValue("fusedOps",node,tensorMap,context),isBiasAdd=extraOp==="biasadd",isPrelu=activationFunc==="prelu",isBatchNorm=extraOp==="fusedbatchnorm",numArgs=getParamValue("numArgs",node,tensorMap,context);if(isBiasAdd){if(isPrelu&&numArgs!==2)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!isPrelu&&numArgs!==1)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(isBatchNorm)throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported.");let stride=getParamValue("strides",node,tensorMap,context),pad3=getPadding(node,tensorMap,context),dataFormat=getParamValue("dataFormat",node,tensorMap,context).toUpperCase(),dilations=getParamValue("dilations",node,tensorMap,context),[biasArg,preluArg]=getParamValue("args",node,tensorMap,context),leakyreluAlpha=getParamValue("leakyreluAlpha",node,tensorMap,context);return{stride,pad:pad3,dataFormat,dilations,biasArg,preluArg,activationFunc,leakyreluAlpha}}var executeOp4=(node,tensorMap,context)=>{switch(node.op){case"Conv1D":{let stride=getParamValue("stride",node,tensorMap,context),pad3=getParamValue("pad",node,tensorMap,context),dataFormat=getParamValue("dataFormat",node,tensorMap,context).toUpperCase(),dilation=getParamValue("dilation",node,tensorMap,context);return[conv1d(getParamValue("x",node,tensorMap,context),getParamValue("filter",node,tensorMap,context),stride,pad3,dataFormat,dilation)]}case"Conv2D":{let stride=getParamValue("strides",node,tensorMap,context),pad3=getPadding(node,tensorMap,context),dataFormat=getParamValue("dataFormat",node,tensorMap,context).toUpperCase(),dilations=getParamValue("dilations",node,tensorMap,context);return[conv2d(getParamValue("x",node,tensorMap,context),getParamValue("filter",node,tensorMap,context),[stride[1],stride[2]],pad3,dataFormat,[dilations[1],dilations[2]])]}case"_FusedConv2D":{let{stride,pad:pad3,dataFormat,dilations,biasArg,preluArg,activationFunc,leakyreluAlpha}=fusedConvAndDepthWiseParams(node,tensorMap,context);return[fused_ops_exports.conv2d({x:getParamValue("x",node,tensorMap,context),filter:getParamValue("filter",node,tensorMap,context),strides:[stride[1],stride[2]],pad:pad3,dataFormat,dilations:[dilations[1],dilations[2]],bias:biasArg,activation:activationFunc,preluActivationWeights:preluArg,leakyreluAlpha})]}case"FusedDepthwiseConv2dNative":{let{stride,pad:pad3,dataFormat,dilations,biasArg,preluArg,activationFunc,leakyreluAlpha}=fusedConvAndDepthWiseParams(node,tensorMap,context);return[fused_ops_exports.depthwiseConv2d({x:getParamValue("x",node,tensorMap,context),filter:getParamValue("filter",node,tensorMap,context),strides:[stride[1],stride[2]],pad:pad3,dataFormat,dilations:[dilations[1],dilations[2]],bias:biasArg,activation:activationFunc,preluActivationWeights:preluArg,leakyreluAlpha})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{let shape=getParamValue("outputShape",node,tensorMap,context),stride=getParamValue("strides",node,tensorMap,context),pad3=getPadding(node,tensorMap,context);return[conv2dTranspose(getParamValue("x",node,tensorMap,context),getParamValue("filter",node,tensorMap,context),shape,[stride[1],stride[2]],pad3)]}case"DepthwiseConv2dNative":case"DepthwiseConv2d":{let stride=getParamValue("strides",node,tensorMap,context),pad3=getPadding(node,tensorMap,context),dilations=getParamValue("dilations",node,tensorMap,context),dataFormat=getParamValue("dataFormat",node,tensorMap,context).toUpperCase();return[depthwiseConv2d(getParamValue("input",node,tensorMap,context),getParamValue("filter",node,tensorMap,context),[stride[1],stride[2]],pad3,dataFormat,[dilations[1],dilations[2]])]}case"Conv3D":{let stride=getParamValue("strides",node,tensorMap,context),pad3=getParamValue("pad",node,tensorMap,context),dataFormat=getParamValue("dataFormat",node,tensorMap,context).toUpperCase(),dilations=getParamValue("dilations",node,tensorMap,context);return[conv3d(getParamValue("x",node,tensorMap,context),getParamValue("filter",node,tensorMap,context),[stride[1],stride[2],stride[3]],pad3,dataFormat,[dilations[1],dilations[2],dilations[3]])]}case"AvgPool":{let stride=getParamValue("strides",node,tensorMap,context),pad3=getParamValue("pad",node,tensorMap,context),kernelSize=getParamValue("kernelSize",node,tensorMap,context);return[avgPool(getParamValue("x",node,tensorMap,context),[kernelSize[1],kernelSize[2]],[stride[1],stride[2]],pad3)]}case"MaxPool":{let stride=getParamValue("strides",node,tensorMap,context),pad3=getParamValue("pad",node,tensorMap,context),kernelSize=getParamValue("kernelSize",node,tensorMap,context);return[maxPool(getParamValue("x",node,tensorMap,context),[kernelSize[1],kernelSize[2]],[stride[1],stride[2]],pad3)]}case"MaxPoolWithArgmax":{let stride=getParamValue("strides",node,tensorMap,context),pad3=getParamValue("pad",node,tensorMap,context),kernelSize=getParamValue("kernelSize",node,tensorMap,context),includeBatchInIndex=getParamValue("includeBatchInIndex",node,tensorMap,context),{result,indexes}=maxPoolWithArgmax(getParamValue("x",node,tensorMap,context),[kernelSize[1],kernelSize[2]],[stride[1],stride[2]],pad3,includeBatchInIndex);return[result,indexes]}case"AvgPool3D":{let stride=getParamValue("strides",node,tensorMap,context),pad3=getParamValue("pad",node,tensorMap,context),kernelSize=getParamValue("kernelSize",node,tensorMap,context);return[avgPool3d(getParamValue("x",node,tensorMap,context),[kernelSize[1],kernelSize[2],kernelSize[3]],[stride[1],stride[2],stride[3]],pad3)]}case"MaxPool3D":{let stride=getParamValue("strides",node,tensorMap,context),pad3=getParamValue("pad",node,tensorMap,context),kernelSize=getParamValue("kernelSize",node,tensorMap,context);return[maxPool3d(getParamValue("x",node,tensorMap,context),[kernelSize[1],kernelSize[2],kernelSize[3]],[stride[1],stride[2],stride[3]],pad3)]}case"Dilation2D":{let strides=getParamValue("strides",node,tensorMap,context),pad3=getParamValue("pad",node,tensorMap,context),dilations=getParamValue("dilations",node,tensorMap,context),strideHeight=strides[1],strideWidth=strides[2],dilationHeight=dilations[1],dilationWidth=dilations[2];return[dilation2d(getParamValue("x",node,tensorMap,context),getParamValue("filter",node,tensorMap,context),[strideHeight,strideWidth],pad3,[dilationHeight,dilationWidth],"NHWC")]}default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp5=(node,tensorMap,context)=>{switch(node.op){case"Fill":{let shape=getParamValue("shape",node,tensorMap,context),dtype=getParamValue("dtype",node,tensorMap,context),value=getParamValue("value",node,tensorMap,context);return[fill(shape,value,dtype)]}case"LinSpace":{let start=getParamValue("start",node,tensorMap,context),stop=getParamValue("stop",node,tensorMap,context),num=getParamValue("num",node,tensorMap,context);return[linspace(start,stop,num)]}case"Multinomial":{let logits=getParamValue("logits",node,tensorMap,context),numSamples=getParamValue("numSamples",node,tensorMap,context),seed=getParamValue("seed",node,tensorMap,context);return[multinomial(logits,numSamples,seed)]}case"OneHot":{let indices=getParamValue("indices",node,tensorMap,context),depth=getParamValue("depth",node,tensorMap,context),onValue=getParamValue("onValue",node,tensorMap,context),offValue=getParamValue("offValue",node,tensorMap,context);return[oneHot(indices,depth,onValue,offValue)]}case"Ones":return[ones2(getParamValue("shape",node,tensorMap,context),getParamValue("dtype",node,tensorMap,context))];case"OnesLike":return[onesLike(getParamValue("x",node,tensorMap,context))];case"RandomUniform":return[randomUniform(getParamValue("shape",node,tensorMap,context),getParamValue("minval",node,tensorMap,context),getParamValue("maxval",node,tensorMap,context),getParamValue("dtype",node,tensorMap,context))];case"Range":{let start=getParamValue("start",node,tensorMap,context),stop=getParamValue("stop",node,tensorMap,context),step4=getParamValue("step",node,tensorMap,context);return[range(start,stop,step4,getParamValue("dtype",node,tensorMap,context))]}case"TruncatedNormal":{let shape=getParamValue("shape",node,tensorMap,context),mean4=getParamValue("mean",node,tensorMap,context),stdDev=getParamValue("stdDev",node,tensorMap,context),seed=getParamValue("seed",node,tensorMap,context);return[truncatedNormal(shape,mean4,stdDev,getParamValue("dtype",node,tensorMap,context),seed)]}case"Zeros":return[zeros(getParamValue("shape",node,tensorMap,context),getParamValue("dtype",node,tensorMap,context))];case"ZerosLike":return[zerosLike(getParamValue("x",node,tensorMap,context))];default:throw TypeError(`Node type ${node.op} is not implemented`)}};function nmsParams(node,tensorMap,context){let boxes=getParamValue("boxes",node,tensorMap,context),scores=getParamValue("scores",node,tensorMap,context),maxOutputSize=getParamValue("maxOutputSize",node,tensorMap,context),iouThreshold=getParamValue("iouThreshold",node,tensorMap,context),scoreThreshold=getParamValue("scoreThreshold",node,tensorMap,context),softNmsSigma=getParamValue("softNmsSigma",node,tensorMap,context);return{boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma}}var executeOp6=async(node,tensorMap,context)=>{switch(node.op){case"NonMaxSuppressionV5":{let{boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma}=nmsParams(node,tensorMap,context),result=await image.nonMaxSuppressionWithScoreAsync(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma);return[result.selectedIndices,result.selectedScores]}case"NonMaxSuppressionV4":{let{boxes,scores,maxOutputSize,iouThreshold,scoreThreshold}=nmsParams(node,tensorMap,context),padToMaxOutputSize=getParamValue("padToMaxOutputSize",node,tensorMap,context),result=await image.nonMaxSuppressionPaddedAsync(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize);return[result.selectedIndices,result.validOutputs]}case"NonMaxSuppressionV3":case"NonMaxSuppressionV2":{let{boxes,scores,maxOutputSize,iouThreshold,scoreThreshold}=nmsParams(node,tensorMap,context);return[await image.nonMaxSuppressionAsync(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold)]}case"Where":{let condition=cast(getParamValue("condition",node,tensorMap,context),"bool"),result=[await whereAsync(condition)];return condition.dispose(),result}case"ListDiff":return setdiff1dAsync(getParamValue("x",node,tensorMap,context),getParamValue("y",node,tensorMap,context));default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp7=(node,tensorMap,context)=>{switch(node.op){case"TopKV2":{let x=getParamValue("x",node,tensorMap,context),k=getParamValue("k",node,tensorMap,context),sorted=getParamValue("sorted",node,tensorMap,context),result=topk(x,k,sorted);return[result.values,result.indices]}case"Unique":{let x=getParamValue("x",node,tensorMap,context),result=unique(x);return[result.values,result.indices]}case"UniqueV2":{let x=getParamValue("x",node,tensorMap,context),axis=getParamValue("axis",node,tensorMap,context),result=unique(x,axis);return[result.values,result.indices]}default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp8=(node,tensorMap,context)=>{switch(node.op){case"Const":return tensorMap[node.name];case"PlaceholderWithDefault":let def=getParamValue("default",node,tensorMap,context);return[getTensor(node.name,tensorMap,context)||def];case"Placeholder":return[getTensor(node.name,tensorMap,context)];case"Identity":case"StopGradient":case"FakeQuantWithMinMaxVars":{let data22=getParamValue("x",node,tensorMap,context);return[cloneTensor(data22)]}case"IdentityN":return getParamValue("x",node,tensorMap,context).map(t=>cloneTensor(t));case"Snapshot":let snapshot=getParamValue("x",node,tensorMap,context);return[cloneTensor(snapshot)];case"Shape":return[tensor1d(getParamValue("x",node,tensorMap,context).shape,"int32")];case"ShapeN":return getParamValue("x",node,tensorMap,context).map(t=>tensor1d(t.shape));case"Size":return[scalar(getParamValue("x",node,tensorMap,context).size,"int32")];case"Rank":return[scalar(getParamValue("x",node,tensorMap,context).rank,"int32")];case"NoOp":return[scalar(1)];case"Print":let input2=getParamValue("x",node,tensorMap,context),data2=getParamValue("data",node,tensorMap,context),message=getParamValue("message",node,tensorMap,context),summarize=getParamValue("summarize",node,tensorMap,context);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."),console.log(message);for(let i=0;ivalue.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}async import(keys,values){this.checkKeyAndValueTensor(keys,values);let $keys=await keys.data();return this.tensorMap.forEach(value=>value.dispose()),this.tensorMap.clear(),tidy(()=>{let $values=unstack(values),keysLength=$keys.length,valuesLength=$values.length;util_exports.assert(keysLength===valuesLength,()=>`The number of elements doesn't match, keys has ${keysLength} elements, the values has ${valuesLength} elements.`);for(let i=0;i{let result=[];for(let i=0;i<$keys.length;i++){let key=$keys[i],value=this.findWithDefault(key,defaultValue);result.push(value)}return stack(result)})}findWithDefault(key,defaultValue){let result=this.tensorMap.get(key);return result!=null?result:defaultValue}checkKeyAndValueTensor(key,value){if(key.dtype!==this.keyDType)throw new Error(`Expect key dtype ${this.keyDType}, but got ${key.dtype}`);if(value.dtype!==this.valueDType)throw new Error(`Expect value dtype ${this.valueDType}, but got ${value.dtype}`)}},executeOp9=async(node,tensorMap,context,resourceManager)=>{switch(node.op){case"HashTable":case"HashTableV2":{let keyDType=getParamValue("keyDType",node,tensorMap,context),valueDType=getParamValue("valueDType",node,tensorMap,context),hashTable2=new HashTable(keyDType,valueDType);return resourceManager.addHashTable(node.name,hashTable2),[hashTable2.handle]}case"LookupTableImport":case"LookupTableImportV2":{let handle=getParamValue("tableHandle",node,tensorMap,context,resourceManager),keys=getParamValue("keys",node,tensorMap,context),values=getParamValue("values",node,tensorMap,context);return[await resourceManager.getHashTableById(handle.id).import(keys,values)]}case"LookupTableFind":case"LookupTableFindV2":{let handle=getParamValue("tableHandle",node,tensorMap,context,resourceManager),keys=getParamValue("keys",node,tensorMap,context),defaultValue=getParamValue("defaultValue",node,tensorMap,context);return[await resourceManager.getHashTableById(handle.id).find(keys,defaultValue)]}default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp10=(node,tensorMap,context)=>{switch(node.op){case"ResizeBilinear":{let images=getParamValue("images",node,tensorMap,context),size=getParamValue("size",node,tensorMap,context),alignCorners=getParamValue("alignCorners",node,tensorMap,context),halfPixelCenters=getParamValue("halfPixelCenters",node,tensorMap,context);return[image.resizeBilinear(images,[size[0],size[1]],alignCorners,halfPixelCenters)]}case"ResizeNearestNeighbor":{let images=getParamValue("images",node,tensorMap,context),size=getParamValue("size",node,tensorMap,context),alignCorners=getParamValue("alignCorners",node,tensorMap,context),halfPixelCenters=getParamValue("halfPixelCenters",node,tensorMap,context);return[image.resizeNearestNeighbor(images,[size[0],size[1]],alignCorners,halfPixelCenters)]}case"CropAndResize":{let image3=getParamValue("image",node,tensorMap,context),boxes=getParamValue("boxes",node,tensorMap,context),boxInd=getParamValue("boxInd",node,tensorMap,context),cropSize=getParamValue("cropSize",node,tensorMap,context),method=getParamValue("method",node,tensorMap,context),extrapolationValue=getParamValue("extrapolationValue",node,tensorMap,context);return[image.cropAndResize(image3,boxes,boxInd,cropSize,method,extrapolationValue)]}default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp11=(node,tensorMap,context)=>{switch(node.op){case"Equal":return[equal(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"NotEqual":return[notEqual(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Greater":return[greater(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"GreaterEqual":return[greaterEqual(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Less":return[less(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"LessEqual":return[lessEqual(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"LogicalAnd":return[logicalAnd(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"LogicalNot":return[logicalNot(getParamValue("a",node,tensorMap,context))];case"LogicalOr":return[logicalOr(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Select":case"SelectV2":return[where(getParamValue("condition",node,tensorMap,context),getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp12=(node,tensorMap,context)=>{switch(node.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[matMul(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context),getParamValue("transposeA",node,tensorMap,context),getParamValue("transposeB",node,tensorMap,context))];case"Transpose":return[transpose(getParamValue("x",node,tensorMap,context),getParamValue("perm",node,tensorMap,context))];case"_FusedMatMul":let[extraOp,activationFunc]=getParamValue("fusedOps",node,tensorMap,context),isBiasAdd=extraOp==="biasadd",isPrelu=activationFunc==="prelu",numArgs=getParamValue("numArgs",node,tensorMap,context),leakyreluAlpha=getParamValue("leakyreluAlpha",node,tensorMap,context);if(isBiasAdd){if(isPrelu&&numArgs!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!isPrelu&&numArgs!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[biasArg,preluArg]=getParamValue("args",node,tensorMap,context);return[fused_ops_exports.matMul({a:getParamValue("a",node,tensorMap,context),b:getParamValue("b",node,tensorMap,context),transposeA:getParamValue("transposeA",node,tensorMap,context),transposeB:getParamValue("transposeB",node,tensorMap,context),bias:biasArg,activation:activationFunc,preluActivationWeights:preluArg,leakyreluAlpha})];default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp13=(node,tensorMap,context)=>{switch(node.op){case"FusedBatchNorm":case"FusedBatchNormV2":return[batchNorm(getParamValue("x",node,tensorMap,context),getParamValue("mean",node,tensorMap,context),getParamValue("variance",node,tensorMap,context),getParamValue("offset",node,tensorMap,context),getParamValue("scale",node,tensorMap,context),getParamValue("epsilon",node,tensorMap,context))];case"FusedBatchNormV3":return[batchNorm(getParamValue("x",node,tensorMap,context),getParamValue("mean",node,tensorMap,context),getParamValue("variance",node,tensorMap,context),getParamValue("offset",node,tensorMap,context),getParamValue("scale",node,tensorMap,context),getParamValue("epsilon",node,tensorMap,context))];case"LRN":return[localResponseNormalization(getParamValue("x",node,tensorMap,context),getParamValue("radius",node,tensorMap,context),getParamValue("bias",node,tensorMap,context),getParamValue("alpha",node,tensorMap,context),getParamValue("beta",node,tensorMap,context))];case"Softmax":return[softmax(getParamValue("x",node,tensorMap,context))];case"LogSoftmax":return[logSoftmax(getParamValue("x",node,tensorMap,context))];case"SparseToDense":return[sparseToDense(getParamValue("sparseIndices",node,tensorMap,context),getParamValue("outputShape",node,tensorMap,context),getParamValue("sparseValues",node,tensorMap,context),getParamValue("defaultValue",node,tensorMap,context))];default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp14=(node,tensorMap,context)=>{switch(node.op){case"Max":{let axis=getParamValue("axis",node,tensorMap,context),keepDims=getParamValue("keepDims",node,tensorMap,context);return[max(getParamValue("x",node,tensorMap,context),axis,keepDims)]}case"Mean":{let axis=getParamValue("axis",node,tensorMap,context),keepDims=getParamValue("keepDims",node,tensorMap,context);return[mean(getParamValue("x",node,tensorMap,context),axis,keepDims)]}case"Min":{let axis=getParamValue("axis",node,tensorMap,context),keepDims=getParamValue("keepDims",node,tensorMap,context);return[min(getParamValue("x",node,tensorMap,context),axis,keepDims)]}case"Sum":{let axis=getParamValue("axis",node,tensorMap,context),keepDims=getParamValue("keepDims",node,tensorMap,context);return[sum2(getParamValue("x",node,tensorMap,context),axis,keepDims)]}case"All":{let axis=getParamValue("axis",node,tensorMap,context),keepDims=getParamValue("keepDims",node,tensorMap,context);return[all(getParamValue("x",node,tensorMap,context),axis,keepDims)]}case"Any":{let axis=getParamValue("axis",node,tensorMap,context),keepDims=getParamValue("keepDims",node,tensorMap,context);return[any(getParamValue("x",node,tensorMap,context),axis,keepDims)]}case"ArgMax":{let axis=getParamValue("axis",node,tensorMap,context);return[argMax(getParamValue("x",node,tensorMap,context),axis)]}case"ArgMin":{let axis=getParamValue("axis",node,tensorMap,context);return[argMin(getParamValue("x",node,tensorMap,context),axis)]}case"Prod":{let axis=getParamValue("axis",node,tensorMap,context),keepDims=getParamValue("keepDims",node,tensorMap,context);return[prod(getParamValue("x",node,tensorMap,context),axis,keepDims)]}case"Cumsum":{let axis=getParamValue("axis",node,tensorMap,context),exclusive=getParamValue("exclusive",node,tensorMap,context),reverse5=getParamValue("reverse",node,tensorMap,context);return[cumsum(getParamValue("x",node,tensorMap,context),axis,exclusive,reverse5)]}case"Bincount":let x=getParamValue("x",node,tensorMap,context),weights=getParamValue("weights",node,tensorMap,context),size=getParamValue("size",node,tensorMap,context);return[bincount(x,weights,size)];case"DenseBincount":{let x2=getParamValue("x",node,tensorMap,context),weights2=getParamValue("weights",node,tensorMap,context),size2=getParamValue("size",node,tensorMap,context),binaryOutput=getParamValue("binaryOutput",node,tensorMap,context);return[denseBincount(x2,weights2,size2,binaryOutput)]}default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp15=(node,tensorMap,context)=>{switch(node.op){case"ConcatV2":case"Concat":{let n=getParamValue("n",node,tensorMap,context),axis=getParamValue("axis",node,tensorMap,context),inputs=getParamValue("tensors",node,tensorMap,context);return inputs=inputs.slice(0,n),[concat(inputs,axis)]}case"Gather":{let input2=getParamValue("x",node,tensorMap,context),indices=getParamValue("indices",node,tensorMap,context);return[gather(input2,cast(indices,"int32"),0)]}case"GatherV2":{let axis=getParamValue("axis",node,tensorMap,context),batchDims=getParamValue("batchDims",node,tensorMap,context),input2=getParamValue("x",node,tensorMap,context),indices=getParamValue("indices",node,tensorMap,context);return[gather(input2,cast(indices,"int32"),axis,batchDims)]}case"Reverse":{let dims=getParamValue("dims",node,tensorMap,context),axis=[];for(let i=0;i{let axis=getParamValue("axis",node,tensorMap,context),tensors=getParamValue("tensors",node,tensorMap,context),shape=tensors[0].shape,squeezedShape=squeeze(tensors[0]).shape,mapped=tensors.map(tensor2=>{let sameShape=util_exports.arraysEqual(tensor2.shape,shape);if(!sameShape&&!util_exports.arraysEqual(squeeze(tensor2).shape,squeezedShape))throw new Error("the input tensors shape does not match");return sameShape?tensor2:reshape(tensor2,shape)});return[stack(mapped,axis)]});case"Unpack":{let axis=getParamValue("axis",node,tensorMap,context),tensor2=getParamValue("tensor",node,tensorMap,context);return unstack(tensor2,axis)}case"Tile":{let reps=getParamValue("reps",node,tensorMap,context);return[tile(getParamValue("x",node,tensorMap,context),reps)]}case"Split":case"SplitV":{let axis=getParamValue("axis",node,tensorMap,context),numOrSizeSplits=getParamValue("numOrSizeSplits",node,tensorMap,context),tensor2=getParamValue("x",node,tensorMap,context);return split(tensor2,numOrSizeSplits,axis)}case"ScatterNd":{let indices=getParamValue("indices",node,tensorMap,context),values=getParamValue("values",node,tensorMap,context),shape=getParamValue("shape",node,tensorMap,context);return[scatterND(indices,values,shape)]}case"GatherNd":{let x=getParamValue("x",node,tensorMap,context),indices=getParamValue("indices",node,tensorMap,context);return[gatherND(x,indices)]}case"SparseToDense":{let indices=getParamValue("sparseIndices",node,tensorMap,context),shape=getParamValue("outputShape",node,tensorMap,context),sparseValues=getParamValue("sparseValues",node,tensorMap,context),defaultValue=getParamValue("defaultValue",node,tensorMap,context);return[sparseToDense(indices,sparseValues,shape,sparseValues.dtype===defaultValue.dtype?defaultValue:cast(defaultValue,sparseValues.dtype))]}default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp16=(node,tensorMap,context)=>{switch(node.op){case"FFT":return[fft(getParamValue("x",node,tensorMap,context))];case"IFFT":return[ifft(getParamValue("x",node,tensorMap,context))];case"RFFT":return[rfft(getParamValue("x",node,tensorMap,context))];case"IRFFT":return[irfft(getParamValue("x",node,tensorMap,context))];default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp17=(node,tensorMap,context)=>{switch(node.op){case"Cast":return[cast(getParamValue("x",node,tensorMap,context),getParamValue("dtype",node,tensorMap,context))];case"ExpandDims":{let axis=getParamValue("axis",node,tensorMap,context);return[expandDims(getParamValue("x",node,tensorMap,context),axis)]}case"Squeeze":{let axis=getParamValue("axis",node,tensorMap,context);return[squeeze(getParamValue("x",node,tensorMap,context),axis)]}case"Reshape":return[reshape(getParamValue("x",node,tensorMap,context),getParamValue("shape",node,tensorMap,context))];case"MirrorPad":return[mirrorPad(getParamValue("x",node,tensorMap,context),getParamValue("padding",node,tensorMap,context),getParamValue("mode",node,tensorMap,context))];case"PadV2":case"Pad":return[pad(getParamValue("x",node,tensorMap,context),getParamValue("padding",node,tensorMap,context),getParamValue("constantValue",node,tensorMap,context))];case"SpaceToBatchND":{let blockShape=getParamValue("blockShape",node,tensorMap,context),paddings=getParamValue("paddings",node,tensorMap,context);return[spaceToBatchND(getParamValue("x",node,tensorMap,context),blockShape,paddings)]}case"BatchToSpaceND":{let blockShape=getParamValue("blockShape",node,tensorMap,context),crops=getParamValue("crops",node,tensorMap,context);return[batchToSpaceND(getParamValue("x",node,tensorMap,context),blockShape,crops)]}case"DepthToSpace":{let blockSize=getParamValue("blockSize",node,tensorMap,context),dataFormat=getParamValue("dataFormat",node,tensorMap,context).toUpperCase();return[depthToSpace(getParamValue("x",node,tensorMap,context),blockSize,dataFormat)]}case"BroadcastTo":return[broadcastTo(getParamValue("x",node,tensorMap,context),getParamValue("shape",node,tensorMap,context))];default:throw TypeError(`Node type ${node.op} is not implemented`)}};function executeOp18(node,tensorMap,context,resourceManager){let value=((node2,tensorMap2,context2)=>{switch(node2.category){case"arithmetic":return tidy(()=>executeOp(node2,tensorMap2,context2));case"basic_math":return tidy(()=>executeOp2(node2,tensorMap2,context2));case"control":return executeOp3(node2,tensorMap2,context2);case"convolution":return tidy(()=>executeOp4(node2,tensorMap2,context2));case"creation":return tidy(()=>executeOp5(node2,tensorMap2,context2));case"dynamic":return executeOp6(node2,tensorMap2,context2);case"evaluation":return tidy(()=>executeOp7(node2,tensorMap2,context2));case"image":return tidy(()=>executeOp10(node2,tensorMap2,context2));case"graph":return tidy(()=>executeOp8(node2,tensorMap2,context2));case"logical":return tidy(()=>executeOp11(node2,tensorMap2,context2));case"matrices":return tidy(()=>executeOp12(node2,tensorMap2,context2));case"normalization":return tidy(()=>executeOp13(node2,tensorMap2,context2));case"reduction":return tidy(()=>executeOp14(node2,tensorMap2,context2));case"slice_join":return tidy(()=>executeOp15(node2,tensorMap2,context2));case"spectral":return tidy(()=>executeOp16(node2,tensorMap2,context2));case"transformation":return tidy(()=>executeOp17(node2,tensorMap2,context2));case"hash_table":return executeOp9(node2,tensorMap2,context2,resourceManager);case"custom":let opMapper=getRegisteredOp(node2.op);if(opMapper&&opMapper.customExecutor)return opMapper.customExecutor(new NodeValueImpl(node2,tensorMap2,context2));throw TypeError(`Custom op ${node2.op} is not registered.`);default:throw TypeError(`Unknown op '${node2.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(node,tensorMap,context);return util_exports.isPromise(value)?value.then(data2=>[].concat(data2)):[].concat(value)}var ExecutionContext=class{constructor(weightMap={},tensorArrayMap={},tensorListMap={},functionMap={}){this.weightMap=weightMap,this.tensorArrayMap=tensorArrayMap,this.tensorListMap=tensorListMap,this.functionMap=functionMap,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(id,frameName){return{id,frameName,iterationId:0}}set currentContext(contexts2){this.contexts!==contexts2&&(this.contexts=contexts2,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let names=[];for(let i=0;icontext.id===0&&context.iterationId===0?"":`${context.frameName}-${context.iterationId}`).join("/"):""}enterFrame(frameId){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,frameId)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let context=Object.assign({},this.contexts[this.contexts.length-1]);context.iterationId+=1,context.id=this.lastId,this.contexts.splice(-1,1,context),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(name){return this.weightMap[name]}addTensorArray(tensorArray){this.tensorArrayMap[tensorArray.id]=tensorArray}getTensorArray(id){return this.tensorArrayMap[id]}addTensorList(tensorList){this.tensorListMap[tensorList.id]=tensorList}getTensorList(id){return this.tensorListMap[id]}dispose(keepIds){for(let key in this.tensorArrayMap)this.tensorArrayMap[key].clearAndClose(keepIds);for(let key in this.tensorListMap)this.tensorListMap[key].clearAndClose(keepIds)}};function getExecutionSubgraph(inputs,outputs,weightMap,initNodes){let usedNodes=new Set,missingInputs=[],dynamicNode=null,syncInputs=null,seen=new Set,inputNodeNames=Object.keys(inputs).map(name=>parseNodeName(name)[0]),initNodeNames=[];initNodes!=null&&(initNodeNames=initNodes.map(node=>parseNodeName(node.name)[0]));let frontier=[...outputs];for(;frontier.length>0;){let node=frontier.pop();if((isControlFlow(node)||isDynamicShape(node)||isHashTable(node))&&dynamicNode==null&&(dynamicNode=node,syncInputs=dynamicNode.children.map(child=>child.name).filter(name=>usedNodes.has(name))),usedNodes.add(node.name),weightMap[node.name]==null&&inputNodeNames.indexOf(node.name)===-1&&initNodeNames.indexOf(node.name)===-1){if(node.inputs.length===0){missingInputs.push(node.name);continue}node.inputs.forEach(input2=>{seen.has(input2.name)||(seen.add(input2.name),frontier.push(input2))})}}return{inputs,outputs,usedNodes,missingInputs,dynamicNode,syncInputs}}function getNodesInTopologicalOrder(graph2,weightMap,executionInfo){let{usedNodes,inputs}=executionInfo,frontier=[],inputNodes=Object.keys(inputs).map(name=>parseNodeName(name)[0]).map(name=>graph2.nodes[name]),initNodes=graph2.initNodes;inputNodes.forEach(input2=>{usedNodes.has(input2.name)&&frontier.push(input2)}),graph2.weights.forEach(weight=>{usedNodes.has(weight.name)&&frontier.push(weight)}),initNodes!=null&&initNodes.forEach(node=>{usedNodes.has(node.name)&&frontier.push(node)});let seen=new Set,orderedNodes=[];for(;frontier.length>0;){let node=frontier.pop();seen.add(node.name),weightMap[node.name]||orderedNodes.push(node),node.children.forEach(child=>{!seen.has(child.name)&&usedNodes.has(child.name)&&child.inputs.every(input2=>seen.has(input2.name))&&frontier.push(child)})}return orderedNodes}var CONTROL_FLOW_OPS=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],DYNAMIC_SHAPE_OPS=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],HASH_TABLE_OPS=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2"];function isControlFlow(node){return CONTROL_FLOW_OPS.indexOf(node.op)>=0}function isDynamicShape(node){return DYNAMIC_SHAPE_OPS.indexOf(node.op)>=0}function isHashTable(node){return HASH_TABLE_OPS.indexOf(node.op)>=0}var GraphExecutor=class{constructor(graph2,parent){this.graph=graph2,this.parent=parent,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this._outputs=graph2.outputs,this._inputs=graph2.inputs,this._initNodes=graph2.initNodes,this._signature=graph2.signature,this._functions=graph2.functions,graph2.functions!=null&&Object.keys(graph2.functions).forEach(name=>{this._functionExecutorMap[name]=new GraphExecutor(graph2.functions[name],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(weightMap){let weightIds=Object.keys(weightMap).map(key=>weightMap[key].map(tensor2=>tensor2.id));this._weightIds=[].concat(...weightIds),this._weightMap=weightMap}set resourceManager(resourceManager){this._resourceManager=resourceManager}get inputs(){return this._inputs.map(node=>({name:node.name,shape:node.attrParams.shape?node.attrParams.shape.value:void 0,dtype:node.attrParams.dtype?node.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(node=>({name:node.name,shape:node.attrParams.shape?node.attrParams.shape.value:void 0,dtype:node.attrParams.dtype?node.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(node=>node.signatureKey||node.name)}get outputNodes(){return this._outputs.map(node=>{let name=node.signatureKey||node.name;return node.defaultOutput?`${name}:${node.defaultOutput}`:name})}get functions(){return Object.keys(this._functions).reduce((map,key)=>(map[key]=this._functions[key].signature,map),{})}getCompilationKey(inputs,outputs){let sortedInputs=inputs.map(node=>node.name).sort(),sortedOutputs=outputs.map(node=>node.name).sort();return sortedInputs.join(this.SEPERATOR)+"--"+sortedOutputs.join(this.SEPERATOR)}compile(inputs,outputs){let executionInfo=getExecutionSubgraph(inputs,outputs,this.weightMap,this._initNodes),{missingInputs,dynamicNode,syncInputs}=executionInfo;if(dynamicNode!=null)throw new Error(`This execution contains the node '${dynamicNode.name}', which has the dynamic op '${dynamicNode.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${syncInputs}]`);if(missingInputs.length>0){let outNames=outputs.map(n=>n.name),inNames=Object.keys(inputs);throw new Error(`Cannot compute the outputs [${outNames}] from the provided inputs [${inNames}]. Missing the following inputs: [${missingInputs}]`)}return getNodesInTopologicalOrder(this.graph,this.weightMap,executionInfo)}execute(inputs,outputs){inputs=this.mapInputs(inputs);let names=Object.keys(inputs).sort();this.checkInputs(inputs),this.checkInputShapeAndType(inputs),outputs=this.mapOutputs(outputs),this.checkOutputs(outputs);let inputNodes=names.map(name=>this.graph.nodes[parseNodeName(name)[0]]),outputNodeNames=outputs.map(name=>parseNodeName(name)[0]),outputNodes=outputNodeNames.map(name=>this.graph.nodes[name]);outputNodes.length===0&&(outputNodes=this._outputs);let compilationKey=this.getCompilationKey(inputNodes,outputNodes),orderedNodes=this.compiledMap.get(compilationKey);orderedNodes==null&&(orderedNodes=this.compile(inputs,outputNodes),this.compiledMap.set(compilationKey,orderedNodes));let tensorArrayMap={},tensorListMap={};return tidy(()=>{let context=new ExecutionContext(this.weightMap,tensorArrayMap,tensorListMap,this.functionExecutorMap),tensorsMap=Object.assign({},this.weightMap);Object.keys(inputs).forEach(name=>{let[nodeName,index]=parseNodeName(name),tensors=[];tensors[index]=inputs[name],tensorsMap[nodeName]=tensors});let tensorsToKeep=this.getFrozenTensorIds(tensorsMap),intermediateTensorConsumerCount={};for(let i=0;igetTensor(name,tensorsMap,context))})}getFrozenTensorIds(tensorMap){let ids=[].concat.apply([],Object.keys(tensorMap).map(key=>tensorMap[key]).map(tensors=>tensors.map(tensor2=>tensor2.id)));return new Set(ids)}checkTensorForDisposal(nodeName,node,tensorMap,context,tensorsToKeep,outputNames,intermediateTensorConsumerCount){node.category==="control"||outputNames.indexOf(nodeName)!==-1||(tensorMap[nodeName].forEach(tensor2=>{tensor2!=null&&(intermediateTensorConsumerCount[tensor2.id]=(intermediateTensorConsumerCount[tensor2.id]||0)+node.children.length)}),node.inputs.forEach(input2=>{if(input2.category!=="control"){let tensors=getTensorsForCurrentContenxt(input2.name,tensorMap,context);tensors!=null&&tensors.forEach(tensor2=>{if(tensor2&&!tensorsToKeep.has(tensor2.id)){let count2=intermediateTensorConsumerCount[tensor2.id];count2===1?(tensor2.dispose(),delete intermediateTensorConsumerCount[tensor2.id]):count2!=null&&intermediateTensorConsumerCount[tensor2.id]--}})}}))}async executeAsync(inputs,outputs){return this._executeAsync(inputs,outputs)}async _executeAsync(inputs,outputs,isFunctionExecution=!1,tensorArrayMap={},tensorListMap={}){isFunctionExecution||(inputs=this.mapInputs(inputs),this.checkInputs(inputs),this.checkInputShapeAndType(inputs),outputs=this.mapOutputs(outputs),this.checkOutputs(outputs));let context=new ExecutionContext(this.weightMap,tensorArrayMap,tensorListMap,this.functionExecutorMap),tensorMap=await this.executeWithControlFlow(inputs,context,outputs,isFunctionExecution),results=outputs.map(name=>getTensor(name,tensorMap,context)),outputIds=results.map(t=>t.id),inputIds=Object.keys(inputs).map(name=>inputs[name].id),keepIds=new Set([...outputIds,...inputIds,...this.weightIds]);return Object.keys(tensorMap).forEach(key=>{tensorMap[key].forEach(tensor2=>{tensor2&&!tensor2.isDisposed&&!keepIds.has(tensor2.id)&&tensor2.dispose()})}),this.parent==null&&context.dispose(keepIds),results}async executeFunctionAsync(inputs,tensorArrayMap,tensorListMap){let mappedInputs=inputs.reduce((map,tensor2,index)=>(map[this.inputs[index].name]=tensor2,map),{});return this._executeAsync(mappedInputs,this.outputNodes,!0,tensorArrayMap,tensorListMap)}async executeWithControlFlow(inputs,context,outputNames,isFunctionExecution){let names=Object.keys(inputs),inputNodes=names.map(name=>this.graph.nodes[parseNodeName(name)[0]]),outputNodeNames=outputNames.map(name=>parseNodeName(name)[0]),outputNodes=outputNodeNames.map(name=>this.graph.nodes[name]);outputNodes.length===0&&(outputNodes=this._outputs);let{usedNodes,missingInputs,dynamicNode,syncInputs}=getExecutionSubgraph(inputs,outputNodes,this.weightMap,this._initNodes),stack2=[...inputNodes,...this.graph.weights,...this._initNodes||[]].map(node=>({node,contexts:context.currentContext})),tensorsMap=Object.assign({},this.weightMap);Object.keys(inputs).forEach(name=>{let[nodeName,index]=parseNodeName(name),tensors=[];tensors[index]=inputs[name],tensorsMap[nodeName]=tensors});let intermediateTensorConsumerCount={},tensorsToKeep=this.getFrozenTensorIds(tensorsMap),added={};for(;stack2.length>0;){let promises=this.processStack(inputNodes,stack2,context,tensorsMap,added,tensorsToKeep,outputNodeNames,intermediateTensorConsumerCount,usedNodes);await Promise.all(promises)}dynamicNode==null&&!isFunctionExecution&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let missingOutputs=outputNodes.filter(node=>!isControlFlow(node)&&!getTensor(node.name,tensorsMap,context)).map(node=>node.name);if(missingOutputs.length>0){let alternativeMsg="";throw dynamicNode!=null&&(alternativeMsg=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${syncInputs}]`),new Error(`Cannot compute the outputs [${missingOutputs}] from the provided inputs [${names}]. Consider providing the following inputs: [${missingInputs}]. ${alternativeMsg}`)}return tensorsMap}processStack(inputNodes,stack2,context,tensorMap,added,tensorsToKeep,outputNames,intermediateTensorConsumerCount,usedNodes){let promises=[];for(;stack2.length>0;){let item=stack2.pop();context.currentContext=item.contexts;let nodeName="";if(item.node.op==="Enter"&&getParamValue("isConstant",item.node,tensorMap,context)&&([nodeName]=getNodeNameAndIndex(item.node.name,context)),tensorMap[item.node.name]==null){let tensors=executeOp18(item.node,tensorMap,context,this._resourceManager);nodeName||([nodeName]=getNodeNameAndIndex(item.node.name,context));let currentContext=context.currentContext;util_exports.isPromise(tensors)?promises.push(tensors.then(t=>(tensorMap[nodeName]=t,context.currentContext=currentContext,this.checkTensorForDisposal(nodeName,item.node,tensorMap,context,tensorsToKeep,outputNames,intermediateTensorConsumerCount),this.processChildNodes(item.node,stack2,context,tensorMap,added,usedNodes),t))):(tensorMap[nodeName]=tensors,this.checkTensorForDisposal(nodeName,item.node,tensorMap,context,tensorsToKeep,outputNames,intermediateTensorConsumerCount),this.processChildNodes(item.node,stack2,context,tensorMap,added,usedNodes))}else this.processChildNodes(item.node,stack2,context,tensorMap,added,usedNodes)}return promises}processChildNodes(node,stack2,context,tensorMap,added,usedNodes){node.children.forEach(childNode=>{let[nodeName]=getNodeNameAndIndex(childNode.name,context);added[nodeName]||!usedNodes.has(childNode.name)||(childNode.op==="Merge"?childNode.inputNames.some(name=>!!getTensor(name,tensorMap,context))&&(added[nodeName]=!0,stack2.push({contexts:context.currentContext,node:childNode})):childNode.inputNames.every(name=>!!getTensor(name,tensorMap,context))&&(added[nodeName]=!0,stack2.push({contexts:context.currentContext,node:childNode})))})}dispose(){Object.keys(this.weightMap).forEach(key=>this.weightMap[key].forEach(tensor2=>tensor2.dispose()))}checkInputShapeAndType(inputs){Object.keys(inputs).forEach(name=>{let input2=inputs[name],[nodeName]=parseNodeName(name),node=this.graph.nodes[nodeName];if(node.attrParams.shape&&node.attrParams.shape.value){let shape=node.attrParams.shape.value,match=shape.length===input2.shape.length&&input2.shape.every((dim,index)=>shape[index]===-1||shape[index]===dim);util_exports.assert(match,()=>`The shape of dict['${node.name}'] provided in model.execute(dict) must be [${shape}], but was [${input2.shape}]`)}node.attrParams.dtype&&node.attrParams.dtype.value&&util_exports.assert(input2.dtype===node.attrParams.dtype.value,()=>`The dtype of dict['${node.name}'] provided in model.execute(dict) must be ${node.attrParams.dtype.value}, but was ${input2.dtype}`)})}mapInputs(inputs){let result={};for(let inputName in inputs)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[inputName]!=null){let tensor2=this._signature.inputs[inputName];result[tensor2.name]=inputs[inputName]}else result[inputName]=inputs[inputName];return result}checkInputs(inputs){let notInGraph=Object.keys(inputs).filter(name=>{let[nodeName]=parseNodeName(name);return this.graph.nodes[nodeName]==null});if(notInGraph.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${notInGraph}] that are not part of graph`)}mapOutputs(outputs){return outputs.map(name=>this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[name]!=null?this._signature.outputs[name].name:name,{})}checkOutputs(outputs){outputs.forEach(name=>{let[normalizedName]=parseNodeName(name);if(!this.graph.nodes[normalizedName])throw new Error(`The output '${name}' is not found in the graph`)})}},ResourceManager=class{constructor(hashTableNameToHandle={},hashTableMap={}){this.hashTableNameToHandle=hashTableNameToHandle,this.hashTableMap=hashTableMap}addHashTable(name,hashTable2){this.hashTableNameToHandle[name]=hashTable2.handle,this.hashTableMap[hashTable2.id]=hashTable2}getHashTableHandleByName(name){return this.hashTableNameToHandle[name]}getHashTableById(id){return this.hashTableMap[id]}dispose(){for(let key in this.hashTableMap)this.hashTableMap[key].clearAndClose(),delete this.hashTableMap[key];for(let name in this.hashTableNameToHandle)this.hashTableNameToHandle[name].dispose(),delete this.hashTableNameToHandle[name]}},TFHUB_SEARCH_PARAM="?tfjs-format=file",DEFAULT_MODEL_NAME="model.json",GraphModel=class{constructor(modelUrl,loadOptions={}){this.modelUrl=modelUrl,this.loadOptions=loadOptions,this.version="n/a",loadOptions==null&&(this.loadOptions={}),this.resourceManager=new ResourceManager}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let path=this.modelUrl;if(path.load!=null)this.handler=path;else if(this.loadOptions.requestInit!=null)this.handler=io_exports.browserHTTPRequest(path,this.loadOptions);else{let handlers=io_exports.getLoadHandlers(path,this.loadOptions);if(handlers.length===0)handlers.push(io_exports.browserHTTPRequest(path,this.loadOptions));else if(handlers.length>1)throw new Error(`Found more than one (${handlers.length}) load handlers for URL '${[path]}'`);this.handler=handlers[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let artifacts=await this.handler.load();return this.loadSync(artifacts)}loadSync(artifacts){this.artifacts=artifacts;let graph2=this.artifacts.modelTopology,signature;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?signature=this.artifacts.userDefinedMetadata.signature:signature=this.artifacts.signature,this.signature=signature,this.version=`${graph2.versions.producer}.${graph2.versions.minConsumer}`;let weightMap=io_exports.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new GraphExecutor(OperationMapper.Instance.transformGraph(graph2,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(weightMap),this.executor.resourceManager=this.resourceManager,artifacts.modelInitializer!=null&&artifacts.modelInitializer.node!=null){let initializer=OperationMapper.Instance.transformGraph(artifacts.modelInitializer);this.initializer=new GraphExecutor(initializer),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(handlerOrURL,config3){if(typeof handlerOrURL=="string"){let handlers=io_exports.getSaveHandlers(handlerOrURL);if(handlers.length===0)throw new Error(`Cannot find any save handlers for URL '${handlerOrURL}'`);if(handlers.length>1)throw new Error(`Found more than one (${handlers.length}) save handlers for URL '${handlerOrURL}'`);handlerOrURL=handlers[0]}if(handlerOrURL.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return handlerOrURL.save(this.artifacts)}predict(inputs,config3){return this.execute(inputs,this.outputNodes)}normalizeInputs(inputs){if(!(inputs instanceof Tensor)&&!Array.isArray(inputs))return inputs;if(inputs=Array.isArray(inputs)?inputs:[inputs],inputs.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${inputs.length} input tensors.`);return this.inputNodes.reduce((map,inputName,i)=>(map[inputName]=inputs[i],map),{})}normalizeOutputs(outputs){return outputs=outputs||this.outputNodes,Array.isArray(outputs)?outputs:[outputs]}execute(inputs,outputs){inputs=this.normalizeInputs(inputs),outputs=this.normalizeOutputs(outputs);let result=this.executor.execute(inputs,outputs);return result.length>1?result:result[0]}async executeAsync(inputs,outputs){inputs=this.normalizeInputs(inputs),outputs=this.normalizeOutputs(outputs);let result=await this.executor.executeAsync(inputs,outputs);return result.length>1?result:result[0]}convertTensorMapToTensorsMap(map){return Object.keys(map).reduce((newMap,key)=>(newMap[key]=[map[key]],newMap),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function loadGraphModel(modelUrl,options={}){if(modelUrl==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");options==null&&(options={}),options.fromTFHub&&modelUrl.load==null&&(modelUrl.endsWith("/")||(modelUrl=modelUrl+"/"),modelUrl=`${modelUrl}${DEFAULT_MODEL_NAME}${TFHUB_SEARCH_PARAM}`);let model2=new GraphModel(modelUrl,options);return await model2.load(),model2}var version11="2.8.3",dist_exports={};__export2(dist_exports,{CSVDataset:()=>CSVDataset,Dataset:()=>Dataset,FileDataSource:()=>FileDataSource,TextLineDataset:()=>TextLineDataset,URLDataSource:()=>URLDataSource,array:()=>array,csv:()=>csv,func:()=>func,generator:()=>generator,microphone:()=>microphone,version_data:()=>version12,webcam:()=>webcam,zip:()=>zip});var seedrandom4=__toModule2(require_seedrandom6()),seedrandom3=__toModule2(require_seedrandom6());function deepMap(input2,mapFn){return deepMapInternal(input2,mapFn)}function deepMapInternal(input2,mapFn,seen=new Map,containedIn=new Set){if(input2==null)return null;if(containedIn.has(input2))throw new Error("Circular references are not supported.");if(seen.has(input2))return seen.get(input2);let result=mapFn(input2);if(result.recurse&&result.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(result.recurse)if(isIterable2(input2)){let mappedIterable=Array.isArray(input2)?[]:{};containedIn.add(input2);for(let k in input2){let child=input2[k],childResult=deepMapInternal(child,mapFn,seen,containedIn);mappedIterable[k]=childResult}return containedIn.delete(input2),mappedIterable}else throw new Error(`Can't recurse into non-iterable type: ${input2}`);else return seen.set(input2,result.value),result.value}function deepZip(inputs,zipFn=zipToList){return deepZipInternal(inputs,zipFn)}function deepZipInternal(inputs,zipFn,containedIn=new Set){let input2=inputs[0];if(containedIn.has(input2))throw new Error("Circular references are not supported.");let result=zipFn(inputs);if(result.recurse&&result.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(result.recurse)if(isIterable2(input2)){let mappedIterable=Array.isArray(input2)?[]:{};containedIn.add(input2);for(let k in input2){let children=inputs.map(x=>x[k]),childResult=deepZipInternal(children,zipFn,containedIn);mappedIterable[k]=childResult}return containedIn.delete(input2),mappedIterable}else throw new Error(`Can't recurse into non-iterable type: ${input2}`);else return result.value}function zipToList(x){return x===null?null:isIterable2(x[0])?{value:null,recurse:!0}:{value:x,recurse:!1}}async function deepMapAndAwaitAll(input2,mapFn){let seen=new Map;deepMapInternal(input2,mapFn,seen);for(let key of Array.from(seen.keys())){let value=seen.get(key);if(util_exports.isPromise(value)){let mappedValue=await value;seen.set(key,mappedValue)}}return deepMapInternal(input2,mapFn,seen)}function isIterable2(obj){return obj!=null&&!ArrayBuffer.isView(obj)&&(Array.isArray(obj)||typeof obj=="object"&&!(obj instanceof Tensor))}function canTensorify(obj){return obj==null||isPrimitive(obj)||Array.isArray(obj)||typeof obj=="object"&&obj instanceof Tensor||util_exports.isTypedArray(obj)}function isPrimitive(value){return value===null||typeof value!="object"&&typeof value!="function"}function deepClone(container){return deepMap(container,cloneIfTensor)}function cloneIfTensor(item){return item instanceof Tensor?{value:item.clone(),recurse:!1}:isIterable2(item)?{value:null,recurse:!0}:{value:item,recurse:!1}}var RingBuffer=class{constructor(capacity){if(this.capacity=capacity,this.begin=0,this.end=0,capacity==null)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(capacity<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(capacity),this.doubledCapacity=2*capacity}wrap(index){for(;index<0;)index+=this.doubledCapacity;return index%this.doubledCapacity}get(index){if(index<0)throw new RangeError("Can't get item at a negative index.");return this.data[index%this.capacity]}set(index,value){if(index<0)throw new RangeError("Can't set item at a negative index.");this.data[index%this.capacity]=value}length(){let length=this.end-this.begin;return length<0&&(length=this.doubledCapacity+length),length}isFull(){return this.length()===this.capacity}isEmpty(){return this.length()===0}push(value){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,value),this.end=this.wrap(this.end+1)}pushAll(values){for(let value of values)this.push(value)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);let result=this.get(this.end);return this.set(this.end,void 0),result}unshift(value){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,value)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let result=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),result}shuffleExcise(relativeIndex){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let index=this.wrap(this.begin+relativeIndex),result=this.get(index);return this.set(index,this.pop()),result}},GrowingRingBuffer=class extends RingBuffer{constructor(){super(GrowingRingBuffer.INITIAL_CAPACITY)}isFull(){return!1}push(value){super.isFull()&&this.expand(),super.push(value)}unshift(value){super.isFull()&&this.expand(),super.unshift(value)}expand(){let newCapacity=this.capacity*2,newData=new Array(newCapacity),len=this.length();for(let i=0;ix===!0)}rowMajorBatch(batchSize,smallLastBatch=!0){return new RowMajorBatchIterator(this,batchSize,smallLastBatch)}columnMajorBatch(batchSize,smallLastBatch=!0,zipFn=zipToList){return this.rowMajorBatch(batchSize,smallLastBatch).map(x=>deepZip(x,zipFn))}concatenate(iterator,baseErrorHandler){return new ChainedIterator(iteratorFromItems([this,iterator]),baseErrorHandler)}take(count2){return count2<0||count2==null?this:new TakeIterator(this,count2)}skip(count2){return count2<0||count2==null?this:new SkipIterator(this,count2)}prefetch(bufferSize){return new PrefetchIterator(this,bufferSize)}shuffle(windowSize,seed){return new ShuffleIterator(this,windowSize,seed)}serial(){return new SerialIterator(this)}},ArrayIterator=class extends LazyIterator{constructor(items){super();this.items=items,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let item=this.items[this.trav];return this.trav++,{value:deepClone(item),done:!1}}},FunctionCallIterator=class extends LazyIterator{constructor(nextFn){super();this.nextFn=nextFn}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: ${e.message}`,e}}},SerialIterator=class extends LazyIterator{constructor(upstream){super();this.upstream=upstream,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},SkipIterator=class extends LazyIterator{constructor(upstream,maxCount){super();this.upstream=upstream,this.maxCount=maxCount,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++ Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},RowMajorBatchIterator=class extends LazyIterator{constructor(upstream,batchSize,enableSmallLastBatch=!0){super();this.upstream=upstream,this.batchSize=batchSize,this.enableSmallLastBatch=enableSmallLastBatch,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let batch=[];for(;batch.length0?{value:batch,done:!1}:{value:null,done:!0};batch.push(item.value)}return{value:batch,done:!1}}},FilterIterator=class extends LazyIterator{constructor(upstream,predicate){super();this.upstream=upstream,this.predicate=predicate,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let item=await this.upstream.next();if(item.done||this.predicate(item.value))return item;dispose(item.value)}}},MapIterator=class extends LazyIterator{constructor(upstream,transform){super();this.upstream=upstream,this.transform=transform}summary(){return`${this.upstream.summary()} -> Map`}async next(){let item=await this.upstream.next();if(item.done)return{value:null,done:!0};let inputTensors=tensor_util_exports.getTensorsInContainer(item.value),mapped=this.transform(item.value),outputTensors=tensor_util_exports.getTensorsInContainer(mapped);for(let t of inputTensors)tensor_util_exports.isTensorInList(t,outputTensors)||t.dispose();return{value:mapped,done:!1}}},ErrorHandlingLazyIterator=class extends LazyIterator{constructor(upstream,handler){super();this.upstream=upstream,this.handler=handler,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},AsyncMapIterator=class extends LazyIterator{constructor(upstream,transform){super();this.upstream=upstream,this.transform=transform}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let item=await this.upstream.next();if(item.done)return{value:null,done:!0};let inputTensors=tensor_util_exports.getTensorsInContainer(item.value),mapped=await this.transform(item.value),outputTensors=tensor_util_exports.getTensorsInContainer(mapped);for(let t of inputTensors)tensor_util_exports.isTensorInList(t,outputTensors)||t.dispose();return{value:mapped,done:!1}}},OneToManyIterator=class extends LazyIterator{constructor(){super();this.outputQueue=new GrowingRingBuffer,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},FlatmapIterator=class extends OneToManyIterator{constructor(upstream,transform){super();this.upstream=upstream,this.transform=transform}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let item=await this.upstream.next();if(item.done)return!1;let inputTensors=tensor_util_exports.getTensorsInContainer(item.value),mappedArray=this.transform(item.value),outputTensors=tensor_util_exports.getTensorsInContainer(mappedArray);this.outputQueue.pushAll(mappedArray);for(let t of inputTensors)tensor_util_exports.isTensorInList(t,outputTensors)||t.dispose();return!0}},ChainedIterator=class extends LazyIterator{constructor(iterators,baseErrorHandler){super();this.baseErrorHandler=baseErrorHandler,this.lastRead=null,this.iterator=null,this.moreIterators=iterators}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(lastRead){if(await lastRead,this.iterator==null){let iteratorResult=await this.moreIterators.next();if(iteratorResult.done)return{value:null,done:!0};this.iterator=iteratorResult.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let itemResult=await this.iterator.next();return itemResult.done?(this.iterator=null,this.readFromChain(lastRead)):itemResult}},ZipMismatchMode;(function(ZipMismatchMode2){ZipMismatchMode2[ZipMismatchMode2.FAIL=0]="FAIL",ZipMismatchMode2[ZipMismatchMode2.SHORTEST=1]="SHORTEST",ZipMismatchMode2[ZipMismatchMode2.LONGEST=2]="LONGEST"})(ZipMismatchMode||(ZipMismatchMode={}));var ZipIterator=class extends LazyIterator{constructor(iterators,mismatchMode=ZipMismatchMode.FAIL){super();this.iterators=iterators,this.mismatchMode=mismatchMode,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(afterState){await afterState;let numIterators=0,iteratorsDone=0;function getNext(container){return container instanceof LazyIterator?{value:container.next().then(x=>(numIterators++,x.done&&iteratorsDone++,x.value)),recurse:!1}:{value:null,recurse:!0}}let mapped=await deepMapAndAwaitAll(this.iterators,getNext);if(numIterators===iteratorsDone)return{value:null,done:!0};if(iteratorsDone>0)switch(this.mismatchMode){case ZipMismatchMode.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case ZipMismatchMode.SHORTEST:return{value:null,done:!0};case ZipMismatchMode.LONGEST:default:}return this.count++,{value:mapped,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},PrefetchIterator=class extends LazyIterator{constructor(upstream,bufferSize){super();this.upstream=upstream,this.bufferSize=bufferSize,this.buffer=new RingBuffer(bufferSize)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let v=this.upstream.next();this.buffer.push(v)}}next(){return this.refill(),this.buffer.shift()}},ShuffleIterator=class extends PrefetchIterator{constructor(upstream,windowSize,seed){super(upstream,windowSize);this.upstream=upstream,this.windowSize=windowSize,this.upstreamExhausted=!1,this.random=seedrandom3.alea(seed||util_exports.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(max6){return Math.floor(this.random()*max6)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let chosenIndex=this.chooseIndex(),result=await this.buffer.shuffleExcise(chosenIndex);if(result.done)this.upstreamExhausted=!0;else return this.refill(),result}return{value:null,done:!0}}},Dataset=class{constructor(){this.size=null}batch(batchSize,smallLastBatch=!0){let base2=this;util_exports.assert(batchSize>0,()=>`batchSize needs to be positive, but it is + ${totalLength}, and tensor's shape is: ${tensor2.shape}`);let elementPerRow=totalLength===0?0:tensor2.size/totalLength,tensors=tidy(()=>{let tensors2=[];tensor2=reshape(tensor2,[1,totalLength,elementPerRow]);for(let i=0;i{switch(node.op){case"If":case"StatelessIf":{let thenFunc=getParamValue("thenBranch",node,tensorMap,context),elseFunc=getParamValue("elseBranch",node,tensorMap,context),cond=getParamValue("cond",node,tensorMap,context),args=getParamValue("args",node,tensorMap,context);return(await cond.data())[0]?context.functionMap[thenFunc].executeFunctionAsync(args,context.tensorArrayMap,context.tensorListMap):context.functionMap[elseFunc].executeFunctionAsync(args,context.tensorArrayMap,context.tensorListMap)}case"While":case"StatelessWhile":{let bodyFunc=getParamValue("body",node,tensorMap,context),condFunc=getParamValue("cond",node,tensorMap,context),args=getParamValue("args",node,tensorMap,context),condResult=await context.functionMap[condFunc].executeFunctionAsync(args,context.tensorArrayMap,context.tensorListMap),argIds=args.map(tensor2=>tensor2.id),condValue=await condResult[0].data();condResult.forEach(tensor2=>{!tensor2.kept&&argIds.indexOf(tensor2.id)===-1&&tensor2.dispose()});let result=args;for(;condValue[0];){let origResult=result;result=await context.functionMap[bodyFunc].executeFunctionAsync(result,context.tensorArrayMap,context.tensorListMap);let resultIds=result.map(tensor2=>tensor2.id);origResult.forEach(tensor2=>{!tensor2.kept&&argIds.indexOf(tensor2.id)===-1&&resultIds.indexOf(tensor2.id)===-1&&tensor2.dispose()});let condResult2=await context.functionMap[condFunc].executeFunctionAsync(result,context.tensorArrayMap,context.tensorListMap);condValue=await condResult2[0].data(),condResult2.forEach(tensor2=>{!tensor2.kept&&argIds.indexOf(tensor2.id)===-1&&resultIds.indexOf(tensor2.id)===-1&&tensor2.dispose()})}return result}case"LoopCond":{let pred=getParamValue("pred",node,tensorMap,context);return[cloneTensor(pred)]}case"Switch":{let pred=getParamValue("pred",node,tensorMap,context),data2=getParamValue("data",node,tensorMap,context);return data2.kept||(data2=cloneTensor(data2)),(await pred.data())[0]?[void 0,data2]:[data2,void 0]}case"Merge":{let inputName=node.inputNames.find(name=>getTensor(name,tensorMap,context)!==void 0);if(inputName){let data2=getTensor(inputName,tensorMap,context);return[cloneTensor(data2)]}return}case"Enter":{let frameId=getParamValue("frameName",node,tensorMap,context),data2=getParamValue("tensor",node,tensorMap,context);return context.enterFrame(frameId),[cloneTensor(data2)]}case"Exit":{let data2=getParamValue("tensor",node,tensorMap,context);return context.exitFrame(),[cloneTensor(data2)]}case"NextIteration":{let data2=getParamValue("tensor",node,tensorMap,context);return context.nextIteration(),[cloneTensor(data2)]}case"TensorArrayV3":{let size=getParamValue("size",node,tensorMap,context),dtype=getParamValue("dtype",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),dynamicSize=getParamValue("dynamicSize",node,tensorMap,context),clearAfterRead=getParamValue("clearAfterRead",node,tensorMap,context),identicalElementShapes=getParamValue("identicalElementShapes",node,tensorMap,context),name=getParamValue("name",node,tensorMap,context),tensorArray=new TensorArray(name,dtype,size,elementShape,identicalElementShapes,dynamicSize,clearAfterRead);return context.addTensorArray(tensorArray),[tensorArray.idTensor,scalar(1)]}case"TensorArrayWriteV3":{let id=getParamValue("tensorArrayId",node,tensorMap,context),index=getParamValue("index",node,tensorMap,context),writeTensor=getParamValue("tensor",node,tensorMap,context),writeTensorArray=context.getTensorArray(id.id);return writeTensorArray.write(index,writeTensor),[writeTensorArray.idTensor]}case"TensorArrayReadV3":{let readId=getParamValue("tensorArrayId",node,tensorMap,context),readIndex=getParamValue("index",node,tensorMap,context);return[context.getTensorArray(readId.id).read(readIndex)]}case"TensorArrayGatherV3":{let gatherId=getParamValue("tensorArrayId",node,tensorMap,context),gatherIndices=getParamValue("indices",node,tensorMap,context),gatherDtype=getParamValue("dtype",node,tensorMap,context);return[context.getTensorArray(gatherId.id).gather(gatherIndices,gatherDtype)]}case"TensorArrayScatterV3":{let scatterId=getParamValue("tensorArrayId",node,tensorMap,context),scatterIndices=getParamValue("indices",node,tensorMap,context),scatterTensor=getParamValue("tensor",node,tensorMap,context),scatterTensorArray=context.getTensorArray(scatterId.id);return scatterTensorArray.scatter(scatterIndices,scatterTensor),[scatterTensorArray.idTensor]}case"TensorArrayConcatV3":{let concatId=getParamValue("tensorArrayId",node,tensorMap,context),concatTensorArray=context.getTensorArray(concatId.id),concatDtype=getParamValue("dtype",node,tensorMap,context);return[concatTensorArray.concat(concatDtype)]}case"TensorArraySplitV3":{let splitId=getParamValue("tensorArrayId",node,tensorMap,context),splitTensor=getParamValue("tensor",node,tensorMap,context),lengths=getParamValue("lengths",node,tensorMap,context),splitTensorArray=context.getTensorArray(splitId.id);return splitTensorArray.split(lengths,splitTensor),[splitTensorArray.idTensor]}case"TensorArraySizeV3":{let sizeId=getParamValue("tensorArrayId",node,tensorMap,context),sizeTensorArray=context.getTensorArray(sizeId.id);return[scalar(sizeTensorArray.size(),"int32")]}case"TensorArrayCloseV3":{let closeId=getParamValue("tensorArrayId",node,tensorMap,context),closeTensorArray=context.getTensorArray(closeId.id);return closeTensorArray.clearAndClose(),[closeTensorArray.idTensor]}case"TensorListSetItem":{let idTensor=getParamValue("tensorListId",node,tensorMap,context),index=getParamValue("index",node,tensorMap,context),writeTensor=getParamValue("tensor",node,tensorMap,context),tensorList=context.getTensorList(idTensor.id);return tensorList.setItem(index,writeTensor),[tensorList.idTensor]}case"TensorListGetItem":{let idTensor=getParamValue("tensorListId",node,tensorMap,context),readIndex=getParamValue("index",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),elementDType=getParamValue("elementDType",node,tensorMap,context);return[context.getTensorList(idTensor.id).getItem(readIndex,elementShape,elementDType)]}case"TensorListScatterV2":case"TensorListScatter":{let scatterIndices=getParamValue("indices",node,tensorMap,context),scatterTensor=getParamValue("tensor",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),numElements=getParamValue("numElements",node,tensorMap,context),tensorList=scatter(scatterTensor,scatterIndices,elementShape,numElements);return context.addTensorList(tensorList),[tensorList.idTensor]}case"TensorListReserve":case"EmptyTensorList":{let elementShape=getParamValue("elementShape",node,tensorMap,context),elementDtype=getParamValue("elementDType",node,tensorMap,context),numElementsParam;node.op==="TensorListReserve"?numElementsParam="numElements":numElementsParam="maxNumElements";let numElements=getParamValue(numElementsParam,node,tensorMap,context),tensorList=reserve(elementShape,elementDtype,numElements);return context.addTensorList(tensorList),[tensorList.idTensor]}case"TensorListGather":{let gatherId=getParamValue("tensorListId",node,tensorMap,context),gatherIndices=getParamValue("indices",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),elementDtype=getParamValue("elementDType",node,tensorMap,context);return[context.getTensorList(gatherId.id).gather(gatherIndices,elementDtype,elementShape)]}case"TensorListStack":{let idTensor=getParamValue("tensorListId",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),elementDtype=getParamValue("elementDType",node,tensorMap,context),numElements=getParamValue("numElements",node,tensorMap,context);return[context.getTensorList(idTensor.id).stack(elementShape,elementDtype,numElements)]}case"TensorListFromTensor":{let tensor2=getParamValue("tensor",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),elementDtype=getParamValue("elementDType",node,tensorMap,context),tensorList=fromTensor(tensor2,elementShape,elementDtype);return context.addTensorList(tensorList),[tensorList.idTensor]}case"TensorListConcat":{let concatId=getParamValue("tensorListId",node,tensorMap,context),tensorList=context.getTensorList(concatId.id),concatDtype=getParamValue("dtype",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context);return[tensorList.concat(concatDtype,elementShape)]}case"TensorListPushBack":{let idTensor=getParamValue("tensorListId",node,tensorMap,context),writeTensor=getParamValue("tensor",node,tensorMap,context),tensorList=context.getTensorList(idTensor.id);return tensorList.pushBack(writeTensor),[tensorList.idTensor]}case"TensorListPopBack":{let idTensor=getParamValue("tensorListId",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),elementDType=getParamValue("elementDType",node,tensorMap,context);return[context.getTensorList(idTensor.id).popBack(elementShape,elementDType)]}case"TensorListSplit":{let splitTensor=getParamValue("tensor",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),lengths=getParamValue("lengths",node,tensorMap,context),tensorList=split2(splitTensor,lengths,elementShape);return context.addTensorList(tensorList),[tensorList.idTensor]}default:throw TypeError(`Node type ${node.op} is not implemented`)}};function fusedConvAndDepthWiseParams(node,tensorMap,context){let[extraOp,activationFunc]=getParamValue("fusedOps",node,tensorMap,context),isBiasAdd=extraOp==="biasadd",isPrelu=activationFunc==="prelu",isBatchNorm=extraOp==="fusedbatchnorm",numArgs=getParamValue("numArgs",node,tensorMap,context);if(isBiasAdd){if(isPrelu&&numArgs!==2)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!isPrelu&&numArgs!==1)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(isBatchNorm)throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported.");let stride=getParamValue("strides",node,tensorMap,context),pad3=getPadding(node,tensorMap,context),dataFormat=getParamValue("dataFormat",node,tensorMap,context).toUpperCase(),dilations=getParamValue("dilations",node,tensorMap,context),[biasArg,preluArg]=getParamValue("args",node,tensorMap,context),leakyreluAlpha=getParamValue("leakyreluAlpha",node,tensorMap,context);return{stride,pad:pad3,dataFormat,dilations,biasArg,preluArg,activationFunc,leakyreluAlpha}}var executeOp4=(node,tensorMap,context)=>{switch(node.op){case"Conv1D":{let stride=getParamValue("stride",node,tensorMap,context),pad3=getParamValue("pad",node,tensorMap,context),dataFormat=getParamValue("dataFormat",node,tensorMap,context).toUpperCase(),dilation=getParamValue("dilation",node,tensorMap,context);return[conv1d(getParamValue("x",node,tensorMap,context),getParamValue("filter",node,tensorMap,context),stride,pad3,dataFormat,dilation)]}case"Conv2D":{let stride=getParamValue("strides",node,tensorMap,context),pad3=getPadding(node,tensorMap,context),dataFormat=getParamValue("dataFormat",node,tensorMap,context).toUpperCase(),dilations=getParamValue("dilations",node,tensorMap,context);return[conv2d(getParamValue("x",node,tensorMap,context),getParamValue("filter",node,tensorMap,context),[stride[1],stride[2]],pad3,dataFormat,[dilations[1],dilations[2]])]}case"_FusedConv2D":{let{stride,pad:pad3,dataFormat,dilations,biasArg,preluArg,activationFunc,leakyreluAlpha}=fusedConvAndDepthWiseParams(node,tensorMap,context);return[fused_ops_exports.conv2d({x:getParamValue("x",node,tensorMap,context),filter:getParamValue("filter",node,tensorMap,context),strides:[stride[1],stride[2]],pad:pad3,dataFormat,dilations:[dilations[1],dilations[2]],bias:biasArg,activation:activationFunc,preluActivationWeights:preluArg,leakyreluAlpha})]}case"FusedDepthwiseConv2dNative":{let{stride,pad:pad3,dataFormat,dilations,biasArg,preluArg,activationFunc,leakyreluAlpha}=fusedConvAndDepthWiseParams(node,tensorMap,context);return[fused_ops_exports.depthwiseConv2d({x:getParamValue("x",node,tensorMap,context),filter:getParamValue("filter",node,tensorMap,context),strides:[stride[1],stride[2]],pad:pad3,dataFormat,dilations:[dilations[1],dilations[2]],bias:biasArg,activation:activationFunc,preluActivationWeights:preluArg,leakyreluAlpha})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{let shape=getParamValue("outputShape",node,tensorMap,context),stride=getParamValue("strides",node,tensorMap,context),pad3=getPadding(node,tensorMap,context);return[conv2dTranspose(getParamValue("x",node,tensorMap,context),getParamValue("filter",node,tensorMap,context),shape,[stride[1],stride[2]],pad3)]}case"DepthwiseConv2dNative":case"DepthwiseConv2d":{let stride=getParamValue("strides",node,tensorMap,context),pad3=getPadding(node,tensorMap,context),dilations=getParamValue("dilations",node,tensorMap,context),dataFormat=getParamValue("dataFormat",node,tensorMap,context).toUpperCase();return[depthwiseConv2d(getParamValue("input",node,tensorMap,context),getParamValue("filter",node,tensorMap,context),[stride[1],stride[2]],pad3,dataFormat,[dilations[1],dilations[2]])]}case"Conv3D":{let stride=getParamValue("strides",node,tensorMap,context),pad3=getParamValue("pad",node,tensorMap,context),dataFormat=getParamValue("dataFormat",node,tensorMap,context).toUpperCase(),dilations=getParamValue("dilations",node,tensorMap,context);return[conv3d(getParamValue("x",node,tensorMap,context),getParamValue("filter",node,tensorMap,context),[stride[1],stride[2],stride[3]],pad3,dataFormat,[dilations[1],dilations[2],dilations[3]])]}case"AvgPool":{let stride=getParamValue("strides",node,tensorMap,context),pad3=getParamValue("pad",node,tensorMap,context),kernelSize=getParamValue("kernelSize",node,tensorMap,context);return[avgPool(getParamValue("x",node,tensorMap,context),[kernelSize[1],kernelSize[2]],[stride[1],stride[2]],pad3)]}case"MaxPool":{let stride=getParamValue("strides",node,tensorMap,context),pad3=getParamValue("pad",node,tensorMap,context),kernelSize=getParamValue("kernelSize",node,tensorMap,context);return[maxPool(getParamValue("x",node,tensorMap,context),[kernelSize[1],kernelSize[2]],[stride[1],stride[2]],pad3)]}case"MaxPoolWithArgmax":{let stride=getParamValue("strides",node,tensorMap,context),pad3=getParamValue("pad",node,tensorMap,context),kernelSize=getParamValue("kernelSize",node,tensorMap,context),includeBatchInIndex=getParamValue("includeBatchInIndex",node,tensorMap,context),{result,indexes}=maxPoolWithArgmax(getParamValue("x",node,tensorMap,context),[kernelSize[1],kernelSize[2]],[stride[1],stride[2]],pad3,includeBatchInIndex);return[result,indexes]}case"AvgPool3D":{let stride=getParamValue("strides",node,tensorMap,context),pad3=getParamValue("pad",node,tensorMap,context),kernelSize=getParamValue("kernelSize",node,tensorMap,context);return[avgPool3d(getParamValue("x",node,tensorMap,context),[kernelSize[1],kernelSize[2],kernelSize[3]],[stride[1],stride[2],stride[3]],pad3)]}case"MaxPool3D":{let stride=getParamValue("strides",node,tensorMap,context),pad3=getParamValue("pad",node,tensorMap,context),kernelSize=getParamValue("kernelSize",node,tensorMap,context);return[maxPool3d(getParamValue("x",node,tensorMap,context),[kernelSize[1],kernelSize[2],kernelSize[3]],[stride[1],stride[2],stride[3]],pad3)]}case"Dilation2D":{let strides=getParamValue("strides",node,tensorMap,context),pad3=getParamValue("pad",node,tensorMap,context),dilations=getParamValue("dilations",node,tensorMap,context),strideHeight=strides[1],strideWidth=strides[2],dilationHeight=dilations[1],dilationWidth=dilations[2];return[dilation2d(getParamValue("x",node,tensorMap,context),getParamValue("filter",node,tensorMap,context),[strideHeight,strideWidth],pad3,[dilationHeight,dilationWidth],"NHWC")]}default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp5=(node,tensorMap,context)=>{switch(node.op){case"Fill":{let shape=getParamValue("shape",node,tensorMap,context),dtype=getParamValue("dtype",node,tensorMap,context),value=getParamValue("value",node,tensorMap,context);return[fill(shape,value,dtype)]}case"LinSpace":{let start=getParamValue("start",node,tensorMap,context),stop=getParamValue("stop",node,tensorMap,context),num=getParamValue("num",node,tensorMap,context);return[linspace(start,stop,num)]}case"Multinomial":{let logits=getParamValue("logits",node,tensorMap,context),numSamples=getParamValue("numSamples",node,tensorMap,context),seed=getParamValue("seed",node,tensorMap,context);return[multinomial(logits,numSamples,seed)]}case"OneHot":{let indices=getParamValue("indices",node,tensorMap,context),depth=getParamValue("depth",node,tensorMap,context),onValue=getParamValue("onValue",node,tensorMap,context),offValue=getParamValue("offValue",node,tensorMap,context);return[oneHot(indices,depth,onValue,offValue)]}case"Ones":return[ones2(getParamValue("shape",node,tensorMap,context),getParamValue("dtype",node,tensorMap,context))];case"OnesLike":return[onesLike(getParamValue("x",node,tensorMap,context))];case"RandomUniform":return[randomUniform(getParamValue("shape",node,tensorMap,context),getParamValue("minval",node,tensorMap,context),getParamValue("maxval",node,tensorMap,context),getParamValue("dtype",node,tensorMap,context))];case"Range":{let start=getParamValue("start",node,tensorMap,context),stop=getParamValue("stop",node,tensorMap,context),step4=getParamValue("step",node,tensorMap,context);return[range(start,stop,step4,getParamValue("dtype",node,tensorMap,context))]}case"TruncatedNormal":{let shape=getParamValue("shape",node,tensorMap,context),mean4=getParamValue("mean",node,tensorMap,context),stdDev=getParamValue("stdDev",node,tensorMap,context),seed=getParamValue("seed",node,tensorMap,context);return[truncatedNormal(shape,mean4,stdDev,getParamValue("dtype",node,tensorMap,context),seed)]}case"Zeros":return[zeros(getParamValue("shape",node,tensorMap,context),getParamValue("dtype",node,tensorMap,context))];case"ZerosLike":return[zerosLike(getParamValue("x",node,tensorMap,context))];default:throw TypeError(`Node type ${node.op} is not implemented`)}};function nmsParams(node,tensorMap,context){let boxes=getParamValue("boxes",node,tensorMap,context),scores=getParamValue("scores",node,tensorMap,context),maxOutputSize=getParamValue("maxOutputSize",node,tensorMap,context),iouThreshold=getParamValue("iouThreshold",node,tensorMap,context),scoreThreshold=getParamValue("scoreThreshold",node,tensorMap,context),softNmsSigma=getParamValue("softNmsSigma",node,tensorMap,context);return{boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma}}var executeOp6=async(node,tensorMap,context)=>{switch(node.op){case"NonMaxSuppressionV5":{let{boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma}=nmsParams(node,tensorMap,context),result=await image.nonMaxSuppressionWithScoreAsync(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma);return[result.selectedIndices,result.selectedScores]}case"NonMaxSuppressionV4":{let{boxes,scores,maxOutputSize,iouThreshold,scoreThreshold}=nmsParams(node,tensorMap,context),padToMaxOutputSize=getParamValue("padToMaxOutputSize",node,tensorMap,context),result=await image.nonMaxSuppressionPaddedAsync(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize);return[result.selectedIndices,result.validOutputs]}case"NonMaxSuppressionV3":case"NonMaxSuppressionV2":{let{boxes,scores,maxOutputSize,iouThreshold,scoreThreshold}=nmsParams(node,tensorMap,context);return[await image.nonMaxSuppressionAsync(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold)]}case"Where":{let condition=cast(getParamValue("condition",node,tensorMap,context),"bool"),result=[await whereAsync(condition)];return condition.dispose(),result}case"ListDiff":return setdiff1dAsync(getParamValue("x",node,tensorMap,context),getParamValue("y",node,tensorMap,context));default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp7=(node,tensorMap,context)=>{switch(node.op){case"TopKV2":{let x=getParamValue("x",node,tensorMap,context),k=getParamValue("k",node,tensorMap,context),sorted=getParamValue("sorted",node,tensorMap,context),result=topk(x,k,sorted);return[result.values,result.indices]}case"Unique":{let x=getParamValue("x",node,tensorMap,context),result=unique(x);return[result.values,result.indices]}case"UniqueV2":{let x=getParamValue("x",node,tensorMap,context),axis=getParamValue("axis",node,tensorMap,context),result=unique(x,axis);return[result.values,result.indices]}default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp8=(node,tensorMap,context)=>{switch(node.op){case"Const":return tensorMap[node.name];case"PlaceholderWithDefault":let def=getParamValue("default",node,tensorMap,context);return[getTensor(node.name,tensorMap,context)||def];case"Placeholder":return[getTensor(node.name,tensorMap,context)];case"Identity":case"StopGradient":case"FakeQuantWithMinMaxVars":{let data22=getParamValue("x",node,tensorMap,context);return[cloneTensor(data22)]}case"IdentityN":return getParamValue("x",node,tensorMap,context).map(t=>cloneTensor(t));case"Snapshot":let snapshot=getParamValue("x",node,tensorMap,context);return[cloneTensor(snapshot)];case"Shape":return[tensor1d(getParamValue("x",node,tensorMap,context).shape,"int32")];case"ShapeN":return getParamValue("x",node,tensorMap,context).map(t=>tensor1d(t.shape));case"Size":return[scalar(getParamValue("x",node,tensorMap,context).size,"int32")];case"Rank":return[scalar(getParamValue("x",node,tensorMap,context).rank,"int32")];case"NoOp":return[scalar(1)];case"Print":let input2=getParamValue("x",node,tensorMap,context),data2=getParamValue("data",node,tensorMap,context),message=getParamValue("message",node,tensorMap,context),summarize=getParamValue("summarize",node,tensorMap,context);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."),console.log(message);for(let i=0;ivalue.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}async import(keys,values){this.checkKeyAndValueTensor(keys,values);let $keys=await keys.data();return this.tensorMap.forEach(value=>value.dispose()),this.tensorMap.clear(),tidy(()=>{let $values=unstack(values),keysLength=$keys.length,valuesLength=$values.length;util_exports.assert(keysLength===valuesLength,()=>`The number of elements doesn't match, keys has ${keysLength} elements, the values has ${valuesLength} elements.`);for(let i=0;i{let result=[];for(let i=0;i<$keys.length;i++){let key=$keys[i],value=this.findWithDefault(key,defaultValue);result.push(value)}return stack(result)})}findWithDefault(key,defaultValue){let result=this.tensorMap.get(key);return result!=null?result:defaultValue}checkKeyAndValueTensor(key,value){if(key.dtype!==this.keyDType)throw new Error(`Expect key dtype ${this.keyDType}, but got ${key.dtype}`);if(value.dtype!==this.valueDType)throw new Error(`Expect value dtype ${this.valueDType}, but got ${value.dtype}`)}},executeOp9=async(node,tensorMap,context,resourceManager)=>{switch(node.op){case"HashTable":case"HashTableV2":{let keyDType=getParamValue("keyDType",node,tensorMap,context),valueDType=getParamValue("valueDType",node,tensorMap,context),hashTable2=new HashTable(keyDType,valueDType);return resourceManager.addHashTable(node.name,hashTable2),[hashTable2.handle]}case"LookupTableImport":case"LookupTableImportV2":{let handle=getParamValue("tableHandle",node,tensorMap,context,resourceManager),keys=getParamValue("keys",node,tensorMap,context),values=getParamValue("values",node,tensorMap,context);return[await resourceManager.getHashTableById(handle.id).import(keys,values)]}case"LookupTableFind":case"LookupTableFindV2":{let handle=getParamValue("tableHandle",node,tensorMap,context,resourceManager),keys=getParamValue("keys",node,tensorMap,context),defaultValue=getParamValue("defaultValue",node,tensorMap,context);return[await resourceManager.getHashTableById(handle.id).find(keys,defaultValue)]}default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp10=(node,tensorMap,context)=>{switch(node.op){case"ResizeBilinear":{let images=getParamValue("images",node,tensorMap,context),size=getParamValue("size",node,tensorMap,context),alignCorners=getParamValue("alignCorners",node,tensorMap,context),halfPixelCenters=getParamValue("halfPixelCenters",node,tensorMap,context);return[image.resizeBilinear(images,[size[0],size[1]],alignCorners,halfPixelCenters)]}case"ResizeNearestNeighbor":{let images=getParamValue("images",node,tensorMap,context),size=getParamValue("size",node,tensorMap,context),alignCorners=getParamValue("alignCorners",node,tensorMap,context),halfPixelCenters=getParamValue("halfPixelCenters",node,tensorMap,context);return[image.resizeNearestNeighbor(images,[size[0],size[1]],alignCorners,halfPixelCenters)]}case"CropAndResize":{let image3=getParamValue("image",node,tensorMap,context),boxes=getParamValue("boxes",node,tensorMap,context),boxInd=getParamValue("boxInd",node,tensorMap,context),cropSize=getParamValue("cropSize",node,tensorMap,context),method=getParamValue("method",node,tensorMap,context),extrapolationValue=getParamValue("extrapolationValue",node,tensorMap,context);return[image.cropAndResize(image3,boxes,boxInd,cropSize,method,extrapolationValue)]}default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp11=(node,tensorMap,context)=>{switch(node.op){case"Equal":return[equal(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"NotEqual":return[notEqual(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Greater":return[greater(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"GreaterEqual":return[greaterEqual(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Less":return[less(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"LessEqual":return[lessEqual(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"LogicalAnd":return[logicalAnd(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"LogicalNot":return[logicalNot(getParamValue("a",node,tensorMap,context))];case"LogicalOr":return[logicalOr(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Select":case"SelectV2":return[where(getParamValue("condition",node,tensorMap,context),getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp12=(node,tensorMap,context)=>{switch(node.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[matMul(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context),getParamValue("transposeA",node,tensorMap,context),getParamValue("transposeB",node,tensorMap,context))];case"Transpose":return[transpose(getParamValue("x",node,tensorMap,context),getParamValue("perm",node,tensorMap,context))];case"_FusedMatMul":let[extraOp,activationFunc]=getParamValue("fusedOps",node,tensorMap,context),isBiasAdd=extraOp==="biasadd",isPrelu=activationFunc==="prelu",numArgs=getParamValue("numArgs",node,tensorMap,context),leakyreluAlpha=getParamValue("leakyreluAlpha",node,tensorMap,context);if(isBiasAdd){if(isPrelu&&numArgs!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!isPrelu&&numArgs!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[biasArg,preluArg]=getParamValue("args",node,tensorMap,context);return[fused_ops_exports.matMul({a:getParamValue("a",node,tensorMap,context),b:getParamValue("b",node,tensorMap,context),transposeA:getParamValue("transposeA",node,tensorMap,context),transposeB:getParamValue("transposeB",node,tensorMap,context),bias:biasArg,activation:activationFunc,preluActivationWeights:preluArg,leakyreluAlpha})];default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp13=(node,tensorMap,context)=>{switch(node.op){case"FusedBatchNorm":case"FusedBatchNormV2":return[batchNorm(getParamValue("x",node,tensorMap,context),getParamValue("mean",node,tensorMap,context),getParamValue("variance",node,tensorMap,context),getParamValue("offset",node,tensorMap,context),getParamValue("scale",node,tensorMap,context),getParamValue("epsilon",node,tensorMap,context))];case"FusedBatchNormV3":return[batchNorm(getParamValue("x",node,tensorMap,context),getParamValue("mean",node,tensorMap,context),getParamValue("variance",node,tensorMap,context),getParamValue("offset",node,tensorMap,context),getParamValue("scale",node,tensorMap,context),getParamValue("epsilon",node,tensorMap,context))];case"LRN":return[localResponseNormalization(getParamValue("x",node,tensorMap,context),getParamValue("radius",node,tensorMap,context),getParamValue("bias",node,tensorMap,context),getParamValue("alpha",node,tensorMap,context),getParamValue("beta",node,tensorMap,context))];case"Softmax":return[softmax(getParamValue("x",node,tensorMap,context))];case"LogSoftmax":return[logSoftmax(getParamValue("x",node,tensorMap,context))];case"SparseToDense":return[sparseToDense(getParamValue("sparseIndices",node,tensorMap,context),getParamValue("outputShape",node,tensorMap,context),getParamValue("sparseValues",node,tensorMap,context),getParamValue("defaultValue",node,tensorMap,context))];default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp14=(node,tensorMap,context)=>{switch(node.op){case"Max":{let axis=getParamValue("axis",node,tensorMap,context),keepDims=getParamValue("keepDims",node,tensorMap,context);return[max(getParamValue("x",node,tensorMap,context),axis,keepDims)]}case"Mean":{let axis=getParamValue("axis",node,tensorMap,context),keepDims=getParamValue("keepDims",node,tensorMap,context);return[mean(getParamValue("x",node,tensorMap,context),axis,keepDims)]}case"Min":{let axis=getParamValue("axis",node,tensorMap,context),keepDims=getParamValue("keepDims",node,tensorMap,context);return[min(getParamValue("x",node,tensorMap,context),axis,keepDims)]}case"Sum":{let axis=getParamValue("axis",node,tensorMap,context),keepDims=getParamValue("keepDims",node,tensorMap,context);return[sum2(getParamValue("x",node,tensorMap,context),axis,keepDims)]}case"All":{let axis=getParamValue("axis",node,tensorMap,context),keepDims=getParamValue("keepDims",node,tensorMap,context);return[all(getParamValue("x",node,tensorMap,context),axis,keepDims)]}case"Any":{let axis=getParamValue("axis",node,tensorMap,context),keepDims=getParamValue("keepDims",node,tensorMap,context);return[any(getParamValue("x",node,tensorMap,context),axis,keepDims)]}case"ArgMax":{let axis=getParamValue("axis",node,tensorMap,context);return[argMax(getParamValue("x",node,tensorMap,context),axis)]}case"ArgMin":{let axis=getParamValue("axis",node,tensorMap,context);return[argMin(getParamValue("x",node,tensorMap,context),axis)]}case"Prod":{let axis=getParamValue("axis",node,tensorMap,context),keepDims=getParamValue("keepDims",node,tensorMap,context);return[prod(getParamValue("x",node,tensorMap,context),axis,keepDims)]}case"Cumsum":{let axis=getParamValue("axis",node,tensorMap,context),exclusive=getParamValue("exclusive",node,tensorMap,context),reverse5=getParamValue("reverse",node,tensorMap,context);return[cumsum(getParamValue("x",node,tensorMap,context),axis,exclusive,reverse5)]}case"Bincount":let x=getParamValue("x",node,tensorMap,context),weights=getParamValue("weights",node,tensorMap,context),size=getParamValue("size",node,tensorMap,context);return[bincount(x,weights,size)];case"DenseBincount":{let x2=getParamValue("x",node,tensorMap,context),weights2=getParamValue("weights",node,tensorMap,context),size2=getParamValue("size",node,tensorMap,context),binaryOutput=getParamValue("binaryOutput",node,tensorMap,context);return[denseBincount(x2,weights2,size2,binaryOutput)]}default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp15=(node,tensorMap,context)=>{switch(node.op){case"ConcatV2":case"Concat":{let n=getParamValue("n",node,tensorMap,context),axis=getParamValue("axis",node,tensorMap,context),inputs=getParamValue("tensors",node,tensorMap,context);return inputs=inputs.slice(0,n),[concat(inputs,axis)]}case"Gather":{let input2=getParamValue("x",node,tensorMap,context),indices=getParamValue("indices",node,tensorMap,context);return[gather(input2,cast(indices,"int32"),0)]}case"GatherV2":{let axis=getParamValue("axis",node,tensorMap,context),batchDims=getParamValue("batchDims",node,tensorMap,context),input2=getParamValue("x",node,tensorMap,context),indices=getParamValue("indices",node,tensorMap,context);return[gather(input2,cast(indices,"int32"),axis,batchDims)]}case"Reverse":{let dims=getParamValue("dims",node,tensorMap,context),axis=[];for(let i=0;i{let axis=getParamValue("axis",node,tensorMap,context),tensors=getParamValue("tensors",node,tensorMap,context),shape=tensors[0].shape,squeezedShape=squeeze(tensors[0]).shape,mapped=tensors.map(tensor2=>{let sameShape=util_exports.arraysEqual(tensor2.shape,shape);if(!sameShape&&!util_exports.arraysEqual(squeeze(tensor2).shape,squeezedShape))throw new Error("the input tensors shape does not match");return sameShape?tensor2:reshape(tensor2,shape)});return[stack(mapped,axis)]});case"Unpack":{let axis=getParamValue("axis",node,tensorMap,context),tensor2=getParamValue("tensor",node,tensorMap,context);return unstack(tensor2,axis)}case"Tile":{let reps=getParamValue("reps",node,tensorMap,context);return[tile(getParamValue("x",node,tensorMap,context),reps)]}case"Split":case"SplitV":{let axis=getParamValue("axis",node,tensorMap,context),numOrSizeSplits=getParamValue("numOrSizeSplits",node,tensorMap,context),tensor2=getParamValue("x",node,tensorMap,context);return split(tensor2,numOrSizeSplits,axis)}case"ScatterNd":{let indices=getParamValue("indices",node,tensorMap,context),values=getParamValue("values",node,tensorMap,context),shape=getParamValue("shape",node,tensorMap,context);return[scatterND(indices,values,shape)]}case"GatherNd":{let x=getParamValue("x",node,tensorMap,context),indices=getParamValue("indices",node,tensorMap,context);return[gatherND(x,indices)]}case"SparseToDense":{let indices=getParamValue("sparseIndices",node,tensorMap,context),shape=getParamValue("outputShape",node,tensorMap,context),sparseValues=getParamValue("sparseValues",node,tensorMap,context),defaultValue=getParamValue("defaultValue",node,tensorMap,context);return[sparseToDense(indices,sparseValues,shape,sparseValues.dtype===defaultValue.dtype?defaultValue:cast(defaultValue,sparseValues.dtype))]}default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp16=(node,tensorMap,context)=>{switch(node.op){case"FFT":return[fft(getParamValue("x",node,tensorMap,context))];case"IFFT":return[ifft(getParamValue("x",node,tensorMap,context))];case"RFFT":return[rfft(getParamValue("x",node,tensorMap,context))];case"IRFFT":return[irfft(getParamValue("x",node,tensorMap,context))];default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp17=(node,tensorMap,context)=>{switch(node.op){case"Cast":return[cast(getParamValue("x",node,tensorMap,context),getParamValue("dtype",node,tensorMap,context))];case"ExpandDims":{let axis=getParamValue("axis",node,tensorMap,context);return[expandDims(getParamValue("x",node,tensorMap,context),axis)]}case"Squeeze":{let axis=getParamValue("axis",node,tensorMap,context);return[squeeze(getParamValue("x",node,tensorMap,context),axis)]}case"Reshape":return[reshape(getParamValue("x",node,tensorMap,context),getParamValue("shape",node,tensorMap,context))];case"MirrorPad":return[mirrorPad(getParamValue("x",node,tensorMap,context),getParamValue("padding",node,tensorMap,context),getParamValue("mode",node,tensorMap,context))];case"PadV2":case"Pad":return[pad(getParamValue("x",node,tensorMap,context),getParamValue("padding",node,tensorMap,context),getParamValue("constantValue",node,tensorMap,context))];case"SpaceToBatchND":{let blockShape=getParamValue("blockShape",node,tensorMap,context),paddings=getParamValue("paddings",node,tensorMap,context);return[spaceToBatchND(getParamValue("x",node,tensorMap,context),blockShape,paddings)]}case"BatchToSpaceND":{let blockShape=getParamValue("blockShape",node,tensorMap,context),crops=getParamValue("crops",node,tensorMap,context);return[batchToSpaceND(getParamValue("x",node,tensorMap,context),blockShape,crops)]}case"DepthToSpace":{let blockSize=getParamValue("blockSize",node,tensorMap,context),dataFormat=getParamValue("dataFormat",node,tensorMap,context).toUpperCase();return[depthToSpace(getParamValue("x",node,tensorMap,context),blockSize,dataFormat)]}case"BroadcastTo":return[broadcastTo(getParamValue("x",node,tensorMap,context),getParamValue("shape",node,tensorMap,context))];default:throw TypeError(`Node type ${node.op} is not implemented`)}};function executeOp18(node,tensorMap,context,resourceManager){let value=((node2,tensorMap2,context2)=>{switch(node2.category){case"arithmetic":return tidy(()=>executeOp(node2,tensorMap2,context2));case"basic_math":return tidy(()=>executeOp2(node2,tensorMap2,context2));case"control":return executeOp3(node2,tensorMap2,context2);case"convolution":return tidy(()=>executeOp4(node2,tensorMap2,context2));case"creation":return tidy(()=>executeOp5(node2,tensorMap2,context2));case"dynamic":return executeOp6(node2,tensorMap2,context2);case"evaluation":return tidy(()=>executeOp7(node2,tensorMap2,context2));case"image":return tidy(()=>executeOp10(node2,tensorMap2,context2));case"graph":return tidy(()=>executeOp8(node2,tensorMap2,context2));case"logical":return tidy(()=>executeOp11(node2,tensorMap2,context2));case"matrices":return tidy(()=>executeOp12(node2,tensorMap2,context2));case"normalization":return tidy(()=>executeOp13(node2,tensorMap2,context2));case"reduction":return tidy(()=>executeOp14(node2,tensorMap2,context2));case"slice_join":return tidy(()=>executeOp15(node2,tensorMap2,context2));case"spectral":return tidy(()=>executeOp16(node2,tensorMap2,context2));case"transformation":return tidy(()=>executeOp17(node2,tensorMap2,context2));case"hash_table":return executeOp9(node2,tensorMap2,context2,resourceManager);case"custom":let opMapper=getRegisteredOp(node2.op);if(opMapper&&opMapper.customExecutor)return opMapper.customExecutor(new NodeValueImpl(node2,tensorMap2,context2));throw TypeError(`Custom op ${node2.op} is not registered.`);default:throw TypeError(`Unknown op '${node2.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(node,tensorMap,context);return util_exports.isPromise(value)?value.then(data2=>[].concat(data2)):[].concat(value)}var ExecutionContext=class{constructor(weightMap={},tensorArrayMap={},tensorListMap={},functionMap={}){this.weightMap=weightMap,this.tensorArrayMap=tensorArrayMap,this.tensorListMap=tensorListMap,this.functionMap=functionMap,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(id,frameName){return{id,frameName,iterationId:0}}set currentContext(contexts2){this.contexts!==contexts2&&(this.contexts=contexts2,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let names=[];for(let i=0;icontext.id===0&&context.iterationId===0?"":`${context.frameName}-${context.iterationId}`).join("/"):""}enterFrame(frameId){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,frameId)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let context=Object.assign({},this.contexts[this.contexts.length-1]);context.iterationId+=1,context.id=this.lastId,this.contexts.splice(-1,1,context),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(name){return this.weightMap[name]}addTensorArray(tensorArray){this.tensorArrayMap[tensorArray.id]=tensorArray}getTensorArray(id){return this.tensorArrayMap[id]}addTensorList(tensorList){this.tensorListMap[tensorList.id]=tensorList}getTensorList(id){return this.tensorListMap[id]}dispose(keepIds){for(let key in this.tensorArrayMap)this.tensorArrayMap[key].clearAndClose(keepIds);for(let key in this.tensorListMap)this.tensorListMap[key].clearAndClose(keepIds)}};function getExecutionSubgraph(inputs,outputs,weightMap,initNodes){let usedNodes=new Set,missingInputs=[],dynamicNode=null,syncInputs=null,seen=new Set,inputNodeNames=Object.keys(inputs).map(name=>parseNodeName(name)[0]),initNodeNames=[];initNodes!=null&&(initNodeNames=initNodes.map(node=>parseNodeName(node.name)[0]));let frontier=[...outputs];for(;frontier.length>0;){let node=frontier.pop();if((isControlFlow(node)||isDynamicShape(node)||isHashTable(node))&&dynamicNode==null&&(dynamicNode=node,syncInputs=dynamicNode.children.map(child=>child.name).filter(name=>usedNodes.has(name))),usedNodes.add(node.name),weightMap[node.name]==null&&inputNodeNames.indexOf(node.name)===-1&&initNodeNames.indexOf(node.name)===-1){if(node.inputs.length===0){missingInputs.push(node.name);continue}node.inputs.forEach(input2=>{seen.has(input2.name)||(seen.add(input2.name),frontier.push(input2))})}}return{inputs,outputs,usedNodes,missingInputs,dynamicNode,syncInputs}}function getNodesInTopologicalOrder(graph2,weightMap,executionInfo){let{usedNodes,inputs}=executionInfo,frontier=[],inputNodes=Object.keys(inputs).map(name=>parseNodeName(name)[0]).map(name=>graph2.nodes[name]),initNodes=graph2.initNodes;inputNodes.forEach(input2=>{usedNodes.has(input2.name)&&frontier.push(input2)}),graph2.weights.forEach(weight=>{usedNodes.has(weight.name)&&frontier.push(weight)}),initNodes!=null&&initNodes.forEach(node=>{usedNodes.has(node.name)&&frontier.push(node)});let seen=new Set,orderedNodes=[];for(;frontier.length>0;){let node=frontier.pop();seen.add(node.name),weightMap[node.name]||orderedNodes.push(node),node.children.forEach(child=>{!seen.has(child.name)&&usedNodes.has(child.name)&&child.inputs.every(input2=>seen.has(input2.name))&&frontier.push(child)})}return orderedNodes}var CONTROL_FLOW_OPS=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],DYNAMIC_SHAPE_OPS=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],HASH_TABLE_OPS=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2"];function isControlFlow(node){return CONTROL_FLOW_OPS.indexOf(node.op)>=0}function isDynamicShape(node){return DYNAMIC_SHAPE_OPS.indexOf(node.op)>=0}function isHashTable(node){return HASH_TABLE_OPS.indexOf(node.op)>=0}var GraphExecutor=class{constructor(graph2,parent){this.graph=graph2,this.parent=parent,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this._outputs=graph2.outputs,this._inputs=graph2.inputs,this._initNodes=graph2.initNodes,this._signature=graph2.signature,this._functions=graph2.functions,graph2.functions!=null&&Object.keys(graph2.functions).forEach(name=>{this._functionExecutorMap[name]=new GraphExecutor(graph2.functions[name],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(weightMap){let weightIds=Object.keys(weightMap).map(key=>weightMap[key].map(tensor2=>tensor2.id));this._weightIds=[].concat(...weightIds),this._weightMap=weightMap}set resourceManager(resourceManager){this._resourceManager=resourceManager}get inputs(){return this._inputs.map(node=>({name:node.name,shape:node.attrParams.shape?node.attrParams.shape.value:void 0,dtype:node.attrParams.dtype?node.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(node=>({name:node.name,shape:node.attrParams.shape?node.attrParams.shape.value:void 0,dtype:node.attrParams.dtype?node.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(node=>node.signatureKey||node.name)}get outputNodes(){return this._outputs.map(node=>{let name=node.signatureKey||node.name;return node.defaultOutput?`${name}:${node.defaultOutput}`:name})}get functions(){return Object.keys(this._functions).reduce((map,key)=>(map[key]=this._functions[key].signature,map),{})}getCompilationKey(inputs,outputs){let sortedInputs=inputs.map(node=>node.name).sort(),sortedOutputs=outputs.map(node=>node.name).sort();return sortedInputs.join(this.SEPERATOR)+"--"+sortedOutputs.join(this.SEPERATOR)}compile(inputs,outputs){let executionInfo=getExecutionSubgraph(inputs,outputs,this.weightMap,this._initNodes),{missingInputs,dynamicNode,syncInputs}=executionInfo;if(dynamicNode!=null)throw new Error(`This execution contains the node '${dynamicNode.name}', which has the dynamic op '${dynamicNode.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${syncInputs}]`);if(missingInputs.length>0){let outNames=outputs.map(n=>n.name),inNames=Object.keys(inputs);throw new Error(`Cannot compute the outputs [${outNames}] from the provided inputs [${inNames}]. Missing the following inputs: [${missingInputs}]`)}return getNodesInTopologicalOrder(this.graph,this.weightMap,executionInfo)}execute(inputs,outputs){inputs=this.mapInputs(inputs);let names=Object.keys(inputs).sort();this.checkInputs(inputs),this.checkInputShapeAndType(inputs),outputs=this.mapOutputs(outputs),this.checkOutputs(outputs);let inputNodes=names.map(name=>this.graph.nodes[parseNodeName(name)[0]]),outputNodeNames=outputs.map(name=>parseNodeName(name)[0]),outputNodes=outputNodeNames.map(name=>this.graph.nodes[name]);outputNodes.length===0&&(outputNodes=this._outputs);let compilationKey=this.getCompilationKey(inputNodes,outputNodes),orderedNodes=this.compiledMap.get(compilationKey);orderedNodes==null&&(orderedNodes=this.compile(inputs,outputNodes),this.compiledMap.set(compilationKey,orderedNodes));let tensorArrayMap={},tensorListMap={};return tidy(()=>{let context=new ExecutionContext(this.weightMap,tensorArrayMap,tensorListMap,this.functionExecutorMap),tensorsMap=Object.assign({},this.weightMap);Object.keys(inputs).forEach(name=>{let[nodeName,index]=parseNodeName(name),tensors=[];tensors[index]=inputs[name],tensorsMap[nodeName]=tensors});let tensorsToKeep=this.getFrozenTensorIds(tensorsMap),intermediateTensorConsumerCount={};for(let i=0;igetTensor(name,tensorsMap,context))})}getFrozenTensorIds(tensorMap){let ids=[].concat.apply([],Object.keys(tensorMap).map(key=>tensorMap[key]).map(tensors=>tensors.map(tensor2=>tensor2.id)));return new Set(ids)}checkTensorForDisposal(nodeName,node,tensorMap,context,tensorsToKeep,outputNames,intermediateTensorConsumerCount){node.category==="control"||outputNames.indexOf(nodeName)!==-1||(tensorMap[nodeName].forEach(tensor2=>{tensor2!=null&&(intermediateTensorConsumerCount[tensor2.id]=(intermediateTensorConsumerCount[tensor2.id]||0)+node.children.length)}),node.inputs.forEach(input2=>{if(input2.category!=="control"){let tensors=getTensorsForCurrentContenxt(input2.name,tensorMap,context);tensors!=null&&tensors.forEach(tensor2=>{if(tensor2&&!tensorsToKeep.has(tensor2.id)){let count2=intermediateTensorConsumerCount[tensor2.id];count2===1?(tensor2.dispose(),delete intermediateTensorConsumerCount[tensor2.id]):count2!=null&&intermediateTensorConsumerCount[tensor2.id]--}})}}))}async executeAsync(inputs,outputs){return this._executeAsync(inputs,outputs)}async _executeAsync(inputs,outputs,isFunctionExecution=!1,tensorArrayMap={},tensorListMap={}){isFunctionExecution||(inputs=this.mapInputs(inputs),this.checkInputs(inputs),this.checkInputShapeAndType(inputs),outputs=this.mapOutputs(outputs),this.checkOutputs(outputs));let context=new ExecutionContext(this.weightMap,tensorArrayMap,tensorListMap,this.functionExecutorMap),tensorMap=await this.executeWithControlFlow(inputs,context,outputs,isFunctionExecution),results=outputs.map(name=>getTensor(name,tensorMap,context)),outputIds=results.map(t=>t.id),inputIds=Object.keys(inputs).map(name=>inputs[name].id),keepIds=new Set([...outputIds,...inputIds,...this.weightIds]);return Object.keys(tensorMap).forEach(key=>{tensorMap[key].forEach(tensor2=>{tensor2&&!tensor2.isDisposed&&!keepIds.has(tensor2.id)&&tensor2.dispose()})}),this.parent==null&&context.dispose(keepIds),results}async executeFunctionAsync(inputs,tensorArrayMap,tensorListMap){let mappedInputs=inputs.reduce((map,tensor2,index)=>(map[this.inputs[index].name]=tensor2,map),{});return this._executeAsync(mappedInputs,this.outputNodes,!0,tensorArrayMap,tensorListMap)}async executeWithControlFlow(inputs,context,outputNames,isFunctionExecution){let names=Object.keys(inputs),inputNodes=names.map(name=>this.graph.nodes[parseNodeName(name)[0]]),outputNodeNames=outputNames.map(name=>parseNodeName(name)[0]),outputNodes=outputNodeNames.map(name=>this.graph.nodes[name]);outputNodes.length===0&&(outputNodes=this._outputs);let{usedNodes,missingInputs,dynamicNode,syncInputs}=getExecutionSubgraph(inputs,outputNodes,this.weightMap,this._initNodes),stack2=[...inputNodes,...this.graph.weights,...this._initNodes||[]].map(node=>({node,contexts:context.currentContext})),tensorsMap=Object.assign({},this.weightMap);Object.keys(inputs).forEach(name=>{let[nodeName,index]=parseNodeName(name),tensors=[];tensors[index]=inputs[name],tensorsMap[nodeName]=tensors});let intermediateTensorConsumerCount={},tensorsToKeep=this.getFrozenTensorIds(tensorsMap),added={};for(;stack2.length>0;){let promises=this.processStack(inputNodes,stack2,context,tensorsMap,added,tensorsToKeep,outputNodeNames,intermediateTensorConsumerCount,usedNodes);await Promise.all(promises)}dynamicNode==null&&!isFunctionExecution&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let missingOutputs=outputNodes.filter(node=>!isControlFlow(node)&&!getTensor(node.name,tensorsMap,context)).map(node=>node.name);if(missingOutputs.length>0){let alternativeMsg="";throw dynamicNode!=null&&(alternativeMsg=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${syncInputs}]`),new Error(`Cannot compute the outputs [${missingOutputs}] from the provided inputs [${names}]. Consider providing the following inputs: [${missingInputs}]. ${alternativeMsg}`)}return tensorsMap}processStack(inputNodes,stack2,context,tensorMap,added,tensorsToKeep,outputNames,intermediateTensorConsumerCount,usedNodes){let promises=[];for(;stack2.length>0;){let item=stack2.pop();context.currentContext=item.contexts;let nodeName="";if(item.node.op==="Enter"&&getParamValue("isConstant",item.node,tensorMap,context)&&([nodeName]=getNodeNameAndIndex(item.node.name,context)),tensorMap[item.node.name]==null){let tensors=executeOp18(item.node,tensorMap,context,this._resourceManager);nodeName||([nodeName]=getNodeNameAndIndex(item.node.name,context));let currentContext=context.currentContext;util_exports.isPromise(tensors)?promises.push(tensors.then(t=>(tensorMap[nodeName]=t,context.currentContext=currentContext,this.checkTensorForDisposal(nodeName,item.node,tensorMap,context,tensorsToKeep,outputNames,intermediateTensorConsumerCount),this.processChildNodes(item.node,stack2,context,tensorMap,added,usedNodes),t))):(tensorMap[nodeName]=tensors,this.checkTensorForDisposal(nodeName,item.node,tensorMap,context,tensorsToKeep,outputNames,intermediateTensorConsumerCount),this.processChildNodes(item.node,stack2,context,tensorMap,added,usedNodes))}else this.processChildNodes(item.node,stack2,context,tensorMap,added,usedNodes)}return promises}processChildNodes(node,stack2,context,tensorMap,added,usedNodes){node.children.forEach(childNode=>{let[nodeName]=getNodeNameAndIndex(childNode.name,context);added[nodeName]||!usedNodes.has(childNode.name)||(childNode.op==="Merge"?childNode.inputNames.some(name=>!!getTensor(name,tensorMap,context))&&(added[nodeName]=!0,stack2.push({contexts:context.currentContext,node:childNode})):childNode.inputNames.every(name=>!!getTensor(name,tensorMap,context))&&(added[nodeName]=!0,stack2.push({contexts:context.currentContext,node:childNode})))})}dispose(){Object.keys(this.weightMap).forEach(key=>this.weightMap[key].forEach(tensor2=>tensor2.dispose()))}checkInputShapeAndType(inputs){Object.keys(inputs).forEach(name=>{let input2=inputs[name],[nodeName]=parseNodeName(name),node=this.graph.nodes[nodeName];if(node.attrParams.shape&&node.attrParams.shape.value){let shape=node.attrParams.shape.value,match=shape.length===input2.shape.length&&input2.shape.every((dim,index)=>shape[index]===-1||shape[index]===dim);util_exports.assert(match,()=>`The shape of dict['${node.name}'] provided in model.execute(dict) must be [${shape}], but was [${input2.shape}]`)}node.attrParams.dtype&&node.attrParams.dtype.value&&util_exports.assert(input2.dtype===node.attrParams.dtype.value,()=>`The dtype of dict['${node.name}'] provided in model.execute(dict) must be ${node.attrParams.dtype.value}, but was ${input2.dtype}`)})}mapInputs(inputs){let result={};for(let inputName in inputs)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[inputName]!=null){let tensor2=this._signature.inputs[inputName];result[tensor2.name]=inputs[inputName]}else result[inputName]=inputs[inputName];return result}checkInputs(inputs){let notInGraph=Object.keys(inputs).filter(name=>{let[nodeName]=parseNodeName(name);return this.graph.nodes[nodeName]==null});if(notInGraph.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${notInGraph}] that are not part of graph`)}mapOutputs(outputs){return outputs.map(name=>this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[name]!=null?this._signature.outputs[name].name:name,{})}checkOutputs(outputs){outputs.forEach(name=>{let[normalizedName]=parseNodeName(name);if(!this.graph.nodes[normalizedName])throw new Error(`The output '${name}' is not found in the graph`)})}},ResourceManager=class{constructor(hashTableNameToHandle={},hashTableMap={}){this.hashTableNameToHandle=hashTableNameToHandle,this.hashTableMap=hashTableMap}addHashTable(name,hashTable2){this.hashTableNameToHandle[name]=hashTable2.handle,this.hashTableMap[hashTable2.id]=hashTable2}getHashTableHandleByName(name){return this.hashTableNameToHandle[name]}getHashTableById(id){return this.hashTableMap[id]}dispose(){for(let key in this.hashTableMap)this.hashTableMap[key].clearAndClose(),delete this.hashTableMap[key];for(let name in this.hashTableNameToHandle)this.hashTableNameToHandle[name].dispose(),delete this.hashTableNameToHandle[name]}},TFHUB_SEARCH_PARAM="?tfjs-format=file",DEFAULT_MODEL_NAME="model.json",GraphModel=class{constructor(modelUrl,loadOptions={}){this.modelUrl=modelUrl,this.loadOptions=loadOptions,this.version="n/a",loadOptions==null&&(this.loadOptions={}),this.resourceManager=new ResourceManager}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let path=this.modelUrl;if(path.load!=null)this.handler=path;else if(this.loadOptions.requestInit!=null)this.handler=io_exports.browserHTTPRequest(path,this.loadOptions);else{let handlers=io_exports.getLoadHandlers(path,this.loadOptions);if(handlers.length===0)handlers.push(io_exports.browserHTTPRequest(path,this.loadOptions));else if(handlers.length>1)throw new Error(`Found more than one (${handlers.length}) load handlers for URL '${[path]}'`);this.handler=handlers[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let artifacts=await this.handler.load();return this.loadSync(artifacts)}loadSync(artifacts){this.artifacts=artifacts;let graph2=this.artifacts.modelTopology,signature;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?signature=this.artifacts.userDefinedMetadata.signature:signature=this.artifacts.signature,this.signature=signature,this.version=`${graph2.versions.producer}.${graph2.versions.minConsumer}`;let weightMap=io_exports.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new GraphExecutor(OperationMapper.Instance.transformGraph(graph2,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(weightMap),this.executor.resourceManager=this.resourceManager,artifacts.modelInitializer!=null&&artifacts.modelInitializer.node!=null){let initializer=OperationMapper.Instance.transformGraph(artifacts.modelInitializer);this.initializer=new GraphExecutor(initializer),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(handlerOrURL,config2){if(typeof handlerOrURL=="string"){let handlers=io_exports.getSaveHandlers(handlerOrURL);if(handlers.length===0)throw new Error(`Cannot find any save handlers for URL '${handlerOrURL}'`);if(handlers.length>1)throw new Error(`Found more than one (${handlers.length}) save handlers for URL '${handlerOrURL}'`);handlerOrURL=handlers[0]}if(handlerOrURL.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return handlerOrURL.save(this.artifacts)}predict(inputs,config2){return this.execute(inputs,this.outputNodes)}normalizeInputs(inputs){if(!(inputs instanceof Tensor)&&!Array.isArray(inputs))return inputs;if(inputs=Array.isArray(inputs)?inputs:[inputs],inputs.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${inputs.length} input tensors.`);return this.inputNodes.reduce((map,inputName,i)=>(map[inputName]=inputs[i],map),{})}normalizeOutputs(outputs){return outputs=outputs||this.outputNodes,Array.isArray(outputs)?outputs:[outputs]}execute(inputs,outputs){inputs=this.normalizeInputs(inputs),outputs=this.normalizeOutputs(outputs);let result=this.executor.execute(inputs,outputs);return result.length>1?result:result[0]}async executeAsync(inputs,outputs){inputs=this.normalizeInputs(inputs),outputs=this.normalizeOutputs(outputs);let result=await this.executor.executeAsync(inputs,outputs);return result.length>1?result:result[0]}convertTensorMapToTensorsMap(map){return Object.keys(map).reduce((newMap,key)=>(newMap[key]=[map[key]],newMap),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function loadGraphModel(modelUrl,options={}){if(modelUrl==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");options==null&&(options={}),options.fromTFHub&&modelUrl.load==null&&(modelUrl.endsWith("/")||(modelUrl=modelUrl+"/"),modelUrl=`${modelUrl}${DEFAULT_MODEL_NAME}${TFHUB_SEARCH_PARAM}`);let model2=new GraphModel(modelUrl,options);return await model2.load(),model2}var version11="2.8.3",dist_exports={};__export2(dist_exports,{CSVDataset:()=>CSVDataset,Dataset:()=>Dataset,FileDataSource:()=>FileDataSource,TextLineDataset:()=>TextLineDataset,URLDataSource:()=>URLDataSource,array:()=>array,csv:()=>csv,func:()=>func,generator:()=>generator,microphone:()=>microphone,version_data:()=>version12,webcam:()=>webcam,zip:()=>zip});var seedrandom4=__toModule2(require_seedrandom6()),seedrandom3=__toModule2(require_seedrandom6());function deepMap(input2,mapFn){return deepMapInternal(input2,mapFn)}function deepMapInternal(input2,mapFn,seen=new Map,containedIn=new Set){if(input2==null)return null;if(containedIn.has(input2))throw new Error("Circular references are not supported.");if(seen.has(input2))return seen.get(input2);let result=mapFn(input2);if(result.recurse&&result.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(result.recurse)if(isIterable2(input2)){let mappedIterable=Array.isArray(input2)?[]:{};containedIn.add(input2);for(let k in input2){let child=input2[k],childResult=deepMapInternal(child,mapFn,seen,containedIn);mappedIterable[k]=childResult}return containedIn.delete(input2),mappedIterable}else throw new Error(`Can't recurse into non-iterable type: ${input2}`);else return seen.set(input2,result.value),result.value}function deepZip(inputs,zipFn=zipToList){return deepZipInternal(inputs,zipFn)}function deepZipInternal(inputs,zipFn,containedIn=new Set){let input2=inputs[0];if(containedIn.has(input2))throw new Error("Circular references are not supported.");let result=zipFn(inputs);if(result.recurse&&result.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(result.recurse)if(isIterable2(input2)){let mappedIterable=Array.isArray(input2)?[]:{};containedIn.add(input2);for(let k in input2){let children=inputs.map(x=>x[k]),childResult=deepZipInternal(children,zipFn,containedIn);mappedIterable[k]=childResult}return containedIn.delete(input2),mappedIterable}else throw new Error(`Can't recurse into non-iterable type: ${input2}`);else return result.value}function zipToList(x){return x===null?null:isIterable2(x[0])?{value:null,recurse:!0}:{value:x,recurse:!1}}async function deepMapAndAwaitAll(input2,mapFn){let seen=new Map;deepMapInternal(input2,mapFn,seen);for(let key of Array.from(seen.keys())){let value=seen.get(key);if(util_exports.isPromise(value)){let mappedValue=await value;seen.set(key,mappedValue)}}return deepMapInternal(input2,mapFn,seen)}function isIterable2(obj){return obj!=null&&!ArrayBuffer.isView(obj)&&(Array.isArray(obj)||typeof obj=="object"&&!(obj instanceof Tensor))}function canTensorify(obj){return obj==null||isPrimitive(obj)||Array.isArray(obj)||typeof obj=="object"&&obj instanceof Tensor||util_exports.isTypedArray(obj)}function isPrimitive(value){return value===null||typeof value!="object"&&typeof value!="function"}function deepClone(container){return deepMap(container,cloneIfTensor)}function cloneIfTensor(item){return item instanceof Tensor?{value:item.clone(),recurse:!1}:isIterable2(item)?{value:null,recurse:!0}:{value:item,recurse:!1}}var RingBuffer=class{constructor(capacity){if(this.capacity=capacity,this.begin=0,this.end=0,capacity==null)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(capacity<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(capacity),this.doubledCapacity=2*capacity}wrap(index){for(;index<0;)index+=this.doubledCapacity;return index%this.doubledCapacity}get(index){if(index<0)throw new RangeError("Can't get item at a negative index.");return this.data[index%this.capacity]}set(index,value){if(index<0)throw new RangeError("Can't set item at a negative index.");this.data[index%this.capacity]=value}length(){let length=this.end-this.begin;return length<0&&(length=this.doubledCapacity+length),length}isFull(){return this.length()===this.capacity}isEmpty(){return this.length()===0}push(value){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,value),this.end=this.wrap(this.end+1)}pushAll(values){for(let value of values)this.push(value)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);let result=this.get(this.end);return this.set(this.end,void 0),result}unshift(value){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,value)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let result=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),result}shuffleExcise(relativeIndex){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let index=this.wrap(this.begin+relativeIndex),result=this.get(index);return this.set(index,this.pop()),result}},GrowingRingBuffer=class extends RingBuffer{constructor(){super(GrowingRingBuffer.INITIAL_CAPACITY)}isFull(){return!1}push(value){super.isFull()&&this.expand(),super.push(value)}unshift(value){super.isFull()&&this.expand(),super.unshift(value)}expand(){let newCapacity=this.capacity*2,newData=new Array(newCapacity),len=this.length();for(let i=0;ix===!0)}rowMajorBatch(batchSize,smallLastBatch=!0){return new RowMajorBatchIterator(this,batchSize,smallLastBatch)}columnMajorBatch(batchSize,smallLastBatch=!0,zipFn=zipToList){return this.rowMajorBatch(batchSize,smallLastBatch).map(x=>deepZip(x,zipFn))}concatenate(iterator,baseErrorHandler){return new ChainedIterator(iteratorFromItems([this,iterator]),baseErrorHandler)}take(count2){return count2<0||count2==null?this:new TakeIterator(this,count2)}skip(count2){return count2<0||count2==null?this:new SkipIterator(this,count2)}prefetch(bufferSize){return new PrefetchIterator(this,bufferSize)}shuffle(windowSize,seed){return new ShuffleIterator(this,windowSize,seed)}serial(){return new SerialIterator(this)}},ArrayIterator=class extends LazyIterator{constructor(items){super();this.items=items,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let item=this.items[this.trav];return this.trav++,{value:deepClone(item),done:!1}}},FunctionCallIterator=class extends LazyIterator{constructor(nextFn){super();this.nextFn=nextFn}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: ${e.message}`,e}}},SerialIterator=class extends LazyIterator{constructor(upstream){super();this.upstream=upstream,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},SkipIterator=class extends LazyIterator{constructor(upstream,maxCount){super();this.upstream=upstream,this.maxCount=maxCount,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++ Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},RowMajorBatchIterator=class extends LazyIterator{constructor(upstream,batchSize,enableSmallLastBatch=!0){super();this.upstream=upstream,this.batchSize=batchSize,this.enableSmallLastBatch=enableSmallLastBatch,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let batch=[];for(;batch.length0?{value:batch,done:!1}:{value:null,done:!0};batch.push(item.value)}return{value:batch,done:!1}}},FilterIterator=class extends LazyIterator{constructor(upstream,predicate){super();this.upstream=upstream,this.predicate=predicate,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let item=await this.upstream.next();if(item.done||this.predicate(item.value))return item;dispose(item.value)}}},MapIterator=class extends LazyIterator{constructor(upstream,transform){super();this.upstream=upstream,this.transform=transform}summary(){return`${this.upstream.summary()} -> Map`}async next(){let item=await this.upstream.next();if(item.done)return{value:null,done:!0};let inputTensors=tensor_util_exports.getTensorsInContainer(item.value),mapped=this.transform(item.value),outputTensors=tensor_util_exports.getTensorsInContainer(mapped);for(let t of inputTensors)tensor_util_exports.isTensorInList(t,outputTensors)||t.dispose();return{value:mapped,done:!1}}},ErrorHandlingLazyIterator=class extends LazyIterator{constructor(upstream,handler){super();this.upstream=upstream,this.handler=handler,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},AsyncMapIterator=class extends LazyIterator{constructor(upstream,transform){super();this.upstream=upstream,this.transform=transform}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let item=await this.upstream.next();if(item.done)return{value:null,done:!0};let inputTensors=tensor_util_exports.getTensorsInContainer(item.value),mapped=await this.transform(item.value),outputTensors=tensor_util_exports.getTensorsInContainer(mapped);for(let t of inputTensors)tensor_util_exports.isTensorInList(t,outputTensors)||t.dispose();return{value:mapped,done:!1}}},OneToManyIterator=class extends LazyIterator{constructor(){super();this.outputQueue=new GrowingRingBuffer,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},FlatmapIterator=class extends OneToManyIterator{constructor(upstream,transform){super();this.upstream=upstream,this.transform=transform}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let item=await this.upstream.next();if(item.done)return!1;let inputTensors=tensor_util_exports.getTensorsInContainer(item.value),mappedArray=this.transform(item.value),outputTensors=tensor_util_exports.getTensorsInContainer(mappedArray);this.outputQueue.pushAll(mappedArray);for(let t of inputTensors)tensor_util_exports.isTensorInList(t,outputTensors)||t.dispose();return!0}},ChainedIterator=class extends LazyIterator{constructor(iterators,baseErrorHandler){super();this.baseErrorHandler=baseErrorHandler,this.lastRead=null,this.iterator=null,this.moreIterators=iterators}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(lastRead){if(await lastRead,this.iterator==null){let iteratorResult=await this.moreIterators.next();if(iteratorResult.done)return{value:null,done:!0};this.iterator=iteratorResult.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let itemResult=await this.iterator.next();return itemResult.done?(this.iterator=null,this.readFromChain(lastRead)):itemResult}},ZipMismatchMode;(function(ZipMismatchMode2){ZipMismatchMode2[ZipMismatchMode2.FAIL=0]="FAIL",ZipMismatchMode2[ZipMismatchMode2.SHORTEST=1]="SHORTEST",ZipMismatchMode2[ZipMismatchMode2.LONGEST=2]="LONGEST"})(ZipMismatchMode||(ZipMismatchMode={}));var ZipIterator=class extends LazyIterator{constructor(iterators,mismatchMode=ZipMismatchMode.FAIL){super();this.iterators=iterators,this.mismatchMode=mismatchMode,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(afterState){await afterState;let numIterators=0,iteratorsDone=0;function getNext(container){return container instanceof LazyIterator?{value:container.next().then(x=>(numIterators++,x.done&&iteratorsDone++,x.value)),recurse:!1}:{value:null,recurse:!0}}let mapped=await deepMapAndAwaitAll(this.iterators,getNext);if(numIterators===iteratorsDone)return{value:null,done:!0};if(iteratorsDone>0)switch(this.mismatchMode){case ZipMismatchMode.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case ZipMismatchMode.SHORTEST:return{value:null,done:!0};case ZipMismatchMode.LONGEST:default:}return this.count++,{value:mapped,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},PrefetchIterator=class extends LazyIterator{constructor(upstream,bufferSize){super();this.upstream=upstream,this.bufferSize=bufferSize,this.buffer=new RingBuffer(bufferSize)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let v=this.upstream.next();this.buffer.push(v)}}next(){return this.refill(),this.buffer.shift()}},ShuffleIterator=class extends PrefetchIterator{constructor(upstream,windowSize,seed){super(upstream,windowSize);this.upstream=upstream,this.windowSize=windowSize,this.upstreamExhausted=!1,this.random=seedrandom3.alea(seed||util_exports.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(max6){return Math.floor(this.random()*max6)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let chosenIndex=this.chooseIndex(),result=await this.buffer.shuffleExcise(chosenIndex);if(result.done)this.upstreamExhausted=!0;else return this.refill(),result}return{value:null,done:!0}}},Dataset=class{constructor(){this.size=null}batch(batchSize,smallLastBatch=!0){let base2=this;util_exports.assert(batchSize>0,()=>`batchSize needs to be positive, but it is ${batchSize}`);let size;return this.size===Infinity||this.size==null?size=this.size:smallLastBatch?size=Math.ceil(this.size/batchSize):size=Math.floor(this.size/batchSize),datasetFromIteratorFn(async()=>(await base2.iterator()).columnMajorBatch(batchSize,smallLastBatch,deepBatchConcat),size)}concatenate(dataset){let base2=this,size;return this.size===Infinity||dataset.size===Infinity?size=Infinity:this.size!=null&&dataset.size!=null?size=this.size+dataset.size:size=null,datasetFromIteratorFn(async()=>(await base2.iterator()).concatenate(await dataset.iterator()),size)}filter(predicate){let base2=this,size;return this.size===Infinity?size=Infinity:size=null,datasetFromIteratorFn(async()=>(await base2.iterator()).filter(x=>tidy(()=>predicate(x))),size)}async forEachAsync(f){return(await this.iterator()).forEachAsync(f)}map(transform){let base2=this;return datasetFromIteratorFn(async()=>(await base2.iterator()).map(x=>tidy(()=>transform(x))),this.size)}mapAsync(transform){let base2=this;return datasetFromIteratorFn(async()=>(await base2.iterator()).mapAsync(transform),this.size)}prefetch(bufferSize){if(bufferSize==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let base2=this;return datasetFromIteratorFn(async()=>(await base2.iterator()).prefetch(bufferSize),this.size)}repeat(count2){let base2=this,size;return this.size!=null&&count2>0?size=this.size*count2:count2===0?size=0:this.size!=null&&(count2===void 0||count2<0)?size=Infinity:size=null,datasetFromIteratorFn(async()=>{let iteratorIterator=iteratorFromFunction(async()=>({value:await base2.iterator(),done:!1}));return iteratorFromConcatenated(iteratorIterator.take(count2))},size)}skip(count2){let base2=this,size;return this.size!=null&&count2>=0&&this.size>=count2?size=this.size-count2:this.size!=null&&(this.size(await base2.iterator()).skip(count2),size)}shuffle(bufferSize,seed,reshuffleEachIteration=!0){if(bufferSize==null||bufferSize<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let base2=this,random=seedrandom4.alea(seed||util_exports.now().toString());return datasetFromIteratorFn(async()=>{let seed2=random.int32();return reshuffleEachIteration&&(seed2+=random.int32()),(await base2.iterator()).shuffle(bufferSize,seed2.toString())},this.size)}take(count2){let base2=this,size;return this.size!=null&&this.size>count2?size=count2:this.size!=null&&this.size<=count2?size=this.size:size=null,datasetFromIteratorFn(async()=>(await base2.iterator()).take(count2),size)}async toArray(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};Dataset.MAX_BUFFER_SIZE=1e4;function datasetFromIteratorFn(iteratorFn,size=null){return new class extends Dataset{constructor(){super(...arguments);this.size=size}async iterator(){return iteratorFn()}}}function array(items){return datasetFromIteratorFn(async()=>iteratorFromItems(items),items.length)}function zip(datasets){if(!isIterable2(datasets))throw new Error("The argument to zip() must be an object or array.");let size;if(Array.isArray(datasets))for(let i=0;i{let streams=await deepMapAndAwaitAll(datasets,d=>{if(d instanceof Dataset)return{value:d.iterator(),recurse:!1};if(isIterable2(d))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return iteratorFromZipped(streams,ZipMismatchMode.SHORTEST)},size)}function deepBatchConcat(rows){if(rows===null)return null;let exampleRow=rows[0];return canTensorify(exampleRow)?{value:batchConcat(rows),recurse:!1}:{value:null,recurse:!0}}function batchConcat(arrays){if(arrays.length===0)throw new Error("Can't make a batch of zero elements.");return arrays[0]instanceof Tensor?stack(arrays):tensor(arrays)}var TextLineDataset=class extends Dataset{constructor(input2){super();this.input=input2}async iterator(){return(await this.input.iterator()).decodeUTF8().split(` -`).map(line=>(line.endsWith("\r")&&(line=line.slice(0,-1)),line))}},CODE_QUOTE='"',STATE_OUT=Symbol("out"),STATE_FIELD=Symbol("field"),STATE_QUOTE=Symbol("quote"),STATE_QUOTE_AFTER_QUOTE=Symbol("quoteafterquote"),STATE_WITHIN_QUOTE_IN_QUOTE=Symbol("quoteinquote"),CSVDataset=class extends Dataset{constructor(input2,csvConfig){super();this.input=input2,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new TextLineDataset(input2),csvConfig||(csvConfig={}),this.hasHeader=csvConfig.hasHeader!==!1,this.fullColumnNames=csvConfig.columnNames,this.columnConfigs=csvConfig.columnConfigs,this.configuredColumnsOnly=csvConfig.configuredColumnsOnly,csvConfig.delimWhitespace?(util_exports.assert(csvConfig.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=csvConfig.delimiter?csvConfig.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let columnNamesFromFile=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!columnNamesFromFile)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&columnNamesFromFile&&util_exports.assert(columnNamesFromFile.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+columnNamesFromFile.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=columnNamesFromFile);let counts=this.fullColumnNames.reduce((countAcc,name)=>(countAcc[name]=countAcc[name]+1||1,countAcc),{}),duplicateNames=Object.keys(counts).filter(name=>counts[name]>1);if(util_exports.assert(duplicateNames.length===0,()=>"Duplicate column names found: "+duplicateNames.toString()),this.columnConfigs){for(let key of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(key)===-1)throw new Error('The key "'+key+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let firstElement=await(await this.base.iterator()).next();if(firstElement.done)throw new Error("No data was found for CSV parsing.");let firstLine=firstElement.value;return this.parseRow(firstLine,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let lines=await this.base.iterator();return this.hasHeader&&(lines=lines.skip(1)),lines.map(x=>this.makeDataElement(x))}makeDataElement(line){let values=this.parseRow(line),features={},labels={};for(let i=0;i14||!Number.isInteger(fftSizeLog2))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=microphoneConfig.numFramesPerSpectrogram||43,this.sampleRateHz=microphoneConfig.sampleRateHz,this.columnTruncateLength=microphoneConfig.columnTruncateLength||this.fftSize,this.audioTrackConstraints=microphoneConfig.audioTrackConstraints,this.smoothingTimeConstant=microphoneConfig.smoothingTimeConstant||0,this.includeSpectrogram=microphoneConfig.includeSpectrogram!==!1,this.includeWaveform=microphoneConfig.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(microphoneConfig={}){if(env().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let microphoneIterator=new MicrophoneIterator(microphoneConfig);return await microphoneIterator.start(),microphoneIterator}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(e){throw new Error(`Error thrown while initializing video stream: ${e.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let ctxConstructor=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new ctxConstructor,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let streamSource=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,streamSource.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let spectrogramTensor,waveformTensor,audioDataQueue=await this.getAudioData();if(this.includeSpectrogram){let freqData=this.flattenQueue(audioDataQueue.freqDataQueue);spectrogramTensor=this.getTensorFromAudioDataArray(freqData,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let timeData=this.flattenQueue(audioDataQueue.timeDataQueue);waveformTensor=this.getTensorFromAudioDataArray(timeData,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:spectrogramTensor,waveform:waveformTensor},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let freqDataQueue=[],timeDataQueue=[],currentFrames=0;return new Promise(resolve=>{let intervalID=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&resolve({freqDataQueue,timeDataQueue}),freqDataQueue.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),timeDataQueue.push(this.timeData.slice())),++currentFrames===this.numFrames&&(clearInterval(intervalID),resolve({freqDataQueue,timeDataQueue}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(queue){let frameSize=queue[0].length,freqData=new Float32Array(queue.length*frameSize);return queue.forEach((data2,i)=>freqData.set(data2,i*frameSize)),freqData}getTensorFromAudioDataArray(freqData,shape){let vals=new Float32Array(util_exports.sizeFromShape(shape));return vals.set(freqData,vals.length-freqData.length),tensor(vals,shape)}},WebcamIterator=class extends LazyIterator{constructor(webcamVideoElement,webcamConfig){super();if(this.webcamVideoElement=webcamVideoElement,this.webcamConfig=webcamConfig,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=tensor1d([0],"int32"),this.webcamConfig.centerCrop){let widthCroppingRatio=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,heightCroppingRatio=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,widthCropStart=(1-widthCroppingRatio)/2,heightCropStart=(1-heightCroppingRatio)/2,widthCropEnd=widthCropStart+widthCroppingRatio,heightCropEnd=heightCroppingRatio+heightCropStart;this.cropBox=tensor2d([heightCropStart,widthCropStart,heightCropEnd,widthCropEnd],[1,4])}else this.cropBox=tensor2d([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(webcamVideoElement,webcamConfig={}){if(env().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!webcamVideoElement){if(webcamVideoElement=document.createElement("video"),!webcamConfig.resizeWidth||!webcamConfig.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");webcamVideoElement.width=webcamConfig.resizeWidth,webcamVideoElement.height=webcamConfig.resizeHeight}let webcamIterator=new WebcamIterator(webcamVideoElement,webcamConfig);return await webcamIterator.start(),webcamIterator}async start(){this.webcamConfig.facingMode&&util_exports.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(error){console.log(error),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(resolve=>{this.webcamVideoElement.onloadedmetadata=()=>{resolve()}})}async next(){if(this.isClosed)return{value:null,done:!0};let img;try{img=browser_exports.fromPixels(this.webcamVideoElement)}catch(e){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(e)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(img),done:!1}}catch(e){throw new Error(`Error thrown cropping the video: ${e.message}`)}finally{img.dispose()}else return{value:img,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(img){return tidy(()=>{let expandedImage=img.toFloat().expandDims(0),resizedImage;resizedImage=image.cropAndResize(expandedImage,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let shape=resizedImage.shape;return resizedImage.reshape(shape.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(track=>track.stop());try{this.webcamVideoElement.srcObject=null}catch(error){console.log(error),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},DataSource=class{},StringIterator=class extends LazyIterator{split(separator){return new SplitIterator(this,separator)}},SplitIterator=class extends StringIterator{constructor(upstream,separator){super();this.upstream=upstream,this.impl=new SplitIteratorImpl(upstream,separator)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},SplitIteratorImpl=class extends OneToManyIterator{constructor(upstream,separator){super();this.upstream=upstream,this.separator=separator,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let chunkResult=await this.upstream.next();if(chunkResult.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let lines=chunkResult.value.split(this.separator);lines[0]=this.carryover+lines[0];for(let line of lines.slice(0,-1))this.outputQueue.push(line);return this.carryover=lines[lines.length-1],!0}},ByteChunkIterator=class extends LazyIterator{decodeUTF8(){return new Utf8Iterator(this)}},Utf8Iterator=class extends StringIterator{constructor(upstream){super();this.upstream=upstream,this.impl=new Utf8IteratorImpl(upstream)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},Utf8IteratorImpl=class extends OneToManyIterator{constructor(upstream){super();if(this.upstream=upstream,env().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder}=require_string_decoder();this.decoder=new StringDecoder("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let chunkResult=await this.upstream.next(),chunk;if(chunkResult.done)return!1;chunk=chunkResult.value;let text;return env().get("IS_BROWSER")?text=this.decoder.decode(chunk,{stream:!0}):text=this.decoder.write(Buffer.from(chunk.buffer)),this.outputQueue.push(text),!0}},FileChunkIterator=class extends ByteChunkIterator{constructor(file,options={}){super();this.file=file,this.options=options,util_exports.assert(file instanceof Uint8Array||(env().get("IS_BROWSER")?file instanceof File||file instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=options.offset||0,this.chunkSize=options.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((resolve,reject)=>{let end=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)resolve(new Uint8Array(this.file.slice(this.offset,end)));else{let fileReader=new FileReader;fileReader.onload=event=>{let data2=fileReader.result;if(data2 instanceof ArrayBuffer&&(data2=new Uint8Array(data2)),!(data2 instanceof Uint8Array))return reject(new TypeError("FileReader returned unknown type."));resolve(data2)},fileReader.onabort=event=>reject(new Error("Aborted")),fileReader.onerror=event=>reject(new Error(event.type));let slice5=this.file.slice(this.offset,end);fileReader.readAsArrayBuffer(slice5)}this.offset=end}),done:!1}}};async function urlChunkIterator(url,options={}){let urlString,requestInit;typeof url=="string"?urlString=url:(urlString=url.url,requestInit=getRequestInitFromRequest(url));let response=await util_exports.fetch(urlString,requestInit);if(response.ok){let uint8Array=new Uint8Array(await response.arrayBuffer());return new FileChunkIterator(uint8Array,options)}else throw new Error(response.statusText)}var getRequestInitFromRequest=request=>({method:request.method,headers:request.headers,body:request.body,mode:request.mode,credentials:request.credentials,cache:request.cache,redirect:request.redirect,referrer:request.referrer,integrity:request.integrity});function isLocalPath(source){return typeof source=="string"&&source.substr(0,7)==="file://"}var FileDataSource=class extends DataSource{constructor(input2,options={}){super();this.input=input2,this.options=options}async iterator(){if(isLocalPath(this.input)&&env().get("IS_NODE")){let fs=require("fs");this.input=fs.readFileSync(this.input.substr(7))}return new FileChunkIterator(this.input,this.options)}},URLDataSource=class extends DataSource{constructor(url,fileOptions={}){super();this.url=url,this.fileOptions=fileOptions}async iterator(){return isLocalPath(this.url)?new FileDataSource(this.url,this.fileOptions).iterator():urlChunkIterator(this.url,this.fileOptions)}};function csv(source,csvConfig={}){return new CSVDataset(new URLDataSource(source),csvConfig)}function func(f){let iter=iteratorFromFunction(f);return datasetFromIteratorFn(async()=>iter)}function generator(generator2){return datasetFromIteratorFn(async()=>{let gen=await generator2();return iteratorFromFunction(()=>gen.next())})}async function webcam(webcamVideoElement,webcamConfig){return WebcamIterator.create(webcamVideoElement,webcamConfig)}async function microphone(microphoneConfig){return MicrophoneIterator.create(microphoneConfig)}var version12="2.8.3",version13={tfjs:version,"tfjs-core":version2,"tfjs-data":version3,"tfjs-layers":version4,"tfjs-converter":version5,"tfjs-backend-cpu":version7,"tfjs-backend-webgl":version8,"tfjs-backend-wasm":version9};var config={name:"humangl",priority:99,canvas:null,width:1024,height:1024,webGLattr:{alpha:!1,antialias:!1,premultipliedAlpha:!1,preserveDrawingBuffer:!1,depth:!1,stencil:!1,failIfMajorPerformanceCaveat:!1,desynchronized:!0}};function register(){if(!findBackend(config.name)){config.canvas=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(config.width,config.height):document.createElement("canvas");let gl=config.canvas.getContext("webgl2",config.webGLattr);setWebGLContext(2,gl);let ctx=new GPGPUContext(gl);registerBackend(config.name,()=>new MathBackendWebGL(ctx),config.priority),getKernelsForBackend("webgl").forEach(kernelConfig=>{let newKernelConfig={...kernelConfig,backendName:config.name};registerKernel(newKernelConfig)}),ENV.set("WEBGL_VERSION",2),ENV.set("WEBGL_MAX_TEXTURE_SIZE",gl.getParameter(gl.MAX_TEXTURE_SIZE)),ENV.set("WEBGL_FORCE_F16_TEXTURES",!0),ENV.set("WEBGL_PACK_DEPTHWISECONV",!0)}}var facemesh=__toModule(require_facemesh()),age=__toModule(require_age()),gender=__toModule(require_gender()),emotion=__toModule(require_emotion()),embedding2=__toModule(require_embedding()),posenet=__toModule(require_posenet());var models={},config2={},anchors=[],kMidHipCenter=0,kPoseDetectKeyNum=2,kFullBodySizeRot=1;function calculateScale(min_scale,max_scale,stride_index,num_strides){return num_strides===1?(min_scale+max_scale)*.5:min_scale+(max_scale-min_scale)*1*stride_index/(num_strides-1)}function generateAnchors(){let options={};options.strides=[],options.aspect_ratios=[],options.feature_map_height=[],options.num_layers=4,options.min_scale=.1484375,options.max_scale=.75,options.input_size_height=128,options.input_size_width=128,options.anchor_offset_x=.5,options.anchor_offset_y=.5,options.strides.push(8),options.strides.push(16),options.strides.push(16),options.strides.push(16),options.aspect_ratios.push(1),options.reduce_boxes_in_lowest_layer=!1,options.interpolated_scale_aspect_ratio=1,options.fixed_anchor_size=!0;let layer_id=0;for(;layer_id0){let scale_next=last_same_stride_layer===options.strides.length-1?1:calculateScale(options.min_scale,options.max_scale,last_same_stride_layer+1,options.strides.length);scales.push(Math.sqrt(scale2*scale_next)),aspect_ratios.push(options.interpolated_scale_aspect_ratio)}}last_same_stride_layer++}for(let i=0;iconfig2.pose.scoreThreshold){let idx=(4+2*kPoseDetectKeyNum)*i,sx=boxes[idx+0],sy=boxes[idx+1],w=boxes[idx+2]/config2.pose.inputSize,h=boxes[idx+3]/config2.pose.inputSize,cx=(sx+anchors[i].x_center*config2.pose.inputSize)/config2.pose.inputSize,cy=(sy+anchors[i].y_center*config2.pose.inputSize)/config2.pose.inputSize;region.score=Math.round(1e3*score)/1e3,region.box=[cx-w*.5,cy-h*.5,w*.5,h*.5];let keys=new Array(kPoseDetectKeyNum);for(let j=0;j{config2=cfg;let resize=image.resizeBilinear(image3,[config2.pose.inputSize,config2.pose.inputSize],!1),enhance=div(resize,127.5).sub(1);dispose(resize);let logits=await models.blazepose.predict(enhance);dispose(enhance);let regions=await decode(logits);logits[0].dispose(),logits[1].dispose(),log("poses",regions),resolve(regions)}):null}function getBoxSize(box){return[Math.abs(box.endPoint[0]-box.startPoint[0]),Math.abs(box.endPoint[1]-box.startPoint[1])]}function getBoxCenter(box){return[box.startPoint[0]+(box.endPoint[0]-box.startPoint[0])/2,box.startPoint[1]+(box.endPoint[1]-box.startPoint[1])/2]}function cutBoxFromImageAndResize(box,image3,cropSize){let h=image3.shape[1],w=image3.shape[2],boxes=[[box.startPoint[1]/h,box.startPoint[0]/w,box.endPoint[1]/h,box.endPoint[0]/w]];return image.cropAndResize(image3,boxes,[0],cropSize)}function scaleBoxCoordinates(box,factor){let startPoint=[box.startPoint[0]*factor[0],box.startPoint[1]*factor[1]],endPoint=[box.endPoint[0]*factor[0],box.endPoint[1]*factor[1]],palmLandmarks=box.palmLandmarks.map(coord=>[coord[0]*factor[0],coord[1]*factor[1]]);return{startPoint,endPoint,palmLandmarks,confidence:box.confidence}}function enlargeBox(box,factor=1.5){let center=getBoxCenter(box),size=getBoxSize(box),newHalfSize=[factor*size[0]/2,factor*size[1]/2],startPoint=[center[0]-newHalfSize[0],center[1]-newHalfSize[1]],endPoint=[center[0]+newHalfSize[0],center[1]+newHalfSize[1]];return{startPoint,endPoint,palmLandmarks:box.palmLandmarks}}function squarifyBox(box){let centers=getBoxCenter(box),size=getBoxSize(box),halfSize=Math.max(...size)/2,startPoint=[centers[0]-halfSize,centers[1]-halfSize],endPoint=[centers[0]+halfSize,centers[1]+halfSize];return{startPoint,endPoint,palmLandmarks:box.palmLandmarks}}function normalizeRadians(angle){return angle-2*Math.PI*Math.floor((angle+Math.PI)/(2*Math.PI))}function computeRotation(point1,point2){let radians=Math.PI/2-Math.atan2(-(point2[1]-point1[1]),point2[0]-point1[0]);return normalizeRadians(radians)}var buildTranslationMatrix=(x,y)=>[[1,0,x],[0,1,y],[0,0,1]];function dot4(v1,v2){let product=0;for(let i=0;iconfig_default});var config_default={backend:"webgl",wasmPath:"../assets/",async:!0,profile:!1,deallocate:!1,scoped:!1,videoOptimized:!0,warmup:"face",filter:{enabled:!0,width:0,height:0,return:!0,brightness:0,contrast:0,sharpness:0,blur:0,saturation:0,hue:0,negative:!1,sepia:!1,vintage:!1,kodachrome:!1,technicolor:!1,polaroid:!1,pixelate:0},gesture:{enabled:!0},face:{enabled:!0,detector:{modelPath:"../models/blazeface-back.json",inputSize:256,rotation:!1,maxFaces:10,skipFrames:11,minConfidence:.5,iouThreshold:.2,scoreThreshold:.5},mesh:{enabled:!0,modelPath:"../models/facemesh.json",inputSize:192,returnRawData:!1},iris:{enabled:!0,modelPath:"../models/iris.json",inputSize:64},age:{enabled:!0,modelPath:"../models/age-ssrnet-imdb.json",inputSize:64,skipFrames:31},gender:{enabled:!0,minConfidence:.1,modelPath:"../models/gender-ssrnet-imdb.json",inputSize:64,skipFrames:41},emotion:{enabled:!0,inputSize:64,minConfidence:.2,skipFrames:21,modelPath:"../models/emotion-large.json"},embedding:{enabled:!1,inputSize:112,modelPath:"../models/mobilefacenet.json"}},body:{enabled:!0,modelPath:"../models/posenet.json",inputSize:257,maxDetections:10,scoreThreshold:.5,nmsRadius:20,outputStride:16,modelType:"MobileNet"},pose:{enabled:!1,scoreThreshold:.6,iouThreshold:.3,modelPath:"../models/blazepose.json",inputSize:128},hand:{enabled:!0,rotation:!1,inputSize:256,skipFrames:12,minConfidence:.1,iouThreshold:.1,scoreThreshold:.5,maxHands:1,landmarks:!0,detector:{modelPath:"../models/handdetect.json"},skeleton:{modelPath:"../models/handskeleton.json"}}};var face=` +`).map(line=>(line.endsWith("\r")&&(line=line.slice(0,-1)),line))}},CODE_QUOTE='"',STATE_OUT=Symbol("out"),STATE_FIELD=Symbol("field"),STATE_QUOTE=Symbol("quote"),STATE_QUOTE_AFTER_QUOTE=Symbol("quoteafterquote"),STATE_WITHIN_QUOTE_IN_QUOTE=Symbol("quoteinquote"),CSVDataset=class extends Dataset{constructor(input2,csvConfig){super();this.input=input2,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new TextLineDataset(input2),csvConfig||(csvConfig={}),this.hasHeader=csvConfig.hasHeader!==!1,this.fullColumnNames=csvConfig.columnNames,this.columnConfigs=csvConfig.columnConfigs,this.configuredColumnsOnly=csvConfig.configuredColumnsOnly,csvConfig.delimWhitespace?(util_exports.assert(csvConfig.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=csvConfig.delimiter?csvConfig.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let columnNamesFromFile=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!columnNamesFromFile)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&columnNamesFromFile&&util_exports.assert(columnNamesFromFile.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+columnNamesFromFile.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=columnNamesFromFile);let counts=this.fullColumnNames.reduce((countAcc,name)=>(countAcc[name]=countAcc[name]+1||1,countAcc),{}),duplicateNames=Object.keys(counts).filter(name=>counts[name]>1);if(util_exports.assert(duplicateNames.length===0,()=>"Duplicate column names found: "+duplicateNames.toString()),this.columnConfigs){for(let key of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(key)===-1)throw new Error('The key "'+key+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let firstElement=await(await this.base.iterator()).next();if(firstElement.done)throw new Error("No data was found for CSV parsing.");let firstLine=firstElement.value;return this.parseRow(firstLine,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let lines=await this.base.iterator();return this.hasHeader&&(lines=lines.skip(1)),lines.map(x=>this.makeDataElement(x))}makeDataElement(line){let values=this.parseRow(line),features={},labels={};for(let i=0;i14||!Number.isInteger(fftSizeLog2))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=microphoneConfig.numFramesPerSpectrogram||43,this.sampleRateHz=microphoneConfig.sampleRateHz,this.columnTruncateLength=microphoneConfig.columnTruncateLength||this.fftSize,this.audioTrackConstraints=microphoneConfig.audioTrackConstraints,this.smoothingTimeConstant=microphoneConfig.smoothingTimeConstant||0,this.includeSpectrogram=microphoneConfig.includeSpectrogram!==!1,this.includeWaveform=microphoneConfig.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(microphoneConfig={}){if(env().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let microphoneIterator=new MicrophoneIterator(microphoneConfig);return await microphoneIterator.start(),microphoneIterator}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(e){throw new Error(`Error thrown while initializing video stream: ${e.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let ctxConstructor=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new ctxConstructor,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let streamSource=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,streamSource.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let spectrogramTensor,waveformTensor,audioDataQueue=await this.getAudioData();if(this.includeSpectrogram){let freqData=this.flattenQueue(audioDataQueue.freqDataQueue);spectrogramTensor=this.getTensorFromAudioDataArray(freqData,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let timeData=this.flattenQueue(audioDataQueue.timeDataQueue);waveformTensor=this.getTensorFromAudioDataArray(timeData,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:spectrogramTensor,waveform:waveformTensor},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let freqDataQueue=[],timeDataQueue=[],currentFrames=0;return new Promise(resolve=>{let intervalID=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&resolve({freqDataQueue,timeDataQueue}),freqDataQueue.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),timeDataQueue.push(this.timeData.slice())),++currentFrames===this.numFrames&&(clearInterval(intervalID),resolve({freqDataQueue,timeDataQueue}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(queue){let frameSize=queue[0].length,freqData=new Float32Array(queue.length*frameSize);return queue.forEach((data2,i)=>freqData.set(data2,i*frameSize)),freqData}getTensorFromAudioDataArray(freqData,shape){let vals=new Float32Array(util_exports.sizeFromShape(shape));return vals.set(freqData,vals.length-freqData.length),tensor(vals,shape)}},WebcamIterator=class extends LazyIterator{constructor(webcamVideoElement,webcamConfig){super();if(this.webcamVideoElement=webcamVideoElement,this.webcamConfig=webcamConfig,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=tensor1d([0],"int32"),this.webcamConfig.centerCrop){let widthCroppingRatio=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,heightCroppingRatio=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,widthCropStart=(1-widthCroppingRatio)/2,heightCropStart=(1-heightCroppingRatio)/2,widthCropEnd=widthCropStart+widthCroppingRatio,heightCropEnd=heightCroppingRatio+heightCropStart;this.cropBox=tensor2d([heightCropStart,widthCropStart,heightCropEnd,widthCropEnd],[1,4])}else this.cropBox=tensor2d([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(webcamVideoElement,webcamConfig={}){if(env().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!webcamVideoElement){if(webcamVideoElement=document.createElement("video"),!webcamConfig.resizeWidth||!webcamConfig.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");webcamVideoElement.width=webcamConfig.resizeWidth,webcamVideoElement.height=webcamConfig.resizeHeight}let webcamIterator=new WebcamIterator(webcamVideoElement,webcamConfig);return await webcamIterator.start(),webcamIterator}async start(){this.webcamConfig.facingMode&&util_exports.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(error){console.log(error),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(resolve=>{this.webcamVideoElement.onloadedmetadata=()=>{resolve()}})}async next(){if(this.isClosed)return{value:null,done:!0};let img;try{img=browser_exports.fromPixels(this.webcamVideoElement)}catch(e){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(e)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(img),done:!1}}catch(e){throw new Error(`Error thrown cropping the video: ${e.message}`)}finally{img.dispose()}else return{value:img,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(img){return tidy(()=>{let expandedImage=img.toFloat().expandDims(0),resizedImage;resizedImage=image.cropAndResize(expandedImage,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let shape=resizedImage.shape;return resizedImage.reshape(shape.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(track=>track.stop());try{this.webcamVideoElement.srcObject=null}catch(error){console.log(error),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},DataSource=class{},StringIterator=class extends LazyIterator{split(separator){return new SplitIterator(this,separator)}},SplitIterator=class extends StringIterator{constructor(upstream,separator){super();this.upstream=upstream,this.impl=new SplitIteratorImpl(upstream,separator)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},SplitIteratorImpl=class extends OneToManyIterator{constructor(upstream,separator){super();this.upstream=upstream,this.separator=separator,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let chunkResult=await this.upstream.next();if(chunkResult.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let lines=chunkResult.value.split(this.separator);lines[0]=this.carryover+lines[0];for(let line of lines.slice(0,-1))this.outputQueue.push(line);return this.carryover=lines[lines.length-1],!0}},ByteChunkIterator=class extends LazyIterator{decodeUTF8(){return new Utf8Iterator(this)}},Utf8Iterator=class extends StringIterator{constructor(upstream){super();this.upstream=upstream,this.impl=new Utf8IteratorImpl(upstream)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},Utf8IteratorImpl=class extends OneToManyIterator{constructor(upstream){super();if(this.upstream=upstream,env().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder}=require_string_decoder();this.decoder=new StringDecoder("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let chunkResult=await this.upstream.next(),chunk;if(chunkResult.done)return!1;chunk=chunkResult.value;let text;return env().get("IS_BROWSER")?text=this.decoder.decode(chunk,{stream:!0}):text=this.decoder.write(Buffer.from(chunk.buffer)),this.outputQueue.push(text),!0}},FileChunkIterator=class extends ByteChunkIterator{constructor(file,options={}){super();this.file=file,this.options=options,util_exports.assert(file instanceof Uint8Array||(env().get("IS_BROWSER")?file instanceof File||file instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=options.offset||0,this.chunkSize=options.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((resolve,reject)=>{let end=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)resolve(new Uint8Array(this.file.slice(this.offset,end)));else{let fileReader=new FileReader;fileReader.onload=event=>{let data2=fileReader.result;if(data2 instanceof ArrayBuffer&&(data2=new Uint8Array(data2)),!(data2 instanceof Uint8Array))return reject(new TypeError("FileReader returned unknown type."));resolve(data2)},fileReader.onabort=event=>reject(new Error("Aborted")),fileReader.onerror=event=>reject(new Error(event.type));let slice5=this.file.slice(this.offset,end);fileReader.readAsArrayBuffer(slice5)}this.offset=end}),done:!1}}};async function urlChunkIterator(url,options={}){let urlString,requestInit;typeof url=="string"?urlString=url:(urlString=url.url,requestInit=getRequestInitFromRequest(url));let response=await util_exports.fetch(urlString,requestInit);if(response.ok){let uint8Array=new Uint8Array(await response.arrayBuffer());return new FileChunkIterator(uint8Array,options)}else throw new Error(response.statusText)}var getRequestInitFromRequest=request=>({method:request.method,headers:request.headers,body:request.body,mode:request.mode,credentials:request.credentials,cache:request.cache,redirect:request.redirect,referrer:request.referrer,integrity:request.integrity});function isLocalPath(source){return typeof source=="string"&&source.substr(0,7)==="file://"}var FileDataSource=class extends DataSource{constructor(input2,options={}){super();this.input=input2,this.options=options}async iterator(){if(isLocalPath(this.input)&&env().get("IS_NODE")){let fs=require("fs");this.input=fs.readFileSync(this.input.substr(7))}return new FileChunkIterator(this.input,this.options)}},URLDataSource=class extends DataSource{constructor(url,fileOptions={}){super();this.url=url,this.fileOptions=fileOptions}async iterator(){return isLocalPath(this.url)?new FileDataSource(this.url,this.fileOptions).iterator():urlChunkIterator(this.url,this.fileOptions)}};function csv(source,csvConfig={}){return new CSVDataset(new URLDataSource(source),csvConfig)}function func(f){let iter=iteratorFromFunction(f);return datasetFromIteratorFn(async()=>iter)}function generator(generator2){return datasetFromIteratorFn(async()=>{let gen=await generator2();return iteratorFromFunction(()=>gen.next())})}async function webcam(webcamVideoElement,webcamConfig){return WebcamIterator.create(webcamVideoElement,webcamConfig)}async function microphone(microphoneConfig){return MicrophoneIterator.create(microphoneConfig)}var version12="2.8.3",version13={tfjs:version,"tfjs-core":version2,"tfjs-data":version3,"tfjs-layers":version4,"tfjs-converter":version5,"tfjs-backend-cpu":version7,"tfjs-backend-webgl":version8,"tfjs-backend-wasm":version9};var config={name:"humangl",priority:99,canvas:null,width:1024,height:1024,webGLattr:{alpha:!1,antialias:!1,premultipliedAlpha:!1,preserveDrawingBuffer:!1,depth:!1,stencil:!1,failIfMajorPerformanceCaveat:!1,desynchronized:!0}};function register(){if(!findBackend(config.name)){config.canvas=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(config.width,config.height):document.createElement("canvas");let gl=config.canvas.getContext("webgl2",config.webGLattr);setWebGLContext(2,gl);let ctx=new GPGPUContext(gl);registerBackend(config.name,()=>new MathBackendWebGL(ctx),config.priority),getKernelsForBackend("webgl").forEach(kernelConfig=>{let newKernelConfig={...kernelConfig,backendName:config.name};registerKernel(newKernelConfig)}),ENV.set("WEBGL_VERSION",2),ENV.set("WEBGL_MAX_TEXTURE_SIZE",gl.getParameter(gl.MAX_TEXTURE_SIZE)),ENV.set("WEBGL_FORCE_F16_TEXTURES",!0),ENV.set("WEBGL_PACK_DEPTHWISECONV",!0)}}var facemesh=__toModule(require_facemesh()),age=__toModule(require_age()),gender=__toModule(require_gender()),emotion=__toModule(require_emotion()),embedding2=__toModule(require_embedding()),posenet=__toModule(require_posenet());var models={};async function load(config2){return models.blazepose||(models.blazepose=await loadGraphModel(config2.pose.modelPath),log(`load model: ${config2.pose.modelPath.match(/\/(.*)\./)[1]}`)),models.blazepose}async function predict(image3,config2){return models.blazepose?new Promise(async resolve=>{let resize=image.resizeBilinear(image3,[config2.pose.inputSize,config2.pose.inputSize],!1),enhance=div(resize,127.5).sub(1);dispose(resize);let logits=await models.blazepose.predict(enhance);dispose(enhance),logits.map(logit=>logit.dispose()),resolve(logits)}):null}function getBoxSize(box){return[Math.abs(box.endPoint[0]-box.startPoint[0]),Math.abs(box.endPoint[1]-box.startPoint[1])]}function getBoxCenter(box){return[box.startPoint[0]+(box.endPoint[0]-box.startPoint[0])/2,box.startPoint[1]+(box.endPoint[1]-box.startPoint[1])/2]}function cutBoxFromImageAndResize(box,image3,cropSize){let h=image3.shape[1],w=image3.shape[2],boxes=[[box.startPoint[1]/h,box.startPoint[0]/w,box.endPoint[1]/h,box.endPoint[0]/w]];return image.cropAndResize(image3,boxes,[0],cropSize)}function scaleBoxCoordinates(box,factor){let startPoint=[box.startPoint[0]*factor[0],box.startPoint[1]*factor[1]],endPoint=[box.endPoint[0]*factor[0],box.endPoint[1]*factor[1]],palmLandmarks=box.palmLandmarks.map(coord=>[coord[0]*factor[0],coord[1]*factor[1]]);return{startPoint,endPoint,palmLandmarks,confidence:box.confidence}}function enlargeBox(box,factor=1.5){let center=getBoxCenter(box),size=getBoxSize(box),newHalfSize=[factor*size[0]/2,factor*size[1]/2],startPoint=[center[0]-newHalfSize[0],center[1]-newHalfSize[1]],endPoint=[center[0]+newHalfSize[0],center[1]+newHalfSize[1]];return{startPoint,endPoint,palmLandmarks:box.palmLandmarks}}function squarifyBox(box){let centers=getBoxCenter(box),size=getBoxSize(box),halfSize=Math.max(...size)/2,startPoint=[centers[0]-halfSize,centers[1]-halfSize],endPoint=[centers[0]+halfSize,centers[1]+halfSize];return{startPoint,endPoint,palmLandmarks:box.palmLandmarks}}function normalizeRadians(angle){return angle-2*Math.PI*Math.floor((angle+Math.PI)/(2*Math.PI))}function computeRotation(point1,point2){let radians=Math.PI/2-Math.atan2(-(point2[1]-point1[1]),point2[0]-point1[0]);return normalizeRadians(radians)}var buildTranslationMatrix=(x,y)=>[[1,0,x],[0,1,y],[0,0,1]];function dot4(v1,v2){let product=0;for(let i=0;iconfig_default});var config_default={backend:"webgl",wasmPath:"../assets/",async:!0,profile:!1,deallocate:!1,scoped:!1,videoOptimized:!0,warmup:"face",filter:{enabled:!0,width:0,height:0,return:!0,brightness:0,contrast:0,sharpness:0,blur:0,saturation:0,hue:0,negative:!1,sepia:!1,vintage:!1,kodachrome:!1,technicolor:!1,polaroid:!1,pixelate:0},gesture:{enabled:!0},face:{enabled:!0,detector:{modelPath:"../models/blazeface-back.json",inputSize:256,rotation:!1,maxFaces:10,skipFrames:11,minConfidence:.5,iouThreshold:.2,scoreThreshold:.5},mesh:{enabled:!0,modelPath:"../models/facemesh.json",inputSize:192,returnRawData:!1},iris:{enabled:!0,modelPath:"../models/iris.json",inputSize:64},age:{enabled:!0,modelPath:"../models/age-ssrnet-imdb.json",inputSize:64,skipFrames:31},gender:{enabled:!0,minConfidence:.1,modelPath:"../models/gender-ssrnet-imdb.json",inputSize:64,skipFrames:41},emotion:{enabled:!0,inputSize:64,minConfidence:.2,skipFrames:21,modelPath:"../models/emotion-large.json"},embedding:{enabled:!1,inputSize:112,modelPath:"../models/mobilefacenet.json"}},body:{enabled:!0,modelPath:"../models/posenet.json",inputSize:257,maxDetections:10,scoreThreshold:.5,nmsRadius:20,outputStride:16,modelType:"MobileNet"},pose:{enabled:!1,scoreThreshold:.6,iouThreshold:.3,modelPath:"../models/blazepose.json",inputSize:256},hand:{enabled:!0,rotation:!1,inputSize:256,skipFrames:12,minConfidence:.1,iouThreshold:.1,scoreThreshold:.5,maxHands:1,landmarks:!0,detector:{modelPath:"../models/handdetect.json"},skeleton:{modelPath:"../models/handskeleton.json"}}};var face=` /9j/4AAQSkZJRgABAQEAYABgAAD/4QBoRXhpZgAATU0AKgAAAAgABAEaAAUAAAABAAAAPgEbAAUA AAABAAAARgEoAAMAAAABAAIAAAExAAIAAAARAAAATgAAAAAAAABgAAAAAQAAAGAAAAABcGFpbnQu bmV0IDQuMi4xMwAA/9sAQwAGBAUGBQQGBgUGBwcGCAoQCgoJCQoUDg8MEBcUGBgXFBYWGh0lHxob @@ -4771,7 +4771,7 @@ AAAAAAJAAAAAAAAAAAAAABAJEAAAAAAAAAAAAAAAIEoBKAAAAAAAAAAAAAAABAlAAAAAAAIAAAAA BAkBAkBAkBAlACEgMZjdjbFW8bWrEx8YWANb6Fp+bfwab+vLDKMFK9qxH5L0bAr8OPRPKz2AY7J2 SbAjYZAI2E7AIEgIEgIEgMdkSy2NgY7MdlmyNoBXsxmFuyNgVTVjNV3KjlBRNTlXTVHKCrlIqt5T lBhEMohlFerLlBjEMohMVTEARDKCITsAk2AEgAAAkAAAAAAAAAAAAAAAAAAAAAAAASAAAAAAAAD/ -2Q==`;var version14="0.9.23";var now2=()=>typeof performance!="undefined"?performance.now():parseInt(Number(process.hrtime.bigint())/1e3/1e3);function mergeDeep(...objects){let isObject=obj=>obj&&typeof obj=="object";return objects.reduce((prev,obj)=>(Object.keys(obj||{}).forEach(key=>{let pVal=prev[key],oVal=obj[key];Array.isArray(pVal)&&Array.isArray(oVal)?prev[key]=pVal.concat(...oVal):isObject(pVal)&&isObject(oVal)?prev[key]=mergeDeep(pVal,oVal):prev[key]=oVal}),prev),{})}var Human=class{constructor(userConfig={}){this.tf=tfjs_esm_exports,this.version=version14,this.config=mergeDeep(config_default,userConfig),this.fx=null,this.state="idle",this.numTensors=0,this.analyzeMemoryLeaks=!1,this.checkSanity=!1,this.firstRun=!0,this.perf={},this.models={facemesh:null,posenet:null,handpose:null,iris:null,age:null,gender:null,emotion:null},this.facemesh=facemesh,this.age=age,this.gender=gender,this.emotion=emotion,this.body=posenet,this.hand=handpose}profile(){return this.config.profile?profile2.data:{}}analyze(...msg){if(!this.analyzeMemoryLeaks)return;let current=engine().state.numTensors,previous=this.numTensors;this.numTensors=current;let leaked=current-previous;leaked!==0&&log(...msg,leaked)}sanity(input2){if(!this.checkSanity)return null;if(!input2)return"input is not defined";if(ENV.flags.IS_NODE&&!(input2 instanceof Tensor))return"input must be a tensor";try{getBackend()}catch(e){return"backend not loaded"}return null}simmilarity(embedding1,embedding22){return this.config.face.embedding.enabled?embedding2.simmilarity(embedding1,embedding22):0}async load(userConfig){this.state="load";let timeStamp=now2();userConfig&&(this.config=mergeDeep(this.config,userConfig)),this.firstRun&&(log(`version: ${this.version} TensorFlow/JS version: ${version6}`),await this.checkBackend(!0),ENV.flags.IS_BROWSER&&(log("configuration:",this.config),log("tf flags:",ENV.flags)),this.firstRun=!1),this.config.async?[this.models.facemesh,this.models.age,this.models.gender,this.models.emotion,this.models.embedding,this.models.posenet,this.models.handpose,this.models.blazepose]=await Promise.all([this.models.facemesh||(this.config.face.enabled?facemesh.load(this.config):null),this.models.age||(this.config.face.enabled&&this.config.face.age.enabled?age.load(this.config):null),this.models.gender||(this.config.face.enabled&&this.config.face.gender.enabled?gender.load(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?emotion.load(this.config):null),this.models.embedding||(this.config.face.enabled&&this.config.face.embedding.enabled?embedding2.load(this.config):null),this.models.posenet||(this.config.body.enabled?posenet.load(this.config):null),this.models.handpose||(this.config.hand.enabled?handpose.load(this.config):null),this.models.blazepose||(this.config.pose.enabled?load(this.config):null)]):(this.config.face.enabled&&!this.models.facemesh&&(this.models.facemesh=await facemesh.load(this.config)),this.config.face.enabled&&this.config.face.age.enabled&&!this.models.age&&(this.models.age=await age.load(this.config)),this.config.face.enabled&&this.config.face.gender.enabled&&!this.models.gender&&(this.models.gender=await gender.load(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await emotion.load(this.config)),this.config.face.enabled&&this.config.face.embedding.enabled&&!this.models.embedding&&(this.models.embedding=await embedding2.load(this.config)),this.config.body.enabled&&!this.models.posenet&&(this.models.posenet=await posenet.load(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await handpose.load(this.config)),this.config.pose.enabled&&!this.models.blazepose&&(this.models.blazepose=await load(this.config)));let current=Math.trunc(now2()-timeStamp);current>(this.perf.load||0)&&(this.perf.load=current)}async checkBackend(force){if(this.config.backend&&this.config.backend!==""&&force||getBackend()!==this.config.backend){let timeStamp=now2();if(this.state="backend",log("setting backend:",this.config.backend),this.config.backend==="wasm"&&(log("settings wasm path:",this.config.wasmPath),setWasmPaths(this.config.wasmPath),await env().getAsync("WASM_HAS_SIMD_SUPPORT")||log("warning: wasm simd support is not enabled")),this.config.backend==="humangl"&&(log("registering humangl backend"),register()),await setBackend(this.config.backend),enableProdMode(),getBackend()==="webgl"){this.config.deallocate&&(log("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",this.config.deallocate),ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",this.config.deallocate?0:-1)),ENV.set("WEBGL_FORCE_F16_TEXTURES",!0),ENV.set("WEBGL_PACK_DEPTHWISECONV",!0);let gl=await backend().getGPGPUContext().gl;log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`)}await ready(),this.perf.backend=Math.trunc(now2()-timeStamp)}}async detectFace(input2){var _a;let timeStamp,ageRes,genderRes,emotionRes,embeddingRes,faceRes=[];this.state="run:face",timeStamp=now2();let faces=await((_a=this.models.facemesh)==null?void 0:_a.estimateFaces(input2,this.config));this.perf.face=Math.trunc(now2()-timeStamp);for(let face3 of faces){if(this.analyze("Get Face"),!face3.image||face3.image.isDisposedInternal){log("Face object is disposed:",face3.image);continue}this.analyze("Start Age:"),this.config.async?ageRes=this.config.face.age.enabled?age.predict(face3.image,this.config):{}:(this.state="run:age",timeStamp=now2(),ageRes=this.config.face.age.enabled?await age.predict(face3.image,this.config):{},this.perf.age=Math.trunc(now2()-timeStamp)),this.analyze("Start Gender:"),this.config.async?genderRes=this.config.face.gender.enabled?gender.predict(face3.image,this.config):{}:(this.state="run:gender",timeStamp=now2(),genderRes=this.config.face.gender.enabled?await gender.predict(face3.image,this.config):{},this.perf.gender=Math.trunc(now2()-timeStamp)),this.analyze("Start Emotion:"),this.config.async?emotionRes=this.config.face.emotion.enabled?emotion.predict(face3.image,this.config):{}:(this.state="run:emotion",timeStamp=now2(),emotionRes=this.config.face.emotion.enabled?await emotion.predict(face3.image,this.config):{},this.perf.emotion=Math.trunc(now2()-timeStamp)),this.analyze("End Emotion:"),this.analyze("Start Embedding:"),this.config.async?embeddingRes=this.config.face.embedding.enabled?embedding2.predict(face3.image,this.config):{}:(this.state="run:embedding",timeStamp=now2(),embeddingRes=this.config.face.embedding.enabled?await embedding2.predict(face3.image,this.config):{},this.perf.embedding=Math.trunc(now2()-timeStamp)),this.analyze("End Emotion:"),this.config.async&&([ageRes,genderRes,emotionRes,embeddingRes]=await Promise.all([ageRes,genderRes,emotionRes,embeddingRes])),this.analyze("Finish Face:"),face3.image.dispose(),this.config.face.iris.enabled||(delete face3.annotations.leftEyeIris,delete face3.annotations.rightEyeIris);let irisSize=face3.annotations.leftEyeIris&&face3.annotations.rightEyeIris?11.7*Math.max(Math.abs(face3.annotations.leftEyeIris[3][0]-face3.annotations.leftEyeIris[1][0]),Math.abs(face3.annotations.rightEyeIris[4][1]-face3.annotations.rightEyeIris[2][1])):0;faceRes.push({confidence:face3.confidence,box:face3.box,mesh:face3.mesh,boxRaw:face3.boxRaw,meshRaw:face3.meshRaw,annotations:face3.annotations,age:ageRes.age,gender:genderRes.gender,genderConfidence:genderRes.confidence,emotion:emotionRes,embedding:embeddingRes,iris:irisSize!==0?Math.trunc(irisSize)/100:0}),this.analyze("End Face")}return this.analyze("End FaceMesh:"),this.config.async&&(this.perf.face&&delete this.perf.face,this.perf.age&&delete this.perf.age,this.perf.gender&&delete this.perf.gender,this.perf.emotion&&delete this.perf.emotion),faceRes}async image(input2,userConfig={}){this.state="image",this.config=mergeDeep(this.config,userConfig);let process3=image2.process(input2,this.config);return process3.tensor.dispose(),process3.canvas}async detect(input2,userConfig={}){return new Promise(async resolve=>{var _a,_b,_c,_d;this.state="config";let timeStamp;this.config=mergeDeep(this.config,userConfig),this.state="check";let error=this.sanity(input2);error&&(log(error,input2),resolve({error}));let poseRes,blazeposeRes,handRes,faceRes,timeStart=now2();await this.checkBackend(),await this.load(),this.config.scoped&&engine().startScope(),this.analyze("Start Scope:"),timeStamp=now2();let process3=image2.process(input2,this.config);if(!process3||!process3.tensor){log("could not convert input to tensor"),resolve({error:"could not convert input to tensor"});return}this.perf.image=Math.trunc(now2()-timeStamp),this.analyze("Get Image:"),this.config.async?(faceRes=this.config.face.enabled?this.detectFace(process3.tensor):[],this.perf.face&&delete this.perf.face):(this.state="run:face",timeStamp=now2(),faceRes=this.config.face.enabled?await this.detectFace(process3.tensor):[],this.perf.face=Math.trunc(now2()-timeStamp)),this.analyze("Start Body:"),this.config.async?(poseRes=this.config.body.enabled?(_a=this.models.posenet)==null?void 0:_a.estimatePoses(process3.tensor,this.config):[],this.perf.body&&delete this.perf.body):(this.state="run:body",timeStamp=now2(),poseRes=this.config.body.enabled?await((_b=this.models.posenet)==null?void 0:_b.estimatePoses(process3.tensor,this.config)):[],this.perf.body=Math.trunc(now2()-timeStamp)),this.analyze("End Body:"),this.analyze("Start Pose:"),this.config.async?(blazeposeRes=this.config.pose.enabled?predict(process3.tensor,this.config):[],this.perf.pose&&delete this.perf.pose):(this.state="run:pose",timeStamp=now2(),blazeposeRes=this.config.pose.enabled?await predict(process3.tensor,this.config):[],this.perf.pose=Math.trunc(now2()-timeStamp)),this.analyze("End Pose:"),this.analyze("Start Hand:"),this.config.async?(handRes=this.config.hand.enabled?(_c=this.models.handpose)==null?void 0:_c.estimateHands(process3.tensor,this.config):[],this.perf.hand&&delete this.perf.hand):(this.state="run:hand",timeStamp=now2(),handRes=this.config.hand.enabled?await((_d=this.models.handpose)==null?void 0:_d.estimateHands(process3.tensor,this.config)):[],this.perf.hand=Math.trunc(now2()-timeStamp)),this.analyze("End Hand:"),this.config.async&&([faceRes,poseRes,blazeposeRes,handRes]=await Promise.all([faceRes,poseRes,blazeposeRes,handRes])),process3.tensor.dispose(),this.config.scoped&&engine().endScope(),this.analyze("End Scope:");let gestureRes=[];this.config.gesture.enabled&&(timeStamp=now2(),gestureRes=[...gesture.face(faceRes),...gesture.body(poseRes),...gesture.hand(handRes),...gesture.iris(faceRes)],this.config.async?this.perf.gesture&&delete this.perf.gesture:this.perf.gesture=Math.trunc(now2()-timeStamp)),this.perf.total=Math.trunc(now2()-timeStart),this.state="idle",resolve({face:faceRes,body:poseRes,hand:handRes,pose:blazeposeRes,gesture:gestureRes,performance:this.perf,canvas:process3.canvas})})}async warmup(userConfig){let b64toBlob=(base64,type="application/octet-stream")=>fetch(`data:${type};base64,${base64}`).then(res=>res.blob());userConfig&&(this.config=mergeDeep(this.config,userConfig));let video=this.config.videoOptimized;this.config.videoOptimized=!1;let blob;switch(this.config.warmup){case"face":blob=await b64toBlob(face);break;case"full":blob=await b64toBlob(body);break;default:blob=null}if(!blob)return null;let bitmap=await createImageBitmap(blob),t0=now2(),warmup=await this.detect(bitmap,config_exports),t1=now2();return bitmap.close(),log("Warmup",this.config.warmup,t1-t0,warmup),this.config.videoOptimized=video,warmup}};export{Human as default}; +2Q==`;var version14="0.9.24";var now2=()=>typeof performance!="undefined"?performance.now():parseInt(Number(process.hrtime.bigint())/1e3/1e3);function mergeDeep(...objects){let isObject=obj=>obj&&typeof obj=="object";return objects.reduce((prev,obj)=>(Object.keys(obj||{}).forEach(key=>{let pVal=prev[key],oVal=obj[key];Array.isArray(pVal)&&Array.isArray(oVal)?prev[key]=pVal.concat(...oVal):isObject(pVal)&&isObject(oVal)?prev[key]=mergeDeep(pVal,oVal):prev[key]=oVal}),prev),{})}var Human=class{constructor(userConfig={}){this.tf=tfjs_esm_exports,this.version=version14,this.config=mergeDeep(config_default,userConfig),this.fx=null,this.state="idle",this.numTensors=0,this.analyzeMemoryLeaks=!1,this.checkSanity=!1,this.firstRun=!0,this.perf={},this.models={facemesh:null,posenet:null,handpose:null,iris:null,age:null,gender:null,emotion:null},this.facemesh=facemesh,this.age=age,this.gender=gender,this.emotion=emotion,this.body=posenet,this.hand=handpose}profile(){return this.config.profile?profile2.data:{}}analyze(...msg){if(!this.analyzeMemoryLeaks)return;let current=engine().state.numTensors,previous=this.numTensors;this.numTensors=current;let leaked=current-previous;leaked!==0&&log(...msg,leaked)}sanity(input2){if(!this.checkSanity)return null;if(!input2)return"input is not defined";if(ENV.flags.IS_NODE&&!(input2 instanceof Tensor))return"input must be a tensor";try{getBackend()}catch(e){return"backend not loaded"}return null}simmilarity(embedding1,embedding22){return this.config.face.embedding.enabled?embedding2.simmilarity(embedding1,embedding22):0}async load(userConfig){this.state="load";let timeStamp=now2();userConfig&&(this.config=mergeDeep(this.config,userConfig)),this.firstRun&&(log(`version: ${this.version} TensorFlow/JS version: ${version6}`),await this.checkBackend(!0),ENV.flags.IS_BROWSER&&(log("configuration:",this.config),log("tf flags:",ENV.flags)),this.firstRun=!1),this.config.async?[this.models.facemesh,this.models.age,this.models.gender,this.models.emotion,this.models.embedding,this.models.posenet,this.models.handpose,this.models.blazepose]=await Promise.all([this.models.facemesh||(this.config.face.enabled?facemesh.load(this.config):null),this.models.age||(this.config.face.enabled&&this.config.face.age.enabled?age.load(this.config):null),this.models.gender||(this.config.face.enabled&&this.config.face.gender.enabled?gender.load(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?emotion.load(this.config):null),this.models.embedding||(this.config.face.enabled&&this.config.face.embedding.enabled?embedding2.load(this.config):null),this.models.posenet||(this.config.body.enabled?posenet.load(this.config):null),this.models.handpose||(this.config.hand.enabled?handpose.load(this.config):null),this.models.blazepose||(this.config.pose.enabled?load(this.config):null)]):(this.config.face.enabled&&!this.models.facemesh&&(this.models.facemesh=await facemesh.load(this.config)),this.config.face.enabled&&this.config.face.age.enabled&&!this.models.age&&(this.models.age=await age.load(this.config)),this.config.face.enabled&&this.config.face.gender.enabled&&!this.models.gender&&(this.models.gender=await gender.load(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await emotion.load(this.config)),this.config.face.enabled&&this.config.face.embedding.enabled&&!this.models.embedding&&(this.models.embedding=await embedding2.load(this.config)),this.config.body.enabled&&!this.models.posenet&&(this.models.posenet=await posenet.load(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await handpose.load(this.config)),this.config.pose.enabled&&!this.models.blazepose&&(this.models.blazepose=await load(this.config)));let current=Math.trunc(now2()-timeStamp);current>(this.perf.load||0)&&(this.perf.load=current)}async checkBackend(force){if(this.config.backend&&this.config.backend!==""&&force||getBackend()!==this.config.backend){let timeStamp=now2();if(this.state="backend",log("setting backend:",this.config.backend),this.config.backend==="wasm"&&(log("settings wasm path:",this.config.wasmPath),setWasmPaths(this.config.wasmPath),await env().getAsync("WASM_HAS_SIMD_SUPPORT")||log("warning: wasm simd support is not enabled")),this.config.backend==="humangl"&&(log("registering humangl backend"),register()),await setBackend(this.config.backend),enableProdMode(),getBackend()==="webgl"){this.config.deallocate&&(log("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",this.config.deallocate),ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",this.config.deallocate?0:-1)),ENV.set("WEBGL_FORCE_F16_TEXTURES",!0),ENV.set("WEBGL_PACK_DEPTHWISECONV",!0);let gl=await backend().getGPGPUContext().gl;log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`)}await ready(),this.perf.backend=Math.trunc(now2()-timeStamp)}}async detectFace(input2){var _a;let timeStamp,ageRes,genderRes,emotionRes,embeddingRes,faceRes=[];this.state="run:face",timeStamp=now2();let faces=await((_a=this.models.facemesh)==null?void 0:_a.estimateFaces(input2,this.config));this.perf.face=Math.trunc(now2()-timeStamp);for(let face3 of faces){if(this.analyze("Get Face"),!face3.image||face3.image.isDisposedInternal){log("Face object is disposed:",face3.image);continue}this.analyze("Start Age:"),this.config.async?ageRes=this.config.face.age.enabled?age.predict(face3.image,this.config):{}:(this.state="run:age",timeStamp=now2(),ageRes=this.config.face.age.enabled?await age.predict(face3.image,this.config):{},this.perf.age=Math.trunc(now2()-timeStamp)),this.analyze("Start Gender:"),this.config.async?genderRes=this.config.face.gender.enabled?gender.predict(face3.image,this.config):{}:(this.state="run:gender",timeStamp=now2(),genderRes=this.config.face.gender.enabled?await gender.predict(face3.image,this.config):{},this.perf.gender=Math.trunc(now2()-timeStamp)),this.analyze("Start Emotion:"),this.config.async?emotionRes=this.config.face.emotion.enabled?emotion.predict(face3.image,this.config):{}:(this.state="run:emotion",timeStamp=now2(),emotionRes=this.config.face.emotion.enabled?await emotion.predict(face3.image,this.config):{},this.perf.emotion=Math.trunc(now2()-timeStamp)),this.analyze("End Emotion:"),this.analyze("Start Embedding:"),this.config.async?embeddingRes=this.config.face.embedding.enabled?embedding2.predict(face3.image,this.config):{}:(this.state="run:embedding",timeStamp=now2(),embeddingRes=this.config.face.embedding.enabled?await embedding2.predict(face3.image,this.config):{},this.perf.embedding=Math.trunc(now2()-timeStamp)),this.analyze("End Emotion:"),this.config.async&&([ageRes,genderRes,emotionRes,embeddingRes]=await Promise.all([ageRes,genderRes,emotionRes,embeddingRes])),this.analyze("Finish Face:"),face3.image.dispose(),this.config.face.iris.enabled||(delete face3.annotations.leftEyeIris,delete face3.annotations.rightEyeIris);let irisSize=face3.annotations.leftEyeIris&&face3.annotations.rightEyeIris?11.7*Math.max(Math.abs(face3.annotations.leftEyeIris[3][0]-face3.annotations.leftEyeIris[1][0]),Math.abs(face3.annotations.rightEyeIris[4][1]-face3.annotations.rightEyeIris[2][1])):0;faceRes.push({confidence:face3.confidence,box:face3.box,mesh:face3.mesh,boxRaw:face3.boxRaw,meshRaw:face3.meshRaw,annotations:face3.annotations,age:ageRes.age,gender:genderRes.gender,genderConfidence:genderRes.confidence,emotion:emotionRes,embedding:embeddingRes,iris:irisSize!==0?Math.trunc(irisSize)/100:0}),this.analyze("End Face")}return this.analyze("End FaceMesh:"),this.config.async&&(this.perf.face&&delete this.perf.face,this.perf.age&&delete this.perf.age,this.perf.gender&&delete this.perf.gender,this.perf.emotion&&delete this.perf.emotion),faceRes}async image(input2,userConfig={}){this.state="image",this.config=mergeDeep(this.config,userConfig);let process3=image2.process(input2,this.config);return process3.tensor.dispose(),process3.canvas}async detect(input2,userConfig={}){return new Promise(async resolve=>{var _a,_b,_c,_d;this.state="config";let timeStamp;this.config=mergeDeep(this.config,userConfig),this.state="check";let error=this.sanity(input2);error&&(log(error,input2),resolve({error}));let poseRes,blazeposeRes,handRes,faceRes,timeStart=now2();await this.checkBackend(),await this.load(),this.config.scoped&&engine().startScope(),this.analyze("Start Scope:"),timeStamp=now2();let process3=image2.process(input2,this.config);if(!process3||!process3.tensor){log("could not convert input to tensor"),resolve({error:"could not convert input to tensor"});return}this.perf.image=Math.trunc(now2()-timeStamp),this.analyze("Get Image:"),this.config.async?(faceRes=this.config.face.enabled?this.detectFace(process3.tensor):[],this.perf.face&&delete this.perf.face):(this.state="run:face",timeStamp=now2(),faceRes=this.config.face.enabled?await this.detectFace(process3.tensor):[],this.perf.face=Math.trunc(now2()-timeStamp)),this.analyze("Start Body:"),this.config.async?(poseRes=this.config.body.enabled?(_a=this.models.posenet)==null?void 0:_a.estimatePoses(process3.tensor,this.config):[],this.perf.body&&delete this.perf.body):(this.state="run:body",timeStamp=now2(),poseRes=this.config.body.enabled?await((_b=this.models.posenet)==null?void 0:_b.estimatePoses(process3.tensor,this.config)):[],this.perf.body=Math.trunc(now2()-timeStamp)),this.analyze("End Body:"),this.analyze("Start Pose:"),this.config.async?(blazeposeRes=this.config.pose.enabled?predict(process3.tensor,this.config):[],this.perf.pose&&delete this.perf.pose):(this.state="run:pose",timeStamp=now2(),blazeposeRes=this.config.pose.enabled?await predict(process3.tensor,this.config):[],this.perf.pose=Math.trunc(now2()-timeStamp)),this.analyze("End Pose:"),this.analyze("Start Hand:"),this.config.async?(handRes=this.config.hand.enabled?(_c=this.models.handpose)==null?void 0:_c.estimateHands(process3.tensor,this.config):[],this.perf.hand&&delete this.perf.hand):(this.state="run:hand",timeStamp=now2(),handRes=this.config.hand.enabled?await((_d=this.models.handpose)==null?void 0:_d.estimateHands(process3.tensor,this.config)):[],this.perf.hand=Math.trunc(now2()-timeStamp)),this.analyze("End Hand:"),this.config.async&&([faceRes,poseRes,blazeposeRes,handRes]=await Promise.all([faceRes,poseRes,blazeposeRes,handRes])),process3.tensor.dispose(),this.config.scoped&&engine().endScope(),this.analyze("End Scope:");let gestureRes=[];this.config.gesture.enabled&&(timeStamp=now2(),gestureRes=[...gesture.face(faceRes),...gesture.body(poseRes),...gesture.hand(handRes),...gesture.iris(faceRes)],this.config.async?this.perf.gesture&&delete this.perf.gesture:this.perf.gesture=Math.trunc(now2()-timeStamp)),this.perf.total=Math.trunc(now2()-timeStart),this.state="idle",resolve({face:faceRes,body:poseRes,hand:handRes,pose:blazeposeRes,gesture:gestureRes,performance:this.perf,canvas:process3.canvas})})}async warmupBitmap(){let b64toBlob=(base64,type="application/octet-stream")=>fetch(`data:${type};base64,${base64}`).then(res2=>res2.blob()),blob,res;switch(this.config.warmup){case"face":blob=await b64toBlob(face);break;case"full":blob=await b64toBlob(body);break;default:blob=null}if(blob){let bitmap=await createImageBitmap(blob);res=await this.detect(bitmap,config_exports),bitmap.close()}return res}async warmupCanvas(){return new Promise(resolve=>{let src,size=0;switch(this.config.warmup){case"face":size=256,src="data:image/jpeg;base64,"+face;break;case"full":size=1200,src="data:image/jpeg;base64,"+body;break;default:src=null}let img=new Image(size,size);img.onload=()=>{let canvas=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(size,size):document.createElement("canvas");canvas.width=size,canvas.height=size;let ctx=canvas.getContext("2d");ctx.drawImage(img,0,0);let data2=ctx.getImageData(0,0,size,size);this.detect(data2,config_exports).then(res=>resolve(res))},src?img.src=src:resolve(null)})}async warmup(userConfig){let t0=now2();userConfig&&(this.config=mergeDeep(this.config,userConfig));let video=this.config.videoOptimized;this.config.videoOptimized=!1;let res;typeof createImageBitmap=="function"?res=await this.warmupBitmap():res=await this.warmupCanvas(),this.config.videoOptimized=video;let t1=now2();return log("Warmup",this.config.warmup,t1-t0,res),res}};export{Human as default}; /** * @license * Copyright 2017 Google LLC. All Rights Reserved. diff --git a/dist/human.esm.js.map b/dist/human.esm.js.map index c7a5d083..592271ce 100644 --- a/dist/human.esm.js.map +++ b/dist/human.esm.js.map @@ -1,7 +1,7 @@ { "version": 3, "sources": ["../src/blazeface/blazeface.js", "../src/blazeface/box.js", "../src/blazeface/util.js", "../src/blazeface/coords.js", "../src/blazeface/facepipeline.js", "../src/blazeface/facemesh.js", "../src/profile.js", "../src/age/age.js", "../src/gender/gender.js", "../src/emotion/emotion.js", "../src/embedding/embedding.js", "../src/posenet/modelBase.js", "../src/posenet/heapSort.js", "../src/posenet/buildParts.js", "../src/posenet/keypoints.js", "../src/posenet/vectors.js", "../src/posenet/decoders.js", "../src/posenet/decodePose.js", "../src/posenet/decodeMultiple.js", "../src/posenet/util.js", "../src/posenet/modelPoseNet.js", "../src/posenet/posenet.js", "../src/handpose/handdetector.js", "../src/handpose/handpipeline.js", "../src/handpose/anchors.js", "../src/handpose/handpose.js", "../src/gesture/gesture.js", "../src/imagefx.js", "../src/image.js", "../src/log.js", "empty:/home/vlado/dev/human/node_modules/node-fetch/browser.js", "../node_modules/@tensorflow/tfjs-core/node_modules/seedrandom/lib/alea.js", "../node_modules/@tensorflow/tfjs-core/node_modules/seedrandom/lib/xor128.js", "../node_modules/@tensorflow/tfjs-core/node_modules/seedrandom/lib/xorwow.js", "../node_modules/@tensorflow/tfjs-core/node_modules/seedrandom/lib/xorshift7.js", "../node_modules/@tensorflow/tfjs-core/node_modules/seedrandom/lib/xor4096.js", "../node_modules/@tensorflow/tfjs-core/node_modules/seedrandom/lib/tychei.js", "empty:crypto", "../node_modules/@tensorflow/tfjs-core/node_modules/seedrandom/seedrandom.js", "../node_modules/@tensorflow/tfjs-core/node_modules/seedrandom/index.js", "../node_modules/@tensorflow/tfjs-backend-cpu/node_modules/seedrandom/lib/alea.js", "../node_modules/@tensorflow/tfjs-backend-cpu/node_modules/seedrandom/lib/xor128.js", "../node_modules/@tensorflow/tfjs-backend-cpu/node_modules/seedrandom/lib/xorwow.js", "../node_modules/@tensorflow/tfjs-backend-cpu/node_modules/seedrandom/lib/xorshift7.js", "../node_modules/@tensorflow/tfjs-backend-cpu/node_modules/seedrandom/lib/xor4096.js", "../node_modules/@tensorflow/tfjs-backend-cpu/node_modules/seedrandom/lib/tychei.js", "../node_modules/@tensorflow/tfjs-backend-cpu/node_modules/seedrandom/seedrandom.js", "../node_modules/@tensorflow/tfjs-backend-cpu/node_modules/seedrandom/index.js", "empty:path", "empty:worker_threads", "empty:perf_hooks", "../node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js", "../node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js", "../node_modules/seedrandom/lib/alea.js", "../node_modules/seedrandom/lib/xor128.js", "../node_modules/seedrandom/lib/xorwow.js", "../node_modules/seedrandom/lib/xorshift7.js", "../node_modules/seedrandom/lib/xor4096.js", "../node_modules/seedrandom/lib/tychei.js", "../node_modules/seedrandom/seedrandom.js", "../node_modules/seedrandom/index.js", "empty:/home/vlado/dev/human/node_modules/string_decoder/lib/string_decoder.js", "../node_modules/@tensorflow/tfjs-core/src/backends/backend.ts", "../node_modules/@tensorflow/tfjs-core/src/util_base.ts", "../node_modules/@tensorflow/tfjs-core/src/environment.ts", "../node_modules/@tensorflow/tfjs-core/src/global_util.ts", "../node_modules/@tensorflow/tfjs-core/src/kernel_names.ts", "../node_modules/@tensorflow/tfjs-core/src/kernel_registry.ts", "../node_modules/@tensorflow/tfjs-core/src/util.ts", "../node_modules/@tensorflow/tfjs-core/src/profiler.ts", "../node_modules/@tensorflow/tfjs-core/src/tape.ts", "../node_modules/@tensorflow/tfjs-core/src/tensor_format.ts", "../node_modules/@tensorflow/tfjs-core/src/tensor.ts", "../node_modules/@tensorflow/tfjs-core/src/tensor_util.ts", "../node_modules/@tensorflow/tfjs-core/src/types.ts", "../node_modules/@tensorflow/tfjs-core/src/engine.ts", "../node_modules/@tensorflow/tfjs-core/src/device_util.ts", "../node_modules/@tensorflow/tfjs-core/src/flags.ts", "../node_modules/@tensorflow/tfjs-core/src/tensor_util_env.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/operation.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/complex.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/tensor_ops_util.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/tensor.ts", "../node_modules/@tensorflow/tfjs-core/src/io/types.ts", "../node_modules/@tensorflow/tfjs-core/src/io/io_utils.ts", "../node_modules/@tensorflow/tfjs-core/src/io/router_registry.ts", "../node_modules/@tensorflow/tfjs-core/src/io/indexed_db.ts", "../node_modules/@tensorflow/tfjs-core/src/io/local_storage.ts", "../node_modules/@tensorflow/tfjs-core/src/io/model_management.ts", "../node_modules/@tensorflow/tfjs-core/src/platforms/platform_browser.ts", "../node_modules/@tensorflow/tfjs-core/src/platforms/platform_node.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/buffer.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/cast.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/clone.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/print.ts", "../node_modules/@tensorflow/tfjs-core/src/base_side_effects.ts", "../node_modules/@tensorflow/tfjs-core/src/io/io.ts", "../node_modules/@tensorflow/tfjs-core/src/io/browser_files.ts", "../node_modules/@tensorflow/tfjs-core/src/io/progress.ts", "../node_modules/@tensorflow/tfjs-core/src/io/weights_loader.ts", "../node_modules/@tensorflow/tfjs-core/src/io/http.ts", "../node_modules/@tensorflow/tfjs-core/src/io/passthrough.ts", "../node_modules/@tensorflow/tfjs-core/src/math.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/mat_mul.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/one_hot.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/transpose.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/confusion_matrix.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/browser.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/tensor3d.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/gather_nd_util.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/scatter_nd_util.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/slice_util.ts", "../node_modules/@tensorflow/tfjs-core/src/serialization.ts", "../node_modules/@tensorflow/tfjs-core/src/test_util.ts", "../node_modules/@tensorflow/tfjs-core/src/version.ts", "../node_modules/@tensorflow/tfjs-core/src/globals.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/add.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/floorDiv.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/div.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/mul.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/abs.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/acos.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/acosh.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/add_n.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/all.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/any.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/arg_max.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/arg_min.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/asin.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/asinh.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/atan.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/atan2.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/atanh.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/conv_util.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/reshape.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/avg_pool.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/avg_pool_3d.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/concat.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/sigmoid.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/slice.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/tanh.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/basic_lstm_cell.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/batch_to_space_nd.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/batchnorm_util.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/batchnorm.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/batchnorm2d.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/batchnorm3d.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/batchnorm4d.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/bincount.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/broadcast_to.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/ceil.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/clip_by_value.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/concat_1d.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/concat_2d.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/concat_3d.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/concat_4d.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/conv2d.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/conv1d.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/conv2d_backprop_input.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/conv2d_transpose.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/conv3d.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/conv3d_backprop_input.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/conv3d_transpose.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/cos.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/cosh.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/cumsum.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/dense_bincount.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/depth_to_space.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/depthwise_conv2d.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/diag.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/dilation2d.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/broadcast_util.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/equal.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/where.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/zeros_like.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/div_no_nan.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/dot.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/elu.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/erf.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/exp.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/expand_dims.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/expm1.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/tile.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/eye.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/fill.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/floor.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/gather.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/greater.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/greater_equal.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/imag.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/is_finite.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/is_inf.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/is_nan.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/leaky_relu.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/less.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/less_equal.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/linspace.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/local_response_normalization.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/log.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/log1p.ts", 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"../node_modules/@tensorflow/tfjs-data/src/sources/file_data_source.ts", "../node_modules/@tensorflow/tfjs-data/src/sources/url_data_source.ts", "../node_modules/@tensorflow/tfjs-data/src/readers.ts", "../node_modules/@tensorflow/tfjs-data/src/version.ts", "../src/tfjs/tf-browser.js", "../src/tfjs/backend.js", "../src/human.js", "../src/blazepose/blazepose.js", "../src/handpose/box.js", "../src/handpose/util.js", "../config.js", "../src/sample.js"], - "sourcesContent": ["import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nconst NUM_LANDMARKS = 6;\n\nfunction generateAnchors(inputSize) {\n const spec = { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] };\n const anchors = [];\n for (let i = 0; i < spec.strides.length; i++) {\n const stride = spec.strides[i];\n const gridRows = Math.floor((inputSize + stride - 1) / stride);\n const gridCols = Math.floor((inputSize + stride - 1) / stride);\n const anchorsNum = spec.anchors[i];\n for (let gridY = 0; gridY < gridRows; gridY++) {\n const anchorY = stride * (gridY + 0.5);\n for (let gridX = 0; gridX < gridCols; gridX++) {\n const anchorX = stride * (gridX + 0.5);\n for (let n = 0; n < anchorsNum; n++) {\n anchors.push([anchorX, anchorY]);\n }\n }\n }\n }\n return anchors;\n}\n\nconst disposeBox = (box) => {\n box.startEndTensor.dispose();\n box.startPoint.dispose();\n box.endPoint.dispose();\n};\n\nconst createBox = (startEndTensor) => ({\n startEndTensor,\n startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]),\n endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]),\n});\n\nconst scaleBox = (box, factors) => {\n const starts = tf.mul(box.startPoint, factors);\n const ends = tf.mul(box.endPoint, factors);\n const newCoordinates = tf.concat2d([starts, ends], 1);\n return createBox(newCoordinates);\n};\n\nfunction decodeBounds(boxOutputs, anchors, inputSize) {\n const boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n const centers = tf.add(boxStarts, anchors);\n const boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n const boxSizesNormalized = tf.div(boxSizes, inputSize);\n const centersNormalized = tf.div(centers, inputSize);\n const halfBoxSize = tf.div(boxSizesNormalized, 2);\n const starts = tf.sub(centersNormalized, halfBoxSize);\n const ends = tf.add(centersNormalized, halfBoxSize);\n const startNormalized = tf.mul(starts, inputSize);\n const endNormalized = tf.mul(ends, inputSize);\n const concatAxis = 1;\n return tf.concat2d([startNormalized, endNormalized], concatAxis);\n}\n\nfunction scaleBoxFromPrediction(face, scaleFactor) {\n return tf.tidy(() => {\n const box = face['box'] ? face['box'] : face;\n return scaleBox(box, scaleFactor).startEndTensor.squeeze();\n });\n}\n\nclass BlazeFaceModel {\n constructor(model, config) {\n this.blazeFaceModel = model;\n this.width = config.face.detector.inputSize;\n this.height = config.face.detector.inputSize;\n this.anchorsData = generateAnchors(config.face.detector.inputSize);\n this.anchors = tf.tensor2d(this.anchorsData);\n this.inputSize = tf.tensor1d([this.width, this.height]);\n this.config = config;\n this.scaleFaces = 0.8;\n }\n\n async getBoundingBoxes(inputImage) {\n // sanity check on input\n if ((!inputImage) || (inputImage.isDisposedInternal) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return null;\n const [detectedOutputs, boxes, scores] = tf.tidy(() => {\n const resizedImage = inputImage.resizeBilinear([this.width, this.height]);\n // const normalizedImage = tf.mul(tf.sub(resizedImage.div(255), 0.5), 2);\n const normalizedImage = tf.sub(resizedImage.div(127.5), 1);\n const batchedPrediction = this.blazeFaceModel.predict(normalizedImage);\n let prediction;\n // are we using tfhub or pinto converted model?\n if (Array.isArray(batchedPrediction)) {\n const sorted = batchedPrediction.sort((a, b) => a.size - b.size);\n const concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n const concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n const concat = tf.concat([concat512, concat384], 1);\n prediction = concat.squeeze(0);\n } else {\n prediction = batchedPrediction.squeeze(); // when using tfhub model\n }\n const decodedBounds = decodeBounds(prediction, this.anchors, this.inputSize);\n const logits = tf.slice(prediction, [0, 0], [-1, 1]);\n const scoresOut = tf.sigmoid(logits).squeeze();\n return [prediction, decodedBounds, scoresOut];\n });\n const boxIndicesTensor = await tf.image.nonMaxSuppressionAsync(boxes, scores, this.config.face.detector.maxFaces, this.config.face.detector.iouThreshold, this.config.face.detector.scoreThreshold);\n const boxIndices = boxIndicesTensor.arraySync();\n boxIndicesTensor.dispose();\n const boundingBoxesMap = boxIndices.map((boxIndex) => tf.slice(boxes, [boxIndex, 0], [1, -1]));\n const boundingBoxes = boundingBoxesMap.map((boundingBox) => {\n const vals = boundingBox.arraySync();\n boundingBox.dispose();\n return vals;\n });\n\n const scoresVal = scores.dataSync();\n const annotatedBoxes = [];\n for (let i = 0; i < boundingBoxes.length; i++) {\n const boxIndex = boxIndices[i];\n const confidence = scoresVal[boxIndex];\n if (confidence > this.config.face.detector.minConfidence) {\n const box = createBox(boundingBoxes[i]);\n const anchor = this.anchorsData[boxIndex];\n const landmarks = tf.tidy(() => tf.slice(detectedOutputs, [boxIndex, NUM_LANDMARKS - 1], [1, -1]).squeeze().reshape([NUM_LANDMARKS, -1]));\n annotatedBoxes.push({ box, landmarks, anchor, confidence });\n }\n }\n detectedOutputs.dispose();\n boxes.dispose();\n scores.dispose();\n detectedOutputs.dispose();\n return {\n boxes: annotatedBoxes,\n scaleFactor: [inputImage.shape[2] / this.width, inputImage.shape[1] / this.height],\n };\n }\n\n async estimateFaces(input) {\n // @ts-ignore\n const { boxes, scaleFactor } = await this.getBoundingBoxes(input);\n const faces = [];\n for (const face of boxes) {\n const landmarkData = face.landmarks.arraySync();\n const scaledBox = scaleBoxFromPrediction(face, scaleFactor);\n // @ts-ignore\n const boxData = scaleBox.arraySync();\n const probabilityData = face.probability.arraySync();\n const anchor = face.anchor;\n const [scaleFactorX, scaleFactorY] = scaleFactor;\n const scaledLandmarks = landmarkData\n .map((landmark) => ([\n (landmark[0] + anchor[0]) * scaleFactorX,\n (landmark[1] + anchor[1]) * scaleFactorY,\n ]));\n const normalizedFace = {\n topLeft: boxData.slice(0, 2),\n bottomRight: boxData.slice(2),\n landmarks: scaledLandmarks,\n probability: probabilityData,\n };\n disposeBox(face.box);\n face.landmarks.dispose();\n face.probability.dispose();\n scaledBox.dispose();\n faces.push(normalizedFace);\n }\n return faces;\n }\n}\n\nasync function load(config) {\n const blazeface = await tf.loadGraphModel(config.face.detector.modelPath, { fromTFHub: config.face.detector.modelPath.includes('tfhub.dev') });\n const model = new BlazeFaceModel(blazeface, config);\n log(`load model: ${config.face.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n return model;\n}\n\nexports.load = load;\nexports.BlazeFaceModel = BlazeFaceModel;\nexports.disposeBox = disposeBox;\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nfunction scaleBoxCoordinates(box, factor) {\n const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n return { startPoint, endPoint };\n}\nexports.scaleBoxCoordinates = scaleBoxCoordinates;\n\nfunction getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\nexports.getBoxSize = getBoxSize;\n\nfunction getBoxCenter(box) {\n return [\n box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n ];\n}\nexports.getBoxCenter = getBoxCenter;\n\nfunction cutBoxFromImageAndResize(box, image, cropSize) {\n const h = image.shape[1];\n const w = image.shape[2];\n const boxes = [[\n box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h,\n box.endPoint[0] / w,\n ]];\n return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\nexports.cutBoxFromImageAndResize = cutBoxFromImageAndResize;\n\nfunction enlargeBox(box, factor = 1.5) {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n return { startPoint, endPoint, landmarks: box.landmarks };\n}\nexports.enlargeBox = enlargeBox;\n\nfunction squarifyBox(box) {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const maxEdge = Math.max(...size);\n const halfSize = maxEdge / 2;\n const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n return { startPoint, endPoint, landmarks: box.landmarks };\n}\nexports.squarifyBox = squarifyBox;\n", "exports.IDENTITY_MATRIX = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n/**\n * Normalizes the provided angle to the range -pi to pi.\n * @param angle The angle in radians to be normalized.\n */\nfunction normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\nexports.normalizeRadians = normalizeRadians;\n\n/**\n * Computes the angle of rotation between two anchor points.\n * @param point1 First anchor point\n * @param point2 Second anchor point\n */\nfunction computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\nexports.computeRotation = computeRotation;\n\nfunction radToDegrees(rad) {\n return rad * 180 / Math.PI;\n}\nexports.radToDegrees = radToDegrees;\n\nfunction buildTranslationMatrix(x, y) {\n return [[1, 0, x], [0, 1, y], [0, 0, 1]];\n}\n\nfunction dot(v1, v2) {\n let product = 0;\n for (let i = 0; i < v1.length; i++) {\n product += v1[i] * v2[i];\n }\n return product;\n}\nexports.dot = dot;\n\nfunction getColumnFrom2DArr(arr, columnIndex) {\n const column = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\nexports.getColumnFrom2DArr = getColumnFrom2DArr;\n\nfunction multiplyTransformMatrices(mat1, mat2) {\n const product = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) {\n product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n }\n return product;\n}\nfunction buildRotationMatrix(rotation, center) {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\nexports.buildRotationMatrix = buildRotationMatrix;\n\nfunction invertTransformMatrix(matrix) {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [\n -dot(rotationComponent[0], translationComponent),\n -dot(rotationComponent[1], translationComponent),\n ];\n return [\n rotationComponent[0].concat(invertedTranslation[0]),\n rotationComponent[1].concat(invertedTranslation[1]),\n [0, 0, 1],\n ];\n}\nexports.invertTransformMatrix = invertTransformMatrix;\n\nfunction rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\nexports.rotatePoint = rotatePoint;\n\nfunction xyDistanceBetweenPoints(a, b) {\n return Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n}\nexports.xyDistanceBetweenPoints = xyDistanceBetweenPoints;\n", "const MESH_ANNOTATIONS = {\n silhouette: [\n 10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n 397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n 172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n ],\n lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291],\n lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291],\n lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308],\n lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],\n rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173],\n rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133],\n rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190],\n rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243],\n rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189],\n rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244],\n rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245],\n rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193],\n rightEyebrowLower: [35, 124, 46, 53, 52, 65],\n rightEyeIris: [473, 474, 475, 476, 477],\n leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n leftEyeIris: [468, 469, 470, 471, 472],\n midwayBetweenEyes: [168],\n noseTip: [1],\n noseBottom: [2],\n noseRightCorner: [98],\n noseLeftCorner: [327],\n rightCheek: [205],\n leftCheek: [425],\n};\n\nconst MESH_TO_IRIS_INDICES_MAP = [ // A mapping from facemesh model keypoints to iris model keypoints.\n { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] },\n { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] },\n { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] },\n { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] },\n { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] },\n { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] },\n { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] },\n // { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] },\n // { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] },\n];\n\nconst UV468 = [\n [0.499976992607117, 0.652534008026123],\n [0.500025987625122, 0.547487020492554],\n [0.499974012374878, 0.602371990680695],\n [0.482113003730774, 0.471979022026062],\n [0.500150978565216, 0.527155995368958],\n [0.499909996986389, 0.498252987861633],\n [0.499523013830185, 0.40106201171875],\n [0.289712011814117, 0.380764007568359],\n [0.499954998493195, 0.312398016452789],\n [0.499987006187439, 0.269918978214264],\n [0.500023007392883, 0.107050001621246],\n [0.500023007392883, 0.666234016418457],\n [0.5000159740448, 0.679224014282227],\n [0.500023007392883, 0.692348003387451],\n [0.499976992607117, 0.695277988910675],\n [0.499976992607117, 0.70593398809433],\n [0.499976992607117, 0.719385027885437],\n [0.499976992607117, 0.737019002437592],\n [0.499967992305756, 0.781370997428894],\n [0.499816000461578, 0.562981009483337],\n [0.473773002624512, 0.573909997940063],\n [0.104906998574734, 0.254140973091125],\n [0.365929991006851, 0.409575998783112],\n [0.338757991790771, 0.41302502155304],\n [0.311120003461838, 0.409460008144379],\n [0.274657994508743, 0.389131009578705],\n [0.393361985683441, 0.403706014156342],\n [0.345234006643295, 0.344011008739471],\n [0.370094001293182, 0.346076011657715],\n [0.319321990013123, 0.347265005111694],\n [0.297903001308441, 0.353591024875641],\n [0.24779200553894, 0.410809993743896],\n [0.396889001131058, 0.842755019664764],\n [0.280097991228104, 0.375599980354309],\n [0.106310002505779, 0.399955987930298],\n [0.2099249958992, 0.391353011131287],\n [0.355807989835739, 0.534406006336212],\n [0.471751004457474, 0.65040397644043],\n [0.474155008792877, 0.680191993713379],\n [0.439785003662109, 0.657229006290436],\n [0.414617002010345, 0.66654098033905],\n [0.450374007225037, 0.680860996246338],\n [0.428770989179611, 0.682690978050232],\n [0.374971002340317, 0.727805018424988],\n [0.486716985702515, 0.547628998756409],\n [0.485300987958908, 0.527395009994507],\n [0.257764995098114, 0.314490020275116],\n [0.401223003864288, 0.455172002315521],\n [0.429818987846375, 0.548614978790283],\n [0.421351999044418, 0.533740997314453],\n [0.276895999908447, 0.532056987285614],\n [0.483370006084442, 0.499586999416351],\n [0.33721199631691, 0.282882988452911],\n [0.296391993761063, 0.293242990970612],\n [0.169294998049736, 0.193813979625702],\n [0.447580009698868, 0.302609980106354],\n [0.392390012741089, 0.353887975215912],\n [0.354490011930466, 0.696784019470215],\n [0.067304998636246, 0.730105042457581],\n [0.442739009857178, 0.572826027870178],\n [0.457098007202148, 0.584792017936707],\n [0.381974011659622, 0.694710969924927],\n [0.392388999462128, 0.694203019142151],\n [0.277076005935669, 0.271932005882263],\n [0.422551989555359, 0.563233017921448],\n [0.385919004678726, 0.281364023685455],\n [0.383103013038635, 0.255840003490448],\n [0.331431001424789, 0.119714021682739],\n [0.229923993349075, 0.232002973556519],\n [0.364500999450684, 0.189113974571228],\n [0.229622006416321, 0.299540996551514],\n [0.173287004232407, 0.278747975826263],\n [0.472878992557526, 0.666198015213013],\n [0.446828007698059, 0.668527007102966],\n [0.422762006521225, 0.673889994621277],\n [0.445307999849319, 0.580065965652466],\n [0.388103008270264, 0.693961024284363],\n [0.403039008378983, 0.706539988517761],\n [0.403629004955292, 0.693953037261963],\n [0.460041999816895, 0.557139039039612],\n [0.431158006191254, 0.692366003990173],\n [0.452181994915009, 0.692366003990173],\n [0.475387006998062, 0.692366003990173],\n [0.465828001499176, 0.779190003871918],\n [0.472328990697861, 0.736225962638855],\n [0.473087012767792, 0.717857003211975],\n [0.473122000694275, 0.704625964164734],\n [0.473033010959625, 0.695277988910675],\n [0.427942007780075, 0.695277988910675],\n [0.426479011774063, 0.703539967536926],\n [0.423162013292313, 0.711845993995667],\n [0.4183090031147, 0.720062971115112],\n [0.390094995498657, 0.639572978019714],\n [0.013953999616206, 0.560034036636353],\n [0.499913990497589, 0.58014702796936],\n [0.413199990987778, 0.69539999961853],\n [0.409626007080078, 0.701822996139526],\n [0.468080013990402, 0.601534962654114],\n [0.422728985548019, 0.585985004901886],\n [0.463079988956451, 0.593783974647522],\n [0.37211999297142, 0.47341400384903],\n [0.334562003612518, 0.496073007583618],\n [0.411671012639999, 0.546965003013611],\n [0.242175996303558, 0.14767599105835],\n [0.290776997804642, 0.201445996761322],\n [0.327338010072708, 0.256527006626129],\n [0.399509996175766, 0.748921036720276],\n [0.441727995872498, 0.261676013469696],\n [0.429764986038208, 0.187834024429321],\n [0.412198007106781, 0.108901023864746],\n [0.288955003023148, 0.398952007293701],\n [0.218936994671822, 0.435410976409912],\n [0.41278201341629, 0.398970007896423],\n [0.257135003805161, 0.355440020561218],\n [0.427684992551804, 0.437960982322693],\n [0.448339998722076, 0.536936044692993],\n [0.178560003638268, 0.45755398273468],\n [0.247308000922203, 0.457193970680237],\n [0.286267012357712, 0.467674970626831],\n [0.332827985286713, 0.460712015628815],\n [0.368755996227264, 0.447206974029541],\n [0.398963987827301, 0.432654976844788],\n [0.476410001516342, 0.405806005001068],\n [0.189241006970406, 0.523923993110657],\n [0.228962004184723, 0.348950982093811],\n [0.490725994110107, 0.562400996685028],\n [0.404670000076294, 0.485132992267609],\n [0.019469000399113, 0.401564002037048],\n [0.426243007183075, 0.420431017875671],\n [0.396993011236191, 0.548797011375427],\n [0.266469985246658, 0.376977026462555],\n [0.439121007919312, 0.51895797252655],\n [0.032313998788595, 0.644356966018677],\n [0.419054001569748, 0.387154996395111],\n [0.462783008813858, 0.505746960639954],\n [0.238978996872902, 0.779744982719421],\n [0.198220998048782, 0.831938028335571],\n [0.107550002634525, 0.540755033493042],\n [0.183610007166862, 0.740257024765015],\n [0.134409993886948, 0.333683013916016],\n [0.385764002799988, 0.883153975009918],\n [0.490967005491257, 0.579378008842468],\n [0.382384985685349, 0.508572995662689],\n [0.174399003386497, 0.397670984268188],\n [0.318785011768341, 0.39623498916626],\n [0.343364000320435, 0.400596976280212],\n [0.396100014448166, 0.710216999053955],\n [0.187885001301765, 0.588537991046906],\n [0.430987000465393, 0.944064974784851],\n [0.318993002176285, 0.898285031318665],\n [0.266247987747192, 0.869701027870178],\n [0.500023007392883, 0.190576016902924],\n [0.499976992607117, 0.954452991485596],\n [0.366169989109039, 0.398822009563446],\n [0.393207013607025, 0.39553701877594],\n [0.410373002290726, 0.391080021858215],\n [0.194993004202843, 0.342101991176605],\n [0.388664990663528, 0.362284004688263],\n [0.365961998701096, 0.355970978736877],\n [0.343364000320435, 0.355356991291046],\n [0.318785011768341, 0.35834002494812],\n [0.301414996385574, 0.363156020641327],\n [0.058132998645306, 0.319076001644135],\n [0.301414996385574, 0.387449026107788],\n [0.499987989664078, 0.618434011936188],\n [0.415838003158569, 0.624195992946625],\n [0.445681989192963, 0.566076993942261],\n [0.465844005346298, 0.620640993118286],\n [0.49992299079895, 0.351523995399475],\n [0.288718998432159, 0.819945991039276],\n [0.335278987884521, 0.852819979190826],\n [0.440512001514435, 0.902418971061707],\n [0.128294005990028, 0.791940987110138],\n [0.408771991729736, 0.373893976211548],\n [0.455606997013092, 0.451801002025604],\n [0.499877005815506, 0.908990025520325],\n [0.375436991453171, 0.924192011356354],\n [0.11421000212431, 0.615022003650665],\n [0.448662012815475, 0.695277988910675],\n [0.4480200111866, 0.704632043838501],\n [0.447111994028091, 0.715808033943176],\n [0.444831997156143, 0.730794012546539],\n [0.430011987686157, 0.766808986663818],\n [0.406787008047104, 0.685672998428345],\n [0.400738000869751, 0.681069016456604],\n [0.392399996519089, 0.677703022956848],\n [0.367855995893478, 0.663918972015381],\n [0.247923001646996, 0.601333022117615],\n [0.452769994735718, 0.420849978923798],\n [0.43639200925827, 0.359887003898621],\n [0.416164010763168, 0.368713974952698],\n [0.413385987281799, 0.692366003990173],\n [0.228018000721931, 0.683571994304657],\n [0.468268007040024, 0.352671027183533],\n [0.411361992359161, 0.804327011108398],\n [0.499989002943039, 0.469825029373169],\n [0.479153990745544, 0.442654013633728],\n [0.499974012374878, 0.439637005329132],\n [0.432112008333206, 0.493588984012604],\n [0.499886006116867, 0.866917014122009],\n [0.49991300702095, 0.821729004383087],\n [0.456548988819122, 0.819200992584229],\n [0.344549000263214, 0.745438992977142],\n [0.37890899181366, 0.574010014533997],\n [0.374292999505997, 0.780184984207153],\n [0.319687992334366, 0.570737957954407],\n [0.357154995203018, 0.604269981384277],\n [0.295284003019333, 0.621580958366394],\n [0.447750002145767, 0.862477004528046],\n [0.410986006259918, 0.508723020553589],\n [0.31395098567009, 0.775308012962341],\n [0.354128003120422, 0.812552988529205],\n [0.324548006057739, 0.703992962837219],\n [0.189096003770828, 0.646299958229065],\n [0.279776990413666, 0.71465802192688],\n [0.1338230073452, 0.682700991630554],\n [0.336768001317978, 0.644733011722565],\n [0.429883986711502, 0.466521978378296],\n [0.455527991056442, 0.548622965812683],\n [0.437114000320435, 0.558896005153656],\n [0.467287987470627, 0.529924988746643],\n [0.414712011814117, 0.335219979286194],\n [0.37704598903656, 0.322777986526489],\n [0.344107985496521, 0.320150971412659],\n [0.312875986099243, 0.32233202457428],\n [0.283526003360748, 0.333190023899078],\n [0.241245999932289, 0.382785975933075],\n [0.102986000478268, 0.468762993812561],\n [0.267612010240555, 0.424560010433197],\n [0.297879010438919, 0.433175981044769],\n [0.333433985710144, 0.433878004550934],\n [0.366427004337311, 0.426115989685059],\n [0.396012008190155, 0.416696012020111],\n [0.420121014118195, 0.41022801399231],\n [0.007561000064015, 0.480777025222778],\n [0.432949006557465, 0.569517970085144],\n [0.458638995885849, 0.479089021682739],\n [0.473466008901596, 0.545744001865387],\n [0.476087987422943, 0.563830018043518],\n [0.468472003936768, 0.555056989192963],\n [0.433990985155106, 0.582361996173859],\n [0.483518004417419, 0.562983989715576],\n [0.482482999563217, 0.57784903049469],\n [0.42645001411438, 0.389798998832703],\n [0.438998997211456, 0.39649498462677],\n [0.450067013502121, 0.400434017181396],\n [0.289712011814117, 0.368252992630005],\n [0.276670008897781, 0.363372981548309],\n [0.517862021923065, 0.471948027610779],\n [0.710287988185883, 0.380764007568359],\n [0.526226997375488, 0.573909997940063],\n [0.895093023777008, 0.254140973091125],\n [0.634069979190826, 0.409575998783112],\n [0.661242008209229, 0.41302502155304],\n [0.688880026340485, 0.409460008144379],\n [0.725341975688934, 0.389131009578705],\n [0.606630027294159, 0.40370500087738],\n [0.654766023159027, 0.344011008739471],\n [0.629905998706818, 0.346076011657715],\n [0.680678009986877, 0.347265005111694],\n [0.702096998691559, 0.353591024875641],\n [0.75221198797226, 0.410804986953735],\n [0.602918028831482, 0.842862963676453],\n [0.719901978969574, 0.375599980354309],\n [0.893692970275879, 0.399959981441498],\n [0.790081977844238, 0.391354024410248],\n [0.643998026847839, 0.534487962722778],\n [0.528249025344849, 0.65040397644043],\n [0.525849997997284, 0.680191040039062],\n [0.560214996337891, 0.657229006290436],\n [0.585384011268616, 0.66654098033905],\n [0.549625992774963, 0.680860996246338],\n [0.57122802734375, 0.682691991329193],\n [0.624852001667023, 0.72809898853302],\n [0.513050019741058, 0.547281980514526],\n [0.51509702205658, 0.527251958847046],\n [0.742246985435486, 0.314507007598877],\n [0.598631024360657, 0.454979002475739],\n [0.570338010787964, 0.548575043678284],\n [0.578631997108459, 0.533622980117798],\n [0.723087012767792, 0.532054007053375],\n [0.516445994377136, 0.499638974666595],\n [0.662801027297974, 0.282917976379395],\n [0.70362401008606, 0.293271005153656],\n [0.830704987049103, 0.193813979625702],\n [0.552385985851288, 0.302568018436432],\n [0.607609987258911, 0.353887975215912],\n [0.645429015159607, 0.696707010269165],\n [0.932694971561432, 0.730105042457581],\n [0.557260990142822, 0.572826027870178],\n [0.542901992797852, 0.584792017936707],\n [0.6180260181427, 0.694710969924927],\n [0.607590973377228, 0.694203019142151],\n [0.722943007946014, 0.271963000297546],\n [0.577413976192474, 0.563166975975037],\n [0.614082992076874, 0.281386971473694],\n [0.616907000541687, 0.255886018276215],\n [0.668509006500244, 0.119913995265961],\n [0.770092010498047, 0.232020974159241],\n [0.635536015033722, 0.189248979091644],\n [0.77039098739624, 0.299556016921997],\n [0.826722025871277, 0.278755009174347],\n [0.527121007442474, 0.666198015213013],\n [0.553171992301941, 0.668527007102966],\n [0.577238023281097, 0.673889994621277],\n [0.554691970348358, 0.580065965652466],\n [0.611896991729736, 0.693961024284363],\n [0.59696102142334, 0.706539988517761],\n [0.596370995044708, 0.693953037261963],\n [0.539958000183105, 0.557139039039612],\n [0.568841993808746, 0.692366003990173],\n [0.547818005084991, 0.692366003990173],\n [0.52461302280426, 0.692366003990173],\n [0.534089982509613, 0.779141008853912],\n [0.527670979499817, 0.736225962638855],\n [0.526912987232208, 0.717857003211975],\n [0.526877999305725, 0.704625964164734],\n [0.526966989040375, 0.695277988910675],\n [0.572058022022247, 0.695277988910675],\n [0.573521018028259, 0.703539967536926],\n [0.57683801651001, 0.711845993995667],\n [0.581691026687622, 0.720062971115112],\n [0.609944999217987, 0.639909982681274],\n [0.986046016216278, 0.560034036636353],\n [0.5867999792099, 0.69539999961853],\n [0.590372025966644, 0.701822996139526],\n [0.531915009021759, 0.601536989212036],\n [0.577268004417419, 0.585934996604919],\n [0.536915004253387, 0.593786001205444],\n [0.627542972564697, 0.473352015018463],\n [0.665585994720459, 0.495950996875763],\n [0.588353991508484, 0.546862006187439],\n [0.757824003696442, 0.14767599105835],\n [0.709249973297119, 0.201507985591888],\n [0.672684013843536, 0.256581008434296],\n [0.600408971309662, 0.74900496006012],\n [0.55826598405838, 0.261672019958496],\n [0.570303976535797, 0.187870979309082],\n [0.588165998458862, 0.109044015407562],\n [0.711045026779175, 0.398952007293701],\n [0.781069993972778, 0.435405015945435],\n [0.587247014045715, 0.398931980133057],\n [0.742869973182678, 0.355445981025696],\n [0.572156012058258, 0.437651991844177],\n [0.55186802148819, 0.536570012569427],\n [0.821442008018494, 0.457556009292603],\n [0.752701997756958, 0.457181990146637],\n [0.71375697851181, 0.467626988887787],\n [0.66711300611496, 0.460672974586487],\n [0.631101012229919, 0.447153985500336],\n [0.6008620262146, 0.432473003864288],\n [0.523481011390686, 0.405627012252808],\n [0.810747981071472, 0.523926019668579],\n [0.771045982837677, 0.348959028720856],\n [0.509127020835876, 0.562718033790588],\n [0.595292985439301, 0.485023975372314],\n [0.980530977249146, 0.401564002037048],\n [0.573499977588654, 0.420000016689301],\n [0.602994978427887, 0.548687994480133],\n [0.733529984951019, 0.376977026462555],\n [0.560611009597778, 0.519016981124878],\n [0.967685997486115, 0.644356966018677],\n [0.580985009670258, 0.387160003185272],\n [0.537728011608124, 0.505385041236877],\n [0.760966002941132, 0.779752969741821],\n [0.801778972148895, 0.831938028335571],\n [0.892440974712372, 0.54076099395752],\n [0.816350996494293, 0.740260004997253],\n [0.865594983100891, 0.333687007427216],\n [0.614073991775513, 0.883246004581451],\n [0.508952975273132, 0.579437971115112],\n [0.617941975593567, 0.508316040039062],\n [0.825608015060425, 0.397674977779388],\n [0.681214988231659, 0.39623498916626],\n [0.656635999679565, 0.400596976280212],\n [0.603900015354156, 0.710216999053955],\n [0.81208598613739, 0.588539004325867],\n [0.56801301240921, 0.944564998149872],\n [0.681007981300354, 0.898285031318665],\n [0.733752012252808, 0.869701027870178],\n [0.633830010890961, 0.398822009563446],\n [0.606792986392975, 0.39553701877594],\n [0.589659988880157, 0.391062021255493],\n [0.805015981197357, 0.342108011245728],\n [0.611334979534149, 0.362284004688263],\n [0.634037971496582, 0.355970978736877],\n [0.656635999679565, 0.355356991291046],\n [0.681214988231659, 0.35834002494812],\n [0.698584973812103, 0.363156020641327],\n [0.941866993904114, 0.319076001644135],\n [0.698584973812103, 0.387449026107788],\n [0.584177017211914, 0.624107003211975],\n [0.554318010807037, 0.566076993942261],\n [0.534153997898102, 0.62064003944397],\n [0.711217999458313, 0.819975018501282],\n [0.664629995822906, 0.852871000766754],\n [0.559099972248077, 0.902631998062134],\n [0.871706008911133, 0.791940987110138],\n [0.591234028339386, 0.373893976211548],\n [0.544341027736664, 0.451583981513977],\n [0.624562978744507, 0.924192011356354],\n [0.88577002286911, 0.615028977394104],\n [0.551338016986847, 0.695277988910675],\n [0.551980018615723, 0.704632043838501],\n [0.552887976169586, 0.715808033943176],\n [0.555167973041534, 0.730794012546539],\n [0.569944024085999, 0.767035007476807],\n [0.593203008174896, 0.685675978660583],\n [0.599261999130249, 0.681069016456604],\n [0.607599973678589, 0.677703022956848],\n [0.631937980651855, 0.663500010967255],\n [0.752032995223999, 0.601315021514893],\n [0.547226011753082, 0.420395016670227],\n [0.563543975353241, 0.359827995300293],\n [0.583841025829315, 0.368713974952698],\n [0.586614012718201, 0.692366003990173],\n [0.771915018558502, 0.683578014373779],\n [0.531597018241882, 0.352482974529266],\n [0.588370978832245, 0.804440975189209],\n [0.52079701423645, 0.442565023899078],\n [0.567984998226166, 0.493479013442993],\n [0.543282985687256, 0.819254994392395],\n [0.655317008495331, 0.745514988899231],\n [0.621008992195129, 0.574018001556396],\n [0.625559985637665, 0.78031200170517],\n [0.680198013782501, 0.570719003677368],\n [0.64276397228241, 0.604337990283966],\n [0.704662978649139, 0.621529996395111],\n [0.552012026309967, 0.862591981887817],\n [0.589071989059448, 0.508637011051178],\n [0.685944974422455, 0.775357007980347],\n [0.645735025405884, 0.812640011310577],\n [0.675342977046967, 0.703978002071381],\n [0.810858011245728, 0.646304965019226],\n [0.72012197971344, 0.714666962623596],\n [0.866151988506317, 0.682704985141754],\n [0.663187026977539, 0.644596993923187],\n [0.570082008838654, 0.466325998306274],\n [0.544561982154846, 0.548375964164734],\n [0.562758982181549, 0.558784961700439],\n [0.531987011432648, 0.530140042304993],\n [0.585271000862122, 0.335177004337311],\n [0.622952997684479, 0.32277899980545],\n [0.655896008014679, 0.320163011550903],\n [0.687132000923157, 0.322345972061157],\n [0.716481983661652, 0.333200991153717],\n [0.758756995201111, 0.382786989212036],\n [0.897013008594513, 0.468769013881683],\n [0.732392013072968, 0.424547016620636],\n [0.70211398601532, 0.433162987232208],\n [0.66652500629425, 0.433866024017334],\n [0.633504986763, 0.426087975502014],\n [0.603875994682312, 0.416586995124817],\n [0.579657971858978, 0.409945011138916],\n [0.992439985275269, 0.480777025222778],\n [0.567192018032074, 0.569419980049133],\n [0.54136598110199, 0.478899002075195],\n [0.526564002037048, 0.546118021011353],\n [0.523913025856018, 0.563830018043518],\n [0.531529009342194, 0.555056989192963],\n [0.566035985946655, 0.582329034805298],\n [0.51631098985672, 0.563053965568542],\n [0.5174720287323, 0.577877044677734],\n [0.573594987392426, 0.389806985855103],\n [0.560697972774506, 0.395331978797913],\n [0.549755990505219, 0.399751007556915],\n [0.710287988185883, 0.368252992630005],\n [0.723330020904541, 0.363372981548309],\n];\n\nconst TRI468 = [\n 127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n 151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n 157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n 181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n 228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n 170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n 155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n 158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n 102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n 90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n 196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n 246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n 117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n 153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n 63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n 135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n 76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n 166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n 190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n 112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n 110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n 348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n 314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n 321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n 466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n 429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n 293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n 427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n 431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n 434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n 348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n 318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n 373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n 340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n 351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n 164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n 327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n 405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n 438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n 449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n 444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n 464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n 243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n 166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n 119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n 77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n 201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n 103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n 217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n 78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n 57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n 142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n 90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n 178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n 224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n 143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n 153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n 63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n 241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n 186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n 141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n 223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n 128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n 451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n 344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n 424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n 321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n 448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n 281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n 283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n 432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n 268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n 6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n 317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n 272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n 336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n 441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n 373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n 425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n 403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n 383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n 254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n 259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n 453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nconst TRI68 = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n 8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n 45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n 23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n 39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n 35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n 48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n 66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\nconst TRI33 = [\n /* eyes */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n /* brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n /* 4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n /* nose */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n /* chin */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n /* cont */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\nconst TRI7 = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nconst VTX68 = [\n /* cont */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n /* nose */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n /* eyes */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n /* lip */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\nconst VTX33 = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\nconst VTX7 = [33, 133, 362, 263, 1, 78, 308];\n\nexports.MESH_ANNOTATIONS = MESH_ANNOTATIONS;\nexports.MESH_TO_IRIS_INDICES_MAP = MESH_TO_IRIS_INDICES_MAP;\n\nexports.TRI468 = TRI468;\nexports.TRI68 = TRI68;\nexports.TRI33 = TRI33;\nexports.TRI7 = TRI7;\n\nexports.UV468 = UV468;\nexports.UV68 = VTX68.map((x) => UV468[x]);\nexports.UV33 = VTX33.map((x) => UV468[x]);\nexports.UV7 = VTX7.map((x) => UV468[x]);\n", "/* eslint-disable class-methods-use-this */\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as bounding from './box';\nimport * as util from './util';\nimport * as coords from './coords.js';\n// eslint-disable-next-line no-unused-vars\nimport { log } from '../log.js';\n\nconst LANDMARKS_COUNT = 468;\nconst MESH_MOUTH_INDEX = 13;\nconst MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES = [MESH_MOUTH_INDEX, coords.MESH_ANNOTATIONS['midwayBetweenEyes'][0]];\nconst BLAZEFACE_MOUTH_INDEX = 3;\nconst BLAZEFACE_NOSE_INDEX = 2;\nconst BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES = [BLAZEFACE_MOUTH_INDEX, BLAZEFACE_NOSE_INDEX];\nconst LEFT_EYE_OUTLINE = coords.MESH_ANNOTATIONS['leftEyeLower0'];\nconst LEFT_EYE_BOUNDS = [LEFT_EYE_OUTLINE[0], LEFT_EYE_OUTLINE[LEFT_EYE_OUTLINE.length - 1]];\nconst RIGHT_EYE_OUTLINE = coords.MESH_ANNOTATIONS['rightEyeLower0'];\nconst RIGHT_EYE_BOUNDS = [RIGHT_EYE_OUTLINE[0], RIGHT_EYE_OUTLINE[RIGHT_EYE_OUTLINE.length - 1]];\nconst IRIS_UPPER_CENTER_INDEX = 3;\nconst IRIS_LOWER_CENTER_INDEX = 4;\nconst IRIS_IRIS_INDEX = 71;\nconst IRIS_NUM_COORDINATES = 76;\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates. Update the z coordinate to be an average of the original and the new. This produces the best visual effect.\nfunction replaceRawCoordinates(rawCoords, newCoords, prefix, keys) {\n for (let i = 0; i < coords.MESH_TO_IRIS_INDICES_MAP.length; i++) {\n const { key, indices } = coords.MESH_TO_IRIS_INDICES_MAP[i];\n const originalIndices = coords.MESH_ANNOTATIONS[`${prefix}${key}`];\n const shouldReplaceAllKeys = keys == null;\n if (shouldReplaceAllKeys || keys.includes(key)) {\n for (let j = 0; j < indices.length; j++) {\n const index = indices[j];\n rawCoords[originalIndices[j]] = [\n newCoords[index][0], newCoords[index][1],\n (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n ];\n }\n }\n }\n}\n// The Pipeline coordinates between the bounding box and skeleton models.\nclass Pipeline {\n constructor(boundingBoxDetector, meshDetector, irisModel, config) {\n // An array of facial bounding boxes.\n this.storedBoxes = [];\n this.runsWithoutFaceDetector = 0;\n this.boundingBoxDetector = boundingBoxDetector;\n this.meshDetector = meshDetector;\n this.irisModel = irisModel;\n this.meshWidth = config.face.mesh.inputSize;\n this.meshHeight = config.face.mesh.inputSize;\n this.irisSize = config.face.iris.inputSize;\n this.irisEnlarge = 2.3;\n this.skipped = 0;\n this.detectedFaces = 0;\n }\n\n transformRawCoords(rawCoords, box, angle, rotationMatrix) {\n // @ts-ignore\n const boxSize = bounding.getBoxSize({ startPoint: box.startPoint, endPoint: box.endPoint });\n const scaleFactor = [boxSize[0] / this.meshWidth, boxSize[1] / this.meshHeight];\n const coordsScaled = rawCoords.map((coord) => ([\n scaleFactor[0] * (coord[0] - this.meshWidth / 2),\n scaleFactor[1] * (coord[1] - this.meshHeight / 2), coord[2],\n ]));\n const coordsRotationMatrix = (angle !== 0) ? util.buildRotationMatrix(angle, [0, 0]) : util.IDENTITY_MATRIX;\n const coordsRotated = (angle !== 0) ? coordsScaled.map((coord) => ([...util.rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n const inverseRotationMatrix = (angle !== 0) ? util.invertTransformMatrix(rotationMatrix) : util.IDENTITY_MATRIX;\n // @ts-ignore\n const boxCenter = [...bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint }), 1];\n return coordsRotated.map((coord) => ([\n coord[0] + util.dot(boxCenter, inverseRotationMatrix[0]),\n coord[1] + util.dot(boxCenter, inverseRotationMatrix[1]),\n coord[2],\n ]));\n }\n\n getLeftToRightEyeDepthDifference(rawCoords) {\n const leftEyeZ = rawCoords[LEFT_EYE_BOUNDS[0]][2];\n const rightEyeZ = rawCoords[RIGHT_EYE_BOUNDS[0]][2];\n return leftEyeZ - rightEyeZ;\n }\n\n // Returns a box describing a cropped region around the eye fit for passing to the iris model.\n getEyeBox(rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, flip = false) {\n // @ts-ignore\n const box = bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), this.irisEnlarge));\n // @ts-ignore\n const boxSize = bounding.getBoxSize(box);\n let crop = tf.image.cropAndResize(face, [[\n box.startPoint[1] / this.meshHeight,\n box.startPoint[0] / this.meshWidth, box.endPoint[1] / this.meshHeight,\n box.endPoint[0] / this.meshWidth,\n ]], [0], [this.irisSize, this.irisSize]);\n if (flip) {\n crop = tf.image.flipLeftRight(crop);\n }\n return { box, boxSize, crop };\n }\n\n // Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\n getEyeCoords(eyeData, eyeBox, eyeBoxSize, flip = false) {\n const eyeRawCoords = [];\n for (let i = 0; i < IRIS_NUM_COORDINATES; i++) {\n const x = eyeData[i * 3];\n const y = eyeData[i * 3 + 1];\n const z = eyeData[i * 3 + 2];\n eyeRawCoords.push([\n (flip\n ? (1 - (x / this.irisSize))\n : (x / this.irisSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n (y / this.irisSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n ]);\n }\n return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(IRIS_IRIS_INDEX) };\n }\n\n // The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\n getAdjustedIrisCoords(rawCoords, irisCoords, direction) {\n const upperCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeUpper0`][IRIS_UPPER_CENTER_INDEX]][2];\n const lowerCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeLower0`][IRIS_LOWER_CENTER_INDEX]][2];\n const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n return irisCoords.map((coord, i) => {\n let z = averageZ;\n if (i === 2) {\n z = upperCenterZ;\n } else if (i === 4) {\n z = lowerCenterZ;\n }\n return [coord[0], coord[1], z];\n });\n }\n\n async predict(input, config) {\n let useFreshBox = false;\n // run new detector every skipFrames unless we only want box to start with\n let detector;\n if ((this.skipped === 0) || (this.skipped > config.face.detector.skipFrames) || !config.face.mesh.enabled || !config.videoOptimized) {\n detector = await this.boundingBoxDetector.getBoundingBoxes(input);\n this.skipped = 0;\n }\n if (config.videoOptimized) this.skipped++;\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (detector && detector.boxes && (detector.boxes.length > 0) && (!config.face.mesh.enabled || (detector.boxes.length !== this.detectedFaces) && (this.detectedFaces !== config.face.detector.maxFaces))) {\n this.storedBoxes = [];\n this.detectedFaces = 0;\n for (const possible of detector.boxes) {\n this.storedBoxes.push({ startPoint: possible.box.startPoint.dataSync(), endPoint: possible.box.endPoint.dataSync(), landmarks: possible.landmarks, confidence: possible.confidence });\n }\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\n\n if (useFreshBox) {\n if (!detector || !detector.boxes || (detector.boxes.length === 0)) {\n this.storedBoxes = [];\n this.detectedFaces = 0;\n return null;\n }\n for (let i = 0; i < this.storedBoxes.length; i++) {\n // @ts-ignore\n const scaledBox = bounding.scaleBoxCoordinates({ startPoint: this.storedBoxes[i].startPoint, endPoint: this.storedBoxes[i].endPoint }, detector.scaleFactor);\n // @ts-ignore\n const enlargedBox = bounding.enlargeBox(scaledBox);\n // @ts-ignore\n const squarifiedBox = bounding.squarifyBox(enlargedBox);\n const landmarks = this.storedBoxes[i].landmarks.arraySync();\n const confidence = this.storedBoxes[i].confidence;\n this.storedBoxes[i] = { ...squarifiedBox, confidence, landmarks };\n }\n this.runsWithoutFaceDetector = 0;\n }\n if (detector && detector.boxes) {\n detector.boxes.forEach((prediction) => {\n prediction.box.startPoint.dispose();\n prediction.box.endPoint.dispose();\n prediction.landmarks.dispose();\n });\n }\n\n // log('face', `skipped: ${this.skipped} max: ${config.face.detector.maxFaces} detected: ${this.detectedFaces} stored: ${this.storedBoxes.length} new: ${detector?.boxes?.length}`);\n let results = tf.tidy(() => this.storedBoxes.map((box, i) => {\n // The facial bounding box landmarks could come either from blazeface (if we are using a fresh box), or from the mesh model (if we are reusing an old box).\n let face;\n let angle = 0;\n let rotationMatrix;\n if (config.face.detector.rotation) {\n const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= LANDMARKS_COUNT) ? MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES : BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES;\n angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n // @ts-ignore\n const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n const rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized);\n rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n // @ts-ignore\n face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshHeight, this.meshWidth]).div(255);\n } else {\n rotationMatrix = util.IDENTITY_MATRIX;\n const cloned = input.clone();\n // @ts-ignore\n face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, cloned, [this.meshHeight, this.meshWidth]).div(255);\n }\n\n // if we're not going to produce mesh, don't spend time with further processing\n if (!config.face.mesh.enabled) {\n const prediction = {\n coords: null,\n box,\n faceConfidence: null,\n confidence: box.confidence,\n image: face,\n };\n return prediction;\n }\n\n const [, confidence, contourCoords] = this.meshDetector.predict(face); // The first returned tensor represents facial contours, which are included in the coordinates.\n const confidenceVal = confidence.dataSync()[0];\n if (confidenceVal < config.face.detector.minConfidence) return null; // if below confidence just exit\n const coordsReshaped = tf.reshape(contourCoords, [-1, 3]);\n let rawCoords = coordsReshaped.arraySync();\n\n if (config.face.iris.enabled) {\n const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = this.getEyeBox(rawCoords, face, LEFT_EYE_BOUNDS[0], LEFT_EYE_BOUNDS[1], true);\n const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = this.getEyeBox(rawCoords, face, RIGHT_EYE_BOUNDS[0], RIGHT_EYE_BOUNDS[1]);\n const eyePredictions = this.irisModel.predict(tf.concat([leftEyeCrop, rightEyeCrop]));\n const eyePredictionsData = eyePredictions.dataSync();\n const leftEyeData = eyePredictionsData.slice(0, IRIS_NUM_COORDINATES * 3);\n const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = this.getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n const rightEyeData = eyePredictionsData.slice(IRIS_NUM_COORDINATES * 3);\n const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = this.getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize);\n const leftToRightEyeDepthDifference = this.getLeftToRightEyeDepthDifference(rawCoords);\n if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left');\n replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right');\n // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged. So we only update a single contour line above and below the eye.\n } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n } else { // User is looking towards the left.\n replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n }\n const adjustedLeftIrisCoords = this.getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n const adjustedRightIrisCoords = this.getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n rawCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n }\n\n const transformedCoordsData = this.transformRawCoords(rawCoords, box, angle, rotationMatrix);\n // @ts-ignore\n const landmarksBox = bounding.enlargeBox(this.calculateLandmarksBoundingBox(transformedCoordsData));\n // @ts-ignore\n const squarifiedLandmarksBox = bounding.squarifyBox(landmarksBox);\n const transformedCoords = tf.tensor2d(transformedCoordsData);\n const prediction = {\n coords: transformedCoords,\n box: landmarksBox,\n faceConfidence: confidenceVal,\n confidence: box.confidence,\n image: face,\n };\n if (config.face.mesh.returnRawData) prediction.rawCoords = rawCoords;\n this.storedBoxes[i] = { ...squarifiedLandmarksBox, landmarks: transformedCoords.arraySync(), confidence: box.confidence, faceConfidence: confidenceVal };\n\n return prediction;\n }));\n results = results.filter((a) => a !== null);\n this.detectedFaces = results.length;\n return results;\n }\n\n calculateLandmarksBoundingBox(landmarks) {\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint, landmarks };\n }\n}\nexports.Pipeline = Pipeline;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as blazeface from './blazeface.js';\nimport * as facepipeline from './facepipeline.js';\nimport * as coords from './coords.js';\n\nclass MediaPipeFaceMesh {\n constructor(blazeFace, blazeMeshModel, irisModel, config) {\n // @ts-ignore\n this.facePipeline = new facepipeline.Pipeline(blazeFace, blazeMeshModel, irisModel, config);\n this.config = config;\n }\n\n async estimateFaces(input, config) {\n const predictions = await this.facePipeline.predict(input, config);\n const results = [];\n for (const prediction of (predictions || [])) {\n if (prediction.isDisposedInternal) continue; // guard against disposed tensors on long running operations such as pause in middle of processing\n const mesh = prediction.coords ? prediction.coords.arraySync() : null;\n const meshRaw = prediction.rawCoords;\n const annotations = {};\n if (mesh && mesh.length > 0) {\n for (const key of Object.keys(coords.MESH_ANNOTATIONS)) {\n annotations[key] = coords.MESH_ANNOTATIONS[key].map((index) => mesh[index]);\n }\n }\n const boxRaw = (config.face.mesh.returnRawData && prediction.box) ? { topLeft: prediction.box.startPoint, bottomRight: prediction.box.endPoint } : null;\n const box = prediction.box ? [\n Math.max(0, prediction.box.startPoint[0]),\n Math.max(0, prediction.box.startPoint[1]),\n Math.min(input.shape[2], prediction.box.endPoint[0]) - prediction.box.startPoint[0],\n Math.min(input.shape[1], prediction.box.endPoint[1]) - prediction.box.startPoint[1],\n ] : 0;\n results.push({\n confidence: prediction.confidence || 0,\n box,\n mesh,\n boxRaw,\n meshRaw,\n annotations,\n image: prediction.image ? tf.clone(prediction.image) : null,\n });\n if (prediction.coords) prediction.coords.dispose();\n if (prediction.image) prediction.image.dispose();\n }\n return results;\n }\n}\n\nlet faceModels = [null, null, null];\nasync function load(config) {\n faceModels = await Promise.all([\n // @ts-ignore\n (!faceModels[0] && config.face.enabled) ? blazeface.load(config) : null,\n (!faceModels[1] && config.face.mesh.enabled) ? tf.loadGraphModel(config.face.mesh.modelPath, { fromTFHub: config.face.mesh.modelPath.includes('tfhub.dev') }) : null,\n (!faceModels[2] && config.face.iris.enabled) ? tf.loadGraphModel(config.face.iris.modelPath, { fromTFHub: config.face.iris.modelPath.includes('tfhub.dev') }) : null,\n ]);\n const faceMesh = new MediaPipeFaceMesh(faceModels[0], faceModels[1], faceModels[2], config);\n if (config.face.mesh.enabled) log(`load model: ${config.face.mesh.modelPath.match(/\\/(.*)\\./)[1]}`);\n if (config.face.iris.enabled) log(`load model: ${config.face.iris.modelPath.match(/\\/(.*)\\./)[1]}`);\n return faceMesh;\n}\n\nexports.load = load;\nexports.MediaPipeFaceMesh = MediaPipeFaceMesh;\nexports.triangulation = coords.TRI468;\n", "import { log } from './log.js';\n\nconst profileData = {};\n\nfunction profile(name, data) {\n if (!data || !data.kernels) return;\n const maxResults = 5;\n const time = data.kernels\n .filter((a) => a.kernelTimeMs > 0)\n .reduce((a, b) => a += b.kernelTimeMs, 0);\n const slowest = data.kernels\n .map((a, i) => { a.id = i; return a; })\n .filter((a) => a.kernelTimeMs > 0)\n .sort((a, b) => b.kernelTimeMs - a.kernelTimeMs);\n const largest = data.kernels\n .map((a, i) => { a.id = i; return a; })\n .filter((a) => a.totalBytesSnapshot > 0)\n .sort((a, b) => b.totalBytesSnapshot - a.totalBytesSnapshot);\n if (slowest.length > maxResults) slowest.length = maxResults;\n if (largest.length > maxResults) largest.length = maxResults;\n const res = { newBytes: data.newBytes, newTensors: data.newTensors, peakBytes: data.peakBytes, numKernelOps: data.kernels.length, timeKernelOps: time, slowestKernelOps: slowest, largestKernelOps: largest };\n profileData[name] = res;\n log('Human profiler', name, res);\n}\n\nexports.run = profile;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile.js';\n\nconst models = {};\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nasync function load(config) {\n if (!models.age) {\n models.age = await tf.loadGraphModel(config.face.age.modelPath);\n log(`load model: ${config.face.age.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.age;\n}\n\nasync function predict(image, config) {\n if (!models.age) return null;\n if ((skipped < config.face.age.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n /*\n const zoom = [0, 0]; // 0..1 meaning 0%..100%\n const box = [[\n (image.shape[1] * zoom[0]) / image.shape[1],\n (image.shape[2] * zoom[1]) / image.shape[2],\n (image.shape[1] - (image.shape[1] * zoom[0])) / image.shape[1],\n (image.shape[2] - (image.shape[2] * zoom[1])) / image.shape[2],\n ]];\n const resize = tf.image.cropAndResize(image, box, [0], [config.face.age.inputSize, config.face.age.inputSize]);\n */\n const resize = tf.image.resizeBilinear(image, [config.face.age.inputSize, config.face.age.inputSize], false);\n const enhance = tf.mul(resize, [255.0]);\n tf.dispose(resize);\n\n let ageT;\n const obj = {};\n\n if (!config.profile) {\n if (config.face.age.enabled) ageT = await models.age.predict(enhance);\n } else {\n const profileAge = config.face.age.enabled ? await tf.profile(() => models.age.predict(enhance)) : {};\n ageT = profileAge.result.clone();\n profileAge.result.dispose();\n // @ts-ignore\n profile.run('age', profileAge);\n }\n enhance.dispose();\n\n if (ageT) {\n const data = ageT.dataSync();\n obj.age = Math.trunc(10 * data[0]) / 10;\n }\n ageT.dispose();\n\n last = obj;\n resolve(obj);\n });\n}\n\nexports.predict = predict;\nexports.load = load;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile.js';\n\nconst models = {};\nlet last = { gender: '' };\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet alternative = false;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nasync function load(config) {\n if (!models.gender) {\n models.gender = await tf.loadGraphModel(config.face.gender.modelPath);\n alternative = models.gender.inputs[0].shape[3] === 1;\n log(`load model: ${config.face.gender.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.gender;\n}\n\nasync function predict(image, config) {\n if (!models.gender) return null;\n if ((skipped < config.face.gender.skipFrames) && config.videoOptimized && last.gender !== '') {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [config.face.gender.inputSize, config.face.gender.inputSize], false);\n let enhance;\n if (alternative) {\n enhance = tf.tidy(() => {\n const [red, green, blue] = tf.split(resize, 3, 3);\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n return grayscale.sub(0.5).mul(2);\n });\n } else {\n enhance = tf.mul(resize, [255.0]);\n }\n tf.dispose(resize);\n\n let genderT;\n const obj = {};\n\n if (!config.profile) {\n if (config.face.gender.enabled) genderT = await models.gender.predict(enhance);\n } else {\n const profileGender = config.face.gender.enabled ? await tf.profile(() => models.gender.predict(enhance)) : {};\n genderT = profileGender.result.clone();\n profileGender.result.dispose();\n // @ts-ignore\n profile.run('gender', profileGender);\n }\n enhance.dispose();\n\n if (genderT) {\n const data = genderT.dataSync();\n if (alternative) {\n // returns two values 0..1, bigger one is prediction\n const confidence = Math.trunc(100 * Math.abs(data[0] - data[1])) / 100;\n if (confidence > config.face.gender.minConfidence) {\n obj.gender = data[0] > data[1] ? 'female' : 'male';\n obj.confidence = confidence;\n }\n } else {\n // returns one value 0..1, .5 is prediction threshold\n const confidence = Math.trunc(200 * Math.abs((data[0] - 0.5))) / 100;\n if (confidence > config.face.gender.minConfidence) {\n obj.gender = data[0] <= 0.5 ? 'female' : 'male';\n obj.confidence = Math.min(0.99, confidence);\n }\n }\n }\n genderT.dispose();\n\n last = obj;\n resolve(obj);\n });\n}\n\nexports.predict = predict;\nexports.load = load;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile.js';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];\nconst models = {};\nlet last = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\nconst scale = 1; // score multiplication factor\n\nasync function load(config) {\n if (!models.emotion) {\n models.emotion = await tf.loadGraphModel(config.face.emotion.modelPath);\n log(`load model: ${config.face.emotion.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.emotion;\n}\n\nasync function predict(image, config) {\n if (!models.emotion) return null;\n if ((skipped < config.face.emotion.skipFrames) && config.videoOptimized && (last.length > 0)) {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n /*\n const zoom = [0, 0]; // 0..1 meaning 0%..100%\n const box = [[\n (image.shape[1] * zoom[0]) / image.shape[1],\n (image.shape[2] * zoom[1]) / image.shape[2],\n (image.shape[1] - (image.shape[1] * zoom[0])) / image.shape[1],\n (image.shape[2] - (image.shape[2] * zoom[1])) / image.shape[2],\n ]];\n const resize = tf.image.cropAndResize(image, box, [0], [config.face.emotion.inputSize, config.face.emotion.inputSize]);\n */\n const resize = tf.image.resizeBilinear(image, [config.face.emotion.inputSize, config.face.emotion.inputSize], false);\n const [red, green, blue] = tf.split(resize, 3, 3);\n resize.dispose();\n // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n red.dispose();\n green.dispose();\n blue.dispose();\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n redNorm.dispose();\n greenNorm.dispose();\n blueNorm.dispose();\n const normalize = tf.tidy(() => grayscale.sub(0.5).mul(2));\n grayscale.dispose();\n const obj = [];\n if (config.face.emotion.enabled) {\n let data;\n if (!config.profile) {\n const emotionT = await models.emotion.predict(normalize);\n data = emotionT.dataSync();\n tf.dispose(emotionT);\n } else {\n const profileData = await tf.profile(() => models.emotion.predict(normalize));\n data = profileData.result.dataSync();\n profileData.result.dispose();\n // @ts-ignore\n profile.run('emotion', profileData);\n }\n for (let i = 0; i < data.length; i++) {\n if (scale * data[i] > config.face.emotion.minConfidence) obj.push({ score: Math.min(0.99, Math.trunc(100 * scale * data[i]) / 100), emotion: annotations[i] });\n }\n obj.sort((a, b) => b.score - a.score);\n }\n normalize.dispose();\n last = obj;\n resolve(obj);\n });\n}\n\nexports.predict = predict;\nexports.load = load;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile.js';\n\n// based on https://github.com/sirius-ai/MobileFaceNet_TF\n// model converted from https://github.com/sirius-ai/MobileFaceNet_TF/files/3551493/FaceMobileNet192_train_false.zip\n\nconst models = {};\n\nasync function load(config) {\n if (!models.embedding) {\n models.embedding = await tf.loadGraphModel(config.face.embedding.modelPath);\n log(`load model: ${config.face.embedding.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.embedding;\n}\n\nfunction simmilarity(embedding1, embedding2) {\n if (embedding1?.length !== embedding2?.length) return 0;\n // general minkowski distance\n // euclidean distance is limited case where order is 2\n const order = 2;\n const distance = 10.0 * ((embedding1.map((val, i) => (val - embedding2[i])).reduce((dist, diff) => dist + (diff ** order), 0) ** (1 / order)));\n return (Math.trunc(1000 * (1 - distance)) / 1000);\n}\n\nasync function predict(image, config) {\n if (!models.embedding) return null;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [config.face.embedding.inputSize, config.face.embedding.inputSize], false);\n // const normalize = tf.tidy(() => resize.div(127.5).sub(0.5)); // this is -0.5...0.5 ???\n let data = [];\n if (config.face.embedding.enabled) {\n if (!config.profile) {\n const embeddingT = await models.embedding.predict({ img_inputs: resize });\n data = [...embeddingT.dataSync()]; // convert object array to standard array\n tf.dispose(embeddingT);\n } else {\n const profileData = await tf.profile(() => models.embedding.predict({ img_inputs: resize }));\n data = [...profileData.result.dataSync()];\n profileData.result.dispose();\n // @ts-ignore\n profile.run('emotion', profileData);\n }\n }\n resize.dispose();\n // normalize.dispose();\n resolve(data);\n });\n}\n\nexports.predict = predict;\nexports.simmilarity = simmilarity;\nexports.load = load;\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nconst imageNetMean = [-123.15, -115.90, -103.06];\n\nfunction nameOutputResultsMobileNet(results) {\n const [offsets, heatmap, displacementFwd, displacementBwd] = results;\n return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nfunction nameOutputResultsResNet(results) {\n const [displacementFwd, displacementBwd, offsets, heatmap] = results;\n return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nclass BaseModel {\n constructor(model) {\n this.model = model;\n }\n\n predict(input, config) {\n return tf.tidy(() => {\n const asFloat = (config.body.modelType === 'ResNet') ? input.toFloat().add(imageNetMean) : input.toFloat().div(127.5).sub(1.0);\n const asBatch = asFloat.expandDims(0);\n const results = this.model.predict(asBatch);\n const results3d = results.map((y) => y.squeeze([0]));\n const namedResults = (config.body.modelType === 'ResNet') ? nameOutputResultsResNet(results3d) : nameOutputResultsMobileNet(results3d);\n return {\n heatmapScores: namedResults.heatmap.sigmoid(),\n offsets: namedResults.offsets,\n displacementFwd: namedResults.displacementFwd,\n displacementBwd: namedResults.displacementBwd,\n };\n });\n }\n\n dispose() {\n this.model.dispose();\n }\n}\nexports.BaseModel = BaseModel;\n", "// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nfunction half(k) {\n return Math.floor(k / 2);\n}\nclass MaxHeap {\n constructor(maxSize, getElementValue) {\n this.priorityQueue = new Array(maxSize);\n this.numberOfElements = -1;\n this.getElementValue = getElementValue;\n }\n\n enqueue(x) {\n this.priorityQueue[++this.numberOfElements] = x;\n this.swim(this.numberOfElements);\n }\n\n dequeue() {\n const max = this.priorityQueue[0];\n this.exchange(0, this.numberOfElements--);\n this.sink(0);\n this.priorityQueue[this.numberOfElements + 1] = null;\n return max;\n }\n\n empty() {\n return this.numberOfElements === -1;\n }\n\n size() {\n return this.numberOfElements + 1;\n }\n\n all() {\n return this.priorityQueue.slice(0, this.numberOfElements + 1);\n }\n\n max() {\n return this.priorityQueue[0];\n }\n\n swim(k) {\n while (k > 0 && this.less(half(k), k)) {\n this.exchange(k, half(k));\n k = half(k);\n }\n }\n\n sink(k) {\n while (2 * k <= this.numberOfElements) {\n let j = 2 * k;\n if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n if (!this.less(k, j)) break;\n this.exchange(k, j);\n k = j;\n }\n }\n\n getValueAt(i) {\n return this.getElementValue(this.priorityQueue[i]);\n }\n\n less(i, j) {\n return this.getValueAt(i) < this.getValueAt(j);\n }\n\n exchange(i, j) {\n const t = this.priorityQueue[i];\n this.priorityQueue[i] = this.priorityQueue[j];\n this.priorityQueue[j] = t;\n }\n}\nexports.MaxHeap = MaxHeap;\n", "import * as heapSort from './heapSort';\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores) {\n const [height, width] = scores.shape;\n let localMaximum = true;\n const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n localMaximum = false;\n break;\n }\n }\n if (!localMaximum) break;\n }\n return localMaximum;\n}\n\nfunction buildPartWithScoreQueue(scoreThreshold, localMaximumRadius, scores) {\n const [height, width, numKeypoints] = scores.shape;\n const queue = new heapSort.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n const score = scores.get(heatmapY, heatmapX, keypointId);\n // Only consider parts with score greater or equal to threshold as root candidates.\n if (score < scoreThreshold) continue;\n // Only consider keypoints whose score is maximum in a local window.\n if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores)) {\n queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n }\n }\n }\n }\n return queue;\n}\nexports.buildPartWithScoreQueue = buildPartWithScoreQueue;\n", "exports.partNames = [\n 'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n 'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n 'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexports.NUM_KEYPOINTS = exports.partNames.length;\n\nexports.partIds = exports.partNames.reduce((result, jointName, i) => {\n result[jointName] = i;\n return result;\n}, {});\n\nconst connectedPartNames = [\n ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexports.connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([exports.partIds[jointNameA], exports.partIds[jointNameB]]));\n\nexports.poseChain = [\n ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n ['rightKnee', 'rightAnkle'],\n];\n\nexports.partChannels = [\n 'left_face',\n 'right_face',\n 'right_upper_leg_front',\n 'right_lower_leg_back',\n 'right_upper_leg_back',\n 'left_lower_leg_front',\n 'left_upper_leg_front',\n 'left_upper_leg_back',\n 'left_lower_leg_back',\n 'right_feet',\n 'right_lower_leg_front',\n 'left_feet',\n 'torso_front',\n 'torso_back',\n 'right_upper_arm_front',\n 'right_upper_arm_back',\n 'right_lower_arm_back',\n 'left_lower_arm_front',\n 'left_upper_arm_front',\n 'left_upper_arm_back',\n 'left_lower_arm_back',\n 'right_hand',\n 'right_lower_arm_front',\n 'left_hand',\n];\n", "import * as kpt from './keypoints';\n\nfunction getOffsetPoint(y, x, keypoint, offsets) {\n return {\n y: offsets.get(y, x, keypoint),\n x: offsets.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n };\n}\nexports.getOffsetPoint = getOffsetPoint;\n\nfunction getImageCoords(part, outputStride, offsets) {\n const { heatmapY, heatmapX, id: keypoint } = part;\n const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n return {\n x: part.heatmapX * outputStride + x,\n y: part.heatmapY * outputStride + y,\n };\n}\nexports.getImageCoords = getImageCoords;\n\nfunction fillArray(element, size) {\n const result = new Array(size);\n for (let i = 0; i < size; i++) {\n result[i] = element;\n }\n return result;\n}\nexports.fillArray = fillArray;\n\nfunction clamp(a, min, max) {\n if (a < min) return min;\n if (a > max) return max;\n return a;\n}\nexports.clamp = clamp;\n\nfunction squaredDistance(y1, x1, y2, x2) {\n const dy = y2 - y1;\n const dx = x2 - x1;\n return dy * dy + dx * dx;\n}\nexports.squaredDistance = squaredDistance;\n\nfunction addVectors(a, b) {\n return { x: a.x + b.x, y: a.y + b.y };\n}\nexports.addVectors = addVectors;\n\nfunction clampVector(a, min, max) {\n return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\nexports.clampVector = clampVector;\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as kpt from './keypoints';\n\nfunction getPointsConfidence(heatmapScores, heatMapCoords) {\n const numKeypoints = heatMapCoords.shape[0];\n const result = new Float32Array(numKeypoints);\n for (let keypoint = 0; keypoint < numKeypoints; keypoint++) {\n const y = heatMapCoords.get(keypoint, 0);\n const x = heatMapCoords.get(keypoint, 1);\n result[keypoint] = heatmapScores.get(y, x, keypoint);\n }\n return result;\n}\nexports.getPointsConfidence = getPointsConfidence;\n\nfunction getOffsetPoint(y, x, keypoint, offsetsBuffer) {\n return {\n y: offsetsBuffer.get(y, x, keypoint),\n x: offsetsBuffer.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n };\n}\n\nfunction getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer) {\n const result = [];\n for (let keypoint = 0; keypoint < kpt.NUM_KEYPOINTS; keypoint++) {\n const heatmapY = heatMapCoordsBuffer.get(keypoint, 0).valueOf();\n const heatmapX = heatMapCoordsBuffer.get(keypoint, 1).valueOf();\n const { x, y } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsetsBuffer);\n result.push(y);\n result.push(x);\n }\n return tf.tensor2d(result, [kpt.NUM_KEYPOINTS, 2]);\n}\nexports.getOffsetVectors = getOffsetVectors;\n\nfunction getOffsetPoints(heatMapCoordsBuffer, outputStride, offsetsBuffer) {\n return tf.tidy(() => heatMapCoordsBuffer.toTensor().mul(tf.scalar(outputStride, 'int32')).toFloat().add(getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer)));\n}\nexports.getOffsetPoints = getOffsetPoints;\n\nfunction mod(a, b) {\n return tf.tidy(() => {\n const floored = a.div(tf.scalar(b, 'int32'));\n return a.sub(floored.mul(tf.scalar(b, 'int32')));\n });\n}\n\nfunction argmax2d(inputs) {\n const [height, width, depth] = inputs.shape;\n return tf.tidy(() => {\n const reshaped = inputs.reshape([height * width, depth]);\n const coords = reshaped.argMax(0);\n const yCoords = coords.div(tf.scalar(width, 'int32')).expandDims(1);\n const xCoords = mod(coords, width).expandDims(1);\n return tf.concat([yCoords, xCoords], 1);\n });\n}\nexports.argmax2d = argmax2d;\n", "import * as keypoints from './keypoints';\nimport * as vectors from './vectors';\nimport * as decoders from './decoders';\n\nconst parentChildrenTuples = keypoints.poseChain.map(([parentJoinName, childJoinName]) => ([keypoints.partIds[parentJoinName], keypoints.partIds[childJoinName]]));\nconst parentToChildEdges = parentChildrenTuples.map(([, childJointId]) => childJointId);\nconst childToParentEdges = parentChildrenTuples.map(([parentJointId]) => parentJointId);\nfunction getDisplacement(edgeId, point, displacements) {\n const numEdges = displacements.shape[2] / 2;\n return {\n y: displacements.get(point.y, point.x, edgeId),\n x: displacements.get(point.y, point.x, numEdges + edgeId),\n };\n}\nfunction getStridedIndexNearPoint(point, outputStride, height, width) {\n return {\n // @ts-ignore\n y: vectors.clamp(Math.round(point.y / outputStride), 0, height - 1),\n // @ts-ignore\n x: vectors.clamp(Math.round(point.x / outputStride), 0, width - 1),\n };\n}\n\nfunction traverseToTargetKeypoint(edgeId, sourceKeypoint, targetKeypointId, scoresBuffer, offsets, outputStride, displacements, offsetRefineStep = 2) {\n const [height, width] = scoresBuffer.shape;\n // Nearest neighbor interpolation for the source->target displacements.\n const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, outputStride, height, width);\n const displacement = getDisplacement(edgeId, sourceKeypointIndices, displacements);\n // @ts-ignore\n const displacedPoint = vectors.addVectors(sourceKeypoint.position, displacement);\n let targetKeypoint = displacedPoint;\n for (let i = 0; i < offsetRefineStep; i++) {\n const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n // @ts-ignore\n const offsetPoint = vectors.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetKeypointId, offsets);\n // @ts-ignore\n targetKeypoint = vectors.addVectors({\n x: targetKeypointIndices.x * outputStride,\n y: targetKeypointIndices.y * outputStride,\n }, { x: offsetPoint.x, y: offsetPoint.y });\n }\n const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n const score = scoresBuffer.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetKeypointId);\n return { position: targetKeypoint, part: keypoints.partNames[targetKeypointId], score };\n}\n\nfunction decodePose(root, scores, offsets, outputStride, displacementsFwd, displacementsBwd) {\n const numParts = scores.shape[2];\n const numEdges = parentToChildEdges.length;\n const instanceKeypoints = new Array(numParts);\n // Start a new detection instance at the position of the root.\n const { part: rootPart, score: rootScore } = root;\n // @ts-ignore\n const rootPoint = vectors.getImageCoords(rootPart, outputStride, offsets);\n instanceKeypoints[rootPart.id] = {\n score: rootScore,\n part: keypoints.partNames[rootPart.id],\n position: rootPoint,\n };\n // Decode the part positions upwards in the tree, following the backward displacements.\n for (let edge = numEdges - 1; edge >= 0; --edge) {\n const sourceKeypointId = parentToChildEdges[edge];\n const targetKeypointId = childToParentEdges[edge];\n if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsBwd);\n }\n }\n // Decode the part positions downwards in the tree, following the forward displacements.\n for (let edge = 0; edge < numEdges; ++edge) {\n const sourceKeypointId = childToParentEdges[edge];\n const targetKeypointId = parentToChildEdges[edge];\n if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsFwd);\n }\n }\n return instanceKeypoints;\n}\nexports.decodePose = decodePose;\n\nasync function decodeSinglePose(heatmapScores, offsets, config) {\n let totalScore = 0.0;\n // @ts-ignore\n const heatmapValues = decoders.argmax2d(heatmapScores);\n const allTensorBuffers = await Promise.all([heatmapScores.buffer(), offsets.buffer(), heatmapValues.buffer()]);\n const scoresBuffer = allTensorBuffers[0];\n const offsetsBuffer = allTensorBuffers[1];\n const heatmapValuesBuffer = allTensorBuffers[2];\n // @ts-ignore\n const offsetPoints = decoders.getOffsetPoints(heatmapValuesBuffer, config.body.outputStride, offsetsBuffer);\n const offsetPointsBuffer = await offsetPoints.buffer();\n // @ts-ignore\n const keypointConfidence = Array.from(decoders.getPointsConfidence(scoresBuffer, heatmapValuesBuffer));\n const instanceKeypoints = keypointConfidence.map((score, i) => {\n totalScore += score;\n return {\n position: {\n y: offsetPointsBuffer.get(i, 0),\n x: offsetPointsBuffer.get(i, 1),\n },\n part: keypoints.partNames[i],\n score,\n };\n });\n const filteredKeypoints = instanceKeypoints.filter((kpt) => kpt.score > config.body.scoreThreshold);\n heatmapValues.dispose();\n offsetPoints.dispose();\n return { keypoints: filteredKeypoints, score: totalScore / instanceKeypoints.length };\n}\nexports.decodeSinglePose = decodeSinglePose;\n", "import * as buildParts from './buildParts';\nimport * as decodePose from './decodePose';\nimport * as vectors from './vectors';\n\nconst kLocalMaximumRadius = 1;\n\nfunction withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, { x, y }, keypointId) {\n return poses.some(({ keypoints }) => {\n const correspondingKeypoint = keypoints[keypointId].position;\n // @ts-ignore\n return vectors.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n });\n}\n\nfunction getInstanceScore(existingPoses, squaredNmsRadius, instanceKeypoints) {\n const notOverlappedKeypointScores = instanceKeypoints.reduce((result, { position, score }, keypointId) => {\n if (!withinNmsRadiusOfCorrespondingPoint(existingPoses, squaredNmsRadius, position, keypointId)) result += score;\n return result;\n }, 0.0);\n return notOverlappedKeypointScores / instanceKeypoints.length;\n}\n\nfunction decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config) {\n const poses = [];\n // @ts-ignore\n const queue = buildParts.buildPartWithScoreQueue(config.body.scoreThreshold, kLocalMaximumRadius, scoresBuffer);\n const squaredNmsRadius = config.body.nmsRadius ^ 2;\n // Generate at most maxDetections object instances per image in decreasing root part score order.\n while (poses.length < config.body.maxDetections && !queue.empty()) {\n // The top element in the queue is the next root candidate.\n const root = queue.dequeue();\n // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n // @ts-ignore\n const rootImageCoords = vectors.getImageCoords(root.part, config.body.outputStride, offsetsBuffer);\n if (withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, rootImageCoords, root.part.id)) continue;\n // Else start a new detection instance at the position of the root.\n // @ts-ignore\n const keypoints = decodePose.decodePose(root, scoresBuffer, offsetsBuffer, config.body.outputStride, displacementsFwdBuffer, displacementsBwdBuffer);\n const score = getInstanceScore(poses, squaredNmsRadius, keypoints);\n if (score > config.body.scoreThreshold) poses.push({ keypoints, score });\n }\n return poses;\n}\nexports.decodeMultiplePoses = decodeMultiplePoses;\n", "import * as kpt from './keypoints';\n\nfunction eitherPointDoesntMeetConfidence(a, b, minConfidence) {\n return (a < minConfidence || b < minConfidence);\n}\n\nfunction getAdjacentKeyPoints(keypoints, minConfidence) {\n return kpt.connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n return result;\n }\n result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n return result;\n }, []);\n}\nexports.getAdjacentKeyPoints = getAdjacentKeyPoints;\n\nconst { NEGATIVE_INFINITY, POSITIVE_INFINITY } = Number;\nfunction getBoundingBox(keypoints) {\n return keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n maxX: Math.max(maxX, x),\n maxY: Math.max(maxY, y),\n minX: Math.min(minX, x),\n minY: Math.min(minY, y),\n }), {\n maxX: NEGATIVE_INFINITY,\n maxY: NEGATIVE_INFINITY,\n minX: POSITIVE_INFINITY,\n minY: POSITIVE_INFINITY,\n });\n}\nexports.getBoundingBox = getBoundingBox;\n\nfunction getBoundingBoxPoints(keypoints) {\n const { minX, minY, maxX, maxY } = getBoundingBox(keypoints);\n return [{ x: minX, y: minY }, { x: maxX, y: minY }, { x: maxX, y: maxY }, { x: minX, y: maxY }];\n}\nexports.getBoundingBoxPoints = getBoundingBoxPoints;\n\nasync function toTensorBuffers3D(tensors) {\n return Promise.all(tensors.map((tensor) => tensor.buffer()));\n}\nexports.toTensorBuffers3D = toTensorBuffers3D;\n\nfunction scalePose(pose, scaleY, scaleX) {\n return {\n score: pose.score,\n keypoints: pose.keypoints.map(({ score, part, position }) => ({\n score,\n part,\n position: { x: position.x * scaleX, y: position.y * scaleY },\n })),\n };\n}\nexports.scalePose = scalePose;\n\nfunction resizeTo(image, [targetH, targetW]) {\n const input = image.squeeze(0);\n const resized = input.resizeBilinear([targetH, targetW]);\n input.dispose();\n return resized;\n}\nexports.resizeTo = resizeTo;\n\nfunction scaleAndFlipPoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) {\n const scaledPoses = poses.map((pose) => scalePose(pose, height / inputResolutionHeight, width / inputResolutionWidth));\n return scaledPoses;\n}\nexports.scaleAndFlipPoses = scaleAndFlipPoses;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as modelBase from './modelBase';\nimport * as decodeMultiple from './decodeMultiple';\nimport * as decodePose from './decodePose';\nimport * as util from './util';\n\nasync function estimateMultiple(input, res, config) {\n return new Promise(async (resolve) => {\n const height = input.shape[1];\n const width = input.shape[2];\n // @ts-ignore\n const allTensorBuffers = await util.toTensorBuffers3D([res.heatmapScores, res.offsets, res.displacementFwd, res.displacementBwd]);\n const scoresBuffer = allTensorBuffers[0];\n const offsetsBuffer = allTensorBuffers[1];\n const displacementsFwdBuffer = allTensorBuffers[2];\n const displacementsBwdBuffer = allTensorBuffers[3];\n // @ts-ignore\n const poses = await decodeMultiple.decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config);\n // @ts-ignore\n const scaled = util.scaleAndFlipPoses(poses, [height, width], [config.body.inputSize, config.body.inputSize]);\n resolve(scaled);\n });\n}\n\nasync function estimateSingle(input, res, config) {\n return new Promise(async (resolve) => {\n const height = input.shape[1];\n const width = input.shape[2];\n // @ts-ignore\n const pose = await decodePose.decodeSinglePose(res.heatmapScores, res.offsets, config);\n const poses = [pose];\n // @ts-ignore\n const scaled = util.scaleAndFlipPoses(poses, [height, width], [config.body.inputSize, config.body.inputSize]);\n resolve(scaled);\n });\n}\n\nclass PoseNet {\n constructor(model) {\n this.baseModel = model;\n }\n\n async estimatePoses(input, config) {\n // @ts-ignore\n const resized = util.resizeTo(input, [config.body.inputSize, config.body.inputSize]);\n const res = this.baseModel.predict(resized, config);\n\n const poses = (config.body.maxDetections < 2) ? await estimateSingle(input, res, config) : await estimateMultiple(input, res, config);\n\n res.heatmapScores.dispose();\n res.offsets.dispose();\n res.displacementFwd.dispose();\n res.displacementBwd.dispose();\n resized.dispose();\n\n return poses;\n }\n\n dispose() {\n this.baseModel.dispose();\n }\n}\nexports.PoseNet = PoseNet;\n\nasync function load(config) {\n const model = await tf.loadGraphModel(config.body.modelPath);\n // @ts-ignore\n const mobilenet = new modelBase.BaseModel(model);\n log(`load model: ${config.body.modelPath.match(/\\/(.*)\\./)[1]}`);\n return new PoseNet(mobilenet);\n}\nexports.load = load;\n", "import * as modelPoseNet from './modelPoseNet';\nimport * as keypoints from './keypoints';\nimport * as util from './util';\n\n// @ts-ignore\nexports.load = modelPoseNet.load;\n// @ts-ignore\nexports.PoseNet = modelPoseNet.PoseNet;\n\nexports.partChannels = keypoints.partChannels;\nexports.partIds = keypoints.partIds;\nexports.partNames = keypoints.partNames;\nexports.poseChain = keypoints.poseChain;\n// @ts-ignore\nexports.getAdjacentKeyPoints = util.getAdjacentKeyPoints;\n// @ts-ignore\nexports.getBoundingBox = util.getBoundingBox;\n// @ts-ignore\nexports.getBoundingBoxPoints = util.getBoundingBoxPoints;\n// @ts-ignore\nexports.scaleAndFlipPoses = util.scaleAndFlipPoses;\n// @ts-ignore\nexports.scalePose = util.scalePose;\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\n\nclass HandDetector {\n constructor(model, inputSize, anchorsAnnotated) {\n this.model = model;\n this.anchors = anchorsAnnotated.map((anchor) => [anchor.x_center, anchor.y_center]);\n this.anchorsTensor = tf.tensor2d(this.anchors);\n this.inputSizeTensor = tf.tensor1d([inputSize, inputSize]);\n this.doubleInputSizeTensor = tf.tensor1d([inputSize * 2, inputSize * 2]);\n }\n\n normalizeBoxes(boxes) {\n return tf.tidy(() => {\n const boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n const boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n const boxCenterPoints = tf.add(tf.div(boxOffsets, this.inputSizeTensor), this.anchorsTensor);\n const halfBoxSizes = tf.div(boxSizes, this.doubleInputSizeTensor);\n const startPoints = tf.mul(tf.sub(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n const endPoints = tf.mul(tf.add(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n return tf.concat2d([startPoints, endPoints], 1);\n });\n }\n\n normalizeLandmarks(rawPalmLandmarks, index) {\n return tf.tidy(() => {\n const landmarks = tf.add(tf.div(rawPalmLandmarks.reshape([-1, 7, 2]), this.inputSizeTensor), this.anchors[index]);\n return tf.mul(landmarks, this.inputSizeTensor);\n });\n }\n\n async getBoxes(input, config) {\n const batched = this.model.predict(input);\n const predictions = batched.squeeze();\n batched.dispose();\n const scoresT = tf.tidy(() => tf.sigmoid(tf.slice(predictions, [0, 0], [-1, 1])).squeeze());\n const scores = scoresT.dataSync();\n const rawBoxes = tf.slice(predictions, [0, 1], [-1, 4]);\n const boxes = this.normalizeBoxes(rawBoxes);\n rawBoxes.dispose();\n const filteredT = await tf.image.nonMaxSuppressionAsync(boxes, scores, config.hand.maxHands, config.hand.iouThreshold, config.hand.scoreThreshold);\n const filtered = filteredT.arraySync();\n\n scoresT.dispose();\n filteredT.dispose();\n const hands = [];\n for (const index of filtered) {\n if (scores[index] >= config.hand.minConfidence) {\n const matchingBox = tf.slice(boxes, [index, 0], [1, -1]);\n const rawPalmLandmarks = tf.slice(predictions, [index, 5], [1, 14]);\n const palmLandmarks = tf.tidy(() => this.normalizeLandmarks(rawPalmLandmarks, index).reshape([-1, 2]));\n rawPalmLandmarks.dispose();\n hands.push({ box: matchingBox, palmLandmarks, confidence: scores[index] });\n }\n }\n predictions.dispose();\n boxes.dispose();\n return hands;\n }\n\n async estimateHandBounds(input, config) {\n const inputHeight = input.shape[1];\n const inputWidth = input.shape[2];\n const image = tf.tidy(() => input.resizeBilinear([config.hand.inputSize, config.hand.inputSize]).div(127.5).sub(1));\n const predictions = await this.getBoxes(image, config);\n image.dispose();\n const hands = [];\n if (!predictions || predictions.length === 0) return hands;\n for (const prediction of predictions) {\n const boxes = prediction.box.dataSync();\n const startPoint = boxes.slice(0, 2);\n const endPoint = boxes.slice(2, 4);\n const palmLandmarks = prediction.palmLandmarks.arraySync();\n prediction.box.dispose();\n prediction.palmLandmarks.dispose();\n hands.push(box.scaleBoxCoordinates({ startPoint, endPoint, palmLandmarks, confidence: prediction.confidence }, [inputWidth / config.hand.inputSize, inputHeight / config.hand.inputSize]));\n }\n return hands;\n }\n}\nexports.HandDetector = HandDetector;\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\nimport * as util from './util';\n// eslint-disable-next-line no-unused-vars\nimport { log } from '../log.js';\n\n// const PALM_BOX_SHIFT_VECTOR = [0, -0.4];\nconst PALM_BOX_ENLARGE_FACTOR = 5; // default 3\n// const HAND_BOX_SHIFT_VECTOR = [0, -0.1]; // move detected hand box by x,y to ease landmark detection\nconst HAND_BOX_ENLARGE_FACTOR = 1.65; // default 1.65\nconst PALM_LANDMARK_IDS = [0, 5, 9, 13, 17, 1, 2];\nconst PALM_LANDMARKS_INDEX_OF_PALM_BASE = 0;\nconst PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE = 2;\n\nclass HandPipeline {\n constructor(handDetector, landmarkDetector, inputSize) {\n this.handDetector = handDetector;\n this.landmarkDetector = landmarkDetector;\n this.inputSize = inputSize;\n this.storedBoxes = [];\n this.skipped = 0;\n this.detectedHands = 0;\n }\n\n getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n // return box.enlargeBox(box.squarifyBox(box.shiftBox(boxAroundPalm, PALM_BOX_SHIFT_VECTOR)), PALM_BOX_ENLARGE_FACTOR);\n return box.enlargeBox(box.squarifyBox(boxAroundPalm), PALM_BOX_ENLARGE_FACTOR);\n }\n\n getBoxForHandLandmarks(landmarks) {\n const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n // const boxAroundHand = box.enlargeBox(box.squarifyBox(box.shiftBox(boundingBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n const boxAroundHand = box.enlargeBox(box.squarifyBox(boundingBox), HAND_BOX_ENLARGE_FACTOR);\n boxAroundHand.palmLandmarks = [];\n for (let i = 0; i < PALM_LANDMARK_IDS.length; i++) {\n boxAroundHand.palmLandmarks.push(landmarks[PALM_LANDMARK_IDS[i]].slice(0, 2));\n }\n return boxAroundHand;\n }\n\n transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n const boxSize = box.getBoxSize(box2);\n const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n const coordsScaled = rawCoords.map((coord) => [\n scaleFactor[0] * (coord[0] - this.inputSize / 2),\n scaleFactor[1] * (coord[1] - this.inputSize / 2),\n scaleFactor[2] * coord[2],\n ]);\n const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n const coordsRotated = coordsScaled.map((coord) => {\n const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n return [...rotated, coord[2]];\n });\n const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n const boxCenter = [...box.getBoxCenter(box2), 1];\n const originalBoxCenter = [\n util.dot(boxCenter, inverseRotationMatrix[0]),\n util.dot(boxCenter, inverseRotationMatrix[1]),\n ];\n return coordsRotated.map((coord) => [\n coord[0] + originalBoxCenter[0],\n coord[1] + originalBoxCenter[1],\n coord[2],\n ]);\n }\n\n async estimateHands(image, config) {\n let useFreshBox = false;\n\n // run new detector every skipFrames unless we only want box to start with\n let boxes;\n if ((this.skipped === 0) || (this.skipped > config.hand.skipFrames) || !config.hand.landmarks || !config.videoOptimized) {\n boxes = await this.handDetector.estimateHandBounds(image, config);\n this.skipped = 0;\n }\n if (config.videoOptimized) this.skipped++;\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxHands) || !config.hand.landmarks)) {\n this.detectedHands = 0;\n this.storedBoxes = [...boxes];\n // for (const possible of boxes) this.storedBoxes.push(possible);\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\n const hands = [];\n // log('hand', `skipped: ${this.skipped} max: ${config.hand.maxHands} detected: ${this.detectedHands} stored: ${this.storedBoxes.length} new: ${boxes?.length}`);\n\n // go through working set of boxes\n for (let i = 0; i < this.storedBoxes.length; i++) {\n const currentBox = this.storedBoxes[i];\n if (!currentBox) continue;\n if (config.hand.landmarks) {\n const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_PALM_BASE], currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE]) : 0;\n const palmCenter = box.getBoxCenter(currentBox);\n const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n const rotatedImage = config.hand.rotation ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n const croppedInput = box.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n const handImage = croppedInput.div(255);\n croppedInput.dispose();\n rotatedImage.dispose();\n const [confidenceT, keypoints] = await this.landmarkDetector.predict(handImage);\n handImage.dispose();\n const confidence = confidenceT.dataSync()[0];\n confidenceT.dispose();\n if (confidence >= config.hand.minConfidence) {\n const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n const rawCoords = keypointsReshaped.arraySync();\n keypoints.dispose();\n keypointsReshaped.dispose();\n const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n this.storedBoxes[i] = nextBoundingBox;\n const result = {\n landmarks: coords,\n confidence,\n box: {\n topLeft: nextBoundingBox.startPoint,\n bottomRight: nextBoundingBox.endPoint,\n },\n };\n hands.push(result);\n } else {\n this.storedBoxes[i] = null;\n }\n keypoints.dispose();\n } else {\n // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n const enlarged = box.enlargeBox(box.squarifyBox(currentBox), HAND_BOX_ENLARGE_FACTOR);\n const result = {\n confidence: currentBox.confidence,\n box: {\n topLeft: enlarged.startPoint,\n bottomRight: enlarged.endPoint,\n },\n };\n hands.push(result);\n }\n }\n this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n this.detectedHands = hands.length;\n return hands;\n }\n\n // eslint-disable-next-line class-methods-use-this\n calculateLandmarksBoundingBox(landmarks) {\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint };\n }\n}\n\nexports.HandPipeline = HandPipeline;\n", "exports.anchors = [\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.796875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.796875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.828125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.828125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.859375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.859375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.890625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.890625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.921875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.921875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.953125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.953125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.984375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.984375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n 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0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n];\n", "// https://storage.googleapis.com/tfjs-models/demos/handpose/index.html\n\nimport { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as handdetector from './handdetector';\nimport * as handpipeline from './handpipeline';\nimport * as anchors from './anchors';\n\nconst MESH_ANNOTATIONS = {\n thumb: [1, 2, 3, 4],\n indexFinger: [5, 6, 7, 8],\n middleFinger: [9, 10, 11, 12],\n ringFinger: [13, 14, 15, 16],\n pinky: [17, 18, 19, 20],\n palmBase: [0],\n};\n\nclass HandPose {\n constructor(handPipeline) {\n this.handPipeline = handPipeline;\n }\n\n static getAnnotations() {\n return MESH_ANNOTATIONS;\n }\n\n async estimateHands(input, config) {\n const predictions = await this.handPipeline.estimateHands(input, config);\n if (!predictions) return [];\n const hands = [];\n for (const prediction of predictions) {\n const annotations = {};\n if (prediction.landmarks) {\n for (const key of Object.keys(MESH_ANNOTATIONS)) {\n annotations[key] = MESH_ANNOTATIONS[key].map((index) => prediction.landmarks[index]);\n }\n }\n const box = prediction.box ? [\n Math.max(0, prediction.box.topLeft[0]),\n Math.max(0, prediction.box.topLeft[1]),\n Math.min(input.shape[2], prediction.box.bottomRight[0]) - prediction.box.topLeft[0],\n Math.min(input.shape[1], prediction.box.bottomRight[1]) - prediction.box.topLeft[1],\n ] : 0;\n hands.push({\n confidence: prediction.confidence,\n box,\n landmarks: prediction.landmarks,\n annotations,\n });\n }\n return hands;\n }\n}\nexports.HandPose = HandPose;\n\nasync function load(config) {\n const [handDetectorModel, handPoseModel] = await Promise.all([\n config.hand.enabled ? tf.loadGraphModel(config.hand.detector.modelPath, { fromTFHub: config.hand.detector.modelPath.includes('tfhub.dev') }) : null,\n config.hand.landmarks ? tf.loadGraphModel(config.hand.skeleton.modelPath, { fromTFHub: config.hand.skeleton.modelPath.includes('tfhub.dev') }) : null,\n ]);\n // @ts-ignore\n const handDetector = new handdetector.HandDetector(handDetectorModel, config.hand.inputSize, anchors.anchors);\n // @ts-ignore\n const handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel, config.hand.inputSize);\n const handPose = new HandPose(handPipeline);\n if (config.hand.enabled) log(`load model: ${config.hand.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n if (config.hand.landmarks) log(`load model: ${config.hand.skeleton.modelPath.match(/\\/(.*)\\./)[1]}`);\n return handPose;\n}\nexports.load = load;\n", "exports.body = (res) => {\n if (!res) return [];\n const gestures = [];\n for (let i = 0; i < res.length; i++) {\n // raising hands\n const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n if (nose && leftWrist && rightWrist && (leftWrist.position.y < nose.position.y) && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'i give up' });\n else if (nose && leftWrist && (leftWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise left hand' });\n else if (nose && rightWrist && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise right hand' });\n\n // leaning\n const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n if (leftShoulder && rightShoulder) gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position.y > rightShoulder.position.y) ? 'left' : 'right'}` });\n }\n return gestures;\n};\n\nexports.face = (res) => {\n if (!res) return [];\n const gestures = [];\n for (let i = 0; i < res.length; i++) {\n if (res[i].mesh && res[i].mesh.length > 0) {\n const eyeFacing = res[i].mesh[35][2] - res[i].mesh[263][2];\n if (Math.abs(eyeFacing) < 10) gestures.push({ face: i, gesture: 'facing camera' });\n else gestures.push({ face: i, gesture: `facing ${eyeFacing < 0 ? 'right' : 'left'}` });\n const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n const chinDepth = res[i].mesh[152][2];\n if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n }\n }\n return gestures;\n};\n\nexports.iris = (res) => {\n if (!res) return [];\n const gestures = [];\n for (let i = 0; i < res.length; i++) {\n if (!res[i].annotations || !res[i].annotations.leftEyeIris || !res[i].annotations.rightEyeIris) continue;\n const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n if (difference < 0.25) gestures.push({ iris: i, gesture: 'looking at camera' });\n }\n return gestures;\n};\n\nexports.hand = (res) => {\n if (!res) return [];\n const gestures = [];\n for (let i = 0; i < res.length; i++) {\n const fingers = [];\n for (const [finger, pos] of Object.entries(res[i]['annotations'])) {\n if (finger !== 'palmBase') fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n }\n if (fingers && fingers.length > 0) {\n const closest = fingers.reduce((best, a) => (best.position[2] < a.position[2] ? best : a));\n const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n gestures.push({ hand: i, gesture: `${closest.name} forward ${highest.name} up` });\n }\n }\n return gestures;\n};\n", "/* eslint-disable no-use-before-define */\n/*\nWebGLImageFilter - MIT Licensed\n2013, Dominic Szablewski - phoboslab.org\n\n*/\n\nconst WebGLProgram = function (gl, vertexSource, fragmentSource) {\n const _collect = function (source, prefix, collection) {\n const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n source.replace(r, (match, name) => {\n collection[name] = 0;\n return match;\n });\n };\n\n const _compile = function (source, type) {\n const shader = gl.createShader(type);\n gl.shaderSource(shader, source);\n gl.compileShader(shader);\n\n if (!gl.getShaderParameter(shader, gl.COMPILE_STATUS)) {\n // @ts-ignore\n throw new Error('Filter: GL compile failed', gl.getShaderInfoLog(shader));\n }\n return shader;\n };\n\n this.uniform = {};\n this.attribute = {};\n\n const _vsh = _compile(vertexSource, gl.VERTEX_SHADER);\n const _fsh = _compile(fragmentSource, gl.FRAGMENT_SHADER);\n\n this.id = gl.createProgram();\n gl.attachShader(this.id, _vsh);\n gl.attachShader(this.id, _fsh);\n gl.linkProgram(this.id);\n\n if (!gl.getProgramParameter(this.id, gl.LINK_STATUS)) {\n // @ts-ignore\n throw new Error('Filter: GL link failed', gl.getProgramInfoLog(this.id));\n }\n\n gl.useProgram(this.id);\n\n // Collect attributes\n _collect(vertexSource, 'attribute', this.attribute);\n for (const a in this.attribute) {\n this.attribute[a] = gl.getAttribLocation(this.id, a);\n }\n\n // Collect uniforms\n _collect(vertexSource, 'uniform', this.uniform);\n _collect(fragmentSource, 'uniform', this.uniform);\n for (const u in this.uniform) {\n this.uniform[u] = gl.getUniformLocation(this.id, u);\n }\n};\n\nconst WebGLImageFilter = function (params) {\n if (!params) params = { };\n let _drawCount = 0;\n let _sourceTexture = null;\n let _lastInChain = false;\n let _currentFramebufferIndex = -1;\n let _tempFramebuffers = [null, null];\n let _filterChain = [];\n let _width = -1;\n let _height = -1;\n let _vertexBuffer = null;\n let _currentProgram = null;\n const _canvas = params.canvas || document.createElement('canvas');\n\n // key is the shader program source, value is the compiled program\n const _shaderProgramCache = { };\n\n const gl = _canvas.getContext('webgl');\n if (!gl) throw new Error('Filter: getContext() failed');\n\n this.addFilter = function (name) {\n // eslint-disable-next-line prefer-rest-params\n const args = Array.prototype.slice.call(arguments, 1);\n const filter = _filter[name];\n\n _filterChain.push({ func: filter, args });\n };\n\n this.reset = function () {\n _filterChain = [];\n };\n\n this.apply = function (image) {\n _resize(image.width, image.height);\n _drawCount = 0;\n\n // Create the texture for the input image if we haven't yet\n if (!_sourceTexture) _sourceTexture = gl.createTexture();\n gl.bindTexture(gl.TEXTURE_2D, _sourceTexture);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n\n // No filters? Just draw\n if (_filterChain.length === 0) {\n // const program = _compileShader(SHADER.FRAGMENT_IDENTITY);\n _draw();\n return _canvas;\n }\n\n for (let i = 0; i < _filterChain.length; i++) {\n _lastInChain = (i === _filterChain.length - 1);\n const f = _filterChain[i];\n f.func.apply(this, f.args || []);\n }\n\n return _canvas;\n };\n\n const _resize = function (width, height) {\n // Same width/height? Nothing to do here\n if (width === _width && height === _height) { return; }\n\n _canvas.width = width;\n _width = width;\n _canvas.height = height;\n _height = height;\n\n // Create the context if we don't have it yet\n if (!_vertexBuffer) {\n // Create the vertex buffer for the two triangles [x, y, u, v] * 6\n const vertices = new Float32Array([\n -1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0,\n -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0,\n ]);\n // eslint-disable-next-line no-unused-expressions\n (_vertexBuffer = gl.createBuffer(), gl.bindBuffer(gl.ARRAY_BUFFER, _vertexBuffer));\n gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);\n\n // Note sure if this is a good idea; at least it makes texture loading\n // in Ejecta instant.\n gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n }\n\n gl.viewport(0, 0, _width, _height);\n\n // Delete old temp framebuffers\n _tempFramebuffers = [null, null];\n };\n\n const _getTempFramebuffer = function (index) {\n // @ts-ignore\n _tempFramebuffers[index] = _tempFramebuffers[index] || _createFramebufferTexture(_width, _height);\n return _tempFramebuffers[index];\n };\n\n const _createFramebufferTexture = function (width, height) {\n const fbo = gl.createFramebuffer();\n gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n\n const renderbuffer = gl.createRenderbuffer();\n gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n\n const texture = gl.createTexture();\n gl.bindTexture(gl.TEXTURE_2D, texture);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n\n gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n\n gl.bindTexture(gl.TEXTURE_2D, null);\n gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n\n return { fbo, texture };\n };\n\n const _draw = function (flags) {\n let source = null;\n let target = null;\n let flipY = false;\n\n // Set up the source\n if (_drawCount === 0) {\n // First draw call - use the source texture\n source = _sourceTexture;\n } else {\n // All following draw calls use the temp buffer last drawn to\n // @ts-ignore\n source = _getTempFramebuffer(_currentFramebufferIndex)?.texture;\n }\n _drawCount++;\n\n // Set up the target\n if (_lastInChain && !(flags & DRAW.INTERMEDIATE)) {\n // Last filter in our chain - draw directly to the WebGL Canvas. We may\n // also have to flip the image vertically now\n target = null;\n flipY = _drawCount % 2 === 0;\n } else {\n // Intermediate draw call - get a temp buffer to draw to\n _currentFramebufferIndex = (_currentFramebufferIndex + 1) % 2;\n // @ts-ignore\n target = _getTempFramebuffer(_currentFramebufferIndex)?.fbo;\n }\n\n // Bind the source and target and draw the two triangles\n gl.bindTexture(gl.TEXTURE_2D, source);\n gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n\n gl.uniform1f(_currentProgram.uniform.flipY, (flipY ? -1 : 1));\n gl.drawArrays(gl.TRIANGLES, 0, 6);\n };\n\n const _compileShader = function (fragmentSource) {\n if (_shaderProgramCache[fragmentSource]) {\n _currentProgram = _shaderProgramCache[fragmentSource];\n gl.useProgram(_currentProgram.id);\n return _currentProgram;\n }\n\n // Compile shaders\n _currentProgram = new WebGLProgram(gl, SHADER.VERTEX_IDENTITY, fragmentSource);\n\n const floatSize = Float32Array.BYTES_PER_ELEMENT;\n const vertSize = 4 * floatSize;\n gl.enableVertexAttribArray(_currentProgram.attribute.pos);\n gl.vertexAttribPointer(_currentProgram.attribute.pos, 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n gl.enableVertexAttribArray(_currentProgram.attribute.uv);\n gl.vertexAttribPointer(_currentProgram.attribute.uv, 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n\n _shaderProgramCache[fragmentSource] = _currentProgram;\n return _currentProgram;\n };\n\n let DRAW = { INTERMEDIATE: 1 };\n\n let SHADER = {};\n SHADER.VERTEX_IDENTITY = [\n 'precision highp float;',\n 'attribute vec2 pos;',\n 'attribute vec2 uv;',\n 'varying vec2 vUv;',\n 'uniform float flipY;',\n\n 'void main(void) {',\n 'vUv = uv;',\n 'gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);',\n '}',\n ].join('\\n');\n\n SHADER.FRAGMENT_IDENTITY = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n\n 'void main(void) {',\n 'gl_FragColor = texture2D(texture, vUv);',\n '}',\n ].join('\\n');\n\n let _filter = {};\n\n // -------------------------------------------------------------------------\n // Color Matrix Filter\n\n _filter.colorMatrix = function (matrix) {\n // Create a Float32 Array and normalize the offset component to 0-1\n const m = new Float32Array(matrix);\n m[4] /= 255;\n m[9] /= 255;\n m[14] /= 255;\n m[19] /= 255;\n\n // Can we ignore the alpha value? Makes things a bit faster.\n const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0)\n ? _filter.colorMatrix.SHADER.WITHOUT_ALPHA\n : _filter.colorMatrix.SHADER.WITH_ALPHA;\n\n const program = _compileShader(shader);\n gl.uniform1fv(program.uniform.m, m);\n _draw();\n };\n\n _filter.colorMatrix.SHADER = {};\n _filter.colorMatrix.SHADER.WITH_ALPHA = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform float m[20];',\n\n 'void main(void) {',\n 'vec4 c = texture2D(texture, vUv);',\n 'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];',\n 'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];',\n 'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];',\n 'gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];',\n '}',\n ].join('\\n');\n _filter.colorMatrix.SHADER.WITHOUT_ALPHA = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform float m[20];',\n\n 'void main(void) {',\n 'vec4 c = texture2D(texture, vUv);',\n 'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];',\n 'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];',\n 'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];',\n 'gl_FragColor.a = c.a;',\n '}',\n ].join('\\n');\n\n _filter.brightness = function (brightness) {\n const b = (brightness || 0) + 1;\n _filter.colorMatrix([\n b, 0, 0, 0, 0,\n 0, b, 0, 0, 0,\n 0, 0, b, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.saturation = function (amount) {\n const x = (amount || 0) * 2 / 3 + 1;\n const y = ((x - 1) * -0.5);\n _filter.colorMatrix([\n x, y, y, 0, 0,\n y, x, y, 0, 0,\n y, y, x, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.desaturate = function () {\n _filter.saturation(-1);\n };\n\n _filter.contrast = function (amount) {\n const v = (amount || 0) + 1;\n const o = -128 * (v - 1);\n\n _filter.colorMatrix([\n v, 0, 0, 0, o,\n 0, v, 0, 0, o,\n 0, 0, v, 0, o,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.negative = function () {\n _filter.contrast(-2);\n };\n\n _filter.hue = function (rotation) {\n rotation = (rotation || 0) / 180 * Math.PI;\n const cos = Math.cos(rotation);\n const sin = Math.sin(rotation);\n const lumR = 0.213;\n const lumG = 0.715;\n const lumB = 0.072;\n\n _filter.colorMatrix([\n lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.desaturateLuminance = function () {\n _filter.colorMatrix([\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.sepia = function () {\n _filter.colorMatrix([\n 0.393, 0.7689999, 0.18899999, 0, 0,\n 0.349, 0.6859999, 0.16799999, 0, 0,\n 0.272, 0.5339999, 0.13099999, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.brownie = function () {\n _filter.colorMatrix([\n 0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n 0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.vintagePinhole = function () {\n _filter.colorMatrix([\n 0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n 0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n 0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.kodachrome = function () {\n _filter.colorMatrix([\n 1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.technicolor = function () {\n _filter.colorMatrix([\n 1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.polaroid = function () {\n _filter.colorMatrix([\n 1.438, -0.062, -0.062, 0, 0,\n -0.122, 1.378, -0.122, 0, 0,\n -0.016, -0.016, 1.483, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.shiftToBGR = function () {\n _filter.colorMatrix([\n 0, 0, 1, 0, 0,\n 0, 1, 0, 0, 0,\n 1, 0, 0, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n // -------------------------------------------------------------------------\n // Convolution Filter\n\n _filter.convolution = function (matrix) {\n const m = new Float32Array(matrix);\n const pixelSizeX = 1 / _width;\n const pixelSizeY = 1 / _height;\n\n const program = _compileShader(_filter.convolution.SHADER);\n gl.uniform1fv(program.uniform.m, m);\n gl.uniform2f(program.uniform.px, pixelSizeX, pixelSizeY);\n _draw();\n };\n\n _filter.convolution.SHADER = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform vec2 px;',\n 'uniform float m[9];',\n\n 'void main(void) {',\n 'vec4 c11 = texture2D(texture, vUv - px);', // top left\n 'vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y));', // top center\n 'vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y));', // top right\n\n 'vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) );', // mid left\n 'vec4 c22 = texture2D(texture, vUv);', // mid center\n 'vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) );', // mid right\n\n 'vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) );', // bottom left\n 'vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) );', // bottom center\n 'vec4 c33 = texture2D(texture, vUv + px );', // bottom right\n\n 'gl_FragColor = ',\n 'c11 * m[0] + c12 * m[1] + c22 * m[2] +',\n 'c21 * m[3] + c22 * m[4] + c23 * m[5] +',\n 'c31 * m[6] + c32 * m[7] + c33 * m[8];',\n 'gl_FragColor.a = c22.a;',\n '}',\n ].join('\\n');\n\n _filter.detectEdges = function () {\n _filter.convolution.call(this, [\n 0, 1, 0,\n 1, -4, 1,\n 0, 1, 0,\n ]);\n };\n\n _filter.sobelX = function () {\n _filter.convolution.call(this, [\n -1, 0, 1,\n -2, 0, 2,\n -1, 0, 1,\n ]);\n };\n\n _filter.sobelY = function () {\n _filter.convolution.call(this, [\n -1, -2, -1,\n 0, 0, 0,\n 1, 2, 1,\n ]);\n };\n\n _filter.sharpen = function (amount) {\n const a = amount || 1;\n _filter.convolution.call(this, [\n 0, -1 * a, 0,\n -1 * a, 1 + 4 * a, -1 * a,\n 0, -1 * a, 0,\n ]);\n };\n\n _filter.emboss = function (size) {\n const s = size || 1;\n _filter.convolution.call(this, [\n -2 * s, -1 * s, 0,\n -1 * s, 1, 1 * s,\n 0, 1 * s, 2 * s,\n ]);\n };\n\n // -------------------------------------------------------------------------\n // Blur Filter\n\n _filter.blur = function (size) {\n const blurSizeX = (size / 7) / _width;\n const blurSizeY = (size / 7) / _height;\n\n const program = _compileShader(_filter.blur.SHADER);\n\n // Vertical\n gl.uniform2f(program.uniform.px, 0, blurSizeY);\n _draw(DRAW.INTERMEDIATE);\n\n // Horizontal\n gl.uniform2f(program.uniform.px, blurSizeX, 0);\n _draw();\n };\n\n _filter.blur.SHADER = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform vec2 px;',\n\n 'void main(void) {',\n 'gl_FragColor = vec4(0.0);',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;',\n 'gl_FragColor += texture2D(texture, vUv )*0.159576912161;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x, 1.0*px.y))*0.147308056121;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x, 2.0*px.y))*0.115876621105;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x, 3.0*px.y))*0.0776744219933;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x, 4.0*px.y))*0.0443683338718;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x, 5.0*px.y))*0.0215963866053;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265;',\n '}',\n ].join('\\n');\n\n // -------------------------------------------------------------------------\n // Pixelate Filter\n\n _filter.pixelate = function (size) {\n const blurSizeX = (size) / _width;\n const blurSizeY = (size) / _height;\n\n const program = _compileShader(_filter.pixelate.SHADER);\n\n // Horizontal\n gl.uniform2f(program.uniform.size, blurSizeX, blurSizeY);\n _draw();\n };\n\n _filter.pixelate.SHADER = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform vec2 size;',\n 'uniform sampler2D texture;',\n\n 'vec2 pixelate(vec2 coord, vec2 size) {',\n 'return floor( coord / size ) * size;',\n '}',\n\n 'void main(void) {',\n 'gl_FragColor = vec4(0.0);',\n 'vec2 coord = pixelate(vUv, size);',\n 'gl_FragColor += texture2D(texture, coord);',\n '}',\n ].join('\\n');\n};\n\nexports.Canvas = WebGLImageFilter;\n", "import { log } from './log.js';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as fxImage from './imagefx.js';\n\n// internal temp canvases\nlet inCanvas = null;\nlet outCanvas = null;\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nfunction process(input, config) {\n let tensor;\n if (input instanceof tf.Tensor) {\n tensor = tf.clone(input);\n } else {\n const originalWidth = input.naturalWidth || input.videoWidth || input.width || (input.shape && (input.shape[1] > 0));\n const originalHeight = input.naturalHeight || input.videoHeight || input.height || (input.shape && (input.shape[2] > 0));\n let targetWidth = originalWidth;\n let targetHeight = originalHeight;\n if (config.filter.width > 0) targetWidth = config.filter.width;\n else if (config.filter.height > 0) targetWidth = originalWidth * (config.filter.height / originalHeight);\n if (config.filter.height > 0) targetHeight = config.filter.height;\n else if (config.filter.width > 0) targetHeight = originalHeight * (config.filter.width / originalWidth);\n if (!targetWidth || !targetHeight) {\n log('Human: invalid input', input);\n return null;\n }\n if (!inCanvas || (inCanvas.width !== targetWidth) || (inCanvas.height !== targetHeight)) {\n inCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n if (inCanvas.width !== targetWidth) inCanvas.width = targetWidth;\n if (inCanvas.height !== targetHeight) inCanvas.height = targetHeight;\n }\n const ctx = inCanvas.getContext('2d');\n if (input instanceof ImageData) ctx.putImageData(input, 0, 0);\n else ctx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);\n if (config.filter.enabled) {\n if (!this.fx || !outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas.height !== outCanvas.height)) {\n outCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(inCanvas.width, inCanvas.height) : document.createElement('canvas');\n if (outCanvas.width !== inCanvas.width) outCanvas.width = inCanvas.width;\n if (outCanvas.height !== inCanvas.height) outCanvas.height = inCanvas.height;\n this.fx = tf.ENV.flags.IS_BROWSER ? new fxImage.Canvas({ canvas: outCanvas }) : null; // && (typeof document !== 'undefined')\n }\n if (!this.fx) return inCanvas;\n this.fx.reset();\n this.fx.addFilter('brightness', config.filter.brightness); // must have at least one filter enabled\n if (config.filter.contrast !== 0) this.fx.addFilter('contrast', config.filter.contrast);\n if (config.filter.sharpness !== 0) this.fx.addFilter('sharpen', config.filter.sharpness);\n if (config.filter.blur !== 0) this.fx.addFilter('blur', config.filter.blur);\n if (config.filter.saturation !== 0) this.fx.addFilter('saturation', config.filter.saturation);\n if (config.filter.hue !== 0) this.fx.addFilter('hue', config.filter.hue);\n if (config.filter.negative) this.fx.addFilter('negative');\n if (config.filter.sepia) this.fx.addFilter('sepia');\n if (config.filter.vintage) this.fx.addFilter('brownie');\n if (config.filter.sepia) this.fx.addFilter('sepia');\n if (config.filter.kodachrome) this.fx.addFilter('kodachrome');\n if (config.filter.technicolor) this.fx.addFilter('technicolor');\n if (config.filter.polaroid) this.fx.addFilter('polaroid');\n if (config.filter.pixelate !== 0) this.fx.addFilter('pixelate', config.filter.pixelate);\n this.fx.apply(inCanvas);\n // read pixel data\n /*\n const gl = outCanvas.getContext('webgl');\n if (gl) {\n const glBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 4);\n const pixBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 3);\n gl.readPixels(0, 0, outCanvas.width, outCanvas.height, gl.RGBA, gl.UNSIGNED_BYTE, glBuffer);\n // gl returns rbga while we only need rgb, so discarding alpha channel\n // gl returns starting point as lower left, so need to invert vertical\n let i = 0;\n for (let y = outCanvas.height - 1; y >= 0; y--) {\n for (let x = 0; x < outCanvas.width; x++) {\n const index = (x + y * outCanvas.width) * 4;\n pixBuffer[i++] = glBuffer[index + 0];\n pixBuffer[i++] = glBuffer[index + 1];\n pixBuffer[i++] = glBuffer[index + 2];\n }\n }\n outCanvas.data = pixBuffer;\n }\n */\n } else {\n outCanvas = inCanvas;\n }\n let pixels;\n if (outCanvas.data) {\n const shape = [outCanvas.height, outCanvas.width, 3];\n pixels = tf.tensor3d(outCanvas.data, shape, 'int32');\n } else if ((config.backend === 'webgl') || (outCanvas instanceof ImageData)) {\n // tf kernel-optimized method to get imagedata, also if input is imagedata, just use it\n pixels = tf.browser.fromPixels(outCanvas);\n } else {\n // cpu and wasm kernel does not implement efficient fromPixels method nor we can use canvas as-is, so we do a silly one more canvas\n const tempCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n tempCanvas.width = targetWidth;\n tempCanvas.height = targetHeight;\n const tempCtx = tempCanvas.getContext('2d');\n tempCtx?.drawImage(outCanvas, 0, 0);\n const data = tempCtx?.getImageData(0, 0, targetWidth, targetHeight);\n pixels = tf.browser.fromPixels(data);\n }\n const casted = pixels.toFloat();\n tensor = casted.expandDims(0);\n pixels.dispose();\n casted.dispose();\n }\n return { tensor, canvas: config.filter.return ? outCanvas : null };\n}\n\nexports.process = process;\n", "// helper function: wrapper around console output\nexport function log(...msg) {\n const dt = new Date();\n const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n // eslint-disable-next-line no-console\n if (msg) console.log(ts, 'Human:', ...msg);\n}\n", "", "// A port of an algorithm by Johannes Baag\u00F8e , 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e \n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n// \n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n// \n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n var me = this, mash = Mash();\n\n me.next = function() {\n var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n me.s0 = me.s1;\n me.s1 = me.s2;\n return me.s2 = t - (me.c = t | 0);\n };\n\n // Apply the seeding algorithm from Baagoe.\n me.c = 1;\n me.s0 = mash(' ');\n me.s1 = mash(' ');\n me.s2 = mash(' ');\n me.s0 -= mash(seed);\n if (me.s0 < 0) { me.s0 += 1; }\n me.s1 -= mash(seed);\n if (me.s1 < 0) { me.s1 += 1; }\n me.s2 -= mash(seed);\n if (me.s2 < 0) { me.s2 += 1; }\n mash = null;\n}\n\nfunction copy(f, t) {\n t.c = f.c;\n t.s0 = f.s0;\n t.s1 = f.s1;\n t.s2 = f.s2;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new Alea(seed),\n state = opts && opts.state,\n prng = xg.next;\n prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n prng.double = function() {\n return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n };\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nfunction Mash() {\n var n = 0xefc8249d;\n\n var mash = function(data) {\n data = data.toString();\n for (var i = 0; i < data.length; i++) {\n n += data.charCodeAt(i);\n var h = 0.02519603282416938 * n;\n n = h >>> 0;\n h -= n;\n h *= n;\n n = h >>> 0;\n h -= n;\n n += h * 0x100000000; // 2^32\n }\n return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n };\n\n return mash;\n}\n\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.alea = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n\n // Set up generator function.\n me.next = function() {\n var t = me.x ^ (me.x << 11);\n me.x = me.y;\n me.y = me.z;\n me.z = me.w;\n return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n };\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor128 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var t = (me.x ^ (me.x >>> 2));\n me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n return (me.d = (me.d + 362437 | 0)) +\n (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n };\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n me.v = 0;\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n if (k == strseed.length) {\n me.d = me.x << 10 ^ me.x >>> 4;\n }\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n t.v = f.v;\n t.d = f.d;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorwow = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n // Update xor generator.\n var X = me.x, i = me.i, t, v, w;\n t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n X[i] = v;\n me.i = (i + 1) & 7;\n return v;\n };\n\n function init(me, seed) {\n var j, w, X = [];\n\n if (seed === (seed | 0)) {\n // Seed state array using a 32-bit integer.\n w = X[0] = seed;\n } else {\n // Seed state using a string.\n seed = '' + seed;\n for (j = 0; j < seed.length; ++j) {\n X[j & 7] = (X[j & 7] << 15) ^\n (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n }\n }\n // Enforce an array length of 8, not all zeroes.\n while (X.length < 8) X.push(0);\n for (j = 0; j < 8 && X[j] === 0; ++j);\n if (j == 8) w = X[7] = -1; else w = X[j];\n\n me.x = X;\n me.i = 0;\n\n // Discard an initial 256 values.\n for (j = 256; j > 0; --j) {\n me.next();\n }\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.x = f.x.slice();\n t.i = f.i;\n return t;\n}\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.x) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorshift7 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated. For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1); // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897); // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C. This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n var w = me.w,\n X = me.X, i = me.i, t, v;\n // Update Weyl generator.\n me.w = w = (w + 0x61c88647) | 0;\n // Update xor generator.\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n // Update Xor generator array state.\n v = X[i] = v ^ t;\n me.i = i;\n // Result is the combination.\n return (v + (w ^ (w >>> 16))) | 0;\n };\n\n function init(me, seed) {\n var t, v, i, j, w, X = [], limit = 128;\n if (seed === (seed | 0)) {\n // Numeric seeds initialize v, which is used to generates X.\n v = seed;\n seed = null;\n } else {\n // String seeds are mixed into v and X one character at a time.\n seed = seed + '\\0';\n v = 0;\n limit = Math.max(limit, seed.length);\n }\n // Initialize circular array and weyl value.\n for (i = 0, j = -32; j < limit; ++j) {\n // Put the unicode characters into the array, and shuffle them.\n if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n // After 32 shuffles, take v as the starting w value.\n if (j === 0) w = v;\n v ^= v << 10;\n v ^= v >>> 15;\n v ^= v << 4;\n v ^= v >>> 13;\n if (j >= 0) {\n w = (w + 0x61c88647) | 0; // Weyl.\n t = (X[j & 127] ^= (v + w)); // Combine xor and weyl to init array.\n i = (0 == t) ? i + 1 : 0; // Count zeroes.\n }\n }\n // We have detected all zeroes; make the key nonzero.\n if (i >= 128) {\n X[(seed && seed.length || 0) & 127] = -1;\n }\n // Run the generator 512 times to further mix the state before using it.\n // Factoring this as a function slows the main generator, so it is just\n // unrolled here. The weyl generator is not advanced while warming up.\n i = 127;\n for (j = 4 * 128; j > 0; --j) {\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n X[i] = v ^ t;\n }\n // Storing state as object members is faster than using closure variables.\n me.w = w;\n me.X = X;\n me.i = i;\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.i = f.i;\n t.w = f.w;\n t.X = f.X.slice();\n return t;\n};\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.X) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor4096 = impl;\n}\n\n})(\n this, // window object or global\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var b = me.b, c = me.c, d = me.d, a = me.a;\n b = (b << 25) ^ (b >>> 7) ^ c;\n c = (c - d) | 0;\n d = (d << 24) ^ (d >>> 8) ^ a;\n a = (a - b) | 0;\n me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n me.c = c = (c - d) | 0;\n me.d = (d << 16) ^ (c >>> 16) ^ a;\n return me.a = (a - b) | 0;\n };\n\n /* The following is non-inverted tyche, which has better internal\n * bit diffusion, but which is about 25% slower than tyche-i in JS.\n me.next = function() {\n var a = me.a, b = me.b, c = me.c, d = me.d;\n a = (me.a + me.b | 0) >>> 0;\n d = me.d ^ a; d = d << 16 ^ d >>> 16;\n c = me.c + d | 0;\n b = me.b ^ c; b = b << 12 ^ d >>> 20;\n me.a = a = a + b | 0;\n d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n me.c = c = c + d | 0;\n b = b ^ c;\n return me.b = (b << 7 ^ b >>> 25);\n }\n */\n\n me.a = 0;\n me.b = 0;\n me.c = 2654435769 | 0;\n me.d = 1367130551;\n\n if (seed === Math.floor(seed)) {\n // Integer seed.\n me.a = (seed / 0x100000000) | 0;\n me.b = seed | 0;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 20; k++) {\n me.b ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.a = f.a;\n t.b = f.b;\n t.c = f.c;\n t.d = f.d;\n return t;\n};\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.tychei = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "", "/*\nCopyright 2014 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\nvar global = this,\n width = 256, // each RC4 output is 0 <= x < 256\n chunks = 6, // at least six RC4 outputs for each double\n digits = 52, // there are 52 significant digits in a double\n rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n startdenom = math.pow(width, chunks),\n significance = math.pow(2, digits),\n overflow = significance * 2,\n mask = width - 1,\n nodecrypto; // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n var key = [];\n options = (options == true) ? { entropy: true } : (options || {});\n\n // Flatten the seed string or build one from local entropy if needed.\n var shortseed = mixkey(flatten(\n options.entropy ? [seed, tostring(pool)] :\n (seed == null) ? autoseed() : seed, 3), key);\n\n // Use the seed to initialize an ARC4 generator.\n var arc4 = new ARC4(key);\n\n // This function returns a random double in [0, 1) that contains\n // randomness in every bit of the mantissa of the IEEE 754 value.\n var prng = function() {\n var n = arc4.g(chunks), // Start with a numerator n < 2 ^ 48\n d = startdenom, // and denominator d = 2 ^ 48.\n x = 0; // and no 'extra last byte'.\n while (n < significance) { // Fill up all significant digits by\n n = (n + x) * width; // shifting numerator and\n d *= width; // denominator and generating a\n x = arc4.g(1); // new least-significant-byte.\n }\n while (n >= overflow) { // To avoid rounding up, before adding\n n /= 2; // last byte, shift everything\n d /= 2; // right using integer math until\n x >>>= 1; // we have exactly the desired bits.\n }\n return (n + x) / d; // Form the number within [0, 1).\n };\n\n prng.int32 = function() { return arc4.g(4) | 0; }\n prng.quick = function() { return arc4.g(4) / 0x100000000; }\n prng.double = prng;\n\n // Mix the randomness into accumulated entropy.\n mixkey(tostring(arc4.S), pool);\n\n // Calling convention: what to return as a function of prng, seed, is_math.\n return (options.pass || callback ||\n function(prng, seed, is_math_call, state) {\n if (state) {\n // Load the arc4 state from the given state if it has an S array.\n if (state.S) { copy(state, arc4); }\n // Only provide the .state method if requested via options.state.\n prng.state = function() { return copy(arc4, {}); }\n }\n\n // If called as a method of Math (Math.seedrandom()), mutate\n // Math.random because that is how seedrandom.js has worked since v1.0.\n if (is_math_call) { math[rngname] = prng; return seed; }\n\n // Otherwise, it is a newer calling convention, so return the\n // prng directly.\n else return prng;\n })(\n prng,\n shortseed,\n 'global' in options ? options.global : (this == math),\n options.state);\n}\nmath['seed' + rngname] = seedrandom;\n\n//\n// ARC4\n//\n// An ARC4 implementation. The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4. Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n var t, keylen = key.length,\n me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n // The empty key [] is treated as [0].\n if (!keylen) { key = [keylen++]; }\n\n // Set up S using the standard key scheduling algorithm.\n while (i < width) {\n s[i] = i++;\n }\n for (i = 0; i < width; i++) {\n s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n s[j] = t;\n }\n\n // The \"g\" method returns the next (count) outputs as one number.\n (me.g = function(count) {\n // Using instance members instead of closure state nearly doubles speed.\n var t, r = 0,\n i = me.i, j = me.j, s = me.S;\n while (count--) {\n t = s[i = mask & (i + 1)];\n r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n }\n me.i = i; me.j = j;\n return r;\n // For robust unpredictability, the function call below automatically\n // discards an initial batch of values. This is called RC4-drop[256].\n // See http://google.com/search?q=rsa+fluhrer+response&btnI\n })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n t.i = f.i;\n t.j = f.j;\n t.S = f.S.slice();\n return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n var result = [], typ = (typeof obj), prop;\n if (depth && typ == 'object') {\n for (prop in obj) {\n try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n }\n }\n return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n var stringseed = seed + '', smear, j = 0;\n while (j < stringseed.length) {\n key[mask & j] =\n mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n }\n return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n try {\n var out;\n if (nodecrypto && (out = nodecrypto.randomBytes)) {\n // The use of 'out' to remember randomBytes makes tight minified code.\n out = out(width);\n } else {\n out = new Uint8Array(width);\n (global.crypto || global.msCrypto).getRandomValues(out);\n }\n return tostring(out);\n } catch (e) {\n var browser = global.navigator,\n plugins = browser && browser.plugins;\n return [+new Date, global, plugins, global.screen, tostring(pool)];\n }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool. Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n module.exports = seedrandom;\n // When in node.js, try using crypto package for autoseeding.\n try {\n nodecrypto = require('crypto');\n } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n define(function() { return seedrandom; });\n}\n\n// End anonymous scope, and pass initial values.\n})(\n [], // pool: entropy pool starts empty\n Math // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random(); // 0 <= x < 1. Every bit is random.\n// var x = random.quick(); // 0 <= x < 1. 32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three. It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures. Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "// A port of an algorithm by Johannes Baag\u00F8e , 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e \n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n// \n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n// \n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n var me = this, mash = Mash();\n\n me.next = function() {\n var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n me.s0 = me.s1;\n me.s1 = me.s2;\n return me.s2 = t - (me.c = t | 0);\n };\n\n // Apply the seeding algorithm from Baagoe.\n me.c = 1;\n me.s0 = mash(' ');\n me.s1 = mash(' ');\n me.s2 = mash(' ');\n me.s0 -= mash(seed);\n if (me.s0 < 0) { me.s0 += 1; }\n me.s1 -= mash(seed);\n if (me.s1 < 0) { me.s1 += 1; }\n me.s2 -= mash(seed);\n if (me.s2 < 0) { me.s2 += 1; }\n mash = null;\n}\n\nfunction copy(f, t) {\n t.c = f.c;\n t.s0 = f.s0;\n t.s1 = f.s1;\n t.s2 = f.s2;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new Alea(seed),\n state = opts && opts.state,\n prng = xg.next;\n prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n prng.double = function() {\n return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n };\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nfunction Mash() {\n var n = 0xefc8249d;\n\n var mash = function(data) {\n data = data.toString();\n for (var i = 0; i < data.length; i++) {\n n += data.charCodeAt(i);\n var h = 0.02519603282416938 * n;\n n = h >>> 0;\n h -= n;\n h *= n;\n n = h >>> 0;\n h -= n;\n n += h * 0x100000000; // 2^32\n }\n return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n };\n\n return mash;\n}\n\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.alea = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n\n // Set up generator function.\n me.next = function() {\n var t = me.x ^ (me.x << 11);\n me.x = me.y;\n me.y = me.z;\n me.z = me.w;\n return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n };\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor128 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var t = (me.x ^ (me.x >>> 2));\n me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n return (me.d = (me.d + 362437 | 0)) +\n (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n };\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n me.v = 0;\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n if (k == strseed.length) {\n me.d = me.x << 10 ^ me.x >>> 4;\n }\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n t.v = f.v;\n t.d = f.d;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorwow = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n // Update xor generator.\n var X = me.x, i = me.i, t, v, w;\n t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n X[i] = v;\n me.i = (i + 1) & 7;\n return v;\n };\n\n function init(me, seed) {\n var j, w, X = [];\n\n if (seed === (seed | 0)) {\n // Seed state array using a 32-bit integer.\n w = X[0] = seed;\n } else {\n // Seed state using a string.\n seed = '' + seed;\n for (j = 0; j < seed.length; ++j) {\n X[j & 7] = (X[j & 7] << 15) ^\n (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n }\n }\n // Enforce an array length of 8, not all zeroes.\n while (X.length < 8) X.push(0);\n for (j = 0; j < 8 && X[j] === 0; ++j);\n if (j == 8) w = X[7] = -1; else w = X[j];\n\n me.x = X;\n me.i = 0;\n\n // Discard an initial 256 values.\n for (j = 256; j > 0; --j) {\n me.next();\n }\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.x = f.x.slice();\n t.i = f.i;\n return t;\n}\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.x) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorshift7 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated. For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1); // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897); // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C. This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n var w = me.w,\n X = me.X, i = me.i, t, v;\n // Update Weyl generator.\n me.w = w = (w + 0x61c88647) | 0;\n // Update xor generator.\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n // Update Xor generator array state.\n v = X[i] = v ^ t;\n me.i = i;\n // Result is the combination.\n return (v + (w ^ (w >>> 16))) | 0;\n };\n\n function init(me, seed) {\n var t, v, i, j, w, X = [], limit = 128;\n if (seed === (seed | 0)) {\n // Numeric seeds initialize v, which is used to generates X.\n v = seed;\n seed = null;\n } else {\n // String seeds are mixed into v and X one character at a time.\n seed = seed + '\\0';\n v = 0;\n limit = Math.max(limit, seed.length);\n }\n // Initialize circular array and weyl value.\n for (i = 0, j = -32; j < limit; ++j) {\n // Put the unicode characters into the array, and shuffle them.\n if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n // After 32 shuffles, take v as the starting w value.\n if (j === 0) w = v;\n v ^= v << 10;\n v ^= v >>> 15;\n v ^= v << 4;\n v ^= v >>> 13;\n if (j >= 0) {\n w = (w + 0x61c88647) | 0; // Weyl.\n t = (X[j & 127] ^= (v + w)); // Combine xor and weyl to init array.\n i = (0 == t) ? i + 1 : 0; // Count zeroes.\n }\n }\n // We have detected all zeroes; make the key nonzero.\n if (i >= 128) {\n X[(seed && seed.length || 0) & 127] = -1;\n }\n // Run the generator 512 times to further mix the state before using it.\n // Factoring this as a function slows the main generator, so it is just\n // unrolled here. The weyl generator is not advanced while warming up.\n i = 127;\n for (j = 4 * 128; j > 0; --j) {\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n X[i] = v ^ t;\n }\n // Storing state as object members is faster than using closure variables.\n me.w = w;\n me.X = X;\n me.i = i;\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.i = f.i;\n t.w = f.w;\n t.X = f.X.slice();\n return t;\n};\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.X) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor4096 = impl;\n}\n\n})(\n this, // window object or global\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var b = me.b, c = me.c, d = me.d, a = me.a;\n b = (b << 25) ^ (b >>> 7) ^ c;\n c = (c - d) | 0;\n d = (d << 24) ^ (d >>> 8) ^ a;\n a = (a - b) | 0;\n me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n me.c = c = (c - d) | 0;\n me.d = (d << 16) ^ (c >>> 16) ^ a;\n return me.a = (a - b) | 0;\n };\n\n /* The following is non-inverted tyche, which has better internal\n * bit diffusion, but which is about 25% slower than tyche-i in JS.\n me.next = function() {\n var a = me.a, b = me.b, c = me.c, d = me.d;\n a = (me.a + me.b | 0) >>> 0;\n d = me.d ^ a; d = d << 16 ^ d >>> 16;\n c = me.c + d | 0;\n b = me.b ^ c; b = b << 12 ^ d >>> 20;\n me.a = a = a + b | 0;\n d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n me.c = c = c + d | 0;\n b = b ^ c;\n return me.b = (b << 7 ^ b >>> 25);\n }\n */\n\n me.a = 0;\n me.b = 0;\n me.c = 2654435769 | 0;\n me.d = 1367130551;\n\n if (seed === Math.floor(seed)) {\n // Integer seed.\n me.a = (seed / 0x100000000) | 0;\n me.b = seed | 0;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 20; k++) {\n me.b ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.a = f.a;\n t.b = f.b;\n t.c = f.c;\n t.d = f.d;\n return t;\n};\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.tychei = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "/*\nCopyright 2014 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\nvar global = this,\n width = 256, // each RC4 output is 0 <= x < 256\n chunks = 6, // at least six RC4 outputs for each double\n digits = 52, // there are 52 significant digits in a double\n rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n startdenom = math.pow(width, chunks),\n significance = math.pow(2, digits),\n overflow = significance * 2,\n mask = width - 1,\n nodecrypto; // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n var key = [];\n options = (options == true) ? { entropy: true } : (options || {});\n\n // Flatten the seed string or build one from local entropy if needed.\n var shortseed = mixkey(flatten(\n options.entropy ? [seed, tostring(pool)] :\n (seed == null) ? autoseed() : seed, 3), key);\n\n // Use the seed to initialize an ARC4 generator.\n var arc4 = new ARC4(key);\n\n // This function returns a random double in [0, 1) that contains\n // randomness in every bit of the mantissa of the IEEE 754 value.\n var prng = function() {\n var n = arc4.g(chunks), // Start with a numerator n < 2 ^ 48\n d = startdenom, // and denominator d = 2 ^ 48.\n x = 0; // and no 'extra last byte'.\n while (n < significance) { // Fill up all significant digits by\n n = (n + x) * width; // shifting numerator and\n d *= width; // denominator and generating a\n x = arc4.g(1); // new least-significant-byte.\n }\n while (n >= overflow) { // To avoid rounding up, before adding\n n /= 2; // last byte, shift everything\n d /= 2; // right using integer math until\n x >>>= 1; // we have exactly the desired bits.\n }\n return (n + x) / d; // Form the number within [0, 1).\n };\n\n prng.int32 = function() { return arc4.g(4) | 0; }\n prng.quick = function() { return arc4.g(4) / 0x100000000; }\n prng.double = prng;\n\n // Mix the randomness into accumulated entropy.\n mixkey(tostring(arc4.S), pool);\n\n // Calling convention: what to return as a function of prng, seed, is_math.\n return (options.pass || callback ||\n function(prng, seed, is_math_call, state) {\n if (state) {\n // Load the arc4 state from the given state if it has an S array.\n if (state.S) { copy(state, arc4); }\n // Only provide the .state method if requested via options.state.\n prng.state = function() { return copy(arc4, {}); }\n }\n\n // If called as a method of Math (Math.seedrandom()), mutate\n // Math.random because that is how seedrandom.js has worked since v1.0.\n if (is_math_call) { math[rngname] = prng; return seed; }\n\n // Otherwise, it is a newer calling convention, so return the\n // prng directly.\n else return prng;\n })(\n prng,\n shortseed,\n 'global' in options ? options.global : (this == math),\n options.state);\n}\nmath['seed' + rngname] = seedrandom;\n\n//\n// ARC4\n//\n// An ARC4 implementation. The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4. Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n var t, keylen = key.length,\n me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n // The empty key [] is treated as [0].\n if (!keylen) { key = [keylen++]; }\n\n // Set up S using the standard key scheduling algorithm.\n while (i < width) {\n s[i] = i++;\n }\n for (i = 0; i < width; i++) {\n s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n s[j] = t;\n }\n\n // The \"g\" method returns the next (count) outputs as one number.\n (me.g = function(count) {\n // Using instance members instead of closure state nearly doubles speed.\n var t, r = 0,\n i = me.i, j = me.j, s = me.S;\n while (count--) {\n t = s[i = mask & (i + 1)];\n r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n }\n me.i = i; me.j = j;\n return r;\n // For robust unpredictability, the function call below automatically\n // discards an initial batch of values. This is called RC4-drop[256].\n // See http://google.com/search?q=rsa+fluhrer+response&btnI\n })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n t.i = f.i;\n t.j = f.j;\n t.S = f.S.slice();\n return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n var result = [], typ = (typeof obj), prop;\n if (depth && typ == 'object') {\n for (prop in obj) {\n try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n }\n }\n return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n var stringseed = seed + '', smear, j = 0;\n while (j < stringseed.length) {\n key[mask & j] =\n mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n }\n return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n try {\n var out;\n if (nodecrypto && (out = nodecrypto.randomBytes)) {\n // The use of 'out' to remember randomBytes makes tight minified code.\n out = out(width);\n } else {\n out = new Uint8Array(width);\n (global.crypto || global.msCrypto).getRandomValues(out);\n }\n return tostring(out);\n } catch (e) {\n var browser = global.navigator,\n plugins = browser && browser.plugins;\n return [+new Date, global, plugins, global.screen, tostring(pool)];\n }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool. Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n module.exports = seedrandom;\n // When in node.js, try using crypto package for autoseeding.\n try {\n nodecrypto = require('crypto');\n } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n define(function() { return seedrandom; });\n}\n\n// End anonymous scope, and pass initial values.\n})(\n [], // pool: entropy pool starts empty\n Math // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random(); // 0 <= x < 1. Every bit is random.\n// var x = random.quick(); // 0 <= x < 1. 32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three. It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures. Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "", "", "", "\nvar WasmBackendModuleThreadedSimd = (function() {\n var _scriptDir = typeof document !== 'undefined' && document.currentScript ? document.currentScript.src : undefined;\n if (typeof __filename !== 'undefined') _scriptDir = _scriptDir || __filename;\n return (\nfunction(WasmBackendModuleThreadedSimd) {\n WasmBackendModuleThreadedSimd = WasmBackendModuleThreadedSimd || {};\n\nfunction GROWABLE_HEAP_I8(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAP8}function GROWABLE_HEAP_U8(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPU8}function GROWABLE_HEAP_I32(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAP32}function GROWABLE_HEAP_U32(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPU32}function GROWABLE_HEAP_F64(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPF64}var Module=typeof WasmBackendModuleThreadedSimd!==\"undefined\"?WasmBackendModuleThreadedSimd:{};var _scriptDir=undefined;var moduleOverrides={};var key;for(key in Module){if(Module.hasOwnProperty(key)){moduleOverrides[key]=Module[key]}}var arguments_=[];var thisProgram=\"./this.program\";var quit_=function(status,toThrow){throw toThrow};var ENVIRONMENT_IS_WEB=false;var ENVIRONMENT_IS_WORKER=false;var ENVIRONMENT_IS_NODE=false;var ENVIRONMENT_IS_SHELL=false;ENVIRONMENT_IS_WEB=typeof window===\"object\";ENVIRONMENT_IS_WORKER=typeof importScripts===\"function\";ENVIRONMENT_IS_NODE=typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\";ENVIRONMENT_IS_SHELL=!ENVIRONMENT_IS_WEB&&!ENVIRONMENT_IS_NODE&&!ENVIRONMENT_IS_WORKER;var ENVIRONMENT_IS_PTHREAD=Module[\"ENVIRONMENT_IS_PTHREAD\"]||false;if(ENVIRONMENT_IS_PTHREAD){buffer=Module[\"buffer\"];DYNAMIC_BASE=Module[\"DYNAMIC_BASE\"];DYNAMICTOP_PTR=Module[\"DYNAMICTOP_PTR\"]}var scriptDirectory=\"\";function locateFile(path){if(Module[\"locateFile\"]){return Module[\"locateFile\"](path,scriptDirectory)}return scriptDirectory+path}var read_,readAsync,readBinary,setWindowTitle;var nodeFS;var nodePath;if(ENVIRONMENT_IS_NODE){if(ENVIRONMENT_IS_WORKER){scriptDirectory=require(\"path\").dirname(scriptDirectory)+\"/\"}else{scriptDirectory=__dirname+\"/\"}read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret};if(process[\"argv\"].length>1){thisProgram=process[\"argv\"][1].replace(/\\\\/g,\"/\")}arguments_=process[\"argv\"].slice(2);process[\"on\"](\"uncaughtException\",function(ex){if(!(ex instanceof ExitStatus)){throw ex}});process[\"on\"](\"unhandledRejection\",abort);quit_=function(status){process[\"exit\"](status)};Module[\"inspect\"]=function(){return\"[Emscripten Module object]\"};var nodeWorkerThreads;try{nodeWorkerThreads=require(\"worker_threads\")}catch(e){console.error('The \"worker_threads\" module is not supported in this node.js build - perhaps a newer version is needed?');throw e}Worker=nodeWorkerThreads.Worker}else if(ENVIRONMENT_IS_SHELL){if(typeof read!=\"undefined\"){read_=function shell_read(f){return read(f)}}readBinary=function readBinary(f){var data;if(typeof readbuffer===\"function\"){return new Uint8Array(readbuffer(f))}data=read(f,\"binary\");assert(typeof data===\"object\");return data};if(typeof scriptArgs!=\"undefined\"){arguments_=scriptArgs}else if(typeof arguments!=\"undefined\"){arguments_=arguments}if(typeof quit===\"function\"){quit_=function(status){quit(status)}}if(typeof print!==\"undefined\"){if(typeof console===\"undefined\")console={};console.log=print;console.warn=console.error=typeof printErr!==\"undefined\"?printErr:print}}else if(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER){if(ENVIRONMENT_IS_WORKER){scriptDirectory=self.location.href}else if(document.currentScript){scriptDirectory=document.currentScript.src}if(_scriptDir){scriptDirectory=_scriptDir}if(scriptDirectory.indexOf(\"blob:\")!==0){scriptDirectory=scriptDirectory.substr(0,scriptDirectory.lastIndexOf(\"/\")+1)}else{scriptDirectory=\"\"}if(ENVIRONMENT_IS_NODE){read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret}}else{read_=function shell_read(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.send(null);return xhr.responseText};if(ENVIRONMENT_IS_WORKER){readBinary=function readBinary(url){var xhr=new 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Atomics_load=Atomics.load;var Atomics_store=Atomics.store;var Atomics_compareExchange=Atomics.compareExchange;var wasmBinary;if(Module[\"wasmBinary\"])wasmBinary=Module[\"wasmBinary\"];var noExitRuntime;if(Module[\"noExitRuntime\"])noExitRuntime=Module[\"noExitRuntime\"];if(typeof WebAssembly!==\"object\"){err(\"no native wasm support detected\")}var wasmMemory;var wasmTable=new WebAssembly.Table({\"initial\":169,\"maximum\":169+0,\"element\":\"anyfunc\"});var wasmModule;var threadInfoStruct=0;var selfThreadId=0;var ABORT=false;var EXITSTATUS=0;function assert(condition,text){if(!condition){abort(\"Assertion failed: \"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i=endIdx)){var u0=heap[idx++];if(!u0)return str;if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224){u0=(u0&15)<<12|u1<<6|u2}else{u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63}if(u0<65536){str+=String.fromCharCode(u0)}else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(GROWABLE_HEAP_U8(),ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var 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alignUp(x,multiple){if(x%multiple>0){x+=multiple-x%multiple}return x}var buffer,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer=buf;Module[\"HEAP8\"]=HEAP8=new Int8Array(buf);Module[\"HEAP16\"]=HEAP16=new Int16Array(buf);Module[\"HEAP32\"]=HEAP32=new Int32Array(buf);Module[\"HEAPU8\"]=HEAPU8=new Uint8Array(buf);Module[\"HEAPU16\"]=HEAPU16=new Uint16Array(buf);Module[\"HEAPU32\"]=HEAPU32=new Uint32Array(buf);Module[\"HEAPF32\"]=HEAPF32=new Float32Array(buf);Module[\"HEAPF64\"]=HEAPF64=new Float64Array(buf)}var STACK_BASE=5256464,STACKTOP=STACK_BASE,STACK_MAX=13584,DYNAMIC_BASE=5256464,DYNAMICTOP_PTR=12656;if(ENVIRONMENT_IS_PTHREAD){}var INITIAL_INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;if(ENVIRONMENT_IS_PTHREAD){wasmMemory=Module[\"wasmMemory\"];buffer=Module[\"buffer\"]}else{if(Module[\"wasmMemory\"]){wasmMemory=Module[\"wasmMemory\"]}else{wasmMemory=new WebAssembly.Memory({\"initial\":INITIAL_INITIAL_MEMORY/WASM_PAGE_SIZE,\"maximum\":2147483648/WASM_PAGE_SIZE,\"shared\":true});if(!(wasmMemory.buffer instanceof SharedArrayBuffer)){err(\"requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag\");if(ENVIRONMENT_IS_NODE){console.log(\"(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)\")}throw Error(\"bad memory\")}}}if(wasmMemory){buffer=wasmMemory.buffer}INITIAL_INITIAL_MEMORY=buffer.byteLength;updateGlobalBufferAndViews(buffer);if(!ENVIRONMENT_IS_PTHREAD){GROWABLE_HEAP_I32()[DYNAMICTOP_PTR>>2]=DYNAMIC_BASE}function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback==\"function\"){callback(Module);continue}var func=callback.func;if(typeof func===\"number\"){if(callback.arg===undefined){Module[\"dynCall_v\"](func)}else{Module[\"dynCall_vi\"](func,callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATEXIT__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;if(ENVIRONMENT_IS_PTHREAD)runtimeInitialized=true;function preRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"preRun\"]){if(typeof Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;callRuntimeCallbacks(__ATINIT__)}function preMain(){if(ENVIRONMENT_IS_PTHREAD)return;callRuntimeCallbacks(__ATMAIN__)}function postRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"postRun\"]){if(typeof Module[\"postRun\"]==\"function\")Module[\"postRun\"]=[Module[\"postRun\"]];while(Module[\"postRun\"].length){addOnPostRun(Module[\"postRun\"].shift())}}callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var Math_ceil=Math.ceil;var Math_floor=Math.floor;var runDependencies=0;var runDependencyWatcher=null;var dependenciesFulfilled=null;function addRunDependency(id){assert(!ENVIRONMENT_IS_PTHREAD,\"addRunDependency cannot be used in a pthread worker\");runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}if(ENVIRONMENT_IS_PTHREAD)console.error(\"Pthread aborting at \"+(new Error).stack);what+=\"\";out(what);err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";throw new WebAssembly.RuntimeError(what)}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm-threaded-simd.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(){try{if(wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(wasmBinaryFile)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary()})}return new Promise(function(resolve,reject){resolve(getBinary())})}function createWasm(){var info={\"a\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmModule=module;if(!ENVIRONMENT_IS_PTHREAD){var numWorkersToLoad=PThread.unusedWorkers.length;PThread.unusedWorkers.forEach(function(w){PThread.loadWasmModuleToWorker(w,function(){if(!--numWorkersToLoad)removeRunDependency(\"wasm-instantiate\")})})}}if(!ENVIRONMENT_IS_PTHREAD){addRunDependency(\"wasm-instantiate\")}function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"],output[\"module\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming===\"function\"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch===\"function\"){fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err(\"wasm streaming compile failed: \"+reason);err(\"falling back to ArrayBuffer instantiation\");instantiateArrayBuffer(receiveInstantiatedSource)})})}else{return instantiateArrayBuffer(receiveInstantiatedSource)}}if(Module[\"instantiateWasm\"]){try{var exports=Module[\"instantiateWasm\"](info,receiveInstance);return exports}catch(e){err(\"Module.instantiateWasm callback failed with error: \"+e);return false}}instantiateAsync();return{}}var ASM_CONSTS={};function initPthreadsJS(){PThread.initRuntime()}if(!ENVIRONMENT_IS_PTHREAD)__ATINIT__.push({func:function(){___wasm_call_ctors()}});var __pthread_ptr=0;var __pthread_is_main_runtime_thread=0;var __pthread_is_main_browser_thread=0;function __register_pthread_ptr(pthreadPtr,isMainBrowserThread,isMainRuntimeThread){pthreadPtr=pthreadPtr|0;isMainBrowserThread=isMainBrowserThread|0;isMainRuntimeThread=isMainRuntimeThread|0;__pthread_ptr=pthreadPtr;__pthread_is_main_browser_thread=isMainBrowserThread;__pthread_is_main_runtime_thread=isMainRuntimeThread}Module[\"__register_pthread_ptr\"]=__register_pthread_ptr;var ERRNO_CODES={EPERM:63,ENOENT:44,ESRCH:71,EINTR:27,EIO:29,ENXIO:60,E2BIG:1,ENOEXEC:45,EBADF:8,ECHILD:12,EAGAIN:6,EWOULDBLOCK:6,ENOMEM:48,EACCES:2,EFAULT:21,ENOTBLK:105,EBUSY:10,EEXIST:20,EXDEV:75,ENODEV:43,ENOTDIR:54,EISDIR:31,EINVAL:28,ENFILE:41,EMFILE:33,ENOTTY:59,ETXTBSY:74,EFBIG:22,ENOSPC:51,ESPIPE:70,EROFS:69,EMLINK:34,EPIPE:64,EDOM:18,ERANGE:68,ENOMSG:49,EIDRM:24,ECHRNG:106,EL2NSYNC:156,EL3HLT:107,EL3RST:108,ELNRNG:109,EUNATCH:110,ENOCSI:111,EL2HLT:112,EDEADLK:16,ENOLCK:46,EBADE:113,EBADR:114,EXFULL:115,ENOANO:104,EBADRQC:103,EBADSLT:102,EDEADLOCK:16,EBFONT:101,ENOSTR:100,ENODATA:116,ETIME:117,ENOSR:118,ENONET:119,ENOPKG:120,EREMOTE:121,ENOLINK:47,EADV:122,ESRMNT:123,ECOMM:124,EPROTO:65,EMULTIHOP:36,EDOTDOT:125,EBADMSG:9,ENOTUNIQ:126,EBADFD:127,EREMCHG:128,ELIBACC:129,ELIBBAD:130,ELIBSCN:131,ELIBMAX:132,ELIBEXEC:133,ENOSYS:52,ENOTEMPTY:55,ENAMETOOLONG:37,ELOOP:32,EOPNOTSUPP:138,EPFNOSUPPORT:139,ECONNRESET:15,ENOBUFS:42,EAFNOSUPPORT:5,EPROTOTYPE:67,ENOTSOCK:57,ENOPROTOOPT:50,ESHUTDOWN:140,ECONNREFUSED:14,EADDRINUSE:3,ECONNABORTED:13,ENETUNREACH:40,ENETDOWN:38,ETIMEDOUT:73,EHOSTDOWN:142,EHOSTUNREACH:23,EINPROGRESS:26,EALREADY:7,EDESTADDRREQ:17,EMSGSIZE:35,EPROTONOSUPPORT:66,ESOCKTNOSUPPORT:137,EADDRNOTAVAIL:4,ENETRESET:39,EISCONN:30,ENOTCONN:53,ETOOMANYREFS:141,EUSERS:136,EDQUOT:19,ESTALE:72,ENOTSUP:138,ENOMEDIUM:148,EILSEQ:25,EOVERFLOW:61,ECANCELED:11,ENOTRECOVERABLE:56,EOWNERDEAD:62,ESTRPIPE:135};var __main_thread_futex_wait_address=13568;function _emscripten_futex_wake(addr,count){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0||count<0)return-28;if(count==0)return 0;if(count>=2147483647)count=Infinity;var mainThreadWaitAddress=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2);var mainThreadWoken=0;if(mainThreadWaitAddress==addr){var loadedAddr=Atomics.compareExchange(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,mainThreadWaitAddress,0);if(loadedAddr==mainThreadWaitAddress){--count;mainThreadWoken=1;if(count<=0)return 1}}var ret=Atomics.notify(GROWABLE_HEAP_I32(),addr>>2,count);if(ret>=0)return ret+mainThreadWoken;throw\"Atomics.notify returned an unexpected value \"+ret}Module[\"_emscripten_futex_wake\"]=_emscripten_futex_wake;function __kill_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _kill_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _kill_thread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];pthread.worker.terminate();PThread.freeThreadData(pthread);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(pthread.worker),1);pthread.worker.pthread=undefined}function __cancel_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _cancel_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _cancel_thread!\";var pthread=PThread.pthreads[pthread_ptr];pthread.worker.postMessage({\"cmd\":\"cancel\"})}function __cleanup_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _cleanup_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _cleanup_thread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];if(pthread){var worker=pthread.worker;PThread.returnWorkerToPool(worker)}}var PThread={MAIN_THREAD_ID:1,mainThreadInfo:{schedPolicy:0,schedPrio:0},unusedWorkers:[],runningWorkers:[],initRuntime:function(){__register_pthread_ptr(PThread.mainThreadBlock,!ENVIRONMENT_IS_WORKER,1);_emscripten_register_main_browser_thread_id(PThread.mainThreadBlock)},initMainThreadBlock:function(){var pthreadPoolSize=8;for(var i=0;i>2]=PThread.mainThreadBlock;var headPtr=PThread.mainThreadBlock+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var tlsMemory=13056;for(var i=0;i<128;++i)GROWABLE_HEAP_U32()[tlsMemory/4+i]=0;Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+104>>2,tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+40>>2,PThread.mainThreadBlock);Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+44>>2,42)},initWorker:function(){},pthreads:{},exitHandlers:null,setThreadStatus:function(){},runExitHandlers:function(){if(PThread.exitHandlers!==null){while(PThread.exitHandlers.length>0){PThread.exitHandlers.pop()()}PThread.exitHandlers=null}if(ENVIRONMENT_IS_PTHREAD&&threadInfoStruct)___pthread_tsd_run_dtors()},threadExit:function(exitCode){var tb=_pthread_self();if(tb){Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,exitCode);Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+60>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+64>>2,0);PThread.runExitHandlers();_emscripten_futex_wake(tb+0,2147483647);__register_pthread_ptr(0,0,0);threadInfoStruct=0;if(ENVIRONMENT_IS_PTHREAD){postMessage({\"cmd\":\"exit\"})}}},threadCancel:function(){PThread.runExitHandlers();Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+4>>2,-1);Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+0>>2,1);_emscripten_futex_wake(threadInfoStruct+0,2147483647);threadInfoStruct=selfThreadId=0;__register_pthread_ptr(0,0,0);postMessage({\"cmd\":\"cancelDone\"})},terminateAllThreads:function(){for(var t in PThread.pthreads){var pthread=PThread.pthreads[t];if(pthread&&pthread.worker){PThread.returnWorkerToPool(pthread.worker)}}PThread.pthreads={};for(var i=0;i>2];GROWABLE_HEAP_I32()[pthread.threadInfoStruct+104>>2]=0;_free(tlsMemory);_free(pthread.threadInfoStruct)}pthread.threadInfoStruct=0;if(pthread.allocatedOwnStack&&pthread.stackBase)_free(pthread.stackBase);pthread.stackBase=0;if(pthread.worker)pthread.worker.pthread=null},returnWorkerToPool:function(worker){delete PThread.pthreads[worker.pthread.thread];PThread.unusedWorkers.push(worker);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker),1);PThread.freeThreadData(worker.pthread);worker.pthread=undefined},receiveObjectTransfer:function(data){},loadWasmModuleToWorker:function(worker,onFinishedLoading){worker.onmessage=function(e){var d=e[\"data\"];var cmd=d[\"cmd\"];if(worker.pthread)PThread.currentProxiedOperationCallerThread=worker.pthread.threadInfoStruct;if(d[\"targetThread\"]&&d[\"targetThread\"]!=_pthread_self()){var thread=PThread.pthreads[d.targetThread];if(thread){thread.worker.postMessage(e.data,d[\"transferList\"])}else{console.error('Internal error! Worker sent a message \"'+cmd+'\" to target pthread '+d[\"targetThread\"]+\", but that thread no longer exists!\")}PThread.currentProxiedOperationCallerThread=undefined;return}if(cmd===\"processQueuedMainThreadWork\"){_emscripten_main_thread_process_queued_calls()}else if(cmd===\"spawnThread\"){__spawn_thread(e.data)}else if(cmd===\"cleanupThread\"){__cleanup_thread(d[\"thread\"])}else if(cmd===\"killThread\"){__kill_thread(d[\"thread\"])}else if(cmd===\"cancelThread\"){__cancel_thread(d[\"thread\"])}else if(cmd===\"loaded\"){worker.loaded=true;if(onFinishedLoading)onFinishedLoading(worker);if(worker.runPthread){worker.runPthread();delete worker.runPthread}}else if(cmd===\"print\"){out(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"printErr\"){err(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"alert\"){alert(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"exit\"){var detached=worker.pthread&&Atomics.load(GROWABLE_HEAP_U32(),worker.pthread.thread+68>>2);if(detached){PThread.returnWorkerToPool(worker)}}else if(cmd===\"cancelDone\"){PThread.returnWorkerToPool(worker)}else if(cmd===\"objectTransfer\"){PThread.receiveObjectTransfer(e.data)}else if(e.data.target===\"setimmediate\"){worker.postMessage(e.data)}else{err(\"worker sent an unknown command \"+cmd)}PThread.currentProxiedOperationCallerThread=undefined};worker.onerror=function(e){err(\"pthread sent an error! \"+e.filename+\":\"+e.lineno+\": \"+e.message)};if(ENVIRONMENT_IS_NODE){worker.on(\"message\",function(data){worker.onmessage({data:data})});worker.on(\"error\",function(data){worker.onerror(data)});worker.on(\"exit\",function(data){console.log(\"worker exited - TODO: update the worker queue?\")})}worker.postMessage({\"cmd\":\"load\",\"urlOrBlob\":Module[\"mainScriptUrlOrBlob\"]||_scriptDir,\"wasmMemory\":wasmMemory,\"wasmModule\":wasmModule,\"DYNAMIC_BASE\":DYNAMIC_BASE,\"DYNAMICTOP_PTR\":DYNAMICTOP_PTR})},allocateUnusedWorker:function(){var pthreadMainJs=locateFile(\"tfjs-backend-wasm-threaded-simd.worker.js\");PThread.unusedWorkers.push(new Worker(pthreadMainJs))},getNewWorker:function(){if(PThread.unusedWorkers.length==0){PThread.allocateUnusedWorker();PThread.loadWasmModuleToWorker(PThread.unusedWorkers[0])}if(PThread.unusedWorkers.length>0)return PThread.unusedWorkers.pop();else return null},busySpinWait:function(msecs){var t=performance.now()+msecs;while(performance.now()>2]=value;return value}function _atexit(func,arg){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(1,1,func,arg);__ATEXIT__.unshift({func:func,arg:arg})}function __emscripten_notify_thread_queue(targetThreadId,mainThreadId){if(targetThreadId==mainThreadId){postMessage({\"cmd\":\"processQueuedMainThreadWork\"})}else if(ENVIRONMENT_IS_PTHREAD){postMessage({\"targetThread\":targetThreadId,\"cmd\":\"processThreadQueue\"})}else{var pthread=PThread.pthreads[targetThreadId];var worker=pthread&&pthread.worker;if(!worker){return}worker.postMessage({\"cmd\":\"processThreadQueue\"})}return 1}function _abort(){abort()}function _emscripten_conditional_set_current_thread_status(expectedStatus,newStatus){expectedStatus=expectedStatus|0;newStatus=newStatus|0}function _emscripten_futex_wait(addr,val,timeout){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0)return-28;if(ENVIRONMENT_IS_WORKER){var ret=Atomics.wait(GROWABLE_HEAP_I32(),addr>>2,val,timeout);if(ret===\"timed-out\")return-73;if(ret===\"not-equal\")return-6;if(ret===\"ok\")return 0;throw\"Atomics.wait returned an unexpected value \"+ret}else{var loadedVal=Atomics.load(GROWABLE_HEAP_I32(),addr>>2);if(val!=loadedVal)return-6;var tNow=performance.now();var tEnd=tNow+timeout;Atomics.store(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,addr);var ourWaitAddress=addr;while(addr==ourWaitAddress){tNow=performance.now();if(tNow>tEnd){return-73}_emscripten_main_thread_process_queued_calls();addr=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2)}return 0}}function _emscripten_is_main_browser_thread(){return __pthread_is_main_browser_thread|0}function _emscripten_is_main_runtime_thread(){return __pthread_is_main_runtime_thread|0}function _emscripten_memcpy_big(dest,src,num){GROWABLE_HEAP_U8().copyWithin(dest,src,src+num)}function _emscripten_num_logical_cores(){return navigator[\"hardwareConcurrency\"]}function _emscripten_proxy_to_main_thread_js(index,sync){var numCallArgs=arguments.length-2;var stack=stackSave();var args=stackAlloc(numCallArgs*8);var b=args>>3;for(var i=0;i>3]);buf+=8}else{buf=buf+3&~3;args.push(GROWABLE_HEAP_I32()[buf>>2]);buf+=4}}return args}function _emscripten_receive_on_main_thread_js(index,numCallArgs,args){_emscripten_receive_on_main_thread_js_callArgs.length=numCallArgs;var b=args>>3;for(var i=0;i>>16);updateGlobalBufferAndViews(wasmMemory.buffer);return 1}catch(e){}}function _emscripten_resize_heap(requestedSize){requestedSize=requestedSize>>>0;var oldSize=_emscripten_get_heap_size();if(requestedSize<=oldSize){return false}var PAGE_MULTIPLE=65536;var maxHeapSize=2147483648;if(requestedSize>maxHeapSize){return false}var minHeapSize=16777216;for(var cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(minHeapSize,requestedSize,overGrownHeapSize),PAGE_MULTIPLE));var replacement=emscripten_realloc_buffer(newSize);if(replacement){return true}}return false}var JSEvents={keyEvent:0,mouseEvent:0,wheelEvent:0,uiEvent:0,focusEvent:0,deviceOrientationEvent:0,deviceMotionEvent:0,fullscreenChangeEvent:0,pointerlockChangeEvent:0,visibilityChangeEvent:0,touchEvent:0,previousFullscreenElement:null,previousScreenX:null,previousScreenY:null,removeEventListenersRegistered:false,removeAllEventListeners:function(){for(var i=JSEvents.eventHandlers.length-1;i>=0;--i){JSEvents._removeHandler(i)}JSEvents.eventHandlers=[];JSEvents.deferredCalls=[]},registerRemoveEventListeners:function(){if(!JSEvents.removeEventListenersRegistered){__ATEXIT__.push(JSEvents.removeAllEventListeners);JSEvents.removeEventListenersRegistered=true}},deferredCalls:[],deferCall:function(targetFunction,precedence,argsList){function arraysHaveEqualContent(arrA,arrB){if(arrA.length!=arrB.length)return false;for(var i in arrA){if(arrA[i]!=arrB[i])return false}return true}for(var i in JSEvents.deferredCalls){var call=JSEvents.deferredCalls[i];if(call.targetFunction==targetFunction&&arraysHaveEqualContent(call.argsList,argsList)){return}}JSEvents.deferredCalls.push({targetFunction:targetFunction,precedence:precedence,argsList:argsList});JSEvents.deferredCalls.sort(function(x,y){return x.precedence>2]=eventTypeId;GROWABLE_HEAP_I32()[varargs+4>>2]=eventData;GROWABLE_HEAP_I32()[varargs+8>>2]=userData;_emscripten_async_queue_on_thread_(targetThread,637534208,eventHandlerFunc,eventData,varargs);stackRestore(stackTop)},getTargetThreadForEventCallback:function(targetThread){switch(targetThread){case 1:return 0;case 2:return PThread.currentProxiedOperationCallerThread;default:return targetThread}},getNodeNameForTarget:function(target){if(!target)return\"\";if(target==window)return\"#window\";if(target==screen)return\"#screen\";return target&&target.nodeName?target.nodeName:\"\"},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function stringToNewUTF8(jsString){var length=lengthBytesUTF8(jsString)+1;var cString=_malloc(length);stringToUTF8(jsString,cString,length);return cString}function _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height){var stackTop=stackSave();var varargs=stackAlloc(12);var targetCanvasPtr=0;if(targetCanvas){targetCanvasPtr=stringToNewUTF8(targetCanvas)}GROWABLE_HEAP_I32()[varargs>>2]=targetCanvasPtr;GROWABLE_HEAP_I32()[varargs+4>>2]=width;GROWABLE_HEAP_I32()[varargs+8>>2]=height;_emscripten_async_queue_on_thread_(targetThread,657457152,0,targetCanvasPtr,varargs);stackRestore(stackTop)}function _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,targetCanvas,width,height){targetCanvas=targetCanvas?UTF8ToString(targetCanvas):\"\";_emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height)}function __maybeCStringToJsString(cString){return cString>2?UTF8ToString(cString):cString}var specialHTMLTargets=[0,typeof document!==\"undefined\"?document:0,typeof window!==\"undefined\"?window:0];function __findEventTarget(target){target=__maybeCStringToJsString(target);var domElement=specialHTMLTargets[target]||(typeof document!==\"undefined\"?document.querySelector(target):undefined);return domElement}function __findCanvasEventTarget(target){return __findEventTarget(target)}function _emscripten_set_canvas_element_size_calling_thread(target,width,height){var canvas=__findCanvasEventTarget(target);if(!canvas)return-4;if(canvas.canvasSharedPtr){GROWABLE_HEAP_I32()[canvas.canvasSharedPtr>>2]=width;GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+4>>2]=height}if(canvas.offscreenCanvas||!canvas.controlTransferredOffscreen){if(canvas.offscreenCanvas)canvas=canvas.offscreenCanvas;var autoResizeViewport=false;if(canvas.GLctxObject&&canvas.GLctxObject.GLctx){var prevViewport=canvas.GLctxObject.GLctx.getParameter(2978);autoResizeViewport=prevViewport[0]===0&&prevViewport[1]===0&&prevViewport[2]===canvas.width&&prevViewport[3]===canvas.height}canvas.width=width;canvas.height=height;if(autoResizeViewport){canvas.GLctxObject.GLctx.viewport(0,0,width,height)}}else if(canvas.canvasSharedPtr){var targetThread=GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+8>>2];_emscripten_set_offscreencanvas_size_on_target_thread(targetThread,target,width,height);return 1}else{return-4}return 0}function _emscripten_set_canvas_element_size_main_thread(target,width,height){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(2,1,target,width,height);return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}function _emscripten_set_canvas_element_size(target,width,height){var canvas=__findCanvasEventTarget(target);if(canvas){return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}else{return _emscripten_set_canvas_element_size_main_thread(target,width,height)}}function _emscripten_set_current_thread_status(newStatus){newStatus=newStatus|0}function _emscripten_set_thread_name(threadId,name){threadId=threadId|0;name=name|0}function __webgl_enable_ANGLE_instanced_arrays(ctx){var ext=ctx.getExtension(\"ANGLE_instanced_arrays\");if(ext){ctx[\"vertexAttribDivisor\"]=function(index,divisor){ext[\"vertexAttribDivisorANGLE\"](index,divisor)};ctx[\"drawArraysInstanced\"]=function(mode,first,count,primcount){ext[\"drawArraysInstancedANGLE\"](mode,first,count,primcount)};ctx[\"drawElementsInstanced\"]=function(mode,count,type,indices,primcount){ext[\"drawElementsInstancedANGLE\"](mode,count,type,indices,primcount)};return 1}}function __webgl_enable_OES_vertex_array_object(ctx){var ext=ctx.getExtension(\"OES_vertex_array_object\");if(ext){ctx[\"createVertexArray\"]=function(){return ext[\"createVertexArrayOES\"]()};ctx[\"deleteVertexArray\"]=function(vao){ext[\"deleteVertexArrayOES\"](vao)};ctx[\"bindVertexArray\"]=function(vao){ext[\"bindVertexArrayOES\"](vao)};ctx[\"isVertexArray\"]=function(vao){return ext[\"isVertexArrayOES\"](vao)};return 1}}function __webgl_enable_WEBGL_draw_buffers(ctx){var ext=ctx.getExtension(\"WEBGL_draw_buffers\");if(ext){ctx[\"drawBuffers\"]=function(n,bufs){ext[\"drawBuffersWEBGL\"](n,bufs)};return 1}}var GL={counter:1,lastError:0,buffers:[],mappedBuffers:{},programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},currentContext:null,offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,init:function(){var miniTempFloatBuffer=new Float32Array(GL.MINI_TEMP_BUFFER_SIZE);for(var i=0;i>2]:-1;source+=UTF8ToString(GROWABLE_HEAP_I32()[string+i*4>>2],len<0?undefined:len)}return source},createContext:function(canvas,webGLContextAttributes){var ctx=canvas.getContext(\"webgl\",webGLContextAttributes);if(!ctx)return 0;var handle=GL.registerContext(ctx,webGLContextAttributes);return handle},registerContext:function(ctx,webGLContextAttributes){var handle=_malloc(8);GROWABLE_HEAP_I32()[handle+4>>2]=_pthread_self();var context={handle:handle,attributes:webGLContextAttributes,version:webGLContextAttributes.majorVersion,GLctx:ctx};if(ctx.canvas)ctx.canvas.GLctxObject=context;GL.contexts[handle]=context;if(typeof webGLContextAttributes.enableExtensionsByDefault===\"undefined\"||webGLContextAttributes.enableExtensionsByDefault){GL.initExtensions(context)}return handle},makeContextCurrent:function(contextHandle){GL.currentContext=GL.contexts[contextHandle];Module.ctx=GLctx=GL.currentContext&&GL.currentContext.GLctx;return!(contextHandle&&!GLctx)},getContext:function(contextHandle){return GL.contexts[contextHandle]},deleteContext:function(contextHandle){if(GL.currentContext===GL.contexts[contextHandle])GL.currentContext=null;if(typeof JSEvents===\"object\")JSEvents.removeAllHandlersOnTarget(GL.contexts[contextHandle].GLctx.canvas);if(GL.contexts[contextHandle]&&GL.contexts[contextHandle].GLctx.canvas)GL.contexts[contextHandle].GLctx.canvas.GLctxObject=undefined;_free(GL.contexts[contextHandle].handle);GL.contexts[contextHandle]=null},initExtensions:function(context){if(!context)context=GL.currentContext;if(context.initExtensionsDone)return;context.initExtensionsDone=true;var GLctx=context.GLctx;__webgl_enable_ANGLE_instanced_arrays(GLctx);__webgl_enable_OES_vertex_array_object(GLctx);__webgl_enable_WEBGL_draw_buffers(GLctx);GLctx.disjointTimerQueryExt=GLctx.getExtension(\"EXT_disjoint_timer_query\");var automaticallyEnabledExtensions=[\"OES_texture_float\",\"OES_texture_half_float\",\"OES_standard_derivatives\",\"OES_vertex_array_object\",\"WEBGL_compressed_texture_s3tc\",\"WEBGL_depth_texture\",\"OES_element_index_uint\",\"EXT_texture_filter_anisotropic\",\"EXT_frag_depth\",\"WEBGL_draw_buffers\",\"ANGLE_instanced_arrays\",\"OES_texture_float_linear\",\"OES_texture_half_float_linear\",\"EXT_blend_minmax\",\"EXT_shader_texture_lod\",\"EXT_texture_norm16\",\"WEBGL_compressed_texture_pvrtc\",\"EXT_color_buffer_half_float\",\"WEBGL_color_buffer_float\",\"EXT_sRGB\",\"WEBGL_compressed_texture_etc1\",\"EXT_disjoint_timer_query\",\"WEBGL_compressed_texture_etc\",\"WEBGL_compressed_texture_astc\",\"EXT_color_buffer_float\",\"WEBGL_compressed_texture_s3tc_srgb\",\"EXT_disjoint_timer_query_webgl2\",\"WEBKIT_WEBGL_compressed_texture_pvrtc\"];var exts=GLctx.getSupportedExtensions()||[];exts.forEach(function(ext){if(automaticallyEnabledExtensions.indexOf(ext)!=-1){GLctx.getExtension(ext)}})},populateUniformTable:function(program){var p=GL.programs[program];var ptable=GL.programInfos[program]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1};var utable=ptable.uniforms;var numUniforms=GLctx.getProgramParameter(p,35718);for(var i=0;i>2;contextAttributes[\"alpha\"]=!!GROWABLE_HEAP_I32()[a+(0>>2)];contextAttributes[\"depth\"]=!!GROWABLE_HEAP_I32()[a+(4>>2)];contextAttributes[\"stencil\"]=!!GROWABLE_HEAP_I32()[a+(8>>2)];contextAttributes[\"antialias\"]=!!GROWABLE_HEAP_I32()[a+(12>>2)];contextAttributes[\"premultipliedAlpha\"]=!!GROWABLE_HEAP_I32()[a+(16>>2)];contextAttributes[\"preserveDrawingBuffer\"]=!!GROWABLE_HEAP_I32()[a+(20>>2)];var powerPreference=GROWABLE_HEAP_I32()[a+(24>>2)];contextAttributes[\"powerPreference\"]=__emscripten_webgl_power_preferences[powerPreference];contextAttributes[\"failIfMajorPerformanceCaveat\"]=!!GROWABLE_HEAP_I32()[a+(28>>2)];contextAttributes.majorVersion=GROWABLE_HEAP_I32()[a+(32>>2)];contextAttributes.minorVersion=GROWABLE_HEAP_I32()[a+(36>>2)];contextAttributes.enableExtensionsByDefault=GROWABLE_HEAP_I32()[a+(40>>2)];contextAttributes.explicitSwapControl=GROWABLE_HEAP_I32()[a+(44>>2)];contextAttributes.proxyContextToMainThread=GROWABLE_HEAP_I32()[a+(48>>2)];contextAttributes.renderViaOffscreenBackBuffer=GROWABLE_HEAP_I32()[a+(52>>2)];var canvas=__findCanvasEventTarget(target);if(!canvas){return-4}if(contextAttributes.explicitSwapControl){return-1}var contextHandle=GL.createContext(canvas,contextAttributes);return contextHandle}function _emscripten_webgl_create_context(a0,a1){return _emscripten_webgl_do_create_context(a0,a1)}var PATH={splitPath:function(filename){var splitPathRe=/^(\\/?|)([\\s\\S]*?)((?:\\.{1,2}|[^\\/]+?|)(\\.[^.\\/]*|))(?:[\\/]*)$/;return splitPathRe.exec(filename).slice(1)},normalizeArray:function(parts,allowAboveRoot){var up=0;for(var i=parts.length-1;i>=0;i--){var last=parts[i];if(last===\".\"){parts.splice(i,1)}else if(last===\"..\"){parts.splice(i,1);up++}else if(up){parts.splice(i,1);up--}}if(allowAboveRoot){for(;up;up--){parts.unshift(\"..\")}}return parts},normalize:function(path){var isAbsolute=path.charAt(0)===\"/\",trailingSlash=path.substr(-1)===\"/\";path=PATH.normalizeArray(path.split(\"/\").filter(function(p){return!!p}),!isAbsolute).join(\"/\");if(!path&&!isAbsolute){path=\".\"}if(path&&trailingSlash){path+=\"/\"}return(isAbsolute?\"/\":\"\")+path},dirname:function(path){var result=PATH.splitPath(path),root=result[0],dir=result[1];if(!root&&!dir){return\".\"}if(dir){dir=dir.substr(0,dir.length-1)}return root+dir},basename:function(path){if(path===\"/\")return\"/\";var lastSlash=path.lastIndexOf(\"/\");if(lastSlash===-1)return path;return path.substr(lastSlash+1)},extname:function(path){return PATH.splitPath(path)[3]},join:function(){var paths=Array.prototype.slice.call(arguments,0);return PATH.normalize(paths.join(\"/\"))},join2:function(l,r){return PATH.normalize(l+\"/\"+r)}};var SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer=SYSCALLS.buffers[stream];if(curr===0||curr===10){(stream===1?out:err)(UTF8ArrayToString(buffer,0));buffer.length=0}else{buffer.push(curr)}},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=GROWABLE_HEAP_I32()[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(3,1,fd);return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(4,1,fd,offset_low,offset_high,whence,newOffset)}function _fd_write(fd,iov,iovcnt,pnum){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(5,1,fd,iov,iovcnt,pnum);var num=0;for(var i=0;i>2];var len=GROWABLE_HEAP_I32()[iov+(i*8+4)>>2];for(var j=0;j>2]=num;return 0}function _pthread_cleanup_pop(execute){var routine=PThread.exitHandlers.pop();if(execute)routine()}function _pthread_cleanup_push(routine,arg){if(PThread.exitHandlers===null){PThread.exitHandlers=[]}PThread.exitHandlers.push(function(){dynCall_vi(routine,arg)})}function __spawn_thread(threadParams){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _spawn_thread() can only ever be called from main application thread!\";var worker=PThread.getNewWorker();if(worker.pthread!==undefined)throw\"Internal error!\";if(!threadParams.pthread_ptr)throw\"Internal error, no pthread ptr!\";PThread.runningWorkers.push(worker);var tlsMemory=_malloc(128*4);for(var i=0;i<128;++i){GROWABLE_HEAP_I32()[tlsMemory+i*4>>2]=0}var stackHigh=threadParams.stackBase+threadParams.stackSize;var pthread=PThread.pthreads[threadParams.pthread_ptr]={worker:worker,stackBase:threadParams.stackBase,stackSize:threadParams.stackSize,allocatedOwnStack:threadParams.allocatedOwnStack,thread:threadParams.pthread_ptr,threadInfoStruct:threadParams.pthread_ptr};var tis=pthread.threadInfoStruct>>2;Atomics.store(GROWABLE_HEAP_U32(),tis+(0>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(4>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(8>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(68>>2),threadParams.detached);Atomics.store(GROWABLE_HEAP_U32(),tis+(104>>2),tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),tis+(48>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(40>>2),pthread.threadInfoStruct);Atomics.store(GROWABLE_HEAP_U32(),tis+(44>>2),42);Atomics.store(GROWABLE_HEAP_U32(),tis+(108>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(84>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(80>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+8>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+12>>2),threadParams.detached);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+20>>2),threadParams.schedPolicy);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+24>>2),threadParams.schedPrio);var global_libc=_emscripten_get_global_libc();var global_locale=global_libc+40;Atomics.store(GROWABLE_HEAP_U32(),tis+(176>>2),global_locale);worker.pthread=pthread;var msg={\"cmd\":\"run\",\"start_routine\":threadParams.startRoutine,\"arg\":threadParams.arg,\"threadInfoStruct\":threadParams.pthread_ptr,\"selfThreadId\":threadParams.pthread_ptr,\"parentThreadId\":threadParams.parent_pthread_ptr,\"stackBase\":threadParams.stackBase,\"stackSize\":threadParams.stackSize};worker.runPthread=function(){msg.time=performance.now();worker.postMessage(msg,threadParams.transferList)};if(worker.loaded){worker.runPthread();delete worker.runPthread}}function _pthread_getschedparam(thread,policy,schedparam){if(!policy&&!schedparam)return ERRNO_CODES.EINVAL;if(!thread){err(\"pthread_getschedparam called with a null thread pointer!\");return ERRNO_CODES.ESRCH}var self=GROWABLE_HEAP_I32()[thread+12>>2];if(self!==thread){err(\"pthread_getschedparam attempted on thread \"+thread+\", which does not point to a valid thread, or does not exist anymore!\");return ERRNO_CODES.ESRCH}var schedPolicy=Atomics.load(GROWABLE_HEAP_U32(),thread+108+20>>2);var schedPrio=Atomics.load(GROWABLE_HEAP_U32(),thread+108+24>>2);if(policy)GROWABLE_HEAP_I32()[policy>>2]=schedPolicy;if(schedparam)GROWABLE_HEAP_I32()[schedparam>>2]=schedPrio;return 0}function _pthread_self(){return __pthread_ptr|0}Module[\"_pthread_self\"]=_pthread_self;function _pthread_create(pthread_ptr,attr,start_routine,arg){if(typeof SharedArrayBuffer===\"undefined\"){err(\"Current environment does not support SharedArrayBuffer, pthreads are not available!\");return 6}if(!pthread_ptr){err(\"pthread_create called with a null thread pointer!\");return 28}var transferList=[];var error=0;if(ENVIRONMENT_IS_PTHREAD&&(transferList.length===0||error)){return _emscripten_sync_run_in_main_thread_4(687865856,pthread_ptr,attr,start_routine,arg)}if(error)return error;var stackSize=0;var stackBase=0;var detached=0;var schedPolicy=0;var schedPrio=0;if(attr){stackSize=GROWABLE_HEAP_I32()[attr>>2];stackSize+=81920;stackBase=GROWABLE_HEAP_I32()[attr+8>>2];detached=GROWABLE_HEAP_I32()[attr+12>>2]!==0;var inheritSched=GROWABLE_HEAP_I32()[attr+16>>2]===0;if(inheritSched){var prevSchedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];var prevSchedPrio=GROWABLE_HEAP_I32()[attr+24>>2];var parentThreadPtr=PThread.currentProxiedOperationCallerThread?PThread.currentProxiedOperationCallerThread:_pthread_self();_pthread_getschedparam(parentThreadPtr,attr+20,attr+24);schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];schedPrio=GROWABLE_HEAP_I32()[attr+24>>2];GROWABLE_HEAP_I32()[attr+20>>2]=prevSchedPolicy;GROWABLE_HEAP_I32()[attr+24>>2]=prevSchedPrio}else{schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];schedPrio=GROWABLE_HEAP_I32()[attr+24>>2]}}else{stackSize=2097152}var allocatedOwnStack=stackBase==0;if(allocatedOwnStack){stackBase=_memalign(16,stackSize)}else{stackBase-=stackSize;assert(stackBase>0)}var threadInfoStruct=_malloc(232);for(var i=0;i<232>>2;++i)GROWABLE_HEAP_U32()[(threadInfoStruct>>2)+i]=0;GROWABLE_HEAP_I32()[pthread_ptr>>2]=threadInfoStruct;GROWABLE_HEAP_I32()[threadInfoStruct+12>>2]=threadInfoStruct;var headPtr=threadInfoStruct+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var threadParams={stackBase:stackBase,stackSize:stackSize,allocatedOwnStack:allocatedOwnStack,schedPolicy:schedPolicy,schedPrio:schedPrio,detached:detached,startRoutine:start_routine,pthread_ptr:threadInfoStruct,parent_pthread_ptr:_pthread_self(),arg:arg,transferList:transferList};if(ENVIRONMENT_IS_PTHREAD){threadParams.cmd=\"spawnThread\";postMessage(threadParams,transferList)}else{__spawn_thread(threadParams)}return 0}function _roundf(d){d=+d;return d>=+0?+Math_floor(d+ +.5):+Math_ceil(d-+.5)}function _sysconf(name){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(6,1,name);switch(name){case 30:return 16384;case 85:var maxHeapSize=2147483648;return maxHeapSize/16384;case 132:case 133:case 12:case 137:case 138:case 15:case 235:case 16:case 17:case 18:case 19:case 20:case 149:case 13:case 10:case 236:case 153:case 9:case 21:case 22:case 159:case 154:case 14:case 77:case 78:case 139:case 80:case 81:case 82:case 68:case 67:case 164:case 11:case 29:case 47:case 48:case 95:case 52:case 51:case 46:case 79:return 200809;case 27:case 246:case 127:case 128:case 23:case 24:case 160:case 161:case 181:case 182:case 242:case 183:case 184:case 243:case 244:case 245:case 165:case 178:case 179:case 49:case 50:case 168:case 169:case 175:case 170:case 171:case 172:case 97:case 76:case 32:case 173:case 35:return-1;case 176:case 177:case 7:case 155:case 8:case 157:case 125:case 126:case 92:case 93:case 129:case 130:case 131:case 94:case 91:return 1;case 74:case 60:case 69:case 70:case 4:return 1024;case 31:case 42:case 72:return 32;case 87:case 26:case 33:return 2147483647;case 34:case 1:return 47839;case 38:case 36:return 99;case 43:case 37:return 2048;case 0:return 2097152;case 3:return 65536;case 28:return 32768;case 44:return 32767;case 75:return 16384;case 39:return 1e3;case 89:return 700;case 71:return 256;case 40:return 255;case 2:return 100;case 180:return 64;case 25:return 20;case 5:return 16;case 6:return 6;case 73:return 4;case 84:{if(typeof navigator===\"object\")return navigator[\"hardwareConcurrency\"]||1;return 1}}setErrNo(28);return-1}if(!ENVIRONMENT_IS_PTHREAD)PThread.initMainThreadBlock();else PThread.initWorker();var GLctx;GL.init();var proxiedFunctionTable=[null,_atexit,_emscripten_set_canvas_element_size_main_thread,_fd_close,_fd_seek,_fd_write,_sysconf];var asmLibraryArg={\"e\":___assert_fail,\"r\":___call_main,\"w\":__emscripten_notify_thread_queue,\"a\":_abort,\"l\":_emscripten_conditional_set_current_thread_status,\"d\":_emscripten_futex_wait,\"c\":_emscripten_futex_wake,\"h\":_emscripten_get_now,\"g\":_emscripten_is_main_browser_thread,\"x\":_emscripten_is_main_runtime_thread,\"q\":_emscripten_memcpy_big,\"B\":_emscripten_num_logical_cores,\"t\":_emscripten_receive_on_main_thread_js,\"A\":_emscripten_resize_heap,\"u\":_emscripten_set_canvas_element_size,\"k\":_emscripten_set_current_thread_status,\"s\":_emscripten_set_thread_name,\"v\":_emscripten_webgl_create_context,\"m\":_fd_close,\"o\":_fd_seek,\"i\":_fd_write,\"p\":initPthreadsJS,\"memory\":wasmMemory||Module[\"wasmMemory\"],\"y\":_pthread_cleanup_pop,\"z\":_pthread_cleanup_push,\"j\":_pthread_create,\"b\":_pthread_self,\"f\":_roundf,\"n\":_sysconf,\"table\":wasmTable};var asm=createWasm();Module[\"asm\"]=asm;var ___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=function(){return(___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=Module[\"asm\"][\"C\"]).apply(null,arguments)};var _init=Module[\"_init\"]=function(){return(_init=Module[\"_init\"]=Module[\"asm\"][\"D\"]).apply(null,arguments)};var _register_tensor=Module[\"_register_tensor\"]=function(){return(_register_tensor=Module[\"_register_tensor\"]=Module[\"asm\"][\"E\"]).apply(null,arguments)};var _dispose_data=Module[\"_dispose_data\"]=function(){return(_dispose_data=Module[\"_dispose_data\"]=Module[\"asm\"][\"F\"]).apply(null,arguments)};var _dispose=Module[\"_dispose\"]=function(){return(_dispose=Module[\"_dispose\"]=Module[\"asm\"][\"G\"]).apply(null,arguments)};var _Abs=Module[\"_Abs\"]=function(){return(_Abs=Module[\"_Abs\"]=Module[\"asm\"][\"H\"]).apply(null,arguments)};var _Add=Module[\"_Add\"]=function(){return(_Add=Module[\"_Add\"]=Module[\"asm\"][\"I\"]).apply(null,arguments)};var 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assert(condition,text){if(!condition){abort(\"Assertion failed: \"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i=endIdx))++endPtr;if(endPtr-idx>16&&heap.subarray&&UTF8Decoder){return UTF8Decoder.decode(heap.subarray(idx,endPtr))}else{var str=\"\";while(idx>10,56320|ch&1023)}}}return str}function UTF8ToString(ptr,maxBytesToRead){return 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addRunDependency(id){runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}what+=\"\";out(what);err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";throw new WebAssembly.RuntimeError(what)}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(){try{if(wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(wasmBinaryFile)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary()})}return new Promise(function(resolve,reject){resolve(getBinary())})}function createWasm(){var info={\"env\":asmLibraryArg,\"wasi_snapshot_preview1\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmMemory=exports[\"memory\"];updateGlobalBufferAndViews(wasmMemory.buffer);removeRunDependency(\"wasm-instantiate\")}addRunDependency(\"wasm-instantiate\");function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming===\"function\"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch===\"function\"){fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err(\"wasm streaming compile failed: \"+reason);err(\"falling back to ArrayBuffer instantiation\");instantiateArrayBuffer(receiveInstantiatedSource)})})}else{return instantiateArrayBuffer(receiveInstantiatedSource)}}if(Module[\"instantiateWasm\"]){try{var exports=Module[\"instantiateWasm\"](info,receiveInstance);return exports}catch(e){err(\"Module.instantiateWasm callback failed with error: \"+e);return false}}instantiateAsync();return{}}__ATINIT__.push();function _emscripten_notify_memory_growth(memoryIndex){updateGlobalBufferAndViews(wasmMemory.buffer)}var PATH={splitPath:function(filename){var splitPathRe=/^(\\/?|)([\\s\\S]*?)((?:\\.{1,2}|[^\\/]+?|)(\\.[^.\\/]*|))(?:[\\/]*)$/;return splitPathRe.exec(filename).slice(1)},normalizeArray:function(parts,allowAboveRoot){var up=0;for(var i=parts.length-1;i>=0;i--){var last=parts[i];if(last===\".\"){parts.splice(i,1)}else if(last===\"..\"){parts.splice(i,1);up++}else if(up){parts.splice(i,1);up--}}if(allowAboveRoot){for(;up;up--){parts.unshift(\"..\")}}return parts},normalize:function(path){var isAbsolute=path.charAt(0)===\"/\",trailingSlash=path.substr(-1)===\"/\";path=PATH.normalizeArray(path.split(\"/\").filter(function(p){return!!p}),!isAbsolute).join(\"/\");if(!path&&!isAbsolute){path=\".\"}if(path&&trailingSlash){path+=\"/\"}return(isAbsolute?\"/\":\"\")+path},dirname:function(path){var result=PATH.splitPath(path),root=result[0],dir=result[1];if(!root&&!dir){return\".\"}if(dir){dir=dir.substr(0,dir.length-1)}return root+dir},basename:function(path){if(path===\"/\")return\"/\";var lastSlash=path.lastIndexOf(\"/\");if(lastSlash===-1)return path;return path.substr(lastSlash+1)},extname:function(path){return PATH.splitPath(path)[3]},join:function(){var paths=Array.prototype.slice.call(arguments,0);return PATH.normalize(paths.join(\"/\"))},join2:function(l,r){return PATH.normalize(l+\"/\"+r)}};var SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer=SYSCALLS.buffers[stream];if(curr===0||curr===10){(stream===1?out:err)(UTF8ArrayToString(buffer,0));buffer.length=0}else{buffer.push(curr)}},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=HEAP32[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){}function _fd_write(fd,iov,iovcnt,pnum){var num=0;for(var i=0;i>2];var len=HEAP32[iov+(i*8+4)>>2];for(var j=0;j>2]=num;return 0}function _exit(status){exit(status)}function _proc_exit(code){_exit(code)}function _roundf(d){d=+d;return d>=+0?+Math_floor(d+ +.5):+Math_ceil(d-+.5)}var asmLibraryArg={\"emscripten_notify_memory_growth\":_emscripten_notify_memory_growth,\"fd_close\":_fd_close,\"fd_seek\":_fd_seek,\"fd_write\":_fd_write,\"proc_exit\":_proc_exit,\"roundf\":_roundf};var asm=createWasm();Module[\"asm\"]=asm;var _init=Module[\"_init\"]=function(){return(_init=Module[\"_init\"]=Module[\"asm\"][\"init\"]).apply(null,arguments)};var _register_tensor=Module[\"_register_tensor\"]=function(){return(_register_tensor=Module[\"_register_tensor\"]=Module[\"asm\"][\"register_tensor\"]).apply(null,arguments)};var _dispose_data=Module[\"_dispose_data\"]=function(){return(_dispose_data=Module[\"_dispose_data\"]=Module[\"asm\"][\"dispose_data\"]).apply(null,arguments)};var _dispose=Module[\"_dispose\"]=function(){return(_dispose=Module[\"_dispose\"]=Module[\"asm\"][\"dispose\"]).apply(null,arguments)};var 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ExitStatus(status){this.name=\"ExitStatus\";this.message=\"Program terminated with exit(\"+status+\")\";this.status=status}var calledMain=false;dependenciesFulfilled=function runCaller(){if(!calledRun)run();if(!calledRun)dependenciesFulfilled=runCaller};function callMain(args){var entryFunction=Module[\"__start\"];try{entryFunction();var ret=0;exit(ret,true)}catch(e){if(e instanceof ExitStatus){return}else if(e==\"unwind\"){noExitRuntime=true;return}else{var toLog=e;if(e&&typeof e===\"object\"&&e.stack){toLog=[e,e.stack]}err(\"exception thrown: \"+toLog);quit_(1,e)}}finally{calledMain=true}}function run(args){args=args||arguments_;if(runDependencies>0){return}preRun();if(runDependencies>0)return;function doRun(){if(calledRun)return;calledRun=true;Module[\"calledRun\"]=true;if(ABORT)return;initRuntime();preMain();if(Module[\"onRuntimeInitialized\"])Module[\"onRuntimeInitialized\"]();if(shouldRunNow)callMain(args);postRun()}if(Module[\"setStatus\"]){Module[\"setStatus\"](\"Running...\");setTimeout(function(){setTimeout(function(){Module[\"setStatus\"](\"\")},1);doRun()},1)}else{doRun()}}Module[\"run\"]=run;function exit(status,implicit){if(implicit&&noExitRuntime&&status===0){return}if(noExitRuntime){}else{ABORT=true;EXITSTATUS=status;exitRuntime();if(Module[\"onExit\"])Module[\"onExit\"](status)}quit_(status,new ExitStatus(status))}if(Module[\"preInit\"]){if(typeof Module[\"preInit\"]==\"function\")Module[\"preInit\"]=[Module[\"preInit\"]];while(Module[\"preInit\"].length>0){Module[\"preInit\"].pop()()}}var shouldRunNow=true;if(Module[\"noInitialRun\"])shouldRunNow=false;noExitRuntime=true;run();\n\n\n return WasmBackendModule\n}\n);\n})();\nif (typeof exports === 'object' && typeof module === 'object')\n module.exports = WasmBackendModule;\n else if (typeof define === 'function' && define['amd'])\n define([], function() { return WasmBackendModule; });\n else if (typeof exports === 'object')\n exports[\"WasmBackendModule\"] = WasmBackendModule;\n ", "// A port of an algorithm by Johannes Baag\u00F8e , 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e \n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n//\n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n//\n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n var me = this, mash = Mash();\n\n me.next = function() {\n var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n me.s0 = me.s1;\n me.s1 = me.s2;\n return me.s2 = t - (me.c = t | 0);\n };\n\n // Apply the seeding algorithm from Baagoe.\n me.c = 1;\n me.s0 = mash(' ');\n me.s1 = mash(' ');\n me.s2 = mash(' ');\n me.s0 -= mash(seed);\n if (me.s0 < 0) { me.s0 += 1; }\n me.s1 -= mash(seed);\n if (me.s1 < 0) { me.s1 += 1; }\n me.s2 -= mash(seed);\n if (me.s2 < 0) { me.s2 += 1; }\n mash = null;\n}\n\nfunction copy(f, t) {\n t.c = f.c;\n t.s0 = f.s0;\n t.s1 = f.s1;\n t.s2 = f.s2;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new Alea(seed),\n state = opts && opts.state,\n prng = xg.next;\n prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n prng.double = function() {\n return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n };\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nfunction Mash() {\n var n = 0xefc8249d;\n\n var mash = function(data) {\n data = String(data);\n for (var i = 0; i < data.length; i++) {\n n += data.charCodeAt(i);\n var h = 0.02519603282416938 * n;\n n = h >>> 0;\n h -= n;\n h *= n;\n n = h >>> 0;\n h -= n;\n n += h * 0x100000000; // 2^32\n }\n return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n };\n\n return mash;\n}\n\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.alea = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n\n // Set up generator function.\n me.next = function() {\n var t = me.x ^ (me.x << 11);\n me.x = me.y;\n me.y = me.z;\n me.z = me.w;\n return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n };\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor128 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var t = (me.x ^ (me.x >>> 2));\n me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n return (me.d = (me.d + 362437 | 0)) +\n (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n };\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n me.v = 0;\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n if (k == strseed.length) {\n me.d = me.x << 10 ^ me.x >>> 4;\n }\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n t.v = f.v;\n t.d = f.d;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorwow = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n // Update xor generator.\n var X = me.x, i = me.i, t, v, w;\n t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n X[i] = v;\n me.i = (i + 1) & 7;\n return v;\n };\n\n function init(me, seed) {\n var j, w, X = [];\n\n if (seed === (seed | 0)) {\n // Seed state array using a 32-bit integer.\n w = X[0] = seed;\n } else {\n // Seed state using a string.\n seed = '' + seed;\n for (j = 0; j < seed.length; ++j) {\n X[j & 7] = (X[j & 7] << 15) ^\n (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n }\n }\n // Enforce an array length of 8, not all zeroes.\n while (X.length < 8) X.push(0);\n for (j = 0; j < 8 && X[j] === 0; ++j);\n if (j == 8) w = X[7] = -1; else w = X[j];\n\n me.x = X;\n me.i = 0;\n\n // Discard an initial 256 values.\n for (j = 256; j > 0; --j) {\n me.next();\n }\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.x = f.x.slice();\n t.i = f.i;\n return t;\n}\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.x) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorshift7 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated. For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1); // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897); // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C. This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n var w = me.w,\n X = me.X, i = me.i, t, v;\n // Update Weyl generator.\n me.w = w = (w + 0x61c88647) | 0;\n // Update xor generator.\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n // Update Xor generator array state.\n v = X[i] = v ^ t;\n me.i = i;\n // Result is the combination.\n return (v + (w ^ (w >>> 16))) | 0;\n };\n\n function init(me, seed) {\n var t, v, i, j, w, X = [], limit = 128;\n if (seed === (seed | 0)) {\n // Numeric seeds initialize v, which is used to generates X.\n v = seed;\n seed = null;\n } else {\n // String seeds are mixed into v and X one character at a time.\n seed = seed + '\\0';\n v = 0;\n limit = Math.max(limit, seed.length);\n }\n // Initialize circular array and weyl value.\n for (i = 0, j = -32; j < limit; ++j) {\n // Put the unicode characters into the array, and shuffle them.\n if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n // After 32 shuffles, take v as the starting w value.\n if (j === 0) w = v;\n v ^= v << 10;\n v ^= v >>> 15;\n v ^= v << 4;\n v ^= v >>> 13;\n if (j >= 0) {\n w = (w + 0x61c88647) | 0; // Weyl.\n t = (X[j & 127] ^= (v + w)); // Combine xor and weyl to init array.\n i = (0 == t) ? i + 1 : 0; // Count zeroes.\n }\n }\n // We have detected all zeroes; make the key nonzero.\n if (i >= 128) {\n X[(seed && seed.length || 0) & 127] = -1;\n }\n // Run the generator 512 times to further mix the state before using it.\n // Factoring this as a function slows the main generator, so it is just\n // unrolled here. The weyl generator is not advanced while warming up.\n i = 127;\n for (j = 4 * 128; j > 0; --j) {\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n X[i] = v ^ t;\n }\n // Storing state as object members is faster than using closure variables.\n me.w = w;\n me.X = X;\n me.i = i;\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.i = f.i;\n t.w = f.w;\n t.X = f.X.slice();\n return t;\n};\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.X) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor4096 = impl;\n}\n\n})(\n this, // window object or global\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var b = me.b, c = me.c, d = me.d, a = me.a;\n b = (b << 25) ^ (b >>> 7) ^ c;\n c = (c - d) | 0;\n d = (d << 24) ^ (d >>> 8) ^ a;\n a = (a - b) | 0;\n me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n me.c = c = (c - d) | 0;\n me.d = (d << 16) ^ (c >>> 16) ^ a;\n return me.a = (a - b) | 0;\n };\n\n /* The following is non-inverted tyche, which has better internal\n * bit diffusion, but which is about 25% slower than tyche-i in JS.\n me.next = function() {\n var a = me.a, b = me.b, c = me.c, d = me.d;\n a = (me.a + me.b | 0) >>> 0;\n d = me.d ^ a; d = d << 16 ^ d >>> 16;\n c = me.c + d | 0;\n b = me.b ^ c; b = b << 12 ^ d >>> 20;\n me.a = a = a + b | 0;\n d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n me.c = c = c + d | 0;\n b = b ^ c;\n return me.b = (b << 7 ^ b >>> 25);\n }\n */\n\n me.a = 0;\n me.b = 0;\n me.c = 2654435769 | 0;\n me.d = 1367130551;\n\n if (seed === Math.floor(seed)) {\n // Integer seed.\n me.a = (seed / 0x100000000) | 0;\n me.b = seed | 0;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 20; k++) {\n me.b ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.a = f.a;\n t.b = f.b;\n t.c = f.c;\n t.d = f.d;\n return t;\n};\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.tychei = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "/*\nCopyright 2019 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (global, pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\n\nvar width = 256, // each RC4 output is 0 <= x < 256\n chunks = 6, // at least six RC4 outputs for each double\n digits = 52, // there are 52 significant digits in a double\n rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n startdenom = math.pow(width, chunks),\n significance = math.pow(2, digits),\n overflow = significance * 2,\n mask = width - 1,\n nodecrypto; // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n var key = [];\n options = (options == true) ? { entropy: true } : (options || {});\n\n // Flatten the seed string or build one from local entropy if needed.\n var shortseed = mixkey(flatten(\n options.entropy ? [seed, tostring(pool)] :\n (seed == null) ? autoseed() : seed, 3), key);\n\n // Use the seed to initialize an ARC4 generator.\n var arc4 = new ARC4(key);\n\n // This function returns a random double in [0, 1) that contains\n // randomness in every bit of the mantissa of the IEEE 754 value.\n var prng = function() {\n var n = arc4.g(chunks), // Start with a numerator n < 2 ^ 48\n d = startdenom, // and denominator d = 2 ^ 48.\n x = 0; // and no 'extra last byte'.\n while (n < significance) { // Fill up all significant digits by\n n = (n + x) * width; // shifting numerator and\n d *= width; // denominator and generating a\n x = arc4.g(1); // new least-significant-byte.\n }\n while (n >= overflow) { // To avoid rounding up, before adding\n n /= 2; // last byte, shift everything\n d /= 2; // right using integer math until\n x >>>= 1; // we have exactly the desired bits.\n }\n return (n + x) / d; // Form the number within [0, 1).\n };\n\n prng.int32 = function() { return arc4.g(4) | 0; }\n prng.quick = function() { return arc4.g(4) / 0x100000000; }\n prng.double = prng;\n\n // Mix the randomness into accumulated entropy.\n mixkey(tostring(arc4.S), pool);\n\n // Calling convention: what to return as a function of prng, seed, is_math.\n return (options.pass || callback ||\n function(prng, seed, is_math_call, state) {\n if (state) {\n // Load the arc4 state from the given state if it has an S array.\n if (state.S) { copy(state, arc4); }\n // Only provide the .state method if requested via options.state.\n prng.state = function() { return copy(arc4, {}); }\n }\n\n // If called as a method of Math (Math.seedrandom()), mutate\n // Math.random because that is how seedrandom.js has worked since v1.0.\n if (is_math_call) { math[rngname] = prng; return seed; }\n\n // Otherwise, it is a newer calling convention, so return the\n // prng directly.\n else return prng;\n })(\n prng,\n shortseed,\n 'global' in options ? options.global : (this == math),\n options.state);\n}\n\n//\n// ARC4\n//\n// An ARC4 implementation. The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4. Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n var t, keylen = key.length,\n me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n // The empty key [] is treated as [0].\n if (!keylen) { key = [keylen++]; }\n\n // Set up S using the standard key scheduling algorithm.\n while (i < width) {\n s[i] = i++;\n }\n for (i = 0; i < width; i++) {\n s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n s[j] = t;\n }\n\n // The \"g\" method returns the next (count) outputs as one number.\n (me.g = function(count) {\n // Using instance members instead of closure state nearly doubles speed.\n var t, r = 0,\n i = me.i, j = me.j, s = me.S;\n while (count--) {\n t = s[i = mask & (i + 1)];\n r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n }\n me.i = i; me.j = j;\n return r;\n // For robust unpredictability, the function call below automatically\n // discards an initial batch of values. This is called RC4-drop[256].\n // See http://google.com/search?q=rsa+fluhrer+response&btnI\n })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n t.i = f.i;\n t.j = f.j;\n t.S = f.S.slice();\n return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n var result = [], typ = (typeof obj), prop;\n if (depth && typ == 'object') {\n for (prop in obj) {\n try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n }\n }\n return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n var stringseed = seed + '', smear, j = 0;\n while (j < stringseed.length) {\n key[mask & j] =\n mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n }\n return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n try {\n var out;\n if (nodecrypto && (out = nodecrypto.randomBytes)) {\n // The use of 'out' to remember randomBytes makes tight minified code.\n out = out(width);\n } else {\n out = new Uint8Array(width);\n (global.crypto || global.msCrypto).getRandomValues(out);\n }\n return tostring(out);\n } catch (e) {\n var browser = global.navigator,\n plugins = browser && browser.plugins;\n return [+new Date, global, plugins, global.screen, tostring(pool)];\n }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool. Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n module.exports = seedrandom;\n // When in node.js, try using crypto package for autoseeding.\n try {\n nodecrypto = require('crypto');\n } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n define(function() { return seedrandom; });\n} else {\n // When included as a plain script, set up Math.seedrandom global.\n math['seed' + rngname] = seedrandom;\n}\n\n\n// End anonymous scope, and pass initial values.\n})(\n // global: `self` in browsers (including strict mode and web workers),\n // otherwise `this` in Node and other environments\n (typeof self !== 'undefined') ? self : this,\n [], // pool: entropy pool starts empty\n Math // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random(); // 0 <= x < 1. Every bit is random.\n// var x = random.quick(); // 0 <= x < 1. 32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three. It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures. Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "export const wasmWorkerContents = 'var threadInfoStruct=0;var selfThreadId=0;var parentThreadId=0;var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(\" \");console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(\" \");postMessage({cmd:\"alert\",text:text,threadId:selfThreadId})}var err=threadPrintErr;this.alert=threadAlert;Module[\"instantiateWasm\"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module[\"wasmModule\"],info);Module[\"wasmModule\"]=null;receiveInstance(instance);return instance.exports};this.onmessage=function(e){try{if(e.data.cmd===\"load\"){Module[\"DYNAMIC_BASE\"]=e.data.DYNAMIC_BASE;Module[\"DYNAMICTOP_PTR\"]=e.data.DYNAMICTOP_PTR;Module[\"wasmModule\"]=e.data.wasmModule;Module[\"wasmMemory\"]=e.data.wasmMemory;Module[\"buffer\"]=Module[\"wasmMemory\"].buffer;Module[\"ENVIRONMENT_IS_PTHREAD\"]=true;if(typeof e.data.urlOrBlob===\"string\"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}Module=WasmBackendModuleThreadedSimd(Module);postMessage({\"cmd\":\"loaded\"})}else if(e.data.cmd===\"objectTransfer\"){Module[\"PThread\"].receiveObjectTransfer(e.data)}else if(e.data.cmd===\"run\"){Module[\"__performance_now_clock_drift\"]=performance.now()-e.data.time;threadInfoStruct=e.data.threadInfoStruct;Module[\"__register_pthread_ptr\"](threadInfoStruct,0,0);selfThreadId=e.data.selfThreadId;parentThreadId=e.data.parentThreadId;var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module[\"establishStackSpace\"](top,max);Module[\"_emscripten_tls_init\"]();Module[\"PThread\"].receiveObjectTransfer(e.data);Module[\"PThread\"].setThreadStatus(Module[\"_pthread_self\"](),1);try{var result=Module[\"dynCall_ii\"](e.data.start_routine,e.data.arg);if(!Module[\"getNoExitRuntime\"]())Module[\"PThread\"].threadExit(result)}catch(ex){if(ex===\"Canceled!\"){Module[\"PThread\"].threadCancel()}else if(ex!=\"unwind\"){Atomics.store(Module[\"HEAPU32\"],threadInfoStruct+4>>2,ex instanceof Module[\"ExitStatus\"]?ex.status:-2);Atomics.store(Module[\"HEAPU32\"],threadInfoStruct+0>>2,1);Module[\"_emscripten_futex_wake\"](threadInfoStruct+0,2147483647);if(!(ex instanceof Module[\"ExitStatus\"]))throw ex}}}else if(e.data.cmd===\"cancel\"){if(threadInfoStruct){Module[\"PThread\"].threadCancel()}}else if(e.data.target===\"setimmediate\"){}else if(e.data.cmd===\"processThreadQueue\"){if(threadInfoStruct){Module[\"_emscripten_current_thread_process_queued_calls\"]()}}else{err(\"worker.js received unknown command \"+e.data.cmd);err(e.data)}}catch(ex){err(\"worker.js onmessage() captured an uncaught exception: \"+ex);if(ex.stack)err(ex.stack);throw ex}};if(typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\"){self={location:{href:__filename}};var onmessage=this.onmessage;var nodeWorkerThreads=require(\"worker_threads\");Worker=nodeWorkerThreads.Worker;var parentPort=nodeWorkerThreads.parentPort;parentPort.on(\"message\",function(data){onmessage({data:data})});var nodeFS=require(\"fs\");var nodeRead=function(filename){return nodeFS.readFileSync(filename,\"utf8\")};function globalEval(x){global.require=require;global.Module=Module;eval.call(null,x)}importScripts=function(f){globalEval(nodeRead(f))};postMessage=function(msg){parentPort.postMessage(msg)};if(typeof performance===\"undefined\"){performance={now:function(){return Date.now()}}}}';", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "// wrapper to load tfjs in a single place so version can be changed quickly\n\n// simplified\n// { modules: 1061, moduleBytes: 3772720, outputBytes: 1531035 }\n\n// export * from '@tensorflow/tfjs/dist/index.js';\n// export * from '@tensorflow/tfjs-backend-wasm';\n\n// modular\n// { modules: 1064, moduleBytes: 3793219, outputBytes: 1535600 }\n\n// get versions of all packages.\nimport { version as tfjs } from '@tensorflow/tfjs/package.json';\nimport { version as versionCore } from '@tensorflow/tfjs-core/package.json';\nimport { version as versionData } from '@tensorflow/tfjs-data/package.json';\nimport { version as versionLayers } from '@tensorflow/tfjs-layers/package.json';\nimport { version as versionConverter } from '@tensorflow/tfjs-converter/package.json';\n// for backends, get version from source so it can register backend during import\nimport { version_cpu } from '@tensorflow/tfjs-backend-cpu/dist/index.js';\nimport { version_webgl } from '@tensorflow/tfjs-backend-webgl/dist/index.js';\nimport { version_wasm } from '@tensorflow/tfjs-backend-wasm/dist/index.js';\n\n// export all\nexport * from '@tensorflow/tfjs-core/dist/index.js';\nexport * from '@tensorflow/tfjs-layers/dist/index.js';\nexport * from '@tensorflow/tfjs-converter/dist/index.js';\nexport * as data from '@tensorflow/tfjs-data/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-cpu/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-webgl/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-wasm/dist/index.js';\n\n// export versions\nexport const version = {\n tfjs,\n 'tfjs-core': versionCore,\n 'tfjs-data': versionData,\n 'tfjs-layers': versionLayers,\n 'tfjs-converter': versionConverter,\n 'tfjs-backend-cpu': version_cpu,\n 'tfjs-backend-webgl': version_webgl,\n 'tfjs-backend-wasm': version_wasm,\n};\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport const config = {\n name: 'humangl',\n priority: 99,\n canvas: null,\n width: 1024,\n height: 1024,\n webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n alpha: false,\n antialias: false,\n premultipliedAlpha: false,\n preserveDrawingBuffer: false,\n depth: false,\n stencil: false,\n failIfMajorPerformanceCaveat: false,\n desynchronized: true,\n },\n};\n\nexport function register() {\n if (!tf.findBackend(config.name)) {\n // @ts-ignore\n config.canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(config.width, config.height) : document.createElement('canvas');\n // @ts-ignore\n const gl = config.canvas.getContext('webgl2', config.webGLattr);\n tf.setWebGLContext(2, gl);\n const ctx = new tf.GPGPUContext(gl);\n tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n const kernels = tf.getKernelsForBackend('webgl');\n kernels.forEach((kernelConfig) => {\n const newKernelConfig = { ...kernelConfig, backendName: config.name };\n tf.registerKernel(newKernelConfig);\n });\n tf.ENV.set('WEBGL_VERSION', 2);\n tf.ENV.set('WEBGL_MAX_TEXTURE_SIZE', gl.getParameter(gl.MAX_TEXTURE_SIZE));\n tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n }\n}\n", "import { log } from './log.js';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as backend from './tfjs/backend.js';\nimport * as facemesh from './blazeface/facemesh.js';\nimport * as age from './age/age.js';\nimport * as gender from './gender/gender.js';\nimport * as emotion from './emotion/emotion.js';\nimport * as embedding from './embedding/embedding.js';\nimport * as posenet from './posenet/posenet.js';\nimport * as blazepose from './blazepose/blazepose.js';\nimport * as handpose from './handpose/handpose.js';\nimport * as gesture from './gesture/gesture.js';\nimport * as image from './image.js';\nimport * as profile from './profile.js';\nimport * as config from '../config.js';\nimport * as sample from './sample.js';\nimport * as app from '../package.json';\n\n// helper function: gets elapsed time on both browser and nodejs\nconst now = () => {\n if (typeof performance !== 'undefined') return performance.now();\n return parseInt(Number(process.hrtime.bigint()) / 1000 / 1000);\n};\n\n// helper function: perform deep merge of multiple objects so it allows full inheriance with overrides\nfunction mergeDeep(...objects) {\n const isObject = (obj) => obj && typeof obj === 'object';\n return objects.reduce((prev, obj) => {\n Object.keys(obj || {}).forEach((key) => {\n const pVal = prev[key];\n const oVal = obj[key];\n if (Array.isArray(pVal) && Array.isArray(oVal)) {\n prev[key] = pVal.concat(...oVal);\n } else if (isObject(pVal) && isObject(oVal)) {\n prev[key] = mergeDeep(pVal, oVal);\n } else {\n prev[key] = oVal;\n }\n });\n return prev;\n }, {});\n}\n\nclass Human {\n constructor(userConfig = {}) {\n this.tf = tf;\n this.version = app.version;\n this.config = mergeDeep(config.default, userConfig);\n this.fx = null;\n this.state = 'idle';\n this.numTensors = 0;\n this.analyzeMemoryLeaks = false;\n this.checkSanity = false;\n this.firstRun = true;\n this.perf = {};\n // object that contains all initialized models\n this.models = {\n facemesh: null,\n posenet: null,\n handpose: null,\n iris: null,\n age: null,\n gender: null,\n emotion: null,\n };\n // export raw access to underlying models\n this.facemesh = facemesh;\n this.age = age;\n this.gender = gender;\n this.emotion = emotion;\n this.body = posenet;\n this.hand = handpose;\n }\n\n profile() {\n if (this.config.profile) return profile.data;\n return {};\n }\n\n // helper function: measure tensor leak\n analyze(...msg) {\n if (!this.analyzeMemoryLeaks) return;\n const current = tf.engine().state.numTensors;\n const previous = this.numTensors;\n this.numTensors = current;\n const leaked = current - previous;\n if (leaked !== 0) log(...msg, leaked);\n }\n\n // quick sanity check on inputs\n sanity(input) {\n if (!this.checkSanity) return null;\n if (!input) return 'input is not defined';\n if (tf.ENV.flags.IS_NODE && !(input instanceof tf.Tensor)) {\n return 'input must be a tensor';\n }\n try {\n tf.getBackend();\n } catch {\n return 'backend not loaded';\n }\n return null;\n }\n\n simmilarity(embedding1, embedding2) {\n if (this.config.face.embedding.enabled) return embedding.simmilarity(embedding1, embedding2);\n return 0;\n }\n\n // preload models, not explicitly required as it's done automatically on first use\n async load(userConfig) {\n this.state = 'load';\n const timeStamp = now();\n if (userConfig) this.config = mergeDeep(this.config, userConfig);\n\n if (this.firstRun) {\n log(`version: ${this.version} TensorFlow/JS version: ${tf.version_core}`);\n await this.checkBackend(true);\n if (tf.ENV.flags.IS_BROWSER) {\n log('configuration:', this.config);\n log('tf flags:', tf.ENV.flags);\n }\n this.firstRun = false;\n }\n\n if (this.config.async) {\n [\n this.models.facemesh,\n this.models.age,\n this.models.gender,\n this.models.emotion,\n this.models.embedding,\n this.models.posenet,\n this.models.handpose,\n this.models.blazepose,\n ] = await Promise.all([\n this.models.facemesh || (this.config.face.enabled ? facemesh.load(this.config) : null),\n this.models.age || ((this.config.face.enabled && this.config.face.age.enabled) ? age.load(this.config) : null),\n this.models.gender || ((this.config.face.enabled && this.config.face.gender.enabled) ? gender.load(this.config) : null),\n this.models.emotion || ((this.config.face.enabled && this.config.face.emotion.enabled) ? emotion.load(this.config) : null),\n this.models.embedding || ((this.config.face.enabled && this.config.face.embedding.enabled) ? embedding.load(this.config) : null),\n this.models.posenet || (this.config.body.enabled ? posenet.load(this.config) : null),\n this.models.handpose || (this.config.hand.enabled ? handpose.load(this.config) : null),\n this.models.blazepose || (this.config.pose.enabled ? blazepose.load(this.config) : null),\n ]);\n } else {\n if (this.config.face.enabled && !this.models.facemesh) this.models.facemesh = await facemesh.load(this.config);\n if (this.config.face.enabled && this.config.face.age.enabled && !this.models.age) this.models.age = await age.load(this.config);\n if (this.config.face.enabled && this.config.face.gender.enabled && !this.models.gender) this.models.gender = await gender.load(this.config);\n if (this.config.face.enabled && this.config.face.emotion.enabled && !this.models.emotion) this.models.emotion = await emotion.load(this.config);\n if (this.config.face.enabled && this.config.face.embedding.enabled && !this.models.embedding) this.models.embedding = await embedding.load(this.config);\n if (this.config.body.enabled && !this.models.posenet) this.models.posenet = await posenet.load(this.config);\n if (this.config.hand.enabled && !this.models.handpose) this.models.handpose = await handpose.load(this.config);\n if (this.config.pose.enabled && !this.models.blazepose) this.models.blazepose = await blazepose.load(this.config);\n }\n const current = Math.trunc(now() - timeStamp);\n if (current > (this.perf.load || 0)) this.perf.load = current;\n }\n\n // check if backend needs initialization if it changed\n async checkBackend(force) {\n if (this.config.backend && (this.config.backend !== '') && force || (tf.getBackend() !== this.config.backend)) {\n const timeStamp = now();\n this.state = 'backend';\n /* force backend reload\n if (this.config.backend in tf.engine().registry) {\n const backendFactory = tf.findBackendFactory(this.config.backend);\n tf.removeBackend(this.config.backend);\n tf.registerBackend(this.config.backend, backendFactory);\n } else {\n log('Backend not registred:', this.config.backend);\n }\n */\n\n log('setting backend:', this.config.backend);\n\n if (this.config.backend === 'wasm') {\n log('settings wasm path:', this.config.wasmPath);\n tf.setWasmPaths(this.config.wasmPath);\n const simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n if (!simd) log('warning: wasm simd support is not enabled');\n }\n\n if (this.config.backend === 'humangl') {\n log('registering humangl backend');\n backend.register();\n }\n\n await tf.setBackend(this.config.backend);\n tf.enableProdMode();\n /* debug mode is really too mcuh\n tf.enableDebugMode();\n */\n if (tf.getBackend() === 'webgl') {\n if (this.config.deallocate) {\n log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', this.config.deallocate);\n tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', this.config.deallocate ? 0 : -1);\n }\n tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n const gl = await tf.backend().getGPGPUContext().gl;\n log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);\n }\n await tf.ready();\n this.perf.backend = Math.trunc(now() - timeStamp);\n }\n }\n\n async detectFace(input) {\n // run facemesh, includes blazeface and iris\n // eslint-disable-next-line no-async-promise-executor\n let timeStamp;\n let ageRes;\n let genderRes;\n let emotionRes;\n let embeddingRes;\n const faceRes = [];\n this.state = 'run:face';\n timeStamp = now();\n const faces = await this.models.facemesh?.estimateFaces(input, this.config);\n this.perf.face = Math.trunc(now() - timeStamp);\n for (const face of faces) {\n this.analyze('Get Face');\n\n // is something went wrong, skip the face\n if (!face.image || face.image.isDisposedInternal) {\n log('Face object is disposed:', face.image);\n continue;\n }\n\n // run age, inherits face from blazeface\n this.analyze('Start Age:');\n if (this.config.async) {\n ageRes = this.config.face.age.enabled ? age.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:age';\n timeStamp = now();\n ageRes = this.config.face.age.enabled ? await age.predict(face.image, this.config) : {};\n this.perf.age = Math.trunc(now() - timeStamp);\n }\n\n // run gender, inherits face from blazeface\n this.analyze('Start Gender:');\n if (this.config.async) {\n genderRes = this.config.face.gender.enabled ? gender.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:gender';\n timeStamp = now();\n genderRes = this.config.face.gender.enabled ? await gender.predict(face.image, this.config) : {};\n this.perf.gender = Math.trunc(now() - timeStamp);\n }\n\n // run emotion, inherits face from blazeface\n this.analyze('Start Emotion:');\n if (this.config.async) {\n emotionRes = this.config.face.emotion.enabled ? emotion.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:emotion';\n timeStamp = now();\n emotionRes = this.config.face.emotion.enabled ? await emotion.predict(face.image, this.config) : {};\n this.perf.emotion = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Emotion:');\n\n // run emotion, inherits face from blazeface\n this.analyze('Start Embedding:');\n if (this.config.async) {\n embeddingRes = this.config.face.embedding.enabled ? embedding.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:embedding';\n timeStamp = now();\n embeddingRes = this.config.face.embedding.enabled ? await embedding.predict(face.image, this.config) : {};\n this.perf.embedding = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Emotion:');\n\n // if async wait for results\n if (this.config.async) {\n [ageRes, genderRes, emotionRes, embeddingRes] = await Promise.all([ageRes, genderRes, emotionRes, embeddingRes]);\n }\n\n this.analyze('Finish Face:');\n // dont need face anymore\n face.image.dispose();\n\n // calculate iris distance\n // iris: array[ center, left, top, right, bottom]\n if (!this.config.face.iris.enabled) {\n delete face.annotations.leftEyeIris;\n delete face.annotations.rightEyeIris;\n }\n const irisSize = (face.annotations.leftEyeIris && face.annotations.rightEyeIris)\n /* average human iris size is 11.7mm */\n ? 11.7 * Math.max(Math.abs(face.annotations.leftEyeIris[3][0] - face.annotations.leftEyeIris[1][0]), Math.abs(face.annotations.rightEyeIris[4][1] - face.annotations.rightEyeIris[2][1]))\n : 0;\n\n // combine results\n faceRes.push({\n confidence: face.confidence,\n box: face.box,\n mesh: face.mesh,\n boxRaw: face.boxRaw,\n meshRaw: face.meshRaw,\n annotations: face.annotations,\n age: ageRes.age,\n gender: genderRes.gender,\n genderConfidence: genderRes.confidence,\n emotion: emotionRes,\n embedding: embeddingRes,\n iris: (irisSize !== 0) ? Math.trunc(irisSize) / 100 : 0,\n });\n this.analyze('End Face');\n }\n this.analyze('End FaceMesh:');\n if (this.config.async) {\n if (this.perf.face) delete this.perf.face;\n if (this.perf.age) delete this.perf.age;\n if (this.perf.gender) delete this.perf.gender;\n if (this.perf.emotion) delete this.perf.emotion;\n }\n return faceRes;\n }\n\n async image(input, userConfig = {}) {\n this.state = 'image';\n this.config = mergeDeep(this.config, userConfig);\n const process = image.process(input, this.config);\n process.tensor.dispose();\n return process.canvas;\n }\n\n // main detect function\n async detect(input, userConfig = {}) {\n // detection happens inside a promise\n return new Promise(async (resolve) => {\n this.state = 'config';\n let timeStamp;\n\n // update configuration\n this.config = mergeDeep(this.config, userConfig);\n\n // sanity checks\n this.state = 'check';\n const error = this.sanity(input);\n if (error) {\n log(error, input);\n resolve({ error });\n }\n\n let poseRes;\n let blazeposeRes;\n let handRes;\n let faceRes;\n\n const timeStart = now();\n\n // configure backend\n await this.checkBackend();\n\n // load models if enabled\n await this.load();\n\n if (this.config.scoped) tf.engine().startScope();\n this.analyze('Start Scope:');\n\n timeStamp = now();\n const process = image.process(input, this.config);\n if (!process || !process.tensor) {\n log('could not convert input to tensor');\n resolve({ error: 'could not convert input to tensor' });\n return;\n }\n this.perf.image = Math.trunc(now() - timeStamp);\n this.analyze('Get Image:');\n\n // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n if (this.config.async) {\n faceRes = this.config.face.enabled ? this.detectFace(process.tensor) : [];\n if (this.perf.face) delete this.perf.face;\n } else {\n this.state = 'run:face';\n timeStamp = now();\n faceRes = this.config.face.enabled ? await this.detectFace(process.tensor) : [];\n this.perf.face = Math.trunc(now() - timeStamp);\n }\n\n // run posenet\n this.analyze('Start Body:');\n if (this.config.async) {\n poseRes = this.config.body.enabled ? this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n if (this.perf.body) delete this.perf.body;\n } else {\n this.state = 'run:body';\n timeStamp = now();\n poseRes = this.config.body.enabled ? await this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n this.perf.body = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Body:');\n\n // run posenet\n this.analyze('Start Pose:');\n if (this.config.async) {\n blazeposeRes = this.config.pose.enabled ? blazepose.predict(process.tensor, this.config) : [];\n if (this.perf.pose) delete this.perf.pose;\n } else {\n this.state = 'run:pose';\n timeStamp = now();\n blazeposeRes = this.config.pose.enabled ? await blazepose.predict(process.tensor, this.config) : [];\n this.perf.pose = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Pose:');\n\n // run handpose\n this.analyze('Start Hand:');\n if (this.config.async) {\n handRes = this.config.hand.enabled ? this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n if (this.perf.hand) delete this.perf.hand;\n } else {\n this.state = 'run:hand';\n timeStamp = now();\n handRes = this.config.hand.enabled ? await this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n this.perf.hand = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Hand:');\n\n // if async wait for results\n if (this.config.async) {\n [faceRes, poseRes, blazeposeRes, handRes] = await Promise.all([faceRes, poseRes, blazeposeRes, handRes]);\n }\n process.tensor.dispose();\n\n if (this.config.scoped) tf.engine().endScope();\n this.analyze('End Scope:');\n\n let gestureRes = [];\n if (this.config.gesture.enabled) {\n timeStamp = now();\n gestureRes = [...gesture.face(faceRes), ...gesture.body(poseRes), ...gesture.hand(handRes), ...gesture.iris(faceRes)];\n if (!this.config.async) this.perf.gesture = Math.trunc(now() - timeStamp);\n else if (this.perf.gesture) delete this.perf.gesture;\n }\n\n this.perf.total = Math.trunc(now() - timeStart);\n this.state = 'idle';\n resolve({ face: faceRes, body: poseRes, hand: handRes, pose: blazeposeRes, gesture: gestureRes, performance: this.perf, canvas: process.canvas });\n });\n }\n\n async warmup(userConfig) {\n const b64toBlob = (base64, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n\n if (userConfig) this.config = mergeDeep(this.config, userConfig);\n const video = this.config.videoOptimized;\n this.config.videoOptimized = false;\n let blob;\n switch (this.config.warmup) {\n case 'face': blob = await b64toBlob(sample.face); break;\n case 'full': blob = await b64toBlob(sample.body); break;\n default: blob = null;\n }\n if (!blob) return null;\n const bitmap = await createImageBitmap(blob);\n const t0 = now();\n const warmup = await this.detect(bitmap, config);\n const t1 = now();\n bitmap.close();\n log('Warmup', this.config.warmup, (t1 - t0), warmup);\n this.config.videoOptimized = video;\n return warmup;\n }\n}\n\nexport { Human as default };\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\n// import * as helpers from './helpers.js';\n// import * as profile from '../profile.js';\n\nconst models = {};\nlet config = {};\nconst anchors = [];\nconst kMidHipCenter = 0;\nconst kPoseDetectKeyNum = 2;\nconst kFullBodySizeRot = 1;\n\nfunction calculateScale(min_scale, max_scale, stride_index, num_strides) {\n if (num_strides === 1) return (min_scale + max_scale) * 0.5;\n return min_scale + (max_scale - min_scale) * 1.0 * stride_index / (num_strides - 1.0);\n}\n\nexport function generateAnchors() {\n const options = {};\n options.strides = [];\n options.aspect_ratios = [];\n options.feature_map_height = [];\n options.num_layers = 4;\n options.min_scale = 0.1484375;\n options.max_scale = 0.75;\n options.input_size_height = 128;\n options.input_size_width = 128;\n options.anchor_offset_x = 0.5;\n options.anchor_offset_y = 0.5;\n options.strides.push(8);\n options.strides.push(16);\n options.strides.push(16);\n options.strides.push(16);\n options.aspect_ratios.push(1.0);\n options.reduce_boxes_in_lowest_layer = false;\n options.interpolated_scale_aspect_ratio = 1.0;\n options.fixed_anchor_size = true;\n let layer_id = 0;\n while (layer_id < options.strides.length) {\n const anchor_height = [];\n const anchor_width = [];\n const aspect_ratios = [];\n const scales = [];\n // For same strides, we merge the anchors in the same order.\n let last_same_stride_layer = layer_id;\n while (last_same_stride_layer < options.strides.length && options.strides[last_same_stride_layer] === options.strides[layer_id]) {\n const scale = calculateScale(options.min_scale, options.max_scale, last_same_stride_layer, options.strides.length);\n if (last_same_stride_layer === 0 && options.reduce_boxes_in_lowest_layer) {\n // For first layer, it can be specified to use predefined anchors.\n aspect_ratios.push(1.0);\n aspect_ratios.push(2.0);\n aspect_ratios.push(0.5);\n scales.push(0.1);\n scales.push(scale);\n scales.push(scale);\n } else {\n for (let aspect_ratio_id = 0; aspect_ratio_id < options.aspect_ratios.length; ++aspect_ratio_id) {\n aspect_ratios.push(options.aspect_ratios[aspect_ratio_id]);\n scales.push(scale);\n }\n if (options.interpolated_scale_aspect_ratio > 0.0) {\n const scale_next = last_same_stride_layer === options.strides.length - 1 ? 1.0 : calculateScale(options.min_scale, options.max_scale, last_same_stride_layer + 1, options.strides.length);\n scales.push(Math.sqrt(scale * scale_next));\n aspect_ratios.push(options.interpolated_scale_aspect_ratio);\n }\n }\n last_same_stride_layer++;\n }\n for (let i = 0; i < aspect_ratios.length; ++i) {\n const ratio_sqrts = Math.sqrt(aspect_ratios[i]);\n anchor_height.push(scales[i] / ratio_sqrts);\n anchor_width.push(scales[i] * ratio_sqrts);\n }\n let feature_map_height = 0;\n let feature_map_width = 0;\n if (options.feature_map_height.length) {\n feature_map_height = options.feature_map_height[layer_id];\n feature_map_width = options.feature_map_width[layer_id];\n } else {\n const stride = options.strides[layer_id];\n feature_map_height = Math.ceil(1.0 * options.input_size_height / stride);\n feature_map_width = Math.ceil(1.0 * options.input_size_width / stride);\n }\n for (let y = 0; y < feature_map_height; ++y) {\n for (let x = 0; x < feature_map_width; ++x) {\n for (let anchor_id = 0; anchor_id < anchor_height.length; ++anchor_id) {\n const x_center = (x + options.anchor_offset_x) * 1.0 / feature_map_width;\n const y_center = (y + options.anchor_offset_y) * 1.0 / feature_map_height;\n const new_anchor = {};\n new_anchor.x_center = x_center;\n new_anchor.y_center = y_center;\n if (options.fixed_anchor_size) {\n new_anchor.w = 1.0;\n new_anchor.h = 1.0;\n } else {\n new_anchor.w = anchor_width[anchor_id];\n new_anchor.h = anchor_height[anchor_id];\n }\n anchors.push(new_anchor);\n }\n }\n }\n layer_id = last_same_stride_layer;\n }\n}\n\nexport async function load(cfg) {\n config = cfg;\n if (!models.blazepose) {\n models.blazepose = await tf.loadGraphModel(config.pose.modelPath);\n log(`load model: ${config.pose.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n generateAnchors();\n return models.blazepose;\n}\n\nfunction rotateRegion(region) {\n const x0 = region.keys[kMidHipCenter].x;\n const y0 = region.keys[kMidHipCenter].y;\n const x1 = (region.box[0] + region.box[2]) * 0.5;\n const y1 = (region.box[1] + region.box[3]) * 0.5;\n const target_angle = Math.PI * 0.5;\n const angle = target_angle - Math.atan2(-(y1 - y0), x1 - x0);\n return Math.round(1000 * (angle - 2 * Math.PI * Math.floor((angle - (-Math.PI)) / (2 * Math.PI)))) / 1000;\n}\n\nfunction rotateVecor(vec, rotation) {\n const sx = vec.x;\n const sy = vec.y;\n vec.x = sx * Math.cos(rotation) - sy * Math.sin(rotation);\n vec.y = sx * Math.sin(rotation) + sy * Math.cos(rotation);\n}\n\nasync function decode(logits) {\n const scores = await logits[0].data();\n const boxes = await logits[1].data();\n // todo: add nms\n // todo scale output with image.shape\n const regions = [];\n for (let i = 0; i < anchors.length; i++) {\n const region = {};\n const score = 1.0 / (1.0 + Math.exp(-scores[i]));\n if (score > config.pose.scoreThreshold) {\n const idx = (4 + 2 * kPoseDetectKeyNum) * i;\n /* boundary box */\n const sx = boxes[idx + 0];\n const sy = boxes[idx + 1];\n const w = boxes[idx + 2] / config.pose.inputSize;\n const h = boxes[idx + 3] / config.pose.inputSize;\n const cx = (sx + anchors[i].x_center * config.pose.inputSize) / config.pose.inputSize;\n const cy = (sy + anchors[i].y_center * config.pose.inputSize) / config.pose.inputSize;\n region.score = Math.round(1000 * score) / 1000;\n region.box = [cx - w * 0.5, cy - h * 0.5, w * 0.5, h * 0.5];\n /* landmark positions (6 keys) */\n const keys = new Array(kPoseDetectKeyNum);\n for (let j = 0; j < kPoseDetectKeyNum; j++) {\n const lx = (boxes[idx + 4 + (2 * j) + 0] + anchors[i].x_center * config.pose.inputSize) / config.pose.inputSize;\n const ly = (boxes[idx + 4 + (2 * j) + 1] + anchors[i].y_center * config.pose.inputSize) / config.pose.inputSize;\n keys[j] = { x: lx, y: ly };\n }\n region.keys = keys;\n region.angle = rotateRegion(region);\n // add points\n const x_center = region.keys[kMidHipCenter].x * config.pose.inputSize;\n const y_center = region.keys[kMidHipCenter].y * config.pose.inputSize;\n const x_scale = region.keys[kFullBodySizeRot].x * config.pose.inputSize;\n const y_scale = region.keys[kFullBodySizeRot].y * config.pose.inputSize;\n // Bounding box size as double distance from center to scale point.\n const box_size = Math.sqrt((x_scale - x_center) * (x_scale - x_center) + (y_scale - y_center) * (y_scale - y_center)) * 2.0;\n /* RectTransformationCalculator::TransformNormalizedRect() */\n const roi_cx = region.angle === 0.0 ? x_center + box_size : x_center + box_size * Math.cos(region.angle) - box_size * Math.sin(region.angle);\n const roi_cy = region.angle === 0.0 ? y_center + box_size : y_center + box_size * Math.sin(region.angle) + box_size * Math.cos(region.angle);\n const long_side = Math.max(box_size, box_size);\n const roi_w = long_side * 1.5;\n const roi_h = long_side * 1.5;\n region.center = { x: roi_cx / config.pose.inputSize, y: roi_cy / config.pose.inputSize };\n region.size = { x: roi_w / config.pose.inputSize, y: roi_h / config.pose.inputSize };\n /* calculate ROI coordinates */\n const dx = roi_w * 0.5;\n const dy = roi_h * 0.5;\n region.coords = [];\n region.coords[0] = { x: -dx, y: -dy };\n region.coords[1] = { x: +dx, y: -dy };\n region.coords[2] = { x: +dx, y: +dy };\n region.coords[3] = { x: -dx, y: +dy };\n for (let j = 0; j < 4; j++) {\n rotateVecor(region.coords[i], region.angle);\n region.coords[j].x = (region.coords[j].x + roi_cx) / config.pose.inputSize;\n region.coords[j].y = (region.coords[j].y + roi_cy) / config.pose.inputSize;\n }\n\n regions.push(region);\n }\n }\n return regions;\n}\n\nexport async function predict(image, cfg) {\n if (!models.blazepose) return null;\n return new Promise(async (resolve) => {\n config = cfg;\n const resize = tf.image.resizeBilinear(image, [config.pose.inputSize, config.pose.inputSize], false);\n const enhance = tf.div(resize, 127.5).sub(1);\n tf.dispose(resize);\n const logits = await models.blazepose.predict(enhance);\n // todo: add landmarks model\n tf.dispose(enhance);\n const regions = await decode(logits);\n logits[0].dispose();\n logits[1].dispose();\n log('poses', regions);\n resolve(regions);\n });\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nfunction getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\nfunction getBoxCenter(box) {\n return [\n box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n ];\n}\nfunction cutBoxFromImageAndResize(box, image, cropSize) {\n const h = image.shape[1];\n const w = image.shape[2];\n const boxes = [[\n box.startPoint[1] / h,\n box.startPoint[0] / w,\n box.endPoint[1] / h,\n box.endPoint[0] / w,\n ]];\n return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\nfunction scaleBoxCoordinates(box, factor) {\n const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n const palmLandmarks = box.palmLandmarks.map((coord) => {\n const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n return scaledCoord;\n });\n return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\nfunction enlargeBox(box, factor = 1.5) {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\nfunction squarifyBox(box) {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const maxEdge = Math.max(...size);\n const halfSize = maxEdge / 2;\n const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\nfunction shiftBox(box, shiftFactor) {\n const boxSize = [\n box.endPoint[0] - box.startPoint[0],\n box.endPoint[1] - box.startPoint[1],\n ];\n const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]];\n const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\nexport {\n cutBoxFromImageAndResize,\n enlargeBox,\n getBoxCenter,\n getBoxSize,\n scaleBoxCoordinates,\n shiftBox,\n squarifyBox,\n};\n", "function normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\nfunction computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\nconst buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\nfunction dot(v1, v2) {\n let product = 0;\n for (let i = 0; i < v1.length; i++) {\n product += v1[i] * v2[i];\n }\n return product;\n}\nfunction getColumnFrom2DArr(arr, columnIndex) {\n const column = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\nfunction multiplyTransformMatrices(mat1, mat2) {\n const product = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) {\n // @ts-ignore\n product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n }\n return product;\n}\nfunction buildRotationMatrix(rotation, center) {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\nfunction invertTransformMatrix(matrix) {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [\n -dot(rotationComponent[0], translationComponent),\n -dot(rotationComponent[1], translationComponent),\n ];\n return [\n rotationComponent[0].concat(invertedTranslation[0]),\n rotationComponent[1].concat(invertedTranslation[1]),\n [0, 0, 1],\n ];\n}\nfunction rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\nexport {\n buildRotationMatrix,\n computeRotation,\n dot,\n getColumnFrom2DArr,\n invertTransformMatrix,\n normalizeRadians,\n rotatePoint,\n};\n", "/* eslint-disable indent */\n/* eslint-disable no-multi-spaces */\n\nexport default {\n backend: 'webgl', // select tfjs backend to use\n wasmPath: '../assets/', // path for wasm binaries\n // only used for backend: wasm\n async: true, // execute enabled models in parallel\n // this disables per-model performance data but\n // slightly increases performance\n // cannot be used if profiling is enabled\n profile: false, // enable tfjs profiling\n // this has significant performance impact\n // only enable for debugging purposes\n // currently only implemented for age,gender,emotion models\n deallocate: false, // aggresively deallocate gpu memory after each usage\n // only valid for webgl backend and only during first call\n // cannot be changed unless library is reloaded\n // this has significant performance impact\n // only enable on low-memory devices\n scoped: false, // enable scoped runs\n // some models *may* have memory leaks,\n // this wrapps everything in a local scope at a cost of performance\n // typically not needed\n videoOptimized: true, // perform additional optimizations when input is video,\n // must be disabled for images\n // basically this skips object box boundary detection for every n frames\n // while maintaining in-box detection since objects cannot move that fast\n warmup: 'face', // what to use for human.warmup(), can be 'none', 'face', 'full'\n // warmup pre-initializes all models for faster inference but can take\n // significant time on startup\n filter: {\n enabled: true, // enable image pre-processing filters\n width: 0, // resize input width\n height: 0, // resize input height\n // if both width and height are set to 0, there is no resizing\n // if just one is set, second one is scaled automatically\n // if both are set, values are used as-is\n return: true, // return processed canvas imagedata in result\n brightness: 0, // range: -1 (darken) to 1 (lighten)\n contrast: 0, // range: -1 (reduce contrast) to 1 (increase contrast)\n sharpness: 0, // range: 0 (no sharpening) to 1 (maximum sharpening)\n blur: 0, // range: 0 (no blur) to N (blur radius in pixels)\n saturation: 0, // range: -1 (reduce saturation) to 1 (increase saturation)\n hue: 0, // range: 0 (no change) to 360 (hue rotation in degrees)\n negative: false, // image negative\n sepia: false, // image sepia colors\n vintage: false, // image vintage colors\n kodachrome: false, // image kodachrome colors\n technicolor: false, // image technicolor colors\n polaroid: false, // image polaroid camera effect\n pixelate: 0, // range: 0 (no pixelate) to N (number of pixels to pixelate)\n },\n\n gesture: {\n enabled: true, // enable simple gesture recognition\n },\n\n face: {\n enabled: true, // controls if specified modul is enabled\n // face.enabled is required for all face models:\n // detector, mesh, iris, age, gender, emotion\n // (note: module is not loaded until it is required)\n detector: {\n modelPath: '../models/blazeface-back.json', // can be 'front' or 'back'.\n // 'front' is optimized for large faces\n // such as front-facing camera and\n // 'back' is optimized for distanct faces.\n inputSize: 256, // fixed value: 128 for front and 256 for 'back'\n rotation: false, // use best-guess rotated face image or just box with rotation as-is\n // false means higher performance, but incorrect mesh mapping if face angle is above 20 degrees\n maxFaces: 10, // maximum number of faces detected in the input\n // should be set to the minimum number for performance\n skipFrames: 11, // how many frames to go without re-running the face bounding box detector\n // only used for video inputs\n // e.g., if model is running st 25 FPS, we can re-use existing bounding\n // box for updated face analysis as the head probably hasn't moved much\n // in short time (10 * 1/25 = 0.25 sec)\n minConfidence: 0.5, // threshold for discarding a prediction\n iouThreshold: 0.2, // threshold for deciding whether boxes overlap too much in\n // non-maximum suppression (0.1 means drop if overlap 10%)\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on score\n // in non-maximum suppression,\n // this is applied on detection objects only and before minConfidence\n },\n\n mesh: {\n enabled: true,\n modelPath: '../models/facemesh.json',\n inputSize: 192, // fixed value\n returnRawData: false, // in addition to standard mesh and box values, return raw normalized values as well\n },\n\n iris: {\n enabled: true,\n modelPath: '../models/iris.json',\n inputSize: 64, // fixed value\n },\n\n age: {\n enabled: true,\n modelPath: '../models/age-ssrnet-imdb.json', // can be 'age-ssrnet-imdb' or 'age-ssrnet-wiki'\n // which determines training set for model\n inputSize: 64, // fixed value\n skipFrames: 31, // how many frames to go without re-running the detector\n // only used for video inputs\n },\n\n gender: {\n enabled: true,\n minConfidence: 0.1, // threshold for discarding a prediction\n modelPath: '../models/gender-ssrnet-imdb.json', // can be 'gender', 'gender-ssrnet-imdb' or 'gender-ssrnet-wiki'\n inputSize: 64, // fixed value\n skipFrames: 41, // how many frames to go without re-running the detector\n // only used for video inputs\n },\n\n emotion: {\n enabled: true,\n inputSize: 64, // fixed value\n minConfidence: 0.2, // threshold for discarding a prediction\n skipFrames: 21, // how many frames to go without re-running the detector\n modelPath: '../models/emotion-large.json', // can be 'mini', 'large'\n },\n\n embedding: {\n enabled: false,\n inputSize: 112, // fixed value\n modelPath: '../models/mobilefacenet.json',\n },\n },\n\n body: {\n enabled: true,\n modelPath: '../models/posenet.json',\n inputSize: 257, // fixed value\n maxDetections: 10, // maximum number of people detected in the input\n // should be set to the minimum number for performance\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on score\n // in non-maximum suppression\n nmsRadius: 20, // radius for deciding points are too close in non-maximum suppression\n outputStride: 16, // size of block in which to run point detectopn, smaller value means higher resolution\n // defined by model itself, can be 8, 16, or 32\n modelType: 'MobileNet', // Human includes MobileNet version, but you can switch to ResNet\n },\n\n pose: {\n enabled: false,\n scoreThreshold: 0.6, // threshold for deciding when to remove boxes based on score\n // in non-maximum suppression\n iouThreshold: 0.3, // threshold for deciding whether boxes overlap too much\n // in non-maximum suppression\n modelPath: '../models/blazepose.json',\n inputSize: 128, // fixed value\n },\n\n hand: {\n enabled: true,\n rotation: false, // use best-guess rotated hand image or just box with rotation as-is\n // false means higher performance, but incorrect finger mapping if hand is inverted\n inputSize: 256, // fixed value\n skipFrames: 12, // how many frames to go without re-running the hand bounding box detector\n // only used for video inputs\n // e.g., if model is running st 25 FPS, we can re-use existing bounding\n // box for updated hand skeleton analysis as the hand probably\n // hasn't moved much in short time (10 * 1/25 = 0.25 sec)\n minConfidence: 0.1, // threshold for discarding a prediction\n iouThreshold: 0.1, // threshold for deciding whether boxes overlap too much\n // in non-maximum suppression\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on\n // score in non-maximum suppression\n maxHands: 1, // maximum number of hands detected in the input\n // should be set to the minimum number for performance\n landmarks: true, // detect hand landmarks or just hand boundary box\n detector: {\n modelPath: '../models/handdetect.json',\n },\n skeleton: {\n modelPath: '../models/handskeleton.json',\n },\n },\n};\n", "// data:image/jpeg;base64,\nexport const face = 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+ "sourcesContent": ["import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nconst NUM_LANDMARKS = 6;\n\nfunction generateAnchors(inputSize) {\n const spec = { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] };\n const anchors = [];\n for (let i = 0; i < spec.strides.length; i++) {\n const stride = spec.strides[i];\n const gridRows = Math.floor((inputSize + stride - 1) / stride);\n const gridCols = Math.floor((inputSize + stride - 1) / stride);\n const anchorsNum = spec.anchors[i];\n for (let gridY = 0; gridY < gridRows; gridY++) {\n const anchorY = stride * (gridY + 0.5);\n for (let gridX = 0; gridX < gridCols; gridX++) {\n const anchorX = stride * (gridX + 0.5);\n for (let n = 0; n < anchorsNum; n++) {\n anchors.push([anchorX, anchorY]);\n }\n }\n }\n }\n return anchors;\n}\n\nconst disposeBox = (box) => {\n box.startEndTensor.dispose();\n box.startPoint.dispose();\n box.endPoint.dispose();\n};\n\nconst createBox = (startEndTensor) => ({\n startEndTensor,\n startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]),\n endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]),\n});\n\nconst scaleBox = (box, factors) => {\n const starts = tf.mul(box.startPoint, factors);\n const ends = tf.mul(box.endPoint, factors);\n const newCoordinates = tf.concat2d([starts, ends], 1);\n return createBox(newCoordinates);\n};\n\nfunction decodeBounds(boxOutputs, anchors, inputSize) {\n const boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n const centers = tf.add(boxStarts, anchors);\n const boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n const boxSizesNormalized = tf.div(boxSizes, inputSize);\n const centersNormalized = tf.div(centers, inputSize);\n const halfBoxSize = tf.div(boxSizesNormalized, 2);\n const starts = tf.sub(centersNormalized, halfBoxSize);\n const ends = tf.add(centersNormalized, halfBoxSize);\n const startNormalized = tf.mul(starts, inputSize);\n const endNormalized = tf.mul(ends, inputSize);\n const concatAxis = 1;\n return tf.concat2d([startNormalized, endNormalized], concatAxis);\n}\n\nfunction scaleBoxFromPrediction(face, scaleFactor) {\n return tf.tidy(() => {\n const box = face['box'] ? face['box'] : face;\n return scaleBox(box, scaleFactor).startEndTensor.squeeze();\n });\n}\n\nclass BlazeFaceModel {\n constructor(model, config) {\n this.blazeFaceModel = model;\n this.width = config.face.detector.inputSize;\n this.height = config.face.detector.inputSize;\n this.anchorsData = generateAnchors(config.face.detector.inputSize);\n this.anchors = tf.tensor2d(this.anchorsData);\n this.inputSize = tf.tensor1d([this.width, this.height]);\n this.config = config;\n this.scaleFaces = 0.8;\n }\n\n async getBoundingBoxes(inputImage) {\n // sanity check on input\n if ((!inputImage) || (inputImage.isDisposedInternal) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return null;\n const [detectedOutputs, boxes, scores] = tf.tidy(() => {\n const resizedImage = inputImage.resizeBilinear([this.width, this.height]);\n // const normalizedImage = tf.mul(tf.sub(resizedImage.div(255), 0.5), 2);\n const normalizedImage = tf.sub(resizedImage.div(127.5), 1);\n const batchedPrediction = this.blazeFaceModel.predict(normalizedImage);\n let prediction;\n // are we using tfhub or pinto converted model?\n if (Array.isArray(batchedPrediction)) {\n const sorted = batchedPrediction.sort((a, b) => a.size - b.size);\n const concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n const concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n const concat = tf.concat([concat512, concat384], 1);\n prediction = concat.squeeze(0);\n } else {\n prediction = batchedPrediction.squeeze(); // when using tfhub model\n }\n const decodedBounds = decodeBounds(prediction, this.anchors, this.inputSize);\n const logits = tf.slice(prediction, [0, 0], [-1, 1]);\n const scoresOut = tf.sigmoid(logits).squeeze();\n return [prediction, decodedBounds, scoresOut];\n });\n const boxIndicesTensor = await tf.image.nonMaxSuppressionAsync(boxes, scores, this.config.face.detector.maxFaces, this.config.face.detector.iouThreshold, this.config.face.detector.scoreThreshold);\n const boxIndices = boxIndicesTensor.arraySync();\n boxIndicesTensor.dispose();\n const boundingBoxesMap = boxIndices.map((boxIndex) => tf.slice(boxes, [boxIndex, 0], [1, -1]));\n const boundingBoxes = boundingBoxesMap.map((boundingBox) => {\n const vals = boundingBox.arraySync();\n boundingBox.dispose();\n return vals;\n });\n\n const scoresVal = scores.dataSync();\n const annotatedBoxes = [];\n for (let i = 0; i < boundingBoxes.length; i++) {\n const boxIndex = boxIndices[i];\n const confidence = scoresVal[boxIndex];\n if (confidence > this.config.face.detector.minConfidence) {\n const box = createBox(boundingBoxes[i]);\n const anchor = this.anchorsData[boxIndex];\n const landmarks = tf.tidy(() => tf.slice(detectedOutputs, [boxIndex, NUM_LANDMARKS - 1], [1, -1]).squeeze().reshape([NUM_LANDMARKS, -1]));\n annotatedBoxes.push({ box, landmarks, anchor, confidence });\n }\n }\n detectedOutputs.dispose();\n boxes.dispose();\n scores.dispose();\n detectedOutputs.dispose();\n return {\n boxes: annotatedBoxes,\n scaleFactor: [inputImage.shape[2] / this.width, inputImage.shape[1] / this.height],\n };\n }\n\n async estimateFaces(input) {\n // @ts-ignore\n const { boxes, scaleFactor } = await this.getBoundingBoxes(input);\n const faces = [];\n for (const face of boxes) {\n const landmarkData = face.landmarks.arraySync();\n const scaledBox = scaleBoxFromPrediction(face, scaleFactor);\n // @ts-ignore\n const boxData = scaleBox.arraySync();\n const probabilityData = face.probability.arraySync();\n const anchor = face.anchor;\n const [scaleFactorX, scaleFactorY] = scaleFactor;\n const scaledLandmarks = landmarkData\n .map((landmark) => ([\n (landmark[0] + anchor[0]) * scaleFactorX,\n (landmark[1] + anchor[1]) * scaleFactorY,\n ]));\n const normalizedFace = {\n topLeft: boxData.slice(0, 2),\n bottomRight: boxData.slice(2),\n landmarks: scaledLandmarks,\n probability: probabilityData,\n };\n disposeBox(face.box);\n face.landmarks.dispose();\n face.probability.dispose();\n scaledBox.dispose();\n faces.push(normalizedFace);\n }\n return faces;\n }\n}\n\nasync function load(config) {\n const blazeface = await tf.loadGraphModel(config.face.detector.modelPath, { fromTFHub: config.face.detector.modelPath.includes('tfhub.dev') });\n const model = new BlazeFaceModel(blazeface, config);\n log(`load model: ${config.face.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n return model;\n}\n\nexports.load = load;\nexports.BlazeFaceModel = BlazeFaceModel;\nexports.disposeBox = disposeBox;\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nfunction scaleBoxCoordinates(box, factor) {\n const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n return { startPoint, endPoint };\n}\nexports.scaleBoxCoordinates = scaleBoxCoordinates;\n\nfunction getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\nexports.getBoxSize = getBoxSize;\n\nfunction getBoxCenter(box) {\n return [\n box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n ];\n}\nexports.getBoxCenter = getBoxCenter;\n\nfunction cutBoxFromImageAndResize(box, image, cropSize) {\n const h = image.shape[1];\n const w = image.shape[2];\n const boxes = [[\n box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h,\n box.endPoint[0] / w,\n ]];\n return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\nexports.cutBoxFromImageAndResize = cutBoxFromImageAndResize;\n\nfunction enlargeBox(box, factor = 1.5) {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n return { startPoint, endPoint, landmarks: box.landmarks };\n}\nexports.enlargeBox = enlargeBox;\n\nfunction squarifyBox(box) {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const maxEdge = Math.max(...size);\n const halfSize = maxEdge / 2;\n const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n return { startPoint, endPoint, landmarks: box.landmarks };\n}\nexports.squarifyBox = squarifyBox;\n", "exports.IDENTITY_MATRIX = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n/**\n * Normalizes the provided angle to the range -pi to pi.\n * @param angle The angle in radians to be normalized.\n */\nfunction normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\nexports.normalizeRadians = normalizeRadians;\n\n/**\n * Computes the angle of rotation between two anchor points.\n * @param point1 First anchor point\n * @param point2 Second anchor point\n */\nfunction computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\nexports.computeRotation = computeRotation;\n\nfunction radToDegrees(rad) {\n return rad * 180 / Math.PI;\n}\nexports.radToDegrees = radToDegrees;\n\nfunction buildTranslationMatrix(x, y) {\n return [[1, 0, x], [0, 1, y], [0, 0, 1]];\n}\n\nfunction dot(v1, v2) {\n let product = 0;\n for (let i = 0; i < v1.length; i++) {\n product += v1[i] * v2[i];\n }\n return product;\n}\nexports.dot = dot;\n\nfunction getColumnFrom2DArr(arr, columnIndex) {\n const column = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\nexports.getColumnFrom2DArr = getColumnFrom2DArr;\n\nfunction multiplyTransformMatrices(mat1, mat2) {\n const product = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) {\n product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n }\n return product;\n}\nfunction buildRotationMatrix(rotation, center) {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\nexports.buildRotationMatrix = buildRotationMatrix;\n\nfunction invertTransformMatrix(matrix) {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [\n -dot(rotationComponent[0], translationComponent),\n -dot(rotationComponent[1], translationComponent),\n ];\n return [\n rotationComponent[0].concat(invertedTranslation[0]),\n rotationComponent[1].concat(invertedTranslation[1]),\n [0, 0, 1],\n ];\n}\nexports.invertTransformMatrix = invertTransformMatrix;\n\nfunction rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\nexports.rotatePoint = rotatePoint;\n\nfunction xyDistanceBetweenPoints(a, b) {\n return Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n}\nexports.xyDistanceBetweenPoints = xyDistanceBetweenPoints;\n", "const MESH_ANNOTATIONS = {\n silhouette: [\n 10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n 397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n 172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n ],\n lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291],\n lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291],\n lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308],\n lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],\n rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173],\n rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133],\n rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190],\n rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243],\n rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189],\n rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244],\n rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245],\n rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193],\n rightEyebrowLower: [35, 124, 46, 53, 52, 65],\n rightEyeIris: [473, 474, 475, 476, 477],\n leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n leftEyeIris: [468, 469, 470, 471, 472],\n midwayBetweenEyes: [168],\n noseTip: [1],\n noseBottom: [2],\n noseRightCorner: [98],\n noseLeftCorner: [327],\n rightCheek: [205],\n leftCheek: [425],\n};\n\nconst MESH_TO_IRIS_INDICES_MAP = [ // A mapping from facemesh model keypoints to iris model keypoints.\n { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] },\n { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] },\n { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] },\n { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] },\n { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] },\n { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] },\n { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] },\n // { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] },\n // { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] },\n];\n\nconst UV468 = [\n [0.499976992607117, 0.652534008026123],\n [0.500025987625122, 0.547487020492554],\n [0.499974012374878, 0.602371990680695],\n [0.482113003730774, 0.471979022026062],\n [0.500150978565216, 0.527155995368958],\n [0.499909996986389, 0.498252987861633],\n [0.499523013830185, 0.40106201171875],\n [0.289712011814117, 0.380764007568359],\n [0.499954998493195, 0.312398016452789],\n [0.499987006187439, 0.269918978214264],\n [0.500023007392883, 0.107050001621246],\n [0.500023007392883, 0.666234016418457],\n [0.5000159740448, 0.679224014282227],\n [0.500023007392883, 0.692348003387451],\n [0.499976992607117, 0.695277988910675],\n [0.499976992607117, 0.70593398809433],\n [0.499976992607117, 0.719385027885437],\n [0.499976992607117, 0.737019002437592],\n [0.499967992305756, 0.781370997428894],\n [0.499816000461578, 0.562981009483337],\n [0.473773002624512, 0.573909997940063],\n [0.104906998574734, 0.254140973091125],\n [0.365929991006851, 0.409575998783112],\n [0.338757991790771, 0.41302502155304],\n [0.311120003461838, 0.409460008144379],\n [0.274657994508743, 0.389131009578705],\n [0.393361985683441, 0.403706014156342],\n [0.345234006643295, 0.344011008739471],\n [0.370094001293182, 0.346076011657715],\n [0.319321990013123, 0.347265005111694],\n [0.297903001308441, 0.353591024875641],\n [0.24779200553894, 0.410809993743896],\n [0.396889001131058, 0.842755019664764],\n [0.280097991228104, 0.375599980354309],\n [0.106310002505779, 0.399955987930298],\n [0.2099249958992, 0.391353011131287],\n [0.355807989835739, 0.534406006336212],\n [0.471751004457474, 0.65040397644043],\n [0.474155008792877, 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[0.614073991775513, 0.883246004581451],\n [0.508952975273132, 0.579437971115112],\n [0.617941975593567, 0.508316040039062],\n [0.825608015060425, 0.397674977779388],\n [0.681214988231659, 0.39623498916626],\n [0.656635999679565, 0.400596976280212],\n [0.603900015354156, 0.710216999053955],\n [0.81208598613739, 0.588539004325867],\n [0.56801301240921, 0.944564998149872],\n [0.681007981300354, 0.898285031318665],\n [0.733752012252808, 0.869701027870178],\n [0.633830010890961, 0.398822009563446],\n [0.606792986392975, 0.39553701877594],\n [0.589659988880157, 0.391062021255493],\n [0.805015981197357, 0.342108011245728],\n [0.611334979534149, 0.362284004688263],\n [0.634037971496582, 0.355970978736877],\n [0.656635999679565, 0.355356991291046],\n [0.681214988231659, 0.35834002494812],\n [0.698584973812103, 0.363156020641327],\n [0.941866993904114, 0.319076001644135],\n [0.698584973812103, 0.387449026107788],\n [0.584177017211914, 0.624107003211975],\n [0.554318010807037, 0.566076993942261],\n [0.534153997898102, 0.62064003944397],\n [0.711217999458313, 0.819975018501282],\n [0.664629995822906, 0.852871000766754],\n [0.559099972248077, 0.902631998062134],\n [0.871706008911133, 0.791940987110138],\n [0.591234028339386, 0.373893976211548],\n [0.544341027736664, 0.451583981513977],\n [0.624562978744507, 0.924192011356354],\n [0.88577002286911, 0.615028977394104],\n [0.551338016986847, 0.695277988910675],\n [0.551980018615723, 0.704632043838501],\n [0.552887976169586, 0.715808033943176],\n [0.555167973041534, 0.730794012546539],\n [0.569944024085999, 0.767035007476807],\n [0.593203008174896, 0.685675978660583],\n [0.599261999130249, 0.681069016456604],\n [0.607599973678589, 0.677703022956848],\n [0.631937980651855, 0.663500010967255],\n [0.752032995223999, 0.601315021514893],\n [0.547226011753082, 0.420395016670227],\n [0.563543975353241, 0.359827995300293],\n [0.583841025829315, 0.368713974952698],\n [0.586614012718201, 0.692366003990173],\n [0.771915018558502, 0.683578014373779],\n [0.531597018241882, 0.352482974529266],\n [0.588370978832245, 0.804440975189209],\n [0.52079701423645, 0.442565023899078],\n [0.567984998226166, 0.493479013442993],\n [0.543282985687256, 0.819254994392395],\n [0.655317008495331, 0.745514988899231],\n [0.621008992195129, 0.574018001556396],\n [0.625559985637665, 0.78031200170517],\n [0.680198013782501, 0.570719003677368],\n [0.64276397228241, 0.604337990283966],\n [0.704662978649139, 0.621529996395111],\n [0.552012026309967, 0.862591981887817],\n [0.589071989059448, 0.508637011051178],\n [0.685944974422455, 0.775357007980347],\n [0.645735025405884, 0.812640011310577],\n [0.675342977046967, 0.703978002071381],\n [0.810858011245728, 0.646304965019226],\n [0.72012197971344, 0.714666962623596],\n [0.866151988506317, 0.682704985141754],\n [0.663187026977539, 0.644596993923187],\n [0.570082008838654, 0.466325998306274],\n [0.544561982154846, 0.548375964164734],\n [0.562758982181549, 0.558784961700439],\n [0.531987011432648, 0.530140042304993],\n [0.585271000862122, 0.335177004337311],\n [0.622952997684479, 0.32277899980545],\n [0.655896008014679, 0.320163011550903],\n [0.687132000923157, 0.322345972061157],\n [0.716481983661652, 0.333200991153717],\n [0.758756995201111, 0.382786989212036],\n [0.897013008594513, 0.468769013881683],\n [0.732392013072968, 0.424547016620636],\n [0.70211398601532, 0.433162987232208],\n [0.66652500629425, 0.433866024017334],\n [0.633504986763, 0.426087975502014],\n [0.603875994682312, 0.416586995124817],\n [0.579657971858978, 0.409945011138916],\n [0.992439985275269, 0.480777025222778],\n [0.567192018032074, 0.569419980049133],\n [0.54136598110199, 0.478899002075195],\n [0.526564002037048, 0.546118021011353],\n [0.523913025856018, 0.563830018043518],\n [0.531529009342194, 0.555056989192963],\n [0.566035985946655, 0.582329034805298],\n [0.51631098985672, 0.563053965568542],\n [0.5174720287323, 0.577877044677734],\n [0.573594987392426, 0.389806985855103],\n [0.560697972774506, 0.395331978797913],\n [0.549755990505219, 0.399751007556915],\n [0.710287988185883, 0.368252992630005],\n [0.723330020904541, 0.363372981548309],\n];\n\nconst TRI468 = [\n 127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n 151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n 157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n 181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n 228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n 170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n 155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n 158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n 102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n 90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n 196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n 246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n 117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n 153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n 63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n 135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n 76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n 166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n 190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n 112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n 110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n 348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n 314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n 321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n 466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n 429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n 293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n 427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n 431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n 434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n 348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n 318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n 373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n 340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n 351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n 164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n 327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n 405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n 438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n 449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n 444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n 464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n 243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n 166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n 119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n 77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n 201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n 103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n 217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n 78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n 57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n 142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n 90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n 178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n 224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n 143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n 153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n 63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n 241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n 186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n 141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n 223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n 128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n 451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n 344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n 424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n 321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n 448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n 281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n 283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n 432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n 268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n 6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n 317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n 272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n 336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n 441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n 373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n 425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n 403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n 383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n 254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n 259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n 453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nconst TRI68 = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n 8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n 45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n 23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n 39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n 35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n 48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n 66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\nconst TRI33 = [\n /* eyes */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n /* brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n /* 4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n /* nose */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n /* chin */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n /* cont */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\nconst TRI7 = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nconst VTX68 = [\n /* cont */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n /* nose */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n /* eyes */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n /* lip */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\nconst VTX33 = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\nconst VTX7 = [33, 133, 362, 263, 1, 78, 308];\n\nexports.MESH_ANNOTATIONS = MESH_ANNOTATIONS;\nexports.MESH_TO_IRIS_INDICES_MAP = MESH_TO_IRIS_INDICES_MAP;\n\nexports.TRI468 = TRI468;\nexports.TRI68 = TRI68;\nexports.TRI33 = TRI33;\nexports.TRI7 = TRI7;\n\nexports.UV468 = UV468;\nexports.UV68 = VTX68.map((x) => UV468[x]);\nexports.UV33 = VTX33.map((x) => UV468[x]);\nexports.UV7 = VTX7.map((x) => UV468[x]);\n", "/* eslint-disable class-methods-use-this */\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as bounding from './box';\nimport * as util from './util';\nimport * as coords from './coords.js';\n// eslint-disable-next-line no-unused-vars\nimport { log } from '../log.js';\n\nconst LANDMARKS_COUNT = 468;\nconst MESH_MOUTH_INDEX = 13;\nconst MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES = [MESH_MOUTH_INDEX, coords.MESH_ANNOTATIONS['midwayBetweenEyes'][0]];\nconst BLAZEFACE_MOUTH_INDEX = 3;\nconst BLAZEFACE_NOSE_INDEX = 2;\nconst BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES = [BLAZEFACE_MOUTH_INDEX, BLAZEFACE_NOSE_INDEX];\nconst LEFT_EYE_OUTLINE = coords.MESH_ANNOTATIONS['leftEyeLower0'];\nconst LEFT_EYE_BOUNDS = [LEFT_EYE_OUTLINE[0], LEFT_EYE_OUTLINE[LEFT_EYE_OUTLINE.length - 1]];\nconst RIGHT_EYE_OUTLINE = coords.MESH_ANNOTATIONS['rightEyeLower0'];\nconst RIGHT_EYE_BOUNDS = [RIGHT_EYE_OUTLINE[0], RIGHT_EYE_OUTLINE[RIGHT_EYE_OUTLINE.length - 1]];\nconst IRIS_UPPER_CENTER_INDEX = 3;\nconst IRIS_LOWER_CENTER_INDEX = 4;\nconst IRIS_IRIS_INDEX = 71;\nconst IRIS_NUM_COORDINATES = 76;\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates. Update the z coordinate to be an average of the original and the new. This produces the best visual effect.\nfunction replaceRawCoordinates(rawCoords, newCoords, prefix, keys) {\n for (let i = 0; i < coords.MESH_TO_IRIS_INDICES_MAP.length; i++) {\n const { key, indices } = coords.MESH_TO_IRIS_INDICES_MAP[i];\n const originalIndices = coords.MESH_ANNOTATIONS[`${prefix}${key}`];\n const shouldReplaceAllKeys = keys == null;\n if (shouldReplaceAllKeys || keys.includes(key)) {\n for (let j = 0; j < indices.length; j++) {\n const index = indices[j];\n rawCoords[originalIndices[j]] = [\n newCoords[index][0], newCoords[index][1],\n (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n ];\n }\n }\n }\n}\n// The Pipeline coordinates between the bounding box and skeleton models.\nclass Pipeline {\n constructor(boundingBoxDetector, meshDetector, irisModel, config) {\n // An array of facial bounding boxes.\n this.storedBoxes = [];\n this.runsWithoutFaceDetector = 0;\n this.boundingBoxDetector = boundingBoxDetector;\n this.meshDetector = meshDetector;\n this.irisModel = irisModel;\n this.meshWidth = config.face.mesh.inputSize;\n this.meshHeight = config.face.mesh.inputSize;\n this.irisSize = config.face.iris.inputSize;\n this.irisEnlarge = 2.3;\n this.skipped = 0;\n this.detectedFaces = 0;\n }\n\n transformRawCoords(rawCoords, box, angle, rotationMatrix) {\n // @ts-ignore\n const boxSize = bounding.getBoxSize({ startPoint: box.startPoint, endPoint: box.endPoint });\n const scaleFactor = [boxSize[0] / this.meshWidth, boxSize[1] / this.meshHeight];\n const coordsScaled = rawCoords.map((coord) => ([\n scaleFactor[0] * (coord[0] - this.meshWidth / 2),\n scaleFactor[1] * (coord[1] - this.meshHeight / 2), coord[2],\n ]));\n const coordsRotationMatrix = (angle !== 0) ? util.buildRotationMatrix(angle, [0, 0]) : util.IDENTITY_MATRIX;\n const coordsRotated = (angle !== 0) ? coordsScaled.map((coord) => ([...util.rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n const inverseRotationMatrix = (angle !== 0) ? util.invertTransformMatrix(rotationMatrix) : util.IDENTITY_MATRIX;\n // @ts-ignore\n const boxCenter = [...bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint }), 1];\n return coordsRotated.map((coord) => ([\n coord[0] + util.dot(boxCenter, inverseRotationMatrix[0]),\n coord[1] + util.dot(boxCenter, inverseRotationMatrix[1]),\n coord[2],\n ]));\n }\n\n getLeftToRightEyeDepthDifference(rawCoords) {\n const leftEyeZ = rawCoords[LEFT_EYE_BOUNDS[0]][2];\n const rightEyeZ = rawCoords[RIGHT_EYE_BOUNDS[0]][2];\n return leftEyeZ - rightEyeZ;\n }\n\n // Returns a box describing a cropped region around the eye fit for passing to the iris model.\n getEyeBox(rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, flip = false) {\n // @ts-ignore\n const box = bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), this.irisEnlarge));\n // @ts-ignore\n const boxSize = bounding.getBoxSize(box);\n let crop = tf.image.cropAndResize(face, [[\n box.startPoint[1] / this.meshHeight,\n box.startPoint[0] / this.meshWidth, box.endPoint[1] / this.meshHeight,\n box.endPoint[0] / this.meshWidth,\n ]], [0], [this.irisSize, this.irisSize]);\n if (flip) {\n crop = tf.image.flipLeftRight(crop);\n }\n return { box, boxSize, crop };\n }\n\n // Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\n getEyeCoords(eyeData, eyeBox, eyeBoxSize, flip = false) {\n const eyeRawCoords = [];\n for (let i = 0; i < IRIS_NUM_COORDINATES; i++) {\n const x = eyeData[i * 3];\n const y = eyeData[i * 3 + 1];\n const z = eyeData[i * 3 + 2];\n eyeRawCoords.push([\n (flip\n ? (1 - (x / this.irisSize))\n : (x / this.irisSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n (y / this.irisSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n ]);\n }\n return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(IRIS_IRIS_INDEX) };\n }\n\n // The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\n getAdjustedIrisCoords(rawCoords, irisCoords, direction) {\n const upperCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeUpper0`][IRIS_UPPER_CENTER_INDEX]][2];\n const lowerCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeLower0`][IRIS_LOWER_CENTER_INDEX]][2];\n const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n return irisCoords.map((coord, i) => {\n let z = averageZ;\n if (i === 2) {\n z = upperCenterZ;\n } else if (i === 4) {\n z = lowerCenterZ;\n }\n return [coord[0], coord[1], z];\n });\n }\n\n async predict(input, config) {\n let useFreshBox = false;\n // run new detector every skipFrames unless we only want box to start with\n let detector;\n if ((this.skipped === 0) || (this.skipped > config.face.detector.skipFrames) || !config.face.mesh.enabled || !config.videoOptimized) {\n detector = await this.boundingBoxDetector.getBoundingBoxes(input);\n this.skipped = 0;\n }\n if (config.videoOptimized) this.skipped++;\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (detector && detector.boxes && (detector.boxes.length > 0) && (!config.face.mesh.enabled || (detector.boxes.length !== this.detectedFaces) && (this.detectedFaces !== config.face.detector.maxFaces))) {\n this.storedBoxes = [];\n this.detectedFaces = 0;\n for (const possible of detector.boxes) {\n this.storedBoxes.push({ startPoint: possible.box.startPoint.dataSync(), endPoint: possible.box.endPoint.dataSync(), landmarks: possible.landmarks, confidence: possible.confidence });\n }\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\n\n if (useFreshBox) {\n if (!detector || !detector.boxes || (detector.boxes.length === 0)) {\n this.storedBoxes = [];\n this.detectedFaces = 0;\n return null;\n }\n for (let i = 0; i < this.storedBoxes.length; i++) {\n // @ts-ignore\n const scaledBox = bounding.scaleBoxCoordinates({ startPoint: this.storedBoxes[i].startPoint, endPoint: this.storedBoxes[i].endPoint }, detector.scaleFactor);\n // @ts-ignore\n const enlargedBox = bounding.enlargeBox(scaledBox);\n // @ts-ignore\n const squarifiedBox = bounding.squarifyBox(enlargedBox);\n const landmarks = this.storedBoxes[i].landmarks.arraySync();\n const confidence = this.storedBoxes[i].confidence;\n this.storedBoxes[i] = { ...squarifiedBox, confidence, landmarks };\n }\n this.runsWithoutFaceDetector = 0;\n }\n if (detector && detector.boxes) {\n detector.boxes.forEach((prediction) => {\n prediction.box.startPoint.dispose();\n prediction.box.endPoint.dispose();\n prediction.landmarks.dispose();\n });\n }\n\n // log('face', `skipped: ${this.skipped} max: ${config.face.detector.maxFaces} detected: ${this.detectedFaces} stored: ${this.storedBoxes.length} new: ${detector?.boxes?.length}`);\n let results = tf.tidy(() => this.storedBoxes.map((box, i) => {\n // The facial bounding box landmarks could come either from blazeface (if we are using a fresh box), or from the mesh model (if we are reusing an old box).\n let face;\n let angle = 0;\n let rotationMatrix;\n if (config.face.detector.rotation) {\n const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= LANDMARKS_COUNT) ? MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES : BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES;\n angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n // @ts-ignore\n const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n const rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized);\n rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n // @ts-ignore\n face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshHeight, this.meshWidth]).div(255);\n } else {\n rotationMatrix = util.IDENTITY_MATRIX;\n const cloned = input.clone();\n // @ts-ignore\n face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, cloned, [this.meshHeight, this.meshWidth]).div(255);\n }\n\n // if we're not going to produce mesh, don't spend time with further processing\n if (!config.face.mesh.enabled) {\n const prediction = {\n coords: null,\n box,\n faceConfidence: null,\n confidence: box.confidence,\n image: face,\n };\n return prediction;\n }\n\n const [, confidence, contourCoords] = this.meshDetector.predict(face); // The first returned tensor represents facial contours, which are included in the coordinates.\n const confidenceVal = confidence.dataSync()[0];\n if (confidenceVal < config.face.detector.minConfidence) return null; // if below confidence just exit\n const coordsReshaped = tf.reshape(contourCoords, [-1, 3]);\n let rawCoords = coordsReshaped.arraySync();\n\n if (config.face.iris.enabled) {\n const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = this.getEyeBox(rawCoords, face, LEFT_EYE_BOUNDS[0], LEFT_EYE_BOUNDS[1], true);\n const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = this.getEyeBox(rawCoords, face, RIGHT_EYE_BOUNDS[0], RIGHT_EYE_BOUNDS[1]);\n const eyePredictions = this.irisModel.predict(tf.concat([leftEyeCrop, rightEyeCrop]));\n const eyePredictionsData = eyePredictions.dataSync();\n const leftEyeData = eyePredictionsData.slice(0, IRIS_NUM_COORDINATES * 3);\n const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = this.getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n const rightEyeData = eyePredictionsData.slice(IRIS_NUM_COORDINATES * 3);\n const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = this.getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize);\n const leftToRightEyeDepthDifference = this.getLeftToRightEyeDepthDifference(rawCoords);\n if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left');\n replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right');\n // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged. So we only update a single contour line above and below the eye.\n } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n } else { // User is looking towards the left.\n replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n }\n const adjustedLeftIrisCoords = this.getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n const adjustedRightIrisCoords = this.getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n rawCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n }\n\n const transformedCoordsData = this.transformRawCoords(rawCoords, box, angle, rotationMatrix);\n // @ts-ignore\n const landmarksBox = bounding.enlargeBox(this.calculateLandmarksBoundingBox(transformedCoordsData));\n // @ts-ignore\n const squarifiedLandmarksBox = bounding.squarifyBox(landmarksBox);\n const transformedCoords = tf.tensor2d(transformedCoordsData);\n const prediction = {\n coords: transformedCoords,\n box: landmarksBox,\n faceConfidence: confidenceVal,\n confidence: box.confidence,\n image: face,\n };\n if (config.face.mesh.returnRawData) prediction.rawCoords = rawCoords;\n this.storedBoxes[i] = { ...squarifiedLandmarksBox, landmarks: transformedCoords.arraySync(), confidence: box.confidence, faceConfidence: confidenceVal };\n\n return prediction;\n }));\n results = results.filter((a) => a !== null);\n this.detectedFaces = results.length;\n return results;\n }\n\n calculateLandmarksBoundingBox(landmarks) {\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint, landmarks };\n }\n}\nexports.Pipeline = Pipeline;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as blazeface from './blazeface.js';\nimport * as facepipeline from './facepipeline.js';\nimport * as coords from './coords.js';\n\nclass MediaPipeFaceMesh {\n constructor(blazeFace, blazeMeshModel, irisModel, config) {\n // @ts-ignore\n this.facePipeline = new facepipeline.Pipeline(blazeFace, blazeMeshModel, irisModel, config);\n this.config = config;\n }\n\n async estimateFaces(input, config) {\n const predictions = await this.facePipeline.predict(input, config);\n const results = [];\n for (const prediction of (predictions || [])) {\n if (prediction.isDisposedInternal) continue; // guard against disposed tensors on long running operations such as pause in middle of processing\n const mesh = prediction.coords ? prediction.coords.arraySync() : null;\n const meshRaw = prediction.rawCoords;\n const annotations = {};\n if (mesh && mesh.length > 0) {\n for (const key of Object.keys(coords.MESH_ANNOTATIONS)) {\n annotations[key] = coords.MESH_ANNOTATIONS[key].map((index) => mesh[index]);\n }\n }\n const boxRaw = (config.face.mesh.returnRawData && prediction.box) ? { topLeft: prediction.box.startPoint, bottomRight: prediction.box.endPoint } : null;\n const box = prediction.box ? [\n Math.max(0, prediction.box.startPoint[0]),\n Math.max(0, prediction.box.startPoint[1]),\n Math.min(input.shape[2], prediction.box.endPoint[0]) - prediction.box.startPoint[0],\n Math.min(input.shape[1], prediction.box.endPoint[1]) - prediction.box.startPoint[1],\n ] : 0;\n results.push({\n confidence: prediction.confidence || 0,\n box,\n mesh,\n boxRaw,\n meshRaw,\n annotations,\n image: prediction.image ? tf.clone(prediction.image) : null,\n });\n if (prediction.coords) prediction.coords.dispose();\n if (prediction.image) prediction.image.dispose();\n }\n return results;\n }\n}\n\nlet faceModels = [null, null, null];\nasync function load(config) {\n faceModels = await Promise.all([\n // @ts-ignore\n (!faceModels[0] && config.face.enabled) ? blazeface.load(config) : null,\n (!faceModels[1] && config.face.mesh.enabled) ? tf.loadGraphModel(config.face.mesh.modelPath, { fromTFHub: config.face.mesh.modelPath.includes('tfhub.dev') }) : null,\n (!faceModels[2] && config.face.iris.enabled) ? tf.loadGraphModel(config.face.iris.modelPath, { fromTFHub: config.face.iris.modelPath.includes('tfhub.dev') }) : null,\n ]);\n const faceMesh = new MediaPipeFaceMesh(faceModels[0], faceModels[1], faceModels[2], config);\n if (config.face.mesh.enabled) log(`load model: ${config.face.mesh.modelPath.match(/\\/(.*)\\./)[1]}`);\n if (config.face.iris.enabled) log(`load model: ${config.face.iris.modelPath.match(/\\/(.*)\\./)[1]}`);\n return faceMesh;\n}\n\nexports.load = load;\nexports.MediaPipeFaceMesh = MediaPipeFaceMesh;\nexports.triangulation = coords.TRI468;\n", "import { log } from './log.js';\n\nconst profileData = {};\n\nfunction profile(name, data) {\n if (!data || !data.kernels) return;\n const maxResults = 5;\n const time = data.kernels\n .filter((a) => a.kernelTimeMs > 0)\n .reduce((a, b) => a += b.kernelTimeMs, 0);\n const slowest = data.kernels\n .map((a, i) => { a.id = i; return a; })\n .filter((a) => a.kernelTimeMs > 0)\n .sort((a, b) => b.kernelTimeMs - a.kernelTimeMs);\n const largest = data.kernels\n .map((a, i) => { a.id = i; return a; })\n .filter((a) => a.totalBytesSnapshot > 0)\n .sort((a, b) => b.totalBytesSnapshot - a.totalBytesSnapshot);\n if (slowest.length > maxResults) slowest.length = maxResults;\n if (largest.length > maxResults) largest.length = maxResults;\n const res = { newBytes: data.newBytes, newTensors: data.newTensors, peakBytes: data.peakBytes, numKernelOps: data.kernels.length, timeKernelOps: time, slowestKernelOps: slowest, largestKernelOps: largest };\n profileData[name] = res;\n log('Human profiler', name, res);\n}\n\nexports.run = profile;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile.js';\n\nconst models = {};\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nasync function load(config) {\n if (!models.age) {\n models.age = await tf.loadGraphModel(config.face.age.modelPath);\n log(`load model: ${config.face.age.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.age;\n}\n\nasync function predict(image, config) {\n if (!models.age) return null;\n if ((skipped < config.face.age.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n /*\n const zoom = [0, 0]; // 0..1 meaning 0%..100%\n const box = [[\n (image.shape[1] * zoom[0]) / image.shape[1],\n (image.shape[2] * zoom[1]) / image.shape[2],\n (image.shape[1] - (image.shape[1] * zoom[0])) / image.shape[1],\n (image.shape[2] - (image.shape[2] * zoom[1])) / image.shape[2],\n ]];\n const resize = tf.image.cropAndResize(image, box, [0], [config.face.age.inputSize, config.face.age.inputSize]);\n */\n const resize = tf.image.resizeBilinear(image, [config.face.age.inputSize, config.face.age.inputSize], false);\n const enhance = tf.mul(resize, [255.0]);\n tf.dispose(resize);\n\n let ageT;\n const obj = {};\n\n if (!config.profile) {\n if (config.face.age.enabled) ageT = await models.age.predict(enhance);\n } else {\n const profileAge = config.face.age.enabled ? await tf.profile(() => models.age.predict(enhance)) : {};\n ageT = profileAge.result.clone();\n profileAge.result.dispose();\n // @ts-ignore\n profile.run('age', profileAge);\n }\n enhance.dispose();\n\n if (ageT) {\n const data = ageT.dataSync();\n obj.age = Math.trunc(10 * data[0]) / 10;\n }\n ageT.dispose();\n\n last = obj;\n resolve(obj);\n });\n}\n\nexports.predict = predict;\nexports.load = load;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile.js';\n\nconst models = {};\nlet last = { gender: '' };\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet alternative = false;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nasync function load(config) {\n if (!models.gender) {\n models.gender = await tf.loadGraphModel(config.face.gender.modelPath);\n alternative = models.gender.inputs[0].shape[3] === 1;\n log(`load model: ${config.face.gender.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.gender;\n}\n\nasync function predict(image, config) {\n if (!models.gender) return null;\n if ((skipped < config.face.gender.skipFrames) && config.videoOptimized && last.gender !== '') {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [config.face.gender.inputSize, config.face.gender.inputSize], false);\n let enhance;\n if (alternative) {\n enhance = tf.tidy(() => {\n const [red, green, blue] = tf.split(resize, 3, 3);\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n return grayscale.sub(0.5).mul(2);\n });\n } else {\n enhance = tf.mul(resize, [255.0]);\n }\n tf.dispose(resize);\n\n let genderT;\n const obj = {};\n\n if (!config.profile) {\n if (config.face.gender.enabled) genderT = await models.gender.predict(enhance);\n } else {\n const profileGender = config.face.gender.enabled ? await tf.profile(() => models.gender.predict(enhance)) : {};\n genderT = profileGender.result.clone();\n profileGender.result.dispose();\n // @ts-ignore\n profile.run('gender', profileGender);\n }\n enhance.dispose();\n\n if (genderT) {\n const data = genderT.dataSync();\n if (alternative) {\n // returns two values 0..1, bigger one is prediction\n const confidence = Math.trunc(100 * Math.abs(data[0] - data[1])) / 100;\n if (confidence > config.face.gender.minConfidence) {\n obj.gender = data[0] > data[1] ? 'female' : 'male';\n obj.confidence = confidence;\n }\n } else {\n // returns one value 0..1, .5 is prediction threshold\n const confidence = Math.trunc(200 * Math.abs((data[0] - 0.5))) / 100;\n if (confidence > config.face.gender.minConfidence) {\n obj.gender = data[0] <= 0.5 ? 'female' : 'male';\n obj.confidence = Math.min(0.99, confidence);\n }\n }\n }\n genderT.dispose();\n\n last = obj;\n resolve(obj);\n });\n}\n\nexports.predict = predict;\nexports.load = load;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile.js';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];\nconst models = {};\nlet last = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\nconst scale = 1; // score multiplication factor\n\nasync function load(config) {\n if (!models.emotion) {\n models.emotion = await tf.loadGraphModel(config.face.emotion.modelPath);\n log(`load model: ${config.face.emotion.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.emotion;\n}\n\nasync function predict(image, config) {\n if (!models.emotion) return null;\n if ((skipped < config.face.emotion.skipFrames) && config.videoOptimized && (last.length > 0)) {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n /*\n const zoom = [0, 0]; // 0..1 meaning 0%..100%\n const box = [[\n (image.shape[1] * zoom[0]) / image.shape[1],\n (image.shape[2] * zoom[1]) / image.shape[2],\n (image.shape[1] - (image.shape[1] * zoom[0])) / image.shape[1],\n (image.shape[2] - (image.shape[2] * zoom[1])) / image.shape[2],\n ]];\n const resize = tf.image.cropAndResize(image, box, [0], [config.face.emotion.inputSize, config.face.emotion.inputSize]);\n */\n const resize = tf.image.resizeBilinear(image, [config.face.emotion.inputSize, config.face.emotion.inputSize], false);\n const [red, green, blue] = tf.split(resize, 3, 3);\n resize.dispose();\n // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n red.dispose();\n green.dispose();\n blue.dispose();\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n redNorm.dispose();\n greenNorm.dispose();\n blueNorm.dispose();\n const normalize = tf.tidy(() => grayscale.sub(0.5).mul(2));\n grayscale.dispose();\n const obj = [];\n if (config.face.emotion.enabled) {\n let data;\n if (!config.profile) {\n const emotionT = await models.emotion.predict(normalize);\n data = emotionT.dataSync();\n tf.dispose(emotionT);\n } else {\n const profileData = await tf.profile(() => models.emotion.predict(normalize));\n data = profileData.result.dataSync();\n profileData.result.dispose();\n // @ts-ignore\n profile.run('emotion', profileData);\n }\n for (let i = 0; i < data.length; i++) {\n if (scale * data[i] > config.face.emotion.minConfidence) obj.push({ score: Math.min(0.99, Math.trunc(100 * scale * data[i]) / 100), emotion: annotations[i] });\n }\n obj.sort((a, b) => b.score - a.score);\n }\n normalize.dispose();\n last = obj;\n resolve(obj);\n });\n}\n\nexports.predict = predict;\nexports.load = load;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile.js';\n\n// based on https://github.com/sirius-ai/MobileFaceNet_TF\n// model converted from https://github.com/sirius-ai/MobileFaceNet_TF/files/3551493/FaceMobileNet192_train_false.zip\n\nconst models = {};\n\nasync function load(config) {\n if (!models.embedding) {\n models.embedding = await tf.loadGraphModel(config.face.embedding.modelPath);\n log(`load model: ${config.face.embedding.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.embedding;\n}\n\nfunction simmilarity(embedding1, embedding2) {\n if (embedding1?.length !== embedding2?.length) return 0;\n // general minkowski distance\n // euclidean distance is limited case where order is 2\n const order = 2;\n const distance = 10.0 * ((embedding1.map((val, i) => (val - embedding2[i])).reduce((dist, diff) => dist + (diff ** order), 0) ** (1 / order)));\n return (Math.trunc(1000 * (1 - distance)) / 1000);\n}\n\nasync function predict(image, config) {\n if (!models.embedding) return null;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [config.face.embedding.inputSize, config.face.embedding.inputSize], false);\n // const normalize = tf.tidy(() => resize.div(127.5).sub(0.5)); // this is -0.5...0.5 ???\n let data = [];\n if (config.face.embedding.enabled) {\n if (!config.profile) {\n const embeddingT = await models.embedding.predict({ img_inputs: resize });\n data = [...embeddingT.dataSync()]; // convert object array to standard array\n tf.dispose(embeddingT);\n } else {\n const profileData = await tf.profile(() => models.embedding.predict({ img_inputs: resize }));\n data = [...profileData.result.dataSync()];\n profileData.result.dispose();\n // @ts-ignore\n profile.run('emotion', profileData);\n }\n }\n resize.dispose();\n // normalize.dispose();\n resolve(data);\n });\n}\n\nexports.predict = predict;\nexports.simmilarity = simmilarity;\nexports.load = load;\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nconst imageNetMean = [-123.15, -115.90, -103.06];\n\nfunction nameOutputResultsMobileNet(results) {\n const [offsets, heatmap, displacementFwd, displacementBwd] = results;\n return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nfunction nameOutputResultsResNet(results) {\n const [displacementFwd, displacementBwd, offsets, heatmap] = results;\n return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nclass BaseModel {\n constructor(model) {\n this.model = model;\n }\n\n predict(input, config) {\n return tf.tidy(() => {\n const asFloat = (config.body.modelType === 'ResNet') ? input.toFloat().add(imageNetMean) : input.toFloat().div(127.5).sub(1.0);\n const asBatch = asFloat.expandDims(0);\n const results = this.model.predict(asBatch);\n const results3d = results.map((y) => y.squeeze([0]));\n const namedResults = (config.body.modelType === 'ResNet') ? nameOutputResultsResNet(results3d) : nameOutputResultsMobileNet(results3d);\n return {\n heatmapScores: namedResults.heatmap.sigmoid(),\n offsets: namedResults.offsets,\n displacementFwd: namedResults.displacementFwd,\n displacementBwd: namedResults.displacementBwd,\n };\n });\n }\n\n dispose() {\n this.model.dispose();\n }\n}\nexports.BaseModel = BaseModel;\n", "// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nfunction half(k) {\n return Math.floor(k / 2);\n}\nclass MaxHeap {\n constructor(maxSize, getElementValue) {\n this.priorityQueue = new Array(maxSize);\n this.numberOfElements = -1;\n this.getElementValue = getElementValue;\n }\n\n enqueue(x) {\n this.priorityQueue[++this.numberOfElements] = x;\n this.swim(this.numberOfElements);\n }\n\n dequeue() {\n const max = this.priorityQueue[0];\n this.exchange(0, this.numberOfElements--);\n this.sink(0);\n this.priorityQueue[this.numberOfElements + 1] = null;\n return max;\n }\n\n empty() {\n return this.numberOfElements === -1;\n }\n\n size() {\n return this.numberOfElements + 1;\n }\n\n all() {\n return this.priorityQueue.slice(0, this.numberOfElements + 1);\n }\n\n max() {\n return this.priorityQueue[0];\n }\n\n swim(k) {\n while (k > 0 && this.less(half(k), k)) {\n this.exchange(k, half(k));\n k = half(k);\n }\n }\n\n sink(k) {\n while (2 * k <= this.numberOfElements) {\n let j = 2 * k;\n if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n if (!this.less(k, j)) break;\n this.exchange(k, j);\n k = j;\n }\n }\n\n getValueAt(i) {\n return this.getElementValue(this.priorityQueue[i]);\n }\n\n less(i, j) {\n return this.getValueAt(i) < this.getValueAt(j);\n }\n\n exchange(i, j) {\n const t = this.priorityQueue[i];\n this.priorityQueue[i] = this.priorityQueue[j];\n this.priorityQueue[j] = t;\n }\n}\nexports.MaxHeap = MaxHeap;\n", "import * as heapSort from './heapSort';\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores) {\n const [height, width] = scores.shape;\n let localMaximum = true;\n const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n localMaximum = false;\n break;\n }\n }\n if (!localMaximum) break;\n }\n return localMaximum;\n}\n\nfunction buildPartWithScoreQueue(scoreThreshold, localMaximumRadius, scores) {\n const [height, width, numKeypoints] = scores.shape;\n const queue = new heapSort.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n const score = scores.get(heatmapY, heatmapX, keypointId);\n // Only consider parts with score greater or equal to threshold as root candidates.\n if (score < scoreThreshold) continue;\n // Only consider keypoints whose score is maximum in a local window.\n if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores)) {\n queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n }\n }\n }\n }\n return queue;\n}\nexports.buildPartWithScoreQueue = buildPartWithScoreQueue;\n", "exports.partNames = [\n 'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n 'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n 'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexports.NUM_KEYPOINTS = exports.partNames.length;\n\nexports.partIds = exports.partNames.reduce((result, jointName, i) => {\n result[jointName] = i;\n return result;\n}, {});\n\nconst connectedPartNames = [\n ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexports.connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([exports.partIds[jointNameA], exports.partIds[jointNameB]]));\n\nexports.poseChain = [\n ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n ['rightKnee', 'rightAnkle'],\n];\n\nexports.partChannels = [\n 'left_face',\n 'right_face',\n 'right_upper_leg_front',\n 'right_lower_leg_back',\n 'right_upper_leg_back',\n 'left_lower_leg_front',\n 'left_upper_leg_front',\n 'left_upper_leg_back',\n 'left_lower_leg_back',\n 'right_feet',\n 'right_lower_leg_front',\n 'left_feet',\n 'torso_front',\n 'torso_back',\n 'right_upper_arm_front',\n 'right_upper_arm_back',\n 'right_lower_arm_back',\n 'left_lower_arm_front',\n 'left_upper_arm_front',\n 'left_upper_arm_back',\n 'left_lower_arm_back',\n 'right_hand',\n 'right_lower_arm_front',\n 'left_hand',\n];\n", "import * as kpt from './keypoints';\n\nfunction getOffsetPoint(y, x, keypoint, offsets) {\n return {\n y: offsets.get(y, x, keypoint),\n x: offsets.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n };\n}\nexports.getOffsetPoint = getOffsetPoint;\n\nfunction getImageCoords(part, outputStride, offsets) {\n const { heatmapY, heatmapX, id: keypoint } = part;\n const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n return {\n x: part.heatmapX * outputStride + x,\n y: part.heatmapY * outputStride + y,\n };\n}\nexports.getImageCoords = getImageCoords;\n\nfunction fillArray(element, size) {\n const result = new Array(size);\n for (let i = 0; i < size; i++) {\n result[i] = element;\n }\n return result;\n}\nexports.fillArray = fillArray;\n\nfunction clamp(a, min, max) {\n if (a < min) return min;\n if (a > max) return max;\n return a;\n}\nexports.clamp = clamp;\n\nfunction squaredDistance(y1, x1, y2, x2) {\n const dy = y2 - y1;\n const dx = x2 - x1;\n return dy * dy + dx * dx;\n}\nexports.squaredDistance = squaredDistance;\n\nfunction addVectors(a, b) {\n return { x: a.x + b.x, y: a.y + b.y };\n}\nexports.addVectors = addVectors;\n\nfunction clampVector(a, min, max) {\n return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\nexports.clampVector = clampVector;\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as kpt from './keypoints';\n\nfunction getPointsConfidence(heatmapScores, heatMapCoords) {\n const numKeypoints = heatMapCoords.shape[0];\n const result = new Float32Array(numKeypoints);\n for (let keypoint = 0; keypoint < numKeypoints; keypoint++) {\n const y = heatMapCoords.get(keypoint, 0);\n const x = heatMapCoords.get(keypoint, 1);\n result[keypoint] = heatmapScores.get(y, x, keypoint);\n }\n return result;\n}\nexports.getPointsConfidence = getPointsConfidence;\n\nfunction getOffsetPoint(y, x, keypoint, offsetsBuffer) {\n return {\n y: offsetsBuffer.get(y, x, keypoint),\n x: offsetsBuffer.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n };\n}\n\nfunction getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer) {\n const result = [];\n for (let keypoint = 0; keypoint < kpt.NUM_KEYPOINTS; keypoint++) {\n const heatmapY = heatMapCoordsBuffer.get(keypoint, 0).valueOf();\n const heatmapX = heatMapCoordsBuffer.get(keypoint, 1).valueOf();\n const { x, y } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsetsBuffer);\n result.push(y);\n result.push(x);\n }\n return tf.tensor2d(result, [kpt.NUM_KEYPOINTS, 2]);\n}\nexports.getOffsetVectors = getOffsetVectors;\n\nfunction getOffsetPoints(heatMapCoordsBuffer, outputStride, offsetsBuffer) {\n return tf.tidy(() => heatMapCoordsBuffer.toTensor().mul(tf.scalar(outputStride, 'int32')).toFloat().add(getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer)));\n}\nexports.getOffsetPoints = getOffsetPoints;\n\nfunction mod(a, b) {\n return tf.tidy(() => {\n const floored = a.div(tf.scalar(b, 'int32'));\n return a.sub(floored.mul(tf.scalar(b, 'int32')));\n });\n}\n\nfunction argmax2d(inputs) {\n const [height, width, depth] = inputs.shape;\n return tf.tidy(() => {\n const reshaped = inputs.reshape([height * width, depth]);\n const coords = reshaped.argMax(0);\n const yCoords = coords.div(tf.scalar(width, 'int32')).expandDims(1);\n const xCoords = mod(coords, width).expandDims(1);\n return tf.concat([yCoords, xCoords], 1);\n });\n}\nexports.argmax2d = argmax2d;\n", "import * as keypoints from './keypoints';\nimport * as vectors from './vectors';\nimport * as decoders from './decoders';\n\nconst parentChildrenTuples = keypoints.poseChain.map(([parentJoinName, childJoinName]) => ([keypoints.partIds[parentJoinName], keypoints.partIds[childJoinName]]));\nconst parentToChildEdges = parentChildrenTuples.map(([, childJointId]) => childJointId);\nconst childToParentEdges = parentChildrenTuples.map(([parentJointId]) => parentJointId);\nfunction getDisplacement(edgeId, point, displacements) {\n const numEdges = displacements.shape[2] / 2;\n return {\n y: displacements.get(point.y, point.x, edgeId),\n x: displacements.get(point.y, point.x, numEdges + edgeId),\n };\n}\nfunction getStridedIndexNearPoint(point, outputStride, height, width) {\n return {\n // @ts-ignore\n y: vectors.clamp(Math.round(point.y / outputStride), 0, height - 1),\n // @ts-ignore\n x: vectors.clamp(Math.round(point.x / outputStride), 0, width - 1),\n };\n}\n\nfunction traverseToTargetKeypoint(edgeId, sourceKeypoint, targetKeypointId, scoresBuffer, offsets, outputStride, displacements, offsetRefineStep = 2) {\n const [height, width] = scoresBuffer.shape;\n // Nearest neighbor interpolation for the source->target displacements.\n const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, outputStride, height, width);\n const displacement = getDisplacement(edgeId, sourceKeypointIndices, displacements);\n // @ts-ignore\n const displacedPoint = vectors.addVectors(sourceKeypoint.position, displacement);\n let targetKeypoint = displacedPoint;\n for (let i = 0; i < offsetRefineStep; i++) {\n const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n // @ts-ignore\n const offsetPoint = vectors.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetKeypointId, offsets);\n // @ts-ignore\n targetKeypoint = vectors.addVectors({\n x: targetKeypointIndices.x * outputStride,\n y: targetKeypointIndices.y * outputStride,\n }, { x: offsetPoint.x, y: offsetPoint.y });\n }\n const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n const score = scoresBuffer.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetKeypointId);\n return { position: targetKeypoint, part: keypoints.partNames[targetKeypointId], score };\n}\n\nfunction decodePose(root, scores, offsets, outputStride, displacementsFwd, displacementsBwd) {\n const numParts = scores.shape[2];\n const numEdges = parentToChildEdges.length;\n const instanceKeypoints = new Array(numParts);\n // Start a new detection instance at the position of the root.\n const { part: rootPart, score: rootScore } = root;\n // @ts-ignore\n const rootPoint = vectors.getImageCoords(rootPart, outputStride, offsets);\n instanceKeypoints[rootPart.id] = {\n score: rootScore,\n part: keypoints.partNames[rootPart.id],\n position: rootPoint,\n };\n // Decode the part positions upwards in the tree, following the backward displacements.\n for (let edge = numEdges - 1; edge >= 0; --edge) {\n const sourceKeypointId = parentToChildEdges[edge];\n const targetKeypointId = childToParentEdges[edge];\n if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsBwd);\n }\n }\n // Decode the part positions downwards in the tree, following the forward displacements.\n for (let edge = 0; edge < numEdges; ++edge) {\n const sourceKeypointId = childToParentEdges[edge];\n const targetKeypointId = parentToChildEdges[edge];\n if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsFwd);\n }\n }\n return instanceKeypoints;\n}\nexports.decodePose = decodePose;\n\nasync function decodeSinglePose(heatmapScores, offsets, config) {\n let totalScore = 0.0;\n // @ts-ignore\n const heatmapValues = decoders.argmax2d(heatmapScores);\n const allTensorBuffers = await Promise.all([heatmapScores.buffer(), offsets.buffer(), heatmapValues.buffer()]);\n const scoresBuffer = allTensorBuffers[0];\n const offsetsBuffer = allTensorBuffers[1];\n const heatmapValuesBuffer = allTensorBuffers[2];\n // @ts-ignore\n const offsetPoints = decoders.getOffsetPoints(heatmapValuesBuffer, config.body.outputStride, offsetsBuffer);\n const offsetPointsBuffer = await offsetPoints.buffer();\n // @ts-ignore\n const keypointConfidence = Array.from(decoders.getPointsConfidence(scoresBuffer, heatmapValuesBuffer));\n const instanceKeypoints = keypointConfidence.map((score, i) => {\n totalScore += score;\n return {\n position: {\n y: offsetPointsBuffer.get(i, 0),\n x: offsetPointsBuffer.get(i, 1),\n },\n part: keypoints.partNames[i],\n score,\n };\n });\n const filteredKeypoints = instanceKeypoints.filter((kpt) => kpt.score > config.body.scoreThreshold);\n heatmapValues.dispose();\n offsetPoints.dispose();\n return { keypoints: filteredKeypoints, score: totalScore / instanceKeypoints.length };\n}\nexports.decodeSinglePose = decodeSinglePose;\n", "import * as buildParts from './buildParts';\nimport * as decodePose from './decodePose';\nimport * as vectors from './vectors';\n\nconst kLocalMaximumRadius = 1;\n\nfunction withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, { x, y }, keypointId) {\n return poses.some(({ keypoints }) => {\n const correspondingKeypoint = keypoints[keypointId].position;\n // @ts-ignore\n return vectors.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n });\n}\n\nfunction getInstanceScore(existingPoses, squaredNmsRadius, instanceKeypoints) {\n const notOverlappedKeypointScores = instanceKeypoints.reduce((result, { position, score }, keypointId) => {\n if (!withinNmsRadiusOfCorrespondingPoint(existingPoses, squaredNmsRadius, position, keypointId)) result += score;\n return result;\n }, 0.0);\n return notOverlappedKeypointScores / instanceKeypoints.length;\n}\n\nfunction decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config) {\n const poses = [];\n // @ts-ignore\n const queue = buildParts.buildPartWithScoreQueue(config.body.scoreThreshold, kLocalMaximumRadius, scoresBuffer);\n const squaredNmsRadius = config.body.nmsRadius ^ 2;\n // Generate at most maxDetections object instances per image in decreasing root part score order.\n while (poses.length < config.body.maxDetections && !queue.empty()) {\n // The top element in the queue is the next root candidate.\n const root = queue.dequeue();\n // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n // @ts-ignore\n const rootImageCoords = vectors.getImageCoords(root.part, config.body.outputStride, offsetsBuffer);\n if (withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, rootImageCoords, root.part.id)) continue;\n // Else start a new detection instance at the position of the root.\n // @ts-ignore\n const keypoints = decodePose.decodePose(root, scoresBuffer, offsetsBuffer, config.body.outputStride, displacementsFwdBuffer, displacementsBwdBuffer);\n const score = getInstanceScore(poses, squaredNmsRadius, keypoints);\n if (score > config.body.scoreThreshold) poses.push({ keypoints, score });\n }\n return poses;\n}\nexports.decodeMultiplePoses = decodeMultiplePoses;\n", "import * as kpt from './keypoints';\n\nfunction eitherPointDoesntMeetConfidence(a, b, minConfidence) {\n return (a < minConfidence || b < minConfidence);\n}\n\nfunction getAdjacentKeyPoints(keypoints, minConfidence) {\n return kpt.connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n return result;\n }\n result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n return result;\n }, []);\n}\nexports.getAdjacentKeyPoints = getAdjacentKeyPoints;\n\nconst { NEGATIVE_INFINITY, POSITIVE_INFINITY } = Number;\nfunction getBoundingBox(keypoints) {\n return keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n maxX: Math.max(maxX, x),\n maxY: Math.max(maxY, y),\n minX: Math.min(minX, x),\n minY: Math.min(minY, y),\n }), {\n maxX: NEGATIVE_INFINITY,\n maxY: NEGATIVE_INFINITY,\n minX: POSITIVE_INFINITY,\n minY: POSITIVE_INFINITY,\n });\n}\nexports.getBoundingBox = getBoundingBox;\n\nfunction getBoundingBoxPoints(keypoints) {\n const { minX, minY, maxX, maxY } = getBoundingBox(keypoints);\n return [{ x: minX, y: minY }, { x: maxX, y: minY }, { x: maxX, y: maxY }, { x: minX, y: maxY }];\n}\nexports.getBoundingBoxPoints = getBoundingBoxPoints;\n\nasync function toTensorBuffers3D(tensors) {\n return Promise.all(tensors.map((tensor) => tensor.buffer()));\n}\nexports.toTensorBuffers3D = toTensorBuffers3D;\n\nfunction scalePose(pose, scaleY, scaleX) {\n return {\n score: pose.score,\n keypoints: pose.keypoints.map(({ score, part, position }) => ({\n score,\n part,\n position: { x: position.x * scaleX, y: position.y * scaleY },\n })),\n };\n}\nexports.scalePose = scalePose;\n\nfunction resizeTo(image, [targetH, targetW]) {\n const input = image.squeeze(0);\n const resized = input.resizeBilinear([targetH, targetW]);\n input.dispose();\n return resized;\n}\nexports.resizeTo = resizeTo;\n\nfunction scaleAndFlipPoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) {\n const scaledPoses = poses.map((pose) => scalePose(pose, height / inputResolutionHeight, width / inputResolutionWidth));\n return scaledPoses;\n}\nexports.scaleAndFlipPoses = scaleAndFlipPoses;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as modelBase from './modelBase';\nimport * as decodeMultiple from './decodeMultiple';\nimport * as decodePose from './decodePose';\nimport * as util from './util';\n\nasync function estimateMultiple(input, res, config) {\n return new Promise(async (resolve) => {\n const height = input.shape[1];\n const width = input.shape[2];\n // @ts-ignore\n const allTensorBuffers = await util.toTensorBuffers3D([res.heatmapScores, res.offsets, res.displacementFwd, res.displacementBwd]);\n const scoresBuffer = allTensorBuffers[0];\n const offsetsBuffer = allTensorBuffers[1];\n const displacementsFwdBuffer = allTensorBuffers[2];\n const displacementsBwdBuffer = allTensorBuffers[3];\n // @ts-ignore\n const poses = await decodeMultiple.decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config);\n // @ts-ignore\n const scaled = util.scaleAndFlipPoses(poses, [height, width], [config.body.inputSize, config.body.inputSize]);\n resolve(scaled);\n });\n}\n\nasync function estimateSingle(input, res, config) {\n return new Promise(async (resolve) => {\n const height = input.shape[1];\n const width = input.shape[2];\n // @ts-ignore\n const pose = await decodePose.decodeSinglePose(res.heatmapScores, res.offsets, config);\n const poses = [pose];\n // @ts-ignore\n const scaled = util.scaleAndFlipPoses(poses, [height, width], [config.body.inputSize, config.body.inputSize]);\n resolve(scaled);\n });\n}\n\nclass PoseNet {\n constructor(model) {\n this.baseModel = model;\n }\n\n async estimatePoses(input, config) {\n // @ts-ignore\n const resized = util.resizeTo(input, [config.body.inputSize, config.body.inputSize]);\n const res = this.baseModel.predict(resized, config);\n\n const poses = (config.body.maxDetections < 2) ? await estimateSingle(input, res, config) : await estimateMultiple(input, res, config);\n\n res.heatmapScores.dispose();\n res.offsets.dispose();\n res.displacementFwd.dispose();\n res.displacementBwd.dispose();\n resized.dispose();\n\n return poses;\n }\n\n dispose() {\n this.baseModel.dispose();\n }\n}\nexports.PoseNet = PoseNet;\n\nasync function load(config) {\n const model = await tf.loadGraphModel(config.body.modelPath);\n // @ts-ignore\n const mobilenet = new modelBase.BaseModel(model);\n log(`load model: ${config.body.modelPath.match(/\\/(.*)\\./)[1]}`);\n return new PoseNet(mobilenet);\n}\nexports.load = load;\n", "import * as modelPoseNet from './modelPoseNet';\nimport * as keypoints from './keypoints';\nimport * as util from './util';\n\n// @ts-ignore\nexports.load = modelPoseNet.load;\n// @ts-ignore\nexports.PoseNet = modelPoseNet.PoseNet;\n\nexports.partChannels = keypoints.partChannels;\nexports.partIds = keypoints.partIds;\nexports.partNames = keypoints.partNames;\nexports.poseChain = keypoints.poseChain;\n// @ts-ignore\nexports.getAdjacentKeyPoints = util.getAdjacentKeyPoints;\n// @ts-ignore\nexports.getBoundingBox = util.getBoundingBox;\n// @ts-ignore\nexports.getBoundingBoxPoints = util.getBoundingBoxPoints;\n// @ts-ignore\nexports.scaleAndFlipPoses = util.scaleAndFlipPoses;\n// @ts-ignore\nexports.scalePose = util.scalePose;\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\n\nclass HandDetector {\n constructor(model, inputSize, anchorsAnnotated) {\n this.model = model;\n this.anchors = anchorsAnnotated.map((anchor) => [anchor.x_center, anchor.y_center]);\n this.anchorsTensor = tf.tensor2d(this.anchors);\n this.inputSizeTensor = tf.tensor1d([inputSize, inputSize]);\n this.doubleInputSizeTensor = tf.tensor1d([inputSize * 2, inputSize * 2]);\n }\n\n normalizeBoxes(boxes) {\n return tf.tidy(() => {\n const boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n const boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n const boxCenterPoints = tf.add(tf.div(boxOffsets, this.inputSizeTensor), this.anchorsTensor);\n const halfBoxSizes = tf.div(boxSizes, this.doubleInputSizeTensor);\n const startPoints = tf.mul(tf.sub(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n const endPoints = tf.mul(tf.add(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n return tf.concat2d([startPoints, endPoints], 1);\n });\n }\n\n normalizeLandmarks(rawPalmLandmarks, index) {\n return tf.tidy(() => {\n const landmarks = tf.add(tf.div(rawPalmLandmarks.reshape([-1, 7, 2]), this.inputSizeTensor), this.anchors[index]);\n return tf.mul(landmarks, this.inputSizeTensor);\n });\n }\n\n async getBoxes(input, config) {\n const batched = this.model.predict(input);\n const predictions = batched.squeeze();\n batched.dispose();\n const scoresT = tf.tidy(() => tf.sigmoid(tf.slice(predictions, [0, 0], [-1, 1])).squeeze());\n const scores = scoresT.dataSync();\n const rawBoxes = tf.slice(predictions, [0, 1], [-1, 4]);\n const boxes = this.normalizeBoxes(rawBoxes);\n rawBoxes.dispose();\n const filteredT = await tf.image.nonMaxSuppressionAsync(boxes, scores, config.hand.maxHands, config.hand.iouThreshold, config.hand.scoreThreshold);\n const filtered = filteredT.arraySync();\n\n scoresT.dispose();\n filteredT.dispose();\n const hands = [];\n for (const index of filtered) {\n if (scores[index] >= config.hand.minConfidence) {\n const matchingBox = tf.slice(boxes, [index, 0], [1, -1]);\n const rawPalmLandmarks = tf.slice(predictions, [index, 5], [1, 14]);\n const palmLandmarks = tf.tidy(() => this.normalizeLandmarks(rawPalmLandmarks, index).reshape([-1, 2]));\n rawPalmLandmarks.dispose();\n hands.push({ box: matchingBox, palmLandmarks, confidence: scores[index] });\n }\n }\n predictions.dispose();\n boxes.dispose();\n return hands;\n }\n\n async estimateHandBounds(input, config) {\n const inputHeight = input.shape[1];\n const inputWidth = input.shape[2];\n const image = tf.tidy(() => input.resizeBilinear([config.hand.inputSize, config.hand.inputSize]).div(127.5).sub(1));\n const predictions = await this.getBoxes(image, config);\n image.dispose();\n const hands = [];\n if (!predictions || predictions.length === 0) return hands;\n for (const prediction of predictions) {\n const boxes = prediction.box.dataSync();\n const startPoint = boxes.slice(0, 2);\n const endPoint = boxes.slice(2, 4);\n const palmLandmarks = prediction.palmLandmarks.arraySync();\n prediction.box.dispose();\n prediction.palmLandmarks.dispose();\n hands.push(box.scaleBoxCoordinates({ startPoint, endPoint, palmLandmarks, confidence: prediction.confidence }, [inputWidth / config.hand.inputSize, inputHeight / config.hand.inputSize]));\n }\n return hands;\n }\n}\nexports.HandDetector = HandDetector;\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\nimport * as util from './util';\n// eslint-disable-next-line no-unused-vars\nimport { log } from '../log.js';\n\n// const PALM_BOX_SHIFT_VECTOR = [0, -0.4];\nconst PALM_BOX_ENLARGE_FACTOR = 5; // default 3\n// const HAND_BOX_SHIFT_VECTOR = [0, -0.1]; // move detected hand box by x,y to ease landmark detection\nconst HAND_BOX_ENLARGE_FACTOR = 1.65; // default 1.65\nconst PALM_LANDMARK_IDS = [0, 5, 9, 13, 17, 1, 2];\nconst PALM_LANDMARKS_INDEX_OF_PALM_BASE = 0;\nconst PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE = 2;\n\nclass HandPipeline {\n constructor(handDetector, landmarkDetector, inputSize) {\n this.handDetector = handDetector;\n this.landmarkDetector = landmarkDetector;\n this.inputSize = inputSize;\n this.storedBoxes = [];\n this.skipped = 0;\n this.detectedHands = 0;\n }\n\n getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n // return box.enlargeBox(box.squarifyBox(box.shiftBox(boxAroundPalm, PALM_BOX_SHIFT_VECTOR)), PALM_BOX_ENLARGE_FACTOR);\n return box.enlargeBox(box.squarifyBox(boxAroundPalm), PALM_BOX_ENLARGE_FACTOR);\n }\n\n getBoxForHandLandmarks(landmarks) {\n const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n // const boxAroundHand = box.enlargeBox(box.squarifyBox(box.shiftBox(boundingBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n const boxAroundHand = box.enlargeBox(box.squarifyBox(boundingBox), HAND_BOX_ENLARGE_FACTOR);\n boxAroundHand.palmLandmarks = [];\n for (let i = 0; i < PALM_LANDMARK_IDS.length; i++) {\n boxAroundHand.palmLandmarks.push(landmarks[PALM_LANDMARK_IDS[i]].slice(0, 2));\n }\n return boxAroundHand;\n }\n\n transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n const boxSize = box.getBoxSize(box2);\n const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n const coordsScaled = rawCoords.map((coord) => [\n scaleFactor[0] * (coord[0] - this.inputSize / 2),\n scaleFactor[1] * (coord[1] - this.inputSize / 2),\n scaleFactor[2] * coord[2],\n ]);\n const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n const coordsRotated = coordsScaled.map((coord) => {\n const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n return [...rotated, coord[2]];\n });\n const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n const boxCenter = [...box.getBoxCenter(box2), 1];\n const originalBoxCenter = [\n util.dot(boxCenter, inverseRotationMatrix[0]),\n util.dot(boxCenter, inverseRotationMatrix[1]),\n ];\n return coordsRotated.map((coord) => [\n coord[0] + originalBoxCenter[0],\n coord[1] + originalBoxCenter[1],\n coord[2],\n ]);\n }\n\n async estimateHands(image, config) {\n let useFreshBox = false;\n\n // run new detector every skipFrames unless we only want box to start with\n let boxes;\n if ((this.skipped === 0) || (this.skipped > config.hand.skipFrames) || !config.hand.landmarks || !config.videoOptimized) {\n boxes = await this.handDetector.estimateHandBounds(image, config);\n this.skipped = 0;\n }\n if (config.videoOptimized) this.skipped++;\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxHands) || !config.hand.landmarks)) {\n this.detectedHands = 0;\n this.storedBoxes = [...boxes];\n // for (const possible of boxes) this.storedBoxes.push(possible);\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\n const hands = [];\n // log('hand', `skipped: ${this.skipped} max: ${config.hand.maxHands} detected: ${this.detectedHands} stored: ${this.storedBoxes.length} new: ${boxes?.length}`);\n\n // go through working set of boxes\n for (let i = 0; i < this.storedBoxes.length; i++) {\n const currentBox = this.storedBoxes[i];\n if (!currentBox) continue;\n if (config.hand.landmarks) {\n const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_PALM_BASE], currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE]) : 0;\n const palmCenter = box.getBoxCenter(currentBox);\n const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n const rotatedImage = config.hand.rotation ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n const croppedInput = box.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n const handImage = croppedInput.div(255);\n croppedInput.dispose();\n rotatedImage.dispose();\n const [confidenceT, keypoints] = await this.landmarkDetector.predict(handImage);\n handImage.dispose();\n const confidence = confidenceT.dataSync()[0];\n confidenceT.dispose();\n if (confidence >= config.hand.minConfidence) {\n const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n const rawCoords = keypointsReshaped.arraySync();\n keypoints.dispose();\n keypointsReshaped.dispose();\n const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n this.storedBoxes[i] = nextBoundingBox;\n const result = {\n landmarks: coords,\n confidence,\n box: {\n topLeft: nextBoundingBox.startPoint,\n bottomRight: nextBoundingBox.endPoint,\n },\n };\n hands.push(result);\n } else {\n this.storedBoxes[i] = null;\n }\n keypoints.dispose();\n } else {\n // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n const enlarged = box.enlargeBox(box.squarifyBox(currentBox), HAND_BOX_ENLARGE_FACTOR);\n const result = {\n confidence: currentBox.confidence,\n box: {\n topLeft: enlarged.startPoint,\n bottomRight: enlarged.endPoint,\n },\n };\n hands.push(result);\n }\n }\n this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n this.detectedHands = hands.length;\n return hands;\n }\n\n // eslint-disable-next-line class-methods-use-this\n calculateLandmarksBoundingBox(landmarks) {\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint };\n }\n}\n\nexports.HandPipeline = HandPipeline;\n", "exports.anchors = [\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.796875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.796875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.828125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.828125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.859375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.859375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.890625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.890625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.921875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.921875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.953125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.953125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.984375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.984375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.796875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.796875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.828125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.828125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.859375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.859375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.890625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.890625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.921875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.921875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.953125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.953125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.984375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.984375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.078125,\n },\n {\n w: 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0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.6875,\n },\n {\n w: 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0.9375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n];\n", "// https://storage.googleapis.com/tfjs-models/demos/handpose/index.html\n\nimport { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as handdetector from './handdetector';\nimport * as handpipeline from './handpipeline';\nimport * as anchors from './anchors';\n\nconst MESH_ANNOTATIONS = {\n thumb: [1, 2, 3, 4],\n indexFinger: [5, 6, 7, 8],\n middleFinger: [9, 10, 11, 12],\n ringFinger: [13, 14, 15, 16],\n pinky: [17, 18, 19, 20],\n palmBase: [0],\n};\n\nclass HandPose {\n constructor(handPipeline) {\n this.handPipeline = handPipeline;\n }\n\n static getAnnotations() {\n return MESH_ANNOTATIONS;\n }\n\n async estimateHands(input, config) {\n const predictions = await this.handPipeline.estimateHands(input, config);\n if (!predictions) return [];\n const hands = [];\n for (const prediction of predictions) {\n const annotations = {};\n if (prediction.landmarks) {\n for (const key of Object.keys(MESH_ANNOTATIONS)) {\n annotations[key] = MESH_ANNOTATIONS[key].map((index) => prediction.landmarks[index]);\n }\n }\n const box = prediction.box ? [\n Math.max(0, prediction.box.topLeft[0]),\n Math.max(0, prediction.box.topLeft[1]),\n Math.min(input.shape[2], prediction.box.bottomRight[0]) - prediction.box.topLeft[0],\n Math.min(input.shape[1], prediction.box.bottomRight[1]) - prediction.box.topLeft[1],\n ] : 0;\n hands.push({\n confidence: prediction.confidence,\n box,\n landmarks: prediction.landmarks,\n annotations,\n });\n }\n return hands;\n }\n}\nexports.HandPose = HandPose;\n\nasync function load(config) {\n const [handDetectorModel, handPoseModel] = await Promise.all([\n config.hand.enabled ? tf.loadGraphModel(config.hand.detector.modelPath, { fromTFHub: config.hand.detector.modelPath.includes('tfhub.dev') }) : null,\n config.hand.landmarks ? tf.loadGraphModel(config.hand.skeleton.modelPath, { fromTFHub: config.hand.skeleton.modelPath.includes('tfhub.dev') }) : null,\n ]);\n // @ts-ignore\n const handDetector = new handdetector.HandDetector(handDetectorModel, config.hand.inputSize, anchors.anchors);\n // @ts-ignore\n const handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel, config.hand.inputSize);\n const handPose = new HandPose(handPipeline);\n if (config.hand.enabled) log(`load model: ${config.hand.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n if (config.hand.landmarks) log(`load model: ${config.hand.skeleton.modelPath.match(/\\/(.*)\\./)[1]}`);\n return handPose;\n}\nexports.load = load;\n", "exports.body = (res) => {\n if (!res) return [];\n const gestures = [];\n for (let i = 0; i < res.length; i++) {\n // raising hands\n const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n if (nose && leftWrist && rightWrist && (leftWrist.position.y < nose.position.y) && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'i give up' });\n else if (nose && leftWrist && (leftWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise left hand' });\n else if (nose && rightWrist && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise right hand' });\n\n // leaning\n const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n if (leftShoulder && rightShoulder) gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position.y > rightShoulder.position.y) ? 'left' : 'right'}` });\n }\n return gestures;\n};\n\nexports.face = (res) => {\n if (!res) return [];\n const gestures = [];\n for (let i = 0; i < res.length; i++) {\n if (res[i].mesh && res[i].mesh.length > 0) {\n const eyeFacing = res[i].mesh[35][2] - res[i].mesh[263][2];\n if (Math.abs(eyeFacing) < 10) gestures.push({ face: i, gesture: 'facing camera' });\n else gestures.push({ face: i, gesture: `facing ${eyeFacing < 0 ? 'right' : 'left'}` });\n const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n const chinDepth = res[i].mesh[152][2];\n if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n }\n }\n return gestures;\n};\n\nexports.iris = (res) => {\n if (!res) return [];\n const gestures = [];\n for (let i = 0; i < res.length; i++) {\n if (!res[i].annotations || !res[i].annotations.leftEyeIris || !res[i].annotations.rightEyeIris) continue;\n const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n if (difference < 0.25) gestures.push({ iris: i, gesture: 'looking at camera' });\n }\n return gestures;\n};\n\nexports.hand = (res) => {\n if (!res) return [];\n const gestures = [];\n for (let i = 0; i < res.length; i++) {\n const fingers = [];\n for (const [finger, pos] of Object.entries(res[i]['annotations'])) {\n if (finger !== 'palmBase') fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n }\n if (fingers && fingers.length > 0) {\n const closest = fingers.reduce((best, a) => (best.position[2] < a.position[2] ? best : a));\n const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n gestures.push({ hand: i, gesture: `${closest.name} forward ${highest.name} up` });\n }\n }\n return gestures;\n};\n", "/* eslint-disable no-use-before-define */\n/*\nWebGLImageFilter - MIT Licensed\n2013, Dominic Szablewski - phoboslab.org\n\n*/\n\nconst WebGLProgram = function (gl, vertexSource, fragmentSource) {\n const _collect = function (source, prefix, collection) {\n const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n source.replace(r, (match, name) => {\n collection[name] = 0;\n return match;\n });\n };\n\n const _compile = function (source, type) {\n const shader = gl.createShader(type);\n gl.shaderSource(shader, source);\n gl.compileShader(shader);\n\n if (!gl.getShaderParameter(shader, gl.COMPILE_STATUS)) {\n // @ts-ignore\n throw new Error('Filter: GL compile failed', gl.getShaderInfoLog(shader));\n }\n return shader;\n };\n\n this.uniform = {};\n this.attribute = {};\n\n const _vsh = _compile(vertexSource, gl.VERTEX_SHADER);\n const _fsh = _compile(fragmentSource, gl.FRAGMENT_SHADER);\n\n this.id = gl.createProgram();\n gl.attachShader(this.id, _vsh);\n gl.attachShader(this.id, _fsh);\n gl.linkProgram(this.id);\n\n if (!gl.getProgramParameter(this.id, gl.LINK_STATUS)) {\n // @ts-ignore\n throw new Error('Filter: GL link failed', gl.getProgramInfoLog(this.id));\n }\n\n gl.useProgram(this.id);\n\n // Collect attributes\n _collect(vertexSource, 'attribute', this.attribute);\n for (const a in this.attribute) {\n this.attribute[a] = gl.getAttribLocation(this.id, a);\n }\n\n // Collect uniforms\n _collect(vertexSource, 'uniform', this.uniform);\n _collect(fragmentSource, 'uniform', this.uniform);\n for (const u in this.uniform) {\n this.uniform[u] = gl.getUniformLocation(this.id, u);\n }\n};\n\nconst WebGLImageFilter = function (params) {\n if (!params) params = { };\n let _drawCount = 0;\n let _sourceTexture = null;\n let _lastInChain = false;\n let _currentFramebufferIndex = -1;\n let _tempFramebuffers = [null, null];\n let _filterChain = [];\n let _width = -1;\n let _height = -1;\n let _vertexBuffer = null;\n let _currentProgram = null;\n const _canvas = params.canvas || document.createElement('canvas');\n\n // key is the shader program source, value is the compiled program\n const _shaderProgramCache = { };\n\n const gl = _canvas.getContext('webgl');\n if (!gl) throw new Error('Filter: getContext() failed');\n\n this.addFilter = function (name) {\n // eslint-disable-next-line prefer-rest-params\n const args = Array.prototype.slice.call(arguments, 1);\n const filter = _filter[name];\n\n _filterChain.push({ func: filter, args });\n };\n\n this.reset = function () {\n _filterChain = [];\n };\n\n this.apply = function (image) {\n _resize(image.width, image.height);\n _drawCount = 0;\n\n // Create the texture for the input image if we haven't yet\n if (!_sourceTexture) _sourceTexture = gl.createTexture();\n gl.bindTexture(gl.TEXTURE_2D, _sourceTexture);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n\n // No filters? Just draw\n if (_filterChain.length === 0) {\n // const program = _compileShader(SHADER.FRAGMENT_IDENTITY);\n _draw();\n return _canvas;\n }\n\n for (let i = 0; i < _filterChain.length; i++) {\n _lastInChain = (i === _filterChain.length - 1);\n const f = _filterChain[i];\n f.func.apply(this, f.args || []);\n }\n\n return _canvas;\n };\n\n const _resize = function (width, height) {\n // Same width/height? Nothing to do here\n if (width === _width && height === _height) { return; }\n\n _canvas.width = width;\n _width = width;\n _canvas.height = height;\n _height = height;\n\n // Create the context if we don't have it yet\n if (!_vertexBuffer) {\n // Create the vertex buffer for the two triangles [x, y, u, v] * 6\n const vertices = new Float32Array([\n -1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0,\n -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0,\n ]);\n // eslint-disable-next-line no-unused-expressions\n (_vertexBuffer = gl.createBuffer(), gl.bindBuffer(gl.ARRAY_BUFFER, _vertexBuffer));\n gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);\n\n // Note sure if this is a good idea; at least it makes texture loading\n // in Ejecta instant.\n gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n }\n\n gl.viewport(0, 0, _width, _height);\n\n // Delete old temp framebuffers\n _tempFramebuffers = [null, null];\n };\n\n const _getTempFramebuffer = function (index) {\n // @ts-ignore\n _tempFramebuffers[index] = _tempFramebuffers[index] || _createFramebufferTexture(_width, _height);\n return _tempFramebuffers[index];\n };\n\n const _createFramebufferTexture = function (width, height) {\n const fbo = gl.createFramebuffer();\n gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n\n const renderbuffer = gl.createRenderbuffer();\n gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n\n const texture = gl.createTexture();\n gl.bindTexture(gl.TEXTURE_2D, texture);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n\n gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n\n gl.bindTexture(gl.TEXTURE_2D, null);\n gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n\n return { fbo, texture };\n };\n\n const _draw = function (flags) {\n let source = null;\n let target = null;\n let flipY = false;\n\n // Set up the source\n if (_drawCount === 0) {\n // First draw call - use the source texture\n source = _sourceTexture;\n } else {\n // All following draw calls use the temp buffer last drawn to\n // @ts-ignore\n source = _getTempFramebuffer(_currentFramebufferIndex)?.texture;\n }\n _drawCount++;\n\n // Set up the target\n if (_lastInChain && !(flags & DRAW.INTERMEDIATE)) {\n // Last filter in our chain - draw directly to the WebGL Canvas. We may\n // also have to flip the image vertically now\n target = null;\n flipY = _drawCount % 2 === 0;\n } else {\n // Intermediate draw call - get a temp buffer to draw to\n _currentFramebufferIndex = (_currentFramebufferIndex + 1) % 2;\n // @ts-ignore\n target = _getTempFramebuffer(_currentFramebufferIndex)?.fbo;\n }\n\n // Bind the source and target and draw the two triangles\n gl.bindTexture(gl.TEXTURE_2D, source);\n gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n\n gl.uniform1f(_currentProgram.uniform.flipY, (flipY ? -1 : 1));\n gl.drawArrays(gl.TRIANGLES, 0, 6);\n };\n\n const _compileShader = function (fragmentSource) {\n if (_shaderProgramCache[fragmentSource]) {\n _currentProgram = _shaderProgramCache[fragmentSource];\n gl.useProgram(_currentProgram.id);\n return _currentProgram;\n }\n\n // Compile shaders\n _currentProgram = new WebGLProgram(gl, SHADER.VERTEX_IDENTITY, fragmentSource);\n\n const floatSize = Float32Array.BYTES_PER_ELEMENT;\n const vertSize = 4 * floatSize;\n gl.enableVertexAttribArray(_currentProgram.attribute.pos);\n gl.vertexAttribPointer(_currentProgram.attribute.pos, 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n gl.enableVertexAttribArray(_currentProgram.attribute.uv);\n gl.vertexAttribPointer(_currentProgram.attribute.uv, 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n\n _shaderProgramCache[fragmentSource] = _currentProgram;\n return _currentProgram;\n };\n\n let DRAW = { INTERMEDIATE: 1 };\n\n let SHADER = {};\n SHADER.VERTEX_IDENTITY = [\n 'precision highp float;',\n 'attribute vec2 pos;',\n 'attribute vec2 uv;',\n 'varying vec2 vUv;',\n 'uniform float flipY;',\n\n 'void main(void) {',\n 'vUv = uv;',\n 'gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);',\n '}',\n ].join('\\n');\n\n SHADER.FRAGMENT_IDENTITY = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n\n 'void main(void) {',\n 'gl_FragColor = texture2D(texture, vUv);',\n '}',\n ].join('\\n');\n\n let _filter = {};\n\n // -------------------------------------------------------------------------\n // Color Matrix Filter\n\n _filter.colorMatrix = function (matrix) {\n // Create a Float32 Array and normalize the offset component to 0-1\n const m = new Float32Array(matrix);\n m[4] /= 255;\n m[9] /= 255;\n m[14] /= 255;\n m[19] /= 255;\n\n // Can we ignore the alpha value? Makes things a bit faster.\n const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0)\n ? _filter.colorMatrix.SHADER.WITHOUT_ALPHA\n : _filter.colorMatrix.SHADER.WITH_ALPHA;\n\n const program = _compileShader(shader);\n gl.uniform1fv(program.uniform.m, m);\n _draw();\n };\n\n _filter.colorMatrix.SHADER = {};\n _filter.colorMatrix.SHADER.WITH_ALPHA = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform float m[20];',\n\n 'void main(void) {',\n 'vec4 c = texture2D(texture, vUv);',\n 'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];',\n 'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];',\n 'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];',\n 'gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];',\n '}',\n ].join('\\n');\n _filter.colorMatrix.SHADER.WITHOUT_ALPHA = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform float m[20];',\n\n 'void main(void) {',\n 'vec4 c = texture2D(texture, vUv);',\n 'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];',\n 'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];',\n 'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];',\n 'gl_FragColor.a = c.a;',\n '}',\n ].join('\\n');\n\n _filter.brightness = function (brightness) {\n const b = (brightness || 0) + 1;\n _filter.colorMatrix([\n b, 0, 0, 0, 0,\n 0, b, 0, 0, 0,\n 0, 0, b, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.saturation = function (amount) {\n const x = (amount || 0) * 2 / 3 + 1;\n const y = ((x - 1) * -0.5);\n _filter.colorMatrix([\n x, y, y, 0, 0,\n y, x, y, 0, 0,\n y, y, x, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.desaturate = function () {\n _filter.saturation(-1);\n };\n\n _filter.contrast = function (amount) {\n const v = (amount || 0) + 1;\n const o = -128 * (v - 1);\n\n _filter.colorMatrix([\n v, 0, 0, 0, o,\n 0, v, 0, 0, o,\n 0, 0, v, 0, o,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.negative = function () {\n _filter.contrast(-2);\n };\n\n _filter.hue = function (rotation) {\n rotation = (rotation || 0) / 180 * Math.PI;\n const cos = Math.cos(rotation);\n const sin = Math.sin(rotation);\n const lumR = 0.213;\n const lumG = 0.715;\n const lumB = 0.072;\n\n _filter.colorMatrix([\n lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.desaturateLuminance = function () {\n _filter.colorMatrix([\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.sepia = function () {\n _filter.colorMatrix([\n 0.393, 0.7689999, 0.18899999, 0, 0,\n 0.349, 0.6859999, 0.16799999, 0, 0,\n 0.272, 0.5339999, 0.13099999, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.brownie = function () {\n _filter.colorMatrix([\n 0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n 0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.vintagePinhole = function () {\n _filter.colorMatrix([\n 0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n 0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n 0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.kodachrome = function () {\n _filter.colorMatrix([\n 1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.technicolor = function () {\n _filter.colorMatrix([\n 1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.polaroid = function () {\n _filter.colorMatrix([\n 1.438, -0.062, -0.062, 0, 0,\n -0.122, 1.378, -0.122, 0, 0,\n -0.016, -0.016, 1.483, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.shiftToBGR = function () {\n _filter.colorMatrix([\n 0, 0, 1, 0, 0,\n 0, 1, 0, 0, 0,\n 1, 0, 0, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n // -------------------------------------------------------------------------\n // Convolution Filter\n\n _filter.convolution = function (matrix) {\n const m = new Float32Array(matrix);\n const pixelSizeX = 1 / _width;\n const pixelSizeY = 1 / _height;\n\n const program = _compileShader(_filter.convolution.SHADER);\n gl.uniform1fv(program.uniform.m, m);\n gl.uniform2f(program.uniform.px, pixelSizeX, pixelSizeY);\n _draw();\n };\n\n _filter.convolution.SHADER = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform vec2 px;',\n 'uniform float m[9];',\n\n 'void main(void) {',\n 'vec4 c11 = texture2D(texture, vUv - px);', // top left\n 'vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y));', // top center\n 'vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y));', // top right\n\n 'vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) );', // mid left\n 'vec4 c22 = texture2D(texture, vUv);', // mid center\n 'vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) );', // mid right\n\n 'vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) );', // bottom left\n 'vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) );', // bottom center\n 'vec4 c33 = texture2D(texture, vUv + px );', // bottom right\n\n 'gl_FragColor = ',\n 'c11 * m[0] + c12 * m[1] + c22 * m[2] +',\n 'c21 * m[3] + c22 * m[4] + c23 * m[5] +',\n 'c31 * m[6] + c32 * m[7] + c33 * m[8];',\n 'gl_FragColor.a = c22.a;',\n '}',\n ].join('\\n');\n\n _filter.detectEdges = function () {\n _filter.convolution.call(this, [\n 0, 1, 0,\n 1, -4, 1,\n 0, 1, 0,\n ]);\n };\n\n _filter.sobelX = function () {\n _filter.convolution.call(this, [\n -1, 0, 1,\n -2, 0, 2,\n -1, 0, 1,\n ]);\n };\n\n _filter.sobelY = function () {\n _filter.convolution.call(this, [\n -1, -2, -1,\n 0, 0, 0,\n 1, 2, 1,\n ]);\n };\n\n _filter.sharpen = function (amount) {\n const a = amount || 1;\n _filter.convolution.call(this, [\n 0, -1 * a, 0,\n -1 * a, 1 + 4 * a, -1 * a,\n 0, -1 * a, 0,\n ]);\n };\n\n _filter.emboss = function (size) {\n const s = size || 1;\n _filter.convolution.call(this, [\n -2 * s, -1 * s, 0,\n -1 * s, 1, 1 * s,\n 0, 1 * s, 2 * s,\n ]);\n };\n\n // -------------------------------------------------------------------------\n // Blur Filter\n\n _filter.blur = function (size) {\n const blurSizeX = (size / 7) / _width;\n const blurSizeY = (size / 7) / _height;\n\n const program = _compileShader(_filter.blur.SHADER);\n\n // Vertical\n gl.uniform2f(program.uniform.px, 0, blurSizeY);\n _draw(DRAW.INTERMEDIATE);\n\n // Horizontal\n gl.uniform2f(program.uniform.px, blurSizeX, 0);\n _draw();\n };\n\n _filter.blur.SHADER = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform vec2 px;',\n\n 'void main(void) {',\n 'gl_FragColor = vec4(0.0);',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;',\n 'gl_FragColor += texture2D(texture, vUv )*0.159576912161;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x, 1.0*px.y))*0.147308056121;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x, 2.0*px.y))*0.115876621105;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x, 3.0*px.y))*0.0776744219933;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x, 4.0*px.y))*0.0443683338718;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x, 5.0*px.y))*0.0215963866053;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265;',\n '}',\n ].join('\\n');\n\n // -------------------------------------------------------------------------\n // Pixelate Filter\n\n _filter.pixelate = function (size) {\n const blurSizeX = (size) / _width;\n const blurSizeY = (size) / _height;\n\n const program = _compileShader(_filter.pixelate.SHADER);\n\n // Horizontal\n gl.uniform2f(program.uniform.size, blurSizeX, blurSizeY);\n _draw();\n };\n\n _filter.pixelate.SHADER = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform vec2 size;',\n 'uniform sampler2D texture;',\n\n 'vec2 pixelate(vec2 coord, vec2 size) {',\n 'return floor( coord / size ) * size;',\n '}',\n\n 'void main(void) {',\n 'gl_FragColor = vec4(0.0);',\n 'vec2 coord = pixelate(vUv, size);',\n 'gl_FragColor += texture2D(texture, coord);',\n '}',\n ].join('\\n');\n};\n\nexports.Canvas = WebGLImageFilter;\n", "import { log } from './log.js';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as fxImage from './imagefx.js';\n\n// internal temp canvases\nlet inCanvas = null;\nlet outCanvas = null;\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nfunction process(input, config) {\n let tensor;\n if (input instanceof tf.Tensor) {\n tensor = tf.clone(input);\n } else {\n const originalWidth = input.naturalWidth || input.videoWidth || input.width || (input.shape && (input.shape[1] > 0));\n const originalHeight = input.naturalHeight || input.videoHeight || input.height || (input.shape && (input.shape[2] > 0));\n let targetWidth = originalWidth;\n let targetHeight = originalHeight;\n if (config.filter.width > 0) targetWidth = config.filter.width;\n else if (config.filter.height > 0) targetWidth = originalWidth * (config.filter.height / originalHeight);\n if (config.filter.height > 0) targetHeight = config.filter.height;\n else if (config.filter.width > 0) targetHeight = originalHeight * (config.filter.width / originalWidth);\n if (!targetWidth || !targetHeight) {\n log('Human: invalid input', input);\n return null;\n }\n if (!inCanvas || (inCanvas.width !== targetWidth) || (inCanvas.height !== targetHeight)) {\n inCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n if (inCanvas.width !== targetWidth) inCanvas.width = targetWidth;\n if (inCanvas.height !== targetHeight) inCanvas.height = targetHeight;\n }\n const ctx = inCanvas.getContext('2d');\n if (input instanceof ImageData) ctx.putImageData(input, 0, 0);\n else ctx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);\n if (config.filter.enabled) {\n if (!this.fx || !outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas.height !== outCanvas.height)) {\n outCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(inCanvas.width, inCanvas.height) : document.createElement('canvas');\n if (outCanvas.width !== inCanvas.width) outCanvas.width = inCanvas.width;\n if (outCanvas.height !== inCanvas.height) outCanvas.height = inCanvas.height;\n this.fx = tf.ENV.flags.IS_BROWSER ? new fxImage.Canvas({ canvas: outCanvas }) : null; // && (typeof document !== 'undefined')\n }\n if (!this.fx) return inCanvas;\n this.fx.reset();\n this.fx.addFilter('brightness', config.filter.brightness); // must have at least one filter enabled\n if (config.filter.contrast !== 0) this.fx.addFilter('contrast', config.filter.contrast);\n if (config.filter.sharpness !== 0) this.fx.addFilter('sharpen', config.filter.sharpness);\n if (config.filter.blur !== 0) this.fx.addFilter('blur', config.filter.blur);\n if (config.filter.saturation !== 0) this.fx.addFilter('saturation', config.filter.saturation);\n if (config.filter.hue !== 0) this.fx.addFilter('hue', config.filter.hue);\n if (config.filter.negative) this.fx.addFilter('negative');\n if (config.filter.sepia) this.fx.addFilter('sepia');\n if (config.filter.vintage) this.fx.addFilter('brownie');\n if (config.filter.sepia) this.fx.addFilter('sepia');\n if (config.filter.kodachrome) this.fx.addFilter('kodachrome');\n if (config.filter.technicolor) this.fx.addFilter('technicolor');\n if (config.filter.polaroid) this.fx.addFilter('polaroid');\n if (config.filter.pixelate !== 0) this.fx.addFilter('pixelate', config.filter.pixelate);\n this.fx.apply(inCanvas);\n // read pixel data\n /*\n const gl = outCanvas.getContext('webgl');\n if (gl) {\n const glBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 4);\n const pixBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 3);\n gl.readPixels(0, 0, outCanvas.width, outCanvas.height, gl.RGBA, gl.UNSIGNED_BYTE, glBuffer);\n // gl returns rbga while we only need rgb, so discarding alpha channel\n // gl returns starting point as lower left, so need to invert vertical\n let i = 0;\n for (let y = outCanvas.height - 1; y >= 0; y--) {\n for (let x = 0; x < outCanvas.width; x++) {\n const index = (x + y * outCanvas.width) * 4;\n pixBuffer[i++] = glBuffer[index + 0];\n pixBuffer[i++] = glBuffer[index + 1];\n pixBuffer[i++] = glBuffer[index + 2];\n }\n }\n outCanvas.data = pixBuffer;\n }\n */\n } else {\n outCanvas = inCanvas;\n }\n let pixels;\n if (outCanvas.data) {\n const shape = [outCanvas.height, outCanvas.width, 3];\n pixels = tf.tensor3d(outCanvas.data, shape, 'int32');\n } else if ((config.backend === 'webgl') || (outCanvas instanceof ImageData)) {\n // tf kernel-optimized method to get imagedata, also if input is imagedata, just use it\n pixels = tf.browser.fromPixels(outCanvas);\n } else {\n // cpu and wasm kernel does not implement efficient fromPixels method nor we can use canvas as-is, so we do a silly one more canvas\n const tempCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n tempCanvas.width = targetWidth;\n tempCanvas.height = targetHeight;\n const tempCtx = tempCanvas.getContext('2d');\n tempCtx?.drawImage(outCanvas, 0, 0);\n const data = tempCtx?.getImageData(0, 0, targetWidth, targetHeight);\n pixels = tf.browser.fromPixels(data);\n }\n const casted = pixels.toFloat();\n tensor = casted.expandDims(0);\n pixels.dispose();\n casted.dispose();\n }\n return { tensor, canvas: config.filter.return ? outCanvas : null };\n}\n\nexports.process = process;\n", "// helper function: wrapper around console output\nexport function log(...msg) {\n const dt = new Date();\n const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n // eslint-disable-next-line no-console\n if (msg) console.log(ts, 'Human:', ...msg);\n}\n", "", "// A port of an algorithm by Johannes Baag\u00F8e , 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e \n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n// \n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n// \n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n var me = this, mash = Mash();\n\n me.next = function() {\n var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n me.s0 = me.s1;\n me.s1 = me.s2;\n return me.s2 = t - (me.c = t | 0);\n };\n\n // Apply the seeding algorithm from Baagoe.\n me.c = 1;\n me.s0 = mash(' ');\n me.s1 = mash(' ');\n me.s2 = mash(' ');\n me.s0 -= mash(seed);\n if (me.s0 < 0) { me.s0 += 1; }\n me.s1 -= mash(seed);\n if (me.s1 < 0) { me.s1 += 1; }\n me.s2 -= mash(seed);\n if (me.s2 < 0) { me.s2 += 1; }\n mash = null;\n}\n\nfunction copy(f, t) {\n t.c = f.c;\n t.s0 = f.s0;\n t.s1 = f.s1;\n t.s2 = f.s2;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new Alea(seed),\n state = opts && opts.state,\n prng = xg.next;\n prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n prng.double = function() {\n return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n };\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nfunction Mash() {\n var n = 0xefc8249d;\n\n var mash = function(data) {\n data = data.toString();\n for (var i = 0; i < data.length; i++) {\n n += data.charCodeAt(i);\n var h = 0.02519603282416938 * n;\n n = h >>> 0;\n h -= n;\n h *= n;\n n = h >>> 0;\n h -= n;\n n += h * 0x100000000; // 2^32\n }\n return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n };\n\n return mash;\n}\n\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.alea = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n\n // Set up generator function.\n me.next = function() {\n var t = me.x ^ (me.x << 11);\n me.x = me.y;\n me.y = me.z;\n me.z = me.w;\n return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n };\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor128 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var t = (me.x ^ (me.x >>> 2));\n me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n return (me.d = (me.d + 362437 | 0)) +\n (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n };\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n me.v = 0;\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n if (k == strseed.length) {\n me.d = me.x << 10 ^ me.x >>> 4;\n }\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n t.v = f.v;\n t.d = f.d;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorwow = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n // Update xor generator.\n var X = me.x, i = me.i, t, v, w;\n t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n X[i] = v;\n me.i = (i + 1) & 7;\n return v;\n };\n\n function init(me, seed) {\n var j, w, X = [];\n\n if (seed === (seed | 0)) {\n // Seed state array using a 32-bit integer.\n w = X[0] = seed;\n } else {\n // Seed state using a string.\n seed = '' + seed;\n for (j = 0; j < seed.length; ++j) {\n X[j & 7] = (X[j & 7] << 15) ^\n (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n }\n }\n // Enforce an array length of 8, not all zeroes.\n while (X.length < 8) X.push(0);\n for (j = 0; j < 8 && X[j] === 0; ++j);\n if (j == 8) w = X[7] = -1; else w = X[j];\n\n me.x = X;\n me.i = 0;\n\n // Discard an initial 256 values.\n for (j = 256; j > 0; --j) {\n me.next();\n }\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.x = f.x.slice();\n t.i = f.i;\n return t;\n}\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.x) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorshift7 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated. For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1); // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897); // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C. This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n var w = me.w,\n X = me.X, i = me.i, t, v;\n // Update Weyl generator.\n me.w = w = (w + 0x61c88647) | 0;\n // Update xor generator.\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n // Update Xor generator array state.\n v = X[i] = v ^ t;\n me.i = i;\n // Result is the combination.\n return (v + (w ^ (w >>> 16))) | 0;\n };\n\n function init(me, seed) {\n var t, v, i, j, w, X = [], limit = 128;\n if (seed === (seed | 0)) {\n // Numeric seeds initialize v, which is used to generates X.\n v = seed;\n seed = null;\n } else {\n // String seeds are mixed into v and X one character at a time.\n seed = seed + '\\0';\n v = 0;\n limit = Math.max(limit, seed.length);\n }\n // Initialize circular array and weyl value.\n for (i = 0, j = -32; j < limit; ++j) {\n // Put the unicode characters into the array, and shuffle them.\n if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n // After 32 shuffles, take v as the starting w value.\n if (j === 0) w = v;\n v ^= v << 10;\n v ^= v >>> 15;\n v ^= v << 4;\n v ^= v >>> 13;\n if (j >= 0) {\n w = (w + 0x61c88647) | 0; // Weyl.\n t = (X[j & 127] ^= (v + w)); // Combine xor and weyl to init array.\n i = (0 == t) ? i + 1 : 0; // Count zeroes.\n }\n }\n // We have detected all zeroes; make the key nonzero.\n if (i >= 128) {\n X[(seed && seed.length || 0) & 127] = -1;\n }\n // Run the generator 512 times to further mix the state before using it.\n // Factoring this as a function slows the main generator, so it is just\n // unrolled here. The weyl generator is not advanced while warming up.\n i = 127;\n for (j = 4 * 128; j > 0; --j) {\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n X[i] = v ^ t;\n }\n // Storing state as object members is faster than using closure variables.\n me.w = w;\n me.X = X;\n me.i = i;\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.i = f.i;\n t.w = f.w;\n t.X = f.X.slice();\n return t;\n};\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.X) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor4096 = impl;\n}\n\n})(\n this, // window object or global\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var b = me.b, c = me.c, d = me.d, a = me.a;\n b = (b << 25) ^ (b >>> 7) ^ c;\n c = (c - d) | 0;\n d = (d << 24) ^ (d >>> 8) ^ a;\n a = (a - b) | 0;\n me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n me.c = c = (c - d) | 0;\n me.d = (d << 16) ^ (c >>> 16) ^ a;\n return me.a = (a - b) | 0;\n };\n\n /* The following is non-inverted tyche, which has better internal\n * bit diffusion, but which is about 25% slower than tyche-i in JS.\n me.next = function() {\n var a = me.a, b = me.b, c = me.c, d = me.d;\n a = (me.a + me.b | 0) >>> 0;\n d = me.d ^ a; d = d << 16 ^ d >>> 16;\n c = me.c + d | 0;\n b = me.b ^ c; b = b << 12 ^ d >>> 20;\n me.a = a = a + b | 0;\n d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n me.c = c = c + d | 0;\n b = b ^ c;\n return me.b = (b << 7 ^ b >>> 25);\n }\n */\n\n me.a = 0;\n me.b = 0;\n me.c = 2654435769 | 0;\n me.d = 1367130551;\n\n if (seed === Math.floor(seed)) {\n // Integer seed.\n me.a = (seed / 0x100000000) | 0;\n me.b = seed | 0;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 20; k++) {\n me.b ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.a = f.a;\n t.b = f.b;\n t.c = f.c;\n t.d = f.d;\n return t;\n};\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.tychei = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "", "/*\nCopyright 2014 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\nvar global = this,\n width = 256, // each RC4 output is 0 <= x < 256\n chunks = 6, // at least six RC4 outputs for each double\n digits = 52, // there are 52 significant digits in a double\n rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n startdenom = math.pow(width, chunks),\n significance = math.pow(2, digits),\n overflow = significance * 2,\n mask = width - 1,\n nodecrypto; // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n var key = [];\n options = (options == true) ? { entropy: true } : (options || {});\n\n // Flatten the seed string or build one from local entropy if needed.\n var shortseed = mixkey(flatten(\n options.entropy ? [seed, tostring(pool)] :\n (seed == null) ? autoseed() : seed, 3), key);\n\n // Use the seed to initialize an ARC4 generator.\n var arc4 = new ARC4(key);\n\n // This function returns a random double in [0, 1) that contains\n // randomness in every bit of the mantissa of the IEEE 754 value.\n var prng = function() {\n var n = arc4.g(chunks), // Start with a numerator n < 2 ^ 48\n d = startdenom, // and denominator d = 2 ^ 48.\n x = 0; // and no 'extra last byte'.\n while (n < significance) { // Fill up all significant digits by\n n = (n + x) * width; // shifting numerator and\n d *= width; // denominator and generating a\n x = arc4.g(1); // new least-significant-byte.\n }\n while (n >= overflow) { // To avoid rounding up, before adding\n n /= 2; // last byte, shift everything\n d /= 2; // right using integer math until\n x >>>= 1; // we have exactly the desired bits.\n }\n return (n + x) / d; // Form the number within [0, 1).\n };\n\n prng.int32 = function() { return arc4.g(4) | 0; }\n prng.quick = function() { return arc4.g(4) / 0x100000000; }\n prng.double = prng;\n\n // Mix the randomness into accumulated entropy.\n mixkey(tostring(arc4.S), pool);\n\n // Calling convention: what to return as a function of prng, seed, is_math.\n return (options.pass || callback ||\n function(prng, seed, is_math_call, state) {\n if (state) {\n // Load the arc4 state from the given state if it has an S array.\n if (state.S) { copy(state, arc4); }\n // Only provide the .state method if requested via options.state.\n prng.state = function() { return copy(arc4, {}); }\n }\n\n // If called as a method of Math (Math.seedrandom()), mutate\n // Math.random because that is how seedrandom.js has worked since v1.0.\n if (is_math_call) { math[rngname] = prng; return seed; }\n\n // Otherwise, it is a newer calling convention, so return the\n // prng directly.\n else return prng;\n })(\n prng,\n shortseed,\n 'global' in options ? options.global : (this == math),\n options.state);\n}\nmath['seed' + rngname] = seedrandom;\n\n//\n// ARC4\n//\n// An ARC4 implementation. The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4. Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n var t, keylen = key.length,\n me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n // The empty key [] is treated as [0].\n if (!keylen) { key = [keylen++]; }\n\n // Set up S using the standard key scheduling algorithm.\n while (i < width) {\n s[i] = i++;\n }\n for (i = 0; i < width; i++) {\n s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n s[j] = t;\n }\n\n // The \"g\" method returns the next (count) outputs as one number.\n (me.g = function(count) {\n // Using instance members instead of closure state nearly doubles speed.\n var t, r = 0,\n i = me.i, j = me.j, s = me.S;\n while (count--) {\n t = s[i = mask & (i + 1)];\n r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n }\n me.i = i; me.j = j;\n return r;\n // For robust unpredictability, the function call below automatically\n // discards an initial batch of values. This is called RC4-drop[256].\n // See http://google.com/search?q=rsa+fluhrer+response&btnI\n })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n t.i = f.i;\n t.j = f.j;\n t.S = f.S.slice();\n return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n var result = [], typ = (typeof obj), prop;\n if (depth && typ == 'object') {\n for (prop in obj) {\n try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n }\n }\n return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n var stringseed = seed + '', smear, j = 0;\n while (j < stringseed.length) {\n key[mask & j] =\n mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n }\n return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n try {\n var out;\n if (nodecrypto && (out = nodecrypto.randomBytes)) {\n // The use of 'out' to remember randomBytes makes tight minified code.\n out = out(width);\n } else {\n out = new Uint8Array(width);\n (global.crypto || global.msCrypto).getRandomValues(out);\n }\n return tostring(out);\n } catch (e) {\n var browser = global.navigator,\n plugins = browser && browser.plugins;\n return [+new Date, global, plugins, global.screen, tostring(pool)];\n }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool. Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n module.exports = seedrandom;\n // When in node.js, try using crypto package for autoseeding.\n try {\n nodecrypto = require('crypto');\n } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n define(function() { return seedrandom; });\n}\n\n// End anonymous scope, and pass initial values.\n})(\n [], // pool: entropy pool starts empty\n Math // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random(); // 0 <= x < 1. Every bit is random.\n// var x = random.quick(); // 0 <= x < 1. 32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three. It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures. Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "// A port of an algorithm by Johannes Baag\u00F8e , 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e \n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n// \n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n// \n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n var me = this, mash = Mash();\n\n me.next = function() {\n var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n me.s0 = me.s1;\n me.s1 = me.s2;\n return me.s2 = t - (me.c = t | 0);\n };\n\n // Apply the seeding algorithm from Baagoe.\n me.c = 1;\n me.s0 = mash(' ');\n me.s1 = mash(' ');\n me.s2 = mash(' ');\n me.s0 -= mash(seed);\n if (me.s0 < 0) { me.s0 += 1; }\n me.s1 -= mash(seed);\n if (me.s1 < 0) { me.s1 += 1; }\n me.s2 -= mash(seed);\n if (me.s2 < 0) { me.s2 += 1; }\n mash = null;\n}\n\nfunction copy(f, t) {\n t.c = f.c;\n t.s0 = f.s0;\n t.s1 = f.s1;\n t.s2 = f.s2;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new Alea(seed),\n state = opts && opts.state,\n prng = xg.next;\n prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n prng.double = function() {\n return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n };\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nfunction Mash() {\n var n = 0xefc8249d;\n\n var mash = function(data) {\n data = data.toString();\n for (var i = 0; i < data.length; i++) {\n n += data.charCodeAt(i);\n var h = 0.02519603282416938 * n;\n n = h >>> 0;\n h -= n;\n h *= n;\n n = h >>> 0;\n h -= n;\n n += h * 0x100000000; // 2^32\n }\n return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n };\n\n return mash;\n}\n\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.alea = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n\n // Set up generator function.\n me.next = function() {\n var t = me.x ^ (me.x << 11);\n me.x = me.y;\n me.y = me.z;\n me.z = me.w;\n return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n };\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor128 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var t = (me.x ^ (me.x >>> 2));\n me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n return (me.d = (me.d + 362437 | 0)) +\n (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n };\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n me.v = 0;\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n if (k == strseed.length) {\n me.d = me.x << 10 ^ me.x >>> 4;\n }\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n t.v = f.v;\n t.d = f.d;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorwow = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n // Update xor generator.\n var X = me.x, i = me.i, t, v, w;\n t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n X[i] = v;\n me.i = (i + 1) & 7;\n return v;\n };\n\n function init(me, seed) {\n var j, w, X = [];\n\n if (seed === (seed | 0)) {\n // Seed state array using a 32-bit integer.\n w = X[0] = seed;\n } else {\n // Seed state using a string.\n seed = '' + seed;\n for (j = 0; j < seed.length; ++j) {\n X[j & 7] = (X[j & 7] << 15) ^\n (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n }\n }\n // Enforce an array length of 8, not all zeroes.\n while (X.length < 8) X.push(0);\n for (j = 0; j < 8 && X[j] === 0; ++j);\n if (j == 8) w = X[7] = -1; else w = X[j];\n\n me.x = X;\n me.i = 0;\n\n // Discard an initial 256 values.\n for (j = 256; j > 0; --j) {\n me.next();\n }\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.x = f.x.slice();\n t.i = f.i;\n return t;\n}\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.x) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorshift7 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated. For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1); // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897); // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C. This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n var w = me.w,\n X = me.X, i = me.i, t, v;\n // Update Weyl generator.\n me.w = w = (w + 0x61c88647) | 0;\n // Update xor generator.\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n // Update Xor generator array state.\n v = X[i] = v ^ t;\n me.i = i;\n // Result is the combination.\n return (v + (w ^ (w >>> 16))) | 0;\n };\n\n function init(me, seed) {\n var t, v, i, j, w, X = [], limit = 128;\n if (seed === (seed | 0)) {\n // Numeric seeds initialize v, which is used to generates X.\n v = seed;\n seed = null;\n } else {\n // String seeds are mixed into v and X one character at a time.\n seed = seed + '\\0';\n v = 0;\n limit = Math.max(limit, seed.length);\n }\n // Initialize circular array and weyl value.\n for (i = 0, j = -32; j < limit; ++j) {\n // Put the unicode characters into the array, and shuffle them.\n if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n // After 32 shuffles, take v as the starting w value.\n if (j === 0) w = v;\n v ^= v << 10;\n v ^= v >>> 15;\n v ^= v << 4;\n v ^= v >>> 13;\n if (j >= 0) {\n w = (w + 0x61c88647) | 0; // Weyl.\n t = (X[j & 127] ^= (v + w)); // Combine xor and weyl to init array.\n i = (0 == t) ? i + 1 : 0; // Count zeroes.\n }\n }\n // We have detected all zeroes; make the key nonzero.\n if (i >= 128) {\n X[(seed && seed.length || 0) & 127] = -1;\n }\n // Run the generator 512 times to further mix the state before using it.\n // Factoring this as a function slows the main generator, so it is just\n // unrolled here. The weyl generator is not advanced while warming up.\n i = 127;\n for (j = 4 * 128; j > 0; --j) {\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n X[i] = v ^ t;\n }\n // Storing state as object members is faster than using closure variables.\n me.w = w;\n me.X = X;\n me.i = i;\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.i = f.i;\n t.w = f.w;\n t.X = f.X.slice();\n return t;\n};\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.X) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor4096 = impl;\n}\n\n})(\n this, // window object or global\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var b = me.b, c = me.c, d = me.d, a = me.a;\n b = (b << 25) ^ (b >>> 7) ^ c;\n c = (c - d) | 0;\n d = (d << 24) ^ (d >>> 8) ^ a;\n a = (a - b) | 0;\n me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n me.c = c = (c - d) | 0;\n me.d = (d << 16) ^ (c >>> 16) ^ a;\n return me.a = (a - b) | 0;\n };\n\n /* The following is non-inverted tyche, which has better internal\n * bit diffusion, but which is about 25% slower than tyche-i in JS.\n me.next = function() {\n var a = me.a, b = me.b, c = me.c, d = me.d;\n a = (me.a + me.b | 0) >>> 0;\n d = me.d ^ a; d = d << 16 ^ d >>> 16;\n c = me.c + d | 0;\n b = me.b ^ c; b = b << 12 ^ d >>> 20;\n me.a = a = a + b | 0;\n d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n me.c = c = c + d | 0;\n b = b ^ c;\n return me.b = (b << 7 ^ b >>> 25);\n }\n */\n\n me.a = 0;\n me.b = 0;\n me.c = 2654435769 | 0;\n me.d = 1367130551;\n\n if (seed === Math.floor(seed)) {\n // Integer seed.\n me.a = (seed / 0x100000000) | 0;\n me.b = seed | 0;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 20; k++) {\n me.b ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.a = f.a;\n t.b = f.b;\n t.c = f.c;\n t.d = f.d;\n return t;\n};\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.tychei = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "/*\nCopyright 2014 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\nvar global = this,\n width = 256, // each RC4 output is 0 <= x < 256\n chunks = 6, // at least six RC4 outputs for each double\n digits = 52, // there are 52 significant digits in a double\n rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n startdenom = math.pow(width, chunks),\n significance = math.pow(2, digits),\n overflow = significance * 2,\n mask = width - 1,\n nodecrypto; // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n var key = [];\n options = (options == true) ? { entropy: true } : (options || {});\n\n // Flatten the seed string or build one from local entropy if needed.\n var shortseed = mixkey(flatten(\n options.entropy ? [seed, tostring(pool)] :\n (seed == null) ? autoseed() : seed, 3), key);\n\n // Use the seed to initialize an ARC4 generator.\n var arc4 = new ARC4(key);\n\n // This function returns a random double in [0, 1) that contains\n // randomness in every bit of the mantissa of the IEEE 754 value.\n var prng = function() {\n var n = arc4.g(chunks), // Start with a numerator n < 2 ^ 48\n d = startdenom, // and denominator d = 2 ^ 48.\n x = 0; // and no 'extra last byte'.\n while (n < significance) { // Fill up all significant digits by\n n = (n + x) * width; // shifting numerator and\n d *= width; // denominator and generating a\n x = arc4.g(1); // new least-significant-byte.\n }\n while (n >= overflow) { // To avoid rounding up, before adding\n n /= 2; // last byte, shift everything\n d /= 2; // right using integer math until\n x >>>= 1; // we have exactly the desired bits.\n }\n return (n + x) / d; // Form the number within [0, 1).\n };\n\n prng.int32 = function() { return arc4.g(4) | 0; }\n prng.quick = function() { return arc4.g(4) / 0x100000000; }\n prng.double = prng;\n\n // Mix the randomness into accumulated entropy.\n mixkey(tostring(arc4.S), pool);\n\n // Calling convention: what to return as a function of prng, seed, is_math.\n return (options.pass || callback ||\n function(prng, seed, is_math_call, state) {\n if (state) {\n // Load the arc4 state from the given state if it has an S array.\n if (state.S) { copy(state, arc4); }\n // Only provide the .state method if requested via options.state.\n prng.state = function() { return copy(arc4, {}); }\n }\n\n // If called as a method of Math (Math.seedrandom()), mutate\n // Math.random because that is how seedrandom.js has worked since v1.0.\n if (is_math_call) { math[rngname] = prng; return seed; }\n\n // Otherwise, it is a newer calling convention, so return the\n // prng directly.\n else return prng;\n })(\n prng,\n shortseed,\n 'global' in options ? options.global : (this == math),\n options.state);\n}\nmath['seed' + rngname] = seedrandom;\n\n//\n// ARC4\n//\n// An ARC4 implementation. The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4. Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n var t, keylen = key.length,\n me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n // The empty key [] is treated as [0].\n if (!keylen) { key = [keylen++]; }\n\n // Set up S using the standard key scheduling algorithm.\n while (i < width) {\n s[i] = i++;\n }\n for (i = 0; i < width; i++) {\n s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n s[j] = t;\n }\n\n // The \"g\" method returns the next (count) outputs as one number.\n (me.g = function(count) {\n // Using instance members instead of closure state nearly doubles speed.\n var t, r = 0,\n i = me.i, j = me.j, s = me.S;\n while (count--) {\n t = s[i = mask & (i + 1)];\n r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n }\n me.i = i; me.j = j;\n return r;\n // For robust unpredictability, the function call below automatically\n // discards an initial batch of values. This is called RC4-drop[256].\n // See http://google.com/search?q=rsa+fluhrer+response&btnI\n })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n t.i = f.i;\n t.j = f.j;\n t.S = f.S.slice();\n return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n var result = [], typ = (typeof obj), prop;\n if (depth && typ == 'object') {\n for (prop in obj) {\n try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n }\n }\n return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n var stringseed = seed + '', smear, j = 0;\n while (j < stringseed.length) {\n key[mask & j] =\n mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n }\n return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n try {\n var out;\n if (nodecrypto && (out = nodecrypto.randomBytes)) {\n // The use of 'out' to remember randomBytes makes tight minified code.\n out = out(width);\n } else {\n out = new Uint8Array(width);\n (global.crypto || global.msCrypto).getRandomValues(out);\n }\n return tostring(out);\n } catch (e) {\n var browser = global.navigator,\n plugins = browser && browser.plugins;\n return [+new Date, global, plugins, global.screen, tostring(pool)];\n }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool. Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n module.exports = seedrandom;\n // When in node.js, try using crypto package for autoseeding.\n try {\n nodecrypto = require('crypto');\n } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n define(function() { return seedrandom; });\n}\n\n// End anonymous scope, and pass initial values.\n})(\n [], // pool: entropy pool starts empty\n Math // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random(); // 0 <= x < 1. Every bit is random.\n// var x = random.quick(); // 0 <= x < 1. 32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three. It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures. Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "", "", "", "\nvar WasmBackendModuleThreadedSimd = (function() {\n var _scriptDir = typeof document !== 'undefined' && document.currentScript ? document.currentScript.src : undefined;\n if (typeof __filename !== 'undefined') _scriptDir = _scriptDir || __filename;\n return (\nfunction(WasmBackendModuleThreadedSimd) {\n WasmBackendModuleThreadedSimd = WasmBackendModuleThreadedSimd || {};\n\nfunction GROWABLE_HEAP_I8(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAP8}function GROWABLE_HEAP_U8(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPU8}function GROWABLE_HEAP_I32(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAP32}function GROWABLE_HEAP_U32(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPU32}function GROWABLE_HEAP_F64(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPF64}var Module=typeof WasmBackendModuleThreadedSimd!==\"undefined\"?WasmBackendModuleThreadedSimd:{};var _scriptDir=undefined;var moduleOverrides={};var key;for(key in Module){if(Module.hasOwnProperty(key)){moduleOverrides[key]=Module[key]}}var arguments_=[];var thisProgram=\"./this.program\";var quit_=function(status,toThrow){throw toThrow};var ENVIRONMENT_IS_WEB=false;var ENVIRONMENT_IS_WORKER=false;var ENVIRONMENT_IS_NODE=false;var ENVIRONMENT_IS_SHELL=false;ENVIRONMENT_IS_WEB=typeof window===\"object\";ENVIRONMENT_IS_WORKER=typeof importScripts===\"function\";ENVIRONMENT_IS_NODE=typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\";ENVIRONMENT_IS_SHELL=!ENVIRONMENT_IS_WEB&&!ENVIRONMENT_IS_NODE&&!ENVIRONMENT_IS_WORKER;var ENVIRONMENT_IS_PTHREAD=Module[\"ENVIRONMENT_IS_PTHREAD\"]||false;if(ENVIRONMENT_IS_PTHREAD){buffer=Module[\"buffer\"];DYNAMIC_BASE=Module[\"DYNAMIC_BASE\"];DYNAMICTOP_PTR=Module[\"DYNAMICTOP_PTR\"]}var scriptDirectory=\"\";function locateFile(path){if(Module[\"locateFile\"]){return Module[\"locateFile\"](path,scriptDirectory)}return scriptDirectory+path}var read_,readAsync,readBinary,setWindowTitle;var nodeFS;var nodePath;if(ENVIRONMENT_IS_NODE){if(ENVIRONMENT_IS_WORKER){scriptDirectory=require(\"path\").dirname(scriptDirectory)+\"/\"}else{scriptDirectory=__dirname+\"/\"}read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret};if(process[\"argv\"].length>1){thisProgram=process[\"argv\"][1].replace(/\\\\/g,\"/\")}arguments_=process[\"argv\"].slice(2);process[\"on\"](\"uncaughtException\",function(ex){if(!(ex instanceof ExitStatus)){throw ex}});process[\"on\"](\"unhandledRejection\",abort);quit_=function(status){process[\"exit\"](status)};Module[\"inspect\"]=function(){return\"[Emscripten Module object]\"};var nodeWorkerThreads;try{nodeWorkerThreads=require(\"worker_threads\")}catch(e){console.error('The \"worker_threads\" module is not supported in this node.js build - perhaps a newer version is needed?');throw e}Worker=nodeWorkerThreads.Worker}else if(ENVIRONMENT_IS_SHELL){if(typeof read!=\"undefined\"){read_=function shell_read(f){return read(f)}}readBinary=function readBinary(f){var data;if(typeof readbuffer===\"function\"){return new Uint8Array(readbuffer(f))}data=read(f,\"binary\");assert(typeof data===\"object\");return data};if(typeof scriptArgs!=\"undefined\"){arguments_=scriptArgs}else if(typeof arguments!=\"undefined\"){arguments_=arguments}if(typeof quit===\"function\"){quit_=function(status){quit(status)}}if(typeof print!==\"undefined\"){if(typeof console===\"undefined\")console={};console.log=print;console.warn=console.error=typeof printErr!==\"undefined\"?printErr:print}}else if(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER){if(ENVIRONMENT_IS_WORKER){scriptDirectory=self.location.href}else if(document.currentScript){scriptDirectory=document.currentScript.src}if(_scriptDir){scriptDirectory=_scriptDir}if(scriptDirectory.indexOf(\"blob:\")!==0){scriptDirectory=scriptDirectory.substr(0,scriptDirectory.lastIndexOf(\"/\")+1)}else{scriptDirectory=\"\"}if(ENVIRONMENT_IS_NODE){read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret}}else{read_=function shell_read(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.send(null);return xhr.responseText};if(ENVIRONMENT_IS_WORKER){readBinary=function readBinary(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.responseType=\"arraybuffer\";xhr.send(null);return new Uint8Array(xhr.response)}}readAsync=function readAsync(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,true);xhr.responseType=\"arraybuffer\";xhr.onload=function xhr_onload(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()};xhr.onerror=onerror;xhr.send(null)}}setWindowTitle=function(title){document.title=title}}else{}if(ENVIRONMENT_IS_NODE){if(typeof performance===\"undefined\"){performance=require(\"perf_hooks\").performance}}var out=Module[\"print\"]||console.log.bind(console);var err=Module[\"printErr\"]||console.warn.bind(console);for(key in moduleOverrides){if(moduleOverrides.hasOwnProperty(key)){Module[key]=moduleOverrides[key]}}moduleOverrides=null;if(Module[\"arguments\"])arguments_=Module[\"arguments\"];if(Module[\"thisProgram\"])thisProgram=Module[\"thisProgram\"];if(Module[\"quit\"])quit_=Module[\"quit\"];var Atomics_load=Atomics.load;var Atomics_store=Atomics.store;var Atomics_compareExchange=Atomics.compareExchange;var wasmBinary;if(Module[\"wasmBinary\"])wasmBinary=Module[\"wasmBinary\"];var noExitRuntime;if(Module[\"noExitRuntime\"])noExitRuntime=Module[\"noExitRuntime\"];if(typeof WebAssembly!==\"object\"){err(\"no native wasm support detected\")}var wasmMemory;var wasmTable=new WebAssembly.Table({\"initial\":169,\"maximum\":169+0,\"element\":\"anyfunc\"});var wasmModule;var threadInfoStruct=0;var selfThreadId=0;var ABORT=false;var EXITSTATUS=0;function assert(condition,text){if(!condition){abort(\"Assertion failed: \"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i=endIdx)){var u0=heap[idx++];if(!u0)return str;if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224){u0=(u0&15)<<12|u1<<6|u2}else{u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63}if(u0<65536){str+=String.fromCharCode(u0)}else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(GROWABLE_HEAP_U8(),ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6;heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18;heap[outIdx++]=128|u>>12&63;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}}heap[outIdx]=0;return outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,GROWABLE_HEAP_U8(),outPtr,maxBytesToWrite)}function lengthBytesUTF8(str){var len=0;for(var i=0;i=55296&&u<=57343)u=65536+((u&1023)<<10)|str.charCodeAt(++i)&1023;if(u<=127)++len;else if(u<=2047)len+=2;else if(u<=65535)len+=3;else len+=4}return len}function writeArrayToMemory(array,buffer){GROWABLE_HEAP_I8().set(array,buffer)}var WASM_PAGE_SIZE=65536;function alignUp(x,multiple){if(x%multiple>0){x+=multiple-x%multiple}return x}var buffer,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer=buf;Module[\"HEAP8\"]=HEAP8=new Int8Array(buf);Module[\"HEAP16\"]=HEAP16=new Int16Array(buf);Module[\"HEAP32\"]=HEAP32=new Int32Array(buf);Module[\"HEAPU8\"]=HEAPU8=new Uint8Array(buf);Module[\"HEAPU16\"]=HEAPU16=new Uint16Array(buf);Module[\"HEAPU32\"]=HEAPU32=new Uint32Array(buf);Module[\"HEAPF32\"]=HEAPF32=new Float32Array(buf);Module[\"HEAPF64\"]=HEAPF64=new Float64Array(buf)}var STACK_BASE=5256464,STACKTOP=STACK_BASE,STACK_MAX=13584,DYNAMIC_BASE=5256464,DYNAMICTOP_PTR=12656;if(ENVIRONMENT_IS_PTHREAD){}var INITIAL_INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;if(ENVIRONMENT_IS_PTHREAD){wasmMemory=Module[\"wasmMemory\"];buffer=Module[\"buffer\"]}else{if(Module[\"wasmMemory\"]){wasmMemory=Module[\"wasmMemory\"]}else{wasmMemory=new WebAssembly.Memory({\"initial\":INITIAL_INITIAL_MEMORY/WASM_PAGE_SIZE,\"maximum\":2147483648/WASM_PAGE_SIZE,\"shared\":true});if(!(wasmMemory.buffer instanceof SharedArrayBuffer)){err(\"requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag\");if(ENVIRONMENT_IS_NODE){console.log(\"(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)\")}throw Error(\"bad memory\")}}}if(wasmMemory){buffer=wasmMemory.buffer}INITIAL_INITIAL_MEMORY=buffer.byteLength;updateGlobalBufferAndViews(buffer);if(!ENVIRONMENT_IS_PTHREAD){GROWABLE_HEAP_I32()[DYNAMICTOP_PTR>>2]=DYNAMIC_BASE}function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback==\"function\"){callback(Module);continue}var func=callback.func;if(typeof func===\"number\"){if(callback.arg===undefined){Module[\"dynCall_v\"](func)}else{Module[\"dynCall_vi\"](func,callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATEXIT__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;if(ENVIRONMENT_IS_PTHREAD)runtimeInitialized=true;function preRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"preRun\"]){if(typeof Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;callRuntimeCallbacks(__ATINIT__)}function preMain(){if(ENVIRONMENT_IS_PTHREAD)return;callRuntimeCallbacks(__ATMAIN__)}function postRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"postRun\"]){if(typeof Module[\"postRun\"]==\"function\")Module[\"postRun\"]=[Module[\"postRun\"]];while(Module[\"postRun\"].length){addOnPostRun(Module[\"postRun\"].shift())}}callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var Math_ceil=Math.ceil;var Math_floor=Math.floor;var runDependencies=0;var runDependencyWatcher=null;var dependenciesFulfilled=null;function addRunDependency(id){assert(!ENVIRONMENT_IS_PTHREAD,\"addRunDependency cannot be used in a pthread worker\");runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}if(ENVIRONMENT_IS_PTHREAD)console.error(\"Pthread aborting at \"+(new Error).stack);what+=\"\";out(what);err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";throw new WebAssembly.RuntimeError(what)}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm-threaded-simd.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(){try{if(wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(wasmBinaryFile)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary()})}return new Promise(function(resolve,reject){resolve(getBinary())})}function createWasm(){var info={\"a\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmModule=module;if(!ENVIRONMENT_IS_PTHREAD){var numWorkersToLoad=PThread.unusedWorkers.length;PThread.unusedWorkers.forEach(function(w){PThread.loadWasmModuleToWorker(w,function(){if(!--numWorkersToLoad)removeRunDependency(\"wasm-instantiate\")})})}}if(!ENVIRONMENT_IS_PTHREAD){addRunDependency(\"wasm-instantiate\")}function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"],output[\"module\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming===\"function\"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch===\"function\"){fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err(\"wasm streaming compile failed: \"+reason);err(\"falling back to ArrayBuffer instantiation\");instantiateArrayBuffer(receiveInstantiatedSource)})})}else{return instantiateArrayBuffer(receiveInstantiatedSource)}}if(Module[\"instantiateWasm\"]){try{var exports=Module[\"instantiateWasm\"](info,receiveInstance);return exports}catch(e){err(\"Module.instantiateWasm callback failed with error: \"+e);return false}}instantiateAsync();return{}}var ASM_CONSTS={};function initPthreadsJS(){PThread.initRuntime()}if(!ENVIRONMENT_IS_PTHREAD)__ATINIT__.push({func:function(){___wasm_call_ctors()}});var __pthread_ptr=0;var __pthread_is_main_runtime_thread=0;var __pthread_is_main_browser_thread=0;function __register_pthread_ptr(pthreadPtr,isMainBrowserThread,isMainRuntimeThread){pthreadPtr=pthreadPtr|0;isMainBrowserThread=isMainBrowserThread|0;isMainRuntimeThread=isMainRuntimeThread|0;__pthread_ptr=pthreadPtr;__pthread_is_main_browser_thread=isMainBrowserThread;__pthread_is_main_runtime_thread=isMainRuntimeThread}Module[\"__register_pthread_ptr\"]=__register_pthread_ptr;var ERRNO_CODES={EPERM:63,ENOENT:44,ESRCH:71,EINTR:27,EIO:29,ENXIO:60,E2BIG:1,ENOEXEC:45,EBADF:8,ECHILD:12,EAGAIN:6,EWOULDBLOCK:6,ENOMEM:48,EACCES:2,EFAULT:21,ENOTBLK:105,EBUSY:10,EEXIST:20,EXDEV:75,ENODEV:43,ENOTDIR:54,EISDIR:31,EINVAL:28,ENFILE:41,EMFILE:33,ENOTTY:59,ETXTBSY:74,EFBIG:22,ENOSPC:51,ESPIPE:70,EROFS:69,EMLINK:34,EPIPE:64,EDOM:18,ERANGE:68,ENOMSG:49,EIDRM:24,ECHRNG:106,EL2NSYNC:156,EL3HLT:107,EL3RST:108,ELNRNG:109,EUNATCH:110,ENOCSI:111,EL2HLT:112,EDEADLK:16,ENOLCK:46,EBADE:113,EBADR:114,EXFULL:115,ENOANO:104,EBADRQC:103,EBADSLT:102,EDEADLOCK:16,EBFONT:101,ENOSTR:100,ENODATA:116,ETIME:117,ENOSR:118,ENONET:119,ENOPKG:120,EREMOTE:121,ENOLINK:47,EADV:122,ESRMNT:123,ECOMM:124,EPROTO:65,EMULTIHOP:36,EDOTDOT:125,EBADMSG:9,ENOTUNIQ:126,EBADFD:127,EREMCHG:128,ELIBACC:129,ELIBBAD:130,ELIBSCN:131,ELIBMAX:132,ELIBEXEC:133,ENOSYS:52,ENOTEMPTY:55,ENAMETOOLONG:37,ELOOP:32,EOPNOTSUPP:138,EPFNOSUPPORT:139,ECONNRESET:15,ENOBUFS:42,EAFNOSUPPORT:5,EPROTOTYPE:67,ENOTSOCK:57,ENOPROTOOPT:50,ESHUTDOWN:140,ECONNREFUSED:14,EADDRINUSE:3,ECONNABORTED:13,ENETUNREACH:40,ENETDOWN:38,ETIMEDOUT:73,EHOSTDOWN:142,EHOSTUNREACH:23,EINPROGRESS:26,EALREADY:7,EDESTADDRREQ:17,EMSGSIZE:35,EPROTONOSUPPORT:66,ESOCKTNOSUPPORT:137,EADDRNOTAVAIL:4,ENETRESET:39,EISCONN:30,ENOTCONN:53,ETOOMANYREFS:141,EUSERS:136,EDQUOT:19,ESTALE:72,ENOTSUP:138,ENOMEDIUM:148,EILSEQ:25,EOVERFLOW:61,ECANCELED:11,ENOTRECOVERABLE:56,EOWNERDEAD:62,ESTRPIPE:135};var __main_thread_futex_wait_address=13568;function _emscripten_futex_wake(addr,count){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0||count<0)return-28;if(count==0)return 0;if(count>=2147483647)count=Infinity;var mainThreadWaitAddress=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2);var mainThreadWoken=0;if(mainThreadWaitAddress==addr){var loadedAddr=Atomics.compareExchange(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,mainThreadWaitAddress,0);if(loadedAddr==mainThreadWaitAddress){--count;mainThreadWoken=1;if(count<=0)return 1}}var ret=Atomics.notify(GROWABLE_HEAP_I32(),addr>>2,count);if(ret>=0)return ret+mainThreadWoken;throw\"Atomics.notify returned an unexpected value \"+ret}Module[\"_emscripten_futex_wake\"]=_emscripten_futex_wake;function __kill_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _kill_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _kill_thread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];pthread.worker.terminate();PThread.freeThreadData(pthread);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(pthread.worker),1);pthread.worker.pthread=undefined}function __cancel_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _cancel_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _cancel_thread!\";var pthread=PThread.pthreads[pthread_ptr];pthread.worker.postMessage({\"cmd\":\"cancel\"})}function __cleanup_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _cleanup_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _cleanup_thread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];if(pthread){var worker=pthread.worker;PThread.returnWorkerToPool(worker)}}var PThread={MAIN_THREAD_ID:1,mainThreadInfo:{schedPolicy:0,schedPrio:0},unusedWorkers:[],runningWorkers:[],initRuntime:function(){__register_pthread_ptr(PThread.mainThreadBlock,!ENVIRONMENT_IS_WORKER,1);_emscripten_register_main_browser_thread_id(PThread.mainThreadBlock)},initMainThreadBlock:function(){var pthreadPoolSize=8;for(var i=0;i>2]=PThread.mainThreadBlock;var headPtr=PThread.mainThreadBlock+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var tlsMemory=13056;for(var i=0;i<128;++i)GROWABLE_HEAP_U32()[tlsMemory/4+i]=0;Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+104>>2,tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+40>>2,PThread.mainThreadBlock);Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+44>>2,42)},initWorker:function(){},pthreads:{},exitHandlers:null,setThreadStatus:function(){},runExitHandlers:function(){if(PThread.exitHandlers!==null){while(PThread.exitHandlers.length>0){PThread.exitHandlers.pop()()}PThread.exitHandlers=null}if(ENVIRONMENT_IS_PTHREAD&&threadInfoStruct)___pthread_tsd_run_dtors()},threadExit:function(exitCode){var tb=_pthread_self();if(tb){Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,exitCode);Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+60>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+64>>2,0);PThread.runExitHandlers();_emscripten_futex_wake(tb+0,2147483647);__register_pthread_ptr(0,0,0);threadInfoStruct=0;if(ENVIRONMENT_IS_PTHREAD){postMessage({\"cmd\":\"exit\"})}}},threadCancel:function(){PThread.runExitHandlers();Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+4>>2,-1);Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+0>>2,1);_emscripten_futex_wake(threadInfoStruct+0,2147483647);threadInfoStruct=selfThreadId=0;__register_pthread_ptr(0,0,0);postMessage({\"cmd\":\"cancelDone\"})},terminateAllThreads:function(){for(var t in PThread.pthreads){var pthread=PThread.pthreads[t];if(pthread&&pthread.worker){PThread.returnWorkerToPool(pthread.worker)}}PThread.pthreads={};for(var i=0;i>2];GROWABLE_HEAP_I32()[pthread.threadInfoStruct+104>>2]=0;_free(tlsMemory);_free(pthread.threadInfoStruct)}pthread.threadInfoStruct=0;if(pthread.allocatedOwnStack&&pthread.stackBase)_free(pthread.stackBase);pthread.stackBase=0;if(pthread.worker)pthread.worker.pthread=null},returnWorkerToPool:function(worker){delete PThread.pthreads[worker.pthread.thread];PThread.unusedWorkers.push(worker);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker),1);PThread.freeThreadData(worker.pthread);worker.pthread=undefined},receiveObjectTransfer:function(data){},loadWasmModuleToWorker:function(worker,onFinishedLoading){worker.onmessage=function(e){var d=e[\"data\"];var cmd=d[\"cmd\"];if(worker.pthread)PThread.currentProxiedOperationCallerThread=worker.pthread.threadInfoStruct;if(d[\"targetThread\"]&&d[\"targetThread\"]!=_pthread_self()){var thread=PThread.pthreads[d.targetThread];if(thread){thread.worker.postMessage(e.data,d[\"transferList\"])}else{console.error('Internal error! Worker sent a message \"'+cmd+'\" to target pthread '+d[\"targetThread\"]+\", but that thread no longer exists!\")}PThread.currentProxiedOperationCallerThread=undefined;return}if(cmd===\"processQueuedMainThreadWork\"){_emscripten_main_thread_process_queued_calls()}else if(cmd===\"spawnThread\"){__spawn_thread(e.data)}else if(cmd===\"cleanupThread\"){__cleanup_thread(d[\"thread\"])}else if(cmd===\"killThread\"){__kill_thread(d[\"thread\"])}else if(cmd===\"cancelThread\"){__cancel_thread(d[\"thread\"])}else if(cmd===\"loaded\"){worker.loaded=true;if(onFinishedLoading)onFinishedLoading(worker);if(worker.runPthread){worker.runPthread();delete worker.runPthread}}else if(cmd===\"print\"){out(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"printErr\"){err(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"alert\"){alert(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"exit\"){var detached=worker.pthread&&Atomics.load(GROWABLE_HEAP_U32(),worker.pthread.thread+68>>2);if(detached){PThread.returnWorkerToPool(worker)}}else if(cmd===\"cancelDone\"){PThread.returnWorkerToPool(worker)}else if(cmd===\"objectTransfer\"){PThread.receiveObjectTransfer(e.data)}else if(e.data.target===\"setimmediate\"){worker.postMessage(e.data)}else{err(\"worker sent an unknown command \"+cmd)}PThread.currentProxiedOperationCallerThread=undefined};worker.onerror=function(e){err(\"pthread sent an error! \"+e.filename+\":\"+e.lineno+\": \"+e.message)};if(ENVIRONMENT_IS_NODE){worker.on(\"message\",function(data){worker.onmessage({data:data})});worker.on(\"error\",function(data){worker.onerror(data)});worker.on(\"exit\",function(data){console.log(\"worker exited - TODO: update the worker queue?\")})}worker.postMessage({\"cmd\":\"load\",\"urlOrBlob\":Module[\"mainScriptUrlOrBlob\"]||_scriptDir,\"wasmMemory\":wasmMemory,\"wasmModule\":wasmModule,\"DYNAMIC_BASE\":DYNAMIC_BASE,\"DYNAMICTOP_PTR\":DYNAMICTOP_PTR})},allocateUnusedWorker:function(){var pthreadMainJs=locateFile(\"tfjs-backend-wasm-threaded-simd.worker.js\");PThread.unusedWorkers.push(new Worker(pthreadMainJs))},getNewWorker:function(){if(PThread.unusedWorkers.length==0){PThread.allocateUnusedWorker();PThread.loadWasmModuleToWorker(PThread.unusedWorkers[0])}if(PThread.unusedWorkers.length>0)return PThread.unusedWorkers.pop();else return null},busySpinWait:function(msecs){var t=performance.now()+msecs;while(performance.now()>2]=value;return value}function _atexit(func,arg){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(1,1,func,arg);__ATEXIT__.unshift({func:func,arg:arg})}function __emscripten_notify_thread_queue(targetThreadId,mainThreadId){if(targetThreadId==mainThreadId){postMessage({\"cmd\":\"processQueuedMainThreadWork\"})}else if(ENVIRONMENT_IS_PTHREAD){postMessage({\"targetThread\":targetThreadId,\"cmd\":\"processThreadQueue\"})}else{var pthread=PThread.pthreads[targetThreadId];var worker=pthread&&pthread.worker;if(!worker){return}worker.postMessage({\"cmd\":\"processThreadQueue\"})}return 1}function _abort(){abort()}function _emscripten_conditional_set_current_thread_status(expectedStatus,newStatus){expectedStatus=expectedStatus|0;newStatus=newStatus|0}function _emscripten_futex_wait(addr,val,timeout){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0)return-28;if(ENVIRONMENT_IS_WORKER){var ret=Atomics.wait(GROWABLE_HEAP_I32(),addr>>2,val,timeout);if(ret===\"timed-out\")return-73;if(ret===\"not-equal\")return-6;if(ret===\"ok\")return 0;throw\"Atomics.wait returned an unexpected value \"+ret}else{var loadedVal=Atomics.load(GROWABLE_HEAP_I32(),addr>>2);if(val!=loadedVal)return-6;var tNow=performance.now();var tEnd=tNow+timeout;Atomics.store(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,addr);var ourWaitAddress=addr;while(addr==ourWaitAddress){tNow=performance.now();if(tNow>tEnd){return-73}_emscripten_main_thread_process_queued_calls();addr=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2)}return 0}}function _emscripten_is_main_browser_thread(){return __pthread_is_main_browser_thread|0}function _emscripten_is_main_runtime_thread(){return __pthread_is_main_runtime_thread|0}function _emscripten_memcpy_big(dest,src,num){GROWABLE_HEAP_U8().copyWithin(dest,src,src+num)}function _emscripten_num_logical_cores(){return navigator[\"hardwareConcurrency\"]}function _emscripten_proxy_to_main_thread_js(index,sync){var numCallArgs=arguments.length-2;var stack=stackSave();var args=stackAlloc(numCallArgs*8);var b=args>>3;for(var i=0;i>3]);buf+=8}else{buf=buf+3&~3;args.push(GROWABLE_HEAP_I32()[buf>>2]);buf+=4}}return args}function _emscripten_receive_on_main_thread_js(index,numCallArgs,args){_emscripten_receive_on_main_thread_js_callArgs.length=numCallArgs;var b=args>>3;for(var i=0;i>>16);updateGlobalBufferAndViews(wasmMemory.buffer);return 1}catch(e){}}function _emscripten_resize_heap(requestedSize){requestedSize=requestedSize>>>0;var oldSize=_emscripten_get_heap_size();if(requestedSize<=oldSize){return false}var PAGE_MULTIPLE=65536;var maxHeapSize=2147483648;if(requestedSize>maxHeapSize){return false}var minHeapSize=16777216;for(var cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(minHeapSize,requestedSize,overGrownHeapSize),PAGE_MULTIPLE));var replacement=emscripten_realloc_buffer(newSize);if(replacement){return true}}return false}var JSEvents={keyEvent:0,mouseEvent:0,wheelEvent:0,uiEvent:0,focusEvent:0,deviceOrientationEvent:0,deviceMotionEvent:0,fullscreenChangeEvent:0,pointerlockChangeEvent:0,visibilityChangeEvent:0,touchEvent:0,previousFullscreenElement:null,previousScreenX:null,previousScreenY:null,removeEventListenersRegistered:false,removeAllEventListeners:function(){for(var i=JSEvents.eventHandlers.length-1;i>=0;--i){JSEvents._removeHandler(i)}JSEvents.eventHandlers=[];JSEvents.deferredCalls=[]},registerRemoveEventListeners:function(){if(!JSEvents.removeEventListenersRegistered){__ATEXIT__.push(JSEvents.removeAllEventListeners);JSEvents.removeEventListenersRegistered=true}},deferredCalls:[],deferCall:function(targetFunction,precedence,argsList){function arraysHaveEqualContent(arrA,arrB){if(arrA.length!=arrB.length)return false;for(var i in arrA){if(arrA[i]!=arrB[i])return false}return true}for(var i in JSEvents.deferredCalls){var call=JSEvents.deferredCalls[i];if(call.targetFunction==targetFunction&&arraysHaveEqualContent(call.argsList,argsList)){return}}JSEvents.deferredCalls.push({targetFunction:targetFunction,precedence:precedence,argsList:argsList});JSEvents.deferredCalls.sort(function(x,y){return x.precedence>2]=eventTypeId;GROWABLE_HEAP_I32()[varargs+4>>2]=eventData;GROWABLE_HEAP_I32()[varargs+8>>2]=userData;_emscripten_async_queue_on_thread_(targetThread,637534208,eventHandlerFunc,eventData,varargs);stackRestore(stackTop)},getTargetThreadForEventCallback:function(targetThread){switch(targetThread){case 1:return 0;case 2:return PThread.currentProxiedOperationCallerThread;default:return targetThread}},getNodeNameForTarget:function(target){if(!target)return\"\";if(target==window)return\"#window\";if(target==screen)return\"#screen\";return target&&target.nodeName?target.nodeName:\"\"},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function stringToNewUTF8(jsString){var length=lengthBytesUTF8(jsString)+1;var cString=_malloc(length);stringToUTF8(jsString,cString,length);return cString}function _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height){var stackTop=stackSave();var varargs=stackAlloc(12);var targetCanvasPtr=0;if(targetCanvas){targetCanvasPtr=stringToNewUTF8(targetCanvas)}GROWABLE_HEAP_I32()[varargs>>2]=targetCanvasPtr;GROWABLE_HEAP_I32()[varargs+4>>2]=width;GROWABLE_HEAP_I32()[varargs+8>>2]=height;_emscripten_async_queue_on_thread_(targetThread,657457152,0,targetCanvasPtr,varargs);stackRestore(stackTop)}function _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,targetCanvas,width,height){targetCanvas=targetCanvas?UTF8ToString(targetCanvas):\"\";_emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height)}function __maybeCStringToJsString(cString){return cString>2?UTF8ToString(cString):cString}var specialHTMLTargets=[0,typeof document!==\"undefined\"?document:0,typeof window!==\"undefined\"?window:0];function __findEventTarget(target){target=__maybeCStringToJsString(target);var domElement=specialHTMLTargets[target]||(typeof document!==\"undefined\"?document.querySelector(target):undefined);return domElement}function __findCanvasEventTarget(target){return __findEventTarget(target)}function _emscripten_set_canvas_element_size_calling_thread(target,width,height){var canvas=__findCanvasEventTarget(target);if(!canvas)return-4;if(canvas.canvasSharedPtr){GROWABLE_HEAP_I32()[canvas.canvasSharedPtr>>2]=width;GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+4>>2]=height}if(canvas.offscreenCanvas||!canvas.controlTransferredOffscreen){if(canvas.offscreenCanvas)canvas=canvas.offscreenCanvas;var autoResizeViewport=false;if(canvas.GLctxObject&&canvas.GLctxObject.GLctx){var prevViewport=canvas.GLctxObject.GLctx.getParameter(2978);autoResizeViewport=prevViewport[0]===0&&prevViewport[1]===0&&prevViewport[2]===canvas.width&&prevViewport[3]===canvas.height}canvas.width=width;canvas.height=height;if(autoResizeViewport){canvas.GLctxObject.GLctx.viewport(0,0,width,height)}}else if(canvas.canvasSharedPtr){var targetThread=GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+8>>2];_emscripten_set_offscreencanvas_size_on_target_thread(targetThread,target,width,height);return 1}else{return-4}return 0}function _emscripten_set_canvas_element_size_main_thread(target,width,height){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(2,1,target,width,height);return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}function _emscripten_set_canvas_element_size(target,width,height){var canvas=__findCanvasEventTarget(target);if(canvas){return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}else{return _emscripten_set_canvas_element_size_main_thread(target,width,height)}}function _emscripten_set_current_thread_status(newStatus){newStatus=newStatus|0}function _emscripten_set_thread_name(threadId,name){threadId=threadId|0;name=name|0}function __webgl_enable_ANGLE_instanced_arrays(ctx){var ext=ctx.getExtension(\"ANGLE_instanced_arrays\");if(ext){ctx[\"vertexAttribDivisor\"]=function(index,divisor){ext[\"vertexAttribDivisorANGLE\"](index,divisor)};ctx[\"drawArraysInstanced\"]=function(mode,first,count,primcount){ext[\"drawArraysInstancedANGLE\"](mode,first,count,primcount)};ctx[\"drawElementsInstanced\"]=function(mode,count,type,indices,primcount){ext[\"drawElementsInstancedANGLE\"](mode,count,type,indices,primcount)};return 1}}function __webgl_enable_OES_vertex_array_object(ctx){var ext=ctx.getExtension(\"OES_vertex_array_object\");if(ext){ctx[\"createVertexArray\"]=function(){return ext[\"createVertexArrayOES\"]()};ctx[\"deleteVertexArray\"]=function(vao){ext[\"deleteVertexArrayOES\"](vao)};ctx[\"bindVertexArray\"]=function(vao){ext[\"bindVertexArrayOES\"](vao)};ctx[\"isVertexArray\"]=function(vao){return ext[\"isVertexArrayOES\"](vao)};return 1}}function __webgl_enable_WEBGL_draw_buffers(ctx){var ext=ctx.getExtension(\"WEBGL_draw_buffers\");if(ext){ctx[\"drawBuffers\"]=function(n,bufs){ext[\"drawBuffersWEBGL\"](n,bufs)};return 1}}var GL={counter:1,lastError:0,buffers:[],mappedBuffers:{},programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},currentContext:null,offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,init:function(){var miniTempFloatBuffer=new Float32Array(GL.MINI_TEMP_BUFFER_SIZE);for(var i=0;i>2]:-1;source+=UTF8ToString(GROWABLE_HEAP_I32()[string+i*4>>2],len<0?undefined:len)}return source},createContext:function(canvas,webGLContextAttributes){var ctx=canvas.getContext(\"webgl\",webGLContextAttributes);if(!ctx)return 0;var handle=GL.registerContext(ctx,webGLContextAttributes);return handle},registerContext:function(ctx,webGLContextAttributes){var handle=_malloc(8);GROWABLE_HEAP_I32()[handle+4>>2]=_pthread_self();var context={handle:handle,attributes:webGLContextAttributes,version:webGLContextAttributes.majorVersion,GLctx:ctx};if(ctx.canvas)ctx.canvas.GLctxObject=context;GL.contexts[handle]=context;if(typeof webGLContextAttributes.enableExtensionsByDefault===\"undefined\"||webGLContextAttributes.enableExtensionsByDefault){GL.initExtensions(context)}return handle},makeContextCurrent:function(contextHandle){GL.currentContext=GL.contexts[contextHandle];Module.ctx=GLctx=GL.currentContext&&GL.currentContext.GLctx;return!(contextHandle&&!GLctx)},getContext:function(contextHandle){return GL.contexts[contextHandle]},deleteContext:function(contextHandle){if(GL.currentContext===GL.contexts[contextHandle])GL.currentContext=null;if(typeof JSEvents===\"object\")JSEvents.removeAllHandlersOnTarget(GL.contexts[contextHandle].GLctx.canvas);if(GL.contexts[contextHandle]&&GL.contexts[contextHandle].GLctx.canvas)GL.contexts[contextHandle].GLctx.canvas.GLctxObject=undefined;_free(GL.contexts[contextHandle].handle);GL.contexts[contextHandle]=null},initExtensions:function(context){if(!context)context=GL.currentContext;if(context.initExtensionsDone)return;context.initExtensionsDone=true;var GLctx=context.GLctx;__webgl_enable_ANGLE_instanced_arrays(GLctx);__webgl_enable_OES_vertex_array_object(GLctx);__webgl_enable_WEBGL_draw_buffers(GLctx);GLctx.disjointTimerQueryExt=GLctx.getExtension(\"EXT_disjoint_timer_query\");var automaticallyEnabledExtensions=[\"OES_texture_float\",\"OES_texture_half_float\",\"OES_standard_derivatives\",\"OES_vertex_array_object\",\"WEBGL_compressed_texture_s3tc\",\"WEBGL_depth_texture\",\"OES_element_index_uint\",\"EXT_texture_filter_anisotropic\",\"EXT_frag_depth\",\"WEBGL_draw_buffers\",\"ANGLE_instanced_arrays\",\"OES_texture_float_linear\",\"OES_texture_half_float_linear\",\"EXT_blend_minmax\",\"EXT_shader_texture_lod\",\"EXT_texture_norm16\",\"WEBGL_compressed_texture_pvrtc\",\"EXT_color_buffer_half_float\",\"WEBGL_color_buffer_float\",\"EXT_sRGB\",\"WEBGL_compressed_texture_etc1\",\"EXT_disjoint_timer_query\",\"WEBGL_compressed_texture_etc\",\"WEBGL_compressed_texture_astc\",\"EXT_color_buffer_float\",\"WEBGL_compressed_texture_s3tc_srgb\",\"EXT_disjoint_timer_query_webgl2\",\"WEBKIT_WEBGL_compressed_texture_pvrtc\"];var exts=GLctx.getSupportedExtensions()||[];exts.forEach(function(ext){if(automaticallyEnabledExtensions.indexOf(ext)!=-1){GLctx.getExtension(ext)}})},populateUniformTable:function(program){var p=GL.programs[program];var ptable=GL.programInfos[program]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1};var utable=ptable.uniforms;var numUniforms=GLctx.getProgramParameter(p,35718);for(var i=0;i>2;contextAttributes[\"alpha\"]=!!GROWABLE_HEAP_I32()[a+(0>>2)];contextAttributes[\"depth\"]=!!GROWABLE_HEAP_I32()[a+(4>>2)];contextAttributes[\"stencil\"]=!!GROWABLE_HEAP_I32()[a+(8>>2)];contextAttributes[\"antialias\"]=!!GROWABLE_HEAP_I32()[a+(12>>2)];contextAttributes[\"premultipliedAlpha\"]=!!GROWABLE_HEAP_I32()[a+(16>>2)];contextAttributes[\"preserveDrawingBuffer\"]=!!GROWABLE_HEAP_I32()[a+(20>>2)];var powerPreference=GROWABLE_HEAP_I32()[a+(24>>2)];contextAttributes[\"powerPreference\"]=__emscripten_webgl_power_preferences[powerPreference];contextAttributes[\"failIfMajorPerformanceCaveat\"]=!!GROWABLE_HEAP_I32()[a+(28>>2)];contextAttributes.majorVersion=GROWABLE_HEAP_I32()[a+(32>>2)];contextAttributes.minorVersion=GROWABLE_HEAP_I32()[a+(36>>2)];contextAttributes.enableExtensionsByDefault=GROWABLE_HEAP_I32()[a+(40>>2)];contextAttributes.explicitSwapControl=GROWABLE_HEAP_I32()[a+(44>>2)];contextAttributes.proxyContextToMainThread=GROWABLE_HEAP_I32()[a+(48>>2)];contextAttributes.renderViaOffscreenBackBuffer=GROWABLE_HEAP_I32()[a+(52>>2)];var canvas=__findCanvasEventTarget(target);if(!canvas){return-4}if(contextAttributes.explicitSwapControl){return-1}var contextHandle=GL.createContext(canvas,contextAttributes);return contextHandle}function _emscripten_webgl_create_context(a0,a1){return _emscripten_webgl_do_create_context(a0,a1)}var PATH={splitPath:function(filename){var splitPathRe=/^(\\/?|)([\\s\\S]*?)((?:\\.{1,2}|[^\\/]+?|)(\\.[^.\\/]*|))(?:[\\/]*)$/;return splitPathRe.exec(filename).slice(1)},normalizeArray:function(parts,allowAboveRoot){var up=0;for(var i=parts.length-1;i>=0;i--){var last=parts[i];if(last===\".\"){parts.splice(i,1)}else if(last===\"..\"){parts.splice(i,1);up++}else if(up){parts.splice(i,1);up--}}if(allowAboveRoot){for(;up;up--){parts.unshift(\"..\")}}return parts},normalize:function(path){var isAbsolute=path.charAt(0)===\"/\",trailingSlash=path.substr(-1)===\"/\";path=PATH.normalizeArray(path.split(\"/\").filter(function(p){return!!p}),!isAbsolute).join(\"/\");if(!path&&!isAbsolute){path=\".\"}if(path&&trailingSlash){path+=\"/\"}return(isAbsolute?\"/\":\"\")+path},dirname:function(path){var result=PATH.splitPath(path),root=result[0],dir=result[1];if(!root&&!dir){return\".\"}if(dir){dir=dir.substr(0,dir.length-1)}return root+dir},basename:function(path){if(path===\"/\")return\"/\";var lastSlash=path.lastIndexOf(\"/\");if(lastSlash===-1)return path;return path.substr(lastSlash+1)},extname:function(path){return PATH.splitPath(path)[3]},join:function(){var paths=Array.prototype.slice.call(arguments,0);return PATH.normalize(paths.join(\"/\"))},join2:function(l,r){return PATH.normalize(l+\"/\"+r)}};var SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer=SYSCALLS.buffers[stream];if(curr===0||curr===10){(stream===1?out:err)(UTF8ArrayToString(buffer,0));buffer.length=0}else{buffer.push(curr)}},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=GROWABLE_HEAP_I32()[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(3,1,fd);return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(4,1,fd,offset_low,offset_high,whence,newOffset)}function _fd_write(fd,iov,iovcnt,pnum){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(5,1,fd,iov,iovcnt,pnum);var num=0;for(var i=0;i>2];var len=GROWABLE_HEAP_I32()[iov+(i*8+4)>>2];for(var j=0;j>2]=num;return 0}function _pthread_cleanup_pop(execute){var routine=PThread.exitHandlers.pop();if(execute)routine()}function _pthread_cleanup_push(routine,arg){if(PThread.exitHandlers===null){PThread.exitHandlers=[]}PThread.exitHandlers.push(function(){dynCall_vi(routine,arg)})}function __spawn_thread(threadParams){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _spawn_thread() can only ever be called from main application thread!\";var worker=PThread.getNewWorker();if(worker.pthread!==undefined)throw\"Internal error!\";if(!threadParams.pthread_ptr)throw\"Internal error, no pthread ptr!\";PThread.runningWorkers.push(worker);var tlsMemory=_malloc(128*4);for(var i=0;i<128;++i){GROWABLE_HEAP_I32()[tlsMemory+i*4>>2]=0}var stackHigh=threadParams.stackBase+threadParams.stackSize;var pthread=PThread.pthreads[threadParams.pthread_ptr]={worker:worker,stackBase:threadParams.stackBase,stackSize:threadParams.stackSize,allocatedOwnStack:threadParams.allocatedOwnStack,thread:threadParams.pthread_ptr,threadInfoStruct:threadParams.pthread_ptr};var tis=pthread.threadInfoStruct>>2;Atomics.store(GROWABLE_HEAP_U32(),tis+(0>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(4>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(8>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(68>>2),threadParams.detached);Atomics.store(GROWABLE_HEAP_U32(),tis+(104>>2),tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),tis+(48>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(40>>2),pthread.threadInfoStruct);Atomics.store(GROWABLE_HEAP_U32(),tis+(44>>2),42);Atomics.store(GROWABLE_HEAP_U32(),tis+(108>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(84>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(80>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+8>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+12>>2),threadParams.detached);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+20>>2),threadParams.schedPolicy);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+24>>2),threadParams.schedPrio);var global_libc=_emscripten_get_global_libc();var global_locale=global_libc+40;Atomics.store(GROWABLE_HEAP_U32(),tis+(176>>2),global_locale);worker.pthread=pthread;var msg={\"cmd\":\"run\",\"start_routine\":threadParams.startRoutine,\"arg\":threadParams.arg,\"threadInfoStruct\":threadParams.pthread_ptr,\"selfThreadId\":threadParams.pthread_ptr,\"parentThreadId\":threadParams.parent_pthread_ptr,\"stackBase\":threadParams.stackBase,\"stackSize\":threadParams.stackSize};worker.runPthread=function(){msg.time=performance.now();worker.postMessage(msg,threadParams.transferList)};if(worker.loaded){worker.runPthread();delete worker.runPthread}}function _pthread_getschedparam(thread,policy,schedparam){if(!policy&&!schedparam)return ERRNO_CODES.EINVAL;if(!thread){err(\"pthread_getschedparam called with a null thread pointer!\");return ERRNO_CODES.ESRCH}var self=GROWABLE_HEAP_I32()[thread+12>>2];if(self!==thread){err(\"pthread_getschedparam attempted on thread \"+thread+\", which does not point to a valid thread, or does not exist anymore!\");return ERRNO_CODES.ESRCH}var schedPolicy=Atomics.load(GROWABLE_HEAP_U32(),thread+108+20>>2);var schedPrio=Atomics.load(GROWABLE_HEAP_U32(),thread+108+24>>2);if(policy)GROWABLE_HEAP_I32()[policy>>2]=schedPolicy;if(schedparam)GROWABLE_HEAP_I32()[schedparam>>2]=schedPrio;return 0}function _pthread_self(){return __pthread_ptr|0}Module[\"_pthread_self\"]=_pthread_self;function _pthread_create(pthread_ptr,attr,start_routine,arg){if(typeof SharedArrayBuffer===\"undefined\"){err(\"Current environment does not support SharedArrayBuffer, pthreads are not available!\");return 6}if(!pthread_ptr){err(\"pthread_create called with a null thread pointer!\");return 28}var transferList=[];var error=0;if(ENVIRONMENT_IS_PTHREAD&&(transferList.length===0||error)){return _emscripten_sync_run_in_main_thread_4(687865856,pthread_ptr,attr,start_routine,arg)}if(error)return error;var stackSize=0;var stackBase=0;var detached=0;var schedPolicy=0;var schedPrio=0;if(attr){stackSize=GROWABLE_HEAP_I32()[attr>>2];stackSize+=81920;stackBase=GROWABLE_HEAP_I32()[attr+8>>2];detached=GROWABLE_HEAP_I32()[attr+12>>2]!==0;var inheritSched=GROWABLE_HEAP_I32()[attr+16>>2]===0;if(inheritSched){var prevSchedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];var prevSchedPrio=GROWABLE_HEAP_I32()[attr+24>>2];var parentThreadPtr=PThread.currentProxiedOperationCallerThread?PThread.currentProxiedOperationCallerThread:_pthread_self();_pthread_getschedparam(parentThreadPtr,attr+20,attr+24);schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];schedPrio=GROWABLE_HEAP_I32()[attr+24>>2];GROWABLE_HEAP_I32()[attr+20>>2]=prevSchedPolicy;GROWABLE_HEAP_I32()[attr+24>>2]=prevSchedPrio}else{schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];schedPrio=GROWABLE_HEAP_I32()[attr+24>>2]}}else{stackSize=2097152}var allocatedOwnStack=stackBase==0;if(allocatedOwnStack){stackBase=_memalign(16,stackSize)}else{stackBase-=stackSize;assert(stackBase>0)}var threadInfoStruct=_malloc(232);for(var i=0;i<232>>2;++i)GROWABLE_HEAP_U32()[(threadInfoStruct>>2)+i]=0;GROWABLE_HEAP_I32()[pthread_ptr>>2]=threadInfoStruct;GROWABLE_HEAP_I32()[threadInfoStruct+12>>2]=threadInfoStruct;var headPtr=threadInfoStruct+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var threadParams={stackBase:stackBase,stackSize:stackSize,allocatedOwnStack:allocatedOwnStack,schedPolicy:schedPolicy,schedPrio:schedPrio,detached:detached,startRoutine:start_routine,pthread_ptr:threadInfoStruct,parent_pthread_ptr:_pthread_self(),arg:arg,transferList:transferList};if(ENVIRONMENT_IS_PTHREAD){threadParams.cmd=\"spawnThread\";postMessage(threadParams,transferList)}else{__spawn_thread(threadParams)}return 0}function _roundf(d){d=+d;return d>=+0?+Math_floor(d+ +.5):+Math_ceil(d-+.5)}function _sysconf(name){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(6,1,name);switch(name){case 30:return 16384;case 85:var maxHeapSize=2147483648;return maxHeapSize/16384;case 132:case 133:case 12:case 137:case 138:case 15:case 235:case 16:case 17:case 18:case 19:case 20:case 149:case 13:case 10:case 236:case 153:case 9:case 21:case 22:case 159:case 154:case 14:case 77:case 78:case 139:case 80:case 81:case 82:case 68:case 67:case 164:case 11:case 29:case 47:case 48:case 95:case 52:case 51:case 46:case 79:return 200809;case 27:case 246:case 127:case 128:case 23:case 24:case 160:case 161:case 181:case 182:case 242:case 183:case 184:case 243:case 244:case 245:case 165:case 178:case 179:case 49:case 50:case 168:case 169:case 175:case 170:case 171:case 172:case 97:case 76:case 32:case 173:case 35:return-1;case 176:case 177:case 7:case 155:case 8:case 157:case 125:case 126:case 92:case 93:case 129:case 130:case 131:case 94:case 91:return 1;case 74:case 60:case 69:case 70:case 4:return 1024;case 31:case 42:case 72:return 32;case 87:case 26:case 33:return 2147483647;case 34:case 1:return 47839;case 38:case 36:return 99;case 43:case 37:return 2048;case 0:return 2097152;case 3:return 65536;case 28:return 32768;case 44:return 32767;case 75:return 16384;case 39:return 1e3;case 89:return 700;case 71:return 256;case 40:return 255;case 2:return 100;case 180:return 64;case 25:return 20;case 5:return 16;case 6:return 6;case 73:return 4;case 84:{if(typeof navigator===\"object\")return navigator[\"hardwareConcurrency\"]||1;return 1}}setErrNo(28);return-1}if(!ENVIRONMENT_IS_PTHREAD)PThread.initMainThreadBlock();else PThread.initWorker();var GLctx;GL.init();var proxiedFunctionTable=[null,_atexit,_emscripten_set_canvas_element_size_main_thread,_fd_close,_fd_seek,_fd_write,_sysconf];var asmLibraryArg={\"e\":___assert_fail,\"r\":___call_main,\"w\":__emscripten_notify_thread_queue,\"a\":_abort,\"l\":_emscripten_conditional_set_current_thread_status,\"d\":_emscripten_futex_wait,\"c\":_emscripten_futex_wake,\"h\":_emscripten_get_now,\"g\":_emscripten_is_main_browser_thread,\"x\":_emscripten_is_main_runtime_thread,\"q\":_emscripten_memcpy_big,\"B\":_emscripten_num_logical_cores,\"t\":_emscripten_receive_on_main_thread_js,\"A\":_emscripten_resize_heap,\"u\":_emscripten_set_canvas_element_size,\"k\":_emscripten_set_current_thread_status,\"s\":_emscripten_set_thread_name,\"v\":_emscripten_webgl_create_context,\"m\":_fd_close,\"o\":_fd_seek,\"i\":_fd_write,\"p\":initPthreadsJS,\"memory\":wasmMemory||Module[\"wasmMemory\"],\"y\":_pthread_cleanup_pop,\"z\":_pthread_cleanup_push,\"j\":_pthread_create,\"b\":_pthread_self,\"f\":_roundf,\"n\":_sysconf,\"table\":wasmTable};var asm=createWasm();Module[\"asm\"]=asm;var ___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=function(){return(___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=Module[\"asm\"][\"C\"]).apply(null,arguments)};var _init=Module[\"_init\"]=function(){return(_init=Module[\"_init\"]=Module[\"asm\"][\"D\"]).apply(null,arguments)};var _register_tensor=Module[\"_register_tensor\"]=function(){return(_register_tensor=Module[\"_register_tensor\"]=Module[\"asm\"][\"E\"]).apply(null,arguments)};var _dispose_data=Module[\"_dispose_data\"]=function(){return(_dispose_data=Module[\"_dispose_data\"]=Module[\"asm\"][\"F\"]).apply(null,arguments)};var _dispose=Module[\"_dispose\"]=function(){return(_dispose=Module[\"_dispose\"]=Module[\"asm\"][\"G\"]).apply(null,arguments)};var _Abs=Module[\"_Abs\"]=function(){return(_Abs=Module[\"_Abs\"]=Module[\"asm\"][\"H\"]).apply(null,arguments)};var _Add=Module[\"_Add\"]=function(){return(_Add=Module[\"_Add\"]=Module[\"asm\"][\"I\"]).apply(null,arguments)};var _AddN=Module[\"_AddN\"]=function(){return(_AddN=Module[\"_AddN\"]=Module[\"asm\"][\"J\"]).apply(null,arguments)};var _ArgMax=Module[\"_ArgMax\"]=function(){return(_ArgMax=Module[\"_ArgMax\"]=Module[\"asm\"][\"K\"]).apply(null,arguments)};var _AvgPool=Module[\"_AvgPool\"]=function(){return(_AvgPool=Module[\"_AvgPool\"]=Module[\"asm\"][\"L\"]).apply(null,arguments)};var _BatchMatMul=Module[\"_BatchMatMul\"]=function(){return(_BatchMatMul=Module[\"_BatchMatMul\"]=Module[\"asm\"][\"M\"]).apply(null,arguments)};var _ClipByValue=Module[\"_ClipByValue\"]=function(){return(_ClipByValue=Module[\"_ClipByValue\"]=Module[\"asm\"][\"N\"]).apply(null,arguments)};var _Conv2D=Module[\"_Conv2D\"]=function(){return(_Conv2D=Module[\"_Conv2D\"]=Module[\"asm\"][\"O\"]).apply(null,arguments)};var _Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=function(){return(_Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=Module[\"asm\"][\"P\"]).apply(null,arguments)};var _Cos=Module[\"_Cos\"]=function(){return(_Cos=Module[\"_Cos\"]=Module[\"asm\"][\"Q\"]).apply(null,arguments)};var _CropAndResize=Module[\"_CropAndResize\"]=function(){return(_CropAndResize=Module[\"_CropAndResize\"]=Module[\"asm\"][\"R\"]).apply(null,arguments)};var _Cumsum=Module[\"_Cumsum\"]=function(){return(_Cumsum=Module[\"_Cumsum\"]=Module[\"asm\"][\"S\"]).apply(null,arguments)};var _DepthToSpace=Module[\"_DepthToSpace\"]=function(){return(_DepthToSpace=Module[\"_DepthToSpace\"]=Module[\"asm\"][\"T\"]).apply(null,arguments)};var _DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=function(){return(_DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=Module[\"asm\"][\"U\"]).apply(null,arguments)};var _Equal=Module[\"_Equal\"]=function(){return(_Equal=Module[\"_Equal\"]=Module[\"asm\"][\"V\"]).apply(null,arguments)};var _Exp=Module[\"_Exp\"]=function(){return(_Exp=Module[\"_Exp\"]=Module[\"asm\"][\"W\"]).apply(null,arguments)};var _FlipLeftRight=Module[\"_FlipLeftRight\"]=function(){return(_FlipLeftRight=Module[\"_FlipLeftRight\"]=Module[\"asm\"][\"X\"]).apply(null,arguments)};var _Floor=Module[\"_Floor\"]=function(){return(_Floor=Module[\"_Floor\"]=Module[\"asm\"][\"Y\"]).apply(null,arguments)};var _FloorDiv=Module[\"_FloorDiv\"]=function(){return(_FloorDiv=Module[\"_FloorDiv\"]=Module[\"asm\"][\"Z\"]).apply(null,arguments)};var _FusedBatchNorm=Module[\"_FusedBatchNorm\"]=function(){return(_FusedBatchNorm=Module[\"_FusedBatchNorm\"]=Module[\"asm\"][\"_\"]).apply(null,arguments)};var _FusedConv2D=Module[\"_FusedConv2D\"]=function(){return(_FusedConv2D=Module[\"_FusedConv2D\"]=Module[\"asm\"][\"$\"]).apply(null,arguments)};var _FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=function(){return(_FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=Module[\"asm\"][\"aa\"]).apply(null,arguments)};var _Gather=Module[\"_Gather\"]=function(){return(_Gather=Module[\"_Gather\"]=Module[\"asm\"][\"ba\"]).apply(null,arguments)};var _GatherNd=Module[\"_GatherNd\"]=function(){return(_GatherNd=Module[\"_GatherNd\"]=Module[\"asm\"][\"ca\"]).apply(null,arguments)};var _Greater=Module[\"_Greater\"]=function(){return(_Greater=Module[\"_Greater\"]=Module[\"asm\"][\"da\"]).apply(null,arguments)};var _GreaterEqual=Module[\"_GreaterEqual\"]=function(){return(_GreaterEqual=Module[\"_GreaterEqual\"]=Module[\"asm\"][\"ea\"]).apply(null,arguments)};var _LeakyRelu=Module[\"_LeakyRelu\"]=function(){return(_LeakyRelu=Module[\"_LeakyRelu\"]=Module[\"asm\"][\"fa\"]).apply(null,arguments)};var _Less=Module[\"_Less\"]=function(){return(_Less=Module[\"_Less\"]=Module[\"asm\"][\"ga\"]).apply(null,arguments)};var _LessEqual=Module[\"_LessEqual\"]=function(){return(_LessEqual=Module[\"_LessEqual\"]=Module[\"asm\"][\"ha\"]).apply(null,arguments)};var _Log=Module[\"_Log\"]=function(){return(_Log=Module[\"_Log\"]=Module[\"asm\"][\"ia\"]).apply(null,arguments)};var _LogicalAnd=Module[\"_LogicalAnd\"]=function(){return(_LogicalAnd=Module[\"_LogicalAnd\"]=Module[\"asm\"][\"ja\"]).apply(null,arguments)};var _Max=Module[\"_Max\"]=function(){return(_Max=Module[\"_Max\"]=Module[\"asm\"][\"ka\"]).apply(null,arguments)};var _MaxPool=Module[\"_MaxPool\"]=function(){return(_MaxPool=Module[\"_MaxPool\"]=Module[\"asm\"][\"la\"]).apply(null,arguments)};var _Maximum=Module[\"_Maximum\"]=function(){return(_Maximum=Module[\"_Maximum\"]=Module[\"asm\"][\"ma\"]).apply(null,arguments)};var _Mean=Module[\"_Mean\"]=function(){return(_Mean=Module[\"_Mean\"]=Module[\"asm\"][\"na\"]).apply(null,arguments)};var _Min=Module[\"_Min\"]=function(){return(_Min=Module[\"_Min\"]=Module[\"asm\"][\"oa\"]).apply(null,arguments)};var _Minimum=Module[\"_Minimum\"]=function(){return(_Minimum=Module[\"_Minimum\"]=Module[\"asm\"][\"pa\"]).apply(null,arguments)};var 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module === 'object')\n module.exports = WasmBackendModule;\n else if (typeof define === 'function' && define['amd'])\n define([], function() { return WasmBackendModule; });\n else if (typeof exports === 'object')\n exports[\"WasmBackendModule\"] = WasmBackendModule;\n ", "// A port of an algorithm by Johannes Baag\u00F8e , 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e \n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n//\n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n//\n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n var me = this, mash = Mash();\n\n me.next = function() {\n var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n me.s0 = me.s1;\n me.s1 = me.s2;\n return me.s2 = t - (me.c = t | 0);\n };\n\n // Apply the seeding algorithm from Baagoe.\n me.c = 1;\n me.s0 = mash(' ');\n me.s1 = mash(' ');\n me.s2 = mash(' ');\n me.s0 -= mash(seed);\n if (me.s0 < 0) { me.s0 += 1; }\n me.s1 -= mash(seed);\n if (me.s1 < 0) { me.s1 += 1; }\n me.s2 -= mash(seed);\n if (me.s2 < 0) { me.s2 += 1; }\n mash = null;\n}\n\nfunction copy(f, t) {\n t.c = f.c;\n t.s0 = f.s0;\n t.s1 = f.s1;\n t.s2 = f.s2;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new Alea(seed),\n state = opts && opts.state,\n prng = xg.next;\n prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n prng.double = function() {\n return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n };\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nfunction Mash() {\n var n = 0xefc8249d;\n\n var mash = function(data) {\n data = String(data);\n for (var i = 0; i < data.length; i++) {\n n += data.charCodeAt(i);\n var h = 0.02519603282416938 * n;\n n = h >>> 0;\n h -= n;\n h *= n;\n n = h >>> 0;\n h -= n;\n n += h * 0x100000000; // 2^32\n }\n return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n };\n\n return mash;\n}\n\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.alea = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n\n // Set up generator function.\n me.next = function() {\n var t = me.x ^ (me.x << 11);\n me.x = me.y;\n me.y = me.z;\n me.z = me.w;\n return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n };\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor128 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var t = (me.x ^ (me.x >>> 2));\n me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n return (me.d = (me.d + 362437 | 0)) +\n (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n };\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n me.v = 0;\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n if (k == strseed.length) {\n me.d = me.x << 10 ^ me.x >>> 4;\n }\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n t.v = f.v;\n t.d = f.d;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorwow = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n // Update xor generator.\n var X = me.x, i = me.i, t, v, w;\n t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n X[i] = v;\n me.i = (i + 1) & 7;\n return v;\n };\n\n function init(me, seed) {\n var j, w, X = [];\n\n if (seed === (seed | 0)) {\n // Seed state array using a 32-bit integer.\n w = X[0] = seed;\n } else {\n // Seed state using a string.\n seed = '' + seed;\n for (j = 0; j < seed.length; ++j) {\n X[j & 7] = (X[j & 7] << 15) ^\n (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n }\n }\n // Enforce an array length of 8, not all zeroes.\n while (X.length < 8) X.push(0);\n for (j = 0; j < 8 && X[j] === 0; ++j);\n if (j == 8) w = X[7] = -1; else w = X[j];\n\n me.x = X;\n me.i = 0;\n\n // Discard an initial 256 values.\n for (j = 256; j > 0; --j) {\n me.next();\n }\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.x = f.x.slice();\n t.i = f.i;\n return t;\n}\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.x) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorshift7 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated. For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1); // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897); // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C. This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n var w = me.w,\n X = me.X, i = me.i, t, v;\n // Update Weyl generator.\n me.w = w = (w + 0x61c88647) | 0;\n // Update xor generator.\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n // Update Xor generator array state.\n v = X[i] = v ^ t;\n me.i = i;\n // Result is the combination.\n return (v + (w ^ (w >>> 16))) | 0;\n };\n\n function init(me, seed) {\n var t, v, i, j, w, X = [], limit = 128;\n if (seed === (seed | 0)) {\n // Numeric seeds initialize v, which is used to generates X.\n v = seed;\n seed = null;\n } else {\n // String seeds are mixed into v and X one character at a time.\n seed = seed + '\\0';\n v = 0;\n limit = Math.max(limit, seed.length);\n }\n // Initialize circular array and weyl value.\n for (i = 0, j = -32; j < limit; ++j) {\n // Put the unicode characters into the array, and shuffle them.\n if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n // After 32 shuffles, take v as the starting w value.\n if (j === 0) w = v;\n v ^= v << 10;\n v ^= v >>> 15;\n v ^= v << 4;\n v ^= v >>> 13;\n if (j >= 0) {\n w = (w + 0x61c88647) | 0; // Weyl.\n t = (X[j & 127] ^= (v + w)); // Combine xor and weyl to init array.\n i = (0 == t) ? i + 1 : 0; // Count zeroes.\n }\n }\n // We have detected all zeroes; make the key nonzero.\n if (i >= 128) {\n X[(seed && seed.length || 0) & 127] = -1;\n }\n // Run the generator 512 times to further mix the state before using it.\n // Factoring this as a function slows the main generator, so it is just\n // unrolled here. The weyl generator is not advanced while warming up.\n i = 127;\n for (j = 4 * 128; j > 0; --j) {\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n X[i] = v ^ t;\n }\n // Storing state as object members is faster than using closure variables.\n me.w = w;\n me.X = X;\n me.i = i;\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.i = f.i;\n t.w = f.w;\n t.X = f.X.slice();\n return t;\n};\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.X) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor4096 = impl;\n}\n\n})(\n this, // window object or global\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var b = me.b, c = me.c, d = me.d, a = me.a;\n b = (b << 25) ^ (b >>> 7) ^ c;\n c = (c - d) | 0;\n d = (d << 24) ^ (d >>> 8) ^ a;\n a = (a - b) | 0;\n me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n me.c = c = (c - d) | 0;\n me.d = (d << 16) ^ (c >>> 16) ^ a;\n return me.a = (a - b) | 0;\n };\n\n /* The following is non-inverted tyche, which has better internal\n * bit diffusion, but which is about 25% slower than tyche-i in JS.\n me.next = function() {\n var a = me.a, b = me.b, c = me.c, d = me.d;\n a = (me.a + me.b | 0) >>> 0;\n d = me.d ^ a; d = d << 16 ^ d >>> 16;\n c = me.c + d | 0;\n b = me.b ^ c; b = b << 12 ^ d >>> 20;\n me.a = a = a + b | 0;\n d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n me.c = c = c + d | 0;\n b = b ^ c;\n return me.b = (b << 7 ^ b >>> 25);\n }\n */\n\n me.a = 0;\n me.b = 0;\n me.c = 2654435769 | 0;\n me.d = 1367130551;\n\n if (seed === Math.floor(seed)) {\n // Integer seed.\n me.a = (seed / 0x100000000) | 0;\n me.b = seed | 0;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 20; k++) {\n me.b ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.a = f.a;\n t.b = f.b;\n t.c = f.c;\n t.d = f.d;\n return t;\n};\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.tychei = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "/*\nCopyright 2019 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (global, pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\n\nvar width = 256, // each RC4 output is 0 <= x < 256\n chunks = 6, // at least six RC4 outputs for each double\n digits = 52, // there are 52 significant digits in a double\n rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n startdenom = math.pow(width, chunks),\n significance = math.pow(2, digits),\n overflow = significance * 2,\n mask = width - 1,\n nodecrypto; // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n var key = [];\n options = (options == true) ? { entropy: true } : (options || {});\n\n // Flatten the seed string or build one from local entropy if needed.\n var shortseed = mixkey(flatten(\n options.entropy ? [seed, tostring(pool)] :\n (seed == null) ? autoseed() : seed, 3), key);\n\n // Use the seed to initialize an ARC4 generator.\n var arc4 = new ARC4(key);\n\n // This function returns a random double in [0, 1) that contains\n // randomness in every bit of the mantissa of the IEEE 754 value.\n var prng = function() {\n var n = arc4.g(chunks), // Start with a numerator n < 2 ^ 48\n d = startdenom, // and denominator d = 2 ^ 48.\n x = 0; // and no 'extra last byte'.\n while (n < significance) { // Fill up all significant digits by\n n = (n + x) * width; // shifting numerator and\n d *= width; // denominator and generating a\n x = arc4.g(1); // new least-significant-byte.\n }\n while (n >= overflow) { // To avoid rounding up, before adding\n n /= 2; // last byte, shift everything\n d /= 2; // right using integer math until\n x >>>= 1; // we have exactly the desired bits.\n }\n return (n + x) / d; // Form the number within [0, 1).\n };\n\n prng.int32 = function() { return arc4.g(4) | 0; }\n prng.quick = function() { return arc4.g(4) / 0x100000000; }\n prng.double = prng;\n\n // Mix the randomness into accumulated entropy.\n mixkey(tostring(arc4.S), pool);\n\n // Calling convention: what to return as a function of prng, seed, is_math.\n return (options.pass || callback ||\n function(prng, seed, is_math_call, state) {\n if (state) {\n // Load the arc4 state from the given state if it has an S array.\n if (state.S) { copy(state, arc4); }\n // Only provide the .state method if requested via options.state.\n prng.state = function() { return copy(arc4, {}); }\n }\n\n // If called as a method of Math (Math.seedrandom()), mutate\n // Math.random because that is how seedrandom.js has worked since v1.0.\n if (is_math_call) { math[rngname] = prng; return seed; }\n\n // Otherwise, it is a newer calling convention, so return the\n // prng directly.\n else return prng;\n })(\n prng,\n shortseed,\n 'global' in options ? options.global : (this == math),\n options.state);\n}\n\n//\n// ARC4\n//\n// An ARC4 implementation. The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4. Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n var t, keylen = key.length,\n me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n // The empty key [] is treated as [0].\n if (!keylen) { key = [keylen++]; }\n\n // Set up S using the standard key scheduling algorithm.\n while (i < width) {\n s[i] = i++;\n }\n for (i = 0; i < width; i++) {\n s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n s[j] = t;\n }\n\n // The \"g\" method returns the next (count) outputs as one number.\n (me.g = function(count) {\n // Using instance members instead of closure state nearly doubles speed.\n var t, r = 0,\n i = me.i, j = me.j, s = me.S;\n while (count--) {\n t = s[i = mask & (i + 1)];\n r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n }\n me.i = i; me.j = j;\n return r;\n // For robust unpredictability, the function call below automatically\n // discards an initial batch of values. This is called RC4-drop[256].\n // See http://google.com/search?q=rsa+fluhrer+response&btnI\n })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n t.i = f.i;\n t.j = f.j;\n t.S = f.S.slice();\n return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n var result = [], typ = (typeof obj), prop;\n if (depth && typ == 'object') {\n for (prop in obj) {\n try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n }\n }\n return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n var stringseed = seed + '', smear, j = 0;\n while (j < stringseed.length) {\n key[mask & j] =\n mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n }\n return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n try {\n var out;\n if (nodecrypto && (out = nodecrypto.randomBytes)) {\n // The use of 'out' to remember randomBytes makes tight minified code.\n out = out(width);\n } else {\n out = new Uint8Array(width);\n (global.crypto || global.msCrypto).getRandomValues(out);\n }\n return tostring(out);\n } catch (e) {\n var browser = global.navigator,\n plugins = browser && browser.plugins;\n return [+new Date, global, plugins, global.screen, tostring(pool)];\n }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool. Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n module.exports = seedrandom;\n // When in node.js, try using crypto package for autoseeding.\n try {\n nodecrypto = require('crypto');\n } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n define(function() { return seedrandom; });\n} else {\n // When included as a plain script, set up Math.seedrandom global.\n math['seed' + rngname] = seedrandom;\n}\n\n\n// End anonymous scope, and pass initial values.\n})(\n // global: `self` in browsers (including strict mode and web workers),\n // otherwise `this` in Node and other environments\n (typeof self !== 'undefined') ? self : this,\n [], // pool: entropy pool starts empty\n Math // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random(); // 0 <= x < 1. Every bit is random.\n// var x = random.quick(); // 0 <= x < 1. 32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three. It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures. Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "export const wasmWorkerContents = 'var threadInfoStruct=0;var selfThreadId=0;var parentThreadId=0;var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(\" \");console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(\" \");postMessage({cmd:\"alert\",text:text,threadId:selfThreadId})}var err=threadPrintErr;this.alert=threadAlert;Module[\"instantiateWasm\"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module[\"wasmModule\"],info);Module[\"wasmModule\"]=null;receiveInstance(instance);return instance.exports};this.onmessage=function(e){try{if(e.data.cmd===\"load\"){Module[\"DYNAMIC_BASE\"]=e.data.DYNAMIC_BASE;Module[\"DYNAMICTOP_PTR\"]=e.data.DYNAMICTOP_PTR;Module[\"wasmModule\"]=e.data.wasmModule;Module[\"wasmMemory\"]=e.data.wasmMemory;Module[\"buffer\"]=Module[\"wasmMemory\"].buffer;Module[\"ENVIRONMENT_IS_PTHREAD\"]=true;if(typeof e.data.urlOrBlob===\"string\"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}Module=WasmBackendModuleThreadedSimd(Module);postMessage({\"cmd\":\"loaded\"})}else if(e.data.cmd===\"objectTransfer\"){Module[\"PThread\"].receiveObjectTransfer(e.data)}else if(e.data.cmd===\"run\"){Module[\"__performance_now_clock_drift\"]=performance.now()-e.data.time;threadInfoStruct=e.data.threadInfoStruct;Module[\"__register_pthread_ptr\"](threadInfoStruct,0,0);selfThreadId=e.data.selfThreadId;parentThreadId=e.data.parentThreadId;var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module[\"establishStackSpace\"](top,max);Module[\"_emscripten_tls_init\"]();Module[\"PThread\"].receiveObjectTransfer(e.data);Module[\"PThread\"].setThreadStatus(Module[\"_pthread_self\"](),1);try{var result=Module[\"dynCall_ii\"](e.data.start_routine,e.data.arg);if(!Module[\"getNoExitRuntime\"]())Module[\"PThread\"].threadExit(result)}catch(ex){if(ex===\"Canceled!\"){Module[\"PThread\"].threadCancel()}else if(ex!=\"unwind\"){Atomics.store(Module[\"HEAPU32\"],threadInfoStruct+4>>2,ex instanceof Module[\"ExitStatus\"]?ex.status:-2);Atomics.store(Module[\"HEAPU32\"],threadInfoStruct+0>>2,1);Module[\"_emscripten_futex_wake\"](threadInfoStruct+0,2147483647);if(!(ex instanceof Module[\"ExitStatus\"]))throw ex}}}else if(e.data.cmd===\"cancel\"){if(threadInfoStruct){Module[\"PThread\"].threadCancel()}}else if(e.data.target===\"setimmediate\"){}else if(e.data.cmd===\"processThreadQueue\"){if(threadInfoStruct){Module[\"_emscripten_current_thread_process_queued_calls\"]()}}else{err(\"worker.js received unknown command \"+e.data.cmd);err(e.data)}}catch(ex){err(\"worker.js onmessage() captured an uncaught exception: \"+ex);if(ex.stack)err(ex.stack);throw ex}};if(typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\"){self={location:{href:__filename}};var onmessage=this.onmessage;var nodeWorkerThreads=require(\"worker_threads\");Worker=nodeWorkerThreads.Worker;var parentPort=nodeWorkerThreads.parentPort;parentPort.on(\"message\",function(data){onmessage({data:data})});var nodeFS=require(\"fs\");var nodeRead=function(filename){return nodeFS.readFileSync(filename,\"utf8\")};function globalEval(x){global.require=require;global.Module=Module;eval.call(null,x)}importScripts=function(f){globalEval(nodeRead(f))};postMessage=function(msg){parentPort.postMessage(msg)};if(typeof performance===\"undefined\"){performance={now:function(){return Date.now()}}}}';", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "// wrapper to load tfjs in a single place so version can be changed quickly\n\n// simplified\n// { modules: 1061, moduleBytes: 3772720, outputBytes: 1531035 }\n\n// export * from '@tensorflow/tfjs/dist/index.js';\n// export * from '@tensorflow/tfjs-backend-wasm';\n\n// modular\n// { modules: 1064, moduleBytes: 3793219, outputBytes: 1535600 }\n\n// get versions of all packages.\nimport { version as tfjs } from '@tensorflow/tfjs/package.json';\nimport { version as versionCore } from '@tensorflow/tfjs-core/package.json';\nimport { version as versionData } from '@tensorflow/tfjs-data/package.json';\nimport { version as versionLayers } from '@tensorflow/tfjs-layers/package.json';\nimport { version as versionConverter } from '@tensorflow/tfjs-converter/package.json';\n// for backends, get version from source so it can register backend during import\nimport { version_cpu } from '@tensorflow/tfjs-backend-cpu/dist/index.js';\nimport { version_webgl } from '@tensorflow/tfjs-backend-webgl/dist/index.js';\nimport { version_wasm } from '@tensorflow/tfjs-backend-wasm/dist/index.js';\n\n// export all\nexport * from '@tensorflow/tfjs-core/dist/index.js';\nexport * from '@tensorflow/tfjs-layers/dist/index.js';\nexport * from '@tensorflow/tfjs-converter/dist/index.js';\nexport * as data from '@tensorflow/tfjs-data/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-cpu/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-webgl/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-wasm/dist/index.js';\n\n// export versions\nexport const version = {\n tfjs,\n 'tfjs-core': versionCore,\n 'tfjs-data': versionData,\n 'tfjs-layers': versionLayers,\n 'tfjs-converter': versionConverter,\n 'tfjs-backend-cpu': version_cpu,\n 'tfjs-backend-webgl': version_webgl,\n 'tfjs-backend-wasm': version_wasm,\n};\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport const config = {\n name: 'humangl',\n priority: 99,\n canvas: null,\n width: 1024,\n height: 1024,\n webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n alpha: false,\n antialias: false,\n premultipliedAlpha: false,\n preserveDrawingBuffer: false,\n depth: false,\n stencil: false,\n failIfMajorPerformanceCaveat: false,\n desynchronized: true,\n },\n};\n\nexport function register() {\n if (!tf.findBackend(config.name)) {\n // @ts-ignore\n config.canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(config.width, config.height) : document.createElement('canvas');\n // @ts-ignore\n const gl = config.canvas.getContext('webgl2', config.webGLattr);\n tf.setWebGLContext(2, gl);\n const ctx = new tf.GPGPUContext(gl);\n tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n const kernels = tf.getKernelsForBackend('webgl');\n kernels.forEach((kernelConfig) => {\n const newKernelConfig = { ...kernelConfig, backendName: config.name };\n tf.registerKernel(newKernelConfig);\n });\n tf.ENV.set('WEBGL_VERSION', 2);\n tf.ENV.set('WEBGL_MAX_TEXTURE_SIZE', gl.getParameter(gl.MAX_TEXTURE_SIZE));\n tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n }\n}\n", "import { log } from './log.js';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as backend from './tfjs/backend.js';\nimport * as facemesh from './blazeface/facemesh.js';\nimport * as age from './age/age.js';\nimport * as gender from './gender/gender.js';\nimport * as emotion from './emotion/emotion.js';\nimport * as embedding from './embedding/embedding.js';\nimport * as posenet from './posenet/posenet.js';\nimport * as blazepose from './blazepose/blazepose.js';\nimport * as handpose from './handpose/handpose.js';\nimport * as gesture from './gesture/gesture.js';\nimport * as image from './image.js';\nimport * as profile from './profile.js';\nimport * as config from '../config.js';\nimport * as sample from './sample.js';\nimport * as app from '../package.json';\n\n// helper function: gets elapsed time on both browser and nodejs\nconst now = () => {\n if (typeof performance !== 'undefined') return performance.now();\n return parseInt(Number(process.hrtime.bigint()) / 1000 / 1000);\n};\n\n// helper function: perform deep merge of multiple objects so it allows full inheriance with overrides\nfunction mergeDeep(...objects) {\n const isObject = (obj) => obj && typeof obj === 'object';\n return objects.reduce((prev, obj) => {\n Object.keys(obj || {}).forEach((key) => {\n const pVal = prev[key];\n const oVal = obj[key];\n if (Array.isArray(pVal) && Array.isArray(oVal)) {\n prev[key] = pVal.concat(...oVal);\n } else if (isObject(pVal) && isObject(oVal)) {\n prev[key] = mergeDeep(pVal, oVal);\n } else {\n prev[key] = oVal;\n }\n });\n return prev;\n }, {});\n}\n\nclass Human {\n constructor(userConfig = {}) {\n this.tf = tf;\n this.version = app.version;\n this.config = mergeDeep(config.default, userConfig);\n this.fx = null;\n this.state = 'idle';\n this.numTensors = 0;\n this.analyzeMemoryLeaks = false;\n this.checkSanity = false;\n this.firstRun = true;\n this.perf = {};\n // object that contains all initialized models\n this.models = {\n facemesh: null,\n posenet: null,\n handpose: null,\n iris: null,\n age: null,\n gender: null,\n emotion: null,\n };\n // export raw access to underlying models\n this.facemesh = facemesh;\n this.age = age;\n this.gender = gender;\n this.emotion = emotion;\n this.body = posenet;\n this.hand = handpose;\n }\n\n profile() {\n if (this.config.profile) return profile.data;\n return {};\n }\n\n // helper function: measure tensor leak\n analyze(...msg) {\n if (!this.analyzeMemoryLeaks) return;\n const current = tf.engine().state.numTensors;\n const previous = this.numTensors;\n this.numTensors = current;\n const leaked = current - previous;\n if (leaked !== 0) log(...msg, leaked);\n }\n\n // quick sanity check on inputs\n sanity(input) {\n if (!this.checkSanity) return null;\n if (!input) return 'input is not defined';\n if (tf.ENV.flags.IS_NODE && !(input instanceof tf.Tensor)) {\n return 'input must be a tensor';\n }\n try {\n tf.getBackend();\n } catch {\n return 'backend not loaded';\n }\n return null;\n }\n\n simmilarity(embedding1, embedding2) {\n if (this.config.face.embedding.enabled) return embedding.simmilarity(embedding1, embedding2);\n return 0;\n }\n\n // preload models, not explicitly required as it's done automatically on first use\n async load(userConfig) {\n this.state = 'load';\n const timeStamp = now();\n if (userConfig) this.config = mergeDeep(this.config, userConfig);\n\n if (this.firstRun) {\n log(`version: ${this.version} TensorFlow/JS version: ${tf.version_core}`);\n await this.checkBackend(true);\n if (tf.ENV.flags.IS_BROWSER) {\n log('configuration:', this.config);\n log('tf flags:', tf.ENV.flags);\n }\n this.firstRun = false;\n }\n\n if (this.config.async) {\n [\n this.models.facemesh,\n this.models.age,\n this.models.gender,\n this.models.emotion,\n this.models.embedding,\n this.models.posenet,\n this.models.handpose,\n this.models.blazepose,\n ] = await Promise.all([\n this.models.facemesh || (this.config.face.enabled ? facemesh.load(this.config) : null),\n this.models.age || ((this.config.face.enabled && this.config.face.age.enabled) ? age.load(this.config) : null),\n this.models.gender || ((this.config.face.enabled && this.config.face.gender.enabled) ? gender.load(this.config) : null),\n this.models.emotion || ((this.config.face.enabled && this.config.face.emotion.enabled) ? emotion.load(this.config) : null),\n this.models.embedding || ((this.config.face.enabled && this.config.face.embedding.enabled) ? embedding.load(this.config) : null),\n this.models.posenet || (this.config.body.enabled ? posenet.load(this.config) : null),\n this.models.handpose || (this.config.hand.enabled ? handpose.load(this.config) : null),\n this.models.blazepose || (this.config.pose.enabled ? blazepose.load(this.config) : null),\n ]);\n } else {\n if (this.config.face.enabled && !this.models.facemesh) this.models.facemesh = await facemesh.load(this.config);\n if (this.config.face.enabled && this.config.face.age.enabled && !this.models.age) this.models.age = await age.load(this.config);\n if (this.config.face.enabled && this.config.face.gender.enabled && !this.models.gender) this.models.gender = await gender.load(this.config);\n if (this.config.face.enabled && this.config.face.emotion.enabled && !this.models.emotion) this.models.emotion = await emotion.load(this.config);\n if (this.config.face.enabled && this.config.face.embedding.enabled && !this.models.embedding) this.models.embedding = await embedding.load(this.config);\n if (this.config.body.enabled && !this.models.posenet) this.models.posenet = await posenet.load(this.config);\n if (this.config.hand.enabled && !this.models.handpose) this.models.handpose = await handpose.load(this.config);\n if (this.config.pose.enabled && !this.models.blazepose) this.models.blazepose = await blazepose.load(this.config);\n }\n const current = Math.trunc(now() - timeStamp);\n if (current > (this.perf.load || 0)) this.perf.load = current;\n }\n\n // check if backend needs initialization if it changed\n async checkBackend(force) {\n if (this.config.backend && (this.config.backend !== '') && force || (tf.getBackend() !== this.config.backend)) {\n const timeStamp = now();\n this.state = 'backend';\n /* force backend reload\n if (this.config.backend in tf.engine().registry) {\n const backendFactory = tf.findBackendFactory(this.config.backend);\n tf.removeBackend(this.config.backend);\n tf.registerBackend(this.config.backend, backendFactory);\n } else {\n log('Backend not registred:', this.config.backend);\n }\n */\n\n log('setting backend:', this.config.backend);\n\n if (this.config.backend === 'wasm') {\n log('settings wasm path:', this.config.wasmPath);\n tf.setWasmPaths(this.config.wasmPath);\n const simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n if (!simd) log('warning: wasm simd support is not enabled');\n }\n\n if (this.config.backend === 'humangl') {\n log('registering humangl backend');\n backend.register();\n }\n\n await tf.setBackend(this.config.backend);\n tf.enableProdMode();\n /* debug mode is really too mcuh\n tf.enableDebugMode();\n */\n if (tf.getBackend() === 'webgl') {\n if (this.config.deallocate) {\n log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', this.config.deallocate);\n tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', this.config.deallocate ? 0 : -1);\n }\n tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n const gl = await tf.backend().getGPGPUContext().gl;\n log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);\n }\n await tf.ready();\n this.perf.backend = Math.trunc(now() - timeStamp);\n }\n }\n\n async detectFace(input) {\n // run facemesh, includes blazeface and iris\n // eslint-disable-next-line no-async-promise-executor\n let timeStamp;\n let ageRes;\n let genderRes;\n let emotionRes;\n let embeddingRes;\n const faceRes = [];\n this.state = 'run:face';\n timeStamp = now();\n const faces = await this.models.facemesh?.estimateFaces(input, this.config);\n this.perf.face = Math.trunc(now() - timeStamp);\n for (const face of faces) {\n this.analyze('Get Face');\n\n // is something went wrong, skip the face\n if (!face.image || face.image.isDisposedInternal) {\n log('Face object is disposed:', face.image);\n continue;\n }\n\n // run age, inherits face from blazeface\n this.analyze('Start Age:');\n if (this.config.async) {\n ageRes = this.config.face.age.enabled ? age.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:age';\n timeStamp = now();\n ageRes = this.config.face.age.enabled ? await age.predict(face.image, this.config) : {};\n this.perf.age = Math.trunc(now() - timeStamp);\n }\n\n // run gender, inherits face from blazeface\n this.analyze('Start Gender:');\n if (this.config.async) {\n genderRes = this.config.face.gender.enabled ? gender.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:gender';\n timeStamp = now();\n genderRes = this.config.face.gender.enabled ? await gender.predict(face.image, this.config) : {};\n this.perf.gender = Math.trunc(now() - timeStamp);\n }\n\n // run emotion, inherits face from blazeface\n this.analyze('Start Emotion:');\n if (this.config.async) {\n emotionRes = this.config.face.emotion.enabled ? emotion.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:emotion';\n timeStamp = now();\n emotionRes = this.config.face.emotion.enabled ? await emotion.predict(face.image, this.config) : {};\n this.perf.emotion = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Emotion:');\n\n // run emotion, inherits face from blazeface\n this.analyze('Start Embedding:');\n if (this.config.async) {\n embeddingRes = this.config.face.embedding.enabled ? embedding.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:embedding';\n timeStamp = now();\n embeddingRes = this.config.face.embedding.enabled ? await embedding.predict(face.image, this.config) : {};\n this.perf.embedding = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Emotion:');\n\n // if async wait for results\n if (this.config.async) {\n [ageRes, genderRes, emotionRes, embeddingRes] = await Promise.all([ageRes, genderRes, emotionRes, embeddingRes]);\n }\n\n this.analyze('Finish Face:');\n // dont need face anymore\n face.image.dispose();\n\n // calculate iris distance\n // iris: array[ center, left, top, right, bottom]\n if (!this.config.face.iris.enabled) {\n delete face.annotations.leftEyeIris;\n delete face.annotations.rightEyeIris;\n }\n const irisSize = (face.annotations.leftEyeIris && face.annotations.rightEyeIris)\n /* average human iris size is 11.7mm */\n ? 11.7 * Math.max(Math.abs(face.annotations.leftEyeIris[3][0] - face.annotations.leftEyeIris[1][0]), Math.abs(face.annotations.rightEyeIris[4][1] - face.annotations.rightEyeIris[2][1]))\n : 0;\n\n // combine results\n faceRes.push({\n confidence: face.confidence,\n box: face.box,\n mesh: face.mesh,\n boxRaw: face.boxRaw,\n meshRaw: face.meshRaw,\n annotations: face.annotations,\n age: ageRes.age,\n gender: genderRes.gender,\n genderConfidence: genderRes.confidence,\n emotion: emotionRes,\n embedding: embeddingRes,\n iris: (irisSize !== 0) ? Math.trunc(irisSize) / 100 : 0,\n });\n this.analyze('End Face');\n }\n this.analyze('End FaceMesh:');\n if (this.config.async) {\n if (this.perf.face) delete this.perf.face;\n if (this.perf.age) delete this.perf.age;\n if (this.perf.gender) delete this.perf.gender;\n if (this.perf.emotion) delete this.perf.emotion;\n }\n return faceRes;\n }\n\n async image(input, userConfig = {}) {\n this.state = 'image';\n this.config = mergeDeep(this.config, userConfig);\n const process = image.process(input, this.config);\n process.tensor.dispose();\n return process.canvas;\n }\n\n // main detect function\n async detect(input, userConfig = {}) {\n // detection happens inside a promise\n return new Promise(async (resolve) => {\n this.state = 'config';\n let timeStamp;\n\n // update configuration\n this.config = mergeDeep(this.config, userConfig);\n\n // sanity checks\n this.state = 'check';\n const error = this.sanity(input);\n if (error) {\n log(error, input);\n resolve({ error });\n }\n\n let poseRes;\n let blazeposeRes;\n let handRes;\n let faceRes;\n\n const timeStart = now();\n\n // configure backend\n await this.checkBackend();\n\n // load models if enabled\n await this.load();\n\n if (this.config.scoped) tf.engine().startScope();\n this.analyze('Start Scope:');\n\n timeStamp = now();\n const process = image.process(input, this.config);\n if (!process || !process.tensor) {\n log('could not convert input to tensor');\n resolve({ error: 'could not convert input to tensor' });\n return;\n }\n this.perf.image = Math.trunc(now() - timeStamp);\n this.analyze('Get Image:');\n\n // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n if (this.config.async) {\n faceRes = this.config.face.enabled ? this.detectFace(process.tensor) : [];\n if (this.perf.face) delete this.perf.face;\n } else {\n this.state = 'run:face';\n timeStamp = now();\n faceRes = this.config.face.enabled ? await this.detectFace(process.tensor) : [];\n this.perf.face = Math.trunc(now() - timeStamp);\n }\n\n // run posenet\n this.analyze('Start Body:');\n if (this.config.async) {\n poseRes = this.config.body.enabled ? this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n if (this.perf.body) delete this.perf.body;\n } else {\n this.state = 'run:body';\n timeStamp = now();\n poseRes = this.config.body.enabled ? await this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n this.perf.body = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Body:');\n\n // run posenet\n this.analyze('Start Pose:');\n if (this.config.async) {\n blazeposeRes = this.config.pose.enabled ? blazepose.predict(process.tensor, this.config) : [];\n if (this.perf.pose) delete this.perf.pose;\n } else {\n this.state = 'run:pose';\n timeStamp = now();\n blazeposeRes = this.config.pose.enabled ? await blazepose.predict(process.tensor, this.config) : [];\n this.perf.pose = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Pose:');\n\n // run handpose\n this.analyze('Start Hand:');\n if (this.config.async) {\n handRes = this.config.hand.enabled ? this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n if (this.perf.hand) delete this.perf.hand;\n } else {\n this.state = 'run:hand';\n timeStamp = now();\n handRes = this.config.hand.enabled ? await this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n this.perf.hand = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Hand:');\n\n // if async wait for results\n if (this.config.async) {\n [faceRes, poseRes, blazeposeRes, handRes] = await Promise.all([faceRes, poseRes, blazeposeRes, handRes]);\n }\n process.tensor.dispose();\n\n if (this.config.scoped) tf.engine().endScope();\n this.analyze('End Scope:');\n\n let gestureRes = [];\n if (this.config.gesture.enabled) {\n timeStamp = now();\n gestureRes = [...gesture.face(faceRes), ...gesture.body(poseRes), ...gesture.hand(handRes), ...gesture.iris(faceRes)];\n if (!this.config.async) this.perf.gesture = Math.trunc(now() - timeStamp);\n else if (this.perf.gesture) delete this.perf.gesture;\n }\n\n this.perf.total = Math.trunc(now() - timeStart);\n this.state = 'idle';\n resolve({ face: faceRes, body: poseRes, hand: handRes, pose: blazeposeRes, gesture: gestureRes, performance: this.perf, canvas: process.canvas });\n });\n }\n\n async warmupBitmap() {\n const b64toBlob = (base64, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n let blob;\n let res;\n switch (this.config.warmup) {\n case 'face': blob = await b64toBlob(sample.face); break;\n case 'full': blob = await b64toBlob(sample.body); break;\n default: blob = null;\n }\n if (blob) {\n const bitmap = await createImageBitmap(blob);\n res = await this.detect(bitmap, config);\n bitmap.close();\n }\n return res;\n }\n\n async warmupCanvas() {\n return new Promise((resolve) => {\n let src;\n let size = 0;\n switch (this.config.warmup) {\n case 'face':\n size = 256;\n src = 'data:image/jpeg;base64,' + sample.face;\n break;\n case 'full':\n size = 1200;\n src = 'data:image/jpeg;base64,' + sample.body;\n break;\n default:\n src = null;\n }\n const img = new Image(size, size);\n img.onload = () => {\n const canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(size, size) : document.createElement('canvas');\n canvas.width = size;\n canvas.height = size;\n const ctx = canvas.getContext('2d');\n ctx.drawImage(img, 0, 0);\n const data = ctx.getImageData(0, 0, size, size);\n this.detect(data, config).then((res) => resolve(res));\n };\n if (src) img.src = src;\n else resolve(null);\n });\n }\n\n async warmup(userConfig) {\n const t0 = now();\n if (userConfig) this.config = mergeDeep(this.config, userConfig);\n const video = this.config.videoOptimized;\n this.config.videoOptimized = false;\n let res;\n if (typeof createImageBitmap === 'function') res = await this.warmupBitmap();\n else res = await this.warmupCanvas();\n this.config.videoOptimized = video;\n const t1 = now();\n log('Warmup', this.config.warmup, (t1 - t0), res);\n return res;\n }\n}\n\nexport { Human as default };\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\n// import * as profile from '../profile.js';\n\nconst models = {};\n\nexport async function load(config) {\n if (!models.blazepose) {\n models.blazepose = await tf.loadGraphModel(config.pose.modelPath);\n log(`load model: ${config.pose.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.blazepose;\n}\n\nexport async function predict(image, config) {\n if (!models.blazepose) return null;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [config.pose.inputSize, config.pose.inputSize], false);\n const enhance = tf.div(resize, 127.5).sub(1);\n tf.dispose(resize);\n const logits = await models.blazepose.predict(enhance);\n //\n tf.dispose(enhance);\n logits.map((logit) => logit.dispose());\n resolve(logits);\n });\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nfunction getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\nfunction getBoxCenter(box) {\n return [\n box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n ];\n}\nfunction cutBoxFromImageAndResize(box, image, cropSize) {\n const h = image.shape[1];\n const w = image.shape[2];\n const boxes = [[\n box.startPoint[1] / h,\n box.startPoint[0] / w,\n box.endPoint[1] / h,\n box.endPoint[0] / w,\n ]];\n return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\nfunction scaleBoxCoordinates(box, factor) {\n const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n const palmLandmarks = box.palmLandmarks.map((coord) => {\n const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n return scaledCoord;\n });\n return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\nfunction enlargeBox(box, factor = 1.5) {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\nfunction squarifyBox(box) {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const maxEdge = Math.max(...size);\n const halfSize = maxEdge / 2;\n const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\nfunction shiftBox(box, shiftFactor) {\n const boxSize = [\n box.endPoint[0] - box.startPoint[0],\n box.endPoint[1] - box.startPoint[1],\n ];\n const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]];\n const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\nexport {\n cutBoxFromImageAndResize,\n enlargeBox,\n getBoxCenter,\n getBoxSize,\n scaleBoxCoordinates,\n shiftBox,\n squarifyBox,\n};\n", "function normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\nfunction computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\nconst buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\nfunction dot(v1, v2) {\n let product = 0;\n for (let i = 0; i < v1.length; i++) {\n product += v1[i] * v2[i];\n }\n return product;\n}\nfunction getColumnFrom2DArr(arr, columnIndex) {\n const column = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\nfunction multiplyTransformMatrices(mat1, mat2) {\n const product = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) {\n // @ts-ignore\n product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n }\n return product;\n}\nfunction buildRotationMatrix(rotation, center) {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\nfunction invertTransformMatrix(matrix) {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [\n -dot(rotationComponent[0], translationComponent),\n -dot(rotationComponent[1], translationComponent),\n ];\n return [\n rotationComponent[0].concat(invertedTranslation[0]),\n rotationComponent[1].concat(invertedTranslation[1]),\n [0, 0, 1],\n ];\n}\nfunction rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\nexport {\n buildRotationMatrix,\n computeRotation,\n dot,\n getColumnFrom2DArr,\n invertTransformMatrix,\n normalizeRadians,\n rotatePoint,\n};\n", "/* eslint-disable indent */\n/* eslint-disable no-multi-spaces */\n\nexport default {\n backend: 'webgl', // select tfjs backend to use\n wasmPath: '../assets/', // path for wasm binaries\n // only used for backend: wasm\n async: true, // execute enabled models in parallel\n // this disables per-model performance data but\n // slightly increases performance\n // cannot be used if profiling is enabled\n profile: false, // enable tfjs profiling\n // this has significant performance impact\n // only enable for debugging purposes\n // currently only implemented for age,gender,emotion models\n deallocate: false, // aggresively deallocate gpu memory after each usage\n // only valid for webgl backend and only during first call\n // cannot be changed unless library is reloaded\n // this has significant performance impact\n // only enable on low-memory devices\n scoped: false, // enable scoped runs\n // some models *may* have memory leaks,\n // this wrapps everything in a local scope at a cost of performance\n // typically not needed\n videoOptimized: true, // perform additional optimizations when input is video,\n // must be disabled for images\n // basically this skips object box boundary detection for every n frames\n // while maintaining in-box detection since objects cannot move that fast\n warmup: 'face', // what to use for human.warmup(), can be 'none', 'face', 'full'\n // warmup pre-initializes all models for faster inference but can take\n // significant time on startup\n filter: {\n enabled: true, // enable image pre-processing filters\n width: 0, // resize input width\n height: 0, // resize input height\n // if both width and height are set to 0, there is no resizing\n // if just one is set, second one is scaled automatically\n // if both are set, values are used as-is\n return: true, // return processed canvas imagedata in result\n brightness: 0, // range: -1 (darken) to 1 (lighten)\n contrast: 0, // range: -1 (reduce contrast) to 1 (increase contrast)\n sharpness: 0, // range: 0 (no sharpening) to 1 (maximum sharpening)\n blur: 0, // range: 0 (no blur) to N (blur radius in pixels)\n saturation: 0, // range: -1 (reduce saturation) to 1 (increase saturation)\n hue: 0, // range: 0 (no change) to 360 (hue rotation in degrees)\n negative: false, // image negative\n sepia: false, // image sepia colors\n vintage: false, // image vintage colors\n kodachrome: false, // image kodachrome colors\n technicolor: false, // image technicolor colors\n polaroid: false, // image polaroid camera effect\n pixelate: 0, // range: 0 (no pixelate) to N (number of pixels to pixelate)\n },\n\n gesture: {\n enabled: true, // enable simple gesture recognition\n },\n\n face: {\n enabled: true, // controls if specified modul is enabled\n // face.enabled is required for all face models:\n // detector, mesh, iris, age, gender, emotion\n // (note: module is not loaded until it is required)\n detector: {\n modelPath: '../models/blazeface-back.json', // can be 'front' or 'back'.\n // 'front' is optimized for large faces\n // such as front-facing camera and\n // 'back' is optimized for distanct faces.\n inputSize: 256, // fixed value: 128 for front and 256 for 'back'\n rotation: false, // use best-guess rotated face image or just box with rotation as-is\n // false means higher performance, but incorrect mesh mapping if face angle is above 20 degrees\n maxFaces: 10, // maximum number of faces detected in the input\n // should be set to the minimum number for performance\n skipFrames: 11, // how many frames to go without re-running the face bounding box detector\n // only used for video inputs\n // e.g., if model is running st 25 FPS, we can re-use existing bounding\n // box for updated face analysis as the head probably hasn't moved much\n // in short time (10 * 1/25 = 0.25 sec)\n minConfidence: 0.5, // threshold for discarding a prediction\n iouThreshold: 0.2, // threshold for deciding whether boxes overlap too much in\n // non-maximum suppression (0.1 means drop if overlap 10%)\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on score\n // in non-maximum suppression,\n // this is applied on detection objects only and before minConfidence\n },\n\n mesh: {\n enabled: true,\n modelPath: '../models/facemesh.json',\n inputSize: 192, // fixed value\n returnRawData: false, // in addition to standard mesh and box values, return raw normalized values as well\n },\n\n iris: {\n enabled: true,\n modelPath: '../models/iris.json',\n inputSize: 64, // fixed value\n },\n\n age: {\n enabled: true,\n modelPath: '../models/age-ssrnet-imdb.json', // can be 'age-ssrnet-imdb' or 'age-ssrnet-wiki'\n // which determines training set for model\n inputSize: 64, // fixed value\n skipFrames: 31, // how many frames to go without re-running the detector\n // only used for video inputs\n },\n\n gender: {\n enabled: true,\n minConfidence: 0.1, // threshold for discarding a prediction\n modelPath: '../models/gender-ssrnet-imdb.json', // can be 'gender', 'gender-ssrnet-imdb' or 'gender-ssrnet-wiki'\n inputSize: 64, // fixed value\n skipFrames: 41, // how many frames to go without re-running the detector\n // only used for video inputs\n },\n\n emotion: {\n enabled: true,\n inputSize: 64, // fixed value\n minConfidence: 0.2, // threshold for discarding a prediction\n skipFrames: 21, // how many frames to go without re-running the detector\n modelPath: '../models/emotion-large.json', // can be 'mini', 'large'\n },\n\n embedding: {\n enabled: false,\n inputSize: 112, // fixed value\n modelPath: '../models/mobilefacenet.json',\n },\n },\n\n body: {\n enabled: true,\n modelPath: '../models/posenet.json',\n inputSize: 257, // fixed value\n maxDetections: 10, // maximum number of people detected in the input\n // should be set to the minimum number for performance\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on score\n // in non-maximum suppression\n nmsRadius: 20, // radius for deciding points are too close in non-maximum suppression\n outputStride: 16, // size of block in which to run point detectopn, smaller value means higher resolution\n // defined by model itself, can be 8, 16, or 32\n modelType: 'MobileNet', // Human includes MobileNet version, but you can switch to ResNet\n },\n\n pose: { // TBD: not currently in use\n enabled: false,\n scoreThreshold: 0.6, // threshold for deciding when to remove boxes based on score\n // in non-maximum suppression\n iouThreshold: 0.3, // threshold for deciding whether boxes overlap too much\n // in non-maximum suppression\n modelPath: '../models/blazepose.json',\n inputSize: 256, // fixed value\n },\n\n hand: {\n enabled: true,\n rotation: false, // use best-guess rotated hand image or just box with rotation as-is\n // false means higher performance, but incorrect finger mapping if hand is inverted\n inputSize: 256, // fixed value\n skipFrames: 12, // how many frames to go without re-running the hand bounding box detector\n // only used for video inputs\n // e.g., if model is running st 25 FPS, we can re-use existing bounding\n // box for updated hand skeleton analysis as the hand probably\n // hasn't moved much in short time (10 * 1/25 = 0.25 sec)\n minConfidence: 0.1, // threshold for discarding a prediction\n iouThreshold: 0.1, // threshold for deciding whether boxes overlap too much\n // in non-maximum suppression\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on\n // score in non-maximum suppression\n maxHands: 1, // maximum number of hands detected in the input\n // should be set to the minimum number for performance\n landmarks: true, // detect hand landmarks or just hand boundary box\n detector: {\n modelPath: '../models/handdetect.json',\n },\n skeleton: {\n modelPath: '../models/handskeleton.json',\n },\n },\n};\n", "// data:image/jpeg;base64,\nexport const face = 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"names": [] } diff --git a/dist/human.esm.json b/dist/human.esm.json index 9a03dc13..5d3755c1 100644 --- a/dist/human.esm.json +++ b/dist/human.esm.json @@ -264,7 +264,7 @@ ] }, "src/blazepose/blazepose.js": { - "bytes": 8999, + "bytes": 856, "imports": [ { "path": "src/log.js" @@ -361,7 +361,7 @@ ] }, "config.js": { - "bytes": 10062, + "bytes": 10110, "imports": [] }, "src/sample.js": { @@ -373,7 +373,7 @@ "imports": [] }, "src/human.js": { - "bytes": 17787, + "bytes": 18921, "imports": [ { "path": "src/log.js" @@ -433,7 +433,7 @@ "dist/human.esm.js.map": { "imports": [], "inputs": {}, - "bytes": 1945558 + "bytes": 1934763 }, "dist/human.esm.js": { "imports": [], @@ -442,7 +442,7 @@ ], "inputs": { "src/blazeface/blazeface.js": { - "bytesInOutput": 5046 + "bytesInOutput": 5039 }, "src/blazeface/box.js": { "bytesInOutput": 1567 @@ -517,7 +517,7 @@ "bytesInOutput": 127037 }, "src/handpose/handpose.js": { - "bytesInOutput": 2025 + "bytesInOutput": 2023 }, "src/gesture/gesture.js": { "bytesInOutput": 3162 @@ -538,10 +538,10 @@ "bytesInOutput": 918 }, "src/human.js": { - "bytesInOutput": 11921 + "bytesInOutput": 12660 }, "src/blazepose/blazepose.js": { - "bytesInOutput": 6201 + "bytesInOutput": 572 }, "src/handpose/box.js": { "bytesInOutput": 1420 @@ -559,7 +559,7 @@ "bytesInOutput": 23 } }, - "bytes": 1912580 + "bytes": 1907681 } } } diff --git a/dist/human.js b/dist/human.js index bea3cb2e..80375fb4 100644 --- a/dist/human.js +++ b/dist/human.js @@ -5,19 +5,19 @@ author: ' */ -var Human=(()=>{var __create=Object.create,__defProp=Object.defineProperty,__getProtoOf=Object.getPrototypeOf,__hasOwnProp=Object.prototype.hasOwnProperty,__getOwnPropNames=Object.getOwnPropertyNames,__getOwnPropDesc=Object.getOwnPropertyDescriptor;var __markAsModule=target=>__defProp(target,"__esModule",{value:!0});var __commonJS=(callback,module)=>()=>(module||(module={exports:{}},callback(module.exports,module)),module.exports),__export=(target,all4)=>{__markAsModule(target);for(var name in all4)__defProp(target,name,{get:all4[name],enumerable:!0})},__exportStar=(target,module,desc)=>{if(__markAsModule(target),module&&typeof module=="object"||typeof module=="function")for(let key of __getOwnPropNames(module))!__hasOwnProp.call(target,key)&&key!=="default"&&__defProp(target,key,{get:()=>module[key],enumerable:!(desc=__getOwnPropDesc(module,key))||desc.enumerable});return target},__toModule=module=>module&&module.__esModule?module:__exportStar(__defProp(module!=null?__create(__getProtoOf(module)):{},"default",{value:module,enumerable:!0}),module);var require_blazeface=__commonJS(exports=>{var NUM_LANDMARKS=6;function generateAnchors2(inputSize){let spec={strides:[inputSize/16,inputSize/8],anchors:[2,6]},anchors2=[];for(let i=0;i{box.startEndTensor.dispose(),box.startPoint.dispose(),box.endPoint.dispose()},createBox=startEndTensor=>({startEndTensor,startPoint:slice(startEndTensor,[0,0],[-1,2]),endPoint:slice(startEndTensor,[0,2],[-1,2])}),scaleBox=(box,factors)=>{let starts=mul(box.startPoint,factors),ends=mul(box.endPoint,factors),newCoordinates=concat2d([starts,ends],1);return createBox(newCoordinates)};function decodeBounds(boxOutputs,anchors2,inputSize){let boxStarts=slice(boxOutputs,[0,1],[-1,2]),centers=add2(boxStarts,anchors2),boxSizes=slice(boxOutputs,[0,3],[-1,2]),boxSizesNormalized=div(boxSizes,inputSize),centersNormalized=div(centers,inputSize),halfBoxSize=div(boxSizesNormalized,2),starts=sub(centersNormalized,halfBoxSize),ends=add2(centersNormalized,halfBoxSize),startNormalized=mul(starts,inputSize),endNormalized=mul(ends,inputSize);return concat2d([startNormalized,endNormalized],1)}function scaleBoxFromPrediction(face3,scaleFactor){return tidy(()=>{let box=face3.box?face3.box:face3;return scaleBox(box,scaleFactor).startEndTensor.squeeze()})}var BlazeFaceModel=class{constructor(model2,config3){this.blazeFaceModel=model2,this.width=config3.face.detector.inputSize,this.height=config3.face.detector.inputSize,this.anchorsData=generateAnchors2(config3.face.detector.inputSize),this.anchors=tensor2d(this.anchorsData),this.inputSize=tensor1d([this.width,this.height]),this.config=config3,this.scaleFaces=.8}async getBoundingBoxes(inputImage){if(!inputImage||inputImage.isDisposedInternal||inputImage.shape.length!==4||inputImage.shape[1]<1||inputImage.shape[2]<1)return null;let[detectedOutputs,boxes,scores]=tidy(()=>{let resizedImage=inputImage.resizeBilinear([this.width,this.height]),normalizedImage=sub(resizedImage.div(127.5),1),batchedPrediction=this.blazeFaceModel.predict(normalizedImage),prediction;if(Array.isArray(batchedPrediction)){let sorted=batchedPrediction.sort((a,b)=>a.size-b.size),concat384=concat([sorted[0],sorted[2]],2),concat512=concat([sorted[1],sorted[3]],2);prediction=concat([concat512,concat384],1).squeeze(0)}else prediction=batchedPrediction.squeeze();let decodedBounds=decodeBounds(prediction,this.anchors,this.inputSize),logits=slice(prediction,[0,0],[-1,1]),scoresOut=sigmoid(logits).squeeze();return[prediction,decodedBounds,scoresOut]}),boxIndicesTensor=await image.nonMaxSuppressionAsync(boxes,scores,this.config.face.detector.maxFaces,this.config.face.detector.iouThreshold,this.config.face.detector.scoreThreshold),boxIndices=boxIndicesTensor.arraySync();boxIndicesTensor.dispose();let boundingBoxes=boxIndices.map(boxIndex=>slice(boxes,[boxIndex,0],[1,-1])).map(boundingBox=>{let vals=boundingBox.arraySync();return boundingBox.dispose(),vals}),scoresVal=scores.dataSync(),annotatedBoxes=[];for(let i=0;ithis.config.face.detector.minConfidence){let box=createBox(boundingBoxes[i]),anchor=this.anchorsData[boxIndex],landmarks=tidy(()=>slice(detectedOutputs,[boxIndex,NUM_LANDMARKS-1],[1,-1]).squeeze().reshape([NUM_LANDMARKS,-1]));annotatedBoxes.push({box,landmarks,anchor,confidence})}}return detectedOutputs.dispose(),boxes.dispose(),scores.dispose(),detectedOutputs.dispose(),{boxes:annotatedBoxes,scaleFactor:[inputImage.shape[2]/this.width,inputImage.shape[1]/this.height]}}async estimateFaces(input2){let{boxes,scaleFactor}=await this.getBoundingBoxes(input2),faces=[];for(let face3 of boxes){let landmarkData=face3.landmarks.arraySync(),scaledBox=scaleBoxFromPrediction(face3,scaleFactor),boxData=scaleBox.arraySync(),probabilityData=face3.probability.arraySync(),anchor=face3.anchor,[scaleFactorX,scaleFactorY]=scaleFactor,scaledLandmarks=landmarkData.map(landmark=>[(landmark[0]+anchor[0])*scaleFactorX,(landmark[1]+anchor[1])*scaleFactorY]),normalizedFace={topLeft:boxData.slice(0,2),bottomRight:boxData.slice(2),landmarks:scaledLandmarks,probability:probabilityData};disposeBox(face3.box),face3.landmarks.dispose(),face3.probability.dispose(),scaledBox.dispose(),faces.push(normalizedFace)}return faces}};async function load3(config3){let blazeface=await loadGraphModel(config3.face.detector.modelPath,{fromTFHub:config3.face.detector.modelPath.includes("tfhub.dev")}),model2=new BlazeFaceModel(blazeface,config3);return log(`load model: ${config3.face.detector.modelPath.match(/\/(.*)\./)[1]}`),model2}exports.load=load3;exports.BlazeFaceModel=BlazeFaceModel;exports.disposeBox=disposeBox});var require_box=__commonJS(exports=>{function scaleBoxCoordinates2(box,factor){let startPoint=[box.startPoint[0]*factor[0],box.startPoint[1]*factor[1]],endPoint=[box.endPoint[0]*factor[0],box.endPoint[1]*factor[1]];return{startPoint,endPoint}}exports.scaleBoxCoordinates=scaleBoxCoordinates2;function getBoxSize2(box){return[Math.abs(box.endPoint[0]-box.startPoint[0]),Math.abs(box.endPoint[1]-box.startPoint[1])]}exports.getBoxSize=getBoxSize2;function getBoxCenter2(box){return[box.startPoint[0]+(box.endPoint[0]-box.startPoint[0])/2,box.startPoint[1]+(box.endPoint[1]-box.startPoint[1])/2]}exports.getBoxCenter=getBoxCenter2;function cutBoxFromImageAndResize2(box,image3,cropSize){let h=image3.shape[1],w=image3.shape[2],boxes=[[box.startPoint[1]/h,box.startPoint[0]/w,box.endPoint[1]/h,box.endPoint[0]/w]];return image.cropAndResize(image3,boxes,[0],cropSize)}exports.cutBoxFromImageAndResize=cutBoxFromImageAndResize2;function enlargeBox2(box,factor=1.5){let center=getBoxCenter2(box),size=getBoxSize2(box),newHalfSize=[factor*size[0]/2,factor*size[1]/2],startPoint=[center[0]-newHalfSize[0],center[1]-newHalfSize[1]],endPoint=[center[0]+newHalfSize[0],center[1]+newHalfSize[1]];return{startPoint,endPoint,landmarks:box.landmarks}}exports.enlargeBox=enlargeBox2;function squarifyBox2(box){let centers=getBoxCenter2(box),size=getBoxSize2(box),halfSize=Math.max(...size)/2,startPoint=[centers[0]-halfSize,centers[1]-halfSize],endPoint=[centers[0]+halfSize,centers[1]+halfSize];return{startPoint,endPoint,landmarks:box.landmarks}}exports.squarifyBox=squarifyBox2});var require_util=__commonJS(exports=>{exports.IDENTITY_MATRIX=[[1,0,0],[0,1,0],[0,0,1]];function normalizeRadians2(angle){return angle-2*Math.PI*Math.floor((angle+Math.PI)/(2*Math.PI))}exports.normalizeRadians=normalizeRadians2;function computeRotation2(point1,point2){let radians=Math.PI/2-Math.atan2(-(point2[1]-point1[1]),point2[0]-point1[0]);return normalizeRadians2(radians)}exports.computeRotation=computeRotation2;function radToDegrees(rad){return rad*180/Math.PI}exports.radToDegrees=radToDegrees;function buildTranslationMatrix2(x,y){return[[1,0,x],[0,1,y],[0,0,1]]}function dot5(v1,v2){let product=0;for(let i=0;i{var 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,378,379,397,288,361,454,356,70,63,105,66,107,336,296,334,293,300,168,6,195,4,98,97,2,326,327,33,160,158,133,153,144,362,385,387,263,373,380,57,40,37,0,267,270,287,321,314,17,84,91,78,81,13,311,308,402,14,178],VTX33=[33,133,362,263,1,62,308,159,145,386,374,6,102,331,2,13,14,70,105,107,336,334,300,54,10,284,50,280,234,454,58,288,152],VTX7=[33,133,362,263,1,78,308];exports.MESH_ANNOTATIONS=MESH_ANNOTATIONS;exports.MESH_TO_IRIS_INDICES_MAP=MESH_TO_IRIS_INDICES_MAP;exports.TRI468=TRI468;exports.TRI68=TRI68;exports.TRI33=TRI33;exports.TRI7=TRI7;exports.UV468=UV468;exports.UV68=VTX68.map(x=>UV468[x]);exports.UV33=VTX33.map(x=>UV468[x]);exports.UV7=VTX7.map(x=>UV468[x])});var require_facepipeline=__commonJS(exports=>{var bounding=__toModule(require_box()),util=__toModule(require_util()),coords2=__toModule(require_coords()),LANDMARKS_COUNT=468,MESH_MOUTH_INDEX=13,MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES=[MESH_MOUTH_INDEX,coords2.MESH_ANNOTATIONS.midwayBetweenEyes[0]],BLAZEFACE_MOUTH_INDEX=3,BLAZEFACE_NOSE_INDEX=2,BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES=[BLAZEFACE_MOUTH_INDEX,BLAZEFACE_NOSE_INDEX],LEFT_EYE_OUTLINE=coords2.MESH_ANNOTATIONS.leftEyeLower0,LEFT_EYE_BOUNDS=[LEFT_EYE_OUTLINE[0],LEFT_EYE_OUTLINE[LEFT_EYE_OUTLINE.length-1]],RIGHT_EYE_OUTLINE=coords2.MESH_ANNOTATIONS.rightEyeLower0,RIGHT_EYE_BOUNDS=[RIGHT_EYE_OUTLINE[0],RIGHT_EYE_OUTLINE[RIGHT_EYE_OUTLINE.length-1]],IRIS_UPPER_CENTER_INDEX=3,IRIS_LOWER_CENTER_INDEX=4,IRIS_IRIS_INDEX=71,IRIS_NUM_COORDINATES=76;function replaceRawCoordinates(rawCoords,newCoords,prefix,keys){for(let i=0;i[scaleFactor[0]*(coord[0]-this.meshWidth/2),scaleFactor[1]*(coord[1]-this.meshHeight/2),coord[2]]),coordsRotationMatrix=angle!==0?util.buildRotationMatrix(angle,[0,0]):util.IDENTITY_MATRIX,coordsRotated=angle!==0?coordsScaled.map(coord=>[...util.rotatePoint(coord,coordsRotationMatrix),coord[2]]):coordsScaled,inverseRotationMatrix=angle!==0?util.invertTransformMatrix(rotationMatrix):util.IDENTITY_MATRIX,boxCenter=[...bounding.getBoxCenter({startPoint:box.startPoint,endPoint:box.endPoint}),1];return coordsRotated.map(coord=>[coord[0]+util.dot(boxCenter,inverseRotationMatrix[0]),coord[1]+util.dot(boxCenter,inverseRotationMatrix[1]),coord[2]])}getLeftToRightEyeDepthDifference(rawCoords){let leftEyeZ=rawCoords[LEFT_EYE_BOUNDS[0]][2],rightEyeZ=rawCoords[RIGHT_EYE_BOUNDS[0]][2];return leftEyeZ-rightEyeZ}getEyeBox(rawCoords,face3,eyeInnerCornerIndex,eyeOuterCornerIndex,flip=!1){let box=bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex],rawCoords[eyeOuterCornerIndex]]),this.irisEnlarge)),boxSize=bounding.getBoxSize(box),crop=image.cropAndResize(face3,[[box.startPoint[1]/this.meshHeight,box.startPoint[0]/this.meshWidth,box.endPoint[1]/this.meshHeight,box.endPoint[0]/this.meshWidth]],[0],[this.irisSize,this.irisSize]);return flip&&(crop=image.flipLeftRight(crop)),{box,boxSize,crop}}getEyeCoords(eyeData,eyeBox,eyeBoxSize,flip=!1){let eyeRawCoords=[];for(let i=0;i{let z=averageZ;return i===2?z=upperCenterZ:i===4&&(z=lowerCenterZ),[coord[0],coord[1],z]})}async predict(input2,config3){let useFreshBox=!1,detector;if((this.skipped===0||this.skipped>config3.face.detector.skipFrames||!config3.face.mesh.enabled||!config3.videoOptimized)&&(detector=await this.boundingBoxDetector.getBoundingBoxes(input2),this.skipped=0),config3.videoOptimized&&this.skipped++,detector&&detector.boxes&&detector.boxes.length>0&&(!config3.face.mesh.enabled||detector.boxes.length!==this.detectedFaces&&this.detectedFaces!==config3.face.detector.maxFaces)){this.storedBoxes=[],this.detectedFaces=0;for(let possible of detector.boxes)this.storedBoxes.push({startPoint:possible.box.startPoint.dataSync(),endPoint:possible.box.endPoint.dataSync(),landmarks:possible.landmarks,confidence:possible.confidence});this.storedBoxes.length>0&&(useFreshBox=!0)}if(useFreshBox){if(!detector||!detector.boxes||detector.boxes.length===0)return this.storedBoxes=[],this.detectedFaces=0,null;for(let i=0;i{prediction.box.startPoint.dispose(),prediction.box.endPoint.dispose(),prediction.landmarks.dispose()});let results=tidy(()=>this.storedBoxes.map((box,i)=>{let face3,angle=0,rotationMatrix;if(config3.face.detector.rotation){let[indexOfMouth,indexOfForehead]=box.landmarks.length>=LANDMARKS_COUNT?MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES:BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES;angle=util.computeRotation(box.landmarks[indexOfMouth],box.landmarks[indexOfForehead]);let faceCenter=bounding.getBoxCenter({startPoint:box.startPoint,endPoint:box.endPoint}),faceCenterNormalized=[faceCenter[0]/input2.shape[2],faceCenter[1]/input2.shape[1]],rotatedImage=image.rotateWithOffset(input2,angle,0,faceCenterNormalized);rotationMatrix=util.buildRotationMatrix(-angle,faceCenter),face3=bounding.cutBoxFromImageAndResize({startPoint:box.startPoint,endPoint:box.endPoint},rotatedImage,[this.meshHeight,this.meshWidth]).div(255)}else{rotationMatrix=util.IDENTITY_MATRIX;let cloned=input2.clone();face3=bounding.cutBoxFromImageAndResize({startPoint:box.startPoint,endPoint:box.endPoint},cloned,[this.meshHeight,this.meshWidth]).div(255)}if(!config3.face.mesh.enabled)return{coords:null,box,faceConfidence:null,confidence:box.confidence,image:face3};let[,confidence,contourCoords]=this.meshDetector.predict(face3),confidenceVal=confidence.dataSync()[0];if(confidenceVala!==null),this.detectedFaces=results.length,results}calculateLandmarksBoundingBox(landmarks){let xs=landmarks.map(d=>d[0]),ys=landmarks.map(d=>d[1]),startPoint=[Math.min(...xs),Math.min(...ys)],endPoint=[Math.max(...xs),Math.max(...ys)];return{startPoint,endPoint,landmarks}}};exports.Pipeline=Pipeline});var require_facemesh=__commonJS(exports=>{var blazeface=__toModule(require_blazeface()),facepipeline=__toModule(require_facepipeline()),coords2=__toModule(require_coords()),MediaPipeFaceMesh=class{constructor(blazeFace,blazeMeshModel,irisModel,config3){this.facePipeline=new facepipeline.Pipeline(blazeFace,blazeMeshModel,irisModel,config3),this.config=config3}async estimateFaces(input2,config3){let predictions=await this.facePipeline.predict(input2,config3),results=[];for(let prediction of predictions||[]){if(prediction.isDisposedInternal)continue;let mesh=prediction.coords?prediction.coords.arraySync():null,meshRaw=prediction.rawCoords,annotations={};if(mesh&&mesh.length>0)for(let key of Object.keys(coords2.MESH_ANNOTATIONS))annotations[key]=coords2.MESH_ANNOTATIONS[key].map(index=>mesh[index]);let boxRaw=config3.face.mesh.returnRawData&&prediction.box?{topLeft:prediction.box.startPoint,bottomRight:prediction.box.endPoint}:null,box=prediction.box?[Math.max(0,prediction.box.startPoint[0]),Math.max(0,prediction.box.startPoint[1]),Math.min(input2.shape[2],prediction.box.endPoint[0])-prediction.box.startPoint[0],Math.min(input2.shape[1],prediction.box.endPoint[1])-prediction.box.startPoint[1]]:0;results.push({confidence:prediction.confidence||0,box,mesh,boxRaw,meshRaw,annotations,image:prediction.image?clone(prediction.image):null}),prediction.coords&&prediction.coords.dispose(),prediction.image&&prediction.image.dispose()}return results}},faceModels=[null,null,null];async function load3(config3){faceModels=await Promise.all([!faceModels[0]&&config3.face.enabled?blazeface.load(config3):null,!faceModels[1]&&config3.face.mesh.enabled?loadGraphModel(config3.face.mesh.modelPath,{fromTFHub:config3.face.mesh.modelPath.includes("tfhub.dev")}):null,!faceModels[2]&&config3.face.iris.enabled?loadGraphModel(config3.face.iris.modelPath,{fromTFHub:config3.face.iris.modelPath.includes("tfhub.dev")}):null]);let faceMesh=new MediaPipeFaceMesh(faceModels[0],faceModels[1],faceModels[2],config3);return config3.face.mesh.enabled&&log(`load model: ${config3.face.mesh.modelPath.match(/\/(.*)\./)[1]}`),config3.face.iris.enabled&&log(`load model: ${config3.face.iris.modelPath.match(/\/(.*)\./)[1]}`),faceMesh}exports.load=load3;exports.MediaPipeFaceMesh=MediaPipeFaceMesh;exports.triangulation=coords2.TRI468});var require_profile=__commonJS(exports=>{var profileData={};function profile3(name,data2){if(!data2||!data2.kernels)return;let maxResults=5,time2=data2.kernels.filter(a=>a.kernelTimeMs>0).reduce((a,b)=>a+=b.kernelTimeMs,0),slowest=data2.kernels.map((a,i)=>(a.id=i,a)).filter(a=>a.kernelTimeMs>0).sort((a,b)=>b.kernelTimeMs-a.kernelTimeMs),largest=data2.kernels.map((a,i)=>(a.id=i,a)).filter(a=>a.totalBytesSnapshot>0).sort((a,b)=>b.totalBytesSnapshot-a.totalBytesSnapshot);slowest.length>maxResults&&(slowest.length=maxResults),largest.length>maxResults&&(largest.length=maxResults);let res={newBytes:data2.newBytes,newTensors:data2.newTensors,peakBytes:data2.peakBytes,numKernelOps:data2.kernels.length,timeKernelOps:time2,slowestKernelOps:slowest,largestKernelOps:largest};profileData[name]=res,log("Human profiler",name,res)}exports.run=profile3});var require_age=__commonJS(exports=>{var profile3=__toModule(require_profile()),models2={},last={age:0},skipped=Number.MAX_SAFE_INTEGER;async function load3(config3){return models2.age||(models2.age=await loadGraphModel(config3.face.age.modelPath),log(`load model: ${config3.face.age.modelPath.match(/\/(.*)\./)[1]}`)),models2.age}async function predict3(image3,config3){return models2.age?skipped0?(skipped++,last):(config3.videoOptimized?skipped=0:skipped=Number.MAX_SAFE_INTEGER,new Promise(async resolve=>{let resize=image.resizeBilinear(image3,[config3.face.age.inputSize,config3.face.age.inputSize],!1),enhance=mul(resize,[255]);dispose(resize);let ageT,obj={};if(!config3.profile)config3.face.age.enabled&&(ageT=await models2.age.predict(enhance));else{let profileAge=config3.face.age.enabled?await profile(()=>models2.age.predict(enhance)):{};ageT=profileAge.result.clone(),profileAge.result.dispose(),profile3.run("age",profileAge)}if(enhance.dispose(),ageT){let data2=ageT.dataSync();obj.age=Math.trunc(10*data2[0])/10}ageT.dispose(),last=obj,resolve(obj)})):null}exports.predict=predict3;exports.load=load3});var require_gender=__commonJS(exports=>{var profile3=__toModule(require_profile()),models2={},last={gender:""},skipped=Number.MAX_SAFE_INTEGER,alternative=!1,rgb=[.2989,.587,.114];async function load3(config3){return models2.gender||(models2.gender=await loadGraphModel(config3.face.gender.modelPath),alternative=models2.gender.inputs[0].shape[3]===1,log(`load model: ${config3.face.gender.modelPath.match(/\/(.*)\./)[1]}`)),models2.gender}async function predict3(image3,config3){return models2.gender?skipped{let resize=image.resizeBilinear(image3,[config3.face.gender.inputSize,config3.face.gender.inputSize],!1),enhance;alternative?enhance=tidy(()=>{let[red,green,blue]=split(resize,3,3),redNorm=mul(red,rgb[0]),greenNorm=mul(green,rgb[1]),blueNorm=mul(blue,rgb[2]);return addN([redNorm,greenNorm,blueNorm]).sub(.5).mul(2)}):enhance=mul(resize,[255]),dispose(resize);let genderT,obj={};if(!config3.profile)config3.face.gender.enabled&&(genderT=await models2.gender.predict(enhance));else{let profileGender=config3.face.gender.enabled?await profile(()=>models2.gender.predict(enhance)):{};genderT=profileGender.result.clone(),profileGender.result.dispose(),profile3.run("gender",profileGender)}if(enhance.dispose(),genderT){let data2=genderT.dataSync();if(alternative){let confidence=Math.trunc(100*Math.abs(data2[0]-data2[1]))/100;confidence>config3.face.gender.minConfidence&&(obj.gender=data2[0]>data2[1]?"female":"male",obj.confidence=confidence)}else{let confidence=Math.trunc(200*Math.abs(data2[0]-.5))/100;confidence>config3.face.gender.minConfidence&&(obj.gender=data2[0]<=.5?"female":"male",obj.confidence=Math.min(.99,confidence))}}genderT.dispose(),last=obj,resolve(obj)})):null}exports.predict=predict3;exports.load=load3});var require_emotion=__commonJS(exports=>{var profile3=__toModule(require_profile()),annotations=["angry","disgust","fear","happy","sad","surprise","neutral"],models2={},last=[],skipped=Number.MAX_SAFE_INTEGER,rgb=[.2989,.587,.114],scale2=1;async function load3(config3){return models2.emotion||(models2.emotion=await loadGraphModel(config3.face.emotion.modelPath),log(`load model: ${config3.face.emotion.modelPath.match(/\/(.*)\./)[1]}`)),models2.emotion}async function predict3(image3,config3){return models2.emotion?skipped0?(skipped++,last):(config3.videoOptimized?skipped=0:skipped=Number.MAX_SAFE_INTEGER,new Promise(async resolve=>{let resize=image.resizeBilinear(image3,[config3.face.emotion.inputSize,config3.face.emotion.inputSize],!1),[red,green,blue]=split(resize,3,3);resize.dispose();let redNorm=mul(red,rgb[0]),greenNorm=mul(green,rgb[1]),blueNorm=mul(blue,rgb[2]);red.dispose(),green.dispose(),blue.dispose();let grayscale=addN([redNorm,greenNorm,blueNorm]);redNorm.dispose(),greenNorm.dispose(),blueNorm.dispose();let normalize=tidy(()=>grayscale.sub(.5).mul(2));grayscale.dispose();let obj=[];if(config3.face.emotion.enabled){let data2;if(config3.profile){let profileData=await profile(()=>models2.emotion.predict(normalize));data2=profileData.result.dataSync(),profileData.result.dispose(),profile3.run("emotion",profileData)}else{let emotionT=await models2.emotion.predict(normalize);data2=emotionT.dataSync(),dispose(emotionT)}for(let i=0;iconfig3.face.emotion.minConfidence&&obj.push({score:Math.min(.99,Math.trunc(100*scale2*data2[i])/100),emotion:annotations[i]});obj.sort((a,b)=>b.score-a.score)}normalize.dispose(),last=obj,resolve(obj)})):null}exports.predict=predict3;exports.load=load3});var require_embedding=__commonJS(exports=>{var profile3=__toModule(require_profile()),models2={};async function load3(config3){return models2.embedding||(models2.embedding=await loadGraphModel(config3.face.embedding.modelPath),log(`load model: ${config3.face.embedding.modelPath.match(/\/(.*)\./)[1]}`)),models2.embedding}function simmilarity2(embedding1,embedding22){if((embedding1==null?void 0:embedding1.length)!==(embedding22==null?void 0:embedding22.length))return 0;let order=2,distance=10*embedding1.map((val,i)=>val-embedding22[i]).reduce((dist,diff)=>dist+diff**order,0)**(1/order);return Math.trunc(1e3*(1-distance))/1e3}async function predict3(image3,config3){return models2.embedding?new Promise(async resolve=>{let resize=image.resizeBilinear(image3,[config3.face.embedding.inputSize,config3.face.embedding.inputSize],!1),data2=[];if(config3.face.embedding.enabled)if(config3.profile){let profileData=await profile(()=>models2.embedding.predict({img_inputs:resize}));data2=[...profileData.result.dataSync()],profileData.result.dispose(),profile3.run("emotion",profileData)}else{let embeddingT=await models2.embedding.predict({img_inputs:resize});data2=[...embeddingT.dataSync()],dispose(embeddingT)}resize.dispose(),resolve(data2)}):null}exports.predict=predict3;exports.simmilarity=simmilarity2;exports.load=load3});var require_modelBase=__commonJS(exports=>{var imageNetMean=[-123.15,-115.9,-103.06];function nameOutputResultsMobileNet(results){let[offsets,heatmap,displacementFwd,displacementBwd]=results;return{offsets,heatmap,displacementFwd,displacementBwd}}function nameOutputResultsResNet(results){let[displacementFwd,displacementBwd,offsets,heatmap]=results;return{offsets,heatmap,displacementFwd,displacementBwd}}var BaseModel=class{constructor(model2){this.model=model2}predict(input2,config3){return tidy(()=>{let asBatch=(config3.body.modelType==="ResNet"?input2.toFloat().add(imageNetMean):input2.toFloat().div(127.5).sub(1)).expandDims(0),results3d=this.model.predict(asBatch).map(y=>y.squeeze([0])),namedResults=config3.body.modelType==="ResNet"?nameOutputResultsResNet(results3d):nameOutputResultsMobileNet(results3d);return{heatmapScores:namedResults.heatmap.sigmoid(),offsets:namedResults.offsets,displacementFwd:namedResults.displacementFwd,displacementBwd:namedResults.displacementBwd}})}dispose(){this.model.dispose()}};exports.BaseModel=BaseModel});var require_heapSort=__commonJS(exports=>{function half(k){return Math.floor(k/2)}var MaxHeap=class{constructor(maxSize,getElementValue){this.priorityQueue=new Array(maxSize),this.numberOfElements=-1,this.getElementValue=getElementValue}enqueue(x){this.priorityQueue[++this.numberOfElements]=x,this.swim(this.numberOfElements)}dequeue(){let max6=this.priorityQueue[0];return this.exchange(0,this.numberOfElements--),this.sink(0),this.priorityQueue[this.numberOfElements+1]=null,max6}empty(){return this.numberOfElements===-1}size(){return this.numberOfElements+1}all(){return this.priorityQueue.slice(0,this.numberOfElements+1)}max(){return this.priorityQueue[0]}swim(k){for(;k>0&&this.less(half(k),k);)this.exchange(k,half(k)),k=half(k)}sink(k){for(;2*k<=this.numberOfElements;){let j=2*k;if(j{var heapSort=__toModule(require_heapSort());function scoreIsMaximumInLocalWindow(keypointId,score,heatmapY,heatmapX,localMaximumRadius,scores){let[height,width]=scores.shape,localMaximum=!0,yStart=Math.max(heatmapY-localMaximumRadius,0),yEnd=Math.min(heatmapY+localMaximumRadius+1,height);for(let yCurrent=yStart;yCurrentscore){localMaximum=!1;break}if(!localMaximum)break}return localMaximum}function buildPartWithScoreQueue(scoreThreshold,localMaximumRadius,scores){let[height,width,numKeypoints]=scores.shape,queue=new heapSort.MaxHeap(height*width*numKeypoints,({score})=>score);for(let heatmapY=0;heatmapY{exports.partNames=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"];exports.NUM_KEYPOINTS=exports.partNames.length;exports.partIds=exports.partNames.reduce((result,jointName,i)=>(result[jointName]=i,result),{});var connectedPartNames=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]];exports.connectedPartIndices=connectedPartNames.map(([jointNameA,jointNameB])=>[exports.partIds[jointNameA],exports.partIds[jointNameB]]);exports.poseChain=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];exports.partChannels=["left_face","right_face","right_upper_leg_front","right_lower_leg_back","right_upper_leg_back","left_lower_leg_front","left_upper_leg_front","left_upper_leg_back","left_lower_leg_back","right_feet","right_lower_leg_front","left_feet","torso_front","torso_back","right_upper_arm_front","right_upper_arm_back","right_lower_arm_back","left_lower_arm_front","left_upper_arm_front","left_upper_arm_back","left_lower_arm_back","right_hand","right_lower_arm_front","left_hand"]});var require_vectors=__commonJS(exports=>{var kpt=__toModule(require_keypoints());function getOffsetPoint(y,x,keypoint,offsets){return{y:offsets.get(y,x,keypoint),x:offsets.get(y,x,keypoint+kpt.NUM_KEYPOINTS)}}exports.getOffsetPoint=getOffsetPoint;function getImageCoords(part,outputStride,offsets){let{heatmapY,heatmapX,id:keypoint}=part,{y,x}=getOffsetPoint(heatmapY,heatmapX,keypoint,offsets);return{x:part.heatmapX*outputStride+x,y:part.heatmapY*outputStride+y}}exports.getImageCoords=getImageCoords;function fillArray(element,size){let result=new Array(size);for(let i=0;imax6?max6:a}exports.clamp=clamp2;function squaredDistance(y1,x1,y2,x2){let dy=y2-y1,dx=x2-x1;return dy*dy+dx*dx}exports.squaredDistance=squaredDistance;function addVectors(a,b){return{x:a.x+b.x,y:a.y+b.y}}exports.addVectors=addVectors;function clampVector(a,min6,max6){return{y:clamp2(a.y,min6,max6),x:clamp2(a.x,min6,max6)}}exports.clampVector=clampVector});var require_decoders=__commonJS(exports=>{var kpt=__toModule(require_keypoints());function getPointsConfidence(heatmapScores,heatMapCoords){let numKeypoints=heatMapCoords.shape[0],result=new Float32Array(numKeypoints);for(let keypoint=0;keypointheatMapCoordsBuffer.toTensor().mul(scalar(outputStride,"int32")).toFloat().add(getOffsetVectors(heatMapCoordsBuffer,offsetsBuffer)))}exports.getOffsetPoints=getOffsetPoints;function mod4(a,b){return tidy(()=>{let floored=a.div(scalar(b,"int32"));return a.sub(floored.mul(scalar(b,"int32")))})}function argmax2d(inputs){let[height,width,depth]=inputs.shape;return tidy(()=>{let coords2=inputs.reshape([height*width,depth]).argMax(0),yCoords=coords2.div(scalar(width,"int32")).expandDims(1),xCoords=mod4(coords2,width).expandDims(1);return concat([yCoords,xCoords],1)})}exports.argmax2d=argmax2d});var require_decodePose=__commonJS(exports=>{var keypoints=__toModule(require_keypoints()),vectors=__toModule(require_vectors()),decoders=__toModule(require_decoders()),parentChildrenTuples=keypoints.poseChain.map(([parentJoinName,childJoinName])=>[keypoints.partIds[parentJoinName],keypoints.partIds[childJoinName]]),parentToChildEdges=parentChildrenTuples.map(([,childJointId])=>childJointId),childToParentEdges=parentChildrenTuples.map(([parentJointId])=>parentJointId);function getDisplacement(edgeId,point,displacements){let numEdges=displacements.shape[2]/2;return{y:displacements.get(point.y,point.x,edgeId),x:displacements.get(point.y,point.x,numEdges+edgeId)}}function getStridedIndexNearPoint(point,outputStride,height,width){return{y:vectors.clamp(Math.round(point.y/outputStride),0,height-1),x:vectors.clamp(Math.round(point.x/outputStride),0,width-1)}}function traverseToTargetKeypoint(edgeId,sourceKeypoint,targetKeypointId,scoresBuffer,offsets,outputStride,displacements,offsetRefineStep=2){let[height,width]=scoresBuffer.shape,sourceKeypointIndices=getStridedIndexNearPoint(sourceKeypoint.position,outputStride,height,width),displacement=getDisplacement(edgeId,sourceKeypointIndices,displacements),targetKeypoint=vectors.addVectors(sourceKeypoint.position,displacement);for(let i=0;i=0;--edge){let sourceKeypointId=parentToChildEdges[edge],targetKeypointId=childToParentEdges[edge];instanceKeypoints[sourceKeypointId]&&!instanceKeypoints[targetKeypointId]&&(instanceKeypoints[targetKeypointId]=traverseToTargetKeypoint(edge,instanceKeypoints[sourceKeypointId],targetKeypointId,scores,offsets,outputStride,displacementsBwd))}for(let edge=0;edge(totalScore+=score,{position:{y:offsetPointsBuffer.get(i,0),x:offsetPointsBuffer.get(i,1)},part:keypoints.partNames[i],score})),filteredKeypoints=instanceKeypoints.filter(kpt=>kpt.score>config3.body.scoreThreshold);return heatmapValues.dispose(),offsetPoints.dispose(),{keypoints:filteredKeypoints,score:totalScore/instanceKeypoints.length}}exports.decodeSinglePose=decodeSinglePose});var require_decodeMultiple=__commonJS(exports=>{var buildParts=__toModule(require_buildParts()),decodePose=__toModule(require_decodePose()),vectors=__toModule(require_vectors()),kLocalMaximumRadius=1;function withinNmsRadiusOfCorrespondingPoint(poses,squaredNmsRadius,{x,y},keypointId){return poses.some(({keypoints})=>{let correspondingKeypoint=keypoints[keypointId].position;return vectors.squaredDistance(y,x,correspondingKeypoint.y,correspondingKeypoint.x)<=squaredNmsRadius})}function getInstanceScore(existingPoses,squaredNmsRadius,instanceKeypoints){return instanceKeypoints.reduce((result,{position,score},keypointId)=>(withinNmsRadiusOfCorrespondingPoint(existingPoses,squaredNmsRadius,position,keypointId)||(result+=score),result),0)/instanceKeypoints.length}function decodeMultiplePoses(scoresBuffer,offsetsBuffer,displacementsFwdBuffer,displacementsBwdBuffer,config3){let poses=[],queue=buildParts.buildPartWithScoreQueue(config3.body.scoreThreshold,kLocalMaximumRadius,scoresBuffer),squaredNmsRadius=config3.body.nmsRadius^2;for(;poses.lengthconfig3.body.scoreThreshold&&poses.push({keypoints,score})}return poses}exports.decodeMultiplePoses=decodeMultiplePoses});var require_util2=__commonJS(exports=>{var kpt=__toModule(require_keypoints());function eitherPointDoesntMeetConfidence(a,b,minConfidence){return a(eitherPointDoesntMeetConfidence(keypoints[leftJoint].score,keypoints[rightJoint].score,minConfidence)||result.push([keypoints[leftJoint],keypoints[rightJoint]]),result),[])}exports.getAdjacentKeyPoints=getAdjacentKeyPoints;var{NEGATIVE_INFINITY,POSITIVE_INFINITY}=Number;function getBoundingBox(keypoints){return keypoints.reduce(({maxX,maxY,minX,minY},{position:{x,y}})=>({maxX:Math.max(maxX,x),maxY:Math.max(maxY,y),minX:Math.min(minX,x),minY:Math.min(minY,y)}),{maxX:NEGATIVE_INFINITY,maxY:NEGATIVE_INFINITY,minX:POSITIVE_INFINITY,minY:POSITIVE_INFINITY})}exports.getBoundingBox=getBoundingBox;function getBoundingBoxPoints(keypoints){let{minX,minY,maxX,maxY}=getBoundingBox(keypoints);return[{x:minX,y:minY},{x:maxX,y:minY},{x:maxX,y:maxY},{x:minX,y:maxY}]}exports.getBoundingBoxPoints=getBoundingBoxPoints;async function toTensorBuffers3D(tensors){return Promise.all(tensors.map(tensor2=>tensor2.buffer()))}exports.toTensorBuffers3D=toTensorBuffers3D;function scalePose(pose,scaleY,scaleX){return{score:pose.score,keypoints:pose.keypoints.map(({score,part,position})=>({score,part,position:{x:position.x*scaleX,y:position.y*scaleY}}))}}exports.scalePose=scalePose;function resizeTo(image3,[targetH,targetW]){let input2=image3.squeeze(0),resized=input2.resizeBilinear([targetH,targetW]);return input2.dispose(),resized}exports.resizeTo=resizeTo;function scaleAndFlipPoses(poses,[height,width],[inputResolutionHeight,inputResolutionWidth]){return poses.map(pose=>scalePose(pose,height/inputResolutionHeight,width/inputResolutionWidth))}exports.scaleAndFlipPoses=scaleAndFlipPoses});var require_modelPoseNet=__commonJS(exports=>{var modelBase=__toModule(require_modelBase()),decodeMultiple=__toModule(require_decodeMultiple()),decodePose=__toModule(require_decodePose()),util=__toModule(require_util2());async function estimateMultiple(input2,res,config3){return new Promise(async resolve=>{let height=input2.shape[1],width=input2.shape[2],allTensorBuffers=await util.toTensorBuffers3D([res.heatmapScores,res.offsets,res.displacementFwd,res.displacementBwd]),scoresBuffer=allTensorBuffers[0],offsetsBuffer=allTensorBuffers[1],displacementsFwdBuffer=allTensorBuffers[2],displacementsBwdBuffer=allTensorBuffers[3],poses=await decodeMultiple.decodeMultiplePoses(scoresBuffer,offsetsBuffer,displacementsFwdBuffer,displacementsBwdBuffer,config3),scaled=util.scaleAndFlipPoses(poses,[height,width],[config3.body.inputSize,config3.body.inputSize]);resolve(scaled)})}async function estimateSingle(input2,res,config3){return new Promise(async resolve=>{let height=input2.shape[1],width=input2.shape[2],poses=[await decodePose.decodeSinglePose(res.heatmapScores,res.offsets,config3)],scaled=util.scaleAndFlipPoses(poses,[height,width],[config3.body.inputSize,config3.body.inputSize]);resolve(scaled)})}var PoseNet=class{constructor(model2){this.baseModel=model2}async estimatePoses(input2,config3){let resized=util.resizeTo(input2,[config3.body.inputSize,config3.body.inputSize]),res=this.baseModel.predict(resized,config3),poses=config3.body.maxDetections<2?await estimateSingle(input2,res,config3):await estimateMultiple(input2,res,config3);return res.heatmapScores.dispose(),res.offsets.dispose(),res.displacementFwd.dispose(),res.displacementBwd.dispose(),resized.dispose(),poses}dispose(){this.baseModel.dispose()}};exports.PoseNet=PoseNet;async function load3(config3){let model2=await loadGraphModel(config3.body.modelPath),mobilenet=new modelBase.BaseModel(model2);return log(`load model: ${config3.body.modelPath.match(/\/(.*)\./)[1]}`),new PoseNet(mobilenet)}exports.load=load3});var require_posenet=__commonJS(exports=>{var modelPoseNet=__toModule(require_modelPoseNet()),keypoints=__toModule(require_keypoints()),util=__toModule(require_util2());exports.load=modelPoseNet.load;exports.PoseNet=modelPoseNet.PoseNet;exports.partChannels=keypoints.partChannels;exports.partIds=keypoints.partIds;exports.partNames=keypoints.partNames;exports.poseChain=keypoints.poseChain;exports.getAdjacentKeyPoints=util.getAdjacentKeyPoints;exports.getBoundingBox=util.getBoundingBox;exports.getBoundingBoxPoints=util.getBoundingBoxPoints;exports.scaleAndFlipPoses=util.scaleAndFlipPoses;exports.scalePose=util.scalePose});var require_handdetector=__commonJS(exports=>{var HandDetector=class{constructor(model2,inputSize,anchorsAnnotated){this.model=model2,this.anchors=anchorsAnnotated.map(anchor=>[anchor.x_center,anchor.y_center]),this.anchorsTensor=tensor2d(this.anchors),this.inputSizeTensor=tensor1d([inputSize,inputSize]),this.doubleInputSizeTensor=tensor1d([inputSize*2,inputSize*2])}normalizeBoxes(boxes){return tidy(()=>{let boxOffsets=slice(boxes,[0,0],[-1,2]),boxSizes=slice(boxes,[0,2],[-1,2]),boxCenterPoints=add2(div(boxOffsets,this.inputSizeTensor),this.anchorsTensor),halfBoxSizes=div(boxSizes,this.doubleInputSizeTensor),startPoints=mul(sub(boxCenterPoints,halfBoxSizes),this.inputSizeTensor),endPoints=mul(add2(boxCenterPoints,halfBoxSizes),this.inputSizeTensor);return concat2d([startPoints,endPoints],1)})}normalizeLandmarks(rawPalmLandmarks,index){return tidy(()=>{let landmarks=add2(div(rawPalmLandmarks.reshape([-1,7,2]),this.inputSizeTensor),this.anchors[index]);return mul(landmarks,this.inputSizeTensor)})}async getBoxes(input2,config3){let batched=this.model.predict(input2),predictions=batched.squeeze();batched.dispose();let scoresT=tidy(()=>sigmoid(slice(predictions,[0,0],[-1,1])).squeeze()),scores=scoresT.dataSync(),rawBoxes=slice(predictions,[0,1],[-1,4]),boxes=this.normalizeBoxes(rawBoxes);rawBoxes.dispose();let filteredT=await image.nonMaxSuppressionAsync(boxes,scores,config3.hand.maxHands,config3.hand.iouThreshold,config3.hand.scoreThreshold),filtered=filteredT.arraySync();scoresT.dispose(),filteredT.dispose();let hands=[];for(let index of filtered)if(scores[index]>=config3.hand.minConfidence){let matchingBox=slice(boxes,[index,0],[1,-1]),rawPalmLandmarks=slice(predictions,[index,5],[1,14]),palmLandmarks=tidy(()=>this.normalizeLandmarks(rawPalmLandmarks,index).reshape([-1,2]));rawPalmLandmarks.dispose(),hands.push({box:matchingBox,palmLandmarks,confidence:scores[index]})}return predictions.dispose(),boxes.dispose(),hands}async estimateHandBounds(input2,config3){let inputHeight=input2.shape[1],inputWidth=input2.shape[2],image3=tidy(()=>input2.resizeBilinear([config3.hand.inputSize,config3.hand.inputSize]).div(127.5).sub(1)),predictions=await this.getBoxes(image3,config3);image3.dispose();let hands=[];if(!predictions||predictions.length===0)return hands;for(let prediction of predictions){let boxes=prediction.box.dataSync(),startPoint=boxes.slice(0,2),endPoint=boxes.slice(2,4),palmLandmarks=prediction.palmLandmarks.arraySync();prediction.box.dispose(),prediction.palmLandmarks.dispose(),hands.push(scaleBoxCoordinates({startPoint,endPoint,palmLandmarks,confidence:prediction.confidence},[inputWidth/config3.hand.inputSize,inputHeight/config3.hand.inputSize]))}return hands}};exports.HandDetector=HandDetector});var require_handpipeline=__commonJS(exports=>{var PALM_BOX_ENLARGE_FACTOR=5,HAND_BOX_ENLARGE_FACTOR=1.65,PALM_LANDMARK_IDS=[0,5,9,13,17,1,2],PALM_LANDMARKS_INDEX_OF_PALM_BASE=0,PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE=2,HandPipeline=class{constructor(handDetector,landmarkDetector,inputSize){this.handDetector=handDetector,this.landmarkDetector=landmarkDetector,this.inputSize=inputSize,this.storedBoxes=[],this.skipped=0,this.detectedHands=0}getBoxForPalmLandmarks(palmLandmarks,rotationMatrix){let rotatedPalmLandmarks=palmLandmarks.map(coord=>rotatePoint([...coord,1],rotationMatrix)),boxAroundPalm=this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);return enlargeBox(squarifyBox(boxAroundPalm),PALM_BOX_ENLARGE_FACTOR)}getBoxForHandLandmarks(landmarks){let boundingBox=this.calculateLandmarksBoundingBox(landmarks),boxAroundHand=enlargeBox(squarifyBox(boundingBox),HAND_BOX_ENLARGE_FACTOR);boxAroundHand.palmLandmarks=[];for(let i=0;i[scaleFactor[0]*(coord[0]-this.inputSize/2),scaleFactor[1]*(coord[1]-this.inputSize/2),scaleFactor[2]*coord[2]]),coordsRotationMatrix=buildRotationMatrix(angle,[0,0]),coordsRotated=coordsScaled.map(coord=>[...rotatePoint(coord,coordsRotationMatrix),coord[2]]),inverseRotationMatrix=invertTransformMatrix(rotationMatrix),boxCenter=[...getBoxCenter(box2),1],originalBoxCenter=[dot4(boxCenter,inverseRotationMatrix[0]),dot4(boxCenter,inverseRotationMatrix[1])];return coordsRotated.map(coord=>[coord[0]+originalBoxCenter[0],coord[1]+originalBoxCenter[1],coord[2]])}async estimateHands(image3,config3){let useFreshBox=!1,boxes;(this.skipped===0||this.skipped>config3.hand.skipFrames||!config3.hand.landmarks||!config3.videoOptimized)&&(boxes=await this.handDetector.estimateHandBounds(image3,config3),this.skipped=0),config3.videoOptimized&&this.skipped++,boxes&&boxes.length>0&&(boxes.length!==this.detectedHands&&this.detectedHands!==config3.hand.maxHands||!config3.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...boxes],this.storedBoxes.length>0&&(useFreshBox=!0));let hands=[];for(let i=0;i=config3.hand.minConfidence){let keypointsReshaped=reshape(keypoints,[-1,3]),rawCoords=keypointsReshaped.arraySync();keypoints.dispose(),keypointsReshaped.dispose();let coords2=this.transformRawCoords(rawCoords,newBox,angle,rotationMatrix),nextBoundingBox=this.getBoxForHandLandmarks(coords2);this.storedBoxes[i]=nextBoundingBox;let result={landmarks:coords2,confidence,box:{topLeft:nextBoundingBox.startPoint,bottomRight:nextBoundingBox.endPoint}};hands.push(result)}else this.storedBoxes[i]=null;keypoints.dispose()}else{let enlarged=enlargeBox(squarifyBox(currentBox),HAND_BOX_ENLARGE_FACTOR),result={confidence:currentBox.confidence,box:{topLeft:enlarged.startPoint,bottomRight:enlarged.endPoint}};hands.push(result)}}return this.storedBoxes=this.storedBoxes.filter(a=>a!==null),this.detectedHands=hands.length,hands}calculateLandmarksBoundingBox(landmarks){let xs=landmarks.map(d=>d[0]),ys=landmarks.map(d=>d[1]),startPoint=[Math.min(...xs),Math.min(...ys)],endPoint=[Math.max(...xs),Math.max(...ys)];return{startPoint,endPoint}}};exports.HandPipeline=HandPipeline});var 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HandPose(handPipeline);return config3.hand.enabled&&log(`load model: ${config3.hand.detector.modelPath.match(/\/(.*)\./)[1]}`),config3.hand.landmarks&&log(`load model: ${config3.hand.skeleton.modelPath.match(/\/(.*)\./)[1]}`),handPose}exports.load=load3});var require_gesture=__commonJS(exports=>{exports.body=res=>{if(!res)return[];let gestures=[];for(let i=0;ia.part==="leftWrist"),rightWrist=res[i].keypoints.find(a=>a.part==="rightWrist"),nose=res[i].keypoints.find(a=>a.part==="nose");nose&&leftWrist&&rightWrist&&leftWrist.position.ya.part==="leftShoulder"),rightShoulder=res[i].keypoints.find(a=>a.part==="rightShoulder");leftShoulder&&rightShoulder&&gestures.push({body:i,gesture:`leaning ${leftShoulder.position.y>rightShoulder.position.y?"left":"right"}`})}return gestures};exports.face=res=>{if(!res)return[];let gestures=[];for(let i=0;i0){let eyeFacing=res[i].mesh[35][2]-res[i].mesh[263][2];Math.abs(eyeFacing)<10?gestures.push({face:i,gesture:"facing camera"}):gestures.push({face:i,gesture:`facing ${eyeFacing<0?"right":"left"}`}),Math.abs(res[i].mesh[374][1]-res[i].mesh[386][1])/Math.abs(res[i].mesh[443][1]-res[i].mesh[450][1])<.2&&gestures.push({face:i,gesture:"blink left eye"}),Math.abs(res[i].mesh[145][1]-res[i].mesh[159][1])/Math.abs(res[i].mesh[223][1]-res[i].mesh[230][1])<.2&&gestures.push({face:i,gesture:"blink right eye"});let mouthOpen=Math.min(100,500*Math.abs(res[i].mesh[13][1]-res[i].mesh[14][1])/Math.abs(res[i].mesh[10][1]-res[i].mesh[152][1]));mouthOpen>10&&gestures.push({face:i,gesture:`mouth ${Math.trunc(mouthOpen)}% open`});let chinDepth=res[i].mesh[152][2];Math.abs(chinDepth)>10&&gestures.push({face:i,gesture:`head ${chinDepth<0?"up":"down"}`})}return gestures};exports.iris=res=>{if(!res)return[];let gestures=[];for(let i=0;i{if(!res)return[];let gestures=[];for(let i=0;i0){let closest=fingers.reduce((best,a)=>best.position[2]best.position[1]{var WebGLProgram=function(gl,vertexSource,fragmentSource){let _collect=function(source,prefix,collection){let r=new RegExp("\\b"+prefix+" \\w+ (\\w+)","ig");source.replace(r,(match,name)=>(collection[name]=0,match))},_compile=function(source,type){let shader=gl.createShader(type);if(gl.shaderSource(shader,source),gl.compileShader(shader),!gl.getShaderParameter(shader,gl.COMPILE_STATUS))throw new Error("Filter: GL compile failed",gl.getShaderInfoLog(shader));return shader};this.uniform={},this.attribute={};let _vsh=_compile(vertexSource,gl.VERTEX_SHADER),_fsh=_compile(fragmentSource,gl.FRAGMENT_SHADER);if(this.id=gl.createProgram(),gl.attachShader(this.id,_vsh),gl.attachShader(this.id,_fsh),gl.linkProgram(this.id),!gl.getProgramParameter(this.id,gl.LINK_STATUS))throw new Error("Filter: GL link failed",gl.getProgramInfoLog(this.id));gl.useProgram(this.id),_collect(vertexSource,"attribute",this.attribute);for(let a in this.attribute)this.attribute[a]=gl.getAttribLocation(this.id,a);_collect(vertexSource,"uniform",this.uniform),_collect(fragmentSource,"uniform",this.uniform);for(let u in this.uniform)this.uniform[u]=gl.getUniformLocation(this.id,u)},WebGLImageFilter=function(params){params||(params={});let _drawCount=0,_sourceTexture=null,_lastInChain=!1,_currentFramebufferIndex=-1,_tempFramebuffers=[null,null],_filterChain=[],_width=-1,_height=-1,_vertexBuffer=null,_currentProgram=null,_canvas=params.canvas||document.createElement("canvas"),_shaderProgramCache={},gl=_canvas.getContext("webgl");if(!gl)throw new Error("Filter: getContext() failed");this.addFilter=function(name){let args=Array.prototype.slice.call(arguments,1),filter=_filter[name];_filterChain.push({func:filter,args})},this.reset=function(){_filterChain=[]},this.apply=function(image3){if(_resize(image3.width,image3.height),_drawCount=0,_sourceTexture||(_sourceTexture=gl.createTexture()),gl.bindTexture(gl.TEXTURE_2D,_sourceTexture),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_WRAP_S,gl.CLAMP_TO_EDGE),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_WRAP_T,gl.CLAMP_TO_EDGE),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_MIN_FILTER,gl.NEAREST),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_MAG_FILTER,gl.NEAREST),gl.texImage2D(gl.TEXTURE_2D,0,gl.RGBA,gl.RGBA,gl.UNSIGNED_BYTE,image3),_filterChain.length===0)return _draw(),_canvas;for(let i=0;i<_filterChain.length;i++){_lastInChain=i===_filterChain.length-1;let f=_filterChain[i];f.func.apply(this,f.args||[])}return _canvas};let _resize=function(width,height){if(!(width===_width&&height===_height)){if(_canvas.width=width,_width=width,_canvas.height=height,_height=height,!_vertexBuffer){let vertices=new Float32Array([-1,-1,0,1,1,-1,1,1,-1,1,0,0,-1,1,0,0,1,-1,1,1,1,1,1,0]);_vertexBuffer=gl.createBuffer(),gl.bindBuffer(gl.ARRAY_BUFFER,_vertexBuffer),gl.bufferData(gl.ARRAY_BUFFER,vertices,gl.STATIC_DRAW),gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL,!0)}gl.viewport(0,0,_width,_height),_tempFramebuffers=[null,null]}},_getTempFramebuffer=function(index){return _tempFramebuffers[index]=_tempFramebuffers[index]||_createFramebufferTexture(_width,_height),_tempFramebuffers[index]},_createFramebufferTexture=function(width,height){let fbo=gl.createFramebuffer();gl.bindFramebuffer(gl.FRAMEBUFFER,fbo);let renderbuffer=gl.createRenderbuffer();gl.bindRenderbuffer(gl.RENDERBUFFER,renderbuffer);let texture=gl.createTexture();return gl.bindTexture(gl.TEXTURE_2D,texture),gl.texImage2D(gl.TEXTURE_2D,0,gl.RGBA,width,height,0,gl.RGBA,gl.UNSIGNED_BYTE,null),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_MAG_FILTER,gl.LINEAR),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_MIN_FILTER,gl.LINEAR),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_WRAP_S,gl.CLAMP_TO_EDGE),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_WRAP_T,gl.CLAMP_TO_EDGE),gl.framebufferTexture2D(gl.FRAMEBUFFER,gl.COLOR_ATTACHMENT0,gl.TEXTURE_2D,texture,0),gl.bindTexture(gl.TEXTURE_2D,null),gl.bindFramebuffer(gl.FRAMEBUFFER,null),{fbo,texture}},_draw=function(flags){var _a,_b;let source=null,target=null,flipY=!1;_drawCount===0?source=_sourceTexture:source=(_a=_getTempFramebuffer(_currentFramebufferIndex))==null?void 0:_a.texture,_drawCount++,_lastInChain&&!(flags&DRAW.INTERMEDIATE)?(target=null,flipY=_drawCount%2==0):(_currentFramebufferIndex=(_currentFramebufferIndex+1)%2,target=(_b=_getTempFramebuffer(_currentFramebufferIndex))==null?void 0:_b.fbo),gl.bindTexture(gl.TEXTURE_2D,source),gl.bindFramebuffer(gl.FRAMEBUFFER,target),gl.uniform1f(_currentProgram.uniform.flipY,flipY?-1:1),gl.drawArrays(gl.TRIANGLES,0,6)},_compileShader=function(fragmentSource){if(_shaderProgramCache[fragmentSource])return _currentProgram=_shaderProgramCache[fragmentSource],gl.useProgram(_currentProgram.id),_currentProgram;_currentProgram=new WebGLProgram(gl,SHADER.VERTEX_IDENTITY,fragmentSource);let floatSize=Float32Array.BYTES_PER_ELEMENT,vertSize=4*floatSize;return gl.enableVertexAttribArray(_currentProgram.attribute.pos),gl.vertexAttribPointer(_currentProgram.attribute.pos,2,gl.FLOAT,!1,vertSize,0*floatSize),gl.enableVertexAttribArray(_currentProgram.attribute.uv),gl.vertexAttribPointer(_currentProgram.attribute.uv,2,gl.FLOAT,!1,vertSize,2*floatSize),_shaderProgramCache[fragmentSource]=_currentProgram,_currentProgram},DRAW={INTERMEDIATE:1},SHADER={};SHADER.VERTEX_IDENTITY=["precision highp float;","attribute vec2 pos;","attribute vec2 uv;","varying vec2 vUv;","uniform float flipY;","void main(void) {","vUv = uv;","gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);","}"].join(` +var Human=(()=>{var __create=Object.create,__defProp=Object.defineProperty,__getProtoOf=Object.getPrototypeOf,__hasOwnProp=Object.prototype.hasOwnProperty,__getOwnPropNames=Object.getOwnPropertyNames,__getOwnPropDesc=Object.getOwnPropertyDescriptor;var __markAsModule=target=>__defProp(target,"__esModule",{value:!0});var __commonJS=(callback,module)=>()=>(module||(module={exports:{}},callback(module.exports,module)),module.exports),__export=(target,all4)=>{__markAsModule(target);for(var name in all4)__defProp(target,name,{get:all4[name],enumerable:!0})},__exportStar=(target,module,desc)=>{if(__markAsModule(target),module&&typeof module=="object"||typeof module=="function")for(let key of __getOwnPropNames(module))!__hasOwnProp.call(target,key)&&key!=="default"&&__defProp(target,key,{get:()=>module[key],enumerable:!(desc=__getOwnPropDesc(module,key))||desc.enumerable});return target},__toModule=module=>module&&module.__esModule?module:__exportStar(__defProp(module!=null?__create(__getProtoOf(module)):{},"default",{value:module,enumerable:!0}),module);var require_blazeface=__commonJS(exports=>{var NUM_LANDMARKS=6;function generateAnchors(inputSize){let spec={strides:[inputSize/16,inputSize/8],anchors:[2,6]},anchors=[];for(let i=0;i{box.startEndTensor.dispose(),box.startPoint.dispose(),box.endPoint.dispose()},createBox=startEndTensor=>({startEndTensor,startPoint:slice(startEndTensor,[0,0],[-1,2]),endPoint:slice(startEndTensor,[0,2],[-1,2])}),scaleBox=(box,factors)=>{let starts=mul(box.startPoint,factors),ends=mul(box.endPoint,factors),newCoordinates=concat2d([starts,ends],1);return createBox(newCoordinates)};function decodeBounds(boxOutputs,anchors,inputSize){let boxStarts=slice(boxOutputs,[0,1],[-1,2]),centers=add2(boxStarts,anchors),boxSizes=slice(boxOutputs,[0,3],[-1,2]),boxSizesNormalized=div(boxSizes,inputSize),centersNormalized=div(centers,inputSize),halfBoxSize=div(boxSizesNormalized,2),starts=sub(centersNormalized,halfBoxSize),ends=add2(centersNormalized,halfBoxSize),startNormalized=mul(starts,inputSize),endNormalized=mul(ends,inputSize);return concat2d([startNormalized,endNormalized],1)}function scaleBoxFromPrediction(face3,scaleFactor){return tidy(()=>{let box=face3.box?face3.box:face3;return scaleBox(box,scaleFactor).startEndTensor.squeeze()})}var BlazeFaceModel=class{constructor(model2,config2){this.blazeFaceModel=model2,this.width=config2.face.detector.inputSize,this.height=config2.face.detector.inputSize,this.anchorsData=generateAnchors(config2.face.detector.inputSize),this.anchors=tensor2d(this.anchorsData),this.inputSize=tensor1d([this.width,this.height]),this.config=config2,this.scaleFaces=.8}async getBoundingBoxes(inputImage){if(!inputImage||inputImage.isDisposedInternal||inputImage.shape.length!==4||inputImage.shape[1]<1||inputImage.shape[2]<1)return null;let[detectedOutputs,boxes,scores]=tidy(()=>{let resizedImage=inputImage.resizeBilinear([this.width,this.height]),normalizedImage=sub(resizedImage.div(127.5),1),batchedPrediction=this.blazeFaceModel.predict(normalizedImage),prediction;if(Array.isArray(batchedPrediction)){let sorted=batchedPrediction.sort((a,b)=>a.size-b.size),concat384=concat([sorted[0],sorted[2]],2),concat512=concat([sorted[1],sorted[3]],2);prediction=concat([concat512,concat384],1).squeeze(0)}else prediction=batchedPrediction.squeeze();let decodedBounds=decodeBounds(prediction,this.anchors,this.inputSize),logits=slice(prediction,[0,0],[-1,1]),scoresOut=sigmoid(logits).squeeze();return[prediction,decodedBounds,scoresOut]}),boxIndicesTensor=await image.nonMaxSuppressionAsync(boxes,scores,this.config.face.detector.maxFaces,this.config.face.detector.iouThreshold,this.config.face.detector.scoreThreshold),boxIndices=boxIndicesTensor.arraySync();boxIndicesTensor.dispose();let boundingBoxes=boxIndices.map(boxIndex=>slice(boxes,[boxIndex,0],[1,-1])).map(boundingBox=>{let vals=boundingBox.arraySync();return boundingBox.dispose(),vals}),scoresVal=scores.dataSync(),annotatedBoxes=[];for(let i=0;ithis.config.face.detector.minConfidence){let box=createBox(boundingBoxes[i]),anchor=this.anchorsData[boxIndex],landmarks=tidy(()=>slice(detectedOutputs,[boxIndex,NUM_LANDMARKS-1],[1,-1]).squeeze().reshape([NUM_LANDMARKS,-1]));annotatedBoxes.push({box,landmarks,anchor,confidence})}}return detectedOutputs.dispose(),boxes.dispose(),scores.dispose(),detectedOutputs.dispose(),{boxes:annotatedBoxes,scaleFactor:[inputImage.shape[2]/this.width,inputImage.shape[1]/this.height]}}async estimateFaces(input2){let{boxes,scaleFactor}=await this.getBoundingBoxes(input2),faces=[];for(let face3 of boxes){let landmarkData=face3.landmarks.arraySync(),scaledBox=scaleBoxFromPrediction(face3,scaleFactor),boxData=scaleBox.arraySync(),probabilityData=face3.probability.arraySync(),anchor=face3.anchor,[scaleFactorX,scaleFactorY]=scaleFactor,scaledLandmarks=landmarkData.map(landmark=>[(landmark[0]+anchor[0])*scaleFactorX,(landmark[1]+anchor[1])*scaleFactorY]),normalizedFace={topLeft:boxData.slice(0,2),bottomRight:boxData.slice(2),landmarks:scaledLandmarks,probability:probabilityData};disposeBox(face3.box),face3.landmarks.dispose(),face3.probability.dispose(),scaledBox.dispose(),faces.push(normalizedFace)}return faces}};async function load3(config2){let blazeface=await loadGraphModel(config2.face.detector.modelPath,{fromTFHub:config2.face.detector.modelPath.includes("tfhub.dev")}),model2=new BlazeFaceModel(blazeface,config2);return log(`load model: ${config2.face.detector.modelPath.match(/\/(.*)\./)[1]}`),model2}exports.load=load3;exports.BlazeFaceModel=BlazeFaceModel;exports.disposeBox=disposeBox});var require_box=__commonJS(exports=>{function scaleBoxCoordinates2(box,factor){let startPoint=[box.startPoint[0]*factor[0],box.startPoint[1]*factor[1]],endPoint=[box.endPoint[0]*factor[0],box.endPoint[1]*factor[1]];return{startPoint,endPoint}}exports.scaleBoxCoordinates=scaleBoxCoordinates2;function getBoxSize2(box){return[Math.abs(box.endPoint[0]-box.startPoint[0]),Math.abs(box.endPoint[1]-box.startPoint[1])]}exports.getBoxSize=getBoxSize2;function getBoxCenter2(box){return[box.startPoint[0]+(box.endPoint[0]-box.startPoint[0])/2,box.startPoint[1]+(box.endPoint[1]-box.startPoint[1])/2]}exports.getBoxCenter=getBoxCenter2;function cutBoxFromImageAndResize2(box,image3,cropSize){let h=image3.shape[1],w=image3.shape[2],boxes=[[box.startPoint[1]/h,box.startPoint[0]/w,box.endPoint[1]/h,box.endPoint[0]/w]];return image.cropAndResize(image3,boxes,[0],cropSize)}exports.cutBoxFromImageAndResize=cutBoxFromImageAndResize2;function enlargeBox2(box,factor=1.5){let center=getBoxCenter2(box),size=getBoxSize2(box),newHalfSize=[factor*size[0]/2,factor*size[1]/2],startPoint=[center[0]-newHalfSize[0],center[1]-newHalfSize[1]],endPoint=[center[0]+newHalfSize[0],center[1]+newHalfSize[1]];return{startPoint,endPoint,landmarks:box.landmarks}}exports.enlargeBox=enlargeBox2;function squarifyBox2(box){let centers=getBoxCenter2(box),size=getBoxSize2(box),halfSize=Math.max(...size)/2,startPoint=[centers[0]-halfSize,centers[1]-halfSize],endPoint=[centers[0]+halfSize,centers[1]+halfSize];return{startPoint,endPoint,landmarks:box.landmarks}}exports.squarifyBox=squarifyBox2});var require_util=__commonJS(exports=>{exports.IDENTITY_MATRIX=[[1,0,0],[0,1,0],[0,0,1]];function normalizeRadians2(angle){return angle-2*Math.PI*Math.floor((angle+Math.PI)/(2*Math.PI))}exports.normalizeRadians=normalizeRadians2;function computeRotation2(point1,point2){let radians=Math.PI/2-Math.atan2(-(point2[1]-point1[1]),point2[0]-point1[0]);return normalizeRadians2(radians)}exports.computeRotation=computeRotation2;function radToDegrees(rad){return rad*180/Math.PI}exports.radToDegrees=radToDegrees;function buildTranslationMatrix2(x,y){return[[1,0,x],[0,1,y],[0,0,1]]}function dot5(v1,v2){let product=0;for(let i=0;i{var 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,378,379,397,288,361,454,356,70,63,105,66,107,336,296,334,293,300,168,6,195,4,98,97,2,326,327,33,160,158,133,153,144,362,385,387,263,373,380,57,40,37,0,267,270,287,321,314,17,84,91,78,81,13,311,308,402,14,178],VTX33=[33,133,362,263,1,62,308,159,145,386,374,6,102,331,2,13,14,70,105,107,336,334,300,54,10,284,50,280,234,454,58,288,152],VTX7=[33,133,362,263,1,78,308];exports.MESH_ANNOTATIONS=MESH_ANNOTATIONS;exports.MESH_TO_IRIS_INDICES_MAP=MESH_TO_IRIS_INDICES_MAP;exports.TRI468=TRI468;exports.TRI68=TRI68;exports.TRI33=TRI33;exports.TRI7=TRI7;exports.UV468=UV468;exports.UV68=VTX68.map(x=>UV468[x]);exports.UV33=VTX33.map(x=>UV468[x]);exports.UV7=VTX7.map(x=>UV468[x])});var require_facepipeline=__commonJS(exports=>{var bounding=__toModule(require_box()),util=__toModule(require_util()),coords2=__toModule(require_coords()),LANDMARKS_COUNT=468,MESH_MOUTH_INDEX=13,MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES=[MESH_MOUTH_INDEX,coords2.MESH_ANNOTATIONS.midwayBetweenEyes[0]],BLAZEFACE_MOUTH_INDEX=3,BLAZEFACE_NOSE_INDEX=2,BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES=[BLAZEFACE_MOUTH_INDEX,BLAZEFACE_NOSE_INDEX],LEFT_EYE_OUTLINE=coords2.MESH_ANNOTATIONS.leftEyeLower0,LEFT_EYE_BOUNDS=[LEFT_EYE_OUTLINE[0],LEFT_EYE_OUTLINE[LEFT_EYE_OUTLINE.length-1]],RIGHT_EYE_OUTLINE=coords2.MESH_ANNOTATIONS.rightEyeLower0,RIGHT_EYE_BOUNDS=[RIGHT_EYE_OUTLINE[0],RIGHT_EYE_OUTLINE[RIGHT_EYE_OUTLINE.length-1]],IRIS_UPPER_CENTER_INDEX=3,IRIS_LOWER_CENTER_INDEX=4,IRIS_IRIS_INDEX=71,IRIS_NUM_COORDINATES=76;function replaceRawCoordinates(rawCoords,newCoords,prefix,keys){for(let i=0;i[scaleFactor[0]*(coord[0]-this.meshWidth/2),scaleFactor[1]*(coord[1]-this.meshHeight/2),coord[2]]),coordsRotationMatrix=angle!==0?util.buildRotationMatrix(angle,[0,0]):util.IDENTITY_MATRIX,coordsRotated=angle!==0?coordsScaled.map(coord=>[...util.rotatePoint(coord,coordsRotationMatrix),coord[2]]):coordsScaled,inverseRotationMatrix=angle!==0?util.invertTransformMatrix(rotationMatrix):util.IDENTITY_MATRIX,boxCenter=[...bounding.getBoxCenter({startPoint:box.startPoint,endPoint:box.endPoint}),1];return coordsRotated.map(coord=>[coord[0]+util.dot(boxCenter,inverseRotationMatrix[0]),coord[1]+util.dot(boxCenter,inverseRotationMatrix[1]),coord[2]])}getLeftToRightEyeDepthDifference(rawCoords){let leftEyeZ=rawCoords[LEFT_EYE_BOUNDS[0]][2],rightEyeZ=rawCoords[RIGHT_EYE_BOUNDS[0]][2];return leftEyeZ-rightEyeZ}getEyeBox(rawCoords,face3,eyeInnerCornerIndex,eyeOuterCornerIndex,flip=!1){let box=bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex],rawCoords[eyeOuterCornerIndex]]),this.irisEnlarge)),boxSize=bounding.getBoxSize(box),crop=image.cropAndResize(face3,[[box.startPoint[1]/this.meshHeight,box.startPoint[0]/this.meshWidth,box.endPoint[1]/this.meshHeight,box.endPoint[0]/this.meshWidth]],[0],[this.irisSize,this.irisSize]);return flip&&(crop=image.flipLeftRight(crop)),{box,boxSize,crop}}getEyeCoords(eyeData,eyeBox,eyeBoxSize,flip=!1){let eyeRawCoords=[];for(let i=0;i{let z=averageZ;return i===2?z=upperCenterZ:i===4&&(z=lowerCenterZ),[coord[0],coord[1],z]})}async predict(input2,config2){let useFreshBox=!1,detector;if((this.skipped===0||this.skipped>config2.face.detector.skipFrames||!config2.face.mesh.enabled||!config2.videoOptimized)&&(detector=await this.boundingBoxDetector.getBoundingBoxes(input2),this.skipped=0),config2.videoOptimized&&this.skipped++,detector&&detector.boxes&&detector.boxes.length>0&&(!config2.face.mesh.enabled||detector.boxes.length!==this.detectedFaces&&this.detectedFaces!==config2.face.detector.maxFaces)){this.storedBoxes=[],this.detectedFaces=0;for(let possible of detector.boxes)this.storedBoxes.push({startPoint:possible.box.startPoint.dataSync(),endPoint:possible.box.endPoint.dataSync(),landmarks:possible.landmarks,confidence:possible.confidence});this.storedBoxes.length>0&&(useFreshBox=!0)}if(useFreshBox){if(!detector||!detector.boxes||detector.boxes.length===0)return this.storedBoxes=[],this.detectedFaces=0,null;for(let i=0;i{prediction.box.startPoint.dispose(),prediction.box.endPoint.dispose(),prediction.landmarks.dispose()});let results=tidy(()=>this.storedBoxes.map((box,i)=>{let face3,angle=0,rotationMatrix;if(config2.face.detector.rotation){let[indexOfMouth,indexOfForehead]=box.landmarks.length>=LANDMARKS_COUNT?MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES:BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES;angle=util.computeRotation(box.landmarks[indexOfMouth],box.landmarks[indexOfForehead]);let faceCenter=bounding.getBoxCenter({startPoint:box.startPoint,endPoint:box.endPoint}),faceCenterNormalized=[faceCenter[0]/input2.shape[2],faceCenter[1]/input2.shape[1]],rotatedImage=image.rotateWithOffset(input2,angle,0,faceCenterNormalized);rotationMatrix=util.buildRotationMatrix(-angle,faceCenter),face3=bounding.cutBoxFromImageAndResize({startPoint:box.startPoint,endPoint:box.endPoint},rotatedImage,[this.meshHeight,this.meshWidth]).div(255)}else{rotationMatrix=util.IDENTITY_MATRIX;let cloned=input2.clone();face3=bounding.cutBoxFromImageAndResize({startPoint:box.startPoint,endPoint:box.endPoint},cloned,[this.meshHeight,this.meshWidth]).div(255)}if(!config2.face.mesh.enabled)return{coords:null,box,faceConfidence:null,confidence:box.confidence,image:face3};let[,confidence,contourCoords]=this.meshDetector.predict(face3),confidenceVal=confidence.dataSync()[0];if(confidenceVala!==null),this.detectedFaces=results.length,results}calculateLandmarksBoundingBox(landmarks){let xs=landmarks.map(d=>d[0]),ys=landmarks.map(d=>d[1]),startPoint=[Math.min(...xs),Math.min(...ys)],endPoint=[Math.max(...xs),Math.max(...ys)];return{startPoint,endPoint,landmarks}}};exports.Pipeline=Pipeline});var require_facemesh=__commonJS(exports=>{var blazeface=__toModule(require_blazeface()),facepipeline=__toModule(require_facepipeline()),coords2=__toModule(require_coords()),MediaPipeFaceMesh=class{constructor(blazeFace,blazeMeshModel,irisModel,config2){this.facePipeline=new facepipeline.Pipeline(blazeFace,blazeMeshModel,irisModel,config2),this.config=config2}async estimateFaces(input2,config2){let predictions=await this.facePipeline.predict(input2,config2),results=[];for(let prediction of predictions||[]){if(prediction.isDisposedInternal)continue;let mesh=prediction.coords?prediction.coords.arraySync():null,meshRaw=prediction.rawCoords,annotations={};if(mesh&&mesh.length>0)for(let key of Object.keys(coords2.MESH_ANNOTATIONS))annotations[key]=coords2.MESH_ANNOTATIONS[key].map(index=>mesh[index]);let boxRaw=config2.face.mesh.returnRawData&&prediction.box?{topLeft:prediction.box.startPoint,bottomRight:prediction.box.endPoint}:null,box=prediction.box?[Math.max(0,prediction.box.startPoint[0]),Math.max(0,prediction.box.startPoint[1]),Math.min(input2.shape[2],prediction.box.endPoint[0])-prediction.box.startPoint[0],Math.min(input2.shape[1],prediction.box.endPoint[1])-prediction.box.startPoint[1]]:0;results.push({confidence:prediction.confidence||0,box,mesh,boxRaw,meshRaw,annotations,image:prediction.image?clone(prediction.image):null}),prediction.coords&&prediction.coords.dispose(),prediction.image&&prediction.image.dispose()}return results}},faceModels=[null,null,null];async function load3(config2){faceModels=await Promise.all([!faceModels[0]&&config2.face.enabled?blazeface.load(config2):null,!faceModels[1]&&config2.face.mesh.enabled?loadGraphModel(config2.face.mesh.modelPath,{fromTFHub:config2.face.mesh.modelPath.includes("tfhub.dev")}):null,!faceModels[2]&&config2.face.iris.enabled?loadGraphModel(config2.face.iris.modelPath,{fromTFHub:config2.face.iris.modelPath.includes("tfhub.dev")}):null]);let faceMesh=new MediaPipeFaceMesh(faceModels[0],faceModels[1],faceModels[2],config2);return config2.face.mesh.enabled&&log(`load model: ${config2.face.mesh.modelPath.match(/\/(.*)\./)[1]}`),config2.face.iris.enabled&&log(`load model: ${config2.face.iris.modelPath.match(/\/(.*)\./)[1]}`),faceMesh}exports.load=load3;exports.MediaPipeFaceMesh=MediaPipeFaceMesh;exports.triangulation=coords2.TRI468});var require_profile=__commonJS(exports=>{var profileData={};function profile3(name,data2){if(!data2||!data2.kernels)return;let maxResults=5,time2=data2.kernels.filter(a=>a.kernelTimeMs>0).reduce((a,b)=>a+=b.kernelTimeMs,0),slowest=data2.kernels.map((a,i)=>(a.id=i,a)).filter(a=>a.kernelTimeMs>0).sort((a,b)=>b.kernelTimeMs-a.kernelTimeMs),largest=data2.kernels.map((a,i)=>(a.id=i,a)).filter(a=>a.totalBytesSnapshot>0).sort((a,b)=>b.totalBytesSnapshot-a.totalBytesSnapshot);slowest.length>maxResults&&(slowest.length=maxResults),largest.length>maxResults&&(largest.length=maxResults);let res={newBytes:data2.newBytes,newTensors:data2.newTensors,peakBytes:data2.peakBytes,numKernelOps:data2.kernels.length,timeKernelOps:time2,slowestKernelOps:slowest,largestKernelOps:largest};profileData[name]=res,log("Human profiler",name,res)}exports.run=profile3});var require_age=__commonJS(exports=>{var profile3=__toModule(require_profile()),models2={},last={age:0},skipped=Number.MAX_SAFE_INTEGER;async function load3(config2){return models2.age||(models2.age=await loadGraphModel(config2.face.age.modelPath),log(`load model: ${config2.face.age.modelPath.match(/\/(.*)\./)[1]}`)),models2.age}async function predict3(image3,config2){return models2.age?skipped0?(skipped++,last):(config2.videoOptimized?skipped=0:skipped=Number.MAX_SAFE_INTEGER,new Promise(async resolve=>{let resize=image.resizeBilinear(image3,[config2.face.age.inputSize,config2.face.age.inputSize],!1),enhance=mul(resize,[255]);dispose(resize);let ageT,obj={};if(!config2.profile)config2.face.age.enabled&&(ageT=await models2.age.predict(enhance));else{let profileAge=config2.face.age.enabled?await profile(()=>models2.age.predict(enhance)):{};ageT=profileAge.result.clone(),profileAge.result.dispose(),profile3.run("age",profileAge)}if(enhance.dispose(),ageT){let data2=ageT.dataSync();obj.age=Math.trunc(10*data2[0])/10}ageT.dispose(),last=obj,resolve(obj)})):null}exports.predict=predict3;exports.load=load3});var require_gender=__commonJS(exports=>{var profile3=__toModule(require_profile()),models2={},last={gender:""},skipped=Number.MAX_SAFE_INTEGER,alternative=!1,rgb=[.2989,.587,.114];async function load3(config2){return models2.gender||(models2.gender=await loadGraphModel(config2.face.gender.modelPath),alternative=models2.gender.inputs[0].shape[3]===1,log(`load model: ${config2.face.gender.modelPath.match(/\/(.*)\./)[1]}`)),models2.gender}async function predict3(image3,config2){return models2.gender?skipped{let resize=image.resizeBilinear(image3,[config2.face.gender.inputSize,config2.face.gender.inputSize],!1),enhance;alternative?enhance=tidy(()=>{let[red,green,blue]=split(resize,3,3),redNorm=mul(red,rgb[0]),greenNorm=mul(green,rgb[1]),blueNorm=mul(blue,rgb[2]);return addN([redNorm,greenNorm,blueNorm]).sub(.5).mul(2)}):enhance=mul(resize,[255]),dispose(resize);let genderT,obj={};if(!config2.profile)config2.face.gender.enabled&&(genderT=await models2.gender.predict(enhance));else{let profileGender=config2.face.gender.enabled?await profile(()=>models2.gender.predict(enhance)):{};genderT=profileGender.result.clone(),profileGender.result.dispose(),profile3.run("gender",profileGender)}if(enhance.dispose(),genderT){let data2=genderT.dataSync();if(alternative){let confidence=Math.trunc(100*Math.abs(data2[0]-data2[1]))/100;confidence>config2.face.gender.minConfidence&&(obj.gender=data2[0]>data2[1]?"female":"male",obj.confidence=confidence)}else{let confidence=Math.trunc(200*Math.abs(data2[0]-.5))/100;confidence>config2.face.gender.minConfidence&&(obj.gender=data2[0]<=.5?"female":"male",obj.confidence=Math.min(.99,confidence))}}genderT.dispose(),last=obj,resolve(obj)})):null}exports.predict=predict3;exports.load=load3});var require_emotion=__commonJS(exports=>{var profile3=__toModule(require_profile()),annotations=["angry","disgust","fear","happy","sad","surprise","neutral"],models2={},last=[],skipped=Number.MAX_SAFE_INTEGER,rgb=[.2989,.587,.114],scale2=1;async function load3(config2){return models2.emotion||(models2.emotion=await loadGraphModel(config2.face.emotion.modelPath),log(`load model: ${config2.face.emotion.modelPath.match(/\/(.*)\./)[1]}`)),models2.emotion}async function predict3(image3,config2){return models2.emotion?skipped0?(skipped++,last):(config2.videoOptimized?skipped=0:skipped=Number.MAX_SAFE_INTEGER,new Promise(async resolve=>{let resize=image.resizeBilinear(image3,[config2.face.emotion.inputSize,config2.face.emotion.inputSize],!1),[red,green,blue]=split(resize,3,3);resize.dispose();let redNorm=mul(red,rgb[0]),greenNorm=mul(green,rgb[1]),blueNorm=mul(blue,rgb[2]);red.dispose(),green.dispose(),blue.dispose();let grayscale=addN([redNorm,greenNorm,blueNorm]);redNorm.dispose(),greenNorm.dispose(),blueNorm.dispose();let normalize=tidy(()=>grayscale.sub(.5).mul(2));grayscale.dispose();let obj=[];if(config2.face.emotion.enabled){let data2;if(config2.profile){let profileData=await profile(()=>models2.emotion.predict(normalize));data2=profileData.result.dataSync(),profileData.result.dispose(),profile3.run("emotion",profileData)}else{let emotionT=await models2.emotion.predict(normalize);data2=emotionT.dataSync(),dispose(emotionT)}for(let i=0;iconfig2.face.emotion.minConfidence&&obj.push({score:Math.min(.99,Math.trunc(100*scale2*data2[i])/100),emotion:annotations[i]});obj.sort((a,b)=>b.score-a.score)}normalize.dispose(),last=obj,resolve(obj)})):null}exports.predict=predict3;exports.load=load3});var require_embedding=__commonJS(exports=>{var profile3=__toModule(require_profile()),models2={};async function load3(config2){return models2.embedding||(models2.embedding=await loadGraphModel(config2.face.embedding.modelPath),log(`load model: ${config2.face.embedding.modelPath.match(/\/(.*)\./)[1]}`)),models2.embedding}function simmilarity2(embedding1,embedding22){if((embedding1==null?void 0:embedding1.length)!==(embedding22==null?void 0:embedding22.length))return 0;let order=2,distance=10*embedding1.map((val,i)=>val-embedding22[i]).reduce((dist,diff)=>dist+diff**order,0)**(1/order);return Math.trunc(1e3*(1-distance))/1e3}async function predict3(image3,config2){return models2.embedding?new Promise(async resolve=>{let resize=image.resizeBilinear(image3,[config2.face.embedding.inputSize,config2.face.embedding.inputSize],!1),data2=[];if(config2.face.embedding.enabled)if(config2.profile){let profileData=await profile(()=>models2.embedding.predict({img_inputs:resize}));data2=[...profileData.result.dataSync()],profileData.result.dispose(),profile3.run("emotion",profileData)}else{let embeddingT=await models2.embedding.predict({img_inputs:resize});data2=[...embeddingT.dataSync()],dispose(embeddingT)}resize.dispose(),resolve(data2)}):null}exports.predict=predict3;exports.simmilarity=simmilarity2;exports.load=load3});var require_modelBase=__commonJS(exports=>{var imageNetMean=[-123.15,-115.9,-103.06];function nameOutputResultsMobileNet(results){let[offsets,heatmap,displacementFwd,displacementBwd]=results;return{offsets,heatmap,displacementFwd,displacementBwd}}function nameOutputResultsResNet(results){let[displacementFwd,displacementBwd,offsets,heatmap]=results;return{offsets,heatmap,displacementFwd,displacementBwd}}var BaseModel=class{constructor(model2){this.model=model2}predict(input2,config2){return tidy(()=>{let asBatch=(config2.body.modelType==="ResNet"?input2.toFloat().add(imageNetMean):input2.toFloat().div(127.5).sub(1)).expandDims(0),results3d=this.model.predict(asBatch).map(y=>y.squeeze([0])),namedResults=config2.body.modelType==="ResNet"?nameOutputResultsResNet(results3d):nameOutputResultsMobileNet(results3d);return{heatmapScores:namedResults.heatmap.sigmoid(),offsets:namedResults.offsets,displacementFwd:namedResults.displacementFwd,displacementBwd:namedResults.displacementBwd}})}dispose(){this.model.dispose()}};exports.BaseModel=BaseModel});var require_heapSort=__commonJS(exports=>{function half(k){return Math.floor(k/2)}var MaxHeap=class{constructor(maxSize,getElementValue){this.priorityQueue=new Array(maxSize),this.numberOfElements=-1,this.getElementValue=getElementValue}enqueue(x){this.priorityQueue[++this.numberOfElements]=x,this.swim(this.numberOfElements)}dequeue(){let max6=this.priorityQueue[0];return this.exchange(0,this.numberOfElements--),this.sink(0),this.priorityQueue[this.numberOfElements+1]=null,max6}empty(){return this.numberOfElements===-1}size(){return this.numberOfElements+1}all(){return this.priorityQueue.slice(0,this.numberOfElements+1)}max(){return this.priorityQueue[0]}swim(k){for(;k>0&&this.less(half(k),k);)this.exchange(k,half(k)),k=half(k)}sink(k){for(;2*k<=this.numberOfElements;){let j=2*k;if(j{var heapSort=__toModule(require_heapSort());function scoreIsMaximumInLocalWindow(keypointId,score,heatmapY,heatmapX,localMaximumRadius,scores){let[height,width]=scores.shape,localMaximum=!0,yStart=Math.max(heatmapY-localMaximumRadius,0),yEnd=Math.min(heatmapY+localMaximumRadius+1,height);for(let yCurrent=yStart;yCurrentscore){localMaximum=!1;break}if(!localMaximum)break}return localMaximum}function buildPartWithScoreQueue(scoreThreshold,localMaximumRadius,scores){let[height,width,numKeypoints]=scores.shape,queue=new heapSort.MaxHeap(height*width*numKeypoints,({score})=>score);for(let heatmapY=0;heatmapY{exports.partNames=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"];exports.NUM_KEYPOINTS=exports.partNames.length;exports.partIds=exports.partNames.reduce((result,jointName,i)=>(result[jointName]=i,result),{});var connectedPartNames=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]];exports.connectedPartIndices=connectedPartNames.map(([jointNameA,jointNameB])=>[exports.partIds[jointNameA],exports.partIds[jointNameB]]);exports.poseChain=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];exports.partChannels=["left_face","right_face","right_upper_leg_front","right_lower_leg_back","right_upper_leg_back","left_lower_leg_front","left_upper_leg_front","left_upper_leg_back","left_lower_leg_back","right_feet","right_lower_leg_front","left_feet","torso_front","torso_back","right_upper_arm_front","right_upper_arm_back","right_lower_arm_back","left_lower_arm_front","left_upper_arm_front","left_upper_arm_back","left_lower_arm_back","right_hand","right_lower_arm_front","left_hand"]});var require_vectors=__commonJS(exports=>{var kpt=__toModule(require_keypoints());function getOffsetPoint(y,x,keypoint,offsets){return{y:offsets.get(y,x,keypoint),x:offsets.get(y,x,keypoint+kpt.NUM_KEYPOINTS)}}exports.getOffsetPoint=getOffsetPoint;function getImageCoords(part,outputStride,offsets){let{heatmapY,heatmapX,id:keypoint}=part,{y,x}=getOffsetPoint(heatmapY,heatmapX,keypoint,offsets);return{x:part.heatmapX*outputStride+x,y:part.heatmapY*outputStride+y}}exports.getImageCoords=getImageCoords;function fillArray(element,size){let result=new Array(size);for(let i=0;imax6?max6:a}exports.clamp=clamp2;function squaredDistance(y1,x1,y2,x2){let dy=y2-y1,dx=x2-x1;return dy*dy+dx*dx}exports.squaredDistance=squaredDistance;function addVectors(a,b){return{x:a.x+b.x,y:a.y+b.y}}exports.addVectors=addVectors;function clampVector(a,min6,max6){return{y:clamp2(a.y,min6,max6),x:clamp2(a.x,min6,max6)}}exports.clampVector=clampVector});var require_decoders=__commonJS(exports=>{var kpt=__toModule(require_keypoints());function getPointsConfidence(heatmapScores,heatMapCoords){let numKeypoints=heatMapCoords.shape[0],result=new Float32Array(numKeypoints);for(let keypoint=0;keypointheatMapCoordsBuffer.toTensor().mul(scalar(outputStride,"int32")).toFloat().add(getOffsetVectors(heatMapCoordsBuffer,offsetsBuffer)))}exports.getOffsetPoints=getOffsetPoints;function mod4(a,b){return tidy(()=>{let floored=a.div(scalar(b,"int32"));return a.sub(floored.mul(scalar(b,"int32")))})}function argmax2d(inputs){let[height,width,depth]=inputs.shape;return tidy(()=>{let coords2=inputs.reshape([height*width,depth]).argMax(0),yCoords=coords2.div(scalar(width,"int32")).expandDims(1),xCoords=mod4(coords2,width).expandDims(1);return concat([yCoords,xCoords],1)})}exports.argmax2d=argmax2d});var require_decodePose=__commonJS(exports=>{var keypoints=__toModule(require_keypoints()),vectors=__toModule(require_vectors()),decoders=__toModule(require_decoders()),parentChildrenTuples=keypoints.poseChain.map(([parentJoinName,childJoinName])=>[keypoints.partIds[parentJoinName],keypoints.partIds[childJoinName]]),parentToChildEdges=parentChildrenTuples.map(([,childJointId])=>childJointId),childToParentEdges=parentChildrenTuples.map(([parentJointId])=>parentJointId);function getDisplacement(edgeId,point,displacements){let numEdges=displacements.shape[2]/2;return{y:displacements.get(point.y,point.x,edgeId),x:displacements.get(point.y,point.x,numEdges+edgeId)}}function getStridedIndexNearPoint(point,outputStride,height,width){return{y:vectors.clamp(Math.round(point.y/outputStride),0,height-1),x:vectors.clamp(Math.round(point.x/outputStride),0,width-1)}}function traverseToTargetKeypoint(edgeId,sourceKeypoint,targetKeypointId,scoresBuffer,offsets,outputStride,displacements,offsetRefineStep=2){let[height,width]=scoresBuffer.shape,sourceKeypointIndices=getStridedIndexNearPoint(sourceKeypoint.position,outputStride,height,width),displacement=getDisplacement(edgeId,sourceKeypointIndices,displacements),targetKeypoint=vectors.addVectors(sourceKeypoint.position,displacement);for(let i=0;i=0;--edge){let sourceKeypointId=parentToChildEdges[edge],targetKeypointId=childToParentEdges[edge];instanceKeypoints[sourceKeypointId]&&!instanceKeypoints[targetKeypointId]&&(instanceKeypoints[targetKeypointId]=traverseToTargetKeypoint(edge,instanceKeypoints[sourceKeypointId],targetKeypointId,scores,offsets,outputStride,displacementsBwd))}for(let edge=0;edge(totalScore+=score,{position:{y:offsetPointsBuffer.get(i,0),x:offsetPointsBuffer.get(i,1)},part:keypoints.partNames[i],score})),filteredKeypoints=instanceKeypoints.filter(kpt=>kpt.score>config2.body.scoreThreshold);return heatmapValues.dispose(),offsetPoints.dispose(),{keypoints:filteredKeypoints,score:totalScore/instanceKeypoints.length}}exports.decodeSinglePose=decodeSinglePose});var require_decodeMultiple=__commonJS(exports=>{var buildParts=__toModule(require_buildParts()),decodePose=__toModule(require_decodePose()),vectors=__toModule(require_vectors()),kLocalMaximumRadius=1;function withinNmsRadiusOfCorrespondingPoint(poses,squaredNmsRadius,{x,y},keypointId){return poses.some(({keypoints})=>{let correspondingKeypoint=keypoints[keypointId].position;return vectors.squaredDistance(y,x,correspondingKeypoint.y,correspondingKeypoint.x)<=squaredNmsRadius})}function getInstanceScore(existingPoses,squaredNmsRadius,instanceKeypoints){return instanceKeypoints.reduce((result,{position,score},keypointId)=>(withinNmsRadiusOfCorrespondingPoint(existingPoses,squaredNmsRadius,position,keypointId)||(result+=score),result),0)/instanceKeypoints.length}function decodeMultiplePoses(scoresBuffer,offsetsBuffer,displacementsFwdBuffer,displacementsBwdBuffer,config2){let poses=[],queue=buildParts.buildPartWithScoreQueue(config2.body.scoreThreshold,kLocalMaximumRadius,scoresBuffer),squaredNmsRadius=config2.body.nmsRadius^2;for(;poses.lengthconfig2.body.scoreThreshold&&poses.push({keypoints,score})}return poses}exports.decodeMultiplePoses=decodeMultiplePoses});var require_util2=__commonJS(exports=>{var kpt=__toModule(require_keypoints());function eitherPointDoesntMeetConfidence(a,b,minConfidence){return a(eitherPointDoesntMeetConfidence(keypoints[leftJoint].score,keypoints[rightJoint].score,minConfidence)||result.push([keypoints[leftJoint],keypoints[rightJoint]]),result),[])}exports.getAdjacentKeyPoints=getAdjacentKeyPoints;var{NEGATIVE_INFINITY,POSITIVE_INFINITY}=Number;function getBoundingBox(keypoints){return keypoints.reduce(({maxX,maxY,minX,minY},{position:{x,y}})=>({maxX:Math.max(maxX,x),maxY:Math.max(maxY,y),minX:Math.min(minX,x),minY:Math.min(minY,y)}),{maxX:NEGATIVE_INFINITY,maxY:NEGATIVE_INFINITY,minX:POSITIVE_INFINITY,minY:POSITIVE_INFINITY})}exports.getBoundingBox=getBoundingBox;function getBoundingBoxPoints(keypoints){let{minX,minY,maxX,maxY}=getBoundingBox(keypoints);return[{x:minX,y:minY},{x:maxX,y:minY},{x:maxX,y:maxY},{x:minX,y:maxY}]}exports.getBoundingBoxPoints=getBoundingBoxPoints;async function toTensorBuffers3D(tensors){return Promise.all(tensors.map(tensor2=>tensor2.buffer()))}exports.toTensorBuffers3D=toTensorBuffers3D;function scalePose(pose,scaleY,scaleX){return{score:pose.score,keypoints:pose.keypoints.map(({score,part,position})=>({score,part,position:{x:position.x*scaleX,y:position.y*scaleY}}))}}exports.scalePose=scalePose;function resizeTo(image3,[targetH,targetW]){let input2=image3.squeeze(0),resized=input2.resizeBilinear([targetH,targetW]);return input2.dispose(),resized}exports.resizeTo=resizeTo;function scaleAndFlipPoses(poses,[height,width],[inputResolutionHeight,inputResolutionWidth]){return poses.map(pose=>scalePose(pose,height/inputResolutionHeight,width/inputResolutionWidth))}exports.scaleAndFlipPoses=scaleAndFlipPoses});var require_modelPoseNet=__commonJS(exports=>{var modelBase=__toModule(require_modelBase()),decodeMultiple=__toModule(require_decodeMultiple()),decodePose=__toModule(require_decodePose()),util=__toModule(require_util2());async function estimateMultiple(input2,res,config2){return new Promise(async resolve=>{let height=input2.shape[1],width=input2.shape[2],allTensorBuffers=await util.toTensorBuffers3D([res.heatmapScores,res.offsets,res.displacementFwd,res.displacementBwd]),scoresBuffer=allTensorBuffers[0],offsetsBuffer=allTensorBuffers[1],displacementsFwdBuffer=allTensorBuffers[2],displacementsBwdBuffer=allTensorBuffers[3],poses=await decodeMultiple.decodeMultiplePoses(scoresBuffer,offsetsBuffer,displacementsFwdBuffer,displacementsBwdBuffer,config2),scaled=util.scaleAndFlipPoses(poses,[height,width],[config2.body.inputSize,config2.body.inputSize]);resolve(scaled)})}async function estimateSingle(input2,res,config2){return new Promise(async resolve=>{let height=input2.shape[1],width=input2.shape[2],poses=[await decodePose.decodeSinglePose(res.heatmapScores,res.offsets,config2)],scaled=util.scaleAndFlipPoses(poses,[height,width],[config2.body.inputSize,config2.body.inputSize]);resolve(scaled)})}var PoseNet=class{constructor(model2){this.baseModel=model2}async estimatePoses(input2,config2){let resized=util.resizeTo(input2,[config2.body.inputSize,config2.body.inputSize]),res=this.baseModel.predict(resized,config2),poses=config2.body.maxDetections<2?await estimateSingle(input2,res,config2):await estimateMultiple(input2,res,config2);return res.heatmapScores.dispose(),res.offsets.dispose(),res.displacementFwd.dispose(),res.displacementBwd.dispose(),resized.dispose(),poses}dispose(){this.baseModel.dispose()}};exports.PoseNet=PoseNet;async function load3(config2){let model2=await loadGraphModel(config2.body.modelPath),mobilenet=new modelBase.BaseModel(model2);return log(`load model: ${config2.body.modelPath.match(/\/(.*)\./)[1]}`),new PoseNet(mobilenet)}exports.load=load3});var require_posenet=__commonJS(exports=>{var modelPoseNet=__toModule(require_modelPoseNet()),keypoints=__toModule(require_keypoints()),util=__toModule(require_util2());exports.load=modelPoseNet.load;exports.PoseNet=modelPoseNet.PoseNet;exports.partChannels=keypoints.partChannels;exports.partIds=keypoints.partIds;exports.partNames=keypoints.partNames;exports.poseChain=keypoints.poseChain;exports.getAdjacentKeyPoints=util.getAdjacentKeyPoints;exports.getBoundingBox=util.getBoundingBox;exports.getBoundingBoxPoints=util.getBoundingBoxPoints;exports.scaleAndFlipPoses=util.scaleAndFlipPoses;exports.scalePose=util.scalePose});var require_handdetector=__commonJS(exports=>{var HandDetector=class{constructor(model2,inputSize,anchorsAnnotated){this.model=model2,this.anchors=anchorsAnnotated.map(anchor=>[anchor.x_center,anchor.y_center]),this.anchorsTensor=tensor2d(this.anchors),this.inputSizeTensor=tensor1d([inputSize,inputSize]),this.doubleInputSizeTensor=tensor1d([inputSize*2,inputSize*2])}normalizeBoxes(boxes){return tidy(()=>{let boxOffsets=slice(boxes,[0,0],[-1,2]),boxSizes=slice(boxes,[0,2],[-1,2]),boxCenterPoints=add2(div(boxOffsets,this.inputSizeTensor),this.anchorsTensor),halfBoxSizes=div(boxSizes,this.doubleInputSizeTensor),startPoints=mul(sub(boxCenterPoints,halfBoxSizes),this.inputSizeTensor),endPoints=mul(add2(boxCenterPoints,halfBoxSizes),this.inputSizeTensor);return concat2d([startPoints,endPoints],1)})}normalizeLandmarks(rawPalmLandmarks,index){return tidy(()=>{let landmarks=add2(div(rawPalmLandmarks.reshape([-1,7,2]),this.inputSizeTensor),this.anchors[index]);return mul(landmarks,this.inputSizeTensor)})}async getBoxes(input2,config2){let batched=this.model.predict(input2),predictions=batched.squeeze();batched.dispose();let scoresT=tidy(()=>sigmoid(slice(predictions,[0,0],[-1,1])).squeeze()),scores=scoresT.dataSync(),rawBoxes=slice(predictions,[0,1],[-1,4]),boxes=this.normalizeBoxes(rawBoxes);rawBoxes.dispose();let filteredT=await image.nonMaxSuppressionAsync(boxes,scores,config2.hand.maxHands,config2.hand.iouThreshold,config2.hand.scoreThreshold),filtered=filteredT.arraySync();scoresT.dispose(),filteredT.dispose();let hands=[];for(let index of filtered)if(scores[index]>=config2.hand.minConfidence){let matchingBox=slice(boxes,[index,0],[1,-1]),rawPalmLandmarks=slice(predictions,[index,5],[1,14]),palmLandmarks=tidy(()=>this.normalizeLandmarks(rawPalmLandmarks,index).reshape([-1,2]));rawPalmLandmarks.dispose(),hands.push({box:matchingBox,palmLandmarks,confidence:scores[index]})}return predictions.dispose(),boxes.dispose(),hands}async estimateHandBounds(input2,config2){let inputHeight=input2.shape[1],inputWidth=input2.shape[2],image3=tidy(()=>input2.resizeBilinear([config2.hand.inputSize,config2.hand.inputSize]).div(127.5).sub(1)),predictions=await this.getBoxes(image3,config2);image3.dispose();let hands=[];if(!predictions||predictions.length===0)return hands;for(let prediction of predictions){let boxes=prediction.box.dataSync(),startPoint=boxes.slice(0,2),endPoint=boxes.slice(2,4),palmLandmarks=prediction.palmLandmarks.arraySync();prediction.box.dispose(),prediction.palmLandmarks.dispose(),hands.push(scaleBoxCoordinates({startPoint,endPoint,palmLandmarks,confidence:prediction.confidence},[inputWidth/config2.hand.inputSize,inputHeight/config2.hand.inputSize]))}return hands}};exports.HandDetector=HandDetector});var require_handpipeline=__commonJS(exports=>{var PALM_BOX_ENLARGE_FACTOR=5,HAND_BOX_ENLARGE_FACTOR=1.65,PALM_LANDMARK_IDS=[0,5,9,13,17,1,2],PALM_LANDMARKS_INDEX_OF_PALM_BASE=0,PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE=2,HandPipeline=class{constructor(handDetector,landmarkDetector,inputSize){this.handDetector=handDetector,this.landmarkDetector=landmarkDetector,this.inputSize=inputSize,this.storedBoxes=[],this.skipped=0,this.detectedHands=0}getBoxForPalmLandmarks(palmLandmarks,rotationMatrix){let rotatedPalmLandmarks=palmLandmarks.map(coord=>rotatePoint([...coord,1],rotationMatrix)),boxAroundPalm=this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);return enlargeBox(squarifyBox(boxAroundPalm),PALM_BOX_ENLARGE_FACTOR)}getBoxForHandLandmarks(landmarks){let boundingBox=this.calculateLandmarksBoundingBox(landmarks),boxAroundHand=enlargeBox(squarifyBox(boundingBox),HAND_BOX_ENLARGE_FACTOR);boxAroundHand.palmLandmarks=[];for(let i=0;i[scaleFactor[0]*(coord[0]-this.inputSize/2),scaleFactor[1]*(coord[1]-this.inputSize/2),scaleFactor[2]*coord[2]]),coordsRotationMatrix=buildRotationMatrix(angle,[0,0]),coordsRotated=coordsScaled.map(coord=>[...rotatePoint(coord,coordsRotationMatrix),coord[2]]),inverseRotationMatrix=invertTransformMatrix(rotationMatrix),boxCenter=[...getBoxCenter(box2),1],originalBoxCenter=[dot4(boxCenter,inverseRotationMatrix[0]),dot4(boxCenter,inverseRotationMatrix[1])];return coordsRotated.map(coord=>[coord[0]+originalBoxCenter[0],coord[1]+originalBoxCenter[1],coord[2]])}async estimateHands(image3,config2){let useFreshBox=!1,boxes;(this.skipped===0||this.skipped>config2.hand.skipFrames||!config2.hand.landmarks||!config2.videoOptimized)&&(boxes=await this.handDetector.estimateHandBounds(image3,config2),this.skipped=0),config2.videoOptimized&&this.skipped++,boxes&&boxes.length>0&&(boxes.length!==this.detectedHands&&this.detectedHands!==config2.hand.maxHands||!config2.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...boxes],this.storedBoxes.length>0&&(useFreshBox=!0));let hands=[];for(let i=0;i=config2.hand.minConfidence){let keypointsReshaped=reshape(keypoints,[-1,3]),rawCoords=keypointsReshaped.arraySync();keypoints.dispose(),keypointsReshaped.dispose();let coords2=this.transformRawCoords(rawCoords,newBox,angle,rotationMatrix),nextBoundingBox=this.getBoxForHandLandmarks(coords2);this.storedBoxes[i]=nextBoundingBox;let result={landmarks:coords2,confidence,box:{topLeft:nextBoundingBox.startPoint,bottomRight:nextBoundingBox.endPoint}};hands.push(result)}else this.storedBoxes[i]=null;keypoints.dispose()}else{let enlarged=enlargeBox(squarifyBox(currentBox),HAND_BOX_ENLARGE_FACTOR),result={confidence:currentBox.confidence,box:{topLeft:enlarged.startPoint,bottomRight:enlarged.endPoint}};hands.push(result)}}return this.storedBoxes=this.storedBoxes.filter(a=>a!==null),this.detectedHands=hands.length,hands}calculateLandmarksBoundingBox(landmarks){let xs=landmarks.map(d=>d[0]),ys=landmarks.map(d=>d[1]),startPoint=[Math.min(...xs),Math.min(...ys)],endPoint=[Math.max(...xs),Math.max(...ys)];return{startPoint,endPoint}}};exports.HandPipeline=HandPipeline});var 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HandPose(handPipeline);return config2.hand.enabled&&log(`load model: ${config2.hand.detector.modelPath.match(/\/(.*)\./)[1]}`),config2.hand.landmarks&&log(`load model: ${config2.hand.skeleton.modelPath.match(/\/(.*)\./)[1]}`),handPose}exports.load=load3});var require_gesture=__commonJS(exports=>{exports.body=res=>{if(!res)return[];let gestures=[];for(let i=0;ia.part==="leftWrist"),rightWrist=res[i].keypoints.find(a=>a.part==="rightWrist"),nose=res[i].keypoints.find(a=>a.part==="nose");nose&&leftWrist&&rightWrist&&leftWrist.position.ya.part==="leftShoulder"),rightShoulder=res[i].keypoints.find(a=>a.part==="rightShoulder");leftShoulder&&rightShoulder&&gestures.push({body:i,gesture:`leaning ${leftShoulder.position.y>rightShoulder.position.y?"left":"right"}`})}return gestures};exports.face=res=>{if(!res)return[];let gestures=[];for(let i=0;i0){let eyeFacing=res[i].mesh[35][2]-res[i].mesh[263][2];Math.abs(eyeFacing)<10?gestures.push({face:i,gesture:"facing camera"}):gestures.push({face:i,gesture:`facing ${eyeFacing<0?"right":"left"}`}),Math.abs(res[i].mesh[374][1]-res[i].mesh[386][1])/Math.abs(res[i].mesh[443][1]-res[i].mesh[450][1])<.2&&gestures.push({face:i,gesture:"blink left eye"}),Math.abs(res[i].mesh[145][1]-res[i].mesh[159][1])/Math.abs(res[i].mesh[223][1]-res[i].mesh[230][1])<.2&&gestures.push({face:i,gesture:"blink right eye"});let mouthOpen=Math.min(100,500*Math.abs(res[i].mesh[13][1]-res[i].mesh[14][1])/Math.abs(res[i].mesh[10][1]-res[i].mesh[152][1]));mouthOpen>10&&gestures.push({face:i,gesture:`mouth ${Math.trunc(mouthOpen)}% open`});let chinDepth=res[i].mesh[152][2];Math.abs(chinDepth)>10&&gestures.push({face:i,gesture:`head ${chinDepth<0?"up":"down"}`})}return gestures};exports.iris=res=>{if(!res)return[];let gestures=[];for(let i=0;i{if(!res)return[];let gestures=[];for(let i=0;i0){let closest=fingers.reduce((best,a)=>best.position[2]best.position[1]{var WebGLProgram=function(gl,vertexSource,fragmentSource){let _collect=function(source,prefix,collection){let r=new RegExp("\\b"+prefix+" \\w+ (\\w+)","ig");source.replace(r,(match,name)=>(collection[name]=0,match))},_compile=function(source,type){let shader=gl.createShader(type);if(gl.shaderSource(shader,source),gl.compileShader(shader),!gl.getShaderParameter(shader,gl.COMPILE_STATUS))throw new Error("Filter: GL compile failed",gl.getShaderInfoLog(shader));return shader};this.uniform={},this.attribute={};let _vsh=_compile(vertexSource,gl.VERTEX_SHADER),_fsh=_compile(fragmentSource,gl.FRAGMENT_SHADER);if(this.id=gl.createProgram(),gl.attachShader(this.id,_vsh),gl.attachShader(this.id,_fsh),gl.linkProgram(this.id),!gl.getProgramParameter(this.id,gl.LINK_STATUS))throw new Error("Filter: GL link failed",gl.getProgramInfoLog(this.id));gl.useProgram(this.id),_collect(vertexSource,"attribute",this.attribute);for(let a in this.attribute)this.attribute[a]=gl.getAttribLocation(this.id,a);_collect(vertexSource,"uniform",this.uniform),_collect(fragmentSource,"uniform",this.uniform);for(let u in this.uniform)this.uniform[u]=gl.getUniformLocation(this.id,u)},WebGLImageFilter=function(params){params||(params={});let _drawCount=0,_sourceTexture=null,_lastInChain=!1,_currentFramebufferIndex=-1,_tempFramebuffers=[null,null],_filterChain=[],_width=-1,_height=-1,_vertexBuffer=null,_currentProgram=null,_canvas=params.canvas||document.createElement("canvas"),_shaderProgramCache={},gl=_canvas.getContext("webgl");if(!gl)throw new Error("Filter: getContext() failed");this.addFilter=function(name){let args=Array.prototype.slice.call(arguments,1),filter=_filter[name];_filterChain.push({func:filter,args})},this.reset=function(){_filterChain=[]},this.apply=function(image3){if(_resize(image3.width,image3.height),_drawCount=0,_sourceTexture||(_sourceTexture=gl.createTexture()),gl.bindTexture(gl.TEXTURE_2D,_sourceTexture),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_WRAP_S,gl.CLAMP_TO_EDGE),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_WRAP_T,gl.CLAMP_TO_EDGE),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_MIN_FILTER,gl.NEAREST),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_MAG_FILTER,gl.NEAREST),gl.texImage2D(gl.TEXTURE_2D,0,gl.RGBA,gl.RGBA,gl.UNSIGNED_BYTE,image3),_filterChain.length===0)return _draw(),_canvas;for(let i=0;i<_filterChain.length;i++){_lastInChain=i===_filterChain.length-1;let f=_filterChain[i];f.func.apply(this,f.args||[])}return _canvas};let _resize=function(width,height){if(!(width===_width&&height===_height)){if(_canvas.width=width,_width=width,_canvas.height=height,_height=height,!_vertexBuffer){let vertices=new Float32Array([-1,-1,0,1,1,-1,1,1,-1,1,0,0,-1,1,0,0,1,-1,1,1,1,1,1,0]);_vertexBuffer=gl.createBuffer(),gl.bindBuffer(gl.ARRAY_BUFFER,_vertexBuffer),gl.bufferData(gl.ARRAY_BUFFER,vertices,gl.STATIC_DRAW),gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL,!0)}gl.viewport(0,0,_width,_height),_tempFramebuffers=[null,null]}},_getTempFramebuffer=function(index){return _tempFramebuffers[index]=_tempFramebuffers[index]||_createFramebufferTexture(_width,_height),_tempFramebuffers[index]},_createFramebufferTexture=function(width,height){let fbo=gl.createFramebuffer();gl.bindFramebuffer(gl.FRAMEBUFFER,fbo);let renderbuffer=gl.createRenderbuffer();gl.bindRenderbuffer(gl.RENDERBUFFER,renderbuffer);let texture=gl.createTexture();return gl.bindTexture(gl.TEXTURE_2D,texture),gl.texImage2D(gl.TEXTURE_2D,0,gl.RGBA,width,height,0,gl.RGBA,gl.UNSIGNED_BYTE,null),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_MAG_FILTER,gl.LINEAR),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_MIN_FILTER,gl.LINEAR),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_WRAP_S,gl.CLAMP_TO_EDGE),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_WRAP_T,gl.CLAMP_TO_EDGE),gl.framebufferTexture2D(gl.FRAMEBUFFER,gl.COLOR_ATTACHMENT0,gl.TEXTURE_2D,texture,0),gl.bindTexture(gl.TEXTURE_2D,null),gl.bindFramebuffer(gl.FRAMEBUFFER,null),{fbo,texture}},_draw=function(flags){var _a,_b;let source=null,target=null,flipY=!1;_drawCount===0?source=_sourceTexture:source=(_a=_getTempFramebuffer(_currentFramebufferIndex))==null?void 0:_a.texture,_drawCount++,_lastInChain&&!(flags&DRAW.INTERMEDIATE)?(target=null,flipY=_drawCount%2==0):(_currentFramebufferIndex=(_currentFramebufferIndex+1)%2,target=(_b=_getTempFramebuffer(_currentFramebufferIndex))==null?void 0:_b.fbo),gl.bindTexture(gl.TEXTURE_2D,source),gl.bindFramebuffer(gl.FRAMEBUFFER,target),gl.uniform1f(_currentProgram.uniform.flipY,flipY?-1:1),gl.drawArrays(gl.TRIANGLES,0,6)},_compileShader=function(fragmentSource){if(_shaderProgramCache[fragmentSource])return _currentProgram=_shaderProgramCache[fragmentSource],gl.useProgram(_currentProgram.id),_currentProgram;_currentProgram=new WebGLProgram(gl,SHADER.VERTEX_IDENTITY,fragmentSource);let floatSize=Float32Array.BYTES_PER_ELEMENT,vertSize=4*floatSize;return gl.enableVertexAttribArray(_currentProgram.attribute.pos),gl.vertexAttribPointer(_currentProgram.attribute.pos,2,gl.FLOAT,!1,vertSize,0*floatSize),gl.enableVertexAttribArray(_currentProgram.attribute.uv),gl.vertexAttribPointer(_currentProgram.attribute.uv,2,gl.FLOAT,!1,vertSize,2*floatSize),_shaderProgramCache[fragmentSource]=_currentProgram,_currentProgram},DRAW={INTERMEDIATE:1},SHADER={};SHADER.VERTEX_IDENTITY=["precision highp float;","attribute vec2 pos;","attribute vec2 uv;","varying vec2 vUv;","uniform float flipY;","void main(void) {","vUv = uv;","gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);","}"].join(` `),SHADER.FRAGMENT_IDENTITY=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","void main(void) {","gl_FragColor = texture2D(texture, vUv);","}"].join(` `);let _filter={};_filter.colorMatrix=function(matrix){let m=new Float32Array(matrix);m[4]/=255,m[9]/=255,m[14]/=255,m[19]/=255;let shader=m[18]===1&&m[3]===0&&m[8]===0&&m[13]===0&&m[15]===0&&m[16]===0&&m[17]===0&&m[19]===0?_filter.colorMatrix.SHADER.WITHOUT_ALPHA:_filter.colorMatrix.SHADER.WITH_ALPHA,program=_compileShader(shader);gl.uniform1fv(program.uniform.m,m),_draw()},_filter.colorMatrix.SHADER={},_filter.colorMatrix.SHADER.WITH_ALPHA=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","uniform float m[20];","void main(void) {","vec4 c = texture2D(texture, vUv);","gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];","gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];","gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];","gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];","}"].join(` `),_filter.colorMatrix.SHADER.WITHOUT_ALPHA=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","uniform float m[20];","void main(void) {","vec4 c = texture2D(texture, vUv);","gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];","gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];","gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];","gl_FragColor.a = c.a;","}"].join(` `),_filter.brightness=function(brightness){let b=(brightness||0)+1;_filter.colorMatrix([b,0,0,0,0,0,b,0,0,0,0,0,b,0,0,0,0,0,1,0])},_filter.saturation=function(amount){let x=(amount||0)*2/3+1,y=(x-1)*-.5;_filter.colorMatrix([x,y,y,0,0,y,x,y,0,0,y,y,x,0,0,0,0,0,1,0])},_filter.desaturate=function(){_filter.saturation(-1)},_filter.contrast=function(amount){let v=(amount||0)+1,o=-128*(v-1);_filter.colorMatrix([v,0,0,0,o,0,v,0,0,o,0,0,v,0,o,0,0,0,1,0])},_filter.negative=function(){_filter.contrast(-2)},_filter.hue=function(rotation){rotation=(rotation||0)/180*Math.PI;let cos4=Math.cos(rotation),sin4=Math.sin(rotation),lumR=.213,lumG=.715,lumB=.072;_filter.colorMatrix([lumR+cos4*(1-lumR)+sin4*-lumR,lumG+cos4*-lumG+sin4*-lumG,lumB+cos4*-lumB+sin4*(1-lumB),0,0,lumR+cos4*-lumR+sin4*.143,lumG+cos4*(1-lumG)+sin4*.14,lumB+cos4*-lumB+sin4*-.283,0,0,lumR+cos4*-lumR+sin4*-(1-lumR),lumG+cos4*-lumG+sin4*lumG,lumB+cos4*(1-lumB)+sin4*lumB,0,0,0,0,0,1,0])},_filter.desaturateLuminance=function(){_filter.colorMatrix([.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,0,0,0,1,0])},_filter.sepia=function(){_filter.colorMatrix([.393,.7689999,.18899999,0,0,.349,.6859999,.16799999,0,0,.272,.5339999,.13099999,0,0,0,0,0,1,0])},_filter.brownie=function(){_filter.colorMatrix([.5997023498159715,.34553243048391263,-.2708298674538042,0,47.43192855600873,-.037703249837783157,.8609577587992641,.15059552388459913,0,-36.96841498319127,.24113635128153335,-.07441037908422492,.44972182064877153,0,-7.562075277591283,0,0,0,1,0])},_filter.vintagePinhole=function(){_filter.colorMatrix([.6279345635605994,.3202183420819367,-.03965408211312453,0,9.651285835294123,.02578397704808868,.6441188644374771,.03259127616149294,0,7.462829176470591,.0466055556782719,-.0851232987247891,.5241648018700465,0,5.159190588235296,0,0,0,1,0])},_filter.kodachrome=function(){_filter.colorMatrix([1.1285582396593525,-.3967382283601348,-.03992559172921793,0,63.72958762196502,-.16404339962244616,1.0835251566291304,-.05498805115633132,0,24.732407896706203,-.16786010706155763,-.5603416277695248,1.6014850761964943,0,35.62982807460946,0,0,0,1,0])},_filter.technicolor=function(){_filter.colorMatrix([1.9125277891456083,-.8545344976951645,-.09155508482755585,0,11.793603434377337,-.3087833385928097,1.7658908555458428,-.10601743074722245,0,-70.35205161461398,-.231103377548616,-.7501899197440212,1.847597816108189,0,30.950940869491138,0,0,0,1,0])},_filter.polaroid=function(){_filter.colorMatrix([1.438,-.062,-.062,0,0,-.122,1.378,-.122,0,0,-.016,-.016,1.483,0,0,0,0,0,1,0])},_filter.shiftToBGR=function(){_filter.colorMatrix([0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0])},_filter.convolution=function(matrix){let m=new Float32Array(matrix),pixelSizeX=1/_width,pixelSizeY=1/_height,program=_compileShader(_filter.convolution.SHADER);gl.uniform1fv(program.uniform.m,m),gl.uniform2f(program.uniform.px,pixelSizeX,pixelSizeY),_draw()},_filter.convolution.SHADER=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","uniform vec2 px;","uniform float m[9];","void main(void) {","vec4 c11 = texture2D(texture, vUv - px);","vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y));","vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y));","vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) );","vec4 c22 = texture2D(texture, vUv);","vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) );","vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) );","vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) );","vec4 c33 = texture2D(texture, vUv + px );","gl_FragColor = ","c11 * m[0] + c12 * m[1] + c22 * m[2] +","c21 * m[3] + c22 * m[4] + c23 * m[5] +","c31 * m[6] + c32 * m[7] + c33 * m[8];","gl_FragColor.a = c22.a;","}"].join(` `),_filter.detectEdges=function(){_filter.convolution.call(this,[0,1,0,1,-4,1,0,1,0])},_filter.sobelX=function(){_filter.convolution.call(this,[-1,0,1,-2,0,2,-1,0,1])},_filter.sobelY=function(){_filter.convolution.call(this,[-1,-2,-1,0,0,0,1,2,1])},_filter.sharpen=function(amount){let a=amount||1;_filter.convolution.call(this,[0,-1*a,0,-1*a,1+4*a,-1*a,0,-1*a,0])},_filter.emboss=function(size){let s=size||1;_filter.convolution.call(this,[-2*s,-1*s,0,-1*s,1,1*s,0,1*s,2*s])},_filter.blur=function(size){let blurSizeX=size/7/_width,blurSizeY=size/7/_height,program=_compileShader(_filter.blur.SHADER);gl.uniform2f(program.uniform.px,0,blurSizeY),_draw(DRAW.INTERMEDIATE),gl.uniform2f(program.uniform.px,blurSizeX,0),_draw()},_filter.blur.SHADER=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","uniform vec2 px;","void main(void) {","gl_FragColor = vec4(0.0);","gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;","gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;","gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;","gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;","gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;","gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;","gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;","gl_FragColor += texture2D(texture, vUv )*0.159576912161;","gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x, 1.0*px.y))*0.147308056121;","gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x, 2.0*px.y))*0.115876621105;","gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x, 3.0*px.y))*0.0776744219933;","gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x, 4.0*px.y))*0.0443683338718;","gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x, 5.0*px.y))*0.0215963866053;","gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794;","gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265;","}"].join(` `),_filter.pixelate=function(size){let blurSizeX=size/_width,blurSizeY=size/_height,program=_compileShader(_filter.pixelate.SHADER);gl.uniform2f(program.uniform.size,blurSizeX,blurSizeY),_draw()},_filter.pixelate.SHADER=["precision highp float;","varying vec2 vUv;","uniform vec2 size;","uniform sampler2D texture;","vec2 pixelate(vec2 coord, vec2 size) {","return floor( coord / size ) * size;","}","void main(void) {","gl_FragColor = vec4(0.0);","vec2 coord = pixelate(vUv, size);","gl_FragColor += texture2D(texture, coord);","}"].join(` -`)};exports.Canvas=WebGLImageFilter});var require_image=__commonJS(exports=>{var fxImage=__toModule(require_imagefx()),inCanvas=null,outCanvas=null;function process3(input2,config3){let tensor2;if(input2 instanceof Tensor)tensor2=clone(input2);else{let originalWidth=input2.naturalWidth||input2.videoWidth||input2.width||input2.shape&&input2.shape[1]>0,originalHeight=input2.naturalHeight||input2.videoHeight||input2.height||input2.shape&&input2.shape[2]>0,targetWidth=originalWidth,targetHeight=originalHeight;if(config3.filter.width>0?targetWidth=config3.filter.width:config3.filter.height>0&&(targetWidth=originalWidth*(config3.filter.height/originalHeight)),config3.filter.height>0?targetHeight=config3.filter.height:config3.filter.width>0&&(targetHeight=originalHeight*(config3.filter.width/originalWidth)),!targetWidth||!targetHeight)return log("Human: invalid input",input2),null;(!inCanvas||inCanvas.width!==targetWidth||inCanvas.height!==targetHeight)&&(inCanvas=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(targetWidth,targetHeight):document.createElement("canvas"),inCanvas.width!==targetWidth&&(inCanvas.width=targetWidth),inCanvas.height!==targetHeight&&(inCanvas.height=targetHeight));let ctx=inCanvas.getContext("2d");if(input2 instanceof ImageData?ctx.putImageData(input2,0,0):ctx.drawImage(input2,0,0,originalWidth,originalHeight,0,0,inCanvas.width,inCanvas.height),config3.filter.enabled){if((!this.fx||!outCanvas||inCanvas.width!==outCanvas.width||inCanvas.height!==outCanvas.height)&&(outCanvas=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(inCanvas.width,inCanvas.height):document.createElement("canvas"),outCanvas.width!==inCanvas.width&&(outCanvas.width=inCanvas.width),outCanvas.height!==inCanvas.height&&(outCanvas.height=inCanvas.height),this.fx=ENV.flags.IS_BROWSER?new fxImage.Canvas({canvas:outCanvas}):null),!this.fx)return inCanvas;this.fx.reset(),this.fx.addFilter("brightness",config3.filter.brightness),config3.filter.contrast!==0&&this.fx.addFilter("contrast",config3.filter.contrast),config3.filter.sharpness!==0&&this.fx.addFilter("sharpen",config3.filter.sharpness),config3.filter.blur!==0&&this.fx.addFilter("blur",config3.filter.blur),config3.filter.saturation!==0&&this.fx.addFilter("saturation",config3.filter.saturation),config3.filter.hue!==0&&this.fx.addFilter("hue",config3.filter.hue),config3.filter.negative&&this.fx.addFilter("negative"),config3.filter.sepia&&this.fx.addFilter("sepia"),config3.filter.vintage&&this.fx.addFilter("brownie"),config3.filter.sepia&&this.fx.addFilter("sepia"),config3.filter.kodachrome&&this.fx.addFilter("kodachrome"),config3.filter.technicolor&&this.fx.addFilter("technicolor"),config3.filter.polaroid&&this.fx.addFilter("polaroid"),config3.filter.pixelate!==0&&this.fx.addFilter("pixelate",config3.filter.pixelate),this.fx.apply(inCanvas)}else outCanvas=inCanvas;let pixels;if(outCanvas.data){let shape=[outCanvas.height,outCanvas.width,3];pixels=tensor3d(outCanvas.data,shape,"int32")}else if(config3.backend==="webgl"||outCanvas instanceof ImageData)pixels=browser_exports.fromPixels(outCanvas);else{let tempCanvas=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(targetWidth,targetHeight):document.createElement("canvas");tempCanvas.width=targetWidth,tempCanvas.height=targetHeight;let tempCtx=tempCanvas.getContext("2d");tempCtx==null||tempCtx.drawImage(outCanvas,0,0);let data2=tempCtx==null?void 0:tempCtx.getImageData(0,0,targetWidth,targetHeight);pixels=browser_exports.fromPixels(data2)}let casted=pixels.toFloat();tensor2=casted.expandDims(0),pixels.dispose(),casted.dispose()}return{tensor:tensor2,canvas:config3.filter.return?outCanvas:null}}exports.process=process3});var human_exports={};__export(human_exports,{default:()=>Human});function log(...msg){let dt=new Date,ts=`${dt.getHours().toString().padStart(2,"0")}:${dt.getMinutes().toString().padStart(2,"0")}:${dt.getSeconds().toString().padStart(2,"0")}.${dt.getMilliseconds().toString().padStart(3,"0")}`;msg&&console.log(ts,"Human:",...msg)}var 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impl}):this.xor4096=impl})(exports,typeof module=="object"&&module,typeof define=="function"&&define)}),require_tychei=__commonJS2((exports,module)=>{(function(global2,module2,define2){function XorGen(seed){var me=this,strseed="";me.next=function(){var b=me.b,c=me.c,d=me.d,a=me.a;return b=b<<25^b>>>7^c,c=c-d|0,d=d<<24^d>>>8^a,a=a-b|0,me.b=b=b<<20^b>>>12^c,me.c=c=c-d|0,me.d=d<<16^c>>>16^a,me.a=a-b|0},me.a=0,me.b=0,me.c=2654435769|0,me.d=1367130551,seed===Math.floor(seed)?(me.a=seed/4294967296|0,me.b=seed|0):strseed+=seed;for(var k=0;k>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state&&(typeof state=="object"&©(state,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.tychei=impl})(exports,typeof module=="object"&&module,typeof define=="function"&&define)}),require_crypto=__commonJS2(()=>{}),require_seedrandom=__commonJS2((exports,module)=>{(function(pool3,math){var global2=this,width=256,chunks=6,digits=52,rngname="random",startdenom=math.pow(width,chunks),significance=math.pow(2,digits),overflow=significance*2,mask=width-1,nodecrypto;function seedrandom5(seed,options,callback){var key=[];options=options==!0?{entropy:!0}:options||{};var shortseed=mixkey(flatten4(options.entropy?[seed,tostring(pool3)]:seed==null?autoseed():seed,3),key),arc4=new ARC4(key),prng=function(){for(var n=arc4.g(chunks),d=startdenom,x=0;n=overflow;)n/=2,d/=2,x>>>=1;return(n+x)/d};return prng.int32=function(){return arc4.g(4)|0},prng.quick=function(){return arc4.g(4)/4294967296},prng.double=prng,mixkey(tostring(arc4.S),pool3),(options.pass||callback||function(prng2,seed2,is_math_call,state){return state&&(state.S&©(state,arc4),prng2.state=function(){return copy(arc4,{})}),is_math_call?(math[rngname]=prng2,seed2):prng2})(prng,shortseed,"global"in options?options.global:this==math,options.state)}math["seed"+rngname]=seedrandom5;function ARC4(key){var t,keylen=key.length,me=this,i=0,j=me.i=me.j=0,s=me.S=[];for(keylen||(key=[keylen++]);i{var alea5=require_alea(),xor128=require_xor128(),xorwow=require_xorwow(),xorshift7=require_xorshift7(),xor4096=require_xor4096(),tychei=require_tychei(),sr=require_seedrandom();sr.alea=alea5,sr.xor128=xor128,sr.xorwow=xorwow,sr.xorshift7=xorshift7,sr.xor4096=xor4096,sr.tychei=tychei,module.exports=sr}),require_alea2=__commonJS2((exports,module)=>{(function(global2,module2,define2){function Alea(seed){var me=this,mash=Mash();me.next=function(){var t=2091639*me.s0+me.c*23283064365386963e-26;return me.s0=me.s1,me.s1=me.s2,me.s2=t-(me.c=t|0)},me.c=1,me.s0=mash(" "),me.s1=mash(" "),me.s2=mash(" "),me.s0-=mash(seed),me.s0<0&&(me.s0+=1),me.s1-=mash(seed),me.s1<0&&(me.s1+=1),me.s2-=mash(seed),me.s2<0&&(me.s2+=1),mash=null}function copy(f,t){return t.c=f.c,t.s0=f.s0,t.s1=f.s1,t.s2=f.s2,t}function impl(seed,opts){var xg=new Alea(seed),state=opts&&opts.state,prng=xg.next;return prng.int32=function(){return xg.next()*4294967296|0},prng.double=function(){return prng()+(prng()*2097152|0)*11102230246251565e-32},prng.quick=prng,state&&(typeof state=="object"&©(state,xg),prng.state=function(){return copy(xg,{})}),prng}function Mash(){var n=4022871197,mash=function(data2){data2=data2.toString();for(var i=0;i>>0,h-=n,h*=n,n=h>>>0,h-=n,n+=h*4294967296}return(n>>>0)*23283064365386963e-26};return mash}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.alea=impl})(exports,typeof module=="object"&&module,typeof define=="function"&&define)}),require_xor1282=__commonJS2((exports,module)=>{(function(global2,module2,define2){function XorGen(seed){var me=this,strseed="";me.x=0,me.y=0,me.z=0,me.w=0,me.next=function(){var t=me.x^me.x<<11;return me.x=me.y,me.y=me.z,me.z=me.w,me.w^=me.w>>>19^t^t>>>8},seed===(seed|0)?me.x=seed:strseed+=seed;for(var k=0;k>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state&&(typeof state=="object"&©(state,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.xor128=impl})(exports,typeof module=="object"&&module,typeof define=="function"&&define)}),require_xorwow2=__commonJS2((exports,module)=>{(function(global2,module2,define2){function XorGen(seed){var me=this,strseed="";me.next=function(){var t=me.x^me.x>>>2;return me.x=me.y,me.y=me.z,me.z=me.w,me.w=me.v,(me.d=me.d+362437|0)+(me.v=me.v^me.v<<4^(t^t<<1))|0},me.x=0,me.y=0,me.z=0,me.w=0,me.v=0,seed===(seed|0)?me.x=seed:strseed+=seed;for(var k=0;k>>4),me.next()}function copy(f,t){return t.x=f.x,t.y=f.y,t.z=f.z,t.w=f.w,t.v=f.v,t.d=f.d,t}function impl(seed,opts){var xg=new XorGen(seed),state=opts&&opts.state,prng=function(){return(xg.next()>>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state&&(typeof state=="object"&©(state,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.xorwow=impl})(exports,typeof module=="object"&&module,typeof define=="function"&&define)}),require_xorshift72=__commonJS2((exports,module)=>{(function(global2,module2,define2){function XorGen(seed){var me=this;me.next=function(){var X=me.x,i=me.i,t,v,w;return t=X[i],t^=t>>>7,v=t^t<<24,t=X[i+1&7],v^=t^t>>>10,t=X[i+3&7],v^=t^t>>>3,t=X[i+4&7],v^=t^t<<7,t=X[i+7&7],t=t^t<<13,v^=t^t<<9,X[i]=v,me.i=i+1&7,v};function init2(me2,seed2){var j,w,X=[];if(seed2===(seed2|0))w=X[0]=seed2;else for(seed2=""+seed2,j=0;j0;--j)me2.next()}init2(me,seed)}function copy(f,t){return t.x=f.x.slice(),t.i=f.i,t}function impl(seed,opts){seed==null&&(seed=+new Date);var xg=new XorGen(seed),state=opts&&opts.state,prng=function(){return(xg.next()>>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state&&(state.x&©(state,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.xorshift7=impl})(exports,typeof module=="object"&&module,typeof define=="function"&&define)}),require_xor40962=__commonJS2((exports,module)=>{(function(global2,module2,define2){function XorGen(seed){var me=this;me.next=function(){var w=me.w,X=me.X,i=me.i,t,v;return me.w=w=w+1640531527|0,v=X[i+34&127],t=X[i=i+1&127],v^=v<<13,t^=t<<17,v^=v>>>15,t^=t>>>12,v=X[i]=v^t,me.i=i,v+(w^w>>>16)|0};function init2(me2,seed2){var t,v,i,j,w,X=[],limit=128;for(seed2===(seed2|0)?(v=seed2,seed2=null):(seed2=seed2+"\0",v=0,limit=Math.max(limit,seed2.length)),i=0,j=-32;j>>15,v^=v<<4,v^=v>>>13,j>=0&&(w=w+1640531527|0,t=X[j&127]^=v+w,i=t==0?i+1:0);for(i>=128&&(X[(seed2&&seed2.length||0)&127]=-1),i=127,j=4*128;j>0;--j)v=X[i+34&127],t=X[i=i+1&127],v^=v<<13,t^=t<<17,v^=v>>>15,t^=t>>>12,X[i]=v^t;me2.w=w,me2.X=X,me2.i=i}init2(me,seed)}function copy(f,t){return t.i=f.i,t.w=f.w,t.X=f.X.slice(),t}function impl(seed,opts){seed==null&&(seed=+new Date);var xg=new XorGen(seed),state=opts&&opts.state,prng=function(){return(xg.next()>>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state&&(state.X&©(state,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.xor4096=impl})(exports,typeof module=="object"&&module,typeof define=="function"&&define)}),require_tychei2=__commonJS2((exports,module)=>{(function(global2,module2,define2){function XorGen(seed){var me=this,strseed="";me.next=function(){var b=me.b,c=me.c,d=me.d,a=me.a;return b=b<<25^b>>>7^c,c=c-d|0,d=d<<24^d>>>8^a,a=a-b|0,me.b=b=b<<20^b>>>12^c,me.c=c=c-d|0,me.d=d<<16^c>>>16^a,me.a=a-b|0},me.a=0,me.b=0,me.c=2654435769|0,me.d=1367130551,seed===Math.floor(seed)?(me.a=seed/4294967296|0,me.b=seed|0):strseed+=seed;for(var k=0;k>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state&&(typeof state=="object"&©(state,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.tychei=impl})(exports,typeof module=="object"&&module,typeof define=="function"&&define)}),require_seedrandom3=__commonJS2((exports,module)=>{(function(pool3,math){var global2=this,width=256,chunks=6,digits=52,rngname="random",startdenom=math.pow(width,chunks),significance=math.pow(2,digits),overflow=significance*2,mask=width-1,nodecrypto;function seedrandom5(seed,options,callback){var key=[];options=options==!0?{entropy:!0}:options||{};var shortseed=mixkey(flatten4(options.entropy?[seed,tostring(pool3)]:seed==null?autoseed():seed,3),key),arc4=new ARC4(key),prng=function(){for(var n=arc4.g(chunks),d=startdenom,x=0;n=overflow;)n/=2,d/=2,x>>>=1;return(n+x)/d};return prng.int32=function(){return arc4.g(4)|0},prng.quick=function(){return arc4.g(4)/4294967296},prng.double=prng,mixkey(tostring(arc4.S),pool3),(options.pass||callback||function(prng2,seed2,is_math_call,state){return state&&(state.S&©(state,arc4),prng2.state=function(){return copy(arc4,{})}),is_math_call?(math[rngname]=prng2,seed2):prng2})(prng,shortseed,"global"in options?options.global:this==math,options.state)}math["seed"+rngname]=seedrandom5;function ARC4(key){var t,keylen=key.length,me=this,i=0,j=me.i=me.j=0,s=me.S=[];for(keylen||(key=[keylen++]);i{var alea5=require_alea2(),xor128=require_xor1282(),xorwow=require_xorwow2(),xorshift7=require_xorshift72(),xor4096=require_xor40962(),tychei=require_tychei2(),sr=require_seedrandom3();sr.alea=alea5,sr.xor128=xor128,sr.xorwow=xorwow,sr.xorshift7=xorshift7,sr.xor4096=xor4096,sr.tychei=tychei,module.exports=sr}),require_path=__commonJS2(()=>{}),require_worker_threads=__commonJS2(()=>{}),require_perf_hooks=__commonJS2(()=>{}),require_tfjs_backend_wasm_threaded_simd=__commonJS2((exports,module)=>{var WasmBackendModuleThreadedSimd=function(){var _scriptDir=typeof document!="undefined"&&document.currentScript?document.currentScript.src:void 0;return typeof __filename!="undefined"&&(_scriptDir=_scriptDir||__filename),function(WasmBackendModuleThreadedSimd2){WasmBackendModuleThreadedSimd2=WasmBackendModuleThreadedSimd2||{};function GROWABLE_HEAP_I8(){return wasmMemory.buffer!=buffer2&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAP8}function GROWABLE_HEAP_U8(){return wasmMemory.buffer!=buffer2&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAPU8}function GROWABLE_HEAP_I32(){return wasmMemory.buffer!=buffer2&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAP32}function GROWABLE_HEAP_U32(){return wasmMemory.buffer!=buffer2&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAPU32}function GROWABLE_HEAP_F64(){return wasmMemory.buffer!=buffer2&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAPF64}var Module=typeof WasmBackendModuleThreadedSimd2!="undefined"?WasmBackendModuleThreadedSimd2:{},_scriptDir2=void 0,moduleOverrides={},key;for(key in Module)Module.hasOwnProperty(key)&&(moduleOverrides[key]=Module[key]);var arguments_=[],thisProgram="./this.program",quit_=function(status,toThrow){throw toThrow},ENVIRONMENT_IS_WEB=!1,ENVIRONMENT_IS_WORKER=!1,ENVIRONMENT_IS_NODE=!1,ENVIRONMENT_IS_SHELL=!1;ENVIRONMENT_IS_WEB=typeof window=="object",ENVIRONMENT_IS_WORKER=typeof importScripts=="function",ENVIRONMENT_IS_NODE=typeof process=="object"&&typeof process.versions=="object"&&typeof process.versions.node=="string",ENVIRONMENT_IS_SHELL=!ENVIRONMENT_IS_WEB&&!ENVIRONMENT_IS_NODE&&!ENVIRONMENT_IS_WORKER;var ENVIRONMENT_IS_PTHREAD=Module.ENVIRONMENT_IS_PTHREAD||!1;ENVIRONMENT_IS_PTHREAD&&(buffer2=Module.buffer,DYNAMIC_BASE=Module.DYNAMIC_BASE,DYNAMICTOP_PTR=Module.DYNAMICTOP_PTR);var scriptDirectory="";function locateFile(path){return Module.locateFile?Module.locateFile(path,scriptDirectory):scriptDirectory+path}var read_,readAsync,readBinary,setWindowTitle,nodeFS,nodePath;if(ENVIRONMENT_IS_NODE){ENVIRONMENT_IS_WORKER?scriptDirectory=require_path().dirname(scriptDirectory)+"/":scriptDirectory=__dirname+"/",read_=function(filename,binary){return nodeFS||(nodeFS=require("fs")),nodePath||(nodePath=require_path()),filename=nodePath.normalize(filename),nodeFS.readFileSync(filename,binary?null:"utf8")},readBinary=function(filename){var ret=read_(filename,!0);return ret.buffer||(ret=new Uint8Array(ret)),assert3(ret.buffer),ret},process.argv.length>1&&(thisProgram=process.argv[1].replace(/\\/g,"/")),arguments_=process.argv.slice(2),process.on("uncaughtException",function(ex){if(!(ex instanceof ExitStatus))throw ex}),process.on("unhandledRejection",abort),quit_=function(status){process.exit(status)},Module.inspect=function(){return"[Emscripten Module object]"};var nodeWorkerThreads;try{nodeWorkerThreads=require_worker_threads()}catch(e){throw console.error('The "worker_threads" module is not supported in this node.js build - perhaps a newer version is needed?'),e}Worker=nodeWorkerThreads.Worker}else ENVIRONMENT_IS_SHELL?(typeof read!="undefined"&&(read_=function(f){return read(f)}),readBinary=function(f){var data2;return typeof readbuffer=="function"?new Uint8Array(readbuffer(f)):(data2=read(f,"binary"),assert3(typeof data2=="object"),data2)},typeof scriptArgs!="undefined"?arguments_=scriptArgs:typeof arguments!="undefined"&&(arguments_=arguments),typeof quit=="function"&&(quit_=function(status){quit(status)}),typeof print!="undefined"&&(typeof console=="undefined"&&(console={}),console.log=print,console.warn=console.error=typeof printErr!="undefined"?printErr:print)):(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&(ENVIRONMENT_IS_WORKER?scriptDirectory=self.location.href:document.currentScript&&(scriptDirectory=document.currentScript.src),_scriptDir2&&(scriptDirectory=_scriptDir2),scriptDirectory.indexOf("blob:")!==0?scriptDirectory=scriptDirectory.substr(0,scriptDirectory.lastIndexOf("/")+1):scriptDirectory="",ENVIRONMENT_IS_NODE?(read_=function(filename,binary){return nodeFS||(nodeFS=require("fs")),nodePath||(nodePath=require_path()),filename=nodePath.normalize(filename),nodeFS.readFileSync(filename,binary?null:"utf8")},readBinary=function(filename){var ret=read_(filename,!0);return ret.buffer||(ret=new Uint8Array(ret)),assert3(ret.buffer),ret}):(read_=function(url){var xhr=new XMLHttpRequest;return xhr.open("GET",url,!1),xhr.send(null),xhr.responseText},ENVIRONMENT_IS_WORKER&&(readBinary=function(url){var xhr=new XMLHttpRequest;return xhr.open("GET",url,!1),xhr.responseType="arraybuffer",xhr.send(null),new Uint8Array(xhr.response)}),readAsync=function(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open("GET",url,!0),xhr.responseType="arraybuffer",xhr.onload=function(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()},xhr.onerror=onerror,xhr.send(null)}),setWindowTitle=function(title){document.title=title});ENVIRONMENT_IS_NODE&&typeof performance=="undefined"&&(performance=require_perf_hooks().performance);var out=Module.print||console.log.bind(console),err=Module.printErr||console.warn.bind(console);for(key in moduleOverrides)moduleOverrides.hasOwnProperty(key)&&(Module[key]=moduleOverrides[key]);moduleOverrides=null,Module.arguments&&(arguments_=Module.arguments),Module.thisProgram&&(thisProgram=Module.thisProgram),Module.quit&&(quit_=Module.quit);var Atomics_load=Atomics.load,Atomics_store=Atomics.store,Atomics_compareExchange=Atomics.compareExchange,wasmBinary;Module.wasmBinary&&(wasmBinary=Module.wasmBinary);var noExitRuntime;Module.noExitRuntime&&(noExitRuntime=Module.noExitRuntime),typeof WebAssembly!="object"&&err("no native wasm support detected");var wasmMemory,wasmTable=new WebAssembly.Table({initial:169,maximum:169+0,element:"anyfunc"}),wasmModule,threadInfoStruct=0,selfThreadId=0,ABORT=!1,EXITSTATUS=0;function assert3(condition,text){condition||abort("Assertion failed: "+text)}function getCFunc(ident){var func2=Module["_"+ident];return assert3(func2,"Cannot call unknown function "+ident+", make sure it is exported"),func2}function ccall(ident,returnType,argTypes,args,opts){var toC={string:function(str){var ret2=0;if(str!=null&&str!==0){var len=(str.length<<2)+1;ret2=stackAlloc(len),stringToUTF8(str,ret2,len)}return ret2},array:function(arr){var ret2=stackAlloc(arr.length);return writeArrayToMemory(arr,ret2),ret2}};function convertReturnValue(ret2){return returnType==="string"?UTF8ToString(ret2):returnType==="boolean"?Boolean(ret2):ret2}var func2=getCFunc(ident),cArgs=[],stack2=0;if(args)for(var i=0;i=endIdx);){var u0=heap[idx++];if(!u0)return str;if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224?u0=(u0&15)<<12|u1<<6|u2:u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63,u0<65536)str+=String.fromCharCode(u0);else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(GROWABLE_HEAP_U8(),ptr,maxBytesToRead):""}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;for(var startIdx=outIdx,endIdx=outIdx+maxBytesToWrite-1,i=0;i=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6,heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12,heap[outIdx++]=128|u>>6&63,heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18,heap[outIdx++]=128|u>>12&63,heap[outIdx++]=128|u>>6&63,heap[outIdx++]=128|u&63}}return heap[outIdx]=0,outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,GROWABLE_HEAP_U8(),outPtr,maxBytesToWrite)}function lengthBytesUTF8(str){for(var len=0,i=0;i=55296&&u<=57343&&(u=65536+((u&1023)<<10)|str.charCodeAt(++i)&1023),u<=127?++len:u<=2047?len+=2:u<=65535?len+=3:len+=4}return len}function writeArrayToMemory(array2,buffer3){GROWABLE_HEAP_I8().set(array2,buffer3)}var WASM_PAGE_SIZE=65536;function alignUp(x,multiple){return x%multiple>0&&(x+=multiple-x%multiple),x}var buffer2,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer2=buf,Module.HEAP8=HEAP8=new Int8Array(buf),Module.HEAP16=HEAP16=new Int16Array(buf),Module.HEAP32=HEAP32=new Int32Array(buf),Module.HEAPU8=HEAPU8=new Uint8Array(buf),Module.HEAPU16=HEAPU16=new Uint16Array(buf),Module.HEAPU32=HEAPU32=new Uint32Array(buf),Module.HEAPF32=HEAPF32=new Float32Array(buf),Module.HEAPF64=HEAPF64=new Float64Array(buf)}var STACK_BASE=5256464,STACKTOP=STACK_BASE,STACK_MAX=13584,DYNAMIC_BASE=5256464,DYNAMICTOP_PTR=12656,INITIAL_INITIAL_MEMORY=Module.INITIAL_MEMORY||16777216;if(ENVIRONMENT_IS_PTHREAD)wasmMemory=Module.wasmMemory,buffer2=Module.buffer;else if(Module.wasmMemory)wasmMemory=Module.wasmMemory;else if(wasmMemory=new WebAssembly.Memory({initial:INITIAL_INITIAL_MEMORY/WASM_PAGE_SIZE,maximum:2147483648/WASM_PAGE_SIZE,shared:!0}),!(wasmMemory.buffer instanceof SharedArrayBuffer))throw err("requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag"),ENVIRONMENT_IS_NODE&&console.log("(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)"),Error("bad memory");wasmMemory&&(buffer2=wasmMemory.buffer),INITIAL_INITIAL_MEMORY=buffer2.byteLength,updateGlobalBufferAndViews(buffer2),ENVIRONMENT_IS_PTHREAD||(GROWABLE_HEAP_I32()[DYNAMICTOP_PTR>>2]=DYNAMIC_BASE);function callRuntimeCallbacks(callbacks2){for(;callbacks2.length>0;){var callback=callbacks2.shift();if(typeof callback=="function"){callback(Module);continue}var func2=callback.func;typeof func2=="number"?callback.arg===void 0?Module.dynCall_v(func2):Module.dynCall_vi(func2,callback.arg):func2(callback.arg===void 0?null:callback.arg)}}var __ATPRERUN__=[],__ATINIT__=[],__ATMAIN__=[],__ATEXIT__=[],__ATPOSTRUN__=[],runtimeInitialized=!1;ENVIRONMENT_IS_PTHREAD&&(runtimeInitialized=!0);function preRun(){if(!ENVIRONMENT_IS_PTHREAD){if(Module.preRun)for(typeof Module.preRun=="function"&&(Module.preRun=[Module.preRun]);Module.preRun.length;)addOnPreRun(Module.preRun.shift());callRuntimeCallbacks(__ATPRERUN__)}}function initRuntime(){runtimeInitialized=!0,callRuntimeCallbacks(__ATINIT__)}function preMain(){ENVIRONMENT_IS_PTHREAD||callRuntimeCallbacks(__ATMAIN__)}function postRun(){if(!ENVIRONMENT_IS_PTHREAD){if(Module.postRun)for(typeof Module.postRun=="function"&&(Module.postRun=[Module.postRun]);Module.postRun.length;)addOnPostRun(Module.postRun.shift());callRuntimeCallbacks(__ATPOSTRUN__)}}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var Math_ceil=Math.ceil,Math_floor=Math.floor,runDependencies=0,runDependencyWatcher=null,dependenciesFulfilled=null;function addRunDependency(id){assert3(!ENVIRONMENT_IS_PTHREAD,"addRunDependency cannot be used in a pthread worker"),runDependencies++,Module.monitorRunDependencies&&Module.monitorRunDependencies(runDependencies)}function removeRunDependency(id){if(runDependencies--,Module.monitorRunDependencies&&Module.monitorRunDependencies(runDependencies),runDependencies==0&&(runDependencyWatcher!==null&&(clearInterval(runDependencyWatcher),runDependencyWatcher=null),dependenciesFulfilled)){var callback=dependenciesFulfilled;dependenciesFulfilled=null,callback()}}Module.preloadedImages={},Module.preloadedAudios={};function abort(what){throw Module.onAbort&&Module.onAbort(what),ENVIRONMENT_IS_PTHREAD&&console.error("Pthread aborting at "+new Error().stack),what+="",out(what),err(what),ABORT=!0,EXITSTATUS=1,what="abort("+what+"). Build with -s ASSERTIONS=1 for more info.",new WebAssembly.RuntimeError(what)}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix="data:application/octet-stream;base64,";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix="file://";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile="tfjs-backend-wasm-threaded-simd.wasm";isDataURI(wasmBinaryFile)||(wasmBinaryFile=locateFile(wasmBinaryFile));function getBinary(){try{if(wasmBinary)return new Uint8Array(wasmBinary);if(readBinary)return readBinary(wasmBinaryFile);throw"both async and sync fetching of the wasm failed"}catch(err2){abort(err2)}}function getBinaryPromise(){return!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&typeof fetch=="function"&&!isFileURI(wasmBinaryFile)?fetch(wasmBinaryFile,{credentials:"same-origin"}).then(function(response){if(!response.ok)throw"failed to load wasm binary file at '"+wasmBinaryFile+"'";return response.arrayBuffer()}).catch(function(){return getBinary()}):new Promise(function(resolve,reject){resolve(getBinary())})}function createWasm(){var info={a:asmLibraryArg};function receiveInstance(instance,module2){var exports3=instance.exports;if(Module.asm=exports3,wasmModule=module2,!ENVIRONMENT_IS_PTHREAD){var numWorkersToLoad=PThread.unusedWorkers.length;PThread.unusedWorkers.forEach(function(w){PThread.loadWasmModuleToWorker(w,function(){--numWorkersToLoad||removeRunDependency("wasm-instantiate")})})}}ENVIRONMENT_IS_PTHREAD||addRunDependency("wasm-instantiate");function receiveInstantiatedSource(output){receiveInstance(output.instance,output.module)}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err("failed to asynchronously prepare wasm: "+reason),abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming=="function"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch=="function")fetch(wasmBinaryFile,{credentials:"same-origin"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err("wasm streaming compile failed: "+reason),err("falling back to ArrayBuffer instantiation"),instantiateArrayBuffer(receiveInstantiatedSource)})});else return instantiateArrayBuffer(receiveInstantiatedSource)}if(Module.instantiateWasm)try{var exports2=Module.instantiateWasm(info,receiveInstance);return exports2}catch(e){return err("Module.instantiateWasm callback failed with error: "+e),!1}return instantiateAsync(),{}}var ASM_CONSTS={};function initPthreadsJS(){PThread.initRuntime()}ENVIRONMENT_IS_PTHREAD||__ATINIT__.push({func:function(){___wasm_call_ctors()}});var __pthread_ptr=0,__pthread_is_main_runtime_thread=0,__pthread_is_main_browser_thread=0;function __register_pthread_ptr(pthreadPtr,isMainBrowserThread,isMainRuntimeThread){pthreadPtr=pthreadPtr|0,isMainBrowserThread=isMainBrowserThread|0,isMainRuntimeThread=isMainRuntimeThread|0,__pthread_ptr=pthreadPtr,__pthread_is_main_browser_thread=isMainBrowserThread,__pthread_is_main_runtime_thread=isMainRuntimeThread}Module.__register_pthread_ptr=__register_pthread_ptr;var ERRNO_CODES={EPERM:63,ENOENT:44,ESRCH:71,EINTR:27,EIO:29,ENXIO:60,E2BIG:1,ENOEXEC:45,EBADF:8,ECHILD:12,EAGAIN:6,EWOULDBLOCK:6,ENOMEM:48,EACCES:2,EFAULT:21,ENOTBLK:105,EBUSY:10,EEXIST:20,EXDEV:75,ENODEV:43,ENOTDIR:54,EISDIR:31,EINVAL:28,ENFILE:41,EMFILE:33,ENOTTY:59,ETXTBSY:74,EFBIG:22,ENOSPC:51,ESPIPE:70,EROFS:69,EMLINK:34,EPIPE:64,EDOM:18,ERANGE:68,ENOMSG:49,EIDRM:24,ECHRNG:106,EL2NSYNC:156,EL3HLT:107,EL3RST:108,ELNRNG:109,EUNATCH:110,ENOCSI:111,EL2HLT:112,EDEADLK:16,ENOLCK:46,EBADE:113,EBADR:114,EXFULL:115,ENOANO:104,EBADRQC:103,EBADSLT:102,EDEADLOCK:16,EBFONT:101,ENOSTR:100,ENODATA:116,ETIME:117,ENOSR:118,ENONET:119,ENOPKG:120,EREMOTE:121,ENOLINK:47,EADV:122,ESRMNT:123,ECOMM:124,EPROTO:65,EMULTIHOP:36,EDOTDOT:125,EBADMSG:9,ENOTUNIQ:126,EBADFD:127,EREMCHG:128,ELIBACC:129,ELIBBAD:130,ELIBSCN:131,ELIBMAX:132,ELIBEXEC:133,ENOSYS:52,ENOTEMPTY:55,ENAMETOOLONG:37,ELOOP:32,EOPNOTSUPP:138,EPFNOSUPPORT:139,ECONNRESET:15,ENOBUFS:42,EAFNOSUPPORT:5,EPROTOTYPE:67,ENOTSOCK:57,ENOPROTOOPT:50,ESHUTDOWN:140,ECONNREFUSED:14,EADDRINUSE:3,ECONNABORTED:13,ENETUNREACH:40,ENETDOWN:38,ETIMEDOUT:73,EHOSTDOWN:142,EHOSTUNREACH:23,EINPROGRESS:26,EALREADY:7,EDESTADDRREQ:17,EMSGSIZE:35,EPROTONOSUPPORT:66,ESOCKTNOSUPPORT:137,EADDRNOTAVAIL:4,ENETRESET:39,EISCONN:30,ENOTCONN:53,ETOOMANYREFS:141,EUSERS:136,EDQUOT:19,ESTALE:72,ENOTSUP:138,ENOMEDIUM:148,EILSEQ:25,EOVERFLOW:61,ECANCELED:11,ENOTRECOVERABLE:56,EOWNERDEAD:62,ESTRPIPE:135},__main_thread_futex_wait_address=13568;function _emscripten_futex_wake(addr,count2){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&!0||count2<0)return-28;if(count2==0)return 0;count2>=2147483647&&(count2=Infinity);var mainThreadWaitAddress=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2),mainThreadWoken=0;if(mainThreadWaitAddress==addr){var loadedAddr=Atomics.compareExchange(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,mainThreadWaitAddress,0);if(loadedAddr==mainThreadWaitAddress&&(--count2,mainThreadWoken=1,count2<=0))return 1}var ret=Atomics.notify(GROWABLE_HEAP_I32(),addr>>2,count2);if(ret>=0)return ret+mainThreadWoken;throw"Atomics.notify returned an unexpected value "+ret}Module._emscripten_futex_wake=_emscripten_futex_wake;function __kill_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw"Internal Error! _kill_thread() can only ever be called from main application thread!";if(!pthread_ptr)throw"Internal Error! Null pthread_ptr in _kill_thread!";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];pthread.worker.terminate(),PThread.freeThreadData(pthread),PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(pthread.worker),1),pthread.worker.pthread=void 0}function __cancel_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw"Internal Error! _cancel_thread() can only ever be called from main application thread!";if(!pthread_ptr)throw"Internal Error! Null pthread_ptr in _cancel_thread!";var pthread=PThread.pthreads[pthread_ptr];pthread.worker.postMessage({cmd:"cancel"})}function __cleanup_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw"Internal Error! _cleanup_thread() can only ever be called from main application thread!";if(!pthread_ptr)throw"Internal Error! Null pthread_ptr in _cleanup_thread!";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];if(pthread){var worker=pthread.worker;PThread.returnWorkerToPool(worker)}}var PThread={MAIN_THREAD_ID:1,mainThreadInfo:{schedPolicy:0,schedPrio:0},unusedWorkers:[],runningWorkers:[],initRuntime:function(){__register_pthread_ptr(PThread.mainThreadBlock,!ENVIRONMENT_IS_WORKER,1),_emscripten_register_main_browser_thread_id(PThread.mainThreadBlock)},initMainThreadBlock:function(){for(var pthreadPoolSize=8,i=0;i>2]=PThread.mainThreadBlock;var headPtr=PThread.mainThreadBlock+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;for(var tlsMemory=13056,i=0;i<128;++i)GROWABLE_HEAP_U32()[tlsMemory/4+i]=0;Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+104>>2,tlsMemory),Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+40>>2,PThread.mainThreadBlock),Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+44>>2,42)},initWorker:function(){},pthreads:{},exitHandlers:null,setThreadStatus:function(){},runExitHandlers:function(){if(PThread.exitHandlers!==null){for(;PThread.exitHandlers.length>0;)PThread.exitHandlers.pop()();PThread.exitHandlers=null}ENVIRONMENT_IS_PTHREAD&&threadInfoStruct&&___pthread_tsd_run_dtors()},threadExit:function(exitCode){var tb=_pthread_self();tb&&(Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,exitCode),Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1),Atomics.store(GROWABLE_HEAP_U32(),tb+60>>2,1),Atomics.store(GROWABLE_HEAP_U32(),tb+64>>2,0),PThread.runExitHandlers(),_emscripten_futex_wake(tb+0,2147483647),__register_pthread_ptr(0,0,0),threadInfoStruct=0,ENVIRONMENT_IS_PTHREAD&&postMessage({cmd:"exit"}))},threadCancel:function(){PThread.runExitHandlers(),Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+4>>2,-1),Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+0>>2,1),_emscripten_futex_wake(threadInfoStruct+0,2147483647),threadInfoStruct=selfThreadId=0,__register_pthread_ptr(0,0,0),postMessage({cmd:"cancelDone"})},terminateAllThreads:function(){for(var t in PThread.pthreads){var pthread=PThread.pthreads[t];pthread&&pthread.worker&&PThread.returnWorkerToPool(pthread.worker)}PThread.pthreads={};for(var i=0;i>2];GROWABLE_HEAP_I32()[pthread.threadInfoStruct+104>>2]=0,_free(tlsMemory),_free(pthread.threadInfoStruct)}pthread.threadInfoStruct=0,pthread.allocatedOwnStack&&pthread.stackBase&&_free(pthread.stackBase),pthread.stackBase=0,pthread.worker&&(pthread.worker.pthread=null)}},returnWorkerToPool:function(worker){delete PThread.pthreads[worker.pthread.thread],PThread.unusedWorkers.push(worker),PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker),1),PThread.freeThreadData(worker.pthread),worker.pthread=void 0},receiveObjectTransfer:function(data2){},loadWasmModuleToWorker:function(worker,onFinishedLoading){worker.onmessage=function(e){var d=e.data,cmd=d.cmd;if(worker.pthread&&(PThread.currentProxiedOperationCallerThread=worker.pthread.threadInfoStruct),d.targetThread&&d.targetThread!=_pthread_self()){var thread=PThread.pthreads[d.targetThread];thread?thread.worker.postMessage(e.data,d.transferList):console.error('Internal error! Worker sent a message "'+cmd+'" to target pthread '+d.targetThread+", but that thread no longer exists!"),PThread.currentProxiedOperationCallerThread=void 0;return}if(cmd==="processQueuedMainThreadWork")_emscripten_main_thread_process_queued_calls();else if(cmd==="spawnThread")__spawn_thread(e.data);else if(cmd==="cleanupThread")__cleanup_thread(d.thread);else if(cmd==="killThread")__kill_thread(d.thread);else if(cmd==="cancelThread")__cancel_thread(d.thread);else if(cmd==="loaded")worker.loaded=!0,onFinishedLoading&&onFinishedLoading(worker),worker.runPthread&&(worker.runPthread(),delete worker.runPthread);else if(cmd==="print")out("Thread "+d.threadId+": "+d.text);else if(cmd==="printErr")err("Thread "+d.threadId+": "+d.text);else if(cmd==="alert")alert("Thread "+d.threadId+": "+d.text);else if(cmd==="exit"){var detached=worker.pthread&&Atomics.load(GROWABLE_HEAP_U32(),worker.pthread.thread+68>>2);detached&&PThread.returnWorkerToPool(worker)}else cmd==="cancelDone"?PThread.returnWorkerToPool(worker):cmd==="objectTransfer"?PThread.receiveObjectTransfer(e.data):e.data.target==="setimmediate"?worker.postMessage(e.data):err("worker sent an unknown command "+cmd);PThread.currentProxiedOperationCallerThread=void 0},worker.onerror=function(e){err("pthread sent an error! "+e.filename+":"+e.lineno+": "+e.message)},ENVIRONMENT_IS_NODE&&(worker.on("message",function(data2){worker.onmessage({data:data2})}),worker.on("error",function(data2){worker.onerror(data2)}),worker.on("exit",function(data2){console.log("worker exited - TODO: update the worker queue?")})),worker.postMessage({cmd:"load",urlOrBlob:Module.mainScriptUrlOrBlob||_scriptDir2,wasmMemory,wasmModule,DYNAMIC_BASE,DYNAMICTOP_PTR})},allocateUnusedWorker:function(){var pthreadMainJs=locateFile("tfjs-backend-wasm-threaded-simd.worker.js");PThread.unusedWorkers.push(new Worker(pthreadMainJs))},getNewWorker:function(){return PThread.unusedWorkers.length==0&&(PThread.allocateUnusedWorker(),PThread.loadWasmModuleToWorker(PThread.unusedWorkers[0])),PThread.unusedWorkers.length>0?PThread.unusedWorkers.pop():null},busySpinWait:function(msecs){for(var t=performance.now()+msecs;performance.now()>2]=value,value}function _atexit(func2,arg){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(1,1,func2,arg);__ATEXIT__.unshift({func:func2,arg})}function __emscripten_notify_thread_queue(targetThreadId,mainThreadId){if(targetThreadId==mainThreadId)postMessage({cmd:"processQueuedMainThreadWork"});else if(ENVIRONMENT_IS_PTHREAD)postMessage({targetThread:targetThreadId,cmd:"processThreadQueue"});else{var pthread=PThread.pthreads[targetThreadId],worker=pthread&&pthread.worker;if(!worker)return;worker.postMessage({cmd:"processThreadQueue"})}return 1}function _abort(){abort()}function _emscripten_conditional_set_current_thread_status(expectedStatus,newStatus){expectedStatus=expectedStatus|0,newStatus=newStatus|0}function _emscripten_futex_wait(addr,val,timeout){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&!0)return-28;if(ENVIRONMENT_IS_WORKER){var ret=Atomics.wait(GROWABLE_HEAP_I32(),addr>>2,val,timeout);if(ret==="timed-out")return-73;if(ret==="not-equal")return-6;if(ret==="ok")return 0;throw"Atomics.wait returned an unexpected value "+ret}else{var loadedVal=Atomics.load(GROWABLE_HEAP_I32(),addr>>2);if(val!=loadedVal)return-6;var tNow=performance.now(),tEnd=tNow+timeout;Atomics.store(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,addr);for(var ourWaitAddress=addr;addr==ourWaitAddress;){if(tNow=performance.now(),tNow>tEnd)return-73;_emscripten_main_thread_process_queued_calls(),addr=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2)}return 0}}function _emscripten_is_main_browser_thread(){return __pthread_is_main_browser_thread|0}function _emscripten_is_main_runtime_thread(){return __pthread_is_main_runtime_thread|0}function _emscripten_memcpy_big(dest,src,num){GROWABLE_HEAP_U8().copyWithin(dest,src,src+num)}function _emscripten_num_logical_cores(){return navigator.hardwareConcurrency}function _emscripten_proxy_to_main_thread_js(index,sync){for(var numCallArgs=arguments.length-2,stack2=stackSave(),args=stackAlloc(numCallArgs*8),b=args>>3,i=0;i>3]),buf+=8):(buf=buf+3&~3,args.push(GROWABLE_HEAP_I32()[buf>>2]),buf+=4);return args}function _emscripten_receive_on_main_thread_js(index,numCallArgs,args){_emscripten_receive_on_main_thread_js_callArgs.length=numCallArgs;for(var b=args>>3,i=0;i>>16),updateGlobalBufferAndViews(wasmMemory.buffer),1}catch(e){}}function _emscripten_resize_heap(requestedSize){requestedSize=requestedSize>>>0;var oldSize=_emscripten_get_heap_size();if(requestedSize<=oldSize)return!1;var PAGE_MULTIPLE=65536,maxHeapSize=2147483648;if(requestedSize>maxHeapSize)return!1;for(var minHeapSize=16777216,cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(minHeapSize,requestedSize,overGrownHeapSize),PAGE_MULTIPLE)),replacement=emscripten_realloc_buffer(newSize);if(replacement)return!0}return!1}var JSEvents={keyEvent:0,mouseEvent:0,wheelEvent:0,uiEvent:0,focusEvent:0,deviceOrientationEvent:0,deviceMotionEvent:0,fullscreenChangeEvent:0,pointerlockChangeEvent:0,visibilityChangeEvent:0,touchEvent:0,previousFullscreenElement:null,previousScreenX:null,previousScreenY:null,removeEventListenersRegistered:!1,removeAllEventListeners:function(){for(var i=JSEvents.eventHandlers.length-1;i>=0;--i)JSEvents._removeHandler(i);JSEvents.eventHandlers=[],JSEvents.deferredCalls=[]},registerRemoveEventListeners:function(){JSEvents.removeEventListenersRegistered||(__ATEXIT__.push(JSEvents.removeAllEventListeners),JSEvents.removeEventListenersRegistered=!0)},deferredCalls:[],deferCall:function(targetFunction,precedence,argsList){function arraysHaveEqualContent(arrA,arrB){if(arrA.length!=arrB.length)return!1;for(var i2 in arrA)if(arrA[i2]!=arrB[i2])return!1;return!0}for(var i in JSEvents.deferredCalls){var call=JSEvents.deferredCalls[i];if(call.targetFunction==targetFunction&&arraysHaveEqualContent(call.argsList,argsList))return}JSEvents.deferredCalls.push({targetFunction,precedence,argsList}),JSEvents.deferredCalls.sort(function(x,y){return x.precedence>2]=eventTypeId,GROWABLE_HEAP_I32()[varargs+4>>2]=eventData,GROWABLE_HEAP_I32()[varargs+8>>2]=userData,_emscripten_async_queue_on_thread_(targetThread,637534208,eventHandlerFunc,eventData,varargs),stackRestore(stackTop)},getTargetThreadForEventCallback:function(targetThread){switch(targetThread){case 1:return 0;case 2:return PThread.currentProxiedOperationCallerThread;default:return targetThread}},getNodeNameForTarget:function(target){return target?target==window?"#window":target==screen?"#screen":target&&target.nodeName?target.nodeName:"":""},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function stringToNewUTF8(jsString){var length=lengthBytesUTF8(jsString)+1,cString=_malloc(length);return stringToUTF8(jsString,cString,length),cString}function _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height){var stackTop=stackSave(),varargs=stackAlloc(12),targetCanvasPtr=0;targetCanvas&&(targetCanvasPtr=stringToNewUTF8(targetCanvas)),GROWABLE_HEAP_I32()[varargs>>2]=targetCanvasPtr,GROWABLE_HEAP_I32()[varargs+4>>2]=width,GROWABLE_HEAP_I32()[varargs+8>>2]=height,_emscripten_async_queue_on_thread_(targetThread,657457152,0,targetCanvasPtr,varargs),stackRestore(stackTop)}function _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,targetCanvas,width,height){targetCanvas=targetCanvas?UTF8ToString(targetCanvas):"",_emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height)}function __maybeCStringToJsString(cString){return cString>2?UTF8ToString(cString):cString}var specialHTMLTargets=[0,typeof document!="undefined"?document:0,typeof window!="undefined"?window:0];function __findEventTarget(target){target=__maybeCStringToJsString(target);var domElement=specialHTMLTargets[target]||(typeof document!="undefined"?document.querySelector(target):void 0);return domElement}function __findCanvasEventTarget(target){return __findEventTarget(target)}function _emscripten_set_canvas_element_size_calling_thread(target,width,height){var canvas=__findCanvasEventTarget(target);if(!canvas)return-4;if(canvas.canvasSharedPtr&&(GROWABLE_HEAP_I32()[canvas.canvasSharedPtr>>2]=width,GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+4>>2]=height),canvas.offscreenCanvas||!canvas.controlTransferredOffscreen){canvas.offscreenCanvas&&(canvas=canvas.offscreenCanvas);var autoResizeViewport=!1;if(canvas.GLctxObject&&canvas.GLctxObject.GLctx){var prevViewport=canvas.GLctxObject.GLctx.getParameter(2978);autoResizeViewport=prevViewport[0]===0&&prevViewport[1]===0&&prevViewport[2]===canvas.width&&prevViewport[3]===canvas.height}canvas.width=width,canvas.height=height,autoResizeViewport&&canvas.GLctxObject.GLctx.viewport(0,0,width,height)}else if(canvas.canvasSharedPtr){var targetThread=GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+8>>2];return _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,target,width,height),1}else return-4;return 0}function _emscripten_set_canvas_element_size_main_thread(target,width,height){return ENVIRONMENT_IS_PTHREAD?_emscripten_proxy_to_main_thread_js(2,1,target,width,height):_emscripten_set_canvas_element_size_calling_thread(target,width,height)}function _emscripten_set_canvas_element_size(target,width,height){var canvas=__findCanvasEventTarget(target);return canvas?_emscripten_set_canvas_element_size_calling_thread(target,width,height):_emscripten_set_canvas_element_size_main_thread(target,width,height)}function _emscripten_set_current_thread_status(newStatus){newStatus=newStatus|0}function _emscripten_set_thread_name(threadId,name){threadId=threadId|0,name=name|0}function __webgl_enable_ANGLE_instanced_arrays(ctx){var ext=ctx.getExtension("ANGLE_instanced_arrays");if(ext)return ctx.vertexAttribDivisor=function(index,divisor){ext.vertexAttribDivisorANGLE(index,divisor)},ctx.drawArraysInstanced=function(mode,first,count2,primcount){ext.drawArraysInstancedANGLE(mode,first,count2,primcount)},ctx.drawElementsInstanced=function(mode,count2,type,indices,primcount){ext.drawElementsInstancedANGLE(mode,count2,type,indices,primcount)},1}function __webgl_enable_OES_vertex_array_object(ctx){var ext=ctx.getExtension("OES_vertex_array_object");if(ext)return ctx.createVertexArray=function(){return ext.createVertexArrayOES()},ctx.deleteVertexArray=function(vao){ext.deleteVertexArrayOES(vao)},ctx.bindVertexArray=function(vao){ext.bindVertexArrayOES(vao)},ctx.isVertexArray=function(vao){return ext.isVertexArrayOES(vao)},1}function __webgl_enable_WEBGL_draw_buffers(ctx){var ext=ctx.getExtension("WEBGL_draw_buffers");if(ext)return ctx.drawBuffers=function(n,bufs){ext.drawBuffersWEBGL(n,bufs)},1}var GL={counter:1,lastError:0,buffers:[],mappedBuffers:{},programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},currentContext:null,offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,init:function(){for(var miniTempFloatBuffer=new Float32Array(GL.MINI_TEMP_BUFFER_SIZE),i=0;i>2]:-1;source+=UTF8ToString(GROWABLE_HEAP_I32()[string+i*4>>2],len<0?void 0:len)}return source},createContext:function(canvas,webGLContextAttributes){var ctx=canvas.getContext("webgl",webGLContextAttributes);if(!ctx)return 0;var handle=GL.registerContext(ctx,webGLContextAttributes);return handle},registerContext:function(ctx,webGLContextAttributes){var handle=_malloc(8);GROWABLE_HEAP_I32()[handle+4>>2]=_pthread_self();var context={handle,attributes:webGLContextAttributes,version:webGLContextAttributes.majorVersion,GLctx:ctx};return ctx.canvas&&(ctx.canvas.GLctxObject=context),GL.contexts[handle]=context,(typeof webGLContextAttributes.enableExtensionsByDefault=="undefined"||webGLContextAttributes.enableExtensionsByDefault)&&GL.initExtensions(context),handle},makeContextCurrent:function(contextHandle){return GL.currentContext=GL.contexts[contextHandle],Module.ctx=GLctx=GL.currentContext&&GL.currentContext.GLctx,!(contextHandle&&!GLctx)},getContext:function(contextHandle){return GL.contexts[contextHandle]},deleteContext:function(contextHandle){GL.currentContext===GL.contexts[contextHandle]&&(GL.currentContext=null),typeof JSEvents=="object"&&JSEvents.removeAllHandlersOnTarget(GL.contexts[contextHandle].GLctx.canvas),GL.contexts[contextHandle]&&GL.contexts[contextHandle].GLctx.canvas&&(GL.contexts[contextHandle].GLctx.canvas.GLctxObject=void 0),_free(GL.contexts[contextHandle].handle),GL.contexts[contextHandle]=null},initExtensions:function(context){if(context||(context=GL.currentContext),!context.initExtensionsDone){context.initExtensionsDone=!0;var GLctx2=context.GLctx;__webgl_enable_ANGLE_instanced_arrays(GLctx2),__webgl_enable_OES_vertex_array_object(GLctx2),__webgl_enable_WEBGL_draw_buffers(GLctx2),GLctx2.disjointTimerQueryExt=GLctx2.getExtension("EXT_disjoint_timer_query");var automaticallyEnabledExtensions=["OES_texture_float","OES_texture_half_float","OES_standard_derivatives","OES_vertex_array_object","WEBGL_compressed_texture_s3tc","WEBGL_depth_texture","OES_element_index_uint","EXT_texture_filter_anisotropic","EXT_frag_depth","WEBGL_draw_buffers","ANGLE_instanced_arrays","OES_texture_float_linear","OES_texture_half_float_linear","EXT_blend_minmax","EXT_shader_texture_lod","EXT_texture_norm16","WEBGL_compressed_texture_pvrtc","EXT_color_buffer_half_float","WEBGL_color_buffer_float","EXT_sRGB","WEBGL_compressed_texture_etc1","EXT_disjoint_timer_query","WEBGL_compressed_texture_etc","WEBGL_compressed_texture_astc","EXT_color_buffer_float","WEBGL_compressed_texture_s3tc_srgb","EXT_disjoint_timer_query_webgl2","WEBKIT_WEBGL_compressed_texture_pvrtc"],exts=GLctx2.getSupportedExtensions()||[];exts.forEach(function(ext){automaticallyEnabledExtensions.indexOf(ext)!=-1&&GLctx2.getExtension(ext)})}},populateUniformTable:function(program){for(var p2=GL.programs[program],ptable=GL.programInfos[program]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1},utable=ptable.uniforms,numUniforms=GLctx.getProgramParameter(p2,35718),i=0;i>2;contextAttributes.alpha=!!GROWABLE_HEAP_I32()[a+(0>>2)],contextAttributes.depth=!!GROWABLE_HEAP_I32()[a+(4>>2)],contextAttributes.stencil=!!GROWABLE_HEAP_I32()[a+(8>>2)],contextAttributes.antialias=!!GROWABLE_HEAP_I32()[a+(12>>2)],contextAttributes.premultipliedAlpha=!!GROWABLE_HEAP_I32()[a+(16>>2)],contextAttributes.preserveDrawingBuffer=!!GROWABLE_HEAP_I32()[a+(20>>2)];var powerPreference=GROWABLE_HEAP_I32()[a+(24>>2)];contextAttributes.powerPreference=__emscripten_webgl_power_preferences[powerPreference],contextAttributes.failIfMajorPerformanceCaveat=!!GROWABLE_HEAP_I32()[a+(28>>2)],contextAttributes.majorVersion=GROWABLE_HEAP_I32()[a+(32>>2)],contextAttributes.minorVersion=GROWABLE_HEAP_I32()[a+(36>>2)],contextAttributes.enableExtensionsByDefault=GROWABLE_HEAP_I32()[a+(40>>2)],contextAttributes.explicitSwapControl=GROWABLE_HEAP_I32()[a+(44>>2)],contextAttributes.proxyContextToMainThread=GROWABLE_HEAP_I32()[a+(48>>2)],contextAttributes.renderViaOffscreenBackBuffer=GROWABLE_HEAP_I32()[a+(52>>2)];var canvas=__findCanvasEventTarget(target);if(!canvas)return-4;if(contextAttributes.explicitSwapControl)return-1;var contextHandle=GL.createContext(canvas,contextAttributes);return contextHandle}function _emscripten_webgl_create_context(a0,a12){return _emscripten_webgl_do_create_context(a0,a12)}var PATH={splitPath:function(filename){var splitPathRe=/^(\/?|)([\s\S]*?)((?:\.{1,2}|[^\/]+?|)(\.[^.\/]*|))(?:[\/]*)$/;return splitPathRe.exec(filename).slice(1)},normalizeArray:function(parts,allowAboveRoot){for(var up=0,i=parts.length-1;i>=0;i--){var last=parts[i];last==="."?parts.splice(i,1):last===".."?(parts.splice(i,1),up++):up&&(parts.splice(i,1),up--)}if(allowAboveRoot)for(;up;up--)parts.unshift("..");return parts},normalize:function(path){var isAbsolute=path.charAt(0)==="/",trailingSlash=path.substr(-1)==="/";return path=PATH.normalizeArray(path.split("/").filter(function(p2){return!!p2}),!isAbsolute).join("/"),!path&&!isAbsolute&&(path="."),path&&trailingSlash&&(path+="/"),(isAbsolute?"/":"")+path},dirname:function(path){var result=PATH.splitPath(path),root=result[0],dir=result[1];return!root&&!dir?".":(dir&&(dir=dir.substr(0,dir.length-1)),root+dir)},basename:function(path){if(path==="/")return"/";var lastSlash=path.lastIndexOf("/");return lastSlash===-1?path:path.substr(lastSlash+1)},extname:function(path){return PATH.splitPath(path)[3]},join:function(){var paths=Array.prototype.slice.call(arguments,0);return PATH.normalize(paths.join("/"))},join2:function(l,r){return PATH.normalize(l+"/"+r)}},SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer3=SYSCALLS.buffers[stream];curr===0||curr===10?((stream===1?out:err)(UTF8ArrayToString(buffer3,0)),buffer3.length=0):buffer3.push(curr)},varargs:void 0,get:function(){SYSCALLS.varargs+=4;var ret=GROWABLE_HEAP_I32()[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){return ENVIRONMENT_IS_PTHREAD?_emscripten_proxy_to_main_thread_js(3,1,fd):0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(4,1,fd,offset_low,offset_high,whence,newOffset)}function _fd_write(fd,iov,iovcnt,pnum){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(5,1,fd,iov,iovcnt,pnum);for(var num=0,i=0;i>2],len=GROWABLE_HEAP_I32()[iov+(i*8+4)>>2],j=0;j>2]=num,0}function _pthread_cleanup_pop(execute2){var routine=PThread.exitHandlers.pop();execute2&&routine()}function _pthread_cleanup_push(routine,arg){PThread.exitHandlers===null&&(PThread.exitHandlers=[]),PThread.exitHandlers.push(function(){dynCall_vi(routine,arg)})}function __spawn_thread(threadParams){if(ENVIRONMENT_IS_PTHREAD)throw"Internal Error! _spawn_thread() can only ever be called from main application thread!";var worker=PThread.getNewWorker();if(worker.pthread!==void 0)throw"Internal error!";if(!threadParams.pthread_ptr)throw"Internal error, no pthread ptr!";PThread.runningWorkers.push(worker);for(var tlsMemory=_malloc(128*4),i=0;i<128;++i)GROWABLE_HEAP_I32()[tlsMemory+i*4>>2]=0;var stackHigh=threadParams.stackBase+threadParams.stackSize,pthread=PThread.pthreads[threadParams.pthread_ptr]={worker,stackBase:threadParams.stackBase,stackSize:threadParams.stackSize,allocatedOwnStack:threadParams.allocatedOwnStack,thread:threadParams.pthread_ptr,threadInfoStruct:threadParams.pthread_ptr},tis=pthread.threadInfoStruct>>2;Atomics.store(GROWABLE_HEAP_U32(),tis+(0>>2),0),Atomics.store(GROWABLE_HEAP_U32(),tis+(4>>2),0),Atomics.store(GROWABLE_HEAP_U32(),tis+(8>>2),0),Atomics.store(GROWABLE_HEAP_U32(),tis+(68>>2),threadParams.detached),Atomics.store(GROWABLE_HEAP_U32(),tis+(104>>2),tlsMemory),Atomics.store(GROWABLE_HEAP_U32(),tis+(48>>2),0),Atomics.store(GROWABLE_HEAP_U32(),tis+(40>>2),pthread.threadInfoStruct),Atomics.store(GROWABLE_HEAP_U32(),tis+(44>>2),42),Atomics.store(GROWABLE_HEAP_U32(),tis+(108>>2),threadParams.stackSize),Atomics.store(GROWABLE_HEAP_U32(),tis+(84>>2),threadParams.stackSize),Atomics.store(GROWABLE_HEAP_U32(),tis+(80>>2),stackHigh),Atomics.store(GROWABLE_HEAP_U32(),tis+(108+8>>2),stackHigh),Atomics.store(GROWABLE_HEAP_U32(),tis+(108+12>>2),threadParams.detached),Atomics.store(GROWABLE_HEAP_U32(),tis+(108+20>>2),threadParams.schedPolicy),Atomics.store(GROWABLE_HEAP_U32(),tis+(108+24>>2),threadParams.schedPrio);var global_libc=_emscripten_get_global_libc(),global_locale=global_libc+40;Atomics.store(GROWABLE_HEAP_U32(),tis+(176>>2),global_locale),worker.pthread=pthread;var msg={cmd:"run",start_routine:threadParams.startRoutine,arg:threadParams.arg,threadInfoStruct:threadParams.pthread_ptr,selfThreadId:threadParams.pthread_ptr,parentThreadId:threadParams.parent_pthread_ptr,stackBase:threadParams.stackBase,stackSize:threadParams.stackSize};worker.runPthread=function(){msg.time=performance.now(),worker.postMessage(msg,threadParams.transferList)},worker.loaded&&(worker.runPthread(),delete worker.runPthread)}function _pthread_getschedparam(thread,policy,schedparam){if(!policy&&!schedparam)return ERRNO_CODES.EINVAL;if(!thread)return err("pthread_getschedparam called with a null thread pointer!"),ERRNO_CODES.ESRCH;var self2=GROWABLE_HEAP_I32()[thread+12>>2];if(self2!==thread)return err("pthread_getschedparam attempted on thread "+thread+", which does not point to a valid thread, or does not exist anymore!"),ERRNO_CODES.ESRCH;var schedPolicy=Atomics.load(GROWABLE_HEAP_U32(),thread+108+20>>2),schedPrio=Atomics.load(GROWABLE_HEAP_U32(),thread+108+24>>2);return policy&&(GROWABLE_HEAP_I32()[policy>>2]=schedPolicy),schedparam&&(GROWABLE_HEAP_I32()[schedparam>>2]=schedPrio),0}function _pthread_self(){return __pthread_ptr|0}Module._pthread_self=_pthread_self;function _pthread_create(pthread_ptr,attr,start_routine,arg){if(typeof SharedArrayBuffer=="undefined")return err("Current environment does not support SharedArrayBuffer, pthreads are not available!"),6;if(!pthread_ptr)return err("pthread_create called with a null thread pointer!"),28;var transferList=[],error=0;if(ENVIRONMENT_IS_PTHREAD&&(transferList.length===0||error))return _emscripten_sync_run_in_main_thread_4(687865856,pthread_ptr,attr,start_routine,arg);if(error)return error;var stackSize=0,stackBase=0,detached=0,schedPolicy=0,schedPrio=0;if(attr){stackSize=GROWABLE_HEAP_I32()[attr>>2],stackSize+=81920,stackBase=GROWABLE_HEAP_I32()[attr+8>>2],detached=GROWABLE_HEAP_I32()[attr+12>>2]!==0;var inheritSched=GROWABLE_HEAP_I32()[attr+16>>2]===0;if(inheritSched){var prevSchedPolicy=GROWABLE_HEAP_I32()[attr+20>>2],prevSchedPrio=GROWABLE_HEAP_I32()[attr+24>>2],parentThreadPtr=PThread.currentProxiedOperationCallerThread?PThread.currentProxiedOperationCallerThread:_pthread_self();_pthread_getschedparam(parentThreadPtr,attr+20,attr+24),schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2],schedPrio=GROWABLE_HEAP_I32()[attr+24>>2],GROWABLE_HEAP_I32()[attr+20>>2]=prevSchedPolicy,GROWABLE_HEAP_I32()[attr+24>>2]=prevSchedPrio}else schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2],schedPrio=GROWABLE_HEAP_I32()[attr+24>>2]}else stackSize=2097152;var allocatedOwnStack=stackBase==0;allocatedOwnStack?stackBase=_memalign(16,stackSize):(stackBase-=stackSize,assert3(stackBase>0));for(var threadInfoStruct2=_malloc(232),i=0;i<232>>2;++i)GROWABLE_HEAP_U32()[(threadInfoStruct2>>2)+i]=0;GROWABLE_HEAP_I32()[pthread_ptr>>2]=threadInfoStruct2,GROWABLE_HEAP_I32()[threadInfoStruct2+12>>2]=threadInfoStruct2;var headPtr=threadInfoStruct2+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var threadParams={stackBase,stackSize,allocatedOwnStack,schedPolicy,schedPrio,detached,startRoutine:start_routine,pthread_ptr:threadInfoStruct2,parent_pthread_ptr:_pthread_self(),arg,transferList};return ENVIRONMENT_IS_PTHREAD?(threadParams.cmd="spawnThread",postMessage(threadParams,transferList)):__spawn_thread(threadParams),0}function _roundf(d){return d=+d,d>=0?+Math_floor(d+.5):+Math_ceil(d-.5)}function _sysconf(name){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(6,1,name);switch(name){case 30:return 16384;case 85:var maxHeapSize=2147483648;return maxHeapSize/16384;case 132:case 133:case 12:case 137:case 138:case 15:case 235:case 16:case 17:case 18:case 19:case 20:case 149:case 13:case 10:case 236:case 153:case 9:case 21:case 22:case 159:case 154:case 14:case 77:case 78:case 139:case 80:case 81:case 82:case 68:case 67:case 164:case 11:case 29:case 47:case 48:case 95:case 52:case 51:case 46:case 79:return 200809;case 27:case 246:case 127:case 128:case 23:case 24:case 160:case 161:case 181:case 182:case 242:case 183:case 184:case 243:case 244:case 245:case 165:case 178:case 179:case 49:case 50:case 168:case 169:case 175:case 170:case 171:case 172:case 97:case 76:case 32:case 173:case 35:return-1;case 176:case 177:case 7:case 155:case 8:case 157:case 125:case 126:case 92:case 93:case 129:case 130:case 131:case 94:case 91:return 1;case 74:case 60:case 69:case 70:case 4:return 1024;case 31:case 42:case 72:return 32;case 87:case 26:case 33:return 2147483647;case 34:case 1:return 47839;case 38:case 36:return 99;case 43:case 37:return 2048;case 0:return 2097152;case 3:return 65536;case 28:return 32768;case 44:return 32767;case 75:return 16384;case 39:return 1e3;case 89:return 700;case 71:return 256;case 40:return 255;case 2:return 100;case 180:return 64;case 25:return 20;case 5:return 16;case 6:return 6;case 73:return 4;case 84:return typeof navigator=="object"&&navigator.hardwareConcurrency||1}return setErrNo(28),-1}ENVIRONMENT_IS_PTHREAD?PThread.initWorker():PThread.initMainThreadBlock();var GLctx;GL.init();var proxiedFunctionTable=[null,_atexit,_emscripten_set_canvas_element_size_main_thread,_fd_close,_fd_seek,_fd_write,_sysconf],asmLibraryArg={e:___assert_fail,r:___call_main,w:__emscripten_notify_thread_queue,a:_abort,l:_emscripten_conditional_set_current_thread_status,d:_emscripten_futex_wait,c:_emscripten_futex_wake,h:_emscripten_get_now,g:_emscripten_is_main_browser_thread,x:_emscripten_is_main_runtime_thread,q:_emscripten_memcpy_big,B:_emscripten_num_logical_cores,t:_emscripten_receive_on_main_thread_js,A:_emscripten_resize_heap,u:_emscripten_set_canvas_element_size,k:_emscripten_set_current_thread_status,s:_emscripten_set_thread_name,v:_emscripten_webgl_create_context,m:_fd_close,o:_fd_seek,i:_fd_write,p:initPthreadsJS,memory:wasmMemory||Module.wasmMemory,y:_pthread_cleanup_pop,z:_pthread_cleanup_push,j:_pthread_create,b:_pthread_self,f:_roundf,n:_sysconf,table:wasmTable},asm=createWasm();Module.asm=asm;var 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Uint8Array?"int32":isNumber(values)?"float32":isString(values)?"string":isBoolean(values)?"bool":"float32"}function isFunction(f){return!!(f&&f.constructor&&f.call&&f.apply)}function nearestDivisor(size,start){for(let i=start;i=0;--i)strides[i]=strides[i+1]*shape[i+1];return strides}function createNestedArray(offset,shape,a){let ret=new Array;if(shape.length===1){let d=shape[0];for(let i=0;iacc*c);for(let i=0;iacc*c);if(size===0)return[];if(size!==a.length)throw new Error(`[${shape}] does not match the input size ${a.length}.`);return createNestedArray(0,shape,a)}function makeOnesTypedArray(size,dtype){let array2=makeZerosTypedArray(size,dtype);for(let i=0;iprev*curr,1);if(dtype==null||dtype==="float32")return toNestedArray(shape,new Float32Array(size));if(dtype==="int32")return toNestedArray(shape,new Int32Array(size));if(dtype==="bool")return toNestedArray(shape,new Uint8Array(size));throw new Error(`Unknown data type ${dtype}`)}function assertNonNegativeIntegerDimensions(shape){shape.forEach(dimSize=>{assert(Number.isInteger(dimSize)&&dimSize>=0,()=>`Tensor must have a shape comprised of positive integers but got shape [${shape}].`)})}function locToIndex(locs,rank,strides){if(rank===0)return 0;if(rank===1)return locs[0];let index=locs[locs.length-1];for(let i=0;i{let[key,value]=keyValue.split(":");this.urlFlags[key]=parseValue(key,value)})}};function getQueryParams(queryString){let params={};return queryString.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g,(s,...t)=>(decodeParam(params,t[0],t[1]),t.join("="))),params}function decodeParam(params,name,value){params[decodeURIComponent(name)]=decodeURIComponent(value||"")}function parseValue(flagName,value){if(value=value.toLowerCase(),value==="true"||value==="false")return value==="true";if(`${+value}`===value)return+value;throw new Error(`Could not parse value flag value ${value} for flag ${flagName}.`)}function env(){return ENV}var ENV=null;function setEnvironmentGlobal(environment){ENV=environment}var globalNameSpace;function getGlobalNamespace(){if(globalNameSpace==null){let ns;if(typeof window!="undefined")ns=window;else if(typeof global!="undefined")ns=global;else if(typeof process!="undefined")ns=process;else if(typeof self!="undefined")ns=self;else throw new Error("Could not find a global object");globalNameSpace=ns}return globalNameSpace}function getGlobalMap(){let ns=getGlobalNamespace();return ns._tfGlobals==null&&(ns._tfGlobals=new Map),ns._tfGlobals}function getGlobal(key,init2){let globalMap=getGlobalMap();if(globalMap.has(key))return globalMap.get(key);{let singleton=init2();return globalMap.set(key,singleton),globalMap.get(key)}}var Abs="Abs",Acos="Acos",Acosh="Acosh",Add="Add",AddN="AddN",All="All",Any="Any",ArgMax="ArgMax",ArgMin="ArgMin",Asin="Asin",Asinh="Asinh",Atan="Atan",Atanh="Atanh",Atan2="Atan2",AvgPool="AvgPool",AvgPoolGrad="AvgPoolGrad",AvgPool3D="AvgPool3D",AvgPool3DGrad="AvgPool3DGrad",BatchMatMul="BatchMatMul",BatchToSpaceND="BatchToSpaceND",Bincount="Bincount",BroadcastTo="BroadcastTo",Cast="Cast",Ceil="Ceil",ClipByValue="ClipByValue",Complex="Complex",ComplexAbs="ComplexAbs",Concat="Concat",Conv2D="Conv2D",Conv2DBackpropFilter="Conv2DBackpropFilter",Conv2DBackpropInput="Conv2DBackpropInput",Conv3D="Conv3D",Conv3DBackpropFilterV2="Conv3DBackpropFilterV2",Conv3DBackpropInputV2="Conv3DBackpropInputV2",Cos="Cos",Cosh="Cosh",Cumsum="Cumsum",CropAndResize="CropAndResize",DenseBincount="DenseBincount",DepthToSpace="DepthToSpace",DepthwiseConv2dNative="DepthwiseConv2dNative",DepthwiseConv2dNativeBackpropFilter="DepthwiseConv2dNativeBackpropFilter",DepthwiseConv2dNativeBackpropInput="DepthwiseConv2dNativeBackpropInput",Diag="Diag",Dilation2D="Dilation2D",Dilation2DBackpropInput="Dilation2DBackpropInput",Dilation2DBackpropFilter="Dilation2DBackpropFilter",RealDiv="RealDiv",Elu="Elu",EluGrad="EluGrad",Erf="Erf",Equal="Equal",Exp="Exp",ExpandDims="ExpandDims",Expm1="Expm1",FFT="FFT",Fill="Fill",FlipLeftRight="FlipLeftRight",Floor="Floor",FloorDiv="FloorDiv",FusedBatchNorm="FusedBatchNorm",GatherV2="GatherV2",GatherNd="GatherNd",Greater="Greater",GreaterEqual="GreaterEqual",Identity="Identity",IFFT="IFFT",Imag="Imag",IsFinite="IsFinite",IsInf="IsInf",IsNan="IsNan",LeakyRelu="LeakyRelu",Less="Less",LessEqual="LessEqual",LinSpace="LinSpace",Log="Log",Log1p="Log1p",LogicalAnd="LogicalAnd",LogicalNot="LogicalNot",LogicalOr="LogicalOr",LogSoftmax="LogSoftmax",LRN="LRN",LRNGrad="LRNGrad",Max="Max",Maximum="Maximum",MaxPool="MaxPool",MaxPoolGrad="MaxPoolGrad",MaxPool3D="MaxPool3D",MaxPool3DGrad="MaxPool3DGrad",MaxPoolWithArgmax="MaxPoolWithArgmax",Mean="Mean",Min="Min",Minimum="Minimum",MirrorPad="MirrorPad",Mod="Mod",Multinomial="Multinomial",Multiply="Multiply",Neg="Neg",NotEqual="NotEqual",NonMaxSuppressionV3="NonMaxSuppressionV3",NonMaxSuppressionV4="NonMaxSuppressionV4",NonMaxSuppressionV5="NonMaxSuppressionV5",OnesLike="OnesLike",OneHot="OneHot",Pack="Pack",PadV2="PadV2",Pool="Pool",Pow="Pow",Prelu="Prelu",Prod="Prod",Range="Range",Real="Real",Reciprocal="Reciprocal",Relu="Relu",Reshape="Reshape",ResizeNearestNeighbor="ResizeNearestNeighbor",ResizeNearestNeighborGrad="ResizeNearestNeighborGrad",ResizeBilinear="ResizeBilinear",ResizeBilinearGrad="ResizeBilinearGrad",Relu6="Relu6",Reverse="Reverse",Round="Round",Rsqrt="Rsqrt",ScatterNd="ScatterNd",Select="Select",Selu="Selu",Slice="Slice",Sin="Sin",Sinh="Sinh",Sign="Sign",Sigmoid="Sigmoid",Softplus="Softplus",Sqrt="Sqrt",Sum="Sum",SpaceToBatchND="SpaceToBatchND",SplitV="SplitV",Softmax="Softmax",SquaredDifference="SquaredDifference",Square="Square",Sub="Sub",SparseToDense="SparseToDense",StridedSlice="StridedSlice",Tan="Tan",Tanh="Tanh",Tile="Tile",TopK="TopK",Transpose="Transpose",Unique="Unique",Unpack="Unpack",UnsortedSegmentSum="UnsortedSegmentSum",ZerosLike="ZerosLike",Step="Step",FromPixels="FromPixels",RotateWithOffset="RotateWithOffset",_FusedMatMul="_FusedMatMul",FusedConv2D="FusedConv2D",FusedDepthwiseConv2D="FusedDepthwiseConv2D",kernelRegistry=getGlobal("kernelRegistry",()=>new Map),gradRegistry=getGlobal("gradRegistry",()=>new Map);function getKernel(kernelName,backendName){let key=makeKey(kernelName,backendName);return kernelRegistry.get(key)}function getGradient(kernelName){return gradRegistry.get(kernelName)}function getKernelsForBackend(backendName){let it=kernelRegistry.entries(),result=[];for(;;){let{done,value}=it.next();if(done)break;let[key,config3]=value,[backend22]=key.split("_");backend22===backendName&&result.push(config3)}return result}function registerKernel(config3){let{kernelName,backendName}=config3,key=makeKey(kernelName,backendName);kernelRegistry.has(key)&&console.warn(`The kernel '${kernelName}' for backend '${backendName}' is already registered`),kernelRegistry.set(key,config3)}function registerGradient(config3){let{kernelName}=config3;gradRegistry.has(kernelName)&&env().getBool("DEBUG")&&console.warn(`Overriding the gradient for '${kernelName}'`),gradRegistry.set(kernelName,config3)}function unregisterKernel(kernelName,backendName){let key=makeKey(kernelName,backendName);if(!kernelRegistry.has(key))throw new Error(`The kernel '${kernelName}' for backend '${backendName}' is not registered`);kernelRegistry.delete(key)}function unregisterGradient(kernelName){if(!gradRegistry.has(kernelName))throw new Error(`The gradient '${kernelName}' for backend is not registered`);gradRegistry.delete(kernelName)}function copyRegisteredKernels(registeredBackendName,newBackendName){getKernelsForBackend(registeredBackendName).forEach(kernelConfig=>{let newKernelConfig=Object.assign({},kernelConfig,{backendName:newBackendName});registerKernel(newKernelConfig)})}function makeKey(kernelName,backendName){return`${backendName}_${kernelName}`}var util_exports={};__export2(util_exports,{arraysEqual:()=>arraysEqual,assert:()=>assert,assertNonNegativeIntegerDimensions:()=>assertNonNegativeIntegerDimensions,assertNonNull:()=>assertNonNull,assertShapesMatch:()=>assertShapesMatch,bytesFromStringArray:()=>bytesFromStringArray,bytesPerElement:()=>bytesPerElement,checkConversionForErrors:()=>checkConversionForErrors,clamp:()=>clamp,computeStrides:()=>computeStrides,createScalarValue:()=>createScalarValue,createShuffledIndices:()=>createShuffledIndices,decodeString:()=>decodeString,distSquared:()=>distSquared,encodeString:()=>encodeString,fetch:()=>fetch2,flatten:()=>flatten,getArrayFromDType:()=>getArrayFromDType,getTypedArrayFromDType:()=>getTypedArrayFromDType,hasEncodingLoss:()=>hasEncodingLoss,indexToLoc:()=>indexToLoc,inferDtype:()=>inferDtype,inferFromImplicitShape:()=>inferFromImplicitShape,isBoolean:()=>isBoolean,isFunction:()=>isFunction,isInt:()=>isInt,isNumber:()=>isNumber,isPromise:()=>isPromise,isScalarShape:()=>isScalarShape,isString:()=>isString,isTypedArray:()=>isTypedArray,isValidDtype:()=>isValidDtype,locToIndex:()=>locToIndex,makeOnesTypedArray:()=>makeOnesTypedArray,makeZerosNestedTypedArray:()=>makeZerosNestedTypedArray,makeZerosTypedArray:()=>makeZerosTypedArray,nearestDivisor:()=>nearestDivisor,nearestLargerEven:()=>nearestLargerEven,now:()=>now,parseAxisParam:()=>parseAxisParam,randUniform:()=>randUniform,repeatedTry:()=>repeatedTry,rightPad:()=>rightPad,shuffle:()=>shuffle,sizeFromShape:()=>sizeFromShape,sizeToSquarishShape:()=>sizeToSquarishShape,squeezeShape:()=>squeezeShape,sum:()=>sum,tanh:()=>tanh,toNestedArray:()=>toNestedArray,toTypedArray:()=>toTypedArray});function createScalarValue(value,dtype){return dtype==="string"?encodeString(value):toTypedArray([value],dtype)}function noConversionNeeded(a,dtype){return a instanceof Float32Array&&dtype==="float32"||a instanceof Int32Array&&dtype==="int32"||a instanceof Uint8Array&&dtype==="bool"}function toTypedArray(a,dtype){if(dtype==="string")throw new Error("Cannot convert a string[] to a TypedArray");if(Array.isArray(a)&&(a=flatten(a)),env().getBool("DEBUG")&&checkConversionForErrors(a,dtype),noConversionNeeded(a,dtype))return a;if(dtype==null||dtype==="float32"||dtype==="complex64")return new Float32Array(a);if(dtype==="int32")return new Int32Array(a);if(dtype==="bool"){let bool=new Uint8Array(a.length);for(let i=0;i{outputs=f()},timer=this.backendTimer.time(holdResultWrapperFn);if(env().getBool("CHECK_COMPUTATION_FOR_ERRORS"))for(let i=0;i{checkComputationForErrors(tensorVals,output.dtype,kernelName)})}return{kernelName,outputs,inputs,timeMs:timer.then(timing=>timing.kernelMs),extraInfo:timer.then(timing=>timing.getExtraProfileInfo!=null?timing.getExtraProfileInfo():"")}}logKernelProfile(kernelProfile){let{kernelName,outputs,timeMs,inputs,extraInfo}=kernelProfile;outputs.forEach(result=>{Promise.all([result.data(),timeMs,extraInfo]).then(valueContainer=>{this.logger.logKernelProfile(kernelName,result,valueContainer[0],valueContainer[1],inputs,valueContainer[2])})})}};function checkComputationForErrors(vals,dtype,kernelName){if(dtype!=="float32")return!1;for(let i=0;i0?inputShape:""} `}}console.log(`%c${paddedName} %c${time2} %c${rank}D ${shape} %c${size} %c${inputShapesDescription} %c${extraInfo}`,"font-weight:bold","color:red","color:blue","color: orange","color: green","color: steelblue")}};function getFilteredNodesXToY(tape,xs,y){let tensorsFromX={},nodesFromX={};for(let i=0;itensorsFromX[output.id]=!0),anyInputFromX=!0,nodesFromX[node.id]=!0;break}if(anyInputFromX)break}}let tensorsLeadToY={};tensorsLeadToY[y.id]=!0;let nodesToY={};for(let i=tape.length-1;i>=0;i--){let node=tape[i],nodeInputs=node.inputs;for(let j=0;j=0;i--){let node=filteredTape[i],dys=[];if(node.outputs.forEach(o=>{let gradTensor=tensorAccumulatedGradientMap[o.id];gradTensor!=null?dys.push(gradTensor):dys.push(null)}),node.gradient==null)throw new Error(`Cannot compute gradient: gradient function not found for ${node.kernelName}.`);let inputGradients=node.gradient(dys);for(let inputName in node.inputs){if(!(inputName in inputGradients))throw new Error(`Cannot backprop through input ${inputName}. Available gradients found: ${Object.keys(inputGradients)}.`);let dx=tidy2(()=>inputGradients[inputName]());if(dx.dtype!=="float32")throw new Error(`Error in gradient for op ${node.kernelName}. The gradient of input ${inputName} must have 'float32' dtype, but has '${dx.dtype}'`);let x=node.inputs[inputName];if(!arraysEqual(dx.shape,x.shape))throw new Error(`Error in gradient for op ${node.kernelName}. The gradient of input '${inputName}' has shape '${dx.shape}', which does not match the shape of the input '${x.shape}'`);if(tensorAccumulatedGradientMap[x.id]==null)tensorAccumulatedGradientMap[x.id]=dx;else{let curGradient=tensorAccumulatedGradientMap[x.id];tensorAccumulatedGradientMap[x.id]=add5(curGradient,dx),curGradient.dispose()}}}}var FORMAT_LIMIT_NUM_VALS=20,FORMAT_NUM_FIRST_LAST_VALS=3,FORMAT_NUM_SIG_DIGITS=7;function tensorToString(vals,shape,dtype,verbose){let strides=computeStrides(shape),padPerCol=computeMaxSizePerColumn(vals,shape,dtype,strides),rank=shape.length,valsLines=subTensorToString(vals,shape,dtype,strides,padPerCol),lines=["Tensor"];return verbose&&(lines.push(` dtype: ${dtype}`),lines.push(` rank: ${rank}`),lines.push(` shape: [${shape}]`),lines.push(" values:")),lines.push(valsLines.map(l=>" "+l).join(` +`)};exports.Canvas=WebGLImageFilter});var require_image=__commonJS(exports=>{var fxImage=__toModule(require_imagefx()),inCanvas=null,outCanvas=null;function process3(input2,config2){let tensor2;if(input2 instanceof Tensor)tensor2=clone(input2);else{let originalWidth=input2.naturalWidth||input2.videoWidth||input2.width||input2.shape&&input2.shape[1]>0,originalHeight=input2.naturalHeight||input2.videoHeight||input2.height||input2.shape&&input2.shape[2]>0,targetWidth=originalWidth,targetHeight=originalHeight;if(config2.filter.width>0?targetWidth=config2.filter.width:config2.filter.height>0&&(targetWidth=originalWidth*(config2.filter.height/originalHeight)),config2.filter.height>0?targetHeight=config2.filter.height:config2.filter.width>0&&(targetHeight=originalHeight*(config2.filter.width/originalWidth)),!targetWidth||!targetHeight)return log("Human: invalid input",input2),null;(!inCanvas||inCanvas.width!==targetWidth||inCanvas.height!==targetHeight)&&(inCanvas=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(targetWidth,targetHeight):document.createElement("canvas"),inCanvas.width!==targetWidth&&(inCanvas.width=targetWidth),inCanvas.height!==targetHeight&&(inCanvas.height=targetHeight));let ctx=inCanvas.getContext("2d");if(input2 instanceof ImageData?ctx.putImageData(input2,0,0):ctx.drawImage(input2,0,0,originalWidth,originalHeight,0,0,inCanvas.width,inCanvas.height),config2.filter.enabled){if((!this.fx||!outCanvas||inCanvas.width!==outCanvas.width||inCanvas.height!==outCanvas.height)&&(outCanvas=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(inCanvas.width,inCanvas.height):document.createElement("canvas"),outCanvas.width!==inCanvas.width&&(outCanvas.width=inCanvas.width),outCanvas.height!==inCanvas.height&&(outCanvas.height=inCanvas.height),this.fx=ENV.flags.IS_BROWSER?new fxImage.Canvas({canvas:outCanvas}):null),!this.fx)return inCanvas;this.fx.reset(),this.fx.addFilter("brightness",config2.filter.brightness),config2.filter.contrast!==0&&this.fx.addFilter("contrast",config2.filter.contrast),config2.filter.sharpness!==0&&this.fx.addFilter("sharpen",config2.filter.sharpness),config2.filter.blur!==0&&this.fx.addFilter("blur",config2.filter.blur),config2.filter.saturation!==0&&this.fx.addFilter("saturation",config2.filter.saturation),config2.filter.hue!==0&&this.fx.addFilter("hue",config2.filter.hue),config2.filter.negative&&this.fx.addFilter("negative"),config2.filter.sepia&&this.fx.addFilter("sepia"),config2.filter.vintage&&this.fx.addFilter("brownie"),config2.filter.sepia&&this.fx.addFilter("sepia"),config2.filter.kodachrome&&this.fx.addFilter("kodachrome"),config2.filter.technicolor&&this.fx.addFilter("technicolor"),config2.filter.polaroid&&this.fx.addFilter("polaroid"),config2.filter.pixelate!==0&&this.fx.addFilter("pixelate",config2.filter.pixelate),this.fx.apply(inCanvas)}else outCanvas=inCanvas;let pixels;if(outCanvas.data){let shape=[outCanvas.height,outCanvas.width,3];pixels=tensor3d(outCanvas.data,shape,"int32")}else if(config2.backend==="webgl"||outCanvas instanceof ImageData)pixels=browser_exports.fromPixels(outCanvas);else{let tempCanvas=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(targetWidth,targetHeight):document.createElement("canvas");tempCanvas.width=targetWidth,tempCanvas.height=targetHeight;let tempCtx=tempCanvas.getContext("2d");tempCtx==null||tempCtx.drawImage(outCanvas,0,0);let data2=tempCtx==null?void 0:tempCtx.getImageData(0,0,targetWidth,targetHeight);pixels=browser_exports.fromPixels(data2)}let casted=pixels.toFloat();tensor2=casted.expandDims(0),pixels.dispose(),casted.dispose()}return{tensor:tensor2,canvas:config2.filter.return?outCanvas:null}}exports.process=process3});var human_exports={};__export(human_exports,{default:()=>Human});function log(...msg){let dt=new Date,ts=`${dt.getHours().toString().padStart(2,"0")}:${dt.getMinutes().toString().padStart(2,"0")}:${dt.getSeconds().toString().padStart(2,"0")}.${dt.getMilliseconds().toString().padStart(3,"0")}`;msg&&console.log(ts,"Human:",...msg)}var tfjs_esm_exports={};__export(tfjs_esm_exports,{Abs:()=>Abs,Acos:()=>Acos,Acosh:()=>Acosh,AdadeltaOptimizer:()=>AdadeltaOptimizer,AdagradOptimizer:()=>AdagradOptimizer,AdamOptimizer:()=>AdamOptimizer,AdamaxOptimizer:()=>AdamaxOptimizer,Add:()=>Add,AddN:()=>AddN,All:()=>All,Any:()=>Any,ArgMax:()=>ArgMax,ArgMin:()=>ArgMin,Asin:()=>Asin,Asinh:()=>Asinh,Atan:()=>Atan,Atan2:()=>Atan2,Atanh:()=>Atanh,AvgPool:()=>AvgPool,AvgPool3D:()=>AvgPool3D,AvgPool3DGrad:()=>AvgPool3DGrad,AvgPoolGrad:()=>AvgPoolGrad,BackendWasm:()=>BackendWasm,BatchMatMul:()=>BatchMatMul,BatchToSpaceND:()=>BatchToSpaceND,Bincount:()=>Bincount,BroadcastTo:()=>BroadcastTo,Callback:()=>Callback,CallbackList:()=>CallbackList,Cast:()=>Cast,Ceil:()=>Ceil,ClipByValue:()=>ClipByValue,Complex:()=>Complex,ComplexAbs:()=>ComplexAbs,Concat:()=>Concat,Conv2D:()=>Conv2D,Conv2DBackpropFilter:()=>Conv2DBackpropFilter,Conv2DBackpropInput:()=>Conv2DBackpropInput,Conv3D:()=>Conv3D,Conv3DBackpropFilterV2:()=>Conv3DBackpropFilterV2,Conv3DBackpropInputV2:()=>Conv3DBackpropInputV2,Cos:()=>Cos,Cosh:()=>Cosh,CropAndResize:()=>CropAndResize,Cumsum:()=>Cumsum,CustomCallback:()=>CustomCallback,DataStorage:()=>DataStorage,DenseBincount:()=>DenseBincount,DepthToSpace:()=>DepthToSpace,DepthwiseConv2dNative:()=>DepthwiseConv2dNative,DepthwiseConv2dNativeBackpropFilter:()=>DepthwiseConv2dNativeBackpropFilter,DepthwiseConv2dNativeBackpropInput:()=>DepthwiseConv2dNativeBackpropInput,Diag:()=>Diag,Dilation2D:()=>Dilation2D,Dilation2DBackpropFilter:()=>Dilation2DBackpropFilter,Dilation2DBackpropInput:()=>Dilation2DBackpropInput,ENV:()=>ENV,EarlyStopping:()=>EarlyStopping,Elu:()=>Elu,EluGrad:()=>EluGrad,Environment:()=>Environment,Equal:()=>Equal,Erf:()=>Erf,Exp:()=>Exp,ExpandDims:()=>ExpandDims,Expm1:()=>Expm1,FFT:()=>FFT,Fill:()=>Fill,FlipLeftRight:()=>FlipLeftRight,Floor:()=>Floor,FloorDiv:()=>FloorDiv,FromPixels:()=>FromPixels,FusedBatchNorm:()=>FusedBatchNorm,FusedConv2D:()=>FusedConv2D,FusedDepthwiseConv2D:()=>FusedDepthwiseConv2D,GPGPUContext:()=>GPGPUContext,GatherNd:()=>GatherNd,GatherV2:()=>GatherV2,GraphModel:()=>GraphModel,Greater:()=>Greater,GreaterEqual:()=>GreaterEqual,History:()=>History,IFFT:()=>IFFT,Identity:()=>Identity,Imag:()=>Imag,InputSpec:()=>InputSpec,IsFinite:()=>IsFinite,IsInf:()=>IsInf,IsNan:()=>IsNan,KernelBackend:()=>KernelBackend,LRN:()=>LRN,LRNGrad:()=>LRNGrad,LayerVariable:()=>LayerVariable,LayersModel:()=>LayersModel,LeakyRelu:()=>LeakyRelu,Less:()=>Less,LessEqual:()=>LessEqual,LinSpace:()=>LinSpace,Log:()=>Log,Log1p:()=>Log1p,LogSoftmax:()=>LogSoftmax,LogicalAnd:()=>LogicalAnd,LogicalNot:()=>LogicalNot,LogicalOr:()=>LogicalOr,MathBackendCPU:()=>MathBackendCPU,MathBackendWebGL:()=>MathBackendWebGL,Max:()=>Max,MaxPool:()=>MaxPool,MaxPool3D:()=>MaxPool3D,MaxPool3DGrad:()=>MaxPool3DGrad,MaxPoolGrad:()=>MaxPoolGrad,MaxPoolWithArgmax:()=>MaxPoolWithArgmax,Maximum:()=>Maximum,Mean:()=>Mean,Min:()=>Min,Minimum:()=>Minimum,MirrorPad:()=>MirrorPad,Mod:()=>Mod,MomentumOptimizer:()=>MomentumOptimizer,Multinomial:()=>Multinomial,Multiply:()=>Multiply,Neg:()=>Neg,NonMaxSuppressionV3:()=>NonMaxSuppressionV3,NonMaxSuppressionV4:()=>NonMaxSuppressionV4,NonMaxSuppressionV5:()=>NonMaxSuppressionV5,NotEqual:()=>NotEqual,OP_SCOPE_SUFFIX:()=>OP_SCOPE_SUFFIX,OneHot:()=>OneHot,OnesLike:()=>OnesLike,Optimizer:()=>Optimizer,Pack:()=>Pack,PadV2:()=>PadV2,Pool:()=>Pool,Pow:()=>Pow,Prelu:()=>Prelu,Prod:()=>Prod,RMSPropOptimizer:()=>RMSPropOptimizer,RNN:()=>RNN,Range:()=>Range,Rank:()=>Rank,Real:()=>Real,RealDiv:()=>RealDiv,Reciprocal:()=>Reciprocal,Reduction:()=>Reduction,Relu:()=>Relu,Relu6:()=>Relu6,Reshape:()=>Reshape,ResizeBilinear:()=>ResizeBilinear,ResizeBilinearGrad:()=>ResizeBilinearGrad,ResizeNearestNeighbor:()=>ResizeNearestNeighbor,ResizeNearestNeighborGrad:()=>ResizeNearestNeighborGrad,Reverse:()=>Reverse,RotateWithOffset:()=>RotateWithOffset,Round:()=>Round,Rsqrt:()=>Rsqrt,SGDOptimizer:()=>SGDOptimizer,ScatterNd:()=>ScatterNd,Select:()=>Select,Selu:()=>Selu,Sequential:()=>Sequential,Sigmoid:()=>Sigmoid,Sign:()=>Sign,Sin:()=>Sin,Sinh:()=>Sinh,Slice:()=>Slice,Softmax:()=>Softmax,Softplus:()=>Softplus,SpaceToBatchND:()=>SpaceToBatchND,SparseToDense:()=>SparseToDense,SplitV:()=>SplitV,Sqrt:()=>Sqrt,Square:()=>Square,SquaredDifference:()=>SquaredDifference,Step:()=>Step,StridedSlice:()=>StridedSlice,Sub:()=>Sub,Sum:()=>Sum,SymbolicTensor:()=>SymbolicTensor,Tan:()=>Tan,Tanh:()=>Tanh,Tensor:()=>Tensor,TensorBuffer:()=>TensorBuffer,Tile:()=>Tile,TopK:()=>TopK,Transpose:()=>Transpose,Unique:()=>Unique,Unpack:()=>Unpack,UnsortedSegmentSum:()=>UnsortedSegmentSum,Variable:()=>Variable,ZerosLike:()=>ZerosLike,_FusedMatMul:()=>_FusedMatMul,abs:()=>abs,acos:()=>acos,acosh:()=>acosh,add:()=>add2,addN:()=>addN,addStrict:()=>addStrict,all:()=>all,any:()=>any,argMax:()=>argMax,argMin:()=>argMin,asin:()=>asin,asinh:()=>asinh,atan:()=>atan,atan2:()=>atan2,atanh:()=>atanh,avgPool:()=>avgPool,avgPool3d:()=>avgPool3d,backend:()=>backend,backend_util:()=>backend_util_exports,basicLSTMCel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define=="function"&&define)}),require_crypto=__commonJS2(()=>{}),require_seedrandom=__commonJS2((exports,module)=>{(function(pool3,math){var global2=this,width=256,chunks=6,digits=52,rngname="random",startdenom=math.pow(width,chunks),significance=math.pow(2,digits),overflow=significance*2,mask=width-1,nodecrypto;function seedrandom5(seed,options,callback){var key=[];options=options==!0?{entropy:!0}:options||{};var shortseed=mixkey(flatten4(options.entropy?[seed,tostring(pool3)]:seed==null?autoseed():seed,3),key),arc4=new ARC4(key),prng=function(){for(var n=arc4.g(chunks),d=startdenom,x=0;n=overflow;)n/=2,d/=2,x>>>=1;return(n+x)/d};return prng.int32=function(){return arc4.g(4)|0},prng.quick=function(){return arc4.g(4)/4294967296},prng.double=prng,mixkey(tostring(arc4.S),pool3),(options.pass||callback||function(prng2,seed2,is_math_call,state){return state&&(state.S&©(state,arc4),prng2.state=function(){return copy(arc4,{})}),is_math_call?(math[rngname]=prng2,seed2):prng2})(prng,shortseed,"global"in options?options.global:this==math,options.state)}math["seed"+rngname]=seedrandom5;function ARC4(key){var t,keylen=key.length,me=this,i=0,j=me.i=me.j=0,s=me.S=[];for(keylen||(key=[keylen++]);i{var alea5=require_alea(),xor128=require_xor128(),xorwow=require_xorwow(),xorshift7=require_xorshift7(),xor4096=require_xor4096(),tychei=require_tychei(),sr=require_seedrandom();sr.alea=alea5,sr.xor128=xor128,sr.xorwow=xorwow,sr.xorshift7=xorshift7,sr.xor4096=xor4096,sr.tychei=tychei,module.exports=sr}),require_alea2=__commonJS2((exports,module)=>{(function(global2,module2,define2){function Alea(seed){var me=this,mash=Mash();me.next=function(){var t=2091639*me.s0+me.c*23283064365386963e-26;return me.s0=me.s1,me.s1=me.s2,me.s2=t-(me.c=t|0)},me.c=1,me.s0=mash(" "),me.s1=mash(" "),me.s2=mash(" "),me.s0-=mash(seed),me.s0<0&&(me.s0+=1),me.s1-=mash(seed),me.s1<0&&(me.s1+=1),me.s2-=mash(seed),me.s2<0&&(me.s2+=1),mash=null}function copy(f,t){return t.c=f.c,t.s0=f.s0,t.s1=f.s1,t.s2=f.s2,t}function impl(seed,opts){var xg=new Alea(seed),state=opts&&opts.state,prng=xg.next;return prng.int32=function(){return xg.next()*4294967296|0},prng.double=function(){return prng()+(prng()*2097152|0)*11102230246251565e-32},prng.quick=prng,state&&(typeof state=="object"&©(state,xg),prng.state=function(){return copy(xg,{})}),prng}function Mash(){var n=4022871197,mash=function(data2){data2=data2.toString();for(var i=0;i>>0,h-=n,h*=n,n=h>>>0,h-=n,n+=h*4294967296}return(n>>>0)*23283064365386963e-26};return mash}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.alea=impl})(exports,typeof module=="object"&&module,typeof define=="function"&&define)}),require_xor1282=__commonJS2((exports,module)=>{(function(global2,module2,define2){function XorGen(seed){var me=this,strseed="";me.x=0,me.y=0,me.z=0,me.w=0,me.next=function(){var t=me.x^me.x<<11;return me.x=me.y,me.y=me.z,me.z=me.w,me.w^=me.w>>>19^t^t>>>8},seed===(seed|0)?me.x=seed:strseed+=seed;for(var k=0;k>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state&&(typeof state=="object"&©(state,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.xor128=impl})(exports,typeof module=="object"&&module,typeof define=="function"&&define)}),require_xorwow2=__commonJS2((exports,module)=>{(function(global2,module2,define2){function XorGen(seed){var me=this,strseed="";me.next=function(){var t=me.x^me.x>>>2;return me.x=me.y,me.y=me.z,me.z=me.w,me.w=me.v,(me.d=me.d+362437|0)+(me.v=me.v^me.v<<4^(t^t<<1))|0},me.x=0,me.y=0,me.z=0,me.w=0,me.v=0,seed===(seed|0)?me.x=seed:strseed+=seed;for(var k=0;k>>4),me.next()}function copy(f,t){return t.x=f.x,t.y=f.y,t.z=f.z,t.w=f.w,t.v=f.v,t.d=f.d,t}function impl(seed,opts){var xg=new XorGen(seed),state=opts&&opts.state,prng=function(){return(xg.next()>>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state&&(typeof state=="object"&©(state,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.xorwow=impl})(exports,typeof module=="object"&&module,typeof define=="function"&&define)}),require_xorshift72=__commonJS2((exports,module)=>{(function(global2,module2,define2){function XorGen(seed){var me=this;me.next=function(){var X=me.x,i=me.i,t,v,w;return t=X[i],t^=t>>>7,v=t^t<<24,t=X[i+1&7],v^=t^t>>>10,t=X[i+3&7],v^=t^t>>>3,t=X[i+4&7],v^=t^t<<7,t=X[i+7&7],t=t^t<<13,v^=t^t<<9,X[i]=v,me.i=i+1&7,v};function init2(me2,seed2){var j,w,X=[];if(seed2===(seed2|0))w=X[0]=seed2;else for(seed2=""+seed2,j=0;j0;--j)me2.next()}init2(me,seed)}function copy(f,t){return t.x=f.x.slice(),t.i=f.i,t}function impl(seed,opts){seed==null&&(seed=+new Date);var xg=new XorGen(seed),state=opts&&opts.state,prng=function(){return(xg.next()>>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state&&(state.x&©(state,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.xorshift7=impl})(exports,typeof module=="object"&&module,typeof define=="function"&&define)}),require_xor40962=__commonJS2((exports,module)=>{(function(global2,module2,define2){function XorGen(seed){var me=this;me.next=function(){var w=me.w,X=me.X,i=me.i,t,v;return me.w=w=w+1640531527|0,v=X[i+34&127],t=X[i=i+1&127],v^=v<<13,t^=t<<17,v^=v>>>15,t^=t>>>12,v=X[i]=v^t,me.i=i,v+(w^w>>>16)|0};function init2(me2,seed2){var t,v,i,j,w,X=[],limit=128;for(seed2===(seed2|0)?(v=seed2,seed2=null):(seed2=seed2+"\0",v=0,limit=Math.max(limit,seed2.length)),i=0,j=-32;j>>15,v^=v<<4,v^=v>>>13,j>=0&&(w=w+1640531527|0,t=X[j&127]^=v+w,i=t==0?i+1:0);for(i>=128&&(X[(seed2&&seed2.length||0)&127]=-1),i=127,j=4*128;j>0;--j)v=X[i+34&127],t=X[i=i+1&127],v^=v<<13,t^=t<<17,v^=v>>>15,t^=t>>>12,X[i]=v^t;me2.w=w,me2.X=X,me2.i=i}init2(me,seed)}function copy(f,t){return t.i=f.i,t.w=f.w,t.X=f.X.slice(),t}function impl(seed,opts){seed==null&&(seed=+new Date);var xg=new XorGen(seed),state=opts&&opts.state,prng=function(){return(xg.next()>>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state&&(state.X&©(state,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.xor4096=impl})(exports,typeof module=="object"&&module,typeof define=="function"&&define)}),require_tychei2=__commonJS2((exports,module)=>{(function(global2,module2,define2){function XorGen(seed){var me=this,strseed="";me.next=function(){var b=me.b,c=me.c,d=me.d,a=me.a;return b=b<<25^b>>>7^c,c=c-d|0,d=d<<24^d>>>8^a,a=a-b|0,me.b=b=b<<20^b>>>12^c,me.c=c=c-d|0,me.d=d<<16^c>>>16^a,me.a=a-b|0},me.a=0,me.b=0,me.c=2654435769|0,me.d=1367130551,seed===Math.floor(seed)?(me.a=seed/4294967296|0,me.b=seed|0):strseed+=seed;for(var k=0;k>>0)/4294967296};return prng.double=function(){do var top=xg.next()>>>11,bot=(xg.next()>>>0)/4294967296,result=(top+bot)/(1<<21);while(result===0);return result},prng.int32=xg.next,prng.quick=prng,state&&(typeof state=="object"&©(state,xg),prng.state=function(){return copy(xg,{})}),prng}module2&&module2.exports?module2.exports=impl:define2&&define2.amd?define2(function(){return impl}):this.tychei=impl})(exports,typeof module=="object"&&module,typeof define=="function"&&define)}),require_seedrandom3=__commonJS2((exports,module)=>{(function(pool3,math){var global2=this,width=256,chunks=6,digits=52,rngname="random",startdenom=math.pow(width,chunks),significance=math.pow(2,digits),overflow=significance*2,mask=width-1,nodecrypto;function seedrandom5(seed,options,callback){var key=[];options=options==!0?{entropy:!0}:options||{};var shortseed=mixkey(flatten4(options.entropy?[seed,tostring(pool3)]:seed==null?autoseed():seed,3),key),arc4=new ARC4(key),prng=function(){for(var n=arc4.g(chunks),d=startdenom,x=0;n=overflow;)n/=2,d/=2,x>>>=1;return(n+x)/d};return prng.int32=function(){return arc4.g(4)|0},prng.quick=function(){return arc4.g(4)/4294967296},prng.double=prng,mixkey(tostring(arc4.S),pool3),(options.pass||callback||function(prng2,seed2,is_math_call,state){return state&&(state.S&©(state,arc4),prng2.state=function(){return copy(arc4,{})}),is_math_call?(math[rngname]=prng2,seed2):prng2})(prng,shortseed,"global"in options?options.global:this==math,options.state)}math["seed"+rngname]=seedrandom5;function ARC4(key){var t,keylen=key.length,me=this,i=0,j=me.i=me.j=0,s=me.S=[];for(keylen||(key=[keylen++]);i{var alea5=require_alea2(),xor128=require_xor1282(),xorwow=require_xorwow2(),xorshift7=require_xorshift72(),xor4096=require_xor40962(),tychei=require_tychei2(),sr=require_seedrandom3();sr.alea=alea5,sr.xor128=xor128,sr.xorwow=xorwow,sr.xorshift7=xorshift7,sr.xor4096=xor4096,sr.tychei=tychei,module.exports=sr}),require_path=__commonJS2(()=>{}),require_worker_threads=__commonJS2(()=>{}),require_perf_hooks=__commonJS2(()=>{}),require_tfjs_backend_wasm_threaded_simd=__commonJS2((exports,module)=>{var WasmBackendModuleThreadedSimd=function(){var _scriptDir=typeof document!="undefined"&&document.currentScript?document.currentScript.src:void 0;return typeof __filename!="undefined"&&(_scriptDir=_scriptDir||__filename),function(WasmBackendModuleThreadedSimd2){WasmBackendModuleThreadedSimd2=WasmBackendModuleThreadedSimd2||{};function GROWABLE_HEAP_I8(){return wasmMemory.buffer!=buffer2&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAP8}function GROWABLE_HEAP_U8(){return wasmMemory.buffer!=buffer2&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAPU8}function GROWABLE_HEAP_I32(){return wasmMemory.buffer!=buffer2&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAP32}function GROWABLE_HEAP_U32(){return wasmMemory.buffer!=buffer2&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAPU32}function GROWABLE_HEAP_F64(){return wasmMemory.buffer!=buffer2&&updateGlobalBufferAndViews(wasmMemory.buffer),HEAPF64}var Module=typeof WasmBackendModuleThreadedSimd2!="undefined"?WasmBackendModuleThreadedSimd2:{},_scriptDir2=void 0,moduleOverrides={},key;for(key in Module)Module.hasOwnProperty(key)&&(moduleOverrides[key]=Module[key]);var arguments_=[],thisProgram="./this.program",quit_=function(status,toThrow){throw toThrow},ENVIRONMENT_IS_WEB=!1,ENVIRONMENT_IS_WORKER=!1,ENVIRONMENT_IS_NODE=!1,ENVIRONMENT_IS_SHELL=!1;ENVIRONMENT_IS_WEB=typeof window=="object",ENVIRONMENT_IS_WORKER=typeof importScripts=="function",ENVIRONMENT_IS_NODE=typeof process=="object"&&typeof process.versions=="object"&&typeof process.versions.node=="string",ENVIRONMENT_IS_SHELL=!ENVIRONMENT_IS_WEB&&!ENVIRONMENT_IS_NODE&&!ENVIRONMENT_IS_WORKER;var ENVIRONMENT_IS_PTHREAD=Module.ENVIRONMENT_IS_PTHREAD||!1;ENVIRONMENT_IS_PTHREAD&&(buffer2=Module.buffer,DYNAMIC_BASE=Module.DYNAMIC_BASE,DYNAMICTOP_PTR=Module.DYNAMICTOP_PTR);var scriptDirectory="";function locateFile(path){return Module.locateFile?Module.locateFile(path,scriptDirectory):scriptDirectory+path}var read_,readAsync,readBinary,setWindowTitle,nodeFS,nodePath;if(ENVIRONMENT_IS_NODE){ENVIRONMENT_IS_WORKER?scriptDirectory=require_path().dirname(scriptDirectory)+"/":scriptDirectory=__dirname+"/",read_=function(filename,binary){return nodeFS||(nodeFS=require("fs")),nodePath||(nodePath=require_path()),filename=nodePath.normalize(filename),nodeFS.readFileSync(filename,binary?null:"utf8")},readBinary=function(filename){var ret=read_(filename,!0);return ret.buffer||(ret=new Uint8Array(ret)),assert3(ret.buffer),ret},process.argv.length>1&&(thisProgram=process.argv[1].replace(/\\/g,"/")),arguments_=process.argv.slice(2),process.on("uncaughtException",function(ex){if(!(ex instanceof ExitStatus))throw ex}),process.on("unhandledRejection",abort),quit_=function(status){process.exit(status)},Module.inspect=function(){return"[Emscripten Module object]"};var nodeWorkerThreads;try{nodeWorkerThreads=require_worker_threads()}catch(e){throw console.error('The "worker_threads" module is not supported in this node.js build - perhaps a newer version is needed?'),e}Worker=nodeWorkerThreads.Worker}else ENVIRONMENT_IS_SHELL?(typeof read!="undefined"&&(read_=function(f){return read(f)}),readBinary=function(f){var data2;return typeof readbuffer=="function"?new Uint8Array(readbuffer(f)):(data2=read(f,"binary"),assert3(typeof data2=="object"),data2)},typeof scriptArgs!="undefined"?arguments_=scriptArgs:typeof arguments!="undefined"&&(arguments_=arguments),typeof quit=="function"&&(quit_=function(status){quit(status)}),typeof print!="undefined"&&(typeof console=="undefined"&&(console={}),console.log=print,console.warn=console.error=typeof printErr!="undefined"?printErr:print)):(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&(ENVIRONMENT_IS_WORKER?scriptDirectory=self.location.href:document.currentScript&&(scriptDirectory=document.currentScript.src),_scriptDir2&&(scriptDirectory=_scriptDir2),scriptDirectory.indexOf("blob:")!==0?scriptDirectory=scriptDirectory.substr(0,scriptDirectory.lastIndexOf("/")+1):scriptDirectory="",ENVIRONMENT_IS_NODE?(read_=function(filename,binary){return nodeFS||(nodeFS=require("fs")),nodePath||(nodePath=require_path()),filename=nodePath.normalize(filename),nodeFS.readFileSync(filename,binary?null:"utf8")},readBinary=function(filename){var ret=read_(filename,!0);return ret.buffer||(ret=new Uint8Array(ret)),assert3(ret.buffer),ret}):(read_=function(url){var xhr=new XMLHttpRequest;return xhr.open("GET",url,!1),xhr.send(null),xhr.responseText},ENVIRONMENT_IS_WORKER&&(readBinary=function(url){var xhr=new XMLHttpRequest;return xhr.open("GET",url,!1),xhr.responseType="arraybuffer",xhr.send(null),new Uint8Array(xhr.response)}),readAsync=function(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open("GET",url,!0),xhr.responseType="arraybuffer",xhr.onload=function(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()},xhr.onerror=onerror,xhr.send(null)}),setWindowTitle=function(title){document.title=title});ENVIRONMENT_IS_NODE&&typeof performance=="undefined"&&(performance=require_perf_hooks().performance);var out=Module.print||console.log.bind(console),err=Module.printErr||console.warn.bind(console);for(key in moduleOverrides)moduleOverrides.hasOwnProperty(key)&&(Module[key]=moduleOverrides[key]);moduleOverrides=null,Module.arguments&&(arguments_=Module.arguments),Module.thisProgram&&(thisProgram=Module.thisProgram),Module.quit&&(quit_=Module.quit);var Atomics_load=Atomics.load,Atomics_store=Atomics.store,Atomics_compareExchange=Atomics.compareExchange,wasmBinary;Module.wasmBinary&&(wasmBinary=Module.wasmBinary);var noExitRuntime;Module.noExitRuntime&&(noExitRuntime=Module.noExitRuntime),typeof WebAssembly!="object"&&err("no native wasm support detected");var wasmMemory,wasmTable=new WebAssembly.Table({initial:169,maximum:169+0,element:"anyfunc"}),wasmModule,threadInfoStruct=0,selfThreadId=0,ABORT=!1,EXITSTATUS=0;function assert3(condition,text){condition||abort("Assertion failed: "+text)}function getCFunc(ident){var func2=Module["_"+ident];return assert3(func2,"Cannot call unknown function "+ident+", make sure it is exported"),func2}function ccall(ident,returnType,argTypes,args,opts){var toC={string:function(str){var ret2=0;if(str!=null&&str!==0){var len=(str.length<<2)+1;ret2=stackAlloc(len),stringToUTF8(str,ret2,len)}return ret2},array:function(arr){var ret2=stackAlloc(arr.length);return writeArrayToMemory(arr,ret2),ret2}};function convertReturnValue(ret2){return returnType==="string"?UTF8ToString(ret2):returnType==="boolean"?Boolean(ret2):ret2}var func2=getCFunc(ident),cArgs=[],stack2=0;if(args)for(var i=0;i=endIdx);){var u0=heap[idx++];if(!u0)return str;if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224?u0=(u0&15)<<12|u1<<6|u2:u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63,u0<65536)str+=String.fromCharCode(u0);else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(GROWABLE_HEAP_U8(),ptr,maxBytesToRead):""}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;for(var startIdx=outIdx,endIdx=outIdx+maxBytesToWrite-1,i=0;i=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6,heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12,heap[outIdx++]=128|u>>6&63,heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18,heap[outIdx++]=128|u>>12&63,heap[outIdx++]=128|u>>6&63,heap[outIdx++]=128|u&63}}return heap[outIdx]=0,outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,GROWABLE_HEAP_U8(),outPtr,maxBytesToWrite)}function lengthBytesUTF8(str){for(var len=0,i=0;i=55296&&u<=57343&&(u=65536+((u&1023)<<10)|str.charCodeAt(++i)&1023),u<=127?++len:u<=2047?len+=2:u<=65535?len+=3:len+=4}return len}function writeArrayToMemory(array2,buffer3){GROWABLE_HEAP_I8().set(array2,buffer3)}var WASM_PAGE_SIZE=65536;function alignUp(x,multiple){return x%multiple>0&&(x+=multiple-x%multiple),x}var buffer2,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer2=buf,Module.HEAP8=HEAP8=new Int8Array(buf),Module.HEAP16=HEAP16=new Int16Array(buf),Module.HEAP32=HEAP32=new Int32Array(buf),Module.HEAPU8=HEAPU8=new Uint8Array(buf),Module.HEAPU16=HEAPU16=new Uint16Array(buf),Module.HEAPU32=HEAPU32=new Uint32Array(buf),Module.HEAPF32=HEAPF32=new Float32Array(buf),Module.HEAPF64=HEAPF64=new Float64Array(buf)}var STACK_BASE=5256464,STACKTOP=STACK_BASE,STACK_MAX=13584,DYNAMIC_BASE=5256464,DYNAMICTOP_PTR=12656,INITIAL_INITIAL_MEMORY=Module.INITIAL_MEMORY||16777216;if(ENVIRONMENT_IS_PTHREAD)wasmMemory=Module.wasmMemory,buffer2=Module.buffer;else if(Module.wasmMemory)wasmMemory=Module.wasmMemory;else if(wasmMemory=new WebAssembly.Memory({initial:INITIAL_INITIAL_MEMORY/WASM_PAGE_SIZE,maximum:2147483648/WASM_PAGE_SIZE,shared:!0}),!(wasmMemory.buffer instanceof SharedArrayBuffer))throw err("requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag"),ENVIRONMENT_IS_NODE&&console.log("(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)"),Error("bad memory");wasmMemory&&(buffer2=wasmMemory.buffer),INITIAL_INITIAL_MEMORY=buffer2.byteLength,updateGlobalBufferAndViews(buffer2),ENVIRONMENT_IS_PTHREAD||(GROWABLE_HEAP_I32()[DYNAMICTOP_PTR>>2]=DYNAMIC_BASE);function callRuntimeCallbacks(callbacks2){for(;callbacks2.length>0;){var callback=callbacks2.shift();if(typeof callback=="function"){callback(Module);continue}var func2=callback.func;typeof func2=="number"?callback.arg===void 0?Module.dynCall_v(func2):Module.dynCall_vi(func2,callback.arg):func2(callback.arg===void 0?null:callback.arg)}}var __ATPRERUN__=[],__ATINIT__=[],__ATMAIN__=[],__ATEXIT__=[],__ATPOSTRUN__=[],runtimeInitialized=!1;ENVIRONMENT_IS_PTHREAD&&(runtimeInitialized=!0);function preRun(){if(!ENVIRONMENT_IS_PTHREAD){if(Module.preRun)for(typeof Module.preRun=="function"&&(Module.preRun=[Module.preRun]);Module.preRun.length;)addOnPreRun(Module.preRun.shift());callRuntimeCallbacks(__ATPRERUN__)}}function initRuntime(){runtimeInitialized=!0,callRuntimeCallbacks(__ATINIT__)}function preMain(){ENVIRONMENT_IS_PTHREAD||callRuntimeCallbacks(__ATMAIN__)}function postRun(){if(!ENVIRONMENT_IS_PTHREAD){if(Module.postRun)for(typeof Module.postRun=="function"&&(Module.postRun=[Module.postRun]);Module.postRun.length;)addOnPostRun(Module.postRun.shift());callRuntimeCallbacks(__ATPOSTRUN__)}}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var Math_ceil=Math.ceil,Math_floor=Math.floor,runDependencies=0,runDependencyWatcher=null,dependenciesFulfilled=null;function addRunDependency(id){assert3(!ENVIRONMENT_IS_PTHREAD,"addRunDependency cannot be used in a pthread worker"),runDependencies++,Module.monitorRunDependencies&&Module.monitorRunDependencies(runDependencies)}function removeRunDependency(id){if(runDependencies--,Module.monitorRunDependencies&&Module.monitorRunDependencies(runDependencies),runDependencies==0&&(runDependencyWatcher!==null&&(clearInterval(runDependencyWatcher),runDependencyWatcher=null),dependenciesFulfilled)){var callback=dependenciesFulfilled;dependenciesFulfilled=null,callback()}}Module.preloadedImages={},Module.preloadedAudios={};function abort(what){throw Module.onAbort&&Module.onAbort(what),ENVIRONMENT_IS_PTHREAD&&console.error("Pthread aborting at "+new Error().stack),what+="",out(what),err(what),ABORT=!0,EXITSTATUS=1,what="abort("+what+"). Build with -s ASSERTIONS=1 for more info.",new WebAssembly.RuntimeError(what)}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix="data:application/octet-stream;base64,";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix="file://";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile="tfjs-backend-wasm-threaded-simd.wasm";isDataURI(wasmBinaryFile)||(wasmBinaryFile=locateFile(wasmBinaryFile));function getBinary(){try{if(wasmBinary)return new Uint8Array(wasmBinary);if(readBinary)return readBinary(wasmBinaryFile);throw"both async and sync fetching of the wasm failed"}catch(err2){abort(err2)}}function getBinaryPromise(){return!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&typeof fetch=="function"&&!isFileURI(wasmBinaryFile)?fetch(wasmBinaryFile,{credentials:"same-origin"}).then(function(response){if(!response.ok)throw"failed to load wasm binary file at '"+wasmBinaryFile+"'";return response.arrayBuffer()}).catch(function(){return getBinary()}):new Promise(function(resolve,reject){resolve(getBinary())})}function createWasm(){var info={a:asmLibraryArg};function receiveInstance(instance,module2){var exports3=instance.exports;if(Module.asm=exports3,wasmModule=module2,!ENVIRONMENT_IS_PTHREAD){var numWorkersToLoad=PThread.unusedWorkers.length;PThread.unusedWorkers.forEach(function(w){PThread.loadWasmModuleToWorker(w,function(){--numWorkersToLoad||removeRunDependency("wasm-instantiate")})})}}ENVIRONMENT_IS_PTHREAD||addRunDependency("wasm-instantiate");function receiveInstantiatedSource(output){receiveInstance(output.instance,output.module)}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err("failed to asynchronously prepare wasm: "+reason),abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming=="function"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch=="function")fetch(wasmBinaryFile,{credentials:"same-origin"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err("wasm streaming compile failed: "+reason),err("falling back to ArrayBuffer instantiation"),instantiateArrayBuffer(receiveInstantiatedSource)})});else return instantiateArrayBuffer(receiveInstantiatedSource)}if(Module.instantiateWasm)try{var exports2=Module.instantiateWasm(info,receiveInstance);return exports2}catch(e){return err("Module.instantiateWasm callback failed with error: "+e),!1}return instantiateAsync(),{}}var ASM_CONSTS={};function initPthreadsJS(){PThread.initRuntime()}ENVIRONMENT_IS_PTHREAD||__ATINIT__.push({func:function(){___wasm_call_ctors()}});var __pthread_ptr=0,__pthread_is_main_runtime_thread=0,__pthread_is_main_browser_thread=0;function __register_pthread_ptr(pthreadPtr,isMainBrowserThread,isMainRuntimeThread){pthreadPtr=pthreadPtr|0,isMainBrowserThread=isMainBrowserThread|0,isMainRuntimeThread=isMainRuntimeThread|0,__pthread_ptr=pthreadPtr,__pthread_is_main_browser_thread=isMainBrowserThread,__pthread_is_main_runtime_thread=isMainRuntimeThread}Module.__register_pthread_ptr=__register_pthread_ptr;var ERRNO_CODES={EPERM:63,ENOENT:44,ESRCH:71,EINTR:27,EIO:29,ENXIO:60,E2BIG:1,ENOEXEC:45,EBADF:8,ECHILD:12,EAGAIN:6,EWOULDBLOCK:6,ENOMEM:48,EACCES:2,EFAULT:21,ENOTBLK:105,EBUSY:10,EEXIST:20,EXDEV:75,ENODEV:43,ENOTDIR:54,EISDIR:31,EINVAL:28,ENFILE:41,EMFILE:33,ENOTTY:59,ETXTBSY:74,EFBIG:22,ENOSPC:51,ESPIPE:70,EROFS:69,EMLINK:34,EPIPE:64,EDOM:18,ERANGE:68,ENOMSG:49,EIDRM:24,ECHRNG:106,EL2NSYNC:156,EL3HLT:107,EL3RST:108,ELNRNG:109,EUNATCH:110,ENOCSI:111,EL2HLT:112,EDEADLK:16,ENOLCK:46,EBADE:113,EBADR:114,EXFULL:115,ENOANO:104,EBADRQC:103,EBADSLT:102,EDEADLOCK:16,EBFONT:101,ENOSTR:100,ENODATA:116,ETIME:117,ENOSR:118,ENONET:119,ENOPKG:120,EREMOTE:121,ENOLINK:47,EADV:122,ESRMNT:123,ECOMM:124,EPROTO:65,EMULTIHOP:36,EDOTDOT:125,EBADMSG:9,ENOTUNIQ:126,EBADFD:127,EREMCHG:128,ELIBACC:129,ELIBBAD:130,ELIBSCN:131,ELIBMAX:132,ELIBEXEC:133,ENOSYS:52,ENOTEMPTY:55,ENAMETOOLONG:37,ELOOP:32,EOPNOTSUPP:138,EPFNOSUPPORT:139,ECONNRESET:15,ENOBUFS:42,EAFNOSUPPORT:5,EPROTOTYPE:67,ENOTSOCK:57,ENOPROTOOPT:50,ESHUTDOWN:140,ECONNREFUSED:14,EADDRINUSE:3,ECONNABORTED:13,ENETUNREACH:40,ENETDOWN:38,ETIMEDOUT:73,EHOSTDOWN:142,EHOSTUNREACH:23,EINPROGRESS:26,EALREADY:7,EDESTADDRREQ:17,EMSGSIZE:35,EPROTONOSUPPORT:66,ESOCKTNOSUPPORT:137,EADDRNOTAVAIL:4,ENETRESET:39,EISCONN:30,ENOTCONN:53,ETOOMANYREFS:141,EUSERS:136,EDQUOT:19,ESTALE:72,ENOTSUP:138,ENOMEDIUM:148,EILSEQ:25,EOVERFLOW:61,ECANCELED:11,ENOTRECOVERABLE:56,EOWNERDEAD:62,ESTRPIPE:135},__main_thread_futex_wait_address=13568;function _emscripten_futex_wake(addr,count2){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&!0||count2<0)return-28;if(count2==0)return 0;count2>=2147483647&&(count2=Infinity);var mainThreadWaitAddress=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2),mainThreadWoken=0;if(mainThreadWaitAddress==addr){var loadedAddr=Atomics.compareExchange(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,mainThreadWaitAddress,0);if(loadedAddr==mainThreadWaitAddress&&(--count2,mainThreadWoken=1,count2<=0))return 1}var ret=Atomics.notify(GROWABLE_HEAP_I32(),addr>>2,count2);if(ret>=0)return ret+mainThreadWoken;throw"Atomics.notify returned an unexpected value "+ret}Module._emscripten_futex_wake=_emscripten_futex_wake;function __kill_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw"Internal Error! _kill_thread() can only ever be called from main application thread!";if(!pthread_ptr)throw"Internal Error! Null pthread_ptr in _kill_thread!";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];pthread.worker.terminate(),PThread.freeThreadData(pthread),PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(pthread.worker),1),pthread.worker.pthread=void 0}function __cancel_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw"Internal Error! _cancel_thread() can only ever be called from main application thread!";if(!pthread_ptr)throw"Internal Error! Null pthread_ptr in _cancel_thread!";var pthread=PThread.pthreads[pthread_ptr];pthread.worker.postMessage({cmd:"cancel"})}function __cleanup_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw"Internal Error! _cleanup_thread() can only ever be called from main application thread!";if(!pthread_ptr)throw"Internal Error! Null pthread_ptr in _cleanup_thread!";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];if(pthread){var worker=pthread.worker;PThread.returnWorkerToPool(worker)}}var PThread={MAIN_THREAD_ID:1,mainThreadInfo:{schedPolicy:0,schedPrio:0},unusedWorkers:[],runningWorkers:[],initRuntime:function(){__register_pthread_ptr(PThread.mainThreadBlock,!ENVIRONMENT_IS_WORKER,1),_emscripten_register_main_browser_thread_id(PThread.mainThreadBlock)},initMainThreadBlock:function(){for(var pthreadPoolSize=8,i=0;i>2]=PThread.mainThreadBlock;var headPtr=PThread.mainThreadBlock+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;for(var tlsMemory=13056,i=0;i<128;++i)GROWABLE_HEAP_U32()[tlsMemory/4+i]=0;Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+104>>2,tlsMemory),Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+40>>2,PThread.mainThreadBlock),Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+44>>2,42)},initWorker:function(){},pthreads:{},exitHandlers:null,setThreadStatus:function(){},runExitHandlers:function(){if(PThread.exitHandlers!==null){for(;PThread.exitHandlers.length>0;)PThread.exitHandlers.pop()();PThread.exitHandlers=null}ENVIRONMENT_IS_PTHREAD&&threadInfoStruct&&___pthread_tsd_run_dtors()},threadExit:function(exitCode){var tb=_pthread_self();tb&&(Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,exitCode),Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1),Atomics.store(GROWABLE_HEAP_U32(),tb+60>>2,1),Atomics.store(GROWABLE_HEAP_U32(),tb+64>>2,0),PThread.runExitHandlers(),_emscripten_futex_wake(tb+0,2147483647),__register_pthread_ptr(0,0,0),threadInfoStruct=0,ENVIRONMENT_IS_PTHREAD&&postMessage({cmd:"exit"}))},threadCancel:function(){PThread.runExitHandlers(),Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+4>>2,-1),Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+0>>2,1),_emscripten_futex_wake(threadInfoStruct+0,2147483647),threadInfoStruct=selfThreadId=0,__register_pthread_ptr(0,0,0),postMessage({cmd:"cancelDone"})},terminateAllThreads:function(){for(var t in PThread.pthreads){var pthread=PThread.pthreads[t];pthread&&pthread.worker&&PThread.returnWorkerToPool(pthread.worker)}PThread.pthreads={};for(var i=0;i>2];GROWABLE_HEAP_I32()[pthread.threadInfoStruct+104>>2]=0,_free(tlsMemory),_free(pthread.threadInfoStruct)}pthread.threadInfoStruct=0,pthread.allocatedOwnStack&&pthread.stackBase&&_free(pthread.stackBase),pthread.stackBase=0,pthread.worker&&(pthread.worker.pthread=null)}},returnWorkerToPool:function(worker){delete PThread.pthreads[worker.pthread.thread],PThread.unusedWorkers.push(worker),PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker),1),PThread.freeThreadData(worker.pthread),worker.pthread=void 0},receiveObjectTransfer:function(data2){},loadWasmModuleToWorker:function(worker,onFinishedLoading){worker.onmessage=function(e){var d=e.data,cmd=d.cmd;if(worker.pthread&&(PThread.currentProxiedOperationCallerThread=worker.pthread.threadInfoStruct),d.targetThread&&d.targetThread!=_pthread_self()){var thread=PThread.pthreads[d.targetThread];thread?thread.worker.postMessage(e.data,d.transferList):console.error('Internal error! Worker sent a message "'+cmd+'" to target pthread '+d.targetThread+", but that thread no longer exists!"),PThread.currentProxiedOperationCallerThread=void 0;return}if(cmd==="processQueuedMainThreadWork")_emscripten_main_thread_process_queued_calls();else if(cmd==="spawnThread")__spawn_thread(e.data);else if(cmd==="cleanupThread")__cleanup_thread(d.thread);else if(cmd==="killThread")__kill_thread(d.thread);else if(cmd==="cancelThread")__cancel_thread(d.thread);else if(cmd==="loaded")worker.loaded=!0,onFinishedLoading&&onFinishedLoading(worker),worker.runPthread&&(worker.runPthread(),delete worker.runPthread);else if(cmd==="print")out("Thread "+d.threadId+": "+d.text);else if(cmd==="printErr")err("Thread "+d.threadId+": "+d.text);else if(cmd==="alert")alert("Thread "+d.threadId+": "+d.text);else if(cmd==="exit"){var detached=worker.pthread&&Atomics.load(GROWABLE_HEAP_U32(),worker.pthread.thread+68>>2);detached&&PThread.returnWorkerToPool(worker)}else cmd==="cancelDone"?PThread.returnWorkerToPool(worker):cmd==="objectTransfer"?PThread.receiveObjectTransfer(e.data):e.data.target==="setimmediate"?worker.postMessage(e.data):err("worker sent an unknown command "+cmd);PThread.currentProxiedOperationCallerThread=void 0},worker.onerror=function(e){err("pthread sent an error! "+e.filename+":"+e.lineno+": "+e.message)},ENVIRONMENT_IS_NODE&&(worker.on("message",function(data2){worker.onmessage({data:data2})}),worker.on("error",function(data2){worker.onerror(data2)}),worker.on("exit",function(data2){console.log("worker exited - TODO: update the worker queue?")})),worker.postMessage({cmd:"load",urlOrBlob:Module.mainScriptUrlOrBlob||_scriptDir2,wasmMemory,wasmModule,DYNAMIC_BASE,DYNAMICTOP_PTR})},allocateUnusedWorker:function(){var pthreadMainJs=locateFile("tfjs-backend-wasm-threaded-simd.worker.js");PThread.unusedWorkers.push(new Worker(pthreadMainJs))},getNewWorker:function(){return PThread.unusedWorkers.length==0&&(PThread.allocateUnusedWorker(),PThread.loadWasmModuleToWorker(PThread.unusedWorkers[0])),PThread.unusedWorkers.length>0?PThread.unusedWorkers.pop():null},busySpinWait:function(msecs){for(var t=performance.now()+msecs;performance.now()>2]=value,value}function _atexit(func2,arg){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(1,1,func2,arg);__ATEXIT__.unshift({func:func2,arg})}function __emscripten_notify_thread_queue(targetThreadId,mainThreadId){if(targetThreadId==mainThreadId)postMessage({cmd:"processQueuedMainThreadWork"});else if(ENVIRONMENT_IS_PTHREAD)postMessage({targetThread:targetThreadId,cmd:"processThreadQueue"});else{var pthread=PThread.pthreads[targetThreadId],worker=pthread&&pthread.worker;if(!worker)return;worker.postMessage({cmd:"processThreadQueue"})}return 1}function _abort(){abort()}function _emscripten_conditional_set_current_thread_status(expectedStatus,newStatus){expectedStatus=expectedStatus|0,newStatus=newStatus|0}function _emscripten_futex_wait(addr,val,timeout){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&!0)return-28;if(ENVIRONMENT_IS_WORKER){var ret=Atomics.wait(GROWABLE_HEAP_I32(),addr>>2,val,timeout);if(ret==="timed-out")return-73;if(ret==="not-equal")return-6;if(ret==="ok")return 0;throw"Atomics.wait returned an unexpected value "+ret}else{var loadedVal=Atomics.load(GROWABLE_HEAP_I32(),addr>>2);if(val!=loadedVal)return-6;var tNow=performance.now(),tEnd=tNow+timeout;Atomics.store(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,addr);for(var ourWaitAddress=addr;addr==ourWaitAddress;){if(tNow=performance.now(),tNow>tEnd)return-73;_emscripten_main_thread_process_queued_calls(),addr=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2)}return 0}}function _emscripten_is_main_browser_thread(){return __pthread_is_main_browser_thread|0}function _emscripten_is_main_runtime_thread(){return __pthread_is_main_runtime_thread|0}function _emscripten_memcpy_big(dest,src,num){GROWABLE_HEAP_U8().copyWithin(dest,src,src+num)}function _emscripten_num_logical_cores(){return navigator.hardwareConcurrency}function _emscripten_proxy_to_main_thread_js(index,sync){for(var numCallArgs=arguments.length-2,stack2=stackSave(),args=stackAlloc(numCallArgs*8),b=args>>3,i=0;i>3]),buf+=8):(buf=buf+3&~3,args.push(GROWABLE_HEAP_I32()[buf>>2]),buf+=4);return args}function _emscripten_receive_on_main_thread_js(index,numCallArgs,args){_emscripten_receive_on_main_thread_js_callArgs.length=numCallArgs;for(var b=args>>3,i=0;i>>16),updateGlobalBufferAndViews(wasmMemory.buffer),1}catch(e){}}function _emscripten_resize_heap(requestedSize){requestedSize=requestedSize>>>0;var oldSize=_emscripten_get_heap_size();if(requestedSize<=oldSize)return!1;var PAGE_MULTIPLE=65536,maxHeapSize=2147483648;if(requestedSize>maxHeapSize)return!1;for(var minHeapSize=16777216,cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(minHeapSize,requestedSize,overGrownHeapSize),PAGE_MULTIPLE)),replacement=emscripten_realloc_buffer(newSize);if(replacement)return!0}return!1}var JSEvents={keyEvent:0,mouseEvent:0,wheelEvent:0,uiEvent:0,focusEvent:0,deviceOrientationEvent:0,deviceMotionEvent:0,fullscreenChangeEvent:0,pointerlockChangeEvent:0,visibilityChangeEvent:0,touchEvent:0,previousFullscreenElement:null,previousScreenX:null,previousScreenY:null,removeEventListenersRegistered:!1,removeAllEventListeners:function(){for(var i=JSEvents.eventHandlers.length-1;i>=0;--i)JSEvents._removeHandler(i);JSEvents.eventHandlers=[],JSEvents.deferredCalls=[]},registerRemoveEventListeners:function(){JSEvents.removeEventListenersRegistered||(__ATEXIT__.push(JSEvents.removeAllEventListeners),JSEvents.removeEventListenersRegistered=!0)},deferredCalls:[],deferCall:function(targetFunction,precedence,argsList){function arraysHaveEqualContent(arrA,arrB){if(arrA.length!=arrB.length)return!1;for(var i2 in arrA)if(arrA[i2]!=arrB[i2])return!1;return!0}for(var i in JSEvents.deferredCalls){var call=JSEvents.deferredCalls[i];if(call.targetFunction==targetFunction&&arraysHaveEqualContent(call.argsList,argsList))return}JSEvents.deferredCalls.push({targetFunction,precedence,argsList}),JSEvents.deferredCalls.sort(function(x,y){return x.precedence>2]=eventTypeId,GROWABLE_HEAP_I32()[varargs+4>>2]=eventData,GROWABLE_HEAP_I32()[varargs+8>>2]=userData,_emscripten_async_queue_on_thread_(targetThread,637534208,eventHandlerFunc,eventData,varargs),stackRestore(stackTop)},getTargetThreadForEventCallback:function(targetThread){switch(targetThread){case 1:return 0;case 2:return PThread.currentProxiedOperationCallerThread;default:return targetThread}},getNodeNameForTarget:function(target){return target?target==window?"#window":target==screen?"#screen":target&&target.nodeName?target.nodeName:"":""},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function stringToNewUTF8(jsString){var length=lengthBytesUTF8(jsString)+1,cString=_malloc(length);return stringToUTF8(jsString,cString,length),cString}function _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height){var stackTop=stackSave(),varargs=stackAlloc(12),targetCanvasPtr=0;targetCanvas&&(targetCanvasPtr=stringToNewUTF8(targetCanvas)),GROWABLE_HEAP_I32()[varargs>>2]=targetCanvasPtr,GROWABLE_HEAP_I32()[varargs+4>>2]=width,GROWABLE_HEAP_I32()[varargs+8>>2]=height,_emscripten_async_queue_on_thread_(targetThread,657457152,0,targetCanvasPtr,varargs),stackRestore(stackTop)}function _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,targetCanvas,width,height){targetCanvas=targetCanvas?UTF8ToString(targetCanvas):"",_emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height)}function __maybeCStringToJsString(cString){return cString>2?UTF8ToString(cString):cString}var specialHTMLTargets=[0,typeof document!="undefined"?document:0,typeof window!="undefined"?window:0];function __findEventTarget(target){target=__maybeCStringToJsString(target);var domElement=specialHTMLTargets[target]||(typeof document!="undefined"?document.querySelector(target):void 0);return domElement}function __findCanvasEventTarget(target){return __findEventTarget(target)}function _emscripten_set_canvas_element_size_calling_thread(target,width,height){var canvas=__findCanvasEventTarget(target);if(!canvas)return-4;if(canvas.canvasSharedPtr&&(GROWABLE_HEAP_I32()[canvas.canvasSharedPtr>>2]=width,GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+4>>2]=height),canvas.offscreenCanvas||!canvas.controlTransferredOffscreen){canvas.offscreenCanvas&&(canvas=canvas.offscreenCanvas);var autoResizeViewport=!1;if(canvas.GLctxObject&&canvas.GLctxObject.GLctx){var prevViewport=canvas.GLctxObject.GLctx.getParameter(2978);autoResizeViewport=prevViewport[0]===0&&prevViewport[1]===0&&prevViewport[2]===canvas.width&&prevViewport[3]===canvas.height}canvas.width=width,canvas.height=height,autoResizeViewport&&canvas.GLctxObject.GLctx.viewport(0,0,width,height)}else if(canvas.canvasSharedPtr){var targetThread=GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+8>>2];return _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,target,width,height),1}else return-4;return 0}function _emscripten_set_canvas_element_size_main_thread(target,width,height){return ENVIRONMENT_IS_PTHREAD?_emscripten_proxy_to_main_thread_js(2,1,target,width,height):_emscripten_set_canvas_element_size_calling_thread(target,width,height)}function _emscripten_set_canvas_element_size(target,width,height){var canvas=__findCanvasEventTarget(target);return canvas?_emscripten_set_canvas_element_size_calling_thread(target,width,height):_emscripten_set_canvas_element_size_main_thread(target,width,height)}function _emscripten_set_current_thread_status(newStatus){newStatus=newStatus|0}function _emscripten_set_thread_name(threadId,name){threadId=threadId|0,name=name|0}function __webgl_enable_ANGLE_instanced_arrays(ctx){var ext=ctx.getExtension("ANGLE_instanced_arrays");if(ext)return ctx.vertexAttribDivisor=function(index,divisor){ext.vertexAttribDivisorANGLE(index,divisor)},ctx.drawArraysInstanced=function(mode,first,count2,primcount){ext.drawArraysInstancedANGLE(mode,first,count2,primcount)},ctx.drawElementsInstanced=function(mode,count2,type,indices,primcount){ext.drawElementsInstancedANGLE(mode,count2,type,indices,primcount)},1}function __webgl_enable_OES_vertex_array_object(ctx){var ext=ctx.getExtension("OES_vertex_array_object");if(ext)return ctx.createVertexArray=function(){return ext.createVertexArrayOES()},ctx.deleteVertexArray=function(vao){ext.deleteVertexArrayOES(vao)},ctx.bindVertexArray=function(vao){ext.bindVertexArrayOES(vao)},ctx.isVertexArray=function(vao){return ext.isVertexArrayOES(vao)},1}function __webgl_enable_WEBGL_draw_buffers(ctx){var ext=ctx.getExtension("WEBGL_draw_buffers");if(ext)return ctx.drawBuffers=function(n,bufs){ext.drawBuffersWEBGL(n,bufs)},1}var GL={counter:1,lastError:0,buffers:[],mappedBuffers:{},programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},currentContext:null,offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,init:function(){for(var miniTempFloatBuffer=new Float32Array(GL.MINI_TEMP_BUFFER_SIZE),i=0;i>2]:-1;source+=UTF8ToString(GROWABLE_HEAP_I32()[string+i*4>>2],len<0?void 0:len)}return source},createContext:function(canvas,webGLContextAttributes){var ctx=canvas.getContext("webgl",webGLContextAttributes);if(!ctx)return 0;var handle=GL.registerContext(ctx,webGLContextAttributes);return handle},registerContext:function(ctx,webGLContextAttributes){var handle=_malloc(8);GROWABLE_HEAP_I32()[handle+4>>2]=_pthread_self();var context={handle,attributes:webGLContextAttributes,version:webGLContextAttributes.majorVersion,GLctx:ctx};return ctx.canvas&&(ctx.canvas.GLctxObject=context),GL.contexts[handle]=context,(typeof webGLContextAttributes.enableExtensionsByDefault=="undefined"||webGLContextAttributes.enableExtensionsByDefault)&&GL.initExtensions(context),handle},makeContextCurrent:function(contextHandle){return GL.currentContext=GL.contexts[contextHandle],Module.ctx=GLctx=GL.currentContext&&GL.currentContext.GLctx,!(contextHandle&&!GLctx)},getContext:function(contextHandle){return GL.contexts[contextHandle]},deleteContext:function(contextHandle){GL.currentContext===GL.contexts[contextHandle]&&(GL.currentContext=null),typeof JSEvents=="object"&&JSEvents.removeAllHandlersOnTarget(GL.contexts[contextHandle].GLctx.canvas),GL.contexts[contextHandle]&&GL.contexts[contextHandle].GLctx.canvas&&(GL.contexts[contextHandle].GLctx.canvas.GLctxObject=void 0),_free(GL.contexts[contextHandle].handle),GL.contexts[contextHandle]=null},initExtensions:function(context){if(context||(context=GL.currentContext),!context.initExtensionsDone){context.initExtensionsDone=!0;var GLctx2=context.GLctx;__webgl_enable_ANGLE_instanced_arrays(GLctx2),__webgl_enable_OES_vertex_array_object(GLctx2),__webgl_enable_WEBGL_draw_buffers(GLctx2),GLctx2.disjointTimerQueryExt=GLctx2.getExtension("EXT_disjoint_timer_query");var automaticallyEnabledExtensions=["OES_texture_float","OES_texture_half_float","OES_standard_derivatives","OES_vertex_array_object","WEBGL_compressed_texture_s3tc","WEBGL_depth_texture","OES_element_index_uint","EXT_texture_filter_anisotropic","EXT_frag_depth","WEBGL_draw_buffers","ANGLE_instanced_arrays","OES_texture_float_linear","OES_texture_half_float_linear","EXT_blend_minmax","EXT_shader_texture_lod","EXT_texture_norm16","WEBGL_compressed_texture_pvrtc","EXT_color_buffer_half_float","WEBGL_color_buffer_float","EXT_sRGB","WEBGL_compressed_texture_etc1","EXT_disjoint_timer_query","WEBGL_compressed_texture_etc","WEBGL_compressed_texture_astc","EXT_color_buffer_float","WEBGL_compressed_texture_s3tc_srgb","EXT_disjoint_timer_query_webgl2","WEBKIT_WEBGL_compressed_texture_pvrtc"],exts=GLctx2.getSupportedExtensions()||[];exts.forEach(function(ext){automaticallyEnabledExtensions.indexOf(ext)!=-1&&GLctx2.getExtension(ext)})}},populateUniformTable:function(program){for(var p2=GL.programs[program],ptable=GL.programInfos[program]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1},utable=ptable.uniforms,numUniforms=GLctx.getProgramParameter(p2,35718),i=0;i>2;contextAttributes.alpha=!!GROWABLE_HEAP_I32()[a+(0>>2)],contextAttributes.depth=!!GROWABLE_HEAP_I32()[a+(4>>2)],contextAttributes.stencil=!!GROWABLE_HEAP_I32()[a+(8>>2)],contextAttributes.antialias=!!GROWABLE_HEAP_I32()[a+(12>>2)],contextAttributes.premultipliedAlpha=!!GROWABLE_HEAP_I32()[a+(16>>2)],contextAttributes.preserveDrawingBuffer=!!GROWABLE_HEAP_I32()[a+(20>>2)];var powerPreference=GROWABLE_HEAP_I32()[a+(24>>2)];contextAttributes.powerPreference=__emscripten_webgl_power_preferences[powerPreference],contextAttributes.failIfMajorPerformanceCaveat=!!GROWABLE_HEAP_I32()[a+(28>>2)],contextAttributes.majorVersion=GROWABLE_HEAP_I32()[a+(32>>2)],contextAttributes.minorVersion=GROWABLE_HEAP_I32()[a+(36>>2)],contextAttributes.enableExtensionsByDefault=GROWABLE_HEAP_I32()[a+(40>>2)],contextAttributes.explicitSwapControl=GROWABLE_HEAP_I32()[a+(44>>2)],contextAttributes.proxyContextToMainThread=GROWABLE_HEAP_I32()[a+(48>>2)],contextAttributes.renderViaOffscreenBackBuffer=GROWABLE_HEAP_I32()[a+(52>>2)];var canvas=__findCanvasEventTarget(target);if(!canvas)return-4;if(contextAttributes.explicitSwapControl)return-1;var contextHandle=GL.createContext(canvas,contextAttributes);return contextHandle}function _emscripten_webgl_create_context(a0,a12){return _emscripten_webgl_do_create_context(a0,a12)}var PATH={splitPath:function(filename){var splitPathRe=/^(\/?|)([\s\S]*?)((?:\.{1,2}|[^\/]+?|)(\.[^.\/]*|))(?:[\/]*)$/;return splitPathRe.exec(filename).slice(1)},normalizeArray:function(parts,allowAboveRoot){for(var up=0,i=parts.length-1;i>=0;i--){var last=parts[i];last==="."?parts.splice(i,1):last===".."?(parts.splice(i,1),up++):up&&(parts.splice(i,1),up--)}if(allowAboveRoot)for(;up;up--)parts.unshift("..");return parts},normalize:function(path){var isAbsolute=path.charAt(0)==="/",trailingSlash=path.substr(-1)==="/";return path=PATH.normalizeArray(path.split("/").filter(function(p2){return!!p2}),!isAbsolute).join("/"),!path&&!isAbsolute&&(path="."),path&&trailingSlash&&(path+="/"),(isAbsolute?"/":"")+path},dirname:function(path){var result=PATH.splitPath(path),root=result[0],dir=result[1];return!root&&!dir?".":(dir&&(dir=dir.substr(0,dir.length-1)),root+dir)},basename:function(path){if(path==="/")return"/";var lastSlash=path.lastIndexOf("/");return lastSlash===-1?path:path.substr(lastSlash+1)},extname:function(path){return PATH.splitPath(path)[3]},join:function(){var paths=Array.prototype.slice.call(arguments,0);return PATH.normalize(paths.join("/"))},join2:function(l,r){return PATH.normalize(l+"/"+r)}},SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer3=SYSCALLS.buffers[stream];curr===0||curr===10?((stream===1?out:err)(UTF8ArrayToString(buffer3,0)),buffer3.length=0):buffer3.push(curr)},varargs:void 0,get:function(){SYSCALLS.varargs+=4;var ret=GROWABLE_HEAP_I32()[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){return ENVIRONMENT_IS_PTHREAD?_emscripten_proxy_to_main_thread_js(3,1,fd):0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(4,1,fd,offset_low,offset_high,whence,newOffset)}function _fd_write(fd,iov,iovcnt,pnum){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(5,1,fd,iov,iovcnt,pnum);for(var num=0,i=0;i>2],len=GROWABLE_HEAP_I32()[iov+(i*8+4)>>2],j=0;j>2]=num,0}function _pthread_cleanup_pop(execute2){var routine=PThread.exitHandlers.pop();execute2&&routine()}function _pthread_cleanup_push(routine,arg){PThread.exitHandlers===null&&(PThread.exitHandlers=[]),PThread.exitHandlers.push(function(){dynCall_vi(routine,arg)})}function __spawn_thread(threadParams){if(ENVIRONMENT_IS_PTHREAD)throw"Internal Error! _spawn_thread() can only ever be called from main application thread!";var worker=PThread.getNewWorker();if(worker.pthread!==void 0)throw"Internal error!";if(!threadParams.pthread_ptr)throw"Internal error, no pthread ptr!";PThread.runningWorkers.push(worker);for(var tlsMemory=_malloc(128*4),i=0;i<128;++i)GROWABLE_HEAP_I32()[tlsMemory+i*4>>2]=0;var stackHigh=threadParams.stackBase+threadParams.stackSize,pthread=PThread.pthreads[threadParams.pthread_ptr]={worker,stackBase:threadParams.stackBase,stackSize:threadParams.stackSize,allocatedOwnStack:threadParams.allocatedOwnStack,thread:threadParams.pthread_ptr,threadInfoStruct:threadParams.pthread_ptr},tis=pthread.threadInfoStruct>>2;Atomics.store(GROWABLE_HEAP_U32(),tis+(0>>2),0),Atomics.store(GROWABLE_HEAP_U32(),tis+(4>>2),0),Atomics.store(GROWABLE_HEAP_U32(),tis+(8>>2),0),Atomics.store(GROWABLE_HEAP_U32(),tis+(68>>2),threadParams.detached),Atomics.store(GROWABLE_HEAP_U32(),tis+(104>>2),tlsMemory),Atomics.store(GROWABLE_HEAP_U32(),tis+(48>>2),0),Atomics.store(GROWABLE_HEAP_U32(),tis+(40>>2),pthread.threadInfoStruct),Atomics.store(GROWABLE_HEAP_U32(),tis+(44>>2),42),Atomics.store(GROWABLE_HEAP_U32(),tis+(108>>2),threadParams.stackSize),Atomics.store(GROWABLE_HEAP_U32(),tis+(84>>2),threadParams.stackSize),Atomics.store(GROWABLE_HEAP_U32(),tis+(80>>2),stackHigh),Atomics.store(GROWABLE_HEAP_U32(),tis+(108+8>>2),stackHigh),Atomics.store(GROWABLE_HEAP_U32(),tis+(108+12>>2),threadParams.detached),Atomics.store(GROWABLE_HEAP_U32(),tis+(108+20>>2),threadParams.schedPolicy),Atomics.store(GROWABLE_HEAP_U32(),tis+(108+24>>2),threadParams.schedPrio);var global_libc=_emscripten_get_global_libc(),global_locale=global_libc+40;Atomics.store(GROWABLE_HEAP_U32(),tis+(176>>2),global_locale),worker.pthread=pthread;var msg={cmd:"run",start_routine:threadParams.startRoutine,arg:threadParams.arg,threadInfoStruct:threadParams.pthread_ptr,selfThreadId:threadParams.pthread_ptr,parentThreadId:threadParams.parent_pthread_ptr,stackBase:threadParams.stackBase,stackSize:threadParams.stackSize};worker.runPthread=function(){msg.time=performance.now(),worker.postMessage(msg,threadParams.transferList)},worker.loaded&&(worker.runPthread(),delete worker.runPthread)}function _pthread_getschedparam(thread,policy,schedparam){if(!policy&&!schedparam)return ERRNO_CODES.EINVAL;if(!thread)return err("pthread_getschedparam called with a null thread pointer!"),ERRNO_CODES.ESRCH;var self2=GROWABLE_HEAP_I32()[thread+12>>2];if(self2!==thread)return err("pthread_getschedparam attempted on thread "+thread+", which does not point to a valid thread, or does not exist anymore!"),ERRNO_CODES.ESRCH;var schedPolicy=Atomics.load(GROWABLE_HEAP_U32(),thread+108+20>>2),schedPrio=Atomics.load(GROWABLE_HEAP_U32(),thread+108+24>>2);return policy&&(GROWABLE_HEAP_I32()[policy>>2]=schedPolicy),schedparam&&(GROWABLE_HEAP_I32()[schedparam>>2]=schedPrio),0}function _pthread_self(){return __pthread_ptr|0}Module._pthread_self=_pthread_self;function _pthread_create(pthread_ptr,attr,start_routine,arg){if(typeof SharedArrayBuffer=="undefined")return err("Current environment does not support SharedArrayBuffer, pthreads are not available!"),6;if(!pthread_ptr)return err("pthread_create called with a null thread pointer!"),28;var transferList=[],error=0;if(ENVIRONMENT_IS_PTHREAD&&(transferList.length===0||error))return _emscripten_sync_run_in_main_thread_4(687865856,pthread_ptr,attr,start_routine,arg);if(error)return error;var stackSize=0,stackBase=0,detached=0,schedPolicy=0,schedPrio=0;if(attr){stackSize=GROWABLE_HEAP_I32()[attr>>2],stackSize+=81920,stackBase=GROWABLE_HEAP_I32()[attr+8>>2],detached=GROWABLE_HEAP_I32()[attr+12>>2]!==0;var inheritSched=GROWABLE_HEAP_I32()[attr+16>>2]===0;if(inheritSched){var prevSchedPolicy=GROWABLE_HEAP_I32()[attr+20>>2],prevSchedPrio=GROWABLE_HEAP_I32()[attr+24>>2],parentThreadPtr=PThread.currentProxiedOperationCallerThread?PThread.currentProxiedOperationCallerThread:_pthread_self();_pthread_getschedparam(parentThreadPtr,attr+20,attr+24),schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2],schedPrio=GROWABLE_HEAP_I32()[attr+24>>2],GROWABLE_HEAP_I32()[attr+20>>2]=prevSchedPolicy,GROWABLE_HEAP_I32()[attr+24>>2]=prevSchedPrio}else schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2],schedPrio=GROWABLE_HEAP_I32()[attr+24>>2]}else stackSize=2097152;var allocatedOwnStack=stackBase==0;allocatedOwnStack?stackBase=_memalign(16,stackSize):(stackBase-=stackSize,assert3(stackBase>0));for(var threadInfoStruct2=_malloc(232),i=0;i<232>>2;++i)GROWABLE_HEAP_U32()[(threadInfoStruct2>>2)+i]=0;GROWABLE_HEAP_I32()[pthread_ptr>>2]=threadInfoStruct2,GROWABLE_HEAP_I32()[threadInfoStruct2+12>>2]=threadInfoStruct2;var headPtr=threadInfoStruct2+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var threadParams={stackBase,stackSize,allocatedOwnStack,schedPolicy,schedPrio,detached,startRoutine:start_routine,pthread_ptr:threadInfoStruct2,parent_pthread_ptr:_pthread_self(),arg,transferList};return ENVIRONMENT_IS_PTHREAD?(threadParams.cmd="spawnThread",postMessage(threadParams,transferList)):__spawn_thread(threadParams),0}function _roundf(d){return d=+d,d>=0?+Math_floor(d+.5):+Math_ceil(d-.5)}function _sysconf(name){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(6,1,name);switch(name){case 30:return 16384;case 85:var maxHeapSize=2147483648;return maxHeapSize/16384;case 132:case 133:case 12:case 137:case 138:case 15:case 235:case 16:case 17:case 18:case 19:case 20:case 149:case 13:case 10:case 236:case 153:case 9:case 21:case 22:case 159:case 154:case 14:case 77:case 78:case 139:case 80:case 81:case 82:case 68:case 67:case 164:case 11:case 29:case 47:case 48:case 95:case 52:case 51:case 46:case 79:return 200809;case 27:case 246:case 127:case 128:case 23:case 24:case 160:case 161:case 181:case 182:case 242:case 183:case 184:case 243:case 244:case 245:case 165:case 178:case 179:case 49:case 50:case 168:case 169:case 175:case 170:case 171:case 172:case 97:case 76:case 32:case 173:case 35:return-1;case 176:case 177:case 7:case 155:case 8:case 157:case 125:case 126:case 92:case 93:case 129:case 130:case 131:case 94:case 91:return 1;case 74:case 60:case 69:case 70:case 4:return 1024;case 31:case 42:case 72:return 32;case 87:case 26:case 33:return 2147483647;case 34:case 1:return 47839;case 38:case 36:return 99;case 43:case 37:return 2048;case 0:return 2097152;case 3:return 65536;case 28:return 32768;case 44:return 32767;case 75:return 16384;case 39:return 1e3;case 89:return 700;case 71:return 256;case 40:return 255;case 2:return 100;case 180:return 64;case 25:return 20;case 5:return 16;case 6:return 6;case 73:return 4;case 84:return typeof navigator=="object"&&navigator.hardwareConcurrency||1}return setErrNo(28),-1}ENVIRONMENT_IS_PTHREAD?PThread.initWorker():PThread.initMainThreadBlock();var GLctx;GL.init();var proxiedFunctionTable=[null,_atexit,_emscripten_set_canvas_element_size_main_thread,_fd_close,_fd_seek,_fd_write,_sysconf],asmLibraryArg={e:___assert_fail,r:___call_main,w:__emscripten_notify_thread_queue,a:_abort,l:_emscripten_conditional_set_current_thread_status,d:_emscripten_futex_wait,c:_emscripten_futex_wake,h:_emscripten_get_now,g:_emscripten_is_main_browser_thread,x:_emscripten_is_main_runtime_thread,q:_emscripten_memcpy_big,B:_emscripten_num_logical_cores,t:_emscripten_receive_on_main_thread_js,A:_emscripten_resize_heap,u:_emscripten_set_canvas_element_size,k:_emscripten_set_current_thread_status,s:_emscripten_set_thread_name,v:_emscripten_webgl_create_context,m:_fd_close,o:_fd_seek,i:_fd_write,p:initPthreadsJS,memory:wasmMemory||Module.wasmMemory,y:_pthread_cleanup_pop,z:_pthread_cleanup_push,j:_pthread_create,b:_pthread_self,f:_roundf,n:_sysconf,table:wasmTable},asm=createWasm();Module.asm=asm;var 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Uint8Array?"int32":isNumber(values)?"float32":isString(values)?"string":isBoolean(values)?"bool":"float32"}function isFunction(f){return!!(f&&f.constructor&&f.call&&f.apply)}function nearestDivisor(size,start){for(let i=start;i=0;--i)strides[i]=strides[i+1]*shape[i+1];return strides}function createNestedArray(offset,shape,a){let ret=new Array;if(shape.length===1){let d=shape[0];for(let i=0;iacc*c);for(let i=0;iacc*c);if(size===0)return[];if(size!==a.length)throw new Error(`[${shape}] does not match the input size ${a.length}.`);return createNestedArray(0,shape,a)}function makeOnesTypedArray(size,dtype){let array2=makeZerosTypedArray(size,dtype);for(let i=0;iprev*curr,1);if(dtype==null||dtype==="float32")return toNestedArray(shape,new Float32Array(size));if(dtype==="int32")return toNestedArray(shape,new Int32Array(size));if(dtype==="bool")return toNestedArray(shape,new Uint8Array(size));throw new Error(`Unknown data type ${dtype}`)}function assertNonNegativeIntegerDimensions(shape){shape.forEach(dimSize=>{assert(Number.isInteger(dimSize)&&dimSize>=0,()=>`Tensor must have a shape comprised of positive integers but got shape [${shape}].`)})}function locToIndex(locs,rank,strides){if(rank===0)return 0;if(rank===1)return locs[0];let index=locs[locs.length-1];for(let i=0;i{let[key,value]=keyValue.split(":");this.urlFlags[key]=parseValue(key,value)})}};function getQueryParams(queryString){let params={};return queryString.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g,(s,...t)=>(decodeParam(params,t[0],t[1]),t.join("="))),params}function decodeParam(params,name,value){params[decodeURIComponent(name)]=decodeURIComponent(value||"")}function parseValue(flagName,value){if(value=value.toLowerCase(),value==="true"||value==="false")return value==="true";if(`${+value}`===value)return+value;throw new Error(`Could not parse value flag value ${value} for flag ${flagName}.`)}function env(){return ENV}var ENV=null;function setEnvironmentGlobal(environment){ENV=environment}var globalNameSpace;function getGlobalNamespace(){if(globalNameSpace==null){let ns;if(typeof window!="undefined")ns=window;else if(typeof global!="undefined")ns=global;else if(typeof process!="undefined")ns=process;else if(typeof self!="undefined")ns=self;else throw new Error("Could not find a global object");globalNameSpace=ns}return globalNameSpace}function getGlobalMap(){let ns=getGlobalNamespace();return ns._tfGlobals==null&&(ns._tfGlobals=new Map),ns._tfGlobals}function getGlobal(key,init2){let globalMap=getGlobalMap();if(globalMap.has(key))return globalMap.get(key);{let singleton=init2();return globalMap.set(key,singleton),globalMap.get(key)}}var Abs="Abs",Acos="Acos",Acosh="Acosh",Add="Add",AddN="AddN",All="All",Any="Any",ArgMax="ArgMax",ArgMin="ArgMin",Asin="Asin",Asinh="Asinh",Atan="Atan",Atanh="Atanh",Atan2="Atan2",AvgPool="AvgPool",AvgPoolGrad="AvgPoolGrad",AvgPool3D="AvgPool3D",AvgPool3DGrad="AvgPool3DGrad",BatchMatMul="BatchMatMul",BatchToSpaceND="BatchToSpaceND",Bincount="Bincount",BroadcastTo="BroadcastTo",Cast="Cast",Ceil="Ceil",ClipByValue="ClipByValue",Complex="Complex",ComplexAbs="ComplexAbs",Concat="Concat",Conv2D="Conv2D",Conv2DBackpropFilter="Conv2DBackpropFilter",Conv2DBackpropInput="Conv2DBackpropInput",Conv3D="Conv3D",Conv3DBackpropFilterV2="Conv3DBackpropFilterV2",Conv3DBackpropInputV2="Conv3DBackpropInputV2",Cos="Cos",Cosh="Cosh",Cumsum="Cumsum",CropAndResize="CropAndResize",DenseBincount="DenseBincount",DepthToSpace="DepthToSpace",DepthwiseConv2dNative="DepthwiseConv2dNative",DepthwiseConv2dNativeBackpropFilter="DepthwiseConv2dNativeBackpropFilter",DepthwiseConv2dNativeBackpropInput="DepthwiseConv2dNativeBackpropInput",Diag="Diag",Dilation2D="Dilation2D",Dilation2DBackpropInput="Dilation2DBackpropInput",Dilation2DBackpropFilter="Dilation2DBackpropFilter",RealDiv="RealDiv",Elu="Elu",EluGrad="EluGrad",Erf="Erf",Equal="Equal",Exp="Exp",ExpandDims="ExpandDims",Expm1="Expm1",FFT="FFT",Fill="Fill",FlipLeftRight="FlipLeftRight",Floor="Floor",FloorDiv="FloorDiv",FusedBatchNorm="FusedBatchNorm",GatherV2="GatherV2",GatherNd="GatherNd",Greater="Greater",GreaterEqual="GreaterEqual",Identity="Identity",IFFT="IFFT",Imag="Imag",IsFinite="IsFinite",IsInf="IsInf",IsNan="IsNan",LeakyRelu="LeakyRelu",Less="Less",LessEqual="LessEqual",LinSpace="LinSpace",Log="Log",Log1p="Log1p",LogicalAnd="LogicalAnd",LogicalNot="LogicalNot",LogicalOr="LogicalOr",LogSoftmax="LogSoftmax",LRN="LRN",LRNGrad="LRNGrad",Max="Max",Maximum="Maximum",MaxPool="MaxPool",MaxPoolGrad="MaxPoolGrad",MaxPool3D="MaxPool3D",MaxPool3DGrad="MaxPool3DGrad",MaxPoolWithArgmax="MaxPoolWithArgmax",Mean="Mean",Min="Min",Minimum="Minimum",MirrorPad="MirrorPad",Mod="Mod",Multinomial="Multinomial",Multiply="Multiply",Neg="Neg",NotEqual="NotEqual",NonMaxSuppressionV3="NonMaxSuppressionV3",NonMaxSuppressionV4="NonMaxSuppressionV4",NonMaxSuppressionV5="NonMaxSuppressionV5",OnesLike="OnesLike",OneHot="OneHot",Pack="Pack",PadV2="PadV2",Pool="Pool",Pow="Pow",Prelu="Prelu",Prod="Prod",Range="Range",Real="Real",Reciprocal="Reciprocal",Relu="Relu",Reshape="Reshape",ResizeNearestNeighbor="ResizeNearestNeighbor",ResizeNearestNeighborGrad="ResizeNearestNeighborGrad",ResizeBilinear="ResizeBilinear",ResizeBilinearGrad="ResizeBilinearGrad",Relu6="Relu6",Reverse="Reverse",Round="Round",Rsqrt="Rsqrt",ScatterNd="ScatterNd",Select="Select",Selu="Selu",Slice="Slice",Sin="Sin",Sinh="Sinh",Sign="Sign",Sigmoid="Sigmoid",Softplus="Softplus",Sqrt="Sqrt",Sum="Sum",SpaceToBatchND="SpaceToBatchND",SplitV="SplitV",Softmax="Softmax",SquaredDifference="SquaredDifference",Square="Square",Sub="Sub",SparseToDense="SparseToDense",StridedSlice="StridedSlice",Tan="Tan",Tanh="Tanh",Tile="Tile",TopK="TopK",Transpose="Transpose",Unique="Unique",Unpack="Unpack",UnsortedSegmentSum="UnsortedSegmentSum",ZerosLike="ZerosLike",Step="Step",FromPixels="FromPixels",RotateWithOffset="RotateWithOffset",_FusedMatMul="_FusedMatMul",FusedConv2D="FusedConv2D",FusedDepthwiseConv2D="FusedDepthwiseConv2D",kernelRegistry=getGlobal("kernelRegistry",()=>new Map),gradRegistry=getGlobal("gradRegistry",()=>new Map);function getKernel(kernelName,backendName){let key=makeKey(kernelName,backendName);return kernelRegistry.get(key)}function getGradient(kernelName){return gradRegistry.get(kernelName)}function getKernelsForBackend(backendName){let it=kernelRegistry.entries(),result=[];for(;;){let{done,value}=it.next();if(done)break;let[key,config2]=value,[backend22]=key.split("_");backend22===backendName&&result.push(config2)}return result}function registerKernel(config2){let{kernelName,backendName}=config2,key=makeKey(kernelName,backendName);kernelRegistry.has(key)&&console.warn(`The kernel '${kernelName}' for backend '${backendName}' is already registered`),kernelRegistry.set(key,config2)}function registerGradient(config2){let{kernelName}=config2;gradRegistry.has(kernelName)&&env().getBool("DEBUG")&&console.warn(`Overriding the gradient for '${kernelName}'`),gradRegistry.set(kernelName,config2)}function unregisterKernel(kernelName,backendName){let key=makeKey(kernelName,backendName);if(!kernelRegistry.has(key))throw new Error(`The kernel '${kernelName}' for backend '${backendName}' is not registered`);kernelRegistry.delete(key)}function unregisterGradient(kernelName){if(!gradRegistry.has(kernelName))throw new Error(`The gradient '${kernelName}' for backend is not registered`);gradRegistry.delete(kernelName)}function copyRegisteredKernels(registeredBackendName,newBackendName){getKernelsForBackend(registeredBackendName).forEach(kernelConfig=>{let newKernelConfig=Object.assign({},kernelConfig,{backendName:newBackendName});registerKernel(newKernelConfig)})}function makeKey(kernelName,backendName){return`${backendName}_${kernelName}`}var util_exports={};__export2(util_exports,{arraysEqual:()=>arraysEqual,assert:()=>assert,assertNonNegativeIntegerDimensions:()=>assertNonNegativeIntegerDimensions,assertNonNull:()=>assertNonNull,assertShapesMatch:()=>assertShapesMatch,bytesFromStringArray:()=>bytesFromStringArray,bytesPerElement:()=>bytesPerElement,checkConversionForErrors:()=>checkConversionForErrors,clamp:()=>clamp,computeStrides:()=>computeStrides,createScalarValue:()=>createScalarValue,createShuffledIndices:()=>createShuffledIndices,decodeString:()=>decodeString,distSquared:()=>distSquared,encodeString:()=>encodeString,fetch:()=>fetch2,flatten:()=>flatten,getArrayFromDType:()=>getArrayFromDType,getTypedArrayFromDType:()=>getTypedArrayFromDType,hasEncodingLoss:()=>hasEncodingLoss,indexToLoc:()=>indexToLoc,inferDtype:()=>inferDtype,inferFromImplicitShape:()=>inferFromImplicitShape,isBoolean:()=>isBoolean,isFunction:()=>isFunction,isInt:()=>isInt,isNumber:()=>isNumber,isPromise:()=>isPromise,isScalarShape:()=>isScalarShape,isString:()=>isString,isTypedArray:()=>isTypedArray,isValidDtype:()=>isValidDtype,locToIndex:()=>locToIndex,makeOnesTypedArray:()=>makeOnesTypedArray,makeZerosNestedTypedArray:()=>makeZerosNestedTypedArray,makeZerosTypedArray:()=>makeZerosTypedArray,nearestDivisor:()=>nearestDivisor,nearestLargerEven:()=>nearestLargerEven,now:()=>now,parseAxisParam:()=>parseAxisParam,randUniform:()=>randUniform,repeatedTry:()=>repeatedTry,rightPad:()=>rightPad,shuffle:()=>shuffle,sizeFromShape:()=>sizeFromShape,sizeToSquarishShape:()=>sizeToSquarishShape,squeezeShape:()=>squeezeShape,sum:()=>sum,tanh:()=>tanh,toNestedArray:()=>toNestedArray,toTypedArray:()=>toTypedArray});function createScalarValue(value,dtype){return dtype==="string"?encodeString(value):toTypedArray([value],dtype)}function noConversionNeeded(a,dtype){return a instanceof Float32Array&&dtype==="float32"||a instanceof Int32Array&&dtype==="int32"||a instanceof Uint8Array&&dtype==="bool"}function toTypedArray(a,dtype){if(dtype==="string")throw new Error("Cannot convert a string[] to a TypedArray");if(Array.isArray(a)&&(a=flatten(a)),env().getBool("DEBUG")&&checkConversionForErrors(a,dtype),noConversionNeeded(a,dtype))return a;if(dtype==null||dtype==="float32"||dtype==="complex64")return new Float32Array(a);if(dtype==="int32")return new Int32Array(a);if(dtype==="bool"){let bool=new Uint8Array(a.length);for(let i=0;i{outputs=f()},timer=this.backendTimer.time(holdResultWrapperFn);if(env().getBool("CHECK_COMPUTATION_FOR_ERRORS"))for(let i=0;i{checkComputationForErrors(tensorVals,output.dtype,kernelName)})}return{kernelName,outputs,inputs,timeMs:timer.then(timing=>timing.kernelMs),extraInfo:timer.then(timing=>timing.getExtraProfileInfo!=null?timing.getExtraProfileInfo():"")}}logKernelProfile(kernelProfile){let{kernelName,outputs,timeMs,inputs,extraInfo}=kernelProfile;outputs.forEach(result=>{Promise.all([result.data(),timeMs,extraInfo]).then(valueContainer=>{this.logger.logKernelProfile(kernelName,result,valueContainer[0],valueContainer[1],inputs,valueContainer[2])})})}};function checkComputationForErrors(vals,dtype,kernelName){if(dtype!=="float32")return!1;for(let i=0;i0?inputShape:""} `}}console.log(`%c${paddedName} %c${time2} %c${rank}D ${shape} %c${size} %c${inputShapesDescription} %c${extraInfo}`,"font-weight:bold","color:red","color:blue","color: orange","color: green","color: steelblue")}};function getFilteredNodesXToY(tape,xs,y){let tensorsFromX={},nodesFromX={};for(let i=0;itensorsFromX[output.id]=!0),anyInputFromX=!0,nodesFromX[node.id]=!0;break}if(anyInputFromX)break}}let tensorsLeadToY={};tensorsLeadToY[y.id]=!0;let nodesToY={};for(let i=tape.length-1;i>=0;i--){let node=tape[i],nodeInputs=node.inputs;for(let j=0;j=0;i--){let node=filteredTape[i],dys=[];if(node.outputs.forEach(o=>{let gradTensor=tensorAccumulatedGradientMap[o.id];gradTensor!=null?dys.push(gradTensor):dys.push(null)}),node.gradient==null)throw new Error(`Cannot compute gradient: gradient function not found for ${node.kernelName}.`);let inputGradients=node.gradient(dys);for(let inputName in node.inputs){if(!(inputName in inputGradients))throw new Error(`Cannot backprop through input ${inputName}. Available gradients found: ${Object.keys(inputGradients)}.`);let dx=tidy2(()=>inputGradients[inputName]());if(dx.dtype!=="float32")throw new Error(`Error in gradient for op ${node.kernelName}. The gradient of input ${inputName} must have 'float32' dtype, but has '${dx.dtype}'`);let x=node.inputs[inputName];if(!arraysEqual(dx.shape,x.shape))throw new Error(`Error in gradient for op ${node.kernelName}. The gradient of input '${inputName}' has shape '${dx.shape}', which does not match the shape of the input '${x.shape}'`);if(tensorAccumulatedGradientMap[x.id]==null)tensorAccumulatedGradientMap[x.id]=dx;else{let curGradient=tensorAccumulatedGradientMap[x.id];tensorAccumulatedGradientMap[x.id]=add5(curGradient,dx),curGradient.dispose()}}}}var FORMAT_LIMIT_NUM_VALS=20,FORMAT_NUM_FIRST_LAST_VALS=3,FORMAT_NUM_SIG_DIGITS=7;function tensorToString(vals,shape,dtype,verbose){let strides=computeStrides(shape),padPerCol=computeMaxSizePerColumn(vals,shape,dtype,strides),rank=shape.length,valsLines=subTensorToString(vals,shape,dtype,strides,padPerCol),lines=["Tensor"];return verbose&&(lines.push(` dtype: ${dtype}`),lines.push(` rank: ${rank}`),lines.push(` shape: [${shape}]`),lines.push(" values:")),lines.push(valsLines.map(l=>" "+l).join(` `)),lines.join(` `)}function computeMaxSizePerColumn(vals,shape,dtype,strides){let n=sizeFromShape(shape),numCols=strides[strides.length-1],padPerCol=new Array(numCols).fill(0),rank=shape.length,valuesOrTuples=dtype==="complex64"?createComplexTuples(vals):vals;if(rank>1)for(let row=0;rowFORMAT_LIMIT_NUM_VALS){let firstValsSize=FORMAT_NUM_FIRST_LAST_VALS*storagePerElement,firstVals=Array.from(vals.slice(0,firstValsSize)),lastVals=Array.from(vals.slice((size-FORMAT_NUM_FIRST_LAST_VALS)*storagePerElement,size*storagePerElement));return dtype==="complex64"&&(firstVals=createComplexTuples(firstVals),lastVals=createComplexTuples(lastVals)),["["+firstVals.map((x,i)=>valToString(x,padPerCol[i],dtype)).join(", ")+", ..., "+lastVals.map((x,i)=>valToString(x,padPerCol[size-FORMAT_NUM_FIRST_LAST_VALS+i],dtype)).join(", ")+"]"]}let displayVals=dtype==="complex64"?createComplexTuples(vals):Array.from(vals);return["["+displayVals.map((x,i)=>valToString(x,padPerCol[i],dtype)).join(", ")+"]"]}let subshape=shape.slice(1),substrides=strides.slice(1),stride=strides[0]*storagePerElement,lines=[];if(size>FORMAT_LIMIT_NUM_VALS){for(let i=0;i`Length of values '${n}' does not match the size inferred by the shape '${this.size}'.`)}if(dtype==="complex64")throw new Error("complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).");this.values=values||getArrayFromDType(dtype,this.size),this.strides=computeStrides(shape)}set(value,...locs){locs.length===0&&(locs=[0]),assert(locs.length===this.rank,()=>`The number of provided coordinates (${locs.length}) must match the rank (${this.rank})`);let index=this.locToIndex(locs);this.values[index]=value}get(...locs){locs.length===0&&(locs=[0]);let i=0;for(let loc of locs){if(loc<0||loc>=this.shape[i]){let msg=`Requested out of range element at ${locs}. Buffer shape=${this.shape}`;throw new Error(msg)}i++}let index=locs[locs.length-1];for(let i2=0;i2decodeString(b))}catch(_a){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}}return data2}dataSync(){this.throwIfDisposed();let data2=trackerFn().readSync(this.dataId);if(this.dtype==="string")try{return data2.map(b=>decodeString(b))}catch(_a){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}return data2}async bytes(){this.throwIfDisposed();let data2=await trackerFn().read(this.dataId);return this.dtype==="string"?data2:new Uint8Array(data2.buffer)}dispose(){this.isDisposed||(trackerFn().disposeTensor(this),this.isDisposedInternal=!0)}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(verbose=!1){return opHandler.print(this,verbose)}clone(){return this.throwIfDisposed(),opHandler.clone(this)}toString(verbose=!1){let vals=this.dataSync();return tensorToString(vals,this.shape,this.dtype,verbose)}cast(dtype){return this.throwIfDisposed(),opHandler.cast(this,dtype)}variable(trainable=!0,name,dtype){return this.throwIfDisposed(),trackerFn().makeVariable(this,trainable,name,dtype)}};Object.defineProperty(Tensor,Symbol.hasInstance,{value:instance=>!!instance&&instance.data!=null&&instance.dataSync!=null&&instance.throwIfDisposed!=null});var Variable=class extends Tensor{constructor(initialValue,trainable,name,tensorId){super(initialValue.shape,initialValue.dtype,initialValue.dataId,tensorId);this.trainable=trainable,this.name=name}assign(newValue){if(newValue.dtype!==this.dtype)throw new Error(`dtype of the new value (${newValue.dtype}) and previous value (${this.dtype}) must match`);if(!arraysEqual(newValue.shape,this.shape))throw new Error(`shape of the new value (${newValue.shape}) and previous value (${this.shape}) must match`);trackerFn().disposeTensor(this),this.dataId=newValue.dataId,trackerFn().incRef(this,null)}dispose(){trackerFn().disposeVariable(this),this.isDisposedInternal=!0}};Object.defineProperty(Variable,Symbol.hasInstance,{value:instance=>instance instanceof Tensor&&instance.assign!=null&&instance.assign instanceof Function});var tensor_util_exports={};__export2(tensor_util_exports,{assertTypesMatch:()=>assertTypesMatch,getTensorsInContainer:()=>getTensorsInContainer,isTensorInList:()=>isTensorInList,makeTypesMatch:()=>makeTypesMatch});var Rank;(function(Rank2){Rank2.R0="R0",Rank2.R1="R1",Rank2.R2="R2",Rank2.R3="R3",Rank2.R4="R4",Rank2.R5="R5",Rank2.R6="R6"})(Rank||(Rank={}));var UpcastInt32AndMap;(function(UpcastInt32AndMap2){UpcastInt32AndMap2.float32="float32",UpcastInt32AndMap2.int32="int32",UpcastInt32AndMap2.bool="int32",UpcastInt32AndMap2.complex64="complex64"})(UpcastInt32AndMap||(UpcastInt32AndMap={}));var UpcastBoolAndMap;(function(UpcastBoolAndMap2){UpcastBoolAndMap2.float32="float32",UpcastBoolAndMap2.int32="int32",UpcastBoolAndMap2.bool="bool",UpcastBoolAndMap2.complex64="complex64"})(UpcastBoolAndMap||(UpcastBoolAndMap={}));var UpcastFloat32AndMap;(function(UpcastFloat32AndMap2){UpcastFloat32AndMap2.float32="float32",UpcastFloat32AndMap2.int32="float32",UpcastFloat32AndMap2.bool="float32",UpcastFloat32AndMap2.complex64="complex64"})(UpcastFloat32AndMap||(UpcastFloat32AndMap={}));var UpcastComplex64AndMap;(function(UpcastComplex64AndMap2){UpcastComplex64AndMap2.float32="complex64",UpcastComplex64AndMap2.int32="complex64",UpcastComplex64AndMap2.bool="complex64",UpcastComplex64AndMap2.complex64="complex64"})(UpcastComplex64AndMap||(UpcastComplex64AndMap={}));var upcastTypeMap={float32:UpcastFloat32AndMap,int32:UpcastInt32AndMap,bool:UpcastBoolAndMap,complex64:UpcastComplex64AndMap};function upcastType(typeA,typeB){if(typeA==="string"||typeB==="string"){if(typeA==="string"&&typeB==="string")return"string";throw new Error(`Can not upcast ${typeA} with ${typeB}`)}return upcastTypeMap[typeA][typeB]}function sumOutType(type){return upcastType(type,"int32")}function makeTypesMatch(a,b){if(a.dtype===b.dtype)return[a,b];let dtype=upcastType(a.dtype,b.dtype);return[a.cast(dtype),b.cast(dtype)]}function assertTypesMatch(a,b){assert(a.dtype===b.dtype,()=>`The dtypes of the first(${a.dtype}) and second(${b.dtype}) input must match`)}function isTensorInList(tensor2,tensorList){return tensorList.some(x=>x.id===tensor2.id)}function getTensorsInContainer(result){let list=[],seen=new Set;return walkTensorContainer(result,list,seen),list}function walkTensorContainer(container,list,seen){if(container==null)return;if(container instanceof Tensor){list.push(container);return}if(!isIterable(container))return;let iterable=container;for(let k in iterable){let val=iterable[k];seen.has(val)||(seen.add(val),walkTensorContainer(val,list,seen))}}function isIterable(obj){return Array.isArray(obj)||typeof obj=="object"}var EngineState=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(k=>k.name)))}}}dispose(){for(let variableName in this.registeredVariables)this.registeredVariables[variableName].dispose()}},Engine=class{constructor(ENV5){this.ENV=ENV5,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new EngineState}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let sortedBackends=this.getSortedBackends();for(let i=0;i{kernel.setupFunc!=null&&kernel.setupFunc(this.backendInstance)})}disposeRegisteredKernels(backendName){getKernelsForBackend(backendName).forEach(kernel=>{kernel.disposeFunc!=null&&kernel.disposeFunc(this.registry[backendName])})}initializeBackend(backendName){let registryFactoryEntry=this.registryFactory[backendName];if(registryFactoryEntry==null)throw new Error(`Cannot initialize backend ${backendName}, no registration found.`);try{let backend22=registryFactoryEntry.factory();if(backend22&&!(backend22 instanceof KernelBackend)&&typeof backend22.then=="function"){let promiseId=++this.pendingBackendInitId,success=backend22.then(backendInstance=>promiseId(promiseIdthis.registryFactory[b].priority-this.registryFactory[a].priority)}initializeBackendsAndReturnBest(){let sortedBackends=this.getSortedBackends();for(let i=0;ithis.startScope(name),()=>this.endScope(result),()=>(result=fn(),result instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),result))}scopedRun(start,end,f){start();try{let res=f();return end(),res}catch(ex){throw end(),ex}}nextTensorId(){return Engine.nextTensorId++}nextVariableId(){return Engine.nextVariableId++}clone(x){let y=this.makeTensorFromDataId(x.dataId,x.shape,x.dtype),inputs={x},grad2=dy=>({x:()=>{let dtype="float32",gradInputs={x:dy},attrs={dtype};return ENGINE.runKernelFunc(backend22=>backend22.cast(dy,dtype),gradInputs,null,Cast,attrs)}}),saved=[];return this.addTapeNode(this.state.activeScope.name,inputs,[y],grad2,saved,{}),y}runKernel(kernelName,inputs,attrs,inputsToSave,outputsToSave){let forwardFunc=null,backwardsFunc=null;return this.runKernelFunc(forwardFunc,inputs,backwardsFunc,kernelName,attrs,inputsToSave,outputsToSave)}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(kernelName,numDataIdsBefore,outInfos){let numDataIdsAfter=this.backend.numDataIds(),numOutputDataIds=0;outInfos.forEach(info=>{numOutputDataIds+=info.dtype==="complex64"?3:1});let numMoves=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],dataIdsLeaked=numDataIdsAfter-numDataIdsBefore-numOutputDataIds-numMoves;if(dataIdsLeaked>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${dataIdsLeaked} data ids) after running '${kernelName}'`)}runKernelFunc(forwardFunc,inputs,backwardsFunc,kernelName,attrs,inputsToSave,outputsToSave){let outputs,saved=[],isTapeOn=this.isTapeOn();kernelName==null&&(kernelName=this.state.activeScope!=null?this.state.activeScope.name:"");let startingBytecount=this.state.numBytes,startingNumTensors=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let kernelFunc3;this.backendName==null&&this.backend;let kernel=getKernel(kernelName,this.backendName),out;if(kernel!=null)kernelFunc3=()=>{let numDataIdsBefore=this.backend.numDataIds();out=kernel.kernelFunc({inputs,attrs,backend:this.backend});let outInfos=Array.isArray(out)?out:[out];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(kernelName,numDataIdsBefore,outInfos);let outTensors=outInfos.map(outInfo=>{if(outInfo.rank!=null)return outInfo;let{dataId,shape,dtype}=outInfo;return this.makeTensorFromDataId(dataId,shape,dtype)});if(isTapeOn){let tensorsToSave=this.getTensorsForGradient(kernelName,inputs,outTensors);if(tensorsToSave==null){outputsToSave==null&&(outputsToSave=[]);let outsToSave=outTensors.filter((_,i)=>outputsToSave[i]);tensorsToSave=(inputsToSave||[]).slice().concat(outsToSave)}saved=this.saveTensorsForBackwardMode(tensorsToSave)}return outTensors};else{if(forwardFunc==null)throw new Error(`Error running ${kernelName}: Neither modular kernel nor forward func passed`);let saveFunc=tensors=>{!isTapeOn||(saved=tensors.map(tensor2=>this.keep(this.clone(tensor2))))};kernelFunc3=()=>{let numDataIdsBefore=this.backend.numDataIds();out=this.tidy(()=>forwardFunc(this.backend,saveFunc));let outs=Array.isArray(out)?out:[out];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(kernelName,numDataIdsBefore,outs),outs}}let kernelProfile;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?outputs=kernelFunc3():(kernelProfile=this.profiler.profileKernel(kernelName,inputs,()=>kernelFunc3()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(kernelProfile),outputs=kernelProfile.outputs)}),isTapeOn&&this.addTapeNode(kernelName,inputs,outputs,backwardsFunc,saved,attrs),this.state.profiling&&this.state.activeProfile.kernels.push({name:kernelName,bytesAdded:this.state.numBytes-startingBytecount,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-startingNumTensors,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(inputs).map(key=>inputs[key]!=null?inputs[key].shape:null),outputShapes:outputs.map(item=>item.shape),kernelTimeMs:kernelProfile.timeMs,extraInfo:kernelProfile.extraInfo}),Array.isArray(out)?outputs:outputs[0]}saveTensorsForBackwardMode(tensors){return tensors.map(tensor2=>this.keep(this.clone(tensor2)))}getTensorsForGradient(kernelName,inputs,outputs){let gradConfig=getGradient(kernelName);if(gradConfig!=null){let inputsToSave=gradConfig.inputsToSave||[],outputsToSave=gradConfig.outputsToSave||[],inputTensorsToSave;gradConfig.saveAllInputs?(assert(Array.isArray(inputs),()=>"saveAllInputs is true, expected inputs to be an array."),inputTensorsToSave=Object.keys(inputs).map(key=>inputs[key])):inputTensorsToSave=inputsToSave.map(inputName=>inputs[inputName]);let outputTensorsToSave=outputs.filter((_,i)=>outputsToSave[i]);return inputTensorsToSave.concat(outputTensorsToSave)}return null}makeTensor(values,shape,dtype,backend22){if(values==null)throw new Error("Values passed to engine.makeTensor() are null");dtype=dtype||"float32",backend22=backend22||this.backend;let backendVals=values;dtype==="string"&&isString(values[0])&&(backendVals=values.map(d=>encodeString(d)));let dataId=backend22.write(backendVals,shape,dtype),t=new Tensor(shape,dtype,dataId,this.nextTensorId());if(this.incRef(t,backend22),dtype==="string"){let info=this.state.tensorInfo.get(dataId),newBytes=bytesFromStringArray(backendVals);this.state.numBytes+=newBytes-info.bytes,info.bytes=newBytes}return t}makeTensorFromDataId(dataId,shape,dtype,backend22){dtype=dtype||"float32";let t=new Tensor(shape,dtype,dataId,this.nextTensorId());return this.incRef(t,backend22),t}makeVariable(initialValue,trainable=!0,name,dtype){name=name||this.nextVariableId().toString(),dtype!=null&&dtype!==initialValue.dtype&&(initialValue=initialValue.cast(dtype));let v=new Variable(initialValue,trainable,name,this.nextTensorId());if(this.state.registeredVariables[v.name]!=null)throw new Error(`Variable with name ${v.name} was already registered`);return this.state.registeredVariables[v.name]=v,this.incRef(v,this.backend),v}incRef(a,backend22){let refCount=this.state.tensorInfo.has(a.dataId)?this.state.tensorInfo.get(a.dataId).refCount:0;if(this.state.numTensors++,a.dtype==="string"&&this.state.numStringTensors++,refCount===0){this.state.numDataBuffers++;let bytes=0;a.dtype!=="complex64"&&a.dtype!=="string"&&(bytes=a.size*bytesPerElement(a.dtype)),this.state.tensorInfo.set(a.dataId,{backend:backend22||this.backend,dtype:a.dtype,shape:a.shape,bytes,refCount:0}),this.state.numBytes+=bytes}this.state.tensorInfo.get(a.dataId).refCount++,a instanceof Variable||this.track(a)}disposeTensor(a){if(!this.state.tensorInfo.has(a.dataId))return;this.state.numTensors--,a.dtype==="string"&&this.state.numStringTensors--;let info=this.state.tensorInfo.get(a.dataId);info.refCount<=1?(a.dtype!=="complex64"&&(this.state.numBytes-=info.bytes),this.state.numDataBuffers--,info.backend.disposeData(a.dataId),this.state.tensorInfo.delete(a.dataId)):this.state.tensorInfo.get(a.dataId).refCount--}disposeVariables(){for(let varName in this.state.registeredVariables){let v=this.state.registeredVariables[varName];this.disposeVariable(v)}}disposeVariable(v){this.disposeTensor(v),this.state.registeredVariables[v.name]!=null&&delete this.state.registeredVariables[v.name]}memory(){let info=this.backend.memory();return info.numTensors=this.state.numTensors,info.numDataBuffers=this.state.numDataBuffers,info.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(info.unreliable=!0,info.reasons==null&&(info.reasons=[]),info.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),info}async profile(query){this.state.profiling=!0;let startBytes=this.state.numBytes,startNumTensors=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await query(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(d=>d.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-startBytes,this.state.activeProfile.newTensors=this.state.numTensors-startNumTensors;for(let kernel of this.state.activeProfile.kernels)kernel.kernelTimeMs=await kernel.kernelTimeMs,kernel.extraInfo=await kernel.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(kernelName,inputs,outputs,gradientsFunc,saved,attrs){let tapeNode={id:this.state.nextTapeNodeId++,kernelName,inputs,outputs,saved},gradConfig=getGradient(kernelName);gradConfig!=null&&(gradientsFunc=gradConfig.gradFunc),gradientsFunc!=null&&(tapeNode.gradient=dys=>(dys=dys.map((dy,i)=>{if(dy==null){let output=outputs[i],vals=makeZerosTypedArray(output.size,output.dtype);return this.makeTensor(vals,output.shape,output.dtype)}return dy}),gradientsFunc(dys.length>1?dys:dys[0],saved,attrs))),this.state.activeTape.push(tapeNode)}keep(result){return result.kept=!0,result}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(name){let scopeInfo={track:[],name:"unnamed scope",id:this.state.nextScopeId++};name&&(scopeInfo.name=name),this.state.scopeStack.push(scopeInfo),this.state.activeScope=scopeInfo}endScope(result){let tensorsToTrackInParent=getTensorsInContainer(result),tensorsToTrackInParentSet=new Set(tensorsToTrackInParent.map(t=>t.id));for(let i=0;i{!tensor2.kept&&tensor2.scopeId===oldScope.id&&this.track(tensor2)})}gradients(f,xs,dy,allowNoGradients=!1){if(assert(xs.length>0,()=>"gradients() received an empty list of xs."),dy!=null&&dy.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${dy.dtype}'`);let y=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",f));assert(y instanceof Tensor,()=>"The result y returned by f() must be a tensor.");let filteredTape=getFilteredNodesXToY(this.state.activeTape,xs,y);if(!allowNoGradients&&filteredTape.length===0&&xs.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.");return this.tidy("backward",()=>{let accumulatedGradientMap={};accumulatedGradientMap[y.id]=dy==null?ones(y.shape):dy,backpropagateGradients(accumulatedGradientMap,filteredTape,f2=>this.tidy(f2),add);let grads2=xs.map(x=>accumulatedGradientMap[x.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(node=>{for(let tensor2 of node.saved)tensor2.dispose()}),this.state.activeTape=null),{value:y,grads:grads2}})}customGrad(f){return assert(isFunction(f),()=>"The f passed in customGrad(f) must be a function."),(...inputs)=>{assert(inputs.every(t=>t instanceof Tensor),()=>"The args passed in customGrad(f)(x1, x2,...) must all be tensors");let res,inputMap={};return inputs.forEach((input2,i)=>{inputMap[i]=input2}),this.runKernelFunc((_,save)=>(res=f(...inputs,save),assert(res.value instanceof Tensor,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),assert(isFunction(res.gradFunc),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."),res.value),inputMap,(dy,saved)=>{let gradRes=res.gradFunc(dy,saved),grads2=Array.isArray(gradRes)?gradRes:[gradRes];assert(grads2.length===inputs.length,()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...)."),assert(grads2.every(t=>t instanceof Tensor),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.");let gradMap={};return grads2.forEach((grad2,i)=>{gradMap[i]=()=>grad2}),gradMap})}}readSync(dataId){return this.state.tensorInfo.get(dataId).backend.readSync(dataId)}read(dataId){return this.state.tensorInfo.get(dataId).backend.read(dataId)}async time(query){let start=now(),timingInfo=await this.backend.time(query);return timingInfo.wallMs=now()-start,timingInfo}track(result){return this.state.activeScope!=null&&(result.scopeId=this.state.activeScope.id,this.state.activeScope.track.push(result)),result}get registeredVariables(){return this.state.registeredVariables}reset(){this.pendingBackendInitId++,this.state.dispose(),this.ENV.reset(),this.state=new EngineState;for(let backendName in this.registry)this.disposeRegisteredKernels(backendName),this.registry[backendName].dispose(),delete this.registry[backendName];this.backendName=null,this.backendInstance=null,this.pendingBackendInit=null}};Engine.nextTensorId=0;Engine.nextVariableId=0;function ones(shape){let values=makeOnesTypedArray(sizeFromShape(shape),"float32");return ENGINE.makeTensor(values,shape,"float32")}function getOrMakeEngine(){let ns=getGlobalNamespace();if(ns._tfengine==null){let environment=new Environment(ns);ns._tfengine=new Engine(environment)}return setEnvironmentGlobal(ns._tfengine.ENV),setTensorTracker(()=>ns._tfengine),ns._tfengine}var ENGINE=getOrMakeEngine();function add(a,b){let inputs={a,b};return ENGINE.runKernel(Add,inputs)}var device_util_exports={};__export2(device_util_exports,{isBrowser:()=>isBrowser,isMobile:()=>isMobile});function _isNavigatorDefined(){return typeof navigator!="undefined"&&navigator!=null}function isMobile(){if(_isNavigatorDefined()){let a=navigator.userAgent||navigator.vendor||window.opera;return/(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i.test(a)||/1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\-m|r |s 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is ON. The output of every math call will be downloaded to CPU and checked for NaNs. This significantly impacts performance.")});ENV2.registerFlag("IS_BROWSER",()=>isBrowser());ENV2.registerFlag("IS_NODE",()=>typeof process!="undefined"&&typeof process.versions!="undefined"&&typeof process.versions.node!="undefined");ENV2.registerFlag("IS_CHROME",()=>typeof navigator!="undefined"&&navigator!=null&&navigator.userAgent!=null&&/Chrome/.test(navigator.userAgent)&&/Google Inc/.test(navigator.vendor));ENV2.registerFlag("PROD",()=>!1);ENV2.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY",()=>ENV2.getBool("DEBUG"));ENV2.registerFlag("DEPRECATION_WARNINGS_ENABLED",()=>!0);ENV2.registerFlag("IS_TEST",()=>!1);ENV2.registerFlag("CHECK_COMPUTATION_FOR_ERRORS",()=>!0);function inferShape(val,dtype){let firstElem=val;if(isTypedArray(val))return dtype==="string"?[]:[val.length];if(!Array.isArray(val))return[];let shape=[];for(;Array.isArray(firstElem)||isTypedArray(firstElem)&&dtype!=="string";)shape.push(firstElem.length),firstElem=firstElem[0];return Array.isArray(val)&&env().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY")&&deepAssertShapeConsistency(val,shape,[]),shape}function deepAssertShapeConsistency(val,shape,indices){if(indices=indices||[],!Array.isArray(val)&&!isTypedArray(val)){assert(shape.length===0,()=>`Element arr[${indices.join("][")}] is a primitive, but should be an array/TypedArray of ${shape[0]} elements`);return}assert(shape.length>0,()=>`Element arr[${indices.join("][")}] should be a primitive, but is an array of ${val.length} elements`),assert(val.length===shape[0],()=>`Element arr[${indices.join("][")}] should have ${shape[0]} elements, but has ${val.length} elements`);let subShape=shape.slice(1);for(let i=0;i=0&&(inferredDtype=parseAsDtype),assertDtype(parseAsDtype,inferredDtype,argName,functionName),x==null||!isTypedArray(x)&&!Array.isArray(x)&&typeof x!="number"&&typeof x!="boolean"&&typeof x!="string"){let type=x==null?"null":x.constructor.name;throw new Error(`Argument '${argName}' passed to '${functionName}' must be a Tensor or TensorLike, but got '${type}'`)}let inferredShape=inferShape(x,inferredDtype);!isTypedArray(x)&&!Array.isArray(x)&&(x=[x]);let values=inferredDtype!=="string"?toTypedArray(x,inferredDtype):flatten(x,[],!0);return ENGINE.makeTensor(values,inferredShape,inferredDtype)}function convertToTensorArray(arg,argName,functionName,parseAsDtype="numeric"){if(!Array.isArray(arg))throw new Error(`Argument ${argName} passed to ${functionName} must be a \`Tensor[]\` or \`TensorLike[]\``);return arg.map((t,i)=>convertToTensor(t,`${argName}[${i}]`,functionName,parseAsDtype))}var OP_SCOPE_SUFFIX="__op";function op(f){let keys=Object.keys(f);if(keys.length!==1)throw new Error(`Please provide an object with a single key (operation name) mapping to a function. Got an object with ${keys.length} keys.`);let opName=keys[0],fn=f[opName];opName.endsWith("_")&&(opName=opName.substring(0,opName.length-1)),opName=opName+OP_SCOPE_SUFFIX;let f2=(...args)=>{ENGINE.startScope(opName);try{let result=fn(...args);return isPromise(result)&&console.error("Cannot return a Promise inside of tidy."),ENGINE.endScope(result),result}catch(ex){throw ENGINE.endScope(null),ex}};return Object.defineProperty(f2,"name",{value:opName,configurable:!0}),f2}function complex_(real4,imag4){let $real=convertToTensor(real4,"real","complex"),$imag=convertToTensor(imag4,"imag","complex");assertShapesMatch($real.shape,$imag.shape,`real and imag shapes, ${$real.shape} and ${$imag.shape}, must match in call to tf.complex().`);let inputs={real:$real,imag:$imag};return ENGINE.runKernel(Complex,inputs)}var complex=op({complex_});function makeTensor(values,shape,inferredShape,dtype){if(dtype==null&&(dtype=inferDtype(values)),dtype==="complex64")throw new Error("Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).");if(!isTypedArray(values)&&!Array.isArray(values)&&typeof values!="number"&&typeof values!="boolean"&&typeof values!="string")throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray");if(shape!=null){assertNonNegativeIntegerDimensions(shape);let providedSize=sizeFromShape(shape),inferredSize=sizeFromShape(inferredShape);assert(providedSize===inferredSize,()=>`Based on the provided shape, [${shape}], the tensor should have ${providedSize} values but has ${inferredSize}`);for(let i=0;i`Error creating a new Tensor. Inferred shape (${inferredShape}) does not match the provided shape (${shape}). `)}}return!isTypedArray(values)&&!Array.isArray(values)&&(values=[values]),shape=shape||inferredShape,values=dtype!=="string"?toTypedArray(values,dtype):flatten(values,[],!0),ENGINE.makeTensor(values,shape,dtype)}function tensor(values,shape,dtype){let inferredShape=inferShape(values,dtype);return makeTensor(values,shape,inferredShape,dtype)}var DTYPE_VALUE_SIZE_MAP={float32:4,float16:2,int32:4,uint16:2,uint8:1,bool:1,complex64:8},NUM_BYTES_STRING_LENGTH=4;async function encodeWeights(tensors,group){let specs=[],dataPromises=[],names=Array.isArray(tensors)?tensors.map(tensor2=>tensor2.name):Object.keys(tensors);for(let i=0;i{let vals=await t.bytes(),totalNumBytes=vals.reduce((p2,c)=>p2+c.length,0)+NUM_BYTES_STRING_LENGTH*vals.length,bytes=new Uint8Array(totalNumBytes),offset=0;for(let i2=0;i2{if(totalByteLength+=x.byteLength,normalizedXs.push(x.byteLength===x.buffer.byteLength?x:new x.constructor(x)),!(x instanceof Float32Array||x instanceof Int32Array||x instanceof Uint8Array))throw new Error(`Unsupported TypedArray subtype: ${x.constructor.name}`)});let y=new Uint8Array(totalByteLength),offset=0;return normalizedXs.forEach(x=>{y.set(new Uint8Array(x.buffer),offset),offset+=x.byteLength}),y.buffer}var useNodeBuffer=typeof Buffer!="undefined"&&(typeof Blob=="undefined"||typeof atob=="undefined"||typeof btoa=="undefined");function stringByteLength(str){return useNodeBuffer?Buffer.byteLength(str):new Blob([str]).size}function arrayBufferToBase64String(buffer2){if(useNodeBuffer)return Buffer.from(buffer2).toString("base64");let buf=new Uint8Array(buffer2),s="";for(let i=0,l=buf.length;i{totalByteLength+=buffer2.byteLength});let temp=new Uint8Array(totalByteLength),offset=0;return buffers.forEach(buffer2=>{temp.set(new Uint8Array(buffer2),offset),offset+=buffer2.byteLength}),temp.buffer}function basename(path){let SEPARATOR="/";for(path=path.trim();path.endsWith(SEPARATOR);)path=path.slice(0,path.length-1);let items=path.split(SEPARATOR);return items[items.length-1]}function getModelArtifactsInfoForJSON(modelArtifacts){if(modelArtifacts.modelTopology instanceof ArrayBuffer)throw new Error("Expected JSON model topology, received ArrayBuffer.");return{dateSaved:new Date,modelTopologyType:"JSON",modelTopologyBytes:modelArtifacts.modelTopology==null?0:stringByteLength(JSON.stringify(modelArtifacts.modelTopology)),weightSpecsBytes:modelArtifacts.weightSpecs==null?0:stringByteLength(JSON.stringify(modelArtifacts.weightSpecs)),weightDataBytes:modelArtifacts.weightData==null?0:modelArtifacts.weightData.byteLength}}function computeFloat16MantisaTable(){let convertMantissa=i=>{let m=i<<13,e=0;for(;(m&8388608)==0;)e-=8388608,m<<=1;return m&=~8388608,e+=947912704,m|e},mantisaTable=new Uint32Array(2048);mantisaTable[0]=0;for(let i=1;i<1024;i++)mantisaTable[i]=convertMantissa(i);for(let i=1024;i<2048;i++)mantisaTable[i]=939524096+(i-1024<<13);return mantisaTable}function computeFloat16ExponentTable(){let exponentTable=new Uint32Array(64);exponentTable[0]=0,exponentTable[31]=1199570944,exponentTable[32]=2147483648,exponentTable[63]=3347054592;for(let i=1;i<31;i++)exponentTable[i]=i<<23;for(let i=33;i<63;i++)exponentTable[i]=2147483648+(i-32<<23);return exponentTable}function computeFloat16OffsetTable(){let offsetTable=new Uint32Array(64);for(let i=0;i<64;i++)offsetTable[i]=1024;return offsetTable[0]=offsetTable[32]=0,offsetTable}function getFloat16Decoder(){let mantisaTable=computeFloat16MantisaTable(),exponentTable=computeFloat16ExponentTable(),offsetTable=computeFloat16OffsetTable();return quantizedArray=>{let buffer2=new ArrayBuffer(4*quantizedArray.length),bufferUint32View=new Uint32Array(buffer2);for(let index=0;index>10]+(float16Bits&1023)]+exponentTable[float16Bits>>10];bufferUint32View[index]=float32Bits}return new Float32Array(buffer2)}}var IORouterRegistry=class{constructor(){this.saveRouters=[],this.loadRouters=[]}static getInstance(){return IORouterRegistry.instance==null&&(IORouterRegistry.instance=new IORouterRegistry),IORouterRegistry.instance}static registerSaveRouter(saveRouter){IORouterRegistry.getInstance().saveRouters.push(saveRouter)}static registerLoadRouter(loadRouter){IORouterRegistry.getInstance().loadRouters.push(loadRouter)}static getSaveHandlers(url){return IORouterRegistry.getHandlers(url,"save")}static getLoadHandlers(url,loadOptions){return IORouterRegistry.getHandlers(url,"load",loadOptions)}static getHandlers(url,handlerType,loadOptions){let validHandlers=[];return(handlerType==="load"?IORouterRegistry.getInstance().loadRouters:IORouterRegistry.getInstance().saveRouters).forEach(router=>{let handler=router(url,loadOptions);handler!==null&&validHandlers.push(handler)}),validHandlers}},registerSaveRouter=loudRouter=>IORouterRegistry.registerSaveRouter(loudRouter),registerLoadRouter=loudRouter=>IORouterRegistry.registerLoadRouter(loudRouter),getSaveHandlers=url=>IORouterRegistry.getSaveHandlers(url),getLoadHandlers=(url,loadOptions)=>IORouterRegistry.getLoadHandlers(url,loadOptions),DATABASE_NAME="tensorflowjs",DATABASE_VERSION=1,MODEL_STORE_NAME="models_store",INFO_STORE_NAME="model_info_store";function getIndexedDBFactory(){if(!env().getBool("IS_BROWSER"))throw new Error("Failed to obtain IndexedDB factory because the current environmentis not a web browser.");let theWindow=typeof window=="undefined"?self:window,factory=theWindow.indexedDB||theWindow.mozIndexedDB||theWindow.webkitIndexedDB||theWindow.msIndexedDB||theWindow.shimIndexedDB;if(factory==null)throw new Error("The current browser does not appear to support IndexedDB.");return factory}function setUpDatabase(openRequest){let db=openRequest.result;db.createObjectStore(MODEL_STORE_NAME,{keyPath:"modelPath"}),db.createObjectStore(INFO_STORE_NAME,{keyPath:"modelPath"})}var BrowserIndexedDB=class{constructor(modelPath){if(this.indexedDB=getIndexedDBFactory(),modelPath==null||!modelPath)throw new Error("For IndexedDB, modelPath must not be null, undefined or empty.");this.modelPath=modelPath}async save(modelArtifacts){if(modelArtifacts.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");return this.databaseAction(this.modelPath,modelArtifacts)}async load(){return this.databaseAction(this.modelPath)}databaseAction(modelPath,modelArtifacts){return new Promise((resolve,reject)=>{let openRequest=this.indexedDB.open(DATABASE_NAME,DATABASE_VERSION);openRequest.onupgradeneeded=()=>setUpDatabase(openRequest),openRequest.onsuccess=()=>{let db=openRequest.result;if(modelArtifacts==null){let modelTx=db.transaction(MODEL_STORE_NAME,"readonly"),getRequest=modelTx.objectStore(MODEL_STORE_NAME).get(this.modelPath);getRequest.onsuccess=()=>{if(getRequest.result==null)return db.close(),reject(new Error(`Cannot find model with path '${this.modelPath}' in IndexedDB.`));resolve(getRequest.result.modelArtifacts)},getRequest.onerror=error=>(db.close(),reject(getRequest.error)),modelTx.oncomplete=()=>db.close()}else{let modelArtifactsInfo=getModelArtifactsInfoForJSON(modelArtifacts),infoTx=db.transaction(INFO_STORE_NAME,"readwrite"),infoStore=infoTx.objectStore(INFO_STORE_NAME),putInfoRequest=infoStore.put({modelPath:this.modelPath,modelArtifactsInfo}),modelTx;putInfoRequest.onsuccess=()=>{modelTx=db.transaction(MODEL_STORE_NAME,"readwrite");let putModelRequest=modelTx.objectStore(MODEL_STORE_NAME).put({modelPath:this.modelPath,modelArtifacts,modelArtifactsInfo});putModelRequest.onsuccess=()=>resolve({modelArtifactsInfo}),putModelRequest.onerror=error=>{infoStore=infoTx.objectStore(INFO_STORE_NAME);let deleteInfoRequest=infoStore.delete(this.modelPath);deleteInfoRequest.onsuccess=()=>(db.close(),reject(putModelRequest.error)),deleteInfoRequest.onerror=error2=>(db.close(),reject(putModelRequest.error))}},putInfoRequest.onerror=error=>(db.close(),reject(putInfoRequest.error)),infoTx.oncomplete=()=>{modelTx==null?db.close():modelTx.oncomplete=()=>db.close()}}},openRequest.onerror=error=>reject(openRequest.error)})}};BrowserIndexedDB.URL_SCHEME="indexeddb://";var indexedDBRouter=url=>env().getBool("IS_BROWSER")&&!Array.isArray(url)&&url.startsWith(BrowserIndexedDB.URL_SCHEME)?browserIndexedDB(url.slice(BrowserIndexedDB.URL_SCHEME.length)):null;IORouterRegistry.registerSaveRouter(indexedDBRouter);IORouterRegistry.registerLoadRouter(indexedDBRouter);function browserIndexedDB(modelPath){return new BrowserIndexedDB(modelPath)}function maybeStripScheme(key){return key.startsWith(BrowserIndexedDB.URL_SCHEME)?key.slice(BrowserIndexedDB.URL_SCHEME.length):key}var BrowserIndexedDBManager=class{constructor(){this.indexedDB=getIndexedDBFactory()}async listModels(){return new Promise((resolve,reject)=>{let openRequest=this.indexedDB.open(DATABASE_NAME,DATABASE_VERSION);openRequest.onupgradeneeded=()=>setUpDatabase(openRequest),openRequest.onsuccess=()=>{let db=openRequest.result,tx=db.transaction(INFO_STORE_NAME,"readonly"),getAllInfoRequest=tx.objectStore(INFO_STORE_NAME).getAll();getAllInfoRequest.onsuccess=()=>{let out={};for(let item of getAllInfoRequest.result)out[item.modelPath]=item.modelArtifactsInfo;resolve(out)},getAllInfoRequest.onerror=error=>(db.close(),reject(getAllInfoRequest.error)),tx.oncomplete=()=>db.close()},openRequest.onerror=error=>reject(openRequest.error)})}async removeModel(path){return path=maybeStripScheme(path),new Promise((resolve,reject)=>{let openRequest=this.indexedDB.open(DATABASE_NAME,DATABASE_VERSION);openRequest.onupgradeneeded=()=>setUpDatabase(openRequest),openRequest.onsuccess=()=>{let db=openRequest.result,infoTx=db.transaction(INFO_STORE_NAME,"readwrite"),infoStore=infoTx.objectStore(INFO_STORE_NAME),getInfoRequest=infoStore.get(path),modelTx;getInfoRequest.onsuccess=()=>{if(getInfoRequest.result==null)return db.close(),reject(new Error(`Cannot find model with path '${path}' in IndexedDB.`));{let deleteInfoRequest=infoStore.delete(path),deleteModelData=()=>{modelTx=db.transaction(MODEL_STORE_NAME,"readwrite");let deleteModelRequest=modelTx.objectStore(MODEL_STORE_NAME).delete(path);deleteModelRequest.onsuccess=()=>resolve(getInfoRequest.result.modelArtifactsInfo),deleteModelRequest.onerror=error=>reject(getInfoRequest.error)};deleteInfoRequest.onsuccess=deleteModelData,deleteInfoRequest.onerror=error=>(deleteModelData(),db.close(),reject(getInfoRequest.error))}},getInfoRequest.onerror=error=>(db.close(),reject(getInfoRequest.error)),infoTx.oncomplete=()=>{modelTx==null?db.close():modelTx.oncomplete=()=>db.close()}},openRequest.onerror=error=>reject(openRequest.error)})}},PATH_SEPARATOR="/",PATH_PREFIX="tensorflowjs_models",INFO_SUFFIX="info",MODEL_TOPOLOGY_SUFFIX="model_topology",WEIGHT_SPECS_SUFFIX="weight_specs",WEIGHT_DATA_SUFFIX="weight_data",MODEL_METADATA_SUFFIX="model_metadata";function getModelKeys(path){return{info:[PATH_PREFIX,path,INFO_SUFFIX].join(PATH_SEPARATOR),topology:[PATH_PREFIX,path,MODEL_TOPOLOGY_SUFFIX].join(PATH_SEPARATOR),weightSpecs:[PATH_PREFIX,path,WEIGHT_SPECS_SUFFIX].join(PATH_SEPARATOR),weightData:[PATH_PREFIX,path,WEIGHT_DATA_SUFFIX].join(PATH_SEPARATOR),modelMetadata:[PATH_PREFIX,path,MODEL_METADATA_SUFFIX].join(PATH_SEPARATOR)}}function getModelPathFromKey(key){let items=key.split(PATH_SEPARATOR);if(items.length<3)throw new Error(`Invalid key format: ${key}`);return items.slice(1,items.length-1).join(PATH_SEPARATOR)}function maybeStripScheme2(key){return key.startsWith(BrowserLocalStorage.URL_SCHEME)?key.slice(BrowserLocalStorage.URL_SCHEME.length):key}var BrowserLocalStorage=class{constructor(modelPath){if(!env().getBool("IS_BROWSER")||typeof window=="undefined"||typeof window.localStorage=="undefined")throw new Error("The current environment does not support local storage.");if(this.LS=window.localStorage,modelPath==null||!modelPath)throw new Error("For local storage, modelPath must not be null, undefined or empty.");this.modelPath=modelPath,this.keys=getModelKeys(this.modelPath)}async save(modelArtifacts){if(modelArtifacts.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");{let topology=JSON.stringify(modelArtifacts.modelTopology),weightSpecs=JSON.stringify(modelArtifacts.weightSpecs),modelArtifactsInfo=getModelArtifactsInfoForJSON(modelArtifacts);try{this.LS.setItem(this.keys.info,JSON.stringify(modelArtifactsInfo)),this.LS.setItem(this.keys.topology,topology),this.LS.setItem(this.keys.weightSpecs,weightSpecs),this.LS.setItem(this.keys.weightData,arrayBufferToBase64String(modelArtifacts.weightData));let result={format:modelArtifacts.format,generatedBy:modelArtifacts.generatedBy,convertedBy:modelArtifacts.convertedBy};return modelArtifacts.signature!=null&&(result.signature=modelArtifacts.signature),modelArtifacts.userDefinedMetadata!=null&&(result.userDefinedMetadata=modelArtifacts.userDefinedMetadata),modelArtifacts.modelInitializer!=null&&(result.modelInitializer=modelArtifacts.modelInitializer),this.LS.setItem(this.keys.modelMetadata,JSON.stringify(result)),{modelArtifactsInfo}}catch(err){throw this.LS.removeItem(this.keys.info),this.LS.removeItem(this.keys.topology),this.LS.removeItem(this.keys.weightSpecs),this.LS.removeItem(this.keys.weightData),this.LS.removeItem(this.keys.modelMetadata),new Error(`Failed to save model '${this.modelPath}' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes=${modelArtifactsInfo.modelTopologyBytes}, weightSpecsBytes=${modelArtifactsInfo.weightSpecsBytes}, weightDataBytes=${modelArtifactsInfo.weightDataBytes}.`)}}}async load(){let info=JSON.parse(this.LS.getItem(this.keys.info));if(info==null)throw new Error(`In local storage, there is no model with name '${this.modelPath}'`);if(info.modelTopologyType!=="JSON")throw new Error("BrowserLocalStorage does not support loading non-JSON model topology yet.");let out={},topology=JSON.parse(this.LS.getItem(this.keys.topology));if(topology==null)throw new Error(`In local storage, the topology of model '${this.modelPath}' is missing.`);out.modelTopology=topology;let weightSpecs=JSON.parse(this.LS.getItem(this.keys.weightSpecs));if(weightSpecs==null)throw new Error(`In local storage, the weight specs of model '${this.modelPath}' are missing.`);out.weightSpecs=weightSpecs;let metadataString=this.LS.getItem(this.keys.modelMetadata);if(metadataString!=null){let metadata=JSON.parse(metadataString);out.format=metadata.format,out.generatedBy=metadata.generatedBy,out.convertedBy=metadata.convertedBy,metadata.signature!=null&&(out.signature=metadata.signature),metadata.userDefinedMetadata!=null&&(out.userDefinedMetadata=metadata.userDefinedMetadata),metadata.modelInitializer!=null&&(out.modelInitializer=metadata.modelInitializer)}let weightDataBase64=this.LS.getItem(this.keys.weightData);if(weightDataBase64==null)throw new Error(`In local storage, the binary weight values of model '${this.modelPath}' are missing.`);return out.weightData=base64StringToArrayBuffer(weightDataBase64),out}};BrowserLocalStorage.URL_SCHEME="localstorage://";var localStorageRouter=url=>env().getBool("IS_BROWSER")&&!Array.isArray(url)&&url.startsWith(BrowserLocalStorage.URL_SCHEME)?browserLocalStorage(url.slice(BrowserLocalStorage.URL_SCHEME.length)):null;IORouterRegistry.registerSaveRouter(localStorageRouter);IORouterRegistry.registerLoadRouter(localStorageRouter);function browserLocalStorage(modelPath){return new BrowserLocalStorage(modelPath)}var BrowserLocalStorageManager=class{constructor(){assert(env().getBool("IS_BROWSER"),()=>"Current environment is not a web browser"),assert(typeof window=="undefined"||typeof window.localStorage!="undefined",()=>"Current browser does not appear to support localStorage"),this.LS=window.localStorage}async listModels(){let out={},prefix=PATH_PREFIX+PATH_SEPARATOR,suffix=PATH_SEPARATOR+INFO_SUFFIX;for(let i=0;i"scheme must not be undefined or null."),scheme.endsWith(URL_SCHEME_SUFFIX)&&(scheme=scheme.slice(0,scheme.indexOf(URL_SCHEME_SUFFIX))),assert(scheme.length>0,()=>"scheme must not be an empty string.");let registry=ModelStoreManagerRegistry.getInstance();assert(registry.managers[scheme]==null,()=>`A model store manager is already registered for scheme '${scheme}'.`),registry.managers[scheme]=manager}static getManager(scheme){let manager=this.getInstance().managers[scheme];if(manager==null)throw new Error(`Cannot find model manager for scheme '${scheme}'`);return manager}static getSchemes(){return Object.keys(this.getInstance().managers)}};function parseURL(url){if(url.indexOf(URL_SCHEME_SUFFIX)===-1)throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${ModelStoreManagerRegistry.getSchemes().join(",")}`);return{scheme:url.split(URL_SCHEME_SUFFIX)[0],path:url.split(URL_SCHEME_SUFFIX)[1]}}async function cloneModelInternal(sourceURL,destURL,deleteSource=!1){assert(sourceURL!==destURL,()=>`Old path and new path are the same: '${sourceURL}'`);let loadHandlers=IORouterRegistry.getLoadHandlers(sourceURL);assert(loadHandlers.length>0,()=>`Copying failed because no load handler is found for source URL ${sourceURL}.`),assert(loadHandlers.length<2,()=>`Copying failed because more than one (${loadHandlers.length}) load handlers for source URL ${sourceURL}.`);let loadHandler=loadHandlers[0],saveHandlers=IORouterRegistry.getSaveHandlers(destURL);assert(saveHandlers.length>0,()=>`Copying failed because no save handler is found for destination URL ${destURL}.`),assert(saveHandlers.length<2,()=>`Copying failed because more than one (${loadHandlers.length}) save handlers for destination URL ${destURL}.`);let saveHandler=saveHandlers[0],sourceScheme=parseURL(sourceURL).scheme,sourcePath=parseURL(sourceURL).path,sameMedium=sourceScheme===parseURL(sourceURL).scheme,modelArtifacts=await loadHandler.load();deleteSource&&sameMedium&&await ModelStoreManagerRegistry.getManager(sourceScheme).removeModel(sourcePath);let saveResult=await saveHandler.save(modelArtifacts);return deleteSource&&!sameMedium&&await ModelStoreManagerRegistry.getManager(sourceScheme).removeModel(sourcePath),saveResult.modelArtifactsInfo}async function listModels(){let schemes=ModelStoreManagerRegistry.getSchemes(),out={};for(let scheme of schemes){let schemeOut=await ModelStoreManagerRegistry.getManager(scheme).listModels();for(let path in schemeOut){let url=scheme+URL_SCHEME_SUFFIX+path;out[url]=schemeOut[path]}}return out}async function removeModel(url){let schemeAndPath=parseURL(url);return ModelStoreManagerRegistry.getManager(schemeAndPath.scheme).removeModel(schemeAndPath.path)}async function copyModel(sourceURL,destURL){return cloneModelInternal(sourceURL,destURL,!1)}async function moveModel(sourceURL,destURL){return cloneModelInternal(sourceURL,destURL,!0)}var PlatformBrowser=class{fetch(path,init2){return fetch(path,init2)}now(){return performance.now()}encode(text,encoding){if(encoding!=="utf-8"&&encoding!=="utf8")throw new Error(`Browser's encoder only supports utf-8, but got ${encoding}`);return this.textEncoder==null&&(this.textEncoder=new TextEncoder),this.textEncoder.encode(text)}decode(bytes,encoding){return new TextDecoder(encoding).decode(bytes)}};if(env().get("IS_BROWSER")){env().setPlatform("browser",new PlatformBrowser);try{ModelStoreManagerRegistry.registerManager(BrowserLocalStorage.URL_SCHEME,new BrowserLocalStorageManager)}catch(err){}try{ModelStoreManagerRegistry.registerManager(BrowserIndexedDB.URL_SCHEME,new BrowserIndexedDBManager)}catch(err){}}var getNodeFetch={importFetch:()=>require_browser()},systemFetch,PlatformNode=class{constructor(){this.util=require("util"),this.textEncoder=new this.util.TextEncoder}fetch(path,requestInits){return env().global.fetch!=null?env().global.fetch(path,requestInits):(systemFetch==null&&(systemFetch=getNodeFetch.importFetch()),systemFetch(path,requestInits))}now(){let time2=process.hrtime();return time2[0]*1e3+time2[1]/1e6}encode(text,encoding){if(encoding!=="utf-8"&&encoding!=="utf8")throw new Error(`Node built-in encoder only supports utf-8, but got ${encoding}`);return this.textEncoder.encode(text)}decode(bytes,encoding){return bytes.length===0?"":new this.util.TextDecoder(encoding).decode(bytes)}};env().get("IS_NODE")&&env().setPlatform("node",new PlatformNode);function buffer(shape,dtype="float32",values){return dtype=dtype||"float32",assertNonNegativeIntegerDimensions(shape),new TensorBuffer(shape,dtype,values)}function cast_(x,dtype){let $x=convertToTensor(x,"x","cast");if(!isValidDtype(dtype))throw new Error(`Failed to cast to unknown dtype ${dtype}`);if(dtype==="string"&&$x.dtype!=="string"||dtype!=="string"&&$x.dtype==="string")throw new Error("Only strings can be casted to strings");let inputs={x:$x},attrs={dtype};return ENGINE.runKernel(Cast,inputs,attrs)}var cast=op({cast_});function clone_(x){let inputs={x:convertToTensor(x,"x","clone","string_or_numeric")};return ENGINE.runKernel(Identity,inputs)}var clone=op({clone_});function print2(x,verbose=!1){console.log(x.toString(verbose))}getOrMakeEngine();var opHandler2={buffer,cast,clone,print:print2};setOpHandler(opHandler2);var io_exports={};__export2(io_exports,{browserFiles:()=>browserFiles,browserHTTPRequest:()=>browserHTTPRequest,concatenateArrayBuffers:()=>concatenateArrayBuffers,copyModel:()=>copyModel,decodeWeights:()=>decodeWeights,encodeWeights:()=>encodeWeights,fromMemory:()=>fromMemory,getLoadHandlers:()=>getLoadHandlers,getModelArtifactsInfoForJSON:()=>getModelArtifactsInfoForJSON,getSaveHandlers:()=>getSaveHandlers,http:()=>http,isHTTPScheme:()=>isHTTPScheme,listModels:()=>listModels,loadWeights:()=>loadWeights,moveModel:()=>moveModel,registerLoadRouter:()=>registerLoadRouter,registerSaveRouter:()=>registerSaveRouter,removeModel:()=>removeModel,weightsLoaderFactory:()=>weightsLoaderFactory,withSaveHandler:()=>withSaveHandler});var DEFAULT_FILE_NAME_PREFIX="model",DEFAULT_JSON_EXTENSION_NAME=".json",DEFAULT_WEIGHT_DATA_EXTENSION_NAME=".weights.bin";function defer(f){return new Promise(resolve=>setTimeout(resolve)).then(f)}var BrowserDownloads=class{constructor(fileNamePrefix){if(!env().getBool("IS_BROWSER"))throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");fileNamePrefix.startsWith(BrowserDownloads.URL_SCHEME)&&(fileNamePrefix=fileNamePrefix.slice(BrowserDownloads.URL_SCHEME.length)),(fileNamePrefix==null||fileNamePrefix.length===0)&&(fileNamePrefix=DEFAULT_FILE_NAME_PREFIX),this.modelTopologyFileName=fileNamePrefix+DEFAULT_JSON_EXTENSION_NAME,this.weightDataFileName=fileNamePrefix+DEFAULT_WEIGHT_DATA_EXTENSION_NAME}async save(modelArtifacts){if(typeof document=="undefined")throw new Error("Browser downloads are not supported in this environment since `document` is not present");let weightsURL=window.URL.createObjectURL(new Blob([modelArtifacts.weightData],{type:"application/octet-stream"}));if(modelArtifacts.modelTopology instanceof ArrayBuffer)throw new Error("BrowserDownloads.save() does not support saving model topology in binary formats yet.");{let weightsManifest=[{paths:["./"+this.weightDataFileName],weights:modelArtifacts.weightSpecs}],modelTopologyAndWeightManifest={modelTopology:modelArtifacts.modelTopology,format:modelArtifacts.format,generatedBy:modelArtifacts.generatedBy,convertedBy:modelArtifacts.convertedBy,weightsManifest};modelArtifacts.signature!=null&&(modelTopologyAndWeightManifest.signature=modelArtifacts.signature),modelArtifacts.userDefinedMetadata!=null&&(modelTopologyAndWeightManifest.userDefinedMetadata=modelArtifacts.userDefinedMetadata),modelArtifacts.modelInitializer!=null&&(modelTopologyAndWeightManifest.modelInitializer=modelArtifacts.modelInitializer);let modelTopologyAndWeightManifestURL=window.URL.createObjectURL(new Blob([JSON.stringify(modelTopologyAndWeightManifest)],{type:"application/json"})),jsonAnchor=this.jsonAnchor==null?document.createElement("a"):this.jsonAnchor;if(jsonAnchor.download=this.modelTopologyFileName,jsonAnchor.href=modelTopologyAndWeightManifestURL,await defer(()=>jsonAnchor.dispatchEvent(new MouseEvent("click"))),modelArtifacts.weightData!=null){let weightDataAnchor=this.weightDataAnchor==null?document.createElement("a"):this.weightDataAnchor;weightDataAnchor.download=this.weightDataFileName,weightDataAnchor.href=weightsURL,await defer(()=>weightDataAnchor.dispatchEvent(new MouseEvent("click")))}return{modelArtifactsInfo:getModelArtifactsInfoForJSON(modelArtifacts)}}}};BrowserDownloads.URL_SCHEME="downloads://";var BrowserFiles=class{constructor(files){if(files==null||files.length<1)throw new Error(`When calling browserFiles, at least 1 file is required, but received ${files}`);this.files=files}async load(){let jsonFile=this.files[0],weightFiles=this.files.slice(1);return new Promise((resolve,reject)=>{let jsonReader=new FileReader;jsonReader.onload=event=>{let modelJSON=JSON.parse(event.target.result),modelTopology=modelJSON.modelTopology;if(modelTopology==null){reject(new Error(`modelTopology field is missing from file ${jsonFile.name}`));return}weightFiles.length===0&&resolve({modelTopology});let weightsManifest=modelJSON.weightsManifest;if(weightsManifest==null){reject(new Error(`weightManifest field is missing from file ${jsonFile.name}`));return}let pathToFile;try{pathToFile=this.checkManifestAndWeightFiles(weightsManifest,weightFiles)}catch(err){reject(err);return}let weightSpecs=[],paths=[],perFileBuffers=[];weightsManifest.forEach(weightsGroup=>{weightsGroup.paths.forEach(path=>{paths.push(path),perFileBuffers.push(null)}),weightSpecs.push(...weightsGroup.weights)}),weightsManifest.forEach(weightsGroup=>{weightsGroup.paths.forEach(path=>{let weightFileReader=new FileReader;weightFileReader.onload=event2=>{let weightData=event2.target.result,index=paths.indexOf(path);if(perFileBuffers[index]=weightData,perFileBuffers.indexOf(null)===-1){let result={modelTopology,weightSpecs,weightData:concatenateArrayBuffers(perFileBuffers),format:modelJSON.format,generatedBy:modelJSON.generatedBy,convertedBy:modelJSON.convertedBy};modelJSON.signature!=null&&(result.signature=modelJSON.signature),modelJSON.userDefinedMetadata!=null&&(result.userDefinedMetadata=modelJSON.userDefinedMetadata),modelJSON.modelInitializer!=null&&(result.modelInitializer=modelJSON.modelInitializer),resolve(result)}},weightFileReader.onerror=error=>reject(`Failed to weights data from file of path '${path}'.`),weightFileReader.readAsArrayBuffer(pathToFile[path])})})},jsonReader.onerror=error=>reject(`Failed to read model topology and weights manifest JSON from file '${jsonFile.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`),jsonReader.readAsText(jsonFile)})}checkManifestAndWeightFiles(manifest,files){let basenames=[],fileNames=files.map(file=>basename(file.name)),pathToFile={};for(let group of manifest)group.paths.forEach(path=>{let pathBasename=basename(path);if(basenames.indexOf(pathBasename)!==-1)throw new Error(`Duplicate file basename found in weights manifest: '${pathBasename}'`);if(basenames.push(pathBasename),fileNames.indexOf(pathBasename)===-1)throw new Error(`Weight file with basename '${pathBasename}' is not provided.`);pathToFile[path]=files[fileNames.indexOf(pathBasename)]});if(basenames.length!==files.length)throw new Error(`Mismatch in the number of files in weights manifest (${basenames.length}) and the number of weight files provided (${files.length}).`);return pathToFile}},browserDownloadsRouter=url=>env().getBool("IS_BROWSER")&&!Array.isArray(url)&&url.startsWith(BrowserDownloads.URL_SCHEME)?browserDownloads(url.slice(BrowserDownloads.URL_SCHEME.length)):null;IORouterRegistry.registerSaveRouter(browserDownloadsRouter);function browserDownloads(fileNamePrefix="model"){return new BrowserDownloads(fileNamePrefix)}function browserFiles(files){return new BrowserFiles(files)}function monitorPromisesProgress(promises,onProgress,startFraction,endFraction){checkPromises(promises),startFraction=startFraction==null?0:startFraction,endFraction=endFraction==null?1:endFraction,checkFraction(startFraction,endFraction);let resolvedPromise=0,registerMonitor=promise=>(promise.then(value=>{let fraction=startFraction+ ++resolvedPromise/promises.length*(endFraction-startFraction);return onProgress(fraction),value}),promise);function checkPromises(promises2){assert(promises2!=null&&Array.isArray(promises2)&&promises2.length>0,()=>"promises must be a none empty array")}function checkFraction(startFraction2,endFraction2){assert(startFraction2>=0&&startFraction2<=1,()=>`Progress fraction must be in range [0, 1], but got startFraction ${startFraction2}`),assert(endFraction2>=0&&endFraction2<=1,()=>`Progress fraction must be in range [0, 1], but got endFraction ${endFraction2}`),assert(endFraction2>=startFraction2,()=>`startFraction must be no more than endFraction, but got startFraction ${startFraction2} and endFraction ${endFraction2}`)}return Promise.all(promises.map(registerMonitor))}async function loadWeightsAsArrayBuffer(fetchURLs,loadOptions){loadOptions==null&&(loadOptions={});let fetchFunc=loadOptions.fetchFunc==null?env().platform.fetch:loadOptions.fetchFunc,requests=fetchURLs.map(fetchURL=>fetchFunc(fetchURL,loadOptions.requestInit,{isBinary:!0})),fetchStartFraction=0,fetchEndFraction=.5,bufferPromises=(loadOptions.onProgress==null?await Promise.all(requests):await monitorPromisesProgress(requests,loadOptions.onProgress,fetchStartFraction,fetchEndFraction)).map(response=>response.arrayBuffer()),bufferStartFraction=.5,bufferEndFraction=1;return loadOptions.onProgress==null?await Promise.all(bufferPromises):await monitorPromisesProgress(bufferPromises,loadOptions.onProgress,bufferStartFraction,bufferEndFraction)}async function loadWeights(manifest,filePathPrefix="",weightNames,requestInit){return weightsLoaderFactory(fetchUrls=>loadWeightsAsArrayBuffer(fetchUrls,{requestInit}))(manifest,filePathPrefix,weightNames)}function weightsLoaderFactory(fetchWeightsFunction){return async(manifest,filePathPrefix="",weightNames)=>{let groupIndicesToFetchMap=manifest.map(()=>!1),groupWeightsToFetch={},weightsFound=weightNames!=null?weightNames.map(()=>!1):[],allManifestWeightNames=[];if(manifest.forEach((manifestGroupConfig,groupIndex)=>{let groupOffset=0;manifestGroupConfig.weights.forEach(weightsEntry=>{let rawDtype="quantization"in weightsEntry?weightsEntry.quantization.dtype:weightsEntry.dtype,weightsBytes=DTYPE_VALUE_SIZE_MAP[rawDtype]*sizeFromShape(weightsEntry.shape),enqueueWeightsForFetchingFn=()=>{groupIndicesToFetchMap[groupIndex]=!0,groupWeightsToFetch[groupIndex]==null&&(groupWeightsToFetch[groupIndex]=[]),groupWeightsToFetch[groupIndex].push({manifestEntry:weightsEntry,groupOffset,sizeBytes:weightsBytes})};weightNames!=null?weightNames.forEach((weightName,weightIndex)=>{weightName===weightsEntry.name&&(enqueueWeightsForFetchingFn(),weightsFound[weightIndex]=!0)}):enqueueWeightsForFetchingFn(),allManifestWeightNames.push(weightsEntry.name),groupOffset+=weightsBytes})}),!weightsFound.every(found=>found)){let weightsNotFound=weightNames.filter((_,i)=>!weightsFound[i]);throw new Error(`Could not find weights in manifest with names: ${weightsNotFound.join(", ")}. -Manifest JSON has weights with names: ${allManifestWeightNames.join(", ")}.`)}let groupIndicesToFetch=groupIndicesToFetchMap.reduce((accumulator,shouldFetch,i)=>(shouldFetch&&accumulator.push(i),accumulator),[]),fetchUrls=[];groupIndicesToFetch.forEach(i=>{manifest[i].paths.forEach(filepath=>{let fetchUrl=filePathPrefix+(filePathPrefix.endsWith("/")?"":"/")+filepath;fetchUrls.push(fetchUrl)})});let buffers=await fetchWeightsFunction(fetchUrls),weightsTensorMap={},bufferIndexOffset=0;return groupIndicesToFetch.forEach(i=>{let numBuffers=manifest[i].paths.length,groupBytes=0;for(let i2=0;i2{let byteBuffer=groupBuffer.slice(weightsEntry.groupOffset,weightsEntry.groupOffset+weightsEntry.sizeBytes),nameToTensorMap=decodeWeights(byteBuffer,[weightsEntry.manifestEntry]);for(let name in nameToTensorMap)weightsTensorMap[name]=nameToTensorMap[name]}),bufferIndexOffset+=numBuffers}),weightsTensorMap}}var OCTET_STREAM_MIME_TYPE="application/octet-stream",JSON_TYPE="application/json",HTTPRequest=class{constructor(path,loadOptions){if(this.DEFAULT_METHOD="POST",loadOptions==null&&(loadOptions={}),this.weightPathPrefix=loadOptions.weightPathPrefix,this.onProgress=loadOptions.onProgress,this.weightUrlConverter=loadOptions.weightUrlConverter,loadOptions.fetchFunc!=null?(assert(typeof loadOptions.fetchFunc=="function",()=>"Must pass a function that matches the signature of `fetch` (see https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)"),this.fetch=loadOptions.fetchFunc):this.fetch=env().platform.fetch,assert(path!=null&&path.length>0,()=>"URL path for http must not be null, undefined or empty."),Array.isArray(path)&&assert(path.length===2,()=>`URL paths for http must have a length of 2, (actual length is ${path.length}).`),this.path=path,loadOptions.requestInit!=null&&loadOptions.requestInit.body!=null)throw new Error("requestInit is expected to have no pre-existing body, but has one.");this.requestInit=loadOptions.requestInit||{}}async save(modelArtifacts){if(modelArtifacts.modelTopology instanceof ArrayBuffer)throw new Error("BrowserHTTPRequest.save() does not support saving model topology in binary formats yet.");let init2=Object.assign({method:this.DEFAULT_METHOD},this.requestInit);init2.body=new FormData;let weightsManifest=[{paths:["./model.weights.bin"],weights:modelArtifacts.weightSpecs}],modelTopologyAndWeightManifest={modelTopology:modelArtifacts.modelTopology,format:modelArtifacts.format,generatedBy:modelArtifacts.generatedBy,convertedBy:modelArtifacts.convertedBy,weightsManifest};modelArtifacts.signature!=null&&(modelTopologyAndWeightManifest.signature=modelArtifacts.signature),modelArtifacts.userDefinedMetadata!=null&&(modelTopologyAndWeightManifest.userDefinedMetadata=modelArtifacts.userDefinedMetadata),modelArtifacts.modelInitializer!=null&&(modelTopologyAndWeightManifest.modelInitializer=modelArtifacts.modelInitializer),init2.body.append("model.json",new Blob([JSON.stringify(modelTopologyAndWeightManifest)],{type:JSON_TYPE}),"model.json"),modelArtifacts.weightData!=null&&init2.body.append("model.weights.bin",new Blob([modelArtifacts.weightData],{type:OCTET_STREAM_MIME_TYPE}),"model.weights.bin");let response=await this.fetch(this.path,init2);if(response.ok)return{modelArtifactsInfo:getModelArtifactsInfoForJSON(modelArtifacts),responses:[response]};throw new Error(`BrowserHTTPRequest.save() failed due to HTTP response status ${response.status}.`)}async load(){let modelConfigRequest=await this.fetch(this.path,this.requestInit);if(!modelConfigRequest.ok)throw new Error(`Request to ${this.path} failed with status code ${modelConfigRequest.status}. Please verify this URL points to the model JSON of the model to load.`);let modelConfig;try{modelConfig=await modelConfigRequest.json()}catch(e){let message=`Failed to parse model JSON of response from ${this.path}.`;throw this.path.endsWith(".pb")?message+=" Your path contains a .pb file extension. Support for .pb models have been removed in TensorFlow.js 1.0 in favor of .json models. You can re-convert your Python TensorFlow model using the TensorFlow.js 1.0 conversion scripts or you can convert your.pb models with the 'pb2json'NPM script in the tensorflow/tfjs-converter repository.":message+=" Please make sure the server is serving valid JSON for this request.",new Error(message)}let modelTopology=modelConfig.modelTopology,weightsManifest=modelConfig.weightsManifest,generatedBy=modelConfig.generatedBy,convertedBy=modelConfig.convertedBy,format=modelConfig.format,signature=modelConfig.signature,userDefinedMetadata=modelConfig.userDefinedMetadata;if(modelTopology==null&&weightsManifest==null)throw new Error(`The JSON from HTTP path ${this.path} contains neither model topology or manifest for weights.`);let weightSpecs,weightData;weightsManifest!=null&&([weightSpecs,weightData]=await this.loadWeights(weightsManifest));let artifacts={modelTopology,weightSpecs,weightData,generatedBy,convertedBy,format};signature!=null&&(artifacts.signature=signature),userDefinedMetadata!=null&&(artifacts.userDefinedMetadata=userDefinedMetadata);let initializer=modelConfig.modelInitializer;return initializer&&(artifacts.modelInitializer=initializer),artifacts}async loadWeights(weightsManifest){let weightPath=Array.isArray(this.path)?this.path[1]:this.path,[prefix,suffix]=parseUrl(weightPath),pathPrefix=this.weightPathPrefix||prefix,weightSpecs=[];for(let entry of weightsManifest)weightSpecs.push(...entry.weights);let fetchURLs=[],urlPromises=[];for(let weightsGroup of weightsManifest)for(let path of weightsGroup.paths)this.weightUrlConverter!=null?urlPromises.push(this.weightUrlConverter(path)):fetchURLs.push(pathPrefix+path+suffix);this.weightUrlConverter&&fetchURLs.push(...await Promise.all(urlPromises));let buffers=await loadWeightsAsArrayBuffer(fetchURLs,{requestInit:this.requestInit,fetchFunc:this.fetch,onProgress:this.onProgress});return[weightSpecs,concatenateArrayBuffers(buffers)]}};HTTPRequest.URL_SCHEME_REGEX=/^https?:\/\//;function parseUrl(url){let lastSlash=url.lastIndexOf("/"),lastSearchParam=url.lastIndexOf("?"),prefix=url.substring(0,lastSlash),suffix=lastSearchParam>lastSlash?url.substring(lastSearchParam):"";return[prefix+"/",suffix]}function isHTTPScheme(url){return url.match(HTTPRequest.URL_SCHEME_REGEX)!=null}var httpRouter=(url,loadOptions)=>{if(typeof fetch=="undefined"&&(loadOptions==null||loadOptions.fetchFunc==null))return null;{let isHTTP=!0;if(Array.isArray(url)?isHTTP=url.every(urlItem=>isHTTPScheme(urlItem)):isHTTP=isHTTPScheme(url),isHTTP)return http(url,loadOptions)}return null};IORouterRegistry.registerSaveRouter(httpRouter);IORouterRegistry.registerLoadRouter(httpRouter);function http(path,loadOptions){return new HTTPRequest(path,loadOptions)}function browserHTTPRequest(path,loadOptions){return http(path,loadOptions)}var PassthroughLoader=class{constructor(modelArtifacts){this.modelArtifacts=modelArtifacts}async load(){return this.modelArtifacts}},PassthroughSaver=class{constructor(saveHandler){this.saveHandler=saveHandler}async save(modelArtifacts){return this.saveHandler(modelArtifacts)}};function fromMemory(modelArtifacts,weightSpecs,weightData,trainingConfig){return arguments.length===1?modelArtifacts.modelTopology!=null||modelArtifacts.weightSpecs!=null?new PassthroughLoader(modelArtifacts):(console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."),new PassthroughLoader({modelTopology:modelArtifacts})):(console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."),new PassthroughLoader({modelTopology:modelArtifacts,weightSpecs,weightData,trainingConfig}))}function withSaveHandler(saveHandler){return new PassthroughSaver(saveHandler)}var math_exports={};__export2(math_exports,{confusionMatrix:()=>confusionMatrix});function matMul_(a,b,transposeA=!1,transposeB=!1){let $a=convertToTensor(a,"a","matMul"),$b=convertToTensor(b,"b","matMul");[$a,$b]=makeTypesMatch($a,$b);let inputs={a:$a,b:$b},attrs={transposeA,transposeB};return ENGINE.runKernel(BatchMatMul,inputs,attrs)}var matMul=op({matMul_});function oneHot_(indices,depth,onValue=1,offValue=0){if(depth<2)throw new Error(`Error in oneHot: depth must be >=2, but it is ${depth}`);let inputs={indices:convertToTensor(indices,"indices","oneHot","int32")},attrs={depth,onValue,offValue};return ENGINE.runKernel(OneHot,inputs,attrs)}var oneHot=op({oneHot_});function transpose_(x,perm){let $x=convertToTensor(x,"x","transpose");if(perm==null&&(perm=$x.shape.map((s,i)=>i).reverse()),assert($x.rank===perm.length,()=>`Error in transpose: rank of input ${$x.rank} must match length of perm ${perm}.`),perm.forEach(axis=>{assert(axis>=0&&axis<$x.rank,()=>`All entries in 'perm' must be between 0 and ${$x.rank-1} but got ${perm}`)}),$x.rank<=1)return $x.clone();let inputs={x:$x},attrs={perm};return ENGINE.runKernel(Transpose,inputs,attrs)}var transpose=op({transpose_});function confusionMatrix_(labels,predictions,numClasses){let $labels=convertToTensor(labels,"labels","confusionMatrix"),$predictions=convertToTensor(predictions,"predictions","confusionMatrix");assert(numClasses==null||numClasses>0&&Number.isInteger(numClasses),()=>`If provided, numClasses must be a positive integer, but got ${numClasses}`),assert($labels.rank===1,()=>`Expected the rank of labels to be 1, but got ${$labels.rank}`),assert($predictions.rank===1,()=>`Expected the rank of predictions to be 1, but got ${$predictions.rank}`),assert($labels.shape[0]===$predictions.shape[0],()=>`Mismatch in the number of examples: ${$labels.shape[0]} vs. ${$predictions.shape[0]}. Labels and predictions should have the same number of elements.`),assert(numClasses>0&&Number.isInteger(numClasses),()=>`numClasses is required to be a positive integer, but got ${numClasses}`);let oneHotLabels=oneHot(cast($labels,"int32"),numClasses),oneHotPredictions=oneHot(cast($predictions,"int32"),numClasses),oneHotLabelsT=transpose(oneHotLabels),product=matMul(oneHotLabelsT,oneHotPredictions);return cast(product,"int32")}var confusionMatrix=op({confusionMatrix_}),browser_exports={};__export2(browser_exports,{fromPixels:()=>fromPixels,toPixels:()=>toPixels});function tensor3d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==3)throw new Error("tensor3d() requires shape to have three numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==3&&inferredShape.length!==1)throw new Error("tensor3d() requires values to be number[][][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor3d() requires shape to be provided when `values` are a flat array");return makeTensor(values,shape,inferredShape,dtype)}var fromPixels2DContext;function fromPixels_(pixels,numChannels=3){if(numChannels>4)throw new Error("Cannot construct Tensor with more than 4 channels from pixels.");if(pixels==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let isPixelData=!1,isImageData=!1,isVideo=!1,isImage=!1,isCanvasLike=!1,isImageBitmap=!1;if(pixels.data instanceof Uint8Array)isPixelData=!0;else if(typeof ImageData!="undefined"&&pixels instanceof ImageData)isImageData=!0;else if(typeof HTMLVideoElement!="undefined"&&pixels instanceof HTMLVideoElement)isVideo=!0;else if(typeof HTMLImageElement!="undefined"&&pixels instanceof HTMLImageElement)isImage=!0;else if(pixels.getContext!=null)isCanvasLike=!0;else if(typeof ImageBitmap!="undefined"&&pixels instanceof ImageBitmap)isImageBitmap=!0;else throw new Error(`pixels passed to tf.browser.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData in browser, or OffscreenCanvas, ImageData in webworker or {data: Uint32Array, width: number, height: number}, but was ${pixels.constructor.name}`);if(isVideo){let HAVE_CURRENT_DATA_READY_STATE=2;if(isVideo&&pixels.readyState element.")}if(getKernel(FromPixels,ENGINE.backendName)!=null){let inputs={pixels},attrs={numChannels};return ENGINE.runKernel(FromPixels,inputs,attrs)}let[width,height]=isVideo?[pixels.videoWidth,pixels.videoHeight]:[pixels.width,pixels.height],vals;isCanvasLike?vals=pixels.getContext("2d").getImageData(0,0,width,height).data:isImageData||isPixelData?vals=pixels.data:(isImage||isVideo||isImageBitmap)&&(fromPixels2DContext==null&&(fromPixels2DContext=document.createElement("canvas").getContext("2d")),fromPixels2DContext.canvas.width=width,fromPixels2DContext.canvas.height=height,fromPixels2DContext.drawImage(pixels,0,0,width,height),vals=fromPixels2DContext.getImageData(0,0,width,height).data);let values;if(numChannels===4)values=new Int32Array(vals);else{let numPixels=width*height;values=new Int32Array(numPixels*numChannels);for(let i=0;i4||depth===2)throw new Error(`toPixels only supports depth of size 1, 3 or 4 but got ${depth}`);if($img.dtype!=="float32"&&$img.dtype!=="int32")throw new Error(`Unsupported type for toPixels: ${$img.dtype}. Please use float32 or int32 tensors.`);let data2=await $img.data(),multiplier=$img.dtype==="float32"?255:1,bytes=new Uint8ClampedArray(width*height*4);for(let i=0;i1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${value}.`)}else if($img.dtype==="int32"&&(value<0||value>255))throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${value}.`);depth===1?(rgba[0]=value*multiplier,rgba[1]=value*multiplier,rgba[2]=value*multiplier):rgba[d]=value*multiplier}let j=i*4;bytes[j+0]=Math.round(rgba[0]),bytes[j+1]=Math.round(rgba[1]),bytes[j+2]=Math.round(rgba[2]),bytes[j+3]=Math.round(rgba[3])}if(canvas!=null){canvas.width=width,canvas.height=height;let ctx=canvas.getContext("2d"),imageData=new ImageData(bytes,width,height);ctx.putImageData(imageData,0,0)}return $img!==img&&$img.dispose(),bytes}var fromPixels=op({fromPixels_}),gather_nd_util_exports={};__export2(gather_nd_util_exports,{prepareAndValidate:()=>prepareAndValidate});function prepareAndValidate(tensor2,indices){let tensorRank=tensor2.shape.length,indicesRank=indices.shape.length;if(tensorRank<1)throw new Error(`tf.gatherND() expects the input to be rank 1 or higher, but the rank was ${tensorRank}.`);if(indicesRank<1)throw new Error(`tf.gatherND() expects the indices to be rank 1 or higher, but the rank was ${indicesRank}.`);if(indices.dtype!=="int32")throw new Error(`tf.gatherND() expects the indices to be int32 type, but the dtype was ${indices.dtype}.`);if(indices.shape[indicesRank-1]>tensorRank)throw new Error(`index innermost dimension length must be <= tensor rank; saw: ${indices.shape[indicesRank-1]} vs. ${tensorRank}`);if(sizeFromShape(tensor2.shape)===0)throw new Error(`Requested more than 0 entries, but input is empty. Input shape: ${tensor2.shape}.`);let indicesShape=indices.shape,sliceRank=indicesShape[indicesShape.length-1],nResult=1;for(let i=0;istride/sliceSize),1].slice(0,sliceRank);return[resultShape,nResult,sliceSize,strides]}var scatter_nd_util_exports={};__export2(scatter_nd_util_exports,{calculateShapes:()=>calculateShapes,validateInput:()=>validateInput,validateUpdateShape:()=>validateUpdateShape});function validateUpdateShape(shape,indices,updates){let sliceDim=indices.rank>1?indices.shape[indices.rank-1]:1,batchDim=indices.rank>1?indices.rank-1:1,shapeError=`Must have updates.shape = indices.shape[:batchDim] + shape[sliceDim:], got updates.shape: ${updates.shape}, indices.shape: ${indices.shape}, shape: ${shape}, sliceDim: ${sliceDim}, and batchDim: ${batchDim}.`;if(updates.rank1?indices.shape[indicesRank-1]:1,totalNd=shape.length,sliceSize=1;for(let i=sliceRank;iassertParamsValid,computeFlatOffset:()=>computeFlatOffset,computeOutShape:()=>computeOutShape,getNormalizedAxes:()=>getNormalizedAxes,isSliceContinous:()=>isSliceContinous,maskToAxes:()=>maskToAxes,parseSliceParams:()=>parseSliceParams,sliceInfo:()=>sliceInfo,startForAxis:()=>startForAxis,startIndicesWithElidedDims:()=>startIndicesWithElidedDims,stopForAxis:()=>stopForAxis,stopIndicesWithElidedDims:()=>stopIndicesWithElidedDims,stridesForAxis:()=>stridesForAxis,stridesWithElidedDims:()=>stridesWithElidedDims});function assertParamsValid(input2,begin,size){let inputRank=input2.shape.length;assert(inputRank===begin.length,()=>`Error in slice${inputRank}D: Length of begin ${begin} must match the rank of the array (${inputRank}).`),assert(inputRank===size.length,()=>`Error in slice${inputRank}D: Length of size ${size} must match the rank of the array (${inputRank}).`);for(let i=0;i`Error in slice${inputRank}D: begin[${i}] + size[${i}] (${begin[i]+size[i]}) would overflow input.shape[${i}] (${input2.shape[i]})`)}function maskToAxes(mask){let axes=[],axis=0;for(;mask>0;)mask&1&&axes.push(axis),mask/=2,axis++;return axes}function computeOutShape(begin,end,strides){let size=[];for(let axis=0;axis0){let fullIndex=ellipsisAxes[0],numElidedAxes=numInterpolatedAxes+1;normalizedBegin=startIndicesWithElidedDims(beginMask,fullIndex,numElidedAxes,begin,inputShape),normalizedEnd=stopIndicesWithElidedDims(endMask,fullIndex,numElidedAxes,end,inputShape),normalizedStrides=stridesWithElidedDims(strides,fullIndex,numElidedAxes,inputShape)}else for(let axis=0;axis-1)newIndices[axis]=0;else{let originalAxis=unnormalizeAxis(ellipsisInsertionIndex,numElidedAxes,axis),originalValue=originalBegin[originalAxis];beginMask&1<-1)newIndices[axis]=Number.MAX_SAFE_INTEGER;else{let originalAxis=unnormalizeAxis(ellipsisInsertionIndex,numElidedAxes,axis),originalValue=originalEnd[originalAxis];endMask&1<0?start=Number.MIN_SAFE_INTEGER:start=Number.MAX_SAFE_INTEGER);let axisSize=inputShape[axis];return start<0&&(start+=axisSize),start=clamp(0,start,axisSize-1),start}function stopForAxis(endMask,stopIndices,strides,inputShape,axis,ellipsisMask){let stop=stopIndices[axis],stride=strides[axis]||1;(endMask&1<0?stop=Number.MAX_SAFE_INTEGER:stop=Number.MIN_SAFE_INTEGER);let axisSize=inputShape[axis];return stop<0&&(stop+=axisSize),stride>0?stop=clamp(0,stop,axisSize):stop=clamp(-1,stop,axisSize-1),stop}function isSliceContinous(shape,begin,size){let firstNonOneAxis=size.length;for(let i=0;i1){firstNonOneAxis=i;break}for(let i=firstNonOneAxis+1;i0||size[i]!==shape[i])return!1;return!0}function computeFlatOffset(begin,strides){let flatOffset=begin.length>0?begin[begin.length-1]:1;for(let i=0;i{assert(d!==-1,()=>"slice() does not support negative begin indexing.")});let size_;return size==null?size_=new Array(xRank).fill(-1):typeof size=="number"?size_=[size,...new Array(xRank-1).fill(-1)]:size.lengthd>=0?d:(assert(d===-1,()=>`Negative size values should be exactly -1 but got ${d} for the slice() size at index ${i}.`),x.shape[i]-begin_[i])),[begin_,size_]}function sliceInfo(xShape,begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask){let $begin=begin.slice(),$end=end.slice(),$strides=strides;strides==null&&($strides=new Array($begin.length));let ellipsisAxes=maskToAxes(ellipsisMask);if(ellipsisAxes.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(ellipsisMask!==0&&newAxisMask!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(ellipsisMask!==0&&shrinkAxisMask!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");let numInterpolatedAxes=xShape.length-$begin.length,expandAxes=maskToAxes(newAxisMask),newShape=xShape.slice();expandAxes.forEach(axis=>{$begin[axis]=0,$end[axis]=1,newShape.splice(axis,0,1)});let{begin:normalizedBegin,end:normalizedEnd,strides:normalizedStrides}=getNormalizedAxes(newShape,ellipsisAxes,numInterpolatedAxes,$begin,$end,$strides,beginMask,endMask,ellipsisMask);$begin=normalizedBegin,$end=normalizedEnd,$strides=normalizedStrides;let shrinkAxes=maskToAxes(shrinkAxisMask);shrinkAxes.forEach(axis=>{$end[axis]=$begin[axis]+1,$strides[axis]=1});let size=computeOutShape($begin,$end,$strides),outShape=size.filter((_,axis)=>shrinkAxes.indexOf(axis)===-1);return{nonStrided:$strides.every(v=>v===1),$begin,$end,$strides,size,newShape,outShape}}var serialization_exports={};__export2(serialization_exports,{Serializable:()=>Serializable,SerializationMap:()=>SerializationMap,registerClass:()=>registerClass});var Serializable=class{getClassName(){return this.constructor.className}static fromConfig(cls,config3){return new cls(config3)}},SerializationMap=class{constructor(){this.classNameMap={}}static getMap(){return SerializationMap.instance==null&&(SerializationMap.instance=new SerializationMap),SerializationMap.instance}static register(cls){SerializationMap.getMap().classNameMap[cls.className]=[cls,cls.fromConfig]}};function registerClass(cls){assert(cls.className!=null,()=>"Class being registered does not have the static className property defined."),assert(typeof cls.className=="string",()=>"className is required to be a string, but got type "+typeof cls.className),assert(cls.className.length>0,()=>"Class being registered has an empty-string as its className, which is disallowed."),SerializationMap.register(cls)}var test_util_exports={};__export2(test_util_exports,{TEST_EPSILON_FLOAT16:()=>TEST_EPSILON_FLOAT16,encodeStrings:()=>encodeStrings,expectArrayBuffersEqual:()=>expectArrayBuffersEqual,expectArraysClose:()=>expectArraysClose,expectArraysEqual:()=>expectArraysEqual,expectNumbersClose:()=>expectNumbersClose,expectPromiseToFail:()=>expectPromiseToFail,expectValuesInRange:()=>expectValuesInRange,testEpsilon:()=>testEpsilon});var TEST_EPSILON_FLOAT32=.001,TEST_EPSILON_FLOAT16=.1;function expectArraysClose(actual,expected,epsilon3){return epsilon3==null&&(epsilon3=testEpsilon()),expectArraysPredicate(actual,expected,(a,b)=>areClose(a,b,epsilon3))}function testEpsilon(){return ENGINE.backend.floatPrecision()===32?TEST_EPSILON_FLOAT32:TEST_EPSILON_FLOAT16}function expectArraysPredicate(actual,expected,predicate){let checkClassType=!0;if((isTypedArray(actual)||isTypedArray(expected))&&(checkClassType=!1),isTypedArray(actual)&&isTypedArray(expected)&&(checkClassType=!0),checkClassType){let aType=actual.constructor.name,bType=expected.constructor.name;if(aType!==bType)throw new Error(`Arrays are of different type. Actual: ${aType}. Expected: ${bType}`)}if(Array.isArray(actual)&&Array.isArray(expected)){let actualShape=inferShape(actual),expectedShape=inferShape(expected);if(!arraysEqual(actualShape,expectedShape))throw new Error(`Arrays have different shapes. Actual: [${actualShape}]. Expected: [${expectedShape}]`)}let actualFlat=isTypedArray(actual)?actual:flatten(actual),expectedFlat=isTypedArray(expected)?expected:flatten(expected);if(actualFlat.length!==expectedFlat.length)throw new Error(`Arrays have different lengths actual: ${actualFlat.length} vs expected: ${expectedFlat.length}. +Manifest JSON has weights with names: ${allManifestWeightNames.join(", ")}.`)}let groupIndicesToFetch=groupIndicesToFetchMap.reduce((accumulator,shouldFetch,i)=>(shouldFetch&&accumulator.push(i),accumulator),[]),fetchUrls=[];groupIndicesToFetch.forEach(i=>{manifest[i].paths.forEach(filepath=>{let fetchUrl=filePathPrefix+(filePathPrefix.endsWith("/")?"":"/")+filepath;fetchUrls.push(fetchUrl)})});let buffers=await fetchWeightsFunction(fetchUrls),weightsTensorMap={},bufferIndexOffset=0;return groupIndicesToFetch.forEach(i=>{let numBuffers=manifest[i].paths.length,groupBytes=0;for(let i2=0;i2{let byteBuffer=groupBuffer.slice(weightsEntry.groupOffset,weightsEntry.groupOffset+weightsEntry.sizeBytes),nameToTensorMap=decodeWeights(byteBuffer,[weightsEntry.manifestEntry]);for(let name in nameToTensorMap)weightsTensorMap[name]=nameToTensorMap[name]}),bufferIndexOffset+=numBuffers}),weightsTensorMap}}var OCTET_STREAM_MIME_TYPE="application/octet-stream",JSON_TYPE="application/json",HTTPRequest=class{constructor(path,loadOptions){if(this.DEFAULT_METHOD="POST",loadOptions==null&&(loadOptions={}),this.weightPathPrefix=loadOptions.weightPathPrefix,this.onProgress=loadOptions.onProgress,this.weightUrlConverter=loadOptions.weightUrlConverter,loadOptions.fetchFunc!=null?(assert(typeof loadOptions.fetchFunc=="function",()=>"Must pass a function that matches the signature of `fetch` (see https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)"),this.fetch=loadOptions.fetchFunc):this.fetch=env().platform.fetch,assert(path!=null&&path.length>0,()=>"URL path for http must not be null, undefined or empty."),Array.isArray(path)&&assert(path.length===2,()=>`URL paths for http must have a length of 2, (actual length is ${path.length}).`),this.path=path,loadOptions.requestInit!=null&&loadOptions.requestInit.body!=null)throw new Error("requestInit is expected to have no pre-existing body, but has one.");this.requestInit=loadOptions.requestInit||{}}async save(modelArtifacts){if(modelArtifacts.modelTopology instanceof ArrayBuffer)throw new Error("BrowserHTTPRequest.save() does not support saving model topology in binary formats yet.");let init2=Object.assign({method:this.DEFAULT_METHOD},this.requestInit);init2.body=new FormData;let weightsManifest=[{paths:["./model.weights.bin"],weights:modelArtifacts.weightSpecs}],modelTopologyAndWeightManifest={modelTopology:modelArtifacts.modelTopology,format:modelArtifacts.format,generatedBy:modelArtifacts.generatedBy,convertedBy:modelArtifacts.convertedBy,weightsManifest};modelArtifacts.signature!=null&&(modelTopologyAndWeightManifest.signature=modelArtifacts.signature),modelArtifacts.userDefinedMetadata!=null&&(modelTopologyAndWeightManifest.userDefinedMetadata=modelArtifacts.userDefinedMetadata),modelArtifacts.modelInitializer!=null&&(modelTopologyAndWeightManifest.modelInitializer=modelArtifacts.modelInitializer),init2.body.append("model.json",new Blob([JSON.stringify(modelTopologyAndWeightManifest)],{type:JSON_TYPE}),"model.json"),modelArtifacts.weightData!=null&&init2.body.append("model.weights.bin",new Blob([modelArtifacts.weightData],{type:OCTET_STREAM_MIME_TYPE}),"model.weights.bin");let response=await this.fetch(this.path,init2);if(response.ok)return{modelArtifactsInfo:getModelArtifactsInfoForJSON(modelArtifacts),responses:[response]};throw new Error(`BrowserHTTPRequest.save() failed due to HTTP response status ${response.status}.`)}async load(){let modelConfigRequest=await this.fetch(this.path,this.requestInit);if(!modelConfigRequest.ok)throw new Error(`Request to ${this.path} failed with status code ${modelConfigRequest.status}. Please verify this URL points to the model JSON of the model to load.`);let modelConfig;try{modelConfig=await modelConfigRequest.json()}catch(e){let message=`Failed to parse model JSON of response from ${this.path}.`;throw this.path.endsWith(".pb")?message+=" Your path contains a .pb file extension. Support for .pb models have been removed in TensorFlow.js 1.0 in favor of .json models. You can re-convert your Python TensorFlow model using the TensorFlow.js 1.0 conversion scripts or you can convert your.pb models with the 'pb2json'NPM script in the tensorflow/tfjs-converter repository.":message+=" Please make sure the server is serving valid JSON for this request.",new Error(message)}let modelTopology=modelConfig.modelTopology,weightsManifest=modelConfig.weightsManifest,generatedBy=modelConfig.generatedBy,convertedBy=modelConfig.convertedBy,format=modelConfig.format,signature=modelConfig.signature,userDefinedMetadata=modelConfig.userDefinedMetadata;if(modelTopology==null&&weightsManifest==null)throw new Error(`The JSON from HTTP path ${this.path} contains neither model topology or manifest for weights.`);let weightSpecs,weightData;weightsManifest!=null&&([weightSpecs,weightData]=await this.loadWeights(weightsManifest));let artifacts={modelTopology,weightSpecs,weightData,generatedBy,convertedBy,format};signature!=null&&(artifacts.signature=signature),userDefinedMetadata!=null&&(artifacts.userDefinedMetadata=userDefinedMetadata);let initializer=modelConfig.modelInitializer;return initializer&&(artifacts.modelInitializer=initializer),artifacts}async loadWeights(weightsManifest){let weightPath=Array.isArray(this.path)?this.path[1]:this.path,[prefix,suffix]=parseUrl(weightPath),pathPrefix=this.weightPathPrefix||prefix,weightSpecs=[];for(let entry of weightsManifest)weightSpecs.push(...entry.weights);let fetchURLs=[],urlPromises=[];for(let weightsGroup of weightsManifest)for(let path of weightsGroup.paths)this.weightUrlConverter!=null?urlPromises.push(this.weightUrlConverter(path)):fetchURLs.push(pathPrefix+path+suffix);this.weightUrlConverter&&fetchURLs.push(...await Promise.all(urlPromises));let buffers=await loadWeightsAsArrayBuffer(fetchURLs,{requestInit:this.requestInit,fetchFunc:this.fetch,onProgress:this.onProgress});return[weightSpecs,concatenateArrayBuffers(buffers)]}};HTTPRequest.URL_SCHEME_REGEX=/^https?:\/\//;function parseUrl(url){let lastSlash=url.lastIndexOf("/"),lastSearchParam=url.lastIndexOf("?"),prefix=url.substring(0,lastSlash),suffix=lastSearchParam>lastSlash?url.substring(lastSearchParam):"";return[prefix+"/",suffix]}function isHTTPScheme(url){return url.match(HTTPRequest.URL_SCHEME_REGEX)!=null}var httpRouter=(url,loadOptions)=>{if(typeof fetch=="undefined"&&(loadOptions==null||loadOptions.fetchFunc==null))return null;{let isHTTP=!0;if(Array.isArray(url)?isHTTP=url.every(urlItem=>isHTTPScheme(urlItem)):isHTTP=isHTTPScheme(url),isHTTP)return http(url,loadOptions)}return null};IORouterRegistry.registerSaveRouter(httpRouter);IORouterRegistry.registerLoadRouter(httpRouter);function http(path,loadOptions){return new HTTPRequest(path,loadOptions)}function browserHTTPRequest(path,loadOptions){return http(path,loadOptions)}var PassthroughLoader=class{constructor(modelArtifacts){this.modelArtifacts=modelArtifacts}async load(){return this.modelArtifacts}},PassthroughSaver=class{constructor(saveHandler){this.saveHandler=saveHandler}async save(modelArtifacts){return this.saveHandler(modelArtifacts)}};function fromMemory(modelArtifacts,weightSpecs,weightData,trainingConfig){return arguments.length===1?modelArtifacts.modelTopology!=null||modelArtifacts.weightSpecs!=null?new PassthroughLoader(modelArtifacts):(console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."),new PassthroughLoader({modelTopology:modelArtifacts})):(console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."),new PassthroughLoader({modelTopology:modelArtifacts,weightSpecs,weightData,trainingConfig}))}function withSaveHandler(saveHandler){return new PassthroughSaver(saveHandler)}var math_exports={};__export2(math_exports,{confusionMatrix:()=>confusionMatrix});function matMul_(a,b,transposeA=!1,transposeB=!1){let $a=convertToTensor(a,"a","matMul"),$b=convertToTensor(b,"b","matMul");[$a,$b]=makeTypesMatch($a,$b);let inputs={a:$a,b:$b},attrs={transposeA,transposeB};return ENGINE.runKernel(BatchMatMul,inputs,attrs)}var matMul=op({matMul_});function oneHot_(indices,depth,onValue=1,offValue=0){if(depth<2)throw new Error(`Error in oneHot: depth must be >=2, but it is ${depth}`);let inputs={indices:convertToTensor(indices,"indices","oneHot","int32")},attrs={depth,onValue,offValue};return ENGINE.runKernel(OneHot,inputs,attrs)}var oneHot=op({oneHot_});function transpose_(x,perm){let $x=convertToTensor(x,"x","transpose");if(perm==null&&(perm=$x.shape.map((s,i)=>i).reverse()),assert($x.rank===perm.length,()=>`Error in transpose: rank of input ${$x.rank} must match length of perm ${perm}.`),perm.forEach(axis=>{assert(axis>=0&&axis<$x.rank,()=>`All entries in 'perm' must be between 0 and ${$x.rank-1} but got ${perm}`)}),$x.rank<=1)return $x.clone();let inputs={x:$x},attrs={perm};return ENGINE.runKernel(Transpose,inputs,attrs)}var transpose=op({transpose_});function confusionMatrix_(labels,predictions,numClasses){let $labels=convertToTensor(labels,"labels","confusionMatrix"),$predictions=convertToTensor(predictions,"predictions","confusionMatrix");assert(numClasses==null||numClasses>0&&Number.isInteger(numClasses),()=>`If provided, numClasses must be a positive integer, but got ${numClasses}`),assert($labels.rank===1,()=>`Expected the rank of labels to be 1, but got ${$labels.rank}`),assert($predictions.rank===1,()=>`Expected the rank of predictions to be 1, but got ${$predictions.rank}`),assert($labels.shape[0]===$predictions.shape[0],()=>`Mismatch in the number of examples: ${$labels.shape[0]} vs. ${$predictions.shape[0]}. Labels and predictions should have the same number of elements.`),assert(numClasses>0&&Number.isInteger(numClasses),()=>`numClasses is required to be a positive integer, but got ${numClasses}`);let oneHotLabels=oneHot(cast($labels,"int32"),numClasses),oneHotPredictions=oneHot(cast($predictions,"int32"),numClasses),oneHotLabelsT=transpose(oneHotLabels),product=matMul(oneHotLabelsT,oneHotPredictions);return cast(product,"int32")}var confusionMatrix=op({confusionMatrix_}),browser_exports={};__export2(browser_exports,{fromPixels:()=>fromPixels,toPixels:()=>toPixels});function tensor3d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==3)throw new Error("tensor3d() requires shape to have three numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==3&&inferredShape.length!==1)throw new Error("tensor3d() requires values to be number[][][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor3d() requires shape to be provided when `values` are a flat array");return makeTensor(values,shape,inferredShape,dtype)}var fromPixels2DContext;function fromPixels_(pixels,numChannels=3){if(numChannels>4)throw new Error("Cannot construct Tensor with more than 4 channels from pixels.");if(pixels==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let isPixelData=!1,isImageData=!1,isVideo=!1,isImage=!1,isCanvasLike=!1,isImageBitmap=!1;if(pixels.data instanceof Uint8Array)isPixelData=!0;else if(typeof ImageData!="undefined"&&pixels instanceof ImageData)isImageData=!0;else if(typeof HTMLVideoElement!="undefined"&&pixels instanceof HTMLVideoElement)isVideo=!0;else if(typeof HTMLImageElement!="undefined"&&pixels instanceof HTMLImageElement)isImage=!0;else if(pixels.getContext!=null)isCanvasLike=!0;else if(typeof ImageBitmap!="undefined"&&pixels instanceof ImageBitmap)isImageBitmap=!0;else throw new Error(`pixels passed to tf.browser.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData in browser, or OffscreenCanvas, ImageData in webworker or {data: Uint32Array, width: number, height: number}, but was ${pixels.constructor.name}`);if(isVideo){let HAVE_CURRENT_DATA_READY_STATE=2;if(isVideo&&pixels.readyState element.")}if(getKernel(FromPixels,ENGINE.backendName)!=null){let inputs={pixels},attrs={numChannels};return ENGINE.runKernel(FromPixels,inputs,attrs)}let[width,height]=isVideo?[pixels.videoWidth,pixels.videoHeight]:[pixels.width,pixels.height],vals;isCanvasLike?vals=pixels.getContext("2d").getImageData(0,0,width,height).data:isImageData||isPixelData?vals=pixels.data:(isImage||isVideo||isImageBitmap)&&(fromPixels2DContext==null&&(fromPixels2DContext=document.createElement("canvas").getContext("2d")),fromPixels2DContext.canvas.width=width,fromPixels2DContext.canvas.height=height,fromPixels2DContext.drawImage(pixels,0,0,width,height),vals=fromPixels2DContext.getImageData(0,0,width,height).data);let values;if(numChannels===4)values=new Int32Array(vals);else{let numPixels=width*height;values=new Int32Array(numPixels*numChannels);for(let i=0;i4||depth===2)throw new Error(`toPixels only supports depth of size 1, 3 or 4 but got ${depth}`);if($img.dtype!=="float32"&&$img.dtype!=="int32")throw new Error(`Unsupported type for toPixels: ${$img.dtype}. Please use float32 or int32 tensors.`);let data2=await $img.data(),multiplier=$img.dtype==="float32"?255:1,bytes=new Uint8ClampedArray(width*height*4);for(let i=0;i1)throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${value}.`)}else if($img.dtype==="int32"&&(value<0||value>255))throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${value}.`);depth===1?(rgba[0]=value*multiplier,rgba[1]=value*multiplier,rgba[2]=value*multiplier):rgba[d]=value*multiplier}let j=i*4;bytes[j+0]=Math.round(rgba[0]),bytes[j+1]=Math.round(rgba[1]),bytes[j+2]=Math.round(rgba[2]),bytes[j+3]=Math.round(rgba[3])}if(canvas!=null){canvas.width=width,canvas.height=height;let ctx=canvas.getContext("2d"),imageData=new ImageData(bytes,width,height);ctx.putImageData(imageData,0,0)}return $img!==img&&$img.dispose(),bytes}var fromPixels=op({fromPixels_}),gather_nd_util_exports={};__export2(gather_nd_util_exports,{prepareAndValidate:()=>prepareAndValidate});function prepareAndValidate(tensor2,indices){let tensorRank=tensor2.shape.length,indicesRank=indices.shape.length;if(tensorRank<1)throw new Error(`tf.gatherND() expects the input to be rank 1 or higher, but the rank was ${tensorRank}.`);if(indicesRank<1)throw new Error(`tf.gatherND() expects the indices to be rank 1 or higher, but the rank was ${indicesRank}.`);if(indices.dtype!=="int32")throw new Error(`tf.gatherND() expects the indices to be int32 type, but the dtype was ${indices.dtype}.`);if(indices.shape[indicesRank-1]>tensorRank)throw new Error(`index innermost dimension length must be <= tensor rank; saw: ${indices.shape[indicesRank-1]} vs. ${tensorRank}`);if(sizeFromShape(tensor2.shape)===0)throw new Error(`Requested more than 0 entries, but input is empty. Input shape: ${tensor2.shape}.`);let indicesShape=indices.shape,sliceRank=indicesShape[indicesShape.length-1],nResult=1;for(let i=0;istride/sliceSize),1].slice(0,sliceRank);return[resultShape,nResult,sliceSize,strides]}var scatter_nd_util_exports={};__export2(scatter_nd_util_exports,{calculateShapes:()=>calculateShapes,validateInput:()=>validateInput,validateUpdateShape:()=>validateUpdateShape});function validateUpdateShape(shape,indices,updates){let sliceDim=indices.rank>1?indices.shape[indices.rank-1]:1,batchDim=indices.rank>1?indices.rank-1:1,shapeError=`Must have updates.shape = indices.shape[:batchDim] + shape[sliceDim:], got updates.shape: ${updates.shape}, indices.shape: ${indices.shape}, shape: ${shape}, sliceDim: ${sliceDim}, and batchDim: ${batchDim}.`;if(updates.rank1?indices.shape[indicesRank-1]:1,totalNd=shape.length,sliceSize=1;for(let i=sliceRank;iassertParamsValid,computeFlatOffset:()=>computeFlatOffset,computeOutShape:()=>computeOutShape,getNormalizedAxes:()=>getNormalizedAxes,isSliceContinous:()=>isSliceContinous,maskToAxes:()=>maskToAxes,parseSliceParams:()=>parseSliceParams,sliceInfo:()=>sliceInfo,startForAxis:()=>startForAxis,startIndicesWithElidedDims:()=>startIndicesWithElidedDims,stopForAxis:()=>stopForAxis,stopIndicesWithElidedDims:()=>stopIndicesWithElidedDims,stridesForAxis:()=>stridesForAxis,stridesWithElidedDims:()=>stridesWithElidedDims});function assertParamsValid(input2,begin,size){let inputRank=input2.shape.length;assert(inputRank===begin.length,()=>`Error in slice${inputRank}D: Length of begin ${begin} must match the rank of the array (${inputRank}).`),assert(inputRank===size.length,()=>`Error in slice${inputRank}D: Length of size ${size} must match the rank of the array (${inputRank}).`);for(let i=0;i`Error in slice${inputRank}D: begin[${i}] + size[${i}] (${begin[i]+size[i]}) would overflow input.shape[${i}] (${input2.shape[i]})`)}function maskToAxes(mask){let axes=[],axis=0;for(;mask>0;)mask&1&&axes.push(axis),mask/=2,axis++;return axes}function computeOutShape(begin,end,strides){let size=[];for(let axis=0;axis0){let fullIndex=ellipsisAxes[0],numElidedAxes=numInterpolatedAxes+1;normalizedBegin=startIndicesWithElidedDims(beginMask,fullIndex,numElidedAxes,begin,inputShape),normalizedEnd=stopIndicesWithElidedDims(endMask,fullIndex,numElidedAxes,end,inputShape),normalizedStrides=stridesWithElidedDims(strides,fullIndex,numElidedAxes,inputShape)}else for(let axis=0;axis-1)newIndices[axis]=0;else{let originalAxis=unnormalizeAxis(ellipsisInsertionIndex,numElidedAxes,axis),originalValue=originalBegin[originalAxis];beginMask&1<-1)newIndices[axis]=Number.MAX_SAFE_INTEGER;else{let originalAxis=unnormalizeAxis(ellipsisInsertionIndex,numElidedAxes,axis),originalValue=originalEnd[originalAxis];endMask&1<0?start=Number.MIN_SAFE_INTEGER:start=Number.MAX_SAFE_INTEGER);let axisSize=inputShape[axis];return start<0&&(start+=axisSize),start=clamp(0,start,axisSize-1),start}function stopForAxis(endMask,stopIndices,strides,inputShape,axis,ellipsisMask){let stop=stopIndices[axis],stride=strides[axis]||1;(endMask&1<0?stop=Number.MAX_SAFE_INTEGER:stop=Number.MIN_SAFE_INTEGER);let axisSize=inputShape[axis];return stop<0&&(stop+=axisSize),stride>0?stop=clamp(0,stop,axisSize):stop=clamp(-1,stop,axisSize-1),stop}function isSliceContinous(shape,begin,size){let firstNonOneAxis=size.length;for(let i=0;i1){firstNonOneAxis=i;break}for(let i=firstNonOneAxis+1;i0||size[i]!==shape[i])return!1;return!0}function computeFlatOffset(begin,strides){let flatOffset=begin.length>0?begin[begin.length-1]:1;for(let i=0;i{assert(d!==-1,()=>"slice() does not support negative begin indexing.")});let size_;return size==null?size_=new Array(xRank).fill(-1):typeof size=="number"?size_=[size,...new Array(xRank-1).fill(-1)]:size.lengthd>=0?d:(assert(d===-1,()=>`Negative size values should be exactly -1 but got ${d} for the slice() size at index ${i}.`),x.shape[i]-begin_[i])),[begin_,size_]}function sliceInfo(xShape,begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask){let $begin=begin.slice(),$end=end.slice(),$strides=strides;strides==null&&($strides=new Array($begin.length));let ellipsisAxes=maskToAxes(ellipsisMask);if(ellipsisAxes.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(ellipsisMask!==0&&newAxisMask!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(ellipsisMask!==0&&shrinkAxisMask!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");let numInterpolatedAxes=xShape.length-$begin.length,expandAxes=maskToAxes(newAxisMask),newShape=xShape.slice();expandAxes.forEach(axis=>{$begin[axis]=0,$end[axis]=1,newShape.splice(axis,0,1)});let{begin:normalizedBegin,end:normalizedEnd,strides:normalizedStrides}=getNormalizedAxes(newShape,ellipsisAxes,numInterpolatedAxes,$begin,$end,$strides,beginMask,endMask,ellipsisMask);$begin=normalizedBegin,$end=normalizedEnd,$strides=normalizedStrides;let shrinkAxes=maskToAxes(shrinkAxisMask);shrinkAxes.forEach(axis=>{$end[axis]=$begin[axis]+1,$strides[axis]=1});let size=computeOutShape($begin,$end,$strides),outShape=size.filter((_,axis)=>shrinkAxes.indexOf(axis)===-1);return{nonStrided:$strides.every(v=>v===1),$begin,$end,$strides,size,newShape,outShape}}var serialization_exports={};__export2(serialization_exports,{Serializable:()=>Serializable,SerializationMap:()=>SerializationMap,registerClass:()=>registerClass});var Serializable=class{getClassName(){return this.constructor.className}static fromConfig(cls,config2){return new cls(config2)}},SerializationMap=class{constructor(){this.classNameMap={}}static getMap(){return SerializationMap.instance==null&&(SerializationMap.instance=new SerializationMap),SerializationMap.instance}static register(cls){SerializationMap.getMap().classNameMap[cls.className]=[cls,cls.fromConfig]}};function registerClass(cls){assert(cls.className!=null,()=>"Class being registered does not have the static className property defined."),assert(typeof cls.className=="string",()=>"className is required to be a string, but got type "+typeof cls.className),assert(cls.className.length>0,()=>"Class being registered has an empty-string as its className, which is disallowed."),SerializationMap.register(cls)}var test_util_exports={};__export2(test_util_exports,{TEST_EPSILON_FLOAT16:()=>TEST_EPSILON_FLOAT16,encodeStrings:()=>encodeStrings,expectArrayBuffersEqual:()=>expectArrayBuffersEqual,expectArraysClose:()=>expectArraysClose,expectArraysEqual:()=>expectArraysEqual,expectNumbersClose:()=>expectNumbersClose,expectPromiseToFail:()=>expectPromiseToFail,expectValuesInRange:()=>expectValuesInRange,testEpsilon:()=>testEpsilon});var TEST_EPSILON_FLOAT32=.001,TEST_EPSILON_FLOAT16=.1;function expectArraysClose(actual,expected,epsilon3){return epsilon3==null&&(epsilon3=testEpsilon()),expectArraysPredicate(actual,expected,(a,b)=>areClose(a,b,epsilon3))}function testEpsilon(){return ENGINE.backend.floatPrecision()===32?TEST_EPSILON_FLOAT32:TEST_EPSILON_FLOAT16}function expectArraysPredicate(actual,expected,predicate){let checkClassType=!0;if((isTypedArray(actual)||isTypedArray(expected))&&(checkClassType=!1),isTypedArray(actual)&&isTypedArray(expected)&&(checkClassType=!0),checkClassType){let aType=actual.constructor.name,bType=expected.constructor.name;if(aType!==bType)throw new Error(`Arrays are of different type. Actual: ${aType}. Expected: ${bType}`)}if(Array.isArray(actual)&&Array.isArray(expected)){let actualShape=inferShape(actual),expectedShape=inferShape(expected);if(!arraysEqual(actualShape,expectedShape))throw new Error(`Arrays have different shapes. Actual: [${actualShape}]. Expected: [${expectedShape}]`)}let actualFlat=isTypedArray(actual)?actual:flatten(actual),expectedFlat=isTypedArray(expected)?expected:flatten(expected);if(actualFlat.length!==expectedFlat.length)throw new Error(`Arrays have different lengths actual: ${actualFlat.length} vs expected: ${expectedFlat.length}. Actual: ${actualFlat}. Expected: ${expectedFlat}.`);for(let i=0;i`Dimension size must be evenly divisible by ${blockSize*blockSize} but is ${inputDepth} for depthToSpace with input shape ${$x.shape}`);let inputs={x:$x},attrs={blockSize,dataFormat};return ENGINE.runKernel(DepthToSpace,inputs,attrs)}var depthToSpace=op({depthToSpace_});function depthwiseConv2d_(x,filter,strides,pad3,dataFormat="NHWC",dilations=[1,1],dimRoundingMode){let $x=convertToTensor(x,"x","depthwiseConv2d"),$filter=convertToTensor(filter,"filter","depthwiseConv2d"),x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in depthwiseConv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert($filter.rank===4,()=>`Error in depthwiseConv2d: filter must be rank 4, but got rank ${$filter.rank}.`),assert(x4D.shape[3]===$filter.shape[2],()=>`Error in depthwiseConv2d: number of input channels (${x4D.shape[3]}) must match the inChannels dimension in filter ${$filter.shape[2]}.`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={x:x4D,filter:$filter},attrs={strides,pad:pad3,dataFormat,dilations,dimRoundingMode},res=ENGINE.runKernel(DepthwiseConv2dNative,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var depthwiseConv2d=op({depthwiseConv2d_});function diag_(x){let inputs={x:convertToTensor(x,"x","diag")};return ENGINE.runKernel(Diag,inputs)}var diag=op({diag_});function dilation2d_(x,filter,strides,pad3,dilations=[1,1],dataFormat="NHWC"){let $x=convertToTensor(x,"x","dilation2d"),$filter=convertToTensor(filter,"filter","dilation2d");assert($x.rank===3||$x.rank===4,()=>`Error in dilation2d: input must be rank 3 or 4, but got rank ${$x.rank}.`),assert($filter.rank===3,()=>`Error in dilation2d: filter must be rank 3, but got rank ${$filter.rank}.`),assert(dataFormat==="NHWC",()=>`Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${dataFormat}`);let x4D=$x,reshapedTo4D=!1;$x.rank===3&&(x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]]),reshapedTo4D=!0);let inputs={x:x4D,filter:$filter},attrs={strides,pad:pad3,dilations},res=ENGINE.runKernel(Dilation2D,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var dilation2d=op({dilation2d_});function getBroadcastDims(inShape,outShape){let inRank=inShape.length,dims=[];for(let i=0;i1&&a===1&&dims.unshift(dim)}return dims}function getReductionAxes(inShape,outShape){let result=[];for(let i=0;i1)&&result.unshift(outAxis)}return result}function assertAndGetBroadcastShape(shapeA,shapeB){let result=[],l=Math.max(shapeA.length,shapeB.length);for(let i=0;i"The first dimension of `a` must match the size of `condition`."),$condition.rank!==1&&assertShapesMatch($condition.shape,$broadcastedB.shape,"Error in where: ");let inputs={condition:$condition,t:$broadcastedA,e:$broadcastedB};return ENGINE.runKernel(Select,inputs)}var where=op({where_});function zerosLike_(x){let inputs={x:convertToTensor(x,"x","zerosLike")};return ENGINE.runKernel(ZerosLike,inputs)}var zerosLike=op({zerosLike_});function divNoNan_(a,b){let $a=convertToTensor(a,"a","div"),$b=convertToTensor(b,"b","div");[$a,$b]=makeTypesMatch($a,$b);let divResult=div($a,$b),zeros4=zerosLike(divResult),bEqualsZero=equal($b,zeros4);return where(bEqualsZero,zeros4,divResult)}var divNoNan=op({divNoNan_});function dot_(t1,t2){let $t1=convertToTensor(t1,"t1","dot"),$t2=convertToTensor(t2,"t2","dot");assert(($t1.rank===1||$t1.rank===2)&&($t2.rank===1||$t2.rank===2),()=>`Error in dot: inputs must all be rank 1 or 2, but got ranks ${$t1.rank} and ${$t2.rank}.`);let t1Inner=$t1.rank===1?$t1.size:$t1.shape[1],t2Inner=$t2.rank===1?$t2.size:$t2.shape[0];if(assert(t1Inner===t2Inner,()=>`Error in dot: inner dimensions of inputs must match, but got ${t1Inner} and ${t2Inner}.`),$t1.rank===1&&$t2.rank===1){let t12D=reshape($t1,[1,-1]),t22D=reshape($t2,[-1,1]),t1t2=matMul(t12D,t22D);return reshape(t1t2,[])}else if($t1.rank===1&&$t2.rank===2){let t12D=reshape($t1,[1,-1]),t22D=reshape($t2,[$t2.shape[0],$t2.shape[1]]),t1t2=matMul(t12D,t22D);return reshape(t1t2,[t1t2.size])}else if($t1.rank===2&&$t2.rank===1){let t22D=reshape($t2,[-1,1]),t1t2=matMul($t1,t22D);return reshape(t1t2,[t1t2.size])}else{let t22D=reshape($t2,[$t2.shape[0],$t2.shape[1]]);return matMul($t1,t22D)}}var dot=op({dot_});function elu_(x){let inputs={x:convertToTensor(x,"x","elu")};return ENGINE.runKernel(Elu,inputs)}var elu=op({elu_});function erf_(x){let $x=convertToTensor(x,"x","erf");assert($x.dtype==="int32"||$x.dtype==="float32",()=>"Input dtype must be `int32` or `float32`."),$x.dtype==="int32"&&($x=cast($x,"float32"));let inputs={x:$x};return ENGINE.runKernel(Erf,inputs)}var erf=op({erf_});function exp_(x){let inputs={x:convertToTensor(x,"x","exp")};return ENGINE.runKernel(Exp,inputs)}var exp=op({exp_});function expandDims_(x,axis=0){let $x=convertToTensor(x,"x","expandDims","string_or_numeric");assert(axis<=$x.rank,()=>"Axis must be <= rank of the tensor");let inputs={input:$x},attrs={dim:axis};return ENGINE.runKernel(ExpandDims,inputs,attrs)}var expandDims=op({expandDims_});function expm1_(x){let inputs={x:convertToTensor(x,"x","expm1")};return ENGINE.runKernel(Expm1,inputs)}var expm1=op({expm1_});function tile_(x,reps){let $x=convertToTensor(x,"x","tile","string_or_numeric");assert($x.rank===reps.length,()=>`Error in transpose: rank of input ${$x.rank} must match length of reps ${reps}.`);let inputs={x:$x},attrs={reps};return ENGINE.runKernel(Tile,inputs,attrs)}var tile=op({tile_});function eye_(numRows,numColumns,batchShape,dtype="float32"){numColumns==null&&(numColumns=numRows);let buff=buffer([numRows,numColumns],dtype),n=numRows<=numColumns?numRows:numColumns;for(let i=0;i`Error in localResponseNormalization: x must be rank 3 or 4 but got rank ${$x.rank}.`),assert(isInt(depthRadius),()=>`Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${depthRadius}.`);let x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]]));let inputs={x:x4D},attrs={depthRadius,bias,alpha,beta},res=ENGINE.runKernel(LRN,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var localResponseNormalization=op({localResponseNormalization_});function log_(x){let inputs={x:convertToTensor(x,"x","log")};return ENGINE.runKernel(Log,inputs)}var log2=op({log_});function log1p_(x){let inputs={x:convertToTensor(x,"x","log1p")};return ENGINE.runKernel(Log1p,inputs)}var log1p=op({log1p_});function grad(f){return assert(isFunction(f),()=>"The f passed in grad(f) must be a function"),(x,dy)=>{let $x=convertToTensor(x,"x","tf.grad","string_or_numeric"),$dy=dy!=null?convertToTensor(dy,"dy","tf.grad"):null;return ENGINE.tidy(()=>{let{value,grads:grads2}=ENGINE.gradients(()=>f($x),[$x],$dy);return $dy!=null&&assertShapesMatch(value.shape,$dy.shape,"The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)"),checkGrads(grads2),grads2[0]})}}function grads(f){return assert(isFunction(f),()=>"The f passed in grads(f) must be a function"),(args,dy)=>{assert(Array.isArray(args),()=>"The args passed in grads(f)(args) must be an array of `Tensor`s or `TensorLike`s");let $args=convertToTensorArray(args,"args","tf.grads","string_or_numeric"),$dy=dy!=null?convertToTensor(dy,"dy","tf.grads"):null;return ENGINE.tidy(()=>{let{value,grads:grads2}=ENGINE.gradients(()=>f(...$args),$args,$dy);return $dy!=null&&assertShapesMatch(value.shape,$dy.shape,"The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),checkGrads(grads2),grads2})}}function valueAndGrad(f){return assert(isFunction(f),()=>"The f passed in valueAndGrad(f) must be a function"),(x,dy)=>{assert(x instanceof Tensor,()=>"The x passed in valueAndGrad(f)(x) must be a tensor"),assert(dy==null||dy instanceof Tensor,()=>"The dy passed in valueAndGrad(f)(x, dy) must be a tensor");let{grads:grads2,value}=ENGINE.gradients(()=>f(x),[x],dy);return checkGrads(grads2),{grad:grads2[0],value}}}function valueAndGrads(f){return assert(isFunction(f),()=>"The f passed in valueAndGrads(f) must be a function"),(args,dy)=>{assert(Array.isArray(args)&&args.every(arg=>arg instanceof Tensor),()=>"The args passed in valueAndGrads(f)(args) must be array of tensors"),assert(dy==null||dy instanceof Tensor,()=>"The dy passed in valueAndGrads(f)(args, dy) must be a tensor");let res=ENGINE.gradients(()=>f(...args),args,dy);return dy!=null&&assertShapesMatch(res.value.shape,dy.shape,"The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),checkGrads(res.grads),res}}function variableGrads(f,varList){assert(isFunction(f),()=>"The f passed in variableGrads(f) must be a function"),assert(varList==null||Array.isArray(varList)&&varList.every(v=>v instanceof Variable),()=>"The varList passed in variableGrads(f, varList) must be an array of variables");let specifiedVarList=varList!=null;if(!specifiedVarList){varList=[];for(let varName in ENGINE.registeredVariables)varList.push(ENGINE.registeredVariables[varName])}let specifiedNonTrainable=specifiedVarList?varList.filter(variable2=>!variable2.trainable):null,originalVarCount=varList.length;varList=varList.filter(variable2=>variable2.trainable),assert(varList.length>0,()=>`variableGrads() expects at least one of the input variables to be trainable, but none of the ${originalVarCount} variables is trainable.`);let allowNoGradients=!0,{value,grads:grads2}=ENGINE.gradients(f,varList,null,allowNoGradients);assert(grads2.some(g=>g!=null),()=>"Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize()."),assert(value.rank===0,()=>`The f passed in variableGrads(f) must return a scalar, but it returned a rank-${value.rank} tensor`);let namedGrads={};return varList.forEach((v,i)=>{grads2[i]!=null&&(namedGrads[v.name]=grads2[i])}),specifiedNonTrainable!=null&&specifiedNonTrainable.forEach(v=>namedGrads[v.name]=null),{value,grads:namedGrads}}function customGrad(f){return ENGINE.customGrad(f)}function checkGrads(grads2){if(grads2.filter(g=>g==null).length>0)throw new Error(`Cannot compute gradient of y=f(x) with respect to x. Make sure that - the f you passed encloses all operations that lead from x to y.`)}function neg_(x){let inputs={x:convertToTensor(x,"x","neg")};return ENGINE.runKernel(Neg,inputs)}var neg=op({neg_});function softplus_(x){let inputs={x:convertToTensor(x,"x","softplus")};return ENGINE.runKernel(Softplus,inputs)}var softplus=op({softplus_});function logSigmoid_(x){let $x=convertToTensor(x,"x","logSigmoid");return customGrad(x2=>({value:neg(softplus(neg(x2))),gradFunc:dy=>mul(dy,sigmoid(neg(x2)))}))($x)}var logSigmoid=op({logSigmoid_});function max_(x,axis=null,keepDims=!1){let inputs={x:convertToTensor(x,"x","max")},attrs={reductionIndices:axis,keepDims};return ENGINE.runKernel(Max,inputs,attrs)}var max=op({max_});function sub_(a,b){let $a=convertToTensor(a,"a","sub"),$b=convertToTensor(b,"b","sub");[$a,$b]=makeTypesMatch($a,$b);let inputs={a:$a,b:$b};return ENGINE.runKernel(Sub,inputs)}var sub=op({sub_});function sum_(x,axis=null,keepDims=!1){let $x=convertToTensor(x,"x","sum");$x.dtype==="bool"&&($x=cast($x,"int32"));let inputs={x:$x},attrs={axis,keepDims};return ENGINE.runKernel(Sum,inputs,attrs)}var sum2=op({sum_});function logSoftmax_(logits,axis=-1){let $logits=convertToTensor(logits,"logits","logSoftmax");if(axis===-1&&(axis=$logits.rank-1),axis!==$logits.rank-1)throw Error(`Log Softmax along a non-last dimension is not yet supported. Logits was rank ${$logits.rank} and axis was ${axis}`);return customGrad((logits2,save)=>{let keepDims=!0,xMax=max(logits2,axis,!0),shifted=sub(logits2,xMax),value=sub(cast(shifted,"float32"),log2(sum2(exp(shifted),axis,keepDims)));return save([value]),{value,gradFunc:(dy,saved)=>{let[value2]=saved,keepDims2=!0,softmax6=exp(value2);return sub(dy,mul(sum2(dy,axis,keepDims2),softmax6))}}})($logits)}var logSoftmax=op({logSoftmax_});function axesAreInnerMostDims(axes,rank){for(let i=0;iaShape[dim]);return[outShape,reduceShape]}function expandShapeToKeepDim(shape,axes){let reduceSubShape=axes.map(x=>1);return combineLocations(shape,reduceSubShape,axes)}function assertAxesAreInnerMostDims(msg,axes,rank){assert(axesAreInnerMostDims(axes,rank),()=>`${msg} supports only inner-most axes for now. Got axes ${axes} and rank-${rank} input.`)}function getAxesPermutation(axes,rank){if(axesAreInnerMostDims(axes,rank))return null;let result=[];for(let i=0;iresult.push(axis)),result}function getUndoAxesPermutation(axes){return axes.map((axis,i)=>[i,axis]).sort((a,b)=>a[1]-b[1]).map(x=>x[0])}function getInnerMostAxes(numAxes,rank){let res=[];for(let i=rank-numAxes;i`Error in maxPool: input must be rank 4 but got rank ${x4D.rank}.`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in maxPool: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={x:x4D},attrs={filterSize,strides,pad:pad3,dimRoundingMode},res=ENGINE.runKernel(MaxPool,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var maxPool=op({maxPool_});function maxPool3d_(x,filterSize=[1,1,1],strides,pad3,dimRoundingMode,dataFormat="NDHWC",dilations){dilations==null?dilations=[1,1,1]:deprecationWarn("dilations is deprecated, this field will be gone in v3.0.0.");let $x=convertToTensor(x,"x","maxPool3d"),x5D=$x,reshapedTo5D=!1;$x.rank===4&&(reshapedTo5D=!0,x5D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2],$x.shape[3]])),assert(x5D.rank===5,()=>`Error in maxPool3d: x must be rank 5 but got rank ${x5D.rank}.`),assert(dataFormat==="NDHWC",()=>`Error in maxPool3d: Only NDHWC is currently supported, but got dataFormat of ${dataFormat}`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in maxPool3d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in maxPool3d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={x:x5D},attrs={filterSize,strides,pad:pad3,dimRoundingMode,dataFormat,dilations},res=ENGINE.runKernel(MaxPool3D,inputs,attrs);return reshapedTo5D?reshape(res,[res.shape[1],res.shape[2],res.shape[3],res.shape[4]]):res}var maxPool3d=op({maxPool3d_});function maxPoolWithArgmax_(x,filterSize,strides,pad3,includeBatchInIndex=!1){let inputs={x:convertToTensor(x,"x","maxPoolWithArgmax")},attrs={filterSize,strides,pad:pad3,includeBatchInIndex},result=ENGINE.runKernel(MaxPoolWithArgmax,inputs,attrs);return{result:result[0],indexes:result[1]}}var maxPoolWithArgmax=op({maxPoolWithArgmax_});function maximum_(a,b){let $a=convertToTensor(a,"a","maximum"),$b=convertToTensor(b,"b","maximum");[$a,$b]=makeTypesMatch($a,$b),$a.dtype==="bool"&&($a=cast($a,"int32"),$b=cast($b,"int32")),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(Maximum,inputs)}var maximum=op({maximum_});function mean_(x,axis=null,keepDims=!1){let inputs={x:convertToTensor(x,"x","mean")},attrs={axis,keepDims};return ENGINE.runKernel(Mean,inputs,attrs)}var mean=op({mean_});function min_(x,axis=null,keepDims=!1){let inputs={x:convertToTensor(x,"x","min")},attrs={axis,keepDims};return ENGINE.runKernel(Min,inputs,attrs)}var min=op({min_});function minimum_(a,b){let $a=convertToTensor(a,"a","minimum"),$b=convertToTensor(b,"b","minimum");[$a,$b]=makeTypesMatch($a,$b),$a.dtype==="bool"&&($a=cast($a,"int32"),$b=cast($b,"int32")),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(Minimum,inputs)}var minimum=op({minimum_});function mirrorPad_(x,paddings,mode){assert(mode==="reflect"||mode==="symmetric",()=>`Invalid mode. Mode must be either reflect or symmetric. Got ${mode}.`);let $x=convertToTensor(x,"x","mirrorPad");if($x.rank===0)throw new Error("mirrorPad(scalar) is not defined. Pass non-scalar to mirrorPad");assert(paddings.length===$x.rank,()=>`Padding doesn't match input. Must be ${$x.rank}. Got ${paddings.length}.`);let shapeOffset=mode==="reflect"?1:0;for(let i=0;i<$x.rank;i++)assert(paddings[i].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),assert(paddings[i][0]>=0&&paddings[i][0]<=$x.shape[i]-shapeOffset&&paddings[i][1]>=0&&paddings[i][1]<=$x.shape[i]-shapeOffset,()=>`Padding in dimension ${i} cannot be greater than or equal to ${$x.shape[i]-shapeOffset} or less than 0 for input of shape ${$x.shape}`);let attrs={paddings,mode},inputs={x:$x};return ENGINE.runKernel(MirrorPad,inputs,attrs)}var mirrorPad=op({mirrorPad_});function mod_(a,b){let $a=convertToTensor(a,"a","mod"),$b=convertToTensor(b,"b","mod");[$a,$b]=makeTypesMatch($a,$b);let inputs={a:$a,b:$b};return ENGINE.runKernel(Mod,inputs)}var mod=op({mod_});function square_(x){let $x=convertToTensor(x,"x","square"),attrs={};return ENGINE.runKernel("Square",{x:$x},attrs)}var square=op({square_});function moments_(x,axis=null,keepDims=!1){x=convertToTensor(x,"x","moments");let axes=parseAxisParam(axis,x.shape),xMean=mean(x,axes,keepDims),keepDimsShape=xMean.shape;keepDims||(keepDimsShape=expandShapeToKeepDim(xMean.shape,axes));let devSquared=square(sub(cast(x,"float32"),reshape(xMean,keepDimsShape))),variance=mean(devSquared,axes,keepDims);return{mean:xMean,variance}}var moments=op({moments_});function multiRNNCell_(lstmCells,data2,c,h){let $data=convertToTensor(data2,"data","multiRNNCell"),$c=convertToTensorArray(c,"c","multiRNNCell"),$h=convertToTensorArray(h,"h","multiRNNCell"),input2=$data,newStates=[];for(let i=0;i2)throw new Error(`Rank of probabilities must be 1 or 2, but is ${origRank}`);seed=seed||Math.random();let inputs={logits:origRank===1?reshape($logits,[1,-1]):$logits},attrs={numSamples,seed,normalized},res=ENGINE.runKernel(Multinomial,inputs,attrs);return origRank===1?reshape(res,[res.size]):res}var multinomial=op({multinomial_});function notEqual_(a,b){let $a=convertToTensor(a,"a","notEqual"),$b=convertToTensor(b,"b","notEqual");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(NotEqual,inputs)}var notEqual=op({notEqual_});function zeros(shape,dtype="float32"){if(dtype==="complex64"){let real4=zeros(shape,"float32"),imag4=zeros(shape,"float32");return complex(real4,imag4)}let values=makeZerosTypedArray(sizeFromShape(shape),dtype);return ENGINE.makeTensor(values,shape,dtype)}function ones2(shape,dtype="float32"){if(dtype==="complex64"){let real4=ones2(shape,"float32"),imag4=zeros(shape,"float32");return complex(real4,imag4)}let values=makeOnesTypedArray(sizeFromShape(shape),dtype);return ENGINE.makeTensor(values,shape,dtype)}function onesLike_(x){let inputs={x:convertToTensor(x,"x","onesLike")};return ENGINE.runKernel(OnesLike,inputs)}var onesLike=op({onesLike_});function outerProduct_(v1,v2){let $v1=convertToTensor(v1,"v1","outerProduct"),$v2=convertToTensor(v2,"v2","outerProduct");assert($v1.rank===1&&$v2.rank===1,()=>`Error in outerProduct: inputs must be rank 1, but got ranks ${$v1.rank} and ${$v2.rank}.`);let v12D=reshape($v1,[-1,1]),v22D=reshape($v2,[1,-1]);return matMul(v12D,v22D)}var outerProduct=op({outerProduct_});function pad_(x,paddings,constantValue=0){let $x=convertToTensor(x,"x","pad");if($x.rank===0)throw new Error("pad(scalar) is not defined. Pass non-scalar to pad");let attrs={paddings,constantValue},inputs={x:$x};return ENGINE.runKernel(PadV2,inputs,attrs)}var pad=op({pad_});function pad1d_(x,paddings,constantValue=0){return assert(paddings.length===2,()=>"Invalid number of paddings. Must be length of 2."),pad(x,[paddings],constantValue)}var pad1d=op({pad1d_});function pad2d_(x,paddings,constantValue=0){return assert(paddings.length===2&&paddings[0].length===2&&paddings[1].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),pad(x,paddings,constantValue)}var pad2d=op({pad2d_});function pad3d_(x,paddings,constantValue=0){return assert(paddings.length===3&&paddings[0].length===2&&paddings[1].length===2&&paddings[2].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),pad(x,paddings,constantValue)}var pad3d=op({pad3d_});function pad4d_(x,paddings,constantValue=0){return assert(paddings.length===4&&paddings[0].length===2&&paddings[1].length===2&&paddings[2].length===2&&paddings[3].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),pad(x,paddings,constantValue)}var pad4d=op({pad4d_});function spaceToBatchND_(x,blockShape,paddings){let $x=convertToTensor(x,"x","spaceToBatchND");assert($x.rank>=1+blockShape.length,()=>`input rank ${$x.rank} should be > than [blockShape] ${blockShape.length}`),assert(paddings.length===blockShape.length,()=>`paddings.shape[0] ${paddings.length} must be equal to [blockShape] ${blockShape.length}`),assert($x.shape.reduce((a,b,i)=>i>0&&i<=blockShape.length?a&&(b+paddings[i-1][0]+paddings[i-1][1])%blockShape[i-1]==0:a,!0),()=>`input spatial dimensions ${$x.shape.slice(1)} with paddings ${paddings.toString()} must be divisible by blockShapes ${blockShape.toString()}`);let inputs={x:$x},attrs={blockShape,paddings};return ENGINE.runKernel(SpaceToBatchND,inputs,attrs)}var spaceToBatchND=op({spaceToBatchND_});function pool_(input2,windowShape,poolingType,pad3,dilations,strides){dilations==null&&(dilations=[1,1]),strides==null&&(strides=1),pad3===0&&(pad3="valid");let $x=convertToTensor(input2,"x","maxPool"),x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in pool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);let convInfo=computePool2DInfo(x4D.shape,windowShape,strides,dilations,pad3),dilation=[convInfo.dilationHeight,convInfo.dilationWidth],basePadding;pad3==="same"?basePadding=withSpaceToBatchBasePaddings([convInfo.filterHeight,convInfo.filterWidth],dilation):basePadding=[[0,0],[0,0]];let isDilationOne=dilation[0]===1&&dilation[1]===1,[adjustedPadding,adjustedCrops]=requiredSpaceToBatchPaddings([convInfo.inHeight,convInfo.inWidth],dilation,basePadding),convertedPad=isDilationOne?pad3:"valid",convertedX=isDilationOne?x4D:spaceToBatchND(x4D,dilation,adjustedPadding),y=(poolingType==="avg"?()=>avgPool(convertedX,windowShape,strides,convertedPad):()=>maxPool(convertedX,windowShape,strides,convertedPad))(),res=isDilationOne?y:batchToSpaceND(y,dilation,adjustedCrops);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}function requiredSpaceToBatchPaddings(inputShape,blockShape,basePadding){let padStart=basePadding.map(b=>b[0]),origPadEnd=basePadding.map(b=>b[1]),fullInputShape=inputShape.concat(padStart,origPadEnd),padEndExtra=blockShape.map((b,i)=>(b-fullInputShape[i]%b)%b),padEnd=origPadEnd.map((s,i)=>s+padEndExtra[i]),paddings=blockShape.map((_,i)=>[padStart[i],padEnd[i]]),crops=blockShape.map((_,i)=>[0,padEndExtra[i]]);return[paddings,crops]}function withSpaceToBatchBasePaddings(filterShape,dilation){let padExtraShape=filterShape.map((s,i)=>s+(s-1)*(dilation[i]-1)).map(s=>s-1),padExtraStart=padExtraShape.map(s=>Math.floor(s/2)),padExtraEnd=padExtraShape.map((s,i)=>s-padExtraStart[i]);return padExtraShape.map((_,i)=>[padExtraStart[i],padExtraEnd[i]])}var pool=op({pool_});function pow_(base2,exp4){let $base=convertToTensor(base2,"base","pow"),$exp=convertToTensor(exp4,"exp","pow");[$base,$exp]=makeTypesMatch($base,$exp);let inputs={a:$base,b:$exp};return ENGINE.runKernel(Pow,inputs)}var pow=op({pow_});function prelu_(x,alpha){let $x=convertToTensor(x,"x","prelu"),$alpha=convertToTensor(alpha,"alpha","prelu"),inputs={x:$x,alpha:$alpha};return ENGINE.runKernel(Prelu,inputs)}var prelu=op({prelu_});function prod_(x,axis=null,keepDims=!1){let $x=convertToTensor(x,"x","prod");$x.dtype==="bool"&&($x=cast($x,"int32"));let inputs={x:$x},attrs={axis,keepDims};return ENGINE.runKernel(Prod,inputs,attrs)}var prod=op({prod_});function rand_(shape,randFunction,dtype){let size=sizeFromShape(shape),values=null;if(dtype==null||dtype==="float32")values=new Float32Array(size);else if(dtype==="int32")values=new Int32Array(size);else if(dtype==="bool")values=new Uint8Array(size);else throw new Error(`Unknown data type ${dtype}`);for(let i=0;i=1||s===0);let mul2=Math.sqrt(-2*Math.log(s)/s);resultX=this.mean+this.stdDev*v1*mul2,resultY=this.mean+this.stdDev*v2*mul2,(!this.truncated||this.isValidTruncated(resultX))&&(isValid=!0)}return(!this.truncated||this.isValidTruncated(resultY))&&(this.nextVal=this.convertValue(resultY)),this.convertValue(resultX)}convertValue(value){return this.dtype==null||this.dtype==="float32"?value:Math.round(value)}isValidTruncated(value){return value<=this.upper&&value>=this.lower}},RandGamma=class{constructor(alpha,beta,dtype,seed){this.alpha=alpha,this.beta=1/beta,this.dtype=dtype;let seedValue=seed||Math.random();this.randu=seedrandom.alea(seedValue.toString()),this.randn=new MPRandGauss(0,1,dtype,!1,this.randu()),alpha<1?this.d=alpha+2/3:this.d=alpha-1/3,this.c=1/Math.sqrt(9*this.d)}nextValue(){let x2,v0,v1,x,u,v;for(;;){do x=this.randn.nextValue(),v=1+this.c*x;while(v<=0);if(v*=v*v,x2=x*x,v0=1-.331*x2*x2,v1=.5*x2+this.d*(1-v+Math.log(v)),u=this.randu(),uthis.dtype==null||this.dtype==="float32",this.min=min6,this.range=max6-min6,this.dtype=dtype,seed==null&&(seed=Math.random()),typeof seed=="number"&&(seed=seed.toString()),!this.canReturnFloat()&&this.range<=1)throw new Error(`The difference between ${min6} - ${max6} <= 1 and dtype is not float`);this.random=seedrandom.alea(seed)}convertValue(value){return this.canReturnFloat()?value:Math.round(value)}nextValue(){return this.convertValue(this.min+this.range*this.random())}};function randomGamma_(shape,alpha,beta=1,dtype="float32",seed){if(beta==null&&(beta=1),dtype==null&&(dtype="float32"),dtype!=="float32"&&dtype!=="int32")throw new Error(`Unsupported data type ${dtype}`);let rgamma=new RandGamma(alpha,beta,dtype,seed),res=buffer(shape,dtype);for(let i=0;i`Error in reverse1D: x must be rank 1 but got rank ${$x.rank}.`),reverse($x,0)}var reverse1d=op({reverse1d_});function reverse2d_(x,axis){let $x=convertToTensor(x,"x","reverse");return assert($x.rank===2,()=>`Error in reverse2D: x must be rank 2 but got rank ${$x.rank}.`),reverse($x,axis)}var reverse2d=op({reverse2d_});function reverse3d_(x,axis){let $x=convertToTensor(x,"x","reverse");return assert($x.rank===3,()=>`Error in reverse3D: x must be rank 3 but got rank ${$x.rank}.`),reverse($x,axis)}var reverse3d=op({reverse3d_});function reverse4d_(x,axis){let $x=convertToTensor(x,"x","reverse");return assert($x.rank===4,()=>`Error in reverse4D: x must be rank 4 but got rank ${$x.rank}.`),reverse($x,axis)}var reverse4d=op({reverse4d_});function round_(x){let inputs={x:convertToTensor(x,"x","round")};return ENGINE.runKernel(Round,inputs)}var round2=op({round_});function rsqrt_(x){let inputs={x:convertToTensor(x,"x","rsqrt")};return ENGINE.runKernel(Rsqrt,inputs)}var rsqrt=op({rsqrt_});function scalar(value,dtype){if((isTypedArray(value)&&dtype!=="string"||Array.isArray(value))&&dtype!=="complex64")throw new Error("Error creating a new Scalar: value must be a primitive (number|boolean|string)");if(dtype==="string"&&isTypedArray(value)&&!(value instanceof Uint8Array))throw new Error("When making a scalar from encoded string, the value must be `Uint8Array`.");return makeTensor(value,[],[],dtype)}function selu_(x){let inputs={x:convertToTensor(x,"x","selu")};return ENGINE.runKernel(Selu,inputs)}var selu=op({selu_});function separableConv2d_(x,depthwiseFilter,pointwiseFilter,strides,pad3,dilation=[1,1],dataFormat="NHWC"){let $x=convertToTensor(x,"x","separableConv2d"),$depthwiseFilter=convertToTensor(depthwiseFilter,"depthwiseFilter","separableConv2d"),$pointwiseFilter=convertToTensor(pointwiseFilter,"pointwiseFilter","separableConv2d"),x4D=$x,reshapedTo4D=!1;if($x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),dataFormat==="NCHW")throw new Error("separableConv2d currently does not support dataFormat NCHW; only NHWC is supported");assert(x4D.rank===4,()=>`Error in separableConv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert($depthwiseFilter.rank===4,()=>`Error in separableConv2d: depthwise filter must be rank 4, but got rank ${$depthwiseFilter.rank}.`),assert($pointwiseFilter.rank===4,()=>`Error in separableConv2d: pointwise filter must be rank 4, but got rank ${$depthwiseFilter.rank}.`),assert($pointwiseFilter.shape[0]===1,()=>`Error in separableConv2d: the first dimension of pointwise filter must be 1, but got ${$pointwiseFilter.shape[0]}.`),assert($pointwiseFilter.shape[1]===1,()=>`Error in separableConv2d: the second dimension of pointwise filter must be 1, but got ${$pointwiseFilter.shape[1]}.`);let inChannels=$depthwiseFilter.shape[2],channelMultiplier=$depthwiseFilter.shape[3];assert($pointwiseFilter.shape[2]===inChannels*channelMultiplier,()=>`Error in separableConv2d: the third dimension of pointwise filter must be ${inChannels*channelMultiplier}, but got ${$pointwiseFilter.shape[2]}.`);let depthwise=depthwiseConv2d(x4D,$depthwiseFilter,strides,pad3,dataFormat,dilation),res=conv2d(depthwise,$pointwiseFilter,1,"valid",dataFormat);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var separableConv2d=op({separableConv2d_});async function setdiff1dAsync_(x,y){let $x=convertToTensor(x,"x","setdiff1d"),$y=convertToTensor(y,"y","setdiff1d");assert($x.dtype===$y.dtype,()=>`x and y should have the same dtype, but got x (${$x.dtype}) and y (${$y.dtype}).`),assert($x.rank===1,()=>`x should be 1D tensor, but got x (${$x.shape}).`),assert($y.rank===1,()=>`y should be 1D tensor, but got y (${$y.shape}).`);let xVals=await $x.data(),yVals=await $y.data(),ySet=new Set(yVals),outputSize=0;for(let i=0;i`slice1d expects a rank-1 tensor, but got a rank-${$x.rank} tensor`),slice($x,[begin],[size])}var slice1d=op({slice1d_});function slice2d_(x,begin,size){let $x=convertToTensor(x,"x","slice2d");return assert($x.rank===2,()=>`slice2d expects a rank-2 tensor, but got a rank-${$x.rank} tensor`),slice($x,begin,size)}var slice2d=op({slice2d_});function slice3d_(x,begin,size){let $x=convertToTensor(x,"x","slice3d");return assert($x.rank===3,()=>`slice3d expects a rank-3 tensor, but got a rank-${$x.rank} tensor`),slice($x,begin,size)}var slice3d=op({slice3d_});function slice4d_(x,begin,size){let $x=convertToTensor(x,"x","slice4d");return assert($x.rank===4,()=>`slice4d expects a rank-4 tensor, but got a rank-${$x.rank} tensor`),slice($x,begin,size)}var slice4d=op({slice4d_});function softmax_(logits,dim=-1){let $logits=convertToTensor(logits,"logits","softmax","float32");if(dim===-1&&(dim=$logits.rank-1),dim!==$logits.rank-1)throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${$logits.rank} and dim was ${dim}`);let inputs={logits:$logits},attrs={dim};return ENGINE.runKernel(Softmax,inputs,attrs)}var softmax=op({softmax_});function fft_(input2){assert(input2.dtype==="complex64",()=>`The dtype for tf.spectral.fft() must be complex64 but got ${input2.dtype}.`);let inputs={input:input2};return ENGINE.runKernel(FFT,inputs)}var fft=op({fft_});function ifft_(input2){assert(input2.dtype==="complex64",()=>`The dtype for tf.spectral.ifft() must be complex64 but got ${input2.dtype}.`);let inputs={input:input2};return ENGINE.runKernel(IFFT,inputs)}var ifft=op({ifft_});function irfft_(input2){let innerDimensionSize=input2.shape[input2.shape.length-1],batch=input2.size/innerDimensionSize,ret;if(innerDimensionSize<=2){let complexInput=reshape(input2,[batch,innerDimensionSize]);ret=ifft(complexInput)}else{let outputShape=[batch,2*(innerDimensionSize-1)],realInput=reshape(real(input2),[batch,innerDimensionSize]),imagInput=reshape(imag(input2),[batch,innerDimensionSize]),realConjugate=reverse(slice(realInput,[0,1],[batch,innerDimensionSize-2]),1),imagConjugate=mul(reverse(slice(imagInput,[0,1],[batch,innerDimensionSize-2]),1),scalar(-1)),r=concat([realInput,realConjugate],1),i=concat([imagInput,imagConjugate],1),complexInput=reshape(complex(r,i),[outputShape[0],outputShape[1]]);ret=ifft(complexInput)}if(ret=real(ret),input2.rank===3&&input2.shape[0]!==0){let temp=ret,batch2=input2.shape[0];ret=reshape(ret,[batch2,ret.shape[0]/batch2,ret.shape[1]]),temp.dispose()}return ret}var irfft=op({irfft_});function split_(x,numOrSizeSplits,axis=0){let inputs={x:convertToTensor(x,"x","split")},attr={numOrSizeSplits,axis};return ENGINE.runKernel(SplitV,inputs,attr)}var split=op({split_});function rfft_(input2,fftLength){assert(input2.dtype==="float32",()=>`The dtype for rfft() must be real value but got ${input2.dtype}`);let innerDimensionSize=input2.shape[input2.shape.length-1],batch=input2.size/innerDimensionSize,adjustedInput;if(fftLength!=null&&fftLength0),size=input2.shape.map(v=>v);size[input2.shape.length-1]=fftLength,adjustedInput=slice(input2,begin,size),innerDimensionSize=fftLength}else if(fftLength!=null&&fftLength>innerDimensionSize){let zerosShape=input2.shape.map(v=>v);zerosShape[input2.shape.length-1]=fftLength-innerDimensionSize,adjustedInput=concat([input2,zeros(zerosShape)],input2.shape.length-1),innerDimensionSize=fftLength}else adjustedInput=input2;let zerosInput=zerosLike(adjustedInput),complexInput=reshape(complex(adjustedInput,zerosInput),[batch,innerDimensionSize]),ret=fft(complexInput),half=Math.floor(innerDimensionSize/2)+1,realValues=real(ret),imagValues=imag(ret),realComplexConjugate=split(realValues,[half,innerDimensionSize-half],realValues.shape.length-1),imagComplexConjugate=split(imagValues,[half,innerDimensionSize-half],imagValues.shape.length-1),outputShape=adjustedInput.shape.slice();return outputShape[adjustedInput.shape.length-1]=half,reshape(complex(realComplexConjugate[0],imagComplexConjugate[0]),outputShape)}var rfft=op({rfft_});function sqrt_(x){let inputs={x:convertToTensor(x,"x","sqrt")};return ENGINE.runKernel(Sqrt,inputs)}var sqrt=op({sqrt_});function squaredDifference_(a,b){let $a=convertToTensor(a,"a","squaredDifference"),$b=convertToTensor(b,"b","squaredDifference");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b},attrs={};return ENGINE.runKernel(SquaredDifference,inputs,attrs)}var squaredDifference=op({squaredDifference_});function squeeze_(x,axis){let $x=convertToTensor(x,"x","squeeze");return reshape($x,squeezeShape($x.shape,axis).newShape)}var squeeze=op({squeeze_});function stack_(tensors,axis=0){let $tensors=convertToTensorArray(tensors,"tensors","stack","string_or_numeric");assert($tensors.length>=1,()=>"Pass at least one tensor to tf.stack"),$tensors.length>0&&assert(axis<=$tensors[0].rank,()=>"Axis must be <= rank of the tensor");let inputs=$tensors,attrs={axis};return ENGINE.runKernel(Pack,inputs,attrs)}var stack=op({stack_});function step_(x,alpha=0){let inputs={x:convertToTensor(x,"x","step")},attrs={alpha};return ENGINE.runKernel(Step,inputs,attrs)}var step=op({step_});function stridedSlice_(x,begin,end,strides,beginMask=0,endMask=0,ellipsisMask=0,newAxisMask=0,shrinkAxisMask=0){let inputs={x:convertToTensor(x,"x","stridedSlice")},attrs={begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask};return ENGINE.runKernel(StridedSlice,inputs,attrs)}var stridedSlice=op({stridedSlice_});function tan_(x){let inputs={x:convertToTensor(x,"x","tan")};return ENGINE.runKernel(Tan,inputs)}var tan=op({tan_});function tensor1d(values,dtype){assertNonNull(values);let inferredShape=inferShape(values,dtype);if(inferredShape.length!==1)throw new Error("tensor1d() requires values to be a flat/TypedArray");return makeTensor(values,null,inferredShape,dtype)}function tensor2d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==2)throw new Error("tensor2d() requires shape to have two numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==2&&inferredShape.length!==1)throw new Error("tensor2d() requires values to be number[][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor2d() requires shape to be provided when `values` are a flat/TypedArray");return makeTensor(values,shape,inferredShape,dtype)}function tensor4d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==4)throw new Error("tensor4d() requires shape to have four numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==4&&inferredShape.length!==1)throw new Error("tensor4d() requires values to be number[][][][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor4d() requires shape to be provided when `values` are a flat array");return makeTensor(values,shape,inferredShape,dtype)}function tensor5d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==5)throw new Error("tensor5d() requires shape to have five numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==5&&inferredShape.length!==1)throw new Error("tensor5d() requires values to be number[][][][][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor5d() requires shape to be provided when `values` are a flat array");return makeTensor(values,shape,inferredShape,dtype)}function tensor6d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==6)throw new Error("tensor6d() requires shape to have six numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==6&&inferredShape.length!==1)throw new Error("tensor6d() requires values to be number[][][][][][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor6d() requires shape to be provided when `values` are a flat array");return shape=shape||inferredShape,makeTensor(values,shape,inferredShape,dtype)}function topk_(x,k=1,sorted=!0){let $x=convertToTensor(x,"x","topk");if($x.rank===0)throw new Error("topk() expects the input to be of rank 1 or higher");let lastDim=$x.shape[$x.shape.length-1];if(k>lastDim)throw new Error(`'k' passed to topk() must be <= the last dimension (${lastDim}) but got ${k}`);let inputs={x:$x},attrs={k,sorted},[values,indices]=ENGINE.runKernel(TopK,inputs,attrs);return{values,indices}}var topk=op({topk_});function truncatedNormal_(shape,mean4=0,stdDev=1,dtype,seed){if(dtype!=null&&dtype==="bool")throw new Error("Unsupported data type $ { dtype }");let randGauss=new MPRandGauss(mean4,stdDev,dtype,!0,seed),res=buffer(shape,dtype);for(let i=0;i0,()=>"The input tensor must be at least 1D");let inputs={x:$x},attrs={axis},[values,indices]=ENGINE.runKernel(Unique,inputs,attrs);return{values,indices}}var unique=op({unique_});function unsortedSegmentSum_(x,segmentIds,numSegments){let $x=convertToTensor(x,"x","unsortedSegmentSum"),$segmentIds=convertToTensor(segmentIds,"segmentIds","unsortedSegmentSum","int32");assert(isInt(numSegments),()=>"numSegments must be of dtype int");let inputs={x:$x,segmentIds:$segmentIds},attrs={numSegments};return ENGINE.runKernel(UnsortedSegmentSum,inputs,attrs)}var unsortedSegmentSum=op({unsortedSegmentSum_});function unstack_(x,axis=0){let $x=convertToTensor(x,"x","unstack","string_or_numeric");assert(axis>=-$x.shape.length&&axis<$x.shape.length,()=>`Axis = ${axis} is not in [-${$x.shape.length}, ${$x.shape.length})`);let inputs={value:$x},attrs={axis};return ENGINE.runKernel(Unpack,inputs,attrs)}var unstack=op({unstack_});function variable(initialValue,trainable=!0,name,dtype){return ENGINE.makeVariable(initialValue,trainable,name,dtype)}function whereImpl(condShape,condVals){let indices=[];for(let i=0;i0,()=>"mask cannot be scalar"),assertShapesMatch(tensorShape.slice(axisFrom,axisFrom+maskDim),$mask.shape,"mask's shape must match the first K dimensions of tensor's shape,");let leadingSize=1;for(let i=axisFrom;i"Shape mismatch in v and x");let one=scalar(1),oneMinusDecay=sub(one,$decay),update=mul(sub($x,$v),oneMinusDecay);if(zeroDebias){assert(step4!=null,()=>"When using zeroDebias: true, step is required.");let $step=convertToTensor(step4,"step","movingAverage");update=div(update,sub(one,pow($decay,$step)))}return add2($v,update)}var movingAverage=op({movingAverage_});function scatterND_(indices,updates,shape){let $indices=convertToTensor(indices,"indices","scatterND","int32"),$updates=convertToTensor(updates,"updates","scatterND");validateInput($updates,$indices,shape);let inputs={indices:$indices,updates:$updates},attrs={shape};return ENGINE.runKernel(ScatterNd,inputs,attrs)}var scatterND=op({scatterND_});function validateInput2(sparseIndices,sparseValues,outputShape,defaultValues){if(sparseIndices.dtype!=="int32")throw new Error(`tf.sparseToDense() expects the indices to be int32 type, but the dtype was ${sparseIndices.dtype}.`);if(sparseIndices.rank>2)throw new Error(`sparseIndices should be a scalar, vector, or matrix, but got shape ${sparseIndices.shape}.`);let numElems=sparseIndices.rank>0?sparseIndices.shape[0]:1,numDims=sparseIndices.rank>1?sparseIndices.shape[1]:1;if(outputShape.length!==numDims)throw new Error(`outputShape has incorrect number of elements:, ${outputShape.length}, should be: ${numDims}.`);let numValues=sparseValues.size;if(!(sparseValues.rank===0||sparseValues.rank===1&&numValues===numElems))throw new Error(`sparseValues has incorrect shape ${sparseValues.shape}, should be [] or [${numElems}]`);if(sparseValues.dtype!==defaultValues.dtype)throw new Error("sparseValues.dtype must match defaultValues.dtype")}function sparseToDense_(sparseIndices,sparseValues,outputShape,defaultValue=0){let $sparseIndices=convertToTensor(sparseIndices,"sparseIndices","sparseToDense","int32"),$sparseValues=convertToTensor(sparseValues,"sparseValues","sparseToDense"),$defaultValue=convertToTensor(defaultValue,"defaultValue","sparseToDense",$sparseValues.dtype);validateInput2($sparseIndices,$sparseValues,outputShape,$defaultValue);let inputs={sparseIndices:$sparseIndices,sparseValues:$sparseValues,defaultValue:$defaultValue},attrs={outputShape};return ENGINE.runKernel(SparseToDense,inputs,attrs)}var sparseToDense=op({sparseToDense_});function gatherND_(x,indices){let $indices=convertToTensor(indices,"indices","gatherND","int32"),inputs={params:convertToTensor(x,"x","gatherND"),indices:$indices};return ENGINE.runKernel(GatherNd,inputs)}var gatherND=op({gatherND_});function getNoiseShape(x,noiseShape){if(noiseShape==null)return x.shape.slice();if(arraysEqual(x.shape,noiseShape))return noiseShape;if(x.shape.length===noiseShape.length){let newDimension=[];for(let i=0;i`x has to be a floating point tensor since it's going to be scaled, but got a ${$x.dtype} tensor instead.`),assert(rate>=0&&rate<1,()=>`rate must be a float in the range [0, 1), but got ${rate}.`),rate===0)return x instanceof Tensor?$x.clone():$x;let $noiseShape=getNoiseShape($x,noiseShape),keepProb=1-rate,multiplier=div(floor(add2(randomUniform($noiseShape,0,1,"float32",seed),keepProb)),keepProb);return mul($x,multiplier)}var dropout=op({dropout_});function enclosingPowerOfTwo(value){return Math.floor(Math.pow(2,Math.ceil(Math.log(value)/Math.log(2))))}function cosineWindow(windowLength,a,b){let even=1-windowLength%2,newValues=new Float32Array(windowLength);for(let i=0;i1,()=>`inTopK() expects the predictions to be of rank 2 or higher, but got ${$predictions.rank}`),assert($predictions.rank-1===$targets.rank,()=>`predictions rank should be 1 larger than targets rank, but got predictions rank ${$predictions.rank} and targets rank ${$targets.rank}`),assertShapesMatch($predictions.shape.slice(0,$predictions.shape.length-1),$targets.shape,"predictions's shape should be align with the targets' shape, except the last dimension.");let lastDim=$predictions.shape[$predictions.shape.length-1];assert(k>0&&k<=lastDim,()=>`'k' passed to inTopK() must be > 0 && <= the predictions last dimension (${lastDim}), but got ${k}`);let predictionsVals=await $predictions.data(),targetsVals=await $targets.data(),[batch,size]=[predictionsVals.length/lastDim,lastDim],precision3=getTypedArrayFromDType("bool",batch);for(let b=0;bb2.value-a.value),precision3[b]=0;for(let i=0;iconv2d2,depthwiseConv2d:()=>depthwiseConv2d2,matMul:()=>matMul2});function conv2DBackpropFilter_(x,dy,filterShape,strides,pad3,dataFormat="NHWC",dimRoundingMode){let x4D=x;x.rank===3&&(x4D=reshape(x,[1,x.shape[0],x.shape[1],x.shape[2]]));let dy4D=dy;dy4D.rank===3&&(dy4D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2]])),assert(x4D.rank===4,()=>`Error in conv2dDerFilter: input must be rank 4, but got shape ${x4D.shape}.`),assert(dy4D.rank===4,()=>`Error in conv2dDerFilter: dy must be rank 4, but got shape ${dy4D.shape}.`),assert(filterShape.length===4,()=>`Error in conv2dDerFilter: filterShape must be length 4, but got ${filterShape}.`);let inDepth=dataFormat==="NHWC"?x4D.shape[3]:x4D.shape[1],outDepth=dataFormat==="NHWC"?dy4D.shape[3]:dy4D.shape[1];assert(inDepth===filterShape[2],()=>`Error in conv2dDerFilter: depth of input ${inDepth}) must match input depth in filter (${filterShape[2]}.`),assert(outDepth===filterShape[3],()=>`Error in conv2dDerFilter: depth of dy (${outDepth}) must match output depth for filter (${filterShape[3]}).`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in conv2dDerFilter: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={x:x4D,dy:dy4D},attrs={strides,pad:pad3,dataFormat,dimRoundingMode,filterShape};return ENGINE.runKernel(Conv2DBackpropFilter,inputs,attrs)}var conv2DBackpropFilter=op({conv2DBackpropFilter_});function getFusedDyActivation(dy,y,activation2){if(activation2==null||activation2==="linear")return dy;if(activation2==="relu")return mul(dy,step(y));throw new Error(`Cannot compute gradient for fused activation ${activation2}.`)}function getFusedBiasGradient(bias,dyActivation){let res=dyActivation,reduceAxes=getReductionAxes(bias.shape,dyActivation.shape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,bias.shape)}function applyActivation(x,activation2,preluActivationWeights,leakyreluAlpha){if(activation2==="linear")return x;if(activation2==="relu")return relu(x);if(activation2==="elu")return elu(x);if(activation2==="relu6")return relu6(x);if(activation2==="prelu")return prelu(x,preluActivationWeights);if(activation2==="leakyrelu")return leakyRelu(x,leakyreluAlpha);throw new Error(`Unknown fused activation ${activation2}.`)}var shouldFuse=(gradientDepth,activation2)=>!(gradientDepth>0)||activation2==="linear";function fusedConv2d_({x,filter,strides,pad:pad3,dataFormat="NHWC",dilations=[1,1],dimRoundingMode,bias,activation:activation2="linear",preluActivationWeights,leakyreluAlpha}){if(activation2=activation2||"linear",shouldFuse(ENGINE.state.gradientDepth,activation2)===!1){let result=conv2d(x,filter,strides,pad3,dataFormat,dilations,dimRoundingMode);return bias!=null&&(result=add2(result,bias)),applyActivation(result,activation2,preluActivationWeights,leakyreluAlpha)}let $x=convertToTensor(x,"x","conv2d"),$filter=convertToTensor(filter,"filter","conv2d"),x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in fused conv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert($filter.rank===4,()=>`Error in fused conv2d: filter must be rank 4, but got rank ${$filter.rank}.`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in fused conv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`),assert(x4D.shape[3]===$filter.shape[2],()=>`Error in conv2d: depth of input (${x4D.shape[3]}) must match input depth for filter ${$filter.shape[2]}.`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),assert(dataFormat==="NHWC",()=>`Error in conv2d: got dataFormat of ${dataFormat} but only NHWC is currently supported.`);let convInfo=computeConv2DInfo(x4D.shape,$filter.shape,strides,dilations,pad3,dimRoundingMode),$bias;bias!=null&&($bias=convertToTensor(bias,"bias","fused conv2d"),[$bias]=makeTypesMatch($bias,$x),assertAndGetBroadcastShape(convInfo.outShape,$bias.shape));let $preluActivationWeights;preluActivationWeights!=null&&($preluActivationWeights=convertToTensor(preluActivationWeights,"prelu weights","fused conv2d"));let grad2=(dy,saved)=>{let[$filter2,x4D2,y,$bias2]=saved,dyActivation=getFusedDyActivation(dy,y,activation2);assert(tupleValuesAreOne(dilations),()=>`Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`);let xDer=conv2DBackpropInput(x4D2.shape,dyActivation,$filter2,strides,pad3),filterDer=conv2DBackpropFilter(x4D2,dyActivation,$filter2.shape,strides,pad3),der=[xDer,filterDer];if($bias2!=null){let biasDer=getFusedBiasGradient($bias2,dyActivation);der.push(biasDer)}return der},inputs={x:x4D,filter:$filter,bias:$bias,preluActivationWeights:$preluActivationWeights},attrs={strides,pad:pad3,dataFormat,dilations,dimRoundingMode,activation:activation2,leakyreluAlpha};return bias==null?customGrad((x4D2,filter2,save)=>{let res=ENGINE.runKernel(FusedConv2D,inputs,attrs);return save([filter2,x4D2,res]),reshapedTo4D&&(res=reshape(res,[res.shape[1],res.shape[2],res.shape[3]])),{value:res,gradFunc:grad2}})(x4D,$filter):customGrad((x4D2,filter2,bias2,save)=>{let res=ENGINE.runKernel(FusedConv2D,inputs,attrs);return save([filter2,x4D2,res,bias2]),reshapedTo4D&&(res=reshape(res,[res.shape[1],res.shape[2],res.shape[3]])),{value:res,gradFunc:grad2}})(x4D,$filter,$bias)}var conv2d2=op({fusedConv2d_});function depthwiseConv2dNativeBackpropFilter_(x,dy,filterShape,strides,pad3,dilations=[1,1],dimRoundingMode){let x4D=x;x.rank===3&&(x4D=reshape(x,[1,x.shape[0],x.shape[1],x.shape[2]]));let dy4D=dy;dy4D.rank===3&&(dy4D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2]]));let inputs={x:x4D,dy:dy4D},attrs={strides,pad:pad3,dimRoundingMode,dilations,filterShape};return ENGINE.runKernel(DepthwiseConv2dNativeBackpropFilter,inputs,attrs)}var depthwiseConv2dNativeBackpropFilter=op({depthwiseConv2dNativeBackpropFilter_});function depthwiseConv2dNativeBackpropInput_(xShape,dy,filter,strides,pad3,dilations=[1,1],dimRoundingMode){let dy4D=dy,reshapedTo4D=!1;dy.rank===3&&(reshapedTo4D=!0,dy4D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2]]));let inputs={dy:dy4D,filter},attrs={strides,pad:pad3,dimRoundingMode,dilations,inputShape:xShape},res=ENGINE.runKernel(DepthwiseConv2dNativeBackpropInput,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var depthwiseConv2dNativeBackpropInput=op({depthwiseConv2dNativeBackpropInput_});function fusedDepthwiseConv2d_({x,filter,strides,pad:pad3,dataFormat="NHWC",dilations=[1,1],dimRoundingMode,bias,activation:activation2="linear",preluActivationWeights,leakyreluAlpha}){if(shouldFuse(ENGINE.state.gradientDepth,activation2)===!1){let result=depthwiseConv2d(x,filter,strides,pad3,dataFormat,dilations,dimRoundingMode);return bias!=null&&(result=add2(result,bias)),applyActivation(result,activation2,preluActivationWeights,leakyreluAlpha)}let $x=convertToTensor(x,"x","depthwiseConv2d"),$filter=convertToTensor(filter,"filter","depthwiseConv2d"),x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in fused depthwiseConv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert($filter.rank===4,()=>`Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${$filter.rank}.`),assert(x4D.shape[3]===$filter.shape[2],()=>`Error in fused depthwiseConv2d: number of input channels (${x4D.shape[3]}) must match the inChannels dimension in filter ${$filter.shape[2]}.`),dilations==null&&(dilations=[1,1]),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in fused depthwiseConv2d: pad must be an integer when using dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let convInfo=computeConv2DInfo(x4D.shape,$filter.shape,strides,dilations,pad3,dimRoundingMode,!0),$bias;bias!=null&&($bias=convertToTensor(bias,"bias","fused conv2d"),[$bias]=makeTypesMatch($bias,$x),assertAndGetBroadcastShape(convInfo.outShape,$bias.shape));let $preluActivationWeights;preluActivationWeights!=null&&($preluActivationWeights=convertToTensor(preluActivationWeights,"prelu weights","fused depthwiseConv2d"));let grad2=(dy,saved)=>{assert(tupleValuesAreOne(dilations),()=>`Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${dilations}'`);let[$filter2,x4D2,y,bias2]=saved,dyActivation=getFusedDyActivation(dy,y,activation2),xDer=depthwiseConv2dNativeBackpropInput(x4D2.shape,dyActivation,$filter2,strides,pad3,dilations,dimRoundingMode),filterDer=depthwiseConv2dNativeBackpropFilter(x4D2,dyActivation,$filter2.shape,strides,pad3,dilations,dimRoundingMode);if(bias2!=null){let biasDer=getFusedBiasGradient($bias,dyActivation);return[xDer,filterDer,biasDer]}return[xDer,filterDer]},inputs={x:x4D,filter:$filter,bias:$bias,preluActivationWeights:$preluActivationWeights},attrs={strides,pad:pad3,dataFormat,dilations,dimRoundingMode,activation:activation2,leakyreluAlpha};return bias==null?customGrad((x4D2,filter2,save)=>{let res=ENGINE.runKernel(FusedDepthwiseConv2D,inputs,attrs);return save([filter2,x4D2,res]),reshapedTo4D&&(res=reshape(res,[res.shape[1],res.shape[2],res.shape[3]])),{value:res,gradFunc:grad2}})(x4D,$filter):customGrad((x4D2,filter2,bias2,save)=>{let res=ENGINE.runKernel(FusedDepthwiseConv2D,inputs,attrs);return save([filter2,x4D2,res,bias2]),reshapedTo4D&&(res=reshape(res,[res.shape[1],res.shape[2],res.shape[3]])),{value:res,gradFunc:grad2}})(x4D,$filter,$bias)}var depthwiseConv2d2=op({fusedDepthwiseConv2d_});function fusedMatMul_({a,b,transposeA=!1,transposeB=!1,bias,activation:activation2="linear",preluActivationWeights,leakyreluAlpha}){if(shouldFuse(ENGINE.state.gradientDepth,activation2)===!1){let result=matMul(a,b,transposeA,transposeB);return bias!=null&&(result=add2(result,bias)),applyActivation(result,activation2,preluActivationWeights,leakyreluAlpha)}let $a=convertToTensor(a,"a","fused matMul"),$b=convertToTensor(b,"b","fused matMul");[$a,$b]=makeTypesMatch($a,$b);let innerShapeA=transposeA?$a.shape[$a.rank-2]:$a.shape[$a.rank-1],innerShapeB=transposeB?$b.shape[$b.rank-1]:$b.shape[$b.rank-2],outerShapeA=transposeA?$a.shape[$a.rank-1]:$a.shape[$a.rank-2],outerShapeB=transposeB?$b.shape[$b.rank-2]:$b.shape[$b.rank-1],outerDimsA=$a.shape.slice(0,-2),outerDimsB=$b.shape.slice(0,-2),batchDimA=sizeFromShape(outerDimsA),batchDimB=sizeFromShape(outerDimsB);assert($a.rank>=2&&$b.rank>=2&&$a.rank===$b.rank,()=>`Error in fused matMul: inputs must have the same rank of at least 2, got ranks ${$a.rank} and ${$b.rank}.`),assert(arraysEqual(outerDimsA,outerDimsB),()=>`Error in fused matMul: outer dimensions (${outerDimsA}) and (${outerDimsB}) of Tensors with shapes ${$a.shape} and ${$b.shape} must match.`),assert(innerShapeA===innerShapeB,()=>`Error in fused matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${$a.shape} and ${$b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);let outShape=$a.shape.slice(0,-2).concat([outerShapeA,outerShapeB]),a3D=transposeA?reshape($a,[batchDimA,innerShapeA,outerShapeA]):reshape($a,[batchDimA,outerShapeA,innerShapeA]),b3D=transposeB?reshape($b,[batchDimB,outerShapeB,innerShapeB]):reshape($b,[batchDimB,innerShapeB,outerShapeB]),$bias;bias!=null&&($bias=convertToTensor(bias,"bias","fused matMul"),[$bias]=makeTypesMatch($bias,$a),assertAndGetBroadcastShape(outShape,$bias.shape));let $preluActivationWeights;preluActivationWeights!=null&&($preluActivationWeights=convertToTensor(preluActivationWeights,"prelu weights","fused matMul"));let grad2=(dy,saved)=>{let[a3D2,b3D2,y,$bias2]=saved,dyActivation=getFusedDyActivation(reshape(dy,y.shape),y,activation2),aDer,bDer;if(!transposeA&&!transposeB?(aDer=matMul(dyActivation,b3D2,!1,!0),bDer=matMul(a3D2,dyActivation,!0,!1)):!transposeA&&transposeB?(aDer=matMul(dyActivation,b3D2,!1,!1),bDer=matMul(dyActivation,a3D2,!0,!1)):transposeA&&!transposeB?(aDer=matMul(b3D2,dyActivation,!1,!0),bDer=matMul(a3D2,dyActivation,!1,!1)):(aDer=matMul(b3D2,dyActivation,!0,!0),bDer=matMul(dyActivation,a3D2,!0,!0)),bias!=null){let biasDer=getFusedBiasGradient($bias2,dyActivation);return[aDer,bDer,biasDer]}else return[aDer,bDer]},inputs={a:a3D,b:b3D,bias:$bias,preluActivationWeights:$preluActivationWeights},attrs={transposeA,transposeB,activation:activation2,leakyreluAlpha};return bias==null?customGrad((a3D2,b3D2,save)=>{let res=ENGINE.runKernel(_FusedMatMul,inputs,attrs);return save([a3D2,b3D2,res]),{value:reshape(res,outShape),gradFunc:grad2}})(a3D,b3D):customGrad((a3D2,b3D2,$bias2,save)=>{let res=ENGINE.runKernel(_FusedMatMul,inputs,attrs);return save([a3D2,b3D2,res,$bias2]),{value:reshape(res,outShape),gradFunc:grad2}})(a3D,b3D,$bias)}var matMul2=op({fusedMatMul_});function hammingWindow_(windowLength){return cosineWindow(windowLength,.54,.46)}var hammingWindow=op({hammingWindow_});function hannWindow_(windowLength){return cosineWindow(windowLength,.5,.5)}var hannWindow=op({hannWindow_});function frame_(signal2,frameLength,frameStep,padEnd=!1,padValue=0){let start=0,output=[];for(;start+frameLength<=signal2.size;)output.push(slice(signal2,start,frameLength)),start+=frameStep;if(padEnd)for(;start`Error in cropAndResize: image must be rank 4,but got rank ${$image.rank}.`),assert($boxes.rank===2&&$boxes.shape[1]===4,()=>`Error in cropAndResize: boxes must be have size [${numBoxes},4] but had shape ${$boxes.shape}.`),assert($boxInd.rank===1&&$boxInd.shape[0]===numBoxes,()=>`Error in cropAndResize: boxInd must be have size [${numBoxes}] but had shape ${$boxes.shape}.`),assert(cropSize.length===2,()=>`Error in cropAndResize: cropSize must be of length 2, but got length ${cropSize.length}.`),assert(cropSize[0]>=1&&cropSize[1]>=1,()=>`cropSize must be atleast [1,1], but was ${cropSize}`),assert(method==="bilinear"||method==="nearest",()=>`method must be bilinear or nearest, but was ${method}`);let inputs={image:$image,boxes:$boxes,boxInd:$boxInd},attrs={method,extrapolationValue,cropSize};return ENGINE.runKernel(CropAndResize,inputs,attrs)}var cropAndResize=op({cropAndResize_});function flipLeftRight_(image3){let $image=convertToTensor(image3,"image","flipLeftRight","float32");assert($image.rank===4,()=>`Error in flipLeftRight: image must be rank 4,but got rank ${$image.rank}.`);let inputs={image:$image};return ENGINE.runKernel(FlipLeftRight,inputs,{})}var flipLeftRight=op({flipLeftRight_});function rotateWithOffset_(image3,radians,fillValue=0,center=.5){let $image=convertToTensor(image3,"image","rotateWithOffset","float32");assert($image.rank===4,()=>`Error in rotateWithOffset: image must be rank 4,but got rank ${$image.rank}.`);let inputs={image:$image},attrs={radians,fillValue,center};return ENGINE.runKernel(RotateWithOffset,inputs,attrs)}var rotateWithOffset=op({rotateWithOffset_});function nonMaxSuppSanityCheck(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma){iouThreshold==null&&(iouThreshold=.5),scoreThreshold==null&&(scoreThreshold=Number.NEGATIVE_INFINITY),softNmsSigma==null&&(softNmsSigma=0);let numBoxes=boxes.shape[0];return maxOutputSize=Math.min(maxOutputSize,numBoxes),assert(0<=iouThreshold&&iouThreshold<=1,()=>`iouThreshold must be in [0, 1], but was '${iouThreshold}'`),assert(boxes.rank===2,()=>`boxes must be a 2D tensor, but was of rank '${boxes.rank}'`),assert(boxes.shape[1]===4,()=>`boxes must have 4 columns, but 2nd dimension was ${boxes.shape[1]}`),assert(scores.rank===1,()=>"scores must be a 1D tensor"),assert(scores.shape[0]===numBoxes,()=>`scores has incompatible shape with boxes. Expected ${numBoxes}, but was ${scores.shape[0]}`),assert(0<=softNmsSigma&&softNmsSigma<=1,()=>`softNmsSigma must be in [0, 1], but was '${softNmsSigma}'`),{maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma}}function nonMaxSuppression_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppression"),$scores=convertToTensor(scores,"scores","nonMaxSuppression"),inputs=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold);maxOutputSize=inputs.maxOutputSize,iouThreshold=inputs.iouThreshold,scoreThreshold=inputs.scoreThreshold;let attrs={maxOutputSize,iouThreshold,scoreThreshold};return ENGINE.runKernel(NonMaxSuppressionV3,{boxes:$boxes,scores:$scores},attrs)}var nonMaxSuppression=op({nonMaxSuppression_});function binaryInsert(arr,element,comparator){let index=binarySearch(arr,element,comparator),insertionPoint=index<0?-(index+1):index;arr.splice(insertionPoint,0,element)}function binarySearch(arr,target,comparator){return binarySearch_(arr,target,comparator||defaultComparator)}function defaultComparator(a,b){return a>b?1:a>>1);let compareResult=comparator(target,arr[middle]);compareResult>0?left=middle+1:(right=middle,found=!compareResult)}return found?left:-left-1}function nonMaxSuppressionV3Impl(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold){return nonMaxSuppressionImpl_(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,0)}function nonMaxSuppressionV4Impl(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize){return nonMaxSuppressionImpl_(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,0,!1,padToMaxOutputSize,!0)}function nonMaxSuppressionV5Impl(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma){return nonMaxSuppressionImpl_(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma,!0)}function nonMaxSuppressionImpl_(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma,returnScoresTensor=!1,padToMaxOutputSize=!1,returnValidOutputs=!1){let candidates=[];for(let i=0;iscoreThreshold&&candidates.push({score:scores[i],boxIndex:i,suppressBeginIndex:0});candidates.sort(ascendingComparator);let scale2=softNmsSigma>0?-.5/softNmsSigma:0,selectedIndices=[],selectedScores=[];for(;selectedIndices.length0;){let candidate=candidates.pop(),{score:originalScore,boxIndex,suppressBeginIndex}=candidate;if(originalScore=suppressBeginIndex;--j){let iou=intersectionOverUnion(boxes,boxIndex,selectedIndices[j]);if(iou>=iouThreshold){ignoreCandidate=!0;break}if(candidate.score=candidate.score*suppressWeight(iouThreshold,scale2,iou),candidate.score<=scoreThreshold)break}candidate.suppressBeginIndex=selectedIndices.length,ignoreCandidate||(candidate.score===originalScore?(selectedIndices.push(boxIndex),selectedScores.push(candidate.score)):candidate.score>scoreThreshold&&binaryInsert(candidates,candidate,ascendingComparator))}let validOutputs=selectedIndices.length,elemsToPad=maxOutputSize-validOutputs;padToMaxOutputSize&&elemsToPad>0&&(selectedIndices.push(...new Array(elemsToPad).fill(0)),selectedScores.push(...new Array(elemsToPad).fill(0)));let result={selectedIndices};return returnScoresTensor&&(result.selectedScores=selectedScores),returnValidOutputs&&(result.validOutputs=validOutputs),result}function intersectionOverUnion(boxes,i,j){let iCoord=boxes.subarray(i*4,i*4+4),jCoord=boxes.subarray(j*4,j*4+4),yminI=Math.min(iCoord[0],iCoord[2]),xminI=Math.min(iCoord[1],iCoord[3]),ymaxI=Math.max(iCoord[0],iCoord[2]),xmaxI=Math.max(iCoord[1],iCoord[3]),yminJ=Math.min(jCoord[0],jCoord[2]),xminJ=Math.min(jCoord[1],jCoord[3]),ymaxJ=Math.max(jCoord[0],jCoord[2]),xmaxJ=Math.max(jCoord[1],jCoord[3]),areaI=(ymaxI-yminI)*(xmaxI-xminI),areaJ=(ymaxJ-yminJ)*(xmaxJ-xminJ);if(areaI<=0||areaJ<=0)return 0;let intersectionYmin=Math.max(yminI,yminJ),intersectionXmin=Math.max(xminI,xminJ),intersectionYmax=Math.min(ymaxI,ymaxJ),intersectionXmax=Math.min(xmaxI,xmaxJ),intersectionArea=Math.max(intersectionYmax-intersectionYmin,0)*Math.max(intersectionXmax-intersectionXmin,0);return intersectionArea/(areaI+areaJ-intersectionArea)}function suppressWeight(iouThreshold,scale2,iou){let weight=Math.exp(scale2*iou*iou);return iou<=iouThreshold?weight:0}function ascendingComparator(c1,c2){return c1.score-c2.score||c1.score===c2.score&&c2.boxIndex-c1.boxIndex}async function nonMaxSuppressionAsync_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppressionAsync"),$scores=convertToTensor(scores,"scores","nonMaxSuppressionAsync"),inputs=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold);maxOutputSize=inputs.maxOutputSize,iouThreshold=inputs.iouThreshold,scoreThreshold=inputs.scoreThreshold;let boxesAndScores=await Promise.all([$boxes.data(),$scores.data()]),boxesVals=boxesAndScores[0],scoresVals=boxesAndScores[1],{selectedIndices}=nonMaxSuppressionV3Impl(boxesVals,scoresVals,maxOutputSize,iouThreshold,scoreThreshold);return $boxes!==boxes&&$boxes.dispose(),$scores!==scores&&$scores.dispose(),tensor1d(selectedIndices,"int32")}var nonMaxSuppressionAsync=nonMaxSuppressionAsync_;function nonMaxSuppressionWithScore_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY,softNmsSigma=0){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppression"),$scores=convertToTensor(scores,"scores","nonMaxSuppression"),params=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma);maxOutputSize=params.maxOutputSize,iouThreshold=params.iouThreshold,scoreThreshold=params.scoreThreshold,softNmsSigma=params.softNmsSigma;let inputs={boxes:$boxes,scores:$scores},attrs={maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma},result=ENGINE.runKernel(NonMaxSuppressionV5,inputs,attrs);return{selectedIndices:result[0],selectedScores:result[1]}}var nonMaxSuppressionWithScore=op({nonMaxSuppressionWithScore_});async function nonMaxSuppressionWithScoreAsync_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY,softNmsSigma=0){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppressionAsync"),$scores=convertToTensor(scores,"scores","nonMaxSuppressionAsync"),params=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma);maxOutputSize=params.maxOutputSize,iouThreshold=params.iouThreshold,scoreThreshold=params.scoreThreshold,softNmsSigma=params.softNmsSigma;let boxesAndScores=await Promise.all([$boxes.data(),$scores.data()]),boxesVals=boxesAndScores[0],scoresVals=boxesAndScores[1],{selectedIndices,selectedScores}=nonMaxSuppressionV5Impl(boxesVals,scoresVals,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma);return $boxes!==boxes&&$boxes.dispose(),$scores!==scores&&$scores.dispose(),{selectedIndices:tensor1d(selectedIndices,"int32"),selectedScores:tensor1d(selectedScores)}}var nonMaxSuppressionWithScoreAsync=nonMaxSuppressionWithScoreAsync_;function nonMaxSuppressionPadded_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY,padToMaxOutputSize=!1){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppression"),$scores=convertToTensor(scores,"scores","nonMaxSuppression"),params=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold,null),$maxOutputSize=params.maxOutputSize,$iouThreshold=params.iouThreshold,$scoreThreshold=params.scoreThreshold,inputs={boxes:$boxes,scores:$scores},attrs={maxOutputSize:$maxOutputSize,iouThreshold:$iouThreshold,scoreThreshold:$scoreThreshold,padToMaxOutputSize},result=ENGINE.runKernel(NonMaxSuppressionV4,inputs,attrs);return{selectedIndices:result[0],validOutputs:result[1]}}var nonMaxSuppressionPadded=op({nonMaxSuppressionPadded_});async function nonMaxSuppressionPaddedAsync_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY,padToMaxOutputSize=!1){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppressionAsync"),$scores=convertToTensor(scores,"scores","nonMaxSuppressionAsync"),params=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold,null),$maxOutputSize=params.maxOutputSize,$iouThreshold=params.iouThreshold,$scoreThreshold=params.scoreThreshold,[boxesVals,scoresVals]=await Promise.all([$boxes.data(),$scores.data()]),{selectedIndices,validOutputs}=nonMaxSuppressionV4Impl(boxesVals,scoresVals,$maxOutputSize,$iouThreshold,$scoreThreshold,padToMaxOutputSize);return $boxes!==boxes&&$boxes.dispose(),$scores!==scores&&$scores.dispose(),{selectedIndices:tensor1d(selectedIndices,"int32"),validOutputs:scalar(validOutputs,"int32")}}var nonMaxSuppressionPaddedAsync=nonMaxSuppressionPaddedAsync_;function resizeBilinear_(images,size,alignCorners=!1,halfPixelCenters=!1){let $images=convertToTensor(images,"images","resizeBilinear");assert($images.rank===3||$images.rank===4,()=>`Error in resizeBilinear: x must be rank 3 or 4, but got rank ${$images.rank}.`),assert(size.length===2,()=>`Error in resizeBilinear: new shape must 2D, but got shape ${size}.`),assert(halfPixelCenters===!1||alignCorners===!1,()=>"Error in resizeBilinear: If halfPixelCenters is true, alignCorners must be false.");let batchImages=$images,reshapedTo4D=!1;$images.rank===3&&(reshapedTo4D=!0,batchImages=reshape($images,[1,$images.shape[0],$images.shape[1],$images.shape[2]]));let[]=size,inputs={images:batchImages},attrs={alignCorners,halfPixelCenters,size},res=ENGINE.runKernel(ResizeBilinear,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var resizeBilinear=op({resizeBilinear_});function resizeNearestNeighbor_(images,size,alignCorners=!1,halfPixelCenters=!1){let $images=convertToTensor(images,"images","resizeNearestNeighbor");assert($images.rank===3||$images.rank===4,()=>`Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${$images.rank}.`),assert(size.length===2,()=>`Error in resizeNearestNeighbor: new shape must 2D, but got shape ${size}.`),assert($images.dtype==="float32"||$images.dtype==="int32",()=>"`images` must have `int32` or `float32` as dtype"),assert(halfPixelCenters===!1||alignCorners===!1,()=>"Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false.");let batchImages=$images,reshapedTo4D=!1;$images.rank===3&&(reshapedTo4D=!0,batchImages=reshape($images,[1,$images.shape[0],$images.shape[1],$images.shape[2]]));let[]=size,inputs={images:batchImages},attrs={alignCorners,halfPixelCenters,size},res=ENGINE.runKernel(ResizeNearestNeighbor,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var resizeNearestNeighbor=op({resizeNearestNeighbor_});function bandPart_(a,numLower,numUpper){assert(numLower%1==0,()=>`bandPart(): numLower must be an integer, got ${numLower}.`),assert(numUpper%1==0,()=>`bandPart(): numUpper must be an integer, got ${numUpper}.`);let $a=convertToTensor(a,"a","bandPart");assert($a.rank>=2,()=>`bandPart(): Rank must be at least 2, got ${$a.rank}.`);let shape=$a.shape,[M,N]=$a.shape.slice(-2);if(!(numLower<=M))throw new Error(`bandPart(): numLower (${numLower}) must not be greater than the number of rows (${M}).`);if(!(numUpper<=N))throw new Error(`bandPart(): numUpper (${numUpper}) must not be greater than the number of columns (${N}).`);numLower<0&&(numLower=M),numUpper<0&&(numUpper=N);let i=reshape(range(0,M,1,"int32"),[-1,1]),j=range(0,N,1,"int32"),ij=sub(i,j),inBand=logicalAnd(lessEqual(ij,scalar(+numLower,"int32")),greaterEqual(ij,scalar(-numUpper,"int32"))),zero=zeros([M,N],$a.dtype);return reshape(stack(unstack(reshape($a,[-1,M,N])).map(mat=>where(inBand,mat,zero))),shape)}var bandPart=op({bandPart_});function gramSchmidt_(xs){let inputIsTensor2D;if(Array.isArray(xs)){inputIsTensor2D=!1,assert(xs!=null&&xs.length>0,()=>"Gram-Schmidt process: input must not be null, undefined, or empty");let dim=xs[0].shape[0];for(let i=1;i`Gram-Schmidt: Non-unique lengths found in the input vectors: (${xs[i].shape[0]} vs. ${dim})`)}else inputIsTensor2D=!0,xs=split(xs,xs.shape[0],0).map(x=>squeeze(x,[0]));assert(xs.length<=xs[0].shape[0],()=>`Gram-Schmidt: Number of vectors (${xs.length}) exceeds number of dimensions (${xs[0].shape[0]}).`);let ys=[],xs1d=xs;for(let i=0;i{let x=xs1d[i];if(i>0)for(let j=0;j=2,()=>`qr() requires input tensor to have a rank >= 2, but got rank ${x.rank}`),x.rank===2)return qr2d(x,fullMatrices);{let outerDimsProd=x.shape.slice(0,x.shape.length-2).reduce((value,prev)=>value*prev),x2ds=unstack(reshape(x,[outerDimsProd,x.shape[x.shape.length-2],x.shape[x.shape.length-1]]),0),q2ds=[],r2ds=[];x2ds.forEach(x2d=>{let[q2d,r2d]=qr2d(x2d,fullMatrices);q2ds.push(q2d),r2ds.push(r2d)});let q=reshape(stack(q2ds,0),x.shape),r=reshape(stack(r2ds,0),x.shape);return[q,r]}}function qr2d(x,fullMatrices=!1){return ENGINE.tidy(()=>{assert(x.shape.length===2,()=>`qr2d() requires a 2D Tensor, but got a ${x.shape.length}D Tensor.`);let m=x.shape[0],n=x.shape[1],q=eye(m),r=clone(x),one2D=tensor2d([[1]],[1,1]),w=clone(one2D),iters=m>=n?n:m;for(let j=0;j{let rjEnd1=slice(r,[j,j],[m-j,1]),normX=norm(rjEnd1),rjj=slice(r,[j,j],[1,1]),s=where(greater(rjj,0),tensor2d([[-1]]),tensor2d([[1]])),u1=sub(rjj,mul(s,normX)),wPre=div(rjEnd1,u1);wPre.shape[0]===1?w=clone(one2D):w=concat([one2D,slice(wPre,[1,0],[wPre.shape[0]-1,wPre.shape[1]])],0);let tau=neg(div(matMul(s,u1),normX)),rjEndAll=slice(r,[j,0],[m-j,n]),tauTimesW=mul(tau,w),wT=transpose(w);if(j===0)r=sub(rjEndAll,matMul(tauTimesW,matMul(wT,rjEndAll)));else{let rTimesTau=sub(rjEndAll,matMul(tauTimesW,matMul(wT,rjEndAll)));r=concat([slice(r,[0,0],[j,n]),rTimesTau],0)}let tawTimesWT=transpose(tauTimesW),qAllJEnd=slice(q,[0,j],[m,q.shape[1]-j]);if(j===0)q=sub(qAllJEnd,matMul(matMul(qAllJEnd,w),tawTimesWT));else{let qTimesTau=sub(qAllJEnd,matMul(matMul(qAllJEnd,w),tawTimesWT));q=concat([slice(q,[0,0],[m,j]),qTimesTau],1)}return[w,r,q]}),dispose([rTemp,wTemp,qTemp])}return!fullMatrices&&m>n&&(q=slice(q,[0,0],[m,n]),r=slice(r,[0,0],[n,n])),[q,r]})}var qr=op({qr_}),Reduction;(function(Reduction2){Reduction2[Reduction2.NONE=0]="NONE",Reduction2[Reduction2.MEAN=1]="MEAN",Reduction2[Reduction2.SUM=2]="SUM",Reduction2[Reduction2.SUM_BY_NONZERO_WEIGHTS=3]="SUM_BY_NONZERO_WEIGHTS"})(Reduction||(Reduction={}));function computeWeightedLoss_(losses4,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $losses=convertToTensor(losses4,"losses","computeWeightedLoss"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","computeWeightedLoss"));let weightedLoss=$weights==null?$losses:mul($losses,$weights);if(reduction2===Reduction.NONE)return weightedLoss;if(reduction2===Reduction.SUM)return sum2(weightedLoss);if(reduction2===Reduction.MEAN){if($weights==null)return mean(weightedLoss);{let broadcastFactor=$losses.size/$weights.size,result=div(sum2(weightedLoss),sum2($weights));return broadcastFactor>1?div(result,scalar(broadcastFactor)):result}}if(reduction2===Reduction.SUM_BY_NONZERO_WEIGHTS){if($weights==null)return div(sum2(weightedLoss),scalar($losses.size));{let broadcastedWeights=mul($weights,ones2($losses.shape)),numNonZeros=cast(sum2(notEqual(broadcastedWeights,scalar(0))),"float32");return div(sum2(weightedLoss),numNonZeros)}}throw Error(`Unknown reduction: ${reduction2}`)}var computeWeightedLoss=op({computeWeightedLoss_});function absoluteDifference_(labels,predictions,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","absoluteDifference"),$predictions=convertToTensor(predictions,"predictions","absoluteDifference"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","absoluteDifference")),assertShapesMatch($labels.shape,$predictions.shape,"Error in absoluteDifference: ");let losses4=abs(sub($labels,$predictions));return computeWeightedLoss(losses4,$weights,reduction2)}var absoluteDifference=op({absoluteDifference_});function cosineDistance_(labels,predictions,axis,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","cosineDistance"),$predictions=convertToTensor(predictions,"predictions","cosineDistance"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","cosineDistance")),assertShapesMatch($labels.shape,$predictions.shape,"Error in cosineDistance: ");let one=scalar(1),losses4=sub(one,sum2(mul($labels,$predictions),axis,!0));return computeWeightedLoss(losses4,$weights,reduction2)}var cosineDistance=op({cosineDistance_});function hingeLoss_(labels,predictions,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","hingeLoss"),$predictions=convertToTensor(predictions,"predictions","hingeLoss"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","hingeLoss")),assertShapesMatch($labels.shape,$predictions.shape,"Error in hingeLoss: ");let one=scalar(1);$labels=sub(mul(scalar(2),$labels),one);let losses4=relu(sub(one,mul($labels,$predictions)));return computeWeightedLoss(losses4,$weights,reduction2)}var hingeLoss=op({hingeLoss_});function huberLoss_(labels,predictions,weights,delta=1,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","huberLoss"),$predictions=convertToTensor(predictions,"predictions","huberLoss"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","huberLoss")),assertShapesMatch($labels.shape,$predictions.shape,"Error in huberLoss: ");let deltaScalar=scalar(delta),error=abs(sub($predictions,$labels)),quadratic=minimum(error,deltaScalar),linear=sub(error,quadratic),losses4=add2(mul(scalar(.5),square(quadratic)),mul(deltaScalar,linear));return computeWeightedLoss(losses4,$weights,reduction2)}var huberLoss=op({huberLoss_});function logLoss_(labels,predictions,weights,epsilon3=1e-7,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","logLoss"),$predictions=convertToTensor(predictions,"predictions","logLoss"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","logLoss")),assertShapesMatch($labels.shape,$predictions.shape,"Error in logLoss: ");let one=scalar(1),epsilonScalar=scalar(epsilon3),l13=neg(mul($labels,log2(add2($predictions,epsilonScalar)))),l23=mul(sub(one,$labels),log2(add2(sub(one,$predictions),epsilonScalar))),losses4=sub(l13,l23);return computeWeightedLoss(losses4,$weights,reduction2)}var logLoss=op({logLoss_});function meanSquaredError_(labels,predictions,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","meanSquaredError"),$predictions=convertToTensor(predictions,"predictions","meanSquaredError"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","meanSquaredError")),assertShapesMatch($labels.shape,$predictions.shape,"Error in meanSquaredError: ");let losses4=squaredDifference($labels,$predictions);return computeWeightedLoss(losses4,$weights,reduction2)}var meanSquaredError=op({meanSquaredError_});function sigmoidCrossEntropyWithLogits_(labels,logits){let $labels=convertToTensor(labels,"labels","sigmoidCrossEntropyWithLogits"),$logits=convertToTensor(logits,"logits","sigmoidCrossEntropyWithLogits");assertShapesMatch($labels.shape,$logits.shape,"Error in sigmoidCrossEntropyWithLogits: ");let maxOutput=relu($logits),outputXTarget=mul($logits,$labels),sigmoidOutput=log1p(exp(neg(abs($logits))));return add2(sub(maxOutput,outputXTarget),sigmoidOutput)}function sigmoidCrossEntropy_(multiClassLabels,logits,weights,labelSmoothing=0,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $multiClassLabels=convertToTensor(multiClassLabels,"multiClassLabels","sigmoidCrossEntropy"),$logits=convertToTensor(logits,"logits","sigmoidCrossEntropy"),$weights=null;if(weights!=null&&($weights=convertToTensor(weights,"weights","sigmoidCrossEntropy")),assertShapesMatch($multiClassLabels.shape,$logits.shape,"Error in sigmoidCrossEntropy: "),labelSmoothing>0){let labelSmoothingScalar=scalar(labelSmoothing),one=scalar(1),half=scalar(.5);$multiClassLabels=add2(mul($multiClassLabels,sub(one,labelSmoothingScalar)),mul(half,labelSmoothingScalar))}let losses4=sigmoidCrossEntropyWithLogits_($multiClassLabels,$logits);return computeWeightedLoss(losses4,$weights,reduction2)}var sigmoidCrossEntropy=op({sigmoidCrossEntropy_});function softmaxCrossEntropyWithLogits_(labels,logits,dim=-1){if(dim===-1&&(dim=logits.rank-1),dim!==logits.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${logits.rank} and dim was ${dim}`);return customGrad((labels2,logits2,save)=>{let lse=logSumExp(logits2,[dim],!0),logResult=sub(cast(logits2,"float32"),lse);save([labels2,logResult]);let costVector=neg(mul(logResult,labels2));return{value:sum2(costVector,[dim]),gradFunc:(dy,saved)=>{let[labels3,logResult2]=saved,dyShape=expandShapeToKeepDim(dy.shape,[dim]);return[mul(reshape(dy,dyShape),sub(cast(labels3,"float32"),exp(logResult2))),mul(reshape(dy,dyShape),sub(exp(logResult2),cast(labels3,"float32")))]}}})(labels,logits)}function softmaxCrossEntropy_(onehotLabels,logits,weights,labelSmoothing=0,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $onehotLabels=convertToTensor(onehotLabels,"onehotLabels","softmaxCrossEntropy"),$logits=convertToTensor(logits,"logits","softmaxCrossEntropy"),$weights=null;if(weights!=null&&($weights=convertToTensor(weights,"weights","softmaxCrossEntropy")),assertShapesMatch($onehotLabels.shape,$logits.shape,"Error in softmaxCrossEntropy: "),labelSmoothing>0){let labelSmoothingScalar=scalar(labelSmoothing),one=scalar(1),numClasses=scalar($onehotLabels.shape[1]);$onehotLabels=add2(mul($onehotLabels,sub(one,labelSmoothingScalar)),div(labelSmoothingScalar,numClasses))}let losses4=softmaxCrossEntropyWithLogits_($onehotLabels,$logits);return computeWeightedLoss(losses4,$weights,reduction2)}var softmaxCrossEntropy=op({softmaxCrossEntropy_}),spectral={fft,ifft,rfft,irfft},signal={hammingWindow,hannWindow,frame,stft},image={flipLeftRight,resizeNearestNeighbor,resizeBilinear,rotateWithOffset,cropAndResize,nonMaxSuppression,nonMaxSuppressionAsync,nonMaxSuppressionWithScore,nonMaxSuppressionWithScoreAsync,nonMaxSuppressionPadded,nonMaxSuppressionPaddedAsync},linalg={bandPart,gramSchmidt,qr},losses={absoluteDifference,computeWeightedLoss,cosineDistance,hingeLoss,huberLoss,logLoss,meanSquaredError,sigmoidCrossEntropy,softmaxCrossEntropy},Optimizer=class extends Serializable{minimize(f,returnCost=!1,varList){let{value,grads:grads2}=this.computeGradients(f,varList);if(varList!=null){let gradArray=varList.map(v=>({name:v.name,tensor:grads2[v.name]}));this.applyGradients(gradArray)}else this.applyGradients(grads2);return dispose(grads2),returnCost?value:(value.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(f,varList){return variableGrads(f,varList)}dispose(){this.iterations_!=null&&dispose(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:scalar(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(weightValues){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(weightValues){return this.iterations_=(await weightValues[0].tensor.data())[0],weightValues.slice(1)}};Object.defineProperty(Optimizer,Symbol.hasInstance,{value:instance=>instance.minimize!=null&&instance.computeGradients!=null&&instance.applyGradients!=null});var AdadeltaOptimizer=class extends Optimizer{constructor(learningRate,rho,epsilon3=null){super();this.learningRate=learningRate,this.rho=rho,this.epsilon=epsilon3,this.accumulatedGrads=[],this.accumulatedUpdates=[],epsilon3==null&&(this.epsilon=ENGINE.backend.epsilon())}applyGradients(variableGradients){(Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients)).forEach((name,i)=>{let value=ENGINE.registeredVariables[name],trainable=!1;this.accumulatedGrads[i]==null&&(this.accumulatedGrads[i]={originalName:`${name}/accum_grad`,variable:tidy(()=>zerosLike(value).variable(trainable))}),this.accumulatedUpdates[i]==null&&(this.accumulatedUpdates[i]={originalName:`${name}/accum_var`,variable:tidy(()=>zerosLike(value).variable(trainable))});let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;let accumulatedGrad=this.accumulatedGrads[i].variable,accumulatedUpdate=this.accumulatedUpdates[i].variable;tidy(()=>{let newAccumulatedGrad=add2(mul(accumulatedGrad,this.rho),mul(square(gradient),1-this.rho)),updates=mul(div(sqrt(add2(accumulatedUpdate,this.epsilon)),sqrt(add2(accumulatedGrad,this.epsilon))),gradient),newAccumulatedUpdate=add2(mul(accumulatedUpdate,this.rho),mul(square(updates),1-this.rho));accumulatedGrad.assign(newAccumulatedGrad),accumulatedUpdate.assign(newAccumulatedUpdate);let newValue=add2(mul(updates,-this.learningRate),value);value.assign(newValue)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(dispose(this.accumulatedGrads.map(v=>v.variable)),dispose(this.accumulatedUpdates.map(v=>v.variable)))}async getWeights(){let variables=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(variables.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues);let variableCount=weightValues.length/2,trainable=!1;this.accumulatedGrads=weightValues.slice(0,variableCount).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})),this.accumulatedUpdates=weightValues.slice(variableCount,variableCount*2).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(cls,config3){return new cls(config3.learningRate,config3.rho,config3.epsilon)}};AdadeltaOptimizer.className="Adadelta";registerClass(AdadeltaOptimizer);var AdagradOptimizer=class extends Optimizer{constructor(learningRate,initialAccumulatorValue=.1){super();this.learningRate=learningRate,this.initialAccumulatorValue=initialAccumulatorValue,this.accumulatedGrads=[]}applyGradients(variableGradients){(Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients)).forEach((name,i)=>{let value=ENGINE.registeredVariables[name];if(this.accumulatedGrads[i]==null){let trainable=!1;this.accumulatedGrads[i]={originalName:`${name}/accumulator`,variable:tidy(()=>fill(value.shape,this.initialAccumulatorValue).variable(trainable))}}let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;let accumulatedGrad=this.accumulatedGrads[i].variable;tidy(()=>{let newAccumulatedGrad=add2(accumulatedGrad,square(gradient));accumulatedGrad.assign(newAccumulatedGrad);let newValue=add2(mul(div(gradient,sqrt(add2(newAccumulatedGrad,ENGINE.backend.epsilon()))),-this.learningRate),value);value.assign(newValue)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&dispose(this.accumulatedGrads.map(v=>v.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues);let trainable=!1;this.accumulatedGrads=weightValues.map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(cls,config3){return new cls(config3.learningRate,config3.initialAccumulatorValue)}};AdagradOptimizer.className="Adagrad";registerClass(AdagradOptimizer);var AdamOptimizer=class extends Optimizer{constructor(learningRate,beta1,beta2,epsilon3=null){super();this.learningRate=learningRate,this.beta1=beta1,this.beta2=beta2,this.epsilon=epsilon3,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],tidy(()=>{this.accBeta1=scalar(beta1).variable(),this.accBeta2=scalar(beta2).variable()}),epsilon3==null&&(this.epsilon=ENGINE.backend.epsilon())}applyGradients(variableGradients){let varNames=Array.isArray(variableGradients)?variableGradients.map(v=>v.name):Object.keys(variableGradients);tidy(()=>{let oneMinusAccBeta1=sub(1,this.accBeta1),oneMinusAccBeta2=sub(1,this.accBeta2);varNames.forEach((name,i)=>{let value=ENGINE.registeredVariables[name],trainable=!1;this.accumulatedFirstMoment[i]==null&&(this.accumulatedFirstMoment[i]={originalName:`${name}/m`,variable:tidy(()=>zerosLike(value).variable(trainable))}),this.accumulatedSecondMoment[i]==null&&(this.accumulatedSecondMoment[i]={originalName:`${name}/v`,variable:tidy(()=>zerosLike(value).variable(trainable))});let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;let firstMoment=this.accumulatedFirstMoment[i].variable,secondMoment=this.accumulatedSecondMoment[i].variable,newFirstMoment=add2(mul(firstMoment,this.beta1),mul(gradient,1-this.beta1)),newSecondMoment=add2(mul(secondMoment,this.beta2),mul(square(gradient),1-this.beta2)),biasCorrectedFirstMoment=div(newFirstMoment,oneMinusAccBeta1),biasCorrectedSecondMoment=div(newSecondMoment,oneMinusAccBeta2);firstMoment.assign(newFirstMoment),secondMoment.assign(newSecondMoment);let newValue=add2(mul(div(biasCorrectedFirstMoment,add2(sqrt(biasCorrectedSecondMoment),this.epsilon)),-this.learningRate),value);value.assign(newValue)}),this.accBeta1.assign(mul(this.accBeta1,this.beta1)),this.accBeta2.assign(mul(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&dispose(this.accumulatedFirstMoment.map(v=>v.variable)),this.accumulatedSecondMoment!=null&&dispose(this.accumulatedSecondMoment.map(v=>v.variable))}async getWeights(){let variables=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(variables.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues),tidy(()=>{this.accBeta1.assign(pow(this.beta1,this.iterations_+1)),this.accBeta2.assign(pow(this.beta2,this.iterations_+1))});let variableCount=weightValues.length/2,trainable=!1;this.accumulatedFirstMoment=weightValues.slice(0,variableCount).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})),this.accumulatedSecondMoment=weightValues.slice(variableCount,variableCount*2).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(cls,config3){return new cls(config3.learningRate,config3.beta1,config3.beta2,config3.epsilon)}};AdamOptimizer.className="Adam";registerClass(AdamOptimizer);var AdamaxOptimizer=class extends Optimizer{constructor(learningRate,beta1,beta2,epsilon3=null,decay=0){super();this.learningRate=learningRate,this.beta1=beta1,this.beta2=beta2,this.epsilon=epsilon3,this.decay=decay,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],tidy(()=>{this.iteration=scalar(0).variable(),this.accBeta1=scalar(beta1).variable()}),epsilon3==null&&(this.epsilon=ENGINE.backend.epsilon())}applyGradients(variableGradients){let variableNames=Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients);tidy(()=>{let oneMinusAccBeta1=sub(1,this.accBeta1),lr=div(-this.learningRate,add2(mul(this.iteration,this.decay),1));variableNames.forEach((name,i)=>{let value=ENGINE.registeredVariables[name],trainable=!1;this.accumulatedFirstMoment[i]==null&&(this.accumulatedFirstMoment[i]={originalName:`${name}/m`,variable:zerosLike(value).variable(trainable)}),this.accumulatedWeightedInfNorm[i]==null&&(this.accumulatedWeightedInfNorm[i]={originalName:`${name}/v`,variable:zerosLike(value).variable(trainable)});let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;let firstMoment=this.accumulatedFirstMoment[i].variable,weightedInfNorm=this.accumulatedWeightedInfNorm[i].variable,newFirstMoment=add2(mul(firstMoment,this.beta1),mul(gradient,1-this.beta1)),ut0=mul(weightedInfNorm,this.beta2),ut1=abs(gradient),newWeightedInfNorm=maximum(ut0,ut1);firstMoment.assign(newFirstMoment),weightedInfNorm.assign(newWeightedInfNorm);let newValue=add2(mul(div(lr,oneMinusAccBeta1),div(newFirstMoment,add2(newWeightedInfNorm,this.epsilon))),value);value.assign(newValue)}),this.iteration.assign(add2(this.iteration,1)),this.accBeta1.assign(mul(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&dispose(this.accumulatedFirstMoment.map(v=>v.variable)),this.accumulatedWeightedInfNorm!=null&&dispose(this.accumulatedWeightedInfNorm.map(v=>v.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(weightValues){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(cls,config3){return new cls(config3.learningRate,config3.beta1,config3.beta2,config3.epsilon,config3.decay)}};AdamaxOptimizer.className="Adamax";registerClass(AdamaxOptimizer);var SGDOptimizer=class extends Optimizer{constructor(learningRate){super();this.learningRate=learningRate,this.setLearningRate(learningRate)}applyGradients(variableGradients){(Array.isArray(variableGradients)?variableGradients.map(v=>v.name):Object.keys(variableGradients)).forEach((name,i)=>{let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;let value=ENGINE.registeredVariables[name];tidy(()=>{let newValue=add2(mul(this.c,gradient),value);value.assign(newValue)})}),this.incrementIterations()}setLearningRate(learningRate){this.learningRate=learningRate,this.c!=null&&this.c.dispose(),this.c=keep(scalar(-learningRate))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(weightValues){if(weightValues=await this.extractIterations(weightValues),weightValues.length!==0)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(cls,config3){return new cls(config3.learningRate)}};SGDOptimizer.className="SGD";registerClass(SGDOptimizer);var MomentumOptimizer=class extends SGDOptimizer{constructor(learningRate,momentum,useNesterov=!1){super(learningRate);this.learningRate=learningRate,this.momentum=momentum,this.useNesterov=useNesterov,this.accumulations=[],this.m=scalar(this.momentum)}applyGradients(variableGradients){(Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients)).forEach((name,i)=>{let value=ENGINE.registeredVariables[name];if(this.accumulations[i]==null){let trainable=!1;this.accumulations[i]={originalName:`${name}/momentum`,variable:tidy(()=>zerosLike(value).variable(trainable))}}let accumulation=this.accumulations[i].variable,gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];gradient!=null&&tidy(()=>{let newValue,newAccumulation=add2(mul(this.m,accumulation),gradient);this.useNesterov?newValue=add2(mul(this.c,add2(gradient,mul(newAccumulation,this.m))),value):newValue=add2(mul(this.c,newAccumulation),value),accumulation.assign(newAccumulation),value.assign(newValue)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&dispose(this.accumulations.map(v=>v.variable))}setMomentum(momentum){this.momentum=momentum}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues);let trainable=!1;this.accumulations=weightValues.map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(cls,config3){return new cls(config3.learningRate,config3.momentum,config3.useNesterov)}};MomentumOptimizer.className="Momentum";registerClass(MomentumOptimizer);var RMSPropOptimizer=class extends Optimizer{constructor(learningRate,decay=.9,momentum=0,epsilon3=null,centered=!1){super();if(this.learningRate=learningRate,this.decay=decay,this.momentum=momentum,this.epsilon=epsilon3,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=centered,epsilon3==null&&(this.epsilon=ENGINE.backend.epsilon()),learningRate==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(variableGradients){(Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients)).forEach((name,i)=>{let value=ENGINE.registeredVariables[name],trainable=!1;this.accumulatedMeanSquares[i]==null&&(this.accumulatedMeanSquares[i]={originalName:`${name}/rms`,variable:tidy(()=>zerosLike(value).variable(trainable))}),this.accumulatedMoments[i]==null&&(this.accumulatedMoments[i]={originalName:`${name}/momentum`,variable:tidy(()=>zerosLike(value).variable(trainable))}),this.accumulatedMeanGrads[i]==null&&this.centered&&(this.accumulatedMeanGrads[i]={originalName:`${name}/mg`,variable:tidy(()=>zerosLike(value).variable(trainable))});let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;let accumulatedMeanSquare=this.accumulatedMeanSquares[i].variable,accumulatedMoments=this.accumulatedMoments[i].variable;tidy(()=>{let newAccumulatedMeanSquare=add2(mul(accumulatedMeanSquare,this.decay),mul(square(gradient),1-this.decay));if(this.centered){let accumulatedMeanGrad=this.accumulatedMeanGrads[i].variable,newAccumulatedMeanGrad=add2(mul(accumulatedMeanGrad,this.decay),mul(gradient,1-this.decay)),gradContribution=div(mul(gradient,this.learningRate),sqrt(sub(newAccumulatedMeanSquare,add2(square(newAccumulatedMeanGrad),this.epsilon)))),newAccumulatedMoments=add2(mul(accumulatedMoments,this.momentum),gradContribution);accumulatedMeanSquare.assign(newAccumulatedMeanSquare),accumulatedMeanGrad.assign(newAccumulatedMeanGrad),accumulatedMoments.assign(newAccumulatedMoments);let newValue=sub(value,newAccumulatedMoments);value.assign(newValue)}else{let newAccumulatedMeanSquare2=add2(mul(accumulatedMeanSquare,this.decay),mul(square(gradient),1-this.decay)),newAccumulatedMoments=add2(mul(accumulatedMoments,this.momentum),div(mul(gradient,this.learningRate),sqrt(add2(newAccumulatedMeanSquare2,this.epsilon))));accumulatedMeanSquare.assign(newAccumulatedMeanSquare2),accumulatedMoments.assign(newAccumulatedMoments);let newValue=sub(value,newAccumulatedMoments);value.assign(newValue)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&dispose(this.accumulatedMeanSquares.map(v=>v.variable)),this.accumulatedMeanGrads!=null&&this.centered&&dispose(this.accumulatedMeanGrads.map(v=>v.variable)),this.accumulatedMoments!=null&&dispose(this.accumulatedMoments.map(v=>v.variable))}async getWeights(){let variables=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&variables.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(variables.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues);let variableCount=this.centered?weightValues.length/3:weightValues.length/2,trainable=!1;this.accumulatedMeanSquares=weightValues.slice(0,variableCount).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})),this.accumulatedMoments=weightValues.slice(variableCount,variableCount*2).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})),this.centered&&(this.accumulatedMeanGrads=weightValues.slice(variableCount*2,variableCount*3).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(cls,config3){return new cls(config3.learningRate,config3.decay,config3.momentum,config3.epsilon,config3.centered)}};RMSPropOptimizer.className="RMSProp";registerClass(RMSPropOptimizer);var OptimizerConstructors=class{static sgd(learningRate){return new SGDOptimizer(learningRate)}static momentum(learningRate,momentum,useNesterov=!1){return new MomentumOptimizer(learningRate,momentum,useNesterov)}static rmsprop(learningRate,decay=.9,momentum=0,epsilon3=null,centered=!1){return new RMSPropOptimizer(learningRate,decay,momentum,epsilon3,centered)}static adam(learningRate=.001,beta1=.9,beta2=.999,epsilon3=null){return new AdamOptimizer(learningRate,beta1,beta2,epsilon3)}static adadelta(learningRate=.001,rho=.95,epsilon3=null){return new AdadeltaOptimizer(learningRate,rho,epsilon3)}static adamax(learningRate=.002,beta1=.9,beta2=.999,epsilon3=null,decay=0){return new AdamaxOptimizer(learningRate,beta1,beta2,epsilon3,decay)}static adagrad(learningRate,initialAccumulatorValue=.1){return new AdagradOptimizer(learningRate,initialAccumulatorValue)}},train={sgd:OptimizerConstructors.sgd,momentum:OptimizerConstructors.momentum,adadelta:OptimizerConstructors.adadelta,adagrad:OptimizerConstructors.adagrad,rmsprop:OptimizerConstructors.rmsprop,adamax:OptimizerConstructors.adamax,adam:OptimizerConstructors.adam},delayCallback=(()=>typeof requestAnimationFrame!="undefined"?requestAnimationFrame:typeof setImmediate!="undefined"?setImmediate:f=>f())();function nextFrame(){return new Promise(resolve=>delayCallback(()=>resolve()))}var backend_util_exports={};__export2(backend_util_exports,{ERF_A1:()=>ERF_A1,ERF_A2:()=>ERF_A2,ERF_A3:()=>ERF_A3,ERF_A4:()=>ERF_A4,ERF_A5:()=>ERF_A5,ERF_P:()=>ERF_P,PARALLELIZE_THRESHOLD:()=>PARALLELIZE_THRESHOLD,SELU_SCALE:()=>SELU_SCALE,SELU_SCALEALPHA:()=>SELU_SCALEALPHA,applyActivation:()=>applyActivation,assertAndGetBroadcastShape:()=>assertAndGetBroadcastShape,assertAxesAreInnerMostDims:()=>assertAxesAreInnerMostDims,assertParamsConsistent:()=>assertParamsConsistent,assignToTypedArray:()=>assignToTypedArray,axesAreInnerMostDims:()=>axesAreInnerMostDims,calculateShapes:()=>calculateShapes,castTensor:()=>castTensor,combineLocations:()=>combineLocations,complexWithEvenIndex:()=>complexWithEvenIndex,complexWithOddIndex:()=>complexWithOddIndex,computeConv2DInfo:()=>computeConv2DInfo,computeConv3DInfo:()=>computeConv3DInfo,computeDefaultPad:()=>computeDefaultPad,computeDilation2DInfo:()=>computeDilation2DInfo,computeOptimalWindowSize:()=>computeOptimalWindowSize,computeOutAndReduceShapes:()=>computeOutAndReduceShapes,computeOutShape:()=>computeOutShape2,computePool2DInfo:()=>computePool2DInfo,computePool3DInfo:()=>computePool3DInfo,convertConv2DDataFormat:()=>convertConv2DDataFormat,eitherStridesOrDilationsAreOne:()=>eitherStridesOrDilationsAreOne,expandShapeToKeepDim:()=>expandShapeToKeepDim,exponent:()=>exponent,exponents:()=>exponents,fromStringArrayToUint8:()=>fromStringArrayToUint8,fromUint8ToStringArray:()=>fromUint8ToStringArray,getAxesPermutation:()=>getAxesPermutation,getBroadcastDims:()=>getBroadcastDims,getComplexWithIndex:()=>getComplexWithIndex,getFusedBiasGradient:()=>getFusedBiasGradient,getFusedDyActivation:()=>getFusedDyActivation,getImageCenter:()=>getImageCenter,getInnerMostAxes:()=>getInnerMostAxes,getPermuted:()=>getPermuted,getReductionAxes:()=>getReductionAxes,getReshaped:()=>getReshaped,getReshapedPermuted:()=>getReshapedPermuted,getSliceBeginCoords:()=>getSliceBeginCoords,getSliceSize:()=>getSliceSize,getUndoAxesPermutation:()=>getUndoAxesPermutation,log:()=>log22,mergeRealAndImagArrays:()=>mergeRealAndImagArrays,prepareAndValidate:()=>prepareAndValidate,prepareSplitSize:()=>prepareSplitSize,reshapeTensor:()=>reshapeTensor,segment_util:()=>segment_util_exports,shouldFuse:()=>shouldFuse,slice_util:()=>slice_util_exports,splitRealAndImagArrays:()=>splitRealAndImagArrays,tupleValuesAreOne:()=>tupleValuesAreOne,upcastType:()=>upcastType,validateInput:()=>validateInput,validateUpdateShape:()=>validateUpdateShape,warn:()=>warn});function assertParamsConsistent(shapes,axis){let rank=shapes[0].length;shapes.forEach((shape,i)=>{assert(shape.length===rank,()=>`Error in concat${rank}D: rank of tensors[${i}] must be the same as the rank of the rest (${rank})`)}),assert(axis>=0&&axis`Error in concat${rank}D: axis must be between 0 and ${rank-1}.`);let firstShape=shapes[0];shapes.forEach((shape,i)=>{for(let r=0;r`Error in concat${rank}D: Shape of tensors[${i}] (${shape}) does not match the shape of the rest (${firstShape}) along the non-concatenated axis ${i}.`)})}function computeOutShape2(shapes,axis){let outputShape=shapes[0].slice();for(let i=1;i=blockShapeRank*2+1||i%2==1?permutedAfterBatch.push(i):permutedBeforeBatch.push(i);permuted.push(...permutedBeforeBatch),permuted.push(0),permuted.push(...permutedAfterBatch)}return permuted}function getReshapedPermuted(inputShape,blockShape,prod5,batchToSpace=!0){let reshapedPermuted=[];batchToSpace?reshapedPermuted.push(inputShape[0]/prod5):reshapedPermuted.push(inputShape[0]*prod5);for(let i=1;i"Number of splits must evenly divide the axis."),splitSizes=new Array(numOrSizeSplits).fill(x.shape[axis]/numOrSizeSplits);else{let numOfNegs=numOrSizeSplits.reduce((count2,value)=>(value===-1&&(count2+=1),count2),0);assert(numOfNegs<=1,()=>"There should be only one negative value in split array.");let negIndex=numOrSizeSplits.indexOf(-1);if(negIndex!==-1){let total=numOrSizeSplits.reduce((a,b)=>b>0?a+b:a);numOrSizeSplits[negIndex]=x.shape[axis]-total}assert(x.shape[axis]===numOrSizeSplits.reduce((a,b)=>a+b),()=>"The sum of sizes must match the size of the axis dimension."),splitSizes=numOrSizeSplits}return splitSizes}var segment_util_exports={};__export2(segment_util_exports,{collectGatherOpShapeInfo:()=>collectGatherOpShapeInfo,computeOutShape:()=>computeOutShape3,segOpComputeOptimalWindowSize:()=>segOpComputeOptimalWindowSize});function segOpComputeOptimalWindowSize(inSize,numSegments){let done=!1,res;for(inSize<=PARALLELIZE_THRESHOLD?(res=inSize,done=!0):res=nearestDivisor(inSize,Math.floor(Math.sqrt(inSize)));!done;)res>numSegments||res===inSize?done=!0:res=nearestDivisor(inSize,res+1);return res}function computeOutShape3(aShape,axis,numSegments){let outShape=[],rank=aShape.length;for(let dim=0;dimindicesRank))throw new Error(`Expect batchDims in the range of [-${indicesRank}, ${indicesRank}], but got ${batchDims}`);if(batchDims<0&&(batchDims+=indicesRank),batchDims>xRank)throw new Error(`batchDims (${batchDims}) must be less than rank(x) ( + the f you passed encloses all operations that lead from x to y.`)}function neg_(x){let inputs={x:convertToTensor(x,"x","neg")};return ENGINE.runKernel(Neg,inputs)}var neg=op({neg_});function softplus_(x){let inputs={x:convertToTensor(x,"x","softplus")};return ENGINE.runKernel(Softplus,inputs)}var softplus=op({softplus_});function logSigmoid_(x){let $x=convertToTensor(x,"x","logSigmoid");return customGrad(x2=>({value:neg(softplus(neg(x2))),gradFunc:dy=>mul(dy,sigmoid(neg(x2)))}))($x)}var logSigmoid=op({logSigmoid_});function max_(x,axis=null,keepDims=!1){let inputs={x:convertToTensor(x,"x","max")},attrs={reductionIndices:axis,keepDims};return ENGINE.runKernel(Max,inputs,attrs)}var max=op({max_});function sub_(a,b){let $a=convertToTensor(a,"a","sub"),$b=convertToTensor(b,"b","sub");[$a,$b]=makeTypesMatch($a,$b);let inputs={a:$a,b:$b};return ENGINE.runKernel(Sub,inputs)}var sub=op({sub_});function sum_(x,axis=null,keepDims=!1){let $x=convertToTensor(x,"x","sum");$x.dtype==="bool"&&($x=cast($x,"int32"));let inputs={x:$x},attrs={axis,keepDims};return ENGINE.runKernel(Sum,inputs,attrs)}var sum2=op({sum_});function logSoftmax_(logits,axis=-1){let $logits=convertToTensor(logits,"logits","logSoftmax");if(axis===-1&&(axis=$logits.rank-1),axis!==$logits.rank-1)throw Error(`Log Softmax along a non-last dimension is not yet supported. Logits was rank ${$logits.rank} and axis was ${axis}`);return customGrad((logits2,save)=>{let keepDims=!0,xMax=max(logits2,axis,!0),shifted=sub(logits2,xMax),value=sub(cast(shifted,"float32"),log2(sum2(exp(shifted),axis,keepDims)));return save([value]),{value,gradFunc:(dy,saved)=>{let[value2]=saved,keepDims2=!0,softmax6=exp(value2);return sub(dy,mul(sum2(dy,axis,keepDims2),softmax6))}}})($logits)}var logSoftmax=op({logSoftmax_});function axesAreInnerMostDims(axes,rank){for(let i=0;iaShape[dim]);return[outShape,reduceShape]}function expandShapeToKeepDim(shape,axes){let reduceSubShape=axes.map(x=>1);return combineLocations(shape,reduceSubShape,axes)}function assertAxesAreInnerMostDims(msg,axes,rank){assert(axesAreInnerMostDims(axes,rank),()=>`${msg} supports only inner-most axes for now. Got axes ${axes} and rank-${rank} input.`)}function getAxesPermutation(axes,rank){if(axesAreInnerMostDims(axes,rank))return null;let result=[];for(let i=0;iresult.push(axis)),result}function getUndoAxesPermutation(axes){return axes.map((axis,i)=>[i,axis]).sort((a,b)=>a[1]-b[1]).map(x=>x[0])}function getInnerMostAxes(numAxes,rank){let res=[];for(let i=rank-numAxes;i`Error in maxPool: input must be rank 4 but got rank ${x4D.rank}.`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in maxPool: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={x:x4D},attrs={filterSize,strides,pad:pad3,dimRoundingMode},res=ENGINE.runKernel(MaxPool,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var maxPool=op({maxPool_});function maxPool3d_(x,filterSize=[1,1,1],strides,pad3,dimRoundingMode,dataFormat="NDHWC",dilations){dilations==null?dilations=[1,1,1]:deprecationWarn("dilations is deprecated, this field will be gone in v3.0.0.");let $x=convertToTensor(x,"x","maxPool3d"),x5D=$x,reshapedTo5D=!1;$x.rank===4&&(reshapedTo5D=!0,x5D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2],$x.shape[3]])),assert(x5D.rank===5,()=>`Error in maxPool3d: x must be rank 5 but got rank ${x5D.rank}.`),assert(dataFormat==="NDHWC",()=>`Error in maxPool3d: Only NDHWC is currently supported, but got dataFormat of ${dataFormat}`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in maxPool3d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in maxPool3d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={x:x5D},attrs={filterSize,strides,pad:pad3,dimRoundingMode,dataFormat,dilations},res=ENGINE.runKernel(MaxPool3D,inputs,attrs);return reshapedTo5D?reshape(res,[res.shape[1],res.shape[2],res.shape[3],res.shape[4]]):res}var maxPool3d=op({maxPool3d_});function maxPoolWithArgmax_(x,filterSize,strides,pad3,includeBatchInIndex=!1){let inputs={x:convertToTensor(x,"x","maxPoolWithArgmax")},attrs={filterSize,strides,pad:pad3,includeBatchInIndex},result=ENGINE.runKernel(MaxPoolWithArgmax,inputs,attrs);return{result:result[0],indexes:result[1]}}var maxPoolWithArgmax=op({maxPoolWithArgmax_});function maximum_(a,b){let $a=convertToTensor(a,"a","maximum"),$b=convertToTensor(b,"b","maximum");[$a,$b]=makeTypesMatch($a,$b),$a.dtype==="bool"&&($a=cast($a,"int32"),$b=cast($b,"int32")),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(Maximum,inputs)}var maximum=op({maximum_});function mean_(x,axis=null,keepDims=!1){let inputs={x:convertToTensor(x,"x","mean")},attrs={axis,keepDims};return ENGINE.runKernel(Mean,inputs,attrs)}var mean=op({mean_});function min_(x,axis=null,keepDims=!1){let inputs={x:convertToTensor(x,"x","min")},attrs={axis,keepDims};return ENGINE.runKernel(Min,inputs,attrs)}var min=op({min_});function minimum_(a,b){let $a=convertToTensor(a,"a","minimum"),$b=convertToTensor(b,"b","minimum");[$a,$b]=makeTypesMatch($a,$b),$a.dtype==="bool"&&($a=cast($a,"int32"),$b=cast($b,"int32")),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(Minimum,inputs)}var minimum=op({minimum_});function mirrorPad_(x,paddings,mode){assert(mode==="reflect"||mode==="symmetric",()=>`Invalid mode. Mode must be either reflect or symmetric. Got ${mode}.`);let $x=convertToTensor(x,"x","mirrorPad");if($x.rank===0)throw new Error("mirrorPad(scalar) is not defined. Pass non-scalar to mirrorPad");assert(paddings.length===$x.rank,()=>`Padding doesn't match input. Must be ${$x.rank}. Got ${paddings.length}.`);let shapeOffset=mode==="reflect"?1:0;for(let i=0;i<$x.rank;i++)assert(paddings[i].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),assert(paddings[i][0]>=0&&paddings[i][0]<=$x.shape[i]-shapeOffset&&paddings[i][1]>=0&&paddings[i][1]<=$x.shape[i]-shapeOffset,()=>`Padding in dimension ${i} cannot be greater than or equal to ${$x.shape[i]-shapeOffset} or less than 0 for input of shape ${$x.shape}`);let attrs={paddings,mode},inputs={x:$x};return ENGINE.runKernel(MirrorPad,inputs,attrs)}var mirrorPad=op({mirrorPad_});function mod_(a,b){let $a=convertToTensor(a,"a","mod"),$b=convertToTensor(b,"b","mod");[$a,$b]=makeTypesMatch($a,$b);let inputs={a:$a,b:$b};return ENGINE.runKernel(Mod,inputs)}var mod=op({mod_});function square_(x){let $x=convertToTensor(x,"x","square"),attrs={};return ENGINE.runKernel("Square",{x:$x},attrs)}var square=op({square_});function moments_(x,axis=null,keepDims=!1){x=convertToTensor(x,"x","moments");let axes=parseAxisParam(axis,x.shape),xMean=mean(x,axes,keepDims),keepDimsShape=xMean.shape;keepDims||(keepDimsShape=expandShapeToKeepDim(xMean.shape,axes));let devSquared=square(sub(cast(x,"float32"),reshape(xMean,keepDimsShape))),variance=mean(devSquared,axes,keepDims);return{mean:xMean,variance}}var moments=op({moments_});function multiRNNCell_(lstmCells,data2,c,h){let $data=convertToTensor(data2,"data","multiRNNCell"),$c=convertToTensorArray(c,"c","multiRNNCell"),$h=convertToTensorArray(h,"h","multiRNNCell"),input2=$data,newStates=[];for(let i=0;i2)throw new Error(`Rank of probabilities must be 1 or 2, but is ${origRank}`);seed=seed||Math.random();let inputs={logits:origRank===1?reshape($logits,[1,-1]):$logits},attrs={numSamples,seed,normalized},res=ENGINE.runKernel(Multinomial,inputs,attrs);return origRank===1?reshape(res,[res.size]):res}var multinomial=op({multinomial_});function notEqual_(a,b){let $a=convertToTensor(a,"a","notEqual"),$b=convertToTensor(b,"b","notEqual");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b};return ENGINE.runKernel(NotEqual,inputs)}var notEqual=op({notEqual_});function zeros(shape,dtype="float32"){if(dtype==="complex64"){let real4=zeros(shape,"float32"),imag4=zeros(shape,"float32");return complex(real4,imag4)}let values=makeZerosTypedArray(sizeFromShape(shape),dtype);return ENGINE.makeTensor(values,shape,dtype)}function ones2(shape,dtype="float32"){if(dtype==="complex64"){let real4=ones2(shape,"float32"),imag4=zeros(shape,"float32");return complex(real4,imag4)}let values=makeOnesTypedArray(sizeFromShape(shape),dtype);return ENGINE.makeTensor(values,shape,dtype)}function onesLike_(x){let inputs={x:convertToTensor(x,"x","onesLike")};return ENGINE.runKernel(OnesLike,inputs)}var onesLike=op({onesLike_});function outerProduct_(v1,v2){let $v1=convertToTensor(v1,"v1","outerProduct"),$v2=convertToTensor(v2,"v2","outerProduct");assert($v1.rank===1&&$v2.rank===1,()=>`Error in outerProduct: inputs must be rank 1, but got ranks ${$v1.rank} and ${$v2.rank}.`);let v12D=reshape($v1,[-1,1]),v22D=reshape($v2,[1,-1]);return matMul(v12D,v22D)}var outerProduct=op({outerProduct_});function pad_(x,paddings,constantValue=0){let $x=convertToTensor(x,"x","pad");if($x.rank===0)throw new Error("pad(scalar) is not defined. Pass non-scalar to pad");let attrs={paddings,constantValue},inputs={x:$x};return ENGINE.runKernel(PadV2,inputs,attrs)}var pad=op({pad_});function pad1d_(x,paddings,constantValue=0){return assert(paddings.length===2,()=>"Invalid number of paddings. Must be length of 2."),pad(x,[paddings],constantValue)}var pad1d=op({pad1d_});function pad2d_(x,paddings,constantValue=0){return assert(paddings.length===2&&paddings[0].length===2&&paddings[1].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),pad(x,paddings,constantValue)}var pad2d=op({pad2d_});function pad3d_(x,paddings,constantValue=0){return assert(paddings.length===3&&paddings[0].length===2&&paddings[1].length===2&&paddings[2].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),pad(x,paddings,constantValue)}var pad3d=op({pad3d_});function pad4d_(x,paddings,constantValue=0){return assert(paddings.length===4&&paddings[0].length===2&&paddings[1].length===2&&paddings[2].length===2&&paddings[3].length===2,()=>"Invalid number of paddings. Must be length of 2 each."),pad(x,paddings,constantValue)}var pad4d=op({pad4d_});function spaceToBatchND_(x,blockShape,paddings){let $x=convertToTensor(x,"x","spaceToBatchND");assert($x.rank>=1+blockShape.length,()=>`input rank ${$x.rank} should be > than [blockShape] ${blockShape.length}`),assert(paddings.length===blockShape.length,()=>`paddings.shape[0] ${paddings.length} must be equal to [blockShape] ${blockShape.length}`),assert($x.shape.reduce((a,b,i)=>i>0&&i<=blockShape.length?a&&(b+paddings[i-1][0]+paddings[i-1][1])%blockShape[i-1]==0:a,!0),()=>`input spatial dimensions ${$x.shape.slice(1)} with paddings ${paddings.toString()} must be divisible by blockShapes ${blockShape.toString()}`);let inputs={x:$x},attrs={blockShape,paddings};return ENGINE.runKernel(SpaceToBatchND,inputs,attrs)}var spaceToBatchND=op({spaceToBatchND_});function pool_(input2,windowShape,poolingType,pad3,dilations,strides){dilations==null&&(dilations=[1,1]),strides==null&&(strides=1),pad3===0&&(pad3="valid");let $x=convertToTensor(input2,"x","maxPool"),x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in pool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);let convInfo=computePool2DInfo(x4D.shape,windowShape,strides,dilations,pad3),dilation=[convInfo.dilationHeight,convInfo.dilationWidth],basePadding;pad3==="same"?basePadding=withSpaceToBatchBasePaddings([convInfo.filterHeight,convInfo.filterWidth],dilation):basePadding=[[0,0],[0,0]];let isDilationOne=dilation[0]===1&&dilation[1]===1,[adjustedPadding,adjustedCrops]=requiredSpaceToBatchPaddings([convInfo.inHeight,convInfo.inWidth],dilation,basePadding),convertedPad=isDilationOne?pad3:"valid",convertedX=isDilationOne?x4D:spaceToBatchND(x4D,dilation,adjustedPadding),y=(poolingType==="avg"?()=>avgPool(convertedX,windowShape,strides,convertedPad):()=>maxPool(convertedX,windowShape,strides,convertedPad))(),res=isDilationOne?y:batchToSpaceND(y,dilation,adjustedCrops);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}function requiredSpaceToBatchPaddings(inputShape,blockShape,basePadding){let padStart=basePadding.map(b=>b[0]),origPadEnd=basePadding.map(b=>b[1]),fullInputShape=inputShape.concat(padStart,origPadEnd),padEndExtra=blockShape.map((b,i)=>(b-fullInputShape[i]%b)%b),padEnd=origPadEnd.map((s,i)=>s+padEndExtra[i]),paddings=blockShape.map((_,i)=>[padStart[i],padEnd[i]]),crops=blockShape.map((_,i)=>[0,padEndExtra[i]]);return[paddings,crops]}function withSpaceToBatchBasePaddings(filterShape,dilation){let padExtraShape=filterShape.map((s,i)=>s+(s-1)*(dilation[i]-1)).map(s=>s-1),padExtraStart=padExtraShape.map(s=>Math.floor(s/2)),padExtraEnd=padExtraShape.map((s,i)=>s-padExtraStart[i]);return padExtraShape.map((_,i)=>[padExtraStart[i],padExtraEnd[i]])}var pool=op({pool_});function pow_(base2,exp4){let $base=convertToTensor(base2,"base","pow"),$exp=convertToTensor(exp4,"exp","pow");[$base,$exp]=makeTypesMatch($base,$exp);let inputs={a:$base,b:$exp};return ENGINE.runKernel(Pow,inputs)}var pow=op({pow_});function prelu_(x,alpha){let $x=convertToTensor(x,"x","prelu"),$alpha=convertToTensor(alpha,"alpha","prelu"),inputs={x:$x,alpha:$alpha};return ENGINE.runKernel(Prelu,inputs)}var prelu=op({prelu_});function prod_(x,axis=null,keepDims=!1){let $x=convertToTensor(x,"x","prod");$x.dtype==="bool"&&($x=cast($x,"int32"));let inputs={x:$x},attrs={axis,keepDims};return ENGINE.runKernel(Prod,inputs,attrs)}var prod=op({prod_});function rand_(shape,randFunction,dtype){let size=sizeFromShape(shape),values=null;if(dtype==null||dtype==="float32")values=new Float32Array(size);else if(dtype==="int32")values=new Int32Array(size);else if(dtype==="bool")values=new Uint8Array(size);else throw new Error(`Unknown data type ${dtype}`);for(let i=0;i=1||s===0);let mul2=Math.sqrt(-2*Math.log(s)/s);resultX=this.mean+this.stdDev*v1*mul2,resultY=this.mean+this.stdDev*v2*mul2,(!this.truncated||this.isValidTruncated(resultX))&&(isValid=!0)}return(!this.truncated||this.isValidTruncated(resultY))&&(this.nextVal=this.convertValue(resultY)),this.convertValue(resultX)}convertValue(value){return this.dtype==null||this.dtype==="float32"?value:Math.round(value)}isValidTruncated(value){return value<=this.upper&&value>=this.lower}},RandGamma=class{constructor(alpha,beta,dtype,seed){this.alpha=alpha,this.beta=1/beta,this.dtype=dtype;let seedValue=seed||Math.random();this.randu=seedrandom.alea(seedValue.toString()),this.randn=new MPRandGauss(0,1,dtype,!1,this.randu()),alpha<1?this.d=alpha+2/3:this.d=alpha-1/3,this.c=1/Math.sqrt(9*this.d)}nextValue(){let x2,v0,v1,x,u,v;for(;;){do x=this.randn.nextValue(),v=1+this.c*x;while(v<=0);if(v*=v*v,x2=x*x,v0=1-.331*x2*x2,v1=.5*x2+this.d*(1-v+Math.log(v)),u=this.randu(),uthis.dtype==null||this.dtype==="float32",this.min=min6,this.range=max6-min6,this.dtype=dtype,seed==null&&(seed=Math.random()),typeof seed=="number"&&(seed=seed.toString()),!this.canReturnFloat()&&this.range<=1)throw new Error(`The difference between ${min6} - ${max6} <= 1 and dtype is not float`);this.random=seedrandom.alea(seed)}convertValue(value){return this.canReturnFloat()?value:Math.round(value)}nextValue(){return this.convertValue(this.min+this.range*this.random())}};function randomGamma_(shape,alpha,beta=1,dtype="float32",seed){if(beta==null&&(beta=1),dtype==null&&(dtype="float32"),dtype!=="float32"&&dtype!=="int32")throw new Error(`Unsupported data type ${dtype}`);let rgamma=new RandGamma(alpha,beta,dtype,seed),res=buffer(shape,dtype);for(let i=0;i`Error in reverse1D: x must be rank 1 but got rank ${$x.rank}.`),reverse($x,0)}var reverse1d=op({reverse1d_});function reverse2d_(x,axis){let $x=convertToTensor(x,"x","reverse");return assert($x.rank===2,()=>`Error in reverse2D: x must be rank 2 but got rank ${$x.rank}.`),reverse($x,axis)}var reverse2d=op({reverse2d_});function reverse3d_(x,axis){let $x=convertToTensor(x,"x","reverse");return assert($x.rank===3,()=>`Error in reverse3D: x must be rank 3 but got rank ${$x.rank}.`),reverse($x,axis)}var reverse3d=op({reverse3d_});function reverse4d_(x,axis){let $x=convertToTensor(x,"x","reverse");return assert($x.rank===4,()=>`Error in reverse4D: x must be rank 4 but got rank ${$x.rank}.`),reverse($x,axis)}var reverse4d=op({reverse4d_});function round_(x){let inputs={x:convertToTensor(x,"x","round")};return ENGINE.runKernel(Round,inputs)}var round2=op({round_});function rsqrt_(x){let inputs={x:convertToTensor(x,"x","rsqrt")};return ENGINE.runKernel(Rsqrt,inputs)}var rsqrt=op({rsqrt_});function scalar(value,dtype){if((isTypedArray(value)&&dtype!=="string"||Array.isArray(value))&&dtype!=="complex64")throw new Error("Error creating a new Scalar: value must be a primitive (number|boolean|string)");if(dtype==="string"&&isTypedArray(value)&&!(value instanceof Uint8Array))throw new Error("When making a scalar from encoded string, the value must be `Uint8Array`.");return makeTensor(value,[],[],dtype)}function selu_(x){let inputs={x:convertToTensor(x,"x","selu")};return ENGINE.runKernel(Selu,inputs)}var selu=op({selu_});function separableConv2d_(x,depthwiseFilter,pointwiseFilter,strides,pad3,dilation=[1,1],dataFormat="NHWC"){let $x=convertToTensor(x,"x","separableConv2d"),$depthwiseFilter=convertToTensor(depthwiseFilter,"depthwiseFilter","separableConv2d"),$pointwiseFilter=convertToTensor(pointwiseFilter,"pointwiseFilter","separableConv2d"),x4D=$x,reshapedTo4D=!1;if($x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),dataFormat==="NCHW")throw new Error("separableConv2d currently does not support dataFormat NCHW; only NHWC is supported");assert(x4D.rank===4,()=>`Error in separableConv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert($depthwiseFilter.rank===4,()=>`Error in separableConv2d: depthwise filter must be rank 4, but got rank ${$depthwiseFilter.rank}.`),assert($pointwiseFilter.rank===4,()=>`Error in separableConv2d: pointwise filter must be rank 4, but got rank ${$depthwiseFilter.rank}.`),assert($pointwiseFilter.shape[0]===1,()=>`Error in separableConv2d: the first dimension of pointwise filter must be 1, but got ${$pointwiseFilter.shape[0]}.`),assert($pointwiseFilter.shape[1]===1,()=>`Error in separableConv2d: the second dimension of pointwise filter must be 1, but got ${$pointwiseFilter.shape[1]}.`);let inChannels=$depthwiseFilter.shape[2],channelMultiplier=$depthwiseFilter.shape[3];assert($pointwiseFilter.shape[2]===inChannels*channelMultiplier,()=>`Error in separableConv2d: the third dimension of pointwise filter must be ${inChannels*channelMultiplier}, but got ${$pointwiseFilter.shape[2]}.`);let depthwise=depthwiseConv2d(x4D,$depthwiseFilter,strides,pad3,dataFormat,dilation),res=conv2d(depthwise,$pointwiseFilter,1,"valid",dataFormat);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var separableConv2d=op({separableConv2d_});async function setdiff1dAsync_(x,y){let $x=convertToTensor(x,"x","setdiff1d"),$y=convertToTensor(y,"y","setdiff1d");assert($x.dtype===$y.dtype,()=>`x and y should have the same dtype, but got x (${$x.dtype}) and y (${$y.dtype}).`),assert($x.rank===1,()=>`x should be 1D tensor, but got x (${$x.shape}).`),assert($y.rank===1,()=>`y should be 1D tensor, but got y (${$y.shape}).`);let xVals=await $x.data(),yVals=await $y.data(),ySet=new Set(yVals),outputSize=0;for(let i=0;i`slice1d expects a rank-1 tensor, but got a rank-${$x.rank} tensor`),slice($x,[begin],[size])}var slice1d=op({slice1d_});function slice2d_(x,begin,size){let $x=convertToTensor(x,"x","slice2d");return assert($x.rank===2,()=>`slice2d expects a rank-2 tensor, but got a rank-${$x.rank} tensor`),slice($x,begin,size)}var slice2d=op({slice2d_});function slice3d_(x,begin,size){let $x=convertToTensor(x,"x","slice3d");return assert($x.rank===3,()=>`slice3d expects a rank-3 tensor, but got a rank-${$x.rank} tensor`),slice($x,begin,size)}var slice3d=op({slice3d_});function slice4d_(x,begin,size){let $x=convertToTensor(x,"x","slice4d");return assert($x.rank===4,()=>`slice4d expects a rank-4 tensor, but got a rank-${$x.rank} tensor`),slice($x,begin,size)}var slice4d=op({slice4d_});function softmax_(logits,dim=-1){let $logits=convertToTensor(logits,"logits","softmax","float32");if(dim===-1&&(dim=$logits.rank-1),dim!==$logits.rank-1)throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${$logits.rank} and dim was ${dim}`);let inputs={logits:$logits},attrs={dim};return ENGINE.runKernel(Softmax,inputs,attrs)}var softmax=op({softmax_});function fft_(input2){assert(input2.dtype==="complex64",()=>`The dtype for tf.spectral.fft() must be complex64 but got ${input2.dtype}.`);let inputs={input:input2};return ENGINE.runKernel(FFT,inputs)}var fft=op({fft_});function ifft_(input2){assert(input2.dtype==="complex64",()=>`The dtype for tf.spectral.ifft() must be complex64 but got ${input2.dtype}.`);let inputs={input:input2};return ENGINE.runKernel(IFFT,inputs)}var ifft=op({ifft_});function irfft_(input2){let innerDimensionSize=input2.shape[input2.shape.length-1],batch=input2.size/innerDimensionSize,ret;if(innerDimensionSize<=2){let complexInput=reshape(input2,[batch,innerDimensionSize]);ret=ifft(complexInput)}else{let outputShape=[batch,2*(innerDimensionSize-1)],realInput=reshape(real(input2),[batch,innerDimensionSize]),imagInput=reshape(imag(input2),[batch,innerDimensionSize]),realConjugate=reverse(slice(realInput,[0,1],[batch,innerDimensionSize-2]),1),imagConjugate=mul(reverse(slice(imagInput,[0,1],[batch,innerDimensionSize-2]),1),scalar(-1)),r=concat([realInput,realConjugate],1),i=concat([imagInput,imagConjugate],1),complexInput=reshape(complex(r,i),[outputShape[0],outputShape[1]]);ret=ifft(complexInput)}if(ret=real(ret),input2.rank===3&&input2.shape[0]!==0){let temp=ret,batch2=input2.shape[0];ret=reshape(ret,[batch2,ret.shape[0]/batch2,ret.shape[1]]),temp.dispose()}return ret}var irfft=op({irfft_});function split_(x,numOrSizeSplits,axis=0){let inputs={x:convertToTensor(x,"x","split")},attr={numOrSizeSplits,axis};return ENGINE.runKernel(SplitV,inputs,attr)}var split=op({split_});function rfft_(input2,fftLength){assert(input2.dtype==="float32",()=>`The dtype for rfft() must be real value but got ${input2.dtype}`);let innerDimensionSize=input2.shape[input2.shape.length-1],batch=input2.size/innerDimensionSize,adjustedInput;if(fftLength!=null&&fftLength0),size=input2.shape.map(v=>v);size[input2.shape.length-1]=fftLength,adjustedInput=slice(input2,begin,size),innerDimensionSize=fftLength}else if(fftLength!=null&&fftLength>innerDimensionSize){let zerosShape=input2.shape.map(v=>v);zerosShape[input2.shape.length-1]=fftLength-innerDimensionSize,adjustedInput=concat([input2,zeros(zerosShape)],input2.shape.length-1),innerDimensionSize=fftLength}else adjustedInput=input2;let zerosInput=zerosLike(adjustedInput),complexInput=reshape(complex(adjustedInput,zerosInput),[batch,innerDimensionSize]),ret=fft(complexInput),half=Math.floor(innerDimensionSize/2)+1,realValues=real(ret),imagValues=imag(ret),realComplexConjugate=split(realValues,[half,innerDimensionSize-half],realValues.shape.length-1),imagComplexConjugate=split(imagValues,[half,innerDimensionSize-half],imagValues.shape.length-1),outputShape=adjustedInput.shape.slice();return outputShape[adjustedInput.shape.length-1]=half,reshape(complex(realComplexConjugate[0],imagComplexConjugate[0]),outputShape)}var rfft=op({rfft_});function sqrt_(x){let inputs={x:convertToTensor(x,"x","sqrt")};return ENGINE.runKernel(Sqrt,inputs)}var sqrt=op({sqrt_});function squaredDifference_(a,b){let $a=convertToTensor(a,"a","squaredDifference"),$b=convertToTensor(b,"b","squaredDifference");[$a,$b]=makeTypesMatch($a,$b),assertAndGetBroadcastShape($a.shape,$b.shape);let inputs={a:$a,b:$b},attrs={};return ENGINE.runKernel(SquaredDifference,inputs,attrs)}var squaredDifference=op({squaredDifference_});function squeeze_(x,axis){let $x=convertToTensor(x,"x","squeeze");return reshape($x,squeezeShape($x.shape,axis).newShape)}var squeeze=op({squeeze_});function stack_(tensors,axis=0){let $tensors=convertToTensorArray(tensors,"tensors","stack","string_or_numeric");assert($tensors.length>=1,()=>"Pass at least one tensor to tf.stack"),$tensors.length>0&&assert(axis<=$tensors[0].rank,()=>"Axis must be <= rank of the tensor");let inputs=$tensors,attrs={axis};return ENGINE.runKernel(Pack,inputs,attrs)}var stack=op({stack_});function step_(x,alpha=0){let inputs={x:convertToTensor(x,"x","step")},attrs={alpha};return ENGINE.runKernel(Step,inputs,attrs)}var step=op({step_});function stridedSlice_(x,begin,end,strides,beginMask=0,endMask=0,ellipsisMask=0,newAxisMask=0,shrinkAxisMask=0){let inputs={x:convertToTensor(x,"x","stridedSlice")},attrs={begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask};return ENGINE.runKernel(StridedSlice,inputs,attrs)}var stridedSlice=op({stridedSlice_});function tan_(x){let inputs={x:convertToTensor(x,"x","tan")};return ENGINE.runKernel(Tan,inputs)}var tan=op({tan_});function tensor1d(values,dtype){assertNonNull(values);let inferredShape=inferShape(values,dtype);if(inferredShape.length!==1)throw new Error("tensor1d() requires values to be a flat/TypedArray");return makeTensor(values,null,inferredShape,dtype)}function tensor2d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==2)throw new Error("tensor2d() requires shape to have two numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==2&&inferredShape.length!==1)throw new Error("tensor2d() requires values to be number[][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor2d() requires shape to be provided when `values` are a flat/TypedArray");return makeTensor(values,shape,inferredShape,dtype)}function tensor4d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==4)throw new Error("tensor4d() requires shape to have four numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==4&&inferredShape.length!==1)throw new Error("tensor4d() requires values to be number[][][][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor4d() requires shape to be provided when `values` are a flat array");return makeTensor(values,shape,inferredShape,dtype)}function tensor5d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==5)throw new Error("tensor5d() requires shape to have five numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==5&&inferredShape.length!==1)throw new Error("tensor5d() requires values to be number[][][][][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor5d() requires shape to be provided when `values` are a flat array");return makeTensor(values,shape,inferredShape,dtype)}function tensor6d(values,shape,dtype){if(assertNonNull(values),shape!=null&&shape.length!==6)throw new Error("tensor6d() requires shape to have six numbers");let inferredShape=inferShape(values,dtype);if(inferredShape.length!==6&&inferredShape.length!==1)throw new Error("tensor6d() requires values to be number[][][][][][] or flat/TypedArray");if(inferredShape.length===1&&shape==null)throw new Error("tensor6d() requires shape to be provided when `values` are a flat array");return shape=shape||inferredShape,makeTensor(values,shape,inferredShape,dtype)}function topk_(x,k=1,sorted=!0){let $x=convertToTensor(x,"x","topk");if($x.rank===0)throw new Error("topk() expects the input to be of rank 1 or higher");let lastDim=$x.shape[$x.shape.length-1];if(k>lastDim)throw new Error(`'k' passed to topk() must be <= the last dimension (${lastDim}) but got ${k}`);let inputs={x:$x},attrs={k,sorted},[values,indices]=ENGINE.runKernel(TopK,inputs,attrs);return{values,indices}}var topk=op({topk_});function truncatedNormal_(shape,mean4=0,stdDev=1,dtype,seed){if(dtype!=null&&dtype==="bool")throw new Error("Unsupported data type $ { dtype }");let randGauss=new MPRandGauss(mean4,stdDev,dtype,!0,seed),res=buffer(shape,dtype);for(let i=0;i0,()=>"The input tensor must be at least 1D");let inputs={x:$x},attrs={axis},[values,indices]=ENGINE.runKernel(Unique,inputs,attrs);return{values,indices}}var unique=op({unique_});function unsortedSegmentSum_(x,segmentIds,numSegments){let $x=convertToTensor(x,"x","unsortedSegmentSum"),$segmentIds=convertToTensor(segmentIds,"segmentIds","unsortedSegmentSum","int32");assert(isInt(numSegments),()=>"numSegments must be of dtype int");let inputs={x:$x,segmentIds:$segmentIds},attrs={numSegments};return ENGINE.runKernel(UnsortedSegmentSum,inputs,attrs)}var unsortedSegmentSum=op({unsortedSegmentSum_});function unstack_(x,axis=0){let $x=convertToTensor(x,"x","unstack","string_or_numeric");assert(axis>=-$x.shape.length&&axis<$x.shape.length,()=>`Axis = ${axis} is not in [-${$x.shape.length}, ${$x.shape.length})`);let inputs={value:$x},attrs={axis};return ENGINE.runKernel(Unpack,inputs,attrs)}var unstack=op({unstack_});function variable(initialValue,trainable=!0,name,dtype){return ENGINE.makeVariable(initialValue,trainable,name,dtype)}function whereImpl(condShape,condVals){let indices=[];for(let i=0;i0,()=>"mask cannot be scalar"),assertShapesMatch(tensorShape.slice(axisFrom,axisFrom+maskDim),$mask.shape,"mask's shape must match the first K dimensions of tensor's shape,");let leadingSize=1;for(let i=axisFrom;i"Shape mismatch in v and x");let one=scalar(1),oneMinusDecay=sub(one,$decay),update=mul(sub($x,$v),oneMinusDecay);if(zeroDebias){assert(step4!=null,()=>"When using zeroDebias: true, step is required.");let $step=convertToTensor(step4,"step","movingAverage");update=div(update,sub(one,pow($decay,$step)))}return add2($v,update)}var movingAverage=op({movingAverage_});function scatterND_(indices,updates,shape){let $indices=convertToTensor(indices,"indices","scatterND","int32"),$updates=convertToTensor(updates,"updates","scatterND");validateInput($updates,$indices,shape);let inputs={indices:$indices,updates:$updates},attrs={shape};return ENGINE.runKernel(ScatterNd,inputs,attrs)}var scatterND=op({scatterND_});function validateInput2(sparseIndices,sparseValues,outputShape,defaultValues){if(sparseIndices.dtype!=="int32")throw new Error(`tf.sparseToDense() expects the indices to be int32 type, but the dtype was ${sparseIndices.dtype}.`);if(sparseIndices.rank>2)throw new Error(`sparseIndices should be a scalar, vector, or matrix, but got shape ${sparseIndices.shape}.`);let numElems=sparseIndices.rank>0?sparseIndices.shape[0]:1,numDims=sparseIndices.rank>1?sparseIndices.shape[1]:1;if(outputShape.length!==numDims)throw new Error(`outputShape has incorrect number of elements:, ${outputShape.length}, should be: ${numDims}.`);let numValues=sparseValues.size;if(!(sparseValues.rank===0||sparseValues.rank===1&&numValues===numElems))throw new Error(`sparseValues has incorrect shape ${sparseValues.shape}, should be [] or [${numElems}]`);if(sparseValues.dtype!==defaultValues.dtype)throw new Error("sparseValues.dtype must match defaultValues.dtype")}function sparseToDense_(sparseIndices,sparseValues,outputShape,defaultValue=0){let $sparseIndices=convertToTensor(sparseIndices,"sparseIndices","sparseToDense","int32"),$sparseValues=convertToTensor(sparseValues,"sparseValues","sparseToDense"),$defaultValue=convertToTensor(defaultValue,"defaultValue","sparseToDense",$sparseValues.dtype);validateInput2($sparseIndices,$sparseValues,outputShape,$defaultValue);let inputs={sparseIndices:$sparseIndices,sparseValues:$sparseValues,defaultValue:$defaultValue},attrs={outputShape};return ENGINE.runKernel(SparseToDense,inputs,attrs)}var sparseToDense=op({sparseToDense_});function gatherND_(x,indices){let $indices=convertToTensor(indices,"indices","gatherND","int32"),inputs={params:convertToTensor(x,"x","gatherND"),indices:$indices};return ENGINE.runKernel(GatherNd,inputs)}var gatherND=op({gatherND_});function getNoiseShape(x,noiseShape){if(noiseShape==null)return x.shape.slice();if(arraysEqual(x.shape,noiseShape))return noiseShape;if(x.shape.length===noiseShape.length){let newDimension=[];for(let i=0;i`x has to be a floating point tensor since it's going to be scaled, but got a ${$x.dtype} tensor instead.`),assert(rate>=0&&rate<1,()=>`rate must be a float in the range [0, 1), but got ${rate}.`),rate===0)return x instanceof Tensor?$x.clone():$x;let $noiseShape=getNoiseShape($x,noiseShape),keepProb=1-rate,multiplier=div(floor(add2(randomUniform($noiseShape,0,1,"float32",seed),keepProb)),keepProb);return mul($x,multiplier)}var dropout=op({dropout_});function enclosingPowerOfTwo(value){return Math.floor(Math.pow(2,Math.ceil(Math.log(value)/Math.log(2))))}function cosineWindow(windowLength,a,b){let even=1-windowLength%2,newValues=new Float32Array(windowLength);for(let i=0;i1,()=>`inTopK() expects the predictions to be of rank 2 or higher, but got ${$predictions.rank}`),assert($predictions.rank-1===$targets.rank,()=>`predictions rank should be 1 larger than targets rank, but got predictions rank ${$predictions.rank} and targets rank ${$targets.rank}`),assertShapesMatch($predictions.shape.slice(0,$predictions.shape.length-1),$targets.shape,"predictions's shape should be align with the targets' shape, except the last dimension.");let lastDim=$predictions.shape[$predictions.shape.length-1];assert(k>0&&k<=lastDim,()=>`'k' passed to inTopK() must be > 0 && <= the predictions last dimension (${lastDim}), but got ${k}`);let predictionsVals=await $predictions.data(),targetsVals=await $targets.data(),[batch,size]=[predictionsVals.length/lastDim,lastDim],precision3=getTypedArrayFromDType("bool",batch);for(let b=0;bb2.value-a.value),precision3[b]=0;for(let i=0;iconv2d2,depthwiseConv2d:()=>depthwiseConv2d2,matMul:()=>matMul2});function conv2DBackpropFilter_(x,dy,filterShape,strides,pad3,dataFormat="NHWC",dimRoundingMode){let x4D=x;x.rank===3&&(x4D=reshape(x,[1,x.shape[0],x.shape[1],x.shape[2]]));let dy4D=dy;dy4D.rank===3&&(dy4D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2]])),assert(x4D.rank===4,()=>`Error in conv2dDerFilter: input must be rank 4, but got shape ${x4D.shape}.`),assert(dy4D.rank===4,()=>`Error in conv2dDerFilter: dy must be rank 4, but got shape ${dy4D.shape}.`),assert(filterShape.length===4,()=>`Error in conv2dDerFilter: filterShape must be length 4, but got ${filterShape}.`);let inDepth=dataFormat==="NHWC"?x4D.shape[3]:x4D.shape[1],outDepth=dataFormat==="NHWC"?dy4D.shape[3]:dy4D.shape[1];assert(inDepth===filterShape[2],()=>`Error in conv2dDerFilter: depth of input ${inDepth}) must match input depth in filter (${filterShape[2]}.`),assert(outDepth===filterShape[3],()=>`Error in conv2dDerFilter: depth of dy (${outDepth}) must match output depth for filter (${filterShape[3]}).`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in conv2dDerFilter: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={x:x4D,dy:dy4D},attrs={strides,pad:pad3,dataFormat,dimRoundingMode,filterShape};return ENGINE.runKernel(Conv2DBackpropFilter,inputs,attrs)}var conv2DBackpropFilter=op({conv2DBackpropFilter_});function getFusedDyActivation(dy,y,activation2){if(activation2==null||activation2==="linear")return dy;if(activation2==="relu")return mul(dy,step(y));throw new Error(`Cannot compute gradient for fused activation ${activation2}.`)}function getFusedBiasGradient(bias,dyActivation){let res=dyActivation,reduceAxes=getReductionAxes(bias.shape,dyActivation.shape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,bias.shape)}function applyActivation(x,activation2,preluActivationWeights,leakyreluAlpha){if(activation2==="linear")return x;if(activation2==="relu")return relu(x);if(activation2==="elu")return elu(x);if(activation2==="relu6")return relu6(x);if(activation2==="prelu")return prelu(x,preluActivationWeights);if(activation2==="leakyrelu")return leakyRelu(x,leakyreluAlpha);throw new Error(`Unknown fused activation ${activation2}.`)}var shouldFuse=(gradientDepth,activation2)=>!(gradientDepth>0)||activation2==="linear";function fusedConv2d_({x,filter,strides,pad:pad3,dataFormat="NHWC",dilations=[1,1],dimRoundingMode,bias,activation:activation2="linear",preluActivationWeights,leakyreluAlpha}){if(activation2=activation2||"linear",shouldFuse(ENGINE.state.gradientDepth,activation2)===!1){let result=conv2d(x,filter,strides,pad3,dataFormat,dilations,dimRoundingMode);return bias!=null&&(result=add2(result,bias)),applyActivation(result,activation2,preluActivationWeights,leakyreluAlpha)}let $x=convertToTensor(x,"x","conv2d"),$filter=convertToTensor(filter,"filter","conv2d"),x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in fused conv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert($filter.rank===4,()=>`Error in fused conv2d: filter must be rank 4, but got rank ${$filter.rank}.`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in fused conv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`),assert(x4D.shape[3]===$filter.shape[2],()=>`Error in conv2d: depth of input (${x4D.shape[3]}) must match input depth for filter ${$filter.shape[2]}.`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),assert(dataFormat==="NHWC",()=>`Error in conv2d: got dataFormat of ${dataFormat} but only NHWC is currently supported.`);let convInfo=computeConv2DInfo(x4D.shape,$filter.shape,strides,dilations,pad3,dimRoundingMode),$bias;bias!=null&&($bias=convertToTensor(bias,"bias","fused conv2d"),[$bias]=makeTypesMatch($bias,$x),assertAndGetBroadcastShape(convInfo.outShape,$bias.shape));let $preluActivationWeights;preluActivationWeights!=null&&($preluActivationWeights=convertToTensor(preluActivationWeights,"prelu weights","fused conv2d"));let grad2=(dy,saved)=>{let[$filter2,x4D2,y,$bias2]=saved,dyActivation=getFusedDyActivation(dy,y,activation2);assert(tupleValuesAreOne(dilations),()=>`Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`);let xDer=conv2DBackpropInput(x4D2.shape,dyActivation,$filter2,strides,pad3),filterDer=conv2DBackpropFilter(x4D2,dyActivation,$filter2.shape,strides,pad3),der=[xDer,filterDer];if($bias2!=null){let biasDer=getFusedBiasGradient($bias2,dyActivation);der.push(biasDer)}return der},inputs={x:x4D,filter:$filter,bias:$bias,preluActivationWeights:$preluActivationWeights},attrs={strides,pad:pad3,dataFormat,dilations,dimRoundingMode,activation:activation2,leakyreluAlpha};return bias==null?customGrad((x4D2,filter2,save)=>{let res=ENGINE.runKernel(FusedConv2D,inputs,attrs);return save([filter2,x4D2,res]),reshapedTo4D&&(res=reshape(res,[res.shape[1],res.shape[2],res.shape[3]])),{value:res,gradFunc:grad2}})(x4D,$filter):customGrad((x4D2,filter2,bias2,save)=>{let res=ENGINE.runKernel(FusedConv2D,inputs,attrs);return save([filter2,x4D2,res,bias2]),reshapedTo4D&&(res=reshape(res,[res.shape[1],res.shape[2],res.shape[3]])),{value:res,gradFunc:grad2}})(x4D,$filter,$bias)}var conv2d2=op({fusedConv2d_});function depthwiseConv2dNativeBackpropFilter_(x,dy,filterShape,strides,pad3,dilations=[1,1],dimRoundingMode){let x4D=x;x.rank===3&&(x4D=reshape(x,[1,x.shape[0],x.shape[1],x.shape[2]]));let dy4D=dy;dy4D.rank===3&&(dy4D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2]]));let inputs={x:x4D,dy:dy4D},attrs={strides,pad:pad3,dimRoundingMode,dilations,filterShape};return ENGINE.runKernel(DepthwiseConv2dNativeBackpropFilter,inputs,attrs)}var depthwiseConv2dNativeBackpropFilter=op({depthwiseConv2dNativeBackpropFilter_});function depthwiseConv2dNativeBackpropInput_(xShape,dy,filter,strides,pad3,dilations=[1,1],dimRoundingMode){let dy4D=dy,reshapedTo4D=!1;dy.rank===3&&(reshapedTo4D=!0,dy4D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2]]));let inputs={dy:dy4D,filter},attrs={strides,pad:pad3,dimRoundingMode,dilations,inputShape:xShape},res=ENGINE.runKernel(DepthwiseConv2dNativeBackpropInput,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var depthwiseConv2dNativeBackpropInput=op({depthwiseConv2dNativeBackpropInput_});function fusedDepthwiseConv2d_({x,filter,strides,pad:pad3,dataFormat="NHWC",dilations=[1,1],dimRoundingMode,bias,activation:activation2="linear",preluActivationWeights,leakyreluAlpha}){if(shouldFuse(ENGINE.state.gradientDepth,activation2)===!1){let result=depthwiseConv2d(x,filter,strides,pad3,dataFormat,dilations,dimRoundingMode);return bias!=null&&(result=add2(result,bias)),applyActivation(result,activation2,preluActivationWeights,leakyreluAlpha)}let $x=convertToTensor(x,"x","depthwiseConv2d"),$filter=convertToTensor(filter,"filter","depthwiseConv2d"),x4D=$x,reshapedTo4D=!1;$x.rank===3&&(reshapedTo4D=!0,x4D=reshape($x,[1,$x.shape[0],$x.shape[1],$x.shape[2]])),assert(x4D.rank===4,()=>`Error in fused depthwiseConv2d: input must be rank 4, but got rank ${x4D.rank}.`),assert($filter.rank===4,()=>`Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${$filter.rank}.`),assert(x4D.shape[3]===$filter.shape[2],()=>`Error in fused depthwiseConv2d: number of input channels (${x4D.shape[3]}) must match the inChannels dimension in filter ${$filter.shape[2]}.`),dilations==null&&(dilations=[1,1]),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in fused depthwiseConv2d: pad must be an integer when using dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let convInfo=computeConv2DInfo(x4D.shape,$filter.shape,strides,dilations,pad3,dimRoundingMode,!0),$bias;bias!=null&&($bias=convertToTensor(bias,"bias","fused conv2d"),[$bias]=makeTypesMatch($bias,$x),assertAndGetBroadcastShape(convInfo.outShape,$bias.shape));let $preluActivationWeights;preluActivationWeights!=null&&($preluActivationWeights=convertToTensor(preluActivationWeights,"prelu weights","fused depthwiseConv2d"));let grad2=(dy,saved)=>{assert(tupleValuesAreOne(dilations),()=>`Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${dilations}'`);let[$filter2,x4D2,y,bias2]=saved,dyActivation=getFusedDyActivation(dy,y,activation2),xDer=depthwiseConv2dNativeBackpropInput(x4D2.shape,dyActivation,$filter2,strides,pad3,dilations,dimRoundingMode),filterDer=depthwiseConv2dNativeBackpropFilter(x4D2,dyActivation,$filter2.shape,strides,pad3,dilations,dimRoundingMode);if(bias2!=null){let biasDer=getFusedBiasGradient($bias,dyActivation);return[xDer,filterDer,biasDer]}return[xDer,filterDer]},inputs={x:x4D,filter:$filter,bias:$bias,preluActivationWeights:$preluActivationWeights},attrs={strides,pad:pad3,dataFormat,dilations,dimRoundingMode,activation:activation2,leakyreluAlpha};return bias==null?customGrad((x4D2,filter2,save)=>{let res=ENGINE.runKernel(FusedDepthwiseConv2D,inputs,attrs);return save([filter2,x4D2,res]),reshapedTo4D&&(res=reshape(res,[res.shape[1],res.shape[2],res.shape[3]])),{value:res,gradFunc:grad2}})(x4D,$filter):customGrad((x4D2,filter2,bias2,save)=>{let res=ENGINE.runKernel(FusedDepthwiseConv2D,inputs,attrs);return save([filter2,x4D2,res,bias2]),reshapedTo4D&&(res=reshape(res,[res.shape[1],res.shape[2],res.shape[3]])),{value:res,gradFunc:grad2}})(x4D,$filter,$bias)}var depthwiseConv2d2=op({fusedDepthwiseConv2d_});function fusedMatMul_({a,b,transposeA=!1,transposeB=!1,bias,activation:activation2="linear",preluActivationWeights,leakyreluAlpha}){if(shouldFuse(ENGINE.state.gradientDepth,activation2)===!1){let result=matMul(a,b,transposeA,transposeB);return bias!=null&&(result=add2(result,bias)),applyActivation(result,activation2,preluActivationWeights,leakyreluAlpha)}let $a=convertToTensor(a,"a","fused matMul"),$b=convertToTensor(b,"b","fused matMul");[$a,$b]=makeTypesMatch($a,$b);let innerShapeA=transposeA?$a.shape[$a.rank-2]:$a.shape[$a.rank-1],innerShapeB=transposeB?$b.shape[$b.rank-1]:$b.shape[$b.rank-2],outerShapeA=transposeA?$a.shape[$a.rank-1]:$a.shape[$a.rank-2],outerShapeB=transposeB?$b.shape[$b.rank-2]:$b.shape[$b.rank-1],outerDimsA=$a.shape.slice(0,-2),outerDimsB=$b.shape.slice(0,-2),batchDimA=sizeFromShape(outerDimsA),batchDimB=sizeFromShape(outerDimsB);assert($a.rank>=2&&$b.rank>=2&&$a.rank===$b.rank,()=>`Error in fused matMul: inputs must have the same rank of at least 2, got ranks ${$a.rank} and ${$b.rank}.`),assert(arraysEqual(outerDimsA,outerDimsB),()=>`Error in fused matMul: outer dimensions (${outerDimsA}) and (${outerDimsB}) of Tensors with shapes ${$a.shape} and ${$b.shape} must match.`),assert(innerShapeA===innerShapeB,()=>`Error in fused matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${$a.shape} and ${$b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);let outShape=$a.shape.slice(0,-2).concat([outerShapeA,outerShapeB]),a3D=transposeA?reshape($a,[batchDimA,innerShapeA,outerShapeA]):reshape($a,[batchDimA,outerShapeA,innerShapeA]),b3D=transposeB?reshape($b,[batchDimB,outerShapeB,innerShapeB]):reshape($b,[batchDimB,innerShapeB,outerShapeB]),$bias;bias!=null&&($bias=convertToTensor(bias,"bias","fused matMul"),[$bias]=makeTypesMatch($bias,$a),assertAndGetBroadcastShape(outShape,$bias.shape));let $preluActivationWeights;preluActivationWeights!=null&&($preluActivationWeights=convertToTensor(preluActivationWeights,"prelu weights","fused matMul"));let grad2=(dy,saved)=>{let[a3D2,b3D2,y,$bias2]=saved,dyActivation=getFusedDyActivation(reshape(dy,y.shape),y,activation2),aDer,bDer;if(!transposeA&&!transposeB?(aDer=matMul(dyActivation,b3D2,!1,!0),bDer=matMul(a3D2,dyActivation,!0,!1)):!transposeA&&transposeB?(aDer=matMul(dyActivation,b3D2,!1,!1),bDer=matMul(dyActivation,a3D2,!0,!1)):transposeA&&!transposeB?(aDer=matMul(b3D2,dyActivation,!1,!0),bDer=matMul(a3D2,dyActivation,!1,!1)):(aDer=matMul(b3D2,dyActivation,!0,!0),bDer=matMul(dyActivation,a3D2,!0,!0)),bias!=null){let biasDer=getFusedBiasGradient($bias2,dyActivation);return[aDer,bDer,biasDer]}else return[aDer,bDer]},inputs={a:a3D,b:b3D,bias:$bias,preluActivationWeights:$preluActivationWeights},attrs={transposeA,transposeB,activation:activation2,leakyreluAlpha};return bias==null?customGrad((a3D2,b3D2,save)=>{let res=ENGINE.runKernel(_FusedMatMul,inputs,attrs);return save([a3D2,b3D2,res]),{value:reshape(res,outShape),gradFunc:grad2}})(a3D,b3D):customGrad((a3D2,b3D2,$bias2,save)=>{let res=ENGINE.runKernel(_FusedMatMul,inputs,attrs);return save([a3D2,b3D2,res,$bias2]),{value:reshape(res,outShape),gradFunc:grad2}})(a3D,b3D,$bias)}var matMul2=op({fusedMatMul_});function hammingWindow_(windowLength){return cosineWindow(windowLength,.54,.46)}var hammingWindow=op({hammingWindow_});function hannWindow_(windowLength){return cosineWindow(windowLength,.5,.5)}var hannWindow=op({hannWindow_});function frame_(signal2,frameLength,frameStep,padEnd=!1,padValue=0){let start=0,output=[];for(;start+frameLength<=signal2.size;)output.push(slice(signal2,start,frameLength)),start+=frameStep;if(padEnd)for(;start`Error in cropAndResize: image must be rank 4,but got rank ${$image.rank}.`),assert($boxes.rank===2&&$boxes.shape[1]===4,()=>`Error in cropAndResize: boxes must be have size [${numBoxes},4] but had shape ${$boxes.shape}.`),assert($boxInd.rank===1&&$boxInd.shape[0]===numBoxes,()=>`Error in cropAndResize: boxInd must be have size [${numBoxes}] but had shape ${$boxes.shape}.`),assert(cropSize.length===2,()=>`Error in cropAndResize: cropSize must be of length 2, but got length ${cropSize.length}.`),assert(cropSize[0]>=1&&cropSize[1]>=1,()=>`cropSize must be atleast [1,1], but was ${cropSize}`),assert(method==="bilinear"||method==="nearest",()=>`method must be bilinear or nearest, but was ${method}`);let inputs={image:$image,boxes:$boxes,boxInd:$boxInd},attrs={method,extrapolationValue,cropSize};return ENGINE.runKernel(CropAndResize,inputs,attrs)}var cropAndResize=op({cropAndResize_});function flipLeftRight_(image3){let $image=convertToTensor(image3,"image","flipLeftRight","float32");assert($image.rank===4,()=>`Error in flipLeftRight: image must be rank 4,but got rank ${$image.rank}.`);let inputs={image:$image};return ENGINE.runKernel(FlipLeftRight,inputs,{})}var flipLeftRight=op({flipLeftRight_});function rotateWithOffset_(image3,radians,fillValue=0,center=.5){let $image=convertToTensor(image3,"image","rotateWithOffset","float32");assert($image.rank===4,()=>`Error in rotateWithOffset: image must be rank 4,but got rank ${$image.rank}.`);let inputs={image:$image},attrs={radians,fillValue,center};return ENGINE.runKernel(RotateWithOffset,inputs,attrs)}var rotateWithOffset=op({rotateWithOffset_});function nonMaxSuppSanityCheck(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma){iouThreshold==null&&(iouThreshold=.5),scoreThreshold==null&&(scoreThreshold=Number.NEGATIVE_INFINITY),softNmsSigma==null&&(softNmsSigma=0);let numBoxes=boxes.shape[0];return maxOutputSize=Math.min(maxOutputSize,numBoxes),assert(0<=iouThreshold&&iouThreshold<=1,()=>`iouThreshold must be in [0, 1], but was '${iouThreshold}'`),assert(boxes.rank===2,()=>`boxes must be a 2D tensor, but was of rank '${boxes.rank}'`),assert(boxes.shape[1]===4,()=>`boxes must have 4 columns, but 2nd dimension was ${boxes.shape[1]}`),assert(scores.rank===1,()=>"scores must be a 1D tensor"),assert(scores.shape[0]===numBoxes,()=>`scores has incompatible shape with boxes. Expected ${numBoxes}, but was ${scores.shape[0]}`),assert(0<=softNmsSigma&&softNmsSigma<=1,()=>`softNmsSigma must be in [0, 1], but was '${softNmsSigma}'`),{maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma}}function nonMaxSuppression_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppression"),$scores=convertToTensor(scores,"scores","nonMaxSuppression"),inputs=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold);maxOutputSize=inputs.maxOutputSize,iouThreshold=inputs.iouThreshold,scoreThreshold=inputs.scoreThreshold;let attrs={maxOutputSize,iouThreshold,scoreThreshold};return ENGINE.runKernel(NonMaxSuppressionV3,{boxes:$boxes,scores:$scores},attrs)}var nonMaxSuppression=op({nonMaxSuppression_});function binaryInsert(arr,element,comparator){let index=binarySearch(arr,element,comparator),insertionPoint=index<0?-(index+1):index;arr.splice(insertionPoint,0,element)}function binarySearch(arr,target,comparator){return binarySearch_(arr,target,comparator||defaultComparator)}function defaultComparator(a,b){return a>b?1:a>>1);let compareResult=comparator(target,arr[middle]);compareResult>0?left=middle+1:(right=middle,found=!compareResult)}return found?left:-left-1}function nonMaxSuppressionV3Impl(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold){return nonMaxSuppressionImpl_(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,0)}function nonMaxSuppressionV4Impl(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize){return nonMaxSuppressionImpl_(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,0,!1,padToMaxOutputSize,!0)}function nonMaxSuppressionV5Impl(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma){return nonMaxSuppressionImpl_(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma,!0)}function nonMaxSuppressionImpl_(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma,returnScoresTensor=!1,padToMaxOutputSize=!1,returnValidOutputs=!1){let candidates=[];for(let i=0;iscoreThreshold&&candidates.push({score:scores[i],boxIndex:i,suppressBeginIndex:0});candidates.sort(ascendingComparator);let scale2=softNmsSigma>0?-.5/softNmsSigma:0,selectedIndices=[],selectedScores=[];for(;selectedIndices.length0;){let candidate=candidates.pop(),{score:originalScore,boxIndex,suppressBeginIndex}=candidate;if(originalScore=suppressBeginIndex;--j){let iou=intersectionOverUnion(boxes,boxIndex,selectedIndices[j]);if(iou>=iouThreshold){ignoreCandidate=!0;break}if(candidate.score=candidate.score*suppressWeight(iouThreshold,scale2,iou),candidate.score<=scoreThreshold)break}candidate.suppressBeginIndex=selectedIndices.length,ignoreCandidate||(candidate.score===originalScore?(selectedIndices.push(boxIndex),selectedScores.push(candidate.score)):candidate.score>scoreThreshold&&binaryInsert(candidates,candidate,ascendingComparator))}let validOutputs=selectedIndices.length,elemsToPad=maxOutputSize-validOutputs;padToMaxOutputSize&&elemsToPad>0&&(selectedIndices.push(...new Array(elemsToPad).fill(0)),selectedScores.push(...new Array(elemsToPad).fill(0)));let result={selectedIndices};return returnScoresTensor&&(result.selectedScores=selectedScores),returnValidOutputs&&(result.validOutputs=validOutputs),result}function intersectionOverUnion(boxes,i,j){let iCoord=boxes.subarray(i*4,i*4+4),jCoord=boxes.subarray(j*4,j*4+4),yminI=Math.min(iCoord[0],iCoord[2]),xminI=Math.min(iCoord[1],iCoord[3]),ymaxI=Math.max(iCoord[0],iCoord[2]),xmaxI=Math.max(iCoord[1],iCoord[3]),yminJ=Math.min(jCoord[0],jCoord[2]),xminJ=Math.min(jCoord[1],jCoord[3]),ymaxJ=Math.max(jCoord[0],jCoord[2]),xmaxJ=Math.max(jCoord[1],jCoord[3]),areaI=(ymaxI-yminI)*(xmaxI-xminI),areaJ=(ymaxJ-yminJ)*(xmaxJ-xminJ);if(areaI<=0||areaJ<=0)return 0;let intersectionYmin=Math.max(yminI,yminJ),intersectionXmin=Math.max(xminI,xminJ),intersectionYmax=Math.min(ymaxI,ymaxJ),intersectionXmax=Math.min(xmaxI,xmaxJ),intersectionArea=Math.max(intersectionYmax-intersectionYmin,0)*Math.max(intersectionXmax-intersectionXmin,0);return intersectionArea/(areaI+areaJ-intersectionArea)}function suppressWeight(iouThreshold,scale2,iou){let weight=Math.exp(scale2*iou*iou);return iou<=iouThreshold?weight:0}function ascendingComparator(c1,c2){return c1.score-c2.score||c1.score===c2.score&&c2.boxIndex-c1.boxIndex}async function nonMaxSuppressionAsync_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppressionAsync"),$scores=convertToTensor(scores,"scores","nonMaxSuppressionAsync"),inputs=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold);maxOutputSize=inputs.maxOutputSize,iouThreshold=inputs.iouThreshold,scoreThreshold=inputs.scoreThreshold;let boxesAndScores=await Promise.all([$boxes.data(),$scores.data()]),boxesVals=boxesAndScores[0],scoresVals=boxesAndScores[1],{selectedIndices}=nonMaxSuppressionV3Impl(boxesVals,scoresVals,maxOutputSize,iouThreshold,scoreThreshold);return $boxes!==boxes&&$boxes.dispose(),$scores!==scores&&$scores.dispose(),tensor1d(selectedIndices,"int32")}var nonMaxSuppressionAsync=nonMaxSuppressionAsync_;function nonMaxSuppressionWithScore_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY,softNmsSigma=0){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppression"),$scores=convertToTensor(scores,"scores","nonMaxSuppression"),params=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma);maxOutputSize=params.maxOutputSize,iouThreshold=params.iouThreshold,scoreThreshold=params.scoreThreshold,softNmsSigma=params.softNmsSigma;let inputs={boxes:$boxes,scores:$scores},attrs={maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma},result=ENGINE.runKernel(NonMaxSuppressionV5,inputs,attrs);return{selectedIndices:result[0],selectedScores:result[1]}}var nonMaxSuppressionWithScore=op({nonMaxSuppressionWithScore_});async function nonMaxSuppressionWithScoreAsync_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY,softNmsSigma=0){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppressionAsync"),$scores=convertToTensor(scores,"scores","nonMaxSuppressionAsync"),params=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma);maxOutputSize=params.maxOutputSize,iouThreshold=params.iouThreshold,scoreThreshold=params.scoreThreshold,softNmsSigma=params.softNmsSigma;let boxesAndScores=await Promise.all([$boxes.data(),$scores.data()]),boxesVals=boxesAndScores[0],scoresVals=boxesAndScores[1],{selectedIndices,selectedScores}=nonMaxSuppressionV5Impl(boxesVals,scoresVals,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma);return $boxes!==boxes&&$boxes.dispose(),$scores!==scores&&$scores.dispose(),{selectedIndices:tensor1d(selectedIndices,"int32"),selectedScores:tensor1d(selectedScores)}}var nonMaxSuppressionWithScoreAsync=nonMaxSuppressionWithScoreAsync_;function nonMaxSuppressionPadded_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY,padToMaxOutputSize=!1){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppression"),$scores=convertToTensor(scores,"scores","nonMaxSuppression"),params=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold,null),$maxOutputSize=params.maxOutputSize,$iouThreshold=params.iouThreshold,$scoreThreshold=params.scoreThreshold,inputs={boxes:$boxes,scores:$scores},attrs={maxOutputSize:$maxOutputSize,iouThreshold:$iouThreshold,scoreThreshold:$scoreThreshold,padToMaxOutputSize},result=ENGINE.runKernel(NonMaxSuppressionV4,inputs,attrs);return{selectedIndices:result[0],validOutputs:result[1]}}var nonMaxSuppressionPadded=op({nonMaxSuppressionPadded_});async function nonMaxSuppressionPaddedAsync_(boxes,scores,maxOutputSize,iouThreshold=.5,scoreThreshold=Number.NEGATIVE_INFINITY,padToMaxOutputSize=!1){let $boxes=convertToTensor(boxes,"boxes","nonMaxSuppressionAsync"),$scores=convertToTensor(scores,"scores","nonMaxSuppressionAsync"),params=nonMaxSuppSanityCheck($boxes,$scores,maxOutputSize,iouThreshold,scoreThreshold,null),$maxOutputSize=params.maxOutputSize,$iouThreshold=params.iouThreshold,$scoreThreshold=params.scoreThreshold,[boxesVals,scoresVals]=await Promise.all([$boxes.data(),$scores.data()]),{selectedIndices,validOutputs}=nonMaxSuppressionV4Impl(boxesVals,scoresVals,$maxOutputSize,$iouThreshold,$scoreThreshold,padToMaxOutputSize);return $boxes!==boxes&&$boxes.dispose(),$scores!==scores&&$scores.dispose(),{selectedIndices:tensor1d(selectedIndices,"int32"),validOutputs:scalar(validOutputs,"int32")}}var nonMaxSuppressionPaddedAsync=nonMaxSuppressionPaddedAsync_;function resizeBilinear_(images,size,alignCorners=!1,halfPixelCenters=!1){let $images=convertToTensor(images,"images","resizeBilinear");assert($images.rank===3||$images.rank===4,()=>`Error in resizeBilinear: x must be rank 3 or 4, but got rank ${$images.rank}.`),assert(size.length===2,()=>`Error in resizeBilinear: new shape must 2D, but got shape ${size}.`),assert(halfPixelCenters===!1||alignCorners===!1,()=>"Error in resizeBilinear: If halfPixelCenters is true, alignCorners must be false.");let batchImages=$images,reshapedTo4D=!1;$images.rank===3&&(reshapedTo4D=!0,batchImages=reshape($images,[1,$images.shape[0],$images.shape[1],$images.shape[2]]));let[]=size,inputs={images:batchImages},attrs={alignCorners,halfPixelCenters,size},res=ENGINE.runKernel(ResizeBilinear,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var resizeBilinear=op({resizeBilinear_});function resizeNearestNeighbor_(images,size,alignCorners=!1,halfPixelCenters=!1){let $images=convertToTensor(images,"images","resizeNearestNeighbor");assert($images.rank===3||$images.rank===4,()=>`Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${$images.rank}.`),assert(size.length===2,()=>`Error in resizeNearestNeighbor: new shape must 2D, but got shape ${size}.`),assert($images.dtype==="float32"||$images.dtype==="int32",()=>"`images` must have `int32` or `float32` as dtype"),assert(halfPixelCenters===!1||alignCorners===!1,()=>"Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false.");let batchImages=$images,reshapedTo4D=!1;$images.rank===3&&(reshapedTo4D=!0,batchImages=reshape($images,[1,$images.shape[0],$images.shape[1],$images.shape[2]]));let[]=size,inputs={images:batchImages},attrs={alignCorners,halfPixelCenters,size},res=ENGINE.runKernel(ResizeNearestNeighbor,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var resizeNearestNeighbor=op({resizeNearestNeighbor_});function bandPart_(a,numLower,numUpper){assert(numLower%1==0,()=>`bandPart(): numLower must be an integer, got ${numLower}.`),assert(numUpper%1==0,()=>`bandPart(): numUpper must be an integer, got ${numUpper}.`);let $a=convertToTensor(a,"a","bandPart");assert($a.rank>=2,()=>`bandPart(): Rank must be at least 2, got ${$a.rank}.`);let shape=$a.shape,[M,N]=$a.shape.slice(-2);if(!(numLower<=M))throw new Error(`bandPart(): numLower (${numLower}) must not be greater than the number of rows (${M}).`);if(!(numUpper<=N))throw new Error(`bandPart(): numUpper (${numUpper}) must not be greater than the number of columns (${N}).`);numLower<0&&(numLower=M),numUpper<0&&(numUpper=N);let i=reshape(range(0,M,1,"int32"),[-1,1]),j=range(0,N,1,"int32"),ij=sub(i,j),inBand=logicalAnd(lessEqual(ij,scalar(+numLower,"int32")),greaterEqual(ij,scalar(-numUpper,"int32"))),zero=zeros([M,N],$a.dtype);return reshape(stack(unstack(reshape($a,[-1,M,N])).map(mat=>where(inBand,mat,zero))),shape)}var bandPart=op({bandPart_});function gramSchmidt_(xs){let inputIsTensor2D;if(Array.isArray(xs)){inputIsTensor2D=!1,assert(xs!=null&&xs.length>0,()=>"Gram-Schmidt process: input must not be null, undefined, or empty");let dim=xs[0].shape[0];for(let i=1;i`Gram-Schmidt: Non-unique lengths found in the input vectors: (${xs[i].shape[0]} vs. ${dim})`)}else inputIsTensor2D=!0,xs=split(xs,xs.shape[0],0).map(x=>squeeze(x,[0]));assert(xs.length<=xs[0].shape[0],()=>`Gram-Schmidt: Number of vectors (${xs.length}) exceeds number of dimensions (${xs[0].shape[0]}).`);let ys=[],xs1d=xs;for(let i=0;i{let x=xs1d[i];if(i>0)for(let j=0;j=2,()=>`qr() requires input tensor to have a rank >= 2, but got rank ${x.rank}`),x.rank===2)return qr2d(x,fullMatrices);{let outerDimsProd=x.shape.slice(0,x.shape.length-2).reduce((value,prev)=>value*prev),x2ds=unstack(reshape(x,[outerDimsProd,x.shape[x.shape.length-2],x.shape[x.shape.length-1]]),0),q2ds=[],r2ds=[];x2ds.forEach(x2d=>{let[q2d,r2d]=qr2d(x2d,fullMatrices);q2ds.push(q2d),r2ds.push(r2d)});let q=reshape(stack(q2ds,0),x.shape),r=reshape(stack(r2ds,0),x.shape);return[q,r]}}function qr2d(x,fullMatrices=!1){return ENGINE.tidy(()=>{assert(x.shape.length===2,()=>`qr2d() requires a 2D Tensor, but got a ${x.shape.length}D Tensor.`);let m=x.shape[0],n=x.shape[1],q=eye(m),r=clone(x),one2D=tensor2d([[1]],[1,1]),w=clone(one2D),iters=m>=n?n:m;for(let j=0;j{let rjEnd1=slice(r,[j,j],[m-j,1]),normX=norm(rjEnd1),rjj=slice(r,[j,j],[1,1]),s=where(greater(rjj,0),tensor2d([[-1]]),tensor2d([[1]])),u1=sub(rjj,mul(s,normX)),wPre=div(rjEnd1,u1);wPre.shape[0]===1?w=clone(one2D):w=concat([one2D,slice(wPre,[1,0],[wPre.shape[0]-1,wPre.shape[1]])],0);let tau=neg(div(matMul(s,u1),normX)),rjEndAll=slice(r,[j,0],[m-j,n]),tauTimesW=mul(tau,w),wT=transpose(w);if(j===0)r=sub(rjEndAll,matMul(tauTimesW,matMul(wT,rjEndAll)));else{let rTimesTau=sub(rjEndAll,matMul(tauTimesW,matMul(wT,rjEndAll)));r=concat([slice(r,[0,0],[j,n]),rTimesTau],0)}let tawTimesWT=transpose(tauTimesW),qAllJEnd=slice(q,[0,j],[m,q.shape[1]-j]);if(j===0)q=sub(qAllJEnd,matMul(matMul(qAllJEnd,w),tawTimesWT));else{let qTimesTau=sub(qAllJEnd,matMul(matMul(qAllJEnd,w),tawTimesWT));q=concat([slice(q,[0,0],[m,j]),qTimesTau],1)}return[w,r,q]}),dispose([rTemp,wTemp,qTemp])}return!fullMatrices&&m>n&&(q=slice(q,[0,0],[m,n]),r=slice(r,[0,0],[n,n])),[q,r]})}var qr=op({qr_}),Reduction;(function(Reduction2){Reduction2[Reduction2.NONE=0]="NONE",Reduction2[Reduction2.MEAN=1]="MEAN",Reduction2[Reduction2.SUM=2]="SUM",Reduction2[Reduction2.SUM_BY_NONZERO_WEIGHTS=3]="SUM_BY_NONZERO_WEIGHTS"})(Reduction||(Reduction={}));function computeWeightedLoss_(losses4,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $losses=convertToTensor(losses4,"losses","computeWeightedLoss"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","computeWeightedLoss"));let weightedLoss=$weights==null?$losses:mul($losses,$weights);if(reduction2===Reduction.NONE)return weightedLoss;if(reduction2===Reduction.SUM)return sum2(weightedLoss);if(reduction2===Reduction.MEAN){if($weights==null)return mean(weightedLoss);{let broadcastFactor=$losses.size/$weights.size,result=div(sum2(weightedLoss),sum2($weights));return broadcastFactor>1?div(result,scalar(broadcastFactor)):result}}if(reduction2===Reduction.SUM_BY_NONZERO_WEIGHTS){if($weights==null)return div(sum2(weightedLoss),scalar($losses.size));{let broadcastedWeights=mul($weights,ones2($losses.shape)),numNonZeros=cast(sum2(notEqual(broadcastedWeights,scalar(0))),"float32");return div(sum2(weightedLoss),numNonZeros)}}throw Error(`Unknown reduction: ${reduction2}`)}var computeWeightedLoss=op({computeWeightedLoss_});function absoluteDifference_(labels,predictions,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","absoluteDifference"),$predictions=convertToTensor(predictions,"predictions","absoluteDifference"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","absoluteDifference")),assertShapesMatch($labels.shape,$predictions.shape,"Error in absoluteDifference: ");let losses4=abs(sub($labels,$predictions));return computeWeightedLoss(losses4,$weights,reduction2)}var absoluteDifference=op({absoluteDifference_});function cosineDistance_(labels,predictions,axis,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","cosineDistance"),$predictions=convertToTensor(predictions,"predictions","cosineDistance"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","cosineDistance")),assertShapesMatch($labels.shape,$predictions.shape,"Error in cosineDistance: ");let one=scalar(1),losses4=sub(one,sum2(mul($labels,$predictions),axis,!0));return computeWeightedLoss(losses4,$weights,reduction2)}var cosineDistance=op({cosineDistance_});function hingeLoss_(labels,predictions,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","hingeLoss"),$predictions=convertToTensor(predictions,"predictions","hingeLoss"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","hingeLoss")),assertShapesMatch($labels.shape,$predictions.shape,"Error in hingeLoss: ");let one=scalar(1);$labels=sub(mul(scalar(2),$labels),one);let losses4=relu(sub(one,mul($labels,$predictions)));return computeWeightedLoss(losses4,$weights,reduction2)}var hingeLoss=op({hingeLoss_});function huberLoss_(labels,predictions,weights,delta=1,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","huberLoss"),$predictions=convertToTensor(predictions,"predictions","huberLoss"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","huberLoss")),assertShapesMatch($labels.shape,$predictions.shape,"Error in huberLoss: ");let deltaScalar=scalar(delta),error=abs(sub($predictions,$labels)),quadratic=minimum(error,deltaScalar),linear=sub(error,quadratic),losses4=add2(mul(scalar(.5),square(quadratic)),mul(deltaScalar,linear));return computeWeightedLoss(losses4,$weights,reduction2)}var huberLoss=op({huberLoss_});function logLoss_(labels,predictions,weights,epsilon3=1e-7,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","logLoss"),$predictions=convertToTensor(predictions,"predictions","logLoss"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","logLoss")),assertShapesMatch($labels.shape,$predictions.shape,"Error in logLoss: ");let one=scalar(1),epsilonScalar=scalar(epsilon3),l13=neg(mul($labels,log2(add2($predictions,epsilonScalar)))),l23=mul(sub(one,$labels),log2(add2(sub(one,$predictions),epsilonScalar))),losses4=sub(l13,l23);return computeWeightedLoss(losses4,$weights,reduction2)}var logLoss=op({logLoss_});function meanSquaredError_(labels,predictions,weights,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $labels=convertToTensor(labels,"labels","meanSquaredError"),$predictions=convertToTensor(predictions,"predictions","meanSquaredError"),$weights=null;weights!=null&&($weights=convertToTensor(weights,"weights","meanSquaredError")),assertShapesMatch($labels.shape,$predictions.shape,"Error in meanSquaredError: ");let losses4=squaredDifference($labels,$predictions);return computeWeightedLoss(losses4,$weights,reduction2)}var meanSquaredError=op({meanSquaredError_});function sigmoidCrossEntropyWithLogits_(labels,logits){let $labels=convertToTensor(labels,"labels","sigmoidCrossEntropyWithLogits"),$logits=convertToTensor(logits,"logits","sigmoidCrossEntropyWithLogits");assertShapesMatch($labels.shape,$logits.shape,"Error in sigmoidCrossEntropyWithLogits: ");let maxOutput=relu($logits),outputXTarget=mul($logits,$labels),sigmoidOutput=log1p(exp(neg(abs($logits))));return add2(sub(maxOutput,outputXTarget),sigmoidOutput)}function sigmoidCrossEntropy_(multiClassLabels,logits,weights,labelSmoothing=0,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $multiClassLabels=convertToTensor(multiClassLabels,"multiClassLabels","sigmoidCrossEntropy"),$logits=convertToTensor(logits,"logits","sigmoidCrossEntropy"),$weights=null;if(weights!=null&&($weights=convertToTensor(weights,"weights","sigmoidCrossEntropy")),assertShapesMatch($multiClassLabels.shape,$logits.shape,"Error in sigmoidCrossEntropy: "),labelSmoothing>0){let labelSmoothingScalar=scalar(labelSmoothing),one=scalar(1),half=scalar(.5);$multiClassLabels=add2(mul($multiClassLabels,sub(one,labelSmoothingScalar)),mul(half,labelSmoothingScalar))}let losses4=sigmoidCrossEntropyWithLogits_($multiClassLabels,$logits);return computeWeightedLoss(losses4,$weights,reduction2)}var sigmoidCrossEntropy=op({sigmoidCrossEntropy_});function softmaxCrossEntropyWithLogits_(labels,logits,dim=-1){if(dim===-1&&(dim=logits.rank-1),dim!==logits.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${logits.rank} and dim was ${dim}`);return customGrad((labels2,logits2,save)=>{let lse=logSumExp(logits2,[dim],!0),logResult=sub(cast(logits2,"float32"),lse);save([labels2,logResult]);let costVector=neg(mul(logResult,labels2));return{value:sum2(costVector,[dim]),gradFunc:(dy,saved)=>{let[labels3,logResult2]=saved,dyShape=expandShapeToKeepDim(dy.shape,[dim]);return[mul(reshape(dy,dyShape),sub(cast(labels3,"float32"),exp(logResult2))),mul(reshape(dy,dyShape),sub(exp(logResult2),cast(labels3,"float32")))]}}})(labels,logits)}function softmaxCrossEntropy_(onehotLabels,logits,weights,labelSmoothing=0,reduction2=Reduction.SUM_BY_NONZERO_WEIGHTS){let $onehotLabels=convertToTensor(onehotLabels,"onehotLabels","softmaxCrossEntropy"),$logits=convertToTensor(logits,"logits","softmaxCrossEntropy"),$weights=null;if(weights!=null&&($weights=convertToTensor(weights,"weights","softmaxCrossEntropy")),assertShapesMatch($onehotLabels.shape,$logits.shape,"Error in softmaxCrossEntropy: "),labelSmoothing>0){let labelSmoothingScalar=scalar(labelSmoothing),one=scalar(1),numClasses=scalar($onehotLabels.shape[1]);$onehotLabels=add2(mul($onehotLabels,sub(one,labelSmoothingScalar)),div(labelSmoothingScalar,numClasses))}let losses4=softmaxCrossEntropyWithLogits_($onehotLabels,$logits);return computeWeightedLoss(losses4,$weights,reduction2)}var softmaxCrossEntropy=op({softmaxCrossEntropy_}),spectral={fft,ifft,rfft,irfft},signal={hammingWindow,hannWindow,frame,stft},image={flipLeftRight,resizeNearestNeighbor,resizeBilinear,rotateWithOffset,cropAndResize,nonMaxSuppression,nonMaxSuppressionAsync,nonMaxSuppressionWithScore,nonMaxSuppressionWithScoreAsync,nonMaxSuppressionPadded,nonMaxSuppressionPaddedAsync},linalg={bandPart,gramSchmidt,qr},losses={absoluteDifference,computeWeightedLoss,cosineDistance,hingeLoss,huberLoss,logLoss,meanSquaredError,sigmoidCrossEntropy,softmaxCrossEntropy},Optimizer=class extends Serializable{minimize(f,returnCost=!1,varList){let{value,grads:grads2}=this.computeGradients(f,varList);if(varList!=null){let gradArray=varList.map(v=>({name:v.name,tensor:grads2[v.name]}));this.applyGradients(gradArray)}else this.applyGradients(grads2);return dispose(grads2),returnCost?value:(value.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(f,varList){return variableGrads(f,varList)}dispose(){this.iterations_!=null&&dispose(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:scalar(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(weightValues){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(weightValues){return this.iterations_=(await weightValues[0].tensor.data())[0],weightValues.slice(1)}};Object.defineProperty(Optimizer,Symbol.hasInstance,{value:instance=>instance.minimize!=null&&instance.computeGradients!=null&&instance.applyGradients!=null});var AdadeltaOptimizer=class extends Optimizer{constructor(learningRate,rho,epsilon3=null){super();this.learningRate=learningRate,this.rho=rho,this.epsilon=epsilon3,this.accumulatedGrads=[],this.accumulatedUpdates=[],epsilon3==null&&(this.epsilon=ENGINE.backend.epsilon())}applyGradients(variableGradients){(Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients)).forEach((name,i)=>{let value=ENGINE.registeredVariables[name],trainable=!1;this.accumulatedGrads[i]==null&&(this.accumulatedGrads[i]={originalName:`${name}/accum_grad`,variable:tidy(()=>zerosLike(value).variable(trainable))}),this.accumulatedUpdates[i]==null&&(this.accumulatedUpdates[i]={originalName:`${name}/accum_var`,variable:tidy(()=>zerosLike(value).variable(trainable))});let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;let accumulatedGrad=this.accumulatedGrads[i].variable,accumulatedUpdate=this.accumulatedUpdates[i].variable;tidy(()=>{let newAccumulatedGrad=add2(mul(accumulatedGrad,this.rho),mul(square(gradient),1-this.rho)),updates=mul(div(sqrt(add2(accumulatedUpdate,this.epsilon)),sqrt(add2(accumulatedGrad,this.epsilon))),gradient),newAccumulatedUpdate=add2(mul(accumulatedUpdate,this.rho),mul(square(updates),1-this.rho));accumulatedGrad.assign(newAccumulatedGrad),accumulatedUpdate.assign(newAccumulatedUpdate);let newValue=add2(mul(updates,-this.learningRate),value);value.assign(newValue)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(dispose(this.accumulatedGrads.map(v=>v.variable)),dispose(this.accumulatedUpdates.map(v=>v.variable)))}async getWeights(){let variables=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(variables.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues);let variableCount=weightValues.length/2,trainable=!1;this.accumulatedGrads=weightValues.slice(0,variableCount).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})),this.accumulatedUpdates=weightValues.slice(variableCount,variableCount*2).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(cls,config2){return new cls(config2.learningRate,config2.rho,config2.epsilon)}};AdadeltaOptimizer.className="Adadelta";registerClass(AdadeltaOptimizer);var AdagradOptimizer=class extends Optimizer{constructor(learningRate,initialAccumulatorValue=.1){super();this.learningRate=learningRate,this.initialAccumulatorValue=initialAccumulatorValue,this.accumulatedGrads=[]}applyGradients(variableGradients){(Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients)).forEach((name,i)=>{let value=ENGINE.registeredVariables[name];if(this.accumulatedGrads[i]==null){let trainable=!1;this.accumulatedGrads[i]={originalName:`${name}/accumulator`,variable:tidy(()=>fill(value.shape,this.initialAccumulatorValue).variable(trainable))}}let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;let accumulatedGrad=this.accumulatedGrads[i].variable;tidy(()=>{let newAccumulatedGrad=add2(accumulatedGrad,square(gradient));accumulatedGrad.assign(newAccumulatedGrad);let newValue=add2(mul(div(gradient,sqrt(add2(newAccumulatedGrad,ENGINE.backend.epsilon()))),-this.learningRate),value);value.assign(newValue)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&dispose(this.accumulatedGrads.map(v=>v.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues);let trainable=!1;this.accumulatedGrads=weightValues.map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(cls,config2){return new cls(config2.learningRate,config2.initialAccumulatorValue)}};AdagradOptimizer.className="Adagrad";registerClass(AdagradOptimizer);var AdamOptimizer=class extends Optimizer{constructor(learningRate,beta1,beta2,epsilon3=null){super();this.learningRate=learningRate,this.beta1=beta1,this.beta2=beta2,this.epsilon=epsilon3,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],tidy(()=>{this.accBeta1=scalar(beta1).variable(),this.accBeta2=scalar(beta2).variable()}),epsilon3==null&&(this.epsilon=ENGINE.backend.epsilon())}applyGradients(variableGradients){let varNames=Array.isArray(variableGradients)?variableGradients.map(v=>v.name):Object.keys(variableGradients);tidy(()=>{let oneMinusAccBeta1=sub(1,this.accBeta1),oneMinusAccBeta2=sub(1,this.accBeta2);varNames.forEach((name,i)=>{let value=ENGINE.registeredVariables[name],trainable=!1;this.accumulatedFirstMoment[i]==null&&(this.accumulatedFirstMoment[i]={originalName:`${name}/m`,variable:tidy(()=>zerosLike(value).variable(trainable))}),this.accumulatedSecondMoment[i]==null&&(this.accumulatedSecondMoment[i]={originalName:`${name}/v`,variable:tidy(()=>zerosLike(value).variable(trainable))});let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;let firstMoment=this.accumulatedFirstMoment[i].variable,secondMoment=this.accumulatedSecondMoment[i].variable,newFirstMoment=add2(mul(firstMoment,this.beta1),mul(gradient,1-this.beta1)),newSecondMoment=add2(mul(secondMoment,this.beta2),mul(square(gradient),1-this.beta2)),biasCorrectedFirstMoment=div(newFirstMoment,oneMinusAccBeta1),biasCorrectedSecondMoment=div(newSecondMoment,oneMinusAccBeta2);firstMoment.assign(newFirstMoment),secondMoment.assign(newSecondMoment);let newValue=add2(mul(div(biasCorrectedFirstMoment,add2(sqrt(biasCorrectedSecondMoment),this.epsilon)),-this.learningRate),value);value.assign(newValue)}),this.accBeta1.assign(mul(this.accBeta1,this.beta1)),this.accBeta2.assign(mul(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&dispose(this.accumulatedFirstMoment.map(v=>v.variable)),this.accumulatedSecondMoment!=null&&dispose(this.accumulatedSecondMoment.map(v=>v.variable))}async getWeights(){let variables=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(variables.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues),tidy(()=>{this.accBeta1.assign(pow(this.beta1,this.iterations_+1)),this.accBeta2.assign(pow(this.beta2,this.iterations_+1))});let variableCount=weightValues.length/2,trainable=!1;this.accumulatedFirstMoment=weightValues.slice(0,variableCount).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})),this.accumulatedSecondMoment=weightValues.slice(variableCount,variableCount*2).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(cls,config2){return new cls(config2.learningRate,config2.beta1,config2.beta2,config2.epsilon)}};AdamOptimizer.className="Adam";registerClass(AdamOptimizer);var AdamaxOptimizer=class extends Optimizer{constructor(learningRate,beta1,beta2,epsilon3=null,decay=0){super();this.learningRate=learningRate,this.beta1=beta1,this.beta2=beta2,this.epsilon=epsilon3,this.decay=decay,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],tidy(()=>{this.iteration=scalar(0).variable(),this.accBeta1=scalar(beta1).variable()}),epsilon3==null&&(this.epsilon=ENGINE.backend.epsilon())}applyGradients(variableGradients){let variableNames=Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients);tidy(()=>{let oneMinusAccBeta1=sub(1,this.accBeta1),lr=div(-this.learningRate,add2(mul(this.iteration,this.decay),1));variableNames.forEach((name,i)=>{let value=ENGINE.registeredVariables[name],trainable=!1;this.accumulatedFirstMoment[i]==null&&(this.accumulatedFirstMoment[i]={originalName:`${name}/m`,variable:zerosLike(value).variable(trainable)}),this.accumulatedWeightedInfNorm[i]==null&&(this.accumulatedWeightedInfNorm[i]={originalName:`${name}/v`,variable:zerosLike(value).variable(trainable)});let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;let firstMoment=this.accumulatedFirstMoment[i].variable,weightedInfNorm=this.accumulatedWeightedInfNorm[i].variable,newFirstMoment=add2(mul(firstMoment,this.beta1),mul(gradient,1-this.beta1)),ut0=mul(weightedInfNorm,this.beta2),ut1=abs(gradient),newWeightedInfNorm=maximum(ut0,ut1);firstMoment.assign(newFirstMoment),weightedInfNorm.assign(newWeightedInfNorm);let newValue=add2(mul(div(lr,oneMinusAccBeta1),div(newFirstMoment,add2(newWeightedInfNorm,this.epsilon))),value);value.assign(newValue)}),this.iteration.assign(add2(this.iteration,1)),this.accBeta1.assign(mul(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&dispose(this.accumulatedFirstMoment.map(v=>v.variable)),this.accumulatedWeightedInfNorm!=null&&dispose(this.accumulatedWeightedInfNorm.map(v=>v.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(weightValues){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(cls,config2){return new cls(config2.learningRate,config2.beta1,config2.beta2,config2.epsilon,config2.decay)}};AdamaxOptimizer.className="Adamax";registerClass(AdamaxOptimizer);var SGDOptimizer=class extends Optimizer{constructor(learningRate){super();this.learningRate=learningRate,this.setLearningRate(learningRate)}applyGradients(variableGradients){(Array.isArray(variableGradients)?variableGradients.map(v=>v.name):Object.keys(variableGradients)).forEach((name,i)=>{let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;let value=ENGINE.registeredVariables[name];tidy(()=>{let newValue=add2(mul(this.c,gradient),value);value.assign(newValue)})}),this.incrementIterations()}setLearningRate(learningRate){this.learningRate=learningRate,this.c!=null&&this.c.dispose(),this.c=keep(scalar(-learningRate))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(weightValues){if(weightValues=await this.extractIterations(weightValues),weightValues.length!==0)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(cls,config2){return new cls(config2.learningRate)}};SGDOptimizer.className="SGD";registerClass(SGDOptimizer);var MomentumOptimizer=class extends SGDOptimizer{constructor(learningRate,momentum,useNesterov=!1){super(learningRate);this.learningRate=learningRate,this.momentum=momentum,this.useNesterov=useNesterov,this.accumulations=[],this.m=scalar(this.momentum)}applyGradients(variableGradients){(Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients)).forEach((name,i)=>{let value=ENGINE.registeredVariables[name];if(this.accumulations[i]==null){let trainable=!1;this.accumulations[i]={originalName:`${name}/momentum`,variable:tidy(()=>zerosLike(value).variable(trainable))}}let accumulation=this.accumulations[i].variable,gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];gradient!=null&&tidy(()=>{let newValue,newAccumulation=add2(mul(this.m,accumulation),gradient);this.useNesterov?newValue=add2(mul(this.c,add2(gradient,mul(newAccumulation,this.m))),value):newValue=add2(mul(this.c,newAccumulation),value),accumulation.assign(newAccumulation),value.assign(newValue)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&dispose(this.accumulations.map(v=>v.variable))}setMomentum(momentum){this.momentum=momentum}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues);let trainable=!1;this.accumulations=weightValues.map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(cls,config2){return new cls(config2.learningRate,config2.momentum,config2.useNesterov)}};MomentumOptimizer.className="Momentum";registerClass(MomentumOptimizer);var RMSPropOptimizer=class extends Optimizer{constructor(learningRate,decay=.9,momentum=0,epsilon3=null,centered=!1){super();if(this.learningRate=learningRate,this.decay=decay,this.momentum=momentum,this.epsilon=epsilon3,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=centered,epsilon3==null&&(this.epsilon=ENGINE.backend.epsilon()),learningRate==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(variableGradients){(Array.isArray(variableGradients)?variableGradients.map(item=>item.name):Object.keys(variableGradients)).forEach((name,i)=>{let value=ENGINE.registeredVariables[name],trainable=!1;this.accumulatedMeanSquares[i]==null&&(this.accumulatedMeanSquares[i]={originalName:`${name}/rms`,variable:tidy(()=>zerosLike(value).variable(trainable))}),this.accumulatedMoments[i]==null&&(this.accumulatedMoments[i]={originalName:`${name}/momentum`,variable:tidy(()=>zerosLike(value).variable(trainable))}),this.accumulatedMeanGrads[i]==null&&this.centered&&(this.accumulatedMeanGrads[i]={originalName:`${name}/mg`,variable:tidy(()=>zerosLike(value).variable(trainable))});let gradient=Array.isArray(variableGradients)?variableGradients[i].tensor:variableGradients[name];if(gradient==null)return;let accumulatedMeanSquare=this.accumulatedMeanSquares[i].variable,accumulatedMoments=this.accumulatedMoments[i].variable;tidy(()=>{let newAccumulatedMeanSquare=add2(mul(accumulatedMeanSquare,this.decay),mul(square(gradient),1-this.decay));if(this.centered){let accumulatedMeanGrad=this.accumulatedMeanGrads[i].variable,newAccumulatedMeanGrad=add2(mul(accumulatedMeanGrad,this.decay),mul(gradient,1-this.decay)),gradContribution=div(mul(gradient,this.learningRate),sqrt(sub(newAccumulatedMeanSquare,add2(square(newAccumulatedMeanGrad),this.epsilon)))),newAccumulatedMoments=add2(mul(accumulatedMoments,this.momentum),gradContribution);accumulatedMeanSquare.assign(newAccumulatedMeanSquare),accumulatedMeanGrad.assign(newAccumulatedMeanGrad),accumulatedMoments.assign(newAccumulatedMoments);let newValue=sub(value,newAccumulatedMoments);value.assign(newValue)}else{let newAccumulatedMeanSquare2=add2(mul(accumulatedMeanSquare,this.decay),mul(square(gradient),1-this.decay)),newAccumulatedMoments=add2(mul(accumulatedMoments,this.momentum),div(mul(gradient,this.learningRate),sqrt(add2(newAccumulatedMeanSquare2,this.epsilon))));accumulatedMeanSquare.assign(newAccumulatedMeanSquare2),accumulatedMoments.assign(newAccumulatedMoments);let newValue=sub(value,newAccumulatedMoments);value.assign(newValue)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&dispose(this.accumulatedMeanSquares.map(v=>v.variable)),this.accumulatedMeanGrads!=null&&this.centered&&dispose(this.accumulatedMeanGrads.map(v=>v.variable)),this.accumulatedMoments!=null&&dispose(this.accumulatedMoments.map(v=>v.variable))}async getWeights(){let variables=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&variables.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(variables.map(v=>({name:v.originalName,tensor:v.variable})))}async setWeights(weightValues){weightValues=await this.extractIterations(weightValues);let variableCount=this.centered?weightValues.length/3:weightValues.length/2,trainable=!1;this.accumulatedMeanSquares=weightValues.slice(0,variableCount).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})),this.accumulatedMoments=weightValues.slice(variableCount,variableCount*2).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})),this.centered&&(this.accumulatedMeanGrads=weightValues.slice(variableCount*2,variableCount*3).map(v=>({originalName:v.name,variable:v.tensor.variable(trainable)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(cls,config2){return new cls(config2.learningRate,config2.decay,config2.momentum,config2.epsilon,config2.centered)}};RMSPropOptimizer.className="RMSProp";registerClass(RMSPropOptimizer);var OptimizerConstructors=class{static sgd(learningRate){return new SGDOptimizer(learningRate)}static momentum(learningRate,momentum,useNesterov=!1){return new MomentumOptimizer(learningRate,momentum,useNesterov)}static rmsprop(learningRate,decay=.9,momentum=0,epsilon3=null,centered=!1){return new RMSPropOptimizer(learningRate,decay,momentum,epsilon3,centered)}static adam(learningRate=.001,beta1=.9,beta2=.999,epsilon3=null){return new AdamOptimizer(learningRate,beta1,beta2,epsilon3)}static adadelta(learningRate=.001,rho=.95,epsilon3=null){return new AdadeltaOptimizer(learningRate,rho,epsilon3)}static adamax(learningRate=.002,beta1=.9,beta2=.999,epsilon3=null,decay=0){return new AdamaxOptimizer(learningRate,beta1,beta2,epsilon3,decay)}static adagrad(learningRate,initialAccumulatorValue=.1){return new AdagradOptimizer(learningRate,initialAccumulatorValue)}},train={sgd:OptimizerConstructors.sgd,momentum:OptimizerConstructors.momentum,adadelta:OptimizerConstructors.adadelta,adagrad:OptimizerConstructors.adagrad,rmsprop:OptimizerConstructors.rmsprop,adamax:OptimizerConstructors.adamax,adam:OptimizerConstructors.adam},delayCallback=(()=>typeof requestAnimationFrame!="undefined"?requestAnimationFrame:typeof setImmediate!="undefined"?setImmediate:f=>f())();function nextFrame(){return new Promise(resolve=>delayCallback(()=>resolve()))}var backend_util_exports={};__export2(backend_util_exports,{ERF_A1:()=>ERF_A1,ERF_A2:()=>ERF_A2,ERF_A3:()=>ERF_A3,ERF_A4:()=>ERF_A4,ERF_A5:()=>ERF_A5,ERF_P:()=>ERF_P,PARALLELIZE_THRESHOLD:()=>PARALLELIZE_THRESHOLD,SELU_SCALE:()=>SELU_SCALE,SELU_SCALEALPHA:()=>SELU_SCALEALPHA,applyActivation:()=>applyActivation,assertAndGetBroadcastShape:()=>assertAndGetBroadcastShape,assertAxesAreInnerMostDims:()=>assertAxesAreInnerMostDims,assertParamsConsistent:()=>assertParamsConsistent,assignToTypedArray:()=>assignToTypedArray,axesAreInnerMostDims:()=>axesAreInnerMostDims,calculateShapes:()=>calculateShapes,castTensor:()=>castTensor,combineLocations:()=>combineLocations,complexWithEvenIndex:()=>complexWithEvenIndex,complexWithOddIndex:()=>complexWithOddIndex,computeConv2DInfo:()=>computeConv2DInfo,computeConv3DInfo:()=>computeConv3DInfo,computeDefaultPad:()=>computeDefaultPad,computeDilation2DInfo:()=>computeDilation2DInfo,computeOptimalWindowSize:()=>computeOptimalWindowSize,computeOutAndReduceShapes:()=>computeOutAndReduceShapes,computeOutShape:()=>computeOutShape2,computePool2DInfo:()=>computePool2DInfo,computePool3DInfo:()=>computePool3DInfo,convertConv2DDataFormat:()=>convertConv2DDataFormat,eitherStridesOrDilationsAreOne:()=>eitherStridesOrDilationsAreOne,expandShapeToKeepDim:()=>expandShapeToKeepDim,exponent:()=>exponent,exponents:()=>exponents,fromStringArrayToUint8:()=>fromStringArrayToUint8,fromUint8ToStringArray:()=>fromUint8ToStringArray,getAxesPermutation:()=>getAxesPermutation,getBroadcastDims:()=>getBroadcastDims,getComplexWithIndex:()=>getComplexWithIndex,getFusedBiasGradient:()=>getFusedBiasGradient,getFusedDyActivation:()=>getFusedDyActivation,getImageCenter:()=>getImageCenter,getInnerMostAxes:()=>getInnerMostAxes,getPermuted:()=>getPermuted,getReductionAxes:()=>getReductionAxes,getReshaped:()=>getReshaped,getReshapedPermuted:()=>getReshapedPermuted,getSliceBeginCoords:()=>getSliceBeginCoords,getSliceSize:()=>getSliceSize,getUndoAxesPermutation:()=>getUndoAxesPermutation,log:()=>log22,mergeRealAndImagArrays:()=>mergeRealAndImagArrays,prepareAndValidate:()=>prepareAndValidate,prepareSplitSize:()=>prepareSplitSize,reshapeTensor:()=>reshapeTensor,segment_util:()=>segment_util_exports,shouldFuse:()=>shouldFuse,slice_util:()=>slice_util_exports,splitRealAndImagArrays:()=>splitRealAndImagArrays,tupleValuesAreOne:()=>tupleValuesAreOne,upcastType:()=>upcastType,validateInput:()=>validateInput,validateUpdateShape:()=>validateUpdateShape,warn:()=>warn});function assertParamsConsistent(shapes,axis){let rank=shapes[0].length;shapes.forEach((shape,i)=>{assert(shape.length===rank,()=>`Error in concat${rank}D: rank of tensors[${i}] must be the same as the rank of the rest (${rank})`)}),assert(axis>=0&&axis`Error in concat${rank}D: axis must be between 0 and ${rank-1}.`);let firstShape=shapes[0];shapes.forEach((shape,i)=>{for(let r=0;r`Error in concat${rank}D: Shape of tensors[${i}] (${shape}) does not match the shape of the rest (${firstShape}) along the non-concatenated axis ${i}.`)})}function computeOutShape2(shapes,axis){let outputShape=shapes[0].slice();for(let i=1;i=blockShapeRank*2+1||i%2==1?permutedAfterBatch.push(i):permutedBeforeBatch.push(i);permuted.push(...permutedBeforeBatch),permuted.push(0),permuted.push(...permutedAfterBatch)}return permuted}function getReshapedPermuted(inputShape,blockShape,prod5,batchToSpace=!0){let reshapedPermuted=[];batchToSpace?reshapedPermuted.push(inputShape[0]/prod5):reshapedPermuted.push(inputShape[0]*prod5);for(let i=1;i"Number of splits must evenly divide the axis."),splitSizes=new Array(numOrSizeSplits).fill(x.shape[axis]/numOrSizeSplits);else{let numOfNegs=numOrSizeSplits.reduce((count2,value)=>(value===-1&&(count2+=1),count2),0);assert(numOfNegs<=1,()=>"There should be only one negative value in split array.");let negIndex=numOrSizeSplits.indexOf(-1);if(negIndex!==-1){let total=numOrSizeSplits.reduce((a,b)=>b>0?a+b:a);numOrSizeSplits[negIndex]=x.shape[axis]-total}assert(x.shape[axis]===numOrSizeSplits.reduce((a,b)=>a+b),()=>"The sum of sizes must match the size of the axis dimension."),splitSizes=numOrSizeSplits}return splitSizes}var segment_util_exports={};__export2(segment_util_exports,{collectGatherOpShapeInfo:()=>collectGatherOpShapeInfo,computeOutShape:()=>computeOutShape3,segOpComputeOptimalWindowSize:()=>segOpComputeOptimalWindowSize});function segOpComputeOptimalWindowSize(inSize,numSegments){let done=!1,res;for(inSize<=PARALLELIZE_THRESHOLD?(res=inSize,done=!0):res=nearestDivisor(inSize,Math.floor(Math.sqrt(inSize)));!done;)res>numSegments||res===inSize?done=!0:res=nearestDivisor(inSize,res+1);return res}function computeOutShape3(aShape,axis,numSegments){let outShape=[],rank=aShape.length;for(let dim=0;dimindicesRank))throw new Error(`Expect batchDims in the range of [-${indicesRank}, ${indicesRank}], but got ${batchDims}`);if(batchDims<0&&(batchDims+=indicesRank),batchDims>xRank)throw new Error(`batchDims (${batchDims}) must be less than rank(x) ( ${xRank}).`);if(axisdecodeString(val))}catch(err){throw new Error(`Failed to decode encoded string bytes into utf-8, error: ${err}`)}}function fromStringArrayToUint8(strings){return strings.map(s=>encodeString(s))}var kernel_impls_exports={};__export2(kernel_impls_exports,{nonMaxSuppressionV3Impl:()=>nonMaxSuppressionV3Impl,nonMaxSuppressionV4Impl:()=>nonMaxSuppressionV4Impl,nonMaxSuppressionV5Impl:()=>nonMaxSuppressionV5Impl,whereImpl:()=>whereImpl});var absGradConfig={kernelName:Abs,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(dy,step(cast(x,"float32"),-1))}}},acosGradConfig={kernelName:Acos,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>{let a=square(cast(x,"float32")),b=sqrt(sub(scalar(1),a));return neg(div(dy,b))}}}},acoshGradConfig={kernelName:Acosh,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>{let a=sqrt(sub(square(cast(x,"float32")),1));return div(dy,a)}}}},addGradConfig={kernelName:Add,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,outShape=assertAndGetBroadcastShape(a.shape,b.shape);return{a:()=>{let res=dy,reduceAxes=getReductionAxes(a.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,a.shape)},b:()=>{let res=dy,reduceAxes=getReductionAxes(b.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,b.shape)}}}},addNGradConfig={kernelName:AddN,saveAllInputs:!0,gradFunc:(dy,saved)=>{let ders={};return saved.forEach((_,i)=>{ders[i]=()=>dy.clone()}),ders}},argMaxGradConfig={kernelName:ArgMax,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>zerosLike(x)}}},argMinGradConfig={kernelName:ArgMin,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>zerosLike(x)}}},asinGradConfig={kernelName:Asin,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div(dy,sqrt(sub(scalar(1),square(cast(x,"float32")))))}}},asinhGradConfig={kernelName:Asinh,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>{let a=sqrt(add2(scalar(1),square(cast(x,"float32"))));return div(dy,a)}}}},atan2GradConfig={kernelName:Atan2,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,outShape=assertAndGetBroadcastShape(a.shape,b.shape);return{a:()=>{let d=add2(square(a),square(b)),res=mul(dy,div(b,d)),reduceAxes=getReductionAxes(a.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,a.shape)},b:()=>{let d=add2(square(a),square(b)),res=neg(mul(dy,div(a,d))),reduceAxes=getReductionAxes(b.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,b.shape)}}}},atanGradConfig={kernelName:Atan,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div(dy,add2(square(cast(x,"float32")),1))}}},atanhGradConfig={kernelName:Atanh,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div(dy,sub(scalar(1),square(cast(x,"float32"))))}}};function avgPool3dGrad_(dy,input2,filterSize,strides,dilations=[1,1,1],pad3,dimRoundingMode){let $dy=convertToTensor(dy,"dy","avgPool3dGrad"),$input=convertToTensor(input2,"input","avgPool3dGrad"),dy5D=$dy,input5D=$input,reshapedTo5D=!1;$input.rank===4&&(reshapedTo5D=!0,dy5D=reshape($dy,[1,$dy.shape[0],$dy.shape[1],$dy.shape[2],$dy.shape[3]]),input5D=reshape($input,[1,$input.shape[0],$input.shape[1],$input.shape[2],$input.shape[3]])),assert(dy5D.rank===5,()=>`Error in avgPool3dGrad: dy must be rank 5 but got rank ${dy5D.rank}.`),assert(input5D.rank===5,()=>`Error in avgPool3dGrad: input must be rank 5 but got rank ${input5D.rank}.`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in avgPool3dGrad: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in avgPool3dGrad: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={dy:dy5D,input:input5D},attrs={filterSize,strides,dilations,pad:pad3,dimRoundingMode},res=ENGINE.runKernel(AvgPool3DGrad,inputs,attrs);return reshapedTo5D?reshape(res,[res.shape[1],res.shape[2],res.shape[3],res.shape[4]]):res}var avgPool3dGrad=op({avgPool3dGrad_}),avgPool3DGradConfig={kernelName:AvgPool3D,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{filterSize,strides,dilations,pad:pad3,dimRoundingMode}=attrs,$dilations=dilations==null?[1,1,1]:dilations;return{x:()=>avgPool3dGrad(dy,x,filterSize,strides,$dilations,pad3,dimRoundingMode)}}};function avgPoolGrad_(dy,input2,filterSize,strides,pad3){let $dy=convertToTensor(dy,"dy","avgPoolGrad"),$input=convertToTensor(input2,"input","avgPoolGrad");assert($input.rank===$dy.rank,()=>`Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`);let input4D=$input,dy4D=$dy,reshapedTo4D=!1;$input.rank===3&&(reshapedTo4D=!0,input4D=reshape($input,[1,$input.shape[0],$input.shape[1],$input.shape[2]]),dy4D=reshape($dy,[1,$dy.shape[0],$dy.shape[1],$dy.shape[2]])),assert(dy4D.rank===4,()=>`Error in avgPoolGrad: dy must be rank 4 but got rank ${dy4D.rank}.`),assert(input4D.rank===4,()=>`Error in avgPoolGrad: input must be rank 4 but got rank ${input4D.rank}.`);let inputs={dy:dy4D,input:input4D},attrs={filterSize,strides,pad:pad3},res=ENGINE.runKernel(AvgPoolGrad,inputs,attrs);return reshapedTo4D?reshape(res,[res.shape[1],res.shape[2],res.shape[3]]):res}var avgPoolGrad=op({avgPoolGrad_}),avgPoolGradConfig={kernelName:AvgPool,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{filterSize,strides,pad:pad3}=attrs;return{x:()=>avgPoolGrad(dy,x,filterSize,strides,pad3)}}},batchMatMulGradConfig={kernelName:BatchMatMul,inputsToSave:["a","b"],gradFunc:(dy,saved,attrs)=>{let[a,b]=saved,{transposeA,transposeB}=attrs;return!transposeA&&!transposeB?{a:()=>matMul(dy,b,!1,!0),b:()=>matMul(a,dy,!0,!1)}:!transposeA&&transposeB?{a:()=>matMul(dy,b,!1,!1),b:()=>matMul(dy,a,!0,!1)}:transposeA&&!transposeB?{a:()=>matMul(b,dy,!1,!0),b:()=>matMul(a,dy,!1,!1)}:{a:()=>matMul(b,dy,!0,!0),b:()=>matMul(dy,a,!0,!0)}}},batchToSpaceNDGradConfig={kernelName:BatchToSpaceND,gradFunc:(dy,saved,attrs)=>{let{blockShape,crops}=attrs;return{x:()=>spaceToBatchND(dy,blockShape,crops)}}},broadcastToGradConfig={kernelName:BroadcastTo,gradFunc:(dy,saved,attrs)=>{let broadCastToAttrs=attrs,inputShape=broadCastToAttrs.inputShape,outputShape=broadCastToAttrs.shape,reps=Array.from(outputShape);for(let i=inputShape.length-1;i>=0;i--)if(inputShape[i]===outputShape[i])reps[i]=1;else if(inputShape[i]!==1)throw new Error(`broadcastTo(): [${inputShape}] cannot be broadcast to [${outputShape}].`);let axes=[];for(let i=0;i1&&axes.push(i);return{x:()=>sum2(dy,axes,!0)}}},castGradConfig={kernelName:Cast,gradFunc:dy=>({x:()=>dy.clone()})},ceilGradConfig={kernelName:Ceil,gradFunc:dy=>({x:()=>zerosLike(dy)})},clipByValueGradConfig={kernelName:ClipByValue,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{clipValueMin,clipValueMax}=attrs;return{x:()=>where(logicalAnd(greaterEqual(x,clipValueMin),lessEqual(x,clipValueMax)),dy,zerosLike(dy))}}},complexAbsGradConfig={kernelName:ComplexAbs,inputsToSave:["x"],gradFunc:absGradConfig.gradFunc},concatGradConfig={kernelName:Concat,saveAllInputs:!0,gradFunc:(dy,saved,attrs)=>{let shapes=saved.map(t=>t.shape),{axis}=attrs,$axis=parseAxisParam(axis,saved[0].shape)[0],sizeSplits=shapes.map(s=>s[$axis]);return split(dy,sizeSplits,$axis).map(t=>()=>t)}},conv2DGradConfig={kernelName:Conv2D,inputsToSave:["x","filter"],gradFunc:(dy,saved,attrs)=>{let[x4D,$filter]=saved,{dilations,strides,pad:pad3,dataFormat}=attrs;return assert(tupleValuesAreOne(dilations),()=>`Error in gradient of conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`),{x:()=>conv2DBackpropInput(x4D.shape,dy,$filter,strides,pad3,dataFormat),filter:()=>conv2DBackpropFilter(x4D,dy,$filter.shape,strides,pad3,dataFormat)}}},conv2DBackpropInputGradConfig={kernelName:Conv2DBackpropInput,inputsToSave:["dy","filter"],gradFunc:(ddx,saved,attrs)=>{let[dy,filter]=saved,{strides,pad:pad3,dataFormat,dimRoundingMode}=attrs;return{dy:()=>conv2d(ddx,filter,strides,pad3,dataFormat,1,dimRoundingMode),filter:()=>conv2DBackpropFilter(ddx,dy,filter.shape,strides,pad3,dataFormat,dimRoundingMode)}}};function conv3DBackpropFilter_(x,dy,filterShape,strides,pad3){let x5D=x;x.rank===4&&(x5D=reshape(x,[1,x.shape[0],x.shape[1],x.shape[2],x.shape[3]]));let dy5D=dy;dy5D.rank===4&&(dy5D=reshape(dy,[1,dy.shape[0],dy.shape[1],dy.shape[2],dy.shape[3]])),assert(x5D.rank===5,()=>`Error in conv3dDerFilter: input must be rank 5, but got shape ${x5D.shape}.`),assert(dy5D.rank===5,()=>`Error in conv3dDerFilter: dy must be rank 5, but got shape ${dy5D.shape}.`),assert(filterShape.length===5,()=>`Error in conv3dDerFilter: filterShape must be length 5, but got ${filterShape}.`),assert(x5D.shape[4]===filterShape[3],()=>`Error in conv3dDerFilter: depth of input ${x5D.shape[4]}) must match input depth in filter (${filterShape[3]}.`),assert(dy5D.shape[4]===filterShape[4],()=>`Error in conv3dDerFilter: depth of dy (${dy5D.shape[4]}) must match output depth for filter (${filterShape[4]}).`);let inputs={x:x5D,dy:dy5D},attrs={strides,pad:pad3,filterShape};return ENGINE.runKernel(Conv3DBackpropFilterV2,inputs,attrs)}var conv3DBackpropFilter=op({conv3DBackpropFilter_}),conv3DGradConfig={kernelName:Conv3D,inputsToSave:["x","filter"],gradFunc:(dy,saved,attrs)=>{let{dilations,strides,pad:pad3}=attrs;assert(tupleValuesAreOne(dilations),()=>`Error in gradient of conv3D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`);let[x5D,$filter]=saved;return{x:()=>conv3DBackpropInput(x5D.shape,dy,$filter,strides,pad3),filter:()=>conv3DBackpropFilter(x5D,dy,$filter.shape,strides,pad3)}}},cosGradConfig={kernelName:Cos,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(neg(sin(cast(x,"float32"))),dy)}}},coshGradConfig={kernelName:Cosh,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(sinh(cast(x,"float32")),dy)}}},cumsumGradConfig={kernelName:Cumsum,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{axis,exclusive,reverse:reverse5}=attrs;return{x:()=>{let permutation=getAxesPermutation([axis],x.rank),out=cumsum(dy,axis,exclusive,!reverse5);return permutation!=null&&(out=transpose(out,permutation)),out}}}},depthwiseConv2dNativeGradConfig={kernelName:DepthwiseConv2dNative,inputsToSave:["x","filter"],gradFunc:(dy,saved,attrs)=>{let{dilations,strides,pad:pad3,dimRoundingMode}=attrs,$dilations=dilations==null?[1,1]:dilations;assert(tupleValuesAreOne($dilations),()=>`Error in gradient of depthwiseConv2dNative: dilation rates greater than 1 are not yet supported. Got dilations '${$dilations}'`);let[x,filter]=saved;return assert(x.rank===4,()=>`Error in gradient of depthwiseConv2dNative: input must be rank 4, but got rank ${x.rank}.`),assert(filter.rank===4,()=>`Error in gradient of depthwiseConv2dNative: filter must be rank 4, but got rank ${filter.rank}.`),assert(x.shape[3]===filter.shape[2],()=>`Error in gradient of depthwiseConv2d: number of input channels (${x.shape[3]}) must match the inChannels dimension in filter ${filter.shape[2]}.`),assert(eitherStridesOrDilationsAreOne(strides,$dilations),()=>`Error in gradient of depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${$dilations}'.`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`),{x:()=>depthwiseConv2dNativeBackpropInput(x.shape,dy,filter,strides,pad3,dilations,dimRoundingMode),filter:()=>depthwiseConv2dNativeBackpropFilter(x,dy,filter.shape,strides,pad3,dilations,dimRoundingMode)}}},dilation2dGradConfig={kernelName:Dilation2D,inputsToSave:["x","filter"],gradFunc:(dy,saved,attrs)=>{let[x,filter]=saved,inputInputs={x,filter,dy},filterInputs={x,filter,dy};return{x:()=>ENGINE.runKernel(Dilation2DBackpropInput,inputInputs,attrs),filter:()=>ENGINE.runKernel(Dilation2DBackpropFilter,filterInputs,attrs)}}},eluGradConfig={kernelName:Elu,outputsToSave:[!0],gradFunc:(dy,saved)=>{let[y]=saved,inputs={dy,y};return{x:()=>ENGINE.runKernel(EluGrad,inputs)}}},erfGradConfig={kernelName:Erf,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved,a=mul(exp(neg(square(x))),2/Math.sqrt(Math.PI));return{x:()=>mul(dy,a)}}},expGradConfig={kernelName:Exp,outputsToSave:[!0],gradFunc:(dy,saved)=>{let[y]=saved;return{x:()=>mul(dy,y)}}},expandDimsGradConfig={kernelName:ExpandDims,inputsToSave:["input"],gradFunc:(dy,saved)=>{let[input2]=saved;return{input:()=>reshape(dy,input2.shape)}}},expm1GradConfig={kernelName:Expm1,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(dy,exp(x))}}},floorGradConfig={kernelName:Floor,gradFunc:dy=>({x:()=>zerosLike(dy)})},floorDivGradConfig={kernelName:FloorDiv,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,outShape=assertAndGetBroadcastShape(a.shape,b.shape);return{a:()=>{let res=div(dy,cast(b,"float32")),reduceAxes=getReductionAxes(a.shape,outShape);return reduceAxes.length>0?reshape(sum2(res,reduceAxes),a.shape):res},b:()=>{let res=mul(dy,cast(a,"float32")),reduceAxes=getReductionAxes(b.shape,outShape);reduceAxes.length>0&&(res=reshape(sum2(res,reduceAxes),b.shape));let tmp=square(b);return neg(div(res,cast(tmp,"float32")))}}}},fusedBatchNormGradConfig={kernelName:FusedBatchNorm,inputsToSave:["x","mean","variance","scale"],gradFunc:(dy,saved,attrs)=>{let{varianceEpsilon}=attrs,[x,mean4,variance,scale2]=saved,scaleValue=scale2==null?scalar(1):scale2,reductionAxes=getReductionAxes(mean4.shape,x.shape),tileShape=[];if(mean4.rank===1){for(let i=0;imean4.rank===1?reshape(mul(mul(dy,tile(reshape(oneOverSqrtVariance,[1,1,1,mean4.shape[0]]),tileShape)),scaleValue),x.shape):reshape(mul(mul(dy,oneOverSqrtVariance),scaleValue),x.shape),mean:()=>{let meanDer=mul(mul(oneOverSqrtVariance,scalar(-1)),dyTimesScaleValue);return mean4.rank===1&&(meanDer=sum2(meanDer,reductionAxes)),reshape(meanDer,mean4.shape)},variance:()=>{let varianceDer=mul(mul(minusHalfRCube,xMinusMean),dyTimesScaleValue);return mean4.rank===1&&(varianceDer=sum2(varianceDer,reductionAxes)),reshape(varianceDer,mean4.shape)},scale:()=>{let xMinusMean2TimesRsqrt=mul(xMinusMean,oneOverSqrtVariance),scaleDer=mul(dy,xMinusMean2TimesRsqrt);return mean4.rank===1&&(scaleDer=sum2(scaleDer,reductionAxes)),reshape(scaleDer,mean4.shape)},offset:()=>{let offsetDer=dy;return mean4.rank===1&&(offsetDer=sum2(offsetDer,reductionAxes)),reshape(offsetDer,mean4.shape)}}}},gatherGradConfig={kernelName:GatherV2,inputsToSave:["x","indices"],gradFunc:(dy,saved,attrs)=>{let[x,indices]=saved,{axis}=attrs,parsedAxis=parseAxisParam(axis,x.shape)[0];return{x:()=>{let paramsShape=x.shape,indicesSize=indices.size,outerShape=paramsShape.slice(0,parsedAxis),outerDims=outerShape.length,innerShape=paramsShape.slice(axis,paramsShape.length).slice(1),innerDims=innerShape.length,outerAxesIndices=arrayRange(0,outerDims),innerAxesIndices=arrayRange(outerDims+1,outerDims+1+innerDims),valuesShape=arrayConcat([outerShape,[indicesSize],innerShape]),values=reshape(dy,valuesShape),reshapedIndices=reshape(indices,[indicesSize]),transposeDims=arrayConcat([[outerDims],outerAxesIndices,innerAxesIndices]),valuesTranspose=transpose(values,transposeDims),paramsGrad=unsortedSegmentSum(valuesTranspose,reshapedIndices,x.shape[parsedAxis]),invertTransposeDims=getUndoAxesPermutation(transposeDims);return paramsGrad=transpose(paramsGrad,invertTransposeDims),paramsGrad},indices:()=>indices}}};function arrayRange(start,stop){let result=[];for(let i=start;i{let[a,b]=saved;return{a:()=>zerosLike(a),b:()=>zerosLike(b)}}},identityGradConfig={kernelName:Identity,gradFunc:dy=>({x:()=>cast(dy,"float32")})},isFiniteGradConfig={kernelName:IsFinite,gradFunc:dy=>({x:()=>zerosLike(dy)})},isInfGradConfig={kernelName:IsInf,gradFunc:dy=>({x:()=>zerosLike(dy)})},isNanGradConfig={kernelName:IsNan,gradFunc:dy=>({x:()=>zerosLike(dy)})},leakyReluGradConfig={kernelName:LeakyRelu,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{alpha}=attrs,mask=greater(x,0);return{x:()=>where(mask,dy,mul(dy,alpha))}}},log1pGradConfig={kernelName:Log1p,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div(dy,add2(x,1))}}},logGradConfig={kernelName:Log,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div(dy,cast(x,"float32"))}}},logSoftmaxGradConfig={kernelName:LogSoftmax,inputsToSave:[],outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let[value]=saved,{axis}=attrs;return{logits:()=>{let keepDims=!0,softmax6=exp(value);return sub(dy,mul(sum2(dy,axis,keepDims),softmax6))}}}};function localResponseNormalizationBackprop_(x,y,dy,depthRadius=5,bias=1,alpha=1,beta=.5){let inputs={x,y,dy},attrs={depthRadius,bias,alpha,beta};return ENGINE.runKernel(LRNGrad,inputs,attrs)}var localResponseNormalizationBackprop=op({localResponseNormalizationBackprop_}),lrnGradConfig={kernelName:LRN,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let[x,y]=saved,{depthRadius,bias,alpha,beta}=attrs;return{x:()=>localResponseNormalizationBackprop(x,y,dy,depthRadius,bias,alpha,beta)}}};function gradForMinAndMax(dy,y,xOrig,origAxes){return y.rankmul(dy,cast(equal(xOrig,y),dy.dtype))}}var maxGradConfig={kernelName:Max,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let maxAttrs=attrs,{reductionIndices}=maxAttrs,x=saved[0],y=saved[1],origAxes=parseAxisParam(reductionIndices,x.shape),maxGrad=gradForMinAndMax(dy,y,x,origAxes);return{x:()=>maxGrad.x()}}},maximumGradConfig={kernelName:Maximum,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved;return{a:()=>mul(dy,cast(greaterEqual(a,b),"float32")),b:()=>mul(dy,cast(less(a,b),"float32"))}}};function maxPool3dGrad_(dy,input2,output,filterSize,strides,dilations=[1,1,1],pad3,dimRoundingMode){let $dy=convertToTensor(dy,"dy","maxPool3dGrad"),$input=convertToTensor(input2,"input","maxPool3dGrad"),$output=convertToTensor(output,"output","maxPool3dGrad"),dy5D=$dy,input5D=$input,output5D=$output,reshapedTo5D=!1;$input.rank===4&&(reshapedTo5D=!0,dy5D=reshape($dy,[1,$dy.shape[0],$dy.shape[1],$dy.shape[2],$dy.shape[3]]),input5D=reshape($input,[1,$input.shape[0],$input.shape[1],$input.shape[2],$input.shape[3]]),output5D=reshape($output,[1,$output.shape[0],$output.shape[1],$output.shape[2],$output.shape[3]])),assert(dy5D.rank===5,()=>`Error in maxPool3dGrad: dy must be rank 5 but got rank ${dy5D.rank}.`),assert(input5D.rank===5,()=>`Error in maxPool3dGrad: input must be rank 5 but got rank ${input5D.rank}.`),assert(output5D.rank===5,()=>`Error in maxPool3dGrad: output must be rank 5 but got rank ${output5D.rank}.`),assert(eitherStridesOrDilationsAreOne(strides,dilations),()=>`Error in maxPool3dGrad: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in maxPool3dGrad: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={dy:dy5D,input:input5D,output:output5D},attrs={filterSize,strides,dilations,pad:pad3,dimRoundingMode},res=ENGINE.runKernel(MaxPool3DGrad,inputs,attrs);return reshapedTo5D?reshape(res,[res.shape[1],res.shape[2],res.shape[3],res.shape[4]]):res}var maxPool3dGrad=op({maxPool3dGrad_}),maxPool3DGradConfig={kernelName:MaxPool3D,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let[x,y]=saved,{filterSize,strides,dilations,pad:pad3,dimRoundingMode}=attrs,$dilations=dilations==null?[1,1,1]:dilations;return{x:()=>maxPool3dGrad(dy,x,y,filterSize,strides,$dilations,pad3,dimRoundingMode)}}};function maxPoolGrad_(dy,input2,output,filterSize,strides,pad3,dimRoundingMode){let $dy=convertToTensor(dy,"dy","maxPoolGrad"),$input=convertToTensor(input2,"input","maxPoolGrad"),$output=convertToTensor(output,"output","maxPoolGrad");assert($input.rank===$dy.rank,()=>`Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`),assert($dy.rank===4,()=>`Error in maxPoolGrad: dy must be rank 4 but got rank ${$dy.rank}.`),assert($input.rank===4,()=>`Error in maxPoolGrad: input must be rank 4 but got rank ${$input.rank}.`),dimRoundingMode!=null&&assert(isInt(pad3),()=>`Error in maxPoolGrad: pad must be an integer when using, dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);let inputs={dy:$dy,input:$input,output:$output},attrs={filterSize,strides,pad:pad3,dimRoundingMode};return ENGINE.runKernel(MaxPoolGrad,inputs,attrs)}var maxPoolGrad=op({maxPoolGrad_}),maxPoolGradConfig={kernelName:MaxPool,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let[x,y]=saved,{filterSize,strides,pad:pad3}=attrs;return{x:()=>maxPoolGrad(dy,x,y,filterSize,strides,pad3)}}},meanGradConfig={kernelName:Mean,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{axis}=attrs,axes=parseAxisParam(axis,x.shape),reduceShape=computeOutAndReduceShapes(x.shape,axes)[1],reduceSize=sizeFromShape(reduceShape);return{x:()=>{let expandedDyShape=x.shape.slice();axes.forEach(axis2=>{expandedDyShape[axis2]=1});let expandedDy=reshape(dy,expandedDyShape);return div(mul(expandedDy,ones2(x.shape,"float32")),reduceSize)}}}},minGradConfig={kernelName:Min,inputsToSave:["x"],outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let minAttrs=attrs,{axis}=minAttrs,[x,y]=saved,origAxes=parseAxisParam(axis,x.shape),minGrad=gradForMinAndMax(dy,y,x,origAxes);return{x:()=>minGrad.x()}}},minimumGradConfig={kernelName:Minimum,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved;return{a:()=>mul(dy,cast(lessEqual(a,b),"float32")),b:()=>mul(dy,cast(greater(a,b),"float32"))}}},mirrorPadGradConfig={kernelName:MirrorPad,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let x=saved[0],{paddings}=attrs,begin=paddings.map(p2=>p2[0]);return{x:()=>slice(dy,begin,x.shape)}}},modGradConfig={kernelName:Mod,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,outShape=assertAndGetBroadcastShape(a.shape,b.shape);return{a:()=>{let reduceAxes=getReductionAxes(a.shape,outShape);return reduceAxes.length>0?reshape(sum2(dy,reduceAxes),a.shape):dy},b:()=>{let res=mul(dy,neg(floor(div(a,b)))),reduceAxes=getReductionAxes(b.shape,outShape);return reduceAxes.length>0?reshape(sum2(res,reduceAxes),b.shape):res}}}},multiplyGradConfig={kernelName:Multiply,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,outShape=assertAndGetBroadcastShape(a.shape,b.shape);return{a:()=>{let res=mul(dy,cast(b,"float32")),reduceAxes=getReductionAxes(a.shape,outShape);return reduceAxes.length>0?reshape(sum2(res,reduceAxes),a.shape):res},b:()=>{let res=mul(dy,cast(a,"float32")),reduceAxes=getReductionAxes(b.shape,outShape);return reduceAxes.length>0?reshape(sum2(res,reduceAxes),b.shape):res}}}},negGradConfig={kernelName:Neg,gradFunc:dy=>({x:()=>neg(dy)})},oneHotGradConfig={kernelName:OneHot,inputsToSave:["indices"],gradFunc:(dy,saved)=>{let indices=saved[0];return{indices:()=>zeros(indices.shape,"float32")}}},onesLikeGradConfig={kernelName:OnesLike,gradFunc:dy=>({x:()=>zerosLike(dy)})},packGradConfig={kernelName:Pack,saveAllInputs:!0,gradFunc:(dy,saved,attrs)=>{let{axis}=attrs;return unstack(dy,axis).map(t=>()=>t)}},padV2GradConfig={kernelName:PadV2,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let x=saved[0],{paddings}=attrs,begin=paddings.map(p2=>p2[0]);return{x:()=>slice(dy,begin,x.shape)}}},powGradConfig={kernelName:Pow,inputsToSave:["a","b"],outputsToSave:[!0],gradFunc:(dy,saved)=>{let[a,b,y]=saved,base2=a,exp4=b,outShape=assertAndGetBroadcastShape(base2.shape,exp4.shape);return{a:()=>{let expFloat=cast(exp4,"float32"),res=mul(dy,mul(expFloat,pow(base2,sub(expFloat,scalar(1))))),reduceAxes=getReductionAxes(base2.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,base2.shape)},b:()=>{let condition=greater(base2,0),logBase=where(condition,log2(base2),zerosLike(base2)),res=mul(dy,mul(y,logBase)),reduceAxes=getReductionAxes(exp4.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,exp4.shape)}}}},preluGradConfig={kernelName:Prelu,inputsToSave:["x","alpha"],gradFunc:(dy,saved)=>{let[x,alpha]=saved,mask=greater(x,0);return{x:()=>where(mask,dy,mul(dy,alpha)),alpha:()=>{let res=where(mask,zerosLike(dy),mul(dy,x)),reduceAxes=getReductionAxes(alpha.shape,dy.shape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,alpha.shape)}}}},divGradConfig={kernelName:RealDiv,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,outShape=assertAndGetBroadcastShape(a.shape,b.shape);return{a:()=>{let res=div(dy,cast(b,"float32")),reduceAxes=getReductionAxes(a.shape,outShape);return reduceAxes.length>0?reshape(sum2(res,reduceAxes),a.shape):res},b:()=>{let res=mul(dy,cast(a,"float32")),reduceAxes=getReductionAxes(b.shape,outShape);reduceAxes.length>0&&(res=reshape(sum2(res,reduceAxes),b.shape));let tmp=square(b);return neg(div(res,cast(tmp,"float32")))}}}},reciprocalGradConfig={kernelName:Reciprocal,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div(dy,neg(square(x)))}}},relu6GradConfig={kernelName:Relu6,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved,mask=mul(lessEqual(x,6),step(x));return{x:()=>mul(dy,cast(mask,"float32"))}}},reluGradConfig={kernelName:Relu,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(dy,cast(step(x),"float32"))}}},reshapeGradConfig={kernelName:Reshape,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>reshape(dy,x.shape)}}},resizeBilinearGradConfig={kernelName:ResizeBilinear,inputsToSave:["images"],gradFunc:(dy,saved,attrs)=>{let[images]=saved,inputs={dy,images};return{images:()=>ENGINE.runKernel(ResizeBilinearGrad,inputs,attrs)}}},resizeNearestNeighborGradConfig={kernelName:ResizeNearestNeighbor,inputsToSave:["images"],gradFunc:(dy,saved,attrs)=>{let[images]=saved,inputs={dy,images};return{images:()=>ENGINE.runKernel(ResizeNearestNeighborGrad,inputs,attrs)}}},reverseGradConfig={kernelName:Reverse,gradFunc:(dy,saved,attrs)=>{let{dims}=attrs,axes=parseAxisParam(dims,dy.shape);return{x:()=>reverse(dy,axes)}}},roundGradConfig={kernelName:Round,gradFunc:dy=>({x:()=>zerosLike(dy)})},rsqrtGradConfig={kernelName:Rsqrt,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>neg(div(dy,mul(pow(x,1.5),2)))}}},selectGradConfig={kernelName:Select,inputsToSave:["condition"],gradFunc:(dy,saved)=>{let[condition]=saved;return{condition:()=>cast(zerosLike(condition),"float32"),t:()=>mul(dy,cast(condition,dy.dtype)),e:()=>mul(dy,cast(logicalNot(condition),dy.dtype))}}},seluGradConfig={kernelName:Selu,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>{let mask=greater(x,scalar(0)),scaleAlpha2=scalar(SELU_SCALEALPHA),scale2=scalar(SELU_SCALE),greaterThanZeroDer=mul(dy,scale2),lessEqualZeroDer=mul(mul(dy,scaleAlpha2),exp(cast(x,"float32")));return where(mask,greaterThanZeroDer,lessEqualZeroDer)}}}},sigmoidGradConfig={kernelName:Sigmoid,outputsToSave:[!0],gradFunc:(dy,saved)=>{let[y]=saved;return{x:()=>mul(dy,mul(y,sub(scalar(1),y)))}}},signGradConfig={kernelName:Sign,gradFunc:dy=>({x:()=>zerosLike(dy)})},sinGradConfig={kernelName:Sin,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(cos(cast(x,"float32")),dy)}}},sinhGradConfig={kernelName:Sinh,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(cosh(cast(x,"float32")),dy)}}},sliceGradConfig={kernelName:Slice,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{begin,size}=attrs,inputShape=x.shape,[begin_,size_]=parseSliceParams(x,begin,size),paddings=[];for(let i=0;ipad(dy,paddings)}}},softmaxGradConfig={kernelName:Softmax,outputsToSave:[!0],gradFunc:(dy,saved,attrs)=>{let[y]=saved,{dim}=attrs,keepDims=!0,dyTimesY=mul(dy,y);return{logits:()=>sub(dyTimesY,mul(sum2(dyTimesY,[dim],keepDims),y))}}},softplusGradConfig={kernelName:Softplus,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(dy,sigmoid(x))}}},spaceToBatchNDGradConfig={kernelName:SpaceToBatchND,gradFunc:(dy,saved,attrs)=>{let{blockShape,paddings}=attrs;return{x:()=>batchToSpaceND(dy,blockShape,paddings)}}},splitVGradConfig={kernelName:SplitV,gradFunc:(dy,saved,attrs)=>{let{axis}=attrs;return{x:()=>concat(dy,axis)}}},sqrtGradConfig={kernelName:Sqrt,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div(dy,mul(sqrt(cast(x,"float32")),2))}}},squareGradConfig={kernelName:Square,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>mul(dy,mul(cast(x,"float32"),2))}}},squaredDifferenceGradConfig={kernelName:SquaredDifference,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,two=scalar(2);return{a:()=>mul(dy,mul(two,sub(a,b))),b:()=>mul(dy,mul(two,sub(b,a)))}}},stepGradConfig={kernelName:Step,gradFunc:dy=>({x:()=>zerosLike(dy)})},subGradConfig={kernelName:Sub,inputsToSave:["a","b"],gradFunc:(dy,saved)=>{let[a,b]=saved,outShape=assertAndGetBroadcastShape(a.shape,b.shape);return{a:()=>{let res=dy,reduceAxes=getReductionAxes(a.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(res,a.shape)},b:()=>{let res=dy,reduceAxes=getReductionAxes(b.shape,outShape);return reduceAxes.length>0&&(res=sum2(res,reduceAxes)),reshape(neg(res),b.shape)}}}},sumGradConfig={kernelName:Sum,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,expandedDyShape=x.shape.slice(),{axis}=attrs;parseAxisParam(axis,x.shape).forEach(axis2=>{expandedDyShape[axis2]=1});let expandedDy=reshape(dy,expandedDyShape),derX=mul(expandedDy,ones2(x.shape,"float32"));return{x:()=>derX}}},tanGradConfig={kernelName:Tan,inputsToSave:["x"],gradFunc:(dy,saved)=>{let[x]=saved;return{x:()=>div(dy,square(cos(x)))}}},tanhGradConfig={kernelName:Tanh,outputsToSave:[!0],gradFunc:(dy,saved)=>{let[y]=saved;return{x:()=>mul(sub(scalar(1),square(y)),dy)}}},tileGradConfig={kernelName:Tile,inputsToSave:["x"],gradFunc:(dy,saved,attrs)=>{let[x]=saved,{reps}=attrs;return{x:()=>{let xGrad=zerosLike(x);if(x.rank===1)for(let i=0;i{let transposeAttrs=attrs,{perm}=transposeAttrs,undoPerm=getUndoAxesPermutation(perm);return{x:()=>transpose(dy,undoPerm)}}},unpackGradConfig={kernelName:Unpack,gradFunc:(dy,saved,attrs)=>{let unpackAttrs=attrs,{axis}=unpackAttrs;return{value:()=>stack(dy,axis)}}},unsortedSegmentSumGradConfig={kernelName:UnsortedSegmentSum,inputsToSave:["segmentIds"],gradFunc:(dy,saved)=>{let[segmentIds]=saved;return{x:()=>gatherDropNegatives(dy,segmentIds)}}};function gatherDropNegatives(x,indices){let zeroClippedIndices=maximum(indices,zerosLike(indices)),gathered=gather(x,zeroClippedIndices),isPositive=greaterEqual(indices,scalar(0,"int32")),numIters=gathered.rank-isPositive.rank;for(let i=0;i({x:()=>zerosLike(dy)})},gradConfigs=[absGradConfig,acosGradConfig,acoshGradConfig,addGradConfig,addNGradConfig,argMaxGradConfig,argMinGradConfig,asinGradConfig,asinhGradConfig,atan2GradConfig,atanGradConfig,atanhGradConfig,avgPool3DGradConfig,avgPoolGradConfig,batchMatMulGradConfig,batchToSpaceNDGradConfig,broadcastToGradConfig,castGradConfig,ceilGradConfig,clipByValueGradConfig,complexAbsGradConfig,concatGradConfig,conv2DBackpropInputGradConfig,conv2DGradConfig,conv3DGradConfig,cosGradConfig,coshGradConfig,cumsumGradConfig,depthwiseConv2dNativeGradConfig,dilation2dGradConfig,divGradConfig,eluGradConfig,erfGradConfig,expGradConfig,expandDimsGradConfig,expm1GradConfig,floorDivGradConfig,floorGradConfig,fusedBatchNormGradConfig,gatherGradConfig,greaterEqualGradConfig,identityGradConfig,isFiniteGradConfig,isInfGradConfig,isNanGradConfig,leakyReluGradConfig,log1pGradConfig,logGradConfig,logSoftmaxGradConfig,lrnGradConfig,maxGradConfig,maxGradConfig,maximumGradConfig,maxPool3DGradConfig,maxPoolGradConfig,meanGradConfig,minGradConfig,minimumGradConfig,mirrorPadGradConfig,modGradConfig,multiplyGradConfig,negGradConfig,oneHotGradConfig,onesLikeGradConfig,packGradConfig,padV2GradConfig,padV2GradConfig,powGradConfig,preluGradConfig,reciprocalGradConfig,relu6GradConfig,reluGradConfig,reshapeGradConfig,resizeBilinearGradConfig,resizeNearestNeighborGradConfig,reverseGradConfig,roundGradConfig,rsqrtGradConfig,selectGradConfig,seluGradConfig,sigmoidGradConfig,signGradConfig,sinGradConfig,sinhGradConfig,sliceGradConfig,softmaxGradConfig,softplusGradConfig,spaceToBatchNDGradConfig,spaceToBatchNDGradConfig,splitVGradConfig,splitVGradConfig,sqrtGradConfig,squaredDifferenceGradConfig,squareGradConfig,stepGradConfig,subGradConfig,sumGradConfig,tanGradConfig,tanhGradConfig,tileGradConfig,transposeGradConfig,unpackGradConfig,unsortedSegmentSumGradConfig,zerosLikeGradConfig];for(let gradientConfig of gradConfigs)registerGradient(gradientConfig);Tensor.prototype.abs=function(){return this.throwIfDisposed(),abs(this)};Tensor.prototype.acos=function(){return this.throwIfDisposed(),acos(this)};Tensor.prototype.acosh=function(){return this.throwIfDisposed(),acosh(this)};Tensor.prototype.addStrict=function(x){return this.throwIfDisposed(),addStrict(this,x)};Tensor.prototype.add=function(b){return this.throwIfDisposed(),add2(this,b)};Tensor.prototype.all=function(axis,keepDims){return this.throwIfDisposed(),all(this,axis,keepDims)};Tensor.prototype.any=function(axis,keepDims){return this.throwIfDisposed(),any(this,axis,keepDims)};Tensor.prototype.argMax=function(axis){return this.throwIfDisposed(),argMax(this,axis)};Tensor.prototype.argMin=function(axis){return this.throwIfDisposed(),argMin(this,axis)};Tensor.prototype.asScalar=function(){return this.throwIfDisposed(),assert(this.size===1,()=>"The array must have only 1 element."),reshape(this,[])};Tensor.prototype.asType=function(dtype){return this.throwIfDisposed(),cast(this,dtype)};Tensor.prototype.as1D=function(){return this.throwIfDisposed(),reshape(this,[this.size])};Tensor.prototype.as2D=function(rows,columns){return this.throwIfDisposed(),reshape(this,[rows,columns])};Tensor.prototype.as3D=function(rows,columns,depth){return this.throwIfDisposed(),reshape(this,[rows,columns,depth])};Tensor.prototype.as4D=function(rows,columns,depth,depth2){return this.throwIfDisposed(),reshape(this,[rows,columns,depth,depth2])};Tensor.prototype.as5D=function(rows,columns,depth,depth2,depth3){return this.throwIfDisposed(),reshape(this,[rows,columns,depth,depth2,depth3])};Tensor.prototype.asin=function(){return this.throwIfDisposed(),asin(this)};Tensor.prototype.asinh=function(){return this.throwIfDisposed(),asinh(this)};Tensor.prototype.atan=function(){return this.throwIfDisposed(),atan(this)};Tensor.prototype.atan2=function(b){return this.throwIfDisposed(),atan2(this,b)};Tensor.prototype.atanh=function(){return this.throwIfDisposed(),atanh(this)};Tensor.prototype.avgPool=function(filterSize,strides,pad3,dimRoundingMode){return this.throwIfDisposed(),avgPool(this,filterSize,strides,pad3,dimRoundingMode)};Tensor.prototype.batchToSpaceND=function(blockShape,crops){return this.throwIfDisposed(),batchToSpaceND(this,blockShape,crops)};Tensor.prototype.batchNorm=function(mean4,variance,offset,scale2,varianceEpsilon){return this.throwIfDisposed(),batchNorm(this,mean4,variance,offset,scale2,varianceEpsilon)};Tensor.prototype.broadcastTo=function(shape){return this.throwIfDisposed(),broadcastTo(this,shape)};Tensor.prototype.cast=function(dtype){return this.throwIfDisposed(),cast(this,dtype)};Tensor.prototype.ceil=function(){return this.throwIfDisposed(),ceil(this)};Tensor.prototype.clipByValue=function(min6,max6){return this.throwIfDisposed(),clipByValue(this,min6,max6)};Tensor.prototype.concat=function(x,axis){return this.throwIfDisposed(),x instanceof Tensor&&(x=[x]),concat([this,...x],axis)};Tensor.prototype.conv1d=function(filter,stride,pad3,dataFormat,dilation,dimRoundingMode){return this.throwIfDisposed(),conv1d(this,filter,stride,pad3,dataFormat,dilation,dimRoundingMode)};Tensor.prototype.conv2dTranspose=function(filter,outputShape,strides,pad3,dimRoundingMode){return this.throwIfDisposed(),conv2dTranspose(this,filter,outputShape,strides,pad3,dimRoundingMode)};Tensor.prototype.conv2d=function(filter,strides,pad3,dataFormat,dilations,dimRoundingMode){return this.throwIfDisposed(),conv2d(this,filter,strides,pad3,dataFormat,dilations,dimRoundingMode)};Tensor.prototype.cos=function(){return this.throwIfDisposed(),cos(this)};Tensor.prototype.cosh=function(){return this.throwIfDisposed(),cosh(this)};Tensor.prototype.cumsum=function(axis,exclusive,reverse5){return this.throwIfDisposed(),cumsum(this,axis,exclusive,reverse5)};Tensor.prototype.depthToSpace=function(blockSize,dataFormat){return this.throwIfDisposed(),depthToSpace(this,blockSize,dataFormat)};Tensor.prototype.depthwiseConv2D=function(filter,strides,pad3,dataFormat,dilations,dimRoundingMode){return deprecationWarn("depthwiseConv2D is deprecated, use depthwiseConv2d instead"),this.throwIfDisposed(),depthwiseConv2d(this,filter,strides,pad3,dataFormat,dilations,dimRoundingMode)};Tensor.prototype.depthwiseConv2d=function(filter,strides,pad3,dataFormat,dilations,dimRoundingMode){return this.throwIfDisposed(),depthwiseConv2d(this,filter,strides,pad3,dataFormat,dilations,dimRoundingMode)};Tensor.prototype.dilation2d=function(filter,strides,pad3,dilations,dataFormat){return this.throwIfDisposed(),dilation2d(this,filter,strides,pad3,dilations,dataFormat)};Tensor.prototype.divNoNan=function(b){return this.throwIfDisposed(),divNoNan(this,b)};Tensor.prototype.divStrict=function(x){return this.throwIfDisposed(),divStrict(this,x)};Tensor.prototype.div=function(b){return this.throwIfDisposed(),div(this,b)};Tensor.prototype.dot=function(b){return this.throwIfDisposed(),dot(this,b)};Tensor.prototype.elu=function(){return this.throwIfDisposed(),elu(this)};Tensor.prototype.equalStrict=function(x){return this.throwIfDisposed(),equalStrict(this,x)};Tensor.prototype.equal=function(b){return this.throwIfDisposed(),equal(this,b)};Tensor.prototype.erf=function(){return this.throwIfDisposed(),erf(this)};Tensor.prototype.exp=function(){return this.throwIfDisposed(),exp(this)};Tensor.prototype.expandDims=function(axis){return this.throwIfDisposed(),expandDims(this,axis)};Tensor.prototype.expm1=function(){return this.throwIfDisposed(),expm1(this)};Tensor.prototype.fft=function(){return this.throwIfDisposed(),fft(this)};Tensor.prototype.flatten=function(){return this.throwIfDisposed(),reshape(this,[this.size])};Tensor.prototype.floor=function(){return this.throwIfDisposed(),floor(this)};Tensor.prototype.floorDiv=function(b){return this.throwIfDisposed(),floorDiv(this,b)};Tensor.prototype.gather=function(indices,axis){return this.throwIfDisposed(),gather(this,indices,axis)};Tensor.prototype.greaterEqualStrict=function(x){return this.throwIfDisposed(),greaterEqualStrict(this,x)};Tensor.prototype.greaterEqual=function(b){return this.throwIfDisposed(),greaterEqual(this,b)};Tensor.prototype.greaterStrict=function(x){return this.throwIfDisposed(),greaterStrict(this,x)};Tensor.prototype.greater=function(b){return this.throwIfDisposed(),greater(this,b)};Tensor.prototype.ifft=function(){return this.throwIfDisposed(),ifft(this)};Tensor.prototype.irfft=function(){return this.throwIfDisposed(),irfft(this)};Tensor.prototype.isFinite=function(){return this.throwIfDisposed(),isFinite2(this)};Tensor.prototype.isInf=function(){return this.throwIfDisposed(),isInf(this)};Tensor.prototype.isNaN=function(){return this.throwIfDisposed(),isNaN2(this)};Tensor.prototype.leakyRelu=function(alpha){return this.throwIfDisposed(),leakyRelu(this,alpha)};Tensor.prototype.lessEqualStrict=function(x){return this.throwIfDisposed(),lessEqualStrict(this,x)};Tensor.prototype.lessEqual=function(b){return this.throwIfDisposed(),lessEqual(this,b)};Tensor.prototype.lessStrict=function(x){return this.throwIfDisposed(),lessStrict(this,x)};Tensor.prototype.less=function(b){return this.throwIfDisposed(),less(this,b)};Tensor.prototype.localResponseNormalization=function(depthRadius,bias,alpha,beta){return this.throwIfDisposed(),localResponseNormalization(this,depthRadius,bias,alpha,beta)};Tensor.prototype.logSigmoid=function(){return this.throwIfDisposed(),logSigmoid(this)};Tensor.prototype.logSoftmax=function(axis){return this.throwIfDisposed(),logSoftmax(this,axis)};Tensor.prototype.logSumExp=function(axis,keepDims){return this.throwIfDisposed(),logSumExp(this,axis,keepDims)};Tensor.prototype.log=function(){return this.throwIfDisposed(),log2(this)};Tensor.prototype.log1p=function(){return this.throwIfDisposed(),log1p(this)};Tensor.prototype.logicalAnd=function(b){return this.throwIfDisposed(),logicalAnd(this,b)};Tensor.prototype.logicalNot=function(){return this.throwIfDisposed(),logicalNot(this)};Tensor.prototype.logicalOr=function(b){return this.throwIfDisposed(),logicalOr(this,b)};Tensor.prototype.logicalXor=function(b){return this.throwIfDisposed(),logicalXor(this,b)};Tensor.prototype.matMul=function(b,transposeA,transposeB){return this.throwIfDisposed(),matMul(this,b,transposeA,transposeB)};Tensor.prototype.maxPool=function(filterSize,strides,pad3,dimRoundingMode){return this.throwIfDisposed(),maxPool(this,filterSize,strides,pad3,dimRoundingMode)};Tensor.prototype.max=function(axis,keepDims){return this.throwIfDisposed(),max(this,axis,keepDims)};Tensor.prototype.maximumStrict=function(x){return this.throwIfDisposed(),maximumStrict(this,x)};Tensor.prototype.maximum=function(b){return this.throwIfDisposed(),maximum(this,b)};Tensor.prototype.mean=function(axis,keepDims){return this.throwIfDisposed(),mean(this,axis,keepDims)};Tensor.prototype.min=function(axis,keepDims){return this.throwIfDisposed(),min(this,axis,keepDims)};Tensor.prototype.minimumStrict=function(x){return this.throwIfDisposed(),minimumStrict(this,x)};Tensor.prototype.minimum=function(b){return this.throwIfDisposed(),minimum(this,b)};Tensor.prototype.mirrorPad=function(paddings,mode){return this.throwIfDisposed(),mirrorPad(this,paddings,mode)};Tensor.prototype.modStrict=function(x){return this.throwIfDisposed(),modStrict(this,x)};Tensor.prototype.mod=function(b){return this.throwIfDisposed(),mod(this,b)};Tensor.prototype.mulStrict=function(x){return this.throwIfDisposed(),mulStrict(this,x)};Tensor.prototype.mul=function(b){return this.throwIfDisposed(),mul(this,b)};Tensor.prototype.neg=function(){return this.throwIfDisposed(),neg(this)};Tensor.prototype.norm=function(ord,axis,keepDims){return this.throwIfDisposed(),norm(this,ord,axis,keepDims)};Tensor.prototype.notEqualStrict=function(x){return this.throwIfDisposed(),notEqualStrict(this,x)};Tensor.prototype.notEqual=function(b){return this.throwIfDisposed(),notEqual(this,b)};Tensor.prototype.oneHot=function(depth,onValue=1,offValue=0){return this.throwIfDisposed(),oneHot(this,depth,onValue,offValue)};Tensor.prototype.onesLike=function(){return this.throwIfDisposed(),onesLike(this)};Tensor.prototype.pad=function(paddings,constantValue){return this.throwIfDisposed(),pad(this,paddings,constantValue)};Tensor.prototype.pool=function(windowShape,poolingType,padding,dilationRate,strides){return this.throwIfDisposed(),pool(this,windowShape,poolingType,padding,dilationRate,strides)};Tensor.prototype.powStrict=function(exp4){return this.throwIfDisposed(),powStrict(this,exp4)};Tensor.prototype.pow=function(exp4){return this.throwIfDisposed(),pow(this,exp4)};Tensor.prototype.prelu=function(alpha){return this.throwIfDisposed(),prelu(this,alpha)};Tensor.prototype.prod=function(axis,keepDims){return this.throwIfDisposed(),prod(this,axis,keepDims)};Tensor.prototype.reciprocal=function(){return this.throwIfDisposed(),reciprocal(this)};Tensor.prototype.relu=function(){return this.throwIfDisposed(),relu(this)};Tensor.prototype.relu6=function(){return this.throwIfDisposed(),relu6(this)};Tensor.prototype.reshapeAs=function(x){return this.throwIfDisposed(),reshape(this,x.shape)};Tensor.prototype.reshape=function(shape){return this.throwIfDisposed(),reshape(this,shape)};Tensor.prototype.resizeBilinear=function(newShape2D,alignCorners,halfPixelCenters){return this.throwIfDisposed(),resizeBilinear(this,newShape2D,alignCorners,halfPixelCenters)};Tensor.prototype.resizeNearestNeighbor=function(newShape2D,alignCorners,halfFloatCenters){return this.throwIfDisposed(),resizeNearestNeighbor(this,newShape2D,alignCorners,halfFloatCenters)};Tensor.prototype.reverse=function(axis){return this.throwIfDisposed(),reverse(this,axis)};Tensor.prototype.rfft=function(){return this.throwIfDisposed(),rfft(this)};Tensor.prototype.round=function(){return this.throwIfDisposed(),round2(this)};Tensor.prototype.rsqrt=function(){return this.throwIfDisposed(),rsqrt(this)};Tensor.prototype.selu=function(){return this.throwIfDisposed(),selu(this)};Tensor.prototype.separableConv2d=function(depthwiseFilter,pointwiseFilter,strides,pad3,dilation,dataFormat){return this.throwIfDisposed(),separableConv2d(this,depthwiseFilter,pointwiseFilter,strides,pad3,dilation,dataFormat)};Tensor.prototype.sigmoid=function(){return this.throwIfDisposed(),sigmoid(this)};Tensor.prototype.sign=function(){return this.throwIfDisposed(),sign(this)};Tensor.prototype.sin=function(){return this.throwIfDisposed(),sin(this)};Tensor.prototype.sinh=function(){return this.throwIfDisposed(),sinh(this)};Tensor.prototype.slice=function(begin,size){return this.throwIfDisposed(),slice(this,begin,size)};Tensor.prototype.softmax=function(dim){return this.throwIfDisposed(),softmax(this,dim)};Tensor.prototype.softplus=function(){return this.throwIfDisposed(),softplus(this)};Tensor.prototype.spaceToBatchND=function(blockShape,paddings){return this.throwIfDisposed(),spaceToBatchND(this,blockShape,paddings)};Tensor.prototype.split=function(numOrSizeSplits,axis){return this.throwIfDisposed(),split(this,numOrSizeSplits,axis)};Tensor.prototype.sqrt=function(){return this.throwIfDisposed(),sqrt(this)};Tensor.prototype.square=function(){return this.throwIfDisposed(),square(this)};Tensor.prototype.squaredDifference=function(b){return this.throwIfDisposed(),squaredDifference(this,b)};Tensor.prototype.squaredDifferenceStrict=function(x){return this.throwIfDisposed(),squaredDifferenceStrict(this,x)};Tensor.prototype.squeeze=function(axis){return this.throwIfDisposed(),squeeze(this,axis)};Tensor.prototype.stack=function(x,axis){this.throwIfDisposed();let tensorsToBeStacked=x instanceof Tensor?[this,x]:[this,...x];return stack(tensorsToBeStacked,axis)};Tensor.prototype.step=function(alpha){return this.throwIfDisposed(),step(this,alpha)};Tensor.prototype.stridedSlice=function(begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask){return this.throwIfDisposed(),stridedSlice(this,begin,end,strides,beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask)};Tensor.prototype.subStrict=function(x){return this.throwIfDisposed(),subStrict(this,x)};Tensor.prototype.sub=function(b){return this.throwIfDisposed(),sub(this,b)};Tensor.prototype.sum=function(axis,keepDims){return this.throwIfDisposed(),sum2(this,axis,keepDims)};Tensor.prototype.tan=function(){return this.throwIfDisposed(),tan(this)};Tensor.prototype.tanh=function(){return this.throwIfDisposed(),tanh2(this)};Tensor.prototype.tile=function(reps){return this.throwIfDisposed(),tile(this,reps)};Tensor.prototype.toBool=function(){return this.throwIfDisposed(),cast(this,"bool")};Tensor.prototype.toFloat=function(){return this.throwIfDisposed(),cast(this,"float32")};Tensor.prototype.toInt=function(){return this.throwIfDisposed(),cast(this,"int32")};Tensor.prototype.topk=function(k,sorted){return this.throwIfDisposed(),topk(this,k,sorted)};Tensor.prototype.transpose=function(perm){return this.throwIfDisposed(),transpose(this,perm)};Tensor.prototype.unique=function(axis){return this.throwIfDisposed(),unique(this,axis)};Tensor.prototype.unsortedSegmentSum=function(segmentIds,numSegments){return this.throwIfDisposed(),unsortedSegmentSum(this,segmentIds,numSegments)};Tensor.prototype.unstack=function(axis){return this.throwIfDisposed(),unstack(this,axis)};Tensor.prototype.where=function(condition,x){return this.throwIfDisposed(),where(condition,this,x)};Tensor.prototype.zerosLike=function(){return this.throwIfDisposed(),zerosLike(this)};function assertNotComplex(tensor2,opName){Array.isArray(tensor2)||(tensor2=[tensor2]),tensor2.forEach(t=>{t!=null&&util_exports.assert(t.dtype!=="complex64",()=>`${opName} does not support complex64 tensors in the CPU backend.`)})}var whereImpl2=kernel_impls_exports.whereImpl,MathBackendCPU=class extends KernelBackend{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new DataStorage(this,engine())}write(values,shape,dtype){this.firstUse&&(this.firstUse=!1,env().get("IS_NODE")&&backend_util_exports.warn(` ============================ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, which binds to TensorFlow C++, by running npm i @tensorflow/tfjs-node, or npm i @tensorflow/tfjs-node-gpu if you have CUDA. Then call require('@tensorflow/tfjs-node'); (-gpu suffix for CUDA) at the start of your program. Visit https://github.com/tensorflow/tfjs-node for more details. @@ -4040,19 +4040,19 @@ return a / b;`,DIV_PACKED=` } setOutput(${returnValue}); } - `}};function unsortedSegmentSum3(args){let{inputs,backend:backend22,attrs}=args,{x,segmentIds}=inputs,{numSegments}=attrs,xRank=x.shape.length,toDispose=[],axis=0,permutation=backend_util_exports.getAxesPermutation([axis],xRank),permutedX=x;permutation!=null&&(permutedX=transpose3({inputs:{x},backend:backend22,attrs:{perm:permutation}}),toDispose.push(permutedX),axis=backend_util_exports.getInnerMostAxes(1,xRank)[0]);let outShape=backend_util_exports.segment_util.computeOutShape(permutedX.shape,axis,numSegments),inSize=util_exports.sizeFromShape([permutedX.shape[axis]]),a2D=reshape3({inputs:{x:permutedX},backend:backend22,attrs:{shape:[-1,inSize]}});toDispose.push(a2D);let outputDType=sumOutType(x.dtype),segOpCompute=(x2,segOpType,segmentIds2,dtype,numSegments2)=>{let batchSize=x2.shape[0],inSize2=x2.shape[1],windowSize=backend_util_exports.segment_util.segOpComputeOptimalWindowSize(inSize2,numSegments2),segOpInfo={windowSize,inSize:inSize2,batchSize,numSegments:numSegments2},program=new SegmentOpProgram(segOpInfo,segOpType),output=backend22.compileAndRun(program,[x2,segmentIds2],dtype);if(toDispose.push(output),output.shape[1]===numSegments2)return output;let rangeInfo=range3({backend:backend22,attrs:{start:0,stop:numSegments2,step:1,dtype:"float32"}}),tileInfo=tile3({inputs:{x:rangeInfo},backend:backend22,attrs:{reps:[inSize2/windowSize]}});return toDispose.push(rangeInfo),toDispose.push(tileInfo),segOpCompute(output,segOpType,tileInfo,dtype,numSegments2)},segOpResult=segOpCompute(a2D,"unsortedSegmentSum",segmentIds,outputDType,numSegments),reshaped=reshape3({inputs:{x:segOpResult},backend:backend22,attrs:{shape:outShape}}),result=reshaped;if(permutation!=null){toDispose.push(reshaped);let perm=backend_util_exports.getUndoAxesPermutation(permutation);result=transpose3({inputs:{x:result},backend:backend22,attrs:{perm}})}return toDispose.forEach(t=>backend22.disposeIntermediateTensorInfo(t)),result}var unsortedSegmentSumConfig2={kernelName:UnsortedSegmentSum,backendName:"webgl",kernelFunc:unsortedSegmentSum3},kernelConfigs2=[LRNConfig,LRNGradConfig,_fusedMatMulConfig2,absConfig2,acosConfig2,acoshConfig2,addConfig2,addNConfig2,allConfig2,anyConfig2,argMaxConfig2,argMinConfig2,asinConfig2,asinhConfig2,atan2Config2,atanConfig2,atanhConfig2,avgPool3DConfig2,avgPoolConfig2,avgPoolGrad3DConfig,avgPoolGradConfig3,batchMatMulConfig2,batchNormConfig2,batchToSpaceNDConfig2,bincountConfig2,castConfig2,ceilConfig2,clipByValueConfig,complexAbsConfig2,complexConfig2,concatConfig2,conv2DBackpropFilterConfig2,conv2DBackpropInputConfig2,conv2DConfig2,conv3DBackpropFilterV2Config2,conv3DBackpropInputConfig,conv3DConfig2,cosConfig2,coshConfig2,cropAndResizeConfig2,cumsumConfig2,denseBincountConfig2,depthToSpaceConfig2,depthwiseConv2dNativeBackpropFilterConfig2,depthwiseConv2dNativeBackpropInputConfig2,depthwiseConv2dNativeConfig2,diagConfig2,dilation2DConfig,eluConfig2,eluGradConfig3,equalConfig2,erfConfig2,expConfig2,expandDimsConfig2,expm1Config2,fftConfig2,fillConfig2,flipLeftRightConfig2,floorConfig2,floorDivConfig2,fromPixelsConfig,fusedConv2DConfig2,fusedDepthwiseConv2DConfig2,gatherNdConfig2,gatherV2Config2,greaterConfig2,greaterEqualConfig2,identityConfig2,ifftConfig2,imagConfig2,isFiniteConfig2,isInfConfig2,isNaNConfig2,leakyReluConfig2,lessConfig2,lessEqualConfig2,linSpaceConfig2,log1pConfig2,logConfig2,logicalAndConfig2,logicalNotConfig2,logicalOrConfig2,maxConfig2,maxPool3DConfig2,maxPoolConfig2,maxPoolGrad3DConfig,maxPoolGradConfig3,maxPoolWithArgmaxConfig2,maximumConfig2,meanConfig2,minConfig2,minimumConfig2,mirrorPadConfig2,modConfig2,multinomialConfig2,multiplyConfig2,negConfig2,nonMaxSuppressionV3Config2,nonMaxSuppressionV4Config2,nonMaxSuppressionV5Config2,notEqualConfig2,oneHotConfig2,onesLikeConfig2,packConfig2,padV2Config2,powConfig2,preluConfig2,prodConfig2,rangeConfig2,realConfig2,realDivConfig2,reciprocalConfig2,relu6Config2,reluConfig2,reshapeConfig2,resizeBilinearConfig2,resizeBilinearGradConfig3,resizeNearestNeighborConfig2,resizeNearestNeighborGradConfig3,reverseConfig2,rotateWithOffsetConfig2,roundConfig2,rsqrtConfig2,scatterNdConfig2,selectConfig2,seluConfig2,sigmoidConfig2,signConfig2,sinConfig2,sinhConfig2,sliceConfig2,softmaxConfig2,softplusConfig2,spaceToBatchNDConfig2,sparseToDenseConfig2,splitVConfig2,sqrtConfig2,squareConfig2,squaredDifferenceConfig2,stepConfig2,stridedSliceConfig2,subConfig2,sumConfig2,tanConfig2,tanhConfig2,tileConfig2,topKConfig2,transposeConfig2,uniqueConfig2,unpackConfig2,unsortedSegmentSumConfig2,zerosLikeConfig2];for(let kernelConfig of kernelConfigs2)registerKernel(kernelConfig);var CppDType;(function(CppDType2){CppDType2[CppDType2.float32=0]="float32",CppDType2[CppDType2.int32=1]="int32",CppDType2[CppDType2.bool=2]="bool",CppDType2[CppDType2.string=3]="string",CppDType2[CppDType2.complex64=4]="complex64"})(CppDType||(CppDType={}));var FusableActivation;(function(FusableActivation2){FusableActivation2[FusableActivation2.linear=0]="linear",FusableActivation2[FusableActivation2.relu=1]="relu",FusableActivation2[FusableActivation2.relu6=2]="relu6",FusableActivation2[FusableActivation2.prelu=3]="prelu",FusableActivation2[FusableActivation2.leakyrelu=4]="leakyrelu"})(FusableActivation||(FusableActivation={}));var wasmFusedMatMul;function setup(backend22){wasmFusedMatMul=backend22.wasm.cwrap(_FusedMatMul,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function fusedBatchMatMul(args){let{inputs,backend:backend22,attrs}=args,{a,b,bias,preluActivationWeights}=inputs;if(a.dtype!=="float32"||b.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA,transposeB,activation:activation2,leakyreluAlpha}=attrs,aId=backend22.dataIdMap.get(a.dataId).id,bId=backend22.dataIdMap.get(b.dataId).id,biasId=0;if(bias!=null){let biasData=backend22.dataIdMap.get(bias.dataId);if(biasData.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${biasData.shape.length}.`);biasId=biasData.id}let preluActivationWeightsId=preluActivationWeights==null?0:backend22.dataIdMap.get(preluActivationWeights.dataId).id,fusedActivation=FusableActivation[activation2];if(fusedActivation==null)throw new Error(`${activation2} activation not yet supported for FusedConv2D in the wasm backend.`);let leftDim=transposeA?a.shape[2]:a.shape[1],rightDim=transposeB?b.shape[1]:b.shape[2],batchDim=a.shape[0],out=backend22.makeOutput([batchDim,leftDim,rightDim],a.dtype),outId=backend22.dataIdMap.get(out.dataId).id,aShapeBytes=new Uint8Array(new Int32Array(a.shape).buffer),bShapeBytes=new Uint8Array(new Int32Array(b.shape).buffer);return wasmFusedMatMul(aId,aShapeBytes,a.shape.length,bId,bShapeBytes,b.shape.length,transposeA,transposeB,fusedActivation,biasId,preluActivationWeightsId,leakyreluAlpha||0,outId),out}var fusedMatMulConfig={kernelName:_FusedMatMul,backendName:"wasm",setupFunc:setup,kernelFunc:fusedBatchMatMul};function createUnaryKernelConfig(kernelName){let wasmFunc9;function setupFunc3(backend22){wasmFunc9=backend22.wasm.cwrap(kernelName,null,["number","number"])}function kernelFunc3(args){let{backend:backend22,inputs:{x}}=args,xId=backend22.dataIdMap.get(x.dataId).id,out=backend22.makeOutput(x.shape,x.dtype),outId=backend22.dataIdMap.get(out.dataId).id;return util_exports.sizeFromShape(out.shape)===0||wasmFunc9(xId,outId),out}return{kernelName,backendName:"wasm",setupFunc:setupFunc3,kernelFunc:kernelFunc3}}var absConfig3=createUnaryKernelConfig(Abs);function createBinaryKernelConfig(kernelName,supportsFullBroadcast17,dtype){let wasmFunc9;function setupFunc3(backend22){wasmFunc9=backend22.wasm.cwrap(kernelName,null,["number","array","number","number","array","number","number","number"])}function kernelFunc3(args){let{backend:backend22,inputs}=args,{a,b}=inputs,aId=backend22.dataIdMap.get(a.dataId).id,bId=backend22.dataIdMap.get(b.dataId).id,outputType=dtype!=null?dtype:a.dtype,newShape=backend_util_exports.assertAndGetBroadcastShape(a.shape,b.shape),out=backend22.makeOutput(newShape,outputType);if(util_exports.sizeFromShape(newShape)===0)return out;let aShapeBytes=new Uint8Array(new Int32Array(a.shape).buffer),bShapeBytes=new Uint8Array(new Int32Array(b.shape).buffer),outId=backend22.dataIdMap.get(out.dataId).id,kernelFunc4=()=>wasmFunc9(aId,aShapeBytes,a.shape.length,bId,bShapeBytes,b.shape.length,CppDType[a.dtype],outId);if(supportsFullBroadcast17&&a.dtype==="float32")return kernelFunc4(),out;let aBroadcastDims=backend_util_exports.getBroadcastDims(a.shape,newShape),bBroadcastDims=backend_util_exports.getBroadcastDims(b.shape,newShape),loopsOverAllOfA=aBroadcastDims.every((v,i)=>v===i),loopsOverAllOfB=bBroadcastDims.every((v,i)=>v===i);if(loopsOverAllOfA&&loopsOverAllOfB)return kernelFunc4(),out;throw new Error(`Broadcasting along outer dims is not yet supported for ${a.dtype} ${kernelName}.`)}return{kernelName,backendName:"wasm",setupFunc:setupFunc3,kernelFunc:kernelFunc3}}var supportsFullBroadcast=!0,addConfig3=createBinaryKernelConfig(Add,supportsFullBroadcast),wasmFunc;function setupFunc(backend22){wasmFunc=backend22.wasm.cwrap(AddN,null,["array","number","number","number"])}function addn(args){let{inputs,backend:backend22}=args,out=backend22.makeOutput(inputs[0].shape,inputs[0].dtype);if(util_exports.sizeFromShape(out.shape)===0)return out;let inputIds=inputs.map(x=>backend22.dataIdMap.get(x.dataId).id),inputIdsBytes=new Uint8Array(new Int32Array(inputIds).buffer),outId=backend22.dataIdMap.get(out.dataId).id;return wasmFunc(inputIdsBytes,inputIds.length,CppDType[out.dtype],outId),out}var addNConfig3={kernelName:AddN,backendName:"wasm",setupFunc,kernelFunc:addn};function identity3(args){let{inputs:{x},backend:backend22}=args,out=backend22.makeOutput(x.shape,x.dtype),inVals=backend22.typedArrayFromHeap(x);return backend22.typedArrayFromHeap(out).set(inVals),out}var identityConfig3={kernelName:Identity,backendName:"wasm",kernelFunc:identity3},wasmTranspose;function setup2(backend22){wasmTranspose=backend22.wasm.cwrap(Transpose,null,["number","array","number","number","number","array","number"])}function transpose4(args){let{inputs,backend:backend22,attrs}=args,[reducedShape,perm]=removeOneSizeDims(inputs.x.shape,attrs.perm),permIsNoOp=!0;for(let i=0;i=i&&(minValIdx===-1||newPerm[minValIdx]>newPerm[j])&&(minValIdx=j);newPerm[minValIdx]=i}return[newShape,newPerm]}var transposeConfig3={kernelName:Transpose,backendName:"wasm",kernelFunc:transpose4,setupFunc:setup2};function permuteAxesAndTranspose(x,axis,backend22){let xShape=x.shape,xRank=x.shape.length,originalAxes=util_exports.parseAxisParam(axis,xShape),axes=originalAxes,permutedAxes=backend_util_exports.getAxesPermutation(axes,xRank),xTransposed=null,inputWasTransposed=!1;if(permutedAxes!=null){let newShape=new Array(xRank);for(let i=0;i`new shape: ${$shape}, old shape: ${x.shape}. New shape and old shape must have the same number of elements.`),{dataId:x.dataId,shape:$shape,dtype:x.dtype}}var reshapeConfig3={kernelName:Reshape,backendName:"wasm",kernelFunc:reshape4},wasmBatchMatMul;function setup5(backend22){wasmBatchMatMul=backend22.wasm.cwrap(BatchMatMul,null,["number","array","number","number","array","number","number","number","number"])}function batchMatMul3(args){let{inputs,backend:backend22,attrs}=args,{a,b}=inputs,{transposeA,transposeB}=attrs;if(a.dtype!=="float32"||b.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let aRank=a.shape.length,bRank=b.shape.length,innerShapeA=transposeA?a.shape[aRank-2]:a.shape[aRank-1],innerShapeB=transposeB?b.shape[bRank-1]:b.shape[bRank-2],outerShapeA=transposeA?a.shape[aRank-1]:a.shape[aRank-2],outerShapeB=transposeB?b.shape[bRank-2]:b.shape[bRank-1],outerDimsA=a.shape.slice(0,-2),outerDimsB=b.shape.slice(0,-2),batchDimA=util_exports.sizeFromShape(outerDimsA),batchDimB=util_exports.sizeFromShape(outerDimsB),batchDimsCompatible=batchDimA===batchDimB||batchDimA===1||batchDimB===1;util_exports.assert(aRank>=2&&bRank>=2&&batchDimsCompatible,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${outerDimsA}) and (${outerDimsB}).`);let outShape=(batchDimA>batchDimB?a.shape.slice(0,-2):b.shape.slice(0,-2)).concat([outerShapeA,outerShapeB]);util_exports.assert(innerShapeA===innerShapeB,()=>`Error in matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${a.shape} and ${b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);let a3dShape=transposeA?[batchDimA,innerShapeA,outerShapeA]:[batchDimA,outerShapeA,innerShapeA],b3dShape=transposeB?[batchDimB,outerShapeB,innerShapeB]:[batchDimB,innerShapeB,outerShapeB],a3d=reshape4({inputs:{x:a},backend:backend22,attrs:{shape:a3dShape}}),b3d=reshape4({inputs:{x:b},backend:backend22,attrs:{shape:b3dShape}}),a3dId=backend22.dataIdMap.get(a3d.dataId).id,b3dId=backend22.dataIdMap.get(b3d.dataId).id,leftDim=transposeA?a3d.shape[2]:a3d.shape[1],rightDim=transposeB?b3d.shape[1]:b3d.shape[2],batchDim=Math.max(batchDimA,batchDimB),out=backend22.makeOutput([batchDim,leftDim,rightDim],a3d.dtype),outId=backend22.dataIdMap.get(out.dataId).id,aShapeBytes=new Uint8Array(new Int32Array(a3d.shape).buffer),bShapeBytes=new Uint8Array(new Int32Array(b3d.shape).buffer);return wasmBatchMatMul(a3dId,aShapeBytes,a3d.shape.length,b3dId,bShapeBytes,b3d.shape.length,transposeA,transposeB,outId),out.shape=outShape,out}var batchMatMulConfig3={kernelName:BatchMatMul,backendName:"wasm",setupFunc:setup5,kernelFunc:batchMatMul3};function cast4(args){let{inputs:{x},attrs:{dtype},backend:backend22}=args,out=backend22.makeOutput(x.shape,dtype),inVals=backend22.typedArrayFromHeap(x);return backend22.typedArrayFromHeap(out).set(inVals),out}var castConfig3={kernelName:Cast,backendName:"wasm",kernelFunc:cast4},wasmClip;function setup6(backend22){wasmClip=backend22.wasm.cwrap(ClipByValue,null,["number","number","number","number"])}function clip2(args){let{inputs,backend:backend22,attrs}=args,{x}=inputs,{clipValueMin,clipValueMax}=attrs,xId=backend22.dataIdMap.get(x.dataId).id,out=backend22.makeOutput(x.shape,x.dtype),outId=backend22.dataIdMap.get(out.dataId).id;return wasmClip(xId,clipValueMin,clipValueMax,outId),out}var clipByValueConfig2={kernelName:ClipByValue,backendName:"wasm",setupFunc:setup6,kernelFunc:clip2};function concat4(args){let{inputs,backend:backend22}=args,axis=util_exports.parseAxisParam(args.attrs.axis,inputs[0].shape)[0],outShape=backend_util_exports.computeOutShape(inputs.map(t=>t.shape),axis),$inputs=inputs.filter(t=>util_exports.sizeFromShape(t.shape)>0);if($inputs.length===1)return identity3({inputs:{x:$inputs[0]},backend:backend22});let out=backend22.makeOutput(outShape,inputs[0].dtype);if(util_exports.sizeFromShape(outShape)===0)return out;let shapes=$inputs.map(t=>t.shape);if(backend_util_exports.assertParamsConsistent(shapes,axis),$inputs[0].dtype==="string"){let inputs2D=$inputs.map(t=>{let innerSize=util_exports.sizeFromShape(t.shape.slice(axis));return reshape4({inputs:{x:t},backend:backend22,attrs:{shape:[-1,innerSize]}})}),inputsValShapes=inputs2D.map(t=>({vals:backend22.readSync(t.dataId),shape:t.shape}));outShape=backend_util_exports.computeOutShape(inputs2D.map(t=>t.shape),1);let simplyConcat=inputs2D[0].shape[0]===1,outVals2=concatImpl(inputsValShapes,outShape,inputs[0].dtype,simplyConcat),finalOutShape=backend_util_exports.computeOutShape($inputs.map(t=>t.shape),axis);out.shape=finalOutShape;let outData=backend22.dataIdMap.get(out.dataId);return outData.stringBytes=backend_util_exports.fromStringArrayToUint8(outVals2),out}let batchDim=util_exports.sizeFromShape($inputs[0].shape.slice(0,axis)),sumInnerDims=0,innerDims=$inputs.map(input2=>{let innerDim=util_exports.sizeFromShape(input2.shape.slice(axis));return sumInnerDims+=innerDim,innerDim}),inVals=$inputs.map(input2=>backend22.typedArrayFromHeap(input2)),outVals=backend22.typedArrayFromHeap(out);for(let b=0;b`cumsum does not support ${x.dtype} tensors in the WASM backend`);let permutation=backend_util_exports.getAxesPermutation([axis],xRank),permutedX=x;permutation!==null&&(permutedX=transpose4({inputs:{x},attrs:{perm:permutation},backend:backend22}));let permutedAxis=backend_util_exports.getInnerMostAxes(1,xRank)[0];backend_util_exports.assertAxesAreInnerMostDims("cumsum",[permutedAxis],xRank);let permutedOut=backend22.makeOutput(permutedX.shape,permutedX.dtype),finalDim=permutedX.shape[permutedAxis],permutedXId=backend22.dataIdMap.get(permutedX.dataId).id,permutedOutId=backend22.dataIdMap.get(permutedOut.dataId).id;wasmCumsum(permutedXId,exclusive?1:0,reverse5?1:0,finalDim,permutedOutId,CppDType[x.dtype]);let out=permutedOut;if(permutation!==null){let undoPermutation=backend_util_exports.getUndoAxesPermutation(permutation);out=transpose4({inputs:{x:permutedOut},attrs:{perm:undoPermutation},backend:backend22}),backend22.disposeData(permutedX.dataId),backend22.disposeData(permutedOut.dataId)}return out}var cumsumConfig3={kernelName:Cumsum,backendName:"wasm",setupFunc:setup10,kernelFunc:cumsum4},wasmDepthToSpace;function setup11(backend22){wasmDepthToSpace=backend22.wasm.cwrap(DepthToSpace,null,["number","number","number","array","number","array","array","number","number"])}function depthToSpace4(args){let{backend:backend22,inputs,attrs}=args,{x}=inputs,{blockSize,dataFormat}=attrs;util_exports.assert(blockSize>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);let batchSize=x.shape[0],inputHeight=dataFormat==="NHWC"?x.shape[1]:x.shape[2],inputWidth=dataFormat==="NHWC"?x.shape[2]:x.shape[3],inputDepth=dataFormat==="NHWC"?x.shape[3]:x.shape[1],outputHeight=inputHeight*blockSize,outputWidth=inputWidth*blockSize,outputDepth=inputDepth/(blockSize*blockSize),outputShape=dataFormat==="NHWC"?[batchSize,outputHeight,outputWidth,outputDepth]:[batchSize,outputDepth,outputHeight,outputWidth],out=backend22.makeOutput(outputShape,"float32"),xId=backend22.dataIdMap.get(x.dataId).id,xStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(x.shape)).buffer),outputShapeBytes=new Uint8Array(new Int32Array(outputShape).buffer),outStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(outputShape)).buffer),outId=backend22.dataIdMap.get(out.dataId).id;return wasmDepthToSpace(xId,blockSize,dataFormat==="NHWC"?1:0,xStridesBytes,x.shape.length-1,outputShapeBytes,outStridesBytes,outputShape.length,outId),out}var depthToSpaceConfig3={kernelName:DepthToSpace,backendName:"wasm",setupFunc:setup11,kernelFunc:depthToSpace4},wasmDepthwiseConv2d;function setup12(backend22){wasmDepthwiseConv2d=backend22.wasm.cwrap(DepthwiseConv2dNative,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function depthwiseConv2d3(args){let{inputs,attrs,backend:backend22}=args,{x,filter}=inputs,xId=backend22.dataIdMap.get(x.dataId).id,filterId=backend22.dataIdMap.get(filter.dataId).id,{strides,dilations,pad:pad3,dimRoundingMode}=attrs,$dilations=dilations==null?[1,1]:dilations,convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,$dilations,pad3,dimRoundingMode,!0),filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,inputChannels=convInfo.inChannels,outputChannels=convInfo.outChannels,isSamePad=convInfo.padInfo.type==="SAME"?1:0;if(convInfo.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);let out=backend22.makeOutput(convInfo.outShape,"float32"),outId=backend22.dataIdMap.get(out.dataId).id;return wasmDepthwiseConv2d(xId,x.shape[0],x.shape[1],x.shape[2],filterId,filterHeight,filterWidth,padTop,padRight,padBottom,padLeft,isSamePad,dilationHeight,dilationWidth,strideHeight,strideWidth,inputChannels,outputChannels,outId),out}var depthwiseConv2dNativeConfig3={kernelName:DepthwiseConv2dNative,backendName:"wasm",setupFunc:setup12,kernelFunc:depthwiseConv2d3},supportsFullBroadcast2=!1,equalConfig3=createBinaryKernelConfig(Equal,supportsFullBroadcast2,"bool"),expConfig3=createUnaryKernelConfig(Exp);function expandDims4(args){let{inputs,attrs,backend:backend22}=args,{input:input2}=inputs,{dim}=attrs,inputRank=input2.shape.length,newShape=input2.shape.slice(),$dim=dim;return dim<0&&(util_exports.assert(-(inputRank+1)<=dim,()=>`Axis must be in the interval [${-(inputRank+1)}, ${inputRank}]`),$dim=inputRank+dim+1),newShape.splice($dim,0,1),reshape4({inputs:{x:input2},backend:backend22,attrs:{shape:newShape}})}var expandDimsConfig3={kernelName:ExpandDims,backendName:"wasm",kernelFunc:expandDims4};function fill4(args){let{attrs:{shape,value,dtype},backend:backend22}=args,out=backend22.makeOutput(shape,dtype);return backend22.typedArrayFromHeap(out).fill(value),out}var fillConfig3={kernelName:Fill,backendName:"wasm",kernelFunc:fill4},wasmFlipLeftRight;function setup13(backend22){wasmFlipLeftRight=backend22.wasm.cwrap(FlipLeftRight,null,["number","number","number","number","number","number"])}function flipLeftRight2(args){let{inputs,backend:backend22}=args,{image:image3}=inputs,out=backend22.makeOutput(image3.shape,image3.dtype),imageId=backend22.dataIdMap.get(image3.dataId).id,outId=backend22.dataIdMap.get(out.dataId).id,[batch,imageHeight,imageWidth,numChannels]=image3.shape;return wasmFlipLeftRight(imageId,batch,imageHeight,imageWidth,numChannels,outId),out}var flipLeftRightConfig3={kernelName:FlipLeftRight,backendName:"wasm",kernelFunc:flipLeftRight2,setupFunc:setup13},floorConfig3=createUnaryKernelConfig(Floor),supportsFullBroadcast3=!1,floorDivConfig3=createBinaryKernelConfig(FloorDiv,supportsFullBroadcast3),wasmBatchNorm;function setup14(backend22){wasmBatchNorm=backend22.wasm.cwrap(FusedBatchNorm,null,["number","number","number","number","number","number","number"])}function fusedBatchNorm(args){let{backend:backend22,inputs,attrs}=args,{varianceEpsilon}=attrs,{x,mean:mean4,variance,offset,scale:scale2}=inputs,xId=backend22.dataIdMap.get(x.dataId).id,meanId=backend22.dataIdMap.get(mean4.dataId).id,varianceId=backend22.dataIdMap.get(variance.dataId).id,offsetId=offset!=null?backend22.dataIdMap.get(offset.dataId).id:0,scaleId=scale2!=null?backend22.dataIdMap.get(scale2.dataId).id:0,out=backend22.makeOutput(x.shape,x.dtype);if(util_exports.sizeFromShape(x.shape)===0)return out;let outId=backend22.dataIdMap.get(out.dataId).id;return wasmBatchNorm(xId,meanId,varianceId,offsetId,scaleId,varianceEpsilon,outId),out}var fusedBatchNormConfig={kernelName:FusedBatchNorm,backendName:"wasm",setupFunc:setup14,kernelFunc:fusedBatchNorm},wasmFusedConv2d;function setup15(backend22){wasmFusedConv2d=backend22.wasm.cwrap(FusedConv2D,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function fusedConv2d2(args){let{inputs,attrs,backend:backend22}=args,{x,filter,bias,preluActivationWeights}=inputs,{strides,pad:pad3,dilations,dataFormat,dimRoundingMode,activation:activation2,leakyreluAlpha}=attrs,convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,dilations,pad3,dimRoundingMode),fusedActivation=FusableActivation[activation2];if(fusedActivation==null)throw new Error(`${activation2} activation not yet supported for FusedConv2D in the wasm backend.`);let xId=backend22.dataIdMap.get(x.dataId).id,filterId=backend22.dataIdMap.get(filter.dataId).id,outputChannels=convInfo.outChannels,biasId=0;if(bias!=null){let biasData=backend22.dataIdMap.get(bias.dataId);if(biasData.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${biasData.shape.length}.`);if(biasData.shape[0]!==outputChannels)throw new Error(`FusedConv2D bias shape (${biasData.shape}) does not match the number of output channels (${outputChannels})`);biasId=biasData.id}let filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,inputChannels=convInfo.inChannels,isSamePad=convInfo.padInfo.type==="SAME"?1:0,batchSize=convInfo.batchSize,inHeight=convInfo.inHeight,inWidth=convInfo.inWidth;if(dataFormat!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${dataFormat}'. Please use 'NHWC'.`);let out=backend22.makeOutput(convInfo.outShape,"float32"),outId=backend22.dataIdMap.get(out.dataId).id,preluActivationWeightsId=preluActivationWeights==null?0:backend22.dataIdMap.get(preluActivationWeights.dataId).id;return wasmFusedConv2d(xId,batchSize,inHeight,inWidth,filterId,filterHeight,filterWidth,biasId,padTop,padRight,padBottom,padLeft,isSamePad,dilationHeight,dilationWidth,strideHeight,strideWidth,inputChannels,outputChannels,fusedActivation,preluActivationWeightsId,leakyreluAlpha||0,outId),out}var fusedConv2DConfig3={kernelName:FusedConv2D,backendName:"wasm",setupFunc:setup15,kernelFunc:fusedConv2d2},wasmFusedDepthwiseConv2d;function setup16(backend22){wasmFusedDepthwiseConv2d=backend22.wasm.cwrap(FusedDepthwiseConv2D,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function fusedDepthwiseConv2d(args){let{inputs,attrs,backend:backend22}=args,{x,filter,bias,preluActivationWeights}=inputs,{strides,pad:pad3,dilations,dataFormat,dimRoundingMode,activation:activation2,leakyreluAlpha}=attrs,convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,dilations,pad3,dimRoundingMode,!0),fusedActivation=FusableActivation[activation2];if(fusedActivation==null)throw new Error(`${activation2} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let xId=backend22.dataIdMap.get(x.dataId).id,filterId=backend22.dataIdMap.get(filter.dataId).id,outputChannels=convInfo.outChannels,biasId=0;if(bias!=null){let biasData=backend22.dataIdMap.get(bias.dataId);if(biasData.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${biasData.shape.length}.`);if(biasData.shape[0]!==outputChannels)throw new Error(`FusedDepthwiseConv2D bias shape (${biasData.shape}) does not match the number of output channels (${outputChannels})`);biasId=biasData.id}let filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,inputChannels=convInfo.inChannels,isSamePad=convInfo.padInfo.type==="SAME"?1:0,batchSize=convInfo.batchSize,inHeight=convInfo.inHeight,inWidth=convInfo.inWidth;if(dataFormat!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${dataFormat}'. Please use 'NHWC'.`);let out=backend22.makeOutput(convInfo.outShape,"float32"),outId=backend22.dataIdMap.get(out.dataId).id,preluActivationWeightsId=preluActivationWeights==null?0:backend22.dataIdMap.get(preluActivationWeights.dataId).id;return wasmFusedDepthwiseConv2d(xId,batchSize,inHeight,inWidth,filterId,filterHeight,filterWidth,biasId,padTop,padRight,padBottom,padLeft,isSamePad,dilationHeight,dilationWidth,strideHeight,strideWidth,inputChannels,outputChannels,fusedActivation,preluActivationWeightsId,leakyreluAlpha||0,outId),out}var fusedDepthwiseConv2DConfig3={kernelName:FusedDepthwiseConv2D,backendName:"wasm",setupFunc:setup16,kernelFunc:fusedDepthwiseConv2d},wasmGatherNd;function setup17(backend22){wasmGatherNd=backend22.wasm.cwrap(GatherNd,null,["number","number","number","number","number","number","array","number"])}function gatherNd3(args){let{backend:backend22,inputs}=args,{params,indices}=inputs,[resultShape,numSlices,sliceSize,strides]=gather_nd_util_exports.prepareAndValidate(params,indices),out=backend22.makeOutput(resultShape,params.dtype);if(numSlices===0)return out;let indicesShape=indices.shape,sliceRank=indicesShape[indicesShape.length-1],xId=backend22.dataIdMap.get(params.dataId).id,indicesId=backend22.dataIdMap.get(indices.dataId).id,stridesBytes=new Uint8Array(new Int32Array(strides).buffer),outId=backend22.dataIdMap.get(out.dataId).id;return wasmGatherNd(xId,CppDType[params.dtype],indicesId,numSlices,sliceRank,sliceSize,stridesBytes,outId),out}var gatherNdConfig3={kernelName:GatherNd,backendName:"wasm",setupFunc:setup17,kernelFunc:gatherNd3},wasmGather;function setup18(backend22){wasmGather=backend22.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function gatherV23(args){let{backend:backend22,inputs,attrs}=args,{x,indices}=inputs,{axis,batchDims}=attrs,parsedAxis=util_exports.parseAxisParam(axis,x.shape)[0],shapeInfo=backend_util_exports.segment_util.collectGatherOpShapeInfo(x,indices,parsedAxis,batchDims),flattenX=reshape4({inputs:{x},attrs:{shape:[shapeInfo.batchSize,shapeInfo.outerSize,shapeInfo.dimSize,shapeInfo.sliceSize]},backend:backend22}),indicesSize=util_exports.sizeFromShape(indices.shape),flattenIndex=reshape4({inputs:{x:indices},attrs:{shape:[shapeInfo.batchSize,indicesSize/shapeInfo.batchSize]},backend:backend22}),flattenOutputShape=[shapeInfo.batchSize,shapeInfo.outerSize,indicesSize/shapeInfo.batchSize,shapeInfo.sliceSize],out=backend22.makeOutput(flattenOutputShape,x.dtype);if(util_exports.sizeFromShape(x.shape)===0)return out;let stridesSize=flattenX.shape.length-1,xId=backend22.dataIdMap.get(flattenX.dataId).id,indicesId=backend22.dataIdMap.get(flattenIndex.dataId).id,outId=backend22.dataIdMap.get(out.dataId).id,xStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(flattenX.shape)).buffer),outStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(flattenOutputShape)).buffer);return wasmGather(xId,CppDType[x.dtype],xStridesBytes,stridesSize,indicesId,shapeInfo.batchSize,outStridesBytes,outId),out.shape=shapeInfo.outputShape,out}var gatherV2Config3={kernelName:GatherV2,backendName:"wasm",setupFunc:setup18,kernelFunc:gatherV23},supportsFullBroadcast4=!1,greaterConfig3=createBinaryKernelConfig(Greater,supportsFullBroadcast4,"bool"),supportsFullBroadcast5=!1,greaterEqualConfig3=createBinaryKernelConfig(GreaterEqual,supportsFullBroadcast5,"bool"),wasmFunc3;function setupFunc2(backend22){wasmFunc3=backend22.wasm.cwrap(LeakyRelu,null,["number","number","number"])}function leakyRelu4(args){let{inputs:{x},attrs:{alpha},backend:backend22}=args,xId=backend22.dataIdMap.get(x.dataId).id,out=backend22.makeOutput(x.shape,x.dtype);if(util_exports.sizeFromShape(x.shape)!==0){let outId=backend22.dataIdMap.get(out.dataId).id;wasmFunc3(xId,alpha,outId)}return out}var leakyReluConfig3={kernelName:LeakyRelu,backendName:"wasm",setupFunc:setupFunc2,kernelFunc:leakyRelu4},supportsFullBroadcast6=!1,lessConfig3=createBinaryKernelConfig(Less,supportsFullBroadcast6,"bool"),supportsFullBroadcast7=!1,lessEqualConfig3=createBinaryKernelConfig(LessEqual,supportsFullBroadcast7,"bool"),logConfig3=createUnaryKernelConfig(Log),supportsFullBroadcast8=!1,logicalAndConfig3=createBinaryKernelConfig(LogicalAnd,supportsFullBroadcast8,"bool"),wasmMax;function setup19(backend22){wasmMax=backend22.wasm.cwrap(Max,null,["number, number, number"])}function max4(args){let{backend:backend22,inputs,attrs}=args,{reductionIndices:axis,keepDims}=attrs,{x}=inputs,inputId=backend22.dataIdMap.get(x.dataId).id,input2=x,{transposed,axes,originalAxes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend22);if(inputWasTransposed){let transposedId=backend22.dataIdMap.get(transposed.dataId).id;input2=transposed,inputId=transposedId}let inputRank=input2.shape.length;backend_util_exports.assertAxesAreInnerMostDims("max",axes,inputRank);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(input2.shape,axes),reduceSize=util_exports.sizeFromShape(reduceShape),out=backend22.makeOutput(outShape,x.dtype);if(util_exports.sizeFromShape(input2.shape)!==0){let outId=backend22.dataIdMap.get(out.dataId).id;wasmMax(inputId,reduceSize,outId)}if(inputWasTransposed&&backend22.disposeData(transposed.dataId),keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(out.shape,originalAxes);out.shape=newShape}return out}var maxConfig3={kernelName:Max,backendName:"wasm",setupFunc:setup19,kernelFunc:max4},supportsFullBroadcast9=!1,maximumConfig3=createBinaryKernelConfig(Maximum,supportsFullBroadcast9),wasmMaxPool;function setup20(backend22){wasmMaxPool=backend22.wasm.cwrap(MaxPool,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function maxPool4(args){let{inputs,attrs,backend:backend22}=args,x=inputs.x,xId=backend22.dataIdMap.get(x.dataId).id,{filterSize,strides,pad:pad3,dimRoundingMode}=attrs,convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,1,pad3,dimRoundingMode),filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,inputChannels=convInfo.inChannels,outputChannels=convInfo.outChannels;if(convInfo.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);let out=backend22.makeOutput(convInfo.outShape,"float32"),outId=backend22.dataIdMap.get(out.dataId).id;return wasmMaxPool(xId,x.shape[0],x.shape[1],x.shape[2],filterHeight,filterWidth,padTop,padRight,padBottom,padLeft,dilationHeight,dilationWidth,strideHeight,strideWidth,inputChannels,outputChannels,outId),out}var maxPoolConfig3={kernelName:MaxPool,backendName:"wasm",setupFunc:setup20,kernelFunc:maxPool4},wasmMean;function setup21(backend22){wasmMean=backend22.wasm.cwrap(Mean,null,["number, number, number"])}function mean3(args){let{backend:backend22,inputs,attrs}=args,{axis,keepDims}=attrs,{x}=inputs,xId=backend22.dataIdMap.get(x.dataId).id,inputId=xId,input2=x,{transposed,axes,originalAxes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend22),reductionAxes=axes;if(inputWasTransposed){let transposedId=backend22.dataIdMap.get(transposed.dataId).id;transposedId!==xId&&(input2=transposed,inputId=transposedId,reductionAxes=backend_util_exports.getInnerMostAxes(reductionAxes.length,input2.shape.length))}backend_util_exports.assertAxesAreInnerMostDims("mean",reductionAxes,input2.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(input2.shape,reductionAxes),reduceSize=util_exports.sizeFromShape(reduceShape),castedInput=input2;input2.dtype!=="float32"&&(castedInput=cast4({backend:backend22,inputs:{x:input2},attrs:{dtype:"float32"}}),inputId=backend22.dataIdMap.get(castedInput.dataId).id);let out=backend22.makeOutput(outShape,"float32");if(util_exports.sizeFromShape(input2.shape)!==0){let outId=backend22.dataIdMap.get(out.dataId).id;wasmMean(inputId,reduceSize,outId)}if(inputWasTransposed&&backend22.disposeData(transposed.dataId),keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(out.shape,originalAxes);out.shape=newShape}return input2.dtype!=="float32"&&backend22.disposeData(castedInput.dataId),out}var meanConfig3={kernelName:Mean,backendName:"wasm",setupFunc:setup21,kernelFunc:mean3},wasmMin;function setup22(backend22){wasmMin=backend22.wasm.cwrap(Min,null,["number, number, number"])}function min4(args){let{backend:backend22,inputs,attrs}=args,{axis,keepDims}=attrs,{x}=inputs,xId=backend22.dataIdMap.get(x.dataId).id,inputId=xId,input2=x,{transposed,axes,originalAxes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend22);if(inputWasTransposed){let transposedId=backend22.dataIdMap.get(transposed.dataId).id;transposedId!==xId&&(input2=transposed,inputId=transposedId)}let inputRank=input2.shape.length;backend_util_exports.assertAxesAreInnerMostDims("min",axes,inputRank);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(input2.shape,axes),reduceSize=util_exports.sizeFromShape(reduceShape),out=backend22.makeOutput(outShape,input2.dtype);if(util_exports.sizeFromShape(input2.shape)!==0){let outId=backend22.dataIdMap.get(out.dataId).id;wasmMin(inputId,reduceSize,outId)}if(inputWasTransposed&&backend22.disposeData(transposed.dataId),keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(out.shape,originalAxes);out.shape=newShape}return out}var minConfig3={kernelName:Min,backendName:"wasm",setupFunc:setup22,kernelFunc:min4},supportsFullBroadcast10=!1,minimumConfig3=createBinaryKernelConfig(Minimum,supportsFullBroadcast10),supportsFullBroadcast11=!0,multiplyConfig3=createBinaryKernelConfig(Multiply,supportsFullBroadcast11),negConfig3=createUnaryKernelConfig(Neg);function parseResultStruct(backend22,resOffset){let result=new Int32Array(backend22.wasm.HEAPU8.buffer,resOffset,4),pSelectedIndices=result[0],selectedSize=result[1],pSelectedScores=result[2],pValidOutputs=result[3];return backend22.wasm._free(resOffset),{pSelectedIndices,selectedSize,pSelectedScores,pValidOutputs}}var wasmFunc4;function setup23(backend22){wasmFunc4=backend22.wasm.cwrap(NonMaxSuppressionV3,"number",["number","number","number","number","number"])}function kernelFunc(args){let{backend:backend22,inputs,attrs}=args,{iouThreshold,maxOutputSize,scoreThreshold}=attrs,{boxes,scores}=inputs,boxesId=backend22.dataIdMap.get(boxes.dataId).id,scoresId=backend22.dataIdMap.get(scores.dataId).id,resOffset=wasmFunc4(boxesId,scoresId,maxOutputSize,iouThreshold,scoreThreshold),{pSelectedIndices,selectedSize,pSelectedScores,pValidOutputs}=parseResultStruct(backend22,resOffset);return backend22.wasm._free(pSelectedScores),backend22.wasm._free(pValidOutputs),backend22.makeOutput([selectedSize],"int32",pSelectedIndices)}var nonMaxSuppressionV3Config3={kernelName:NonMaxSuppressionV3,backendName:"wasm",setupFunc:setup23,kernelFunc},wasmFunc5;function setup24(backend22){wasmFunc5=backend22.wasm.cwrap(NonMaxSuppressionV4,"number",["number","number","number","number","number","bool"])}function nonMaxSuppressionV43(args){let{backend:backend22,inputs,attrs}=args,{iouThreshold,maxOutputSize,scoreThreshold,padToMaxOutputSize}=attrs,{boxes,scores}=inputs,boxesId=backend22.dataIdMap.get(boxes.dataId).id,scoresId=backend22.dataIdMap.get(scores.dataId).id,resOffset=wasmFunc5(boxesId,scoresId,maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize),{pSelectedIndices,selectedSize,pSelectedScores,pValidOutputs}=parseResultStruct(backend22,resOffset);backend22.wasm._free(pSelectedScores);let selectedIndicesTensor=backend22.makeOutput([selectedSize],"int32",pSelectedIndices),validOutputsTensor=backend22.makeOutput([],"int32",pValidOutputs);return[selectedIndicesTensor,validOutputsTensor]}var nonMaxSuppressionV4Config3={kernelName:NonMaxSuppressionV4,backendName:"wasm",setupFunc:setup24,kernelFunc:nonMaxSuppressionV43},wasmFunc6;function setup25(backend22){wasmFunc6=backend22.wasm.cwrap(NonMaxSuppressionV5,"number",["number","number","number","number","number","number"])}function kernelFunc2(args){let{backend:backend22,inputs,attrs}=args,{iouThreshold,maxOutputSize,scoreThreshold,softNmsSigma}=attrs,{boxes,scores}=inputs,boxesId=backend22.dataIdMap.get(boxes.dataId).id,scoresId=backend22.dataIdMap.get(scores.dataId).id,resOffset=wasmFunc6(boxesId,scoresId,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma),{pSelectedIndices,selectedSize,pSelectedScores,pValidOutputs}=parseResultStruct(backend22,resOffset);backend22.wasm._free(pValidOutputs);let selectedIndicesTensor=backend22.makeOutput([selectedSize],"int32",pSelectedIndices),selectedScoresTensor=backend22.makeOutput([selectedSize],"float32",pSelectedScores);return[selectedIndicesTensor,selectedScoresTensor]}var nonMaxSuppressionV5Config3={kernelName:NonMaxSuppressionV5,backendName:"wasm",setupFunc:setup25,kernelFunc:kernelFunc2},supportsFullBroadcast12=!1,notEqualConfig3=createBinaryKernelConfig(NotEqual,supportsFullBroadcast12,"bool"),wasmOneHot;function setup26(backend22){wasmOneHot=backend22.wasm.cwrap(OneHot,null,["number","number","number","number","number"])}function oneHot4(args){let{inputs,backend:backend22,attrs}=args,{indices}=inputs,{depth,onValue,offValue}=attrs,out=backend22.makeOutput([...indices.shape,depth],"int32"),outId=backend22.dataIdMap.get(out.dataId).id,indicesId=backend22.dataIdMap.get(indices.dataId).id;return wasmOneHot(indicesId,depth,onValue,offValue,outId),out}var oneHotConfig3={kernelName:OneHot,backendName:"wasm",setupFunc:setup26,kernelFunc:oneHot4};function onesLike4(args){let{inputs:{x},backend:backend22}=args,out=backend22.makeOutput(x.shape,x.dtype);return backend22.typedArrayFromHeap(out).fill(1),out}var onesLikeConfig3={kernelName:OnesLike,backendName:"wasm",kernelFunc:onesLike4};function pack3(args){let{inputs,backend:backend22,attrs}=args,{axis}=attrs;if(inputs.length===1)return expandDims4({inputs:{input:inputs[0]},backend:backend22,attrs:{dim:axis}});let shape=inputs[0].shape,dtype=inputs[0].dtype;inputs.forEach(t=>{util_exports.assertShapesMatch(shape,t.shape,"All tensors passed to stack must have matching shapes"),util_exports.assert(dtype===t.dtype,()=>"All tensors passed to stack must have matching dtypes")});let expandedTensors=inputs.map(t=>expandDims4({inputs:{input:t},backend:backend22,attrs:{dim:axis}}));return concat4({inputs:expandedTensors,backend:backend22,attrs:{axis}})}var packConfig3={kernelName:Pack,backendName:"wasm",kernelFunc:pack3},wasmPadV2;function setup27(backend22){wasmPadV2=backend22.wasm.cwrap(PadV2,null,["number","array","number","number","array","array","number","number"])}function pad2(args){let{inputs:{x},backend:backend22,attrs:{paddings,constantValue}}=args,outShape=paddings.map((p2,i)=>p2[0]+x.shape[i]+p2[1]),xId=backend22.dataIdMap.get(x.dataId).id,out=backend22.makeOutput(outShape,x.dtype),outId=backend22.dataIdMap.get(out.dataId).id,xShapeBytes=new Uint8Array(new Int32Array(x.shape).buffer),prePaddingsFlat=paddings.map(padTuple=>padTuple[0]),postPaddingsFlat=paddings.map(padTuple=>padTuple[1]),prePaddingsBytes=new Uint8Array(new Int32Array(prePaddingsFlat).buffer),postPaddingsBytes=new Uint8Array(new Int32Array(postPaddingsFlat).buffer);return wasmPadV2(xId,xShapeBytes,x.shape.length,CppDType[x.dtype],prePaddingsBytes,postPaddingsBytes,constantValue,outId),out}var padV2Config3={kernelName:PadV2,backendName:"wasm",kernelFunc:pad2,setupFunc:setup27},supportsFullBroadcast13=!1,powConfig3=createBinaryKernelConfig(Pow,supportsFullBroadcast13),wasmPrelu;function setup28(backend22){wasmPrelu=backend22.wasm.cwrap(Prelu,null,["number","number","number"])}function prelu4(args){let{inputs,backend:backend22}=args,{x,alpha}=inputs,xId=backend22.dataIdMap.get(x.dataId).id,weightsId=backend22.dataIdMap.get(alpha.dataId).id,out=backend22.makeOutput(x.shape,"float32"),outId=backend22.dataIdMap.get(out.dataId).id;return wasmPrelu(xId,weightsId,outId),out}var preluConfig3={kernelName:Prelu,backendName:"wasm",setupFunc:setup28,kernelFunc:prelu4},wasmProd;function setup29(backend22){wasmProd=backend22.wasm.cwrap(Prod,null,["number","number","number","number"])}function prod4(args){let{backend:backend22,inputs,attrs}=args,{axis,keepDims}=attrs,{x}=inputs,xId=backend22.dataIdMap.get(x.dataId).id,inputId=xId,input2=x,{transposed,axes,originalAxes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend22),reductionAxes=axes;if(inputWasTransposed){let transposedId=backend22.dataIdMap.get(transposed.dataId).id;transposedId!==xId&&(input2=transposed,inputId=transposedId,reductionAxes=backend_util_exports.getInnerMostAxes(reductionAxes.length,input2.shape.length))}backend_util_exports.assertAxesAreInnerMostDims("prod",reductionAxes,input2.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(input2.shape,reductionAxes),reduceSize=util_exports.sizeFromShape(reduceShape),out=backend22.makeOutput(outShape,input2.dtype);if(util_exports.sizeFromShape(input2.shape)!==0){let outId=backend22.dataIdMap.get(out.dataId).id;wasmProd(inputId,reduceSize,CppDType[out.dtype],outId)}if(inputWasTransposed&&backend22.disposeData(transposed.dataId),keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(out.shape,originalAxes);out.shape=newShape}return out}var prodConfig3={kernelName:Prod,backendName:"wasm",setupFunc:setup29,kernelFunc:prod4},range4=args=>{let{backend:backend22,attrs}=args,{start,stop,step:step4,dtype}=attrs,values=rangeImpl(start,stop,step4,dtype),out=backend22.makeOutput([values.length],dtype);return backend22.typedArrayFromHeap(out).set(values),out},rangeConfig3={kernelName:Range,backendName:"wasm",kernelFunc:range4},supportsFullBroadcast14=!0,realDivConfig3=createBinaryKernelConfig(RealDiv,supportsFullBroadcast14),reluConfig3=createUnaryKernelConfig(Relu),relu6Config3=createUnaryKernelConfig(Relu6),wasmResizeBilinear;function setup30(backend22){wasmResizeBilinear=backend22.wasm.cwrap(ResizeBilinear,null,["number","number","number","number","number","number","number","number","number","number"])}function resizeBilinear4(args){let{backend:backend22,inputs,attrs}=args,{images}=inputs,{alignCorners,halfPixelCenters,size}=attrs,[newHeight,newWidth]=size,[batch,oldHeight,oldWidth,numChannels]=images.shape,outShape=[batch,newHeight,newWidth,numChannels],xData=backend22.dataIdMap.get(images.dataId),castedData;xData.dtype!=="float32"&&(castedData=cast4({backend:backend22,inputs:{x:images},attrs:{dtype:"float32"}}),xData=backend22.dataIdMap.get(castedData.dataId));let xId=xData.id,out=backend22.makeOutput(outShape,"float32");if(util_exports.sizeFromShape(images.shape)===0)return out;let outId=backend22.dataIdMap.get(out.dataId).id;return wasmResizeBilinear(xId,batch,oldHeight,oldWidth,numChannels,newHeight,newWidth,alignCorners?1:0,halfPixelCenters?1:0,outId),castedData!=null&&backend22.disposeData(castedData.dataId),out}var resizeBilinearConfig3={kernelName:ResizeBilinear,backendName:"wasm",setupFunc:setup30,kernelFunc:resizeBilinear4},wasmReverse;function setup31(backend22){wasmReverse=backend22.wasm.cwrap(Reverse,null,["number","array","number","array","number","number"])}function reverse4(args){let{inputs,backend:backend22,attrs}=args,{x}=inputs,{dims}=attrs,axes=util_exports.parseAxisParam(dims,x.shape);if(x.shape.length===0)return identity3({inputs:{x},backend:backend22});let out=backend22.makeOutput(x.shape,x.dtype),xId=backend22.dataIdMap.get(x.dataId).id,outId=backend22.dataIdMap.get(out.dataId).id,axesBytes=new Uint8Array(new Int32Array(axes).buffer),outShapeBytes=new Uint8Array(new Int32Array(x.shape).buffer);return wasmReverse(xId,axesBytes,axes.length,outShapeBytes,x.shape.length,outId),reshape4({inputs:{x:out},attrs:{shape:x.shape},backend:backend22})}var reverseConfig3={kernelName:Reverse,backendName:"wasm",kernelFunc:reverse4,setupFunc:setup31},wasmRotate;function setup32(backend22){wasmRotate=backend22.wasm.cwrap(RotateWithOffset,null,["number","number","number","number","number","number","number","number","array","number","number"])}function rotateWithOffset2(args){let{inputs,backend:backend22,attrs}=args,{image:image3}=inputs,{radians,fillValue,center}=attrs,out=backend22.makeOutput(image3.shape,image3.dtype),imageId=backend22.dataIdMap.get(image3.dataId).id,outId=backend22.dataIdMap.get(out.dataId).id,[batch,imageHeight,imageWidth,numChannels]=image3.shape,[centerX,centerY]=backend_util_exports.getImageCenter(center,imageHeight,imageWidth),fillIsBlack=fillValue===0,fullOpacityValue=255,fillValues2=typeof fillValue=="number"?[fillValue,fillValue,fillValue,fillIsBlack?0:fullOpacityValue]:[...fillValue,fullOpacityValue],fillBytes=new Uint8Array(new Int32Array(fillValues2).buffer);return wasmRotate(imageId,batch,imageHeight,imageWidth,numChannels,radians,centerX,centerY,fillBytes,fillValues2.length,outId),out}var rotateWithOffsetConfig3={kernelName:RotateWithOffset,backendName:"wasm",kernelFunc:rotateWithOffset2,setupFunc:setup32},roundConfig3=createUnaryKernelConfig(Round),rsqrtConfig3=createUnaryKernelConfig(Rsqrt),wasmScatterNd;function setup33(backend22){wasmScatterNd=backend22.wasm.cwrap(ScatterNd,null,["number","number","number","number","number","number","array","number","number"])}function scatterNd3(args){let{backend:backend22,inputs,attrs}=args,{indices,updates}=inputs,{shape}=attrs,out=backend22.makeOutput(shape,updates.dtype);if(util_exports.sizeFromShape(shape)===0)return out;let{sliceRank,numUpdates,sliceSize,strides,outputSize}=scatter_nd_util_exports.calculateShapes(updates,indices,shape),indicesId=backend22.dataIdMap.get(indices.dataId).id,updatesId=backend22.dataIdMap.get(updates.dataId).id,stridesBytes=new Uint8Array(new Int32Array(strides).buffer),outId=backend22.dataIdMap.get(out.dataId).id;return wasmScatterNd(indicesId,updatesId,CppDType[updates.dtype],sliceRank,numUpdates,sliceSize,stridesBytes,outputSize,outId),out}var scatterNdConfig3={kernelName:ScatterNd,backendName:"wasm",setupFunc:setup33,kernelFunc:scatterNd3},wasmSelect;function setup34(backend22){wasmSelect=backend22.wasm.cwrap("SelectV2",null,["number","number","number","number","number"])}function select3(args){let{inputs,backend:backend22}=args,{condition,t,e}=inputs,conditionId=backend22.dataIdMap.get(condition.dataId).id,tId=backend22.dataIdMap.get(t.dataId).id,eId=backend22.dataIdMap.get(e.dataId).id,out=backend22.makeOutput(t.shape,t.dtype),outId=backend22.dataIdMap.get(out.dataId).id,cRank=condition.shape.length,tRank=t.shape.length,offset=cRank===0||cRank>1||tRank===1?1:util_exports.sizeFromShape(t.shape.slice(1));return wasmSelect(conditionId,tId,eId,offset,outId),out}var selectConfig3={kernelName:Select,backendName:"wasm",kernelFunc:select3,setupFunc:setup34},wasmFunc7;function setup35(backend22){wasmFunc7=backend22.wasm.cwrap(Sigmoid,null,["number","number"])}function sigmoid4(args){let{backend:backend22,inputs:{x}}=args,xId=backend22.dataIdMap.get(x.dataId).id,out=backend22.makeOutput(x.shape,x.dtype),outId=backend22.dataIdMap.get(out.dataId).id;return util_exports.sizeFromShape(out.shape)===0||wasmFunc7(xId,outId),out}var sigmoidConfig3={kernelName:"Sigmoid",backendName:"wasm",setupFunc:setup35,kernelFunc:sigmoid4},sinConfig3=createUnaryKernelConfig(Sin);function slice4(args){let{inputs:{x},attrs:{begin,size},backend:backend22}=args,[begin_,size_]=slice_util_exports.parseSliceParams(x,begin,size),isContinous=slice_util_exports.isSliceContinous(x.shape,begin_,size_),xVals=backend22.readSync(x.dataId),out=backend22.makeOutput(size_,x.dtype),xStrides=util_exports.computeStrides(x.shape),outData=backend22.dataIdMap.get(out.dataId);if(isContinous){let flatOffset=slice_util_exports.computeFlatOffset(begin_,xStrides);return x.dtype==="string"?outData.stringBytes=xVals.slice(flatOffset,flatOffset+util_exports.sizeFromShape(size_)):backend22.typedArrayFromHeap(out).set(xVals.subarray(flatOffset,flatOffset+util_exports.sizeFromShape(size_))),out}if(x.dtype==="string"){let res=sliceImpl(xVals,begin_,size_,x.shape,x.dtype);return outData.stringBytes=res,out}let outVals=backend22.typedArrayFromHeap(out),rank=x.shape.length;if(rank===2)slice2d2(xVals,xStrides[0],outVals,begin_,size_);else if(rank===3)slice3d2(xVals,xStrides[0],xStrides[1],outVals,begin_,size_);else if(rank===4)slice4d2(xVals,xStrides[0],xStrides[1],xStrides[2],outVals,begin_,size_);else{let res=sliceImpl(xVals,begin_,size_,x.shape,x.dtype);outVals.set(res)}return out}function slice2d2(xVals,xStride,outVals,begin,size){let outOffset=0,beginI=begin[0],beginJ=begin[1],endI=beginI+size[0];for(let i=beginI;i{let xSliceSize=[...size];xSliceSize[$axis]=s;let xSlice=slice4({inputs:{x},attrs:{begin,size:xSliceSize},backend:backend22});return begin[$axis]+=s,xSlice})}var splitVConfig3={kernelName:SplitV,backendName:"wasm",kernelFunc:splitV3},sqrtConfig3=createUnaryKernelConfig(Sqrt),squareConfig3=createUnaryKernelConfig(Square),supportsFullBroadcast15=!0,squaredDifferenceConfig3=createBinaryKernelConfig(SquaredDifference,supportsFullBroadcast15),wasmStridedSlice;function setup37(backend22){wasmStridedSlice=backend22.wasm.cwrap(StridedSlice,null,["number","array","number","array","array","array","array","array","number","number"])}function stridedSlice4(args){let{backend:backend22,inputs,attrs}=args,{x}=inputs,{begin,end,strides}=attrs;strides==null&&(strides=new Array(begin.length));let{beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask}=attrs,ellipsisAxes=backend_util_exports.slice_util.maskToAxes(ellipsisMask);if(ellipsisAxes.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(ellipsisMask!==0&&newAxisMask!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(ellipsisMask!==0&&shrinkAxisMask!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");let numInterpolatedAxes=x.shape.length-begin.length,expandAxes=backend_util_exports.slice_util.maskToAxes(newAxisMask),newShape=x.shape.slice();expandAxes.forEach(axis=>{begin[axis]=0,end[axis]=1,newShape.splice(axis,0,1)});let xReshaped=reshape4({inputs:{x},attrs:{shape:newShape},backend:backend22}),{begin:normalizedBegin,end:normalizedEnd,strides:normalizedStrides}=backend_util_exports.slice_util.getNormalizedAxes(xReshaped.shape,ellipsisAxes,numInterpolatedAxes,begin,end,strides,beginMask,endMask,ellipsisMask);begin=normalizedBegin,end=normalizedEnd,strides=normalizedStrides;let shrinkAxes=backend_util_exports.slice_util.maskToAxes(shrinkAxisMask);shrinkAxes.forEach(axis=>{end[axis]=begin[axis]+1,strides[axis]=1});let size=backend_util_exports.slice_util.computeOutShape(begin,end,strides),outShape=size.filter((_,axis)=>shrinkAxes.indexOf(axis)===-1);if(strides.every(v=>v===1)){let xSliced=slice4({inputs:{x},attrs:{begin,size},backend:backend22});return reshape4({inputs:{x:xSliced},attrs:{shape:outShape},backend:backend22})}let out=backend22.makeOutput(outShape,"float32");if(!outShape.some(axis=>axis===0)){let xId=backend22.dataIdMap.get(xReshaped.dataId).id,xStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(xReshaped.shape)).buffer),beginBytes=new Uint8Array(new Int32Array(begin).buffer),endBytes=new Uint8Array(new Int32Array(end).buffer),stridesBytes=new Uint8Array(new Int32Array(strides).buffer),outputShapeBytes=new Uint8Array(new Int32Array(outShape).buffer),outStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(outShape)).buffer),outId=backend22.dataIdMap.get(out.dataId).id;wasmStridedSlice(xId,xStridesBytes,xReshaped.shape.length,beginBytes,endBytes,stridesBytes,outputShapeBytes,outStridesBytes,outShape.length,outId)}return reshape4({inputs:{x:out},attrs:{shape:outShape},backend:backend22})}var stridedSliceConfig3={kernelName:StridedSlice,backendName:"wasm",setupFunc:setup37,kernelFunc:stridedSlice4},supportsFullBroadcast16=!0,subConfig3=createBinaryKernelConfig(Sub,supportsFullBroadcast16),wasmSum;function setup38(backend22){wasmSum=backend22.wasm.cwrap(Sum,null,["number, number, number"])}function sum5(args){let{backend:backend22,inputs,attrs}=args,{axis,keepDims}=attrs,{x}=inputs,xId=backend22.dataIdMap.get(x.dataId).id,inputId=xId,input2=x,{transposed,axes,originalAxes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend22),reductionAxes=axes;if(inputWasTransposed){let transposedId=backend22.dataIdMap.get(transposed.dataId).id;transposedId!==xId&&(input2=transposed,inputId=transposedId,reductionAxes=backend_util_exports.getInnerMostAxes(reductionAxes.length,input2.shape.length))}backend_util_exports.assertAxesAreInnerMostDims("sum",reductionAxes,input2.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(input2.shape,reductionAxes),reduceSize=util_exports.sizeFromShape(reduceShape),out=backend22.makeOutput(outShape,input2.dtype);if(util_exports.sizeFromShape(input2.shape)!==0){let outId=backend22.dataIdMap.get(out.dataId).id;wasmSum(inputId,reduceSize,outId)}if(inputWasTransposed&&backend22.disposeData(transposed.dataId),keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(out.shape,originalAxes);out.shape=newShape}return out}var sumConfig3={kernelName:Sum,backendName:"wasm",setupFunc:setup38,kernelFunc:sum5},tanhConfig3=createUnaryKernelConfig(Tanh),wasmTile;function setup39(backend22){wasmTile=backend22.wasm.cwrap(Tile,null,["number","array","number","array","number","number"])}function tile4(args){let{inputs,backend:backend22,attrs}=args,{x}=inputs,xId=backend22.dataIdMap.get(x.dataId).id,{reps}=attrs,newShape=new Array(x.shape.length);for(let i=0;i{let{x}=inputs,{k,sorted}=attrs,xId=backend22.dataIdMap.get(x.dataId).id,xShapeBytes=new Uint8Array(new Int32Array(x.shape).buffer),outputShape=x.shape.slice();outputShape[outputShape.length-1]=k;let outValues=backend22.makeOutput(outputShape,x.dtype),outValuesId=backend22.dataIdMap.get(outValues.dataId).id,outIndices=backend22.makeOutput(outputShape,"int32"),outIndicesId=backend22.dataIdMap.get(outIndices.dataId).id;return wasmTopK(xId,xShapeBytes,x.shape.length,CppDType[x.dtype],k,sorted,outValuesId,outIndicesId),[outValues,outIndices]},topKConfig3={kernelName:TopK,backendName:"wasm",setupFunc:setup40,kernelFunc:topk2};function unpack3(args){let{inputs,backend:backend22,attrs}=args,{value}=inputs,{axis}=attrs;axis<0&&(axis+=value.shape.length);let numOutputs=value.shape[axis],rank=value.shape.length,outShape=new Array(rank-1),outIndex=0;for(let i=0;i({dataId,dtype,shape:outShape}))}var unpackConfig3={kernelName:Unpack,backendName:"wasm",kernelFunc:unpack3};function zerosLike4(args){let{inputs:{x},backend:backend22}=args,out=backend22.makeOutput(x.shape,x.dtype);return backend22.typedArrayFromHeap(out).fill(0),out}var zerosLikeConfig3={kernelName:ZerosLike,backendName:"wasm",kernelFunc:zerosLike4},kernelConfigs3=[absConfig3,addConfig3,addNConfig3,argMaxConfig3,avgPoolConfig3,batchMatMulConfig3,castConfig3,clipByValueConfig2,concatConfig3,conv2DConfig3,conv2DBackpropInputConfig3,cosConfig3,cropAndResizeConfig3,cumsumConfig3,depthToSpaceConfig3,depthwiseConv2dNativeConfig3,equalConfig3,expConfig3,expandDimsConfig3,fillConfig3,flipLeftRightConfig3,floorConfig3,floorDivConfig3,fusedMatMulConfig,fusedBatchNormConfig,fusedConv2DConfig3,fusedDepthwiseConv2DConfig3,gatherNdConfig3,gatherV2Config3,greaterConfig3,greaterEqualConfig3,identityConfig3,leakyReluConfig3,lessConfig3,lessEqualConfig3,logConfig3,logicalAndConfig3,maxConfig3,maximumConfig3,maxPoolConfig3,meanConfig3,minConfig3,minimumConfig3,multiplyConfig3,negConfig3,nonMaxSuppressionV3Config3,nonMaxSuppressionV4Config3,nonMaxSuppressionV5Config3,notEqualConfig3,oneHotConfig3,onesLikeConfig3,packConfig3,padV2Config3,powConfig3,preluConfig3,prodConfig3,rangeConfig3,realDivConfig3,reluConfig3,relu6Config3,reshapeConfig3,resizeBilinearConfig3,reverseConfig3,rotateWithOffsetConfig3,rsqrtConfig3,roundConfig3,scatterNdConfig3,selectConfig3,sigmoidConfig3,sinConfig3,sliceConfig3,softmaxConfig3,splitVConfig3,sqrtConfig3,squareConfig3,squaredDifferenceConfig3,stridedSliceConfig3,subConfig3,sumConfig3,tanhConfig3,tileConfig3,topKConfig3,transposeConfig3,unpackConfig3,zerosLikeConfig3];for(let kernelConfig of kernelConfigs3)registerKernel(kernelConfig);var ENV4=env();ENV4.registerFlag("WASM_HAS_SIMD_SUPPORT",async()=>WebAssembly.validate(new Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,2,1,0,10,9,1,7,0,65,0,253,15,26,11])));ENV4.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT",async()=>{if(ENV4.get("IS_NODE"))return!1;try{return new MessageChannel().port1.postMessage(new SharedArrayBuffer(1)),WebAssembly.validate(new Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,2,1,0,5,4,1,3,1,1,10,11,1,9,0,65,0,254,16,2,0,26,11]))}catch(e){return!1}});var import_tfjs_backend_wasm_threaded_simd=__toModule2(require_tfjs_backend_wasm_threaded_simd()),wasmWorkerContents='var threadInfoStruct=0;var selfThreadId=0;var parentThreadId=0;var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(" ");console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(" ");postMessage({cmd:"alert",text:text,threadId:selfThreadId})}var err=threadPrintErr;this.alert=threadAlert;Module["instantiateWasm"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module["wasmModule"],info);Module["wasmModule"]=null;receiveInstance(instance);return instance.exports};this.onmessage=function(e){try{if(e.data.cmd==="load"){Module["DYNAMIC_BASE"]=e.data.DYNAMIC_BASE;Module["DYNAMICTOP_PTR"]=e.data.DYNAMICTOP_PTR;Module["wasmModule"]=e.data.wasmModule;Module["wasmMemory"]=e.data.wasmMemory;Module["buffer"]=Module["wasmMemory"].buffer;Module["ENVIRONMENT_IS_PTHREAD"]=true;if(typeof e.data.urlOrBlob==="string"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}Module=WasmBackendModuleThreadedSimd(Module);postMessage({"cmd":"loaded"})}else if(e.data.cmd==="objectTransfer"){Module["PThread"].receiveObjectTransfer(e.data)}else if(e.data.cmd==="run"){Module["__performance_now_clock_drift"]=performance.now()-e.data.time;threadInfoStruct=e.data.threadInfoStruct;Module["__register_pthread_ptr"](threadInfoStruct,0,0);selfThreadId=e.data.selfThreadId;parentThreadId=e.data.parentThreadId;var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module["establishStackSpace"](top,max);Module["_emscripten_tls_init"]();Module["PThread"].receiveObjectTransfer(e.data);Module["PThread"].setThreadStatus(Module["_pthread_self"](),1);try{var result=Module["dynCall_ii"](e.data.start_routine,e.data.arg);if(!Module["getNoExitRuntime"]())Module["PThread"].threadExit(result)}catch(ex){if(ex==="Canceled!"){Module["PThread"].threadCancel()}else if(ex!="unwind"){Atomics.store(Module["HEAPU32"],threadInfoStruct+4>>2,ex instanceof Module["ExitStatus"]?ex.status:-2);Atomics.store(Module["HEAPU32"],threadInfoStruct+0>>2,1);Module["_emscripten_futex_wake"](threadInfoStruct+0,2147483647);if(!(ex instanceof Module["ExitStatus"]))throw ex}}}else if(e.data.cmd==="cancel"){if(threadInfoStruct){Module["PThread"].threadCancel()}}else if(e.data.target==="setimmediate"){}else if(e.data.cmd==="processThreadQueue"){if(threadInfoStruct){Module["_emscripten_current_thread_process_queued_calls"]()}}else{err("worker.js received unknown command "+e.data.cmd);err(e.data)}}catch(ex){err("worker.js onmessage() captured an uncaught exception: "+ex);if(ex.stack)err(ex.stack);throw ex}};if(typeof process==="object"&&typeof process.versions==="object"&&typeof process.versions.node==="string"){self={location:{href:__filename}};var onmessage=this.onmessage;var nodeWorkerThreads=require("worker_threads");Worker=nodeWorkerThreads.Worker;var parentPort=nodeWorkerThreads.parentPort;parentPort.on("message",function(data){onmessage({data:data})});var nodeFS=require("fs");var nodeRead=function(filename){return nodeFS.readFileSync(filename,"utf8")};function globalEval(x){global.require=require;global.Module=Module;eval.call(null,x)}importScripts=function(f){globalEval(nodeRead(f))};postMessage=function(msg){parentPort.postMessage(msg)};if(typeof performance==="undefined"){performance={now:function(){return Date.now()}}}}',import_tfjs_backend_wasm=__toModule2(require_tfjs_backend_wasm()),BackendWasm=class extends KernelBackend{constructor(wasm){super();this.wasm=wasm,this.dataIdNextNumber=1,this.wasm.tfjs.init(),this.dataIdMap=new DataStorage(this,engine())}write(values,shape,dtype){let dataId={};return this.move(dataId,values,shape,dtype),dataId}numDataIds(){return this.dataIdMap.numDataIds()}async time(f){let start=util_exports.now();return f(),{kernelMs:util_exports.now()-start}}move(dataId,values,shape,dtype){let id=this.dataIdNextNumber++;if(dtype==="string"){let stringBytes=values;this.dataIdMap.set(dataId,{id,stringBytes,shape,dtype,memoryOffset:null});return}let size=util_exports.sizeFromShape(shape),numBytes=size*util_exports.bytesPerElement(dtype),memoryOffset=this.wasm._malloc(numBytes);this.dataIdMap.set(dataId,{id,memoryOffset,shape,dtype}),this.wasm.tfjs.registerTensor(id,size,memoryOffset),values!=null&&this.wasm.HEAPU8.set(new Uint8Array(values.buffer,values.byteOffset,numBytes),memoryOffset)}async read(dataId){return this.readSync(dataId)}readSync(dataId){let{memoryOffset,dtype,shape,stringBytes}=this.dataIdMap.get(dataId);if(dtype==="string")return stringBytes;let bytes=this.wasm.HEAPU8.slice(memoryOffset,memoryOffset+util_exports.sizeFromShape(shape)*util_exports.bytesPerElement(dtype));return typedArrayFromBuffer(bytes.buffer,dtype)}disposeData(dataId){let data2=this.dataIdMap.get(dataId);this.wasm._free(data2.memoryOffset),this.wasm.tfjs.disposeData(data2.id),this.dataIdMap.delete(dataId)}floatPrecision(){return 32}getMemoryOffset(dataId){return this.dataIdMap.get(dataId).memoryOffset}dispose(){this.wasm.tfjs.dispose(),this.wasm=null}memory(){return{unreliable:!1}}makeOutput(shape,dtype,memoryOffset){let dataId;if(memoryOffset==null)dataId=this.write(null,shape,dtype);else{dataId={};let id=this.dataIdNextNumber++;this.dataIdMap.set(dataId,{id,memoryOffset,shape,dtype});let size=util_exports.sizeFromShape(shape);this.wasm.tfjs.registerTensor(id,size,memoryOffset)}return{dataId,shape,dtype}}typedArrayFromHeap({shape,dtype,dataId}){let buffer2=this.wasm.HEAPU8.buffer,{memoryOffset}=this.dataIdMap.get(dataId),size=util_exports.sizeFromShape(shape);switch(dtype){case"float32":return new Float32Array(buffer2,memoryOffset,size);case"int32":return new Int32Array(buffer2,memoryOffset,size);case"bool":return new Uint8Array(buffer2,memoryOffset,size);default:throw new Error(`Unknown dtype ${dtype}`)}}};function createInstantiateWasmFunc(path){return(imports,callback)=>(util_exports.fetch(path,{credentials:"same-origin"}).then(response=>{response.ok||imports.env.a(`failed to load wasm binary file at '${path}'`),response.arrayBuffer().then(binary=>{WebAssembly.instantiate(binary,imports).then(output=>{callback(output.instance)})})}),{})}function getPathToWasmBinary(simdSupported,threadsSupported,wasmModuleFolder){if(wasmPath!=null)return wasmPath;let path="tfjs-backend-wasm.wasm";return simdSupported&&threadsSupported?path="tfjs-backend-wasm-threaded-simd.wasm":simdSupported&&(path="tfjs-backend-wasm-simd.wasm"),wasmFileMap!=null&&wasmFileMap[path]!=null?wasmFileMap[path]:wasmModuleFolder+path}async function init(){let[simdSupported,threadsSupported]=await Promise.all([env().getAsync("WASM_HAS_SIMD_SUPPORT"),env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]);return new Promise((resolve,reject)=>{let factoryConfig={};factoryConfig.locateFile=(path,prefix)=>{if(path.endsWith(".worker.js")){let response=wasmWorkerContents,blob=new Blob([response],{type:"application/javascript"});return URL.createObjectURL(blob)}return path.endsWith(".wasm")?getPathToWasmBinary(simdSupported,threadsSupported,wasmPathPrefix!=null?wasmPathPrefix:prefix):prefix+path},customFetch&&(factoryConfig.instantiateWasm=createInstantiateWasmFunc(getPathToWasmBinary(simdSupported,threadsSupported,wasmPathPrefix!=null?wasmPathPrefix:"")));let wasm;threadsSupported&&simdSupported&&wasmPath==null?(wasm=import_tfjs_backend_wasm_threaded_simd.default(factoryConfig),wasm.mainScriptUrlOrBlob=new Blob(["var WasmBackendModuleThreadedSimd = "+import_tfjs_backend_wasm_threaded_simd.default.toString()],{type:"text/javascript"})):wasm=import_tfjs_backend_wasm.default(factoryConfig);let voidReturnType=null;wasm.tfjs={init:wasm.cwrap("init",null,[]),registerTensor:wasm.cwrap("register_tensor",null,["number","number","number"]),disposeData:wasm.cwrap("dispose_data",voidReturnType,["number"]),dispose:wasm.cwrap("dispose",voidReturnType,[])};let initialized=!1;wasm.onRuntimeInitialized=()=>{initialized=!0,initAborted=!1,resolve({wasm})},wasm.onAbort=()=>{initialized||initAborted||(initAborted=!0,reject({message:"Make sure the server can serve the `.wasm` file relative to the bundled js file. For more details see https://github.com/tensorflow/tfjs/blob/master/tfjs-backend-wasm/README.md#using-bundlers"}))}})}function typedArrayFromBuffer(buffer2,dtype){switch(dtype){case"float32":return new Float32Array(buffer2);case"int32":return new Int32Array(buffer2);case"bool":return new Uint8Array(buffer2);default:throw new Error(`Unknown dtype ${dtype}`)}}var wasmBinaryNames=["tfjs-backend-wasm.wasm","tfjs-backend-wasm-simd.wasm","tfjs-backend-wasm-threaded-simd.wasm"],wasmPath=null,wasmPathPrefix=null,wasmFileMap={},initAborted=!1,customFetch=!1;function setWasmPath(path,usePlatformFetch=!1){if(deprecationWarn("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release."),initAborted)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`");wasmPath=path,customFetch=usePlatformFetch}function setWasmPaths(prefixOrFileMap,usePlatformFetch=!1){if(initAborted)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPaths()` before you call `tf.setBackend()` or `tf.ready()`");if(typeof prefixOrFileMap=="string")wasmPathPrefix=prefixOrFileMap;else{wasmFileMap=prefixOrFileMap;let missingPaths=wasmBinaryNames.filter(name=>wasmFileMap[name]==null);if(missingPaths.length>0)throw new Error(`There were no entries found for the following binaries: ${missingPaths.join(",")}. Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`)}customFetch=usePlatformFetch}var version9="2.8.3",WASM_PRIORITY=2;registerBackend("wasm",async()=>{let{wasm}=await init();return new BackendWasm(wasm)},WASM_PRIORITY);var exports_constraints_exports={};__export2(exports_constraints_exports,{maxNorm:()=>maxNorm,minMaxNorm:()=>minMaxNorm,nonNeg:()=>nonNeg,unitNorm:()=>unitNorm});var _epsilon;function epsilon2(){return _epsilon==null&&(_epsilon=backend().epsilon()),_epsilon}function imageDataFormat(){return"channelsLast"}var AttributeError=class extends Error{constructor(message){super(message);Object.setPrototypeOf(this,AttributeError.prototype)}},RuntimeError=class extends Error{constructor(message){super(message);Object.setPrototypeOf(this,RuntimeError.prototype)}},ValueError=class extends Error{constructor(message){super(message);Object.setPrototypeOf(this,ValueError.prototype)}},NotImplementedError=class extends Error{constructor(message){super(message);Object.setPrototypeOf(this,NotImplementedError.prototype)}},AssertionError=class extends Error{constructor(message){super(message);Object.setPrototypeOf(this,AssertionError.prototype)}},IndexError=class extends Error{constructor(message){super(message);Object.setPrototypeOf(this,IndexError.prototype)}};function pyListRepeat(value,numValues){if(Array.isArray(value)){let newArray=[];for(let i=0;ip1.toUpperCase())}var _GLOBAL_CUSTOM_OBJECTS={};function serializeKerasObject(instance){if(instance==null)return null;let dict={};return dict.className=instance.getClassName(),dict.config=instance.getConfig(),dict}function convertNDArrayScalarsInConfig(config3){if(!(config3==null||typeof config3!="object"))if(Array.isArray(config3))config3.forEach(configItem=>convertNDArrayScalarsInConfig(configItem));else{let fields=Object.keys(config3);for(let field of fields){let value=config3[field];value!=null&&typeof value=="object"&&(!Array.isArray(value)&&value.type==="ndarray"&&typeof value.value=="number"?config3[field]=value.value:convertNDArrayScalarsInConfig(value))}}}function deserializeKerasObject(identifier,moduleObjects={},customObjects={},printableModuleName="object",fastWeightInit=!1){if(typeof identifier=="string"){let functionName=identifier,fn;if(functionName in customObjects)fn=customObjects[functionName];else if(functionName in _GLOBAL_CUSTOM_OBJECTS)fn=_GLOBAL_CUSTOM_OBJECTS[functionName];else if(fn=moduleObjects[functionName],fn==null)throw new ValueError(`Unknown ${printableModuleName}: ${identifier}. This may be due to one of the following reasons: + `}};function unsortedSegmentSum3(args){let{inputs,backend:backend22,attrs}=args,{x,segmentIds}=inputs,{numSegments}=attrs,xRank=x.shape.length,toDispose=[],axis=0,permutation=backend_util_exports.getAxesPermutation([axis],xRank),permutedX=x;permutation!=null&&(permutedX=transpose3({inputs:{x},backend:backend22,attrs:{perm:permutation}}),toDispose.push(permutedX),axis=backend_util_exports.getInnerMostAxes(1,xRank)[0]);let outShape=backend_util_exports.segment_util.computeOutShape(permutedX.shape,axis,numSegments),inSize=util_exports.sizeFromShape([permutedX.shape[axis]]),a2D=reshape3({inputs:{x:permutedX},backend:backend22,attrs:{shape:[-1,inSize]}});toDispose.push(a2D);let outputDType=sumOutType(x.dtype),segOpCompute=(x2,segOpType,segmentIds2,dtype,numSegments2)=>{let batchSize=x2.shape[0],inSize2=x2.shape[1],windowSize=backend_util_exports.segment_util.segOpComputeOptimalWindowSize(inSize2,numSegments2),segOpInfo={windowSize,inSize:inSize2,batchSize,numSegments:numSegments2},program=new SegmentOpProgram(segOpInfo,segOpType),output=backend22.compileAndRun(program,[x2,segmentIds2],dtype);if(toDispose.push(output),output.shape[1]===numSegments2)return output;let rangeInfo=range3({backend:backend22,attrs:{start:0,stop:numSegments2,step:1,dtype:"float32"}}),tileInfo=tile3({inputs:{x:rangeInfo},backend:backend22,attrs:{reps:[inSize2/windowSize]}});return toDispose.push(rangeInfo),toDispose.push(tileInfo),segOpCompute(output,segOpType,tileInfo,dtype,numSegments2)},segOpResult=segOpCompute(a2D,"unsortedSegmentSum",segmentIds,outputDType,numSegments),reshaped=reshape3({inputs:{x:segOpResult},backend:backend22,attrs:{shape:outShape}}),result=reshaped;if(permutation!=null){toDispose.push(reshaped);let perm=backend_util_exports.getUndoAxesPermutation(permutation);result=transpose3({inputs:{x:result},backend:backend22,attrs:{perm}})}return toDispose.forEach(t=>backend22.disposeIntermediateTensorInfo(t)),result}var unsortedSegmentSumConfig2={kernelName:UnsortedSegmentSum,backendName:"webgl",kernelFunc:unsortedSegmentSum3},kernelConfigs2=[LRNConfig,LRNGradConfig,_fusedMatMulConfig2,absConfig2,acosConfig2,acoshConfig2,addConfig2,addNConfig2,allConfig2,anyConfig2,argMaxConfig2,argMinConfig2,asinConfig2,asinhConfig2,atan2Config2,atanConfig2,atanhConfig2,avgPool3DConfig2,avgPoolConfig2,avgPoolGrad3DConfig,avgPoolGradConfig3,batchMatMulConfig2,batchNormConfig2,batchToSpaceNDConfig2,bincountConfig2,castConfig2,ceilConfig2,clipByValueConfig,complexAbsConfig2,complexConfig2,concatConfig2,conv2DBackpropFilterConfig2,conv2DBackpropInputConfig2,conv2DConfig2,conv3DBackpropFilterV2Config2,conv3DBackpropInputConfig,conv3DConfig2,cosConfig2,coshConfig2,cropAndResizeConfig2,cumsumConfig2,denseBincountConfig2,depthToSpaceConfig2,depthwiseConv2dNativeBackpropFilterConfig2,depthwiseConv2dNativeBackpropInputConfig2,depthwiseConv2dNativeConfig2,diagConfig2,dilation2DConfig,eluConfig2,eluGradConfig3,equalConfig2,erfConfig2,expConfig2,expandDimsConfig2,expm1Config2,fftConfig2,fillConfig2,flipLeftRightConfig2,floorConfig2,floorDivConfig2,fromPixelsConfig,fusedConv2DConfig2,fusedDepthwiseConv2DConfig2,gatherNdConfig2,gatherV2Config2,greaterConfig2,greaterEqualConfig2,identityConfig2,ifftConfig2,imagConfig2,isFiniteConfig2,isInfConfig2,isNaNConfig2,leakyReluConfig2,lessConfig2,lessEqualConfig2,linSpaceConfig2,log1pConfig2,logConfig2,logicalAndConfig2,logicalNotConfig2,logicalOrConfig2,maxConfig2,maxPool3DConfig2,maxPoolConfig2,maxPoolGrad3DConfig,maxPoolGradConfig3,maxPoolWithArgmaxConfig2,maximumConfig2,meanConfig2,minConfig2,minimumConfig2,mirrorPadConfig2,modConfig2,multinomialConfig2,multiplyConfig2,negConfig2,nonMaxSuppressionV3Config2,nonMaxSuppressionV4Config2,nonMaxSuppressionV5Config2,notEqualConfig2,oneHotConfig2,onesLikeConfig2,packConfig2,padV2Config2,powConfig2,preluConfig2,prodConfig2,rangeConfig2,realConfig2,realDivConfig2,reciprocalConfig2,relu6Config2,reluConfig2,reshapeConfig2,resizeBilinearConfig2,resizeBilinearGradConfig3,resizeNearestNeighborConfig2,resizeNearestNeighborGradConfig3,reverseConfig2,rotateWithOffsetConfig2,roundConfig2,rsqrtConfig2,scatterNdConfig2,selectConfig2,seluConfig2,sigmoidConfig2,signConfig2,sinConfig2,sinhConfig2,sliceConfig2,softmaxConfig2,softplusConfig2,spaceToBatchNDConfig2,sparseToDenseConfig2,splitVConfig2,sqrtConfig2,squareConfig2,squaredDifferenceConfig2,stepConfig2,stridedSliceConfig2,subConfig2,sumConfig2,tanConfig2,tanhConfig2,tileConfig2,topKConfig2,transposeConfig2,uniqueConfig2,unpackConfig2,unsortedSegmentSumConfig2,zerosLikeConfig2];for(let kernelConfig of kernelConfigs2)registerKernel(kernelConfig);var CppDType;(function(CppDType2){CppDType2[CppDType2.float32=0]="float32",CppDType2[CppDType2.int32=1]="int32",CppDType2[CppDType2.bool=2]="bool",CppDType2[CppDType2.string=3]="string",CppDType2[CppDType2.complex64=4]="complex64"})(CppDType||(CppDType={}));var FusableActivation;(function(FusableActivation2){FusableActivation2[FusableActivation2.linear=0]="linear",FusableActivation2[FusableActivation2.relu=1]="relu",FusableActivation2[FusableActivation2.relu6=2]="relu6",FusableActivation2[FusableActivation2.prelu=3]="prelu",FusableActivation2[FusableActivation2.leakyrelu=4]="leakyrelu"})(FusableActivation||(FusableActivation={}));var wasmFusedMatMul;function setup(backend22){wasmFusedMatMul=backend22.wasm.cwrap(_FusedMatMul,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function fusedBatchMatMul(args){let{inputs,backend:backend22,attrs}=args,{a,b,bias,preluActivationWeights}=inputs;if(a.dtype!=="float32"||b.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA,transposeB,activation:activation2,leakyreluAlpha}=attrs,aId=backend22.dataIdMap.get(a.dataId).id,bId=backend22.dataIdMap.get(b.dataId).id,biasId=0;if(bias!=null){let biasData=backend22.dataIdMap.get(bias.dataId);if(biasData.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${biasData.shape.length}.`);biasId=biasData.id}let preluActivationWeightsId=preluActivationWeights==null?0:backend22.dataIdMap.get(preluActivationWeights.dataId).id,fusedActivation=FusableActivation[activation2];if(fusedActivation==null)throw new Error(`${activation2} activation not yet supported for FusedConv2D in the wasm backend.`);let leftDim=transposeA?a.shape[2]:a.shape[1],rightDim=transposeB?b.shape[1]:b.shape[2],batchDim=a.shape[0],out=backend22.makeOutput([batchDim,leftDim,rightDim],a.dtype),outId=backend22.dataIdMap.get(out.dataId).id,aShapeBytes=new Uint8Array(new Int32Array(a.shape).buffer),bShapeBytes=new Uint8Array(new Int32Array(b.shape).buffer);return wasmFusedMatMul(aId,aShapeBytes,a.shape.length,bId,bShapeBytes,b.shape.length,transposeA,transposeB,fusedActivation,biasId,preluActivationWeightsId,leakyreluAlpha||0,outId),out}var fusedMatMulConfig={kernelName:_FusedMatMul,backendName:"wasm",setupFunc:setup,kernelFunc:fusedBatchMatMul};function createUnaryKernelConfig(kernelName){let wasmFunc9;function setupFunc3(backend22){wasmFunc9=backend22.wasm.cwrap(kernelName,null,["number","number"])}function kernelFunc3(args){let{backend:backend22,inputs:{x}}=args,xId=backend22.dataIdMap.get(x.dataId).id,out=backend22.makeOutput(x.shape,x.dtype),outId=backend22.dataIdMap.get(out.dataId).id;return util_exports.sizeFromShape(out.shape)===0||wasmFunc9(xId,outId),out}return{kernelName,backendName:"wasm",setupFunc:setupFunc3,kernelFunc:kernelFunc3}}var absConfig3=createUnaryKernelConfig(Abs);function createBinaryKernelConfig(kernelName,supportsFullBroadcast17,dtype){let wasmFunc9;function setupFunc3(backend22){wasmFunc9=backend22.wasm.cwrap(kernelName,null,["number","array","number","number","array","number","number","number"])}function kernelFunc3(args){let{backend:backend22,inputs}=args,{a,b}=inputs,aId=backend22.dataIdMap.get(a.dataId).id,bId=backend22.dataIdMap.get(b.dataId).id,outputType=dtype!=null?dtype:a.dtype,newShape=backend_util_exports.assertAndGetBroadcastShape(a.shape,b.shape),out=backend22.makeOutput(newShape,outputType);if(util_exports.sizeFromShape(newShape)===0)return out;let aShapeBytes=new Uint8Array(new Int32Array(a.shape).buffer),bShapeBytes=new Uint8Array(new Int32Array(b.shape).buffer),outId=backend22.dataIdMap.get(out.dataId).id,kernelFunc4=()=>wasmFunc9(aId,aShapeBytes,a.shape.length,bId,bShapeBytes,b.shape.length,CppDType[a.dtype],outId);if(supportsFullBroadcast17&&a.dtype==="float32")return kernelFunc4(),out;let aBroadcastDims=backend_util_exports.getBroadcastDims(a.shape,newShape),bBroadcastDims=backend_util_exports.getBroadcastDims(b.shape,newShape),loopsOverAllOfA=aBroadcastDims.every((v,i)=>v===i),loopsOverAllOfB=bBroadcastDims.every((v,i)=>v===i);if(loopsOverAllOfA&&loopsOverAllOfB)return kernelFunc4(),out;throw new Error(`Broadcasting along outer dims is not yet supported for ${a.dtype} ${kernelName}.`)}return{kernelName,backendName:"wasm",setupFunc:setupFunc3,kernelFunc:kernelFunc3}}var supportsFullBroadcast=!0,addConfig3=createBinaryKernelConfig(Add,supportsFullBroadcast),wasmFunc;function setupFunc(backend22){wasmFunc=backend22.wasm.cwrap(AddN,null,["array","number","number","number"])}function addn(args){let{inputs,backend:backend22}=args,out=backend22.makeOutput(inputs[0].shape,inputs[0].dtype);if(util_exports.sizeFromShape(out.shape)===0)return out;let inputIds=inputs.map(x=>backend22.dataIdMap.get(x.dataId).id),inputIdsBytes=new Uint8Array(new Int32Array(inputIds).buffer),outId=backend22.dataIdMap.get(out.dataId).id;return wasmFunc(inputIdsBytes,inputIds.length,CppDType[out.dtype],outId),out}var addNConfig3={kernelName:AddN,backendName:"wasm",setupFunc,kernelFunc:addn};function identity3(args){let{inputs:{x},backend:backend22}=args,out=backend22.makeOutput(x.shape,x.dtype),inVals=backend22.typedArrayFromHeap(x);return backend22.typedArrayFromHeap(out).set(inVals),out}var identityConfig3={kernelName:Identity,backendName:"wasm",kernelFunc:identity3},wasmTranspose;function setup2(backend22){wasmTranspose=backend22.wasm.cwrap(Transpose,null,["number","array","number","number","number","array","number"])}function transpose4(args){let{inputs,backend:backend22,attrs}=args,[reducedShape,perm]=removeOneSizeDims(inputs.x.shape,attrs.perm),permIsNoOp=!0;for(let i=0;i=i&&(minValIdx===-1||newPerm[minValIdx]>newPerm[j])&&(minValIdx=j);newPerm[minValIdx]=i}return[newShape,newPerm]}var transposeConfig3={kernelName:Transpose,backendName:"wasm",kernelFunc:transpose4,setupFunc:setup2};function permuteAxesAndTranspose(x,axis,backend22){let xShape=x.shape,xRank=x.shape.length,originalAxes=util_exports.parseAxisParam(axis,xShape),axes=originalAxes,permutedAxes=backend_util_exports.getAxesPermutation(axes,xRank),xTransposed=null,inputWasTransposed=!1;if(permutedAxes!=null){let newShape=new Array(xRank);for(let i=0;i`new shape: ${$shape}, old shape: ${x.shape}. New shape and old shape must have the same number of elements.`),{dataId:x.dataId,shape:$shape,dtype:x.dtype}}var reshapeConfig3={kernelName:Reshape,backendName:"wasm",kernelFunc:reshape4},wasmBatchMatMul;function setup5(backend22){wasmBatchMatMul=backend22.wasm.cwrap(BatchMatMul,null,["number","array","number","number","array","number","number","number","number"])}function batchMatMul3(args){let{inputs,backend:backend22,attrs}=args,{a,b}=inputs,{transposeA,transposeB}=attrs;if(a.dtype!=="float32"||b.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let aRank=a.shape.length,bRank=b.shape.length,innerShapeA=transposeA?a.shape[aRank-2]:a.shape[aRank-1],innerShapeB=transposeB?b.shape[bRank-1]:b.shape[bRank-2],outerShapeA=transposeA?a.shape[aRank-1]:a.shape[aRank-2],outerShapeB=transposeB?b.shape[bRank-2]:b.shape[bRank-1],outerDimsA=a.shape.slice(0,-2),outerDimsB=b.shape.slice(0,-2),batchDimA=util_exports.sizeFromShape(outerDimsA),batchDimB=util_exports.sizeFromShape(outerDimsB),batchDimsCompatible=batchDimA===batchDimB||batchDimA===1||batchDimB===1;util_exports.assert(aRank>=2&&bRank>=2&&batchDimsCompatible,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${outerDimsA}) and (${outerDimsB}).`);let outShape=(batchDimA>batchDimB?a.shape.slice(0,-2):b.shape.slice(0,-2)).concat([outerShapeA,outerShapeB]);util_exports.assert(innerShapeA===innerShapeB,()=>`Error in matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${a.shape} and ${b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);let a3dShape=transposeA?[batchDimA,innerShapeA,outerShapeA]:[batchDimA,outerShapeA,innerShapeA],b3dShape=transposeB?[batchDimB,outerShapeB,innerShapeB]:[batchDimB,innerShapeB,outerShapeB],a3d=reshape4({inputs:{x:a},backend:backend22,attrs:{shape:a3dShape}}),b3d=reshape4({inputs:{x:b},backend:backend22,attrs:{shape:b3dShape}}),a3dId=backend22.dataIdMap.get(a3d.dataId).id,b3dId=backend22.dataIdMap.get(b3d.dataId).id,leftDim=transposeA?a3d.shape[2]:a3d.shape[1],rightDim=transposeB?b3d.shape[1]:b3d.shape[2],batchDim=Math.max(batchDimA,batchDimB),out=backend22.makeOutput([batchDim,leftDim,rightDim],a3d.dtype),outId=backend22.dataIdMap.get(out.dataId).id,aShapeBytes=new Uint8Array(new Int32Array(a3d.shape).buffer),bShapeBytes=new Uint8Array(new Int32Array(b3d.shape).buffer);return wasmBatchMatMul(a3dId,aShapeBytes,a3d.shape.length,b3dId,bShapeBytes,b3d.shape.length,transposeA,transposeB,outId),out.shape=outShape,out}var batchMatMulConfig3={kernelName:BatchMatMul,backendName:"wasm",setupFunc:setup5,kernelFunc:batchMatMul3};function cast4(args){let{inputs:{x},attrs:{dtype},backend:backend22}=args,out=backend22.makeOutput(x.shape,dtype),inVals=backend22.typedArrayFromHeap(x);return backend22.typedArrayFromHeap(out).set(inVals),out}var castConfig3={kernelName:Cast,backendName:"wasm",kernelFunc:cast4},wasmClip;function setup6(backend22){wasmClip=backend22.wasm.cwrap(ClipByValue,null,["number","number","number","number"])}function clip2(args){let{inputs,backend:backend22,attrs}=args,{x}=inputs,{clipValueMin,clipValueMax}=attrs,xId=backend22.dataIdMap.get(x.dataId).id,out=backend22.makeOutput(x.shape,x.dtype),outId=backend22.dataIdMap.get(out.dataId).id;return wasmClip(xId,clipValueMin,clipValueMax,outId),out}var clipByValueConfig2={kernelName:ClipByValue,backendName:"wasm",setupFunc:setup6,kernelFunc:clip2};function concat4(args){let{inputs,backend:backend22}=args,axis=util_exports.parseAxisParam(args.attrs.axis,inputs[0].shape)[0],outShape=backend_util_exports.computeOutShape(inputs.map(t=>t.shape),axis),$inputs=inputs.filter(t=>util_exports.sizeFromShape(t.shape)>0);if($inputs.length===1)return identity3({inputs:{x:$inputs[0]},backend:backend22});let out=backend22.makeOutput(outShape,inputs[0].dtype);if(util_exports.sizeFromShape(outShape)===0)return out;let shapes=$inputs.map(t=>t.shape);if(backend_util_exports.assertParamsConsistent(shapes,axis),$inputs[0].dtype==="string"){let inputs2D=$inputs.map(t=>{let innerSize=util_exports.sizeFromShape(t.shape.slice(axis));return reshape4({inputs:{x:t},backend:backend22,attrs:{shape:[-1,innerSize]}})}),inputsValShapes=inputs2D.map(t=>({vals:backend22.readSync(t.dataId),shape:t.shape}));outShape=backend_util_exports.computeOutShape(inputs2D.map(t=>t.shape),1);let simplyConcat=inputs2D[0].shape[0]===1,outVals2=concatImpl(inputsValShapes,outShape,inputs[0].dtype,simplyConcat),finalOutShape=backend_util_exports.computeOutShape($inputs.map(t=>t.shape),axis);out.shape=finalOutShape;let outData=backend22.dataIdMap.get(out.dataId);return outData.stringBytes=backend_util_exports.fromStringArrayToUint8(outVals2),out}let batchDim=util_exports.sizeFromShape($inputs[0].shape.slice(0,axis)),sumInnerDims=0,innerDims=$inputs.map(input2=>{let innerDim=util_exports.sizeFromShape(input2.shape.slice(axis));return sumInnerDims+=innerDim,innerDim}),inVals=$inputs.map(input2=>backend22.typedArrayFromHeap(input2)),outVals=backend22.typedArrayFromHeap(out);for(let b=0;b`cumsum does not support ${x.dtype} tensors in the WASM backend`);let permutation=backend_util_exports.getAxesPermutation([axis],xRank),permutedX=x;permutation!==null&&(permutedX=transpose4({inputs:{x},attrs:{perm:permutation},backend:backend22}));let permutedAxis=backend_util_exports.getInnerMostAxes(1,xRank)[0];backend_util_exports.assertAxesAreInnerMostDims("cumsum",[permutedAxis],xRank);let permutedOut=backend22.makeOutput(permutedX.shape,permutedX.dtype),finalDim=permutedX.shape[permutedAxis],permutedXId=backend22.dataIdMap.get(permutedX.dataId).id,permutedOutId=backend22.dataIdMap.get(permutedOut.dataId).id;wasmCumsum(permutedXId,exclusive?1:0,reverse5?1:0,finalDim,permutedOutId,CppDType[x.dtype]);let out=permutedOut;if(permutation!==null){let undoPermutation=backend_util_exports.getUndoAxesPermutation(permutation);out=transpose4({inputs:{x:permutedOut},attrs:{perm:undoPermutation},backend:backend22}),backend22.disposeData(permutedX.dataId),backend22.disposeData(permutedOut.dataId)}return out}var cumsumConfig3={kernelName:Cumsum,backendName:"wasm",setupFunc:setup10,kernelFunc:cumsum4},wasmDepthToSpace;function setup11(backend22){wasmDepthToSpace=backend22.wasm.cwrap(DepthToSpace,null,["number","number","number","array","number","array","array","number","number"])}function depthToSpace4(args){let{backend:backend22,inputs,attrs}=args,{x}=inputs,{blockSize,dataFormat}=attrs;util_exports.assert(blockSize>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);let batchSize=x.shape[0],inputHeight=dataFormat==="NHWC"?x.shape[1]:x.shape[2],inputWidth=dataFormat==="NHWC"?x.shape[2]:x.shape[3],inputDepth=dataFormat==="NHWC"?x.shape[3]:x.shape[1],outputHeight=inputHeight*blockSize,outputWidth=inputWidth*blockSize,outputDepth=inputDepth/(blockSize*blockSize),outputShape=dataFormat==="NHWC"?[batchSize,outputHeight,outputWidth,outputDepth]:[batchSize,outputDepth,outputHeight,outputWidth],out=backend22.makeOutput(outputShape,"float32"),xId=backend22.dataIdMap.get(x.dataId).id,xStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(x.shape)).buffer),outputShapeBytes=new Uint8Array(new Int32Array(outputShape).buffer),outStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(outputShape)).buffer),outId=backend22.dataIdMap.get(out.dataId).id;return wasmDepthToSpace(xId,blockSize,dataFormat==="NHWC"?1:0,xStridesBytes,x.shape.length-1,outputShapeBytes,outStridesBytes,outputShape.length,outId),out}var depthToSpaceConfig3={kernelName:DepthToSpace,backendName:"wasm",setupFunc:setup11,kernelFunc:depthToSpace4},wasmDepthwiseConv2d;function setup12(backend22){wasmDepthwiseConv2d=backend22.wasm.cwrap(DepthwiseConv2dNative,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function depthwiseConv2d3(args){let{inputs,attrs,backend:backend22}=args,{x,filter}=inputs,xId=backend22.dataIdMap.get(x.dataId).id,filterId=backend22.dataIdMap.get(filter.dataId).id,{strides,dilations,pad:pad3,dimRoundingMode}=attrs,$dilations=dilations==null?[1,1]:dilations,convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,$dilations,pad3,dimRoundingMode,!0),filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,inputChannels=convInfo.inChannels,outputChannels=convInfo.outChannels,isSamePad=convInfo.padInfo.type==="SAME"?1:0;if(convInfo.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);let out=backend22.makeOutput(convInfo.outShape,"float32"),outId=backend22.dataIdMap.get(out.dataId).id;return wasmDepthwiseConv2d(xId,x.shape[0],x.shape[1],x.shape[2],filterId,filterHeight,filterWidth,padTop,padRight,padBottom,padLeft,isSamePad,dilationHeight,dilationWidth,strideHeight,strideWidth,inputChannels,outputChannels,outId),out}var depthwiseConv2dNativeConfig3={kernelName:DepthwiseConv2dNative,backendName:"wasm",setupFunc:setup12,kernelFunc:depthwiseConv2d3},supportsFullBroadcast2=!1,equalConfig3=createBinaryKernelConfig(Equal,supportsFullBroadcast2,"bool"),expConfig3=createUnaryKernelConfig(Exp);function expandDims4(args){let{inputs,attrs,backend:backend22}=args,{input:input2}=inputs,{dim}=attrs,inputRank=input2.shape.length,newShape=input2.shape.slice(),$dim=dim;return dim<0&&(util_exports.assert(-(inputRank+1)<=dim,()=>`Axis must be in the interval [${-(inputRank+1)}, ${inputRank}]`),$dim=inputRank+dim+1),newShape.splice($dim,0,1),reshape4({inputs:{x:input2},backend:backend22,attrs:{shape:newShape}})}var expandDimsConfig3={kernelName:ExpandDims,backendName:"wasm",kernelFunc:expandDims4};function fill4(args){let{attrs:{shape,value,dtype},backend:backend22}=args,out=backend22.makeOutput(shape,dtype);return backend22.typedArrayFromHeap(out).fill(value),out}var fillConfig3={kernelName:Fill,backendName:"wasm",kernelFunc:fill4},wasmFlipLeftRight;function setup13(backend22){wasmFlipLeftRight=backend22.wasm.cwrap(FlipLeftRight,null,["number","number","number","number","number","number"])}function flipLeftRight2(args){let{inputs,backend:backend22}=args,{image:image3}=inputs,out=backend22.makeOutput(image3.shape,image3.dtype),imageId=backend22.dataIdMap.get(image3.dataId).id,outId=backend22.dataIdMap.get(out.dataId).id,[batch,imageHeight,imageWidth,numChannels]=image3.shape;return wasmFlipLeftRight(imageId,batch,imageHeight,imageWidth,numChannels,outId),out}var flipLeftRightConfig3={kernelName:FlipLeftRight,backendName:"wasm",kernelFunc:flipLeftRight2,setupFunc:setup13},floorConfig3=createUnaryKernelConfig(Floor),supportsFullBroadcast3=!1,floorDivConfig3=createBinaryKernelConfig(FloorDiv,supportsFullBroadcast3),wasmBatchNorm;function setup14(backend22){wasmBatchNorm=backend22.wasm.cwrap(FusedBatchNorm,null,["number","number","number","number","number","number","number"])}function fusedBatchNorm(args){let{backend:backend22,inputs,attrs}=args,{varianceEpsilon}=attrs,{x,mean:mean4,variance,offset,scale:scale2}=inputs,xId=backend22.dataIdMap.get(x.dataId).id,meanId=backend22.dataIdMap.get(mean4.dataId).id,varianceId=backend22.dataIdMap.get(variance.dataId).id,offsetId=offset!=null?backend22.dataIdMap.get(offset.dataId).id:0,scaleId=scale2!=null?backend22.dataIdMap.get(scale2.dataId).id:0,out=backend22.makeOutput(x.shape,x.dtype);if(util_exports.sizeFromShape(x.shape)===0)return out;let outId=backend22.dataIdMap.get(out.dataId).id;return wasmBatchNorm(xId,meanId,varianceId,offsetId,scaleId,varianceEpsilon,outId),out}var fusedBatchNormConfig={kernelName:FusedBatchNorm,backendName:"wasm",setupFunc:setup14,kernelFunc:fusedBatchNorm},wasmFusedConv2d;function setup15(backend22){wasmFusedConv2d=backend22.wasm.cwrap(FusedConv2D,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function fusedConv2d2(args){let{inputs,attrs,backend:backend22}=args,{x,filter,bias,preluActivationWeights}=inputs,{strides,pad:pad3,dilations,dataFormat,dimRoundingMode,activation:activation2,leakyreluAlpha}=attrs,convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,dilations,pad3,dimRoundingMode),fusedActivation=FusableActivation[activation2];if(fusedActivation==null)throw new Error(`${activation2} activation not yet supported for FusedConv2D in the wasm backend.`);let xId=backend22.dataIdMap.get(x.dataId).id,filterId=backend22.dataIdMap.get(filter.dataId).id,outputChannels=convInfo.outChannels,biasId=0;if(bias!=null){let biasData=backend22.dataIdMap.get(bias.dataId);if(biasData.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${biasData.shape.length}.`);if(biasData.shape[0]!==outputChannels)throw new Error(`FusedConv2D bias shape (${biasData.shape}) does not match the number of output channels (${outputChannels})`);biasId=biasData.id}let filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,inputChannels=convInfo.inChannels,isSamePad=convInfo.padInfo.type==="SAME"?1:0,batchSize=convInfo.batchSize,inHeight=convInfo.inHeight,inWidth=convInfo.inWidth;if(dataFormat!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${dataFormat}'. Please use 'NHWC'.`);let out=backend22.makeOutput(convInfo.outShape,"float32"),outId=backend22.dataIdMap.get(out.dataId).id,preluActivationWeightsId=preluActivationWeights==null?0:backend22.dataIdMap.get(preluActivationWeights.dataId).id;return wasmFusedConv2d(xId,batchSize,inHeight,inWidth,filterId,filterHeight,filterWidth,biasId,padTop,padRight,padBottom,padLeft,isSamePad,dilationHeight,dilationWidth,strideHeight,strideWidth,inputChannels,outputChannels,fusedActivation,preluActivationWeightsId,leakyreluAlpha||0,outId),out}var fusedConv2DConfig3={kernelName:FusedConv2D,backendName:"wasm",setupFunc:setup15,kernelFunc:fusedConv2d2},wasmFusedDepthwiseConv2d;function setup16(backend22){wasmFusedDepthwiseConv2d=backend22.wasm.cwrap(FusedDepthwiseConv2D,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function fusedDepthwiseConv2d(args){let{inputs,attrs,backend:backend22}=args,{x,filter,bias,preluActivationWeights}=inputs,{strides,pad:pad3,dilations,dataFormat,dimRoundingMode,activation:activation2,leakyreluAlpha}=attrs,convInfo=backend_util_exports.computeConv2DInfo(x.shape,filter.shape,strides,dilations,pad3,dimRoundingMode,!0),fusedActivation=FusableActivation[activation2];if(fusedActivation==null)throw new Error(`${activation2} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let xId=backend22.dataIdMap.get(x.dataId).id,filterId=backend22.dataIdMap.get(filter.dataId).id,outputChannels=convInfo.outChannels,biasId=0;if(bias!=null){let biasData=backend22.dataIdMap.get(bias.dataId);if(biasData.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${biasData.shape.length}.`);if(biasData.shape[0]!==outputChannels)throw new Error(`FusedDepthwiseConv2D bias shape (${biasData.shape}) does not match the number of output channels (${outputChannels})`);biasId=biasData.id}let filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,inputChannels=convInfo.inChannels,isSamePad=convInfo.padInfo.type==="SAME"?1:0,batchSize=convInfo.batchSize,inHeight=convInfo.inHeight,inWidth=convInfo.inWidth;if(dataFormat!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${dataFormat}'. Please use 'NHWC'.`);let out=backend22.makeOutput(convInfo.outShape,"float32"),outId=backend22.dataIdMap.get(out.dataId).id,preluActivationWeightsId=preluActivationWeights==null?0:backend22.dataIdMap.get(preluActivationWeights.dataId).id;return wasmFusedDepthwiseConv2d(xId,batchSize,inHeight,inWidth,filterId,filterHeight,filterWidth,biasId,padTop,padRight,padBottom,padLeft,isSamePad,dilationHeight,dilationWidth,strideHeight,strideWidth,inputChannels,outputChannels,fusedActivation,preluActivationWeightsId,leakyreluAlpha||0,outId),out}var fusedDepthwiseConv2DConfig3={kernelName:FusedDepthwiseConv2D,backendName:"wasm",setupFunc:setup16,kernelFunc:fusedDepthwiseConv2d},wasmGatherNd;function setup17(backend22){wasmGatherNd=backend22.wasm.cwrap(GatherNd,null,["number","number","number","number","number","number","array","number"])}function gatherNd3(args){let{backend:backend22,inputs}=args,{params,indices}=inputs,[resultShape,numSlices,sliceSize,strides]=gather_nd_util_exports.prepareAndValidate(params,indices),out=backend22.makeOutput(resultShape,params.dtype);if(numSlices===0)return out;let indicesShape=indices.shape,sliceRank=indicesShape[indicesShape.length-1],xId=backend22.dataIdMap.get(params.dataId).id,indicesId=backend22.dataIdMap.get(indices.dataId).id,stridesBytes=new Uint8Array(new Int32Array(strides).buffer),outId=backend22.dataIdMap.get(out.dataId).id;return wasmGatherNd(xId,CppDType[params.dtype],indicesId,numSlices,sliceRank,sliceSize,stridesBytes,outId),out}var gatherNdConfig3={kernelName:GatherNd,backendName:"wasm",setupFunc:setup17,kernelFunc:gatherNd3},wasmGather;function setup18(backend22){wasmGather=backend22.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function gatherV23(args){let{backend:backend22,inputs,attrs}=args,{x,indices}=inputs,{axis,batchDims}=attrs,parsedAxis=util_exports.parseAxisParam(axis,x.shape)[0],shapeInfo=backend_util_exports.segment_util.collectGatherOpShapeInfo(x,indices,parsedAxis,batchDims),flattenX=reshape4({inputs:{x},attrs:{shape:[shapeInfo.batchSize,shapeInfo.outerSize,shapeInfo.dimSize,shapeInfo.sliceSize]},backend:backend22}),indicesSize=util_exports.sizeFromShape(indices.shape),flattenIndex=reshape4({inputs:{x:indices},attrs:{shape:[shapeInfo.batchSize,indicesSize/shapeInfo.batchSize]},backend:backend22}),flattenOutputShape=[shapeInfo.batchSize,shapeInfo.outerSize,indicesSize/shapeInfo.batchSize,shapeInfo.sliceSize],out=backend22.makeOutput(flattenOutputShape,x.dtype);if(util_exports.sizeFromShape(x.shape)===0)return out;let stridesSize=flattenX.shape.length-1,xId=backend22.dataIdMap.get(flattenX.dataId).id,indicesId=backend22.dataIdMap.get(flattenIndex.dataId).id,outId=backend22.dataIdMap.get(out.dataId).id,xStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(flattenX.shape)).buffer),outStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(flattenOutputShape)).buffer);return wasmGather(xId,CppDType[x.dtype],xStridesBytes,stridesSize,indicesId,shapeInfo.batchSize,outStridesBytes,outId),out.shape=shapeInfo.outputShape,out}var gatherV2Config3={kernelName:GatherV2,backendName:"wasm",setupFunc:setup18,kernelFunc:gatherV23},supportsFullBroadcast4=!1,greaterConfig3=createBinaryKernelConfig(Greater,supportsFullBroadcast4,"bool"),supportsFullBroadcast5=!1,greaterEqualConfig3=createBinaryKernelConfig(GreaterEqual,supportsFullBroadcast5,"bool"),wasmFunc3;function setupFunc2(backend22){wasmFunc3=backend22.wasm.cwrap(LeakyRelu,null,["number","number","number"])}function leakyRelu4(args){let{inputs:{x},attrs:{alpha},backend:backend22}=args,xId=backend22.dataIdMap.get(x.dataId).id,out=backend22.makeOutput(x.shape,x.dtype);if(util_exports.sizeFromShape(x.shape)!==0){let outId=backend22.dataIdMap.get(out.dataId).id;wasmFunc3(xId,alpha,outId)}return out}var leakyReluConfig3={kernelName:LeakyRelu,backendName:"wasm",setupFunc:setupFunc2,kernelFunc:leakyRelu4},supportsFullBroadcast6=!1,lessConfig3=createBinaryKernelConfig(Less,supportsFullBroadcast6,"bool"),supportsFullBroadcast7=!1,lessEqualConfig3=createBinaryKernelConfig(LessEqual,supportsFullBroadcast7,"bool"),logConfig3=createUnaryKernelConfig(Log),supportsFullBroadcast8=!1,logicalAndConfig3=createBinaryKernelConfig(LogicalAnd,supportsFullBroadcast8,"bool"),wasmMax;function setup19(backend22){wasmMax=backend22.wasm.cwrap(Max,null,["number, number, number"])}function max4(args){let{backend:backend22,inputs,attrs}=args,{reductionIndices:axis,keepDims}=attrs,{x}=inputs,inputId=backend22.dataIdMap.get(x.dataId).id,input2=x,{transposed,axes,originalAxes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend22);if(inputWasTransposed){let transposedId=backend22.dataIdMap.get(transposed.dataId).id;input2=transposed,inputId=transposedId}let inputRank=input2.shape.length;backend_util_exports.assertAxesAreInnerMostDims("max",axes,inputRank);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(input2.shape,axes),reduceSize=util_exports.sizeFromShape(reduceShape),out=backend22.makeOutput(outShape,x.dtype);if(util_exports.sizeFromShape(input2.shape)!==0){let outId=backend22.dataIdMap.get(out.dataId).id;wasmMax(inputId,reduceSize,outId)}if(inputWasTransposed&&backend22.disposeData(transposed.dataId),keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(out.shape,originalAxes);out.shape=newShape}return out}var maxConfig3={kernelName:Max,backendName:"wasm",setupFunc:setup19,kernelFunc:max4},supportsFullBroadcast9=!1,maximumConfig3=createBinaryKernelConfig(Maximum,supportsFullBroadcast9),wasmMaxPool;function setup20(backend22){wasmMaxPool=backend22.wasm.cwrap(MaxPool,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function maxPool4(args){let{inputs,attrs,backend:backend22}=args,x=inputs.x,xId=backend22.dataIdMap.get(x.dataId).id,{filterSize,strides,pad:pad3,dimRoundingMode}=attrs,convInfo=backend_util_exports.computePool2DInfo(x.shape,filterSize,strides,1,pad3,dimRoundingMode),filterHeight=convInfo.filterHeight,filterWidth=convInfo.filterWidth,padTop=convInfo.padInfo.top,padRight=convInfo.padInfo.right,padBottom=convInfo.padInfo.bottom,padLeft=convInfo.padInfo.left,dilationHeight=convInfo.dilationHeight,dilationWidth=convInfo.dilationWidth,strideHeight=convInfo.strideHeight,strideWidth=convInfo.strideWidth,inputChannels=convInfo.inChannels,outputChannels=convInfo.outChannels;if(convInfo.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);let out=backend22.makeOutput(convInfo.outShape,"float32"),outId=backend22.dataIdMap.get(out.dataId).id;return wasmMaxPool(xId,x.shape[0],x.shape[1],x.shape[2],filterHeight,filterWidth,padTop,padRight,padBottom,padLeft,dilationHeight,dilationWidth,strideHeight,strideWidth,inputChannels,outputChannels,outId),out}var maxPoolConfig3={kernelName:MaxPool,backendName:"wasm",setupFunc:setup20,kernelFunc:maxPool4},wasmMean;function setup21(backend22){wasmMean=backend22.wasm.cwrap(Mean,null,["number, number, number"])}function mean3(args){let{backend:backend22,inputs,attrs}=args,{axis,keepDims}=attrs,{x}=inputs,xId=backend22.dataIdMap.get(x.dataId).id,inputId=xId,input2=x,{transposed,axes,originalAxes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend22),reductionAxes=axes;if(inputWasTransposed){let transposedId=backend22.dataIdMap.get(transposed.dataId).id;transposedId!==xId&&(input2=transposed,inputId=transposedId,reductionAxes=backend_util_exports.getInnerMostAxes(reductionAxes.length,input2.shape.length))}backend_util_exports.assertAxesAreInnerMostDims("mean",reductionAxes,input2.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(input2.shape,reductionAxes),reduceSize=util_exports.sizeFromShape(reduceShape),castedInput=input2;input2.dtype!=="float32"&&(castedInput=cast4({backend:backend22,inputs:{x:input2},attrs:{dtype:"float32"}}),inputId=backend22.dataIdMap.get(castedInput.dataId).id);let out=backend22.makeOutput(outShape,"float32");if(util_exports.sizeFromShape(input2.shape)!==0){let outId=backend22.dataIdMap.get(out.dataId).id;wasmMean(inputId,reduceSize,outId)}if(inputWasTransposed&&backend22.disposeData(transposed.dataId),keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(out.shape,originalAxes);out.shape=newShape}return input2.dtype!=="float32"&&backend22.disposeData(castedInput.dataId),out}var meanConfig3={kernelName:Mean,backendName:"wasm",setupFunc:setup21,kernelFunc:mean3},wasmMin;function setup22(backend22){wasmMin=backend22.wasm.cwrap(Min,null,["number, number, number"])}function min4(args){let{backend:backend22,inputs,attrs}=args,{axis,keepDims}=attrs,{x}=inputs,xId=backend22.dataIdMap.get(x.dataId).id,inputId=xId,input2=x,{transposed,axes,originalAxes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend22);if(inputWasTransposed){let transposedId=backend22.dataIdMap.get(transposed.dataId).id;transposedId!==xId&&(input2=transposed,inputId=transposedId)}let inputRank=input2.shape.length;backend_util_exports.assertAxesAreInnerMostDims("min",axes,inputRank);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(input2.shape,axes),reduceSize=util_exports.sizeFromShape(reduceShape),out=backend22.makeOutput(outShape,input2.dtype);if(util_exports.sizeFromShape(input2.shape)!==0){let outId=backend22.dataIdMap.get(out.dataId).id;wasmMin(inputId,reduceSize,outId)}if(inputWasTransposed&&backend22.disposeData(transposed.dataId),keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(out.shape,originalAxes);out.shape=newShape}return out}var minConfig3={kernelName:Min,backendName:"wasm",setupFunc:setup22,kernelFunc:min4},supportsFullBroadcast10=!1,minimumConfig3=createBinaryKernelConfig(Minimum,supportsFullBroadcast10),supportsFullBroadcast11=!0,multiplyConfig3=createBinaryKernelConfig(Multiply,supportsFullBroadcast11),negConfig3=createUnaryKernelConfig(Neg);function parseResultStruct(backend22,resOffset){let result=new Int32Array(backend22.wasm.HEAPU8.buffer,resOffset,4),pSelectedIndices=result[0],selectedSize=result[1],pSelectedScores=result[2],pValidOutputs=result[3];return backend22.wasm._free(resOffset),{pSelectedIndices,selectedSize,pSelectedScores,pValidOutputs}}var wasmFunc4;function setup23(backend22){wasmFunc4=backend22.wasm.cwrap(NonMaxSuppressionV3,"number",["number","number","number","number","number"])}function kernelFunc(args){let{backend:backend22,inputs,attrs}=args,{iouThreshold,maxOutputSize,scoreThreshold}=attrs,{boxes,scores}=inputs,boxesId=backend22.dataIdMap.get(boxes.dataId).id,scoresId=backend22.dataIdMap.get(scores.dataId).id,resOffset=wasmFunc4(boxesId,scoresId,maxOutputSize,iouThreshold,scoreThreshold),{pSelectedIndices,selectedSize,pSelectedScores,pValidOutputs}=parseResultStruct(backend22,resOffset);return backend22.wasm._free(pSelectedScores),backend22.wasm._free(pValidOutputs),backend22.makeOutput([selectedSize],"int32",pSelectedIndices)}var nonMaxSuppressionV3Config3={kernelName:NonMaxSuppressionV3,backendName:"wasm",setupFunc:setup23,kernelFunc},wasmFunc5;function setup24(backend22){wasmFunc5=backend22.wasm.cwrap(NonMaxSuppressionV4,"number",["number","number","number","number","number","bool"])}function nonMaxSuppressionV43(args){let{backend:backend22,inputs,attrs}=args,{iouThreshold,maxOutputSize,scoreThreshold,padToMaxOutputSize}=attrs,{boxes,scores}=inputs,boxesId=backend22.dataIdMap.get(boxes.dataId).id,scoresId=backend22.dataIdMap.get(scores.dataId).id,resOffset=wasmFunc5(boxesId,scoresId,maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize),{pSelectedIndices,selectedSize,pSelectedScores,pValidOutputs}=parseResultStruct(backend22,resOffset);backend22.wasm._free(pSelectedScores);let selectedIndicesTensor=backend22.makeOutput([selectedSize],"int32",pSelectedIndices),validOutputsTensor=backend22.makeOutput([],"int32",pValidOutputs);return[selectedIndicesTensor,validOutputsTensor]}var nonMaxSuppressionV4Config3={kernelName:NonMaxSuppressionV4,backendName:"wasm",setupFunc:setup24,kernelFunc:nonMaxSuppressionV43},wasmFunc6;function setup25(backend22){wasmFunc6=backend22.wasm.cwrap(NonMaxSuppressionV5,"number",["number","number","number","number","number","number"])}function kernelFunc2(args){let{backend:backend22,inputs,attrs}=args,{iouThreshold,maxOutputSize,scoreThreshold,softNmsSigma}=attrs,{boxes,scores}=inputs,boxesId=backend22.dataIdMap.get(boxes.dataId).id,scoresId=backend22.dataIdMap.get(scores.dataId).id,resOffset=wasmFunc6(boxesId,scoresId,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma),{pSelectedIndices,selectedSize,pSelectedScores,pValidOutputs}=parseResultStruct(backend22,resOffset);backend22.wasm._free(pValidOutputs);let selectedIndicesTensor=backend22.makeOutput([selectedSize],"int32",pSelectedIndices),selectedScoresTensor=backend22.makeOutput([selectedSize],"float32",pSelectedScores);return[selectedIndicesTensor,selectedScoresTensor]}var nonMaxSuppressionV5Config3={kernelName:NonMaxSuppressionV5,backendName:"wasm",setupFunc:setup25,kernelFunc:kernelFunc2},supportsFullBroadcast12=!1,notEqualConfig3=createBinaryKernelConfig(NotEqual,supportsFullBroadcast12,"bool"),wasmOneHot;function setup26(backend22){wasmOneHot=backend22.wasm.cwrap(OneHot,null,["number","number","number","number","number"])}function oneHot4(args){let{inputs,backend:backend22,attrs}=args,{indices}=inputs,{depth,onValue,offValue}=attrs,out=backend22.makeOutput([...indices.shape,depth],"int32"),outId=backend22.dataIdMap.get(out.dataId).id,indicesId=backend22.dataIdMap.get(indices.dataId).id;return wasmOneHot(indicesId,depth,onValue,offValue,outId),out}var oneHotConfig3={kernelName:OneHot,backendName:"wasm",setupFunc:setup26,kernelFunc:oneHot4};function onesLike4(args){let{inputs:{x},backend:backend22}=args,out=backend22.makeOutput(x.shape,x.dtype);return backend22.typedArrayFromHeap(out).fill(1),out}var onesLikeConfig3={kernelName:OnesLike,backendName:"wasm",kernelFunc:onesLike4};function pack3(args){let{inputs,backend:backend22,attrs}=args,{axis}=attrs;if(inputs.length===1)return expandDims4({inputs:{input:inputs[0]},backend:backend22,attrs:{dim:axis}});let shape=inputs[0].shape,dtype=inputs[0].dtype;inputs.forEach(t=>{util_exports.assertShapesMatch(shape,t.shape,"All tensors passed to stack must have matching shapes"),util_exports.assert(dtype===t.dtype,()=>"All tensors passed to stack must have matching dtypes")});let expandedTensors=inputs.map(t=>expandDims4({inputs:{input:t},backend:backend22,attrs:{dim:axis}}));return concat4({inputs:expandedTensors,backend:backend22,attrs:{axis}})}var packConfig3={kernelName:Pack,backendName:"wasm",kernelFunc:pack3},wasmPadV2;function setup27(backend22){wasmPadV2=backend22.wasm.cwrap(PadV2,null,["number","array","number","number","array","array","number","number"])}function pad2(args){let{inputs:{x},backend:backend22,attrs:{paddings,constantValue}}=args,outShape=paddings.map((p2,i)=>p2[0]+x.shape[i]+p2[1]),xId=backend22.dataIdMap.get(x.dataId).id,out=backend22.makeOutput(outShape,x.dtype),outId=backend22.dataIdMap.get(out.dataId).id,xShapeBytes=new Uint8Array(new Int32Array(x.shape).buffer),prePaddingsFlat=paddings.map(padTuple=>padTuple[0]),postPaddingsFlat=paddings.map(padTuple=>padTuple[1]),prePaddingsBytes=new Uint8Array(new Int32Array(prePaddingsFlat).buffer),postPaddingsBytes=new Uint8Array(new Int32Array(postPaddingsFlat).buffer);return wasmPadV2(xId,xShapeBytes,x.shape.length,CppDType[x.dtype],prePaddingsBytes,postPaddingsBytes,constantValue,outId),out}var padV2Config3={kernelName:PadV2,backendName:"wasm",kernelFunc:pad2,setupFunc:setup27},supportsFullBroadcast13=!1,powConfig3=createBinaryKernelConfig(Pow,supportsFullBroadcast13),wasmPrelu;function setup28(backend22){wasmPrelu=backend22.wasm.cwrap(Prelu,null,["number","number","number"])}function prelu4(args){let{inputs,backend:backend22}=args,{x,alpha}=inputs,xId=backend22.dataIdMap.get(x.dataId).id,weightsId=backend22.dataIdMap.get(alpha.dataId).id,out=backend22.makeOutput(x.shape,"float32"),outId=backend22.dataIdMap.get(out.dataId).id;return wasmPrelu(xId,weightsId,outId),out}var preluConfig3={kernelName:Prelu,backendName:"wasm",setupFunc:setup28,kernelFunc:prelu4},wasmProd;function setup29(backend22){wasmProd=backend22.wasm.cwrap(Prod,null,["number","number","number","number"])}function prod4(args){let{backend:backend22,inputs,attrs}=args,{axis,keepDims}=attrs,{x}=inputs,xId=backend22.dataIdMap.get(x.dataId).id,inputId=xId,input2=x,{transposed,axes,originalAxes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend22),reductionAxes=axes;if(inputWasTransposed){let transposedId=backend22.dataIdMap.get(transposed.dataId).id;transposedId!==xId&&(input2=transposed,inputId=transposedId,reductionAxes=backend_util_exports.getInnerMostAxes(reductionAxes.length,input2.shape.length))}backend_util_exports.assertAxesAreInnerMostDims("prod",reductionAxes,input2.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(input2.shape,reductionAxes),reduceSize=util_exports.sizeFromShape(reduceShape),out=backend22.makeOutput(outShape,input2.dtype);if(util_exports.sizeFromShape(input2.shape)!==0){let outId=backend22.dataIdMap.get(out.dataId).id;wasmProd(inputId,reduceSize,CppDType[out.dtype],outId)}if(inputWasTransposed&&backend22.disposeData(transposed.dataId),keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(out.shape,originalAxes);out.shape=newShape}return out}var prodConfig3={kernelName:Prod,backendName:"wasm",setupFunc:setup29,kernelFunc:prod4},range4=args=>{let{backend:backend22,attrs}=args,{start,stop,step:step4,dtype}=attrs,values=rangeImpl(start,stop,step4,dtype),out=backend22.makeOutput([values.length],dtype);return backend22.typedArrayFromHeap(out).set(values),out},rangeConfig3={kernelName:Range,backendName:"wasm",kernelFunc:range4},supportsFullBroadcast14=!0,realDivConfig3=createBinaryKernelConfig(RealDiv,supportsFullBroadcast14),reluConfig3=createUnaryKernelConfig(Relu),relu6Config3=createUnaryKernelConfig(Relu6),wasmResizeBilinear;function setup30(backend22){wasmResizeBilinear=backend22.wasm.cwrap(ResizeBilinear,null,["number","number","number","number","number","number","number","number","number","number"])}function resizeBilinear4(args){let{backend:backend22,inputs,attrs}=args,{images}=inputs,{alignCorners,halfPixelCenters,size}=attrs,[newHeight,newWidth]=size,[batch,oldHeight,oldWidth,numChannels]=images.shape,outShape=[batch,newHeight,newWidth,numChannels],xData=backend22.dataIdMap.get(images.dataId),castedData;xData.dtype!=="float32"&&(castedData=cast4({backend:backend22,inputs:{x:images},attrs:{dtype:"float32"}}),xData=backend22.dataIdMap.get(castedData.dataId));let xId=xData.id,out=backend22.makeOutput(outShape,"float32");if(util_exports.sizeFromShape(images.shape)===0)return out;let outId=backend22.dataIdMap.get(out.dataId).id;return wasmResizeBilinear(xId,batch,oldHeight,oldWidth,numChannels,newHeight,newWidth,alignCorners?1:0,halfPixelCenters?1:0,outId),castedData!=null&&backend22.disposeData(castedData.dataId),out}var resizeBilinearConfig3={kernelName:ResizeBilinear,backendName:"wasm",setupFunc:setup30,kernelFunc:resizeBilinear4},wasmReverse;function setup31(backend22){wasmReverse=backend22.wasm.cwrap(Reverse,null,["number","array","number","array","number","number"])}function reverse4(args){let{inputs,backend:backend22,attrs}=args,{x}=inputs,{dims}=attrs,axes=util_exports.parseAxisParam(dims,x.shape);if(x.shape.length===0)return identity3({inputs:{x},backend:backend22});let out=backend22.makeOutput(x.shape,x.dtype),xId=backend22.dataIdMap.get(x.dataId).id,outId=backend22.dataIdMap.get(out.dataId).id,axesBytes=new Uint8Array(new Int32Array(axes).buffer),outShapeBytes=new Uint8Array(new Int32Array(x.shape).buffer);return wasmReverse(xId,axesBytes,axes.length,outShapeBytes,x.shape.length,outId),reshape4({inputs:{x:out},attrs:{shape:x.shape},backend:backend22})}var reverseConfig3={kernelName:Reverse,backendName:"wasm",kernelFunc:reverse4,setupFunc:setup31},wasmRotate;function setup32(backend22){wasmRotate=backend22.wasm.cwrap(RotateWithOffset,null,["number","number","number","number","number","number","number","number","array","number","number"])}function rotateWithOffset2(args){let{inputs,backend:backend22,attrs}=args,{image:image3}=inputs,{radians,fillValue,center}=attrs,out=backend22.makeOutput(image3.shape,image3.dtype),imageId=backend22.dataIdMap.get(image3.dataId).id,outId=backend22.dataIdMap.get(out.dataId).id,[batch,imageHeight,imageWidth,numChannels]=image3.shape,[centerX,centerY]=backend_util_exports.getImageCenter(center,imageHeight,imageWidth),fillIsBlack=fillValue===0,fullOpacityValue=255,fillValues2=typeof fillValue=="number"?[fillValue,fillValue,fillValue,fillIsBlack?0:fullOpacityValue]:[...fillValue,fullOpacityValue],fillBytes=new Uint8Array(new Int32Array(fillValues2).buffer);return wasmRotate(imageId,batch,imageHeight,imageWidth,numChannels,radians,centerX,centerY,fillBytes,fillValues2.length,outId),out}var rotateWithOffsetConfig3={kernelName:RotateWithOffset,backendName:"wasm",kernelFunc:rotateWithOffset2,setupFunc:setup32},roundConfig3=createUnaryKernelConfig(Round),rsqrtConfig3=createUnaryKernelConfig(Rsqrt),wasmScatterNd;function setup33(backend22){wasmScatterNd=backend22.wasm.cwrap(ScatterNd,null,["number","number","number","number","number","number","array","number","number"])}function scatterNd3(args){let{backend:backend22,inputs,attrs}=args,{indices,updates}=inputs,{shape}=attrs,out=backend22.makeOutput(shape,updates.dtype);if(util_exports.sizeFromShape(shape)===0)return out;let{sliceRank,numUpdates,sliceSize,strides,outputSize}=scatter_nd_util_exports.calculateShapes(updates,indices,shape),indicesId=backend22.dataIdMap.get(indices.dataId).id,updatesId=backend22.dataIdMap.get(updates.dataId).id,stridesBytes=new Uint8Array(new Int32Array(strides).buffer),outId=backend22.dataIdMap.get(out.dataId).id;return wasmScatterNd(indicesId,updatesId,CppDType[updates.dtype],sliceRank,numUpdates,sliceSize,stridesBytes,outputSize,outId),out}var scatterNdConfig3={kernelName:ScatterNd,backendName:"wasm",setupFunc:setup33,kernelFunc:scatterNd3},wasmSelect;function setup34(backend22){wasmSelect=backend22.wasm.cwrap("SelectV2",null,["number","number","number","number","number"])}function select3(args){let{inputs,backend:backend22}=args,{condition,t,e}=inputs,conditionId=backend22.dataIdMap.get(condition.dataId).id,tId=backend22.dataIdMap.get(t.dataId).id,eId=backend22.dataIdMap.get(e.dataId).id,out=backend22.makeOutput(t.shape,t.dtype),outId=backend22.dataIdMap.get(out.dataId).id,cRank=condition.shape.length,tRank=t.shape.length,offset=cRank===0||cRank>1||tRank===1?1:util_exports.sizeFromShape(t.shape.slice(1));return wasmSelect(conditionId,tId,eId,offset,outId),out}var selectConfig3={kernelName:Select,backendName:"wasm",kernelFunc:select3,setupFunc:setup34},wasmFunc7;function setup35(backend22){wasmFunc7=backend22.wasm.cwrap(Sigmoid,null,["number","number"])}function sigmoid4(args){let{backend:backend22,inputs:{x}}=args,xId=backend22.dataIdMap.get(x.dataId).id,out=backend22.makeOutput(x.shape,x.dtype),outId=backend22.dataIdMap.get(out.dataId).id;return util_exports.sizeFromShape(out.shape)===0||wasmFunc7(xId,outId),out}var sigmoidConfig3={kernelName:"Sigmoid",backendName:"wasm",setupFunc:setup35,kernelFunc:sigmoid4},sinConfig3=createUnaryKernelConfig(Sin);function slice4(args){let{inputs:{x},attrs:{begin,size},backend:backend22}=args,[begin_,size_]=slice_util_exports.parseSliceParams(x,begin,size),isContinous=slice_util_exports.isSliceContinous(x.shape,begin_,size_),xVals=backend22.readSync(x.dataId),out=backend22.makeOutput(size_,x.dtype),xStrides=util_exports.computeStrides(x.shape),outData=backend22.dataIdMap.get(out.dataId);if(isContinous){let flatOffset=slice_util_exports.computeFlatOffset(begin_,xStrides);return x.dtype==="string"?outData.stringBytes=xVals.slice(flatOffset,flatOffset+util_exports.sizeFromShape(size_)):backend22.typedArrayFromHeap(out).set(xVals.subarray(flatOffset,flatOffset+util_exports.sizeFromShape(size_))),out}if(x.dtype==="string"){let res=sliceImpl(xVals,begin_,size_,x.shape,x.dtype);return outData.stringBytes=res,out}let outVals=backend22.typedArrayFromHeap(out),rank=x.shape.length;if(rank===2)slice2d2(xVals,xStrides[0],outVals,begin_,size_);else if(rank===3)slice3d2(xVals,xStrides[0],xStrides[1],outVals,begin_,size_);else if(rank===4)slice4d2(xVals,xStrides[0],xStrides[1],xStrides[2],outVals,begin_,size_);else{let res=sliceImpl(xVals,begin_,size_,x.shape,x.dtype);outVals.set(res)}return out}function slice2d2(xVals,xStride,outVals,begin,size){let outOffset=0,beginI=begin[0],beginJ=begin[1],endI=beginI+size[0];for(let i=beginI;i{let xSliceSize=[...size];xSliceSize[$axis]=s;let xSlice=slice4({inputs:{x},attrs:{begin,size:xSliceSize},backend:backend22});return begin[$axis]+=s,xSlice})}var splitVConfig3={kernelName:SplitV,backendName:"wasm",kernelFunc:splitV3},sqrtConfig3=createUnaryKernelConfig(Sqrt),squareConfig3=createUnaryKernelConfig(Square),supportsFullBroadcast15=!0,squaredDifferenceConfig3=createBinaryKernelConfig(SquaredDifference,supportsFullBroadcast15),wasmStridedSlice;function setup37(backend22){wasmStridedSlice=backend22.wasm.cwrap(StridedSlice,null,["number","array","number","array","array","array","array","array","number","number"])}function stridedSlice4(args){let{backend:backend22,inputs,attrs}=args,{x}=inputs,{begin,end,strides}=attrs;strides==null&&(strides=new Array(begin.length));let{beginMask,endMask,ellipsisMask,newAxisMask,shrinkAxisMask}=attrs,ellipsisAxes=backend_util_exports.slice_util.maskToAxes(ellipsisMask);if(ellipsisAxes.length>1)throw new Error("Multiple ellipses in slice is not allowed.");if(ellipsisMask!==0&&newAxisMask!==0)throw new Error("Using both ellipsisMask and newAxisMask is not yet supported.");if(ellipsisMask!==0&&shrinkAxisMask!==0)throw new Error("Using both ellipsisMask and shrinkAxisMask is not yet supported.");let numInterpolatedAxes=x.shape.length-begin.length,expandAxes=backend_util_exports.slice_util.maskToAxes(newAxisMask),newShape=x.shape.slice();expandAxes.forEach(axis=>{begin[axis]=0,end[axis]=1,newShape.splice(axis,0,1)});let xReshaped=reshape4({inputs:{x},attrs:{shape:newShape},backend:backend22}),{begin:normalizedBegin,end:normalizedEnd,strides:normalizedStrides}=backend_util_exports.slice_util.getNormalizedAxes(xReshaped.shape,ellipsisAxes,numInterpolatedAxes,begin,end,strides,beginMask,endMask,ellipsisMask);begin=normalizedBegin,end=normalizedEnd,strides=normalizedStrides;let shrinkAxes=backend_util_exports.slice_util.maskToAxes(shrinkAxisMask);shrinkAxes.forEach(axis=>{end[axis]=begin[axis]+1,strides[axis]=1});let size=backend_util_exports.slice_util.computeOutShape(begin,end,strides),outShape=size.filter((_,axis)=>shrinkAxes.indexOf(axis)===-1);if(strides.every(v=>v===1)){let xSliced=slice4({inputs:{x},attrs:{begin,size},backend:backend22});return reshape4({inputs:{x:xSliced},attrs:{shape:outShape},backend:backend22})}let out=backend22.makeOutput(outShape,"float32");if(!outShape.some(axis=>axis===0)){let xId=backend22.dataIdMap.get(xReshaped.dataId).id,xStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(xReshaped.shape)).buffer),beginBytes=new Uint8Array(new Int32Array(begin).buffer),endBytes=new Uint8Array(new Int32Array(end).buffer),stridesBytes=new Uint8Array(new Int32Array(strides).buffer),outputShapeBytes=new Uint8Array(new Int32Array(outShape).buffer),outStridesBytes=new Uint8Array(new Int32Array(util_exports.computeStrides(outShape)).buffer),outId=backend22.dataIdMap.get(out.dataId).id;wasmStridedSlice(xId,xStridesBytes,xReshaped.shape.length,beginBytes,endBytes,stridesBytes,outputShapeBytes,outStridesBytes,outShape.length,outId)}return reshape4({inputs:{x:out},attrs:{shape:outShape},backend:backend22})}var stridedSliceConfig3={kernelName:StridedSlice,backendName:"wasm",setupFunc:setup37,kernelFunc:stridedSlice4},supportsFullBroadcast16=!0,subConfig3=createBinaryKernelConfig(Sub,supportsFullBroadcast16),wasmSum;function setup38(backend22){wasmSum=backend22.wasm.cwrap(Sum,null,["number, number, number"])}function sum5(args){let{backend:backend22,inputs,attrs}=args,{axis,keepDims}=attrs,{x}=inputs,xId=backend22.dataIdMap.get(x.dataId).id,inputId=xId,input2=x,{transposed,axes,originalAxes,inputWasTransposed}=permuteAxesAndTranspose(x,axis,backend22),reductionAxes=axes;if(inputWasTransposed){let transposedId=backend22.dataIdMap.get(transposed.dataId).id;transposedId!==xId&&(input2=transposed,inputId=transposedId,reductionAxes=backend_util_exports.getInnerMostAxes(reductionAxes.length,input2.shape.length))}backend_util_exports.assertAxesAreInnerMostDims("sum",reductionAxes,input2.shape.length);let[outShape,reduceShape]=backend_util_exports.computeOutAndReduceShapes(input2.shape,reductionAxes),reduceSize=util_exports.sizeFromShape(reduceShape),out=backend22.makeOutput(outShape,input2.dtype);if(util_exports.sizeFromShape(input2.shape)!==0){let outId=backend22.dataIdMap.get(out.dataId).id;wasmSum(inputId,reduceSize,outId)}if(inputWasTransposed&&backend22.disposeData(transposed.dataId),keepDims){let newShape=backend_util_exports.expandShapeToKeepDim(out.shape,originalAxes);out.shape=newShape}return out}var sumConfig3={kernelName:Sum,backendName:"wasm",setupFunc:setup38,kernelFunc:sum5},tanhConfig3=createUnaryKernelConfig(Tanh),wasmTile;function setup39(backend22){wasmTile=backend22.wasm.cwrap(Tile,null,["number","array","number","array","number","number"])}function tile4(args){let{inputs,backend:backend22,attrs}=args,{x}=inputs,xId=backend22.dataIdMap.get(x.dataId).id,{reps}=attrs,newShape=new Array(x.shape.length);for(let i=0;i{let{x}=inputs,{k,sorted}=attrs,xId=backend22.dataIdMap.get(x.dataId).id,xShapeBytes=new Uint8Array(new Int32Array(x.shape).buffer),outputShape=x.shape.slice();outputShape[outputShape.length-1]=k;let outValues=backend22.makeOutput(outputShape,x.dtype),outValuesId=backend22.dataIdMap.get(outValues.dataId).id,outIndices=backend22.makeOutput(outputShape,"int32"),outIndicesId=backend22.dataIdMap.get(outIndices.dataId).id;return wasmTopK(xId,xShapeBytes,x.shape.length,CppDType[x.dtype],k,sorted,outValuesId,outIndicesId),[outValues,outIndices]},topKConfig3={kernelName:TopK,backendName:"wasm",setupFunc:setup40,kernelFunc:topk2};function unpack3(args){let{inputs,backend:backend22,attrs}=args,{value}=inputs,{axis}=attrs;axis<0&&(axis+=value.shape.length);let numOutputs=value.shape[axis],rank=value.shape.length,outShape=new Array(rank-1),outIndex=0;for(let i=0;i({dataId,dtype,shape:outShape}))}var unpackConfig3={kernelName:Unpack,backendName:"wasm",kernelFunc:unpack3};function zerosLike4(args){let{inputs:{x},backend:backend22}=args,out=backend22.makeOutput(x.shape,x.dtype);return backend22.typedArrayFromHeap(out).fill(0),out}var zerosLikeConfig3={kernelName:ZerosLike,backendName:"wasm",kernelFunc:zerosLike4},kernelConfigs3=[absConfig3,addConfig3,addNConfig3,argMaxConfig3,avgPoolConfig3,batchMatMulConfig3,castConfig3,clipByValueConfig2,concatConfig3,conv2DConfig3,conv2DBackpropInputConfig3,cosConfig3,cropAndResizeConfig3,cumsumConfig3,depthToSpaceConfig3,depthwiseConv2dNativeConfig3,equalConfig3,expConfig3,expandDimsConfig3,fillConfig3,flipLeftRightConfig3,floorConfig3,floorDivConfig3,fusedMatMulConfig,fusedBatchNormConfig,fusedConv2DConfig3,fusedDepthwiseConv2DConfig3,gatherNdConfig3,gatherV2Config3,greaterConfig3,greaterEqualConfig3,identityConfig3,leakyReluConfig3,lessConfig3,lessEqualConfig3,logConfig3,logicalAndConfig3,maxConfig3,maximumConfig3,maxPoolConfig3,meanConfig3,minConfig3,minimumConfig3,multiplyConfig3,negConfig3,nonMaxSuppressionV3Config3,nonMaxSuppressionV4Config3,nonMaxSuppressionV5Config3,notEqualConfig3,oneHotConfig3,onesLikeConfig3,packConfig3,padV2Config3,powConfig3,preluConfig3,prodConfig3,rangeConfig3,realDivConfig3,reluConfig3,relu6Config3,reshapeConfig3,resizeBilinearConfig3,reverseConfig3,rotateWithOffsetConfig3,rsqrtConfig3,roundConfig3,scatterNdConfig3,selectConfig3,sigmoidConfig3,sinConfig3,sliceConfig3,softmaxConfig3,splitVConfig3,sqrtConfig3,squareConfig3,squaredDifferenceConfig3,stridedSliceConfig3,subConfig3,sumConfig3,tanhConfig3,tileConfig3,topKConfig3,transposeConfig3,unpackConfig3,zerosLikeConfig3];for(let kernelConfig of kernelConfigs3)registerKernel(kernelConfig);var ENV4=env();ENV4.registerFlag("WASM_HAS_SIMD_SUPPORT",async()=>WebAssembly.validate(new Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,2,1,0,10,9,1,7,0,65,0,253,15,26,11])));ENV4.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT",async()=>{if(ENV4.get("IS_NODE"))return!1;try{return new MessageChannel().port1.postMessage(new SharedArrayBuffer(1)),WebAssembly.validate(new Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,2,1,0,5,4,1,3,1,1,10,11,1,9,0,65,0,254,16,2,0,26,11]))}catch(e){return!1}});var import_tfjs_backend_wasm_threaded_simd=__toModule2(require_tfjs_backend_wasm_threaded_simd()),wasmWorkerContents='var threadInfoStruct=0;var selfThreadId=0;var parentThreadId=0;var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(" ");console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(" ");postMessage({cmd:"alert",text:text,threadId:selfThreadId})}var err=threadPrintErr;this.alert=threadAlert;Module["instantiateWasm"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module["wasmModule"],info);Module["wasmModule"]=null;receiveInstance(instance);return instance.exports};this.onmessage=function(e){try{if(e.data.cmd==="load"){Module["DYNAMIC_BASE"]=e.data.DYNAMIC_BASE;Module["DYNAMICTOP_PTR"]=e.data.DYNAMICTOP_PTR;Module["wasmModule"]=e.data.wasmModule;Module["wasmMemory"]=e.data.wasmMemory;Module["buffer"]=Module["wasmMemory"].buffer;Module["ENVIRONMENT_IS_PTHREAD"]=true;if(typeof e.data.urlOrBlob==="string"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}Module=WasmBackendModuleThreadedSimd(Module);postMessage({"cmd":"loaded"})}else if(e.data.cmd==="objectTransfer"){Module["PThread"].receiveObjectTransfer(e.data)}else if(e.data.cmd==="run"){Module["__performance_now_clock_drift"]=performance.now()-e.data.time;threadInfoStruct=e.data.threadInfoStruct;Module["__register_pthread_ptr"](threadInfoStruct,0,0);selfThreadId=e.data.selfThreadId;parentThreadId=e.data.parentThreadId;var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module["establishStackSpace"](top,max);Module["_emscripten_tls_init"]();Module["PThread"].receiveObjectTransfer(e.data);Module["PThread"].setThreadStatus(Module["_pthread_self"](),1);try{var result=Module["dynCall_ii"](e.data.start_routine,e.data.arg);if(!Module["getNoExitRuntime"]())Module["PThread"].threadExit(result)}catch(ex){if(ex==="Canceled!"){Module["PThread"].threadCancel()}else if(ex!="unwind"){Atomics.store(Module["HEAPU32"],threadInfoStruct+4>>2,ex instanceof Module["ExitStatus"]?ex.status:-2);Atomics.store(Module["HEAPU32"],threadInfoStruct+0>>2,1);Module["_emscripten_futex_wake"](threadInfoStruct+0,2147483647);if(!(ex instanceof Module["ExitStatus"]))throw ex}}}else if(e.data.cmd==="cancel"){if(threadInfoStruct){Module["PThread"].threadCancel()}}else if(e.data.target==="setimmediate"){}else if(e.data.cmd==="processThreadQueue"){if(threadInfoStruct){Module["_emscripten_current_thread_process_queued_calls"]()}}else{err("worker.js received unknown command "+e.data.cmd);err(e.data)}}catch(ex){err("worker.js onmessage() captured an uncaught exception: "+ex);if(ex.stack)err(ex.stack);throw ex}};if(typeof process==="object"&&typeof process.versions==="object"&&typeof process.versions.node==="string"){self={location:{href:__filename}};var onmessage=this.onmessage;var nodeWorkerThreads=require("worker_threads");Worker=nodeWorkerThreads.Worker;var parentPort=nodeWorkerThreads.parentPort;parentPort.on("message",function(data){onmessage({data:data})});var nodeFS=require("fs");var nodeRead=function(filename){return nodeFS.readFileSync(filename,"utf8")};function globalEval(x){global.require=require;global.Module=Module;eval.call(null,x)}importScripts=function(f){globalEval(nodeRead(f))};postMessage=function(msg){parentPort.postMessage(msg)};if(typeof performance==="undefined"){performance={now:function(){return Date.now()}}}}',import_tfjs_backend_wasm=__toModule2(require_tfjs_backend_wasm()),BackendWasm=class extends KernelBackend{constructor(wasm){super();this.wasm=wasm,this.dataIdNextNumber=1,this.wasm.tfjs.init(),this.dataIdMap=new DataStorage(this,engine())}write(values,shape,dtype){let dataId={};return this.move(dataId,values,shape,dtype),dataId}numDataIds(){return this.dataIdMap.numDataIds()}async time(f){let start=util_exports.now();return f(),{kernelMs:util_exports.now()-start}}move(dataId,values,shape,dtype){let id=this.dataIdNextNumber++;if(dtype==="string"){let stringBytes=values;this.dataIdMap.set(dataId,{id,stringBytes,shape,dtype,memoryOffset:null});return}let size=util_exports.sizeFromShape(shape),numBytes=size*util_exports.bytesPerElement(dtype),memoryOffset=this.wasm._malloc(numBytes);this.dataIdMap.set(dataId,{id,memoryOffset,shape,dtype}),this.wasm.tfjs.registerTensor(id,size,memoryOffset),values!=null&&this.wasm.HEAPU8.set(new Uint8Array(values.buffer,values.byteOffset,numBytes),memoryOffset)}async read(dataId){return this.readSync(dataId)}readSync(dataId){let{memoryOffset,dtype,shape,stringBytes}=this.dataIdMap.get(dataId);if(dtype==="string")return stringBytes;let bytes=this.wasm.HEAPU8.slice(memoryOffset,memoryOffset+util_exports.sizeFromShape(shape)*util_exports.bytesPerElement(dtype));return typedArrayFromBuffer(bytes.buffer,dtype)}disposeData(dataId){let data2=this.dataIdMap.get(dataId);this.wasm._free(data2.memoryOffset),this.wasm.tfjs.disposeData(data2.id),this.dataIdMap.delete(dataId)}floatPrecision(){return 32}getMemoryOffset(dataId){return this.dataIdMap.get(dataId).memoryOffset}dispose(){this.wasm.tfjs.dispose(),this.wasm=null}memory(){return{unreliable:!1}}makeOutput(shape,dtype,memoryOffset){let dataId;if(memoryOffset==null)dataId=this.write(null,shape,dtype);else{dataId={};let id=this.dataIdNextNumber++;this.dataIdMap.set(dataId,{id,memoryOffset,shape,dtype});let size=util_exports.sizeFromShape(shape);this.wasm.tfjs.registerTensor(id,size,memoryOffset)}return{dataId,shape,dtype}}typedArrayFromHeap({shape,dtype,dataId}){let buffer2=this.wasm.HEAPU8.buffer,{memoryOffset}=this.dataIdMap.get(dataId),size=util_exports.sizeFromShape(shape);switch(dtype){case"float32":return new Float32Array(buffer2,memoryOffset,size);case"int32":return new Int32Array(buffer2,memoryOffset,size);case"bool":return new Uint8Array(buffer2,memoryOffset,size);default:throw new Error(`Unknown dtype ${dtype}`)}}};function createInstantiateWasmFunc(path){return(imports,callback)=>(util_exports.fetch(path,{credentials:"same-origin"}).then(response=>{response.ok||imports.env.a(`failed to load wasm binary file at '${path}'`),response.arrayBuffer().then(binary=>{WebAssembly.instantiate(binary,imports).then(output=>{callback(output.instance)})})}),{})}function getPathToWasmBinary(simdSupported,threadsSupported,wasmModuleFolder){if(wasmPath!=null)return wasmPath;let path="tfjs-backend-wasm.wasm";return simdSupported&&threadsSupported?path="tfjs-backend-wasm-threaded-simd.wasm":simdSupported&&(path="tfjs-backend-wasm-simd.wasm"),wasmFileMap!=null&&wasmFileMap[path]!=null?wasmFileMap[path]:wasmModuleFolder+path}async function init(){let[simdSupported,threadsSupported]=await Promise.all([env().getAsync("WASM_HAS_SIMD_SUPPORT"),env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]);return new Promise((resolve,reject)=>{let factoryConfig={};factoryConfig.locateFile=(path,prefix)=>{if(path.endsWith(".worker.js")){let response=wasmWorkerContents,blob=new Blob([response],{type:"application/javascript"});return URL.createObjectURL(blob)}return path.endsWith(".wasm")?getPathToWasmBinary(simdSupported,threadsSupported,wasmPathPrefix!=null?wasmPathPrefix:prefix):prefix+path},customFetch&&(factoryConfig.instantiateWasm=createInstantiateWasmFunc(getPathToWasmBinary(simdSupported,threadsSupported,wasmPathPrefix!=null?wasmPathPrefix:"")));let wasm;threadsSupported&&simdSupported&&wasmPath==null?(wasm=import_tfjs_backend_wasm_threaded_simd.default(factoryConfig),wasm.mainScriptUrlOrBlob=new Blob(["var WasmBackendModuleThreadedSimd = "+import_tfjs_backend_wasm_threaded_simd.default.toString()],{type:"text/javascript"})):wasm=import_tfjs_backend_wasm.default(factoryConfig);let voidReturnType=null;wasm.tfjs={init:wasm.cwrap("init",null,[]),registerTensor:wasm.cwrap("register_tensor",null,["number","number","number"]),disposeData:wasm.cwrap("dispose_data",voidReturnType,["number"]),dispose:wasm.cwrap("dispose",voidReturnType,[])};let initialized=!1;wasm.onRuntimeInitialized=()=>{initialized=!0,initAborted=!1,resolve({wasm})},wasm.onAbort=()=>{initialized||initAborted||(initAborted=!0,reject({message:"Make sure the server can serve the `.wasm` file relative to the bundled js file. For more details see https://github.com/tensorflow/tfjs/blob/master/tfjs-backend-wasm/README.md#using-bundlers"}))}})}function typedArrayFromBuffer(buffer2,dtype){switch(dtype){case"float32":return new Float32Array(buffer2);case"int32":return new Int32Array(buffer2);case"bool":return new Uint8Array(buffer2);default:throw new Error(`Unknown dtype ${dtype}`)}}var wasmBinaryNames=["tfjs-backend-wasm.wasm","tfjs-backend-wasm-simd.wasm","tfjs-backend-wasm-threaded-simd.wasm"],wasmPath=null,wasmPathPrefix=null,wasmFileMap={},initAborted=!1,customFetch=!1;function setWasmPath(path,usePlatformFetch=!1){if(deprecationWarn("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release."),initAborted)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`");wasmPath=path,customFetch=usePlatformFetch}function setWasmPaths(prefixOrFileMap,usePlatformFetch=!1){if(initAborted)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPaths()` before you call `tf.setBackend()` or `tf.ready()`");if(typeof prefixOrFileMap=="string")wasmPathPrefix=prefixOrFileMap;else{wasmFileMap=prefixOrFileMap;let missingPaths=wasmBinaryNames.filter(name=>wasmFileMap[name]==null);if(missingPaths.length>0)throw new Error(`There were no entries found for the following binaries: ${missingPaths.join(",")}. Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`)}customFetch=usePlatformFetch}var version9="2.8.3",WASM_PRIORITY=2;registerBackend("wasm",async()=>{let{wasm}=await init();return new BackendWasm(wasm)},WASM_PRIORITY);var exports_constraints_exports={};__export2(exports_constraints_exports,{maxNorm:()=>maxNorm,minMaxNorm:()=>minMaxNorm,nonNeg:()=>nonNeg,unitNorm:()=>unitNorm});var _epsilon;function epsilon2(){return _epsilon==null&&(_epsilon=backend().epsilon()),_epsilon}function imageDataFormat(){return"channelsLast"}var AttributeError=class extends Error{constructor(message){super(message);Object.setPrototypeOf(this,AttributeError.prototype)}},RuntimeError=class extends Error{constructor(message){super(message);Object.setPrototypeOf(this,RuntimeError.prototype)}},ValueError=class extends Error{constructor(message){super(message);Object.setPrototypeOf(this,ValueError.prototype)}},NotImplementedError=class extends Error{constructor(message){super(message);Object.setPrototypeOf(this,NotImplementedError.prototype)}},AssertionError=class extends Error{constructor(message){super(message);Object.setPrototypeOf(this,AssertionError.prototype)}},IndexError=class extends Error{constructor(message){super(message);Object.setPrototypeOf(this,IndexError.prototype)}};function pyListRepeat(value,numValues){if(Array.isArray(value)){let newArray=[];for(let i=0;ip1.toUpperCase())}var _GLOBAL_CUSTOM_OBJECTS={};function serializeKerasObject(instance){if(instance==null)return null;let dict={};return dict.className=instance.getClassName(),dict.config=instance.getConfig(),dict}function convertNDArrayScalarsInConfig(config2){if(!(config2==null||typeof config2!="object"))if(Array.isArray(config2))config2.forEach(configItem=>convertNDArrayScalarsInConfig(configItem));else{let fields=Object.keys(config2);for(let field of fields){let value=config2[field];value!=null&&typeof value=="object"&&(!Array.isArray(value)&&value.type==="ndarray"&&typeof value.value=="number"?config2[field]=value.value:convertNDArrayScalarsInConfig(value))}}}function deserializeKerasObject(identifier,moduleObjects={},customObjects={},printableModuleName="object",fastWeightInit=!1){if(typeof identifier=="string"){let functionName=identifier,fn;if(functionName in customObjects)fn=customObjects[functionName];else if(functionName in _GLOBAL_CUSTOM_OBJECTS)fn=_GLOBAL_CUSTOM_OBJECTS[functionName];else if(fn=moduleObjects[functionName],fn==null)throw new ValueError(`Unknown ${printableModuleName}: ${identifier}. This may be due to one of the following reasons: 1. The ${printableModuleName} is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code. -2. The custom ${printableModuleName} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);return fn}else{let config3=identifier;if(config3.className==null||config3.config==null)throw new ValueError(`${printableModuleName}: Improper config format: ${JSON.stringify(config3)}. -'className' and 'config' must set.`);let className=config3.className,cls,fromConfig;if(className in customObjects?[cls,fromConfig]=customObjects[className]:className in _GLOBAL_CUSTOM_OBJECTS?[cls,fromConfig]=_GLOBAL_CUSTOM_OBJECTS.className:className in moduleObjects&&([cls,fromConfig]=moduleObjects[className]),cls==null)throw new ValueError(`Unknown ${printableModuleName}: ${className}. This may be due to one of the following reasons: +2. The custom ${printableModuleName} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);return fn}else{let config2=identifier;if(config2.className==null||config2.config==null)throw new ValueError(`${printableModuleName}: Improper config format: ${JSON.stringify(config2)}. +'className' and 'config' must set.`);let className=config2.className,cls,fromConfig;if(className in customObjects?[cls,fromConfig]=customObjects[className]:className in _GLOBAL_CUSTOM_OBJECTS?[cls,fromConfig]=_GLOBAL_CUSTOM_OBJECTS.className:className in moduleObjects&&([cls,fromConfig]=moduleObjects[className]),cls==null)throw new ValueError(`Unknown ${printableModuleName}: ${className}. This may be due to one of the following reasons: 1. The ${printableModuleName} is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code. -2. The custom ${printableModuleName} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);if(fromConfig!=null){let customObjectsCombined={};for(let key of Object.keys(_GLOBAL_CUSTOM_OBJECTS))customObjectsCombined[key]=_GLOBAL_CUSTOM_OBJECTS[key];for(let key of Object.keys(customObjects))customObjectsCombined[key]=customObjects[key];let nestedConfig=config3.config;nestedConfig.customObjects=customObjectsCombined;let backupCustomObjects=Object.assign({},_GLOBAL_CUSTOM_OBJECTS);for(let key of Object.keys(customObjects))_GLOBAL_CUSTOM_OBJECTS[key]=customObjects[key];convertNDArrayScalarsInConfig(config3.config);let returnObj=fromConfig(cls,config3.config,customObjects,fastWeightInit);return _GLOBAL_CUSTOM_OBJECTS=Object.assign({},backupCustomObjects),returnObj}else{let backupCustomObjects=Object.assign({},_GLOBAL_CUSTOM_OBJECTS);for(let key of Object.keys(customObjects))_GLOBAL_CUSTOM_OBJECTS[key]=customObjects[key];let returnObj=new cls(config3.config);return _GLOBAL_CUSTOM_OBJECTS=Object.assign({},backupCustomObjects),returnObj}}}function numberCompare(a,b){return ab?1:0}function reverseNumberCompare(a,b){return-1*numberCompare(a,b)}function unique4(xs){if(xs==null)return xs;let out=[];for(let x of xs)out.indexOf(x)===-1&&out.push(x);return out}function isObjectEmpty(obj){if(obj==null)throw new ValueError(`Invalid value in obj: ${JSON.stringify(obj)}`);for(let key in obj)if(obj.hasOwnProperty(key))return!1;return!0}function checkStringTypeUnionValue(values,label,value){if(value!=null&&values.indexOf(value)<0)throw new ValueError(`${value} is not a valid ${label}. Valid values are ${values} or null/undefined.`)}function checkArrayTypeAndLength(x,expectedType,minLength=0,maxLength=Infinity){return assert2(minLength>=0),assert2(maxLength>=minLength),Array.isArray(x)&&x.length>=minLength&&x.length<=maxLength&&x.every(e=>typeof e===expectedType)}function assertPositiveInteger(value,name){Array.isArray(value)?(util_exports.assert(value.length>0,()=>`${name} is unexpectedly an empty array.`),value.forEach((v,i)=>assertPositiveInteger(v,`element ${i+1} of ${name}`))):util_exports.assert(Number.isInteger(value)&&value>0,()=>`Expected ${name} to be a positive integer, but got ${formatAsFriendlyString(value)}.`)}function formatAsFriendlyString(value){return value===null?"null":Array.isArray(value)?"["+value.map(v=>formatAsFriendlyString(v)).join(",")+"]":typeof value=="string"?`"${value}"`:`${value}`}function debounce(f,waitMs){let lastTime=util_exports.now(),lastResult;return(...args)=>{let now22=util_exports.now();return now22-lastTimesqrt(sum2(mul(w,w),axis,!0)))}var Constraint=class extends serialization_exports.Serializable{getConfig(){return{}}},MaxNorm=class extends Constraint{constructor(args){super();this.defaultMaxValue=2,this.defaultAxis=0,this.maxValue=args.maxValue!=null?args.maxValue:this.defaultMaxValue,this.axis=args.axis!=null?args.axis:this.defaultAxis}apply(w){return tidy(()=>{let norms=calcL2Norms(w,this.axis),desired=clipByValue(norms,0,this.maxValue);return mul(w,div(desired,add2(epsilon2(),norms)))})}getConfig(){return{maxValue:this.maxValue,axis:this.axis}}};MaxNorm.className="MaxNorm";serialization_exports.registerClass(MaxNorm);var UnitNorm=class extends Constraint{constructor(args){super();this.defaultAxis=0,this.axis=args.axis!=null?args.axis:this.defaultAxis}apply(w){return tidy(()=>div(w,add2(epsilon2(),calcL2Norms(w,this.axis))))}getConfig(){return{axis:this.axis}}};UnitNorm.className="UnitNorm";serialization_exports.registerClass(UnitNorm);var NonNeg=class extends Constraint{apply(w){return relu(w)}};NonNeg.className="NonNeg";serialization_exports.registerClass(NonNeg);var MinMaxNorm=class extends Constraint{constructor(args){super();this.defaultMinValue=0,this.defaultMaxValue=1,this.defaultRate=1,this.defaultAxis=0,this.minValue=args.minValue!=null?args.minValue:this.defaultMinValue,this.maxValue=args.maxValue!=null?args.maxValue:this.defaultMaxValue,this.rate=args.rate!=null?args.rate:this.defaultRate,this.axis=args.axis!=null?args.axis:this.defaultAxis}apply(w){return tidy(()=>{let norms=calcL2Norms(w,this.axis),desired=add2(mul(this.rate,clipByValue(norms,this.minValue,this.maxValue)),mul(1-this.rate,norms));return mul(w,div(desired,add2(epsilon2(),norms)))})}getConfig(){return{minValue:this.minValue,maxValue:this.maxValue,rate:this.rate,axis:this.axis}}};MinMaxNorm.className="MinMaxNorm";serialization_exports.registerClass(MinMaxNorm);var CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP={maxNorm:"MaxNorm",minMaxNorm:"MinMaxNorm",nonNeg:"NonNeg",unitNorm:"UnitNorm"};function serializeConstraint(constraint){return serializeKerasObject(constraint)}function deserializeConstraint(config3,customObjects={}){return deserializeKerasObject(config3,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"constraint")}function getConstraint(identifier){if(identifier==null)return null;if(typeof identifier=="string"){let config3={className:identifier in CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP?CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier]:identifier,config:{}};return deserializeConstraint(config3)}else return identifier instanceof Constraint?identifier:deserializeConstraint(identifier)}function maxNorm(args){return new MaxNorm(args)}function unitNorm(args){return new UnitNorm(args)}function nonNeg(){return new NonNeg}function minMaxNorm(config3){return new MinMaxNorm(config3)}var exports_initializers_exports={};__export2(exports_initializers_exports,{constant:()=>constant,glorotNormal:()=>glorotNormal,glorotUniform:()=>glorotUniform,heNormal:()=>heNormal,heUniform:()=>heUniform,identity:()=>identity4,leCunNormal:()=>leCunNormal,leCunUniform:()=>leCunUniform,ones:()=>ones3,orthogonal:()=>orthogonal,randomNormal:()=>randomNormal3,randomUniform:()=>randomUniform2,truncatedNormal:()=>truncatedNormal2,varianceScaling:()=>varianceScaling,zeros:()=>zeros3});var VALID_DATA_FORMAT_VALUES=["channelsFirst","channelsLast"],VALID_INTERPOLATION_FORMAT_VALUES=["nearest","bilinear"],VALID_PADDING_MODE_VALUES=["valid","same","causal"],VALID_POOL_MODE_VALUES=["max","avg"],VALID_BIDIRECTIONAL_MERGE_MODES=["sum","mul","concat","ave"],nameMap=new Map;function checkDataFormat(value){checkStringTypeUnionValue(VALID_DATA_FORMAT_VALUES,"DataFormat",value)}function checkInterpolationFormat(value){checkStringTypeUnionValue(VALID_INTERPOLATION_FORMAT_VALUES,"InterpolationFormat",value)}function checkPaddingMode(value){checkStringTypeUnionValue(VALID_PADDING_MODE_VALUES,"PaddingMode",value)}function checkPoolMode(value){checkStringTypeUnionValue(VALID_POOL_MODE_VALUES,"PoolMode",value)}var _nameScopeStack=[],_nameScopeDivider="/";function nameScope(name,fn){_nameScopeStack.push(name);try{let val=fn();return _nameScopeStack.pop(),val}catch(e){throw _nameScopeStack.pop(),e}}function currentNameScopePrefix(){return _nameScopeStack.length===0?"":_nameScopeStack.join(_nameScopeDivider)+_nameScopeDivider}function getScopedTensorName(tensorName){if(!isValidTensorName(tensorName))throw new Error("Not a valid tensor name: '"+tensorName+"'");return currentNameScopePrefix()+tensorName}function getUniqueTensorName(scopedName){if(!isValidTensorName(scopedName))throw new Error("Not a valid tensor name: '"+scopedName+"'");nameMap.has(scopedName)||nameMap.set(scopedName,0);let index=nameMap.get(scopedName);if(nameMap.set(scopedName,nameMap.get(scopedName)+1),index>0){let result=`${scopedName}_${index}`;return nameMap.set(result,1),result}else return scopedName}var tensorNameRegex=new RegExp(/^[A-Za-z0-9][-A-Za-z0-9\._\/]*$/);function isValidTensorName(name){return!!name.match(tensorNameRegex)}function isInteger(x){return x===parseInt(x.toString(),10)}function arrayProd(array2,begin,end){begin==null&&(begin=0),end==null&&(end=array2.length);let prod5=1;for(let i=begin;i{if(x.shape.length!==2)throw new ValueError(`repeat() expects a rank-2 tensor, but received a rank-${x.shape.length} tensor.`);let y=expandDims5(x,1);return tile5(y,[1,n,1])})}function flatten2(x){let newShape=[arrayProd(x.shape)];return x.reshape(newShape)}function batchFlatten(x){if(x.rank<=1)throw new ValueError(`batchFlatten requires a minimum rank of 2. Got rank: ${x.rank}.`);let newShape=[x.shape[0],arrayProd(x.shape,1)];return x.reshape(newShape)}function sliceAlongFirstAxis(array2,start,size){return tidy(()=>{switch(array2.rank){case 1:return slice1d(array2,start,size);case 2:return slice2d(array2,[start,0],[size,array2.shape[1]]);case 3:return slice3d(array2,[start,0,0],[size,array2.shape[1],array2.shape[2]]);case 4:return slice4d(array2,[start,0,0,0],[size,array2.shape[1],array2.shape[2],array2.shape[3]]);case 5:return slice(array2,[start,0,0,0,0],[size,array2.shape[1],array2.shape[2],array2.shape[3],array2.shape[4]]);case 6:return slice(array2,[start,0,0,0,0,0],[size,array2.shape[1],array2.shape[2],array2.shape[3],array2.shape[4],array2.shape[5]]);default:throw new ValueError(`sliceAlongFirstAxis() received an unsupported tensor rank: ${array2.rank}`)}})}function sliceAlongLastAxis(array2,start,size){return tidy(()=>{switch(array2.rank){case 1:return slice1d(array2,start,size);case 2:return slice2d(array2,[0,start],[array2.shape[0],size]);case 3:return slice3d(array2,[0,0,start],[array2.shape[0],array2.shape[1],size]);case 4:return slice4d(array2,[0,0,0,start],[array2.shape[0],array2.shape[1],array2.shape[2],size]);default:throw new ValueError(`sliceAlongLastAxis() received an unsupported tensor rank: ${array2.rank}`)}})}function sliceAlongAxis(array2,start,size,axis){return tidy(()=>{switch(array2.rank){case 1:return slice1d(array2,start,size);case 2:switch(axis){case 1:return sliceAlongFirstAxis(array2,start,size);case 2:return sliceAlongLastAxis(array2,start,size);default:throw new ValueError(`The axis is not within the rank of the tensor ${axis}`)}case 3:switch(axis){case 1:return sliceAlongFirstAxis(array2,start,size);case 2:return slice3d(array2,[0,start,0],[array2.shape[0],size,array2.shape[2]]);case 3:return sliceAlongLastAxis(array2,start,size);default:throw new ValueError(`The axis is not within the rank of the tensor ${axis}`)}case 4:switch(axis){case 1:return sliceAlongFirstAxis(array2,start,size);case 2:return slice4d(array2,[0,start,0,0],[array2.shape[0],size,array2.shape[2],array2.shape[3]]);case 3:return slice4d(array2,[0,0,start,0],[array2.shape[0],array2.shape[1],size,array2.shape[3]]);case 4:return sliceAlongLastAxis(array2,start,size);default:throw new ValueError(`The axis is not within the rank of the tensor ${axis}`)}default:throw new ValueError(`sliceAlongLastAxis() received an unsupported tensor rank: ${array2.rank}`)}})}function concatenate(tensors,axis=-1){let rank;return axis<0&&(rank=tensors[0].rank,rank!==0?axis=rank:axis=0),axis===tensors[0].rank&&(axis=-1),concat(tensors,axis)}function concatAlongFirstAxis(a,b){switch(a.rank){case 1:return concat1d([a,b]);case 2:return concat2d([a,b],0);case 3:return concat3d([a,b],0);case 4:return concat4d([a,b],0);default:throw new ValueError(`concatAlongFirstAxis() received an unsupported tensor rank: ${a.rank}`)}}function tile5(x,n){if(Array.isArray(n)||(n=[n]),x.rank!==n.length)throw new ValueError(`The length of input n (${n.length}) does not match the number of dimensions in input x (${x.rank})`);return tile(x,n)}function randomNormal2(shape,mean4=0,stddev=1,dtype,seed){return randomNormal(shape,mean4,stddev,dtype,seed)}function dot2(a,b,activation2,bias){if(a.rank<2||b.rank<2)throw new NotImplementedError(`dot requires both inputs to be rank >= 2 but got x shape = ${a.shape} and y shape = ${b.shape}`);if(b.rank>=3){let xLastDim=a.shape.slice(-1)[0],ySecondLastDim=b.shape.slice(-2)[0];if(xLastDim!==ySecondLastDim)throw new NotImplementedError(`If rank y >= 3, then the second last dim of y must equal the last dim of x but got x shape = ${a.shape} and y shape = ${b.shape}`)}if(a.rank===2&&b.rank===2){let transposeA=!1,transposeB=!1;return fused_ops_exports.matMul({a,b,transposeA,transposeB,bias:bias?reshapeBias(a.rank,bias,imageDataFormat()):null,activation:activation2})}else{let aFirstDims=a.shape.slice(),aLastDim=aFirstDims.pop();a=a.reshape([-1,aLastDim]);let bShape=b.shape.slice(),bLastDim=bShape.pop(),ySecondLastDim=bShape.pop(),yOtherDims=[...bShape,bLastDim],perm=Array.from({length:b.rank},(_,i)=>i===0?b.rank-2:i<=b.rank-2?i-1:i);b=b.transpose(perm).reshape([ySecondLastDim,-1]);let outputShape=[...aFirstDims,...yOtherDims],transposeA=!1,transposeB=!1;return fused_ops_exports.matMul({a,b,transposeA,transposeB,bias:bias?reshapeBias(a.rank,bias,imageDataFormat()):null,activation:activation2}).reshape(outputShape)}}function gather2(reference,indices,axis){return tidy(()=>(Array.isArray(indices)?indices=tensor1d(indices,"int32"):indices=indices.toInt(),gather(reference,indices,axis)))}function square3(x){return mul(x,x)}function reshapeBias(xRank,bias,dataFormat){let biasShape=bias.shape;if(bias.rank!==1&&bias.rank!==xRank)throw new ValueError(`Unexpected bias dimensions: ${bias.rank}; expected it to be 1 or ${xRank}`);if(xRank===5){if(dataFormat==="channelsFirst")return biasShape.length===1?bias.reshape([1,biasShape[0],1,1,1]):bias.reshape([1,biasShape[3],biasShape[0],biasShape[1],biasShape[2]]);if(dataFormat==="channelsLast")return biasShape.length===1?bias.reshape([1,1,1,1,biasShape[0]]):bias.reshape([1].concat(biasShape))}else if(xRank===4){if(dataFormat==="channelsFirst")return biasShape.length===1?bias.reshape([1,biasShape[0],1,1]):bias.reshape([1,biasShape[2],biasShape[0],biasShape[1]]);if(dataFormat==="channelsLast")return biasShape.length===1?bias.reshape([1,1,1,biasShape[0]]):bias.reshape([1].concat(biasShape))}else if(xRank===3){if(dataFormat==="channelsFirst")return biasShape.length===1?bias.reshape([1,biasShape[0],1]):bias.reshape([1,biasShape[1],biasShape[0]]);if(dataFormat==="channelsLast")return biasShape.length===1?bias.reshape([1,1,biasShape[0]]):bias.reshape([1].concat(biasShape))}else if(xRank<3)return bias;throw new ValueError(`Unsupported input rank by biasAdd: ${bias.rank}`)}function biasAdd(x,bias,dataFormat){return tidy(()=>(dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat),x.add(reshapeBias(x.rank,bias,dataFormat))))}function elu4(x,alpha=1){if(alpha!==1)throw new NotImplementedError(`Support for alpha values other than 1 (${alpha}) is not implemented yet.`);return elu(x)}function softsign(x){return tidy(()=>div(x,abs(x).add(1)))}function dropout2(x,level,noiseShape,seed){return tidy(()=>dropout(x,level,noiseShape,seed))}function hardSigmoid(x){return tidy(()=>{let y=add2(.5,mul(.2,x));return clipByValue(y,0,1)})}function inTrainPhase(x,alt,training=!1){return training?x():alt()}var VALID_FAN_MODE_VALUES=["fanIn","fanOut","fanAvg"],VALID_DISTRIBUTION_VALUES=["normal","uniform","truncatedNormal"];function checkFanMode(value){checkStringTypeUnionValue(VALID_FAN_MODE_VALUES,"FanMode",value)}function checkDistribution(value){checkStringTypeUnionValue(VALID_DISTRIBUTION_VALUES,"Distribution",value)}var Initializer=class extends serialization_exports.Serializable{fromConfigUsesCustomObjects(){return!1}getConfig(){return{}}},Zeros=class extends Initializer{apply(shape,dtype){return zeros(shape,dtype)}};Zeros.className="Zeros";serialization_exports.registerClass(Zeros);var Ones=class extends Initializer{apply(shape,dtype){return ones2(shape,dtype)}};Ones.className="Ones";serialization_exports.registerClass(Ones);var Constant=class extends Initializer{constructor(args){super();if(typeof args!="object")throw new ValueError(`Expected argument of type ConstantConfig but got ${args}`);if(args.value===void 0)throw new ValueError(`config must have value set but got ${args}`);this.value=args.value}apply(shape,dtype){return tidy(()=>mul(scalar(this.value),ones2(shape,dtype)))}getConfig(){return{value:this.value}}};Constant.className="Constant";serialization_exports.registerClass(Constant);var RandomUniform=class extends Initializer{constructor(args){super();this.DEFAULT_MINVAL=-.05,this.DEFAULT_MAXVAL=.05,this.minval=args.minval||this.DEFAULT_MINVAL,this.maxval=args.maxval||this.DEFAULT_MAXVAL,this.seed=args.seed}apply(shape,dtype){return randomUniform(shape,this.minval,this.maxval,dtype)}getConfig(){return{minval:this.minval,maxval:this.maxval,seed:this.seed}}};RandomUniform.className="RandomUniform";serialization_exports.registerClass(RandomUniform);var RandomNormal=class extends Initializer{constructor(args){super();this.DEFAULT_MEAN=0,this.DEFAULT_STDDEV=.05,this.mean=args.mean||this.DEFAULT_MEAN,this.stddev=args.stddev||this.DEFAULT_STDDEV,this.seed=args.seed}apply(shape,dtype){if(dtype=dtype||"float32",dtype!=="float32"&&dtype!=="int32")throw new NotImplementedError(`randomNormal does not support dType ${dtype}.`);return randomNormal2(shape,this.mean,this.stddev,dtype,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}};RandomNormal.className="RandomNormal";serialization_exports.registerClass(RandomNormal);var TruncatedNormal=class extends Initializer{constructor(args){super();this.DEFAULT_MEAN=0,this.DEFAULT_STDDEV=.05,this.mean=args.mean||this.DEFAULT_MEAN,this.stddev=args.stddev||this.DEFAULT_STDDEV,this.seed=args.seed}apply(shape,dtype){if(dtype=dtype||"float32",dtype!=="float32"&&dtype!=="int32")throw new NotImplementedError(`truncatedNormal does not support dType ${dtype}.`);return truncatedNormal(shape,this.mean,this.stddev,dtype,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}};TruncatedNormal.className="TruncatedNormal";serialization_exports.registerClass(TruncatedNormal);var Identity2=class extends Initializer{constructor(args){super();this.gain=args.gain!=null?args.gain:1}apply(shape,dtype){return tidy(()=>{if(shape.length!==2||shape[0]!==shape[1])throw new ValueError("Identity matrix initializer can only be used for 2D square matrices.");return mul(this.gain,eye(shape[0]))})}getConfig(){return{gain:this.gain}}};Identity2.className="Identity";serialization_exports.registerClass(Identity2);function computeFans(shape,dataFormat="channelsLast"){let fanIn,fanOut;if(checkDataFormat(dataFormat),shape.length===2)fanIn=shape[0],fanOut=shape[1];else if([3,4,5].indexOf(shape.length)!==-1){if(dataFormat==="channelsFirst"){let receptiveFieldSize=arrayProd(shape,2);fanIn=shape[1]*receptiveFieldSize,fanOut=shape[0]*receptiveFieldSize}else if(dataFormat==="channelsLast"){let receptiveFieldSize=arrayProd(shape,0,shape.length-2);fanIn=shape[shape.length-2]*receptiveFieldSize,fanOut=shape[shape.length-1]*receptiveFieldSize}}else{let shapeProd=arrayProd(shape);fanIn=Math.sqrt(shapeProd),fanOut=Math.sqrt(shapeProd)}return[fanIn,fanOut]}var VarianceScaling=class extends Initializer{constructor(args){super();if(args.scale<0)throw new ValueError(`scale must be a positive float. Got: ${args.scale}`);this.scale=args.scale==null?1:args.scale,this.mode=args.mode==null?"fanIn":args.mode,checkFanMode(this.mode),this.distribution=args.distribution==null?"normal":args.distribution,checkDistribution(this.distribution),this.seed=args.seed}apply(shape,dtype){let fans=computeFans(shape),fanIn=fans[0],fanOut=fans[1],scale2=this.scale;if(this.mode==="fanIn"?scale2/=Math.max(1,fanIn):this.mode==="fanOut"?scale2/=Math.max(1,fanOut):scale2/=Math.max(1,(fanIn+fanOut)/2),this.distribution==="normal"){let stddev=Math.sqrt(scale2);if(dtype=dtype||"float32",dtype!=="float32"&&dtype!=="int32")throw new NotImplementedError(`${this.getClassName()} does not support dType ${dtype}.`);return truncatedNormal(shape,0,stddev,dtype,this.seed)}else{let limit=Math.sqrt(3*scale2);return randomUniform(shape,-limit,limit,dtype)}}getConfig(){return{scale:this.scale,mode:this.mode,distribution:this.distribution,seed:this.seed}}};VarianceScaling.className="VarianceScaling";serialization_exports.registerClass(VarianceScaling);var GlorotUniform=class extends VarianceScaling{constructor(args){super({scale:1,mode:"fanAvg",distribution:"uniform",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};GlorotUniform.className="GlorotUniform";serialization_exports.registerClass(GlorotUniform);var GlorotNormal=class extends VarianceScaling{constructor(args){super({scale:1,mode:"fanAvg",distribution:"normal",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};GlorotNormal.className="GlorotNormal";serialization_exports.registerClass(GlorotNormal);var HeNormal=class extends VarianceScaling{constructor(args){super({scale:2,mode:"fanIn",distribution:"normal",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};HeNormal.className="HeNormal";serialization_exports.registerClass(HeNormal);var HeUniform=class extends VarianceScaling{constructor(args){super({scale:2,mode:"fanIn",distribution:"uniform",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};HeUniform.className="HeUniform";serialization_exports.registerClass(HeUniform);var LeCunNormal=class extends VarianceScaling{constructor(args){super({scale:1,mode:"fanIn",distribution:"normal",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};LeCunNormal.className="LeCunNormal";serialization_exports.registerClass(LeCunNormal);var LeCunUniform=class extends VarianceScaling{constructor(args){super({scale:1,mode:"fanIn",distribution:"uniform",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};LeCunUniform.className="LeCunNormal";serialization_exports.registerClass(LeCunUniform);var Orthogonal=class extends Initializer{constructor(args){super();if(this.DEFAULT_GAIN=1,this.gain=args.gain==null?this.DEFAULT_GAIN:args.gain,this.seed=args.seed,this.seed!=null)throw new NotImplementedError("Random seed is not implemented for Orthogonal Initializer yet.")}apply(shape,dtype){return tidy(()=>{if(shape.length<2)throw new NotImplementedError("Shape must be at least 2D.");shape[0]*shape[1]>2e3&&console.warn(`Orthogonal initializer is being called on a matrix with more than 2000 (${shape[0]*shape[1]}) elements: Slowness may result.`);let normalizedShape=shape[0]>shape[1]?[shape[1],shape[0]]:shape,a=randomNormal2(normalizedShape,0,1,"float32"),q=linalg.gramSchmidt(a);return shape[0]>shape[1]&&(q=q.transpose()),mul(this.gain,q)})}getConfig(){return{gain:this.gain,seed:this.seed}}};Orthogonal.className="Orthogonal";serialization_exports.registerClass(Orthogonal);var INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP={constant:"Constant",glorotNormal:"GlorotNormal",glorotUniform:"GlorotUniform",heNormal:"HeNormal",heUniform:"HeUniform",identity:"Identity",leCunNormal:"LeCunNormal",leCunUniform:"LeCunUniform",ones:"Ones",orthogonal:"Orthogonal",randomNormal:"RandomNormal",randomUniform:"RandomUniform",truncatedNormal:"TruncatedNormal",varianceScaling:"VarianceScaling",zeros:"Zeros"};function deserializeInitializer(config3,customObjects={}){return deserializeKerasObject(config3,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"initializer")}function serializeInitializer(initializer){return serializeKerasObject(initializer)}function getInitializer(identifier){if(typeof identifier=="string"){let className=identifier in INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP?INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier]:identifier;if(className==="GlorotNormal")return new GlorotNormal;if(className==="GlorotUniform")return new GlorotUniform;if(className==="HeNormal")return new HeNormal;if(className==="HeUniform")return new HeUniform;if(className==="LeCunNormal")return new LeCunNormal;if(className==="LeCunUniform")return new LeCunUniform;{let config3={};return config3.className=className,config3.config={},deserializeInitializer(config3)}}else return identifier instanceof Initializer?identifier:deserializeInitializer(identifier)}function zeros3(){return new Zeros}function ones3(){return new Ones}function constant(args){return new Constant(args)}function randomUniform2(args){return new RandomUniform(args)}function randomNormal3(args){return new RandomNormal(args)}function truncatedNormal2(args){return new TruncatedNormal(args)}function identity4(args){return new Identity2(args)}function varianceScaling(config3){return new VarianceScaling(config3)}function glorotUniform(args){return new GlorotUniform(args)}function glorotNormal(args){return new GlorotNormal(args)}function heNormal(args){return new HeNormal(args)}function heUniform(args){return new HeUniform(args)}function leCunNormal(args){return new LeCunNormal(args)}function leCunUniform(args){return new LeCunUniform(args)}function orthogonal(args){return new Orthogonal(args)}var exports_layers_exports={};__export2(exports_layers_exports,{Layer:()=>Layer,RNN:()=>RNN,RNNCell:()=>RNNCell,activation:()=>activation,add:()=>add4,alphaDropout:()=>alphaDropout,average:()=>average,averagePooling1d:()=>averagePooling1d,averagePooling2d:()=>averagePooling2d,averagePooling3d:()=>averagePooling3d,avgPool1d:()=>avgPool1d,avgPool2d:()=>avgPool2d,avgPool3d:()=>avgPool3d2,avgPooling1d:()=>avgPooling1d,avgPooling2d:()=>avgPooling2d,avgPooling3d:()=>avgPooling3d,batchNormalization:()=>batchNormalization2,bidirectional:()=>bidirectional,concatenate:()=>concatenate2,conv1d:()=>conv1d2,conv2d:()=>conv2d5,conv2dTranspose:()=>conv2dTranspose2,conv3d:()=>conv3d2,convLstm2d:()=>convLstm2d,convLstm2dCell:()=>convLstm2dCell,cropping2D:()=>cropping2D,dense:()=>dense,depthwiseConv2d:()=>depthwiseConv2d5,dot:()=>dot3,dropout:()=>dropout3,elu:()=>elu5,embedding:()=>embedding,flatten:()=>flatten3,gaussianDropout:()=>gaussianDropout,gaussianNoise:()=>gaussianNoise,globalAveragePooling1d:()=>globalAveragePooling1d,globalAveragePooling2d:()=>globalAveragePooling2d,globalMaxPool1d:()=>globalMaxPool1d,globalMaxPool2d:()=>globalMaxPool2d,globalMaxPooling1d:()=>globalMaxPooling1d,globalMaxPooling2d:()=>globalMaxPooling2d,gru:()=>gru,gruCell:()=>gruCell,input:()=>input,inputLayer:()=>inputLayer,layerNormalization:()=>layerNormalization,leakyReLU:()=>leakyReLU,lstm:()=>lstm,lstmCell:()=>lstmCell,masking:()=>masking,maxPool1d:()=>maxPool1d,maxPool2d:()=>maxPool2d,maxPooling1d:()=>maxPooling1d,maxPooling2d:()=>maxPooling2d,maxPooling3d:()=>maxPooling3d,maximum:()=>maximum4,minimum:()=>minimum4,multiply:()=>multiply3,permute:()=>permute,prelu:()=>prelu5,reLU:()=>reLU,repeatVector:()=>repeatVector,reshape:()=>reshape5,rnn:()=>rnn2,separableConv2d:()=>separableConv2d2,simpleRNN:()=>simpleRNN,simpleRNNCell:()=>simpleRNNCell,softmax:()=>softmax5,spatialDropout1d:()=>spatialDropout1d,stackedRNNCells:()=>stackedRNNCells,thresholdedReLU:()=>thresholdedReLU,timeDistributed:()=>timeDistributed,upSampling2d:()=>upSampling2d,zeroPadding2d:()=>zeroPadding2d});var _nextUniqueTensorId=0;function getNextUniqueTensorId(){return _nextUniqueTensorId++}var _uidPrefixes={};function getUid(prefix=""){return prefix in _uidPrefixes||(_uidPrefixes[prefix]=0),_uidPrefixes[prefix]+=1,prefix+_uidPrefixes[prefix].toString()}function isArrayOfShapes(x){return Array.isArray(x)&&Array.isArray(x[0])}function normalizeShapeList(x){return x.length===0?[]:Array.isArray(x[0])?x:[x]}function getExactlyOneTensor(xs){let x;if(Array.isArray(xs)){if(xs.length!==1)throw new ValueError(`Expected Tensor length to be 1; got ${xs.length}`);x=xs[0]}else x=xs;return x}function getExactlyOneShape(shapes){if(Array.isArray(shapes)&&Array.isArray(shapes[0])){if(shapes.length===1)return shapes=shapes,shapes[0];throw new ValueError(`Expected exactly 1 Shape; got ${shapes.length}`)}else return shapes}function countParamsInWeights(weights){let count2=0;for(let weight of weights)weight.shape.length===0?count2+=1:count2+=weight.shape.reduce((a,b)=>a*b);return count2}var DEFAULT_VARIABLE_NAME_PREFIX="Variable",LayerVariable=class{constructor(val,dtype="float32",name=DEFAULT_VARIABLE_NAME_PREFIX,trainable=!0,constraint=null){this.dtype=dtype==null?"float32":dtype,this.shape=val.shape,this.id=getNextUniqueTensorId(),name=name==null?DEFAULT_VARIABLE_NAME_PREFIX:name,this.originalName=getScopedTensorName(name),this.name=getUniqueTensorName(this.originalName),this.trainable_=trainable,this.constraint=constraint,this.val=variable(val,this.trainable_,this.name,this.dtype)}read(){return this.assertNotDisposed(),this.val}write(newVal){return this.assertNotDisposed(),checkShapesMatch(this.val,newVal),this.val.id!==newVal.id&&(this.val.assign(newVal),this.constraint!=null&&this.val.assign(this.constraint.apply(this.val))),this}dispose(){this.assertNotDisposed(),this.val.dispose()}assertNotDisposed(){if(this.val.isDisposed)throw new Error(`LayersVariable ${this.name} is already disposed.`)}get trainable(){return this.trainable_}set trainable(trainable){this.trainable_=trainable,this.val.trainable=trainable}};function checkShapesMatch(x,y){if(x.shape.toString()!==y.shape.toString())throw new Error("Shape mismatch: "+JSON.stringify(x.shape)+" vs. "+JSON.stringify(y.shape))}function batchGetValue(xs){return xs.map(x=>x.read())}function batchSetValue(variablesAndValues){variablesAndValues.forEach(variableAndValue=>{variableAndValue[0].write(variableAndValue[1])})}var InputSpec=class{constructor(args){this.dtype=args.dtype,this.shape=args.shape,args.shape!=null?this.ndim=args.shape.length:this.ndim=args.ndim,this.maxNDim=args.maxNDim,this.minNDim=args.minNDim,this.axes=args.axes||{}}},SymbolicTensor=class{constructor(dtype,shape,sourceLayer,inputs,callArgs,name,outputTensorIndex){this.dtype=dtype,this.shape=shape,this.sourceLayer=sourceLayer,this.inputs=inputs,this.callArgs=callArgs,this.outputTensorIndex=outputTensorIndex,this.id=getNextUniqueTensorId(),name!=null&&(this.originalName=getScopedTensorName(name),this.name=getUniqueTensorName(this.originalName)),this.rank=shape.length}},_nextNodeID=0,Node=class{constructor(args,callArgs){this.callArgs=callArgs,this.id=_nextNodeID++,this.outboundLayer=args.outboundLayer,this.inboundLayers=args.inboundLayers,this.nodeIndices=args.nodeIndices,this.tensorIndices=args.tensorIndices,this.inputTensors=args.inputTensors,this.outputTensors=args.outputTensors,this.inputMasks=args.inputMasks,this.outputMasks=args.outputMasks,this.inputShapes=args.inputShapes,this.outputShapes=args.outputShapes;for(let layer of args.inboundLayers)layer!=null&&layer.outboundNodes.push(this);args.outboundLayer.inboundNodes.push(this)}getConfig(){let inboundNames=[];for(let layer of this.inboundLayers)layer!=null?inboundNames.push(layer.name):inboundNames.push(null);return{outboundLayer:this.outboundLayer?this.outboundLayer.name:null,inboundLayers:inboundNames,nodeIndices:this.nodeIndices,tensorIndices:this.tensorIndices}}},_nextLayerID=0,Layer=class extends serialization_exports.Serializable{constructor(args={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=_nextLayerID++,this.activityRegularizer=null,this.inputSpec=null,this.supportsMasking=!1,this._trainableWeights=[],this._nonTrainableWeights=[],this._losses=[],this._updates=[],this._built=!1,this.inboundNodes=[],this.outboundNodes=[];let name=args.name;if(!name){let prefix=this.getClassName();name=toSnakeCase(prefix)+"_"+getUid(prefix)}if(this.name=name,this.trainable_=args.trainable==null?!0:args.trainable,args.inputShape!=null||args.batchInputShape!=null){let batchInputShape;if(args.batchInputShape!=null)batchInputShape=args.batchInputShape;else if(args.inputShape!=null){let batchSize=null;args.batchSize!=null&&(batchSize=args.batchSize),batchInputShape=[batchSize].concat(args.inputShape)}this.batchInputShape=batchInputShape;let dtype=args.dtype;dtype==null&&(dtype=args.inputDType),dtype==null&&(dtype="float32"),this.dtype=dtype}args.weights!=null?this.initialWeights=args.weights:this.initialWeights=null,this._refCount=null,this.fastWeightInitDuringBuild=!1}static nodeKey(layer,nodeIndex){return layer.name+"_ib-"+nodeIndex.toString()}getNodeAtIndex(nodeIndex,attrName){if(this.inboundNodes.length===0)throw new RuntimeError(`The layer has never been called and thus has no defined ${attrName}.`);if(this.inboundNodes.length<=nodeIndex)throw new ValueError(`Asked to get ${attrName} at node ${nodeIndex}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[nodeIndex]}getInputAt(nodeIndex){return singletonOrArray(this.getNodeAtIndex(nodeIndex,"input").inputTensors)}getOutputAt(nodeIndex){return singletonOrArray(this.getNodeAtIndex(nodeIndex,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new AttributeError(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer input" is ill-defined. Use \`getInputAt(nodeIndex)\` instead.`);if(this.inboundNodes.length===0)throw new AttributeError(`Layer ${this.name} is not connected, no input to return.`);return singletonOrArray(this.getNodeAtIndex(0,"input").inputTensors)}get output(){if(this.inboundNodes.length===0)throw new AttributeError(`Layer ${this.name} has no inbound nodes.`);if(this.inboundNodes.length>1)throw new AttributeError(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer output" is ill-defined. Use \`getOutputAt(nodeIndex)\` instead.`);return singletonOrArray(this.getNodeAtIndex(0,"output").outputTensors)}get losses(){return this._losses}calculateLosses(){return this.losses.map(lossFn=>lossFn())}get updates(){return this._updates}get built(){return this._built}set built(built){this._built=built}get trainable(){return this.trainable_}set trainable(trainable){this._trainableWeights.forEach(w=>w.trainable=trainable),this.trainable_=trainable}get trainableWeights(){return this.trainable_?this._trainableWeights.filter(w=>w.trainable):[]}set trainableWeights(weights){this._trainableWeights=weights}get nonTrainableWeights(){return this.trainable?this._trainableWeights.filter(w=>!w.trainable).concat(this._nonTrainableWeights):this._trainableWeights.concat(this._nonTrainableWeights)}set nonTrainableWeights(weights){this._nonTrainableWeights=weights}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}get stateful(){return this._stateful}resetStates(){if(!this.stateful)throw new Error("Cannot call the resetStates() method of a non-stateful Layer object.")}assertInputCompatibility(inputs){if(inputs=toList(inputs),this.inputSpec==null||this.inputSpec.length===0)return;let inputSpec=toList(this.inputSpec);if(inputs.length!==inputSpec.length)throw new ValueError(`Layer ${this.name} expects ${inputSpec.length} inputs, but it received ${inputs.length} input tensors. Input received: ${inputs}`);for(let inputIndex=0;inputIndexspec.maxNDim)throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected max_ndim=${spec.maxNDim}, found ndim=${ndim}`);if(spec.minNDim!=null&&ndim=0?xShape[axis]:xShape[xShape.length+axis];if(value!=null&&[value,null].indexOf(xShapeAtAxis)===-1)throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected axis ${axis} of input shape to have value ${value} but got shape ${xShape}.`)}}if(spec.shape!=null)for(let i=0;i{if(!this.built){this.assertInputCompatibility(inputs);let inputShapes=[];for(let xElem of toList(inputs))inputShapes.push(xElem.shape);this.build(singletonOrArray(inputShapes)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&noneAreSymbolic&&(this._refCount=1)}if(this.assertInputCompatibility(inputs),noneAreSymbolic){let output=this.call(inputs,kwargs),outputList=toList(output),outputListCopy=[];for(let x of outputList)inputsList.indexOf(x)!==-1&&(x=x.clone()),outputListCopy.push(x);if(output=singletonOrArray(outputListCopy),this.activityRegularizer!=null)throw new NotImplementedError("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return output}else{let inputShape=collectInputShape(inputs),outputShape=this.computeOutputShape(inputShape),output,outputDType=guessOutputDType(inputs);if(this.warnOnIncompatibleInputShape(Array.isArray(inputs)?inputShape[0]:inputShape),outputShape!=null&&outputShape.length>0&&Array.isArray(outputShape[0])?output=outputShape.map((shape,index)=>new SymbolicTensor(outputDType,shape,this,toList(inputs),kwargs,this.name,index)):output=new SymbolicTensor(outputDType,outputShape,this,toList(inputs),kwargs,this.name),this.addInboundNode(inputs,output,null,null,inputShape,outputShape,kwargs),this._refCount++,this.activityRegularizer!=null)throw new NotImplementedError("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return output}})}warnOnIncompatibleInputShape(inputShape){if(this.batchInputShape!=null)if(inputShape.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(inputShape)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let dimMismatch=!1;this.batchInputShape.forEach((dimension,i)=>{dimension!=null&&inputShape[i]!=null&&inputShape[i]!==dimension&&(dimMismatch=!0)}),dimMismatch&&console.warn(`The shape of the input tensor (${JSON.stringify(inputShape)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}get outputShape(){if(this.inboundNodes==null||this.inboundNodes.length===0)throw new AttributeError(`The layer ${this.name} has never been called and thus has no defined output shape.`);let allOutputShapes=[];for(let node of this.inboundNodes){let shapeString=JSON.stringify(node.outputShapes);allOutputShapes.indexOf(shapeString)===-1&&allOutputShapes.push(shapeString)}if(allOutputShapes.length===1){let outputShapes=this.inboundNodes[0].outputShapes;return Array.isArray(outputShapes)&&Array.isArray(outputShapes[0])&&outputShapes.length===1?outputShapes[0]:outputShapes}else throw new AttributeError(`The layer ${this.name} has multiple inbound nodes with different output shapes. Hence the notion of "output shape" is ill-defined for the layer.`)}countParams(){if(!this.built)throw new RuntimeError(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return countParamsInWeights(this.weights)}build(inputShape){this.built=!0}getWeights(trainableOnly=!1){return batchGetValue(trainableOnly?this.trainableWeights:this.weights)}setWeights(weights){tidy(()=>{let params=this.weights;if(params.length!==weights.length)throw new ValueError(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${weights.length}, but the layer was expecting ${params.length} weights. Provided weights: ${weights}...`);if(params.length===0)return;let weightValueTuples=[],paramValues=batchGetValue(params);for(let i=0;iregularizer.apply(weight.read())),trainable==null&&(trainable=!0),trainable?this._trainableWeights.push(weight):this._nonTrainableWeights.push(weight),weight}setFastWeightInitDuringBuild(value){this.fastWeightInitDuringBuild=value}addLoss(losses4){losses4==null||Array.isArray(losses4)&&losses4.length===0||(losses4=toList(losses4),this._losses!==void 0&&this._losses!==null&&this.losses.push(...losses4))}computeOutputShape(inputShape){return inputShape}computeMask(inputs,mask){if(!this.supportsMasking){if(mask!=null)if(Array.isArray(mask))mask.forEach(maskElement=>{if(maskElement!=null)throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`)});else throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);return null}return mask}addInboundNode(inputTensors,outputTensors,inputMasks,outputMasks,inputShapes,outputShapes,kwargs=null){let inputTensorList=toList(inputTensors);outputTensors=toList(outputTensors),inputMasks=toList(inputMasks),outputMasks=toList(outputMasks),inputShapes=normalizeShapeList(inputShapes),outputShapes=normalizeShapeList(outputShapes);let inboundLayers=[],nodeIndices=[],tensorIndices=[];for(let x of inputTensorList)inboundLayers.push(x.sourceLayer),nodeIndices.push(x.nodeIndex),tensorIndices.push(x.tensorIndex);new Node({outboundLayer:this,inboundLayers,nodeIndices,tensorIndices,inputTensors:inputTensorList,outputTensors,inputMasks,outputMasks,inputShapes,outputShapes},kwargs);for(let i=0;iweight.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${this.name}' is already disposed.`)}dispose(){if(!this.built)throw new Error(`Cannot dispose Layer ${this.name} because it has not been built yet.`);if(this._refCount===null)throw new Error(`Cannot dispose Layer ${this.name} because it has not been used yet.`);this.assertNotDisposed();let numDisposedVariables=0;return--this._refCount==0&&(numDisposedVariables=this.disposeWeights()),{refCountAfterDispose:this._refCount,numDisposedVariables}}};function collectInputShape(inputTensors){inputTensors=toList(inputTensors);let shapes=[];for(let x of inputTensors)shapes.push(x.shape);return singletonOrArray(shapes)}function guessOutputDType(inputTensors){return"float32"}function getSourceInputs(tensor2,layer,nodeIndex){if((layer==null||nodeIndex!=null&&nodeIndex>0)&&(layer=tensor2.sourceLayer,nodeIndex=tensor2.nodeIndex),layer.inboundNodes.length===0)return[tensor2];{let node=layer.inboundNodes[nodeIndex];if(node.inboundLayers.length===0)return node.inputTensors;{let sourceTensors=[];for(let i=0;i0){let values=await Promise.all(promises);for(let i=0;iadd2(this.totals[key],mul(value,batchSize)));this.totals[key]=total,oldTotalsToDispose!=null&&oldTotalsToDispose.dispose()}}}async onEpochEnd(epoch,logs){if(logs!=null)for(let key of this.params.metrics)this.totals[key]!=null&&(typeof this.totals[key]=="number"?logs[key]=this.totals[key]/this.seen:tidy(()=>{let log5=mul(div(1,this.seen),this.totals[key]);logs[key]=log5,this.totals[key].dispose(),keep(logs[key])}))}},History=class extends BaseCallback{async onTrainBegin(logs){this.epoch=[],this.history={}}async onEpochEnd(epoch,logs){logs==null&&(logs={}),this.epoch.push(epoch);for(let key in logs)this.history[key]==null&&(this.history[key]=[]),this.history[key].push(logs[key])}async syncData(){let promises=[],keys=[],indices=[];for(let key in this.history){let valueArray=this.history[key];for(let i=0;inew CustomCallback(callbackConfig,yieldEvery))}var CallbackConstructorRegistry=class{constructor(){}static registerCallbackConstructor(verbosityLevel,callbackConstructor){util_exports.assert(verbosityLevel>=0&&Number.isInteger(verbosityLevel),()=>`Verbosity level is expected to be an integer >= 0, but got ${verbosityLevel}`),CallbackConstructorRegistry.checkForDuplicate(callbackConstructor),CallbackConstructorRegistry.constructors[verbosityLevel]==null&&(CallbackConstructorRegistry.constructors[verbosityLevel]=[]),CallbackConstructorRegistry.constructors[verbosityLevel].push(callbackConstructor)}static checkForDuplicate(callbackConstructor){for(let levelName in CallbackConstructorRegistry.constructors)CallbackConstructorRegistry.constructors[+levelName].forEach(ctor=>{if(ctor===callbackConstructor)throw new ValueError("Duplicate callback constructor.")})}static clear(){CallbackConstructorRegistry.constructors={}}static createCallbacks(verbosityLevel){let constructors=[];for(let levelName in CallbackConstructorRegistry.constructors){let level=+levelName;verbosityLevel>=level&&constructors.push(...CallbackConstructorRegistry.constructors[level])}return constructors.map(ctor=>new ctor)}};CallbackConstructorRegistry.constructors={};function configureCallbacks(callbacks2,verbose,epochs,initialEpoch,numTrainSamples,stepsPerEpoch,batchSize,doValidation,callbackMetrics){let history=new History,actualCallbacks=[new BaseLogger,...CallbackConstructorRegistry.createCallbacks(verbose)];callbacks2!=null&&actualCallbacks.push(...callbacks2),actualCallbacks.push(history);let callbackList=new CallbackList(actualCallbacks);return callbackList.setParams({epochs,initialEpoch,samples:numTrainSamples,steps:stepsPerEpoch,batchSize,verbose,doValidation,metrics:callbackMetrics}),{callbackList,history}}function deserialize(config3,customObjects={},fastWeightInit=!1){return deserializeKerasObject(config3,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"layer",fastWeightInit)}function l2Normalize(x,axis){return tidy(()=>{x.dtype!=="float32"&&(x=x.asType("float32"));let squareSum=sum2(square3(x),axis,!0),epsilonTensor=fill(squareSum.shape,epsilon2()),norm2=sqrt(maximum(squareSum,epsilonTensor));return div(x,norm2)})}function meanSquaredError2(yTrue,yPred){return tidy(()=>mean(square3(sub(yPred,yTrue)),-1))}function meanAbsoluteError(yTrue,yPred){return tidy(()=>mean(abs(sub(yPred,yTrue)),-1))}function meanAbsolutePercentageError(yTrue,yPred){return tidy(()=>{let diff=sub(yTrue,yPred),clippedTrue=clipByValue(abs(yTrue),epsilon2(),Number.MAX_VALUE),absResult=abs(div(diff,clippedTrue));return mul(100,mean(absResult,-1))})}function meanSquaredLogarithmicError(yTrue,yPred){return tidy(()=>{let clippedPred=clipByValue(yPred,epsilon2(),Number.MAX_VALUE),firstLog=log2(add2(1,clippedPred)),clippedTrue=clipByValue(yTrue,epsilon2(),Number.MAX_VALUE),secondLog=log2(add2(1,clippedTrue));return mean(square3(sub(firstLog,secondLog)),-1)})}function squaredHinge(yTrue,yPred){return tidy(()=>{let maxResult=maximum(0,sub(1,mul(yTrue,yPred)));return mean(square3(maxResult),-1)})}function hinge(yTrue,yPred){return tidy(()=>{let maxResult=maximum(0,sub(1,mul(yTrue,yPred)));return mean(maxResult,-1)})}function categoricalHinge(yTrue,yPred){return tidy(()=>{let pos=sum2(mul(yTrue,yPred),-1),neg4=max(mul(sub(1,yTrue),yPred),-1);return maximum(0,add2(1,sub(neg4,pos)))})}function logcosh(yTrue,yPred){return tidy(()=>{let log222=Math.log(2),predictionDiff=sub(yPred,yTrue),logcoshResult=sub(add2(predictionDiff,softplus(mul(-2,predictionDiff))),log222);return mean(logcoshResult,-1)})}function categoricalCrossentropy(target,output,fromLogits=!1){return tidy(()=>{if(fromLogits)output=softmax(output);else{let outputSum=sum2(output,output.shape.length-1,!0);output=div(output,outputSum)}return output=clipByValue(output,epsilon2(),1-epsilon2()),neg(sum2(mul(target.toFloat(),log2(output)),output.shape.length-1))})}function sparseCategoricalCrossentropy(target,output,fromLogits=!1){return tidy(()=>{let flatTarget=floor(flatten2(target)).toInt();output=clipByValue(output,epsilon2(),1-epsilon2());let outputShape=output.shape,oneHotTarget=oneHot(flatTarget,outputShape[outputShape.length-1]).reshape(outputShape);return categoricalCrossentropy(oneHotTarget,output,fromLogits)})}function sigmoidCrossEntropyWithLogits(labels,logits){if(!util_exports.arraysEqual(labels.shape,logits.shape))throw new ValueError(`logits and labels must have the same shape, but got shapes ${JSON.stringify(labels.shape)} and ${JSON.stringify(logits.shape)}`);return tidy(()=>{let reluLogits=logits.relu(),negAbsLogits=logits.abs().neg();return reluLogits.sub(logits.mul(labels)).add(negAbsLogits.exp().log1p())})}function binaryCrossentropy(yTrue,yPred){return tidy(()=>{let y;return y=clipByValue(yPred,epsilon2(),1-epsilon2()),y=log2(div(y,sub(1,y))),mean(sigmoidCrossEntropyWithLogits(yTrue,y),-1)})}function kullbackLeiblerDivergence(yTrue,yPred){return tidy(()=>{let clippedTrue=clipByValue(yTrue,epsilon2(),1),clippedPred=clipByValue(yPred,epsilon2(),1);return sum2(mul(yTrue,log2(div(clippedTrue,clippedPred))),-1)})}function poisson(yTrue,yPred){return tidy(()=>{let logPred=log2(add2(epsilon2(),yPred));return mean(sub(yPred,mul(yTrue,logPred)),-1)})}function cosineProximity(yTrue,yPred){return tidy(()=>{let trueNormalized=l2Normalize(yTrue,-1),predNormalized=l2Normalize(yPred,-1),trueXPred=mul(trueNormalized,predNormalized);return neg(sum2(trueXPred,-1))})}var lossesMap={meanSquaredError:meanSquaredError2,meanAbsoluteError,meanAbsolutePercentageError,meanSquaredLogarithmicError,squaredHinge,hinge,categoricalHinge,logcosh,categoricalCrossentropy,sparseCategoricalCrossentropy,binaryCrossentropy,kullbackLeiblerDivergence,poisson,cosineProximity};function get(identifierOrFn){if(typeof identifierOrFn=="string"){if(identifierOrFn in lossesMap)return lossesMap[identifierOrFn];let errMsg=`Unknown loss ${identifierOrFn}`;throw identifierOrFn.toLowerCase().includes("softmaxcrossentropy")&&(errMsg=`Unknown loss ${identifierOrFn}. Use "categoricalCrossentropy" as the string name for tf.losses.softmaxCrossEntropy`),new ValueError(errMsg)}else return identifierOrFn}function binaryAccuracy(yTrue,yPred){return tidy(()=>{let threshold2=mul(.5,onesLike(yPred)),yPredThresholded=cast5(greater(yPred,threshold2),yTrue.dtype);return mean(equal(yTrue,yPredThresholded),-1)})}function categoricalAccuracy(yTrue,yPred){return tidy(()=>cast5(equal(argMax(yTrue,-1),argMax(yPred,-1)),"float32"))}function truePositives(yTrue,yPred){return tidy(()=>logicalAnd(yTrue.equal(1),yPred.equal(1)).sum().cast("float32"))}function falseNegatives(yTrue,yPred){return tidy(()=>logicalAnd(yTrue.equal(1),yPred.equal(0)).sum().cast("float32"))}function falsePositives(yTrue,yPred){return tidy(()=>logicalAnd(yTrue.equal(0),yPred.equal(1)).sum().cast("float32"))}function precision(yTrue,yPred){return tidy(()=>{let tp=truePositives(yTrue,yPred),fp=falsePositives(yTrue,yPred),denominator=tp.add(fp);return where(greater(denominator,0),tp.div(denominator),0).cast("float32")})}function recall(yTrue,yPred){return tidy(()=>{let tp=truePositives(yTrue,yPred),fn=falseNegatives(yTrue,yPred),denominator=tp.add(fn);return where(greater(denominator,0),tp.div(denominator),0).cast("float32")})}function binaryCrossentropy2(yTrue,yPred){return binaryCrossentropy(yTrue,yPred)}function sparseCategoricalAccuracy(yTrue,yPred){return yTrue.rank===yPred.rank&&(yTrue=yTrue.squeeze([yTrue.rank-1])),yPred=yPred.argMax(-1),yPred.dtype!==yTrue.dtype&&(yPred=yPred.asType(yTrue.dtype)),equal(yTrue,yPred).asType("float32")}var mse=meanSquaredError2,MSE=meanSquaredError2,mae=meanAbsoluteError,MAE=meanAbsoluteError,mape=meanAbsolutePercentageError,MAPE=meanAbsolutePercentageError,categoricalCrossentropy2=categoricalCrossentropy,cosine=cosineProximity,sparseCategoricalCrossentropy2=sparseCategoricalCrossentropy,metricsMap={binaryAccuracy,categoricalAccuracy,precision,categoricalCrossentropy:categoricalCrossentropy2,sparseCategoricalCrossentropy:sparseCategoricalCrossentropy2,mse,MSE,mae,MAE,mape,MAPE,cosine};function get2(identifier){if(typeof identifier=="string"&&identifier in metricsMap)return metricsMap[identifier];if(typeof identifier!="string"&&identifier!=null)return identifier;throw new ValueError(`Unknown metric ${identifier}`)}function getLossOrMetricName(fn){if(assert2(fn!==null,`Unknown LossOrMetricFn ${fn}`),typeof fn=="string")return fn;{let fnName;for(let key of Object.keys(lossesMap))if(lossesMap[key]===fn){fnName=key;break}if(fnName!==void 0)return fnName;for(let key of Object.keys(metricsMap))if(metricsMap[key]===fn){fnName=key;break}return fnName!==void 0?fnName:fn.name}}function getOptimizer(identifier){let optimizerMap={Adagrad:()=>train.adagrad(.01),Adadelta:()=>train.adadelta(1,.95,epsilon2()),Adam:()=>train.adam(.001,.9,.999,epsilon2()),Adamax:()=>train.adamax(.002,.9,.999,epsilon2(),0),RMSProp:()=>train.rmsprop(.001,.9,0,epsilon2()),SGD:()=>train.sgd(.01)};if(optimizerMap.adagrad=optimizerMap.Adagrad,optimizerMap.adadelta=optimizerMap.Adadelta,optimizerMap.adam=optimizerMap.Adam,optimizerMap.adamax=optimizerMap.Adamax,optimizerMap.rmsprop=optimizerMap.RMSProp,optimizerMap.sgd=optimizerMap.SGD,identifier in optimizerMap)return optimizerMap[identifier]();throw new ValueError(`Unknown Optimizer ${identifier}`)}var MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH=1*1024*1024;function checkUserDefinedMetadata(userDefinedMetadata,modelName,checkSize=!1){if(userDefinedMetadata==null||typeof userDefinedMetadata!="object"||Object.getPrototypeOf(userDefinedMetadata)!==Object.prototype||!plainObjectCheck(userDefinedMetadata))throw new Error("User-defined metadata is expected to be a JSON object, but is not.");if(checkSize){let out=JSON.stringify(userDefinedMetadata);out.length>MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH&&console.warn(`User-defined metadata of model "${modelName}" is too large in size (length=${out.length} when serialized). It is not recommended to store such large objects in user-defined metadata. Please make sure its serialized length is <= ${MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH}.`)}}function plainObjectCheck(x){if(x===null)return!0;if(typeof x=="object")if(Object.getPrototypeOf(x)===Object.prototype){let keys=Object.keys(x);for(let key of keys)if(typeof key!="string"||!plainObjectCheck(x[key]))return!1;return!0}else if(Array.isArray(x)){for(let item of x)if(!plainObjectCheck(item))return!1;return!0}else return!1;else{let xType=typeof x;return xType==="string"||xType==="number"||xType==="boolean"}}function printSummary(model2,lineLength,positions,printFn=console.log){let sequentialLike=isModelSequentialLike(model2),toDisplay=["Layer (type)","Output shape","Param #"];sequentialLike?(lineLength=lineLength||65,positions=positions||[.45,.85,1]):(lineLength=lineLength||98,positions=positions||[.33,.55,.67,1]),positions[positions.length-1]<=1&&(positions=positions.map(p2=>Math.floor(lineLength*p2)));let relevantNodes;if(!sequentialLike){toDisplay.push("Receives inputs"),relevantNodes=[];for(let depth in model2.nodesByDepth)relevantNodes.push(...model2.nodesByDepth[depth])}printFn("_".repeat(lineLength)),printRow(toDisplay,positions,printFn),printFn("=".repeat(lineLength));let layers=model2.layers;for(let i=0;i1||depthNodes.length===1&&depthNodes[0].inboundLayers.length>1){sequentialLike=!1;break}nodes.push(...depthNodes)}if(sequentialLike)for(let layer of model2.layers){let flag=!1;for(let node of layer.inboundNodes)if(nodes.indexOf(node)!==-1)if(flag){sequentialLike=!1;break}else flag=!0;if(!sequentialLike)break}return sequentialLike}function printRow(fields,positions,printFn=console.log){let line="";for(let i=0;i0&&(line=line.slice(0,line.length-1)+" "),line+=fields[i],line=line.slice(0,positions[i]),line+=" ".repeat(positions[i]-line.length);printFn(line)}function printLayerSummary(layer,positions,printFn){let outputShape;try{outputShape=JSON.stringify(layer.outputShape)}catch(err){outputShape="multiple"}let name=layer.name,className=layer.getClassName(),fields=[`${name} (${className})`,outputShape,layer.countParams().toString()];printRow(fields,positions,printFn)}function printLayerSummaryWithConnections(layer,positions,relevantNodes,printFn){let outputShape;try{outputShape=JSON.stringify(layer.outputShape)}catch(err){outputShape="multiple"}let connections=[];for(let node of layer.inboundNodes)if(!(relevantNodes!=null&&relevantNodes.length>0&&relevantNodes.indexOf(node)===-1))for(let i=0;it.name),finalOutputs=[],feedNames=feedDict.names();for(let outputName of outputNames)feedNames.indexOf(outputName)!==-1?finalOutputs.push(feedDict.getValue(outputName)):finalOutputs.push(null);probe!=null&&(probe.maxNumTensors=-Infinity,probe.minNumTensors=Infinity);let fetchAndFeedKey=outputNames.join(",")+"|"+feedDict.names().join(","),sorted,recipientCounts;if(cachedSorted[fetchAndFeedKey]==null){let out=getTopologicalSortAndRecipientCounts(fetchArray,feedDict);sorted=out.sorted,recipientCounts=out.recipientCounts,cachedSorted[fetchAndFeedKey]=sorted,cachedRecipientCounts[fetchAndFeedKey]=recipientCounts}sorted=cachedSorted[fetchAndFeedKey],recipientCounts={},training||Object.assign(recipientCounts,cachedRecipientCounts[fetchAndFeedKey]);let internalFeedDict=new FeedDict(feedDict);for(let i=0;iprobe.maxNumTensors&&(probe.maxNumTensors=numTensors),numTensors0,()=>"Expected at least one fetch, got none");let finalSorted=[],finalRecipientMap={};if(fetches.length===1){let out=getTopologicalSortAndRecipientCountsForOneFetch(fetches[0],feedDict);finalSorted=out.sorted,finalRecipientMap=out.recipientMap}else{let visited=new Set;for(let fetch3 of fetches){let{sorted,recipientMap}=getTopologicalSortAndRecipientCountsForOneFetch(fetch3,feedDict);for(let symbolicTensor of sorted)visited.has(symbolicTensor.name)||(finalSorted.push(symbolicTensor),visited.add(symbolicTensor.name));for(let name in recipientMap)finalRecipientMap[name]==null&&(finalRecipientMap[name]=new Set),recipientMap[name].forEach(recipient=>finalRecipientMap[name].add(recipient))}}return{sorted:finalSorted,recipientCounts:recipientMap2Counts(finalRecipientMap)}}function recipientMap2Counts(recipientMap){let recipientCounts={};for(let name in recipientMap)recipientCounts[name]=recipientMap[name].size;return recipientCounts}function getTopologicalSortAndRecipientCountsForOneFetch(fetch3,feedDict){let visited=new Set,sorted=[],recipientMap={};for(let key of feedDict.names())visited.add(key);let stack2=[],marks=[];for(stack2.push(fetch3);stack2.length>0;){let top=stack2[stack2.length-1];if(visited.has(top.name)){stack2.pop();continue}let topIsMarked=marks[marks.length-1]===stack2.length-1;if(top.inputs.length===0||topIsMarked)stack2.pop(),sorted.push(top),visited.add(top.name),topIsMarked&&marks.pop();else{marks.push(stack2.length-1);for(let input2 of top.inputs)recipientMap[input2.name]==null&&(recipientMap[input2.name]=new Set),recipientMap[input2.name].add(top.name),!visited.has(input2.name)&&stack2.push(input2)}}return{sorted,recipientMap}}function getNodeOutputs(fetch3){let layerOutputs;if(fetch3.sourceLayer.inboundNodes.length===1)layerOutputs=fetch3.sourceLayer.output;else{let nodeIndex=null;for(let i=0;ix.name)}`);unique4(this.outputs).length!==this.outputs.length&&console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map(x=>x.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(let x of this.outputs){let layer=x.sourceLayer,nodeIndex=x.nodeIndex,tensorIndex=x.tensorIndex;this.outputLayers.push(layer),this.outputLayersNodeIndices.push(nodeIndex),this.outputLayersTensorIndices.push(tensorIndex)}for(let x of this.inputs){let layer=x.sourceLayer,nodeIndex=x.nodeIndex,tensorIndex=x.tensorIndex;assert2(nodeIndex===0,"input layer has >1 nodes"),assert2(tensorIndex===0,"input layer has >1 tensors"),this.inputLayers.push(layer),this.inputLayersNodeIndices.push(nodeIndex),this.inputLayersTensorIndices.push(tensorIndex)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let i=0;ix.shape),this.internalOutputShapes=this.outputs.map(x=>x.shape);let nodesDepths={},nodeIDToNode={},layersDepths={},layerIDToLayer={},layerIndices={},nodesInDecreasingDepth=[],buildMapOfGraph=(tensor2,finishedNodes2,nodesInProgress2,layer,nodeIndex,tensorIndex)=>{(layer==null||nodeIndex==null||tensorIndex==null)&&(layer=tensor2.sourceLayer,nodeIndex=tensor2.nodeIndex,tensorIndex=tensor2.tensorIndex);let node=layer.inboundNodes[nodeIndex];if(nodesInProgress2.indexOf(node)!==-1)throw new RuntimeError(`The tensor ${tensor2.name} at layer "${layer.name}" is part of a cycle.`);if(finishedNodes2.indexOf(node)!==-1)return;this.containerNodes.add(Container.nodeKey(layer,nodeIndex)),layer.id in layerIndices||(layerIndices[layer.id]=Object.keys(layerIndices).length),nodesInProgress2.indexOf(node)===-1&&nodesInProgress2.push(node);let numInboundLayers=node.inboundLayers.length;for(let i=0;i=0;)nodesInProgress2.splice(nodesInProgress2.indexOf(node),1);nodesInDecreasingDepth.push(node)},finishedNodes=[],nodesInProgress=[];for(let x of this.outputs)buildMapOfGraph(x,finishedNodes,nodesInProgress);let reversedNodesInDecreasingDepth=nodesInDecreasingDepth.slice().reverse();for(let node of reversedNodesInDecreasingDepth){nodeIDToNode[node.id]=node,node.id in nodesDepths||(nodesDepths[node.id]=0);let depth=nodesDepths[node.id],previousDepth=layersDepths[node.outboundLayer.id]==null?0:layersDepths[node.outboundLayer.id];depth=Math.max(depth,previousDepth),layersDepths[node.outboundLayer.id]=depth,layerIDToLayer[node.outboundLayer.id]=node.outboundLayer,nodesDepths[node.id]=depth;for(let i=0;iparseInt(x,10)).sort(reverseNumberCompare);this.layers=[];for(let depth of depthKeys){let layersForDepth=layersByDepth[depth];layersForDepth.sort((a,b)=>{let aIndex=layerIndices[a.id],bIndex=layerIndices[b.id];return aIndexbIndex?1:0});for(let layer of layersForDepth)layer instanceof Container&&this.internalContainerRefs.push(layer),this.layers.push(layer)}this.layersByDepth=layersByDepth,depthKeys=Object.keys(nodesByDepth).map(x=>parseInt(x,10)).sort(reverseNumberCompare);let computableTensors=this.inputs.slice(),layersWithCompleteInput=[];for(let depth of depthKeys)for(let node of nodesByDepth[depth]){let layer=node.outboundLayer;if(layer!=null){for(let x of node.inputTensors)if(computableTensors.indexOf(x)===-1)throw new RuntimeError(`Graph disconnected: cannot obtain value for tensor ${x} at layer "${layer.name}". The following previous layers were accessed without issue: ${layersWithCompleteInput}`);for(let x of node.outputTensors)computableTensors.push(x);layersWithCompleteInput.push(layer.name)}}this.nodesByDepth=nodesByDepth;let allNames=this.layers.map(x=>x.name);for(let name of allNames){let numOccurrences=allNames.filter(x=>x===name).length;if(numOccurrences!==1)throw new RuntimeError(`The name "${name}" is used ${numOccurrences} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(allNames))}this.outboundNodes=[],this.inboundNodes=[],new Node({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(x=>null),outputMasks:this.outputs.map(x=>null),inputShapes:this.inputs.map(x=>x.shape),outputShapes:this.outputs.map(x=>x.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();let result={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount==0){for(let layer of this.layers)result.numDisposedVariables+=layer.dispose().numDisposedVariables;for(let container of this.internalContainerRefs)result.numDisposedVariables+=container.dispose().numDisposedVariables}return result.refCountAfterDispose=this._refCount,result}get trainable(){return this.trainable_}set trainable(trainable){this.layers.forEach(layer=>{layer._trainableWeights.forEach(w=>w.trainable=trainable)}),this.trainable_=trainable}get trainableWeights(){if(this._trainableWeights.length>0)throw new ValueError("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let weights=[];for(let layer of this.layers)weights=weights.concat(layer.trainableWeights);return weights}get nonTrainableWeights(){let weights=[];for(let layer of this.layers)weights.push(...layer.nonTrainableWeights);if(!this.trainable){let trainableWeights=[];for(let layer of this.layers)trainableWeights.push(...layer.trainableWeights);return trainableWeights.concat(weights)}return weights}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(weights,strict=!0){let nameToWeight={},totalWeightsCount=0;for(let layer of this.layers)for(let weight of layer.weights){if(nameToWeight[weight.originalName]!=null)throw new ValueError(`Duplicate weight name: ${weight.originalName}`);nameToWeight[weight.originalName]=weight,totalWeightsCount++}let weightValueTuples=[];for(let name in weights){let validatedName=name;if(nameToWeight[name]==null){let tokens=name.split("/");validatedName=tokens.slice(0,-2).concat([tokens[tokens.length-1]]).join("/")}if(nameToWeight[validatedName]!=null)weightValueTuples.push([nameToWeight[validatedName],weights[name]]);else if(strict)throw new ValueError(`Provided weight data has no target variable: ${name}`);delete nameToWeight[validatedName]}if(strict){let unsetNames=[];for(let name in nameToWeight)unsetNames.push(name);if(unsetNames.length>0)throw new ValueError(`${unsetNames.length} of ${totalWeightsCount} weights are not set: ${unsetNames}`)}batchSetValue(weightValueTuples)}updatedConfig(){let theConfig=this.getConfig(),modelConfig={};return modelConfig.className=this.getClassName(),modelConfig.config=theConfig,modelConfig.kerasVersion=`tfjs-layers ${version10}`,modelConfig.backend="TensorFlow.js",modelConfig}toJSON(unused,returnString=!0){let modelConfig=convertTsToPythonic(this.updatedConfig());return returnString?JSON.stringify(modelConfig):modelConfig}call(inputs,kwargs){return tidy(()=>{inputs=toList(inputs);let feedDict=new FeedDict;for(let i=0;i{inputs=toList(inputs);let masks;return mask==null?masks=pyListRepeat(null,inputs.length):masks=toList(mask),this.runInternalGraph(inputs,masks)[1]})}computeOutputShape(inputShape){let inputShapes=normalizeShapeList(inputShape);if(inputShapes.length!==this.inputLayers.length)throw new ValueError(`Invalid inputShape argument ${inputShape}: model has ${this.inputLayers.length} tensor inputs.`);let layersToOutputShapes={};for(let i=0;iparseInt(x,10)).sort(reverseNumberCompare);if(depthKeys.length>1)for(let depth of depthKeys){let nodes=this.nodesByDepth[depth];for(let node of nodes){let layer=node.outboundLayer;if(this.inputLayers.map(x=>x.id).indexOf(layer.id)!==-1)continue;let inputShapes2=[];for(let j=0;jparseInt(x,10)).sort(reverseNumberCompare);for(let depth of depthKeys){let nodes=this.nodesByDepth[depth];for(let node of nodes){let layer=node.outboundLayer,referenceInputTensors=node.inputTensors,referenceOutputTensors=node.outputTensors,computedData=new Array;for(let x of referenceInputTensors)x.id in tensorMap&&computedData.push(tensorMap[x.id]);if(computedData.length===referenceInputTensors.length){let kwargs={},computedTensors,computedMasks,outputTensors2,outputMasks2;if(node.callArgs!=null&&(kwargs=node.callArgs),computedData.length===1){let[computedTensor,computedMask]=computedData[0];kwargs.mask==null&&(kwargs.mask=computedMask),outputTensors2=toList(layer.call(computedTensor,kwargs)),outputMasks2=toList(layer.computeMask(computedTensor,computedMask)),computedTensors=[computedTensor],computedMasks=[computedMask]}else computedTensors=computedData.map(x=>x[0]),computedMasks=computedData.map(x=>x[1]),kwargs.mask==null&&(kwargs.mask=computedMasks),outputTensors2=toList(layer.call(computedTensors,kwargs)),outputMasks2=toList(layer.computeMask(computedTensors,computedMasks));if(layer.activityRegularizer)throw new NotImplementedError("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let i=0;i{let losses4=[];for(let layer of this.layers)for(let nodeIndex=0;nodeIndex0){let nodeData=[];for(let i=0;i0&&layer.apply(singletonOrArray(inputTensors2),kwargs)}function processLayer(layerData){let layerName=layerData.name,layer=deserialize(layerData,config3.customObjects!=null?config3.customObjects:{});layer.setFastWeightInitDuringBuild(fastWeightInit),createdLayers[layerName]=layer,layerData.inboundNodes.forEach(nodeData=>{if(!(nodeData instanceof Array))throw new ValueError(`Corrupted configuration, expected array for nodeData: ${nodeData}`);addUnprocessedNode(layer,nodeData)})}let name=config3.name,layersFromConfig=config3.layers;for(let layerData of layersFromConfig)processLayer(layerData);for(;!isObjectEmpty(unprocessedNodes);)for(let layerData of layersFromConfig){let layer=createdLayers[layerData.name];if(layer.name in unprocessedNodes){let currentUnprocessedNodesForLayer=unprocessedNodes[layer.name];delete unprocessedNodes[layer.name];for(let nodeData of currentUnprocessedNodesForLayer)processNode(layer,nodeData)}}let inputTensors=[],outputTensors=[],inputLayersFromConfig=config3.inputLayers;for(let layerData of inputLayersFromConfig){let layerName=layerData[0],nodeIndex=layerData[1],tensorIndex=layerData[2];assert2(layerName in createdLayers);let layerOutputTensors=createdLayers[layerName].inboundNodes[nodeIndex].outputTensors;inputTensors.push(layerOutputTensors[tensorIndex])}let outputLayersFromConfig=config3.outputLayers;for(let layerData of outputLayersFromConfig){let layerName=layerData[0],nodeIndex=layerData[1],tensorIndex=layerData[2];assert2(layerName in createdLayers);let layerOutputTensors=createdLayers[layerName].inboundNodes[nodeIndex].outputTensors;outputTensors.push(layerOutputTensors[tensorIndex])}return new cls({inputs:inputTensors,outputs:outputTensors,name})}get stateful(){if(this._stateful)throw new ValueError("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(let layer of this.layers)if(layer.stateful)return!0;return!1}resetStates(){tidy(()=>{this.layers.forEach(layer=>{layer.stateful&&layer.resetStates()})})}};function standardizeSampleOrClassWeights(xWeight,outputNames,weightType){let numOutputs=outputNames.length;if(xWeight==null||Array.isArray(xWeight)&&xWeight.length===0)return outputNames.map(name=>null);if(numOutputs===1)return Array.isArray(xWeight)&&xWeight.length===1?xWeight:typeof xWeight=="object"&&outputNames[0]in xWeight?[xWeight[outputNames[0]]]:[xWeight];if(Array.isArray(xWeight)){if(xWeight.length!==numOutputs)throw new Error(`Provided ${weightType} is an array of ${xWeight.length} element(s), but the model has ${numOutputs} outputs. Make sure a set of weights is provided for each model output.`);return xWeight}else if(typeof xWeight=="object"&&Object.keys(xWeight).length>0&&typeof xWeight[Object.keys(xWeight)[0]]=="object"){let output=[];return outputNames.forEach(outputName=>{outputName in xWeight?output.push(xWeight[outputName]):output.push(null)}),output}else throw new Error(`The model has multiple (${numOutputs}) outputs, so ${weightType} must be either an array with ${numOutputs} elements or an object with ${outputNames} keys. Provided ${weightType} not understood: ${JSON.stringify(xWeight)}`)}function standardizeClassWeights(classWeight,outputNames){return standardizeSampleOrClassWeights(classWeight,outputNames,"classWeight")}async function standardizeWeights(y,sampleWeight,classWeight,sampleWeightMode){if(sampleWeight!=null||sampleWeightMode!=null)throw new Error("Support sampleWeight is not implemented yet");if(classWeight!=null){let yClasses=tidy(()=>{if(y.shape.length===1)return y.clone();if(y.shape.length===2)if(y.shape[1]>1){let axis=1;return y.argMax(axis)}else{if(y.shape[1]===1)return y.reshape([y.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${y.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${y.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),yClassIndices=Array.from(await yClasses.data());dispose(yClasses);let classSampleWeight=[];return yClassIndices.forEach(classIndex=>{if(classWeight[classIndex]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${classIndex} exists in the data but not in classWeight`);classSampleWeight.push(classWeight[classIndex])}),tensor1d(classSampleWeight,"float32")}else return null}function computeWeightedLoss2(losses4,sampleWeights){return mul(losses4,sampleWeights)}var DEFAULT_VALIDATION_BATCH_SIZE=32;function standardizeDataIteratorOutput(model2,iteratorOut){let xs,ys,iteratorOutObj=iteratorOut;xs=iteratorOutObj.xs,ys=iteratorOutObj.ys,util_exports.assert(xs!=null&&ys!=null,()=>`A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor. The provided Dataset instead generates ${iteratorOut}`);let flattenedXs=flattenTensorOrArrayOrMap("input",model2.inputNames,xs),flattenedYs=flattenTensorOrArrayOrMap("output",model2.outputNames,ys),batchSize=flattenedXs[0].shape[0];util_exports.assert(flattenedXs.length===model2.inputs.length,()=>`LayersModel has ${model2.inputs.length} inputs, but the dataset provides ${flattenedXs.length} inputs. (Expected input keys: ${JSON.stringify(model2.inputNames)})`),util_exports.assert(flattenedYs.length===model2.outputs.length,()=>`LayersModel has ${model2.outputs.length} outputs, but the dataset provides ${flattenedYs.length} outputs. (Expected output keys: ${JSON.stringify(model2.outputNames)})`);for(let xIndex=0;xIndex`Batch size mismatch: input ${model2.inputNames[xIndex]} has ${flattenedXs[xIndex].shape[0]}; expected ${batchSize} based on input ${model2.inputNames[0]}.`);for(let yIndex=0;yIndex`Batch size mismatch: output ${model2.outputNames[yIndex]} has ${flattenedYs[yIndex].shape[0]}; expected ${batchSize} based on input ${model2.inputNames[0]}.`);return{xs:flattenedXs,ys:flattenedYs}}function flattenTensorOrArrayOrMap(inputOrOutput,names,values){if(values instanceof Tensor)return[values];if(Array.isArray(values))return util_exports.assert(values.length===names.length,()=>`Received an array of ${values.length} Tensors, but expected ${names.length} to match the ${inputOrOutput} keys ${names}.`),values;{let result=[];for(let name of names){if(values[name]==null)throw new ValueError(`The feature data generated by the dataset lacks the required ${inputOrOutput} key '${name}'.`);result.push(values[name])}return result}}function standardizeTensorValidationData(data2){if(data2.length===3)throw new NotImplementedError("Validation with sample weights is not implemented yet.");return{xs:data2[0],ys:data2[1]}}async function fitDataset(model2,dataset,args){let hasBatchesPerEpoch=args.batchesPerEpoch!=null;if(util_exports.assert(model2.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),util_exports.assert(args!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),util_exports.assert(args.epochs!=null&&args.epochs>0&&Number.isInteger(args.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${args.epochs}`),util_exports.assert(!hasBatchesPerEpoch||args.batchesPerEpoch>0&&Number.isInteger(args.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${args.batchesPerEpoch}`),util_exports.assert(args.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. Use validationData instead."),model2.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");model2.isTraining=!0;try{let doValidation=args.validationData!=null,valXs,valYs;if(doValidation)if(isDatasetObject(args.validationData))util_exports.assert(args.validationBatches==null||args.validationBatches>0&&Number.isInteger(args.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${args.validationBatches}`);else{let validationData=standardizeTensorValidationData(args.validationData);valXs=validationData.xs,valYs=validationData.ys}let trainFunction=model2.makeTrainFunction(),outLabels=model2.getDedupedMetricsNames(),callbackMetrics;doValidation?callbackMetrics=outLabels.slice().concat(outLabels.map(n=>"val_"+n)):callbackMetrics=outLabels.slice();let callbacks2=standardizeCallbacks(args.callbacks,args.yieldEvery),verbose=args.verbose==null?1:args.verbose,{callbackList,history}=configureCallbacks(callbacks2,verbose,args.epochs,null,null,getStepsPerEpoch(dataset,args),null,doValidation,callbackMetrics);callbackList.setModel(model2),model2.history=history,await callbackList.onTrainBegin(),model2.stopTraining_=!1;let epoch=args.initialEpoch==null?0:args.initialEpoch,dataIterator=await dataset.iterator();for(;epoch=args.batchesPerEpoch:iteratorOut.done){if(doValidation){let valOuts;isDatasetObject(args.validationData)?valOuts=toList(await model2.evaluateDataset(args.validationData,{batches:args.validationBatches})):valOuts=toList(model2.evaluate(valXs,valYs,{batchSize:args.validationBatchSize==null?DEFAULT_VALIDATION_BATCH_SIZE:args.validationBatchSize,verbose:0}));for(let i=0;i0)throw new NotImplementedError("Verbose mode is not implemented yet.");util_exports.assert(!hasBatches||args.batches>0&&Number.isInteger(args.batches),()=>`Test loop expects \`batches\` to be a positive integer, but received ${JSON.stringify(args.batches)}`);let dataIterator=isLazyIteratorObject(dataset)?dataset:await dataset.iterator(),numExamples=0,batch=0;for(;hasBatches?batch{if(iteratorOut.value){let{xs,ys}=standardizeDataIteratorOutput(model2,iteratorOut.value),xsAndYs=xs.concat(ys),batchOuts=tidy(()=>f(xsAndYs));if(dispose(xsAndYs),batch===0)for(let i=0;iadd2(outs[i],mul(batchSize,batchOut))),batch>0&&dispose(oldScalar)}dispose(batchOuts),numExamples+=batchSize,++batch}return outs}),iteratorOut.done){hasBatches&&console.warn(`Your dataset iterator ran out of data during evaluateDataset(). Interrupting evalution. Make sure that your dataset can generate at least \`batches\` batches (in this case, ${args.batches} batches). You may need to use the repeat() function when building your dataset.`);break}}for(let i=0;i0&&Number.isInteger(batchSize),()=>`batchSize is required to be a positive integer, but got ${batchSize}`)}function sliceArrays(arrays,start,stop){return arrays==null?[null]:Array.isArray(arrays)?arrays.map(array2=>sliceAlongFirstAxis(array2,start,stop-start)):sliceAlongFirstAxis(arrays,start,stop-start)}function sliceArraysByIndices(arrays,indices){return tidy(()=>arrays==null?null:Array.isArray(arrays)?arrays.map(array2=>sliceArraysByIndices(array2,indices)):gather2(arrays,indices.dtype==="int32"?indices:indices.toInt()))}function makeBatches(size,batchSize){let output=[],batchStart=0,batchEnd=null;for(;batchStart=size&&(batchEnd=size),output.push([batchStart,batchEnd]),batchStart=batchEnd;return output}async function fitLoop(model2,f,ins,outLabels,batchSize,epochs,verbose,callbacks2,valF,valIns,shuffle2,callbackMetrics,initialEpoch,stepsPerEpoch,validationSteps){batchSize==null&&(batchSize=32),epochs==null&&(epochs=1),shuffle2==null&&(shuffle2=!0),initialEpoch==null&&(initialEpoch=0);let doValidation=!1;if(valF!=null&&valIns!=null&&(doValidation=!0),validationSteps!=null&&(doValidation=!0,stepsPerEpoch==null))throw new ValueError("Can only use `validationSteps` when doing step-wise training, i.e., `stepsPerEpoch` must be set.");let numTrainSamples=model2.checkNumSamples(ins,batchSize,stepsPerEpoch,"steps_per_epoch"),indexArray;numTrainSamples!=null&&(indexArray=range5(0,numTrainSamples)),verbose==null&&(verbose=1);let{callbackList,history}=configureCallbacks(callbacks2,verbose,epochs,initialEpoch,numTrainSamples,stepsPerEpoch,batchSize,doValidation,callbackMetrics);callbackList.setModel(model2),model2.history=history,await callbackList.onTrainBegin(),model2.stopTraining_=!1;for(let epoch=initialEpoch;epoch{let batchStart=batches[batchIndex][0],batchEnd=batches[batchIndex][1],batchIds=sliceAlongFirstAxis(epochIndexArray1D,batchStart,batchEnd-batchStart);batchLogs.batch=batchIndex,batchLogs.size=batchEnd-batchStart;let insBatch=sliceArraysByIndices(ins,batchIds),outs=f(insBatch);for(let i=0;i0){if(doValidation=!0,args.validationData.length===2)inputValX=args.validationData[0],inputValY=args.validationData[1];else throw args.validationData.length===3?new NotImplementedError("validationData including sample weights is not supported yet."):new ValueError(`When passing validation data, it must contain 2 (valX, valY) or 3 (valX, valY, valSampleWeight) items; ${args.validationData} is invalid.`);let checkBatchAxis2=!0,valStandardized=await model2.standardizeUserData(inputValX,inputValY,null,null,checkBatchAxis2,batchSize);valX=valStandardized[0],valY=valStandardized[1],valIns=valX.concat(valY)}else if(args.validationSplit!=null&&args.validationSplit>0&&args.validationSplit<1){doValidation=!0;let splitAt=Math.floor(inputs[0].shape[0]*(1-args.validationSplit)),originalBatchSize=inputs[0].shape[0];valX=sliceArrays(inputs,splitAt,originalBatchSize),inputs=sliceArrays(inputs,0,splitAt),valY=sliceArrays(targets,splitAt,originalBatchSize),targets=sliceArrays(targets,0,splitAt),valIns=valX.concat(valY)}else args.validationSteps!=null&&(doValidation=!0);let ins=inputs.concat(targets).concat(sampleWeights);model2.checkTrainableWeightsConsistency();let trainFunction=model2.makeTrainFunction(),outLabels=model2.getDedupedMetricsNames(),valFunction,callbackMetrics;doValidation?(model2.makeTestFunction(),valFunction=model2.testFunction,callbackMetrics=outLabels.slice().concat(outLabels.map(n=>"val_"+n))):(valFunction=null,valIns=[],callbackMetrics=outLabels.slice());let callbacks2=standardizeCallbacks(args.callbacks,args.yieldEvery);return await fitLoop(model2,trainFunction,ins,outLabels,batchSize,args.epochs,args.verbose,callbacks2,valFunction,valIns,args.shuffle,callbackMetrics,args.initialEpoch,null,null)}finally{model2.isTraining=!1,disposeNewTensors(inputs,x),disposeNewTensors(targets,y),disposeNewTensors(valX,inputValX),disposeNewTensors(valY,inputValY),sampleWeights!=null&&dispose(sampleWeights)}}function ensureTensorsRank2OrHigher(tensors){let outs=[];tensors instanceof Tensor&&(tensors=[tensors]);for(let i=0;ioldTensorIds.push(t.id));else if(refTensors!=null)for(let name in refTensors){let oldTensor=refTensors[name];oldTensorIds.push(oldTensor.id)}let tensorsToDispose=[];if(tensors instanceof Tensor)oldTensorIds.indexOf(tensors.id)===-1&&tensorsToDispose.push(tensors);else if(Array.isArray(tensors))tensors.forEach(t=>{oldTensorIds.indexOf(t.id)===-1&&tensorsToDispose.push(t)});else if(tensors!=null)for(let name in tensors){let tensor2=tensors[name];oldTensorIds.indexOf(tensor2.id)===-1&&tensorsToDispose.push(tensor2)}tensorsToDispose.forEach(t=>{t.isDisposed||t.dispose()})}function isDataTensor(x){return x instanceof Tensor}function isDataArray(x){return Array.isArray(x)}function isDataDict(x){return!isDataTensor(x)&&!isDataArray(x)}function standardizeInputData(data2,names,shapes,checkBatchAxis=!0,exceptionPrefix=""){if(names==null||names.length===0){if(data2!=null){let gotUnexpectedData=!1;if(isDataArray(data2)&&data2.length>0)gotUnexpectedData=!0;else if(isDataDict(data2)){for(let key in data2)if(data2.hasOwnProperty(key)){gotUnexpectedData=!0;break}}else gotUnexpectedData=!0;if(gotUnexpectedData)throw new ValueError(`Error when checking model ${exceptionPrefix} expected no data, but got ${data2}`)}return[]}if(data2==null)return names.map(name=>null);let arrays;if(isDataDict(data2)){data2=data2,arrays=[];for(let name of names){if(data2[name]==null)throw new ValueError(`No data provided for "${name}". Need data for each key in: ${names}`);arrays.push(data2[name])}}else if(isDataArray(data2)){if(data2=data2,data2.length!==names.length)throw new ValueError(`Error when checking model ${exceptionPrefix}: the Array of Tensors that you are passing to your model is not the size the model expected. Expected to see ${names.length} Tensor(s), but instead got the following list of Tensor(s): ${data2}`);arrays=data2}else{if(data2=data2,names.length>1)throw new ValueError(`The model ${exceptionPrefix} expects ${names.length} Tensor(s), but only received one Tensor. Found: Tensor with shape ${data2.shape}`);arrays=[data2]}if(arrays=ensureTensorsRank2OrHigher(arrays),shapes!=null)for(let i=0;i=0&&dim!==refDim)throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} to have shape [${shapes[i]}], but got array with shape [${array2.shape}].`)}}return arrays}function checkArrayLengths(inputs,targets,weights){let setX=unique4(inputs.map(input2=>input2.shape[0]));setX.sort();let setY=unique4(targets.map(target=>target.shape[0]));if(setY.sort(),setX.length>1)throw new ValueError(`All input Tensors (x) should have the same number of samples. Got array shapes: ${JSON.stringify(inputs.map(input2=>input2.shape))}`);if(setY.length>1)throw new ValueError(`All target Tensors (y) should have the same number of samples. Got array shapes: ${JSON.stringify(targets.map(target=>target.shape))}`);if(setX.length>0&&setY.length>0&&!util_exports.arraysEqual(setX,setY))throw new ValueError(`Input Tensors should have the same number of samples as target Tensors. Found ${setX[0]} input sample(s) and ${setY[0]} target sample(s).`)}function checkLossAndTargetCompatibility(targets,lossFns,outputShapes){let keyLosses=[meanSquaredError2,binaryCrossentropy,categoricalCrossentropy];for(let i=0;i1)throw new ValueError(`The model expects ${names.length} ${exceptionPrefix} Tensors, but only received one Tensor. Found: array with shape ${JSON.stringify(data2.shape)}.`);arrays=[data2]}if(shapes!=null)for(let i=0;i[]);let wrappedMetrics;if(typeof metrics2=="string"||typeof metrics2=="function")wrappedMetrics=[metrics2];else if(Array.isArray(metrics2)||typeof metrics2=="object")wrappedMetrics=metrics2;else throw new TypeError(`Type of metrics argument not understood. Expected an string,function, Array, or Object, found: ${metrics2}`);if(Array.isArray(wrappedMetrics))return outputNames.map(name=>wrappedMetrics);{let nestedMetrics=[];for(let name of outputNames){let outputMetrics=wrappedMetrics.hasOwnProperty(name)?wrappedMetrics[name]:[];Array.isArray(outputMetrics)||(outputMetrics=[outputMetrics]),nestedMetrics.push(outputMetrics)}return nestedMetrics}}var LAYERS_MODEL_FORMAT_NAME="layers-model",LayersModel=class extends Container{constructor(args){super(args);this.isTraining=!1}summary(lineLength,positions,printFn=console.log){if(!this.built)throw new ValueError("This model has never been called, thus its weights have not been created yet. So no summary can be displayed. Build the model first (e.g., by calling it on some test data).");printSummary(this,lineLength,positions,printFn)}compile(args){if(args.loss==null&&(args.loss=[]),this.loss=args.loss,typeof args.optimizer=="string")this.optimizer_=getOptimizer(args.optimizer),this.isOptimizerOwned=!0;else{if(!(args.optimizer instanceof Optimizer))throw new ValueError("User-defined optimizer must be an instance of tf.Optimizer.");this.optimizer_=args.optimizer,this.isOptimizerOwned=!1}let lossFunctions=[];if(!Array.isArray(args.loss)&&typeof args.loss!="string"&&typeof args.loss!="function"){args.loss=args.loss;for(let name in args.loss)if(this.outputNames.indexOf(name)===-1)throw new ValueError(`Unknown entry in loss dictionary: "${name}". Only expected the following keys: ${this.outputNames}`);for(let name of this.outputNames)args.loss[name]==null&&console.warn(`Output "${name}" is missing from loss dictionary. We assume this was done on purpose, and we will not be expecting data to be passed to ${name} during training`),lossFunctions.push(get(args.loss[name]))}else if(Array.isArray(args.loss)){if(args.loss.length!==this.outputs.length)throw new ValueError(`When passing an Array as loss, it should have one entry per model output. The model has ${this.outputs.length} output(s), but you passed loss=${args.loss}.`);lossFunctions=args.loss.map(l=>get(l))}else{let lossFunction=get(args.loss);this.outputs.forEach(_=>{lossFunctions.push(lossFunction)})}this.lossFunctions=lossFunctions,this.feedOutputNames=[],this.feedOutputShapes=[],this.feedLossFns=[];for(let i=0;i{for(let i=0;i1&&(this.metricsTensors.push([weightedLoss,i]),this.metricsNames.push(this.outputNames[i]+"_loss"))}});let nestedMetrics=collectMetrics(args.metrics,this.outputNames),appendMetric=(outputIndex,metricName,metricTensor)=>{this.outputNames.length>1&&(metricName=this.outputNames[outputIndex]+"_"+metricName),this.metricsNames.push(metricName),this.metricsTensors.push([metricTensor,outputIndex])};nameScope("metric",()=>{for(let i=0;i{let metricNamePrefix="",metricName,accFn,weightedMetricFn;for(let metric of metrics2){if(typeof metric=="string"&&["accuracy","acc","crossentropy","ce"].indexOf(metric)!==-1){let outputShape=this.internalOutputShapes[i];outputShape[outputShape.length-1]===1||this.lossFunctions[i]===binaryCrossentropy?["accuracy","acc"].indexOf(metric)!==-1?accFn=binaryAccuracy:["crossentropy","ce"].indexOf(metric)!==-1&&(accFn=binaryCrossentropy2):this.lossFunctions[i]===sparseCategoricalCrossentropy?["accuracy","acc"].indexOf(metric)!==-1?accFn=sparseCategoricalAccuracy:["crossentropy","ce"].indexOf(metric)!==-1&&(accFn=sparseCategoricalCrossentropy2):["accuracy","acc"].indexOf(metric)!==-1?accFn=categoricalAccuracy:["crossentropy","ce"].indexOf(metric)!==-1&&(accFn=categoricalCrossentropy2);let suffix;["accuracy","acc"].indexOf(metric)!==-1?suffix="acc":["crossentropy","ce"].indexOf(metric)!==-1&&(suffix="ce"),weightedMetricFn=accFn,metricName=metricNamePrefix+suffix}else weightedMetricFn=get2(metric),metricName=metricNamePrefix+getLossOrMetricName(metric);let metricResult;nameScope(metricName,()=>{metricResult=weightedMetricFn}),appendMetric(i,metricName,metricResult)}})(outputMetrics)}}),this.collectedTrainableWeights=this.trainableWeights}checkTrainableWeightsConsistency(){this.collectedTrainableWeights!=null&&this.trainableWeights.length!==this.collectedTrainableWeights.length&&console.warn("Discrepancy between trainableweights and collected trainable weights. Did you set `model.trainable` without calling `model.compile()` afterwards?")}evaluate(x,y,args={}){let batchSize=args.batchSize==null?32:args.batchSize;checkBatchSize(batchSize);let checkBatchAxis=!0,standardizedOuts=this.standardizeUserDataXY(x,y,checkBatchAxis,batchSize);try{let ins=standardizedOuts[0].concat(standardizedOuts[1]);this.makeTestFunction();let f=this.testFunction,testOuts=this.testLoop(f,ins,batchSize,args.verbose,args.steps);return singletonOrArray(testOuts)}finally{disposeNewTensors(standardizedOuts[0],x),disposeNewTensors(standardizedOuts[1],y)}}async evaluateDataset(dataset,args){return this.makeTestFunction(),evaluateDataset(this,dataset,args)}checkNumSamples(ins,batchSize,steps,stepsName="steps"){let numSamples;if(steps!=null){if(numSamples=null,batchSize!=null)throw new ValueError(`If ${stepsName} is set, batchSize must be null or undefined.Got batchSize = ${batchSize}`)}else if(ins!=null)Array.isArray(ins)?numSamples=ins[0].shape[0]:numSamples=ins.shape[0];else throw new ValueError(`Either the input data should have a defined shape, or ${stepsName} shoud be specified.`);return numSamples}execute(inputs,outputs){if(Array.isArray(outputs)&&outputs.length===0)throw new ValueError("`outputs` is an empty Array, which is not allowed.");let outputsIsArray=Array.isArray(outputs),outputNames=outputsIsArray?outputs:[outputs],outputSymbolicTensors=this.retrieveSymbolicTensors(outputNames),feedDict=new FeedDict;if(inputs instanceof Tensor&&(inputs=[inputs]),Array.isArray(inputs)){if(inputs.length!==this.inputs.length)throw new ValueError(`The number of inputs provided (${inputs.length}) does not match the number of inputs of this model (${this.inputs.length}).`);for(let i=0;ioutput.name);for(let i=0;i0){let remainingNames=[];throw outputSymbolicTensors.forEach((tensor2,i)=>{tensor2==null&&remainingNames.push(symbolicTensorNames[i])}),new ValueError(`Cannot find SymbolicTensors for output name(s): ${JSON.stringify(remainingNames)}`)}return outputSymbolicTensors}predictLoop(ins,batchSize=32,verbose=!1){return tidy(()=>{let numSamples=this.checkNumSamples(ins);if(verbose)throw new NotImplementedError("Verbose predictLoop() is not implemented yet.");let batches=makeBatches(numSamples,batchSize),outsBatches=this.outputs.map(output=>[]);for(let batchIndex=0;batchIndex{let batchStart=batches[batchIndex][0],batchEnd=batches[batchIndex][1],insBatch=sliceArrays(ins,batchStart,batchEnd),feeds=[];if(Array.isArray(insBatch))for(let i=0;ioutsBatches[i].push(batchOut));return singletonOrArray(outsBatches.map(batches2=>concat(batches2,0)))})}predict(x,args={}){let xsRank2OrHigher=ensureTensorsRank2OrHigher(x);checkInputData(xsRank2OrHigher,this.inputNames,this.feedInputShapes,!1);try{let batchSize=args.batchSize==null?32:args.batchSize;return checkBatchSize(batchSize),this.predictLoop(xsRank2OrHigher,batchSize)}finally{disposeNewTensors(xsRank2OrHigher,x)}}predictOnBatch(x){checkInputData(x,this.inputNames,this.feedInputShapes,!0);let batchSize=(Array.isArray(x)?x[0]:x).shape[0];return this.predictLoop(x,batchSize)}standardizeUserDataXY(x,y,checkBatchAxis=!0,batchSize){if(this.optimizer_==null)throw new RuntimeError("You must compile a model before training/testing. Use LayersModel.compile(modelCompileArgs).");let outputShapes=[];for(let i=0;i0&&x[0].shape[0]%batchSize!=0)throw new ValueError(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${batchSize}. Found: ${x[0].shape[0]} sample(s).`);return[x,y]}async standardizeUserData(x,y,sampleWeight,classWeight,checkBatchAxis=!0,batchSize){let[standardXs,standardYs]=this.standardizeUserDataXY(x,y,checkBatchAxis,batchSize);if(sampleWeight!=null)throw new Error("sample weight is not supported yet.");let standardSampleWeights=null;if(classWeight!=null){let classWeights=standardizeClassWeights(classWeight,this.outputNames);standardSampleWeights=[];for(let i=0;i{let numSamples=this.checkNumSamples(ins,batchSize,steps,"steps"),outs=[];if(verbose>0)throw new NotImplementedError("Verbose mode is not implemented yet.");if(steps!=null)throw new NotImplementedError("steps mode in testLoop() is not implemented yet");{let batches=makeBatches(numSamples,batchSize),indexArray=tensor1d(range5(0,numSamples));for(let batchIndex=0;batchIndex1&&(newLabel+=`_${count(outLabels.slice(0,i),label)}`),dedupedOutLabels.push(newLabel)}return dedupedOutLabels}makeTrainFunction(){return data2=>{let lossValues=[],inputs=data2.slice(0,this.inputs.length),targets=data2.slice(this.inputs.length,this.inputs.length+this.outputs.length),sampleWeights=data2.slice(this.inputs.length+this.outputs.length,this.inputs.length+this.outputs.length*2),metricsValues=[],totalLossFunction=()=>{let feeds=[];for(let i=0;i1&&i{totalLoss=add2(totalLoss,regularizerLoss)}),totalLoss},variables=this.collectedTrainableWeights.map(param=>param.read()),returnCost=!0;return[this.optimizer_.minimize(totalLossFunction,returnCost,variables)].concat(metricsValues)}}makeTestFunction(){this.testFunction=data2=>tidy(()=>{let valOutputs=[],totalLoss,inputs=data2.slice(0,this.inputs.length),targets=data2.slice(this.inputs.length,this.inputs.length+this.outputs.length),feeds=[];for(let i=0;itoSnakeCase(name))}else{let outputNames=Object.keys(this.loss);lossNames={};let losses4=this.loss;for(let outputName of outputNames)if(typeof losses4[outputName]=="string")lossNames[outputName]=toSnakeCase(losses4[outputName]);else throw new Error("Serialization of non-string loss is not supported.")}return lossNames}getMetricIdentifiers(){if(typeof this.metrics=="string"||typeof this.metrics=="function")return[toSnakeCase(getLossOrMetricName(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(metric=>toSnakeCase(getLossOrMetricName(metric)));{let metricsIdentifiers={};for(let key in this.metrics)metricsIdentifiers[key]=toSnakeCase(getLossOrMetricName(this.metrics[key]));return metricsIdentifiers}}getTrainingConfig(){return{loss:this.getLossIdentifiers(),metrics:this.getMetricIdentifiers(),optimizer_config:{class_name:this.optimizer.getClassName(),config:this.optimizer.getConfig()}}}loadTrainingConfig(trainingConfig){if(trainingConfig.weighted_metrics!=null)throw new Error("Loading weight_metrics is not supported yet.");if(trainingConfig.loss_weights!=null)throw new Error("Loading loss_weights is not supported yet.");if(trainingConfig.sample_weight_mode!=null)throw new Error("Loading sample_weight_mode is not supported yet.");let tsConfig=convertPythonicToTs(trainingConfig.optimizer_config),optimizer=deserialize(tsConfig),loss;if(typeof trainingConfig.loss=="string")loss=toCamelCase(trainingConfig.loss);else if(Array.isArray(trainingConfig.loss))loss=trainingConfig.loss.map(lossEntry=>toCamelCase(lossEntry));else if(trainingConfig.loss!=null){loss={};for(let key in trainingConfig.loss)loss[key]=toCamelCase(trainingConfig.loss[key])}let metrics2;if(Array.isArray(trainingConfig.metrics))metrics2=trainingConfig.metrics.map(metric=>toCamelCase(metric));else if(trainingConfig.metrics!=null){metrics2={};for(let key in trainingConfig.metrics)metrics2[key]=toCamelCase(trainingConfig.metrics[key])}this.compile({loss,metrics:metrics2,optimizer})}async save(handlerOrURL,config3){if(typeof handlerOrURL=="string"){let handlers=io_exports.getSaveHandlers(handlerOrURL);if(handlers.length===0)throw new ValueError(`Cannot find any save handlers for URL '${handlerOrURL}'`);if(handlers.length>1)throw new ValueError(`Found more than one (${handlers.length}) save handlers for URL '${handlerOrURL}'`);handlerOrURL=handlers[0]}if(handlerOrURL.save==null)throw new ValueError("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let weightDataAndSpecs=await io_exports.encodeWeights(this.getNamedWeights(config3)),returnString=!1,unusedArg=null,modelArtifacts={modelTopology:this.toJSON(unusedArg,returnString),format:LAYERS_MODEL_FORMAT_NAME,generatedBy:`TensorFlow.js tfjs-layers v${version10}`,convertedBy:null};if((config3==null?!1:config3.includeOptimizer)&&this.optimizer!=null){modelArtifacts.trainingConfig=this.getTrainingConfig();let weightType="optimizer",{data:optimizerWeightData,specs:optimizerWeightSpecs}=await io_exports.encodeWeights(await this.optimizer.getWeights(),weightType);weightDataAndSpecs.specs.push(...optimizerWeightSpecs),weightDataAndSpecs.data=io_exports.concatenateArrayBuffers([weightDataAndSpecs.data,optimizerWeightData])}if(this.userDefinedMetadata!=null){let checkSize=!0;checkUserDefinedMetadata(this.userDefinedMetadata,this.name,checkSize),modelArtifacts.userDefinedMetadata=this.userDefinedMetadata}return modelArtifacts.weightData=weightDataAndSpecs.data,modelArtifacts.weightSpecs=weightDataAndSpecs.specs,handlerOrURL.save(modelArtifacts)}setUserDefinedMetadata(userDefinedMetadata){checkUserDefinedMetadata(userDefinedMetadata,this.name),this.userDefinedMetadata=userDefinedMetadata}getUserDefinedMetadata(){return this.userDefinedMetadata}};LayersModel.className="Model";serialization_exports.registerClass(LayersModel);var Functional=class extends LayersModel{};Functional.className="Functional";serialization_exports.registerClass(Functional);async function modelFromJSON(modelAndWeightsConfig,customObjects){"modelTopology"in modelAndWeightsConfig||(modelAndWeightsConfig={modelTopology:modelAndWeightsConfig}),modelAndWeightsConfig=modelAndWeightsConfig;let modelTopology=modelAndWeightsConfig.modelTopology;modelTopology.model_config!=null&&(modelTopology=modelTopology.model_config);let tsConfig=convertPythonicToTs(modelTopology),model2=deserialize(tsConfig,customObjects);if(modelAndWeightsConfig.weightsManifest!=null){let weightValues=await io_exports.loadWeights(modelAndWeightsConfig.weightsManifest,modelAndWeightsConfig.pathPrefix,model2.weights.map(weight=>weight.originalName)),uniqueWeightValues={};for(let weight of model2.weights)uniqueWeightValues[weight.originalName]=weightValues[weight.originalName];model2.loadWeights(uniqueWeightValues),dispose(weightValues)}return model2}async function loadLayersModelInternal(pathOrIOHandler,options){if(options==null&&(options={}),typeof pathOrIOHandler=="string"){let handlers=io_exports.getLoadHandlers(pathOrIOHandler,options);if(handlers.length===0)handlers.push(io_exports.browserHTTPRequest(pathOrIOHandler,options));else if(handlers.length>1)throw new ValueError(`Found more than one (${handlers.length}) load handlers for URL '${pathOrIOHandler}'`);pathOrIOHandler=handlers[0]}return loadLayersModelFromIOHandler(pathOrIOHandler,void 0,options)}async function loadLayersModelFromIOHandler(handler,customObjects,options){if(options==null&&(options={}),handler.load==null)throw new ValueError("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let artifacts=await handler.load(),modelTopology=artifacts.modelTopology;modelTopology.model_config!=null&&(modelTopology=modelTopology.model_config);let strict=options.strict==null?!0:options.strict,fastWeightInit=artifacts.weightData!=null&&artifacts.weightSpecs!=null&&strict,model2=deserialize(convertPythonicToTs(modelTopology),customObjects,fastWeightInit),trainingConfig=artifacts.trainingConfig;if(trainingConfig!=null&&model2.loadTrainingConfig(trainingConfig),artifacts.userDefinedMetadata!=null&&model2.setUserDefinedMetadata(artifacts.userDefinedMetadata),artifacts.weightData!=null){if(artifacts.weightSpecs==null)throw new ValueError("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");let{modelWeights,optimizerWeights}=decodeModelAndOptimizerWeights(artifacts.weightData,artifacts.weightSpecs);model2.loadWeights(modelWeights,strict),model2.optimizer!=null&&optimizerWeights.length>0&&await model2.optimizer.setWeights(optimizerWeights),dispose(modelWeights),dispose(optimizerWeights.map(w=>w.tensor))}return model2}function decodeModelAndOptimizerWeights(buffer2,specs){let name2Tensor=io_exports.decodeWeights(buffer2,specs),modelWeights={},optimizerWeights=[];return specs.forEach(spec=>{spec.group==="optimizer"?optimizerWeights.push({name:spec.name,tensor:name2Tensor[spec.name]}):modelWeights[spec.name]=name2Tensor[spec.name]}),{modelWeights,optimizerWeights}}var Sequential=class extends LayersModel{constructor(args){super({inputs:[],outputs:[]});if(args=args||{},this.trainable=!0,this.built=!1,this.name=args.name!=null?args.name:getUid("sequential_"),args.layers!=null)for(let layer of args.layers)this.add(layer)}checkShape(layer){if(layer.inboundNodes[0].outputTensors[0].shape.some(x=>x<0))throw new ValueError(`Negative dimension size caused by adding layer ${layer.name} with input shape [${layer.inboundNodes[0].inputTensors[0].shape}]`)}add(layer){let isLayerModelInstance=layer instanceof Sequential||layer instanceof LayersModel,modelLayer;if(isLayerModelInstance){if(modelLayer=layer,modelLayer.outputs.length!==1)throw new ValueError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");if(modelLayer.inputs.length!==1)throw new ValueError("All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.")}if(this.outputs.length===0){if(layer.inboundNodes.length===0){if(layer.batchInputShape==null)throw new ValueError("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");let x=Input({batchShape:layer.batchInputShape,dtype:layer.dtype,name:layer.name+"_input"});layer.apply(x)}if(isLayerModelInstance)this.outputs=modelLayer.outputs,this.inputs=modelLayer.inputs;else{if(layer.inboundNodes.length!==1)throw new ValueError(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${layer.name} which has ${layer.inboundNodes.length} pre-existing inbound connections.`);if(layer.inboundNodes[0].outputTensors.length!==1)throw new ValueError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(layer),this.outputs=[layer.inboundNodes[0].outputTensors[0]],this.inputs=getSourceInputs(this.outputs[0])}this.inboundNodes=[],new Node({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:pyListRepeat(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map(x=>x.shape),outputShapes:this.outputs[0].shape})}else{let outputTensor=layer.apply(this.outputs[0]);if(Array.isArray(outputTensor))throw new TypeError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(layer),this.outputs=[outputTensor],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}this.layers.push(layer),this.built=!1}pop(){if(this.layers.length===0)throw new TypeError("There are no layers in the model.");if(this.layers.pop(),this.layers.length===0)this.outputs=[],this.inboundNodes=[],this.outboundNodes=[];else{let lastLayerIndex=this.layers.length-1;this.layers[lastLayerIndex].outboundNodes=[],this.outputs=[this.layers[lastLayerIndex].output],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}}call(inputs,kwargs){return this.model==null&&this.build(),this.model.call(inputs,kwargs)}build(inputShape){if(getExactlyOneShape(inputShape),this.inputs.length===0||this.outputs.length===0)throw new TypeError("Sequential model cannot be built: model is empty. Add some layers first.");this.model=new LayersModel({inputs:this.inputs,outputs:this.outputs[0],name:this.name+"_model"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0}countParams(){return this.built||this.build(),super.countParams()}summary(lineLength,positions,printFn=console.log){this.built||this.build(),super.summary(lineLength,positions,printFn)}setWeights(weights){this.model==null&&this.build(),this.model.setWeights(weights)}evaluate(x,y,args={}){if(!this.built)throw new RuntimeError("The model needs to be compiled before being used.");return this.model.evaluate(x,y,args)}async evaluateDataset(dataset,args){if(!this.built)throw new RuntimeError("The model needs to be compiled before being used.");return this.model.evaluateDataset(dataset,args)}predict(x,args={}){return this.model==null&&this.build(),this.model.predict(x,args)}predictOnBatch(x){return this.model==null&&this.build(),this.model.predictOnBatch(x)}compile(args){this.build(),this.model.compile(args),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(optimizer){this.model.optimizer=optimizer}async fit(x,y,args={}){if(!this.built)throw new RuntimeError("The model needs to be compiled before being used.");return this.model.fit(x,y,args)}async fitDataset(dataset,args){if(!this.built)throw new RuntimeError("The model needs to be compiled before being used.");return this.model.fitDataset(dataset,args)}async trainOnBatch(x,y){return this.model.trainOnBatch(x,y)}static fromConfig(cls,config3,customObjects={},fastWeightInit=!1){let configArray,extraModelConfig={};if(config3 instanceof Array){if(config3[0].className==null||config3[0].className==="Merge")throw new ValueError("Legacy serialization format not supported yet.");configArray=config3}else util_exports.assert(config3.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),configArray=config3.layers,delete config3.layers,extraModelConfig=config3;let model2=new cls(extraModelConfig);if(!(model2 instanceof Sequential))throw new NotImplementedError(`Sequential.fromConfig called on non-Sequential input: ${model2}`);for(let conf of configArray){let layer=deserialize(conf,void 0,fastWeightInit);fastWeightInit&&layer.setFastWeightInitDuringBuild(!0),model2.add(layer)}return model2}set stopTraining(stop){if(this.model==null)throw new ValueError("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=stop}get stopTraining(){if(this.model==null)throw new ValueError("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let layers=[];for(let layer of this.layers){let dict={};dict.className=layer.getClassName(),dict.config=layer.getConfig(),layers.push(dict)}return{name:this.name,layers}}};Sequential.className="Sequential";serialization_exports.registerClass(Sequential);function model(args){return new LayersModel(args)}function sequential(config3){return new Sequential(config3)}function loadLayersModel(pathOrIOHandler,options){return options==null&&(options={}),loadLayersModelInternal(pathOrIOHandler,options)}function input(config3){return Input(config3)}function registerCallbackConstructor(verbosityLevel,callbackConstructor){CallbackConstructorRegistry.registerCallbackConstructor(verbosityLevel,callbackConstructor)}var Activation=class extends serialization_exports.Serializable{getConfig(){return{}}},Elu2=class extends Activation{apply(x,alpha=1){return elu4(x,alpha)}};Elu2.className="elu";serialization_exports.registerClass(Elu2);var Selu2=class extends Activation{apply(x){return selu(x)}};Selu2.className="selu";serialization_exports.registerClass(Selu2);var Relu2=class extends Activation{apply(x){return relu(x)}};Relu2.className="relu";serialization_exports.registerClass(Relu2);var Relu62=class extends Activation{apply(x){return tidy(()=>minimum(6,relu(x)))}};Relu62.className="relu6";serialization_exports.registerClass(Relu62);var Linear=class extends Activation{apply(x){return x}};Linear.className="linear";serialization_exports.registerClass(Linear);var Sigmoid2=class extends Activation{apply(x){return sigmoid(x)}};Sigmoid2.className="sigmoid";serialization_exports.registerClass(Sigmoid2);var HardSigmoid=class extends Activation{apply(x){return hardSigmoid(x)}};HardSigmoid.className="hardSigmoid";serialization_exports.registerClass(HardSigmoid);var Softplus2=class extends Activation{apply(x){return softplus(x)}};Softplus2.className="softplus";serialization_exports.registerClass(Softplus2);var Softsign=class extends Activation{apply(x){return softsign(x)}};Softsign.className="softsign";serialization_exports.registerClass(Softsign);var Tanh2=class extends Activation{apply(x){return tanh2(x)}};Tanh2.className="tanh";serialization_exports.registerClass(Tanh2);var Softmax2=class extends Activation{apply(x,axis=-1){return softmax(x,axis)}};Softmax2.className="softmax";serialization_exports.registerClass(Softmax2);var LogSoftmax2=class extends Activation{apply(x,axis=-1){return logSoftmax(x,axis)}};LogSoftmax2.className="logSoftmax";serialization_exports.registerClass(LogSoftmax2);var Swish=class extends Activation{apply(x,alpha=1){return tidy(()=>sigmoid(x.mul(alpha)).mul(x))}};Swish.className="swish";serialization_exports.registerClass(Swish);function serializeActivation(activation2){return activation2.getClassName()}function deserializeActivation(config3,customObjects={}){return deserializeKerasObject(config3,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"activation")}function getActivation(identifier){if(identifier==null){let config3={};return config3.className="linear",config3.config={},deserializeActivation(config3)}if(typeof identifier=="string"){let config3={};return config3.className=identifier,config3.config={},deserializeActivation(config3)}else return identifier instanceof Activation?identifier:deserializeActivation(identifier)}function assertObjectArgs(args){if(args!=null&&typeof args!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${args}`)}var Regularizer=class extends serialization_exports.Serializable{},L1L2=class extends Regularizer{constructor(args){super();assertObjectArgs(args),this.l1=args==null||args.l1==null?.01:args.l1,this.l2=args==null||args.l2==null?.01:args.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(x){return tidy(()=>{let regularization=zeros([1]);return this.hasL1&&(regularization=add2(regularization,sum2(mul(this.l1,abs(x))))),this.hasL2&&(regularization=add2(regularization,sum2(mul(this.l2,square3(x))))),regularization.asScalar()})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(cls,config3){return new cls({l1:config3.l1,l2:config3.l2})}};L1L2.className="L1L2";serialization_exports.registerClass(L1L2);function l1(args){return assertObjectArgs(args),new L1L2({l1:args!=null?args.l1:null,l2:0})}function l2(args){return assertObjectArgs(args),new L1L2({l2:args!=null?args.l2:null,l1:0})}var REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP={l1l2:"L1L2"};function serializeRegularizer(constraint){return serializeKerasObject(constraint)}function deserializeRegularizer(config3,customObjects={}){return deserializeKerasObject(config3,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"regularizer")}function getRegularizer(identifier){if(identifier==null)return null;if(typeof identifier=="string"){let config3={className:identifier in REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP?REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier]:identifier,config:{}};return deserializeRegularizer(config3)}else return identifier instanceof Regularizer?identifier:deserializeRegularizer(identifier)}var ReLU=class extends Layer{constructor(args){super(args==null?{}:args);this.supportsMasking=!0,args!=null&&(this.maxValue=args.maxValue)}call(inputs,kwargs){inputs=getExactlyOneTensor(inputs);let output=relu(inputs);return this.maxValue!=null&&(output=clipByValue(output,0,this.maxValue)),output}computeOutputShape(inputShape){return inputShape}getConfig(){let config3={maxValue:this.maxValue},baseConfig=super.getConfig();return Object.assign(config3,baseConfig),config3}};ReLU.className="ReLU";serialization_exports.registerClass(ReLU);var LeakyReLU=class extends Layer{constructor(args){super(args==null?{}:args);this.DEFAULT_ALPHA=.3,args==null&&(args={}),this.alpha=args.alpha==null?this.DEFAULT_ALPHA:args.alpha}call(inputs,kwargs){let x=getExactlyOneTensor(inputs);return leakyRelu(x,this.alpha)}computeOutputShape(inputShape){return inputShape}getConfig(){let config3={alpha:this.alpha},baseConfig=super.getConfig();return Object.assign(config3,baseConfig),config3}};LeakyReLU.className="LeakyReLU";serialization_exports.registerClass(LeakyReLU);var PReLU=class extends Layer{constructor(args){super(args==null?{}:args);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",args==null&&(args={}),this.supportsMasking=!0,this.alphaInitializer=getInitializer(args.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=getRegularizer(args.alphaRegularizer),this.alphaConstraint=getConstraint(args.alphaConstraint),args.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(args.sharedAxes))this.sharedAxes=args.sharedAxes;else if(typeof args.sharedAxes=="number")this.sharedAxes=[args.sharedAxes];else throw new ValueError(`Expected sharedAxes to be a number or an array of numbers, but got ${args.sharedAxes}`)}build(inputShape){inputShape=getExactlyOneShape(inputShape);let paramShape=inputShape.slice(1);if(this.sharedAxes!=null)for(let i of this.sharedAxes)paramShape[i-1]=1;this.alpha=this.addWeight("alpha",paramShape,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let axes={};if(this.sharedAxes!=null)for(let i=1;i(checkDataFormat(dataFormat),dataFormat==="channelsFirst"?transpose(x,[0,2,3,1]):x))}function preprocessConv3DInput(x,dataFormat){return tidy(()=>(checkDataFormat(dataFormat),dataFormat==="channelsFirst"?transpose(x,[0,2,3,4,1]):x))}function conv1dWithBias(x,kernel,bias,strides=1,padding="valid",dataFormat,dilationRate=1){return tidy(()=>{if(dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat),x.shape.length!==3)throw new ValueError(`The input of a conv1dWithBias operation should be 3, but is ${x.shape.length} instead.`);if(kernel.shape.length!==3)throw new ValueError(`The kernel for a conv1dWithBias operation should be 3, but is ${kernel.shape.length} instead`);if(bias!=null&&bias.shape.length!==1)throw new ValueError(`The bias for a conv1dWithBias operation should be 1, but is ${kernel.shape.length} instead`);if(dataFormat==="channelsFirst"&&(x=transpose(x,[0,2,1])),padding==="causal")throw new NotImplementedError("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let y=conv1d(x,kernel,strides,padding==="same"?"same":"valid","NWC",dilationRate);return bias!=null&&(y=biasAdd(y,bias)),y})}function conv2dWithBiasActivation(x,kernel,bias,strides=[1,1],padding="valid",dataFormat,dilationRate,activation2=null){return tidy(()=>{if(dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat),x.rank!==3&&x.rank!==4)throw new ValueError(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${x.rank}.`);if(kernel.rank!==3&&kernel.rank!==4)throw new ValueError(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${x.rank}.`);let y=preprocessConv2DInput(x,dataFormat);if(padding==="causal")throw new NotImplementedError("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return y=fused_ops_exports.conv2d({x:y,filter:kernel,strides,pad:padding==="same"?"same":"valid",dilations:dilationRate,dataFormat:"NHWC",bias,activation:activation2}),dataFormat==="channelsFirst"&&(y=transpose(y,[0,3,1,2])),y})}function conv3dWithBias(x,kernel,bias,strides=[1,1,1],padding="valid",dataFormat,dilationRate){return tidy(()=>{if(dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat),x.rank!==4&&x.rank!==5)throw new ValueError(`conv3dWithBias expects input to be of rank 4 or 5, but received ${x.rank}.`);if(kernel.rank!==4&&kernel.rank!==5)throw new ValueError(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${x.rank}.`);let y=preprocessConv3DInput(x,dataFormat);if(padding==="causal")throw new NotImplementedError("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return y=conv3d(y,kernel,strides,padding==="same"?"same":"valid","NDHWC",dilationRate),bias!=null&&(y=biasAdd(y,bias)),dataFormat==="channelsFirst"&&(y=transpose(y,[0,4,1,2,3])),y})}var BaseConv=class extends Layer{constructor(rank,args){super(args);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",BaseConv.verifyArgs(args),this.rank=rank,assertPositiveInteger(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new NotImplementedError(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=normalizeArray(args.kernelSize,rank,"kernelSize"),this.strides=normalizeArray(args.strides==null?1:args.strides,rank,"strides"),this.padding=args.padding==null?"valid":args.padding,checkPaddingMode(this.padding),this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),this.activation=getActivation(args.activation),this.useBias=args.useBias==null?!0:args.useBias,this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=getConstraint(args.biasConstraint),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.activityRegularizer=getRegularizer(args.activityRegularizer),this.dilationRate=normalizeArray(args.dilationRate==null?1:args.dilationRate,rank,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new ValueError(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new ValueError(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new ValueError(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(args){if(assert2("kernelSize"in args,"required key 'kernelSize' not in config"),typeof args.kernelSize!="number"&&!checkArrayTypeAndLength(args.kernelSize,"number",1,3))throw new ValueError(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(args.kernelSize)}.`)}getConfig(){let config3={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:serializeActivation(this.activation),useBias:this.useBias,biasInitializer:serializeInitializer(this.biasInitializer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),biasConstraint:serializeConstraint(this.biasConstraint)},baseConfig=super.getConfig();return Object.assign(config3,baseConfig),config3}},Conv=class extends BaseConv{constructor(rank,args){super(rank,args);this.kernel=null,Conv.verifyArgs(args),this.filters=args.filters,assertPositiveInteger(this.filters,"filters"),this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=getConstraint(args.kernelConstraint),this.kernelRegularizer=getRegularizer(args.kernelRegularizer)}build(inputShape){inputShape=getExactlyOneShape(inputShape);let channelAxis=this.dataFormat==="channelsFirst"?1:inputShape.length-1;if(inputShape[channelAxis]==null)throw new ValueError(`The channel dimension of the input should be defined. Found ${inputShape[channelAxis]}`);let inputDim=inputShape[channelAxis],kernelShape=this.kernelSize.concat([inputDim,this.filters]);this.kernel=this.addWeight("kernel",kernelShape,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[channelAxis]:inputDim}}],this.built=!0}call(inputs,kwargs){return tidy(()=>{inputs=getExactlyOneTensor(inputs);let outputs,biasValue=this.bias==null?null:this.bias.read(),fusedActivationName=mapActivationToFusedKernel(this.activation.getClassName());if(fusedActivationName!=null&&this.rank===2)outputs=conv2dWithBiasActivation(inputs,this.kernel.read(),biasValue,this.strides,this.padding,this.dataFormat,this.dilationRate,fusedActivationName);else{if(this.rank===1)outputs=conv1dWithBias(inputs,this.kernel.read(),biasValue,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)outputs=conv2dWithBiasActivation(inputs,this.kernel.read(),biasValue,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)outputs=conv3dWithBias(inputs,this.kernel.read(),biasValue,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new NotImplementedError("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(outputs=this.activation.apply(outputs))}return outputs})}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let newSpace=[],space=this.dataFormat==="channelsLast"?inputShape.slice(1,inputShape.length-1):inputShape.slice(2);for(let i=0;i 0 but got ${JSON.stringify(args.filters)}`)}},Conv2D2=class extends Conv{constructor(args){super(2,args);Conv2D2.verifyArgs(args)}getConfig(){let config3=super.getConfig();return delete config3.rank,config3}static verifyArgs(args){if(typeof args.kernelSize!="number"&&!checkArrayTypeAndLength(args.kernelSize,"number",1,2))throw new ValueError(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(args.kernelSize)}.`)}};Conv2D2.className="Conv2D";serialization_exports.registerClass(Conv2D2);var Conv3D2=class extends Conv{constructor(args){super(3,args);Conv3D2.verifyArgs(args)}getConfig(){let config3=super.getConfig();return delete config3.rank,config3}static verifyArgs(args){if(typeof args.kernelSize!="number"&&!(Array.isArray(args.kernelSize)&&(args.kernelSize.length===1||args.kernelSize.length===3)))throw new ValueError(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(args.kernelSize)}.`)}};Conv3D2.className="Conv3D";serialization_exports.registerClass(Conv3D2);var Conv2DTranspose=class extends Conv2D2{constructor(args){super(args);if(this.inputSpec=[new InputSpec({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new ValueError(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(inputShape){if(inputShape=getExactlyOneShape(inputShape),inputShape.length!==4)throw new ValueError("Input should have rank 4; Received input shape: "+JSON.stringify(inputShape));let channelAxis=this.dataFormat==="channelsFirst"?1:inputShape.length-1;if(inputShape[channelAxis]==null)throw new ValueError("The channel dimension of the inputs should be defined. Found `None`.");let inputDim=inputShape[channelAxis],kernelShape=this.kernelSize.concat([this.filters,inputDim]);this.kernel=this.addWeight("kernel",kernelShape,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new InputSpec({ndim:4,axes:{[channelAxis]:inputDim}})],this.built=!0}call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);if(input2.shape.length!==4)throw new ValueError(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${input2.shape.length}`);let inputShape=input2.shape,batchSize=inputShape[0],hAxis,wAxis;this.dataFormat==="channelsFirst"?(hAxis=2,wAxis=3):(hAxis=1,wAxis=2);let height=inputShape[hAxis],width=inputShape[wAxis],kernelH=this.kernelSize[0],kernelW=this.kernelSize[1],strideH=this.strides[0],strideW=this.strides[1],outHeight=deconvLength(height,strideH,kernelH,this.padding),outWidth=deconvLength(width,strideW,kernelW,this.padding),outputShape=[batchSize,outHeight,outWidth,this.filters];this.dataFormat!=="channelsLast"&&(input2=transpose(input2,[0,2,3,1]));let outputs=conv2dTranspose(input2,this.kernel.read(),outputShape,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(outputs=transpose(outputs,[0,3,1,2])),this.bias!=null&&(outputs=biasAdd(outputs,this.bias.read(),this.dataFormat)),this.activation!=null&&(outputs=this.activation.apply(outputs)),outputs})}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let outputShape=inputShape.slice(),channelAxis,heightAxis,widthAxis;this.dataFormat==="channelsFirst"?(channelAxis=1,heightAxis=2,widthAxis=3):(channelAxis=3,heightAxis=1,widthAxis=2);let kernelH=this.kernelSize[0],kernelW=this.kernelSize[1],strideH=this.strides[0],strideW=this.strides[1];return outputShape[channelAxis]=this.filters,outputShape[heightAxis]=deconvLength(outputShape[heightAxis],strideH,kernelH,this.padding),outputShape[widthAxis]=deconvLength(outputShape[widthAxis],strideW,kernelW,this.padding),outputShape}getConfig(){let config3=super.getConfig();return delete config3.dilationRate,config3}};Conv2DTranspose.className="Conv2DTranspose";serialization_exports.registerClass(Conv2DTranspose);var SeparableConv=class extends Conv{constructor(rank,config3){super(rank,config3);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,config3.filters==null)throw new ValueError("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(config3.kernelInitializer!=null||config3.kernelRegularizer!=null||config3.kernelConstraint!=null)throw new ValueError("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(config3.padding!=null&&config3.padding!=="same"&&config3.padding!=="valid")throw new ValueError(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(config3.padding)}`);this.depthMultiplier=config3.depthMultiplier==null?1:config3.depthMultiplier,this.depthwiseInitializer=getInitializer(config3.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=getRegularizer(config3.depthwiseRegularizer),this.depthwiseConstraint=getConstraint(config3.depthwiseConstraint),this.pointwiseInitializer=getInitializer(config3.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=getRegularizer(config3.pointwiseRegularizer),this.pointwiseConstraint=getConstraint(config3.pointwiseConstraint)}build(inputShape){if(inputShape=getExactlyOneShape(inputShape),inputShape.length{inputs=getExactlyOneTensor(inputs);let output;if(this.rank===1)throw new NotImplementedError("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(inputs=transpose(inputs,[0,2,3,1])),output=separableConv2d(inputs,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(output=biasAdd(output,this.bias.read(),this.dataFormat)),this.activation!=null&&(output=this.activation.apply(output)),this.dataFormat==="channelsFirst"&&(output=transpose(output,[0,3,1,2])),output})}getConfig(){let config3=super.getConfig();return delete config3.rank,delete config3.kernelInitializer,delete config3.kernelRegularizer,delete config3.kernelConstraint,config3.depthwiseInitializer=serializeInitializer(this.depthwiseInitializer),config3.pointwiseInitializer=serializeInitializer(this.pointwiseInitializer),config3.depthwiseRegularizer=serializeRegularizer(this.depthwiseRegularizer),config3.pointwiseRegularizer=serializeRegularizer(this.pointwiseRegularizer),config3.depthwiseConstraint=serializeConstraint(this.depthwiseConstraint),config3.pointwiseConstraint=serializeConstraint(this.pointwiseConstraint),config3}};SeparableConv.className="SeparableConv";var SeparableConv2D=class extends SeparableConv{constructor(args){super(2,args)}};SeparableConv2D.className="SeparableConv2D";serialization_exports.registerClass(SeparableConv2D);var Conv1D=class extends Conv{constructor(args){super(1,args);Conv1D.verifyArgs(args),this.inputSpec=[{ndim:3}]}getConfig(){let config3=super.getConfig();return delete config3.rank,delete config3.dataFormat,config3}static verifyArgs(args){if(typeof args.kernelSize!="number"&&!checkArrayTypeAndLength(args.kernelSize,"number",1,1))throw new ValueError(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(args.kernelSize)}.`)}};Conv1D.className="Conv1D";serialization_exports.registerClass(Conv1D);var Cropping2D=class extends Layer{constructor(args){super(args);typeof args.cropping=="number"?this.cropping=[[args.cropping,args.cropping],[args.cropping,args.cropping]]:typeof args.cropping[0]=="number"?this.cropping=[[args.cropping[0],args.cropping[0]],[args.cropping[1],args.cropping[1]]]:this.cropping=args.cropping,this.dataFormat=args.dataFormat===void 0?"channelsLast":args.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(inputShape){return this.dataFormat==="channelsFirst"?[inputShape[0],inputShape[1],inputShape[2]-this.cropping[0][0]-this.cropping[0][1],inputShape[3]-this.cropping[1][0]-this.cropping[1][1]]:[inputShape[0],inputShape[1]-this.cropping[0][0]-this.cropping[0][1],inputShape[2]-this.cropping[1][0]-this.cropping[1][1],inputShape[3]]}call(inputs,kwargs){return tidy(()=>{if(inputs=getExactlyOneTensor(inputs),this.dataFormat==="channelsLast"){let hSliced=sliceAlongAxis(inputs,this.cropping[0][0],inputs.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return sliceAlongAxis(hSliced,this.cropping[1][0],inputs.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let hSliced=sliceAlongAxis(inputs,this.cropping[0][0],inputs.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return sliceAlongAxis(hSliced,this.cropping[1][0],inputs.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let config3={cropping:this.cropping,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config3,baseConfig),config3}};Cropping2D.className="Cropping2D";serialization_exports.registerClass(Cropping2D);var UpSampling2D=class extends Layer{constructor(args){super(args);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=args.size==null?this.DEFAULT_SIZE:args.size,this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),this.interpolation=args.interpolation==null?"nearest":args.interpolation,checkInterpolationFormat(this.interpolation)}computeOutputShape(inputShape){if(this.dataFormat==="channelsFirst"){let height=inputShape[2]==null?null:this.size[0]*inputShape[2],width=inputShape[3]==null?null:this.size[1]*inputShape[3];return[inputShape[0],inputShape[1],height,width]}else{let height=inputShape[1]==null?null:this.size[0]*inputShape[1],width=inputShape[2]==null?null:this.size[1]*inputShape[2];return[inputShape[0],height,width,inputShape[3]]}}call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs),inputShape=input2.shape;if(this.dataFormat==="channelsFirst"){input2=transpose(input2,[0,2,3,1]);let height=this.size[0]*inputShape[2],width=this.size[1]*inputShape[3],resized=this.interpolation==="nearest"?input2.resizeNearestNeighbor([height,width]):input2.resizeBilinear([height,width]);return transpose(resized,[0,3,1,2])}else{let height=this.size[0]*inputShape[1],width=this.size[1]*inputShape[2];return this.interpolation==="nearest"?input2.resizeNearestNeighbor([height,width]):input2.resizeBilinear([height,width])}})}getConfig(){let config3={size:this.size,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config3,baseConfig),config3}};UpSampling2D.className="UpSampling2D";serialization_exports.registerClass(UpSampling2D);function depthwiseConv2d4(x,depthwiseKernel,strides=[1,1],padding="valid",dataFormat,dilationRate){return tidy(()=>{dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat);let y=preprocessConv2DInput(x,dataFormat);if(x.rank!==4)throw new ValueError(`Input for depthwiseConv2d is required to be 4-D, but is instead ${x.rank}-D`);if(depthwiseKernel.rank!==4)throw new ValueError(`depthwiseKernel is required to be 4-D, but is instead ${depthwiseKernel.rank}-D`);return y=depthwiseConv2d(y,depthwiseKernel,strides,padding==="same"?"same":"valid","NHWC",dilationRate),dataFormat==="channelsFirst"&&(y=transpose(y,[0,3,1,2])),y})}var DepthwiseConv2D=class extends BaseConv{constructor(args){super(2,args);this.depthwiseKernel=null,this.depthMultiplier=args.depthMultiplier==null?1:args.depthMultiplier,this.depthwiseInitializer=getInitializer(args.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=getConstraint(args.depthwiseConstraint),this.depthwiseRegularizer=getRegularizer(args.depthwiseRegularizer)}build(inputShape){if(inputShape=getExactlyOneShape(inputShape),inputShape.length<4)throw new ValueError(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(inputShape)}.`);let channelAxis=this.dataFormat==="channelsFirst"?1:3;if(inputShape[channelAxis]==null||inputShape[channelAxis]<0)throw new ValueError(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${inputShape[channelAxis]}).`);let inputDim=inputShape[channelAxis],depthwiseKernelShape=[this.kernelSize[0],this.kernelSize[1],inputDim,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",depthwiseKernelShape,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[inputDim*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(inputs,kwargs){return tidy(()=>{inputs=getExactlyOneTensor(inputs);let outputs=depthwiseConv2d4(inputs,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(outputs=biasAdd(outputs,this.bias.read(),this.dataFormat)),this.activation!=null&&(outputs=this.activation.apply(outputs)),outputs})}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let rows=this.dataFormat==="channelsFirst"?inputShape[2]:inputShape[1],cols=this.dataFormat==="channelsFirst"?inputShape[3]:inputShape[2],outFilters=this.dataFormat==="channelsFirst"?inputShape[1]*this.depthMultiplier:inputShape[3]*this.depthMultiplier,outRows=convOutputLength(rows,this.kernelSize[0],this.padding,this.strides[0]),outCols=convOutputLength(cols,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[inputShape[0],outFilters,outRows,outCols]:[inputShape[0],outRows,outCols,outFilters]}getConfig(){let config3=super.getConfig();return config3.depthMultiplier=this.depthMultiplier,config3.depthwiseInitializer=serializeInitializer(this.depthwiseInitializer),config3.depthwiseRegularizer=serializeRegularizer(this.depthwiseRegularizer),config3.depthwiseConstraint=serializeConstraint(this.depthwiseRegularizer),config3}};DepthwiseConv2D.className="DepthwiseConv2D";serialization_exports.registerClass(DepthwiseConv2D);function standardizeArgs(inputs,initialState,constants,numConstants){if(Array.isArray(inputs)){if(initialState!=null||constants!=null)throw new ValueError("When inputs is an array, neither initialState or constants should be provided");numConstants!=null&&(constants=inputs.slice(inputs.length-numConstants,inputs.length),inputs=inputs.slice(0,inputs.length-numConstants)),inputs.length>1&&(initialState=inputs.slice(1,inputs.length)),inputs=inputs[0]}function toListOrNull(x){return x==null||Array.isArray(x)?x:[x]}return initialState=toListOrNull(initialState),constants=toListOrNull(constants),{inputs,initialState,constants}}function rnn(stepFunction,inputs,initialStates,goBackwards=!1,mask,constants,unroll=!1,needPerStepOutputs=!1){return tidy(()=>{let ndim=inputs.shape.length;if(ndim<3)throw new ValueError(`Input should be at least 3D, but is ${ndim}D.`);let axes=[1,0].concat(range5(2,ndim));if(inputs=transpose(inputs,axes),constants!=null)throw new NotImplementedError("The rnn() functoin of the deeplearn.js backend does not support constants yet.");unroll&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),mask!=null&&(mask=mask.asType("bool").asType("float32"),mask.rank===ndim-1&&(mask=expandDims(mask,-1)),mask=transpose(mask,axes)),goBackwards&&(inputs=reverse(inputs,0),mask!=null&&(mask=reverse(mask,0)));let perStepOutputs=[],lastOutput,states=initialStates,timeSteps=inputs.shape[0],perStepInputs=unstack(inputs),perStepMasks;mask!=null&&(perStepMasks=unstack(mask));for(let t=0;tstepFunction(currentInput,states));if(mask==null)lastOutput=stepOutputs[0],states=stepOutputs[1];else{let maskedOutputs=tidy(()=>{let stepMask=perStepMasks[t],negStepMask=onesLike(stepMask).sub(stepMask),output=stepOutputs[0].mul(stepMask).add(states[0].mul(negStepMask)),newStates=states.map((state,i)=>stepOutputs[1][i].mul(stepMask).add(state.mul(negStepMask)));return{output,newStates}});lastOutput=maskedOutputs.output,states=maskedOutputs.newStates}needPerStepOutputs&&perStepOutputs.push(lastOutput)}let outputs;return needPerStepOutputs&&(outputs=stack(perStepOutputs,1)),[lastOutput,outputs,states]})}var RNN=class extends Layer{constructor(args){super(args);let cell;if(args.cell==null)throw new ValueError("cell property is missing for the constructor of RNN.");if(Array.isArray(args.cell)?cell=new StackedRNNCells({cells:args.cell}):cell=args.cell,cell.stateSize==null)throw new ValueError("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=cell,this.returnSequences=args.returnSequences==null?!1:args.returnSequences,this.returnState=args.returnState==null?!1:args.returnState,this.goBackwards=args.goBackwards==null?!1:args.goBackwards,this._stateful=args.stateful==null?!1:args.stateful,this.unroll=args.unroll==null?!1:args.unroll,this.supportsMasking=!0,this.inputSpec=[new InputSpec({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let numStates=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return range5(0,numStates).map(x=>null)}else return this.states_}setStates(states){this.states_=states}computeOutputShape(inputShape){isArrayOfShapes(inputShape)&&(inputShape=inputShape[0]),inputShape=inputShape;let stateSize=this.cell.stateSize;Array.isArray(stateSize)||(stateSize=[stateSize]);let outputDim=stateSize[0],outputShape;if(this.returnSequences?outputShape=[inputShape[0],inputShape[1],outputDim]:outputShape=[inputShape[0],outputDim],this.returnState){let stateShape=[];for(let dim of stateSize)stateShape.push([inputShape[0],dim]);return[outputShape].concat(stateShape)}else return outputShape}computeMask(inputs,mask){return tidy(()=>{Array.isArray(mask)&&(mask=mask[0]);let outputMask=this.returnSequences?mask:null;if(this.returnState){let stateMask=this.states.map(s=>null);return[outputMask].concat(stateMask)}else return outputMask})}get states(){if(this.states_==null){let numStates=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,output=[];for(let i=0;ispec.shape[spec.shape.length-1]),stateSize))throw new ValueError(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=stateSize.map(dim=>new InputSpec({shape:[null,dim]}));this.stateful&&this.resetStates()}resetStates(states,training=!1){tidy(()=>{if(!this.stateful)throw new AttributeError("Cannot call resetStates() on an RNN Layer that is not stateful.");let batchSize=this.inputSpec[0].shape[0];if(batchSize==null)throw new ValueError("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(dim=>zeros([batchSize,dim])):this.states_=[zeros([batchSize,this.cell.stateSize])];else if(states==null)dispose(this.states_),this.keptStates!=null&&(dispose(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(dim=>zeros([batchSize,dim])):this.states_[0]=zeros([batchSize,this.cell.stateSize]);else{if(Array.isArray(states)||(states=[states]),states.length!==this.states_.length)throw new ValueError(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${states.length} state value(s). Input received: ${states}`);training===!0?this.keptStates.push(this.states_.slice()):dispose(this.states_);for(let index=0;indexkeep(state.clone()))})}apply(inputs,kwargs){let initialState=kwargs==null?null:kwargs.initialState,constants=kwargs==null?null:kwargs.constants;kwargs==null&&(kwargs={});let standardized=standardizeArgs(inputs,initialState,constants,this.numConstants);inputs=standardized.inputs,initialState=standardized.initialState,constants=standardized.constants;let additionalInputs=[],additionalSpecs=[];if(initialState!=null){kwargs.initialState=initialState,additionalInputs=additionalInputs.concat(initialState),this.stateSpec=[];for(let state of initialState)this.stateSpec.push(new InputSpec({shape:state.shape}));additionalSpecs=additionalSpecs.concat(this.stateSpec)}if(constants!=null&&(kwargs.constants=constants,additionalInputs=additionalInputs.concat(constants),this.numConstants=constants.length),additionalInputs[0]instanceof SymbolicTensor){let fullInput=[inputs].concat(additionalInputs),fullInputSpec=this.inputSpec.concat(additionalSpecs),originalInputSpec=this.inputSpec;this.inputSpec=fullInputSpec;let output=super.apply(fullInput,kwargs);return this.inputSpec=originalInputSpec,output}else return super.apply(inputs,kwargs)}call(inputs,kwargs){return tidy(()=>{let mask=kwargs==null?null:kwargs.mask,training=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;inputs=getExactlyOneTensor(inputs),initialState==null&&(this.stateful?initialState=this.states_:initialState=this.getInitialState(inputs));let numStates=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(initialState.length!==numStates)throw new ValueError(`RNN Layer has ${numStates} state(s) but was passed ${initialState.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let cellCallKwargs={training},rnnOutputs=rnn((inputs2,states2)=>{let outputs2=this.cell.call([inputs2].concat(states2),cellCallKwargs);return[outputs2[0],outputs2.slice(1)]},inputs,initialState,this.goBackwards,mask,null,this.unroll,this.returnSequences),lastOutput=rnnOutputs[0],outputs=rnnOutputs[1],states=rnnOutputs[2];this.stateful&&this.resetStates(states,training);let output=this.returnSequences?outputs:lastOutput;return this.returnState?[output].concat(states):output})}getInitialState(inputs){return tidy(()=>{let initialState=zeros(inputs.shape);return initialState=sum2(initialState,[1,2]),initialState=expandDims5(initialState),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(dim=>dim>1?tile5(initialState,[1,dim]):initialState):this.cell.stateSize>1?[tile5(initialState,[1,this.cell.stateSize])]:[initialState]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(value){super.setFastWeightInitDuringBuild(value),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(value)}getConfig(){let baseConfig=super.getConfig(),config3={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(config3.numConstants=this.numConstants);let cellConfig=this.cell.getConfig();return this.getClassName()===RNN.className&&(config3.cell={className:this.cell.getClassName(),config:cellConfig}),Object.assign({},cellConfig,baseConfig,config3)}static fromConfig(cls,config3,customObjects={}){let cellConfig=config3.cell,cell=deserialize(cellConfig,customObjects);return new cls(Object.assign(config3,{cell}))}};RNN.className="RNN";serialization_exports.registerClass(RNN);var RNNCell=class extends Layer{},SimpleRNNCell=class extends RNNCell{constructor(args){super(args);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=args.units,assertPositiveInteger(this.units,"units"),this.activation=getActivation(args.activation==null?this.DEFAULT_ACTIVATION:args.activation),this.useBias=args.useBias==null?!0:args.useBias,this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=getInitializer(args.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=getRegularizer(args.kernelRegularizer),this.recurrentRegularizer=getRegularizer(args.recurrentRegularizer),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.kernelConstraint=getConstraint(args.kernelConstraint),this.recurrentConstraint=getConstraint(args.recurrentConstraint),this.biasConstraint=getConstraint(args.biasConstraint),this.dropout=min5([1,max5([0,args.dropout==null?0:args.dropout])]),this.recurrentDropout=min5([1,max5([0,args.recurrentDropout==null?0:args.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(inputShape){inputShape=getExactlyOneShape(inputShape),this.kernel=this.addWeight("kernel",[inputShape[inputShape.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(inputs,kwargs){return tidy(()=>{if(inputs=inputs,inputs.length!==2)throw new ValueError(`SimpleRNNCell expects 2 input Tensors, got ${inputs.length}.`);let prevOutput=inputs[1];inputs=inputs[0];let training=kwargs.training==null?!1:kwargs.training;0onesLike(inputs),rate:this.dropout,training})),0onesLike(prevOutput),rate:this.recurrentDropout,training}));let h,dpMask=this.dropoutMask,recDpMask=this.recurrentDropoutMask;dpMask!=null?h=dot2(mul(inputs,dpMask),this.kernel.read()):h=dot2(inputs,this.kernel.read()),this.bias!=null&&(h=biasAdd(h,this.bias.read())),recDpMask!=null&&(prevOutput=mul(prevOutput,recDpMask));let output=add2(h,dot2(prevOutput,this.recurrentKernel.read()));return this.activation!=null&&(output=this.activation.apply(output)),[output,output]})}getConfig(){let baseConfig=super.getConfig(),config3={units:this.units,activation:serializeActivation(this.activation),useBias:this.useBias,kernelInitializer:serializeInitializer(this.kernelInitializer),recurrentInitializer:serializeInitializer(this.recurrentInitializer),biasInitializer:serializeInitializer(this.biasInitializer),kernelRegularizer:serializeRegularizer(this.kernelRegularizer),recurrentRegularizer:serializeRegularizer(this.recurrentRegularizer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),kernelConstraint:serializeConstraint(this.kernelConstraint),recurrentConstraint:serializeConstraint(this.recurrentConstraint),biasConstraint:serializeConstraint(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},baseConfig,config3)}};SimpleRNNCell.className="SimpleRNNCell";serialization_exports.registerClass(SimpleRNNCell);var SimpleRNN=class extends RNN{constructor(args){args.cell=new SimpleRNNCell(args),super(args)}call(inputs,kwargs){return tidy(()=>{this.cell.dropoutMask!=null&&(dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let mask=kwargs==null?null:kwargs.mask,training=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;return super.call(inputs,{mask,training,initialState})})}static fromConfig(cls,config3){return new cls(config3)}};SimpleRNN.className="SimpleRNN";serialization_exports.registerClass(SimpleRNN);var GRUCell=class extends RNNCell{constructor(args){super(args);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",args.resetAfter)throw new ValueError("GRUCell does not support reset_after parameter set to true.");this.units=args.units,assertPositiveInteger(this.units,"units"),this.activation=getActivation(args.activation===void 0?this.DEFAULT_ACTIVATION:args.activation),this.recurrentActivation=getActivation(args.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:args.recurrentActivation),this.useBias=args.useBias==null?!0:args.useBias,this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=getInitializer(args.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=getRegularizer(args.kernelRegularizer),this.recurrentRegularizer=getRegularizer(args.recurrentRegularizer),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.kernelConstraint=getConstraint(args.kernelConstraint),this.recurrentConstraint=getConstraint(args.recurrentConstraint),this.biasConstraint=getConstraint(args.biasConstraint),this.dropout=min5([1,max5([0,args.dropout==null?0:args.dropout])]),this.recurrentDropout=min5([1,max5([0,args.recurrentDropout==null?0:args.recurrentDropout])]),this.implementation=args.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(inputShape){inputShape=getExactlyOneShape(inputShape);let inputDim=inputShape[inputShape.length-1];this.kernel=this.addWeight("kernel",[inputDim,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(inputs,kwargs){return tidy(()=>{if(inputs=inputs,inputs.length!==2)throw new ValueError(`GRUCell expects 2 input Tensors (inputs, h, c), got ${inputs.length}.`);let training=kwargs.training==null?!1:kwargs.training,hTMinus1=inputs[1];inputs=inputs[0],0onesLike(inputs),rate:this.dropout,training,count:3})),0onesLike(hTMinus1),rate:this.recurrentDropout,training,count:3}));let dpMask=this.dropoutMask,recDpMask=this.recurrentDropoutMask,z,r,hh;0{this.cell.dropoutMask!=null&&(dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let mask=kwargs==null?null:kwargs.mask,training=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;return super.call(inputs,{mask,training,initialState})})}static fromConfig(cls,config3){return config3.implmentation===0&&(config3.implementation=1),new cls(config3)}};GRU.className="GRU";serialization_exports.registerClass(GRU);var LSTMCell=class extends RNNCell{constructor(args){super(args);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=args.units,assertPositiveInteger(this.units,"units"),this.activation=getActivation(args.activation===void 0?this.DEFAULT_ACTIVATION:args.activation),this.recurrentActivation=getActivation(args.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:args.recurrentActivation),this.useBias=args.useBias==null?!0:args.useBias,this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=getInitializer(args.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=args.unitForgetBias,this.kernelRegularizer=getRegularizer(args.kernelRegularizer),this.recurrentRegularizer=getRegularizer(args.recurrentRegularizer),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.kernelConstraint=getConstraint(args.kernelConstraint),this.recurrentConstraint=getConstraint(args.recurrentConstraint),this.biasConstraint=getConstraint(args.biasConstraint),this.dropout=min5([1,max5([0,args.dropout==null?0:args.dropout])]),this.recurrentDropout=min5([1,max5([0,args.recurrentDropout==null?0:args.recurrentDropout])]),this.implementation=args.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(inputShape){var _a;inputShape=getExactlyOneShape(inputShape);let inputDim=inputShape[inputShape.length-1];this.kernel=this.addWeight("kernel",[inputDim,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let biasInitializer;if(this.useBias){if(this.unitForgetBias){let capturedBiasInit=this.biasInitializer,capturedUnits=this.units;biasInitializer=new(_a=class extends Initializer{apply(shape,dtype){let bI=capturedBiasInit.apply([capturedUnits]),bF=new Ones().apply([capturedUnits]),bCAndH=capturedBiasInit.apply([capturedUnits*2]);return concatAlongFirstAxis(concatAlongFirstAxis(bI,bF),bCAndH)}},_a.className="CustomInit",_a)}else biasInitializer=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,biasInitializer,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(inputs,kwargs){return tidy(()=>{let training=kwargs.training==null?!1:kwargs.training;if(inputs=inputs,inputs.length!==3)throw new ValueError(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${inputs.length}.`);let hTMinus1=inputs[1],cTMinus1=inputs[2];inputs=inputs[0],0onesLike(inputs),rate:this.dropout,training,count:4})),0onesLike(hTMinus1),rate:this.recurrentDropout,training,count:4}));let dpMask=this.dropoutMask,recDpMask=this.recurrentDropoutMask,i,f,c,o;0{this.cell.dropoutMask!=null&&(dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let mask=kwargs==null?null:kwargs.mask,training=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;return super.call(inputs,{mask,training,initialState})})}static fromConfig(cls,config3){return config3.implmentation===0&&(config3.implementation=1),new cls(config3)}};LSTM.className="LSTM";serialization_exports.registerClass(LSTM);var StackedRNNCells=class extends RNNCell{constructor(args){super(args);this.cells=args.cells}get stateSize(){let stateSize=[];for(let cell of this.cells.slice().reverse())Array.isArray(cell.stateSize)?stateSize.push(...cell.stateSize):stateSize.push(cell.stateSize);return stateSize}call(inputs,kwargs){return tidy(()=>{inputs=inputs;let states=inputs.slice(1),nestedStates=[];for(let cell of this.cells.slice().reverse())Array.isArray(cell.stateSize)?nestedStates.push(states.splice(0,cell.stateSize.length)):nestedStates.push(states.splice(0,1));nestedStates.reverse();let newNestedStates=[],callInputs;for(let i=0;i{nameScope(`RNNCell_${i}`,()=>{cell.build(inputShape),Array.isArray(cell.stateSize)?outputDim=cell.stateSize[0]:outputDim=cell.stateSize,inputShape=[inputShape[0],outputDim]})}),this.built=!0}getConfig(){let baseConfig=super.getConfig(),getCellConfig=cell=>({className:cell.getClassName(),config:cell.getConfig()}),config3={cells:this.cells.map(getCellConfig)};return Object.assign({},baseConfig,config3)}static fromConfig(cls,config3,customObjects={}){let cells=[];for(let cellConfig of config3.cells)cells.push(deserialize(cellConfig,customObjects));return new cls({cells})}get trainableWeights(){if(!this.trainable)return[];let weights=[];for(let cell of this.cells)weights.push(...cell.trainableWeights);return weights}get nonTrainableWeights(){let weights=[];for(let cell of this.cells)weights.push(...cell.nonTrainableWeights);if(!this.trainable){let trainableWeights=[];for(let cell of this.cells)trainableWeights.push(...cell.trainableWeights);return trainableWeights.concat(weights)}return weights}getWeights(){let weights=[];for(let cell of this.cells)weights.push(...cell.weights);return batchGetValue(weights)}setWeights(weights){let tuples=[];for(let cell of this.cells){let numParams=cell.weights.length,inputWeights=weights.splice(numParams);for(let i=0;idropout2(ones4(),rate),createMask=()=>inTrainPhase(droppedInputs,ones4,training);return!count2||count2<=1?keep(createMask().clone()):Array(count2).fill(void 0).map(createMask).map(m=>keep(m.clone()))}var __rest=function(s,e){var t={};for(var p2 in s)Object.prototype.hasOwnProperty.call(s,p2)&&e.indexOf(p2)<0&&(t[p2]=s[p2]);if(s!=null&&typeof Object.getOwnPropertySymbols=="function")for(var i=0,p2=Object.getOwnPropertySymbols(s);i{if(this.cell.dropoutMask!=null&&(dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),kwargs&&kwargs.constants)throw new ValueError("ConvRNN2D cell does not support constants");let mask=kwargs==null?null:kwargs.mask,training=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;return super.call(inputs,{mask,training,initialState})})}computeOutputShape(inputShape){let outShape=this.computeSingleOutputShape(inputShape);return this.returnSequences||(outShape=[outShape[0],...outShape.slice(2)]),this.returnState&&(outShape=[outShape,...Array(2).fill([inputShape[0],...outShape.slice(-3)])]),outShape}getInitialState(inputs){return tidy(()=>{let{stateSize}=this.cell,inputShape=inputs.shape,outputShape=this.computeSingleOutputShape(inputShape),stateShape=[outputShape[0],...outputShape.slice(2)],initialState=zeros(stateShape);return Array.isArray(stateSize)?Array(stateSize.length).fill(initialState):[initialState]})}resetStates(states,training=!1){tidy(()=>{if(!this.stateful)throw new AttributeError("Cannot call resetStates() on an RNN Layer that is not stateful.");let inputShape=this.inputSpec[0].shape,outputShape=this.computeSingleOutputShape(inputShape),stateShape=[outputShape[0],...outputShape.slice(2)];if(inputShape[0]==null)throw new ValueError("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>zeros(stateShape)):this.states_=[zeros(stateShape)];else if(states==null)dispose(this.states_),this.keptStates!=null&&(dispose(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>zeros(stateShape)):this.states_[0]=zeros(stateShape);else{if(Array.isArray(states)||(states=[states]),states.length!==this.states_.length)throw new ValueError(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${states.length} state value(s). Input received: ${states}`);training?this.keptStates.push(this.states_.slice()):dispose(this.states_);for(let index=0;indexkeep(state.clone()))})}computeSingleOutputShape(inputShape){let{dataFormat,filters,kernelSize,padding,strides,dilationRate}=this.cell,isChannelsFirst=dataFormat==="channelsFirst",h=inputShape[isChannelsFirst?3:2],w=inputShape[isChannelsFirst?4:3],hOut=convOutputLength(h,kernelSize[0],padding,strides[0],dilationRate[0]),wOut=convOutputLength(w,kernelSize[1],padding,strides[1],dilationRate[1]);return[...inputShape.slice(0,2),...isChannelsFirst?[filters,hOut,wOut]:[hOut,wOut,filters]]}};ConvRNN2D.className="ConvRNN2D";var ConvLSTM2DCell=class extends LSTMCell{constructor(args){let{filters,kernelSize,strides,padding,dataFormat,dilationRate}=args;super(Object.assign({},args,{units:filters}));this.filters=filters,assertPositiveInteger(this.filters,"filters"),this.kernelSize=normalizeArray(kernelSize,2,"kernelSize"),this.kernelSize.forEach(size=>assertPositiveInteger(size,"kernelSize")),this.strides=normalizeArray(strides||1,2,"strides"),this.strides.forEach(stride=>assertPositiveInteger(stride,"strides")),this.padding=padding||"valid",checkPaddingMode(this.padding),this.dataFormat=dataFormat||"channelsLast",checkDataFormat(this.dataFormat),this.dilationRate=normalizeArray(dilationRate||1,2,"dilationRate"),this.dilationRate.forEach(rate=>assertPositiveInteger(rate,"dilationRate"))}build(inputShape){var _a;inputShape=getExactlyOneShape(inputShape);let channelAxis=this.dataFormat==="channelsFirst"?1:inputShape.length-1;if(inputShape[channelAxis]==null)throw new ValueError(`The channel dimension of the input should be defined. Found ${inputShape[channelAxis]}`);let inputDim=inputShape[channelAxis],numOfKernels=4,kernelShape=this.kernelSize.concat([inputDim,this.filters*numOfKernels]);this.kernel=this.addWeight("kernel",kernelShape,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let recurrentKernelShape=this.kernelSize.concat([this.filters,this.filters*numOfKernels]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",recurrentKernelShape,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let biasInitializer;if(this.unitForgetBias){let init2=this.biasInitializer,filters=this.filters;biasInitializer=new(_a=class extends Initializer{apply(shape,dtype){let biasI=init2.apply([filters]),biasF=ones2([filters]),biasCAndO=init2.apply([filters*2]);return concatenate([biasI,biasF,biasCAndO])}},_a.className="CustomInit",_a)}else biasInitializer=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*numOfKernels],null,biasInitializer,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(inputs,kwargs){return tidy(()=>{if(inputs.length!==3)throw new ValueError(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${inputs.length}.`);let training=kwargs.training||!1,x=inputs[0],hTMinus1=inputs[1],cTMinus1=inputs[2],numOfKernels=4;0onesLike(x),rate:this.dropout,training,count:numOfKernels}));let dropoutMask=this.dropoutMask,applyDropout=(x2,mask,index)=>!mask||!mask[index]?x2:mul(mask[index],x2),xI=applyDropout(x,dropoutMask,0),xF=applyDropout(x,dropoutMask,1),xC=applyDropout(x,dropoutMask,2),xO=applyDropout(x,dropoutMask,3);0onesLike(hTMinus1),rate:this.recurrentDropout,training,count:numOfKernels}));let recDropoutMask=this.recurrentDropoutMask,hI=applyDropout(hTMinus1,recDropoutMask,0),hF=applyDropout(hTMinus1,recDropoutMask,1),hC=applyDropout(hTMinus1,recDropoutMask,2),hO=applyDropout(hTMinus1,recDropoutMask,3),kernelChannelAxis=3,[kernelI,kernelF,kernelC,kernelO]=split(this.kernel.read(),numOfKernels,kernelChannelAxis),[biasI,biasF,biasC,biasO]=this.useBias?split(this.bias.read(),numOfKernels):[null,null,null,null];xI=this.inputConv(xI,kernelI,biasI,this.padding),xF=this.inputConv(xF,kernelF,biasF,this.padding),xC=this.inputConv(xC,kernelC,biasC,this.padding),xO=this.inputConv(xO,kernelO,biasO,this.padding);let[recKernelI,recKernelF,recKernelC,recKernelO]=split(this.recurrentKernel.read(),numOfKernels,kernelChannelAxis);hI=this.recurrentConv(hI,recKernelI),hF=this.recurrentConv(hF,recKernelF),hC=this.recurrentConv(hC,recKernelC),hO=this.recurrentConv(hO,recKernelO);let i=this.recurrentActivation.apply(add2(xI,hI)),f=this.recurrentActivation.apply(add2(xF,hF)),c=add2(mul(f,cTMinus1),mul(i,this.activation.apply(add2(xC,hC)))),h=mul(this.recurrentActivation.apply(add2(xO,hO)),this.activation.apply(c));return[h,h,c]})}getConfig(){let _a=super.getConfig(),{units:_}=_a,baseConfig=__rest(_a,["units"]),config3={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},baseConfig,config3)}inputConv(x,w,b,padding){let out=conv2d(x,w,this.strides,padding||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return b?biasAdd(out,b,this.dataFormat):out}recurrentConv(x,w){return conv2d(x,w,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};ConvLSTM2DCell.className="ConvLSTM2DCell";serialization_exports.registerClass(ConvLSTM2DCell);var ConvLSTM2D=class extends ConvRNN2D{constructor(args){let cell=new ConvLSTM2DCell(args);super(Object.assign({},args,{cell}))}static fromConfig(cls,config3){return new cls(config3)}};ConvLSTM2D.className="ConvLSTM2D";serialization_exports.registerClass(ConvLSTM2D);var Dropout=class extends Layer{constructor(args){super(args);this.rate=Math.max(Math.min(args.rate,1),0),this.noiseShape=args.noiseShape,this.seed=args.seed,this.supportsMasking=!0}getNoiseShape(input2){if(this.noiseShape==null)return this.noiseShape;let inputShape=input2.shape,noiseShape=[];for(let i=0;i{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);if(0dropout2(input2,this.rate,noiseShape,this.seed),()=>input2,training)}return inputs})}getConfig(){let config3={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},baseConfig=super.getConfig();return Object.assign(config3,baseConfig),config3}dispose(){return super.dispose()}};Dropout.className="Dropout";serialization_exports.registerClass(Dropout);var SpatialDropout1D=class extends Dropout{constructor(args){super(args);this.inputSpec=[{ndim:3}]}getNoiseShape(input2){let inputShape=input2.shape;return[inputShape[0],1,inputShape[2]]}};SpatialDropout1D.className="SpatialDropout1D";serialization_exports.registerClass(SpatialDropout1D);var Dense=class extends Layer{constructor(args){super(args);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",args.batchInputShape==null&&args.inputShape==null&&args.inputDim!=null){let batchSize=null;args.batchSize!=null&&(batchSize=args.batchSize),this.batchInputShape=[batchSize,args.inputDim]}this.units=args.units,assertPositiveInteger(this.units,"units"),this.activation=getActivation(args.activation),args.useBias!=null&&(this.useBias=args.useBias),this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=getConstraint(args.kernelConstraint),this.biasConstraint=getConstraint(args.biasConstraint),this.kernelRegularizer=getRegularizer(args.kernelRegularizer),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.activityRegularizer=getRegularizer(args.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(inputShape){inputShape=getExactlyOneShape(inputShape);let inputLastDim=inputShape[inputShape.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[inputLastDim,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:inputLastDim}}],this.built=!0}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let outputShape=inputShape.slice();return outputShape[outputShape.length-1]=this.units,outputShape}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs),fusedActivationName=mapActivationToFusedKernel(this.activation.getClassName()),output;return fusedActivationName!=null?output=dot2(input2,this.kernel.read(),fusedActivationName,this.bias?this.bias.read():null):(output=dot2(input2,this.kernel.read()),this.bias!=null&&(output=biasAdd(output,this.bias.read())),this.activation!=null&&(output=this.activation.apply(output))),output})}getConfig(){let config3={units:this.units,activation:serializeActivation(this.activation),useBias:this.useBias,kernelInitializer:serializeInitializer(this.kernelInitializer),biasInitializer:serializeInitializer(this.biasInitializer),kernelRegularizer:serializeRegularizer(this.kernelRegularizer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),kernelConstraint:serializeConstraint(this.kernelConstraint),biasConstraint:serializeConstraint(this.biasConstraint)},baseConfig=super.getConfig();return Object.assign(config3,baseConfig),config3}};Dense.className="Dense";serialization_exports.registerClass(Dense);var Flatten=class extends Layer{constructor(args){args=args||{},super(args),this.inputSpec=[{minNDim:3}],this.dataFormat=args.dataFormat}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);for(let dim of inputShape.slice(1))if(dim==null)throw new ValueError(`The shape of the input to "Flatten" is not fully defined (got ${inputShape.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[inputShape[0],arrayProd(inputShape,1)]}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);if(this.dataFormat==="channelsFirst"&&input2.rank>1){let permutation=[0];for(let i=2;i{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);return this.activation.apply(input2)})}getConfig(){let config3={activation:serializeActivation(this.activation)},baseConfig=super.getConfig();return Object.assign(config3,baseConfig),config3}};Activation2.className="Activation";serialization_exports.registerClass(Activation2);var RepeatVector=class extends Layer{constructor(args){super(args);this.n=args.n,this.inputSpec=[{ndim:2}]}computeOutputShape(inputShape){return[inputShape[0],this.n,inputShape[1]]}call(inputs,kwargs){return tidy(()=>(inputs=getExactlyOneTensor(inputs),repeat(inputs,this.n)))}getConfig(){let config3={n:this.n},baseConfig=super.getConfig();return Object.assign(config3,baseConfig),config3}};RepeatVector.className="RepeatVector";serialization_exports.registerClass(RepeatVector);var Reshape2=class extends Layer{constructor(args){super(args);this.targetShape=args.targetShape;for(let i=0;i{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs),inputShape=input2.shape,outputShape=inputShape.slice(0,1).concat(this.fixUnknownDimension(inputShape.slice(1),this.targetShape));return input2.reshape(outputShape)})}getConfig(){let config3={targetShape:this.targetShape},baseConfig=super.getConfig();return Object.assign(config3,baseConfig),config3}};Reshape2.className="Reshape";serialization_exports.registerClass(Reshape2);var Permute=class extends Layer{constructor(args){super(args);if(args.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(args.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${args.dims} instead.`);let expectedSortedIndices=range5(1,args.dims.length+1);if(!util_exports.arraysEqual(args.dims.slice().sort(),expectedSortedIndices))throw new Error("Invalid permutation `dims`: "+JSON.stringify(args.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=args.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new InputSpec({ndim:this.dims.length+1})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let outputShape=inputShape.slice();return this.dims.forEach((dim,i)=>{outputShape[i+1]=inputShape[dim]}),outputShape}call(inputs,kwargs){return transpose(getExactlyOneTensor(inputs),this.dimsIncludingBatch)}getConfig(){let config3={dims:this.dims},baseConfig=super.getConfig();return Object.assign(config3,baseConfig),config3}};Permute.className="Permute";serialization_exports.registerClass(Permute);var Masking=class extends Layer{constructor(args){super(args==null?{}:args);this.supportsMasking=!0,args!=null?this.maskValue=args.maskValue==null?0:args.maskValue:this.maskValue=0}computeOutputShape(inputShape){return inputShape}getConfig(){let baseConfig=super.getConfig(),config3={maskValue:this.maskValue};return Object.assign(config3,baseConfig),config3}computeMask(inputs,mask){let input2=getExactlyOneTensor(inputs),axis=-1;return any(notEqual(input2,this.maskValue),axis)}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs),axis=-1,keepDims=!0,booleanMask=any(notEqual(input2,this.maskValue),axis,keepDims);return input2.mul(booleanMask.asType(input2.dtype))})}};Masking.className="Masking";serialization_exports.registerClass(Masking);var Embedding=class extends Layer{constructor(args){super(args);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",args.batchInputShape==null&&args.inputShape==null){let batchSize=null;args.batchSize!=null&&(batchSize=args.batchSize),args.inputLength==null?this.batchInputShape=[batchSize,null]:this.batchInputShape=[batchSize].concat(toList(args.inputLength))}this.inputDim=args.inputDim,assertPositiveInteger(this.inputDim,"inputDim"),this.outputDim=args.outputDim,assertPositiveInteger(this.outputDim,"outputDim"),this.embeddingsInitializer=getInitializer(args.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=getRegularizer(args.embeddingsRegularizer),this.activityRegularizer=getRegularizer(args.activityRegularizer),this.embeddingsConstraint=getConstraint(args.embeddingsConstraint),this.maskZero=args.maskZero,this.supportsMasking=args.maskZero,this.inputLength=args.inputLength}build(inputShape){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(inputShape){}computeMask(inputs,mask){return tidy(()=>this.maskZero?(inputs=getExactlyOneTensor(inputs),notEqual(inputs,zerosLike(inputs))):null)}computeOutputShape(inputShape){if(inputShape=getExactlyOneShape(inputShape),this.inputLength==null)return[...inputShape,this.outputDim];let inLens=toList(this.inputLength);if(inLens.length!==inputShape.length-1)throw new ValueError(`"inputLength" is ${this.inputLength}, but received input shape has shape ${inputShape}`);{let i=0;for(let k=0;k{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);return input2.dtype!=="int32"&&(input2=cast5(input2,"int32")),gather2(this.embeddings.read(),input2.as1D()).reshape(getExactlyOneShape(this.computeOutputShape(input2.shape)))})}getConfig(){let config3={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:serializeInitializer(this.embeddingsInitializer),embeddingsRegularizer:serializeRegularizer(this.embeddingsRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),embeddingsConstraint:serializeConstraint(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},baseConfig=super.getConfig();return Object.assign(config3,baseConfig),config3}};Embedding.className="Embedding";serialization_exports.registerClass(Embedding);var Merge=class extends Layer{constructor(args){super(args||{});this.supportsMasking=!0}mergeFunction(inputs){throw new NotImplementedError}computeElementwiseOpOutputShape(shape1,shape2){if(shape1==null||shape2==null)return null;if(shape1.length1)throw new ValueError(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(inputShape)}.`);let outputShape=inputShape[0]==null?null:inputShape[0].slice(1);for(let i=1;ishape.length);inputShape.indexOf(null)===-1&&unique4(allRanks).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(inputs,kwargs){return tidy(()=>{if(inputs=inputs,this.reshapeRequired){let reshapedInputs=[],inputDims=inputs.map(input2=>input2.rank);if(inputDims.indexOf(null)===-1){let maxNDim=max5(inputDims);for(let x of inputs){let xNDim=x.rank;for(let k=0;k1){let dims=range5(1,xNDim).concat([0]);reshapedInputs.push(transpose(x,dims)),transposed=!0}else reshapedInputs.push(x)}let y=this.mergeFunction(reshapedInputs),yNDim=y.rank;if(transposed){if(yNDim==null){let yShape=y.shape,yNDim2=yShape.length,batchSize=yShape[yNDim2-1],newShape=[batchSize].concat(yShape.slice(0,yShape.length-1));y=transpose(y.reshape([-1,batchSize]),[1,0]).reshape(newShape)}else if(yNDim>1){let dims=[yNDim-1].concat(range5(0,yNDim-1));y=transpose(y,dims)}}return y}}else return this.mergeFunction(inputs)})}computeOutputShape(inputShape){inputShape=inputShape;let outputShape;inputShape[0]==null?outputShape=null:outputShape=inputShape[0].slice(1);for(let i=1;i{if(mask==null)return null;if(!Array.isArray(mask))throw new ValueError("`mask` should be an Array");if(!Array.isArray(inputs))throw new ValueError("`inputs` should be an Array");if(mask.length!==inputs.length)throw new ValueError(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${inputs.length} vs ${mask.length})`);if(mask.every(m=>m==null))return null;mask=mask.map(m=>m==null?m:expandDims(m,0));let output=mask[0];for(let i=1;i{let output=inputs[0].clone();for(let i=1;i{let output=inputs[0].clone();for(let i=1;i{let output=inputs[0].clone();for(let i=1;i{let output=inputs[0];for(let i=1;i{let output=inputs[0];for(let i=1;i1)throw new ValueError("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(inputShape))}mergeFunction(inputs){return tidy(()=>concatenate(inputs,this.axis))}computeOutputShape(inputShape){if(!(Array.isArray(inputShape)&&Array.isArray(inputShape[0])))throw new ValueError("A `Concatenate` layer should be called on a list of inputs.");let inputShapes=inputShape,outputShape=inputShapes[0].slice(),axis=this.axis<0?outputShape.length+this.axis:this.axis;for(let shape of inputShapes.slice(1)){if(outputShape[axis]==null||shape[axis]==null){outputShape[axis]=null;break}outputShape[axis]+=shape[axis]}return outputShape}computeMask(inputs,mask){if(mask==null)return null;if(!Array.isArray(mask))throw new ValueError("`mask` should be an array for Concatenate");if(!Array.isArray(inputs))throw new ValueError("`inputs` should be an array for Concatenate");if(mask.length!==inputs.length)throw new ValueError(`Mismatch in the length of mask (${mask.length}) and the legnth of inputs (${inputs.length})`);return tidy(()=>{let allNullMasks=!0;if(mask.forEach(m=>{if(m!=null){allNullMasks=!1;return}}),allNullMasks)return null;let outputMasks=[];for(let i=0;i3||y.shape.length>3)throw new NotImplementedError("batchDot is not implemented for tensors of 4D or higher rank yet");if(util_exports.assert(x.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${x.shape.length}`),util_exports.assert(x.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${y.shape.length}`),typeof axes=="number"&&(axes=[axes,axes]),x.dtype==="complex64"||y.dtype==="complex64")throw new NotImplementedError("batchDot is not implemented for complex64-type Tensors yet.");let xNDim=x.shape.length,yNDim=y.shape.length;axes==null&&(axes=[xNDim-1,yNDim-2]);let axesArray=axes;return tidy(()=>{let diff;if(xNDim>yNDim){diff=xNDim-yNDim;let diffShape=[];for(let i=0;ixNDim){diff=yNDim-xNDim;let diffShape=[];for(let i=0;i0){let idx;xNDim>yNDim?idx=xNDim+yNDim-3:idx=xNDim-1;let squeezeAxes=[];for(let i=idx;i"A `Dot` layer should be called on a list of exactly 2 inputs.");let shape1=inputShape[0],shape2=inputShape[1];if(shape1.length>3||shape2.length>3)throw new NotImplementedError("Dot layer does not support tensors of 4D or higher rank yet.");let axes=this.interpretAxes(shape1,shape2);if(shape1[axes[0]]!==shape2[axes[1]])throw new ValueError(`Dimension incompatibility: ${shape1[axes[0]]} !== ${shape2[axes[1]]}`)}mergeFunction(inputs){if(inputs.length!==2)throw new ValueError(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${inputs.length} input(s).`);let x1=inputs[0],x2=inputs[1],axes;return Array.isArray(this.axes)?axes=this.axes.map((axis,i)=>interpretAxis(axis,inputs[i].shape.length)):axes=[interpretAxis(this.axes,x1.shape.length),interpretAxis(this.axes,x2.shape.length)],this.normalize&&(x1=l2Normalize(x1,axes[0]),x2=l2Normalize(x2,axes[1])),batchDot(x1,x2,axes)}interpretAxes(shape1,shape2){let axes;return Array.isArray(this.axes)?axes=this.axes:axes=[interpretAxis(this.axes,shape1.length),interpretAxis(this.axes,shape2.length)],axes}computeOutputShape(inputShape){util_exports.assert(Array.isArray(inputShape)&&inputShape.length===2&&Array.isArray(inputShape[0])&&Array.isArray(inputShape[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let shape1=inputShape[0].slice(),shape2=inputShape[1].slice();if(shape1.length>3||shape2.length>3)throw new NotImplementedError("Dot layer does not support tensors of 4D or higher rank yet.");let axes=this.interpretAxes(shape1,shape2);shape1.splice(axes[0],1),shape2.splice(axes[1],1),shape2.splice(0,1);let outputShape=shape1.concat(shape2);return outputShape.length===1&&outputShape.push(1),outputShape}computeMask(inputs,mask){return null}getConfig(){let config3={axes:this.axes,normalize:this.normalize},baseConfig=super.getConfig();return Object.assign(config3,baseConfig),config3}};Dot.className="Dot";serialization_exports.registerClass(Dot);var GaussianNoise=class extends Layer{constructor(args){super(args);this.supportsMasking=!0,this.stddev=args.stddev}computeOutputShape(inputShape){return inputShape}getConfig(){let baseConfig=super.getConfig(),config3={stddev:this.stddev};return Object.assign(config3,baseConfig),config3}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);return inTrainPhase(()=>randomNormal2(input2.shape,0,this.stddev).add(input2),()=>input2,kwargs.training||!1)})}};GaussianNoise.className="GaussianNoise";serialization_exports.registerClass(GaussianNoise);var GaussianDropout=class extends Layer{constructor(args){super(args);this.supportsMasking=!0,this.rate=args.rate}computeOutputShape(inputShape){return inputShape}getConfig(){let baseConfig=super.getConfig(),config3={rate:this.rate};return Object.assign(config3,baseConfig),config3}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);return this.rate>0&&this.rate<1?inTrainPhase(()=>{let stddev=Math.sqrt(this.rate/(1-this.rate));return input2.mul(randomNormal2(input2.shape,1,stddev))},()=>input2,kwargs.training||!1):input2})}};GaussianDropout.className="GaussianDropout";serialization_exports.registerClass(GaussianDropout);var AlphaDropout=class extends Layer{constructor(args){super(args);this.supportsMasking=!0,this.rate=args.rate,this.noiseShape=args.noiseShape}_getNoiseShape(inputs){return this.noiseShape||getExactlyOneTensor(inputs).shape}computeOutputShape(inputShape){return inputShape}getConfig(){let baseConfig=super.getConfig(),config3={rate:this.rate};return Object.assign(config3,baseConfig),config3}call(inputs,kwargs){return tidy(()=>{if(this.rate<1&&this.rate>0){let noiseShape=this._getNoiseShape(inputs);return inTrainPhase(()=>{let input2=getExactlyOneTensor(inputs),alpha=1.6732632423543772,scale2=1.0507009873554805,alphaP=-alpha*scale2,keptIdx=greaterEqual(randomUniform(noiseShape),this.rate);keptIdx=cast5(keptIdx,"float32");let a=((1-this.rate)*(1+this.rate*alphaP**2))**-.5,b=-a*alphaP*this.rate;return input2.mul(keptIdx).add(keptIdx.add(-1).mul(alphaP)).mul(a).add(b)},()=>getExactlyOneTensor(inputs),kwargs.training||!1)}return inputs})}};AlphaDropout.className="AlphaDropout";serialization_exports.registerClass(AlphaDropout);function batchNormalization(x,mean4,variance,beta,gamma,epsilon3=.001){let out;if(x.rank===2)out=batchNorm2d(x,mean4,variance,beta,gamma,epsilon3);else if(x.rank===3)out=batchNorm3d(x,mean4,variance,beta,gamma,epsilon3);else if(x.rank===4)out=batchNorm4d(x,mean4,variance,beta,gamma,epsilon3);else throw new NotImplementedError(`batchNormalization is not implemented for array of rank ${x.rank} yet`);return out}function regularNormalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon3=.001){return tidy(()=>{let meanAndVariance=moments(x,reductionAxes),mean4=meanAndVariance.mean,variance=meanAndVariance.variance;return[batchNormalization(x,mean4,variance,beta,gamma,epsilon3),mean4,variance]})}function broadcastNormalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon3=.001){return tidy(()=>{let meanAndVariance=moments(x,reductionAxes),mean4=meanAndVariance.mean,variance=meanAndVariance.variance,targetShape=[];for(let axis of range5(0,x.rank))reductionAxes.indexOf(axis)!==-1?targetShape.push(1):targetShape.push(x.shape[axis]);let broadcastMean=mean4.reshape(targetShape),broadcastVariance=variance.reshape(targetShape),broadcastGamma=gamma==null?null:gamma.reshape(targetShape),broadcastBeta=beta==null?null:beta.reshape(targetShape);return[batchNormalization(x,broadcastMean,broadcastVariance,broadcastBeta,broadcastGamma,epsilon3),mean4,variance]})}function normalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon3=.001){return util_exports.arraysEqual(reductionAxes.slice().sort(),range5(0,x.rank-1))?regularNormalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon3):broadcastNormalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon3)}var BatchNormalization=class extends Layer{constructor(args){args==null&&(args={}),super(args),this.supportsMasking=!0,this.axis=args.axis==null?-1:args.axis,this.momentum=args.momentum==null?.99:args.momentum,this.epsilon=args.epsilon==null?.001:args.epsilon,this.center=args.center==null?!0:args.center,this.scale=args.scale==null?!0:args.scale,this.betaInitializer=getInitializer(args.betaInitializer||"zeros"),this.gammaInitializer=getInitializer(args.gammaInitializer||"ones"),this.movingMeanInitializer=getInitializer(args.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=getInitializer(args.movingVarianceInitializer||"ones"),this.betaConstraint=getConstraint(args.betaConstraint),this.gammaConstraint=getConstraint(args.gammaConstraint),this.betaRegularizer=getRegularizer(args.betaRegularizer),this.gammaRegularizer=getRegularizer(args.gammaRegularizer)}build(inputShape){inputShape=getExactlyOneShape(inputShape);let axis=this.axis>=0?this.axis:this.axis+inputShape.length,dim=inputShape[axis];if(dim==null)throw new ValueError(`Axis ${axis} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(inputShape)}.`);this.inputSpec=[new InputSpec({ndim:inputShape.length,axes:{[axis]:dim}})];let shape=[dim];this.scale&&(this.gamma=this.addWeight("gamma",shape,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",shape,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",shape,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",shape,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(inputs,kwargs){return tidy(()=>{let training=kwargs.training==null?!1:kwargs.training,input2=getExactlyOneTensor(inputs),inputShape=input2.shape,ndim=inputShape.length,reductionAxes=range5(0,ndim),axis=this.axis>=0?this.axis:this.axis+ndim;reductionAxes.splice(axis,1);let broadcastShape=pyListRepeat(1,ndim);broadcastShape[axis]=inputShape[axis];let sortedReductionAxes=reductionAxes.slice();sortedReductionAxes.sort();let needsBroadcasting=!util_exports.arraysEqual(sortedReductionAxes,range5(0,ndim).slice(0,ndim-1)),normalizeInference=()=>{if(needsBroadcasting){let broadcastMovingMean=this.movingMean.read().reshape(broadcastShape),broadcastMovingVariance=this.movingVariance.read().reshape(broadcastShape),broadcastBeta=this.center?this.beta.read().reshape(broadcastShape):null,broadcastGamma=this.scale?this.gamma.read().reshape(broadcastShape):null;return batchNormalization(input2,broadcastMovingMean,broadcastMovingVariance,broadcastBeta,broadcastGamma,this.epsilon)}else return batchNormalization(input2,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!training)return normalizeInference();let[normedTraining,mean4,variance]=normalizeBatchInTraining(input2,this.gamma.read(),this.beta.read(),reductionAxes,this.epsilon),doMovingAverage=(variable2,value,momentum)=>{tidy(()=>{let decay=1-momentum,origValue=variable2.read(),updateDelta=origValue.sub(value).mul(decay);variable2.write(origValue.sub(updateDelta))})};return(()=>{doMovingAverage(this.movingMean,mean4,this.momentum),doMovingAverage(this.movingVariance,variance,this.momentum)})(),normedTraining})}getConfig(){let config3={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:serializeInitializer(this.betaInitializer),gammaInitializer:serializeInitializer(this.gammaInitializer),movingMeanInitializer:serializeInitializer(this.movingMeanInitializer),movingVarianceInitializer:serializeInitializer(this.movingVarianceInitializer),betaRegularizer:serializeRegularizer(this.betaRegularizer),gammaRegularizer:serializeRegularizer(this.gammaRegularizer),betaConstraint:serializeConstraint(this.betaConstraint),gammaConstraint:serializeConstraint(this.gammaConstraint)},baseConfig=super.getConfig();return Object.assign(config3,baseConfig),config3}};BatchNormalization.className="BatchNormalization";serialization_exports.registerClass(BatchNormalization);var LayerNormalization=class extends Layer{constructor(args){if(args==null&&(args={}),super(args),this.axis=args.axis==null?-1:args.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let axis of this.axis)if(!Number.isInteger(axis))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=args.epsilon==null?.001:args.epsilon,this.center=args.center==null?!0:args.center,this.scale=args.scale==null?!0:args.scale,this.betaInitializer=getInitializer(args.betaInitializer||"zeros"),this.gammaInitializer=getInitializer(args.gammaInitializer||"ones"),this.betaRegularizer=getRegularizer(args.betaRegularizer),this.gammaRegularizer=getRegularizer(args.gammaRegularizer),this.supportsMasking=!0}build(inputShape){inputShape=getExactlyOneShape(inputShape);let nDims=inputShape.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let i=0;i=nDims)throw new Error(`Invalid axis: ${axis}`);if(this.axis.length!==unique4(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let paramShape=this.axis.map(axis=>inputShape[axis]),trainable=!0;this.scale?this.gamma=this.addWeight("gamma",paramShape,"float32",this.gammaInitializer,this.gammaRegularizer,trainable):this.gamma=null,this.center?this.beta=this.addWeight("beta",paramShape,"float32",this.betaInitializer,this.betaRegularizer,trainable):this.beta=null,this.built=!0}call(inputs,kwargs){let input2=getExactlyOneTensor(inputs),inputShape=input2.shape,nDims=inputShape.length;return tidy(()=>{let keepDims=!0,{mean:mean4,variance}=moments(input2,this.axis,keepDims),broadcastShape=pyListRepeat(1,nDims);for(let dim of this.axis)broadcastShape[dim]=inputShape[dim];let broadcast=v=>v!=null&&v.shape.length!==nDims&&this.axis!==[nDims-1]?v.reshape(broadcastShape):v,scale2=broadcast(this.gamma.read()),offset=broadcast(this.beta.read()),momentsTiling=[],scaleOffsetTiling=[];for(let i=0;i{if(x.rank!==4)throw new ValueError(`temporalPadding expects input tensor to be 4-D, but received a ${x.rank}-D tensor.`);if(padding==null&&(padding=[[1,1],[1,1]]),padding.length!==2||padding[0].length!==2||padding[1].length!==2)throw new ValueError("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(dataFormat==null&&(dataFormat=imageDataFormat()),dataFormat!=="channelsLast"&&dataFormat!=="channelsFirst")throw new ValueError(`Unknown data format: ${dataFormat}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let pattern;return dataFormat==="channelsFirst"?pattern=[[0,0],[0,0],padding[0],padding[1]]:pattern=[[0,0],padding[0],padding[1],[0,0]],pad(x,pattern)})}var ZeroPadding2D=class extends Layer{constructor(args){if(args==null&&(args={}),super(args),this.dataFormat=args.dataFormat==null?imageDataFormat():args.dataFormat,args.padding==null)this.padding=[[1,1],[1,1]];else if(typeof args.padding=="number")this.padding=[[args.padding,args.padding],[args.padding,args.padding]];else{if(args.padding=args.padding,args.padding.length!==2)throw new ValueError(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${args.padding.length} array.`);let heightPadding,widthPadding;if(typeof args.padding[0]=="number")heightPadding=[args.padding[0],args.padding[0]],widthPadding=[args.padding[1],args.padding[1]];else{if(args.padding=args.padding,args.padding[0].length!==2)throw new ValueError(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${args.padding[0].length} array.`);if(heightPadding=args.padding[0],args.padding[1].length!==2)throw new ValueError(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${args.padding[1].length} array.`);widthPadding=args.padding[1]}this.padding=[heightPadding,widthPadding]}this.inputSpec=[new InputSpec({ndim:4})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let rows,cols;return this.dataFormat==="channelsFirst"?(inputShape[2]!=null&&inputShape[2]>=0?rows=inputShape[2]+this.padding[0][0]+this.padding[0][1]:rows=null,inputShape[3]!=null&&inputShape[3]>=0?cols=inputShape[3]+this.padding[1][0]+this.padding[1][1]:cols=null,[inputShape[0],inputShape[1],rows,cols]):(inputShape[1]!=null&&inputShape[1]>=0?rows=inputShape[1]+this.padding[0][0]+this.padding[0][1]:rows=null,inputShape[2]!=null&&inputShape[2]>=0?cols=inputShape[2]+this.padding[1][0]+this.padding[1][1]:cols=null,[inputShape[0],rows,cols,inputShape[3]])}call(inputs,kwargs){return tidy(()=>spatial2dPadding(getExactlyOneTensor(inputs),this.padding,this.dataFormat))}getConfig(){let config3={padding:this.padding,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config3,baseConfig),config3}};ZeroPadding2D.className="ZeroPadding2D";serialization_exports.registerClass(ZeroPadding2D);function pool2d(x,poolSize,strides,padding,dataFormat,poolMode){return tidy(()=>{checkDataFormat(dataFormat),checkPoolMode(poolMode),checkPaddingMode(padding),strides==null&&(strides=[1,1]),padding==null&&(padding="valid"),dataFormat==null&&(dataFormat=imageDataFormat()),poolMode==null&&(poolMode="max"),x=preprocessConv2DInput(x,dataFormat);let y,paddingString=padding==="same"?"same":"valid";return poolMode==="max"?y=maxPool(x,poolSize,strides,paddingString):y=avgPool(x,poolSize,strides,paddingString),dataFormat==="channelsFirst"&&(y=transpose(y,[0,3,1,2])),y})}function pool3d2(x,poolSize,strides,padding,dataFormat,poolMode){return tidy(()=>{checkDataFormat(dataFormat),checkPoolMode(poolMode),checkPaddingMode(padding),strides==null&&(strides=[1,1,1]),padding==null&&(padding="valid"),dataFormat==null&&(dataFormat=imageDataFormat()),poolMode==null&&(poolMode="max"),x=preprocessConv3DInput(x,dataFormat);let y,paddingString=padding==="same"?"same":"valid";return poolMode==="max"?y=maxPool3d(x,poolSize,strides,paddingString):y=avgPool3d(x,poolSize,strides,paddingString),dataFormat==="channelsFirst"&&(y=transpose(y,[0,4,1,2,3])),y})}var Pooling1D=class extends Layer{constructor(args){if(args.poolSize==null&&(args.poolSize=2),super(args),typeof args.poolSize=="number")this.poolSize=[args.poolSize];else if(Array.isArray(args.poolSize)&&args.poolSize.length===1&&typeof args.poolSize[0]=="number")this.poolSize=args.poolSize;else throw new ValueError(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(args.poolSize)}`);if(assertPositiveInteger(this.poolSize,"poolSize"),args.strides==null)this.strides=this.poolSize;else if(typeof args.strides=="number")this.strides=[args.strides];else if(Array.isArray(args.strides)&&args.strides.length===1&&typeof args.strides[0]=="number")this.strides=args.strides;else throw new ValueError(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(args.strides)}`);assertPositiveInteger(this.strides,"strides"),this.padding=args.padding==null?"valid":args.padding,checkPaddingMode(this.padding),this.inputSpec=[new InputSpec({ndim:3})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let length=convOutputLength(inputShape[1],this.poolSize[0],this.padding,this.strides[0]);return[inputShape[0],length,inputShape[2]]}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs),inputs=expandDims5(getExactlyOneTensor(inputs),2);let output=this.poolingFunction(getExactlyOneTensor(inputs),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return squeeze(output,[2])})}getConfig(){let config3={poolSize:this.poolSize,padding:this.padding,strides:this.strides},baseConfig=super.getConfig();return Object.assign(config3,baseConfig),config3}},MaxPooling1D=class extends Pooling1D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding),pool2d(inputs,poolSize,strides,padding,dataFormat,"max")}};MaxPooling1D.className="MaxPooling1D";serialization_exports.registerClass(MaxPooling1D);var AveragePooling1D=class extends Pooling1D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding),pool2d(inputs,poolSize,strides,padding,dataFormat,"avg")}};AveragePooling1D.className="AveragePooling1D";serialization_exports.registerClass(AveragePooling1D);var Pooling2D=class extends Layer{constructor(args){if(args.poolSize==null&&(args.poolSize=[2,2]),super(args),this.poolSize=Array.isArray(args.poolSize)?args.poolSize:[args.poolSize,args.poolSize],args.strides==null)this.strides=this.poolSize;else if(Array.isArray(args.strides)){if(args.strides.length!==2)throw new ValueError(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${args.strides.length}.`);this.strides=args.strides}else this.strides=[args.strides,args.strides];assertPositiveInteger(this.poolSize,"poolSize"),assertPositiveInteger(this.strides,"strides"),this.padding=args.padding==null?"valid":args.padding,this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),checkPaddingMode(this.padding),this.inputSpec=[new InputSpec({ndim:4})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let rows=this.dataFormat==="channelsFirst"?inputShape[2]:inputShape[1],cols=this.dataFormat==="channelsFirst"?inputShape[3]:inputShape[2];return rows=convOutputLength(rows,this.poolSize[0],this.padding,this.strides[0]),cols=convOutputLength(cols,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[inputShape[0],inputShape[1],rows,cols]:[inputShape[0],rows,cols,inputShape[3]]}call(inputs,kwargs){return tidy(()=>(this.invokeCallHook(inputs,kwargs),this.poolingFunction(getExactlyOneTensor(inputs),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let config3={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config3,baseConfig),config3}},MaxPooling2D=class extends Pooling2D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding),pool2d(inputs,poolSize,strides,padding,dataFormat,"max")}};MaxPooling2D.className="MaxPooling2D";serialization_exports.registerClass(MaxPooling2D);var AveragePooling2D=class extends Pooling2D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding),pool2d(inputs,poolSize,strides,padding,dataFormat,"avg")}};AveragePooling2D.className="AveragePooling2D";serialization_exports.registerClass(AveragePooling2D);var Pooling3D=class extends Layer{constructor(args){if(args.poolSize==null&&(args.poolSize=[2,2,2]),super(args),this.poolSize=Array.isArray(args.poolSize)?args.poolSize:[args.poolSize,args.poolSize,args.poolSize],args.strides==null)this.strides=this.poolSize;else if(Array.isArray(args.strides)){if(args.strides.length!==3)throw new ValueError(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${args.strides.length}.`);this.strides=args.strides}else this.strides=[args.strides,args.strides,args.strides];assertPositiveInteger(this.poolSize,"poolSize"),assertPositiveInteger(this.strides,"strides"),this.padding=args.padding==null?"valid":args.padding,this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),checkPaddingMode(this.padding),this.inputSpec=[new InputSpec({ndim:5})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let depths=this.dataFormat==="channelsFirst"?inputShape[2]:inputShape[1],rows=this.dataFormat==="channelsFirst"?inputShape[3]:inputShape[2],cols=this.dataFormat==="channelsFirst"?inputShape[4]:inputShape[3];return depths=convOutputLength(depths,this.poolSize[0],this.padding,this.strides[0]),rows=convOutputLength(rows,this.poolSize[1],this.padding,this.strides[1]),cols=convOutputLength(cols,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[inputShape[0],inputShape[1],depths,rows,cols]:[inputShape[0],depths,rows,cols,inputShape[4]]}call(inputs,kwargs){return tidy(()=>(this.invokeCallHook(inputs,kwargs),this.poolingFunction(getExactlyOneTensor(inputs),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let config3={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config3,baseConfig),config3}},MaxPooling3D=class extends Pooling3D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding),pool3d2(inputs,poolSize,strides,padding,dataFormat,"max")}};MaxPooling3D.className="MaxPooling3D";serialization_exports.registerClass(MaxPooling3D);var AveragePooling3D=class extends Pooling3D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding),pool3d2(inputs,poolSize,strides,padding,dataFormat,"avg")}};AveragePooling3D.className="AveragePooling3D";serialization_exports.registerClass(AveragePooling3D);var GlobalPooling1D=class extends Layer{constructor(args){super(args);this.inputSpec=[new InputSpec({ndim:3})]}computeOutputShape(inputShape){return[inputShape[0],inputShape[2]]}call(inputs,kwargs){throw new NotImplementedError}},GlobalAveragePooling1D=class extends GlobalPooling1D{constructor(args){super(args||{})}call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);return mean(input2,1)})}};GlobalAveragePooling1D.className="GlobalAveragePooling1D";serialization_exports.registerClass(GlobalAveragePooling1D);var GlobalMaxPooling1D=class extends GlobalPooling1D{constructor(args){super(args||{})}call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);return max(input2,1)})}};GlobalMaxPooling1D.className="GlobalMaxPooling1D";serialization_exports.registerClass(GlobalMaxPooling1D);var GlobalPooling2D=class extends Layer{constructor(args){super(args);this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),this.inputSpec=[new InputSpec({ndim:4})]}computeOutputShape(inputShape){return inputShape=inputShape,this.dataFormat==="channelsLast"?[inputShape[0],inputShape[3]]:[inputShape[0],inputShape[1]]}call(inputs,kwargs){throw new NotImplementedError}getConfig(){let config3={dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config3,baseConfig),config3}},GlobalAveragePooling2D=class extends GlobalPooling2D{call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);return this.dataFormat==="channelsLast"?mean(input2,[1,2]):mean(input2,[2,3])})}};GlobalAveragePooling2D.className="GlobalAveragePooling2D";serialization_exports.registerClass(GlobalAveragePooling2D);var GlobalMaxPooling2D=class extends GlobalPooling2D{call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);return this.dataFormat==="channelsLast"?max(input2,[1,2]):max(input2,[2,3])})}};GlobalMaxPooling2D.className="GlobalMaxPooling2D";serialization_exports.registerClass(GlobalMaxPooling2D);var Wrapper=class extends Layer{constructor(args){super(args);this.layer=args.layer}build(inputShape){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(value){this.layer!=null&&(this.layer.trainable=value)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(weights){this.layer.setWeights(weights)}getConfig(){let config3={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},baseConfig=super.getConfig();return Object.assign(config3,baseConfig),config3}setFastWeightInitDuringBuild(value){super.setFastWeightInitDuringBuild(value),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(value)}static fromConfig(cls,config3,customObjects={}){let layerConfig=config3.layer,layer=deserialize(layerConfig,customObjects);delete config3.layer;let newConfig={layer};return Object.assign(newConfig,config3),new cls(newConfig)}},TimeDistributed=class extends Wrapper{constructor(args){super(args);this.supportsMasking=!0}build(inputShape){if(inputShape=getExactlyOneShape(inputShape),inputShape.length<3)throw new ValueError(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(inputShape)}`);this.inputSpec=[{shape:inputShape}];let childInputShape=[inputShape[0]].concat(inputShape.slice(2));this.layer.built||(this.layer.build(childInputShape),this.layer.built=!0),super.build(inputShape)}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let childInputShape=[inputShape[0]].concat(inputShape.slice(2)),childOutputShape=this.layer.computeOutputShape(childInputShape),timesteps=inputShape[1];return[childOutputShape[0],timesteps].concat(childOutputShape.slice(1))}call(inputs,kwargs){return tidy(()=>(inputs=getExactlyOneTensor(inputs),rnn((inputs2,states)=>[getExactlyOneTensor(this.layer.call(inputs2,kwargs)),[]],inputs,[],!1,null,null,!1,!0)[1]))}};TimeDistributed.className="TimeDistributed";serialization_exports.registerClass(TimeDistributed);function checkBidirectionalMergeMode(value){checkStringTypeUnionValue(VALID_BIDIRECTIONAL_MERGE_MODES,"BidirectionalMergeMode",value)}var DEFAULT_BIDIRECTIONAL_MERGE_MODE="concat",Bidirectional=class extends Wrapper{constructor(args){super(args);let layerConfig=args.layer.getConfig(),forwDict={};forwDict.className=args.layer.getClassName(),forwDict.config=layerConfig,this.forwardLayer=deserialize(forwDict),layerConfig.goBackwards=layerConfig.goBackwards!==!0;let backDict={};if(backDict.className=args.layer.getClassName(),backDict.config=layerConfig,this.backwardLayer=deserialize(backDict),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=args.mergeMode===void 0?DEFAULT_BIDIRECTIONAL_MERGE_MODE:args.mergeMode,checkBidirectionalMergeMode(this.mergeMode),args.weights)throw new NotImplementedError("weights support is not implemented for Bidirectional layer yet.");this._stateful=args.layer.stateful,this.returnSequences=args.layer.returnSequences,this.returnState=args.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=args.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(value){this._trainable=value,this.forwardLayer!=null&&(this.forwardLayer.trainable=value),this.backwardLayer!=null&&(this.backwardLayer.trainable=value)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(weights){let numWeights=weights.length,numeightsOver2=Math.floor(numWeights/2);this.forwardLayer.setWeights(weights.slice(0,numeightsOver2)),this.backwardLayer.setWeights(weights.slice(numeightsOver2))}computeOutputShape(inputShape){let layerShapes=this.forwardLayer.computeOutputShape(inputShape);Array.isArray(layerShapes)&&Array.isArray(layerShapes[0])||(layerShapes=[layerShapes]),layerShapes=layerShapes;let outputShape,outputShapes,stateShape;return this.returnState&&(stateShape=layerShapes.slice(1)),outputShape=layerShapes[0],outputShape=outputShape,this.mergeMode==="concat"?(outputShape[outputShape.length-1]*=2,outputShapes=[outputShape]):this.mergeMode==null?outputShapes=[outputShape,outputShape.slice()]:outputShapes=[outputShape],this.returnState?this.mergeMode==null?outputShapes.concat(stateShape).concat(stateShape.slice()):[outputShape].concat(stateShape).concat(stateShape.slice()):singletonOrArray(outputShapes)}apply(inputs,kwargs){let initialState=kwargs==null?null:kwargs.initialState,constants=kwargs==null?null:kwargs.constants;kwargs==null&&(kwargs={});let standardized=standardizeArgs(inputs,initialState,constants,this.numConstants);if(inputs=standardized.inputs,initialState=standardized.initialState,constants=standardized.constants,Array.isArray(inputs)&&(initialState=inputs.slice(1),inputs=inputs[0]),(initialState==null||initialState.length===0)&&constants==null)return super.apply(inputs,kwargs);let additionalInputs=[],additionalSpecs=[];if(initialState!=null){let numStates=initialState.length;if(numStates%2>0)throw new ValueError("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");kwargs.initialState=initialState,additionalInputs.push(...initialState);let stateSpecs=initialState.map(state=>new InputSpec({shape:state.shape}));this.forwardLayer.stateSpec=stateSpecs.slice(0,numStates/2),this.backwardLayer.stateSpec=stateSpecs.slice(numStates/2),additionalSpecs.push(...stateSpecs)}if(constants!=null)throw new NotImplementedError("Support for constants in Bidirectional layers is not implemented yet.");let isSymbolicTensor=additionalInputs[0]instanceof SymbolicTensor;for(let tensor2 of additionalInputs)if(tensor2 instanceof SymbolicTensor!==isSymbolicTensor)throw new ValueError("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(isSymbolicTensor){let fullInput=[inputs].concat(additionalInputs),fullInputSpec=this.inputSpec.concat(additionalSpecs),originalInputSpec=this.inputSpec;this.inputSpec=fullInputSpec;let output=super.apply(fullInput,kwargs);return this.inputSpec=originalInputSpec,output}else return super.apply(inputs,kwargs)}call(inputs,kwargs){return tidy(()=>{let initialState=kwargs.initialState,y,yRev;if(initialState==null)y=this.forwardLayer.call(inputs,kwargs),yRev=this.backwardLayer.call(inputs,kwargs);else{let forwardState=initialState.slice(0,initialState.length/2),backwardState=initialState.slice(initialState.length/2);y=this.forwardLayer.call(inputs,Object.assign(kwargs,{initialState:forwardState})),yRev=this.backwardLayer.call(inputs,Object.assign(kwargs,{initialState:backwardState}))}let states;this.returnState&&(Array.isArray(y)&&(states=y.slice(1).concat(yRev.slice(1))),y=y[0],yRev=yRev[0]),this.returnSequences&&(yRev=reverse(yRev,1));let output;return this.mergeMode==="concat"?output=concatenate([y,yRev]):this.mergeMode==="sum"?output=add2(y,yRev):this.mergeMode==="ave"?output=mul(.5,add2(y,yRev)):this.mergeMode==="mul"?output=mul(y,yRev):this.mergeMode==null&&(output=[y,yRev]),this.returnState?this.mergeMode==null?output.concat(states):[output].concat(states):output})}resetStates(states){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(inputShape){nameScope(this.forwardLayer.name,()=>{this.forwardLayer.build(inputShape)}),nameScope(this.backwardLayer.name,()=>{this.backwardLayer.build(inputShape)}),this.built=!0}computeMask(inputs,mask){Array.isArray(mask)&&(mask=mask[0]);let outputMask;if(this.returnSequences?this.mergeMode==null?outputMask=[mask,mask]:outputMask=mask:this.mergeMode==null?outputMask=[null,null]:outputMask=null,this.returnState){let stateMask=this.forwardLayer.states.map(state=>null);return Array.isArray(outputMask)?outputMask.concat(stateMask).concat(stateMask):[outputMask].concat(stateMask).concat(stateMask)}else return outputMask}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)}setFastWeightInitDuringBuild(value){super.setFastWeightInitDuringBuild(value),this.forwardLayer!=null&&this.forwardLayer.setFastWeightInitDuringBuild(value),this.backwardLayer!=null&&this.backwardLayer.setFastWeightInitDuringBuild(value)}getConfig(){let config3={mergeMode:this.mergeMode},baseConfig=super.getConfig();return Object.assign(config3,baseConfig),config3}static fromConfig(cls,config3){let rnnLayer=deserialize(config3.layer);if(delete config3.layer,config3.numConstants!=null)throw new NotImplementedError("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");let newConfig=config3;return newConfig.layer=rnnLayer,new cls(newConfig)}};Bidirectional.className="Bidirectional";serialization_exports.registerClass(Bidirectional);function inputLayer(args){return new InputLayer(args)}function elu5(args){return new ELU4(args)}function reLU(args){return new ReLU(args)}function leakyReLU(args){return new LeakyReLU(args)}function prelu5(args){return new PReLU(args)}function softmax5(args){return new Softmax3(args)}function thresholdedReLU(args){return new ThresholdedReLU(args)}function conv1d2(args){return new Conv1D(args)}function conv2d5(args){return new Conv2D2(args)}function conv2dTranspose2(args){return new Conv2DTranspose(args)}function conv3d2(args){return new Conv3D2(args)}function separableConv2d2(args){return new SeparableConv2D(args)}function cropping2D(args){return new Cropping2D(args)}function upSampling2d(args){return new UpSampling2D(args)}function depthwiseConv2d5(args){return new DepthwiseConv2D(args)}function activation(args){return new Activation2(args)}function dense(args){return new Dense(args)}function dropout3(args){return new Dropout(args)}function spatialDropout1d(args){return new SpatialDropout1D(args)}function flatten3(args){return new Flatten(args)}function repeatVector(args){return new RepeatVector(args)}function reshape5(args){return new Reshape2(args)}function permute(args){return new Permute(args)}function embedding(args){return new Embedding(args)}function add4(args){return new Add2(args)}function average(args){return new Average(args)}function concatenate2(args){return new Concatenate(args)}function maximum4(args){return new Maximum2(args)}function minimum4(args){return new Minimum2(args)}function multiply3(args){return new Multiply2(args)}function dot3(args){return new Dot(args)}function batchNormalization2(args){return new BatchNormalization(args)}function layerNormalization(args){return new LayerNormalization(args)}function zeroPadding2d(args){return new ZeroPadding2D(args)}function averagePooling1d(args){return new AveragePooling1D(args)}function avgPool1d(args){return averagePooling1d(args)}function avgPooling1d(args){return averagePooling1d(args)}function averagePooling2d(args){return new AveragePooling2D(args)}function avgPool2d(args){return averagePooling2d(args)}function avgPooling2d(args){return averagePooling2d(args)}function averagePooling3d(args){return new AveragePooling3D(args)}function avgPool3d2(args){return averagePooling3d(args)}function avgPooling3d(args){return averagePooling3d(args)}function globalAveragePooling1d(args){return new GlobalAveragePooling1D(args)}function globalAveragePooling2d(args){return new GlobalAveragePooling2D(args)}function globalMaxPooling1d(args){return new GlobalMaxPooling1D(args)}function globalMaxPooling2d(args){return new GlobalMaxPooling2D(args)}function maxPooling1d(args){return new MaxPooling1D(args)}function maxPooling2d(args){return new MaxPooling2D(args)}function maxPooling3d(args){return new MaxPooling3D(args)}function gru(args){return new GRU(args)}function gruCell(args){return new GRUCell(args)}function lstm(args){return new LSTM(args)}function lstmCell(args){return new LSTMCell(args)}function simpleRNN(args){return new SimpleRNN(args)}function simpleRNNCell(args){return new SimpleRNNCell(args)}function convLstm2d(args){return new ConvLSTM2D(args)}function convLstm2dCell(args){return new ConvLSTM2DCell(args)}function rnn2(args){return new RNN(args)}function stackedRNNCells(args){return new StackedRNNCells(args)}function bidirectional(args){return new Bidirectional(args)}function timeDistributed(args){return new TimeDistributed(args)}var globalMaxPool1d=globalMaxPooling1d,globalMaxPool2d=globalMaxPooling2d,maxPool1d=maxPooling1d,maxPool2d=maxPooling2d;function gaussianNoise(args){return new GaussianNoise(args)}function gaussianDropout(args){return new GaussianDropout(args)}function alphaDropout(args){return new AlphaDropout(args)}function masking(args){return new Masking(args)}var exports_metrics_exports={};__export2(exports_metrics_exports,{MAPE:()=>MAPE2,MSE:()=>MSE2,binaryAccuracy:()=>binaryAccuracy2,binaryCrossentropy:()=>binaryCrossentropy3,categoricalAccuracy:()=>categoricalAccuracy2,categoricalCrossentropy:()=>categoricalCrossentropy3,cosineProximity:()=>cosineProximity2,mape:()=>mape2,meanAbsoluteError:()=>meanAbsoluteError2,meanAbsolutePercentageError:()=>meanAbsolutePercentageError2,meanSquaredError:()=>meanSquaredError3,mse:()=>mse2,precision:()=>precision2,recall:()=>recall2,sparseCategoricalAccuracy:()=>sparseCategoricalAccuracy2});function binaryAccuracy2(yTrue,yPred){return binaryAccuracy(yTrue,yPred)}function binaryCrossentropy3(yTrue,yPred){return binaryCrossentropy2(yTrue,yPred)}function sparseCategoricalAccuracy2(yTrue,yPred){return sparseCategoricalAccuracy(yTrue,yPred)}function categoricalAccuracy2(yTrue,yPred){return categoricalAccuracy(yTrue,yPred)}function categoricalCrossentropy3(yTrue,yPred){return categoricalCrossentropy2(yTrue,yPred)}function precision2(yTrue,yPred){return precision(yTrue,yPred)}function recall2(yTrue,yPred){return recall(yTrue,yPred)}function cosineProximity2(yTrue,yPred){return cosineProximity(yTrue,yPred)}function meanAbsoluteError2(yTrue,yPred){return meanAbsoluteError(yTrue,yPred)}function meanAbsolutePercentageError2(yTrue,yPred){return meanAbsolutePercentageError(yTrue,yPred)}function MAPE2(yTrue,yPred){return meanAbsolutePercentageError(yTrue,yPred)}function mape2(yTrue,yPred){return meanAbsolutePercentageError(yTrue,yPred)}function meanSquaredError3(yTrue,yPred){return meanSquaredError2(yTrue,yPred)}function MSE2(yTrue,yPred){return meanSquaredError2(yTrue,yPred)}function mse2(yTrue,yPred){return meanSquaredError2(yTrue,yPred)}var exports_models_exports={};__export2(exports_models_exports,{modelFromJSON:()=>modelFromJSON});var exports_regularizers_exports={};__export2(exports_regularizers_exports,{l1:()=>l12,l1l2:()=>l1l2,l2:()=>l22});function l1l2(config3){return new L1L2(config3)}function l12(config3){return l1(config3)}function l22(config3){return l2(config3)}var Callback=class extends BaseCallback{constructor(){super(...arguments);this.model=null}setModel(model2){if(!(model2 instanceof LayersModel))throw new Error("model must be a LayersModel, not some other Container");this.model=model2}};function less4(currVal,prevVal){return currValprevVal}var EarlyStopping=class extends Callback{constructor(args){super();if(args==null&&(args={}),args.restoreBestWeights)throw new NotImplementedError("restoreBestWeights = True is not implemented in EarlyStopping yet.");this.monitor=args.monitor||"val_loss",this.minDelta=Math.abs(args.minDelta||0),this.patience=args.patience||0,this.verbose=args.verbose||0,this.mode=args.mode||"auto",this.baseline=args.baseline,["auto","min","max"].indexOf(this.mode)===-1&&(console.warn(`EarlyStopping mode '${this.mode}' is invalid. Falling back to mode 'auto'.`),this.mode="auto"),this.mode==="min"?this.monitorFunc=less4:this.mode==="max"?this.monitorFunc=greater4:this.monitor.indexOf("acc")!==-1?this.monitorFunc=greater4:this.monitorFunc=less4,this.monitorFunc===less4&&(this.minDelta*=-1)}async onTrainBegin(logs){this.wait=0,this.stoppedEpoch=0,this.baseline!=null?this.best=this.baseline:this.best=this.monitorFunc===less4?Infinity:-Infinity}async onEpochEnd(epoch,logs){await resolveScalarsInLogs(logs);let current=this.getMonitorValue(logs);current!=null&&(this.monitorFunc(current-this.minDelta,this.best)?(this.best=current,this.wait=0):(this.wait++,this.wait>=this.patience&&(this.stoppedEpoch=epoch,this.model.stopTraining=!0)))}async onTrainEnd(logs){this.stoppedEpoch>0&&this.verbose&&console.log(`Epoch ${this.stoppedEpoch}: early stopping.`)}getMonitorValue(logs){logs==null&&(logs={});let monitorValue=logs[this.monitor];return monitorValue==null&&console.warn(`Metric for EarlyStopping ${this.monitor} is not available. Available metrics are: ${Object.keys(logs)}`),monitorValue}};function earlyStopping(args){return new EarlyStopping(args)}var callbacks={earlyStopping},DataType;(function(DataType2){DataType2[DataType2.DT_INVALID=0]="DT_INVALID",DataType2[DataType2.DT_FLOAT=1]="DT_FLOAT",DataType2[DataType2.DT_DOUBLE=2]="DT_DOUBLE",DataType2[DataType2.DT_INT32=3]="DT_INT32",DataType2[DataType2.DT_UINT8=4]="DT_UINT8",DataType2[DataType2.DT_INT16=5]="DT_INT16",DataType2[DataType2.DT_INT8=6]="DT_INT8",DataType2[DataType2.DT_STRING=7]="DT_STRING",DataType2[DataType2.DT_COMPLEX64=8]="DT_COMPLEX64",DataType2[DataType2.DT_INT64=9]="DT_INT64",DataType2[DataType2.DT_BOOL=10]="DT_BOOL",DataType2[DataType2.DT_QINT8=11]="DT_QINT8",DataType2[DataType2.DT_QUINT8=12]="DT_QUINT8",DataType2[DataType2.DT_QINT32=13]="DT_QINT32",DataType2[DataType2.DT_BFLOAT16=14]="DT_BFLOAT16",DataType2[DataType2.DT_FLOAT_REF=101]="DT_FLOAT_REF",DataType2[DataType2.DT_DOUBLE_REF=102]="DT_DOUBLE_REF",DataType2[DataType2.DT_INT32_REF=103]="DT_INT32_REF",DataType2[DataType2.DT_UINT8_REF=104]="DT_UINT8_REF",DataType2[DataType2.DT_INT16_REF=105]="DT_INT16_REF",DataType2[DataType2.DT_INT8_REF=106]="DT_INT8_REF",DataType2[DataType2.DT_STRING_REF=107]="DT_STRING_REF",DataType2[DataType2.DT_COMPLEX64_REF=108]="DT_COMPLEX64_REF",DataType2[DataType2.DT_INT64_REF=109]="DT_INT64_REF",DataType2[DataType2.DT_BOOL_REF=110]="DT_BOOL_REF",DataType2[DataType2.DT_QINT8_REF=111]="DT_QINT8_REF",DataType2[DataType2.DT_QUINT8_REF=112]="DT_QUINT8_REF",DataType2[DataType2.DT_QINT32_REF=113]="DT_QINT32_REF",DataType2[DataType2.DT_BFLOAT16_REF=114]="DT_BFLOAT16_REF"})(DataType||(DataType={}));var SaverDef;(function(SaverDef2){let CheckpointFormatVersion;(function(CheckpointFormatVersion2){CheckpointFormatVersion2[CheckpointFormatVersion2.LEGACY=0]="LEGACY",CheckpointFormatVersion2[CheckpointFormatVersion2.V1=1]="V1",CheckpointFormatVersion2[CheckpointFormatVersion2.V2=2]="V2"})(CheckpointFormatVersion=SaverDef2.CheckpointFormatVersion||(SaverDef2.CheckpointFormatVersion={}))})(SaverDef||(SaverDef={}));var CUSTOM_OPS={};function registerOp(name,opFunc){let opMapper={tfOpName:name,category:"custom",inputs:[],attrs:[],customExecutor:opFunc};CUSTOM_OPS[name]=opMapper}function getRegisteredOp(name){return CUSTOM_OPS[name]}function deregisterOp(name){delete CUSTOM_OPS[name]}function getParamValue(paramName,node,tensorMap,context,resourceManager){let inputParam=node.inputParams[paramName];if(inputParam&&inputParam.inputIndexStart!==void 0){let start=inputParam.inputIndexStart,end=inputParam.inputIndexEnd===0?void 0:inputParam.inputIndexEnd===void 0?start+1:inputParam.inputIndexEnd;if(inputParam.type==="tensor")return getTensor(node.inputNames[inputParam.inputIndexStart],tensorMap,context,resourceManager);if(inputParam.type==="tensors")return node.inputNames.slice(start,end).map(name=>getTensor(name,tensorMap,context,resourceManager));let tensor2=getTensor(node.inputNames.slice(start)[0],tensorMap,context,resourceManager),data2=tensor2.dataSync();return inputParam.type==="number"?data2[0]:util_exports.toNestedArray(tensor2.shape,data2)}let attrParam=node.attrParams[paramName];return attrParam&&attrParam.value}function getTensor(name,tensorsMap,context,resourceManager){let[nodeName,index]=parseNodeName(name);if(resourceManager!=null){let tensor2=resourceManager.getHashTableHandleByName(nodeName);if(tensor2!=null)return tensor2}let contextId=context.currentContextIds.find(contextId2=>!!tensorsMap[getNodeNameWithContextId(nodeName,contextId2)]);return contextId!==void 0?tensorsMap[getNodeNameWithContextId(nodeName,contextId)][index]:void 0}function getTensorsForCurrentContenxt(name,tensorsMap,context){return tensorsMap[getNodeNameWithContextId(name,context.currentContextId)]}function getNodeNameAndIndex(inputName,context){let[nodeName,index]=parseNodeName(inputName);return[getNodeNameWithContextId(nodeName,context&&context.currentContextId),index]}function getNodeNameWithContextId(name,contextId){return contextId?`${name}-${contextId}`:name}function parseNodeName(name){let parts=name.split(":");return parts.length===1?[name,0]:[parts[0],Number(parts[parts.length-1])]}function getPadding(node,tensorMap,context){let pad3=getParamValue("pad",node,tensorMap,context);if(pad3==="explicit"){pad3=getParamValue("explicitPaddings",node,tensorMap,context);let explicitPadding=[[0,0],[0,0],[0,0],[0,0]];for(let i=0;i<4;i++)explicitPadding[i][0]=pad3[i*2],explicitPadding[i][1]=pad3[i*2+1];return explicitPadding}return pad3}function cloneTensor(tensor2){return tensor2.kept?tensor2:clone(tensor2)}var arithmetic_exports={};__export2(arithmetic_exports,{json:()=>json});var json=[{tfOpName:"Add",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"AddV2",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"AddN",category:"arithmetic",inputs:[{start:0,end:0,name:"tensors",type:"tensors"}]},{tfOpName:"BiasAdd",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"Sub",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"RealDiv",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Div",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"DivNoNan",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"FloorDiv",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Mul",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Maximum",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Minimum",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Pow",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"SquaredDifference",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Mod",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"FloorMod",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]}],basic_math_exports={};__export2(basic_math_exports,{json:()=>json2});var json2=[{tfOpName:"Abs",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Acos",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Asin",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Atan",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Atan2",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"y",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Ceil",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"ClipByValue",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"clipValueMin",type:"number"},{start:2,name:"clipValueMax",type:"number"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Complex",category:"basic_math",inputs:[{start:0,name:"real",type:"tensor"},{start:1,name:"imag",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"ComplexAbs",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Cos",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Cosh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Elu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Exp",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Floor",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Log",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Imag",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"Tout",name:"outputType",type:"dtype",notSupported:!0}]},{tfOpName:"Neg",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Real",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"Tout",name:"outputType",type:"dtype",notSupported:!0}]},{tfOpName:"Prelu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"alpha",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Relu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Relu6",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Selu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sigmoid",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sin",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sinh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sqrt",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Rsqrt",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Square",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Tan",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Tanh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sign",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Round",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Expm1",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Log1p",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Reciprocal",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Softplus",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Asinh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Acosh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Atanh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Erf",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Prod",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axes",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool",notSupported:!0},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LeakyRelu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"alpha",name:"alpha",type:"number",defaultValue:.2},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]}],control_exports={};__export2(control_exports,{json:()=>json3});var 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json11=[{tfOpName:"Equal",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"NotEqual",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Greater",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"GreaterEqual",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Less",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LessEqual",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LogicalAnd",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LogicalNot",category:"logical",inputs:[{start:0,name:"a",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LogicalOr",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Select",category:"logical",inputs:[{start:0,name:"condition",type:"tensor"},{start:1,name:"a",type:"tensor"},{start:2,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"SelectV2",category:"logical",inputs:[{start:0,name:"condition",type:"tensor"},{start:1,name:"a",type:"tensor"},{start:2,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]}],matrices_exports={};__export2(matrices_exports,{json:()=>json12});var json12=[{tfOpName:"_FusedMatMul",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"},{start:2,end:0,name:"args",type:"tensors"}],attrs:[{tfName:"num_args",name:"numArgs",type:"number"},{tfName:"fused_ops",name:"fusedOps",type:"string[]",defaultValue:[]},{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:1e-4},{tfName:"transpose_a",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"transpose_b",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"MatMul",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"transpose_a",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"transpose_b",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"BatchMatMul",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"adj_x",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"adj_y",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"BatchMatMulV2",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"adj_x",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"adj_y",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Transpose",category:"matrices",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"perm",type:"number[]"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]}],normalization_exports={};__export2(normalization_exports,{json:()=>json13});var json13=[{tfOpName:"FusedBatchNorm",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"scale",type:"tensor"},{start:2,name:"offset",type:"tensor"},{start:3,name:"mean",type:"tensor"},{start:4,name:"variance",type:"tensor"}],attrs:[{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:.001},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"FusedBatchNormV2",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"scale",type:"tensor"},{start:2,name:"offset",type:"tensor"},{start:3,name:"mean",type:"tensor"},{start:4,name:"variance",type:"tensor"}],attrs:[{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:.001},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"FusedBatchNormV3",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"scale",type:"tensor"},{start:2,name:"offset",type:"tensor"},{start:3,name:"mean",type:"tensor"},{start:4,name:"variance",type:"tensor"}],attrs:[{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:.001},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"LRN",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"depth_radius",name:"radius",type:"number",defaultValue:5},{tfName:"bias",name:"bias",type:"number",defaultValue:1},{tfName:"alpha",name:"alpha",type:"number",defaultValue:1},{tfName:"beta",name:"beta",type:"number",defaultValue:.5}]},{tfOpName:"Softmax",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"LogSoftmax",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"SparseToDense",category:"normalization",inputs:[{start:0,name:"sparseIndices",type:"tensor"},{start:1,name:"outputShape",type:"number[]"},{start:2,name:"sparseValues",type:"tensor"},{start:3,name:"defaultValue",type:"tensor"}],attrs:[{tfName:"validate_indices",name:"validateIndices",type:"bool",defaultValue:!0,notSupported:!0}]}],reduction_exports={};__export2(reduction_exports,{json:()=>json14});var json14=[{tfOpName:"Bincount",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"size",type:"number"},{start:2,name:"weights",type:"tensor"}]},{tfOpName:"DenseBincount",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"size",type:"number"},{start:2,name:"weights",type:"tensor"}],attrs:[{tfName:"binary_output",name:"binaryOutput",type:"bool"}]},{tfOpName:"Max",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Mean",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Min",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Sum",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"All",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Any",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"ArgMax",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]},{tfOpName:"ArgMin",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]},{tfOpName:"Prod",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Cumsum",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}],attrs:[{tfName:"exclusive",name:"exclusive",type:"bool"},{tfName:"reverse",name:"reverse",type:"bool"}]}],slice_join_exports={};__export2(slice_join_exports,{json:()=>json15});var json15=[{tfOpName:"ConcatV2",category:"slice_join",inputs:[{start:0,end:-1,name:"tensors",type:"tensors"},{start:-1,name:"axis",type:"number"}],attrs:[{tfName:"N",name:"n",type:"number",defaultValue:2}]},{tfOpName:"Concat",category:"slice_join",inputs:[{start:1,end:0,name:"tensors",type:"tensors"},{start:0,name:"axis",type:"number"}],attrs:[{tfName:"N",name:"n",type:"number",defaultValue:2}]},{tfOpName:"GatherV2",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"indices",type:"tensor"},{start:2,name:"axis",type:"number",defaultValue:0}],attrs:[{tfName:"batch_dims",name:"batchDims",type:"number",defaultValue:0}]},{tfOpName:"Gather",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"indices",type:"tensor"}],attrs:[{tfName:"validate_indices",name:"validateIndices",type:"bool",notSupported:!0}]},{tfOpName:"Reverse",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"dims",type:"bool[]"}]},{tfOpName:"ReverseV2",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}]},{tfOpName:"Slice",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"begin",type:"number[]"},{start:2,name:"size",type:"number[]"}]},{tfOpName:"StridedSlice",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"begin",type:"number[]"},{start:2,name:"end",type:"number[]"},{start:3,name:"strides",type:"number[]"}],attrs:[{tfName:"begin_mask",name:"beginMask",type:"number",defaultValue:0},{tfName:"end_mask",name:"endMask",type:"number",defaultValue:0},{tfName:"new_axis_mask",name:"newAxisMask",type:"number",defaultValue:0},{tfName:"ellipsis_mask",name:"ellipsisMask",type:"number",defaultValue:0},{tfName:"shrink_axis_mask",name:"shrinkAxisMask",type:"number",defaultValue:0}]},{tfOpName:"Pack",category:"slice_join",inputs:[{start:0,end:0,name:"tensors",type:"tensors"}],attrs:[{tfName:"axis",name:"axis",type:"number",defaultValue:0}]},{tfOpName:"Unpack",category:"slice_join",inputs:[{start:0,name:"tensor",type:"tensor"}],attrs:[{tfName:"axis",name:"axis",type:"number",defaultValue:0},{tfName:"num",name:"num",type:"number",defaultValue:0,notSupported:!0}]},{tfOpName:"Tile",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"reps",type:"number[]"}]},{tfOpName:"Split",category:"slice_join",inputs:[{start:0,name:"axis",type:"number",defaultValue:0},{start:1,name:"x",type:"tensor"}],attrs:[{tfName:"num_split",name:"numOrSizeSplits",type:"number",defaultValue:1}]},{tfOpName:"SplitV",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"numOrSizeSplits",type:"number[]"},{start:2,name:"axis",type:"number",defaultValue:0}]},{tfOpName:"ScatterNd",category:"slice_join",inputs:[{start:0,name:"indices",type:"tensor"},{start:1,name:"values",type:"tensor"},{start:2,name:"shape",type:"number[]"}]},{tfOpName:"GatherNd",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"indices",type:"tensor"}]},{tfOpName:"SparseToDense",category:"slice_join",inputs:[{start:0,name:"sparseIndices",type:"tensor"},{start:1,name:"outputShape",type:"number[]"},{start:2,name:"sparseValues",type:"tensor"},{start:3,name:"defaultValue",type:"tensor"}],attrs:[{tfName:"validate_indices",name:"validateIndices",type:"bool",defaultValue:!1,notSupported:!0}]}],spectral_exports={};__export2(spectral_exports,{json:()=>json16});var json16=[{tfOpName:"FFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"IFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"RFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"fft_length",type:"number",notSupported:!0}]},{tfOpName:"IRFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"fft_length",type:"number",notSupported:!0}]}],transformation_exports={};__export2(transformation_exports,{json:()=>json17});var json17=[{tfOpName:"Cast",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"SrcT",name:"sdtype",type:"dtype",notSupported:!0},{tfName:"DstT",name:"dtype",type:"dtype"}]},{tfOpName:"ExpandDims",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]},{tfOpName:"MirrorPad",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"}],attrs:[{tfName:"mode",name:"mode",type:"string"}]},{tfOpName:"Pad",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"}],attrs:[{tfName:"constant_value",name:"constantValue",type:"number",defaultValue:0}]},{tfOpName:"PadV2",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"},{start:2,name:"constantValue",type:"number",defaultValue:0}]},{tfOpName:"Reshape",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"shape",type:"number[]"}]},{tfOpName:"Squeeze",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"axis",tfDeprecatedName:"squeeze_dims",name:"axis",type:"number[]"}]},{tfOpName:"SpaceToBatchND",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"blockShape",type:"number[]"},{start:2,name:"paddings",type:"number[]"}]},{tfOpName:"BatchToSpaceND",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"blockShape",type:"number[]"},{start:2,name:"crops",type:"number[]"}]},{tfOpName:"DepthToSpace",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"block_size",name:"blockSize",type:"number"},{tfName:"data_format",name:"dataFormat",type:"string"}]},{tfOpName:"BroadcastTo",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"shape",type:"number[]"}],attrs:[]}],OperationMapper=class{static get Instance(){return this._instance||(this._instance=new this)}constructor(){let ops=[arithmetic_exports,basic_math_exports,control_exports,convolution_exports,creation_exports,dynamic_exports,evaluation_exports,logical_exports,image_exports,graph_exports,matrices_exports,normalization_exports,reduction_exports,slice_join_exports,spectral_exports,transformation_exports,hash_table_exports],mappersJson=[].concat(...ops.map(op2=>op2.json));this.opMappers=mappersJson.reduce((map,mapper)=>(map[mapper.tfOpName]=mapper,map),{})}transformGraph(graph2,signature={}){let tfNodes=graph2.node,placeholders=[],weights=[],initNodes=[],nodes=tfNodes.reduce((map,node)=>(map[node.name]=this.mapNode(node),node.op.startsWith("Placeholder")?placeholders.push(map[node.name]):node.op==="Const"?weights.push(map[node.name]):(node.input==null||node.input.length===0)&&initNodes.push(map[node.name]),map),{}),inputs=[],outputs=[],inputNodeNameToKey={},outputNodeNameToKey={};signature!=null&&(inputNodeNameToKey=this.mapSignatureEntries(signature.inputs),outputNodeNameToKey=this.mapSignatureEntries(signature.outputs));let allNodes=Object.keys(nodes);allNodes.forEach(key=>{let node=nodes[key];node.inputNames.forEach(name=>{let[nodeName]=getNodeNameAndIndex(name);node.inputs.push(nodes[nodeName]),nodes[nodeName].children.push(node)})}),Object.keys(outputNodeNameToKey).length===0?allNodes.forEach(key=>{let node=nodes[key];node.children.length===0&&outputs.push(node)}):Object.keys(outputNodeNameToKey).forEach(name=>{let[nodeName]=getNodeNameAndIndex(name),node=nodes[nodeName];node!=null&&(node.signatureKey=outputNodeNameToKey[name],outputs.push(node))}),Object.keys(inputNodeNameToKey).length>0?Object.keys(inputNodeNameToKey).forEach(name=>{let[nodeName]=getNodeNameAndIndex(name),node=nodes[nodeName];node&&(node.signatureKey=inputNodeNameToKey[name],inputs.push(node))}):inputs=placeholders;let functions={};graph2.library!=null&&graph2.library.function!=null&&(functions=graph2.library.function.reduce((functions2,func2)=>(functions2[func2.signature.name]=this.mapFunction(func2),functions2),{}));let result={nodes,inputs,outputs,weights,placeholders,signature,functions};return initNodes.length>0&&(result.initNodes=initNodes),result}mapSignatureEntries(entries){return Object.keys(entries||{}).reduce((prev,curr)=>(prev[entries[curr].name]=curr,prev),{})}mapNode(node){let mapper=getRegisteredOp(node.op)||this.opMappers[node.op]||{};node.attr==null&&(node.attr={});let newNode={name:node.name,op:node.op,category:mapper.category,inputNames:(node.input||[]).map(input2=>input2.startsWith("^")?input2.substr(1):input2),inputs:[],children:[],inputParams:{},attrParams:{},rawAttrs:node.attr};return mapper.inputs!=null&&(newNode.inputParams=mapper.inputs.reduce((map,param)=>(map[param.name]={type:param.type,inputIndexStart:param.start,inputIndexEnd:param.end},map),{})),mapper.attrs!=null&&(newNode.attrParams=mapper.attrs.reduce((map,param)=>{let type=param.type,value;switch(param.type){case"string":value=getStringParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getStringParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"string[]":value=getStringArrayParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getStringArrayParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"number":value=getNumberParam(node.attr,param.tfName,param.defaultValue||0),value===void 0&&!!param.tfDeprecatedName&&(value=getNumberParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"number[]":value=getNumericArrayParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getNumericArrayParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"bool":value=getBoolParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getBoolParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"bool[]":value=getBoolArrayParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getBoolArrayParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"shape":value=getTensorShapeParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getTensorShapeParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"shape[]":value=getTensorShapeArrayParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getTensorShapeArrayParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"dtype":value=getDtypeParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getDtypeParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"dtype[]":value=getDtypeArrayParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getDtypeArrayParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"func":value=getFuncParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getFuncParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"tensor":case"tensors":break;default:throw new Error(`Unsupported param type: ${param.type} for op: ${node.op}`)}return map[param.name]={value,type},map},{})),newNode}mapFunction(functionDef){let tfNodes=functionDef.nodeDef,placeholders=[],weights=[],nodes={};tfNodes!=null&&(nodes=tfNodes.reduce((map,node)=>(map[node.name]=this.mapNode(node),node.op==="Const"&&weights.push(map[node.name]),map),{}));let inputs=[],outputs=[];functionDef.signature.inputArg.forEach(arg=>{let[nodeName]=getNodeNameAndIndex(arg.name),node={name:nodeName,op:"Placeholder",inputs:[],inputNames:[],category:"graph",inputParams:{},attrParams:{dtype:{value:parseDtypeParam(arg.type),type:"dtype"}},children:[]};node.signatureKey=arg.name,inputs.push(node),nodes[nodeName]=node}),Object.keys(nodes).forEach(key=>{let node=nodes[key];node.inputNames.forEach(name=>{let[nodeName]=getNodeNameAndIndex(name);node.inputs.push(nodes[nodeName]),nodes[nodeName].children.push(node)})});let returnNodeMap=functionDef.ret;functionDef.signature.outputArg.forEach(output=>{let[nodeName,index]=getNodeNameAndIndex(returnNodeMap[output.name]),node=nodes[nodeName];node!=null&&(node.defaultOutput=index,outputs.push(node))});let signature=this.mapArgsToSignature(functionDef);return{nodes,inputs,outputs,weights,placeholders,signature}}mapArgsToSignature(functionDef){return{methodName:functionDef.signature.name,inputs:functionDef.signature.inputArg.reduce((map,arg)=>(map[arg.name]=this.mapArgToTensorInfo(arg),map),{}),outputs:functionDef.signature.outputArg.reduce((map,arg)=>(map[arg.name]=this.mapArgToTensorInfo(arg,functionDef.ret),map),{})}}mapArgToTensorInfo(arg,nameMap2){let name=arg.name;return nameMap2!=null&&(name=nameMap2[name]),{name,dtype:arg.type}}};function decodeBase64(text){let global2=env().global;if(typeof global2.atob!="undefined")return global2.atob(text);if(typeof Buffer!="undefined")return new Buffer(text,"base64").toString();throw new Error("Unable to decode base64 in this environment. Missing built-in atob() or Buffer()")}function parseStringParam(s,keepCase){let value=Array.isArray(s)?String.fromCharCode.apply(null,s):decodeBase64(s);return keepCase?value:value.toLowerCase()}function getStringParam(attrs,name,def,keepCase=!1){let param=attrs[name];return param!=null?parseStringParam(param.s,keepCase):def}function getBoolParam(attrs,name,def){let param=attrs[name];return param?param.b:def}function getNumberParam(attrs,name,def){let param=attrs[name]||{},value=param.i!=null?param.i:param.f!=null?param.f:def;return typeof value=="number"?value:parseInt(value,10)}function parseDtypeParam(value){switch(typeof value=="string"&&(value=DataType[value]),value){case DataType.DT_FLOAT:return"float32";case DataType.DT_INT32:case DataType.DT_INT64:case DataType.DT_INT8:case DataType.DT_UINT8:return"int32";case DataType.DT_BOOL:return"bool";case DataType.DT_DOUBLE:return"float32";case DataType.DT_STRING:return"string";default:return null}}function getFuncParam(attrs,name,def){let param=attrs[name];return param&¶m.func?param.func.name:def}function getDtypeParam(attrs,name,def){let param=attrs[name];return param&¶m.type?parseDtypeParam(param.type):def}function getDtypeArrayParam(attrs,name,def){let param=attrs[name];return param&¶m.list&¶m.list.type?param.list.type.map(v=>parseDtypeParam(v)):def}function parseTensorShapeParam(shape){if(!shape.unknownRank)return shape.dim!=null?shape.dim.map(dim=>typeof dim.size=="number"?dim.size:parseInt(dim.size,10)):[]}function getTensorShapeParam(attrs,name,def){let param=attrs[name];return param&¶m.shape?parseTensorShapeParam(param.shape):def}function getNumericArrayParam(attrs,name,def){let param=attrs[name];return param?((param.list.f&¶m.list.f.length?param.list.f:param.list.i)||[]).map(v=>typeof v=="number"?v:parseInt(v,10)):def}function getStringArrayParam(attrs,name,def,keepCase=!1){let param=attrs[name];return param&¶m.list&¶m.list.s?param.list.s.map(v=>parseStringParam(v,keepCase)):def}function getTensorShapeArrayParam(attrs,name,def){let param=attrs[name];return param&¶m.list&¶m.list.shape?param.list.shape.map(v=>parseTensorShapeParam(v)):def}function getBoolArrayParam(attrs,name,def){let param=attrs[name];return param&¶m.list&¶m.list.b?param.list.b:def}var NodeValueImpl=class{constructor(node,tensorMap,context){this.node=node,this.tensorMap=tensorMap,this.context=context,this.inputs=[],this.attrs={},this.inputs=node.inputNames.map(name=>this.getInput(name)),node.rawAttrs!=null&&(this.attrs=Object.keys(node.rawAttrs).reduce((attrs,key)=>(attrs[key]=this.getAttr(key),attrs),{}))}getInput(name){return getTensor(name,this.tensorMap,this.context)}getAttr(name,defaultValue){let value=this.node.rawAttrs[name];if(value.tensor!=null)return getTensor(name,this.tensorMap,this.context);if(value.i!=null||value.f!=null)return getNumberParam(this.node.rawAttrs,name,defaultValue);if(value.s!=null)return getStringParam(this.node.rawAttrs,name,defaultValue);if(value.b!=null)return getBoolParam(this.node.rawAttrs,name,defaultValue);if(value.shape!=null)return getTensorShapeParam(this.node.rawAttrs,name,defaultValue);if(value.type!=null)return getDtypeParam(this.node.rawAttrs,name,defaultValue);if(value.list!=null){if(value.list.i!=null||value.list.f!=null)return getNumericArrayParam(this.node.rawAttrs,name,defaultValue);if(value.list.s!=null)return getStringArrayParam(this.node.rawAttrs,name,defaultValue);if(value.list.shape!=null)return getTensorShapeArrayParam(this.node.rawAttrs,name,defaultValue);if(value.list.b!=null)return getBoolArrayParam(this.node.rawAttrs,name,defaultValue);if(value.list.type!=null)return getDtypeArrayParam(this.node.rawAttrs,name,defaultValue)}return defaultValue}},executeOp=(node,tensorMap,context)=>{switch(node.op){case"BiasAdd":case"AddV2":case"Add":return[add2(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"AddN":return[addN(getParamValue("tensors",node,tensorMap,context))];case"FloorMod":case"Mod":return[mod(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Mul":return[mul(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"RealDiv":case"Div":return[div(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"DivNoNan":return[divNoNan(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"FloorDiv":return[floorDiv(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Sub":return[sub(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Minimum":return[minimum(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Maximum":return[maximum(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Pow":return[pow(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"SquaredDifference":return[squaredDifference(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp2=(node,tensorMap,context)=>{switch(node.op){case"Abs":case"ComplexAbs":return[abs(getParamValue("x",node,tensorMap,context))];case"Acos":return[acos(getParamValue("x",node,tensorMap,context))];case"Acosh":return[acosh(getParamValue("x",node,tensorMap,context))];case"Asin":return[asin(getParamValue("x",node,tensorMap,context))];case"Asinh":return[asinh(getParamValue("x",node,tensorMap,context))];case"Atan":return[atan(getParamValue("x",node,tensorMap,context))];case"Atan2":return[atan2(getParamValue("x",node,tensorMap,context),getParamValue("y",node,tensorMap,context))];case"Atanh":return[atanh(getParamValue("x",node,tensorMap,context))];case"Ceil":return[ceil(getParamValue("x",node,tensorMap,context))];case"Complex":return[complex(getParamValue("real",node,tensorMap,context),getParamValue("imag",node,tensorMap,context))];case"Cos":return[cos(getParamValue("x",node,tensorMap,context))];case"Cosh":return[cosh(getParamValue("x",node,tensorMap,context))];case"Elu":return[elu(getParamValue("x",node,tensorMap,context))];case"Erf":return[erf(getParamValue("x",node,tensorMap,context))];case"Exp":return[exp(getParamValue("x",node,tensorMap,context))];case"Expm1":return[expm1(getParamValue("x",node,tensorMap,context))];case"Floor":return[floor(getParamValue("x",node,tensorMap,context))];case"Log":return[log2(getParamValue("x",node,tensorMap,context))];case"Log1p":return[log1p(getParamValue("x",node,tensorMap,context))];case"Imag":return[imag(getParamValue("x",node,tensorMap,context))];case"Neg":return[neg(getParamValue("x",node,tensorMap,context))];case"Reciprocal":return[reciprocal(getParamValue("x",node,tensorMap,context))];case"Real":return[real(getParamValue("x",node,tensorMap,context))];case"Relu":return[relu(getParamValue("x",node,tensorMap,context))];case"Round":return[round2(getParamValue("x",node,tensorMap,context))];case"Selu":return[selu(getParamValue("x",node,tensorMap,context))];case"Sigmoid":return[sigmoid(getParamValue("x",node,tensorMap,context))];case"Sin":return[sin(getParamValue("x",node,tensorMap,context))];case"Sign":return[sign(getParamValue("x",node,tensorMap,context))];case"Sinh":return[sinh(getParamValue("x",node,tensorMap,context))];case"Softplus":return[softplus(getParamValue("x",node,tensorMap,context))];case"Sqrt":return[sqrt(getParamValue("x",node,tensorMap,context))];case"Square":return[square(getParamValue("x",node,tensorMap,context))];case"Tanh":return[tanh2(getParamValue("x",node,tensorMap,context))];case"Tan":return[tan(getParamValue("x",node,tensorMap,context))];case"ClipByValue":return[clipByValue(getParamValue("x",node,tensorMap,context),getParamValue("clipValueMin",node,tensorMap,context),getParamValue("clipValueMax",node,tensorMap,context))];case"Relu6":return[relu6(getParamValue("x",node,tensorMap,context))];case"Rsqrt":return[rsqrt(getTensor(node.inputNames[0],tensorMap,context))];case"Prod":return[prod(getParamValue("x",node,tensorMap,context),getParamValue("axes",node,tensorMap,context))];case"LeakyRelu":return[leakyRelu(getParamValue("x",node,tensorMap,context),getParamValue("alpha",node,tensorMap,context))];case"Prelu":return[prelu(getParamValue("x",node,tensorMap,context),getParamValue("alpha",node,tensorMap,context))];default:throw TypeError(`Node type ${node.op} is not implemented`)}};function assertShapesMatchAllowUndefinedSize(shapeA,shapeB,errorMessagePrefix=""){util_exports.assert(shapesEqualAllowUndefinedSize(shapeA,shapeB),()=>errorMessagePrefix+` Shapes ${shapeA} and ${shapeB} must match`)}function shapesEqualAllowUndefinedSize(n1,n2){if(n1.length!==n2.length)return!1;for(let i=0;i{(keepIds==null||!keepIds.has(tensor2.tensor.id))&&tensor2.tensor.dispose()}),this.tensors=[],this.closed_=!0,this.idTensor.dispose()}size(){return this.tensors.length}read(index){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(index<0||index>=this.size())throw new Error(`Tried to read from index ${index}, but array size is: ${this.size()}`);let tensorWithState=this.tensors[index];if(tensorWithState.cleared)throw new Error(`TensorArray ${this.name}: Could not read index ${index} twice because it was cleared after a previous read (perhaps try setting clear_after_read = false?).`);return this.clearAfterRead&&(tensorWithState.cleared=!0),tensorWithState.read=!0,tensorWithState.tensor}readMany(indices){return indices.map(index=>this.read(index))}write(index,tensor2){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(index<0||!this.dynamicSize&&index>=this.maxSize)throw new Error(`Tried to write to index ${index}, but array is not resizeable and size is: ${this.maxSize}`);let t=this.tensors[index]||{};if(tensor2.dtype!==this.dtype)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index}, +2. The custom ${printableModuleName} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);if(fromConfig!=null){let customObjectsCombined={};for(let key of Object.keys(_GLOBAL_CUSTOM_OBJECTS))customObjectsCombined[key]=_GLOBAL_CUSTOM_OBJECTS[key];for(let key of Object.keys(customObjects))customObjectsCombined[key]=customObjects[key];let nestedConfig=config2.config;nestedConfig.customObjects=customObjectsCombined;let backupCustomObjects=Object.assign({},_GLOBAL_CUSTOM_OBJECTS);for(let key of Object.keys(customObjects))_GLOBAL_CUSTOM_OBJECTS[key]=customObjects[key];convertNDArrayScalarsInConfig(config2.config);let returnObj=fromConfig(cls,config2.config,customObjects,fastWeightInit);return _GLOBAL_CUSTOM_OBJECTS=Object.assign({},backupCustomObjects),returnObj}else{let backupCustomObjects=Object.assign({},_GLOBAL_CUSTOM_OBJECTS);for(let key of Object.keys(customObjects))_GLOBAL_CUSTOM_OBJECTS[key]=customObjects[key];let returnObj=new cls(config2.config);return _GLOBAL_CUSTOM_OBJECTS=Object.assign({},backupCustomObjects),returnObj}}}function numberCompare(a,b){return ab?1:0}function reverseNumberCompare(a,b){return-1*numberCompare(a,b)}function unique4(xs){if(xs==null)return xs;let out=[];for(let x of xs)out.indexOf(x)===-1&&out.push(x);return out}function isObjectEmpty(obj){if(obj==null)throw new ValueError(`Invalid value in obj: ${JSON.stringify(obj)}`);for(let key in obj)if(obj.hasOwnProperty(key))return!1;return!0}function checkStringTypeUnionValue(values,label,value){if(value!=null&&values.indexOf(value)<0)throw new ValueError(`${value} is not a valid ${label}. Valid values are ${values} or null/undefined.`)}function checkArrayTypeAndLength(x,expectedType,minLength=0,maxLength=Infinity){return assert2(minLength>=0),assert2(maxLength>=minLength),Array.isArray(x)&&x.length>=minLength&&x.length<=maxLength&&x.every(e=>typeof e===expectedType)}function assertPositiveInteger(value,name){Array.isArray(value)?(util_exports.assert(value.length>0,()=>`${name} is unexpectedly an empty array.`),value.forEach((v,i)=>assertPositiveInteger(v,`element ${i+1} of ${name}`))):util_exports.assert(Number.isInteger(value)&&value>0,()=>`Expected ${name} to be a positive integer, but got ${formatAsFriendlyString(value)}.`)}function formatAsFriendlyString(value){return value===null?"null":Array.isArray(value)?"["+value.map(v=>formatAsFriendlyString(v)).join(",")+"]":typeof value=="string"?`"${value}"`:`${value}`}function debounce(f,waitMs){let lastTime=util_exports.now(),lastResult;return(...args)=>{let now22=util_exports.now();return now22-lastTimesqrt(sum2(mul(w,w),axis,!0)))}var Constraint=class extends serialization_exports.Serializable{getConfig(){return{}}},MaxNorm=class extends Constraint{constructor(args){super();this.defaultMaxValue=2,this.defaultAxis=0,this.maxValue=args.maxValue!=null?args.maxValue:this.defaultMaxValue,this.axis=args.axis!=null?args.axis:this.defaultAxis}apply(w){return tidy(()=>{let norms=calcL2Norms(w,this.axis),desired=clipByValue(norms,0,this.maxValue);return mul(w,div(desired,add2(epsilon2(),norms)))})}getConfig(){return{maxValue:this.maxValue,axis:this.axis}}};MaxNorm.className="MaxNorm";serialization_exports.registerClass(MaxNorm);var UnitNorm=class extends Constraint{constructor(args){super();this.defaultAxis=0,this.axis=args.axis!=null?args.axis:this.defaultAxis}apply(w){return tidy(()=>div(w,add2(epsilon2(),calcL2Norms(w,this.axis))))}getConfig(){return{axis:this.axis}}};UnitNorm.className="UnitNorm";serialization_exports.registerClass(UnitNorm);var NonNeg=class extends Constraint{apply(w){return relu(w)}};NonNeg.className="NonNeg";serialization_exports.registerClass(NonNeg);var MinMaxNorm=class extends Constraint{constructor(args){super();this.defaultMinValue=0,this.defaultMaxValue=1,this.defaultRate=1,this.defaultAxis=0,this.minValue=args.minValue!=null?args.minValue:this.defaultMinValue,this.maxValue=args.maxValue!=null?args.maxValue:this.defaultMaxValue,this.rate=args.rate!=null?args.rate:this.defaultRate,this.axis=args.axis!=null?args.axis:this.defaultAxis}apply(w){return tidy(()=>{let norms=calcL2Norms(w,this.axis),desired=add2(mul(this.rate,clipByValue(norms,this.minValue,this.maxValue)),mul(1-this.rate,norms));return mul(w,div(desired,add2(epsilon2(),norms)))})}getConfig(){return{minValue:this.minValue,maxValue:this.maxValue,rate:this.rate,axis:this.axis}}};MinMaxNorm.className="MinMaxNorm";serialization_exports.registerClass(MinMaxNorm);var CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP={maxNorm:"MaxNorm",minMaxNorm:"MinMaxNorm",nonNeg:"NonNeg",unitNorm:"UnitNorm"};function serializeConstraint(constraint){return serializeKerasObject(constraint)}function deserializeConstraint(config2,customObjects={}){return deserializeKerasObject(config2,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"constraint")}function getConstraint(identifier){if(identifier==null)return null;if(typeof identifier=="string"){let config2={className:identifier in CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP?CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier]:identifier,config:{}};return deserializeConstraint(config2)}else return identifier instanceof Constraint?identifier:deserializeConstraint(identifier)}function maxNorm(args){return new MaxNorm(args)}function unitNorm(args){return new UnitNorm(args)}function nonNeg(){return new NonNeg}function minMaxNorm(config2){return new MinMaxNorm(config2)}var exports_initializers_exports={};__export2(exports_initializers_exports,{constant:()=>constant,glorotNormal:()=>glorotNormal,glorotUniform:()=>glorotUniform,heNormal:()=>heNormal,heUniform:()=>heUniform,identity:()=>identity4,leCunNormal:()=>leCunNormal,leCunUniform:()=>leCunUniform,ones:()=>ones3,orthogonal:()=>orthogonal,randomNormal:()=>randomNormal3,randomUniform:()=>randomUniform2,truncatedNormal:()=>truncatedNormal2,varianceScaling:()=>varianceScaling,zeros:()=>zeros3});var VALID_DATA_FORMAT_VALUES=["channelsFirst","channelsLast"],VALID_INTERPOLATION_FORMAT_VALUES=["nearest","bilinear"],VALID_PADDING_MODE_VALUES=["valid","same","causal"],VALID_POOL_MODE_VALUES=["max","avg"],VALID_BIDIRECTIONAL_MERGE_MODES=["sum","mul","concat","ave"],nameMap=new Map;function checkDataFormat(value){checkStringTypeUnionValue(VALID_DATA_FORMAT_VALUES,"DataFormat",value)}function checkInterpolationFormat(value){checkStringTypeUnionValue(VALID_INTERPOLATION_FORMAT_VALUES,"InterpolationFormat",value)}function checkPaddingMode(value){checkStringTypeUnionValue(VALID_PADDING_MODE_VALUES,"PaddingMode",value)}function checkPoolMode(value){checkStringTypeUnionValue(VALID_POOL_MODE_VALUES,"PoolMode",value)}var _nameScopeStack=[],_nameScopeDivider="/";function nameScope(name,fn){_nameScopeStack.push(name);try{let val=fn();return _nameScopeStack.pop(),val}catch(e){throw _nameScopeStack.pop(),e}}function currentNameScopePrefix(){return _nameScopeStack.length===0?"":_nameScopeStack.join(_nameScopeDivider)+_nameScopeDivider}function getScopedTensorName(tensorName){if(!isValidTensorName(tensorName))throw new Error("Not a valid tensor name: '"+tensorName+"'");return currentNameScopePrefix()+tensorName}function getUniqueTensorName(scopedName){if(!isValidTensorName(scopedName))throw new Error("Not a valid tensor name: '"+scopedName+"'");nameMap.has(scopedName)||nameMap.set(scopedName,0);let index=nameMap.get(scopedName);if(nameMap.set(scopedName,nameMap.get(scopedName)+1),index>0){let result=`${scopedName}_${index}`;return nameMap.set(result,1),result}else return scopedName}var tensorNameRegex=new RegExp(/^[A-Za-z0-9][-A-Za-z0-9\._\/]*$/);function isValidTensorName(name){return!!name.match(tensorNameRegex)}function isInteger(x){return x===parseInt(x.toString(),10)}function arrayProd(array2,begin,end){begin==null&&(begin=0),end==null&&(end=array2.length);let prod5=1;for(let i=begin;i{if(x.shape.length!==2)throw new ValueError(`repeat() expects a rank-2 tensor, but received a rank-${x.shape.length} tensor.`);let y=expandDims5(x,1);return tile5(y,[1,n,1])})}function flatten2(x){let newShape=[arrayProd(x.shape)];return x.reshape(newShape)}function batchFlatten(x){if(x.rank<=1)throw new ValueError(`batchFlatten requires a minimum rank of 2. Got rank: ${x.rank}.`);let newShape=[x.shape[0],arrayProd(x.shape,1)];return x.reshape(newShape)}function sliceAlongFirstAxis(array2,start,size){return tidy(()=>{switch(array2.rank){case 1:return slice1d(array2,start,size);case 2:return slice2d(array2,[start,0],[size,array2.shape[1]]);case 3:return slice3d(array2,[start,0,0],[size,array2.shape[1],array2.shape[2]]);case 4:return slice4d(array2,[start,0,0,0],[size,array2.shape[1],array2.shape[2],array2.shape[3]]);case 5:return slice(array2,[start,0,0,0,0],[size,array2.shape[1],array2.shape[2],array2.shape[3],array2.shape[4]]);case 6:return slice(array2,[start,0,0,0,0,0],[size,array2.shape[1],array2.shape[2],array2.shape[3],array2.shape[4],array2.shape[5]]);default:throw new ValueError(`sliceAlongFirstAxis() received an unsupported tensor rank: ${array2.rank}`)}})}function sliceAlongLastAxis(array2,start,size){return tidy(()=>{switch(array2.rank){case 1:return slice1d(array2,start,size);case 2:return slice2d(array2,[0,start],[array2.shape[0],size]);case 3:return slice3d(array2,[0,0,start],[array2.shape[0],array2.shape[1],size]);case 4:return slice4d(array2,[0,0,0,start],[array2.shape[0],array2.shape[1],array2.shape[2],size]);default:throw new ValueError(`sliceAlongLastAxis() received an unsupported tensor rank: ${array2.rank}`)}})}function sliceAlongAxis(array2,start,size,axis){return tidy(()=>{switch(array2.rank){case 1:return slice1d(array2,start,size);case 2:switch(axis){case 1:return sliceAlongFirstAxis(array2,start,size);case 2:return sliceAlongLastAxis(array2,start,size);default:throw new ValueError(`The axis is not within the rank of the tensor ${axis}`)}case 3:switch(axis){case 1:return sliceAlongFirstAxis(array2,start,size);case 2:return slice3d(array2,[0,start,0],[array2.shape[0],size,array2.shape[2]]);case 3:return sliceAlongLastAxis(array2,start,size);default:throw new ValueError(`The axis is not within the rank of the tensor ${axis}`)}case 4:switch(axis){case 1:return sliceAlongFirstAxis(array2,start,size);case 2:return slice4d(array2,[0,start,0,0],[array2.shape[0],size,array2.shape[2],array2.shape[3]]);case 3:return slice4d(array2,[0,0,start,0],[array2.shape[0],array2.shape[1],size,array2.shape[3]]);case 4:return sliceAlongLastAxis(array2,start,size);default:throw new ValueError(`The axis is not within the rank of the tensor ${axis}`)}default:throw new ValueError(`sliceAlongLastAxis() received an unsupported tensor rank: ${array2.rank}`)}})}function concatenate(tensors,axis=-1){let rank;return axis<0&&(rank=tensors[0].rank,rank!==0?axis=rank:axis=0),axis===tensors[0].rank&&(axis=-1),concat(tensors,axis)}function concatAlongFirstAxis(a,b){switch(a.rank){case 1:return concat1d([a,b]);case 2:return concat2d([a,b],0);case 3:return concat3d([a,b],0);case 4:return concat4d([a,b],0);default:throw new ValueError(`concatAlongFirstAxis() received an unsupported tensor rank: ${a.rank}`)}}function tile5(x,n){if(Array.isArray(n)||(n=[n]),x.rank!==n.length)throw new ValueError(`The length of input n (${n.length}) does not match the number of dimensions in input x (${x.rank})`);return tile(x,n)}function randomNormal2(shape,mean4=0,stddev=1,dtype,seed){return randomNormal(shape,mean4,stddev,dtype,seed)}function dot2(a,b,activation2,bias){if(a.rank<2||b.rank<2)throw new NotImplementedError(`dot requires both inputs to be rank >= 2 but got x shape = ${a.shape} and y shape = ${b.shape}`);if(b.rank>=3){let xLastDim=a.shape.slice(-1)[0],ySecondLastDim=b.shape.slice(-2)[0];if(xLastDim!==ySecondLastDim)throw new NotImplementedError(`If rank y >= 3, then the second last dim of y must equal the last dim of x but got x shape = ${a.shape} and y shape = ${b.shape}`)}if(a.rank===2&&b.rank===2){let transposeA=!1,transposeB=!1;return fused_ops_exports.matMul({a,b,transposeA,transposeB,bias:bias?reshapeBias(a.rank,bias,imageDataFormat()):null,activation:activation2})}else{let aFirstDims=a.shape.slice(),aLastDim=aFirstDims.pop();a=a.reshape([-1,aLastDim]);let bShape=b.shape.slice(),bLastDim=bShape.pop(),ySecondLastDim=bShape.pop(),yOtherDims=[...bShape,bLastDim],perm=Array.from({length:b.rank},(_,i)=>i===0?b.rank-2:i<=b.rank-2?i-1:i);b=b.transpose(perm).reshape([ySecondLastDim,-1]);let outputShape=[...aFirstDims,...yOtherDims],transposeA=!1,transposeB=!1;return fused_ops_exports.matMul({a,b,transposeA,transposeB,bias:bias?reshapeBias(a.rank,bias,imageDataFormat()):null,activation:activation2}).reshape(outputShape)}}function gather2(reference,indices,axis){return tidy(()=>(Array.isArray(indices)?indices=tensor1d(indices,"int32"):indices=indices.toInt(),gather(reference,indices,axis)))}function square3(x){return mul(x,x)}function reshapeBias(xRank,bias,dataFormat){let biasShape=bias.shape;if(bias.rank!==1&&bias.rank!==xRank)throw new ValueError(`Unexpected bias dimensions: ${bias.rank}; expected it to be 1 or ${xRank}`);if(xRank===5){if(dataFormat==="channelsFirst")return biasShape.length===1?bias.reshape([1,biasShape[0],1,1,1]):bias.reshape([1,biasShape[3],biasShape[0],biasShape[1],biasShape[2]]);if(dataFormat==="channelsLast")return biasShape.length===1?bias.reshape([1,1,1,1,biasShape[0]]):bias.reshape([1].concat(biasShape))}else if(xRank===4){if(dataFormat==="channelsFirst")return biasShape.length===1?bias.reshape([1,biasShape[0],1,1]):bias.reshape([1,biasShape[2],biasShape[0],biasShape[1]]);if(dataFormat==="channelsLast")return biasShape.length===1?bias.reshape([1,1,1,biasShape[0]]):bias.reshape([1].concat(biasShape))}else if(xRank===3){if(dataFormat==="channelsFirst")return biasShape.length===1?bias.reshape([1,biasShape[0],1]):bias.reshape([1,biasShape[1],biasShape[0]]);if(dataFormat==="channelsLast")return biasShape.length===1?bias.reshape([1,1,biasShape[0]]):bias.reshape([1].concat(biasShape))}else if(xRank<3)return bias;throw new ValueError(`Unsupported input rank by biasAdd: ${bias.rank}`)}function biasAdd(x,bias,dataFormat){return tidy(()=>(dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat),x.add(reshapeBias(x.rank,bias,dataFormat))))}function elu4(x,alpha=1){if(alpha!==1)throw new NotImplementedError(`Support for alpha values other than 1 (${alpha}) is not implemented yet.`);return elu(x)}function softsign(x){return tidy(()=>div(x,abs(x).add(1)))}function dropout2(x,level,noiseShape,seed){return tidy(()=>dropout(x,level,noiseShape,seed))}function hardSigmoid(x){return tidy(()=>{let y=add2(.5,mul(.2,x));return clipByValue(y,0,1)})}function inTrainPhase(x,alt,training=!1){return training?x():alt()}var VALID_FAN_MODE_VALUES=["fanIn","fanOut","fanAvg"],VALID_DISTRIBUTION_VALUES=["normal","uniform","truncatedNormal"];function checkFanMode(value){checkStringTypeUnionValue(VALID_FAN_MODE_VALUES,"FanMode",value)}function checkDistribution(value){checkStringTypeUnionValue(VALID_DISTRIBUTION_VALUES,"Distribution",value)}var Initializer=class extends serialization_exports.Serializable{fromConfigUsesCustomObjects(){return!1}getConfig(){return{}}},Zeros=class extends Initializer{apply(shape,dtype){return zeros(shape,dtype)}};Zeros.className="Zeros";serialization_exports.registerClass(Zeros);var Ones=class extends Initializer{apply(shape,dtype){return ones2(shape,dtype)}};Ones.className="Ones";serialization_exports.registerClass(Ones);var Constant=class extends Initializer{constructor(args){super();if(typeof args!="object")throw new ValueError(`Expected argument of type ConstantConfig but got ${args}`);if(args.value===void 0)throw new ValueError(`config must have value set but got ${args}`);this.value=args.value}apply(shape,dtype){return tidy(()=>mul(scalar(this.value),ones2(shape,dtype)))}getConfig(){return{value:this.value}}};Constant.className="Constant";serialization_exports.registerClass(Constant);var RandomUniform=class extends Initializer{constructor(args){super();this.DEFAULT_MINVAL=-.05,this.DEFAULT_MAXVAL=.05,this.minval=args.minval||this.DEFAULT_MINVAL,this.maxval=args.maxval||this.DEFAULT_MAXVAL,this.seed=args.seed}apply(shape,dtype){return randomUniform(shape,this.minval,this.maxval,dtype)}getConfig(){return{minval:this.minval,maxval:this.maxval,seed:this.seed}}};RandomUniform.className="RandomUniform";serialization_exports.registerClass(RandomUniform);var RandomNormal=class extends Initializer{constructor(args){super();this.DEFAULT_MEAN=0,this.DEFAULT_STDDEV=.05,this.mean=args.mean||this.DEFAULT_MEAN,this.stddev=args.stddev||this.DEFAULT_STDDEV,this.seed=args.seed}apply(shape,dtype){if(dtype=dtype||"float32",dtype!=="float32"&&dtype!=="int32")throw new NotImplementedError(`randomNormal does not support dType ${dtype}.`);return randomNormal2(shape,this.mean,this.stddev,dtype,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}};RandomNormal.className="RandomNormal";serialization_exports.registerClass(RandomNormal);var TruncatedNormal=class extends Initializer{constructor(args){super();this.DEFAULT_MEAN=0,this.DEFAULT_STDDEV=.05,this.mean=args.mean||this.DEFAULT_MEAN,this.stddev=args.stddev||this.DEFAULT_STDDEV,this.seed=args.seed}apply(shape,dtype){if(dtype=dtype||"float32",dtype!=="float32"&&dtype!=="int32")throw new NotImplementedError(`truncatedNormal does not support dType ${dtype}.`);return truncatedNormal(shape,this.mean,this.stddev,dtype,this.seed)}getConfig(){return{mean:this.mean,stddev:this.stddev,seed:this.seed}}};TruncatedNormal.className="TruncatedNormal";serialization_exports.registerClass(TruncatedNormal);var Identity2=class extends Initializer{constructor(args){super();this.gain=args.gain!=null?args.gain:1}apply(shape,dtype){return tidy(()=>{if(shape.length!==2||shape[0]!==shape[1])throw new ValueError("Identity matrix initializer can only be used for 2D square matrices.");return mul(this.gain,eye(shape[0]))})}getConfig(){return{gain:this.gain}}};Identity2.className="Identity";serialization_exports.registerClass(Identity2);function computeFans(shape,dataFormat="channelsLast"){let fanIn,fanOut;if(checkDataFormat(dataFormat),shape.length===2)fanIn=shape[0],fanOut=shape[1];else if([3,4,5].indexOf(shape.length)!==-1){if(dataFormat==="channelsFirst"){let receptiveFieldSize=arrayProd(shape,2);fanIn=shape[1]*receptiveFieldSize,fanOut=shape[0]*receptiveFieldSize}else if(dataFormat==="channelsLast"){let receptiveFieldSize=arrayProd(shape,0,shape.length-2);fanIn=shape[shape.length-2]*receptiveFieldSize,fanOut=shape[shape.length-1]*receptiveFieldSize}}else{let shapeProd=arrayProd(shape);fanIn=Math.sqrt(shapeProd),fanOut=Math.sqrt(shapeProd)}return[fanIn,fanOut]}var VarianceScaling=class extends Initializer{constructor(args){super();if(args.scale<0)throw new ValueError(`scale must be a positive float. Got: ${args.scale}`);this.scale=args.scale==null?1:args.scale,this.mode=args.mode==null?"fanIn":args.mode,checkFanMode(this.mode),this.distribution=args.distribution==null?"normal":args.distribution,checkDistribution(this.distribution),this.seed=args.seed}apply(shape,dtype){let fans=computeFans(shape),fanIn=fans[0],fanOut=fans[1],scale2=this.scale;if(this.mode==="fanIn"?scale2/=Math.max(1,fanIn):this.mode==="fanOut"?scale2/=Math.max(1,fanOut):scale2/=Math.max(1,(fanIn+fanOut)/2),this.distribution==="normal"){let stddev=Math.sqrt(scale2);if(dtype=dtype||"float32",dtype!=="float32"&&dtype!=="int32")throw new NotImplementedError(`${this.getClassName()} does not support dType ${dtype}.`);return truncatedNormal(shape,0,stddev,dtype,this.seed)}else{let limit=Math.sqrt(3*scale2);return randomUniform(shape,-limit,limit,dtype)}}getConfig(){return{scale:this.scale,mode:this.mode,distribution:this.distribution,seed:this.seed}}};VarianceScaling.className="VarianceScaling";serialization_exports.registerClass(VarianceScaling);var GlorotUniform=class extends VarianceScaling{constructor(args){super({scale:1,mode:"fanAvg",distribution:"uniform",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};GlorotUniform.className="GlorotUniform";serialization_exports.registerClass(GlorotUniform);var GlorotNormal=class extends VarianceScaling{constructor(args){super({scale:1,mode:"fanAvg",distribution:"normal",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};GlorotNormal.className="GlorotNormal";serialization_exports.registerClass(GlorotNormal);var HeNormal=class extends VarianceScaling{constructor(args){super({scale:2,mode:"fanIn",distribution:"normal",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};HeNormal.className="HeNormal";serialization_exports.registerClass(HeNormal);var HeUniform=class extends VarianceScaling{constructor(args){super({scale:2,mode:"fanIn",distribution:"uniform",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};HeUniform.className="HeUniform";serialization_exports.registerClass(HeUniform);var LeCunNormal=class extends VarianceScaling{constructor(args){super({scale:1,mode:"fanIn",distribution:"normal",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};LeCunNormal.className="LeCunNormal";serialization_exports.registerClass(LeCunNormal);var LeCunUniform=class extends VarianceScaling{constructor(args){super({scale:1,mode:"fanIn",distribution:"uniform",seed:args==null?null:args.seed})}getClassName(){return VarianceScaling.className}};LeCunUniform.className="LeCunNormal";serialization_exports.registerClass(LeCunUniform);var Orthogonal=class extends Initializer{constructor(args){super();if(this.DEFAULT_GAIN=1,this.gain=args.gain==null?this.DEFAULT_GAIN:args.gain,this.seed=args.seed,this.seed!=null)throw new NotImplementedError("Random seed is not implemented for Orthogonal Initializer yet.")}apply(shape,dtype){return tidy(()=>{if(shape.length<2)throw new NotImplementedError("Shape must be at least 2D.");shape[0]*shape[1]>2e3&&console.warn(`Orthogonal initializer is being called on a matrix with more than 2000 (${shape[0]*shape[1]}) elements: Slowness may result.`);let normalizedShape=shape[0]>shape[1]?[shape[1],shape[0]]:shape,a=randomNormal2(normalizedShape,0,1,"float32"),q=linalg.gramSchmidt(a);return shape[0]>shape[1]&&(q=q.transpose()),mul(this.gain,q)})}getConfig(){return{gain:this.gain,seed:this.seed}}};Orthogonal.className="Orthogonal";serialization_exports.registerClass(Orthogonal);var INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP={constant:"Constant",glorotNormal:"GlorotNormal",glorotUniform:"GlorotUniform",heNormal:"HeNormal",heUniform:"HeUniform",identity:"Identity",leCunNormal:"LeCunNormal",leCunUniform:"LeCunUniform",ones:"Ones",orthogonal:"Orthogonal",randomNormal:"RandomNormal",randomUniform:"RandomUniform",truncatedNormal:"TruncatedNormal",varianceScaling:"VarianceScaling",zeros:"Zeros"};function deserializeInitializer(config2,customObjects={}){return deserializeKerasObject(config2,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"initializer")}function serializeInitializer(initializer){return serializeKerasObject(initializer)}function getInitializer(identifier){if(typeof identifier=="string"){let className=identifier in INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP?INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier]:identifier;if(className==="GlorotNormal")return new GlorotNormal;if(className==="GlorotUniform")return new GlorotUniform;if(className==="HeNormal")return new HeNormal;if(className==="HeUniform")return new HeUniform;if(className==="LeCunNormal")return new LeCunNormal;if(className==="LeCunUniform")return new LeCunUniform;{let config2={};return config2.className=className,config2.config={},deserializeInitializer(config2)}}else return identifier instanceof Initializer?identifier:deserializeInitializer(identifier)}function zeros3(){return new Zeros}function ones3(){return new Ones}function constant(args){return new Constant(args)}function randomUniform2(args){return new RandomUniform(args)}function randomNormal3(args){return new RandomNormal(args)}function truncatedNormal2(args){return new TruncatedNormal(args)}function identity4(args){return new Identity2(args)}function varianceScaling(config2){return new VarianceScaling(config2)}function glorotUniform(args){return new GlorotUniform(args)}function glorotNormal(args){return new GlorotNormal(args)}function heNormal(args){return new HeNormal(args)}function heUniform(args){return new HeUniform(args)}function leCunNormal(args){return new LeCunNormal(args)}function leCunUniform(args){return new LeCunUniform(args)}function orthogonal(args){return new Orthogonal(args)}var exports_layers_exports={};__export2(exports_layers_exports,{Layer:()=>Layer,RNN:()=>RNN,RNNCell:()=>RNNCell,activation:()=>activation,add:()=>add4,alphaDropout:()=>alphaDropout,average:()=>average,averagePooling1d:()=>averagePooling1d,averagePooling2d:()=>averagePooling2d,averagePooling3d:()=>averagePooling3d,avgPool1d:()=>avgPool1d,avgPool2d:()=>avgPool2d,avgPool3d:()=>avgPool3d2,avgPooling1d:()=>avgPooling1d,avgPooling2d:()=>avgPooling2d,avgPooling3d:()=>avgPooling3d,batchNormalization:()=>batchNormalization2,bidirectional:()=>bidirectional,concatenate:()=>concatenate2,conv1d:()=>conv1d2,conv2d:()=>conv2d5,conv2dTranspose:()=>conv2dTranspose2,conv3d:()=>conv3d2,convLstm2d:()=>convLstm2d,convLstm2dCell:()=>convLstm2dCell,cropping2D:()=>cropping2D,dense:()=>dense,depthwiseConv2d:()=>depthwiseConv2d5,dot:()=>dot3,dropout:()=>dropout3,elu:()=>elu5,embedding:()=>embedding,flatten:()=>flatten3,gaussianDropout:()=>gaussianDropout,gaussianNoise:()=>gaussianNoise,globalAveragePooling1d:()=>globalAveragePooling1d,globalAveragePooling2d:()=>globalAveragePooling2d,globalMaxPool1d:()=>globalMaxPool1d,globalMaxPool2d:()=>globalMaxPool2d,globalMaxPooling1d:()=>globalMaxPooling1d,globalMaxPooling2d:()=>globalMaxPooling2d,gru:()=>gru,gruCell:()=>gruCell,input:()=>input,inputLayer:()=>inputLayer,layerNormalization:()=>layerNormalization,leakyReLU:()=>leakyReLU,lstm:()=>lstm,lstmCell:()=>lstmCell,masking:()=>masking,maxPool1d:()=>maxPool1d,maxPool2d:()=>maxPool2d,maxPooling1d:()=>maxPooling1d,maxPooling2d:()=>maxPooling2d,maxPooling3d:()=>maxPooling3d,maximum:()=>maximum4,minimum:()=>minimum4,multiply:()=>multiply3,permute:()=>permute,prelu:()=>prelu5,reLU:()=>reLU,repeatVector:()=>repeatVector,reshape:()=>reshape5,rnn:()=>rnn2,separableConv2d:()=>separableConv2d2,simpleRNN:()=>simpleRNN,simpleRNNCell:()=>simpleRNNCell,softmax:()=>softmax5,spatialDropout1d:()=>spatialDropout1d,stackedRNNCells:()=>stackedRNNCells,thresholdedReLU:()=>thresholdedReLU,timeDistributed:()=>timeDistributed,upSampling2d:()=>upSampling2d,zeroPadding2d:()=>zeroPadding2d});var _nextUniqueTensorId=0;function getNextUniqueTensorId(){return _nextUniqueTensorId++}var _uidPrefixes={};function getUid(prefix=""){return prefix in _uidPrefixes||(_uidPrefixes[prefix]=0),_uidPrefixes[prefix]+=1,prefix+_uidPrefixes[prefix].toString()}function isArrayOfShapes(x){return Array.isArray(x)&&Array.isArray(x[0])}function normalizeShapeList(x){return x.length===0?[]:Array.isArray(x[0])?x:[x]}function getExactlyOneTensor(xs){let x;if(Array.isArray(xs)){if(xs.length!==1)throw new ValueError(`Expected Tensor length to be 1; got ${xs.length}`);x=xs[0]}else x=xs;return x}function getExactlyOneShape(shapes){if(Array.isArray(shapes)&&Array.isArray(shapes[0])){if(shapes.length===1)return shapes=shapes,shapes[0];throw new ValueError(`Expected exactly 1 Shape; got ${shapes.length}`)}else return shapes}function countParamsInWeights(weights){let count2=0;for(let weight of weights)weight.shape.length===0?count2+=1:count2+=weight.shape.reduce((a,b)=>a*b);return count2}var DEFAULT_VARIABLE_NAME_PREFIX="Variable",LayerVariable=class{constructor(val,dtype="float32",name=DEFAULT_VARIABLE_NAME_PREFIX,trainable=!0,constraint=null){this.dtype=dtype==null?"float32":dtype,this.shape=val.shape,this.id=getNextUniqueTensorId(),name=name==null?DEFAULT_VARIABLE_NAME_PREFIX:name,this.originalName=getScopedTensorName(name),this.name=getUniqueTensorName(this.originalName),this.trainable_=trainable,this.constraint=constraint,this.val=variable(val,this.trainable_,this.name,this.dtype)}read(){return this.assertNotDisposed(),this.val}write(newVal){return this.assertNotDisposed(),checkShapesMatch(this.val,newVal),this.val.id!==newVal.id&&(this.val.assign(newVal),this.constraint!=null&&this.val.assign(this.constraint.apply(this.val))),this}dispose(){this.assertNotDisposed(),this.val.dispose()}assertNotDisposed(){if(this.val.isDisposed)throw new Error(`LayersVariable ${this.name} is already disposed.`)}get trainable(){return this.trainable_}set trainable(trainable){this.trainable_=trainable,this.val.trainable=trainable}};function checkShapesMatch(x,y){if(x.shape.toString()!==y.shape.toString())throw new Error("Shape mismatch: "+JSON.stringify(x.shape)+" vs. "+JSON.stringify(y.shape))}function batchGetValue(xs){return xs.map(x=>x.read())}function batchSetValue(variablesAndValues){variablesAndValues.forEach(variableAndValue=>{variableAndValue[0].write(variableAndValue[1])})}var InputSpec=class{constructor(args){this.dtype=args.dtype,this.shape=args.shape,args.shape!=null?this.ndim=args.shape.length:this.ndim=args.ndim,this.maxNDim=args.maxNDim,this.minNDim=args.minNDim,this.axes=args.axes||{}}},SymbolicTensor=class{constructor(dtype,shape,sourceLayer,inputs,callArgs,name,outputTensorIndex){this.dtype=dtype,this.shape=shape,this.sourceLayer=sourceLayer,this.inputs=inputs,this.callArgs=callArgs,this.outputTensorIndex=outputTensorIndex,this.id=getNextUniqueTensorId(),name!=null&&(this.originalName=getScopedTensorName(name),this.name=getUniqueTensorName(this.originalName)),this.rank=shape.length}},_nextNodeID=0,Node=class{constructor(args,callArgs){this.callArgs=callArgs,this.id=_nextNodeID++,this.outboundLayer=args.outboundLayer,this.inboundLayers=args.inboundLayers,this.nodeIndices=args.nodeIndices,this.tensorIndices=args.tensorIndices,this.inputTensors=args.inputTensors,this.outputTensors=args.outputTensors,this.inputMasks=args.inputMasks,this.outputMasks=args.outputMasks,this.inputShapes=args.inputShapes,this.outputShapes=args.outputShapes;for(let layer of args.inboundLayers)layer!=null&&layer.outboundNodes.push(this);args.outboundLayer.inboundNodes.push(this)}getConfig(){let inboundNames=[];for(let layer of this.inboundLayers)layer!=null?inboundNames.push(layer.name):inboundNames.push(null);return{outboundLayer:this.outboundLayer?this.outboundLayer.name:null,inboundLayers:inboundNames,nodeIndices:this.nodeIndices,tensorIndices:this.tensorIndices}}},_nextLayerID=0,Layer=class extends serialization_exports.Serializable{constructor(args={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=_nextLayerID++,this.activityRegularizer=null,this.inputSpec=null,this.supportsMasking=!1,this._trainableWeights=[],this._nonTrainableWeights=[],this._losses=[],this._updates=[],this._built=!1,this.inboundNodes=[],this.outboundNodes=[];let name=args.name;if(!name){let prefix=this.getClassName();name=toSnakeCase(prefix)+"_"+getUid(prefix)}if(this.name=name,this.trainable_=args.trainable==null?!0:args.trainable,args.inputShape!=null||args.batchInputShape!=null){let batchInputShape;if(args.batchInputShape!=null)batchInputShape=args.batchInputShape;else if(args.inputShape!=null){let batchSize=null;args.batchSize!=null&&(batchSize=args.batchSize),batchInputShape=[batchSize].concat(args.inputShape)}this.batchInputShape=batchInputShape;let dtype=args.dtype;dtype==null&&(dtype=args.inputDType),dtype==null&&(dtype="float32"),this.dtype=dtype}args.weights!=null?this.initialWeights=args.weights:this.initialWeights=null,this._refCount=null,this.fastWeightInitDuringBuild=!1}static nodeKey(layer,nodeIndex){return layer.name+"_ib-"+nodeIndex.toString()}getNodeAtIndex(nodeIndex,attrName){if(this.inboundNodes.length===0)throw new RuntimeError(`The layer has never been called and thus has no defined ${attrName}.`);if(this.inboundNodes.length<=nodeIndex)throw new ValueError(`Asked to get ${attrName} at node ${nodeIndex}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[nodeIndex]}getInputAt(nodeIndex){return singletonOrArray(this.getNodeAtIndex(nodeIndex,"input").inputTensors)}getOutputAt(nodeIndex){return singletonOrArray(this.getNodeAtIndex(nodeIndex,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new AttributeError(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer input" is ill-defined. Use \`getInputAt(nodeIndex)\` instead.`);if(this.inboundNodes.length===0)throw new AttributeError(`Layer ${this.name} is not connected, no input to return.`);return singletonOrArray(this.getNodeAtIndex(0,"input").inputTensors)}get output(){if(this.inboundNodes.length===0)throw new AttributeError(`Layer ${this.name} has no inbound nodes.`);if(this.inboundNodes.length>1)throw new AttributeError(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer output" is ill-defined. Use \`getOutputAt(nodeIndex)\` instead.`);return singletonOrArray(this.getNodeAtIndex(0,"output").outputTensors)}get losses(){return this._losses}calculateLosses(){return this.losses.map(lossFn=>lossFn())}get updates(){return this._updates}get built(){return this._built}set built(built){this._built=built}get trainable(){return this.trainable_}set trainable(trainable){this._trainableWeights.forEach(w=>w.trainable=trainable),this.trainable_=trainable}get trainableWeights(){return this.trainable_?this._trainableWeights.filter(w=>w.trainable):[]}set trainableWeights(weights){this._trainableWeights=weights}get nonTrainableWeights(){return this.trainable?this._trainableWeights.filter(w=>!w.trainable).concat(this._nonTrainableWeights):this._trainableWeights.concat(this._nonTrainableWeights)}set nonTrainableWeights(weights){this._nonTrainableWeights=weights}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}get stateful(){return this._stateful}resetStates(){if(!this.stateful)throw new Error("Cannot call the resetStates() method of a non-stateful Layer object.")}assertInputCompatibility(inputs){if(inputs=toList(inputs),this.inputSpec==null||this.inputSpec.length===0)return;let inputSpec=toList(this.inputSpec);if(inputs.length!==inputSpec.length)throw new ValueError(`Layer ${this.name} expects ${inputSpec.length} inputs, but it received ${inputs.length} input tensors. Input received: ${inputs}`);for(let inputIndex=0;inputIndexspec.maxNDim)throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected max_ndim=${spec.maxNDim}, found ndim=${ndim}`);if(spec.minNDim!=null&&ndim=0?xShape[axis]:xShape[xShape.length+axis];if(value!=null&&[value,null].indexOf(xShapeAtAxis)===-1)throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected axis ${axis} of input shape to have value ${value} but got shape ${xShape}.`)}}if(spec.shape!=null)for(let i=0;i{if(!this.built){this.assertInputCompatibility(inputs);let inputShapes=[];for(let xElem of toList(inputs))inputShapes.push(xElem.shape);this.build(singletonOrArray(inputShapes)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&noneAreSymbolic&&(this._refCount=1)}if(this.assertInputCompatibility(inputs),noneAreSymbolic){let output=this.call(inputs,kwargs),outputList=toList(output),outputListCopy=[];for(let x of outputList)inputsList.indexOf(x)!==-1&&(x=x.clone()),outputListCopy.push(x);if(output=singletonOrArray(outputListCopy),this.activityRegularizer!=null)throw new NotImplementedError("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return output}else{let inputShape=collectInputShape(inputs),outputShape=this.computeOutputShape(inputShape),output,outputDType=guessOutputDType(inputs);if(this.warnOnIncompatibleInputShape(Array.isArray(inputs)?inputShape[0]:inputShape),outputShape!=null&&outputShape.length>0&&Array.isArray(outputShape[0])?output=outputShape.map((shape,index)=>new SymbolicTensor(outputDType,shape,this,toList(inputs),kwargs,this.name,index)):output=new SymbolicTensor(outputDType,outputShape,this,toList(inputs),kwargs,this.name),this.addInboundNode(inputs,output,null,null,inputShape,outputShape,kwargs),this._refCount++,this.activityRegularizer!=null)throw new NotImplementedError("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return output}})}warnOnIncompatibleInputShape(inputShape){if(this.batchInputShape!=null)if(inputShape.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(inputShape)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let dimMismatch=!1;this.batchInputShape.forEach((dimension,i)=>{dimension!=null&&inputShape[i]!=null&&inputShape[i]!==dimension&&(dimMismatch=!0)}),dimMismatch&&console.warn(`The shape of the input tensor (${JSON.stringify(inputShape)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}get outputShape(){if(this.inboundNodes==null||this.inboundNodes.length===0)throw new AttributeError(`The layer ${this.name} has never been called and thus has no defined output shape.`);let allOutputShapes=[];for(let node of this.inboundNodes){let shapeString=JSON.stringify(node.outputShapes);allOutputShapes.indexOf(shapeString)===-1&&allOutputShapes.push(shapeString)}if(allOutputShapes.length===1){let outputShapes=this.inboundNodes[0].outputShapes;return Array.isArray(outputShapes)&&Array.isArray(outputShapes[0])&&outputShapes.length===1?outputShapes[0]:outputShapes}else throw new AttributeError(`The layer ${this.name} has multiple inbound nodes with different output shapes. Hence the notion of "output shape" is ill-defined for the layer.`)}countParams(){if(!this.built)throw new RuntimeError(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return countParamsInWeights(this.weights)}build(inputShape){this.built=!0}getWeights(trainableOnly=!1){return batchGetValue(trainableOnly?this.trainableWeights:this.weights)}setWeights(weights){tidy(()=>{let params=this.weights;if(params.length!==weights.length)throw new ValueError(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${weights.length}, but the layer was expecting ${params.length} weights. Provided weights: ${weights}...`);if(params.length===0)return;let weightValueTuples=[],paramValues=batchGetValue(params);for(let i=0;iregularizer.apply(weight.read())),trainable==null&&(trainable=!0),trainable?this._trainableWeights.push(weight):this._nonTrainableWeights.push(weight),weight}setFastWeightInitDuringBuild(value){this.fastWeightInitDuringBuild=value}addLoss(losses4){losses4==null||Array.isArray(losses4)&&losses4.length===0||(losses4=toList(losses4),this._losses!==void 0&&this._losses!==null&&this.losses.push(...losses4))}computeOutputShape(inputShape){return inputShape}computeMask(inputs,mask){if(!this.supportsMasking){if(mask!=null)if(Array.isArray(mask))mask.forEach(maskElement=>{if(maskElement!=null)throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`)});else throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);return null}return mask}addInboundNode(inputTensors,outputTensors,inputMasks,outputMasks,inputShapes,outputShapes,kwargs=null){let inputTensorList=toList(inputTensors);outputTensors=toList(outputTensors),inputMasks=toList(inputMasks),outputMasks=toList(outputMasks),inputShapes=normalizeShapeList(inputShapes),outputShapes=normalizeShapeList(outputShapes);let inboundLayers=[],nodeIndices=[],tensorIndices=[];for(let x of inputTensorList)inboundLayers.push(x.sourceLayer),nodeIndices.push(x.nodeIndex),tensorIndices.push(x.tensorIndex);new Node({outboundLayer:this,inboundLayers,nodeIndices,tensorIndices,inputTensors:inputTensorList,outputTensors,inputMasks,outputMasks,inputShapes,outputShapes},kwargs);for(let i=0;iweight.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${this.name}' is already disposed.`)}dispose(){if(!this.built)throw new Error(`Cannot dispose Layer ${this.name} because it has not been built yet.`);if(this._refCount===null)throw new Error(`Cannot dispose Layer ${this.name} because it has not been used yet.`);this.assertNotDisposed();let numDisposedVariables=0;return--this._refCount==0&&(numDisposedVariables=this.disposeWeights()),{refCountAfterDispose:this._refCount,numDisposedVariables}}};function collectInputShape(inputTensors){inputTensors=toList(inputTensors);let shapes=[];for(let x of inputTensors)shapes.push(x.shape);return singletonOrArray(shapes)}function guessOutputDType(inputTensors){return"float32"}function getSourceInputs(tensor2,layer,nodeIndex){if((layer==null||nodeIndex!=null&&nodeIndex>0)&&(layer=tensor2.sourceLayer,nodeIndex=tensor2.nodeIndex),layer.inboundNodes.length===0)return[tensor2];{let node=layer.inboundNodes[nodeIndex];if(node.inboundLayers.length===0)return node.inputTensors;{let sourceTensors=[];for(let i=0;i0){let values=await Promise.all(promises);for(let i=0;iadd2(this.totals[key],mul(value,batchSize)));this.totals[key]=total,oldTotalsToDispose!=null&&oldTotalsToDispose.dispose()}}}async onEpochEnd(epoch,logs){if(logs!=null)for(let key of this.params.metrics)this.totals[key]!=null&&(typeof this.totals[key]=="number"?logs[key]=this.totals[key]/this.seen:tidy(()=>{let log5=mul(div(1,this.seen),this.totals[key]);logs[key]=log5,this.totals[key].dispose(),keep(logs[key])}))}},History=class extends BaseCallback{async onTrainBegin(logs){this.epoch=[],this.history={}}async onEpochEnd(epoch,logs){logs==null&&(logs={}),this.epoch.push(epoch);for(let key in logs)this.history[key]==null&&(this.history[key]=[]),this.history[key].push(logs[key])}async syncData(){let promises=[],keys=[],indices=[];for(let key in this.history){let valueArray=this.history[key];for(let i=0;inew CustomCallback(callbackConfig,yieldEvery))}var CallbackConstructorRegistry=class{constructor(){}static registerCallbackConstructor(verbosityLevel,callbackConstructor){util_exports.assert(verbosityLevel>=0&&Number.isInteger(verbosityLevel),()=>`Verbosity level is expected to be an integer >= 0, but got ${verbosityLevel}`),CallbackConstructorRegistry.checkForDuplicate(callbackConstructor),CallbackConstructorRegistry.constructors[verbosityLevel]==null&&(CallbackConstructorRegistry.constructors[verbosityLevel]=[]),CallbackConstructorRegistry.constructors[verbosityLevel].push(callbackConstructor)}static checkForDuplicate(callbackConstructor){for(let levelName in CallbackConstructorRegistry.constructors)CallbackConstructorRegistry.constructors[+levelName].forEach(ctor=>{if(ctor===callbackConstructor)throw new ValueError("Duplicate callback constructor.")})}static clear(){CallbackConstructorRegistry.constructors={}}static createCallbacks(verbosityLevel){let constructors=[];for(let levelName in CallbackConstructorRegistry.constructors){let level=+levelName;verbosityLevel>=level&&constructors.push(...CallbackConstructorRegistry.constructors[level])}return constructors.map(ctor=>new ctor)}};CallbackConstructorRegistry.constructors={};function configureCallbacks(callbacks2,verbose,epochs,initialEpoch,numTrainSamples,stepsPerEpoch,batchSize,doValidation,callbackMetrics){let history=new History,actualCallbacks=[new BaseLogger,...CallbackConstructorRegistry.createCallbacks(verbose)];callbacks2!=null&&actualCallbacks.push(...callbacks2),actualCallbacks.push(history);let callbackList=new CallbackList(actualCallbacks);return callbackList.setParams({epochs,initialEpoch,samples:numTrainSamples,steps:stepsPerEpoch,batchSize,verbose,doValidation,metrics:callbackMetrics}),{callbackList,history}}function deserialize(config2,customObjects={},fastWeightInit=!1){return deserializeKerasObject(config2,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"layer",fastWeightInit)}function l2Normalize(x,axis){return tidy(()=>{x.dtype!=="float32"&&(x=x.asType("float32"));let squareSum=sum2(square3(x),axis,!0),epsilonTensor=fill(squareSum.shape,epsilon2()),norm2=sqrt(maximum(squareSum,epsilonTensor));return div(x,norm2)})}function meanSquaredError2(yTrue,yPred){return tidy(()=>mean(square3(sub(yPred,yTrue)),-1))}function meanAbsoluteError(yTrue,yPred){return tidy(()=>mean(abs(sub(yPred,yTrue)),-1))}function meanAbsolutePercentageError(yTrue,yPred){return tidy(()=>{let diff=sub(yTrue,yPred),clippedTrue=clipByValue(abs(yTrue),epsilon2(),Number.MAX_VALUE),absResult=abs(div(diff,clippedTrue));return mul(100,mean(absResult,-1))})}function meanSquaredLogarithmicError(yTrue,yPred){return tidy(()=>{let clippedPred=clipByValue(yPred,epsilon2(),Number.MAX_VALUE),firstLog=log2(add2(1,clippedPred)),clippedTrue=clipByValue(yTrue,epsilon2(),Number.MAX_VALUE),secondLog=log2(add2(1,clippedTrue));return mean(square3(sub(firstLog,secondLog)),-1)})}function squaredHinge(yTrue,yPred){return tidy(()=>{let maxResult=maximum(0,sub(1,mul(yTrue,yPred)));return mean(square3(maxResult),-1)})}function hinge(yTrue,yPred){return tidy(()=>{let maxResult=maximum(0,sub(1,mul(yTrue,yPred)));return mean(maxResult,-1)})}function categoricalHinge(yTrue,yPred){return tidy(()=>{let pos=sum2(mul(yTrue,yPred),-1),neg4=max(mul(sub(1,yTrue),yPred),-1);return maximum(0,add2(1,sub(neg4,pos)))})}function logcosh(yTrue,yPred){return tidy(()=>{let log222=Math.log(2),predictionDiff=sub(yPred,yTrue),logcoshResult=sub(add2(predictionDiff,softplus(mul(-2,predictionDiff))),log222);return mean(logcoshResult,-1)})}function categoricalCrossentropy(target,output,fromLogits=!1){return tidy(()=>{if(fromLogits)output=softmax(output);else{let outputSum=sum2(output,output.shape.length-1,!0);output=div(output,outputSum)}return output=clipByValue(output,epsilon2(),1-epsilon2()),neg(sum2(mul(target.toFloat(),log2(output)),output.shape.length-1))})}function sparseCategoricalCrossentropy(target,output,fromLogits=!1){return tidy(()=>{let flatTarget=floor(flatten2(target)).toInt();output=clipByValue(output,epsilon2(),1-epsilon2());let outputShape=output.shape,oneHotTarget=oneHot(flatTarget,outputShape[outputShape.length-1]).reshape(outputShape);return categoricalCrossentropy(oneHotTarget,output,fromLogits)})}function sigmoidCrossEntropyWithLogits(labels,logits){if(!util_exports.arraysEqual(labels.shape,logits.shape))throw new ValueError(`logits and labels must have the same shape, but got shapes ${JSON.stringify(labels.shape)} and ${JSON.stringify(logits.shape)}`);return tidy(()=>{let reluLogits=logits.relu(),negAbsLogits=logits.abs().neg();return reluLogits.sub(logits.mul(labels)).add(negAbsLogits.exp().log1p())})}function binaryCrossentropy(yTrue,yPred){return tidy(()=>{let y;return y=clipByValue(yPred,epsilon2(),1-epsilon2()),y=log2(div(y,sub(1,y))),mean(sigmoidCrossEntropyWithLogits(yTrue,y),-1)})}function kullbackLeiblerDivergence(yTrue,yPred){return tidy(()=>{let clippedTrue=clipByValue(yTrue,epsilon2(),1),clippedPred=clipByValue(yPred,epsilon2(),1);return sum2(mul(yTrue,log2(div(clippedTrue,clippedPred))),-1)})}function poisson(yTrue,yPred){return tidy(()=>{let logPred=log2(add2(epsilon2(),yPred));return mean(sub(yPred,mul(yTrue,logPred)),-1)})}function cosineProximity(yTrue,yPred){return tidy(()=>{let trueNormalized=l2Normalize(yTrue,-1),predNormalized=l2Normalize(yPred,-1),trueXPred=mul(trueNormalized,predNormalized);return neg(sum2(trueXPred,-1))})}var lossesMap={meanSquaredError:meanSquaredError2,meanAbsoluteError,meanAbsolutePercentageError,meanSquaredLogarithmicError,squaredHinge,hinge,categoricalHinge,logcosh,categoricalCrossentropy,sparseCategoricalCrossentropy,binaryCrossentropy,kullbackLeiblerDivergence,poisson,cosineProximity};function get(identifierOrFn){if(typeof identifierOrFn=="string"){if(identifierOrFn in lossesMap)return lossesMap[identifierOrFn];let errMsg=`Unknown loss ${identifierOrFn}`;throw identifierOrFn.toLowerCase().includes("softmaxcrossentropy")&&(errMsg=`Unknown loss ${identifierOrFn}. Use "categoricalCrossentropy" as the string name for tf.losses.softmaxCrossEntropy`),new ValueError(errMsg)}else return identifierOrFn}function binaryAccuracy(yTrue,yPred){return tidy(()=>{let threshold2=mul(.5,onesLike(yPred)),yPredThresholded=cast5(greater(yPred,threshold2),yTrue.dtype);return mean(equal(yTrue,yPredThresholded),-1)})}function categoricalAccuracy(yTrue,yPred){return tidy(()=>cast5(equal(argMax(yTrue,-1),argMax(yPred,-1)),"float32"))}function truePositives(yTrue,yPred){return tidy(()=>logicalAnd(yTrue.equal(1),yPred.equal(1)).sum().cast("float32"))}function falseNegatives(yTrue,yPred){return tidy(()=>logicalAnd(yTrue.equal(1),yPred.equal(0)).sum().cast("float32"))}function falsePositives(yTrue,yPred){return tidy(()=>logicalAnd(yTrue.equal(0),yPred.equal(1)).sum().cast("float32"))}function precision(yTrue,yPred){return tidy(()=>{let tp=truePositives(yTrue,yPred),fp=falsePositives(yTrue,yPred),denominator=tp.add(fp);return where(greater(denominator,0),tp.div(denominator),0).cast("float32")})}function recall(yTrue,yPred){return tidy(()=>{let tp=truePositives(yTrue,yPred),fn=falseNegatives(yTrue,yPred),denominator=tp.add(fn);return where(greater(denominator,0),tp.div(denominator),0).cast("float32")})}function binaryCrossentropy2(yTrue,yPred){return binaryCrossentropy(yTrue,yPred)}function sparseCategoricalAccuracy(yTrue,yPred){return yTrue.rank===yPred.rank&&(yTrue=yTrue.squeeze([yTrue.rank-1])),yPred=yPred.argMax(-1),yPred.dtype!==yTrue.dtype&&(yPred=yPred.asType(yTrue.dtype)),equal(yTrue,yPred).asType("float32")}var mse=meanSquaredError2,MSE=meanSquaredError2,mae=meanAbsoluteError,MAE=meanAbsoluteError,mape=meanAbsolutePercentageError,MAPE=meanAbsolutePercentageError,categoricalCrossentropy2=categoricalCrossentropy,cosine=cosineProximity,sparseCategoricalCrossentropy2=sparseCategoricalCrossentropy,metricsMap={binaryAccuracy,categoricalAccuracy,precision,categoricalCrossentropy:categoricalCrossentropy2,sparseCategoricalCrossentropy:sparseCategoricalCrossentropy2,mse,MSE,mae,MAE,mape,MAPE,cosine};function get2(identifier){if(typeof identifier=="string"&&identifier in metricsMap)return metricsMap[identifier];if(typeof identifier!="string"&&identifier!=null)return identifier;throw new ValueError(`Unknown metric ${identifier}`)}function getLossOrMetricName(fn){if(assert2(fn!==null,`Unknown LossOrMetricFn ${fn}`),typeof fn=="string")return fn;{let fnName;for(let key of Object.keys(lossesMap))if(lossesMap[key]===fn){fnName=key;break}if(fnName!==void 0)return fnName;for(let key of Object.keys(metricsMap))if(metricsMap[key]===fn){fnName=key;break}return fnName!==void 0?fnName:fn.name}}function getOptimizer(identifier){let optimizerMap={Adagrad:()=>train.adagrad(.01),Adadelta:()=>train.adadelta(1,.95,epsilon2()),Adam:()=>train.adam(.001,.9,.999,epsilon2()),Adamax:()=>train.adamax(.002,.9,.999,epsilon2(),0),RMSProp:()=>train.rmsprop(.001,.9,0,epsilon2()),SGD:()=>train.sgd(.01)};if(optimizerMap.adagrad=optimizerMap.Adagrad,optimizerMap.adadelta=optimizerMap.Adadelta,optimizerMap.adam=optimizerMap.Adam,optimizerMap.adamax=optimizerMap.Adamax,optimizerMap.rmsprop=optimizerMap.RMSProp,optimizerMap.sgd=optimizerMap.SGD,identifier in optimizerMap)return optimizerMap[identifier]();throw new ValueError(`Unknown Optimizer ${identifier}`)}var MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH=1*1024*1024;function checkUserDefinedMetadata(userDefinedMetadata,modelName,checkSize=!1){if(userDefinedMetadata==null||typeof userDefinedMetadata!="object"||Object.getPrototypeOf(userDefinedMetadata)!==Object.prototype||!plainObjectCheck(userDefinedMetadata))throw new Error("User-defined metadata is expected to be a JSON object, but is not.");if(checkSize){let out=JSON.stringify(userDefinedMetadata);out.length>MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH&&console.warn(`User-defined metadata of model "${modelName}" is too large in size (length=${out.length} when serialized). It is not recommended to store such large objects in user-defined metadata. Please make sure its serialized length is <= ${MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH}.`)}}function plainObjectCheck(x){if(x===null)return!0;if(typeof x=="object")if(Object.getPrototypeOf(x)===Object.prototype){let keys=Object.keys(x);for(let key of keys)if(typeof key!="string"||!plainObjectCheck(x[key]))return!1;return!0}else if(Array.isArray(x)){for(let item of x)if(!plainObjectCheck(item))return!1;return!0}else return!1;else{let xType=typeof x;return xType==="string"||xType==="number"||xType==="boolean"}}function printSummary(model2,lineLength,positions,printFn=console.log){let sequentialLike=isModelSequentialLike(model2),toDisplay=["Layer (type)","Output shape","Param #"];sequentialLike?(lineLength=lineLength||65,positions=positions||[.45,.85,1]):(lineLength=lineLength||98,positions=positions||[.33,.55,.67,1]),positions[positions.length-1]<=1&&(positions=positions.map(p2=>Math.floor(lineLength*p2)));let relevantNodes;if(!sequentialLike){toDisplay.push("Receives inputs"),relevantNodes=[];for(let depth in model2.nodesByDepth)relevantNodes.push(...model2.nodesByDepth[depth])}printFn("_".repeat(lineLength)),printRow(toDisplay,positions,printFn),printFn("=".repeat(lineLength));let layers=model2.layers;for(let i=0;i1||depthNodes.length===1&&depthNodes[0].inboundLayers.length>1){sequentialLike=!1;break}nodes.push(...depthNodes)}if(sequentialLike)for(let layer of model2.layers){let flag=!1;for(let node of layer.inboundNodes)if(nodes.indexOf(node)!==-1)if(flag){sequentialLike=!1;break}else flag=!0;if(!sequentialLike)break}return sequentialLike}function printRow(fields,positions,printFn=console.log){let line="";for(let i=0;i0&&(line=line.slice(0,line.length-1)+" "),line+=fields[i],line=line.slice(0,positions[i]),line+=" ".repeat(positions[i]-line.length);printFn(line)}function printLayerSummary(layer,positions,printFn){let outputShape;try{outputShape=JSON.stringify(layer.outputShape)}catch(err){outputShape="multiple"}let name=layer.name,className=layer.getClassName(),fields=[`${name} (${className})`,outputShape,layer.countParams().toString()];printRow(fields,positions,printFn)}function printLayerSummaryWithConnections(layer,positions,relevantNodes,printFn){let outputShape;try{outputShape=JSON.stringify(layer.outputShape)}catch(err){outputShape="multiple"}let connections=[];for(let node of layer.inboundNodes)if(!(relevantNodes!=null&&relevantNodes.length>0&&relevantNodes.indexOf(node)===-1))for(let i=0;it.name),finalOutputs=[],feedNames=feedDict.names();for(let outputName of outputNames)feedNames.indexOf(outputName)!==-1?finalOutputs.push(feedDict.getValue(outputName)):finalOutputs.push(null);probe!=null&&(probe.maxNumTensors=-Infinity,probe.minNumTensors=Infinity);let fetchAndFeedKey=outputNames.join(",")+"|"+feedDict.names().join(","),sorted,recipientCounts;if(cachedSorted[fetchAndFeedKey]==null){let out=getTopologicalSortAndRecipientCounts(fetchArray,feedDict);sorted=out.sorted,recipientCounts=out.recipientCounts,cachedSorted[fetchAndFeedKey]=sorted,cachedRecipientCounts[fetchAndFeedKey]=recipientCounts}sorted=cachedSorted[fetchAndFeedKey],recipientCounts={},training||Object.assign(recipientCounts,cachedRecipientCounts[fetchAndFeedKey]);let internalFeedDict=new FeedDict(feedDict);for(let i=0;iprobe.maxNumTensors&&(probe.maxNumTensors=numTensors),numTensors0,()=>"Expected at least one fetch, got none");let finalSorted=[],finalRecipientMap={};if(fetches.length===1){let out=getTopologicalSortAndRecipientCountsForOneFetch(fetches[0],feedDict);finalSorted=out.sorted,finalRecipientMap=out.recipientMap}else{let visited=new Set;for(let fetch3 of fetches){let{sorted,recipientMap}=getTopologicalSortAndRecipientCountsForOneFetch(fetch3,feedDict);for(let symbolicTensor of sorted)visited.has(symbolicTensor.name)||(finalSorted.push(symbolicTensor),visited.add(symbolicTensor.name));for(let name in recipientMap)finalRecipientMap[name]==null&&(finalRecipientMap[name]=new Set),recipientMap[name].forEach(recipient=>finalRecipientMap[name].add(recipient))}}return{sorted:finalSorted,recipientCounts:recipientMap2Counts(finalRecipientMap)}}function recipientMap2Counts(recipientMap){let recipientCounts={};for(let name in recipientMap)recipientCounts[name]=recipientMap[name].size;return recipientCounts}function getTopologicalSortAndRecipientCountsForOneFetch(fetch3,feedDict){let visited=new Set,sorted=[],recipientMap={};for(let key of feedDict.names())visited.add(key);let stack2=[],marks=[];for(stack2.push(fetch3);stack2.length>0;){let top=stack2[stack2.length-1];if(visited.has(top.name)){stack2.pop();continue}let topIsMarked=marks[marks.length-1]===stack2.length-1;if(top.inputs.length===0||topIsMarked)stack2.pop(),sorted.push(top),visited.add(top.name),topIsMarked&&marks.pop();else{marks.push(stack2.length-1);for(let input2 of top.inputs)recipientMap[input2.name]==null&&(recipientMap[input2.name]=new Set),recipientMap[input2.name].add(top.name),!visited.has(input2.name)&&stack2.push(input2)}}return{sorted,recipientMap}}function getNodeOutputs(fetch3){let layerOutputs;if(fetch3.sourceLayer.inboundNodes.length===1)layerOutputs=fetch3.sourceLayer.output;else{let nodeIndex=null;for(let i=0;ix.name)}`);unique4(this.outputs).length!==this.outputs.length&&console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map(x=>x.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(let x of this.outputs){let layer=x.sourceLayer,nodeIndex=x.nodeIndex,tensorIndex=x.tensorIndex;this.outputLayers.push(layer),this.outputLayersNodeIndices.push(nodeIndex),this.outputLayersTensorIndices.push(tensorIndex)}for(let x of this.inputs){let layer=x.sourceLayer,nodeIndex=x.nodeIndex,tensorIndex=x.tensorIndex;assert2(nodeIndex===0,"input layer has >1 nodes"),assert2(tensorIndex===0,"input layer has >1 tensors"),this.inputLayers.push(layer),this.inputLayersNodeIndices.push(nodeIndex),this.inputLayersTensorIndices.push(tensorIndex)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let i=0;ix.shape),this.internalOutputShapes=this.outputs.map(x=>x.shape);let nodesDepths={},nodeIDToNode={},layersDepths={},layerIDToLayer={},layerIndices={},nodesInDecreasingDepth=[],buildMapOfGraph=(tensor2,finishedNodes2,nodesInProgress2,layer,nodeIndex,tensorIndex)=>{(layer==null||nodeIndex==null||tensorIndex==null)&&(layer=tensor2.sourceLayer,nodeIndex=tensor2.nodeIndex,tensorIndex=tensor2.tensorIndex);let node=layer.inboundNodes[nodeIndex];if(nodesInProgress2.indexOf(node)!==-1)throw new RuntimeError(`The tensor ${tensor2.name} at layer "${layer.name}" is part of a cycle.`);if(finishedNodes2.indexOf(node)!==-1)return;this.containerNodes.add(Container.nodeKey(layer,nodeIndex)),layer.id in layerIndices||(layerIndices[layer.id]=Object.keys(layerIndices).length),nodesInProgress2.indexOf(node)===-1&&nodesInProgress2.push(node);let numInboundLayers=node.inboundLayers.length;for(let i=0;i=0;)nodesInProgress2.splice(nodesInProgress2.indexOf(node),1);nodesInDecreasingDepth.push(node)},finishedNodes=[],nodesInProgress=[];for(let x of this.outputs)buildMapOfGraph(x,finishedNodes,nodesInProgress);let reversedNodesInDecreasingDepth=nodesInDecreasingDepth.slice().reverse();for(let node of reversedNodesInDecreasingDepth){nodeIDToNode[node.id]=node,node.id in nodesDepths||(nodesDepths[node.id]=0);let depth=nodesDepths[node.id],previousDepth=layersDepths[node.outboundLayer.id]==null?0:layersDepths[node.outboundLayer.id];depth=Math.max(depth,previousDepth),layersDepths[node.outboundLayer.id]=depth,layerIDToLayer[node.outboundLayer.id]=node.outboundLayer,nodesDepths[node.id]=depth;for(let i=0;iparseInt(x,10)).sort(reverseNumberCompare);this.layers=[];for(let depth of depthKeys){let layersForDepth=layersByDepth[depth];layersForDepth.sort((a,b)=>{let aIndex=layerIndices[a.id],bIndex=layerIndices[b.id];return aIndexbIndex?1:0});for(let layer of layersForDepth)layer instanceof Container&&this.internalContainerRefs.push(layer),this.layers.push(layer)}this.layersByDepth=layersByDepth,depthKeys=Object.keys(nodesByDepth).map(x=>parseInt(x,10)).sort(reverseNumberCompare);let computableTensors=this.inputs.slice(),layersWithCompleteInput=[];for(let depth of depthKeys)for(let node of nodesByDepth[depth]){let layer=node.outboundLayer;if(layer!=null){for(let x of node.inputTensors)if(computableTensors.indexOf(x)===-1)throw new RuntimeError(`Graph disconnected: cannot obtain value for tensor ${x} at layer "${layer.name}". The following previous layers were accessed without issue: ${layersWithCompleteInput}`);for(let x of node.outputTensors)computableTensors.push(x);layersWithCompleteInput.push(layer.name)}}this.nodesByDepth=nodesByDepth;let allNames=this.layers.map(x=>x.name);for(let name of allNames){let numOccurrences=allNames.filter(x=>x===name).length;if(numOccurrences!==1)throw new RuntimeError(`The name "${name}" is used ${numOccurrences} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(allNames))}this.outboundNodes=[],this.inboundNodes=[],new Node({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(x=>null),outputMasks:this.outputs.map(x=>null),inputShapes:this.inputs.map(x=>x.shape),outputShapes:this.outputs.map(x=>x.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();let result={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount==0){for(let layer of this.layers)result.numDisposedVariables+=layer.dispose().numDisposedVariables;for(let container of this.internalContainerRefs)result.numDisposedVariables+=container.dispose().numDisposedVariables}return result.refCountAfterDispose=this._refCount,result}get trainable(){return this.trainable_}set trainable(trainable){this.layers.forEach(layer=>{layer._trainableWeights.forEach(w=>w.trainable=trainable)}),this.trainable_=trainable}get trainableWeights(){if(this._trainableWeights.length>0)throw new ValueError("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let weights=[];for(let layer of this.layers)weights=weights.concat(layer.trainableWeights);return weights}get nonTrainableWeights(){let weights=[];for(let layer of this.layers)weights.push(...layer.nonTrainableWeights);if(!this.trainable){let trainableWeights=[];for(let layer of this.layers)trainableWeights.push(...layer.trainableWeights);return trainableWeights.concat(weights)}return weights}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(weights,strict=!0){let nameToWeight={},totalWeightsCount=0;for(let layer of this.layers)for(let weight of layer.weights){if(nameToWeight[weight.originalName]!=null)throw new ValueError(`Duplicate weight name: ${weight.originalName}`);nameToWeight[weight.originalName]=weight,totalWeightsCount++}let weightValueTuples=[];for(let name in weights){let validatedName=name;if(nameToWeight[name]==null){let tokens=name.split("/");validatedName=tokens.slice(0,-2).concat([tokens[tokens.length-1]]).join("/")}if(nameToWeight[validatedName]!=null)weightValueTuples.push([nameToWeight[validatedName],weights[name]]);else if(strict)throw new ValueError(`Provided weight data has no target variable: ${name}`);delete nameToWeight[validatedName]}if(strict){let unsetNames=[];for(let name in nameToWeight)unsetNames.push(name);if(unsetNames.length>0)throw new ValueError(`${unsetNames.length} of ${totalWeightsCount} weights are not set: ${unsetNames}`)}batchSetValue(weightValueTuples)}updatedConfig(){let theConfig=this.getConfig(),modelConfig={};return modelConfig.className=this.getClassName(),modelConfig.config=theConfig,modelConfig.kerasVersion=`tfjs-layers ${version10}`,modelConfig.backend="TensorFlow.js",modelConfig}toJSON(unused,returnString=!0){let modelConfig=convertTsToPythonic(this.updatedConfig());return returnString?JSON.stringify(modelConfig):modelConfig}call(inputs,kwargs){return tidy(()=>{inputs=toList(inputs);let feedDict=new FeedDict;for(let i=0;i{inputs=toList(inputs);let masks;return mask==null?masks=pyListRepeat(null,inputs.length):masks=toList(mask),this.runInternalGraph(inputs,masks)[1]})}computeOutputShape(inputShape){let inputShapes=normalizeShapeList(inputShape);if(inputShapes.length!==this.inputLayers.length)throw new ValueError(`Invalid inputShape argument ${inputShape}: model has ${this.inputLayers.length} tensor inputs.`);let layersToOutputShapes={};for(let i=0;iparseInt(x,10)).sort(reverseNumberCompare);if(depthKeys.length>1)for(let depth of depthKeys){let nodes=this.nodesByDepth[depth];for(let node of nodes){let layer=node.outboundLayer;if(this.inputLayers.map(x=>x.id).indexOf(layer.id)!==-1)continue;let inputShapes2=[];for(let j=0;jparseInt(x,10)).sort(reverseNumberCompare);for(let depth of depthKeys){let nodes=this.nodesByDepth[depth];for(let node of nodes){let layer=node.outboundLayer,referenceInputTensors=node.inputTensors,referenceOutputTensors=node.outputTensors,computedData=new Array;for(let x of referenceInputTensors)x.id in tensorMap&&computedData.push(tensorMap[x.id]);if(computedData.length===referenceInputTensors.length){let kwargs={},computedTensors,computedMasks,outputTensors2,outputMasks2;if(node.callArgs!=null&&(kwargs=node.callArgs),computedData.length===1){let[computedTensor,computedMask]=computedData[0];kwargs.mask==null&&(kwargs.mask=computedMask),outputTensors2=toList(layer.call(computedTensor,kwargs)),outputMasks2=toList(layer.computeMask(computedTensor,computedMask)),computedTensors=[computedTensor],computedMasks=[computedMask]}else computedTensors=computedData.map(x=>x[0]),computedMasks=computedData.map(x=>x[1]),kwargs.mask==null&&(kwargs.mask=computedMasks),outputTensors2=toList(layer.call(computedTensors,kwargs)),outputMasks2=toList(layer.computeMask(computedTensors,computedMasks));if(layer.activityRegularizer)throw new NotImplementedError("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let i=0;i{let losses4=[];for(let layer of this.layers)for(let nodeIndex=0;nodeIndex0){let nodeData=[];for(let i=0;i0&&layer.apply(singletonOrArray(inputTensors2),kwargs)}function processLayer(layerData){let layerName=layerData.name,layer=deserialize(layerData,config2.customObjects!=null?config2.customObjects:{});layer.setFastWeightInitDuringBuild(fastWeightInit),createdLayers[layerName]=layer,layerData.inboundNodes.forEach(nodeData=>{if(!(nodeData instanceof Array))throw new ValueError(`Corrupted configuration, expected array for nodeData: ${nodeData}`);addUnprocessedNode(layer,nodeData)})}let name=config2.name,layersFromConfig=config2.layers;for(let layerData of layersFromConfig)processLayer(layerData);for(;!isObjectEmpty(unprocessedNodes);)for(let layerData of layersFromConfig){let layer=createdLayers[layerData.name];if(layer.name in unprocessedNodes){let currentUnprocessedNodesForLayer=unprocessedNodes[layer.name];delete unprocessedNodes[layer.name];for(let nodeData of currentUnprocessedNodesForLayer)processNode(layer,nodeData)}}let inputTensors=[],outputTensors=[],inputLayersFromConfig=config2.inputLayers;for(let layerData of inputLayersFromConfig){let layerName=layerData[0],nodeIndex=layerData[1],tensorIndex=layerData[2];assert2(layerName in createdLayers);let layerOutputTensors=createdLayers[layerName].inboundNodes[nodeIndex].outputTensors;inputTensors.push(layerOutputTensors[tensorIndex])}let outputLayersFromConfig=config2.outputLayers;for(let layerData of outputLayersFromConfig){let layerName=layerData[0],nodeIndex=layerData[1],tensorIndex=layerData[2];assert2(layerName in createdLayers);let layerOutputTensors=createdLayers[layerName].inboundNodes[nodeIndex].outputTensors;outputTensors.push(layerOutputTensors[tensorIndex])}return new cls({inputs:inputTensors,outputs:outputTensors,name})}get stateful(){if(this._stateful)throw new ValueError("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(let layer of this.layers)if(layer.stateful)return!0;return!1}resetStates(){tidy(()=>{this.layers.forEach(layer=>{layer.stateful&&layer.resetStates()})})}};function standardizeSampleOrClassWeights(xWeight,outputNames,weightType){let numOutputs=outputNames.length;if(xWeight==null||Array.isArray(xWeight)&&xWeight.length===0)return outputNames.map(name=>null);if(numOutputs===1)return Array.isArray(xWeight)&&xWeight.length===1?xWeight:typeof xWeight=="object"&&outputNames[0]in xWeight?[xWeight[outputNames[0]]]:[xWeight];if(Array.isArray(xWeight)){if(xWeight.length!==numOutputs)throw new Error(`Provided ${weightType} is an array of ${xWeight.length} element(s), but the model has ${numOutputs} outputs. Make sure a set of weights is provided for each model output.`);return xWeight}else if(typeof xWeight=="object"&&Object.keys(xWeight).length>0&&typeof xWeight[Object.keys(xWeight)[0]]=="object"){let output=[];return outputNames.forEach(outputName=>{outputName in xWeight?output.push(xWeight[outputName]):output.push(null)}),output}else throw new Error(`The model has multiple (${numOutputs}) outputs, so ${weightType} must be either an array with ${numOutputs} elements or an object with ${outputNames} keys. Provided ${weightType} not understood: ${JSON.stringify(xWeight)}`)}function standardizeClassWeights(classWeight,outputNames){return standardizeSampleOrClassWeights(classWeight,outputNames,"classWeight")}async function standardizeWeights(y,sampleWeight,classWeight,sampleWeightMode){if(sampleWeight!=null||sampleWeightMode!=null)throw new Error("Support sampleWeight is not implemented yet");if(classWeight!=null){let yClasses=tidy(()=>{if(y.shape.length===1)return y.clone();if(y.shape.length===2)if(y.shape[1]>1){let axis=1;return y.argMax(axis)}else{if(y.shape[1]===1)return y.reshape([y.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${y.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${y.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),yClassIndices=Array.from(await yClasses.data());dispose(yClasses);let classSampleWeight=[];return yClassIndices.forEach(classIndex=>{if(classWeight[classIndex]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${classIndex} exists in the data but not in classWeight`);classSampleWeight.push(classWeight[classIndex])}),tensor1d(classSampleWeight,"float32")}else return null}function computeWeightedLoss2(losses4,sampleWeights){return mul(losses4,sampleWeights)}var DEFAULT_VALIDATION_BATCH_SIZE=32;function standardizeDataIteratorOutput(model2,iteratorOut){let xs,ys,iteratorOutObj=iteratorOut;xs=iteratorOutObj.xs,ys=iteratorOutObj.ys,util_exports.assert(xs!=null&&ys!=null,()=>`A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor. The provided Dataset instead generates ${iteratorOut}`);let flattenedXs=flattenTensorOrArrayOrMap("input",model2.inputNames,xs),flattenedYs=flattenTensorOrArrayOrMap("output",model2.outputNames,ys),batchSize=flattenedXs[0].shape[0];util_exports.assert(flattenedXs.length===model2.inputs.length,()=>`LayersModel has ${model2.inputs.length} inputs, but the dataset provides ${flattenedXs.length} inputs. (Expected input keys: ${JSON.stringify(model2.inputNames)})`),util_exports.assert(flattenedYs.length===model2.outputs.length,()=>`LayersModel has ${model2.outputs.length} outputs, but the dataset provides ${flattenedYs.length} outputs. (Expected output keys: ${JSON.stringify(model2.outputNames)})`);for(let xIndex=0;xIndex`Batch size mismatch: input ${model2.inputNames[xIndex]} has ${flattenedXs[xIndex].shape[0]}; expected ${batchSize} based on input ${model2.inputNames[0]}.`);for(let yIndex=0;yIndex`Batch size mismatch: output ${model2.outputNames[yIndex]} has ${flattenedYs[yIndex].shape[0]}; expected ${batchSize} based on input ${model2.inputNames[0]}.`);return{xs:flattenedXs,ys:flattenedYs}}function flattenTensorOrArrayOrMap(inputOrOutput,names,values){if(values instanceof Tensor)return[values];if(Array.isArray(values))return util_exports.assert(values.length===names.length,()=>`Received an array of ${values.length} Tensors, but expected ${names.length} to match the ${inputOrOutput} keys ${names}.`),values;{let result=[];for(let name of names){if(values[name]==null)throw new ValueError(`The feature data generated by the dataset lacks the required ${inputOrOutput} key '${name}'.`);result.push(values[name])}return result}}function standardizeTensorValidationData(data2){if(data2.length===3)throw new NotImplementedError("Validation with sample weights is not implemented yet.");return{xs:data2[0],ys:data2[1]}}async function fitDataset(model2,dataset,args){let hasBatchesPerEpoch=args.batchesPerEpoch!=null;if(util_exports.assert(model2.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),util_exports.assert(args!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),util_exports.assert(args.epochs!=null&&args.epochs>0&&Number.isInteger(args.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${args.epochs}`),util_exports.assert(!hasBatchesPerEpoch||args.batchesPerEpoch>0&&Number.isInteger(args.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${args.batchesPerEpoch}`),util_exports.assert(args.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. Use validationData instead."),model2.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");model2.isTraining=!0;try{let doValidation=args.validationData!=null,valXs,valYs;if(doValidation)if(isDatasetObject(args.validationData))util_exports.assert(args.validationBatches==null||args.validationBatches>0&&Number.isInteger(args.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${args.validationBatches}`);else{let validationData=standardizeTensorValidationData(args.validationData);valXs=validationData.xs,valYs=validationData.ys}let trainFunction=model2.makeTrainFunction(),outLabels=model2.getDedupedMetricsNames(),callbackMetrics;doValidation?callbackMetrics=outLabels.slice().concat(outLabels.map(n=>"val_"+n)):callbackMetrics=outLabels.slice();let callbacks2=standardizeCallbacks(args.callbacks,args.yieldEvery),verbose=args.verbose==null?1:args.verbose,{callbackList,history}=configureCallbacks(callbacks2,verbose,args.epochs,null,null,getStepsPerEpoch(dataset,args),null,doValidation,callbackMetrics);callbackList.setModel(model2),model2.history=history,await callbackList.onTrainBegin(),model2.stopTraining_=!1;let epoch=args.initialEpoch==null?0:args.initialEpoch,dataIterator=await dataset.iterator();for(;epoch=args.batchesPerEpoch:iteratorOut.done){if(doValidation){let valOuts;isDatasetObject(args.validationData)?valOuts=toList(await model2.evaluateDataset(args.validationData,{batches:args.validationBatches})):valOuts=toList(model2.evaluate(valXs,valYs,{batchSize:args.validationBatchSize==null?DEFAULT_VALIDATION_BATCH_SIZE:args.validationBatchSize,verbose:0}));for(let i=0;i0)throw new NotImplementedError("Verbose mode is not implemented yet.");util_exports.assert(!hasBatches||args.batches>0&&Number.isInteger(args.batches),()=>`Test loop expects \`batches\` to be a positive integer, but received ${JSON.stringify(args.batches)}`);let dataIterator=isLazyIteratorObject(dataset)?dataset:await dataset.iterator(),numExamples=0,batch=0;for(;hasBatches?batch{if(iteratorOut.value){let{xs,ys}=standardizeDataIteratorOutput(model2,iteratorOut.value),xsAndYs=xs.concat(ys),batchOuts=tidy(()=>f(xsAndYs));if(dispose(xsAndYs),batch===0)for(let i=0;iadd2(outs[i],mul(batchSize,batchOut))),batch>0&&dispose(oldScalar)}dispose(batchOuts),numExamples+=batchSize,++batch}return outs}),iteratorOut.done){hasBatches&&console.warn(`Your dataset iterator ran out of data during evaluateDataset(). Interrupting evalution. Make sure that your dataset can generate at least \`batches\` batches (in this case, ${args.batches} batches). You may need to use the repeat() function when building your dataset.`);break}}for(let i=0;i0&&Number.isInteger(batchSize),()=>`batchSize is required to be a positive integer, but got ${batchSize}`)}function sliceArrays(arrays,start,stop){return arrays==null?[null]:Array.isArray(arrays)?arrays.map(array2=>sliceAlongFirstAxis(array2,start,stop-start)):sliceAlongFirstAxis(arrays,start,stop-start)}function sliceArraysByIndices(arrays,indices){return tidy(()=>arrays==null?null:Array.isArray(arrays)?arrays.map(array2=>sliceArraysByIndices(array2,indices)):gather2(arrays,indices.dtype==="int32"?indices:indices.toInt()))}function makeBatches(size,batchSize){let output=[],batchStart=0,batchEnd=null;for(;batchStart=size&&(batchEnd=size),output.push([batchStart,batchEnd]),batchStart=batchEnd;return output}async function fitLoop(model2,f,ins,outLabels,batchSize,epochs,verbose,callbacks2,valF,valIns,shuffle2,callbackMetrics,initialEpoch,stepsPerEpoch,validationSteps){batchSize==null&&(batchSize=32),epochs==null&&(epochs=1),shuffle2==null&&(shuffle2=!0),initialEpoch==null&&(initialEpoch=0);let doValidation=!1;if(valF!=null&&valIns!=null&&(doValidation=!0),validationSteps!=null&&(doValidation=!0,stepsPerEpoch==null))throw new ValueError("Can only use `validationSteps` when doing step-wise training, i.e., `stepsPerEpoch` must be set.");let numTrainSamples=model2.checkNumSamples(ins,batchSize,stepsPerEpoch,"steps_per_epoch"),indexArray;numTrainSamples!=null&&(indexArray=range5(0,numTrainSamples)),verbose==null&&(verbose=1);let{callbackList,history}=configureCallbacks(callbacks2,verbose,epochs,initialEpoch,numTrainSamples,stepsPerEpoch,batchSize,doValidation,callbackMetrics);callbackList.setModel(model2),model2.history=history,await callbackList.onTrainBegin(),model2.stopTraining_=!1;for(let epoch=initialEpoch;epoch{let batchStart=batches[batchIndex][0],batchEnd=batches[batchIndex][1],batchIds=sliceAlongFirstAxis(epochIndexArray1D,batchStart,batchEnd-batchStart);batchLogs.batch=batchIndex,batchLogs.size=batchEnd-batchStart;let insBatch=sliceArraysByIndices(ins,batchIds),outs=f(insBatch);for(let i=0;i0){if(doValidation=!0,args.validationData.length===2)inputValX=args.validationData[0],inputValY=args.validationData[1];else throw args.validationData.length===3?new NotImplementedError("validationData including sample weights is not supported yet."):new ValueError(`When passing validation data, it must contain 2 (valX, valY) or 3 (valX, valY, valSampleWeight) items; ${args.validationData} is invalid.`);let checkBatchAxis2=!0,valStandardized=await model2.standardizeUserData(inputValX,inputValY,null,null,checkBatchAxis2,batchSize);valX=valStandardized[0],valY=valStandardized[1],valIns=valX.concat(valY)}else if(args.validationSplit!=null&&args.validationSplit>0&&args.validationSplit<1){doValidation=!0;let splitAt=Math.floor(inputs[0].shape[0]*(1-args.validationSplit)),originalBatchSize=inputs[0].shape[0];valX=sliceArrays(inputs,splitAt,originalBatchSize),inputs=sliceArrays(inputs,0,splitAt),valY=sliceArrays(targets,splitAt,originalBatchSize),targets=sliceArrays(targets,0,splitAt),valIns=valX.concat(valY)}else args.validationSteps!=null&&(doValidation=!0);let ins=inputs.concat(targets).concat(sampleWeights);model2.checkTrainableWeightsConsistency();let trainFunction=model2.makeTrainFunction(),outLabels=model2.getDedupedMetricsNames(),valFunction,callbackMetrics;doValidation?(model2.makeTestFunction(),valFunction=model2.testFunction,callbackMetrics=outLabels.slice().concat(outLabels.map(n=>"val_"+n))):(valFunction=null,valIns=[],callbackMetrics=outLabels.slice());let callbacks2=standardizeCallbacks(args.callbacks,args.yieldEvery);return await fitLoop(model2,trainFunction,ins,outLabels,batchSize,args.epochs,args.verbose,callbacks2,valFunction,valIns,args.shuffle,callbackMetrics,args.initialEpoch,null,null)}finally{model2.isTraining=!1,disposeNewTensors(inputs,x),disposeNewTensors(targets,y),disposeNewTensors(valX,inputValX),disposeNewTensors(valY,inputValY),sampleWeights!=null&&dispose(sampleWeights)}}function ensureTensorsRank2OrHigher(tensors){let outs=[];tensors instanceof Tensor&&(tensors=[tensors]);for(let i=0;ioldTensorIds.push(t.id));else if(refTensors!=null)for(let name in refTensors){let oldTensor=refTensors[name];oldTensorIds.push(oldTensor.id)}let tensorsToDispose=[];if(tensors instanceof Tensor)oldTensorIds.indexOf(tensors.id)===-1&&tensorsToDispose.push(tensors);else if(Array.isArray(tensors))tensors.forEach(t=>{oldTensorIds.indexOf(t.id)===-1&&tensorsToDispose.push(t)});else if(tensors!=null)for(let name in tensors){let tensor2=tensors[name];oldTensorIds.indexOf(tensor2.id)===-1&&tensorsToDispose.push(tensor2)}tensorsToDispose.forEach(t=>{t.isDisposed||t.dispose()})}function isDataTensor(x){return x instanceof Tensor}function isDataArray(x){return Array.isArray(x)}function isDataDict(x){return!isDataTensor(x)&&!isDataArray(x)}function standardizeInputData(data2,names,shapes,checkBatchAxis=!0,exceptionPrefix=""){if(names==null||names.length===0){if(data2!=null){let gotUnexpectedData=!1;if(isDataArray(data2)&&data2.length>0)gotUnexpectedData=!0;else if(isDataDict(data2)){for(let key in data2)if(data2.hasOwnProperty(key)){gotUnexpectedData=!0;break}}else gotUnexpectedData=!0;if(gotUnexpectedData)throw new ValueError(`Error when checking model ${exceptionPrefix} expected no data, but got ${data2}`)}return[]}if(data2==null)return names.map(name=>null);let arrays;if(isDataDict(data2)){data2=data2,arrays=[];for(let name of names){if(data2[name]==null)throw new ValueError(`No data provided for "${name}". Need data for each key in: ${names}`);arrays.push(data2[name])}}else if(isDataArray(data2)){if(data2=data2,data2.length!==names.length)throw new ValueError(`Error when checking model ${exceptionPrefix}: the Array of Tensors that you are passing to your model is not the size the model expected. Expected to see ${names.length} Tensor(s), but instead got the following list of Tensor(s): ${data2}`);arrays=data2}else{if(data2=data2,names.length>1)throw new ValueError(`The model ${exceptionPrefix} expects ${names.length} Tensor(s), but only received one Tensor. Found: Tensor with shape ${data2.shape}`);arrays=[data2]}if(arrays=ensureTensorsRank2OrHigher(arrays),shapes!=null)for(let i=0;i=0&&dim!==refDim)throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} to have shape [${shapes[i]}], but got array with shape [${array2.shape}].`)}}return arrays}function checkArrayLengths(inputs,targets,weights){let setX=unique4(inputs.map(input2=>input2.shape[0]));setX.sort();let setY=unique4(targets.map(target=>target.shape[0]));if(setY.sort(),setX.length>1)throw new ValueError(`All input Tensors (x) should have the same number of samples. Got array shapes: ${JSON.stringify(inputs.map(input2=>input2.shape))}`);if(setY.length>1)throw new ValueError(`All target Tensors (y) should have the same number of samples. Got array shapes: ${JSON.stringify(targets.map(target=>target.shape))}`);if(setX.length>0&&setY.length>0&&!util_exports.arraysEqual(setX,setY))throw new ValueError(`Input Tensors should have the same number of samples as target Tensors. Found ${setX[0]} input sample(s) and ${setY[0]} target sample(s).`)}function checkLossAndTargetCompatibility(targets,lossFns,outputShapes){let keyLosses=[meanSquaredError2,binaryCrossentropy,categoricalCrossentropy];for(let i=0;i1)throw new ValueError(`The model expects ${names.length} ${exceptionPrefix} Tensors, but only received one Tensor. Found: array with shape ${JSON.stringify(data2.shape)}.`);arrays=[data2]}if(shapes!=null)for(let i=0;i[]);let wrappedMetrics;if(typeof metrics2=="string"||typeof metrics2=="function")wrappedMetrics=[metrics2];else if(Array.isArray(metrics2)||typeof metrics2=="object")wrappedMetrics=metrics2;else throw new TypeError(`Type of metrics argument not understood. Expected an string,function, Array, or Object, found: ${metrics2}`);if(Array.isArray(wrappedMetrics))return outputNames.map(name=>wrappedMetrics);{let nestedMetrics=[];for(let name of outputNames){let outputMetrics=wrappedMetrics.hasOwnProperty(name)?wrappedMetrics[name]:[];Array.isArray(outputMetrics)||(outputMetrics=[outputMetrics]),nestedMetrics.push(outputMetrics)}return nestedMetrics}}var LAYERS_MODEL_FORMAT_NAME="layers-model",LayersModel=class extends Container{constructor(args){super(args);this.isTraining=!1}summary(lineLength,positions,printFn=console.log){if(!this.built)throw new ValueError("This model has never been called, thus its weights have not been created yet. So no summary can be displayed. Build the model first (e.g., by calling it on some test data).");printSummary(this,lineLength,positions,printFn)}compile(args){if(args.loss==null&&(args.loss=[]),this.loss=args.loss,typeof args.optimizer=="string")this.optimizer_=getOptimizer(args.optimizer),this.isOptimizerOwned=!0;else{if(!(args.optimizer instanceof Optimizer))throw new ValueError("User-defined optimizer must be an instance of tf.Optimizer.");this.optimizer_=args.optimizer,this.isOptimizerOwned=!1}let lossFunctions=[];if(!Array.isArray(args.loss)&&typeof args.loss!="string"&&typeof args.loss!="function"){args.loss=args.loss;for(let name in args.loss)if(this.outputNames.indexOf(name)===-1)throw new ValueError(`Unknown entry in loss dictionary: "${name}". Only expected the following keys: ${this.outputNames}`);for(let name of this.outputNames)args.loss[name]==null&&console.warn(`Output "${name}" is missing from loss dictionary. We assume this was done on purpose, and we will not be expecting data to be passed to ${name} during training`),lossFunctions.push(get(args.loss[name]))}else if(Array.isArray(args.loss)){if(args.loss.length!==this.outputs.length)throw new ValueError(`When passing an Array as loss, it should have one entry per model output. The model has ${this.outputs.length} output(s), but you passed loss=${args.loss}.`);lossFunctions=args.loss.map(l=>get(l))}else{let lossFunction=get(args.loss);this.outputs.forEach(_=>{lossFunctions.push(lossFunction)})}this.lossFunctions=lossFunctions,this.feedOutputNames=[],this.feedOutputShapes=[],this.feedLossFns=[];for(let i=0;i{for(let i=0;i1&&(this.metricsTensors.push([weightedLoss,i]),this.metricsNames.push(this.outputNames[i]+"_loss"))}});let nestedMetrics=collectMetrics(args.metrics,this.outputNames),appendMetric=(outputIndex,metricName,metricTensor)=>{this.outputNames.length>1&&(metricName=this.outputNames[outputIndex]+"_"+metricName),this.metricsNames.push(metricName),this.metricsTensors.push([metricTensor,outputIndex])};nameScope("metric",()=>{for(let i=0;i{let metricNamePrefix="",metricName,accFn,weightedMetricFn;for(let metric of metrics2){if(typeof metric=="string"&&["accuracy","acc","crossentropy","ce"].indexOf(metric)!==-1){let outputShape=this.internalOutputShapes[i];outputShape[outputShape.length-1]===1||this.lossFunctions[i]===binaryCrossentropy?["accuracy","acc"].indexOf(metric)!==-1?accFn=binaryAccuracy:["crossentropy","ce"].indexOf(metric)!==-1&&(accFn=binaryCrossentropy2):this.lossFunctions[i]===sparseCategoricalCrossentropy?["accuracy","acc"].indexOf(metric)!==-1?accFn=sparseCategoricalAccuracy:["crossentropy","ce"].indexOf(metric)!==-1&&(accFn=sparseCategoricalCrossentropy2):["accuracy","acc"].indexOf(metric)!==-1?accFn=categoricalAccuracy:["crossentropy","ce"].indexOf(metric)!==-1&&(accFn=categoricalCrossentropy2);let suffix;["accuracy","acc"].indexOf(metric)!==-1?suffix="acc":["crossentropy","ce"].indexOf(metric)!==-1&&(suffix="ce"),weightedMetricFn=accFn,metricName=metricNamePrefix+suffix}else weightedMetricFn=get2(metric),metricName=metricNamePrefix+getLossOrMetricName(metric);let metricResult;nameScope(metricName,()=>{metricResult=weightedMetricFn}),appendMetric(i,metricName,metricResult)}})(outputMetrics)}}),this.collectedTrainableWeights=this.trainableWeights}checkTrainableWeightsConsistency(){this.collectedTrainableWeights!=null&&this.trainableWeights.length!==this.collectedTrainableWeights.length&&console.warn("Discrepancy between trainableweights and collected trainable weights. Did you set `model.trainable` without calling `model.compile()` afterwards?")}evaluate(x,y,args={}){let batchSize=args.batchSize==null?32:args.batchSize;checkBatchSize(batchSize);let checkBatchAxis=!0,standardizedOuts=this.standardizeUserDataXY(x,y,checkBatchAxis,batchSize);try{let ins=standardizedOuts[0].concat(standardizedOuts[1]);this.makeTestFunction();let f=this.testFunction,testOuts=this.testLoop(f,ins,batchSize,args.verbose,args.steps);return singletonOrArray(testOuts)}finally{disposeNewTensors(standardizedOuts[0],x),disposeNewTensors(standardizedOuts[1],y)}}async evaluateDataset(dataset,args){return this.makeTestFunction(),evaluateDataset(this,dataset,args)}checkNumSamples(ins,batchSize,steps,stepsName="steps"){let numSamples;if(steps!=null){if(numSamples=null,batchSize!=null)throw new ValueError(`If ${stepsName} is set, batchSize must be null or undefined.Got batchSize = ${batchSize}`)}else if(ins!=null)Array.isArray(ins)?numSamples=ins[0].shape[0]:numSamples=ins.shape[0];else throw new ValueError(`Either the input data should have a defined shape, or ${stepsName} shoud be specified.`);return numSamples}execute(inputs,outputs){if(Array.isArray(outputs)&&outputs.length===0)throw new ValueError("`outputs` is an empty Array, which is not allowed.");let outputsIsArray=Array.isArray(outputs),outputNames=outputsIsArray?outputs:[outputs],outputSymbolicTensors=this.retrieveSymbolicTensors(outputNames),feedDict=new FeedDict;if(inputs instanceof Tensor&&(inputs=[inputs]),Array.isArray(inputs)){if(inputs.length!==this.inputs.length)throw new ValueError(`The number of inputs provided (${inputs.length}) does not match the number of inputs of this model (${this.inputs.length}).`);for(let i=0;ioutput.name);for(let i=0;i0){let remainingNames=[];throw outputSymbolicTensors.forEach((tensor2,i)=>{tensor2==null&&remainingNames.push(symbolicTensorNames[i])}),new ValueError(`Cannot find SymbolicTensors for output name(s): ${JSON.stringify(remainingNames)}`)}return outputSymbolicTensors}predictLoop(ins,batchSize=32,verbose=!1){return tidy(()=>{let numSamples=this.checkNumSamples(ins);if(verbose)throw new NotImplementedError("Verbose predictLoop() is not implemented yet.");let batches=makeBatches(numSamples,batchSize),outsBatches=this.outputs.map(output=>[]);for(let batchIndex=0;batchIndex{let batchStart=batches[batchIndex][0],batchEnd=batches[batchIndex][1],insBatch=sliceArrays(ins,batchStart,batchEnd),feeds=[];if(Array.isArray(insBatch))for(let i=0;ioutsBatches[i].push(batchOut));return singletonOrArray(outsBatches.map(batches2=>concat(batches2,0)))})}predict(x,args={}){let xsRank2OrHigher=ensureTensorsRank2OrHigher(x);checkInputData(xsRank2OrHigher,this.inputNames,this.feedInputShapes,!1);try{let batchSize=args.batchSize==null?32:args.batchSize;return checkBatchSize(batchSize),this.predictLoop(xsRank2OrHigher,batchSize)}finally{disposeNewTensors(xsRank2OrHigher,x)}}predictOnBatch(x){checkInputData(x,this.inputNames,this.feedInputShapes,!0);let batchSize=(Array.isArray(x)?x[0]:x).shape[0];return this.predictLoop(x,batchSize)}standardizeUserDataXY(x,y,checkBatchAxis=!0,batchSize){if(this.optimizer_==null)throw new RuntimeError("You must compile a model before training/testing. Use LayersModel.compile(modelCompileArgs).");let outputShapes=[];for(let i=0;i0&&x[0].shape[0]%batchSize!=0)throw new ValueError(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${batchSize}. Found: ${x[0].shape[0]} sample(s).`);return[x,y]}async standardizeUserData(x,y,sampleWeight,classWeight,checkBatchAxis=!0,batchSize){let[standardXs,standardYs]=this.standardizeUserDataXY(x,y,checkBatchAxis,batchSize);if(sampleWeight!=null)throw new Error("sample weight is not supported yet.");let standardSampleWeights=null;if(classWeight!=null){let classWeights=standardizeClassWeights(classWeight,this.outputNames);standardSampleWeights=[];for(let i=0;i{let numSamples=this.checkNumSamples(ins,batchSize,steps,"steps"),outs=[];if(verbose>0)throw new NotImplementedError("Verbose mode is not implemented yet.");if(steps!=null)throw new NotImplementedError("steps mode in testLoop() is not implemented yet");{let batches=makeBatches(numSamples,batchSize),indexArray=tensor1d(range5(0,numSamples));for(let batchIndex=0;batchIndex1&&(newLabel+=`_${count(outLabels.slice(0,i),label)}`),dedupedOutLabels.push(newLabel)}return dedupedOutLabels}makeTrainFunction(){return data2=>{let lossValues=[],inputs=data2.slice(0,this.inputs.length),targets=data2.slice(this.inputs.length,this.inputs.length+this.outputs.length),sampleWeights=data2.slice(this.inputs.length+this.outputs.length,this.inputs.length+this.outputs.length*2),metricsValues=[],totalLossFunction=()=>{let feeds=[];for(let i=0;i1&&i{totalLoss=add2(totalLoss,regularizerLoss)}),totalLoss},variables=this.collectedTrainableWeights.map(param=>param.read()),returnCost=!0;return[this.optimizer_.minimize(totalLossFunction,returnCost,variables)].concat(metricsValues)}}makeTestFunction(){this.testFunction=data2=>tidy(()=>{let valOutputs=[],totalLoss,inputs=data2.slice(0,this.inputs.length),targets=data2.slice(this.inputs.length,this.inputs.length+this.outputs.length),feeds=[];for(let i=0;itoSnakeCase(name))}else{let outputNames=Object.keys(this.loss);lossNames={};let losses4=this.loss;for(let outputName of outputNames)if(typeof losses4[outputName]=="string")lossNames[outputName]=toSnakeCase(losses4[outputName]);else throw new Error("Serialization of non-string loss is not supported.")}return lossNames}getMetricIdentifiers(){if(typeof this.metrics=="string"||typeof this.metrics=="function")return[toSnakeCase(getLossOrMetricName(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(metric=>toSnakeCase(getLossOrMetricName(metric)));{let metricsIdentifiers={};for(let key in this.metrics)metricsIdentifiers[key]=toSnakeCase(getLossOrMetricName(this.metrics[key]));return metricsIdentifiers}}getTrainingConfig(){return{loss:this.getLossIdentifiers(),metrics:this.getMetricIdentifiers(),optimizer_config:{class_name:this.optimizer.getClassName(),config:this.optimizer.getConfig()}}}loadTrainingConfig(trainingConfig){if(trainingConfig.weighted_metrics!=null)throw new Error("Loading weight_metrics is not supported yet.");if(trainingConfig.loss_weights!=null)throw new Error("Loading loss_weights is not supported yet.");if(trainingConfig.sample_weight_mode!=null)throw new Error("Loading sample_weight_mode is not supported yet.");let tsConfig=convertPythonicToTs(trainingConfig.optimizer_config),optimizer=deserialize(tsConfig),loss;if(typeof trainingConfig.loss=="string")loss=toCamelCase(trainingConfig.loss);else if(Array.isArray(trainingConfig.loss))loss=trainingConfig.loss.map(lossEntry=>toCamelCase(lossEntry));else if(trainingConfig.loss!=null){loss={};for(let key in trainingConfig.loss)loss[key]=toCamelCase(trainingConfig.loss[key])}let metrics2;if(Array.isArray(trainingConfig.metrics))metrics2=trainingConfig.metrics.map(metric=>toCamelCase(metric));else if(trainingConfig.metrics!=null){metrics2={};for(let key in trainingConfig.metrics)metrics2[key]=toCamelCase(trainingConfig.metrics[key])}this.compile({loss,metrics:metrics2,optimizer})}async save(handlerOrURL,config2){if(typeof handlerOrURL=="string"){let handlers=io_exports.getSaveHandlers(handlerOrURL);if(handlers.length===0)throw new ValueError(`Cannot find any save handlers for URL '${handlerOrURL}'`);if(handlers.length>1)throw new ValueError(`Found more than one (${handlers.length}) save handlers for URL '${handlerOrURL}'`);handlerOrURL=handlers[0]}if(handlerOrURL.save==null)throw new ValueError("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let weightDataAndSpecs=await io_exports.encodeWeights(this.getNamedWeights(config2)),returnString=!1,unusedArg=null,modelArtifacts={modelTopology:this.toJSON(unusedArg,returnString),format:LAYERS_MODEL_FORMAT_NAME,generatedBy:`TensorFlow.js tfjs-layers v${version10}`,convertedBy:null};if((config2==null?!1:config2.includeOptimizer)&&this.optimizer!=null){modelArtifacts.trainingConfig=this.getTrainingConfig();let weightType="optimizer",{data:optimizerWeightData,specs:optimizerWeightSpecs}=await io_exports.encodeWeights(await this.optimizer.getWeights(),weightType);weightDataAndSpecs.specs.push(...optimizerWeightSpecs),weightDataAndSpecs.data=io_exports.concatenateArrayBuffers([weightDataAndSpecs.data,optimizerWeightData])}if(this.userDefinedMetadata!=null){let checkSize=!0;checkUserDefinedMetadata(this.userDefinedMetadata,this.name,checkSize),modelArtifacts.userDefinedMetadata=this.userDefinedMetadata}return modelArtifacts.weightData=weightDataAndSpecs.data,modelArtifacts.weightSpecs=weightDataAndSpecs.specs,handlerOrURL.save(modelArtifacts)}setUserDefinedMetadata(userDefinedMetadata){checkUserDefinedMetadata(userDefinedMetadata,this.name),this.userDefinedMetadata=userDefinedMetadata}getUserDefinedMetadata(){return this.userDefinedMetadata}};LayersModel.className="Model";serialization_exports.registerClass(LayersModel);var Functional=class extends LayersModel{};Functional.className="Functional";serialization_exports.registerClass(Functional);async function modelFromJSON(modelAndWeightsConfig,customObjects){"modelTopology"in modelAndWeightsConfig||(modelAndWeightsConfig={modelTopology:modelAndWeightsConfig}),modelAndWeightsConfig=modelAndWeightsConfig;let modelTopology=modelAndWeightsConfig.modelTopology;modelTopology.model_config!=null&&(modelTopology=modelTopology.model_config);let tsConfig=convertPythonicToTs(modelTopology),model2=deserialize(tsConfig,customObjects);if(modelAndWeightsConfig.weightsManifest!=null){let weightValues=await io_exports.loadWeights(modelAndWeightsConfig.weightsManifest,modelAndWeightsConfig.pathPrefix,model2.weights.map(weight=>weight.originalName)),uniqueWeightValues={};for(let weight of model2.weights)uniqueWeightValues[weight.originalName]=weightValues[weight.originalName];model2.loadWeights(uniqueWeightValues),dispose(weightValues)}return model2}async function loadLayersModelInternal(pathOrIOHandler,options){if(options==null&&(options={}),typeof pathOrIOHandler=="string"){let handlers=io_exports.getLoadHandlers(pathOrIOHandler,options);if(handlers.length===0)handlers.push(io_exports.browserHTTPRequest(pathOrIOHandler,options));else if(handlers.length>1)throw new ValueError(`Found more than one (${handlers.length}) load handlers for URL '${pathOrIOHandler}'`);pathOrIOHandler=handlers[0]}return loadLayersModelFromIOHandler(pathOrIOHandler,void 0,options)}async function loadLayersModelFromIOHandler(handler,customObjects,options){if(options==null&&(options={}),handler.load==null)throw new ValueError("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let artifacts=await handler.load(),modelTopology=artifacts.modelTopology;modelTopology.model_config!=null&&(modelTopology=modelTopology.model_config);let strict=options.strict==null?!0:options.strict,fastWeightInit=artifacts.weightData!=null&&artifacts.weightSpecs!=null&&strict,model2=deserialize(convertPythonicToTs(modelTopology),customObjects,fastWeightInit),trainingConfig=artifacts.trainingConfig;if(trainingConfig!=null&&model2.loadTrainingConfig(trainingConfig),artifacts.userDefinedMetadata!=null&&model2.setUserDefinedMetadata(artifacts.userDefinedMetadata),artifacts.weightData!=null){if(artifacts.weightSpecs==null)throw new ValueError("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");let{modelWeights,optimizerWeights}=decodeModelAndOptimizerWeights(artifacts.weightData,artifacts.weightSpecs);model2.loadWeights(modelWeights,strict),model2.optimizer!=null&&optimizerWeights.length>0&&await model2.optimizer.setWeights(optimizerWeights),dispose(modelWeights),dispose(optimizerWeights.map(w=>w.tensor))}return model2}function decodeModelAndOptimizerWeights(buffer2,specs){let name2Tensor=io_exports.decodeWeights(buffer2,specs),modelWeights={},optimizerWeights=[];return specs.forEach(spec=>{spec.group==="optimizer"?optimizerWeights.push({name:spec.name,tensor:name2Tensor[spec.name]}):modelWeights[spec.name]=name2Tensor[spec.name]}),{modelWeights,optimizerWeights}}var Sequential=class extends LayersModel{constructor(args){super({inputs:[],outputs:[]});if(args=args||{},this.trainable=!0,this.built=!1,this.name=args.name!=null?args.name:getUid("sequential_"),args.layers!=null)for(let layer of args.layers)this.add(layer)}checkShape(layer){if(layer.inboundNodes[0].outputTensors[0].shape.some(x=>x<0))throw new ValueError(`Negative dimension size caused by adding layer ${layer.name} with input shape [${layer.inboundNodes[0].inputTensors[0].shape}]`)}add(layer){let isLayerModelInstance=layer instanceof Sequential||layer instanceof LayersModel,modelLayer;if(isLayerModelInstance){if(modelLayer=layer,modelLayer.outputs.length!==1)throw new ValueError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");if(modelLayer.inputs.length!==1)throw new ValueError("All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.")}if(this.outputs.length===0){if(layer.inboundNodes.length===0){if(layer.batchInputShape==null)throw new ValueError("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");let x=Input({batchShape:layer.batchInputShape,dtype:layer.dtype,name:layer.name+"_input"});layer.apply(x)}if(isLayerModelInstance)this.outputs=modelLayer.outputs,this.inputs=modelLayer.inputs;else{if(layer.inboundNodes.length!==1)throw new ValueError(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${layer.name} which has ${layer.inboundNodes.length} pre-existing inbound connections.`);if(layer.inboundNodes[0].outputTensors.length!==1)throw new ValueError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(layer),this.outputs=[layer.inboundNodes[0].outputTensors[0]],this.inputs=getSourceInputs(this.outputs[0])}this.inboundNodes=[],new Node({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:pyListRepeat(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map(x=>x.shape),outputShapes:this.outputs[0].shape})}else{let outputTensor=layer.apply(this.outputs[0]);if(Array.isArray(outputTensor))throw new TypeError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(layer),this.outputs=[outputTensor],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}this.layers.push(layer),this.built=!1}pop(){if(this.layers.length===0)throw new TypeError("There are no layers in the model.");if(this.layers.pop(),this.layers.length===0)this.outputs=[],this.inboundNodes=[],this.outboundNodes=[];else{let lastLayerIndex=this.layers.length-1;this.layers[lastLayerIndex].outboundNodes=[],this.outputs=[this.layers[lastLayerIndex].output],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}}call(inputs,kwargs){return this.model==null&&this.build(),this.model.call(inputs,kwargs)}build(inputShape){if(getExactlyOneShape(inputShape),this.inputs.length===0||this.outputs.length===0)throw new TypeError("Sequential model cannot be built: model is empty. Add some layers first.");this.model=new LayersModel({inputs:this.inputs,outputs:this.outputs[0],name:this.name+"_model"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0}countParams(){return this.built||this.build(),super.countParams()}summary(lineLength,positions,printFn=console.log){this.built||this.build(),super.summary(lineLength,positions,printFn)}setWeights(weights){this.model==null&&this.build(),this.model.setWeights(weights)}evaluate(x,y,args={}){if(!this.built)throw new RuntimeError("The model needs to be compiled before being used.");return this.model.evaluate(x,y,args)}async evaluateDataset(dataset,args){if(!this.built)throw new RuntimeError("The model needs to be compiled before being used.");return this.model.evaluateDataset(dataset,args)}predict(x,args={}){return this.model==null&&this.build(),this.model.predict(x,args)}predictOnBatch(x){return this.model==null&&this.build(),this.model.predictOnBatch(x)}compile(args){this.build(),this.model.compile(args),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(optimizer){this.model.optimizer=optimizer}async fit(x,y,args={}){if(!this.built)throw new RuntimeError("The model needs to be compiled before being used.");return this.model.fit(x,y,args)}async fitDataset(dataset,args){if(!this.built)throw new RuntimeError("The model needs to be compiled before being used.");return this.model.fitDataset(dataset,args)}async trainOnBatch(x,y){return this.model.trainOnBatch(x,y)}static fromConfig(cls,config2,customObjects={},fastWeightInit=!1){let configArray,extraModelConfig={};if(config2 instanceof Array){if(config2[0].className==null||config2[0].className==="Merge")throw new ValueError("Legacy serialization format not supported yet.");configArray=config2}else util_exports.assert(config2.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),configArray=config2.layers,delete config2.layers,extraModelConfig=config2;let model2=new cls(extraModelConfig);if(!(model2 instanceof Sequential))throw new NotImplementedError(`Sequential.fromConfig called on non-Sequential input: ${model2}`);for(let conf of configArray){let layer=deserialize(conf,void 0,fastWeightInit);fastWeightInit&&layer.setFastWeightInitDuringBuild(!0),model2.add(layer)}return model2}set stopTraining(stop){if(this.model==null)throw new ValueError("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=stop}get stopTraining(){if(this.model==null)throw new ValueError("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let layers=[];for(let layer of this.layers){let dict={};dict.className=layer.getClassName(),dict.config=layer.getConfig(),layers.push(dict)}return{name:this.name,layers}}};Sequential.className="Sequential";serialization_exports.registerClass(Sequential);function model(args){return new LayersModel(args)}function sequential(config2){return new Sequential(config2)}function loadLayersModel(pathOrIOHandler,options){return options==null&&(options={}),loadLayersModelInternal(pathOrIOHandler,options)}function input(config2){return Input(config2)}function registerCallbackConstructor(verbosityLevel,callbackConstructor){CallbackConstructorRegistry.registerCallbackConstructor(verbosityLevel,callbackConstructor)}var Activation=class extends serialization_exports.Serializable{getConfig(){return{}}},Elu2=class extends Activation{apply(x,alpha=1){return elu4(x,alpha)}};Elu2.className="elu";serialization_exports.registerClass(Elu2);var Selu2=class extends Activation{apply(x){return selu(x)}};Selu2.className="selu";serialization_exports.registerClass(Selu2);var Relu2=class extends Activation{apply(x){return relu(x)}};Relu2.className="relu";serialization_exports.registerClass(Relu2);var Relu62=class extends Activation{apply(x){return tidy(()=>minimum(6,relu(x)))}};Relu62.className="relu6";serialization_exports.registerClass(Relu62);var Linear=class extends Activation{apply(x){return x}};Linear.className="linear";serialization_exports.registerClass(Linear);var Sigmoid2=class extends Activation{apply(x){return sigmoid(x)}};Sigmoid2.className="sigmoid";serialization_exports.registerClass(Sigmoid2);var HardSigmoid=class extends Activation{apply(x){return hardSigmoid(x)}};HardSigmoid.className="hardSigmoid";serialization_exports.registerClass(HardSigmoid);var Softplus2=class extends Activation{apply(x){return softplus(x)}};Softplus2.className="softplus";serialization_exports.registerClass(Softplus2);var Softsign=class extends Activation{apply(x){return softsign(x)}};Softsign.className="softsign";serialization_exports.registerClass(Softsign);var Tanh2=class extends Activation{apply(x){return tanh2(x)}};Tanh2.className="tanh";serialization_exports.registerClass(Tanh2);var Softmax2=class extends Activation{apply(x,axis=-1){return softmax(x,axis)}};Softmax2.className="softmax";serialization_exports.registerClass(Softmax2);var LogSoftmax2=class extends Activation{apply(x,axis=-1){return logSoftmax(x,axis)}};LogSoftmax2.className="logSoftmax";serialization_exports.registerClass(LogSoftmax2);var Swish=class extends Activation{apply(x,alpha=1){return tidy(()=>sigmoid(x.mul(alpha)).mul(x))}};Swish.className="swish";serialization_exports.registerClass(Swish);function serializeActivation(activation2){return activation2.getClassName()}function deserializeActivation(config2,customObjects={}){return deserializeKerasObject(config2,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"activation")}function getActivation(identifier){if(identifier==null){let config2={};return config2.className="linear",config2.config={},deserializeActivation(config2)}if(typeof identifier=="string"){let config2={};return config2.className=identifier,config2.config={},deserializeActivation(config2)}else return identifier instanceof Activation?identifier:deserializeActivation(identifier)}function assertObjectArgs(args){if(args!=null&&typeof args!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${args}`)}var Regularizer=class extends serialization_exports.Serializable{},L1L2=class extends Regularizer{constructor(args){super();assertObjectArgs(args),this.l1=args==null||args.l1==null?.01:args.l1,this.l2=args==null||args.l2==null?.01:args.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(x){return tidy(()=>{let regularization=zeros([1]);return this.hasL1&&(regularization=add2(regularization,sum2(mul(this.l1,abs(x))))),this.hasL2&&(regularization=add2(regularization,sum2(mul(this.l2,square3(x))))),regularization.asScalar()})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(cls,config2){return new cls({l1:config2.l1,l2:config2.l2})}};L1L2.className="L1L2";serialization_exports.registerClass(L1L2);function l1(args){return assertObjectArgs(args),new L1L2({l1:args!=null?args.l1:null,l2:0})}function l2(args){return assertObjectArgs(args),new L1L2({l2:args!=null?args.l2:null,l1:0})}var REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP={l1l2:"L1L2"};function serializeRegularizer(constraint){return serializeKerasObject(constraint)}function deserializeRegularizer(config2,customObjects={}){return deserializeKerasObject(config2,serialization_exports.SerializationMap.getMap().classNameMap,customObjects,"regularizer")}function getRegularizer(identifier){if(identifier==null)return null;if(typeof identifier=="string"){let config2={className:identifier in REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP?REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier]:identifier,config:{}};return deserializeRegularizer(config2)}else return identifier instanceof Regularizer?identifier:deserializeRegularizer(identifier)}var ReLU=class extends Layer{constructor(args){super(args==null?{}:args);this.supportsMasking=!0,args!=null&&(this.maxValue=args.maxValue)}call(inputs,kwargs){inputs=getExactlyOneTensor(inputs);let output=relu(inputs);return this.maxValue!=null&&(output=clipByValue(output,0,this.maxValue)),output}computeOutputShape(inputShape){return inputShape}getConfig(){let config2={maxValue:this.maxValue},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};ReLU.className="ReLU";serialization_exports.registerClass(ReLU);var LeakyReLU=class extends Layer{constructor(args){super(args==null?{}:args);this.DEFAULT_ALPHA=.3,args==null&&(args={}),this.alpha=args.alpha==null?this.DEFAULT_ALPHA:args.alpha}call(inputs,kwargs){let x=getExactlyOneTensor(inputs);return leakyRelu(x,this.alpha)}computeOutputShape(inputShape){return inputShape}getConfig(){let config2={alpha:this.alpha},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};LeakyReLU.className="LeakyReLU";serialization_exports.registerClass(LeakyReLU);var PReLU=class extends Layer{constructor(args){super(args==null?{}:args);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",args==null&&(args={}),this.supportsMasking=!0,this.alphaInitializer=getInitializer(args.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=getRegularizer(args.alphaRegularizer),this.alphaConstraint=getConstraint(args.alphaConstraint),args.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(args.sharedAxes))this.sharedAxes=args.sharedAxes;else if(typeof args.sharedAxes=="number")this.sharedAxes=[args.sharedAxes];else throw new ValueError(`Expected sharedAxes to be a number or an array of numbers, but got ${args.sharedAxes}`)}build(inputShape){inputShape=getExactlyOneShape(inputShape);let paramShape=inputShape.slice(1);if(this.sharedAxes!=null)for(let i of this.sharedAxes)paramShape[i-1]=1;this.alpha=this.addWeight("alpha",paramShape,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let axes={};if(this.sharedAxes!=null)for(let i=1;i(checkDataFormat(dataFormat),dataFormat==="channelsFirst"?transpose(x,[0,2,3,1]):x))}function preprocessConv3DInput(x,dataFormat){return tidy(()=>(checkDataFormat(dataFormat),dataFormat==="channelsFirst"?transpose(x,[0,2,3,4,1]):x))}function conv1dWithBias(x,kernel,bias,strides=1,padding="valid",dataFormat,dilationRate=1){return tidy(()=>{if(dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat),x.shape.length!==3)throw new ValueError(`The input of a conv1dWithBias operation should be 3, but is ${x.shape.length} instead.`);if(kernel.shape.length!==3)throw new ValueError(`The kernel for a conv1dWithBias operation should be 3, but is ${kernel.shape.length} instead`);if(bias!=null&&bias.shape.length!==1)throw new ValueError(`The bias for a conv1dWithBias operation should be 1, but is ${kernel.shape.length} instead`);if(dataFormat==="channelsFirst"&&(x=transpose(x,[0,2,1])),padding==="causal")throw new NotImplementedError("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let y=conv1d(x,kernel,strides,padding==="same"?"same":"valid","NWC",dilationRate);return bias!=null&&(y=biasAdd(y,bias)),y})}function conv2dWithBiasActivation(x,kernel,bias,strides=[1,1],padding="valid",dataFormat,dilationRate,activation2=null){return tidy(()=>{if(dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat),x.rank!==3&&x.rank!==4)throw new ValueError(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${x.rank}.`);if(kernel.rank!==3&&kernel.rank!==4)throw new ValueError(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${x.rank}.`);let y=preprocessConv2DInput(x,dataFormat);if(padding==="causal")throw new NotImplementedError("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return y=fused_ops_exports.conv2d({x:y,filter:kernel,strides,pad:padding==="same"?"same":"valid",dilations:dilationRate,dataFormat:"NHWC",bias,activation:activation2}),dataFormat==="channelsFirst"&&(y=transpose(y,[0,3,1,2])),y})}function conv3dWithBias(x,kernel,bias,strides=[1,1,1],padding="valid",dataFormat,dilationRate){return tidy(()=>{if(dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat),x.rank!==4&&x.rank!==5)throw new ValueError(`conv3dWithBias expects input to be of rank 4 or 5, but received ${x.rank}.`);if(kernel.rank!==4&&kernel.rank!==5)throw new ValueError(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${x.rank}.`);let y=preprocessConv3DInput(x,dataFormat);if(padding==="causal")throw new NotImplementedError("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return y=conv3d(y,kernel,strides,padding==="same"?"same":"valid","NDHWC",dilationRate),bias!=null&&(y=biasAdd(y,bias)),dataFormat==="channelsFirst"&&(y=transpose(y,[0,4,1,2,3])),y})}var BaseConv=class extends Layer{constructor(rank,args){super(args);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",BaseConv.verifyArgs(args),this.rank=rank,assertPositiveInteger(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new NotImplementedError(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=normalizeArray(args.kernelSize,rank,"kernelSize"),this.strides=normalizeArray(args.strides==null?1:args.strides,rank,"strides"),this.padding=args.padding==null?"valid":args.padding,checkPaddingMode(this.padding),this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),this.activation=getActivation(args.activation),this.useBias=args.useBias==null?!0:args.useBias,this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=getConstraint(args.biasConstraint),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.activityRegularizer=getRegularizer(args.activityRegularizer),this.dilationRate=normalizeArray(args.dilationRate==null?1:args.dilationRate,rank,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new ValueError(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new ValueError(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new ValueError(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(args){if(assert2("kernelSize"in args,"required key 'kernelSize' not in config"),typeof args.kernelSize!="number"&&!checkArrayTypeAndLength(args.kernelSize,"number",1,3))throw new ValueError(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(args.kernelSize)}.`)}getConfig(){let config2={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:serializeActivation(this.activation),useBias:this.useBias,biasInitializer:serializeInitializer(this.biasInitializer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),biasConstraint:serializeConstraint(this.biasConstraint)},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}},Conv=class extends BaseConv{constructor(rank,args){super(rank,args);this.kernel=null,Conv.verifyArgs(args),this.filters=args.filters,assertPositiveInteger(this.filters,"filters"),this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=getConstraint(args.kernelConstraint),this.kernelRegularizer=getRegularizer(args.kernelRegularizer)}build(inputShape){inputShape=getExactlyOneShape(inputShape);let channelAxis=this.dataFormat==="channelsFirst"?1:inputShape.length-1;if(inputShape[channelAxis]==null)throw new ValueError(`The channel dimension of the input should be defined. Found ${inputShape[channelAxis]}`);let inputDim=inputShape[channelAxis],kernelShape=this.kernelSize.concat([inputDim,this.filters]);this.kernel=this.addWeight("kernel",kernelShape,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[channelAxis]:inputDim}}],this.built=!0}call(inputs,kwargs){return tidy(()=>{inputs=getExactlyOneTensor(inputs);let outputs,biasValue=this.bias==null?null:this.bias.read(),fusedActivationName=mapActivationToFusedKernel(this.activation.getClassName());if(fusedActivationName!=null&&this.rank===2)outputs=conv2dWithBiasActivation(inputs,this.kernel.read(),biasValue,this.strides,this.padding,this.dataFormat,this.dilationRate,fusedActivationName);else{if(this.rank===1)outputs=conv1dWithBias(inputs,this.kernel.read(),biasValue,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)outputs=conv2dWithBiasActivation(inputs,this.kernel.read(),biasValue,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)outputs=conv3dWithBias(inputs,this.kernel.read(),biasValue,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new NotImplementedError("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(outputs=this.activation.apply(outputs))}return outputs})}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let newSpace=[],space=this.dataFormat==="channelsLast"?inputShape.slice(1,inputShape.length-1):inputShape.slice(2);for(let i=0;i 0 but got ${JSON.stringify(args.filters)}`)}},Conv2D2=class extends Conv{constructor(args){super(2,args);Conv2D2.verifyArgs(args)}getConfig(){let config2=super.getConfig();return delete config2.rank,config2}static verifyArgs(args){if(typeof args.kernelSize!="number"&&!checkArrayTypeAndLength(args.kernelSize,"number",1,2))throw new ValueError(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(args.kernelSize)}.`)}};Conv2D2.className="Conv2D";serialization_exports.registerClass(Conv2D2);var Conv3D2=class extends Conv{constructor(args){super(3,args);Conv3D2.verifyArgs(args)}getConfig(){let config2=super.getConfig();return delete config2.rank,config2}static verifyArgs(args){if(typeof args.kernelSize!="number"&&!(Array.isArray(args.kernelSize)&&(args.kernelSize.length===1||args.kernelSize.length===3)))throw new ValueError(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(args.kernelSize)}.`)}};Conv3D2.className="Conv3D";serialization_exports.registerClass(Conv3D2);var Conv2DTranspose=class extends Conv2D2{constructor(args){super(args);if(this.inputSpec=[new InputSpec({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new ValueError(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(inputShape){if(inputShape=getExactlyOneShape(inputShape),inputShape.length!==4)throw new ValueError("Input should have rank 4; Received input shape: "+JSON.stringify(inputShape));let channelAxis=this.dataFormat==="channelsFirst"?1:inputShape.length-1;if(inputShape[channelAxis]==null)throw new ValueError("The channel dimension of the inputs should be defined. Found `None`.");let inputDim=inputShape[channelAxis],kernelShape=this.kernelSize.concat([this.filters,inputDim]);this.kernel=this.addWeight("kernel",kernelShape,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new InputSpec({ndim:4,axes:{[channelAxis]:inputDim}})],this.built=!0}call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);if(input2.shape.length!==4)throw new ValueError(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${input2.shape.length}`);let inputShape=input2.shape,batchSize=inputShape[0],hAxis,wAxis;this.dataFormat==="channelsFirst"?(hAxis=2,wAxis=3):(hAxis=1,wAxis=2);let height=inputShape[hAxis],width=inputShape[wAxis],kernelH=this.kernelSize[0],kernelW=this.kernelSize[1],strideH=this.strides[0],strideW=this.strides[1],outHeight=deconvLength(height,strideH,kernelH,this.padding),outWidth=deconvLength(width,strideW,kernelW,this.padding),outputShape=[batchSize,outHeight,outWidth,this.filters];this.dataFormat!=="channelsLast"&&(input2=transpose(input2,[0,2,3,1]));let outputs=conv2dTranspose(input2,this.kernel.read(),outputShape,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(outputs=transpose(outputs,[0,3,1,2])),this.bias!=null&&(outputs=biasAdd(outputs,this.bias.read(),this.dataFormat)),this.activation!=null&&(outputs=this.activation.apply(outputs)),outputs})}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let outputShape=inputShape.slice(),channelAxis,heightAxis,widthAxis;this.dataFormat==="channelsFirst"?(channelAxis=1,heightAxis=2,widthAxis=3):(channelAxis=3,heightAxis=1,widthAxis=2);let kernelH=this.kernelSize[0],kernelW=this.kernelSize[1],strideH=this.strides[0],strideW=this.strides[1];return outputShape[channelAxis]=this.filters,outputShape[heightAxis]=deconvLength(outputShape[heightAxis],strideH,kernelH,this.padding),outputShape[widthAxis]=deconvLength(outputShape[widthAxis],strideW,kernelW,this.padding),outputShape}getConfig(){let config2=super.getConfig();return delete config2.dilationRate,config2}};Conv2DTranspose.className="Conv2DTranspose";serialization_exports.registerClass(Conv2DTranspose);var SeparableConv=class extends Conv{constructor(rank,config2){super(rank,config2);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,config2.filters==null)throw new ValueError("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(config2.kernelInitializer!=null||config2.kernelRegularizer!=null||config2.kernelConstraint!=null)throw new ValueError("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(config2.padding!=null&&config2.padding!=="same"&&config2.padding!=="valid")throw new ValueError(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(config2.padding)}`);this.depthMultiplier=config2.depthMultiplier==null?1:config2.depthMultiplier,this.depthwiseInitializer=getInitializer(config2.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=getRegularizer(config2.depthwiseRegularizer),this.depthwiseConstraint=getConstraint(config2.depthwiseConstraint),this.pointwiseInitializer=getInitializer(config2.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=getRegularizer(config2.pointwiseRegularizer),this.pointwiseConstraint=getConstraint(config2.pointwiseConstraint)}build(inputShape){if(inputShape=getExactlyOneShape(inputShape),inputShape.length{inputs=getExactlyOneTensor(inputs);let output;if(this.rank===1)throw new NotImplementedError("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(inputs=transpose(inputs,[0,2,3,1])),output=separableConv2d(inputs,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(output=biasAdd(output,this.bias.read(),this.dataFormat)),this.activation!=null&&(output=this.activation.apply(output)),this.dataFormat==="channelsFirst"&&(output=transpose(output,[0,3,1,2])),output})}getConfig(){let config2=super.getConfig();return delete config2.rank,delete config2.kernelInitializer,delete config2.kernelRegularizer,delete config2.kernelConstraint,config2.depthwiseInitializer=serializeInitializer(this.depthwiseInitializer),config2.pointwiseInitializer=serializeInitializer(this.pointwiseInitializer),config2.depthwiseRegularizer=serializeRegularizer(this.depthwiseRegularizer),config2.pointwiseRegularizer=serializeRegularizer(this.pointwiseRegularizer),config2.depthwiseConstraint=serializeConstraint(this.depthwiseConstraint),config2.pointwiseConstraint=serializeConstraint(this.pointwiseConstraint),config2}};SeparableConv.className="SeparableConv";var SeparableConv2D=class extends SeparableConv{constructor(args){super(2,args)}};SeparableConv2D.className="SeparableConv2D";serialization_exports.registerClass(SeparableConv2D);var Conv1D=class extends Conv{constructor(args){super(1,args);Conv1D.verifyArgs(args),this.inputSpec=[{ndim:3}]}getConfig(){let config2=super.getConfig();return delete config2.rank,delete config2.dataFormat,config2}static verifyArgs(args){if(typeof args.kernelSize!="number"&&!checkArrayTypeAndLength(args.kernelSize,"number",1,1))throw new ValueError(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(args.kernelSize)}.`)}};Conv1D.className="Conv1D";serialization_exports.registerClass(Conv1D);var Cropping2D=class extends Layer{constructor(args){super(args);typeof args.cropping=="number"?this.cropping=[[args.cropping,args.cropping],[args.cropping,args.cropping]]:typeof args.cropping[0]=="number"?this.cropping=[[args.cropping[0],args.cropping[0]],[args.cropping[1],args.cropping[1]]]:this.cropping=args.cropping,this.dataFormat=args.dataFormat===void 0?"channelsLast":args.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(inputShape){return this.dataFormat==="channelsFirst"?[inputShape[0],inputShape[1],inputShape[2]-this.cropping[0][0]-this.cropping[0][1],inputShape[3]-this.cropping[1][0]-this.cropping[1][1]]:[inputShape[0],inputShape[1]-this.cropping[0][0]-this.cropping[0][1],inputShape[2]-this.cropping[1][0]-this.cropping[1][1],inputShape[3]]}call(inputs,kwargs){return tidy(()=>{if(inputs=getExactlyOneTensor(inputs),this.dataFormat==="channelsLast"){let hSliced=sliceAlongAxis(inputs,this.cropping[0][0],inputs.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return sliceAlongAxis(hSliced,this.cropping[1][0],inputs.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let hSliced=sliceAlongAxis(inputs,this.cropping[0][0],inputs.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return sliceAlongAxis(hSliced,this.cropping[1][0],inputs.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let config2={cropping:this.cropping,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};Cropping2D.className="Cropping2D";serialization_exports.registerClass(Cropping2D);var UpSampling2D=class extends Layer{constructor(args){super(args);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=args.size==null?this.DEFAULT_SIZE:args.size,this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),this.interpolation=args.interpolation==null?"nearest":args.interpolation,checkInterpolationFormat(this.interpolation)}computeOutputShape(inputShape){if(this.dataFormat==="channelsFirst"){let height=inputShape[2]==null?null:this.size[0]*inputShape[2],width=inputShape[3]==null?null:this.size[1]*inputShape[3];return[inputShape[0],inputShape[1],height,width]}else{let height=inputShape[1]==null?null:this.size[0]*inputShape[1],width=inputShape[2]==null?null:this.size[1]*inputShape[2];return[inputShape[0],height,width,inputShape[3]]}}call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs),inputShape=input2.shape;if(this.dataFormat==="channelsFirst"){input2=transpose(input2,[0,2,3,1]);let height=this.size[0]*inputShape[2],width=this.size[1]*inputShape[3],resized=this.interpolation==="nearest"?input2.resizeNearestNeighbor([height,width]):input2.resizeBilinear([height,width]);return transpose(resized,[0,3,1,2])}else{let height=this.size[0]*inputShape[1],width=this.size[1]*inputShape[2];return this.interpolation==="nearest"?input2.resizeNearestNeighbor([height,width]):input2.resizeBilinear([height,width])}})}getConfig(){let config2={size:this.size,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};UpSampling2D.className="UpSampling2D";serialization_exports.registerClass(UpSampling2D);function depthwiseConv2d4(x,depthwiseKernel,strides=[1,1],padding="valid",dataFormat,dilationRate){return tidy(()=>{dataFormat==null&&(dataFormat=imageDataFormat()),checkDataFormat(dataFormat);let y=preprocessConv2DInput(x,dataFormat);if(x.rank!==4)throw new ValueError(`Input for depthwiseConv2d is required to be 4-D, but is instead ${x.rank}-D`);if(depthwiseKernel.rank!==4)throw new ValueError(`depthwiseKernel is required to be 4-D, but is instead ${depthwiseKernel.rank}-D`);return y=depthwiseConv2d(y,depthwiseKernel,strides,padding==="same"?"same":"valid","NHWC",dilationRate),dataFormat==="channelsFirst"&&(y=transpose(y,[0,3,1,2])),y})}var DepthwiseConv2D=class extends BaseConv{constructor(args){super(2,args);this.depthwiseKernel=null,this.depthMultiplier=args.depthMultiplier==null?1:args.depthMultiplier,this.depthwiseInitializer=getInitializer(args.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=getConstraint(args.depthwiseConstraint),this.depthwiseRegularizer=getRegularizer(args.depthwiseRegularizer)}build(inputShape){if(inputShape=getExactlyOneShape(inputShape),inputShape.length<4)throw new ValueError(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(inputShape)}.`);let channelAxis=this.dataFormat==="channelsFirst"?1:3;if(inputShape[channelAxis]==null||inputShape[channelAxis]<0)throw new ValueError(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${inputShape[channelAxis]}).`);let inputDim=inputShape[channelAxis],depthwiseKernelShape=[this.kernelSize[0],this.kernelSize[1],inputDim,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",depthwiseKernelShape,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[inputDim*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(inputs,kwargs){return tidy(()=>{inputs=getExactlyOneTensor(inputs);let outputs=depthwiseConv2d4(inputs,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(outputs=biasAdd(outputs,this.bias.read(),this.dataFormat)),this.activation!=null&&(outputs=this.activation.apply(outputs)),outputs})}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let rows=this.dataFormat==="channelsFirst"?inputShape[2]:inputShape[1],cols=this.dataFormat==="channelsFirst"?inputShape[3]:inputShape[2],outFilters=this.dataFormat==="channelsFirst"?inputShape[1]*this.depthMultiplier:inputShape[3]*this.depthMultiplier,outRows=convOutputLength(rows,this.kernelSize[0],this.padding,this.strides[0]),outCols=convOutputLength(cols,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[inputShape[0],outFilters,outRows,outCols]:[inputShape[0],outRows,outCols,outFilters]}getConfig(){let config2=super.getConfig();return config2.depthMultiplier=this.depthMultiplier,config2.depthwiseInitializer=serializeInitializer(this.depthwiseInitializer),config2.depthwiseRegularizer=serializeRegularizer(this.depthwiseRegularizer),config2.depthwiseConstraint=serializeConstraint(this.depthwiseRegularizer),config2}};DepthwiseConv2D.className="DepthwiseConv2D";serialization_exports.registerClass(DepthwiseConv2D);function standardizeArgs(inputs,initialState,constants,numConstants){if(Array.isArray(inputs)){if(initialState!=null||constants!=null)throw new ValueError("When inputs is an array, neither initialState or constants should be provided");numConstants!=null&&(constants=inputs.slice(inputs.length-numConstants,inputs.length),inputs=inputs.slice(0,inputs.length-numConstants)),inputs.length>1&&(initialState=inputs.slice(1,inputs.length)),inputs=inputs[0]}function toListOrNull(x){return x==null||Array.isArray(x)?x:[x]}return initialState=toListOrNull(initialState),constants=toListOrNull(constants),{inputs,initialState,constants}}function rnn(stepFunction,inputs,initialStates,goBackwards=!1,mask,constants,unroll=!1,needPerStepOutputs=!1){return tidy(()=>{let ndim=inputs.shape.length;if(ndim<3)throw new ValueError(`Input should be at least 3D, but is ${ndim}D.`);let axes=[1,0].concat(range5(2,ndim));if(inputs=transpose(inputs,axes),constants!=null)throw new NotImplementedError("The rnn() functoin of the deeplearn.js backend does not support constants yet.");unroll&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),mask!=null&&(mask=mask.asType("bool").asType("float32"),mask.rank===ndim-1&&(mask=expandDims(mask,-1)),mask=transpose(mask,axes)),goBackwards&&(inputs=reverse(inputs,0),mask!=null&&(mask=reverse(mask,0)));let perStepOutputs=[],lastOutput,states=initialStates,timeSteps=inputs.shape[0],perStepInputs=unstack(inputs),perStepMasks;mask!=null&&(perStepMasks=unstack(mask));for(let t=0;tstepFunction(currentInput,states));if(mask==null)lastOutput=stepOutputs[0],states=stepOutputs[1];else{let maskedOutputs=tidy(()=>{let stepMask=perStepMasks[t],negStepMask=onesLike(stepMask).sub(stepMask),output=stepOutputs[0].mul(stepMask).add(states[0].mul(negStepMask)),newStates=states.map((state,i)=>stepOutputs[1][i].mul(stepMask).add(state.mul(negStepMask)));return{output,newStates}});lastOutput=maskedOutputs.output,states=maskedOutputs.newStates}needPerStepOutputs&&perStepOutputs.push(lastOutput)}let outputs;return needPerStepOutputs&&(outputs=stack(perStepOutputs,1)),[lastOutput,outputs,states]})}var RNN=class extends Layer{constructor(args){super(args);let cell;if(args.cell==null)throw new ValueError("cell property is missing for the constructor of RNN.");if(Array.isArray(args.cell)?cell=new StackedRNNCells({cells:args.cell}):cell=args.cell,cell.stateSize==null)throw new ValueError("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=cell,this.returnSequences=args.returnSequences==null?!1:args.returnSequences,this.returnState=args.returnState==null?!1:args.returnState,this.goBackwards=args.goBackwards==null?!1:args.goBackwards,this._stateful=args.stateful==null?!1:args.stateful,this.unroll=args.unroll==null?!1:args.unroll,this.supportsMasking=!0,this.inputSpec=[new InputSpec({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let numStates=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return range5(0,numStates).map(x=>null)}else return this.states_}setStates(states){this.states_=states}computeOutputShape(inputShape){isArrayOfShapes(inputShape)&&(inputShape=inputShape[0]),inputShape=inputShape;let stateSize=this.cell.stateSize;Array.isArray(stateSize)||(stateSize=[stateSize]);let outputDim=stateSize[0],outputShape;if(this.returnSequences?outputShape=[inputShape[0],inputShape[1],outputDim]:outputShape=[inputShape[0],outputDim],this.returnState){let stateShape=[];for(let dim of stateSize)stateShape.push([inputShape[0],dim]);return[outputShape].concat(stateShape)}else return outputShape}computeMask(inputs,mask){return tidy(()=>{Array.isArray(mask)&&(mask=mask[0]);let outputMask=this.returnSequences?mask:null;if(this.returnState){let stateMask=this.states.map(s=>null);return[outputMask].concat(stateMask)}else return outputMask})}get states(){if(this.states_==null){let numStates=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,output=[];for(let i=0;ispec.shape[spec.shape.length-1]),stateSize))throw new ValueError(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=stateSize.map(dim=>new InputSpec({shape:[null,dim]}));this.stateful&&this.resetStates()}resetStates(states,training=!1){tidy(()=>{if(!this.stateful)throw new AttributeError("Cannot call resetStates() on an RNN Layer that is not stateful.");let batchSize=this.inputSpec[0].shape[0];if(batchSize==null)throw new ValueError("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(dim=>zeros([batchSize,dim])):this.states_=[zeros([batchSize,this.cell.stateSize])];else if(states==null)dispose(this.states_),this.keptStates!=null&&(dispose(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(dim=>zeros([batchSize,dim])):this.states_[0]=zeros([batchSize,this.cell.stateSize]);else{if(Array.isArray(states)||(states=[states]),states.length!==this.states_.length)throw new ValueError(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${states.length} state value(s). Input received: ${states}`);training===!0?this.keptStates.push(this.states_.slice()):dispose(this.states_);for(let index=0;indexkeep(state.clone()))})}apply(inputs,kwargs){let initialState=kwargs==null?null:kwargs.initialState,constants=kwargs==null?null:kwargs.constants;kwargs==null&&(kwargs={});let standardized=standardizeArgs(inputs,initialState,constants,this.numConstants);inputs=standardized.inputs,initialState=standardized.initialState,constants=standardized.constants;let additionalInputs=[],additionalSpecs=[];if(initialState!=null){kwargs.initialState=initialState,additionalInputs=additionalInputs.concat(initialState),this.stateSpec=[];for(let state of initialState)this.stateSpec.push(new InputSpec({shape:state.shape}));additionalSpecs=additionalSpecs.concat(this.stateSpec)}if(constants!=null&&(kwargs.constants=constants,additionalInputs=additionalInputs.concat(constants),this.numConstants=constants.length),additionalInputs[0]instanceof SymbolicTensor){let fullInput=[inputs].concat(additionalInputs),fullInputSpec=this.inputSpec.concat(additionalSpecs),originalInputSpec=this.inputSpec;this.inputSpec=fullInputSpec;let output=super.apply(fullInput,kwargs);return this.inputSpec=originalInputSpec,output}else return super.apply(inputs,kwargs)}call(inputs,kwargs){return tidy(()=>{let mask=kwargs==null?null:kwargs.mask,training=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;inputs=getExactlyOneTensor(inputs),initialState==null&&(this.stateful?initialState=this.states_:initialState=this.getInitialState(inputs));let numStates=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(initialState.length!==numStates)throw new ValueError(`RNN Layer has ${numStates} state(s) but was passed ${initialState.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let cellCallKwargs={training},rnnOutputs=rnn((inputs2,states2)=>{let outputs2=this.cell.call([inputs2].concat(states2),cellCallKwargs);return[outputs2[0],outputs2.slice(1)]},inputs,initialState,this.goBackwards,mask,null,this.unroll,this.returnSequences),lastOutput=rnnOutputs[0],outputs=rnnOutputs[1],states=rnnOutputs[2];this.stateful&&this.resetStates(states,training);let output=this.returnSequences?outputs:lastOutput;return this.returnState?[output].concat(states):output})}getInitialState(inputs){return tidy(()=>{let initialState=zeros(inputs.shape);return initialState=sum2(initialState,[1,2]),initialState=expandDims5(initialState),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(dim=>dim>1?tile5(initialState,[1,dim]):initialState):this.cell.stateSize>1?[tile5(initialState,[1,this.cell.stateSize])]:[initialState]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(value){super.setFastWeightInitDuringBuild(value),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(value)}getConfig(){let baseConfig=super.getConfig(),config2={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(config2.numConstants=this.numConstants);let cellConfig=this.cell.getConfig();return this.getClassName()===RNN.className&&(config2.cell={className:this.cell.getClassName(),config:cellConfig}),Object.assign({},cellConfig,baseConfig,config2)}static fromConfig(cls,config2,customObjects={}){let cellConfig=config2.cell,cell=deserialize(cellConfig,customObjects);return new cls(Object.assign(config2,{cell}))}};RNN.className="RNN";serialization_exports.registerClass(RNN);var RNNCell=class extends Layer{},SimpleRNNCell=class extends RNNCell{constructor(args){super(args);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=args.units,assertPositiveInteger(this.units,"units"),this.activation=getActivation(args.activation==null?this.DEFAULT_ACTIVATION:args.activation),this.useBias=args.useBias==null?!0:args.useBias,this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=getInitializer(args.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=getRegularizer(args.kernelRegularizer),this.recurrentRegularizer=getRegularizer(args.recurrentRegularizer),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.kernelConstraint=getConstraint(args.kernelConstraint),this.recurrentConstraint=getConstraint(args.recurrentConstraint),this.biasConstraint=getConstraint(args.biasConstraint),this.dropout=min5([1,max5([0,args.dropout==null?0:args.dropout])]),this.recurrentDropout=min5([1,max5([0,args.recurrentDropout==null?0:args.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(inputShape){inputShape=getExactlyOneShape(inputShape),this.kernel=this.addWeight("kernel",[inputShape[inputShape.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(inputs,kwargs){return tidy(()=>{if(inputs=inputs,inputs.length!==2)throw new ValueError(`SimpleRNNCell expects 2 input Tensors, got ${inputs.length}.`);let prevOutput=inputs[1];inputs=inputs[0];let training=kwargs.training==null?!1:kwargs.training;0onesLike(inputs),rate:this.dropout,training})),0onesLike(prevOutput),rate:this.recurrentDropout,training}));let h,dpMask=this.dropoutMask,recDpMask=this.recurrentDropoutMask;dpMask!=null?h=dot2(mul(inputs,dpMask),this.kernel.read()):h=dot2(inputs,this.kernel.read()),this.bias!=null&&(h=biasAdd(h,this.bias.read())),recDpMask!=null&&(prevOutput=mul(prevOutput,recDpMask));let output=add2(h,dot2(prevOutput,this.recurrentKernel.read()));return this.activation!=null&&(output=this.activation.apply(output)),[output,output]})}getConfig(){let baseConfig=super.getConfig(),config2={units:this.units,activation:serializeActivation(this.activation),useBias:this.useBias,kernelInitializer:serializeInitializer(this.kernelInitializer),recurrentInitializer:serializeInitializer(this.recurrentInitializer),biasInitializer:serializeInitializer(this.biasInitializer),kernelRegularizer:serializeRegularizer(this.kernelRegularizer),recurrentRegularizer:serializeRegularizer(this.recurrentRegularizer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),kernelConstraint:serializeConstraint(this.kernelConstraint),recurrentConstraint:serializeConstraint(this.recurrentConstraint),biasConstraint:serializeConstraint(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},baseConfig,config2)}};SimpleRNNCell.className="SimpleRNNCell";serialization_exports.registerClass(SimpleRNNCell);var SimpleRNN=class extends RNN{constructor(args){args.cell=new SimpleRNNCell(args),super(args)}call(inputs,kwargs){return tidy(()=>{this.cell.dropoutMask!=null&&(dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let mask=kwargs==null?null:kwargs.mask,training=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;return super.call(inputs,{mask,training,initialState})})}static fromConfig(cls,config2){return new cls(config2)}};SimpleRNN.className="SimpleRNN";serialization_exports.registerClass(SimpleRNN);var GRUCell=class extends RNNCell{constructor(args){super(args);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",args.resetAfter)throw new ValueError("GRUCell does not support reset_after parameter set to true.");this.units=args.units,assertPositiveInteger(this.units,"units"),this.activation=getActivation(args.activation===void 0?this.DEFAULT_ACTIVATION:args.activation),this.recurrentActivation=getActivation(args.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:args.recurrentActivation),this.useBias=args.useBias==null?!0:args.useBias,this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=getInitializer(args.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=getRegularizer(args.kernelRegularizer),this.recurrentRegularizer=getRegularizer(args.recurrentRegularizer),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.kernelConstraint=getConstraint(args.kernelConstraint),this.recurrentConstraint=getConstraint(args.recurrentConstraint),this.biasConstraint=getConstraint(args.biasConstraint),this.dropout=min5([1,max5([0,args.dropout==null?0:args.dropout])]),this.recurrentDropout=min5([1,max5([0,args.recurrentDropout==null?0:args.recurrentDropout])]),this.implementation=args.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(inputShape){inputShape=getExactlyOneShape(inputShape);let inputDim=inputShape[inputShape.length-1];this.kernel=this.addWeight("kernel",[inputDim,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(inputs,kwargs){return tidy(()=>{if(inputs=inputs,inputs.length!==2)throw new ValueError(`GRUCell expects 2 input Tensors (inputs, h, c), got ${inputs.length}.`);let training=kwargs.training==null?!1:kwargs.training,hTMinus1=inputs[1];inputs=inputs[0],0onesLike(inputs),rate:this.dropout,training,count:3})),0onesLike(hTMinus1),rate:this.recurrentDropout,training,count:3}));let dpMask=this.dropoutMask,recDpMask=this.recurrentDropoutMask,z,r,hh;0{this.cell.dropoutMask!=null&&(dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let mask=kwargs==null?null:kwargs.mask,training=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;return super.call(inputs,{mask,training,initialState})})}static fromConfig(cls,config2){return config2.implmentation===0&&(config2.implementation=1),new cls(config2)}};GRU.className="GRU";serialization_exports.registerClass(GRU);var LSTMCell=class extends RNNCell{constructor(args){super(args);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=args.units,assertPositiveInteger(this.units,"units"),this.activation=getActivation(args.activation===void 0?this.DEFAULT_ACTIVATION:args.activation),this.recurrentActivation=getActivation(args.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:args.recurrentActivation),this.useBias=args.useBias==null?!0:args.useBias,this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=getInitializer(args.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=args.unitForgetBias,this.kernelRegularizer=getRegularizer(args.kernelRegularizer),this.recurrentRegularizer=getRegularizer(args.recurrentRegularizer),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.kernelConstraint=getConstraint(args.kernelConstraint),this.recurrentConstraint=getConstraint(args.recurrentConstraint),this.biasConstraint=getConstraint(args.biasConstraint),this.dropout=min5([1,max5([0,args.dropout==null?0:args.dropout])]),this.recurrentDropout=min5([1,max5([0,args.recurrentDropout==null?0:args.recurrentDropout])]),this.implementation=args.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(inputShape){var _a;inputShape=getExactlyOneShape(inputShape);let inputDim=inputShape[inputShape.length-1];this.kernel=this.addWeight("kernel",[inputDim,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let biasInitializer;if(this.useBias){if(this.unitForgetBias){let capturedBiasInit=this.biasInitializer,capturedUnits=this.units;biasInitializer=new(_a=class extends Initializer{apply(shape,dtype){let bI=capturedBiasInit.apply([capturedUnits]),bF=new Ones().apply([capturedUnits]),bCAndH=capturedBiasInit.apply([capturedUnits*2]);return concatAlongFirstAxis(concatAlongFirstAxis(bI,bF),bCAndH)}},_a.className="CustomInit",_a)}else biasInitializer=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,biasInitializer,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(inputs,kwargs){return tidy(()=>{let training=kwargs.training==null?!1:kwargs.training;if(inputs=inputs,inputs.length!==3)throw new ValueError(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${inputs.length}.`);let hTMinus1=inputs[1],cTMinus1=inputs[2];inputs=inputs[0],0onesLike(inputs),rate:this.dropout,training,count:4})),0onesLike(hTMinus1),rate:this.recurrentDropout,training,count:4}));let dpMask=this.dropoutMask,recDpMask=this.recurrentDropoutMask,i,f,c,o;0{this.cell.dropoutMask!=null&&(dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let mask=kwargs==null?null:kwargs.mask,training=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;return super.call(inputs,{mask,training,initialState})})}static fromConfig(cls,config2){return config2.implmentation===0&&(config2.implementation=1),new cls(config2)}};LSTM.className="LSTM";serialization_exports.registerClass(LSTM);var StackedRNNCells=class extends RNNCell{constructor(args){super(args);this.cells=args.cells}get stateSize(){let stateSize=[];for(let cell of this.cells.slice().reverse())Array.isArray(cell.stateSize)?stateSize.push(...cell.stateSize):stateSize.push(cell.stateSize);return stateSize}call(inputs,kwargs){return tidy(()=>{inputs=inputs;let states=inputs.slice(1),nestedStates=[];for(let cell of this.cells.slice().reverse())Array.isArray(cell.stateSize)?nestedStates.push(states.splice(0,cell.stateSize.length)):nestedStates.push(states.splice(0,1));nestedStates.reverse();let newNestedStates=[],callInputs;for(let i=0;i{nameScope(`RNNCell_${i}`,()=>{cell.build(inputShape),Array.isArray(cell.stateSize)?outputDim=cell.stateSize[0]:outputDim=cell.stateSize,inputShape=[inputShape[0],outputDim]})}),this.built=!0}getConfig(){let baseConfig=super.getConfig(),getCellConfig=cell=>({className:cell.getClassName(),config:cell.getConfig()}),config2={cells:this.cells.map(getCellConfig)};return Object.assign({},baseConfig,config2)}static fromConfig(cls,config2,customObjects={}){let cells=[];for(let cellConfig of config2.cells)cells.push(deserialize(cellConfig,customObjects));return new cls({cells})}get trainableWeights(){if(!this.trainable)return[];let weights=[];for(let cell of this.cells)weights.push(...cell.trainableWeights);return weights}get nonTrainableWeights(){let weights=[];for(let cell of this.cells)weights.push(...cell.nonTrainableWeights);if(!this.trainable){let trainableWeights=[];for(let cell of this.cells)trainableWeights.push(...cell.trainableWeights);return trainableWeights.concat(weights)}return weights}getWeights(){let weights=[];for(let cell of this.cells)weights.push(...cell.weights);return batchGetValue(weights)}setWeights(weights){let tuples=[];for(let cell of this.cells){let numParams=cell.weights.length,inputWeights=weights.splice(numParams);for(let i=0;idropout2(ones4(),rate),createMask=()=>inTrainPhase(droppedInputs,ones4,training);return!count2||count2<=1?keep(createMask().clone()):Array(count2).fill(void 0).map(createMask).map(m=>keep(m.clone()))}var __rest=function(s,e){var t={};for(var p2 in s)Object.prototype.hasOwnProperty.call(s,p2)&&e.indexOf(p2)<0&&(t[p2]=s[p2]);if(s!=null&&typeof Object.getOwnPropertySymbols=="function")for(var i=0,p2=Object.getOwnPropertySymbols(s);i{if(this.cell.dropoutMask!=null&&(dispose(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(dispose(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),kwargs&&kwargs.constants)throw new ValueError("ConvRNN2D cell does not support constants");let mask=kwargs==null?null:kwargs.mask,training=kwargs==null?null:kwargs.training,initialState=kwargs==null?null:kwargs.initialState;return super.call(inputs,{mask,training,initialState})})}computeOutputShape(inputShape){let outShape=this.computeSingleOutputShape(inputShape);return this.returnSequences||(outShape=[outShape[0],...outShape.slice(2)]),this.returnState&&(outShape=[outShape,...Array(2).fill([inputShape[0],...outShape.slice(-3)])]),outShape}getInitialState(inputs){return tidy(()=>{let{stateSize}=this.cell,inputShape=inputs.shape,outputShape=this.computeSingleOutputShape(inputShape),stateShape=[outputShape[0],...outputShape.slice(2)],initialState=zeros(stateShape);return Array.isArray(stateSize)?Array(stateSize.length).fill(initialState):[initialState]})}resetStates(states,training=!1){tidy(()=>{if(!this.stateful)throw new AttributeError("Cannot call resetStates() on an RNN Layer that is not stateful.");let inputShape=this.inputSpec[0].shape,outputShape=this.computeSingleOutputShape(inputShape),stateShape=[outputShape[0],...outputShape.slice(2)];if(inputShape[0]==null)throw new ValueError("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>zeros(stateShape)):this.states_=[zeros(stateShape)];else if(states==null)dispose(this.states_),this.keptStates!=null&&(dispose(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>zeros(stateShape)):this.states_[0]=zeros(stateShape);else{if(Array.isArray(states)||(states=[states]),states.length!==this.states_.length)throw new ValueError(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${states.length} state value(s). Input received: ${states}`);training?this.keptStates.push(this.states_.slice()):dispose(this.states_);for(let index=0;indexkeep(state.clone()))})}computeSingleOutputShape(inputShape){let{dataFormat,filters,kernelSize,padding,strides,dilationRate}=this.cell,isChannelsFirst=dataFormat==="channelsFirst",h=inputShape[isChannelsFirst?3:2],w=inputShape[isChannelsFirst?4:3],hOut=convOutputLength(h,kernelSize[0],padding,strides[0],dilationRate[0]),wOut=convOutputLength(w,kernelSize[1],padding,strides[1],dilationRate[1]);return[...inputShape.slice(0,2),...isChannelsFirst?[filters,hOut,wOut]:[hOut,wOut,filters]]}};ConvRNN2D.className="ConvRNN2D";var ConvLSTM2DCell=class extends LSTMCell{constructor(args){let{filters,kernelSize,strides,padding,dataFormat,dilationRate}=args;super(Object.assign({},args,{units:filters}));this.filters=filters,assertPositiveInteger(this.filters,"filters"),this.kernelSize=normalizeArray(kernelSize,2,"kernelSize"),this.kernelSize.forEach(size=>assertPositiveInteger(size,"kernelSize")),this.strides=normalizeArray(strides||1,2,"strides"),this.strides.forEach(stride=>assertPositiveInteger(stride,"strides")),this.padding=padding||"valid",checkPaddingMode(this.padding),this.dataFormat=dataFormat||"channelsLast",checkDataFormat(this.dataFormat),this.dilationRate=normalizeArray(dilationRate||1,2,"dilationRate"),this.dilationRate.forEach(rate=>assertPositiveInteger(rate,"dilationRate"))}build(inputShape){var _a;inputShape=getExactlyOneShape(inputShape);let channelAxis=this.dataFormat==="channelsFirst"?1:inputShape.length-1;if(inputShape[channelAxis]==null)throw new ValueError(`The channel dimension of the input should be defined. Found ${inputShape[channelAxis]}`);let inputDim=inputShape[channelAxis],numOfKernels=4,kernelShape=this.kernelSize.concat([inputDim,this.filters*numOfKernels]);this.kernel=this.addWeight("kernel",kernelShape,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let recurrentKernelShape=this.kernelSize.concat([this.filters,this.filters*numOfKernels]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",recurrentKernelShape,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let biasInitializer;if(this.unitForgetBias){let init2=this.biasInitializer,filters=this.filters;biasInitializer=new(_a=class extends Initializer{apply(shape,dtype){let biasI=init2.apply([filters]),biasF=ones2([filters]),biasCAndO=init2.apply([filters*2]);return concatenate([biasI,biasF,biasCAndO])}},_a.className="CustomInit",_a)}else biasInitializer=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*numOfKernels],null,biasInitializer,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(inputs,kwargs){return tidy(()=>{if(inputs.length!==3)throw new ValueError(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${inputs.length}.`);let training=kwargs.training||!1,x=inputs[0],hTMinus1=inputs[1],cTMinus1=inputs[2],numOfKernels=4;0onesLike(x),rate:this.dropout,training,count:numOfKernels}));let dropoutMask=this.dropoutMask,applyDropout=(x2,mask,index)=>!mask||!mask[index]?x2:mul(mask[index],x2),xI=applyDropout(x,dropoutMask,0),xF=applyDropout(x,dropoutMask,1),xC=applyDropout(x,dropoutMask,2),xO=applyDropout(x,dropoutMask,3);0onesLike(hTMinus1),rate:this.recurrentDropout,training,count:numOfKernels}));let recDropoutMask=this.recurrentDropoutMask,hI=applyDropout(hTMinus1,recDropoutMask,0),hF=applyDropout(hTMinus1,recDropoutMask,1),hC=applyDropout(hTMinus1,recDropoutMask,2),hO=applyDropout(hTMinus1,recDropoutMask,3),kernelChannelAxis=3,[kernelI,kernelF,kernelC,kernelO]=split(this.kernel.read(),numOfKernels,kernelChannelAxis),[biasI,biasF,biasC,biasO]=this.useBias?split(this.bias.read(),numOfKernels):[null,null,null,null];xI=this.inputConv(xI,kernelI,biasI,this.padding),xF=this.inputConv(xF,kernelF,biasF,this.padding),xC=this.inputConv(xC,kernelC,biasC,this.padding),xO=this.inputConv(xO,kernelO,biasO,this.padding);let[recKernelI,recKernelF,recKernelC,recKernelO]=split(this.recurrentKernel.read(),numOfKernels,kernelChannelAxis);hI=this.recurrentConv(hI,recKernelI),hF=this.recurrentConv(hF,recKernelF),hC=this.recurrentConv(hC,recKernelC),hO=this.recurrentConv(hO,recKernelO);let i=this.recurrentActivation.apply(add2(xI,hI)),f=this.recurrentActivation.apply(add2(xF,hF)),c=add2(mul(f,cTMinus1),mul(i,this.activation.apply(add2(xC,hC)))),h=mul(this.recurrentActivation.apply(add2(xO,hO)),this.activation.apply(c));return[h,h,c]})}getConfig(){let _a=super.getConfig(),{units:_}=_a,baseConfig=__rest(_a,["units"]),config2={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},baseConfig,config2)}inputConv(x,w,b,padding){let out=conv2d(x,w,this.strides,padding||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return b?biasAdd(out,b,this.dataFormat):out}recurrentConv(x,w){return conv2d(x,w,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};ConvLSTM2DCell.className="ConvLSTM2DCell";serialization_exports.registerClass(ConvLSTM2DCell);var ConvLSTM2D=class extends ConvRNN2D{constructor(args){let cell=new ConvLSTM2DCell(args);super(Object.assign({},args,{cell}))}static fromConfig(cls,config2){return new cls(config2)}};ConvLSTM2D.className="ConvLSTM2D";serialization_exports.registerClass(ConvLSTM2D);var Dropout=class extends Layer{constructor(args){super(args);this.rate=Math.max(Math.min(args.rate,1),0),this.noiseShape=args.noiseShape,this.seed=args.seed,this.supportsMasking=!0}getNoiseShape(input2){if(this.noiseShape==null)return this.noiseShape;let inputShape=input2.shape,noiseShape=[];for(let i=0;i{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);if(0dropout2(input2,this.rate,noiseShape,this.seed),()=>input2,training)}return inputs})}getConfig(){let config2={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}dispose(){return super.dispose()}};Dropout.className="Dropout";serialization_exports.registerClass(Dropout);var SpatialDropout1D=class extends Dropout{constructor(args){super(args);this.inputSpec=[{ndim:3}]}getNoiseShape(input2){let inputShape=input2.shape;return[inputShape[0],1,inputShape[2]]}};SpatialDropout1D.className="SpatialDropout1D";serialization_exports.registerClass(SpatialDropout1D);var Dense=class extends Layer{constructor(args){super(args);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",args.batchInputShape==null&&args.inputShape==null&&args.inputDim!=null){let batchSize=null;args.batchSize!=null&&(batchSize=args.batchSize),this.batchInputShape=[batchSize,args.inputDim]}this.units=args.units,assertPositiveInteger(this.units,"units"),this.activation=getActivation(args.activation),args.useBias!=null&&(this.useBias=args.useBias),this.kernelInitializer=getInitializer(args.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=getInitializer(args.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=getConstraint(args.kernelConstraint),this.biasConstraint=getConstraint(args.biasConstraint),this.kernelRegularizer=getRegularizer(args.kernelRegularizer),this.biasRegularizer=getRegularizer(args.biasRegularizer),this.activityRegularizer=getRegularizer(args.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(inputShape){inputShape=getExactlyOneShape(inputShape);let inputLastDim=inputShape[inputShape.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[inputLastDim,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:inputLastDim}}],this.built=!0}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let outputShape=inputShape.slice();return outputShape[outputShape.length-1]=this.units,outputShape}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs),fusedActivationName=mapActivationToFusedKernel(this.activation.getClassName()),output;return fusedActivationName!=null?output=dot2(input2,this.kernel.read(),fusedActivationName,this.bias?this.bias.read():null):(output=dot2(input2,this.kernel.read()),this.bias!=null&&(output=biasAdd(output,this.bias.read())),this.activation!=null&&(output=this.activation.apply(output))),output})}getConfig(){let config2={units:this.units,activation:serializeActivation(this.activation),useBias:this.useBias,kernelInitializer:serializeInitializer(this.kernelInitializer),biasInitializer:serializeInitializer(this.biasInitializer),kernelRegularizer:serializeRegularizer(this.kernelRegularizer),biasRegularizer:serializeRegularizer(this.biasRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),kernelConstraint:serializeConstraint(this.kernelConstraint),biasConstraint:serializeConstraint(this.biasConstraint)},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};Dense.className="Dense";serialization_exports.registerClass(Dense);var Flatten=class extends Layer{constructor(args){args=args||{},super(args),this.inputSpec=[{minNDim:3}],this.dataFormat=args.dataFormat}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);for(let dim of inputShape.slice(1))if(dim==null)throw new ValueError(`The shape of the input to "Flatten" is not fully defined (got ${inputShape.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[inputShape[0],arrayProd(inputShape,1)]}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);if(this.dataFormat==="channelsFirst"&&input2.rank>1){let permutation=[0];for(let i=2;i{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);return this.activation.apply(input2)})}getConfig(){let config2={activation:serializeActivation(this.activation)},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};Activation2.className="Activation";serialization_exports.registerClass(Activation2);var RepeatVector=class extends Layer{constructor(args){super(args);this.n=args.n,this.inputSpec=[{ndim:2}]}computeOutputShape(inputShape){return[inputShape[0],this.n,inputShape[1]]}call(inputs,kwargs){return tidy(()=>(inputs=getExactlyOneTensor(inputs),repeat(inputs,this.n)))}getConfig(){let config2={n:this.n},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};RepeatVector.className="RepeatVector";serialization_exports.registerClass(RepeatVector);var Reshape2=class extends Layer{constructor(args){super(args);this.targetShape=args.targetShape;for(let i=0;i{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs),inputShape=input2.shape,outputShape=inputShape.slice(0,1).concat(this.fixUnknownDimension(inputShape.slice(1),this.targetShape));return input2.reshape(outputShape)})}getConfig(){let config2={targetShape:this.targetShape},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};Reshape2.className="Reshape";serialization_exports.registerClass(Reshape2);var Permute=class extends Layer{constructor(args){super(args);if(args.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(args.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${args.dims} instead.`);let expectedSortedIndices=range5(1,args.dims.length+1);if(!util_exports.arraysEqual(args.dims.slice().sort(),expectedSortedIndices))throw new Error("Invalid permutation `dims`: "+JSON.stringify(args.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=args.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new InputSpec({ndim:this.dims.length+1})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let outputShape=inputShape.slice();return this.dims.forEach((dim,i)=>{outputShape[i+1]=inputShape[dim]}),outputShape}call(inputs,kwargs){return transpose(getExactlyOneTensor(inputs),this.dimsIncludingBatch)}getConfig(){let config2={dims:this.dims},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};Permute.className="Permute";serialization_exports.registerClass(Permute);var Masking=class extends Layer{constructor(args){super(args==null?{}:args);this.supportsMasking=!0,args!=null?this.maskValue=args.maskValue==null?0:args.maskValue:this.maskValue=0}computeOutputShape(inputShape){return inputShape}getConfig(){let baseConfig=super.getConfig(),config2={maskValue:this.maskValue};return Object.assign(config2,baseConfig),config2}computeMask(inputs,mask){let input2=getExactlyOneTensor(inputs),axis=-1;return any(notEqual(input2,this.maskValue),axis)}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs),axis=-1,keepDims=!0,booleanMask=any(notEqual(input2,this.maskValue),axis,keepDims);return input2.mul(booleanMask.asType(input2.dtype))})}};Masking.className="Masking";serialization_exports.registerClass(Masking);var Embedding=class extends Layer{constructor(args){super(args);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",args.batchInputShape==null&&args.inputShape==null){let batchSize=null;args.batchSize!=null&&(batchSize=args.batchSize),args.inputLength==null?this.batchInputShape=[batchSize,null]:this.batchInputShape=[batchSize].concat(toList(args.inputLength))}this.inputDim=args.inputDim,assertPositiveInteger(this.inputDim,"inputDim"),this.outputDim=args.outputDim,assertPositiveInteger(this.outputDim,"outputDim"),this.embeddingsInitializer=getInitializer(args.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=getRegularizer(args.embeddingsRegularizer),this.activityRegularizer=getRegularizer(args.activityRegularizer),this.embeddingsConstraint=getConstraint(args.embeddingsConstraint),this.maskZero=args.maskZero,this.supportsMasking=args.maskZero,this.inputLength=args.inputLength}build(inputShape){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(inputShape){}computeMask(inputs,mask){return tidy(()=>this.maskZero?(inputs=getExactlyOneTensor(inputs),notEqual(inputs,zerosLike(inputs))):null)}computeOutputShape(inputShape){if(inputShape=getExactlyOneShape(inputShape),this.inputLength==null)return[...inputShape,this.outputDim];let inLens=toList(this.inputLength);if(inLens.length!==inputShape.length-1)throw new ValueError(`"inputLength" is ${this.inputLength}, but received input shape has shape ${inputShape}`);{let i=0;for(let k=0;k{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);return input2.dtype!=="int32"&&(input2=cast5(input2,"int32")),gather2(this.embeddings.read(),input2.as1D()).reshape(getExactlyOneShape(this.computeOutputShape(input2.shape)))})}getConfig(){let config2={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:serializeInitializer(this.embeddingsInitializer),embeddingsRegularizer:serializeRegularizer(this.embeddingsRegularizer),activityRegularizer:serializeRegularizer(this.activityRegularizer),embeddingsConstraint:serializeConstraint(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};Embedding.className="Embedding";serialization_exports.registerClass(Embedding);var Merge=class extends Layer{constructor(args){super(args||{});this.supportsMasking=!0}mergeFunction(inputs){throw new NotImplementedError}computeElementwiseOpOutputShape(shape1,shape2){if(shape1==null||shape2==null)return null;if(shape1.length1)throw new ValueError(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(inputShape)}.`);let outputShape=inputShape[0]==null?null:inputShape[0].slice(1);for(let i=1;ishape.length);inputShape.indexOf(null)===-1&&unique4(allRanks).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(inputs,kwargs){return tidy(()=>{if(inputs=inputs,this.reshapeRequired){let reshapedInputs=[],inputDims=inputs.map(input2=>input2.rank);if(inputDims.indexOf(null)===-1){let maxNDim=max5(inputDims);for(let x of inputs){let xNDim=x.rank;for(let k=0;k1){let dims=range5(1,xNDim).concat([0]);reshapedInputs.push(transpose(x,dims)),transposed=!0}else reshapedInputs.push(x)}let y=this.mergeFunction(reshapedInputs),yNDim=y.rank;if(transposed){if(yNDim==null){let yShape=y.shape,yNDim2=yShape.length,batchSize=yShape[yNDim2-1],newShape=[batchSize].concat(yShape.slice(0,yShape.length-1));y=transpose(y.reshape([-1,batchSize]),[1,0]).reshape(newShape)}else if(yNDim>1){let dims=[yNDim-1].concat(range5(0,yNDim-1));y=transpose(y,dims)}}return y}}else return this.mergeFunction(inputs)})}computeOutputShape(inputShape){inputShape=inputShape;let outputShape;inputShape[0]==null?outputShape=null:outputShape=inputShape[0].slice(1);for(let i=1;i{if(mask==null)return null;if(!Array.isArray(mask))throw new ValueError("`mask` should be an Array");if(!Array.isArray(inputs))throw new ValueError("`inputs` should be an Array");if(mask.length!==inputs.length)throw new ValueError(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${inputs.length} vs ${mask.length})`);if(mask.every(m=>m==null))return null;mask=mask.map(m=>m==null?m:expandDims(m,0));let output=mask[0];for(let i=1;i{let output=inputs[0].clone();for(let i=1;i{let output=inputs[0].clone();for(let i=1;i{let output=inputs[0].clone();for(let i=1;i{let output=inputs[0];for(let i=1;i{let output=inputs[0];for(let i=1;i1)throw new ValueError("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(inputShape))}mergeFunction(inputs){return tidy(()=>concatenate(inputs,this.axis))}computeOutputShape(inputShape){if(!(Array.isArray(inputShape)&&Array.isArray(inputShape[0])))throw new ValueError("A `Concatenate` layer should be called on a list of inputs.");let inputShapes=inputShape,outputShape=inputShapes[0].slice(),axis=this.axis<0?outputShape.length+this.axis:this.axis;for(let shape of inputShapes.slice(1)){if(outputShape[axis]==null||shape[axis]==null){outputShape[axis]=null;break}outputShape[axis]+=shape[axis]}return outputShape}computeMask(inputs,mask){if(mask==null)return null;if(!Array.isArray(mask))throw new ValueError("`mask` should be an array for Concatenate");if(!Array.isArray(inputs))throw new ValueError("`inputs` should be an array for Concatenate");if(mask.length!==inputs.length)throw new ValueError(`Mismatch in the length of mask (${mask.length}) and the legnth of inputs (${inputs.length})`);return tidy(()=>{let allNullMasks=!0;if(mask.forEach(m=>{if(m!=null){allNullMasks=!1;return}}),allNullMasks)return null;let outputMasks=[];for(let i=0;i3||y.shape.length>3)throw new NotImplementedError("batchDot is not implemented for tensors of 4D or higher rank yet");if(util_exports.assert(x.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${x.shape.length}`),util_exports.assert(x.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${y.shape.length}`),typeof axes=="number"&&(axes=[axes,axes]),x.dtype==="complex64"||y.dtype==="complex64")throw new NotImplementedError("batchDot is not implemented for complex64-type Tensors yet.");let xNDim=x.shape.length,yNDim=y.shape.length;axes==null&&(axes=[xNDim-1,yNDim-2]);let axesArray=axes;return tidy(()=>{let diff;if(xNDim>yNDim){diff=xNDim-yNDim;let diffShape=[];for(let i=0;ixNDim){diff=yNDim-xNDim;let diffShape=[];for(let i=0;i0){let idx;xNDim>yNDim?idx=xNDim+yNDim-3:idx=xNDim-1;let squeezeAxes=[];for(let i=idx;i"A `Dot` layer should be called on a list of exactly 2 inputs.");let shape1=inputShape[0],shape2=inputShape[1];if(shape1.length>3||shape2.length>3)throw new NotImplementedError("Dot layer does not support tensors of 4D or higher rank yet.");let axes=this.interpretAxes(shape1,shape2);if(shape1[axes[0]]!==shape2[axes[1]])throw new ValueError(`Dimension incompatibility: ${shape1[axes[0]]} !== ${shape2[axes[1]]}`)}mergeFunction(inputs){if(inputs.length!==2)throw new ValueError(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${inputs.length} input(s).`);let x1=inputs[0],x2=inputs[1],axes;return Array.isArray(this.axes)?axes=this.axes.map((axis,i)=>interpretAxis(axis,inputs[i].shape.length)):axes=[interpretAxis(this.axes,x1.shape.length),interpretAxis(this.axes,x2.shape.length)],this.normalize&&(x1=l2Normalize(x1,axes[0]),x2=l2Normalize(x2,axes[1])),batchDot(x1,x2,axes)}interpretAxes(shape1,shape2){let axes;return Array.isArray(this.axes)?axes=this.axes:axes=[interpretAxis(this.axes,shape1.length),interpretAxis(this.axes,shape2.length)],axes}computeOutputShape(inputShape){util_exports.assert(Array.isArray(inputShape)&&inputShape.length===2&&Array.isArray(inputShape[0])&&Array.isArray(inputShape[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let shape1=inputShape[0].slice(),shape2=inputShape[1].slice();if(shape1.length>3||shape2.length>3)throw new NotImplementedError("Dot layer does not support tensors of 4D or higher rank yet.");let axes=this.interpretAxes(shape1,shape2);shape1.splice(axes[0],1),shape2.splice(axes[1],1),shape2.splice(0,1);let outputShape=shape1.concat(shape2);return outputShape.length===1&&outputShape.push(1),outputShape}computeMask(inputs,mask){return null}getConfig(){let config2={axes:this.axes,normalize:this.normalize},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};Dot.className="Dot";serialization_exports.registerClass(Dot);var GaussianNoise=class extends Layer{constructor(args){super(args);this.supportsMasking=!0,this.stddev=args.stddev}computeOutputShape(inputShape){return inputShape}getConfig(){let baseConfig=super.getConfig(),config2={stddev:this.stddev};return Object.assign(config2,baseConfig),config2}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);return inTrainPhase(()=>randomNormal2(input2.shape,0,this.stddev).add(input2),()=>input2,kwargs.training||!1)})}};GaussianNoise.className="GaussianNoise";serialization_exports.registerClass(GaussianNoise);var GaussianDropout=class extends Layer{constructor(args){super(args);this.supportsMasking=!0,this.rate=args.rate}computeOutputShape(inputShape){return inputShape}getConfig(){let baseConfig=super.getConfig(),config2={rate:this.rate};return Object.assign(config2,baseConfig),config2}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs);let input2=getExactlyOneTensor(inputs);return this.rate>0&&this.rate<1?inTrainPhase(()=>{let stddev=Math.sqrt(this.rate/(1-this.rate));return input2.mul(randomNormal2(input2.shape,1,stddev))},()=>input2,kwargs.training||!1):input2})}};GaussianDropout.className="GaussianDropout";serialization_exports.registerClass(GaussianDropout);var AlphaDropout=class extends Layer{constructor(args){super(args);this.supportsMasking=!0,this.rate=args.rate,this.noiseShape=args.noiseShape}_getNoiseShape(inputs){return this.noiseShape||getExactlyOneTensor(inputs).shape}computeOutputShape(inputShape){return inputShape}getConfig(){let baseConfig=super.getConfig(),config2={rate:this.rate};return Object.assign(config2,baseConfig),config2}call(inputs,kwargs){return tidy(()=>{if(this.rate<1&&this.rate>0){let noiseShape=this._getNoiseShape(inputs);return inTrainPhase(()=>{let input2=getExactlyOneTensor(inputs),alpha=1.6732632423543772,scale2=1.0507009873554805,alphaP=-alpha*scale2,keptIdx=greaterEqual(randomUniform(noiseShape),this.rate);keptIdx=cast5(keptIdx,"float32");let a=((1-this.rate)*(1+this.rate*alphaP**2))**-.5,b=-a*alphaP*this.rate;return input2.mul(keptIdx).add(keptIdx.add(-1).mul(alphaP)).mul(a).add(b)},()=>getExactlyOneTensor(inputs),kwargs.training||!1)}return inputs})}};AlphaDropout.className="AlphaDropout";serialization_exports.registerClass(AlphaDropout);function batchNormalization(x,mean4,variance,beta,gamma,epsilon3=.001){let out;if(x.rank===2)out=batchNorm2d(x,mean4,variance,beta,gamma,epsilon3);else if(x.rank===3)out=batchNorm3d(x,mean4,variance,beta,gamma,epsilon3);else if(x.rank===4)out=batchNorm4d(x,mean4,variance,beta,gamma,epsilon3);else throw new NotImplementedError(`batchNormalization is not implemented for array of rank ${x.rank} yet`);return out}function regularNormalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon3=.001){return tidy(()=>{let meanAndVariance=moments(x,reductionAxes),mean4=meanAndVariance.mean,variance=meanAndVariance.variance;return[batchNormalization(x,mean4,variance,beta,gamma,epsilon3),mean4,variance]})}function broadcastNormalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon3=.001){return tidy(()=>{let meanAndVariance=moments(x,reductionAxes),mean4=meanAndVariance.mean,variance=meanAndVariance.variance,targetShape=[];for(let axis of range5(0,x.rank))reductionAxes.indexOf(axis)!==-1?targetShape.push(1):targetShape.push(x.shape[axis]);let broadcastMean=mean4.reshape(targetShape),broadcastVariance=variance.reshape(targetShape),broadcastGamma=gamma==null?null:gamma.reshape(targetShape),broadcastBeta=beta==null?null:beta.reshape(targetShape);return[batchNormalization(x,broadcastMean,broadcastVariance,broadcastBeta,broadcastGamma,epsilon3),mean4,variance]})}function normalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon3=.001){return util_exports.arraysEqual(reductionAxes.slice().sort(),range5(0,x.rank-1))?regularNormalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon3):broadcastNormalizeBatchInTraining(x,gamma,beta,reductionAxes,epsilon3)}var BatchNormalization=class extends Layer{constructor(args){args==null&&(args={}),super(args),this.supportsMasking=!0,this.axis=args.axis==null?-1:args.axis,this.momentum=args.momentum==null?.99:args.momentum,this.epsilon=args.epsilon==null?.001:args.epsilon,this.center=args.center==null?!0:args.center,this.scale=args.scale==null?!0:args.scale,this.betaInitializer=getInitializer(args.betaInitializer||"zeros"),this.gammaInitializer=getInitializer(args.gammaInitializer||"ones"),this.movingMeanInitializer=getInitializer(args.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=getInitializer(args.movingVarianceInitializer||"ones"),this.betaConstraint=getConstraint(args.betaConstraint),this.gammaConstraint=getConstraint(args.gammaConstraint),this.betaRegularizer=getRegularizer(args.betaRegularizer),this.gammaRegularizer=getRegularizer(args.gammaRegularizer)}build(inputShape){inputShape=getExactlyOneShape(inputShape);let axis=this.axis>=0?this.axis:this.axis+inputShape.length,dim=inputShape[axis];if(dim==null)throw new ValueError(`Axis ${axis} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(inputShape)}.`);this.inputSpec=[new InputSpec({ndim:inputShape.length,axes:{[axis]:dim}})];let shape=[dim];this.scale&&(this.gamma=this.addWeight("gamma",shape,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",shape,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",shape,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",shape,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(inputs,kwargs){return tidy(()=>{let training=kwargs.training==null?!1:kwargs.training,input2=getExactlyOneTensor(inputs),inputShape=input2.shape,ndim=inputShape.length,reductionAxes=range5(0,ndim),axis=this.axis>=0?this.axis:this.axis+ndim;reductionAxes.splice(axis,1);let broadcastShape=pyListRepeat(1,ndim);broadcastShape[axis]=inputShape[axis];let sortedReductionAxes=reductionAxes.slice();sortedReductionAxes.sort();let needsBroadcasting=!util_exports.arraysEqual(sortedReductionAxes,range5(0,ndim).slice(0,ndim-1)),normalizeInference=()=>{if(needsBroadcasting){let broadcastMovingMean=this.movingMean.read().reshape(broadcastShape),broadcastMovingVariance=this.movingVariance.read().reshape(broadcastShape),broadcastBeta=this.center?this.beta.read().reshape(broadcastShape):null,broadcastGamma=this.scale?this.gamma.read().reshape(broadcastShape):null;return batchNormalization(input2,broadcastMovingMean,broadcastMovingVariance,broadcastBeta,broadcastGamma,this.epsilon)}else return batchNormalization(input2,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!training)return normalizeInference();let[normedTraining,mean4,variance]=normalizeBatchInTraining(input2,this.gamma.read(),this.beta.read(),reductionAxes,this.epsilon),doMovingAverage=(variable2,value,momentum)=>{tidy(()=>{let decay=1-momentum,origValue=variable2.read(),updateDelta=origValue.sub(value).mul(decay);variable2.write(origValue.sub(updateDelta))})};return(()=>{doMovingAverage(this.movingMean,mean4,this.momentum),doMovingAverage(this.movingVariance,variance,this.momentum)})(),normedTraining})}getConfig(){let config2={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:serializeInitializer(this.betaInitializer),gammaInitializer:serializeInitializer(this.gammaInitializer),movingMeanInitializer:serializeInitializer(this.movingMeanInitializer),movingVarianceInitializer:serializeInitializer(this.movingVarianceInitializer),betaRegularizer:serializeRegularizer(this.betaRegularizer),gammaRegularizer:serializeRegularizer(this.gammaRegularizer),betaConstraint:serializeConstraint(this.betaConstraint),gammaConstraint:serializeConstraint(this.gammaConstraint)},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};BatchNormalization.className="BatchNormalization";serialization_exports.registerClass(BatchNormalization);var LayerNormalization=class extends Layer{constructor(args){if(args==null&&(args={}),super(args),this.axis=args.axis==null?-1:args.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let axis of this.axis)if(!Number.isInteger(axis))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=args.epsilon==null?.001:args.epsilon,this.center=args.center==null?!0:args.center,this.scale=args.scale==null?!0:args.scale,this.betaInitializer=getInitializer(args.betaInitializer||"zeros"),this.gammaInitializer=getInitializer(args.gammaInitializer||"ones"),this.betaRegularizer=getRegularizer(args.betaRegularizer),this.gammaRegularizer=getRegularizer(args.gammaRegularizer),this.supportsMasking=!0}build(inputShape){inputShape=getExactlyOneShape(inputShape);let nDims=inputShape.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let i=0;i=nDims)throw new Error(`Invalid axis: ${axis}`);if(this.axis.length!==unique4(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let paramShape=this.axis.map(axis=>inputShape[axis]),trainable=!0;this.scale?this.gamma=this.addWeight("gamma",paramShape,"float32",this.gammaInitializer,this.gammaRegularizer,trainable):this.gamma=null,this.center?this.beta=this.addWeight("beta",paramShape,"float32",this.betaInitializer,this.betaRegularizer,trainable):this.beta=null,this.built=!0}call(inputs,kwargs){let input2=getExactlyOneTensor(inputs),inputShape=input2.shape,nDims=inputShape.length;return tidy(()=>{let keepDims=!0,{mean:mean4,variance}=moments(input2,this.axis,keepDims),broadcastShape=pyListRepeat(1,nDims);for(let dim of this.axis)broadcastShape[dim]=inputShape[dim];let broadcast=v=>v!=null&&v.shape.length!==nDims&&this.axis!==[nDims-1]?v.reshape(broadcastShape):v,scale2=broadcast(this.gamma.read()),offset=broadcast(this.beta.read()),momentsTiling=[],scaleOffsetTiling=[];for(let i=0;i{if(x.rank!==4)throw new ValueError(`temporalPadding expects input tensor to be 4-D, but received a ${x.rank}-D tensor.`);if(padding==null&&(padding=[[1,1],[1,1]]),padding.length!==2||padding[0].length!==2||padding[1].length!==2)throw new ValueError("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(dataFormat==null&&(dataFormat=imageDataFormat()),dataFormat!=="channelsLast"&&dataFormat!=="channelsFirst")throw new ValueError(`Unknown data format: ${dataFormat}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let pattern;return dataFormat==="channelsFirst"?pattern=[[0,0],[0,0],padding[0],padding[1]]:pattern=[[0,0],padding[0],padding[1],[0,0]],pad(x,pattern)})}var ZeroPadding2D=class extends Layer{constructor(args){if(args==null&&(args={}),super(args),this.dataFormat=args.dataFormat==null?imageDataFormat():args.dataFormat,args.padding==null)this.padding=[[1,1],[1,1]];else if(typeof args.padding=="number")this.padding=[[args.padding,args.padding],[args.padding,args.padding]];else{if(args.padding=args.padding,args.padding.length!==2)throw new ValueError(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${args.padding.length} array.`);let heightPadding,widthPadding;if(typeof args.padding[0]=="number")heightPadding=[args.padding[0],args.padding[0]],widthPadding=[args.padding[1],args.padding[1]];else{if(args.padding=args.padding,args.padding[0].length!==2)throw new ValueError(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${args.padding[0].length} array.`);if(heightPadding=args.padding[0],args.padding[1].length!==2)throw new ValueError(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${args.padding[1].length} array.`);widthPadding=args.padding[1]}this.padding=[heightPadding,widthPadding]}this.inputSpec=[new InputSpec({ndim:4})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let rows,cols;return this.dataFormat==="channelsFirst"?(inputShape[2]!=null&&inputShape[2]>=0?rows=inputShape[2]+this.padding[0][0]+this.padding[0][1]:rows=null,inputShape[3]!=null&&inputShape[3]>=0?cols=inputShape[3]+this.padding[1][0]+this.padding[1][1]:cols=null,[inputShape[0],inputShape[1],rows,cols]):(inputShape[1]!=null&&inputShape[1]>=0?rows=inputShape[1]+this.padding[0][0]+this.padding[0][1]:rows=null,inputShape[2]!=null&&inputShape[2]>=0?cols=inputShape[2]+this.padding[1][0]+this.padding[1][1]:cols=null,[inputShape[0],rows,cols,inputShape[3]])}call(inputs,kwargs){return tidy(()=>spatial2dPadding(getExactlyOneTensor(inputs),this.padding,this.dataFormat))}getConfig(){let config2={padding:this.padding,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}};ZeroPadding2D.className="ZeroPadding2D";serialization_exports.registerClass(ZeroPadding2D);function pool2d(x,poolSize,strides,padding,dataFormat,poolMode){return tidy(()=>{checkDataFormat(dataFormat),checkPoolMode(poolMode),checkPaddingMode(padding),strides==null&&(strides=[1,1]),padding==null&&(padding="valid"),dataFormat==null&&(dataFormat=imageDataFormat()),poolMode==null&&(poolMode="max"),x=preprocessConv2DInput(x,dataFormat);let y,paddingString=padding==="same"?"same":"valid";return poolMode==="max"?y=maxPool(x,poolSize,strides,paddingString):y=avgPool(x,poolSize,strides,paddingString),dataFormat==="channelsFirst"&&(y=transpose(y,[0,3,1,2])),y})}function pool3d2(x,poolSize,strides,padding,dataFormat,poolMode){return tidy(()=>{checkDataFormat(dataFormat),checkPoolMode(poolMode),checkPaddingMode(padding),strides==null&&(strides=[1,1,1]),padding==null&&(padding="valid"),dataFormat==null&&(dataFormat=imageDataFormat()),poolMode==null&&(poolMode="max"),x=preprocessConv3DInput(x,dataFormat);let y,paddingString=padding==="same"?"same":"valid";return poolMode==="max"?y=maxPool3d(x,poolSize,strides,paddingString):y=avgPool3d(x,poolSize,strides,paddingString),dataFormat==="channelsFirst"&&(y=transpose(y,[0,4,1,2,3])),y})}var Pooling1D=class extends Layer{constructor(args){if(args.poolSize==null&&(args.poolSize=2),super(args),typeof args.poolSize=="number")this.poolSize=[args.poolSize];else if(Array.isArray(args.poolSize)&&args.poolSize.length===1&&typeof args.poolSize[0]=="number")this.poolSize=args.poolSize;else throw new ValueError(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(args.poolSize)}`);if(assertPositiveInteger(this.poolSize,"poolSize"),args.strides==null)this.strides=this.poolSize;else if(typeof args.strides=="number")this.strides=[args.strides];else if(Array.isArray(args.strides)&&args.strides.length===1&&typeof args.strides[0]=="number")this.strides=args.strides;else throw new ValueError(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(args.strides)}`);assertPositiveInteger(this.strides,"strides"),this.padding=args.padding==null?"valid":args.padding,checkPaddingMode(this.padding),this.inputSpec=[new InputSpec({ndim:3})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let length=convOutputLength(inputShape[1],this.poolSize[0],this.padding,this.strides[0]);return[inputShape[0],length,inputShape[2]]}call(inputs,kwargs){return tidy(()=>{this.invokeCallHook(inputs,kwargs),inputs=expandDims5(getExactlyOneTensor(inputs),2);let output=this.poolingFunction(getExactlyOneTensor(inputs),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return squeeze(output,[2])})}getConfig(){let config2={poolSize:this.poolSize,padding:this.padding,strides:this.strides},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}},MaxPooling1D=class extends Pooling1D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding),pool2d(inputs,poolSize,strides,padding,dataFormat,"max")}};MaxPooling1D.className="MaxPooling1D";serialization_exports.registerClass(MaxPooling1D);var AveragePooling1D=class extends Pooling1D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding),pool2d(inputs,poolSize,strides,padding,dataFormat,"avg")}};AveragePooling1D.className="AveragePooling1D";serialization_exports.registerClass(AveragePooling1D);var Pooling2D=class extends Layer{constructor(args){if(args.poolSize==null&&(args.poolSize=[2,2]),super(args),this.poolSize=Array.isArray(args.poolSize)?args.poolSize:[args.poolSize,args.poolSize],args.strides==null)this.strides=this.poolSize;else if(Array.isArray(args.strides)){if(args.strides.length!==2)throw new ValueError(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${args.strides.length}.`);this.strides=args.strides}else this.strides=[args.strides,args.strides];assertPositiveInteger(this.poolSize,"poolSize"),assertPositiveInteger(this.strides,"strides"),this.padding=args.padding==null?"valid":args.padding,this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),checkPaddingMode(this.padding),this.inputSpec=[new InputSpec({ndim:4})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let rows=this.dataFormat==="channelsFirst"?inputShape[2]:inputShape[1],cols=this.dataFormat==="channelsFirst"?inputShape[3]:inputShape[2];return rows=convOutputLength(rows,this.poolSize[0],this.padding,this.strides[0]),cols=convOutputLength(cols,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[inputShape[0],inputShape[1],rows,cols]:[inputShape[0],rows,cols,inputShape[3]]}call(inputs,kwargs){return tidy(()=>(this.invokeCallHook(inputs,kwargs),this.poolingFunction(getExactlyOneTensor(inputs),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let config2={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}},MaxPooling2D=class extends Pooling2D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding),pool2d(inputs,poolSize,strides,padding,dataFormat,"max")}};MaxPooling2D.className="MaxPooling2D";serialization_exports.registerClass(MaxPooling2D);var AveragePooling2D=class extends Pooling2D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding),pool2d(inputs,poolSize,strides,padding,dataFormat,"avg")}};AveragePooling2D.className="AveragePooling2D";serialization_exports.registerClass(AveragePooling2D);var Pooling3D=class extends Layer{constructor(args){if(args.poolSize==null&&(args.poolSize=[2,2,2]),super(args),this.poolSize=Array.isArray(args.poolSize)?args.poolSize:[args.poolSize,args.poolSize,args.poolSize],args.strides==null)this.strides=this.poolSize;else if(Array.isArray(args.strides)){if(args.strides.length!==3)throw new ValueError(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${args.strides.length}.`);this.strides=args.strides}else this.strides=[args.strides,args.strides,args.strides];assertPositiveInteger(this.poolSize,"poolSize"),assertPositiveInteger(this.strides,"strides"),this.padding=args.padding==null?"valid":args.padding,this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),checkPaddingMode(this.padding),this.inputSpec=[new InputSpec({ndim:5})]}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let depths=this.dataFormat==="channelsFirst"?inputShape[2]:inputShape[1],rows=this.dataFormat==="channelsFirst"?inputShape[3]:inputShape[2],cols=this.dataFormat==="channelsFirst"?inputShape[4]:inputShape[3];return depths=convOutputLength(depths,this.poolSize[0],this.padding,this.strides[0]),rows=convOutputLength(rows,this.poolSize[1],this.padding,this.strides[1]),cols=convOutputLength(cols,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[inputShape[0],inputShape[1],depths,rows,cols]:[inputShape[0],depths,rows,cols,inputShape[4]]}call(inputs,kwargs){return tidy(()=>(this.invokeCallHook(inputs,kwargs),this.poolingFunction(getExactlyOneTensor(inputs),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let config2={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}},MaxPooling3D=class extends Pooling3D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding),pool3d2(inputs,poolSize,strides,padding,dataFormat,"max")}};MaxPooling3D.className="MaxPooling3D";serialization_exports.registerClass(MaxPooling3D);var AveragePooling3D=class extends Pooling3D{constructor(args){super(args)}poolingFunction(inputs,poolSize,strides,padding,dataFormat){return checkDataFormat(dataFormat),checkPaddingMode(padding),pool3d2(inputs,poolSize,strides,padding,dataFormat,"avg")}};AveragePooling3D.className="AveragePooling3D";serialization_exports.registerClass(AveragePooling3D);var GlobalPooling1D=class extends Layer{constructor(args){super(args);this.inputSpec=[new InputSpec({ndim:3})]}computeOutputShape(inputShape){return[inputShape[0],inputShape[2]]}call(inputs,kwargs){throw new NotImplementedError}},GlobalAveragePooling1D=class extends GlobalPooling1D{constructor(args){super(args||{})}call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);return mean(input2,1)})}};GlobalAveragePooling1D.className="GlobalAveragePooling1D";serialization_exports.registerClass(GlobalAveragePooling1D);var GlobalMaxPooling1D=class extends GlobalPooling1D{constructor(args){super(args||{})}call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);return max(input2,1)})}};GlobalMaxPooling1D.className="GlobalMaxPooling1D";serialization_exports.registerClass(GlobalMaxPooling1D);var GlobalPooling2D=class extends Layer{constructor(args){super(args);this.dataFormat=args.dataFormat==null?"channelsLast":args.dataFormat,checkDataFormat(this.dataFormat),this.inputSpec=[new InputSpec({ndim:4})]}computeOutputShape(inputShape){return inputShape=inputShape,this.dataFormat==="channelsLast"?[inputShape[0],inputShape[3]]:[inputShape[0],inputShape[1]]}call(inputs,kwargs){throw new NotImplementedError}getConfig(){let config2={dataFormat:this.dataFormat},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}},GlobalAveragePooling2D=class extends GlobalPooling2D{call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);return this.dataFormat==="channelsLast"?mean(input2,[1,2]):mean(input2,[2,3])})}};GlobalAveragePooling2D.className="GlobalAveragePooling2D";serialization_exports.registerClass(GlobalAveragePooling2D);var GlobalMaxPooling2D=class extends GlobalPooling2D{call(inputs,kwargs){return tidy(()=>{let input2=getExactlyOneTensor(inputs);return this.dataFormat==="channelsLast"?max(input2,[1,2]):max(input2,[2,3])})}};GlobalMaxPooling2D.className="GlobalMaxPooling2D";serialization_exports.registerClass(GlobalMaxPooling2D);var Wrapper=class extends Layer{constructor(args){super(args);this.layer=args.layer}build(inputShape){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(value){this.layer!=null&&(this.layer.trainable=value)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(weights){this.layer.setWeights(weights)}getConfig(){let config2={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}setFastWeightInitDuringBuild(value){super.setFastWeightInitDuringBuild(value),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(value)}static fromConfig(cls,config2,customObjects={}){let layerConfig=config2.layer,layer=deserialize(layerConfig,customObjects);delete config2.layer;let newConfig={layer};return Object.assign(newConfig,config2),new cls(newConfig)}},TimeDistributed=class extends Wrapper{constructor(args){super(args);this.supportsMasking=!0}build(inputShape){if(inputShape=getExactlyOneShape(inputShape),inputShape.length<3)throw new ValueError(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(inputShape)}`);this.inputSpec=[{shape:inputShape}];let childInputShape=[inputShape[0]].concat(inputShape.slice(2));this.layer.built||(this.layer.build(childInputShape),this.layer.built=!0),super.build(inputShape)}computeOutputShape(inputShape){inputShape=getExactlyOneShape(inputShape);let childInputShape=[inputShape[0]].concat(inputShape.slice(2)),childOutputShape=this.layer.computeOutputShape(childInputShape),timesteps=inputShape[1];return[childOutputShape[0],timesteps].concat(childOutputShape.slice(1))}call(inputs,kwargs){return tidy(()=>(inputs=getExactlyOneTensor(inputs),rnn((inputs2,states)=>[getExactlyOneTensor(this.layer.call(inputs2,kwargs)),[]],inputs,[],!1,null,null,!1,!0)[1]))}};TimeDistributed.className="TimeDistributed";serialization_exports.registerClass(TimeDistributed);function checkBidirectionalMergeMode(value){checkStringTypeUnionValue(VALID_BIDIRECTIONAL_MERGE_MODES,"BidirectionalMergeMode",value)}var DEFAULT_BIDIRECTIONAL_MERGE_MODE="concat",Bidirectional=class extends Wrapper{constructor(args){super(args);let layerConfig=args.layer.getConfig(),forwDict={};forwDict.className=args.layer.getClassName(),forwDict.config=layerConfig,this.forwardLayer=deserialize(forwDict),layerConfig.goBackwards=layerConfig.goBackwards!==!0;let backDict={};if(backDict.className=args.layer.getClassName(),backDict.config=layerConfig,this.backwardLayer=deserialize(backDict),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=args.mergeMode===void 0?DEFAULT_BIDIRECTIONAL_MERGE_MODE:args.mergeMode,checkBidirectionalMergeMode(this.mergeMode),args.weights)throw new NotImplementedError("weights support is not implemented for Bidirectional layer yet.");this._stateful=args.layer.stateful,this.returnSequences=args.layer.returnSequences,this.returnState=args.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=args.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(value){this._trainable=value,this.forwardLayer!=null&&(this.forwardLayer.trainable=value),this.backwardLayer!=null&&(this.backwardLayer.trainable=value)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(weights){let numWeights=weights.length,numeightsOver2=Math.floor(numWeights/2);this.forwardLayer.setWeights(weights.slice(0,numeightsOver2)),this.backwardLayer.setWeights(weights.slice(numeightsOver2))}computeOutputShape(inputShape){let layerShapes=this.forwardLayer.computeOutputShape(inputShape);Array.isArray(layerShapes)&&Array.isArray(layerShapes[0])||(layerShapes=[layerShapes]),layerShapes=layerShapes;let outputShape,outputShapes,stateShape;return this.returnState&&(stateShape=layerShapes.slice(1)),outputShape=layerShapes[0],outputShape=outputShape,this.mergeMode==="concat"?(outputShape[outputShape.length-1]*=2,outputShapes=[outputShape]):this.mergeMode==null?outputShapes=[outputShape,outputShape.slice()]:outputShapes=[outputShape],this.returnState?this.mergeMode==null?outputShapes.concat(stateShape).concat(stateShape.slice()):[outputShape].concat(stateShape).concat(stateShape.slice()):singletonOrArray(outputShapes)}apply(inputs,kwargs){let initialState=kwargs==null?null:kwargs.initialState,constants=kwargs==null?null:kwargs.constants;kwargs==null&&(kwargs={});let standardized=standardizeArgs(inputs,initialState,constants,this.numConstants);if(inputs=standardized.inputs,initialState=standardized.initialState,constants=standardized.constants,Array.isArray(inputs)&&(initialState=inputs.slice(1),inputs=inputs[0]),(initialState==null||initialState.length===0)&&constants==null)return super.apply(inputs,kwargs);let additionalInputs=[],additionalSpecs=[];if(initialState!=null){let numStates=initialState.length;if(numStates%2>0)throw new ValueError("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");kwargs.initialState=initialState,additionalInputs.push(...initialState);let stateSpecs=initialState.map(state=>new InputSpec({shape:state.shape}));this.forwardLayer.stateSpec=stateSpecs.slice(0,numStates/2),this.backwardLayer.stateSpec=stateSpecs.slice(numStates/2),additionalSpecs.push(...stateSpecs)}if(constants!=null)throw new NotImplementedError("Support for constants in Bidirectional layers is not implemented yet.");let isSymbolicTensor=additionalInputs[0]instanceof SymbolicTensor;for(let tensor2 of additionalInputs)if(tensor2 instanceof SymbolicTensor!==isSymbolicTensor)throw new ValueError("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(isSymbolicTensor){let fullInput=[inputs].concat(additionalInputs),fullInputSpec=this.inputSpec.concat(additionalSpecs),originalInputSpec=this.inputSpec;this.inputSpec=fullInputSpec;let output=super.apply(fullInput,kwargs);return this.inputSpec=originalInputSpec,output}else return super.apply(inputs,kwargs)}call(inputs,kwargs){return tidy(()=>{let initialState=kwargs.initialState,y,yRev;if(initialState==null)y=this.forwardLayer.call(inputs,kwargs),yRev=this.backwardLayer.call(inputs,kwargs);else{let forwardState=initialState.slice(0,initialState.length/2),backwardState=initialState.slice(initialState.length/2);y=this.forwardLayer.call(inputs,Object.assign(kwargs,{initialState:forwardState})),yRev=this.backwardLayer.call(inputs,Object.assign(kwargs,{initialState:backwardState}))}let states;this.returnState&&(Array.isArray(y)&&(states=y.slice(1).concat(yRev.slice(1))),y=y[0],yRev=yRev[0]),this.returnSequences&&(yRev=reverse(yRev,1));let output;return this.mergeMode==="concat"?output=concatenate([y,yRev]):this.mergeMode==="sum"?output=add2(y,yRev):this.mergeMode==="ave"?output=mul(.5,add2(y,yRev)):this.mergeMode==="mul"?output=mul(y,yRev):this.mergeMode==null&&(output=[y,yRev]),this.returnState?this.mergeMode==null?output.concat(states):[output].concat(states):output})}resetStates(states){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(inputShape){nameScope(this.forwardLayer.name,()=>{this.forwardLayer.build(inputShape)}),nameScope(this.backwardLayer.name,()=>{this.backwardLayer.build(inputShape)}),this.built=!0}computeMask(inputs,mask){Array.isArray(mask)&&(mask=mask[0]);let outputMask;if(this.returnSequences?this.mergeMode==null?outputMask=[mask,mask]:outputMask=mask:this.mergeMode==null?outputMask=[null,null]:outputMask=null,this.returnState){let stateMask=this.forwardLayer.states.map(state=>null);return Array.isArray(outputMask)?outputMask.concat(stateMask).concat(stateMask):[outputMask].concat(stateMask).concat(stateMask)}else return outputMask}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)}setFastWeightInitDuringBuild(value){super.setFastWeightInitDuringBuild(value),this.forwardLayer!=null&&this.forwardLayer.setFastWeightInitDuringBuild(value),this.backwardLayer!=null&&this.backwardLayer.setFastWeightInitDuringBuild(value)}getConfig(){let config2={mergeMode:this.mergeMode},baseConfig=super.getConfig();return Object.assign(config2,baseConfig),config2}static fromConfig(cls,config2){let rnnLayer=deserialize(config2.layer);if(delete config2.layer,config2.numConstants!=null)throw new NotImplementedError("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");let newConfig=config2;return newConfig.layer=rnnLayer,new cls(newConfig)}};Bidirectional.className="Bidirectional";serialization_exports.registerClass(Bidirectional);function inputLayer(args){return new InputLayer(args)}function elu5(args){return new ELU4(args)}function reLU(args){return new ReLU(args)}function leakyReLU(args){return new LeakyReLU(args)}function prelu5(args){return new PReLU(args)}function softmax5(args){return new Softmax3(args)}function thresholdedReLU(args){return new ThresholdedReLU(args)}function conv1d2(args){return new Conv1D(args)}function conv2d5(args){return new Conv2D2(args)}function conv2dTranspose2(args){return new Conv2DTranspose(args)}function conv3d2(args){return new Conv3D2(args)}function separableConv2d2(args){return new SeparableConv2D(args)}function cropping2D(args){return new Cropping2D(args)}function upSampling2d(args){return new UpSampling2D(args)}function depthwiseConv2d5(args){return new DepthwiseConv2D(args)}function activation(args){return new Activation2(args)}function dense(args){return new Dense(args)}function dropout3(args){return new Dropout(args)}function spatialDropout1d(args){return new SpatialDropout1D(args)}function flatten3(args){return new Flatten(args)}function repeatVector(args){return new RepeatVector(args)}function reshape5(args){return new Reshape2(args)}function permute(args){return new Permute(args)}function embedding(args){return new Embedding(args)}function add4(args){return new Add2(args)}function average(args){return new Average(args)}function concatenate2(args){return new Concatenate(args)}function maximum4(args){return new Maximum2(args)}function minimum4(args){return new Minimum2(args)}function multiply3(args){return new Multiply2(args)}function dot3(args){return new Dot(args)}function batchNormalization2(args){return new BatchNormalization(args)}function layerNormalization(args){return new LayerNormalization(args)}function zeroPadding2d(args){return new ZeroPadding2D(args)}function averagePooling1d(args){return new AveragePooling1D(args)}function avgPool1d(args){return averagePooling1d(args)}function avgPooling1d(args){return averagePooling1d(args)}function averagePooling2d(args){return new AveragePooling2D(args)}function avgPool2d(args){return averagePooling2d(args)}function avgPooling2d(args){return averagePooling2d(args)}function averagePooling3d(args){return new AveragePooling3D(args)}function avgPool3d2(args){return averagePooling3d(args)}function avgPooling3d(args){return averagePooling3d(args)}function globalAveragePooling1d(args){return new GlobalAveragePooling1D(args)}function globalAveragePooling2d(args){return new GlobalAveragePooling2D(args)}function globalMaxPooling1d(args){return new GlobalMaxPooling1D(args)}function globalMaxPooling2d(args){return new GlobalMaxPooling2D(args)}function maxPooling1d(args){return new MaxPooling1D(args)}function maxPooling2d(args){return new MaxPooling2D(args)}function maxPooling3d(args){return new MaxPooling3D(args)}function gru(args){return new GRU(args)}function gruCell(args){return new GRUCell(args)}function lstm(args){return new LSTM(args)}function lstmCell(args){return new LSTMCell(args)}function simpleRNN(args){return new SimpleRNN(args)}function simpleRNNCell(args){return new SimpleRNNCell(args)}function convLstm2d(args){return new ConvLSTM2D(args)}function convLstm2dCell(args){return new ConvLSTM2DCell(args)}function rnn2(args){return new RNN(args)}function stackedRNNCells(args){return new StackedRNNCells(args)}function bidirectional(args){return new Bidirectional(args)}function timeDistributed(args){return new TimeDistributed(args)}var globalMaxPool1d=globalMaxPooling1d,globalMaxPool2d=globalMaxPooling2d,maxPool1d=maxPooling1d,maxPool2d=maxPooling2d;function gaussianNoise(args){return new GaussianNoise(args)}function gaussianDropout(args){return new GaussianDropout(args)}function alphaDropout(args){return new AlphaDropout(args)}function masking(args){return new Masking(args)}var exports_metrics_exports={};__export2(exports_metrics_exports,{MAPE:()=>MAPE2,MSE:()=>MSE2,binaryAccuracy:()=>binaryAccuracy2,binaryCrossentropy:()=>binaryCrossentropy3,categoricalAccuracy:()=>categoricalAccuracy2,categoricalCrossentropy:()=>categoricalCrossentropy3,cosineProximity:()=>cosineProximity2,mape:()=>mape2,meanAbsoluteError:()=>meanAbsoluteError2,meanAbsolutePercentageError:()=>meanAbsolutePercentageError2,meanSquaredError:()=>meanSquaredError3,mse:()=>mse2,precision:()=>precision2,recall:()=>recall2,sparseCategoricalAccuracy:()=>sparseCategoricalAccuracy2});function binaryAccuracy2(yTrue,yPred){return binaryAccuracy(yTrue,yPred)}function binaryCrossentropy3(yTrue,yPred){return binaryCrossentropy2(yTrue,yPred)}function sparseCategoricalAccuracy2(yTrue,yPred){return sparseCategoricalAccuracy(yTrue,yPred)}function categoricalAccuracy2(yTrue,yPred){return categoricalAccuracy(yTrue,yPred)}function categoricalCrossentropy3(yTrue,yPred){return categoricalCrossentropy2(yTrue,yPred)}function precision2(yTrue,yPred){return precision(yTrue,yPred)}function recall2(yTrue,yPred){return recall(yTrue,yPred)}function cosineProximity2(yTrue,yPred){return cosineProximity(yTrue,yPred)}function meanAbsoluteError2(yTrue,yPred){return meanAbsoluteError(yTrue,yPred)}function meanAbsolutePercentageError2(yTrue,yPred){return meanAbsolutePercentageError(yTrue,yPred)}function MAPE2(yTrue,yPred){return meanAbsolutePercentageError(yTrue,yPred)}function mape2(yTrue,yPred){return meanAbsolutePercentageError(yTrue,yPred)}function meanSquaredError3(yTrue,yPred){return meanSquaredError2(yTrue,yPred)}function MSE2(yTrue,yPred){return meanSquaredError2(yTrue,yPred)}function mse2(yTrue,yPred){return meanSquaredError2(yTrue,yPred)}var exports_models_exports={};__export2(exports_models_exports,{modelFromJSON:()=>modelFromJSON});var exports_regularizers_exports={};__export2(exports_regularizers_exports,{l1:()=>l12,l1l2:()=>l1l2,l2:()=>l22});function l1l2(config2){return new L1L2(config2)}function l12(config2){return l1(config2)}function l22(config2){return l2(config2)}var Callback=class extends BaseCallback{constructor(){super(...arguments);this.model=null}setModel(model2){if(!(model2 instanceof LayersModel))throw new Error("model must be a LayersModel, not some other Container");this.model=model2}};function less4(currVal,prevVal){return currValprevVal}var EarlyStopping=class extends Callback{constructor(args){super();if(args==null&&(args={}),args.restoreBestWeights)throw new NotImplementedError("restoreBestWeights = True is not implemented in EarlyStopping yet.");this.monitor=args.monitor||"val_loss",this.minDelta=Math.abs(args.minDelta||0),this.patience=args.patience||0,this.verbose=args.verbose||0,this.mode=args.mode||"auto",this.baseline=args.baseline,["auto","min","max"].indexOf(this.mode)===-1&&(console.warn(`EarlyStopping mode '${this.mode}' is invalid. Falling back to mode 'auto'.`),this.mode="auto"),this.mode==="min"?this.monitorFunc=less4:this.mode==="max"?this.monitorFunc=greater4:this.monitor.indexOf("acc")!==-1?this.monitorFunc=greater4:this.monitorFunc=less4,this.monitorFunc===less4&&(this.minDelta*=-1)}async onTrainBegin(logs){this.wait=0,this.stoppedEpoch=0,this.baseline!=null?this.best=this.baseline:this.best=this.monitorFunc===less4?Infinity:-Infinity}async onEpochEnd(epoch,logs){await resolveScalarsInLogs(logs);let current=this.getMonitorValue(logs);current!=null&&(this.monitorFunc(current-this.minDelta,this.best)?(this.best=current,this.wait=0):(this.wait++,this.wait>=this.patience&&(this.stoppedEpoch=epoch,this.model.stopTraining=!0)))}async onTrainEnd(logs){this.stoppedEpoch>0&&this.verbose&&console.log(`Epoch ${this.stoppedEpoch}: early stopping.`)}getMonitorValue(logs){logs==null&&(logs={});let monitorValue=logs[this.monitor];return monitorValue==null&&console.warn(`Metric for EarlyStopping ${this.monitor} is not available. Available metrics are: ${Object.keys(logs)}`),monitorValue}};function earlyStopping(args){return new EarlyStopping(args)}var callbacks={earlyStopping},DataType;(function(DataType2){DataType2[DataType2.DT_INVALID=0]="DT_INVALID",DataType2[DataType2.DT_FLOAT=1]="DT_FLOAT",DataType2[DataType2.DT_DOUBLE=2]="DT_DOUBLE",DataType2[DataType2.DT_INT32=3]="DT_INT32",DataType2[DataType2.DT_UINT8=4]="DT_UINT8",DataType2[DataType2.DT_INT16=5]="DT_INT16",DataType2[DataType2.DT_INT8=6]="DT_INT8",DataType2[DataType2.DT_STRING=7]="DT_STRING",DataType2[DataType2.DT_COMPLEX64=8]="DT_COMPLEX64",DataType2[DataType2.DT_INT64=9]="DT_INT64",DataType2[DataType2.DT_BOOL=10]="DT_BOOL",DataType2[DataType2.DT_QINT8=11]="DT_QINT8",DataType2[DataType2.DT_QUINT8=12]="DT_QUINT8",DataType2[DataType2.DT_QINT32=13]="DT_QINT32",DataType2[DataType2.DT_BFLOAT16=14]="DT_BFLOAT16",DataType2[DataType2.DT_FLOAT_REF=101]="DT_FLOAT_REF",DataType2[DataType2.DT_DOUBLE_REF=102]="DT_DOUBLE_REF",DataType2[DataType2.DT_INT32_REF=103]="DT_INT32_REF",DataType2[DataType2.DT_UINT8_REF=104]="DT_UINT8_REF",DataType2[DataType2.DT_INT16_REF=105]="DT_INT16_REF",DataType2[DataType2.DT_INT8_REF=106]="DT_INT8_REF",DataType2[DataType2.DT_STRING_REF=107]="DT_STRING_REF",DataType2[DataType2.DT_COMPLEX64_REF=108]="DT_COMPLEX64_REF",DataType2[DataType2.DT_INT64_REF=109]="DT_INT64_REF",DataType2[DataType2.DT_BOOL_REF=110]="DT_BOOL_REF",DataType2[DataType2.DT_QINT8_REF=111]="DT_QINT8_REF",DataType2[DataType2.DT_QUINT8_REF=112]="DT_QUINT8_REF",DataType2[DataType2.DT_QINT32_REF=113]="DT_QINT32_REF",DataType2[DataType2.DT_BFLOAT16_REF=114]="DT_BFLOAT16_REF"})(DataType||(DataType={}));var SaverDef;(function(SaverDef2){let CheckpointFormatVersion;(function(CheckpointFormatVersion2){CheckpointFormatVersion2[CheckpointFormatVersion2.LEGACY=0]="LEGACY",CheckpointFormatVersion2[CheckpointFormatVersion2.V1=1]="V1",CheckpointFormatVersion2[CheckpointFormatVersion2.V2=2]="V2"})(CheckpointFormatVersion=SaverDef2.CheckpointFormatVersion||(SaverDef2.CheckpointFormatVersion={}))})(SaverDef||(SaverDef={}));var CUSTOM_OPS={};function registerOp(name,opFunc){let opMapper={tfOpName:name,category:"custom",inputs:[],attrs:[],customExecutor:opFunc};CUSTOM_OPS[name]=opMapper}function getRegisteredOp(name){return CUSTOM_OPS[name]}function deregisterOp(name){delete CUSTOM_OPS[name]}function getParamValue(paramName,node,tensorMap,context,resourceManager){let inputParam=node.inputParams[paramName];if(inputParam&&inputParam.inputIndexStart!==void 0){let start=inputParam.inputIndexStart,end=inputParam.inputIndexEnd===0?void 0:inputParam.inputIndexEnd===void 0?start+1:inputParam.inputIndexEnd;if(inputParam.type==="tensor")return getTensor(node.inputNames[inputParam.inputIndexStart],tensorMap,context,resourceManager);if(inputParam.type==="tensors")return node.inputNames.slice(start,end).map(name=>getTensor(name,tensorMap,context,resourceManager));let tensor2=getTensor(node.inputNames.slice(start)[0],tensorMap,context,resourceManager),data2=tensor2.dataSync();return inputParam.type==="number"?data2[0]:util_exports.toNestedArray(tensor2.shape,data2)}let attrParam=node.attrParams[paramName];return attrParam&&attrParam.value}function getTensor(name,tensorsMap,context,resourceManager){let[nodeName,index]=parseNodeName(name);if(resourceManager!=null){let tensor2=resourceManager.getHashTableHandleByName(nodeName);if(tensor2!=null)return tensor2}let contextId=context.currentContextIds.find(contextId2=>!!tensorsMap[getNodeNameWithContextId(nodeName,contextId2)]);return contextId!==void 0?tensorsMap[getNodeNameWithContextId(nodeName,contextId)][index]:void 0}function getTensorsForCurrentContenxt(name,tensorsMap,context){return tensorsMap[getNodeNameWithContextId(name,context.currentContextId)]}function getNodeNameAndIndex(inputName,context){let[nodeName,index]=parseNodeName(inputName);return[getNodeNameWithContextId(nodeName,context&&context.currentContextId),index]}function getNodeNameWithContextId(name,contextId){return contextId?`${name}-${contextId}`:name}function parseNodeName(name){let parts=name.split(":");return parts.length===1?[name,0]:[parts[0],Number(parts[parts.length-1])]}function getPadding(node,tensorMap,context){let pad3=getParamValue("pad",node,tensorMap,context);if(pad3==="explicit"){pad3=getParamValue("explicitPaddings",node,tensorMap,context);let explicitPadding=[[0,0],[0,0],[0,0],[0,0]];for(let i=0;i<4;i++)explicitPadding[i][0]=pad3[i*2],explicitPadding[i][1]=pad3[i*2+1];return explicitPadding}return pad3}function cloneTensor(tensor2){return tensor2.kept?tensor2:clone(tensor2)}var arithmetic_exports={};__export2(arithmetic_exports,{json:()=>json});var json=[{tfOpName:"Add",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"AddV2",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"AddN",category:"arithmetic",inputs:[{start:0,end:0,name:"tensors",type:"tensors"}]},{tfOpName:"BiasAdd",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"Sub",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"RealDiv",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Div",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"DivNoNan",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"FloorDiv",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Mul",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Maximum",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Minimum",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Pow",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"SquaredDifference",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Mod",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"FloorMod",category:"arithmetic",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]}],basic_math_exports={};__export2(basic_math_exports,{json:()=>json2});var json2=[{tfOpName:"Abs",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Acos",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Asin",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Atan",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Atan2",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"y",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Ceil",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"ClipByValue",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"clipValueMin",type:"number"},{start:2,name:"clipValueMax",type:"number"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Complex",category:"basic_math",inputs:[{start:0,name:"real",type:"tensor"},{start:1,name:"imag",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"ComplexAbs",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Cos",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Cosh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Elu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Exp",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Floor",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Log",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Imag",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"Tout",name:"outputType",type:"dtype",notSupported:!0}]},{tfOpName:"Neg",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Real",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0},{tfName:"Tout",name:"outputType",type:"dtype",notSupported:!0}]},{tfOpName:"Prelu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"alpha",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Relu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Relu6",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Selu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sigmoid",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sin",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sinh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sqrt",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Rsqrt",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Square",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Tan",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Tanh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Sign",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Round",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Expm1",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Log1p",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Reciprocal",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Softplus",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Asinh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Acosh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Atanh",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Erf",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Prod",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axes",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool",notSupported:!0},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LeakyRelu",category:"basic_math",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"alpha",name:"alpha",type:"number",defaultValue:.2},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]}],control_exports={};__export2(control_exports,{json:()=>json3});var 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json11=[{tfOpName:"Equal",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"NotEqual",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Greater",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"GreaterEqual",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Less",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LessEqual",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LogicalAnd",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LogicalNot",category:"logical",inputs:[{start:0,name:"a",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"LogicalOr",category:"logical",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Select",category:"logical",inputs:[{start:0,name:"condition",type:"tensor"},{start:1,name:"a",type:"tensor"},{start:2,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"SelectV2",category:"logical",inputs:[{start:0,name:"condition",type:"tensor"},{start:1,name:"a",type:"tensor"},{start:2,name:"b",type:"tensor"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]}],matrices_exports={};__export2(matrices_exports,{json:()=>json12});var json12=[{tfOpName:"_FusedMatMul",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"},{start:2,end:0,name:"args",type:"tensors"}],attrs:[{tfName:"num_args",name:"numArgs",type:"number"},{tfName:"fused_ops",name:"fusedOps",type:"string[]",defaultValue:[]},{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:1e-4},{tfName:"transpose_a",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"transpose_b",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"MatMul",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"transpose_a",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"transpose_b",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"BatchMatMul",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"adj_x",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"adj_y",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"BatchMatMulV2",category:"matrices",inputs:[{start:0,name:"a",type:"tensor"},{start:1,name:"b",type:"tensor"}],attrs:[{tfName:"adj_x",name:"transposeA",type:"bool",defaultValue:!1},{tfName:"adj_y",name:"transposeB",type:"bool",defaultValue:!1},{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]},{tfOpName:"Transpose",category:"matrices",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"perm",type:"number[]"}],attrs:[{tfName:"T",name:"dtype",type:"dtype",notSupported:!0}]}],normalization_exports={};__export2(normalization_exports,{json:()=>json13});var json13=[{tfOpName:"FusedBatchNorm",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"scale",type:"tensor"},{start:2,name:"offset",type:"tensor"},{start:3,name:"mean",type:"tensor"},{start:4,name:"variance",type:"tensor"}],attrs:[{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:.001},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"FusedBatchNormV2",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"scale",type:"tensor"},{start:2,name:"offset",type:"tensor"},{start:3,name:"mean",type:"tensor"},{start:4,name:"variance",type:"tensor"}],attrs:[{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:.001},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"FusedBatchNormV3",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"scale",type:"tensor"},{start:2,name:"offset",type:"tensor"},{start:3,name:"mean",type:"tensor"},{start:4,name:"variance",type:"tensor"}],attrs:[{tfName:"epsilon",name:"epsilon",type:"number",defaultValue:.001},{tfName:"data_format",name:"dataFormat",type:"string",notSupported:!0}]},{tfOpName:"LRN",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"depth_radius",name:"radius",type:"number",defaultValue:5},{tfName:"bias",name:"bias",type:"number",defaultValue:1},{tfName:"alpha",name:"alpha",type:"number",defaultValue:1},{tfName:"beta",name:"beta",type:"number",defaultValue:.5}]},{tfOpName:"Softmax",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"LogSoftmax",category:"normalization",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"SparseToDense",category:"normalization",inputs:[{start:0,name:"sparseIndices",type:"tensor"},{start:1,name:"outputShape",type:"number[]"},{start:2,name:"sparseValues",type:"tensor"},{start:3,name:"defaultValue",type:"tensor"}],attrs:[{tfName:"validate_indices",name:"validateIndices",type:"bool",defaultValue:!0,notSupported:!0}]}],reduction_exports={};__export2(reduction_exports,{json:()=>json14});var json14=[{tfOpName:"Bincount",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"size",type:"number"},{start:2,name:"weights",type:"tensor"}]},{tfOpName:"DenseBincount",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"size",type:"number"},{start:2,name:"weights",type:"tensor"}],attrs:[{tfName:"binary_output",name:"binaryOutput",type:"bool"}]},{tfOpName:"Max",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Mean",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Min",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Sum",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"All",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Any",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"ArgMax",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]},{tfOpName:"ArgMin",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]},{tfOpName:"Prod",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}],attrs:[{tfName:"keep_dims",name:"keepDims",type:"bool"}]},{tfOpName:"Cumsum",category:"reduction",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}],attrs:[{tfName:"exclusive",name:"exclusive",type:"bool"},{tfName:"reverse",name:"reverse",type:"bool"}]}],slice_join_exports={};__export2(slice_join_exports,{json:()=>json15});var json15=[{tfOpName:"ConcatV2",category:"slice_join",inputs:[{start:0,end:-1,name:"tensors",type:"tensors"},{start:-1,name:"axis",type:"number"}],attrs:[{tfName:"N",name:"n",type:"number",defaultValue:2}]},{tfOpName:"Concat",category:"slice_join",inputs:[{start:1,end:0,name:"tensors",type:"tensors"},{start:0,name:"axis",type:"number"}],attrs:[{tfName:"N",name:"n",type:"number",defaultValue:2}]},{tfOpName:"GatherV2",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"indices",type:"tensor"},{start:2,name:"axis",type:"number",defaultValue:0}],attrs:[{tfName:"batch_dims",name:"batchDims",type:"number",defaultValue:0}]},{tfOpName:"Gather",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"indices",type:"tensor"}],attrs:[{tfName:"validate_indices",name:"validateIndices",type:"bool",notSupported:!0}]},{tfOpName:"Reverse",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"dims",type:"bool[]"}]},{tfOpName:"ReverseV2",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number[]"}]},{tfOpName:"Slice",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"begin",type:"number[]"},{start:2,name:"size",type:"number[]"}]},{tfOpName:"StridedSlice",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"begin",type:"number[]"},{start:2,name:"end",type:"number[]"},{start:3,name:"strides",type:"number[]"}],attrs:[{tfName:"begin_mask",name:"beginMask",type:"number",defaultValue:0},{tfName:"end_mask",name:"endMask",type:"number",defaultValue:0},{tfName:"new_axis_mask",name:"newAxisMask",type:"number",defaultValue:0},{tfName:"ellipsis_mask",name:"ellipsisMask",type:"number",defaultValue:0},{tfName:"shrink_axis_mask",name:"shrinkAxisMask",type:"number",defaultValue:0}]},{tfOpName:"Pack",category:"slice_join",inputs:[{start:0,end:0,name:"tensors",type:"tensors"}],attrs:[{tfName:"axis",name:"axis",type:"number",defaultValue:0}]},{tfOpName:"Unpack",category:"slice_join",inputs:[{start:0,name:"tensor",type:"tensor"}],attrs:[{tfName:"axis",name:"axis",type:"number",defaultValue:0},{tfName:"num",name:"num",type:"number",defaultValue:0,notSupported:!0}]},{tfOpName:"Tile",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"reps",type:"number[]"}]},{tfOpName:"Split",category:"slice_join",inputs:[{start:0,name:"axis",type:"number",defaultValue:0},{start:1,name:"x",type:"tensor"}],attrs:[{tfName:"num_split",name:"numOrSizeSplits",type:"number",defaultValue:1}]},{tfOpName:"SplitV",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"numOrSizeSplits",type:"number[]"},{start:2,name:"axis",type:"number",defaultValue:0}]},{tfOpName:"ScatterNd",category:"slice_join",inputs:[{start:0,name:"indices",type:"tensor"},{start:1,name:"values",type:"tensor"},{start:2,name:"shape",type:"number[]"}]},{tfOpName:"GatherNd",category:"slice_join",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"indices",type:"tensor"}]},{tfOpName:"SparseToDense",category:"slice_join",inputs:[{start:0,name:"sparseIndices",type:"tensor"},{start:1,name:"outputShape",type:"number[]"},{start:2,name:"sparseValues",type:"tensor"},{start:3,name:"defaultValue",type:"tensor"}],attrs:[{tfName:"validate_indices",name:"validateIndices",type:"bool",defaultValue:!1,notSupported:!0}]}],spectral_exports={};__export2(spectral_exports,{json:()=>json16});var json16=[{tfOpName:"FFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"IFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"}]},{tfOpName:"RFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"fft_length",type:"number",notSupported:!0}]},{tfOpName:"IRFFT",category:"spectral",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"fft_length",type:"number",notSupported:!0}]}],transformation_exports={};__export2(transformation_exports,{json:()=>json17});var json17=[{tfOpName:"Cast",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"SrcT",name:"sdtype",type:"dtype",notSupported:!0},{tfName:"DstT",name:"dtype",type:"dtype"}]},{tfOpName:"ExpandDims",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"axis",type:"number"}]},{tfOpName:"MirrorPad",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"}],attrs:[{tfName:"mode",name:"mode",type:"string"}]},{tfOpName:"Pad",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"}],attrs:[{tfName:"constant_value",name:"constantValue",type:"number",defaultValue:0}]},{tfOpName:"PadV2",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"padding",type:"number[]"},{start:2,name:"constantValue",type:"number",defaultValue:0}]},{tfOpName:"Reshape",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"shape",type:"number[]"}]},{tfOpName:"Squeeze",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"axis",tfDeprecatedName:"squeeze_dims",name:"axis",type:"number[]"}]},{tfOpName:"SpaceToBatchND",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"blockShape",type:"number[]"},{start:2,name:"paddings",type:"number[]"}]},{tfOpName:"BatchToSpaceND",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"blockShape",type:"number[]"},{start:2,name:"crops",type:"number[]"}]},{tfOpName:"DepthToSpace",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"}],attrs:[{tfName:"block_size",name:"blockSize",type:"number"},{tfName:"data_format",name:"dataFormat",type:"string"}]},{tfOpName:"BroadcastTo",category:"transformation",inputs:[{start:0,name:"x",type:"tensor"},{start:1,name:"shape",type:"number[]"}],attrs:[]}],OperationMapper=class{static get Instance(){return this._instance||(this._instance=new this)}constructor(){let ops=[arithmetic_exports,basic_math_exports,control_exports,convolution_exports,creation_exports,dynamic_exports,evaluation_exports,logical_exports,image_exports,graph_exports,matrices_exports,normalization_exports,reduction_exports,slice_join_exports,spectral_exports,transformation_exports,hash_table_exports],mappersJson=[].concat(...ops.map(op2=>op2.json));this.opMappers=mappersJson.reduce((map,mapper)=>(map[mapper.tfOpName]=mapper,map),{})}transformGraph(graph2,signature={}){let tfNodes=graph2.node,placeholders=[],weights=[],initNodes=[],nodes=tfNodes.reduce((map,node)=>(map[node.name]=this.mapNode(node),node.op.startsWith("Placeholder")?placeholders.push(map[node.name]):node.op==="Const"?weights.push(map[node.name]):(node.input==null||node.input.length===0)&&initNodes.push(map[node.name]),map),{}),inputs=[],outputs=[],inputNodeNameToKey={},outputNodeNameToKey={};signature!=null&&(inputNodeNameToKey=this.mapSignatureEntries(signature.inputs),outputNodeNameToKey=this.mapSignatureEntries(signature.outputs));let allNodes=Object.keys(nodes);allNodes.forEach(key=>{let node=nodes[key];node.inputNames.forEach(name=>{let[nodeName]=getNodeNameAndIndex(name);node.inputs.push(nodes[nodeName]),nodes[nodeName].children.push(node)})}),Object.keys(outputNodeNameToKey).length===0?allNodes.forEach(key=>{let node=nodes[key];node.children.length===0&&outputs.push(node)}):Object.keys(outputNodeNameToKey).forEach(name=>{let[nodeName]=getNodeNameAndIndex(name),node=nodes[nodeName];node!=null&&(node.signatureKey=outputNodeNameToKey[name],outputs.push(node))}),Object.keys(inputNodeNameToKey).length>0?Object.keys(inputNodeNameToKey).forEach(name=>{let[nodeName]=getNodeNameAndIndex(name),node=nodes[nodeName];node&&(node.signatureKey=inputNodeNameToKey[name],inputs.push(node))}):inputs=placeholders;let functions={};graph2.library!=null&&graph2.library.function!=null&&(functions=graph2.library.function.reduce((functions2,func2)=>(functions2[func2.signature.name]=this.mapFunction(func2),functions2),{}));let result={nodes,inputs,outputs,weights,placeholders,signature,functions};return initNodes.length>0&&(result.initNodes=initNodes),result}mapSignatureEntries(entries){return Object.keys(entries||{}).reduce((prev,curr)=>(prev[entries[curr].name]=curr,prev),{})}mapNode(node){let mapper=getRegisteredOp(node.op)||this.opMappers[node.op]||{};node.attr==null&&(node.attr={});let newNode={name:node.name,op:node.op,category:mapper.category,inputNames:(node.input||[]).map(input2=>input2.startsWith("^")?input2.substr(1):input2),inputs:[],children:[],inputParams:{},attrParams:{},rawAttrs:node.attr};return mapper.inputs!=null&&(newNode.inputParams=mapper.inputs.reduce((map,param)=>(map[param.name]={type:param.type,inputIndexStart:param.start,inputIndexEnd:param.end},map),{})),mapper.attrs!=null&&(newNode.attrParams=mapper.attrs.reduce((map,param)=>{let type=param.type,value;switch(param.type){case"string":value=getStringParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getStringParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"string[]":value=getStringArrayParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getStringArrayParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"number":value=getNumberParam(node.attr,param.tfName,param.defaultValue||0),value===void 0&&!!param.tfDeprecatedName&&(value=getNumberParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"number[]":value=getNumericArrayParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getNumericArrayParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"bool":value=getBoolParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getBoolParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"bool[]":value=getBoolArrayParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getBoolArrayParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"shape":value=getTensorShapeParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getTensorShapeParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"shape[]":value=getTensorShapeArrayParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getTensorShapeArrayParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"dtype":value=getDtypeParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getDtypeParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"dtype[]":value=getDtypeArrayParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getDtypeArrayParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"func":value=getFuncParam(node.attr,param.tfName,param.defaultValue),value===void 0&&!!param.tfDeprecatedName&&(value=getFuncParam(node.attr,param.tfDeprecatedName,param.defaultValue));break;case"tensor":case"tensors":break;default:throw new Error(`Unsupported param type: ${param.type} for op: ${node.op}`)}return map[param.name]={value,type},map},{})),newNode}mapFunction(functionDef){let tfNodes=functionDef.nodeDef,placeholders=[],weights=[],nodes={};tfNodes!=null&&(nodes=tfNodes.reduce((map,node)=>(map[node.name]=this.mapNode(node),node.op==="Const"&&weights.push(map[node.name]),map),{}));let inputs=[],outputs=[];functionDef.signature.inputArg.forEach(arg=>{let[nodeName]=getNodeNameAndIndex(arg.name),node={name:nodeName,op:"Placeholder",inputs:[],inputNames:[],category:"graph",inputParams:{},attrParams:{dtype:{value:parseDtypeParam(arg.type),type:"dtype"}},children:[]};node.signatureKey=arg.name,inputs.push(node),nodes[nodeName]=node}),Object.keys(nodes).forEach(key=>{let node=nodes[key];node.inputNames.forEach(name=>{let[nodeName]=getNodeNameAndIndex(name);node.inputs.push(nodes[nodeName]),nodes[nodeName].children.push(node)})});let returnNodeMap=functionDef.ret;functionDef.signature.outputArg.forEach(output=>{let[nodeName,index]=getNodeNameAndIndex(returnNodeMap[output.name]),node=nodes[nodeName];node!=null&&(node.defaultOutput=index,outputs.push(node))});let signature=this.mapArgsToSignature(functionDef);return{nodes,inputs,outputs,weights,placeholders,signature}}mapArgsToSignature(functionDef){return{methodName:functionDef.signature.name,inputs:functionDef.signature.inputArg.reduce((map,arg)=>(map[arg.name]=this.mapArgToTensorInfo(arg),map),{}),outputs:functionDef.signature.outputArg.reduce((map,arg)=>(map[arg.name]=this.mapArgToTensorInfo(arg,functionDef.ret),map),{})}}mapArgToTensorInfo(arg,nameMap2){let name=arg.name;return nameMap2!=null&&(name=nameMap2[name]),{name,dtype:arg.type}}};function decodeBase64(text){let global2=env().global;if(typeof global2.atob!="undefined")return global2.atob(text);if(typeof Buffer!="undefined")return new Buffer(text,"base64").toString();throw new Error("Unable to decode base64 in this environment. Missing built-in atob() or Buffer()")}function parseStringParam(s,keepCase){let value=Array.isArray(s)?String.fromCharCode.apply(null,s):decodeBase64(s);return keepCase?value:value.toLowerCase()}function getStringParam(attrs,name,def,keepCase=!1){let param=attrs[name];return param!=null?parseStringParam(param.s,keepCase):def}function getBoolParam(attrs,name,def){let param=attrs[name];return param?param.b:def}function getNumberParam(attrs,name,def){let param=attrs[name]||{},value=param.i!=null?param.i:param.f!=null?param.f:def;return typeof value=="number"?value:parseInt(value,10)}function parseDtypeParam(value){switch(typeof value=="string"&&(value=DataType[value]),value){case DataType.DT_FLOAT:return"float32";case DataType.DT_INT32:case DataType.DT_INT64:case DataType.DT_INT8:case DataType.DT_UINT8:return"int32";case DataType.DT_BOOL:return"bool";case DataType.DT_DOUBLE:return"float32";case DataType.DT_STRING:return"string";default:return null}}function getFuncParam(attrs,name,def){let param=attrs[name];return param&¶m.func?param.func.name:def}function getDtypeParam(attrs,name,def){let param=attrs[name];return param&¶m.type?parseDtypeParam(param.type):def}function getDtypeArrayParam(attrs,name,def){let param=attrs[name];return param&¶m.list&¶m.list.type?param.list.type.map(v=>parseDtypeParam(v)):def}function parseTensorShapeParam(shape){if(!shape.unknownRank)return shape.dim!=null?shape.dim.map(dim=>typeof dim.size=="number"?dim.size:parseInt(dim.size,10)):[]}function getTensorShapeParam(attrs,name,def){let param=attrs[name];return param&¶m.shape?parseTensorShapeParam(param.shape):def}function getNumericArrayParam(attrs,name,def){let param=attrs[name];return param?((param.list.f&¶m.list.f.length?param.list.f:param.list.i)||[]).map(v=>typeof v=="number"?v:parseInt(v,10)):def}function getStringArrayParam(attrs,name,def,keepCase=!1){let param=attrs[name];return param&¶m.list&¶m.list.s?param.list.s.map(v=>parseStringParam(v,keepCase)):def}function getTensorShapeArrayParam(attrs,name,def){let param=attrs[name];return param&¶m.list&¶m.list.shape?param.list.shape.map(v=>parseTensorShapeParam(v)):def}function getBoolArrayParam(attrs,name,def){let param=attrs[name];return param&¶m.list&¶m.list.b?param.list.b:def}var NodeValueImpl=class{constructor(node,tensorMap,context){this.node=node,this.tensorMap=tensorMap,this.context=context,this.inputs=[],this.attrs={},this.inputs=node.inputNames.map(name=>this.getInput(name)),node.rawAttrs!=null&&(this.attrs=Object.keys(node.rawAttrs).reduce((attrs,key)=>(attrs[key]=this.getAttr(key),attrs),{}))}getInput(name){return getTensor(name,this.tensorMap,this.context)}getAttr(name,defaultValue){let value=this.node.rawAttrs[name];if(value.tensor!=null)return getTensor(name,this.tensorMap,this.context);if(value.i!=null||value.f!=null)return getNumberParam(this.node.rawAttrs,name,defaultValue);if(value.s!=null)return getStringParam(this.node.rawAttrs,name,defaultValue);if(value.b!=null)return getBoolParam(this.node.rawAttrs,name,defaultValue);if(value.shape!=null)return getTensorShapeParam(this.node.rawAttrs,name,defaultValue);if(value.type!=null)return getDtypeParam(this.node.rawAttrs,name,defaultValue);if(value.list!=null){if(value.list.i!=null||value.list.f!=null)return getNumericArrayParam(this.node.rawAttrs,name,defaultValue);if(value.list.s!=null)return getStringArrayParam(this.node.rawAttrs,name,defaultValue);if(value.list.shape!=null)return getTensorShapeArrayParam(this.node.rawAttrs,name,defaultValue);if(value.list.b!=null)return getBoolArrayParam(this.node.rawAttrs,name,defaultValue);if(value.list.type!=null)return getDtypeArrayParam(this.node.rawAttrs,name,defaultValue)}return defaultValue}},executeOp=(node,tensorMap,context)=>{switch(node.op){case"BiasAdd":case"AddV2":case"Add":return[add2(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"AddN":return[addN(getParamValue("tensors",node,tensorMap,context))];case"FloorMod":case"Mod":return[mod(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Mul":return[mul(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"RealDiv":case"Div":return[div(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"DivNoNan":return[divNoNan(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"FloorDiv":return[floorDiv(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Sub":return[sub(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Minimum":return[minimum(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Maximum":return[maximum(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Pow":return[pow(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"SquaredDifference":return[squaredDifference(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp2=(node,tensorMap,context)=>{switch(node.op){case"Abs":case"ComplexAbs":return[abs(getParamValue("x",node,tensorMap,context))];case"Acos":return[acos(getParamValue("x",node,tensorMap,context))];case"Acosh":return[acosh(getParamValue("x",node,tensorMap,context))];case"Asin":return[asin(getParamValue("x",node,tensorMap,context))];case"Asinh":return[asinh(getParamValue("x",node,tensorMap,context))];case"Atan":return[atan(getParamValue("x",node,tensorMap,context))];case"Atan2":return[atan2(getParamValue("x",node,tensorMap,context),getParamValue("y",node,tensorMap,context))];case"Atanh":return[atanh(getParamValue("x",node,tensorMap,context))];case"Ceil":return[ceil(getParamValue("x",node,tensorMap,context))];case"Complex":return[complex(getParamValue("real",node,tensorMap,context),getParamValue("imag",node,tensorMap,context))];case"Cos":return[cos(getParamValue("x",node,tensorMap,context))];case"Cosh":return[cosh(getParamValue("x",node,tensorMap,context))];case"Elu":return[elu(getParamValue("x",node,tensorMap,context))];case"Erf":return[erf(getParamValue("x",node,tensorMap,context))];case"Exp":return[exp(getParamValue("x",node,tensorMap,context))];case"Expm1":return[expm1(getParamValue("x",node,tensorMap,context))];case"Floor":return[floor(getParamValue("x",node,tensorMap,context))];case"Log":return[log2(getParamValue("x",node,tensorMap,context))];case"Log1p":return[log1p(getParamValue("x",node,tensorMap,context))];case"Imag":return[imag(getParamValue("x",node,tensorMap,context))];case"Neg":return[neg(getParamValue("x",node,tensorMap,context))];case"Reciprocal":return[reciprocal(getParamValue("x",node,tensorMap,context))];case"Real":return[real(getParamValue("x",node,tensorMap,context))];case"Relu":return[relu(getParamValue("x",node,tensorMap,context))];case"Round":return[round2(getParamValue("x",node,tensorMap,context))];case"Selu":return[selu(getParamValue("x",node,tensorMap,context))];case"Sigmoid":return[sigmoid(getParamValue("x",node,tensorMap,context))];case"Sin":return[sin(getParamValue("x",node,tensorMap,context))];case"Sign":return[sign(getParamValue("x",node,tensorMap,context))];case"Sinh":return[sinh(getParamValue("x",node,tensorMap,context))];case"Softplus":return[softplus(getParamValue("x",node,tensorMap,context))];case"Sqrt":return[sqrt(getParamValue("x",node,tensorMap,context))];case"Square":return[square(getParamValue("x",node,tensorMap,context))];case"Tanh":return[tanh2(getParamValue("x",node,tensorMap,context))];case"Tan":return[tan(getParamValue("x",node,tensorMap,context))];case"ClipByValue":return[clipByValue(getParamValue("x",node,tensorMap,context),getParamValue("clipValueMin",node,tensorMap,context),getParamValue("clipValueMax",node,tensorMap,context))];case"Relu6":return[relu6(getParamValue("x",node,tensorMap,context))];case"Rsqrt":return[rsqrt(getTensor(node.inputNames[0],tensorMap,context))];case"Prod":return[prod(getParamValue("x",node,tensorMap,context),getParamValue("axes",node,tensorMap,context))];case"LeakyRelu":return[leakyRelu(getParamValue("x",node,tensorMap,context),getParamValue("alpha",node,tensorMap,context))];case"Prelu":return[prelu(getParamValue("x",node,tensorMap,context),getParamValue("alpha",node,tensorMap,context))];default:throw TypeError(`Node type ${node.op} is not implemented`)}};function assertShapesMatchAllowUndefinedSize(shapeA,shapeB,errorMessagePrefix=""){util_exports.assert(shapesEqualAllowUndefinedSize(shapeA,shapeB),()=>errorMessagePrefix+` Shapes ${shapeA} and ${shapeB} must match`)}function shapesEqualAllowUndefinedSize(n1,n2){if(n1.length!==n2.length)return!1;for(let i=0;i{(keepIds==null||!keepIds.has(tensor2.tensor.id))&&tensor2.tensor.dispose()}),this.tensors=[],this.closed_=!0,this.idTensor.dispose()}size(){return this.tensors.length}read(index){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(index<0||index>=this.size())throw new Error(`Tried to read from index ${index}, but array size is: ${this.size()}`);let tensorWithState=this.tensors[index];if(tensorWithState.cleared)throw new Error(`TensorArray ${this.name}: Could not read index ${index} twice because it was cleared after a previous read (perhaps try setting clear_after_read = false?).`);return this.clearAfterRead&&(tensorWithState.cleared=!0),tensorWithState.read=!0,tensorWithState.tensor}readMany(indices){return indices.map(index=>this.read(index))}write(index,tensor2){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(index<0||!this.dynamicSize&&index>=this.maxSize)throw new Error(`Tried to write to index ${index}, but array is not resizeable and size is: ${this.maxSize}`);let t=this.tensors[index]||{};if(tensor2.dtype!==this.dtype)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index}, because the value dtype is ${tensor2.dtype}, but TensorArray dtype is ${this.dtype}.`);if(this.size()===0&&(this.elementShape==null||this.elementShape.length===0)&&(this.elementShape=tensor2.shape),assertShapesMatchAllowUndefinedSize(this.elementShape,tensor2.shape,`TensorArray ${this.name}: Could not write to TensorArray index ${index}.`),t.read)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index}, because it has already been read.`);if(t.written)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index}, because it has already been written.`);t.tensor=tensor2,keep(tensor2),t.written=!0,this.tensors[index]=t}writeMany(indices,tensors){if(indices.length!==tensors.length)throw new Error(`TensorArray ${this.name}: could not write multiple tensors,because the index size: ${indices.length} is not the same as tensors size: ${tensors.length}.`);indices.forEach((i,index)=>this.write(i,tensors[index]))}gather(indices,dtype){if(!!dtype&&dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but gather requested dtype ${dtype}`);if(indices)indices=indices.slice(0,this.size());else{indices=[];for(let i=0;i=this.maxSize)throw new Error(`Max index must be < array size (${maxIndex} vs. ${this.maxSize})`);this.writeMany(indices,unstack(tensor2,0))}split(length,tensor2){if(tensor2.dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${tensor2.dtype}`);let totalLength=0,cumulativeLengths=length.map(len=>(totalLength+=len,totalLength));if(totalLength!==tensor2.shape[0])throw new Error(`Expected sum of lengths to be equal to tensor.shape[0], but sum of lengths is ${totalLength}, and tensor's shape is: ${tensor2.shape}`);if(!this.dynamicSize&&length.length!==this.maxSize)throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${length.length}), and the TensorArray is not marked as dynamically resizeable`);let elementPerRow=totalLength===0?0:tensor2.size/totalLength,tensors=[];tidy(()=>{tensor2=reshape(tensor2,[1,totalLength,elementPerRow]);for(let i=0;i{if(elementDtype!==tensor2.dtype)throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${tensor2.dtype}`);assertShapesMatchAllowUndefinedSize(elementShape,tensor2.shape,"TensorList shape mismatch: "),keep(tensor2)}),this.idTensor=scalar(0),this.maxNumElements=maxNumElements,keep(this.idTensor)}get id(){return this.idTensor.id}copy(){return new TensorList([...this.tensors],this.elementShape,this.elementDtype)}clearAndClose(keepIds){this.tensors.forEach(tensor2=>{(keepIds==null||!keepIds.has(tensor2.id))&&tensor2.dispose()}),this.tensors.length=0,this.idTensor.dispose()}size(){return this.tensors.length}stack(elementShape,elementDtype,numElements=-1){if(elementDtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);if(numElements!==-1&&this.tensors.length!==numElements)throw new Error(`Operation expected a list with ${numElements} elements but got a list with ${this.tensors.length} elements.`);return assertShapesMatchAllowUndefinedSize(elementShape,this.elementShape,"TensorList shape mismatch: "),tidy(()=>{let reshapedTensors=this.tensors.map(tensor2=>reshape(tensor2,elementShape));return stack(reshapedTensors,0)})}popBack(elementShape,elementDtype){if(elementDtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);if(this.size()===0)throw new Error("Trying to pop from an empty list.");let tensor2=this.tensors.pop();return assertShapesMatchAllowUndefinedSize(tensor2.shape,elementShape,"TensorList shape mismatch: "),reshape(tensor2,elementShape)}pushBack(tensor2){if(tensor2.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${tensor2.dtype}, but list elements ${this.elementDtype}`);if(assertShapesMatchAllowUndefinedSize(tensor2.shape,this.elementShape,"TensorList shape mismatch: "),this.maxNumElements===this.size())throw new Error("Trying to push element into a full list.");keep(tensor2),this.tensors.push(tensor2)}resize(size){if(size<0)throw new Error(`TensorListResize expects size to be non-negative. Got: ${size}`);if(this.maxNumElements!==-1&&size>this.maxNumElements)throw new Error(`TensorListResize input size ${size} is greater maxNumElement ${this.maxNumElements}.`);this.tensors.length=size}getItem(elementIndex,elementShape,elementDtype){if(elementDtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);if(elementIndex<0||elementIndex>this.tensors.length)throw new Error(`Trying to access element ${elementIndex} in a list with ${this.tensors.length} elements.`);if(this.tensors[elementIndex]==null)throw new Error(`element at index ${elementIndex} is null.`);return assertShapesMatchAllowUndefinedSize(this.tensors[elementIndex].shape,elementShape,"TensorList shape mismatch: "),this.tensors[elementIndex]}setItem(elementIndex,tensor2){if(tensor2.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${tensor2.dtype}, but list elements ${this.elementDtype}`);if(elementIndex<0||this.maxNumElements!==-1&&elementIndex>=this.maxNumElements)throw new Error(`Trying to set element ${elementIndex} in a list with max ${this.maxNumElements} elements.`);assertShapesMatchAllowUndefinedSize(this.elementShape,tensor2.shape,"TensorList shape mismatch: "),keep(tensor2),this.tensors[elementIndex]=tensor2}gather(indices,elementDtype,elementShape){if(elementDtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);return assertShapesMatchAllowUndefinedSize(this.elementShape,elementShape,"TensorList shape mismatch: "),indices=indices.slice(0,this.size()),indices.length===0?tensor([],[0].concat(this.elementShape)):tidy(()=>{let tensors=indices.map(i=>reshape(this.tensors[i],elementShape));return stack(tensors,0)})}concat(elementDtype,elementShape){if(!!elementDtype&&elementDtype!==this.elementDtype)throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${elementDtype}`);return assertShapesMatchAllowUndefinedSize(this.elementShape,elementShape,"TensorList shape mismatch: "),this.size()===0?tensor([],[0].concat(this.elementShape)):tidy(()=>{let tensors=this.tensors.map(t=>reshape(t,elementShape));return concat(tensors,0)})}};function fromTensor(tensor2,elementShape,elementDtype){let dtype=tensor2.dtype;if(tensor2.shape.length<1)throw new Error(`Tensor must be at least a vector, but saw shape: ${tensor2.shape}`);if(tensor2.dtype!==elementDtype)throw new Error(`Invalid data types; op elements ${tensor2.dtype}, but list elements ${elementDtype}`);let outputShape=tensor2.shape.slice(1);assertShapesMatchAllowUndefinedSize(outputShape,elementShape,"TensorList shape mismatch: ");let tensorList=unstack(tensor2);return new TensorList(tensorList,elementShape,dtype)}function reserve(elementShape,elementDtype,numElements){return new TensorList([],elementShape,elementDtype,numElements)}function scatter(tensor2,indices,elementShape,numElements){if(indices.length!==tensor2.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${indices.length} vs. ${tensor2.shape[0]}`);let maxIndex=Math.max(...indices);if(numElements!=null&&numElements!==-1&&maxIndex>=numElements)throw new Error(`Max index must be < array size (${maxIndex} vs. ${numElements})`);let list=new TensorList([],elementShape,tensor2.dtype,numElements),tensors=unstack(tensor2,0);return indices.forEach((value,index)=>{list.setItem(value,tensors[index])}),list}function split2(tensor2,length,elementShape){let totalLength=0,cumulativeLengths=length.map(len=>(totalLength+=len,totalLength));if(totalLength!==tensor2.shape[0])throw new Error(`Expected sum of lengths to be equal to tensor.shape[0], but sum of lengths is - ${totalLength}, and tensor's shape is: ${tensor2.shape}`);let elementPerRow=totalLength===0?0:tensor2.size/totalLength,tensors=tidy(()=>{let tensors2=[];tensor2=reshape(tensor2,[1,totalLength,elementPerRow]);for(let i=0;i{switch(node.op){case"If":case"StatelessIf":{let thenFunc=getParamValue("thenBranch",node,tensorMap,context),elseFunc=getParamValue("elseBranch",node,tensorMap,context),cond=getParamValue("cond",node,tensorMap,context),args=getParamValue("args",node,tensorMap,context);return(await cond.data())[0]?context.functionMap[thenFunc].executeFunctionAsync(args,context.tensorArrayMap,context.tensorListMap):context.functionMap[elseFunc].executeFunctionAsync(args,context.tensorArrayMap,context.tensorListMap)}case"While":case"StatelessWhile":{let bodyFunc=getParamValue("body",node,tensorMap,context),condFunc=getParamValue("cond",node,tensorMap,context),args=getParamValue("args",node,tensorMap,context),condResult=await context.functionMap[condFunc].executeFunctionAsync(args,context.tensorArrayMap,context.tensorListMap),argIds=args.map(tensor2=>tensor2.id),condValue=await condResult[0].data();condResult.forEach(tensor2=>{!tensor2.kept&&argIds.indexOf(tensor2.id)===-1&&tensor2.dispose()});let result=args;for(;condValue[0];){let origResult=result;result=await context.functionMap[bodyFunc].executeFunctionAsync(result,context.tensorArrayMap,context.tensorListMap);let resultIds=result.map(tensor2=>tensor2.id);origResult.forEach(tensor2=>{!tensor2.kept&&argIds.indexOf(tensor2.id)===-1&&resultIds.indexOf(tensor2.id)===-1&&tensor2.dispose()});let condResult2=await context.functionMap[condFunc].executeFunctionAsync(result,context.tensorArrayMap,context.tensorListMap);condValue=await condResult2[0].data(),condResult2.forEach(tensor2=>{!tensor2.kept&&argIds.indexOf(tensor2.id)===-1&&resultIds.indexOf(tensor2.id)===-1&&tensor2.dispose()})}return result}case"LoopCond":{let pred=getParamValue("pred",node,tensorMap,context);return[cloneTensor(pred)]}case"Switch":{let pred=getParamValue("pred",node,tensorMap,context),data2=getParamValue("data",node,tensorMap,context);return data2.kept||(data2=cloneTensor(data2)),(await pred.data())[0]?[void 0,data2]:[data2,void 0]}case"Merge":{let inputName=node.inputNames.find(name=>getTensor(name,tensorMap,context)!==void 0);if(inputName){let data2=getTensor(inputName,tensorMap,context);return[cloneTensor(data2)]}return}case"Enter":{let frameId=getParamValue("frameName",node,tensorMap,context),data2=getParamValue("tensor",node,tensorMap,context);return context.enterFrame(frameId),[cloneTensor(data2)]}case"Exit":{let data2=getParamValue("tensor",node,tensorMap,context);return context.exitFrame(),[cloneTensor(data2)]}case"NextIteration":{let data2=getParamValue("tensor",node,tensorMap,context);return context.nextIteration(),[cloneTensor(data2)]}case"TensorArrayV3":{let size=getParamValue("size",node,tensorMap,context),dtype=getParamValue("dtype",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),dynamicSize=getParamValue("dynamicSize",node,tensorMap,context),clearAfterRead=getParamValue("clearAfterRead",node,tensorMap,context),identicalElementShapes=getParamValue("identicalElementShapes",node,tensorMap,context),name=getParamValue("name",node,tensorMap,context),tensorArray=new TensorArray(name,dtype,size,elementShape,identicalElementShapes,dynamicSize,clearAfterRead);return context.addTensorArray(tensorArray),[tensorArray.idTensor,scalar(1)]}case"TensorArrayWriteV3":{let id=getParamValue("tensorArrayId",node,tensorMap,context),index=getParamValue("index",node,tensorMap,context),writeTensor=getParamValue("tensor",node,tensorMap,context),writeTensorArray=context.getTensorArray(id.id);return writeTensorArray.write(index,writeTensor),[writeTensorArray.idTensor]}case"TensorArrayReadV3":{let readId=getParamValue("tensorArrayId",node,tensorMap,context),readIndex=getParamValue("index",node,tensorMap,context);return[context.getTensorArray(readId.id).read(readIndex)]}case"TensorArrayGatherV3":{let gatherId=getParamValue("tensorArrayId",node,tensorMap,context),gatherIndices=getParamValue("indices",node,tensorMap,context),gatherDtype=getParamValue("dtype",node,tensorMap,context);return[context.getTensorArray(gatherId.id).gather(gatherIndices,gatherDtype)]}case"TensorArrayScatterV3":{let scatterId=getParamValue("tensorArrayId",node,tensorMap,context),scatterIndices=getParamValue("indices",node,tensorMap,context),scatterTensor=getParamValue("tensor",node,tensorMap,context),scatterTensorArray=context.getTensorArray(scatterId.id);return scatterTensorArray.scatter(scatterIndices,scatterTensor),[scatterTensorArray.idTensor]}case"TensorArrayConcatV3":{let concatId=getParamValue("tensorArrayId",node,tensorMap,context),concatTensorArray=context.getTensorArray(concatId.id),concatDtype=getParamValue("dtype",node,tensorMap,context);return[concatTensorArray.concat(concatDtype)]}case"TensorArraySplitV3":{let splitId=getParamValue("tensorArrayId",node,tensorMap,context),splitTensor=getParamValue("tensor",node,tensorMap,context),lengths=getParamValue("lengths",node,tensorMap,context),splitTensorArray=context.getTensorArray(splitId.id);return splitTensorArray.split(lengths,splitTensor),[splitTensorArray.idTensor]}case"TensorArraySizeV3":{let sizeId=getParamValue("tensorArrayId",node,tensorMap,context),sizeTensorArray=context.getTensorArray(sizeId.id);return[scalar(sizeTensorArray.size(),"int32")]}case"TensorArrayCloseV3":{let closeId=getParamValue("tensorArrayId",node,tensorMap,context),closeTensorArray=context.getTensorArray(closeId.id);return closeTensorArray.clearAndClose(),[closeTensorArray.idTensor]}case"TensorListSetItem":{let idTensor=getParamValue("tensorListId",node,tensorMap,context),index=getParamValue("index",node,tensorMap,context),writeTensor=getParamValue("tensor",node,tensorMap,context),tensorList=context.getTensorList(idTensor.id);return tensorList.setItem(index,writeTensor),[tensorList.idTensor]}case"TensorListGetItem":{let idTensor=getParamValue("tensorListId",node,tensorMap,context),readIndex=getParamValue("index",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),elementDType=getParamValue("elementDType",node,tensorMap,context);return[context.getTensorList(idTensor.id).getItem(readIndex,elementShape,elementDType)]}case"TensorListScatterV2":case"TensorListScatter":{let scatterIndices=getParamValue("indices",node,tensorMap,context),scatterTensor=getParamValue("tensor",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),numElements=getParamValue("numElements",node,tensorMap,context),tensorList=scatter(scatterTensor,scatterIndices,elementShape,numElements);return context.addTensorList(tensorList),[tensorList.idTensor]}case"TensorListReserve":case"EmptyTensorList":{let elementShape=getParamValue("elementShape",node,tensorMap,context),elementDtype=getParamValue("elementDType",node,tensorMap,context),numElementsParam;node.op==="TensorListReserve"?numElementsParam="numElements":numElementsParam="maxNumElements";let numElements=getParamValue(numElementsParam,node,tensorMap,context),tensorList=reserve(elementShape,elementDtype,numElements);return context.addTensorList(tensorList),[tensorList.idTensor]}case"TensorListGather":{let gatherId=getParamValue("tensorListId",node,tensorMap,context),gatherIndices=getParamValue("indices",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),elementDtype=getParamValue("elementDType",node,tensorMap,context);return[context.getTensorList(gatherId.id).gather(gatherIndices,elementDtype,elementShape)]}case"TensorListStack":{let idTensor=getParamValue("tensorListId",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),elementDtype=getParamValue("elementDType",node,tensorMap,context),numElements=getParamValue("numElements",node,tensorMap,context);return[context.getTensorList(idTensor.id).stack(elementShape,elementDtype,numElements)]}case"TensorListFromTensor":{let tensor2=getParamValue("tensor",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),elementDtype=getParamValue("elementDType",node,tensorMap,context),tensorList=fromTensor(tensor2,elementShape,elementDtype);return context.addTensorList(tensorList),[tensorList.idTensor]}case"TensorListConcat":{let concatId=getParamValue("tensorListId",node,tensorMap,context),tensorList=context.getTensorList(concatId.id),concatDtype=getParamValue("dtype",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context);return[tensorList.concat(concatDtype,elementShape)]}case"TensorListPushBack":{let idTensor=getParamValue("tensorListId",node,tensorMap,context),writeTensor=getParamValue("tensor",node,tensorMap,context),tensorList=context.getTensorList(idTensor.id);return tensorList.pushBack(writeTensor),[tensorList.idTensor]}case"TensorListPopBack":{let idTensor=getParamValue("tensorListId",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),elementDType=getParamValue("elementDType",node,tensorMap,context);return[context.getTensorList(idTensor.id).popBack(elementShape,elementDType)]}case"TensorListSplit":{let splitTensor=getParamValue("tensor",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),lengths=getParamValue("lengths",node,tensorMap,context),tensorList=split2(splitTensor,lengths,elementShape);return context.addTensorList(tensorList),[tensorList.idTensor]}default:throw TypeError(`Node type ${node.op} is not implemented`)}};function fusedConvAndDepthWiseParams(node,tensorMap,context){let[extraOp,activationFunc]=getParamValue("fusedOps",node,tensorMap,context),isBiasAdd=extraOp==="biasadd",isPrelu=activationFunc==="prelu",isBatchNorm=extraOp==="fusedbatchnorm",numArgs=getParamValue("numArgs",node,tensorMap,context);if(isBiasAdd){if(isPrelu&&numArgs!==2)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!isPrelu&&numArgs!==1)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(isBatchNorm)throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported.");let stride=getParamValue("strides",node,tensorMap,context),pad3=getPadding(node,tensorMap,context),dataFormat=getParamValue("dataFormat",node,tensorMap,context).toUpperCase(),dilations=getParamValue("dilations",node,tensorMap,context),[biasArg,preluArg]=getParamValue("args",node,tensorMap,context),leakyreluAlpha=getParamValue("leakyreluAlpha",node,tensorMap,context);return{stride,pad:pad3,dataFormat,dilations,biasArg,preluArg,activationFunc,leakyreluAlpha}}var executeOp4=(node,tensorMap,context)=>{switch(node.op){case"Conv1D":{let stride=getParamValue("stride",node,tensorMap,context),pad3=getParamValue("pad",node,tensorMap,context),dataFormat=getParamValue("dataFormat",node,tensorMap,context).toUpperCase(),dilation=getParamValue("dilation",node,tensorMap,context);return[conv1d(getParamValue("x",node,tensorMap,context),getParamValue("filter",node,tensorMap,context),stride,pad3,dataFormat,dilation)]}case"Conv2D":{let stride=getParamValue("strides",node,tensorMap,context),pad3=getPadding(node,tensorMap,context),dataFormat=getParamValue("dataFormat",node,tensorMap,context).toUpperCase(),dilations=getParamValue("dilations",node,tensorMap,context);return[conv2d(getParamValue("x",node,tensorMap,context),getParamValue("filter",node,tensorMap,context),[stride[1],stride[2]],pad3,dataFormat,[dilations[1],dilations[2]])]}case"_FusedConv2D":{let{stride,pad:pad3,dataFormat,dilations,biasArg,preluArg,activationFunc,leakyreluAlpha}=fusedConvAndDepthWiseParams(node,tensorMap,context);return[fused_ops_exports.conv2d({x:getParamValue("x",node,tensorMap,context),filter:getParamValue("filter",node,tensorMap,context),strides:[stride[1],stride[2]],pad:pad3,dataFormat,dilations:[dilations[1],dilations[2]],bias:biasArg,activation:activationFunc,preluActivationWeights:preluArg,leakyreluAlpha})]}case"FusedDepthwiseConv2dNative":{let{stride,pad:pad3,dataFormat,dilations,biasArg,preluArg,activationFunc,leakyreluAlpha}=fusedConvAndDepthWiseParams(node,tensorMap,context);return[fused_ops_exports.depthwiseConv2d({x:getParamValue("x",node,tensorMap,context),filter:getParamValue("filter",node,tensorMap,context),strides:[stride[1],stride[2]],pad:pad3,dataFormat,dilations:[dilations[1],dilations[2]],bias:biasArg,activation:activationFunc,preluActivationWeights:preluArg,leakyreluAlpha})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{let shape=getParamValue("outputShape",node,tensorMap,context),stride=getParamValue("strides",node,tensorMap,context),pad3=getPadding(node,tensorMap,context);return[conv2dTranspose(getParamValue("x",node,tensorMap,context),getParamValue("filter",node,tensorMap,context),shape,[stride[1],stride[2]],pad3)]}case"DepthwiseConv2dNative":case"DepthwiseConv2d":{let stride=getParamValue("strides",node,tensorMap,context),pad3=getPadding(node,tensorMap,context),dilations=getParamValue("dilations",node,tensorMap,context),dataFormat=getParamValue("dataFormat",node,tensorMap,context).toUpperCase();return[depthwiseConv2d(getParamValue("input",node,tensorMap,context),getParamValue("filter",node,tensorMap,context),[stride[1],stride[2]],pad3,dataFormat,[dilations[1],dilations[2]])]}case"Conv3D":{let stride=getParamValue("strides",node,tensorMap,context),pad3=getParamValue("pad",node,tensorMap,context),dataFormat=getParamValue("dataFormat",node,tensorMap,context).toUpperCase(),dilations=getParamValue("dilations",node,tensorMap,context);return[conv3d(getParamValue("x",node,tensorMap,context),getParamValue("filter",node,tensorMap,context),[stride[1],stride[2],stride[3]],pad3,dataFormat,[dilations[1],dilations[2],dilations[3]])]}case"AvgPool":{let stride=getParamValue("strides",node,tensorMap,context),pad3=getParamValue("pad",node,tensorMap,context),kernelSize=getParamValue("kernelSize",node,tensorMap,context);return[avgPool(getParamValue("x",node,tensorMap,context),[kernelSize[1],kernelSize[2]],[stride[1],stride[2]],pad3)]}case"MaxPool":{let stride=getParamValue("strides",node,tensorMap,context),pad3=getParamValue("pad",node,tensorMap,context),kernelSize=getParamValue("kernelSize",node,tensorMap,context);return[maxPool(getParamValue("x",node,tensorMap,context),[kernelSize[1],kernelSize[2]],[stride[1],stride[2]],pad3)]}case"MaxPoolWithArgmax":{let stride=getParamValue("strides",node,tensorMap,context),pad3=getParamValue("pad",node,tensorMap,context),kernelSize=getParamValue("kernelSize",node,tensorMap,context),includeBatchInIndex=getParamValue("includeBatchInIndex",node,tensorMap,context),{result,indexes}=maxPoolWithArgmax(getParamValue("x",node,tensorMap,context),[kernelSize[1],kernelSize[2]],[stride[1],stride[2]],pad3,includeBatchInIndex);return[result,indexes]}case"AvgPool3D":{let stride=getParamValue("strides",node,tensorMap,context),pad3=getParamValue("pad",node,tensorMap,context),kernelSize=getParamValue("kernelSize",node,tensorMap,context);return[avgPool3d(getParamValue("x",node,tensorMap,context),[kernelSize[1],kernelSize[2],kernelSize[3]],[stride[1],stride[2],stride[3]],pad3)]}case"MaxPool3D":{let stride=getParamValue("strides",node,tensorMap,context),pad3=getParamValue("pad",node,tensorMap,context),kernelSize=getParamValue("kernelSize",node,tensorMap,context);return[maxPool3d(getParamValue("x",node,tensorMap,context),[kernelSize[1],kernelSize[2],kernelSize[3]],[stride[1],stride[2],stride[3]],pad3)]}case"Dilation2D":{let strides=getParamValue("strides",node,tensorMap,context),pad3=getParamValue("pad",node,tensorMap,context),dilations=getParamValue("dilations",node,tensorMap,context),strideHeight=strides[1],strideWidth=strides[2],dilationHeight=dilations[1],dilationWidth=dilations[2];return[dilation2d(getParamValue("x",node,tensorMap,context),getParamValue("filter",node,tensorMap,context),[strideHeight,strideWidth],pad3,[dilationHeight,dilationWidth],"NHWC")]}default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp5=(node,tensorMap,context)=>{switch(node.op){case"Fill":{let shape=getParamValue("shape",node,tensorMap,context),dtype=getParamValue("dtype",node,tensorMap,context),value=getParamValue("value",node,tensorMap,context);return[fill(shape,value,dtype)]}case"LinSpace":{let start=getParamValue("start",node,tensorMap,context),stop=getParamValue("stop",node,tensorMap,context),num=getParamValue("num",node,tensorMap,context);return[linspace(start,stop,num)]}case"Multinomial":{let logits=getParamValue("logits",node,tensorMap,context),numSamples=getParamValue("numSamples",node,tensorMap,context),seed=getParamValue("seed",node,tensorMap,context);return[multinomial(logits,numSamples,seed)]}case"OneHot":{let indices=getParamValue("indices",node,tensorMap,context),depth=getParamValue("depth",node,tensorMap,context),onValue=getParamValue("onValue",node,tensorMap,context),offValue=getParamValue("offValue",node,tensorMap,context);return[oneHot(indices,depth,onValue,offValue)]}case"Ones":return[ones2(getParamValue("shape",node,tensorMap,context),getParamValue("dtype",node,tensorMap,context))];case"OnesLike":return[onesLike(getParamValue("x",node,tensorMap,context))];case"RandomUniform":return[randomUniform(getParamValue("shape",node,tensorMap,context),getParamValue("minval",node,tensorMap,context),getParamValue("maxval",node,tensorMap,context),getParamValue("dtype",node,tensorMap,context))];case"Range":{let start=getParamValue("start",node,tensorMap,context),stop=getParamValue("stop",node,tensorMap,context),step4=getParamValue("step",node,tensorMap,context);return[range(start,stop,step4,getParamValue("dtype",node,tensorMap,context))]}case"TruncatedNormal":{let shape=getParamValue("shape",node,tensorMap,context),mean4=getParamValue("mean",node,tensorMap,context),stdDev=getParamValue("stdDev",node,tensorMap,context),seed=getParamValue("seed",node,tensorMap,context);return[truncatedNormal(shape,mean4,stdDev,getParamValue("dtype",node,tensorMap,context),seed)]}case"Zeros":return[zeros(getParamValue("shape",node,tensorMap,context),getParamValue("dtype",node,tensorMap,context))];case"ZerosLike":return[zerosLike(getParamValue("x",node,tensorMap,context))];default:throw TypeError(`Node type ${node.op} is not implemented`)}};function nmsParams(node,tensorMap,context){let boxes=getParamValue("boxes",node,tensorMap,context),scores=getParamValue("scores",node,tensorMap,context),maxOutputSize=getParamValue("maxOutputSize",node,tensorMap,context),iouThreshold=getParamValue("iouThreshold",node,tensorMap,context),scoreThreshold=getParamValue("scoreThreshold",node,tensorMap,context),softNmsSigma=getParamValue("softNmsSigma",node,tensorMap,context);return{boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma}}var executeOp6=async(node,tensorMap,context)=>{switch(node.op){case"NonMaxSuppressionV5":{let{boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma}=nmsParams(node,tensorMap,context),result=await image.nonMaxSuppressionWithScoreAsync(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma);return[result.selectedIndices,result.selectedScores]}case"NonMaxSuppressionV4":{let{boxes,scores,maxOutputSize,iouThreshold,scoreThreshold}=nmsParams(node,tensorMap,context),padToMaxOutputSize=getParamValue("padToMaxOutputSize",node,tensorMap,context),result=await image.nonMaxSuppressionPaddedAsync(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize);return[result.selectedIndices,result.validOutputs]}case"NonMaxSuppressionV3":case"NonMaxSuppressionV2":{let{boxes,scores,maxOutputSize,iouThreshold,scoreThreshold}=nmsParams(node,tensorMap,context);return[await image.nonMaxSuppressionAsync(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold)]}case"Where":{let condition=cast(getParamValue("condition",node,tensorMap,context),"bool"),result=[await whereAsync(condition)];return condition.dispose(),result}case"ListDiff":return setdiff1dAsync(getParamValue("x",node,tensorMap,context),getParamValue("y",node,tensorMap,context));default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp7=(node,tensorMap,context)=>{switch(node.op){case"TopKV2":{let x=getParamValue("x",node,tensorMap,context),k=getParamValue("k",node,tensorMap,context),sorted=getParamValue("sorted",node,tensorMap,context),result=topk(x,k,sorted);return[result.values,result.indices]}case"Unique":{let x=getParamValue("x",node,tensorMap,context),result=unique(x);return[result.values,result.indices]}case"UniqueV2":{let x=getParamValue("x",node,tensorMap,context),axis=getParamValue("axis",node,tensorMap,context),result=unique(x,axis);return[result.values,result.indices]}default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp8=(node,tensorMap,context)=>{switch(node.op){case"Const":return tensorMap[node.name];case"PlaceholderWithDefault":let def=getParamValue("default",node,tensorMap,context);return[getTensor(node.name,tensorMap,context)||def];case"Placeholder":return[getTensor(node.name,tensorMap,context)];case"Identity":case"StopGradient":case"FakeQuantWithMinMaxVars":{let data22=getParamValue("x",node,tensorMap,context);return[cloneTensor(data22)]}case"IdentityN":return getParamValue("x",node,tensorMap,context).map(t=>cloneTensor(t));case"Snapshot":let snapshot=getParamValue("x",node,tensorMap,context);return[cloneTensor(snapshot)];case"Shape":return[tensor1d(getParamValue("x",node,tensorMap,context).shape,"int32")];case"ShapeN":return getParamValue("x",node,tensorMap,context).map(t=>tensor1d(t.shape));case"Size":return[scalar(getParamValue("x",node,tensorMap,context).size,"int32")];case"Rank":return[scalar(getParamValue("x",node,tensorMap,context).rank,"int32")];case"NoOp":return[scalar(1)];case"Print":let input2=getParamValue("x",node,tensorMap,context),data2=getParamValue("data",node,tensorMap,context),message=getParamValue("message",node,tensorMap,context),summarize=getParamValue("summarize",node,tensorMap,context);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."),console.log(message);for(let i=0;ivalue.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}async import(keys,values){this.checkKeyAndValueTensor(keys,values);let $keys=await keys.data();return this.tensorMap.forEach(value=>value.dispose()),this.tensorMap.clear(),tidy(()=>{let $values=unstack(values),keysLength=$keys.length,valuesLength=$values.length;util_exports.assert(keysLength===valuesLength,()=>`The number of elements doesn't match, keys has ${keysLength} elements, the values has ${valuesLength} elements.`);for(let i=0;i{let result=[];for(let i=0;i<$keys.length;i++){let key=$keys[i],value=this.findWithDefault(key,defaultValue);result.push(value)}return stack(result)})}findWithDefault(key,defaultValue){let result=this.tensorMap.get(key);return result!=null?result:defaultValue}checkKeyAndValueTensor(key,value){if(key.dtype!==this.keyDType)throw new Error(`Expect key dtype ${this.keyDType}, but got ${key.dtype}`);if(value.dtype!==this.valueDType)throw new Error(`Expect value dtype ${this.valueDType}, but got ${value.dtype}`)}},executeOp9=async(node,tensorMap,context,resourceManager)=>{switch(node.op){case"HashTable":case"HashTableV2":{let keyDType=getParamValue("keyDType",node,tensorMap,context),valueDType=getParamValue("valueDType",node,tensorMap,context),hashTable2=new HashTable(keyDType,valueDType);return resourceManager.addHashTable(node.name,hashTable2),[hashTable2.handle]}case"LookupTableImport":case"LookupTableImportV2":{let handle=getParamValue("tableHandle",node,tensorMap,context,resourceManager),keys=getParamValue("keys",node,tensorMap,context),values=getParamValue("values",node,tensorMap,context);return[await resourceManager.getHashTableById(handle.id).import(keys,values)]}case"LookupTableFind":case"LookupTableFindV2":{let handle=getParamValue("tableHandle",node,tensorMap,context,resourceManager),keys=getParamValue("keys",node,tensorMap,context),defaultValue=getParamValue("defaultValue",node,tensorMap,context);return[await resourceManager.getHashTableById(handle.id).find(keys,defaultValue)]}default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp10=(node,tensorMap,context)=>{switch(node.op){case"ResizeBilinear":{let images=getParamValue("images",node,tensorMap,context),size=getParamValue("size",node,tensorMap,context),alignCorners=getParamValue("alignCorners",node,tensorMap,context),halfPixelCenters=getParamValue("halfPixelCenters",node,tensorMap,context);return[image.resizeBilinear(images,[size[0],size[1]],alignCorners,halfPixelCenters)]}case"ResizeNearestNeighbor":{let images=getParamValue("images",node,tensorMap,context),size=getParamValue("size",node,tensorMap,context),alignCorners=getParamValue("alignCorners",node,tensorMap,context),halfPixelCenters=getParamValue("halfPixelCenters",node,tensorMap,context);return[image.resizeNearestNeighbor(images,[size[0],size[1]],alignCorners,halfPixelCenters)]}case"CropAndResize":{let image3=getParamValue("image",node,tensorMap,context),boxes=getParamValue("boxes",node,tensorMap,context),boxInd=getParamValue("boxInd",node,tensorMap,context),cropSize=getParamValue("cropSize",node,tensorMap,context),method=getParamValue("method",node,tensorMap,context),extrapolationValue=getParamValue("extrapolationValue",node,tensorMap,context);return[image.cropAndResize(image3,boxes,boxInd,cropSize,method,extrapolationValue)]}default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp11=(node,tensorMap,context)=>{switch(node.op){case"Equal":return[equal(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"NotEqual":return[notEqual(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Greater":return[greater(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"GreaterEqual":return[greaterEqual(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Less":return[less(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"LessEqual":return[lessEqual(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"LogicalAnd":return[logicalAnd(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"LogicalNot":return[logicalNot(getParamValue("a",node,tensorMap,context))];case"LogicalOr":return[logicalOr(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Select":case"SelectV2":return[where(getParamValue("condition",node,tensorMap,context),getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp12=(node,tensorMap,context)=>{switch(node.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[matMul(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context),getParamValue("transposeA",node,tensorMap,context),getParamValue("transposeB",node,tensorMap,context))];case"Transpose":return[transpose(getParamValue("x",node,tensorMap,context),getParamValue("perm",node,tensorMap,context))];case"_FusedMatMul":let[extraOp,activationFunc]=getParamValue("fusedOps",node,tensorMap,context),isBiasAdd=extraOp==="biasadd",isPrelu=activationFunc==="prelu",numArgs=getParamValue("numArgs",node,tensorMap,context),leakyreluAlpha=getParamValue("leakyreluAlpha",node,tensorMap,context);if(isBiasAdd){if(isPrelu&&numArgs!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!isPrelu&&numArgs!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[biasArg,preluArg]=getParamValue("args",node,tensorMap,context);return[fused_ops_exports.matMul({a:getParamValue("a",node,tensorMap,context),b:getParamValue("b",node,tensorMap,context),transposeA:getParamValue("transposeA",node,tensorMap,context),transposeB:getParamValue("transposeB",node,tensorMap,context),bias:biasArg,activation:activationFunc,preluActivationWeights:preluArg,leakyreluAlpha})];default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp13=(node,tensorMap,context)=>{switch(node.op){case"FusedBatchNorm":case"FusedBatchNormV2":return[batchNorm(getParamValue("x",node,tensorMap,context),getParamValue("mean",node,tensorMap,context),getParamValue("variance",node,tensorMap,context),getParamValue("offset",node,tensorMap,context),getParamValue("scale",node,tensorMap,context),getParamValue("epsilon",node,tensorMap,context))];case"FusedBatchNormV3":return[batchNorm(getParamValue("x",node,tensorMap,context),getParamValue("mean",node,tensorMap,context),getParamValue("variance",node,tensorMap,context),getParamValue("offset",node,tensorMap,context),getParamValue("scale",node,tensorMap,context),getParamValue("epsilon",node,tensorMap,context))];case"LRN":return[localResponseNormalization(getParamValue("x",node,tensorMap,context),getParamValue("radius",node,tensorMap,context),getParamValue("bias",node,tensorMap,context),getParamValue("alpha",node,tensorMap,context),getParamValue("beta",node,tensorMap,context))];case"Softmax":return[softmax(getParamValue("x",node,tensorMap,context))];case"LogSoftmax":return[logSoftmax(getParamValue("x",node,tensorMap,context))];case"SparseToDense":return[sparseToDense(getParamValue("sparseIndices",node,tensorMap,context),getParamValue("outputShape",node,tensorMap,context),getParamValue("sparseValues",node,tensorMap,context),getParamValue("defaultValue",node,tensorMap,context))];default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp14=(node,tensorMap,context)=>{switch(node.op){case"Max":{let axis=getParamValue("axis",node,tensorMap,context),keepDims=getParamValue("keepDims",node,tensorMap,context);return[max(getParamValue("x",node,tensorMap,context),axis,keepDims)]}case"Mean":{let axis=getParamValue("axis",node,tensorMap,context),keepDims=getParamValue("keepDims",node,tensorMap,context);return[mean(getParamValue("x",node,tensorMap,context),axis,keepDims)]}case"Min":{let axis=getParamValue("axis",node,tensorMap,context),keepDims=getParamValue("keepDims",node,tensorMap,context);return[min(getParamValue("x",node,tensorMap,context),axis,keepDims)]}case"Sum":{let axis=getParamValue("axis",node,tensorMap,context),keepDims=getParamValue("keepDims",node,tensorMap,context);return[sum2(getParamValue("x",node,tensorMap,context),axis,keepDims)]}case"All":{let axis=getParamValue("axis",node,tensorMap,context),keepDims=getParamValue("keepDims",node,tensorMap,context);return[all(getParamValue("x",node,tensorMap,context),axis,keepDims)]}case"Any":{let axis=getParamValue("axis",node,tensorMap,context),keepDims=getParamValue("keepDims",node,tensorMap,context);return[any(getParamValue("x",node,tensorMap,context),axis,keepDims)]}case"ArgMax":{let axis=getParamValue("axis",node,tensorMap,context);return[argMax(getParamValue("x",node,tensorMap,context),axis)]}case"ArgMin":{let axis=getParamValue("axis",node,tensorMap,context);return[argMin(getParamValue("x",node,tensorMap,context),axis)]}case"Prod":{let axis=getParamValue("axis",node,tensorMap,context),keepDims=getParamValue("keepDims",node,tensorMap,context);return[prod(getParamValue("x",node,tensorMap,context),axis,keepDims)]}case"Cumsum":{let axis=getParamValue("axis",node,tensorMap,context),exclusive=getParamValue("exclusive",node,tensorMap,context),reverse5=getParamValue("reverse",node,tensorMap,context);return[cumsum(getParamValue("x",node,tensorMap,context),axis,exclusive,reverse5)]}case"Bincount":let x=getParamValue("x",node,tensorMap,context),weights=getParamValue("weights",node,tensorMap,context),size=getParamValue("size",node,tensorMap,context);return[bincount(x,weights,size)];case"DenseBincount":{let x2=getParamValue("x",node,tensorMap,context),weights2=getParamValue("weights",node,tensorMap,context),size2=getParamValue("size",node,tensorMap,context),binaryOutput=getParamValue("binaryOutput",node,tensorMap,context);return[denseBincount(x2,weights2,size2,binaryOutput)]}default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp15=(node,tensorMap,context)=>{switch(node.op){case"ConcatV2":case"Concat":{let n=getParamValue("n",node,tensorMap,context),axis=getParamValue("axis",node,tensorMap,context),inputs=getParamValue("tensors",node,tensorMap,context);return inputs=inputs.slice(0,n),[concat(inputs,axis)]}case"Gather":{let input2=getParamValue("x",node,tensorMap,context),indices=getParamValue("indices",node,tensorMap,context);return[gather(input2,cast(indices,"int32"),0)]}case"GatherV2":{let axis=getParamValue("axis",node,tensorMap,context),batchDims=getParamValue("batchDims",node,tensorMap,context),input2=getParamValue("x",node,tensorMap,context),indices=getParamValue("indices",node,tensorMap,context);return[gather(input2,cast(indices,"int32"),axis,batchDims)]}case"Reverse":{let dims=getParamValue("dims",node,tensorMap,context),axis=[];for(let i=0;i{let axis=getParamValue("axis",node,tensorMap,context),tensors=getParamValue("tensors",node,tensorMap,context),shape=tensors[0].shape,squeezedShape=squeeze(tensors[0]).shape,mapped=tensors.map(tensor2=>{let sameShape=util_exports.arraysEqual(tensor2.shape,shape);if(!sameShape&&!util_exports.arraysEqual(squeeze(tensor2).shape,squeezedShape))throw new Error("the input tensors shape does not match");return sameShape?tensor2:reshape(tensor2,shape)});return[stack(mapped,axis)]});case"Unpack":{let axis=getParamValue("axis",node,tensorMap,context),tensor2=getParamValue("tensor",node,tensorMap,context);return unstack(tensor2,axis)}case"Tile":{let reps=getParamValue("reps",node,tensorMap,context);return[tile(getParamValue("x",node,tensorMap,context),reps)]}case"Split":case"SplitV":{let axis=getParamValue("axis",node,tensorMap,context),numOrSizeSplits=getParamValue("numOrSizeSplits",node,tensorMap,context),tensor2=getParamValue("x",node,tensorMap,context);return split(tensor2,numOrSizeSplits,axis)}case"ScatterNd":{let indices=getParamValue("indices",node,tensorMap,context),values=getParamValue("values",node,tensorMap,context),shape=getParamValue("shape",node,tensorMap,context);return[scatterND(indices,values,shape)]}case"GatherNd":{let x=getParamValue("x",node,tensorMap,context),indices=getParamValue("indices",node,tensorMap,context);return[gatherND(x,indices)]}case"SparseToDense":{let indices=getParamValue("sparseIndices",node,tensorMap,context),shape=getParamValue("outputShape",node,tensorMap,context),sparseValues=getParamValue("sparseValues",node,tensorMap,context),defaultValue=getParamValue("defaultValue",node,tensorMap,context);return[sparseToDense(indices,sparseValues,shape,sparseValues.dtype===defaultValue.dtype?defaultValue:cast(defaultValue,sparseValues.dtype))]}default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp16=(node,tensorMap,context)=>{switch(node.op){case"FFT":return[fft(getParamValue("x",node,tensorMap,context))];case"IFFT":return[ifft(getParamValue("x",node,tensorMap,context))];case"RFFT":return[rfft(getParamValue("x",node,tensorMap,context))];case"IRFFT":return[irfft(getParamValue("x",node,tensorMap,context))];default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp17=(node,tensorMap,context)=>{switch(node.op){case"Cast":return[cast(getParamValue("x",node,tensorMap,context),getParamValue("dtype",node,tensorMap,context))];case"ExpandDims":{let axis=getParamValue("axis",node,tensorMap,context);return[expandDims(getParamValue("x",node,tensorMap,context),axis)]}case"Squeeze":{let axis=getParamValue("axis",node,tensorMap,context);return[squeeze(getParamValue("x",node,tensorMap,context),axis)]}case"Reshape":return[reshape(getParamValue("x",node,tensorMap,context),getParamValue("shape",node,tensorMap,context))];case"MirrorPad":return[mirrorPad(getParamValue("x",node,tensorMap,context),getParamValue("padding",node,tensorMap,context),getParamValue("mode",node,tensorMap,context))];case"PadV2":case"Pad":return[pad(getParamValue("x",node,tensorMap,context),getParamValue("padding",node,tensorMap,context),getParamValue("constantValue",node,tensorMap,context))];case"SpaceToBatchND":{let blockShape=getParamValue("blockShape",node,tensorMap,context),paddings=getParamValue("paddings",node,tensorMap,context);return[spaceToBatchND(getParamValue("x",node,tensorMap,context),blockShape,paddings)]}case"BatchToSpaceND":{let blockShape=getParamValue("blockShape",node,tensorMap,context),crops=getParamValue("crops",node,tensorMap,context);return[batchToSpaceND(getParamValue("x",node,tensorMap,context),blockShape,crops)]}case"DepthToSpace":{let blockSize=getParamValue("blockSize",node,tensorMap,context),dataFormat=getParamValue("dataFormat",node,tensorMap,context).toUpperCase();return[depthToSpace(getParamValue("x",node,tensorMap,context),blockSize,dataFormat)]}case"BroadcastTo":return[broadcastTo(getParamValue("x",node,tensorMap,context),getParamValue("shape",node,tensorMap,context))];default:throw TypeError(`Node type ${node.op} is not implemented`)}};function executeOp18(node,tensorMap,context,resourceManager){let value=((node2,tensorMap2,context2)=>{switch(node2.category){case"arithmetic":return tidy(()=>executeOp(node2,tensorMap2,context2));case"basic_math":return tidy(()=>executeOp2(node2,tensorMap2,context2));case"control":return executeOp3(node2,tensorMap2,context2);case"convolution":return tidy(()=>executeOp4(node2,tensorMap2,context2));case"creation":return tidy(()=>executeOp5(node2,tensorMap2,context2));case"dynamic":return executeOp6(node2,tensorMap2,context2);case"evaluation":return tidy(()=>executeOp7(node2,tensorMap2,context2));case"image":return tidy(()=>executeOp10(node2,tensorMap2,context2));case"graph":return tidy(()=>executeOp8(node2,tensorMap2,context2));case"logical":return tidy(()=>executeOp11(node2,tensorMap2,context2));case"matrices":return tidy(()=>executeOp12(node2,tensorMap2,context2));case"normalization":return tidy(()=>executeOp13(node2,tensorMap2,context2));case"reduction":return tidy(()=>executeOp14(node2,tensorMap2,context2));case"slice_join":return tidy(()=>executeOp15(node2,tensorMap2,context2));case"spectral":return tidy(()=>executeOp16(node2,tensorMap2,context2));case"transformation":return tidy(()=>executeOp17(node2,tensorMap2,context2));case"hash_table":return executeOp9(node2,tensorMap2,context2,resourceManager);case"custom":let opMapper=getRegisteredOp(node2.op);if(opMapper&&opMapper.customExecutor)return opMapper.customExecutor(new NodeValueImpl(node2,tensorMap2,context2));throw TypeError(`Custom op ${node2.op} is not registered.`);default:throw TypeError(`Unknown op '${node2.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(node,tensorMap,context);return util_exports.isPromise(value)?value.then(data2=>[].concat(data2)):[].concat(value)}var ExecutionContext=class{constructor(weightMap={},tensorArrayMap={},tensorListMap={},functionMap={}){this.weightMap=weightMap,this.tensorArrayMap=tensorArrayMap,this.tensorListMap=tensorListMap,this.functionMap=functionMap,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(id,frameName){return{id,frameName,iterationId:0}}set currentContext(contexts2){this.contexts!==contexts2&&(this.contexts=contexts2,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let names=[];for(let i=0;icontext.id===0&&context.iterationId===0?"":`${context.frameName}-${context.iterationId}`).join("/"):""}enterFrame(frameId){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,frameId)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let context=Object.assign({},this.contexts[this.contexts.length-1]);context.iterationId+=1,context.id=this.lastId,this.contexts.splice(-1,1,context),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(name){return this.weightMap[name]}addTensorArray(tensorArray){this.tensorArrayMap[tensorArray.id]=tensorArray}getTensorArray(id){return this.tensorArrayMap[id]}addTensorList(tensorList){this.tensorListMap[tensorList.id]=tensorList}getTensorList(id){return this.tensorListMap[id]}dispose(keepIds){for(let key in this.tensorArrayMap)this.tensorArrayMap[key].clearAndClose(keepIds);for(let key in this.tensorListMap)this.tensorListMap[key].clearAndClose(keepIds)}};function getExecutionSubgraph(inputs,outputs,weightMap,initNodes){let usedNodes=new Set,missingInputs=[],dynamicNode=null,syncInputs=null,seen=new Set,inputNodeNames=Object.keys(inputs).map(name=>parseNodeName(name)[0]),initNodeNames=[];initNodes!=null&&(initNodeNames=initNodes.map(node=>parseNodeName(node.name)[0]));let frontier=[...outputs];for(;frontier.length>0;){let node=frontier.pop();if((isControlFlow(node)||isDynamicShape(node)||isHashTable(node))&&dynamicNode==null&&(dynamicNode=node,syncInputs=dynamicNode.children.map(child=>child.name).filter(name=>usedNodes.has(name))),usedNodes.add(node.name),weightMap[node.name]==null&&inputNodeNames.indexOf(node.name)===-1&&initNodeNames.indexOf(node.name)===-1){if(node.inputs.length===0){missingInputs.push(node.name);continue}node.inputs.forEach(input2=>{seen.has(input2.name)||(seen.add(input2.name),frontier.push(input2))})}}return{inputs,outputs,usedNodes,missingInputs,dynamicNode,syncInputs}}function getNodesInTopologicalOrder(graph2,weightMap,executionInfo){let{usedNodes,inputs}=executionInfo,frontier=[],inputNodes=Object.keys(inputs).map(name=>parseNodeName(name)[0]).map(name=>graph2.nodes[name]),initNodes=graph2.initNodes;inputNodes.forEach(input2=>{usedNodes.has(input2.name)&&frontier.push(input2)}),graph2.weights.forEach(weight=>{usedNodes.has(weight.name)&&frontier.push(weight)}),initNodes!=null&&initNodes.forEach(node=>{usedNodes.has(node.name)&&frontier.push(node)});let seen=new Set,orderedNodes=[];for(;frontier.length>0;){let node=frontier.pop();seen.add(node.name),weightMap[node.name]||orderedNodes.push(node),node.children.forEach(child=>{!seen.has(child.name)&&usedNodes.has(child.name)&&child.inputs.every(input2=>seen.has(input2.name))&&frontier.push(child)})}return orderedNodes}var CONTROL_FLOW_OPS=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],DYNAMIC_SHAPE_OPS=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],HASH_TABLE_OPS=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2"];function isControlFlow(node){return CONTROL_FLOW_OPS.indexOf(node.op)>=0}function isDynamicShape(node){return DYNAMIC_SHAPE_OPS.indexOf(node.op)>=0}function isHashTable(node){return HASH_TABLE_OPS.indexOf(node.op)>=0}var GraphExecutor=class{constructor(graph2,parent){this.graph=graph2,this.parent=parent,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this._outputs=graph2.outputs,this._inputs=graph2.inputs,this._initNodes=graph2.initNodes,this._signature=graph2.signature,this._functions=graph2.functions,graph2.functions!=null&&Object.keys(graph2.functions).forEach(name=>{this._functionExecutorMap[name]=new GraphExecutor(graph2.functions[name],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(weightMap){let weightIds=Object.keys(weightMap).map(key=>weightMap[key].map(tensor2=>tensor2.id));this._weightIds=[].concat(...weightIds),this._weightMap=weightMap}set resourceManager(resourceManager){this._resourceManager=resourceManager}get inputs(){return this._inputs.map(node=>({name:node.name,shape:node.attrParams.shape?node.attrParams.shape.value:void 0,dtype:node.attrParams.dtype?node.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(node=>({name:node.name,shape:node.attrParams.shape?node.attrParams.shape.value:void 0,dtype:node.attrParams.dtype?node.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(node=>node.signatureKey||node.name)}get outputNodes(){return this._outputs.map(node=>{let name=node.signatureKey||node.name;return node.defaultOutput?`${name}:${node.defaultOutput}`:name})}get functions(){return Object.keys(this._functions).reduce((map,key)=>(map[key]=this._functions[key].signature,map),{})}getCompilationKey(inputs,outputs){let sortedInputs=inputs.map(node=>node.name).sort(),sortedOutputs=outputs.map(node=>node.name).sort();return sortedInputs.join(this.SEPERATOR)+"--"+sortedOutputs.join(this.SEPERATOR)}compile(inputs,outputs){let executionInfo=getExecutionSubgraph(inputs,outputs,this.weightMap,this._initNodes),{missingInputs,dynamicNode,syncInputs}=executionInfo;if(dynamicNode!=null)throw new Error(`This execution contains the node '${dynamicNode.name}', which has the dynamic op '${dynamicNode.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${syncInputs}]`);if(missingInputs.length>0){let outNames=outputs.map(n=>n.name),inNames=Object.keys(inputs);throw new Error(`Cannot compute the outputs [${outNames}] from the provided inputs [${inNames}]. Missing the following inputs: [${missingInputs}]`)}return getNodesInTopologicalOrder(this.graph,this.weightMap,executionInfo)}execute(inputs,outputs){inputs=this.mapInputs(inputs);let names=Object.keys(inputs).sort();this.checkInputs(inputs),this.checkInputShapeAndType(inputs),outputs=this.mapOutputs(outputs),this.checkOutputs(outputs);let inputNodes=names.map(name=>this.graph.nodes[parseNodeName(name)[0]]),outputNodeNames=outputs.map(name=>parseNodeName(name)[0]),outputNodes=outputNodeNames.map(name=>this.graph.nodes[name]);outputNodes.length===0&&(outputNodes=this._outputs);let compilationKey=this.getCompilationKey(inputNodes,outputNodes),orderedNodes=this.compiledMap.get(compilationKey);orderedNodes==null&&(orderedNodes=this.compile(inputs,outputNodes),this.compiledMap.set(compilationKey,orderedNodes));let tensorArrayMap={},tensorListMap={};return tidy(()=>{let context=new ExecutionContext(this.weightMap,tensorArrayMap,tensorListMap,this.functionExecutorMap),tensorsMap=Object.assign({},this.weightMap);Object.keys(inputs).forEach(name=>{let[nodeName,index]=parseNodeName(name),tensors=[];tensors[index]=inputs[name],tensorsMap[nodeName]=tensors});let tensorsToKeep=this.getFrozenTensorIds(tensorsMap),intermediateTensorConsumerCount={};for(let i=0;igetTensor(name,tensorsMap,context))})}getFrozenTensorIds(tensorMap){let ids=[].concat.apply([],Object.keys(tensorMap).map(key=>tensorMap[key]).map(tensors=>tensors.map(tensor2=>tensor2.id)));return new Set(ids)}checkTensorForDisposal(nodeName,node,tensorMap,context,tensorsToKeep,outputNames,intermediateTensorConsumerCount){node.category==="control"||outputNames.indexOf(nodeName)!==-1||(tensorMap[nodeName].forEach(tensor2=>{tensor2!=null&&(intermediateTensorConsumerCount[tensor2.id]=(intermediateTensorConsumerCount[tensor2.id]||0)+node.children.length)}),node.inputs.forEach(input2=>{if(input2.category!=="control"){let tensors=getTensorsForCurrentContenxt(input2.name,tensorMap,context);tensors!=null&&tensors.forEach(tensor2=>{if(tensor2&&!tensorsToKeep.has(tensor2.id)){let count2=intermediateTensorConsumerCount[tensor2.id];count2===1?(tensor2.dispose(),delete intermediateTensorConsumerCount[tensor2.id]):count2!=null&&intermediateTensorConsumerCount[tensor2.id]--}})}}))}async executeAsync(inputs,outputs){return this._executeAsync(inputs,outputs)}async _executeAsync(inputs,outputs,isFunctionExecution=!1,tensorArrayMap={},tensorListMap={}){isFunctionExecution||(inputs=this.mapInputs(inputs),this.checkInputs(inputs),this.checkInputShapeAndType(inputs),outputs=this.mapOutputs(outputs),this.checkOutputs(outputs));let context=new ExecutionContext(this.weightMap,tensorArrayMap,tensorListMap,this.functionExecutorMap),tensorMap=await this.executeWithControlFlow(inputs,context,outputs,isFunctionExecution),results=outputs.map(name=>getTensor(name,tensorMap,context)),outputIds=results.map(t=>t.id),inputIds=Object.keys(inputs).map(name=>inputs[name].id),keepIds=new Set([...outputIds,...inputIds,...this.weightIds]);return Object.keys(tensorMap).forEach(key=>{tensorMap[key].forEach(tensor2=>{tensor2&&!tensor2.isDisposed&&!keepIds.has(tensor2.id)&&tensor2.dispose()})}),this.parent==null&&context.dispose(keepIds),results}async executeFunctionAsync(inputs,tensorArrayMap,tensorListMap){let mappedInputs=inputs.reduce((map,tensor2,index)=>(map[this.inputs[index].name]=tensor2,map),{});return this._executeAsync(mappedInputs,this.outputNodes,!0,tensorArrayMap,tensorListMap)}async executeWithControlFlow(inputs,context,outputNames,isFunctionExecution){let names=Object.keys(inputs),inputNodes=names.map(name=>this.graph.nodes[parseNodeName(name)[0]]),outputNodeNames=outputNames.map(name=>parseNodeName(name)[0]),outputNodes=outputNodeNames.map(name=>this.graph.nodes[name]);outputNodes.length===0&&(outputNodes=this._outputs);let{usedNodes,missingInputs,dynamicNode,syncInputs}=getExecutionSubgraph(inputs,outputNodes,this.weightMap,this._initNodes),stack2=[...inputNodes,...this.graph.weights,...this._initNodes||[]].map(node=>({node,contexts:context.currentContext})),tensorsMap=Object.assign({},this.weightMap);Object.keys(inputs).forEach(name=>{let[nodeName,index]=parseNodeName(name),tensors=[];tensors[index]=inputs[name],tensorsMap[nodeName]=tensors});let intermediateTensorConsumerCount={},tensorsToKeep=this.getFrozenTensorIds(tensorsMap),added={};for(;stack2.length>0;){let promises=this.processStack(inputNodes,stack2,context,tensorsMap,added,tensorsToKeep,outputNodeNames,intermediateTensorConsumerCount,usedNodes);await Promise.all(promises)}dynamicNode==null&&!isFunctionExecution&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let missingOutputs=outputNodes.filter(node=>!isControlFlow(node)&&!getTensor(node.name,tensorsMap,context)).map(node=>node.name);if(missingOutputs.length>0){let alternativeMsg="";throw dynamicNode!=null&&(alternativeMsg=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${syncInputs}]`),new Error(`Cannot compute the outputs [${missingOutputs}] from the provided inputs [${names}]. Consider providing the following inputs: [${missingInputs}]. ${alternativeMsg}`)}return tensorsMap}processStack(inputNodes,stack2,context,tensorMap,added,tensorsToKeep,outputNames,intermediateTensorConsumerCount,usedNodes){let promises=[];for(;stack2.length>0;){let item=stack2.pop();context.currentContext=item.contexts;let nodeName="";if(item.node.op==="Enter"&&getParamValue("isConstant",item.node,tensorMap,context)&&([nodeName]=getNodeNameAndIndex(item.node.name,context)),tensorMap[item.node.name]==null){let tensors=executeOp18(item.node,tensorMap,context,this._resourceManager);nodeName||([nodeName]=getNodeNameAndIndex(item.node.name,context));let currentContext=context.currentContext;util_exports.isPromise(tensors)?promises.push(tensors.then(t=>(tensorMap[nodeName]=t,context.currentContext=currentContext,this.checkTensorForDisposal(nodeName,item.node,tensorMap,context,tensorsToKeep,outputNames,intermediateTensorConsumerCount),this.processChildNodes(item.node,stack2,context,tensorMap,added,usedNodes),t))):(tensorMap[nodeName]=tensors,this.checkTensorForDisposal(nodeName,item.node,tensorMap,context,tensorsToKeep,outputNames,intermediateTensorConsumerCount),this.processChildNodes(item.node,stack2,context,tensorMap,added,usedNodes))}else this.processChildNodes(item.node,stack2,context,tensorMap,added,usedNodes)}return promises}processChildNodes(node,stack2,context,tensorMap,added,usedNodes){node.children.forEach(childNode=>{let[nodeName]=getNodeNameAndIndex(childNode.name,context);added[nodeName]||!usedNodes.has(childNode.name)||(childNode.op==="Merge"?childNode.inputNames.some(name=>!!getTensor(name,tensorMap,context))&&(added[nodeName]=!0,stack2.push({contexts:context.currentContext,node:childNode})):childNode.inputNames.every(name=>!!getTensor(name,tensorMap,context))&&(added[nodeName]=!0,stack2.push({contexts:context.currentContext,node:childNode})))})}dispose(){Object.keys(this.weightMap).forEach(key=>this.weightMap[key].forEach(tensor2=>tensor2.dispose()))}checkInputShapeAndType(inputs){Object.keys(inputs).forEach(name=>{let input2=inputs[name],[nodeName]=parseNodeName(name),node=this.graph.nodes[nodeName];if(node.attrParams.shape&&node.attrParams.shape.value){let shape=node.attrParams.shape.value,match=shape.length===input2.shape.length&&input2.shape.every((dim,index)=>shape[index]===-1||shape[index]===dim);util_exports.assert(match,()=>`The shape of dict['${node.name}'] provided in model.execute(dict) must be [${shape}], but was [${input2.shape}]`)}node.attrParams.dtype&&node.attrParams.dtype.value&&util_exports.assert(input2.dtype===node.attrParams.dtype.value,()=>`The dtype of dict['${node.name}'] provided in model.execute(dict) must be ${node.attrParams.dtype.value}, but was ${input2.dtype}`)})}mapInputs(inputs){let result={};for(let inputName in inputs)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[inputName]!=null){let tensor2=this._signature.inputs[inputName];result[tensor2.name]=inputs[inputName]}else result[inputName]=inputs[inputName];return result}checkInputs(inputs){let notInGraph=Object.keys(inputs).filter(name=>{let[nodeName]=parseNodeName(name);return this.graph.nodes[nodeName]==null});if(notInGraph.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${notInGraph}] that are not part of graph`)}mapOutputs(outputs){return outputs.map(name=>this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[name]!=null?this._signature.outputs[name].name:name,{})}checkOutputs(outputs){outputs.forEach(name=>{let[normalizedName]=parseNodeName(name);if(!this.graph.nodes[normalizedName])throw new Error(`The output '${name}' is not found in the graph`)})}},ResourceManager=class{constructor(hashTableNameToHandle={},hashTableMap={}){this.hashTableNameToHandle=hashTableNameToHandle,this.hashTableMap=hashTableMap}addHashTable(name,hashTable2){this.hashTableNameToHandle[name]=hashTable2.handle,this.hashTableMap[hashTable2.id]=hashTable2}getHashTableHandleByName(name){return this.hashTableNameToHandle[name]}getHashTableById(id){return this.hashTableMap[id]}dispose(){for(let key in this.hashTableMap)this.hashTableMap[key].clearAndClose(),delete this.hashTableMap[key];for(let name in this.hashTableNameToHandle)this.hashTableNameToHandle[name].dispose(),delete this.hashTableNameToHandle[name]}},TFHUB_SEARCH_PARAM="?tfjs-format=file",DEFAULT_MODEL_NAME="model.json",GraphModel=class{constructor(modelUrl,loadOptions={}){this.modelUrl=modelUrl,this.loadOptions=loadOptions,this.version="n/a",loadOptions==null&&(this.loadOptions={}),this.resourceManager=new ResourceManager}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let path=this.modelUrl;if(path.load!=null)this.handler=path;else if(this.loadOptions.requestInit!=null)this.handler=io_exports.browserHTTPRequest(path,this.loadOptions);else{let handlers=io_exports.getLoadHandlers(path,this.loadOptions);if(handlers.length===0)handlers.push(io_exports.browserHTTPRequest(path,this.loadOptions));else if(handlers.length>1)throw new Error(`Found more than one (${handlers.length}) load handlers for URL '${[path]}'`);this.handler=handlers[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let artifacts=await this.handler.load();return this.loadSync(artifacts)}loadSync(artifacts){this.artifacts=artifacts;let graph2=this.artifacts.modelTopology,signature;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?signature=this.artifacts.userDefinedMetadata.signature:signature=this.artifacts.signature,this.signature=signature,this.version=`${graph2.versions.producer}.${graph2.versions.minConsumer}`;let weightMap=io_exports.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new GraphExecutor(OperationMapper.Instance.transformGraph(graph2,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(weightMap),this.executor.resourceManager=this.resourceManager,artifacts.modelInitializer!=null&&artifacts.modelInitializer.node!=null){let initializer=OperationMapper.Instance.transformGraph(artifacts.modelInitializer);this.initializer=new GraphExecutor(initializer),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(handlerOrURL,config3){if(typeof handlerOrURL=="string"){let handlers=io_exports.getSaveHandlers(handlerOrURL);if(handlers.length===0)throw new Error(`Cannot find any save handlers for URL '${handlerOrURL}'`);if(handlers.length>1)throw new Error(`Found more than one (${handlers.length}) save handlers for URL '${handlerOrURL}'`);handlerOrURL=handlers[0]}if(handlerOrURL.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return handlerOrURL.save(this.artifacts)}predict(inputs,config3){return this.execute(inputs,this.outputNodes)}normalizeInputs(inputs){if(!(inputs instanceof Tensor)&&!Array.isArray(inputs))return inputs;if(inputs=Array.isArray(inputs)?inputs:[inputs],inputs.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${inputs.length} input tensors.`);return this.inputNodes.reduce((map,inputName,i)=>(map[inputName]=inputs[i],map),{})}normalizeOutputs(outputs){return outputs=outputs||this.outputNodes,Array.isArray(outputs)?outputs:[outputs]}execute(inputs,outputs){inputs=this.normalizeInputs(inputs),outputs=this.normalizeOutputs(outputs);let result=this.executor.execute(inputs,outputs);return result.length>1?result:result[0]}async executeAsync(inputs,outputs){inputs=this.normalizeInputs(inputs),outputs=this.normalizeOutputs(outputs);let result=await this.executor.executeAsync(inputs,outputs);return result.length>1?result:result[0]}convertTensorMapToTensorsMap(map){return Object.keys(map).reduce((newMap,key)=>(newMap[key]=[map[key]],newMap),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function loadGraphModel(modelUrl,options={}){if(modelUrl==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");options==null&&(options={}),options.fromTFHub&&modelUrl.load==null&&(modelUrl.endsWith("/")||(modelUrl=modelUrl+"/"),modelUrl=`${modelUrl}${DEFAULT_MODEL_NAME}${TFHUB_SEARCH_PARAM}`);let model2=new GraphModel(modelUrl,options);return await model2.load(),model2}var version11="2.8.3",dist_exports={};__export2(dist_exports,{CSVDataset:()=>CSVDataset,Dataset:()=>Dataset,FileDataSource:()=>FileDataSource,TextLineDataset:()=>TextLineDataset,URLDataSource:()=>URLDataSource,array:()=>array,csv:()=>csv,func:()=>func,generator:()=>generator,microphone:()=>microphone,version_data:()=>version12,webcam:()=>webcam,zip:()=>zip});var seedrandom4=__toModule2(require_seedrandom6()),seedrandom3=__toModule2(require_seedrandom6());function deepMap(input2,mapFn){return deepMapInternal(input2,mapFn)}function deepMapInternal(input2,mapFn,seen=new Map,containedIn=new Set){if(input2==null)return null;if(containedIn.has(input2))throw new Error("Circular references are not supported.");if(seen.has(input2))return seen.get(input2);let result=mapFn(input2);if(result.recurse&&result.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(result.recurse)if(isIterable2(input2)){let mappedIterable=Array.isArray(input2)?[]:{};containedIn.add(input2);for(let k in input2){let child=input2[k],childResult=deepMapInternal(child,mapFn,seen,containedIn);mappedIterable[k]=childResult}return containedIn.delete(input2),mappedIterable}else throw new Error(`Can't recurse into non-iterable type: ${input2}`);else return seen.set(input2,result.value),result.value}function deepZip(inputs,zipFn=zipToList){return deepZipInternal(inputs,zipFn)}function deepZipInternal(inputs,zipFn,containedIn=new Set){let input2=inputs[0];if(containedIn.has(input2))throw new Error("Circular references are not supported.");let result=zipFn(inputs);if(result.recurse&&result.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(result.recurse)if(isIterable2(input2)){let mappedIterable=Array.isArray(input2)?[]:{};containedIn.add(input2);for(let k in input2){let children=inputs.map(x=>x[k]),childResult=deepZipInternal(children,zipFn,containedIn);mappedIterable[k]=childResult}return containedIn.delete(input2),mappedIterable}else throw new Error(`Can't recurse into non-iterable type: ${input2}`);else return result.value}function zipToList(x){return x===null?null:isIterable2(x[0])?{value:null,recurse:!0}:{value:x,recurse:!1}}async function deepMapAndAwaitAll(input2,mapFn){let seen=new Map;deepMapInternal(input2,mapFn,seen);for(let key of Array.from(seen.keys())){let value=seen.get(key);if(util_exports.isPromise(value)){let mappedValue=await value;seen.set(key,mappedValue)}}return deepMapInternal(input2,mapFn,seen)}function isIterable2(obj){return obj!=null&&!ArrayBuffer.isView(obj)&&(Array.isArray(obj)||typeof obj=="object"&&!(obj instanceof Tensor))}function canTensorify(obj){return obj==null||isPrimitive(obj)||Array.isArray(obj)||typeof obj=="object"&&obj instanceof Tensor||util_exports.isTypedArray(obj)}function isPrimitive(value){return value===null||typeof value!="object"&&typeof value!="function"}function deepClone(container){return deepMap(container,cloneIfTensor)}function cloneIfTensor(item){return item instanceof Tensor?{value:item.clone(),recurse:!1}:isIterable2(item)?{value:null,recurse:!0}:{value:item,recurse:!1}}var RingBuffer=class{constructor(capacity){if(this.capacity=capacity,this.begin=0,this.end=0,capacity==null)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(capacity<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(capacity),this.doubledCapacity=2*capacity}wrap(index){for(;index<0;)index+=this.doubledCapacity;return index%this.doubledCapacity}get(index){if(index<0)throw new RangeError("Can't get item at a negative index.");return this.data[index%this.capacity]}set(index,value){if(index<0)throw new RangeError("Can't set item at a negative index.");this.data[index%this.capacity]=value}length(){let length=this.end-this.begin;return length<0&&(length=this.doubledCapacity+length),length}isFull(){return this.length()===this.capacity}isEmpty(){return this.length()===0}push(value){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,value),this.end=this.wrap(this.end+1)}pushAll(values){for(let value of values)this.push(value)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);let result=this.get(this.end);return this.set(this.end,void 0),result}unshift(value){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,value)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let result=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),result}shuffleExcise(relativeIndex){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let index=this.wrap(this.begin+relativeIndex),result=this.get(index);return this.set(index,this.pop()),result}},GrowingRingBuffer=class extends RingBuffer{constructor(){super(GrowingRingBuffer.INITIAL_CAPACITY)}isFull(){return!1}push(value){super.isFull()&&this.expand(),super.push(value)}unshift(value){super.isFull()&&this.expand(),super.unshift(value)}expand(){let newCapacity=this.capacity*2,newData=new Array(newCapacity),len=this.length();for(let i=0;ix===!0)}rowMajorBatch(batchSize,smallLastBatch=!0){return new RowMajorBatchIterator(this,batchSize,smallLastBatch)}columnMajorBatch(batchSize,smallLastBatch=!0,zipFn=zipToList){return this.rowMajorBatch(batchSize,smallLastBatch).map(x=>deepZip(x,zipFn))}concatenate(iterator,baseErrorHandler){return new ChainedIterator(iteratorFromItems([this,iterator]),baseErrorHandler)}take(count2){return count2<0||count2==null?this:new TakeIterator(this,count2)}skip(count2){return count2<0||count2==null?this:new SkipIterator(this,count2)}prefetch(bufferSize){return new PrefetchIterator(this,bufferSize)}shuffle(windowSize,seed){return new ShuffleIterator(this,windowSize,seed)}serial(){return new SerialIterator(this)}},ArrayIterator=class extends LazyIterator{constructor(items){super();this.items=items,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let item=this.items[this.trav];return this.trav++,{value:deepClone(item),done:!1}}},FunctionCallIterator=class extends LazyIterator{constructor(nextFn){super();this.nextFn=nextFn}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: ${e.message}`,e}}},SerialIterator=class extends LazyIterator{constructor(upstream){super();this.upstream=upstream,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},SkipIterator=class extends LazyIterator{constructor(upstream,maxCount){super();this.upstream=upstream,this.maxCount=maxCount,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++ Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},RowMajorBatchIterator=class extends LazyIterator{constructor(upstream,batchSize,enableSmallLastBatch=!0){super();this.upstream=upstream,this.batchSize=batchSize,this.enableSmallLastBatch=enableSmallLastBatch,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let batch=[];for(;batch.length0?{value:batch,done:!1}:{value:null,done:!0};batch.push(item.value)}return{value:batch,done:!1}}},FilterIterator=class extends LazyIterator{constructor(upstream,predicate){super();this.upstream=upstream,this.predicate=predicate,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let item=await this.upstream.next();if(item.done||this.predicate(item.value))return item;dispose(item.value)}}},MapIterator=class extends LazyIterator{constructor(upstream,transform){super();this.upstream=upstream,this.transform=transform}summary(){return`${this.upstream.summary()} -> Map`}async next(){let item=await this.upstream.next();if(item.done)return{value:null,done:!0};let inputTensors=tensor_util_exports.getTensorsInContainer(item.value),mapped=this.transform(item.value),outputTensors=tensor_util_exports.getTensorsInContainer(mapped);for(let t of inputTensors)tensor_util_exports.isTensorInList(t,outputTensors)||t.dispose();return{value:mapped,done:!1}}},ErrorHandlingLazyIterator=class extends LazyIterator{constructor(upstream,handler){super();this.upstream=upstream,this.handler=handler,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},AsyncMapIterator=class extends LazyIterator{constructor(upstream,transform){super();this.upstream=upstream,this.transform=transform}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let item=await this.upstream.next();if(item.done)return{value:null,done:!0};let inputTensors=tensor_util_exports.getTensorsInContainer(item.value),mapped=await this.transform(item.value),outputTensors=tensor_util_exports.getTensorsInContainer(mapped);for(let t of inputTensors)tensor_util_exports.isTensorInList(t,outputTensors)||t.dispose();return{value:mapped,done:!1}}},OneToManyIterator=class extends LazyIterator{constructor(){super();this.outputQueue=new GrowingRingBuffer,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},FlatmapIterator=class extends OneToManyIterator{constructor(upstream,transform){super();this.upstream=upstream,this.transform=transform}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let item=await this.upstream.next();if(item.done)return!1;let inputTensors=tensor_util_exports.getTensorsInContainer(item.value),mappedArray=this.transform(item.value),outputTensors=tensor_util_exports.getTensorsInContainer(mappedArray);this.outputQueue.pushAll(mappedArray);for(let t of inputTensors)tensor_util_exports.isTensorInList(t,outputTensors)||t.dispose();return!0}},ChainedIterator=class extends LazyIterator{constructor(iterators,baseErrorHandler){super();this.baseErrorHandler=baseErrorHandler,this.lastRead=null,this.iterator=null,this.moreIterators=iterators}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(lastRead){if(await lastRead,this.iterator==null){let iteratorResult=await this.moreIterators.next();if(iteratorResult.done)return{value:null,done:!0};this.iterator=iteratorResult.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let itemResult=await this.iterator.next();return itemResult.done?(this.iterator=null,this.readFromChain(lastRead)):itemResult}},ZipMismatchMode;(function(ZipMismatchMode2){ZipMismatchMode2[ZipMismatchMode2.FAIL=0]="FAIL",ZipMismatchMode2[ZipMismatchMode2.SHORTEST=1]="SHORTEST",ZipMismatchMode2[ZipMismatchMode2.LONGEST=2]="LONGEST"})(ZipMismatchMode||(ZipMismatchMode={}));var ZipIterator=class extends LazyIterator{constructor(iterators,mismatchMode=ZipMismatchMode.FAIL){super();this.iterators=iterators,this.mismatchMode=mismatchMode,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(afterState){await afterState;let numIterators=0,iteratorsDone=0;function getNext(container){return container instanceof LazyIterator?{value:container.next().then(x=>(numIterators++,x.done&&iteratorsDone++,x.value)),recurse:!1}:{value:null,recurse:!0}}let mapped=await deepMapAndAwaitAll(this.iterators,getNext);if(numIterators===iteratorsDone)return{value:null,done:!0};if(iteratorsDone>0)switch(this.mismatchMode){case ZipMismatchMode.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case ZipMismatchMode.SHORTEST:return{value:null,done:!0};case ZipMismatchMode.LONGEST:default:}return this.count++,{value:mapped,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},PrefetchIterator=class extends LazyIterator{constructor(upstream,bufferSize){super();this.upstream=upstream,this.bufferSize=bufferSize,this.buffer=new RingBuffer(bufferSize)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let v=this.upstream.next();this.buffer.push(v)}}next(){return this.refill(),this.buffer.shift()}},ShuffleIterator=class extends PrefetchIterator{constructor(upstream,windowSize,seed){super(upstream,windowSize);this.upstream=upstream,this.windowSize=windowSize,this.upstreamExhausted=!1,this.random=seedrandom3.alea(seed||util_exports.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(max6){return Math.floor(this.random()*max6)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let chosenIndex=this.chooseIndex(),result=await this.buffer.shuffleExcise(chosenIndex);if(result.done)this.upstreamExhausted=!0;else return this.refill(),result}return{value:null,done:!0}}},Dataset=class{constructor(){this.size=null}batch(batchSize,smallLastBatch=!0){let base2=this;util_exports.assert(batchSize>0,()=>`batchSize needs to be positive, but it is + ${totalLength}, and tensor's shape is: ${tensor2.shape}`);let elementPerRow=totalLength===0?0:tensor2.size/totalLength,tensors=tidy(()=>{let tensors2=[];tensor2=reshape(tensor2,[1,totalLength,elementPerRow]);for(let i=0;i{switch(node.op){case"If":case"StatelessIf":{let thenFunc=getParamValue("thenBranch",node,tensorMap,context),elseFunc=getParamValue("elseBranch",node,tensorMap,context),cond=getParamValue("cond",node,tensorMap,context),args=getParamValue("args",node,tensorMap,context);return(await cond.data())[0]?context.functionMap[thenFunc].executeFunctionAsync(args,context.tensorArrayMap,context.tensorListMap):context.functionMap[elseFunc].executeFunctionAsync(args,context.tensorArrayMap,context.tensorListMap)}case"While":case"StatelessWhile":{let bodyFunc=getParamValue("body",node,tensorMap,context),condFunc=getParamValue("cond",node,tensorMap,context),args=getParamValue("args",node,tensorMap,context),condResult=await context.functionMap[condFunc].executeFunctionAsync(args,context.tensorArrayMap,context.tensorListMap),argIds=args.map(tensor2=>tensor2.id),condValue=await condResult[0].data();condResult.forEach(tensor2=>{!tensor2.kept&&argIds.indexOf(tensor2.id)===-1&&tensor2.dispose()});let result=args;for(;condValue[0];){let origResult=result;result=await context.functionMap[bodyFunc].executeFunctionAsync(result,context.tensorArrayMap,context.tensorListMap);let resultIds=result.map(tensor2=>tensor2.id);origResult.forEach(tensor2=>{!tensor2.kept&&argIds.indexOf(tensor2.id)===-1&&resultIds.indexOf(tensor2.id)===-1&&tensor2.dispose()});let condResult2=await context.functionMap[condFunc].executeFunctionAsync(result,context.tensorArrayMap,context.tensorListMap);condValue=await condResult2[0].data(),condResult2.forEach(tensor2=>{!tensor2.kept&&argIds.indexOf(tensor2.id)===-1&&resultIds.indexOf(tensor2.id)===-1&&tensor2.dispose()})}return result}case"LoopCond":{let pred=getParamValue("pred",node,tensorMap,context);return[cloneTensor(pred)]}case"Switch":{let pred=getParamValue("pred",node,tensorMap,context),data2=getParamValue("data",node,tensorMap,context);return data2.kept||(data2=cloneTensor(data2)),(await pred.data())[0]?[void 0,data2]:[data2,void 0]}case"Merge":{let inputName=node.inputNames.find(name=>getTensor(name,tensorMap,context)!==void 0);if(inputName){let data2=getTensor(inputName,tensorMap,context);return[cloneTensor(data2)]}return}case"Enter":{let frameId=getParamValue("frameName",node,tensorMap,context),data2=getParamValue("tensor",node,tensorMap,context);return context.enterFrame(frameId),[cloneTensor(data2)]}case"Exit":{let data2=getParamValue("tensor",node,tensorMap,context);return context.exitFrame(),[cloneTensor(data2)]}case"NextIteration":{let data2=getParamValue("tensor",node,tensorMap,context);return context.nextIteration(),[cloneTensor(data2)]}case"TensorArrayV3":{let size=getParamValue("size",node,tensorMap,context),dtype=getParamValue("dtype",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),dynamicSize=getParamValue("dynamicSize",node,tensorMap,context),clearAfterRead=getParamValue("clearAfterRead",node,tensorMap,context),identicalElementShapes=getParamValue("identicalElementShapes",node,tensorMap,context),name=getParamValue("name",node,tensorMap,context),tensorArray=new TensorArray(name,dtype,size,elementShape,identicalElementShapes,dynamicSize,clearAfterRead);return context.addTensorArray(tensorArray),[tensorArray.idTensor,scalar(1)]}case"TensorArrayWriteV3":{let id=getParamValue("tensorArrayId",node,tensorMap,context),index=getParamValue("index",node,tensorMap,context),writeTensor=getParamValue("tensor",node,tensorMap,context),writeTensorArray=context.getTensorArray(id.id);return writeTensorArray.write(index,writeTensor),[writeTensorArray.idTensor]}case"TensorArrayReadV3":{let readId=getParamValue("tensorArrayId",node,tensorMap,context),readIndex=getParamValue("index",node,tensorMap,context);return[context.getTensorArray(readId.id).read(readIndex)]}case"TensorArrayGatherV3":{let gatherId=getParamValue("tensorArrayId",node,tensorMap,context),gatherIndices=getParamValue("indices",node,tensorMap,context),gatherDtype=getParamValue("dtype",node,tensorMap,context);return[context.getTensorArray(gatherId.id).gather(gatherIndices,gatherDtype)]}case"TensorArrayScatterV3":{let scatterId=getParamValue("tensorArrayId",node,tensorMap,context),scatterIndices=getParamValue("indices",node,tensorMap,context),scatterTensor=getParamValue("tensor",node,tensorMap,context),scatterTensorArray=context.getTensorArray(scatterId.id);return scatterTensorArray.scatter(scatterIndices,scatterTensor),[scatterTensorArray.idTensor]}case"TensorArrayConcatV3":{let concatId=getParamValue("tensorArrayId",node,tensorMap,context),concatTensorArray=context.getTensorArray(concatId.id),concatDtype=getParamValue("dtype",node,tensorMap,context);return[concatTensorArray.concat(concatDtype)]}case"TensorArraySplitV3":{let splitId=getParamValue("tensorArrayId",node,tensorMap,context),splitTensor=getParamValue("tensor",node,tensorMap,context),lengths=getParamValue("lengths",node,tensorMap,context),splitTensorArray=context.getTensorArray(splitId.id);return splitTensorArray.split(lengths,splitTensor),[splitTensorArray.idTensor]}case"TensorArraySizeV3":{let sizeId=getParamValue("tensorArrayId",node,tensorMap,context),sizeTensorArray=context.getTensorArray(sizeId.id);return[scalar(sizeTensorArray.size(),"int32")]}case"TensorArrayCloseV3":{let closeId=getParamValue("tensorArrayId",node,tensorMap,context),closeTensorArray=context.getTensorArray(closeId.id);return closeTensorArray.clearAndClose(),[closeTensorArray.idTensor]}case"TensorListSetItem":{let idTensor=getParamValue("tensorListId",node,tensorMap,context),index=getParamValue("index",node,tensorMap,context),writeTensor=getParamValue("tensor",node,tensorMap,context),tensorList=context.getTensorList(idTensor.id);return tensorList.setItem(index,writeTensor),[tensorList.idTensor]}case"TensorListGetItem":{let idTensor=getParamValue("tensorListId",node,tensorMap,context),readIndex=getParamValue("index",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),elementDType=getParamValue("elementDType",node,tensorMap,context);return[context.getTensorList(idTensor.id).getItem(readIndex,elementShape,elementDType)]}case"TensorListScatterV2":case"TensorListScatter":{let scatterIndices=getParamValue("indices",node,tensorMap,context),scatterTensor=getParamValue("tensor",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),numElements=getParamValue("numElements",node,tensorMap,context),tensorList=scatter(scatterTensor,scatterIndices,elementShape,numElements);return context.addTensorList(tensorList),[tensorList.idTensor]}case"TensorListReserve":case"EmptyTensorList":{let elementShape=getParamValue("elementShape",node,tensorMap,context),elementDtype=getParamValue("elementDType",node,tensorMap,context),numElementsParam;node.op==="TensorListReserve"?numElementsParam="numElements":numElementsParam="maxNumElements";let numElements=getParamValue(numElementsParam,node,tensorMap,context),tensorList=reserve(elementShape,elementDtype,numElements);return context.addTensorList(tensorList),[tensorList.idTensor]}case"TensorListGather":{let gatherId=getParamValue("tensorListId",node,tensorMap,context),gatherIndices=getParamValue("indices",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),elementDtype=getParamValue("elementDType",node,tensorMap,context);return[context.getTensorList(gatherId.id).gather(gatherIndices,elementDtype,elementShape)]}case"TensorListStack":{let idTensor=getParamValue("tensorListId",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),elementDtype=getParamValue("elementDType",node,tensorMap,context),numElements=getParamValue("numElements",node,tensorMap,context);return[context.getTensorList(idTensor.id).stack(elementShape,elementDtype,numElements)]}case"TensorListFromTensor":{let tensor2=getParamValue("tensor",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),elementDtype=getParamValue("elementDType",node,tensorMap,context),tensorList=fromTensor(tensor2,elementShape,elementDtype);return context.addTensorList(tensorList),[tensorList.idTensor]}case"TensorListConcat":{let concatId=getParamValue("tensorListId",node,tensorMap,context),tensorList=context.getTensorList(concatId.id),concatDtype=getParamValue("dtype",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context);return[tensorList.concat(concatDtype,elementShape)]}case"TensorListPushBack":{let idTensor=getParamValue("tensorListId",node,tensorMap,context),writeTensor=getParamValue("tensor",node,tensorMap,context),tensorList=context.getTensorList(idTensor.id);return tensorList.pushBack(writeTensor),[tensorList.idTensor]}case"TensorListPopBack":{let idTensor=getParamValue("tensorListId",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),elementDType=getParamValue("elementDType",node,tensorMap,context);return[context.getTensorList(idTensor.id).popBack(elementShape,elementDType)]}case"TensorListSplit":{let splitTensor=getParamValue("tensor",node,tensorMap,context),elementShape=getParamValue("elementShape",node,tensorMap,context),lengths=getParamValue("lengths",node,tensorMap,context),tensorList=split2(splitTensor,lengths,elementShape);return context.addTensorList(tensorList),[tensorList.idTensor]}default:throw TypeError(`Node type ${node.op} is not implemented`)}};function fusedConvAndDepthWiseParams(node,tensorMap,context){let[extraOp,activationFunc]=getParamValue("fusedOps",node,tensorMap,context),isBiasAdd=extraOp==="biasadd",isPrelu=activationFunc==="prelu",isBatchNorm=extraOp==="fusedbatchnorm",numArgs=getParamValue("numArgs",node,tensorMap,context);if(isBiasAdd){if(isPrelu&&numArgs!==2)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!isPrelu&&numArgs!==1)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(isBatchNorm)throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported.");let stride=getParamValue("strides",node,tensorMap,context),pad3=getPadding(node,tensorMap,context),dataFormat=getParamValue("dataFormat",node,tensorMap,context).toUpperCase(),dilations=getParamValue("dilations",node,tensorMap,context),[biasArg,preluArg]=getParamValue("args",node,tensorMap,context),leakyreluAlpha=getParamValue("leakyreluAlpha",node,tensorMap,context);return{stride,pad:pad3,dataFormat,dilations,biasArg,preluArg,activationFunc,leakyreluAlpha}}var executeOp4=(node,tensorMap,context)=>{switch(node.op){case"Conv1D":{let stride=getParamValue("stride",node,tensorMap,context),pad3=getParamValue("pad",node,tensorMap,context),dataFormat=getParamValue("dataFormat",node,tensorMap,context).toUpperCase(),dilation=getParamValue("dilation",node,tensorMap,context);return[conv1d(getParamValue("x",node,tensorMap,context),getParamValue("filter",node,tensorMap,context),stride,pad3,dataFormat,dilation)]}case"Conv2D":{let stride=getParamValue("strides",node,tensorMap,context),pad3=getPadding(node,tensorMap,context),dataFormat=getParamValue("dataFormat",node,tensorMap,context).toUpperCase(),dilations=getParamValue("dilations",node,tensorMap,context);return[conv2d(getParamValue("x",node,tensorMap,context),getParamValue("filter",node,tensorMap,context),[stride[1],stride[2]],pad3,dataFormat,[dilations[1],dilations[2]])]}case"_FusedConv2D":{let{stride,pad:pad3,dataFormat,dilations,biasArg,preluArg,activationFunc,leakyreluAlpha}=fusedConvAndDepthWiseParams(node,tensorMap,context);return[fused_ops_exports.conv2d({x:getParamValue("x",node,tensorMap,context),filter:getParamValue("filter",node,tensorMap,context),strides:[stride[1],stride[2]],pad:pad3,dataFormat,dilations:[dilations[1],dilations[2]],bias:biasArg,activation:activationFunc,preluActivationWeights:preluArg,leakyreluAlpha})]}case"FusedDepthwiseConv2dNative":{let{stride,pad:pad3,dataFormat,dilations,biasArg,preluArg,activationFunc,leakyreluAlpha}=fusedConvAndDepthWiseParams(node,tensorMap,context);return[fused_ops_exports.depthwiseConv2d({x:getParamValue("x",node,tensorMap,context),filter:getParamValue("filter",node,tensorMap,context),strides:[stride[1],stride[2]],pad:pad3,dataFormat,dilations:[dilations[1],dilations[2]],bias:biasArg,activation:activationFunc,preluActivationWeights:preluArg,leakyreluAlpha})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{let shape=getParamValue("outputShape",node,tensorMap,context),stride=getParamValue("strides",node,tensorMap,context),pad3=getPadding(node,tensorMap,context);return[conv2dTranspose(getParamValue("x",node,tensorMap,context),getParamValue("filter",node,tensorMap,context),shape,[stride[1],stride[2]],pad3)]}case"DepthwiseConv2dNative":case"DepthwiseConv2d":{let stride=getParamValue("strides",node,tensorMap,context),pad3=getPadding(node,tensorMap,context),dilations=getParamValue("dilations",node,tensorMap,context),dataFormat=getParamValue("dataFormat",node,tensorMap,context).toUpperCase();return[depthwiseConv2d(getParamValue("input",node,tensorMap,context),getParamValue("filter",node,tensorMap,context),[stride[1],stride[2]],pad3,dataFormat,[dilations[1],dilations[2]])]}case"Conv3D":{let stride=getParamValue("strides",node,tensorMap,context),pad3=getParamValue("pad",node,tensorMap,context),dataFormat=getParamValue("dataFormat",node,tensorMap,context).toUpperCase(),dilations=getParamValue("dilations",node,tensorMap,context);return[conv3d(getParamValue("x",node,tensorMap,context),getParamValue("filter",node,tensorMap,context),[stride[1],stride[2],stride[3]],pad3,dataFormat,[dilations[1],dilations[2],dilations[3]])]}case"AvgPool":{let stride=getParamValue("strides",node,tensorMap,context),pad3=getParamValue("pad",node,tensorMap,context),kernelSize=getParamValue("kernelSize",node,tensorMap,context);return[avgPool(getParamValue("x",node,tensorMap,context),[kernelSize[1],kernelSize[2]],[stride[1],stride[2]],pad3)]}case"MaxPool":{let stride=getParamValue("strides",node,tensorMap,context),pad3=getParamValue("pad",node,tensorMap,context),kernelSize=getParamValue("kernelSize",node,tensorMap,context);return[maxPool(getParamValue("x",node,tensorMap,context),[kernelSize[1],kernelSize[2]],[stride[1],stride[2]],pad3)]}case"MaxPoolWithArgmax":{let stride=getParamValue("strides",node,tensorMap,context),pad3=getParamValue("pad",node,tensorMap,context),kernelSize=getParamValue("kernelSize",node,tensorMap,context),includeBatchInIndex=getParamValue("includeBatchInIndex",node,tensorMap,context),{result,indexes}=maxPoolWithArgmax(getParamValue("x",node,tensorMap,context),[kernelSize[1],kernelSize[2]],[stride[1],stride[2]],pad3,includeBatchInIndex);return[result,indexes]}case"AvgPool3D":{let stride=getParamValue("strides",node,tensorMap,context),pad3=getParamValue("pad",node,tensorMap,context),kernelSize=getParamValue("kernelSize",node,tensorMap,context);return[avgPool3d(getParamValue("x",node,tensorMap,context),[kernelSize[1],kernelSize[2],kernelSize[3]],[stride[1],stride[2],stride[3]],pad3)]}case"MaxPool3D":{let stride=getParamValue("strides",node,tensorMap,context),pad3=getParamValue("pad",node,tensorMap,context),kernelSize=getParamValue("kernelSize",node,tensorMap,context);return[maxPool3d(getParamValue("x",node,tensorMap,context),[kernelSize[1],kernelSize[2],kernelSize[3]],[stride[1],stride[2],stride[3]],pad3)]}case"Dilation2D":{let strides=getParamValue("strides",node,tensorMap,context),pad3=getParamValue("pad",node,tensorMap,context),dilations=getParamValue("dilations",node,tensorMap,context),strideHeight=strides[1],strideWidth=strides[2],dilationHeight=dilations[1],dilationWidth=dilations[2];return[dilation2d(getParamValue("x",node,tensorMap,context),getParamValue("filter",node,tensorMap,context),[strideHeight,strideWidth],pad3,[dilationHeight,dilationWidth],"NHWC")]}default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp5=(node,tensorMap,context)=>{switch(node.op){case"Fill":{let shape=getParamValue("shape",node,tensorMap,context),dtype=getParamValue("dtype",node,tensorMap,context),value=getParamValue("value",node,tensorMap,context);return[fill(shape,value,dtype)]}case"LinSpace":{let start=getParamValue("start",node,tensorMap,context),stop=getParamValue("stop",node,tensorMap,context),num=getParamValue("num",node,tensorMap,context);return[linspace(start,stop,num)]}case"Multinomial":{let logits=getParamValue("logits",node,tensorMap,context),numSamples=getParamValue("numSamples",node,tensorMap,context),seed=getParamValue("seed",node,tensorMap,context);return[multinomial(logits,numSamples,seed)]}case"OneHot":{let indices=getParamValue("indices",node,tensorMap,context),depth=getParamValue("depth",node,tensorMap,context),onValue=getParamValue("onValue",node,tensorMap,context),offValue=getParamValue("offValue",node,tensorMap,context);return[oneHot(indices,depth,onValue,offValue)]}case"Ones":return[ones2(getParamValue("shape",node,tensorMap,context),getParamValue("dtype",node,tensorMap,context))];case"OnesLike":return[onesLike(getParamValue("x",node,tensorMap,context))];case"RandomUniform":return[randomUniform(getParamValue("shape",node,tensorMap,context),getParamValue("minval",node,tensorMap,context),getParamValue("maxval",node,tensorMap,context),getParamValue("dtype",node,tensorMap,context))];case"Range":{let start=getParamValue("start",node,tensorMap,context),stop=getParamValue("stop",node,tensorMap,context),step4=getParamValue("step",node,tensorMap,context);return[range(start,stop,step4,getParamValue("dtype",node,tensorMap,context))]}case"TruncatedNormal":{let shape=getParamValue("shape",node,tensorMap,context),mean4=getParamValue("mean",node,tensorMap,context),stdDev=getParamValue("stdDev",node,tensorMap,context),seed=getParamValue("seed",node,tensorMap,context);return[truncatedNormal(shape,mean4,stdDev,getParamValue("dtype",node,tensorMap,context),seed)]}case"Zeros":return[zeros(getParamValue("shape",node,tensorMap,context),getParamValue("dtype",node,tensorMap,context))];case"ZerosLike":return[zerosLike(getParamValue("x",node,tensorMap,context))];default:throw TypeError(`Node type ${node.op} is not implemented`)}};function nmsParams(node,tensorMap,context){let boxes=getParamValue("boxes",node,tensorMap,context),scores=getParamValue("scores",node,tensorMap,context),maxOutputSize=getParamValue("maxOutputSize",node,tensorMap,context),iouThreshold=getParamValue("iouThreshold",node,tensorMap,context),scoreThreshold=getParamValue("scoreThreshold",node,tensorMap,context),softNmsSigma=getParamValue("softNmsSigma",node,tensorMap,context);return{boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma}}var executeOp6=async(node,tensorMap,context)=>{switch(node.op){case"NonMaxSuppressionV5":{let{boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma}=nmsParams(node,tensorMap,context),result=await image.nonMaxSuppressionWithScoreAsync(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,softNmsSigma);return[result.selectedIndices,result.selectedScores]}case"NonMaxSuppressionV4":{let{boxes,scores,maxOutputSize,iouThreshold,scoreThreshold}=nmsParams(node,tensorMap,context),padToMaxOutputSize=getParamValue("padToMaxOutputSize",node,tensorMap,context),result=await image.nonMaxSuppressionPaddedAsync(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold,padToMaxOutputSize);return[result.selectedIndices,result.validOutputs]}case"NonMaxSuppressionV3":case"NonMaxSuppressionV2":{let{boxes,scores,maxOutputSize,iouThreshold,scoreThreshold}=nmsParams(node,tensorMap,context);return[await image.nonMaxSuppressionAsync(boxes,scores,maxOutputSize,iouThreshold,scoreThreshold)]}case"Where":{let condition=cast(getParamValue("condition",node,tensorMap,context),"bool"),result=[await whereAsync(condition)];return condition.dispose(),result}case"ListDiff":return setdiff1dAsync(getParamValue("x",node,tensorMap,context),getParamValue("y",node,tensorMap,context));default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp7=(node,tensorMap,context)=>{switch(node.op){case"TopKV2":{let x=getParamValue("x",node,tensorMap,context),k=getParamValue("k",node,tensorMap,context),sorted=getParamValue("sorted",node,tensorMap,context),result=topk(x,k,sorted);return[result.values,result.indices]}case"Unique":{let x=getParamValue("x",node,tensorMap,context),result=unique(x);return[result.values,result.indices]}case"UniqueV2":{let x=getParamValue("x",node,tensorMap,context),axis=getParamValue("axis",node,tensorMap,context),result=unique(x,axis);return[result.values,result.indices]}default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp8=(node,tensorMap,context)=>{switch(node.op){case"Const":return tensorMap[node.name];case"PlaceholderWithDefault":let def=getParamValue("default",node,tensorMap,context);return[getTensor(node.name,tensorMap,context)||def];case"Placeholder":return[getTensor(node.name,tensorMap,context)];case"Identity":case"StopGradient":case"FakeQuantWithMinMaxVars":{let data22=getParamValue("x",node,tensorMap,context);return[cloneTensor(data22)]}case"IdentityN":return getParamValue("x",node,tensorMap,context).map(t=>cloneTensor(t));case"Snapshot":let snapshot=getParamValue("x",node,tensorMap,context);return[cloneTensor(snapshot)];case"Shape":return[tensor1d(getParamValue("x",node,tensorMap,context).shape,"int32")];case"ShapeN":return getParamValue("x",node,tensorMap,context).map(t=>tensor1d(t.shape));case"Size":return[scalar(getParamValue("x",node,tensorMap,context).size,"int32")];case"Rank":return[scalar(getParamValue("x",node,tensorMap,context).rank,"int32")];case"NoOp":return[scalar(1)];case"Print":let input2=getParamValue("x",node,tensorMap,context),data2=getParamValue("data",node,tensorMap,context),message=getParamValue("message",node,tensorMap,context),summarize=getParamValue("summarize",node,tensorMap,context);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."),console.log(message);for(let i=0;ivalue.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}async import(keys,values){this.checkKeyAndValueTensor(keys,values);let $keys=await keys.data();return this.tensorMap.forEach(value=>value.dispose()),this.tensorMap.clear(),tidy(()=>{let $values=unstack(values),keysLength=$keys.length,valuesLength=$values.length;util_exports.assert(keysLength===valuesLength,()=>`The number of elements doesn't match, keys has ${keysLength} elements, the values has ${valuesLength} elements.`);for(let i=0;i{let result=[];for(let i=0;i<$keys.length;i++){let key=$keys[i],value=this.findWithDefault(key,defaultValue);result.push(value)}return stack(result)})}findWithDefault(key,defaultValue){let result=this.tensorMap.get(key);return result!=null?result:defaultValue}checkKeyAndValueTensor(key,value){if(key.dtype!==this.keyDType)throw new Error(`Expect key dtype ${this.keyDType}, but got ${key.dtype}`);if(value.dtype!==this.valueDType)throw new Error(`Expect value dtype ${this.valueDType}, but got ${value.dtype}`)}},executeOp9=async(node,tensorMap,context,resourceManager)=>{switch(node.op){case"HashTable":case"HashTableV2":{let keyDType=getParamValue("keyDType",node,tensorMap,context),valueDType=getParamValue("valueDType",node,tensorMap,context),hashTable2=new HashTable(keyDType,valueDType);return resourceManager.addHashTable(node.name,hashTable2),[hashTable2.handle]}case"LookupTableImport":case"LookupTableImportV2":{let handle=getParamValue("tableHandle",node,tensorMap,context,resourceManager),keys=getParamValue("keys",node,tensorMap,context),values=getParamValue("values",node,tensorMap,context);return[await resourceManager.getHashTableById(handle.id).import(keys,values)]}case"LookupTableFind":case"LookupTableFindV2":{let handle=getParamValue("tableHandle",node,tensorMap,context,resourceManager),keys=getParamValue("keys",node,tensorMap,context),defaultValue=getParamValue("defaultValue",node,tensorMap,context);return[await resourceManager.getHashTableById(handle.id).find(keys,defaultValue)]}default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp10=(node,tensorMap,context)=>{switch(node.op){case"ResizeBilinear":{let images=getParamValue("images",node,tensorMap,context),size=getParamValue("size",node,tensorMap,context),alignCorners=getParamValue("alignCorners",node,tensorMap,context),halfPixelCenters=getParamValue("halfPixelCenters",node,tensorMap,context);return[image.resizeBilinear(images,[size[0],size[1]],alignCorners,halfPixelCenters)]}case"ResizeNearestNeighbor":{let images=getParamValue("images",node,tensorMap,context),size=getParamValue("size",node,tensorMap,context),alignCorners=getParamValue("alignCorners",node,tensorMap,context),halfPixelCenters=getParamValue("halfPixelCenters",node,tensorMap,context);return[image.resizeNearestNeighbor(images,[size[0],size[1]],alignCorners,halfPixelCenters)]}case"CropAndResize":{let image3=getParamValue("image",node,tensorMap,context),boxes=getParamValue("boxes",node,tensorMap,context),boxInd=getParamValue("boxInd",node,tensorMap,context),cropSize=getParamValue("cropSize",node,tensorMap,context),method=getParamValue("method",node,tensorMap,context),extrapolationValue=getParamValue("extrapolationValue",node,tensorMap,context);return[image.cropAndResize(image3,boxes,boxInd,cropSize,method,extrapolationValue)]}default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp11=(node,tensorMap,context)=>{switch(node.op){case"Equal":return[equal(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"NotEqual":return[notEqual(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Greater":return[greater(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"GreaterEqual":return[greaterEqual(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Less":return[less(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"LessEqual":return[lessEqual(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"LogicalAnd":return[logicalAnd(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"LogicalNot":return[logicalNot(getParamValue("a",node,tensorMap,context))];case"LogicalOr":return[logicalOr(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];case"Select":case"SelectV2":return[where(getParamValue("condition",node,tensorMap,context),getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context))];default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp12=(node,tensorMap,context)=>{switch(node.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[matMul(getParamValue("a",node,tensorMap,context),getParamValue("b",node,tensorMap,context),getParamValue("transposeA",node,tensorMap,context),getParamValue("transposeB",node,tensorMap,context))];case"Transpose":return[transpose(getParamValue("x",node,tensorMap,context),getParamValue("perm",node,tensorMap,context))];case"_FusedMatMul":let[extraOp,activationFunc]=getParamValue("fusedOps",node,tensorMap,context),isBiasAdd=extraOp==="biasadd",isPrelu=activationFunc==="prelu",numArgs=getParamValue("numArgs",node,tensorMap,context),leakyreluAlpha=getParamValue("leakyreluAlpha",node,tensorMap,context);if(isBiasAdd){if(isPrelu&&numArgs!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!isPrelu&&numArgs!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[biasArg,preluArg]=getParamValue("args",node,tensorMap,context);return[fused_ops_exports.matMul({a:getParamValue("a",node,tensorMap,context),b:getParamValue("b",node,tensorMap,context),transposeA:getParamValue("transposeA",node,tensorMap,context),transposeB:getParamValue("transposeB",node,tensorMap,context),bias:biasArg,activation:activationFunc,preluActivationWeights:preluArg,leakyreluAlpha})];default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp13=(node,tensorMap,context)=>{switch(node.op){case"FusedBatchNorm":case"FusedBatchNormV2":return[batchNorm(getParamValue("x",node,tensorMap,context),getParamValue("mean",node,tensorMap,context),getParamValue("variance",node,tensorMap,context),getParamValue("offset",node,tensorMap,context),getParamValue("scale",node,tensorMap,context),getParamValue("epsilon",node,tensorMap,context))];case"FusedBatchNormV3":return[batchNorm(getParamValue("x",node,tensorMap,context),getParamValue("mean",node,tensorMap,context),getParamValue("variance",node,tensorMap,context),getParamValue("offset",node,tensorMap,context),getParamValue("scale",node,tensorMap,context),getParamValue("epsilon",node,tensorMap,context))];case"LRN":return[localResponseNormalization(getParamValue("x",node,tensorMap,context),getParamValue("radius",node,tensorMap,context),getParamValue("bias",node,tensorMap,context),getParamValue("alpha",node,tensorMap,context),getParamValue("beta",node,tensorMap,context))];case"Softmax":return[softmax(getParamValue("x",node,tensorMap,context))];case"LogSoftmax":return[logSoftmax(getParamValue("x",node,tensorMap,context))];case"SparseToDense":return[sparseToDense(getParamValue("sparseIndices",node,tensorMap,context),getParamValue("outputShape",node,tensorMap,context),getParamValue("sparseValues",node,tensorMap,context),getParamValue("defaultValue",node,tensorMap,context))];default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp14=(node,tensorMap,context)=>{switch(node.op){case"Max":{let axis=getParamValue("axis",node,tensorMap,context),keepDims=getParamValue("keepDims",node,tensorMap,context);return[max(getParamValue("x",node,tensorMap,context),axis,keepDims)]}case"Mean":{let axis=getParamValue("axis",node,tensorMap,context),keepDims=getParamValue("keepDims",node,tensorMap,context);return[mean(getParamValue("x",node,tensorMap,context),axis,keepDims)]}case"Min":{let axis=getParamValue("axis",node,tensorMap,context),keepDims=getParamValue("keepDims",node,tensorMap,context);return[min(getParamValue("x",node,tensorMap,context),axis,keepDims)]}case"Sum":{let axis=getParamValue("axis",node,tensorMap,context),keepDims=getParamValue("keepDims",node,tensorMap,context);return[sum2(getParamValue("x",node,tensorMap,context),axis,keepDims)]}case"All":{let axis=getParamValue("axis",node,tensorMap,context),keepDims=getParamValue("keepDims",node,tensorMap,context);return[all(getParamValue("x",node,tensorMap,context),axis,keepDims)]}case"Any":{let axis=getParamValue("axis",node,tensorMap,context),keepDims=getParamValue("keepDims",node,tensorMap,context);return[any(getParamValue("x",node,tensorMap,context),axis,keepDims)]}case"ArgMax":{let axis=getParamValue("axis",node,tensorMap,context);return[argMax(getParamValue("x",node,tensorMap,context),axis)]}case"ArgMin":{let axis=getParamValue("axis",node,tensorMap,context);return[argMin(getParamValue("x",node,tensorMap,context),axis)]}case"Prod":{let axis=getParamValue("axis",node,tensorMap,context),keepDims=getParamValue("keepDims",node,tensorMap,context);return[prod(getParamValue("x",node,tensorMap,context),axis,keepDims)]}case"Cumsum":{let axis=getParamValue("axis",node,tensorMap,context),exclusive=getParamValue("exclusive",node,tensorMap,context),reverse5=getParamValue("reverse",node,tensorMap,context);return[cumsum(getParamValue("x",node,tensorMap,context),axis,exclusive,reverse5)]}case"Bincount":let x=getParamValue("x",node,tensorMap,context),weights=getParamValue("weights",node,tensorMap,context),size=getParamValue("size",node,tensorMap,context);return[bincount(x,weights,size)];case"DenseBincount":{let x2=getParamValue("x",node,tensorMap,context),weights2=getParamValue("weights",node,tensorMap,context),size2=getParamValue("size",node,tensorMap,context),binaryOutput=getParamValue("binaryOutput",node,tensorMap,context);return[denseBincount(x2,weights2,size2,binaryOutput)]}default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp15=(node,tensorMap,context)=>{switch(node.op){case"ConcatV2":case"Concat":{let n=getParamValue("n",node,tensorMap,context),axis=getParamValue("axis",node,tensorMap,context),inputs=getParamValue("tensors",node,tensorMap,context);return inputs=inputs.slice(0,n),[concat(inputs,axis)]}case"Gather":{let input2=getParamValue("x",node,tensorMap,context),indices=getParamValue("indices",node,tensorMap,context);return[gather(input2,cast(indices,"int32"),0)]}case"GatherV2":{let axis=getParamValue("axis",node,tensorMap,context),batchDims=getParamValue("batchDims",node,tensorMap,context),input2=getParamValue("x",node,tensorMap,context),indices=getParamValue("indices",node,tensorMap,context);return[gather(input2,cast(indices,"int32"),axis,batchDims)]}case"Reverse":{let dims=getParamValue("dims",node,tensorMap,context),axis=[];for(let i=0;i{let axis=getParamValue("axis",node,tensorMap,context),tensors=getParamValue("tensors",node,tensorMap,context),shape=tensors[0].shape,squeezedShape=squeeze(tensors[0]).shape,mapped=tensors.map(tensor2=>{let sameShape=util_exports.arraysEqual(tensor2.shape,shape);if(!sameShape&&!util_exports.arraysEqual(squeeze(tensor2).shape,squeezedShape))throw new Error("the input tensors shape does not match");return sameShape?tensor2:reshape(tensor2,shape)});return[stack(mapped,axis)]});case"Unpack":{let axis=getParamValue("axis",node,tensorMap,context),tensor2=getParamValue("tensor",node,tensorMap,context);return unstack(tensor2,axis)}case"Tile":{let reps=getParamValue("reps",node,tensorMap,context);return[tile(getParamValue("x",node,tensorMap,context),reps)]}case"Split":case"SplitV":{let axis=getParamValue("axis",node,tensorMap,context),numOrSizeSplits=getParamValue("numOrSizeSplits",node,tensorMap,context),tensor2=getParamValue("x",node,tensorMap,context);return split(tensor2,numOrSizeSplits,axis)}case"ScatterNd":{let indices=getParamValue("indices",node,tensorMap,context),values=getParamValue("values",node,tensorMap,context),shape=getParamValue("shape",node,tensorMap,context);return[scatterND(indices,values,shape)]}case"GatherNd":{let x=getParamValue("x",node,tensorMap,context),indices=getParamValue("indices",node,tensorMap,context);return[gatherND(x,indices)]}case"SparseToDense":{let indices=getParamValue("sparseIndices",node,tensorMap,context),shape=getParamValue("outputShape",node,tensorMap,context),sparseValues=getParamValue("sparseValues",node,tensorMap,context),defaultValue=getParamValue("defaultValue",node,tensorMap,context);return[sparseToDense(indices,sparseValues,shape,sparseValues.dtype===defaultValue.dtype?defaultValue:cast(defaultValue,sparseValues.dtype))]}default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp16=(node,tensorMap,context)=>{switch(node.op){case"FFT":return[fft(getParamValue("x",node,tensorMap,context))];case"IFFT":return[ifft(getParamValue("x",node,tensorMap,context))];case"RFFT":return[rfft(getParamValue("x",node,tensorMap,context))];case"IRFFT":return[irfft(getParamValue("x",node,tensorMap,context))];default:throw TypeError(`Node type ${node.op} is not implemented`)}},executeOp17=(node,tensorMap,context)=>{switch(node.op){case"Cast":return[cast(getParamValue("x",node,tensorMap,context),getParamValue("dtype",node,tensorMap,context))];case"ExpandDims":{let axis=getParamValue("axis",node,tensorMap,context);return[expandDims(getParamValue("x",node,tensorMap,context),axis)]}case"Squeeze":{let axis=getParamValue("axis",node,tensorMap,context);return[squeeze(getParamValue("x",node,tensorMap,context),axis)]}case"Reshape":return[reshape(getParamValue("x",node,tensorMap,context),getParamValue("shape",node,tensorMap,context))];case"MirrorPad":return[mirrorPad(getParamValue("x",node,tensorMap,context),getParamValue("padding",node,tensorMap,context),getParamValue("mode",node,tensorMap,context))];case"PadV2":case"Pad":return[pad(getParamValue("x",node,tensorMap,context),getParamValue("padding",node,tensorMap,context),getParamValue("constantValue",node,tensorMap,context))];case"SpaceToBatchND":{let blockShape=getParamValue("blockShape",node,tensorMap,context),paddings=getParamValue("paddings",node,tensorMap,context);return[spaceToBatchND(getParamValue("x",node,tensorMap,context),blockShape,paddings)]}case"BatchToSpaceND":{let blockShape=getParamValue("blockShape",node,tensorMap,context),crops=getParamValue("crops",node,tensorMap,context);return[batchToSpaceND(getParamValue("x",node,tensorMap,context),blockShape,crops)]}case"DepthToSpace":{let blockSize=getParamValue("blockSize",node,tensorMap,context),dataFormat=getParamValue("dataFormat",node,tensorMap,context).toUpperCase();return[depthToSpace(getParamValue("x",node,tensorMap,context),blockSize,dataFormat)]}case"BroadcastTo":return[broadcastTo(getParamValue("x",node,tensorMap,context),getParamValue("shape",node,tensorMap,context))];default:throw TypeError(`Node type ${node.op} is not implemented`)}};function executeOp18(node,tensorMap,context,resourceManager){let value=((node2,tensorMap2,context2)=>{switch(node2.category){case"arithmetic":return tidy(()=>executeOp(node2,tensorMap2,context2));case"basic_math":return tidy(()=>executeOp2(node2,tensorMap2,context2));case"control":return executeOp3(node2,tensorMap2,context2);case"convolution":return tidy(()=>executeOp4(node2,tensorMap2,context2));case"creation":return tidy(()=>executeOp5(node2,tensorMap2,context2));case"dynamic":return executeOp6(node2,tensorMap2,context2);case"evaluation":return tidy(()=>executeOp7(node2,tensorMap2,context2));case"image":return tidy(()=>executeOp10(node2,tensorMap2,context2));case"graph":return tidy(()=>executeOp8(node2,tensorMap2,context2));case"logical":return tidy(()=>executeOp11(node2,tensorMap2,context2));case"matrices":return tidy(()=>executeOp12(node2,tensorMap2,context2));case"normalization":return tidy(()=>executeOp13(node2,tensorMap2,context2));case"reduction":return tidy(()=>executeOp14(node2,tensorMap2,context2));case"slice_join":return tidy(()=>executeOp15(node2,tensorMap2,context2));case"spectral":return tidy(()=>executeOp16(node2,tensorMap2,context2));case"transformation":return tidy(()=>executeOp17(node2,tensorMap2,context2));case"hash_table":return executeOp9(node2,tensorMap2,context2,resourceManager);case"custom":let opMapper=getRegisteredOp(node2.op);if(opMapper&&opMapper.customExecutor)return opMapper.customExecutor(new NodeValueImpl(node2,tensorMap2,context2));throw TypeError(`Custom op ${node2.op} is not registered.`);default:throw TypeError(`Unknown op '${node2.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(node,tensorMap,context);return util_exports.isPromise(value)?value.then(data2=>[].concat(data2)):[].concat(value)}var ExecutionContext=class{constructor(weightMap={},tensorArrayMap={},tensorListMap={},functionMap={}){this.weightMap=weightMap,this.tensorArrayMap=tensorArrayMap,this.tensorListMap=tensorListMap,this.functionMap=functionMap,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(id,frameName){return{id,frameName,iterationId:0}}set currentContext(contexts2){this.contexts!==contexts2&&(this.contexts=contexts2,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let names=[];for(let i=0;icontext.id===0&&context.iterationId===0?"":`${context.frameName}-${context.iterationId}`).join("/"):""}enterFrame(frameId){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,frameId)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let context=Object.assign({},this.contexts[this.contexts.length-1]);context.iterationId+=1,context.id=this.lastId,this.contexts.splice(-1,1,context),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(name){return this.weightMap[name]}addTensorArray(tensorArray){this.tensorArrayMap[tensorArray.id]=tensorArray}getTensorArray(id){return this.tensorArrayMap[id]}addTensorList(tensorList){this.tensorListMap[tensorList.id]=tensorList}getTensorList(id){return this.tensorListMap[id]}dispose(keepIds){for(let key in this.tensorArrayMap)this.tensorArrayMap[key].clearAndClose(keepIds);for(let key in this.tensorListMap)this.tensorListMap[key].clearAndClose(keepIds)}};function getExecutionSubgraph(inputs,outputs,weightMap,initNodes){let usedNodes=new Set,missingInputs=[],dynamicNode=null,syncInputs=null,seen=new Set,inputNodeNames=Object.keys(inputs).map(name=>parseNodeName(name)[0]),initNodeNames=[];initNodes!=null&&(initNodeNames=initNodes.map(node=>parseNodeName(node.name)[0]));let frontier=[...outputs];for(;frontier.length>0;){let node=frontier.pop();if((isControlFlow(node)||isDynamicShape(node)||isHashTable(node))&&dynamicNode==null&&(dynamicNode=node,syncInputs=dynamicNode.children.map(child=>child.name).filter(name=>usedNodes.has(name))),usedNodes.add(node.name),weightMap[node.name]==null&&inputNodeNames.indexOf(node.name)===-1&&initNodeNames.indexOf(node.name)===-1){if(node.inputs.length===0){missingInputs.push(node.name);continue}node.inputs.forEach(input2=>{seen.has(input2.name)||(seen.add(input2.name),frontier.push(input2))})}}return{inputs,outputs,usedNodes,missingInputs,dynamicNode,syncInputs}}function getNodesInTopologicalOrder(graph2,weightMap,executionInfo){let{usedNodes,inputs}=executionInfo,frontier=[],inputNodes=Object.keys(inputs).map(name=>parseNodeName(name)[0]).map(name=>graph2.nodes[name]),initNodes=graph2.initNodes;inputNodes.forEach(input2=>{usedNodes.has(input2.name)&&frontier.push(input2)}),graph2.weights.forEach(weight=>{usedNodes.has(weight.name)&&frontier.push(weight)}),initNodes!=null&&initNodes.forEach(node=>{usedNodes.has(node.name)&&frontier.push(node)});let seen=new Set,orderedNodes=[];for(;frontier.length>0;){let node=frontier.pop();seen.add(node.name),weightMap[node.name]||orderedNodes.push(node),node.children.forEach(child=>{!seen.has(child.name)&&usedNodes.has(child.name)&&child.inputs.every(input2=>seen.has(input2.name))&&frontier.push(child)})}return orderedNodes}var CONTROL_FLOW_OPS=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],DYNAMIC_SHAPE_OPS=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],HASH_TABLE_OPS=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2"];function isControlFlow(node){return CONTROL_FLOW_OPS.indexOf(node.op)>=0}function isDynamicShape(node){return DYNAMIC_SHAPE_OPS.indexOf(node.op)>=0}function isHashTable(node){return HASH_TABLE_OPS.indexOf(node.op)>=0}var GraphExecutor=class{constructor(graph2,parent){this.graph=graph2,this.parent=parent,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this._outputs=graph2.outputs,this._inputs=graph2.inputs,this._initNodes=graph2.initNodes,this._signature=graph2.signature,this._functions=graph2.functions,graph2.functions!=null&&Object.keys(graph2.functions).forEach(name=>{this._functionExecutorMap[name]=new GraphExecutor(graph2.functions[name],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(weightMap){let weightIds=Object.keys(weightMap).map(key=>weightMap[key].map(tensor2=>tensor2.id));this._weightIds=[].concat(...weightIds),this._weightMap=weightMap}set resourceManager(resourceManager){this._resourceManager=resourceManager}get inputs(){return this._inputs.map(node=>({name:node.name,shape:node.attrParams.shape?node.attrParams.shape.value:void 0,dtype:node.attrParams.dtype?node.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(node=>({name:node.name,shape:node.attrParams.shape?node.attrParams.shape.value:void 0,dtype:node.attrParams.dtype?node.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(node=>node.signatureKey||node.name)}get outputNodes(){return this._outputs.map(node=>{let name=node.signatureKey||node.name;return node.defaultOutput?`${name}:${node.defaultOutput}`:name})}get functions(){return Object.keys(this._functions).reduce((map,key)=>(map[key]=this._functions[key].signature,map),{})}getCompilationKey(inputs,outputs){let sortedInputs=inputs.map(node=>node.name).sort(),sortedOutputs=outputs.map(node=>node.name).sort();return sortedInputs.join(this.SEPERATOR)+"--"+sortedOutputs.join(this.SEPERATOR)}compile(inputs,outputs){let executionInfo=getExecutionSubgraph(inputs,outputs,this.weightMap,this._initNodes),{missingInputs,dynamicNode,syncInputs}=executionInfo;if(dynamicNode!=null)throw new Error(`This execution contains the node '${dynamicNode.name}', which has the dynamic op '${dynamicNode.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${syncInputs}]`);if(missingInputs.length>0){let outNames=outputs.map(n=>n.name),inNames=Object.keys(inputs);throw new Error(`Cannot compute the outputs [${outNames}] from the provided inputs [${inNames}]. Missing the following inputs: [${missingInputs}]`)}return getNodesInTopologicalOrder(this.graph,this.weightMap,executionInfo)}execute(inputs,outputs){inputs=this.mapInputs(inputs);let names=Object.keys(inputs).sort();this.checkInputs(inputs),this.checkInputShapeAndType(inputs),outputs=this.mapOutputs(outputs),this.checkOutputs(outputs);let inputNodes=names.map(name=>this.graph.nodes[parseNodeName(name)[0]]),outputNodeNames=outputs.map(name=>parseNodeName(name)[0]),outputNodes=outputNodeNames.map(name=>this.graph.nodes[name]);outputNodes.length===0&&(outputNodes=this._outputs);let compilationKey=this.getCompilationKey(inputNodes,outputNodes),orderedNodes=this.compiledMap.get(compilationKey);orderedNodes==null&&(orderedNodes=this.compile(inputs,outputNodes),this.compiledMap.set(compilationKey,orderedNodes));let tensorArrayMap={},tensorListMap={};return tidy(()=>{let context=new ExecutionContext(this.weightMap,tensorArrayMap,tensorListMap,this.functionExecutorMap),tensorsMap=Object.assign({},this.weightMap);Object.keys(inputs).forEach(name=>{let[nodeName,index]=parseNodeName(name),tensors=[];tensors[index]=inputs[name],tensorsMap[nodeName]=tensors});let tensorsToKeep=this.getFrozenTensorIds(tensorsMap),intermediateTensorConsumerCount={};for(let i=0;igetTensor(name,tensorsMap,context))})}getFrozenTensorIds(tensorMap){let ids=[].concat.apply([],Object.keys(tensorMap).map(key=>tensorMap[key]).map(tensors=>tensors.map(tensor2=>tensor2.id)));return new Set(ids)}checkTensorForDisposal(nodeName,node,tensorMap,context,tensorsToKeep,outputNames,intermediateTensorConsumerCount){node.category==="control"||outputNames.indexOf(nodeName)!==-1||(tensorMap[nodeName].forEach(tensor2=>{tensor2!=null&&(intermediateTensorConsumerCount[tensor2.id]=(intermediateTensorConsumerCount[tensor2.id]||0)+node.children.length)}),node.inputs.forEach(input2=>{if(input2.category!=="control"){let tensors=getTensorsForCurrentContenxt(input2.name,tensorMap,context);tensors!=null&&tensors.forEach(tensor2=>{if(tensor2&&!tensorsToKeep.has(tensor2.id)){let count2=intermediateTensorConsumerCount[tensor2.id];count2===1?(tensor2.dispose(),delete intermediateTensorConsumerCount[tensor2.id]):count2!=null&&intermediateTensorConsumerCount[tensor2.id]--}})}}))}async executeAsync(inputs,outputs){return this._executeAsync(inputs,outputs)}async _executeAsync(inputs,outputs,isFunctionExecution=!1,tensorArrayMap={},tensorListMap={}){isFunctionExecution||(inputs=this.mapInputs(inputs),this.checkInputs(inputs),this.checkInputShapeAndType(inputs),outputs=this.mapOutputs(outputs),this.checkOutputs(outputs));let context=new ExecutionContext(this.weightMap,tensorArrayMap,tensorListMap,this.functionExecutorMap),tensorMap=await this.executeWithControlFlow(inputs,context,outputs,isFunctionExecution),results=outputs.map(name=>getTensor(name,tensorMap,context)),outputIds=results.map(t=>t.id),inputIds=Object.keys(inputs).map(name=>inputs[name].id),keepIds=new Set([...outputIds,...inputIds,...this.weightIds]);return Object.keys(tensorMap).forEach(key=>{tensorMap[key].forEach(tensor2=>{tensor2&&!tensor2.isDisposed&&!keepIds.has(tensor2.id)&&tensor2.dispose()})}),this.parent==null&&context.dispose(keepIds),results}async executeFunctionAsync(inputs,tensorArrayMap,tensorListMap){let mappedInputs=inputs.reduce((map,tensor2,index)=>(map[this.inputs[index].name]=tensor2,map),{});return this._executeAsync(mappedInputs,this.outputNodes,!0,tensorArrayMap,tensorListMap)}async executeWithControlFlow(inputs,context,outputNames,isFunctionExecution){let names=Object.keys(inputs),inputNodes=names.map(name=>this.graph.nodes[parseNodeName(name)[0]]),outputNodeNames=outputNames.map(name=>parseNodeName(name)[0]),outputNodes=outputNodeNames.map(name=>this.graph.nodes[name]);outputNodes.length===0&&(outputNodes=this._outputs);let{usedNodes,missingInputs,dynamicNode,syncInputs}=getExecutionSubgraph(inputs,outputNodes,this.weightMap,this._initNodes),stack2=[...inputNodes,...this.graph.weights,...this._initNodes||[]].map(node=>({node,contexts:context.currentContext})),tensorsMap=Object.assign({},this.weightMap);Object.keys(inputs).forEach(name=>{let[nodeName,index]=parseNodeName(name),tensors=[];tensors[index]=inputs[name],tensorsMap[nodeName]=tensors});let intermediateTensorConsumerCount={},tensorsToKeep=this.getFrozenTensorIds(tensorsMap),added={};for(;stack2.length>0;){let promises=this.processStack(inputNodes,stack2,context,tensorsMap,added,tensorsToKeep,outputNodeNames,intermediateTensorConsumerCount,usedNodes);await Promise.all(promises)}dynamicNode==null&&!isFunctionExecution&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let missingOutputs=outputNodes.filter(node=>!isControlFlow(node)&&!getTensor(node.name,tensorsMap,context)).map(node=>node.name);if(missingOutputs.length>0){let alternativeMsg="";throw dynamicNode!=null&&(alternativeMsg=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${syncInputs}]`),new Error(`Cannot compute the outputs [${missingOutputs}] from the provided inputs [${names}]. Consider providing the following inputs: [${missingInputs}]. ${alternativeMsg}`)}return tensorsMap}processStack(inputNodes,stack2,context,tensorMap,added,tensorsToKeep,outputNames,intermediateTensorConsumerCount,usedNodes){let promises=[];for(;stack2.length>0;){let item=stack2.pop();context.currentContext=item.contexts;let nodeName="";if(item.node.op==="Enter"&&getParamValue("isConstant",item.node,tensorMap,context)&&([nodeName]=getNodeNameAndIndex(item.node.name,context)),tensorMap[item.node.name]==null){let tensors=executeOp18(item.node,tensorMap,context,this._resourceManager);nodeName||([nodeName]=getNodeNameAndIndex(item.node.name,context));let currentContext=context.currentContext;util_exports.isPromise(tensors)?promises.push(tensors.then(t=>(tensorMap[nodeName]=t,context.currentContext=currentContext,this.checkTensorForDisposal(nodeName,item.node,tensorMap,context,tensorsToKeep,outputNames,intermediateTensorConsumerCount),this.processChildNodes(item.node,stack2,context,tensorMap,added,usedNodes),t))):(tensorMap[nodeName]=tensors,this.checkTensorForDisposal(nodeName,item.node,tensorMap,context,tensorsToKeep,outputNames,intermediateTensorConsumerCount),this.processChildNodes(item.node,stack2,context,tensorMap,added,usedNodes))}else this.processChildNodes(item.node,stack2,context,tensorMap,added,usedNodes)}return promises}processChildNodes(node,stack2,context,tensorMap,added,usedNodes){node.children.forEach(childNode=>{let[nodeName]=getNodeNameAndIndex(childNode.name,context);added[nodeName]||!usedNodes.has(childNode.name)||(childNode.op==="Merge"?childNode.inputNames.some(name=>!!getTensor(name,tensorMap,context))&&(added[nodeName]=!0,stack2.push({contexts:context.currentContext,node:childNode})):childNode.inputNames.every(name=>!!getTensor(name,tensorMap,context))&&(added[nodeName]=!0,stack2.push({contexts:context.currentContext,node:childNode})))})}dispose(){Object.keys(this.weightMap).forEach(key=>this.weightMap[key].forEach(tensor2=>tensor2.dispose()))}checkInputShapeAndType(inputs){Object.keys(inputs).forEach(name=>{let input2=inputs[name],[nodeName]=parseNodeName(name),node=this.graph.nodes[nodeName];if(node.attrParams.shape&&node.attrParams.shape.value){let shape=node.attrParams.shape.value,match=shape.length===input2.shape.length&&input2.shape.every((dim,index)=>shape[index]===-1||shape[index]===dim);util_exports.assert(match,()=>`The shape of dict['${node.name}'] provided in model.execute(dict) must be [${shape}], but was [${input2.shape}]`)}node.attrParams.dtype&&node.attrParams.dtype.value&&util_exports.assert(input2.dtype===node.attrParams.dtype.value,()=>`The dtype of dict['${node.name}'] provided in model.execute(dict) must be ${node.attrParams.dtype.value}, but was ${input2.dtype}`)})}mapInputs(inputs){let result={};for(let inputName in inputs)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[inputName]!=null){let tensor2=this._signature.inputs[inputName];result[tensor2.name]=inputs[inputName]}else result[inputName]=inputs[inputName];return result}checkInputs(inputs){let notInGraph=Object.keys(inputs).filter(name=>{let[nodeName]=parseNodeName(name);return this.graph.nodes[nodeName]==null});if(notInGraph.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${notInGraph}] that are not part of graph`)}mapOutputs(outputs){return outputs.map(name=>this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[name]!=null?this._signature.outputs[name].name:name,{})}checkOutputs(outputs){outputs.forEach(name=>{let[normalizedName]=parseNodeName(name);if(!this.graph.nodes[normalizedName])throw new Error(`The output '${name}' is not found in the graph`)})}},ResourceManager=class{constructor(hashTableNameToHandle={},hashTableMap={}){this.hashTableNameToHandle=hashTableNameToHandle,this.hashTableMap=hashTableMap}addHashTable(name,hashTable2){this.hashTableNameToHandle[name]=hashTable2.handle,this.hashTableMap[hashTable2.id]=hashTable2}getHashTableHandleByName(name){return this.hashTableNameToHandle[name]}getHashTableById(id){return this.hashTableMap[id]}dispose(){for(let key in this.hashTableMap)this.hashTableMap[key].clearAndClose(),delete this.hashTableMap[key];for(let name in this.hashTableNameToHandle)this.hashTableNameToHandle[name].dispose(),delete this.hashTableNameToHandle[name]}},TFHUB_SEARCH_PARAM="?tfjs-format=file",DEFAULT_MODEL_NAME="model.json",GraphModel=class{constructor(modelUrl,loadOptions={}){this.modelUrl=modelUrl,this.loadOptions=loadOptions,this.version="n/a",loadOptions==null&&(this.loadOptions={}),this.resourceManager=new ResourceManager}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let path=this.modelUrl;if(path.load!=null)this.handler=path;else if(this.loadOptions.requestInit!=null)this.handler=io_exports.browserHTTPRequest(path,this.loadOptions);else{let handlers=io_exports.getLoadHandlers(path,this.loadOptions);if(handlers.length===0)handlers.push(io_exports.browserHTTPRequest(path,this.loadOptions));else if(handlers.length>1)throw new Error(`Found more than one (${handlers.length}) load handlers for URL '${[path]}'`);this.handler=handlers[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let artifacts=await this.handler.load();return this.loadSync(artifacts)}loadSync(artifacts){this.artifacts=artifacts;let graph2=this.artifacts.modelTopology,signature;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?signature=this.artifacts.userDefinedMetadata.signature:signature=this.artifacts.signature,this.signature=signature,this.version=`${graph2.versions.producer}.${graph2.versions.minConsumer}`;let weightMap=io_exports.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new GraphExecutor(OperationMapper.Instance.transformGraph(graph2,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(weightMap),this.executor.resourceManager=this.resourceManager,artifacts.modelInitializer!=null&&artifacts.modelInitializer.node!=null){let initializer=OperationMapper.Instance.transformGraph(artifacts.modelInitializer);this.initializer=new GraphExecutor(initializer),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(handlerOrURL,config2){if(typeof handlerOrURL=="string"){let handlers=io_exports.getSaveHandlers(handlerOrURL);if(handlers.length===0)throw new Error(`Cannot find any save handlers for URL '${handlerOrURL}'`);if(handlers.length>1)throw new Error(`Found more than one (${handlers.length}) save handlers for URL '${handlerOrURL}'`);handlerOrURL=handlers[0]}if(handlerOrURL.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return handlerOrURL.save(this.artifacts)}predict(inputs,config2){return this.execute(inputs,this.outputNodes)}normalizeInputs(inputs){if(!(inputs instanceof Tensor)&&!Array.isArray(inputs))return inputs;if(inputs=Array.isArray(inputs)?inputs:[inputs],inputs.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${inputs.length} input tensors.`);return this.inputNodes.reduce((map,inputName,i)=>(map[inputName]=inputs[i],map),{})}normalizeOutputs(outputs){return outputs=outputs||this.outputNodes,Array.isArray(outputs)?outputs:[outputs]}execute(inputs,outputs){inputs=this.normalizeInputs(inputs),outputs=this.normalizeOutputs(outputs);let result=this.executor.execute(inputs,outputs);return result.length>1?result:result[0]}async executeAsync(inputs,outputs){inputs=this.normalizeInputs(inputs),outputs=this.normalizeOutputs(outputs);let result=await this.executor.executeAsync(inputs,outputs);return result.length>1?result:result[0]}convertTensorMapToTensorsMap(map){return Object.keys(map).reduce((newMap,key)=>(newMap[key]=[map[key]],newMap),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function loadGraphModel(modelUrl,options={}){if(modelUrl==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");options==null&&(options={}),options.fromTFHub&&modelUrl.load==null&&(modelUrl.endsWith("/")||(modelUrl=modelUrl+"/"),modelUrl=`${modelUrl}${DEFAULT_MODEL_NAME}${TFHUB_SEARCH_PARAM}`);let model2=new GraphModel(modelUrl,options);return await model2.load(),model2}var version11="2.8.3",dist_exports={};__export2(dist_exports,{CSVDataset:()=>CSVDataset,Dataset:()=>Dataset,FileDataSource:()=>FileDataSource,TextLineDataset:()=>TextLineDataset,URLDataSource:()=>URLDataSource,array:()=>array,csv:()=>csv,func:()=>func,generator:()=>generator,microphone:()=>microphone,version_data:()=>version12,webcam:()=>webcam,zip:()=>zip});var seedrandom4=__toModule2(require_seedrandom6()),seedrandom3=__toModule2(require_seedrandom6());function deepMap(input2,mapFn){return deepMapInternal(input2,mapFn)}function deepMapInternal(input2,mapFn,seen=new Map,containedIn=new Set){if(input2==null)return null;if(containedIn.has(input2))throw new Error("Circular references are not supported.");if(seen.has(input2))return seen.get(input2);let result=mapFn(input2);if(result.recurse&&result.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(result.recurse)if(isIterable2(input2)){let mappedIterable=Array.isArray(input2)?[]:{};containedIn.add(input2);for(let k in input2){let child=input2[k],childResult=deepMapInternal(child,mapFn,seen,containedIn);mappedIterable[k]=childResult}return containedIn.delete(input2),mappedIterable}else throw new Error(`Can't recurse into non-iterable type: ${input2}`);else return seen.set(input2,result.value),result.value}function deepZip(inputs,zipFn=zipToList){return deepZipInternal(inputs,zipFn)}function deepZipInternal(inputs,zipFn,containedIn=new Set){let input2=inputs[0];if(containedIn.has(input2))throw new Error("Circular references are not supported.");let result=zipFn(inputs);if(result.recurse&&result.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(result.recurse)if(isIterable2(input2)){let mappedIterable=Array.isArray(input2)?[]:{};containedIn.add(input2);for(let k in input2){let children=inputs.map(x=>x[k]),childResult=deepZipInternal(children,zipFn,containedIn);mappedIterable[k]=childResult}return containedIn.delete(input2),mappedIterable}else throw new Error(`Can't recurse into non-iterable type: ${input2}`);else return result.value}function zipToList(x){return x===null?null:isIterable2(x[0])?{value:null,recurse:!0}:{value:x,recurse:!1}}async function deepMapAndAwaitAll(input2,mapFn){let seen=new Map;deepMapInternal(input2,mapFn,seen);for(let key of Array.from(seen.keys())){let value=seen.get(key);if(util_exports.isPromise(value)){let mappedValue=await value;seen.set(key,mappedValue)}}return deepMapInternal(input2,mapFn,seen)}function isIterable2(obj){return obj!=null&&!ArrayBuffer.isView(obj)&&(Array.isArray(obj)||typeof obj=="object"&&!(obj instanceof Tensor))}function canTensorify(obj){return obj==null||isPrimitive(obj)||Array.isArray(obj)||typeof obj=="object"&&obj instanceof Tensor||util_exports.isTypedArray(obj)}function isPrimitive(value){return value===null||typeof value!="object"&&typeof value!="function"}function deepClone(container){return deepMap(container,cloneIfTensor)}function cloneIfTensor(item){return item instanceof Tensor?{value:item.clone(),recurse:!1}:isIterable2(item)?{value:null,recurse:!0}:{value:item,recurse:!1}}var RingBuffer=class{constructor(capacity){if(this.capacity=capacity,this.begin=0,this.end=0,capacity==null)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(capacity<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(capacity),this.doubledCapacity=2*capacity}wrap(index){for(;index<0;)index+=this.doubledCapacity;return index%this.doubledCapacity}get(index){if(index<0)throw new RangeError("Can't get item at a negative index.");return this.data[index%this.capacity]}set(index,value){if(index<0)throw new RangeError("Can't set item at a negative index.");this.data[index%this.capacity]=value}length(){let length=this.end-this.begin;return length<0&&(length=this.doubledCapacity+length),length}isFull(){return this.length()===this.capacity}isEmpty(){return this.length()===0}push(value){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,value),this.end=this.wrap(this.end+1)}pushAll(values){for(let value of values)this.push(value)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);let result=this.get(this.end);return this.set(this.end,void 0),result}unshift(value){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,value)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let result=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),result}shuffleExcise(relativeIndex){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let index=this.wrap(this.begin+relativeIndex),result=this.get(index);return this.set(index,this.pop()),result}},GrowingRingBuffer=class extends RingBuffer{constructor(){super(GrowingRingBuffer.INITIAL_CAPACITY)}isFull(){return!1}push(value){super.isFull()&&this.expand(),super.push(value)}unshift(value){super.isFull()&&this.expand(),super.unshift(value)}expand(){let newCapacity=this.capacity*2,newData=new Array(newCapacity),len=this.length();for(let i=0;ix===!0)}rowMajorBatch(batchSize,smallLastBatch=!0){return new RowMajorBatchIterator(this,batchSize,smallLastBatch)}columnMajorBatch(batchSize,smallLastBatch=!0,zipFn=zipToList){return this.rowMajorBatch(batchSize,smallLastBatch).map(x=>deepZip(x,zipFn))}concatenate(iterator,baseErrorHandler){return new ChainedIterator(iteratorFromItems([this,iterator]),baseErrorHandler)}take(count2){return count2<0||count2==null?this:new TakeIterator(this,count2)}skip(count2){return count2<0||count2==null?this:new SkipIterator(this,count2)}prefetch(bufferSize){return new PrefetchIterator(this,bufferSize)}shuffle(windowSize,seed){return new ShuffleIterator(this,windowSize,seed)}serial(){return new SerialIterator(this)}},ArrayIterator=class extends LazyIterator{constructor(items){super();this.items=items,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let item=this.items[this.trav];return this.trav++,{value:deepClone(item),done:!1}}},FunctionCallIterator=class extends LazyIterator{constructor(nextFn){super();this.nextFn=nextFn}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: ${e.message}`,e}}},SerialIterator=class extends LazyIterator{constructor(upstream){super();this.upstream=upstream,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},SkipIterator=class extends LazyIterator{constructor(upstream,maxCount){super();this.upstream=upstream,this.maxCount=maxCount,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++ Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},RowMajorBatchIterator=class extends LazyIterator{constructor(upstream,batchSize,enableSmallLastBatch=!0){super();this.upstream=upstream,this.batchSize=batchSize,this.enableSmallLastBatch=enableSmallLastBatch,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let batch=[];for(;batch.length0?{value:batch,done:!1}:{value:null,done:!0};batch.push(item.value)}return{value:batch,done:!1}}},FilterIterator=class extends LazyIterator{constructor(upstream,predicate){super();this.upstream=upstream,this.predicate=predicate,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let item=await this.upstream.next();if(item.done||this.predicate(item.value))return item;dispose(item.value)}}},MapIterator=class extends LazyIterator{constructor(upstream,transform){super();this.upstream=upstream,this.transform=transform}summary(){return`${this.upstream.summary()} -> Map`}async next(){let item=await this.upstream.next();if(item.done)return{value:null,done:!0};let inputTensors=tensor_util_exports.getTensorsInContainer(item.value),mapped=this.transform(item.value),outputTensors=tensor_util_exports.getTensorsInContainer(mapped);for(let t of inputTensors)tensor_util_exports.isTensorInList(t,outputTensors)||t.dispose();return{value:mapped,done:!1}}},ErrorHandlingLazyIterator=class extends LazyIterator{constructor(upstream,handler){super();this.upstream=upstream,this.handler=handler,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},AsyncMapIterator=class extends LazyIterator{constructor(upstream,transform){super();this.upstream=upstream,this.transform=transform}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let item=await this.upstream.next();if(item.done)return{value:null,done:!0};let inputTensors=tensor_util_exports.getTensorsInContainer(item.value),mapped=await this.transform(item.value),outputTensors=tensor_util_exports.getTensorsInContainer(mapped);for(let t of inputTensors)tensor_util_exports.isTensorInList(t,outputTensors)||t.dispose();return{value:mapped,done:!1}}},OneToManyIterator=class extends LazyIterator{constructor(){super();this.outputQueue=new GrowingRingBuffer,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},FlatmapIterator=class extends OneToManyIterator{constructor(upstream,transform){super();this.upstream=upstream,this.transform=transform}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let item=await this.upstream.next();if(item.done)return!1;let inputTensors=tensor_util_exports.getTensorsInContainer(item.value),mappedArray=this.transform(item.value),outputTensors=tensor_util_exports.getTensorsInContainer(mappedArray);this.outputQueue.pushAll(mappedArray);for(let t of inputTensors)tensor_util_exports.isTensorInList(t,outputTensors)||t.dispose();return!0}},ChainedIterator=class extends LazyIterator{constructor(iterators,baseErrorHandler){super();this.baseErrorHandler=baseErrorHandler,this.lastRead=null,this.iterator=null,this.moreIterators=iterators}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(lastRead){if(await lastRead,this.iterator==null){let iteratorResult=await this.moreIterators.next();if(iteratorResult.done)return{value:null,done:!0};this.iterator=iteratorResult.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let itemResult=await this.iterator.next();return itemResult.done?(this.iterator=null,this.readFromChain(lastRead)):itemResult}},ZipMismatchMode;(function(ZipMismatchMode2){ZipMismatchMode2[ZipMismatchMode2.FAIL=0]="FAIL",ZipMismatchMode2[ZipMismatchMode2.SHORTEST=1]="SHORTEST",ZipMismatchMode2[ZipMismatchMode2.LONGEST=2]="LONGEST"})(ZipMismatchMode||(ZipMismatchMode={}));var ZipIterator=class extends LazyIterator{constructor(iterators,mismatchMode=ZipMismatchMode.FAIL){super();this.iterators=iterators,this.mismatchMode=mismatchMode,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(afterState){await afterState;let numIterators=0,iteratorsDone=0;function getNext(container){return container instanceof LazyIterator?{value:container.next().then(x=>(numIterators++,x.done&&iteratorsDone++,x.value)),recurse:!1}:{value:null,recurse:!0}}let mapped=await deepMapAndAwaitAll(this.iterators,getNext);if(numIterators===iteratorsDone)return{value:null,done:!0};if(iteratorsDone>0)switch(this.mismatchMode){case ZipMismatchMode.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case ZipMismatchMode.SHORTEST:return{value:null,done:!0};case ZipMismatchMode.LONGEST:default:}return this.count++,{value:mapped,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},PrefetchIterator=class extends LazyIterator{constructor(upstream,bufferSize){super();this.upstream=upstream,this.bufferSize=bufferSize,this.buffer=new RingBuffer(bufferSize)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let v=this.upstream.next();this.buffer.push(v)}}next(){return this.refill(),this.buffer.shift()}},ShuffleIterator=class extends PrefetchIterator{constructor(upstream,windowSize,seed){super(upstream,windowSize);this.upstream=upstream,this.windowSize=windowSize,this.upstreamExhausted=!1,this.random=seedrandom3.alea(seed||util_exports.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(max6){return Math.floor(this.random()*max6)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let chosenIndex=this.chooseIndex(),result=await this.buffer.shuffleExcise(chosenIndex);if(result.done)this.upstreamExhausted=!0;else return this.refill(),result}return{value:null,done:!0}}},Dataset=class{constructor(){this.size=null}batch(batchSize,smallLastBatch=!0){let base2=this;util_exports.assert(batchSize>0,()=>`batchSize needs to be positive, but it is ${batchSize}`);let size;return this.size===Infinity||this.size==null?size=this.size:smallLastBatch?size=Math.ceil(this.size/batchSize):size=Math.floor(this.size/batchSize),datasetFromIteratorFn(async()=>(await base2.iterator()).columnMajorBatch(batchSize,smallLastBatch,deepBatchConcat),size)}concatenate(dataset){let base2=this,size;return this.size===Infinity||dataset.size===Infinity?size=Infinity:this.size!=null&&dataset.size!=null?size=this.size+dataset.size:size=null,datasetFromIteratorFn(async()=>(await base2.iterator()).concatenate(await dataset.iterator()),size)}filter(predicate){let base2=this,size;return this.size===Infinity?size=Infinity:size=null,datasetFromIteratorFn(async()=>(await base2.iterator()).filter(x=>tidy(()=>predicate(x))),size)}async forEachAsync(f){return(await this.iterator()).forEachAsync(f)}map(transform){let base2=this;return datasetFromIteratorFn(async()=>(await base2.iterator()).map(x=>tidy(()=>transform(x))),this.size)}mapAsync(transform){let base2=this;return datasetFromIteratorFn(async()=>(await base2.iterator()).mapAsync(transform),this.size)}prefetch(bufferSize){if(bufferSize==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let base2=this;return datasetFromIteratorFn(async()=>(await base2.iterator()).prefetch(bufferSize),this.size)}repeat(count2){let base2=this,size;return this.size!=null&&count2>0?size=this.size*count2:count2===0?size=0:this.size!=null&&(count2===void 0||count2<0)?size=Infinity:size=null,datasetFromIteratorFn(async()=>{let iteratorIterator=iteratorFromFunction(async()=>({value:await base2.iterator(),done:!1}));return iteratorFromConcatenated(iteratorIterator.take(count2))},size)}skip(count2){let base2=this,size;return this.size!=null&&count2>=0&&this.size>=count2?size=this.size-count2:this.size!=null&&(this.size(await base2.iterator()).skip(count2),size)}shuffle(bufferSize,seed,reshuffleEachIteration=!0){if(bufferSize==null||bufferSize<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let base2=this,random=seedrandom4.alea(seed||util_exports.now().toString());return datasetFromIteratorFn(async()=>{let seed2=random.int32();return reshuffleEachIteration&&(seed2+=random.int32()),(await base2.iterator()).shuffle(bufferSize,seed2.toString())},this.size)}take(count2){let base2=this,size;return this.size!=null&&this.size>count2?size=count2:this.size!=null&&this.size<=count2?size=this.size:size=null,datasetFromIteratorFn(async()=>(await base2.iterator()).take(count2),size)}async toArray(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};Dataset.MAX_BUFFER_SIZE=1e4;function datasetFromIteratorFn(iteratorFn,size=null){return new class extends Dataset{constructor(){super(...arguments);this.size=size}async iterator(){return iteratorFn()}}}function array(items){return datasetFromIteratorFn(async()=>iteratorFromItems(items),items.length)}function zip(datasets){if(!isIterable2(datasets))throw new Error("The argument to zip() must be an object or array.");let size;if(Array.isArray(datasets))for(let i=0;i{let streams=await deepMapAndAwaitAll(datasets,d=>{if(d instanceof Dataset)return{value:d.iterator(),recurse:!1};if(isIterable2(d))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return iteratorFromZipped(streams,ZipMismatchMode.SHORTEST)},size)}function deepBatchConcat(rows){if(rows===null)return null;let exampleRow=rows[0];return canTensorify(exampleRow)?{value:batchConcat(rows),recurse:!1}:{value:null,recurse:!0}}function batchConcat(arrays){if(arrays.length===0)throw new Error("Can't make a batch of zero elements.");return arrays[0]instanceof Tensor?stack(arrays):tensor(arrays)}var TextLineDataset=class extends Dataset{constructor(input2){super();this.input=input2}async iterator(){return(await this.input.iterator()).decodeUTF8().split(` -`).map(line=>(line.endsWith("\r")&&(line=line.slice(0,-1)),line))}},CODE_QUOTE='"',STATE_OUT=Symbol("out"),STATE_FIELD=Symbol("field"),STATE_QUOTE=Symbol("quote"),STATE_QUOTE_AFTER_QUOTE=Symbol("quoteafterquote"),STATE_WITHIN_QUOTE_IN_QUOTE=Symbol("quoteinquote"),CSVDataset=class extends Dataset{constructor(input2,csvConfig){super();this.input=input2,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new TextLineDataset(input2),csvConfig||(csvConfig={}),this.hasHeader=csvConfig.hasHeader!==!1,this.fullColumnNames=csvConfig.columnNames,this.columnConfigs=csvConfig.columnConfigs,this.configuredColumnsOnly=csvConfig.configuredColumnsOnly,csvConfig.delimWhitespace?(util_exports.assert(csvConfig.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=csvConfig.delimiter?csvConfig.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let columnNamesFromFile=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!columnNamesFromFile)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&columnNamesFromFile&&util_exports.assert(columnNamesFromFile.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+columnNamesFromFile.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=columnNamesFromFile);let counts=this.fullColumnNames.reduce((countAcc,name)=>(countAcc[name]=countAcc[name]+1||1,countAcc),{}),duplicateNames=Object.keys(counts).filter(name=>counts[name]>1);if(util_exports.assert(duplicateNames.length===0,()=>"Duplicate column names found: "+duplicateNames.toString()),this.columnConfigs){for(let key of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(key)===-1)throw new Error('The key "'+key+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let firstElement=await(await this.base.iterator()).next();if(firstElement.done)throw new Error("No data was found for CSV parsing.");let firstLine=firstElement.value;return this.parseRow(firstLine,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let lines=await this.base.iterator();return this.hasHeader&&(lines=lines.skip(1)),lines.map(x=>this.makeDataElement(x))}makeDataElement(line){let values=this.parseRow(line),features={},labels={};for(let i=0;i14||!Number.isInteger(fftSizeLog2))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=microphoneConfig.numFramesPerSpectrogram||43,this.sampleRateHz=microphoneConfig.sampleRateHz,this.columnTruncateLength=microphoneConfig.columnTruncateLength||this.fftSize,this.audioTrackConstraints=microphoneConfig.audioTrackConstraints,this.smoothingTimeConstant=microphoneConfig.smoothingTimeConstant||0,this.includeSpectrogram=microphoneConfig.includeSpectrogram!==!1,this.includeWaveform=microphoneConfig.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(microphoneConfig={}){if(env().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let microphoneIterator=new MicrophoneIterator(microphoneConfig);return await microphoneIterator.start(),microphoneIterator}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(e){throw new Error(`Error thrown while initializing video stream: ${e.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let ctxConstructor=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new ctxConstructor,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let streamSource=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,streamSource.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let spectrogramTensor,waveformTensor,audioDataQueue=await this.getAudioData();if(this.includeSpectrogram){let freqData=this.flattenQueue(audioDataQueue.freqDataQueue);spectrogramTensor=this.getTensorFromAudioDataArray(freqData,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let timeData=this.flattenQueue(audioDataQueue.timeDataQueue);waveformTensor=this.getTensorFromAudioDataArray(timeData,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:spectrogramTensor,waveform:waveformTensor},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let freqDataQueue=[],timeDataQueue=[],currentFrames=0;return new Promise(resolve=>{let intervalID=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&resolve({freqDataQueue,timeDataQueue}),freqDataQueue.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),timeDataQueue.push(this.timeData.slice())),++currentFrames===this.numFrames&&(clearInterval(intervalID),resolve({freqDataQueue,timeDataQueue}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(queue){let frameSize=queue[0].length,freqData=new Float32Array(queue.length*frameSize);return queue.forEach((data2,i)=>freqData.set(data2,i*frameSize)),freqData}getTensorFromAudioDataArray(freqData,shape){let vals=new Float32Array(util_exports.sizeFromShape(shape));return vals.set(freqData,vals.length-freqData.length),tensor(vals,shape)}},WebcamIterator=class extends LazyIterator{constructor(webcamVideoElement,webcamConfig){super();if(this.webcamVideoElement=webcamVideoElement,this.webcamConfig=webcamConfig,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=tensor1d([0],"int32"),this.webcamConfig.centerCrop){let widthCroppingRatio=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,heightCroppingRatio=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,widthCropStart=(1-widthCroppingRatio)/2,heightCropStart=(1-heightCroppingRatio)/2,widthCropEnd=widthCropStart+widthCroppingRatio,heightCropEnd=heightCroppingRatio+heightCropStart;this.cropBox=tensor2d([heightCropStart,widthCropStart,heightCropEnd,widthCropEnd],[1,4])}else this.cropBox=tensor2d([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(webcamVideoElement,webcamConfig={}){if(env().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!webcamVideoElement){if(webcamVideoElement=document.createElement("video"),!webcamConfig.resizeWidth||!webcamConfig.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");webcamVideoElement.width=webcamConfig.resizeWidth,webcamVideoElement.height=webcamConfig.resizeHeight}let webcamIterator=new WebcamIterator(webcamVideoElement,webcamConfig);return await webcamIterator.start(),webcamIterator}async start(){this.webcamConfig.facingMode&&util_exports.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(error){console.log(error),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(resolve=>{this.webcamVideoElement.onloadedmetadata=()=>{resolve()}})}async next(){if(this.isClosed)return{value:null,done:!0};let img;try{img=browser_exports.fromPixels(this.webcamVideoElement)}catch(e){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(e)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(img),done:!1}}catch(e){throw new Error(`Error thrown cropping the video: ${e.message}`)}finally{img.dispose()}else return{value:img,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(img){return tidy(()=>{let expandedImage=img.toFloat().expandDims(0),resizedImage;resizedImage=image.cropAndResize(expandedImage,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let shape=resizedImage.shape;return resizedImage.reshape(shape.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(track=>track.stop());try{this.webcamVideoElement.srcObject=null}catch(error){console.log(error),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},DataSource=class{},StringIterator=class extends LazyIterator{split(separator){return new SplitIterator(this,separator)}},SplitIterator=class extends StringIterator{constructor(upstream,separator){super();this.upstream=upstream,this.impl=new SplitIteratorImpl(upstream,separator)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},SplitIteratorImpl=class extends OneToManyIterator{constructor(upstream,separator){super();this.upstream=upstream,this.separator=separator,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let chunkResult=await this.upstream.next();if(chunkResult.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let lines=chunkResult.value.split(this.separator);lines[0]=this.carryover+lines[0];for(let line of lines.slice(0,-1))this.outputQueue.push(line);return this.carryover=lines[lines.length-1],!0}},ByteChunkIterator=class extends LazyIterator{decodeUTF8(){return new Utf8Iterator(this)}},Utf8Iterator=class extends StringIterator{constructor(upstream){super();this.upstream=upstream,this.impl=new Utf8IteratorImpl(upstream)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},Utf8IteratorImpl=class extends OneToManyIterator{constructor(upstream){super();if(this.upstream=upstream,env().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder}=require_string_decoder();this.decoder=new StringDecoder("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let chunkResult=await this.upstream.next(),chunk;if(chunkResult.done)return!1;chunk=chunkResult.value;let text;return env().get("IS_BROWSER")?text=this.decoder.decode(chunk,{stream:!0}):text=this.decoder.write(Buffer.from(chunk.buffer)),this.outputQueue.push(text),!0}},FileChunkIterator=class extends ByteChunkIterator{constructor(file,options={}){super();this.file=file,this.options=options,util_exports.assert(file instanceof Uint8Array||(env().get("IS_BROWSER")?file instanceof File||file instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=options.offset||0,this.chunkSize=options.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((resolve,reject)=>{let end=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)resolve(new Uint8Array(this.file.slice(this.offset,end)));else{let fileReader=new FileReader;fileReader.onload=event=>{let data2=fileReader.result;if(data2 instanceof ArrayBuffer&&(data2=new Uint8Array(data2)),!(data2 instanceof Uint8Array))return reject(new TypeError("FileReader returned unknown type."));resolve(data2)},fileReader.onabort=event=>reject(new Error("Aborted")),fileReader.onerror=event=>reject(new Error(event.type));let slice5=this.file.slice(this.offset,end);fileReader.readAsArrayBuffer(slice5)}this.offset=end}),done:!1}}};async function urlChunkIterator(url,options={}){let urlString,requestInit;typeof url=="string"?urlString=url:(urlString=url.url,requestInit=getRequestInitFromRequest(url));let response=await util_exports.fetch(urlString,requestInit);if(response.ok){let uint8Array=new Uint8Array(await response.arrayBuffer());return new FileChunkIterator(uint8Array,options)}else throw new Error(response.statusText)}var getRequestInitFromRequest=request=>({method:request.method,headers:request.headers,body:request.body,mode:request.mode,credentials:request.credentials,cache:request.cache,redirect:request.redirect,referrer:request.referrer,integrity:request.integrity});function isLocalPath(source){return typeof source=="string"&&source.substr(0,7)==="file://"}var FileDataSource=class extends DataSource{constructor(input2,options={}){super();this.input=input2,this.options=options}async iterator(){if(isLocalPath(this.input)&&env().get("IS_NODE")){let fs=require("fs");this.input=fs.readFileSync(this.input.substr(7))}return new FileChunkIterator(this.input,this.options)}},URLDataSource=class extends DataSource{constructor(url,fileOptions={}){super();this.url=url,this.fileOptions=fileOptions}async iterator(){return isLocalPath(this.url)?new FileDataSource(this.url,this.fileOptions).iterator():urlChunkIterator(this.url,this.fileOptions)}};function csv(source,csvConfig={}){return new CSVDataset(new URLDataSource(source),csvConfig)}function func(f){let iter=iteratorFromFunction(f);return datasetFromIteratorFn(async()=>iter)}function generator(generator2){return datasetFromIteratorFn(async()=>{let gen=await generator2();return iteratorFromFunction(()=>gen.next())})}async function webcam(webcamVideoElement,webcamConfig){return WebcamIterator.create(webcamVideoElement,webcamConfig)}async function microphone(microphoneConfig){return MicrophoneIterator.create(microphoneConfig)}var version12="2.8.3",version13={tfjs:version,"tfjs-core":version2,"tfjs-data":version3,"tfjs-layers":version4,"tfjs-converter":version5,"tfjs-backend-cpu":version7,"tfjs-backend-webgl":version8,"tfjs-backend-wasm":version9};var config={name:"humangl",priority:99,canvas:null,width:1024,height:1024,webGLattr:{alpha:!1,antialias:!1,premultipliedAlpha:!1,preserveDrawingBuffer:!1,depth:!1,stencil:!1,failIfMajorPerformanceCaveat:!1,desynchronized:!0}};function register(){if(!findBackend(config.name)){config.canvas=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(config.width,config.height):document.createElement("canvas");let gl=config.canvas.getContext("webgl2",config.webGLattr);setWebGLContext(2,gl);let ctx=new GPGPUContext(gl);registerBackend(config.name,()=>new MathBackendWebGL(ctx),config.priority),getKernelsForBackend("webgl").forEach(kernelConfig=>{let newKernelConfig={...kernelConfig,backendName:config.name};registerKernel(newKernelConfig)}),ENV.set("WEBGL_VERSION",2),ENV.set("WEBGL_MAX_TEXTURE_SIZE",gl.getParameter(gl.MAX_TEXTURE_SIZE)),ENV.set("WEBGL_FORCE_F16_TEXTURES",!0),ENV.set("WEBGL_PACK_DEPTHWISECONV",!0)}}var facemesh=__toModule(require_facemesh()),age=__toModule(require_age()),gender=__toModule(require_gender()),emotion=__toModule(require_emotion()),embedding2=__toModule(require_embedding()),posenet=__toModule(require_posenet());var models={},config2={},anchors=[],kMidHipCenter=0,kPoseDetectKeyNum=2,kFullBodySizeRot=1;function calculateScale(min_scale,max_scale,stride_index,num_strides){return num_strides===1?(min_scale+max_scale)*.5:min_scale+(max_scale-min_scale)*1*stride_index/(num_strides-1)}function generateAnchors(){let options={};options.strides=[],options.aspect_ratios=[],options.feature_map_height=[],options.num_layers=4,options.min_scale=.1484375,options.max_scale=.75,options.input_size_height=128,options.input_size_width=128,options.anchor_offset_x=.5,options.anchor_offset_y=.5,options.strides.push(8),options.strides.push(16),options.strides.push(16),options.strides.push(16),options.aspect_ratios.push(1),options.reduce_boxes_in_lowest_layer=!1,options.interpolated_scale_aspect_ratio=1,options.fixed_anchor_size=!0;let layer_id=0;for(;layer_id0){let scale_next=last_same_stride_layer===options.strides.length-1?1:calculateScale(options.min_scale,options.max_scale,last_same_stride_layer+1,options.strides.length);scales.push(Math.sqrt(scale2*scale_next)),aspect_ratios.push(options.interpolated_scale_aspect_ratio)}}last_same_stride_layer++}for(let i=0;iconfig2.pose.scoreThreshold){let idx=(4+2*kPoseDetectKeyNum)*i,sx=boxes[idx+0],sy=boxes[idx+1],w=boxes[idx+2]/config2.pose.inputSize,h=boxes[idx+3]/config2.pose.inputSize,cx=(sx+anchors[i].x_center*config2.pose.inputSize)/config2.pose.inputSize,cy=(sy+anchors[i].y_center*config2.pose.inputSize)/config2.pose.inputSize;region.score=Math.round(1e3*score)/1e3,region.box=[cx-w*.5,cy-h*.5,w*.5,h*.5];let keys=new Array(kPoseDetectKeyNum);for(let j=0;j{config2=cfg;let resize=image.resizeBilinear(image3,[config2.pose.inputSize,config2.pose.inputSize],!1),enhance=div(resize,127.5).sub(1);dispose(resize);let logits=await models.blazepose.predict(enhance);dispose(enhance);let regions=await decode(logits);logits[0].dispose(),logits[1].dispose(),log("poses",regions),resolve(regions)}):null}function getBoxSize(box){return[Math.abs(box.endPoint[0]-box.startPoint[0]),Math.abs(box.endPoint[1]-box.startPoint[1])]}function getBoxCenter(box){return[box.startPoint[0]+(box.endPoint[0]-box.startPoint[0])/2,box.startPoint[1]+(box.endPoint[1]-box.startPoint[1])/2]}function cutBoxFromImageAndResize(box,image3,cropSize){let h=image3.shape[1],w=image3.shape[2],boxes=[[box.startPoint[1]/h,box.startPoint[0]/w,box.endPoint[1]/h,box.endPoint[0]/w]];return image.cropAndResize(image3,boxes,[0],cropSize)}function scaleBoxCoordinates(box,factor){let startPoint=[box.startPoint[0]*factor[0],box.startPoint[1]*factor[1]],endPoint=[box.endPoint[0]*factor[0],box.endPoint[1]*factor[1]],palmLandmarks=box.palmLandmarks.map(coord=>[coord[0]*factor[0],coord[1]*factor[1]]);return{startPoint,endPoint,palmLandmarks,confidence:box.confidence}}function enlargeBox(box,factor=1.5){let center=getBoxCenter(box),size=getBoxSize(box),newHalfSize=[factor*size[0]/2,factor*size[1]/2],startPoint=[center[0]-newHalfSize[0],center[1]-newHalfSize[1]],endPoint=[center[0]+newHalfSize[0],center[1]+newHalfSize[1]];return{startPoint,endPoint,palmLandmarks:box.palmLandmarks}}function squarifyBox(box){let centers=getBoxCenter(box),size=getBoxSize(box),halfSize=Math.max(...size)/2,startPoint=[centers[0]-halfSize,centers[1]-halfSize],endPoint=[centers[0]+halfSize,centers[1]+halfSize];return{startPoint,endPoint,palmLandmarks:box.palmLandmarks}}function normalizeRadians(angle){return angle-2*Math.PI*Math.floor((angle+Math.PI)/(2*Math.PI))}function computeRotation(point1,point2){let radians=Math.PI/2-Math.atan2(-(point2[1]-point1[1]),point2[0]-point1[0]);return normalizeRadians(radians)}var buildTranslationMatrix=(x,y)=>[[1,0,x],[0,1,y],[0,0,1]];function dot4(v1,v2){let product=0;for(let i=0;iconfig_default});var config_default={backend:"webgl",wasmPath:"../assets/",async:!0,profile:!1,deallocate:!1,scoped:!1,videoOptimized:!0,warmup:"face",filter:{enabled:!0,width:0,height:0,return:!0,brightness:0,contrast:0,sharpness:0,blur:0,saturation:0,hue:0,negative:!1,sepia:!1,vintage:!1,kodachrome:!1,technicolor:!1,polaroid:!1,pixelate:0},gesture:{enabled:!0},face:{enabled:!0,detector:{modelPath:"../models/blazeface-back.json",inputSize:256,rotation:!1,maxFaces:10,skipFrames:11,minConfidence:.5,iouThreshold:.2,scoreThreshold:.5},mesh:{enabled:!0,modelPath:"../models/facemesh.json",inputSize:192,returnRawData:!1},iris:{enabled:!0,modelPath:"../models/iris.json",inputSize:64},age:{enabled:!0,modelPath:"../models/age-ssrnet-imdb.json",inputSize:64,skipFrames:31},gender:{enabled:!0,minConfidence:.1,modelPath:"../models/gender-ssrnet-imdb.json",inputSize:64,skipFrames:41},emotion:{enabled:!0,inputSize:64,minConfidence:.2,skipFrames:21,modelPath:"../models/emotion-large.json"},embedding:{enabled:!1,inputSize:112,modelPath:"../models/mobilefacenet.json"}},body:{enabled:!0,modelPath:"../models/posenet.json",inputSize:257,maxDetections:10,scoreThreshold:.5,nmsRadius:20,outputStride:16,modelType:"MobileNet"},pose:{enabled:!1,scoreThreshold:.6,iouThreshold:.3,modelPath:"../models/blazepose.json",inputSize:128},hand:{enabled:!0,rotation:!1,inputSize:256,skipFrames:12,minConfidence:.1,iouThreshold:.1,scoreThreshold:.5,maxHands:1,landmarks:!0,detector:{modelPath:"../models/handdetect.json"},skeleton:{modelPath:"../models/handskeleton.json"}}};var face=` +`).map(line=>(line.endsWith("\r")&&(line=line.slice(0,-1)),line))}},CODE_QUOTE='"',STATE_OUT=Symbol("out"),STATE_FIELD=Symbol("field"),STATE_QUOTE=Symbol("quote"),STATE_QUOTE_AFTER_QUOTE=Symbol("quoteafterquote"),STATE_WITHIN_QUOTE_IN_QUOTE=Symbol("quoteinquote"),CSVDataset=class extends Dataset{constructor(input2,csvConfig){super();this.input=input2,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new TextLineDataset(input2),csvConfig||(csvConfig={}),this.hasHeader=csvConfig.hasHeader!==!1,this.fullColumnNames=csvConfig.columnNames,this.columnConfigs=csvConfig.columnConfigs,this.configuredColumnsOnly=csvConfig.configuredColumnsOnly,csvConfig.delimWhitespace?(util_exports.assert(csvConfig.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=csvConfig.delimiter?csvConfig.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let columnNamesFromFile=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!columnNamesFromFile)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&columnNamesFromFile&&util_exports.assert(columnNamesFromFile.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+columnNamesFromFile.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=columnNamesFromFile);let counts=this.fullColumnNames.reduce((countAcc,name)=>(countAcc[name]=countAcc[name]+1||1,countAcc),{}),duplicateNames=Object.keys(counts).filter(name=>counts[name]>1);if(util_exports.assert(duplicateNames.length===0,()=>"Duplicate column names found: "+duplicateNames.toString()),this.columnConfigs){for(let key of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(key)===-1)throw new Error('The key "'+key+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let firstElement=await(await this.base.iterator()).next();if(firstElement.done)throw new Error("No data was found for CSV parsing.");let firstLine=firstElement.value;return this.parseRow(firstLine,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let lines=await this.base.iterator();return this.hasHeader&&(lines=lines.skip(1)),lines.map(x=>this.makeDataElement(x))}makeDataElement(line){let values=this.parseRow(line),features={},labels={};for(let i=0;i14||!Number.isInteger(fftSizeLog2))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=microphoneConfig.numFramesPerSpectrogram||43,this.sampleRateHz=microphoneConfig.sampleRateHz,this.columnTruncateLength=microphoneConfig.columnTruncateLength||this.fftSize,this.audioTrackConstraints=microphoneConfig.audioTrackConstraints,this.smoothingTimeConstant=microphoneConfig.smoothingTimeConstant||0,this.includeSpectrogram=microphoneConfig.includeSpectrogram!==!1,this.includeWaveform=microphoneConfig.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(microphoneConfig={}){if(env().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let microphoneIterator=new MicrophoneIterator(microphoneConfig);return await microphoneIterator.start(),microphoneIterator}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(e){throw new Error(`Error thrown while initializing video stream: ${e.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let ctxConstructor=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new ctxConstructor,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let streamSource=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,streamSource.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let spectrogramTensor,waveformTensor,audioDataQueue=await this.getAudioData();if(this.includeSpectrogram){let freqData=this.flattenQueue(audioDataQueue.freqDataQueue);spectrogramTensor=this.getTensorFromAudioDataArray(freqData,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let timeData=this.flattenQueue(audioDataQueue.timeDataQueue);waveformTensor=this.getTensorFromAudioDataArray(timeData,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:spectrogramTensor,waveform:waveformTensor},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let freqDataQueue=[],timeDataQueue=[],currentFrames=0;return new Promise(resolve=>{let intervalID=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&resolve({freqDataQueue,timeDataQueue}),freqDataQueue.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),timeDataQueue.push(this.timeData.slice())),++currentFrames===this.numFrames&&(clearInterval(intervalID),resolve({freqDataQueue,timeDataQueue}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(queue){let frameSize=queue[0].length,freqData=new Float32Array(queue.length*frameSize);return queue.forEach((data2,i)=>freqData.set(data2,i*frameSize)),freqData}getTensorFromAudioDataArray(freqData,shape){let vals=new Float32Array(util_exports.sizeFromShape(shape));return vals.set(freqData,vals.length-freqData.length),tensor(vals,shape)}},WebcamIterator=class extends LazyIterator{constructor(webcamVideoElement,webcamConfig){super();if(this.webcamVideoElement=webcamVideoElement,this.webcamConfig=webcamConfig,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=tensor1d([0],"int32"),this.webcamConfig.centerCrop){let widthCroppingRatio=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,heightCroppingRatio=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,widthCropStart=(1-widthCroppingRatio)/2,heightCropStart=(1-heightCroppingRatio)/2,widthCropEnd=widthCropStart+widthCroppingRatio,heightCropEnd=heightCroppingRatio+heightCropStart;this.cropBox=tensor2d([heightCropStart,widthCropStart,heightCropEnd,widthCropEnd],[1,4])}else this.cropBox=tensor2d([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(webcamVideoElement,webcamConfig={}){if(env().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!webcamVideoElement){if(webcamVideoElement=document.createElement("video"),!webcamConfig.resizeWidth||!webcamConfig.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");webcamVideoElement.width=webcamConfig.resizeWidth,webcamVideoElement.height=webcamConfig.resizeHeight}let webcamIterator=new WebcamIterator(webcamVideoElement,webcamConfig);return await webcamIterator.start(),webcamIterator}async start(){this.webcamConfig.facingMode&&util_exports.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(error){console.log(error),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(resolve=>{this.webcamVideoElement.onloadedmetadata=()=>{resolve()}})}async next(){if(this.isClosed)return{value:null,done:!0};let img;try{img=browser_exports.fromPixels(this.webcamVideoElement)}catch(e){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(e)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(img),done:!1}}catch(e){throw new Error(`Error thrown cropping the video: ${e.message}`)}finally{img.dispose()}else return{value:img,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(img){return tidy(()=>{let expandedImage=img.toFloat().expandDims(0),resizedImage;resizedImage=image.cropAndResize(expandedImage,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let shape=resizedImage.shape;return resizedImage.reshape(shape.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(track=>track.stop());try{this.webcamVideoElement.srcObject=null}catch(error){console.log(error),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},DataSource=class{},StringIterator=class extends LazyIterator{split(separator){return new SplitIterator(this,separator)}},SplitIterator=class extends StringIterator{constructor(upstream,separator){super();this.upstream=upstream,this.impl=new SplitIteratorImpl(upstream,separator)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},SplitIteratorImpl=class extends OneToManyIterator{constructor(upstream,separator){super();this.upstream=upstream,this.separator=separator,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let chunkResult=await this.upstream.next();if(chunkResult.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let lines=chunkResult.value.split(this.separator);lines[0]=this.carryover+lines[0];for(let line of lines.slice(0,-1))this.outputQueue.push(line);return this.carryover=lines[lines.length-1],!0}},ByteChunkIterator=class extends LazyIterator{decodeUTF8(){return new Utf8Iterator(this)}},Utf8Iterator=class extends StringIterator{constructor(upstream){super();this.upstream=upstream,this.impl=new Utf8IteratorImpl(upstream)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},Utf8IteratorImpl=class extends OneToManyIterator{constructor(upstream){super();if(this.upstream=upstream,env().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder}=require_string_decoder();this.decoder=new StringDecoder("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let chunkResult=await this.upstream.next(),chunk;if(chunkResult.done)return!1;chunk=chunkResult.value;let text;return env().get("IS_BROWSER")?text=this.decoder.decode(chunk,{stream:!0}):text=this.decoder.write(Buffer.from(chunk.buffer)),this.outputQueue.push(text),!0}},FileChunkIterator=class extends ByteChunkIterator{constructor(file,options={}){super();this.file=file,this.options=options,util_exports.assert(file instanceof Uint8Array||(env().get("IS_BROWSER")?file instanceof File||file instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=options.offset||0,this.chunkSize=options.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((resolve,reject)=>{let end=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)resolve(new Uint8Array(this.file.slice(this.offset,end)));else{let fileReader=new FileReader;fileReader.onload=event=>{let data2=fileReader.result;if(data2 instanceof ArrayBuffer&&(data2=new Uint8Array(data2)),!(data2 instanceof Uint8Array))return reject(new TypeError("FileReader returned unknown type."));resolve(data2)},fileReader.onabort=event=>reject(new Error("Aborted")),fileReader.onerror=event=>reject(new Error(event.type));let slice5=this.file.slice(this.offset,end);fileReader.readAsArrayBuffer(slice5)}this.offset=end}),done:!1}}};async function urlChunkIterator(url,options={}){let urlString,requestInit;typeof url=="string"?urlString=url:(urlString=url.url,requestInit=getRequestInitFromRequest(url));let response=await util_exports.fetch(urlString,requestInit);if(response.ok){let uint8Array=new Uint8Array(await response.arrayBuffer());return new FileChunkIterator(uint8Array,options)}else throw new Error(response.statusText)}var getRequestInitFromRequest=request=>({method:request.method,headers:request.headers,body:request.body,mode:request.mode,credentials:request.credentials,cache:request.cache,redirect:request.redirect,referrer:request.referrer,integrity:request.integrity});function isLocalPath(source){return typeof source=="string"&&source.substr(0,7)==="file://"}var FileDataSource=class extends DataSource{constructor(input2,options={}){super();this.input=input2,this.options=options}async iterator(){if(isLocalPath(this.input)&&env().get("IS_NODE")){let fs=require("fs");this.input=fs.readFileSync(this.input.substr(7))}return new FileChunkIterator(this.input,this.options)}},URLDataSource=class extends DataSource{constructor(url,fileOptions={}){super();this.url=url,this.fileOptions=fileOptions}async iterator(){return isLocalPath(this.url)?new FileDataSource(this.url,this.fileOptions).iterator():urlChunkIterator(this.url,this.fileOptions)}};function csv(source,csvConfig={}){return new CSVDataset(new URLDataSource(source),csvConfig)}function func(f){let iter=iteratorFromFunction(f);return datasetFromIteratorFn(async()=>iter)}function generator(generator2){return datasetFromIteratorFn(async()=>{let gen=await generator2();return iteratorFromFunction(()=>gen.next())})}async function webcam(webcamVideoElement,webcamConfig){return WebcamIterator.create(webcamVideoElement,webcamConfig)}async function microphone(microphoneConfig){return MicrophoneIterator.create(microphoneConfig)}var version12="2.8.3",version13={tfjs:version,"tfjs-core":version2,"tfjs-data":version3,"tfjs-layers":version4,"tfjs-converter":version5,"tfjs-backend-cpu":version7,"tfjs-backend-webgl":version8,"tfjs-backend-wasm":version9};var config={name:"humangl",priority:99,canvas:null,width:1024,height:1024,webGLattr:{alpha:!1,antialias:!1,premultipliedAlpha:!1,preserveDrawingBuffer:!1,depth:!1,stencil:!1,failIfMajorPerformanceCaveat:!1,desynchronized:!0}};function register(){if(!findBackend(config.name)){config.canvas=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(config.width,config.height):document.createElement("canvas");let gl=config.canvas.getContext("webgl2",config.webGLattr);setWebGLContext(2,gl);let ctx=new GPGPUContext(gl);registerBackend(config.name,()=>new MathBackendWebGL(ctx),config.priority),getKernelsForBackend("webgl").forEach(kernelConfig=>{let newKernelConfig={...kernelConfig,backendName:config.name};registerKernel(newKernelConfig)}),ENV.set("WEBGL_VERSION",2),ENV.set("WEBGL_MAX_TEXTURE_SIZE",gl.getParameter(gl.MAX_TEXTURE_SIZE)),ENV.set("WEBGL_FORCE_F16_TEXTURES",!0),ENV.set("WEBGL_PACK_DEPTHWISECONV",!0)}}var facemesh=__toModule(require_facemesh()),age=__toModule(require_age()),gender=__toModule(require_gender()),emotion=__toModule(require_emotion()),embedding2=__toModule(require_embedding()),posenet=__toModule(require_posenet());var models={};async function load(config2){return models.blazepose||(models.blazepose=await loadGraphModel(config2.pose.modelPath),log(`load model: ${config2.pose.modelPath.match(/\/(.*)\./)[1]}`)),models.blazepose}async function predict(image3,config2){return models.blazepose?new Promise(async resolve=>{let resize=image.resizeBilinear(image3,[config2.pose.inputSize,config2.pose.inputSize],!1),enhance=div(resize,127.5).sub(1);dispose(resize);let logits=await models.blazepose.predict(enhance);dispose(enhance),logits.map(logit=>logit.dispose()),resolve(logits)}):null}function getBoxSize(box){return[Math.abs(box.endPoint[0]-box.startPoint[0]),Math.abs(box.endPoint[1]-box.startPoint[1])]}function getBoxCenter(box){return[box.startPoint[0]+(box.endPoint[0]-box.startPoint[0])/2,box.startPoint[1]+(box.endPoint[1]-box.startPoint[1])/2]}function cutBoxFromImageAndResize(box,image3,cropSize){let h=image3.shape[1],w=image3.shape[2],boxes=[[box.startPoint[1]/h,box.startPoint[0]/w,box.endPoint[1]/h,box.endPoint[0]/w]];return image.cropAndResize(image3,boxes,[0],cropSize)}function scaleBoxCoordinates(box,factor){let startPoint=[box.startPoint[0]*factor[0],box.startPoint[1]*factor[1]],endPoint=[box.endPoint[0]*factor[0],box.endPoint[1]*factor[1]],palmLandmarks=box.palmLandmarks.map(coord=>[coord[0]*factor[0],coord[1]*factor[1]]);return{startPoint,endPoint,palmLandmarks,confidence:box.confidence}}function enlargeBox(box,factor=1.5){let center=getBoxCenter(box),size=getBoxSize(box),newHalfSize=[factor*size[0]/2,factor*size[1]/2],startPoint=[center[0]-newHalfSize[0],center[1]-newHalfSize[1]],endPoint=[center[0]+newHalfSize[0],center[1]+newHalfSize[1]];return{startPoint,endPoint,palmLandmarks:box.palmLandmarks}}function squarifyBox(box){let centers=getBoxCenter(box),size=getBoxSize(box),halfSize=Math.max(...size)/2,startPoint=[centers[0]-halfSize,centers[1]-halfSize],endPoint=[centers[0]+halfSize,centers[1]+halfSize];return{startPoint,endPoint,palmLandmarks:box.palmLandmarks}}function normalizeRadians(angle){return angle-2*Math.PI*Math.floor((angle+Math.PI)/(2*Math.PI))}function computeRotation(point1,point2){let radians=Math.PI/2-Math.atan2(-(point2[1]-point1[1]),point2[0]-point1[0]);return normalizeRadians(radians)}var buildTranslationMatrix=(x,y)=>[[1,0,x],[0,1,y],[0,0,1]];function dot4(v1,v2){let product=0;for(let i=0;iconfig_default});var config_default={backend:"webgl",wasmPath:"../assets/",async:!0,profile:!1,deallocate:!1,scoped:!1,videoOptimized:!0,warmup:"face",filter:{enabled:!0,width:0,height:0,return:!0,brightness:0,contrast:0,sharpness:0,blur:0,saturation:0,hue:0,negative:!1,sepia:!1,vintage:!1,kodachrome:!1,technicolor:!1,polaroid:!1,pixelate:0},gesture:{enabled:!0},face:{enabled:!0,detector:{modelPath:"../models/blazeface-back.json",inputSize:256,rotation:!1,maxFaces:10,skipFrames:11,minConfidence:.5,iouThreshold:.2,scoreThreshold:.5},mesh:{enabled:!0,modelPath:"../models/facemesh.json",inputSize:192,returnRawData:!1},iris:{enabled:!0,modelPath:"../models/iris.json",inputSize:64},age:{enabled:!0,modelPath:"../models/age-ssrnet-imdb.json",inputSize:64,skipFrames:31},gender:{enabled:!0,minConfidence:.1,modelPath:"../models/gender-ssrnet-imdb.json",inputSize:64,skipFrames:41},emotion:{enabled:!0,inputSize:64,minConfidence:.2,skipFrames:21,modelPath:"../models/emotion-large.json"},embedding:{enabled:!1,inputSize:112,modelPath:"../models/mobilefacenet.json"}},body:{enabled:!0,modelPath:"../models/posenet.json",inputSize:257,maxDetections:10,scoreThreshold:.5,nmsRadius:20,outputStride:16,modelType:"MobileNet"},pose:{enabled:!1,scoreThreshold:.6,iouThreshold:.3,modelPath:"../models/blazepose.json",inputSize:256},hand:{enabled:!0,rotation:!1,inputSize:256,skipFrames:12,minConfidence:.1,iouThreshold:.1,scoreThreshold:.5,maxHands:1,landmarks:!0,detector:{modelPath:"../models/handdetect.json"},skeleton:{modelPath:"../models/handskeleton.json"}}};var face=` /9j/4AAQSkZJRgABAQEAYABgAAD/4QBoRXhpZgAATU0AKgAAAAgABAEaAAUAAAABAAAAPgEbAAUA AAABAAAARgEoAAMAAAABAAIAAAExAAIAAAARAAAATgAAAAAAAABgAAAAAQAAAGAAAAABcGFpbnQu bmV0IDQuMi4xMwAA/9sAQwAGBAUGBQQGBgUGBwcGCAoQCgoJCQoUDg8MEBcUGBgXFBYWGh0lHxob @@ -4771,7 +4771,7 @@ AAAAAAJAAAAAAAAAAAAAABAJEAAAAAAAAAAAAAAAIEoBKAAAAAAAAAAAAAAABAlAAAAAAAIAAAAA BAkBAkBAkBAlACEgMZjdjbFW8bWrEx8YWANb6Fp+bfwab+vLDKMFK9qxH5L0bAr8OPRPKz2AY7J2 SbAjYZAI2E7AIEgIEgIEgMdkSy2NgY7MdlmyNoBXsxmFuyNgVTVjNV3KjlBRNTlXTVHKCrlIqt5T lBhEMohlFerLlBjEMohMVTEARDKCITsAk2AEgAAAkAAAAAAAAAAAAAAAAAAAAAAAASAAAAAAAAD/ -2Q==`;var version14="0.9.23";var now2=()=>typeof performance!="undefined"?performance.now():parseInt(Number(process.hrtime.bigint())/1e3/1e3);function mergeDeep(...objects){let isObject=obj=>obj&&typeof obj=="object";return objects.reduce((prev,obj)=>(Object.keys(obj||{}).forEach(key=>{let pVal=prev[key],oVal=obj[key];Array.isArray(pVal)&&Array.isArray(oVal)?prev[key]=pVal.concat(...oVal):isObject(pVal)&&isObject(oVal)?prev[key]=mergeDeep(pVal,oVal):prev[key]=oVal}),prev),{})}var Human=class{constructor(userConfig={}){this.tf=tfjs_esm_exports,this.version=version14,this.config=mergeDeep(config_default,userConfig),this.fx=null,this.state="idle",this.numTensors=0,this.analyzeMemoryLeaks=!1,this.checkSanity=!1,this.firstRun=!0,this.perf={},this.models={facemesh:null,posenet:null,handpose:null,iris:null,age:null,gender:null,emotion:null},this.facemesh=facemesh,this.age=age,this.gender=gender,this.emotion=emotion,this.body=posenet,this.hand=handpose}profile(){return this.config.profile?profile2.data:{}}analyze(...msg){if(!this.analyzeMemoryLeaks)return;let current=engine().state.numTensors,previous=this.numTensors;this.numTensors=current;let leaked=current-previous;leaked!==0&&log(...msg,leaked)}sanity(input2){if(!this.checkSanity)return null;if(!input2)return"input is not defined";if(ENV.flags.IS_NODE&&!(input2 instanceof Tensor))return"input must be a tensor";try{getBackend()}catch(e){return"backend not loaded"}return null}simmilarity(embedding1,embedding22){return this.config.face.embedding.enabled?embedding2.simmilarity(embedding1,embedding22):0}async load(userConfig){this.state="load";let timeStamp=now2();userConfig&&(this.config=mergeDeep(this.config,userConfig)),this.firstRun&&(log(`version: ${this.version} TensorFlow/JS version: ${version6}`),await this.checkBackend(!0),ENV.flags.IS_BROWSER&&(log("configuration:",this.config),log("tf flags:",ENV.flags)),this.firstRun=!1),this.config.async?[this.models.facemesh,this.models.age,this.models.gender,this.models.emotion,this.models.embedding,this.models.posenet,this.models.handpose,this.models.blazepose]=await Promise.all([this.models.facemesh||(this.config.face.enabled?facemesh.load(this.config):null),this.models.age||(this.config.face.enabled&&this.config.face.age.enabled?age.load(this.config):null),this.models.gender||(this.config.face.enabled&&this.config.face.gender.enabled?gender.load(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?emotion.load(this.config):null),this.models.embedding||(this.config.face.enabled&&this.config.face.embedding.enabled?embedding2.load(this.config):null),this.models.posenet||(this.config.body.enabled?posenet.load(this.config):null),this.models.handpose||(this.config.hand.enabled?handpose.load(this.config):null),this.models.blazepose||(this.config.pose.enabled?load(this.config):null)]):(this.config.face.enabled&&!this.models.facemesh&&(this.models.facemesh=await facemesh.load(this.config)),this.config.face.enabled&&this.config.face.age.enabled&&!this.models.age&&(this.models.age=await age.load(this.config)),this.config.face.enabled&&this.config.face.gender.enabled&&!this.models.gender&&(this.models.gender=await gender.load(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await emotion.load(this.config)),this.config.face.enabled&&this.config.face.embedding.enabled&&!this.models.embedding&&(this.models.embedding=await embedding2.load(this.config)),this.config.body.enabled&&!this.models.posenet&&(this.models.posenet=await posenet.load(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await handpose.load(this.config)),this.config.pose.enabled&&!this.models.blazepose&&(this.models.blazepose=await load(this.config)));let current=Math.trunc(now2()-timeStamp);current>(this.perf.load||0)&&(this.perf.load=current)}async checkBackend(force){if(this.config.backend&&this.config.backend!==""&&force||getBackend()!==this.config.backend){let timeStamp=now2();if(this.state="backend",log("setting backend:",this.config.backend),this.config.backend==="wasm"&&(log("settings wasm path:",this.config.wasmPath),setWasmPaths(this.config.wasmPath),await env().getAsync("WASM_HAS_SIMD_SUPPORT")||log("warning: wasm simd support is not enabled")),this.config.backend==="humangl"&&(log("registering humangl backend"),register()),await setBackend(this.config.backend),enableProdMode(),getBackend()==="webgl"){this.config.deallocate&&(log("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",this.config.deallocate),ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",this.config.deallocate?0:-1)),ENV.set("WEBGL_FORCE_F16_TEXTURES",!0),ENV.set("WEBGL_PACK_DEPTHWISECONV",!0);let gl=await backend().getGPGPUContext().gl;log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`)}await ready(),this.perf.backend=Math.trunc(now2()-timeStamp)}}async detectFace(input2){var _a;let timeStamp,ageRes,genderRes,emotionRes,embeddingRes,faceRes=[];this.state="run:face",timeStamp=now2();let faces=await((_a=this.models.facemesh)==null?void 0:_a.estimateFaces(input2,this.config));this.perf.face=Math.trunc(now2()-timeStamp);for(let face3 of faces){if(this.analyze("Get Face"),!face3.image||face3.image.isDisposedInternal){log("Face object is disposed:",face3.image);continue}this.analyze("Start Age:"),this.config.async?ageRes=this.config.face.age.enabled?age.predict(face3.image,this.config):{}:(this.state="run:age",timeStamp=now2(),ageRes=this.config.face.age.enabled?await age.predict(face3.image,this.config):{},this.perf.age=Math.trunc(now2()-timeStamp)),this.analyze("Start Gender:"),this.config.async?genderRes=this.config.face.gender.enabled?gender.predict(face3.image,this.config):{}:(this.state="run:gender",timeStamp=now2(),genderRes=this.config.face.gender.enabled?await gender.predict(face3.image,this.config):{},this.perf.gender=Math.trunc(now2()-timeStamp)),this.analyze("Start Emotion:"),this.config.async?emotionRes=this.config.face.emotion.enabled?emotion.predict(face3.image,this.config):{}:(this.state="run:emotion",timeStamp=now2(),emotionRes=this.config.face.emotion.enabled?await emotion.predict(face3.image,this.config):{},this.perf.emotion=Math.trunc(now2()-timeStamp)),this.analyze("End Emotion:"),this.analyze("Start Embedding:"),this.config.async?embeddingRes=this.config.face.embedding.enabled?embedding2.predict(face3.image,this.config):{}:(this.state="run:embedding",timeStamp=now2(),embeddingRes=this.config.face.embedding.enabled?await embedding2.predict(face3.image,this.config):{},this.perf.embedding=Math.trunc(now2()-timeStamp)),this.analyze("End Emotion:"),this.config.async&&([ageRes,genderRes,emotionRes,embeddingRes]=await Promise.all([ageRes,genderRes,emotionRes,embeddingRes])),this.analyze("Finish Face:"),face3.image.dispose(),this.config.face.iris.enabled||(delete face3.annotations.leftEyeIris,delete face3.annotations.rightEyeIris);let irisSize=face3.annotations.leftEyeIris&&face3.annotations.rightEyeIris?11.7*Math.max(Math.abs(face3.annotations.leftEyeIris[3][0]-face3.annotations.leftEyeIris[1][0]),Math.abs(face3.annotations.rightEyeIris[4][1]-face3.annotations.rightEyeIris[2][1])):0;faceRes.push({confidence:face3.confidence,box:face3.box,mesh:face3.mesh,boxRaw:face3.boxRaw,meshRaw:face3.meshRaw,annotations:face3.annotations,age:ageRes.age,gender:genderRes.gender,genderConfidence:genderRes.confidence,emotion:emotionRes,embedding:embeddingRes,iris:irisSize!==0?Math.trunc(irisSize)/100:0}),this.analyze("End Face")}return this.analyze("End FaceMesh:"),this.config.async&&(this.perf.face&&delete this.perf.face,this.perf.age&&delete this.perf.age,this.perf.gender&&delete this.perf.gender,this.perf.emotion&&delete this.perf.emotion),faceRes}async image(input2,userConfig={}){this.state="image",this.config=mergeDeep(this.config,userConfig);let process3=image2.process(input2,this.config);return process3.tensor.dispose(),process3.canvas}async detect(input2,userConfig={}){return new Promise(async resolve=>{var _a,_b,_c,_d;this.state="config";let timeStamp;this.config=mergeDeep(this.config,userConfig),this.state="check";let error=this.sanity(input2);error&&(log(error,input2),resolve({error}));let poseRes,blazeposeRes,handRes,faceRes,timeStart=now2();await this.checkBackend(),await this.load(),this.config.scoped&&engine().startScope(),this.analyze("Start Scope:"),timeStamp=now2();let process3=image2.process(input2,this.config);if(!process3||!process3.tensor){log("could not convert input to tensor"),resolve({error:"could not convert input to tensor"});return}this.perf.image=Math.trunc(now2()-timeStamp),this.analyze("Get Image:"),this.config.async?(faceRes=this.config.face.enabled?this.detectFace(process3.tensor):[],this.perf.face&&delete this.perf.face):(this.state="run:face",timeStamp=now2(),faceRes=this.config.face.enabled?await this.detectFace(process3.tensor):[],this.perf.face=Math.trunc(now2()-timeStamp)),this.analyze("Start Body:"),this.config.async?(poseRes=this.config.body.enabled?(_a=this.models.posenet)==null?void 0:_a.estimatePoses(process3.tensor,this.config):[],this.perf.body&&delete this.perf.body):(this.state="run:body",timeStamp=now2(),poseRes=this.config.body.enabled?await((_b=this.models.posenet)==null?void 0:_b.estimatePoses(process3.tensor,this.config)):[],this.perf.body=Math.trunc(now2()-timeStamp)),this.analyze("End Body:"),this.analyze("Start Pose:"),this.config.async?(blazeposeRes=this.config.pose.enabled?predict(process3.tensor,this.config):[],this.perf.pose&&delete this.perf.pose):(this.state="run:pose",timeStamp=now2(),blazeposeRes=this.config.pose.enabled?await predict(process3.tensor,this.config):[],this.perf.pose=Math.trunc(now2()-timeStamp)),this.analyze("End Pose:"),this.analyze("Start Hand:"),this.config.async?(handRes=this.config.hand.enabled?(_c=this.models.handpose)==null?void 0:_c.estimateHands(process3.tensor,this.config):[],this.perf.hand&&delete this.perf.hand):(this.state="run:hand",timeStamp=now2(),handRes=this.config.hand.enabled?await((_d=this.models.handpose)==null?void 0:_d.estimateHands(process3.tensor,this.config)):[],this.perf.hand=Math.trunc(now2()-timeStamp)),this.analyze("End Hand:"),this.config.async&&([faceRes,poseRes,blazeposeRes,handRes]=await Promise.all([faceRes,poseRes,blazeposeRes,handRes])),process3.tensor.dispose(),this.config.scoped&&engine().endScope(),this.analyze("End Scope:");let gestureRes=[];this.config.gesture.enabled&&(timeStamp=now2(),gestureRes=[...gesture.face(faceRes),...gesture.body(poseRes),...gesture.hand(handRes),...gesture.iris(faceRes)],this.config.async?this.perf.gesture&&delete this.perf.gesture:this.perf.gesture=Math.trunc(now2()-timeStamp)),this.perf.total=Math.trunc(now2()-timeStart),this.state="idle",resolve({face:faceRes,body:poseRes,hand:handRes,pose:blazeposeRes,gesture:gestureRes,performance:this.perf,canvas:process3.canvas})})}async warmup(userConfig){let b64toBlob=(base64,type="application/octet-stream")=>fetch(`data:${type};base64,${base64}`).then(res=>res.blob());userConfig&&(this.config=mergeDeep(this.config,userConfig));let video=this.config.videoOptimized;this.config.videoOptimized=!1;let blob;switch(this.config.warmup){case"face":blob=await b64toBlob(face);break;case"full":blob=await b64toBlob(body);break;default:blob=null}if(!blob)return null;let bitmap=await createImageBitmap(blob),t0=now2(),warmup=await this.detect(bitmap,config_exports),t1=now2();return bitmap.close(),log("Warmup",this.config.warmup,t1-t0,warmup),this.config.videoOptimized=video,warmup}};return human_exports;})(); +2Q==`;var version14="0.9.24";var now2=()=>typeof performance!="undefined"?performance.now():parseInt(Number(process.hrtime.bigint())/1e3/1e3);function mergeDeep(...objects){let isObject=obj=>obj&&typeof obj=="object";return objects.reduce((prev,obj)=>(Object.keys(obj||{}).forEach(key=>{let pVal=prev[key],oVal=obj[key];Array.isArray(pVal)&&Array.isArray(oVal)?prev[key]=pVal.concat(...oVal):isObject(pVal)&&isObject(oVal)?prev[key]=mergeDeep(pVal,oVal):prev[key]=oVal}),prev),{})}var Human=class{constructor(userConfig={}){this.tf=tfjs_esm_exports,this.version=version14,this.config=mergeDeep(config_default,userConfig),this.fx=null,this.state="idle",this.numTensors=0,this.analyzeMemoryLeaks=!1,this.checkSanity=!1,this.firstRun=!0,this.perf={},this.models={facemesh:null,posenet:null,handpose:null,iris:null,age:null,gender:null,emotion:null},this.facemesh=facemesh,this.age=age,this.gender=gender,this.emotion=emotion,this.body=posenet,this.hand=handpose}profile(){return this.config.profile?profile2.data:{}}analyze(...msg){if(!this.analyzeMemoryLeaks)return;let current=engine().state.numTensors,previous=this.numTensors;this.numTensors=current;let leaked=current-previous;leaked!==0&&log(...msg,leaked)}sanity(input2){if(!this.checkSanity)return null;if(!input2)return"input is not defined";if(ENV.flags.IS_NODE&&!(input2 instanceof Tensor))return"input must be a tensor";try{getBackend()}catch(e){return"backend not loaded"}return null}simmilarity(embedding1,embedding22){return this.config.face.embedding.enabled?embedding2.simmilarity(embedding1,embedding22):0}async load(userConfig){this.state="load";let timeStamp=now2();userConfig&&(this.config=mergeDeep(this.config,userConfig)),this.firstRun&&(log(`version: ${this.version} TensorFlow/JS version: ${version6}`),await this.checkBackend(!0),ENV.flags.IS_BROWSER&&(log("configuration:",this.config),log("tf flags:",ENV.flags)),this.firstRun=!1),this.config.async?[this.models.facemesh,this.models.age,this.models.gender,this.models.emotion,this.models.embedding,this.models.posenet,this.models.handpose,this.models.blazepose]=await Promise.all([this.models.facemesh||(this.config.face.enabled?facemesh.load(this.config):null),this.models.age||(this.config.face.enabled&&this.config.face.age.enabled?age.load(this.config):null),this.models.gender||(this.config.face.enabled&&this.config.face.gender.enabled?gender.load(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?emotion.load(this.config):null),this.models.embedding||(this.config.face.enabled&&this.config.face.embedding.enabled?embedding2.load(this.config):null),this.models.posenet||(this.config.body.enabled?posenet.load(this.config):null),this.models.handpose||(this.config.hand.enabled?handpose.load(this.config):null),this.models.blazepose||(this.config.pose.enabled?load(this.config):null)]):(this.config.face.enabled&&!this.models.facemesh&&(this.models.facemesh=await facemesh.load(this.config)),this.config.face.enabled&&this.config.face.age.enabled&&!this.models.age&&(this.models.age=await age.load(this.config)),this.config.face.enabled&&this.config.face.gender.enabled&&!this.models.gender&&(this.models.gender=await gender.load(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await emotion.load(this.config)),this.config.face.enabled&&this.config.face.embedding.enabled&&!this.models.embedding&&(this.models.embedding=await embedding2.load(this.config)),this.config.body.enabled&&!this.models.posenet&&(this.models.posenet=await posenet.load(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await handpose.load(this.config)),this.config.pose.enabled&&!this.models.blazepose&&(this.models.blazepose=await load(this.config)));let current=Math.trunc(now2()-timeStamp);current>(this.perf.load||0)&&(this.perf.load=current)}async checkBackend(force){if(this.config.backend&&this.config.backend!==""&&force||getBackend()!==this.config.backend){let timeStamp=now2();if(this.state="backend",log("setting backend:",this.config.backend),this.config.backend==="wasm"&&(log("settings wasm path:",this.config.wasmPath),setWasmPaths(this.config.wasmPath),await env().getAsync("WASM_HAS_SIMD_SUPPORT")||log("warning: wasm simd support is not enabled")),this.config.backend==="humangl"&&(log("registering humangl backend"),register()),await setBackend(this.config.backend),enableProdMode(),getBackend()==="webgl"){this.config.deallocate&&(log("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",this.config.deallocate),ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",this.config.deallocate?0:-1)),ENV.set("WEBGL_FORCE_F16_TEXTURES",!0),ENV.set("WEBGL_PACK_DEPTHWISECONV",!0);let gl=await backend().getGPGPUContext().gl;log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`)}await ready(),this.perf.backend=Math.trunc(now2()-timeStamp)}}async detectFace(input2){var _a;let timeStamp,ageRes,genderRes,emotionRes,embeddingRes,faceRes=[];this.state="run:face",timeStamp=now2();let faces=await((_a=this.models.facemesh)==null?void 0:_a.estimateFaces(input2,this.config));this.perf.face=Math.trunc(now2()-timeStamp);for(let face3 of faces){if(this.analyze("Get Face"),!face3.image||face3.image.isDisposedInternal){log("Face object is disposed:",face3.image);continue}this.analyze("Start Age:"),this.config.async?ageRes=this.config.face.age.enabled?age.predict(face3.image,this.config):{}:(this.state="run:age",timeStamp=now2(),ageRes=this.config.face.age.enabled?await age.predict(face3.image,this.config):{},this.perf.age=Math.trunc(now2()-timeStamp)),this.analyze("Start Gender:"),this.config.async?genderRes=this.config.face.gender.enabled?gender.predict(face3.image,this.config):{}:(this.state="run:gender",timeStamp=now2(),genderRes=this.config.face.gender.enabled?await gender.predict(face3.image,this.config):{},this.perf.gender=Math.trunc(now2()-timeStamp)),this.analyze("Start Emotion:"),this.config.async?emotionRes=this.config.face.emotion.enabled?emotion.predict(face3.image,this.config):{}:(this.state="run:emotion",timeStamp=now2(),emotionRes=this.config.face.emotion.enabled?await emotion.predict(face3.image,this.config):{},this.perf.emotion=Math.trunc(now2()-timeStamp)),this.analyze("End Emotion:"),this.analyze("Start Embedding:"),this.config.async?embeddingRes=this.config.face.embedding.enabled?embedding2.predict(face3.image,this.config):{}:(this.state="run:embedding",timeStamp=now2(),embeddingRes=this.config.face.embedding.enabled?await embedding2.predict(face3.image,this.config):{},this.perf.embedding=Math.trunc(now2()-timeStamp)),this.analyze("End Emotion:"),this.config.async&&([ageRes,genderRes,emotionRes,embeddingRes]=await Promise.all([ageRes,genderRes,emotionRes,embeddingRes])),this.analyze("Finish Face:"),face3.image.dispose(),this.config.face.iris.enabled||(delete face3.annotations.leftEyeIris,delete face3.annotations.rightEyeIris);let irisSize=face3.annotations.leftEyeIris&&face3.annotations.rightEyeIris?11.7*Math.max(Math.abs(face3.annotations.leftEyeIris[3][0]-face3.annotations.leftEyeIris[1][0]),Math.abs(face3.annotations.rightEyeIris[4][1]-face3.annotations.rightEyeIris[2][1])):0;faceRes.push({confidence:face3.confidence,box:face3.box,mesh:face3.mesh,boxRaw:face3.boxRaw,meshRaw:face3.meshRaw,annotations:face3.annotations,age:ageRes.age,gender:genderRes.gender,genderConfidence:genderRes.confidence,emotion:emotionRes,embedding:embeddingRes,iris:irisSize!==0?Math.trunc(irisSize)/100:0}),this.analyze("End Face")}return this.analyze("End FaceMesh:"),this.config.async&&(this.perf.face&&delete this.perf.face,this.perf.age&&delete this.perf.age,this.perf.gender&&delete this.perf.gender,this.perf.emotion&&delete this.perf.emotion),faceRes}async image(input2,userConfig={}){this.state="image",this.config=mergeDeep(this.config,userConfig);let process3=image2.process(input2,this.config);return process3.tensor.dispose(),process3.canvas}async detect(input2,userConfig={}){return new Promise(async resolve=>{var _a,_b,_c,_d;this.state="config";let timeStamp;this.config=mergeDeep(this.config,userConfig),this.state="check";let error=this.sanity(input2);error&&(log(error,input2),resolve({error}));let poseRes,blazeposeRes,handRes,faceRes,timeStart=now2();await this.checkBackend(),await this.load(),this.config.scoped&&engine().startScope(),this.analyze("Start Scope:"),timeStamp=now2();let process3=image2.process(input2,this.config);if(!process3||!process3.tensor){log("could not convert input to tensor"),resolve({error:"could not convert input to tensor"});return}this.perf.image=Math.trunc(now2()-timeStamp),this.analyze("Get Image:"),this.config.async?(faceRes=this.config.face.enabled?this.detectFace(process3.tensor):[],this.perf.face&&delete this.perf.face):(this.state="run:face",timeStamp=now2(),faceRes=this.config.face.enabled?await this.detectFace(process3.tensor):[],this.perf.face=Math.trunc(now2()-timeStamp)),this.analyze("Start Body:"),this.config.async?(poseRes=this.config.body.enabled?(_a=this.models.posenet)==null?void 0:_a.estimatePoses(process3.tensor,this.config):[],this.perf.body&&delete this.perf.body):(this.state="run:body",timeStamp=now2(),poseRes=this.config.body.enabled?await((_b=this.models.posenet)==null?void 0:_b.estimatePoses(process3.tensor,this.config)):[],this.perf.body=Math.trunc(now2()-timeStamp)),this.analyze("End Body:"),this.analyze("Start Pose:"),this.config.async?(blazeposeRes=this.config.pose.enabled?predict(process3.tensor,this.config):[],this.perf.pose&&delete this.perf.pose):(this.state="run:pose",timeStamp=now2(),blazeposeRes=this.config.pose.enabled?await predict(process3.tensor,this.config):[],this.perf.pose=Math.trunc(now2()-timeStamp)),this.analyze("End Pose:"),this.analyze("Start Hand:"),this.config.async?(handRes=this.config.hand.enabled?(_c=this.models.handpose)==null?void 0:_c.estimateHands(process3.tensor,this.config):[],this.perf.hand&&delete this.perf.hand):(this.state="run:hand",timeStamp=now2(),handRes=this.config.hand.enabled?await((_d=this.models.handpose)==null?void 0:_d.estimateHands(process3.tensor,this.config)):[],this.perf.hand=Math.trunc(now2()-timeStamp)),this.analyze("End Hand:"),this.config.async&&([faceRes,poseRes,blazeposeRes,handRes]=await Promise.all([faceRes,poseRes,blazeposeRes,handRes])),process3.tensor.dispose(),this.config.scoped&&engine().endScope(),this.analyze("End Scope:");let gestureRes=[];this.config.gesture.enabled&&(timeStamp=now2(),gestureRes=[...gesture.face(faceRes),...gesture.body(poseRes),...gesture.hand(handRes),...gesture.iris(faceRes)],this.config.async?this.perf.gesture&&delete this.perf.gesture:this.perf.gesture=Math.trunc(now2()-timeStamp)),this.perf.total=Math.trunc(now2()-timeStart),this.state="idle",resolve({face:faceRes,body:poseRes,hand:handRes,pose:blazeposeRes,gesture:gestureRes,performance:this.perf,canvas:process3.canvas})})}async warmupBitmap(){let b64toBlob=(base64,type="application/octet-stream")=>fetch(`data:${type};base64,${base64}`).then(res2=>res2.blob()),blob,res;switch(this.config.warmup){case"face":blob=await b64toBlob(face);break;case"full":blob=await b64toBlob(body);break;default:blob=null}if(blob){let bitmap=await createImageBitmap(blob);res=await this.detect(bitmap,config_exports),bitmap.close()}return res}async warmupCanvas(){return new Promise(resolve=>{let src,size=0;switch(this.config.warmup){case"face":size=256,src="data:image/jpeg;base64,"+face;break;case"full":size=1200,src="data:image/jpeg;base64,"+body;break;default:src=null}let img=new Image(size,size);img.onload=()=>{let canvas=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(size,size):document.createElement("canvas");canvas.width=size,canvas.height=size;let ctx=canvas.getContext("2d");ctx.drawImage(img,0,0);let data2=ctx.getImageData(0,0,size,size);this.detect(data2,config_exports).then(res=>resolve(res))},src?img.src=src:resolve(null)})}async warmup(userConfig){let t0=now2();userConfig&&(this.config=mergeDeep(this.config,userConfig));let video=this.config.videoOptimized;this.config.videoOptimized=!1;let res;typeof createImageBitmap=="function"?res=await this.warmupBitmap():res=await this.warmupCanvas(),this.config.videoOptimized=video;let t1=now2();return log("Warmup",this.config.warmup,t1-t0,res),res}};return human_exports;})(); /** * @license * Copyright 2017 Google LLC. All Rights Reserved. diff --git a/dist/human.js.map b/dist/human.js.map index 08f071dc..cad38ef4 100644 --- a/dist/human.js.map +++ b/dist/human.js.map @@ -1,7 +1,7 @@ { "version": 3, "sources": ["../src/blazeface/blazeface.js", "../src/blazeface/box.js", "../src/blazeface/util.js", "../src/blazeface/coords.js", "../src/blazeface/facepipeline.js", "../src/blazeface/facemesh.js", "../src/profile.js", "../src/age/age.js", "../src/gender/gender.js", "../src/emotion/emotion.js", "../src/embedding/embedding.js", "../src/posenet/modelBase.js", "../src/posenet/heapSort.js", "../src/posenet/buildParts.js", "../src/posenet/keypoints.js", "../src/posenet/vectors.js", "../src/posenet/decoders.js", "../src/posenet/decodePose.js", "../src/posenet/decodeMultiple.js", "../src/posenet/util.js", "../src/posenet/modelPoseNet.js", "../src/posenet/posenet.js", "../src/handpose/handdetector.js", "../src/handpose/handpipeline.js", "../src/handpose/anchors.js", "../src/handpose/handpose.js", 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"../node_modules/seedrandom/lib/tychei.js", "../node_modules/seedrandom/seedrandom.js", "../node_modules/seedrandom/index.js", "empty:/home/vlado/dev/human/node_modules/string_decoder/lib/string_decoder.js", "../node_modules/@tensorflow/tfjs-core/src/backends/backend.ts", "../node_modules/@tensorflow/tfjs-core/src/util_base.ts", "../node_modules/@tensorflow/tfjs-core/src/environment.ts", "../node_modules/@tensorflow/tfjs-core/src/global_util.ts", "../node_modules/@tensorflow/tfjs-core/src/kernel_names.ts", "../node_modules/@tensorflow/tfjs-core/src/kernel_registry.ts", "../node_modules/@tensorflow/tfjs-core/src/util.ts", "../node_modules/@tensorflow/tfjs-core/src/profiler.ts", "../node_modules/@tensorflow/tfjs-core/src/tape.ts", "../node_modules/@tensorflow/tfjs-core/src/tensor_format.ts", "../node_modules/@tensorflow/tfjs-core/src/tensor.ts", "../node_modules/@tensorflow/tfjs-core/src/tensor_util.ts", "../node_modules/@tensorflow/tfjs-core/src/types.ts", "../node_modules/@tensorflow/tfjs-core/src/engine.ts", "../node_modules/@tensorflow/tfjs-core/src/device_util.ts", "../node_modules/@tensorflow/tfjs-core/src/flags.ts", "../node_modules/@tensorflow/tfjs-core/src/tensor_util_env.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/operation.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/complex.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/tensor_ops_util.ts", "../node_modules/@tensorflow/tfjs-core/src/ops/tensor.ts", "../node_modules/@tensorflow/tfjs-core/src/io/types.ts", "../node_modules/@tensorflow/tfjs-core/src/io/io_utils.ts", "../node_modules/@tensorflow/tfjs-core/src/io/router_registry.ts", "../node_modules/@tensorflow/tfjs-core/src/io/indexed_db.ts", "../node_modules/@tensorflow/tfjs-core/src/io/local_storage.ts", "../node_modules/@tensorflow/tfjs-core/src/io/model_management.ts", "../node_modules/@tensorflow/tfjs-core/src/platforms/platform_browser.ts", 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"../node_modules/@tensorflow/tfjs-data/src/sources/file_data_source.ts", "../node_modules/@tensorflow/tfjs-data/src/sources/url_data_source.ts", "../node_modules/@tensorflow/tfjs-data/src/readers.ts", "../node_modules/@tensorflow/tfjs-data/src/version.ts", "../src/tfjs/tf-browser.js", "../src/tfjs/backend.js", "../src/blazepose/blazepose.js", "../src/handpose/box.js", "../src/handpose/util.js", "../config.js", "../src/sample.js"], - "sourcesContent": ["import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nconst NUM_LANDMARKS = 6;\n\nfunction generateAnchors(inputSize) {\n const spec = { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] };\n const anchors = [];\n for (let i = 0; i < spec.strides.length; i++) {\n const stride = spec.strides[i];\n const gridRows = Math.floor((inputSize + stride - 1) / stride);\n const gridCols = Math.floor((inputSize + stride - 1) / stride);\n const anchorsNum = spec.anchors[i];\n for (let gridY = 0; gridY < gridRows; gridY++) {\n const anchorY = stride * (gridY + 0.5);\n for (let gridX = 0; gridX < gridCols; gridX++) {\n const anchorX = stride * (gridX + 0.5);\n for (let n = 0; n < anchorsNum; n++) {\n anchors.push([anchorX, anchorY]);\n }\n }\n }\n }\n return anchors;\n}\n\nconst disposeBox = (box) => {\n box.startEndTensor.dispose();\n box.startPoint.dispose();\n box.endPoint.dispose();\n};\n\nconst createBox = (startEndTensor) => ({\n startEndTensor,\n startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]),\n endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]),\n});\n\nconst scaleBox = (box, factors) => {\n const starts = tf.mul(box.startPoint, factors);\n const ends = tf.mul(box.endPoint, factors);\n const newCoordinates = tf.concat2d([starts, ends], 1);\n return createBox(newCoordinates);\n};\n\nfunction decodeBounds(boxOutputs, anchors, inputSize) {\n const boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n const centers = tf.add(boxStarts, anchors);\n const boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n const boxSizesNormalized = tf.div(boxSizes, inputSize);\n const centersNormalized = tf.div(centers, inputSize);\n const halfBoxSize = tf.div(boxSizesNormalized, 2);\n const starts = tf.sub(centersNormalized, halfBoxSize);\n const ends = tf.add(centersNormalized, halfBoxSize);\n const startNormalized = tf.mul(starts, inputSize);\n const endNormalized = tf.mul(ends, inputSize);\n const concatAxis = 1;\n return tf.concat2d([startNormalized, endNormalized], concatAxis);\n}\n\nfunction scaleBoxFromPrediction(face, scaleFactor) {\n return tf.tidy(() => {\n const box = face['box'] ? face['box'] : face;\n return scaleBox(box, scaleFactor).startEndTensor.squeeze();\n });\n}\n\nclass BlazeFaceModel {\n constructor(model, config) {\n this.blazeFaceModel = model;\n this.width = config.face.detector.inputSize;\n this.height = config.face.detector.inputSize;\n this.anchorsData = generateAnchors(config.face.detector.inputSize);\n this.anchors = tf.tensor2d(this.anchorsData);\n this.inputSize = tf.tensor1d([this.width, this.height]);\n this.config = config;\n this.scaleFaces = 0.8;\n }\n\n async getBoundingBoxes(inputImage) {\n // sanity check on input\n if ((!inputImage) || (inputImage.isDisposedInternal) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return null;\n const [detectedOutputs, boxes, scores] = tf.tidy(() => {\n const resizedImage = inputImage.resizeBilinear([this.width, this.height]);\n // const normalizedImage = tf.mul(tf.sub(resizedImage.div(255), 0.5), 2);\n const normalizedImage = tf.sub(resizedImage.div(127.5), 1);\n const batchedPrediction = this.blazeFaceModel.predict(normalizedImage);\n let prediction;\n // are we using tfhub or pinto converted model?\n if (Array.isArray(batchedPrediction)) {\n const sorted = batchedPrediction.sort((a, b) => a.size - b.size);\n const concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n const concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n const concat = tf.concat([concat512, concat384], 1);\n prediction = concat.squeeze(0);\n } else {\n prediction = batchedPrediction.squeeze(); // when using tfhub model\n }\n const decodedBounds = decodeBounds(prediction, this.anchors, this.inputSize);\n const logits = tf.slice(prediction, [0, 0], [-1, 1]);\n const scoresOut = tf.sigmoid(logits).squeeze();\n return [prediction, decodedBounds, scoresOut];\n });\n const boxIndicesTensor = await tf.image.nonMaxSuppressionAsync(boxes, scores, this.config.face.detector.maxFaces, this.config.face.detector.iouThreshold, this.config.face.detector.scoreThreshold);\n const boxIndices = boxIndicesTensor.arraySync();\n boxIndicesTensor.dispose();\n const boundingBoxesMap = boxIndices.map((boxIndex) => tf.slice(boxes, [boxIndex, 0], [1, -1]));\n const boundingBoxes = boundingBoxesMap.map((boundingBox) => {\n const vals = boundingBox.arraySync();\n boundingBox.dispose();\n return vals;\n });\n\n const scoresVal = scores.dataSync();\n const annotatedBoxes = [];\n for (let i = 0; i < boundingBoxes.length; i++) {\n const boxIndex = boxIndices[i];\n const confidence = scoresVal[boxIndex];\n if (confidence > this.config.face.detector.minConfidence) {\n const box = createBox(boundingBoxes[i]);\n const anchor = this.anchorsData[boxIndex];\n const landmarks = tf.tidy(() => tf.slice(detectedOutputs, [boxIndex, NUM_LANDMARKS - 1], [1, -1]).squeeze().reshape([NUM_LANDMARKS, -1]));\n annotatedBoxes.push({ box, landmarks, anchor, confidence });\n }\n }\n detectedOutputs.dispose();\n boxes.dispose();\n scores.dispose();\n detectedOutputs.dispose();\n return {\n boxes: annotatedBoxes,\n scaleFactor: [inputImage.shape[2] / this.width, inputImage.shape[1] / this.height],\n };\n }\n\n async estimateFaces(input) {\n // @ts-ignore\n const { boxes, scaleFactor } = await this.getBoundingBoxes(input);\n const faces = [];\n for (const face of boxes) {\n const landmarkData = face.landmarks.arraySync();\n const scaledBox = scaleBoxFromPrediction(face, scaleFactor);\n // @ts-ignore\n const boxData = scaleBox.arraySync();\n const probabilityData = face.probability.arraySync();\n const anchor = face.anchor;\n const [scaleFactorX, scaleFactorY] = scaleFactor;\n const scaledLandmarks = landmarkData\n .map((landmark) => ([\n (landmark[0] + anchor[0]) * scaleFactorX,\n (landmark[1] + anchor[1]) * scaleFactorY,\n ]));\n const normalizedFace = {\n topLeft: boxData.slice(0, 2),\n bottomRight: boxData.slice(2),\n landmarks: scaledLandmarks,\n probability: probabilityData,\n };\n disposeBox(face.box);\n face.landmarks.dispose();\n face.probability.dispose();\n scaledBox.dispose();\n faces.push(normalizedFace);\n }\n return faces;\n }\n}\n\nasync function load(config) {\n const blazeface = await tf.loadGraphModel(config.face.detector.modelPath, { fromTFHub: config.face.detector.modelPath.includes('tfhub.dev') });\n const model = new BlazeFaceModel(blazeface, config);\n log(`load model: ${config.face.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n return model;\n}\n\nexports.load = load;\nexports.BlazeFaceModel = BlazeFaceModel;\nexports.disposeBox = disposeBox;\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nfunction scaleBoxCoordinates(box, factor) {\n const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n return { startPoint, endPoint };\n}\nexports.scaleBoxCoordinates = scaleBoxCoordinates;\n\nfunction getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\nexports.getBoxSize = getBoxSize;\n\nfunction getBoxCenter(box) {\n return [\n box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n ];\n}\nexports.getBoxCenter = getBoxCenter;\n\nfunction cutBoxFromImageAndResize(box, image, cropSize) {\n const h = image.shape[1];\n const w = image.shape[2];\n const boxes = [[\n box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h,\n box.endPoint[0] / w,\n ]];\n return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\nexports.cutBoxFromImageAndResize = cutBoxFromImageAndResize;\n\nfunction enlargeBox(box, factor = 1.5) {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n return { startPoint, endPoint, landmarks: box.landmarks };\n}\nexports.enlargeBox = enlargeBox;\n\nfunction squarifyBox(box) {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const maxEdge = Math.max(...size);\n const halfSize = maxEdge / 2;\n const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n return { startPoint, endPoint, landmarks: box.landmarks };\n}\nexports.squarifyBox = squarifyBox;\n", "exports.IDENTITY_MATRIX = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n/**\n * Normalizes the provided angle to the range -pi to pi.\n * @param angle The angle in radians to be normalized.\n */\nfunction normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\nexports.normalizeRadians = normalizeRadians;\n\n/**\n * Computes the angle of rotation between two anchor points.\n * @param point1 First anchor point\n * @param point2 Second anchor point\n */\nfunction computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\nexports.computeRotation = computeRotation;\n\nfunction radToDegrees(rad) {\n return rad * 180 / Math.PI;\n}\nexports.radToDegrees = radToDegrees;\n\nfunction buildTranslationMatrix(x, y) {\n return [[1, 0, x], [0, 1, y], [0, 0, 1]];\n}\n\nfunction dot(v1, v2) {\n let product = 0;\n for (let i = 0; i < v1.length; i++) {\n product += v1[i] * v2[i];\n }\n return product;\n}\nexports.dot = dot;\n\nfunction getColumnFrom2DArr(arr, columnIndex) {\n const column = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\nexports.getColumnFrom2DArr = getColumnFrom2DArr;\n\nfunction multiplyTransformMatrices(mat1, mat2) {\n const product = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) {\n product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n }\n return product;\n}\nfunction buildRotationMatrix(rotation, center) {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\nexports.buildRotationMatrix = buildRotationMatrix;\n\nfunction invertTransformMatrix(matrix) {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [\n -dot(rotationComponent[0], translationComponent),\n -dot(rotationComponent[1], translationComponent),\n ];\n return [\n rotationComponent[0].concat(invertedTranslation[0]),\n rotationComponent[1].concat(invertedTranslation[1]),\n [0, 0, 1],\n ];\n}\nexports.invertTransformMatrix = invertTransformMatrix;\n\nfunction rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\nexports.rotatePoint = rotatePoint;\n\nfunction xyDistanceBetweenPoints(a, b) {\n return Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n}\nexports.xyDistanceBetweenPoints = xyDistanceBetweenPoints;\n", "const MESH_ANNOTATIONS = {\n silhouette: [\n 10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n 397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n 172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n ],\n lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291],\n lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291],\n lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308],\n lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],\n rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173],\n rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133],\n rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190],\n rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243],\n rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189],\n rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244],\n rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245],\n rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193],\n rightEyebrowLower: [35, 124, 46, 53, 52, 65],\n rightEyeIris: [473, 474, 475, 476, 477],\n leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n leftEyeIris: [468, 469, 470, 471, 472],\n midwayBetweenEyes: [168],\n noseTip: [1],\n noseBottom: [2],\n noseRightCorner: [98],\n noseLeftCorner: [327],\n rightCheek: [205],\n leftCheek: [425],\n};\n\nconst MESH_TO_IRIS_INDICES_MAP = [ // A mapping from facemesh model keypoints to iris model keypoints.\n { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] },\n { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] },\n { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] },\n { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] },\n { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] },\n { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] },\n { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] },\n // { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] },\n // { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] },\n];\n\nconst UV468 = [\n [0.499976992607117, 0.652534008026123],\n [0.500025987625122, 0.547487020492554],\n [0.499974012374878, 0.602371990680695],\n [0.482113003730774, 0.471979022026062],\n [0.500150978565216, 0.527155995368958],\n [0.499909996986389, 0.498252987861633],\n [0.499523013830185, 0.40106201171875],\n [0.289712011814117, 0.380764007568359],\n [0.499954998493195, 0.312398016452789],\n [0.499987006187439, 0.269918978214264],\n [0.500023007392883, 0.107050001621246],\n [0.500023007392883, 0.666234016418457],\n [0.5000159740448, 0.679224014282227],\n [0.500023007392883, 0.692348003387451],\n [0.499976992607117, 0.695277988910675],\n [0.499976992607117, 0.70593398809433],\n [0.499976992607117, 0.719385027885437],\n [0.499976992607117, 0.737019002437592],\n [0.499967992305756, 0.781370997428894],\n [0.499816000461578, 0.562981009483337],\n [0.473773002624512, 0.573909997940063],\n [0.104906998574734, 0.254140973091125],\n [0.365929991006851, 0.409575998783112],\n [0.338757991790771, 0.41302502155304],\n [0.311120003461838, 0.409460008144379],\n [0.274657994508743, 0.389131009578705],\n [0.393361985683441, 0.403706014156342],\n [0.345234006643295, 0.344011008739471],\n [0.370094001293182, 0.346076011657715],\n [0.319321990013123, 0.347265005111694],\n [0.297903001308441, 0.353591024875641],\n [0.24779200553894, 0.410809993743896],\n [0.396889001131058, 0.842755019664764],\n [0.280097991228104, 0.375599980354309],\n [0.106310002505779, 0.399955987930298],\n [0.2099249958992, 0.391353011131287],\n [0.355807989835739, 0.534406006336212],\n [0.471751004457474, 0.65040397644043],\n [0.474155008792877, 0.680191993713379],\n [0.439785003662109, 0.657229006290436],\n [0.414617002010345, 0.66654098033905],\n [0.450374007225037, 0.680860996246338],\n [0.428770989179611, 0.682690978050232],\n [0.374971002340317, 0.727805018424988],\n [0.486716985702515, 0.547628998756409],\n [0.485300987958908, 0.527395009994507],\n [0.257764995098114, 0.314490020275116],\n [0.401223003864288, 0.455172002315521],\n [0.429818987846375, 0.548614978790283],\n [0.421351999044418, 0.533740997314453],\n [0.276895999908447, 0.532056987285614],\n [0.483370006084442, 0.499586999416351],\n [0.33721199631691, 0.282882988452911],\n [0.296391993761063, 0.293242990970612],\n [0.169294998049736, 0.193813979625702],\n [0.447580009698868, 0.302609980106354],\n [0.392390012741089, 0.353887975215912],\n [0.354490011930466, 0.696784019470215],\n [0.067304998636246, 0.730105042457581],\n [0.442739009857178, 0.572826027870178],\n [0.457098007202148, 0.584792017936707],\n [0.381974011659622, 0.694710969924927],\n [0.392388999462128, 0.694203019142151],\n [0.277076005935669, 0.271932005882263],\n [0.422551989555359, 0.563233017921448],\n [0.385919004678726, 0.281364023685455],\n [0.383103013038635, 0.255840003490448],\n [0.331431001424789, 0.119714021682739],\n [0.229923993349075, 0.232002973556519],\n [0.364500999450684, 0.189113974571228],\n [0.229622006416321, 0.299540996551514],\n [0.173287004232407, 0.278747975826263],\n [0.472878992557526, 0.666198015213013],\n [0.446828007698059, 0.668527007102966],\n [0.422762006521225, 0.673889994621277],\n [0.445307999849319, 0.580065965652466],\n [0.388103008270264, 0.693961024284363],\n [0.403039008378983, 0.706539988517761],\n [0.403629004955292, 0.693953037261963],\n [0.460041999816895, 0.557139039039612],\n [0.431158006191254, 0.692366003990173],\n [0.452181994915009, 0.692366003990173],\n [0.475387006998062, 0.692366003990173],\n [0.465828001499176, 0.779190003871918],\n [0.472328990697861, 0.736225962638855],\n [0.473087012767792, 0.717857003211975],\n [0.473122000694275, 0.704625964164734],\n [0.473033010959625, 0.695277988910675],\n [0.427942007780075, 0.695277988910675],\n [0.426479011774063, 0.703539967536926],\n [0.423162013292313, 0.711845993995667],\n [0.4183090031147, 0.720062971115112],\n [0.390094995498657, 0.639572978019714],\n [0.013953999616206, 0.560034036636353],\n [0.499913990497589, 0.58014702796936],\n [0.413199990987778, 0.69539999961853],\n [0.409626007080078, 0.701822996139526],\n [0.468080013990402, 0.601534962654114],\n [0.422728985548019, 0.585985004901886],\n [0.463079988956451, 0.593783974647522],\n [0.37211999297142, 0.47341400384903],\n [0.334562003612518, 0.496073007583618],\n [0.411671012639999, 0.546965003013611],\n [0.242175996303558, 0.14767599105835],\n [0.290776997804642, 0.201445996761322],\n [0.327338010072708, 0.256527006626129],\n [0.399509996175766, 0.748921036720276],\n [0.441727995872498, 0.261676013469696],\n [0.429764986038208, 0.187834024429321],\n [0.412198007106781, 0.108901023864746],\n [0.288955003023148, 0.398952007293701],\n [0.218936994671822, 0.435410976409912],\n [0.41278201341629, 0.398970007896423],\n [0.257135003805161, 0.355440020561218],\n [0.427684992551804, 0.437960982322693],\n [0.448339998722076, 0.536936044692993],\n [0.178560003638268, 0.45755398273468],\n [0.247308000922203, 0.457193970680237],\n [0.286267012357712, 0.467674970626831],\n [0.332827985286713, 0.460712015628815],\n [0.368755996227264, 0.447206974029541],\n [0.398963987827301, 0.432654976844788],\n [0.476410001516342, 0.405806005001068],\n [0.189241006970406, 0.523923993110657],\n [0.228962004184723, 0.348950982093811],\n [0.490725994110107, 0.562400996685028],\n [0.404670000076294, 0.485132992267609],\n [0.019469000399113, 0.401564002037048],\n [0.426243007183075, 0.420431017875671],\n [0.396993011236191, 0.548797011375427],\n [0.266469985246658, 0.376977026462555],\n [0.439121007919312, 0.51895797252655],\n [0.032313998788595, 0.644356966018677],\n [0.419054001569748, 0.387154996395111],\n [0.462783008813858, 0.505746960639954],\n [0.238978996872902, 0.779744982719421],\n [0.198220998048782, 0.831938028335571],\n [0.107550002634525, 0.540755033493042],\n [0.183610007166862, 0.740257024765015],\n [0.134409993886948, 0.333683013916016],\n [0.385764002799988, 0.883153975009918],\n [0.490967005491257, 0.579378008842468],\n [0.382384985685349, 0.508572995662689],\n [0.174399003386497, 0.397670984268188],\n [0.318785011768341, 0.39623498916626],\n [0.343364000320435, 0.400596976280212],\n [0.396100014448166, 0.710216999053955],\n [0.187885001301765, 0.588537991046906],\n [0.430987000465393, 0.944064974784851],\n [0.318993002176285, 0.898285031318665],\n [0.266247987747192, 0.869701027870178],\n [0.500023007392883, 0.190576016902924],\n [0.499976992607117, 0.954452991485596],\n [0.366169989109039, 0.398822009563446],\n [0.393207013607025, 0.39553701877594],\n [0.410373002290726, 0.391080021858215],\n [0.194993004202843, 0.342101991176605],\n [0.388664990663528, 0.362284004688263],\n [0.365961998701096, 0.355970978736877],\n [0.343364000320435, 0.355356991291046],\n [0.318785011768341, 0.35834002494812],\n [0.301414996385574, 0.363156020641327],\n [0.058132998645306, 0.319076001644135],\n [0.301414996385574, 0.387449026107788],\n [0.499987989664078, 0.618434011936188],\n [0.415838003158569, 0.624195992946625],\n [0.445681989192963, 0.566076993942261],\n [0.465844005346298, 0.620640993118286],\n [0.49992299079895, 0.351523995399475],\n [0.288718998432159, 0.819945991039276],\n [0.335278987884521, 0.852819979190826],\n [0.440512001514435, 0.902418971061707],\n [0.128294005990028, 0.791940987110138],\n [0.408771991729736, 0.373893976211548],\n [0.455606997013092, 0.451801002025604],\n [0.499877005815506, 0.908990025520325],\n [0.375436991453171, 0.924192011356354],\n [0.11421000212431, 0.615022003650665],\n [0.448662012815475, 0.695277988910675],\n [0.4480200111866, 0.704632043838501],\n [0.447111994028091, 0.715808033943176],\n [0.444831997156143, 0.730794012546539],\n [0.430011987686157, 0.766808986663818],\n [0.406787008047104, 0.685672998428345],\n [0.400738000869751, 0.681069016456604],\n [0.392399996519089, 0.677703022956848],\n [0.367855995893478, 0.663918972015381],\n [0.247923001646996, 0.601333022117615],\n [0.452769994735718, 0.420849978923798],\n [0.43639200925827, 0.359887003898621],\n [0.416164010763168, 0.368713974952698],\n [0.413385987281799, 0.692366003990173],\n [0.228018000721931, 0.683571994304657],\n [0.468268007040024, 0.352671027183533],\n [0.411361992359161, 0.804327011108398],\n [0.499989002943039, 0.469825029373169],\n [0.479153990745544, 0.442654013633728],\n [0.499974012374878, 0.439637005329132],\n [0.432112008333206, 0.493588984012604],\n [0.499886006116867, 0.866917014122009],\n [0.49991300702095, 0.821729004383087],\n [0.456548988819122, 0.819200992584229],\n [0.344549000263214, 0.745438992977142],\n [0.37890899181366, 0.574010014533997],\n [0.374292999505997, 0.780184984207153],\n [0.319687992334366, 0.570737957954407],\n [0.357154995203018, 0.604269981384277],\n [0.295284003019333, 0.621580958366394],\n [0.447750002145767, 0.862477004528046],\n [0.410986006259918, 0.508723020553589],\n [0.31395098567009, 0.775308012962341],\n [0.354128003120422, 0.812552988529205],\n [0.324548006057739, 0.703992962837219],\n [0.189096003770828, 0.646299958229065],\n [0.279776990413666, 0.71465802192688],\n [0.1338230073452, 0.682700991630554],\n [0.336768001317978, 0.644733011722565],\n [0.429883986711502, 0.466521978378296],\n [0.455527991056442, 0.548622965812683],\n [0.437114000320435, 0.558896005153656],\n [0.467287987470627, 0.529924988746643],\n [0.414712011814117, 0.335219979286194],\n [0.37704598903656, 0.322777986526489],\n [0.344107985496521, 0.320150971412659],\n [0.312875986099243, 0.32233202457428],\n [0.283526003360748, 0.333190023899078],\n [0.241245999932289, 0.382785975933075],\n [0.102986000478268, 0.468762993812561],\n [0.267612010240555, 0.424560010433197],\n [0.297879010438919, 0.433175981044769],\n [0.333433985710144, 0.433878004550934],\n [0.366427004337311, 0.426115989685059],\n [0.396012008190155, 0.416696012020111],\n [0.420121014118195, 0.41022801399231],\n [0.007561000064015, 0.480777025222778],\n [0.432949006557465, 0.569517970085144],\n [0.458638995885849, 0.479089021682739],\n [0.473466008901596, 0.545744001865387],\n [0.476087987422943, 0.563830018043518],\n [0.468472003936768, 0.555056989192963],\n [0.433990985155106, 0.582361996173859],\n [0.483518004417419, 0.562983989715576],\n [0.482482999563217, 0.57784903049469],\n [0.42645001411438, 0.389798998832703],\n [0.438998997211456, 0.39649498462677],\n [0.450067013502121, 0.400434017181396],\n [0.289712011814117, 0.368252992630005],\n [0.276670008897781, 0.363372981548309],\n [0.517862021923065, 0.471948027610779],\n [0.710287988185883, 0.380764007568359],\n [0.526226997375488, 0.573909997940063],\n [0.895093023777008, 0.254140973091125],\n [0.634069979190826, 0.409575998783112],\n [0.661242008209229, 0.41302502155304],\n [0.688880026340485, 0.409460008144379],\n [0.725341975688934, 0.389131009578705],\n [0.606630027294159, 0.40370500087738],\n [0.654766023159027, 0.344011008739471],\n [0.629905998706818, 0.346076011657715],\n [0.680678009986877, 0.347265005111694],\n [0.702096998691559, 0.353591024875641],\n [0.75221198797226, 0.410804986953735],\n [0.602918028831482, 0.842862963676453],\n [0.719901978969574, 0.375599980354309],\n [0.893692970275879, 0.399959981441498],\n [0.790081977844238, 0.391354024410248],\n [0.643998026847839, 0.534487962722778],\n [0.528249025344849, 0.65040397644043],\n [0.525849997997284, 0.680191040039062],\n [0.560214996337891, 0.657229006290436],\n [0.585384011268616, 0.66654098033905],\n [0.549625992774963, 0.680860996246338],\n [0.57122802734375, 0.682691991329193],\n [0.624852001667023, 0.72809898853302],\n [0.513050019741058, 0.547281980514526],\n [0.51509702205658, 0.527251958847046],\n [0.742246985435486, 0.314507007598877],\n [0.598631024360657, 0.454979002475739],\n [0.570338010787964, 0.548575043678284],\n [0.578631997108459, 0.533622980117798],\n [0.723087012767792, 0.532054007053375],\n [0.516445994377136, 0.499638974666595],\n [0.662801027297974, 0.282917976379395],\n [0.70362401008606, 0.293271005153656],\n [0.830704987049103, 0.193813979625702],\n [0.552385985851288, 0.302568018436432],\n [0.607609987258911, 0.353887975215912],\n [0.645429015159607, 0.696707010269165],\n [0.932694971561432, 0.730105042457581],\n [0.557260990142822, 0.572826027870178],\n [0.542901992797852, 0.584792017936707],\n [0.6180260181427, 0.694710969924927],\n [0.607590973377228, 0.694203019142151],\n [0.722943007946014, 0.271963000297546],\n [0.577413976192474, 0.563166975975037],\n [0.614082992076874, 0.281386971473694],\n [0.616907000541687, 0.255886018276215],\n [0.668509006500244, 0.119913995265961],\n [0.770092010498047, 0.232020974159241],\n [0.635536015033722, 0.189248979091644],\n [0.77039098739624, 0.299556016921997],\n [0.826722025871277, 0.278755009174347],\n [0.527121007442474, 0.666198015213013],\n [0.553171992301941, 0.668527007102966],\n [0.577238023281097, 0.673889994621277],\n [0.554691970348358, 0.580065965652466],\n [0.611896991729736, 0.693961024284363],\n [0.59696102142334, 0.706539988517761],\n [0.596370995044708, 0.693953037261963],\n [0.539958000183105, 0.557139039039612],\n [0.568841993808746, 0.692366003990173],\n [0.547818005084991, 0.692366003990173],\n [0.52461302280426, 0.692366003990173],\n [0.534089982509613, 0.779141008853912],\n [0.527670979499817, 0.736225962638855],\n [0.526912987232208, 0.717857003211975],\n [0.526877999305725, 0.704625964164734],\n [0.526966989040375, 0.695277988910675],\n [0.572058022022247, 0.695277988910675],\n [0.573521018028259, 0.703539967536926],\n [0.57683801651001, 0.711845993995667],\n [0.581691026687622, 0.720062971115112],\n [0.609944999217987, 0.639909982681274],\n [0.986046016216278, 0.560034036636353],\n [0.5867999792099, 0.69539999961853],\n [0.590372025966644, 0.701822996139526],\n [0.531915009021759, 0.601536989212036],\n [0.577268004417419, 0.585934996604919],\n [0.536915004253387, 0.593786001205444],\n [0.627542972564697, 0.473352015018463],\n [0.665585994720459, 0.495950996875763],\n [0.588353991508484, 0.546862006187439],\n [0.757824003696442, 0.14767599105835],\n [0.709249973297119, 0.201507985591888],\n [0.672684013843536, 0.256581008434296],\n [0.600408971309662, 0.74900496006012],\n [0.55826598405838, 0.261672019958496],\n [0.570303976535797, 0.187870979309082],\n [0.588165998458862, 0.109044015407562],\n [0.711045026779175, 0.398952007293701],\n [0.781069993972778, 0.435405015945435],\n [0.587247014045715, 0.398931980133057],\n [0.742869973182678, 0.355445981025696],\n [0.572156012058258, 0.437651991844177],\n [0.55186802148819, 0.536570012569427],\n [0.821442008018494, 0.457556009292603],\n [0.752701997756958, 0.457181990146637],\n [0.71375697851181, 0.467626988887787],\n [0.66711300611496, 0.460672974586487],\n [0.631101012229919, 0.447153985500336],\n [0.6008620262146, 0.432473003864288],\n [0.523481011390686, 0.405627012252808],\n [0.810747981071472, 0.523926019668579],\n [0.771045982837677, 0.348959028720856],\n [0.509127020835876, 0.562718033790588],\n [0.595292985439301, 0.485023975372314],\n [0.980530977249146, 0.401564002037048],\n [0.573499977588654, 0.420000016689301],\n [0.602994978427887, 0.548687994480133],\n [0.733529984951019, 0.376977026462555],\n [0.560611009597778, 0.519016981124878],\n [0.967685997486115, 0.644356966018677],\n [0.580985009670258, 0.387160003185272],\n [0.537728011608124, 0.505385041236877],\n [0.760966002941132, 0.779752969741821],\n [0.801778972148895, 0.831938028335571],\n [0.892440974712372, 0.54076099395752],\n [0.816350996494293, 0.740260004997253],\n [0.865594983100891, 0.333687007427216],\n [0.614073991775513, 0.883246004581451],\n [0.508952975273132, 0.579437971115112],\n [0.617941975593567, 0.508316040039062],\n [0.825608015060425, 0.397674977779388],\n [0.681214988231659, 0.39623498916626],\n [0.656635999679565, 0.400596976280212],\n [0.603900015354156, 0.710216999053955],\n [0.81208598613739, 0.588539004325867],\n [0.56801301240921, 0.944564998149872],\n [0.681007981300354, 0.898285031318665],\n [0.733752012252808, 0.869701027870178],\n [0.633830010890961, 0.398822009563446],\n [0.606792986392975, 0.39553701877594],\n [0.589659988880157, 0.391062021255493],\n [0.805015981197357, 0.342108011245728],\n [0.611334979534149, 0.362284004688263],\n [0.634037971496582, 0.355970978736877],\n [0.656635999679565, 0.355356991291046],\n [0.681214988231659, 0.35834002494812],\n [0.698584973812103, 0.363156020641327],\n [0.941866993904114, 0.319076001644135],\n [0.698584973812103, 0.387449026107788],\n [0.584177017211914, 0.624107003211975],\n [0.554318010807037, 0.566076993942261],\n [0.534153997898102, 0.62064003944397],\n [0.711217999458313, 0.819975018501282],\n [0.664629995822906, 0.852871000766754],\n [0.559099972248077, 0.902631998062134],\n [0.871706008911133, 0.791940987110138],\n [0.591234028339386, 0.373893976211548],\n [0.544341027736664, 0.451583981513977],\n [0.624562978744507, 0.924192011356354],\n [0.88577002286911, 0.615028977394104],\n [0.551338016986847, 0.695277988910675],\n [0.551980018615723, 0.704632043838501],\n [0.552887976169586, 0.715808033943176],\n [0.555167973041534, 0.730794012546539],\n [0.569944024085999, 0.767035007476807],\n [0.593203008174896, 0.685675978660583],\n [0.599261999130249, 0.681069016456604],\n [0.607599973678589, 0.677703022956848],\n [0.631937980651855, 0.663500010967255],\n [0.752032995223999, 0.601315021514893],\n [0.547226011753082, 0.420395016670227],\n [0.563543975353241, 0.359827995300293],\n [0.583841025829315, 0.368713974952698],\n [0.586614012718201, 0.692366003990173],\n [0.771915018558502, 0.683578014373779],\n [0.531597018241882, 0.352482974529266],\n [0.588370978832245, 0.804440975189209],\n [0.52079701423645, 0.442565023899078],\n [0.567984998226166, 0.493479013442993],\n [0.543282985687256, 0.819254994392395],\n [0.655317008495331, 0.745514988899231],\n [0.621008992195129, 0.574018001556396],\n [0.625559985637665, 0.78031200170517],\n [0.680198013782501, 0.570719003677368],\n [0.64276397228241, 0.604337990283966],\n [0.704662978649139, 0.621529996395111],\n [0.552012026309967, 0.862591981887817],\n [0.589071989059448, 0.508637011051178],\n [0.685944974422455, 0.775357007980347],\n [0.645735025405884, 0.812640011310577],\n [0.675342977046967, 0.703978002071381],\n [0.810858011245728, 0.646304965019226],\n [0.72012197971344, 0.714666962623596],\n [0.866151988506317, 0.682704985141754],\n [0.663187026977539, 0.644596993923187],\n [0.570082008838654, 0.466325998306274],\n [0.544561982154846, 0.548375964164734],\n [0.562758982181549, 0.558784961700439],\n [0.531987011432648, 0.530140042304993],\n [0.585271000862122, 0.335177004337311],\n [0.622952997684479, 0.32277899980545],\n [0.655896008014679, 0.320163011550903],\n [0.687132000923157, 0.322345972061157],\n [0.716481983661652, 0.333200991153717],\n [0.758756995201111, 0.382786989212036],\n [0.897013008594513, 0.468769013881683],\n [0.732392013072968, 0.424547016620636],\n [0.70211398601532, 0.433162987232208],\n [0.66652500629425, 0.433866024017334],\n [0.633504986763, 0.426087975502014],\n [0.603875994682312, 0.416586995124817],\n [0.579657971858978, 0.409945011138916],\n [0.992439985275269, 0.480777025222778],\n [0.567192018032074, 0.569419980049133],\n [0.54136598110199, 0.478899002075195],\n [0.526564002037048, 0.546118021011353],\n [0.523913025856018, 0.563830018043518],\n [0.531529009342194, 0.555056989192963],\n [0.566035985946655, 0.582329034805298],\n [0.51631098985672, 0.563053965568542],\n [0.5174720287323, 0.577877044677734],\n [0.573594987392426, 0.389806985855103],\n [0.560697972774506, 0.395331978797913],\n [0.549755990505219, 0.399751007556915],\n [0.710287988185883, 0.368252992630005],\n [0.723330020904541, 0.363372981548309],\n];\n\nconst TRI468 = [\n 127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n 151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n 157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n 181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n 228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n 170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n 155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n 158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n 102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n 90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n 196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n 246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n 117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n 153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n 63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n 135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n 76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n 166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n 190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n 112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n 110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n 348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n 314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n 321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n 466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n 429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n 293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n 427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n 431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n 434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n 348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n 318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n 373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n 340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n 351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n 164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n 327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n 405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n 438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n 449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n 444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n 464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n 243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n 166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n 119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n 77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n 201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n 103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n 217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n 78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n 57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n 142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n 90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n 178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n 224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n 143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n 153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n 63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n 241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n 186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n 141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n 223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n 128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n 451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n 344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n 424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n 321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n 448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n 281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n 283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n 432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n 268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n 6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n 317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n 272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n 336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n 441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n 373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n 425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n 403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n 383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n 254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n 259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n 453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nconst TRI68 = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n 8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n 45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n 23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n 39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n 35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n 48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n 66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\nconst TRI33 = [\n /* eyes */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n /* brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n /* 4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n /* nose */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n /* chin */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n /* cont */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\nconst TRI7 = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nconst VTX68 = [\n /* cont */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n /* nose */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n /* eyes */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n /* lip */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\nconst VTX33 = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\nconst VTX7 = [33, 133, 362, 263, 1, 78, 308];\n\nexports.MESH_ANNOTATIONS = MESH_ANNOTATIONS;\nexports.MESH_TO_IRIS_INDICES_MAP = MESH_TO_IRIS_INDICES_MAP;\n\nexports.TRI468 = TRI468;\nexports.TRI68 = TRI68;\nexports.TRI33 = TRI33;\nexports.TRI7 = TRI7;\n\nexports.UV468 = UV468;\nexports.UV68 = VTX68.map((x) => UV468[x]);\nexports.UV33 = VTX33.map((x) => UV468[x]);\nexports.UV7 = VTX7.map((x) => UV468[x]);\n", "/* eslint-disable class-methods-use-this */\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as bounding from './box';\nimport * as util from './util';\nimport * as coords from './coords.js';\n// eslint-disable-next-line no-unused-vars\nimport { log } from '../log.js';\n\nconst LANDMARKS_COUNT = 468;\nconst MESH_MOUTH_INDEX = 13;\nconst MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES = [MESH_MOUTH_INDEX, coords.MESH_ANNOTATIONS['midwayBetweenEyes'][0]];\nconst BLAZEFACE_MOUTH_INDEX = 3;\nconst BLAZEFACE_NOSE_INDEX = 2;\nconst BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES = [BLAZEFACE_MOUTH_INDEX, BLAZEFACE_NOSE_INDEX];\nconst LEFT_EYE_OUTLINE = coords.MESH_ANNOTATIONS['leftEyeLower0'];\nconst LEFT_EYE_BOUNDS = [LEFT_EYE_OUTLINE[0], LEFT_EYE_OUTLINE[LEFT_EYE_OUTLINE.length - 1]];\nconst RIGHT_EYE_OUTLINE = coords.MESH_ANNOTATIONS['rightEyeLower0'];\nconst RIGHT_EYE_BOUNDS = [RIGHT_EYE_OUTLINE[0], RIGHT_EYE_OUTLINE[RIGHT_EYE_OUTLINE.length - 1]];\nconst IRIS_UPPER_CENTER_INDEX = 3;\nconst IRIS_LOWER_CENTER_INDEX = 4;\nconst IRIS_IRIS_INDEX = 71;\nconst IRIS_NUM_COORDINATES = 76;\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates. Update the z coordinate to be an average of the original and the new. This produces the best visual effect.\nfunction replaceRawCoordinates(rawCoords, newCoords, prefix, keys) {\n for (let i = 0; i < coords.MESH_TO_IRIS_INDICES_MAP.length; i++) {\n const { key, indices } = coords.MESH_TO_IRIS_INDICES_MAP[i];\n const originalIndices = coords.MESH_ANNOTATIONS[`${prefix}${key}`];\n const shouldReplaceAllKeys = keys == null;\n if (shouldReplaceAllKeys || keys.includes(key)) {\n for (let j = 0; j < indices.length; j++) {\n const index = indices[j];\n rawCoords[originalIndices[j]] = [\n newCoords[index][0], newCoords[index][1],\n (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n ];\n }\n }\n }\n}\n// The Pipeline coordinates between the bounding box and skeleton models.\nclass Pipeline {\n constructor(boundingBoxDetector, meshDetector, irisModel, config) {\n // An array of facial bounding boxes.\n this.storedBoxes = [];\n this.runsWithoutFaceDetector = 0;\n this.boundingBoxDetector = boundingBoxDetector;\n this.meshDetector = meshDetector;\n this.irisModel = irisModel;\n this.meshWidth = config.face.mesh.inputSize;\n this.meshHeight = config.face.mesh.inputSize;\n this.irisSize = config.face.iris.inputSize;\n this.irisEnlarge = 2.3;\n this.skipped = 0;\n this.detectedFaces = 0;\n }\n\n transformRawCoords(rawCoords, box, angle, rotationMatrix) {\n // @ts-ignore\n const boxSize = bounding.getBoxSize({ startPoint: box.startPoint, endPoint: box.endPoint });\n const scaleFactor = [boxSize[0] / this.meshWidth, boxSize[1] / this.meshHeight];\n const coordsScaled = rawCoords.map((coord) => ([\n scaleFactor[0] * (coord[0] - this.meshWidth / 2),\n scaleFactor[1] * (coord[1] - this.meshHeight / 2), coord[2],\n ]));\n const coordsRotationMatrix = (angle !== 0) ? util.buildRotationMatrix(angle, [0, 0]) : util.IDENTITY_MATRIX;\n const coordsRotated = (angle !== 0) ? coordsScaled.map((coord) => ([...util.rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n const inverseRotationMatrix = (angle !== 0) ? util.invertTransformMatrix(rotationMatrix) : util.IDENTITY_MATRIX;\n // @ts-ignore\n const boxCenter = [...bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint }), 1];\n return coordsRotated.map((coord) => ([\n coord[0] + util.dot(boxCenter, inverseRotationMatrix[0]),\n coord[1] + util.dot(boxCenter, inverseRotationMatrix[1]),\n coord[2],\n ]));\n }\n\n getLeftToRightEyeDepthDifference(rawCoords) {\n const leftEyeZ = rawCoords[LEFT_EYE_BOUNDS[0]][2];\n const rightEyeZ = rawCoords[RIGHT_EYE_BOUNDS[0]][2];\n return leftEyeZ - rightEyeZ;\n }\n\n // Returns a box describing a cropped region around the eye fit for passing to the iris model.\n getEyeBox(rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, flip = false) {\n // @ts-ignore\n const box = bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), this.irisEnlarge));\n // @ts-ignore\n const boxSize = bounding.getBoxSize(box);\n let crop = tf.image.cropAndResize(face, [[\n box.startPoint[1] / this.meshHeight,\n box.startPoint[0] / this.meshWidth, box.endPoint[1] / this.meshHeight,\n box.endPoint[0] / this.meshWidth,\n ]], [0], [this.irisSize, this.irisSize]);\n if (flip) {\n crop = tf.image.flipLeftRight(crop);\n }\n return { box, boxSize, crop };\n }\n\n // Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\n getEyeCoords(eyeData, eyeBox, eyeBoxSize, flip = false) {\n const eyeRawCoords = [];\n for (let i = 0; i < IRIS_NUM_COORDINATES; i++) {\n const x = eyeData[i * 3];\n const y = eyeData[i * 3 + 1];\n const z = eyeData[i * 3 + 2];\n eyeRawCoords.push([\n (flip\n ? (1 - (x / this.irisSize))\n : (x / this.irisSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n (y / this.irisSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n ]);\n }\n return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(IRIS_IRIS_INDEX) };\n }\n\n // The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\n getAdjustedIrisCoords(rawCoords, irisCoords, direction) {\n const upperCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeUpper0`][IRIS_UPPER_CENTER_INDEX]][2];\n const lowerCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeLower0`][IRIS_LOWER_CENTER_INDEX]][2];\n const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n return irisCoords.map((coord, i) => {\n let z = averageZ;\n if (i === 2) {\n z = upperCenterZ;\n } else if (i === 4) {\n z = lowerCenterZ;\n }\n return [coord[0], coord[1], z];\n });\n }\n\n async predict(input, config) {\n let useFreshBox = false;\n // run new detector every skipFrames unless we only want box to start with\n let detector;\n if ((this.skipped === 0) || (this.skipped > config.face.detector.skipFrames) || !config.face.mesh.enabled || !config.videoOptimized) {\n detector = await this.boundingBoxDetector.getBoundingBoxes(input);\n this.skipped = 0;\n }\n if (config.videoOptimized) this.skipped++;\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (detector && detector.boxes && (detector.boxes.length > 0) && (!config.face.mesh.enabled || (detector.boxes.length !== this.detectedFaces) && (this.detectedFaces !== config.face.detector.maxFaces))) {\n this.storedBoxes = [];\n this.detectedFaces = 0;\n for (const possible of detector.boxes) {\n this.storedBoxes.push({ startPoint: possible.box.startPoint.dataSync(), endPoint: possible.box.endPoint.dataSync(), landmarks: possible.landmarks, confidence: possible.confidence });\n }\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\n\n if (useFreshBox) {\n if (!detector || !detector.boxes || (detector.boxes.length === 0)) {\n this.storedBoxes = [];\n this.detectedFaces = 0;\n return null;\n }\n for (let i = 0; i < this.storedBoxes.length; i++) {\n // @ts-ignore\n const scaledBox = bounding.scaleBoxCoordinates({ startPoint: this.storedBoxes[i].startPoint, endPoint: this.storedBoxes[i].endPoint }, detector.scaleFactor);\n // @ts-ignore\n const enlargedBox = bounding.enlargeBox(scaledBox);\n // @ts-ignore\n const squarifiedBox = bounding.squarifyBox(enlargedBox);\n const landmarks = this.storedBoxes[i].landmarks.arraySync();\n const confidence = this.storedBoxes[i].confidence;\n this.storedBoxes[i] = { ...squarifiedBox, confidence, landmarks };\n }\n this.runsWithoutFaceDetector = 0;\n }\n if (detector && detector.boxes) {\n detector.boxes.forEach((prediction) => {\n prediction.box.startPoint.dispose();\n prediction.box.endPoint.dispose();\n prediction.landmarks.dispose();\n });\n }\n\n // log('face', `skipped: ${this.skipped} max: ${config.face.detector.maxFaces} detected: ${this.detectedFaces} stored: ${this.storedBoxes.length} new: ${detector?.boxes?.length}`);\n let results = tf.tidy(() => this.storedBoxes.map((box, i) => {\n // The facial bounding box landmarks could come either from blazeface (if we are using a fresh box), or from the mesh model (if we are reusing an old box).\n let face;\n let angle = 0;\n let rotationMatrix;\n if (config.face.detector.rotation) {\n const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= LANDMARKS_COUNT) ? MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES : BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES;\n angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n // @ts-ignore\n const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n const rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized);\n rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n // @ts-ignore\n face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshHeight, this.meshWidth]).div(255);\n } else {\n rotationMatrix = util.IDENTITY_MATRIX;\n const cloned = input.clone();\n // @ts-ignore\n face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, cloned, [this.meshHeight, this.meshWidth]).div(255);\n }\n\n // if we're not going to produce mesh, don't spend time with further processing\n if (!config.face.mesh.enabled) {\n const prediction = {\n coords: null,\n box,\n faceConfidence: null,\n confidence: box.confidence,\n image: face,\n };\n return prediction;\n }\n\n const [, confidence, contourCoords] = this.meshDetector.predict(face); // The first returned tensor represents facial contours, which are included in the coordinates.\n const confidenceVal = confidence.dataSync()[0];\n if (confidenceVal < config.face.detector.minConfidence) return null; // if below confidence just exit\n const coordsReshaped = tf.reshape(contourCoords, [-1, 3]);\n let rawCoords = coordsReshaped.arraySync();\n\n if (config.face.iris.enabled) {\n const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = this.getEyeBox(rawCoords, face, LEFT_EYE_BOUNDS[0], LEFT_EYE_BOUNDS[1], true);\n const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = this.getEyeBox(rawCoords, face, RIGHT_EYE_BOUNDS[0], RIGHT_EYE_BOUNDS[1]);\n const eyePredictions = this.irisModel.predict(tf.concat([leftEyeCrop, rightEyeCrop]));\n const eyePredictionsData = eyePredictions.dataSync();\n const leftEyeData = eyePredictionsData.slice(0, IRIS_NUM_COORDINATES * 3);\n const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = this.getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n const rightEyeData = eyePredictionsData.slice(IRIS_NUM_COORDINATES * 3);\n const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = this.getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize);\n const leftToRightEyeDepthDifference = this.getLeftToRightEyeDepthDifference(rawCoords);\n if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left');\n replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right');\n // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged. So we only update a single contour line above and below the eye.\n } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n } else { // User is looking towards the left.\n replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n }\n const adjustedLeftIrisCoords = this.getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n const adjustedRightIrisCoords = this.getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n rawCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n }\n\n const transformedCoordsData = this.transformRawCoords(rawCoords, box, angle, rotationMatrix);\n // @ts-ignore\n const landmarksBox = bounding.enlargeBox(this.calculateLandmarksBoundingBox(transformedCoordsData));\n // @ts-ignore\n const squarifiedLandmarksBox = bounding.squarifyBox(landmarksBox);\n const transformedCoords = tf.tensor2d(transformedCoordsData);\n const prediction = {\n coords: transformedCoords,\n box: landmarksBox,\n faceConfidence: confidenceVal,\n confidence: box.confidence,\n image: face,\n };\n if (config.face.mesh.returnRawData) prediction.rawCoords = rawCoords;\n this.storedBoxes[i] = { ...squarifiedLandmarksBox, landmarks: transformedCoords.arraySync(), confidence: box.confidence, faceConfidence: confidenceVal };\n\n return prediction;\n }));\n results = results.filter((a) => a !== null);\n this.detectedFaces = results.length;\n return results;\n }\n\n calculateLandmarksBoundingBox(landmarks) {\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint, landmarks };\n }\n}\nexports.Pipeline = Pipeline;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as blazeface from './blazeface.js';\nimport * as facepipeline from './facepipeline.js';\nimport * as coords from './coords.js';\n\nclass MediaPipeFaceMesh {\n constructor(blazeFace, blazeMeshModel, irisModel, config) {\n // @ts-ignore\n this.facePipeline = new facepipeline.Pipeline(blazeFace, blazeMeshModel, irisModel, config);\n this.config = config;\n }\n\n async estimateFaces(input, config) {\n const predictions = await this.facePipeline.predict(input, config);\n const results = [];\n for (const prediction of (predictions || [])) {\n if (prediction.isDisposedInternal) continue; // guard against disposed tensors on long running operations such as pause in middle of processing\n const mesh = prediction.coords ? prediction.coords.arraySync() : null;\n const meshRaw = prediction.rawCoords;\n const annotations = {};\n if (mesh && mesh.length > 0) {\n for (const key of Object.keys(coords.MESH_ANNOTATIONS)) {\n annotations[key] = coords.MESH_ANNOTATIONS[key].map((index) => mesh[index]);\n }\n }\n const boxRaw = (config.face.mesh.returnRawData && prediction.box) ? { topLeft: prediction.box.startPoint, bottomRight: prediction.box.endPoint } : null;\n const box = prediction.box ? [\n Math.max(0, prediction.box.startPoint[0]),\n Math.max(0, prediction.box.startPoint[1]),\n Math.min(input.shape[2], prediction.box.endPoint[0]) - prediction.box.startPoint[0],\n Math.min(input.shape[1], prediction.box.endPoint[1]) - prediction.box.startPoint[1],\n ] : 0;\n results.push({\n confidence: prediction.confidence || 0,\n box,\n mesh,\n boxRaw,\n meshRaw,\n annotations,\n image: prediction.image ? tf.clone(prediction.image) : null,\n });\n if (prediction.coords) prediction.coords.dispose();\n if (prediction.image) prediction.image.dispose();\n }\n return results;\n }\n}\n\nlet faceModels = [null, null, null];\nasync function load(config) {\n faceModels = await Promise.all([\n // @ts-ignore\n (!faceModels[0] && config.face.enabled) ? blazeface.load(config) : null,\n (!faceModels[1] && config.face.mesh.enabled) ? tf.loadGraphModel(config.face.mesh.modelPath, { fromTFHub: config.face.mesh.modelPath.includes('tfhub.dev') }) : null,\n (!faceModels[2] && config.face.iris.enabled) ? tf.loadGraphModel(config.face.iris.modelPath, { fromTFHub: config.face.iris.modelPath.includes('tfhub.dev') }) : null,\n ]);\n const faceMesh = new MediaPipeFaceMesh(faceModels[0], faceModels[1], faceModels[2], config);\n if (config.face.mesh.enabled) log(`load model: ${config.face.mesh.modelPath.match(/\\/(.*)\\./)[1]}`);\n if (config.face.iris.enabled) log(`load model: ${config.face.iris.modelPath.match(/\\/(.*)\\./)[1]}`);\n return faceMesh;\n}\n\nexports.load = load;\nexports.MediaPipeFaceMesh = MediaPipeFaceMesh;\nexports.triangulation = coords.TRI468;\n", "import { log } from './log.js';\n\nconst profileData = {};\n\nfunction profile(name, data) {\n if (!data || !data.kernels) return;\n const maxResults = 5;\n const time = data.kernels\n .filter((a) => a.kernelTimeMs > 0)\n .reduce((a, b) => a += b.kernelTimeMs, 0);\n const slowest = data.kernels\n .map((a, i) => { a.id = i; return a; })\n .filter((a) => a.kernelTimeMs > 0)\n .sort((a, b) => b.kernelTimeMs - a.kernelTimeMs);\n const largest = data.kernels\n .map((a, i) => { a.id = i; return a; })\n .filter((a) => a.totalBytesSnapshot > 0)\n .sort((a, b) => b.totalBytesSnapshot - a.totalBytesSnapshot);\n if (slowest.length > maxResults) slowest.length = maxResults;\n if (largest.length > maxResults) largest.length = maxResults;\n const res = { newBytes: data.newBytes, newTensors: data.newTensors, peakBytes: data.peakBytes, numKernelOps: data.kernels.length, timeKernelOps: time, slowestKernelOps: slowest, largestKernelOps: largest };\n profileData[name] = res;\n log('Human profiler', name, res);\n}\n\nexports.run = profile;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile.js';\n\nconst models = {};\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nasync function load(config) {\n if (!models.age) {\n models.age = await tf.loadGraphModel(config.face.age.modelPath);\n log(`load model: ${config.face.age.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.age;\n}\n\nasync function predict(image, config) {\n if (!models.age) return null;\n if ((skipped < config.face.age.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n /*\n const zoom = [0, 0]; // 0..1 meaning 0%..100%\n const box = [[\n (image.shape[1] * zoom[0]) / image.shape[1],\n (image.shape[2] * zoom[1]) / image.shape[2],\n (image.shape[1] - (image.shape[1] * zoom[0])) / image.shape[1],\n (image.shape[2] - (image.shape[2] * zoom[1])) / image.shape[2],\n ]];\n const resize = tf.image.cropAndResize(image, box, [0], [config.face.age.inputSize, config.face.age.inputSize]);\n */\n const resize = tf.image.resizeBilinear(image, [config.face.age.inputSize, config.face.age.inputSize], false);\n const enhance = tf.mul(resize, [255.0]);\n tf.dispose(resize);\n\n let ageT;\n const obj = {};\n\n if (!config.profile) {\n if (config.face.age.enabled) ageT = await models.age.predict(enhance);\n } else {\n const profileAge = config.face.age.enabled ? await tf.profile(() => models.age.predict(enhance)) : {};\n ageT = profileAge.result.clone();\n profileAge.result.dispose();\n // @ts-ignore\n profile.run('age', profileAge);\n }\n enhance.dispose();\n\n if (ageT) {\n const data = ageT.dataSync();\n obj.age = Math.trunc(10 * data[0]) / 10;\n }\n ageT.dispose();\n\n last = obj;\n resolve(obj);\n });\n}\n\nexports.predict = predict;\nexports.load = load;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile.js';\n\nconst models = {};\nlet last = { gender: '' };\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet alternative = false;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nasync function load(config) {\n if (!models.gender) {\n models.gender = await tf.loadGraphModel(config.face.gender.modelPath);\n alternative = models.gender.inputs[0].shape[3] === 1;\n log(`load model: ${config.face.gender.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.gender;\n}\n\nasync function predict(image, config) {\n if (!models.gender) return null;\n if ((skipped < config.face.gender.skipFrames) && config.videoOptimized && last.gender !== '') {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [config.face.gender.inputSize, config.face.gender.inputSize], false);\n let enhance;\n if (alternative) {\n enhance = tf.tidy(() => {\n const [red, green, blue] = tf.split(resize, 3, 3);\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n return grayscale.sub(0.5).mul(2);\n });\n } else {\n enhance = tf.mul(resize, [255.0]);\n }\n tf.dispose(resize);\n\n let genderT;\n const obj = {};\n\n if (!config.profile) {\n if (config.face.gender.enabled) genderT = await models.gender.predict(enhance);\n } else {\n const profileGender = config.face.gender.enabled ? await tf.profile(() => models.gender.predict(enhance)) : {};\n genderT = profileGender.result.clone();\n profileGender.result.dispose();\n // @ts-ignore\n profile.run('gender', profileGender);\n }\n enhance.dispose();\n\n if (genderT) {\n const data = genderT.dataSync();\n if (alternative) {\n // returns two values 0..1, bigger one is prediction\n const confidence = Math.trunc(100 * Math.abs(data[0] - data[1])) / 100;\n if (confidence > config.face.gender.minConfidence) {\n obj.gender = data[0] > data[1] ? 'female' : 'male';\n obj.confidence = confidence;\n }\n } else {\n // returns one value 0..1, .5 is prediction threshold\n const confidence = Math.trunc(200 * Math.abs((data[0] - 0.5))) / 100;\n if (confidence > config.face.gender.minConfidence) {\n obj.gender = data[0] <= 0.5 ? 'female' : 'male';\n obj.confidence = Math.min(0.99, confidence);\n }\n }\n }\n genderT.dispose();\n\n last = obj;\n resolve(obj);\n });\n}\n\nexports.predict = predict;\nexports.load = load;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile.js';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];\nconst models = {};\nlet last = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\nconst scale = 1; // score multiplication factor\n\nasync function load(config) {\n if (!models.emotion) {\n models.emotion = await tf.loadGraphModel(config.face.emotion.modelPath);\n log(`load model: ${config.face.emotion.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.emotion;\n}\n\nasync function predict(image, config) {\n if (!models.emotion) return null;\n if ((skipped < config.face.emotion.skipFrames) && config.videoOptimized && (last.length > 0)) {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n /*\n const zoom = [0, 0]; // 0..1 meaning 0%..100%\n const box = [[\n (image.shape[1] * zoom[0]) / image.shape[1],\n (image.shape[2] * zoom[1]) / image.shape[2],\n (image.shape[1] - (image.shape[1] * zoom[0])) / image.shape[1],\n (image.shape[2] - (image.shape[2] * zoom[1])) / image.shape[2],\n ]];\n const resize = tf.image.cropAndResize(image, box, [0], [config.face.emotion.inputSize, config.face.emotion.inputSize]);\n */\n const resize = tf.image.resizeBilinear(image, [config.face.emotion.inputSize, config.face.emotion.inputSize], false);\n const [red, green, blue] = tf.split(resize, 3, 3);\n resize.dispose();\n // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n red.dispose();\n green.dispose();\n blue.dispose();\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n redNorm.dispose();\n greenNorm.dispose();\n blueNorm.dispose();\n const normalize = tf.tidy(() => grayscale.sub(0.5).mul(2));\n grayscale.dispose();\n const obj = [];\n if (config.face.emotion.enabled) {\n let data;\n if (!config.profile) {\n const emotionT = await models.emotion.predict(normalize);\n data = emotionT.dataSync();\n tf.dispose(emotionT);\n } else {\n const profileData = await tf.profile(() => models.emotion.predict(normalize));\n data = profileData.result.dataSync();\n profileData.result.dispose();\n // @ts-ignore\n profile.run('emotion', profileData);\n }\n for (let i = 0; i < data.length; i++) {\n if (scale * data[i] > config.face.emotion.minConfidence) obj.push({ score: Math.min(0.99, Math.trunc(100 * scale * data[i]) / 100), emotion: annotations[i] });\n }\n obj.sort((a, b) => b.score - a.score);\n }\n normalize.dispose();\n last = obj;\n resolve(obj);\n });\n}\n\nexports.predict = predict;\nexports.load = load;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile.js';\n\n// based on https://github.com/sirius-ai/MobileFaceNet_TF\n// model converted from https://github.com/sirius-ai/MobileFaceNet_TF/files/3551493/FaceMobileNet192_train_false.zip\n\nconst models = {};\n\nasync function load(config) {\n if (!models.embedding) {\n models.embedding = await tf.loadGraphModel(config.face.embedding.modelPath);\n log(`load model: ${config.face.embedding.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.embedding;\n}\n\nfunction simmilarity(embedding1, embedding2) {\n if (embedding1?.length !== embedding2?.length) return 0;\n // general minkowski distance\n // euclidean distance is limited case where order is 2\n const order = 2;\n const distance = 10.0 * ((embedding1.map((val, i) => (val - embedding2[i])).reduce((dist, diff) => dist + (diff ** order), 0) ** (1 / order)));\n return (Math.trunc(1000 * (1 - distance)) / 1000);\n}\n\nasync function predict(image, config) {\n if (!models.embedding) return null;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [config.face.embedding.inputSize, config.face.embedding.inputSize], false);\n // const normalize = tf.tidy(() => resize.div(127.5).sub(0.5)); // this is -0.5...0.5 ???\n let data = [];\n if (config.face.embedding.enabled) {\n if (!config.profile) {\n const embeddingT = await models.embedding.predict({ img_inputs: resize });\n data = [...embeddingT.dataSync()]; // convert object array to standard array\n tf.dispose(embeddingT);\n } else {\n const profileData = await tf.profile(() => models.embedding.predict({ img_inputs: resize }));\n data = [...profileData.result.dataSync()];\n profileData.result.dispose();\n // @ts-ignore\n profile.run('emotion', profileData);\n }\n }\n resize.dispose();\n // normalize.dispose();\n resolve(data);\n });\n}\n\nexports.predict = predict;\nexports.simmilarity = simmilarity;\nexports.load = load;\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nconst imageNetMean = [-123.15, -115.90, -103.06];\n\nfunction nameOutputResultsMobileNet(results) {\n const [offsets, heatmap, displacementFwd, displacementBwd] = results;\n return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nfunction nameOutputResultsResNet(results) {\n const [displacementFwd, displacementBwd, offsets, heatmap] = results;\n return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nclass BaseModel {\n constructor(model) {\n this.model = model;\n }\n\n predict(input, config) {\n return tf.tidy(() => {\n const asFloat = (config.body.modelType === 'ResNet') ? input.toFloat().add(imageNetMean) : input.toFloat().div(127.5).sub(1.0);\n const asBatch = asFloat.expandDims(0);\n const results = this.model.predict(asBatch);\n const results3d = results.map((y) => y.squeeze([0]));\n const namedResults = (config.body.modelType === 'ResNet') ? nameOutputResultsResNet(results3d) : nameOutputResultsMobileNet(results3d);\n return {\n heatmapScores: namedResults.heatmap.sigmoid(),\n offsets: namedResults.offsets,\n displacementFwd: namedResults.displacementFwd,\n displacementBwd: namedResults.displacementBwd,\n };\n });\n }\n\n dispose() {\n this.model.dispose();\n }\n}\nexports.BaseModel = BaseModel;\n", "// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nfunction half(k) {\n return Math.floor(k / 2);\n}\nclass MaxHeap {\n constructor(maxSize, getElementValue) {\n this.priorityQueue = new Array(maxSize);\n this.numberOfElements = -1;\n this.getElementValue = getElementValue;\n }\n\n enqueue(x) {\n this.priorityQueue[++this.numberOfElements] = x;\n this.swim(this.numberOfElements);\n }\n\n dequeue() {\n const max = this.priorityQueue[0];\n this.exchange(0, this.numberOfElements--);\n this.sink(0);\n this.priorityQueue[this.numberOfElements + 1] = null;\n return max;\n }\n\n empty() {\n return this.numberOfElements === -1;\n }\n\n size() {\n return this.numberOfElements + 1;\n }\n\n all() {\n return this.priorityQueue.slice(0, this.numberOfElements + 1);\n }\n\n max() {\n return this.priorityQueue[0];\n }\n\n swim(k) {\n while (k > 0 && this.less(half(k), k)) {\n this.exchange(k, half(k));\n k = half(k);\n }\n }\n\n sink(k) {\n while (2 * k <= this.numberOfElements) {\n let j = 2 * k;\n if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n if (!this.less(k, j)) break;\n this.exchange(k, j);\n k = j;\n }\n }\n\n getValueAt(i) {\n return this.getElementValue(this.priorityQueue[i]);\n }\n\n less(i, j) {\n return this.getValueAt(i) < this.getValueAt(j);\n }\n\n exchange(i, j) {\n const t = this.priorityQueue[i];\n this.priorityQueue[i] = this.priorityQueue[j];\n this.priorityQueue[j] = t;\n }\n}\nexports.MaxHeap = MaxHeap;\n", "import * as heapSort from './heapSort';\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores) {\n const [height, width] = scores.shape;\n let localMaximum = true;\n const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n localMaximum = false;\n break;\n }\n }\n if (!localMaximum) break;\n }\n return localMaximum;\n}\n\nfunction buildPartWithScoreQueue(scoreThreshold, localMaximumRadius, scores) {\n const [height, width, numKeypoints] = scores.shape;\n const queue = new heapSort.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n const score = scores.get(heatmapY, heatmapX, keypointId);\n // Only consider parts with score greater or equal to threshold as root candidates.\n if (score < scoreThreshold) continue;\n // Only consider keypoints whose score is maximum in a local window.\n if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores)) {\n queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n }\n }\n }\n }\n return queue;\n}\nexports.buildPartWithScoreQueue = buildPartWithScoreQueue;\n", "exports.partNames = [\n 'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n 'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n 'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexports.NUM_KEYPOINTS = exports.partNames.length;\n\nexports.partIds = exports.partNames.reduce((result, jointName, i) => {\n result[jointName] = i;\n return result;\n}, {});\n\nconst connectedPartNames = [\n ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexports.connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([exports.partIds[jointNameA], exports.partIds[jointNameB]]));\n\nexports.poseChain = [\n ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n ['rightKnee', 'rightAnkle'],\n];\n\nexports.partChannels = [\n 'left_face',\n 'right_face',\n 'right_upper_leg_front',\n 'right_lower_leg_back',\n 'right_upper_leg_back',\n 'left_lower_leg_front',\n 'left_upper_leg_front',\n 'left_upper_leg_back',\n 'left_lower_leg_back',\n 'right_feet',\n 'right_lower_leg_front',\n 'left_feet',\n 'torso_front',\n 'torso_back',\n 'right_upper_arm_front',\n 'right_upper_arm_back',\n 'right_lower_arm_back',\n 'left_lower_arm_front',\n 'left_upper_arm_front',\n 'left_upper_arm_back',\n 'left_lower_arm_back',\n 'right_hand',\n 'right_lower_arm_front',\n 'left_hand',\n];\n", "import * as kpt from './keypoints';\n\nfunction getOffsetPoint(y, x, keypoint, offsets) {\n return {\n y: offsets.get(y, x, keypoint),\n x: offsets.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n };\n}\nexports.getOffsetPoint = getOffsetPoint;\n\nfunction getImageCoords(part, outputStride, offsets) {\n const { heatmapY, heatmapX, id: keypoint } = part;\n const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n return {\n x: part.heatmapX * outputStride + x,\n y: part.heatmapY * outputStride + y,\n };\n}\nexports.getImageCoords = getImageCoords;\n\nfunction fillArray(element, size) {\n const result = new Array(size);\n for (let i = 0; i < size; i++) {\n result[i] = element;\n }\n return result;\n}\nexports.fillArray = fillArray;\n\nfunction clamp(a, min, max) {\n if (a < min) return min;\n if (a > max) return max;\n return a;\n}\nexports.clamp = clamp;\n\nfunction squaredDistance(y1, x1, y2, x2) {\n const dy = y2 - y1;\n const dx = x2 - x1;\n return dy * dy + dx * dx;\n}\nexports.squaredDistance = squaredDistance;\n\nfunction addVectors(a, b) {\n return { x: a.x + b.x, y: a.y + b.y };\n}\nexports.addVectors = addVectors;\n\nfunction clampVector(a, min, max) {\n return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\nexports.clampVector = clampVector;\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as kpt from './keypoints';\n\nfunction getPointsConfidence(heatmapScores, heatMapCoords) {\n const numKeypoints = heatMapCoords.shape[0];\n const result = new Float32Array(numKeypoints);\n for (let keypoint = 0; keypoint < numKeypoints; keypoint++) {\n const y = heatMapCoords.get(keypoint, 0);\n const x = heatMapCoords.get(keypoint, 1);\n result[keypoint] = heatmapScores.get(y, x, keypoint);\n }\n return result;\n}\nexports.getPointsConfidence = getPointsConfidence;\n\nfunction getOffsetPoint(y, x, keypoint, offsetsBuffer) {\n return {\n y: offsetsBuffer.get(y, x, keypoint),\n x: offsetsBuffer.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n };\n}\n\nfunction getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer) {\n const result = [];\n for (let keypoint = 0; keypoint < kpt.NUM_KEYPOINTS; keypoint++) {\n const heatmapY = heatMapCoordsBuffer.get(keypoint, 0).valueOf();\n const heatmapX = heatMapCoordsBuffer.get(keypoint, 1).valueOf();\n const { x, y } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsetsBuffer);\n result.push(y);\n result.push(x);\n }\n return tf.tensor2d(result, [kpt.NUM_KEYPOINTS, 2]);\n}\nexports.getOffsetVectors = getOffsetVectors;\n\nfunction getOffsetPoints(heatMapCoordsBuffer, outputStride, offsetsBuffer) {\n return tf.tidy(() => heatMapCoordsBuffer.toTensor().mul(tf.scalar(outputStride, 'int32')).toFloat().add(getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer)));\n}\nexports.getOffsetPoints = getOffsetPoints;\n\nfunction mod(a, b) {\n return tf.tidy(() => {\n const floored = a.div(tf.scalar(b, 'int32'));\n return a.sub(floored.mul(tf.scalar(b, 'int32')));\n });\n}\n\nfunction argmax2d(inputs) {\n const [height, width, depth] = inputs.shape;\n return tf.tidy(() => {\n const reshaped = inputs.reshape([height * width, depth]);\n const coords = reshaped.argMax(0);\n const yCoords = coords.div(tf.scalar(width, 'int32')).expandDims(1);\n const xCoords = mod(coords, width).expandDims(1);\n return tf.concat([yCoords, xCoords], 1);\n });\n}\nexports.argmax2d = argmax2d;\n", "import * as keypoints from './keypoints';\nimport * as vectors from './vectors';\nimport * as decoders from './decoders';\n\nconst parentChildrenTuples = keypoints.poseChain.map(([parentJoinName, childJoinName]) => ([keypoints.partIds[parentJoinName], keypoints.partIds[childJoinName]]));\nconst parentToChildEdges = parentChildrenTuples.map(([, childJointId]) => childJointId);\nconst childToParentEdges = parentChildrenTuples.map(([parentJointId]) => parentJointId);\nfunction getDisplacement(edgeId, point, displacements) {\n const numEdges = displacements.shape[2] / 2;\n return {\n y: displacements.get(point.y, point.x, edgeId),\n x: displacements.get(point.y, point.x, numEdges + edgeId),\n };\n}\nfunction getStridedIndexNearPoint(point, outputStride, height, width) {\n return {\n // @ts-ignore\n y: vectors.clamp(Math.round(point.y / outputStride), 0, height - 1),\n // @ts-ignore\n x: vectors.clamp(Math.round(point.x / outputStride), 0, width - 1),\n };\n}\n\nfunction traverseToTargetKeypoint(edgeId, sourceKeypoint, targetKeypointId, scoresBuffer, offsets, outputStride, displacements, offsetRefineStep = 2) {\n const [height, width] = scoresBuffer.shape;\n // Nearest neighbor interpolation for the source->target displacements.\n const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, outputStride, height, width);\n const displacement = getDisplacement(edgeId, sourceKeypointIndices, displacements);\n // @ts-ignore\n const displacedPoint = vectors.addVectors(sourceKeypoint.position, displacement);\n let targetKeypoint = displacedPoint;\n for (let i = 0; i < offsetRefineStep; i++) {\n const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n // @ts-ignore\n const offsetPoint = vectors.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetKeypointId, offsets);\n // @ts-ignore\n targetKeypoint = vectors.addVectors({\n x: targetKeypointIndices.x * outputStride,\n y: targetKeypointIndices.y * outputStride,\n }, { x: offsetPoint.x, y: offsetPoint.y });\n }\n const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n const score = scoresBuffer.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetKeypointId);\n return { position: targetKeypoint, part: keypoints.partNames[targetKeypointId], score };\n}\n\nfunction decodePose(root, scores, offsets, outputStride, displacementsFwd, displacementsBwd) {\n const numParts = scores.shape[2];\n const numEdges = parentToChildEdges.length;\n const instanceKeypoints = new Array(numParts);\n // Start a new detection instance at the position of the root.\n const { part: rootPart, score: rootScore } = root;\n // @ts-ignore\n const rootPoint = vectors.getImageCoords(rootPart, outputStride, offsets);\n instanceKeypoints[rootPart.id] = {\n score: rootScore,\n part: keypoints.partNames[rootPart.id],\n position: rootPoint,\n };\n // Decode the part positions upwards in the tree, following the backward displacements.\n for (let edge = numEdges - 1; edge >= 0; --edge) {\n const sourceKeypointId = parentToChildEdges[edge];\n const targetKeypointId = childToParentEdges[edge];\n if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsBwd);\n }\n }\n // Decode the part positions downwards in the tree, following the forward displacements.\n for (let edge = 0; edge < numEdges; ++edge) {\n const sourceKeypointId = childToParentEdges[edge];\n const targetKeypointId = parentToChildEdges[edge];\n if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsFwd);\n }\n }\n return instanceKeypoints;\n}\nexports.decodePose = decodePose;\n\nasync function decodeSinglePose(heatmapScores, offsets, config) {\n let totalScore = 0.0;\n // @ts-ignore\n const heatmapValues = decoders.argmax2d(heatmapScores);\n const allTensorBuffers = await Promise.all([heatmapScores.buffer(), offsets.buffer(), heatmapValues.buffer()]);\n const scoresBuffer = allTensorBuffers[0];\n const offsetsBuffer = allTensorBuffers[1];\n const heatmapValuesBuffer = allTensorBuffers[2];\n // @ts-ignore\n const offsetPoints = decoders.getOffsetPoints(heatmapValuesBuffer, config.body.outputStride, offsetsBuffer);\n const offsetPointsBuffer = await offsetPoints.buffer();\n // @ts-ignore\n const keypointConfidence = Array.from(decoders.getPointsConfidence(scoresBuffer, heatmapValuesBuffer));\n const instanceKeypoints = keypointConfidence.map((score, i) => {\n totalScore += score;\n return {\n position: {\n y: offsetPointsBuffer.get(i, 0),\n x: offsetPointsBuffer.get(i, 1),\n },\n part: keypoints.partNames[i],\n score,\n };\n });\n const filteredKeypoints = instanceKeypoints.filter((kpt) => kpt.score > config.body.scoreThreshold);\n heatmapValues.dispose();\n offsetPoints.dispose();\n return { keypoints: filteredKeypoints, score: totalScore / instanceKeypoints.length };\n}\nexports.decodeSinglePose = decodeSinglePose;\n", "import * as buildParts from './buildParts';\nimport * as decodePose from './decodePose';\nimport * as vectors from './vectors';\n\nconst kLocalMaximumRadius = 1;\n\nfunction withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, { x, y }, keypointId) {\n return poses.some(({ keypoints }) => {\n const correspondingKeypoint = keypoints[keypointId].position;\n // @ts-ignore\n return vectors.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n });\n}\n\nfunction getInstanceScore(existingPoses, squaredNmsRadius, instanceKeypoints) {\n const notOverlappedKeypointScores = instanceKeypoints.reduce((result, { position, score }, keypointId) => {\n if (!withinNmsRadiusOfCorrespondingPoint(existingPoses, squaredNmsRadius, position, keypointId)) result += score;\n return result;\n }, 0.0);\n return notOverlappedKeypointScores / instanceKeypoints.length;\n}\n\nfunction decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config) {\n const poses = [];\n // @ts-ignore\n const queue = buildParts.buildPartWithScoreQueue(config.body.scoreThreshold, kLocalMaximumRadius, scoresBuffer);\n const squaredNmsRadius = config.body.nmsRadius ^ 2;\n // Generate at most maxDetections object instances per image in decreasing root part score order.\n while (poses.length < config.body.maxDetections && !queue.empty()) {\n // The top element in the queue is the next root candidate.\n const root = queue.dequeue();\n // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n // @ts-ignore\n const rootImageCoords = vectors.getImageCoords(root.part, config.body.outputStride, offsetsBuffer);\n if (withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, rootImageCoords, root.part.id)) continue;\n // Else start a new detection instance at the position of the root.\n // @ts-ignore\n const keypoints = decodePose.decodePose(root, scoresBuffer, offsetsBuffer, config.body.outputStride, displacementsFwdBuffer, displacementsBwdBuffer);\n const score = getInstanceScore(poses, squaredNmsRadius, keypoints);\n if (score > config.body.scoreThreshold) poses.push({ keypoints, score });\n }\n return poses;\n}\nexports.decodeMultiplePoses = decodeMultiplePoses;\n", "import * as kpt from './keypoints';\n\nfunction eitherPointDoesntMeetConfidence(a, b, minConfidence) {\n return (a < minConfidence || b < minConfidence);\n}\n\nfunction getAdjacentKeyPoints(keypoints, minConfidence) {\n return kpt.connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n return result;\n }\n result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n return result;\n }, []);\n}\nexports.getAdjacentKeyPoints = getAdjacentKeyPoints;\n\nconst { NEGATIVE_INFINITY, POSITIVE_INFINITY } = Number;\nfunction getBoundingBox(keypoints) {\n return keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n maxX: Math.max(maxX, x),\n maxY: Math.max(maxY, y),\n minX: Math.min(minX, x),\n minY: Math.min(minY, y),\n }), {\n maxX: NEGATIVE_INFINITY,\n maxY: NEGATIVE_INFINITY,\n minX: POSITIVE_INFINITY,\n minY: POSITIVE_INFINITY,\n });\n}\nexports.getBoundingBox = getBoundingBox;\n\nfunction getBoundingBoxPoints(keypoints) {\n const { minX, minY, maxX, maxY } = getBoundingBox(keypoints);\n return [{ x: minX, y: minY }, { x: maxX, y: minY }, { x: maxX, y: maxY }, { x: minX, y: maxY }];\n}\nexports.getBoundingBoxPoints = getBoundingBoxPoints;\n\nasync function toTensorBuffers3D(tensors) {\n return Promise.all(tensors.map((tensor) => tensor.buffer()));\n}\nexports.toTensorBuffers3D = toTensorBuffers3D;\n\nfunction scalePose(pose, scaleY, scaleX) {\n return {\n score: pose.score,\n keypoints: pose.keypoints.map(({ score, part, position }) => ({\n score,\n part,\n position: { x: position.x * scaleX, y: position.y * scaleY },\n })),\n };\n}\nexports.scalePose = scalePose;\n\nfunction resizeTo(image, [targetH, targetW]) {\n const input = image.squeeze(0);\n const resized = input.resizeBilinear([targetH, targetW]);\n input.dispose();\n return resized;\n}\nexports.resizeTo = resizeTo;\n\nfunction scaleAndFlipPoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) {\n const scaledPoses = poses.map((pose) => scalePose(pose, height / inputResolutionHeight, width / inputResolutionWidth));\n return scaledPoses;\n}\nexports.scaleAndFlipPoses = scaleAndFlipPoses;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as modelBase from './modelBase';\nimport * as decodeMultiple from './decodeMultiple';\nimport * as decodePose from './decodePose';\nimport * as util from './util';\n\nasync function estimateMultiple(input, res, config) {\n return new Promise(async (resolve) => {\n const height = input.shape[1];\n const width = input.shape[2];\n // @ts-ignore\n const allTensorBuffers = await util.toTensorBuffers3D([res.heatmapScores, res.offsets, res.displacementFwd, res.displacementBwd]);\n const scoresBuffer = allTensorBuffers[0];\n const offsetsBuffer = allTensorBuffers[1];\n const displacementsFwdBuffer = allTensorBuffers[2];\n const displacementsBwdBuffer = allTensorBuffers[3];\n // @ts-ignore\n const poses = await decodeMultiple.decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config);\n // @ts-ignore\n const scaled = util.scaleAndFlipPoses(poses, [height, width], [config.body.inputSize, config.body.inputSize]);\n resolve(scaled);\n });\n}\n\nasync function estimateSingle(input, res, config) {\n return new Promise(async (resolve) => {\n const height = input.shape[1];\n const width = input.shape[2];\n // @ts-ignore\n const pose = await decodePose.decodeSinglePose(res.heatmapScores, res.offsets, config);\n const poses = [pose];\n // @ts-ignore\n const scaled = util.scaleAndFlipPoses(poses, [height, width], [config.body.inputSize, config.body.inputSize]);\n resolve(scaled);\n });\n}\n\nclass PoseNet {\n constructor(model) {\n this.baseModel = model;\n }\n\n async estimatePoses(input, config) {\n // @ts-ignore\n const resized = util.resizeTo(input, [config.body.inputSize, config.body.inputSize]);\n const res = this.baseModel.predict(resized, config);\n\n const poses = (config.body.maxDetections < 2) ? await estimateSingle(input, res, config) : await estimateMultiple(input, res, config);\n\n res.heatmapScores.dispose();\n res.offsets.dispose();\n res.displacementFwd.dispose();\n res.displacementBwd.dispose();\n resized.dispose();\n\n return poses;\n }\n\n dispose() {\n this.baseModel.dispose();\n }\n}\nexports.PoseNet = PoseNet;\n\nasync function load(config) {\n const model = await tf.loadGraphModel(config.body.modelPath);\n // @ts-ignore\n const mobilenet = new modelBase.BaseModel(model);\n log(`load model: ${config.body.modelPath.match(/\\/(.*)\\./)[1]}`);\n return new PoseNet(mobilenet);\n}\nexports.load = load;\n", "import * as modelPoseNet from './modelPoseNet';\nimport * as keypoints from './keypoints';\nimport * as util from './util';\n\n// @ts-ignore\nexports.load = modelPoseNet.load;\n// @ts-ignore\nexports.PoseNet = modelPoseNet.PoseNet;\n\nexports.partChannels = keypoints.partChannels;\nexports.partIds = keypoints.partIds;\nexports.partNames = keypoints.partNames;\nexports.poseChain = keypoints.poseChain;\n// @ts-ignore\nexports.getAdjacentKeyPoints = util.getAdjacentKeyPoints;\n// @ts-ignore\nexports.getBoundingBox = util.getBoundingBox;\n// @ts-ignore\nexports.getBoundingBoxPoints = util.getBoundingBoxPoints;\n// @ts-ignore\nexports.scaleAndFlipPoses = util.scaleAndFlipPoses;\n// @ts-ignore\nexports.scalePose = util.scalePose;\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\n\nclass HandDetector {\n constructor(model, inputSize, anchorsAnnotated) {\n this.model = model;\n this.anchors = anchorsAnnotated.map((anchor) => [anchor.x_center, anchor.y_center]);\n this.anchorsTensor = tf.tensor2d(this.anchors);\n this.inputSizeTensor = tf.tensor1d([inputSize, inputSize]);\n this.doubleInputSizeTensor = tf.tensor1d([inputSize * 2, inputSize * 2]);\n }\n\n normalizeBoxes(boxes) {\n return tf.tidy(() => {\n const boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n const boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n const boxCenterPoints = tf.add(tf.div(boxOffsets, this.inputSizeTensor), this.anchorsTensor);\n const halfBoxSizes = tf.div(boxSizes, this.doubleInputSizeTensor);\n const startPoints = tf.mul(tf.sub(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n const endPoints = tf.mul(tf.add(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n return tf.concat2d([startPoints, endPoints], 1);\n });\n }\n\n normalizeLandmarks(rawPalmLandmarks, index) {\n return tf.tidy(() => {\n const landmarks = tf.add(tf.div(rawPalmLandmarks.reshape([-1, 7, 2]), this.inputSizeTensor), this.anchors[index]);\n return tf.mul(landmarks, this.inputSizeTensor);\n });\n }\n\n async getBoxes(input, config) {\n const batched = this.model.predict(input);\n const predictions = batched.squeeze();\n batched.dispose();\n const scoresT = tf.tidy(() => tf.sigmoid(tf.slice(predictions, [0, 0], [-1, 1])).squeeze());\n const scores = scoresT.dataSync();\n const rawBoxes = tf.slice(predictions, [0, 1], [-1, 4]);\n const boxes = this.normalizeBoxes(rawBoxes);\n rawBoxes.dispose();\n const filteredT = await tf.image.nonMaxSuppressionAsync(boxes, scores, config.hand.maxHands, config.hand.iouThreshold, config.hand.scoreThreshold);\n const filtered = filteredT.arraySync();\n\n scoresT.dispose();\n filteredT.dispose();\n const hands = [];\n for (const index of filtered) {\n if (scores[index] >= config.hand.minConfidence) {\n const matchingBox = tf.slice(boxes, [index, 0], [1, -1]);\n const rawPalmLandmarks = tf.slice(predictions, [index, 5], [1, 14]);\n const palmLandmarks = tf.tidy(() => this.normalizeLandmarks(rawPalmLandmarks, index).reshape([-1, 2]));\n rawPalmLandmarks.dispose();\n hands.push({ box: matchingBox, palmLandmarks, confidence: scores[index] });\n }\n }\n predictions.dispose();\n boxes.dispose();\n return hands;\n }\n\n async estimateHandBounds(input, config) {\n const inputHeight = input.shape[1];\n const inputWidth = input.shape[2];\n const image = tf.tidy(() => input.resizeBilinear([config.hand.inputSize, config.hand.inputSize]).div(127.5).sub(1));\n const predictions = await this.getBoxes(image, config);\n image.dispose();\n const hands = [];\n if (!predictions || predictions.length === 0) return hands;\n for (const prediction of predictions) {\n const boxes = prediction.box.dataSync();\n const startPoint = boxes.slice(0, 2);\n const endPoint = boxes.slice(2, 4);\n const palmLandmarks = prediction.palmLandmarks.arraySync();\n prediction.box.dispose();\n prediction.palmLandmarks.dispose();\n hands.push(box.scaleBoxCoordinates({ startPoint, endPoint, palmLandmarks, confidence: prediction.confidence }, [inputWidth / config.hand.inputSize, inputHeight / config.hand.inputSize]));\n }\n return hands;\n }\n}\nexports.HandDetector = HandDetector;\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\nimport * as util from './util';\n// eslint-disable-next-line no-unused-vars\nimport { log } from '../log.js';\n\n// const PALM_BOX_SHIFT_VECTOR = [0, -0.4];\nconst PALM_BOX_ENLARGE_FACTOR = 5; // default 3\n// const HAND_BOX_SHIFT_VECTOR = [0, -0.1]; // move detected hand box by x,y to ease landmark detection\nconst HAND_BOX_ENLARGE_FACTOR = 1.65; // default 1.65\nconst PALM_LANDMARK_IDS = [0, 5, 9, 13, 17, 1, 2];\nconst PALM_LANDMARKS_INDEX_OF_PALM_BASE = 0;\nconst PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE = 2;\n\nclass HandPipeline {\n constructor(handDetector, landmarkDetector, inputSize) {\n this.handDetector = handDetector;\n this.landmarkDetector = landmarkDetector;\n this.inputSize = inputSize;\n this.storedBoxes = [];\n this.skipped = 0;\n this.detectedHands = 0;\n }\n\n getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n // return box.enlargeBox(box.squarifyBox(box.shiftBox(boxAroundPalm, PALM_BOX_SHIFT_VECTOR)), PALM_BOX_ENLARGE_FACTOR);\n return box.enlargeBox(box.squarifyBox(boxAroundPalm), PALM_BOX_ENLARGE_FACTOR);\n }\n\n getBoxForHandLandmarks(landmarks) {\n const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n // const boxAroundHand = box.enlargeBox(box.squarifyBox(box.shiftBox(boundingBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n const boxAroundHand = box.enlargeBox(box.squarifyBox(boundingBox), HAND_BOX_ENLARGE_FACTOR);\n boxAroundHand.palmLandmarks = [];\n for (let i = 0; i < PALM_LANDMARK_IDS.length; i++) {\n boxAroundHand.palmLandmarks.push(landmarks[PALM_LANDMARK_IDS[i]].slice(0, 2));\n }\n return boxAroundHand;\n }\n\n transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n const boxSize = box.getBoxSize(box2);\n const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n const coordsScaled = rawCoords.map((coord) => [\n scaleFactor[0] * (coord[0] - this.inputSize / 2),\n scaleFactor[1] * (coord[1] - this.inputSize / 2),\n scaleFactor[2] * coord[2],\n ]);\n const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n const coordsRotated = coordsScaled.map((coord) => {\n const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n return [...rotated, coord[2]];\n });\n const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n const boxCenter = [...box.getBoxCenter(box2), 1];\n const originalBoxCenter = [\n util.dot(boxCenter, inverseRotationMatrix[0]),\n util.dot(boxCenter, inverseRotationMatrix[1]),\n ];\n return coordsRotated.map((coord) => [\n coord[0] + originalBoxCenter[0],\n coord[1] + originalBoxCenter[1],\n coord[2],\n ]);\n }\n\n async estimateHands(image, config) {\n let useFreshBox = false;\n\n // run new detector every skipFrames unless we only want box to start with\n let boxes;\n if ((this.skipped === 0) || (this.skipped > config.hand.skipFrames) || !config.hand.landmarks || !config.videoOptimized) {\n boxes = await this.handDetector.estimateHandBounds(image, config);\n this.skipped = 0;\n }\n if (config.videoOptimized) this.skipped++;\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxHands) || !config.hand.landmarks)) {\n this.detectedHands = 0;\n this.storedBoxes = [...boxes];\n // for (const possible of boxes) this.storedBoxes.push(possible);\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\n const hands = [];\n // log('hand', `skipped: ${this.skipped} max: ${config.hand.maxHands} detected: ${this.detectedHands} stored: ${this.storedBoxes.length} new: ${boxes?.length}`);\n\n // go through working set of boxes\n for (let i = 0; i < this.storedBoxes.length; i++) {\n const currentBox = this.storedBoxes[i];\n if (!currentBox) continue;\n if (config.hand.landmarks) {\n const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_PALM_BASE], currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE]) : 0;\n const palmCenter = box.getBoxCenter(currentBox);\n const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n const rotatedImage = config.hand.rotation ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n const croppedInput = box.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n const handImage = croppedInput.div(255);\n croppedInput.dispose();\n rotatedImage.dispose();\n const [confidenceT, keypoints] = await this.landmarkDetector.predict(handImage);\n handImage.dispose();\n const confidence = confidenceT.dataSync()[0];\n confidenceT.dispose();\n if (confidence >= config.hand.minConfidence) {\n const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n const rawCoords = keypointsReshaped.arraySync();\n keypoints.dispose();\n keypointsReshaped.dispose();\n const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n this.storedBoxes[i] = nextBoundingBox;\n const result = {\n landmarks: coords,\n confidence,\n box: {\n topLeft: nextBoundingBox.startPoint,\n bottomRight: nextBoundingBox.endPoint,\n },\n };\n hands.push(result);\n } else {\n this.storedBoxes[i] = null;\n }\n keypoints.dispose();\n } else {\n // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n const enlarged = box.enlargeBox(box.squarifyBox(currentBox), HAND_BOX_ENLARGE_FACTOR);\n const result = {\n confidence: currentBox.confidence,\n box: {\n topLeft: enlarged.startPoint,\n bottomRight: enlarged.endPoint,\n },\n };\n hands.push(result);\n }\n }\n this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n this.detectedHands = hands.length;\n return hands;\n }\n\n // eslint-disable-next-line class-methods-use-this\n calculateLandmarksBoundingBox(landmarks) {\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint };\n }\n}\n\nexports.HandPipeline = HandPipeline;\n", "exports.anchors = [\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.796875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.796875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.828125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.828125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.859375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.859375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.890625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.890625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.921875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.921875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.953125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.953125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.984375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.984375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n 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0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n];\n", "// https://storage.googleapis.com/tfjs-models/demos/handpose/index.html\n\nimport { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as handdetector from './handdetector';\nimport * as handpipeline from './handpipeline';\nimport * as anchors from './anchors';\n\nconst MESH_ANNOTATIONS = {\n thumb: [1, 2, 3, 4],\n indexFinger: [5, 6, 7, 8],\n middleFinger: [9, 10, 11, 12],\n ringFinger: [13, 14, 15, 16],\n pinky: [17, 18, 19, 20],\n palmBase: [0],\n};\n\nclass HandPose {\n constructor(handPipeline) {\n this.handPipeline = handPipeline;\n }\n\n static getAnnotations() {\n return MESH_ANNOTATIONS;\n }\n\n async estimateHands(input, config) {\n const predictions = await this.handPipeline.estimateHands(input, config);\n if (!predictions) return [];\n const hands = [];\n for (const prediction of predictions) {\n const annotations = {};\n if (prediction.landmarks) {\n for (const key of Object.keys(MESH_ANNOTATIONS)) {\n annotations[key] = MESH_ANNOTATIONS[key].map((index) => prediction.landmarks[index]);\n }\n }\n const box = prediction.box ? [\n Math.max(0, prediction.box.topLeft[0]),\n Math.max(0, prediction.box.topLeft[1]),\n Math.min(input.shape[2], prediction.box.bottomRight[0]) - prediction.box.topLeft[0],\n Math.min(input.shape[1], prediction.box.bottomRight[1]) - prediction.box.topLeft[1],\n ] : 0;\n hands.push({\n confidence: prediction.confidence,\n box,\n landmarks: prediction.landmarks,\n annotations,\n });\n }\n return hands;\n }\n}\nexports.HandPose = HandPose;\n\nasync function load(config) {\n const [handDetectorModel, handPoseModel] = await Promise.all([\n config.hand.enabled ? tf.loadGraphModel(config.hand.detector.modelPath, { fromTFHub: config.hand.detector.modelPath.includes('tfhub.dev') }) : null,\n config.hand.landmarks ? tf.loadGraphModel(config.hand.skeleton.modelPath, { fromTFHub: config.hand.skeleton.modelPath.includes('tfhub.dev') }) : null,\n ]);\n // @ts-ignore\n const handDetector = new handdetector.HandDetector(handDetectorModel, config.hand.inputSize, anchors.anchors);\n // @ts-ignore\n const handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel, config.hand.inputSize);\n const handPose = new HandPose(handPipeline);\n if (config.hand.enabled) log(`load model: ${config.hand.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n if (config.hand.landmarks) log(`load model: ${config.hand.skeleton.modelPath.match(/\\/(.*)\\./)[1]}`);\n return handPose;\n}\nexports.load = load;\n", "exports.body = (res) => {\n if (!res) return [];\n const gestures = [];\n for (let i = 0; i < res.length; i++) {\n // raising hands\n const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n if (nose && leftWrist && rightWrist && (leftWrist.position.y < nose.position.y) && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'i give up' });\n else if (nose && leftWrist && (leftWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise left hand' });\n else if (nose && rightWrist && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise right hand' });\n\n // leaning\n const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n if (leftShoulder && rightShoulder) gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position.y > rightShoulder.position.y) ? 'left' : 'right'}` });\n }\n return gestures;\n};\n\nexports.face = (res) => {\n if (!res) return [];\n const gestures = [];\n for (let i = 0; i < res.length; i++) {\n if (res[i].mesh && res[i].mesh.length > 0) {\n const eyeFacing = res[i].mesh[35][2] - res[i].mesh[263][2];\n if (Math.abs(eyeFacing) < 10) gestures.push({ face: i, gesture: 'facing camera' });\n else gestures.push({ face: i, gesture: `facing ${eyeFacing < 0 ? 'right' : 'left'}` });\n const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n const chinDepth = res[i].mesh[152][2];\n if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n }\n }\n return gestures;\n};\n\nexports.iris = (res) => {\n if (!res) return [];\n const gestures = [];\n for (let i = 0; i < res.length; i++) {\n if (!res[i].annotations || !res[i].annotations.leftEyeIris || !res[i].annotations.rightEyeIris) continue;\n const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n if (difference < 0.25) gestures.push({ iris: i, gesture: 'looking at camera' });\n }\n return gestures;\n};\n\nexports.hand = (res) => {\n if (!res) return [];\n const gestures = [];\n for (let i = 0; i < res.length; i++) {\n const fingers = [];\n for (const [finger, pos] of Object.entries(res[i]['annotations'])) {\n if (finger !== 'palmBase') fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n }\n if (fingers && fingers.length > 0) {\n const closest = fingers.reduce((best, a) => (best.position[2] < a.position[2] ? best : a));\n const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n gestures.push({ hand: i, gesture: `${closest.name} forward ${highest.name} up` });\n }\n }\n return gestures;\n};\n", "/* eslint-disable no-use-before-define */\n/*\nWebGLImageFilter - MIT Licensed\n2013, Dominic Szablewski - phoboslab.org\n\n*/\n\nconst WebGLProgram = function (gl, vertexSource, fragmentSource) {\n const _collect = function (source, prefix, collection) {\n const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n source.replace(r, (match, name) => {\n collection[name] = 0;\n return match;\n });\n };\n\n const _compile = function (source, type) {\n const shader = gl.createShader(type);\n gl.shaderSource(shader, source);\n gl.compileShader(shader);\n\n if (!gl.getShaderParameter(shader, gl.COMPILE_STATUS)) {\n // @ts-ignore\n throw new Error('Filter: GL compile failed', gl.getShaderInfoLog(shader));\n }\n return shader;\n };\n\n this.uniform = {};\n this.attribute = {};\n\n const _vsh = _compile(vertexSource, gl.VERTEX_SHADER);\n const _fsh = _compile(fragmentSource, gl.FRAGMENT_SHADER);\n\n this.id = gl.createProgram();\n gl.attachShader(this.id, _vsh);\n gl.attachShader(this.id, _fsh);\n gl.linkProgram(this.id);\n\n if (!gl.getProgramParameter(this.id, gl.LINK_STATUS)) {\n // @ts-ignore\n throw new Error('Filter: GL link failed', gl.getProgramInfoLog(this.id));\n }\n\n gl.useProgram(this.id);\n\n // Collect attributes\n _collect(vertexSource, 'attribute', this.attribute);\n for (const a in this.attribute) {\n this.attribute[a] = gl.getAttribLocation(this.id, a);\n }\n\n // Collect uniforms\n _collect(vertexSource, 'uniform', this.uniform);\n _collect(fragmentSource, 'uniform', this.uniform);\n for (const u in this.uniform) {\n this.uniform[u] = gl.getUniformLocation(this.id, u);\n }\n};\n\nconst WebGLImageFilter = function (params) {\n if (!params) params = { };\n let _drawCount = 0;\n let _sourceTexture = null;\n let _lastInChain = false;\n let _currentFramebufferIndex = -1;\n let _tempFramebuffers = [null, null];\n let _filterChain = [];\n let _width = -1;\n let _height = -1;\n let _vertexBuffer = null;\n let _currentProgram = null;\n const _canvas = params.canvas || document.createElement('canvas');\n\n // key is the shader program source, value is the compiled program\n const _shaderProgramCache = { };\n\n const gl = _canvas.getContext('webgl');\n if (!gl) throw new Error('Filter: getContext() failed');\n\n this.addFilter = function (name) {\n // eslint-disable-next-line prefer-rest-params\n const args = Array.prototype.slice.call(arguments, 1);\n const filter = _filter[name];\n\n _filterChain.push({ func: filter, args });\n };\n\n this.reset = function () {\n _filterChain = [];\n };\n\n this.apply = function (image) {\n _resize(image.width, image.height);\n _drawCount = 0;\n\n // Create the texture for the input image if we haven't yet\n if (!_sourceTexture) _sourceTexture = gl.createTexture();\n gl.bindTexture(gl.TEXTURE_2D, _sourceTexture);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n\n // No filters? Just draw\n if (_filterChain.length === 0) {\n // const program = _compileShader(SHADER.FRAGMENT_IDENTITY);\n _draw();\n return _canvas;\n }\n\n for (let i = 0; i < _filterChain.length; i++) {\n _lastInChain = (i === _filterChain.length - 1);\n const f = _filterChain[i];\n f.func.apply(this, f.args || []);\n }\n\n return _canvas;\n };\n\n const _resize = function (width, height) {\n // Same width/height? Nothing to do here\n if (width === _width && height === _height) { return; }\n\n _canvas.width = width;\n _width = width;\n _canvas.height = height;\n _height = height;\n\n // Create the context if we don't have it yet\n if (!_vertexBuffer) {\n // Create the vertex buffer for the two triangles [x, y, u, v] * 6\n const vertices = new Float32Array([\n -1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0,\n -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0,\n ]);\n // eslint-disable-next-line no-unused-expressions\n (_vertexBuffer = gl.createBuffer(), gl.bindBuffer(gl.ARRAY_BUFFER, _vertexBuffer));\n gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);\n\n // Note sure if this is a good idea; at least it makes texture loading\n // in Ejecta instant.\n gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n }\n\n gl.viewport(0, 0, _width, _height);\n\n // Delete old temp framebuffers\n _tempFramebuffers = [null, null];\n };\n\n const _getTempFramebuffer = function (index) {\n // @ts-ignore\n _tempFramebuffers[index] = _tempFramebuffers[index] || _createFramebufferTexture(_width, _height);\n return _tempFramebuffers[index];\n };\n\n const _createFramebufferTexture = function (width, height) {\n const fbo = gl.createFramebuffer();\n gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n\n const renderbuffer = gl.createRenderbuffer();\n gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n\n const texture = gl.createTexture();\n gl.bindTexture(gl.TEXTURE_2D, texture);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n\n gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n\n gl.bindTexture(gl.TEXTURE_2D, null);\n gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n\n return { fbo, texture };\n };\n\n const _draw = function (flags) {\n let source = null;\n let target = null;\n let flipY = false;\n\n // Set up the source\n if (_drawCount === 0) {\n // First draw call - use the source texture\n source = _sourceTexture;\n } else {\n // All following draw calls use the temp buffer last drawn to\n // @ts-ignore\n source = _getTempFramebuffer(_currentFramebufferIndex)?.texture;\n }\n _drawCount++;\n\n // Set up the target\n if (_lastInChain && !(flags & DRAW.INTERMEDIATE)) {\n // Last filter in our chain - draw directly to the WebGL Canvas. We may\n // also have to flip the image vertically now\n target = null;\n flipY = _drawCount % 2 === 0;\n } else {\n // Intermediate draw call - get a temp buffer to draw to\n _currentFramebufferIndex = (_currentFramebufferIndex + 1) % 2;\n // @ts-ignore\n target = _getTempFramebuffer(_currentFramebufferIndex)?.fbo;\n }\n\n // Bind the source and target and draw the two triangles\n gl.bindTexture(gl.TEXTURE_2D, source);\n gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n\n gl.uniform1f(_currentProgram.uniform.flipY, (flipY ? -1 : 1));\n gl.drawArrays(gl.TRIANGLES, 0, 6);\n };\n\n const _compileShader = function (fragmentSource) {\n if (_shaderProgramCache[fragmentSource]) {\n _currentProgram = _shaderProgramCache[fragmentSource];\n gl.useProgram(_currentProgram.id);\n return _currentProgram;\n }\n\n // Compile shaders\n _currentProgram = new WebGLProgram(gl, SHADER.VERTEX_IDENTITY, fragmentSource);\n\n const floatSize = Float32Array.BYTES_PER_ELEMENT;\n const vertSize = 4 * floatSize;\n gl.enableVertexAttribArray(_currentProgram.attribute.pos);\n gl.vertexAttribPointer(_currentProgram.attribute.pos, 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n gl.enableVertexAttribArray(_currentProgram.attribute.uv);\n gl.vertexAttribPointer(_currentProgram.attribute.uv, 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n\n _shaderProgramCache[fragmentSource] = _currentProgram;\n return _currentProgram;\n };\n\n let DRAW = { INTERMEDIATE: 1 };\n\n let SHADER = {};\n SHADER.VERTEX_IDENTITY = [\n 'precision highp float;',\n 'attribute vec2 pos;',\n 'attribute vec2 uv;',\n 'varying vec2 vUv;',\n 'uniform float flipY;',\n\n 'void main(void) {',\n 'vUv = uv;',\n 'gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);',\n '}',\n ].join('\\n');\n\n SHADER.FRAGMENT_IDENTITY = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n\n 'void main(void) {',\n 'gl_FragColor = texture2D(texture, vUv);',\n '}',\n ].join('\\n');\n\n let _filter = {};\n\n // -------------------------------------------------------------------------\n // Color Matrix Filter\n\n _filter.colorMatrix = function (matrix) {\n // Create a Float32 Array and normalize the offset component to 0-1\n const m = new Float32Array(matrix);\n m[4] /= 255;\n m[9] /= 255;\n m[14] /= 255;\n m[19] /= 255;\n\n // Can we ignore the alpha value? Makes things a bit faster.\n const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0)\n ? _filter.colorMatrix.SHADER.WITHOUT_ALPHA\n : _filter.colorMatrix.SHADER.WITH_ALPHA;\n\n const program = _compileShader(shader);\n gl.uniform1fv(program.uniform.m, m);\n _draw();\n };\n\n _filter.colorMatrix.SHADER = {};\n _filter.colorMatrix.SHADER.WITH_ALPHA = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform float m[20];',\n\n 'void main(void) {',\n 'vec4 c = texture2D(texture, vUv);',\n 'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];',\n 'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];',\n 'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];',\n 'gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];',\n '}',\n ].join('\\n');\n _filter.colorMatrix.SHADER.WITHOUT_ALPHA = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform float m[20];',\n\n 'void main(void) {',\n 'vec4 c = texture2D(texture, vUv);',\n 'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];',\n 'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];',\n 'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];',\n 'gl_FragColor.a = c.a;',\n '}',\n ].join('\\n');\n\n _filter.brightness = function (brightness) {\n const b = (brightness || 0) + 1;\n _filter.colorMatrix([\n b, 0, 0, 0, 0,\n 0, b, 0, 0, 0,\n 0, 0, b, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.saturation = function (amount) {\n const x = (amount || 0) * 2 / 3 + 1;\n const y = ((x - 1) * -0.5);\n _filter.colorMatrix([\n x, y, y, 0, 0,\n y, x, y, 0, 0,\n y, y, x, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.desaturate = function () {\n _filter.saturation(-1);\n };\n\n _filter.contrast = function (amount) {\n const v = (amount || 0) + 1;\n const o = -128 * (v - 1);\n\n _filter.colorMatrix([\n v, 0, 0, 0, o,\n 0, v, 0, 0, o,\n 0, 0, v, 0, o,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.negative = function () {\n _filter.contrast(-2);\n };\n\n _filter.hue = function (rotation) {\n rotation = (rotation || 0) / 180 * Math.PI;\n const cos = Math.cos(rotation);\n const sin = Math.sin(rotation);\n const lumR = 0.213;\n const lumG = 0.715;\n const lumB = 0.072;\n\n _filter.colorMatrix([\n lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.desaturateLuminance = function () {\n _filter.colorMatrix([\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.sepia = function () {\n _filter.colorMatrix([\n 0.393, 0.7689999, 0.18899999, 0, 0,\n 0.349, 0.6859999, 0.16799999, 0, 0,\n 0.272, 0.5339999, 0.13099999, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.brownie = function () {\n _filter.colorMatrix([\n 0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n 0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.vintagePinhole = function () {\n _filter.colorMatrix([\n 0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n 0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n 0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.kodachrome = function () {\n _filter.colorMatrix([\n 1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.technicolor = function () {\n _filter.colorMatrix([\n 1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.polaroid = function () {\n _filter.colorMatrix([\n 1.438, -0.062, -0.062, 0, 0,\n -0.122, 1.378, -0.122, 0, 0,\n -0.016, -0.016, 1.483, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.shiftToBGR = function () {\n _filter.colorMatrix([\n 0, 0, 1, 0, 0,\n 0, 1, 0, 0, 0,\n 1, 0, 0, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n // -------------------------------------------------------------------------\n // Convolution Filter\n\n _filter.convolution = function (matrix) {\n const m = new Float32Array(matrix);\n const pixelSizeX = 1 / _width;\n const pixelSizeY = 1 / _height;\n\n const program = _compileShader(_filter.convolution.SHADER);\n gl.uniform1fv(program.uniform.m, m);\n gl.uniform2f(program.uniform.px, pixelSizeX, pixelSizeY);\n _draw();\n };\n\n _filter.convolution.SHADER = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform vec2 px;',\n 'uniform float m[9];',\n\n 'void main(void) {',\n 'vec4 c11 = texture2D(texture, vUv - px);', // top left\n 'vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y));', // top center\n 'vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y));', // top right\n\n 'vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) );', // mid left\n 'vec4 c22 = texture2D(texture, vUv);', // mid center\n 'vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) );', // mid right\n\n 'vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) );', // bottom left\n 'vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) );', // bottom center\n 'vec4 c33 = texture2D(texture, vUv + px );', // bottom right\n\n 'gl_FragColor = ',\n 'c11 * m[0] + c12 * m[1] + c22 * m[2] +',\n 'c21 * m[3] + c22 * m[4] + c23 * m[5] +',\n 'c31 * m[6] + c32 * m[7] + c33 * m[8];',\n 'gl_FragColor.a = c22.a;',\n '}',\n ].join('\\n');\n\n _filter.detectEdges = function () {\n _filter.convolution.call(this, [\n 0, 1, 0,\n 1, -4, 1,\n 0, 1, 0,\n ]);\n };\n\n _filter.sobelX = function () {\n _filter.convolution.call(this, [\n -1, 0, 1,\n -2, 0, 2,\n -1, 0, 1,\n ]);\n };\n\n _filter.sobelY = function () {\n _filter.convolution.call(this, [\n -1, -2, -1,\n 0, 0, 0,\n 1, 2, 1,\n ]);\n };\n\n _filter.sharpen = function (amount) {\n const a = amount || 1;\n _filter.convolution.call(this, [\n 0, -1 * a, 0,\n -1 * a, 1 + 4 * a, -1 * a,\n 0, -1 * a, 0,\n ]);\n };\n\n _filter.emboss = function (size) {\n const s = size || 1;\n _filter.convolution.call(this, [\n -2 * s, -1 * s, 0,\n -1 * s, 1, 1 * s,\n 0, 1 * s, 2 * s,\n ]);\n };\n\n // -------------------------------------------------------------------------\n // Blur Filter\n\n _filter.blur = function (size) {\n const blurSizeX = (size / 7) / _width;\n const blurSizeY = (size / 7) / _height;\n\n const program = _compileShader(_filter.blur.SHADER);\n\n // Vertical\n gl.uniform2f(program.uniform.px, 0, blurSizeY);\n _draw(DRAW.INTERMEDIATE);\n\n // Horizontal\n gl.uniform2f(program.uniform.px, blurSizeX, 0);\n _draw();\n };\n\n _filter.blur.SHADER = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform vec2 px;',\n\n 'void main(void) {',\n 'gl_FragColor = vec4(0.0);',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;',\n 'gl_FragColor += texture2D(texture, vUv )*0.159576912161;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x, 1.0*px.y))*0.147308056121;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x, 2.0*px.y))*0.115876621105;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x, 3.0*px.y))*0.0776744219933;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x, 4.0*px.y))*0.0443683338718;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x, 5.0*px.y))*0.0215963866053;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265;',\n '}',\n ].join('\\n');\n\n // -------------------------------------------------------------------------\n // Pixelate Filter\n\n _filter.pixelate = function (size) {\n const blurSizeX = (size) / _width;\n const blurSizeY = (size) / _height;\n\n const program = _compileShader(_filter.pixelate.SHADER);\n\n // Horizontal\n gl.uniform2f(program.uniform.size, blurSizeX, blurSizeY);\n _draw();\n };\n\n _filter.pixelate.SHADER = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform vec2 size;',\n 'uniform sampler2D texture;',\n\n 'vec2 pixelate(vec2 coord, vec2 size) {',\n 'return floor( coord / size ) * size;',\n '}',\n\n 'void main(void) {',\n 'gl_FragColor = vec4(0.0);',\n 'vec2 coord = pixelate(vUv, size);',\n 'gl_FragColor += texture2D(texture, coord);',\n '}',\n ].join('\\n');\n};\n\nexports.Canvas = WebGLImageFilter;\n", "import { log } from './log.js';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as fxImage from './imagefx.js';\n\n// internal temp canvases\nlet inCanvas = null;\nlet outCanvas = null;\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nfunction process(input, config) {\n let tensor;\n if (input instanceof tf.Tensor) {\n tensor = tf.clone(input);\n } else {\n const originalWidth = input.naturalWidth || input.videoWidth || input.width || (input.shape && (input.shape[1] > 0));\n const originalHeight = input.naturalHeight || input.videoHeight || input.height || (input.shape && (input.shape[2] > 0));\n let targetWidth = originalWidth;\n let targetHeight = originalHeight;\n if (config.filter.width > 0) targetWidth = config.filter.width;\n else if (config.filter.height > 0) targetWidth = originalWidth * (config.filter.height / originalHeight);\n if (config.filter.height > 0) targetHeight = config.filter.height;\n else if (config.filter.width > 0) targetHeight = originalHeight * (config.filter.width / originalWidth);\n if (!targetWidth || !targetHeight) {\n log('Human: invalid input', input);\n return null;\n }\n if (!inCanvas || (inCanvas.width !== targetWidth) || (inCanvas.height !== targetHeight)) {\n inCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n if (inCanvas.width !== targetWidth) inCanvas.width = targetWidth;\n if (inCanvas.height !== targetHeight) inCanvas.height = targetHeight;\n }\n const ctx = inCanvas.getContext('2d');\n if (input instanceof ImageData) ctx.putImageData(input, 0, 0);\n else ctx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);\n if (config.filter.enabled) {\n if (!this.fx || !outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas.height !== outCanvas.height)) {\n outCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(inCanvas.width, inCanvas.height) : document.createElement('canvas');\n if (outCanvas.width !== inCanvas.width) outCanvas.width = inCanvas.width;\n if (outCanvas.height !== inCanvas.height) outCanvas.height = inCanvas.height;\n this.fx = tf.ENV.flags.IS_BROWSER ? new fxImage.Canvas({ canvas: outCanvas }) : null; // && (typeof document !== 'undefined')\n }\n if (!this.fx) return inCanvas;\n this.fx.reset();\n this.fx.addFilter('brightness', config.filter.brightness); // must have at least one filter enabled\n if (config.filter.contrast !== 0) this.fx.addFilter('contrast', config.filter.contrast);\n if (config.filter.sharpness !== 0) this.fx.addFilter('sharpen', config.filter.sharpness);\n if (config.filter.blur !== 0) this.fx.addFilter('blur', config.filter.blur);\n if (config.filter.saturation !== 0) this.fx.addFilter('saturation', config.filter.saturation);\n if (config.filter.hue !== 0) this.fx.addFilter('hue', config.filter.hue);\n if (config.filter.negative) this.fx.addFilter('negative');\n if (config.filter.sepia) this.fx.addFilter('sepia');\n if (config.filter.vintage) this.fx.addFilter('brownie');\n if (config.filter.sepia) this.fx.addFilter('sepia');\n if (config.filter.kodachrome) this.fx.addFilter('kodachrome');\n if (config.filter.technicolor) this.fx.addFilter('technicolor');\n if (config.filter.polaroid) this.fx.addFilter('polaroid');\n if (config.filter.pixelate !== 0) this.fx.addFilter('pixelate', config.filter.pixelate);\n this.fx.apply(inCanvas);\n // read pixel data\n /*\n const gl = outCanvas.getContext('webgl');\n if (gl) {\n const glBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 4);\n const pixBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 3);\n gl.readPixels(0, 0, outCanvas.width, outCanvas.height, gl.RGBA, gl.UNSIGNED_BYTE, glBuffer);\n // gl returns rbga while we only need rgb, so discarding alpha channel\n // gl returns starting point as lower left, so need to invert vertical\n let i = 0;\n for (let y = outCanvas.height - 1; y >= 0; y--) {\n for (let x = 0; x < outCanvas.width; x++) {\n const index = (x + y * outCanvas.width) * 4;\n pixBuffer[i++] = glBuffer[index + 0];\n pixBuffer[i++] = glBuffer[index + 1];\n pixBuffer[i++] = glBuffer[index + 2];\n }\n }\n outCanvas.data = pixBuffer;\n }\n */\n } else {\n outCanvas = inCanvas;\n }\n let pixels;\n if (outCanvas.data) {\n const shape = [outCanvas.height, outCanvas.width, 3];\n pixels = tf.tensor3d(outCanvas.data, shape, 'int32');\n } else if ((config.backend === 'webgl') || (outCanvas instanceof ImageData)) {\n // tf kernel-optimized method to get imagedata, also if input is imagedata, just use it\n pixels = tf.browser.fromPixels(outCanvas);\n } else {\n // cpu and wasm kernel does not implement efficient fromPixels method nor we can use canvas as-is, so we do a silly one more canvas\n const tempCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n tempCanvas.width = targetWidth;\n tempCanvas.height = targetHeight;\n const tempCtx = tempCanvas.getContext('2d');\n tempCtx?.drawImage(outCanvas, 0, 0);\n const data = tempCtx?.getImageData(0, 0, targetWidth, targetHeight);\n pixels = tf.browser.fromPixels(data);\n }\n const casted = pixels.toFloat();\n tensor = casted.expandDims(0);\n pixels.dispose();\n casted.dispose();\n }\n return { tensor, canvas: config.filter.return ? outCanvas : null };\n}\n\nexports.process = process;\n", "import { log } from './log.js';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as backend from './tfjs/backend.js';\nimport * as facemesh from './blazeface/facemesh.js';\nimport * as age from './age/age.js';\nimport * as gender from './gender/gender.js';\nimport * as emotion from './emotion/emotion.js';\nimport * as embedding from './embedding/embedding.js';\nimport * as posenet from './posenet/posenet.js';\nimport * as blazepose from './blazepose/blazepose.js';\nimport * as handpose from './handpose/handpose.js';\nimport * as gesture from './gesture/gesture.js';\nimport * as image from './image.js';\nimport * as profile from './profile.js';\nimport * as config from '../config.js';\nimport * as sample from './sample.js';\nimport * as app from '../package.json';\n\n// helper function: gets elapsed time on both browser and nodejs\nconst now = () => {\n if (typeof performance !== 'undefined') return performance.now();\n return parseInt(Number(process.hrtime.bigint()) / 1000 / 1000);\n};\n\n// helper function: perform deep merge of multiple objects so it allows full inheriance with overrides\nfunction mergeDeep(...objects) {\n const isObject = (obj) => obj && typeof obj === 'object';\n return objects.reduce((prev, obj) => {\n Object.keys(obj || {}).forEach((key) => {\n const pVal = prev[key];\n const oVal = obj[key];\n if (Array.isArray(pVal) && Array.isArray(oVal)) {\n prev[key] = pVal.concat(...oVal);\n } else if (isObject(pVal) && isObject(oVal)) {\n prev[key] = mergeDeep(pVal, oVal);\n } else {\n prev[key] = oVal;\n }\n });\n return prev;\n }, {});\n}\n\nclass Human {\n constructor(userConfig = {}) {\n this.tf = tf;\n this.version = app.version;\n this.config = mergeDeep(config.default, userConfig);\n this.fx = null;\n this.state = 'idle';\n this.numTensors = 0;\n this.analyzeMemoryLeaks = false;\n this.checkSanity = false;\n this.firstRun = true;\n this.perf = {};\n // object that contains all initialized models\n this.models = {\n facemesh: null,\n posenet: null,\n handpose: null,\n iris: null,\n age: null,\n gender: null,\n emotion: null,\n };\n // export raw access to underlying models\n this.facemesh = facemesh;\n this.age = age;\n this.gender = gender;\n this.emotion = emotion;\n this.body = posenet;\n this.hand = handpose;\n }\n\n profile() {\n if (this.config.profile) return profile.data;\n return {};\n }\n\n // helper function: measure tensor leak\n analyze(...msg) {\n if (!this.analyzeMemoryLeaks) return;\n const current = tf.engine().state.numTensors;\n const previous = this.numTensors;\n this.numTensors = current;\n const leaked = current - previous;\n if (leaked !== 0) log(...msg, leaked);\n }\n\n // quick sanity check on inputs\n sanity(input) {\n if (!this.checkSanity) return null;\n if (!input) return 'input is not defined';\n if (tf.ENV.flags.IS_NODE && !(input instanceof tf.Tensor)) {\n return 'input must be a tensor';\n }\n try {\n tf.getBackend();\n } catch {\n return 'backend not loaded';\n }\n return null;\n }\n\n simmilarity(embedding1, embedding2) {\n if (this.config.face.embedding.enabled) return embedding.simmilarity(embedding1, embedding2);\n return 0;\n }\n\n // preload models, not explicitly required as it's done automatically on first use\n async load(userConfig) {\n this.state = 'load';\n const timeStamp = now();\n if (userConfig) this.config = mergeDeep(this.config, userConfig);\n\n if (this.firstRun) {\n log(`version: ${this.version} TensorFlow/JS version: ${tf.version_core}`);\n await this.checkBackend(true);\n if (tf.ENV.flags.IS_BROWSER) {\n log('configuration:', this.config);\n log('tf flags:', tf.ENV.flags);\n }\n this.firstRun = false;\n }\n\n if (this.config.async) {\n [\n this.models.facemesh,\n this.models.age,\n this.models.gender,\n this.models.emotion,\n this.models.embedding,\n this.models.posenet,\n this.models.handpose,\n this.models.blazepose,\n ] = await Promise.all([\n this.models.facemesh || (this.config.face.enabled ? facemesh.load(this.config) : null),\n this.models.age || ((this.config.face.enabled && this.config.face.age.enabled) ? age.load(this.config) : null),\n this.models.gender || ((this.config.face.enabled && this.config.face.gender.enabled) ? gender.load(this.config) : null),\n this.models.emotion || ((this.config.face.enabled && this.config.face.emotion.enabled) ? emotion.load(this.config) : null),\n this.models.embedding || ((this.config.face.enabled && this.config.face.embedding.enabled) ? embedding.load(this.config) : null),\n this.models.posenet || (this.config.body.enabled ? posenet.load(this.config) : null),\n this.models.handpose || (this.config.hand.enabled ? handpose.load(this.config) : null),\n this.models.blazepose || (this.config.pose.enabled ? blazepose.load(this.config) : null),\n ]);\n } else {\n if (this.config.face.enabled && !this.models.facemesh) this.models.facemesh = await facemesh.load(this.config);\n if (this.config.face.enabled && this.config.face.age.enabled && !this.models.age) this.models.age = await age.load(this.config);\n if (this.config.face.enabled && this.config.face.gender.enabled && !this.models.gender) this.models.gender = await gender.load(this.config);\n if (this.config.face.enabled && this.config.face.emotion.enabled && !this.models.emotion) this.models.emotion = await emotion.load(this.config);\n if (this.config.face.enabled && this.config.face.embedding.enabled && !this.models.embedding) this.models.embedding = await embedding.load(this.config);\n if (this.config.body.enabled && !this.models.posenet) this.models.posenet = await posenet.load(this.config);\n if (this.config.hand.enabled && !this.models.handpose) this.models.handpose = await handpose.load(this.config);\n if (this.config.pose.enabled && !this.models.blazepose) this.models.blazepose = await blazepose.load(this.config);\n }\n const current = Math.trunc(now() - timeStamp);\n if (current > (this.perf.load || 0)) this.perf.load = current;\n }\n\n // check if backend needs initialization if it changed\n async checkBackend(force) {\n if (this.config.backend && (this.config.backend !== '') && force || (tf.getBackend() !== this.config.backend)) {\n const timeStamp = now();\n this.state = 'backend';\n /* force backend reload\n if (this.config.backend in tf.engine().registry) {\n const backendFactory = tf.findBackendFactory(this.config.backend);\n tf.removeBackend(this.config.backend);\n tf.registerBackend(this.config.backend, backendFactory);\n } else {\n log('Backend not registred:', this.config.backend);\n }\n */\n\n log('setting backend:', this.config.backend);\n\n if (this.config.backend === 'wasm') {\n log('settings wasm path:', this.config.wasmPath);\n tf.setWasmPaths(this.config.wasmPath);\n const simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n if (!simd) log('warning: wasm simd support is not enabled');\n }\n\n if (this.config.backend === 'humangl') {\n log('registering humangl backend');\n backend.register();\n }\n\n await tf.setBackend(this.config.backend);\n tf.enableProdMode();\n /* debug mode is really too mcuh\n tf.enableDebugMode();\n */\n if (tf.getBackend() === 'webgl') {\n if (this.config.deallocate) {\n log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', this.config.deallocate);\n tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', this.config.deallocate ? 0 : -1);\n }\n tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n const gl = await tf.backend().getGPGPUContext().gl;\n log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);\n }\n await tf.ready();\n this.perf.backend = Math.trunc(now() - timeStamp);\n }\n }\n\n async detectFace(input) {\n // run facemesh, includes blazeface and iris\n // eslint-disable-next-line no-async-promise-executor\n let timeStamp;\n let ageRes;\n let genderRes;\n let emotionRes;\n let embeddingRes;\n const faceRes = [];\n this.state = 'run:face';\n timeStamp = now();\n const faces = await this.models.facemesh?.estimateFaces(input, this.config);\n this.perf.face = Math.trunc(now() - timeStamp);\n for (const face of faces) {\n this.analyze('Get Face');\n\n // is something went wrong, skip the face\n if (!face.image || face.image.isDisposedInternal) {\n log('Face object is disposed:', face.image);\n continue;\n }\n\n // run age, inherits face from blazeface\n this.analyze('Start Age:');\n if (this.config.async) {\n ageRes = this.config.face.age.enabled ? age.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:age';\n timeStamp = now();\n ageRes = this.config.face.age.enabled ? await age.predict(face.image, this.config) : {};\n this.perf.age = Math.trunc(now() - timeStamp);\n }\n\n // run gender, inherits face from blazeface\n this.analyze('Start Gender:');\n if (this.config.async) {\n genderRes = this.config.face.gender.enabled ? gender.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:gender';\n timeStamp = now();\n genderRes = this.config.face.gender.enabled ? await gender.predict(face.image, this.config) : {};\n this.perf.gender = Math.trunc(now() - timeStamp);\n }\n\n // run emotion, inherits face from blazeface\n this.analyze('Start Emotion:');\n if (this.config.async) {\n emotionRes = this.config.face.emotion.enabled ? emotion.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:emotion';\n timeStamp = now();\n emotionRes = this.config.face.emotion.enabled ? await emotion.predict(face.image, this.config) : {};\n this.perf.emotion = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Emotion:');\n\n // run emotion, inherits face from blazeface\n this.analyze('Start Embedding:');\n if (this.config.async) {\n embeddingRes = this.config.face.embedding.enabled ? embedding.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:embedding';\n timeStamp = now();\n embeddingRes = this.config.face.embedding.enabled ? await embedding.predict(face.image, this.config) : {};\n this.perf.embedding = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Emotion:');\n\n // if async wait for results\n if (this.config.async) {\n [ageRes, genderRes, emotionRes, embeddingRes] = await Promise.all([ageRes, genderRes, emotionRes, embeddingRes]);\n }\n\n this.analyze('Finish Face:');\n // dont need face anymore\n face.image.dispose();\n\n // calculate iris distance\n // iris: array[ center, left, top, right, bottom]\n if (!this.config.face.iris.enabled) {\n delete face.annotations.leftEyeIris;\n delete face.annotations.rightEyeIris;\n }\n const irisSize = (face.annotations.leftEyeIris && face.annotations.rightEyeIris)\n /* average human iris size is 11.7mm */\n ? 11.7 * Math.max(Math.abs(face.annotations.leftEyeIris[3][0] - face.annotations.leftEyeIris[1][0]), Math.abs(face.annotations.rightEyeIris[4][1] - face.annotations.rightEyeIris[2][1]))\n : 0;\n\n // combine results\n faceRes.push({\n confidence: face.confidence,\n box: face.box,\n mesh: face.mesh,\n boxRaw: face.boxRaw,\n meshRaw: face.meshRaw,\n annotations: face.annotations,\n age: ageRes.age,\n gender: genderRes.gender,\n genderConfidence: genderRes.confidence,\n emotion: emotionRes,\n embedding: embeddingRes,\n iris: (irisSize !== 0) ? Math.trunc(irisSize) / 100 : 0,\n });\n this.analyze('End Face');\n }\n this.analyze('End FaceMesh:');\n if (this.config.async) {\n if (this.perf.face) delete this.perf.face;\n if (this.perf.age) delete this.perf.age;\n if (this.perf.gender) delete this.perf.gender;\n if (this.perf.emotion) delete this.perf.emotion;\n }\n return faceRes;\n }\n\n async image(input, userConfig = {}) {\n this.state = 'image';\n this.config = mergeDeep(this.config, userConfig);\n const process = image.process(input, this.config);\n process.tensor.dispose();\n return process.canvas;\n }\n\n // main detect function\n async detect(input, userConfig = {}) {\n // detection happens inside a promise\n return new Promise(async (resolve) => {\n this.state = 'config';\n let timeStamp;\n\n // update configuration\n this.config = mergeDeep(this.config, userConfig);\n\n // sanity checks\n this.state = 'check';\n const error = this.sanity(input);\n if (error) {\n log(error, input);\n resolve({ error });\n }\n\n let poseRes;\n let blazeposeRes;\n let handRes;\n let faceRes;\n\n const timeStart = now();\n\n // configure backend\n await this.checkBackend();\n\n // load models if enabled\n await this.load();\n\n if (this.config.scoped) tf.engine().startScope();\n this.analyze('Start Scope:');\n\n timeStamp = now();\n const process = image.process(input, this.config);\n if (!process || !process.tensor) {\n log('could not convert input to tensor');\n resolve({ error: 'could not convert input to tensor' });\n return;\n }\n this.perf.image = Math.trunc(now() - timeStamp);\n this.analyze('Get Image:');\n\n // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n if (this.config.async) {\n faceRes = this.config.face.enabled ? this.detectFace(process.tensor) : [];\n if (this.perf.face) delete this.perf.face;\n } else {\n this.state = 'run:face';\n timeStamp = now();\n faceRes = this.config.face.enabled ? await this.detectFace(process.tensor) : [];\n this.perf.face = Math.trunc(now() - timeStamp);\n }\n\n // run posenet\n this.analyze('Start Body:');\n if (this.config.async) {\n poseRes = this.config.body.enabled ? this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n if (this.perf.body) delete this.perf.body;\n } else {\n this.state = 'run:body';\n timeStamp = now();\n poseRes = this.config.body.enabled ? await this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n this.perf.body = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Body:');\n\n // run posenet\n this.analyze('Start Pose:');\n if (this.config.async) {\n blazeposeRes = this.config.pose.enabled ? blazepose.predict(process.tensor, this.config) : [];\n if (this.perf.pose) delete this.perf.pose;\n } else {\n this.state = 'run:pose';\n timeStamp = now();\n blazeposeRes = this.config.pose.enabled ? await blazepose.predict(process.tensor, this.config) : [];\n this.perf.pose = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Pose:');\n\n // run handpose\n this.analyze('Start Hand:');\n if (this.config.async) {\n handRes = this.config.hand.enabled ? this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n if (this.perf.hand) delete this.perf.hand;\n } else {\n this.state = 'run:hand';\n timeStamp = now();\n handRes = this.config.hand.enabled ? await this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n this.perf.hand = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Hand:');\n\n // if async wait for results\n if (this.config.async) {\n [faceRes, poseRes, blazeposeRes, handRes] = await Promise.all([faceRes, poseRes, blazeposeRes, handRes]);\n }\n process.tensor.dispose();\n\n if (this.config.scoped) tf.engine().endScope();\n this.analyze('End Scope:');\n\n let gestureRes = [];\n if (this.config.gesture.enabled) {\n timeStamp = now();\n gestureRes = [...gesture.face(faceRes), ...gesture.body(poseRes), ...gesture.hand(handRes), ...gesture.iris(faceRes)];\n if (!this.config.async) this.perf.gesture = Math.trunc(now() - timeStamp);\n else if (this.perf.gesture) delete this.perf.gesture;\n }\n\n this.perf.total = Math.trunc(now() - timeStart);\n this.state = 'idle';\n resolve({ face: faceRes, body: poseRes, hand: handRes, pose: blazeposeRes, gesture: gestureRes, performance: this.perf, canvas: process.canvas });\n });\n }\n\n async warmup(userConfig) {\n const b64toBlob = (base64, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n\n if (userConfig) this.config = mergeDeep(this.config, userConfig);\n const video = this.config.videoOptimized;\n this.config.videoOptimized = false;\n let blob;\n switch (this.config.warmup) {\n case 'face': blob = await b64toBlob(sample.face); break;\n case 'full': blob = await b64toBlob(sample.body); break;\n default: blob = null;\n }\n if (!blob) return null;\n const bitmap = await createImageBitmap(blob);\n const t0 = now();\n const warmup = await this.detect(bitmap, config);\n const t1 = now();\n bitmap.close();\n log('Warmup', this.config.warmup, (t1 - t0), warmup);\n this.config.videoOptimized = video;\n return warmup;\n }\n}\n\nexport { Human as default };\n", "// helper function: wrapper around console output\nexport function log(...msg) {\n const dt = new Date();\n const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n // eslint-disable-next-line no-console\n if (msg) console.log(ts, 'Human:', ...msg);\n}\n", "", "// A port of an algorithm by Johannes Baag\u00F8e , 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e \n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n// \n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n// \n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n var me = this, mash = Mash();\n\n me.next = function() {\n var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n me.s0 = me.s1;\n me.s1 = me.s2;\n return me.s2 = t - (me.c = t | 0);\n };\n\n // Apply the seeding algorithm from Baagoe.\n me.c = 1;\n me.s0 = mash(' ');\n me.s1 = mash(' ');\n me.s2 = mash(' ');\n me.s0 -= mash(seed);\n if (me.s0 < 0) { me.s0 += 1; }\n me.s1 -= mash(seed);\n if (me.s1 < 0) { me.s1 += 1; }\n me.s2 -= mash(seed);\n if (me.s2 < 0) { me.s2 += 1; }\n mash = null;\n}\n\nfunction copy(f, t) {\n t.c = f.c;\n t.s0 = f.s0;\n t.s1 = f.s1;\n t.s2 = f.s2;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new Alea(seed),\n state = opts && opts.state,\n prng = xg.next;\n prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n prng.double = function() {\n return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n };\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nfunction Mash() {\n var n = 0xefc8249d;\n\n var mash = function(data) {\n data = data.toString();\n for (var i = 0; i < data.length; i++) {\n n += data.charCodeAt(i);\n var h = 0.02519603282416938 * n;\n n = h >>> 0;\n h -= n;\n h *= n;\n n = h >>> 0;\n h -= n;\n n += h * 0x100000000; // 2^32\n }\n return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n };\n\n return mash;\n}\n\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.alea = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n\n // Set up generator function.\n me.next = function() {\n var t = me.x ^ (me.x << 11);\n me.x = me.y;\n me.y = me.z;\n me.z = me.w;\n return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n };\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor128 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var t = (me.x ^ (me.x >>> 2));\n me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n return (me.d = (me.d + 362437 | 0)) +\n (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n };\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n me.v = 0;\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n if (k == strseed.length) {\n me.d = me.x << 10 ^ me.x >>> 4;\n }\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n t.v = f.v;\n t.d = f.d;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorwow = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n // Update xor generator.\n var X = me.x, i = me.i, t, v, w;\n t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n X[i] = v;\n me.i = (i + 1) & 7;\n return v;\n };\n\n function init(me, seed) {\n var j, w, X = [];\n\n if (seed === (seed | 0)) {\n // Seed state array using a 32-bit integer.\n w = X[0] = seed;\n } else {\n // Seed state using a string.\n seed = '' + seed;\n for (j = 0; j < seed.length; ++j) {\n X[j & 7] = (X[j & 7] << 15) ^\n (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n }\n }\n // Enforce an array length of 8, not all zeroes.\n while (X.length < 8) X.push(0);\n for (j = 0; j < 8 && X[j] === 0; ++j);\n if (j == 8) w = X[7] = -1; else w = X[j];\n\n me.x = X;\n me.i = 0;\n\n // Discard an initial 256 values.\n for (j = 256; j > 0; --j) {\n me.next();\n }\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.x = f.x.slice();\n t.i = f.i;\n return t;\n}\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.x) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorshift7 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated. For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1); // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897); // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C. This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n var w = me.w,\n X = me.X, i = me.i, t, v;\n // Update Weyl generator.\n me.w = w = (w + 0x61c88647) | 0;\n // Update xor generator.\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n // Update Xor generator array state.\n v = X[i] = v ^ t;\n me.i = i;\n // Result is the combination.\n return (v + (w ^ (w >>> 16))) | 0;\n };\n\n function init(me, seed) {\n var t, v, i, j, w, X = [], limit = 128;\n if (seed === (seed | 0)) {\n // Numeric seeds initialize v, which is used to generates X.\n v = seed;\n seed = null;\n } else {\n // String seeds are mixed into v and X one character at a time.\n seed = seed + '\\0';\n v = 0;\n limit = Math.max(limit, seed.length);\n }\n // Initialize circular array and weyl value.\n for (i = 0, j = -32; j < limit; ++j) {\n // Put the unicode characters into the array, and shuffle them.\n if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n // After 32 shuffles, take v as the starting w value.\n if (j === 0) w = v;\n v ^= v << 10;\n v ^= v >>> 15;\n v ^= v << 4;\n v ^= v >>> 13;\n if (j >= 0) {\n w = (w + 0x61c88647) | 0; // Weyl.\n t = (X[j & 127] ^= (v + w)); // Combine xor and weyl to init array.\n i = (0 == t) ? i + 1 : 0; // Count zeroes.\n }\n }\n // We have detected all zeroes; make the key nonzero.\n if (i >= 128) {\n X[(seed && seed.length || 0) & 127] = -1;\n }\n // Run the generator 512 times to further mix the state before using it.\n // Factoring this as a function slows the main generator, so it is just\n // unrolled here. The weyl generator is not advanced while warming up.\n i = 127;\n for (j = 4 * 128; j > 0; --j) {\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n X[i] = v ^ t;\n }\n // Storing state as object members is faster than using closure variables.\n me.w = w;\n me.X = X;\n me.i = i;\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.i = f.i;\n t.w = f.w;\n t.X = f.X.slice();\n return t;\n};\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.X) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor4096 = impl;\n}\n\n})(\n this, // window object or global\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var b = me.b, c = me.c, d = me.d, a = me.a;\n b = (b << 25) ^ (b >>> 7) ^ c;\n c = (c - d) | 0;\n d = (d << 24) ^ (d >>> 8) ^ a;\n a = (a - b) | 0;\n me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n me.c = c = (c - d) | 0;\n me.d = (d << 16) ^ (c >>> 16) ^ a;\n return me.a = (a - b) | 0;\n };\n\n /* The following is non-inverted tyche, which has better internal\n * bit diffusion, but which is about 25% slower than tyche-i in JS.\n me.next = function() {\n var a = me.a, b = me.b, c = me.c, d = me.d;\n a = (me.a + me.b | 0) >>> 0;\n d = me.d ^ a; d = d << 16 ^ d >>> 16;\n c = me.c + d | 0;\n b = me.b ^ c; b = b << 12 ^ d >>> 20;\n me.a = a = a + b | 0;\n d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n me.c = c = c + d | 0;\n b = b ^ c;\n return me.b = (b << 7 ^ b >>> 25);\n }\n */\n\n me.a = 0;\n me.b = 0;\n me.c = 2654435769 | 0;\n me.d = 1367130551;\n\n if (seed === Math.floor(seed)) {\n // Integer seed.\n me.a = (seed / 0x100000000) | 0;\n me.b = seed | 0;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 20; k++) {\n me.b ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.a = f.a;\n t.b = f.b;\n t.c = f.c;\n t.d = f.d;\n return t;\n};\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.tychei = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "", "/*\nCopyright 2014 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\nvar global = this,\n width = 256, // each RC4 output is 0 <= x < 256\n chunks = 6, // at least six RC4 outputs for each double\n digits = 52, // there are 52 significant digits in a double\n rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n startdenom = math.pow(width, chunks),\n significance = math.pow(2, digits),\n overflow = significance * 2,\n mask = width - 1,\n nodecrypto; // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n var key = [];\n options = (options == true) ? { entropy: true } : (options || {});\n\n // Flatten the seed string or build one from local entropy if needed.\n var shortseed = mixkey(flatten(\n options.entropy ? [seed, tostring(pool)] :\n (seed == null) ? autoseed() : seed, 3), key);\n\n // Use the seed to initialize an ARC4 generator.\n var arc4 = new ARC4(key);\n\n // This function returns a random double in [0, 1) that contains\n // randomness in every bit of the mantissa of the IEEE 754 value.\n var prng = function() {\n var n = arc4.g(chunks), // Start with a numerator n < 2 ^ 48\n d = startdenom, // and denominator d = 2 ^ 48.\n x = 0; // and no 'extra last byte'.\n while (n < significance) { // Fill up all significant digits by\n n = (n + x) * width; // shifting numerator and\n d *= width; // denominator and generating a\n x = arc4.g(1); // new least-significant-byte.\n }\n while (n >= overflow) { // To avoid rounding up, before adding\n n /= 2; // last byte, shift everything\n d /= 2; // right using integer math until\n x >>>= 1; // we have exactly the desired bits.\n }\n return (n + x) / d; // Form the number within [0, 1).\n };\n\n prng.int32 = function() { return arc4.g(4) | 0; }\n prng.quick = function() { return arc4.g(4) / 0x100000000; }\n prng.double = prng;\n\n // Mix the randomness into accumulated entropy.\n mixkey(tostring(arc4.S), pool);\n\n // Calling convention: what to return as a function of prng, seed, is_math.\n return (options.pass || callback ||\n function(prng, seed, is_math_call, state) {\n if (state) {\n // Load the arc4 state from the given state if it has an S array.\n if (state.S) { copy(state, arc4); }\n // Only provide the .state method if requested via options.state.\n prng.state = function() { return copy(arc4, {}); }\n }\n\n // If called as a method of Math (Math.seedrandom()), mutate\n // Math.random because that is how seedrandom.js has worked since v1.0.\n if (is_math_call) { math[rngname] = prng; return seed; }\n\n // Otherwise, it is a newer calling convention, so return the\n // prng directly.\n else return prng;\n })(\n prng,\n shortseed,\n 'global' in options ? options.global : (this == math),\n options.state);\n}\nmath['seed' + rngname] = seedrandom;\n\n//\n// ARC4\n//\n// An ARC4 implementation. The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4. Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n var t, keylen = key.length,\n me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n // The empty key [] is treated as [0].\n if (!keylen) { key = [keylen++]; }\n\n // Set up S using the standard key scheduling algorithm.\n while (i < width) {\n s[i] = i++;\n }\n for (i = 0; i < width; i++) {\n s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n s[j] = t;\n }\n\n // The \"g\" method returns the next (count) outputs as one number.\n (me.g = function(count) {\n // Using instance members instead of closure state nearly doubles speed.\n var t, r = 0,\n i = me.i, j = me.j, s = me.S;\n while (count--) {\n t = s[i = mask & (i + 1)];\n r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n }\n me.i = i; me.j = j;\n return r;\n // For robust unpredictability, the function call below automatically\n // discards an initial batch of values. This is called RC4-drop[256].\n // See http://google.com/search?q=rsa+fluhrer+response&btnI\n })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n t.i = f.i;\n t.j = f.j;\n t.S = f.S.slice();\n return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n var result = [], typ = (typeof obj), prop;\n if (depth && typ == 'object') {\n for (prop in obj) {\n try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n }\n }\n return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n var stringseed = seed + '', smear, j = 0;\n while (j < stringseed.length) {\n key[mask & j] =\n mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n }\n return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n try {\n var out;\n if (nodecrypto && (out = nodecrypto.randomBytes)) {\n // The use of 'out' to remember randomBytes makes tight minified code.\n out = out(width);\n } else {\n out = new Uint8Array(width);\n (global.crypto || global.msCrypto).getRandomValues(out);\n }\n return tostring(out);\n } catch (e) {\n var browser = global.navigator,\n plugins = browser && browser.plugins;\n return [+new Date, global, plugins, global.screen, tostring(pool)];\n }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool. Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n module.exports = seedrandom;\n // When in node.js, try using crypto package for autoseeding.\n try {\n nodecrypto = require('crypto');\n } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n define(function() { return seedrandom; });\n}\n\n// End anonymous scope, and pass initial values.\n})(\n [], // pool: entropy pool starts empty\n Math // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random(); // 0 <= x < 1. Every bit is random.\n// var x = random.quick(); // 0 <= x < 1. 32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three. It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures. Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "// A port of an algorithm by Johannes Baag\u00F8e , 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e \n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n// \n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n// \n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n var me = this, mash = Mash();\n\n me.next = function() {\n var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n me.s0 = me.s1;\n me.s1 = me.s2;\n return me.s2 = t - (me.c = t | 0);\n };\n\n // Apply the seeding algorithm from Baagoe.\n me.c = 1;\n me.s0 = mash(' ');\n me.s1 = mash(' ');\n me.s2 = mash(' ');\n me.s0 -= mash(seed);\n if (me.s0 < 0) { me.s0 += 1; }\n me.s1 -= mash(seed);\n if (me.s1 < 0) { me.s1 += 1; }\n me.s2 -= mash(seed);\n if (me.s2 < 0) { me.s2 += 1; }\n mash = null;\n}\n\nfunction copy(f, t) {\n t.c = f.c;\n t.s0 = f.s0;\n t.s1 = f.s1;\n t.s2 = f.s2;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new Alea(seed),\n state = opts && opts.state,\n prng = xg.next;\n prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n prng.double = function() {\n return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n };\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nfunction Mash() {\n var n = 0xefc8249d;\n\n var mash = function(data) {\n data = data.toString();\n for (var i = 0; i < data.length; i++) {\n n += data.charCodeAt(i);\n var h = 0.02519603282416938 * n;\n n = h >>> 0;\n h -= n;\n h *= n;\n n = h >>> 0;\n h -= n;\n n += h * 0x100000000; // 2^32\n }\n return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n };\n\n return mash;\n}\n\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.alea = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n\n // Set up generator function.\n me.next = function() {\n var t = me.x ^ (me.x << 11);\n me.x = me.y;\n me.y = me.z;\n me.z = me.w;\n return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n };\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor128 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var t = (me.x ^ (me.x >>> 2));\n me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n return (me.d = (me.d + 362437 | 0)) +\n (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n };\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n me.v = 0;\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n if (k == strseed.length) {\n me.d = me.x << 10 ^ me.x >>> 4;\n }\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n t.v = f.v;\n t.d = f.d;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorwow = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n // Update xor generator.\n var X = me.x, i = me.i, t, v, w;\n t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n X[i] = v;\n me.i = (i + 1) & 7;\n return v;\n };\n\n function init(me, seed) {\n var j, w, X = [];\n\n if (seed === (seed | 0)) {\n // Seed state array using a 32-bit integer.\n w = X[0] = seed;\n } else {\n // Seed state using a string.\n seed = '' + seed;\n for (j = 0; j < seed.length; ++j) {\n X[j & 7] = (X[j & 7] << 15) ^\n (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n }\n }\n // Enforce an array length of 8, not all zeroes.\n while (X.length < 8) X.push(0);\n for (j = 0; j < 8 && X[j] === 0; ++j);\n if (j == 8) w = X[7] = -1; else w = X[j];\n\n me.x = X;\n me.i = 0;\n\n // Discard an initial 256 values.\n for (j = 256; j > 0; --j) {\n me.next();\n }\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.x = f.x.slice();\n t.i = f.i;\n return t;\n}\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.x) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorshift7 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated. For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1); // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897); // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C. This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n var w = me.w,\n X = me.X, i = me.i, t, v;\n // Update Weyl generator.\n me.w = w = (w + 0x61c88647) | 0;\n // Update xor generator.\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n // Update Xor generator array state.\n v = X[i] = v ^ t;\n me.i = i;\n // Result is the combination.\n return (v + (w ^ (w >>> 16))) | 0;\n };\n\n function init(me, seed) {\n var t, v, i, j, w, X = [], limit = 128;\n if (seed === (seed | 0)) {\n // Numeric seeds initialize v, which is used to generates X.\n v = seed;\n seed = null;\n } else {\n // String seeds are mixed into v and X one character at a time.\n seed = seed + '\\0';\n v = 0;\n limit = Math.max(limit, seed.length);\n }\n // Initialize circular array and weyl value.\n for (i = 0, j = -32; j < limit; ++j) {\n // Put the unicode characters into the array, and shuffle them.\n if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n // After 32 shuffles, take v as the starting w value.\n if (j === 0) w = v;\n v ^= v << 10;\n v ^= v >>> 15;\n v ^= v << 4;\n v ^= v >>> 13;\n if (j >= 0) {\n w = (w + 0x61c88647) | 0; // Weyl.\n t = (X[j & 127] ^= (v + w)); // Combine xor and weyl to init array.\n i = (0 == t) ? i + 1 : 0; // Count zeroes.\n }\n }\n // We have detected all zeroes; make the key nonzero.\n if (i >= 128) {\n X[(seed && seed.length || 0) & 127] = -1;\n }\n // Run the generator 512 times to further mix the state before using it.\n // Factoring this as a function slows the main generator, so it is just\n // unrolled here. The weyl generator is not advanced while warming up.\n i = 127;\n for (j = 4 * 128; j > 0; --j) {\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n X[i] = v ^ t;\n }\n // Storing state as object members is faster than using closure variables.\n me.w = w;\n me.X = X;\n me.i = i;\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.i = f.i;\n t.w = f.w;\n t.X = f.X.slice();\n return t;\n};\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.X) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor4096 = impl;\n}\n\n})(\n this, // window object or global\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var b = me.b, c = me.c, d = me.d, a = me.a;\n b = (b << 25) ^ (b >>> 7) ^ c;\n c = (c - d) | 0;\n d = (d << 24) ^ (d >>> 8) ^ a;\n a = (a - b) | 0;\n me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n me.c = c = (c - d) | 0;\n me.d = (d << 16) ^ (c >>> 16) ^ a;\n return me.a = (a - b) | 0;\n };\n\n /* The following is non-inverted tyche, which has better internal\n * bit diffusion, but which is about 25% slower than tyche-i in JS.\n me.next = function() {\n var a = me.a, b = me.b, c = me.c, d = me.d;\n a = (me.a + me.b | 0) >>> 0;\n d = me.d ^ a; d = d << 16 ^ d >>> 16;\n c = me.c + d | 0;\n b = me.b ^ c; b = b << 12 ^ d >>> 20;\n me.a = a = a + b | 0;\n d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n me.c = c = c + d | 0;\n b = b ^ c;\n return me.b = (b << 7 ^ b >>> 25);\n }\n */\n\n me.a = 0;\n me.b = 0;\n me.c = 2654435769 | 0;\n me.d = 1367130551;\n\n if (seed === Math.floor(seed)) {\n // Integer seed.\n me.a = (seed / 0x100000000) | 0;\n me.b = seed | 0;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 20; k++) {\n me.b ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.a = f.a;\n t.b = f.b;\n t.c = f.c;\n t.d = f.d;\n return t;\n};\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.tychei = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "/*\nCopyright 2014 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\nvar global = this,\n width = 256, // each RC4 output is 0 <= x < 256\n chunks = 6, // at least six RC4 outputs for each double\n digits = 52, // there are 52 significant digits in a double\n rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n startdenom = math.pow(width, chunks),\n significance = math.pow(2, digits),\n overflow = significance * 2,\n mask = width - 1,\n nodecrypto; // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n var key = [];\n options = (options == true) ? { entropy: true } : (options || {});\n\n // Flatten the seed string or build one from local entropy if needed.\n var shortseed = mixkey(flatten(\n options.entropy ? [seed, tostring(pool)] :\n (seed == null) ? autoseed() : seed, 3), key);\n\n // Use the seed to initialize an ARC4 generator.\n var arc4 = new ARC4(key);\n\n // This function returns a random double in [0, 1) that contains\n // randomness in every bit of the mantissa of the IEEE 754 value.\n var prng = function() {\n var n = arc4.g(chunks), // Start with a numerator n < 2 ^ 48\n d = startdenom, // and denominator d = 2 ^ 48.\n x = 0; // and no 'extra last byte'.\n while (n < significance) { // Fill up all significant digits by\n n = (n + x) * width; // shifting numerator and\n d *= width; // denominator and generating a\n x = arc4.g(1); // new least-significant-byte.\n }\n while (n >= overflow) { // To avoid rounding up, before adding\n n /= 2; // last byte, shift everything\n d /= 2; // right using integer math until\n x >>>= 1; // we have exactly the desired bits.\n }\n return (n + x) / d; // Form the number within [0, 1).\n };\n\n prng.int32 = function() { return arc4.g(4) | 0; }\n prng.quick = function() { return arc4.g(4) / 0x100000000; }\n prng.double = prng;\n\n // Mix the randomness into accumulated entropy.\n mixkey(tostring(arc4.S), pool);\n\n // Calling convention: what to return as a function of prng, seed, is_math.\n return (options.pass || callback ||\n function(prng, seed, is_math_call, state) {\n if (state) {\n // Load the arc4 state from the given state if it has an S array.\n if (state.S) { copy(state, arc4); }\n // Only provide the .state method if requested via options.state.\n prng.state = function() { return copy(arc4, {}); }\n }\n\n // If called as a method of Math (Math.seedrandom()), mutate\n // Math.random because that is how seedrandom.js has worked since v1.0.\n if (is_math_call) { math[rngname] = prng; return seed; }\n\n // Otherwise, it is a newer calling convention, so return the\n // prng directly.\n else return prng;\n })(\n prng,\n shortseed,\n 'global' in options ? options.global : (this == math),\n options.state);\n}\nmath['seed' + rngname] = seedrandom;\n\n//\n// ARC4\n//\n// An ARC4 implementation. The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4. Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n var t, keylen = key.length,\n me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n // The empty key [] is treated as [0].\n if (!keylen) { key = [keylen++]; }\n\n // Set up S using the standard key scheduling algorithm.\n while (i < width) {\n s[i] = i++;\n }\n for (i = 0; i < width; i++) {\n s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n s[j] = t;\n }\n\n // The \"g\" method returns the next (count) outputs as one number.\n (me.g = function(count) {\n // Using instance members instead of closure state nearly doubles speed.\n var t, r = 0,\n i = me.i, j = me.j, s = me.S;\n while (count--) {\n t = s[i = mask & (i + 1)];\n r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n }\n me.i = i; me.j = j;\n return r;\n // For robust unpredictability, the function call below automatically\n // discards an initial batch of values. This is called RC4-drop[256].\n // See http://google.com/search?q=rsa+fluhrer+response&btnI\n })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n t.i = f.i;\n t.j = f.j;\n t.S = f.S.slice();\n return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n var result = [], typ = (typeof obj), prop;\n if (depth && typ == 'object') {\n for (prop in obj) {\n try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n }\n }\n return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n var stringseed = seed + '', smear, j = 0;\n while (j < stringseed.length) {\n key[mask & j] =\n mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n }\n return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n try {\n var out;\n if (nodecrypto && (out = nodecrypto.randomBytes)) {\n // The use of 'out' to remember randomBytes makes tight minified code.\n out = out(width);\n } else {\n out = new Uint8Array(width);\n (global.crypto || global.msCrypto).getRandomValues(out);\n }\n return tostring(out);\n } catch (e) {\n var browser = global.navigator,\n plugins = browser && browser.plugins;\n return [+new Date, global, plugins, global.screen, tostring(pool)];\n }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool. Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n module.exports = seedrandom;\n // When in node.js, try using crypto package for autoseeding.\n try {\n nodecrypto = require('crypto');\n } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n define(function() { return seedrandom; });\n}\n\n// End anonymous scope, and pass initial values.\n})(\n [], // pool: entropy pool starts empty\n Math // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random(); // 0 <= x < 1. Every bit is random.\n// var x = random.quick(); // 0 <= x < 1. 32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three. It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures. Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "", "", "", "\nvar WasmBackendModuleThreadedSimd = (function() {\n var _scriptDir = typeof document !== 'undefined' && document.currentScript ? document.currentScript.src : undefined;\n if (typeof __filename !== 'undefined') _scriptDir = _scriptDir || __filename;\n return (\nfunction(WasmBackendModuleThreadedSimd) {\n WasmBackendModuleThreadedSimd = WasmBackendModuleThreadedSimd || {};\n\nfunction GROWABLE_HEAP_I8(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAP8}function GROWABLE_HEAP_U8(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPU8}function GROWABLE_HEAP_I32(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAP32}function GROWABLE_HEAP_U32(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPU32}function GROWABLE_HEAP_F64(){if(wasmMemory.buffer!=buffer){updateGlobalBufferAndViews(wasmMemory.buffer)}return HEAPF64}var Module=typeof WasmBackendModuleThreadedSimd!==\"undefined\"?WasmBackendModuleThreadedSimd:{};var _scriptDir=undefined;var moduleOverrides={};var key;for(key in Module){if(Module.hasOwnProperty(key)){moduleOverrides[key]=Module[key]}}var arguments_=[];var thisProgram=\"./this.program\";var quit_=function(status,toThrow){throw toThrow};var ENVIRONMENT_IS_WEB=false;var ENVIRONMENT_IS_WORKER=false;var ENVIRONMENT_IS_NODE=false;var ENVIRONMENT_IS_SHELL=false;ENVIRONMENT_IS_WEB=typeof window===\"object\";ENVIRONMENT_IS_WORKER=typeof importScripts===\"function\";ENVIRONMENT_IS_NODE=typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\";ENVIRONMENT_IS_SHELL=!ENVIRONMENT_IS_WEB&&!ENVIRONMENT_IS_NODE&&!ENVIRONMENT_IS_WORKER;var ENVIRONMENT_IS_PTHREAD=Module[\"ENVIRONMENT_IS_PTHREAD\"]||false;if(ENVIRONMENT_IS_PTHREAD){buffer=Module[\"buffer\"];DYNAMIC_BASE=Module[\"DYNAMIC_BASE\"];DYNAMICTOP_PTR=Module[\"DYNAMICTOP_PTR\"]}var scriptDirectory=\"\";function locateFile(path){if(Module[\"locateFile\"]){return Module[\"locateFile\"](path,scriptDirectory)}return scriptDirectory+path}var read_,readAsync,readBinary,setWindowTitle;var nodeFS;var nodePath;if(ENVIRONMENT_IS_NODE){if(ENVIRONMENT_IS_WORKER){scriptDirectory=require(\"path\").dirname(scriptDirectory)+\"/\"}else{scriptDirectory=__dirname+\"/\"}read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret};if(process[\"argv\"].length>1){thisProgram=process[\"argv\"][1].replace(/\\\\/g,\"/\")}arguments_=process[\"argv\"].slice(2);process[\"on\"](\"uncaughtException\",function(ex){if(!(ex instanceof ExitStatus)){throw ex}});process[\"on\"](\"unhandledRejection\",abort);quit_=function(status){process[\"exit\"](status)};Module[\"inspect\"]=function(){return\"[Emscripten Module object]\"};var nodeWorkerThreads;try{nodeWorkerThreads=require(\"worker_threads\")}catch(e){console.error('The \"worker_threads\" module is not supported in this node.js build - perhaps a newer version is needed?');throw e}Worker=nodeWorkerThreads.Worker}else if(ENVIRONMENT_IS_SHELL){if(typeof read!=\"undefined\"){read_=function shell_read(f){return read(f)}}readBinary=function readBinary(f){var data;if(typeof readbuffer===\"function\"){return new Uint8Array(readbuffer(f))}data=read(f,\"binary\");assert(typeof data===\"object\");return data};if(typeof scriptArgs!=\"undefined\"){arguments_=scriptArgs}else if(typeof arguments!=\"undefined\"){arguments_=arguments}if(typeof quit===\"function\"){quit_=function(status){quit(status)}}if(typeof print!==\"undefined\"){if(typeof console===\"undefined\")console={};console.log=print;console.warn=console.error=typeof printErr!==\"undefined\"?printErr:print}}else if(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER){if(ENVIRONMENT_IS_WORKER){scriptDirectory=self.location.href}else if(document.currentScript){scriptDirectory=document.currentScript.src}if(_scriptDir){scriptDirectory=_scriptDir}if(scriptDirectory.indexOf(\"blob:\")!==0){scriptDirectory=scriptDirectory.substr(0,scriptDirectory.lastIndexOf(\"/\")+1)}else{scriptDirectory=\"\"}if(ENVIRONMENT_IS_NODE){read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret}}else{read_=function shell_read(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.send(null);return xhr.responseText};if(ENVIRONMENT_IS_WORKER){readBinary=function readBinary(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.responseType=\"arraybuffer\";xhr.send(null);return new Uint8Array(xhr.response)}}readAsync=function readAsync(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,true);xhr.responseType=\"arraybuffer\";xhr.onload=function xhr_onload(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()};xhr.onerror=onerror;xhr.send(null)}}setWindowTitle=function(title){document.title=title}}else{}if(ENVIRONMENT_IS_NODE){if(typeof performance===\"undefined\"){performance=require(\"perf_hooks\").performance}}var out=Module[\"print\"]||console.log.bind(console);var err=Module[\"printErr\"]||console.warn.bind(console);for(key in moduleOverrides){if(moduleOverrides.hasOwnProperty(key)){Module[key]=moduleOverrides[key]}}moduleOverrides=null;if(Module[\"arguments\"])arguments_=Module[\"arguments\"];if(Module[\"thisProgram\"])thisProgram=Module[\"thisProgram\"];if(Module[\"quit\"])quit_=Module[\"quit\"];var Atomics_load=Atomics.load;var Atomics_store=Atomics.store;var Atomics_compareExchange=Atomics.compareExchange;var wasmBinary;if(Module[\"wasmBinary\"])wasmBinary=Module[\"wasmBinary\"];var noExitRuntime;if(Module[\"noExitRuntime\"])noExitRuntime=Module[\"noExitRuntime\"];if(typeof WebAssembly!==\"object\"){err(\"no native wasm support detected\")}var wasmMemory;var wasmTable=new WebAssembly.Table({\"initial\":169,\"maximum\":169+0,\"element\":\"anyfunc\"});var wasmModule;var threadInfoStruct=0;var selfThreadId=0;var ABORT=false;var EXITSTATUS=0;function assert(condition,text){if(!condition){abort(\"Assertion failed: \"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i=endIdx)){var u0=heap[idx++];if(!u0)return str;if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224){u0=(u0&15)<<12|u1<<6|u2}else{u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63}if(u0<65536){str+=String.fromCharCode(u0)}else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(GROWABLE_HEAP_U8(),ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6;heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18;heap[outIdx++]=128|u>>12&63;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}}heap[outIdx]=0;return outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,GROWABLE_HEAP_U8(),outPtr,maxBytesToWrite)}function lengthBytesUTF8(str){var len=0;for(var i=0;i=55296&&u<=57343)u=65536+((u&1023)<<10)|str.charCodeAt(++i)&1023;if(u<=127)++len;else if(u<=2047)len+=2;else if(u<=65535)len+=3;else len+=4}return len}function writeArrayToMemory(array,buffer){GROWABLE_HEAP_I8().set(array,buffer)}var WASM_PAGE_SIZE=65536;function alignUp(x,multiple){if(x%multiple>0){x+=multiple-x%multiple}return x}var buffer,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer=buf;Module[\"HEAP8\"]=HEAP8=new Int8Array(buf);Module[\"HEAP16\"]=HEAP16=new Int16Array(buf);Module[\"HEAP32\"]=HEAP32=new Int32Array(buf);Module[\"HEAPU8\"]=HEAPU8=new Uint8Array(buf);Module[\"HEAPU16\"]=HEAPU16=new Uint16Array(buf);Module[\"HEAPU32\"]=HEAPU32=new Uint32Array(buf);Module[\"HEAPF32\"]=HEAPF32=new Float32Array(buf);Module[\"HEAPF64\"]=HEAPF64=new Float64Array(buf)}var STACK_BASE=5256464,STACKTOP=STACK_BASE,STACK_MAX=13584,DYNAMIC_BASE=5256464,DYNAMICTOP_PTR=12656;if(ENVIRONMENT_IS_PTHREAD){}var INITIAL_INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;if(ENVIRONMENT_IS_PTHREAD){wasmMemory=Module[\"wasmMemory\"];buffer=Module[\"buffer\"]}else{if(Module[\"wasmMemory\"]){wasmMemory=Module[\"wasmMemory\"]}else{wasmMemory=new WebAssembly.Memory({\"initial\":INITIAL_INITIAL_MEMORY/WASM_PAGE_SIZE,\"maximum\":2147483648/WASM_PAGE_SIZE,\"shared\":true});if(!(wasmMemory.buffer instanceof SharedArrayBuffer)){err(\"requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag\");if(ENVIRONMENT_IS_NODE){console.log(\"(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)\")}throw Error(\"bad memory\")}}}if(wasmMemory){buffer=wasmMemory.buffer}INITIAL_INITIAL_MEMORY=buffer.byteLength;updateGlobalBufferAndViews(buffer);if(!ENVIRONMENT_IS_PTHREAD){GROWABLE_HEAP_I32()[DYNAMICTOP_PTR>>2]=DYNAMIC_BASE}function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback==\"function\"){callback(Module);continue}var func=callback.func;if(typeof func===\"number\"){if(callback.arg===undefined){Module[\"dynCall_v\"](func)}else{Module[\"dynCall_vi\"](func,callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATEXIT__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;if(ENVIRONMENT_IS_PTHREAD)runtimeInitialized=true;function preRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"preRun\"]){if(typeof Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;callRuntimeCallbacks(__ATINIT__)}function preMain(){if(ENVIRONMENT_IS_PTHREAD)return;callRuntimeCallbacks(__ATMAIN__)}function postRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"postRun\"]){if(typeof Module[\"postRun\"]==\"function\")Module[\"postRun\"]=[Module[\"postRun\"]];while(Module[\"postRun\"].length){addOnPostRun(Module[\"postRun\"].shift())}}callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var Math_ceil=Math.ceil;var Math_floor=Math.floor;var runDependencies=0;var runDependencyWatcher=null;var dependenciesFulfilled=null;function addRunDependency(id){assert(!ENVIRONMENT_IS_PTHREAD,\"addRunDependency cannot be used in a pthread worker\");runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}if(ENVIRONMENT_IS_PTHREAD)console.error(\"Pthread aborting at \"+(new Error).stack);what+=\"\";out(what);err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";throw new WebAssembly.RuntimeError(what)}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm-threaded-simd.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(){try{if(wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(wasmBinaryFile)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary()})}return new Promise(function(resolve,reject){resolve(getBinary())})}function createWasm(){var info={\"a\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmModule=module;if(!ENVIRONMENT_IS_PTHREAD){var numWorkersToLoad=PThread.unusedWorkers.length;PThread.unusedWorkers.forEach(function(w){PThread.loadWasmModuleToWorker(w,function(){if(!--numWorkersToLoad)removeRunDependency(\"wasm-instantiate\")})})}}if(!ENVIRONMENT_IS_PTHREAD){addRunDependency(\"wasm-instantiate\")}function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"],output[\"module\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming===\"function\"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch===\"function\"){fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err(\"wasm streaming compile failed: \"+reason);err(\"falling back to ArrayBuffer instantiation\");instantiateArrayBuffer(receiveInstantiatedSource)})})}else{return instantiateArrayBuffer(receiveInstantiatedSource)}}if(Module[\"instantiateWasm\"]){try{var exports=Module[\"instantiateWasm\"](info,receiveInstance);return exports}catch(e){err(\"Module.instantiateWasm callback failed with error: \"+e);return false}}instantiateAsync();return{}}var ASM_CONSTS={};function initPthreadsJS(){PThread.initRuntime()}if(!ENVIRONMENT_IS_PTHREAD)__ATINIT__.push({func:function(){___wasm_call_ctors()}});var __pthread_ptr=0;var __pthread_is_main_runtime_thread=0;var __pthread_is_main_browser_thread=0;function __register_pthread_ptr(pthreadPtr,isMainBrowserThread,isMainRuntimeThread){pthreadPtr=pthreadPtr|0;isMainBrowserThread=isMainBrowserThread|0;isMainRuntimeThread=isMainRuntimeThread|0;__pthread_ptr=pthreadPtr;__pthread_is_main_browser_thread=isMainBrowserThread;__pthread_is_main_runtime_thread=isMainRuntimeThread}Module[\"__register_pthread_ptr\"]=__register_pthread_ptr;var ERRNO_CODES={EPERM:63,ENOENT:44,ESRCH:71,EINTR:27,EIO:29,ENXIO:60,E2BIG:1,ENOEXEC:45,EBADF:8,ECHILD:12,EAGAIN:6,EWOULDBLOCK:6,ENOMEM:48,EACCES:2,EFAULT:21,ENOTBLK:105,EBUSY:10,EEXIST:20,EXDEV:75,ENODEV:43,ENOTDIR:54,EISDIR:31,EINVAL:28,ENFILE:41,EMFILE:33,ENOTTY:59,ETXTBSY:74,EFBIG:22,ENOSPC:51,ESPIPE:70,EROFS:69,EMLINK:34,EPIPE:64,EDOM:18,ERANGE:68,ENOMSG:49,EIDRM:24,ECHRNG:106,EL2NSYNC:156,EL3HLT:107,EL3RST:108,ELNRNG:109,EUNATCH:110,ENOCSI:111,EL2HLT:112,EDEADLK:16,ENOLCK:46,EBADE:113,EBADR:114,EXFULL:115,ENOANO:104,EBADRQC:103,EBADSLT:102,EDEADLOCK:16,EBFONT:101,ENOSTR:100,ENODATA:116,ETIME:117,ENOSR:118,ENONET:119,ENOPKG:120,EREMOTE:121,ENOLINK:47,EADV:122,ESRMNT:123,ECOMM:124,EPROTO:65,EMULTIHOP:36,EDOTDOT:125,EBADMSG:9,ENOTUNIQ:126,EBADFD:127,EREMCHG:128,ELIBACC:129,ELIBBAD:130,ELIBSCN:131,ELIBMAX:132,ELIBEXEC:133,ENOSYS:52,ENOTEMPTY:55,ENAMETOOLONG:37,ELOOP:32,EOPNOTSUPP:138,EPFNOSUPPORT:139,ECONNRESET:15,ENOBUFS:42,EAFNOSUPPORT:5,EPROTOTYPE:67,ENOTSOCK:57,ENOPROTOOPT:50,ESHUTDOWN:140,ECONNREFUSED:14,EADDRINUSE:3,ECONNABORTED:13,ENETUNREACH:40,ENETDOWN:38,ETIMEDOUT:73,EHOSTDOWN:142,EHOSTUNREACH:23,EINPROGRESS:26,EALREADY:7,EDESTADDRREQ:17,EMSGSIZE:35,EPROTONOSUPPORT:66,ESOCKTNOSUPPORT:137,EADDRNOTAVAIL:4,ENETRESET:39,EISCONN:30,ENOTCONN:53,ETOOMANYREFS:141,EUSERS:136,EDQUOT:19,ESTALE:72,ENOTSUP:138,ENOMEDIUM:148,EILSEQ:25,EOVERFLOW:61,ECANCELED:11,ENOTRECOVERABLE:56,EOWNERDEAD:62,ESTRPIPE:135};var __main_thread_futex_wait_address=13568;function _emscripten_futex_wake(addr,count){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0||count<0)return-28;if(count==0)return 0;if(count>=2147483647)count=Infinity;var mainThreadWaitAddress=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2);var mainThreadWoken=0;if(mainThreadWaitAddress==addr){var loadedAddr=Atomics.compareExchange(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,mainThreadWaitAddress,0);if(loadedAddr==mainThreadWaitAddress){--count;mainThreadWoken=1;if(count<=0)return 1}}var ret=Atomics.notify(GROWABLE_HEAP_I32(),addr>>2,count);if(ret>=0)return ret+mainThreadWoken;throw\"Atomics.notify returned an unexpected value \"+ret}Module[\"_emscripten_futex_wake\"]=_emscripten_futex_wake;function __kill_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _kill_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _kill_thread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];pthread.worker.terminate();PThread.freeThreadData(pthread);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(pthread.worker),1);pthread.worker.pthread=undefined}function __cancel_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _cancel_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _cancel_thread!\";var pthread=PThread.pthreads[pthread_ptr];pthread.worker.postMessage({\"cmd\":\"cancel\"})}function __cleanup_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _cleanup_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _cleanup_thread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];if(pthread){var worker=pthread.worker;PThread.returnWorkerToPool(worker)}}var PThread={MAIN_THREAD_ID:1,mainThreadInfo:{schedPolicy:0,schedPrio:0},unusedWorkers:[],runningWorkers:[],initRuntime:function(){__register_pthread_ptr(PThread.mainThreadBlock,!ENVIRONMENT_IS_WORKER,1);_emscripten_register_main_browser_thread_id(PThread.mainThreadBlock)},initMainThreadBlock:function(){var pthreadPoolSize=8;for(var i=0;i>2]=PThread.mainThreadBlock;var headPtr=PThread.mainThreadBlock+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var tlsMemory=13056;for(var i=0;i<128;++i)GROWABLE_HEAP_U32()[tlsMemory/4+i]=0;Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+104>>2,tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+40>>2,PThread.mainThreadBlock);Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+44>>2,42)},initWorker:function(){},pthreads:{},exitHandlers:null,setThreadStatus:function(){},runExitHandlers:function(){if(PThread.exitHandlers!==null){while(PThread.exitHandlers.length>0){PThread.exitHandlers.pop()()}PThread.exitHandlers=null}if(ENVIRONMENT_IS_PTHREAD&&threadInfoStruct)___pthread_tsd_run_dtors()},threadExit:function(exitCode){var tb=_pthread_self();if(tb){Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,exitCode);Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+60>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+64>>2,0);PThread.runExitHandlers();_emscripten_futex_wake(tb+0,2147483647);__register_pthread_ptr(0,0,0);threadInfoStruct=0;if(ENVIRONMENT_IS_PTHREAD){postMessage({\"cmd\":\"exit\"})}}},threadCancel:function(){PThread.runExitHandlers();Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+4>>2,-1);Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+0>>2,1);_emscripten_futex_wake(threadInfoStruct+0,2147483647);threadInfoStruct=selfThreadId=0;__register_pthread_ptr(0,0,0);postMessage({\"cmd\":\"cancelDone\"})},terminateAllThreads:function(){for(var t in PThread.pthreads){var pthread=PThread.pthreads[t];if(pthread&&pthread.worker){PThread.returnWorkerToPool(pthread.worker)}}PThread.pthreads={};for(var i=0;i>2];GROWABLE_HEAP_I32()[pthread.threadInfoStruct+104>>2]=0;_free(tlsMemory);_free(pthread.threadInfoStruct)}pthread.threadInfoStruct=0;if(pthread.allocatedOwnStack&&pthread.stackBase)_free(pthread.stackBase);pthread.stackBase=0;if(pthread.worker)pthread.worker.pthread=null},returnWorkerToPool:function(worker){delete PThread.pthreads[worker.pthread.thread];PThread.unusedWorkers.push(worker);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker),1);PThread.freeThreadData(worker.pthread);worker.pthread=undefined},receiveObjectTransfer:function(data){},loadWasmModuleToWorker:function(worker,onFinishedLoading){worker.onmessage=function(e){var d=e[\"data\"];var cmd=d[\"cmd\"];if(worker.pthread)PThread.currentProxiedOperationCallerThread=worker.pthread.threadInfoStruct;if(d[\"targetThread\"]&&d[\"targetThread\"]!=_pthread_self()){var thread=PThread.pthreads[d.targetThread];if(thread){thread.worker.postMessage(e.data,d[\"transferList\"])}else{console.error('Internal error! Worker sent a message \"'+cmd+'\" to target pthread '+d[\"targetThread\"]+\", but that thread no longer exists!\")}PThread.currentProxiedOperationCallerThread=undefined;return}if(cmd===\"processQueuedMainThreadWork\"){_emscripten_main_thread_process_queued_calls()}else if(cmd===\"spawnThread\"){__spawn_thread(e.data)}else if(cmd===\"cleanupThread\"){__cleanup_thread(d[\"thread\"])}else if(cmd===\"killThread\"){__kill_thread(d[\"thread\"])}else if(cmd===\"cancelThread\"){__cancel_thread(d[\"thread\"])}else if(cmd===\"loaded\"){worker.loaded=true;if(onFinishedLoading)onFinishedLoading(worker);if(worker.runPthread){worker.runPthread();delete worker.runPthread}}else if(cmd===\"print\"){out(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"printErr\"){err(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"alert\"){alert(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"exit\"){var detached=worker.pthread&&Atomics.load(GROWABLE_HEAP_U32(),worker.pthread.thread+68>>2);if(detached){PThread.returnWorkerToPool(worker)}}else if(cmd===\"cancelDone\"){PThread.returnWorkerToPool(worker)}else if(cmd===\"objectTransfer\"){PThread.receiveObjectTransfer(e.data)}else if(e.data.target===\"setimmediate\"){worker.postMessage(e.data)}else{err(\"worker sent an unknown command \"+cmd)}PThread.currentProxiedOperationCallerThread=undefined};worker.onerror=function(e){err(\"pthread sent an error! \"+e.filename+\":\"+e.lineno+\": \"+e.message)};if(ENVIRONMENT_IS_NODE){worker.on(\"message\",function(data){worker.onmessage({data:data})});worker.on(\"error\",function(data){worker.onerror(data)});worker.on(\"exit\",function(data){console.log(\"worker exited - TODO: update the worker queue?\")})}worker.postMessage({\"cmd\":\"load\",\"urlOrBlob\":Module[\"mainScriptUrlOrBlob\"]||_scriptDir,\"wasmMemory\":wasmMemory,\"wasmModule\":wasmModule,\"DYNAMIC_BASE\":DYNAMIC_BASE,\"DYNAMICTOP_PTR\":DYNAMICTOP_PTR})},allocateUnusedWorker:function(){var pthreadMainJs=locateFile(\"tfjs-backend-wasm-threaded-simd.worker.js\");PThread.unusedWorkers.push(new Worker(pthreadMainJs))},getNewWorker:function(){if(PThread.unusedWorkers.length==0){PThread.allocateUnusedWorker();PThread.loadWasmModuleToWorker(PThread.unusedWorkers[0])}if(PThread.unusedWorkers.length>0)return PThread.unusedWorkers.pop();else return null},busySpinWait:function(msecs){var t=performance.now()+msecs;while(performance.now()>2]=value;return value}function _atexit(func,arg){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(1,1,func,arg);__ATEXIT__.unshift({func:func,arg:arg})}function __emscripten_notify_thread_queue(targetThreadId,mainThreadId){if(targetThreadId==mainThreadId){postMessage({\"cmd\":\"processQueuedMainThreadWork\"})}else if(ENVIRONMENT_IS_PTHREAD){postMessage({\"targetThread\":targetThreadId,\"cmd\":\"processThreadQueue\"})}else{var pthread=PThread.pthreads[targetThreadId];var worker=pthread&&pthread.worker;if(!worker){return}worker.postMessage({\"cmd\":\"processThreadQueue\"})}return 1}function _abort(){abort()}function _emscripten_conditional_set_current_thread_status(expectedStatus,newStatus){expectedStatus=expectedStatus|0;newStatus=newStatus|0}function _emscripten_futex_wait(addr,val,timeout){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0)return-28;if(ENVIRONMENT_IS_WORKER){var ret=Atomics.wait(GROWABLE_HEAP_I32(),addr>>2,val,timeout);if(ret===\"timed-out\")return-73;if(ret===\"not-equal\")return-6;if(ret===\"ok\")return 0;throw\"Atomics.wait returned an unexpected value \"+ret}else{var loadedVal=Atomics.load(GROWABLE_HEAP_I32(),addr>>2);if(val!=loadedVal)return-6;var tNow=performance.now();var tEnd=tNow+timeout;Atomics.store(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,addr);var ourWaitAddress=addr;while(addr==ourWaitAddress){tNow=performance.now();if(tNow>tEnd){return-73}_emscripten_main_thread_process_queued_calls();addr=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2)}return 0}}function _emscripten_is_main_browser_thread(){return __pthread_is_main_browser_thread|0}function _emscripten_is_main_runtime_thread(){return __pthread_is_main_runtime_thread|0}function _emscripten_memcpy_big(dest,src,num){GROWABLE_HEAP_U8().copyWithin(dest,src,src+num)}function _emscripten_num_logical_cores(){return navigator[\"hardwareConcurrency\"]}function _emscripten_proxy_to_main_thread_js(index,sync){var numCallArgs=arguments.length-2;var stack=stackSave();var args=stackAlloc(numCallArgs*8);var b=args>>3;for(var i=0;i>3]);buf+=8}else{buf=buf+3&~3;args.push(GROWABLE_HEAP_I32()[buf>>2]);buf+=4}}return args}function _emscripten_receive_on_main_thread_js(index,numCallArgs,args){_emscripten_receive_on_main_thread_js_callArgs.length=numCallArgs;var b=args>>3;for(var i=0;i>>16);updateGlobalBufferAndViews(wasmMemory.buffer);return 1}catch(e){}}function _emscripten_resize_heap(requestedSize){requestedSize=requestedSize>>>0;var oldSize=_emscripten_get_heap_size();if(requestedSize<=oldSize){return false}var PAGE_MULTIPLE=65536;var maxHeapSize=2147483648;if(requestedSize>maxHeapSize){return false}var minHeapSize=16777216;for(var cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(minHeapSize,requestedSize,overGrownHeapSize),PAGE_MULTIPLE));var replacement=emscripten_realloc_buffer(newSize);if(replacement){return true}}return false}var JSEvents={keyEvent:0,mouseEvent:0,wheelEvent:0,uiEvent:0,focusEvent:0,deviceOrientationEvent:0,deviceMotionEvent:0,fullscreenChangeEvent:0,pointerlockChangeEvent:0,visibilityChangeEvent:0,touchEvent:0,previousFullscreenElement:null,previousScreenX:null,previousScreenY:null,removeEventListenersRegistered:false,removeAllEventListeners:function(){for(var i=JSEvents.eventHandlers.length-1;i>=0;--i){JSEvents._removeHandler(i)}JSEvents.eventHandlers=[];JSEvents.deferredCalls=[]},registerRemoveEventListeners:function(){if(!JSEvents.removeEventListenersRegistered){__ATEXIT__.push(JSEvents.removeAllEventListeners);JSEvents.removeEventListenersRegistered=true}},deferredCalls:[],deferCall:function(targetFunction,precedence,argsList){function arraysHaveEqualContent(arrA,arrB){if(arrA.length!=arrB.length)return false;for(var i in arrA){if(arrA[i]!=arrB[i])return false}return true}for(var i in JSEvents.deferredCalls){var call=JSEvents.deferredCalls[i];if(call.targetFunction==targetFunction&&arraysHaveEqualContent(call.argsList,argsList)){return}}JSEvents.deferredCalls.push({targetFunction:targetFunction,precedence:precedence,argsList:argsList});JSEvents.deferredCalls.sort(function(x,y){return x.precedence>2]=eventTypeId;GROWABLE_HEAP_I32()[varargs+4>>2]=eventData;GROWABLE_HEAP_I32()[varargs+8>>2]=userData;_emscripten_async_queue_on_thread_(targetThread,637534208,eventHandlerFunc,eventData,varargs);stackRestore(stackTop)},getTargetThreadForEventCallback:function(targetThread){switch(targetThread){case 1:return 0;case 2:return PThread.currentProxiedOperationCallerThread;default:return targetThread}},getNodeNameForTarget:function(target){if(!target)return\"\";if(target==window)return\"#window\";if(target==screen)return\"#screen\";return target&&target.nodeName?target.nodeName:\"\"},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function stringToNewUTF8(jsString){var length=lengthBytesUTF8(jsString)+1;var cString=_malloc(length);stringToUTF8(jsString,cString,length);return cString}function _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height){var stackTop=stackSave();var varargs=stackAlloc(12);var targetCanvasPtr=0;if(targetCanvas){targetCanvasPtr=stringToNewUTF8(targetCanvas)}GROWABLE_HEAP_I32()[varargs>>2]=targetCanvasPtr;GROWABLE_HEAP_I32()[varargs+4>>2]=width;GROWABLE_HEAP_I32()[varargs+8>>2]=height;_emscripten_async_queue_on_thread_(targetThread,657457152,0,targetCanvasPtr,varargs);stackRestore(stackTop)}function _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,targetCanvas,width,height){targetCanvas=targetCanvas?UTF8ToString(targetCanvas):\"\";_emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height)}function __maybeCStringToJsString(cString){return cString>2?UTF8ToString(cString):cString}var specialHTMLTargets=[0,typeof document!==\"undefined\"?document:0,typeof window!==\"undefined\"?window:0];function __findEventTarget(target){target=__maybeCStringToJsString(target);var domElement=specialHTMLTargets[target]||(typeof document!==\"undefined\"?document.querySelector(target):undefined);return domElement}function __findCanvasEventTarget(target){return __findEventTarget(target)}function _emscripten_set_canvas_element_size_calling_thread(target,width,height){var canvas=__findCanvasEventTarget(target);if(!canvas)return-4;if(canvas.canvasSharedPtr){GROWABLE_HEAP_I32()[canvas.canvasSharedPtr>>2]=width;GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+4>>2]=height}if(canvas.offscreenCanvas||!canvas.controlTransferredOffscreen){if(canvas.offscreenCanvas)canvas=canvas.offscreenCanvas;var autoResizeViewport=false;if(canvas.GLctxObject&&canvas.GLctxObject.GLctx){var prevViewport=canvas.GLctxObject.GLctx.getParameter(2978);autoResizeViewport=prevViewport[0]===0&&prevViewport[1]===0&&prevViewport[2]===canvas.width&&prevViewport[3]===canvas.height}canvas.width=width;canvas.height=height;if(autoResizeViewport){canvas.GLctxObject.GLctx.viewport(0,0,width,height)}}else if(canvas.canvasSharedPtr){var targetThread=GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+8>>2];_emscripten_set_offscreencanvas_size_on_target_thread(targetThread,target,width,height);return 1}else{return-4}return 0}function _emscripten_set_canvas_element_size_main_thread(target,width,height){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(2,1,target,width,height);return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}function _emscripten_set_canvas_element_size(target,width,height){var canvas=__findCanvasEventTarget(target);if(canvas){return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}else{return _emscripten_set_canvas_element_size_main_thread(target,width,height)}}function _emscripten_set_current_thread_status(newStatus){newStatus=newStatus|0}function _emscripten_set_thread_name(threadId,name){threadId=threadId|0;name=name|0}function __webgl_enable_ANGLE_instanced_arrays(ctx){var ext=ctx.getExtension(\"ANGLE_instanced_arrays\");if(ext){ctx[\"vertexAttribDivisor\"]=function(index,divisor){ext[\"vertexAttribDivisorANGLE\"](index,divisor)};ctx[\"drawArraysInstanced\"]=function(mode,first,count,primcount){ext[\"drawArraysInstancedANGLE\"](mode,first,count,primcount)};ctx[\"drawElementsInstanced\"]=function(mode,count,type,indices,primcount){ext[\"drawElementsInstancedANGLE\"](mode,count,type,indices,primcount)};return 1}}function __webgl_enable_OES_vertex_array_object(ctx){var ext=ctx.getExtension(\"OES_vertex_array_object\");if(ext){ctx[\"createVertexArray\"]=function(){return ext[\"createVertexArrayOES\"]()};ctx[\"deleteVertexArray\"]=function(vao){ext[\"deleteVertexArrayOES\"](vao)};ctx[\"bindVertexArray\"]=function(vao){ext[\"bindVertexArrayOES\"](vao)};ctx[\"isVertexArray\"]=function(vao){return ext[\"isVertexArrayOES\"](vao)};return 1}}function __webgl_enable_WEBGL_draw_buffers(ctx){var ext=ctx.getExtension(\"WEBGL_draw_buffers\");if(ext){ctx[\"drawBuffers\"]=function(n,bufs){ext[\"drawBuffersWEBGL\"](n,bufs)};return 1}}var GL={counter:1,lastError:0,buffers:[],mappedBuffers:{},programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},currentContext:null,offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,init:function(){var miniTempFloatBuffer=new Float32Array(GL.MINI_TEMP_BUFFER_SIZE);for(var i=0;i>2]:-1;source+=UTF8ToString(GROWABLE_HEAP_I32()[string+i*4>>2],len<0?undefined:len)}return source},createContext:function(canvas,webGLContextAttributes){var ctx=canvas.getContext(\"webgl\",webGLContextAttributes);if(!ctx)return 0;var handle=GL.registerContext(ctx,webGLContextAttributes);return handle},registerContext:function(ctx,webGLContextAttributes){var handle=_malloc(8);GROWABLE_HEAP_I32()[handle+4>>2]=_pthread_self();var context={handle:handle,attributes:webGLContextAttributes,version:webGLContextAttributes.majorVersion,GLctx:ctx};if(ctx.canvas)ctx.canvas.GLctxObject=context;GL.contexts[handle]=context;if(typeof webGLContextAttributes.enableExtensionsByDefault===\"undefined\"||webGLContextAttributes.enableExtensionsByDefault){GL.initExtensions(context)}return handle},makeContextCurrent:function(contextHandle){GL.currentContext=GL.contexts[contextHandle];Module.ctx=GLctx=GL.currentContext&&GL.currentContext.GLctx;return!(contextHandle&&!GLctx)},getContext:function(contextHandle){return GL.contexts[contextHandle]},deleteContext:function(contextHandle){if(GL.currentContext===GL.contexts[contextHandle])GL.currentContext=null;if(typeof JSEvents===\"object\")JSEvents.removeAllHandlersOnTarget(GL.contexts[contextHandle].GLctx.canvas);if(GL.contexts[contextHandle]&&GL.contexts[contextHandle].GLctx.canvas)GL.contexts[contextHandle].GLctx.canvas.GLctxObject=undefined;_free(GL.contexts[contextHandle].handle);GL.contexts[contextHandle]=null},initExtensions:function(context){if(!context)context=GL.currentContext;if(context.initExtensionsDone)return;context.initExtensionsDone=true;var GLctx=context.GLctx;__webgl_enable_ANGLE_instanced_arrays(GLctx);__webgl_enable_OES_vertex_array_object(GLctx);__webgl_enable_WEBGL_draw_buffers(GLctx);GLctx.disjointTimerQueryExt=GLctx.getExtension(\"EXT_disjoint_timer_query\");var automaticallyEnabledExtensions=[\"OES_texture_float\",\"OES_texture_half_float\",\"OES_standard_derivatives\",\"OES_vertex_array_object\",\"WEBGL_compressed_texture_s3tc\",\"WEBGL_depth_texture\",\"OES_element_index_uint\",\"EXT_texture_filter_anisotropic\",\"EXT_frag_depth\",\"WEBGL_draw_buffers\",\"ANGLE_instanced_arrays\",\"OES_texture_float_linear\",\"OES_texture_half_float_linear\",\"EXT_blend_minmax\",\"EXT_shader_texture_lod\",\"EXT_texture_norm16\",\"WEBGL_compressed_texture_pvrtc\",\"EXT_color_buffer_half_float\",\"WEBGL_color_buffer_float\",\"EXT_sRGB\",\"WEBGL_compressed_texture_etc1\",\"EXT_disjoint_timer_query\",\"WEBGL_compressed_texture_etc\",\"WEBGL_compressed_texture_astc\",\"EXT_color_buffer_float\",\"WEBGL_compressed_texture_s3tc_srgb\",\"EXT_disjoint_timer_query_webgl2\",\"WEBKIT_WEBGL_compressed_texture_pvrtc\"];var exts=GLctx.getSupportedExtensions()||[];exts.forEach(function(ext){if(automaticallyEnabledExtensions.indexOf(ext)!=-1){GLctx.getExtension(ext)}})},populateUniformTable:function(program){var p=GL.programs[program];var ptable=GL.programInfos[program]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1};var utable=ptable.uniforms;var numUniforms=GLctx.getProgramParameter(p,35718);for(var i=0;i>2;contextAttributes[\"alpha\"]=!!GROWABLE_HEAP_I32()[a+(0>>2)];contextAttributes[\"depth\"]=!!GROWABLE_HEAP_I32()[a+(4>>2)];contextAttributes[\"stencil\"]=!!GROWABLE_HEAP_I32()[a+(8>>2)];contextAttributes[\"antialias\"]=!!GROWABLE_HEAP_I32()[a+(12>>2)];contextAttributes[\"premultipliedAlpha\"]=!!GROWABLE_HEAP_I32()[a+(16>>2)];contextAttributes[\"preserveDrawingBuffer\"]=!!GROWABLE_HEAP_I32()[a+(20>>2)];var powerPreference=GROWABLE_HEAP_I32()[a+(24>>2)];contextAttributes[\"powerPreference\"]=__emscripten_webgl_power_preferences[powerPreference];contextAttributes[\"failIfMajorPerformanceCaveat\"]=!!GROWABLE_HEAP_I32()[a+(28>>2)];contextAttributes.majorVersion=GROWABLE_HEAP_I32()[a+(32>>2)];contextAttributes.minorVersion=GROWABLE_HEAP_I32()[a+(36>>2)];contextAttributes.enableExtensionsByDefault=GROWABLE_HEAP_I32()[a+(40>>2)];contextAttributes.explicitSwapControl=GROWABLE_HEAP_I32()[a+(44>>2)];contextAttributes.proxyContextToMainThread=GROWABLE_HEAP_I32()[a+(48>>2)];contextAttributes.renderViaOffscreenBackBuffer=GROWABLE_HEAP_I32()[a+(52>>2)];var canvas=__findCanvasEventTarget(target);if(!canvas){return-4}if(contextAttributes.explicitSwapControl){return-1}var contextHandle=GL.createContext(canvas,contextAttributes);return contextHandle}function _emscripten_webgl_create_context(a0,a1){return _emscripten_webgl_do_create_context(a0,a1)}var PATH={splitPath:function(filename){var splitPathRe=/^(\\/?|)([\\s\\S]*?)((?:\\.{1,2}|[^\\/]+?|)(\\.[^.\\/]*|))(?:[\\/]*)$/;return splitPathRe.exec(filename).slice(1)},normalizeArray:function(parts,allowAboveRoot){var up=0;for(var i=parts.length-1;i>=0;i--){var last=parts[i];if(last===\".\"){parts.splice(i,1)}else if(last===\"..\"){parts.splice(i,1);up++}else if(up){parts.splice(i,1);up--}}if(allowAboveRoot){for(;up;up--){parts.unshift(\"..\")}}return parts},normalize:function(path){var isAbsolute=path.charAt(0)===\"/\",trailingSlash=path.substr(-1)===\"/\";path=PATH.normalizeArray(path.split(\"/\").filter(function(p){return!!p}),!isAbsolute).join(\"/\");if(!path&&!isAbsolute){path=\".\"}if(path&&trailingSlash){path+=\"/\"}return(isAbsolute?\"/\":\"\")+path},dirname:function(path){var result=PATH.splitPath(path),root=result[0],dir=result[1];if(!root&&!dir){return\".\"}if(dir){dir=dir.substr(0,dir.length-1)}return root+dir},basename:function(path){if(path===\"/\")return\"/\";var lastSlash=path.lastIndexOf(\"/\");if(lastSlash===-1)return path;return path.substr(lastSlash+1)},extname:function(path){return PATH.splitPath(path)[3]},join:function(){var paths=Array.prototype.slice.call(arguments,0);return PATH.normalize(paths.join(\"/\"))},join2:function(l,r){return PATH.normalize(l+\"/\"+r)}};var SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer=SYSCALLS.buffers[stream];if(curr===0||curr===10){(stream===1?out:err)(UTF8ArrayToString(buffer,0));buffer.length=0}else{buffer.push(curr)}},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=GROWABLE_HEAP_I32()[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(3,1,fd);return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(4,1,fd,offset_low,offset_high,whence,newOffset)}function _fd_write(fd,iov,iovcnt,pnum){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(5,1,fd,iov,iovcnt,pnum);var num=0;for(var i=0;i>2];var len=GROWABLE_HEAP_I32()[iov+(i*8+4)>>2];for(var j=0;j>2]=num;return 0}function _pthread_cleanup_pop(execute){var routine=PThread.exitHandlers.pop();if(execute)routine()}function _pthread_cleanup_push(routine,arg){if(PThread.exitHandlers===null){PThread.exitHandlers=[]}PThread.exitHandlers.push(function(){dynCall_vi(routine,arg)})}function __spawn_thread(threadParams){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _spawn_thread() can only ever be called from main application thread!\";var worker=PThread.getNewWorker();if(worker.pthread!==undefined)throw\"Internal error!\";if(!threadParams.pthread_ptr)throw\"Internal error, no pthread ptr!\";PThread.runningWorkers.push(worker);var tlsMemory=_malloc(128*4);for(var i=0;i<128;++i){GROWABLE_HEAP_I32()[tlsMemory+i*4>>2]=0}var stackHigh=threadParams.stackBase+threadParams.stackSize;var pthread=PThread.pthreads[threadParams.pthread_ptr]={worker:worker,stackBase:threadParams.stackBase,stackSize:threadParams.stackSize,allocatedOwnStack:threadParams.allocatedOwnStack,thread:threadParams.pthread_ptr,threadInfoStruct:threadParams.pthread_ptr};var tis=pthread.threadInfoStruct>>2;Atomics.store(GROWABLE_HEAP_U32(),tis+(0>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(4>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(8>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(68>>2),threadParams.detached);Atomics.store(GROWABLE_HEAP_U32(),tis+(104>>2),tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),tis+(48>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(40>>2),pthread.threadInfoStruct);Atomics.store(GROWABLE_HEAP_U32(),tis+(44>>2),42);Atomics.store(GROWABLE_HEAP_U32(),tis+(108>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(84>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(80>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+8>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+12>>2),threadParams.detached);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+20>>2),threadParams.schedPolicy);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+24>>2),threadParams.schedPrio);var global_libc=_emscripten_get_global_libc();var global_locale=global_libc+40;Atomics.store(GROWABLE_HEAP_U32(),tis+(176>>2),global_locale);worker.pthread=pthread;var msg={\"cmd\":\"run\",\"start_routine\":threadParams.startRoutine,\"arg\":threadParams.arg,\"threadInfoStruct\":threadParams.pthread_ptr,\"selfThreadId\":threadParams.pthread_ptr,\"parentThreadId\":threadParams.parent_pthread_ptr,\"stackBase\":threadParams.stackBase,\"stackSize\":threadParams.stackSize};worker.runPthread=function(){msg.time=performance.now();worker.postMessage(msg,threadParams.transferList)};if(worker.loaded){worker.runPthread();delete worker.runPthread}}function _pthread_getschedparam(thread,policy,schedparam){if(!policy&&!schedparam)return ERRNO_CODES.EINVAL;if(!thread){err(\"pthread_getschedparam called with a null thread pointer!\");return ERRNO_CODES.ESRCH}var self=GROWABLE_HEAP_I32()[thread+12>>2];if(self!==thread){err(\"pthread_getschedparam attempted on thread \"+thread+\", which does not point to a valid thread, or does not exist anymore!\");return ERRNO_CODES.ESRCH}var schedPolicy=Atomics.load(GROWABLE_HEAP_U32(),thread+108+20>>2);var schedPrio=Atomics.load(GROWABLE_HEAP_U32(),thread+108+24>>2);if(policy)GROWABLE_HEAP_I32()[policy>>2]=schedPolicy;if(schedparam)GROWABLE_HEAP_I32()[schedparam>>2]=schedPrio;return 0}function _pthread_self(){return __pthread_ptr|0}Module[\"_pthread_self\"]=_pthread_self;function _pthread_create(pthread_ptr,attr,start_routine,arg){if(typeof SharedArrayBuffer===\"undefined\"){err(\"Current environment does not support SharedArrayBuffer, pthreads are not available!\");return 6}if(!pthread_ptr){err(\"pthread_create called with a null thread pointer!\");return 28}var transferList=[];var error=0;if(ENVIRONMENT_IS_PTHREAD&&(transferList.length===0||error)){return _emscripten_sync_run_in_main_thread_4(687865856,pthread_ptr,attr,start_routine,arg)}if(error)return error;var stackSize=0;var stackBase=0;var detached=0;var schedPolicy=0;var schedPrio=0;if(attr){stackSize=GROWABLE_HEAP_I32()[attr>>2];stackSize+=81920;stackBase=GROWABLE_HEAP_I32()[attr+8>>2];detached=GROWABLE_HEAP_I32()[attr+12>>2]!==0;var inheritSched=GROWABLE_HEAP_I32()[attr+16>>2]===0;if(inheritSched){var prevSchedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];var prevSchedPrio=GROWABLE_HEAP_I32()[attr+24>>2];var parentThreadPtr=PThread.currentProxiedOperationCallerThread?PThread.currentProxiedOperationCallerThread:_pthread_self();_pthread_getschedparam(parentThreadPtr,attr+20,attr+24);schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];schedPrio=GROWABLE_HEAP_I32()[attr+24>>2];GROWABLE_HEAP_I32()[attr+20>>2]=prevSchedPolicy;GROWABLE_HEAP_I32()[attr+24>>2]=prevSchedPrio}else{schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];schedPrio=GROWABLE_HEAP_I32()[attr+24>>2]}}else{stackSize=2097152}var allocatedOwnStack=stackBase==0;if(allocatedOwnStack){stackBase=_memalign(16,stackSize)}else{stackBase-=stackSize;assert(stackBase>0)}var threadInfoStruct=_malloc(232);for(var i=0;i<232>>2;++i)GROWABLE_HEAP_U32()[(threadInfoStruct>>2)+i]=0;GROWABLE_HEAP_I32()[pthread_ptr>>2]=threadInfoStruct;GROWABLE_HEAP_I32()[threadInfoStruct+12>>2]=threadInfoStruct;var headPtr=threadInfoStruct+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var threadParams={stackBase:stackBase,stackSize:stackSize,allocatedOwnStack:allocatedOwnStack,schedPolicy:schedPolicy,schedPrio:schedPrio,detached:detached,startRoutine:start_routine,pthread_ptr:threadInfoStruct,parent_pthread_ptr:_pthread_self(),arg:arg,transferList:transferList};if(ENVIRONMENT_IS_PTHREAD){threadParams.cmd=\"spawnThread\";postMessage(threadParams,transferList)}else{__spawn_thread(threadParams)}return 0}function _roundf(d){d=+d;return d>=+0?+Math_floor(d+ +.5):+Math_ceil(d-+.5)}function _sysconf(name){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(6,1,name);switch(name){case 30:return 16384;case 85:var maxHeapSize=2147483648;return maxHeapSize/16384;case 132:case 133:case 12:case 137:case 138:case 15:case 235:case 16:case 17:case 18:case 19:case 20:case 149:case 13:case 10:case 236:case 153:case 9:case 21:case 22:case 159:case 154:case 14:case 77:case 78:case 139:case 80:case 81:case 82:case 68:case 67:case 164:case 11:case 29:case 47:case 48:case 95:case 52:case 51:case 46:case 79:return 200809;case 27:case 246:case 127:case 128:case 23:case 24:case 160:case 161:case 181:case 182:case 242:case 183:case 184:case 243:case 244:case 245:case 165:case 178:case 179:case 49:case 50:case 168:case 169:case 175:case 170:case 171:case 172:case 97:case 76:case 32:case 173:case 35:return-1;case 176:case 177:case 7:case 155:case 8:case 157:case 125:case 126:case 92:case 93:case 129:case 130:case 131:case 94:case 91:return 1;case 74:case 60:case 69:case 70:case 4:return 1024;case 31:case 42:case 72:return 32;case 87:case 26:case 33:return 2147483647;case 34:case 1:return 47839;case 38:case 36:return 99;case 43:case 37:return 2048;case 0:return 2097152;case 3:return 65536;case 28:return 32768;case 44:return 32767;case 75:return 16384;case 39:return 1e3;case 89:return 700;case 71:return 256;case 40:return 255;case 2:return 100;case 180:return 64;case 25:return 20;case 5:return 16;case 6:return 6;case 73:return 4;case 84:{if(typeof navigator===\"object\")return navigator[\"hardwareConcurrency\"]||1;return 1}}setErrNo(28);return-1}if(!ENVIRONMENT_IS_PTHREAD)PThread.initMainThreadBlock();else PThread.initWorker();var GLctx;GL.init();var proxiedFunctionTable=[null,_atexit,_emscripten_set_canvas_element_size_main_thread,_fd_close,_fd_seek,_fd_write,_sysconf];var asmLibraryArg={\"e\":___assert_fail,\"r\":___call_main,\"w\":__emscripten_notify_thread_queue,\"a\":_abort,\"l\":_emscripten_conditional_set_current_thread_status,\"d\":_emscripten_futex_wait,\"c\":_emscripten_futex_wake,\"h\":_emscripten_get_now,\"g\":_emscripten_is_main_browser_thread,\"x\":_emscripten_is_main_runtime_thread,\"q\":_emscripten_memcpy_big,\"B\":_emscripten_num_logical_cores,\"t\":_emscripten_receive_on_main_thread_js,\"A\":_emscripten_resize_heap,\"u\":_emscripten_set_canvas_element_size,\"k\":_emscripten_set_current_thread_status,\"s\":_emscripten_set_thread_name,\"v\":_emscripten_webgl_create_context,\"m\":_fd_close,\"o\":_fd_seek,\"i\":_fd_write,\"p\":initPthreadsJS,\"memory\":wasmMemory||Module[\"wasmMemory\"],\"y\":_pthread_cleanup_pop,\"z\":_pthread_cleanup_push,\"j\":_pthread_create,\"b\":_pthread_self,\"f\":_roundf,\"n\":_sysconf,\"table\":wasmTable};var asm=createWasm();Module[\"asm\"]=asm;var ___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=function(){return(___wasm_call_ctors=Module[\"___wasm_call_ctors\"]=Module[\"asm\"][\"C\"]).apply(null,arguments)};var _init=Module[\"_init\"]=function(){return(_init=Module[\"_init\"]=Module[\"asm\"][\"D\"]).apply(null,arguments)};var _register_tensor=Module[\"_register_tensor\"]=function(){return(_register_tensor=Module[\"_register_tensor\"]=Module[\"asm\"][\"E\"]).apply(null,arguments)};var _dispose_data=Module[\"_dispose_data\"]=function(){return(_dispose_data=Module[\"_dispose_data\"]=Module[\"asm\"][\"F\"]).apply(null,arguments)};var _dispose=Module[\"_dispose\"]=function(){return(_dispose=Module[\"_dispose\"]=Module[\"asm\"][\"G\"]).apply(null,arguments)};var _Abs=Module[\"_Abs\"]=function(){return(_Abs=Module[\"_Abs\"]=Module[\"asm\"][\"H\"]).apply(null,arguments)};var _Add=Module[\"_Add\"]=function(){return(_Add=Module[\"_Add\"]=Module[\"asm\"][\"I\"]).apply(null,arguments)};var _AddN=Module[\"_AddN\"]=function(){return(_AddN=Module[\"_AddN\"]=Module[\"asm\"][\"J\"]).apply(null,arguments)};var _ArgMax=Module[\"_ArgMax\"]=function(){return(_ArgMax=Module[\"_ArgMax\"]=Module[\"asm\"][\"K\"]).apply(null,arguments)};var _AvgPool=Module[\"_AvgPool\"]=function(){return(_AvgPool=Module[\"_AvgPool\"]=Module[\"asm\"][\"L\"]).apply(null,arguments)};var _BatchMatMul=Module[\"_BatchMatMul\"]=function(){return(_BatchMatMul=Module[\"_BatchMatMul\"]=Module[\"asm\"][\"M\"]).apply(null,arguments)};var _ClipByValue=Module[\"_ClipByValue\"]=function(){return(_ClipByValue=Module[\"_ClipByValue\"]=Module[\"asm\"][\"N\"]).apply(null,arguments)};var _Conv2D=Module[\"_Conv2D\"]=function(){return(_Conv2D=Module[\"_Conv2D\"]=Module[\"asm\"][\"O\"]).apply(null,arguments)};var _Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=function(){return(_Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=Module[\"asm\"][\"P\"]).apply(null,arguments)};var _Cos=Module[\"_Cos\"]=function(){return(_Cos=Module[\"_Cos\"]=Module[\"asm\"][\"Q\"]).apply(null,arguments)};var _CropAndResize=Module[\"_CropAndResize\"]=function(){return(_CropAndResize=Module[\"_CropAndResize\"]=Module[\"asm\"][\"R\"]).apply(null,arguments)};var _Cumsum=Module[\"_Cumsum\"]=function(){return(_Cumsum=Module[\"_Cumsum\"]=Module[\"asm\"][\"S\"]).apply(null,arguments)};var _DepthToSpace=Module[\"_DepthToSpace\"]=function(){return(_DepthToSpace=Module[\"_DepthToSpace\"]=Module[\"asm\"][\"T\"]).apply(null,arguments)};var _DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=function(){return(_DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=Module[\"asm\"][\"U\"]).apply(null,arguments)};var _Equal=Module[\"_Equal\"]=function(){return(_Equal=Module[\"_Equal\"]=Module[\"asm\"][\"V\"]).apply(null,arguments)};var _Exp=Module[\"_Exp\"]=function(){return(_Exp=Module[\"_Exp\"]=Module[\"asm\"][\"W\"]).apply(null,arguments)};var _FlipLeftRight=Module[\"_FlipLeftRight\"]=function(){return(_FlipLeftRight=Module[\"_FlipLeftRight\"]=Module[\"asm\"][\"X\"]).apply(null,arguments)};var _Floor=Module[\"_Floor\"]=function(){return(_Floor=Module[\"_Floor\"]=Module[\"asm\"][\"Y\"]).apply(null,arguments)};var _FloorDiv=Module[\"_FloorDiv\"]=function(){return(_FloorDiv=Module[\"_FloorDiv\"]=Module[\"asm\"][\"Z\"]).apply(null,arguments)};var _FusedBatchNorm=Module[\"_FusedBatchNorm\"]=function(){return(_FusedBatchNorm=Module[\"_FusedBatchNorm\"]=Module[\"asm\"][\"_\"]).apply(null,arguments)};var _FusedConv2D=Module[\"_FusedConv2D\"]=function(){return(_FusedConv2D=Module[\"_FusedConv2D\"]=Module[\"asm\"][\"$\"]).apply(null,arguments)};var _FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=function(){return(_FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=Module[\"asm\"][\"aa\"]).apply(null,arguments)};var _Gather=Module[\"_Gather\"]=function(){return(_Gather=Module[\"_Gather\"]=Module[\"asm\"][\"ba\"]).apply(null,arguments)};var _GatherNd=Module[\"_GatherNd\"]=function(){return(_GatherNd=Module[\"_GatherNd\"]=Module[\"asm\"][\"ca\"]).apply(null,arguments)};var _Greater=Module[\"_Greater\"]=function(){return(_Greater=Module[\"_Greater\"]=Module[\"asm\"][\"da\"]).apply(null,arguments)};var _GreaterEqual=Module[\"_GreaterEqual\"]=function(){return(_GreaterEqual=Module[\"_GreaterEqual\"]=Module[\"asm\"][\"ea\"]).apply(null,arguments)};var _LeakyRelu=Module[\"_LeakyRelu\"]=function(){return(_LeakyRelu=Module[\"_LeakyRelu\"]=Module[\"asm\"][\"fa\"]).apply(null,arguments)};var _Less=Module[\"_Less\"]=function(){return(_Less=Module[\"_Less\"]=Module[\"asm\"][\"ga\"]).apply(null,arguments)};var _LessEqual=Module[\"_LessEqual\"]=function(){return(_LessEqual=Module[\"_LessEqual\"]=Module[\"asm\"][\"ha\"]).apply(null,arguments)};var _Log=Module[\"_Log\"]=function(){return(_Log=Module[\"_Log\"]=Module[\"asm\"][\"ia\"]).apply(null,arguments)};var _LogicalAnd=Module[\"_LogicalAnd\"]=function(){return(_LogicalAnd=Module[\"_LogicalAnd\"]=Module[\"asm\"][\"ja\"]).apply(null,arguments)};var _Max=Module[\"_Max\"]=function(){return(_Max=Module[\"_Max\"]=Module[\"asm\"][\"ka\"]).apply(null,arguments)};var _MaxPool=Module[\"_MaxPool\"]=function(){return(_MaxPool=Module[\"_MaxPool\"]=Module[\"asm\"][\"la\"]).apply(null,arguments)};var _Maximum=Module[\"_Maximum\"]=function(){return(_Maximum=Module[\"_Maximum\"]=Module[\"asm\"][\"ma\"]).apply(null,arguments)};var _Mean=Module[\"_Mean\"]=function(){return(_Mean=Module[\"_Mean\"]=Module[\"asm\"][\"na\"]).apply(null,arguments)};var _Min=Module[\"_Min\"]=function(){return(_Min=Module[\"_Min\"]=Module[\"asm\"][\"oa\"]).apply(null,arguments)};var _Minimum=Module[\"_Minimum\"]=function(){return(_Minimum=Module[\"_Minimum\"]=Module[\"asm\"][\"pa\"]).apply(null,arguments)};var 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module === 'object')\n module.exports = WasmBackendModule;\n else if (typeof define === 'function' && define['amd'])\n define([], function() { return WasmBackendModule; });\n else if (typeof exports === 'object')\n exports[\"WasmBackendModule\"] = WasmBackendModule;\n ", "// A port of an algorithm by Johannes Baag\u00F8e , 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e \n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n//\n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n//\n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n var me = this, mash = Mash();\n\n me.next = function() {\n var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n me.s0 = me.s1;\n me.s1 = me.s2;\n return me.s2 = t - (me.c = t | 0);\n };\n\n // Apply the seeding algorithm from Baagoe.\n me.c = 1;\n me.s0 = mash(' ');\n me.s1 = mash(' ');\n me.s2 = mash(' ');\n me.s0 -= mash(seed);\n if (me.s0 < 0) { me.s0 += 1; }\n me.s1 -= mash(seed);\n if (me.s1 < 0) { me.s1 += 1; }\n me.s2 -= mash(seed);\n if (me.s2 < 0) { me.s2 += 1; }\n mash = null;\n}\n\nfunction copy(f, t) {\n t.c = f.c;\n t.s0 = f.s0;\n t.s1 = f.s1;\n t.s2 = f.s2;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new Alea(seed),\n state = opts && opts.state,\n prng = xg.next;\n prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n prng.double = function() {\n return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n };\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nfunction Mash() {\n var n = 0xefc8249d;\n\n var mash = function(data) {\n data = String(data);\n for (var i = 0; i < data.length; i++) {\n n += data.charCodeAt(i);\n var h = 0.02519603282416938 * n;\n n = h >>> 0;\n h -= n;\n h *= n;\n n = h >>> 0;\n h -= n;\n n += h * 0x100000000; // 2^32\n }\n return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n };\n\n return mash;\n}\n\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.alea = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n\n // Set up generator function.\n me.next = function() {\n var t = me.x ^ (me.x << 11);\n me.x = me.y;\n me.y = me.z;\n me.z = me.w;\n return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n };\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor128 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var t = (me.x ^ (me.x >>> 2));\n me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n return (me.d = (me.d + 362437 | 0)) +\n (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n };\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n me.v = 0;\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n if (k == strseed.length) {\n me.d = me.x << 10 ^ me.x >>> 4;\n }\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n t.v = f.v;\n t.d = f.d;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorwow = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n // Update xor generator.\n var X = me.x, i = me.i, t, v, w;\n t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n X[i] = v;\n me.i = (i + 1) & 7;\n return v;\n };\n\n function init(me, seed) {\n var j, w, X = [];\n\n if (seed === (seed | 0)) {\n // Seed state array using a 32-bit integer.\n w = X[0] = seed;\n } else {\n // Seed state using a string.\n seed = '' + seed;\n for (j = 0; j < seed.length; ++j) {\n X[j & 7] = (X[j & 7] << 15) ^\n (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n }\n }\n // Enforce an array length of 8, not all zeroes.\n while (X.length < 8) X.push(0);\n for (j = 0; j < 8 && X[j] === 0; ++j);\n if (j == 8) w = X[7] = -1; else w = X[j];\n\n me.x = X;\n me.i = 0;\n\n // Discard an initial 256 values.\n for (j = 256; j > 0; --j) {\n me.next();\n }\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.x = f.x.slice();\n t.i = f.i;\n return t;\n}\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.x) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorshift7 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated. For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1); // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897); // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C. This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n var w = me.w,\n X = me.X, i = me.i, t, v;\n // Update Weyl generator.\n me.w = w = (w + 0x61c88647) | 0;\n // Update xor generator.\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n // Update Xor generator array state.\n v = X[i] = v ^ t;\n me.i = i;\n // Result is the combination.\n return (v + (w ^ (w >>> 16))) | 0;\n };\n\n function init(me, seed) {\n var t, v, i, j, w, X = [], limit = 128;\n if (seed === (seed | 0)) {\n // Numeric seeds initialize v, which is used to generates X.\n v = seed;\n seed = null;\n } else {\n // String seeds are mixed into v and X one character at a time.\n seed = seed + '\\0';\n v = 0;\n limit = Math.max(limit, seed.length);\n }\n // Initialize circular array and weyl value.\n for (i = 0, j = -32; j < limit; ++j) {\n // Put the unicode characters into the array, and shuffle them.\n if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n // After 32 shuffles, take v as the starting w value.\n if (j === 0) w = v;\n v ^= v << 10;\n v ^= v >>> 15;\n v ^= v << 4;\n v ^= v >>> 13;\n if (j >= 0) {\n w = (w + 0x61c88647) | 0; // Weyl.\n t = (X[j & 127] ^= (v + w)); // Combine xor and weyl to init array.\n i = (0 == t) ? i + 1 : 0; // Count zeroes.\n }\n }\n // We have detected all zeroes; make the key nonzero.\n if (i >= 128) {\n X[(seed && seed.length || 0) & 127] = -1;\n }\n // Run the generator 512 times to further mix the state before using it.\n // Factoring this as a function slows the main generator, so it is just\n // unrolled here. The weyl generator is not advanced while warming up.\n i = 127;\n for (j = 4 * 128; j > 0; --j) {\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n X[i] = v ^ t;\n }\n // Storing state as object members is faster than using closure variables.\n me.w = w;\n me.X = X;\n me.i = i;\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.i = f.i;\n t.w = f.w;\n t.X = f.X.slice();\n return t;\n};\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.X) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor4096 = impl;\n}\n\n})(\n this, // window object or global\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var b = me.b, c = me.c, d = me.d, a = me.a;\n b = (b << 25) ^ (b >>> 7) ^ c;\n c = (c - d) | 0;\n d = (d << 24) ^ (d >>> 8) ^ a;\n a = (a - b) | 0;\n me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n me.c = c = (c - d) | 0;\n me.d = (d << 16) ^ (c >>> 16) ^ a;\n return me.a = (a - b) | 0;\n };\n\n /* The following is non-inverted tyche, which has better internal\n * bit diffusion, but which is about 25% slower than tyche-i in JS.\n me.next = function() {\n var a = me.a, b = me.b, c = me.c, d = me.d;\n a = (me.a + me.b | 0) >>> 0;\n d = me.d ^ a; d = d << 16 ^ d >>> 16;\n c = me.c + d | 0;\n b = me.b ^ c; b = b << 12 ^ d >>> 20;\n me.a = a = a + b | 0;\n d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n me.c = c = c + d | 0;\n b = b ^ c;\n return me.b = (b << 7 ^ b >>> 25);\n }\n */\n\n me.a = 0;\n me.b = 0;\n me.c = 2654435769 | 0;\n me.d = 1367130551;\n\n if (seed === Math.floor(seed)) {\n // Integer seed.\n me.a = (seed / 0x100000000) | 0;\n me.b = seed | 0;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 20; k++) {\n me.b ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.a = f.a;\n t.b = f.b;\n t.c = f.c;\n t.d = f.d;\n return t;\n};\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.tychei = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "/*\nCopyright 2019 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (global, pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\n\nvar width = 256, // each RC4 output is 0 <= x < 256\n chunks = 6, // at least six RC4 outputs for each double\n digits = 52, // there are 52 significant digits in a double\n rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n startdenom = math.pow(width, chunks),\n significance = math.pow(2, digits),\n overflow = significance * 2,\n mask = width - 1,\n nodecrypto; // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n var key = [];\n options = (options == true) ? { entropy: true } : (options || {});\n\n // Flatten the seed string or build one from local entropy if needed.\n var shortseed = mixkey(flatten(\n options.entropy ? [seed, tostring(pool)] :\n (seed == null) ? autoseed() : seed, 3), key);\n\n // Use the seed to initialize an ARC4 generator.\n var arc4 = new ARC4(key);\n\n // This function returns a random double in [0, 1) that contains\n // randomness in every bit of the mantissa of the IEEE 754 value.\n var prng = function() {\n var n = arc4.g(chunks), // Start with a numerator n < 2 ^ 48\n d = startdenom, // and denominator d = 2 ^ 48.\n x = 0; // and no 'extra last byte'.\n while (n < significance) { // Fill up all significant digits by\n n = (n + x) * width; // shifting numerator and\n d *= width; // denominator and generating a\n x = arc4.g(1); // new least-significant-byte.\n }\n while (n >= overflow) { // To avoid rounding up, before adding\n n /= 2; // last byte, shift everything\n d /= 2; // right using integer math until\n x >>>= 1; // we have exactly the desired bits.\n }\n return (n + x) / d; // Form the number within [0, 1).\n };\n\n prng.int32 = function() { return arc4.g(4) | 0; }\n prng.quick = function() { return arc4.g(4) / 0x100000000; }\n prng.double = prng;\n\n // Mix the randomness into accumulated entropy.\n mixkey(tostring(arc4.S), pool);\n\n // Calling convention: what to return as a function of prng, seed, is_math.\n return (options.pass || callback ||\n function(prng, seed, is_math_call, state) {\n if (state) {\n // Load the arc4 state from the given state if it has an S array.\n if (state.S) { copy(state, arc4); }\n // Only provide the .state method if requested via options.state.\n prng.state = function() { return copy(arc4, {}); }\n }\n\n // If called as a method of Math (Math.seedrandom()), mutate\n // Math.random because that is how seedrandom.js has worked since v1.0.\n if (is_math_call) { math[rngname] = prng; return seed; }\n\n // Otherwise, it is a newer calling convention, so return the\n // prng directly.\n else return prng;\n })(\n prng,\n shortseed,\n 'global' in options ? options.global : (this == math),\n options.state);\n}\n\n//\n// ARC4\n//\n// An ARC4 implementation. The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4. Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n var t, keylen = key.length,\n me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n // The empty key [] is treated as [0].\n if (!keylen) { key = [keylen++]; }\n\n // Set up S using the standard key scheduling algorithm.\n while (i < width) {\n s[i] = i++;\n }\n for (i = 0; i < width; i++) {\n s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n s[j] = t;\n }\n\n // The \"g\" method returns the next (count) outputs as one number.\n (me.g = function(count) {\n // Using instance members instead of closure state nearly doubles speed.\n var t, r = 0,\n i = me.i, j = me.j, s = me.S;\n while (count--) {\n t = s[i = mask & (i + 1)];\n r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n }\n me.i = i; me.j = j;\n return r;\n // For robust unpredictability, the function call below automatically\n // discards an initial batch of values. This is called RC4-drop[256].\n // See http://google.com/search?q=rsa+fluhrer+response&btnI\n })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n t.i = f.i;\n t.j = f.j;\n t.S = f.S.slice();\n return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n var result = [], typ = (typeof obj), prop;\n if (depth && typ == 'object') {\n for (prop in obj) {\n try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n }\n }\n return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n var stringseed = seed + '', smear, j = 0;\n while (j < stringseed.length) {\n key[mask & j] =\n mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n }\n return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n try {\n var out;\n if (nodecrypto && (out = nodecrypto.randomBytes)) {\n // The use of 'out' to remember randomBytes makes tight minified code.\n out = out(width);\n } else {\n out = new Uint8Array(width);\n (global.crypto || global.msCrypto).getRandomValues(out);\n }\n return tostring(out);\n } catch (e) {\n var browser = global.navigator,\n plugins = browser && browser.plugins;\n return [+new Date, global, plugins, global.screen, tostring(pool)];\n }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool. Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n module.exports = seedrandom;\n // When in node.js, try using crypto package for autoseeding.\n try {\n nodecrypto = require('crypto');\n } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n define(function() { return seedrandom; });\n} else {\n // When included as a plain script, set up Math.seedrandom global.\n math['seed' + rngname] = seedrandom;\n}\n\n\n// End anonymous scope, and pass initial values.\n})(\n // global: `self` in browsers (including strict mode and web workers),\n // otherwise `this` in Node and other environments\n (typeof self !== 'undefined') ? self : this,\n [], // pool: entropy pool starts empty\n Math // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random(); // 0 <= x < 1. Every bit is random.\n// var x = random.quick(); // 0 <= x < 1. 32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three. It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures. Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "export const wasmWorkerContents = 'var threadInfoStruct=0;var selfThreadId=0;var parentThreadId=0;var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(\" \");console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(\" \");postMessage({cmd:\"alert\",text:text,threadId:selfThreadId})}var err=threadPrintErr;this.alert=threadAlert;Module[\"instantiateWasm\"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module[\"wasmModule\"],info);Module[\"wasmModule\"]=null;receiveInstance(instance);return instance.exports};this.onmessage=function(e){try{if(e.data.cmd===\"load\"){Module[\"DYNAMIC_BASE\"]=e.data.DYNAMIC_BASE;Module[\"DYNAMICTOP_PTR\"]=e.data.DYNAMICTOP_PTR;Module[\"wasmModule\"]=e.data.wasmModule;Module[\"wasmMemory\"]=e.data.wasmMemory;Module[\"buffer\"]=Module[\"wasmMemory\"].buffer;Module[\"ENVIRONMENT_IS_PTHREAD\"]=true;if(typeof e.data.urlOrBlob===\"string\"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}Module=WasmBackendModuleThreadedSimd(Module);postMessage({\"cmd\":\"loaded\"})}else if(e.data.cmd===\"objectTransfer\"){Module[\"PThread\"].receiveObjectTransfer(e.data)}else if(e.data.cmd===\"run\"){Module[\"__performance_now_clock_drift\"]=performance.now()-e.data.time;threadInfoStruct=e.data.threadInfoStruct;Module[\"__register_pthread_ptr\"](threadInfoStruct,0,0);selfThreadId=e.data.selfThreadId;parentThreadId=e.data.parentThreadId;var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module[\"establishStackSpace\"](top,max);Module[\"_emscripten_tls_init\"]();Module[\"PThread\"].receiveObjectTransfer(e.data);Module[\"PThread\"].setThreadStatus(Module[\"_pthread_self\"](),1);try{var result=Module[\"dynCall_ii\"](e.data.start_routine,e.data.arg);if(!Module[\"getNoExitRuntime\"]())Module[\"PThread\"].threadExit(result)}catch(ex){if(ex===\"Canceled!\"){Module[\"PThread\"].threadCancel()}else if(ex!=\"unwind\"){Atomics.store(Module[\"HEAPU32\"],threadInfoStruct+4>>2,ex instanceof Module[\"ExitStatus\"]?ex.status:-2);Atomics.store(Module[\"HEAPU32\"],threadInfoStruct+0>>2,1);Module[\"_emscripten_futex_wake\"](threadInfoStruct+0,2147483647);if(!(ex instanceof Module[\"ExitStatus\"]))throw ex}}}else if(e.data.cmd===\"cancel\"){if(threadInfoStruct){Module[\"PThread\"].threadCancel()}}else if(e.data.target===\"setimmediate\"){}else if(e.data.cmd===\"processThreadQueue\"){if(threadInfoStruct){Module[\"_emscripten_current_thread_process_queued_calls\"]()}}else{err(\"worker.js received unknown command \"+e.data.cmd);err(e.data)}}catch(ex){err(\"worker.js onmessage() captured an uncaught exception: \"+ex);if(ex.stack)err(ex.stack);throw ex}};if(typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\"){self={location:{href:__filename}};var onmessage=this.onmessage;var nodeWorkerThreads=require(\"worker_threads\");Worker=nodeWorkerThreads.Worker;var parentPort=nodeWorkerThreads.parentPort;parentPort.on(\"message\",function(data){onmessage({data:data})});var nodeFS=require(\"fs\");var nodeRead=function(filename){return nodeFS.readFileSync(filename,\"utf8\")};function globalEval(x){global.require=require;global.Module=Module;eval.call(null,x)}importScripts=function(f){globalEval(nodeRead(f))};postMessage=function(msg){parentPort.postMessage(msg)};if(typeof performance===\"undefined\"){performance={now:function(){return Date.now()}}}}';", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "// wrapper to load tfjs in a single place so version can be changed quickly\n\n// simplified\n// { modules: 1061, moduleBytes: 3772720, outputBytes: 1531035 }\n\n// export * from '@tensorflow/tfjs/dist/index.js';\n// export * from '@tensorflow/tfjs-backend-wasm';\n\n// modular\n// { modules: 1064, moduleBytes: 3793219, outputBytes: 1535600 }\n\n// get versions of all packages.\nimport { version as tfjs } from '@tensorflow/tfjs/package.json';\nimport { version as versionCore } from '@tensorflow/tfjs-core/package.json';\nimport { version as versionData } from '@tensorflow/tfjs-data/package.json';\nimport { version as versionLayers } from '@tensorflow/tfjs-layers/package.json';\nimport { version as versionConverter } from '@tensorflow/tfjs-converter/package.json';\n// for backends, get version from source so it can register backend during import\nimport { version_cpu } from '@tensorflow/tfjs-backend-cpu/dist/index.js';\nimport { version_webgl } from '@tensorflow/tfjs-backend-webgl/dist/index.js';\nimport { version_wasm } from '@tensorflow/tfjs-backend-wasm/dist/index.js';\n\n// export all\nexport * from '@tensorflow/tfjs-core/dist/index.js';\nexport * from '@tensorflow/tfjs-layers/dist/index.js';\nexport * from '@tensorflow/tfjs-converter/dist/index.js';\nexport * as data from '@tensorflow/tfjs-data/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-cpu/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-webgl/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-wasm/dist/index.js';\n\n// export versions\nexport const version = {\n tfjs,\n 'tfjs-core': versionCore,\n 'tfjs-data': versionData,\n 'tfjs-layers': versionLayers,\n 'tfjs-converter': versionConverter,\n 'tfjs-backend-cpu': version_cpu,\n 'tfjs-backend-webgl': version_webgl,\n 'tfjs-backend-wasm': version_wasm,\n};\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport const config = {\n name: 'humangl',\n priority: 99,\n canvas: null,\n width: 1024,\n height: 1024,\n webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n alpha: false,\n antialias: false,\n premultipliedAlpha: false,\n preserveDrawingBuffer: false,\n depth: false,\n stencil: false,\n failIfMajorPerformanceCaveat: false,\n desynchronized: true,\n },\n};\n\nexport function register() {\n if (!tf.findBackend(config.name)) {\n // @ts-ignore\n config.canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(config.width, config.height) : document.createElement('canvas');\n // @ts-ignore\n const gl = config.canvas.getContext('webgl2', config.webGLattr);\n tf.setWebGLContext(2, gl);\n const ctx = new tf.GPGPUContext(gl);\n tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n const kernels = tf.getKernelsForBackend('webgl');\n kernels.forEach((kernelConfig) => {\n const newKernelConfig = { ...kernelConfig, backendName: config.name };\n tf.registerKernel(newKernelConfig);\n });\n tf.ENV.set('WEBGL_VERSION', 2);\n tf.ENV.set('WEBGL_MAX_TEXTURE_SIZE', gl.getParameter(gl.MAX_TEXTURE_SIZE));\n tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n }\n}\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\n// import * as helpers from './helpers.js';\n// import * as profile from '../profile.js';\n\nconst models = {};\nlet config = {};\nconst anchors = [];\nconst kMidHipCenter = 0;\nconst kPoseDetectKeyNum = 2;\nconst kFullBodySizeRot = 1;\n\nfunction calculateScale(min_scale, max_scale, stride_index, num_strides) {\n if (num_strides === 1) return (min_scale + max_scale) * 0.5;\n return min_scale + (max_scale - min_scale) * 1.0 * stride_index / (num_strides - 1.0);\n}\n\nexport function generateAnchors() {\n const options = {};\n options.strides = [];\n options.aspect_ratios = [];\n options.feature_map_height = [];\n options.num_layers = 4;\n options.min_scale = 0.1484375;\n options.max_scale = 0.75;\n options.input_size_height = 128;\n options.input_size_width = 128;\n options.anchor_offset_x = 0.5;\n options.anchor_offset_y = 0.5;\n options.strides.push(8);\n options.strides.push(16);\n options.strides.push(16);\n options.strides.push(16);\n options.aspect_ratios.push(1.0);\n options.reduce_boxes_in_lowest_layer = false;\n options.interpolated_scale_aspect_ratio = 1.0;\n options.fixed_anchor_size = true;\n let layer_id = 0;\n while (layer_id < options.strides.length) {\n const anchor_height = [];\n const anchor_width = [];\n const aspect_ratios = [];\n const scales = [];\n // For same strides, we merge the anchors in the same order.\n let last_same_stride_layer = layer_id;\n while (last_same_stride_layer < options.strides.length && options.strides[last_same_stride_layer] === options.strides[layer_id]) {\n const scale = calculateScale(options.min_scale, options.max_scale, last_same_stride_layer, options.strides.length);\n if (last_same_stride_layer === 0 && options.reduce_boxes_in_lowest_layer) {\n // For first layer, it can be specified to use predefined anchors.\n aspect_ratios.push(1.0);\n aspect_ratios.push(2.0);\n aspect_ratios.push(0.5);\n scales.push(0.1);\n scales.push(scale);\n scales.push(scale);\n } else {\n for (let aspect_ratio_id = 0; aspect_ratio_id < options.aspect_ratios.length; ++aspect_ratio_id) {\n aspect_ratios.push(options.aspect_ratios[aspect_ratio_id]);\n scales.push(scale);\n }\n if (options.interpolated_scale_aspect_ratio > 0.0) {\n const scale_next = last_same_stride_layer === options.strides.length - 1 ? 1.0 : calculateScale(options.min_scale, options.max_scale, last_same_stride_layer + 1, options.strides.length);\n scales.push(Math.sqrt(scale * scale_next));\n aspect_ratios.push(options.interpolated_scale_aspect_ratio);\n }\n }\n last_same_stride_layer++;\n }\n for (let i = 0; i < aspect_ratios.length; ++i) {\n const ratio_sqrts = Math.sqrt(aspect_ratios[i]);\n anchor_height.push(scales[i] / ratio_sqrts);\n anchor_width.push(scales[i] * ratio_sqrts);\n }\n let feature_map_height = 0;\n let feature_map_width = 0;\n if (options.feature_map_height.length) {\n feature_map_height = options.feature_map_height[layer_id];\n feature_map_width = options.feature_map_width[layer_id];\n } else {\n const stride = options.strides[layer_id];\n feature_map_height = Math.ceil(1.0 * options.input_size_height / stride);\n feature_map_width = Math.ceil(1.0 * options.input_size_width / stride);\n }\n for (let y = 0; y < feature_map_height; ++y) {\n for (let x = 0; x < feature_map_width; ++x) {\n for (let anchor_id = 0; anchor_id < anchor_height.length; ++anchor_id) {\n const x_center = (x + options.anchor_offset_x) * 1.0 / feature_map_width;\n const y_center = (y + options.anchor_offset_y) * 1.0 / feature_map_height;\n const new_anchor = {};\n new_anchor.x_center = x_center;\n new_anchor.y_center = y_center;\n if (options.fixed_anchor_size) {\n new_anchor.w = 1.0;\n new_anchor.h = 1.0;\n } else {\n new_anchor.w = anchor_width[anchor_id];\n new_anchor.h = anchor_height[anchor_id];\n }\n anchors.push(new_anchor);\n }\n }\n }\n layer_id = last_same_stride_layer;\n }\n}\n\nexport async function load(cfg) {\n config = cfg;\n if (!models.blazepose) {\n models.blazepose = await tf.loadGraphModel(config.pose.modelPath);\n log(`load model: ${config.pose.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n generateAnchors();\n return models.blazepose;\n}\n\nfunction rotateRegion(region) {\n const x0 = region.keys[kMidHipCenter].x;\n const y0 = region.keys[kMidHipCenter].y;\n const x1 = (region.box[0] + region.box[2]) * 0.5;\n const y1 = (region.box[1] + region.box[3]) * 0.5;\n const target_angle = Math.PI * 0.5;\n const angle = target_angle - Math.atan2(-(y1 - y0), x1 - x0);\n return Math.round(1000 * (angle - 2 * Math.PI * Math.floor((angle - (-Math.PI)) / (2 * Math.PI)))) / 1000;\n}\n\nfunction rotateVecor(vec, rotation) {\n const sx = vec.x;\n const sy = vec.y;\n vec.x = sx * Math.cos(rotation) - sy * Math.sin(rotation);\n vec.y = sx * Math.sin(rotation) + sy * Math.cos(rotation);\n}\n\nasync function decode(logits) {\n const scores = await logits[0].data();\n const boxes = await logits[1].data();\n // todo: add nms\n // todo scale output with image.shape\n const regions = [];\n for (let i = 0; i < anchors.length; i++) {\n const region = {};\n const score = 1.0 / (1.0 + Math.exp(-scores[i]));\n if (score > config.pose.scoreThreshold) {\n const idx = (4 + 2 * kPoseDetectKeyNum) * i;\n /* boundary box */\n const sx = boxes[idx + 0];\n const sy = boxes[idx + 1];\n const w = boxes[idx + 2] / config.pose.inputSize;\n const h = boxes[idx + 3] / config.pose.inputSize;\n const cx = (sx + anchors[i].x_center * config.pose.inputSize) / config.pose.inputSize;\n const cy = (sy + anchors[i].y_center * config.pose.inputSize) / config.pose.inputSize;\n region.score = Math.round(1000 * score) / 1000;\n region.box = [cx - w * 0.5, cy - h * 0.5, w * 0.5, h * 0.5];\n /* landmark positions (6 keys) */\n const keys = new Array(kPoseDetectKeyNum);\n for (let j = 0; j < kPoseDetectKeyNum; j++) {\n const lx = (boxes[idx + 4 + (2 * j) + 0] + anchors[i].x_center * config.pose.inputSize) / config.pose.inputSize;\n const ly = (boxes[idx + 4 + (2 * j) + 1] + anchors[i].y_center * config.pose.inputSize) / config.pose.inputSize;\n keys[j] = { x: lx, y: ly };\n }\n region.keys = keys;\n region.angle = rotateRegion(region);\n // add points\n const x_center = region.keys[kMidHipCenter].x * config.pose.inputSize;\n const y_center = region.keys[kMidHipCenter].y * config.pose.inputSize;\n const x_scale = region.keys[kFullBodySizeRot].x * config.pose.inputSize;\n const y_scale = region.keys[kFullBodySizeRot].y * config.pose.inputSize;\n // Bounding box size as double distance from center to scale point.\n const box_size = Math.sqrt((x_scale - x_center) * (x_scale - x_center) + (y_scale - y_center) * (y_scale - y_center)) * 2.0;\n /* RectTransformationCalculator::TransformNormalizedRect() */\n const roi_cx = region.angle === 0.0 ? x_center + box_size : x_center + box_size * Math.cos(region.angle) - box_size * Math.sin(region.angle);\n const roi_cy = region.angle === 0.0 ? y_center + box_size : y_center + box_size * Math.sin(region.angle) + box_size * Math.cos(region.angle);\n const long_side = Math.max(box_size, box_size);\n const roi_w = long_side * 1.5;\n const roi_h = long_side * 1.5;\n region.center = { x: roi_cx / config.pose.inputSize, y: roi_cy / config.pose.inputSize };\n region.size = { x: roi_w / config.pose.inputSize, y: roi_h / config.pose.inputSize };\n /* calculate ROI coordinates */\n const dx = roi_w * 0.5;\n const dy = roi_h * 0.5;\n region.coords = [];\n region.coords[0] = { x: -dx, y: -dy };\n region.coords[1] = { x: +dx, y: -dy };\n region.coords[2] = { x: +dx, y: +dy };\n region.coords[3] = { x: -dx, y: +dy };\n for (let j = 0; j < 4; j++) {\n rotateVecor(region.coords[i], region.angle);\n region.coords[j].x = (region.coords[j].x + roi_cx) / config.pose.inputSize;\n region.coords[j].y = (region.coords[j].y + roi_cy) / config.pose.inputSize;\n }\n\n regions.push(region);\n }\n }\n return regions;\n}\n\nexport async function predict(image, cfg) {\n if (!models.blazepose) return null;\n return new Promise(async (resolve) => {\n config = cfg;\n const resize = tf.image.resizeBilinear(image, [config.pose.inputSize, config.pose.inputSize], false);\n const enhance = tf.div(resize, 127.5).sub(1);\n tf.dispose(resize);\n const logits = await models.blazepose.predict(enhance);\n // todo: add landmarks model\n tf.dispose(enhance);\n const regions = await decode(logits);\n logits[0].dispose();\n logits[1].dispose();\n log('poses', regions);\n resolve(regions);\n });\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nfunction getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\nfunction getBoxCenter(box) {\n return [\n box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n ];\n}\nfunction cutBoxFromImageAndResize(box, image, cropSize) {\n const h = image.shape[1];\n const w = image.shape[2];\n const boxes = [[\n box.startPoint[1] / h,\n box.startPoint[0] / w,\n box.endPoint[1] / h,\n box.endPoint[0] / w,\n ]];\n return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\nfunction scaleBoxCoordinates(box, factor) {\n const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n const palmLandmarks = box.palmLandmarks.map((coord) => {\n const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n return scaledCoord;\n });\n return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\nfunction enlargeBox(box, factor = 1.5) {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\nfunction squarifyBox(box) {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const maxEdge = Math.max(...size);\n const halfSize = maxEdge / 2;\n const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\nfunction shiftBox(box, shiftFactor) {\n const boxSize = [\n box.endPoint[0] - box.startPoint[0],\n box.endPoint[1] - box.startPoint[1],\n ];\n const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]];\n const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\nexport {\n cutBoxFromImageAndResize,\n enlargeBox,\n getBoxCenter,\n getBoxSize,\n scaleBoxCoordinates,\n shiftBox,\n squarifyBox,\n};\n", "function normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\nfunction computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\nconst buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\nfunction dot(v1, v2) {\n let product = 0;\n for (let i = 0; i < v1.length; i++) {\n product += v1[i] * v2[i];\n }\n return product;\n}\nfunction getColumnFrom2DArr(arr, columnIndex) {\n const column = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\nfunction multiplyTransformMatrices(mat1, mat2) {\n const product = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) {\n // @ts-ignore\n product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n }\n return product;\n}\nfunction buildRotationMatrix(rotation, center) {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\nfunction invertTransformMatrix(matrix) {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [\n -dot(rotationComponent[0], translationComponent),\n -dot(rotationComponent[1], translationComponent),\n ];\n return [\n rotationComponent[0].concat(invertedTranslation[0]),\n rotationComponent[1].concat(invertedTranslation[1]),\n [0, 0, 1],\n ];\n}\nfunction rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\nexport {\n buildRotationMatrix,\n computeRotation,\n dot,\n getColumnFrom2DArr,\n invertTransformMatrix,\n normalizeRadians,\n rotatePoint,\n};\n", "/* eslint-disable indent */\n/* eslint-disable no-multi-spaces */\n\nexport default {\n backend: 'webgl', // select tfjs backend to use\n wasmPath: '../assets/', // path for wasm binaries\n // only used for backend: wasm\n async: true, // execute enabled models in parallel\n // this disables per-model performance data but\n // slightly increases performance\n // cannot be used if profiling is enabled\n profile: false, // enable tfjs profiling\n // this has significant performance impact\n // only enable for debugging purposes\n // currently only implemented for age,gender,emotion models\n deallocate: false, // aggresively deallocate gpu memory after each usage\n // only valid for webgl backend and only during first call\n // cannot be changed unless library is reloaded\n // this has significant performance impact\n // only enable on low-memory devices\n scoped: false, // enable scoped runs\n // some models *may* have memory leaks,\n // this wrapps everything in a local scope at a cost of performance\n // typically not needed\n videoOptimized: true, // perform additional optimizations when input is video,\n // must be disabled for images\n // basically this skips object box boundary detection for every n frames\n // while maintaining in-box detection since objects cannot move that fast\n warmup: 'face', // what to use for human.warmup(), can be 'none', 'face', 'full'\n // warmup pre-initializes all models for faster inference but can take\n // significant time on startup\n filter: {\n enabled: true, // enable image pre-processing filters\n width: 0, // resize input width\n height: 0, // resize input height\n // if both width and height are set to 0, there is no resizing\n // if just one is set, second one is scaled automatically\n // if both are set, values are used as-is\n return: true, // return processed canvas imagedata in result\n brightness: 0, // range: -1 (darken) to 1 (lighten)\n contrast: 0, // range: -1 (reduce contrast) to 1 (increase contrast)\n sharpness: 0, // range: 0 (no sharpening) to 1 (maximum sharpening)\n blur: 0, // range: 0 (no blur) to N (blur radius in pixels)\n saturation: 0, // range: -1 (reduce saturation) to 1 (increase saturation)\n hue: 0, // range: 0 (no change) to 360 (hue rotation in degrees)\n negative: false, // image negative\n sepia: false, // image sepia colors\n vintage: false, // image vintage colors\n kodachrome: false, // image kodachrome colors\n technicolor: false, // image technicolor colors\n polaroid: false, // image polaroid camera effect\n pixelate: 0, // range: 0 (no pixelate) to N (number of pixels to pixelate)\n },\n\n gesture: {\n enabled: true, // enable simple gesture recognition\n },\n\n face: {\n enabled: true, // controls if specified modul is enabled\n // face.enabled is required for all face models:\n // detector, mesh, iris, age, gender, emotion\n // (note: module is not loaded until it is required)\n detector: {\n modelPath: '../models/blazeface-back.json', // can be 'front' or 'back'.\n // 'front' is optimized for large faces\n // such as front-facing camera and\n // 'back' is optimized for distanct faces.\n inputSize: 256, // fixed value: 128 for front and 256 for 'back'\n rotation: false, // use best-guess rotated face image or just box with rotation as-is\n // false means higher performance, but incorrect mesh mapping if face angle is above 20 degrees\n maxFaces: 10, // maximum number of faces detected in the input\n // should be set to the minimum number for performance\n skipFrames: 11, // how many frames to go without re-running the face bounding box detector\n // only used for video inputs\n // e.g., if model is running st 25 FPS, we can re-use existing bounding\n // box for updated face analysis as the head probably hasn't moved much\n // in short time (10 * 1/25 = 0.25 sec)\n minConfidence: 0.5, // threshold for discarding a prediction\n iouThreshold: 0.2, // threshold for deciding whether boxes overlap too much in\n // non-maximum suppression (0.1 means drop if overlap 10%)\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on score\n // in non-maximum suppression,\n // this is applied on detection objects only and before minConfidence\n },\n\n mesh: {\n enabled: true,\n modelPath: '../models/facemesh.json',\n inputSize: 192, // fixed value\n returnRawData: false, // in addition to standard mesh and box values, return raw normalized values as well\n },\n\n iris: {\n enabled: true,\n modelPath: '../models/iris.json',\n inputSize: 64, // fixed value\n },\n\n age: {\n enabled: true,\n modelPath: '../models/age-ssrnet-imdb.json', // can be 'age-ssrnet-imdb' or 'age-ssrnet-wiki'\n // which determines training set for model\n inputSize: 64, // fixed value\n skipFrames: 31, // how many frames to go without re-running the detector\n // only used for video inputs\n },\n\n gender: {\n enabled: true,\n minConfidence: 0.1, // threshold for discarding a prediction\n modelPath: '../models/gender-ssrnet-imdb.json', // can be 'gender', 'gender-ssrnet-imdb' or 'gender-ssrnet-wiki'\n inputSize: 64, // fixed value\n skipFrames: 41, // how many frames to go without re-running the detector\n // only used for video inputs\n },\n\n emotion: {\n enabled: true,\n inputSize: 64, // fixed value\n minConfidence: 0.2, // threshold for discarding a prediction\n skipFrames: 21, // how many frames to go without re-running the detector\n modelPath: '../models/emotion-large.json', // can be 'mini', 'large'\n },\n\n embedding: {\n enabled: false,\n inputSize: 112, // fixed value\n modelPath: '../models/mobilefacenet.json',\n },\n },\n\n body: {\n enabled: true,\n modelPath: '../models/posenet.json',\n inputSize: 257, // fixed value\n maxDetections: 10, // maximum number of people detected in the input\n // should be set to the minimum number for performance\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on score\n // in non-maximum suppression\n nmsRadius: 20, // radius for deciding points are too close in non-maximum suppression\n outputStride: 16, // size of block in which to run point detectopn, smaller value means higher resolution\n // defined by model itself, can be 8, 16, or 32\n modelType: 'MobileNet', // Human includes MobileNet version, but you can switch to ResNet\n },\n\n pose: {\n enabled: false,\n scoreThreshold: 0.6, // threshold for deciding when to remove boxes based on score\n // in non-maximum suppression\n iouThreshold: 0.3, // threshold for deciding whether boxes overlap too much\n // in non-maximum suppression\n modelPath: '../models/blazepose.json',\n inputSize: 128, // fixed value\n },\n\n hand: {\n enabled: true,\n rotation: false, // use best-guess rotated hand image or just box with rotation as-is\n // false means higher performance, but incorrect finger mapping if hand is inverted\n inputSize: 256, // fixed value\n skipFrames: 12, // how many frames to go without re-running the hand bounding box detector\n // only used for video inputs\n // e.g., if model is running st 25 FPS, we can re-use existing bounding\n // box for updated hand skeleton analysis as the hand probably\n // hasn't moved much in short time (10 * 1/25 = 0.25 sec)\n minConfidence: 0.1, // threshold for discarding a prediction\n iouThreshold: 0.1, // threshold for deciding whether boxes overlap too much\n // in non-maximum suppression\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on\n // score in non-maximum suppression\n maxHands: 1, // maximum number of hands detected in the input\n // should be set to the minimum number for performance\n landmarks: true, // detect hand landmarks or just hand boundary box\n detector: {\n modelPath: '../models/handdetect.json',\n },\n skeleton: {\n modelPath: '../models/handskeleton.json',\n },\n },\n};\n", "// data:image/jpeg;base64,\nexport const face = 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+ "sourcesContent": ["import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nconst NUM_LANDMARKS = 6;\n\nfunction generateAnchors(inputSize) {\n const spec = { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] };\n const anchors = [];\n for (let i = 0; i < spec.strides.length; i++) {\n const stride = spec.strides[i];\n const gridRows = Math.floor((inputSize + stride - 1) / stride);\n const gridCols = Math.floor((inputSize + stride - 1) / stride);\n const anchorsNum = spec.anchors[i];\n for (let gridY = 0; gridY < gridRows; gridY++) {\n const anchorY = stride * (gridY + 0.5);\n for (let gridX = 0; gridX < gridCols; gridX++) {\n const anchorX = stride * (gridX + 0.5);\n for (let n = 0; n < anchorsNum; n++) {\n anchors.push([anchorX, anchorY]);\n }\n }\n }\n }\n return anchors;\n}\n\nconst disposeBox = (box) => {\n box.startEndTensor.dispose();\n box.startPoint.dispose();\n box.endPoint.dispose();\n};\n\nconst createBox = (startEndTensor) => ({\n startEndTensor,\n startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]),\n endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]),\n});\n\nconst scaleBox = (box, factors) => {\n const starts = tf.mul(box.startPoint, factors);\n const ends = tf.mul(box.endPoint, factors);\n const newCoordinates = tf.concat2d([starts, ends], 1);\n return createBox(newCoordinates);\n};\n\nfunction decodeBounds(boxOutputs, anchors, inputSize) {\n const boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n const centers = tf.add(boxStarts, anchors);\n const boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n const boxSizesNormalized = tf.div(boxSizes, inputSize);\n const centersNormalized = tf.div(centers, inputSize);\n const halfBoxSize = tf.div(boxSizesNormalized, 2);\n const starts = tf.sub(centersNormalized, halfBoxSize);\n const ends = tf.add(centersNormalized, halfBoxSize);\n const startNormalized = tf.mul(starts, inputSize);\n const endNormalized = tf.mul(ends, inputSize);\n const concatAxis = 1;\n return tf.concat2d([startNormalized, endNormalized], concatAxis);\n}\n\nfunction scaleBoxFromPrediction(face, scaleFactor) {\n return tf.tidy(() => {\n const box = face['box'] ? face['box'] : face;\n return scaleBox(box, scaleFactor).startEndTensor.squeeze();\n });\n}\n\nclass BlazeFaceModel {\n constructor(model, config) {\n this.blazeFaceModel = model;\n this.width = config.face.detector.inputSize;\n this.height = config.face.detector.inputSize;\n this.anchorsData = generateAnchors(config.face.detector.inputSize);\n this.anchors = tf.tensor2d(this.anchorsData);\n this.inputSize = tf.tensor1d([this.width, this.height]);\n this.config = config;\n this.scaleFaces = 0.8;\n }\n\n async getBoundingBoxes(inputImage) {\n // sanity check on input\n if ((!inputImage) || (inputImage.isDisposedInternal) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return null;\n const [detectedOutputs, boxes, scores] = tf.tidy(() => {\n const resizedImage = inputImage.resizeBilinear([this.width, this.height]);\n // const normalizedImage = tf.mul(tf.sub(resizedImage.div(255), 0.5), 2);\n const normalizedImage = tf.sub(resizedImage.div(127.5), 1);\n const batchedPrediction = this.blazeFaceModel.predict(normalizedImage);\n let prediction;\n // are we using tfhub or pinto converted model?\n if (Array.isArray(batchedPrediction)) {\n const sorted = batchedPrediction.sort((a, b) => a.size - b.size);\n const concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n const concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n const concat = tf.concat([concat512, concat384], 1);\n prediction = concat.squeeze(0);\n } else {\n prediction = batchedPrediction.squeeze(); // when using tfhub model\n }\n const decodedBounds = decodeBounds(prediction, this.anchors, this.inputSize);\n const logits = tf.slice(prediction, [0, 0], [-1, 1]);\n const scoresOut = tf.sigmoid(logits).squeeze();\n return [prediction, decodedBounds, scoresOut];\n });\n const boxIndicesTensor = await tf.image.nonMaxSuppressionAsync(boxes, scores, this.config.face.detector.maxFaces, this.config.face.detector.iouThreshold, this.config.face.detector.scoreThreshold);\n const boxIndices = boxIndicesTensor.arraySync();\n boxIndicesTensor.dispose();\n const boundingBoxesMap = boxIndices.map((boxIndex) => tf.slice(boxes, [boxIndex, 0], [1, -1]));\n const boundingBoxes = boundingBoxesMap.map((boundingBox) => {\n const vals = boundingBox.arraySync();\n boundingBox.dispose();\n return vals;\n });\n\n const scoresVal = scores.dataSync();\n const annotatedBoxes = [];\n for (let i = 0; i < boundingBoxes.length; i++) {\n const boxIndex = boxIndices[i];\n const confidence = scoresVal[boxIndex];\n if (confidence > this.config.face.detector.minConfidence) {\n const box = createBox(boundingBoxes[i]);\n const anchor = this.anchorsData[boxIndex];\n const landmarks = tf.tidy(() => tf.slice(detectedOutputs, [boxIndex, NUM_LANDMARKS - 1], [1, -1]).squeeze().reshape([NUM_LANDMARKS, -1]));\n annotatedBoxes.push({ box, landmarks, anchor, confidence });\n }\n }\n detectedOutputs.dispose();\n boxes.dispose();\n scores.dispose();\n detectedOutputs.dispose();\n return {\n boxes: annotatedBoxes,\n scaleFactor: [inputImage.shape[2] / this.width, inputImage.shape[1] / this.height],\n };\n }\n\n async estimateFaces(input) {\n // @ts-ignore\n const { boxes, scaleFactor } = await this.getBoundingBoxes(input);\n const faces = [];\n for (const face of boxes) {\n const landmarkData = face.landmarks.arraySync();\n const scaledBox = scaleBoxFromPrediction(face, scaleFactor);\n // @ts-ignore\n const boxData = scaleBox.arraySync();\n const probabilityData = face.probability.arraySync();\n const anchor = face.anchor;\n const [scaleFactorX, scaleFactorY] = scaleFactor;\n const scaledLandmarks = landmarkData\n .map((landmark) => ([\n (landmark[0] + anchor[0]) * scaleFactorX,\n (landmark[1] + anchor[1]) * scaleFactorY,\n ]));\n const normalizedFace = {\n topLeft: boxData.slice(0, 2),\n bottomRight: boxData.slice(2),\n landmarks: scaledLandmarks,\n probability: probabilityData,\n };\n disposeBox(face.box);\n face.landmarks.dispose();\n face.probability.dispose();\n scaledBox.dispose();\n faces.push(normalizedFace);\n }\n return faces;\n }\n}\n\nasync function load(config) {\n const blazeface = await tf.loadGraphModel(config.face.detector.modelPath, { fromTFHub: config.face.detector.modelPath.includes('tfhub.dev') });\n const model = new BlazeFaceModel(blazeface, config);\n log(`load model: ${config.face.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n return model;\n}\n\nexports.load = load;\nexports.BlazeFaceModel = BlazeFaceModel;\nexports.disposeBox = disposeBox;\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nfunction scaleBoxCoordinates(box, factor) {\n const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n return { startPoint, endPoint };\n}\nexports.scaleBoxCoordinates = scaleBoxCoordinates;\n\nfunction getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\nexports.getBoxSize = getBoxSize;\n\nfunction getBoxCenter(box) {\n return [\n box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n ];\n}\nexports.getBoxCenter = getBoxCenter;\n\nfunction cutBoxFromImageAndResize(box, image, cropSize) {\n const h = image.shape[1];\n const w = image.shape[2];\n const boxes = [[\n box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h,\n box.endPoint[0] / w,\n ]];\n return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\nexports.cutBoxFromImageAndResize = cutBoxFromImageAndResize;\n\nfunction enlargeBox(box, factor = 1.5) {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n return { startPoint, endPoint, landmarks: box.landmarks };\n}\nexports.enlargeBox = enlargeBox;\n\nfunction squarifyBox(box) {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const maxEdge = Math.max(...size);\n const halfSize = maxEdge / 2;\n const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n return { startPoint, endPoint, landmarks: box.landmarks };\n}\nexports.squarifyBox = squarifyBox;\n", "exports.IDENTITY_MATRIX = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n/**\n * Normalizes the provided angle to the range -pi to pi.\n * @param angle The angle in radians to be normalized.\n */\nfunction normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\nexports.normalizeRadians = normalizeRadians;\n\n/**\n * Computes the angle of rotation between two anchor points.\n * @param point1 First anchor point\n * @param point2 Second anchor point\n */\nfunction computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\nexports.computeRotation = computeRotation;\n\nfunction radToDegrees(rad) {\n return rad * 180 / Math.PI;\n}\nexports.radToDegrees = radToDegrees;\n\nfunction buildTranslationMatrix(x, y) {\n return [[1, 0, x], [0, 1, y], [0, 0, 1]];\n}\n\nfunction dot(v1, v2) {\n let product = 0;\n for (let i = 0; i < v1.length; i++) {\n product += v1[i] * v2[i];\n }\n return product;\n}\nexports.dot = dot;\n\nfunction getColumnFrom2DArr(arr, columnIndex) {\n const column = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\nexports.getColumnFrom2DArr = getColumnFrom2DArr;\n\nfunction multiplyTransformMatrices(mat1, mat2) {\n const product = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) {\n product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n }\n return product;\n}\nfunction buildRotationMatrix(rotation, center) {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\nexports.buildRotationMatrix = buildRotationMatrix;\n\nfunction invertTransformMatrix(matrix) {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [\n -dot(rotationComponent[0], translationComponent),\n -dot(rotationComponent[1], translationComponent),\n ];\n return [\n rotationComponent[0].concat(invertedTranslation[0]),\n rotationComponent[1].concat(invertedTranslation[1]),\n [0, 0, 1],\n ];\n}\nexports.invertTransformMatrix = invertTransformMatrix;\n\nfunction rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\nexports.rotatePoint = rotatePoint;\n\nfunction xyDistanceBetweenPoints(a, b) {\n return Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n}\nexports.xyDistanceBetweenPoints = xyDistanceBetweenPoints;\n", "const MESH_ANNOTATIONS = {\n silhouette: [\n 10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n 397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n 172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n ],\n lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291],\n lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291],\n lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308],\n lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],\n rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173],\n rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133],\n rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190],\n rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243],\n rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189],\n rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244],\n rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245],\n rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193],\n rightEyebrowLower: [35, 124, 46, 53, 52, 65],\n rightEyeIris: [473, 474, 475, 476, 477],\n leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n leftEyeIris: [468, 469, 470, 471, 472],\n midwayBetweenEyes: [168],\n noseTip: [1],\n noseBottom: [2],\n noseRightCorner: [98],\n noseLeftCorner: [327],\n rightCheek: [205],\n leftCheek: [425],\n};\n\nconst MESH_TO_IRIS_INDICES_MAP = [ // A mapping from facemesh model keypoints to iris model keypoints.\n { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] },\n { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] },\n { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] },\n { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] },\n { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] },\n { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] },\n { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] },\n // { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] },\n // { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] },\n];\n\nconst UV468 = [\n [0.499976992607117, 0.652534008026123],\n [0.500025987625122, 0.547487020492554],\n [0.499974012374878, 0.602371990680695],\n [0.482113003730774, 0.471979022026062],\n [0.500150978565216, 0.527155995368958],\n [0.499909996986389, 0.498252987861633],\n [0.499523013830185, 0.40106201171875],\n [0.289712011814117, 0.380764007568359],\n [0.499954998493195, 0.312398016452789],\n [0.499987006187439, 0.269918978214264],\n [0.500023007392883, 0.107050001621246],\n [0.500023007392883, 0.666234016418457],\n [0.5000159740448, 0.679224014282227],\n [0.500023007392883, 0.692348003387451],\n [0.499976992607117, 0.695277988910675],\n [0.499976992607117, 0.70593398809433],\n [0.499976992607117, 0.719385027885437],\n [0.499976992607117, 0.737019002437592],\n [0.499967992305756, 0.781370997428894],\n [0.499816000461578, 0.562981009483337],\n [0.473773002624512, 0.573909997940063],\n [0.104906998574734, 0.254140973091125],\n [0.365929991006851, 0.409575998783112],\n [0.338757991790771, 0.41302502155304],\n [0.311120003461838, 0.409460008144379],\n [0.274657994508743, 0.389131009578705],\n [0.393361985683441, 0.403706014156342],\n [0.345234006643295, 0.344011008739471],\n [0.370094001293182, 0.346076011657715],\n [0.319321990013123, 0.347265005111694],\n [0.297903001308441, 0.353591024875641],\n [0.24779200553894, 0.410809993743896],\n [0.396889001131058, 0.842755019664764],\n [0.280097991228104, 0.375599980354309],\n [0.106310002505779, 0.399955987930298],\n [0.2099249958992, 0.391353011131287],\n [0.355807989835739, 0.534406006336212],\n [0.471751004457474, 0.65040397644043],\n [0.474155008792877, 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[0.614073991775513, 0.883246004581451],\n [0.508952975273132, 0.579437971115112],\n [0.617941975593567, 0.508316040039062],\n [0.825608015060425, 0.397674977779388],\n [0.681214988231659, 0.39623498916626],\n [0.656635999679565, 0.400596976280212],\n [0.603900015354156, 0.710216999053955],\n [0.81208598613739, 0.588539004325867],\n [0.56801301240921, 0.944564998149872],\n [0.681007981300354, 0.898285031318665],\n [0.733752012252808, 0.869701027870178],\n [0.633830010890961, 0.398822009563446],\n [0.606792986392975, 0.39553701877594],\n [0.589659988880157, 0.391062021255493],\n [0.805015981197357, 0.342108011245728],\n [0.611334979534149, 0.362284004688263],\n [0.634037971496582, 0.355970978736877],\n [0.656635999679565, 0.355356991291046],\n [0.681214988231659, 0.35834002494812],\n [0.698584973812103, 0.363156020641327],\n [0.941866993904114, 0.319076001644135],\n [0.698584973812103, 0.387449026107788],\n [0.584177017211914, 0.624107003211975],\n [0.554318010807037, 0.566076993942261],\n [0.534153997898102, 0.62064003944397],\n [0.711217999458313, 0.819975018501282],\n [0.664629995822906, 0.852871000766754],\n [0.559099972248077, 0.902631998062134],\n [0.871706008911133, 0.791940987110138],\n [0.591234028339386, 0.373893976211548],\n [0.544341027736664, 0.451583981513977],\n [0.624562978744507, 0.924192011356354],\n [0.88577002286911, 0.615028977394104],\n [0.551338016986847, 0.695277988910675],\n [0.551980018615723, 0.704632043838501],\n [0.552887976169586, 0.715808033943176],\n [0.555167973041534, 0.730794012546539],\n [0.569944024085999, 0.767035007476807],\n [0.593203008174896, 0.685675978660583],\n [0.599261999130249, 0.681069016456604],\n [0.607599973678589, 0.677703022956848],\n [0.631937980651855, 0.663500010967255],\n [0.752032995223999, 0.601315021514893],\n [0.547226011753082, 0.420395016670227],\n [0.563543975353241, 0.359827995300293],\n [0.583841025829315, 0.368713974952698],\n [0.586614012718201, 0.692366003990173],\n [0.771915018558502, 0.683578014373779],\n [0.531597018241882, 0.352482974529266],\n [0.588370978832245, 0.804440975189209],\n [0.52079701423645, 0.442565023899078],\n [0.567984998226166, 0.493479013442993],\n [0.543282985687256, 0.819254994392395],\n [0.655317008495331, 0.745514988899231],\n [0.621008992195129, 0.574018001556396],\n [0.625559985637665, 0.78031200170517],\n [0.680198013782501, 0.570719003677368],\n [0.64276397228241, 0.604337990283966],\n [0.704662978649139, 0.621529996395111],\n [0.552012026309967, 0.862591981887817],\n [0.589071989059448, 0.508637011051178],\n [0.685944974422455, 0.775357007980347],\n [0.645735025405884, 0.812640011310577],\n [0.675342977046967, 0.703978002071381],\n [0.810858011245728, 0.646304965019226],\n [0.72012197971344, 0.714666962623596],\n [0.866151988506317, 0.682704985141754],\n [0.663187026977539, 0.644596993923187],\n [0.570082008838654, 0.466325998306274],\n [0.544561982154846, 0.548375964164734],\n [0.562758982181549, 0.558784961700439],\n [0.531987011432648, 0.530140042304993],\n [0.585271000862122, 0.335177004337311],\n [0.622952997684479, 0.32277899980545],\n [0.655896008014679, 0.320163011550903],\n [0.687132000923157, 0.322345972061157],\n [0.716481983661652, 0.333200991153717],\n [0.758756995201111, 0.382786989212036],\n [0.897013008594513, 0.468769013881683],\n [0.732392013072968, 0.424547016620636],\n [0.70211398601532, 0.433162987232208],\n [0.66652500629425, 0.433866024017334],\n [0.633504986763, 0.426087975502014],\n [0.603875994682312, 0.416586995124817],\n [0.579657971858978, 0.409945011138916],\n [0.992439985275269, 0.480777025222778],\n [0.567192018032074, 0.569419980049133],\n [0.54136598110199, 0.478899002075195],\n [0.526564002037048, 0.546118021011353],\n [0.523913025856018, 0.563830018043518],\n [0.531529009342194, 0.555056989192963],\n [0.566035985946655, 0.582329034805298],\n [0.51631098985672, 0.563053965568542],\n [0.5174720287323, 0.577877044677734],\n [0.573594987392426, 0.389806985855103],\n [0.560697972774506, 0.395331978797913],\n [0.549755990505219, 0.399751007556915],\n [0.710287988185883, 0.368252992630005],\n [0.723330020904541, 0.363372981548309],\n];\n\nconst TRI468 = [\n 127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n 151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n 157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n 181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n 228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n 170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n 155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n 158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n 102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n 90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n 196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n 246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n 117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n 153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n 63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n 135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n 76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n 166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n 190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n 112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n 110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n 348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n 314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n 321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n 466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n 429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n 293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n 427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n 431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n 434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n 348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n 318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n 373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n 340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n 351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n 164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n 327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n 405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n 438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n 449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n 444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n 464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n 243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n 166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n 119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n 77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n 201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n 103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n 217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n 78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n 57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n 142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n 90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n 178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n 224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n 143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n 153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n 63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n 241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n 186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n 141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n 223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n 128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n 451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n 344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n 424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n 321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n 448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n 281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n 283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n 432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n 268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n 6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n 317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n 272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n 336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n 441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n 373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n 425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n 403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n 383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n 254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n 259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n 453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nconst TRI68 = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n 8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n 45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n 23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n 39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n 35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n 48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n 66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\nconst TRI33 = [\n /* eyes */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n /* brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n /* 4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n /* nose */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n /* chin */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n /* cont */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\nconst TRI7 = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nconst VTX68 = [\n /* cont */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n /* nose */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n /* eyes */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n /* lip */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\nconst VTX33 = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\nconst VTX7 = [33, 133, 362, 263, 1, 78, 308];\n\nexports.MESH_ANNOTATIONS = MESH_ANNOTATIONS;\nexports.MESH_TO_IRIS_INDICES_MAP = MESH_TO_IRIS_INDICES_MAP;\n\nexports.TRI468 = TRI468;\nexports.TRI68 = TRI68;\nexports.TRI33 = TRI33;\nexports.TRI7 = TRI7;\n\nexports.UV468 = UV468;\nexports.UV68 = VTX68.map((x) => UV468[x]);\nexports.UV33 = VTX33.map((x) => UV468[x]);\nexports.UV7 = VTX7.map((x) => UV468[x]);\n", "/* eslint-disable class-methods-use-this */\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as bounding from './box';\nimport * as util from './util';\nimport * as coords from './coords.js';\n// eslint-disable-next-line no-unused-vars\nimport { log } from '../log.js';\n\nconst LANDMARKS_COUNT = 468;\nconst MESH_MOUTH_INDEX = 13;\nconst MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES = [MESH_MOUTH_INDEX, coords.MESH_ANNOTATIONS['midwayBetweenEyes'][0]];\nconst BLAZEFACE_MOUTH_INDEX = 3;\nconst BLAZEFACE_NOSE_INDEX = 2;\nconst BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES = [BLAZEFACE_MOUTH_INDEX, BLAZEFACE_NOSE_INDEX];\nconst LEFT_EYE_OUTLINE = coords.MESH_ANNOTATIONS['leftEyeLower0'];\nconst LEFT_EYE_BOUNDS = [LEFT_EYE_OUTLINE[0], LEFT_EYE_OUTLINE[LEFT_EYE_OUTLINE.length - 1]];\nconst RIGHT_EYE_OUTLINE = coords.MESH_ANNOTATIONS['rightEyeLower0'];\nconst RIGHT_EYE_BOUNDS = [RIGHT_EYE_OUTLINE[0], RIGHT_EYE_OUTLINE[RIGHT_EYE_OUTLINE.length - 1]];\nconst IRIS_UPPER_CENTER_INDEX = 3;\nconst IRIS_LOWER_CENTER_INDEX = 4;\nconst IRIS_IRIS_INDEX = 71;\nconst IRIS_NUM_COORDINATES = 76;\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates. Update the z coordinate to be an average of the original and the new. This produces the best visual effect.\nfunction replaceRawCoordinates(rawCoords, newCoords, prefix, keys) {\n for (let i = 0; i < coords.MESH_TO_IRIS_INDICES_MAP.length; i++) {\n const { key, indices } = coords.MESH_TO_IRIS_INDICES_MAP[i];\n const originalIndices = coords.MESH_ANNOTATIONS[`${prefix}${key}`];\n const shouldReplaceAllKeys = keys == null;\n if (shouldReplaceAllKeys || keys.includes(key)) {\n for (let j = 0; j < indices.length; j++) {\n const index = indices[j];\n rawCoords[originalIndices[j]] = [\n newCoords[index][0], newCoords[index][1],\n (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n ];\n }\n }\n }\n}\n// The Pipeline coordinates between the bounding box and skeleton models.\nclass Pipeline {\n constructor(boundingBoxDetector, meshDetector, irisModel, config) {\n // An array of facial bounding boxes.\n this.storedBoxes = [];\n this.runsWithoutFaceDetector = 0;\n this.boundingBoxDetector = boundingBoxDetector;\n this.meshDetector = meshDetector;\n this.irisModel = irisModel;\n this.meshWidth = config.face.mesh.inputSize;\n this.meshHeight = config.face.mesh.inputSize;\n this.irisSize = config.face.iris.inputSize;\n this.irisEnlarge = 2.3;\n this.skipped = 0;\n this.detectedFaces = 0;\n }\n\n transformRawCoords(rawCoords, box, angle, rotationMatrix) {\n // @ts-ignore\n const boxSize = bounding.getBoxSize({ startPoint: box.startPoint, endPoint: box.endPoint });\n const scaleFactor = [boxSize[0] / this.meshWidth, boxSize[1] / this.meshHeight];\n const coordsScaled = rawCoords.map((coord) => ([\n scaleFactor[0] * (coord[0] - this.meshWidth / 2),\n scaleFactor[1] * (coord[1] - this.meshHeight / 2), coord[2],\n ]));\n const coordsRotationMatrix = (angle !== 0) ? util.buildRotationMatrix(angle, [0, 0]) : util.IDENTITY_MATRIX;\n const coordsRotated = (angle !== 0) ? coordsScaled.map((coord) => ([...util.rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n const inverseRotationMatrix = (angle !== 0) ? util.invertTransformMatrix(rotationMatrix) : util.IDENTITY_MATRIX;\n // @ts-ignore\n const boxCenter = [...bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint }), 1];\n return coordsRotated.map((coord) => ([\n coord[0] + util.dot(boxCenter, inverseRotationMatrix[0]),\n coord[1] + util.dot(boxCenter, inverseRotationMatrix[1]),\n coord[2],\n ]));\n }\n\n getLeftToRightEyeDepthDifference(rawCoords) {\n const leftEyeZ = rawCoords[LEFT_EYE_BOUNDS[0]][2];\n const rightEyeZ = rawCoords[RIGHT_EYE_BOUNDS[0]][2];\n return leftEyeZ - rightEyeZ;\n }\n\n // Returns a box describing a cropped region around the eye fit for passing to the iris model.\n getEyeBox(rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, flip = false) {\n // @ts-ignore\n const box = bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), this.irisEnlarge));\n // @ts-ignore\n const boxSize = bounding.getBoxSize(box);\n let crop = tf.image.cropAndResize(face, [[\n box.startPoint[1] / this.meshHeight,\n box.startPoint[0] / this.meshWidth, box.endPoint[1] / this.meshHeight,\n box.endPoint[0] / this.meshWidth,\n ]], [0], [this.irisSize, this.irisSize]);\n if (flip) {\n crop = tf.image.flipLeftRight(crop);\n }\n return { box, boxSize, crop };\n }\n\n // Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\n getEyeCoords(eyeData, eyeBox, eyeBoxSize, flip = false) {\n const eyeRawCoords = [];\n for (let i = 0; i < IRIS_NUM_COORDINATES; i++) {\n const x = eyeData[i * 3];\n const y = eyeData[i * 3 + 1];\n const z = eyeData[i * 3 + 2];\n eyeRawCoords.push([\n (flip\n ? (1 - (x / this.irisSize))\n : (x / this.irisSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n (y / this.irisSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n ]);\n }\n return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(IRIS_IRIS_INDEX) };\n }\n\n // The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\n getAdjustedIrisCoords(rawCoords, irisCoords, direction) {\n const upperCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeUpper0`][IRIS_UPPER_CENTER_INDEX]][2];\n const lowerCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeLower0`][IRIS_LOWER_CENTER_INDEX]][2];\n const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n return irisCoords.map((coord, i) => {\n let z = averageZ;\n if (i === 2) {\n z = upperCenterZ;\n } else if (i === 4) {\n z = lowerCenterZ;\n }\n return [coord[0], coord[1], z];\n });\n }\n\n async predict(input, config) {\n let useFreshBox = false;\n // run new detector every skipFrames unless we only want box to start with\n let detector;\n if ((this.skipped === 0) || (this.skipped > config.face.detector.skipFrames) || !config.face.mesh.enabled || !config.videoOptimized) {\n detector = await this.boundingBoxDetector.getBoundingBoxes(input);\n this.skipped = 0;\n }\n if (config.videoOptimized) this.skipped++;\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (detector && detector.boxes && (detector.boxes.length > 0) && (!config.face.mesh.enabled || (detector.boxes.length !== this.detectedFaces) && (this.detectedFaces !== config.face.detector.maxFaces))) {\n this.storedBoxes = [];\n this.detectedFaces = 0;\n for (const possible of detector.boxes) {\n this.storedBoxes.push({ startPoint: possible.box.startPoint.dataSync(), endPoint: possible.box.endPoint.dataSync(), landmarks: possible.landmarks, confidence: possible.confidence });\n }\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\n\n if (useFreshBox) {\n if (!detector || !detector.boxes || (detector.boxes.length === 0)) {\n this.storedBoxes = [];\n this.detectedFaces = 0;\n return null;\n }\n for (let i = 0; i < this.storedBoxes.length; i++) {\n // @ts-ignore\n const scaledBox = bounding.scaleBoxCoordinates({ startPoint: this.storedBoxes[i].startPoint, endPoint: this.storedBoxes[i].endPoint }, detector.scaleFactor);\n // @ts-ignore\n const enlargedBox = bounding.enlargeBox(scaledBox);\n // @ts-ignore\n const squarifiedBox = bounding.squarifyBox(enlargedBox);\n const landmarks = this.storedBoxes[i].landmarks.arraySync();\n const confidence = this.storedBoxes[i].confidence;\n this.storedBoxes[i] = { ...squarifiedBox, confidence, landmarks };\n }\n this.runsWithoutFaceDetector = 0;\n }\n if (detector && detector.boxes) {\n detector.boxes.forEach((prediction) => {\n prediction.box.startPoint.dispose();\n prediction.box.endPoint.dispose();\n prediction.landmarks.dispose();\n });\n }\n\n // log('face', `skipped: ${this.skipped} max: ${config.face.detector.maxFaces} detected: ${this.detectedFaces} stored: ${this.storedBoxes.length} new: ${detector?.boxes?.length}`);\n let results = tf.tidy(() => this.storedBoxes.map((box, i) => {\n // The facial bounding box landmarks could come either from blazeface (if we are using a fresh box), or from the mesh model (if we are reusing an old box).\n let face;\n let angle = 0;\n let rotationMatrix;\n if (config.face.detector.rotation) {\n const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= LANDMARKS_COUNT) ? MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES : BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES;\n angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n // @ts-ignore\n const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n const rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized);\n rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n // @ts-ignore\n face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshHeight, this.meshWidth]).div(255);\n } else {\n rotationMatrix = util.IDENTITY_MATRIX;\n const cloned = input.clone();\n // @ts-ignore\n face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, cloned, [this.meshHeight, this.meshWidth]).div(255);\n }\n\n // if we're not going to produce mesh, don't spend time with further processing\n if (!config.face.mesh.enabled) {\n const prediction = {\n coords: null,\n box,\n faceConfidence: null,\n confidence: box.confidence,\n image: face,\n };\n return prediction;\n }\n\n const [, confidence, contourCoords] = this.meshDetector.predict(face); // The first returned tensor represents facial contours, which are included in the coordinates.\n const confidenceVal = confidence.dataSync()[0];\n if (confidenceVal < config.face.detector.minConfidence) return null; // if below confidence just exit\n const coordsReshaped = tf.reshape(contourCoords, [-1, 3]);\n let rawCoords = coordsReshaped.arraySync();\n\n if (config.face.iris.enabled) {\n const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = this.getEyeBox(rawCoords, face, LEFT_EYE_BOUNDS[0], LEFT_EYE_BOUNDS[1], true);\n const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = this.getEyeBox(rawCoords, face, RIGHT_EYE_BOUNDS[0], RIGHT_EYE_BOUNDS[1]);\n const eyePredictions = this.irisModel.predict(tf.concat([leftEyeCrop, rightEyeCrop]));\n const eyePredictionsData = eyePredictions.dataSync();\n const leftEyeData = eyePredictionsData.slice(0, IRIS_NUM_COORDINATES * 3);\n const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = this.getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n const rightEyeData = eyePredictionsData.slice(IRIS_NUM_COORDINATES * 3);\n const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = this.getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize);\n const leftToRightEyeDepthDifference = this.getLeftToRightEyeDepthDifference(rawCoords);\n if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left');\n replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right');\n // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged. So we only update a single contour line above and below the eye.\n } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n } else { // User is looking towards the left.\n replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n }\n const adjustedLeftIrisCoords = this.getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n const adjustedRightIrisCoords = this.getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n rawCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n }\n\n const transformedCoordsData = this.transformRawCoords(rawCoords, box, angle, rotationMatrix);\n // @ts-ignore\n const landmarksBox = bounding.enlargeBox(this.calculateLandmarksBoundingBox(transformedCoordsData));\n // @ts-ignore\n const squarifiedLandmarksBox = bounding.squarifyBox(landmarksBox);\n const transformedCoords = tf.tensor2d(transformedCoordsData);\n const prediction = {\n coords: transformedCoords,\n box: landmarksBox,\n faceConfidence: confidenceVal,\n confidence: box.confidence,\n image: face,\n };\n if (config.face.mesh.returnRawData) prediction.rawCoords = rawCoords;\n this.storedBoxes[i] = { ...squarifiedLandmarksBox, landmarks: transformedCoords.arraySync(), confidence: box.confidence, faceConfidence: confidenceVal };\n\n return prediction;\n }));\n results = results.filter((a) => a !== null);\n this.detectedFaces = results.length;\n return results;\n }\n\n calculateLandmarksBoundingBox(landmarks) {\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint, landmarks };\n }\n}\nexports.Pipeline = Pipeline;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as blazeface from './blazeface.js';\nimport * as facepipeline from './facepipeline.js';\nimport * as coords from './coords.js';\n\nclass MediaPipeFaceMesh {\n constructor(blazeFace, blazeMeshModel, irisModel, config) {\n // @ts-ignore\n this.facePipeline = new facepipeline.Pipeline(blazeFace, blazeMeshModel, irisModel, config);\n this.config = config;\n }\n\n async estimateFaces(input, config) {\n const predictions = await this.facePipeline.predict(input, config);\n const results = [];\n for (const prediction of (predictions || [])) {\n if (prediction.isDisposedInternal) continue; // guard against disposed tensors on long running operations such as pause in middle of processing\n const mesh = prediction.coords ? prediction.coords.arraySync() : null;\n const meshRaw = prediction.rawCoords;\n const annotations = {};\n if (mesh && mesh.length > 0) {\n for (const key of Object.keys(coords.MESH_ANNOTATIONS)) {\n annotations[key] = coords.MESH_ANNOTATIONS[key].map((index) => mesh[index]);\n }\n }\n const boxRaw = (config.face.mesh.returnRawData && prediction.box) ? { topLeft: prediction.box.startPoint, bottomRight: prediction.box.endPoint } : null;\n const box = prediction.box ? [\n Math.max(0, prediction.box.startPoint[0]),\n Math.max(0, prediction.box.startPoint[1]),\n Math.min(input.shape[2], prediction.box.endPoint[0]) - prediction.box.startPoint[0],\n Math.min(input.shape[1], prediction.box.endPoint[1]) - prediction.box.startPoint[1],\n ] : 0;\n results.push({\n confidence: prediction.confidence || 0,\n box,\n mesh,\n boxRaw,\n meshRaw,\n annotations,\n image: prediction.image ? tf.clone(prediction.image) : null,\n });\n if (prediction.coords) prediction.coords.dispose();\n if (prediction.image) prediction.image.dispose();\n }\n return results;\n }\n}\n\nlet faceModels = [null, null, null];\nasync function load(config) {\n faceModels = await Promise.all([\n // @ts-ignore\n (!faceModels[0] && config.face.enabled) ? blazeface.load(config) : null,\n (!faceModels[1] && config.face.mesh.enabled) ? tf.loadGraphModel(config.face.mesh.modelPath, { fromTFHub: config.face.mesh.modelPath.includes('tfhub.dev') }) : null,\n (!faceModels[2] && config.face.iris.enabled) ? tf.loadGraphModel(config.face.iris.modelPath, { fromTFHub: config.face.iris.modelPath.includes('tfhub.dev') }) : null,\n ]);\n const faceMesh = new MediaPipeFaceMesh(faceModels[0], faceModels[1], faceModels[2], config);\n if (config.face.mesh.enabled) log(`load model: ${config.face.mesh.modelPath.match(/\\/(.*)\\./)[1]}`);\n if (config.face.iris.enabled) log(`load model: ${config.face.iris.modelPath.match(/\\/(.*)\\./)[1]}`);\n return faceMesh;\n}\n\nexports.load = load;\nexports.MediaPipeFaceMesh = MediaPipeFaceMesh;\nexports.triangulation = coords.TRI468;\n", "import { log } from './log.js';\n\nconst profileData = {};\n\nfunction profile(name, data) {\n if (!data || !data.kernels) return;\n const maxResults = 5;\n const time = data.kernels\n .filter((a) => a.kernelTimeMs > 0)\n .reduce((a, b) => a += b.kernelTimeMs, 0);\n const slowest = data.kernels\n .map((a, i) => { a.id = i; return a; })\n .filter((a) => a.kernelTimeMs > 0)\n .sort((a, b) => b.kernelTimeMs - a.kernelTimeMs);\n const largest = data.kernels\n .map((a, i) => { a.id = i; return a; })\n .filter((a) => a.totalBytesSnapshot > 0)\n .sort((a, b) => b.totalBytesSnapshot - a.totalBytesSnapshot);\n if (slowest.length > maxResults) slowest.length = maxResults;\n if (largest.length > maxResults) largest.length = maxResults;\n const res = { newBytes: data.newBytes, newTensors: data.newTensors, peakBytes: data.peakBytes, numKernelOps: data.kernels.length, timeKernelOps: time, slowestKernelOps: slowest, largestKernelOps: largest };\n profileData[name] = res;\n log('Human profiler', name, res);\n}\n\nexports.run = profile;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile.js';\n\nconst models = {};\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nasync function load(config) {\n if (!models.age) {\n models.age = await tf.loadGraphModel(config.face.age.modelPath);\n log(`load model: ${config.face.age.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.age;\n}\n\nasync function predict(image, config) {\n if (!models.age) return null;\n if ((skipped < config.face.age.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n /*\n const zoom = [0, 0]; // 0..1 meaning 0%..100%\n const box = [[\n (image.shape[1] * zoom[0]) / image.shape[1],\n (image.shape[2] * zoom[1]) / image.shape[2],\n (image.shape[1] - (image.shape[1] * zoom[0])) / image.shape[1],\n (image.shape[2] - (image.shape[2] * zoom[1])) / image.shape[2],\n ]];\n const resize = tf.image.cropAndResize(image, box, [0], [config.face.age.inputSize, config.face.age.inputSize]);\n */\n const resize = tf.image.resizeBilinear(image, [config.face.age.inputSize, config.face.age.inputSize], false);\n const enhance = tf.mul(resize, [255.0]);\n tf.dispose(resize);\n\n let ageT;\n const obj = {};\n\n if (!config.profile) {\n if (config.face.age.enabled) ageT = await models.age.predict(enhance);\n } else {\n const profileAge = config.face.age.enabled ? await tf.profile(() => models.age.predict(enhance)) : {};\n ageT = profileAge.result.clone();\n profileAge.result.dispose();\n // @ts-ignore\n profile.run('age', profileAge);\n }\n enhance.dispose();\n\n if (ageT) {\n const data = ageT.dataSync();\n obj.age = Math.trunc(10 * data[0]) / 10;\n }\n ageT.dispose();\n\n last = obj;\n resolve(obj);\n });\n}\n\nexports.predict = predict;\nexports.load = load;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile.js';\n\nconst models = {};\nlet last = { gender: '' };\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet alternative = false;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nasync function load(config) {\n if (!models.gender) {\n models.gender = await tf.loadGraphModel(config.face.gender.modelPath);\n alternative = models.gender.inputs[0].shape[3] === 1;\n log(`load model: ${config.face.gender.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.gender;\n}\n\nasync function predict(image, config) {\n if (!models.gender) return null;\n if ((skipped < config.face.gender.skipFrames) && config.videoOptimized && last.gender !== '') {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [config.face.gender.inputSize, config.face.gender.inputSize], false);\n let enhance;\n if (alternative) {\n enhance = tf.tidy(() => {\n const [red, green, blue] = tf.split(resize, 3, 3);\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n return grayscale.sub(0.5).mul(2);\n });\n } else {\n enhance = tf.mul(resize, [255.0]);\n }\n tf.dispose(resize);\n\n let genderT;\n const obj = {};\n\n if (!config.profile) {\n if (config.face.gender.enabled) genderT = await models.gender.predict(enhance);\n } else {\n const profileGender = config.face.gender.enabled ? await tf.profile(() => models.gender.predict(enhance)) : {};\n genderT = profileGender.result.clone();\n profileGender.result.dispose();\n // @ts-ignore\n profile.run('gender', profileGender);\n }\n enhance.dispose();\n\n if (genderT) {\n const data = genderT.dataSync();\n if (alternative) {\n // returns two values 0..1, bigger one is prediction\n const confidence = Math.trunc(100 * Math.abs(data[0] - data[1])) / 100;\n if (confidence > config.face.gender.minConfidence) {\n obj.gender = data[0] > data[1] ? 'female' : 'male';\n obj.confidence = confidence;\n }\n } else {\n // returns one value 0..1, .5 is prediction threshold\n const confidence = Math.trunc(200 * Math.abs((data[0] - 0.5))) / 100;\n if (confidence > config.face.gender.minConfidence) {\n obj.gender = data[0] <= 0.5 ? 'female' : 'male';\n obj.confidence = Math.min(0.99, confidence);\n }\n }\n }\n genderT.dispose();\n\n last = obj;\n resolve(obj);\n });\n}\n\nexports.predict = predict;\nexports.load = load;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile.js';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];\nconst models = {};\nlet last = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\nconst scale = 1; // score multiplication factor\n\nasync function load(config) {\n if (!models.emotion) {\n models.emotion = await tf.loadGraphModel(config.face.emotion.modelPath);\n log(`load model: ${config.face.emotion.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.emotion;\n}\n\nasync function predict(image, config) {\n if (!models.emotion) return null;\n if ((skipped < config.face.emotion.skipFrames) && config.videoOptimized && (last.length > 0)) {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n /*\n const zoom = [0, 0]; // 0..1 meaning 0%..100%\n const box = [[\n (image.shape[1] * zoom[0]) / image.shape[1],\n (image.shape[2] * zoom[1]) / image.shape[2],\n (image.shape[1] - (image.shape[1] * zoom[0])) / image.shape[1],\n (image.shape[2] - (image.shape[2] * zoom[1])) / image.shape[2],\n ]];\n const resize = tf.image.cropAndResize(image, box, [0], [config.face.emotion.inputSize, config.face.emotion.inputSize]);\n */\n const resize = tf.image.resizeBilinear(image, [config.face.emotion.inputSize, config.face.emotion.inputSize], false);\n const [red, green, blue] = tf.split(resize, 3, 3);\n resize.dispose();\n // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n red.dispose();\n green.dispose();\n blue.dispose();\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n redNorm.dispose();\n greenNorm.dispose();\n blueNorm.dispose();\n const normalize = tf.tidy(() => grayscale.sub(0.5).mul(2));\n grayscale.dispose();\n const obj = [];\n if (config.face.emotion.enabled) {\n let data;\n if (!config.profile) {\n const emotionT = await models.emotion.predict(normalize);\n data = emotionT.dataSync();\n tf.dispose(emotionT);\n } else {\n const profileData = await tf.profile(() => models.emotion.predict(normalize));\n data = profileData.result.dataSync();\n profileData.result.dispose();\n // @ts-ignore\n profile.run('emotion', profileData);\n }\n for (let i = 0; i < data.length; i++) {\n if (scale * data[i] > config.face.emotion.minConfidence) obj.push({ score: Math.min(0.99, Math.trunc(100 * scale * data[i]) / 100), emotion: annotations[i] });\n }\n obj.sort((a, b) => b.score - a.score);\n }\n normalize.dispose();\n last = obj;\n resolve(obj);\n });\n}\n\nexports.predict = predict;\nexports.load = load;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile.js';\n\n// based on https://github.com/sirius-ai/MobileFaceNet_TF\n// model converted from https://github.com/sirius-ai/MobileFaceNet_TF/files/3551493/FaceMobileNet192_train_false.zip\n\nconst models = {};\n\nasync function load(config) {\n if (!models.embedding) {\n models.embedding = await tf.loadGraphModel(config.face.embedding.modelPath);\n log(`load model: ${config.face.embedding.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.embedding;\n}\n\nfunction simmilarity(embedding1, embedding2) {\n if (embedding1?.length !== embedding2?.length) return 0;\n // general minkowski distance\n // euclidean distance is limited case where order is 2\n const order = 2;\n const distance = 10.0 * ((embedding1.map((val, i) => (val - embedding2[i])).reduce((dist, diff) => dist + (diff ** order), 0) ** (1 / order)));\n return (Math.trunc(1000 * (1 - distance)) / 1000);\n}\n\nasync function predict(image, config) {\n if (!models.embedding) return null;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [config.face.embedding.inputSize, config.face.embedding.inputSize], false);\n // const normalize = tf.tidy(() => resize.div(127.5).sub(0.5)); // this is -0.5...0.5 ???\n let data = [];\n if (config.face.embedding.enabled) {\n if (!config.profile) {\n const embeddingT = await models.embedding.predict({ img_inputs: resize });\n data = [...embeddingT.dataSync()]; // convert object array to standard array\n tf.dispose(embeddingT);\n } else {\n const profileData = await tf.profile(() => models.embedding.predict({ img_inputs: resize }));\n data = [...profileData.result.dataSync()];\n profileData.result.dispose();\n // @ts-ignore\n profile.run('emotion', profileData);\n }\n }\n resize.dispose();\n // normalize.dispose();\n resolve(data);\n });\n}\n\nexports.predict = predict;\nexports.simmilarity = simmilarity;\nexports.load = load;\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nconst imageNetMean = [-123.15, -115.90, -103.06];\n\nfunction nameOutputResultsMobileNet(results) {\n const [offsets, heatmap, displacementFwd, displacementBwd] = results;\n return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nfunction nameOutputResultsResNet(results) {\n const [displacementFwd, displacementBwd, offsets, heatmap] = results;\n return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nclass BaseModel {\n constructor(model) {\n this.model = model;\n }\n\n predict(input, config) {\n return tf.tidy(() => {\n const asFloat = (config.body.modelType === 'ResNet') ? input.toFloat().add(imageNetMean) : input.toFloat().div(127.5).sub(1.0);\n const asBatch = asFloat.expandDims(0);\n const results = this.model.predict(asBatch);\n const results3d = results.map((y) => y.squeeze([0]));\n const namedResults = (config.body.modelType === 'ResNet') ? nameOutputResultsResNet(results3d) : nameOutputResultsMobileNet(results3d);\n return {\n heatmapScores: namedResults.heatmap.sigmoid(),\n offsets: namedResults.offsets,\n displacementFwd: namedResults.displacementFwd,\n displacementBwd: namedResults.displacementBwd,\n };\n });\n }\n\n dispose() {\n this.model.dispose();\n }\n}\nexports.BaseModel = BaseModel;\n", "// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nfunction half(k) {\n return Math.floor(k / 2);\n}\nclass MaxHeap {\n constructor(maxSize, getElementValue) {\n this.priorityQueue = new Array(maxSize);\n this.numberOfElements = -1;\n this.getElementValue = getElementValue;\n }\n\n enqueue(x) {\n this.priorityQueue[++this.numberOfElements] = x;\n this.swim(this.numberOfElements);\n }\n\n dequeue() {\n const max = this.priorityQueue[0];\n this.exchange(0, this.numberOfElements--);\n this.sink(0);\n this.priorityQueue[this.numberOfElements + 1] = null;\n return max;\n }\n\n empty() {\n return this.numberOfElements === -1;\n }\n\n size() {\n return this.numberOfElements + 1;\n }\n\n all() {\n return this.priorityQueue.slice(0, this.numberOfElements + 1);\n }\n\n max() {\n return this.priorityQueue[0];\n }\n\n swim(k) {\n while (k > 0 && this.less(half(k), k)) {\n this.exchange(k, half(k));\n k = half(k);\n }\n }\n\n sink(k) {\n while (2 * k <= this.numberOfElements) {\n let j = 2 * k;\n if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n if (!this.less(k, j)) break;\n this.exchange(k, j);\n k = j;\n }\n }\n\n getValueAt(i) {\n return this.getElementValue(this.priorityQueue[i]);\n }\n\n less(i, j) {\n return this.getValueAt(i) < this.getValueAt(j);\n }\n\n exchange(i, j) {\n const t = this.priorityQueue[i];\n this.priorityQueue[i] = this.priorityQueue[j];\n this.priorityQueue[j] = t;\n }\n}\nexports.MaxHeap = MaxHeap;\n", "import * as heapSort from './heapSort';\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores) {\n const [height, width] = scores.shape;\n let localMaximum = true;\n const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n localMaximum = false;\n break;\n }\n }\n if (!localMaximum) break;\n }\n return localMaximum;\n}\n\nfunction buildPartWithScoreQueue(scoreThreshold, localMaximumRadius, scores) {\n const [height, width, numKeypoints] = scores.shape;\n const queue = new heapSort.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n const score = scores.get(heatmapY, heatmapX, keypointId);\n // Only consider parts with score greater or equal to threshold as root candidates.\n if (score < scoreThreshold) continue;\n // Only consider keypoints whose score is maximum in a local window.\n if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores)) {\n queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n }\n }\n }\n }\n return queue;\n}\nexports.buildPartWithScoreQueue = buildPartWithScoreQueue;\n", "exports.partNames = [\n 'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n 'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n 'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexports.NUM_KEYPOINTS = exports.partNames.length;\n\nexports.partIds = exports.partNames.reduce((result, jointName, i) => {\n result[jointName] = i;\n return result;\n}, {});\n\nconst connectedPartNames = [\n ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexports.connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([exports.partIds[jointNameA], exports.partIds[jointNameB]]));\n\nexports.poseChain = [\n ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n ['rightKnee', 'rightAnkle'],\n];\n\nexports.partChannels = [\n 'left_face',\n 'right_face',\n 'right_upper_leg_front',\n 'right_lower_leg_back',\n 'right_upper_leg_back',\n 'left_lower_leg_front',\n 'left_upper_leg_front',\n 'left_upper_leg_back',\n 'left_lower_leg_back',\n 'right_feet',\n 'right_lower_leg_front',\n 'left_feet',\n 'torso_front',\n 'torso_back',\n 'right_upper_arm_front',\n 'right_upper_arm_back',\n 'right_lower_arm_back',\n 'left_lower_arm_front',\n 'left_upper_arm_front',\n 'left_upper_arm_back',\n 'left_lower_arm_back',\n 'right_hand',\n 'right_lower_arm_front',\n 'left_hand',\n];\n", "import * as kpt from './keypoints';\n\nfunction getOffsetPoint(y, x, keypoint, offsets) {\n return {\n y: offsets.get(y, x, keypoint),\n x: offsets.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n };\n}\nexports.getOffsetPoint = getOffsetPoint;\n\nfunction getImageCoords(part, outputStride, offsets) {\n const { heatmapY, heatmapX, id: keypoint } = part;\n const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n return {\n x: part.heatmapX * outputStride + x,\n y: part.heatmapY * outputStride + y,\n };\n}\nexports.getImageCoords = getImageCoords;\n\nfunction fillArray(element, size) {\n const result = new Array(size);\n for (let i = 0; i < size; i++) {\n result[i] = element;\n }\n return result;\n}\nexports.fillArray = fillArray;\n\nfunction clamp(a, min, max) {\n if (a < min) return min;\n if (a > max) return max;\n return a;\n}\nexports.clamp = clamp;\n\nfunction squaredDistance(y1, x1, y2, x2) {\n const dy = y2 - y1;\n const dx = x2 - x1;\n return dy * dy + dx * dx;\n}\nexports.squaredDistance = squaredDistance;\n\nfunction addVectors(a, b) {\n return { x: a.x + b.x, y: a.y + b.y };\n}\nexports.addVectors = addVectors;\n\nfunction clampVector(a, min, max) {\n return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\nexports.clampVector = clampVector;\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as kpt from './keypoints';\n\nfunction getPointsConfidence(heatmapScores, heatMapCoords) {\n const numKeypoints = heatMapCoords.shape[0];\n const result = new Float32Array(numKeypoints);\n for (let keypoint = 0; keypoint < numKeypoints; keypoint++) {\n const y = heatMapCoords.get(keypoint, 0);\n const x = heatMapCoords.get(keypoint, 1);\n result[keypoint] = heatmapScores.get(y, x, keypoint);\n }\n return result;\n}\nexports.getPointsConfidence = getPointsConfidence;\n\nfunction getOffsetPoint(y, x, keypoint, offsetsBuffer) {\n return {\n y: offsetsBuffer.get(y, x, keypoint),\n x: offsetsBuffer.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n };\n}\n\nfunction getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer) {\n const result = [];\n for (let keypoint = 0; keypoint < kpt.NUM_KEYPOINTS; keypoint++) {\n const heatmapY = heatMapCoordsBuffer.get(keypoint, 0).valueOf();\n const heatmapX = heatMapCoordsBuffer.get(keypoint, 1).valueOf();\n const { x, y } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsetsBuffer);\n result.push(y);\n result.push(x);\n }\n return tf.tensor2d(result, [kpt.NUM_KEYPOINTS, 2]);\n}\nexports.getOffsetVectors = getOffsetVectors;\n\nfunction getOffsetPoints(heatMapCoordsBuffer, outputStride, offsetsBuffer) {\n return tf.tidy(() => heatMapCoordsBuffer.toTensor().mul(tf.scalar(outputStride, 'int32')).toFloat().add(getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer)));\n}\nexports.getOffsetPoints = getOffsetPoints;\n\nfunction mod(a, b) {\n return tf.tidy(() => {\n const floored = a.div(tf.scalar(b, 'int32'));\n return a.sub(floored.mul(tf.scalar(b, 'int32')));\n });\n}\n\nfunction argmax2d(inputs) {\n const [height, width, depth] = inputs.shape;\n return tf.tidy(() => {\n const reshaped = inputs.reshape([height * width, depth]);\n const coords = reshaped.argMax(0);\n const yCoords = coords.div(tf.scalar(width, 'int32')).expandDims(1);\n const xCoords = mod(coords, width).expandDims(1);\n return tf.concat([yCoords, xCoords], 1);\n });\n}\nexports.argmax2d = argmax2d;\n", "import * as keypoints from './keypoints';\nimport * as vectors from './vectors';\nimport * as decoders from './decoders';\n\nconst parentChildrenTuples = keypoints.poseChain.map(([parentJoinName, childJoinName]) => ([keypoints.partIds[parentJoinName], keypoints.partIds[childJoinName]]));\nconst parentToChildEdges = parentChildrenTuples.map(([, childJointId]) => childJointId);\nconst childToParentEdges = parentChildrenTuples.map(([parentJointId]) => parentJointId);\nfunction getDisplacement(edgeId, point, displacements) {\n const numEdges = displacements.shape[2] / 2;\n return {\n y: displacements.get(point.y, point.x, edgeId),\n x: displacements.get(point.y, point.x, numEdges + edgeId),\n };\n}\nfunction getStridedIndexNearPoint(point, outputStride, height, width) {\n return {\n // @ts-ignore\n y: vectors.clamp(Math.round(point.y / outputStride), 0, height - 1),\n // @ts-ignore\n x: vectors.clamp(Math.round(point.x / outputStride), 0, width - 1),\n };\n}\n\nfunction traverseToTargetKeypoint(edgeId, sourceKeypoint, targetKeypointId, scoresBuffer, offsets, outputStride, displacements, offsetRefineStep = 2) {\n const [height, width] = scoresBuffer.shape;\n // Nearest neighbor interpolation for the source->target displacements.\n const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, outputStride, height, width);\n const displacement = getDisplacement(edgeId, sourceKeypointIndices, displacements);\n // @ts-ignore\n const displacedPoint = vectors.addVectors(sourceKeypoint.position, displacement);\n let targetKeypoint = displacedPoint;\n for (let i = 0; i < offsetRefineStep; i++) {\n const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n // @ts-ignore\n const offsetPoint = vectors.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetKeypointId, offsets);\n // @ts-ignore\n targetKeypoint = vectors.addVectors({\n x: targetKeypointIndices.x * outputStride,\n y: targetKeypointIndices.y * outputStride,\n }, { x: offsetPoint.x, y: offsetPoint.y });\n }\n const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n const score = scoresBuffer.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetKeypointId);\n return { position: targetKeypoint, part: keypoints.partNames[targetKeypointId], score };\n}\n\nfunction decodePose(root, scores, offsets, outputStride, displacementsFwd, displacementsBwd) {\n const numParts = scores.shape[2];\n const numEdges = parentToChildEdges.length;\n const instanceKeypoints = new Array(numParts);\n // Start a new detection instance at the position of the root.\n const { part: rootPart, score: rootScore } = root;\n // @ts-ignore\n const rootPoint = vectors.getImageCoords(rootPart, outputStride, offsets);\n instanceKeypoints[rootPart.id] = {\n score: rootScore,\n part: keypoints.partNames[rootPart.id],\n position: rootPoint,\n };\n // Decode the part positions upwards in the tree, following the backward displacements.\n for (let edge = numEdges - 1; edge >= 0; --edge) {\n const sourceKeypointId = parentToChildEdges[edge];\n const targetKeypointId = childToParentEdges[edge];\n if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsBwd);\n }\n }\n // Decode the part positions downwards in the tree, following the forward displacements.\n for (let edge = 0; edge < numEdges; ++edge) {\n const sourceKeypointId = childToParentEdges[edge];\n const targetKeypointId = parentToChildEdges[edge];\n if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsFwd);\n }\n }\n return instanceKeypoints;\n}\nexports.decodePose = decodePose;\n\nasync function decodeSinglePose(heatmapScores, offsets, config) {\n let totalScore = 0.0;\n // @ts-ignore\n const heatmapValues = decoders.argmax2d(heatmapScores);\n const allTensorBuffers = await Promise.all([heatmapScores.buffer(), offsets.buffer(), heatmapValues.buffer()]);\n const scoresBuffer = allTensorBuffers[0];\n const offsetsBuffer = allTensorBuffers[1];\n const heatmapValuesBuffer = allTensorBuffers[2];\n // @ts-ignore\n const offsetPoints = decoders.getOffsetPoints(heatmapValuesBuffer, config.body.outputStride, offsetsBuffer);\n const offsetPointsBuffer = await offsetPoints.buffer();\n // @ts-ignore\n const keypointConfidence = Array.from(decoders.getPointsConfidence(scoresBuffer, heatmapValuesBuffer));\n const instanceKeypoints = keypointConfidence.map((score, i) => {\n totalScore += score;\n return {\n position: {\n y: offsetPointsBuffer.get(i, 0),\n x: offsetPointsBuffer.get(i, 1),\n },\n part: keypoints.partNames[i],\n score,\n };\n });\n const filteredKeypoints = instanceKeypoints.filter((kpt) => kpt.score > config.body.scoreThreshold);\n heatmapValues.dispose();\n offsetPoints.dispose();\n return { keypoints: filteredKeypoints, score: totalScore / instanceKeypoints.length };\n}\nexports.decodeSinglePose = decodeSinglePose;\n", "import * as buildParts from './buildParts';\nimport * as decodePose from './decodePose';\nimport * as vectors from './vectors';\n\nconst kLocalMaximumRadius = 1;\n\nfunction withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, { x, y }, keypointId) {\n return poses.some(({ keypoints }) => {\n const correspondingKeypoint = keypoints[keypointId].position;\n // @ts-ignore\n return vectors.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n });\n}\n\nfunction getInstanceScore(existingPoses, squaredNmsRadius, instanceKeypoints) {\n const notOverlappedKeypointScores = instanceKeypoints.reduce((result, { position, score }, keypointId) => {\n if (!withinNmsRadiusOfCorrespondingPoint(existingPoses, squaredNmsRadius, position, keypointId)) result += score;\n return result;\n }, 0.0);\n return notOverlappedKeypointScores / instanceKeypoints.length;\n}\n\nfunction decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config) {\n const poses = [];\n // @ts-ignore\n const queue = buildParts.buildPartWithScoreQueue(config.body.scoreThreshold, kLocalMaximumRadius, scoresBuffer);\n const squaredNmsRadius = config.body.nmsRadius ^ 2;\n // Generate at most maxDetections object instances per image in decreasing root part score order.\n while (poses.length < config.body.maxDetections && !queue.empty()) {\n // The top element in the queue is the next root candidate.\n const root = queue.dequeue();\n // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n // @ts-ignore\n const rootImageCoords = vectors.getImageCoords(root.part, config.body.outputStride, offsetsBuffer);\n if (withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, rootImageCoords, root.part.id)) continue;\n // Else start a new detection instance at the position of the root.\n // @ts-ignore\n const keypoints = decodePose.decodePose(root, scoresBuffer, offsetsBuffer, config.body.outputStride, displacementsFwdBuffer, displacementsBwdBuffer);\n const score = getInstanceScore(poses, squaredNmsRadius, keypoints);\n if (score > config.body.scoreThreshold) poses.push({ keypoints, score });\n }\n return poses;\n}\nexports.decodeMultiplePoses = decodeMultiplePoses;\n", "import * as kpt from './keypoints';\n\nfunction eitherPointDoesntMeetConfidence(a, b, minConfidence) {\n return (a < minConfidence || b < minConfidence);\n}\n\nfunction getAdjacentKeyPoints(keypoints, minConfidence) {\n return kpt.connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n return result;\n }\n result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n return result;\n }, []);\n}\nexports.getAdjacentKeyPoints = getAdjacentKeyPoints;\n\nconst { NEGATIVE_INFINITY, POSITIVE_INFINITY } = Number;\nfunction getBoundingBox(keypoints) {\n return keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n maxX: Math.max(maxX, x),\n maxY: Math.max(maxY, y),\n minX: Math.min(minX, x),\n minY: Math.min(minY, y),\n }), {\n maxX: NEGATIVE_INFINITY,\n maxY: NEGATIVE_INFINITY,\n minX: POSITIVE_INFINITY,\n minY: POSITIVE_INFINITY,\n });\n}\nexports.getBoundingBox = getBoundingBox;\n\nfunction getBoundingBoxPoints(keypoints) {\n const { minX, minY, maxX, maxY } = getBoundingBox(keypoints);\n return [{ x: minX, y: minY }, { x: maxX, y: minY }, { x: maxX, y: maxY }, { x: minX, y: maxY }];\n}\nexports.getBoundingBoxPoints = getBoundingBoxPoints;\n\nasync function toTensorBuffers3D(tensors) {\n return Promise.all(tensors.map((tensor) => tensor.buffer()));\n}\nexports.toTensorBuffers3D = toTensorBuffers3D;\n\nfunction scalePose(pose, scaleY, scaleX) {\n return {\n score: pose.score,\n keypoints: pose.keypoints.map(({ score, part, position }) => ({\n score,\n part,\n position: { x: position.x * scaleX, y: position.y * scaleY },\n })),\n };\n}\nexports.scalePose = scalePose;\n\nfunction resizeTo(image, [targetH, targetW]) {\n const input = image.squeeze(0);\n const resized = input.resizeBilinear([targetH, targetW]);\n input.dispose();\n return resized;\n}\nexports.resizeTo = resizeTo;\n\nfunction scaleAndFlipPoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) {\n const scaledPoses = poses.map((pose) => scalePose(pose, height / inputResolutionHeight, width / inputResolutionWidth));\n return scaledPoses;\n}\nexports.scaleAndFlipPoses = scaleAndFlipPoses;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as modelBase from './modelBase';\nimport * as decodeMultiple from './decodeMultiple';\nimport * as decodePose from './decodePose';\nimport * as util from './util';\n\nasync function estimateMultiple(input, res, config) {\n return new Promise(async (resolve) => {\n const height = input.shape[1];\n const width = input.shape[2];\n // @ts-ignore\n const allTensorBuffers = await util.toTensorBuffers3D([res.heatmapScores, res.offsets, res.displacementFwd, res.displacementBwd]);\n const scoresBuffer = allTensorBuffers[0];\n const offsetsBuffer = allTensorBuffers[1];\n const displacementsFwdBuffer = allTensorBuffers[2];\n const displacementsBwdBuffer = allTensorBuffers[3];\n // @ts-ignore\n const poses = await decodeMultiple.decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config);\n // @ts-ignore\n const scaled = util.scaleAndFlipPoses(poses, [height, width], [config.body.inputSize, config.body.inputSize]);\n resolve(scaled);\n });\n}\n\nasync function estimateSingle(input, res, config) {\n return new Promise(async (resolve) => {\n const height = input.shape[1];\n const width = input.shape[2];\n // @ts-ignore\n const pose = await decodePose.decodeSinglePose(res.heatmapScores, res.offsets, config);\n const poses = [pose];\n // @ts-ignore\n const scaled = util.scaleAndFlipPoses(poses, [height, width], [config.body.inputSize, config.body.inputSize]);\n resolve(scaled);\n });\n}\n\nclass PoseNet {\n constructor(model) {\n this.baseModel = model;\n }\n\n async estimatePoses(input, config) {\n // @ts-ignore\n const resized = util.resizeTo(input, [config.body.inputSize, config.body.inputSize]);\n const res = this.baseModel.predict(resized, config);\n\n const poses = (config.body.maxDetections < 2) ? await estimateSingle(input, res, config) : await estimateMultiple(input, res, config);\n\n res.heatmapScores.dispose();\n res.offsets.dispose();\n res.displacementFwd.dispose();\n res.displacementBwd.dispose();\n resized.dispose();\n\n return poses;\n }\n\n dispose() {\n this.baseModel.dispose();\n }\n}\nexports.PoseNet = PoseNet;\n\nasync function load(config) {\n const model = await tf.loadGraphModel(config.body.modelPath);\n // @ts-ignore\n const mobilenet = new modelBase.BaseModel(model);\n log(`load model: ${config.body.modelPath.match(/\\/(.*)\\./)[1]}`);\n return new PoseNet(mobilenet);\n}\nexports.load = load;\n", "import * as modelPoseNet from './modelPoseNet';\nimport * as keypoints from './keypoints';\nimport * as util from './util';\n\n// @ts-ignore\nexports.load = modelPoseNet.load;\n// @ts-ignore\nexports.PoseNet = modelPoseNet.PoseNet;\n\nexports.partChannels = keypoints.partChannels;\nexports.partIds = keypoints.partIds;\nexports.partNames = keypoints.partNames;\nexports.poseChain = keypoints.poseChain;\n// @ts-ignore\nexports.getAdjacentKeyPoints = util.getAdjacentKeyPoints;\n// @ts-ignore\nexports.getBoundingBox = util.getBoundingBox;\n// @ts-ignore\nexports.getBoundingBoxPoints = util.getBoundingBoxPoints;\n// @ts-ignore\nexports.scaleAndFlipPoses = util.scaleAndFlipPoses;\n// @ts-ignore\nexports.scalePose = util.scalePose;\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\n\nclass HandDetector {\n constructor(model, inputSize, anchorsAnnotated) {\n this.model = model;\n this.anchors = anchorsAnnotated.map((anchor) => [anchor.x_center, anchor.y_center]);\n this.anchorsTensor = tf.tensor2d(this.anchors);\n this.inputSizeTensor = tf.tensor1d([inputSize, inputSize]);\n this.doubleInputSizeTensor = tf.tensor1d([inputSize * 2, inputSize * 2]);\n }\n\n normalizeBoxes(boxes) {\n return tf.tidy(() => {\n const boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n const boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n const boxCenterPoints = tf.add(tf.div(boxOffsets, this.inputSizeTensor), this.anchorsTensor);\n const halfBoxSizes = tf.div(boxSizes, this.doubleInputSizeTensor);\n const startPoints = tf.mul(tf.sub(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n const endPoints = tf.mul(tf.add(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n return tf.concat2d([startPoints, endPoints], 1);\n });\n }\n\n normalizeLandmarks(rawPalmLandmarks, index) {\n return tf.tidy(() => {\n const landmarks = tf.add(tf.div(rawPalmLandmarks.reshape([-1, 7, 2]), this.inputSizeTensor), this.anchors[index]);\n return tf.mul(landmarks, this.inputSizeTensor);\n });\n }\n\n async getBoxes(input, config) {\n const batched = this.model.predict(input);\n const predictions = batched.squeeze();\n batched.dispose();\n const scoresT = tf.tidy(() => tf.sigmoid(tf.slice(predictions, [0, 0], [-1, 1])).squeeze());\n const scores = scoresT.dataSync();\n const rawBoxes = tf.slice(predictions, [0, 1], [-1, 4]);\n const boxes = this.normalizeBoxes(rawBoxes);\n rawBoxes.dispose();\n const filteredT = await tf.image.nonMaxSuppressionAsync(boxes, scores, config.hand.maxHands, config.hand.iouThreshold, config.hand.scoreThreshold);\n const filtered = filteredT.arraySync();\n\n scoresT.dispose();\n filteredT.dispose();\n const hands = [];\n for (const index of filtered) {\n if (scores[index] >= config.hand.minConfidence) {\n const matchingBox = tf.slice(boxes, [index, 0], [1, -1]);\n const rawPalmLandmarks = tf.slice(predictions, [index, 5], [1, 14]);\n const palmLandmarks = tf.tidy(() => this.normalizeLandmarks(rawPalmLandmarks, index).reshape([-1, 2]));\n rawPalmLandmarks.dispose();\n hands.push({ box: matchingBox, palmLandmarks, confidence: scores[index] });\n }\n }\n predictions.dispose();\n boxes.dispose();\n return hands;\n }\n\n async estimateHandBounds(input, config) {\n const inputHeight = input.shape[1];\n const inputWidth = input.shape[2];\n const image = tf.tidy(() => input.resizeBilinear([config.hand.inputSize, config.hand.inputSize]).div(127.5).sub(1));\n const predictions = await this.getBoxes(image, config);\n image.dispose();\n const hands = [];\n if (!predictions || predictions.length === 0) return hands;\n for (const prediction of predictions) {\n const boxes = prediction.box.dataSync();\n const startPoint = boxes.slice(0, 2);\n const endPoint = boxes.slice(2, 4);\n const palmLandmarks = prediction.palmLandmarks.arraySync();\n prediction.box.dispose();\n prediction.palmLandmarks.dispose();\n hands.push(box.scaleBoxCoordinates({ startPoint, endPoint, palmLandmarks, confidence: prediction.confidence }, [inputWidth / config.hand.inputSize, inputHeight / config.hand.inputSize]));\n }\n return hands;\n }\n}\nexports.HandDetector = HandDetector;\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\nimport * as util from './util';\n// eslint-disable-next-line no-unused-vars\nimport { log } from '../log.js';\n\n// const PALM_BOX_SHIFT_VECTOR = [0, -0.4];\nconst PALM_BOX_ENLARGE_FACTOR = 5; // default 3\n// const HAND_BOX_SHIFT_VECTOR = [0, -0.1]; // move detected hand box by x,y to ease landmark detection\nconst HAND_BOX_ENLARGE_FACTOR = 1.65; // default 1.65\nconst PALM_LANDMARK_IDS = [0, 5, 9, 13, 17, 1, 2];\nconst PALM_LANDMARKS_INDEX_OF_PALM_BASE = 0;\nconst PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE = 2;\n\nclass HandPipeline {\n constructor(handDetector, landmarkDetector, inputSize) {\n this.handDetector = handDetector;\n this.landmarkDetector = landmarkDetector;\n this.inputSize = inputSize;\n this.storedBoxes = [];\n this.skipped = 0;\n this.detectedHands = 0;\n }\n\n getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n // return box.enlargeBox(box.squarifyBox(box.shiftBox(boxAroundPalm, PALM_BOX_SHIFT_VECTOR)), PALM_BOX_ENLARGE_FACTOR);\n return box.enlargeBox(box.squarifyBox(boxAroundPalm), PALM_BOX_ENLARGE_FACTOR);\n }\n\n getBoxForHandLandmarks(landmarks) {\n const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n // const boxAroundHand = box.enlargeBox(box.squarifyBox(box.shiftBox(boundingBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n const boxAroundHand = box.enlargeBox(box.squarifyBox(boundingBox), HAND_BOX_ENLARGE_FACTOR);\n boxAroundHand.palmLandmarks = [];\n for (let i = 0; i < PALM_LANDMARK_IDS.length; i++) {\n boxAroundHand.palmLandmarks.push(landmarks[PALM_LANDMARK_IDS[i]].slice(0, 2));\n }\n return boxAroundHand;\n }\n\n transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n const boxSize = box.getBoxSize(box2);\n const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n const coordsScaled = rawCoords.map((coord) => [\n scaleFactor[0] * (coord[0] - this.inputSize / 2),\n scaleFactor[1] * (coord[1] - this.inputSize / 2),\n scaleFactor[2] * coord[2],\n ]);\n const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n const coordsRotated = coordsScaled.map((coord) => {\n const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n return [...rotated, coord[2]];\n });\n const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n const boxCenter = [...box.getBoxCenter(box2), 1];\n const originalBoxCenter = [\n util.dot(boxCenter, inverseRotationMatrix[0]),\n util.dot(boxCenter, inverseRotationMatrix[1]),\n ];\n return coordsRotated.map((coord) => [\n coord[0] + originalBoxCenter[0],\n coord[1] + originalBoxCenter[1],\n coord[2],\n ]);\n }\n\n async estimateHands(image, config) {\n let useFreshBox = false;\n\n // run new detector every skipFrames unless we only want box to start with\n let boxes;\n if ((this.skipped === 0) || (this.skipped > config.hand.skipFrames) || !config.hand.landmarks || !config.videoOptimized) {\n boxes = await this.handDetector.estimateHandBounds(image, config);\n this.skipped = 0;\n }\n if (config.videoOptimized) this.skipped++;\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxHands) || !config.hand.landmarks)) {\n this.detectedHands = 0;\n this.storedBoxes = [...boxes];\n // for (const possible of boxes) this.storedBoxes.push(possible);\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\n const hands = [];\n // log('hand', `skipped: ${this.skipped} max: ${config.hand.maxHands} detected: ${this.detectedHands} stored: ${this.storedBoxes.length} new: ${boxes?.length}`);\n\n // go through working set of boxes\n for (let i = 0; i < this.storedBoxes.length; i++) {\n const currentBox = this.storedBoxes[i];\n if (!currentBox) continue;\n if (config.hand.landmarks) {\n const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_PALM_BASE], currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE]) : 0;\n const palmCenter = box.getBoxCenter(currentBox);\n const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n const rotatedImage = config.hand.rotation ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n const croppedInput = box.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n const handImage = croppedInput.div(255);\n croppedInput.dispose();\n rotatedImage.dispose();\n const [confidenceT, keypoints] = await this.landmarkDetector.predict(handImage);\n handImage.dispose();\n const confidence = confidenceT.dataSync()[0];\n confidenceT.dispose();\n if (confidence >= config.hand.minConfidence) {\n const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n const rawCoords = keypointsReshaped.arraySync();\n keypoints.dispose();\n keypointsReshaped.dispose();\n const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n this.storedBoxes[i] = nextBoundingBox;\n const result = {\n landmarks: coords,\n confidence,\n box: {\n topLeft: nextBoundingBox.startPoint,\n bottomRight: nextBoundingBox.endPoint,\n },\n };\n hands.push(result);\n } else {\n this.storedBoxes[i] = null;\n }\n keypoints.dispose();\n } else {\n // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n const enlarged = box.enlargeBox(box.squarifyBox(currentBox), HAND_BOX_ENLARGE_FACTOR);\n const result = {\n confidence: currentBox.confidence,\n box: {\n topLeft: enlarged.startPoint,\n bottomRight: enlarged.endPoint,\n },\n };\n hands.push(result);\n }\n }\n this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n this.detectedHands = hands.length;\n return hands;\n }\n\n // eslint-disable-next-line class-methods-use-this\n calculateLandmarksBoundingBox(landmarks) {\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint };\n }\n}\n\nexports.HandPipeline = HandPipeline;\n", "exports.anchors = [\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.796875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.796875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.828125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.828125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.859375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.859375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.890625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.890625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.921875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.921875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.953125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.953125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.984375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.984375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.796875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.796875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.828125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.828125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.859375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.859375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.890625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.890625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.921875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.921875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.953125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.953125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.984375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.984375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.078125,\n },\n {\n w: 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0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.6875,\n },\n {\n w: 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0.9375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n];\n", "// https://storage.googleapis.com/tfjs-models/demos/handpose/index.html\n\nimport { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as handdetector from './handdetector';\nimport * as handpipeline from './handpipeline';\nimport * as anchors from './anchors';\n\nconst MESH_ANNOTATIONS = {\n thumb: [1, 2, 3, 4],\n indexFinger: [5, 6, 7, 8],\n middleFinger: [9, 10, 11, 12],\n ringFinger: [13, 14, 15, 16],\n pinky: [17, 18, 19, 20],\n palmBase: [0],\n};\n\nclass HandPose {\n constructor(handPipeline) {\n this.handPipeline = handPipeline;\n }\n\n static getAnnotations() {\n return MESH_ANNOTATIONS;\n }\n\n async estimateHands(input, config) {\n const predictions = await this.handPipeline.estimateHands(input, config);\n if (!predictions) return [];\n const hands = [];\n for (const prediction of predictions) {\n const annotations = {};\n if (prediction.landmarks) {\n for (const key of Object.keys(MESH_ANNOTATIONS)) {\n annotations[key] = MESH_ANNOTATIONS[key].map((index) => prediction.landmarks[index]);\n }\n }\n const box = prediction.box ? [\n Math.max(0, prediction.box.topLeft[0]),\n Math.max(0, prediction.box.topLeft[1]),\n Math.min(input.shape[2], prediction.box.bottomRight[0]) - prediction.box.topLeft[0],\n Math.min(input.shape[1], prediction.box.bottomRight[1]) - prediction.box.topLeft[1],\n ] : 0;\n hands.push({\n confidence: prediction.confidence,\n box,\n landmarks: prediction.landmarks,\n annotations,\n });\n }\n return hands;\n }\n}\nexports.HandPose = HandPose;\n\nasync function load(config) {\n const [handDetectorModel, handPoseModel] = await Promise.all([\n config.hand.enabled ? tf.loadGraphModel(config.hand.detector.modelPath, { fromTFHub: config.hand.detector.modelPath.includes('tfhub.dev') }) : null,\n config.hand.landmarks ? tf.loadGraphModel(config.hand.skeleton.modelPath, { fromTFHub: config.hand.skeleton.modelPath.includes('tfhub.dev') }) : null,\n ]);\n // @ts-ignore\n const handDetector = new handdetector.HandDetector(handDetectorModel, config.hand.inputSize, anchors.anchors);\n // @ts-ignore\n const handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel, config.hand.inputSize);\n const handPose = new HandPose(handPipeline);\n if (config.hand.enabled) log(`load model: ${config.hand.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n if (config.hand.landmarks) log(`load model: ${config.hand.skeleton.modelPath.match(/\\/(.*)\\./)[1]}`);\n return handPose;\n}\nexports.load = load;\n", "exports.body = (res) => {\n if (!res) return [];\n const gestures = [];\n for (let i = 0; i < res.length; i++) {\n // raising hands\n const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n if (nose && leftWrist && rightWrist && (leftWrist.position.y < nose.position.y) && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'i give up' });\n else if (nose && leftWrist && (leftWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise left hand' });\n else if (nose && rightWrist && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise right hand' });\n\n // leaning\n const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n if (leftShoulder && rightShoulder) gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position.y > rightShoulder.position.y) ? 'left' : 'right'}` });\n }\n return gestures;\n};\n\nexports.face = (res) => {\n if (!res) return [];\n const gestures = [];\n for (let i = 0; i < res.length; i++) {\n if (res[i].mesh && res[i].mesh.length > 0) {\n const eyeFacing = res[i].mesh[35][2] - res[i].mesh[263][2];\n if (Math.abs(eyeFacing) < 10) gestures.push({ face: i, gesture: 'facing camera' });\n else gestures.push({ face: i, gesture: `facing ${eyeFacing < 0 ? 'right' : 'left'}` });\n const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n const chinDepth = res[i].mesh[152][2];\n if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n }\n }\n return gestures;\n};\n\nexports.iris = (res) => {\n if (!res) return [];\n const gestures = [];\n for (let i = 0; i < res.length; i++) {\n if (!res[i].annotations || !res[i].annotations.leftEyeIris || !res[i].annotations.rightEyeIris) continue;\n const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n if (difference < 0.25) gestures.push({ iris: i, gesture: 'looking at camera' });\n }\n return gestures;\n};\n\nexports.hand = (res) => {\n if (!res) return [];\n const gestures = [];\n for (let i = 0; i < res.length; i++) {\n const fingers = [];\n for (const [finger, pos] of Object.entries(res[i]['annotations'])) {\n if (finger !== 'palmBase') fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n }\n if (fingers && fingers.length > 0) {\n const closest = fingers.reduce((best, a) => (best.position[2] < a.position[2] ? best : a));\n const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n gestures.push({ hand: i, gesture: `${closest.name} forward ${highest.name} up` });\n }\n }\n return gestures;\n};\n", "/* eslint-disable no-use-before-define */\n/*\nWebGLImageFilter - MIT Licensed\n2013, Dominic Szablewski - phoboslab.org\n\n*/\n\nconst WebGLProgram = function (gl, vertexSource, fragmentSource) {\n const _collect = function (source, prefix, collection) {\n const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n source.replace(r, (match, name) => {\n collection[name] = 0;\n return match;\n });\n };\n\n const _compile = function (source, type) {\n const shader = gl.createShader(type);\n gl.shaderSource(shader, source);\n gl.compileShader(shader);\n\n if (!gl.getShaderParameter(shader, gl.COMPILE_STATUS)) {\n // @ts-ignore\n throw new Error('Filter: GL compile failed', gl.getShaderInfoLog(shader));\n }\n return shader;\n };\n\n this.uniform = {};\n this.attribute = {};\n\n const _vsh = _compile(vertexSource, gl.VERTEX_SHADER);\n const _fsh = _compile(fragmentSource, gl.FRAGMENT_SHADER);\n\n this.id = gl.createProgram();\n gl.attachShader(this.id, _vsh);\n gl.attachShader(this.id, _fsh);\n gl.linkProgram(this.id);\n\n if (!gl.getProgramParameter(this.id, gl.LINK_STATUS)) {\n // @ts-ignore\n throw new Error('Filter: GL link failed', gl.getProgramInfoLog(this.id));\n }\n\n gl.useProgram(this.id);\n\n // Collect attributes\n _collect(vertexSource, 'attribute', this.attribute);\n for (const a in this.attribute) {\n this.attribute[a] = gl.getAttribLocation(this.id, a);\n }\n\n // Collect uniforms\n _collect(vertexSource, 'uniform', this.uniform);\n _collect(fragmentSource, 'uniform', this.uniform);\n for (const u in this.uniform) {\n this.uniform[u] = gl.getUniformLocation(this.id, u);\n }\n};\n\nconst WebGLImageFilter = function (params) {\n if (!params) params = { };\n let _drawCount = 0;\n let _sourceTexture = null;\n let _lastInChain = false;\n let _currentFramebufferIndex = -1;\n let _tempFramebuffers = [null, null];\n let _filterChain = [];\n let _width = -1;\n let _height = -1;\n let _vertexBuffer = null;\n let _currentProgram = null;\n const _canvas = params.canvas || document.createElement('canvas');\n\n // key is the shader program source, value is the compiled program\n const _shaderProgramCache = { };\n\n const gl = _canvas.getContext('webgl');\n if (!gl) throw new Error('Filter: getContext() failed');\n\n this.addFilter = function (name) {\n // eslint-disable-next-line prefer-rest-params\n const args = Array.prototype.slice.call(arguments, 1);\n const filter = _filter[name];\n\n _filterChain.push({ func: filter, args });\n };\n\n this.reset = function () {\n _filterChain = [];\n };\n\n this.apply = function (image) {\n _resize(image.width, image.height);\n _drawCount = 0;\n\n // Create the texture for the input image if we haven't yet\n if (!_sourceTexture) _sourceTexture = gl.createTexture();\n gl.bindTexture(gl.TEXTURE_2D, _sourceTexture);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n\n // No filters? Just draw\n if (_filterChain.length === 0) {\n // const program = _compileShader(SHADER.FRAGMENT_IDENTITY);\n _draw();\n return _canvas;\n }\n\n for (let i = 0; i < _filterChain.length; i++) {\n _lastInChain = (i === _filterChain.length - 1);\n const f = _filterChain[i];\n f.func.apply(this, f.args || []);\n }\n\n return _canvas;\n };\n\n const _resize = function (width, height) {\n // Same width/height? Nothing to do here\n if (width === _width && height === _height) { return; }\n\n _canvas.width = width;\n _width = width;\n _canvas.height = height;\n _height = height;\n\n // Create the context if we don't have it yet\n if (!_vertexBuffer) {\n // Create the vertex buffer for the two triangles [x, y, u, v] * 6\n const vertices = new Float32Array([\n -1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0,\n -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0,\n ]);\n // eslint-disable-next-line no-unused-expressions\n (_vertexBuffer = gl.createBuffer(), gl.bindBuffer(gl.ARRAY_BUFFER, _vertexBuffer));\n gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);\n\n // Note sure if this is a good idea; at least it makes texture loading\n // in Ejecta instant.\n gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n }\n\n gl.viewport(0, 0, _width, _height);\n\n // Delete old temp framebuffers\n _tempFramebuffers = [null, null];\n };\n\n const _getTempFramebuffer = function (index) {\n // @ts-ignore\n _tempFramebuffers[index] = _tempFramebuffers[index] || _createFramebufferTexture(_width, _height);\n return _tempFramebuffers[index];\n };\n\n const _createFramebufferTexture = function (width, height) {\n const fbo = gl.createFramebuffer();\n gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n\n const renderbuffer = gl.createRenderbuffer();\n gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n\n const texture = gl.createTexture();\n gl.bindTexture(gl.TEXTURE_2D, texture);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n\n gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n\n gl.bindTexture(gl.TEXTURE_2D, null);\n gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n\n return { fbo, texture };\n };\n\n const _draw = function (flags) {\n let source = null;\n let target = null;\n let flipY = false;\n\n // Set up the source\n if (_drawCount === 0) {\n // First draw call - use the source texture\n source = _sourceTexture;\n } else {\n // All following draw calls use the temp buffer last drawn to\n // @ts-ignore\n source = _getTempFramebuffer(_currentFramebufferIndex)?.texture;\n }\n _drawCount++;\n\n // Set up the target\n if (_lastInChain && !(flags & DRAW.INTERMEDIATE)) {\n // Last filter in our chain - draw directly to the WebGL Canvas. We may\n // also have to flip the image vertically now\n target = null;\n flipY = _drawCount % 2 === 0;\n } else {\n // Intermediate draw call - get a temp buffer to draw to\n _currentFramebufferIndex = (_currentFramebufferIndex + 1) % 2;\n // @ts-ignore\n target = _getTempFramebuffer(_currentFramebufferIndex)?.fbo;\n }\n\n // Bind the source and target and draw the two triangles\n gl.bindTexture(gl.TEXTURE_2D, source);\n gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n\n gl.uniform1f(_currentProgram.uniform.flipY, (flipY ? -1 : 1));\n gl.drawArrays(gl.TRIANGLES, 0, 6);\n };\n\n const _compileShader = function (fragmentSource) {\n if (_shaderProgramCache[fragmentSource]) {\n _currentProgram = _shaderProgramCache[fragmentSource];\n gl.useProgram(_currentProgram.id);\n return _currentProgram;\n }\n\n // Compile shaders\n _currentProgram = new WebGLProgram(gl, SHADER.VERTEX_IDENTITY, fragmentSource);\n\n const floatSize = Float32Array.BYTES_PER_ELEMENT;\n const vertSize = 4 * floatSize;\n gl.enableVertexAttribArray(_currentProgram.attribute.pos);\n gl.vertexAttribPointer(_currentProgram.attribute.pos, 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n gl.enableVertexAttribArray(_currentProgram.attribute.uv);\n gl.vertexAttribPointer(_currentProgram.attribute.uv, 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n\n _shaderProgramCache[fragmentSource] = _currentProgram;\n return _currentProgram;\n };\n\n let DRAW = { INTERMEDIATE: 1 };\n\n let SHADER = {};\n SHADER.VERTEX_IDENTITY = [\n 'precision highp float;',\n 'attribute vec2 pos;',\n 'attribute vec2 uv;',\n 'varying vec2 vUv;',\n 'uniform float flipY;',\n\n 'void main(void) {',\n 'vUv = uv;',\n 'gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);',\n '}',\n ].join('\\n');\n\n SHADER.FRAGMENT_IDENTITY = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n\n 'void main(void) {',\n 'gl_FragColor = texture2D(texture, vUv);',\n '}',\n ].join('\\n');\n\n let _filter = {};\n\n // -------------------------------------------------------------------------\n // Color Matrix Filter\n\n _filter.colorMatrix = function (matrix) {\n // Create a Float32 Array and normalize the offset component to 0-1\n const m = new Float32Array(matrix);\n m[4] /= 255;\n m[9] /= 255;\n m[14] /= 255;\n m[19] /= 255;\n\n // Can we ignore the alpha value? Makes things a bit faster.\n const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0)\n ? _filter.colorMatrix.SHADER.WITHOUT_ALPHA\n : _filter.colorMatrix.SHADER.WITH_ALPHA;\n\n const program = _compileShader(shader);\n gl.uniform1fv(program.uniform.m, m);\n _draw();\n };\n\n _filter.colorMatrix.SHADER = {};\n _filter.colorMatrix.SHADER.WITH_ALPHA = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform float m[20];',\n\n 'void main(void) {',\n 'vec4 c = texture2D(texture, vUv);',\n 'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];',\n 'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];',\n 'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];',\n 'gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];',\n '}',\n ].join('\\n');\n _filter.colorMatrix.SHADER.WITHOUT_ALPHA = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform float m[20];',\n\n 'void main(void) {',\n 'vec4 c = texture2D(texture, vUv);',\n 'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];',\n 'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];',\n 'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];',\n 'gl_FragColor.a = c.a;',\n '}',\n ].join('\\n');\n\n _filter.brightness = function (brightness) {\n const b = (brightness || 0) + 1;\n _filter.colorMatrix([\n b, 0, 0, 0, 0,\n 0, b, 0, 0, 0,\n 0, 0, b, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.saturation = function (amount) {\n const x = (amount || 0) * 2 / 3 + 1;\n const y = ((x - 1) * -0.5);\n _filter.colorMatrix([\n x, y, y, 0, 0,\n y, x, y, 0, 0,\n y, y, x, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.desaturate = function () {\n _filter.saturation(-1);\n };\n\n _filter.contrast = function (amount) {\n const v = (amount || 0) + 1;\n const o = -128 * (v - 1);\n\n _filter.colorMatrix([\n v, 0, 0, 0, o,\n 0, v, 0, 0, o,\n 0, 0, v, 0, o,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.negative = function () {\n _filter.contrast(-2);\n };\n\n _filter.hue = function (rotation) {\n rotation = (rotation || 0) / 180 * Math.PI;\n const cos = Math.cos(rotation);\n const sin = Math.sin(rotation);\n const lumR = 0.213;\n const lumG = 0.715;\n const lumB = 0.072;\n\n _filter.colorMatrix([\n lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.desaturateLuminance = function () {\n _filter.colorMatrix([\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.sepia = function () {\n _filter.colorMatrix([\n 0.393, 0.7689999, 0.18899999, 0, 0,\n 0.349, 0.6859999, 0.16799999, 0, 0,\n 0.272, 0.5339999, 0.13099999, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.brownie = function () {\n _filter.colorMatrix([\n 0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n 0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.vintagePinhole = function () {\n _filter.colorMatrix([\n 0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n 0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n 0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.kodachrome = function () {\n _filter.colorMatrix([\n 1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.technicolor = function () {\n _filter.colorMatrix([\n 1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.polaroid = function () {\n _filter.colorMatrix([\n 1.438, -0.062, -0.062, 0, 0,\n -0.122, 1.378, -0.122, 0, 0,\n -0.016, -0.016, 1.483, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.shiftToBGR = function () {\n _filter.colorMatrix([\n 0, 0, 1, 0, 0,\n 0, 1, 0, 0, 0,\n 1, 0, 0, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n // -------------------------------------------------------------------------\n // Convolution Filter\n\n _filter.convolution = function (matrix) {\n const m = new Float32Array(matrix);\n const pixelSizeX = 1 / _width;\n const pixelSizeY = 1 / _height;\n\n const program = _compileShader(_filter.convolution.SHADER);\n gl.uniform1fv(program.uniform.m, m);\n gl.uniform2f(program.uniform.px, pixelSizeX, pixelSizeY);\n _draw();\n };\n\n _filter.convolution.SHADER = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform vec2 px;',\n 'uniform float m[9];',\n\n 'void main(void) {',\n 'vec4 c11 = texture2D(texture, vUv - px);', // top left\n 'vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y));', // top center\n 'vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y));', // top right\n\n 'vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) );', // mid left\n 'vec4 c22 = texture2D(texture, vUv);', // mid center\n 'vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) );', // mid right\n\n 'vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) );', // bottom left\n 'vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) );', // bottom center\n 'vec4 c33 = texture2D(texture, vUv + px );', // bottom right\n\n 'gl_FragColor = ',\n 'c11 * m[0] + c12 * m[1] + c22 * m[2] +',\n 'c21 * m[3] + c22 * m[4] + c23 * m[5] +',\n 'c31 * m[6] + c32 * m[7] + c33 * m[8];',\n 'gl_FragColor.a = c22.a;',\n '}',\n ].join('\\n');\n\n _filter.detectEdges = function () {\n _filter.convolution.call(this, [\n 0, 1, 0,\n 1, -4, 1,\n 0, 1, 0,\n ]);\n };\n\n _filter.sobelX = function () {\n _filter.convolution.call(this, [\n -1, 0, 1,\n -2, 0, 2,\n -1, 0, 1,\n ]);\n };\n\n _filter.sobelY = function () {\n _filter.convolution.call(this, [\n -1, -2, -1,\n 0, 0, 0,\n 1, 2, 1,\n ]);\n };\n\n _filter.sharpen = function (amount) {\n const a = amount || 1;\n _filter.convolution.call(this, [\n 0, -1 * a, 0,\n -1 * a, 1 + 4 * a, -1 * a,\n 0, -1 * a, 0,\n ]);\n };\n\n _filter.emboss = function (size) {\n const s = size || 1;\n _filter.convolution.call(this, [\n -2 * s, -1 * s, 0,\n -1 * s, 1, 1 * s,\n 0, 1 * s, 2 * s,\n ]);\n };\n\n // -------------------------------------------------------------------------\n // Blur Filter\n\n _filter.blur = function (size) {\n const blurSizeX = (size / 7) / _width;\n const blurSizeY = (size / 7) / _height;\n\n const program = _compileShader(_filter.blur.SHADER);\n\n // Vertical\n gl.uniform2f(program.uniform.px, 0, blurSizeY);\n _draw(DRAW.INTERMEDIATE);\n\n // Horizontal\n gl.uniform2f(program.uniform.px, blurSizeX, 0);\n _draw();\n };\n\n _filter.blur.SHADER = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform vec2 px;',\n\n 'void main(void) {',\n 'gl_FragColor = vec4(0.0);',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;',\n 'gl_FragColor += texture2D(texture, vUv )*0.159576912161;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x, 1.0*px.y))*0.147308056121;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x, 2.0*px.y))*0.115876621105;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x, 3.0*px.y))*0.0776744219933;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x, 4.0*px.y))*0.0443683338718;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x, 5.0*px.y))*0.0215963866053;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265;',\n '}',\n ].join('\\n');\n\n // -------------------------------------------------------------------------\n // Pixelate Filter\n\n _filter.pixelate = function (size) {\n const blurSizeX = (size) / _width;\n const blurSizeY = (size) / _height;\n\n const program = _compileShader(_filter.pixelate.SHADER);\n\n // Horizontal\n gl.uniform2f(program.uniform.size, blurSizeX, blurSizeY);\n _draw();\n };\n\n _filter.pixelate.SHADER = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform vec2 size;',\n 'uniform sampler2D texture;',\n\n 'vec2 pixelate(vec2 coord, vec2 size) {',\n 'return floor( coord / size ) * size;',\n '}',\n\n 'void main(void) {',\n 'gl_FragColor = vec4(0.0);',\n 'vec2 coord = pixelate(vUv, size);',\n 'gl_FragColor += texture2D(texture, coord);',\n '}',\n ].join('\\n');\n};\n\nexports.Canvas = WebGLImageFilter;\n", "import { log } from './log.js';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as fxImage from './imagefx.js';\n\n// internal temp canvases\nlet inCanvas = null;\nlet outCanvas = null;\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nfunction process(input, config) {\n let tensor;\n if (input instanceof tf.Tensor) {\n tensor = tf.clone(input);\n } else {\n const originalWidth = input.naturalWidth || input.videoWidth || input.width || (input.shape && (input.shape[1] > 0));\n const originalHeight = input.naturalHeight || input.videoHeight || input.height || (input.shape && (input.shape[2] > 0));\n let targetWidth = originalWidth;\n let targetHeight = originalHeight;\n if (config.filter.width > 0) targetWidth = config.filter.width;\n else if (config.filter.height > 0) targetWidth = originalWidth * (config.filter.height / originalHeight);\n if (config.filter.height > 0) targetHeight = config.filter.height;\n else if (config.filter.width > 0) targetHeight = originalHeight * (config.filter.width / originalWidth);\n if (!targetWidth || !targetHeight) {\n log('Human: invalid input', input);\n return null;\n }\n if (!inCanvas || (inCanvas.width !== targetWidth) || (inCanvas.height !== targetHeight)) {\n inCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n if (inCanvas.width !== targetWidth) inCanvas.width = targetWidth;\n if (inCanvas.height !== targetHeight) inCanvas.height = targetHeight;\n }\n const ctx = inCanvas.getContext('2d');\n if (input instanceof ImageData) ctx.putImageData(input, 0, 0);\n else ctx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);\n if (config.filter.enabled) {\n if (!this.fx || !outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas.height !== outCanvas.height)) {\n outCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(inCanvas.width, inCanvas.height) : document.createElement('canvas');\n if (outCanvas.width !== inCanvas.width) outCanvas.width = inCanvas.width;\n if (outCanvas.height !== inCanvas.height) outCanvas.height = inCanvas.height;\n this.fx = tf.ENV.flags.IS_BROWSER ? new fxImage.Canvas({ canvas: outCanvas }) : null; // && (typeof document !== 'undefined')\n }\n if (!this.fx) return inCanvas;\n this.fx.reset();\n this.fx.addFilter('brightness', config.filter.brightness); // must have at least one filter enabled\n if (config.filter.contrast !== 0) this.fx.addFilter('contrast', config.filter.contrast);\n if (config.filter.sharpness !== 0) this.fx.addFilter('sharpen', config.filter.sharpness);\n if (config.filter.blur !== 0) this.fx.addFilter('blur', config.filter.blur);\n if (config.filter.saturation !== 0) this.fx.addFilter('saturation', config.filter.saturation);\n if (config.filter.hue !== 0) this.fx.addFilter('hue', config.filter.hue);\n if (config.filter.negative) this.fx.addFilter('negative');\n if (config.filter.sepia) this.fx.addFilter('sepia');\n if (config.filter.vintage) this.fx.addFilter('brownie');\n if (config.filter.sepia) this.fx.addFilter('sepia');\n if (config.filter.kodachrome) this.fx.addFilter('kodachrome');\n if (config.filter.technicolor) this.fx.addFilter('technicolor');\n if (config.filter.polaroid) this.fx.addFilter('polaroid');\n if (config.filter.pixelate !== 0) this.fx.addFilter('pixelate', config.filter.pixelate);\n this.fx.apply(inCanvas);\n // read pixel data\n /*\n const gl = outCanvas.getContext('webgl');\n if (gl) {\n const glBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 4);\n const pixBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 3);\n gl.readPixels(0, 0, outCanvas.width, outCanvas.height, gl.RGBA, gl.UNSIGNED_BYTE, glBuffer);\n // gl returns rbga while we only need rgb, so discarding alpha channel\n // gl returns starting point as lower left, so need to invert vertical\n let i = 0;\n for (let y = outCanvas.height - 1; y >= 0; y--) {\n for (let x = 0; x < outCanvas.width; x++) {\n const index = (x + y * outCanvas.width) * 4;\n pixBuffer[i++] = glBuffer[index + 0];\n pixBuffer[i++] = glBuffer[index + 1];\n pixBuffer[i++] = glBuffer[index + 2];\n }\n }\n outCanvas.data = pixBuffer;\n }\n */\n } else {\n outCanvas = inCanvas;\n }\n let pixels;\n if (outCanvas.data) {\n const shape = [outCanvas.height, outCanvas.width, 3];\n pixels = tf.tensor3d(outCanvas.data, shape, 'int32');\n } else if ((config.backend === 'webgl') || (outCanvas instanceof ImageData)) {\n // tf kernel-optimized method to get imagedata, also if input is imagedata, just use it\n pixels = tf.browser.fromPixels(outCanvas);\n } else {\n // cpu and wasm kernel does not implement efficient fromPixels method nor we can use canvas as-is, so we do a silly one more canvas\n const tempCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n tempCanvas.width = targetWidth;\n tempCanvas.height = targetHeight;\n const tempCtx = tempCanvas.getContext('2d');\n tempCtx?.drawImage(outCanvas, 0, 0);\n const data = tempCtx?.getImageData(0, 0, targetWidth, targetHeight);\n pixels = tf.browser.fromPixels(data);\n }\n const casted = pixels.toFloat();\n tensor = casted.expandDims(0);\n pixels.dispose();\n casted.dispose();\n }\n return { tensor, canvas: config.filter.return ? outCanvas : null };\n}\n\nexports.process = process;\n", "import { log } from './log.js';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as backend from './tfjs/backend.js';\nimport * as facemesh from './blazeface/facemesh.js';\nimport * as age from './age/age.js';\nimport * as gender from './gender/gender.js';\nimport * as emotion from './emotion/emotion.js';\nimport * as embedding from './embedding/embedding.js';\nimport * as posenet from './posenet/posenet.js';\nimport * as blazepose from './blazepose/blazepose.js';\nimport * as handpose from './handpose/handpose.js';\nimport * as gesture from './gesture/gesture.js';\nimport * as image from './image.js';\nimport * as profile from './profile.js';\nimport * as config from '../config.js';\nimport * as sample from './sample.js';\nimport * as app from '../package.json';\n\n// helper function: gets elapsed time on both browser and nodejs\nconst now = () => {\n if (typeof performance !== 'undefined') return performance.now();\n return parseInt(Number(process.hrtime.bigint()) / 1000 / 1000);\n};\n\n// helper function: perform deep merge of multiple objects so it allows full inheriance with overrides\nfunction mergeDeep(...objects) {\n const isObject = (obj) => obj && typeof obj === 'object';\n return objects.reduce((prev, obj) => {\n Object.keys(obj || {}).forEach((key) => {\n const pVal = prev[key];\n const oVal = obj[key];\n if (Array.isArray(pVal) && Array.isArray(oVal)) {\n prev[key] = pVal.concat(...oVal);\n } else if (isObject(pVal) && isObject(oVal)) {\n prev[key] = mergeDeep(pVal, oVal);\n } else {\n prev[key] = oVal;\n }\n });\n return prev;\n }, {});\n}\n\nclass Human {\n constructor(userConfig = {}) {\n this.tf = tf;\n this.version = app.version;\n this.config = mergeDeep(config.default, userConfig);\n this.fx = null;\n this.state = 'idle';\n this.numTensors = 0;\n this.analyzeMemoryLeaks = false;\n this.checkSanity = false;\n this.firstRun = true;\n this.perf = {};\n // object that contains all initialized models\n this.models = {\n facemesh: null,\n posenet: null,\n handpose: null,\n iris: null,\n age: null,\n gender: null,\n emotion: null,\n };\n // export raw access to underlying models\n this.facemesh = facemesh;\n this.age = age;\n this.gender = gender;\n this.emotion = emotion;\n this.body = posenet;\n this.hand = handpose;\n }\n\n profile() {\n if (this.config.profile) return profile.data;\n return {};\n }\n\n // helper function: measure tensor leak\n analyze(...msg) {\n if (!this.analyzeMemoryLeaks) return;\n const current = tf.engine().state.numTensors;\n const previous = this.numTensors;\n this.numTensors = current;\n const leaked = current - previous;\n if (leaked !== 0) log(...msg, leaked);\n }\n\n // quick sanity check on inputs\n sanity(input) {\n if (!this.checkSanity) return null;\n if (!input) return 'input is not defined';\n if (tf.ENV.flags.IS_NODE && !(input instanceof tf.Tensor)) {\n return 'input must be a tensor';\n }\n try {\n tf.getBackend();\n } catch {\n return 'backend not loaded';\n }\n return null;\n }\n\n simmilarity(embedding1, embedding2) {\n if (this.config.face.embedding.enabled) return embedding.simmilarity(embedding1, embedding2);\n return 0;\n }\n\n // preload models, not explicitly required as it's done automatically on first use\n async load(userConfig) {\n this.state = 'load';\n const timeStamp = now();\n if (userConfig) this.config = mergeDeep(this.config, userConfig);\n\n if (this.firstRun) {\n log(`version: ${this.version} TensorFlow/JS version: ${tf.version_core}`);\n await this.checkBackend(true);\n if (tf.ENV.flags.IS_BROWSER) {\n log('configuration:', this.config);\n log('tf flags:', tf.ENV.flags);\n }\n this.firstRun = false;\n }\n\n if (this.config.async) {\n [\n this.models.facemesh,\n this.models.age,\n this.models.gender,\n this.models.emotion,\n this.models.embedding,\n this.models.posenet,\n this.models.handpose,\n this.models.blazepose,\n ] = await Promise.all([\n this.models.facemesh || (this.config.face.enabled ? facemesh.load(this.config) : null),\n this.models.age || ((this.config.face.enabled && this.config.face.age.enabled) ? age.load(this.config) : null),\n this.models.gender || ((this.config.face.enabled && this.config.face.gender.enabled) ? gender.load(this.config) : null),\n this.models.emotion || ((this.config.face.enabled && this.config.face.emotion.enabled) ? emotion.load(this.config) : null),\n this.models.embedding || ((this.config.face.enabled && this.config.face.embedding.enabled) ? embedding.load(this.config) : null),\n this.models.posenet || (this.config.body.enabled ? posenet.load(this.config) : null),\n this.models.handpose || (this.config.hand.enabled ? handpose.load(this.config) : null),\n this.models.blazepose || (this.config.pose.enabled ? blazepose.load(this.config) : null),\n ]);\n } else {\n if (this.config.face.enabled && !this.models.facemesh) this.models.facemesh = await facemesh.load(this.config);\n if (this.config.face.enabled && this.config.face.age.enabled && !this.models.age) this.models.age = await age.load(this.config);\n if (this.config.face.enabled && this.config.face.gender.enabled && !this.models.gender) this.models.gender = await gender.load(this.config);\n if (this.config.face.enabled && this.config.face.emotion.enabled && !this.models.emotion) this.models.emotion = await emotion.load(this.config);\n if (this.config.face.enabled && this.config.face.embedding.enabled && !this.models.embedding) this.models.embedding = await embedding.load(this.config);\n if (this.config.body.enabled && !this.models.posenet) this.models.posenet = await posenet.load(this.config);\n if (this.config.hand.enabled && !this.models.handpose) this.models.handpose = await handpose.load(this.config);\n if (this.config.pose.enabled && !this.models.blazepose) this.models.blazepose = await blazepose.load(this.config);\n }\n const current = Math.trunc(now() - timeStamp);\n if (current > (this.perf.load || 0)) this.perf.load = current;\n }\n\n // check if backend needs initialization if it changed\n async checkBackend(force) {\n if (this.config.backend && (this.config.backend !== '') && force || (tf.getBackend() !== this.config.backend)) {\n const timeStamp = now();\n this.state = 'backend';\n /* force backend reload\n if (this.config.backend in tf.engine().registry) {\n const backendFactory = tf.findBackendFactory(this.config.backend);\n tf.removeBackend(this.config.backend);\n tf.registerBackend(this.config.backend, backendFactory);\n } else {\n log('Backend not registred:', this.config.backend);\n }\n */\n\n log('setting backend:', this.config.backend);\n\n if (this.config.backend === 'wasm') {\n log('settings wasm path:', this.config.wasmPath);\n tf.setWasmPaths(this.config.wasmPath);\n const simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n if (!simd) log('warning: wasm simd support is not enabled');\n }\n\n if (this.config.backend === 'humangl') {\n log('registering humangl backend');\n backend.register();\n }\n\n await tf.setBackend(this.config.backend);\n tf.enableProdMode();\n /* debug mode is really too mcuh\n tf.enableDebugMode();\n */\n if (tf.getBackend() === 'webgl') {\n if (this.config.deallocate) {\n log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', this.config.deallocate);\n tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', this.config.deallocate ? 0 : -1);\n }\n tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n const gl = await tf.backend().getGPGPUContext().gl;\n log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);\n }\n await tf.ready();\n this.perf.backend = Math.trunc(now() - timeStamp);\n }\n }\n\n async detectFace(input) {\n // run facemesh, includes blazeface and iris\n // eslint-disable-next-line no-async-promise-executor\n let timeStamp;\n let ageRes;\n let genderRes;\n let emotionRes;\n let embeddingRes;\n const faceRes = [];\n this.state = 'run:face';\n timeStamp = now();\n const faces = await this.models.facemesh?.estimateFaces(input, this.config);\n this.perf.face = Math.trunc(now() - timeStamp);\n for (const face of faces) {\n this.analyze('Get Face');\n\n // is something went wrong, skip the face\n if (!face.image || face.image.isDisposedInternal) {\n log('Face object is disposed:', face.image);\n continue;\n }\n\n // run age, inherits face from blazeface\n this.analyze('Start Age:');\n if (this.config.async) {\n ageRes = this.config.face.age.enabled ? age.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:age';\n timeStamp = now();\n ageRes = this.config.face.age.enabled ? await age.predict(face.image, this.config) : {};\n this.perf.age = Math.trunc(now() - timeStamp);\n }\n\n // run gender, inherits face from blazeface\n this.analyze('Start Gender:');\n if (this.config.async) {\n genderRes = this.config.face.gender.enabled ? gender.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:gender';\n timeStamp = now();\n genderRes = this.config.face.gender.enabled ? await gender.predict(face.image, this.config) : {};\n this.perf.gender = Math.trunc(now() - timeStamp);\n }\n\n // run emotion, inherits face from blazeface\n this.analyze('Start Emotion:');\n if (this.config.async) {\n emotionRes = this.config.face.emotion.enabled ? emotion.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:emotion';\n timeStamp = now();\n emotionRes = this.config.face.emotion.enabled ? await emotion.predict(face.image, this.config) : {};\n this.perf.emotion = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Emotion:');\n\n // run emotion, inherits face from blazeface\n this.analyze('Start Embedding:');\n if (this.config.async) {\n embeddingRes = this.config.face.embedding.enabled ? embedding.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:embedding';\n timeStamp = now();\n embeddingRes = this.config.face.embedding.enabled ? await embedding.predict(face.image, this.config) : {};\n this.perf.embedding = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Emotion:');\n\n // if async wait for results\n if (this.config.async) {\n [ageRes, genderRes, emotionRes, embeddingRes] = await Promise.all([ageRes, genderRes, emotionRes, embeddingRes]);\n }\n\n this.analyze('Finish Face:');\n // dont need face anymore\n face.image.dispose();\n\n // calculate iris distance\n // iris: array[ center, left, top, right, bottom]\n if (!this.config.face.iris.enabled) {\n delete face.annotations.leftEyeIris;\n delete face.annotations.rightEyeIris;\n }\n const irisSize = (face.annotations.leftEyeIris && face.annotations.rightEyeIris)\n /* average human iris size is 11.7mm */\n ? 11.7 * Math.max(Math.abs(face.annotations.leftEyeIris[3][0] - face.annotations.leftEyeIris[1][0]), Math.abs(face.annotations.rightEyeIris[4][1] - face.annotations.rightEyeIris[2][1]))\n : 0;\n\n // combine results\n faceRes.push({\n confidence: face.confidence,\n box: face.box,\n mesh: face.mesh,\n boxRaw: face.boxRaw,\n meshRaw: face.meshRaw,\n annotations: face.annotations,\n age: ageRes.age,\n gender: genderRes.gender,\n genderConfidence: genderRes.confidence,\n emotion: emotionRes,\n embedding: embeddingRes,\n iris: (irisSize !== 0) ? Math.trunc(irisSize) / 100 : 0,\n });\n this.analyze('End Face');\n }\n this.analyze('End FaceMesh:');\n if (this.config.async) {\n if (this.perf.face) delete this.perf.face;\n if (this.perf.age) delete this.perf.age;\n if (this.perf.gender) delete this.perf.gender;\n if (this.perf.emotion) delete this.perf.emotion;\n }\n return faceRes;\n }\n\n async image(input, userConfig = {}) {\n this.state = 'image';\n this.config = mergeDeep(this.config, userConfig);\n const process = image.process(input, this.config);\n process.tensor.dispose();\n return process.canvas;\n }\n\n // main detect function\n async detect(input, userConfig = {}) {\n // detection happens inside a promise\n return new Promise(async (resolve) => {\n this.state = 'config';\n let timeStamp;\n\n // update configuration\n this.config = mergeDeep(this.config, userConfig);\n\n // sanity checks\n this.state = 'check';\n const error = this.sanity(input);\n if (error) {\n log(error, input);\n resolve({ error });\n }\n\n let poseRes;\n let blazeposeRes;\n let handRes;\n let faceRes;\n\n const timeStart = now();\n\n // configure backend\n await this.checkBackend();\n\n // load models if enabled\n await this.load();\n\n if (this.config.scoped) tf.engine().startScope();\n this.analyze('Start Scope:');\n\n timeStamp = now();\n const process = image.process(input, this.config);\n if (!process || !process.tensor) {\n log('could not convert input to tensor');\n resolve({ error: 'could not convert input to tensor' });\n return;\n }\n this.perf.image = Math.trunc(now() - timeStamp);\n this.analyze('Get Image:');\n\n // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n if (this.config.async) {\n faceRes = this.config.face.enabled ? this.detectFace(process.tensor) : [];\n if (this.perf.face) delete this.perf.face;\n } else {\n this.state = 'run:face';\n timeStamp = now();\n faceRes = this.config.face.enabled ? await this.detectFace(process.tensor) : [];\n this.perf.face = Math.trunc(now() - timeStamp);\n }\n\n // run posenet\n this.analyze('Start Body:');\n if (this.config.async) {\n poseRes = this.config.body.enabled ? this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n if (this.perf.body) delete this.perf.body;\n } else {\n this.state = 'run:body';\n timeStamp = now();\n poseRes = this.config.body.enabled ? await this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n this.perf.body = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Body:');\n\n // run posenet\n this.analyze('Start Pose:');\n if (this.config.async) {\n blazeposeRes = this.config.pose.enabled ? blazepose.predict(process.tensor, this.config) : [];\n if (this.perf.pose) delete this.perf.pose;\n } else {\n this.state = 'run:pose';\n timeStamp = now();\n blazeposeRes = this.config.pose.enabled ? await blazepose.predict(process.tensor, this.config) : [];\n this.perf.pose = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Pose:');\n\n // run handpose\n this.analyze('Start Hand:');\n if (this.config.async) {\n handRes = this.config.hand.enabled ? this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n if (this.perf.hand) delete this.perf.hand;\n } else {\n this.state = 'run:hand';\n timeStamp = now();\n handRes = this.config.hand.enabled ? await this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n this.perf.hand = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Hand:');\n\n // if async wait for results\n if (this.config.async) {\n [faceRes, poseRes, blazeposeRes, handRes] = await Promise.all([faceRes, poseRes, blazeposeRes, handRes]);\n }\n process.tensor.dispose();\n\n if (this.config.scoped) tf.engine().endScope();\n this.analyze('End Scope:');\n\n let gestureRes = [];\n if (this.config.gesture.enabled) {\n timeStamp = now();\n gestureRes = [...gesture.face(faceRes), ...gesture.body(poseRes), ...gesture.hand(handRes), ...gesture.iris(faceRes)];\n if (!this.config.async) this.perf.gesture = Math.trunc(now() - timeStamp);\n else if (this.perf.gesture) delete this.perf.gesture;\n }\n\n this.perf.total = Math.trunc(now() - timeStart);\n this.state = 'idle';\n resolve({ face: faceRes, body: poseRes, hand: handRes, pose: blazeposeRes, gesture: gestureRes, performance: this.perf, canvas: process.canvas });\n });\n }\n\n async warmupBitmap() {\n const b64toBlob = (base64, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n let blob;\n let res;\n switch (this.config.warmup) {\n case 'face': blob = await b64toBlob(sample.face); break;\n case 'full': blob = await b64toBlob(sample.body); break;\n default: blob = null;\n }\n if (blob) {\n const bitmap = await createImageBitmap(blob);\n res = await this.detect(bitmap, config);\n bitmap.close();\n }\n return res;\n }\n\n async warmupCanvas() {\n return new Promise((resolve) => {\n let src;\n let size = 0;\n switch (this.config.warmup) {\n case 'face':\n size = 256;\n src = 'data:image/jpeg;base64,' + sample.face;\n break;\n case 'full':\n size = 1200;\n src = 'data:image/jpeg;base64,' + sample.body;\n break;\n default:\n src = null;\n }\n const img = new Image(size, size);\n img.onload = () => {\n const canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(size, size) : document.createElement('canvas');\n canvas.width = size;\n canvas.height = size;\n const ctx = canvas.getContext('2d');\n ctx.drawImage(img, 0, 0);\n const data = ctx.getImageData(0, 0, size, size);\n this.detect(data, config).then((res) => resolve(res));\n };\n if (src) img.src = src;\n else resolve(null);\n });\n }\n\n async warmup(userConfig) {\n const t0 = now();\n if (userConfig) this.config = mergeDeep(this.config, userConfig);\n const video = this.config.videoOptimized;\n this.config.videoOptimized = false;\n let res;\n if (typeof createImageBitmap === 'function') res = await this.warmupBitmap();\n else res = await this.warmupCanvas();\n this.config.videoOptimized = video;\n const t1 = now();\n log('Warmup', this.config.warmup, (t1 - t0), res);\n return res;\n }\n}\n\nexport { Human as default };\n", "// helper function: wrapper around console output\nexport function log(...msg) {\n const dt = new Date();\n const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n // eslint-disable-next-line no-console\n if (msg) console.log(ts, 'Human:', ...msg);\n}\n", "", "// A port of an algorithm by Johannes Baag\u00F8e , 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e \n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n// \n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n// \n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n var me = this, mash = Mash();\n\n me.next = function() {\n var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n me.s0 = me.s1;\n me.s1 = me.s2;\n return me.s2 = t - (me.c = t | 0);\n };\n\n // Apply the seeding algorithm from Baagoe.\n me.c = 1;\n me.s0 = mash(' ');\n me.s1 = mash(' ');\n me.s2 = mash(' ');\n me.s0 -= mash(seed);\n if (me.s0 < 0) { me.s0 += 1; }\n me.s1 -= mash(seed);\n if (me.s1 < 0) { me.s1 += 1; }\n me.s2 -= mash(seed);\n if (me.s2 < 0) { me.s2 += 1; }\n mash = null;\n}\n\nfunction copy(f, t) {\n t.c = f.c;\n t.s0 = f.s0;\n t.s1 = f.s1;\n t.s2 = f.s2;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new Alea(seed),\n state = opts && opts.state,\n prng = xg.next;\n prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n prng.double = function() {\n return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n };\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nfunction Mash() {\n var n = 0xefc8249d;\n\n var mash = function(data) {\n data = data.toString();\n for (var i = 0; i < data.length; i++) {\n n += data.charCodeAt(i);\n var h = 0.02519603282416938 * n;\n n = h >>> 0;\n h -= n;\n h *= n;\n n = h >>> 0;\n h -= n;\n n += h * 0x100000000; // 2^32\n }\n return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n };\n\n return mash;\n}\n\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.alea = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n\n // Set up generator function.\n me.next = function() {\n var t = me.x ^ (me.x << 11);\n me.x = me.y;\n me.y = me.z;\n me.z = me.w;\n return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n };\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor128 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var t = (me.x ^ (me.x >>> 2));\n me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n return (me.d = (me.d + 362437 | 0)) +\n (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n };\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n me.v = 0;\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n if (k == strseed.length) {\n me.d = me.x << 10 ^ me.x >>> 4;\n }\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n t.v = f.v;\n t.d = f.d;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorwow = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n // Update xor generator.\n var X = me.x, i = me.i, t, v, w;\n t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n X[i] = v;\n me.i = (i + 1) & 7;\n return v;\n };\n\n function init(me, seed) {\n var j, w, X = [];\n\n if (seed === (seed | 0)) {\n // Seed state array using a 32-bit integer.\n w = X[0] = seed;\n } else {\n // Seed state using a string.\n seed = '' + seed;\n for (j = 0; j < seed.length; ++j) {\n X[j & 7] = (X[j & 7] << 15) ^\n (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n }\n }\n // Enforce an array length of 8, not all zeroes.\n while (X.length < 8) X.push(0);\n for (j = 0; j < 8 && X[j] === 0; ++j);\n if (j == 8) w = X[7] = -1; else w = X[j];\n\n me.x = X;\n me.i = 0;\n\n // Discard an initial 256 values.\n for (j = 256; j > 0; --j) {\n me.next();\n }\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.x = f.x.slice();\n t.i = f.i;\n return t;\n}\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.x) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorshift7 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated. For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1); // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897); // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C. This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n var w = me.w,\n X = me.X, i = me.i, t, v;\n // Update Weyl generator.\n me.w = w = (w + 0x61c88647) | 0;\n // Update xor generator.\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n // Update Xor generator array state.\n v = X[i] = v ^ t;\n me.i = i;\n // Result is the combination.\n return (v + (w ^ (w >>> 16))) | 0;\n };\n\n function init(me, seed) {\n var t, v, i, j, w, X = [], limit = 128;\n if (seed === (seed | 0)) {\n // Numeric seeds initialize v, which is used to generates X.\n v = seed;\n seed = null;\n } else {\n // String seeds are mixed into v and X one character at a time.\n seed = seed + '\\0';\n v = 0;\n limit = Math.max(limit, seed.length);\n }\n // Initialize circular array and weyl value.\n for (i = 0, j = -32; j < limit; ++j) {\n // Put the unicode characters into the array, and shuffle them.\n if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n // After 32 shuffles, take v as the starting w value.\n if (j === 0) w = v;\n v ^= v << 10;\n v ^= v >>> 15;\n v ^= v << 4;\n v ^= v >>> 13;\n if (j >= 0) {\n w = (w + 0x61c88647) | 0; // Weyl.\n t = (X[j & 127] ^= (v + w)); // Combine xor and weyl to init array.\n i = (0 == t) ? i + 1 : 0; // Count zeroes.\n }\n }\n // We have detected all zeroes; make the key nonzero.\n if (i >= 128) {\n X[(seed && seed.length || 0) & 127] = -1;\n }\n // Run the generator 512 times to further mix the state before using it.\n // Factoring this as a function slows the main generator, so it is just\n // unrolled here. The weyl generator is not advanced while warming up.\n i = 127;\n for (j = 4 * 128; j > 0; --j) {\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n X[i] = v ^ t;\n }\n // Storing state as object members is faster than using closure variables.\n me.w = w;\n me.X = X;\n me.i = i;\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.i = f.i;\n t.w = f.w;\n t.X = f.X.slice();\n return t;\n};\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.X) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor4096 = impl;\n}\n\n})(\n this, // window object or global\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var b = me.b, c = me.c, d = me.d, a = me.a;\n b = (b << 25) ^ (b >>> 7) ^ c;\n c = (c - d) | 0;\n d = (d << 24) ^ (d >>> 8) ^ a;\n a = (a - b) | 0;\n me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n me.c = c = (c - d) | 0;\n me.d = (d << 16) ^ (c >>> 16) ^ a;\n return me.a = (a - b) | 0;\n };\n\n /* The following is non-inverted tyche, which has better internal\n * bit diffusion, but which is about 25% slower than tyche-i in JS.\n me.next = function() {\n var a = me.a, b = me.b, c = me.c, d = me.d;\n a = (me.a + me.b | 0) >>> 0;\n d = me.d ^ a; d = d << 16 ^ d >>> 16;\n c = me.c + d | 0;\n b = me.b ^ c; b = b << 12 ^ d >>> 20;\n me.a = a = a + b | 0;\n d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n me.c = c = c + d | 0;\n b = b ^ c;\n return me.b = (b << 7 ^ b >>> 25);\n }\n */\n\n me.a = 0;\n me.b = 0;\n me.c = 2654435769 | 0;\n me.d = 1367130551;\n\n if (seed === Math.floor(seed)) {\n // Integer seed.\n me.a = (seed / 0x100000000) | 0;\n me.b = seed | 0;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 20; k++) {\n me.b ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.a = f.a;\n t.b = f.b;\n t.c = f.c;\n t.d = f.d;\n return t;\n};\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.tychei = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "", "/*\nCopyright 2014 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\nvar global = this,\n width = 256, // each RC4 output is 0 <= x < 256\n chunks = 6, // at least six RC4 outputs for each double\n digits = 52, // there are 52 significant digits in a double\n rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n startdenom = math.pow(width, chunks),\n significance = math.pow(2, digits),\n overflow = significance * 2,\n mask = width - 1,\n nodecrypto; // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n var key = [];\n options = (options == true) ? { entropy: true } : (options || {});\n\n // Flatten the seed string or build one from local entropy if needed.\n var shortseed = mixkey(flatten(\n options.entropy ? [seed, tostring(pool)] :\n (seed == null) ? autoseed() : seed, 3), key);\n\n // Use the seed to initialize an ARC4 generator.\n var arc4 = new ARC4(key);\n\n // This function returns a random double in [0, 1) that contains\n // randomness in every bit of the mantissa of the IEEE 754 value.\n var prng = function() {\n var n = arc4.g(chunks), // Start with a numerator n < 2 ^ 48\n d = startdenom, // and denominator d = 2 ^ 48.\n x = 0; // and no 'extra last byte'.\n while (n < significance) { // Fill up all significant digits by\n n = (n + x) * width; // shifting numerator and\n d *= width; // denominator and generating a\n x = arc4.g(1); // new least-significant-byte.\n }\n while (n >= overflow) { // To avoid rounding up, before adding\n n /= 2; // last byte, shift everything\n d /= 2; // right using integer math until\n x >>>= 1; // we have exactly the desired bits.\n }\n return (n + x) / d; // Form the number within [0, 1).\n };\n\n prng.int32 = function() { return arc4.g(4) | 0; }\n prng.quick = function() { return arc4.g(4) / 0x100000000; }\n prng.double = prng;\n\n // Mix the randomness into accumulated entropy.\n mixkey(tostring(arc4.S), pool);\n\n // Calling convention: what to return as a function of prng, seed, is_math.\n return (options.pass || callback ||\n function(prng, seed, is_math_call, state) {\n if (state) {\n // Load the arc4 state from the given state if it has an S array.\n if (state.S) { copy(state, arc4); }\n // Only provide the .state method if requested via options.state.\n prng.state = function() { return copy(arc4, {}); }\n }\n\n // If called as a method of Math (Math.seedrandom()), mutate\n // Math.random because that is how seedrandom.js has worked since v1.0.\n if (is_math_call) { math[rngname] = prng; return seed; }\n\n // Otherwise, it is a newer calling convention, so return the\n // prng directly.\n else return prng;\n })(\n prng,\n shortseed,\n 'global' in options ? options.global : (this == math),\n options.state);\n}\nmath['seed' + rngname] = seedrandom;\n\n//\n// ARC4\n//\n// An ARC4 implementation. The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4. Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n var t, keylen = key.length,\n me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n // The empty key [] is treated as [0].\n if (!keylen) { key = [keylen++]; }\n\n // Set up S using the standard key scheduling algorithm.\n while (i < width) {\n s[i] = i++;\n }\n for (i = 0; i < width; i++) {\n s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n s[j] = t;\n }\n\n // The \"g\" method returns the next (count) outputs as one number.\n (me.g = function(count) {\n // Using instance members instead of closure state nearly doubles speed.\n var t, r = 0,\n i = me.i, j = me.j, s = me.S;\n while (count--) {\n t = s[i = mask & (i + 1)];\n r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n }\n me.i = i; me.j = j;\n return r;\n // For robust unpredictability, the function call below automatically\n // discards an initial batch of values. This is called RC4-drop[256].\n // See http://google.com/search?q=rsa+fluhrer+response&btnI\n })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n t.i = f.i;\n t.j = f.j;\n t.S = f.S.slice();\n return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n var result = [], typ = (typeof obj), prop;\n if (depth && typ == 'object') {\n for (prop in obj) {\n try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n }\n }\n return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n var stringseed = seed + '', smear, j = 0;\n while (j < stringseed.length) {\n key[mask & j] =\n mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n }\n return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n try {\n var out;\n if (nodecrypto && (out = nodecrypto.randomBytes)) {\n // The use of 'out' to remember randomBytes makes tight minified code.\n out = out(width);\n } else {\n out = new Uint8Array(width);\n (global.crypto || global.msCrypto).getRandomValues(out);\n }\n return tostring(out);\n } catch (e) {\n var browser = global.navigator,\n plugins = browser && browser.plugins;\n return [+new Date, global, plugins, global.screen, tostring(pool)];\n }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool. Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n module.exports = seedrandom;\n // When in node.js, try using crypto package for autoseeding.\n try {\n nodecrypto = require('crypto');\n } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n define(function() { return seedrandom; });\n}\n\n// End anonymous scope, and pass initial values.\n})(\n [], // pool: entropy pool starts empty\n Math // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random(); // 0 <= x < 1. Every bit is random.\n// var x = random.quick(); // 0 <= x < 1. 32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three. It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures. Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "// A port of an algorithm by Johannes Baag\u00F8e , 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e \n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n// \n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n// \n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n var me = this, mash = Mash();\n\n me.next = function() {\n var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n me.s0 = me.s1;\n me.s1 = me.s2;\n return me.s2 = t - (me.c = t | 0);\n };\n\n // Apply the seeding algorithm from Baagoe.\n me.c = 1;\n me.s0 = mash(' ');\n me.s1 = mash(' ');\n me.s2 = mash(' ');\n me.s0 -= mash(seed);\n if (me.s0 < 0) { me.s0 += 1; }\n me.s1 -= mash(seed);\n if (me.s1 < 0) { me.s1 += 1; }\n me.s2 -= mash(seed);\n if (me.s2 < 0) { me.s2 += 1; }\n mash = null;\n}\n\nfunction copy(f, t) {\n t.c = f.c;\n t.s0 = f.s0;\n t.s1 = f.s1;\n t.s2 = f.s2;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new Alea(seed),\n state = opts && opts.state,\n prng = xg.next;\n prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n prng.double = function() {\n return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n };\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nfunction Mash() {\n var n = 0xefc8249d;\n\n var mash = function(data) {\n data = data.toString();\n for (var i = 0; i < data.length; i++) {\n n += data.charCodeAt(i);\n var h = 0.02519603282416938 * n;\n n = h >>> 0;\n h -= n;\n h *= n;\n n = h >>> 0;\n h -= n;\n n += h * 0x100000000; // 2^32\n }\n return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n };\n\n return mash;\n}\n\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.alea = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n\n // Set up generator function.\n me.next = function() {\n var t = me.x ^ (me.x << 11);\n me.x = me.y;\n me.y = me.z;\n me.z = me.w;\n return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n };\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor128 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var t = (me.x ^ (me.x >>> 2));\n me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n return (me.d = (me.d + 362437 | 0)) +\n (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n };\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n me.v = 0;\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n if (k == strseed.length) {\n me.d = me.x << 10 ^ me.x >>> 4;\n }\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n t.v = f.v;\n t.d = f.d;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorwow = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n // Update xor generator.\n var X = me.x, i = me.i, t, v, w;\n t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n X[i] = v;\n me.i = (i + 1) & 7;\n return v;\n };\n\n function init(me, seed) {\n var j, w, X = [];\n\n if (seed === (seed | 0)) {\n // Seed state array using a 32-bit integer.\n w = X[0] = seed;\n } else {\n // Seed state using a string.\n seed = '' + seed;\n for (j = 0; j < seed.length; ++j) {\n X[j & 7] = (X[j & 7] << 15) ^\n (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n }\n }\n // Enforce an array length of 8, not all zeroes.\n while (X.length < 8) X.push(0);\n for (j = 0; j < 8 && X[j] === 0; ++j);\n if (j == 8) w = X[7] = -1; else w = X[j];\n\n me.x = X;\n me.i = 0;\n\n // Discard an initial 256 values.\n for (j = 256; j > 0; --j) {\n me.next();\n }\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.x = f.x.slice();\n t.i = f.i;\n return t;\n}\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.x) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorshift7 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated. For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1); // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897); // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C. This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n var w = me.w,\n X = me.X, i = me.i, t, v;\n // Update Weyl generator.\n me.w = w = (w + 0x61c88647) | 0;\n // Update xor generator.\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n // Update Xor generator array state.\n v = X[i] = v ^ t;\n me.i = i;\n // Result is the combination.\n return (v + (w ^ (w >>> 16))) | 0;\n };\n\n function init(me, seed) {\n var t, v, i, j, w, X = [], limit = 128;\n if (seed === (seed | 0)) {\n // Numeric seeds initialize v, which is used to generates X.\n v = seed;\n seed = null;\n } else {\n // String seeds are mixed into v and X one character at a time.\n seed = seed + '\\0';\n v = 0;\n limit = Math.max(limit, seed.length);\n }\n // Initialize circular array and weyl value.\n for (i = 0, j = -32; j < limit; ++j) {\n // Put the unicode characters into the array, and shuffle them.\n if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n // After 32 shuffles, take v as the starting w value.\n if (j === 0) w = v;\n v ^= v << 10;\n v ^= v >>> 15;\n v ^= v << 4;\n v ^= v >>> 13;\n if (j >= 0) {\n w = (w + 0x61c88647) | 0; // Weyl.\n t = (X[j & 127] ^= (v + w)); // Combine xor and weyl to init array.\n i = (0 == t) ? i + 1 : 0; // Count zeroes.\n }\n }\n // We have detected all zeroes; make the key nonzero.\n if (i >= 128) {\n X[(seed && seed.length || 0) & 127] = -1;\n }\n // Run the generator 512 times to further mix the state before using it.\n // Factoring this as a function slows the main generator, so it is just\n // unrolled here. The weyl generator is not advanced while warming up.\n i = 127;\n for (j = 4 * 128; j > 0; --j) {\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n X[i] = v ^ t;\n }\n // Storing state as object members is faster than using closure variables.\n me.w = w;\n me.X = X;\n me.i = i;\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.i = f.i;\n t.w = f.w;\n t.X = f.X.slice();\n return t;\n};\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.X) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor4096 = impl;\n}\n\n})(\n this, // window object or global\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var b = me.b, c = me.c, d = me.d, a = me.a;\n b = (b << 25) ^ (b >>> 7) ^ c;\n c = (c - d) | 0;\n d = (d << 24) ^ (d >>> 8) ^ a;\n a = (a - b) | 0;\n me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n me.c = c = (c - d) | 0;\n me.d = (d << 16) ^ (c >>> 16) ^ a;\n return me.a = (a - b) | 0;\n };\n\n /* The following is non-inverted tyche, which has better internal\n * bit diffusion, but which is about 25% slower than tyche-i in JS.\n me.next = function() {\n var a = me.a, b = me.b, c = me.c, d = me.d;\n a = (me.a + me.b | 0) >>> 0;\n d = me.d ^ a; d = d << 16 ^ d >>> 16;\n c = me.c + d | 0;\n b = me.b ^ c; b = b << 12 ^ d >>> 20;\n me.a = a = a + b | 0;\n d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n me.c = c = c + d | 0;\n b = b ^ c;\n return me.b = (b << 7 ^ b >>> 25);\n }\n */\n\n me.a = 0;\n me.b = 0;\n me.c = 2654435769 | 0;\n me.d = 1367130551;\n\n if (seed === Math.floor(seed)) {\n // Integer seed.\n me.a = (seed / 0x100000000) | 0;\n me.b = seed | 0;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 20; k++) {\n me.b ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.a = f.a;\n t.b = f.b;\n t.c = f.c;\n t.d = f.d;\n return t;\n};\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.tychei = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "/*\nCopyright 2014 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\nvar global = this,\n width = 256, // each RC4 output is 0 <= x < 256\n chunks = 6, // at least six RC4 outputs for each double\n digits = 52, // there are 52 significant digits in a double\n rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n startdenom = math.pow(width, chunks),\n significance = math.pow(2, digits),\n overflow = significance * 2,\n mask = width - 1,\n nodecrypto; // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n var key = [];\n options = (options == true) ? { entropy: true } : (options || {});\n\n // Flatten the seed string or build one from local entropy if needed.\n var shortseed = mixkey(flatten(\n options.entropy ? [seed, tostring(pool)] :\n (seed == null) ? autoseed() : seed, 3), key);\n\n // Use the seed to initialize an ARC4 generator.\n var arc4 = new ARC4(key);\n\n // This function returns a random double in [0, 1) that contains\n // randomness in every bit of the mantissa of the IEEE 754 value.\n var prng = function() {\n var n = arc4.g(chunks), // Start with a numerator n < 2 ^ 48\n d = startdenom, // and denominator d = 2 ^ 48.\n x = 0; // and no 'extra last byte'.\n while (n < significance) { // Fill up all significant digits by\n n = (n + x) * width; // shifting numerator and\n d *= width; // denominator and generating a\n x = arc4.g(1); // new least-significant-byte.\n }\n while (n >= overflow) { // To avoid rounding up, before adding\n n /= 2; // last byte, shift everything\n d /= 2; // right using integer math until\n x >>>= 1; // we have exactly the desired bits.\n }\n return (n + x) / d; // Form the number within [0, 1).\n };\n\n prng.int32 = function() { return arc4.g(4) | 0; }\n prng.quick = function() { return arc4.g(4) / 0x100000000; }\n prng.double = prng;\n\n // Mix the randomness into accumulated entropy.\n mixkey(tostring(arc4.S), pool);\n\n // Calling convention: what to return as a function of prng, seed, is_math.\n return (options.pass || callback ||\n function(prng, seed, is_math_call, state) {\n if (state) {\n // Load the arc4 state from the given state if it has an S array.\n if (state.S) { copy(state, arc4); }\n // Only provide the .state method if requested via options.state.\n prng.state = function() { return copy(arc4, {}); }\n }\n\n // If called as a method of Math (Math.seedrandom()), mutate\n // Math.random because that is how seedrandom.js has worked since v1.0.\n if (is_math_call) { math[rngname] = prng; return seed; }\n\n // Otherwise, it is a newer calling convention, so return the\n // prng directly.\n else return prng;\n })(\n prng,\n shortseed,\n 'global' in options ? options.global : (this == math),\n options.state);\n}\nmath['seed' + rngname] = seedrandom;\n\n//\n// ARC4\n//\n// An ARC4 implementation. The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4. Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n var t, keylen = key.length,\n me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n // The empty key [] is treated as [0].\n if (!keylen) { key = [keylen++]; }\n\n // Set up S using the standard key scheduling algorithm.\n while (i < width) {\n s[i] = i++;\n }\n for (i = 0; i < width; i++) {\n s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n s[j] = t;\n }\n\n // The \"g\" method returns the next (count) outputs as one number.\n (me.g = function(count) {\n // Using instance members instead of closure state nearly doubles speed.\n var t, r = 0,\n i = me.i, j = me.j, s = me.S;\n while (count--) {\n t = s[i = mask & (i + 1)];\n r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n }\n me.i = i; me.j = j;\n return r;\n // For robust unpredictability, the function call below automatically\n // discards an initial batch of values. This is called RC4-drop[256].\n // See http://google.com/search?q=rsa+fluhrer+response&btnI\n })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n t.i = f.i;\n t.j = f.j;\n t.S = f.S.slice();\n return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n var result = [], typ = (typeof obj), prop;\n if (depth && typ == 'object') {\n for (prop in obj) {\n try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n }\n }\n return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n var stringseed = seed + '', smear, j = 0;\n while (j < stringseed.length) {\n key[mask & j] =\n mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n }\n return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n try {\n var out;\n if (nodecrypto && (out = nodecrypto.randomBytes)) {\n // The use of 'out' to remember randomBytes makes tight minified code.\n out = out(width);\n } else {\n out = new Uint8Array(width);\n (global.crypto || global.msCrypto).getRandomValues(out);\n }\n return tostring(out);\n } catch (e) {\n var browser = global.navigator,\n plugins = browser && browser.plugins;\n return [+new Date, global, plugins, global.screen, tostring(pool)];\n }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool. Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n module.exports = seedrandom;\n // When in node.js, try using crypto package for autoseeding.\n try {\n nodecrypto = require('crypto');\n } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n define(function() { return seedrandom; });\n}\n\n// End anonymous scope, and pass initial values.\n})(\n [], // pool: entropy pool starts empty\n Math // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random(); // 0 <= x < 1. Every bit is random.\n// var x = random.quick(); // 0 <= x < 1. 32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three. It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures. Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "", "", "", "\nvar WasmBackendModuleThreadedSimd = (function() {\n var _scriptDir = typeof document !== 'undefined' && document.currentScript ? document.currentScript.src : undefined;\n if (typeof __filename !== 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XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.responseType=\"arraybuffer\";xhr.send(null);return new Uint8Array(xhr.response)}}readAsync=function readAsync(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,true);xhr.responseType=\"arraybuffer\";xhr.onload=function xhr_onload(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()};xhr.onerror=onerror;xhr.send(null)}}setWindowTitle=function(title){document.title=title}}else{}if(ENVIRONMENT_IS_NODE){if(typeof performance===\"undefined\"){performance=require(\"perf_hooks\").performance}}var out=Module[\"print\"]||console.log.bind(console);var err=Module[\"printErr\"]||console.warn.bind(console);for(key in moduleOverrides){if(moduleOverrides.hasOwnProperty(key)){Module[key]=moduleOverrides[key]}}moduleOverrides=null;if(Module[\"arguments\"])arguments_=Module[\"arguments\"];if(Module[\"thisProgram\"])thisProgram=Module[\"thisProgram\"];if(Module[\"quit\"])quit_=Module[\"quit\"];var Atomics_load=Atomics.load;var Atomics_store=Atomics.store;var Atomics_compareExchange=Atomics.compareExchange;var wasmBinary;if(Module[\"wasmBinary\"])wasmBinary=Module[\"wasmBinary\"];var noExitRuntime;if(Module[\"noExitRuntime\"])noExitRuntime=Module[\"noExitRuntime\"];if(typeof WebAssembly!==\"object\"){err(\"no native wasm support detected\")}var wasmMemory;var wasmTable=new WebAssembly.Table({\"initial\":169,\"maximum\":169+0,\"element\":\"anyfunc\"});var wasmModule;var threadInfoStruct=0;var selfThreadId=0;var ABORT=false;var EXITSTATUS=0;function assert(condition,text){if(!condition){abort(\"Assertion failed: \"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i=endIdx)){var u0=heap[idx++];if(!u0)return str;if(!(u0&128)){str+=String.fromCharCode(u0);continue}var u1=heap[idx++]&63;if((u0&224)==192){str+=String.fromCharCode((u0&31)<<6|u1);continue}var u2=heap[idx++]&63;if((u0&240)==224){u0=(u0&15)<<12|u1<<6|u2}else{u0=(u0&7)<<18|u1<<12|u2<<6|heap[idx++]&63}if(u0<65536){str+=String.fromCharCode(u0)}else{var ch=u0-65536;str+=String.fromCharCode(55296|ch>>10,56320|ch&1023)}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(GROWABLE_HEAP_U8(),ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6;heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18;heap[outIdx++]=128|u>>12&63;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}}heap[outIdx]=0;return outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,GROWABLE_HEAP_U8(),outPtr,maxBytesToWrite)}function lengthBytesUTF8(str){var len=0;for(var i=0;i=55296&&u<=57343)u=65536+((u&1023)<<10)|str.charCodeAt(++i)&1023;if(u<=127)++len;else if(u<=2047)len+=2;else if(u<=65535)len+=3;else len+=4}return len}function writeArrayToMemory(array,buffer){GROWABLE_HEAP_I8().set(array,buffer)}var WASM_PAGE_SIZE=65536;function alignUp(x,multiple){if(x%multiple>0){x+=multiple-x%multiple}return x}var buffer,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer=buf;Module[\"HEAP8\"]=HEAP8=new Int8Array(buf);Module[\"HEAP16\"]=HEAP16=new Int16Array(buf);Module[\"HEAP32\"]=HEAP32=new Int32Array(buf);Module[\"HEAPU8\"]=HEAPU8=new Uint8Array(buf);Module[\"HEAPU16\"]=HEAPU16=new Uint16Array(buf);Module[\"HEAPU32\"]=HEAPU32=new Uint32Array(buf);Module[\"HEAPF32\"]=HEAPF32=new Float32Array(buf);Module[\"HEAPF64\"]=HEAPF64=new Float64Array(buf)}var STACK_BASE=5256464,STACKTOP=STACK_BASE,STACK_MAX=13584,DYNAMIC_BASE=5256464,DYNAMICTOP_PTR=12656;if(ENVIRONMENT_IS_PTHREAD){}var INITIAL_INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;if(ENVIRONMENT_IS_PTHREAD){wasmMemory=Module[\"wasmMemory\"];buffer=Module[\"buffer\"]}else{if(Module[\"wasmMemory\"]){wasmMemory=Module[\"wasmMemory\"]}else{wasmMemory=new WebAssembly.Memory({\"initial\":INITIAL_INITIAL_MEMORY/WASM_PAGE_SIZE,\"maximum\":2147483648/WASM_PAGE_SIZE,\"shared\":true});if(!(wasmMemory.buffer instanceof SharedArrayBuffer)){err(\"requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag\");if(ENVIRONMENT_IS_NODE){console.log(\"(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)\")}throw Error(\"bad memory\")}}}if(wasmMemory){buffer=wasmMemory.buffer}INITIAL_INITIAL_MEMORY=buffer.byteLength;updateGlobalBufferAndViews(buffer);if(!ENVIRONMENT_IS_PTHREAD){GROWABLE_HEAP_I32()[DYNAMICTOP_PTR>>2]=DYNAMIC_BASE}function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback==\"function\"){callback(Module);continue}var func=callback.func;if(typeof func===\"number\"){if(callback.arg===undefined){Module[\"dynCall_v\"](func)}else{Module[\"dynCall_vi\"](func,callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATEXIT__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;if(ENVIRONMENT_IS_PTHREAD)runtimeInitialized=true;function preRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"preRun\"]){if(typeof Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;callRuntimeCallbacks(__ATINIT__)}function preMain(){if(ENVIRONMENT_IS_PTHREAD)return;callRuntimeCallbacks(__ATMAIN__)}function postRun(){if(ENVIRONMENT_IS_PTHREAD)return;if(Module[\"postRun\"]){if(typeof Module[\"postRun\"]==\"function\")Module[\"postRun\"]=[Module[\"postRun\"]];while(Module[\"postRun\"].length){addOnPostRun(Module[\"postRun\"].shift())}}callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var Math_ceil=Math.ceil;var Math_floor=Math.floor;var runDependencies=0;var runDependencyWatcher=null;var dependenciesFulfilled=null;function addRunDependency(id){assert(!ENVIRONMENT_IS_PTHREAD,\"addRunDependency cannot be used in a pthread worker\");runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}if(ENVIRONMENT_IS_PTHREAD)console.error(\"Pthread aborting at \"+(new Error).stack);what+=\"\";out(what);err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";throw new WebAssembly.RuntimeError(what)}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm-threaded-simd.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(){try{if(wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(wasmBinaryFile)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary()})}return new Promise(function(resolve,reject){resolve(getBinary())})}function createWasm(){var info={\"a\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmModule=module;if(!ENVIRONMENT_IS_PTHREAD){var numWorkersToLoad=PThread.unusedWorkers.length;PThread.unusedWorkers.forEach(function(w){PThread.loadWasmModuleToWorker(w,function(){if(!--numWorkersToLoad)removeRunDependency(\"wasm-instantiate\")})})}}if(!ENVIRONMENT_IS_PTHREAD){addRunDependency(\"wasm-instantiate\")}function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"],output[\"module\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming===\"function\"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch===\"function\"){fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err(\"wasm streaming compile failed: \"+reason);err(\"falling back to ArrayBuffer instantiation\");instantiateArrayBuffer(receiveInstantiatedSource)})})}else{return instantiateArrayBuffer(receiveInstantiatedSource)}}if(Module[\"instantiateWasm\"]){try{var exports=Module[\"instantiateWasm\"](info,receiveInstance);return exports}catch(e){err(\"Module.instantiateWasm callback failed with error: \"+e);return false}}instantiateAsync();return{}}var ASM_CONSTS={};function initPthreadsJS(){PThread.initRuntime()}if(!ENVIRONMENT_IS_PTHREAD)__ATINIT__.push({func:function(){___wasm_call_ctors()}});var __pthread_ptr=0;var __pthread_is_main_runtime_thread=0;var __pthread_is_main_browser_thread=0;function __register_pthread_ptr(pthreadPtr,isMainBrowserThread,isMainRuntimeThread){pthreadPtr=pthreadPtr|0;isMainBrowserThread=isMainBrowserThread|0;isMainRuntimeThread=isMainRuntimeThread|0;__pthread_ptr=pthreadPtr;__pthread_is_main_browser_thread=isMainBrowserThread;__pthread_is_main_runtime_thread=isMainRuntimeThread}Module[\"__register_pthread_ptr\"]=__register_pthread_ptr;var ERRNO_CODES={EPERM:63,ENOENT:44,ESRCH:71,EINTR:27,EIO:29,ENXIO:60,E2BIG:1,ENOEXEC:45,EBADF:8,ECHILD:12,EAGAIN:6,EWOULDBLOCK:6,ENOMEM:48,EACCES:2,EFAULT:21,ENOTBLK:105,EBUSY:10,EEXIST:20,EXDEV:75,ENODEV:43,ENOTDIR:54,EISDIR:31,EINVAL:28,ENFILE:41,EMFILE:33,ENOTTY:59,ETXTBSY:74,EFBIG:22,ENOSPC:51,ESPIPE:70,EROFS:69,EMLINK:34,EPIPE:64,EDOM:18,ERANGE:68,ENOMSG:49,EIDRM:24,ECHRNG:106,EL2NSYNC:156,EL3HLT:107,EL3RST:108,ELNRNG:109,EUNATCH:110,ENOCSI:111,EL2HLT:112,EDEADLK:16,ENOLCK:46,EBADE:113,EBADR:114,EXFULL:115,ENOANO:104,EBADRQC:103,EBADSLT:102,EDEADLOCK:16,EBFONT:101,ENOSTR:100,ENODATA:116,ETIME:117,ENOSR:118,ENONET:119,ENOPKG:120,EREMOTE:121,ENOLINK:47,EADV:122,ESRMNT:123,ECOMM:124,EPROTO:65,EMULTIHOP:36,EDOTDOT:125,EBADMSG:9,ENOTUNIQ:126,EBADFD:127,EREMCHG:128,ELIBACC:129,ELIBBAD:130,ELIBSCN:131,ELIBMAX:132,ELIBEXEC:133,ENOSYS:52,ENOTEMPTY:55,ENAMETOOLONG:37,ELOOP:32,EOPNOTSUPP:138,EPFNOSUPPORT:139,ECONNRESET:15,ENOBUFS:42,EAFNOSUPPORT:5,EPROTOTYPE:67,ENOTSOCK:57,ENOPROTOOPT:50,ESHUTDOWN:140,ECONNREFUSED:14,EADDRINUSE:3,ECONNABORTED:13,ENETUNREACH:40,ENETDOWN:38,ETIMEDOUT:73,EHOSTDOWN:142,EHOSTUNREACH:23,EINPROGRESS:26,EALREADY:7,EDESTADDRREQ:17,EMSGSIZE:35,EPROTONOSUPPORT:66,ESOCKTNOSUPPORT:137,EADDRNOTAVAIL:4,ENETRESET:39,EISCONN:30,ENOTCONN:53,ETOOMANYREFS:141,EUSERS:136,EDQUOT:19,ESTALE:72,ENOTSUP:138,ENOMEDIUM:148,EILSEQ:25,EOVERFLOW:61,ECANCELED:11,ENOTRECOVERABLE:56,EOWNERDEAD:62,ESTRPIPE:135};var __main_thread_futex_wait_address=13568;function _emscripten_futex_wake(addr,count){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0||count<0)return-28;if(count==0)return 0;if(count>=2147483647)count=Infinity;var mainThreadWaitAddress=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2);var mainThreadWoken=0;if(mainThreadWaitAddress==addr){var loadedAddr=Atomics.compareExchange(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,mainThreadWaitAddress,0);if(loadedAddr==mainThreadWaitAddress){--count;mainThreadWoken=1;if(count<=0)return 1}}var ret=Atomics.notify(GROWABLE_HEAP_I32(),addr>>2,count);if(ret>=0)return ret+mainThreadWoken;throw\"Atomics.notify returned an unexpected value \"+ret}Module[\"_emscripten_futex_wake\"]=_emscripten_futex_wake;function __kill_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _kill_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _kill_thread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];pthread.worker.terminate();PThread.freeThreadData(pthread);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(pthread.worker),1);pthread.worker.pthread=undefined}function __cancel_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _cancel_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _cancel_thread!\";var pthread=PThread.pthreads[pthread_ptr];pthread.worker.postMessage({\"cmd\":\"cancel\"})}function __cleanup_thread(pthread_ptr){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _cleanup_thread() can only ever be called from main application thread!\";if(!pthread_ptr)throw\"Internal Error! Null pthread_ptr in _cleanup_thread!\";GROWABLE_HEAP_I32()[pthread_ptr+12>>2]=0;var pthread=PThread.pthreads[pthread_ptr];if(pthread){var worker=pthread.worker;PThread.returnWorkerToPool(worker)}}var PThread={MAIN_THREAD_ID:1,mainThreadInfo:{schedPolicy:0,schedPrio:0},unusedWorkers:[],runningWorkers:[],initRuntime:function(){__register_pthread_ptr(PThread.mainThreadBlock,!ENVIRONMENT_IS_WORKER,1);_emscripten_register_main_browser_thread_id(PThread.mainThreadBlock)},initMainThreadBlock:function(){var pthreadPoolSize=8;for(var i=0;i>2]=PThread.mainThreadBlock;var headPtr=PThread.mainThreadBlock+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var tlsMemory=13056;for(var i=0;i<128;++i)GROWABLE_HEAP_U32()[tlsMemory/4+i]=0;Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+104>>2,tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+40>>2,PThread.mainThreadBlock);Atomics.store(GROWABLE_HEAP_U32(),PThread.mainThreadBlock+44>>2,42)},initWorker:function(){},pthreads:{},exitHandlers:null,setThreadStatus:function(){},runExitHandlers:function(){if(PThread.exitHandlers!==null){while(PThread.exitHandlers.length>0){PThread.exitHandlers.pop()()}PThread.exitHandlers=null}if(ENVIRONMENT_IS_PTHREAD&&threadInfoStruct)___pthread_tsd_run_dtors()},threadExit:function(exitCode){var tb=_pthread_self();if(tb){Atomics.store(GROWABLE_HEAP_U32(),tb+4>>2,exitCode);Atomics.store(GROWABLE_HEAP_U32(),tb+0>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+60>>2,1);Atomics.store(GROWABLE_HEAP_U32(),tb+64>>2,0);PThread.runExitHandlers();_emscripten_futex_wake(tb+0,2147483647);__register_pthread_ptr(0,0,0);threadInfoStruct=0;if(ENVIRONMENT_IS_PTHREAD){postMessage({\"cmd\":\"exit\"})}}},threadCancel:function(){PThread.runExitHandlers();Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+4>>2,-1);Atomics.store(GROWABLE_HEAP_U32(),threadInfoStruct+0>>2,1);_emscripten_futex_wake(threadInfoStruct+0,2147483647);threadInfoStruct=selfThreadId=0;__register_pthread_ptr(0,0,0);postMessage({\"cmd\":\"cancelDone\"})},terminateAllThreads:function(){for(var t in PThread.pthreads){var pthread=PThread.pthreads[t];if(pthread&&pthread.worker){PThread.returnWorkerToPool(pthread.worker)}}PThread.pthreads={};for(var i=0;i>2];GROWABLE_HEAP_I32()[pthread.threadInfoStruct+104>>2]=0;_free(tlsMemory);_free(pthread.threadInfoStruct)}pthread.threadInfoStruct=0;if(pthread.allocatedOwnStack&&pthread.stackBase)_free(pthread.stackBase);pthread.stackBase=0;if(pthread.worker)pthread.worker.pthread=null},returnWorkerToPool:function(worker){delete PThread.pthreads[worker.pthread.thread];PThread.unusedWorkers.push(worker);PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker),1);PThread.freeThreadData(worker.pthread);worker.pthread=undefined},receiveObjectTransfer:function(data){},loadWasmModuleToWorker:function(worker,onFinishedLoading){worker.onmessage=function(e){var d=e[\"data\"];var cmd=d[\"cmd\"];if(worker.pthread)PThread.currentProxiedOperationCallerThread=worker.pthread.threadInfoStruct;if(d[\"targetThread\"]&&d[\"targetThread\"]!=_pthread_self()){var thread=PThread.pthreads[d.targetThread];if(thread){thread.worker.postMessage(e.data,d[\"transferList\"])}else{console.error('Internal error! Worker sent a message \"'+cmd+'\" to target pthread '+d[\"targetThread\"]+\", but that thread no longer exists!\")}PThread.currentProxiedOperationCallerThread=undefined;return}if(cmd===\"processQueuedMainThreadWork\"){_emscripten_main_thread_process_queued_calls()}else if(cmd===\"spawnThread\"){__spawn_thread(e.data)}else if(cmd===\"cleanupThread\"){__cleanup_thread(d[\"thread\"])}else if(cmd===\"killThread\"){__kill_thread(d[\"thread\"])}else if(cmd===\"cancelThread\"){__cancel_thread(d[\"thread\"])}else if(cmd===\"loaded\"){worker.loaded=true;if(onFinishedLoading)onFinishedLoading(worker);if(worker.runPthread){worker.runPthread();delete worker.runPthread}}else if(cmd===\"print\"){out(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"printErr\"){err(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"alert\"){alert(\"Thread \"+d[\"threadId\"]+\": \"+d[\"text\"])}else if(cmd===\"exit\"){var detached=worker.pthread&&Atomics.load(GROWABLE_HEAP_U32(),worker.pthread.thread+68>>2);if(detached){PThread.returnWorkerToPool(worker)}}else if(cmd===\"cancelDone\"){PThread.returnWorkerToPool(worker)}else if(cmd===\"objectTransfer\"){PThread.receiveObjectTransfer(e.data)}else if(e.data.target===\"setimmediate\"){worker.postMessage(e.data)}else{err(\"worker sent an unknown command \"+cmd)}PThread.currentProxiedOperationCallerThread=undefined};worker.onerror=function(e){err(\"pthread sent an error! \"+e.filename+\":\"+e.lineno+\": \"+e.message)};if(ENVIRONMENT_IS_NODE){worker.on(\"message\",function(data){worker.onmessage({data:data})});worker.on(\"error\",function(data){worker.onerror(data)});worker.on(\"exit\",function(data){console.log(\"worker exited - TODO: update the worker queue?\")})}worker.postMessage({\"cmd\":\"load\",\"urlOrBlob\":Module[\"mainScriptUrlOrBlob\"]||_scriptDir,\"wasmMemory\":wasmMemory,\"wasmModule\":wasmModule,\"DYNAMIC_BASE\":DYNAMIC_BASE,\"DYNAMICTOP_PTR\":DYNAMICTOP_PTR})},allocateUnusedWorker:function(){var pthreadMainJs=locateFile(\"tfjs-backend-wasm-threaded-simd.worker.js\");PThread.unusedWorkers.push(new Worker(pthreadMainJs))},getNewWorker:function(){if(PThread.unusedWorkers.length==0){PThread.allocateUnusedWorker();PThread.loadWasmModuleToWorker(PThread.unusedWorkers[0])}if(PThread.unusedWorkers.length>0)return PThread.unusedWorkers.pop();else return null},busySpinWait:function(msecs){var t=performance.now()+msecs;while(performance.now()>2]=value;return value}function _atexit(func,arg){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(1,1,func,arg);__ATEXIT__.unshift({func:func,arg:arg})}function __emscripten_notify_thread_queue(targetThreadId,mainThreadId){if(targetThreadId==mainThreadId){postMessage({\"cmd\":\"processQueuedMainThreadWork\"})}else if(ENVIRONMENT_IS_PTHREAD){postMessage({\"targetThread\":targetThreadId,\"cmd\":\"processThreadQueue\"})}else{var pthread=PThread.pthreads[targetThreadId];var worker=pthread&&pthread.worker;if(!worker){return}worker.postMessage({\"cmd\":\"processThreadQueue\"})}return 1}function _abort(){abort()}function _emscripten_conditional_set_current_thread_status(expectedStatus,newStatus){expectedStatus=expectedStatus|0;newStatus=newStatus|0}function _emscripten_futex_wait(addr,val,timeout){if(addr<=0||addr>GROWABLE_HEAP_I8().length||addr&3!=0)return-28;if(ENVIRONMENT_IS_WORKER){var ret=Atomics.wait(GROWABLE_HEAP_I32(),addr>>2,val,timeout);if(ret===\"timed-out\")return-73;if(ret===\"not-equal\")return-6;if(ret===\"ok\")return 0;throw\"Atomics.wait returned an unexpected value \"+ret}else{var loadedVal=Atomics.load(GROWABLE_HEAP_I32(),addr>>2);if(val!=loadedVal)return-6;var tNow=performance.now();var tEnd=tNow+timeout;Atomics.store(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2,addr);var ourWaitAddress=addr;while(addr==ourWaitAddress){tNow=performance.now();if(tNow>tEnd){return-73}_emscripten_main_thread_process_queued_calls();addr=Atomics.load(GROWABLE_HEAP_I32(),__main_thread_futex_wait_address>>2)}return 0}}function _emscripten_is_main_browser_thread(){return __pthread_is_main_browser_thread|0}function _emscripten_is_main_runtime_thread(){return __pthread_is_main_runtime_thread|0}function _emscripten_memcpy_big(dest,src,num){GROWABLE_HEAP_U8().copyWithin(dest,src,src+num)}function _emscripten_num_logical_cores(){return navigator[\"hardwareConcurrency\"]}function _emscripten_proxy_to_main_thread_js(index,sync){var numCallArgs=arguments.length-2;var stack=stackSave();var args=stackAlloc(numCallArgs*8);var b=args>>3;for(var i=0;i>3]);buf+=8}else{buf=buf+3&~3;args.push(GROWABLE_HEAP_I32()[buf>>2]);buf+=4}}return args}function _emscripten_receive_on_main_thread_js(index,numCallArgs,args){_emscripten_receive_on_main_thread_js_callArgs.length=numCallArgs;var b=args>>3;for(var i=0;i>>16);updateGlobalBufferAndViews(wasmMemory.buffer);return 1}catch(e){}}function _emscripten_resize_heap(requestedSize){requestedSize=requestedSize>>>0;var oldSize=_emscripten_get_heap_size();if(requestedSize<=oldSize){return false}var PAGE_MULTIPLE=65536;var maxHeapSize=2147483648;if(requestedSize>maxHeapSize){return false}var minHeapSize=16777216;for(var cutDown=1;cutDown<=4;cutDown*=2){var overGrownHeapSize=oldSize*(1+.2/cutDown);overGrownHeapSize=Math.min(overGrownHeapSize,requestedSize+100663296);var newSize=Math.min(maxHeapSize,alignUp(Math.max(minHeapSize,requestedSize,overGrownHeapSize),PAGE_MULTIPLE));var replacement=emscripten_realloc_buffer(newSize);if(replacement){return true}}return false}var JSEvents={keyEvent:0,mouseEvent:0,wheelEvent:0,uiEvent:0,focusEvent:0,deviceOrientationEvent:0,deviceMotionEvent:0,fullscreenChangeEvent:0,pointerlockChangeEvent:0,visibilityChangeEvent:0,touchEvent:0,previousFullscreenElement:null,previousScreenX:null,previousScreenY:null,removeEventListenersRegistered:false,removeAllEventListeners:function(){for(var i=JSEvents.eventHandlers.length-1;i>=0;--i){JSEvents._removeHandler(i)}JSEvents.eventHandlers=[];JSEvents.deferredCalls=[]},registerRemoveEventListeners:function(){if(!JSEvents.removeEventListenersRegistered){__ATEXIT__.push(JSEvents.removeAllEventListeners);JSEvents.removeEventListenersRegistered=true}},deferredCalls:[],deferCall:function(targetFunction,precedence,argsList){function arraysHaveEqualContent(arrA,arrB){if(arrA.length!=arrB.length)return false;for(var i in arrA){if(arrA[i]!=arrB[i])return false}return true}for(var i in JSEvents.deferredCalls){var call=JSEvents.deferredCalls[i];if(call.targetFunction==targetFunction&&arraysHaveEqualContent(call.argsList,argsList)){return}}JSEvents.deferredCalls.push({targetFunction:targetFunction,precedence:precedence,argsList:argsList});JSEvents.deferredCalls.sort(function(x,y){return x.precedence>2]=eventTypeId;GROWABLE_HEAP_I32()[varargs+4>>2]=eventData;GROWABLE_HEAP_I32()[varargs+8>>2]=userData;_emscripten_async_queue_on_thread_(targetThread,637534208,eventHandlerFunc,eventData,varargs);stackRestore(stackTop)},getTargetThreadForEventCallback:function(targetThread){switch(targetThread){case 1:return 0;case 2:return PThread.currentProxiedOperationCallerThread;default:return targetThread}},getNodeNameForTarget:function(target){if(!target)return\"\";if(target==window)return\"#window\";if(target==screen)return\"#screen\";return target&&target.nodeName?target.nodeName:\"\"},fullscreenEnabled:function(){return document.fullscreenEnabled||document.webkitFullscreenEnabled}};function stringToNewUTF8(jsString){var length=lengthBytesUTF8(jsString)+1;var cString=_malloc(length);stringToUTF8(jsString,cString,length);return cString}function _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height){var stackTop=stackSave();var varargs=stackAlloc(12);var targetCanvasPtr=0;if(targetCanvas){targetCanvasPtr=stringToNewUTF8(targetCanvas)}GROWABLE_HEAP_I32()[varargs>>2]=targetCanvasPtr;GROWABLE_HEAP_I32()[varargs+4>>2]=width;GROWABLE_HEAP_I32()[varargs+8>>2]=height;_emscripten_async_queue_on_thread_(targetThread,657457152,0,targetCanvasPtr,varargs);stackRestore(stackTop)}function _emscripten_set_offscreencanvas_size_on_target_thread(targetThread,targetCanvas,width,height){targetCanvas=targetCanvas?UTF8ToString(targetCanvas):\"\";_emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread,targetCanvas,width,height)}function __maybeCStringToJsString(cString){return cString>2?UTF8ToString(cString):cString}var specialHTMLTargets=[0,typeof document!==\"undefined\"?document:0,typeof window!==\"undefined\"?window:0];function __findEventTarget(target){target=__maybeCStringToJsString(target);var domElement=specialHTMLTargets[target]||(typeof document!==\"undefined\"?document.querySelector(target):undefined);return domElement}function __findCanvasEventTarget(target){return __findEventTarget(target)}function _emscripten_set_canvas_element_size_calling_thread(target,width,height){var canvas=__findCanvasEventTarget(target);if(!canvas)return-4;if(canvas.canvasSharedPtr){GROWABLE_HEAP_I32()[canvas.canvasSharedPtr>>2]=width;GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+4>>2]=height}if(canvas.offscreenCanvas||!canvas.controlTransferredOffscreen){if(canvas.offscreenCanvas)canvas=canvas.offscreenCanvas;var autoResizeViewport=false;if(canvas.GLctxObject&&canvas.GLctxObject.GLctx){var prevViewport=canvas.GLctxObject.GLctx.getParameter(2978);autoResizeViewport=prevViewport[0]===0&&prevViewport[1]===0&&prevViewport[2]===canvas.width&&prevViewport[3]===canvas.height}canvas.width=width;canvas.height=height;if(autoResizeViewport){canvas.GLctxObject.GLctx.viewport(0,0,width,height)}}else if(canvas.canvasSharedPtr){var targetThread=GROWABLE_HEAP_I32()[canvas.canvasSharedPtr+8>>2];_emscripten_set_offscreencanvas_size_on_target_thread(targetThread,target,width,height);return 1}else{return-4}return 0}function _emscripten_set_canvas_element_size_main_thread(target,width,height){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(2,1,target,width,height);return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}function _emscripten_set_canvas_element_size(target,width,height){var canvas=__findCanvasEventTarget(target);if(canvas){return _emscripten_set_canvas_element_size_calling_thread(target,width,height)}else{return _emscripten_set_canvas_element_size_main_thread(target,width,height)}}function _emscripten_set_current_thread_status(newStatus){newStatus=newStatus|0}function _emscripten_set_thread_name(threadId,name){threadId=threadId|0;name=name|0}function __webgl_enable_ANGLE_instanced_arrays(ctx){var ext=ctx.getExtension(\"ANGLE_instanced_arrays\");if(ext){ctx[\"vertexAttribDivisor\"]=function(index,divisor){ext[\"vertexAttribDivisorANGLE\"](index,divisor)};ctx[\"drawArraysInstanced\"]=function(mode,first,count,primcount){ext[\"drawArraysInstancedANGLE\"](mode,first,count,primcount)};ctx[\"drawElementsInstanced\"]=function(mode,count,type,indices,primcount){ext[\"drawElementsInstancedANGLE\"](mode,count,type,indices,primcount)};return 1}}function __webgl_enable_OES_vertex_array_object(ctx){var ext=ctx.getExtension(\"OES_vertex_array_object\");if(ext){ctx[\"createVertexArray\"]=function(){return ext[\"createVertexArrayOES\"]()};ctx[\"deleteVertexArray\"]=function(vao){ext[\"deleteVertexArrayOES\"](vao)};ctx[\"bindVertexArray\"]=function(vao){ext[\"bindVertexArrayOES\"](vao)};ctx[\"isVertexArray\"]=function(vao){return ext[\"isVertexArrayOES\"](vao)};return 1}}function __webgl_enable_WEBGL_draw_buffers(ctx){var ext=ctx.getExtension(\"WEBGL_draw_buffers\");if(ext){ctx[\"drawBuffers\"]=function(n,bufs){ext[\"drawBuffersWEBGL\"](n,bufs)};return 1}}var GL={counter:1,lastError:0,buffers:[],mappedBuffers:{},programs:[],framebuffers:[],renderbuffers:[],textures:[],uniforms:[],shaders:[],vaos:[],contexts:{},currentContext:null,offscreenCanvases:{},timerQueriesEXT:[],programInfos:{},stringCache:{},unpackAlignment:4,init:function(){var miniTempFloatBuffer=new Float32Array(GL.MINI_TEMP_BUFFER_SIZE);for(var i=0;i>2]:-1;source+=UTF8ToString(GROWABLE_HEAP_I32()[string+i*4>>2],len<0?undefined:len)}return source},createContext:function(canvas,webGLContextAttributes){var ctx=canvas.getContext(\"webgl\",webGLContextAttributes);if(!ctx)return 0;var handle=GL.registerContext(ctx,webGLContextAttributes);return handle},registerContext:function(ctx,webGLContextAttributes){var handle=_malloc(8);GROWABLE_HEAP_I32()[handle+4>>2]=_pthread_self();var context={handle:handle,attributes:webGLContextAttributes,version:webGLContextAttributes.majorVersion,GLctx:ctx};if(ctx.canvas)ctx.canvas.GLctxObject=context;GL.contexts[handle]=context;if(typeof webGLContextAttributes.enableExtensionsByDefault===\"undefined\"||webGLContextAttributes.enableExtensionsByDefault){GL.initExtensions(context)}return handle},makeContextCurrent:function(contextHandle){GL.currentContext=GL.contexts[contextHandle];Module.ctx=GLctx=GL.currentContext&&GL.currentContext.GLctx;return!(contextHandle&&!GLctx)},getContext:function(contextHandle){return GL.contexts[contextHandle]},deleteContext:function(contextHandle){if(GL.currentContext===GL.contexts[contextHandle])GL.currentContext=null;if(typeof JSEvents===\"object\")JSEvents.removeAllHandlersOnTarget(GL.contexts[contextHandle].GLctx.canvas);if(GL.contexts[contextHandle]&&GL.contexts[contextHandle].GLctx.canvas)GL.contexts[contextHandle].GLctx.canvas.GLctxObject=undefined;_free(GL.contexts[contextHandle].handle);GL.contexts[contextHandle]=null},initExtensions:function(context){if(!context)context=GL.currentContext;if(context.initExtensionsDone)return;context.initExtensionsDone=true;var GLctx=context.GLctx;__webgl_enable_ANGLE_instanced_arrays(GLctx);__webgl_enable_OES_vertex_array_object(GLctx);__webgl_enable_WEBGL_draw_buffers(GLctx);GLctx.disjointTimerQueryExt=GLctx.getExtension(\"EXT_disjoint_timer_query\");var automaticallyEnabledExtensions=[\"OES_texture_float\",\"OES_texture_half_float\",\"OES_standard_derivatives\",\"OES_vertex_array_object\",\"WEBGL_compressed_texture_s3tc\",\"WEBGL_depth_texture\",\"OES_element_index_uint\",\"EXT_texture_filter_anisotropic\",\"EXT_frag_depth\",\"WEBGL_draw_buffers\",\"ANGLE_instanced_arrays\",\"OES_texture_float_linear\",\"OES_texture_half_float_linear\",\"EXT_blend_minmax\",\"EXT_shader_texture_lod\",\"EXT_texture_norm16\",\"WEBGL_compressed_texture_pvrtc\",\"EXT_color_buffer_half_float\",\"WEBGL_color_buffer_float\",\"EXT_sRGB\",\"WEBGL_compressed_texture_etc1\",\"EXT_disjoint_timer_query\",\"WEBGL_compressed_texture_etc\",\"WEBGL_compressed_texture_astc\",\"EXT_color_buffer_float\",\"WEBGL_compressed_texture_s3tc_srgb\",\"EXT_disjoint_timer_query_webgl2\",\"WEBKIT_WEBGL_compressed_texture_pvrtc\"];var exts=GLctx.getSupportedExtensions()||[];exts.forEach(function(ext){if(automaticallyEnabledExtensions.indexOf(ext)!=-1){GLctx.getExtension(ext)}})},populateUniformTable:function(program){var p=GL.programs[program];var ptable=GL.programInfos[program]={uniforms:{},maxUniformLength:0,maxAttributeLength:-1,maxUniformBlockNameLength:-1};var utable=ptable.uniforms;var numUniforms=GLctx.getProgramParameter(p,35718);for(var i=0;i>2;contextAttributes[\"alpha\"]=!!GROWABLE_HEAP_I32()[a+(0>>2)];contextAttributes[\"depth\"]=!!GROWABLE_HEAP_I32()[a+(4>>2)];contextAttributes[\"stencil\"]=!!GROWABLE_HEAP_I32()[a+(8>>2)];contextAttributes[\"antialias\"]=!!GROWABLE_HEAP_I32()[a+(12>>2)];contextAttributes[\"premultipliedAlpha\"]=!!GROWABLE_HEAP_I32()[a+(16>>2)];contextAttributes[\"preserveDrawingBuffer\"]=!!GROWABLE_HEAP_I32()[a+(20>>2)];var powerPreference=GROWABLE_HEAP_I32()[a+(24>>2)];contextAttributes[\"powerPreference\"]=__emscripten_webgl_power_preferences[powerPreference];contextAttributes[\"failIfMajorPerformanceCaveat\"]=!!GROWABLE_HEAP_I32()[a+(28>>2)];contextAttributes.majorVersion=GROWABLE_HEAP_I32()[a+(32>>2)];contextAttributes.minorVersion=GROWABLE_HEAP_I32()[a+(36>>2)];contextAttributes.enableExtensionsByDefault=GROWABLE_HEAP_I32()[a+(40>>2)];contextAttributes.explicitSwapControl=GROWABLE_HEAP_I32()[a+(44>>2)];contextAttributes.proxyContextToMainThread=GROWABLE_HEAP_I32()[a+(48>>2)];contextAttributes.renderViaOffscreenBackBuffer=GROWABLE_HEAP_I32()[a+(52>>2)];var canvas=__findCanvasEventTarget(target);if(!canvas){return-4}if(contextAttributes.explicitSwapControl){return-1}var contextHandle=GL.createContext(canvas,contextAttributes);return contextHandle}function _emscripten_webgl_create_context(a0,a1){return _emscripten_webgl_do_create_context(a0,a1)}var PATH={splitPath:function(filename){var splitPathRe=/^(\\/?|)([\\s\\S]*?)((?:\\.{1,2}|[^\\/]+?|)(\\.[^.\\/]*|))(?:[\\/]*)$/;return splitPathRe.exec(filename).slice(1)},normalizeArray:function(parts,allowAboveRoot){var up=0;for(var i=parts.length-1;i>=0;i--){var last=parts[i];if(last===\".\"){parts.splice(i,1)}else if(last===\"..\"){parts.splice(i,1);up++}else if(up){parts.splice(i,1);up--}}if(allowAboveRoot){for(;up;up--){parts.unshift(\"..\")}}return parts},normalize:function(path){var isAbsolute=path.charAt(0)===\"/\",trailingSlash=path.substr(-1)===\"/\";path=PATH.normalizeArray(path.split(\"/\").filter(function(p){return!!p}),!isAbsolute).join(\"/\");if(!path&&!isAbsolute){path=\".\"}if(path&&trailingSlash){path+=\"/\"}return(isAbsolute?\"/\":\"\")+path},dirname:function(path){var result=PATH.splitPath(path),root=result[0],dir=result[1];if(!root&&!dir){return\".\"}if(dir){dir=dir.substr(0,dir.length-1)}return root+dir},basename:function(path){if(path===\"/\")return\"/\";var lastSlash=path.lastIndexOf(\"/\");if(lastSlash===-1)return path;return path.substr(lastSlash+1)},extname:function(path){return PATH.splitPath(path)[3]},join:function(){var paths=Array.prototype.slice.call(arguments,0);return PATH.normalize(paths.join(\"/\"))},join2:function(l,r){return PATH.normalize(l+\"/\"+r)}};var SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer=SYSCALLS.buffers[stream];if(curr===0||curr===10){(stream===1?out:err)(UTF8ArrayToString(buffer,0));buffer.length=0}else{buffer.push(curr)}},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=GROWABLE_HEAP_I32()[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(3,1,fd);return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(4,1,fd,offset_low,offset_high,whence,newOffset)}function _fd_write(fd,iov,iovcnt,pnum){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(5,1,fd,iov,iovcnt,pnum);var num=0;for(var i=0;i>2];var len=GROWABLE_HEAP_I32()[iov+(i*8+4)>>2];for(var j=0;j>2]=num;return 0}function _pthread_cleanup_pop(execute){var routine=PThread.exitHandlers.pop();if(execute)routine()}function _pthread_cleanup_push(routine,arg){if(PThread.exitHandlers===null){PThread.exitHandlers=[]}PThread.exitHandlers.push(function(){dynCall_vi(routine,arg)})}function __spawn_thread(threadParams){if(ENVIRONMENT_IS_PTHREAD)throw\"Internal Error! _spawn_thread() can only ever be called from main application thread!\";var worker=PThread.getNewWorker();if(worker.pthread!==undefined)throw\"Internal error!\";if(!threadParams.pthread_ptr)throw\"Internal error, no pthread ptr!\";PThread.runningWorkers.push(worker);var tlsMemory=_malloc(128*4);for(var i=0;i<128;++i){GROWABLE_HEAP_I32()[tlsMemory+i*4>>2]=0}var stackHigh=threadParams.stackBase+threadParams.stackSize;var pthread=PThread.pthreads[threadParams.pthread_ptr]={worker:worker,stackBase:threadParams.stackBase,stackSize:threadParams.stackSize,allocatedOwnStack:threadParams.allocatedOwnStack,thread:threadParams.pthread_ptr,threadInfoStruct:threadParams.pthread_ptr};var tis=pthread.threadInfoStruct>>2;Atomics.store(GROWABLE_HEAP_U32(),tis+(0>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(4>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(8>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(68>>2),threadParams.detached);Atomics.store(GROWABLE_HEAP_U32(),tis+(104>>2),tlsMemory);Atomics.store(GROWABLE_HEAP_U32(),tis+(48>>2),0);Atomics.store(GROWABLE_HEAP_U32(),tis+(40>>2),pthread.threadInfoStruct);Atomics.store(GROWABLE_HEAP_U32(),tis+(44>>2),42);Atomics.store(GROWABLE_HEAP_U32(),tis+(108>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(84>>2),threadParams.stackSize);Atomics.store(GROWABLE_HEAP_U32(),tis+(80>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+8>>2),stackHigh);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+12>>2),threadParams.detached);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+20>>2),threadParams.schedPolicy);Atomics.store(GROWABLE_HEAP_U32(),tis+(108+24>>2),threadParams.schedPrio);var global_libc=_emscripten_get_global_libc();var global_locale=global_libc+40;Atomics.store(GROWABLE_HEAP_U32(),tis+(176>>2),global_locale);worker.pthread=pthread;var msg={\"cmd\":\"run\",\"start_routine\":threadParams.startRoutine,\"arg\":threadParams.arg,\"threadInfoStruct\":threadParams.pthread_ptr,\"selfThreadId\":threadParams.pthread_ptr,\"parentThreadId\":threadParams.parent_pthread_ptr,\"stackBase\":threadParams.stackBase,\"stackSize\":threadParams.stackSize};worker.runPthread=function(){msg.time=performance.now();worker.postMessage(msg,threadParams.transferList)};if(worker.loaded){worker.runPthread();delete worker.runPthread}}function _pthread_getschedparam(thread,policy,schedparam){if(!policy&&!schedparam)return ERRNO_CODES.EINVAL;if(!thread){err(\"pthread_getschedparam called with a null thread pointer!\");return ERRNO_CODES.ESRCH}var self=GROWABLE_HEAP_I32()[thread+12>>2];if(self!==thread){err(\"pthread_getschedparam attempted on thread \"+thread+\", which does not point to a valid thread, or does not exist anymore!\");return ERRNO_CODES.ESRCH}var schedPolicy=Atomics.load(GROWABLE_HEAP_U32(),thread+108+20>>2);var schedPrio=Atomics.load(GROWABLE_HEAP_U32(),thread+108+24>>2);if(policy)GROWABLE_HEAP_I32()[policy>>2]=schedPolicy;if(schedparam)GROWABLE_HEAP_I32()[schedparam>>2]=schedPrio;return 0}function _pthread_self(){return __pthread_ptr|0}Module[\"_pthread_self\"]=_pthread_self;function _pthread_create(pthread_ptr,attr,start_routine,arg){if(typeof SharedArrayBuffer===\"undefined\"){err(\"Current environment does not support SharedArrayBuffer, pthreads are not available!\");return 6}if(!pthread_ptr){err(\"pthread_create called with a null thread pointer!\");return 28}var transferList=[];var error=0;if(ENVIRONMENT_IS_PTHREAD&&(transferList.length===0||error)){return _emscripten_sync_run_in_main_thread_4(687865856,pthread_ptr,attr,start_routine,arg)}if(error)return error;var stackSize=0;var stackBase=0;var detached=0;var schedPolicy=0;var schedPrio=0;if(attr){stackSize=GROWABLE_HEAP_I32()[attr>>2];stackSize+=81920;stackBase=GROWABLE_HEAP_I32()[attr+8>>2];detached=GROWABLE_HEAP_I32()[attr+12>>2]!==0;var inheritSched=GROWABLE_HEAP_I32()[attr+16>>2]===0;if(inheritSched){var prevSchedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];var prevSchedPrio=GROWABLE_HEAP_I32()[attr+24>>2];var parentThreadPtr=PThread.currentProxiedOperationCallerThread?PThread.currentProxiedOperationCallerThread:_pthread_self();_pthread_getschedparam(parentThreadPtr,attr+20,attr+24);schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];schedPrio=GROWABLE_HEAP_I32()[attr+24>>2];GROWABLE_HEAP_I32()[attr+20>>2]=prevSchedPolicy;GROWABLE_HEAP_I32()[attr+24>>2]=prevSchedPrio}else{schedPolicy=GROWABLE_HEAP_I32()[attr+20>>2];schedPrio=GROWABLE_HEAP_I32()[attr+24>>2]}}else{stackSize=2097152}var allocatedOwnStack=stackBase==0;if(allocatedOwnStack){stackBase=_memalign(16,stackSize)}else{stackBase-=stackSize;assert(stackBase>0)}var threadInfoStruct=_malloc(232);for(var i=0;i<232>>2;++i)GROWABLE_HEAP_U32()[(threadInfoStruct>>2)+i]=0;GROWABLE_HEAP_I32()[pthread_ptr>>2]=threadInfoStruct;GROWABLE_HEAP_I32()[threadInfoStruct+12>>2]=threadInfoStruct;var headPtr=threadInfoStruct+156;GROWABLE_HEAP_I32()[headPtr>>2]=headPtr;var threadParams={stackBase:stackBase,stackSize:stackSize,allocatedOwnStack:allocatedOwnStack,schedPolicy:schedPolicy,schedPrio:schedPrio,detached:detached,startRoutine:start_routine,pthread_ptr:threadInfoStruct,parent_pthread_ptr:_pthread_self(),arg:arg,transferList:transferList};if(ENVIRONMENT_IS_PTHREAD){threadParams.cmd=\"spawnThread\";postMessage(threadParams,transferList)}else{__spawn_thread(threadParams)}return 0}function _roundf(d){d=+d;return d>=+0?+Math_floor(d+ +.5):+Math_ceil(d-+.5)}function _sysconf(name){if(ENVIRONMENT_IS_PTHREAD)return _emscripten_proxy_to_main_thread_js(6,1,name);switch(name){case 30:return 16384;case 85:var maxHeapSize=2147483648;return maxHeapSize/16384;case 132:case 133:case 12:case 137:case 138:case 15:case 235:case 16:case 17:case 18:case 19:case 20:case 149:case 13:case 10:case 236:case 153:case 9:case 21:case 22:case 159:case 154:case 14:case 77:case 78:case 139:case 80:case 81:case 82:case 68:case 67:case 164:case 11:case 29:case 47:case 48:case 95:case 52:case 51:case 46:case 79:return 200809;case 27:case 246:case 127:case 128:case 23:case 24:case 160:case 161:case 181:case 182:case 242:case 183:case 184:case 243:case 244:case 245:case 165:case 178:case 179:case 49:case 50:case 168:case 169:case 175:case 170:case 171:case 172:case 97:case 76:case 32:case 173:case 35:return-1;case 176:case 177:case 7:case 155:case 8:case 157:case 125:case 126:case 92:case 93:case 129:case 130:case 131:case 94:case 91:return 1;case 74:case 60:case 69:case 70:case 4:return 1024;case 31:case 42:case 72:return 32;case 87:case 26:case 33:return 2147483647;case 34:case 1:return 47839;case 38:case 36:return 99;case 43:case 37:return 2048;case 0:return 2097152;case 3:return 65536;case 28:return 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WasmBackendModuleThreadedSimd; });\n else if (typeof exports === 'object')\n exports[\"WasmBackendModuleThreadedSimd\"] = WasmBackendModuleThreadedSimd;\n ", "\nvar WasmBackendModule = (function() {\n var _scriptDir = typeof document !== 'undefined' && document.currentScript ? document.currentScript.src : undefined;\n if (typeof __filename !== 'undefined') _scriptDir = _scriptDir || __filename;\n return (\nfunction(WasmBackendModule) {\n WasmBackendModule = WasmBackendModule || {};\n\nvar Module=typeof WasmBackendModule!==\"undefined\"?WasmBackendModule:{};var moduleOverrides={};var key;for(key in Module){if(Module.hasOwnProperty(key)){moduleOverrides[key]=Module[key]}}var arguments_=[];var thisProgram=\"./this.program\";var quit_=function(status,toThrow){throw toThrow};var ENVIRONMENT_IS_WEB=false;var ENVIRONMENT_IS_WORKER=false;var ENVIRONMENT_IS_NODE=false;var ENVIRONMENT_IS_SHELL=false;ENVIRONMENT_IS_WEB=typeof window===\"object\";ENVIRONMENT_IS_WORKER=typeof importScripts===\"function\";ENVIRONMENT_IS_NODE=typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\";ENVIRONMENT_IS_SHELL=!ENVIRONMENT_IS_WEB&&!ENVIRONMENT_IS_NODE&&!ENVIRONMENT_IS_WORKER;var scriptDirectory=\"\";function locateFile(path){if(Module[\"locateFile\"]){return Module[\"locateFile\"](path,scriptDirectory)}return scriptDirectory+path}var read_,readAsync,readBinary,setWindowTitle;var nodeFS;var nodePath;if(ENVIRONMENT_IS_NODE){if(ENVIRONMENT_IS_WORKER){scriptDirectory=require(\"path\").dirname(scriptDirectory)+\"/\"}else{scriptDirectory=__dirname+\"/\"}read_=function shell_read(filename,binary){if(!nodeFS)nodeFS=require(\"fs\");if(!nodePath)nodePath=require(\"path\");filename=nodePath[\"normalize\"](filename);return nodeFS[\"readFileSync\"](filename,binary?null:\"utf8\")};readBinary=function readBinary(filename){var ret=read_(filename,true);if(!ret.buffer){ret=new Uint8Array(ret)}assert(ret.buffer);return ret};if(process[\"argv\"].length>1){thisProgram=process[\"argv\"][1].replace(/\\\\/g,\"/\")}arguments_=process[\"argv\"].slice(2);process[\"on\"](\"uncaughtException\",function(ex){if(!(ex instanceof ExitStatus)){throw ex}});process[\"on\"](\"unhandledRejection\",abort);quit_=function(status){process[\"exit\"](status)};Module[\"inspect\"]=function(){return\"[Emscripten Module object]\"}}else if(ENVIRONMENT_IS_SHELL){if(typeof read!=\"undefined\"){read_=function shell_read(f){return read(f)}}readBinary=function readBinary(f){var data;if(typeof readbuffer===\"function\"){return new Uint8Array(readbuffer(f))}data=read(f,\"binary\");assert(typeof data===\"object\");return data};if(typeof scriptArgs!=\"undefined\"){arguments_=scriptArgs}else if(typeof arguments!=\"undefined\"){arguments_=arguments}if(typeof quit===\"function\"){quit_=function(status){quit(status)}}if(typeof print!==\"undefined\"){if(typeof console===\"undefined\")console={};console.log=print;console.warn=console.error=typeof printErr!==\"undefined\"?printErr:print}}else if(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER){if(ENVIRONMENT_IS_WORKER){scriptDirectory=self.location.href}else if(document.currentScript){scriptDirectory=document.currentScript.src}if(_scriptDir){scriptDirectory=_scriptDir}if(scriptDirectory.indexOf(\"blob:\")!==0){scriptDirectory=scriptDirectory.substr(0,scriptDirectory.lastIndexOf(\"/\")+1)}else{scriptDirectory=\"\"}{read_=function shell_read(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.send(null);return xhr.responseText};if(ENVIRONMENT_IS_WORKER){readBinary=function readBinary(url){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,false);xhr.responseType=\"arraybuffer\";xhr.send(null);return new Uint8Array(xhr.response)}}readAsync=function readAsync(url,onload,onerror){var xhr=new XMLHttpRequest;xhr.open(\"GET\",url,true);xhr.responseType=\"arraybuffer\";xhr.onload=function xhr_onload(){if(xhr.status==200||xhr.status==0&&xhr.response){onload(xhr.response);return}onerror()};xhr.onerror=onerror;xhr.send(null)}}setWindowTitle=function(title){document.title=title}}else{}var out=Module[\"print\"]||console.log.bind(console);var err=Module[\"printErr\"]||console.warn.bind(console);for(key in moduleOverrides){if(moduleOverrides.hasOwnProperty(key)){Module[key]=moduleOverrides[key]}}moduleOverrides=null;if(Module[\"arguments\"])arguments_=Module[\"arguments\"];if(Module[\"thisProgram\"])thisProgram=Module[\"thisProgram\"];if(Module[\"quit\"])quit_=Module[\"quit\"];var wasmBinary;if(Module[\"wasmBinary\"])wasmBinary=Module[\"wasmBinary\"];var noExitRuntime;if(Module[\"noExitRuntime\"])noExitRuntime=Module[\"noExitRuntime\"];if(typeof WebAssembly!==\"object\"){err(\"no native wasm support detected\")}var wasmMemory;var wasmTable=new WebAssembly.Table({\"initial\":151,\"maximum\":151+0,\"element\":\"anyfunc\"});var ABORT=false;var EXITSTATUS=0;function assert(condition,text){if(!condition){abort(\"Assertion failed: \"+text)}}function getCFunc(ident){var func=Module[\"_\"+ident];assert(func,\"Cannot call unknown function \"+ident+\", make sure it is exported\");return func}function ccall(ident,returnType,argTypes,args,opts){var toC={\"string\":function(str){var ret=0;if(str!==null&&str!==undefined&&str!==0){var len=(str.length<<2)+1;ret=stackAlloc(len);stringToUTF8(str,ret,len)}return ret},\"array\":function(arr){var ret=stackAlloc(arr.length);writeArrayToMemory(arr,ret);return ret}};function convertReturnValue(ret){if(returnType===\"string\")return UTF8ToString(ret);if(returnType===\"boolean\")return Boolean(ret);return ret}var func=getCFunc(ident);var cArgs=[];var stack=0;if(args){for(var i=0;i=endIdx))++endPtr;if(endPtr-idx>16&&heap.subarray&&UTF8Decoder){return UTF8Decoder.decode(heap.subarray(idx,endPtr))}else{var str=\"\";while(idx>10,56320|ch&1023)}}}return str}function UTF8ToString(ptr,maxBytesToRead){return ptr?UTF8ArrayToString(HEAPU8,ptr,maxBytesToRead):\"\"}function stringToUTF8Array(str,heap,outIdx,maxBytesToWrite){if(!(maxBytesToWrite>0))return 0;var startIdx=outIdx;var endIdx=outIdx+maxBytesToWrite-1;for(var i=0;i=55296&&u<=57343){var u1=str.charCodeAt(++i);u=65536+((u&1023)<<10)|u1&1023}if(u<=127){if(outIdx>=endIdx)break;heap[outIdx++]=u}else if(u<=2047){if(outIdx+1>=endIdx)break;heap[outIdx++]=192|u>>6;heap[outIdx++]=128|u&63}else if(u<=65535){if(outIdx+2>=endIdx)break;heap[outIdx++]=224|u>>12;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}else{if(outIdx+3>=endIdx)break;heap[outIdx++]=240|u>>18;heap[outIdx++]=128|u>>12&63;heap[outIdx++]=128|u>>6&63;heap[outIdx++]=128|u&63}}heap[outIdx]=0;return outIdx-startIdx}function stringToUTF8(str,outPtr,maxBytesToWrite){return stringToUTF8Array(str,HEAPU8,outPtr,maxBytesToWrite)}function writeArrayToMemory(array,buffer){HEAP8.set(array,buffer)}var buffer,HEAP8,HEAPU8,HEAP16,HEAPU16,HEAP32,HEAPU32,HEAPF32,HEAPF64;function updateGlobalBufferAndViews(buf){buffer=buf;Module[\"HEAP8\"]=HEAP8=new Int8Array(buf);Module[\"HEAP16\"]=HEAP16=new Int16Array(buf);Module[\"HEAP32\"]=HEAP32=new Int32Array(buf);Module[\"HEAPU8\"]=HEAPU8=new Uint8Array(buf);Module[\"HEAPU16\"]=HEAPU16=new Uint16Array(buf);Module[\"HEAPU32\"]=HEAPU32=new Uint32Array(buf);Module[\"HEAPF32\"]=HEAPF32=new Float32Array(buf);Module[\"HEAPF64\"]=HEAPF64=new Float64Array(buf)}var INITIAL_INITIAL_MEMORY=Module[\"INITIAL_MEMORY\"]||16777216;function callRuntimeCallbacks(callbacks){while(callbacks.length>0){var callback=callbacks.shift();if(typeof callback==\"function\"){callback(Module);continue}var func=callback.func;if(typeof func===\"number\"){if(callback.arg===undefined){Module[\"dynCall_v\"](func)}else{Module[\"dynCall_vi\"](func,callback.arg)}}else{func(callback.arg===undefined?null:callback.arg)}}}var __ATPRERUN__=[];var __ATINIT__=[];var __ATMAIN__=[];var __ATPOSTRUN__=[];var runtimeInitialized=false;var runtimeExited=false;function preRun(){if(Module[\"preRun\"]){if(typeof Module[\"preRun\"]==\"function\")Module[\"preRun\"]=[Module[\"preRun\"]];while(Module[\"preRun\"].length){addOnPreRun(Module[\"preRun\"].shift())}}callRuntimeCallbacks(__ATPRERUN__)}function initRuntime(){runtimeInitialized=true;callRuntimeCallbacks(__ATINIT__)}function preMain(){callRuntimeCallbacks(__ATMAIN__)}function exitRuntime(){runtimeExited=true}function postRun(){if(Module[\"postRun\"]){if(typeof Module[\"postRun\"]==\"function\")Module[\"postRun\"]=[Module[\"postRun\"]];while(Module[\"postRun\"].length){addOnPostRun(Module[\"postRun\"].shift())}}callRuntimeCallbacks(__ATPOSTRUN__)}function addOnPreRun(cb){__ATPRERUN__.unshift(cb)}function addOnPostRun(cb){__ATPOSTRUN__.unshift(cb)}var Math_ceil=Math.ceil;var Math_floor=Math.floor;var runDependencies=0;var runDependencyWatcher=null;var dependenciesFulfilled=null;function addRunDependency(id){runDependencies++;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}}function removeRunDependency(id){runDependencies--;if(Module[\"monitorRunDependencies\"]){Module[\"monitorRunDependencies\"](runDependencies)}if(runDependencies==0){if(runDependencyWatcher!==null){clearInterval(runDependencyWatcher);runDependencyWatcher=null}if(dependenciesFulfilled){var callback=dependenciesFulfilled;dependenciesFulfilled=null;callback()}}}Module[\"preloadedImages\"]={};Module[\"preloadedAudios\"]={};function abort(what){if(Module[\"onAbort\"]){Module[\"onAbort\"](what)}what+=\"\";out(what);err(what);ABORT=true;EXITSTATUS=1;what=\"abort(\"+what+\"). Build with -s ASSERTIONS=1 for more info.\";throw new WebAssembly.RuntimeError(what)}function hasPrefix(str,prefix){return String.prototype.startsWith?str.startsWith(prefix):str.indexOf(prefix)===0}var dataURIPrefix=\"data:application/octet-stream;base64,\";function isDataURI(filename){return hasPrefix(filename,dataURIPrefix)}var fileURIPrefix=\"file://\";function isFileURI(filename){return hasPrefix(filename,fileURIPrefix)}var wasmBinaryFile=\"tfjs-backend-wasm.wasm\";if(!isDataURI(wasmBinaryFile)){wasmBinaryFile=locateFile(wasmBinaryFile)}function getBinary(){try{if(wasmBinary){return new Uint8Array(wasmBinary)}if(readBinary){return readBinary(wasmBinaryFile)}else{throw\"both async and sync fetching of the wasm failed\"}}catch(err){abort(err)}}function getBinaryPromise(){if(!wasmBinary&&(ENVIRONMENT_IS_WEB||ENVIRONMENT_IS_WORKER)&&typeof fetch===\"function\"&&!isFileURI(wasmBinaryFile)){return fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){if(!response[\"ok\"]){throw\"failed to load wasm binary file at '\"+wasmBinaryFile+\"'\"}return response[\"arrayBuffer\"]()}).catch(function(){return getBinary()})}return new Promise(function(resolve,reject){resolve(getBinary())})}function createWasm(){var info={\"env\":asmLibraryArg,\"wasi_snapshot_preview1\":asmLibraryArg};function receiveInstance(instance,module){var exports=instance.exports;Module[\"asm\"]=exports;wasmMemory=exports[\"memory\"];updateGlobalBufferAndViews(wasmMemory.buffer);removeRunDependency(\"wasm-instantiate\")}addRunDependency(\"wasm-instantiate\");function receiveInstantiatedSource(output){receiveInstance(output[\"instance\"])}function instantiateArrayBuffer(receiver){return getBinaryPromise().then(function(binary){return WebAssembly.instantiate(binary,info)}).then(receiver,function(reason){err(\"failed to asynchronously prepare wasm: \"+reason);abort(reason)})}function instantiateAsync(){if(!wasmBinary&&typeof WebAssembly.instantiateStreaming===\"function\"&&!isDataURI(wasmBinaryFile)&&!isFileURI(wasmBinaryFile)&&typeof fetch===\"function\"){fetch(wasmBinaryFile,{credentials:\"same-origin\"}).then(function(response){var result=WebAssembly.instantiateStreaming(response,info);return result.then(receiveInstantiatedSource,function(reason){err(\"wasm streaming compile failed: \"+reason);err(\"falling back to ArrayBuffer instantiation\");instantiateArrayBuffer(receiveInstantiatedSource)})})}else{return instantiateArrayBuffer(receiveInstantiatedSource)}}if(Module[\"instantiateWasm\"]){try{var exports=Module[\"instantiateWasm\"](info,receiveInstance);return exports}catch(e){err(\"Module.instantiateWasm callback failed with error: \"+e);return false}}instantiateAsync();return{}}__ATINIT__.push();function _emscripten_notify_memory_growth(memoryIndex){updateGlobalBufferAndViews(wasmMemory.buffer)}var PATH={splitPath:function(filename){var splitPathRe=/^(\\/?|)([\\s\\S]*?)((?:\\.{1,2}|[^\\/]+?|)(\\.[^.\\/]*|))(?:[\\/]*)$/;return splitPathRe.exec(filename).slice(1)},normalizeArray:function(parts,allowAboveRoot){var up=0;for(var i=parts.length-1;i>=0;i--){var last=parts[i];if(last===\".\"){parts.splice(i,1)}else if(last===\"..\"){parts.splice(i,1);up++}else if(up){parts.splice(i,1);up--}}if(allowAboveRoot){for(;up;up--){parts.unshift(\"..\")}}return parts},normalize:function(path){var isAbsolute=path.charAt(0)===\"/\",trailingSlash=path.substr(-1)===\"/\";path=PATH.normalizeArray(path.split(\"/\").filter(function(p){return!!p}),!isAbsolute).join(\"/\");if(!path&&!isAbsolute){path=\".\"}if(path&&trailingSlash){path+=\"/\"}return(isAbsolute?\"/\":\"\")+path},dirname:function(path){var result=PATH.splitPath(path),root=result[0],dir=result[1];if(!root&&!dir){return\".\"}if(dir){dir=dir.substr(0,dir.length-1)}return root+dir},basename:function(path){if(path===\"/\")return\"/\";var lastSlash=path.lastIndexOf(\"/\");if(lastSlash===-1)return path;return path.substr(lastSlash+1)},extname:function(path){return PATH.splitPath(path)[3]},join:function(){var paths=Array.prototype.slice.call(arguments,0);return PATH.normalize(paths.join(\"/\"))},join2:function(l,r){return PATH.normalize(l+\"/\"+r)}};var SYSCALLS={mappings:{},buffers:[null,[],[]],printChar:function(stream,curr){var buffer=SYSCALLS.buffers[stream];if(curr===0||curr===10){(stream===1?out:err)(UTF8ArrayToString(buffer,0));buffer.length=0}else{buffer.push(curr)}},varargs:undefined,get:function(){SYSCALLS.varargs+=4;var ret=HEAP32[SYSCALLS.varargs-4>>2];return ret},getStr:function(ptr){var ret=UTF8ToString(ptr);return ret},get64:function(low,high){return low}};function _fd_close(fd){return 0}function _fd_seek(fd,offset_low,offset_high,whence,newOffset){}function _fd_write(fd,iov,iovcnt,pnum){var num=0;for(var i=0;i>2];var len=HEAP32[iov+(i*8+4)>>2];for(var j=0;j>2]=num;return 0}function _exit(status){exit(status)}function _proc_exit(code){_exit(code)}function _roundf(d){d=+d;return d>=+0?+Math_floor(d+ +.5):+Math_ceil(d-+.5)}var asmLibraryArg={\"emscripten_notify_memory_growth\":_emscripten_notify_memory_growth,\"fd_close\":_fd_close,\"fd_seek\":_fd_seek,\"fd_write\":_fd_write,\"proc_exit\":_proc_exit,\"roundf\":_roundf};var asm=createWasm();Module[\"asm\"]=asm;var _init=Module[\"_init\"]=function(){return(_init=Module[\"_init\"]=Module[\"asm\"][\"init\"]).apply(null,arguments)};var _register_tensor=Module[\"_register_tensor\"]=function(){return(_register_tensor=Module[\"_register_tensor\"]=Module[\"asm\"][\"register_tensor\"]).apply(null,arguments)};var _dispose_data=Module[\"_dispose_data\"]=function(){return(_dispose_data=Module[\"_dispose_data\"]=Module[\"asm\"][\"dispose_data\"]).apply(null,arguments)};var _dispose=Module[\"_dispose\"]=function(){return(_dispose=Module[\"_dispose\"]=Module[\"asm\"][\"dispose\"]).apply(null,arguments)};var _Abs=Module[\"_Abs\"]=function(){return(_Abs=Module[\"_Abs\"]=Module[\"asm\"][\"Abs\"]).apply(null,arguments)};var _Add=Module[\"_Add\"]=function(){return(_Add=Module[\"_Add\"]=Module[\"asm\"][\"Add\"]).apply(null,arguments)};var _AddN=Module[\"_AddN\"]=function(){return(_AddN=Module[\"_AddN\"]=Module[\"asm\"][\"AddN\"]).apply(null,arguments)};var _ArgMax=Module[\"_ArgMax\"]=function(){return(_ArgMax=Module[\"_ArgMax\"]=Module[\"asm\"][\"ArgMax\"]).apply(null,arguments)};var _AvgPool=Module[\"_AvgPool\"]=function(){return(_AvgPool=Module[\"_AvgPool\"]=Module[\"asm\"][\"AvgPool\"]).apply(null,arguments)};var _BatchMatMul=Module[\"_BatchMatMul\"]=function(){return(_BatchMatMul=Module[\"_BatchMatMul\"]=Module[\"asm\"][\"BatchMatMul\"]).apply(null,arguments)};var _ClipByValue=Module[\"_ClipByValue\"]=function(){return(_ClipByValue=Module[\"_ClipByValue\"]=Module[\"asm\"][\"ClipByValue\"]).apply(null,arguments)};var _Conv2D=Module[\"_Conv2D\"]=function(){return(_Conv2D=Module[\"_Conv2D\"]=Module[\"asm\"][\"Conv2D\"]).apply(null,arguments)};var _Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=function(){return(_Conv2DBackpropInput=Module[\"_Conv2DBackpropInput\"]=Module[\"asm\"][\"Conv2DBackpropInput\"]).apply(null,arguments)};var _Cos=Module[\"_Cos\"]=function(){return(_Cos=Module[\"_Cos\"]=Module[\"asm\"][\"Cos\"]).apply(null,arguments)};var _CropAndResize=Module[\"_CropAndResize\"]=function(){return(_CropAndResize=Module[\"_CropAndResize\"]=Module[\"asm\"][\"CropAndResize\"]).apply(null,arguments)};var _Cumsum=Module[\"_Cumsum\"]=function(){return(_Cumsum=Module[\"_Cumsum\"]=Module[\"asm\"][\"Cumsum\"]).apply(null,arguments)};var _DepthToSpace=Module[\"_DepthToSpace\"]=function(){return(_DepthToSpace=Module[\"_DepthToSpace\"]=Module[\"asm\"][\"DepthToSpace\"]).apply(null,arguments)};var _DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=function(){return(_DepthwiseConv2dNative=Module[\"_DepthwiseConv2dNative\"]=Module[\"asm\"][\"DepthwiseConv2dNative\"]).apply(null,arguments)};var _Equal=Module[\"_Equal\"]=function(){return(_Equal=Module[\"_Equal\"]=Module[\"asm\"][\"Equal\"]).apply(null,arguments)};var _Exp=Module[\"_Exp\"]=function(){return(_Exp=Module[\"_Exp\"]=Module[\"asm\"][\"Exp\"]).apply(null,arguments)};var _FlipLeftRight=Module[\"_FlipLeftRight\"]=function(){return(_FlipLeftRight=Module[\"_FlipLeftRight\"]=Module[\"asm\"][\"FlipLeftRight\"]).apply(null,arguments)};var _Floor=Module[\"_Floor\"]=function(){return(_Floor=Module[\"_Floor\"]=Module[\"asm\"][\"Floor\"]).apply(null,arguments)};var _FloorDiv=Module[\"_FloorDiv\"]=function(){return(_FloorDiv=Module[\"_FloorDiv\"]=Module[\"asm\"][\"FloorDiv\"]).apply(null,arguments)};var _FusedBatchNorm=Module[\"_FusedBatchNorm\"]=function(){return(_FusedBatchNorm=Module[\"_FusedBatchNorm\"]=Module[\"asm\"][\"FusedBatchNorm\"]).apply(null,arguments)};var _FusedConv2D=Module[\"_FusedConv2D\"]=function(){return(_FusedConv2D=Module[\"_FusedConv2D\"]=Module[\"asm\"][\"FusedConv2D\"]).apply(null,arguments)};var _FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=function(){return(_FusedDepthwiseConv2D=Module[\"_FusedDepthwiseConv2D\"]=Module[\"asm\"][\"FusedDepthwiseConv2D\"]).apply(null,arguments)};var _Gather=Module[\"_Gather\"]=function(){return(_Gather=Module[\"_Gather\"]=Module[\"asm\"][\"Gather\"]).apply(null,arguments)};var _GatherNd=Module[\"_GatherNd\"]=function(){return(_GatherNd=Module[\"_GatherNd\"]=Module[\"asm\"][\"GatherNd\"]).apply(null,arguments)};var _Greater=Module[\"_Greater\"]=function(){return(_Greater=Module[\"_Greater\"]=Module[\"asm\"][\"Greater\"]).apply(null,arguments)};var _GreaterEqual=Module[\"_GreaterEqual\"]=function(){return(_GreaterEqual=Module[\"_GreaterEqual\"]=Module[\"asm\"][\"GreaterEqual\"]).apply(null,arguments)};var _LeakyRelu=Module[\"_LeakyRelu\"]=function(){return(_LeakyRelu=Module[\"_LeakyRelu\"]=Module[\"asm\"][\"LeakyRelu\"]).apply(null,arguments)};var _Less=Module[\"_Less\"]=function(){return(_Less=Module[\"_Less\"]=Module[\"asm\"][\"Less\"]).apply(null,arguments)};var _LessEqual=Module[\"_LessEqual\"]=function(){return(_LessEqual=Module[\"_LessEqual\"]=Module[\"asm\"][\"LessEqual\"]).apply(null,arguments)};var _Log=Module[\"_Log\"]=function(){return(_Log=Module[\"_Log\"]=Module[\"asm\"][\"Log\"]).apply(null,arguments)};var _LogicalAnd=Module[\"_LogicalAnd\"]=function(){return(_LogicalAnd=Module[\"_LogicalAnd\"]=Module[\"asm\"][\"LogicalAnd\"]).apply(null,arguments)};var _Max=Module[\"_Max\"]=function(){return(_Max=Module[\"_Max\"]=Module[\"asm\"][\"Max\"]).apply(null,arguments)};var 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module === 'object')\n module.exports = WasmBackendModule;\n else if (typeof define === 'function' && define['amd'])\n define([], function() { return WasmBackendModule; });\n else if (typeof exports === 'object')\n exports[\"WasmBackendModule\"] = WasmBackendModule;\n ", "// A port of an algorithm by Johannes Baag\u00F8e , 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baag\u00F8e \n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n//\n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n//\n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n var me = this, mash = Mash();\n\n me.next = function() {\n var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n me.s0 = me.s1;\n me.s1 = me.s2;\n return me.s2 = t - (me.c = t | 0);\n };\n\n // Apply the seeding algorithm from Baagoe.\n me.c = 1;\n me.s0 = mash(' ');\n me.s1 = mash(' ');\n me.s2 = mash(' ');\n me.s0 -= mash(seed);\n if (me.s0 < 0) { me.s0 += 1; }\n me.s1 -= mash(seed);\n if (me.s1 < 0) { me.s1 += 1; }\n me.s2 -= mash(seed);\n if (me.s2 < 0) { me.s2 += 1; }\n mash = null;\n}\n\nfunction copy(f, t) {\n t.c = f.c;\n t.s0 = f.s0;\n t.s1 = f.s1;\n t.s2 = f.s2;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new Alea(seed),\n state = opts && opts.state,\n prng = xg.next;\n prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n prng.double = function() {\n return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n };\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nfunction Mash() {\n var n = 0xefc8249d;\n\n var mash = function(data) {\n data = String(data);\n for (var i = 0; i < data.length; i++) {\n n += data.charCodeAt(i);\n var h = 0.02519603282416938 * n;\n n = h >>> 0;\n h -= n;\n h *= n;\n n = h >>> 0;\n h -= n;\n n += h * 0x100000000; // 2^32\n }\n return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n };\n\n return mash;\n}\n\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.alea = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n\n // Set up generator function.\n me.next = function() {\n var t = me.x ^ (me.x << 11);\n me.x = me.y;\n me.y = me.z;\n me.z = me.w;\n return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n };\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor128 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var t = (me.x ^ (me.x >>> 2));\n me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n return (me.d = (me.d + 362437 | 0)) +\n (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n };\n\n me.x = 0;\n me.y = 0;\n me.z = 0;\n me.w = 0;\n me.v = 0;\n\n if (seed === (seed | 0)) {\n // Integer seed.\n me.x = seed;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 64; k++) {\n me.x ^= strseed.charCodeAt(k) | 0;\n if (k == strseed.length) {\n me.d = me.x << 10 ^ me.x >>> 4;\n }\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.x = f.x;\n t.y = f.y;\n t.z = f.z;\n t.w = f.w;\n t.v = f.v;\n t.d = f.d;\n return t;\n}\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorwow = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "// A Javascript implementaion of the \"xorshift7\" algorithm by\n// Fran\u00E7ois Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n // Update xor generator.\n var X = me.x, i = me.i, t, v, w;\n t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n X[i] = v;\n me.i = (i + 1) & 7;\n return v;\n };\n\n function init(me, seed) {\n var j, w, X = [];\n\n if (seed === (seed | 0)) {\n // Seed state array using a 32-bit integer.\n w = X[0] = seed;\n } else {\n // Seed state using a string.\n seed = '' + seed;\n for (j = 0; j < seed.length; ++j) {\n X[j & 7] = (X[j & 7] << 15) ^\n (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n }\n }\n // Enforce an array length of 8, not all zeroes.\n while (X.length < 8) X.push(0);\n for (j = 0; j < 8 && X[j] === 0; ++j);\n if (j == 8) w = X[7] = -1; else w = X[j];\n\n me.x = X;\n me.i = 0;\n\n // Discard an initial 256 values.\n for (j = 256; j > 0; --j) {\n me.next();\n }\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.x = f.x.slice();\n t.i = f.i;\n return t;\n}\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.x) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xorshift7 = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n", "// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated. For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1); // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897); // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C. This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this;\n\n // Set up generator function.\n me.next = function() {\n var w = me.w,\n X = me.X, i = me.i, t, v;\n // Update Weyl generator.\n me.w = w = (w + 0x61c88647) | 0;\n // Update xor generator.\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n // Update Xor generator array state.\n v = X[i] = v ^ t;\n me.i = i;\n // Result is the combination.\n return (v + (w ^ (w >>> 16))) | 0;\n };\n\n function init(me, seed) {\n var t, v, i, j, w, X = [], limit = 128;\n if (seed === (seed | 0)) {\n // Numeric seeds initialize v, which is used to generates X.\n v = seed;\n seed = null;\n } else {\n // String seeds are mixed into v and X one character at a time.\n seed = seed + '\\0';\n v = 0;\n limit = Math.max(limit, seed.length);\n }\n // Initialize circular array and weyl value.\n for (i = 0, j = -32; j < limit; ++j) {\n // Put the unicode characters into the array, and shuffle them.\n if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n // After 32 shuffles, take v as the starting w value.\n if (j === 0) w = v;\n v ^= v << 10;\n v ^= v >>> 15;\n v ^= v << 4;\n v ^= v >>> 13;\n if (j >= 0) {\n w = (w + 0x61c88647) | 0; // Weyl.\n t = (X[j & 127] ^= (v + w)); // Combine xor and weyl to init array.\n i = (0 == t) ? i + 1 : 0; // Count zeroes.\n }\n }\n // We have detected all zeroes; make the key nonzero.\n if (i >= 128) {\n X[(seed && seed.length || 0) & 127] = -1;\n }\n // Run the generator 512 times to further mix the state before using it.\n // Factoring this as a function slows the main generator, so it is just\n // unrolled here. The weyl generator is not advanced while warming up.\n i = 127;\n for (j = 4 * 128; j > 0; --j) {\n v = X[(i + 34) & 127];\n t = X[i = ((i + 1) & 127)];\n v ^= v << 13;\n t ^= t << 17;\n v ^= v >>> 15;\n t ^= t >>> 12;\n X[i] = v ^ t;\n }\n // Storing state as object members is faster than using closure variables.\n me.w = w;\n me.X = X;\n me.i = i;\n }\n\n init(me, seed);\n}\n\nfunction copy(f, t) {\n t.i = f.i;\n t.w = f.w;\n t.X = f.X.slice();\n return t;\n};\n\nfunction impl(seed, opts) {\n if (seed == null) seed = +(new Date);\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (state.X) copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.xor4096 = impl;\n}\n\n})(\n this, // window object or global\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n", "// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n var me = this, strseed = '';\n\n // Set up generator function.\n me.next = function() {\n var b = me.b, c = me.c, d = me.d, a = me.a;\n b = (b << 25) ^ (b >>> 7) ^ c;\n c = (c - d) | 0;\n d = (d << 24) ^ (d >>> 8) ^ a;\n a = (a - b) | 0;\n me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n me.c = c = (c - d) | 0;\n me.d = (d << 16) ^ (c >>> 16) ^ a;\n return me.a = (a - b) | 0;\n };\n\n /* The following is non-inverted tyche, which has better internal\n * bit diffusion, but which is about 25% slower than tyche-i in JS.\n me.next = function() {\n var a = me.a, b = me.b, c = me.c, d = me.d;\n a = (me.a + me.b | 0) >>> 0;\n d = me.d ^ a; d = d << 16 ^ d >>> 16;\n c = me.c + d | 0;\n b = me.b ^ c; b = b << 12 ^ d >>> 20;\n me.a = a = a + b | 0;\n d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n me.c = c = c + d | 0;\n b = b ^ c;\n return me.b = (b << 7 ^ b >>> 25);\n }\n */\n\n me.a = 0;\n me.b = 0;\n me.c = 2654435769 | 0;\n me.d = 1367130551;\n\n if (seed === Math.floor(seed)) {\n // Integer seed.\n me.a = (seed / 0x100000000) | 0;\n me.b = seed | 0;\n } else {\n // String seed.\n strseed += seed;\n }\n\n // Mix in string seed, then discard an initial batch of 64 values.\n for (var k = 0; k < strseed.length + 20; k++) {\n me.b ^= strseed.charCodeAt(k) | 0;\n me.next();\n }\n}\n\nfunction copy(f, t) {\n t.a = f.a;\n t.b = f.b;\n t.c = f.c;\n t.d = f.d;\n return t;\n};\n\nfunction impl(seed, opts) {\n var xg = new XorGen(seed),\n state = opts && opts.state,\n prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n prng.double = function() {\n do {\n var top = xg.next() >>> 11,\n bot = (xg.next() >>> 0) / 0x100000000,\n result = (top + bot) / (1 << 21);\n } while (result === 0);\n return result;\n };\n prng.int32 = xg.next;\n prng.quick = prng;\n if (state) {\n if (typeof(state) == 'object') copy(state, xg);\n prng.state = function() { return copy(xg, {}); }\n }\n return prng;\n}\n\nif (module && module.exports) {\n module.exports = impl;\n} else if (define && define.amd) {\n define(function() { return impl; });\n} else {\n this.tychei = impl;\n}\n\n})(\n this,\n (typeof module) == 'object' && module, // present in node.js\n (typeof define) == 'function' && define // present with an AMD loader\n);\n\n\n", "/*\nCopyright 2019 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (global, pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\n\nvar width = 256, // each RC4 output is 0 <= x < 256\n chunks = 6, // at least six RC4 outputs for each double\n digits = 52, // there are 52 significant digits in a double\n rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n startdenom = math.pow(width, chunks),\n significance = math.pow(2, digits),\n overflow = significance * 2,\n mask = width - 1,\n nodecrypto; // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n var key = [];\n options = (options == true) ? { entropy: true } : (options || {});\n\n // Flatten the seed string or build one from local entropy if needed.\n var shortseed = mixkey(flatten(\n options.entropy ? [seed, tostring(pool)] :\n (seed == null) ? autoseed() : seed, 3), key);\n\n // Use the seed to initialize an ARC4 generator.\n var arc4 = new ARC4(key);\n\n // This function returns a random double in [0, 1) that contains\n // randomness in every bit of the mantissa of the IEEE 754 value.\n var prng = function() {\n var n = arc4.g(chunks), // Start with a numerator n < 2 ^ 48\n d = startdenom, // and denominator d = 2 ^ 48.\n x = 0; // and no 'extra last byte'.\n while (n < significance) { // Fill up all significant digits by\n n = (n + x) * width; // shifting numerator and\n d *= width; // denominator and generating a\n x = arc4.g(1); // new least-significant-byte.\n }\n while (n >= overflow) { // To avoid rounding up, before adding\n n /= 2; // last byte, shift everything\n d /= 2; // right using integer math until\n x >>>= 1; // we have exactly the desired bits.\n }\n return (n + x) / d; // Form the number within [0, 1).\n };\n\n prng.int32 = function() { return arc4.g(4) | 0; }\n prng.quick = function() { return arc4.g(4) / 0x100000000; }\n prng.double = prng;\n\n // Mix the randomness into accumulated entropy.\n mixkey(tostring(arc4.S), pool);\n\n // Calling convention: what to return as a function of prng, seed, is_math.\n return (options.pass || callback ||\n function(prng, seed, is_math_call, state) {\n if (state) {\n // Load the arc4 state from the given state if it has an S array.\n if (state.S) { copy(state, arc4); }\n // Only provide the .state method if requested via options.state.\n prng.state = function() { return copy(arc4, {}); }\n }\n\n // If called as a method of Math (Math.seedrandom()), mutate\n // Math.random because that is how seedrandom.js has worked since v1.0.\n if (is_math_call) { math[rngname] = prng; return seed; }\n\n // Otherwise, it is a newer calling convention, so return the\n // prng directly.\n else return prng;\n })(\n prng,\n shortseed,\n 'global' in options ? options.global : (this == math),\n options.state);\n}\n\n//\n// ARC4\n//\n// An ARC4 implementation. The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4. Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n var t, keylen = key.length,\n me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n // The empty key [] is treated as [0].\n if (!keylen) { key = [keylen++]; }\n\n // Set up S using the standard key scheduling algorithm.\n while (i < width) {\n s[i] = i++;\n }\n for (i = 0; i < width; i++) {\n s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n s[j] = t;\n }\n\n // The \"g\" method returns the next (count) outputs as one number.\n (me.g = function(count) {\n // Using instance members instead of closure state nearly doubles speed.\n var t, r = 0,\n i = me.i, j = me.j, s = me.S;\n while (count--) {\n t = s[i = mask & (i + 1)];\n r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n }\n me.i = i; me.j = j;\n return r;\n // For robust unpredictability, the function call below automatically\n // discards an initial batch of values. This is called RC4-drop[256].\n // See http://google.com/search?q=rsa+fluhrer+response&btnI\n })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n t.i = f.i;\n t.j = f.j;\n t.S = f.S.slice();\n return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n var result = [], typ = (typeof obj), prop;\n if (depth && typ == 'object') {\n for (prop in obj) {\n try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n }\n }\n return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n var stringseed = seed + '', smear, j = 0;\n while (j < stringseed.length) {\n key[mask & j] =\n mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n }\n return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n try {\n var out;\n if (nodecrypto && (out = nodecrypto.randomBytes)) {\n // The use of 'out' to remember randomBytes makes tight minified code.\n out = out(width);\n } else {\n out = new Uint8Array(width);\n (global.crypto || global.msCrypto).getRandomValues(out);\n }\n return tostring(out);\n } catch (e) {\n var browser = global.navigator,\n plugins = browser && browser.plugins;\n return [+new Date, global, plugins, global.screen, tostring(pool)];\n }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool. Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n module.exports = seedrandom;\n // When in node.js, try using crypto package for autoseeding.\n try {\n nodecrypto = require('crypto');\n } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n define(function() { return seedrandom; });\n} else {\n // When included as a plain script, set up Math.seedrandom global.\n math['seed' + rngname] = seedrandom;\n}\n\n\n// End anonymous scope, and pass initial values.\n})(\n // global: `self` in browsers (including strict mode and web workers),\n // otherwise `this` in Node and other environments\n (typeof self !== 'undefined') ? self : this,\n [], // pool: entropy pool starts empty\n Math // math: package containing random, pow, and seedrandom\n);\n", "// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random(); // 0 <= x < 1. Every bit is random.\n// var x = random.quick(); // 0 <= x < 1. 32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baag\u00F8e.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by Fran\u00E7ois Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three. It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures. Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n", "", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "export const wasmWorkerContents = 'var threadInfoStruct=0;var selfThreadId=0;var parentThreadId=0;var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(\" \");console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(\" \");postMessage({cmd:\"alert\",text:text,threadId:selfThreadId})}var err=threadPrintErr;this.alert=threadAlert;Module[\"instantiateWasm\"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module[\"wasmModule\"],info);Module[\"wasmModule\"]=null;receiveInstance(instance);return instance.exports};this.onmessage=function(e){try{if(e.data.cmd===\"load\"){Module[\"DYNAMIC_BASE\"]=e.data.DYNAMIC_BASE;Module[\"DYNAMICTOP_PTR\"]=e.data.DYNAMICTOP_PTR;Module[\"wasmModule\"]=e.data.wasmModule;Module[\"wasmMemory\"]=e.data.wasmMemory;Module[\"buffer\"]=Module[\"wasmMemory\"].buffer;Module[\"ENVIRONMENT_IS_PTHREAD\"]=true;if(typeof e.data.urlOrBlob===\"string\"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}Module=WasmBackendModuleThreadedSimd(Module);postMessage({\"cmd\":\"loaded\"})}else if(e.data.cmd===\"objectTransfer\"){Module[\"PThread\"].receiveObjectTransfer(e.data)}else if(e.data.cmd===\"run\"){Module[\"__performance_now_clock_drift\"]=performance.now()-e.data.time;threadInfoStruct=e.data.threadInfoStruct;Module[\"__register_pthread_ptr\"](threadInfoStruct,0,0);selfThreadId=e.data.selfThreadId;parentThreadId=e.data.parentThreadId;var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module[\"establishStackSpace\"](top,max);Module[\"_emscripten_tls_init\"]();Module[\"PThread\"].receiveObjectTransfer(e.data);Module[\"PThread\"].setThreadStatus(Module[\"_pthread_self\"](),1);try{var result=Module[\"dynCall_ii\"](e.data.start_routine,e.data.arg);if(!Module[\"getNoExitRuntime\"]())Module[\"PThread\"].threadExit(result)}catch(ex){if(ex===\"Canceled!\"){Module[\"PThread\"].threadCancel()}else if(ex!=\"unwind\"){Atomics.store(Module[\"HEAPU32\"],threadInfoStruct+4>>2,ex instanceof Module[\"ExitStatus\"]?ex.status:-2);Atomics.store(Module[\"HEAPU32\"],threadInfoStruct+0>>2,1);Module[\"_emscripten_futex_wake\"](threadInfoStruct+0,2147483647);if(!(ex instanceof Module[\"ExitStatus\"]))throw ex}}}else if(e.data.cmd===\"cancel\"){if(threadInfoStruct){Module[\"PThread\"].threadCancel()}}else if(e.data.target===\"setimmediate\"){}else if(e.data.cmd===\"processThreadQueue\"){if(threadInfoStruct){Module[\"_emscripten_current_thread_process_queued_calls\"]()}}else{err(\"worker.js received unknown command \"+e.data.cmd);err(e.data)}}catch(ex){err(\"worker.js onmessage() captured an uncaught exception: \"+ex);if(ex.stack)err(ex.stack);throw ex}};if(typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\"){self={location:{href:__filename}};var onmessage=this.onmessage;var nodeWorkerThreads=require(\"worker_threads\");Worker=nodeWorkerThreads.Worker;var parentPort=nodeWorkerThreads.parentPort;parentPort.on(\"message\",function(data){onmessage({data:data})});var nodeFS=require(\"fs\");var nodeRead=function(filename){return nodeFS.readFileSync(filename,\"utf8\")};function globalEval(x){global.require=require;global.Module=Module;eval.call(null,x)}importScripts=function(f){globalEval(nodeRead(f))};postMessage=function(msg){parentPort.postMessage(msg)};if(typeof performance===\"undefined\"){performance={now:function(){return Date.now()}}}}';", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "// wrapper to load tfjs in a single place so version can be changed quickly\n\n// simplified\n// { modules: 1061, moduleBytes: 3772720, outputBytes: 1531035 }\n\n// export * from '@tensorflow/tfjs/dist/index.js';\n// export * from '@tensorflow/tfjs-backend-wasm';\n\n// modular\n// { modules: 1064, moduleBytes: 3793219, outputBytes: 1535600 }\n\n// get versions of all packages.\nimport { version as tfjs } from '@tensorflow/tfjs/package.json';\nimport { version as versionCore } from '@tensorflow/tfjs-core/package.json';\nimport { version as versionData } from '@tensorflow/tfjs-data/package.json';\nimport { version as versionLayers } from '@tensorflow/tfjs-layers/package.json';\nimport { version as versionConverter } from '@tensorflow/tfjs-converter/package.json';\n// for backends, get version from source so it can register backend during import\nimport { version_cpu } from '@tensorflow/tfjs-backend-cpu/dist/index.js';\nimport { version_webgl } from '@tensorflow/tfjs-backend-webgl/dist/index.js';\nimport { version_wasm } from '@tensorflow/tfjs-backend-wasm/dist/index.js';\n\n// export all\nexport * from '@tensorflow/tfjs-core/dist/index.js';\nexport * from '@tensorflow/tfjs-layers/dist/index.js';\nexport * from '@tensorflow/tfjs-converter/dist/index.js';\nexport * as data from '@tensorflow/tfjs-data/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-cpu/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-webgl/dist/index.js';\nexport * from '@tensorflow/tfjs-backend-wasm/dist/index.js';\n\n// export versions\nexport const version = {\n tfjs,\n 'tfjs-core': versionCore,\n 'tfjs-data': versionData,\n 'tfjs-layers': versionLayers,\n 'tfjs-converter': versionConverter,\n 'tfjs-backend-cpu': version_cpu,\n 'tfjs-backend-webgl': version_webgl,\n 'tfjs-backend-wasm': version_wasm,\n};\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport const config = {\n name: 'humangl',\n priority: 99,\n canvas: null,\n width: 1024,\n height: 1024,\n webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n alpha: false,\n antialias: false,\n premultipliedAlpha: false,\n preserveDrawingBuffer: false,\n depth: false,\n stencil: false,\n failIfMajorPerformanceCaveat: false,\n desynchronized: true,\n },\n};\n\nexport function register() {\n if (!tf.findBackend(config.name)) {\n // @ts-ignore\n config.canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(config.width, config.height) : document.createElement('canvas');\n // @ts-ignore\n const gl = config.canvas.getContext('webgl2', config.webGLattr);\n tf.setWebGLContext(2, gl);\n const ctx = new tf.GPGPUContext(gl);\n tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n const kernels = tf.getKernelsForBackend('webgl');\n kernels.forEach((kernelConfig) => {\n const newKernelConfig = { ...kernelConfig, backendName: config.name };\n tf.registerKernel(newKernelConfig);\n });\n tf.ENV.set('WEBGL_VERSION', 2);\n tf.ENV.set('WEBGL_MAX_TEXTURE_SIZE', gl.getParameter(gl.MAX_TEXTURE_SIZE));\n tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n }\n}\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\n// import * as profile from '../profile.js';\n\nconst models = {};\n\nexport async function load(config) {\n if (!models.blazepose) {\n models.blazepose = await tf.loadGraphModel(config.pose.modelPath);\n log(`load model: ${config.pose.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.blazepose;\n}\n\nexport async function predict(image, config) {\n if (!models.blazepose) return null;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [config.pose.inputSize, config.pose.inputSize], false);\n const enhance = tf.div(resize, 127.5).sub(1);\n tf.dispose(resize);\n const logits = await models.blazepose.predict(enhance);\n //\n tf.dispose(enhance);\n logits.map((logit) => logit.dispose());\n resolve(logits);\n });\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nfunction getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\nfunction getBoxCenter(box) {\n return [\n box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n ];\n}\nfunction cutBoxFromImageAndResize(box, image, cropSize) {\n const h = image.shape[1];\n const w = image.shape[2];\n const boxes = [[\n box.startPoint[1] / h,\n box.startPoint[0] / w,\n box.endPoint[1] / h,\n box.endPoint[0] / w,\n ]];\n return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\nfunction scaleBoxCoordinates(box, factor) {\n const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n const palmLandmarks = box.palmLandmarks.map((coord) => {\n const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n return scaledCoord;\n });\n return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\nfunction enlargeBox(box, factor = 1.5) {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\nfunction squarifyBox(box) {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const maxEdge = Math.max(...size);\n const halfSize = maxEdge / 2;\n const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\nfunction shiftBox(box, shiftFactor) {\n const boxSize = [\n box.endPoint[0] - box.startPoint[0],\n box.endPoint[1] - box.startPoint[1],\n ];\n const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]];\n const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\nexport {\n cutBoxFromImageAndResize,\n enlargeBox,\n getBoxCenter,\n getBoxSize,\n scaleBoxCoordinates,\n shiftBox,\n squarifyBox,\n};\n", "function normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\nfunction computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\nconst buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\nfunction dot(v1, v2) {\n let product = 0;\n for (let i = 0; i < v1.length; i++) {\n product += v1[i] * v2[i];\n }\n return product;\n}\nfunction getColumnFrom2DArr(arr, columnIndex) {\n const column = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\nfunction multiplyTransformMatrices(mat1, mat2) {\n const product = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) {\n // @ts-ignore\n product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n }\n return product;\n}\nfunction buildRotationMatrix(rotation, center) {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\nfunction invertTransformMatrix(matrix) {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [\n -dot(rotationComponent[0], translationComponent),\n -dot(rotationComponent[1], translationComponent),\n ];\n return [\n rotationComponent[0].concat(invertedTranslation[0]),\n rotationComponent[1].concat(invertedTranslation[1]),\n [0, 0, 1],\n ];\n}\nfunction rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\nexport {\n buildRotationMatrix,\n computeRotation,\n dot,\n getColumnFrom2DArr,\n invertTransformMatrix,\n normalizeRadians,\n rotatePoint,\n};\n", "/* eslint-disable indent */\n/* eslint-disable no-multi-spaces */\n\nexport default {\n backend: 'webgl', // select tfjs backend to use\n wasmPath: '../assets/', // path for wasm binaries\n // only used for backend: wasm\n async: true, // execute enabled models in parallel\n // this disables per-model performance data but\n // slightly increases performance\n // cannot be used if profiling is enabled\n profile: false, // enable tfjs profiling\n // this has significant performance impact\n // only enable for debugging purposes\n // currently only implemented for age,gender,emotion models\n deallocate: false, // aggresively deallocate gpu memory after each usage\n // only valid for webgl backend and only during first call\n // cannot be changed unless library is reloaded\n // this has significant performance impact\n // only enable on low-memory devices\n scoped: false, // enable scoped runs\n // some models *may* have memory leaks,\n // this wrapps everything in a local scope at a cost of performance\n // typically not needed\n videoOptimized: true, // perform additional optimizations when input is video,\n // must be disabled for images\n // basically this skips object box boundary detection for every n frames\n // while maintaining in-box detection since objects cannot move that fast\n warmup: 'face', // what to use for human.warmup(), can be 'none', 'face', 'full'\n // warmup pre-initializes all models for faster inference but can take\n // significant time on startup\n filter: {\n enabled: true, // enable image pre-processing filters\n width: 0, // resize input width\n height: 0, // resize input height\n // if both width and height are set to 0, there is no resizing\n // if just one is set, second one is scaled automatically\n // if both are set, values are used as-is\n return: true, // return processed canvas imagedata in result\n brightness: 0, // range: -1 (darken) to 1 (lighten)\n contrast: 0, // range: -1 (reduce contrast) to 1 (increase contrast)\n sharpness: 0, // range: 0 (no sharpening) to 1 (maximum sharpening)\n blur: 0, // range: 0 (no blur) to N (blur radius in pixels)\n saturation: 0, // range: -1 (reduce saturation) to 1 (increase saturation)\n hue: 0, // range: 0 (no change) to 360 (hue rotation in degrees)\n negative: false, // image negative\n sepia: false, // image sepia colors\n vintage: false, // image vintage colors\n kodachrome: false, // image kodachrome colors\n technicolor: false, // image technicolor colors\n polaroid: false, // image polaroid camera effect\n pixelate: 0, // range: 0 (no pixelate) to N (number of pixels to pixelate)\n },\n\n gesture: {\n enabled: true, // enable simple gesture recognition\n },\n\n face: {\n enabled: true, // controls if specified modul is enabled\n // face.enabled is required for all face models:\n // detector, mesh, iris, age, gender, emotion\n // (note: module is not loaded until it is required)\n detector: {\n modelPath: '../models/blazeface-back.json', // can be 'front' or 'back'.\n // 'front' is optimized for large faces\n // such as front-facing camera and\n // 'back' is optimized for distanct faces.\n inputSize: 256, // fixed value: 128 for front and 256 for 'back'\n rotation: false, // use best-guess rotated face image or just box with rotation as-is\n // false means higher performance, but incorrect mesh mapping if face angle is above 20 degrees\n maxFaces: 10, // maximum number of faces detected in the input\n // should be set to the minimum number for performance\n skipFrames: 11, // how many frames to go without re-running the face bounding box detector\n // only used for video inputs\n // e.g., if model is running st 25 FPS, we can re-use existing bounding\n // box for updated face analysis as the head probably hasn't moved much\n // in short time (10 * 1/25 = 0.25 sec)\n minConfidence: 0.5, // threshold for discarding a prediction\n iouThreshold: 0.2, // threshold for deciding whether boxes overlap too much in\n // non-maximum suppression (0.1 means drop if overlap 10%)\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on score\n // in non-maximum suppression,\n // this is applied on detection objects only and before minConfidence\n },\n\n mesh: {\n enabled: true,\n modelPath: '../models/facemesh.json',\n inputSize: 192, // fixed value\n returnRawData: false, // in addition to standard mesh and box values, return raw normalized values as well\n },\n\n iris: {\n enabled: true,\n modelPath: '../models/iris.json',\n inputSize: 64, // fixed value\n },\n\n age: {\n enabled: true,\n modelPath: '../models/age-ssrnet-imdb.json', // can be 'age-ssrnet-imdb' or 'age-ssrnet-wiki'\n // which determines training set for model\n inputSize: 64, // fixed value\n skipFrames: 31, // how many frames to go without re-running the detector\n // only used for video inputs\n },\n\n gender: {\n enabled: true,\n minConfidence: 0.1, // threshold for discarding a prediction\n modelPath: '../models/gender-ssrnet-imdb.json', // can be 'gender', 'gender-ssrnet-imdb' or 'gender-ssrnet-wiki'\n inputSize: 64, // fixed value\n skipFrames: 41, // how many frames to go without re-running the detector\n // only used for video inputs\n },\n\n emotion: {\n enabled: true,\n inputSize: 64, // fixed value\n minConfidence: 0.2, // threshold for discarding a prediction\n skipFrames: 21, // how many frames to go without re-running the detector\n modelPath: '../models/emotion-large.json', // can be 'mini', 'large'\n },\n\n embedding: {\n enabled: false,\n inputSize: 112, // fixed value\n modelPath: '../models/mobilefacenet.json',\n },\n },\n\n body: {\n enabled: true,\n modelPath: '../models/posenet.json',\n inputSize: 257, // fixed value\n maxDetections: 10, // maximum number of people detected in the input\n // should be set to the minimum number for performance\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on score\n // in non-maximum suppression\n nmsRadius: 20, // radius for deciding points are too close in non-maximum suppression\n outputStride: 16, // size of block in which to run point detectopn, smaller value means higher resolution\n // defined by model itself, can be 8, 16, or 32\n modelType: 'MobileNet', // Human includes MobileNet version, but you can switch to ResNet\n },\n\n pose: { // TBD: not currently in use\n enabled: false,\n scoreThreshold: 0.6, // threshold for deciding when to remove boxes based on score\n // in non-maximum suppression\n iouThreshold: 0.3, // threshold for deciding whether boxes overlap too much\n // in non-maximum suppression\n modelPath: '../models/blazepose.json',\n inputSize: 256, // fixed value\n },\n\n hand: {\n enabled: true,\n rotation: false, // use best-guess rotated hand image or just box with rotation as-is\n // false means higher performance, but incorrect finger mapping if hand is inverted\n inputSize: 256, // fixed value\n skipFrames: 12, // how many frames to go without re-running the hand bounding box detector\n // only used for video inputs\n // e.g., if model is running st 25 FPS, we can re-use existing bounding\n // box for updated hand skeleton analysis as the hand probably\n // hasn't moved much in short time (10 * 1/25 = 0.25 sec)\n minConfidence: 0.1, // threshold for discarding a prediction\n iouThreshold: 0.1, // threshold for deciding whether boxes overlap too much\n // in non-maximum suppression\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on\n // score in non-maximum suppression\n maxHands: 1, // maximum number of hands detected in the input\n // should be set to the minimum number for performance\n landmarks: true, // detect hand landmarks or just hand boundary box\n detector: {\n modelPath: '../models/handdetect.json',\n },\n skeleton: {\n modelPath: '../models/handskeleton.json',\n },\n },\n};\n", "// data:image/jpeg;base64,\nexport const face = 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"names": [] } diff --git a/dist/human.json b/dist/human.json index 63520762..2ed854d6 100644 --- a/dist/human.json +++ b/dist/human.json @@ -264,7 +264,7 @@ ] }, "src/blazepose/blazepose.js": { - "bytes": 8999, + "bytes": 856, "imports": [ { "path": "src/log.js" @@ -361,7 +361,7 @@ ] }, "config.js": { - "bytes": 10062, + "bytes": 10110, "imports": [] }, "src/sample.js": { @@ -373,7 +373,7 @@ "imports": [] }, "src/human.js": { - "bytes": 17787, + "bytes": 18921, "imports": [ { "path": "src/log.js" @@ -433,14 +433,14 @@ "dist/human.js.map": { "imports": [], "inputs": {}, - "bytes": 1945575 + "bytes": 1934780 }, "dist/human.js": { "imports": [], "exports": [], "inputs": { "src/blazeface/blazeface.js": { - "bytesInOutput": 5046 + "bytesInOutput": 5039 }, "src/blazeface/box.js": { "bytesInOutput": 1567 @@ -515,7 +515,7 @@ "bytesInOutput": 127037 }, "src/handpose/handpose.js": { - "bytesInOutput": 2025 + "bytesInOutput": 2023 }, "src/gesture/gesture.js": { "bytesInOutput": 3162 @@ -527,7 +527,7 @@ "bytesInOutput": 3652 }, "src/human.js": { - "bytesInOutput": 11986 + "bytesInOutput": 12725 }, "src/log.js": { "bytesInOutput": 266 @@ -539,7 +539,7 @@ "bytesInOutput": 918 }, "src/blazepose/blazepose.js": { - "bytesInOutput": 6201 + "bytesInOutput": 572 }, "src/handpose/box.js": { "bytesInOutput": 1420 @@ -557,7 +557,7 @@ "bytesInOutput": 23 } }, - "bytes": 1912658 + "bytes": 1907759 } } } diff --git a/dist/human.node-gpu.js b/dist/human.node-gpu.js index 57af33af..cff59892 100644 --- a/dist/human.node-gpu.js +++ b/dist/human.node-gpu.js @@ -5,14 +5,14 @@ author: ' */ -var __create=Object.create,__defProp=Object.defineProperty,__getProtoOf=Object.getPrototypeOf,__hasOwnProp=Object.prototype.hasOwnProperty,__getOwnPropNames=Object.getOwnPropertyNames,__getOwnPropDesc=Object.getOwnPropertyDescriptor;var __markAsModule=target=>__defProp(target,"__esModule",{value:!0});var __commonJS=(callback,module2)=>()=>(module2||(module2={exports:{}},callback(module2.exports,module2)),module2.exports),__export=(target,all)=>{__markAsModule(target);for(var name in all)__defProp(target,name,{get:all[name],enumerable:!0})},__exportStar=(target,module2,desc)=>{if(__markAsModule(target),module2&&typeof module2=="object"||typeof module2=="function")for(let key of __getOwnPropNames(module2))!__hasOwnProp.call(target,key)&&key!=="default"&&__defProp(target,key,{get:()=>module2[key],enumerable:!(desc=__getOwnPropDesc(module2,key))||desc.enumerable});return target},__toModule=module2=>module2&&module2.__esModule?module2:__exportStar(__defProp(module2!=null?__create(__getProtoOf(module2)):{},"default",{value:module2,enumerable:!0}),module2);var require_tfjs_esm=__commonJS(exports2=>{var __create2=Object.create,__defProp2=Object.defineProperty,__getProtoOf2=Object.getPrototypeOf,__hasOwnProp2=Object.prototype.hasOwnProperty,__getOwnPropNames2=Object.getOwnPropertyNames,__getOwnPropDesc2=Object.getOwnPropertyDescriptor,__markAsModule2=target=>__defProp2(target,"__esModule",{value:!0}),__exportStar2=(target,module22,desc)=>{if(__markAsModule2(target),module22&&typeof module22=="object"||typeof module22=="function")for(let key of __getOwnPropNames2(module22))!__hasOwnProp2.call(target,key)&&key!=="default"&&__defProp2(target,key,{get:()=>module22[key],enumerable:!(desc=__getOwnPropDesc2(module22,key))||desc.enumerable});return target},__toModule2=module22=>module22&&module22.__esModule?module22:__exportStar2(__defProp2(module22!=null?__create2(__getProtoOf2(module22)):{},"default",{value:module22,enumerable:!0}),module22);__exportStar2(exports2,__toModule2(require("@tensorflow/tfjs-node-gpu")))});var require_blazeface=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm()),NUM_LANDMARKS=6;function generateAnchors(inputSize){let spec={strides:[inputSize/16,inputSize/8],anchors:[2,6]},anchors=[];for(let i=0;i{box.startEndTensor.dispose(),box.startPoint.dispose(),box.endPoint.dispose()},createBox=startEndTensor=>({startEndTensor,startPoint:tf4.slice(startEndTensor,[0,0],[-1,2]),endPoint:tf4.slice(startEndTensor,[0,2],[-1,2])}),scaleBox=(box,factors)=>{let starts=tf4.mul(box.startPoint,factors),ends=tf4.mul(box.endPoint,factors),newCoordinates=tf4.concat2d([starts,ends],1);return createBox(newCoordinates)};function decodeBounds(boxOutputs,anchors,inputSize){let boxStarts=tf4.slice(boxOutputs,[0,1],[-1,2]),centers=tf4.add(boxStarts,anchors),boxSizes=tf4.slice(boxOutputs,[0,3],[-1,2]),boxSizesNormalized=tf4.div(boxSizes,inputSize),centersNormalized=tf4.div(centers,inputSize),halfBoxSize=tf4.div(boxSizesNormalized,2),starts=tf4.sub(centersNormalized,halfBoxSize),ends=tf4.add(centersNormalized,halfBoxSize),startNormalized=tf4.mul(starts,inputSize),endNormalized=tf4.mul(ends,inputSize);return tf4.concat2d([startNormalized,endNormalized],1)}function scaleBoxFromPrediction(face3,scaleFactor){return tf4.tidy(()=>{let box=face3.box?face3.box:face3;return scaleBox(box,scaleFactor).startEndTensor.squeeze()})}var BlazeFaceModel=class{constructor(model,config2){this.blazeFaceModel=model,this.width=config2.face.detector.inputSize,this.height=config2.face.detector.inputSize,this.anchorsData=generateAnchors(config2.face.detector.inputSize),this.anchors=tf4.tensor2d(this.anchorsData),this.inputSize=tf4.tensor1d([this.width,this.height]),this.config=config2,this.scaleFaces=.8}async getBoundingBoxes(inputImage){if(!inputImage||inputImage.isDisposedInternal||inputImage.shape.length!==4||inputImage.shape[1]<1||inputImage.shape[2]<1)return null;let[detectedOutputs,boxes,scores]=tf4.tidy(()=>{let resizedImage=inputImage.resizeBilinear([this.width,this.height]),normalizedImage=tf4.sub(resizedImage.div(127.5),1),batchedPrediction=this.blazeFaceModel.predict(normalizedImage),prediction;if(Array.isArray(batchedPrediction)){let sorted=batchedPrediction.sort((a,b)=>a.size-b.size),concat384=tf4.concat([sorted[0],sorted[2]],2),concat512=tf4.concat([sorted[1],sorted[3]],2);prediction=tf4.concat([concat512,concat384],1).squeeze(0)}else prediction=batchedPrediction.squeeze();let decodedBounds=decodeBounds(prediction,this.anchors,this.inputSize),logits=tf4.slice(prediction,[0,0],[-1,1]),scoresOut=tf4.sigmoid(logits).squeeze();return[prediction,decodedBounds,scoresOut]}),boxIndicesTensor=await tf4.image.nonMaxSuppressionAsync(boxes,scores,this.config.face.detector.maxFaces,this.config.face.detector.iouThreshold,this.config.face.detector.scoreThreshold),boxIndices=boxIndicesTensor.arraySync();boxIndicesTensor.dispose();let boundingBoxes=boxIndices.map(boxIndex=>tf4.slice(boxes,[boxIndex,0],[1,-1])).map(boundingBox=>{let vals=boundingBox.arraySync();return boundingBox.dispose(),vals}),scoresVal=scores.dataSync(),annotatedBoxes=[];for(let i=0;ithis.config.face.detector.minConfidence){let box=createBox(boundingBoxes[i]),anchor=this.anchorsData[boxIndex],landmarks=tf4.tidy(()=>tf4.slice(detectedOutputs,[boxIndex,NUM_LANDMARKS-1],[1,-1]).squeeze().reshape([NUM_LANDMARKS,-1]));annotatedBoxes.push({box,landmarks,anchor,confidence})}}return detectedOutputs.dispose(),boxes.dispose(),scores.dispose(),detectedOutputs.dispose(),{boxes:annotatedBoxes,scaleFactor:[inputImage.shape[2]/this.width,inputImage.shape[1]/this.height]}}async estimateFaces(input){let{boxes,scaleFactor}=await this.getBoundingBoxes(input),faces=[];for(let face3 of boxes){let landmarkData=face3.landmarks.arraySync(),scaledBox=scaleBoxFromPrediction(face3,scaleFactor),boxData=scaleBox.arraySync(),probabilityData=face3.probability.arraySync(),anchor=face3.anchor,[scaleFactorX,scaleFactorY]=scaleFactor,scaledLandmarks=landmarkData.map(landmark=>[(landmark[0]+anchor[0])*scaleFactorX,(landmark[1]+anchor[1])*scaleFactorY]),normalizedFace={topLeft:boxData.slice(0,2),bottomRight:boxData.slice(2),landmarks:scaledLandmarks,probability:probabilityData};disposeBox(face3.box),face3.landmarks.dispose(),face3.probability.dispose(),scaledBox.dispose(),faces.push(normalizedFace)}return faces}};async function load2(config2){let blazeface=await tf4.loadGraphModel(config2.face.detector.modelPath,{fromTFHub:config2.face.detector.modelPath.includes("tfhub.dev")}),model=new BlazeFaceModel(blazeface,config2);return log(`load model: ${config2.face.detector.modelPath.match(/\/(.*)\./)[1]}`),model}exports2.load=load2;exports2.BlazeFaceModel=BlazeFaceModel;exports2.disposeBox=disposeBox});var require_box=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm());function scaleBoxCoordinates2(box,factor){let startPoint=[box.startPoint[0]*factor[0],box.startPoint[1]*factor[1]],endPoint=[box.endPoint[0]*factor[0],box.endPoint[1]*factor[1]];return{startPoint,endPoint}}exports2.scaleBoxCoordinates=scaleBoxCoordinates2;function getBoxSize2(box){return[Math.abs(box.endPoint[0]-box.startPoint[0]),Math.abs(box.endPoint[1]-box.startPoint[1])]}exports2.getBoxSize=getBoxSize2;function getBoxCenter2(box){return[box.startPoint[0]+(box.endPoint[0]-box.startPoint[0])/2,box.startPoint[1]+(box.endPoint[1]-box.startPoint[1])/2]}exports2.getBoxCenter=getBoxCenter2;function cutBoxFromImageAndResize2(box,image4,cropSize){let h=image4.shape[1],w=image4.shape[2],boxes=[[box.startPoint[1]/h,box.startPoint[0]/w,box.endPoint[1]/h,box.endPoint[0]/w]];return tf4.image.cropAndResize(image4,boxes,[0],cropSize)}exports2.cutBoxFromImageAndResize=cutBoxFromImageAndResize2;function enlargeBox2(box,factor=1.5){let center=getBoxCenter2(box),size=getBoxSize2(box),newHalfSize=[factor*size[0]/2,factor*size[1]/2],startPoint=[center[0]-newHalfSize[0],center[1]-newHalfSize[1]],endPoint=[center[0]+newHalfSize[0],center[1]+newHalfSize[1]];return{startPoint,endPoint,landmarks:box.landmarks}}exports2.enlargeBox=enlargeBox2;function squarifyBox2(box){let centers=getBoxCenter2(box),size=getBoxSize2(box),halfSize=Math.max(...size)/2,startPoint=[centers[0]-halfSize,centers[1]-halfSize],endPoint=[centers[0]+halfSize,centers[1]+halfSize];return{startPoint,endPoint,landmarks:box.landmarks}}exports2.squarifyBox=squarifyBox2});var require_util=__commonJS(exports2=>{exports2.IDENTITY_MATRIX=[[1,0,0],[0,1,0],[0,0,1]];function normalizeRadians2(angle){return angle-2*Math.PI*Math.floor((angle+Math.PI)/(2*Math.PI))}exports2.normalizeRadians=normalizeRadians2;function computeRotation2(point1,point2){let radians=Math.PI/2-Math.atan2(-(point2[1]-point1[1]),point2[0]-point1[0]);return normalizeRadians2(radians)}exports2.computeRotation=computeRotation2;function radToDegrees(rad){return rad*180/Math.PI}exports2.radToDegrees=radToDegrees;function buildTranslationMatrix2(x,y){return[[1,0,x],[0,1,y],[0,0,1]]}function dot2(v1,v2){let product=0;for(let i=0;i{var 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,378,379,397,288,361,454,356,70,63,105,66,107,336,296,334,293,300,168,6,195,4,98,97,2,326,327,33,160,158,133,153,144,362,385,387,263,373,380,57,40,37,0,267,270,287,321,314,17,84,91,78,81,13,311,308,402,14,178],VTX33=[33,133,362,263,1,62,308,159,145,386,374,6,102,331,2,13,14,70,105,107,336,334,300,54,10,284,50,280,234,454,58,288,152],VTX7=[33,133,362,263,1,78,308];exports2.MESH_ANNOTATIONS=MESH_ANNOTATIONS;exports2.MESH_TO_IRIS_INDICES_MAP=MESH_TO_IRIS_INDICES_MAP;exports2.TRI468=TRI468;exports2.TRI68=TRI68;exports2.TRI33=TRI33;exports2.TRI7=TRI7;exports2.UV468=UV468;exports2.UV68=VTX68.map(x=>UV468[x]);exports2.UV33=VTX33.map(x=>UV468[x]);exports2.UV7=VTX7.map(x=>UV468[x])});var require_facepipeline=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm()),bounding=__toModule(require_box()),util=__toModule(require_util()),coords=__toModule(require_coords()),LANDMARKS_COUNT=468,MESH_MOUTH_INDEX=13,MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES=[MESH_MOUTH_INDEX,coords.MESH_ANNOTATIONS.midwayBetweenEyes[0]],BLAZEFACE_MOUTH_INDEX=3,BLAZEFACE_NOSE_INDEX=2,BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES=[BLAZEFACE_MOUTH_INDEX,BLAZEFACE_NOSE_INDEX],LEFT_EYE_OUTLINE=coords.MESH_ANNOTATIONS.leftEyeLower0,LEFT_EYE_BOUNDS=[LEFT_EYE_OUTLINE[0],LEFT_EYE_OUTLINE[LEFT_EYE_OUTLINE.length-1]],RIGHT_EYE_OUTLINE=coords.MESH_ANNOTATIONS.rightEyeLower0,RIGHT_EYE_BOUNDS=[RIGHT_EYE_OUTLINE[0],RIGHT_EYE_OUTLINE[RIGHT_EYE_OUTLINE.length-1]],IRIS_UPPER_CENTER_INDEX=3,IRIS_LOWER_CENTER_INDEX=4,IRIS_IRIS_INDEX=71,IRIS_NUM_COORDINATES=76;function replaceRawCoordinates(rawCoords,newCoords,prefix,keys){for(let i=0;i[scaleFactor[0]*(coord[0]-this.meshWidth/2),scaleFactor[1]*(coord[1]-this.meshHeight/2),coord[2]]),coordsRotationMatrix=angle!==0?util.buildRotationMatrix(angle,[0,0]):util.IDENTITY_MATRIX,coordsRotated=angle!==0?coordsScaled.map(coord=>[...util.rotatePoint(coord,coordsRotationMatrix),coord[2]]):coordsScaled,inverseRotationMatrix=angle!==0?util.invertTransformMatrix(rotationMatrix):util.IDENTITY_MATRIX,boxCenter=[...bounding.getBoxCenter({startPoint:box.startPoint,endPoint:box.endPoint}),1];return coordsRotated.map(coord=>[coord[0]+util.dot(boxCenter,inverseRotationMatrix[0]),coord[1]+util.dot(boxCenter,inverseRotationMatrix[1]),coord[2]])}getLeftToRightEyeDepthDifference(rawCoords){let leftEyeZ=rawCoords[LEFT_EYE_BOUNDS[0]][2],rightEyeZ=rawCoords[RIGHT_EYE_BOUNDS[0]][2];return leftEyeZ-rightEyeZ}getEyeBox(rawCoords,face3,eyeInnerCornerIndex,eyeOuterCornerIndex,flip=!1){let box=bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex],rawCoords[eyeOuterCornerIndex]]),this.irisEnlarge)),boxSize=bounding.getBoxSize(box),crop=tf4.image.cropAndResize(face3,[[box.startPoint[1]/this.meshHeight,box.startPoint[0]/this.meshWidth,box.endPoint[1]/this.meshHeight,box.endPoint[0]/this.meshWidth]],[0],[this.irisSize,this.irisSize]);return flip&&(crop=tf4.image.flipLeftRight(crop)),{box,boxSize,crop}}getEyeCoords(eyeData,eyeBox,eyeBoxSize,flip=!1){let eyeRawCoords=[];for(let i=0;i{let z=averageZ;return i===2?z=upperCenterZ:i===4&&(z=lowerCenterZ),[coord[0],coord[1],z]})}async predict(input,config2){let useFreshBox=!1,detector;if((this.skipped===0||this.skipped>config2.face.detector.skipFrames||!config2.face.mesh.enabled||!config2.videoOptimized)&&(detector=await this.boundingBoxDetector.getBoundingBoxes(input),this.skipped=0),config2.videoOptimized&&this.skipped++,detector&&detector.boxes&&detector.boxes.length>0&&(!config2.face.mesh.enabled||detector.boxes.length!==this.detectedFaces&&this.detectedFaces!==config2.face.detector.maxFaces)){this.storedBoxes=[],this.detectedFaces=0;for(let possible of detector.boxes)this.storedBoxes.push({startPoint:possible.box.startPoint.dataSync(),endPoint:possible.box.endPoint.dataSync(),landmarks:possible.landmarks,confidence:possible.confidence});this.storedBoxes.length>0&&(useFreshBox=!0)}if(useFreshBox){if(!detector||!detector.boxes||detector.boxes.length===0)return this.storedBoxes=[],this.detectedFaces=0,null;for(let i=0;i{prediction.box.startPoint.dispose(),prediction.box.endPoint.dispose(),prediction.landmarks.dispose()});let results=tf4.tidy(()=>this.storedBoxes.map((box,i)=>{let face3,angle=0,rotationMatrix;if(config2.face.detector.rotation){let[indexOfMouth,indexOfForehead]=box.landmarks.length>=LANDMARKS_COUNT?MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES:BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES;angle=util.computeRotation(box.landmarks[indexOfMouth],box.landmarks[indexOfForehead]);let faceCenter=bounding.getBoxCenter({startPoint:box.startPoint,endPoint:box.endPoint}),faceCenterNormalized=[faceCenter[0]/input.shape[2],faceCenter[1]/input.shape[1]],rotatedImage=tf4.image.rotateWithOffset(input,angle,0,faceCenterNormalized);rotationMatrix=util.buildRotationMatrix(-angle,faceCenter),face3=bounding.cutBoxFromImageAndResize({startPoint:box.startPoint,endPoint:box.endPoint},rotatedImage,[this.meshHeight,this.meshWidth]).div(255)}else{rotationMatrix=util.IDENTITY_MATRIX;let cloned=input.clone();face3=bounding.cutBoxFromImageAndResize({startPoint:box.startPoint,endPoint:box.endPoint},cloned,[this.meshHeight,this.meshWidth]).div(255)}if(!config2.face.mesh.enabled)return{coords:null,box,faceConfidence:null,confidence:box.confidence,image:face3};let[,confidence,contourCoords]=this.meshDetector.predict(face3),confidenceVal=confidence.dataSync()[0];if(confidenceVala!==null),this.detectedFaces=results.length,results}calculateLandmarksBoundingBox(landmarks){let xs=landmarks.map(d=>d[0]),ys=landmarks.map(d=>d[1]),startPoint=[Math.min(...xs),Math.min(...ys)],endPoint=[Math.max(...xs),Math.max(...ys)];return{startPoint,endPoint,landmarks}}};exports2.Pipeline=Pipeline});var require_facemesh=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm()),blazeface=__toModule(require_blazeface()),facepipeline=__toModule(require_facepipeline()),coords=__toModule(require_coords()),MediaPipeFaceMesh=class{constructor(blazeFace,blazeMeshModel,irisModel,config2){this.facePipeline=new facepipeline.Pipeline(blazeFace,blazeMeshModel,irisModel,config2),this.config=config2}async estimateFaces(input,config2){let predictions=await this.facePipeline.predict(input,config2),results=[];for(let prediction of predictions||[]){if(prediction.isDisposedInternal)continue;let mesh=prediction.coords?prediction.coords.arraySync():null,meshRaw=prediction.rawCoords,annotations={};if(mesh&&mesh.length>0)for(let key of Object.keys(coords.MESH_ANNOTATIONS))annotations[key]=coords.MESH_ANNOTATIONS[key].map(index=>mesh[index]);let boxRaw=config2.face.mesh.returnRawData&&prediction.box?{topLeft:prediction.box.startPoint,bottomRight:prediction.box.endPoint}:null,box=prediction.box?[Math.max(0,prediction.box.startPoint[0]),Math.max(0,prediction.box.startPoint[1]),Math.min(input.shape[2],prediction.box.endPoint[0])-prediction.box.startPoint[0],Math.min(input.shape[1],prediction.box.endPoint[1])-prediction.box.startPoint[1]]:0;results.push({confidence:prediction.confidence||0,box,mesh,boxRaw,meshRaw,annotations,image:prediction.image?tf4.clone(prediction.image):null}),prediction.coords&&prediction.coords.dispose(),prediction.image&&prediction.image.dispose()}return results}},faceModels=[null,null,null];async function load2(config2){faceModels=await Promise.all([!faceModels[0]&&config2.face.enabled?blazeface.load(config2):null,!faceModels[1]&&config2.face.mesh.enabled?tf4.loadGraphModel(config2.face.mesh.modelPath,{fromTFHub:config2.face.mesh.modelPath.includes("tfhub.dev")}):null,!faceModels[2]&&config2.face.iris.enabled?tf4.loadGraphModel(config2.face.iris.modelPath,{fromTFHub:config2.face.iris.modelPath.includes("tfhub.dev")}):null]);let faceMesh=new MediaPipeFaceMesh(faceModels[0],faceModels[1],faceModels[2],config2);return config2.face.mesh.enabled&&log(`load model: ${config2.face.mesh.modelPath.match(/\/(.*)\./)[1]}`),config2.face.iris.enabled&&log(`load model: ${config2.face.iris.modelPath.match(/\/(.*)\./)[1]}`),faceMesh}exports2.load=load2;exports2.MediaPipeFaceMesh=MediaPipeFaceMesh;exports2.triangulation=coords.TRI468});var require_profile=__commonJS(exports2=>{var profileData={};function profile2(name,data2){if(!data2||!data2.kernels)return;let maxResults=5,time=data2.kernels.filter(a=>a.kernelTimeMs>0).reduce((a,b)=>a+=b.kernelTimeMs,0),slowest=data2.kernels.map((a,i)=>(a.id=i,a)).filter(a=>a.kernelTimeMs>0).sort((a,b)=>b.kernelTimeMs-a.kernelTimeMs),largest=data2.kernels.map((a,i)=>(a.id=i,a)).filter(a=>a.totalBytesSnapshot>0).sort((a,b)=>b.totalBytesSnapshot-a.totalBytesSnapshot);slowest.length>maxResults&&(slowest.length=maxResults),largest.length>maxResults&&(largest.length=maxResults);let res={newBytes:data2.newBytes,newTensors:data2.newTensors,peakBytes:data2.peakBytes,numKernelOps:data2.kernels.length,timeKernelOps:time,slowestKernelOps:slowest,largestKernelOps:largest};profileData[name]=res,log("Human profiler",name,res)}exports2.run=profile2});var require_age=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm()),profile2=__toModule(require_profile()),models={},last={age:0},skipped=Number.MAX_SAFE_INTEGER;async function load2(config2){return models.age||(models.age=await tf4.loadGraphModel(config2.face.age.modelPath),log(`load model: ${config2.face.age.modelPath.match(/\/(.*)\./)[1]}`)),models.age}async function predict2(image4,config2){return models.age?skipped0?(skipped++,last):(config2.videoOptimized?skipped=0:skipped=Number.MAX_SAFE_INTEGER,new Promise(async resolve=>{let resize=tf4.image.resizeBilinear(image4,[config2.face.age.inputSize,config2.face.age.inputSize],!1),enhance=tf4.mul(resize,[255]);tf4.dispose(resize);let ageT,obj={};if(!config2.profile)config2.face.age.enabled&&(ageT=await models.age.predict(enhance));else{let profileAge=config2.face.age.enabled?await tf4.profile(()=>models.age.predict(enhance)):{};ageT=profileAge.result.clone(),profileAge.result.dispose(),profile2.run("age",profileAge)}if(enhance.dispose(),ageT){let data2=ageT.dataSync();obj.age=Math.trunc(10*data2[0])/10}ageT.dispose(),last=obj,resolve(obj)})):null}exports2.predict=predict2;exports2.load=load2});var require_gender=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm()),profile2=__toModule(require_profile()),models={},last={gender:""},skipped=Number.MAX_SAFE_INTEGER,alternative=!1,rgb=[.2989,.587,.114];async function load2(config2){return models.gender||(models.gender=await tf4.loadGraphModel(config2.face.gender.modelPath),alternative=models.gender.inputs[0].shape[3]===1,log(`load model: ${config2.face.gender.modelPath.match(/\/(.*)\./)[1]}`)),models.gender}async function predict2(image4,config2){return models.gender?skipped{let resize=tf4.image.resizeBilinear(image4,[config2.face.gender.inputSize,config2.face.gender.inputSize],!1),enhance;alternative?enhance=tf4.tidy(()=>{let[red,green,blue]=tf4.split(resize,3,3),redNorm=tf4.mul(red,rgb[0]),greenNorm=tf4.mul(green,rgb[1]),blueNorm=tf4.mul(blue,rgb[2]);return tf4.addN([redNorm,greenNorm,blueNorm]).sub(.5).mul(2)}):enhance=tf4.mul(resize,[255]),tf4.dispose(resize);let genderT,obj={};if(!config2.profile)config2.face.gender.enabled&&(genderT=await models.gender.predict(enhance));else{let profileGender=config2.face.gender.enabled?await tf4.profile(()=>models.gender.predict(enhance)):{};genderT=profileGender.result.clone(),profileGender.result.dispose(),profile2.run("gender",profileGender)}if(enhance.dispose(),genderT){let data2=genderT.dataSync();if(alternative){let confidence=Math.trunc(100*Math.abs(data2[0]-data2[1]))/100;confidence>config2.face.gender.minConfidence&&(obj.gender=data2[0]>data2[1]?"female":"male",obj.confidence=confidence)}else{let confidence=Math.trunc(200*Math.abs(data2[0]-.5))/100;confidence>config2.face.gender.minConfidence&&(obj.gender=data2[0]<=.5?"female":"male",obj.confidence=Math.min(.99,confidence))}}genderT.dispose(),last=obj,resolve(obj)})):null}exports2.predict=predict2;exports2.load=load2});var require_emotion=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm()),profile2=__toModule(require_profile()),annotations=["angry","disgust","fear","happy","sad","surprise","neutral"],models={},last=[],skipped=Number.MAX_SAFE_INTEGER,rgb=[.2989,.587,.114],scale=1;async function load2(config2){return models.emotion||(models.emotion=await tf4.loadGraphModel(config2.face.emotion.modelPath),log(`load model: ${config2.face.emotion.modelPath.match(/\/(.*)\./)[1]}`)),models.emotion}async function predict2(image4,config2){return models.emotion?skipped0?(skipped++,last):(config2.videoOptimized?skipped=0:skipped=Number.MAX_SAFE_INTEGER,new Promise(async resolve=>{let resize=tf4.image.resizeBilinear(image4,[config2.face.emotion.inputSize,config2.face.emotion.inputSize],!1),[red,green,blue]=tf4.split(resize,3,3);resize.dispose();let redNorm=tf4.mul(red,rgb[0]),greenNorm=tf4.mul(green,rgb[1]),blueNorm=tf4.mul(blue,rgb[2]);red.dispose(),green.dispose(),blue.dispose();let grayscale=tf4.addN([redNorm,greenNorm,blueNorm]);redNorm.dispose(),greenNorm.dispose(),blueNorm.dispose();let normalize=tf4.tidy(()=>grayscale.sub(.5).mul(2));grayscale.dispose();let obj=[];if(config2.face.emotion.enabled){let data2;if(config2.profile){let profileData=await tf4.profile(()=>models.emotion.predict(normalize));data2=profileData.result.dataSync(),profileData.result.dispose(),profile2.run("emotion",profileData)}else{let emotionT=await models.emotion.predict(normalize);data2=emotionT.dataSync(),tf4.dispose(emotionT)}for(let i=0;iconfig2.face.emotion.minConfidence&&obj.push({score:Math.min(.99,Math.trunc(100*scale*data2[i])/100),emotion:annotations[i]});obj.sort((a,b)=>b.score-a.score)}normalize.dispose(),last=obj,resolve(obj)})):null}exports2.predict=predict2;exports2.load=load2});var require_embedding=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm()),profile2=__toModule(require_profile()),models={};async function load2(config2){return models.embedding||(models.embedding=await tf4.loadGraphModel(config2.face.embedding.modelPath),log(`load model: ${config2.face.embedding.modelPath.match(/\/(.*)\./)[1]}`)),models.embedding}function simmilarity2(embedding1,embedding2){if((embedding1==null?void 0:embedding1.length)!==(embedding2==null?void 0:embedding2.length))return 0;let order=2,distance=10*embedding1.map((val,i)=>val-embedding2[i]).reduce((dist,diff)=>dist+diff**order,0)**(1/order);return Math.trunc(1e3*(1-distance))/1e3}async function predict2(image4,config2){return models.embedding?new Promise(async resolve=>{let resize=tf4.image.resizeBilinear(image4,[config2.face.embedding.inputSize,config2.face.embedding.inputSize],!1),data2=[];if(config2.face.embedding.enabled)if(config2.profile){let profileData=await tf4.profile(()=>models.embedding.predict({img_inputs:resize}));data2=[...profileData.result.dataSync()],profileData.result.dispose(),profile2.run("emotion",profileData)}else{let embeddingT=await models.embedding.predict({img_inputs:resize});data2=[...embeddingT.dataSync()],tf4.dispose(embeddingT)}resize.dispose(),resolve(data2)}):null}exports2.predict=predict2;exports2.simmilarity=simmilarity2;exports2.load=load2});var require_modelBase=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm()),imageNetMean=[-123.15,-115.9,-103.06];function nameOutputResultsMobileNet(results){let[offsets,heatmap,displacementFwd,displacementBwd]=results;return{offsets,heatmap,displacementFwd,displacementBwd}}function nameOutputResultsResNet(results){let[displacementFwd,displacementBwd,offsets,heatmap]=results;return{offsets,heatmap,displacementFwd,displacementBwd}}var BaseModel=class{constructor(model){this.model=model}predict(input,config2){return tf4.tidy(()=>{let asBatch=(config2.body.modelType==="ResNet"?input.toFloat().add(imageNetMean):input.toFloat().div(127.5).sub(1)).expandDims(0),results3d=this.model.predict(asBatch).map(y=>y.squeeze([0])),namedResults=config2.body.modelType==="ResNet"?nameOutputResultsResNet(results3d):nameOutputResultsMobileNet(results3d);return{heatmapScores:namedResults.heatmap.sigmoid(),offsets:namedResults.offsets,displacementFwd:namedResults.displacementFwd,displacementBwd:namedResults.displacementBwd}})}dispose(){this.model.dispose()}};exports2.BaseModel=BaseModel});var require_heapSort=__commonJS(exports2=>{function half(k){return Math.floor(k/2)}var MaxHeap=class{constructor(maxSize,getElementValue){this.priorityQueue=new Array(maxSize),this.numberOfElements=-1,this.getElementValue=getElementValue}enqueue(x){this.priorityQueue[++this.numberOfElements]=x,this.swim(this.numberOfElements)}dequeue(){let max=this.priorityQueue[0];return this.exchange(0,this.numberOfElements--),this.sink(0),this.priorityQueue[this.numberOfElements+1]=null,max}empty(){return this.numberOfElements===-1}size(){return this.numberOfElements+1}all(){return this.priorityQueue.slice(0,this.numberOfElements+1)}max(){return this.priorityQueue[0]}swim(k){for(;k>0&&this.less(half(k),k);)this.exchange(k,half(k)),k=half(k)}sink(k){for(;2*k<=this.numberOfElements;){let j=2*k;if(j{var heapSort=__toModule(require_heapSort());function scoreIsMaximumInLocalWindow(keypointId,score,heatmapY,heatmapX,localMaximumRadius,scores){let[height,width]=scores.shape,localMaximum=!0,yStart=Math.max(heatmapY-localMaximumRadius,0),yEnd=Math.min(heatmapY+localMaximumRadius+1,height);for(let yCurrent=yStart;yCurrentscore){localMaximum=!1;break}if(!localMaximum)break}return localMaximum}function buildPartWithScoreQueue(scoreThreshold,localMaximumRadius,scores){let[height,width,numKeypoints]=scores.shape,queue=new heapSort.MaxHeap(height*width*numKeypoints,({score})=>score);for(let heatmapY=0;heatmapY{exports2.partNames=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"];exports2.NUM_KEYPOINTS=exports2.partNames.length;exports2.partIds=exports2.partNames.reduce((result,jointName,i)=>(result[jointName]=i,result),{});var connectedPartNames=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]];exports2.connectedPartIndices=connectedPartNames.map(([jointNameA,jointNameB])=>[exports2.partIds[jointNameA],exports2.partIds[jointNameB]]);exports2.poseChain=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];exports2.partChannels=["left_face","right_face","right_upper_leg_front","right_lower_leg_back","right_upper_leg_back","left_lower_leg_front","left_upper_leg_front","left_upper_leg_back","left_lower_leg_back","right_feet","right_lower_leg_front","left_feet","torso_front","torso_back","right_upper_arm_front","right_upper_arm_back","right_lower_arm_back","left_lower_arm_front","left_upper_arm_front","left_upper_arm_back","left_lower_arm_back","right_hand","right_lower_arm_front","left_hand"]});var require_vectors=__commonJS(exports2=>{var kpt=__toModule(require_keypoints());function getOffsetPoint(y,x,keypoint,offsets){return{y:offsets.get(y,x,keypoint),x:offsets.get(y,x,keypoint+kpt.NUM_KEYPOINTS)}}exports2.getOffsetPoint=getOffsetPoint;function getImageCoords(part,outputStride,offsets){let{heatmapY,heatmapX,id:keypoint}=part,{y,x}=getOffsetPoint(heatmapY,heatmapX,keypoint,offsets);return{x:part.heatmapX*outputStride+x,y:part.heatmapY*outputStride+y}}exports2.getImageCoords=getImageCoords;function fillArray(element,size){let result=new Array(size);for(let i=0;imax?max:a}exports2.clamp=clamp;function squaredDistance(y1,x1,y2,x2){let dy=y2-y1,dx=x2-x1;return dy*dy+dx*dx}exports2.squaredDistance=squaredDistance;function addVectors(a,b){return{x:a.x+b.x,y:a.y+b.y}}exports2.addVectors=addVectors;function clampVector(a,min,max){return{y:clamp(a.y,min,max),x:clamp(a.x,min,max)}}exports2.clampVector=clampVector});var require_decoders=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm()),kpt=__toModule(require_keypoints());function getPointsConfidence(heatmapScores,heatMapCoords){let numKeypoints=heatMapCoords.shape[0],result=new Float32Array(numKeypoints);for(let keypoint=0;keypointheatMapCoordsBuffer.toTensor().mul(tf4.scalar(outputStride,"int32")).toFloat().add(getOffsetVectors(heatMapCoordsBuffer,offsetsBuffer)))}exports2.getOffsetPoints=getOffsetPoints;function mod(a,b){return tf4.tidy(()=>{let floored=a.div(tf4.scalar(b,"int32"));return a.sub(floored.mul(tf4.scalar(b,"int32")))})}function argmax2d(inputs){let[height,width,depth]=inputs.shape;return tf4.tidy(()=>{let coords=inputs.reshape([height*width,depth]).argMax(0),yCoords=coords.div(tf4.scalar(width,"int32")).expandDims(1),xCoords=mod(coords,width).expandDims(1);return tf4.concat([yCoords,xCoords],1)})}exports2.argmax2d=argmax2d});var require_decodePose=__commonJS(exports2=>{var keypoints=__toModule(require_keypoints()),vectors=__toModule(require_vectors()),decoders=__toModule(require_decoders()),parentChildrenTuples=keypoints.poseChain.map(([parentJoinName,childJoinName])=>[keypoints.partIds[parentJoinName],keypoints.partIds[childJoinName]]),parentToChildEdges=parentChildrenTuples.map(([,childJointId])=>childJointId),childToParentEdges=parentChildrenTuples.map(([parentJointId])=>parentJointId);function getDisplacement(edgeId,point,displacements){let numEdges=displacements.shape[2]/2;return{y:displacements.get(point.y,point.x,edgeId),x:displacements.get(point.y,point.x,numEdges+edgeId)}}function getStridedIndexNearPoint(point,outputStride,height,width){return{y:vectors.clamp(Math.round(point.y/outputStride),0,height-1),x:vectors.clamp(Math.round(point.x/outputStride),0,width-1)}}function traverseToTargetKeypoint(edgeId,sourceKeypoint,targetKeypointId,scoresBuffer,offsets,outputStride,displacements,offsetRefineStep=2){let[height,width]=scoresBuffer.shape,sourceKeypointIndices=getStridedIndexNearPoint(sourceKeypoint.position,outputStride,height,width),displacement=getDisplacement(edgeId,sourceKeypointIndices,displacements),targetKeypoint=vectors.addVectors(sourceKeypoint.position,displacement);for(let i=0;i=0;--edge){let sourceKeypointId=parentToChildEdges[edge],targetKeypointId=childToParentEdges[edge];instanceKeypoints[sourceKeypointId]&&!instanceKeypoints[targetKeypointId]&&(instanceKeypoints[targetKeypointId]=traverseToTargetKeypoint(edge,instanceKeypoints[sourceKeypointId],targetKeypointId,scores,offsets,outputStride,displacementsBwd))}for(let edge=0;edge(totalScore+=score,{position:{y:offsetPointsBuffer.get(i,0),x:offsetPointsBuffer.get(i,1)},part:keypoints.partNames[i],score})),filteredKeypoints=instanceKeypoints.filter(kpt=>kpt.score>config2.body.scoreThreshold);return heatmapValues.dispose(),offsetPoints.dispose(),{keypoints:filteredKeypoints,score:totalScore/instanceKeypoints.length}}exports2.decodeSinglePose=decodeSinglePose});var require_decodeMultiple=__commonJS(exports2=>{var buildParts=__toModule(require_buildParts()),decodePose=__toModule(require_decodePose()),vectors=__toModule(require_vectors()),kLocalMaximumRadius=1;function withinNmsRadiusOfCorrespondingPoint(poses,squaredNmsRadius,{x,y},keypointId){return poses.some(({keypoints})=>{let correspondingKeypoint=keypoints[keypointId].position;return vectors.squaredDistance(y,x,correspondingKeypoint.y,correspondingKeypoint.x)<=squaredNmsRadius})}function getInstanceScore(existingPoses,squaredNmsRadius,instanceKeypoints){return instanceKeypoints.reduce((result,{position,score},keypointId)=>(withinNmsRadiusOfCorrespondingPoint(existingPoses,squaredNmsRadius,position,keypointId)||(result+=score),result),0)/instanceKeypoints.length}function decodeMultiplePoses(scoresBuffer,offsetsBuffer,displacementsFwdBuffer,displacementsBwdBuffer,config2){let poses=[],queue=buildParts.buildPartWithScoreQueue(config2.body.scoreThreshold,kLocalMaximumRadius,scoresBuffer),squaredNmsRadius=config2.body.nmsRadius^2;for(;poses.lengthconfig2.body.scoreThreshold&&poses.push({keypoints,score})}return poses}exports2.decodeMultiplePoses=decodeMultiplePoses});var require_util2=__commonJS(exports2=>{var kpt=__toModule(require_keypoints());function eitherPointDoesntMeetConfidence(a,b,minConfidence){return a(eitherPointDoesntMeetConfidence(keypoints[leftJoint].score,keypoints[rightJoint].score,minConfidence)||result.push([keypoints[leftJoint],keypoints[rightJoint]]),result),[])}exports2.getAdjacentKeyPoints=getAdjacentKeyPoints;var{NEGATIVE_INFINITY,POSITIVE_INFINITY}=Number;function getBoundingBox(keypoints){return keypoints.reduce(({maxX,maxY,minX,minY},{position:{x,y}})=>({maxX:Math.max(maxX,x),maxY:Math.max(maxY,y),minX:Math.min(minX,x),minY:Math.min(minY,y)}),{maxX:NEGATIVE_INFINITY,maxY:NEGATIVE_INFINITY,minX:POSITIVE_INFINITY,minY:POSITIVE_INFINITY})}exports2.getBoundingBox=getBoundingBox;function getBoundingBoxPoints(keypoints){let{minX,minY,maxX,maxY}=getBoundingBox(keypoints);return[{x:minX,y:minY},{x:maxX,y:minY},{x:maxX,y:maxY},{x:minX,y:maxY}]}exports2.getBoundingBoxPoints=getBoundingBoxPoints;async function toTensorBuffers3D(tensors){return Promise.all(tensors.map(tensor=>tensor.buffer()))}exports2.toTensorBuffers3D=toTensorBuffers3D;function scalePose(pose,scaleY,scaleX){return{score:pose.score,keypoints:pose.keypoints.map(({score,part,position})=>({score,part,position:{x:position.x*scaleX,y:position.y*scaleY}}))}}exports2.scalePose=scalePose;function resizeTo(image3,[targetH,targetW]){let input=image3.squeeze(0),resized=input.resizeBilinear([targetH,targetW]);return input.dispose(),resized}exports2.resizeTo=resizeTo;function scaleAndFlipPoses(poses,[height,width],[inputResolutionHeight,inputResolutionWidth]){return poses.map(pose=>scalePose(pose,height/inputResolutionHeight,width/inputResolutionWidth))}exports2.scaleAndFlipPoses=scaleAndFlipPoses});var require_modelPoseNet=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm()),modelBase=__toModule(require_modelBase()),decodeMultiple=__toModule(require_decodeMultiple()),decodePose=__toModule(require_decodePose()),util=__toModule(require_util2());async function estimateMultiple(input,res,config2){return new Promise(async resolve=>{let height=input.shape[1],width=input.shape[2],allTensorBuffers=await util.toTensorBuffers3D([res.heatmapScores,res.offsets,res.displacementFwd,res.displacementBwd]),scoresBuffer=allTensorBuffers[0],offsetsBuffer=allTensorBuffers[1],displacementsFwdBuffer=allTensorBuffers[2],displacementsBwdBuffer=allTensorBuffers[3],poses=await decodeMultiple.decodeMultiplePoses(scoresBuffer,offsetsBuffer,displacementsFwdBuffer,displacementsBwdBuffer,config2),scaled=util.scaleAndFlipPoses(poses,[height,width],[config2.body.inputSize,config2.body.inputSize]);resolve(scaled)})}async function estimateSingle(input,res,config2){return new Promise(async resolve=>{let height=input.shape[1],width=input.shape[2],poses=[await decodePose.decodeSinglePose(res.heatmapScores,res.offsets,config2)],scaled=util.scaleAndFlipPoses(poses,[height,width],[config2.body.inputSize,config2.body.inputSize]);resolve(scaled)})}var PoseNet=class{constructor(model){this.baseModel=model}async estimatePoses(input,config2){let resized=util.resizeTo(input,[config2.body.inputSize,config2.body.inputSize]),res=this.baseModel.predict(resized,config2),poses=config2.body.maxDetections<2?await estimateSingle(input,res,config2):await estimateMultiple(input,res,config2);return res.heatmapScores.dispose(),res.offsets.dispose(),res.displacementFwd.dispose(),res.displacementBwd.dispose(),resized.dispose(),poses}dispose(){this.baseModel.dispose()}};exports2.PoseNet=PoseNet;async function load2(config2){let model=await tf4.loadGraphModel(config2.body.modelPath),mobilenet=new modelBase.BaseModel(model);return log(`load model: ${config2.body.modelPath.match(/\/(.*)\./)[1]}`),new PoseNet(mobilenet)}exports2.load=load2});var require_posenet=__commonJS(exports2=>{var modelPoseNet=__toModule(require_modelPoseNet()),keypoints=__toModule(require_keypoints()),util=__toModule(require_util2());exports2.load=modelPoseNet.load;exports2.PoseNet=modelPoseNet.PoseNet;exports2.partChannels=keypoints.partChannels;exports2.partIds=keypoints.partIds;exports2.partNames=keypoints.partNames;exports2.poseChain=keypoints.poseChain;exports2.getAdjacentKeyPoints=util.getAdjacentKeyPoints;exports2.getBoundingBox=util.getBoundingBox;exports2.getBoundingBoxPoints=util.getBoundingBoxPoints;exports2.scaleAndFlipPoses=util.scaleAndFlipPoses;exports2.scalePose=util.scalePose});var require_handdetector=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm()),HandDetector=class{constructor(model,inputSize,anchorsAnnotated){this.model=model,this.anchors=anchorsAnnotated.map(anchor=>[anchor.x_center,anchor.y_center]),this.anchorsTensor=tf4.tensor2d(this.anchors),this.inputSizeTensor=tf4.tensor1d([inputSize,inputSize]),this.doubleInputSizeTensor=tf4.tensor1d([inputSize*2,inputSize*2])}normalizeBoxes(boxes){return tf4.tidy(()=>{let boxOffsets=tf4.slice(boxes,[0,0],[-1,2]),boxSizes=tf4.slice(boxes,[0,2],[-1,2]),boxCenterPoints=tf4.add(tf4.div(boxOffsets,this.inputSizeTensor),this.anchorsTensor),halfBoxSizes=tf4.div(boxSizes,this.doubleInputSizeTensor),startPoints=tf4.mul(tf4.sub(boxCenterPoints,halfBoxSizes),this.inputSizeTensor),endPoints=tf4.mul(tf4.add(boxCenterPoints,halfBoxSizes),this.inputSizeTensor);return tf4.concat2d([startPoints,endPoints],1)})}normalizeLandmarks(rawPalmLandmarks,index){return tf4.tidy(()=>{let landmarks=tf4.add(tf4.div(rawPalmLandmarks.reshape([-1,7,2]),this.inputSizeTensor),this.anchors[index]);return tf4.mul(landmarks,this.inputSizeTensor)})}async getBoxes(input,config2){let batched=this.model.predict(input),predictions=batched.squeeze();batched.dispose();let scoresT=tf4.tidy(()=>tf4.sigmoid(tf4.slice(predictions,[0,0],[-1,1])).squeeze()),scores=scoresT.dataSync(),rawBoxes=tf4.slice(predictions,[0,1],[-1,4]),boxes=this.normalizeBoxes(rawBoxes);rawBoxes.dispose();let filteredT=await tf4.image.nonMaxSuppressionAsync(boxes,scores,config2.hand.maxHands,config2.hand.iouThreshold,config2.hand.scoreThreshold),filtered=filteredT.arraySync();scoresT.dispose(),filteredT.dispose();let hands=[];for(let index of filtered)if(scores[index]>=config2.hand.minConfidence){let matchingBox=tf4.slice(boxes,[index,0],[1,-1]),rawPalmLandmarks=tf4.slice(predictions,[index,5],[1,14]),palmLandmarks=tf4.tidy(()=>this.normalizeLandmarks(rawPalmLandmarks,index).reshape([-1,2]));rawPalmLandmarks.dispose(),hands.push({box:matchingBox,palmLandmarks,confidence:scores[index]})}return predictions.dispose(),boxes.dispose(),hands}async estimateHandBounds(input,config2){let inputHeight=input.shape[1],inputWidth=input.shape[2],image4=tf4.tidy(()=>input.resizeBilinear([config2.hand.inputSize,config2.hand.inputSize]).div(127.5).sub(1)),predictions=await this.getBoxes(image4,config2);image4.dispose();let hands=[];if(!predictions||predictions.length===0)return hands;for(let prediction of predictions){let boxes=prediction.box.dataSync(),startPoint=boxes.slice(0,2),endPoint=boxes.slice(2,4),palmLandmarks=prediction.palmLandmarks.arraySync();prediction.box.dispose(),prediction.palmLandmarks.dispose(),hands.push(scaleBoxCoordinates({startPoint,endPoint,palmLandmarks,confidence:prediction.confidence},[inputWidth/config2.hand.inputSize,inputHeight/config2.hand.inputSize]))}return hands}};exports2.HandDetector=HandDetector});var require_handpipeline=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm()),PALM_BOX_ENLARGE_FACTOR=5,HAND_BOX_ENLARGE_FACTOR=1.65,PALM_LANDMARK_IDS=[0,5,9,13,17,1,2],PALM_LANDMARKS_INDEX_OF_PALM_BASE=0,PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE=2,HandPipeline=class{constructor(handDetector,landmarkDetector,inputSize){this.handDetector=handDetector,this.landmarkDetector=landmarkDetector,this.inputSize=inputSize,this.storedBoxes=[],this.skipped=0,this.detectedHands=0}getBoxForPalmLandmarks(palmLandmarks,rotationMatrix){let rotatedPalmLandmarks=palmLandmarks.map(coord=>rotatePoint([...coord,1],rotationMatrix)),boxAroundPalm=this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);return enlargeBox(squarifyBox(boxAroundPalm),PALM_BOX_ENLARGE_FACTOR)}getBoxForHandLandmarks(landmarks){let boundingBox=this.calculateLandmarksBoundingBox(landmarks),boxAroundHand=enlargeBox(squarifyBox(boundingBox),HAND_BOX_ENLARGE_FACTOR);boxAroundHand.palmLandmarks=[];for(let i=0;i[scaleFactor[0]*(coord[0]-this.inputSize/2),scaleFactor[1]*(coord[1]-this.inputSize/2),scaleFactor[2]*coord[2]]),coordsRotationMatrix=buildRotationMatrix(angle,[0,0]),coordsRotated=coordsScaled.map(coord=>[...rotatePoint(coord,coordsRotationMatrix),coord[2]]),inverseRotationMatrix=invertTransformMatrix(rotationMatrix),boxCenter=[...getBoxCenter(box2),1],originalBoxCenter=[dot(boxCenter,inverseRotationMatrix[0]),dot(boxCenter,inverseRotationMatrix[1])];return coordsRotated.map(coord=>[coord[0]+originalBoxCenter[0],coord[1]+originalBoxCenter[1],coord[2]])}async estimateHands(image4,config2){let useFreshBox=!1,boxes;(this.skipped===0||this.skipped>config2.hand.skipFrames||!config2.hand.landmarks||!config2.videoOptimized)&&(boxes=await this.handDetector.estimateHandBounds(image4,config2),this.skipped=0),config2.videoOptimized&&this.skipped++,boxes&&boxes.length>0&&(boxes.length!==this.detectedHands&&this.detectedHands!==config2.hand.maxHands||!config2.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...boxes],this.storedBoxes.length>0&&(useFreshBox=!0));let hands=[];for(let i=0;i=config2.hand.minConfidence){let keypointsReshaped=tf4.reshape(keypoints,[-1,3]),rawCoords=keypointsReshaped.arraySync();keypoints.dispose(),keypointsReshaped.dispose();let coords=this.transformRawCoords(rawCoords,newBox,angle,rotationMatrix),nextBoundingBox=this.getBoxForHandLandmarks(coords);this.storedBoxes[i]=nextBoundingBox;let result={landmarks:coords,confidence,box:{topLeft:nextBoundingBox.startPoint,bottomRight:nextBoundingBox.endPoint}};hands.push(result)}else this.storedBoxes[i]=null;keypoints.dispose()}else{let enlarged=enlargeBox(squarifyBox(currentBox),HAND_BOX_ENLARGE_FACTOR),result={confidence:currentBox.confidence,box:{topLeft:enlarged.startPoint,bottomRight:enlarged.endPoint}};hands.push(result)}}return this.storedBoxes=this.storedBoxes.filter(a=>a!==null),this.detectedHands=hands.length,hands}calculateLandmarksBoundingBox(landmarks){let xs=landmarks.map(d=>d[0]),ys=landmarks.map(d=>d[1]),startPoint=[Math.min(...xs),Math.min(...ys)],endPoint=[Math.max(...xs),Math.max(...ys)];return{startPoint,endPoint}}};exports2.HandPipeline=HandPipeline});var 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HandPose(handPipeline);return config2.hand.enabled&&log(`load model: ${config2.hand.detector.modelPath.match(/\/(.*)\./)[1]}`),config2.hand.landmarks&&log(`load model: ${config2.hand.skeleton.modelPath.match(/\/(.*)\./)[1]}`),handPose}exports2.load=load2});var require_gesture=__commonJS(exports2=>{exports2.body=res=>{if(!res)return[];let gestures=[];for(let i=0;ia.part==="leftWrist"),rightWrist=res[i].keypoints.find(a=>a.part==="rightWrist"),nose=res[i].keypoints.find(a=>a.part==="nose");nose&&leftWrist&&rightWrist&&leftWrist.position.ya.part==="leftShoulder"),rightShoulder=res[i].keypoints.find(a=>a.part==="rightShoulder");leftShoulder&&rightShoulder&&gestures.push({body:i,gesture:`leaning ${leftShoulder.position.y>rightShoulder.position.y?"left":"right"}`})}return gestures};exports2.face=res=>{if(!res)return[];let gestures=[];for(let i=0;i0){let eyeFacing=res[i].mesh[35][2]-res[i].mesh[263][2];Math.abs(eyeFacing)<10?gestures.push({face:i,gesture:"facing camera"}):gestures.push({face:i,gesture:`facing ${eyeFacing<0?"right":"left"}`}),Math.abs(res[i].mesh[374][1]-res[i].mesh[386][1])/Math.abs(res[i].mesh[443][1]-res[i].mesh[450][1])<.2&&gestures.push({face:i,gesture:"blink left eye"}),Math.abs(res[i].mesh[145][1]-res[i].mesh[159][1])/Math.abs(res[i].mesh[223][1]-res[i].mesh[230][1])<.2&&gestures.push({face:i,gesture:"blink right eye"});let mouthOpen=Math.min(100,500*Math.abs(res[i].mesh[13][1]-res[i].mesh[14][1])/Math.abs(res[i].mesh[10][1]-res[i].mesh[152][1]));mouthOpen>10&&gestures.push({face:i,gesture:`mouth ${Math.trunc(mouthOpen)}% open`});let chinDepth=res[i].mesh[152][2];Math.abs(chinDepth)>10&&gestures.push({face:i,gesture:`head ${chinDepth<0?"up":"down"}`})}return gestures};exports2.iris=res=>{if(!res)return[];let gestures=[];for(let i=0;i{if(!res)return[];let gestures=[];for(let i=0;i0){let closest=fingers.reduce((best,a)=>best.position[2]best.position[1]{var WebGLProgram=function(gl,vertexSource,fragmentSource){let _collect=function(source,prefix,collection){let r=new RegExp("\\b"+prefix+" \\w+ (\\w+)","ig");source.replace(r,(match,name)=>(collection[name]=0,match))},_compile=function(source,type){let shader=gl.createShader(type);if(gl.shaderSource(shader,source),gl.compileShader(shader),!gl.getShaderParameter(shader,gl.COMPILE_STATUS))throw new Error("Filter: GL compile failed",gl.getShaderInfoLog(shader));return shader};this.uniform={},this.attribute={};let _vsh=_compile(vertexSource,gl.VERTEX_SHADER),_fsh=_compile(fragmentSource,gl.FRAGMENT_SHADER);if(this.id=gl.createProgram(),gl.attachShader(this.id,_vsh),gl.attachShader(this.id,_fsh),gl.linkProgram(this.id),!gl.getProgramParameter(this.id,gl.LINK_STATUS))throw new Error("Filter: GL link failed",gl.getProgramInfoLog(this.id));gl.useProgram(this.id),_collect(vertexSource,"attribute",this.attribute);for(let a in this.attribute)this.attribute[a]=gl.getAttribLocation(this.id,a);_collect(vertexSource,"uniform",this.uniform),_collect(fragmentSource,"uniform",this.uniform);for(let u in this.uniform)this.uniform[u]=gl.getUniformLocation(this.id,u)},WebGLImageFilter=function(params){params||(params={});let _drawCount=0,_sourceTexture=null,_lastInChain=!1,_currentFramebufferIndex=-1,_tempFramebuffers=[null,null],_filterChain=[],_width=-1,_height=-1,_vertexBuffer=null,_currentProgram=null,_canvas=params.canvas||document.createElement("canvas"),_shaderProgramCache={},gl=_canvas.getContext("webgl");if(!gl)throw new Error("Filter: getContext() failed");this.addFilter=function(name){let args=Array.prototype.slice.call(arguments,1),filter=_filter[name];_filterChain.push({func:filter,args})},this.reset=function(){_filterChain=[]},this.apply=function(image3){if(_resize(image3.width,image3.height),_drawCount=0,_sourceTexture||(_sourceTexture=gl.createTexture()),gl.bindTexture(gl.TEXTURE_2D,_sourceTexture),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_WRAP_S,gl.CLAMP_TO_EDGE),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_WRAP_T,gl.CLAMP_TO_EDGE),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_MIN_FILTER,gl.NEAREST),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_MAG_FILTER,gl.NEAREST),gl.texImage2D(gl.TEXTURE_2D,0,gl.RGBA,gl.RGBA,gl.UNSIGNED_BYTE,image3),_filterChain.length===0)return _draw(),_canvas;for(let i=0;i<_filterChain.length;i++){_lastInChain=i===_filterChain.length-1;let f=_filterChain[i];f.func.apply(this,f.args||[])}return _canvas};let _resize=function(width,height){if(!(width===_width&&height===_height)){if(_canvas.width=width,_width=width,_canvas.height=height,_height=height,!_vertexBuffer){let vertices=new Float32Array([-1,-1,0,1,1,-1,1,1,-1,1,0,0,-1,1,0,0,1,-1,1,1,1,1,1,0]);_vertexBuffer=gl.createBuffer(),gl.bindBuffer(gl.ARRAY_BUFFER,_vertexBuffer),gl.bufferData(gl.ARRAY_BUFFER,vertices,gl.STATIC_DRAW),gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL,!0)}gl.viewport(0,0,_width,_height),_tempFramebuffers=[null,null]}},_getTempFramebuffer=function(index){return _tempFramebuffers[index]=_tempFramebuffers[index]||_createFramebufferTexture(_width,_height),_tempFramebuffers[index]},_createFramebufferTexture=function(width,height){let fbo=gl.createFramebuffer();gl.bindFramebuffer(gl.FRAMEBUFFER,fbo);let renderbuffer=gl.createRenderbuffer();gl.bindRenderbuffer(gl.RENDERBUFFER,renderbuffer);let texture=gl.createTexture();return gl.bindTexture(gl.TEXTURE_2D,texture),gl.texImage2D(gl.TEXTURE_2D,0,gl.RGBA,width,height,0,gl.RGBA,gl.UNSIGNED_BYTE,null),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_MAG_FILTER,gl.LINEAR),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_MIN_FILTER,gl.LINEAR),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_WRAP_S,gl.CLAMP_TO_EDGE),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_WRAP_T,gl.CLAMP_TO_EDGE),gl.framebufferTexture2D(gl.FRAMEBUFFER,gl.COLOR_ATTACHMENT0,gl.TEXTURE_2D,texture,0),gl.bindTexture(gl.TEXTURE_2D,null),gl.bindFramebuffer(gl.FRAMEBUFFER,null),{fbo,texture}},_draw=function(flags){var _a,_b;let source=null,target=null,flipY=!1;_drawCount===0?source=_sourceTexture:source=(_a=_getTempFramebuffer(_currentFramebufferIndex))==null?void 0:_a.texture,_drawCount++,_lastInChain&&!(flags&DRAW.INTERMEDIATE)?(target=null,flipY=_drawCount%2==0):(_currentFramebufferIndex=(_currentFramebufferIndex+1)%2,target=(_b=_getTempFramebuffer(_currentFramebufferIndex))==null?void 0:_b.fbo),gl.bindTexture(gl.TEXTURE_2D,source),gl.bindFramebuffer(gl.FRAMEBUFFER,target),gl.uniform1f(_currentProgram.uniform.flipY,flipY?-1:1),gl.drawArrays(gl.TRIANGLES,0,6)},_compileShader=function(fragmentSource){if(_shaderProgramCache[fragmentSource])return _currentProgram=_shaderProgramCache[fragmentSource],gl.useProgram(_currentProgram.id),_currentProgram;_currentProgram=new WebGLProgram(gl,SHADER.VERTEX_IDENTITY,fragmentSource);let floatSize=Float32Array.BYTES_PER_ELEMENT,vertSize=4*floatSize;return gl.enableVertexAttribArray(_currentProgram.attribute.pos),gl.vertexAttribPointer(_currentProgram.attribute.pos,2,gl.FLOAT,!1,vertSize,0*floatSize),gl.enableVertexAttribArray(_currentProgram.attribute.uv),gl.vertexAttribPointer(_currentProgram.attribute.uv,2,gl.FLOAT,!1,vertSize,2*floatSize),_shaderProgramCache[fragmentSource]=_currentProgram,_currentProgram},DRAW={INTERMEDIATE:1},SHADER={};SHADER.VERTEX_IDENTITY=["precision highp float;","attribute vec2 pos;","attribute vec2 uv;","varying vec2 vUv;","uniform float flipY;","void main(void) {","vUv = uv;","gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);","}"].join(` +var __create=Object.create,__defProp=Object.defineProperty,__getProtoOf=Object.getPrototypeOf,__hasOwnProp=Object.prototype.hasOwnProperty,__getOwnPropNames=Object.getOwnPropertyNames,__getOwnPropDesc=Object.getOwnPropertyDescriptor;var __markAsModule=target=>__defProp(target,"__esModule",{value:!0});var __commonJS=(callback,module2)=>()=>(module2||(module2={exports:{}},callback(module2.exports,module2)),module2.exports),__export=(target,all)=>{__markAsModule(target);for(var name in all)__defProp(target,name,{get:all[name],enumerable:!0})},__exportStar=(target,module2,desc)=>{if(__markAsModule(target),module2&&typeof module2=="object"||typeof module2=="function")for(let key of __getOwnPropNames(module2))!__hasOwnProp.call(target,key)&&key!=="default"&&__defProp(target,key,{get:()=>module2[key],enumerable:!(desc=__getOwnPropDesc(module2,key))||desc.enumerable});return target},__toModule=module2=>module2&&module2.__esModule?module2:__exportStar(__defProp(module2!=null?__create(__getProtoOf(module2)):{},"default",{value:module2,enumerable:!0}),module2);var require_tfjs_esm=__commonJS(exports2=>{var __create2=Object.create,__defProp2=Object.defineProperty,__getProtoOf2=Object.getPrototypeOf,__hasOwnProp2=Object.prototype.hasOwnProperty,__getOwnPropNames2=Object.getOwnPropertyNames,__getOwnPropDesc2=Object.getOwnPropertyDescriptor,__markAsModule2=target=>__defProp2(target,"__esModule",{value:!0}),__exportStar2=(target,module22,desc)=>{if(__markAsModule2(target),module22&&typeof module22=="object"||typeof module22=="function")for(let key of __getOwnPropNames2(module22))!__hasOwnProp2.call(target,key)&&key!=="default"&&__defProp2(target,key,{get:()=>module22[key],enumerable:!(desc=__getOwnPropDesc2(module22,key))||desc.enumerable});return target},__toModule2=module22=>module22&&module22.__esModule?module22:__exportStar2(__defProp2(module22!=null?__create2(__getProtoOf2(module22)):{},"default",{value:module22,enumerable:!0}),module22);__exportStar2(exports2,__toModule2(require("@tensorflow/tfjs-node-gpu")))});var require_blazeface=__commonJS(exports2=>{var tf5=__toModule(require_tfjs_esm()),NUM_LANDMARKS=6;function generateAnchors(inputSize){let spec={strides:[inputSize/16,inputSize/8],anchors:[2,6]},anchors=[];for(let i=0;i{box.startEndTensor.dispose(),box.startPoint.dispose(),box.endPoint.dispose()},createBox=startEndTensor=>({startEndTensor,startPoint:tf5.slice(startEndTensor,[0,0],[-1,2]),endPoint:tf5.slice(startEndTensor,[0,2],[-1,2])}),scaleBox=(box,factors)=>{let starts=tf5.mul(box.startPoint,factors),ends=tf5.mul(box.endPoint,factors),newCoordinates=tf5.concat2d([starts,ends],1);return createBox(newCoordinates)};function decodeBounds(boxOutputs,anchors,inputSize){let boxStarts=tf5.slice(boxOutputs,[0,1],[-1,2]),centers=tf5.add(boxStarts,anchors),boxSizes=tf5.slice(boxOutputs,[0,3],[-1,2]),boxSizesNormalized=tf5.div(boxSizes,inputSize),centersNormalized=tf5.div(centers,inputSize),halfBoxSize=tf5.div(boxSizesNormalized,2),starts=tf5.sub(centersNormalized,halfBoxSize),ends=tf5.add(centersNormalized,halfBoxSize),startNormalized=tf5.mul(starts,inputSize),endNormalized=tf5.mul(ends,inputSize);return tf5.concat2d([startNormalized,endNormalized],1)}function scaleBoxFromPrediction(face3,scaleFactor){return tf5.tidy(()=>{let box=face3.box?face3.box:face3;return scaleBox(box,scaleFactor).startEndTensor.squeeze()})}var BlazeFaceModel=class{constructor(model,config2){this.blazeFaceModel=model,this.width=config2.face.detector.inputSize,this.height=config2.face.detector.inputSize,this.anchorsData=generateAnchors(config2.face.detector.inputSize),this.anchors=tf5.tensor2d(this.anchorsData),this.inputSize=tf5.tensor1d([this.width,this.height]),this.config=config2,this.scaleFaces=.8}async getBoundingBoxes(inputImage){if(!inputImage||inputImage.isDisposedInternal||inputImage.shape.length!==4||inputImage.shape[1]<1||inputImage.shape[2]<1)return null;let[detectedOutputs,boxes,scores]=tf5.tidy(()=>{let resizedImage=inputImage.resizeBilinear([this.width,this.height]),normalizedImage=tf5.sub(resizedImage.div(127.5),1),batchedPrediction=this.blazeFaceModel.predict(normalizedImage),prediction;if(Array.isArray(batchedPrediction)){let sorted=batchedPrediction.sort((a,b)=>a.size-b.size),concat384=tf5.concat([sorted[0],sorted[2]],2),concat512=tf5.concat([sorted[1],sorted[3]],2);prediction=tf5.concat([concat512,concat384],1).squeeze(0)}else prediction=batchedPrediction.squeeze();let decodedBounds=decodeBounds(prediction,this.anchors,this.inputSize),logits=tf5.slice(prediction,[0,0],[-1,1]),scoresOut=tf5.sigmoid(logits).squeeze();return[prediction,decodedBounds,scoresOut]}),boxIndicesTensor=await tf5.image.nonMaxSuppressionAsync(boxes,scores,this.config.face.detector.maxFaces,this.config.face.detector.iouThreshold,this.config.face.detector.scoreThreshold),boxIndices=boxIndicesTensor.arraySync();boxIndicesTensor.dispose();let boundingBoxes=boxIndices.map(boxIndex=>tf5.slice(boxes,[boxIndex,0],[1,-1])).map(boundingBox=>{let vals=boundingBox.arraySync();return boundingBox.dispose(),vals}),scoresVal=scores.dataSync(),annotatedBoxes=[];for(let i=0;ithis.config.face.detector.minConfidence){let box=createBox(boundingBoxes[i]),anchor=this.anchorsData[boxIndex],landmarks=tf5.tidy(()=>tf5.slice(detectedOutputs,[boxIndex,NUM_LANDMARKS-1],[1,-1]).squeeze().reshape([NUM_LANDMARKS,-1]));annotatedBoxes.push({box,landmarks,anchor,confidence})}}return detectedOutputs.dispose(),boxes.dispose(),scores.dispose(),detectedOutputs.dispose(),{boxes:annotatedBoxes,scaleFactor:[inputImage.shape[2]/this.width,inputImage.shape[1]/this.height]}}async estimateFaces(input){let{boxes,scaleFactor}=await this.getBoundingBoxes(input),faces=[];for(let face3 of boxes){let landmarkData=face3.landmarks.arraySync(),scaledBox=scaleBoxFromPrediction(face3,scaleFactor),boxData=scaleBox.arraySync(),probabilityData=face3.probability.arraySync(),anchor=face3.anchor,[scaleFactorX,scaleFactorY]=scaleFactor,scaledLandmarks=landmarkData.map(landmark=>[(landmark[0]+anchor[0])*scaleFactorX,(landmark[1]+anchor[1])*scaleFactorY]),normalizedFace={topLeft:boxData.slice(0,2),bottomRight:boxData.slice(2),landmarks:scaledLandmarks,probability:probabilityData};disposeBox(face3.box),face3.landmarks.dispose(),face3.probability.dispose(),scaledBox.dispose(),faces.push(normalizedFace)}return faces}};async function load3(config2){let blazeface=await tf5.loadGraphModel(config2.face.detector.modelPath,{fromTFHub:config2.face.detector.modelPath.includes("tfhub.dev")}),model=new BlazeFaceModel(blazeface,config2);return log(`load model: ${config2.face.detector.modelPath.match(/\/(.*)\./)[1]}`),model}exports2.load=load3;exports2.BlazeFaceModel=BlazeFaceModel;exports2.disposeBox=disposeBox});var require_box=__commonJS(exports2=>{var tf5=__toModule(require_tfjs_esm());function scaleBoxCoordinates2(box,factor){let startPoint=[box.startPoint[0]*factor[0],box.startPoint[1]*factor[1]],endPoint=[box.endPoint[0]*factor[0],box.endPoint[1]*factor[1]];return{startPoint,endPoint}}exports2.scaleBoxCoordinates=scaleBoxCoordinates2;function getBoxSize2(box){return[Math.abs(box.endPoint[0]-box.startPoint[0]),Math.abs(box.endPoint[1]-box.startPoint[1])]}exports2.getBoxSize=getBoxSize2;function getBoxCenter2(box){return[box.startPoint[0]+(box.endPoint[0]-box.startPoint[0])/2,box.startPoint[1]+(box.endPoint[1]-box.startPoint[1])/2]}exports2.getBoxCenter=getBoxCenter2;function cutBoxFromImageAndResize2(box,image5,cropSize){let h=image5.shape[1],w=image5.shape[2],boxes=[[box.startPoint[1]/h,box.startPoint[0]/w,box.endPoint[1]/h,box.endPoint[0]/w]];return tf5.image.cropAndResize(image5,boxes,[0],cropSize)}exports2.cutBoxFromImageAndResize=cutBoxFromImageAndResize2;function enlargeBox2(box,factor=1.5){let center=getBoxCenter2(box),size=getBoxSize2(box),newHalfSize=[factor*size[0]/2,factor*size[1]/2],startPoint=[center[0]-newHalfSize[0],center[1]-newHalfSize[1]],endPoint=[center[0]+newHalfSize[0],center[1]+newHalfSize[1]];return{startPoint,endPoint,landmarks:box.landmarks}}exports2.enlargeBox=enlargeBox2;function squarifyBox2(box){let centers=getBoxCenter2(box),size=getBoxSize2(box),halfSize=Math.max(...size)/2,startPoint=[centers[0]-halfSize,centers[1]-halfSize],endPoint=[centers[0]+halfSize,centers[1]+halfSize];return{startPoint,endPoint,landmarks:box.landmarks}}exports2.squarifyBox=squarifyBox2});var require_util=__commonJS(exports2=>{exports2.IDENTITY_MATRIX=[[1,0,0],[0,1,0],[0,0,1]];function normalizeRadians2(angle){return angle-2*Math.PI*Math.floor((angle+Math.PI)/(2*Math.PI))}exports2.normalizeRadians=normalizeRadians2;function computeRotation2(point1,point2){let radians=Math.PI/2-Math.atan2(-(point2[1]-point1[1]),point2[0]-point1[0]);return normalizeRadians2(radians)}exports2.computeRotation=computeRotation2;function radToDegrees(rad){return rad*180/Math.PI}exports2.radToDegrees=radToDegrees;function buildTranslationMatrix2(x,y){return[[1,0,x],[0,1,y],[0,0,1]]}function dot2(v1,v2){let product=0;for(let i=0;i{var 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,378,379,397,288,361,454,356,70,63,105,66,107,336,296,334,293,300,168,6,195,4,98,97,2,326,327,33,160,158,133,153,144,362,385,387,263,373,380,57,40,37,0,267,270,287,321,314,17,84,91,78,81,13,311,308,402,14,178],VTX33=[33,133,362,263,1,62,308,159,145,386,374,6,102,331,2,13,14,70,105,107,336,334,300,54,10,284,50,280,234,454,58,288,152],VTX7=[33,133,362,263,1,78,308];exports2.MESH_ANNOTATIONS=MESH_ANNOTATIONS;exports2.MESH_TO_IRIS_INDICES_MAP=MESH_TO_IRIS_INDICES_MAP;exports2.TRI468=TRI468;exports2.TRI68=TRI68;exports2.TRI33=TRI33;exports2.TRI7=TRI7;exports2.UV468=UV468;exports2.UV68=VTX68.map(x=>UV468[x]);exports2.UV33=VTX33.map(x=>UV468[x]);exports2.UV7=VTX7.map(x=>UV468[x])});var require_facepipeline=__commonJS(exports2=>{var tf5=__toModule(require_tfjs_esm()),bounding=__toModule(require_box()),util=__toModule(require_util()),coords=__toModule(require_coords()),LANDMARKS_COUNT=468,MESH_MOUTH_INDEX=13,MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES=[MESH_MOUTH_INDEX,coords.MESH_ANNOTATIONS.midwayBetweenEyes[0]],BLAZEFACE_MOUTH_INDEX=3,BLAZEFACE_NOSE_INDEX=2,BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES=[BLAZEFACE_MOUTH_INDEX,BLAZEFACE_NOSE_INDEX],LEFT_EYE_OUTLINE=coords.MESH_ANNOTATIONS.leftEyeLower0,LEFT_EYE_BOUNDS=[LEFT_EYE_OUTLINE[0],LEFT_EYE_OUTLINE[LEFT_EYE_OUTLINE.length-1]],RIGHT_EYE_OUTLINE=coords.MESH_ANNOTATIONS.rightEyeLower0,RIGHT_EYE_BOUNDS=[RIGHT_EYE_OUTLINE[0],RIGHT_EYE_OUTLINE[RIGHT_EYE_OUTLINE.length-1]],IRIS_UPPER_CENTER_INDEX=3,IRIS_LOWER_CENTER_INDEX=4,IRIS_IRIS_INDEX=71,IRIS_NUM_COORDINATES=76;function replaceRawCoordinates(rawCoords,newCoords,prefix,keys){for(let i=0;i[scaleFactor[0]*(coord[0]-this.meshWidth/2),scaleFactor[1]*(coord[1]-this.meshHeight/2),coord[2]]),coordsRotationMatrix=angle!==0?util.buildRotationMatrix(angle,[0,0]):util.IDENTITY_MATRIX,coordsRotated=angle!==0?coordsScaled.map(coord=>[...util.rotatePoint(coord,coordsRotationMatrix),coord[2]]):coordsScaled,inverseRotationMatrix=angle!==0?util.invertTransformMatrix(rotationMatrix):util.IDENTITY_MATRIX,boxCenter=[...bounding.getBoxCenter({startPoint:box.startPoint,endPoint:box.endPoint}),1];return coordsRotated.map(coord=>[coord[0]+util.dot(boxCenter,inverseRotationMatrix[0]),coord[1]+util.dot(boxCenter,inverseRotationMatrix[1]),coord[2]])}getLeftToRightEyeDepthDifference(rawCoords){let leftEyeZ=rawCoords[LEFT_EYE_BOUNDS[0]][2],rightEyeZ=rawCoords[RIGHT_EYE_BOUNDS[0]][2];return leftEyeZ-rightEyeZ}getEyeBox(rawCoords,face3,eyeInnerCornerIndex,eyeOuterCornerIndex,flip=!1){let box=bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex],rawCoords[eyeOuterCornerIndex]]),this.irisEnlarge)),boxSize=bounding.getBoxSize(box),crop=tf5.image.cropAndResize(face3,[[box.startPoint[1]/this.meshHeight,box.startPoint[0]/this.meshWidth,box.endPoint[1]/this.meshHeight,box.endPoint[0]/this.meshWidth]],[0],[this.irisSize,this.irisSize]);return flip&&(crop=tf5.image.flipLeftRight(crop)),{box,boxSize,crop}}getEyeCoords(eyeData,eyeBox,eyeBoxSize,flip=!1){let eyeRawCoords=[];for(let i=0;i{let z=averageZ;return i===2?z=upperCenterZ:i===4&&(z=lowerCenterZ),[coord[0],coord[1],z]})}async predict(input,config2){let useFreshBox=!1,detector;if((this.skipped===0||this.skipped>config2.face.detector.skipFrames||!config2.face.mesh.enabled||!config2.videoOptimized)&&(detector=await this.boundingBoxDetector.getBoundingBoxes(input),this.skipped=0),config2.videoOptimized&&this.skipped++,detector&&detector.boxes&&detector.boxes.length>0&&(!config2.face.mesh.enabled||detector.boxes.length!==this.detectedFaces&&this.detectedFaces!==config2.face.detector.maxFaces)){this.storedBoxes=[],this.detectedFaces=0;for(let possible of detector.boxes)this.storedBoxes.push({startPoint:possible.box.startPoint.dataSync(),endPoint:possible.box.endPoint.dataSync(),landmarks:possible.landmarks,confidence:possible.confidence});this.storedBoxes.length>0&&(useFreshBox=!0)}if(useFreshBox){if(!detector||!detector.boxes||detector.boxes.length===0)return this.storedBoxes=[],this.detectedFaces=0,null;for(let i=0;i{prediction.box.startPoint.dispose(),prediction.box.endPoint.dispose(),prediction.landmarks.dispose()});let results=tf5.tidy(()=>this.storedBoxes.map((box,i)=>{let face3,angle=0,rotationMatrix;if(config2.face.detector.rotation){let[indexOfMouth,indexOfForehead]=box.landmarks.length>=LANDMARKS_COUNT?MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES:BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES;angle=util.computeRotation(box.landmarks[indexOfMouth],box.landmarks[indexOfForehead]);let faceCenter=bounding.getBoxCenter({startPoint:box.startPoint,endPoint:box.endPoint}),faceCenterNormalized=[faceCenter[0]/input.shape[2],faceCenter[1]/input.shape[1]],rotatedImage=tf5.image.rotateWithOffset(input,angle,0,faceCenterNormalized);rotationMatrix=util.buildRotationMatrix(-angle,faceCenter),face3=bounding.cutBoxFromImageAndResize({startPoint:box.startPoint,endPoint:box.endPoint},rotatedImage,[this.meshHeight,this.meshWidth]).div(255)}else{rotationMatrix=util.IDENTITY_MATRIX;let cloned=input.clone();face3=bounding.cutBoxFromImageAndResize({startPoint:box.startPoint,endPoint:box.endPoint},cloned,[this.meshHeight,this.meshWidth]).div(255)}if(!config2.face.mesh.enabled)return{coords:null,box,faceConfidence:null,confidence:box.confidence,image:face3};let[,confidence,contourCoords]=this.meshDetector.predict(face3),confidenceVal=confidence.dataSync()[0];if(confidenceVala!==null),this.detectedFaces=results.length,results}calculateLandmarksBoundingBox(landmarks){let xs=landmarks.map(d=>d[0]),ys=landmarks.map(d=>d[1]),startPoint=[Math.min(...xs),Math.min(...ys)],endPoint=[Math.max(...xs),Math.max(...ys)];return{startPoint,endPoint,landmarks}}};exports2.Pipeline=Pipeline});var require_facemesh=__commonJS(exports2=>{var tf5=__toModule(require_tfjs_esm()),blazeface=__toModule(require_blazeface()),facepipeline=__toModule(require_facepipeline()),coords=__toModule(require_coords()),MediaPipeFaceMesh=class{constructor(blazeFace,blazeMeshModel,irisModel,config2){this.facePipeline=new facepipeline.Pipeline(blazeFace,blazeMeshModel,irisModel,config2),this.config=config2}async estimateFaces(input,config2){let predictions=await this.facePipeline.predict(input,config2),results=[];for(let prediction of predictions||[]){if(prediction.isDisposedInternal)continue;let mesh=prediction.coords?prediction.coords.arraySync():null,meshRaw=prediction.rawCoords,annotations={};if(mesh&&mesh.length>0)for(let key of Object.keys(coords.MESH_ANNOTATIONS))annotations[key]=coords.MESH_ANNOTATIONS[key].map(index=>mesh[index]);let boxRaw=config2.face.mesh.returnRawData&&prediction.box?{topLeft:prediction.box.startPoint,bottomRight:prediction.box.endPoint}:null,box=prediction.box?[Math.max(0,prediction.box.startPoint[0]),Math.max(0,prediction.box.startPoint[1]),Math.min(input.shape[2],prediction.box.endPoint[0])-prediction.box.startPoint[0],Math.min(input.shape[1],prediction.box.endPoint[1])-prediction.box.startPoint[1]]:0;results.push({confidence:prediction.confidence||0,box,mesh,boxRaw,meshRaw,annotations,image:prediction.image?tf5.clone(prediction.image):null}),prediction.coords&&prediction.coords.dispose(),prediction.image&&prediction.image.dispose()}return results}},faceModels=[null,null,null];async function load3(config2){faceModels=await Promise.all([!faceModels[0]&&config2.face.enabled?blazeface.load(config2):null,!faceModels[1]&&config2.face.mesh.enabled?tf5.loadGraphModel(config2.face.mesh.modelPath,{fromTFHub:config2.face.mesh.modelPath.includes("tfhub.dev")}):null,!faceModels[2]&&config2.face.iris.enabled?tf5.loadGraphModel(config2.face.iris.modelPath,{fromTFHub:config2.face.iris.modelPath.includes("tfhub.dev")}):null]);let faceMesh=new MediaPipeFaceMesh(faceModels[0],faceModels[1],faceModels[2],config2);return config2.face.mesh.enabled&&log(`load model: ${config2.face.mesh.modelPath.match(/\/(.*)\./)[1]}`),config2.face.iris.enabled&&log(`load model: ${config2.face.iris.modelPath.match(/\/(.*)\./)[1]}`),faceMesh}exports2.load=load3;exports2.MediaPipeFaceMesh=MediaPipeFaceMesh;exports2.triangulation=coords.TRI468});var require_profile=__commonJS(exports2=>{var profileData={};function profile2(name,data2){if(!data2||!data2.kernels)return;let maxResults=5,time=data2.kernels.filter(a=>a.kernelTimeMs>0).reduce((a,b)=>a+=b.kernelTimeMs,0),slowest=data2.kernels.map((a,i)=>(a.id=i,a)).filter(a=>a.kernelTimeMs>0).sort((a,b)=>b.kernelTimeMs-a.kernelTimeMs),largest=data2.kernels.map((a,i)=>(a.id=i,a)).filter(a=>a.totalBytesSnapshot>0).sort((a,b)=>b.totalBytesSnapshot-a.totalBytesSnapshot);slowest.length>maxResults&&(slowest.length=maxResults),largest.length>maxResults&&(largest.length=maxResults);let res={newBytes:data2.newBytes,newTensors:data2.newTensors,peakBytes:data2.peakBytes,numKernelOps:data2.kernels.length,timeKernelOps:time,slowestKernelOps:slowest,largestKernelOps:largest};profileData[name]=res,log("Human profiler",name,res)}exports2.run=profile2});var require_age=__commonJS(exports2=>{var tf5=__toModule(require_tfjs_esm()),profile2=__toModule(require_profile()),models2={},last={age:0},skipped=Number.MAX_SAFE_INTEGER;async function load3(config2){return models2.age||(models2.age=await tf5.loadGraphModel(config2.face.age.modelPath),log(`load model: ${config2.face.age.modelPath.match(/\/(.*)\./)[1]}`)),models2.age}async function predict3(image5,config2){return models2.age?skipped0?(skipped++,last):(config2.videoOptimized?skipped=0:skipped=Number.MAX_SAFE_INTEGER,new Promise(async resolve=>{let resize=tf5.image.resizeBilinear(image5,[config2.face.age.inputSize,config2.face.age.inputSize],!1),enhance=tf5.mul(resize,[255]);tf5.dispose(resize);let ageT,obj={};if(!config2.profile)config2.face.age.enabled&&(ageT=await models2.age.predict(enhance));else{let profileAge=config2.face.age.enabled?await tf5.profile(()=>models2.age.predict(enhance)):{};ageT=profileAge.result.clone(),profileAge.result.dispose(),profile2.run("age",profileAge)}if(enhance.dispose(),ageT){let data2=ageT.dataSync();obj.age=Math.trunc(10*data2[0])/10}ageT.dispose(),last=obj,resolve(obj)})):null}exports2.predict=predict3;exports2.load=load3});var require_gender=__commonJS(exports2=>{var tf5=__toModule(require_tfjs_esm()),profile2=__toModule(require_profile()),models2={},last={gender:""},skipped=Number.MAX_SAFE_INTEGER,alternative=!1,rgb=[.2989,.587,.114];async function load3(config2){return models2.gender||(models2.gender=await tf5.loadGraphModel(config2.face.gender.modelPath),alternative=models2.gender.inputs[0].shape[3]===1,log(`load model: ${config2.face.gender.modelPath.match(/\/(.*)\./)[1]}`)),models2.gender}async function predict3(image5,config2){return models2.gender?skipped{let resize=tf5.image.resizeBilinear(image5,[config2.face.gender.inputSize,config2.face.gender.inputSize],!1),enhance;alternative?enhance=tf5.tidy(()=>{let[red,green,blue]=tf5.split(resize,3,3),redNorm=tf5.mul(red,rgb[0]),greenNorm=tf5.mul(green,rgb[1]),blueNorm=tf5.mul(blue,rgb[2]);return tf5.addN([redNorm,greenNorm,blueNorm]).sub(.5).mul(2)}):enhance=tf5.mul(resize,[255]),tf5.dispose(resize);let genderT,obj={};if(!config2.profile)config2.face.gender.enabled&&(genderT=await models2.gender.predict(enhance));else{let profileGender=config2.face.gender.enabled?await tf5.profile(()=>models2.gender.predict(enhance)):{};genderT=profileGender.result.clone(),profileGender.result.dispose(),profile2.run("gender",profileGender)}if(enhance.dispose(),genderT){let data2=genderT.dataSync();if(alternative){let confidence=Math.trunc(100*Math.abs(data2[0]-data2[1]))/100;confidence>config2.face.gender.minConfidence&&(obj.gender=data2[0]>data2[1]?"female":"male",obj.confidence=confidence)}else{let confidence=Math.trunc(200*Math.abs(data2[0]-.5))/100;confidence>config2.face.gender.minConfidence&&(obj.gender=data2[0]<=.5?"female":"male",obj.confidence=Math.min(.99,confidence))}}genderT.dispose(),last=obj,resolve(obj)})):null}exports2.predict=predict3;exports2.load=load3});var require_emotion=__commonJS(exports2=>{var tf5=__toModule(require_tfjs_esm()),profile2=__toModule(require_profile()),annotations=["angry","disgust","fear","happy","sad","surprise","neutral"],models2={},last=[],skipped=Number.MAX_SAFE_INTEGER,rgb=[.2989,.587,.114],scale=1;async function load3(config2){return models2.emotion||(models2.emotion=await tf5.loadGraphModel(config2.face.emotion.modelPath),log(`load model: ${config2.face.emotion.modelPath.match(/\/(.*)\./)[1]}`)),models2.emotion}async function predict3(image5,config2){return models2.emotion?skipped0?(skipped++,last):(config2.videoOptimized?skipped=0:skipped=Number.MAX_SAFE_INTEGER,new Promise(async resolve=>{let resize=tf5.image.resizeBilinear(image5,[config2.face.emotion.inputSize,config2.face.emotion.inputSize],!1),[red,green,blue]=tf5.split(resize,3,3);resize.dispose();let redNorm=tf5.mul(red,rgb[0]),greenNorm=tf5.mul(green,rgb[1]),blueNorm=tf5.mul(blue,rgb[2]);red.dispose(),green.dispose(),blue.dispose();let grayscale=tf5.addN([redNorm,greenNorm,blueNorm]);redNorm.dispose(),greenNorm.dispose(),blueNorm.dispose();let normalize=tf5.tidy(()=>grayscale.sub(.5).mul(2));grayscale.dispose();let obj=[];if(config2.face.emotion.enabled){let data2;if(config2.profile){let profileData=await tf5.profile(()=>models2.emotion.predict(normalize));data2=profileData.result.dataSync(),profileData.result.dispose(),profile2.run("emotion",profileData)}else{let emotionT=await models2.emotion.predict(normalize);data2=emotionT.dataSync(),tf5.dispose(emotionT)}for(let i=0;iconfig2.face.emotion.minConfidence&&obj.push({score:Math.min(.99,Math.trunc(100*scale*data2[i])/100),emotion:annotations[i]});obj.sort((a,b)=>b.score-a.score)}normalize.dispose(),last=obj,resolve(obj)})):null}exports2.predict=predict3;exports2.load=load3});var require_embedding=__commonJS(exports2=>{var tf5=__toModule(require_tfjs_esm()),profile2=__toModule(require_profile()),models2={};async function load3(config2){return models2.embedding||(models2.embedding=await tf5.loadGraphModel(config2.face.embedding.modelPath),log(`load model: ${config2.face.embedding.modelPath.match(/\/(.*)\./)[1]}`)),models2.embedding}function simmilarity2(embedding1,embedding2){if((embedding1==null?void 0:embedding1.length)!==(embedding2==null?void 0:embedding2.length))return 0;let order=2,distance=10*embedding1.map((val,i)=>val-embedding2[i]).reduce((dist,diff)=>dist+diff**order,0)**(1/order);return Math.trunc(1e3*(1-distance))/1e3}async function predict3(image5,config2){return models2.embedding?new Promise(async resolve=>{let resize=tf5.image.resizeBilinear(image5,[config2.face.embedding.inputSize,config2.face.embedding.inputSize],!1),data2=[];if(config2.face.embedding.enabled)if(config2.profile){let profileData=await tf5.profile(()=>models2.embedding.predict({img_inputs:resize}));data2=[...profileData.result.dataSync()],profileData.result.dispose(),profile2.run("emotion",profileData)}else{let embeddingT=await models2.embedding.predict({img_inputs:resize});data2=[...embeddingT.dataSync()],tf5.dispose(embeddingT)}resize.dispose(),resolve(data2)}):null}exports2.predict=predict3;exports2.simmilarity=simmilarity2;exports2.load=load3});var require_modelBase=__commonJS(exports2=>{var tf5=__toModule(require_tfjs_esm()),imageNetMean=[-123.15,-115.9,-103.06];function nameOutputResultsMobileNet(results){let[offsets,heatmap,displacementFwd,displacementBwd]=results;return{offsets,heatmap,displacementFwd,displacementBwd}}function nameOutputResultsResNet(results){let[displacementFwd,displacementBwd,offsets,heatmap]=results;return{offsets,heatmap,displacementFwd,displacementBwd}}var BaseModel=class{constructor(model){this.model=model}predict(input,config2){return tf5.tidy(()=>{let asBatch=(config2.body.modelType==="ResNet"?input.toFloat().add(imageNetMean):input.toFloat().div(127.5).sub(1)).expandDims(0),results3d=this.model.predict(asBatch).map(y=>y.squeeze([0])),namedResults=config2.body.modelType==="ResNet"?nameOutputResultsResNet(results3d):nameOutputResultsMobileNet(results3d);return{heatmapScores:namedResults.heatmap.sigmoid(),offsets:namedResults.offsets,displacementFwd:namedResults.displacementFwd,displacementBwd:namedResults.displacementBwd}})}dispose(){this.model.dispose()}};exports2.BaseModel=BaseModel});var require_heapSort=__commonJS(exports2=>{function half(k){return Math.floor(k/2)}var MaxHeap=class{constructor(maxSize,getElementValue){this.priorityQueue=new Array(maxSize),this.numberOfElements=-1,this.getElementValue=getElementValue}enqueue(x){this.priorityQueue[++this.numberOfElements]=x,this.swim(this.numberOfElements)}dequeue(){let max=this.priorityQueue[0];return this.exchange(0,this.numberOfElements--),this.sink(0),this.priorityQueue[this.numberOfElements+1]=null,max}empty(){return this.numberOfElements===-1}size(){return this.numberOfElements+1}all(){return this.priorityQueue.slice(0,this.numberOfElements+1)}max(){return this.priorityQueue[0]}swim(k){for(;k>0&&this.less(half(k),k);)this.exchange(k,half(k)),k=half(k)}sink(k){for(;2*k<=this.numberOfElements;){let j=2*k;if(j{var heapSort=__toModule(require_heapSort());function scoreIsMaximumInLocalWindow(keypointId,score,heatmapY,heatmapX,localMaximumRadius,scores){let[height,width]=scores.shape,localMaximum=!0,yStart=Math.max(heatmapY-localMaximumRadius,0),yEnd=Math.min(heatmapY+localMaximumRadius+1,height);for(let yCurrent=yStart;yCurrentscore){localMaximum=!1;break}if(!localMaximum)break}return localMaximum}function buildPartWithScoreQueue(scoreThreshold,localMaximumRadius,scores){let[height,width,numKeypoints]=scores.shape,queue=new heapSort.MaxHeap(height*width*numKeypoints,({score})=>score);for(let heatmapY=0;heatmapY{exports2.partNames=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"];exports2.NUM_KEYPOINTS=exports2.partNames.length;exports2.partIds=exports2.partNames.reduce((result,jointName,i)=>(result[jointName]=i,result),{});var connectedPartNames=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]];exports2.connectedPartIndices=connectedPartNames.map(([jointNameA,jointNameB])=>[exports2.partIds[jointNameA],exports2.partIds[jointNameB]]);exports2.poseChain=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];exports2.partChannels=["left_face","right_face","right_upper_leg_front","right_lower_leg_back","right_upper_leg_back","left_lower_leg_front","left_upper_leg_front","left_upper_leg_back","left_lower_leg_back","right_feet","right_lower_leg_front","left_feet","torso_front","torso_back","right_upper_arm_front","right_upper_arm_back","right_lower_arm_back","left_lower_arm_front","left_upper_arm_front","left_upper_arm_back","left_lower_arm_back","right_hand","right_lower_arm_front","left_hand"]});var require_vectors=__commonJS(exports2=>{var kpt=__toModule(require_keypoints());function getOffsetPoint(y,x,keypoint,offsets){return{y:offsets.get(y,x,keypoint),x:offsets.get(y,x,keypoint+kpt.NUM_KEYPOINTS)}}exports2.getOffsetPoint=getOffsetPoint;function getImageCoords(part,outputStride,offsets){let{heatmapY,heatmapX,id:keypoint}=part,{y,x}=getOffsetPoint(heatmapY,heatmapX,keypoint,offsets);return{x:part.heatmapX*outputStride+x,y:part.heatmapY*outputStride+y}}exports2.getImageCoords=getImageCoords;function fillArray(element,size){let result=new Array(size);for(let i=0;imax?max:a}exports2.clamp=clamp;function squaredDistance(y1,x1,y2,x2){let dy=y2-y1,dx=x2-x1;return dy*dy+dx*dx}exports2.squaredDistance=squaredDistance;function addVectors(a,b){return{x:a.x+b.x,y:a.y+b.y}}exports2.addVectors=addVectors;function clampVector(a,min,max){return{y:clamp(a.y,min,max),x:clamp(a.x,min,max)}}exports2.clampVector=clampVector});var require_decoders=__commonJS(exports2=>{var tf5=__toModule(require_tfjs_esm()),kpt=__toModule(require_keypoints());function getPointsConfidence(heatmapScores,heatMapCoords){let numKeypoints=heatMapCoords.shape[0],result=new Float32Array(numKeypoints);for(let keypoint=0;keypointheatMapCoordsBuffer.toTensor().mul(tf5.scalar(outputStride,"int32")).toFloat().add(getOffsetVectors(heatMapCoordsBuffer,offsetsBuffer)))}exports2.getOffsetPoints=getOffsetPoints;function mod(a,b){return tf5.tidy(()=>{let floored=a.div(tf5.scalar(b,"int32"));return a.sub(floored.mul(tf5.scalar(b,"int32")))})}function argmax2d(inputs){let[height,width,depth]=inputs.shape;return tf5.tidy(()=>{let coords=inputs.reshape([height*width,depth]).argMax(0),yCoords=coords.div(tf5.scalar(width,"int32")).expandDims(1),xCoords=mod(coords,width).expandDims(1);return tf5.concat([yCoords,xCoords],1)})}exports2.argmax2d=argmax2d});var require_decodePose=__commonJS(exports2=>{var keypoints=__toModule(require_keypoints()),vectors=__toModule(require_vectors()),decoders=__toModule(require_decoders()),parentChildrenTuples=keypoints.poseChain.map(([parentJoinName,childJoinName])=>[keypoints.partIds[parentJoinName],keypoints.partIds[childJoinName]]),parentToChildEdges=parentChildrenTuples.map(([,childJointId])=>childJointId),childToParentEdges=parentChildrenTuples.map(([parentJointId])=>parentJointId);function getDisplacement(edgeId,point,displacements){let numEdges=displacements.shape[2]/2;return{y:displacements.get(point.y,point.x,edgeId),x:displacements.get(point.y,point.x,numEdges+edgeId)}}function getStridedIndexNearPoint(point,outputStride,height,width){return{y:vectors.clamp(Math.round(point.y/outputStride),0,height-1),x:vectors.clamp(Math.round(point.x/outputStride),0,width-1)}}function traverseToTargetKeypoint(edgeId,sourceKeypoint,targetKeypointId,scoresBuffer,offsets,outputStride,displacements,offsetRefineStep=2){let[height,width]=scoresBuffer.shape,sourceKeypointIndices=getStridedIndexNearPoint(sourceKeypoint.position,outputStride,height,width),displacement=getDisplacement(edgeId,sourceKeypointIndices,displacements),targetKeypoint=vectors.addVectors(sourceKeypoint.position,displacement);for(let i=0;i=0;--edge){let sourceKeypointId=parentToChildEdges[edge],targetKeypointId=childToParentEdges[edge];instanceKeypoints[sourceKeypointId]&&!instanceKeypoints[targetKeypointId]&&(instanceKeypoints[targetKeypointId]=traverseToTargetKeypoint(edge,instanceKeypoints[sourceKeypointId],targetKeypointId,scores,offsets,outputStride,displacementsBwd))}for(let edge=0;edge(totalScore+=score,{position:{y:offsetPointsBuffer.get(i,0),x:offsetPointsBuffer.get(i,1)},part:keypoints.partNames[i],score})),filteredKeypoints=instanceKeypoints.filter(kpt=>kpt.score>config2.body.scoreThreshold);return heatmapValues.dispose(),offsetPoints.dispose(),{keypoints:filteredKeypoints,score:totalScore/instanceKeypoints.length}}exports2.decodeSinglePose=decodeSinglePose});var require_decodeMultiple=__commonJS(exports2=>{var buildParts=__toModule(require_buildParts()),decodePose=__toModule(require_decodePose()),vectors=__toModule(require_vectors()),kLocalMaximumRadius=1;function withinNmsRadiusOfCorrespondingPoint(poses,squaredNmsRadius,{x,y},keypointId){return poses.some(({keypoints})=>{let correspondingKeypoint=keypoints[keypointId].position;return vectors.squaredDistance(y,x,correspondingKeypoint.y,correspondingKeypoint.x)<=squaredNmsRadius})}function getInstanceScore(existingPoses,squaredNmsRadius,instanceKeypoints){return instanceKeypoints.reduce((result,{position,score},keypointId)=>(withinNmsRadiusOfCorrespondingPoint(existingPoses,squaredNmsRadius,position,keypointId)||(result+=score),result),0)/instanceKeypoints.length}function decodeMultiplePoses(scoresBuffer,offsetsBuffer,displacementsFwdBuffer,displacementsBwdBuffer,config2){let poses=[],queue=buildParts.buildPartWithScoreQueue(config2.body.scoreThreshold,kLocalMaximumRadius,scoresBuffer),squaredNmsRadius=config2.body.nmsRadius^2;for(;poses.lengthconfig2.body.scoreThreshold&&poses.push({keypoints,score})}return poses}exports2.decodeMultiplePoses=decodeMultiplePoses});var require_util2=__commonJS(exports2=>{var kpt=__toModule(require_keypoints());function eitherPointDoesntMeetConfidence(a,b,minConfidence){return a(eitherPointDoesntMeetConfidence(keypoints[leftJoint].score,keypoints[rightJoint].score,minConfidence)||result.push([keypoints[leftJoint],keypoints[rightJoint]]),result),[])}exports2.getAdjacentKeyPoints=getAdjacentKeyPoints;var{NEGATIVE_INFINITY,POSITIVE_INFINITY}=Number;function getBoundingBox(keypoints){return keypoints.reduce(({maxX,maxY,minX,minY},{position:{x,y}})=>({maxX:Math.max(maxX,x),maxY:Math.max(maxY,y),minX:Math.min(minX,x),minY:Math.min(minY,y)}),{maxX:NEGATIVE_INFINITY,maxY:NEGATIVE_INFINITY,minX:POSITIVE_INFINITY,minY:POSITIVE_INFINITY})}exports2.getBoundingBox=getBoundingBox;function getBoundingBoxPoints(keypoints){let{minX,minY,maxX,maxY}=getBoundingBox(keypoints);return[{x:minX,y:minY},{x:maxX,y:minY},{x:maxX,y:maxY},{x:minX,y:maxY}]}exports2.getBoundingBoxPoints=getBoundingBoxPoints;async function toTensorBuffers3D(tensors){return Promise.all(tensors.map(tensor=>tensor.buffer()))}exports2.toTensorBuffers3D=toTensorBuffers3D;function scalePose(pose,scaleY,scaleX){return{score:pose.score,keypoints:pose.keypoints.map(({score,part,position})=>({score,part,position:{x:position.x*scaleX,y:position.y*scaleY}}))}}exports2.scalePose=scalePose;function resizeTo(image4,[targetH,targetW]){let input=image4.squeeze(0),resized=input.resizeBilinear([targetH,targetW]);return input.dispose(),resized}exports2.resizeTo=resizeTo;function scaleAndFlipPoses(poses,[height,width],[inputResolutionHeight,inputResolutionWidth]){return poses.map(pose=>scalePose(pose,height/inputResolutionHeight,width/inputResolutionWidth))}exports2.scaleAndFlipPoses=scaleAndFlipPoses});var require_modelPoseNet=__commonJS(exports2=>{var tf5=__toModule(require_tfjs_esm()),modelBase=__toModule(require_modelBase()),decodeMultiple=__toModule(require_decodeMultiple()),decodePose=__toModule(require_decodePose()),util=__toModule(require_util2());async function estimateMultiple(input,res,config2){return new Promise(async resolve=>{let height=input.shape[1],width=input.shape[2],allTensorBuffers=await util.toTensorBuffers3D([res.heatmapScores,res.offsets,res.displacementFwd,res.displacementBwd]),scoresBuffer=allTensorBuffers[0],offsetsBuffer=allTensorBuffers[1],displacementsFwdBuffer=allTensorBuffers[2],displacementsBwdBuffer=allTensorBuffers[3],poses=await decodeMultiple.decodeMultiplePoses(scoresBuffer,offsetsBuffer,displacementsFwdBuffer,displacementsBwdBuffer,config2),scaled=util.scaleAndFlipPoses(poses,[height,width],[config2.body.inputSize,config2.body.inputSize]);resolve(scaled)})}async function estimateSingle(input,res,config2){return new Promise(async resolve=>{let height=input.shape[1],width=input.shape[2],poses=[await decodePose.decodeSinglePose(res.heatmapScores,res.offsets,config2)],scaled=util.scaleAndFlipPoses(poses,[height,width],[config2.body.inputSize,config2.body.inputSize]);resolve(scaled)})}var PoseNet=class{constructor(model){this.baseModel=model}async estimatePoses(input,config2){let resized=util.resizeTo(input,[config2.body.inputSize,config2.body.inputSize]),res=this.baseModel.predict(resized,config2),poses=config2.body.maxDetections<2?await estimateSingle(input,res,config2):await estimateMultiple(input,res,config2);return res.heatmapScores.dispose(),res.offsets.dispose(),res.displacementFwd.dispose(),res.displacementBwd.dispose(),resized.dispose(),poses}dispose(){this.baseModel.dispose()}};exports2.PoseNet=PoseNet;async function load3(config2){let model=await tf5.loadGraphModel(config2.body.modelPath),mobilenet=new modelBase.BaseModel(model);return log(`load model: ${config2.body.modelPath.match(/\/(.*)\./)[1]}`),new PoseNet(mobilenet)}exports2.load=load3});var require_posenet=__commonJS(exports2=>{var modelPoseNet=__toModule(require_modelPoseNet()),keypoints=__toModule(require_keypoints()),util=__toModule(require_util2());exports2.load=modelPoseNet.load;exports2.PoseNet=modelPoseNet.PoseNet;exports2.partChannels=keypoints.partChannels;exports2.partIds=keypoints.partIds;exports2.partNames=keypoints.partNames;exports2.poseChain=keypoints.poseChain;exports2.getAdjacentKeyPoints=util.getAdjacentKeyPoints;exports2.getBoundingBox=util.getBoundingBox;exports2.getBoundingBoxPoints=util.getBoundingBoxPoints;exports2.scaleAndFlipPoses=util.scaleAndFlipPoses;exports2.scalePose=util.scalePose});var require_handdetector=__commonJS(exports2=>{var tf5=__toModule(require_tfjs_esm()),HandDetector=class{constructor(model,inputSize,anchorsAnnotated){this.model=model,this.anchors=anchorsAnnotated.map(anchor=>[anchor.x_center,anchor.y_center]),this.anchorsTensor=tf5.tensor2d(this.anchors),this.inputSizeTensor=tf5.tensor1d([inputSize,inputSize]),this.doubleInputSizeTensor=tf5.tensor1d([inputSize*2,inputSize*2])}normalizeBoxes(boxes){return tf5.tidy(()=>{let boxOffsets=tf5.slice(boxes,[0,0],[-1,2]),boxSizes=tf5.slice(boxes,[0,2],[-1,2]),boxCenterPoints=tf5.add(tf5.div(boxOffsets,this.inputSizeTensor),this.anchorsTensor),halfBoxSizes=tf5.div(boxSizes,this.doubleInputSizeTensor),startPoints=tf5.mul(tf5.sub(boxCenterPoints,halfBoxSizes),this.inputSizeTensor),endPoints=tf5.mul(tf5.add(boxCenterPoints,halfBoxSizes),this.inputSizeTensor);return tf5.concat2d([startPoints,endPoints],1)})}normalizeLandmarks(rawPalmLandmarks,index){return tf5.tidy(()=>{let landmarks=tf5.add(tf5.div(rawPalmLandmarks.reshape([-1,7,2]),this.inputSizeTensor),this.anchors[index]);return tf5.mul(landmarks,this.inputSizeTensor)})}async getBoxes(input,config2){let batched=this.model.predict(input),predictions=batched.squeeze();batched.dispose();let scoresT=tf5.tidy(()=>tf5.sigmoid(tf5.slice(predictions,[0,0],[-1,1])).squeeze()),scores=scoresT.dataSync(),rawBoxes=tf5.slice(predictions,[0,1],[-1,4]),boxes=this.normalizeBoxes(rawBoxes);rawBoxes.dispose();let filteredT=await tf5.image.nonMaxSuppressionAsync(boxes,scores,config2.hand.maxHands,config2.hand.iouThreshold,config2.hand.scoreThreshold),filtered=filteredT.arraySync();scoresT.dispose(),filteredT.dispose();let hands=[];for(let index of filtered)if(scores[index]>=config2.hand.minConfidence){let matchingBox=tf5.slice(boxes,[index,0],[1,-1]),rawPalmLandmarks=tf5.slice(predictions,[index,5],[1,14]),palmLandmarks=tf5.tidy(()=>this.normalizeLandmarks(rawPalmLandmarks,index).reshape([-1,2]));rawPalmLandmarks.dispose(),hands.push({box:matchingBox,palmLandmarks,confidence:scores[index]})}return predictions.dispose(),boxes.dispose(),hands}async estimateHandBounds(input,config2){let inputHeight=input.shape[1],inputWidth=input.shape[2],image5=tf5.tidy(()=>input.resizeBilinear([config2.hand.inputSize,config2.hand.inputSize]).div(127.5).sub(1)),predictions=await this.getBoxes(image5,config2);image5.dispose();let hands=[];if(!predictions||predictions.length===0)return hands;for(let prediction of predictions){let boxes=prediction.box.dataSync(),startPoint=boxes.slice(0,2),endPoint=boxes.slice(2,4),palmLandmarks=prediction.palmLandmarks.arraySync();prediction.box.dispose(),prediction.palmLandmarks.dispose(),hands.push(scaleBoxCoordinates({startPoint,endPoint,palmLandmarks,confidence:prediction.confidence},[inputWidth/config2.hand.inputSize,inputHeight/config2.hand.inputSize]))}return hands}};exports2.HandDetector=HandDetector});var require_handpipeline=__commonJS(exports2=>{var tf5=__toModule(require_tfjs_esm()),PALM_BOX_ENLARGE_FACTOR=5,HAND_BOX_ENLARGE_FACTOR=1.65,PALM_LANDMARK_IDS=[0,5,9,13,17,1,2],PALM_LANDMARKS_INDEX_OF_PALM_BASE=0,PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE=2,HandPipeline=class{constructor(handDetector,landmarkDetector,inputSize){this.handDetector=handDetector,this.landmarkDetector=landmarkDetector,this.inputSize=inputSize,this.storedBoxes=[],this.skipped=0,this.detectedHands=0}getBoxForPalmLandmarks(palmLandmarks,rotationMatrix){let rotatedPalmLandmarks=palmLandmarks.map(coord=>rotatePoint([...coord,1],rotationMatrix)),boxAroundPalm=this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);return enlargeBox(squarifyBox(boxAroundPalm),PALM_BOX_ENLARGE_FACTOR)}getBoxForHandLandmarks(landmarks){let boundingBox=this.calculateLandmarksBoundingBox(landmarks),boxAroundHand=enlargeBox(squarifyBox(boundingBox),HAND_BOX_ENLARGE_FACTOR);boxAroundHand.palmLandmarks=[];for(let i=0;i[scaleFactor[0]*(coord[0]-this.inputSize/2),scaleFactor[1]*(coord[1]-this.inputSize/2),scaleFactor[2]*coord[2]]),coordsRotationMatrix=buildRotationMatrix(angle,[0,0]),coordsRotated=coordsScaled.map(coord=>[...rotatePoint(coord,coordsRotationMatrix),coord[2]]),inverseRotationMatrix=invertTransformMatrix(rotationMatrix),boxCenter=[...getBoxCenter(box2),1],originalBoxCenter=[dot(boxCenter,inverseRotationMatrix[0]),dot(boxCenter,inverseRotationMatrix[1])];return coordsRotated.map(coord=>[coord[0]+originalBoxCenter[0],coord[1]+originalBoxCenter[1],coord[2]])}async estimateHands(image5,config2){let useFreshBox=!1,boxes;(this.skipped===0||this.skipped>config2.hand.skipFrames||!config2.hand.landmarks||!config2.videoOptimized)&&(boxes=await this.handDetector.estimateHandBounds(image5,config2),this.skipped=0),config2.videoOptimized&&this.skipped++,boxes&&boxes.length>0&&(boxes.length!==this.detectedHands&&this.detectedHands!==config2.hand.maxHands||!config2.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...boxes],this.storedBoxes.length>0&&(useFreshBox=!0));let hands=[];for(let i=0;i=config2.hand.minConfidence){let keypointsReshaped=tf5.reshape(keypoints,[-1,3]),rawCoords=keypointsReshaped.arraySync();keypoints.dispose(),keypointsReshaped.dispose();let coords=this.transformRawCoords(rawCoords,newBox,angle,rotationMatrix),nextBoundingBox=this.getBoxForHandLandmarks(coords);this.storedBoxes[i]=nextBoundingBox;let result={landmarks:coords,confidence,box:{topLeft:nextBoundingBox.startPoint,bottomRight:nextBoundingBox.endPoint}};hands.push(result)}else this.storedBoxes[i]=null;keypoints.dispose()}else{let enlarged=enlargeBox(squarifyBox(currentBox),HAND_BOX_ENLARGE_FACTOR),result={confidence:currentBox.confidence,box:{topLeft:enlarged.startPoint,bottomRight:enlarged.endPoint}};hands.push(result)}}return this.storedBoxes=this.storedBoxes.filter(a=>a!==null),this.detectedHands=hands.length,hands}calculateLandmarksBoundingBox(landmarks){let xs=landmarks.map(d=>d[0]),ys=landmarks.map(d=>d[1]),startPoint=[Math.min(...xs),Math.min(...ys)],endPoint=[Math.max(...xs),Math.max(...ys)];return{startPoint,endPoint}}};exports2.HandPipeline=HandPipeline});var 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HandPose(handPipeline);return config2.hand.enabled&&log(`load model: ${config2.hand.detector.modelPath.match(/\/(.*)\./)[1]}`),config2.hand.landmarks&&log(`load model: ${config2.hand.skeleton.modelPath.match(/\/(.*)\./)[1]}`),handPose}exports2.load=load3});var require_gesture=__commonJS(exports2=>{exports2.body=res=>{if(!res)return[];let gestures=[];for(let i=0;ia.part==="leftWrist"),rightWrist=res[i].keypoints.find(a=>a.part==="rightWrist"),nose=res[i].keypoints.find(a=>a.part==="nose");nose&&leftWrist&&rightWrist&&leftWrist.position.ya.part==="leftShoulder"),rightShoulder=res[i].keypoints.find(a=>a.part==="rightShoulder");leftShoulder&&rightShoulder&&gestures.push({body:i,gesture:`leaning ${leftShoulder.position.y>rightShoulder.position.y?"left":"right"}`})}return gestures};exports2.face=res=>{if(!res)return[];let gestures=[];for(let i=0;i0){let eyeFacing=res[i].mesh[35][2]-res[i].mesh[263][2];Math.abs(eyeFacing)<10?gestures.push({face:i,gesture:"facing camera"}):gestures.push({face:i,gesture:`facing ${eyeFacing<0?"right":"left"}`}),Math.abs(res[i].mesh[374][1]-res[i].mesh[386][1])/Math.abs(res[i].mesh[443][1]-res[i].mesh[450][1])<.2&&gestures.push({face:i,gesture:"blink left eye"}),Math.abs(res[i].mesh[145][1]-res[i].mesh[159][1])/Math.abs(res[i].mesh[223][1]-res[i].mesh[230][1])<.2&&gestures.push({face:i,gesture:"blink right eye"});let mouthOpen=Math.min(100,500*Math.abs(res[i].mesh[13][1]-res[i].mesh[14][1])/Math.abs(res[i].mesh[10][1]-res[i].mesh[152][1]));mouthOpen>10&&gestures.push({face:i,gesture:`mouth ${Math.trunc(mouthOpen)}% open`});let chinDepth=res[i].mesh[152][2];Math.abs(chinDepth)>10&&gestures.push({face:i,gesture:`head ${chinDepth<0?"up":"down"}`})}return gestures};exports2.iris=res=>{if(!res)return[];let gestures=[];for(let i=0;i{if(!res)return[];let gestures=[];for(let i=0;i0){let closest=fingers.reduce((best,a)=>best.position[2]best.position[1]{var WebGLProgram=function(gl,vertexSource,fragmentSource){let _collect=function(source,prefix,collection){let r=new RegExp("\\b"+prefix+" \\w+ (\\w+)","ig");source.replace(r,(match,name)=>(collection[name]=0,match))},_compile=function(source,type){let shader=gl.createShader(type);if(gl.shaderSource(shader,source),gl.compileShader(shader),!gl.getShaderParameter(shader,gl.COMPILE_STATUS))throw new Error("Filter: GL compile failed",gl.getShaderInfoLog(shader));return shader};this.uniform={},this.attribute={};let _vsh=_compile(vertexSource,gl.VERTEX_SHADER),_fsh=_compile(fragmentSource,gl.FRAGMENT_SHADER);if(this.id=gl.createProgram(),gl.attachShader(this.id,_vsh),gl.attachShader(this.id,_fsh),gl.linkProgram(this.id),!gl.getProgramParameter(this.id,gl.LINK_STATUS))throw new Error("Filter: GL link failed",gl.getProgramInfoLog(this.id));gl.useProgram(this.id),_collect(vertexSource,"attribute",this.attribute);for(let a in this.attribute)this.attribute[a]=gl.getAttribLocation(this.id,a);_collect(vertexSource,"uniform",this.uniform),_collect(fragmentSource,"uniform",this.uniform);for(let u in this.uniform)this.uniform[u]=gl.getUniformLocation(this.id,u)},WebGLImageFilter=function(params){params||(params={});let _drawCount=0,_sourceTexture=null,_lastInChain=!1,_currentFramebufferIndex=-1,_tempFramebuffers=[null,null],_filterChain=[],_width=-1,_height=-1,_vertexBuffer=null,_currentProgram=null,_canvas=params.canvas||document.createElement("canvas"),_shaderProgramCache={},gl=_canvas.getContext("webgl");if(!gl)throw new Error("Filter: getContext() failed");this.addFilter=function(name){let args=Array.prototype.slice.call(arguments,1),filter=_filter[name];_filterChain.push({func:filter,args})},this.reset=function(){_filterChain=[]},this.apply=function(image4){if(_resize(image4.width,image4.height),_drawCount=0,_sourceTexture||(_sourceTexture=gl.createTexture()),gl.bindTexture(gl.TEXTURE_2D,_sourceTexture),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_WRAP_S,gl.CLAMP_TO_EDGE),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_WRAP_T,gl.CLAMP_TO_EDGE),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_MIN_FILTER,gl.NEAREST),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_MAG_FILTER,gl.NEAREST),gl.texImage2D(gl.TEXTURE_2D,0,gl.RGBA,gl.RGBA,gl.UNSIGNED_BYTE,image4),_filterChain.length===0)return _draw(),_canvas;for(let i=0;i<_filterChain.length;i++){_lastInChain=i===_filterChain.length-1;let f=_filterChain[i];f.func.apply(this,f.args||[])}return _canvas};let _resize=function(width,height){if(!(width===_width&&height===_height)){if(_canvas.width=width,_width=width,_canvas.height=height,_height=height,!_vertexBuffer){let vertices=new Float32Array([-1,-1,0,1,1,-1,1,1,-1,1,0,0,-1,1,0,0,1,-1,1,1,1,1,1,0]);_vertexBuffer=gl.createBuffer(),gl.bindBuffer(gl.ARRAY_BUFFER,_vertexBuffer),gl.bufferData(gl.ARRAY_BUFFER,vertices,gl.STATIC_DRAW),gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL,!0)}gl.viewport(0,0,_width,_height),_tempFramebuffers=[null,null]}},_getTempFramebuffer=function(index){return _tempFramebuffers[index]=_tempFramebuffers[index]||_createFramebufferTexture(_width,_height),_tempFramebuffers[index]},_createFramebufferTexture=function(width,height){let fbo=gl.createFramebuffer();gl.bindFramebuffer(gl.FRAMEBUFFER,fbo);let renderbuffer=gl.createRenderbuffer();gl.bindRenderbuffer(gl.RENDERBUFFER,renderbuffer);let texture=gl.createTexture();return gl.bindTexture(gl.TEXTURE_2D,texture),gl.texImage2D(gl.TEXTURE_2D,0,gl.RGBA,width,height,0,gl.RGBA,gl.UNSIGNED_BYTE,null),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_MAG_FILTER,gl.LINEAR),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_MIN_FILTER,gl.LINEAR),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_WRAP_S,gl.CLAMP_TO_EDGE),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_WRAP_T,gl.CLAMP_TO_EDGE),gl.framebufferTexture2D(gl.FRAMEBUFFER,gl.COLOR_ATTACHMENT0,gl.TEXTURE_2D,texture,0),gl.bindTexture(gl.TEXTURE_2D,null),gl.bindFramebuffer(gl.FRAMEBUFFER,null),{fbo,texture}},_draw=function(flags){var _a,_b;let source=null,target=null,flipY=!1;_drawCount===0?source=_sourceTexture:source=(_a=_getTempFramebuffer(_currentFramebufferIndex))==null?void 0:_a.texture,_drawCount++,_lastInChain&&!(flags&DRAW.INTERMEDIATE)?(target=null,flipY=_drawCount%2==0):(_currentFramebufferIndex=(_currentFramebufferIndex+1)%2,target=(_b=_getTempFramebuffer(_currentFramebufferIndex))==null?void 0:_b.fbo),gl.bindTexture(gl.TEXTURE_2D,source),gl.bindFramebuffer(gl.FRAMEBUFFER,target),gl.uniform1f(_currentProgram.uniform.flipY,flipY?-1:1),gl.drawArrays(gl.TRIANGLES,0,6)},_compileShader=function(fragmentSource){if(_shaderProgramCache[fragmentSource])return _currentProgram=_shaderProgramCache[fragmentSource],gl.useProgram(_currentProgram.id),_currentProgram;_currentProgram=new WebGLProgram(gl,SHADER.VERTEX_IDENTITY,fragmentSource);let floatSize=Float32Array.BYTES_PER_ELEMENT,vertSize=4*floatSize;return gl.enableVertexAttribArray(_currentProgram.attribute.pos),gl.vertexAttribPointer(_currentProgram.attribute.pos,2,gl.FLOAT,!1,vertSize,0*floatSize),gl.enableVertexAttribArray(_currentProgram.attribute.uv),gl.vertexAttribPointer(_currentProgram.attribute.uv,2,gl.FLOAT,!1,vertSize,2*floatSize),_shaderProgramCache[fragmentSource]=_currentProgram,_currentProgram},DRAW={INTERMEDIATE:1},SHADER={};SHADER.VERTEX_IDENTITY=["precision highp float;","attribute vec2 pos;","attribute vec2 uv;","varying vec2 vUv;","uniform float flipY;","void main(void) {","vUv = uv;","gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);","}"].join(` `),SHADER.FRAGMENT_IDENTITY=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","void main(void) {","gl_FragColor = texture2D(texture, vUv);","}"].join(` `);let _filter={};_filter.colorMatrix=function(matrix){let m=new Float32Array(matrix);m[4]/=255,m[9]/=255,m[14]/=255,m[19]/=255;let shader=m[18]===1&&m[3]===0&&m[8]===0&&m[13]===0&&m[15]===0&&m[16]===0&&m[17]===0&&m[19]===0?_filter.colorMatrix.SHADER.WITHOUT_ALPHA:_filter.colorMatrix.SHADER.WITH_ALPHA,program=_compileShader(shader);gl.uniform1fv(program.uniform.m,m),_draw()},_filter.colorMatrix.SHADER={},_filter.colorMatrix.SHADER.WITH_ALPHA=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","uniform float m[20];","void main(void) {","vec4 c = texture2D(texture, vUv);","gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];","gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];","gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];","gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];","}"].join(` `),_filter.colorMatrix.SHADER.WITHOUT_ALPHA=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","uniform float m[20];","void main(void) {","vec4 c = texture2D(texture, vUv);","gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];","gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];","gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];","gl_FragColor.a = c.a;","}"].join(` `),_filter.brightness=function(brightness){let b=(brightness||0)+1;_filter.colorMatrix([b,0,0,0,0,0,b,0,0,0,0,0,b,0,0,0,0,0,1,0])},_filter.saturation=function(amount){let x=(amount||0)*2/3+1,y=(x-1)*-.5;_filter.colorMatrix([x,y,y,0,0,y,x,y,0,0,y,y,x,0,0,0,0,0,1,0])},_filter.desaturate=function(){_filter.saturation(-1)},_filter.contrast=function(amount){let v=(amount||0)+1,o=-128*(v-1);_filter.colorMatrix([v,0,0,0,o,0,v,0,0,o,0,0,v,0,o,0,0,0,1,0])},_filter.negative=function(){_filter.contrast(-2)},_filter.hue=function(rotation){rotation=(rotation||0)/180*Math.PI;let cos=Math.cos(rotation),sin=Math.sin(rotation),lumR=.213,lumG=.715,lumB=.072;_filter.colorMatrix([lumR+cos*(1-lumR)+sin*-lumR,lumG+cos*-lumG+sin*-lumG,lumB+cos*-lumB+sin*(1-lumB),0,0,lumR+cos*-lumR+sin*.143,lumG+cos*(1-lumG)+sin*.14,lumB+cos*-lumB+sin*-.283,0,0,lumR+cos*-lumR+sin*-(1-lumR),lumG+cos*-lumG+sin*lumG,lumB+cos*(1-lumB)+sin*lumB,0,0,0,0,0,1,0])},_filter.desaturateLuminance=function(){_filter.colorMatrix([.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,0,0,0,1,0])},_filter.sepia=function(){_filter.colorMatrix([.393,.7689999,.18899999,0,0,.349,.6859999,.16799999,0,0,.272,.5339999,.13099999,0,0,0,0,0,1,0])},_filter.brownie=function(){_filter.colorMatrix([.5997023498159715,.34553243048391263,-.2708298674538042,0,47.43192855600873,-.037703249837783157,.8609577587992641,.15059552388459913,0,-36.96841498319127,.24113635128153335,-.07441037908422492,.44972182064877153,0,-7.562075277591283,0,0,0,1,0])},_filter.vintagePinhole=function(){_filter.colorMatrix([.6279345635605994,.3202183420819367,-.03965408211312453,0,9.651285835294123,.02578397704808868,.6441188644374771,.03259127616149294,0,7.462829176470591,.0466055556782719,-.0851232987247891,.5241648018700465,0,5.159190588235296,0,0,0,1,0])},_filter.kodachrome=function(){_filter.colorMatrix([1.1285582396593525,-.3967382283601348,-.03992559172921793,0,63.72958762196502,-.16404339962244616,1.0835251566291304,-.05498805115633132,0,24.732407896706203,-.16786010706155763,-.5603416277695248,1.6014850761964943,0,35.62982807460946,0,0,0,1,0])},_filter.technicolor=function(){_filter.colorMatrix([1.9125277891456083,-.8545344976951645,-.09155508482755585,0,11.793603434377337,-.3087833385928097,1.7658908555458428,-.10601743074722245,0,-70.35205161461398,-.231103377548616,-.7501899197440212,1.847597816108189,0,30.950940869491138,0,0,0,1,0])},_filter.polaroid=function(){_filter.colorMatrix([1.438,-.062,-.062,0,0,-.122,1.378,-.122,0,0,-.016,-.016,1.483,0,0,0,0,0,1,0])},_filter.shiftToBGR=function(){_filter.colorMatrix([0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0])},_filter.convolution=function(matrix){let m=new Float32Array(matrix),pixelSizeX=1/_width,pixelSizeY=1/_height,program=_compileShader(_filter.convolution.SHADER);gl.uniform1fv(program.uniform.m,m),gl.uniform2f(program.uniform.px,pixelSizeX,pixelSizeY),_draw()},_filter.convolution.SHADER=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","uniform vec2 px;","uniform float m[9];","void main(void) {","vec4 c11 = texture2D(texture, vUv - px);","vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y));","vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y));","vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) );","vec4 c22 = texture2D(texture, vUv);","vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) );","vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) );","vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) );","vec4 c33 = texture2D(texture, vUv + px );","gl_FragColor = ","c11 * m[0] + c12 * m[1] + c22 * m[2] +","c21 * m[3] + c22 * m[4] + c23 * m[5] +","c31 * m[6] + c32 * m[7] + c33 * m[8];","gl_FragColor.a = c22.a;","}"].join(` `),_filter.detectEdges=function(){_filter.convolution.call(this,[0,1,0,1,-4,1,0,1,0])},_filter.sobelX=function(){_filter.convolution.call(this,[-1,0,1,-2,0,2,-1,0,1])},_filter.sobelY=function(){_filter.convolution.call(this,[-1,-2,-1,0,0,0,1,2,1])},_filter.sharpen=function(amount){let a=amount||1;_filter.convolution.call(this,[0,-1*a,0,-1*a,1+4*a,-1*a,0,-1*a,0])},_filter.emboss=function(size){let s=size||1;_filter.convolution.call(this,[-2*s,-1*s,0,-1*s,1,1*s,0,1*s,2*s])},_filter.blur=function(size){let blurSizeX=size/7/_width,blurSizeY=size/7/_height,program=_compileShader(_filter.blur.SHADER);gl.uniform2f(program.uniform.px,0,blurSizeY),_draw(DRAW.INTERMEDIATE),gl.uniform2f(program.uniform.px,blurSizeX,0),_draw()},_filter.blur.SHADER=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","uniform vec2 px;","void main(void) {","gl_FragColor = vec4(0.0);","gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;","gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;","gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;","gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;","gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;","gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;","gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;","gl_FragColor += texture2D(texture, vUv )*0.159576912161;","gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x, 1.0*px.y))*0.147308056121;","gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x, 2.0*px.y))*0.115876621105;","gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x, 3.0*px.y))*0.0776744219933;","gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x, 4.0*px.y))*0.0443683338718;","gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x, 5.0*px.y))*0.0215963866053;","gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794;","gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265;","}"].join(` `),_filter.pixelate=function(size){let blurSizeX=size/_width,blurSizeY=size/_height,program=_compileShader(_filter.pixelate.SHADER);gl.uniform2f(program.uniform.size,blurSizeX,blurSizeY),_draw()},_filter.pixelate.SHADER=["precision highp float;","varying vec2 vUv;","uniform vec2 size;","uniform sampler2D texture;","vec2 pixelate(vec2 coord, vec2 size) {","return floor( coord / size ) * size;","}","void main(void) {","gl_FragColor = vec4(0.0);","vec2 coord = pixelate(vUv, size);","gl_FragColor += texture2D(texture, coord);","}"].join(` -`)};exports2.Canvas=WebGLImageFilter});var require_image=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm()),fxImage=__toModule(require_imagefx()),inCanvas=null,outCanvas=null;function process3(input,config2){let tensor;if(input instanceof tf4.Tensor)tensor=tf4.clone(input);else{let originalWidth=input.naturalWidth||input.videoWidth||input.width||input.shape&&input.shape[1]>0,originalHeight=input.naturalHeight||input.videoHeight||input.height||input.shape&&input.shape[2]>0,targetWidth=originalWidth,targetHeight=originalHeight;if(config2.filter.width>0?targetWidth=config2.filter.width:config2.filter.height>0&&(targetWidth=originalWidth*(config2.filter.height/originalHeight)),config2.filter.height>0?targetHeight=config2.filter.height:config2.filter.width>0&&(targetHeight=originalHeight*(config2.filter.width/originalWidth)),!targetWidth||!targetHeight)return log("Human: invalid input",input),null;(!inCanvas||inCanvas.width!==targetWidth||inCanvas.height!==targetHeight)&&(inCanvas=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(targetWidth,targetHeight):document.createElement("canvas"),inCanvas.width!==targetWidth&&(inCanvas.width=targetWidth),inCanvas.height!==targetHeight&&(inCanvas.height=targetHeight));let ctx=inCanvas.getContext("2d");if(input instanceof ImageData?ctx.putImageData(input,0,0):ctx.drawImage(input,0,0,originalWidth,originalHeight,0,0,inCanvas.width,inCanvas.height),config2.filter.enabled){if((!this.fx||!outCanvas||inCanvas.width!==outCanvas.width||inCanvas.height!==outCanvas.height)&&(outCanvas=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(inCanvas.width,inCanvas.height):document.createElement("canvas"),outCanvas.width!==inCanvas.width&&(outCanvas.width=inCanvas.width),outCanvas.height!==inCanvas.height&&(outCanvas.height=inCanvas.height),this.fx=tf4.ENV.flags.IS_BROWSER?new fxImage.Canvas({canvas:outCanvas}):null),!this.fx)return inCanvas;this.fx.reset(),this.fx.addFilter("brightness",config2.filter.brightness),config2.filter.contrast!==0&&this.fx.addFilter("contrast",config2.filter.contrast),config2.filter.sharpness!==0&&this.fx.addFilter("sharpen",config2.filter.sharpness),config2.filter.blur!==0&&this.fx.addFilter("blur",config2.filter.blur),config2.filter.saturation!==0&&this.fx.addFilter("saturation",config2.filter.saturation),config2.filter.hue!==0&&this.fx.addFilter("hue",config2.filter.hue),config2.filter.negative&&this.fx.addFilter("negative"),config2.filter.sepia&&this.fx.addFilter("sepia"),config2.filter.vintage&&this.fx.addFilter("brownie"),config2.filter.sepia&&this.fx.addFilter("sepia"),config2.filter.kodachrome&&this.fx.addFilter("kodachrome"),config2.filter.technicolor&&this.fx.addFilter("technicolor"),config2.filter.polaroid&&this.fx.addFilter("polaroid"),config2.filter.pixelate!==0&&this.fx.addFilter("pixelate",config2.filter.pixelate),this.fx.apply(inCanvas)}else outCanvas=inCanvas;let pixels;if(outCanvas.data){let shape=[outCanvas.height,outCanvas.width,3];pixels=tf4.tensor3d(outCanvas.data,shape,"int32")}else if(config2.backend==="webgl"||outCanvas instanceof ImageData)pixels=tf4.browser.fromPixels(outCanvas);else{let tempCanvas=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(targetWidth,targetHeight):document.createElement("canvas");tempCanvas.width=targetWidth,tempCanvas.height=targetHeight;let tempCtx=tempCanvas.getContext("2d");tempCtx==null||tempCtx.drawImage(outCanvas,0,0);let data2=tempCtx==null?void 0:tempCtx.getImageData(0,0,targetWidth,targetHeight);pixels=tf4.browser.fromPixels(data2)}let casted=pixels.toFloat();tensor=casted.expandDims(0),pixels.dispose(),casted.dispose()}return{tensor,canvas:config2.filter.return?outCanvas:null}}exports2.process=process3});__export(exports,{default:()=>Human});function log(...msg){let dt=new Date,ts=`${dt.getHours().toString().padStart(2,"0")}:${dt.getMinutes().toString().padStart(2,"0")}:${dt.getSeconds().toString().padStart(2,"0")}.${dt.getMilliseconds().toString().padStart(3,"0")}`;msg&&console.log(ts,"Human:",...msg)}var tf3=__toModule(require_tfjs_esm());var tf=__toModule(require_tfjs_esm()),config={name:"humangl",priority:99,canvas:null,width:1024,height:1024,webGLattr:{alpha:!1,antialias:!1,premultipliedAlpha:!1,preserveDrawingBuffer:!1,depth:!1,stencil:!1,failIfMajorPerformanceCaveat:!1,desynchronized:!0}};function register(){if(!tf.findBackend(config.name)){config.canvas=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(config.width,config.height):document.createElement("canvas");let gl=config.canvas.getContext("webgl2",config.webGLattr);tf.setWebGLContext(2,gl);let ctx=new tf.GPGPUContext(gl);tf.registerBackend(config.name,()=>new tf.MathBackendWebGL(ctx),config.priority),tf.getKernelsForBackend("webgl").forEach(kernelConfig=>{let newKernelConfig={...kernelConfig,backendName:config.name};tf.registerKernel(newKernelConfig)}),tf.ENV.set("WEBGL_VERSION",2),tf.ENV.set("WEBGL_MAX_TEXTURE_SIZE",gl.getParameter(gl.MAX_TEXTURE_SIZE)),tf.ENV.set("WEBGL_FORCE_F16_TEXTURES",!0),tf.ENV.set("WEBGL_PACK_DEPTHWISECONV",!0)}}var facemesh=__toModule(require_facemesh()),age=__toModule(require_age()),gender=__toModule(require_gender()),emotion=__toModule(require_emotion()),embedding=__toModule(require_embedding()),posenet=__toModule(require_posenet());var tf2=__toModule(require_tfjs_esm());function getBoxSize(box){return[Math.abs(box.endPoint[0]-box.startPoint[0]),Math.abs(box.endPoint[1]-box.startPoint[1])]}function getBoxCenter(box){return[box.startPoint[0]+(box.endPoint[0]-box.startPoint[0])/2,box.startPoint[1]+(box.endPoint[1]-box.startPoint[1])/2]}function cutBoxFromImageAndResize(box,image3,cropSize){let h=image3.shape[1],w=image3.shape[2],boxes=[[box.startPoint[1]/h,box.startPoint[0]/w,box.endPoint[1]/h,box.endPoint[0]/w]];return tf2.image.cropAndResize(image3,boxes,[0],cropSize)}function scaleBoxCoordinates(box,factor){let startPoint=[box.startPoint[0]*factor[0],box.startPoint[1]*factor[1]],endPoint=[box.endPoint[0]*factor[0],box.endPoint[1]*factor[1]],palmLandmarks=box.palmLandmarks.map(coord=>[coord[0]*factor[0],coord[1]*factor[1]]);return{startPoint,endPoint,palmLandmarks,confidence:box.confidence}}function enlargeBox(box,factor=1.5){let center=getBoxCenter(box),size=getBoxSize(box),newHalfSize=[factor*size[0]/2,factor*size[1]/2],startPoint=[center[0]-newHalfSize[0],center[1]-newHalfSize[1]],endPoint=[center[0]+newHalfSize[0],center[1]+newHalfSize[1]];return{startPoint,endPoint,palmLandmarks:box.palmLandmarks}}function squarifyBox(box){let centers=getBoxCenter(box),size=getBoxSize(box),halfSize=Math.max(...size)/2,startPoint=[centers[0]-halfSize,centers[1]-halfSize],endPoint=[centers[0]+halfSize,centers[1]+halfSize];return{startPoint,endPoint,palmLandmarks:box.palmLandmarks}}function normalizeRadians(angle){return angle-2*Math.PI*Math.floor((angle+Math.PI)/(2*Math.PI))}function computeRotation(point1,point2){let radians=Math.PI/2-Math.atan2(-(point2[1]-point1[1]),point2[0]-point1[0]);return normalizeRadians(radians)}var buildTranslationMatrix=(x,y)=>[[1,0,x],[0,1,y],[0,0,1]];function dot(v1,v2){let product=0;for(let i=0;iconfig_default});var config_default={backend:"webgl",wasmPath:"../assets/",async:!0,profile:!1,deallocate:!1,scoped:!1,videoOptimized:!0,warmup:"face",filter:{enabled:!0,width:0,height:0,return:!0,brightness:0,contrast:0,sharpness:0,blur:0,saturation:0,hue:0,negative:!1,sepia:!1,vintage:!1,kodachrome:!1,technicolor:!1,polaroid:!1,pixelate:0},gesture:{enabled:!0},face:{enabled:!0,detector:{modelPath:"../models/blazeface-back.json",inputSize:256,rotation:!1,maxFaces:10,skipFrames:11,minConfidence:.5,iouThreshold:.2,scoreThreshold:.5},mesh:{enabled:!0,modelPath:"../models/facemesh.json",inputSize:192,returnRawData:!1},iris:{enabled:!0,modelPath:"../models/iris.json",inputSize:64},age:{enabled:!0,modelPath:"../models/age-ssrnet-imdb.json",inputSize:64,skipFrames:31},gender:{enabled:!0,minConfidence:.1,modelPath:"../models/gender-ssrnet-imdb.json",inputSize:64,skipFrames:41},emotion:{enabled:!0,inputSize:64,minConfidence:.2,skipFrames:21,modelPath:"../models/emotion-large.json"},embedding:{enabled:!1,inputSize:112,modelPath:"../models/mobilefacenet.json"}},body:{enabled:!0,modelPath:"../models/posenet.json",inputSize:257,maxDetections:10,scoreThreshold:.5,nmsRadius:20,outputStride:16,modelType:"MobileNet"},hand:{enabled:!0,rotation:!1,inputSize:256,skipFrames:12,minConfidence:.1,iouThreshold:.1,scoreThreshold:.5,maxHands:1,landmarks:!0,detector:{modelPath:"../models/handdetect.json"},skeleton:{modelPath:"../models/handskeleton.json"}}};var face=` +`)};exports2.Canvas=WebGLImageFilter});var require_image=__commonJS(exports2=>{var tf5=__toModule(require_tfjs_esm()),fxImage=__toModule(require_imagefx()),inCanvas=null,outCanvas=null;function process3(input,config2){let tensor;if(input instanceof tf5.Tensor)tensor=tf5.clone(input);else{let originalWidth=input.naturalWidth||input.videoWidth||input.width||input.shape&&input.shape[1]>0,originalHeight=input.naturalHeight||input.videoHeight||input.height||input.shape&&input.shape[2]>0,targetWidth=originalWidth,targetHeight=originalHeight;if(config2.filter.width>0?targetWidth=config2.filter.width:config2.filter.height>0&&(targetWidth=originalWidth*(config2.filter.height/originalHeight)),config2.filter.height>0?targetHeight=config2.filter.height:config2.filter.width>0&&(targetHeight=originalHeight*(config2.filter.width/originalWidth)),!targetWidth||!targetHeight)return log("Human: invalid input",input),null;(!inCanvas||inCanvas.width!==targetWidth||inCanvas.height!==targetHeight)&&(inCanvas=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(targetWidth,targetHeight):document.createElement("canvas"),inCanvas.width!==targetWidth&&(inCanvas.width=targetWidth),inCanvas.height!==targetHeight&&(inCanvas.height=targetHeight));let ctx=inCanvas.getContext("2d");if(input instanceof ImageData?ctx.putImageData(input,0,0):ctx.drawImage(input,0,0,originalWidth,originalHeight,0,0,inCanvas.width,inCanvas.height),config2.filter.enabled){if((!this.fx||!outCanvas||inCanvas.width!==outCanvas.width||inCanvas.height!==outCanvas.height)&&(outCanvas=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(inCanvas.width,inCanvas.height):document.createElement("canvas"),outCanvas.width!==inCanvas.width&&(outCanvas.width=inCanvas.width),outCanvas.height!==inCanvas.height&&(outCanvas.height=inCanvas.height),this.fx=tf5.ENV.flags.IS_BROWSER?new fxImage.Canvas({canvas:outCanvas}):null),!this.fx)return inCanvas;this.fx.reset(),this.fx.addFilter("brightness",config2.filter.brightness),config2.filter.contrast!==0&&this.fx.addFilter("contrast",config2.filter.contrast),config2.filter.sharpness!==0&&this.fx.addFilter("sharpen",config2.filter.sharpness),config2.filter.blur!==0&&this.fx.addFilter("blur",config2.filter.blur),config2.filter.saturation!==0&&this.fx.addFilter("saturation",config2.filter.saturation),config2.filter.hue!==0&&this.fx.addFilter("hue",config2.filter.hue),config2.filter.negative&&this.fx.addFilter("negative"),config2.filter.sepia&&this.fx.addFilter("sepia"),config2.filter.vintage&&this.fx.addFilter("brownie"),config2.filter.sepia&&this.fx.addFilter("sepia"),config2.filter.kodachrome&&this.fx.addFilter("kodachrome"),config2.filter.technicolor&&this.fx.addFilter("technicolor"),config2.filter.polaroid&&this.fx.addFilter("polaroid"),config2.filter.pixelate!==0&&this.fx.addFilter("pixelate",config2.filter.pixelate),this.fx.apply(inCanvas)}else outCanvas=inCanvas;let pixels;if(outCanvas.data){let shape=[outCanvas.height,outCanvas.width,3];pixels=tf5.tensor3d(outCanvas.data,shape,"int32")}else if(config2.backend==="webgl"||outCanvas instanceof ImageData)pixels=tf5.browser.fromPixels(outCanvas);else{let tempCanvas=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(targetWidth,targetHeight):document.createElement("canvas");tempCanvas.width=targetWidth,tempCanvas.height=targetHeight;let tempCtx=tempCanvas.getContext("2d");tempCtx==null||tempCtx.drawImage(outCanvas,0,0);let data2=tempCtx==null?void 0:tempCtx.getImageData(0,0,targetWidth,targetHeight);pixels=tf5.browser.fromPixels(data2)}let casted=pixels.toFloat();tensor=casted.expandDims(0),pixels.dispose(),casted.dispose()}return{tensor,canvas:config2.filter.return?outCanvas:null}}exports2.process=process3});__export(exports,{default:()=>Human});function log(...msg){let dt=new Date,ts=`${dt.getHours().toString().padStart(2,"0")}:${dt.getMinutes().toString().padStart(2,"0")}:${dt.getSeconds().toString().padStart(2,"0")}.${dt.getMilliseconds().toString().padStart(3,"0")}`;msg&&console.log(ts,"Human:",...msg)}var tf4=__toModule(require_tfjs_esm());var tf=__toModule(require_tfjs_esm()),config={name:"humangl",priority:99,canvas:null,width:1024,height:1024,webGLattr:{alpha:!1,antialias:!1,premultipliedAlpha:!1,preserveDrawingBuffer:!1,depth:!1,stencil:!1,failIfMajorPerformanceCaveat:!1,desynchronized:!0}};function register(){if(!tf.findBackend(config.name)){config.canvas=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(config.width,config.height):document.createElement("canvas");let gl=config.canvas.getContext("webgl2",config.webGLattr);tf.setWebGLContext(2,gl);let ctx=new tf.GPGPUContext(gl);tf.registerBackend(config.name,()=>new tf.MathBackendWebGL(ctx),config.priority),tf.getKernelsForBackend("webgl").forEach(kernelConfig=>{let newKernelConfig={...kernelConfig,backendName:config.name};tf.registerKernel(newKernelConfig)}),tf.ENV.set("WEBGL_VERSION",2),tf.ENV.set("WEBGL_MAX_TEXTURE_SIZE",gl.getParameter(gl.MAX_TEXTURE_SIZE)),tf.ENV.set("WEBGL_FORCE_F16_TEXTURES",!0),tf.ENV.set("WEBGL_PACK_DEPTHWISECONV",!0)}}var facemesh=__toModule(require_facemesh()),age=__toModule(require_age()),gender=__toModule(require_gender()),emotion=__toModule(require_emotion()),embedding=__toModule(require_embedding()),posenet=__toModule(require_posenet());var tf2=__toModule(require_tfjs_esm()),models={};async function load(config2){return models.blazepose||(models.blazepose=await tf2.loadGraphModel(config2.pose.modelPath),log(`load model: ${config2.pose.modelPath.match(/\/(.*)\./)[1]}`)),models.blazepose}async function predict(image4,config2){return models.blazepose?new Promise(async resolve=>{let resize=tf2.image.resizeBilinear(image4,[config2.pose.inputSize,config2.pose.inputSize],!1),enhance=tf2.div(resize,127.5).sub(1);tf2.dispose(resize);let logits=await models.blazepose.predict(enhance);tf2.dispose(enhance),logits.map(logit=>logit.dispose()),resolve(logits)}):null}var tf3=__toModule(require_tfjs_esm());function getBoxSize(box){return[Math.abs(box.endPoint[0]-box.startPoint[0]),Math.abs(box.endPoint[1]-box.startPoint[1])]}function getBoxCenter(box){return[box.startPoint[0]+(box.endPoint[0]-box.startPoint[0])/2,box.startPoint[1]+(box.endPoint[1]-box.startPoint[1])/2]}function cutBoxFromImageAndResize(box,image4,cropSize){let h=image4.shape[1],w=image4.shape[2],boxes=[[box.startPoint[1]/h,box.startPoint[0]/w,box.endPoint[1]/h,box.endPoint[0]/w]];return tf3.image.cropAndResize(image4,boxes,[0],cropSize)}function scaleBoxCoordinates(box,factor){let startPoint=[box.startPoint[0]*factor[0],box.startPoint[1]*factor[1]],endPoint=[box.endPoint[0]*factor[0],box.endPoint[1]*factor[1]],palmLandmarks=box.palmLandmarks.map(coord=>[coord[0]*factor[0],coord[1]*factor[1]]);return{startPoint,endPoint,palmLandmarks,confidence:box.confidence}}function enlargeBox(box,factor=1.5){let center=getBoxCenter(box),size=getBoxSize(box),newHalfSize=[factor*size[0]/2,factor*size[1]/2],startPoint=[center[0]-newHalfSize[0],center[1]-newHalfSize[1]],endPoint=[center[0]+newHalfSize[0],center[1]+newHalfSize[1]];return{startPoint,endPoint,palmLandmarks:box.palmLandmarks}}function squarifyBox(box){let centers=getBoxCenter(box),size=getBoxSize(box),halfSize=Math.max(...size)/2,startPoint=[centers[0]-halfSize,centers[1]-halfSize],endPoint=[centers[0]+halfSize,centers[1]+halfSize];return{startPoint,endPoint,palmLandmarks:box.palmLandmarks}}function normalizeRadians(angle){return angle-2*Math.PI*Math.floor((angle+Math.PI)/(2*Math.PI))}function computeRotation(point1,point2){let radians=Math.PI/2-Math.atan2(-(point2[1]-point1[1]),point2[0]-point1[0]);return normalizeRadians(radians)}var buildTranslationMatrix=(x,y)=>[[1,0,x],[0,1,y],[0,0,1]];function dot(v1,v2){let product=0;for(let i=0;iconfig_default});var config_default={backend:"webgl",wasmPath:"../assets/",async:!0,profile:!1,deallocate:!1,scoped:!1,videoOptimized:!0,warmup:"face",filter:{enabled:!0,width:0,height:0,return:!0,brightness:0,contrast:0,sharpness:0,blur:0,saturation:0,hue:0,negative:!1,sepia:!1,vintage:!1,kodachrome:!1,technicolor:!1,polaroid:!1,pixelate:0},gesture:{enabled:!0},face:{enabled:!0,detector:{modelPath:"../models/blazeface-back.json",inputSize:256,rotation:!1,maxFaces:10,skipFrames:11,minConfidence:.5,iouThreshold:.2,scoreThreshold:.5},mesh:{enabled:!0,modelPath:"../models/facemesh.json",inputSize:192,returnRawData:!1},iris:{enabled:!0,modelPath:"../models/iris.json",inputSize:64},age:{enabled:!0,modelPath:"../models/age-ssrnet-imdb.json",inputSize:64,skipFrames:31},gender:{enabled:!0,minConfidence:.1,modelPath:"../models/gender-ssrnet-imdb.json",inputSize:64,skipFrames:41},emotion:{enabled:!0,inputSize:64,minConfidence:.2,skipFrames:21,modelPath:"../models/emotion-large.json"},embedding:{enabled:!1,inputSize:112,modelPath:"../models/mobilefacenet.json"}},body:{enabled:!0,modelPath:"../models/posenet.json",inputSize:257,maxDetections:10,scoreThreshold:.5,nmsRadius:20,outputStride:16,modelType:"MobileNet"},pose:{enabled:!1,scoreThreshold:.6,iouThreshold:.3,modelPath:"../models/blazepose.json",inputSize:256},hand:{enabled:!0,rotation:!1,inputSize:256,skipFrames:12,minConfidence:.1,iouThreshold:.1,scoreThreshold:.5,maxHands:1,landmarks:!0,detector:{modelPath:"../models/handdetect.json"},skeleton:{modelPath:"../models/handskeleton.json"}}};var face=` /9j/4AAQSkZJRgABAQEAYABgAAD/4QBoRXhpZgAATU0AKgAAAAgABAEaAAUAAAABAAAAPgEbAAUA AAABAAAARgEoAAMAAAABAAIAAAExAAIAAAARAAAATgAAAAAAAABgAAAAAQAAAGAAAAABcGFpbnQu bmV0IDQuMi4xMwAA/9sAQwAGBAUGBQQGBgUGBwcGCAoQCgoJCQoUDg8MEBcUGBgXFBYWGh0lHxob @@ -731,5 +731,5 @@ AAAAAAJAAAAAAAAAAAAAABAJEAAAAAAAAAAAAAAAIEoBKAAAAAAAAAAAAAAABAlAAAAAAAIAAAAA BAkBAkBAkBAlACEgMZjdjbFW8bWrEx8YWANb6Fp+bfwab+vLDKMFK9qxH5L0bAr8OPRPKz2AY7J2 SbAjYZAI2E7AIEgIEgIEgMdkSy2NgY7MdlmyNoBXsxmFuyNgVTVjNV3KjlBRNTlXTVHKCrlIqt5T lBhEMohlFerLlBjEMohMVTEARDKCITsAk2AEgAAAkAAAAAAAAAAAAAAAAAAAAAAAASAAAAAAAAD/ -2Q==`;var version="0.9.23";var now=()=>typeof performance!="undefined"?performance.now():parseInt(Number(process.hrtime.bigint())/1e3/1e3);function mergeDeep(...objects){let isObject=obj=>obj&&typeof obj=="object";return objects.reduce((prev,obj)=>(Object.keys(obj||{}).forEach(key=>{let pVal=prev[key],oVal=obj[key];Array.isArray(pVal)&&Array.isArray(oVal)?prev[key]=pVal.concat(...oVal):isObject(pVal)&&isObject(oVal)?prev[key]=mergeDeep(pVal,oVal):prev[key]=oVal}),prev),{})}var Human=class{constructor(userConfig={}){this.tf=tf3,this.version=version,this.config=mergeDeep(config_default,userConfig),this.fx=null,this.state="idle",this.numTensors=0,this.analyzeMemoryLeaks=!1,this.checkSanity=!1,this.firstRun=!0,this.perf={},this.models={facemesh:null,posenet:null,handpose:null,iris:null,age:null,gender:null,emotion:null},this.facemesh=facemesh,this.age=age,this.gender=gender,this.emotion=emotion,this.body=posenet,this.hand=handpose}profile(){return this.config.profile?profile.data:{}}analyze(...msg){if(!this.analyzeMemoryLeaks)return;let current=tf3.engine().state.numTensors,previous=this.numTensors;this.numTensors=current;let leaked=current-previous;leaked!==0&&log(...msg,leaked)}sanity(input){if(!this.checkSanity)return null;if(!input)return"input is not defined";if(tf3.ENV.flags.IS_NODE&&!(input instanceof tf3.Tensor))return"input must be a tensor";try{tf3.getBackend()}catch(e){return"backend not loaded"}return null}simmilarity(embedding1,embedding2){return this.config.face.embedding.enabled?embedding.simmilarity(embedding1,embedding2):0}async load(userConfig){this.state="load";let timeStamp=now();userConfig&&(this.config=mergeDeep(this.config,userConfig)),this.firstRun&&(log(`version: ${this.version} TensorFlow/JS version: ${tf3.version_core}`),await this.checkBackend(!0),tf3.ENV.flags.IS_BROWSER&&(log("configuration:",this.config),log("tf flags:",tf3.ENV.flags)),this.firstRun=!1),this.config.async?[this.models.facemesh,this.models.age,this.models.gender,this.models.emotion,this.models.embedding,this.models.posenet,this.models.handpose]=await Promise.all([this.models.facemesh||(this.config.face.enabled?facemesh.load(this.config):null),this.models.age||(this.config.face.enabled&&this.config.face.age.enabled?age.load(this.config):null),this.models.gender||(this.config.face.enabled&&this.config.face.gender.enabled?gender.load(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?emotion.load(this.config):null),this.models.embedding||(this.config.face.enabled&&this.config.face.embedding.enabled?embedding.load(this.config):null),this.models.posenet||(this.config.body.enabled?posenet.load(this.config):null),this.models.handpose||(this.config.hand.enabled?handpose.load(this.config):null)]):(this.config.face.enabled&&!this.models.facemesh&&(this.models.facemesh=await facemesh.load(this.config)),this.config.face.enabled&&this.config.face.age.enabled&&!this.models.age&&(this.models.age=await age.load(this.config)),this.config.face.enabled&&this.config.face.gender.enabled&&!this.models.gender&&(this.models.gender=await gender.load(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await emotion.load(this.config)),this.config.face.enabled&&this.config.face.embedding.enabled&&!this.models.embedding&&(this.models.embedding=await embedding.load(this.config)),this.config.body.enabled&&!this.models.posenet&&(this.models.posenet=await posenet.load(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await handpose.load(this.config)));let current=Math.trunc(now()-timeStamp);current>(this.perf.load||0)&&(this.perf.load=current)}async checkBackend(force){if(this.config.backend&&this.config.backend!==""&&force||tf3.getBackend()!==this.config.backend){let timeStamp=now();if(this.state="backend",log("setting backend:",this.config.backend),this.config.backend==="wasm"&&(log("settings wasm path:",this.config.wasmPath),tf3.setWasmPaths(this.config.wasmPath),await tf3.env().getAsync("WASM_HAS_SIMD_SUPPORT")||log("warning: wasm simd support is not enabled")),this.config.backend==="humangl"&&(log("registering humangl backend"),register()),await tf3.setBackend(this.config.backend),tf3.enableProdMode(),tf3.getBackend()==="webgl"){this.config.deallocate&&(log("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",this.config.deallocate),tf3.ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",this.config.deallocate?0:-1)),tf3.ENV.set("WEBGL_FORCE_F16_TEXTURES",!0),tf3.ENV.set("WEBGL_PACK_DEPTHWISECONV",!0);let gl=await tf3.backend().getGPGPUContext().gl;log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`)}await tf3.ready(),this.perf.backend=Math.trunc(now()-timeStamp)}}async detectFace(input){var _a;let timeStamp,ageRes,genderRes,emotionRes,embeddingRes,faceRes=[];this.state="run:face",timeStamp=now();let faces=await((_a=this.models.facemesh)==null?void 0:_a.estimateFaces(input,this.config));this.perf.face=Math.trunc(now()-timeStamp);for(let face3 of faces){if(this.analyze("Get Face"),!face3.image||face3.image.isDisposedInternal){log("Face object is disposed:",face3.image);continue}this.analyze("Start Age:"),this.config.async?ageRes=this.config.face.age.enabled?age.predict(face3.image,this.config):{}:(this.state="run:age",timeStamp=now(),ageRes=this.config.face.age.enabled?await age.predict(face3.image,this.config):{},this.perf.age=Math.trunc(now()-timeStamp)),this.analyze("Start Gender:"),this.config.async?genderRes=this.config.face.gender.enabled?gender.predict(face3.image,this.config):{}:(this.state="run:gender",timeStamp=now(),genderRes=this.config.face.gender.enabled?await gender.predict(face3.image,this.config):{},this.perf.gender=Math.trunc(now()-timeStamp)),this.analyze("Start Emotion:"),this.config.async?emotionRes=this.config.face.emotion.enabled?emotion.predict(face3.image,this.config):{}:(this.state="run:emotion",timeStamp=now(),emotionRes=this.config.face.emotion.enabled?await emotion.predict(face3.image,this.config):{},this.perf.emotion=Math.trunc(now()-timeStamp)),this.analyze("End Emotion:"),this.analyze("Start Embedding:"),this.config.async?embeddingRes=this.config.face.embedding.enabled?embedding.predict(face3.image,this.config):{}:(this.state="run:embedding",timeStamp=now(),embeddingRes=this.config.face.embedding.enabled?await embedding.predict(face3.image,this.config):{},this.perf.embedding=Math.trunc(now()-timeStamp)),this.analyze("End Emotion:"),this.config.async&&([ageRes,genderRes,emotionRes,embeddingRes]=await Promise.all([ageRes,genderRes,emotionRes,embeddingRes])),this.analyze("Finish Face:"),face3.image.dispose(),this.config.face.iris.enabled||(delete face3.annotations.leftEyeIris,delete face3.annotations.rightEyeIris);let irisSize=face3.annotations.leftEyeIris&&face3.annotations.rightEyeIris?11.7*Math.max(Math.abs(face3.annotations.leftEyeIris[3][0]-face3.annotations.leftEyeIris[1][0]),Math.abs(face3.annotations.rightEyeIris[4][1]-face3.annotations.rightEyeIris[2][1])):0;faceRes.push({confidence:face3.confidence,box:face3.box,mesh:face3.mesh,boxRaw:face3.boxRaw,meshRaw:face3.meshRaw,annotations:face3.annotations,age:ageRes.age,gender:genderRes.gender,genderConfidence:genderRes.confidence,emotion:emotionRes,embedding:embeddingRes,iris:irisSize!==0?Math.trunc(irisSize)/100:0}),this.analyze("End Face")}return this.analyze("End FaceMesh:"),this.config.async&&(this.perf.face&&delete this.perf.face,this.perf.age&&delete this.perf.age,this.perf.gender&&delete this.perf.gender,this.perf.emotion&&delete this.perf.emotion),faceRes}async image(input,userConfig={}){this.state="image",this.config=mergeDeep(this.config,userConfig);let process3=image2.process(input,this.config);return process3.tensor.dispose(),process3.canvas}async detect(input,userConfig={}){return new Promise(async resolve=>{var _a,_b,_c,_d;this.state="config";let timeStamp;this.config=mergeDeep(this.config,userConfig),this.state="check";let error=this.sanity(input);error&&(log(error,input),resolve({error}));let poseRes,handRes,faceRes,timeStart=now();await this.checkBackend(),await this.load(),this.config.scoped&&tf3.engine().startScope(),this.analyze("Start Scope:"),timeStamp=now();let process3=image2.process(input,this.config);if(!process3||!process3.tensor){log("could not convert input to tensor"),resolve({error:"could not convert input to tensor"});return}this.perf.image=Math.trunc(now()-timeStamp),this.analyze("Get Image:"),this.config.async?(faceRes=this.config.face.enabled?this.detectFace(process3.tensor):[],this.perf.face&&delete this.perf.face):(this.state="run:face",timeStamp=now(),faceRes=this.config.face.enabled?await this.detectFace(process3.tensor):[],this.perf.face=Math.trunc(now()-timeStamp)),this.analyze("Start Body:"),this.config.async?(poseRes=this.config.body.enabled?(_a=this.models.posenet)==null?void 0:_a.estimatePoses(process3.tensor,this.config):[],this.perf.body&&delete this.perf.body):(this.state="run:body",timeStamp=now(),poseRes=this.config.body.enabled?await((_b=this.models.posenet)==null?void 0:_b.estimatePoses(process3.tensor,this.config)):[],this.perf.body=Math.trunc(now()-timeStamp)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(handRes=this.config.hand.enabled?(_c=this.models.handpose)==null?void 0:_c.estimateHands(process3.tensor,this.config):[],this.perf.hand&&delete this.perf.hand):(this.state="run:hand",timeStamp=now(),handRes=this.config.hand.enabled?await((_d=this.models.handpose)==null?void 0:_d.estimateHands(process3.tensor,this.config)):[],this.perf.hand=Math.trunc(now()-timeStamp)),this.config.async&&([faceRes,poseRes,handRes]=await Promise.all([faceRes,poseRes,handRes])),process3.tensor.dispose(),this.config.scoped&&tf3.engine().endScope(),this.analyze("End Scope:");let gestureRes=[];this.config.gesture.enabled&&(timeStamp=now(),gestureRes=[...gesture.face(faceRes),...gesture.body(poseRes),...gesture.hand(handRes),...gesture.iris(faceRes)],this.config.async?this.perf.gesture&&delete this.perf.gesture:this.perf.gesture=Math.trunc(now()-timeStamp)),this.perf.total=Math.trunc(now()-timeStart),this.state="idle",resolve({face:faceRes,body:poseRes,hand:handRes,gesture:gestureRes,performance:this.perf,canvas:process3.canvas})})}async warmup(userConfig){let b64toBlob=(base64,type="application/octet-stream")=>fetch(`data:${type};base64,${base64}`).then(res=>res.blob());userConfig&&(this.config=mergeDeep(this.config,userConfig));let video=this.config.videoOptimized;this.config.videoOptimized=!1;let blob;switch(this.config.warmup){case"face":blob=await b64toBlob(face);break;case"full":blob=await b64toBlob(body);break;default:blob=null}if(!blob)return null;let bitmap=await createImageBitmap(blob),t0=now(),warmup=await this.detect(bitmap,config_exports),t1=now();return bitmap.close(),log("Warmup",this.config.warmup,t1-t0,warmup),this.config.videoOptimized=video,warmup}}; +2Q==`;var version="0.9.24";var now=()=>typeof performance!="undefined"?performance.now():parseInt(Number(process.hrtime.bigint())/1e3/1e3);function mergeDeep(...objects){let isObject=obj=>obj&&typeof obj=="object";return objects.reduce((prev,obj)=>(Object.keys(obj||{}).forEach(key=>{let pVal=prev[key],oVal=obj[key];Array.isArray(pVal)&&Array.isArray(oVal)?prev[key]=pVal.concat(...oVal):isObject(pVal)&&isObject(oVal)?prev[key]=mergeDeep(pVal,oVal):prev[key]=oVal}),prev),{})}var Human=class{constructor(userConfig={}){this.tf=tf4,this.version=version,this.config=mergeDeep(config_default,userConfig),this.fx=null,this.state="idle",this.numTensors=0,this.analyzeMemoryLeaks=!1,this.checkSanity=!1,this.firstRun=!0,this.perf={},this.models={facemesh:null,posenet:null,handpose:null,iris:null,age:null,gender:null,emotion:null},this.facemesh=facemesh,this.age=age,this.gender=gender,this.emotion=emotion,this.body=posenet,this.hand=handpose}profile(){return this.config.profile?profile.data:{}}analyze(...msg){if(!this.analyzeMemoryLeaks)return;let current=tf4.engine().state.numTensors,previous=this.numTensors;this.numTensors=current;let leaked=current-previous;leaked!==0&&log(...msg,leaked)}sanity(input){if(!this.checkSanity)return null;if(!input)return"input is not defined";if(tf4.ENV.flags.IS_NODE&&!(input instanceof tf4.Tensor))return"input must be a tensor";try{tf4.getBackend()}catch(e){return"backend not loaded"}return null}simmilarity(embedding1,embedding2){return this.config.face.embedding.enabled?embedding.simmilarity(embedding1,embedding2):0}async load(userConfig){this.state="load";let timeStamp=now();userConfig&&(this.config=mergeDeep(this.config,userConfig)),this.firstRun&&(log(`version: ${this.version} TensorFlow/JS version: ${tf4.version_core}`),await this.checkBackend(!0),tf4.ENV.flags.IS_BROWSER&&(log("configuration:",this.config),log("tf flags:",tf4.ENV.flags)),this.firstRun=!1),this.config.async?[this.models.facemesh,this.models.age,this.models.gender,this.models.emotion,this.models.embedding,this.models.posenet,this.models.handpose,this.models.blazepose]=await Promise.all([this.models.facemesh||(this.config.face.enabled?facemesh.load(this.config):null),this.models.age||(this.config.face.enabled&&this.config.face.age.enabled?age.load(this.config):null),this.models.gender||(this.config.face.enabled&&this.config.face.gender.enabled?gender.load(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?emotion.load(this.config):null),this.models.embedding||(this.config.face.enabled&&this.config.face.embedding.enabled?embedding.load(this.config):null),this.models.posenet||(this.config.body.enabled?posenet.load(this.config):null),this.models.handpose||(this.config.hand.enabled?handpose.load(this.config):null),this.models.blazepose||(this.config.pose.enabled?load(this.config):null)]):(this.config.face.enabled&&!this.models.facemesh&&(this.models.facemesh=await facemesh.load(this.config)),this.config.face.enabled&&this.config.face.age.enabled&&!this.models.age&&(this.models.age=await age.load(this.config)),this.config.face.enabled&&this.config.face.gender.enabled&&!this.models.gender&&(this.models.gender=await gender.load(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await emotion.load(this.config)),this.config.face.enabled&&this.config.face.embedding.enabled&&!this.models.embedding&&(this.models.embedding=await embedding.load(this.config)),this.config.body.enabled&&!this.models.posenet&&(this.models.posenet=await posenet.load(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await handpose.load(this.config)),this.config.pose.enabled&&!this.models.blazepose&&(this.models.blazepose=await load(this.config)));let current=Math.trunc(now()-timeStamp);current>(this.perf.load||0)&&(this.perf.load=current)}async checkBackend(force){if(this.config.backend&&this.config.backend!==""&&force||tf4.getBackend()!==this.config.backend){let timeStamp=now();if(this.state="backend",log("setting backend:",this.config.backend),this.config.backend==="wasm"&&(log("settings wasm path:",this.config.wasmPath),tf4.setWasmPaths(this.config.wasmPath),await tf4.env().getAsync("WASM_HAS_SIMD_SUPPORT")||log("warning: wasm simd support is not enabled")),this.config.backend==="humangl"&&(log("registering humangl backend"),register()),await tf4.setBackend(this.config.backend),tf4.enableProdMode(),tf4.getBackend()==="webgl"){this.config.deallocate&&(log("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",this.config.deallocate),tf4.ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",this.config.deallocate?0:-1)),tf4.ENV.set("WEBGL_FORCE_F16_TEXTURES",!0),tf4.ENV.set("WEBGL_PACK_DEPTHWISECONV",!0);let gl=await tf4.backend().getGPGPUContext().gl;log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`)}await tf4.ready(),this.perf.backend=Math.trunc(now()-timeStamp)}}async detectFace(input){var _a;let timeStamp,ageRes,genderRes,emotionRes,embeddingRes,faceRes=[];this.state="run:face",timeStamp=now();let faces=await((_a=this.models.facemesh)==null?void 0:_a.estimateFaces(input,this.config));this.perf.face=Math.trunc(now()-timeStamp);for(let face3 of faces){if(this.analyze("Get Face"),!face3.image||face3.image.isDisposedInternal){log("Face object is disposed:",face3.image);continue}this.analyze("Start Age:"),this.config.async?ageRes=this.config.face.age.enabled?age.predict(face3.image,this.config):{}:(this.state="run:age",timeStamp=now(),ageRes=this.config.face.age.enabled?await age.predict(face3.image,this.config):{},this.perf.age=Math.trunc(now()-timeStamp)),this.analyze("Start Gender:"),this.config.async?genderRes=this.config.face.gender.enabled?gender.predict(face3.image,this.config):{}:(this.state="run:gender",timeStamp=now(),genderRes=this.config.face.gender.enabled?await gender.predict(face3.image,this.config):{},this.perf.gender=Math.trunc(now()-timeStamp)),this.analyze("Start Emotion:"),this.config.async?emotionRes=this.config.face.emotion.enabled?emotion.predict(face3.image,this.config):{}:(this.state="run:emotion",timeStamp=now(),emotionRes=this.config.face.emotion.enabled?await emotion.predict(face3.image,this.config):{},this.perf.emotion=Math.trunc(now()-timeStamp)),this.analyze("End Emotion:"),this.analyze("Start Embedding:"),this.config.async?embeddingRes=this.config.face.embedding.enabled?embedding.predict(face3.image,this.config):{}:(this.state="run:embedding",timeStamp=now(),embeddingRes=this.config.face.embedding.enabled?await embedding.predict(face3.image,this.config):{},this.perf.embedding=Math.trunc(now()-timeStamp)),this.analyze("End Emotion:"),this.config.async&&([ageRes,genderRes,emotionRes,embeddingRes]=await Promise.all([ageRes,genderRes,emotionRes,embeddingRes])),this.analyze("Finish Face:"),face3.image.dispose(),this.config.face.iris.enabled||(delete face3.annotations.leftEyeIris,delete face3.annotations.rightEyeIris);let irisSize=face3.annotations.leftEyeIris&&face3.annotations.rightEyeIris?11.7*Math.max(Math.abs(face3.annotations.leftEyeIris[3][0]-face3.annotations.leftEyeIris[1][0]),Math.abs(face3.annotations.rightEyeIris[4][1]-face3.annotations.rightEyeIris[2][1])):0;faceRes.push({confidence:face3.confidence,box:face3.box,mesh:face3.mesh,boxRaw:face3.boxRaw,meshRaw:face3.meshRaw,annotations:face3.annotations,age:ageRes.age,gender:genderRes.gender,genderConfidence:genderRes.confidence,emotion:emotionRes,embedding:embeddingRes,iris:irisSize!==0?Math.trunc(irisSize)/100:0}),this.analyze("End Face")}return this.analyze("End FaceMesh:"),this.config.async&&(this.perf.face&&delete this.perf.face,this.perf.age&&delete this.perf.age,this.perf.gender&&delete this.perf.gender,this.perf.emotion&&delete this.perf.emotion),faceRes}async image(input,userConfig={}){this.state="image",this.config=mergeDeep(this.config,userConfig);let process3=image3.process(input,this.config);return process3.tensor.dispose(),process3.canvas}async detect(input,userConfig={}){return new Promise(async resolve=>{var _a,_b,_c,_d;this.state="config";let timeStamp;this.config=mergeDeep(this.config,userConfig),this.state="check";let error=this.sanity(input);error&&(log(error,input),resolve({error}));let poseRes,blazeposeRes,handRes,faceRes,timeStart=now();await this.checkBackend(),await this.load(),this.config.scoped&&tf4.engine().startScope(),this.analyze("Start Scope:"),timeStamp=now();let process3=image3.process(input,this.config);if(!process3||!process3.tensor){log("could not convert input to tensor"),resolve({error:"could not convert input to tensor"});return}this.perf.image=Math.trunc(now()-timeStamp),this.analyze("Get Image:"),this.config.async?(faceRes=this.config.face.enabled?this.detectFace(process3.tensor):[],this.perf.face&&delete this.perf.face):(this.state="run:face",timeStamp=now(),faceRes=this.config.face.enabled?await this.detectFace(process3.tensor):[],this.perf.face=Math.trunc(now()-timeStamp)),this.analyze("Start Body:"),this.config.async?(poseRes=this.config.body.enabled?(_a=this.models.posenet)==null?void 0:_a.estimatePoses(process3.tensor,this.config):[],this.perf.body&&delete this.perf.body):(this.state="run:body",timeStamp=now(),poseRes=this.config.body.enabled?await((_b=this.models.posenet)==null?void 0:_b.estimatePoses(process3.tensor,this.config)):[],this.perf.body=Math.trunc(now()-timeStamp)),this.analyze("End Body:"),this.analyze("Start Pose:"),this.config.async?(blazeposeRes=this.config.pose.enabled?predict(process3.tensor,this.config):[],this.perf.pose&&delete this.perf.pose):(this.state="run:pose",timeStamp=now(),blazeposeRes=this.config.pose.enabled?await predict(process3.tensor,this.config):[],this.perf.pose=Math.trunc(now()-timeStamp)),this.analyze("End Pose:"),this.analyze("Start Hand:"),this.config.async?(handRes=this.config.hand.enabled?(_c=this.models.handpose)==null?void 0:_c.estimateHands(process3.tensor,this.config):[],this.perf.hand&&delete this.perf.hand):(this.state="run:hand",timeStamp=now(),handRes=this.config.hand.enabled?await((_d=this.models.handpose)==null?void 0:_d.estimateHands(process3.tensor,this.config)):[],this.perf.hand=Math.trunc(now()-timeStamp)),this.analyze("End Hand:"),this.config.async&&([faceRes,poseRes,blazeposeRes,handRes]=await Promise.all([faceRes,poseRes,blazeposeRes,handRes])),process3.tensor.dispose(),this.config.scoped&&tf4.engine().endScope(),this.analyze("End Scope:");let gestureRes=[];this.config.gesture.enabled&&(timeStamp=now(),gestureRes=[...gesture.face(faceRes),...gesture.body(poseRes),...gesture.hand(handRes),...gesture.iris(faceRes)],this.config.async?this.perf.gesture&&delete this.perf.gesture:this.perf.gesture=Math.trunc(now()-timeStamp)),this.perf.total=Math.trunc(now()-timeStart),this.state="idle",resolve({face:faceRes,body:poseRes,hand:handRes,pose:blazeposeRes,gesture:gestureRes,performance:this.perf,canvas:process3.canvas})})}async warmupBitmap(){let b64toBlob=(base64,type="application/octet-stream")=>fetch(`data:${type};base64,${base64}`).then(res2=>res2.blob()),blob,res;switch(this.config.warmup){case"face":blob=await b64toBlob(face);break;case"full":blob=await b64toBlob(body);break;default:blob=null}if(blob){let bitmap=await createImageBitmap(blob);res=await this.detect(bitmap,config_exports),bitmap.close()}return res}async warmupCanvas(){return new Promise(resolve=>{let src,size=0;switch(this.config.warmup){case"face":size=256,src="data:image/jpeg;base64,"+face;break;case"full":size=1200,src="data:image/jpeg;base64,"+body;break;default:src=null}let img=new Image(size,size);img.onload=()=>{let canvas=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(size,size):document.createElement("canvas");canvas.width=size,canvas.height=size;let ctx=canvas.getContext("2d");ctx.drawImage(img,0,0);let data2=ctx.getImageData(0,0,size,size);this.detect(data2,config_exports).then(res=>resolve(res))},src?img.src=src:resolve(null)})}async warmup(userConfig){let t0=now();userConfig&&(this.config=mergeDeep(this.config,userConfig));let video=this.config.videoOptimized;this.config.videoOptimized=!1;let res;typeof createImageBitmap=="function"?res=await this.warmupBitmap():res=await this.warmupCanvas(),this.config.videoOptimized=video;let t1=now();return log("Warmup",this.config.warmup,t1-t0,res),res}}; //# sourceMappingURL=human.node-gpu.js.map diff --git a/dist/human.node-gpu.js.map b/dist/human.node-gpu.js.map index bf86702e..dfca1bd7 100644 --- a/dist/human.node-gpu.js.map +++ b/dist/human.node-gpu.js.map @@ -1,7 +1,7 @@ { "version": 3, - "sources": ["../src/tfjs/tf-node-gpu.js", "../src/face/blazeface.js", "../src/face/box.js", "../src/face/util.js", "../src/face/coords.js", "../src/face/facepipeline.js", "../src/face/facemesh.js", "../src/profile.js", "../src/age/age.js", "../src/gender/gender.js", "../src/emotion/emotion.js", "../src/embedding/embedding.js", "../src/body/modelBase.js", "../src/body/heapSort.js", "../src/body/buildParts.js", "../src/body/keypoints.js", "../src/body/vectors.js", "../src/body/decoders.js", "../src/body/decodePose.js", "../src/body/decodeMultiple.js", "../src/body/util.js", "../src/body/modelPoseNet.js", "../src/body/posenet.js", "../src/hand/handdetector.js", "../src/hand/handpipeline.js", "../src/hand/anchors.js", "../src/hand/handpose.js", "../src/gesture/gesture.js", "../src/imagefx.js", "../src/image.js", "../src/human.js", "../src/log.js", "../src/tfjs/backend.js", "../src/hand/box.js", "../src/hand/util.js", "../config.js", "../src/sample.js"], - "sourcesContent": ["export * from '@tensorflow/tfjs-node-gpu';\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nconst NUM_LANDMARKS = 6;\n\nfunction generateAnchors(inputSize) {\n const spec = { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] };\n const anchors = [];\n for (let i = 0; i < spec.strides.length; i++) {\n const stride = spec.strides[i];\n const gridRows = Math.floor((inputSize + stride - 1) / stride);\n const gridCols = Math.floor((inputSize + stride - 1) / stride);\n const anchorsNum = spec.anchors[i];\n for (let gridY = 0; gridY < gridRows; gridY++) {\n const anchorY = stride * (gridY + 0.5);\n for (let gridX = 0; gridX < gridCols; gridX++) {\n const anchorX = stride * (gridX + 0.5);\n for (let n = 0; n < anchorsNum; n++) {\n anchors.push([anchorX, anchorY]);\n }\n }\n }\n }\n return anchors;\n}\n\nconst disposeBox = (box) => {\n box.startEndTensor.dispose();\n box.startPoint.dispose();\n box.endPoint.dispose();\n};\n\nconst createBox = (startEndTensor) => ({\n startEndTensor,\n startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]),\n endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]),\n});\n\nconst scaleBox = (box, factors) => {\n const starts = tf.mul(box.startPoint, factors);\n const ends = tf.mul(box.endPoint, factors);\n const newCoordinates = tf.concat2d([starts, ends], 1);\n return createBox(newCoordinates);\n};\n\nfunction decodeBounds(boxOutputs, anchors, inputSize) {\n const boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n const centers = tf.add(boxStarts, anchors);\n const boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n const boxSizesNormalized = tf.div(boxSizes, inputSize);\n const centersNormalized = tf.div(centers, inputSize);\n const halfBoxSize = tf.div(boxSizesNormalized, 2);\n const starts = tf.sub(centersNormalized, halfBoxSize);\n const ends = tf.add(centersNormalized, halfBoxSize);\n const startNormalized = tf.mul(starts, inputSize);\n const endNormalized = tf.mul(ends, inputSize);\n const concatAxis = 1;\n return tf.concat2d([startNormalized, endNormalized], concatAxis);\n}\n\nfunction scaleBoxFromPrediction(face, scaleFactor) {\n return tf.tidy(() => {\n const box = face['box'] ? face['box'] : face;\n return scaleBox(box, scaleFactor).startEndTensor.squeeze();\n });\n}\n\nclass BlazeFaceModel {\n constructor(model, config) {\n this.blazeFaceModel = model;\n this.width = config.face.detector.inputSize;\n this.height = config.face.detector.inputSize;\n this.anchorsData = generateAnchors(config.face.detector.inputSize);\n this.anchors = tf.tensor2d(this.anchorsData);\n this.inputSize = tf.tensor1d([this.width, this.height]);\n this.config = config;\n this.scaleFaces = 0.8;\n }\n\n async getBoundingBoxes(inputImage) {\n // sanity check on input\n if ((!inputImage) || (inputImage.isDisposedInternal) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return null;\n const [detectedOutputs, boxes, scores] = tf.tidy(() => {\n const resizedImage = inputImage.resizeBilinear([this.width, this.height]);\n // const normalizedImage = tf.mul(tf.sub(resizedImage.div(255), 0.5), 2);\n const normalizedImage = tf.sub(resizedImage.div(127.5), 1);\n const batchedPrediction = this.blazeFaceModel.predict(normalizedImage);\n let prediction;\n // are we using tfhub or pinto converted model?\n if (Array.isArray(batchedPrediction)) {\n const sorted = batchedPrediction.sort((a, b) => a.size - b.size);\n const concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n const concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n const concat = tf.concat([concat512, concat384], 1);\n prediction = concat.squeeze(0);\n } else {\n prediction = batchedPrediction.squeeze(); // when using tfhub model\n }\n const decodedBounds = decodeBounds(prediction, this.anchors, this.inputSize);\n const logits = tf.slice(prediction, [0, 0], [-1, 1]);\n const scoresOut = tf.sigmoid(logits).squeeze();\n return [prediction, decodedBounds, scoresOut];\n });\n const boxIndicesTensor = await tf.image.nonMaxSuppressionAsync(boxes, scores, this.config.face.detector.maxFaces, this.config.face.detector.iouThreshold, this.config.face.detector.scoreThreshold);\n const boxIndices = boxIndicesTensor.arraySync();\n boxIndicesTensor.dispose();\n const boundingBoxesMap = boxIndices.map((boxIndex) => tf.slice(boxes, [boxIndex, 0], [1, -1]));\n const boundingBoxes = boundingBoxesMap.map((boundingBox) => {\n const vals = boundingBox.arraySync();\n boundingBox.dispose();\n return vals;\n });\n\n const scoresVal = scores.dataSync();\n const annotatedBoxes = [];\n for (let i = 0; i < boundingBoxes.length; i++) {\n const boxIndex = boxIndices[i];\n const confidence = scoresVal[boxIndex];\n if (confidence > this.config.face.detector.minConfidence) {\n const box = createBox(boundingBoxes[i]);\n const anchor = this.anchorsData[boxIndex];\n const landmarks = tf.tidy(() => tf.slice(detectedOutputs, [boxIndex, NUM_LANDMARKS - 1], [1, -1]).squeeze().reshape([NUM_LANDMARKS, -1]));\n annotatedBoxes.push({ box, landmarks, anchor, confidence });\n }\n }\n detectedOutputs.dispose();\n boxes.dispose();\n scores.dispose();\n detectedOutputs.dispose();\n return {\n boxes: annotatedBoxes,\n scaleFactor: [inputImage.shape[2] / this.width, inputImage.shape[1] / this.height],\n };\n }\n\n async estimateFaces(input) {\n // @ts-ignore\n const { boxes, scaleFactor } = await this.getBoundingBoxes(input);\n const faces = [];\n for (const face of boxes) {\n const landmarkData = face.landmarks.arraySync();\n const scaledBox = scaleBoxFromPrediction(face, scaleFactor);\n // @ts-ignore\n const boxData = scaleBox.arraySync();\n const probabilityData = face.probability.arraySync();\n const anchor = face.anchor;\n const [scaleFactorX, scaleFactorY] = scaleFactor;\n const scaledLandmarks = landmarkData\n .map((landmark) => ([\n (landmark[0] + anchor[0]) * scaleFactorX,\n (landmark[1] + anchor[1]) * scaleFactorY,\n ]));\n const normalizedFace = {\n topLeft: boxData.slice(0, 2),\n bottomRight: boxData.slice(2),\n landmarks: scaledLandmarks,\n probability: probabilityData,\n };\n disposeBox(face.box);\n face.landmarks.dispose();\n face.probability.dispose();\n scaledBox.dispose();\n faces.push(normalizedFace);\n }\n return faces;\n }\n}\n\nasync function load(config) {\n const blazeface = await tf.loadGraphModel(config.face.detector.modelPath, { fromTFHub: config.face.detector.modelPath.includes('tfhub.dev') });\n const model = new BlazeFaceModel(blazeface, config);\n log(`load model: ${config.face.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n return model;\n}\n\nexports.load = load;\nexports.BlazeFaceModel = BlazeFaceModel;\nexports.disposeBox = disposeBox;\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nfunction scaleBoxCoordinates(box, factor) {\n const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n return { startPoint, endPoint };\n}\nexports.scaleBoxCoordinates = scaleBoxCoordinates;\n\nfunction getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\nexports.getBoxSize = getBoxSize;\n\nfunction getBoxCenter(box) {\n return [\n box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n ];\n}\nexports.getBoxCenter = getBoxCenter;\n\nfunction cutBoxFromImageAndResize(box, image, cropSize) {\n const h = image.shape[1];\n const w = image.shape[2];\n const boxes = [[\n box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h,\n box.endPoint[0] / w,\n ]];\n return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\nexports.cutBoxFromImageAndResize = cutBoxFromImageAndResize;\n\nfunction enlargeBox(box, factor = 1.5) {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n return { startPoint, endPoint, landmarks: box.landmarks };\n}\nexports.enlargeBox = enlargeBox;\n\nfunction squarifyBox(box) {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const maxEdge = Math.max(...size);\n const halfSize = maxEdge / 2;\n const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n return { startPoint, endPoint, landmarks: box.landmarks };\n}\nexports.squarifyBox = squarifyBox;\n", "exports.IDENTITY_MATRIX = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n/**\n * Normalizes the provided angle to the range -pi to pi.\n * @param angle The angle in radians to be normalized.\n */\nfunction normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\nexports.normalizeRadians = normalizeRadians;\n\n/**\n * Computes the angle of rotation between two anchor points.\n * @param point1 First anchor point\n * @param point2 Second anchor point\n */\nfunction computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\nexports.computeRotation = computeRotation;\n\nfunction radToDegrees(rad) {\n return rad * 180 / Math.PI;\n}\nexports.radToDegrees = radToDegrees;\n\nfunction buildTranslationMatrix(x, y) {\n return [[1, 0, x], [0, 1, y], [0, 0, 1]];\n}\n\nfunction dot(v1, v2) {\n let product = 0;\n for (let i = 0; i < v1.length; i++) {\n product += v1[i] * v2[i];\n }\n return product;\n}\nexports.dot = dot;\n\nfunction getColumnFrom2DArr(arr, columnIndex) {\n const column = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\nexports.getColumnFrom2DArr = getColumnFrom2DArr;\n\nfunction multiplyTransformMatrices(mat1, mat2) {\n const product = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) {\n product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n }\n return product;\n}\nfunction buildRotationMatrix(rotation, center) {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\nexports.buildRotationMatrix = buildRotationMatrix;\n\nfunction invertTransformMatrix(matrix) {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [\n -dot(rotationComponent[0], translationComponent),\n -dot(rotationComponent[1], translationComponent),\n ];\n return [\n rotationComponent[0].concat(invertedTranslation[0]),\n rotationComponent[1].concat(invertedTranslation[1]),\n [0, 0, 1],\n ];\n}\nexports.invertTransformMatrix = invertTransformMatrix;\n\nfunction rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\nexports.rotatePoint = rotatePoint;\n\nfunction xyDistanceBetweenPoints(a, b) {\n return Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n}\nexports.xyDistanceBetweenPoints = xyDistanceBetweenPoints;\n", "const MESH_ANNOTATIONS = {\n silhouette: [\n 10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n 397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n 172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n ],\n lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291],\n lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291],\n lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308],\n lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],\n rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173],\n rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133],\n rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190],\n rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243],\n rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189],\n rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244],\n rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245],\n rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193],\n rightEyebrowLower: [35, 124, 46, 53, 52, 65],\n rightEyeIris: [473, 474, 475, 476, 477],\n leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n leftEyeIris: [468, 469, 470, 471, 472],\n midwayBetweenEyes: [168],\n noseTip: [1],\n noseBottom: [2],\n noseRightCorner: [98],\n noseLeftCorner: [327],\n rightCheek: [205],\n leftCheek: [425],\n};\n\nconst MESH_TO_IRIS_INDICES_MAP = [ // A mapping from facemesh model keypoints to iris model keypoints.\n { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] },\n { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] },\n { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] },\n { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] },\n { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] },\n { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] },\n { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] },\n // { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] },\n // { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] },\n];\n\nconst UV468 = [\n [0.499976992607117, 0.652534008026123],\n [0.500025987625122, 0.547487020492554],\n [0.499974012374878, 0.602371990680695],\n [0.482113003730774, 0.471979022026062],\n [0.500150978565216, 0.527155995368958],\n [0.499909996986389, 0.498252987861633],\n [0.499523013830185, 0.40106201171875],\n [0.289712011814117, 0.380764007568359],\n [0.499954998493195, 0.312398016452789],\n [0.499987006187439, 0.269918978214264],\n [0.500023007392883, 0.107050001621246],\n [0.500023007392883, 0.666234016418457],\n [0.5000159740448, 0.679224014282227],\n [0.500023007392883, 0.692348003387451],\n [0.499976992607117, 0.695277988910675],\n [0.499976992607117, 0.70593398809433],\n [0.499976992607117, 0.719385027885437],\n [0.499976992607117, 0.737019002437592],\n [0.499967992305756, 0.781370997428894],\n [0.499816000461578, 0.562981009483337],\n [0.473773002624512, 0.573909997940063],\n [0.104906998574734, 0.254140973091125],\n [0.365929991006851, 0.409575998783112],\n [0.338757991790771, 0.41302502155304],\n [0.311120003461838, 0.409460008144379],\n [0.274657994508743, 0.389131009578705],\n [0.393361985683441, 0.403706014156342],\n [0.345234006643295, 0.344011008739471],\n [0.370094001293182, 0.346076011657715],\n [0.319321990013123, 0.347265005111694],\n [0.297903001308441, 0.353591024875641],\n [0.24779200553894, 0.410809993743896],\n [0.396889001131058, 0.842755019664764],\n [0.280097991228104, 0.375599980354309],\n [0.106310002505779, 0.399955987930298],\n [0.2099249958992, 0.391353011131287],\n [0.355807989835739, 0.534406006336212],\n [0.471751004457474, 0.65040397644043],\n [0.474155008792877, 0.680191993713379],\n [0.439785003662109, 0.657229006290436],\n [0.414617002010345, 0.66654098033905],\n [0.450374007225037, 0.680860996246338],\n [0.428770989179611, 0.682690978050232],\n [0.374971002340317, 0.727805018424988],\n [0.486716985702515, 0.547628998756409],\n [0.485300987958908, 0.527395009994507],\n [0.257764995098114, 0.314490020275116],\n [0.401223003864288, 0.455172002315521],\n [0.429818987846375, 0.548614978790283],\n [0.421351999044418, 0.533740997314453],\n [0.276895999908447, 0.532056987285614],\n [0.483370006084442, 0.499586999416351],\n [0.33721199631691, 0.282882988452911],\n [0.296391993761063, 0.293242990970612],\n [0.169294998049736, 0.193813979625702],\n [0.447580009698868, 0.302609980106354],\n [0.392390012741089, 0.353887975215912],\n [0.354490011930466, 0.696784019470215],\n [0.067304998636246, 0.730105042457581],\n [0.442739009857178, 0.572826027870178],\n [0.457098007202148, 0.584792017936707],\n [0.381974011659622, 0.694710969924927],\n [0.392388999462128, 0.694203019142151],\n [0.277076005935669, 0.271932005882263],\n [0.422551989555359, 0.563233017921448],\n [0.385919004678726, 0.281364023685455],\n [0.383103013038635, 0.255840003490448],\n [0.331431001424789, 0.119714021682739],\n [0.229923993349075, 0.232002973556519],\n [0.364500999450684, 0.189113974571228],\n [0.229622006416321, 0.299540996551514],\n [0.173287004232407, 0.278747975826263],\n [0.472878992557526, 0.666198015213013],\n [0.446828007698059, 0.668527007102966],\n [0.422762006521225, 0.673889994621277],\n [0.445307999849319, 0.580065965652466],\n [0.388103008270264, 0.693961024284363],\n [0.403039008378983, 0.706539988517761],\n [0.403629004955292, 0.693953037261963],\n [0.460041999816895, 0.557139039039612],\n [0.431158006191254, 0.692366003990173],\n [0.452181994915009, 0.692366003990173],\n [0.475387006998062, 0.692366003990173],\n [0.465828001499176, 0.779190003871918],\n [0.472328990697861, 0.736225962638855],\n [0.473087012767792, 0.717857003211975],\n [0.473122000694275, 0.704625964164734],\n [0.473033010959625, 0.695277988910675],\n [0.427942007780075, 0.695277988910675],\n [0.426479011774063, 0.703539967536926],\n [0.423162013292313, 0.711845993995667],\n [0.4183090031147, 0.720062971115112],\n [0.390094995498657, 0.639572978019714],\n [0.013953999616206, 0.560034036636353],\n [0.499913990497589, 0.58014702796936],\n [0.413199990987778, 0.69539999961853],\n [0.409626007080078, 0.701822996139526],\n [0.468080013990402, 0.601534962654114],\n [0.422728985548019, 0.585985004901886],\n [0.463079988956451, 0.593783974647522],\n [0.37211999297142, 0.47341400384903],\n [0.334562003612518, 0.496073007583618],\n [0.411671012639999, 0.546965003013611],\n [0.242175996303558, 0.14767599105835],\n [0.290776997804642, 0.201445996761322],\n [0.327338010072708, 0.256527006626129],\n [0.399509996175766, 0.748921036720276],\n [0.441727995872498, 0.261676013469696],\n [0.429764986038208, 0.187834024429321],\n [0.412198007106781, 0.108901023864746],\n [0.288955003023148, 0.398952007293701],\n [0.218936994671822, 0.435410976409912],\n [0.41278201341629, 0.398970007896423],\n [0.257135003805161, 0.355440020561218],\n [0.427684992551804, 0.437960982322693],\n [0.448339998722076, 0.536936044692993],\n [0.178560003638268, 0.45755398273468],\n [0.247308000922203, 0.457193970680237],\n [0.286267012357712, 0.467674970626831],\n [0.332827985286713, 0.460712015628815],\n [0.368755996227264, 0.447206974029541],\n [0.398963987827301, 0.432654976844788],\n [0.476410001516342, 0.405806005001068],\n [0.189241006970406, 0.523923993110657],\n [0.228962004184723, 0.348950982093811],\n [0.490725994110107, 0.562400996685028],\n [0.404670000076294, 0.485132992267609],\n [0.019469000399113, 0.401564002037048],\n [0.426243007183075, 0.420431017875671],\n [0.396993011236191, 0.548797011375427],\n [0.266469985246658, 0.376977026462555],\n [0.439121007919312, 0.51895797252655],\n [0.032313998788595, 0.644356966018677],\n [0.419054001569748, 0.387154996395111],\n [0.462783008813858, 0.505746960639954],\n [0.238978996872902, 0.779744982719421],\n [0.198220998048782, 0.831938028335571],\n [0.107550002634525, 0.540755033493042],\n [0.183610007166862, 0.740257024765015],\n [0.134409993886948, 0.333683013916016],\n [0.385764002799988, 0.883153975009918],\n [0.490967005491257, 0.579378008842468],\n [0.382384985685349, 0.508572995662689],\n [0.174399003386497, 0.397670984268188],\n [0.318785011768341, 0.39623498916626],\n [0.343364000320435, 0.400596976280212],\n [0.396100014448166, 0.710216999053955],\n [0.187885001301765, 0.588537991046906],\n [0.430987000465393, 0.944064974784851],\n [0.318993002176285, 0.898285031318665],\n [0.266247987747192, 0.869701027870178],\n [0.500023007392883, 0.190576016902924],\n [0.499976992607117, 0.954452991485596],\n [0.366169989109039, 0.398822009563446],\n [0.393207013607025, 0.39553701877594],\n [0.410373002290726, 0.391080021858215],\n [0.194993004202843, 0.342101991176605],\n [0.388664990663528, 0.362284004688263],\n [0.365961998701096, 0.355970978736877],\n [0.343364000320435, 0.355356991291046],\n [0.318785011768341, 0.35834002494812],\n [0.301414996385574, 0.363156020641327],\n [0.058132998645306, 0.319076001644135],\n [0.301414996385574, 0.387449026107788],\n [0.499987989664078, 0.618434011936188],\n [0.415838003158569, 0.624195992946625],\n [0.445681989192963, 0.566076993942261],\n [0.465844005346298, 0.620640993118286],\n [0.49992299079895, 0.351523995399475],\n [0.288718998432159, 0.819945991039276],\n [0.335278987884521, 0.852819979190826],\n [0.440512001514435, 0.902418971061707],\n [0.128294005990028, 0.791940987110138],\n [0.408771991729736, 0.373893976211548],\n [0.455606997013092, 0.451801002025604],\n [0.499877005815506, 0.908990025520325],\n [0.375436991453171, 0.924192011356354],\n [0.11421000212431, 0.615022003650665],\n [0.448662012815475, 0.695277988910675],\n [0.4480200111866, 0.704632043838501],\n [0.447111994028091, 0.715808033943176],\n [0.444831997156143, 0.730794012546539],\n [0.430011987686157, 0.766808986663818],\n [0.406787008047104, 0.685672998428345],\n [0.400738000869751, 0.681069016456604],\n [0.392399996519089, 0.677703022956848],\n [0.367855995893478, 0.663918972015381],\n [0.247923001646996, 0.601333022117615],\n [0.452769994735718, 0.420849978923798],\n [0.43639200925827, 0.359887003898621],\n [0.416164010763168, 0.368713974952698],\n [0.413385987281799, 0.692366003990173],\n [0.228018000721931, 0.683571994304657],\n [0.468268007040024, 0.352671027183533],\n [0.411361992359161, 0.804327011108398],\n [0.499989002943039, 0.469825029373169],\n [0.479153990745544, 0.442654013633728],\n [0.499974012374878, 0.439637005329132],\n [0.432112008333206, 0.493588984012604],\n [0.499886006116867, 0.866917014122009],\n [0.49991300702095, 0.821729004383087],\n [0.456548988819122, 0.819200992584229],\n [0.344549000263214, 0.745438992977142],\n [0.37890899181366, 0.574010014533997],\n [0.374292999505997, 0.780184984207153],\n [0.319687992334366, 0.570737957954407],\n [0.357154995203018, 0.604269981384277],\n [0.295284003019333, 0.621580958366394],\n [0.447750002145767, 0.862477004528046],\n [0.410986006259918, 0.508723020553589],\n [0.31395098567009, 0.775308012962341],\n [0.354128003120422, 0.812552988529205],\n [0.324548006057739, 0.703992962837219],\n [0.189096003770828, 0.646299958229065],\n [0.279776990413666, 0.71465802192688],\n [0.1338230073452, 0.682700991630554],\n [0.336768001317978, 0.644733011722565],\n [0.429883986711502, 0.466521978378296],\n [0.455527991056442, 0.548622965812683],\n [0.437114000320435, 0.558896005153656],\n [0.467287987470627, 0.529924988746643],\n [0.414712011814117, 0.335219979286194],\n [0.37704598903656, 0.322777986526489],\n [0.344107985496521, 0.320150971412659],\n [0.312875986099243, 0.32233202457428],\n [0.283526003360748, 0.333190023899078],\n [0.241245999932289, 0.382785975933075],\n [0.102986000478268, 0.468762993812561],\n [0.267612010240555, 0.424560010433197],\n [0.297879010438919, 0.433175981044769],\n [0.333433985710144, 0.433878004550934],\n [0.366427004337311, 0.426115989685059],\n [0.396012008190155, 0.416696012020111],\n [0.420121014118195, 0.41022801399231],\n [0.007561000064015, 0.480777025222778],\n [0.432949006557465, 0.569517970085144],\n [0.458638995885849, 0.479089021682739],\n [0.473466008901596, 0.545744001865387],\n [0.476087987422943, 0.563830018043518],\n [0.468472003936768, 0.555056989192963],\n [0.433990985155106, 0.582361996173859],\n [0.483518004417419, 0.562983989715576],\n [0.482482999563217, 0.57784903049469],\n [0.42645001411438, 0.389798998832703],\n [0.438998997211456, 0.39649498462677],\n [0.450067013502121, 0.400434017181396],\n [0.289712011814117, 0.368252992630005],\n [0.276670008897781, 0.363372981548309],\n [0.517862021923065, 0.471948027610779],\n [0.710287988185883, 0.380764007568359],\n [0.526226997375488, 0.573909997940063],\n [0.895093023777008, 0.254140973091125],\n [0.634069979190826, 0.409575998783112],\n [0.661242008209229, 0.41302502155304],\n [0.688880026340485, 0.409460008144379],\n [0.725341975688934, 0.389131009578705],\n [0.606630027294159, 0.40370500087738],\n [0.654766023159027, 0.344011008739471],\n [0.629905998706818, 0.346076011657715],\n [0.680678009986877, 0.347265005111694],\n [0.702096998691559, 0.353591024875641],\n [0.75221198797226, 0.410804986953735],\n [0.602918028831482, 0.842862963676453],\n [0.719901978969574, 0.375599980354309],\n [0.893692970275879, 0.399959981441498],\n [0.790081977844238, 0.391354024410248],\n [0.643998026847839, 0.534487962722778],\n [0.528249025344849, 0.65040397644043],\n [0.525849997997284, 0.680191040039062],\n [0.560214996337891, 0.657229006290436],\n [0.585384011268616, 0.66654098033905],\n [0.549625992774963, 0.680860996246338],\n [0.57122802734375, 0.682691991329193],\n [0.624852001667023, 0.72809898853302],\n [0.513050019741058, 0.547281980514526],\n [0.51509702205658, 0.527251958847046],\n [0.742246985435486, 0.314507007598877],\n [0.598631024360657, 0.454979002475739],\n [0.570338010787964, 0.548575043678284],\n [0.578631997108459, 0.533622980117798],\n [0.723087012767792, 0.532054007053375],\n [0.516445994377136, 0.499638974666595],\n [0.662801027297974, 0.282917976379395],\n [0.70362401008606, 0.293271005153656],\n [0.830704987049103, 0.193813979625702],\n [0.552385985851288, 0.302568018436432],\n [0.607609987258911, 0.353887975215912],\n [0.645429015159607, 0.696707010269165],\n [0.932694971561432, 0.730105042457581],\n [0.557260990142822, 0.572826027870178],\n [0.542901992797852, 0.584792017936707],\n [0.6180260181427, 0.694710969924927],\n [0.607590973377228, 0.694203019142151],\n [0.722943007946014, 0.271963000297546],\n [0.577413976192474, 0.563166975975037],\n [0.614082992076874, 0.281386971473694],\n [0.616907000541687, 0.255886018276215],\n [0.668509006500244, 0.119913995265961],\n [0.770092010498047, 0.232020974159241],\n [0.635536015033722, 0.189248979091644],\n [0.77039098739624, 0.299556016921997],\n [0.826722025871277, 0.278755009174347],\n [0.527121007442474, 0.666198015213013],\n [0.553171992301941, 0.668527007102966],\n [0.577238023281097, 0.673889994621277],\n [0.554691970348358, 0.580065965652466],\n [0.611896991729736, 0.693961024284363],\n [0.59696102142334, 0.706539988517761],\n [0.596370995044708, 0.693953037261963],\n [0.539958000183105, 0.557139039039612],\n [0.568841993808746, 0.692366003990173],\n [0.547818005084991, 0.692366003990173],\n [0.52461302280426, 0.692366003990173],\n [0.534089982509613, 0.779141008853912],\n [0.527670979499817, 0.736225962638855],\n [0.526912987232208, 0.717857003211975],\n [0.526877999305725, 0.704625964164734],\n [0.526966989040375, 0.695277988910675],\n [0.572058022022247, 0.695277988910675],\n [0.573521018028259, 0.703539967536926],\n [0.57683801651001, 0.711845993995667],\n [0.581691026687622, 0.720062971115112],\n [0.609944999217987, 0.639909982681274],\n [0.986046016216278, 0.560034036636353],\n [0.5867999792099, 0.69539999961853],\n [0.590372025966644, 0.701822996139526],\n [0.531915009021759, 0.601536989212036],\n [0.577268004417419, 0.585934996604919],\n [0.536915004253387, 0.593786001205444],\n [0.627542972564697, 0.473352015018463],\n [0.665585994720459, 0.495950996875763],\n [0.588353991508484, 0.546862006187439],\n [0.757824003696442, 0.14767599105835],\n [0.709249973297119, 0.201507985591888],\n [0.672684013843536, 0.256581008434296],\n [0.600408971309662, 0.74900496006012],\n [0.55826598405838, 0.261672019958496],\n [0.570303976535797, 0.187870979309082],\n [0.588165998458862, 0.109044015407562],\n [0.711045026779175, 0.398952007293701],\n [0.781069993972778, 0.435405015945435],\n [0.587247014045715, 0.398931980133057],\n [0.742869973182678, 0.355445981025696],\n [0.572156012058258, 0.437651991844177],\n [0.55186802148819, 0.536570012569427],\n [0.821442008018494, 0.457556009292603],\n [0.752701997756958, 0.457181990146637],\n [0.71375697851181, 0.467626988887787],\n [0.66711300611496, 0.460672974586487],\n [0.631101012229919, 0.447153985500336],\n [0.6008620262146, 0.432473003864288],\n [0.523481011390686, 0.405627012252808],\n [0.810747981071472, 0.523926019668579],\n [0.771045982837677, 0.348959028720856],\n [0.509127020835876, 0.562718033790588],\n [0.595292985439301, 0.485023975372314],\n [0.980530977249146, 0.401564002037048],\n [0.573499977588654, 0.420000016689301],\n [0.602994978427887, 0.548687994480133],\n [0.733529984951019, 0.376977026462555],\n [0.560611009597778, 0.519016981124878],\n [0.967685997486115, 0.644356966018677],\n [0.580985009670258, 0.387160003185272],\n [0.537728011608124, 0.505385041236877],\n [0.760966002941132, 0.779752969741821],\n [0.801778972148895, 0.831938028335571],\n [0.892440974712372, 0.54076099395752],\n [0.816350996494293, 0.740260004997253],\n [0.865594983100891, 0.333687007427216],\n [0.614073991775513, 0.883246004581451],\n [0.508952975273132, 0.579437971115112],\n [0.617941975593567, 0.508316040039062],\n [0.825608015060425, 0.397674977779388],\n [0.681214988231659, 0.39623498916626],\n [0.656635999679565, 0.400596976280212],\n [0.603900015354156, 0.710216999053955],\n [0.81208598613739, 0.588539004325867],\n [0.56801301240921, 0.944564998149872],\n [0.681007981300354, 0.898285031318665],\n [0.733752012252808, 0.869701027870178],\n [0.633830010890961, 0.398822009563446],\n [0.606792986392975, 0.39553701877594],\n [0.589659988880157, 0.391062021255493],\n [0.805015981197357, 0.342108011245728],\n [0.611334979534149, 0.362284004688263],\n [0.634037971496582, 0.355970978736877],\n [0.656635999679565, 0.355356991291046],\n [0.681214988231659, 0.35834002494812],\n [0.698584973812103, 0.363156020641327],\n [0.941866993904114, 0.319076001644135],\n [0.698584973812103, 0.387449026107788],\n [0.584177017211914, 0.624107003211975],\n [0.554318010807037, 0.566076993942261],\n [0.534153997898102, 0.62064003944397],\n [0.711217999458313, 0.819975018501282],\n [0.664629995822906, 0.852871000766754],\n [0.559099972248077, 0.902631998062134],\n [0.871706008911133, 0.791940987110138],\n [0.591234028339386, 0.373893976211548],\n [0.544341027736664, 0.451583981513977],\n [0.624562978744507, 0.924192011356354],\n [0.88577002286911, 0.615028977394104],\n [0.551338016986847, 0.695277988910675],\n [0.551980018615723, 0.704632043838501],\n [0.552887976169586, 0.715808033943176],\n [0.555167973041534, 0.730794012546539],\n [0.569944024085999, 0.767035007476807],\n [0.593203008174896, 0.685675978660583],\n [0.599261999130249, 0.681069016456604],\n [0.607599973678589, 0.677703022956848],\n [0.631937980651855, 0.663500010967255],\n [0.752032995223999, 0.601315021514893],\n [0.547226011753082, 0.420395016670227],\n [0.563543975353241, 0.359827995300293],\n [0.583841025829315, 0.368713974952698],\n [0.586614012718201, 0.692366003990173],\n [0.771915018558502, 0.683578014373779],\n [0.531597018241882, 0.352482974529266],\n [0.588370978832245, 0.804440975189209],\n [0.52079701423645, 0.442565023899078],\n [0.567984998226166, 0.493479013442993],\n [0.543282985687256, 0.819254994392395],\n [0.655317008495331, 0.745514988899231],\n [0.621008992195129, 0.574018001556396],\n [0.625559985637665, 0.78031200170517],\n [0.680198013782501, 0.570719003677368],\n [0.64276397228241, 0.604337990283966],\n [0.704662978649139, 0.621529996395111],\n [0.552012026309967, 0.862591981887817],\n [0.589071989059448, 0.508637011051178],\n [0.685944974422455, 0.775357007980347],\n [0.645735025405884, 0.812640011310577],\n [0.675342977046967, 0.703978002071381],\n [0.810858011245728, 0.646304965019226],\n [0.72012197971344, 0.714666962623596],\n [0.866151988506317, 0.682704985141754],\n [0.663187026977539, 0.644596993923187],\n [0.570082008838654, 0.466325998306274],\n [0.544561982154846, 0.548375964164734],\n [0.562758982181549, 0.558784961700439],\n [0.531987011432648, 0.530140042304993],\n [0.585271000862122, 0.335177004337311],\n [0.622952997684479, 0.32277899980545],\n [0.655896008014679, 0.320163011550903],\n [0.687132000923157, 0.322345972061157],\n [0.716481983661652, 0.333200991153717],\n [0.758756995201111, 0.382786989212036],\n [0.897013008594513, 0.468769013881683],\n [0.732392013072968, 0.424547016620636],\n [0.70211398601532, 0.433162987232208],\n [0.66652500629425, 0.433866024017334],\n [0.633504986763, 0.426087975502014],\n [0.603875994682312, 0.416586995124817],\n [0.579657971858978, 0.409945011138916],\n [0.992439985275269, 0.480777025222778],\n [0.567192018032074, 0.569419980049133],\n [0.54136598110199, 0.478899002075195],\n [0.526564002037048, 0.546118021011353],\n [0.523913025856018, 0.563830018043518],\n [0.531529009342194, 0.555056989192963],\n [0.566035985946655, 0.582329034805298],\n [0.51631098985672, 0.563053965568542],\n [0.5174720287323, 0.577877044677734],\n [0.573594987392426, 0.389806985855103],\n [0.560697972774506, 0.395331978797913],\n [0.549755990505219, 0.399751007556915],\n [0.710287988185883, 0.368252992630005],\n [0.723330020904541, 0.363372981548309],\n];\n\nconst TRI468 = [\n 127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n 151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n 157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n 181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n 228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n 170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n 155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n 158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n 102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n 90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n 196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n 246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n 117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n 153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n 63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n 135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n 76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n 166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n 190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n 112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n 110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n 348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n 314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n 321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n 466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n 429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n 293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n 427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n 431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n 434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n 348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n 318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n 373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n 340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n 351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n 164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n 327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n 405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n 438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n 449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n 444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n 464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n 243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n 166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n 119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n 77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n 201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n 103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n 217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n 78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n 57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n 142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n 90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n 178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n 224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n 143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n 153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n 63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n 241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n 186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n 141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n 223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n 128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n 451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n 344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n 424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n 321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n 448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n 281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n 283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n 432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n 268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n 6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n 317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n 272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n 336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n 441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n 373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n 425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n 403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n 383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n 254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n 259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n 453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nconst TRI68 = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n 8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n 45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n 23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n 39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n 35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n 48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n 66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\nconst TRI33 = [\n /* eyes */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n /* brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n /* 4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n /* nose */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n /* chin */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n /* cont */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\nconst TRI7 = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nconst VTX68 = [\n /* cont */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n /* nose */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n /* eyes */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n /* lip */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\nconst VTX33 = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\nconst VTX7 = [33, 133, 362, 263, 1, 78, 308];\n\nexports.MESH_ANNOTATIONS = MESH_ANNOTATIONS;\nexports.MESH_TO_IRIS_INDICES_MAP = MESH_TO_IRIS_INDICES_MAP;\n\nexports.TRI468 = TRI468;\nexports.TRI68 = TRI68;\nexports.TRI33 = TRI33;\nexports.TRI7 = TRI7;\n\nexports.UV468 = UV468;\nexports.UV68 = VTX68.map((x) => UV468[x]);\nexports.UV33 = VTX33.map((x) => UV468[x]);\nexports.UV7 = VTX7.map((x) => UV468[x]);\n", "/* eslint-disable class-methods-use-this */\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as bounding from './box';\nimport * as util from './util';\nimport * as coords from './coords.js';\n// eslint-disable-next-line no-unused-vars\nimport { log } from '../log.js';\n\nconst LANDMARKS_COUNT = 468;\nconst MESH_MOUTH_INDEX = 13;\nconst MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES = [MESH_MOUTH_INDEX, coords.MESH_ANNOTATIONS['midwayBetweenEyes'][0]];\nconst BLAZEFACE_MOUTH_INDEX = 3;\nconst BLAZEFACE_NOSE_INDEX = 2;\nconst BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES = [BLAZEFACE_MOUTH_INDEX, BLAZEFACE_NOSE_INDEX];\nconst LEFT_EYE_OUTLINE = coords.MESH_ANNOTATIONS['leftEyeLower0'];\nconst LEFT_EYE_BOUNDS = [LEFT_EYE_OUTLINE[0], LEFT_EYE_OUTLINE[LEFT_EYE_OUTLINE.length - 1]];\nconst RIGHT_EYE_OUTLINE = coords.MESH_ANNOTATIONS['rightEyeLower0'];\nconst RIGHT_EYE_BOUNDS = [RIGHT_EYE_OUTLINE[0], RIGHT_EYE_OUTLINE[RIGHT_EYE_OUTLINE.length - 1]];\nconst IRIS_UPPER_CENTER_INDEX = 3;\nconst IRIS_LOWER_CENTER_INDEX = 4;\nconst IRIS_IRIS_INDEX = 71;\nconst IRIS_NUM_COORDINATES = 76;\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates. Update the z coordinate to be an average of the original and the new. This produces the best visual effect.\nfunction replaceRawCoordinates(rawCoords, newCoords, prefix, keys) {\n for (let i = 0; i < coords.MESH_TO_IRIS_INDICES_MAP.length; i++) {\n const { key, indices } = coords.MESH_TO_IRIS_INDICES_MAP[i];\n const originalIndices = coords.MESH_ANNOTATIONS[`${prefix}${key}`];\n const shouldReplaceAllKeys = keys == null;\n if (shouldReplaceAllKeys || keys.includes(key)) {\n for (let j = 0; j < indices.length; j++) {\n const index = indices[j];\n rawCoords[originalIndices[j]] = [\n newCoords[index][0], newCoords[index][1],\n (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n ];\n }\n }\n }\n}\n// The Pipeline coordinates between the bounding box and skeleton models.\nclass Pipeline {\n constructor(boundingBoxDetector, meshDetector, irisModel, config) {\n // An array of facial bounding boxes.\n this.storedBoxes = [];\n this.runsWithoutFaceDetector = 0;\n this.boundingBoxDetector = boundingBoxDetector;\n this.meshDetector = meshDetector;\n this.irisModel = irisModel;\n this.meshWidth = config.face.mesh.inputSize;\n this.meshHeight = config.face.mesh.inputSize;\n this.irisSize = config.face.iris.inputSize;\n this.irisEnlarge = 2.3;\n this.skipped = 0;\n this.detectedFaces = 0;\n }\n\n transformRawCoords(rawCoords, box, angle, rotationMatrix) {\n // @ts-ignore\n const boxSize = bounding.getBoxSize({ startPoint: box.startPoint, endPoint: box.endPoint });\n const scaleFactor = [boxSize[0] / this.meshWidth, boxSize[1] / this.meshHeight];\n const coordsScaled = rawCoords.map((coord) => ([\n scaleFactor[0] * (coord[0] - this.meshWidth / 2),\n scaleFactor[1] * (coord[1] - this.meshHeight / 2), coord[2],\n ]));\n const coordsRotationMatrix = (angle !== 0) ? util.buildRotationMatrix(angle, [0, 0]) : util.IDENTITY_MATRIX;\n const coordsRotated = (angle !== 0) ? coordsScaled.map((coord) => ([...util.rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n const inverseRotationMatrix = (angle !== 0) ? util.invertTransformMatrix(rotationMatrix) : util.IDENTITY_MATRIX;\n // @ts-ignore\n const boxCenter = [...bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint }), 1];\n return coordsRotated.map((coord) => ([\n coord[0] + util.dot(boxCenter, inverseRotationMatrix[0]),\n coord[1] + util.dot(boxCenter, inverseRotationMatrix[1]),\n coord[2],\n ]));\n }\n\n getLeftToRightEyeDepthDifference(rawCoords) {\n const leftEyeZ = rawCoords[LEFT_EYE_BOUNDS[0]][2];\n const rightEyeZ = rawCoords[RIGHT_EYE_BOUNDS[0]][2];\n return leftEyeZ - rightEyeZ;\n }\n\n // Returns a box describing a cropped region around the eye fit for passing to the iris model.\n getEyeBox(rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, flip = false) {\n // @ts-ignore\n const box = bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), this.irisEnlarge));\n // @ts-ignore\n const boxSize = bounding.getBoxSize(box);\n let crop = tf.image.cropAndResize(face, [[\n box.startPoint[1] / this.meshHeight,\n box.startPoint[0] / this.meshWidth, box.endPoint[1] / this.meshHeight,\n box.endPoint[0] / this.meshWidth,\n ]], [0], [this.irisSize, this.irisSize]);\n if (flip) {\n crop = tf.image.flipLeftRight(crop);\n }\n return { box, boxSize, crop };\n }\n\n // Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\n getEyeCoords(eyeData, eyeBox, eyeBoxSize, flip = false) {\n const eyeRawCoords = [];\n for (let i = 0; i < IRIS_NUM_COORDINATES; i++) {\n const x = eyeData[i * 3];\n const y = eyeData[i * 3 + 1];\n const z = eyeData[i * 3 + 2];\n eyeRawCoords.push([\n (flip\n ? (1 - (x / this.irisSize))\n : (x / this.irisSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n (y / this.irisSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n ]);\n }\n return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(IRIS_IRIS_INDEX) };\n }\n\n // The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\n getAdjustedIrisCoords(rawCoords, irisCoords, direction) {\n const upperCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeUpper0`][IRIS_UPPER_CENTER_INDEX]][2];\n const lowerCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeLower0`][IRIS_LOWER_CENTER_INDEX]][2];\n const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n return irisCoords.map((coord, i) => {\n let z = averageZ;\n if (i === 2) {\n z = upperCenterZ;\n } else if (i === 4) {\n z = lowerCenterZ;\n }\n return [coord[0], coord[1], z];\n });\n }\n\n async predict(input, config) {\n let useFreshBox = false;\n // run new detector every skipFrames unless we only want box to start with\n let detector;\n if ((this.skipped === 0) || (this.skipped > config.face.detector.skipFrames) || !config.face.mesh.enabled || !config.videoOptimized) {\n detector = await this.boundingBoxDetector.getBoundingBoxes(input);\n this.skipped = 0;\n }\n if (config.videoOptimized) this.skipped++;\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (detector && detector.boxes && (detector.boxes.length > 0) && (!config.face.mesh.enabled || (detector.boxes.length !== this.detectedFaces) && (this.detectedFaces !== config.face.detector.maxFaces))) {\n this.storedBoxes = [];\n this.detectedFaces = 0;\n for (const possible of detector.boxes) {\n this.storedBoxes.push({ startPoint: possible.box.startPoint.dataSync(), endPoint: possible.box.endPoint.dataSync(), landmarks: possible.landmarks, confidence: possible.confidence });\n }\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\n\n if (useFreshBox) {\n if (!detector || !detector.boxes || (detector.boxes.length === 0)) {\n this.storedBoxes = [];\n this.detectedFaces = 0;\n return null;\n }\n for (let i = 0; i < this.storedBoxes.length; i++) {\n // @ts-ignore\n const scaledBox = bounding.scaleBoxCoordinates({ startPoint: this.storedBoxes[i].startPoint, endPoint: this.storedBoxes[i].endPoint }, detector.scaleFactor);\n // @ts-ignore\n const enlargedBox = bounding.enlargeBox(scaledBox);\n // @ts-ignore\n const squarifiedBox = bounding.squarifyBox(enlargedBox);\n const landmarks = this.storedBoxes[i].landmarks.arraySync();\n const confidence = this.storedBoxes[i].confidence;\n this.storedBoxes[i] = { ...squarifiedBox, confidence, landmarks };\n }\n this.runsWithoutFaceDetector = 0;\n }\n if (detector && detector.boxes) {\n detector.boxes.forEach((prediction) => {\n prediction.box.startPoint.dispose();\n prediction.box.endPoint.dispose();\n prediction.landmarks.dispose();\n });\n }\n\n // log('face', `skipped: ${this.skipped} max: ${config.face.detector.maxFaces} detected: ${this.detectedFaces} stored: ${this.storedBoxes.length} new: ${detector?.boxes?.length}`);\n let results = tf.tidy(() => this.storedBoxes.map((box, i) => {\n // The facial bounding box landmarks could come either from blazeface (if we are using a fresh box), or from the mesh model (if we are reusing an old box).\n let face;\n let angle = 0;\n let rotationMatrix;\n if (config.face.detector.rotation) {\n const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= LANDMARKS_COUNT) ? MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES : BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES;\n angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n // @ts-ignore\n const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n const rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized);\n rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n // @ts-ignore\n face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshHeight, this.meshWidth]).div(255);\n } else {\n rotationMatrix = util.IDENTITY_MATRIX;\n const cloned = input.clone();\n // @ts-ignore\n face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, cloned, [this.meshHeight, this.meshWidth]).div(255);\n }\n\n // if we're not going to produce mesh, don't spend time with further processing\n if (!config.face.mesh.enabled) {\n const prediction = {\n coords: null,\n box,\n faceConfidence: null,\n confidence: box.confidence,\n image: face,\n };\n return prediction;\n }\n\n const [, confidence, contourCoords] = this.meshDetector.predict(face); // The first returned tensor represents facial contours, which are included in the coordinates.\n const confidenceVal = confidence.dataSync()[0];\n if (confidenceVal < config.face.detector.minConfidence) return null; // if below confidence just exit\n const coordsReshaped = tf.reshape(contourCoords, [-1, 3]);\n let rawCoords = coordsReshaped.arraySync();\n\n if (config.face.iris.enabled) {\n const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = this.getEyeBox(rawCoords, face, LEFT_EYE_BOUNDS[0], LEFT_EYE_BOUNDS[1], true);\n const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = this.getEyeBox(rawCoords, face, RIGHT_EYE_BOUNDS[0], RIGHT_EYE_BOUNDS[1]);\n const eyePredictions = this.irisModel.predict(tf.concat([leftEyeCrop, rightEyeCrop]));\n const eyePredictionsData = eyePredictions.dataSync();\n const leftEyeData = eyePredictionsData.slice(0, IRIS_NUM_COORDINATES * 3);\n const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = this.getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n const rightEyeData = eyePredictionsData.slice(IRIS_NUM_COORDINATES * 3);\n const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = this.getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize);\n const leftToRightEyeDepthDifference = this.getLeftToRightEyeDepthDifference(rawCoords);\n if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left');\n replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right');\n // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged. So we only update a single contour line above and below the eye.\n } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n } else { // User is looking towards the left.\n replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n }\n const adjustedLeftIrisCoords = this.getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n const adjustedRightIrisCoords = this.getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n rawCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n }\n\n const transformedCoordsData = this.transformRawCoords(rawCoords, box, angle, rotationMatrix);\n // @ts-ignore\n const landmarksBox = bounding.enlargeBox(this.calculateLandmarksBoundingBox(transformedCoordsData));\n // @ts-ignore\n const squarifiedLandmarksBox = bounding.squarifyBox(landmarksBox);\n const transformedCoords = tf.tensor2d(transformedCoordsData);\n const prediction = {\n coords: transformedCoords,\n box: landmarksBox,\n faceConfidence: confidenceVal,\n confidence: box.confidence,\n image: face,\n };\n if (config.face.mesh.returnRawData) prediction.rawCoords = rawCoords;\n this.storedBoxes[i] = { ...squarifiedLandmarksBox, landmarks: transformedCoords.arraySync(), confidence: box.confidence, faceConfidence: confidenceVal };\n\n return prediction;\n }));\n results = results.filter((a) => a !== null);\n this.detectedFaces = results.length;\n return results;\n }\n\n calculateLandmarksBoundingBox(landmarks) {\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint, landmarks };\n }\n}\nexports.Pipeline = Pipeline;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as blazeface from './blazeface.js';\nimport * as facepipeline from './facepipeline.js';\nimport * as coords from './coords.js';\n\nclass MediaPipeFaceMesh {\n constructor(blazeFace, blazeMeshModel, irisModel, config) {\n // @ts-ignore\n this.facePipeline = new facepipeline.Pipeline(blazeFace, blazeMeshModel, irisModel, config);\n this.config = config;\n }\n\n async estimateFaces(input, config) {\n const predictions = await this.facePipeline.predict(input, config);\n const results = [];\n for (const prediction of (predictions || [])) {\n if (prediction.isDisposedInternal) continue; // guard against disposed tensors on long running operations such as pause in middle of processing\n const mesh = prediction.coords ? prediction.coords.arraySync() : null;\n const meshRaw = prediction.rawCoords;\n const annotations = {};\n if (mesh && mesh.length > 0) {\n for (const key of Object.keys(coords.MESH_ANNOTATIONS)) {\n annotations[key] = coords.MESH_ANNOTATIONS[key].map((index) => mesh[index]);\n }\n }\n const boxRaw = (config.face.mesh.returnRawData && prediction.box) ? { topLeft: prediction.box.startPoint, bottomRight: prediction.box.endPoint } : null;\n const box = prediction.box ? [\n Math.max(0, prediction.box.startPoint[0]),\n Math.max(0, prediction.box.startPoint[1]),\n Math.min(input.shape[2], prediction.box.endPoint[0]) - prediction.box.startPoint[0],\n Math.min(input.shape[1], prediction.box.endPoint[1]) - prediction.box.startPoint[1],\n ] : 0;\n results.push({\n confidence: prediction.confidence || 0,\n box,\n mesh,\n boxRaw,\n meshRaw,\n annotations,\n image: prediction.image ? tf.clone(prediction.image) : null,\n });\n if (prediction.coords) prediction.coords.dispose();\n if (prediction.image) prediction.image.dispose();\n }\n return results;\n }\n}\n\nlet faceModels = [null, null, null];\nasync function load(config) {\n faceModels = await Promise.all([\n // @ts-ignore\n (!faceModels[0] && config.face.enabled) ? blazeface.load(config) : null,\n (!faceModels[1] && config.face.mesh.enabled) ? tf.loadGraphModel(config.face.mesh.modelPath, { fromTFHub: config.face.mesh.modelPath.includes('tfhub.dev') }) : null,\n (!faceModels[2] && config.face.iris.enabled) ? tf.loadGraphModel(config.face.iris.modelPath, { fromTFHub: config.face.iris.modelPath.includes('tfhub.dev') }) : null,\n ]);\n const faceMesh = new MediaPipeFaceMesh(faceModels[0], faceModels[1], faceModels[2], config);\n if (config.face.mesh.enabled) log(`load model: ${config.face.mesh.modelPath.match(/\\/(.*)\\./)[1]}`);\n if (config.face.iris.enabled) log(`load model: ${config.face.iris.modelPath.match(/\\/(.*)\\./)[1]}`);\n return faceMesh;\n}\n\nexports.load = load;\nexports.MediaPipeFaceMesh = MediaPipeFaceMesh;\nexports.triangulation = coords.TRI468;\n", "import { log } from './log.js';\n\nconst profileData = {};\n\nfunction profile(name, data) {\n if (!data || !data.kernels) return;\n const maxResults = 5;\n const time = data.kernels\n .filter((a) => a.kernelTimeMs > 0)\n .reduce((a, b) => a += b.kernelTimeMs, 0);\n const slowest = data.kernels\n .map((a, i) => { a.id = i; return a; })\n .filter((a) => a.kernelTimeMs > 0)\n .sort((a, b) => b.kernelTimeMs - a.kernelTimeMs);\n const largest = data.kernels\n .map((a, i) => { a.id = i; return a; })\n .filter((a) => a.totalBytesSnapshot > 0)\n .sort((a, b) => b.totalBytesSnapshot - a.totalBytesSnapshot);\n if (slowest.length > maxResults) slowest.length = maxResults;\n if (largest.length > maxResults) largest.length = maxResults;\n const res = { newBytes: data.newBytes, newTensors: data.newTensors, peakBytes: data.peakBytes, numKernelOps: data.kernels.length, timeKernelOps: time, slowestKernelOps: slowest, largestKernelOps: largest };\n profileData[name] = res;\n log('Human profiler', name, res);\n}\n\nexports.run = profile;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile.js';\n\nconst models = {};\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nasync function load(config) {\n if (!models.age) {\n models.age = await tf.loadGraphModel(config.face.age.modelPath);\n log(`load model: ${config.face.age.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.age;\n}\n\nasync function predict(image, config) {\n if (!models.age) return null;\n if ((skipped < config.face.age.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n /*\n const zoom = [0, 0]; // 0..1 meaning 0%..100%\n const box = [[\n (image.shape[1] * zoom[0]) / image.shape[1],\n (image.shape[2] * zoom[1]) / image.shape[2],\n (image.shape[1] - (image.shape[1] * zoom[0])) / image.shape[1],\n (image.shape[2] - (image.shape[2] * zoom[1])) / image.shape[2],\n ]];\n const resize = tf.image.cropAndResize(image, box, [0], [config.face.age.inputSize, config.face.age.inputSize]);\n */\n const resize = tf.image.resizeBilinear(image, [config.face.age.inputSize, config.face.age.inputSize], false);\n const enhance = tf.mul(resize, [255.0]);\n tf.dispose(resize);\n\n let ageT;\n const obj = {};\n\n if (!config.profile) {\n if (config.face.age.enabled) ageT = await models.age.predict(enhance);\n } else {\n const profileAge = config.face.age.enabled ? await tf.profile(() => models.age.predict(enhance)) : {};\n ageT = profileAge.result.clone();\n profileAge.result.dispose();\n // @ts-ignore\n profile.run('age', profileAge);\n }\n enhance.dispose();\n\n if (ageT) {\n const data = ageT.dataSync();\n obj.age = Math.trunc(10 * data[0]) / 10;\n }\n ageT.dispose();\n\n last = obj;\n resolve(obj);\n });\n}\n\nexports.predict = predict;\nexports.load = load;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile.js';\n\nconst models = {};\nlet last = { gender: '' };\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet alternative = false;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nasync function load(config) {\n if (!models.gender) {\n models.gender = await tf.loadGraphModel(config.face.gender.modelPath);\n alternative = models.gender.inputs[0].shape[3] === 1;\n log(`load model: ${config.face.gender.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.gender;\n}\n\nasync function predict(image, config) {\n if (!models.gender) return null;\n if ((skipped < config.face.gender.skipFrames) && config.videoOptimized && last.gender !== '') {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [config.face.gender.inputSize, config.face.gender.inputSize], false);\n let enhance;\n if (alternative) {\n enhance = tf.tidy(() => {\n const [red, green, blue] = tf.split(resize, 3, 3);\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n return grayscale.sub(0.5).mul(2);\n });\n } else {\n enhance = tf.mul(resize, [255.0]);\n }\n tf.dispose(resize);\n\n let genderT;\n const obj = {};\n\n if (!config.profile) {\n if (config.face.gender.enabled) genderT = await models.gender.predict(enhance);\n } else {\n const profileGender = config.face.gender.enabled ? await tf.profile(() => models.gender.predict(enhance)) : {};\n genderT = profileGender.result.clone();\n profileGender.result.dispose();\n // @ts-ignore\n profile.run('gender', profileGender);\n }\n enhance.dispose();\n\n if (genderT) {\n const data = genderT.dataSync();\n if (alternative) {\n // returns two values 0..1, bigger one is prediction\n const confidence = Math.trunc(100 * Math.abs(data[0] - data[1])) / 100;\n if (confidence > config.face.gender.minConfidence) {\n obj.gender = data[0] > data[1] ? 'female' : 'male';\n obj.confidence = confidence;\n }\n } else {\n // returns one value 0..1, .5 is prediction threshold\n const confidence = Math.trunc(200 * Math.abs((data[0] - 0.5))) / 100;\n if (confidence > config.face.gender.minConfidence) {\n obj.gender = data[0] <= 0.5 ? 'female' : 'male';\n obj.confidence = Math.min(0.99, confidence);\n }\n }\n }\n genderT.dispose();\n\n last = obj;\n resolve(obj);\n });\n}\n\nexports.predict = predict;\nexports.load = load;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile.js';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];\nconst models = {};\nlet last = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\nconst scale = 1; // score multiplication factor\n\nasync function load(config) {\n if (!models.emotion) {\n models.emotion = await tf.loadGraphModel(config.face.emotion.modelPath);\n log(`load model: ${config.face.emotion.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.emotion;\n}\n\nasync function predict(image, config) {\n if (!models.emotion) return null;\n if ((skipped < config.face.emotion.skipFrames) && config.videoOptimized && (last.length > 0)) {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n /*\n const zoom = [0, 0]; // 0..1 meaning 0%..100%\n const box = [[\n (image.shape[1] * zoom[0]) / image.shape[1],\n (image.shape[2] * zoom[1]) / image.shape[2],\n (image.shape[1] - (image.shape[1] * zoom[0])) / image.shape[1],\n (image.shape[2] - (image.shape[2] * zoom[1])) / image.shape[2],\n ]];\n const resize = tf.image.cropAndResize(image, box, [0], [config.face.emotion.inputSize, config.face.emotion.inputSize]);\n */\n const resize = tf.image.resizeBilinear(image, [config.face.emotion.inputSize, config.face.emotion.inputSize], false);\n const [red, green, blue] = tf.split(resize, 3, 3);\n resize.dispose();\n // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n red.dispose();\n green.dispose();\n blue.dispose();\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n redNorm.dispose();\n greenNorm.dispose();\n blueNorm.dispose();\n const normalize = tf.tidy(() => grayscale.sub(0.5).mul(2));\n grayscale.dispose();\n const obj = [];\n if (config.face.emotion.enabled) {\n let data;\n if (!config.profile) {\n const emotionT = await models.emotion.predict(normalize);\n data = emotionT.dataSync();\n tf.dispose(emotionT);\n } else {\n const profileData = await tf.profile(() => models.emotion.predict(normalize));\n data = profileData.result.dataSync();\n profileData.result.dispose();\n // @ts-ignore\n profile.run('emotion', profileData);\n }\n for (let i = 0; i < data.length; i++) {\n if (scale * data[i] > config.face.emotion.minConfidence) obj.push({ score: Math.min(0.99, Math.trunc(100 * scale * data[i]) / 100), emotion: annotations[i] });\n }\n obj.sort((a, b) => b.score - a.score);\n }\n normalize.dispose();\n last = obj;\n resolve(obj);\n });\n}\n\nexports.predict = predict;\nexports.load = load;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile.js';\n\n// based on https://github.com/sirius-ai/MobileFaceNet_TF\n// model converted from https://github.com/sirius-ai/MobileFaceNet_TF/files/3551493/FaceMobileNet192_train_false.zip\n\nconst models = {};\n\nasync function load(config) {\n if (!models.embedding) {\n models.embedding = await tf.loadGraphModel(config.face.embedding.modelPath);\n log(`load model: ${config.face.embedding.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.embedding;\n}\n\nfunction simmilarity(embedding1, embedding2) {\n if (embedding1?.length !== embedding2?.length) return 0;\n // general minkowski distance\n // euclidean distance is limited case where order is 2\n const order = 2;\n const distance = 10.0 * ((embedding1.map((val, i) => (val - embedding2[i])).reduce((dist, diff) => dist + (diff ** order), 0) ** (1 / order)));\n return (Math.trunc(1000 * (1 - distance)) / 1000);\n}\n\nasync function predict(image, config) {\n if (!models.embedding) return null;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [config.face.embedding.inputSize, config.face.embedding.inputSize], false);\n // const normalize = tf.tidy(() => resize.div(127.5).sub(0.5)); // this is -0.5...0.5 ???\n let data = [];\n if (config.face.embedding.enabled) {\n if (!config.profile) {\n const embeddingT = await models.embedding.predict({ img_inputs: resize });\n data = [...embeddingT.dataSync()]; // convert object array to standard array\n tf.dispose(embeddingT);\n } else {\n const profileData = await tf.profile(() => models.embedding.predict({ img_inputs: resize }));\n data = [...profileData.result.dataSync()];\n profileData.result.dispose();\n // @ts-ignore\n profile.run('emotion', profileData);\n }\n }\n resize.dispose();\n // normalize.dispose();\n resolve(data);\n });\n}\n\nexports.predict = predict;\nexports.simmilarity = simmilarity;\nexports.load = load;\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nconst imageNetMean = [-123.15, -115.90, -103.06];\n\nfunction nameOutputResultsMobileNet(results) {\n const [offsets, heatmap, displacementFwd, displacementBwd] = results;\n return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nfunction nameOutputResultsResNet(results) {\n const [displacementFwd, displacementBwd, offsets, heatmap] = results;\n return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nclass BaseModel {\n constructor(model) {\n this.model = model;\n }\n\n predict(input, config) {\n return tf.tidy(() => {\n const asFloat = (config.body.modelType === 'ResNet') ? input.toFloat().add(imageNetMean) : input.toFloat().div(127.5).sub(1.0);\n const asBatch = asFloat.expandDims(0);\n const results = this.model.predict(asBatch);\n const results3d = results.map((y) => y.squeeze([0]));\n const namedResults = (config.body.modelType === 'ResNet') ? nameOutputResultsResNet(results3d) : nameOutputResultsMobileNet(results3d);\n return {\n heatmapScores: namedResults.heatmap.sigmoid(),\n offsets: namedResults.offsets,\n displacementFwd: namedResults.displacementFwd,\n displacementBwd: namedResults.displacementBwd,\n };\n });\n }\n\n dispose() {\n this.model.dispose();\n }\n}\nexports.BaseModel = BaseModel;\n", "// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nfunction half(k) {\n return Math.floor(k / 2);\n}\nclass MaxHeap {\n constructor(maxSize, getElementValue) {\n this.priorityQueue = new Array(maxSize);\n this.numberOfElements = -1;\n this.getElementValue = getElementValue;\n }\n\n enqueue(x) {\n this.priorityQueue[++this.numberOfElements] = x;\n this.swim(this.numberOfElements);\n }\n\n dequeue() {\n const max = this.priorityQueue[0];\n this.exchange(0, this.numberOfElements--);\n this.sink(0);\n this.priorityQueue[this.numberOfElements + 1] = null;\n return max;\n }\n\n empty() {\n return this.numberOfElements === -1;\n }\n\n size() {\n return this.numberOfElements + 1;\n }\n\n all() {\n return this.priorityQueue.slice(0, this.numberOfElements + 1);\n }\n\n max() {\n return this.priorityQueue[0];\n }\n\n swim(k) {\n while (k > 0 && this.less(half(k), k)) {\n this.exchange(k, half(k));\n k = half(k);\n }\n }\n\n sink(k) {\n while (2 * k <= this.numberOfElements) {\n let j = 2 * k;\n if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n if (!this.less(k, j)) break;\n this.exchange(k, j);\n k = j;\n }\n }\n\n getValueAt(i) {\n return this.getElementValue(this.priorityQueue[i]);\n }\n\n less(i, j) {\n return this.getValueAt(i) < this.getValueAt(j);\n }\n\n exchange(i, j) {\n const t = this.priorityQueue[i];\n this.priorityQueue[i] = this.priorityQueue[j];\n this.priorityQueue[j] = t;\n }\n}\nexports.MaxHeap = MaxHeap;\n", "import * as heapSort from './heapSort';\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores) {\n const [height, width] = scores.shape;\n let localMaximum = true;\n const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n localMaximum = false;\n break;\n }\n }\n if (!localMaximum) break;\n }\n return localMaximum;\n}\n\nfunction buildPartWithScoreQueue(scoreThreshold, localMaximumRadius, scores) {\n const [height, width, numKeypoints] = scores.shape;\n const queue = new heapSort.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n const score = scores.get(heatmapY, heatmapX, keypointId);\n // Only consider parts with score greater or equal to threshold as root candidates.\n if (score < scoreThreshold) continue;\n // Only consider keypoints whose score is maximum in a local window.\n if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores)) {\n queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n }\n }\n }\n }\n return queue;\n}\nexports.buildPartWithScoreQueue = buildPartWithScoreQueue;\n", "exports.partNames = [\n 'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n 'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n 'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexports.NUM_KEYPOINTS = exports.partNames.length;\n\nexports.partIds = exports.partNames.reduce((result, jointName, i) => {\n result[jointName] = i;\n return result;\n}, {});\n\nconst connectedPartNames = [\n ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexports.connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([exports.partIds[jointNameA], exports.partIds[jointNameB]]));\n\nexports.poseChain = [\n ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n ['rightKnee', 'rightAnkle'],\n];\n\nexports.partChannels = [\n 'left_face',\n 'right_face',\n 'right_upper_leg_front',\n 'right_lower_leg_back',\n 'right_upper_leg_back',\n 'left_lower_leg_front',\n 'left_upper_leg_front',\n 'left_upper_leg_back',\n 'left_lower_leg_back',\n 'right_feet',\n 'right_lower_leg_front',\n 'left_feet',\n 'torso_front',\n 'torso_back',\n 'right_upper_arm_front',\n 'right_upper_arm_back',\n 'right_lower_arm_back',\n 'left_lower_arm_front',\n 'left_upper_arm_front',\n 'left_upper_arm_back',\n 'left_lower_arm_back',\n 'right_hand',\n 'right_lower_arm_front',\n 'left_hand',\n];\n", "import * as kpt from './keypoints';\n\nfunction getOffsetPoint(y, x, keypoint, offsets) {\n return {\n y: offsets.get(y, x, keypoint),\n x: offsets.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n };\n}\nexports.getOffsetPoint = getOffsetPoint;\n\nfunction getImageCoords(part, outputStride, offsets) {\n const { heatmapY, heatmapX, id: keypoint } = part;\n const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n return {\n x: part.heatmapX * outputStride + x,\n y: part.heatmapY * outputStride + y,\n };\n}\nexports.getImageCoords = getImageCoords;\n\nfunction fillArray(element, size) {\n const result = new Array(size);\n for (let i = 0; i < size; i++) {\n result[i] = element;\n }\n return result;\n}\nexports.fillArray = fillArray;\n\nfunction clamp(a, min, max) {\n if (a < min) return min;\n if (a > max) return max;\n return a;\n}\nexports.clamp = clamp;\n\nfunction squaredDistance(y1, x1, y2, x2) {\n const dy = y2 - y1;\n const dx = x2 - x1;\n return dy * dy + dx * dx;\n}\nexports.squaredDistance = squaredDistance;\n\nfunction addVectors(a, b) {\n return { x: a.x + b.x, y: a.y + b.y };\n}\nexports.addVectors = addVectors;\n\nfunction clampVector(a, min, max) {\n return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\nexports.clampVector = clampVector;\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as kpt from './keypoints';\n\nfunction getPointsConfidence(heatmapScores, heatMapCoords) {\n const numKeypoints = heatMapCoords.shape[0];\n const result = new Float32Array(numKeypoints);\n for (let keypoint = 0; keypoint < numKeypoints; keypoint++) {\n const y = heatMapCoords.get(keypoint, 0);\n const x = heatMapCoords.get(keypoint, 1);\n result[keypoint] = heatmapScores.get(y, x, keypoint);\n }\n return result;\n}\nexports.getPointsConfidence = getPointsConfidence;\n\nfunction getOffsetPoint(y, x, keypoint, offsetsBuffer) {\n return {\n y: offsetsBuffer.get(y, x, keypoint),\n x: offsetsBuffer.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n };\n}\n\nfunction getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer) {\n const result = [];\n for (let keypoint = 0; keypoint < kpt.NUM_KEYPOINTS; keypoint++) {\n const heatmapY = heatMapCoordsBuffer.get(keypoint, 0).valueOf();\n const heatmapX = heatMapCoordsBuffer.get(keypoint, 1).valueOf();\n const { x, y } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsetsBuffer);\n result.push(y);\n result.push(x);\n }\n return tf.tensor2d(result, [kpt.NUM_KEYPOINTS, 2]);\n}\nexports.getOffsetVectors = getOffsetVectors;\n\nfunction getOffsetPoints(heatMapCoordsBuffer, outputStride, offsetsBuffer) {\n return tf.tidy(() => heatMapCoordsBuffer.toTensor().mul(tf.scalar(outputStride, 'int32')).toFloat().add(getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer)));\n}\nexports.getOffsetPoints = getOffsetPoints;\n\nfunction mod(a, b) {\n return tf.tidy(() => {\n const floored = a.div(tf.scalar(b, 'int32'));\n return a.sub(floored.mul(tf.scalar(b, 'int32')));\n });\n}\n\nfunction argmax2d(inputs) {\n const [height, width, depth] = inputs.shape;\n return tf.tidy(() => {\n const reshaped = inputs.reshape([height * width, depth]);\n const coords = reshaped.argMax(0);\n const yCoords = coords.div(tf.scalar(width, 'int32')).expandDims(1);\n const xCoords = mod(coords, width).expandDims(1);\n return tf.concat([yCoords, xCoords], 1);\n });\n}\nexports.argmax2d = argmax2d;\n", "import * as keypoints from './keypoints';\nimport * as vectors from './vectors';\nimport * as decoders from './decoders';\n\nconst parentChildrenTuples = keypoints.poseChain.map(([parentJoinName, childJoinName]) => ([keypoints.partIds[parentJoinName], keypoints.partIds[childJoinName]]));\nconst parentToChildEdges = parentChildrenTuples.map(([, childJointId]) => childJointId);\nconst childToParentEdges = parentChildrenTuples.map(([parentJointId]) => parentJointId);\nfunction getDisplacement(edgeId, point, displacements) {\n const numEdges = displacements.shape[2] / 2;\n return {\n y: displacements.get(point.y, point.x, edgeId),\n x: displacements.get(point.y, point.x, numEdges + edgeId),\n };\n}\nfunction getStridedIndexNearPoint(point, outputStride, height, width) {\n return {\n // @ts-ignore\n y: vectors.clamp(Math.round(point.y / outputStride), 0, height - 1),\n // @ts-ignore\n x: vectors.clamp(Math.round(point.x / outputStride), 0, width - 1),\n };\n}\n\nfunction traverseToTargetKeypoint(edgeId, sourceKeypoint, targetKeypointId, scoresBuffer, offsets, outputStride, displacements, offsetRefineStep = 2) {\n const [height, width] = scoresBuffer.shape;\n // Nearest neighbor interpolation for the source->target displacements.\n const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, outputStride, height, width);\n const displacement = getDisplacement(edgeId, sourceKeypointIndices, displacements);\n // @ts-ignore\n const displacedPoint = vectors.addVectors(sourceKeypoint.position, displacement);\n let targetKeypoint = displacedPoint;\n for (let i = 0; i < offsetRefineStep; i++) {\n const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n // @ts-ignore\n const offsetPoint = vectors.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetKeypointId, offsets);\n // @ts-ignore\n targetKeypoint = vectors.addVectors({\n x: targetKeypointIndices.x * outputStride,\n y: targetKeypointIndices.y * outputStride,\n }, { x: offsetPoint.x, y: offsetPoint.y });\n }\n const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n const score = scoresBuffer.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetKeypointId);\n return { position: targetKeypoint, part: keypoints.partNames[targetKeypointId], score };\n}\n\nfunction decodePose(root, scores, offsets, outputStride, displacementsFwd, displacementsBwd) {\n const numParts = scores.shape[2];\n const numEdges = parentToChildEdges.length;\n const instanceKeypoints = new Array(numParts);\n // Start a new detection instance at the position of the root.\n const { part: rootPart, score: rootScore } = root;\n // @ts-ignore\n const rootPoint = vectors.getImageCoords(rootPart, outputStride, offsets);\n instanceKeypoints[rootPart.id] = {\n score: rootScore,\n part: keypoints.partNames[rootPart.id],\n position: rootPoint,\n };\n // Decode the part positions upwards in the tree, following the backward displacements.\n for (let edge = numEdges - 1; edge >= 0; --edge) {\n const sourceKeypointId = parentToChildEdges[edge];\n const targetKeypointId = childToParentEdges[edge];\n if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsBwd);\n }\n }\n // Decode the part positions downwards in the tree, following the forward displacements.\n for (let edge = 0; edge < numEdges; ++edge) {\n const sourceKeypointId = childToParentEdges[edge];\n const targetKeypointId = parentToChildEdges[edge];\n if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsFwd);\n }\n }\n return instanceKeypoints;\n}\nexports.decodePose = decodePose;\n\nasync function decodeSinglePose(heatmapScores, offsets, config) {\n let totalScore = 0.0;\n // @ts-ignore\n const heatmapValues = decoders.argmax2d(heatmapScores);\n const allTensorBuffers = await Promise.all([heatmapScores.buffer(), offsets.buffer(), heatmapValues.buffer()]);\n const scoresBuffer = allTensorBuffers[0];\n const offsetsBuffer = allTensorBuffers[1];\n const heatmapValuesBuffer = allTensorBuffers[2];\n // @ts-ignore\n const offsetPoints = decoders.getOffsetPoints(heatmapValuesBuffer, config.body.outputStride, offsetsBuffer);\n const offsetPointsBuffer = await offsetPoints.buffer();\n // @ts-ignore\n const keypointConfidence = Array.from(decoders.getPointsConfidence(scoresBuffer, heatmapValuesBuffer));\n const instanceKeypoints = keypointConfidence.map((score, i) => {\n totalScore += score;\n return {\n position: {\n y: offsetPointsBuffer.get(i, 0),\n x: offsetPointsBuffer.get(i, 1),\n },\n part: keypoints.partNames[i],\n score,\n };\n });\n const filteredKeypoints = instanceKeypoints.filter((kpt) => kpt.score > config.body.scoreThreshold);\n heatmapValues.dispose();\n offsetPoints.dispose();\n return { keypoints: filteredKeypoints, score: totalScore / instanceKeypoints.length };\n}\nexports.decodeSinglePose = decodeSinglePose;\n", "import * as buildParts from './buildParts';\nimport * as decodePose from './decodePose';\nimport * as vectors from './vectors';\n\nconst kLocalMaximumRadius = 1;\n\nfunction withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, { x, y }, keypointId) {\n return poses.some(({ keypoints }) => {\n const correspondingKeypoint = keypoints[keypointId].position;\n // @ts-ignore\n return vectors.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n });\n}\n\nfunction getInstanceScore(existingPoses, squaredNmsRadius, instanceKeypoints) {\n const notOverlappedKeypointScores = instanceKeypoints.reduce((result, { position, score }, keypointId) => {\n if (!withinNmsRadiusOfCorrespondingPoint(existingPoses, squaredNmsRadius, position, keypointId)) result += score;\n return result;\n }, 0.0);\n return notOverlappedKeypointScores / instanceKeypoints.length;\n}\n\nfunction decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config) {\n const poses = [];\n // @ts-ignore\n const queue = buildParts.buildPartWithScoreQueue(config.body.scoreThreshold, kLocalMaximumRadius, scoresBuffer);\n const squaredNmsRadius = config.body.nmsRadius ^ 2;\n // Generate at most maxDetections object instances per image in decreasing root part score order.\n while (poses.length < config.body.maxDetections && !queue.empty()) {\n // The top element in the queue is the next root candidate.\n const root = queue.dequeue();\n // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n // @ts-ignore\n const rootImageCoords = vectors.getImageCoords(root.part, config.body.outputStride, offsetsBuffer);\n if (withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, rootImageCoords, root.part.id)) continue;\n // Else start a new detection instance at the position of the root.\n // @ts-ignore\n const keypoints = decodePose.decodePose(root, scoresBuffer, offsetsBuffer, config.body.outputStride, displacementsFwdBuffer, displacementsBwdBuffer);\n const score = getInstanceScore(poses, squaredNmsRadius, keypoints);\n if (score > config.body.scoreThreshold) poses.push({ keypoints, score });\n }\n return poses;\n}\nexports.decodeMultiplePoses = decodeMultiplePoses;\n", "import * as kpt from './keypoints';\n\nfunction eitherPointDoesntMeetConfidence(a, b, minConfidence) {\n return (a < minConfidence || b < minConfidence);\n}\n\nfunction getAdjacentKeyPoints(keypoints, minConfidence) {\n return kpt.connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n return result;\n }\n result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n return result;\n }, []);\n}\nexports.getAdjacentKeyPoints = getAdjacentKeyPoints;\n\nconst { NEGATIVE_INFINITY, POSITIVE_INFINITY } = Number;\nfunction getBoundingBox(keypoints) {\n return keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n maxX: Math.max(maxX, x),\n maxY: Math.max(maxY, y),\n minX: Math.min(minX, x),\n minY: Math.min(minY, y),\n }), {\n maxX: NEGATIVE_INFINITY,\n maxY: NEGATIVE_INFINITY,\n minX: POSITIVE_INFINITY,\n minY: POSITIVE_INFINITY,\n });\n}\nexports.getBoundingBox = getBoundingBox;\n\nfunction getBoundingBoxPoints(keypoints) {\n const { minX, minY, maxX, maxY } = getBoundingBox(keypoints);\n return [{ x: minX, y: minY }, { x: maxX, y: minY }, { x: maxX, y: maxY }, { x: minX, y: maxY }];\n}\nexports.getBoundingBoxPoints = getBoundingBoxPoints;\n\nasync function toTensorBuffers3D(tensors) {\n return Promise.all(tensors.map((tensor) => tensor.buffer()));\n}\nexports.toTensorBuffers3D = toTensorBuffers3D;\n\nfunction scalePose(pose, scaleY, scaleX) {\n return {\n score: pose.score,\n keypoints: pose.keypoints.map(({ score, part, position }) => ({\n score,\n part,\n position: { x: position.x * scaleX, y: position.y * scaleY },\n })),\n };\n}\nexports.scalePose = scalePose;\n\nfunction resizeTo(image, [targetH, targetW]) {\n const input = image.squeeze(0);\n const resized = input.resizeBilinear([targetH, targetW]);\n input.dispose();\n return resized;\n}\nexports.resizeTo = resizeTo;\n\nfunction scaleAndFlipPoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) {\n const scaledPoses = poses.map((pose) => scalePose(pose, height / inputResolutionHeight, width / inputResolutionWidth));\n return scaledPoses;\n}\nexports.scaleAndFlipPoses = scaleAndFlipPoses;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as modelBase from './modelBase';\nimport * as decodeMultiple from './decodeMultiple';\nimport * as decodePose from './decodePose';\nimport * as util from './util';\n\nasync function estimateMultiple(input, res, config) {\n return new Promise(async (resolve) => {\n const height = input.shape[1];\n const width = input.shape[2];\n // @ts-ignore\n const allTensorBuffers = await util.toTensorBuffers3D([res.heatmapScores, res.offsets, res.displacementFwd, res.displacementBwd]);\n const scoresBuffer = allTensorBuffers[0];\n const offsetsBuffer = allTensorBuffers[1];\n const displacementsFwdBuffer = allTensorBuffers[2];\n const displacementsBwdBuffer = allTensorBuffers[3];\n // @ts-ignore\n const poses = await decodeMultiple.decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config);\n // @ts-ignore\n const scaled = util.scaleAndFlipPoses(poses, [height, width], [config.body.inputSize, config.body.inputSize]);\n resolve(scaled);\n });\n}\n\nasync function estimateSingle(input, res, config) {\n return new Promise(async (resolve) => {\n const height = input.shape[1];\n const width = input.shape[2];\n // @ts-ignore\n const pose = await decodePose.decodeSinglePose(res.heatmapScores, res.offsets, config);\n const poses = [pose];\n // @ts-ignore\n const scaled = util.scaleAndFlipPoses(poses, [height, width], [config.body.inputSize, config.body.inputSize]);\n resolve(scaled);\n });\n}\n\nclass PoseNet {\n constructor(model) {\n this.baseModel = model;\n }\n\n async estimatePoses(input, config) {\n // @ts-ignore\n const resized = util.resizeTo(input, [config.body.inputSize, config.body.inputSize]);\n const res = this.baseModel.predict(resized, config);\n\n const poses = (config.body.maxDetections < 2) ? await estimateSingle(input, res, config) : await estimateMultiple(input, res, config);\n\n res.heatmapScores.dispose();\n res.offsets.dispose();\n res.displacementFwd.dispose();\n res.displacementBwd.dispose();\n resized.dispose();\n\n return poses;\n }\n\n dispose() {\n this.baseModel.dispose();\n }\n}\nexports.PoseNet = PoseNet;\n\nasync function load(config) {\n const model = await tf.loadGraphModel(config.body.modelPath);\n // @ts-ignore\n const mobilenet = new modelBase.BaseModel(model);\n log(`load model: ${config.body.modelPath.match(/\\/(.*)\\./)[1]}`);\n return new PoseNet(mobilenet);\n}\nexports.load = load;\n", "import * as modelPoseNet from './modelPoseNet';\nimport * as keypoints from './keypoints';\nimport * as util from './util';\n\n// @ts-ignore\nexports.load = modelPoseNet.load;\n// @ts-ignore\nexports.PoseNet = modelPoseNet.PoseNet;\n\nexports.partChannels = keypoints.partChannels;\nexports.partIds = keypoints.partIds;\nexports.partNames = keypoints.partNames;\nexports.poseChain = keypoints.poseChain;\n// @ts-ignore\nexports.getAdjacentKeyPoints = util.getAdjacentKeyPoints;\n// @ts-ignore\nexports.getBoundingBox = util.getBoundingBox;\n// @ts-ignore\nexports.getBoundingBoxPoints = util.getBoundingBoxPoints;\n// @ts-ignore\nexports.scaleAndFlipPoses = util.scaleAndFlipPoses;\n// @ts-ignore\nexports.scalePose = util.scalePose;\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\n\nclass HandDetector {\n constructor(model, inputSize, anchorsAnnotated) {\n this.model = model;\n this.anchors = anchorsAnnotated.map((anchor) => [anchor.x_center, anchor.y_center]);\n this.anchorsTensor = tf.tensor2d(this.anchors);\n this.inputSizeTensor = tf.tensor1d([inputSize, inputSize]);\n this.doubleInputSizeTensor = tf.tensor1d([inputSize * 2, inputSize * 2]);\n }\n\n normalizeBoxes(boxes) {\n return tf.tidy(() => {\n const boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n const boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n const boxCenterPoints = tf.add(tf.div(boxOffsets, this.inputSizeTensor), this.anchorsTensor);\n const halfBoxSizes = tf.div(boxSizes, this.doubleInputSizeTensor);\n const startPoints = tf.mul(tf.sub(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n const endPoints = tf.mul(tf.add(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n return tf.concat2d([startPoints, endPoints], 1);\n });\n }\n\n normalizeLandmarks(rawPalmLandmarks, index) {\n return tf.tidy(() => {\n const landmarks = tf.add(tf.div(rawPalmLandmarks.reshape([-1, 7, 2]), this.inputSizeTensor), this.anchors[index]);\n return tf.mul(landmarks, this.inputSizeTensor);\n });\n }\n\n async getBoxes(input, config) {\n const batched = this.model.predict(input);\n const predictions = batched.squeeze();\n batched.dispose();\n const scoresT = tf.tidy(() => tf.sigmoid(tf.slice(predictions, [0, 0], [-1, 1])).squeeze());\n const scores = scoresT.dataSync();\n const rawBoxes = tf.slice(predictions, [0, 1], [-1, 4]);\n const boxes = this.normalizeBoxes(rawBoxes);\n rawBoxes.dispose();\n const filteredT = await tf.image.nonMaxSuppressionAsync(boxes, scores, config.hand.maxHands, config.hand.iouThreshold, config.hand.scoreThreshold);\n const filtered = filteredT.arraySync();\n\n scoresT.dispose();\n filteredT.dispose();\n const hands = [];\n for (const index of filtered) {\n if (scores[index] >= config.hand.minConfidence) {\n const matchingBox = tf.slice(boxes, [index, 0], [1, -1]);\n const rawPalmLandmarks = tf.slice(predictions, [index, 5], [1, 14]);\n const palmLandmarks = tf.tidy(() => this.normalizeLandmarks(rawPalmLandmarks, index).reshape([-1, 2]));\n rawPalmLandmarks.dispose();\n hands.push({ box: matchingBox, palmLandmarks, confidence: scores[index] });\n }\n }\n predictions.dispose();\n boxes.dispose();\n return hands;\n }\n\n async estimateHandBounds(input, config) {\n const inputHeight = input.shape[1];\n const inputWidth = input.shape[2];\n const image = tf.tidy(() => input.resizeBilinear([config.hand.inputSize, config.hand.inputSize]).div(127.5).sub(1));\n const predictions = await this.getBoxes(image, config);\n image.dispose();\n const hands = [];\n if (!predictions || predictions.length === 0) return hands;\n for (const prediction of predictions) {\n const boxes = prediction.box.dataSync();\n const startPoint = boxes.slice(0, 2);\n const endPoint = boxes.slice(2, 4);\n const palmLandmarks = prediction.palmLandmarks.arraySync();\n prediction.box.dispose();\n prediction.palmLandmarks.dispose();\n hands.push(box.scaleBoxCoordinates({ startPoint, endPoint, palmLandmarks, confidence: prediction.confidence }, [inputWidth / config.hand.inputSize, inputHeight / config.hand.inputSize]));\n }\n return hands;\n }\n}\nexports.HandDetector = HandDetector;\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\nimport * as util from './util';\n// eslint-disable-next-line no-unused-vars\nimport { log } from '../log.js';\n\n// const PALM_BOX_SHIFT_VECTOR = [0, -0.4];\nconst PALM_BOX_ENLARGE_FACTOR = 5; // default 3\n// const HAND_BOX_SHIFT_VECTOR = [0, -0.1]; // move detected hand box by x,y to ease landmark detection\nconst HAND_BOX_ENLARGE_FACTOR = 1.65; // default 1.65\nconst PALM_LANDMARK_IDS = [0, 5, 9, 13, 17, 1, 2];\nconst PALM_LANDMARKS_INDEX_OF_PALM_BASE = 0;\nconst PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE = 2;\n\nclass HandPipeline {\n constructor(handDetector, landmarkDetector, inputSize) {\n this.handDetector = handDetector;\n this.landmarkDetector = landmarkDetector;\n this.inputSize = inputSize;\n this.storedBoxes = [];\n this.skipped = 0;\n this.detectedHands = 0;\n }\n\n getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n // return box.enlargeBox(box.squarifyBox(box.shiftBox(boxAroundPalm, PALM_BOX_SHIFT_VECTOR)), PALM_BOX_ENLARGE_FACTOR);\n return box.enlargeBox(box.squarifyBox(boxAroundPalm), PALM_BOX_ENLARGE_FACTOR);\n }\n\n getBoxForHandLandmarks(landmarks) {\n const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n // const boxAroundHand = box.enlargeBox(box.squarifyBox(box.shiftBox(boundingBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n const boxAroundHand = box.enlargeBox(box.squarifyBox(boundingBox), HAND_BOX_ENLARGE_FACTOR);\n boxAroundHand.palmLandmarks = [];\n for (let i = 0; i < PALM_LANDMARK_IDS.length; i++) {\n boxAroundHand.palmLandmarks.push(landmarks[PALM_LANDMARK_IDS[i]].slice(0, 2));\n }\n return boxAroundHand;\n }\n\n transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n const boxSize = box.getBoxSize(box2);\n const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n const coordsScaled = rawCoords.map((coord) => [\n scaleFactor[0] * (coord[0] - this.inputSize / 2),\n scaleFactor[1] * (coord[1] - this.inputSize / 2),\n scaleFactor[2] * coord[2],\n ]);\n const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n const coordsRotated = coordsScaled.map((coord) => {\n const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n return [...rotated, coord[2]];\n });\n const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n const boxCenter = [...box.getBoxCenter(box2), 1];\n const originalBoxCenter = [\n util.dot(boxCenter, inverseRotationMatrix[0]),\n util.dot(boxCenter, inverseRotationMatrix[1]),\n ];\n return coordsRotated.map((coord) => [\n coord[0] + originalBoxCenter[0],\n coord[1] + originalBoxCenter[1],\n coord[2],\n ]);\n }\n\n async estimateHands(image, config) {\n let useFreshBox = false;\n\n // run new detector every skipFrames unless we only want box to start with\n let boxes;\n if ((this.skipped === 0) || (this.skipped > config.hand.skipFrames) || !config.hand.landmarks || !config.videoOptimized) {\n boxes = await this.handDetector.estimateHandBounds(image, config);\n this.skipped = 0;\n }\n if (config.videoOptimized) this.skipped++;\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxHands) || !config.hand.landmarks)) {\n this.detectedHands = 0;\n this.storedBoxes = [...boxes];\n // for (const possible of boxes) this.storedBoxes.push(possible);\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\n const hands = [];\n // log('hand', `skipped: ${this.skipped} max: ${config.hand.maxHands} detected: ${this.detectedHands} stored: ${this.storedBoxes.length} new: ${boxes?.length}`);\n\n // go through working set of boxes\n for (let i = 0; i < this.storedBoxes.length; i++) {\n const currentBox = this.storedBoxes[i];\n if (!currentBox) continue;\n if (config.hand.landmarks) {\n const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_PALM_BASE], currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE]) : 0;\n const palmCenter = box.getBoxCenter(currentBox);\n const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n const rotatedImage = config.hand.rotation ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n const croppedInput = box.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n const handImage = croppedInput.div(255);\n croppedInput.dispose();\n rotatedImage.dispose();\n const [confidenceT, keypoints] = await this.landmarkDetector.predict(handImage);\n handImage.dispose();\n const confidence = confidenceT.dataSync()[0];\n confidenceT.dispose();\n if (confidence >= config.hand.minConfidence) {\n const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n const rawCoords = keypointsReshaped.arraySync();\n keypoints.dispose();\n keypointsReshaped.dispose();\n const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n this.storedBoxes[i] = nextBoundingBox;\n const result = {\n landmarks: coords,\n confidence,\n box: {\n topLeft: nextBoundingBox.startPoint,\n bottomRight: nextBoundingBox.endPoint,\n },\n };\n hands.push(result);\n } else {\n this.storedBoxes[i] = null;\n }\n keypoints.dispose();\n } else {\n // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n const enlarged = box.enlargeBox(box.squarifyBox(currentBox), HAND_BOX_ENLARGE_FACTOR);\n const result = {\n confidence: currentBox.confidence,\n box: {\n topLeft: enlarged.startPoint,\n bottomRight: enlarged.endPoint,\n },\n };\n hands.push(result);\n }\n }\n this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n this.detectedHands = hands.length;\n return hands;\n }\n\n // eslint-disable-next-line class-methods-use-this\n calculateLandmarksBoundingBox(landmarks) {\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint };\n }\n}\n\nexports.HandPipeline = HandPipeline;\n", "exports.anchors = [\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.796875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.796875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.828125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.828125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.859375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.859375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.890625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.890625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.921875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.921875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.953125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.953125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.984375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.984375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n 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0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n];\n", "// https://storage.googleapis.com/tfjs-models/demos/handpose/index.html\n\nimport { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as handdetector from './handdetector';\nimport * as handpipeline from './handpipeline';\nimport * as anchors from './anchors';\n\nconst MESH_ANNOTATIONS = {\n thumb: [1, 2, 3, 4],\n indexFinger: [5, 6, 7, 8],\n middleFinger: [9, 10, 11, 12],\n ringFinger: [13, 14, 15, 16],\n pinky: [17, 18, 19, 20],\n palmBase: [0],\n};\n\nclass HandPose {\n constructor(handPipeline) {\n this.handPipeline = handPipeline;\n }\n\n static getAnnotations() {\n return MESH_ANNOTATIONS;\n }\n\n async estimateHands(input, config) {\n const predictions = await this.handPipeline.estimateHands(input, config);\n if (!predictions) return [];\n const hands = [];\n for (const prediction of predictions) {\n const annotations = {};\n if (prediction.landmarks) {\n for (const key of Object.keys(MESH_ANNOTATIONS)) {\n annotations[key] = MESH_ANNOTATIONS[key].map((index) => prediction.landmarks[index]);\n }\n }\n const box = prediction.box ? [\n Math.max(0, prediction.box.topLeft[0]),\n Math.max(0, prediction.box.topLeft[1]),\n Math.min(input.shape[2], prediction.box.bottomRight[0]) - prediction.box.topLeft[0],\n Math.min(input.shape[1], prediction.box.bottomRight[1]) - prediction.box.topLeft[1],\n ] : 0;\n hands.push({\n confidence: prediction.confidence,\n box,\n landmarks: prediction.landmarks,\n annotations,\n });\n }\n return hands;\n }\n}\nexports.HandPose = HandPose;\n\nasync function load(config) {\n const [handDetectorModel, handPoseModel] = await Promise.all([\n config.hand.enabled ? tf.loadGraphModel(config.hand.detector.modelPath, { fromTFHub: config.hand.detector.modelPath.includes('tfhub.dev') }) : null,\n config.hand.landmarks ? tf.loadGraphModel(config.hand.skeleton.modelPath, { fromTFHub: config.hand.skeleton.modelPath.includes('tfhub.dev') }) : null,\n ]);\n // @ts-ignore\n const handDetector = new handdetector.HandDetector(handDetectorModel, config.hand.inputSize, anchors.anchors);\n // @ts-ignore\n const handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel, config.hand.inputSize);\n const handPose = new HandPose(handPipeline);\n if (config.hand.enabled) log(`load model: ${config.hand.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n if (config.hand.landmarks) log(`load model: ${config.hand.skeleton.modelPath.match(/\\/(.*)\\./)[1]}`);\n return handPose;\n}\nexports.load = load;\n", "exports.body = (res) => {\n if (!res) return [];\n const gestures = [];\n for (let i = 0; i < res.length; i++) {\n // raising hands\n const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n if (nose && leftWrist && rightWrist && (leftWrist.position.y < nose.position.y) && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'i give up' });\n else if (nose && leftWrist && (leftWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise left hand' });\n else if (nose && rightWrist && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise right hand' });\n\n // leaning\n const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n if (leftShoulder && rightShoulder) gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position.y > rightShoulder.position.y) ? 'left' : 'right'}` });\n }\n return gestures;\n};\n\nexports.face = (res) => {\n if (!res) return [];\n const gestures = [];\n for (let i = 0; i < res.length; i++) {\n if (res[i].mesh && res[i].mesh.length > 0) {\n const eyeFacing = res[i].mesh[35][2] - res[i].mesh[263][2];\n if (Math.abs(eyeFacing) < 10) gestures.push({ face: i, gesture: 'facing camera' });\n else gestures.push({ face: i, gesture: `facing ${eyeFacing < 0 ? 'right' : 'left'}` });\n const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n const chinDepth = res[i].mesh[152][2];\n if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n }\n }\n return gestures;\n};\n\nexports.iris = (res) => {\n if (!res) return [];\n const gestures = [];\n for (let i = 0; i < res.length; i++) {\n if (!res[i].annotations || !res[i].annotations.leftEyeIris || !res[i].annotations.rightEyeIris) continue;\n const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n if (difference < 0.25) gestures.push({ iris: i, gesture: 'looking at camera' });\n }\n return gestures;\n};\n\nexports.hand = (res) => {\n if (!res) return [];\n const gestures = [];\n for (let i = 0; i < res.length; i++) {\n const fingers = [];\n for (const [finger, pos] of Object.entries(res[i]['annotations'])) {\n if (finger !== 'palmBase') fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n }\n if (fingers && fingers.length > 0) {\n const closest = fingers.reduce((best, a) => (best.position[2] < a.position[2] ? best : a));\n const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n gestures.push({ hand: i, gesture: `${closest.name} forward ${highest.name} up` });\n }\n }\n return gestures;\n};\n", "/* eslint-disable no-use-before-define */\n/*\nWebGLImageFilter - MIT Licensed\n2013, Dominic Szablewski - phoboslab.org\n\n*/\n\nconst WebGLProgram = function (gl, vertexSource, fragmentSource) {\n const _collect = function (source, prefix, collection) {\n const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n source.replace(r, (match, name) => {\n collection[name] = 0;\n return match;\n });\n };\n\n const _compile = function (source, type) {\n const shader = gl.createShader(type);\n gl.shaderSource(shader, source);\n gl.compileShader(shader);\n\n if (!gl.getShaderParameter(shader, gl.COMPILE_STATUS)) {\n // @ts-ignore\n throw new Error('Filter: GL compile failed', gl.getShaderInfoLog(shader));\n }\n return shader;\n };\n\n this.uniform = {};\n this.attribute = {};\n\n const _vsh = _compile(vertexSource, gl.VERTEX_SHADER);\n const _fsh = _compile(fragmentSource, gl.FRAGMENT_SHADER);\n\n this.id = gl.createProgram();\n gl.attachShader(this.id, _vsh);\n gl.attachShader(this.id, _fsh);\n gl.linkProgram(this.id);\n\n if (!gl.getProgramParameter(this.id, gl.LINK_STATUS)) {\n // @ts-ignore\n throw new Error('Filter: GL link failed', gl.getProgramInfoLog(this.id));\n }\n\n gl.useProgram(this.id);\n\n // Collect attributes\n _collect(vertexSource, 'attribute', this.attribute);\n for (const a in this.attribute) {\n this.attribute[a] = gl.getAttribLocation(this.id, a);\n }\n\n // Collect uniforms\n _collect(vertexSource, 'uniform', this.uniform);\n _collect(fragmentSource, 'uniform', this.uniform);\n for (const u in this.uniform) {\n this.uniform[u] = gl.getUniformLocation(this.id, u);\n }\n};\n\nconst WebGLImageFilter = function (params) {\n if (!params) params = { };\n let _drawCount = 0;\n let _sourceTexture = null;\n let _lastInChain = false;\n let _currentFramebufferIndex = -1;\n let _tempFramebuffers = [null, null];\n let _filterChain = [];\n let _width = -1;\n let _height = -1;\n let _vertexBuffer = null;\n let _currentProgram = null;\n const _canvas = params.canvas || document.createElement('canvas');\n\n // key is the shader program source, value is the compiled program\n const _shaderProgramCache = { };\n\n const gl = _canvas.getContext('webgl');\n if (!gl) throw new Error('Filter: getContext() failed');\n\n this.addFilter = function (name) {\n // eslint-disable-next-line prefer-rest-params\n const args = Array.prototype.slice.call(arguments, 1);\n const filter = _filter[name];\n\n _filterChain.push({ func: filter, args });\n };\n\n this.reset = function () {\n _filterChain = [];\n };\n\n this.apply = function (image) {\n _resize(image.width, image.height);\n _drawCount = 0;\n\n // Create the texture for the input image if we haven't yet\n if (!_sourceTexture) _sourceTexture = gl.createTexture();\n gl.bindTexture(gl.TEXTURE_2D, _sourceTexture);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n\n // No filters? Just draw\n if (_filterChain.length === 0) {\n // const program = _compileShader(SHADER.FRAGMENT_IDENTITY);\n _draw();\n return _canvas;\n }\n\n for (let i = 0; i < _filterChain.length; i++) {\n _lastInChain = (i === _filterChain.length - 1);\n const f = _filterChain[i];\n f.func.apply(this, f.args || []);\n }\n\n return _canvas;\n };\n\n const _resize = function (width, height) {\n // Same width/height? Nothing to do here\n if (width === _width && height === _height) { return; }\n\n _canvas.width = width;\n _width = width;\n _canvas.height = height;\n _height = height;\n\n // Create the context if we don't have it yet\n if (!_vertexBuffer) {\n // Create the vertex buffer for the two triangles [x, y, u, v] * 6\n const vertices = new Float32Array([\n -1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0,\n -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0,\n ]);\n // eslint-disable-next-line no-unused-expressions\n (_vertexBuffer = gl.createBuffer(), gl.bindBuffer(gl.ARRAY_BUFFER, _vertexBuffer));\n gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);\n\n // Note sure if this is a good idea; at least it makes texture loading\n // in Ejecta instant.\n gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n }\n\n gl.viewport(0, 0, _width, _height);\n\n // Delete old temp framebuffers\n _tempFramebuffers = [null, null];\n };\n\n const _getTempFramebuffer = function (index) {\n // @ts-ignore\n _tempFramebuffers[index] = _tempFramebuffers[index] || _createFramebufferTexture(_width, _height);\n return _tempFramebuffers[index];\n };\n\n const _createFramebufferTexture = function (width, height) {\n const fbo = gl.createFramebuffer();\n gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n\n const renderbuffer = gl.createRenderbuffer();\n gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n\n const texture = gl.createTexture();\n gl.bindTexture(gl.TEXTURE_2D, texture);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n\n gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n\n gl.bindTexture(gl.TEXTURE_2D, null);\n gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n\n return { fbo, texture };\n };\n\n const _draw = function (flags) {\n let source = null;\n let target = null;\n let flipY = false;\n\n // Set up the source\n if (_drawCount === 0) {\n // First draw call - use the source texture\n source = _sourceTexture;\n } else {\n // All following draw calls use the temp buffer last drawn to\n // @ts-ignore\n source = _getTempFramebuffer(_currentFramebufferIndex)?.texture;\n }\n _drawCount++;\n\n // Set up the target\n if (_lastInChain && !(flags & DRAW.INTERMEDIATE)) {\n // Last filter in our chain - draw directly to the WebGL Canvas. We may\n // also have to flip the image vertically now\n target = null;\n flipY = _drawCount % 2 === 0;\n } else {\n // Intermediate draw call - get a temp buffer to draw to\n _currentFramebufferIndex = (_currentFramebufferIndex + 1) % 2;\n // @ts-ignore\n target = _getTempFramebuffer(_currentFramebufferIndex)?.fbo;\n }\n\n // Bind the source and target and draw the two triangles\n gl.bindTexture(gl.TEXTURE_2D, source);\n gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n\n gl.uniform1f(_currentProgram.uniform.flipY, (flipY ? -1 : 1));\n gl.drawArrays(gl.TRIANGLES, 0, 6);\n };\n\n const _compileShader = function (fragmentSource) {\n if (_shaderProgramCache[fragmentSource]) {\n _currentProgram = _shaderProgramCache[fragmentSource];\n gl.useProgram(_currentProgram.id);\n return _currentProgram;\n }\n\n // Compile shaders\n _currentProgram = new WebGLProgram(gl, SHADER.VERTEX_IDENTITY, fragmentSource);\n\n const floatSize = Float32Array.BYTES_PER_ELEMENT;\n const vertSize = 4 * floatSize;\n gl.enableVertexAttribArray(_currentProgram.attribute.pos);\n gl.vertexAttribPointer(_currentProgram.attribute.pos, 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n gl.enableVertexAttribArray(_currentProgram.attribute.uv);\n gl.vertexAttribPointer(_currentProgram.attribute.uv, 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n\n _shaderProgramCache[fragmentSource] = _currentProgram;\n return _currentProgram;\n };\n\n let DRAW = { INTERMEDIATE: 1 };\n\n let SHADER = {};\n SHADER.VERTEX_IDENTITY = [\n 'precision highp float;',\n 'attribute vec2 pos;',\n 'attribute vec2 uv;',\n 'varying vec2 vUv;',\n 'uniform float flipY;',\n\n 'void main(void) {',\n 'vUv = uv;',\n 'gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);',\n '}',\n ].join('\\n');\n\n SHADER.FRAGMENT_IDENTITY = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n\n 'void main(void) {',\n 'gl_FragColor = texture2D(texture, vUv);',\n '}',\n ].join('\\n');\n\n let _filter = {};\n\n // -------------------------------------------------------------------------\n // Color Matrix Filter\n\n _filter.colorMatrix = function (matrix) {\n // Create a Float32 Array and normalize the offset component to 0-1\n const m = new Float32Array(matrix);\n m[4] /= 255;\n m[9] /= 255;\n m[14] /= 255;\n m[19] /= 255;\n\n // Can we ignore the alpha value? Makes things a bit faster.\n const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0)\n ? _filter.colorMatrix.SHADER.WITHOUT_ALPHA\n : _filter.colorMatrix.SHADER.WITH_ALPHA;\n\n const program = _compileShader(shader);\n gl.uniform1fv(program.uniform.m, m);\n _draw();\n };\n\n _filter.colorMatrix.SHADER = {};\n _filter.colorMatrix.SHADER.WITH_ALPHA = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform float m[20];',\n\n 'void main(void) {',\n 'vec4 c = texture2D(texture, vUv);',\n 'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];',\n 'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];',\n 'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];',\n 'gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];',\n '}',\n ].join('\\n');\n _filter.colorMatrix.SHADER.WITHOUT_ALPHA = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform float m[20];',\n\n 'void main(void) {',\n 'vec4 c = texture2D(texture, vUv);',\n 'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];',\n 'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];',\n 'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];',\n 'gl_FragColor.a = c.a;',\n '}',\n ].join('\\n');\n\n _filter.brightness = function (brightness) {\n const b = (brightness || 0) + 1;\n _filter.colorMatrix([\n b, 0, 0, 0, 0,\n 0, b, 0, 0, 0,\n 0, 0, b, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.saturation = function (amount) {\n const x = (amount || 0) * 2 / 3 + 1;\n const y = ((x - 1) * -0.5);\n _filter.colorMatrix([\n x, y, y, 0, 0,\n y, x, y, 0, 0,\n y, y, x, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.desaturate = function () {\n _filter.saturation(-1);\n };\n\n _filter.contrast = function (amount) {\n const v = (amount || 0) + 1;\n const o = -128 * (v - 1);\n\n _filter.colorMatrix([\n v, 0, 0, 0, o,\n 0, v, 0, 0, o,\n 0, 0, v, 0, o,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.negative = function () {\n _filter.contrast(-2);\n };\n\n _filter.hue = function (rotation) {\n rotation = (rotation || 0) / 180 * Math.PI;\n const cos = Math.cos(rotation);\n const sin = Math.sin(rotation);\n const lumR = 0.213;\n const lumG = 0.715;\n const lumB = 0.072;\n\n _filter.colorMatrix([\n lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.desaturateLuminance = function () {\n _filter.colorMatrix([\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.sepia = function () {\n _filter.colorMatrix([\n 0.393, 0.7689999, 0.18899999, 0, 0,\n 0.349, 0.6859999, 0.16799999, 0, 0,\n 0.272, 0.5339999, 0.13099999, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.brownie = function () {\n _filter.colorMatrix([\n 0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n 0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.vintagePinhole = function () {\n _filter.colorMatrix([\n 0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n 0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n 0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.kodachrome = function () {\n _filter.colorMatrix([\n 1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.technicolor = function () {\n _filter.colorMatrix([\n 1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.polaroid = function () {\n _filter.colorMatrix([\n 1.438, -0.062, -0.062, 0, 0,\n -0.122, 1.378, -0.122, 0, 0,\n -0.016, -0.016, 1.483, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.shiftToBGR = function () {\n _filter.colorMatrix([\n 0, 0, 1, 0, 0,\n 0, 1, 0, 0, 0,\n 1, 0, 0, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n // -------------------------------------------------------------------------\n // Convolution Filter\n\n _filter.convolution = function (matrix) {\n const m = new Float32Array(matrix);\n const pixelSizeX = 1 / _width;\n const pixelSizeY = 1 / _height;\n\n const program = _compileShader(_filter.convolution.SHADER);\n gl.uniform1fv(program.uniform.m, m);\n gl.uniform2f(program.uniform.px, pixelSizeX, pixelSizeY);\n _draw();\n };\n\n _filter.convolution.SHADER = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform vec2 px;',\n 'uniform float m[9];',\n\n 'void main(void) {',\n 'vec4 c11 = texture2D(texture, vUv - px);', // top left\n 'vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y));', // top center\n 'vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y));', // top right\n\n 'vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) );', // mid left\n 'vec4 c22 = texture2D(texture, vUv);', // mid center\n 'vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) );', // mid right\n\n 'vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) );', // bottom left\n 'vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) );', // bottom center\n 'vec4 c33 = texture2D(texture, vUv + px );', // bottom right\n\n 'gl_FragColor = ',\n 'c11 * m[0] + c12 * m[1] + c22 * m[2] +',\n 'c21 * m[3] + c22 * m[4] + c23 * m[5] +',\n 'c31 * m[6] + c32 * m[7] + c33 * m[8];',\n 'gl_FragColor.a = c22.a;',\n '}',\n ].join('\\n');\n\n _filter.detectEdges = function () {\n _filter.convolution.call(this, [\n 0, 1, 0,\n 1, -4, 1,\n 0, 1, 0,\n ]);\n };\n\n _filter.sobelX = function () {\n _filter.convolution.call(this, [\n -1, 0, 1,\n -2, 0, 2,\n -1, 0, 1,\n ]);\n };\n\n _filter.sobelY = function () {\n _filter.convolution.call(this, [\n -1, -2, -1,\n 0, 0, 0,\n 1, 2, 1,\n ]);\n };\n\n _filter.sharpen = function (amount) {\n const a = amount || 1;\n _filter.convolution.call(this, [\n 0, -1 * a, 0,\n -1 * a, 1 + 4 * a, -1 * a,\n 0, -1 * a, 0,\n ]);\n };\n\n _filter.emboss = function (size) {\n const s = size || 1;\n _filter.convolution.call(this, [\n -2 * s, -1 * s, 0,\n -1 * s, 1, 1 * s,\n 0, 1 * s, 2 * s,\n ]);\n };\n\n // -------------------------------------------------------------------------\n // Blur Filter\n\n _filter.blur = function (size) {\n const blurSizeX = (size / 7) / _width;\n const blurSizeY = (size / 7) / _height;\n\n const program = _compileShader(_filter.blur.SHADER);\n\n // Vertical\n gl.uniform2f(program.uniform.px, 0, blurSizeY);\n _draw(DRAW.INTERMEDIATE);\n\n // Horizontal\n gl.uniform2f(program.uniform.px, blurSizeX, 0);\n _draw();\n };\n\n _filter.blur.SHADER = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform vec2 px;',\n\n 'void main(void) {',\n 'gl_FragColor = vec4(0.0);',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;',\n 'gl_FragColor += texture2D(texture, vUv )*0.159576912161;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x, 1.0*px.y))*0.147308056121;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x, 2.0*px.y))*0.115876621105;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x, 3.0*px.y))*0.0776744219933;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x, 4.0*px.y))*0.0443683338718;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x, 5.0*px.y))*0.0215963866053;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265;',\n '}',\n ].join('\\n');\n\n // -------------------------------------------------------------------------\n // Pixelate Filter\n\n _filter.pixelate = function (size) {\n const blurSizeX = (size) / _width;\n const blurSizeY = (size) / _height;\n\n const program = _compileShader(_filter.pixelate.SHADER);\n\n // Horizontal\n gl.uniform2f(program.uniform.size, blurSizeX, blurSizeY);\n _draw();\n };\n\n _filter.pixelate.SHADER = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform vec2 size;',\n 'uniform sampler2D texture;',\n\n 'vec2 pixelate(vec2 coord, vec2 size) {',\n 'return floor( coord / size ) * size;',\n '}',\n\n 'void main(void) {',\n 'gl_FragColor = vec4(0.0);',\n 'vec2 coord = pixelate(vUv, size);',\n 'gl_FragColor += texture2D(texture, coord);',\n '}',\n ].join('\\n');\n};\n\nexports.Canvas = WebGLImageFilter;\n", "import { log } from './log.js';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as fxImage from './imagefx.js';\n\n// internal temp canvases\nlet inCanvas = null;\nlet outCanvas = null;\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nfunction process(input, config) {\n let tensor;\n if (input instanceof tf.Tensor) {\n tensor = tf.clone(input);\n } else {\n const originalWidth = input.naturalWidth || input.videoWidth || input.width || (input.shape && (input.shape[1] > 0));\n const originalHeight = input.naturalHeight || input.videoHeight || input.height || (input.shape && (input.shape[2] > 0));\n let targetWidth = originalWidth;\n let targetHeight = originalHeight;\n if (config.filter.width > 0) targetWidth = config.filter.width;\n else if (config.filter.height > 0) targetWidth = originalWidth * (config.filter.height / originalHeight);\n if (config.filter.height > 0) targetHeight = config.filter.height;\n else if (config.filter.width > 0) targetHeight = originalHeight * (config.filter.width / originalWidth);\n if (!targetWidth || !targetHeight) {\n log('Human: invalid input', input);\n return null;\n }\n if (!inCanvas || (inCanvas.width !== targetWidth) || (inCanvas.height !== targetHeight)) {\n inCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n if (inCanvas.width !== targetWidth) inCanvas.width = targetWidth;\n if (inCanvas.height !== targetHeight) inCanvas.height = targetHeight;\n }\n const ctx = inCanvas.getContext('2d');\n if (input instanceof ImageData) ctx.putImageData(input, 0, 0);\n else ctx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);\n if (config.filter.enabled) {\n if (!this.fx || !outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas.height !== outCanvas.height)) {\n outCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(inCanvas.width, inCanvas.height) : document.createElement('canvas');\n if (outCanvas.width !== inCanvas.width) outCanvas.width = inCanvas.width;\n if (outCanvas.height !== inCanvas.height) outCanvas.height = inCanvas.height;\n this.fx = tf.ENV.flags.IS_BROWSER ? new fxImage.Canvas({ canvas: outCanvas }) : null; // && (typeof document !== 'undefined')\n }\n if (!this.fx) return inCanvas;\n this.fx.reset();\n this.fx.addFilter('brightness', config.filter.brightness); // must have at least one filter enabled\n if (config.filter.contrast !== 0) this.fx.addFilter('contrast', config.filter.contrast);\n if (config.filter.sharpness !== 0) this.fx.addFilter('sharpen', config.filter.sharpness);\n if (config.filter.blur !== 0) this.fx.addFilter('blur', config.filter.blur);\n if (config.filter.saturation !== 0) this.fx.addFilter('saturation', config.filter.saturation);\n if (config.filter.hue !== 0) this.fx.addFilter('hue', config.filter.hue);\n if (config.filter.negative) this.fx.addFilter('negative');\n if (config.filter.sepia) this.fx.addFilter('sepia');\n if (config.filter.vintage) this.fx.addFilter('brownie');\n if (config.filter.sepia) this.fx.addFilter('sepia');\n if (config.filter.kodachrome) this.fx.addFilter('kodachrome');\n if (config.filter.technicolor) this.fx.addFilter('technicolor');\n if (config.filter.polaroid) this.fx.addFilter('polaroid');\n if (config.filter.pixelate !== 0) this.fx.addFilter('pixelate', config.filter.pixelate);\n this.fx.apply(inCanvas);\n // read pixel data\n /*\n const gl = outCanvas.getContext('webgl');\n if (gl) {\n const glBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 4);\n const pixBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 3);\n gl.readPixels(0, 0, outCanvas.width, outCanvas.height, gl.RGBA, gl.UNSIGNED_BYTE, glBuffer);\n // gl returns rbga while we only need rgb, so discarding alpha channel\n // gl returns starting point as lower left, so need to invert vertical\n let i = 0;\n for (let y = outCanvas.height - 1; y >= 0; y--) {\n for (let x = 0; x < outCanvas.width; x++) {\n const index = (x + y * outCanvas.width) * 4;\n pixBuffer[i++] = glBuffer[index + 0];\n pixBuffer[i++] = glBuffer[index + 1];\n pixBuffer[i++] = glBuffer[index + 2];\n }\n }\n outCanvas.data = pixBuffer;\n }\n */\n } else {\n outCanvas = inCanvas;\n }\n let pixels;\n if (outCanvas.data) {\n const shape = [outCanvas.height, outCanvas.width, 3];\n pixels = tf.tensor3d(outCanvas.data, shape, 'int32');\n } else if ((config.backend === 'webgl') || (outCanvas instanceof ImageData)) {\n // tf kernel-optimized method to get imagedata, also if input is imagedata, just use it\n pixels = tf.browser.fromPixels(outCanvas);\n } else {\n // cpu and wasm kernel does not implement efficient fromPixels method nor we can use canvas as-is, so we do a silly one more canvas\n const tempCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n tempCanvas.width = targetWidth;\n tempCanvas.height = targetHeight;\n const tempCtx = tempCanvas.getContext('2d');\n tempCtx?.drawImage(outCanvas, 0, 0);\n const data = tempCtx?.getImageData(0, 0, targetWidth, targetHeight);\n pixels = tf.browser.fromPixels(data);\n }\n const casted = pixels.toFloat();\n tensor = casted.expandDims(0);\n pixels.dispose();\n casted.dispose();\n }\n return { tensor, canvas: config.filter.return ? outCanvas : null };\n}\n\nexports.process = process;\n", "import { log } from './log.js';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as backend from './tfjs/backend.js';\nimport * as facemesh from './face/facemesh.js';\nimport * as age from './age/age.js';\nimport * as gender from './gender/gender.js';\nimport * as emotion from './emotion/emotion.js';\nimport * as embedding from './embedding/embedding.js';\nimport * as posenet from './body/posenet.js';\nimport * as handpose from './hand/handpose.js';\nimport * as gesture from './gesture/gesture.js';\nimport * as image from './image.js';\nimport * as profile from './profile.js';\nimport * as config from '../config.js';\nimport * as sample from './sample.js';\nimport * as app from '../package.json';\n\n// helper function: gets elapsed time on both browser and nodejs\nconst now = () => {\n if (typeof performance !== 'undefined') return performance.now();\n // @ts-ignore\n return parseInt(Number(process.hrtime.bigint()) / 1000 / 1000);\n};\n\n// helper function: perform deep merge of multiple objects so it allows full inheriance with overrides\nfunction mergeDeep(...objects) {\n const isObject = (obj) => obj && typeof obj === 'object';\n return objects.reduce((prev, obj) => {\n Object.keys(obj || {}).forEach((key) => {\n const pVal = prev[key];\n const oVal = obj[key];\n if (Array.isArray(pVal) && Array.isArray(oVal)) {\n prev[key] = pVal.concat(...oVal);\n } else if (isObject(pVal) && isObject(oVal)) {\n prev[key] = mergeDeep(pVal, oVal);\n } else {\n prev[key] = oVal;\n }\n });\n return prev;\n }, {});\n}\n\nclass Human {\n constructor(userConfig = {}) {\n this.tf = tf;\n this.version = app.version;\n this.config = mergeDeep(config.default, userConfig);\n this.fx = null;\n this.state = 'idle';\n this.numTensors = 0;\n this.analyzeMemoryLeaks = false;\n this.checkSanity = false;\n this.firstRun = true;\n this.perf = {};\n // object that contains all initialized models\n this.models = {\n facemesh: null,\n posenet: null,\n handpose: null,\n iris: null,\n age: null,\n gender: null,\n emotion: null,\n };\n // export raw access to underlying models\n this.facemesh = facemesh;\n this.age = age;\n this.gender = gender;\n this.emotion = emotion;\n this.body = posenet;\n this.hand = handpose;\n }\n\n profile() {\n // @ts-ignore\n if (this.config.profile) return profile.data;\n return {};\n }\n\n // helper function: measure tensor leak\n analyze(...msg) {\n if (!this.analyzeMemoryLeaks) return;\n const current = tf.engine().state.numTensors;\n const previous = this.numTensors;\n this.numTensors = current;\n const leaked = current - previous;\n if (leaked !== 0) log(...msg, leaked);\n }\n\n // quick sanity check on inputs\n sanity(input) {\n if (!this.checkSanity) return null;\n if (!input) return 'input is not defined';\n if (tf.ENV.flags.IS_NODE && !(input instanceof tf.Tensor)) {\n return 'input must be a tensor';\n }\n try {\n tf.getBackend();\n } catch {\n return 'backend not loaded';\n }\n return null;\n }\n\n simmilarity(embedding1, embedding2) {\n // @ts-ignore\n if (this.config.face.embedding.enabled) return embedding.simmilarity(embedding1, embedding2);\n return 0;\n }\n\n // preload models, not explicitly required as it's done automatically on first use\n async load(userConfig) {\n this.state = 'load';\n const timeStamp = now();\n if (userConfig) this.config = mergeDeep(this.config, userConfig);\n\n if (this.firstRun) {\n log(`version: ${this.version} TensorFlow/JS version: ${tf.version_core}`);\n await this.checkBackend(true);\n if (tf.ENV.flags.IS_BROWSER) {\n log('configuration:', this.config);\n log('tf flags:', tf.ENV.flags);\n }\n this.firstRun = false;\n }\n\n if (this.config.async) {\n [\n this.models.facemesh,\n this.models.age,\n this.models.gender,\n this.models.emotion,\n this.models.embedding,\n this.models.posenet,\n this.models.handpose,\n ] = await Promise.all([\n // @ts-ignore\n this.models.facemesh || (this.config.face.enabled ? facemesh.load(this.config) : null),\n // @ts-ignore\n this.models.age || ((this.config.face.enabled && this.config.face.age.enabled) ? age.load(this.config) : null),\n // @ts-ignore\n this.models.gender || ((this.config.face.enabled && this.config.face.gender.enabled) ? gender.load(this.config) : null),\n // @ts-ignore\n this.models.emotion || ((this.config.face.enabled && this.config.face.emotion.enabled) ? emotion.load(this.config) : null),\n // @ts-ignore\n this.models.embedding || ((this.config.face.enabled && this.config.face.embedding.enabled) ? embedding.load(this.config) : null),\n // @ts-ignore\n this.models.posenet || (this.config.body.enabled ? posenet.load(this.config) : null),\n // @ts-ignore\n this.models.handpose || (this.config.hand.enabled ? handpose.load(this.config) : null),\n ]);\n } else {\n // @ts-ignore\n if (this.config.face.enabled && !this.models.facemesh) this.models.facemesh = await facemesh.load(this.config);\n // @ts-ignore\n if (this.config.face.enabled && this.config.face.age.enabled && !this.models.age) this.models.age = await age.load(this.config);\n // @ts-ignore\n if (this.config.face.enabled && this.config.face.gender.enabled && !this.models.gender) this.models.gender = await gender.load(this.config);\n // @ts-ignore\n if (this.config.face.enabled && this.config.face.emotion.enabled && !this.models.emotion) this.models.emotion = await emotion.load(this.config);\n // @ts-ignore\n if (this.config.face.enabled && this.config.face.embedding.enabled && !this.models.embedding) this.models.embedding = await embedding.load(this.config);\n // @ts-ignore\n if (this.config.body.enabled && !this.models.posenet) this.models.posenet = await posenet.load(this.config);\n // @ts-ignore\n if (this.config.hand.enabled && !this.models.handpose) this.models.handpose = await handpose.load(this.config);\n }\n const current = Math.trunc(now() - timeStamp);\n if (current > (this.perf.load || 0)) this.perf.load = current;\n }\n\n // check if backend needs initialization if it changed\n async checkBackend(force) {\n if (this.config.backend && (this.config.backend !== '') && force || (tf.getBackend() !== this.config.backend)) {\n const timeStamp = now();\n this.state = 'backend';\n /* force backend reload\n if (this.config.backend in tf.engine().registry) {\n const backendFactory = tf.findBackendFactory(this.config.backend);\n tf.removeBackend(this.config.backend);\n tf.registerBackend(this.config.backend, backendFactory);\n } else {\n log('Backend not registred:', this.config.backend);\n }\n */\n\n log('setting backend:', this.config.backend);\n\n if (this.config.backend === 'wasm') {\n log('settings wasm path:', this.config.wasmPath);\n tf.setWasmPaths(this.config.wasmPath);\n const simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n if (!simd) log('warning: wasm simd support is not enabled');\n }\n\n if (this.config.backend === 'humangl') {\n log('registering humangl backend');\n backend.register();\n }\n\n await tf.setBackend(this.config.backend);\n tf.enableProdMode();\n /* debug mode is really too mcuh\n tf.enableDebugMode();\n */\n if (tf.getBackend() === 'webgl') {\n if (this.config.deallocate) {\n log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', this.config.deallocate);\n tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', this.config.deallocate ? 0 : -1);\n }\n tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n const gl = await tf.backend().getGPGPUContext().gl;\n log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);\n }\n await tf.ready();\n this.perf.backend = Math.trunc(now() - timeStamp);\n }\n }\n\n async detectFace(input) {\n // run facemesh, includes blazeface and iris\n // eslint-disable-next-line no-async-promise-executor\n let timeStamp;\n let ageRes;\n let genderRes;\n let emotionRes;\n let embeddingRes;\n const faceRes = [];\n this.state = 'run:face';\n timeStamp = now();\n // @ts-ignore\n const faces = await this.models.facemesh?.estimateFaces(input, this.config);\n this.perf.face = Math.trunc(now() - timeStamp);\n for (const face of faces) {\n this.analyze('Get Face');\n\n // is something went wrong, skip the face\n if (!face.image || face.image.isDisposedInternal) {\n log('Face object is disposed:', face.image);\n continue;\n }\n\n // run age, inherits face from blazeface\n this.analyze('Start Age:');\n if (this.config.async) {\n // @ts-ignore\n ageRes = this.config.face.age.enabled ? age.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:age';\n timeStamp = now();\n // @ts-ignore\n ageRes = this.config.face.age.enabled ? await age.predict(face.image, this.config) : {};\n this.perf.age = Math.trunc(now() - timeStamp);\n }\n\n // run gender, inherits face from blazeface\n this.analyze('Start Gender:');\n if (this.config.async) {\n // @ts-ignore\n genderRes = this.config.face.gender.enabled ? gender.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:gender';\n timeStamp = now();\n // @ts-ignore\n genderRes = this.config.face.gender.enabled ? await gender.predict(face.image, this.config) : {};\n this.perf.gender = Math.trunc(now() - timeStamp);\n }\n\n // run emotion, inherits face from blazeface\n this.analyze('Start Emotion:');\n if (this.config.async) {\n // @ts-ignore\n emotionRes = this.config.face.emotion.enabled ? emotion.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:emotion';\n timeStamp = now();\n // @ts-ignore\n emotionRes = this.config.face.emotion.enabled ? await emotion.predict(face.image, this.config) : {};\n this.perf.emotion = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Emotion:');\n\n // run emotion, inherits face from blazeface\n this.analyze('Start Embedding:');\n if (this.config.async) {\n // @ts-ignore\n embeddingRes = this.config.face.embedding.enabled ? embedding.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:embedding';\n timeStamp = now();\n // @ts-ignore\n embeddingRes = this.config.face.embedding.enabled ? await embedding.predict(face.image, this.config) : {};\n this.perf.embedding = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Emotion:');\n\n // if async wait for results\n if (this.config.async) {\n [ageRes, genderRes, emotionRes, embeddingRes] = await Promise.all([ageRes, genderRes, emotionRes, embeddingRes]);\n }\n\n this.analyze('Finish Face:');\n // dont need face anymore\n face.image.dispose();\n\n // calculate iris distance\n // iris: array[ center, left, top, right, bottom]\n if (!this.config.face.iris.enabled) {\n delete face.annotations.leftEyeIris;\n delete face.annotations.rightEyeIris;\n }\n const irisSize = (face.annotations.leftEyeIris && face.annotations.rightEyeIris)\n /* average human iris size is 11.7mm */\n ? 11.7 * Math.max(Math.abs(face.annotations.leftEyeIris[3][0] - face.annotations.leftEyeIris[1][0]), Math.abs(face.annotations.rightEyeIris[4][1] - face.annotations.rightEyeIris[2][1]))\n : 0;\n\n // combine results\n faceRes.push({\n confidence: face.confidence,\n box: face.box,\n mesh: face.mesh,\n boxRaw: face.boxRaw,\n meshRaw: face.meshRaw,\n annotations: face.annotations,\n age: ageRes.age,\n gender: genderRes.gender,\n genderConfidence: genderRes.confidence,\n emotion: emotionRes,\n embedding: embeddingRes,\n iris: (irisSize !== 0) ? Math.trunc(irisSize) / 100 : 0,\n });\n this.analyze('End Face');\n }\n this.analyze('End FaceMesh:');\n if (this.config.async) {\n if (this.perf.face) delete this.perf.face;\n if (this.perf.age) delete this.perf.age;\n if (this.perf.gender) delete this.perf.gender;\n if (this.perf.emotion) delete this.perf.emotion;\n }\n return faceRes;\n }\n\n async image(input, userConfig = {}) {\n this.state = 'image';\n this.config = mergeDeep(this.config, userConfig);\n // @ts-ignore\n const process = image.process(input, this.config);\n process.tensor.dispose();\n return process.canvas;\n }\n\n // main detect function\n async detect(input, userConfig = {}) {\n // detection happens inside a promise\n return new Promise(async (resolve) => {\n this.state = 'config';\n let timeStamp;\n\n // update configuration\n this.config = mergeDeep(this.config, userConfig);\n\n // sanity checks\n this.state = 'check';\n const error = this.sanity(input);\n if (error) {\n log(error, input);\n resolve({ error });\n }\n\n let poseRes;\n let handRes;\n let faceRes;\n\n const timeStart = now();\n\n // configure backend\n await this.checkBackend();\n\n // load models if enabled\n await this.load();\n\n if (this.config.scoped) tf.engine().startScope();\n this.analyze('Start Scope:');\n\n timeStamp = now();\n // @ts-ignore\n const process = image.process(input, this.config);\n if (!process || !process.tensor) {\n log('could not convert input to tensor');\n resolve({ error: 'could not convert input to tensor' });\n return;\n }\n this.perf.image = Math.trunc(now() - timeStamp);\n this.analyze('Get Image:');\n\n // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n if (this.config.async) {\n faceRes = this.config.face.enabled ? this.detectFace(process.tensor) : [];\n if (this.perf.face) delete this.perf.face;\n } else {\n this.state = 'run:face';\n timeStamp = now();\n faceRes = this.config.face.enabled ? await this.detectFace(process.tensor) : [];\n this.perf.face = Math.trunc(now() - timeStamp);\n }\n\n // run posenet\n this.analyze('Start Body:');\n if (this.config.async) {\n // @ts-ignore\n poseRes = this.config.body.enabled ? this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n if (this.perf.body) delete this.perf.body;\n } else {\n this.state = 'run:body';\n timeStamp = now();\n // @ts-ignore\n poseRes = this.config.body.enabled ? await this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n this.perf.body = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Body:');\n\n // run handpose\n this.analyze('Start Hand:');\n if (this.config.async) {\n // @ts-ignore\n handRes = this.config.hand.enabled ? this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n if (this.perf.hand) delete this.perf.hand;\n } else {\n this.state = 'run:hand';\n timeStamp = now();\n // @ts-ignore\n handRes = this.config.hand.enabled ? await this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n this.perf.hand = Math.trunc(now() - timeStamp);\n }\n // this.analyze('End Hand:');\n\n // if async wait for results\n if (this.config.async) {\n [faceRes, poseRes, handRes] = await Promise.all([faceRes, poseRes, handRes]);\n }\n process.tensor.dispose();\n\n if (this.config.scoped) tf.engine().endScope();\n this.analyze('End Scope:');\n\n let gestureRes = [];\n if (this.config.gesture.enabled) {\n timeStamp = now();\n gestureRes = [...gesture.face(faceRes), ...gesture.body(poseRes), ...gesture.hand(handRes), ...gesture.iris(faceRes)];\n if (!this.config.async) this.perf.gesture = Math.trunc(now() - timeStamp);\n else if (this.perf.gesture) delete this.perf.gesture;\n }\n\n this.perf.total = Math.trunc(now() - timeStart);\n this.state = 'idle';\n resolve({ face: faceRes, body: poseRes, hand: handRes, gesture: gestureRes, performance: this.perf, canvas: process.canvas });\n });\n }\n\n async warmup(userConfig) {\n const b64toBlob = (base64, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n\n if (userConfig) this.config = mergeDeep(this.config, userConfig);\n const video = this.config.videoOptimized;\n this.config.videoOptimized = false;\n let blob;\n switch (this.config.warmup) {\n case 'face': blob = await b64toBlob(sample.face); break;\n case 'full': blob = await b64toBlob(sample.body); break;\n default: blob = null;\n }\n if (!blob) return null;\n const bitmap = await createImageBitmap(blob);\n const t0 = now();\n const warmup = await this.detect(bitmap, config);\n const t1 = now();\n bitmap.close();\n log('Warmup', this.config.warmup, (t1 - t0), warmup);\n this.config.videoOptimized = video;\n return warmup;\n }\n}\n\nexport { Human as default };\n", "// helper function: wrapper around console output\nexport function log(...msg) {\n const dt = new Date();\n const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n // eslint-disable-next-line no-console\n if (msg) console.log(ts, 'Human:', ...msg);\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport const config = {\n name: 'humangl',\n priority: 99,\n canvas: null,\n width: 1024,\n height: 1024,\n webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n alpha: false,\n antialias: false,\n premultipliedAlpha: false,\n preserveDrawingBuffer: false,\n depth: false,\n stencil: false,\n failIfMajorPerformanceCaveat: false,\n desynchronized: true,\n },\n};\n\nexport function register() {\n if (!tf.findBackend(config.name)) {\n // @ts-ignore\n config.canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(config.width, config.height) : document.createElement('canvas');\n // @ts-ignore\n const gl = config.canvas.getContext('webgl2', config.webGLattr);\n tf.setWebGLContext(2, gl);\n const ctx = new tf.GPGPUContext(gl);\n tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n const kernels = tf.getKernelsForBackend('webgl');\n kernels.forEach((kernelConfig) => {\n const newKernelConfig = { ...kernelConfig, backendName: config.name };\n tf.registerKernel(newKernelConfig);\n });\n tf.ENV.set('WEBGL_VERSION', 2);\n tf.ENV.set('WEBGL_MAX_TEXTURE_SIZE', gl.getParameter(gl.MAX_TEXTURE_SIZE));\n tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n }\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nfunction getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\nfunction getBoxCenter(box) {\n return [\n box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n ];\n}\nfunction cutBoxFromImageAndResize(box, image, cropSize) {\n const h = image.shape[1];\n const w = image.shape[2];\n const boxes = [[\n box.startPoint[1] / h,\n box.startPoint[0] / w,\n box.endPoint[1] / h,\n box.endPoint[0] / w,\n ]];\n return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\nfunction scaleBoxCoordinates(box, factor) {\n const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n const palmLandmarks = box.palmLandmarks.map((coord) => {\n const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n return scaledCoord;\n });\n return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\nfunction enlargeBox(box, factor = 1.5) {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\nfunction squarifyBox(box) {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const maxEdge = Math.max(...size);\n const halfSize = maxEdge / 2;\n const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\nfunction shiftBox(box, shiftFactor) {\n const boxSize = [\n box.endPoint[0] - box.startPoint[0],\n box.endPoint[1] - box.startPoint[1],\n ];\n const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]];\n const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\nexport {\n cutBoxFromImageAndResize,\n enlargeBox,\n getBoxCenter,\n getBoxSize,\n scaleBoxCoordinates,\n shiftBox,\n squarifyBox,\n};\n", "function normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\nfunction computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\nconst buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\nfunction dot(v1, v2) {\n let product = 0;\n for (let i = 0; i < v1.length; i++) {\n product += v1[i] * v2[i];\n }\n return product;\n}\nfunction getColumnFrom2DArr(arr, columnIndex) {\n const column = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\nfunction multiplyTransformMatrices(mat1, mat2) {\n const product = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) {\n // @ts-ignore\n product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n }\n return product;\n}\nfunction buildRotationMatrix(rotation, center) {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\nfunction invertTransformMatrix(matrix) {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [\n -dot(rotationComponent[0], translationComponent),\n -dot(rotationComponent[1], translationComponent),\n ];\n return [\n rotationComponent[0].concat(invertedTranslation[0]),\n rotationComponent[1].concat(invertedTranslation[1]),\n [0, 0, 1],\n ];\n}\nfunction rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\nexport {\n buildRotationMatrix,\n computeRotation,\n dot,\n getColumnFrom2DArr,\n invertTransformMatrix,\n normalizeRadians,\n rotatePoint,\n};\n", "/* eslint-disable indent */\n/* eslint-disable no-multi-spaces */\n\nexport default {\n backend: 'webgl', // select tfjs backend to use\n wasmPath: '../assets/', // path for wasm binaries\n // only used for backend: wasm\n async: true, // execute enabled models in parallel\n // this disables per-model performance data but\n // slightly increases performance\n // cannot be used if profiling is enabled\n profile: false, // enable tfjs profiling\n // this has significant performance impact\n // only enable for debugging purposes\n // currently only implemented for age,gender,emotion models\n deallocate: false, // aggresively deallocate gpu memory after each usage\n // only valid for webgl backend and only during first call\n // cannot be changed unless library is reloaded\n // this has significant performance impact\n // only enable on low-memory devices\n scoped: false, // enable scoped runs\n // some models *may* have memory leaks,\n // this wrapps everything in a local scope at a cost of performance\n // typically not needed\n videoOptimized: true, // perform additional optimizations when input is video,\n // must be disabled for images\n // basically this skips object box boundary detection for every n frames\n // while maintaining in-box detection since objects cannot move that fast\n warmup: 'face', // what to use for human.warmup(), can be 'none', 'face', 'full'\n // warmup pre-initializes all models for faster inference but can take\n // significant time on startup\n filter: {\n enabled: true, // enable image pre-processing filters\n width: 0, // resize input width\n height: 0, // resize input height\n // if both width and height are set to 0, there is no resizing\n // if just one is set, second one is scaled automatically\n // if both are set, values are used as-is\n return: true, // return processed canvas imagedata in result\n brightness: 0, // range: -1 (darken) to 1 (lighten)\n contrast: 0, // range: -1 (reduce contrast) to 1 (increase contrast)\n sharpness: 0, // range: 0 (no sharpening) to 1 (maximum sharpening)\n blur: 0, // range: 0 (no blur) to N (blur radius in pixels)\n saturation: 0, // range: -1 (reduce saturation) to 1 (increase saturation)\n hue: 0, // range: 0 (no change) to 360 (hue rotation in degrees)\n negative: false, // image negative\n sepia: false, // image sepia colors\n vintage: false, // image vintage colors\n kodachrome: false, // image kodachrome colors\n technicolor: false, // image technicolor colors\n polaroid: false, // image polaroid camera effect\n pixelate: 0, // range: 0 (no pixelate) to N (number of pixels to pixelate)\n },\n\n gesture: {\n enabled: true, // enable simple gesture recognition\n },\n\n face: {\n enabled: true, // controls if specified modul is enabled\n // face.enabled is required for all face models:\n // detector, mesh, iris, age, gender, emotion\n // (note: module is not loaded until it is required)\n detector: {\n modelPath: '../models/blazeface-back.json', // can be 'front' or 'back'.\n // 'front' is optimized for large faces\n // such as front-facing camera and\n // 'back' is optimized for distanct faces.\n inputSize: 256, // fixed value: 128 for front and 256 for 'back'\n rotation: false, // use best-guess rotated face image or just box with rotation as-is\n // false means higher performance, but incorrect mesh mapping if face angle is above 20 degrees\n maxFaces: 10, // maximum number of faces detected in the input\n // should be set to the minimum number for performance\n skipFrames: 11, // how many frames to go without re-running the face bounding box detector\n // only used for video inputs\n // e.g., if model is running st 25 FPS, we can re-use existing bounding\n // box for updated face analysis as the head probably hasn't moved much\n // in short time (10 * 1/25 = 0.25 sec)\n minConfidence: 0.5, // threshold for discarding a prediction\n iouThreshold: 0.2, // threshold for deciding whether boxes overlap too much in\n // non-maximum suppression (0.1 means drop if overlap 10%)\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on score\n // in non-maximum suppression,\n // this is applied on detection objects only and before minConfidence\n },\n\n mesh: {\n enabled: true,\n modelPath: '../models/facemesh.json',\n inputSize: 192, // fixed value\n returnRawData: false, // in addition to standard mesh and box values, return raw normalized values as well\n },\n\n iris: {\n enabled: true,\n modelPath: '../models/iris.json',\n inputSize: 64, // fixed value\n },\n\n age: {\n enabled: true,\n modelPath: '../models/age-ssrnet-imdb.json', // can be 'age-ssrnet-imdb' or 'age-ssrnet-wiki'\n // which determines training set for model\n inputSize: 64, // fixed value\n skipFrames: 31, // how many frames to go without re-running the detector\n // only used for video inputs\n },\n\n gender: {\n enabled: true,\n minConfidence: 0.1, // threshold for discarding a prediction\n modelPath: '../models/gender-ssrnet-imdb.json', // can be 'gender', 'gender-ssrnet-imdb' or 'gender-ssrnet-wiki'\n inputSize: 64, // fixed value\n skipFrames: 41, // how many frames to go without re-running the detector\n // only used for video inputs\n },\n\n emotion: {\n enabled: true,\n inputSize: 64, // fixed value\n minConfidence: 0.2, // threshold for discarding a prediction\n skipFrames: 21, // how many frames to go without re-running the detector\n modelPath: '../models/emotion-large.json', // can be 'mini', 'large'\n },\n\n embedding: {\n enabled: false,\n inputSize: 112, // fixed value\n modelPath: '../models/mobilefacenet.json',\n },\n },\n\n body: {\n enabled: true,\n modelPath: '../models/posenet.json',\n inputSize: 257, // fixed value\n maxDetections: 10, // maximum number of people detected in the input\n // should be set to the minimum number for performance\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on score\n // in non-maximum suppression\n nmsRadius: 20, // radius for deciding points are too close in non-maximum suppression\n outputStride: 16, // size of block in which to run point detectopn, smaller value means higher resolution\n // defined by model itself, can be 8, 16, or 32\n modelType: 'MobileNet', // Human includes MobileNet version, but you can switch to ResNet\n },\n\n hand: {\n enabled: true,\n rotation: false, // use best-guess rotated hand image or just box with rotation as-is\n // false means higher performance, but incorrect finger mapping if hand is inverted\n inputSize: 256, // fixed value\n skipFrames: 12, // how many frames to go without re-running the hand bounding box detector\n // only used for video inputs\n // e.g., if model is running st 25 FPS, we can re-use existing bounding\n // box for updated hand skeleton analysis as the hand probably\n // hasn't moved much in short time (10 * 1/25 = 0.25 sec)\n minConfidence: 0.1, // threshold for discarding a prediction\n iouThreshold: 0.1, // threshold for deciding whether boxes overlap too much\n // in non-maximum suppression\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on\n // score in non-maximum suppression\n maxHands: 1, // maximum number of hands detected in the input\n // should be set to the minimum number for performance\n landmarks: true, // detect hand landmarks or just hand boundary box\n detector: {\n modelPath: '../models/handdetect.json',\n },\n skeleton: {\n modelPath: '../models/handskeleton.json',\n },\n },\n};\n", "// data:image/jpeg;base64,\nexport const face = 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+ "sources": ["../src/tfjs/tf-node-gpu.js", "../src/blazeface/blazeface.js", "../src/blazeface/box.js", "../src/blazeface/util.js", "../src/blazeface/coords.js", "../src/blazeface/facepipeline.js", "../src/blazeface/facemesh.js", "../src/profile.js", "../src/age/age.js", "../src/gender/gender.js", "../src/emotion/emotion.js", "../src/embedding/embedding.js", "../src/posenet/modelBase.js", "../src/posenet/heapSort.js", "../src/posenet/buildParts.js", "../src/posenet/keypoints.js", "../src/posenet/vectors.js", "../src/posenet/decoders.js", "../src/posenet/decodePose.js", "../src/posenet/decodeMultiple.js", "../src/posenet/util.js", "../src/posenet/modelPoseNet.js", "../src/posenet/posenet.js", "../src/handpose/handdetector.js", "../src/handpose/handpipeline.js", "../src/handpose/anchors.js", "../src/handpose/handpose.js", "../src/gesture/gesture.js", "../src/imagefx.js", "../src/image.js", "../src/human.js", "../src/log.js", "../src/tfjs/backend.js", "../src/blazepose/blazepose.js", "../src/handpose/box.js", "../src/handpose/util.js", "../config.js", "../src/sample.js"], + "sourcesContent": ["export * from '@tensorflow/tfjs-node-gpu';\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nconst NUM_LANDMARKS = 6;\n\nfunction generateAnchors(inputSize) {\n const spec = { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] };\n const anchors = [];\n for (let i = 0; i < spec.strides.length; i++) {\n const stride = spec.strides[i];\n const gridRows = Math.floor((inputSize + stride - 1) / stride);\n const gridCols = Math.floor((inputSize + stride - 1) / stride);\n const anchorsNum = spec.anchors[i];\n for (let gridY = 0; gridY < gridRows; gridY++) {\n const anchorY = stride * (gridY + 0.5);\n for (let gridX = 0; gridX < gridCols; gridX++) {\n const anchorX = stride * (gridX + 0.5);\n for (let n = 0; n < anchorsNum; n++) {\n anchors.push([anchorX, anchorY]);\n }\n }\n }\n }\n return anchors;\n}\n\nconst disposeBox = (box) => {\n box.startEndTensor.dispose();\n box.startPoint.dispose();\n box.endPoint.dispose();\n};\n\nconst createBox = (startEndTensor) => ({\n startEndTensor,\n startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]),\n endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]),\n});\n\nconst scaleBox = (box, factors) => {\n const starts = tf.mul(box.startPoint, factors);\n const ends = tf.mul(box.endPoint, factors);\n const newCoordinates = tf.concat2d([starts, ends], 1);\n return createBox(newCoordinates);\n};\n\nfunction decodeBounds(boxOutputs, anchors, inputSize) {\n const boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n const centers = tf.add(boxStarts, anchors);\n const boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n const boxSizesNormalized = tf.div(boxSizes, inputSize);\n const centersNormalized = tf.div(centers, inputSize);\n const halfBoxSize = tf.div(boxSizesNormalized, 2);\n const starts = tf.sub(centersNormalized, halfBoxSize);\n const ends = tf.add(centersNormalized, halfBoxSize);\n const startNormalized = tf.mul(starts, inputSize);\n const endNormalized = tf.mul(ends, inputSize);\n const concatAxis = 1;\n return tf.concat2d([startNormalized, endNormalized], concatAxis);\n}\n\nfunction scaleBoxFromPrediction(face, scaleFactor) {\n return tf.tidy(() => {\n const box = face['box'] ? face['box'] : face;\n return scaleBox(box, scaleFactor).startEndTensor.squeeze();\n });\n}\n\nclass BlazeFaceModel {\n constructor(model, config) {\n this.blazeFaceModel = model;\n this.width = config.face.detector.inputSize;\n this.height = config.face.detector.inputSize;\n this.anchorsData = generateAnchors(config.face.detector.inputSize);\n this.anchors = tf.tensor2d(this.anchorsData);\n this.inputSize = tf.tensor1d([this.width, this.height]);\n this.config = config;\n this.scaleFaces = 0.8;\n }\n\n async getBoundingBoxes(inputImage) {\n // sanity check on input\n if ((!inputImage) || (inputImage.isDisposedInternal) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return null;\n const [detectedOutputs, boxes, scores] = tf.tidy(() => {\n const resizedImage = inputImage.resizeBilinear([this.width, this.height]);\n // const normalizedImage = tf.mul(tf.sub(resizedImage.div(255), 0.5), 2);\n const normalizedImage = tf.sub(resizedImage.div(127.5), 1);\n const batchedPrediction = this.blazeFaceModel.predict(normalizedImage);\n let prediction;\n // are we using tfhub or pinto converted model?\n if (Array.isArray(batchedPrediction)) {\n const sorted = batchedPrediction.sort((a, b) => a.size - b.size);\n const concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n const concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n const concat = tf.concat([concat512, concat384], 1);\n prediction = concat.squeeze(0);\n } else {\n prediction = batchedPrediction.squeeze(); // when using tfhub model\n }\n const decodedBounds = decodeBounds(prediction, this.anchors, this.inputSize);\n const logits = tf.slice(prediction, [0, 0], [-1, 1]);\n const scoresOut = tf.sigmoid(logits).squeeze();\n return [prediction, decodedBounds, scoresOut];\n });\n const boxIndicesTensor = await tf.image.nonMaxSuppressionAsync(boxes, scores, this.config.face.detector.maxFaces, this.config.face.detector.iouThreshold, this.config.face.detector.scoreThreshold);\n const boxIndices = boxIndicesTensor.arraySync();\n boxIndicesTensor.dispose();\n const boundingBoxesMap = boxIndices.map((boxIndex) => tf.slice(boxes, [boxIndex, 0], [1, -1]));\n const boundingBoxes = boundingBoxesMap.map((boundingBox) => {\n const vals = boundingBox.arraySync();\n boundingBox.dispose();\n return vals;\n });\n\n const scoresVal = scores.dataSync();\n const annotatedBoxes = [];\n for (let i = 0; i < boundingBoxes.length; i++) {\n const boxIndex = boxIndices[i];\n const confidence = scoresVal[boxIndex];\n if (confidence > this.config.face.detector.minConfidence) {\n const box = createBox(boundingBoxes[i]);\n const anchor = this.anchorsData[boxIndex];\n const landmarks = tf.tidy(() => tf.slice(detectedOutputs, [boxIndex, NUM_LANDMARKS - 1], [1, -1]).squeeze().reshape([NUM_LANDMARKS, -1]));\n annotatedBoxes.push({ box, landmarks, anchor, confidence });\n }\n }\n detectedOutputs.dispose();\n boxes.dispose();\n scores.dispose();\n detectedOutputs.dispose();\n return {\n boxes: annotatedBoxes,\n scaleFactor: [inputImage.shape[2] / this.width, inputImage.shape[1] / this.height],\n };\n }\n\n async estimateFaces(input) {\n // @ts-ignore\n const { boxes, scaleFactor } = await this.getBoundingBoxes(input);\n const faces = [];\n for (const face of boxes) {\n const landmarkData = face.landmarks.arraySync();\n const scaledBox = scaleBoxFromPrediction(face, scaleFactor);\n // @ts-ignore\n const boxData = scaleBox.arraySync();\n const probabilityData = face.probability.arraySync();\n const anchor = face.anchor;\n const [scaleFactorX, scaleFactorY] = scaleFactor;\n const scaledLandmarks = landmarkData\n .map((landmark) => ([\n (landmark[0] + anchor[0]) * scaleFactorX,\n (landmark[1] + anchor[1]) * scaleFactorY,\n ]));\n const normalizedFace = {\n topLeft: boxData.slice(0, 2),\n bottomRight: boxData.slice(2),\n landmarks: scaledLandmarks,\n probability: probabilityData,\n };\n disposeBox(face.box);\n face.landmarks.dispose();\n face.probability.dispose();\n scaledBox.dispose();\n faces.push(normalizedFace);\n }\n return faces;\n }\n}\n\nasync function load(config) {\n const blazeface = await tf.loadGraphModel(config.face.detector.modelPath, { fromTFHub: config.face.detector.modelPath.includes('tfhub.dev') });\n const model = new BlazeFaceModel(blazeface, config);\n log(`load model: ${config.face.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n return model;\n}\n\nexports.load = load;\nexports.BlazeFaceModel = BlazeFaceModel;\nexports.disposeBox = disposeBox;\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nfunction scaleBoxCoordinates(box, factor) {\n const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n return { startPoint, endPoint };\n}\nexports.scaleBoxCoordinates = scaleBoxCoordinates;\n\nfunction getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\nexports.getBoxSize = getBoxSize;\n\nfunction getBoxCenter(box) {\n return [\n box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n ];\n}\nexports.getBoxCenter = getBoxCenter;\n\nfunction cutBoxFromImageAndResize(box, image, cropSize) {\n const h = image.shape[1];\n const w = image.shape[2];\n const boxes = [[\n box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h,\n box.endPoint[0] / w,\n ]];\n return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\nexports.cutBoxFromImageAndResize = cutBoxFromImageAndResize;\n\nfunction enlargeBox(box, factor = 1.5) {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n return { startPoint, endPoint, landmarks: box.landmarks };\n}\nexports.enlargeBox = enlargeBox;\n\nfunction squarifyBox(box) {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const maxEdge = Math.max(...size);\n const halfSize = maxEdge / 2;\n const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n return { startPoint, endPoint, landmarks: box.landmarks };\n}\nexports.squarifyBox = squarifyBox;\n", "exports.IDENTITY_MATRIX = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n/**\n * Normalizes the provided angle to the range -pi to pi.\n * @param angle The angle in radians to be normalized.\n */\nfunction normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\nexports.normalizeRadians = normalizeRadians;\n\n/**\n * Computes the angle of rotation between two anchor points.\n * @param point1 First anchor point\n * @param point2 Second anchor point\n */\nfunction computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\nexports.computeRotation = computeRotation;\n\nfunction radToDegrees(rad) {\n return rad * 180 / Math.PI;\n}\nexports.radToDegrees = radToDegrees;\n\nfunction buildTranslationMatrix(x, y) {\n return [[1, 0, x], [0, 1, y], [0, 0, 1]];\n}\n\nfunction dot(v1, v2) {\n let product = 0;\n for (let i = 0; i < v1.length; i++) {\n product += v1[i] * v2[i];\n }\n return product;\n}\nexports.dot = dot;\n\nfunction getColumnFrom2DArr(arr, columnIndex) {\n const column = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\nexports.getColumnFrom2DArr = getColumnFrom2DArr;\n\nfunction multiplyTransformMatrices(mat1, mat2) {\n const product = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) {\n product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n }\n return product;\n}\nfunction buildRotationMatrix(rotation, center) {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\nexports.buildRotationMatrix = buildRotationMatrix;\n\nfunction invertTransformMatrix(matrix) {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [\n -dot(rotationComponent[0], translationComponent),\n -dot(rotationComponent[1], translationComponent),\n ];\n return [\n rotationComponent[0].concat(invertedTranslation[0]),\n rotationComponent[1].concat(invertedTranslation[1]),\n [0, 0, 1],\n ];\n}\nexports.invertTransformMatrix = invertTransformMatrix;\n\nfunction rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\nexports.rotatePoint = rotatePoint;\n\nfunction xyDistanceBetweenPoints(a, b) {\n return Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n}\nexports.xyDistanceBetweenPoints = xyDistanceBetweenPoints;\n", "const MESH_ANNOTATIONS = {\n silhouette: [\n 10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n 397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n 172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n ],\n lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291],\n lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291],\n lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308],\n lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],\n rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173],\n rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133],\n rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190],\n rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243],\n rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189],\n rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244],\n rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245],\n rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193],\n rightEyebrowLower: [35, 124, 46, 53, 52, 65],\n rightEyeIris: [473, 474, 475, 476, 477],\n leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n leftEyeIris: [468, 469, 470, 471, 472],\n midwayBetweenEyes: [168],\n noseTip: [1],\n noseBottom: [2],\n noseRightCorner: [98],\n noseLeftCorner: [327],\n rightCheek: [205],\n leftCheek: [425],\n};\n\nconst MESH_TO_IRIS_INDICES_MAP = [ // A mapping from facemesh model keypoints to iris model keypoints.\n { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] },\n { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] },\n { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] },\n { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] },\n { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] },\n { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] },\n { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] },\n // { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] },\n // { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] },\n];\n\nconst UV468 = [\n [0.499976992607117, 0.652534008026123],\n [0.500025987625122, 0.547487020492554],\n [0.499974012374878, 0.602371990680695],\n [0.482113003730774, 0.471979022026062],\n [0.500150978565216, 0.527155995368958],\n [0.499909996986389, 0.498252987861633],\n [0.499523013830185, 0.40106201171875],\n [0.289712011814117, 0.380764007568359],\n [0.499954998493195, 0.312398016452789],\n [0.499987006187439, 0.269918978214264],\n [0.500023007392883, 0.107050001621246],\n [0.500023007392883, 0.666234016418457],\n [0.5000159740448, 0.679224014282227],\n [0.500023007392883, 0.692348003387451],\n [0.499976992607117, 0.695277988910675],\n [0.499976992607117, 0.70593398809433],\n [0.499976992607117, 0.719385027885437],\n [0.499976992607117, 0.737019002437592],\n [0.499967992305756, 0.781370997428894],\n [0.499816000461578, 0.562981009483337],\n [0.473773002624512, 0.573909997940063],\n [0.104906998574734, 0.254140973091125],\n [0.365929991006851, 0.409575998783112],\n [0.338757991790771, 0.41302502155304],\n [0.311120003461838, 0.409460008144379],\n [0.274657994508743, 0.389131009578705],\n [0.393361985683441, 0.403706014156342],\n [0.345234006643295, 0.344011008739471],\n [0.370094001293182, 0.346076011657715],\n [0.319321990013123, 0.347265005111694],\n [0.297903001308441, 0.353591024875641],\n [0.24779200553894, 0.410809993743896],\n [0.396889001131058, 0.842755019664764],\n [0.280097991228104, 0.375599980354309],\n [0.106310002505779, 0.399955987930298],\n [0.2099249958992, 0.391353011131287],\n [0.355807989835739, 0.534406006336212],\n [0.471751004457474, 0.65040397644043],\n [0.474155008792877, 0.680191993713379],\n [0.439785003662109, 0.657229006290436],\n [0.414617002010345, 0.66654098033905],\n [0.450374007225037, 0.680860996246338],\n [0.428770989179611, 0.682690978050232],\n [0.374971002340317, 0.727805018424988],\n [0.486716985702515, 0.547628998756409],\n [0.485300987958908, 0.527395009994507],\n [0.257764995098114, 0.314490020275116],\n [0.401223003864288, 0.455172002315521],\n [0.429818987846375, 0.548614978790283],\n [0.421351999044418, 0.533740997314453],\n [0.276895999908447, 0.532056987285614],\n [0.483370006084442, 0.499586999416351],\n [0.33721199631691, 0.282882988452911],\n [0.296391993761063, 0.293242990970612],\n [0.169294998049736, 0.193813979625702],\n [0.447580009698868, 0.302609980106354],\n [0.392390012741089, 0.353887975215912],\n [0.354490011930466, 0.696784019470215],\n [0.067304998636246, 0.730105042457581],\n [0.442739009857178, 0.572826027870178],\n [0.457098007202148, 0.584792017936707],\n [0.381974011659622, 0.694710969924927],\n [0.392388999462128, 0.694203019142151],\n [0.277076005935669, 0.271932005882263],\n [0.422551989555359, 0.563233017921448],\n [0.385919004678726, 0.281364023685455],\n [0.383103013038635, 0.255840003490448],\n [0.331431001424789, 0.119714021682739],\n [0.229923993349075, 0.232002973556519],\n [0.364500999450684, 0.189113974571228],\n [0.229622006416321, 0.299540996551514],\n [0.173287004232407, 0.278747975826263],\n [0.472878992557526, 0.666198015213013],\n [0.446828007698059, 0.668527007102966],\n [0.422762006521225, 0.673889994621277],\n [0.445307999849319, 0.580065965652466],\n [0.388103008270264, 0.693961024284363],\n [0.403039008378983, 0.706539988517761],\n [0.403629004955292, 0.693953037261963],\n [0.460041999816895, 0.557139039039612],\n [0.431158006191254, 0.692366003990173],\n 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[0.547226011753082, 0.420395016670227],\n [0.563543975353241, 0.359827995300293],\n [0.583841025829315, 0.368713974952698],\n [0.586614012718201, 0.692366003990173],\n [0.771915018558502, 0.683578014373779],\n [0.531597018241882, 0.352482974529266],\n [0.588370978832245, 0.804440975189209],\n [0.52079701423645, 0.442565023899078],\n [0.567984998226166, 0.493479013442993],\n [0.543282985687256, 0.819254994392395],\n [0.655317008495331, 0.745514988899231],\n [0.621008992195129, 0.574018001556396],\n [0.625559985637665, 0.78031200170517],\n [0.680198013782501, 0.570719003677368],\n [0.64276397228241, 0.604337990283966],\n [0.704662978649139, 0.621529996395111],\n [0.552012026309967, 0.862591981887817],\n [0.589071989059448, 0.508637011051178],\n [0.685944974422455, 0.775357007980347],\n [0.645735025405884, 0.812640011310577],\n [0.675342977046967, 0.703978002071381],\n [0.810858011245728, 0.646304965019226],\n [0.72012197971344, 0.714666962623596],\n [0.866151988506317, 0.682704985141754],\n [0.663187026977539, 0.644596993923187],\n [0.570082008838654, 0.466325998306274],\n [0.544561982154846, 0.548375964164734],\n [0.562758982181549, 0.558784961700439],\n [0.531987011432648, 0.530140042304993],\n [0.585271000862122, 0.335177004337311],\n [0.622952997684479, 0.32277899980545],\n [0.655896008014679, 0.320163011550903],\n [0.687132000923157, 0.322345972061157],\n [0.716481983661652, 0.333200991153717],\n [0.758756995201111, 0.382786989212036],\n [0.897013008594513, 0.468769013881683],\n [0.732392013072968, 0.424547016620636],\n [0.70211398601532, 0.433162987232208],\n [0.66652500629425, 0.433866024017334],\n [0.633504986763, 0.426087975502014],\n [0.603875994682312, 0.416586995124817],\n [0.579657971858978, 0.409945011138916],\n [0.992439985275269, 0.480777025222778],\n [0.567192018032074, 0.569419980049133],\n [0.54136598110199, 0.478899002075195],\n [0.526564002037048, 0.546118021011353],\n [0.523913025856018, 0.563830018043518],\n [0.531529009342194, 0.555056989192963],\n [0.566035985946655, 0.582329034805298],\n [0.51631098985672, 0.563053965568542],\n [0.5174720287323, 0.577877044677734],\n [0.573594987392426, 0.389806985855103],\n [0.560697972774506, 0.395331978797913],\n [0.549755990505219, 0.399751007556915],\n [0.710287988185883, 0.368252992630005],\n [0.723330020904541, 0.363372981548309],\n];\n\nconst TRI468 = [\n 127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n 151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n 157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n 181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n 228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n 170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n 155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n 158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n 102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n 90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n 196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n 246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n 117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n 153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n 63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n 135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n 76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n 166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n 190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n 112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n 110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n 348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n 314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n 321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n 466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n 429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n 293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n 427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n 431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n 434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n 348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n 318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n 373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n 340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n 351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n 164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n 327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n 405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n 438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n 449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n 444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n 464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n 243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n 166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n 119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n 77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n 201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n 103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n 217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n 78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n 57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n 142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n 90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n 178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n 224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n 143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n 153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n 63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n 241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n 186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n 141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n 223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n 128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n 451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n 344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n 424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n 321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n 448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n 281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n 283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n 432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n 268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n 6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n 317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n 272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n 336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n 441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n 373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n 425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n 403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n 383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n 254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n 259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n 453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nconst TRI68 = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n 8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n 45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n 23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n 39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n 35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n 48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n 66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\nconst TRI33 = [\n /* eyes */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n /* brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n /* 4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n /* nose */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n /* chin */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n /* cont */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\nconst TRI7 = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nconst VTX68 = [\n /* cont */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n /* nose */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n /* eyes */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n /* lip */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\nconst VTX33 = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\nconst VTX7 = [33, 133, 362, 263, 1, 78, 308];\n\nexports.MESH_ANNOTATIONS = MESH_ANNOTATIONS;\nexports.MESH_TO_IRIS_INDICES_MAP = MESH_TO_IRIS_INDICES_MAP;\n\nexports.TRI468 = TRI468;\nexports.TRI68 = TRI68;\nexports.TRI33 = TRI33;\nexports.TRI7 = TRI7;\n\nexports.UV468 = UV468;\nexports.UV68 = VTX68.map((x) => UV468[x]);\nexports.UV33 = VTX33.map((x) => UV468[x]);\nexports.UV7 = VTX7.map((x) => UV468[x]);\n", "/* eslint-disable class-methods-use-this */\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as bounding from './box';\nimport * as util from './util';\nimport * as coords from './coords.js';\n// eslint-disable-next-line no-unused-vars\nimport { log } from '../log.js';\n\nconst LANDMARKS_COUNT = 468;\nconst MESH_MOUTH_INDEX = 13;\nconst MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES = [MESH_MOUTH_INDEX, coords.MESH_ANNOTATIONS['midwayBetweenEyes'][0]];\nconst BLAZEFACE_MOUTH_INDEX = 3;\nconst BLAZEFACE_NOSE_INDEX = 2;\nconst BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES = [BLAZEFACE_MOUTH_INDEX, BLAZEFACE_NOSE_INDEX];\nconst LEFT_EYE_OUTLINE = coords.MESH_ANNOTATIONS['leftEyeLower0'];\nconst LEFT_EYE_BOUNDS = [LEFT_EYE_OUTLINE[0], LEFT_EYE_OUTLINE[LEFT_EYE_OUTLINE.length - 1]];\nconst RIGHT_EYE_OUTLINE = coords.MESH_ANNOTATIONS['rightEyeLower0'];\nconst RIGHT_EYE_BOUNDS = [RIGHT_EYE_OUTLINE[0], RIGHT_EYE_OUTLINE[RIGHT_EYE_OUTLINE.length - 1]];\nconst IRIS_UPPER_CENTER_INDEX = 3;\nconst IRIS_LOWER_CENTER_INDEX = 4;\nconst IRIS_IRIS_INDEX = 71;\nconst IRIS_NUM_COORDINATES = 76;\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates. Update the z coordinate to be an average of the original and the new. This produces the best visual effect.\nfunction replaceRawCoordinates(rawCoords, newCoords, prefix, keys) {\n for (let i = 0; i < coords.MESH_TO_IRIS_INDICES_MAP.length; i++) {\n const { key, indices } = coords.MESH_TO_IRIS_INDICES_MAP[i];\n const originalIndices = coords.MESH_ANNOTATIONS[`${prefix}${key}`];\n const shouldReplaceAllKeys = keys == null;\n if (shouldReplaceAllKeys || keys.includes(key)) {\n for (let j = 0; j < indices.length; j++) {\n const index = indices[j];\n rawCoords[originalIndices[j]] = [\n newCoords[index][0], newCoords[index][1],\n (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n ];\n }\n }\n }\n}\n// The Pipeline coordinates between the bounding box and skeleton models.\nclass Pipeline {\n constructor(boundingBoxDetector, meshDetector, irisModel, config) {\n // An array of facial bounding boxes.\n this.storedBoxes = [];\n this.runsWithoutFaceDetector = 0;\n this.boundingBoxDetector = boundingBoxDetector;\n this.meshDetector = meshDetector;\n this.irisModel = irisModel;\n this.meshWidth = config.face.mesh.inputSize;\n this.meshHeight = config.face.mesh.inputSize;\n this.irisSize = config.face.iris.inputSize;\n this.irisEnlarge = 2.3;\n this.skipped = 0;\n this.detectedFaces = 0;\n }\n\n transformRawCoords(rawCoords, box, angle, rotationMatrix) {\n // @ts-ignore\n const boxSize = bounding.getBoxSize({ startPoint: box.startPoint, endPoint: box.endPoint });\n const scaleFactor = [boxSize[0] / this.meshWidth, boxSize[1] / this.meshHeight];\n const coordsScaled = rawCoords.map((coord) => ([\n scaleFactor[0] * (coord[0] - this.meshWidth / 2),\n scaleFactor[1] * (coord[1] - this.meshHeight / 2), coord[2],\n ]));\n const coordsRotationMatrix = (angle !== 0) ? util.buildRotationMatrix(angle, [0, 0]) : util.IDENTITY_MATRIX;\n const coordsRotated = (angle !== 0) ? coordsScaled.map((coord) => ([...util.rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n const inverseRotationMatrix = (angle !== 0) ? util.invertTransformMatrix(rotationMatrix) : util.IDENTITY_MATRIX;\n // @ts-ignore\n const boxCenter = [...bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint }), 1];\n return coordsRotated.map((coord) => ([\n coord[0] + util.dot(boxCenter, inverseRotationMatrix[0]),\n coord[1] + util.dot(boxCenter, inverseRotationMatrix[1]),\n coord[2],\n ]));\n }\n\n getLeftToRightEyeDepthDifference(rawCoords) {\n const leftEyeZ = rawCoords[LEFT_EYE_BOUNDS[0]][2];\n const rightEyeZ = rawCoords[RIGHT_EYE_BOUNDS[0]][2];\n return leftEyeZ - rightEyeZ;\n }\n\n // Returns a box describing a cropped region around the eye fit for passing to the iris model.\n getEyeBox(rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, flip = false) {\n // @ts-ignore\n const box = bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), this.irisEnlarge));\n // @ts-ignore\n const boxSize = bounding.getBoxSize(box);\n let crop = tf.image.cropAndResize(face, [[\n box.startPoint[1] / this.meshHeight,\n box.startPoint[0] / this.meshWidth, box.endPoint[1] / this.meshHeight,\n box.endPoint[0] / this.meshWidth,\n ]], [0], [this.irisSize, this.irisSize]);\n if (flip) {\n crop = tf.image.flipLeftRight(crop);\n }\n return { box, boxSize, crop };\n }\n\n // Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\n getEyeCoords(eyeData, eyeBox, eyeBoxSize, flip = false) {\n const eyeRawCoords = [];\n for (let i = 0; i < IRIS_NUM_COORDINATES; i++) {\n const x = eyeData[i * 3];\n const y = eyeData[i * 3 + 1];\n const z = eyeData[i * 3 + 2];\n eyeRawCoords.push([\n (flip\n ? (1 - (x / this.irisSize))\n : (x / this.irisSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n (y / this.irisSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n ]);\n }\n return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(IRIS_IRIS_INDEX) };\n }\n\n // The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\n getAdjustedIrisCoords(rawCoords, irisCoords, direction) {\n const upperCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeUpper0`][IRIS_UPPER_CENTER_INDEX]][2];\n const lowerCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeLower0`][IRIS_LOWER_CENTER_INDEX]][2];\n const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n return irisCoords.map((coord, i) => {\n let z = averageZ;\n if (i === 2) {\n z = upperCenterZ;\n } else if (i === 4) {\n z = lowerCenterZ;\n }\n return [coord[0], coord[1], z];\n });\n }\n\n async predict(input, config) {\n let useFreshBox = false;\n // run new detector every skipFrames unless we only want box to start with\n let detector;\n if ((this.skipped === 0) || (this.skipped > config.face.detector.skipFrames) || !config.face.mesh.enabled || !config.videoOptimized) {\n detector = await this.boundingBoxDetector.getBoundingBoxes(input);\n this.skipped = 0;\n }\n if (config.videoOptimized) this.skipped++;\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (detector && detector.boxes && (detector.boxes.length > 0) && (!config.face.mesh.enabled || (detector.boxes.length !== this.detectedFaces) && (this.detectedFaces !== config.face.detector.maxFaces))) {\n this.storedBoxes = [];\n this.detectedFaces = 0;\n for (const possible of detector.boxes) {\n this.storedBoxes.push({ startPoint: possible.box.startPoint.dataSync(), endPoint: possible.box.endPoint.dataSync(), landmarks: possible.landmarks, confidence: possible.confidence });\n }\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\n\n if (useFreshBox) {\n if (!detector || !detector.boxes || (detector.boxes.length === 0)) {\n this.storedBoxes = [];\n this.detectedFaces = 0;\n return null;\n }\n for (let i = 0; i < this.storedBoxes.length; i++) {\n // @ts-ignore\n const scaledBox = bounding.scaleBoxCoordinates({ startPoint: this.storedBoxes[i].startPoint, endPoint: this.storedBoxes[i].endPoint }, detector.scaleFactor);\n // @ts-ignore\n const enlargedBox = bounding.enlargeBox(scaledBox);\n // @ts-ignore\n const squarifiedBox = bounding.squarifyBox(enlargedBox);\n const landmarks = this.storedBoxes[i].landmarks.arraySync();\n const confidence = this.storedBoxes[i].confidence;\n this.storedBoxes[i] = { ...squarifiedBox, confidence, landmarks };\n }\n this.runsWithoutFaceDetector = 0;\n }\n if (detector && detector.boxes) {\n detector.boxes.forEach((prediction) => {\n prediction.box.startPoint.dispose();\n prediction.box.endPoint.dispose();\n prediction.landmarks.dispose();\n });\n }\n\n // log('face', `skipped: ${this.skipped} max: ${config.face.detector.maxFaces} detected: ${this.detectedFaces} stored: ${this.storedBoxes.length} new: ${detector?.boxes?.length}`);\n let results = tf.tidy(() => this.storedBoxes.map((box, i) => {\n // The facial bounding box landmarks could come either from blazeface (if we are using a fresh box), or from the mesh model (if we are reusing an old box).\n let face;\n let angle = 0;\n let rotationMatrix;\n if (config.face.detector.rotation) {\n const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= LANDMARKS_COUNT) ? MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES : BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES;\n angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n // @ts-ignore\n const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n const rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized);\n rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n // @ts-ignore\n face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshHeight, this.meshWidth]).div(255);\n } else {\n rotationMatrix = util.IDENTITY_MATRIX;\n const cloned = input.clone();\n // @ts-ignore\n face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, cloned, [this.meshHeight, this.meshWidth]).div(255);\n }\n\n // if we're not going to produce mesh, don't spend time with further processing\n if (!config.face.mesh.enabled) {\n const prediction = {\n coords: null,\n box,\n faceConfidence: null,\n confidence: box.confidence,\n image: face,\n };\n return prediction;\n }\n\n const [, confidence, contourCoords] = this.meshDetector.predict(face); // The first returned tensor represents facial contours, which are included in the coordinates.\n const confidenceVal = confidence.dataSync()[0];\n if (confidenceVal < config.face.detector.minConfidence) return null; // if below confidence just exit\n const coordsReshaped = tf.reshape(contourCoords, [-1, 3]);\n let rawCoords = coordsReshaped.arraySync();\n\n if (config.face.iris.enabled) {\n const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = this.getEyeBox(rawCoords, face, LEFT_EYE_BOUNDS[0], LEFT_EYE_BOUNDS[1], true);\n const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = this.getEyeBox(rawCoords, face, RIGHT_EYE_BOUNDS[0], RIGHT_EYE_BOUNDS[1]);\n const eyePredictions = this.irisModel.predict(tf.concat([leftEyeCrop, rightEyeCrop]));\n const eyePredictionsData = eyePredictions.dataSync();\n const leftEyeData = eyePredictionsData.slice(0, IRIS_NUM_COORDINATES * 3);\n const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = this.getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n const rightEyeData = eyePredictionsData.slice(IRIS_NUM_COORDINATES * 3);\n const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = this.getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize);\n const leftToRightEyeDepthDifference = this.getLeftToRightEyeDepthDifference(rawCoords);\n if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left');\n replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right');\n // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged. So we only update a single contour line above and below the eye.\n } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n } else { // User is looking towards the left.\n replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n }\n const adjustedLeftIrisCoords = this.getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n const adjustedRightIrisCoords = this.getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n rawCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n }\n\n const transformedCoordsData = this.transformRawCoords(rawCoords, box, angle, rotationMatrix);\n // @ts-ignore\n const landmarksBox = bounding.enlargeBox(this.calculateLandmarksBoundingBox(transformedCoordsData));\n // @ts-ignore\n const squarifiedLandmarksBox = bounding.squarifyBox(landmarksBox);\n const transformedCoords = tf.tensor2d(transformedCoordsData);\n const prediction = {\n coords: transformedCoords,\n box: landmarksBox,\n faceConfidence: confidenceVal,\n confidence: box.confidence,\n image: face,\n };\n if (config.face.mesh.returnRawData) prediction.rawCoords = rawCoords;\n this.storedBoxes[i] = { ...squarifiedLandmarksBox, landmarks: transformedCoords.arraySync(), confidence: box.confidence, faceConfidence: confidenceVal };\n\n return prediction;\n }));\n results = results.filter((a) => a !== null);\n this.detectedFaces = results.length;\n return results;\n }\n\n calculateLandmarksBoundingBox(landmarks) {\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint, landmarks };\n }\n}\nexports.Pipeline = Pipeline;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as blazeface from './blazeface.js';\nimport * as facepipeline from './facepipeline.js';\nimport * as coords from './coords.js';\n\nclass MediaPipeFaceMesh {\n constructor(blazeFace, blazeMeshModel, irisModel, config) {\n // @ts-ignore\n this.facePipeline = new facepipeline.Pipeline(blazeFace, blazeMeshModel, irisModel, config);\n this.config = config;\n }\n\n async estimateFaces(input, config) {\n const predictions = await this.facePipeline.predict(input, config);\n const results = [];\n for (const prediction of (predictions || [])) {\n if (prediction.isDisposedInternal) continue; // guard against disposed tensors on long running operations such as pause in middle of processing\n const mesh = prediction.coords ? prediction.coords.arraySync() : null;\n const meshRaw = prediction.rawCoords;\n const annotations = {};\n if (mesh && mesh.length > 0) {\n for (const key of Object.keys(coords.MESH_ANNOTATIONS)) {\n annotations[key] = coords.MESH_ANNOTATIONS[key].map((index) => mesh[index]);\n }\n }\n const boxRaw = (config.face.mesh.returnRawData && prediction.box) ? { topLeft: prediction.box.startPoint, bottomRight: prediction.box.endPoint } : null;\n const box = prediction.box ? [\n Math.max(0, prediction.box.startPoint[0]),\n Math.max(0, prediction.box.startPoint[1]),\n Math.min(input.shape[2], prediction.box.endPoint[0]) - prediction.box.startPoint[0],\n Math.min(input.shape[1], prediction.box.endPoint[1]) - prediction.box.startPoint[1],\n ] : 0;\n results.push({\n confidence: prediction.confidence || 0,\n box,\n mesh,\n boxRaw,\n meshRaw,\n annotations,\n image: prediction.image ? tf.clone(prediction.image) : null,\n });\n if (prediction.coords) prediction.coords.dispose();\n if (prediction.image) prediction.image.dispose();\n }\n return results;\n }\n}\n\nlet faceModels = [null, null, null];\nasync function load(config) {\n faceModels = await Promise.all([\n // @ts-ignore\n (!faceModels[0] && config.face.enabled) ? blazeface.load(config) : null,\n (!faceModels[1] && config.face.mesh.enabled) ? tf.loadGraphModel(config.face.mesh.modelPath, { fromTFHub: config.face.mesh.modelPath.includes('tfhub.dev') }) : null,\n (!faceModels[2] && config.face.iris.enabled) ? tf.loadGraphModel(config.face.iris.modelPath, { fromTFHub: config.face.iris.modelPath.includes('tfhub.dev') }) : null,\n ]);\n const faceMesh = new MediaPipeFaceMesh(faceModels[0], faceModels[1], faceModels[2], config);\n if (config.face.mesh.enabled) log(`load model: ${config.face.mesh.modelPath.match(/\\/(.*)\\./)[1]}`);\n if (config.face.iris.enabled) log(`load model: ${config.face.iris.modelPath.match(/\\/(.*)\\./)[1]}`);\n return faceMesh;\n}\n\nexports.load = load;\nexports.MediaPipeFaceMesh = MediaPipeFaceMesh;\nexports.triangulation = coords.TRI468;\n", "import { log } from './log.js';\n\nconst profileData = {};\n\nfunction profile(name, data) {\n if (!data || !data.kernels) return;\n const maxResults = 5;\n const time = data.kernels\n .filter((a) => a.kernelTimeMs > 0)\n .reduce((a, b) => a += b.kernelTimeMs, 0);\n const slowest = data.kernels\n .map((a, i) => { a.id = i; return a; })\n .filter((a) => a.kernelTimeMs > 0)\n .sort((a, b) => b.kernelTimeMs - a.kernelTimeMs);\n const largest = data.kernels\n .map((a, i) => { a.id = i; return a; })\n .filter((a) => a.totalBytesSnapshot > 0)\n .sort((a, b) => b.totalBytesSnapshot - a.totalBytesSnapshot);\n if (slowest.length > maxResults) slowest.length = maxResults;\n if (largest.length > maxResults) largest.length = maxResults;\n const res = { newBytes: data.newBytes, newTensors: data.newTensors, peakBytes: data.peakBytes, numKernelOps: data.kernels.length, timeKernelOps: time, slowestKernelOps: slowest, largestKernelOps: largest };\n profileData[name] = res;\n log('Human profiler', name, res);\n}\n\nexports.run = profile;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile.js';\n\nconst models = {};\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nasync function load(config) {\n if (!models.age) {\n models.age = await tf.loadGraphModel(config.face.age.modelPath);\n log(`load model: ${config.face.age.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.age;\n}\n\nasync function predict(image, config) {\n if (!models.age) return null;\n if ((skipped < config.face.age.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n /*\n const zoom = [0, 0]; // 0..1 meaning 0%..100%\n const box = [[\n (image.shape[1] * zoom[0]) / image.shape[1],\n (image.shape[2] * zoom[1]) / image.shape[2],\n (image.shape[1] - (image.shape[1] * zoom[0])) / image.shape[1],\n (image.shape[2] - (image.shape[2] * zoom[1])) / image.shape[2],\n ]];\n const resize = tf.image.cropAndResize(image, box, [0], [config.face.age.inputSize, config.face.age.inputSize]);\n */\n const resize = tf.image.resizeBilinear(image, [config.face.age.inputSize, config.face.age.inputSize], false);\n const enhance = tf.mul(resize, [255.0]);\n tf.dispose(resize);\n\n let ageT;\n const obj = {};\n\n if (!config.profile) {\n if (config.face.age.enabled) ageT = await models.age.predict(enhance);\n } else {\n const profileAge = config.face.age.enabled ? await tf.profile(() => models.age.predict(enhance)) : {};\n ageT = profileAge.result.clone();\n profileAge.result.dispose();\n // @ts-ignore\n profile.run('age', profileAge);\n }\n enhance.dispose();\n\n if (ageT) {\n const data = ageT.dataSync();\n obj.age = Math.trunc(10 * data[0]) / 10;\n }\n ageT.dispose();\n\n last = obj;\n resolve(obj);\n });\n}\n\nexports.predict = predict;\nexports.load = load;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile.js';\n\nconst models = {};\nlet last = { gender: '' };\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet alternative = false;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nasync function load(config) {\n if (!models.gender) {\n models.gender = await tf.loadGraphModel(config.face.gender.modelPath);\n alternative = models.gender.inputs[0].shape[3] === 1;\n log(`load model: ${config.face.gender.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.gender;\n}\n\nasync function predict(image, config) {\n if (!models.gender) return null;\n if ((skipped < config.face.gender.skipFrames) && config.videoOptimized && last.gender !== '') {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [config.face.gender.inputSize, config.face.gender.inputSize], false);\n let enhance;\n if (alternative) {\n enhance = tf.tidy(() => {\n const [red, green, blue] = tf.split(resize, 3, 3);\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n return grayscale.sub(0.5).mul(2);\n });\n } else {\n enhance = tf.mul(resize, [255.0]);\n }\n tf.dispose(resize);\n\n let genderT;\n const obj = {};\n\n if (!config.profile) {\n if (config.face.gender.enabled) genderT = await models.gender.predict(enhance);\n } else {\n const profileGender = config.face.gender.enabled ? await tf.profile(() => models.gender.predict(enhance)) : {};\n genderT = profileGender.result.clone();\n profileGender.result.dispose();\n // @ts-ignore\n profile.run('gender', profileGender);\n }\n enhance.dispose();\n\n if (genderT) {\n const data = genderT.dataSync();\n if (alternative) {\n // returns two values 0..1, bigger one is prediction\n const confidence = Math.trunc(100 * Math.abs(data[0] - data[1])) / 100;\n if (confidence > config.face.gender.minConfidence) {\n obj.gender = data[0] > data[1] ? 'female' : 'male';\n obj.confidence = confidence;\n }\n } else {\n // returns one value 0..1, .5 is prediction threshold\n const confidence = Math.trunc(200 * Math.abs((data[0] - 0.5))) / 100;\n if (confidence > config.face.gender.minConfidence) {\n obj.gender = data[0] <= 0.5 ? 'female' : 'male';\n obj.confidence = Math.min(0.99, confidence);\n }\n }\n }\n genderT.dispose();\n\n last = obj;\n resolve(obj);\n });\n}\n\nexports.predict = predict;\nexports.load = load;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile.js';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];\nconst models = {};\nlet last = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\nconst scale = 1; // score multiplication factor\n\nasync function load(config) {\n if (!models.emotion) {\n models.emotion = await tf.loadGraphModel(config.face.emotion.modelPath);\n log(`load model: ${config.face.emotion.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.emotion;\n}\n\nasync function predict(image, config) {\n if (!models.emotion) return null;\n if ((skipped < config.face.emotion.skipFrames) && config.videoOptimized && (last.length > 0)) {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n /*\n const zoom = [0, 0]; // 0..1 meaning 0%..100%\n const box = [[\n (image.shape[1] * zoom[0]) / image.shape[1],\n (image.shape[2] * zoom[1]) / image.shape[2],\n (image.shape[1] - (image.shape[1] * zoom[0])) / image.shape[1],\n (image.shape[2] - (image.shape[2] * zoom[1])) / image.shape[2],\n ]];\n const resize = tf.image.cropAndResize(image, box, [0], [config.face.emotion.inputSize, config.face.emotion.inputSize]);\n */\n const resize = tf.image.resizeBilinear(image, [config.face.emotion.inputSize, config.face.emotion.inputSize], false);\n const [red, green, blue] = tf.split(resize, 3, 3);\n resize.dispose();\n // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n red.dispose();\n green.dispose();\n blue.dispose();\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n redNorm.dispose();\n greenNorm.dispose();\n blueNorm.dispose();\n const normalize = tf.tidy(() => grayscale.sub(0.5).mul(2));\n grayscale.dispose();\n const obj = [];\n if (config.face.emotion.enabled) {\n let data;\n if (!config.profile) {\n const emotionT = await models.emotion.predict(normalize);\n data = emotionT.dataSync();\n tf.dispose(emotionT);\n } else {\n const profileData = await tf.profile(() => models.emotion.predict(normalize));\n data = profileData.result.dataSync();\n profileData.result.dispose();\n // @ts-ignore\n profile.run('emotion', profileData);\n }\n for (let i = 0; i < data.length; i++) {\n if (scale * data[i] > config.face.emotion.minConfidence) obj.push({ score: Math.min(0.99, Math.trunc(100 * scale * data[i]) / 100), emotion: annotations[i] });\n }\n obj.sort((a, b) => b.score - a.score);\n }\n normalize.dispose();\n last = obj;\n resolve(obj);\n });\n}\n\nexports.predict = predict;\nexports.load = load;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile.js';\n\n// based on https://github.com/sirius-ai/MobileFaceNet_TF\n// model converted from https://github.com/sirius-ai/MobileFaceNet_TF/files/3551493/FaceMobileNet192_train_false.zip\n\nconst models = {};\n\nasync function load(config) {\n if (!models.embedding) {\n models.embedding = await tf.loadGraphModel(config.face.embedding.modelPath);\n log(`load model: ${config.face.embedding.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.embedding;\n}\n\nfunction simmilarity(embedding1, embedding2) {\n if (embedding1?.length !== embedding2?.length) return 0;\n // general minkowski distance\n // euclidean distance is limited case where order is 2\n const order = 2;\n const distance = 10.0 * ((embedding1.map((val, i) => (val - embedding2[i])).reduce((dist, diff) => dist + (diff ** order), 0) ** (1 / order)));\n return (Math.trunc(1000 * (1 - distance)) / 1000);\n}\n\nasync function predict(image, config) {\n if (!models.embedding) return null;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [config.face.embedding.inputSize, config.face.embedding.inputSize], false);\n // const normalize = tf.tidy(() => resize.div(127.5).sub(0.5)); // this is -0.5...0.5 ???\n let data = [];\n if (config.face.embedding.enabled) {\n if (!config.profile) {\n const embeddingT = await models.embedding.predict({ img_inputs: resize });\n data = [...embeddingT.dataSync()]; // convert object array to standard array\n tf.dispose(embeddingT);\n } else {\n const profileData = await tf.profile(() => models.embedding.predict({ img_inputs: resize }));\n data = [...profileData.result.dataSync()];\n profileData.result.dispose();\n // @ts-ignore\n profile.run('emotion', profileData);\n }\n }\n resize.dispose();\n // normalize.dispose();\n resolve(data);\n });\n}\n\nexports.predict = predict;\nexports.simmilarity = simmilarity;\nexports.load = load;\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nconst imageNetMean = [-123.15, -115.90, -103.06];\n\nfunction nameOutputResultsMobileNet(results) {\n const [offsets, heatmap, displacementFwd, displacementBwd] = results;\n return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nfunction nameOutputResultsResNet(results) {\n const [displacementFwd, displacementBwd, offsets, heatmap] = results;\n return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nclass BaseModel {\n constructor(model) {\n this.model = model;\n }\n\n predict(input, config) {\n return tf.tidy(() => {\n const asFloat = (config.body.modelType === 'ResNet') ? input.toFloat().add(imageNetMean) : input.toFloat().div(127.5).sub(1.0);\n const asBatch = asFloat.expandDims(0);\n const results = this.model.predict(asBatch);\n const results3d = results.map((y) => y.squeeze([0]));\n const namedResults = (config.body.modelType === 'ResNet') ? nameOutputResultsResNet(results3d) : nameOutputResultsMobileNet(results3d);\n return {\n heatmapScores: namedResults.heatmap.sigmoid(),\n offsets: namedResults.offsets,\n displacementFwd: namedResults.displacementFwd,\n displacementBwd: namedResults.displacementBwd,\n };\n });\n }\n\n dispose() {\n this.model.dispose();\n }\n}\nexports.BaseModel = BaseModel;\n", "// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nfunction half(k) {\n return Math.floor(k / 2);\n}\nclass MaxHeap {\n constructor(maxSize, getElementValue) {\n this.priorityQueue = new Array(maxSize);\n this.numberOfElements = -1;\n this.getElementValue = getElementValue;\n }\n\n enqueue(x) {\n this.priorityQueue[++this.numberOfElements] = x;\n this.swim(this.numberOfElements);\n }\n\n dequeue() {\n const max = this.priorityQueue[0];\n this.exchange(0, this.numberOfElements--);\n this.sink(0);\n this.priorityQueue[this.numberOfElements + 1] = null;\n return max;\n }\n\n empty() {\n return this.numberOfElements === -1;\n }\n\n size() {\n return this.numberOfElements + 1;\n }\n\n all() {\n return this.priorityQueue.slice(0, this.numberOfElements + 1);\n }\n\n max() {\n return this.priorityQueue[0];\n }\n\n swim(k) {\n while (k > 0 && this.less(half(k), k)) {\n this.exchange(k, half(k));\n k = half(k);\n }\n }\n\n sink(k) {\n while (2 * k <= this.numberOfElements) {\n let j = 2 * k;\n if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n if (!this.less(k, j)) break;\n this.exchange(k, j);\n k = j;\n }\n }\n\n getValueAt(i) {\n return this.getElementValue(this.priorityQueue[i]);\n }\n\n less(i, j) {\n return this.getValueAt(i) < this.getValueAt(j);\n }\n\n exchange(i, j) {\n const t = this.priorityQueue[i];\n this.priorityQueue[i] = this.priorityQueue[j];\n this.priorityQueue[j] = t;\n }\n}\nexports.MaxHeap = MaxHeap;\n", "import * as heapSort from './heapSort';\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores) {\n const [height, width] = scores.shape;\n let localMaximum = true;\n const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n localMaximum = false;\n break;\n }\n }\n if (!localMaximum) break;\n }\n return localMaximum;\n}\n\nfunction buildPartWithScoreQueue(scoreThreshold, localMaximumRadius, scores) {\n const [height, width, numKeypoints] = scores.shape;\n const queue = new heapSort.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n const score = scores.get(heatmapY, heatmapX, keypointId);\n // Only consider parts with score greater or equal to threshold as root candidates.\n if (score < scoreThreshold) continue;\n // Only consider keypoints whose score is maximum in a local window.\n if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores)) {\n queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n }\n }\n }\n }\n return queue;\n}\nexports.buildPartWithScoreQueue = buildPartWithScoreQueue;\n", "exports.partNames = [\n 'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n 'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n 'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexports.NUM_KEYPOINTS = exports.partNames.length;\n\nexports.partIds = exports.partNames.reduce((result, jointName, i) => {\n result[jointName] = i;\n return result;\n}, {});\n\nconst connectedPartNames = [\n ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexports.connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([exports.partIds[jointNameA], exports.partIds[jointNameB]]));\n\nexports.poseChain = [\n ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n ['rightKnee', 'rightAnkle'],\n];\n\nexports.partChannels = [\n 'left_face',\n 'right_face',\n 'right_upper_leg_front',\n 'right_lower_leg_back',\n 'right_upper_leg_back',\n 'left_lower_leg_front',\n 'left_upper_leg_front',\n 'left_upper_leg_back',\n 'left_lower_leg_back',\n 'right_feet',\n 'right_lower_leg_front',\n 'left_feet',\n 'torso_front',\n 'torso_back',\n 'right_upper_arm_front',\n 'right_upper_arm_back',\n 'right_lower_arm_back',\n 'left_lower_arm_front',\n 'left_upper_arm_front',\n 'left_upper_arm_back',\n 'left_lower_arm_back',\n 'right_hand',\n 'right_lower_arm_front',\n 'left_hand',\n];\n", "import * as kpt from './keypoints';\n\nfunction getOffsetPoint(y, x, keypoint, offsets) {\n return {\n y: offsets.get(y, x, keypoint),\n x: offsets.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n };\n}\nexports.getOffsetPoint = getOffsetPoint;\n\nfunction getImageCoords(part, outputStride, offsets) {\n const { heatmapY, heatmapX, id: keypoint } = part;\n const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n return {\n x: part.heatmapX * outputStride + x,\n y: part.heatmapY * outputStride + y,\n };\n}\nexports.getImageCoords = getImageCoords;\n\nfunction fillArray(element, size) {\n const result = new Array(size);\n for (let i = 0; i < size; i++) {\n result[i] = element;\n }\n return result;\n}\nexports.fillArray = fillArray;\n\nfunction clamp(a, min, max) {\n if (a < min) return min;\n if (a > max) return max;\n return a;\n}\nexports.clamp = clamp;\n\nfunction squaredDistance(y1, x1, y2, x2) {\n const dy = y2 - y1;\n const dx = x2 - x1;\n return dy * dy + dx * dx;\n}\nexports.squaredDistance = squaredDistance;\n\nfunction addVectors(a, b) {\n return { x: a.x + b.x, y: a.y + b.y };\n}\nexports.addVectors = addVectors;\n\nfunction clampVector(a, min, max) {\n return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\nexports.clampVector = clampVector;\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as kpt from './keypoints';\n\nfunction getPointsConfidence(heatmapScores, heatMapCoords) {\n const numKeypoints = heatMapCoords.shape[0];\n const result = new Float32Array(numKeypoints);\n for (let keypoint = 0; keypoint < numKeypoints; keypoint++) {\n const y = heatMapCoords.get(keypoint, 0);\n const x = heatMapCoords.get(keypoint, 1);\n result[keypoint] = heatmapScores.get(y, x, keypoint);\n }\n return result;\n}\nexports.getPointsConfidence = getPointsConfidence;\n\nfunction getOffsetPoint(y, x, keypoint, offsetsBuffer) {\n return {\n y: offsetsBuffer.get(y, x, keypoint),\n x: offsetsBuffer.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n };\n}\n\nfunction getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer) {\n const result = [];\n for (let keypoint = 0; keypoint < kpt.NUM_KEYPOINTS; keypoint++) {\n const heatmapY = heatMapCoordsBuffer.get(keypoint, 0).valueOf();\n const heatmapX = heatMapCoordsBuffer.get(keypoint, 1).valueOf();\n const { x, y } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsetsBuffer);\n result.push(y);\n result.push(x);\n }\n return tf.tensor2d(result, [kpt.NUM_KEYPOINTS, 2]);\n}\nexports.getOffsetVectors = getOffsetVectors;\n\nfunction getOffsetPoints(heatMapCoordsBuffer, outputStride, offsetsBuffer) {\n return tf.tidy(() => heatMapCoordsBuffer.toTensor().mul(tf.scalar(outputStride, 'int32')).toFloat().add(getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer)));\n}\nexports.getOffsetPoints = getOffsetPoints;\n\nfunction mod(a, b) {\n return tf.tidy(() => {\n const floored = a.div(tf.scalar(b, 'int32'));\n return a.sub(floored.mul(tf.scalar(b, 'int32')));\n });\n}\n\nfunction argmax2d(inputs) {\n const [height, width, depth] = inputs.shape;\n return tf.tidy(() => {\n const reshaped = inputs.reshape([height * width, depth]);\n const coords = reshaped.argMax(0);\n const yCoords = coords.div(tf.scalar(width, 'int32')).expandDims(1);\n const xCoords = mod(coords, width).expandDims(1);\n return tf.concat([yCoords, xCoords], 1);\n });\n}\nexports.argmax2d = argmax2d;\n", "import * as keypoints from './keypoints';\nimport * as vectors from './vectors';\nimport * as decoders from './decoders';\n\nconst parentChildrenTuples = keypoints.poseChain.map(([parentJoinName, childJoinName]) => ([keypoints.partIds[parentJoinName], keypoints.partIds[childJoinName]]));\nconst parentToChildEdges = parentChildrenTuples.map(([, childJointId]) => childJointId);\nconst childToParentEdges = parentChildrenTuples.map(([parentJointId]) => parentJointId);\nfunction getDisplacement(edgeId, point, displacements) {\n const numEdges = displacements.shape[2] / 2;\n return {\n y: displacements.get(point.y, point.x, edgeId),\n x: displacements.get(point.y, point.x, numEdges + edgeId),\n };\n}\nfunction getStridedIndexNearPoint(point, outputStride, height, width) {\n return {\n // @ts-ignore\n y: vectors.clamp(Math.round(point.y / outputStride), 0, height - 1),\n // @ts-ignore\n x: vectors.clamp(Math.round(point.x / outputStride), 0, width - 1),\n };\n}\n\nfunction traverseToTargetKeypoint(edgeId, sourceKeypoint, targetKeypointId, scoresBuffer, offsets, outputStride, displacements, offsetRefineStep = 2) {\n const [height, width] = scoresBuffer.shape;\n // Nearest neighbor interpolation for the source->target displacements.\n const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, outputStride, height, width);\n const displacement = getDisplacement(edgeId, sourceKeypointIndices, displacements);\n // @ts-ignore\n const displacedPoint = vectors.addVectors(sourceKeypoint.position, displacement);\n let targetKeypoint = displacedPoint;\n for (let i = 0; i < offsetRefineStep; i++) {\n const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n // @ts-ignore\n const offsetPoint = vectors.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetKeypointId, offsets);\n // @ts-ignore\n targetKeypoint = vectors.addVectors({\n x: targetKeypointIndices.x * outputStride,\n y: targetKeypointIndices.y * outputStride,\n }, { x: offsetPoint.x, y: offsetPoint.y });\n }\n const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n const score = scoresBuffer.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetKeypointId);\n return { position: targetKeypoint, part: keypoints.partNames[targetKeypointId], score };\n}\n\nfunction decodePose(root, scores, offsets, outputStride, displacementsFwd, displacementsBwd) {\n const numParts = scores.shape[2];\n const numEdges = parentToChildEdges.length;\n const instanceKeypoints = new Array(numParts);\n // Start a new detection instance at the position of the root.\n const { part: rootPart, score: rootScore } = root;\n // @ts-ignore\n const rootPoint = vectors.getImageCoords(rootPart, outputStride, offsets);\n instanceKeypoints[rootPart.id] = {\n score: rootScore,\n part: keypoints.partNames[rootPart.id],\n position: rootPoint,\n };\n // Decode the part positions upwards in the tree, following the backward displacements.\n for (let edge = numEdges - 1; edge >= 0; --edge) {\n const sourceKeypointId = parentToChildEdges[edge];\n const targetKeypointId = childToParentEdges[edge];\n if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsBwd);\n }\n }\n // Decode the part positions downwards in the tree, following the forward displacements.\n for (let edge = 0; edge < numEdges; ++edge) {\n const sourceKeypointId = childToParentEdges[edge];\n const targetKeypointId = parentToChildEdges[edge];\n if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsFwd);\n }\n }\n return instanceKeypoints;\n}\nexports.decodePose = decodePose;\n\nasync function decodeSinglePose(heatmapScores, offsets, config) {\n let totalScore = 0.0;\n // @ts-ignore\n const heatmapValues = decoders.argmax2d(heatmapScores);\n const allTensorBuffers = await Promise.all([heatmapScores.buffer(), offsets.buffer(), heatmapValues.buffer()]);\n const scoresBuffer = allTensorBuffers[0];\n const offsetsBuffer = allTensorBuffers[1];\n const heatmapValuesBuffer = allTensorBuffers[2];\n // @ts-ignore\n const offsetPoints = decoders.getOffsetPoints(heatmapValuesBuffer, config.body.outputStride, offsetsBuffer);\n const offsetPointsBuffer = await offsetPoints.buffer();\n // @ts-ignore\n const keypointConfidence = Array.from(decoders.getPointsConfidence(scoresBuffer, heatmapValuesBuffer));\n const instanceKeypoints = keypointConfidence.map((score, i) => {\n totalScore += score;\n return {\n position: {\n y: offsetPointsBuffer.get(i, 0),\n x: offsetPointsBuffer.get(i, 1),\n },\n part: keypoints.partNames[i],\n score,\n };\n });\n const filteredKeypoints = instanceKeypoints.filter((kpt) => kpt.score > config.body.scoreThreshold);\n heatmapValues.dispose();\n offsetPoints.dispose();\n return { keypoints: filteredKeypoints, score: totalScore / instanceKeypoints.length };\n}\nexports.decodeSinglePose = decodeSinglePose;\n", "import * as buildParts from './buildParts';\nimport * as decodePose from './decodePose';\nimport * as vectors from './vectors';\n\nconst kLocalMaximumRadius = 1;\n\nfunction withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, { x, y }, keypointId) {\n return poses.some(({ keypoints }) => {\n const correspondingKeypoint = keypoints[keypointId].position;\n // @ts-ignore\n return vectors.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n });\n}\n\nfunction getInstanceScore(existingPoses, squaredNmsRadius, instanceKeypoints) {\n const notOverlappedKeypointScores = instanceKeypoints.reduce((result, { position, score }, keypointId) => {\n if (!withinNmsRadiusOfCorrespondingPoint(existingPoses, squaredNmsRadius, position, keypointId)) result += score;\n return result;\n }, 0.0);\n return notOverlappedKeypointScores / instanceKeypoints.length;\n}\n\nfunction decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config) {\n const poses = [];\n // @ts-ignore\n const queue = buildParts.buildPartWithScoreQueue(config.body.scoreThreshold, kLocalMaximumRadius, scoresBuffer);\n const squaredNmsRadius = config.body.nmsRadius ^ 2;\n // Generate at most maxDetections object instances per image in decreasing root part score order.\n while (poses.length < config.body.maxDetections && !queue.empty()) {\n // The top element in the queue is the next root candidate.\n const root = queue.dequeue();\n // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n // @ts-ignore\n const rootImageCoords = vectors.getImageCoords(root.part, config.body.outputStride, offsetsBuffer);\n if (withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, rootImageCoords, root.part.id)) continue;\n // Else start a new detection instance at the position of the root.\n // @ts-ignore\n const keypoints = decodePose.decodePose(root, scoresBuffer, offsetsBuffer, config.body.outputStride, displacementsFwdBuffer, displacementsBwdBuffer);\n const score = getInstanceScore(poses, squaredNmsRadius, keypoints);\n if (score > config.body.scoreThreshold) poses.push({ keypoints, score });\n }\n return poses;\n}\nexports.decodeMultiplePoses = decodeMultiplePoses;\n", "import * as kpt from './keypoints';\n\nfunction eitherPointDoesntMeetConfidence(a, b, minConfidence) {\n return (a < minConfidence || b < minConfidence);\n}\n\nfunction getAdjacentKeyPoints(keypoints, minConfidence) {\n return kpt.connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n return result;\n }\n result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n return result;\n }, []);\n}\nexports.getAdjacentKeyPoints = getAdjacentKeyPoints;\n\nconst { NEGATIVE_INFINITY, POSITIVE_INFINITY } = Number;\nfunction getBoundingBox(keypoints) {\n return keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n maxX: Math.max(maxX, x),\n maxY: Math.max(maxY, y),\n minX: Math.min(minX, x),\n minY: Math.min(minY, y),\n }), {\n maxX: NEGATIVE_INFINITY,\n maxY: NEGATIVE_INFINITY,\n minX: POSITIVE_INFINITY,\n minY: POSITIVE_INFINITY,\n });\n}\nexports.getBoundingBox = getBoundingBox;\n\nfunction getBoundingBoxPoints(keypoints) {\n const { minX, minY, maxX, maxY } = getBoundingBox(keypoints);\n return [{ x: minX, y: minY }, { x: maxX, y: minY }, { x: maxX, y: maxY }, { x: minX, y: maxY }];\n}\nexports.getBoundingBoxPoints = getBoundingBoxPoints;\n\nasync function toTensorBuffers3D(tensors) {\n return Promise.all(tensors.map((tensor) => tensor.buffer()));\n}\nexports.toTensorBuffers3D = toTensorBuffers3D;\n\nfunction scalePose(pose, scaleY, scaleX) {\n return {\n score: pose.score,\n keypoints: pose.keypoints.map(({ score, part, position }) => ({\n score,\n part,\n position: { x: position.x * scaleX, y: position.y * scaleY },\n })),\n };\n}\nexports.scalePose = scalePose;\n\nfunction resizeTo(image, [targetH, targetW]) {\n const input = image.squeeze(0);\n const resized = input.resizeBilinear([targetH, targetW]);\n input.dispose();\n return resized;\n}\nexports.resizeTo = resizeTo;\n\nfunction scaleAndFlipPoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) {\n const scaledPoses = poses.map((pose) => scalePose(pose, height / inputResolutionHeight, width / inputResolutionWidth));\n return scaledPoses;\n}\nexports.scaleAndFlipPoses = scaleAndFlipPoses;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as modelBase from './modelBase';\nimport * as decodeMultiple from './decodeMultiple';\nimport * as decodePose from './decodePose';\nimport * as util from './util';\n\nasync function estimateMultiple(input, res, config) {\n return new Promise(async (resolve) => {\n const height = input.shape[1];\n const width = input.shape[2];\n // @ts-ignore\n const allTensorBuffers = await util.toTensorBuffers3D([res.heatmapScores, res.offsets, res.displacementFwd, res.displacementBwd]);\n const scoresBuffer = allTensorBuffers[0];\n const offsetsBuffer = allTensorBuffers[1];\n const displacementsFwdBuffer = allTensorBuffers[2];\n const displacementsBwdBuffer = allTensorBuffers[3];\n // @ts-ignore\n const poses = await decodeMultiple.decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config);\n // @ts-ignore\n const scaled = util.scaleAndFlipPoses(poses, [height, width], [config.body.inputSize, config.body.inputSize]);\n resolve(scaled);\n });\n}\n\nasync function estimateSingle(input, res, config) {\n return new Promise(async (resolve) => {\n const height = input.shape[1];\n const width = input.shape[2];\n // @ts-ignore\n const pose = await decodePose.decodeSinglePose(res.heatmapScores, res.offsets, config);\n const poses = [pose];\n // @ts-ignore\n const scaled = util.scaleAndFlipPoses(poses, [height, width], [config.body.inputSize, config.body.inputSize]);\n resolve(scaled);\n });\n}\n\nclass PoseNet {\n constructor(model) {\n this.baseModel = model;\n }\n\n async estimatePoses(input, config) {\n // @ts-ignore\n const resized = util.resizeTo(input, [config.body.inputSize, config.body.inputSize]);\n const res = this.baseModel.predict(resized, config);\n\n const poses = (config.body.maxDetections < 2) ? await estimateSingle(input, res, config) : await estimateMultiple(input, res, config);\n\n res.heatmapScores.dispose();\n res.offsets.dispose();\n res.displacementFwd.dispose();\n res.displacementBwd.dispose();\n resized.dispose();\n\n return poses;\n }\n\n dispose() {\n this.baseModel.dispose();\n }\n}\nexports.PoseNet = PoseNet;\n\nasync function load(config) {\n const model = await tf.loadGraphModel(config.body.modelPath);\n // @ts-ignore\n const mobilenet = new modelBase.BaseModel(model);\n log(`load model: ${config.body.modelPath.match(/\\/(.*)\\./)[1]}`);\n return new PoseNet(mobilenet);\n}\nexports.load = load;\n", "import * as modelPoseNet from './modelPoseNet';\nimport * as keypoints from './keypoints';\nimport * as util from './util';\n\n// @ts-ignore\nexports.load = modelPoseNet.load;\n// @ts-ignore\nexports.PoseNet = modelPoseNet.PoseNet;\n\nexports.partChannels = keypoints.partChannels;\nexports.partIds = keypoints.partIds;\nexports.partNames = keypoints.partNames;\nexports.poseChain = keypoints.poseChain;\n// @ts-ignore\nexports.getAdjacentKeyPoints = util.getAdjacentKeyPoints;\n// @ts-ignore\nexports.getBoundingBox = util.getBoundingBox;\n// @ts-ignore\nexports.getBoundingBoxPoints = util.getBoundingBoxPoints;\n// @ts-ignore\nexports.scaleAndFlipPoses = util.scaleAndFlipPoses;\n// @ts-ignore\nexports.scalePose = util.scalePose;\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\n\nclass HandDetector {\n constructor(model, inputSize, anchorsAnnotated) {\n this.model = model;\n this.anchors = anchorsAnnotated.map((anchor) => [anchor.x_center, anchor.y_center]);\n this.anchorsTensor = tf.tensor2d(this.anchors);\n this.inputSizeTensor = tf.tensor1d([inputSize, inputSize]);\n this.doubleInputSizeTensor = tf.tensor1d([inputSize * 2, inputSize * 2]);\n }\n\n normalizeBoxes(boxes) {\n return tf.tidy(() => {\n const boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n const boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n const boxCenterPoints = tf.add(tf.div(boxOffsets, this.inputSizeTensor), this.anchorsTensor);\n const halfBoxSizes = tf.div(boxSizes, this.doubleInputSizeTensor);\n const startPoints = tf.mul(tf.sub(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n const endPoints = tf.mul(tf.add(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n return tf.concat2d([startPoints, endPoints], 1);\n });\n }\n\n normalizeLandmarks(rawPalmLandmarks, index) {\n return tf.tidy(() => {\n const landmarks = tf.add(tf.div(rawPalmLandmarks.reshape([-1, 7, 2]), this.inputSizeTensor), this.anchors[index]);\n return tf.mul(landmarks, this.inputSizeTensor);\n });\n }\n\n async getBoxes(input, config) {\n const batched = this.model.predict(input);\n const predictions = batched.squeeze();\n batched.dispose();\n const scoresT = tf.tidy(() => tf.sigmoid(tf.slice(predictions, [0, 0], [-1, 1])).squeeze());\n const scores = scoresT.dataSync();\n const rawBoxes = tf.slice(predictions, [0, 1], [-1, 4]);\n const boxes = this.normalizeBoxes(rawBoxes);\n rawBoxes.dispose();\n const filteredT = await tf.image.nonMaxSuppressionAsync(boxes, scores, config.hand.maxHands, config.hand.iouThreshold, config.hand.scoreThreshold);\n const filtered = filteredT.arraySync();\n\n scoresT.dispose();\n filteredT.dispose();\n const hands = [];\n for (const index of filtered) {\n if (scores[index] >= config.hand.minConfidence) {\n const matchingBox = tf.slice(boxes, [index, 0], [1, -1]);\n const rawPalmLandmarks = tf.slice(predictions, [index, 5], [1, 14]);\n const palmLandmarks = tf.tidy(() => this.normalizeLandmarks(rawPalmLandmarks, index).reshape([-1, 2]));\n rawPalmLandmarks.dispose();\n hands.push({ box: matchingBox, palmLandmarks, confidence: scores[index] });\n }\n }\n predictions.dispose();\n boxes.dispose();\n return hands;\n }\n\n async estimateHandBounds(input, config) {\n const inputHeight = input.shape[1];\n const inputWidth = input.shape[2];\n const image = tf.tidy(() => input.resizeBilinear([config.hand.inputSize, config.hand.inputSize]).div(127.5).sub(1));\n const predictions = await this.getBoxes(image, config);\n image.dispose();\n const hands = [];\n if (!predictions || predictions.length === 0) return hands;\n for (const prediction of predictions) {\n const boxes = prediction.box.dataSync();\n const startPoint = boxes.slice(0, 2);\n const endPoint = boxes.slice(2, 4);\n const palmLandmarks = prediction.palmLandmarks.arraySync();\n prediction.box.dispose();\n prediction.palmLandmarks.dispose();\n hands.push(box.scaleBoxCoordinates({ startPoint, endPoint, palmLandmarks, confidence: prediction.confidence }, [inputWidth / config.hand.inputSize, inputHeight / config.hand.inputSize]));\n }\n return hands;\n }\n}\nexports.HandDetector = HandDetector;\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\nimport * as util from './util';\n// eslint-disable-next-line no-unused-vars\nimport { log } from '../log.js';\n\n// const PALM_BOX_SHIFT_VECTOR = [0, -0.4];\nconst PALM_BOX_ENLARGE_FACTOR = 5; // default 3\n// const HAND_BOX_SHIFT_VECTOR = [0, -0.1]; // move detected hand box by x,y to ease landmark detection\nconst HAND_BOX_ENLARGE_FACTOR = 1.65; // default 1.65\nconst PALM_LANDMARK_IDS = [0, 5, 9, 13, 17, 1, 2];\nconst PALM_LANDMARKS_INDEX_OF_PALM_BASE = 0;\nconst PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE = 2;\n\nclass HandPipeline {\n constructor(handDetector, landmarkDetector, inputSize) {\n this.handDetector = handDetector;\n this.landmarkDetector = landmarkDetector;\n this.inputSize = inputSize;\n this.storedBoxes = [];\n this.skipped = 0;\n this.detectedHands = 0;\n }\n\n getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n // return box.enlargeBox(box.squarifyBox(box.shiftBox(boxAroundPalm, PALM_BOX_SHIFT_VECTOR)), PALM_BOX_ENLARGE_FACTOR);\n return box.enlargeBox(box.squarifyBox(boxAroundPalm), PALM_BOX_ENLARGE_FACTOR);\n }\n\n getBoxForHandLandmarks(landmarks) {\n const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n // const boxAroundHand = box.enlargeBox(box.squarifyBox(box.shiftBox(boundingBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n const boxAroundHand = box.enlargeBox(box.squarifyBox(boundingBox), HAND_BOX_ENLARGE_FACTOR);\n boxAroundHand.palmLandmarks = [];\n for (let i = 0; i < PALM_LANDMARK_IDS.length; i++) {\n boxAroundHand.palmLandmarks.push(landmarks[PALM_LANDMARK_IDS[i]].slice(0, 2));\n }\n return boxAroundHand;\n }\n\n transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n const boxSize = box.getBoxSize(box2);\n const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n const coordsScaled = rawCoords.map((coord) => [\n scaleFactor[0] * (coord[0] - this.inputSize / 2),\n scaleFactor[1] * (coord[1] - this.inputSize / 2),\n scaleFactor[2] * coord[2],\n ]);\n const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n const coordsRotated = coordsScaled.map((coord) => {\n const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n return [...rotated, coord[2]];\n });\n const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n const boxCenter = [...box.getBoxCenter(box2), 1];\n const originalBoxCenter = [\n util.dot(boxCenter, inverseRotationMatrix[0]),\n util.dot(boxCenter, inverseRotationMatrix[1]),\n ];\n return coordsRotated.map((coord) => [\n coord[0] + originalBoxCenter[0],\n coord[1] + originalBoxCenter[1],\n coord[2],\n ]);\n }\n\n async estimateHands(image, config) {\n let useFreshBox = false;\n\n // run new detector every skipFrames unless we only want box to start with\n let boxes;\n if ((this.skipped === 0) || (this.skipped > config.hand.skipFrames) || !config.hand.landmarks || !config.videoOptimized) {\n boxes = await this.handDetector.estimateHandBounds(image, config);\n this.skipped = 0;\n }\n if (config.videoOptimized) this.skipped++;\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxHands) || !config.hand.landmarks)) {\n this.detectedHands = 0;\n this.storedBoxes = [...boxes];\n // for (const possible of boxes) this.storedBoxes.push(possible);\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\n const hands = [];\n // log('hand', `skipped: ${this.skipped} max: ${config.hand.maxHands} detected: ${this.detectedHands} stored: ${this.storedBoxes.length} new: ${boxes?.length}`);\n\n // go through working set of boxes\n for (let i = 0; i < this.storedBoxes.length; i++) {\n const currentBox = this.storedBoxes[i];\n if (!currentBox) continue;\n if (config.hand.landmarks) {\n const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_PALM_BASE], currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE]) : 0;\n const palmCenter = box.getBoxCenter(currentBox);\n const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n const rotatedImage = config.hand.rotation ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n const croppedInput = box.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n const handImage = croppedInput.div(255);\n croppedInput.dispose();\n rotatedImage.dispose();\n const [confidenceT, keypoints] = await this.landmarkDetector.predict(handImage);\n handImage.dispose();\n const confidence = confidenceT.dataSync()[0];\n confidenceT.dispose();\n if (confidence >= config.hand.minConfidence) {\n const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n const rawCoords = keypointsReshaped.arraySync();\n keypoints.dispose();\n keypointsReshaped.dispose();\n const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n this.storedBoxes[i] = nextBoundingBox;\n const result = {\n landmarks: coords,\n confidence,\n box: {\n topLeft: nextBoundingBox.startPoint,\n bottomRight: nextBoundingBox.endPoint,\n },\n };\n hands.push(result);\n } else {\n this.storedBoxes[i] = null;\n }\n keypoints.dispose();\n } else {\n // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n const enlarged = box.enlargeBox(box.squarifyBox(currentBox), HAND_BOX_ENLARGE_FACTOR);\n const result = {\n confidence: currentBox.confidence,\n box: {\n topLeft: enlarged.startPoint,\n bottomRight: enlarged.endPoint,\n },\n };\n hands.push(result);\n }\n }\n this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n this.detectedHands = hands.length;\n return hands;\n }\n\n // eslint-disable-next-line class-methods-use-this\n calculateLandmarksBoundingBox(landmarks) {\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint };\n }\n}\n\nexports.HandPipeline = HandPipeline;\n", "exports.anchors = [\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.796875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.796875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.828125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.828125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.859375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.859375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.890625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.890625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.921875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.921875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.953125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.953125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.984375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.984375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.796875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.796875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.828125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.828125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.859375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.859375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.890625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.890625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.921875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.921875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.953125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.953125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.984375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.984375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.796875,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.796875,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.828125,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.828125,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.859375,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.859375,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.890625,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.890625,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.921875,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.921875,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.953125,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.953125,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.984375,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.984375,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.109375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.109375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.109375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.109375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.109375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.109375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.109375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.109375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.109375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.109375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.109375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.109375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.109375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.109375,\n },\n {\n w: 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0.9375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n];\n", "// https://storage.googleapis.com/tfjs-models/demos/handpose/index.html\n\nimport { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as handdetector from './handdetector';\nimport * as handpipeline from './handpipeline';\nimport * as anchors from './anchors';\n\nconst MESH_ANNOTATIONS = {\n thumb: [1, 2, 3, 4],\n indexFinger: [5, 6, 7, 8],\n middleFinger: [9, 10, 11, 12],\n ringFinger: [13, 14, 15, 16],\n pinky: [17, 18, 19, 20],\n palmBase: [0],\n};\n\nclass HandPose {\n constructor(handPipeline) {\n this.handPipeline = handPipeline;\n }\n\n static getAnnotations() {\n return MESH_ANNOTATIONS;\n }\n\n async estimateHands(input, config) {\n const predictions = await this.handPipeline.estimateHands(input, config);\n if (!predictions) return [];\n const hands = [];\n for (const prediction of predictions) {\n const annotations = {};\n if (prediction.landmarks) {\n for (const key of Object.keys(MESH_ANNOTATIONS)) {\n annotations[key] = MESH_ANNOTATIONS[key].map((index) => prediction.landmarks[index]);\n }\n }\n const box = prediction.box ? [\n Math.max(0, prediction.box.topLeft[0]),\n Math.max(0, prediction.box.topLeft[1]),\n Math.min(input.shape[2], prediction.box.bottomRight[0]) - prediction.box.topLeft[0],\n Math.min(input.shape[1], prediction.box.bottomRight[1]) - prediction.box.topLeft[1],\n ] : 0;\n hands.push({\n confidence: prediction.confidence,\n box,\n landmarks: prediction.landmarks,\n annotations,\n });\n }\n return hands;\n }\n}\nexports.HandPose = HandPose;\n\nasync function load(config) {\n const [handDetectorModel, handPoseModel] = await Promise.all([\n config.hand.enabled ? tf.loadGraphModel(config.hand.detector.modelPath, { fromTFHub: config.hand.detector.modelPath.includes('tfhub.dev') }) : null,\n config.hand.landmarks ? tf.loadGraphModel(config.hand.skeleton.modelPath, { fromTFHub: config.hand.skeleton.modelPath.includes('tfhub.dev') }) : null,\n ]);\n // @ts-ignore\n const handDetector = new handdetector.HandDetector(handDetectorModel, config.hand.inputSize, anchors.anchors);\n // @ts-ignore\n const handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel, config.hand.inputSize);\n const handPose = new HandPose(handPipeline);\n if (config.hand.enabled) log(`load model: ${config.hand.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n if (config.hand.landmarks) log(`load model: ${config.hand.skeleton.modelPath.match(/\\/(.*)\\./)[1]}`);\n return handPose;\n}\nexports.load = load;\n", "exports.body = (res) => {\n if (!res) return [];\n const gestures = [];\n for (let i = 0; i < res.length; i++) {\n // raising hands\n const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n if (nose && leftWrist && rightWrist && (leftWrist.position.y < nose.position.y) && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'i give up' });\n else if (nose && leftWrist && (leftWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise left hand' });\n else if (nose && rightWrist && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise right hand' });\n\n // leaning\n const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n if (leftShoulder && rightShoulder) gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position.y > rightShoulder.position.y) ? 'left' : 'right'}` });\n }\n return gestures;\n};\n\nexports.face = (res) => {\n if (!res) return [];\n const gestures = [];\n for (let i = 0; i < res.length; i++) {\n if (res[i].mesh && res[i].mesh.length > 0) {\n const eyeFacing = res[i].mesh[35][2] - res[i].mesh[263][2];\n if (Math.abs(eyeFacing) < 10) gestures.push({ face: i, gesture: 'facing camera' });\n else gestures.push({ face: i, gesture: `facing ${eyeFacing < 0 ? 'right' : 'left'}` });\n const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n const chinDepth = res[i].mesh[152][2];\n if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n }\n }\n return gestures;\n};\n\nexports.iris = (res) => {\n if (!res) return [];\n const gestures = [];\n for (let i = 0; i < res.length; i++) {\n if (!res[i].annotations || !res[i].annotations.leftEyeIris || !res[i].annotations.rightEyeIris) continue;\n const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n if (difference < 0.25) gestures.push({ iris: i, gesture: 'looking at camera' });\n }\n return gestures;\n};\n\nexports.hand = (res) => {\n if (!res) return [];\n const gestures = [];\n for (let i = 0; i < res.length; i++) {\n const fingers = [];\n for (const [finger, pos] of Object.entries(res[i]['annotations'])) {\n if (finger !== 'palmBase') fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n }\n if (fingers && fingers.length > 0) {\n const closest = fingers.reduce((best, a) => (best.position[2] < a.position[2] ? best : a));\n const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n gestures.push({ hand: i, gesture: `${closest.name} forward ${highest.name} up` });\n }\n }\n return gestures;\n};\n", "/* eslint-disable no-use-before-define */\n/*\nWebGLImageFilter - MIT Licensed\n2013, Dominic Szablewski - phoboslab.org\n\n*/\n\nconst WebGLProgram = function (gl, vertexSource, fragmentSource) {\n const _collect = function (source, prefix, collection) {\n const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n source.replace(r, (match, name) => {\n collection[name] = 0;\n return match;\n });\n };\n\n const _compile = function (source, type) {\n const shader = gl.createShader(type);\n gl.shaderSource(shader, source);\n gl.compileShader(shader);\n\n if (!gl.getShaderParameter(shader, gl.COMPILE_STATUS)) {\n // @ts-ignore\n throw new Error('Filter: GL compile failed', gl.getShaderInfoLog(shader));\n }\n return shader;\n };\n\n this.uniform = {};\n this.attribute = {};\n\n const _vsh = _compile(vertexSource, gl.VERTEX_SHADER);\n const _fsh = _compile(fragmentSource, gl.FRAGMENT_SHADER);\n\n this.id = gl.createProgram();\n gl.attachShader(this.id, _vsh);\n gl.attachShader(this.id, _fsh);\n gl.linkProgram(this.id);\n\n if (!gl.getProgramParameter(this.id, gl.LINK_STATUS)) {\n // @ts-ignore\n throw new Error('Filter: GL link failed', gl.getProgramInfoLog(this.id));\n }\n\n gl.useProgram(this.id);\n\n // Collect attributes\n _collect(vertexSource, 'attribute', this.attribute);\n for (const a in this.attribute) {\n this.attribute[a] = gl.getAttribLocation(this.id, a);\n }\n\n // Collect uniforms\n _collect(vertexSource, 'uniform', this.uniform);\n _collect(fragmentSource, 'uniform', this.uniform);\n for (const u in this.uniform) {\n this.uniform[u] = gl.getUniformLocation(this.id, u);\n }\n};\n\nconst WebGLImageFilter = function (params) {\n if (!params) params = { };\n let _drawCount = 0;\n let _sourceTexture = null;\n let _lastInChain = false;\n let _currentFramebufferIndex = -1;\n let _tempFramebuffers = [null, null];\n let _filterChain = [];\n let _width = -1;\n let _height = -1;\n let _vertexBuffer = null;\n let _currentProgram = null;\n const _canvas = params.canvas || document.createElement('canvas');\n\n // key is the shader program source, value is the compiled program\n const _shaderProgramCache = { };\n\n const gl = _canvas.getContext('webgl');\n if (!gl) throw new Error('Filter: getContext() failed');\n\n this.addFilter = function (name) {\n // eslint-disable-next-line prefer-rest-params\n const args = Array.prototype.slice.call(arguments, 1);\n const filter = _filter[name];\n\n _filterChain.push({ func: filter, args });\n };\n\n this.reset = function () {\n _filterChain = [];\n };\n\n this.apply = function (image) {\n _resize(image.width, image.height);\n _drawCount = 0;\n\n // Create the texture for the input image if we haven't yet\n if (!_sourceTexture) _sourceTexture = gl.createTexture();\n gl.bindTexture(gl.TEXTURE_2D, _sourceTexture);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n\n // No filters? Just draw\n if (_filterChain.length === 0) {\n // const program = _compileShader(SHADER.FRAGMENT_IDENTITY);\n _draw();\n return _canvas;\n }\n\n for (let i = 0; i < _filterChain.length; i++) {\n _lastInChain = (i === _filterChain.length - 1);\n const f = _filterChain[i];\n f.func.apply(this, f.args || []);\n }\n\n return _canvas;\n };\n\n const _resize = function (width, height) {\n // Same width/height? Nothing to do here\n if (width === _width && height === _height) { return; }\n\n _canvas.width = width;\n _width = width;\n _canvas.height = height;\n _height = height;\n\n // Create the context if we don't have it yet\n if (!_vertexBuffer) {\n // Create the vertex buffer for the two triangles [x, y, u, v] * 6\n const vertices = new Float32Array([\n -1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0,\n -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0,\n ]);\n // eslint-disable-next-line no-unused-expressions\n (_vertexBuffer = gl.createBuffer(), gl.bindBuffer(gl.ARRAY_BUFFER, _vertexBuffer));\n gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);\n\n // Note sure if this is a good idea; at least it makes texture loading\n // in Ejecta instant.\n gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n }\n\n gl.viewport(0, 0, _width, _height);\n\n // Delete old temp framebuffers\n _tempFramebuffers = [null, null];\n };\n\n const _getTempFramebuffer = function (index) {\n // @ts-ignore\n _tempFramebuffers[index] = _tempFramebuffers[index] || _createFramebufferTexture(_width, _height);\n return _tempFramebuffers[index];\n };\n\n const _createFramebufferTexture = function (width, height) {\n const fbo = gl.createFramebuffer();\n gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n\n const renderbuffer = gl.createRenderbuffer();\n gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n\n const texture = gl.createTexture();\n gl.bindTexture(gl.TEXTURE_2D, texture);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n\n gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n\n gl.bindTexture(gl.TEXTURE_2D, null);\n gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n\n return { fbo, texture };\n };\n\n const _draw = function (flags) {\n let source = null;\n let target = null;\n let flipY = false;\n\n // Set up the source\n if (_drawCount === 0) {\n // First draw call - use the source texture\n source = _sourceTexture;\n } else {\n // All following draw calls use the temp buffer last drawn to\n // @ts-ignore\n source = _getTempFramebuffer(_currentFramebufferIndex)?.texture;\n }\n _drawCount++;\n\n // Set up the target\n if (_lastInChain && !(flags & DRAW.INTERMEDIATE)) {\n // Last filter in our chain - draw directly to the WebGL Canvas. We may\n // also have to flip the image vertically now\n target = null;\n flipY = _drawCount % 2 === 0;\n } else {\n // Intermediate draw call - get a temp buffer to draw to\n _currentFramebufferIndex = (_currentFramebufferIndex + 1) % 2;\n // @ts-ignore\n target = _getTempFramebuffer(_currentFramebufferIndex)?.fbo;\n }\n\n // Bind the source and target and draw the two triangles\n gl.bindTexture(gl.TEXTURE_2D, source);\n gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n\n gl.uniform1f(_currentProgram.uniform.flipY, (flipY ? -1 : 1));\n gl.drawArrays(gl.TRIANGLES, 0, 6);\n };\n\n const _compileShader = function (fragmentSource) {\n if (_shaderProgramCache[fragmentSource]) {\n _currentProgram = _shaderProgramCache[fragmentSource];\n gl.useProgram(_currentProgram.id);\n return _currentProgram;\n }\n\n // Compile shaders\n _currentProgram = new WebGLProgram(gl, SHADER.VERTEX_IDENTITY, fragmentSource);\n\n const floatSize = Float32Array.BYTES_PER_ELEMENT;\n const vertSize = 4 * floatSize;\n gl.enableVertexAttribArray(_currentProgram.attribute.pos);\n gl.vertexAttribPointer(_currentProgram.attribute.pos, 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n gl.enableVertexAttribArray(_currentProgram.attribute.uv);\n gl.vertexAttribPointer(_currentProgram.attribute.uv, 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n\n _shaderProgramCache[fragmentSource] = _currentProgram;\n return _currentProgram;\n };\n\n let DRAW = { INTERMEDIATE: 1 };\n\n let SHADER = {};\n SHADER.VERTEX_IDENTITY = [\n 'precision highp float;',\n 'attribute vec2 pos;',\n 'attribute vec2 uv;',\n 'varying vec2 vUv;',\n 'uniform float flipY;',\n\n 'void main(void) {',\n 'vUv = uv;',\n 'gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);',\n '}',\n ].join('\\n');\n\n SHADER.FRAGMENT_IDENTITY = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n\n 'void main(void) {',\n 'gl_FragColor = texture2D(texture, vUv);',\n '}',\n ].join('\\n');\n\n let _filter = {};\n\n // -------------------------------------------------------------------------\n // Color Matrix Filter\n\n _filter.colorMatrix = function (matrix) {\n // Create a Float32 Array and normalize the offset component to 0-1\n const m = new Float32Array(matrix);\n m[4] /= 255;\n m[9] /= 255;\n m[14] /= 255;\n m[19] /= 255;\n\n // Can we ignore the alpha value? Makes things a bit faster.\n const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0)\n ? _filter.colorMatrix.SHADER.WITHOUT_ALPHA\n : _filter.colorMatrix.SHADER.WITH_ALPHA;\n\n const program = _compileShader(shader);\n gl.uniform1fv(program.uniform.m, m);\n _draw();\n };\n\n _filter.colorMatrix.SHADER = {};\n _filter.colorMatrix.SHADER.WITH_ALPHA = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform float m[20];',\n\n 'void main(void) {',\n 'vec4 c = texture2D(texture, vUv);',\n 'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];',\n 'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];',\n 'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];',\n 'gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];',\n '}',\n ].join('\\n');\n _filter.colorMatrix.SHADER.WITHOUT_ALPHA = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform float m[20];',\n\n 'void main(void) {',\n 'vec4 c = texture2D(texture, vUv);',\n 'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];',\n 'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];',\n 'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];',\n 'gl_FragColor.a = c.a;',\n '}',\n ].join('\\n');\n\n _filter.brightness = function (brightness) {\n const b = (brightness || 0) + 1;\n _filter.colorMatrix([\n b, 0, 0, 0, 0,\n 0, b, 0, 0, 0,\n 0, 0, b, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.saturation = function (amount) {\n const x = (amount || 0) * 2 / 3 + 1;\n const y = ((x - 1) * -0.5);\n _filter.colorMatrix([\n x, y, y, 0, 0,\n y, x, y, 0, 0,\n y, y, x, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.desaturate = function () {\n _filter.saturation(-1);\n };\n\n _filter.contrast = function (amount) {\n const v = (amount || 0) + 1;\n const o = -128 * (v - 1);\n\n _filter.colorMatrix([\n v, 0, 0, 0, o,\n 0, v, 0, 0, o,\n 0, 0, v, 0, o,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.negative = function () {\n _filter.contrast(-2);\n };\n\n _filter.hue = function (rotation) {\n rotation = (rotation || 0) / 180 * Math.PI;\n const cos = Math.cos(rotation);\n const sin = Math.sin(rotation);\n const lumR = 0.213;\n const lumG = 0.715;\n const lumB = 0.072;\n\n _filter.colorMatrix([\n lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.desaturateLuminance = function () {\n _filter.colorMatrix([\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.sepia = function () {\n _filter.colorMatrix([\n 0.393, 0.7689999, 0.18899999, 0, 0,\n 0.349, 0.6859999, 0.16799999, 0, 0,\n 0.272, 0.5339999, 0.13099999, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.brownie = function () {\n _filter.colorMatrix([\n 0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n 0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.vintagePinhole = function () {\n _filter.colorMatrix([\n 0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n 0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n 0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.kodachrome = function () {\n _filter.colorMatrix([\n 1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.technicolor = function () {\n _filter.colorMatrix([\n 1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.polaroid = function () {\n _filter.colorMatrix([\n 1.438, -0.062, -0.062, 0, 0,\n -0.122, 1.378, -0.122, 0, 0,\n -0.016, -0.016, 1.483, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.shiftToBGR = function () {\n _filter.colorMatrix([\n 0, 0, 1, 0, 0,\n 0, 1, 0, 0, 0,\n 1, 0, 0, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n // -------------------------------------------------------------------------\n // Convolution Filter\n\n _filter.convolution = function (matrix) {\n const m = new Float32Array(matrix);\n const pixelSizeX = 1 / _width;\n const pixelSizeY = 1 / _height;\n\n const program = _compileShader(_filter.convolution.SHADER);\n gl.uniform1fv(program.uniform.m, m);\n gl.uniform2f(program.uniform.px, pixelSizeX, pixelSizeY);\n _draw();\n };\n\n _filter.convolution.SHADER = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform vec2 px;',\n 'uniform float m[9];',\n\n 'void main(void) {',\n 'vec4 c11 = texture2D(texture, vUv - px);', // top left\n 'vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y));', // top center\n 'vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y));', // top right\n\n 'vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) );', // mid left\n 'vec4 c22 = texture2D(texture, vUv);', // mid center\n 'vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) );', // mid right\n\n 'vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) );', // bottom left\n 'vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) );', // bottom center\n 'vec4 c33 = texture2D(texture, vUv + px );', // bottom right\n\n 'gl_FragColor = ',\n 'c11 * m[0] + c12 * m[1] + c22 * m[2] +',\n 'c21 * m[3] + c22 * m[4] + c23 * m[5] +',\n 'c31 * m[6] + c32 * m[7] + c33 * m[8];',\n 'gl_FragColor.a = c22.a;',\n '}',\n ].join('\\n');\n\n _filter.detectEdges = function () {\n _filter.convolution.call(this, [\n 0, 1, 0,\n 1, -4, 1,\n 0, 1, 0,\n ]);\n };\n\n _filter.sobelX = function () {\n _filter.convolution.call(this, [\n -1, 0, 1,\n -2, 0, 2,\n -1, 0, 1,\n ]);\n };\n\n _filter.sobelY = function () {\n _filter.convolution.call(this, [\n -1, -2, -1,\n 0, 0, 0,\n 1, 2, 1,\n ]);\n };\n\n _filter.sharpen = function (amount) {\n const a = amount || 1;\n _filter.convolution.call(this, [\n 0, -1 * a, 0,\n -1 * a, 1 + 4 * a, -1 * a,\n 0, -1 * a, 0,\n ]);\n };\n\n _filter.emboss = function (size) {\n const s = size || 1;\n _filter.convolution.call(this, [\n -2 * s, -1 * s, 0,\n -1 * s, 1, 1 * s,\n 0, 1 * s, 2 * s,\n ]);\n };\n\n // -------------------------------------------------------------------------\n // Blur Filter\n\n _filter.blur = function (size) {\n const blurSizeX = (size / 7) / _width;\n const blurSizeY = (size / 7) / _height;\n\n const program = _compileShader(_filter.blur.SHADER);\n\n // Vertical\n gl.uniform2f(program.uniform.px, 0, blurSizeY);\n _draw(DRAW.INTERMEDIATE);\n\n // Horizontal\n gl.uniform2f(program.uniform.px, blurSizeX, 0);\n _draw();\n };\n\n _filter.blur.SHADER = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform vec2 px;',\n\n 'void main(void) {',\n 'gl_FragColor = vec4(0.0);',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;',\n 'gl_FragColor += texture2D(texture, vUv )*0.159576912161;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x, 1.0*px.y))*0.147308056121;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x, 2.0*px.y))*0.115876621105;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x, 3.0*px.y))*0.0776744219933;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x, 4.0*px.y))*0.0443683338718;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x, 5.0*px.y))*0.0215963866053;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265;',\n '}',\n ].join('\\n');\n\n // -------------------------------------------------------------------------\n // Pixelate Filter\n\n _filter.pixelate = function (size) {\n const blurSizeX = (size) / _width;\n const blurSizeY = (size) / _height;\n\n const program = _compileShader(_filter.pixelate.SHADER);\n\n // Horizontal\n gl.uniform2f(program.uniform.size, blurSizeX, blurSizeY);\n _draw();\n };\n\n _filter.pixelate.SHADER = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform vec2 size;',\n 'uniform sampler2D texture;',\n\n 'vec2 pixelate(vec2 coord, vec2 size) {',\n 'return floor( coord / size ) * size;',\n '}',\n\n 'void main(void) {',\n 'gl_FragColor = vec4(0.0);',\n 'vec2 coord = pixelate(vUv, size);',\n 'gl_FragColor += texture2D(texture, coord);',\n '}',\n ].join('\\n');\n};\n\nexports.Canvas = WebGLImageFilter;\n", "import { log } from './log.js';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as fxImage from './imagefx.js';\n\n// internal temp canvases\nlet inCanvas = null;\nlet outCanvas = null;\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nfunction process(input, config) {\n let tensor;\n if (input instanceof tf.Tensor) {\n tensor = tf.clone(input);\n } else {\n const originalWidth = input.naturalWidth || input.videoWidth || input.width || (input.shape && (input.shape[1] > 0));\n const originalHeight = input.naturalHeight || input.videoHeight || input.height || (input.shape && (input.shape[2] > 0));\n let targetWidth = originalWidth;\n let targetHeight = originalHeight;\n if (config.filter.width > 0) targetWidth = config.filter.width;\n else if (config.filter.height > 0) targetWidth = originalWidth * (config.filter.height / originalHeight);\n if (config.filter.height > 0) targetHeight = config.filter.height;\n else if (config.filter.width > 0) targetHeight = originalHeight * (config.filter.width / originalWidth);\n if (!targetWidth || !targetHeight) {\n log('Human: invalid input', input);\n return null;\n }\n if (!inCanvas || (inCanvas.width !== targetWidth) || (inCanvas.height !== targetHeight)) {\n inCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n if (inCanvas.width !== targetWidth) inCanvas.width = targetWidth;\n if (inCanvas.height !== targetHeight) inCanvas.height = targetHeight;\n }\n const ctx = inCanvas.getContext('2d');\n if (input instanceof ImageData) ctx.putImageData(input, 0, 0);\n else ctx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);\n if (config.filter.enabled) {\n if (!this.fx || !outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas.height !== outCanvas.height)) {\n outCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(inCanvas.width, inCanvas.height) : document.createElement('canvas');\n if (outCanvas.width !== inCanvas.width) outCanvas.width = inCanvas.width;\n if (outCanvas.height !== inCanvas.height) outCanvas.height = inCanvas.height;\n this.fx = tf.ENV.flags.IS_BROWSER ? new fxImage.Canvas({ canvas: outCanvas }) : null; // && (typeof document !== 'undefined')\n }\n if (!this.fx) return inCanvas;\n this.fx.reset();\n this.fx.addFilter('brightness', config.filter.brightness); // must have at least one filter enabled\n if (config.filter.contrast !== 0) this.fx.addFilter('contrast', config.filter.contrast);\n if (config.filter.sharpness !== 0) this.fx.addFilter('sharpen', config.filter.sharpness);\n if (config.filter.blur !== 0) this.fx.addFilter('blur', config.filter.blur);\n if (config.filter.saturation !== 0) this.fx.addFilter('saturation', config.filter.saturation);\n if (config.filter.hue !== 0) this.fx.addFilter('hue', config.filter.hue);\n if (config.filter.negative) this.fx.addFilter('negative');\n if (config.filter.sepia) this.fx.addFilter('sepia');\n if (config.filter.vintage) this.fx.addFilter('brownie');\n if (config.filter.sepia) this.fx.addFilter('sepia');\n if (config.filter.kodachrome) this.fx.addFilter('kodachrome');\n if (config.filter.technicolor) this.fx.addFilter('technicolor');\n if (config.filter.polaroid) this.fx.addFilter('polaroid');\n if (config.filter.pixelate !== 0) this.fx.addFilter('pixelate', config.filter.pixelate);\n this.fx.apply(inCanvas);\n // read pixel data\n /*\n const gl = outCanvas.getContext('webgl');\n if (gl) {\n const glBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 4);\n const pixBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 3);\n gl.readPixels(0, 0, outCanvas.width, outCanvas.height, gl.RGBA, gl.UNSIGNED_BYTE, glBuffer);\n // gl returns rbga while we only need rgb, so discarding alpha channel\n // gl returns starting point as lower left, so need to invert vertical\n let i = 0;\n for (let y = outCanvas.height - 1; y >= 0; y--) {\n for (let x = 0; x < outCanvas.width; x++) {\n const index = (x + y * outCanvas.width) * 4;\n pixBuffer[i++] = glBuffer[index + 0];\n pixBuffer[i++] = glBuffer[index + 1];\n pixBuffer[i++] = glBuffer[index + 2];\n }\n }\n outCanvas.data = pixBuffer;\n }\n */\n } else {\n outCanvas = inCanvas;\n }\n let pixels;\n if (outCanvas.data) {\n const shape = [outCanvas.height, outCanvas.width, 3];\n pixels = tf.tensor3d(outCanvas.data, shape, 'int32');\n } else if ((config.backend === 'webgl') || (outCanvas instanceof ImageData)) {\n // tf kernel-optimized method to get imagedata, also if input is imagedata, just use it\n pixels = tf.browser.fromPixels(outCanvas);\n } else {\n // cpu and wasm kernel does not implement efficient fromPixels method nor we can use canvas as-is, so we do a silly one more canvas\n const tempCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n tempCanvas.width = targetWidth;\n tempCanvas.height = targetHeight;\n const tempCtx = tempCanvas.getContext('2d');\n tempCtx?.drawImage(outCanvas, 0, 0);\n const data = tempCtx?.getImageData(0, 0, targetWidth, targetHeight);\n pixels = tf.browser.fromPixels(data);\n }\n const casted = pixels.toFloat();\n tensor = casted.expandDims(0);\n pixels.dispose();\n casted.dispose();\n }\n return { tensor, canvas: config.filter.return ? outCanvas : null };\n}\n\nexports.process = process;\n", "import { log } from './log.js';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as backend from './tfjs/backend.js';\nimport * as facemesh from './blazeface/facemesh.js';\nimport * as age from './age/age.js';\nimport * as gender from './gender/gender.js';\nimport * as emotion from './emotion/emotion.js';\nimport * as embedding from './embedding/embedding.js';\nimport * as posenet from './posenet/posenet.js';\nimport * as blazepose from './blazepose/blazepose.js';\nimport * as handpose from './handpose/handpose.js';\nimport * as gesture from './gesture/gesture.js';\nimport * as image from './image.js';\nimport * as profile from './profile.js';\nimport * as config from '../config.js';\nimport * as sample from './sample.js';\nimport * as app from '../package.json';\n\n// helper function: gets elapsed time on both browser and nodejs\nconst now = () => {\n if (typeof performance !== 'undefined') return performance.now();\n return parseInt(Number(process.hrtime.bigint()) / 1000 / 1000);\n};\n\n// helper function: perform deep merge of multiple objects so it allows full inheriance with overrides\nfunction mergeDeep(...objects) {\n const isObject = (obj) => obj && typeof obj === 'object';\n return objects.reduce((prev, obj) => {\n Object.keys(obj || {}).forEach((key) => {\n const pVal = prev[key];\n const oVal = obj[key];\n if (Array.isArray(pVal) && Array.isArray(oVal)) {\n prev[key] = pVal.concat(...oVal);\n } else if (isObject(pVal) && isObject(oVal)) {\n prev[key] = mergeDeep(pVal, oVal);\n } else {\n prev[key] = oVal;\n }\n });\n return prev;\n }, {});\n}\n\nclass Human {\n constructor(userConfig = {}) {\n this.tf = tf;\n this.version = app.version;\n this.config = mergeDeep(config.default, userConfig);\n this.fx = null;\n this.state = 'idle';\n this.numTensors = 0;\n this.analyzeMemoryLeaks = false;\n this.checkSanity = false;\n this.firstRun = true;\n this.perf = {};\n // object that contains all initialized models\n this.models = {\n facemesh: null,\n posenet: null,\n handpose: null,\n iris: null,\n age: null,\n gender: null,\n emotion: null,\n };\n // export raw access to underlying models\n this.facemesh = facemesh;\n this.age = age;\n this.gender = gender;\n this.emotion = emotion;\n this.body = posenet;\n this.hand = handpose;\n }\n\n profile() {\n if (this.config.profile) return profile.data;\n return {};\n }\n\n // helper function: measure tensor leak\n analyze(...msg) {\n if (!this.analyzeMemoryLeaks) return;\n const current = tf.engine().state.numTensors;\n const previous = this.numTensors;\n this.numTensors = current;\n const leaked = current - previous;\n if (leaked !== 0) log(...msg, leaked);\n }\n\n // quick sanity check on inputs\n sanity(input) {\n if (!this.checkSanity) return null;\n if (!input) return 'input is not defined';\n if (tf.ENV.flags.IS_NODE && !(input instanceof tf.Tensor)) {\n return 'input must be a tensor';\n }\n try {\n tf.getBackend();\n } catch {\n return 'backend not loaded';\n }\n return null;\n }\n\n simmilarity(embedding1, embedding2) {\n if (this.config.face.embedding.enabled) return embedding.simmilarity(embedding1, embedding2);\n return 0;\n }\n\n // preload models, not explicitly required as it's done automatically on first use\n async load(userConfig) {\n this.state = 'load';\n const timeStamp = now();\n if (userConfig) this.config = mergeDeep(this.config, userConfig);\n\n if (this.firstRun) {\n log(`version: ${this.version} TensorFlow/JS version: ${tf.version_core}`);\n await this.checkBackend(true);\n if (tf.ENV.flags.IS_BROWSER) {\n log('configuration:', this.config);\n log('tf flags:', tf.ENV.flags);\n }\n this.firstRun = false;\n }\n\n if (this.config.async) {\n [\n this.models.facemesh,\n this.models.age,\n this.models.gender,\n this.models.emotion,\n this.models.embedding,\n this.models.posenet,\n this.models.handpose,\n this.models.blazepose,\n ] = await Promise.all([\n this.models.facemesh || (this.config.face.enabled ? facemesh.load(this.config) : null),\n this.models.age || ((this.config.face.enabled && this.config.face.age.enabled) ? age.load(this.config) : null),\n this.models.gender || ((this.config.face.enabled && this.config.face.gender.enabled) ? gender.load(this.config) : null),\n this.models.emotion || ((this.config.face.enabled && this.config.face.emotion.enabled) ? emotion.load(this.config) : null),\n this.models.embedding || ((this.config.face.enabled && this.config.face.embedding.enabled) ? embedding.load(this.config) : null),\n this.models.posenet || (this.config.body.enabled ? posenet.load(this.config) : null),\n this.models.handpose || (this.config.hand.enabled ? handpose.load(this.config) : null),\n this.models.blazepose || (this.config.pose.enabled ? blazepose.load(this.config) : null),\n ]);\n } else {\n if (this.config.face.enabled && !this.models.facemesh) this.models.facemesh = await facemesh.load(this.config);\n if (this.config.face.enabled && this.config.face.age.enabled && !this.models.age) this.models.age = await age.load(this.config);\n if (this.config.face.enabled && this.config.face.gender.enabled && !this.models.gender) this.models.gender = await gender.load(this.config);\n if (this.config.face.enabled && this.config.face.emotion.enabled && !this.models.emotion) this.models.emotion = await emotion.load(this.config);\n if (this.config.face.enabled && this.config.face.embedding.enabled && !this.models.embedding) this.models.embedding = await embedding.load(this.config);\n if (this.config.body.enabled && !this.models.posenet) this.models.posenet = await posenet.load(this.config);\n if (this.config.hand.enabled && !this.models.handpose) this.models.handpose = await handpose.load(this.config);\n if (this.config.pose.enabled && !this.models.blazepose) this.models.blazepose = await blazepose.load(this.config);\n }\n const current = Math.trunc(now() - timeStamp);\n if (current > (this.perf.load || 0)) this.perf.load = current;\n }\n\n // check if backend needs initialization if it changed\n async checkBackend(force) {\n if (this.config.backend && (this.config.backend !== '') && force || (tf.getBackend() !== this.config.backend)) {\n const timeStamp = now();\n this.state = 'backend';\n /* force backend reload\n if (this.config.backend in tf.engine().registry) {\n const backendFactory = tf.findBackendFactory(this.config.backend);\n tf.removeBackend(this.config.backend);\n tf.registerBackend(this.config.backend, backendFactory);\n } else {\n log('Backend not registred:', this.config.backend);\n }\n */\n\n log('setting backend:', this.config.backend);\n\n if (this.config.backend === 'wasm') {\n log('settings wasm path:', this.config.wasmPath);\n tf.setWasmPaths(this.config.wasmPath);\n const simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n if (!simd) log('warning: wasm simd support is not enabled');\n }\n\n if (this.config.backend === 'humangl') {\n log('registering humangl backend');\n backend.register();\n }\n\n await tf.setBackend(this.config.backend);\n tf.enableProdMode();\n /* debug mode is really too mcuh\n tf.enableDebugMode();\n */\n if (tf.getBackend() === 'webgl') {\n if (this.config.deallocate) {\n log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', this.config.deallocate);\n tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', this.config.deallocate ? 0 : -1);\n }\n tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n const gl = await tf.backend().getGPGPUContext().gl;\n log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);\n }\n await tf.ready();\n this.perf.backend = Math.trunc(now() - timeStamp);\n }\n }\n\n async detectFace(input) {\n // run facemesh, includes blazeface and iris\n // eslint-disable-next-line no-async-promise-executor\n let timeStamp;\n let ageRes;\n let genderRes;\n let emotionRes;\n let embeddingRes;\n const faceRes = [];\n this.state = 'run:face';\n timeStamp = now();\n const faces = await this.models.facemesh?.estimateFaces(input, this.config);\n this.perf.face = Math.trunc(now() - timeStamp);\n for (const face of faces) {\n this.analyze('Get Face');\n\n // is something went wrong, skip the face\n if (!face.image || face.image.isDisposedInternal) {\n log('Face object is disposed:', face.image);\n continue;\n }\n\n // run age, inherits face from blazeface\n this.analyze('Start Age:');\n if (this.config.async) {\n ageRes = this.config.face.age.enabled ? age.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:age';\n timeStamp = now();\n ageRes = this.config.face.age.enabled ? await age.predict(face.image, this.config) : {};\n this.perf.age = Math.trunc(now() - timeStamp);\n }\n\n // run gender, inherits face from blazeface\n this.analyze('Start Gender:');\n if (this.config.async) {\n genderRes = this.config.face.gender.enabled ? gender.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:gender';\n timeStamp = now();\n genderRes = this.config.face.gender.enabled ? await gender.predict(face.image, this.config) : {};\n this.perf.gender = Math.trunc(now() - timeStamp);\n }\n\n // run emotion, inherits face from blazeface\n this.analyze('Start Emotion:');\n if (this.config.async) {\n emotionRes = this.config.face.emotion.enabled ? emotion.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:emotion';\n timeStamp = now();\n emotionRes = this.config.face.emotion.enabled ? await emotion.predict(face.image, this.config) : {};\n this.perf.emotion = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Emotion:');\n\n // run emotion, inherits face from blazeface\n this.analyze('Start Embedding:');\n if (this.config.async) {\n embeddingRes = this.config.face.embedding.enabled ? embedding.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:embedding';\n timeStamp = now();\n embeddingRes = this.config.face.embedding.enabled ? await embedding.predict(face.image, this.config) : {};\n this.perf.embedding = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Emotion:');\n\n // if async wait for results\n if (this.config.async) {\n [ageRes, genderRes, emotionRes, embeddingRes] = await Promise.all([ageRes, genderRes, emotionRes, embeddingRes]);\n }\n\n this.analyze('Finish Face:');\n // dont need face anymore\n face.image.dispose();\n\n // calculate iris distance\n // iris: array[ center, left, top, right, bottom]\n if (!this.config.face.iris.enabled) {\n delete face.annotations.leftEyeIris;\n delete face.annotations.rightEyeIris;\n }\n const irisSize = (face.annotations.leftEyeIris && face.annotations.rightEyeIris)\n /* average human iris size is 11.7mm */\n ? 11.7 * Math.max(Math.abs(face.annotations.leftEyeIris[3][0] - face.annotations.leftEyeIris[1][0]), Math.abs(face.annotations.rightEyeIris[4][1] - face.annotations.rightEyeIris[2][1]))\n : 0;\n\n // combine results\n faceRes.push({\n confidence: face.confidence,\n box: face.box,\n mesh: face.mesh,\n boxRaw: face.boxRaw,\n meshRaw: face.meshRaw,\n annotations: face.annotations,\n age: ageRes.age,\n gender: genderRes.gender,\n genderConfidence: genderRes.confidence,\n emotion: emotionRes,\n embedding: embeddingRes,\n iris: (irisSize !== 0) ? Math.trunc(irisSize) / 100 : 0,\n });\n this.analyze('End Face');\n }\n this.analyze('End FaceMesh:');\n if (this.config.async) {\n if (this.perf.face) delete this.perf.face;\n if (this.perf.age) delete this.perf.age;\n if (this.perf.gender) delete this.perf.gender;\n if (this.perf.emotion) delete this.perf.emotion;\n }\n return faceRes;\n }\n\n async image(input, userConfig = {}) {\n this.state = 'image';\n this.config = mergeDeep(this.config, userConfig);\n const process = image.process(input, this.config);\n process.tensor.dispose();\n return process.canvas;\n }\n\n // main detect function\n async detect(input, userConfig = {}) {\n // detection happens inside a promise\n return new Promise(async (resolve) => {\n this.state = 'config';\n let timeStamp;\n\n // update configuration\n this.config = mergeDeep(this.config, userConfig);\n\n // sanity checks\n this.state = 'check';\n const error = this.sanity(input);\n if (error) {\n log(error, input);\n resolve({ error });\n }\n\n let poseRes;\n let blazeposeRes;\n let handRes;\n let faceRes;\n\n const timeStart = now();\n\n // configure backend\n await this.checkBackend();\n\n // load models if enabled\n await this.load();\n\n if (this.config.scoped) tf.engine().startScope();\n this.analyze('Start Scope:');\n\n timeStamp = now();\n const process = image.process(input, this.config);\n if (!process || !process.tensor) {\n log('could not convert input to tensor');\n resolve({ error: 'could not convert input to tensor' });\n return;\n }\n this.perf.image = Math.trunc(now() - timeStamp);\n this.analyze('Get Image:');\n\n // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n if (this.config.async) {\n faceRes = this.config.face.enabled ? this.detectFace(process.tensor) : [];\n if (this.perf.face) delete this.perf.face;\n } else {\n this.state = 'run:face';\n timeStamp = now();\n faceRes = this.config.face.enabled ? await this.detectFace(process.tensor) : [];\n this.perf.face = Math.trunc(now() - timeStamp);\n }\n\n // run posenet\n this.analyze('Start Body:');\n if (this.config.async) {\n poseRes = this.config.body.enabled ? this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n if (this.perf.body) delete this.perf.body;\n } else {\n this.state = 'run:body';\n timeStamp = now();\n poseRes = this.config.body.enabled ? await this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n this.perf.body = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Body:');\n\n // run posenet\n this.analyze('Start Pose:');\n if (this.config.async) {\n blazeposeRes = this.config.pose.enabled ? blazepose.predict(process.tensor, this.config) : [];\n if (this.perf.pose) delete this.perf.pose;\n } else {\n this.state = 'run:pose';\n timeStamp = now();\n blazeposeRes = this.config.pose.enabled ? await blazepose.predict(process.tensor, this.config) : [];\n this.perf.pose = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Pose:');\n\n // run handpose\n this.analyze('Start Hand:');\n if (this.config.async) {\n handRes = this.config.hand.enabled ? this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n if (this.perf.hand) delete this.perf.hand;\n } else {\n this.state = 'run:hand';\n timeStamp = now();\n handRes = this.config.hand.enabled ? await this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n this.perf.hand = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Hand:');\n\n // if async wait for results\n if (this.config.async) {\n [faceRes, poseRes, blazeposeRes, handRes] = await Promise.all([faceRes, poseRes, blazeposeRes, handRes]);\n }\n process.tensor.dispose();\n\n if (this.config.scoped) tf.engine().endScope();\n this.analyze('End Scope:');\n\n let gestureRes = [];\n if (this.config.gesture.enabled) {\n timeStamp = now();\n gestureRes = [...gesture.face(faceRes), ...gesture.body(poseRes), ...gesture.hand(handRes), ...gesture.iris(faceRes)];\n if (!this.config.async) this.perf.gesture = Math.trunc(now() - timeStamp);\n else if (this.perf.gesture) delete this.perf.gesture;\n }\n\n this.perf.total = Math.trunc(now() - timeStart);\n this.state = 'idle';\n resolve({ face: faceRes, body: poseRes, hand: handRes, pose: blazeposeRes, gesture: gestureRes, performance: this.perf, canvas: process.canvas });\n });\n }\n\n async warmupBitmap() {\n const b64toBlob = (base64, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n let blob;\n let res;\n switch (this.config.warmup) {\n case 'face': blob = await b64toBlob(sample.face); break;\n case 'full': blob = await b64toBlob(sample.body); break;\n default: blob = null;\n }\n if (blob) {\n const bitmap = await createImageBitmap(blob);\n res = await this.detect(bitmap, config);\n bitmap.close();\n }\n return res;\n }\n\n async warmupCanvas() {\n return new Promise((resolve) => {\n let src;\n let size = 0;\n switch (this.config.warmup) {\n case 'face':\n size = 256;\n src = 'data:image/jpeg;base64,' + sample.face;\n break;\n case 'full':\n size = 1200;\n src = 'data:image/jpeg;base64,' + sample.body;\n break;\n default:\n src = null;\n }\n const img = new Image(size, size);\n img.onload = () => {\n const canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(size, size) : document.createElement('canvas');\n canvas.width = size;\n canvas.height = size;\n const ctx = canvas.getContext('2d');\n ctx.drawImage(img, 0, 0);\n const data = ctx.getImageData(0, 0, size, size);\n this.detect(data, config).then((res) => resolve(res));\n };\n if (src) img.src = src;\n else resolve(null);\n });\n }\n\n async warmup(userConfig) {\n const t0 = now();\n if (userConfig) this.config = mergeDeep(this.config, userConfig);\n const video = this.config.videoOptimized;\n this.config.videoOptimized = false;\n let res;\n if (typeof createImageBitmap === 'function') res = await this.warmupBitmap();\n else res = await this.warmupCanvas();\n this.config.videoOptimized = video;\n const t1 = now();\n log('Warmup', this.config.warmup, (t1 - t0), res);\n return res;\n }\n}\n\nexport { Human as default };\n", "// helper function: wrapper around console output\nexport function log(...msg) {\n const dt = new Date();\n const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n // eslint-disable-next-line no-console\n if (msg) console.log(ts, 'Human:', ...msg);\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport const config = {\n name: 'humangl',\n priority: 99,\n canvas: null,\n width: 1024,\n height: 1024,\n webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n alpha: false,\n antialias: false,\n premultipliedAlpha: false,\n preserveDrawingBuffer: false,\n depth: false,\n stencil: false,\n failIfMajorPerformanceCaveat: false,\n desynchronized: true,\n },\n};\n\nexport function register() {\n if (!tf.findBackend(config.name)) {\n // @ts-ignore\n config.canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(config.width, config.height) : document.createElement('canvas');\n // @ts-ignore\n const gl = config.canvas.getContext('webgl2', config.webGLattr);\n tf.setWebGLContext(2, gl);\n const ctx = new tf.GPGPUContext(gl);\n tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n const kernels = tf.getKernelsForBackend('webgl');\n kernels.forEach((kernelConfig) => {\n const newKernelConfig = { ...kernelConfig, backendName: config.name };\n tf.registerKernel(newKernelConfig);\n });\n tf.ENV.set('WEBGL_VERSION', 2);\n tf.ENV.set('WEBGL_MAX_TEXTURE_SIZE', gl.getParameter(gl.MAX_TEXTURE_SIZE));\n tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n }\n}\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\n// import * as profile from '../profile.js';\n\nconst models = {};\n\nexport async function load(config) {\n if (!models.blazepose) {\n models.blazepose = await tf.loadGraphModel(config.pose.modelPath);\n log(`load model: ${config.pose.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.blazepose;\n}\n\nexport async function predict(image, config) {\n if (!models.blazepose) return null;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [config.pose.inputSize, config.pose.inputSize], false);\n const enhance = tf.div(resize, 127.5).sub(1);\n tf.dispose(resize);\n const logits = await models.blazepose.predict(enhance);\n //\n tf.dispose(enhance);\n logits.map((logit) => logit.dispose());\n resolve(logits);\n });\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nfunction getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\nfunction getBoxCenter(box) {\n return [\n box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n ];\n}\nfunction cutBoxFromImageAndResize(box, image, cropSize) {\n const h = image.shape[1];\n const w = image.shape[2];\n const boxes = [[\n box.startPoint[1] / h,\n box.startPoint[0] / w,\n box.endPoint[1] / h,\n box.endPoint[0] / w,\n ]];\n return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\nfunction scaleBoxCoordinates(box, factor) {\n const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n const palmLandmarks = box.palmLandmarks.map((coord) => {\n const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n return scaledCoord;\n });\n return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\nfunction enlargeBox(box, factor = 1.5) {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\nfunction squarifyBox(box) {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const maxEdge = Math.max(...size);\n const halfSize = maxEdge / 2;\n const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\nfunction shiftBox(box, shiftFactor) {\n const boxSize = [\n box.endPoint[0] - box.startPoint[0],\n box.endPoint[1] - box.startPoint[1],\n ];\n const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]];\n const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\nexport {\n cutBoxFromImageAndResize,\n enlargeBox,\n getBoxCenter,\n getBoxSize,\n scaleBoxCoordinates,\n shiftBox,\n squarifyBox,\n};\n", "function normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\nfunction computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\nconst buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\nfunction dot(v1, v2) {\n let product = 0;\n for (let i = 0; i < v1.length; i++) {\n product += v1[i] * v2[i];\n }\n return product;\n}\nfunction getColumnFrom2DArr(arr, columnIndex) {\n const column = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\nfunction multiplyTransformMatrices(mat1, mat2) {\n const product = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) {\n // @ts-ignore\n product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n }\n return product;\n}\nfunction buildRotationMatrix(rotation, center) {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\nfunction invertTransformMatrix(matrix) {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [\n -dot(rotationComponent[0], translationComponent),\n -dot(rotationComponent[1], translationComponent),\n ];\n return [\n rotationComponent[0].concat(invertedTranslation[0]),\n rotationComponent[1].concat(invertedTranslation[1]),\n [0, 0, 1],\n ];\n}\nfunction rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\nexport {\n buildRotationMatrix,\n computeRotation,\n dot,\n getColumnFrom2DArr,\n invertTransformMatrix,\n normalizeRadians,\n rotatePoint,\n};\n", "/* eslint-disable indent */\n/* eslint-disable no-multi-spaces */\n\nexport default {\n backend: 'webgl', // select tfjs backend to use\n wasmPath: '../assets/', // path for wasm binaries\n // only used for backend: wasm\n async: true, // execute enabled models in parallel\n // this disables per-model performance data but\n // slightly increases performance\n // cannot be used if profiling is enabled\n profile: false, // enable tfjs profiling\n // this has significant performance impact\n // only enable for debugging purposes\n // currently only implemented for age,gender,emotion models\n deallocate: false, // aggresively deallocate gpu memory after each usage\n // only valid for webgl backend and only during first call\n // cannot be changed unless library is reloaded\n // this has significant performance impact\n // only enable on low-memory devices\n scoped: false, // enable scoped runs\n // some models *may* have memory leaks,\n // this wrapps everything in a local scope at a cost of performance\n // typically not needed\n videoOptimized: true, // perform additional optimizations when input is video,\n // must be disabled for images\n // basically this skips object box boundary detection for every n frames\n // while maintaining in-box detection since objects cannot move that fast\n warmup: 'face', // what to use for human.warmup(), can be 'none', 'face', 'full'\n // warmup pre-initializes all models for faster inference but can take\n // significant time on startup\n filter: {\n enabled: true, // enable image pre-processing filters\n width: 0, // resize input width\n height: 0, // resize input height\n // if both width and height are set to 0, there is no resizing\n // if just one is set, second one is scaled automatically\n // if both are set, values are used as-is\n return: true, // return processed canvas imagedata in result\n brightness: 0, // range: -1 (darken) to 1 (lighten)\n contrast: 0, // range: -1 (reduce contrast) to 1 (increase contrast)\n sharpness: 0, // range: 0 (no sharpening) to 1 (maximum sharpening)\n blur: 0, // range: 0 (no blur) to N (blur radius in pixels)\n saturation: 0, // range: -1 (reduce saturation) to 1 (increase saturation)\n hue: 0, // range: 0 (no change) to 360 (hue rotation in degrees)\n negative: false, // image negative\n sepia: false, // image sepia colors\n vintage: false, // image vintage colors\n kodachrome: false, // image kodachrome colors\n technicolor: false, // image technicolor colors\n polaroid: false, // image polaroid camera effect\n pixelate: 0, // range: 0 (no pixelate) to N (number of pixels to pixelate)\n },\n\n gesture: {\n enabled: true, // enable simple gesture recognition\n },\n\n face: {\n enabled: true, // controls if specified modul is enabled\n // face.enabled is required for all face models:\n // detector, mesh, iris, age, gender, emotion\n // (note: module is not loaded until it is required)\n detector: {\n modelPath: '../models/blazeface-back.json', // can be 'front' or 'back'.\n // 'front' is optimized for large faces\n // such as front-facing camera and\n // 'back' is optimized for distanct faces.\n inputSize: 256, // fixed value: 128 for front and 256 for 'back'\n rotation: false, // use best-guess rotated face image or just box with rotation as-is\n // false means higher performance, but incorrect mesh mapping if face angle is above 20 degrees\n maxFaces: 10, // maximum number of faces detected in the input\n // should be set to the minimum number for performance\n skipFrames: 11, // how many frames to go without re-running the face bounding box detector\n // only used for video inputs\n // e.g., if model is running st 25 FPS, we can re-use existing bounding\n // box for updated face analysis as the head probably hasn't moved much\n // in short time (10 * 1/25 = 0.25 sec)\n minConfidence: 0.5, // threshold for discarding a prediction\n iouThreshold: 0.2, // threshold for deciding whether boxes overlap too much in\n // non-maximum suppression (0.1 means drop if overlap 10%)\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on score\n // in non-maximum suppression,\n // this is applied on detection objects only and before minConfidence\n },\n\n mesh: {\n enabled: true,\n modelPath: '../models/facemesh.json',\n inputSize: 192, // fixed value\n returnRawData: false, // in addition to standard mesh and box values, return raw normalized values as well\n },\n\n iris: {\n enabled: true,\n modelPath: '../models/iris.json',\n inputSize: 64, // fixed value\n },\n\n age: {\n enabled: true,\n modelPath: '../models/age-ssrnet-imdb.json', // can be 'age-ssrnet-imdb' or 'age-ssrnet-wiki'\n // which determines training set for model\n inputSize: 64, // fixed value\n skipFrames: 31, // how many frames to go without re-running the detector\n // only used for video inputs\n },\n\n gender: {\n enabled: true,\n minConfidence: 0.1, // threshold for discarding a prediction\n modelPath: '../models/gender-ssrnet-imdb.json', // can be 'gender', 'gender-ssrnet-imdb' or 'gender-ssrnet-wiki'\n inputSize: 64, // fixed value\n skipFrames: 41, // how many frames to go without re-running the detector\n // only used for video inputs\n },\n\n emotion: {\n enabled: true,\n inputSize: 64, // fixed value\n minConfidence: 0.2, // threshold for discarding a prediction\n skipFrames: 21, // how many frames to go without re-running the detector\n modelPath: '../models/emotion-large.json', // can be 'mini', 'large'\n },\n\n embedding: {\n enabled: false,\n inputSize: 112, // fixed value\n modelPath: '../models/mobilefacenet.json',\n },\n },\n\n body: {\n enabled: true,\n modelPath: '../models/posenet.json',\n inputSize: 257, // fixed value\n maxDetections: 10, // maximum number of people detected in the input\n // should be set to the minimum number for performance\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on score\n // in non-maximum suppression\n nmsRadius: 20, // radius for deciding points are too close in non-maximum suppression\n outputStride: 16, // size of block in which to run point detectopn, smaller value means higher resolution\n // defined by model itself, can be 8, 16, or 32\n modelType: 'MobileNet', // Human includes MobileNet version, but you can switch to ResNet\n },\n\n pose: { // TBD: not currently in use\n enabled: false,\n scoreThreshold: 0.6, // threshold for deciding when to remove boxes based on score\n // in non-maximum suppression\n iouThreshold: 0.3, // threshold for deciding whether boxes overlap too much\n // in non-maximum suppression\n modelPath: '../models/blazepose.json',\n inputSize: 256, // fixed value\n },\n\n hand: {\n enabled: true,\n rotation: false, // use best-guess rotated hand image or just box with rotation as-is\n // false means higher performance, but incorrect finger mapping if hand is inverted\n inputSize: 256, // fixed value\n skipFrames: 12, // how many frames to go without re-running the hand bounding box detector\n // only used for video inputs\n // e.g., if model is running st 25 FPS, we can re-use existing bounding\n // box for updated hand skeleton analysis as the hand probably\n // hasn't moved much in short time (10 * 1/25 = 0.25 sec)\n minConfidence: 0.1, // threshold for discarding a prediction\n iouThreshold: 0.1, // threshold for deciding whether boxes overlap too much\n // in non-maximum suppression\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on\n // score in non-maximum suppression\n maxHands: 1, // maximum number of hands detected in the input\n // should be set to the minimum number for performance\n landmarks: true, // detect hand landmarks or just hand boundary box\n detector: {\n modelPath: '../models/handdetect.json',\n },\n skeleton: {\n modelPath: '../models/handskeleton.json',\n },\n },\n};\n", "// data:image/jpeg;base64,\nexport const face = 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"names": [] } diff --git a/dist/human.node.js b/dist/human.node.js index 37739bad..2b050be1 100644 --- a/dist/human.node.js +++ b/dist/human.node.js @@ -5,14 +5,14 @@ author: ' */ -var __create=Object.create,__defProp=Object.defineProperty,__getProtoOf=Object.getPrototypeOf,__hasOwnProp=Object.prototype.hasOwnProperty,__getOwnPropNames=Object.getOwnPropertyNames,__getOwnPropDesc=Object.getOwnPropertyDescriptor;var __markAsModule=target=>__defProp(target,"__esModule",{value:!0});var __commonJS=(callback,module2)=>()=>(module2||(module2={exports:{}},callback(module2.exports,module2)),module2.exports),__export=(target,all)=>{__markAsModule(target);for(var name in all)__defProp(target,name,{get:all[name],enumerable:!0})},__exportStar=(target,module2,desc)=>{if(__markAsModule(target),module2&&typeof module2=="object"||typeof module2=="function")for(let key of __getOwnPropNames(module2))!__hasOwnProp.call(target,key)&&key!=="default"&&__defProp(target,key,{get:()=>module2[key],enumerable:!(desc=__getOwnPropDesc(module2,key))||desc.enumerable});return target},__toModule=module2=>module2&&module2.__esModule?module2:__exportStar(__defProp(module2!=null?__create(__getProtoOf(module2)):{},"default",{value:module2,enumerable:!0}),module2);var require_tfjs_esm=__commonJS(exports2=>{var __create2=Object.create,__defProp2=Object.defineProperty,__getProtoOf2=Object.getPrototypeOf,__hasOwnProp2=Object.prototype.hasOwnProperty,__getOwnPropNames2=Object.getOwnPropertyNames,__getOwnPropDesc2=Object.getOwnPropertyDescriptor,__markAsModule2=target=>__defProp2(target,"__esModule",{value:!0}),__exportStar2=(target,module22,desc)=>{if(__markAsModule2(target),module22&&typeof module22=="object"||typeof module22=="function")for(let key of __getOwnPropNames2(module22))!__hasOwnProp2.call(target,key)&&key!=="default"&&__defProp2(target,key,{get:()=>module22[key],enumerable:!(desc=__getOwnPropDesc2(module22,key))||desc.enumerable});return target},__toModule2=module22=>module22&&module22.__esModule?module22:__exportStar2(__defProp2(module22!=null?__create2(__getProtoOf2(module22)):{},"default",{value:module22,enumerable:!0}),module22);__exportStar2(exports2,__toModule2(require("@tensorflow/tfjs-node")))});var require_blazeface=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm()),NUM_LANDMARKS=6;function generateAnchors(inputSize){let spec={strides:[inputSize/16,inputSize/8],anchors:[2,6]},anchors=[];for(let i=0;i{box.startEndTensor.dispose(),box.startPoint.dispose(),box.endPoint.dispose()},createBox=startEndTensor=>({startEndTensor,startPoint:tf4.slice(startEndTensor,[0,0],[-1,2]),endPoint:tf4.slice(startEndTensor,[0,2],[-1,2])}),scaleBox=(box,factors)=>{let starts=tf4.mul(box.startPoint,factors),ends=tf4.mul(box.endPoint,factors),newCoordinates=tf4.concat2d([starts,ends],1);return createBox(newCoordinates)};function decodeBounds(boxOutputs,anchors,inputSize){let boxStarts=tf4.slice(boxOutputs,[0,1],[-1,2]),centers=tf4.add(boxStarts,anchors),boxSizes=tf4.slice(boxOutputs,[0,3],[-1,2]),boxSizesNormalized=tf4.div(boxSizes,inputSize),centersNormalized=tf4.div(centers,inputSize),halfBoxSize=tf4.div(boxSizesNormalized,2),starts=tf4.sub(centersNormalized,halfBoxSize),ends=tf4.add(centersNormalized,halfBoxSize),startNormalized=tf4.mul(starts,inputSize),endNormalized=tf4.mul(ends,inputSize);return tf4.concat2d([startNormalized,endNormalized],1)}function scaleBoxFromPrediction(face3,scaleFactor){return tf4.tidy(()=>{let box=face3.box?face3.box:face3;return scaleBox(box,scaleFactor).startEndTensor.squeeze()})}var BlazeFaceModel=class{constructor(model,config2){this.blazeFaceModel=model,this.width=config2.face.detector.inputSize,this.height=config2.face.detector.inputSize,this.anchorsData=generateAnchors(config2.face.detector.inputSize),this.anchors=tf4.tensor2d(this.anchorsData),this.inputSize=tf4.tensor1d([this.width,this.height]),this.config=config2,this.scaleFaces=.8}async getBoundingBoxes(inputImage){if(!inputImage||inputImage.isDisposedInternal||inputImage.shape.length!==4||inputImage.shape[1]<1||inputImage.shape[2]<1)return null;let[detectedOutputs,boxes,scores]=tf4.tidy(()=>{let resizedImage=inputImage.resizeBilinear([this.width,this.height]),normalizedImage=tf4.sub(resizedImage.div(127.5),1),batchedPrediction=this.blazeFaceModel.predict(normalizedImage),prediction;if(Array.isArray(batchedPrediction)){let sorted=batchedPrediction.sort((a,b)=>a.size-b.size),concat384=tf4.concat([sorted[0],sorted[2]],2),concat512=tf4.concat([sorted[1],sorted[3]],2);prediction=tf4.concat([concat512,concat384],1).squeeze(0)}else prediction=batchedPrediction.squeeze();let decodedBounds=decodeBounds(prediction,this.anchors,this.inputSize),logits=tf4.slice(prediction,[0,0],[-1,1]),scoresOut=tf4.sigmoid(logits).squeeze();return[prediction,decodedBounds,scoresOut]}),boxIndicesTensor=await tf4.image.nonMaxSuppressionAsync(boxes,scores,this.config.face.detector.maxFaces,this.config.face.detector.iouThreshold,this.config.face.detector.scoreThreshold),boxIndices=boxIndicesTensor.arraySync();boxIndicesTensor.dispose();let boundingBoxes=boxIndices.map(boxIndex=>tf4.slice(boxes,[boxIndex,0],[1,-1])).map(boundingBox=>{let vals=boundingBox.arraySync();return boundingBox.dispose(),vals}),scoresVal=scores.dataSync(),annotatedBoxes=[];for(let i=0;ithis.config.face.detector.minConfidence){let box=createBox(boundingBoxes[i]),anchor=this.anchorsData[boxIndex],landmarks=tf4.tidy(()=>tf4.slice(detectedOutputs,[boxIndex,NUM_LANDMARKS-1],[1,-1]).squeeze().reshape([NUM_LANDMARKS,-1]));annotatedBoxes.push({box,landmarks,anchor,confidence})}}return detectedOutputs.dispose(),boxes.dispose(),scores.dispose(),detectedOutputs.dispose(),{boxes:annotatedBoxes,scaleFactor:[inputImage.shape[2]/this.width,inputImage.shape[1]/this.height]}}async estimateFaces(input){let{boxes,scaleFactor}=await this.getBoundingBoxes(input),faces=[];for(let face3 of boxes){let landmarkData=face3.landmarks.arraySync(),scaledBox=scaleBoxFromPrediction(face3,scaleFactor),boxData=scaleBox.arraySync(),probabilityData=face3.probability.arraySync(),anchor=face3.anchor,[scaleFactorX,scaleFactorY]=scaleFactor,scaledLandmarks=landmarkData.map(landmark=>[(landmark[0]+anchor[0])*scaleFactorX,(landmark[1]+anchor[1])*scaleFactorY]),normalizedFace={topLeft:boxData.slice(0,2),bottomRight:boxData.slice(2),landmarks:scaledLandmarks,probability:probabilityData};disposeBox(face3.box),face3.landmarks.dispose(),face3.probability.dispose(),scaledBox.dispose(),faces.push(normalizedFace)}return faces}};async function load2(config2){let blazeface=await tf4.loadGraphModel(config2.face.detector.modelPath,{fromTFHub:config2.face.detector.modelPath.includes("tfhub.dev")}),model=new BlazeFaceModel(blazeface,config2);return log(`load model: ${config2.face.detector.modelPath.match(/\/(.*)\./)[1]}`),model}exports2.load=load2;exports2.BlazeFaceModel=BlazeFaceModel;exports2.disposeBox=disposeBox});var require_box=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm());function scaleBoxCoordinates2(box,factor){let startPoint=[box.startPoint[0]*factor[0],box.startPoint[1]*factor[1]],endPoint=[box.endPoint[0]*factor[0],box.endPoint[1]*factor[1]];return{startPoint,endPoint}}exports2.scaleBoxCoordinates=scaleBoxCoordinates2;function getBoxSize2(box){return[Math.abs(box.endPoint[0]-box.startPoint[0]),Math.abs(box.endPoint[1]-box.startPoint[1])]}exports2.getBoxSize=getBoxSize2;function getBoxCenter2(box){return[box.startPoint[0]+(box.endPoint[0]-box.startPoint[0])/2,box.startPoint[1]+(box.endPoint[1]-box.startPoint[1])/2]}exports2.getBoxCenter=getBoxCenter2;function cutBoxFromImageAndResize2(box,image4,cropSize){let h=image4.shape[1],w=image4.shape[2],boxes=[[box.startPoint[1]/h,box.startPoint[0]/w,box.endPoint[1]/h,box.endPoint[0]/w]];return tf4.image.cropAndResize(image4,boxes,[0],cropSize)}exports2.cutBoxFromImageAndResize=cutBoxFromImageAndResize2;function enlargeBox2(box,factor=1.5){let center=getBoxCenter2(box),size=getBoxSize2(box),newHalfSize=[factor*size[0]/2,factor*size[1]/2],startPoint=[center[0]-newHalfSize[0],center[1]-newHalfSize[1]],endPoint=[center[0]+newHalfSize[0],center[1]+newHalfSize[1]];return{startPoint,endPoint,landmarks:box.landmarks}}exports2.enlargeBox=enlargeBox2;function squarifyBox2(box){let centers=getBoxCenter2(box),size=getBoxSize2(box),halfSize=Math.max(...size)/2,startPoint=[centers[0]-halfSize,centers[1]-halfSize],endPoint=[centers[0]+halfSize,centers[1]+halfSize];return{startPoint,endPoint,landmarks:box.landmarks}}exports2.squarifyBox=squarifyBox2});var require_util=__commonJS(exports2=>{exports2.IDENTITY_MATRIX=[[1,0,0],[0,1,0],[0,0,1]];function normalizeRadians2(angle){return angle-2*Math.PI*Math.floor((angle+Math.PI)/(2*Math.PI))}exports2.normalizeRadians=normalizeRadians2;function computeRotation2(point1,point2){let radians=Math.PI/2-Math.atan2(-(point2[1]-point1[1]),point2[0]-point1[0]);return normalizeRadians2(radians)}exports2.computeRotation=computeRotation2;function radToDegrees(rad){return rad*180/Math.PI}exports2.radToDegrees=radToDegrees;function buildTranslationMatrix2(x,y){return[[1,0,x],[0,1,y],[0,0,1]]}function dot2(v1,v2){let product=0;for(let i=0;i{var 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,378,379,397,288,361,454,356,70,63,105,66,107,336,296,334,293,300,168,6,195,4,98,97,2,326,327,33,160,158,133,153,144,362,385,387,263,373,380,57,40,37,0,267,270,287,321,314,17,84,91,78,81,13,311,308,402,14,178],VTX33=[33,133,362,263,1,62,308,159,145,386,374,6,102,331,2,13,14,70,105,107,336,334,300,54,10,284,50,280,234,454,58,288,152],VTX7=[33,133,362,263,1,78,308];exports2.MESH_ANNOTATIONS=MESH_ANNOTATIONS;exports2.MESH_TO_IRIS_INDICES_MAP=MESH_TO_IRIS_INDICES_MAP;exports2.TRI468=TRI468;exports2.TRI68=TRI68;exports2.TRI33=TRI33;exports2.TRI7=TRI7;exports2.UV468=UV468;exports2.UV68=VTX68.map(x=>UV468[x]);exports2.UV33=VTX33.map(x=>UV468[x]);exports2.UV7=VTX7.map(x=>UV468[x])});var require_facepipeline=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm()),bounding=__toModule(require_box()),util=__toModule(require_util()),coords=__toModule(require_coords()),LANDMARKS_COUNT=468,MESH_MOUTH_INDEX=13,MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES=[MESH_MOUTH_INDEX,coords.MESH_ANNOTATIONS.midwayBetweenEyes[0]],BLAZEFACE_MOUTH_INDEX=3,BLAZEFACE_NOSE_INDEX=2,BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES=[BLAZEFACE_MOUTH_INDEX,BLAZEFACE_NOSE_INDEX],LEFT_EYE_OUTLINE=coords.MESH_ANNOTATIONS.leftEyeLower0,LEFT_EYE_BOUNDS=[LEFT_EYE_OUTLINE[0],LEFT_EYE_OUTLINE[LEFT_EYE_OUTLINE.length-1]],RIGHT_EYE_OUTLINE=coords.MESH_ANNOTATIONS.rightEyeLower0,RIGHT_EYE_BOUNDS=[RIGHT_EYE_OUTLINE[0],RIGHT_EYE_OUTLINE[RIGHT_EYE_OUTLINE.length-1]],IRIS_UPPER_CENTER_INDEX=3,IRIS_LOWER_CENTER_INDEX=4,IRIS_IRIS_INDEX=71,IRIS_NUM_COORDINATES=76;function replaceRawCoordinates(rawCoords,newCoords,prefix,keys){for(let i=0;i[scaleFactor[0]*(coord[0]-this.meshWidth/2),scaleFactor[1]*(coord[1]-this.meshHeight/2),coord[2]]),coordsRotationMatrix=angle!==0?util.buildRotationMatrix(angle,[0,0]):util.IDENTITY_MATRIX,coordsRotated=angle!==0?coordsScaled.map(coord=>[...util.rotatePoint(coord,coordsRotationMatrix),coord[2]]):coordsScaled,inverseRotationMatrix=angle!==0?util.invertTransformMatrix(rotationMatrix):util.IDENTITY_MATRIX,boxCenter=[...bounding.getBoxCenter({startPoint:box.startPoint,endPoint:box.endPoint}),1];return coordsRotated.map(coord=>[coord[0]+util.dot(boxCenter,inverseRotationMatrix[0]),coord[1]+util.dot(boxCenter,inverseRotationMatrix[1]),coord[2]])}getLeftToRightEyeDepthDifference(rawCoords){let leftEyeZ=rawCoords[LEFT_EYE_BOUNDS[0]][2],rightEyeZ=rawCoords[RIGHT_EYE_BOUNDS[0]][2];return leftEyeZ-rightEyeZ}getEyeBox(rawCoords,face3,eyeInnerCornerIndex,eyeOuterCornerIndex,flip=!1){let box=bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex],rawCoords[eyeOuterCornerIndex]]),this.irisEnlarge)),boxSize=bounding.getBoxSize(box),crop=tf4.image.cropAndResize(face3,[[box.startPoint[1]/this.meshHeight,box.startPoint[0]/this.meshWidth,box.endPoint[1]/this.meshHeight,box.endPoint[0]/this.meshWidth]],[0],[this.irisSize,this.irisSize]);return flip&&(crop=tf4.image.flipLeftRight(crop)),{box,boxSize,crop}}getEyeCoords(eyeData,eyeBox,eyeBoxSize,flip=!1){let eyeRawCoords=[];for(let i=0;i{let z=averageZ;return i===2?z=upperCenterZ:i===4&&(z=lowerCenterZ),[coord[0],coord[1],z]})}async predict(input,config2){let useFreshBox=!1,detector;if((this.skipped===0||this.skipped>config2.face.detector.skipFrames||!config2.face.mesh.enabled||!config2.videoOptimized)&&(detector=await this.boundingBoxDetector.getBoundingBoxes(input),this.skipped=0),config2.videoOptimized&&this.skipped++,detector&&detector.boxes&&detector.boxes.length>0&&(!config2.face.mesh.enabled||detector.boxes.length!==this.detectedFaces&&this.detectedFaces!==config2.face.detector.maxFaces)){this.storedBoxes=[],this.detectedFaces=0;for(let possible of detector.boxes)this.storedBoxes.push({startPoint:possible.box.startPoint.dataSync(),endPoint:possible.box.endPoint.dataSync(),landmarks:possible.landmarks,confidence:possible.confidence});this.storedBoxes.length>0&&(useFreshBox=!0)}if(useFreshBox){if(!detector||!detector.boxes||detector.boxes.length===0)return this.storedBoxes=[],this.detectedFaces=0,null;for(let i=0;i{prediction.box.startPoint.dispose(),prediction.box.endPoint.dispose(),prediction.landmarks.dispose()});let results=tf4.tidy(()=>this.storedBoxes.map((box,i)=>{let face3,angle=0,rotationMatrix;if(config2.face.detector.rotation){let[indexOfMouth,indexOfForehead]=box.landmarks.length>=LANDMARKS_COUNT?MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES:BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES;angle=util.computeRotation(box.landmarks[indexOfMouth],box.landmarks[indexOfForehead]);let faceCenter=bounding.getBoxCenter({startPoint:box.startPoint,endPoint:box.endPoint}),faceCenterNormalized=[faceCenter[0]/input.shape[2],faceCenter[1]/input.shape[1]],rotatedImage=tf4.image.rotateWithOffset(input,angle,0,faceCenterNormalized);rotationMatrix=util.buildRotationMatrix(-angle,faceCenter),face3=bounding.cutBoxFromImageAndResize({startPoint:box.startPoint,endPoint:box.endPoint},rotatedImage,[this.meshHeight,this.meshWidth]).div(255)}else{rotationMatrix=util.IDENTITY_MATRIX;let cloned=input.clone();face3=bounding.cutBoxFromImageAndResize({startPoint:box.startPoint,endPoint:box.endPoint},cloned,[this.meshHeight,this.meshWidth]).div(255)}if(!config2.face.mesh.enabled)return{coords:null,box,faceConfidence:null,confidence:box.confidence,image:face3};let[,confidence,contourCoords]=this.meshDetector.predict(face3),confidenceVal=confidence.dataSync()[0];if(confidenceVala!==null),this.detectedFaces=results.length,results}calculateLandmarksBoundingBox(landmarks){let xs=landmarks.map(d=>d[0]),ys=landmarks.map(d=>d[1]),startPoint=[Math.min(...xs),Math.min(...ys)],endPoint=[Math.max(...xs),Math.max(...ys)];return{startPoint,endPoint,landmarks}}};exports2.Pipeline=Pipeline});var require_facemesh=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm()),blazeface=__toModule(require_blazeface()),facepipeline=__toModule(require_facepipeline()),coords=__toModule(require_coords()),MediaPipeFaceMesh=class{constructor(blazeFace,blazeMeshModel,irisModel,config2){this.facePipeline=new facepipeline.Pipeline(blazeFace,blazeMeshModel,irisModel,config2),this.config=config2}async estimateFaces(input,config2){let predictions=await this.facePipeline.predict(input,config2),results=[];for(let prediction of predictions||[]){if(prediction.isDisposedInternal)continue;let mesh=prediction.coords?prediction.coords.arraySync():null,meshRaw=prediction.rawCoords,annotations={};if(mesh&&mesh.length>0)for(let key of Object.keys(coords.MESH_ANNOTATIONS))annotations[key]=coords.MESH_ANNOTATIONS[key].map(index=>mesh[index]);let boxRaw=config2.face.mesh.returnRawData&&prediction.box?{topLeft:prediction.box.startPoint,bottomRight:prediction.box.endPoint}:null,box=prediction.box?[Math.max(0,prediction.box.startPoint[0]),Math.max(0,prediction.box.startPoint[1]),Math.min(input.shape[2],prediction.box.endPoint[0])-prediction.box.startPoint[0],Math.min(input.shape[1],prediction.box.endPoint[1])-prediction.box.startPoint[1]]:0;results.push({confidence:prediction.confidence||0,box,mesh,boxRaw,meshRaw,annotations,image:prediction.image?tf4.clone(prediction.image):null}),prediction.coords&&prediction.coords.dispose(),prediction.image&&prediction.image.dispose()}return results}},faceModels=[null,null,null];async function load2(config2){faceModels=await Promise.all([!faceModels[0]&&config2.face.enabled?blazeface.load(config2):null,!faceModels[1]&&config2.face.mesh.enabled?tf4.loadGraphModel(config2.face.mesh.modelPath,{fromTFHub:config2.face.mesh.modelPath.includes("tfhub.dev")}):null,!faceModels[2]&&config2.face.iris.enabled?tf4.loadGraphModel(config2.face.iris.modelPath,{fromTFHub:config2.face.iris.modelPath.includes("tfhub.dev")}):null]);let faceMesh=new MediaPipeFaceMesh(faceModels[0],faceModels[1],faceModels[2],config2);return config2.face.mesh.enabled&&log(`load model: ${config2.face.mesh.modelPath.match(/\/(.*)\./)[1]}`),config2.face.iris.enabled&&log(`load model: ${config2.face.iris.modelPath.match(/\/(.*)\./)[1]}`),faceMesh}exports2.load=load2;exports2.MediaPipeFaceMesh=MediaPipeFaceMesh;exports2.triangulation=coords.TRI468});var require_profile=__commonJS(exports2=>{var profileData={};function profile2(name,data2){if(!data2||!data2.kernels)return;let maxResults=5,time=data2.kernels.filter(a=>a.kernelTimeMs>0).reduce((a,b)=>a+=b.kernelTimeMs,0),slowest=data2.kernels.map((a,i)=>(a.id=i,a)).filter(a=>a.kernelTimeMs>0).sort((a,b)=>b.kernelTimeMs-a.kernelTimeMs),largest=data2.kernels.map((a,i)=>(a.id=i,a)).filter(a=>a.totalBytesSnapshot>0).sort((a,b)=>b.totalBytesSnapshot-a.totalBytesSnapshot);slowest.length>maxResults&&(slowest.length=maxResults),largest.length>maxResults&&(largest.length=maxResults);let res={newBytes:data2.newBytes,newTensors:data2.newTensors,peakBytes:data2.peakBytes,numKernelOps:data2.kernels.length,timeKernelOps:time,slowestKernelOps:slowest,largestKernelOps:largest};profileData[name]=res,log("Human profiler",name,res)}exports2.run=profile2});var require_age=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm()),profile2=__toModule(require_profile()),models={},last={age:0},skipped=Number.MAX_SAFE_INTEGER;async function load2(config2){return models.age||(models.age=await tf4.loadGraphModel(config2.face.age.modelPath),log(`load model: ${config2.face.age.modelPath.match(/\/(.*)\./)[1]}`)),models.age}async function predict2(image4,config2){return models.age?skipped0?(skipped++,last):(config2.videoOptimized?skipped=0:skipped=Number.MAX_SAFE_INTEGER,new Promise(async resolve=>{let resize=tf4.image.resizeBilinear(image4,[config2.face.age.inputSize,config2.face.age.inputSize],!1),enhance=tf4.mul(resize,[255]);tf4.dispose(resize);let ageT,obj={};if(!config2.profile)config2.face.age.enabled&&(ageT=await models.age.predict(enhance));else{let profileAge=config2.face.age.enabled?await tf4.profile(()=>models.age.predict(enhance)):{};ageT=profileAge.result.clone(),profileAge.result.dispose(),profile2.run("age",profileAge)}if(enhance.dispose(),ageT){let data2=ageT.dataSync();obj.age=Math.trunc(10*data2[0])/10}ageT.dispose(),last=obj,resolve(obj)})):null}exports2.predict=predict2;exports2.load=load2});var require_gender=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm()),profile2=__toModule(require_profile()),models={},last={gender:""},skipped=Number.MAX_SAFE_INTEGER,alternative=!1,rgb=[.2989,.587,.114];async function load2(config2){return models.gender||(models.gender=await tf4.loadGraphModel(config2.face.gender.modelPath),alternative=models.gender.inputs[0].shape[3]===1,log(`load model: ${config2.face.gender.modelPath.match(/\/(.*)\./)[1]}`)),models.gender}async function predict2(image4,config2){return models.gender?skipped{let resize=tf4.image.resizeBilinear(image4,[config2.face.gender.inputSize,config2.face.gender.inputSize],!1),enhance;alternative?enhance=tf4.tidy(()=>{let[red,green,blue]=tf4.split(resize,3,3),redNorm=tf4.mul(red,rgb[0]),greenNorm=tf4.mul(green,rgb[1]),blueNorm=tf4.mul(blue,rgb[2]);return tf4.addN([redNorm,greenNorm,blueNorm]).sub(.5).mul(2)}):enhance=tf4.mul(resize,[255]),tf4.dispose(resize);let genderT,obj={};if(!config2.profile)config2.face.gender.enabled&&(genderT=await models.gender.predict(enhance));else{let profileGender=config2.face.gender.enabled?await tf4.profile(()=>models.gender.predict(enhance)):{};genderT=profileGender.result.clone(),profileGender.result.dispose(),profile2.run("gender",profileGender)}if(enhance.dispose(),genderT){let data2=genderT.dataSync();if(alternative){let confidence=Math.trunc(100*Math.abs(data2[0]-data2[1]))/100;confidence>config2.face.gender.minConfidence&&(obj.gender=data2[0]>data2[1]?"female":"male",obj.confidence=confidence)}else{let confidence=Math.trunc(200*Math.abs(data2[0]-.5))/100;confidence>config2.face.gender.minConfidence&&(obj.gender=data2[0]<=.5?"female":"male",obj.confidence=Math.min(.99,confidence))}}genderT.dispose(),last=obj,resolve(obj)})):null}exports2.predict=predict2;exports2.load=load2});var require_emotion=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm()),profile2=__toModule(require_profile()),annotations=["angry","disgust","fear","happy","sad","surprise","neutral"],models={},last=[],skipped=Number.MAX_SAFE_INTEGER,rgb=[.2989,.587,.114],scale=1;async function load2(config2){return models.emotion||(models.emotion=await tf4.loadGraphModel(config2.face.emotion.modelPath),log(`load model: ${config2.face.emotion.modelPath.match(/\/(.*)\./)[1]}`)),models.emotion}async function predict2(image4,config2){return models.emotion?skipped0?(skipped++,last):(config2.videoOptimized?skipped=0:skipped=Number.MAX_SAFE_INTEGER,new Promise(async resolve=>{let resize=tf4.image.resizeBilinear(image4,[config2.face.emotion.inputSize,config2.face.emotion.inputSize],!1),[red,green,blue]=tf4.split(resize,3,3);resize.dispose();let redNorm=tf4.mul(red,rgb[0]),greenNorm=tf4.mul(green,rgb[1]),blueNorm=tf4.mul(blue,rgb[2]);red.dispose(),green.dispose(),blue.dispose();let grayscale=tf4.addN([redNorm,greenNorm,blueNorm]);redNorm.dispose(),greenNorm.dispose(),blueNorm.dispose();let normalize=tf4.tidy(()=>grayscale.sub(.5).mul(2));grayscale.dispose();let obj=[];if(config2.face.emotion.enabled){let data2;if(config2.profile){let profileData=await tf4.profile(()=>models.emotion.predict(normalize));data2=profileData.result.dataSync(),profileData.result.dispose(),profile2.run("emotion",profileData)}else{let emotionT=await models.emotion.predict(normalize);data2=emotionT.dataSync(),tf4.dispose(emotionT)}for(let i=0;iconfig2.face.emotion.minConfidence&&obj.push({score:Math.min(.99,Math.trunc(100*scale*data2[i])/100),emotion:annotations[i]});obj.sort((a,b)=>b.score-a.score)}normalize.dispose(),last=obj,resolve(obj)})):null}exports2.predict=predict2;exports2.load=load2});var require_embedding=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm()),profile2=__toModule(require_profile()),models={};async function load2(config2){return models.embedding||(models.embedding=await tf4.loadGraphModel(config2.face.embedding.modelPath),log(`load model: ${config2.face.embedding.modelPath.match(/\/(.*)\./)[1]}`)),models.embedding}function simmilarity2(embedding1,embedding2){if((embedding1==null?void 0:embedding1.length)!==(embedding2==null?void 0:embedding2.length))return 0;let order=2,distance=10*embedding1.map((val,i)=>val-embedding2[i]).reduce((dist,diff)=>dist+diff**order,0)**(1/order);return Math.trunc(1e3*(1-distance))/1e3}async function predict2(image4,config2){return models.embedding?new Promise(async resolve=>{let resize=tf4.image.resizeBilinear(image4,[config2.face.embedding.inputSize,config2.face.embedding.inputSize],!1),data2=[];if(config2.face.embedding.enabled)if(config2.profile){let profileData=await tf4.profile(()=>models.embedding.predict({img_inputs:resize}));data2=[...profileData.result.dataSync()],profileData.result.dispose(),profile2.run("emotion",profileData)}else{let embeddingT=await models.embedding.predict({img_inputs:resize});data2=[...embeddingT.dataSync()],tf4.dispose(embeddingT)}resize.dispose(),resolve(data2)}):null}exports2.predict=predict2;exports2.simmilarity=simmilarity2;exports2.load=load2});var require_modelBase=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm()),imageNetMean=[-123.15,-115.9,-103.06];function nameOutputResultsMobileNet(results){let[offsets,heatmap,displacementFwd,displacementBwd]=results;return{offsets,heatmap,displacementFwd,displacementBwd}}function nameOutputResultsResNet(results){let[displacementFwd,displacementBwd,offsets,heatmap]=results;return{offsets,heatmap,displacementFwd,displacementBwd}}var BaseModel=class{constructor(model){this.model=model}predict(input,config2){return tf4.tidy(()=>{let asBatch=(config2.body.modelType==="ResNet"?input.toFloat().add(imageNetMean):input.toFloat().div(127.5).sub(1)).expandDims(0),results3d=this.model.predict(asBatch).map(y=>y.squeeze([0])),namedResults=config2.body.modelType==="ResNet"?nameOutputResultsResNet(results3d):nameOutputResultsMobileNet(results3d);return{heatmapScores:namedResults.heatmap.sigmoid(),offsets:namedResults.offsets,displacementFwd:namedResults.displacementFwd,displacementBwd:namedResults.displacementBwd}})}dispose(){this.model.dispose()}};exports2.BaseModel=BaseModel});var require_heapSort=__commonJS(exports2=>{function half(k){return Math.floor(k/2)}var MaxHeap=class{constructor(maxSize,getElementValue){this.priorityQueue=new Array(maxSize),this.numberOfElements=-1,this.getElementValue=getElementValue}enqueue(x){this.priorityQueue[++this.numberOfElements]=x,this.swim(this.numberOfElements)}dequeue(){let max=this.priorityQueue[0];return this.exchange(0,this.numberOfElements--),this.sink(0),this.priorityQueue[this.numberOfElements+1]=null,max}empty(){return this.numberOfElements===-1}size(){return this.numberOfElements+1}all(){return this.priorityQueue.slice(0,this.numberOfElements+1)}max(){return this.priorityQueue[0]}swim(k){for(;k>0&&this.less(half(k),k);)this.exchange(k,half(k)),k=half(k)}sink(k){for(;2*k<=this.numberOfElements;){let j=2*k;if(j{var heapSort=__toModule(require_heapSort());function scoreIsMaximumInLocalWindow(keypointId,score,heatmapY,heatmapX,localMaximumRadius,scores){let[height,width]=scores.shape,localMaximum=!0,yStart=Math.max(heatmapY-localMaximumRadius,0),yEnd=Math.min(heatmapY+localMaximumRadius+1,height);for(let yCurrent=yStart;yCurrentscore){localMaximum=!1;break}if(!localMaximum)break}return localMaximum}function buildPartWithScoreQueue(scoreThreshold,localMaximumRadius,scores){let[height,width,numKeypoints]=scores.shape,queue=new heapSort.MaxHeap(height*width*numKeypoints,({score})=>score);for(let heatmapY=0;heatmapY{exports2.partNames=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"];exports2.NUM_KEYPOINTS=exports2.partNames.length;exports2.partIds=exports2.partNames.reduce((result,jointName,i)=>(result[jointName]=i,result),{});var connectedPartNames=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]];exports2.connectedPartIndices=connectedPartNames.map(([jointNameA,jointNameB])=>[exports2.partIds[jointNameA],exports2.partIds[jointNameB]]);exports2.poseChain=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];exports2.partChannels=["left_face","right_face","right_upper_leg_front","right_lower_leg_back","right_upper_leg_back","left_lower_leg_front","left_upper_leg_front","left_upper_leg_back","left_lower_leg_back","right_feet","right_lower_leg_front","left_feet","torso_front","torso_back","right_upper_arm_front","right_upper_arm_back","right_lower_arm_back","left_lower_arm_front","left_upper_arm_front","left_upper_arm_back","left_lower_arm_back","right_hand","right_lower_arm_front","left_hand"]});var require_vectors=__commonJS(exports2=>{var kpt=__toModule(require_keypoints());function getOffsetPoint(y,x,keypoint,offsets){return{y:offsets.get(y,x,keypoint),x:offsets.get(y,x,keypoint+kpt.NUM_KEYPOINTS)}}exports2.getOffsetPoint=getOffsetPoint;function getImageCoords(part,outputStride,offsets){let{heatmapY,heatmapX,id:keypoint}=part,{y,x}=getOffsetPoint(heatmapY,heatmapX,keypoint,offsets);return{x:part.heatmapX*outputStride+x,y:part.heatmapY*outputStride+y}}exports2.getImageCoords=getImageCoords;function fillArray(element,size){let result=new Array(size);for(let i=0;imax?max:a}exports2.clamp=clamp;function squaredDistance(y1,x1,y2,x2){let dy=y2-y1,dx=x2-x1;return dy*dy+dx*dx}exports2.squaredDistance=squaredDistance;function addVectors(a,b){return{x:a.x+b.x,y:a.y+b.y}}exports2.addVectors=addVectors;function clampVector(a,min,max){return{y:clamp(a.y,min,max),x:clamp(a.x,min,max)}}exports2.clampVector=clampVector});var require_decoders=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm()),kpt=__toModule(require_keypoints());function getPointsConfidence(heatmapScores,heatMapCoords){let numKeypoints=heatMapCoords.shape[0],result=new Float32Array(numKeypoints);for(let keypoint=0;keypointheatMapCoordsBuffer.toTensor().mul(tf4.scalar(outputStride,"int32")).toFloat().add(getOffsetVectors(heatMapCoordsBuffer,offsetsBuffer)))}exports2.getOffsetPoints=getOffsetPoints;function mod(a,b){return tf4.tidy(()=>{let floored=a.div(tf4.scalar(b,"int32"));return a.sub(floored.mul(tf4.scalar(b,"int32")))})}function argmax2d(inputs){let[height,width,depth]=inputs.shape;return tf4.tidy(()=>{let coords=inputs.reshape([height*width,depth]).argMax(0),yCoords=coords.div(tf4.scalar(width,"int32")).expandDims(1),xCoords=mod(coords,width).expandDims(1);return tf4.concat([yCoords,xCoords],1)})}exports2.argmax2d=argmax2d});var require_decodePose=__commonJS(exports2=>{var keypoints=__toModule(require_keypoints()),vectors=__toModule(require_vectors()),decoders=__toModule(require_decoders()),parentChildrenTuples=keypoints.poseChain.map(([parentJoinName,childJoinName])=>[keypoints.partIds[parentJoinName],keypoints.partIds[childJoinName]]),parentToChildEdges=parentChildrenTuples.map(([,childJointId])=>childJointId),childToParentEdges=parentChildrenTuples.map(([parentJointId])=>parentJointId);function getDisplacement(edgeId,point,displacements){let numEdges=displacements.shape[2]/2;return{y:displacements.get(point.y,point.x,edgeId),x:displacements.get(point.y,point.x,numEdges+edgeId)}}function getStridedIndexNearPoint(point,outputStride,height,width){return{y:vectors.clamp(Math.round(point.y/outputStride),0,height-1),x:vectors.clamp(Math.round(point.x/outputStride),0,width-1)}}function traverseToTargetKeypoint(edgeId,sourceKeypoint,targetKeypointId,scoresBuffer,offsets,outputStride,displacements,offsetRefineStep=2){let[height,width]=scoresBuffer.shape,sourceKeypointIndices=getStridedIndexNearPoint(sourceKeypoint.position,outputStride,height,width),displacement=getDisplacement(edgeId,sourceKeypointIndices,displacements),targetKeypoint=vectors.addVectors(sourceKeypoint.position,displacement);for(let i=0;i=0;--edge){let sourceKeypointId=parentToChildEdges[edge],targetKeypointId=childToParentEdges[edge];instanceKeypoints[sourceKeypointId]&&!instanceKeypoints[targetKeypointId]&&(instanceKeypoints[targetKeypointId]=traverseToTargetKeypoint(edge,instanceKeypoints[sourceKeypointId],targetKeypointId,scores,offsets,outputStride,displacementsBwd))}for(let edge=0;edge(totalScore+=score,{position:{y:offsetPointsBuffer.get(i,0),x:offsetPointsBuffer.get(i,1)},part:keypoints.partNames[i],score})),filteredKeypoints=instanceKeypoints.filter(kpt=>kpt.score>config2.body.scoreThreshold);return heatmapValues.dispose(),offsetPoints.dispose(),{keypoints:filteredKeypoints,score:totalScore/instanceKeypoints.length}}exports2.decodeSinglePose=decodeSinglePose});var require_decodeMultiple=__commonJS(exports2=>{var buildParts=__toModule(require_buildParts()),decodePose=__toModule(require_decodePose()),vectors=__toModule(require_vectors()),kLocalMaximumRadius=1;function withinNmsRadiusOfCorrespondingPoint(poses,squaredNmsRadius,{x,y},keypointId){return poses.some(({keypoints})=>{let correspondingKeypoint=keypoints[keypointId].position;return vectors.squaredDistance(y,x,correspondingKeypoint.y,correspondingKeypoint.x)<=squaredNmsRadius})}function getInstanceScore(existingPoses,squaredNmsRadius,instanceKeypoints){return instanceKeypoints.reduce((result,{position,score},keypointId)=>(withinNmsRadiusOfCorrespondingPoint(existingPoses,squaredNmsRadius,position,keypointId)||(result+=score),result),0)/instanceKeypoints.length}function decodeMultiplePoses(scoresBuffer,offsetsBuffer,displacementsFwdBuffer,displacementsBwdBuffer,config2){let poses=[],queue=buildParts.buildPartWithScoreQueue(config2.body.scoreThreshold,kLocalMaximumRadius,scoresBuffer),squaredNmsRadius=config2.body.nmsRadius^2;for(;poses.lengthconfig2.body.scoreThreshold&&poses.push({keypoints,score})}return poses}exports2.decodeMultiplePoses=decodeMultiplePoses});var require_util2=__commonJS(exports2=>{var kpt=__toModule(require_keypoints());function eitherPointDoesntMeetConfidence(a,b,minConfidence){return a(eitherPointDoesntMeetConfidence(keypoints[leftJoint].score,keypoints[rightJoint].score,minConfidence)||result.push([keypoints[leftJoint],keypoints[rightJoint]]),result),[])}exports2.getAdjacentKeyPoints=getAdjacentKeyPoints;var{NEGATIVE_INFINITY,POSITIVE_INFINITY}=Number;function getBoundingBox(keypoints){return keypoints.reduce(({maxX,maxY,minX,minY},{position:{x,y}})=>({maxX:Math.max(maxX,x),maxY:Math.max(maxY,y),minX:Math.min(minX,x),minY:Math.min(minY,y)}),{maxX:NEGATIVE_INFINITY,maxY:NEGATIVE_INFINITY,minX:POSITIVE_INFINITY,minY:POSITIVE_INFINITY})}exports2.getBoundingBox=getBoundingBox;function getBoundingBoxPoints(keypoints){let{minX,minY,maxX,maxY}=getBoundingBox(keypoints);return[{x:minX,y:minY},{x:maxX,y:minY},{x:maxX,y:maxY},{x:minX,y:maxY}]}exports2.getBoundingBoxPoints=getBoundingBoxPoints;async function toTensorBuffers3D(tensors){return Promise.all(tensors.map(tensor=>tensor.buffer()))}exports2.toTensorBuffers3D=toTensorBuffers3D;function scalePose(pose,scaleY,scaleX){return{score:pose.score,keypoints:pose.keypoints.map(({score,part,position})=>({score,part,position:{x:position.x*scaleX,y:position.y*scaleY}}))}}exports2.scalePose=scalePose;function resizeTo(image3,[targetH,targetW]){let input=image3.squeeze(0),resized=input.resizeBilinear([targetH,targetW]);return input.dispose(),resized}exports2.resizeTo=resizeTo;function scaleAndFlipPoses(poses,[height,width],[inputResolutionHeight,inputResolutionWidth]){return poses.map(pose=>scalePose(pose,height/inputResolutionHeight,width/inputResolutionWidth))}exports2.scaleAndFlipPoses=scaleAndFlipPoses});var require_modelPoseNet=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm()),modelBase=__toModule(require_modelBase()),decodeMultiple=__toModule(require_decodeMultiple()),decodePose=__toModule(require_decodePose()),util=__toModule(require_util2());async function estimateMultiple(input,res,config2){return new Promise(async resolve=>{let height=input.shape[1],width=input.shape[2],allTensorBuffers=await util.toTensorBuffers3D([res.heatmapScores,res.offsets,res.displacementFwd,res.displacementBwd]),scoresBuffer=allTensorBuffers[0],offsetsBuffer=allTensorBuffers[1],displacementsFwdBuffer=allTensorBuffers[2],displacementsBwdBuffer=allTensorBuffers[3],poses=await decodeMultiple.decodeMultiplePoses(scoresBuffer,offsetsBuffer,displacementsFwdBuffer,displacementsBwdBuffer,config2),scaled=util.scaleAndFlipPoses(poses,[height,width],[config2.body.inputSize,config2.body.inputSize]);resolve(scaled)})}async function estimateSingle(input,res,config2){return new Promise(async resolve=>{let height=input.shape[1],width=input.shape[2],poses=[await decodePose.decodeSinglePose(res.heatmapScores,res.offsets,config2)],scaled=util.scaleAndFlipPoses(poses,[height,width],[config2.body.inputSize,config2.body.inputSize]);resolve(scaled)})}var PoseNet=class{constructor(model){this.baseModel=model}async estimatePoses(input,config2){let resized=util.resizeTo(input,[config2.body.inputSize,config2.body.inputSize]),res=this.baseModel.predict(resized,config2),poses=config2.body.maxDetections<2?await estimateSingle(input,res,config2):await estimateMultiple(input,res,config2);return res.heatmapScores.dispose(),res.offsets.dispose(),res.displacementFwd.dispose(),res.displacementBwd.dispose(),resized.dispose(),poses}dispose(){this.baseModel.dispose()}};exports2.PoseNet=PoseNet;async function load2(config2){let model=await tf4.loadGraphModel(config2.body.modelPath),mobilenet=new modelBase.BaseModel(model);return log(`load model: ${config2.body.modelPath.match(/\/(.*)\./)[1]}`),new PoseNet(mobilenet)}exports2.load=load2});var require_posenet=__commonJS(exports2=>{var modelPoseNet=__toModule(require_modelPoseNet()),keypoints=__toModule(require_keypoints()),util=__toModule(require_util2());exports2.load=modelPoseNet.load;exports2.PoseNet=modelPoseNet.PoseNet;exports2.partChannels=keypoints.partChannels;exports2.partIds=keypoints.partIds;exports2.partNames=keypoints.partNames;exports2.poseChain=keypoints.poseChain;exports2.getAdjacentKeyPoints=util.getAdjacentKeyPoints;exports2.getBoundingBox=util.getBoundingBox;exports2.getBoundingBoxPoints=util.getBoundingBoxPoints;exports2.scaleAndFlipPoses=util.scaleAndFlipPoses;exports2.scalePose=util.scalePose});var require_handdetector=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm()),HandDetector=class{constructor(model,inputSize,anchorsAnnotated){this.model=model,this.anchors=anchorsAnnotated.map(anchor=>[anchor.x_center,anchor.y_center]),this.anchorsTensor=tf4.tensor2d(this.anchors),this.inputSizeTensor=tf4.tensor1d([inputSize,inputSize]),this.doubleInputSizeTensor=tf4.tensor1d([inputSize*2,inputSize*2])}normalizeBoxes(boxes){return tf4.tidy(()=>{let boxOffsets=tf4.slice(boxes,[0,0],[-1,2]),boxSizes=tf4.slice(boxes,[0,2],[-1,2]),boxCenterPoints=tf4.add(tf4.div(boxOffsets,this.inputSizeTensor),this.anchorsTensor),halfBoxSizes=tf4.div(boxSizes,this.doubleInputSizeTensor),startPoints=tf4.mul(tf4.sub(boxCenterPoints,halfBoxSizes),this.inputSizeTensor),endPoints=tf4.mul(tf4.add(boxCenterPoints,halfBoxSizes),this.inputSizeTensor);return tf4.concat2d([startPoints,endPoints],1)})}normalizeLandmarks(rawPalmLandmarks,index){return tf4.tidy(()=>{let landmarks=tf4.add(tf4.div(rawPalmLandmarks.reshape([-1,7,2]),this.inputSizeTensor),this.anchors[index]);return tf4.mul(landmarks,this.inputSizeTensor)})}async getBoxes(input,config2){let batched=this.model.predict(input),predictions=batched.squeeze();batched.dispose();let scoresT=tf4.tidy(()=>tf4.sigmoid(tf4.slice(predictions,[0,0],[-1,1])).squeeze()),scores=scoresT.dataSync(),rawBoxes=tf4.slice(predictions,[0,1],[-1,4]),boxes=this.normalizeBoxes(rawBoxes);rawBoxes.dispose();let filteredT=await tf4.image.nonMaxSuppressionAsync(boxes,scores,config2.hand.maxHands,config2.hand.iouThreshold,config2.hand.scoreThreshold),filtered=filteredT.arraySync();scoresT.dispose(),filteredT.dispose();let hands=[];for(let index of filtered)if(scores[index]>=config2.hand.minConfidence){let matchingBox=tf4.slice(boxes,[index,0],[1,-1]),rawPalmLandmarks=tf4.slice(predictions,[index,5],[1,14]),palmLandmarks=tf4.tidy(()=>this.normalizeLandmarks(rawPalmLandmarks,index).reshape([-1,2]));rawPalmLandmarks.dispose(),hands.push({box:matchingBox,palmLandmarks,confidence:scores[index]})}return predictions.dispose(),boxes.dispose(),hands}async estimateHandBounds(input,config2){let inputHeight=input.shape[1],inputWidth=input.shape[2],image4=tf4.tidy(()=>input.resizeBilinear([config2.hand.inputSize,config2.hand.inputSize]).div(127.5).sub(1)),predictions=await this.getBoxes(image4,config2);image4.dispose();let hands=[];if(!predictions||predictions.length===0)return hands;for(let prediction of predictions){let boxes=prediction.box.dataSync(),startPoint=boxes.slice(0,2),endPoint=boxes.slice(2,4),palmLandmarks=prediction.palmLandmarks.arraySync();prediction.box.dispose(),prediction.palmLandmarks.dispose(),hands.push(scaleBoxCoordinates({startPoint,endPoint,palmLandmarks,confidence:prediction.confidence},[inputWidth/config2.hand.inputSize,inputHeight/config2.hand.inputSize]))}return hands}};exports2.HandDetector=HandDetector});var require_handpipeline=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm()),PALM_BOX_ENLARGE_FACTOR=5,HAND_BOX_ENLARGE_FACTOR=1.65,PALM_LANDMARK_IDS=[0,5,9,13,17,1,2],PALM_LANDMARKS_INDEX_OF_PALM_BASE=0,PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE=2,HandPipeline=class{constructor(handDetector,landmarkDetector,inputSize){this.handDetector=handDetector,this.landmarkDetector=landmarkDetector,this.inputSize=inputSize,this.storedBoxes=[],this.skipped=0,this.detectedHands=0}getBoxForPalmLandmarks(palmLandmarks,rotationMatrix){let rotatedPalmLandmarks=palmLandmarks.map(coord=>rotatePoint([...coord,1],rotationMatrix)),boxAroundPalm=this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);return enlargeBox(squarifyBox(boxAroundPalm),PALM_BOX_ENLARGE_FACTOR)}getBoxForHandLandmarks(landmarks){let boundingBox=this.calculateLandmarksBoundingBox(landmarks),boxAroundHand=enlargeBox(squarifyBox(boundingBox),HAND_BOX_ENLARGE_FACTOR);boxAroundHand.palmLandmarks=[];for(let i=0;i[scaleFactor[0]*(coord[0]-this.inputSize/2),scaleFactor[1]*(coord[1]-this.inputSize/2),scaleFactor[2]*coord[2]]),coordsRotationMatrix=buildRotationMatrix(angle,[0,0]),coordsRotated=coordsScaled.map(coord=>[...rotatePoint(coord,coordsRotationMatrix),coord[2]]),inverseRotationMatrix=invertTransformMatrix(rotationMatrix),boxCenter=[...getBoxCenter(box2),1],originalBoxCenter=[dot(boxCenter,inverseRotationMatrix[0]),dot(boxCenter,inverseRotationMatrix[1])];return coordsRotated.map(coord=>[coord[0]+originalBoxCenter[0],coord[1]+originalBoxCenter[1],coord[2]])}async estimateHands(image4,config2){let useFreshBox=!1,boxes;(this.skipped===0||this.skipped>config2.hand.skipFrames||!config2.hand.landmarks||!config2.videoOptimized)&&(boxes=await this.handDetector.estimateHandBounds(image4,config2),this.skipped=0),config2.videoOptimized&&this.skipped++,boxes&&boxes.length>0&&(boxes.length!==this.detectedHands&&this.detectedHands!==config2.hand.maxHands||!config2.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...boxes],this.storedBoxes.length>0&&(useFreshBox=!0));let hands=[];for(let i=0;i=config2.hand.minConfidence){let keypointsReshaped=tf4.reshape(keypoints,[-1,3]),rawCoords=keypointsReshaped.arraySync();keypoints.dispose(),keypointsReshaped.dispose();let coords=this.transformRawCoords(rawCoords,newBox,angle,rotationMatrix),nextBoundingBox=this.getBoxForHandLandmarks(coords);this.storedBoxes[i]=nextBoundingBox;let result={landmarks:coords,confidence,box:{topLeft:nextBoundingBox.startPoint,bottomRight:nextBoundingBox.endPoint}};hands.push(result)}else this.storedBoxes[i]=null;keypoints.dispose()}else{let enlarged=enlargeBox(squarifyBox(currentBox),HAND_BOX_ENLARGE_FACTOR),result={confidence:currentBox.confidence,box:{topLeft:enlarged.startPoint,bottomRight:enlarged.endPoint}};hands.push(result)}}return this.storedBoxes=this.storedBoxes.filter(a=>a!==null),this.detectedHands=hands.length,hands}calculateLandmarksBoundingBox(landmarks){let xs=landmarks.map(d=>d[0]),ys=landmarks.map(d=>d[1]),startPoint=[Math.min(...xs),Math.min(...ys)],endPoint=[Math.max(...xs),Math.max(...ys)];return{startPoint,endPoint}}};exports2.HandPipeline=HandPipeline});var 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HandPose(handPipeline);return config2.hand.enabled&&log(`load model: ${config2.hand.detector.modelPath.match(/\/(.*)\./)[1]}`),config2.hand.landmarks&&log(`load model: ${config2.hand.skeleton.modelPath.match(/\/(.*)\./)[1]}`),handPose}exports2.load=load2});var require_gesture=__commonJS(exports2=>{exports2.body=res=>{if(!res)return[];let gestures=[];for(let i=0;ia.part==="leftWrist"),rightWrist=res[i].keypoints.find(a=>a.part==="rightWrist"),nose=res[i].keypoints.find(a=>a.part==="nose");nose&&leftWrist&&rightWrist&&leftWrist.position.ya.part==="leftShoulder"),rightShoulder=res[i].keypoints.find(a=>a.part==="rightShoulder");leftShoulder&&rightShoulder&&gestures.push({body:i,gesture:`leaning ${leftShoulder.position.y>rightShoulder.position.y?"left":"right"}`})}return gestures};exports2.face=res=>{if(!res)return[];let gestures=[];for(let i=0;i0){let eyeFacing=res[i].mesh[35][2]-res[i].mesh[263][2];Math.abs(eyeFacing)<10?gestures.push({face:i,gesture:"facing camera"}):gestures.push({face:i,gesture:`facing ${eyeFacing<0?"right":"left"}`}),Math.abs(res[i].mesh[374][1]-res[i].mesh[386][1])/Math.abs(res[i].mesh[443][1]-res[i].mesh[450][1])<.2&&gestures.push({face:i,gesture:"blink left eye"}),Math.abs(res[i].mesh[145][1]-res[i].mesh[159][1])/Math.abs(res[i].mesh[223][1]-res[i].mesh[230][1])<.2&&gestures.push({face:i,gesture:"blink right eye"});let mouthOpen=Math.min(100,500*Math.abs(res[i].mesh[13][1]-res[i].mesh[14][1])/Math.abs(res[i].mesh[10][1]-res[i].mesh[152][1]));mouthOpen>10&&gestures.push({face:i,gesture:`mouth ${Math.trunc(mouthOpen)}% open`});let chinDepth=res[i].mesh[152][2];Math.abs(chinDepth)>10&&gestures.push({face:i,gesture:`head ${chinDepth<0?"up":"down"}`})}return gestures};exports2.iris=res=>{if(!res)return[];let gestures=[];for(let i=0;i{if(!res)return[];let gestures=[];for(let i=0;i0){let closest=fingers.reduce((best,a)=>best.position[2]best.position[1]{var WebGLProgram=function(gl,vertexSource,fragmentSource){let _collect=function(source,prefix,collection){let r=new RegExp("\\b"+prefix+" \\w+ (\\w+)","ig");source.replace(r,(match,name)=>(collection[name]=0,match))},_compile=function(source,type){let shader=gl.createShader(type);if(gl.shaderSource(shader,source),gl.compileShader(shader),!gl.getShaderParameter(shader,gl.COMPILE_STATUS))throw new Error("Filter: GL compile failed",gl.getShaderInfoLog(shader));return shader};this.uniform={},this.attribute={};let _vsh=_compile(vertexSource,gl.VERTEX_SHADER),_fsh=_compile(fragmentSource,gl.FRAGMENT_SHADER);if(this.id=gl.createProgram(),gl.attachShader(this.id,_vsh),gl.attachShader(this.id,_fsh),gl.linkProgram(this.id),!gl.getProgramParameter(this.id,gl.LINK_STATUS))throw new Error("Filter: GL link failed",gl.getProgramInfoLog(this.id));gl.useProgram(this.id),_collect(vertexSource,"attribute",this.attribute);for(let a in this.attribute)this.attribute[a]=gl.getAttribLocation(this.id,a);_collect(vertexSource,"uniform",this.uniform),_collect(fragmentSource,"uniform",this.uniform);for(let u in this.uniform)this.uniform[u]=gl.getUniformLocation(this.id,u)},WebGLImageFilter=function(params){params||(params={});let _drawCount=0,_sourceTexture=null,_lastInChain=!1,_currentFramebufferIndex=-1,_tempFramebuffers=[null,null],_filterChain=[],_width=-1,_height=-1,_vertexBuffer=null,_currentProgram=null,_canvas=params.canvas||document.createElement("canvas"),_shaderProgramCache={},gl=_canvas.getContext("webgl");if(!gl)throw new Error("Filter: getContext() failed");this.addFilter=function(name){let args=Array.prototype.slice.call(arguments,1),filter=_filter[name];_filterChain.push({func:filter,args})},this.reset=function(){_filterChain=[]},this.apply=function(image3){if(_resize(image3.width,image3.height),_drawCount=0,_sourceTexture||(_sourceTexture=gl.createTexture()),gl.bindTexture(gl.TEXTURE_2D,_sourceTexture),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_WRAP_S,gl.CLAMP_TO_EDGE),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_WRAP_T,gl.CLAMP_TO_EDGE),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_MIN_FILTER,gl.NEAREST),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_MAG_FILTER,gl.NEAREST),gl.texImage2D(gl.TEXTURE_2D,0,gl.RGBA,gl.RGBA,gl.UNSIGNED_BYTE,image3),_filterChain.length===0)return _draw(),_canvas;for(let i=0;i<_filterChain.length;i++){_lastInChain=i===_filterChain.length-1;let f=_filterChain[i];f.func.apply(this,f.args||[])}return _canvas};let _resize=function(width,height){if(!(width===_width&&height===_height)){if(_canvas.width=width,_width=width,_canvas.height=height,_height=height,!_vertexBuffer){let vertices=new Float32Array([-1,-1,0,1,1,-1,1,1,-1,1,0,0,-1,1,0,0,1,-1,1,1,1,1,1,0]);_vertexBuffer=gl.createBuffer(),gl.bindBuffer(gl.ARRAY_BUFFER,_vertexBuffer),gl.bufferData(gl.ARRAY_BUFFER,vertices,gl.STATIC_DRAW),gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL,!0)}gl.viewport(0,0,_width,_height),_tempFramebuffers=[null,null]}},_getTempFramebuffer=function(index){return _tempFramebuffers[index]=_tempFramebuffers[index]||_createFramebufferTexture(_width,_height),_tempFramebuffers[index]},_createFramebufferTexture=function(width,height){let fbo=gl.createFramebuffer();gl.bindFramebuffer(gl.FRAMEBUFFER,fbo);let renderbuffer=gl.createRenderbuffer();gl.bindRenderbuffer(gl.RENDERBUFFER,renderbuffer);let texture=gl.createTexture();return gl.bindTexture(gl.TEXTURE_2D,texture),gl.texImage2D(gl.TEXTURE_2D,0,gl.RGBA,width,height,0,gl.RGBA,gl.UNSIGNED_BYTE,null),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_MAG_FILTER,gl.LINEAR),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_MIN_FILTER,gl.LINEAR),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_WRAP_S,gl.CLAMP_TO_EDGE),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_WRAP_T,gl.CLAMP_TO_EDGE),gl.framebufferTexture2D(gl.FRAMEBUFFER,gl.COLOR_ATTACHMENT0,gl.TEXTURE_2D,texture,0),gl.bindTexture(gl.TEXTURE_2D,null),gl.bindFramebuffer(gl.FRAMEBUFFER,null),{fbo,texture}},_draw=function(flags){var _a,_b;let source=null,target=null,flipY=!1;_drawCount===0?source=_sourceTexture:source=(_a=_getTempFramebuffer(_currentFramebufferIndex))==null?void 0:_a.texture,_drawCount++,_lastInChain&&!(flags&DRAW.INTERMEDIATE)?(target=null,flipY=_drawCount%2==0):(_currentFramebufferIndex=(_currentFramebufferIndex+1)%2,target=(_b=_getTempFramebuffer(_currentFramebufferIndex))==null?void 0:_b.fbo),gl.bindTexture(gl.TEXTURE_2D,source),gl.bindFramebuffer(gl.FRAMEBUFFER,target),gl.uniform1f(_currentProgram.uniform.flipY,flipY?-1:1),gl.drawArrays(gl.TRIANGLES,0,6)},_compileShader=function(fragmentSource){if(_shaderProgramCache[fragmentSource])return _currentProgram=_shaderProgramCache[fragmentSource],gl.useProgram(_currentProgram.id),_currentProgram;_currentProgram=new WebGLProgram(gl,SHADER.VERTEX_IDENTITY,fragmentSource);let floatSize=Float32Array.BYTES_PER_ELEMENT,vertSize=4*floatSize;return gl.enableVertexAttribArray(_currentProgram.attribute.pos),gl.vertexAttribPointer(_currentProgram.attribute.pos,2,gl.FLOAT,!1,vertSize,0*floatSize),gl.enableVertexAttribArray(_currentProgram.attribute.uv),gl.vertexAttribPointer(_currentProgram.attribute.uv,2,gl.FLOAT,!1,vertSize,2*floatSize),_shaderProgramCache[fragmentSource]=_currentProgram,_currentProgram},DRAW={INTERMEDIATE:1},SHADER={};SHADER.VERTEX_IDENTITY=["precision highp float;","attribute vec2 pos;","attribute vec2 uv;","varying vec2 vUv;","uniform float flipY;","void main(void) {","vUv = uv;","gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);","}"].join(` +var __create=Object.create,__defProp=Object.defineProperty,__getProtoOf=Object.getPrototypeOf,__hasOwnProp=Object.prototype.hasOwnProperty,__getOwnPropNames=Object.getOwnPropertyNames,__getOwnPropDesc=Object.getOwnPropertyDescriptor;var __markAsModule=target=>__defProp(target,"__esModule",{value:!0});var __commonJS=(callback,module2)=>()=>(module2||(module2={exports:{}},callback(module2.exports,module2)),module2.exports),__export=(target,all)=>{__markAsModule(target);for(var name in all)__defProp(target,name,{get:all[name],enumerable:!0})},__exportStar=(target,module2,desc)=>{if(__markAsModule(target),module2&&typeof module2=="object"||typeof module2=="function")for(let key of __getOwnPropNames(module2))!__hasOwnProp.call(target,key)&&key!=="default"&&__defProp(target,key,{get:()=>module2[key],enumerable:!(desc=__getOwnPropDesc(module2,key))||desc.enumerable});return target},__toModule=module2=>module2&&module2.__esModule?module2:__exportStar(__defProp(module2!=null?__create(__getProtoOf(module2)):{},"default",{value:module2,enumerable:!0}),module2);var require_tfjs_esm=__commonJS(exports2=>{var __create2=Object.create,__defProp2=Object.defineProperty,__getProtoOf2=Object.getPrototypeOf,__hasOwnProp2=Object.prototype.hasOwnProperty,__getOwnPropNames2=Object.getOwnPropertyNames,__getOwnPropDesc2=Object.getOwnPropertyDescriptor,__markAsModule2=target=>__defProp2(target,"__esModule",{value:!0}),__exportStar2=(target,module22,desc)=>{if(__markAsModule2(target),module22&&typeof module22=="object"||typeof module22=="function")for(let key of __getOwnPropNames2(module22))!__hasOwnProp2.call(target,key)&&key!=="default"&&__defProp2(target,key,{get:()=>module22[key],enumerable:!(desc=__getOwnPropDesc2(module22,key))||desc.enumerable});return target},__toModule2=module22=>module22&&module22.__esModule?module22:__exportStar2(__defProp2(module22!=null?__create2(__getProtoOf2(module22)):{},"default",{value:module22,enumerable:!0}),module22);__exportStar2(exports2,__toModule2(require("@tensorflow/tfjs-node")))});var require_blazeface=__commonJS(exports2=>{var tf5=__toModule(require_tfjs_esm()),NUM_LANDMARKS=6;function generateAnchors(inputSize){let spec={strides:[inputSize/16,inputSize/8],anchors:[2,6]},anchors=[];for(let i=0;i{box.startEndTensor.dispose(),box.startPoint.dispose(),box.endPoint.dispose()},createBox=startEndTensor=>({startEndTensor,startPoint:tf5.slice(startEndTensor,[0,0],[-1,2]),endPoint:tf5.slice(startEndTensor,[0,2],[-1,2])}),scaleBox=(box,factors)=>{let starts=tf5.mul(box.startPoint,factors),ends=tf5.mul(box.endPoint,factors),newCoordinates=tf5.concat2d([starts,ends],1);return createBox(newCoordinates)};function decodeBounds(boxOutputs,anchors,inputSize){let boxStarts=tf5.slice(boxOutputs,[0,1],[-1,2]),centers=tf5.add(boxStarts,anchors),boxSizes=tf5.slice(boxOutputs,[0,3],[-1,2]),boxSizesNormalized=tf5.div(boxSizes,inputSize),centersNormalized=tf5.div(centers,inputSize),halfBoxSize=tf5.div(boxSizesNormalized,2),starts=tf5.sub(centersNormalized,halfBoxSize),ends=tf5.add(centersNormalized,halfBoxSize),startNormalized=tf5.mul(starts,inputSize),endNormalized=tf5.mul(ends,inputSize);return tf5.concat2d([startNormalized,endNormalized],1)}function scaleBoxFromPrediction(face3,scaleFactor){return tf5.tidy(()=>{let box=face3.box?face3.box:face3;return scaleBox(box,scaleFactor).startEndTensor.squeeze()})}var BlazeFaceModel=class{constructor(model,config2){this.blazeFaceModel=model,this.width=config2.face.detector.inputSize,this.height=config2.face.detector.inputSize,this.anchorsData=generateAnchors(config2.face.detector.inputSize),this.anchors=tf5.tensor2d(this.anchorsData),this.inputSize=tf5.tensor1d([this.width,this.height]),this.config=config2,this.scaleFaces=.8}async getBoundingBoxes(inputImage){if(!inputImage||inputImage.isDisposedInternal||inputImage.shape.length!==4||inputImage.shape[1]<1||inputImage.shape[2]<1)return null;let[detectedOutputs,boxes,scores]=tf5.tidy(()=>{let resizedImage=inputImage.resizeBilinear([this.width,this.height]),normalizedImage=tf5.sub(resizedImage.div(127.5),1),batchedPrediction=this.blazeFaceModel.predict(normalizedImage),prediction;if(Array.isArray(batchedPrediction)){let sorted=batchedPrediction.sort((a,b)=>a.size-b.size),concat384=tf5.concat([sorted[0],sorted[2]],2),concat512=tf5.concat([sorted[1],sorted[3]],2);prediction=tf5.concat([concat512,concat384],1).squeeze(0)}else prediction=batchedPrediction.squeeze();let decodedBounds=decodeBounds(prediction,this.anchors,this.inputSize),logits=tf5.slice(prediction,[0,0],[-1,1]),scoresOut=tf5.sigmoid(logits).squeeze();return[prediction,decodedBounds,scoresOut]}),boxIndicesTensor=await tf5.image.nonMaxSuppressionAsync(boxes,scores,this.config.face.detector.maxFaces,this.config.face.detector.iouThreshold,this.config.face.detector.scoreThreshold),boxIndices=boxIndicesTensor.arraySync();boxIndicesTensor.dispose();let boundingBoxes=boxIndices.map(boxIndex=>tf5.slice(boxes,[boxIndex,0],[1,-1])).map(boundingBox=>{let vals=boundingBox.arraySync();return boundingBox.dispose(),vals}),scoresVal=scores.dataSync(),annotatedBoxes=[];for(let i=0;ithis.config.face.detector.minConfidence){let box=createBox(boundingBoxes[i]),anchor=this.anchorsData[boxIndex],landmarks=tf5.tidy(()=>tf5.slice(detectedOutputs,[boxIndex,NUM_LANDMARKS-1],[1,-1]).squeeze().reshape([NUM_LANDMARKS,-1]));annotatedBoxes.push({box,landmarks,anchor,confidence})}}return detectedOutputs.dispose(),boxes.dispose(),scores.dispose(),detectedOutputs.dispose(),{boxes:annotatedBoxes,scaleFactor:[inputImage.shape[2]/this.width,inputImage.shape[1]/this.height]}}async estimateFaces(input){let{boxes,scaleFactor}=await this.getBoundingBoxes(input),faces=[];for(let face3 of boxes){let landmarkData=face3.landmarks.arraySync(),scaledBox=scaleBoxFromPrediction(face3,scaleFactor),boxData=scaleBox.arraySync(),probabilityData=face3.probability.arraySync(),anchor=face3.anchor,[scaleFactorX,scaleFactorY]=scaleFactor,scaledLandmarks=landmarkData.map(landmark=>[(landmark[0]+anchor[0])*scaleFactorX,(landmark[1]+anchor[1])*scaleFactorY]),normalizedFace={topLeft:boxData.slice(0,2),bottomRight:boxData.slice(2),landmarks:scaledLandmarks,probability:probabilityData};disposeBox(face3.box),face3.landmarks.dispose(),face3.probability.dispose(),scaledBox.dispose(),faces.push(normalizedFace)}return faces}};async function load3(config2){let blazeface=await tf5.loadGraphModel(config2.face.detector.modelPath,{fromTFHub:config2.face.detector.modelPath.includes("tfhub.dev")}),model=new BlazeFaceModel(blazeface,config2);return log(`load model: ${config2.face.detector.modelPath.match(/\/(.*)\./)[1]}`),model}exports2.load=load3;exports2.BlazeFaceModel=BlazeFaceModel;exports2.disposeBox=disposeBox});var require_box=__commonJS(exports2=>{var tf5=__toModule(require_tfjs_esm());function scaleBoxCoordinates2(box,factor){let startPoint=[box.startPoint[0]*factor[0],box.startPoint[1]*factor[1]],endPoint=[box.endPoint[0]*factor[0],box.endPoint[1]*factor[1]];return{startPoint,endPoint}}exports2.scaleBoxCoordinates=scaleBoxCoordinates2;function getBoxSize2(box){return[Math.abs(box.endPoint[0]-box.startPoint[0]),Math.abs(box.endPoint[1]-box.startPoint[1])]}exports2.getBoxSize=getBoxSize2;function getBoxCenter2(box){return[box.startPoint[0]+(box.endPoint[0]-box.startPoint[0])/2,box.startPoint[1]+(box.endPoint[1]-box.startPoint[1])/2]}exports2.getBoxCenter=getBoxCenter2;function cutBoxFromImageAndResize2(box,image5,cropSize){let h=image5.shape[1],w=image5.shape[2],boxes=[[box.startPoint[1]/h,box.startPoint[0]/w,box.endPoint[1]/h,box.endPoint[0]/w]];return tf5.image.cropAndResize(image5,boxes,[0],cropSize)}exports2.cutBoxFromImageAndResize=cutBoxFromImageAndResize2;function enlargeBox2(box,factor=1.5){let center=getBoxCenter2(box),size=getBoxSize2(box),newHalfSize=[factor*size[0]/2,factor*size[1]/2],startPoint=[center[0]-newHalfSize[0],center[1]-newHalfSize[1]],endPoint=[center[0]+newHalfSize[0],center[1]+newHalfSize[1]];return{startPoint,endPoint,landmarks:box.landmarks}}exports2.enlargeBox=enlargeBox2;function squarifyBox2(box){let centers=getBoxCenter2(box),size=getBoxSize2(box),halfSize=Math.max(...size)/2,startPoint=[centers[0]-halfSize,centers[1]-halfSize],endPoint=[centers[0]+halfSize,centers[1]+halfSize];return{startPoint,endPoint,landmarks:box.landmarks}}exports2.squarifyBox=squarifyBox2});var require_util=__commonJS(exports2=>{exports2.IDENTITY_MATRIX=[[1,0,0],[0,1,0],[0,0,1]];function normalizeRadians2(angle){return angle-2*Math.PI*Math.floor((angle+Math.PI)/(2*Math.PI))}exports2.normalizeRadians=normalizeRadians2;function computeRotation2(point1,point2){let radians=Math.PI/2-Math.atan2(-(point2[1]-point1[1]),point2[0]-point1[0]);return normalizeRadians2(radians)}exports2.computeRotation=computeRotation2;function radToDegrees(rad){return rad*180/Math.PI}exports2.radToDegrees=radToDegrees;function buildTranslationMatrix2(x,y){return[[1,0,x],[0,1,y],[0,0,1]]}function dot2(v1,v2){let product=0;for(let i=0;i{var 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,378,379,397,288,361,454,356,70,63,105,66,107,336,296,334,293,300,168,6,195,4,98,97,2,326,327,33,160,158,133,153,144,362,385,387,263,373,380,57,40,37,0,267,270,287,321,314,17,84,91,78,81,13,311,308,402,14,178],VTX33=[33,133,362,263,1,62,308,159,145,386,374,6,102,331,2,13,14,70,105,107,336,334,300,54,10,284,50,280,234,454,58,288,152],VTX7=[33,133,362,263,1,78,308];exports2.MESH_ANNOTATIONS=MESH_ANNOTATIONS;exports2.MESH_TO_IRIS_INDICES_MAP=MESH_TO_IRIS_INDICES_MAP;exports2.TRI468=TRI468;exports2.TRI68=TRI68;exports2.TRI33=TRI33;exports2.TRI7=TRI7;exports2.UV468=UV468;exports2.UV68=VTX68.map(x=>UV468[x]);exports2.UV33=VTX33.map(x=>UV468[x]);exports2.UV7=VTX7.map(x=>UV468[x])});var require_facepipeline=__commonJS(exports2=>{var tf5=__toModule(require_tfjs_esm()),bounding=__toModule(require_box()),util=__toModule(require_util()),coords=__toModule(require_coords()),LANDMARKS_COUNT=468,MESH_MOUTH_INDEX=13,MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES=[MESH_MOUTH_INDEX,coords.MESH_ANNOTATIONS.midwayBetweenEyes[0]],BLAZEFACE_MOUTH_INDEX=3,BLAZEFACE_NOSE_INDEX=2,BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES=[BLAZEFACE_MOUTH_INDEX,BLAZEFACE_NOSE_INDEX],LEFT_EYE_OUTLINE=coords.MESH_ANNOTATIONS.leftEyeLower0,LEFT_EYE_BOUNDS=[LEFT_EYE_OUTLINE[0],LEFT_EYE_OUTLINE[LEFT_EYE_OUTLINE.length-1]],RIGHT_EYE_OUTLINE=coords.MESH_ANNOTATIONS.rightEyeLower0,RIGHT_EYE_BOUNDS=[RIGHT_EYE_OUTLINE[0],RIGHT_EYE_OUTLINE[RIGHT_EYE_OUTLINE.length-1]],IRIS_UPPER_CENTER_INDEX=3,IRIS_LOWER_CENTER_INDEX=4,IRIS_IRIS_INDEX=71,IRIS_NUM_COORDINATES=76;function replaceRawCoordinates(rawCoords,newCoords,prefix,keys){for(let i=0;i[scaleFactor[0]*(coord[0]-this.meshWidth/2),scaleFactor[1]*(coord[1]-this.meshHeight/2),coord[2]]),coordsRotationMatrix=angle!==0?util.buildRotationMatrix(angle,[0,0]):util.IDENTITY_MATRIX,coordsRotated=angle!==0?coordsScaled.map(coord=>[...util.rotatePoint(coord,coordsRotationMatrix),coord[2]]):coordsScaled,inverseRotationMatrix=angle!==0?util.invertTransformMatrix(rotationMatrix):util.IDENTITY_MATRIX,boxCenter=[...bounding.getBoxCenter({startPoint:box.startPoint,endPoint:box.endPoint}),1];return coordsRotated.map(coord=>[coord[0]+util.dot(boxCenter,inverseRotationMatrix[0]),coord[1]+util.dot(boxCenter,inverseRotationMatrix[1]),coord[2]])}getLeftToRightEyeDepthDifference(rawCoords){let leftEyeZ=rawCoords[LEFT_EYE_BOUNDS[0]][2],rightEyeZ=rawCoords[RIGHT_EYE_BOUNDS[0]][2];return leftEyeZ-rightEyeZ}getEyeBox(rawCoords,face3,eyeInnerCornerIndex,eyeOuterCornerIndex,flip=!1){let box=bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex],rawCoords[eyeOuterCornerIndex]]),this.irisEnlarge)),boxSize=bounding.getBoxSize(box),crop=tf5.image.cropAndResize(face3,[[box.startPoint[1]/this.meshHeight,box.startPoint[0]/this.meshWidth,box.endPoint[1]/this.meshHeight,box.endPoint[0]/this.meshWidth]],[0],[this.irisSize,this.irisSize]);return flip&&(crop=tf5.image.flipLeftRight(crop)),{box,boxSize,crop}}getEyeCoords(eyeData,eyeBox,eyeBoxSize,flip=!1){let eyeRawCoords=[];for(let i=0;i{let z=averageZ;return i===2?z=upperCenterZ:i===4&&(z=lowerCenterZ),[coord[0],coord[1],z]})}async predict(input,config2){let useFreshBox=!1,detector;if((this.skipped===0||this.skipped>config2.face.detector.skipFrames||!config2.face.mesh.enabled||!config2.videoOptimized)&&(detector=await this.boundingBoxDetector.getBoundingBoxes(input),this.skipped=0),config2.videoOptimized&&this.skipped++,detector&&detector.boxes&&detector.boxes.length>0&&(!config2.face.mesh.enabled||detector.boxes.length!==this.detectedFaces&&this.detectedFaces!==config2.face.detector.maxFaces)){this.storedBoxes=[],this.detectedFaces=0;for(let possible of detector.boxes)this.storedBoxes.push({startPoint:possible.box.startPoint.dataSync(),endPoint:possible.box.endPoint.dataSync(),landmarks:possible.landmarks,confidence:possible.confidence});this.storedBoxes.length>0&&(useFreshBox=!0)}if(useFreshBox){if(!detector||!detector.boxes||detector.boxes.length===0)return this.storedBoxes=[],this.detectedFaces=0,null;for(let i=0;i{prediction.box.startPoint.dispose(),prediction.box.endPoint.dispose(),prediction.landmarks.dispose()});let results=tf5.tidy(()=>this.storedBoxes.map((box,i)=>{let face3,angle=0,rotationMatrix;if(config2.face.detector.rotation){let[indexOfMouth,indexOfForehead]=box.landmarks.length>=LANDMARKS_COUNT?MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES:BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES;angle=util.computeRotation(box.landmarks[indexOfMouth],box.landmarks[indexOfForehead]);let faceCenter=bounding.getBoxCenter({startPoint:box.startPoint,endPoint:box.endPoint}),faceCenterNormalized=[faceCenter[0]/input.shape[2],faceCenter[1]/input.shape[1]],rotatedImage=tf5.image.rotateWithOffset(input,angle,0,faceCenterNormalized);rotationMatrix=util.buildRotationMatrix(-angle,faceCenter),face3=bounding.cutBoxFromImageAndResize({startPoint:box.startPoint,endPoint:box.endPoint},rotatedImage,[this.meshHeight,this.meshWidth]).div(255)}else{rotationMatrix=util.IDENTITY_MATRIX;let cloned=input.clone();face3=bounding.cutBoxFromImageAndResize({startPoint:box.startPoint,endPoint:box.endPoint},cloned,[this.meshHeight,this.meshWidth]).div(255)}if(!config2.face.mesh.enabled)return{coords:null,box,faceConfidence:null,confidence:box.confidence,image:face3};let[,confidence,contourCoords]=this.meshDetector.predict(face3),confidenceVal=confidence.dataSync()[0];if(confidenceVala!==null),this.detectedFaces=results.length,results}calculateLandmarksBoundingBox(landmarks){let xs=landmarks.map(d=>d[0]),ys=landmarks.map(d=>d[1]),startPoint=[Math.min(...xs),Math.min(...ys)],endPoint=[Math.max(...xs),Math.max(...ys)];return{startPoint,endPoint,landmarks}}};exports2.Pipeline=Pipeline});var require_facemesh=__commonJS(exports2=>{var tf5=__toModule(require_tfjs_esm()),blazeface=__toModule(require_blazeface()),facepipeline=__toModule(require_facepipeline()),coords=__toModule(require_coords()),MediaPipeFaceMesh=class{constructor(blazeFace,blazeMeshModel,irisModel,config2){this.facePipeline=new facepipeline.Pipeline(blazeFace,blazeMeshModel,irisModel,config2),this.config=config2}async estimateFaces(input,config2){let predictions=await this.facePipeline.predict(input,config2),results=[];for(let prediction of predictions||[]){if(prediction.isDisposedInternal)continue;let mesh=prediction.coords?prediction.coords.arraySync():null,meshRaw=prediction.rawCoords,annotations={};if(mesh&&mesh.length>0)for(let key of Object.keys(coords.MESH_ANNOTATIONS))annotations[key]=coords.MESH_ANNOTATIONS[key].map(index=>mesh[index]);let boxRaw=config2.face.mesh.returnRawData&&prediction.box?{topLeft:prediction.box.startPoint,bottomRight:prediction.box.endPoint}:null,box=prediction.box?[Math.max(0,prediction.box.startPoint[0]),Math.max(0,prediction.box.startPoint[1]),Math.min(input.shape[2],prediction.box.endPoint[0])-prediction.box.startPoint[0],Math.min(input.shape[1],prediction.box.endPoint[1])-prediction.box.startPoint[1]]:0;results.push({confidence:prediction.confidence||0,box,mesh,boxRaw,meshRaw,annotations,image:prediction.image?tf5.clone(prediction.image):null}),prediction.coords&&prediction.coords.dispose(),prediction.image&&prediction.image.dispose()}return results}},faceModels=[null,null,null];async function load3(config2){faceModels=await Promise.all([!faceModels[0]&&config2.face.enabled?blazeface.load(config2):null,!faceModels[1]&&config2.face.mesh.enabled?tf5.loadGraphModel(config2.face.mesh.modelPath,{fromTFHub:config2.face.mesh.modelPath.includes("tfhub.dev")}):null,!faceModels[2]&&config2.face.iris.enabled?tf5.loadGraphModel(config2.face.iris.modelPath,{fromTFHub:config2.face.iris.modelPath.includes("tfhub.dev")}):null]);let faceMesh=new MediaPipeFaceMesh(faceModels[0],faceModels[1],faceModels[2],config2);return config2.face.mesh.enabled&&log(`load model: ${config2.face.mesh.modelPath.match(/\/(.*)\./)[1]}`),config2.face.iris.enabled&&log(`load model: ${config2.face.iris.modelPath.match(/\/(.*)\./)[1]}`),faceMesh}exports2.load=load3;exports2.MediaPipeFaceMesh=MediaPipeFaceMesh;exports2.triangulation=coords.TRI468});var require_profile=__commonJS(exports2=>{var profileData={};function profile2(name,data2){if(!data2||!data2.kernels)return;let maxResults=5,time=data2.kernels.filter(a=>a.kernelTimeMs>0).reduce((a,b)=>a+=b.kernelTimeMs,0),slowest=data2.kernels.map((a,i)=>(a.id=i,a)).filter(a=>a.kernelTimeMs>0).sort((a,b)=>b.kernelTimeMs-a.kernelTimeMs),largest=data2.kernels.map((a,i)=>(a.id=i,a)).filter(a=>a.totalBytesSnapshot>0).sort((a,b)=>b.totalBytesSnapshot-a.totalBytesSnapshot);slowest.length>maxResults&&(slowest.length=maxResults),largest.length>maxResults&&(largest.length=maxResults);let res={newBytes:data2.newBytes,newTensors:data2.newTensors,peakBytes:data2.peakBytes,numKernelOps:data2.kernels.length,timeKernelOps:time,slowestKernelOps:slowest,largestKernelOps:largest};profileData[name]=res,log("Human profiler",name,res)}exports2.run=profile2});var require_age=__commonJS(exports2=>{var tf5=__toModule(require_tfjs_esm()),profile2=__toModule(require_profile()),models2={},last={age:0},skipped=Number.MAX_SAFE_INTEGER;async function load3(config2){return models2.age||(models2.age=await tf5.loadGraphModel(config2.face.age.modelPath),log(`load model: ${config2.face.age.modelPath.match(/\/(.*)\./)[1]}`)),models2.age}async function predict3(image5,config2){return models2.age?skipped0?(skipped++,last):(config2.videoOptimized?skipped=0:skipped=Number.MAX_SAFE_INTEGER,new Promise(async resolve=>{let resize=tf5.image.resizeBilinear(image5,[config2.face.age.inputSize,config2.face.age.inputSize],!1),enhance=tf5.mul(resize,[255]);tf5.dispose(resize);let ageT,obj={};if(!config2.profile)config2.face.age.enabled&&(ageT=await models2.age.predict(enhance));else{let profileAge=config2.face.age.enabled?await tf5.profile(()=>models2.age.predict(enhance)):{};ageT=profileAge.result.clone(),profileAge.result.dispose(),profile2.run("age",profileAge)}if(enhance.dispose(),ageT){let data2=ageT.dataSync();obj.age=Math.trunc(10*data2[0])/10}ageT.dispose(),last=obj,resolve(obj)})):null}exports2.predict=predict3;exports2.load=load3});var require_gender=__commonJS(exports2=>{var tf5=__toModule(require_tfjs_esm()),profile2=__toModule(require_profile()),models2={},last={gender:""},skipped=Number.MAX_SAFE_INTEGER,alternative=!1,rgb=[.2989,.587,.114];async function load3(config2){return models2.gender||(models2.gender=await tf5.loadGraphModel(config2.face.gender.modelPath),alternative=models2.gender.inputs[0].shape[3]===1,log(`load model: ${config2.face.gender.modelPath.match(/\/(.*)\./)[1]}`)),models2.gender}async function predict3(image5,config2){return models2.gender?skipped{let resize=tf5.image.resizeBilinear(image5,[config2.face.gender.inputSize,config2.face.gender.inputSize],!1),enhance;alternative?enhance=tf5.tidy(()=>{let[red,green,blue]=tf5.split(resize,3,3),redNorm=tf5.mul(red,rgb[0]),greenNorm=tf5.mul(green,rgb[1]),blueNorm=tf5.mul(blue,rgb[2]);return tf5.addN([redNorm,greenNorm,blueNorm]).sub(.5).mul(2)}):enhance=tf5.mul(resize,[255]),tf5.dispose(resize);let genderT,obj={};if(!config2.profile)config2.face.gender.enabled&&(genderT=await models2.gender.predict(enhance));else{let profileGender=config2.face.gender.enabled?await tf5.profile(()=>models2.gender.predict(enhance)):{};genderT=profileGender.result.clone(),profileGender.result.dispose(),profile2.run("gender",profileGender)}if(enhance.dispose(),genderT){let data2=genderT.dataSync();if(alternative){let confidence=Math.trunc(100*Math.abs(data2[0]-data2[1]))/100;confidence>config2.face.gender.minConfidence&&(obj.gender=data2[0]>data2[1]?"female":"male",obj.confidence=confidence)}else{let confidence=Math.trunc(200*Math.abs(data2[0]-.5))/100;confidence>config2.face.gender.minConfidence&&(obj.gender=data2[0]<=.5?"female":"male",obj.confidence=Math.min(.99,confidence))}}genderT.dispose(),last=obj,resolve(obj)})):null}exports2.predict=predict3;exports2.load=load3});var require_emotion=__commonJS(exports2=>{var tf5=__toModule(require_tfjs_esm()),profile2=__toModule(require_profile()),annotations=["angry","disgust","fear","happy","sad","surprise","neutral"],models2={},last=[],skipped=Number.MAX_SAFE_INTEGER,rgb=[.2989,.587,.114],scale=1;async function load3(config2){return models2.emotion||(models2.emotion=await tf5.loadGraphModel(config2.face.emotion.modelPath),log(`load model: ${config2.face.emotion.modelPath.match(/\/(.*)\./)[1]}`)),models2.emotion}async function predict3(image5,config2){return models2.emotion?skipped0?(skipped++,last):(config2.videoOptimized?skipped=0:skipped=Number.MAX_SAFE_INTEGER,new Promise(async resolve=>{let resize=tf5.image.resizeBilinear(image5,[config2.face.emotion.inputSize,config2.face.emotion.inputSize],!1),[red,green,blue]=tf5.split(resize,3,3);resize.dispose();let redNorm=tf5.mul(red,rgb[0]),greenNorm=tf5.mul(green,rgb[1]),blueNorm=tf5.mul(blue,rgb[2]);red.dispose(),green.dispose(),blue.dispose();let grayscale=tf5.addN([redNorm,greenNorm,blueNorm]);redNorm.dispose(),greenNorm.dispose(),blueNorm.dispose();let normalize=tf5.tidy(()=>grayscale.sub(.5).mul(2));grayscale.dispose();let obj=[];if(config2.face.emotion.enabled){let data2;if(config2.profile){let profileData=await tf5.profile(()=>models2.emotion.predict(normalize));data2=profileData.result.dataSync(),profileData.result.dispose(),profile2.run("emotion",profileData)}else{let emotionT=await models2.emotion.predict(normalize);data2=emotionT.dataSync(),tf5.dispose(emotionT)}for(let i=0;iconfig2.face.emotion.minConfidence&&obj.push({score:Math.min(.99,Math.trunc(100*scale*data2[i])/100),emotion:annotations[i]});obj.sort((a,b)=>b.score-a.score)}normalize.dispose(),last=obj,resolve(obj)})):null}exports2.predict=predict3;exports2.load=load3});var require_embedding=__commonJS(exports2=>{var tf5=__toModule(require_tfjs_esm()),profile2=__toModule(require_profile()),models2={};async function load3(config2){return models2.embedding||(models2.embedding=await tf5.loadGraphModel(config2.face.embedding.modelPath),log(`load model: ${config2.face.embedding.modelPath.match(/\/(.*)\./)[1]}`)),models2.embedding}function simmilarity2(embedding1,embedding2){if((embedding1==null?void 0:embedding1.length)!==(embedding2==null?void 0:embedding2.length))return 0;let order=2,distance=10*embedding1.map((val,i)=>val-embedding2[i]).reduce((dist,diff)=>dist+diff**order,0)**(1/order);return Math.trunc(1e3*(1-distance))/1e3}async function predict3(image5,config2){return models2.embedding?new Promise(async resolve=>{let resize=tf5.image.resizeBilinear(image5,[config2.face.embedding.inputSize,config2.face.embedding.inputSize],!1),data2=[];if(config2.face.embedding.enabled)if(config2.profile){let profileData=await tf5.profile(()=>models2.embedding.predict({img_inputs:resize}));data2=[...profileData.result.dataSync()],profileData.result.dispose(),profile2.run("emotion",profileData)}else{let embeddingT=await models2.embedding.predict({img_inputs:resize});data2=[...embeddingT.dataSync()],tf5.dispose(embeddingT)}resize.dispose(),resolve(data2)}):null}exports2.predict=predict3;exports2.simmilarity=simmilarity2;exports2.load=load3});var require_modelBase=__commonJS(exports2=>{var tf5=__toModule(require_tfjs_esm()),imageNetMean=[-123.15,-115.9,-103.06];function nameOutputResultsMobileNet(results){let[offsets,heatmap,displacementFwd,displacementBwd]=results;return{offsets,heatmap,displacementFwd,displacementBwd}}function nameOutputResultsResNet(results){let[displacementFwd,displacementBwd,offsets,heatmap]=results;return{offsets,heatmap,displacementFwd,displacementBwd}}var BaseModel=class{constructor(model){this.model=model}predict(input,config2){return tf5.tidy(()=>{let asBatch=(config2.body.modelType==="ResNet"?input.toFloat().add(imageNetMean):input.toFloat().div(127.5).sub(1)).expandDims(0),results3d=this.model.predict(asBatch).map(y=>y.squeeze([0])),namedResults=config2.body.modelType==="ResNet"?nameOutputResultsResNet(results3d):nameOutputResultsMobileNet(results3d);return{heatmapScores:namedResults.heatmap.sigmoid(),offsets:namedResults.offsets,displacementFwd:namedResults.displacementFwd,displacementBwd:namedResults.displacementBwd}})}dispose(){this.model.dispose()}};exports2.BaseModel=BaseModel});var require_heapSort=__commonJS(exports2=>{function half(k){return Math.floor(k/2)}var MaxHeap=class{constructor(maxSize,getElementValue){this.priorityQueue=new Array(maxSize),this.numberOfElements=-1,this.getElementValue=getElementValue}enqueue(x){this.priorityQueue[++this.numberOfElements]=x,this.swim(this.numberOfElements)}dequeue(){let max=this.priorityQueue[0];return this.exchange(0,this.numberOfElements--),this.sink(0),this.priorityQueue[this.numberOfElements+1]=null,max}empty(){return this.numberOfElements===-1}size(){return this.numberOfElements+1}all(){return this.priorityQueue.slice(0,this.numberOfElements+1)}max(){return this.priorityQueue[0]}swim(k){for(;k>0&&this.less(half(k),k);)this.exchange(k,half(k)),k=half(k)}sink(k){for(;2*k<=this.numberOfElements;){let j=2*k;if(j{var heapSort=__toModule(require_heapSort());function scoreIsMaximumInLocalWindow(keypointId,score,heatmapY,heatmapX,localMaximumRadius,scores){let[height,width]=scores.shape,localMaximum=!0,yStart=Math.max(heatmapY-localMaximumRadius,0),yEnd=Math.min(heatmapY+localMaximumRadius+1,height);for(let yCurrent=yStart;yCurrentscore){localMaximum=!1;break}if(!localMaximum)break}return localMaximum}function buildPartWithScoreQueue(scoreThreshold,localMaximumRadius,scores){let[height,width,numKeypoints]=scores.shape,queue=new heapSort.MaxHeap(height*width*numKeypoints,({score})=>score);for(let heatmapY=0;heatmapY{exports2.partNames=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"];exports2.NUM_KEYPOINTS=exports2.partNames.length;exports2.partIds=exports2.partNames.reduce((result,jointName,i)=>(result[jointName]=i,result),{});var connectedPartNames=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]];exports2.connectedPartIndices=connectedPartNames.map(([jointNameA,jointNameB])=>[exports2.partIds[jointNameA],exports2.partIds[jointNameB]]);exports2.poseChain=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];exports2.partChannels=["left_face","right_face","right_upper_leg_front","right_lower_leg_back","right_upper_leg_back","left_lower_leg_front","left_upper_leg_front","left_upper_leg_back","left_lower_leg_back","right_feet","right_lower_leg_front","left_feet","torso_front","torso_back","right_upper_arm_front","right_upper_arm_back","right_lower_arm_back","left_lower_arm_front","left_upper_arm_front","left_upper_arm_back","left_lower_arm_back","right_hand","right_lower_arm_front","left_hand"]});var require_vectors=__commonJS(exports2=>{var kpt=__toModule(require_keypoints());function getOffsetPoint(y,x,keypoint,offsets){return{y:offsets.get(y,x,keypoint),x:offsets.get(y,x,keypoint+kpt.NUM_KEYPOINTS)}}exports2.getOffsetPoint=getOffsetPoint;function getImageCoords(part,outputStride,offsets){let{heatmapY,heatmapX,id:keypoint}=part,{y,x}=getOffsetPoint(heatmapY,heatmapX,keypoint,offsets);return{x:part.heatmapX*outputStride+x,y:part.heatmapY*outputStride+y}}exports2.getImageCoords=getImageCoords;function fillArray(element,size){let result=new Array(size);for(let i=0;imax?max:a}exports2.clamp=clamp;function squaredDistance(y1,x1,y2,x2){let dy=y2-y1,dx=x2-x1;return dy*dy+dx*dx}exports2.squaredDistance=squaredDistance;function addVectors(a,b){return{x:a.x+b.x,y:a.y+b.y}}exports2.addVectors=addVectors;function clampVector(a,min,max){return{y:clamp(a.y,min,max),x:clamp(a.x,min,max)}}exports2.clampVector=clampVector});var require_decoders=__commonJS(exports2=>{var tf5=__toModule(require_tfjs_esm()),kpt=__toModule(require_keypoints());function getPointsConfidence(heatmapScores,heatMapCoords){let numKeypoints=heatMapCoords.shape[0],result=new Float32Array(numKeypoints);for(let keypoint=0;keypointheatMapCoordsBuffer.toTensor().mul(tf5.scalar(outputStride,"int32")).toFloat().add(getOffsetVectors(heatMapCoordsBuffer,offsetsBuffer)))}exports2.getOffsetPoints=getOffsetPoints;function mod(a,b){return tf5.tidy(()=>{let floored=a.div(tf5.scalar(b,"int32"));return a.sub(floored.mul(tf5.scalar(b,"int32")))})}function argmax2d(inputs){let[height,width,depth]=inputs.shape;return tf5.tidy(()=>{let coords=inputs.reshape([height*width,depth]).argMax(0),yCoords=coords.div(tf5.scalar(width,"int32")).expandDims(1),xCoords=mod(coords,width).expandDims(1);return tf5.concat([yCoords,xCoords],1)})}exports2.argmax2d=argmax2d});var require_decodePose=__commonJS(exports2=>{var keypoints=__toModule(require_keypoints()),vectors=__toModule(require_vectors()),decoders=__toModule(require_decoders()),parentChildrenTuples=keypoints.poseChain.map(([parentJoinName,childJoinName])=>[keypoints.partIds[parentJoinName],keypoints.partIds[childJoinName]]),parentToChildEdges=parentChildrenTuples.map(([,childJointId])=>childJointId),childToParentEdges=parentChildrenTuples.map(([parentJointId])=>parentJointId);function getDisplacement(edgeId,point,displacements){let numEdges=displacements.shape[2]/2;return{y:displacements.get(point.y,point.x,edgeId),x:displacements.get(point.y,point.x,numEdges+edgeId)}}function getStridedIndexNearPoint(point,outputStride,height,width){return{y:vectors.clamp(Math.round(point.y/outputStride),0,height-1),x:vectors.clamp(Math.round(point.x/outputStride),0,width-1)}}function traverseToTargetKeypoint(edgeId,sourceKeypoint,targetKeypointId,scoresBuffer,offsets,outputStride,displacements,offsetRefineStep=2){let[height,width]=scoresBuffer.shape,sourceKeypointIndices=getStridedIndexNearPoint(sourceKeypoint.position,outputStride,height,width),displacement=getDisplacement(edgeId,sourceKeypointIndices,displacements),targetKeypoint=vectors.addVectors(sourceKeypoint.position,displacement);for(let i=0;i=0;--edge){let sourceKeypointId=parentToChildEdges[edge],targetKeypointId=childToParentEdges[edge];instanceKeypoints[sourceKeypointId]&&!instanceKeypoints[targetKeypointId]&&(instanceKeypoints[targetKeypointId]=traverseToTargetKeypoint(edge,instanceKeypoints[sourceKeypointId],targetKeypointId,scores,offsets,outputStride,displacementsBwd))}for(let edge=0;edge(totalScore+=score,{position:{y:offsetPointsBuffer.get(i,0),x:offsetPointsBuffer.get(i,1)},part:keypoints.partNames[i],score})),filteredKeypoints=instanceKeypoints.filter(kpt=>kpt.score>config2.body.scoreThreshold);return heatmapValues.dispose(),offsetPoints.dispose(),{keypoints:filteredKeypoints,score:totalScore/instanceKeypoints.length}}exports2.decodeSinglePose=decodeSinglePose});var require_decodeMultiple=__commonJS(exports2=>{var buildParts=__toModule(require_buildParts()),decodePose=__toModule(require_decodePose()),vectors=__toModule(require_vectors()),kLocalMaximumRadius=1;function withinNmsRadiusOfCorrespondingPoint(poses,squaredNmsRadius,{x,y},keypointId){return poses.some(({keypoints})=>{let correspondingKeypoint=keypoints[keypointId].position;return vectors.squaredDistance(y,x,correspondingKeypoint.y,correspondingKeypoint.x)<=squaredNmsRadius})}function getInstanceScore(existingPoses,squaredNmsRadius,instanceKeypoints){return instanceKeypoints.reduce((result,{position,score},keypointId)=>(withinNmsRadiusOfCorrespondingPoint(existingPoses,squaredNmsRadius,position,keypointId)||(result+=score),result),0)/instanceKeypoints.length}function decodeMultiplePoses(scoresBuffer,offsetsBuffer,displacementsFwdBuffer,displacementsBwdBuffer,config2){let poses=[],queue=buildParts.buildPartWithScoreQueue(config2.body.scoreThreshold,kLocalMaximumRadius,scoresBuffer),squaredNmsRadius=config2.body.nmsRadius^2;for(;poses.lengthconfig2.body.scoreThreshold&&poses.push({keypoints,score})}return poses}exports2.decodeMultiplePoses=decodeMultiplePoses});var require_util2=__commonJS(exports2=>{var kpt=__toModule(require_keypoints());function eitherPointDoesntMeetConfidence(a,b,minConfidence){return a(eitherPointDoesntMeetConfidence(keypoints[leftJoint].score,keypoints[rightJoint].score,minConfidence)||result.push([keypoints[leftJoint],keypoints[rightJoint]]),result),[])}exports2.getAdjacentKeyPoints=getAdjacentKeyPoints;var{NEGATIVE_INFINITY,POSITIVE_INFINITY}=Number;function getBoundingBox(keypoints){return keypoints.reduce(({maxX,maxY,minX,minY},{position:{x,y}})=>({maxX:Math.max(maxX,x),maxY:Math.max(maxY,y),minX:Math.min(minX,x),minY:Math.min(minY,y)}),{maxX:NEGATIVE_INFINITY,maxY:NEGATIVE_INFINITY,minX:POSITIVE_INFINITY,minY:POSITIVE_INFINITY})}exports2.getBoundingBox=getBoundingBox;function getBoundingBoxPoints(keypoints){let{minX,minY,maxX,maxY}=getBoundingBox(keypoints);return[{x:minX,y:minY},{x:maxX,y:minY},{x:maxX,y:maxY},{x:minX,y:maxY}]}exports2.getBoundingBoxPoints=getBoundingBoxPoints;async function toTensorBuffers3D(tensors){return Promise.all(tensors.map(tensor=>tensor.buffer()))}exports2.toTensorBuffers3D=toTensorBuffers3D;function scalePose(pose,scaleY,scaleX){return{score:pose.score,keypoints:pose.keypoints.map(({score,part,position})=>({score,part,position:{x:position.x*scaleX,y:position.y*scaleY}}))}}exports2.scalePose=scalePose;function resizeTo(image4,[targetH,targetW]){let input=image4.squeeze(0),resized=input.resizeBilinear([targetH,targetW]);return input.dispose(),resized}exports2.resizeTo=resizeTo;function scaleAndFlipPoses(poses,[height,width],[inputResolutionHeight,inputResolutionWidth]){return poses.map(pose=>scalePose(pose,height/inputResolutionHeight,width/inputResolutionWidth))}exports2.scaleAndFlipPoses=scaleAndFlipPoses});var require_modelPoseNet=__commonJS(exports2=>{var tf5=__toModule(require_tfjs_esm()),modelBase=__toModule(require_modelBase()),decodeMultiple=__toModule(require_decodeMultiple()),decodePose=__toModule(require_decodePose()),util=__toModule(require_util2());async function estimateMultiple(input,res,config2){return new Promise(async resolve=>{let height=input.shape[1],width=input.shape[2],allTensorBuffers=await util.toTensorBuffers3D([res.heatmapScores,res.offsets,res.displacementFwd,res.displacementBwd]),scoresBuffer=allTensorBuffers[0],offsetsBuffer=allTensorBuffers[1],displacementsFwdBuffer=allTensorBuffers[2],displacementsBwdBuffer=allTensorBuffers[3],poses=await decodeMultiple.decodeMultiplePoses(scoresBuffer,offsetsBuffer,displacementsFwdBuffer,displacementsBwdBuffer,config2),scaled=util.scaleAndFlipPoses(poses,[height,width],[config2.body.inputSize,config2.body.inputSize]);resolve(scaled)})}async function estimateSingle(input,res,config2){return new Promise(async resolve=>{let height=input.shape[1],width=input.shape[2],poses=[await decodePose.decodeSinglePose(res.heatmapScores,res.offsets,config2)],scaled=util.scaleAndFlipPoses(poses,[height,width],[config2.body.inputSize,config2.body.inputSize]);resolve(scaled)})}var PoseNet=class{constructor(model){this.baseModel=model}async estimatePoses(input,config2){let resized=util.resizeTo(input,[config2.body.inputSize,config2.body.inputSize]),res=this.baseModel.predict(resized,config2),poses=config2.body.maxDetections<2?await estimateSingle(input,res,config2):await estimateMultiple(input,res,config2);return res.heatmapScores.dispose(),res.offsets.dispose(),res.displacementFwd.dispose(),res.displacementBwd.dispose(),resized.dispose(),poses}dispose(){this.baseModel.dispose()}};exports2.PoseNet=PoseNet;async function load3(config2){let model=await tf5.loadGraphModel(config2.body.modelPath),mobilenet=new modelBase.BaseModel(model);return log(`load model: ${config2.body.modelPath.match(/\/(.*)\./)[1]}`),new PoseNet(mobilenet)}exports2.load=load3});var require_posenet=__commonJS(exports2=>{var modelPoseNet=__toModule(require_modelPoseNet()),keypoints=__toModule(require_keypoints()),util=__toModule(require_util2());exports2.load=modelPoseNet.load;exports2.PoseNet=modelPoseNet.PoseNet;exports2.partChannels=keypoints.partChannels;exports2.partIds=keypoints.partIds;exports2.partNames=keypoints.partNames;exports2.poseChain=keypoints.poseChain;exports2.getAdjacentKeyPoints=util.getAdjacentKeyPoints;exports2.getBoundingBox=util.getBoundingBox;exports2.getBoundingBoxPoints=util.getBoundingBoxPoints;exports2.scaleAndFlipPoses=util.scaleAndFlipPoses;exports2.scalePose=util.scalePose});var require_handdetector=__commonJS(exports2=>{var tf5=__toModule(require_tfjs_esm()),HandDetector=class{constructor(model,inputSize,anchorsAnnotated){this.model=model,this.anchors=anchorsAnnotated.map(anchor=>[anchor.x_center,anchor.y_center]),this.anchorsTensor=tf5.tensor2d(this.anchors),this.inputSizeTensor=tf5.tensor1d([inputSize,inputSize]),this.doubleInputSizeTensor=tf5.tensor1d([inputSize*2,inputSize*2])}normalizeBoxes(boxes){return tf5.tidy(()=>{let boxOffsets=tf5.slice(boxes,[0,0],[-1,2]),boxSizes=tf5.slice(boxes,[0,2],[-1,2]),boxCenterPoints=tf5.add(tf5.div(boxOffsets,this.inputSizeTensor),this.anchorsTensor),halfBoxSizes=tf5.div(boxSizes,this.doubleInputSizeTensor),startPoints=tf5.mul(tf5.sub(boxCenterPoints,halfBoxSizes),this.inputSizeTensor),endPoints=tf5.mul(tf5.add(boxCenterPoints,halfBoxSizes),this.inputSizeTensor);return tf5.concat2d([startPoints,endPoints],1)})}normalizeLandmarks(rawPalmLandmarks,index){return tf5.tidy(()=>{let landmarks=tf5.add(tf5.div(rawPalmLandmarks.reshape([-1,7,2]),this.inputSizeTensor),this.anchors[index]);return tf5.mul(landmarks,this.inputSizeTensor)})}async getBoxes(input,config2){let batched=this.model.predict(input),predictions=batched.squeeze();batched.dispose();let scoresT=tf5.tidy(()=>tf5.sigmoid(tf5.slice(predictions,[0,0],[-1,1])).squeeze()),scores=scoresT.dataSync(),rawBoxes=tf5.slice(predictions,[0,1],[-1,4]),boxes=this.normalizeBoxes(rawBoxes);rawBoxes.dispose();let filteredT=await tf5.image.nonMaxSuppressionAsync(boxes,scores,config2.hand.maxHands,config2.hand.iouThreshold,config2.hand.scoreThreshold),filtered=filteredT.arraySync();scoresT.dispose(),filteredT.dispose();let hands=[];for(let index of filtered)if(scores[index]>=config2.hand.minConfidence){let matchingBox=tf5.slice(boxes,[index,0],[1,-1]),rawPalmLandmarks=tf5.slice(predictions,[index,5],[1,14]),palmLandmarks=tf5.tidy(()=>this.normalizeLandmarks(rawPalmLandmarks,index).reshape([-1,2]));rawPalmLandmarks.dispose(),hands.push({box:matchingBox,palmLandmarks,confidence:scores[index]})}return predictions.dispose(),boxes.dispose(),hands}async estimateHandBounds(input,config2){let inputHeight=input.shape[1],inputWidth=input.shape[2],image5=tf5.tidy(()=>input.resizeBilinear([config2.hand.inputSize,config2.hand.inputSize]).div(127.5).sub(1)),predictions=await this.getBoxes(image5,config2);image5.dispose();let hands=[];if(!predictions||predictions.length===0)return hands;for(let prediction of predictions){let boxes=prediction.box.dataSync(),startPoint=boxes.slice(0,2),endPoint=boxes.slice(2,4),palmLandmarks=prediction.palmLandmarks.arraySync();prediction.box.dispose(),prediction.palmLandmarks.dispose(),hands.push(scaleBoxCoordinates({startPoint,endPoint,palmLandmarks,confidence:prediction.confidence},[inputWidth/config2.hand.inputSize,inputHeight/config2.hand.inputSize]))}return hands}};exports2.HandDetector=HandDetector});var require_handpipeline=__commonJS(exports2=>{var tf5=__toModule(require_tfjs_esm()),PALM_BOX_ENLARGE_FACTOR=5,HAND_BOX_ENLARGE_FACTOR=1.65,PALM_LANDMARK_IDS=[0,5,9,13,17,1,2],PALM_LANDMARKS_INDEX_OF_PALM_BASE=0,PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE=2,HandPipeline=class{constructor(handDetector,landmarkDetector,inputSize){this.handDetector=handDetector,this.landmarkDetector=landmarkDetector,this.inputSize=inputSize,this.storedBoxes=[],this.skipped=0,this.detectedHands=0}getBoxForPalmLandmarks(palmLandmarks,rotationMatrix){let rotatedPalmLandmarks=palmLandmarks.map(coord=>rotatePoint([...coord,1],rotationMatrix)),boxAroundPalm=this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);return enlargeBox(squarifyBox(boxAroundPalm),PALM_BOX_ENLARGE_FACTOR)}getBoxForHandLandmarks(landmarks){let boundingBox=this.calculateLandmarksBoundingBox(landmarks),boxAroundHand=enlargeBox(squarifyBox(boundingBox),HAND_BOX_ENLARGE_FACTOR);boxAroundHand.palmLandmarks=[];for(let i=0;i[scaleFactor[0]*(coord[0]-this.inputSize/2),scaleFactor[1]*(coord[1]-this.inputSize/2),scaleFactor[2]*coord[2]]),coordsRotationMatrix=buildRotationMatrix(angle,[0,0]),coordsRotated=coordsScaled.map(coord=>[...rotatePoint(coord,coordsRotationMatrix),coord[2]]),inverseRotationMatrix=invertTransformMatrix(rotationMatrix),boxCenter=[...getBoxCenter(box2),1],originalBoxCenter=[dot(boxCenter,inverseRotationMatrix[0]),dot(boxCenter,inverseRotationMatrix[1])];return coordsRotated.map(coord=>[coord[0]+originalBoxCenter[0],coord[1]+originalBoxCenter[1],coord[2]])}async estimateHands(image5,config2){let useFreshBox=!1,boxes;(this.skipped===0||this.skipped>config2.hand.skipFrames||!config2.hand.landmarks||!config2.videoOptimized)&&(boxes=await this.handDetector.estimateHandBounds(image5,config2),this.skipped=0),config2.videoOptimized&&this.skipped++,boxes&&boxes.length>0&&(boxes.length!==this.detectedHands&&this.detectedHands!==config2.hand.maxHands||!config2.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...boxes],this.storedBoxes.length>0&&(useFreshBox=!0));let hands=[];for(let i=0;i=config2.hand.minConfidence){let keypointsReshaped=tf5.reshape(keypoints,[-1,3]),rawCoords=keypointsReshaped.arraySync();keypoints.dispose(),keypointsReshaped.dispose();let coords=this.transformRawCoords(rawCoords,newBox,angle,rotationMatrix),nextBoundingBox=this.getBoxForHandLandmarks(coords);this.storedBoxes[i]=nextBoundingBox;let result={landmarks:coords,confidence,box:{topLeft:nextBoundingBox.startPoint,bottomRight:nextBoundingBox.endPoint}};hands.push(result)}else this.storedBoxes[i]=null;keypoints.dispose()}else{let enlarged=enlargeBox(squarifyBox(currentBox),HAND_BOX_ENLARGE_FACTOR),result={confidence:currentBox.confidence,box:{topLeft:enlarged.startPoint,bottomRight:enlarged.endPoint}};hands.push(result)}}return this.storedBoxes=this.storedBoxes.filter(a=>a!==null),this.detectedHands=hands.length,hands}calculateLandmarksBoundingBox(landmarks){let xs=landmarks.map(d=>d[0]),ys=landmarks.map(d=>d[1]),startPoint=[Math.min(...xs),Math.min(...ys)],endPoint=[Math.max(...xs),Math.max(...ys)];return{startPoint,endPoint}}};exports2.HandPipeline=HandPipeline});var 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HandPose(handPipeline);return config2.hand.enabled&&log(`load model: ${config2.hand.detector.modelPath.match(/\/(.*)\./)[1]}`),config2.hand.landmarks&&log(`load model: ${config2.hand.skeleton.modelPath.match(/\/(.*)\./)[1]}`),handPose}exports2.load=load3});var require_gesture=__commonJS(exports2=>{exports2.body=res=>{if(!res)return[];let gestures=[];for(let i=0;ia.part==="leftWrist"),rightWrist=res[i].keypoints.find(a=>a.part==="rightWrist"),nose=res[i].keypoints.find(a=>a.part==="nose");nose&&leftWrist&&rightWrist&&leftWrist.position.ya.part==="leftShoulder"),rightShoulder=res[i].keypoints.find(a=>a.part==="rightShoulder");leftShoulder&&rightShoulder&&gestures.push({body:i,gesture:`leaning ${leftShoulder.position.y>rightShoulder.position.y?"left":"right"}`})}return gestures};exports2.face=res=>{if(!res)return[];let gestures=[];for(let i=0;i0){let eyeFacing=res[i].mesh[35][2]-res[i].mesh[263][2];Math.abs(eyeFacing)<10?gestures.push({face:i,gesture:"facing camera"}):gestures.push({face:i,gesture:`facing ${eyeFacing<0?"right":"left"}`}),Math.abs(res[i].mesh[374][1]-res[i].mesh[386][1])/Math.abs(res[i].mesh[443][1]-res[i].mesh[450][1])<.2&&gestures.push({face:i,gesture:"blink left eye"}),Math.abs(res[i].mesh[145][1]-res[i].mesh[159][1])/Math.abs(res[i].mesh[223][1]-res[i].mesh[230][1])<.2&&gestures.push({face:i,gesture:"blink right eye"});let mouthOpen=Math.min(100,500*Math.abs(res[i].mesh[13][1]-res[i].mesh[14][1])/Math.abs(res[i].mesh[10][1]-res[i].mesh[152][1]));mouthOpen>10&&gestures.push({face:i,gesture:`mouth ${Math.trunc(mouthOpen)}% open`});let chinDepth=res[i].mesh[152][2];Math.abs(chinDepth)>10&&gestures.push({face:i,gesture:`head ${chinDepth<0?"up":"down"}`})}return gestures};exports2.iris=res=>{if(!res)return[];let gestures=[];for(let i=0;i{if(!res)return[];let gestures=[];for(let i=0;i0){let closest=fingers.reduce((best,a)=>best.position[2]best.position[1]{var WebGLProgram=function(gl,vertexSource,fragmentSource){let _collect=function(source,prefix,collection){let r=new RegExp("\\b"+prefix+" \\w+ (\\w+)","ig");source.replace(r,(match,name)=>(collection[name]=0,match))},_compile=function(source,type){let shader=gl.createShader(type);if(gl.shaderSource(shader,source),gl.compileShader(shader),!gl.getShaderParameter(shader,gl.COMPILE_STATUS))throw new Error("Filter: GL compile failed",gl.getShaderInfoLog(shader));return shader};this.uniform={},this.attribute={};let _vsh=_compile(vertexSource,gl.VERTEX_SHADER),_fsh=_compile(fragmentSource,gl.FRAGMENT_SHADER);if(this.id=gl.createProgram(),gl.attachShader(this.id,_vsh),gl.attachShader(this.id,_fsh),gl.linkProgram(this.id),!gl.getProgramParameter(this.id,gl.LINK_STATUS))throw new Error("Filter: GL link failed",gl.getProgramInfoLog(this.id));gl.useProgram(this.id),_collect(vertexSource,"attribute",this.attribute);for(let a in this.attribute)this.attribute[a]=gl.getAttribLocation(this.id,a);_collect(vertexSource,"uniform",this.uniform),_collect(fragmentSource,"uniform",this.uniform);for(let u in this.uniform)this.uniform[u]=gl.getUniformLocation(this.id,u)},WebGLImageFilter=function(params){params||(params={});let _drawCount=0,_sourceTexture=null,_lastInChain=!1,_currentFramebufferIndex=-1,_tempFramebuffers=[null,null],_filterChain=[],_width=-1,_height=-1,_vertexBuffer=null,_currentProgram=null,_canvas=params.canvas||document.createElement("canvas"),_shaderProgramCache={},gl=_canvas.getContext("webgl");if(!gl)throw new Error("Filter: getContext() failed");this.addFilter=function(name){let args=Array.prototype.slice.call(arguments,1),filter=_filter[name];_filterChain.push({func:filter,args})},this.reset=function(){_filterChain=[]},this.apply=function(image4){if(_resize(image4.width,image4.height),_drawCount=0,_sourceTexture||(_sourceTexture=gl.createTexture()),gl.bindTexture(gl.TEXTURE_2D,_sourceTexture),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_WRAP_S,gl.CLAMP_TO_EDGE),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_WRAP_T,gl.CLAMP_TO_EDGE),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_MIN_FILTER,gl.NEAREST),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_MAG_FILTER,gl.NEAREST),gl.texImage2D(gl.TEXTURE_2D,0,gl.RGBA,gl.RGBA,gl.UNSIGNED_BYTE,image4),_filterChain.length===0)return _draw(),_canvas;for(let i=0;i<_filterChain.length;i++){_lastInChain=i===_filterChain.length-1;let f=_filterChain[i];f.func.apply(this,f.args||[])}return _canvas};let _resize=function(width,height){if(!(width===_width&&height===_height)){if(_canvas.width=width,_width=width,_canvas.height=height,_height=height,!_vertexBuffer){let vertices=new Float32Array([-1,-1,0,1,1,-1,1,1,-1,1,0,0,-1,1,0,0,1,-1,1,1,1,1,1,0]);_vertexBuffer=gl.createBuffer(),gl.bindBuffer(gl.ARRAY_BUFFER,_vertexBuffer),gl.bufferData(gl.ARRAY_BUFFER,vertices,gl.STATIC_DRAW),gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL,!0)}gl.viewport(0,0,_width,_height),_tempFramebuffers=[null,null]}},_getTempFramebuffer=function(index){return _tempFramebuffers[index]=_tempFramebuffers[index]||_createFramebufferTexture(_width,_height),_tempFramebuffers[index]},_createFramebufferTexture=function(width,height){let fbo=gl.createFramebuffer();gl.bindFramebuffer(gl.FRAMEBUFFER,fbo);let renderbuffer=gl.createRenderbuffer();gl.bindRenderbuffer(gl.RENDERBUFFER,renderbuffer);let texture=gl.createTexture();return gl.bindTexture(gl.TEXTURE_2D,texture),gl.texImage2D(gl.TEXTURE_2D,0,gl.RGBA,width,height,0,gl.RGBA,gl.UNSIGNED_BYTE,null),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_MAG_FILTER,gl.LINEAR),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_MIN_FILTER,gl.LINEAR),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_WRAP_S,gl.CLAMP_TO_EDGE),gl.texParameteri(gl.TEXTURE_2D,gl.TEXTURE_WRAP_T,gl.CLAMP_TO_EDGE),gl.framebufferTexture2D(gl.FRAMEBUFFER,gl.COLOR_ATTACHMENT0,gl.TEXTURE_2D,texture,0),gl.bindTexture(gl.TEXTURE_2D,null),gl.bindFramebuffer(gl.FRAMEBUFFER,null),{fbo,texture}},_draw=function(flags){var _a,_b;let source=null,target=null,flipY=!1;_drawCount===0?source=_sourceTexture:source=(_a=_getTempFramebuffer(_currentFramebufferIndex))==null?void 0:_a.texture,_drawCount++,_lastInChain&&!(flags&DRAW.INTERMEDIATE)?(target=null,flipY=_drawCount%2==0):(_currentFramebufferIndex=(_currentFramebufferIndex+1)%2,target=(_b=_getTempFramebuffer(_currentFramebufferIndex))==null?void 0:_b.fbo),gl.bindTexture(gl.TEXTURE_2D,source),gl.bindFramebuffer(gl.FRAMEBUFFER,target),gl.uniform1f(_currentProgram.uniform.flipY,flipY?-1:1),gl.drawArrays(gl.TRIANGLES,0,6)},_compileShader=function(fragmentSource){if(_shaderProgramCache[fragmentSource])return _currentProgram=_shaderProgramCache[fragmentSource],gl.useProgram(_currentProgram.id),_currentProgram;_currentProgram=new WebGLProgram(gl,SHADER.VERTEX_IDENTITY,fragmentSource);let floatSize=Float32Array.BYTES_PER_ELEMENT,vertSize=4*floatSize;return gl.enableVertexAttribArray(_currentProgram.attribute.pos),gl.vertexAttribPointer(_currentProgram.attribute.pos,2,gl.FLOAT,!1,vertSize,0*floatSize),gl.enableVertexAttribArray(_currentProgram.attribute.uv),gl.vertexAttribPointer(_currentProgram.attribute.uv,2,gl.FLOAT,!1,vertSize,2*floatSize),_shaderProgramCache[fragmentSource]=_currentProgram,_currentProgram},DRAW={INTERMEDIATE:1},SHADER={};SHADER.VERTEX_IDENTITY=["precision highp float;","attribute vec2 pos;","attribute vec2 uv;","varying vec2 vUv;","uniform float flipY;","void main(void) {","vUv = uv;","gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);","}"].join(` `),SHADER.FRAGMENT_IDENTITY=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","void main(void) {","gl_FragColor = texture2D(texture, vUv);","}"].join(` `);let _filter={};_filter.colorMatrix=function(matrix){let m=new Float32Array(matrix);m[4]/=255,m[9]/=255,m[14]/=255,m[19]/=255;let shader=m[18]===1&&m[3]===0&&m[8]===0&&m[13]===0&&m[15]===0&&m[16]===0&&m[17]===0&&m[19]===0?_filter.colorMatrix.SHADER.WITHOUT_ALPHA:_filter.colorMatrix.SHADER.WITH_ALPHA,program=_compileShader(shader);gl.uniform1fv(program.uniform.m,m),_draw()},_filter.colorMatrix.SHADER={},_filter.colorMatrix.SHADER.WITH_ALPHA=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","uniform float m[20];","void main(void) {","vec4 c = texture2D(texture, vUv);","gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];","gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];","gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];","gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];","}"].join(` `),_filter.colorMatrix.SHADER.WITHOUT_ALPHA=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","uniform float m[20];","void main(void) {","vec4 c = texture2D(texture, vUv);","gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];","gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];","gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];","gl_FragColor.a = c.a;","}"].join(` `),_filter.brightness=function(brightness){let b=(brightness||0)+1;_filter.colorMatrix([b,0,0,0,0,0,b,0,0,0,0,0,b,0,0,0,0,0,1,0])},_filter.saturation=function(amount){let x=(amount||0)*2/3+1,y=(x-1)*-.5;_filter.colorMatrix([x,y,y,0,0,y,x,y,0,0,y,y,x,0,0,0,0,0,1,0])},_filter.desaturate=function(){_filter.saturation(-1)},_filter.contrast=function(amount){let v=(amount||0)+1,o=-128*(v-1);_filter.colorMatrix([v,0,0,0,o,0,v,0,0,o,0,0,v,0,o,0,0,0,1,0])},_filter.negative=function(){_filter.contrast(-2)},_filter.hue=function(rotation){rotation=(rotation||0)/180*Math.PI;let cos=Math.cos(rotation),sin=Math.sin(rotation),lumR=.213,lumG=.715,lumB=.072;_filter.colorMatrix([lumR+cos*(1-lumR)+sin*-lumR,lumG+cos*-lumG+sin*-lumG,lumB+cos*-lumB+sin*(1-lumB),0,0,lumR+cos*-lumR+sin*.143,lumG+cos*(1-lumG)+sin*.14,lumB+cos*-lumB+sin*-.283,0,0,lumR+cos*-lumR+sin*-(1-lumR),lumG+cos*-lumG+sin*lumG,lumB+cos*(1-lumB)+sin*lumB,0,0,0,0,0,1,0])},_filter.desaturateLuminance=function(){_filter.colorMatrix([.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,0,0,0,1,0])},_filter.sepia=function(){_filter.colorMatrix([.393,.7689999,.18899999,0,0,.349,.6859999,.16799999,0,0,.272,.5339999,.13099999,0,0,0,0,0,1,0])},_filter.brownie=function(){_filter.colorMatrix([.5997023498159715,.34553243048391263,-.2708298674538042,0,47.43192855600873,-.037703249837783157,.8609577587992641,.15059552388459913,0,-36.96841498319127,.24113635128153335,-.07441037908422492,.44972182064877153,0,-7.562075277591283,0,0,0,1,0])},_filter.vintagePinhole=function(){_filter.colorMatrix([.6279345635605994,.3202183420819367,-.03965408211312453,0,9.651285835294123,.02578397704808868,.6441188644374771,.03259127616149294,0,7.462829176470591,.0466055556782719,-.0851232987247891,.5241648018700465,0,5.159190588235296,0,0,0,1,0])},_filter.kodachrome=function(){_filter.colorMatrix([1.1285582396593525,-.3967382283601348,-.03992559172921793,0,63.72958762196502,-.16404339962244616,1.0835251566291304,-.05498805115633132,0,24.732407896706203,-.16786010706155763,-.5603416277695248,1.6014850761964943,0,35.62982807460946,0,0,0,1,0])},_filter.technicolor=function(){_filter.colorMatrix([1.9125277891456083,-.8545344976951645,-.09155508482755585,0,11.793603434377337,-.3087833385928097,1.7658908555458428,-.10601743074722245,0,-70.35205161461398,-.231103377548616,-.7501899197440212,1.847597816108189,0,30.950940869491138,0,0,0,1,0])},_filter.polaroid=function(){_filter.colorMatrix([1.438,-.062,-.062,0,0,-.122,1.378,-.122,0,0,-.016,-.016,1.483,0,0,0,0,0,1,0])},_filter.shiftToBGR=function(){_filter.colorMatrix([0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0])},_filter.convolution=function(matrix){let m=new Float32Array(matrix),pixelSizeX=1/_width,pixelSizeY=1/_height,program=_compileShader(_filter.convolution.SHADER);gl.uniform1fv(program.uniform.m,m),gl.uniform2f(program.uniform.px,pixelSizeX,pixelSizeY),_draw()},_filter.convolution.SHADER=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","uniform vec2 px;","uniform float m[9];","void main(void) {","vec4 c11 = texture2D(texture, vUv - px);","vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y));","vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y));","vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) );","vec4 c22 = texture2D(texture, vUv);","vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) );","vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) );","vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) );","vec4 c33 = texture2D(texture, vUv + px );","gl_FragColor = ","c11 * m[0] + c12 * m[1] + c22 * m[2] +","c21 * m[3] + c22 * m[4] + c23 * m[5] +","c31 * m[6] + c32 * m[7] + c33 * m[8];","gl_FragColor.a = c22.a;","}"].join(` `),_filter.detectEdges=function(){_filter.convolution.call(this,[0,1,0,1,-4,1,0,1,0])},_filter.sobelX=function(){_filter.convolution.call(this,[-1,0,1,-2,0,2,-1,0,1])},_filter.sobelY=function(){_filter.convolution.call(this,[-1,-2,-1,0,0,0,1,2,1])},_filter.sharpen=function(amount){let a=amount||1;_filter.convolution.call(this,[0,-1*a,0,-1*a,1+4*a,-1*a,0,-1*a,0])},_filter.emboss=function(size){let s=size||1;_filter.convolution.call(this,[-2*s,-1*s,0,-1*s,1,1*s,0,1*s,2*s])},_filter.blur=function(size){let blurSizeX=size/7/_width,blurSizeY=size/7/_height,program=_compileShader(_filter.blur.SHADER);gl.uniform2f(program.uniform.px,0,blurSizeY),_draw(DRAW.INTERMEDIATE),gl.uniform2f(program.uniform.px,blurSizeX,0),_draw()},_filter.blur.SHADER=["precision highp float;","varying vec2 vUv;","uniform sampler2D texture;","uniform vec2 px;","void main(void) {","gl_FragColor = vec4(0.0);","gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;","gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;","gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;","gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;","gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;","gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;","gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;","gl_FragColor += texture2D(texture, vUv )*0.159576912161;","gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x, 1.0*px.y))*0.147308056121;","gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x, 2.0*px.y))*0.115876621105;","gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x, 3.0*px.y))*0.0776744219933;","gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x, 4.0*px.y))*0.0443683338718;","gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x, 5.0*px.y))*0.0215963866053;","gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794;","gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265;","}"].join(` `),_filter.pixelate=function(size){let blurSizeX=size/_width,blurSizeY=size/_height,program=_compileShader(_filter.pixelate.SHADER);gl.uniform2f(program.uniform.size,blurSizeX,blurSizeY),_draw()},_filter.pixelate.SHADER=["precision highp float;","varying vec2 vUv;","uniform vec2 size;","uniform sampler2D texture;","vec2 pixelate(vec2 coord, vec2 size) {","return floor( coord / size ) * size;","}","void main(void) {","gl_FragColor = vec4(0.0);","vec2 coord = pixelate(vUv, size);","gl_FragColor += texture2D(texture, coord);","}"].join(` -`)};exports2.Canvas=WebGLImageFilter});var require_image=__commonJS(exports2=>{var tf4=__toModule(require_tfjs_esm()),fxImage=__toModule(require_imagefx()),inCanvas=null,outCanvas=null;function process3(input,config2){let tensor;if(input instanceof tf4.Tensor)tensor=tf4.clone(input);else{let originalWidth=input.naturalWidth||input.videoWidth||input.width||input.shape&&input.shape[1]>0,originalHeight=input.naturalHeight||input.videoHeight||input.height||input.shape&&input.shape[2]>0,targetWidth=originalWidth,targetHeight=originalHeight;if(config2.filter.width>0?targetWidth=config2.filter.width:config2.filter.height>0&&(targetWidth=originalWidth*(config2.filter.height/originalHeight)),config2.filter.height>0?targetHeight=config2.filter.height:config2.filter.width>0&&(targetHeight=originalHeight*(config2.filter.width/originalWidth)),!targetWidth||!targetHeight)return log("Human: invalid input",input),null;(!inCanvas||inCanvas.width!==targetWidth||inCanvas.height!==targetHeight)&&(inCanvas=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(targetWidth,targetHeight):document.createElement("canvas"),inCanvas.width!==targetWidth&&(inCanvas.width=targetWidth),inCanvas.height!==targetHeight&&(inCanvas.height=targetHeight));let ctx=inCanvas.getContext("2d");if(input instanceof ImageData?ctx.putImageData(input,0,0):ctx.drawImage(input,0,0,originalWidth,originalHeight,0,0,inCanvas.width,inCanvas.height),config2.filter.enabled){if((!this.fx||!outCanvas||inCanvas.width!==outCanvas.width||inCanvas.height!==outCanvas.height)&&(outCanvas=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(inCanvas.width,inCanvas.height):document.createElement("canvas"),outCanvas.width!==inCanvas.width&&(outCanvas.width=inCanvas.width),outCanvas.height!==inCanvas.height&&(outCanvas.height=inCanvas.height),this.fx=tf4.ENV.flags.IS_BROWSER?new fxImage.Canvas({canvas:outCanvas}):null),!this.fx)return inCanvas;this.fx.reset(),this.fx.addFilter("brightness",config2.filter.brightness),config2.filter.contrast!==0&&this.fx.addFilter("contrast",config2.filter.contrast),config2.filter.sharpness!==0&&this.fx.addFilter("sharpen",config2.filter.sharpness),config2.filter.blur!==0&&this.fx.addFilter("blur",config2.filter.blur),config2.filter.saturation!==0&&this.fx.addFilter("saturation",config2.filter.saturation),config2.filter.hue!==0&&this.fx.addFilter("hue",config2.filter.hue),config2.filter.negative&&this.fx.addFilter("negative"),config2.filter.sepia&&this.fx.addFilter("sepia"),config2.filter.vintage&&this.fx.addFilter("brownie"),config2.filter.sepia&&this.fx.addFilter("sepia"),config2.filter.kodachrome&&this.fx.addFilter("kodachrome"),config2.filter.technicolor&&this.fx.addFilter("technicolor"),config2.filter.polaroid&&this.fx.addFilter("polaroid"),config2.filter.pixelate!==0&&this.fx.addFilter("pixelate",config2.filter.pixelate),this.fx.apply(inCanvas)}else outCanvas=inCanvas;let pixels;if(outCanvas.data){let shape=[outCanvas.height,outCanvas.width,3];pixels=tf4.tensor3d(outCanvas.data,shape,"int32")}else if(config2.backend==="webgl"||outCanvas instanceof ImageData)pixels=tf4.browser.fromPixels(outCanvas);else{let tempCanvas=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(targetWidth,targetHeight):document.createElement("canvas");tempCanvas.width=targetWidth,tempCanvas.height=targetHeight;let tempCtx=tempCanvas.getContext("2d");tempCtx==null||tempCtx.drawImage(outCanvas,0,0);let data2=tempCtx==null?void 0:tempCtx.getImageData(0,0,targetWidth,targetHeight);pixels=tf4.browser.fromPixels(data2)}let casted=pixels.toFloat();tensor=casted.expandDims(0),pixels.dispose(),casted.dispose()}return{tensor,canvas:config2.filter.return?outCanvas:null}}exports2.process=process3});__export(exports,{default:()=>Human});function log(...msg){let dt=new Date,ts=`${dt.getHours().toString().padStart(2,"0")}:${dt.getMinutes().toString().padStart(2,"0")}:${dt.getSeconds().toString().padStart(2,"0")}.${dt.getMilliseconds().toString().padStart(3,"0")}`;msg&&console.log(ts,"Human:",...msg)}var tf3=__toModule(require_tfjs_esm());var tf=__toModule(require_tfjs_esm()),config={name:"humangl",priority:99,canvas:null,width:1024,height:1024,webGLattr:{alpha:!1,antialias:!1,premultipliedAlpha:!1,preserveDrawingBuffer:!1,depth:!1,stencil:!1,failIfMajorPerformanceCaveat:!1,desynchronized:!0}};function register(){if(!tf.findBackend(config.name)){config.canvas=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(config.width,config.height):document.createElement("canvas");let gl=config.canvas.getContext("webgl2",config.webGLattr);tf.setWebGLContext(2,gl);let ctx=new tf.GPGPUContext(gl);tf.registerBackend(config.name,()=>new tf.MathBackendWebGL(ctx),config.priority),tf.getKernelsForBackend("webgl").forEach(kernelConfig=>{let newKernelConfig={...kernelConfig,backendName:config.name};tf.registerKernel(newKernelConfig)}),tf.ENV.set("WEBGL_VERSION",2),tf.ENV.set("WEBGL_MAX_TEXTURE_SIZE",gl.getParameter(gl.MAX_TEXTURE_SIZE)),tf.ENV.set("WEBGL_FORCE_F16_TEXTURES",!0),tf.ENV.set("WEBGL_PACK_DEPTHWISECONV",!0)}}var facemesh=__toModule(require_facemesh()),age=__toModule(require_age()),gender=__toModule(require_gender()),emotion=__toModule(require_emotion()),embedding=__toModule(require_embedding()),posenet=__toModule(require_posenet());var tf2=__toModule(require_tfjs_esm());function getBoxSize(box){return[Math.abs(box.endPoint[0]-box.startPoint[0]),Math.abs(box.endPoint[1]-box.startPoint[1])]}function getBoxCenter(box){return[box.startPoint[0]+(box.endPoint[0]-box.startPoint[0])/2,box.startPoint[1]+(box.endPoint[1]-box.startPoint[1])/2]}function cutBoxFromImageAndResize(box,image3,cropSize){let h=image3.shape[1],w=image3.shape[2],boxes=[[box.startPoint[1]/h,box.startPoint[0]/w,box.endPoint[1]/h,box.endPoint[0]/w]];return tf2.image.cropAndResize(image3,boxes,[0],cropSize)}function scaleBoxCoordinates(box,factor){let startPoint=[box.startPoint[0]*factor[0],box.startPoint[1]*factor[1]],endPoint=[box.endPoint[0]*factor[0],box.endPoint[1]*factor[1]],palmLandmarks=box.palmLandmarks.map(coord=>[coord[0]*factor[0],coord[1]*factor[1]]);return{startPoint,endPoint,palmLandmarks,confidence:box.confidence}}function enlargeBox(box,factor=1.5){let center=getBoxCenter(box),size=getBoxSize(box),newHalfSize=[factor*size[0]/2,factor*size[1]/2],startPoint=[center[0]-newHalfSize[0],center[1]-newHalfSize[1]],endPoint=[center[0]+newHalfSize[0],center[1]+newHalfSize[1]];return{startPoint,endPoint,palmLandmarks:box.palmLandmarks}}function squarifyBox(box){let centers=getBoxCenter(box),size=getBoxSize(box),halfSize=Math.max(...size)/2,startPoint=[centers[0]-halfSize,centers[1]-halfSize],endPoint=[centers[0]+halfSize,centers[1]+halfSize];return{startPoint,endPoint,palmLandmarks:box.palmLandmarks}}function normalizeRadians(angle){return angle-2*Math.PI*Math.floor((angle+Math.PI)/(2*Math.PI))}function computeRotation(point1,point2){let radians=Math.PI/2-Math.atan2(-(point2[1]-point1[1]),point2[0]-point1[0]);return normalizeRadians(radians)}var buildTranslationMatrix=(x,y)=>[[1,0,x],[0,1,y],[0,0,1]];function dot(v1,v2){let product=0;for(let i=0;iconfig_default});var config_default={backend:"webgl",wasmPath:"../assets/",async:!0,profile:!1,deallocate:!1,scoped:!1,videoOptimized:!0,warmup:"face",filter:{enabled:!0,width:0,height:0,return:!0,brightness:0,contrast:0,sharpness:0,blur:0,saturation:0,hue:0,negative:!1,sepia:!1,vintage:!1,kodachrome:!1,technicolor:!1,polaroid:!1,pixelate:0},gesture:{enabled:!0},face:{enabled:!0,detector:{modelPath:"../models/blazeface-back.json",inputSize:256,rotation:!1,maxFaces:10,skipFrames:11,minConfidence:.5,iouThreshold:.2,scoreThreshold:.5},mesh:{enabled:!0,modelPath:"../models/facemesh.json",inputSize:192,returnRawData:!1},iris:{enabled:!0,modelPath:"../models/iris.json",inputSize:64},age:{enabled:!0,modelPath:"../models/age-ssrnet-imdb.json",inputSize:64,skipFrames:31},gender:{enabled:!0,minConfidence:.1,modelPath:"../models/gender-ssrnet-imdb.json",inputSize:64,skipFrames:41},emotion:{enabled:!0,inputSize:64,minConfidence:.2,skipFrames:21,modelPath:"../models/emotion-large.json"},embedding:{enabled:!1,inputSize:112,modelPath:"../models/mobilefacenet.json"}},body:{enabled:!0,modelPath:"../models/posenet.json",inputSize:257,maxDetections:10,scoreThreshold:.5,nmsRadius:20,outputStride:16,modelType:"MobileNet"},hand:{enabled:!0,rotation:!1,inputSize:256,skipFrames:12,minConfidence:.1,iouThreshold:.1,scoreThreshold:.5,maxHands:1,landmarks:!0,detector:{modelPath:"../models/handdetect.json"},skeleton:{modelPath:"../models/handskeleton.json"}}};var face=` +`)};exports2.Canvas=WebGLImageFilter});var require_image=__commonJS(exports2=>{var tf5=__toModule(require_tfjs_esm()),fxImage=__toModule(require_imagefx()),inCanvas=null,outCanvas=null;function process3(input,config2){let tensor;if(input instanceof tf5.Tensor)tensor=tf5.clone(input);else{let originalWidth=input.naturalWidth||input.videoWidth||input.width||input.shape&&input.shape[1]>0,originalHeight=input.naturalHeight||input.videoHeight||input.height||input.shape&&input.shape[2]>0,targetWidth=originalWidth,targetHeight=originalHeight;if(config2.filter.width>0?targetWidth=config2.filter.width:config2.filter.height>0&&(targetWidth=originalWidth*(config2.filter.height/originalHeight)),config2.filter.height>0?targetHeight=config2.filter.height:config2.filter.width>0&&(targetHeight=originalHeight*(config2.filter.width/originalWidth)),!targetWidth||!targetHeight)return log("Human: invalid input",input),null;(!inCanvas||inCanvas.width!==targetWidth||inCanvas.height!==targetHeight)&&(inCanvas=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(targetWidth,targetHeight):document.createElement("canvas"),inCanvas.width!==targetWidth&&(inCanvas.width=targetWidth),inCanvas.height!==targetHeight&&(inCanvas.height=targetHeight));let ctx=inCanvas.getContext("2d");if(input instanceof ImageData?ctx.putImageData(input,0,0):ctx.drawImage(input,0,0,originalWidth,originalHeight,0,0,inCanvas.width,inCanvas.height),config2.filter.enabled){if((!this.fx||!outCanvas||inCanvas.width!==outCanvas.width||inCanvas.height!==outCanvas.height)&&(outCanvas=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(inCanvas.width,inCanvas.height):document.createElement("canvas"),outCanvas.width!==inCanvas.width&&(outCanvas.width=inCanvas.width),outCanvas.height!==inCanvas.height&&(outCanvas.height=inCanvas.height),this.fx=tf5.ENV.flags.IS_BROWSER?new fxImage.Canvas({canvas:outCanvas}):null),!this.fx)return inCanvas;this.fx.reset(),this.fx.addFilter("brightness",config2.filter.brightness),config2.filter.contrast!==0&&this.fx.addFilter("contrast",config2.filter.contrast),config2.filter.sharpness!==0&&this.fx.addFilter("sharpen",config2.filter.sharpness),config2.filter.blur!==0&&this.fx.addFilter("blur",config2.filter.blur),config2.filter.saturation!==0&&this.fx.addFilter("saturation",config2.filter.saturation),config2.filter.hue!==0&&this.fx.addFilter("hue",config2.filter.hue),config2.filter.negative&&this.fx.addFilter("negative"),config2.filter.sepia&&this.fx.addFilter("sepia"),config2.filter.vintage&&this.fx.addFilter("brownie"),config2.filter.sepia&&this.fx.addFilter("sepia"),config2.filter.kodachrome&&this.fx.addFilter("kodachrome"),config2.filter.technicolor&&this.fx.addFilter("technicolor"),config2.filter.polaroid&&this.fx.addFilter("polaroid"),config2.filter.pixelate!==0&&this.fx.addFilter("pixelate",config2.filter.pixelate),this.fx.apply(inCanvas)}else outCanvas=inCanvas;let pixels;if(outCanvas.data){let shape=[outCanvas.height,outCanvas.width,3];pixels=tf5.tensor3d(outCanvas.data,shape,"int32")}else if(config2.backend==="webgl"||outCanvas instanceof ImageData)pixels=tf5.browser.fromPixels(outCanvas);else{let tempCanvas=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(targetWidth,targetHeight):document.createElement("canvas");tempCanvas.width=targetWidth,tempCanvas.height=targetHeight;let tempCtx=tempCanvas.getContext("2d");tempCtx==null||tempCtx.drawImage(outCanvas,0,0);let data2=tempCtx==null?void 0:tempCtx.getImageData(0,0,targetWidth,targetHeight);pixels=tf5.browser.fromPixels(data2)}let casted=pixels.toFloat();tensor=casted.expandDims(0),pixels.dispose(),casted.dispose()}return{tensor,canvas:config2.filter.return?outCanvas:null}}exports2.process=process3});__export(exports,{default:()=>Human});function log(...msg){let dt=new Date,ts=`${dt.getHours().toString().padStart(2,"0")}:${dt.getMinutes().toString().padStart(2,"0")}:${dt.getSeconds().toString().padStart(2,"0")}.${dt.getMilliseconds().toString().padStart(3,"0")}`;msg&&console.log(ts,"Human:",...msg)}var tf4=__toModule(require_tfjs_esm());var tf=__toModule(require_tfjs_esm()),config={name:"humangl",priority:99,canvas:null,width:1024,height:1024,webGLattr:{alpha:!1,antialias:!1,premultipliedAlpha:!1,preserveDrawingBuffer:!1,depth:!1,stencil:!1,failIfMajorPerformanceCaveat:!1,desynchronized:!0}};function register(){if(!tf.findBackend(config.name)){config.canvas=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(config.width,config.height):document.createElement("canvas");let gl=config.canvas.getContext("webgl2",config.webGLattr);tf.setWebGLContext(2,gl);let ctx=new tf.GPGPUContext(gl);tf.registerBackend(config.name,()=>new tf.MathBackendWebGL(ctx),config.priority),tf.getKernelsForBackend("webgl").forEach(kernelConfig=>{let newKernelConfig={...kernelConfig,backendName:config.name};tf.registerKernel(newKernelConfig)}),tf.ENV.set("WEBGL_VERSION",2),tf.ENV.set("WEBGL_MAX_TEXTURE_SIZE",gl.getParameter(gl.MAX_TEXTURE_SIZE)),tf.ENV.set("WEBGL_FORCE_F16_TEXTURES",!0),tf.ENV.set("WEBGL_PACK_DEPTHWISECONV",!0)}}var facemesh=__toModule(require_facemesh()),age=__toModule(require_age()),gender=__toModule(require_gender()),emotion=__toModule(require_emotion()),embedding=__toModule(require_embedding()),posenet=__toModule(require_posenet());var tf2=__toModule(require_tfjs_esm()),models={};async function load(config2){return models.blazepose||(models.blazepose=await tf2.loadGraphModel(config2.pose.modelPath),log(`load model: ${config2.pose.modelPath.match(/\/(.*)\./)[1]}`)),models.blazepose}async function predict(image4,config2){return models.blazepose?new Promise(async resolve=>{let resize=tf2.image.resizeBilinear(image4,[config2.pose.inputSize,config2.pose.inputSize],!1),enhance=tf2.div(resize,127.5).sub(1);tf2.dispose(resize);let logits=await models.blazepose.predict(enhance);tf2.dispose(enhance),logits.map(logit=>logit.dispose()),resolve(logits)}):null}var tf3=__toModule(require_tfjs_esm());function getBoxSize(box){return[Math.abs(box.endPoint[0]-box.startPoint[0]),Math.abs(box.endPoint[1]-box.startPoint[1])]}function getBoxCenter(box){return[box.startPoint[0]+(box.endPoint[0]-box.startPoint[0])/2,box.startPoint[1]+(box.endPoint[1]-box.startPoint[1])/2]}function cutBoxFromImageAndResize(box,image4,cropSize){let h=image4.shape[1],w=image4.shape[2],boxes=[[box.startPoint[1]/h,box.startPoint[0]/w,box.endPoint[1]/h,box.endPoint[0]/w]];return tf3.image.cropAndResize(image4,boxes,[0],cropSize)}function scaleBoxCoordinates(box,factor){let startPoint=[box.startPoint[0]*factor[0],box.startPoint[1]*factor[1]],endPoint=[box.endPoint[0]*factor[0],box.endPoint[1]*factor[1]],palmLandmarks=box.palmLandmarks.map(coord=>[coord[0]*factor[0],coord[1]*factor[1]]);return{startPoint,endPoint,palmLandmarks,confidence:box.confidence}}function enlargeBox(box,factor=1.5){let center=getBoxCenter(box),size=getBoxSize(box),newHalfSize=[factor*size[0]/2,factor*size[1]/2],startPoint=[center[0]-newHalfSize[0],center[1]-newHalfSize[1]],endPoint=[center[0]+newHalfSize[0],center[1]+newHalfSize[1]];return{startPoint,endPoint,palmLandmarks:box.palmLandmarks}}function squarifyBox(box){let centers=getBoxCenter(box),size=getBoxSize(box),halfSize=Math.max(...size)/2,startPoint=[centers[0]-halfSize,centers[1]-halfSize],endPoint=[centers[0]+halfSize,centers[1]+halfSize];return{startPoint,endPoint,palmLandmarks:box.palmLandmarks}}function normalizeRadians(angle){return angle-2*Math.PI*Math.floor((angle+Math.PI)/(2*Math.PI))}function computeRotation(point1,point2){let radians=Math.PI/2-Math.atan2(-(point2[1]-point1[1]),point2[0]-point1[0]);return normalizeRadians(radians)}var buildTranslationMatrix=(x,y)=>[[1,0,x],[0,1,y],[0,0,1]];function dot(v1,v2){let product=0;for(let i=0;iconfig_default});var config_default={backend:"webgl",wasmPath:"../assets/",async:!0,profile:!1,deallocate:!1,scoped:!1,videoOptimized:!0,warmup:"face",filter:{enabled:!0,width:0,height:0,return:!0,brightness:0,contrast:0,sharpness:0,blur:0,saturation:0,hue:0,negative:!1,sepia:!1,vintage:!1,kodachrome:!1,technicolor:!1,polaroid:!1,pixelate:0},gesture:{enabled:!0},face:{enabled:!0,detector:{modelPath:"../models/blazeface-back.json",inputSize:256,rotation:!1,maxFaces:10,skipFrames:11,minConfidence:.5,iouThreshold:.2,scoreThreshold:.5},mesh:{enabled:!0,modelPath:"../models/facemesh.json",inputSize:192,returnRawData:!1},iris:{enabled:!0,modelPath:"../models/iris.json",inputSize:64},age:{enabled:!0,modelPath:"../models/age-ssrnet-imdb.json",inputSize:64,skipFrames:31},gender:{enabled:!0,minConfidence:.1,modelPath:"../models/gender-ssrnet-imdb.json",inputSize:64,skipFrames:41},emotion:{enabled:!0,inputSize:64,minConfidence:.2,skipFrames:21,modelPath:"../models/emotion-large.json"},embedding:{enabled:!1,inputSize:112,modelPath:"../models/mobilefacenet.json"}},body:{enabled:!0,modelPath:"../models/posenet.json",inputSize:257,maxDetections:10,scoreThreshold:.5,nmsRadius:20,outputStride:16,modelType:"MobileNet"},pose:{enabled:!1,scoreThreshold:.6,iouThreshold:.3,modelPath:"../models/blazepose.json",inputSize:256},hand:{enabled:!0,rotation:!1,inputSize:256,skipFrames:12,minConfidence:.1,iouThreshold:.1,scoreThreshold:.5,maxHands:1,landmarks:!0,detector:{modelPath:"../models/handdetect.json"},skeleton:{modelPath:"../models/handskeleton.json"}}};var face=` /9j/4AAQSkZJRgABAQEAYABgAAD/4QBoRXhpZgAATU0AKgAAAAgABAEaAAUAAAABAAAAPgEbAAUA AAABAAAARgEoAAMAAAABAAIAAAExAAIAAAARAAAATgAAAAAAAABgAAAAAQAAAGAAAAABcGFpbnQu bmV0IDQuMi4xMwAA/9sAQwAGBAUGBQQGBgUGBwcGCAoQCgoJCQoUDg8MEBcUGBgXFBYWGh0lHxob @@ -731,5 +731,5 @@ AAAAAAJAAAAAAAAAAAAAABAJEAAAAAAAAAAAAAAAIEoBKAAAAAAAAAAAAAAABAlAAAAAAAIAAAAA BAkBAkBAkBAlACEgMZjdjbFW8bWrEx8YWANb6Fp+bfwab+vLDKMFK9qxH5L0bAr8OPRPKz2AY7J2 SbAjYZAI2E7AIEgIEgIEgMdkSy2NgY7MdlmyNoBXsxmFuyNgVTVjNV3KjlBRNTlXTVHKCrlIqt5T lBhEMohlFerLlBjEMohMVTEARDKCITsAk2AEgAAAkAAAAAAAAAAAAAAAAAAAAAAAASAAAAAAAAD/ -2Q==`;var version="0.9.23";var now=()=>typeof performance!="undefined"?performance.now():parseInt(Number(process.hrtime.bigint())/1e3/1e3);function mergeDeep(...objects){let isObject=obj=>obj&&typeof obj=="object";return objects.reduce((prev,obj)=>(Object.keys(obj||{}).forEach(key=>{let pVal=prev[key],oVal=obj[key];Array.isArray(pVal)&&Array.isArray(oVal)?prev[key]=pVal.concat(...oVal):isObject(pVal)&&isObject(oVal)?prev[key]=mergeDeep(pVal,oVal):prev[key]=oVal}),prev),{})}var Human=class{constructor(userConfig={}){this.tf=tf3,this.version=version,this.config=mergeDeep(config_default,userConfig),this.fx=null,this.state="idle",this.numTensors=0,this.analyzeMemoryLeaks=!1,this.checkSanity=!1,this.firstRun=!0,this.perf={},this.models={facemesh:null,posenet:null,handpose:null,iris:null,age:null,gender:null,emotion:null},this.facemesh=facemesh,this.age=age,this.gender=gender,this.emotion=emotion,this.body=posenet,this.hand=handpose}profile(){return this.config.profile?profile.data:{}}analyze(...msg){if(!this.analyzeMemoryLeaks)return;let current=tf3.engine().state.numTensors,previous=this.numTensors;this.numTensors=current;let leaked=current-previous;leaked!==0&&log(...msg,leaked)}sanity(input){if(!this.checkSanity)return null;if(!input)return"input is not defined";if(tf3.ENV.flags.IS_NODE&&!(input instanceof tf3.Tensor))return"input must be a tensor";try{tf3.getBackend()}catch(e){return"backend not loaded"}return null}simmilarity(embedding1,embedding2){return this.config.face.embedding.enabled?embedding.simmilarity(embedding1,embedding2):0}async load(userConfig){this.state="load";let timeStamp=now();userConfig&&(this.config=mergeDeep(this.config,userConfig)),this.firstRun&&(log(`version: ${this.version} TensorFlow/JS version: ${tf3.version_core}`),await this.checkBackend(!0),tf3.ENV.flags.IS_BROWSER&&(log("configuration:",this.config),log("tf flags:",tf3.ENV.flags)),this.firstRun=!1),this.config.async?[this.models.facemesh,this.models.age,this.models.gender,this.models.emotion,this.models.embedding,this.models.posenet,this.models.handpose]=await Promise.all([this.models.facemesh||(this.config.face.enabled?facemesh.load(this.config):null),this.models.age||(this.config.face.enabled&&this.config.face.age.enabled?age.load(this.config):null),this.models.gender||(this.config.face.enabled&&this.config.face.gender.enabled?gender.load(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?emotion.load(this.config):null),this.models.embedding||(this.config.face.enabled&&this.config.face.embedding.enabled?embedding.load(this.config):null),this.models.posenet||(this.config.body.enabled?posenet.load(this.config):null),this.models.handpose||(this.config.hand.enabled?handpose.load(this.config):null)]):(this.config.face.enabled&&!this.models.facemesh&&(this.models.facemesh=await facemesh.load(this.config)),this.config.face.enabled&&this.config.face.age.enabled&&!this.models.age&&(this.models.age=await age.load(this.config)),this.config.face.enabled&&this.config.face.gender.enabled&&!this.models.gender&&(this.models.gender=await gender.load(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await emotion.load(this.config)),this.config.face.enabled&&this.config.face.embedding.enabled&&!this.models.embedding&&(this.models.embedding=await embedding.load(this.config)),this.config.body.enabled&&!this.models.posenet&&(this.models.posenet=await posenet.load(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await handpose.load(this.config)));let current=Math.trunc(now()-timeStamp);current>(this.perf.load||0)&&(this.perf.load=current)}async checkBackend(force){if(this.config.backend&&this.config.backend!==""&&force||tf3.getBackend()!==this.config.backend){let timeStamp=now();if(this.state="backend",log("setting backend:",this.config.backend),this.config.backend==="wasm"&&(log("settings wasm path:",this.config.wasmPath),tf3.setWasmPaths(this.config.wasmPath),await tf3.env().getAsync("WASM_HAS_SIMD_SUPPORT")||log("warning: wasm simd support is not enabled")),this.config.backend==="humangl"&&(log("registering humangl backend"),register()),await tf3.setBackend(this.config.backend),tf3.enableProdMode(),tf3.getBackend()==="webgl"){this.config.deallocate&&(log("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",this.config.deallocate),tf3.ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",this.config.deallocate?0:-1)),tf3.ENV.set("WEBGL_FORCE_F16_TEXTURES",!0),tf3.ENV.set("WEBGL_PACK_DEPTHWISECONV",!0);let gl=await tf3.backend().getGPGPUContext().gl;log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`)}await tf3.ready(),this.perf.backend=Math.trunc(now()-timeStamp)}}async detectFace(input){var _a;let timeStamp,ageRes,genderRes,emotionRes,embeddingRes,faceRes=[];this.state="run:face",timeStamp=now();let faces=await((_a=this.models.facemesh)==null?void 0:_a.estimateFaces(input,this.config));this.perf.face=Math.trunc(now()-timeStamp);for(let face3 of faces){if(this.analyze("Get Face"),!face3.image||face3.image.isDisposedInternal){log("Face object is disposed:",face3.image);continue}this.analyze("Start Age:"),this.config.async?ageRes=this.config.face.age.enabled?age.predict(face3.image,this.config):{}:(this.state="run:age",timeStamp=now(),ageRes=this.config.face.age.enabled?await age.predict(face3.image,this.config):{},this.perf.age=Math.trunc(now()-timeStamp)),this.analyze("Start Gender:"),this.config.async?genderRes=this.config.face.gender.enabled?gender.predict(face3.image,this.config):{}:(this.state="run:gender",timeStamp=now(),genderRes=this.config.face.gender.enabled?await gender.predict(face3.image,this.config):{},this.perf.gender=Math.trunc(now()-timeStamp)),this.analyze("Start Emotion:"),this.config.async?emotionRes=this.config.face.emotion.enabled?emotion.predict(face3.image,this.config):{}:(this.state="run:emotion",timeStamp=now(),emotionRes=this.config.face.emotion.enabled?await emotion.predict(face3.image,this.config):{},this.perf.emotion=Math.trunc(now()-timeStamp)),this.analyze("End Emotion:"),this.analyze("Start Embedding:"),this.config.async?embeddingRes=this.config.face.embedding.enabled?embedding.predict(face3.image,this.config):{}:(this.state="run:embedding",timeStamp=now(),embeddingRes=this.config.face.embedding.enabled?await embedding.predict(face3.image,this.config):{},this.perf.embedding=Math.trunc(now()-timeStamp)),this.analyze("End Emotion:"),this.config.async&&([ageRes,genderRes,emotionRes,embeddingRes]=await Promise.all([ageRes,genderRes,emotionRes,embeddingRes])),this.analyze("Finish Face:"),face3.image.dispose(),this.config.face.iris.enabled||(delete face3.annotations.leftEyeIris,delete face3.annotations.rightEyeIris);let irisSize=face3.annotations.leftEyeIris&&face3.annotations.rightEyeIris?11.7*Math.max(Math.abs(face3.annotations.leftEyeIris[3][0]-face3.annotations.leftEyeIris[1][0]),Math.abs(face3.annotations.rightEyeIris[4][1]-face3.annotations.rightEyeIris[2][1])):0;faceRes.push({confidence:face3.confidence,box:face3.box,mesh:face3.mesh,boxRaw:face3.boxRaw,meshRaw:face3.meshRaw,annotations:face3.annotations,age:ageRes.age,gender:genderRes.gender,genderConfidence:genderRes.confidence,emotion:emotionRes,embedding:embeddingRes,iris:irisSize!==0?Math.trunc(irisSize)/100:0}),this.analyze("End Face")}return this.analyze("End FaceMesh:"),this.config.async&&(this.perf.face&&delete this.perf.face,this.perf.age&&delete this.perf.age,this.perf.gender&&delete this.perf.gender,this.perf.emotion&&delete this.perf.emotion),faceRes}async image(input,userConfig={}){this.state="image",this.config=mergeDeep(this.config,userConfig);let process3=image2.process(input,this.config);return process3.tensor.dispose(),process3.canvas}async detect(input,userConfig={}){return new Promise(async resolve=>{var _a,_b,_c,_d;this.state="config";let timeStamp;this.config=mergeDeep(this.config,userConfig),this.state="check";let error=this.sanity(input);error&&(log(error,input),resolve({error}));let poseRes,handRes,faceRes,timeStart=now();await this.checkBackend(),await this.load(),this.config.scoped&&tf3.engine().startScope(),this.analyze("Start Scope:"),timeStamp=now();let process3=image2.process(input,this.config);if(!process3||!process3.tensor){log("could not convert input to tensor"),resolve({error:"could not convert input to tensor"});return}this.perf.image=Math.trunc(now()-timeStamp),this.analyze("Get Image:"),this.config.async?(faceRes=this.config.face.enabled?this.detectFace(process3.tensor):[],this.perf.face&&delete this.perf.face):(this.state="run:face",timeStamp=now(),faceRes=this.config.face.enabled?await this.detectFace(process3.tensor):[],this.perf.face=Math.trunc(now()-timeStamp)),this.analyze("Start Body:"),this.config.async?(poseRes=this.config.body.enabled?(_a=this.models.posenet)==null?void 0:_a.estimatePoses(process3.tensor,this.config):[],this.perf.body&&delete this.perf.body):(this.state="run:body",timeStamp=now(),poseRes=this.config.body.enabled?await((_b=this.models.posenet)==null?void 0:_b.estimatePoses(process3.tensor,this.config)):[],this.perf.body=Math.trunc(now()-timeStamp)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(handRes=this.config.hand.enabled?(_c=this.models.handpose)==null?void 0:_c.estimateHands(process3.tensor,this.config):[],this.perf.hand&&delete this.perf.hand):(this.state="run:hand",timeStamp=now(),handRes=this.config.hand.enabled?await((_d=this.models.handpose)==null?void 0:_d.estimateHands(process3.tensor,this.config)):[],this.perf.hand=Math.trunc(now()-timeStamp)),this.config.async&&([faceRes,poseRes,handRes]=await Promise.all([faceRes,poseRes,handRes])),process3.tensor.dispose(),this.config.scoped&&tf3.engine().endScope(),this.analyze("End Scope:");let gestureRes=[];this.config.gesture.enabled&&(timeStamp=now(),gestureRes=[...gesture.face(faceRes),...gesture.body(poseRes),...gesture.hand(handRes),...gesture.iris(faceRes)],this.config.async?this.perf.gesture&&delete this.perf.gesture:this.perf.gesture=Math.trunc(now()-timeStamp)),this.perf.total=Math.trunc(now()-timeStart),this.state="idle",resolve({face:faceRes,body:poseRes,hand:handRes,gesture:gestureRes,performance:this.perf,canvas:process3.canvas})})}async warmup(userConfig){let b64toBlob=(base64,type="application/octet-stream")=>fetch(`data:${type};base64,${base64}`).then(res=>res.blob());userConfig&&(this.config=mergeDeep(this.config,userConfig));let video=this.config.videoOptimized;this.config.videoOptimized=!1;let blob;switch(this.config.warmup){case"face":blob=await b64toBlob(face);break;case"full":blob=await b64toBlob(body);break;default:blob=null}if(!blob)return null;let bitmap=await createImageBitmap(blob),t0=now(),warmup=await this.detect(bitmap,config_exports),t1=now();return bitmap.close(),log("Warmup",this.config.warmup,t1-t0,warmup),this.config.videoOptimized=video,warmup}}; +2Q==`;var version="0.9.24";var now=()=>typeof performance!="undefined"?performance.now():parseInt(Number(process.hrtime.bigint())/1e3/1e3);function mergeDeep(...objects){let isObject=obj=>obj&&typeof obj=="object";return objects.reduce((prev,obj)=>(Object.keys(obj||{}).forEach(key=>{let pVal=prev[key],oVal=obj[key];Array.isArray(pVal)&&Array.isArray(oVal)?prev[key]=pVal.concat(...oVal):isObject(pVal)&&isObject(oVal)?prev[key]=mergeDeep(pVal,oVal):prev[key]=oVal}),prev),{})}var Human=class{constructor(userConfig={}){this.tf=tf4,this.version=version,this.config=mergeDeep(config_default,userConfig),this.fx=null,this.state="idle",this.numTensors=0,this.analyzeMemoryLeaks=!1,this.checkSanity=!1,this.firstRun=!0,this.perf={},this.models={facemesh:null,posenet:null,handpose:null,iris:null,age:null,gender:null,emotion:null},this.facemesh=facemesh,this.age=age,this.gender=gender,this.emotion=emotion,this.body=posenet,this.hand=handpose}profile(){return this.config.profile?profile.data:{}}analyze(...msg){if(!this.analyzeMemoryLeaks)return;let current=tf4.engine().state.numTensors,previous=this.numTensors;this.numTensors=current;let leaked=current-previous;leaked!==0&&log(...msg,leaked)}sanity(input){if(!this.checkSanity)return null;if(!input)return"input is not defined";if(tf4.ENV.flags.IS_NODE&&!(input instanceof tf4.Tensor))return"input must be a tensor";try{tf4.getBackend()}catch(e){return"backend not loaded"}return null}simmilarity(embedding1,embedding2){return this.config.face.embedding.enabled?embedding.simmilarity(embedding1,embedding2):0}async load(userConfig){this.state="load";let timeStamp=now();userConfig&&(this.config=mergeDeep(this.config,userConfig)),this.firstRun&&(log(`version: ${this.version} TensorFlow/JS version: ${tf4.version_core}`),await this.checkBackend(!0),tf4.ENV.flags.IS_BROWSER&&(log("configuration:",this.config),log("tf flags:",tf4.ENV.flags)),this.firstRun=!1),this.config.async?[this.models.facemesh,this.models.age,this.models.gender,this.models.emotion,this.models.embedding,this.models.posenet,this.models.handpose,this.models.blazepose]=await Promise.all([this.models.facemesh||(this.config.face.enabled?facemesh.load(this.config):null),this.models.age||(this.config.face.enabled&&this.config.face.age.enabled?age.load(this.config):null),this.models.gender||(this.config.face.enabled&&this.config.face.gender.enabled?gender.load(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?emotion.load(this.config):null),this.models.embedding||(this.config.face.enabled&&this.config.face.embedding.enabled?embedding.load(this.config):null),this.models.posenet||(this.config.body.enabled?posenet.load(this.config):null),this.models.handpose||(this.config.hand.enabled?handpose.load(this.config):null),this.models.blazepose||(this.config.pose.enabled?load(this.config):null)]):(this.config.face.enabled&&!this.models.facemesh&&(this.models.facemesh=await facemesh.load(this.config)),this.config.face.enabled&&this.config.face.age.enabled&&!this.models.age&&(this.models.age=await age.load(this.config)),this.config.face.enabled&&this.config.face.gender.enabled&&!this.models.gender&&(this.models.gender=await gender.load(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await emotion.load(this.config)),this.config.face.enabled&&this.config.face.embedding.enabled&&!this.models.embedding&&(this.models.embedding=await embedding.load(this.config)),this.config.body.enabled&&!this.models.posenet&&(this.models.posenet=await posenet.load(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await handpose.load(this.config)),this.config.pose.enabled&&!this.models.blazepose&&(this.models.blazepose=await load(this.config)));let current=Math.trunc(now()-timeStamp);current>(this.perf.load||0)&&(this.perf.load=current)}async checkBackend(force){if(this.config.backend&&this.config.backend!==""&&force||tf4.getBackend()!==this.config.backend){let timeStamp=now();if(this.state="backend",log("setting backend:",this.config.backend),this.config.backend==="wasm"&&(log("settings wasm path:",this.config.wasmPath),tf4.setWasmPaths(this.config.wasmPath),await tf4.env().getAsync("WASM_HAS_SIMD_SUPPORT")||log("warning: wasm simd support is not enabled")),this.config.backend==="humangl"&&(log("registering humangl backend"),register()),await tf4.setBackend(this.config.backend),tf4.enableProdMode(),tf4.getBackend()==="webgl"){this.config.deallocate&&(log("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",this.config.deallocate),tf4.ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",this.config.deallocate?0:-1)),tf4.ENV.set("WEBGL_FORCE_F16_TEXTURES",!0),tf4.ENV.set("WEBGL_PACK_DEPTHWISECONV",!0);let gl=await tf4.backend().getGPGPUContext().gl;log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`)}await tf4.ready(),this.perf.backend=Math.trunc(now()-timeStamp)}}async detectFace(input){var _a;let timeStamp,ageRes,genderRes,emotionRes,embeddingRes,faceRes=[];this.state="run:face",timeStamp=now();let faces=await((_a=this.models.facemesh)==null?void 0:_a.estimateFaces(input,this.config));this.perf.face=Math.trunc(now()-timeStamp);for(let face3 of faces){if(this.analyze("Get Face"),!face3.image||face3.image.isDisposedInternal){log("Face object is disposed:",face3.image);continue}this.analyze("Start Age:"),this.config.async?ageRes=this.config.face.age.enabled?age.predict(face3.image,this.config):{}:(this.state="run:age",timeStamp=now(),ageRes=this.config.face.age.enabled?await age.predict(face3.image,this.config):{},this.perf.age=Math.trunc(now()-timeStamp)),this.analyze("Start Gender:"),this.config.async?genderRes=this.config.face.gender.enabled?gender.predict(face3.image,this.config):{}:(this.state="run:gender",timeStamp=now(),genderRes=this.config.face.gender.enabled?await gender.predict(face3.image,this.config):{},this.perf.gender=Math.trunc(now()-timeStamp)),this.analyze("Start Emotion:"),this.config.async?emotionRes=this.config.face.emotion.enabled?emotion.predict(face3.image,this.config):{}:(this.state="run:emotion",timeStamp=now(),emotionRes=this.config.face.emotion.enabled?await emotion.predict(face3.image,this.config):{},this.perf.emotion=Math.trunc(now()-timeStamp)),this.analyze("End Emotion:"),this.analyze("Start Embedding:"),this.config.async?embeddingRes=this.config.face.embedding.enabled?embedding.predict(face3.image,this.config):{}:(this.state="run:embedding",timeStamp=now(),embeddingRes=this.config.face.embedding.enabled?await embedding.predict(face3.image,this.config):{},this.perf.embedding=Math.trunc(now()-timeStamp)),this.analyze("End Emotion:"),this.config.async&&([ageRes,genderRes,emotionRes,embeddingRes]=await Promise.all([ageRes,genderRes,emotionRes,embeddingRes])),this.analyze("Finish Face:"),face3.image.dispose(),this.config.face.iris.enabled||(delete face3.annotations.leftEyeIris,delete face3.annotations.rightEyeIris);let irisSize=face3.annotations.leftEyeIris&&face3.annotations.rightEyeIris?11.7*Math.max(Math.abs(face3.annotations.leftEyeIris[3][0]-face3.annotations.leftEyeIris[1][0]),Math.abs(face3.annotations.rightEyeIris[4][1]-face3.annotations.rightEyeIris[2][1])):0;faceRes.push({confidence:face3.confidence,box:face3.box,mesh:face3.mesh,boxRaw:face3.boxRaw,meshRaw:face3.meshRaw,annotations:face3.annotations,age:ageRes.age,gender:genderRes.gender,genderConfidence:genderRes.confidence,emotion:emotionRes,embedding:embeddingRes,iris:irisSize!==0?Math.trunc(irisSize)/100:0}),this.analyze("End Face")}return this.analyze("End FaceMesh:"),this.config.async&&(this.perf.face&&delete this.perf.face,this.perf.age&&delete this.perf.age,this.perf.gender&&delete this.perf.gender,this.perf.emotion&&delete this.perf.emotion),faceRes}async image(input,userConfig={}){this.state="image",this.config=mergeDeep(this.config,userConfig);let process3=image3.process(input,this.config);return process3.tensor.dispose(),process3.canvas}async detect(input,userConfig={}){return new Promise(async resolve=>{var _a,_b,_c,_d;this.state="config";let timeStamp;this.config=mergeDeep(this.config,userConfig),this.state="check";let error=this.sanity(input);error&&(log(error,input),resolve({error}));let poseRes,blazeposeRes,handRes,faceRes,timeStart=now();await this.checkBackend(),await this.load(),this.config.scoped&&tf4.engine().startScope(),this.analyze("Start Scope:"),timeStamp=now();let process3=image3.process(input,this.config);if(!process3||!process3.tensor){log("could not convert input to tensor"),resolve({error:"could not convert input to tensor"});return}this.perf.image=Math.trunc(now()-timeStamp),this.analyze("Get Image:"),this.config.async?(faceRes=this.config.face.enabled?this.detectFace(process3.tensor):[],this.perf.face&&delete this.perf.face):(this.state="run:face",timeStamp=now(),faceRes=this.config.face.enabled?await this.detectFace(process3.tensor):[],this.perf.face=Math.trunc(now()-timeStamp)),this.analyze("Start Body:"),this.config.async?(poseRes=this.config.body.enabled?(_a=this.models.posenet)==null?void 0:_a.estimatePoses(process3.tensor,this.config):[],this.perf.body&&delete this.perf.body):(this.state="run:body",timeStamp=now(),poseRes=this.config.body.enabled?await((_b=this.models.posenet)==null?void 0:_b.estimatePoses(process3.tensor,this.config)):[],this.perf.body=Math.trunc(now()-timeStamp)),this.analyze("End Body:"),this.analyze("Start Pose:"),this.config.async?(blazeposeRes=this.config.pose.enabled?predict(process3.tensor,this.config):[],this.perf.pose&&delete this.perf.pose):(this.state="run:pose",timeStamp=now(),blazeposeRes=this.config.pose.enabled?await predict(process3.tensor,this.config):[],this.perf.pose=Math.trunc(now()-timeStamp)),this.analyze("End Pose:"),this.analyze("Start Hand:"),this.config.async?(handRes=this.config.hand.enabled?(_c=this.models.handpose)==null?void 0:_c.estimateHands(process3.tensor,this.config):[],this.perf.hand&&delete this.perf.hand):(this.state="run:hand",timeStamp=now(),handRes=this.config.hand.enabled?await((_d=this.models.handpose)==null?void 0:_d.estimateHands(process3.tensor,this.config)):[],this.perf.hand=Math.trunc(now()-timeStamp)),this.analyze("End Hand:"),this.config.async&&([faceRes,poseRes,blazeposeRes,handRes]=await Promise.all([faceRes,poseRes,blazeposeRes,handRes])),process3.tensor.dispose(),this.config.scoped&&tf4.engine().endScope(),this.analyze("End Scope:");let gestureRes=[];this.config.gesture.enabled&&(timeStamp=now(),gestureRes=[...gesture.face(faceRes),...gesture.body(poseRes),...gesture.hand(handRes),...gesture.iris(faceRes)],this.config.async?this.perf.gesture&&delete this.perf.gesture:this.perf.gesture=Math.trunc(now()-timeStamp)),this.perf.total=Math.trunc(now()-timeStart),this.state="idle",resolve({face:faceRes,body:poseRes,hand:handRes,pose:blazeposeRes,gesture:gestureRes,performance:this.perf,canvas:process3.canvas})})}async warmupBitmap(){let b64toBlob=(base64,type="application/octet-stream")=>fetch(`data:${type};base64,${base64}`).then(res2=>res2.blob()),blob,res;switch(this.config.warmup){case"face":blob=await b64toBlob(face);break;case"full":blob=await b64toBlob(body);break;default:blob=null}if(blob){let bitmap=await createImageBitmap(blob);res=await this.detect(bitmap,config_exports),bitmap.close()}return res}async warmupCanvas(){return new Promise(resolve=>{let src,size=0;switch(this.config.warmup){case"face":size=256,src="data:image/jpeg;base64,"+face;break;case"full":size=1200,src="data:image/jpeg;base64,"+body;break;default:src=null}let img=new Image(size,size);img.onload=()=>{let canvas=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(size,size):document.createElement("canvas");canvas.width=size,canvas.height=size;let ctx=canvas.getContext("2d");ctx.drawImage(img,0,0);let data2=ctx.getImageData(0,0,size,size);this.detect(data2,config_exports).then(res=>resolve(res))},src?img.src=src:resolve(null)})}async warmup(userConfig){let t0=now();userConfig&&(this.config=mergeDeep(this.config,userConfig));let video=this.config.videoOptimized;this.config.videoOptimized=!1;let res;typeof createImageBitmap=="function"?res=await this.warmupBitmap():res=await this.warmupCanvas(),this.config.videoOptimized=video;let t1=now();return log("Warmup",this.config.warmup,t1-t0,res),res}}; //# sourceMappingURL=human.node.js.map diff --git a/dist/human.node.js.map b/dist/human.node.js.map index 0c77fe60..b6287c34 100644 --- a/dist/human.node.js.map +++ b/dist/human.node.js.map @@ -1,7 +1,7 @@ { "version": 3, - "sources": ["../src/tfjs/tf-node.js", "../src/face/blazeface.js", "../src/face/box.js", "../src/face/util.js", "../src/face/coords.js", "../src/face/facepipeline.js", "../src/face/facemesh.js", "../src/profile.js", "../src/age/age.js", "../src/gender/gender.js", "../src/emotion/emotion.js", "../src/embedding/embedding.js", "../src/body/modelBase.js", "../src/body/heapSort.js", "../src/body/buildParts.js", "../src/body/keypoints.js", "../src/body/vectors.js", "../src/body/decoders.js", "../src/body/decodePose.js", "../src/body/decodeMultiple.js", "../src/body/util.js", "../src/body/modelPoseNet.js", "../src/body/posenet.js", "../src/hand/handdetector.js", "../src/hand/handpipeline.js", "../src/hand/anchors.js", "../src/hand/handpose.js", "../src/gesture/gesture.js", "../src/imagefx.js", "../src/image.js", "../src/human.js", "../src/log.js", "../src/tfjs/backend.js", "../src/hand/box.js", "../src/hand/util.js", "../config.js", "../src/sample.js"], - "sourcesContent": ["export * from '@tensorflow/tfjs-node';\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nconst NUM_LANDMARKS = 6;\n\nfunction generateAnchors(inputSize) {\n const spec = { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] };\n const anchors = [];\n for (let i = 0; i < spec.strides.length; i++) {\n const stride = spec.strides[i];\n const gridRows = Math.floor((inputSize + stride - 1) / stride);\n const gridCols = Math.floor((inputSize + stride - 1) / stride);\n const anchorsNum = spec.anchors[i];\n for (let gridY = 0; gridY < gridRows; gridY++) {\n const anchorY = stride * (gridY + 0.5);\n for (let gridX = 0; gridX < gridCols; gridX++) {\n const anchorX = stride * (gridX + 0.5);\n for (let n = 0; n < anchorsNum; n++) {\n anchors.push([anchorX, anchorY]);\n }\n }\n }\n }\n return anchors;\n}\n\nconst disposeBox = (box) => {\n box.startEndTensor.dispose();\n box.startPoint.dispose();\n box.endPoint.dispose();\n};\n\nconst createBox = (startEndTensor) => ({\n startEndTensor,\n startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]),\n endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]),\n});\n\nconst scaleBox = (box, factors) => {\n const starts = tf.mul(box.startPoint, factors);\n const ends = tf.mul(box.endPoint, factors);\n const newCoordinates = tf.concat2d([starts, ends], 1);\n return createBox(newCoordinates);\n};\n\nfunction decodeBounds(boxOutputs, anchors, inputSize) {\n const boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n const centers = tf.add(boxStarts, anchors);\n const boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n const boxSizesNormalized = tf.div(boxSizes, inputSize);\n const centersNormalized = tf.div(centers, inputSize);\n const halfBoxSize = tf.div(boxSizesNormalized, 2);\n const starts = tf.sub(centersNormalized, halfBoxSize);\n const ends = tf.add(centersNormalized, halfBoxSize);\n const startNormalized = tf.mul(starts, inputSize);\n const endNormalized = tf.mul(ends, inputSize);\n const concatAxis = 1;\n return tf.concat2d([startNormalized, endNormalized], concatAxis);\n}\n\nfunction scaleBoxFromPrediction(face, scaleFactor) {\n return tf.tidy(() => {\n const box = face['box'] ? face['box'] : face;\n return scaleBox(box, scaleFactor).startEndTensor.squeeze();\n });\n}\n\nclass BlazeFaceModel {\n constructor(model, config) {\n this.blazeFaceModel = model;\n this.width = config.face.detector.inputSize;\n this.height = config.face.detector.inputSize;\n this.anchorsData = generateAnchors(config.face.detector.inputSize);\n this.anchors = tf.tensor2d(this.anchorsData);\n this.inputSize = tf.tensor1d([this.width, this.height]);\n this.config = config;\n this.scaleFaces = 0.8;\n }\n\n async getBoundingBoxes(inputImage) {\n // sanity check on input\n if ((!inputImage) || (inputImage.isDisposedInternal) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return null;\n const [detectedOutputs, boxes, scores] = tf.tidy(() => {\n const resizedImage = inputImage.resizeBilinear([this.width, this.height]);\n // const normalizedImage = tf.mul(tf.sub(resizedImage.div(255), 0.5), 2);\n const normalizedImage = tf.sub(resizedImage.div(127.5), 1);\n const batchedPrediction = this.blazeFaceModel.predict(normalizedImage);\n let prediction;\n // are we using tfhub or pinto converted model?\n if (Array.isArray(batchedPrediction)) {\n const sorted = batchedPrediction.sort((a, b) => a.size - b.size);\n const concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n const concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n const concat = tf.concat([concat512, concat384], 1);\n prediction = concat.squeeze(0);\n } else {\n prediction = batchedPrediction.squeeze(); // when using tfhub model\n }\n const decodedBounds = decodeBounds(prediction, this.anchors, this.inputSize);\n const logits = tf.slice(prediction, [0, 0], [-1, 1]);\n const scoresOut = tf.sigmoid(logits).squeeze();\n return [prediction, decodedBounds, scoresOut];\n });\n const boxIndicesTensor = await tf.image.nonMaxSuppressionAsync(boxes, scores, this.config.face.detector.maxFaces, this.config.face.detector.iouThreshold, this.config.face.detector.scoreThreshold);\n const boxIndices = boxIndicesTensor.arraySync();\n boxIndicesTensor.dispose();\n const boundingBoxesMap = boxIndices.map((boxIndex) => tf.slice(boxes, [boxIndex, 0], [1, -1]));\n const boundingBoxes = boundingBoxesMap.map((boundingBox) => {\n const vals = boundingBox.arraySync();\n boundingBox.dispose();\n return vals;\n });\n\n const scoresVal = scores.dataSync();\n const annotatedBoxes = [];\n for (let i = 0; i < boundingBoxes.length; i++) {\n const boxIndex = boxIndices[i];\n const confidence = scoresVal[boxIndex];\n if (confidence > this.config.face.detector.minConfidence) {\n const box = createBox(boundingBoxes[i]);\n const anchor = this.anchorsData[boxIndex];\n const landmarks = tf.tidy(() => tf.slice(detectedOutputs, [boxIndex, NUM_LANDMARKS - 1], [1, -1]).squeeze().reshape([NUM_LANDMARKS, -1]));\n annotatedBoxes.push({ box, landmarks, anchor, confidence });\n }\n }\n detectedOutputs.dispose();\n boxes.dispose();\n scores.dispose();\n detectedOutputs.dispose();\n return {\n boxes: annotatedBoxes,\n scaleFactor: [inputImage.shape[2] / this.width, inputImage.shape[1] / this.height],\n };\n }\n\n async estimateFaces(input) {\n // @ts-ignore\n const { boxes, scaleFactor } = await this.getBoundingBoxes(input);\n const faces = [];\n for (const face of boxes) {\n const landmarkData = face.landmarks.arraySync();\n const scaledBox = scaleBoxFromPrediction(face, scaleFactor);\n // @ts-ignore\n const boxData = scaleBox.arraySync();\n const probabilityData = face.probability.arraySync();\n const anchor = face.anchor;\n const [scaleFactorX, scaleFactorY] = scaleFactor;\n const scaledLandmarks = landmarkData\n .map((landmark) => ([\n (landmark[0] + anchor[0]) * scaleFactorX,\n (landmark[1] + anchor[1]) * scaleFactorY,\n ]));\n const normalizedFace = {\n topLeft: boxData.slice(0, 2),\n bottomRight: boxData.slice(2),\n landmarks: scaledLandmarks,\n probability: probabilityData,\n };\n disposeBox(face.box);\n face.landmarks.dispose();\n face.probability.dispose();\n scaledBox.dispose();\n faces.push(normalizedFace);\n }\n return faces;\n }\n}\n\nasync function load(config) {\n const blazeface = await tf.loadGraphModel(config.face.detector.modelPath, { fromTFHub: config.face.detector.modelPath.includes('tfhub.dev') });\n const model = new BlazeFaceModel(blazeface, config);\n log(`load model: ${config.face.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n return model;\n}\n\nexports.load = load;\nexports.BlazeFaceModel = BlazeFaceModel;\nexports.disposeBox = disposeBox;\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nfunction scaleBoxCoordinates(box, factor) {\n const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n return { startPoint, endPoint };\n}\nexports.scaleBoxCoordinates = scaleBoxCoordinates;\n\nfunction getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\nexports.getBoxSize = getBoxSize;\n\nfunction getBoxCenter(box) {\n return [\n box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n ];\n}\nexports.getBoxCenter = getBoxCenter;\n\nfunction cutBoxFromImageAndResize(box, image, cropSize) {\n const h = image.shape[1];\n const w = image.shape[2];\n const boxes = [[\n box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h,\n box.endPoint[0] / w,\n ]];\n return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\nexports.cutBoxFromImageAndResize = cutBoxFromImageAndResize;\n\nfunction enlargeBox(box, factor = 1.5) {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n return { startPoint, endPoint, landmarks: box.landmarks };\n}\nexports.enlargeBox = enlargeBox;\n\nfunction squarifyBox(box) {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const maxEdge = Math.max(...size);\n const halfSize = maxEdge / 2;\n const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n return { startPoint, endPoint, landmarks: box.landmarks };\n}\nexports.squarifyBox = squarifyBox;\n", "exports.IDENTITY_MATRIX = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n/**\n * Normalizes the provided angle to the range -pi to pi.\n * @param angle The angle in radians to be normalized.\n */\nfunction normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\nexports.normalizeRadians = normalizeRadians;\n\n/**\n * Computes the angle of rotation between two anchor points.\n * @param point1 First anchor point\n * @param point2 Second anchor point\n */\nfunction computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\nexports.computeRotation = computeRotation;\n\nfunction radToDegrees(rad) {\n return rad * 180 / Math.PI;\n}\nexports.radToDegrees = radToDegrees;\n\nfunction buildTranslationMatrix(x, y) {\n return [[1, 0, x], [0, 1, y], [0, 0, 1]];\n}\n\nfunction dot(v1, v2) {\n let product = 0;\n for (let i = 0; i < v1.length; i++) {\n product += v1[i] * v2[i];\n }\n return product;\n}\nexports.dot = dot;\n\nfunction getColumnFrom2DArr(arr, columnIndex) {\n const column = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\nexports.getColumnFrom2DArr = getColumnFrom2DArr;\n\nfunction multiplyTransformMatrices(mat1, mat2) {\n const product = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) {\n product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n }\n return product;\n}\nfunction buildRotationMatrix(rotation, center) {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\nexports.buildRotationMatrix = buildRotationMatrix;\n\nfunction invertTransformMatrix(matrix) {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [\n -dot(rotationComponent[0], translationComponent),\n -dot(rotationComponent[1], translationComponent),\n ];\n return [\n rotationComponent[0].concat(invertedTranslation[0]),\n rotationComponent[1].concat(invertedTranslation[1]),\n [0, 0, 1],\n ];\n}\nexports.invertTransformMatrix = invertTransformMatrix;\n\nfunction rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\nexports.rotatePoint = rotatePoint;\n\nfunction xyDistanceBetweenPoints(a, b) {\n return Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n}\nexports.xyDistanceBetweenPoints = xyDistanceBetweenPoints;\n", "const MESH_ANNOTATIONS = {\n silhouette: [\n 10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n 397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n 172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n ],\n lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291],\n lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291],\n lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308],\n lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],\n rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173],\n rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133],\n rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190],\n rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243],\n rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189],\n rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244],\n rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245],\n rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193],\n rightEyebrowLower: [35, 124, 46, 53, 52, 65],\n rightEyeIris: [473, 474, 475, 476, 477],\n leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n leftEyeIris: [468, 469, 470, 471, 472],\n midwayBetweenEyes: [168],\n noseTip: [1],\n noseBottom: [2],\n noseRightCorner: [98],\n noseLeftCorner: [327],\n rightCheek: [205],\n leftCheek: [425],\n};\n\nconst MESH_TO_IRIS_INDICES_MAP = [ // A mapping from facemesh model keypoints to iris model keypoints.\n { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] },\n { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] },\n { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] },\n { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] },\n { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] },\n { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] },\n { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] },\n // { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] },\n // { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] },\n];\n\nconst UV468 = [\n [0.499976992607117, 0.652534008026123],\n [0.500025987625122, 0.547487020492554],\n [0.499974012374878, 0.602371990680695],\n [0.482113003730774, 0.471979022026062],\n [0.500150978565216, 0.527155995368958],\n [0.499909996986389, 0.498252987861633],\n [0.499523013830185, 0.40106201171875],\n [0.289712011814117, 0.380764007568359],\n [0.499954998493195, 0.312398016452789],\n [0.499987006187439, 0.269918978214264],\n [0.500023007392883, 0.107050001621246],\n [0.500023007392883, 0.666234016418457],\n [0.5000159740448, 0.679224014282227],\n [0.500023007392883, 0.692348003387451],\n [0.499976992607117, 0.695277988910675],\n [0.499976992607117, 0.70593398809433],\n [0.499976992607117, 0.719385027885437],\n [0.499976992607117, 0.737019002437592],\n [0.499967992305756, 0.781370997428894],\n [0.499816000461578, 0.562981009483337],\n [0.473773002624512, 0.573909997940063],\n [0.104906998574734, 0.254140973091125],\n [0.365929991006851, 0.409575998783112],\n [0.338757991790771, 0.41302502155304],\n [0.311120003461838, 0.409460008144379],\n [0.274657994508743, 0.389131009578705],\n [0.393361985683441, 0.403706014156342],\n [0.345234006643295, 0.344011008739471],\n [0.370094001293182, 0.346076011657715],\n [0.319321990013123, 0.347265005111694],\n [0.297903001308441, 0.353591024875641],\n [0.24779200553894, 0.410809993743896],\n [0.396889001131058, 0.842755019664764],\n [0.280097991228104, 0.375599980354309],\n [0.106310002505779, 0.399955987930298],\n [0.2099249958992, 0.391353011131287],\n [0.355807989835739, 0.534406006336212],\n [0.471751004457474, 0.65040397644043],\n [0.474155008792877, 0.680191993713379],\n [0.439785003662109, 0.657229006290436],\n [0.414617002010345, 0.66654098033905],\n [0.450374007225037, 0.680860996246338],\n [0.428770989179611, 0.682690978050232],\n [0.374971002340317, 0.727805018424988],\n [0.486716985702515, 0.547628998756409],\n [0.485300987958908, 0.527395009994507],\n [0.257764995098114, 0.314490020275116],\n [0.401223003864288, 0.455172002315521],\n [0.429818987846375, 0.548614978790283],\n [0.421351999044418, 0.533740997314453],\n [0.276895999908447, 0.532056987285614],\n [0.483370006084442, 0.499586999416351],\n [0.33721199631691, 0.282882988452911],\n [0.296391993761063, 0.293242990970612],\n [0.169294998049736, 0.193813979625702],\n [0.447580009698868, 0.302609980106354],\n [0.392390012741089, 0.353887975215912],\n [0.354490011930466, 0.696784019470215],\n [0.067304998636246, 0.730105042457581],\n [0.442739009857178, 0.572826027870178],\n [0.457098007202148, 0.584792017936707],\n [0.381974011659622, 0.694710969924927],\n [0.392388999462128, 0.694203019142151],\n [0.277076005935669, 0.271932005882263],\n [0.422551989555359, 0.563233017921448],\n [0.385919004678726, 0.281364023685455],\n [0.383103013038635, 0.255840003490448],\n [0.331431001424789, 0.119714021682739],\n [0.229923993349075, 0.232002973556519],\n [0.364500999450684, 0.189113974571228],\n [0.229622006416321, 0.299540996551514],\n [0.173287004232407, 0.278747975826263],\n [0.472878992557526, 0.666198015213013],\n [0.446828007698059, 0.668527007102966],\n [0.422762006521225, 0.673889994621277],\n [0.445307999849319, 0.580065965652466],\n [0.388103008270264, 0.693961024284363],\n [0.403039008378983, 0.706539988517761],\n [0.403629004955292, 0.693953037261963],\n [0.460041999816895, 0.557139039039612],\n [0.431158006191254, 0.692366003990173],\n [0.452181994915009, 0.692366003990173],\n [0.475387006998062, 0.692366003990173],\n [0.465828001499176, 0.779190003871918],\n [0.472328990697861, 0.736225962638855],\n [0.473087012767792, 0.717857003211975],\n [0.473122000694275, 0.704625964164734],\n [0.473033010959625, 0.695277988910675],\n [0.427942007780075, 0.695277988910675],\n [0.426479011774063, 0.703539967536926],\n [0.423162013292313, 0.711845993995667],\n [0.4183090031147, 0.720062971115112],\n [0.390094995498657, 0.639572978019714],\n [0.013953999616206, 0.560034036636353],\n [0.499913990497589, 0.58014702796936],\n [0.413199990987778, 0.69539999961853],\n [0.409626007080078, 0.701822996139526],\n [0.468080013990402, 0.601534962654114],\n [0.422728985548019, 0.585985004901886],\n [0.463079988956451, 0.593783974647522],\n [0.37211999297142, 0.47341400384903],\n [0.334562003612518, 0.496073007583618],\n [0.411671012639999, 0.546965003013611],\n [0.242175996303558, 0.14767599105835],\n [0.290776997804642, 0.201445996761322],\n [0.327338010072708, 0.256527006626129],\n [0.399509996175766, 0.748921036720276],\n [0.441727995872498, 0.261676013469696],\n [0.429764986038208, 0.187834024429321],\n [0.412198007106781, 0.108901023864746],\n [0.288955003023148, 0.398952007293701],\n [0.218936994671822, 0.435410976409912],\n [0.41278201341629, 0.398970007896423],\n [0.257135003805161, 0.355440020561218],\n [0.427684992551804, 0.437960982322693],\n [0.448339998722076, 0.536936044692993],\n [0.178560003638268, 0.45755398273468],\n [0.247308000922203, 0.457193970680237],\n [0.286267012357712, 0.467674970626831],\n [0.332827985286713, 0.460712015628815],\n [0.368755996227264, 0.447206974029541],\n [0.398963987827301, 0.432654976844788],\n [0.476410001516342, 0.405806005001068],\n [0.189241006970406, 0.523923993110657],\n [0.228962004184723, 0.348950982093811],\n [0.490725994110107, 0.562400996685028],\n [0.404670000076294, 0.485132992267609],\n [0.019469000399113, 0.401564002037048],\n [0.426243007183075, 0.420431017875671],\n [0.396993011236191, 0.548797011375427],\n [0.266469985246658, 0.376977026462555],\n [0.439121007919312, 0.51895797252655],\n [0.032313998788595, 0.644356966018677],\n [0.419054001569748, 0.387154996395111],\n [0.462783008813858, 0.505746960639954],\n [0.238978996872902, 0.779744982719421],\n [0.198220998048782, 0.831938028335571],\n [0.107550002634525, 0.540755033493042],\n [0.183610007166862, 0.740257024765015],\n [0.134409993886948, 0.333683013916016],\n [0.385764002799988, 0.883153975009918],\n [0.490967005491257, 0.579378008842468],\n [0.382384985685349, 0.508572995662689],\n [0.174399003386497, 0.397670984268188],\n [0.318785011768341, 0.39623498916626],\n [0.343364000320435, 0.400596976280212],\n [0.396100014448166, 0.710216999053955],\n [0.187885001301765, 0.588537991046906],\n [0.430987000465393, 0.944064974784851],\n [0.318993002176285, 0.898285031318665],\n [0.266247987747192, 0.869701027870178],\n [0.500023007392883, 0.190576016902924],\n [0.499976992607117, 0.954452991485596],\n [0.366169989109039, 0.398822009563446],\n [0.393207013607025, 0.39553701877594],\n [0.410373002290726, 0.391080021858215],\n [0.194993004202843, 0.342101991176605],\n [0.388664990663528, 0.362284004688263],\n [0.365961998701096, 0.355970978736877],\n [0.343364000320435, 0.355356991291046],\n [0.318785011768341, 0.35834002494812],\n [0.301414996385574, 0.363156020641327],\n [0.058132998645306, 0.319076001644135],\n [0.301414996385574, 0.387449026107788],\n [0.499987989664078, 0.618434011936188],\n [0.415838003158569, 0.624195992946625],\n [0.445681989192963, 0.566076993942261],\n [0.465844005346298, 0.620640993118286],\n [0.49992299079895, 0.351523995399475],\n [0.288718998432159, 0.819945991039276],\n [0.335278987884521, 0.852819979190826],\n [0.440512001514435, 0.902418971061707],\n [0.128294005990028, 0.791940987110138],\n [0.408771991729736, 0.373893976211548],\n [0.455606997013092, 0.451801002025604],\n [0.499877005815506, 0.908990025520325],\n [0.375436991453171, 0.924192011356354],\n [0.11421000212431, 0.615022003650665],\n [0.448662012815475, 0.695277988910675],\n [0.4480200111866, 0.704632043838501],\n [0.447111994028091, 0.715808033943176],\n [0.444831997156143, 0.730794012546539],\n [0.430011987686157, 0.766808986663818],\n [0.406787008047104, 0.685672998428345],\n [0.400738000869751, 0.681069016456604],\n [0.392399996519089, 0.677703022956848],\n [0.367855995893478, 0.663918972015381],\n [0.247923001646996, 0.601333022117615],\n [0.452769994735718, 0.420849978923798],\n [0.43639200925827, 0.359887003898621],\n [0.416164010763168, 0.368713974952698],\n [0.413385987281799, 0.692366003990173],\n [0.228018000721931, 0.683571994304657],\n [0.468268007040024, 0.352671027183533],\n [0.411361992359161, 0.804327011108398],\n [0.499989002943039, 0.469825029373169],\n [0.479153990745544, 0.442654013633728],\n [0.499974012374878, 0.439637005329132],\n [0.432112008333206, 0.493588984012604],\n [0.499886006116867, 0.866917014122009],\n [0.49991300702095, 0.821729004383087],\n [0.456548988819122, 0.819200992584229],\n [0.344549000263214, 0.745438992977142],\n [0.37890899181366, 0.574010014533997],\n [0.374292999505997, 0.780184984207153],\n [0.319687992334366, 0.570737957954407],\n [0.357154995203018, 0.604269981384277],\n [0.295284003019333, 0.621580958366394],\n [0.447750002145767, 0.862477004528046],\n [0.410986006259918, 0.508723020553589],\n [0.31395098567009, 0.775308012962341],\n [0.354128003120422, 0.812552988529205],\n [0.324548006057739, 0.703992962837219],\n [0.189096003770828, 0.646299958229065],\n [0.279776990413666, 0.71465802192688],\n [0.1338230073452, 0.682700991630554],\n [0.336768001317978, 0.644733011722565],\n [0.429883986711502, 0.466521978378296],\n [0.455527991056442, 0.548622965812683],\n [0.437114000320435, 0.558896005153656],\n [0.467287987470627, 0.529924988746643],\n [0.414712011814117, 0.335219979286194],\n [0.37704598903656, 0.322777986526489],\n [0.344107985496521, 0.320150971412659],\n [0.312875986099243, 0.32233202457428],\n [0.283526003360748, 0.333190023899078],\n [0.241245999932289, 0.382785975933075],\n [0.102986000478268, 0.468762993812561],\n [0.267612010240555, 0.424560010433197],\n [0.297879010438919, 0.433175981044769],\n [0.333433985710144, 0.433878004550934],\n [0.366427004337311, 0.426115989685059],\n [0.396012008190155, 0.416696012020111],\n [0.420121014118195, 0.41022801399231],\n [0.007561000064015, 0.480777025222778],\n [0.432949006557465, 0.569517970085144],\n [0.458638995885849, 0.479089021682739],\n [0.473466008901596, 0.545744001865387],\n [0.476087987422943, 0.563830018043518],\n [0.468472003936768, 0.555056989192963],\n [0.433990985155106, 0.582361996173859],\n [0.483518004417419, 0.562983989715576],\n [0.482482999563217, 0.57784903049469],\n [0.42645001411438, 0.389798998832703],\n [0.438998997211456, 0.39649498462677],\n [0.450067013502121, 0.400434017181396],\n [0.289712011814117, 0.368252992630005],\n [0.276670008897781, 0.363372981548309],\n [0.517862021923065, 0.471948027610779],\n [0.710287988185883, 0.380764007568359],\n [0.526226997375488, 0.573909997940063],\n [0.895093023777008, 0.254140973091125],\n [0.634069979190826, 0.409575998783112],\n [0.661242008209229, 0.41302502155304],\n [0.688880026340485, 0.409460008144379],\n [0.725341975688934, 0.389131009578705],\n [0.606630027294159, 0.40370500087738],\n [0.654766023159027, 0.344011008739471],\n [0.629905998706818, 0.346076011657715],\n [0.680678009986877, 0.347265005111694],\n [0.702096998691559, 0.353591024875641],\n [0.75221198797226, 0.410804986953735],\n [0.602918028831482, 0.842862963676453],\n [0.719901978969574, 0.375599980354309],\n [0.893692970275879, 0.399959981441498],\n [0.790081977844238, 0.391354024410248],\n [0.643998026847839, 0.534487962722778],\n [0.528249025344849, 0.65040397644043],\n [0.525849997997284, 0.680191040039062],\n [0.560214996337891, 0.657229006290436],\n [0.585384011268616, 0.66654098033905],\n [0.549625992774963, 0.680860996246338],\n [0.57122802734375, 0.682691991329193],\n [0.624852001667023, 0.72809898853302],\n [0.513050019741058, 0.547281980514526],\n [0.51509702205658, 0.527251958847046],\n [0.742246985435486, 0.314507007598877],\n [0.598631024360657, 0.454979002475739],\n [0.570338010787964, 0.548575043678284],\n [0.578631997108459, 0.533622980117798],\n [0.723087012767792, 0.532054007053375],\n [0.516445994377136, 0.499638974666595],\n [0.662801027297974, 0.282917976379395],\n [0.70362401008606, 0.293271005153656],\n [0.830704987049103, 0.193813979625702],\n [0.552385985851288, 0.302568018436432],\n [0.607609987258911, 0.353887975215912],\n [0.645429015159607, 0.696707010269165],\n [0.932694971561432, 0.730105042457581],\n [0.557260990142822, 0.572826027870178],\n [0.542901992797852, 0.584792017936707],\n [0.6180260181427, 0.694710969924927],\n [0.607590973377228, 0.694203019142151],\n [0.722943007946014, 0.271963000297546],\n [0.577413976192474, 0.563166975975037],\n [0.614082992076874, 0.281386971473694],\n [0.616907000541687, 0.255886018276215],\n [0.668509006500244, 0.119913995265961],\n [0.770092010498047, 0.232020974159241],\n [0.635536015033722, 0.189248979091644],\n [0.77039098739624, 0.299556016921997],\n [0.826722025871277, 0.278755009174347],\n [0.527121007442474, 0.666198015213013],\n [0.553171992301941, 0.668527007102966],\n [0.577238023281097, 0.673889994621277],\n [0.554691970348358, 0.580065965652466],\n [0.611896991729736, 0.693961024284363],\n [0.59696102142334, 0.706539988517761],\n [0.596370995044708, 0.693953037261963],\n [0.539958000183105, 0.557139039039612],\n [0.568841993808746, 0.692366003990173],\n [0.547818005084991, 0.692366003990173],\n [0.52461302280426, 0.692366003990173],\n [0.534089982509613, 0.779141008853912],\n [0.527670979499817, 0.736225962638855],\n [0.526912987232208, 0.717857003211975],\n [0.526877999305725, 0.704625964164734],\n [0.526966989040375, 0.695277988910675],\n [0.572058022022247, 0.695277988910675],\n [0.573521018028259, 0.703539967536926],\n [0.57683801651001, 0.711845993995667],\n [0.581691026687622, 0.720062971115112],\n [0.609944999217987, 0.639909982681274],\n [0.986046016216278, 0.560034036636353],\n [0.5867999792099, 0.69539999961853],\n [0.590372025966644, 0.701822996139526],\n [0.531915009021759, 0.601536989212036],\n [0.577268004417419, 0.585934996604919],\n [0.536915004253387, 0.593786001205444],\n [0.627542972564697, 0.473352015018463],\n [0.665585994720459, 0.495950996875763],\n [0.588353991508484, 0.546862006187439],\n [0.757824003696442, 0.14767599105835],\n [0.709249973297119, 0.201507985591888],\n [0.672684013843536, 0.256581008434296],\n [0.600408971309662, 0.74900496006012],\n [0.55826598405838, 0.261672019958496],\n [0.570303976535797, 0.187870979309082],\n [0.588165998458862, 0.109044015407562],\n [0.711045026779175, 0.398952007293701],\n [0.781069993972778, 0.435405015945435],\n [0.587247014045715, 0.398931980133057],\n [0.742869973182678, 0.355445981025696],\n [0.572156012058258, 0.437651991844177],\n [0.55186802148819, 0.536570012569427],\n [0.821442008018494, 0.457556009292603],\n [0.752701997756958, 0.457181990146637],\n [0.71375697851181, 0.467626988887787],\n [0.66711300611496, 0.460672974586487],\n [0.631101012229919, 0.447153985500336],\n [0.6008620262146, 0.432473003864288],\n [0.523481011390686, 0.405627012252808],\n [0.810747981071472, 0.523926019668579],\n [0.771045982837677, 0.348959028720856],\n [0.509127020835876, 0.562718033790588],\n [0.595292985439301, 0.485023975372314],\n [0.980530977249146, 0.401564002037048],\n [0.573499977588654, 0.420000016689301],\n [0.602994978427887, 0.548687994480133],\n [0.733529984951019, 0.376977026462555],\n [0.560611009597778, 0.519016981124878],\n [0.967685997486115, 0.644356966018677],\n [0.580985009670258, 0.387160003185272],\n [0.537728011608124, 0.505385041236877],\n [0.760966002941132, 0.779752969741821],\n [0.801778972148895, 0.831938028335571],\n [0.892440974712372, 0.54076099395752],\n [0.816350996494293, 0.740260004997253],\n [0.865594983100891, 0.333687007427216],\n [0.614073991775513, 0.883246004581451],\n [0.508952975273132, 0.579437971115112],\n [0.617941975593567, 0.508316040039062],\n [0.825608015060425, 0.397674977779388],\n [0.681214988231659, 0.39623498916626],\n [0.656635999679565, 0.400596976280212],\n [0.603900015354156, 0.710216999053955],\n [0.81208598613739, 0.588539004325867],\n [0.56801301240921, 0.944564998149872],\n [0.681007981300354, 0.898285031318665],\n [0.733752012252808, 0.869701027870178],\n [0.633830010890961, 0.398822009563446],\n [0.606792986392975, 0.39553701877594],\n [0.589659988880157, 0.391062021255493],\n [0.805015981197357, 0.342108011245728],\n [0.611334979534149, 0.362284004688263],\n [0.634037971496582, 0.355970978736877],\n [0.656635999679565, 0.355356991291046],\n [0.681214988231659, 0.35834002494812],\n [0.698584973812103, 0.363156020641327],\n [0.941866993904114, 0.319076001644135],\n [0.698584973812103, 0.387449026107788],\n [0.584177017211914, 0.624107003211975],\n [0.554318010807037, 0.566076993942261],\n [0.534153997898102, 0.62064003944397],\n [0.711217999458313, 0.819975018501282],\n [0.664629995822906, 0.852871000766754],\n [0.559099972248077, 0.902631998062134],\n [0.871706008911133, 0.791940987110138],\n [0.591234028339386, 0.373893976211548],\n [0.544341027736664, 0.451583981513977],\n [0.624562978744507, 0.924192011356354],\n [0.88577002286911, 0.615028977394104],\n [0.551338016986847, 0.695277988910675],\n [0.551980018615723, 0.704632043838501],\n [0.552887976169586, 0.715808033943176],\n [0.555167973041534, 0.730794012546539],\n [0.569944024085999, 0.767035007476807],\n [0.593203008174896, 0.685675978660583],\n [0.599261999130249, 0.681069016456604],\n [0.607599973678589, 0.677703022956848],\n [0.631937980651855, 0.663500010967255],\n [0.752032995223999, 0.601315021514893],\n [0.547226011753082, 0.420395016670227],\n [0.563543975353241, 0.359827995300293],\n [0.583841025829315, 0.368713974952698],\n [0.586614012718201, 0.692366003990173],\n [0.771915018558502, 0.683578014373779],\n [0.531597018241882, 0.352482974529266],\n [0.588370978832245, 0.804440975189209],\n [0.52079701423645, 0.442565023899078],\n [0.567984998226166, 0.493479013442993],\n [0.543282985687256, 0.819254994392395],\n [0.655317008495331, 0.745514988899231],\n [0.621008992195129, 0.574018001556396],\n [0.625559985637665, 0.78031200170517],\n [0.680198013782501, 0.570719003677368],\n [0.64276397228241, 0.604337990283966],\n [0.704662978649139, 0.621529996395111],\n [0.552012026309967, 0.862591981887817],\n [0.589071989059448, 0.508637011051178],\n [0.685944974422455, 0.775357007980347],\n [0.645735025405884, 0.812640011310577],\n [0.675342977046967, 0.703978002071381],\n [0.810858011245728, 0.646304965019226],\n [0.72012197971344, 0.714666962623596],\n [0.866151988506317, 0.682704985141754],\n [0.663187026977539, 0.644596993923187],\n [0.570082008838654, 0.466325998306274],\n [0.544561982154846, 0.548375964164734],\n [0.562758982181549, 0.558784961700439],\n [0.531987011432648, 0.530140042304993],\n [0.585271000862122, 0.335177004337311],\n [0.622952997684479, 0.32277899980545],\n [0.655896008014679, 0.320163011550903],\n [0.687132000923157, 0.322345972061157],\n [0.716481983661652, 0.333200991153717],\n [0.758756995201111, 0.382786989212036],\n [0.897013008594513, 0.468769013881683],\n [0.732392013072968, 0.424547016620636],\n [0.70211398601532, 0.433162987232208],\n [0.66652500629425, 0.433866024017334],\n [0.633504986763, 0.426087975502014],\n [0.603875994682312, 0.416586995124817],\n [0.579657971858978, 0.409945011138916],\n [0.992439985275269, 0.480777025222778],\n [0.567192018032074, 0.569419980049133],\n [0.54136598110199, 0.478899002075195],\n [0.526564002037048, 0.546118021011353],\n [0.523913025856018, 0.563830018043518],\n [0.531529009342194, 0.555056989192963],\n [0.566035985946655, 0.582329034805298],\n [0.51631098985672, 0.563053965568542],\n [0.5174720287323, 0.577877044677734],\n [0.573594987392426, 0.389806985855103],\n [0.560697972774506, 0.395331978797913],\n [0.549755990505219, 0.399751007556915],\n [0.710287988185883, 0.368252992630005],\n [0.723330020904541, 0.363372981548309],\n];\n\nconst TRI468 = [\n 127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n 151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n 157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n 181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n 228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n 170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n 155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n 158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n 102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n 90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n 196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n 246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n 117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n 153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n 63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n 135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n 76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n 166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n 190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n 112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n 110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n 348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n 314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n 321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n 466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n 429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n 293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n 427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n 431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n 434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n 348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n 318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n 373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n 340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n 351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n 164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n 327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n 405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n 438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n 449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n 444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n 464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n 243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n 166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n 119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n 77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n 201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n 103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n 217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n 78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n 57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n 142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n 90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n 178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n 224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n 143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n 153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n 63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n 241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n 186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n 141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n 223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n 128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n 451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n 344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n 424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n 321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n 448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n 281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n 283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n 432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n 268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n 6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n 317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n 272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n 336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n 441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n 373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n 425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n 403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n 383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n 254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n 259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n 453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nconst TRI68 = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n 8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n 45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n 23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n 39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n 35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n 48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n 66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\nconst TRI33 = [\n /* eyes */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n /* brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n /* 4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n /* nose */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n /* chin */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n /* cont */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\nconst TRI7 = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nconst VTX68 = [\n /* cont */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n /* nose */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n /* eyes */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n /* lip */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\nconst VTX33 = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\nconst VTX7 = [33, 133, 362, 263, 1, 78, 308];\n\nexports.MESH_ANNOTATIONS = MESH_ANNOTATIONS;\nexports.MESH_TO_IRIS_INDICES_MAP = MESH_TO_IRIS_INDICES_MAP;\n\nexports.TRI468 = TRI468;\nexports.TRI68 = TRI68;\nexports.TRI33 = TRI33;\nexports.TRI7 = TRI7;\n\nexports.UV468 = UV468;\nexports.UV68 = VTX68.map((x) => UV468[x]);\nexports.UV33 = VTX33.map((x) => UV468[x]);\nexports.UV7 = VTX7.map((x) => UV468[x]);\n", "/* eslint-disable class-methods-use-this */\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as bounding from './box';\nimport * as util from './util';\nimport * as coords from './coords.js';\n// eslint-disable-next-line no-unused-vars\nimport { log } from '../log.js';\n\nconst LANDMARKS_COUNT = 468;\nconst MESH_MOUTH_INDEX = 13;\nconst MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES = [MESH_MOUTH_INDEX, coords.MESH_ANNOTATIONS['midwayBetweenEyes'][0]];\nconst BLAZEFACE_MOUTH_INDEX = 3;\nconst BLAZEFACE_NOSE_INDEX = 2;\nconst BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES = [BLAZEFACE_MOUTH_INDEX, BLAZEFACE_NOSE_INDEX];\nconst LEFT_EYE_OUTLINE = coords.MESH_ANNOTATIONS['leftEyeLower0'];\nconst LEFT_EYE_BOUNDS = [LEFT_EYE_OUTLINE[0], LEFT_EYE_OUTLINE[LEFT_EYE_OUTLINE.length - 1]];\nconst RIGHT_EYE_OUTLINE = coords.MESH_ANNOTATIONS['rightEyeLower0'];\nconst RIGHT_EYE_BOUNDS = [RIGHT_EYE_OUTLINE[0], RIGHT_EYE_OUTLINE[RIGHT_EYE_OUTLINE.length - 1]];\nconst IRIS_UPPER_CENTER_INDEX = 3;\nconst IRIS_LOWER_CENTER_INDEX = 4;\nconst IRIS_IRIS_INDEX = 71;\nconst IRIS_NUM_COORDINATES = 76;\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates. Update the z coordinate to be an average of the original and the new. This produces the best visual effect.\nfunction replaceRawCoordinates(rawCoords, newCoords, prefix, keys) {\n for (let i = 0; i < coords.MESH_TO_IRIS_INDICES_MAP.length; i++) {\n const { key, indices } = coords.MESH_TO_IRIS_INDICES_MAP[i];\n const originalIndices = coords.MESH_ANNOTATIONS[`${prefix}${key}`];\n const shouldReplaceAllKeys = keys == null;\n if (shouldReplaceAllKeys || keys.includes(key)) {\n for (let j = 0; j < indices.length; j++) {\n const index = indices[j];\n rawCoords[originalIndices[j]] = [\n newCoords[index][0], newCoords[index][1],\n (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n ];\n }\n }\n }\n}\n// The Pipeline coordinates between the bounding box and skeleton models.\nclass Pipeline {\n constructor(boundingBoxDetector, meshDetector, irisModel, config) {\n // An array of facial bounding boxes.\n this.storedBoxes = [];\n this.runsWithoutFaceDetector = 0;\n this.boundingBoxDetector = boundingBoxDetector;\n this.meshDetector = meshDetector;\n this.irisModel = irisModel;\n this.meshWidth = config.face.mesh.inputSize;\n this.meshHeight = config.face.mesh.inputSize;\n this.irisSize = config.face.iris.inputSize;\n this.irisEnlarge = 2.3;\n this.skipped = 0;\n this.detectedFaces = 0;\n }\n\n transformRawCoords(rawCoords, box, angle, rotationMatrix) {\n // @ts-ignore\n const boxSize = bounding.getBoxSize({ startPoint: box.startPoint, endPoint: box.endPoint });\n const scaleFactor = [boxSize[0] / this.meshWidth, boxSize[1] / this.meshHeight];\n const coordsScaled = rawCoords.map((coord) => ([\n scaleFactor[0] * (coord[0] - this.meshWidth / 2),\n scaleFactor[1] * (coord[1] - this.meshHeight / 2), coord[2],\n ]));\n const coordsRotationMatrix = (angle !== 0) ? util.buildRotationMatrix(angle, [0, 0]) : util.IDENTITY_MATRIX;\n const coordsRotated = (angle !== 0) ? coordsScaled.map((coord) => ([...util.rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n const inverseRotationMatrix = (angle !== 0) ? util.invertTransformMatrix(rotationMatrix) : util.IDENTITY_MATRIX;\n // @ts-ignore\n const boxCenter = [...bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint }), 1];\n return coordsRotated.map((coord) => ([\n coord[0] + util.dot(boxCenter, inverseRotationMatrix[0]),\n coord[1] + util.dot(boxCenter, inverseRotationMatrix[1]),\n coord[2],\n ]));\n }\n\n getLeftToRightEyeDepthDifference(rawCoords) {\n const leftEyeZ = rawCoords[LEFT_EYE_BOUNDS[0]][2];\n const rightEyeZ = rawCoords[RIGHT_EYE_BOUNDS[0]][2];\n return leftEyeZ - rightEyeZ;\n }\n\n // Returns a box describing a cropped region around the eye fit for passing to the iris model.\n getEyeBox(rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, flip = false) {\n // @ts-ignore\n const box = bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), this.irisEnlarge));\n // @ts-ignore\n const boxSize = bounding.getBoxSize(box);\n let crop = tf.image.cropAndResize(face, [[\n box.startPoint[1] / this.meshHeight,\n box.startPoint[0] / this.meshWidth, box.endPoint[1] / this.meshHeight,\n box.endPoint[0] / this.meshWidth,\n ]], [0], [this.irisSize, this.irisSize]);\n if (flip) {\n crop = tf.image.flipLeftRight(crop);\n }\n return { box, boxSize, crop };\n }\n\n // Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\n getEyeCoords(eyeData, eyeBox, eyeBoxSize, flip = false) {\n const eyeRawCoords = [];\n for (let i = 0; i < IRIS_NUM_COORDINATES; i++) {\n const x = eyeData[i * 3];\n const y = eyeData[i * 3 + 1];\n const z = eyeData[i * 3 + 2];\n eyeRawCoords.push([\n (flip\n ? (1 - (x / this.irisSize))\n : (x / this.irisSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n (y / this.irisSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n ]);\n }\n return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(IRIS_IRIS_INDEX) };\n }\n\n // The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\n getAdjustedIrisCoords(rawCoords, irisCoords, direction) {\n const upperCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeUpper0`][IRIS_UPPER_CENTER_INDEX]][2];\n const lowerCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeLower0`][IRIS_LOWER_CENTER_INDEX]][2];\n const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n return irisCoords.map((coord, i) => {\n let z = averageZ;\n if (i === 2) {\n z = upperCenterZ;\n } else if (i === 4) {\n z = lowerCenterZ;\n }\n return [coord[0], coord[1], z];\n });\n }\n\n async predict(input, config) {\n let useFreshBox = false;\n // run new detector every skipFrames unless we only want box to start with\n let detector;\n if ((this.skipped === 0) || (this.skipped > config.face.detector.skipFrames) || !config.face.mesh.enabled || !config.videoOptimized) {\n detector = await this.boundingBoxDetector.getBoundingBoxes(input);\n this.skipped = 0;\n }\n if (config.videoOptimized) this.skipped++;\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (detector && detector.boxes && (detector.boxes.length > 0) && (!config.face.mesh.enabled || (detector.boxes.length !== this.detectedFaces) && (this.detectedFaces !== config.face.detector.maxFaces))) {\n this.storedBoxes = [];\n this.detectedFaces = 0;\n for (const possible of detector.boxes) {\n this.storedBoxes.push({ startPoint: possible.box.startPoint.dataSync(), endPoint: possible.box.endPoint.dataSync(), landmarks: possible.landmarks, confidence: possible.confidence });\n }\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\n\n if (useFreshBox) {\n if (!detector || !detector.boxes || (detector.boxes.length === 0)) {\n this.storedBoxes = [];\n this.detectedFaces = 0;\n return null;\n }\n for (let i = 0; i < this.storedBoxes.length; i++) {\n // @ts-ignore\n const scaledBox = bounding.scaleBoxCoordinates({ startPoint: this.storedBoxes[i].startPoint, endPoint: this.storedBoxes[i].endPoint }, detector.scaleFactor);\n // @ts-ignore\n const enlargedBox = bounding.enlargeBox(scaledBox);\n // @ts-ignore\n const squarifiedBox = bounding.squarifyBox(enlargedBox);\n const landmarks = this.storedBoxes[i].landmarks.arraySync();\n const confidence = this.storedBoxes[i].confidence;\n this.storedBoxes[i] = { ...squarifiedBox, confidence, landmarks };\n }\n this.runsWithoutFaceDetector = 0;\n }\n if (detector && detector.boxes) {\n detector.boxes.forEach((prediction) => {\n prediction.box.startPoint.dispose();\n prediction.box.endPoint.dispose();\n prediction.landmarks.dispose();\n });\n }\n\n // log('face', `skipped: ${this.skipped} max: ${config.face.detector.maxFaces} detected: ${this.detectedFaces} stored: ${this.storedBoxes.length} new: ${detector?.boxes?.length}`);\n let results = tf.tidy(() => this.storedBoxes.map((box, i) => {\n // The facial bounding box landmarks could come either from blazeface (if we are using a fresh box), or from the mesh model (if we are reusing an old box).\n let face;\n let angle = 0;\n let rotationMatrix;\n if (config.face.detector.rotation) {\n const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= LANDMARKS_COUNT) ? MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES : BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES;\n angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n // @ts-ignore\n const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n const rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized);\n rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n // @ts-ignore\n face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshHeight, this.meshWidth]).div(255);\n } else {\n rotationMatrix = util.IDENTITY_MATRIX;\n const cloned = input.clone();\n // @ts-ignore\n face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, cloned, [this.meshHeight, this.meshWidth]).div(255);\n }\n\n // if we're not going to produce mesh, don't spend time with further processing\n if (!config.face.mesh.enabled) {\n const prediction = {\n coords: null,\n box,\n faceConfidence: null,\n confidence: box.confidence,\n image: face,\n };\n return prediction;\n }\n\n const [, confidence, contourCoords] = this.meshDetector.predict(face); // The first returned tensor represents facial contours, which are included in the coordinates.\n const confidenceVal = confidence.dataSync()[0];\n if (confidenceVal < config.face.detector.minConfidence) return null; // if below confidence just exit\n const coordsReshaped = tf.reshape(contourCoords, [-1, 3]);\n let rawCoords = coordsReshaped.arraySync();\n\n if (config.face.iris.enabled) {\n const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = this.getEyeBox(rawCoords, face, LEFT_EYE_BOUNDS[0], LEFT_EYE_BOUNDS[1], true);\n const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = this.getEyeBox(rawCoords, face, RIGHT_EYE_BOUNDS[0], RIGHT_EYE_BOUNDS[1]);\n const eyePredictions = this.irisModel.predict(tf.concat([leftEyeCrop, rightEyeCrop]));\n const eyePredictionsData = eyePredictions.dataSync();\n const leftEyeData = eyePredictionsData.slice(0, IRIS_NUM_COORDINATES * 3);\n const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = this.getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n const rightEyeData = eyePredictionsData.slice(IRIS_NUM_COORDINATES * 3);\n const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = this.getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize);\n const leftToRightEyeDepthDifference = this.getLeftToRightEyeDepthDifference(rawCoords);\n if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left');\n replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right');\n // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged. So we only update a single contour line above and below the eye.\n } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n } else { // User is looking towards the left.\n replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n }\n const adjustedLeftIrisCoords = this.getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n const adjustedRightIrisCoords = this.getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n rawCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n }\n\n const transformedCoordsData = this.transformRawCoords(rawCoords, box, angle, rotationMatrix);\n // @ts-ignore\n const landmarksBox = bounding.enlargeBox(this.calculateLandmarksBoundingBox(transformedCoordsData));\n // @ts-ignore\n const squarifiedLandmarksBox = bounding.squarifyBox(landmarksBox);\n const transformedCoords = tf.tensor2d(transformedCoordsData);\n const prediction = {\n coords: transformedCoords,\n box: landmarksBox,\n faceConfidence: confidenceVal,\n confidence: box.confidence,\n image: face,\n };\n if (config.face.mesh.returnRawData) prediction.rawCoords = rawCoords;\n this.storedBoxes[i] = { ...squarifiedLandmarksBox, landmarks: transformedCoords.arraySync(), confidence: box.confidence, faceConfidence: confidenceVal };\n\n return prediction;\n }));\n results = results.filter((a) => a !== null);\n this.detectedFaces = results.length;\n return results;\n }\n\n calculateLandmarksBoundingBox(landmarks) {\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint, landmarks };\n }\n}\nexports.Pipeline = Pipeline;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as blazeface from './blazeface.js';\nimport * as facepipeline from './facepipeline.js';\nimport * as coords from './coords.js';\n\nclass MediaPipeFaceMesh {\n constructor(blazeFace, blazeMeshModel, irisModel, config) {\n // @ts-ignore\n this.facePipeline = new facepipeline.Pipeline(blazeFace, blazeMeshModel, irisModel, config);\n this.config = config;\n }\n\n async estimateFaces(input, config) {\n const predictions = await this.facePipeline.predict(input, config);\n const results = [];\n for (const prediction of (predictions || [])) {\n if (prediction.isDisposedInternal) continue; // guard against disposed tensors on long running operations such as pause in middle of processing\n const mesh = prediction.coords ? prediction.coords.arraySync() : null;\n const meshRaw = prediction.rawCoords;\n const annotations = {};\n if (mesh && mesh.length > 0) {\n for (const key of Object.keys(coords.MESH_ANNOTATIONS)) {\n annotations[key] = coords.MESH_ANNOTATIONS[key].map((index) => mesh[index]);\n }\n }\n const boxRaw = (config.face.mesh.returnRawData && prediction.box) ? { topLeft: prediction.box.startPoint, bottomRight: prediction.box.endPoint } : null;\n const box = prediction.box ? [\n Math.max(0, prediction.box.startPoint[0]),\n Math.max(0, prediction.box.startPoint[1]),\n Math.min(input.shape[2], prediction.box.endPoint[0]) - prediction.box.startPoint[0],\n Math.min(input.shape[1], prediction.box.endPoint[1]) - prediction.box.startPoint[1],\n ] : 0;\n results.push({\n confidence: prediction.confidence || 0,\n box,\n mesh,\n boxRaw,\n meshRaw,\n annotations,\n image: prediction.image ? tf.clone(prediction.image) : null,\n });\n if (prediction.coords) prediction.coords.dispose();\n if (prediction.image) prediction.image.dispose();\n }\n return results;\n }\n}\n\nlet faceModels = [null, null, null];\nasync function load(config) {\n faceModels = await Promise.all([\n // @ts-ignore\n (!faceModels[0] && config.face.enabled) ? blazeface.load(config) : null,\n (!faceModels[1] && config.face.mesh.enabled) ? tf.loadGraphModel(config.face.mesh.modelPath, { fromTFHub: config.face.mesh.modelPath.includes('tfhub.dev') }) : null,\n (!faceModels[2] && config.face.iris.enabled) ? tf.loadGraphModel(config.face.iris.modelPath, { fromTFHub: config.face.iris.modelPath.includes('tfhub.dev') }) : null,\n ]);\n const faceMesh = new MediaPipeFaceMesh(faceModels[0], faceModels[1], faceModels[2], config);\n if (config.face.mesh.enabled) log(`load model: ${config.face.mesh.modelPath.match(/\\/(.*)\\./)[1]}`);\n if (config.face.iris.enabled) log(`load model: ${config.face.iris.modelPath.match(/\\/(.*)\\./)[1]}`);\n return faceMesh;\n}\n\nexports.load = load;\nexports.MediaPipeFaceMesh = MediaPipeFaceMesh;\nexports.triangulation = coords.TRI468;\n", "import { log } from './log.js';\n\nconst profileData = {};\n\nfunction profile(name, data) {\n if (!data || !data.kernels) return;\n const maxResults = 5;\n const time = data.kernels\n .filter((a) => a.kernelTimeMs > 0)\n .reduce((a, b) => a += b.kernelTimeMs, 0);\n const slowest = data.kernels\n .map((a, i) => { a.id = i; return a; })\n .filter((a) => a.kernelTimeMs > 0)\n .sort((a, b) => b.kernelTimeMs - a.kernelTimeMs);\n const largest = data.kernels\n .map((a, i) => { a.id = i; return a; })\n .filter((a) => a.totalBytesSnapshot > 0)\n .sort((a, b) => b.totalBytesSnapshot - a.totalBytesSnapshot);\n if (slowest.length > maxResults) slowest.length = maxResults;\n if (largest.length > maxResults) largest.length = maxResults;\n const res = { newBytes: data.newBytes, newTensors: data.newTensors, peakBytes: data.peakBytes, numKernelOps: data.kernels.length, timeKernelOps: time, slowestKernelOps: slowest, largestKernelOps: largest };\n profileData[name] = res;\n log('Human profiler', name, res);\n}\n\nexports.run = profile;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile.js';\n\nconst models = {};\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nasync function load(config) {\n if (!models.age) {\n models.age = await tf.loadGraphModel(config.face.age.modelPath);\n log(`load model: ${config.face.age.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.age;\n}\n\nasync function predict(image, config) {\n if (!models.age) return null;\n if ((skipped < config.face.age.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n /*\n const zoom = [0, 0]; // 0..1 meaning 0%..100%\n const box = [[\n (image.shape[1] * zoom[0]) / image.shape[1],\n (image.shape[2] * zoom[1]) / image.shape[2],\n (image.shape[1] - (image.shape[1] * zoom[0])) / image.shape[1],\n (image.shape[2] - (image.shape[2] * zoom[1])) / image.shape[2],\n ]];\n const resize = tf.image.cropAndResize(image, box, [0], [config.face.age.inputSize, config.face.age.inputSize]);\n */\n const resize = tf.image.resizeBilinear(image, [config.face.age.inputSize, config.face.age.inputSize], false);\n const enhance = tf.mul(resize, [255.0]);\n tf.dispose(resize);\n\n let ageT;\n const obj = {};\n\n if (!config.profile) {\n if (config.face.age.enabled) ageT = await models.age.predict(enhance);\n } else {\n const profileAge = config.face.age.enabled ? await tf.profile(() => models.age.predict(enhance)) : {};\n ageT = profileAge.result.clone();\n profileAge.result.dispose();\n // @ts-ignore\n profile.run('age', profileAge);\n }\n enhance.dispose();\n\n if (ageT) {\n const data = ageT.dataSync();\n obj.age = Math.trunc(10 * data[0]) / 10;\n }\n ageT.dispose();\n\n last = obj;\n resolve(obj);\n });\n}\n\nexports.predict = predict;\nexports.load = load;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile.js';\n\nconst models = {};\nlet last = { gender: '' };\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet alternative = false;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nasync function load(config) {\n if (!models.gender) {\n models.gender = await tf.loadGraphModel(config.face.gender.modelPath);\n alternative = models.gender.inputs[0].shape[3] === 1;\n log(`load model: ${config.face.gender.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.gender;\n}\n\nasync function predict(image, config) {\n if (!models.gender) return null;\n if ((skipped < config.face.gender.skipFrames) && config.videoOptimized && last.gender !== '') {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [config.face.gender.inputSize, config.face.gender.inputSize], false);\n let enhance;\n if (alternative) {\n enhance = tf.tidy(() => {\n const [red, green, blue] = tf.split(resize, 3, 3);\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n return grayscale.sub(0.5).mul(2);\n });\n } else {\n enhance = tf.mul(resize, [255.0]);\n }\n tf.dispose(resize);\n\n let genderT;\n const obj = {};\n\n if (!config.profile) {\n if (config.face.gender.enabled) genderT = await models.gender.predict(enhance);\n } else {\n const profileGender = config.face.gender.enabled ? await tf.profile(() => models.gender.predict(enhance)) : {};\n genderT = profileGender.result.clone();\n profileGender.result.dispose();\n // @ts-ignore\n profile.run('gender', profileGender);\n }\n enhance.dispose();\n\n if (genderT) {\n const data = genderT.dataSync();\n if (alternative) {\n // returns two values 0..1, bigger one is prediction\n const confidence = Math.trunc(100 * Math.abs(data[0] - data[1])) / 100;\n if (confidence > config.face.gender.minConfidence) {\n obj.gender = data[0] > data[1] ? 'female' : 'male';\n obj.confidence = confidence;\n }\n } else {\n // returns one value 0..1, .5 is prediction threshold\n const confidence = Math.trunc(200 * Math.abs((data[0] - 0.5))) / 100;\n if (confidence > config.face.gender.minConfidence) {\n obj.gender = data[0] <= 0.5 ? 'female' : 'male';\n obj.confidence = Math.min(0.99, confidence);\n }\n }\n }\n genderT.dispose();\n\n last = obj;\n resolve(obj);\n });\n}\n\nexports.predict = predict;\nexports.load = load;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile.js';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];\nconst models = {};\nlet last = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\nconst scale = 1; // score multiplication factor\n\nasync function load(config) {\n if (!models.emotion) {\n models.emotion = await tf.loadGraphModel(config.face.emotion.modelPath);\n log(`load model: ${config.face.emotion.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.emotion;\n}\n\nasync function predict(image, config) {\n if (!models.emotion) return null;\n if ((skipped < config.face.emotion.skipFrames) && config.videoOptimized && (last.length > 0)) {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n /*\n const zoom = [0, 0]; // 0..1 meaning 0%..100%\n const box = [[\n (image.shape[1] * zoom[0]) / image.shape[1],\n (image.shape[2] * zoom[1]) / image.shape[2],\n (image.shape[1] - (image.shape[1] * zoom[0])) / image.shape[1],\n (image.shape[2] - (image.shape[2] * zoom[1])) / image.shape[2],\n ]];\n const resize = tf.image.cropAndResize(image, box, [0], [config.face.emotion.inputSize, config.face.emotion.inputSize]);\n */\n const resize = tf.image.resizeBilinear(image, [config.face.emotion.inputSize, config.face.emotion.inputSize], false);\n const [red, green, blue] = tf.split(resize, 3, 3);\n resize.dispose();\n // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n red.dispose();\n green.dispose();\n blue.dispose();\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n redNorm.dispose();\n greenNorm.dispose();\n blueNorm.dispose();\n const normalize = tf.tidy(() => grayscale.sub(0.5).mul(2));\n grayscale.dispose();\n const obj = [];\n if (config.face.emotion.enabled) {\n let data;\n if (!config.profile) {\n const emotionT = await models.emotion.predict(normalize);\n data = emotionT.dataSync();\n tf.dispose(emotionT);\n } else {\n const profileData = await tf.profile(() => models.emotion.predict(normalize));\n data = profileData.result.dataSync();\n profileData.result.dispose();\n // @ts-ignore\n profile.run('emotion', profileData);\n }\n for (let i = 0; i < data.length; i++) {\n if (scale * data[i] > config.face.emotion.minConfidence) obj.push({ score: Math.min(0.99, Math.trunc(100 * scale * data[i]) / 100), emotion: annotations[i] });\n }\n obj.sort((a, b) => b.score - a.score);\n }\n normalize.dispose();\n last = obj;\n resolve(obj);\n });\n}\n\nexports.predict = predict;\nexports.load = load;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile.js';\n\n// based on https://github.com/sirius-ai/MobileFaceNet_TF\n// model converted from https://github.com/sirius-ai/MobileFaceNet_TF/files/3551493/FaceMobileNet192_train_false.zip\n\nconst models = {};\n\nasync function load(config) {\n if (!models.embedding) {\n models.embedding = await tf.loadGraphModel(config.face.embedding.modelPath);\n log(`load model: ${config.face.embedding.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.embedding;\n}\n\nfunction simmilarity(embedding1, embedding2) {\n if (embedding1?.length !== embedding2?.length) return 0;\n // general minkowski distance\n // euclidean distance is limited case where order is 2\n const order = 2;\n const distance = 10.0 * ((embedding1.map((val, i) => (val - embedding2[i])).reduce((dist, diff) => dist + (diff ** order), 0) ** (1 / order)));\n return (Math.trunc(1000 * (1 - distance)) / 1000);\n}\n\nasync function predict(image, config) {\n if (!models.embedding) return null;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [config.face.embedding.inputSize, config.face.embedding.inputSize], false);\n // const normalize = tf.tidy(() => resize.div(127.5).sub(0.5)); // this is -0.5...0.5 ???\n let data = [];\n if (config.face.embedding.enabled) {\n if (!config.profile) {\n const embeddingT = await models.embedding.predict({ img_inputs: resize });\n data = [...embeddingT.dataSync()]; // convert object array to standard array\n tf.dispose(embeddingT);\n } else {\n const profileData = await tf.profile(() => models.embedding.predict({ img_inputs: resize }));\n data = [...profileData.result.dataSync()];\n profileData.result.dispose();\n // @ts-ignore\n profile.run('emotion', profileData);\n }\n }\n resize.dispose();\n // normalize.dispose();\n resolve(data);\n });\n}\n\nexports.predict = predict;\nexports.simmilarity = simmilarity;\nexports.load = load;\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nconst imageNetMean = [-123.15, -115.90, -103.06];\n\nfunction nameOutputResultsMobileNet(results) {\n const [offsets, heatmap, displacementFwd, displacementBwd] = results;\n return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nfunction nameOutputResultsResNet(results) {\n const [displacementFwd, displacementBwd, offsets, heatmap] = results;\n return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nclass BaseModel {\n constructor(model) {\n this.model = model;\n }\n\n predict(input, config) {\n return tf.tidy(() => {\n const asFloat = (config.body.modelType === 'ResNet') ? input.toFloat().add(imageNetMean) : input.toFloat().div(127.5).sub(1.0);\n const asBatch = asFloat.expandDims(0);\n const results = this.model.predict(asBatch);\n const results3d = results.map((y) => y.squeeze([0]));\n const namedResults = (config.body.modelType === 'ResNet') ? nameOutputResultsResNet(results3d) : nameOutputResultsMobileNet(results3d);\n return {\n heatmapScores: namedResults.heatmap.sigmoid(),\n offsets: namedResults.offsets,\n displacementFwd: namedResults.displacementFwd,\n displacementBwd: namedResults.displacementBwd,\n };\n });\n }\n\n dispose() {\n this.model.dispose();\n }\n}\nexports.BaseModel = BaseModel;\n", "// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nfunction half(k) {\n return Math.floor(k / 2);\n}\nclass MaxHeap {\n constructor(maxSize, getElementValue) {\n this.priorityQueue = new Array(maxSize);\n this.numberOfElements = -1;\n this.getElementValue = getElementValue;\n }\n\n enqueue(x) {\n this.priorityQueue[++this.numberOfElements] = x;\n this.swim(this.numberOfElements);\n }\n\n dequeue() {\n const max = this.priorityQueue[0];\n this.exchange(0, this.numberOfElements--);\n this.sink(0);\n this.priorityQueue[this.numberOfElements + 1] = null;\n return max;\n }\n\n empty() {\n return this.numberOfElements === -1;\n }\n\n size() {\n return this.numberOfElements + 1;\n }\n\n all() {\n return this.priorityQueue.slice(0, this.numberOfElements + 1);\n }\n\n max() {\n return this.priorityQueue[0];\n }\n\n swim(k) {\n while (k > 0 && this.less(half(k), k)) {\n this.exchange(k, half(k));\n k = half(k);\n }\n }\n\n sink(k) {\n while (2 * k <= this.numberOfElements) {\n let j = 2 * k;\n if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n if (!this.less(k, j)) break;\n this.exchange(k, j);\n k = j;\n }\n }\n\n getValueAt(i) {\n return this.getElementValue(this.priorityQueue[i]);\n }\n\n less(i, j) {\n return this.getValueAt(i) < this.getValueAt(j);\n }\n\n exchange(i, j) {\n const t = this.priorityQueue[i];\n this.priorityQueue[i] = this.priorityQueue[j];\n this.priorityQueue[j] = t;\n }\n}\nexports.MaxHeap = MaxHeap;\n", "import * as heapSort from './heapSort';\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores) {\n const [height, width] = scores.shape;\n let localMaximum = true;\n const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n localMaximum = false;\n break;\n }\n }\n if (!localMaximum) break;\n }\n return localMaximum;\n}\n\nfunction buildPartWithScoreQueue(scoreThreshold, localMaximumRadius, scores) {\n const [height, width, numKeypoints] = scores.shape;\n const queue = new heapSort.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n const score = scores.get(heatmapY, heatmapX, keypointId);\n // Only consider parts with score greater or equal to threshold as root candidates.\n if (score < scoreThreshold) continue;\n // Only consider keypoints whose score is maximum in a local window.\n if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores)) {\n queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n }\n }\n }\n }\n return queue;\n}\nexports.buildPartWithScoreQueue = buildPartWithScoreQueue;\n", "exports.partNames = [\n 'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n 'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n 'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexports.NUM_KEYPOINTS = exports.partNames.length;\n\nexports.partIds = exports.partNames.reduce((result, jointName, i) => {\n result[jointName] = i;\n return result;\n}, {});\n\nconst connectedPartNames = [\n ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexports.connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([exports.partIds[jointNameA], exports.partIds[jointNameB]]));\n\nexports.poseChain = [\n ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n ['rightKnee', 'rightAnkle'],\n];\n\nexports.partChannels = [\n 'left_face',\n 'right_face',\n 'right_upper_leg_front',\n 'right_lower_leg_back',\n 'right_upper_leg_back',\n 'left_lower_leg_front',\n 'left_upper_leg_front',\n 'left_upper_leg_back',\n 'left_lower_leg_back',\n 'right_feet',\n 'right_lower_leg_front',\n 'left_feet',\n 'torso_front',\n 'torso_back',\n 'right_upper_arm_front',\n 'right_upper_arm_back',\n 'right_lower_arm_back',\n 'left_lower_arm_front',\n 'left_upper_arm_front',\n 'left_upper_arm_back',\n 'left_lower_arm_back',\n 'right_hand',\n 'right_lower_arm_front',\n 'left_hand',\n];\n", "import * as kpt from './keypoints';\n\nfunction getOffsetPoint(y, x, keypoint, offsets) {\n return {\n y: offsets.get(y, x, keypoint),\n x: offsets.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n };\n}\nexports.getOffsetPoint = getOffsetPoint;\n\nfunction getImageCoords(part, outputStride, offsets) {\n const { heatmapY, heatmapX, id: keypoint } = part;\n const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n return {\n x: part.heatmapX * outputStride + x,\n y: part.heatmapY * outputStride + y,\n };\n}\nexports.getImageCoords = getImageCoords;\n\nfunction fillArray(element, size) {\n const result = new Array(size);\n for (let i = 0; i < size; i++) {\n result[i] = element;\n }\n return result;\n}\nexports.fillArray = fillArray;\n\nfunction clamp(a, min, max) {\n if (a < min) return min;\n if (a > max) return max;\n return a;\n}\nexports.clamp = clamp;\n\nfunction squaredDistance(y1, x1, y2, x2) {\n const dy = y2 - y1;\n const dx = x2 - x1;\n return dy * dy + dx * dx;\n}\nexports.squaredDistance = squaredDistance;\n\nfunction addVectors(a, b) {\n return { x: a.x + b.x, y: a.y + b.y };\n}\nexports.addVectors = addVectors;\n\nfunction clampVector(a, min, max) {\n return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\nexports.clampVector = clampVector;\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as kpt from './keypoints';\n\nfunction getPointsConfidence(heatmapScores, heatMapCoords) {\n const numKeypoints = heatMapCoords.shape[0];\n const result = new Float32Array(numKeypoints);\n for (let keypoint = 0; keypoint < numKeypoints; keypoint++) {\n const y = heatMapCoords.get(keypoint, 0);\n const x = heatMapCoords.get(keypoint, 1);\n result[keypoint] = heatmapScores.get(y, x, keypoint);\n }\n return result;\n}\nexports.getPointsConfidence = getPointsConfidence;\n\nfunction getOffsetPoint(y, x, keypoint, offsetsBuffer) {\n return {\n y: offsetsBuffer.get(y, x, keypoint),\n x: offsetsBuffer.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n };\n}\n\nfunction getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer) {\n const result = [];\n for (let keypoint = 0; keypoint < kpt.NUM_KEYPOINTS; keypoint++) {\n const heatmapY = heatMapCoordsBuffer.get(keypoint, 0).valueOf();\n const heatmapX = heatMapCoordsBuffer.get(keypoint, 1).valueOf();\n const { x, y } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsetsBuffer);\n result.push(y);\n result.push(x);\n }\n return tf.tensor2d(result, [kpt.NUM_KEYPOINTS, 2]);\n}\nexports.getOffsetVectors = getOffsetVectors;\n\nfunction getOffsetPoints(heatMapCoordsBuffer, outputStride, offsetsBuffer) {\n return tf.tidy(() => heatMapCoordsBuffer.toTensor().mul(tf.scalar(outputStride, 'int32')).toFloat().add(getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer)));\n}\nexports.getOffsetPoints = getOffsetPoints;\n\nfunction mod(a, b) {\n return tf.tidy(() => {\n const floored = a.div(tf.scalar(b, 'int32'));\n return a.sub(floored.mul(tf.scalar(b, 'int32')));\n });\n}\n\nfunction argmax2d(inputs) {\n const [height, width, depth] = inputs.shape;\n return tf.tidy(() => {\n const reshaped = inputs.reshape([height * width, depth]);\n const coords = reshaped.argMax(0);\n const yCoords = coords.div(tf.scalar(width, 'int32')).expandDims(1);\n const xCoords = mod(coords, width).expandDims(1);\n return tf.concat([yCoords, xCoords], 1);\n });\n}\nexports.argmax2d = argmax2d;\n", "import * as keypoints from './keypoints';\nimport * as vectors from './vectors';\nimport * as decoders from './decoders';\n\nconst parentChildrenTuples = keypoints.poseChain.map(([parentJoinName, childJoinName]) => ([keypoints.partIds[parentJoinName], keypoints.partIds[childJoinName]]));\nconst parentToChildEdges = parentChildrenTuples.map(([, childJointId]) => childJointId);\nconst childToParentEdges = parentChildrenTuples.map(([parentJointId]) => parentJointId);\nfunction getDisplacement(edgeId, point, displacements) {\n const numEdges = displacements.shape[2] / 2;\n return {\n y: displacements.get(point.y, point.x, edgeId),\n x: displacements.get(point.y, point.x, numEdges + edgeId),\n };\n}\nfunction getStridedIndexNearPoint(point, outputStride, height, width) {\n return {\n // @ts-ignore\n y: vectors.clamp(Math.round(point.y / outputStride), 0, height - 1),\n // @ts-ignore\n x: vectors.clamp(Math.round(point.x / outputStride), 0, width - 1),\n };\n}\n\nfunction traverseToTargetKeypoint(edgeId, sourceKeypoint, targetKeypointId, scoresBuffer, offsets, outputStride, displacements, offsetRefineStep = 2) {\n const [height, width] = scoresBuffer.shape;\n // Nearest neighbor interpolation for the source->target displacements.\n const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, outputStride, height, width);\n const displacement = getDisplacement(edgeId, sourceKeypointIndices, displacements);\n // @ts-ignore\n const displacedPoint = vectors.addVectors(sourceKeypoint.position, displacement);\n let targetKeypoint = displacedPoint;\n for (let i = 0; i < offsetRefineStep; i++) {\n const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n // @ts-ignore\n const offsetPoint = vectors.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetKeypointId, offsets);\n // @ts-ignore\n targetKeypoint = vectors.addVectors({\n x: targetKeypointIndices.x * outputStride,\n y: targetKeypointIndices.y * outputStride,\n }, { x: offsetPoint.x, y: offsetPoint.y });\n }\n const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n const score = scoresBuffer.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetKeypointId);\n return { position: targetKeypoint, part: keypoints.partNames[targetKeypointId], score };\n}\n\nfunction decodePose(root, scores, offsets, outputStride, displacementsFwd, displacementsBwd) {\n const numParts = scores.shape[2];\n const numEdges = parentToChildEdges.length;\n const instanceKeypoints = new Array(numParts);\n // Start a new detection instance at the position of the root.\n const { part: rootPart, score: rootScore } = root;\n // @ts-ignore\n const rootPoint = vectors.getImageCoords(rootPart, outputStride, offsets);\n instanceKeypoints[rootPart.id] = {\n score: rootScore,\n part: keypoints.partNames[rootPart.id],\n position: rootPoint,\n };\n // Decode the part positions upwards in the tree, following the backward displacements.\n for (let edge = numEdges - 1; edge >= 0; --edge) {\n const sourceKeypointId = parentToChildEdges[edge];\n const targetKeypointId = childToParentEdges[edge];\n if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsBwd);\n }\n }\n // Decode the part positions downwards in the tree, following the forward displacements.\n for (let edge = 0; edge < numEdges; ++edge) {\n const sourceKeypointId = childToParentEdges[edge];\n const targetKeypointId = parentToChildEdges[edge];\n if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsFwd);\n }\n }\n return instanceKeypoints;\n}\nexports.decodePose = decodePose;\n\nasync function decodeSinglePose(heatmapScores, offsets, config) {\n let totalScore = 0.0;\n // @ts-ignore\n const heatmapValues = decoders.argmax2d(heatmapScores);\n const allTensorBuffers = await Promise.all([heatmapScores.buffer(), offsets.buffer(), heatmapValues.buffer()]);\n const scoresBuffer = allTensorBuffers[0];\n const offsetsBuffer = allTensorBuffers[1];\n const heatmapValuesBuffer = allTensorBuffers[2];\n // @ts-ignore\n const offsetPoints = decoders.getOffsetPoints(heatmapValuesBuffer, config.body.outputStride, offsetsBuffer);\n const offsetPointsBuffer = await offsetPoints.buffer();\n // @ts-ignore\n const keypointConfidence = Array.from(decoders.getPointsConfidence(scoresBuffer, heatmapValuesBuffer));\n const instanceKeypoints = keypointConfidence.map((score, i) => {\n totalScore += score;\n return {\n position: {\n y: offsetPointsBuffer.get(i, 0),\n x: offsetPointsBuffer.get(i, 1),\n },\n part: keypoints.partNames[i],\n score,\n };\n });\n const filteredKeypoints = instanceKeypoints.filter((kpt) => kpt.score > config.body.scoreThreshold);\n heatmapValues.dispose();\n offsetPoints.dispose();\n return { keypoints: filteredKeypoints, score: totalScore / instanceKeypoints.length };\n}\nexports.decodeSinglePose = decodeSinglePose;\n", "import * as buildParts from './buildParts';\nimport * as decodePose from './decodePose';\nimport * as vectors from './vectors';\n\nconst kLocalMaximumRadius = 1;\n\nfunction withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, { x, y }, keypointId) {\n return poses.some(({ keypoints }) => {\n const correspondingKeypoint = keypoints[keypointId].position;\n // @ts-ignore\n return vectors.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n });\n}\n\nfunction getInstanceScore(existingPoses, squaredNmsRadius, instanceKeypoints) {\n const notOverlappedKeypointScores = instanceKeypoints.reduce((result, { position, score }, keypointId) => {\n if (!withinNmsRadiusOfCorrespondingPoint(existingPoses, squaredNmsRadius, position, keypointId)) result += score;\n return result;\n }, 0.0);\n return notOverlappedKeypointScores / instanceKeypoints.length;\n}\n\nfunction decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config) {\n const poses = [];\n // @ts-ignore\n const queue = buildParts.buildPartWithScoreQueue(config.body.scoreThreshold, kLocalMaximumRadius, scoresBuffer);\n const squaredNmsRadius = config.body.nmsRadius ^ 2;\n // Generate at most maxDetections object instances per image in decreasing root part score order.\n while (poses.length < config.body.maxDetections && !queue.empty()) {\n // The top element in the queue is the next root candidate.\n const root = queue.dequeue();\n // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n // @ts-ignore\n const rootImageCoords = vectors.getImageCoords(root.part, config.body.outputStride, offsetsBuffer);\n if (withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, rootImageCoords, root.part.id)) continue;\n // Else start a new detection instance at the position of the root.\n // @ts-ignore\n const keypoints = decodePose.decodePose(root, scoresBuffer, offsetsBuffer, config.body.outputStride, displacementsFwdBuffer, displacementsBwdBuffer);\n const score = getInstanceScore(poses, squaredNmsRadius, keypoints);\n if (score > config.body.scoreThreshold) poses.push({ keypoints, score });\n }\n return poses;\n}\nexports.decodeMultiplePoses = decodeMultiplePoses;\n", "import * as kpt from './keypoints';\n\nfunction eitherPointDoesntMeetConfidence(a, b, minConfidence) {\n return (a < minConfidence || b < minConfidence);\n}\n\nfunction getAdjacentKeyPoints(keypoints, minConfidence) {\n return kpt.connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n return result;\n }\n result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n return result;\n }, []);\n}\nexports.getAdjacentKeyPoints = getAdjacentKeyPoints;\n\nconst { NEGATIVE_INFINITY, POSITIVE_INFINITY } = Number;\nfunction getBoundingBox(keypoints) {\n return keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n maxX: Math.max(maxX, x),\n maxY: Math.max(maxY, y),\n minX: Math.min(minX, x),\n minY: Math.min(minY, y),\n }), {\n maxX: NEGATIVE_INFINITY,\n maxY: NEGATIVE_INFINITY,\n minX: POSITIVE_INFINITY,\n minY: POSITIVE_INFINITY,\n });\n}\nexports.getBoundingBox = getBoundingBox;\n\nfunction getBoundingBoxPoints(keypoints) {\n const { minX, minY, maxX, maxY } = getBoundingBox(keypoints);\n return [{ x: minX, y: minY }, { x: maxX, y: minY }, { x: maxX, y: maxY }, { x: minX, y: maxY }];\n}\nexports.getBoundingBoxPoints = getBoundingBoxPoints;\n\nasync function toTensorBuffers3D(tensors) {\n return Promise.all(tensors.map((tensor) => tensor.buffer()));\n}\nexports.toTensorBuffers3D = toTensorBuffers3D;\n\nfunction scalePose(pose, scaleY, scaleX) {\n return {\n score: pose.score,\n keypoints: pose.keypoints.map(({ score, part, position }) => ({\n score,\n part,\n position: { x: position.x * scaleX, y: position.y * scaleY },\n })),\n };\n}\nexports.scalePose = scalePose;\n\nfunction resizeTo(image, [targetH, targetW]) {\n const input = image.squeeze(0);\n const resized = input.resizeBilinear([targetH, targetW]);\n input.dispose();\n return resized;\n}\nexports.resizeTo = resizeTo;\n\nfunction scaleAndFlipPoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) {\n const scaledPoses = poses.map((pose) => scalePose(pose, height / inputResolutionHeight, width / inputResolutionWidth));\n return scaledPoses;\n}\nexports.scaleAndFlipPoses = scaleAndFlipPoses;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as modelBase from './modelBase';\nimport * as decodeMultiple from './decodeMultiple';\nimport * as decodePose from './decodePose';\nimport * as util from './util';\n\nasync function estimateMultiple(input, res, config) {\n return new Promise(async (resolve) => {\n const height = input.shape[1];\n const width = input.shape[2];\n // @ts-ignore\n const allTensorBuffers = await util.toTensorBuffers3D([res.heatmapScores, res.offsets, res.displacementFwd, res.displacementBwd]);\n const scoresBuffer = allTensorBuffers[0];\n const offsetsBuffer = allTensorBuffers[1];\n const displacementsFwdBuffer = allTensorBuffers[2];\n const displacementsBwdBuffer = allTensorBuffers[3];\n // @ts-ignore\n const poses = await decodeMultiple.decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config);\n // @ts-ignore\n const scaled = util.scaleAndFlipPoses(poses, [height, width], [config.body.inputSize, config.body.inputSize]);\n resolve(scaled);\n });\n}\n\nasync function estimateSingle(input, res, config) {\n return new Promise(async (resolve) => {\n const height = input.shape[1];\n const width = input.shape[2];\n // @ts-ignore\n const pose = await decodePose.decodeSinglePose(res.heatmapScores, res.offsets, config);\n const poses = [pose];\n // @ts-ignore\n const scaled = util.scaleAndFlipPoses(poses, [height, width], [config.body.inputSize, config.body.inputSize]);\n resolve(scaled);\n });\n}\n\nclass PoseNet {\n constructor(model) {\n this.baseModel = model;\n }\n\n async estimatePoses(input, config) {\n // @ts-ignore\n const resized = util.resizeTo(input, [config.body.inputSize, config.body.inputSize]);\n const res = this.baseModel.predict(resized, config);\n\n const poses = (config.body.maxDetections < 2) ? await estimateSingle(input, res, config) : await estimateMultiple(input, res, config);\n\n res.heatmapScores.dispose();\n res.offsets.dispose();\n res.displacementFwd.dispose();\n res.displacementBwd.dispose();\n resized.dispose();\n\n return poses;\n }\n\n dispose() {\n this.baseModel.dispose();\n }\n}\nexports.PoseNet = PoseNet;\n\nasync function load(config) {\n const model = await tf.loadGraphModel(config.body.modelPath);\n // @ts-ignore\n const mobilenet = new modelBase.BaseModel(model);\n log(`load model: ${config.body.modelPath.match(/\\/(.*)\\./)[1]}`);\n return new PoseNet(mobilenet);\n}\nexports.load = load;\n", "import * as modelPoseNet from './modelPoseNet';\nimport * as keypoints from './keypoints';\nimport * as util from './util';\n\n// @ts-ignore\nexports.load = modelPoseNet.load;\n// @ts-ignore\nexports.PoseNet = modelPoseNet.PoseNet;\n\nexports.partChannels = keypoints.partChannels;\nexports.partIds = keypoints.partIds;\nexports.partNames = keypoints.partNames;\nexports.poseChain = keypoints.poseChain;\n// @ts-ignore\nexports.getAdjacentKeyPoints = util.getAdjacentKeyPoints;\n// @ts-ignore\nexports.getBoundingBox = util.getBoundingBox;\n// @ts-ignore\nexports.getBoundingBoxPoints = util.getBoundingBoxPoints;\n// @ts-ignore\nexports.scaleAndFlipPoses = util.scaleAndFlipPoses;\n// @ts-ignore\nexports.scalePose = util.scalePose;\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\n\nclass HandDetector {\n constructor(model, inputSize, anchorsAnnotated) {\n this.model = model;\n this.anchors = anchorsAnnotated.map((anchor) => [anchor.x_center, anchor.y_center]);\n this.anchorsTensor = tf.tensor2d(this.anchors);\n this.inputSizeTensor = tf.tensor1d([inputSize, inputSize]);\n this.doubleInputSizeTensor = tf.tensor1d([inputSize * 2, inputSize * 2]);\n }\n\n normalizeBoxes(boxes) {\n return tf.tidy(() => {\n const boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n const boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n const boxCenterPoints = tf.add(tf.div(boxOffsets, this.inputSizeTensor), this.anchorsTensor);\n const halfBoxSizes = tf.div(boxSizes, this.doubleInputSizeTensor);\n const startPoints = tf.mul(tf.sub(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n const endPoints = tf.mul(tf.add(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n return tf.concat2d([startPoints, endPoints], 1);\n });\n }\n\n normalizeLandmarks(rawPalmLandmarks, index) {\n return tf.tidy(() => {\n const landmarks = tf.add(tf.div(rawPalmLandmarks.reshape([-1, 7, 2]), this.inputSizeTensor), this.anchors[index]);\n return tf.mul(landmarks, this.inputSizeTensor);\n });\n }\n\n async getBoxes(input, config) {\n const batched = this.model.predict(input);\n const predictions = batched.squeeze();\n batched.dispose();\n const scoresT = tf.tidy(() => tf.sigmoid(tf.slice(predictions, [0, 0], [-1, 1])).squeeze());\n const scores = scoresT.dataSync();\n const rawBoxes = tf.slice(predictions, [0, 1], [-1, 4]);\n const boxes = this.normalizeBoxes(rawBoxes);\n rawBoxes.dispose();\n const filteredT = await tf.image.nonMaxSuppressionAsync(boxes, scores, config.hand.maxHands, config.hand.iouThreshold, config.hand.scoreThreshold);\n const filtered = filteredT.arraySync();\n\n scoresT.dispose();\n filteredT.dispose();\n const hands = [];\n for (const index of filtered) {\n if (scores[index] >= config.hand.minConfidence) {\n const matchingBox = tf.slice(boxes, [index, 0], [1, -1]);\n const rawPalmLandmarks = tf.slice(predictions, [index, 5], [1, 14]);\n const palmLandmarks = tf.tidy(() => this.normalizeLandmarks(rawPalmLandmarks, index).reshape([-1, 2]));\n rawPalmLandmarks.dispose();\n hands.push({ box: matchingBox, palmLandmarks, confidence: scores[index] });\n }\n }\n predictions.dispose();\n boxes.dispose();\n return hands;\n }\n\n async estimateHandBounds(input, config) {\n const inputHeight = input.shape[1];\n const inputWidth = input.shape[2];\n const image = tf.tidy(() => input.resizeBilinear([config.hand.inputSize, config.hand.inputSize]).div(127.5).sub(1));\n const predictions = await this.getBoxes(image, config);\n image.dispose();\n const hands = [];\n if (!predictions || predictions.length === 0) return hands;\n for (const prediction of predictions) {\n const boxes = prediction.box.dataSync();\n const startPoint = boxes.slice(0, 2);\n const endPoint = boxes.slice(2, 4);\n const palmLandmarks = prediction.palmLandmarks.arraySync();\n prediction.box.dispose();\n prediction.palmLandmarks.dispose();\n hands.push(box.scaleBoxCoordinates({ startPoint, endPoint, palmLandmarks, confidence: prediction.confidence }, [inputWidth / config.hand.inputSize, inputHeight / config.hand.inputSize]));\n }\n return hands;\n }\n}\nexports.HandDetector = HandDetector;\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\nimport * as util from './util';\n// eslint-disable-next-line no-unused-vars\nimport { log } from '../log.js';\n\n// const PALM_BOX_SHIFT_VECTOR = [0, -0.4];\nconst PALM_BOX_ENLARGE_FACTOR = 5; // default 3\n// const HAND_BOX_SHIFT_VECTOR = [0, -0.1]; // move detected hand box by x,y to ease landmark detection\nconst HAND_BOX_ENLARGE_FACTOR = 1.65; // default 1.65\nconst PALM_LANDMARK_IDS = [0, 5, 9, 13, 17, 1, 2];\nconst PALM_LANDMARKS_INDEX_OF_PALM_BASE = 0;\nconst PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE = 2;\n\nclass HandPipeline {\n constructor(handDetector, landmarkDetector, inputSize) {\n this.handDetector = handDetector;\n this.landmarkDetector = landmarkDetector;\n this.inputSize = inputSize;\n this.storedBoxes = [];\n this.skipped = 0;\n this.detectedHands = 0;\n }\n\n getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n // return box.enlargeBox(box.squarifyBox(box.shiftBox(boxAroundPalm, PALM_BOX_SHIFT_VECTOR)), PALM_BOX_ENLARGE_FACTOR);\n return box.enlargeBox(box.squarifyBox(boxAroundPalm), PALM_BOX_ENLARGE_FACTOR);\n }\n\n getBoxForHandLandmarks(landmarks) {\n const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n // const boxAroundHand = box.enlargeBox(box.squarifyBox(box.shiftBox(boundingBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n const boxAroundHand = box.enlargeBox(box.squarifyBox(boundingBox), HAND_BOX_ENLARGE_FACTOR);\n boxAroundHand.palmLandmarks = [];\n for (let i = 0; i < PALM_LANDMARK_IDS.length; i++) {\n boxAroundHand.palmLandmarks.push(landmarks[PALM_LANDMARK_IDS[i]].slice(0, 2));\n }\n return boxAroundHand;\n }\n\n transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n const boxSize = box.getBoxSize(box2);\n const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n const coordsScaled = rawCoords.map((coord) => [\n scaleFactor[0] * (coord[0] - this.inputSize / 2),\n scaleFactor[1] * (coord[1] - this.inputSize / 2),\n scaleFactor[2] * coord[2],\n ]);\n const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n const coordsRotated = coordsScaled.map((coord) => {\n const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n return [...rotated, coord[2]];\n });\n const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n const boxCenter = [...box.getBoxCenter(box2), 1];\n const originalBoxCenter = [\n util.dot(boxCenter, inverseRotationMatrix[0]),\n util.dot(boxCenter, inverseRotationMatrix[1]),\n ];\n return coordsRotated.map((coord) => [\n coord[0] + originalBoxCenter[0],\n coord[1] + originalBoxCenter[1],\n coord[2],\n ]);\n }\n\n async estimateHands(image, config) {\n let useFreshBox = false;\n\n // run new detector every skipFrames unless we only want box to start with\n let boxes;\n if ((this.skipped === 0) || (this.skipped > config.hand.skipFrames) || !config.hand.landmarks || !config.videoOptimized) {\n boxes = await this.handDetector.estimateHandBounds(image, config);\n this.skipped = 0;\n }\n if (config.videoOptimized) this.skipped++;\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxHands) || !config.hand.landmarks)) {\n this.detectedHands = 0;\n this.storedBoxes = [...boxes];\n // for (const possible of boxes) this.storedBoxes.push(possible);\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\n const hands = [];\n // log('hand', `skipped: ${this.skipped} max: ${config.hand.maxHands} detected: ${this.detectedHands} stored: ${this.storedBoxes.length} new: ${boxes?.length}`);\n\n // go through working set of boxes\n for (let i = 0; i < this.storedBoxes.length; i++) {\n const currentBox = this.storedBoxes[i];\n if (!currentBox) continue;\n if (config.hand.landmarks) {\n const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_PALM_BASE], currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE]) : 0;\n const palmCenter = box.getBoxCenter(currentBox);\n const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n const rotatedImage = config.hand.rotation ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n const croppedInput = box.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n const handImage = croppedInput.div(255);\n croppedInput.dispose();\n rotatedImage.dispose();\n const [confidenceT, keypoints] = await this.landmarkDetector.predict(handImage);\n handImage.dispose();\n const confidence = confidenceT.dataSync()[0];\n confidenceT.dispose();\n if (confidence >= config.hand.minConfidence) {\n const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n const rawCoords = keypointsReshaped.arraySync();\n keypoints.dispose();\n keypointsReshaped.dispose();\n const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n this.storedBoxes[i] = nextBoundingBox;\n const result = {\n landmarks: coords,\n confidence,\n box: {\n topLeft: nextBoundingBox.startPoint,\n bottomRight: nextBoundingBox.endPoint,\n },\n };\n hands.push(result);\n } else {\n this.storedBoxes[i] = null;\n }\n keypoints.dispose();\n } else {\n // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n const enlarged = box.enlargeBox(box.squarifyBox(currentBox), HAND_BOX_ENLARGE_FACTOR);\n const result = {\n confidence: currentBox.confidence,\n box: {\n topLeft: enlarged.startPoint,\n bottomRight: enlarged.endPoint,\n },\n };\n hands.push(result);\n }\n }\n this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n this.detectedHands = hands.length;\n return hands;\n }\n\n // eslint-disable-next-line class-methods-use-this\n calculateLandmarksBoundingBox(landmarks) {\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint };\n }\n}\n\nexports.HandPipeline = HandPipeline;\n", "exports.anchors = [\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.796875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.796875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.828125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.828125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.859375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.859375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.890625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.890625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.921875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.921875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.953125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.953125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.984375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.984375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n 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0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n];\n", "// https://storage.googleapis.com/tfjs-models/demos/handpose/index.html\n\nimport { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as handdetector from './handdetector';\nimport * as handpipeline from './handpipeline';\nimport * as anchors from './anchors';\n\nconst MESH_ANNOTATIONS = {\n thumb: [1, 2, 3, 4],\n indexFinger: [5, 6, 7, 8],\n middleFinger: [9, 10, 11, 12],\n ringFinger: [13, 14, 15, 16],\n pinky: [17, 18, 19, 20],\n palmBase: [0],\n};\n\nclass HandPose {\n constructor(handPipeline) {\n this.handPipeline = handPipeline;\n }\n\n static getAnnotations() {\n return MESH_ANNOTATIONS;\n }\n\n async estimateHands(input, config) {\n const predictions = await this.handPipeline.estimateHands(input, config);\n if (!predictions) return [];\n const hands = [];\n for (const prediction of predictions) {\n const annotations = {};\n if (prediction.landmarks) {\n for (const key of Object.keys(MESH_ANNOTATIONS)) {\n annotations[key] = MESH_ANNOTATIONS[key].map((index) => prediction.landmarks[index]);\n }\n }\n const box = prediction.box ? [\n Math.max(0, prediction.box.topLeft[0]),\n Math.max(0, prediction.box.topLeft[1]),\n Math.min(input.shape[2], prediction.box.bottomRight[0]) - prediction.box.topLeft[0],\n Math.min(input.shape[1], prediction.box.bottomRight[1]) - prediction.box.topLeft[1],\n ] : 0;\n hands.push({\n confidence: prediction.confidence,\n box,\n landmarks: prediction.landmarks,\n annotations,\n });\n }\n return hands;\n }\n}\nexports.HandPose = HandPose;\n\nasync function load(config) {\n const [handDetectorModel, handPoseModel] = await Promise.all([\n config.hand.enabled ? tf.loadGraphModel(config.hand.detector.modelPath, { fromTFHub: config.hand.detector.modelPath.includes('tfhub.dev') }) : null,\n config.hand.landmarks ? tf.loadGraphModel(config.hand.skeleton.modelPath, { fromTFHub: config.hand.skeleton.modelPath.includes('tfhub.dev') }) : null,\n ]);\n // @ts-ignore\n const handDetector = new handdetector.HandDetector(handDetectorModel, config.hand.inputSize, anchors.anchors);\n // @ts-ignore\n const handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel, config.hand.inputSize);\n const handPose = new HandPose(handPipeline);\n if (config.hand.enabled) log(`load model: ${config.hand.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n if (config.hand.landmarks) log(`load model: ${config.hand.skeleton.modelPath.match(/\\/(.*)\\./)[1]}`);\n return handPose;\n}\nexports.load = load;\n", "exports.body = (res) => {\n if (!res) return [];\n const gestures = [];\n for (let i = 0; i < res.length; i++) {\n // raising hands\n const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n if (nose && leftWrist && rightWrist && (leftWrist.position.y < nose.position.y) && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'i give up' });\n else if (nose && leftWrist && (leftWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise left hand' });\n else if (nose && rightWrist && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise right hand' });\n\n // leaning\n const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n if (leftShoulder && rightShoulder) gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position.y > rightShoulder.position.y) ? 'left' : 'right'}` });\n }\n return gestures;\n};\n\nexports.face = (res) => {\n if (!res) return [];\n const gestures = [];\n for (let i = 0; i < res.length; i++) {\n if (res[i].mesh && res[i].mesh.length > 0) {\n const eyeFacing = res[i].mesh[35][2] - res[i].mesh[263][2];\n if (Math.abs(eyeFacing) < 10) gestures.push({ face: i, gesture: 'facing camera' });\n else gestures.push({ face: i, gesture: `facing ${eyeFacing < 0 ? 'right' : 'left'}` });\n const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n const chinDepth = res[i].mesh[152][2];\n if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n }\n }\n return gestures;\n};\n\nexports.iris = (res) => {\n if (!res) return [];\n const gestures = [];\n for (let i = 0; i < res.length; i++) {\n if (!res[i].annotations || !res[i].annotations.leftEyeIris || !res[i].annotations.rightEyeIris) continue;\n const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n if (difference < 0.25) gestures.push({ iris: i, gesture: 'looking at camera' });\n }\n return gestures;\n};\n\nexports.hand = (res) => {\n if (!res) return [];\n const gestures = [];\n for (let i = 0; i < res.length; i++) {\n const fingers = [];\n for (const [finger, pos] of Object.entries(res[i]['annotations'])) {\n if (finger !== 'palmBase') fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n }\n if (fingers && fingers.length > 0) {\n const closest = fingers.reduce((best, a) => (best.position[2] < a.position[2] ? best : a));\n const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n gestures.push({ hand: i, gesture: `${closest.name} forward ${highest.name} up` });\n }\n }\n return gestures;\n};\n", "/* eslint-disable no-use-before-define */\n/*\nWebGLImageFilter - MIT Licensed\n2013, Dominic Szablewski - phoboslab.org\n\n*/\n\nconst WebGLProgram = function (gl, vertexSource, fragmentSource) {\n const _collect = function (source, prefix, collection) {\n const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n source.replace(r, (match, name) => {\n collection[name] = 0;\n return match;\n });\n };\n\n const _compile = function (source, type) {\n const shader = gl.createShader(type);\n gl.shaderSource(shader, source);\n gl.compileShader(shader);\n\n if (!gl.getShaderParameter(shader, gl.COMPILE_STATUS)) {\n // @ts-ignore\n throw new Error('Filter: GL compile failed', gl.getShaderInfoLog(shader));\n }\n return shader;\n };\n\n this.uniform = {};\n this.attribute = {};\n\n const _vsh = _compile(vertexSource, gl.VERTEX_SHADER);\n const _fsh = _compile(fragmentSource, gl.FRAGMENT_SHADER);\n\n this.id = gl.createProgram();\n gl.attachShader(this.id, _vsh);\n gl.attachShader(this.id, _fsh);\n gl.linkProgram(this.id);\n\n if (!gl.getProgramParameter(this.id, gl.LINK_STATUS)) {\n // @ts-ignore\n throw new Error('Filter: GL link failed', gl.getProgramInfoLog(this.id));\n }\n\n gl.useProgram(this.id);\n\n // Collect attributes\n _collect(vertexSource, 'attribute', this.attribute);\n for (const a in this.attribute) {\n this.attribute[a] = gl.getAttribLocation(this.id, a);\n }\n\n // Collect uniforms\n _collect(vertexSource, 'uniform', this.uniform);\n _collect(fragmentSource, 'uniform', this.uniform);\n for (const u in this.uniform) {\n this.uniform[u] = gl.getUniformLocation(this.id, u);\n }\n};\n\nconst WebGLImageFilter = function (params) {\n if (!params) params = { };\n let _drawCount = 0;\n let _sourceTexture = null;\n let _lastInChain = false;\n let _currentFramebufferIndex = -1;\n let _tempFramebuffers = [null, null];\n let _filterChain = [];\n let _width = -1;\n let _height = -1;\n let _vertexBuffer = null;\n let _currentProgram = null;\n const _canvas = params.canvas || document.createElement('canvas');\n\n // key is the shader program source, value is the compiled program\n const _shaderProgramCache = { };\n\n const gl = _canvas.getContext('webgl');\n if (!gl) throw new Error('Filter: getContext() failed');\n\n this.addFilter = function (name) {\n // eslint-disable-next-line prefer-rest-params\n const args = Array.prototype.slice.call(arguments, 1);\n const filter = _filter[name];\n\n _filterChain.push({ func: filter, args });\n };\n\n this.reset = function () {\n _filterChain = [];\n };\n\n this.apply = function (image) {\n _resize(image.width, image.height);\n _drawCount = 0;\n\n // Create the texture for the input image if we haven't yet\n if (!_sourceTexture) _sourceTexture = gl.createTexture();\n gl.bindTexture(gl.TEXTURE_2D, _sourceTexture);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n\n // No filters? Just draw\n if (_filterChain.length === 0) {\n // const program = _compileShader(SHADER.FRAGMENT_IDENTITY);\n _draw();\n return _canvas;\n }\n\n for (let i = 0; i < _filterChain.length; i++) {\n _lastInChain = (i === _filterChain.length - 1);\n const f = _filterChain[i];\n f.func.apply(this, f.args || []);\n }\n\n return _canvas;\n };\n\n const _resize = function (width, height) {\n // Same width/height? Nothing to do here\n if (width === _width && height === _height) { return; }\n\n _canvas.width = width;\n _width = width;\n _canvas.height = height;\n _height = height;\n\n // Create the context if we don't have it yet\n if (!_vertexBuffer) {\n // Create the vertex buffer for the two triangles [x, y, u, v] * 6\n const vertices = new Float32Array([\n -1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0,\n -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0,\n ]);\n // eslint-disable-next-line no-unused-expressions\n (_vertexBuffer = gl.createBuffer(), gl.bindBuffer(gl.ARRAY_BUFFER, _vertexBuffer));\n gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);\n\n // Note sure if this is a good idea; at least it makes texture loading\n // in Ejecta instant.\n gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n }\n\n gl.viewport(0, 0, _width, _height);\n\n // Delete old temp framebuffers\n _tempFramebuffers = [null, null];\n };\n\n const _getTempFramebuffer = function (index) {\n // @ts-ignore\n _tempFramebuffers[index] = _tempFramebuffers[index] || _createFramebufferTexture(_width, _height);\n return _tempFramebuffers[index];\n };\n\n const _createFramebufferTexture = function (width, height) {\n const fbo = gl.createFramebuffer();\n gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n\n const renderbuffer = gl.createRenderbuffer();\n gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n\n const texture = gl.createTexture();\n gl.bindTexture(gl.TEXTURE_2D, texture);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n\n gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n\n gl.bindTexture(gl.TEXTURE_2D, null);\n gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n\n return { fbo, texture };\n };\n\n const _draw = function (flags) {\n let source = null;\n let target = null;\n let flipY = false;\n\n // Set up the source\n if (_drawCount === 0) {\n // First draw call - use the source texture\n source = _sourceTexture;\n } else {\n // All following draw calls use the temp buffer last drawn to\n // @ts-ignore\n source = _getTempFramebuffer(_currentFramebufferIndex)?.texture;\n }\n _drawCount++;\n\n // Set up the target\n if (_lastInChain && !(flags & DRAW.INTERMEDIATE)) {\n // Last filter in our chain - draw directly to the WebGL Canvas. We may\n // also have to flip the image vertically now\n target = null;\n flipY = _drawCount % 2 === 0;\n } else {\n // Intermediate draw call - get a temp buffer to draw to\n _currentFramebufferIndex = (_currentFramebufferIndex + 1) % 2;\n // @ts-ignore\n target = _getTempFramebuffer(_currentFramebufferIndex)?.fbo;\n }\n\n // Bind the source and target and draw the two triangles\n gl.bindTexture(gl.TEXTURE_2D, source);\n gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n\n gl.uniform1f(_currentProgram.uniform.flipY, (flipY ? -1 : 1));\n gl.drawArrays(gl.TRIANGLES, 0, 6);\n };\n\n const _compileShader = function (fragmentSource) {\n if (_shaderProgramCache[fragmentSource]) {\n _currentProgram = _shaderProgramCache[fragmentSource];\n gl.useProgram(_currentProgram.id);\n return _currentProgram;\n }\n\n // Compile shaders\n _currentProgram = new WebGLProgram(gl, SHADER.VERTEX_IDENTITY, fragmentSource);\n\n const floatSize = Float32Array.BYTES_PER_ELEMENT;\n const vertSize = 4 * floatSize;\n gl.enableVertexAttribArray(_currentProgram.attribute.pos);\n gl.vertexAttribPointer(_currentProgram.attribute.pos, 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n gl.enableVertexAttribArray(_currentProgram.attribute.uv);\n gl.vertexAttribPointer(_currentProgram.attribute.uv, 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n\n _shaderProgramCache[fragmentSource] = _currentProgram;\n return _currentProgram;\n };\n\n let DRAW = { INTERMEDIATE: 1 };\n\n let SHADER = {};\n SHADER.VERTEX_IDENTITY = [\n 'precision highp float;',\n 'attribute vec2 pos;',\n 'attribute vec2 uv;',\n 'varying vec2 vUv;',\n 'uniform float flipY;',\n\n 'void main(void) {',\n 'vUv = uv;',\n 'gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);',\n '}',\n ].join('\\n');\n\n SHADER.FRAGMENT_IDENTITY = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n\n 'void main(void) {',\n 'gl_FragColor = texture2D(texture, vUv);',\n '}',\n ].join('\\n');\n\n let _filter = {};\n\n // -------------------------------------------------------------------------\n // Color Matrix Filter\n\n _filter.colorMatrix = function (matrix) {\n // Create a Float32 Array and normalize the offset component to 0-1\n const m = new Float32Array(matrix);\n m[4] /= 255;\n m[9] /= 255;\n m[14] /= 255;\n m[19] /= 255;\n\n // Can we ignore the alpha value? Makes things a bit faster.\n const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0)\n ? _filter.colorMatrix.SHADER.WITHOUT_ALPHA\n : _filter.colorMatrix.SHADER.WITH_ALPHA;\n\n const program = _compileShader(shader);\n gl.uniform1fv(program.uniform.m, m);\n _draw();\n };\n\n _filter.colorMatrix.SHADER = {};\n _filter.colorMatrix.SHADER.WITH_ALPHA = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform float m[20];',\n\n 'void main(void) {',\n 'vec4 c = texture2D(texture, vUv);',\n 'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];',\n 'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];',\n 'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];',\n 'gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];',\n '}',\n ].join('\\n');\n _filter.colorMatrix.SHADER.WITHOUT_ALPHA = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform float m[20];',\n\n 'void main(void) {',\n 'vec4 c = texture2D(texture, vUv);',\n 'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];',\n 'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];',\n 'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];',\n 'gl_FragColor.a = c.a;',\n '}',\n ].join('\\n');\n\n _filter.brightness = function (brightness) {\n const b = (brightness || 0) + 1;\n _filter.colorMatrix([\n b, 0, 0, 0, 0,\n 0, b, 0, 0, 0,\n 0, 0, b, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.saturation = function (amount) {\n const x = (amount || 0) * 2 / 3 + 1;\n const y = ((x - 1) * -0.5);\n _filter.colorMatrix([\n x, y, y, 0, 0,\n y, x, y, 0, 0,\n y, y, x, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.desaturate = function () {\n _filter.saturation(-1);\n };\n\n _filter.contrast = function (amount) {\n const v = (amount || 0) + 1;\n const o = -128 * (v - 1);\n\n _filter.colorMatrix([\n v, 0, 0, 0, o,\n 0, v, 0, 0, o,\n 0, 0, v, 0, o,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.negative = function () {\n _filter.contrast(-2);\n };\n\n _filter.hue = function (rotation) {\n rotation = (rotation || 0) / 180 * Math.PI;\n const cos = Math.cos(rotation);\n const sin = Math.sin(rotation);\n const lumR = 0.213;\n const lumG = 0.715;\n const lumB = 0.072;\n\n _filter.colorMatrix([\n lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.desaturateLuminance = function () {\n _filter.colorMatrix([\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.sepia = function () {\n _filter.colorMatrix([\n 0.393, 0.7689999, 0.18899999, 0, 0,\n 0.349, 0.6859999, 0.16799999, 0, 0,\n 0.272, 0.5339999, 0.13099999, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.brownie = function () {\n _filter.colorMatrix([\n 0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n 0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.vintagePinhole = function () {\n _filter.colorMatrix([\n 0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n 0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n 0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.kodachrome = function () {\n _filter.colorMatrix([\n 1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.technicolor = function () {\n _filter.colorMatrix([\n 1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.polaroid = function () {\n _filter.colorMatrix([\n 1.438, -0.062, -0.062, 0, 0,\n -0.122, 1.378, -0.122, 0, 0,\n -0.016, -0.016, 1.483, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.shiftToBGR = function () {\n _filter.colorMatrix([\n 0, 0, 1, 0, 0,\n 0, 1, 0, 0, 0,\n 1, 0, 0, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n // -------------------------------------------------------------------------\n // Convolution Filter\n\n _filter.convolution = function (matrix) {\n const m = new Float32Array(matrix);\n const pixelSizeX = 1 / _width;\n const pixelSizeY = 1 / _height;\n\n const program = _compileShader(_filter.convolution.SHADER);\n gl.uniform1fv(program.uniform.m, m);\n gl.uniform2f(program.uniform.px, pixelSizeX, pixelSizeY);\n _draw();\n };\n\n _filter.convolution.SHADER = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform vec2 px;',\n 'uniform float m[9];',\n\n 'void main(void) {',\n 'vec4 c11 = texture2D(texture, vUv - px);', // top left\n 'vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y));', // top center\n 'vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y));', // top right\n\n 'vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) );', // mid left\n 'vec4 c22 = texture2D(texture, vUv);', // mid center\n 'vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) );', // mid right\n\n 'vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) );', // bottom left\n 'vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) );', // bottom center\n 'vec4 c33 = texture2D(texture, vUv + px );', // bottom right\n\n 'gl_FragColor = ',\n 'c11 * m[0] + c12 * m[1] + c22 * m[2] +',\n 'c21 * m[3] + c22 * m[4] + c23 * m[5] +',\n 'c31 * m[6] + c32 * m[7] + c33 * m[8];',\n 'gl_FragColor.a = c22.a;',\n '}',\n ].join('\\n');\n\n _filter.detectEdges = function () {\n _filter.convolution.call(this, [\n 0, 1, 0,\n 1, -4, 1,\n 0, 1, 0,\n ]);\n };\n\n _filter.sobelX = function () {\n _filter.convolution.call(this, [\n -1, 0, 1,\n -2, 0, 2,\n -1, 0, 1,\n ]);\n };\n\n _filter.sobelY = function () {\n _filter.convolution.call(this, [\n -1, -2, -1,\n 0, 0, 0,\n 1, 2, 1,\n ]);\n };\n\n _filter.sharpen = function (amount) {\n const a = amount || 1;\n _filter.convolution.call(this, [\n 0, -1 * a, 0,\n -1 * a, 1 + 4 * a, -1 * a,\n 0, -1 * a, 0,\n ]);\n };\n\n _filter.emboss = function (size) {\n const s = size || 1;\n _filter.convolution.call(this, [\n -2 * s, -1 * s, 0,\n -1 * s, 1, 1 * s,\n 0, 1 * s, 2 * s,\n ]);\n };\n\n // -------------------------------------------------------------------------\n // Blur Filter\n\n _filter.blur = function (size) {\n const blurSizeX = (size / 7) / _width;\n const blurSizeY = (size / 7) / _height;\n\n const program = _compileShader(_filter.blur.SHADER);\n\n // Vertical\n gl.uniform2f(program.uniform.px, 0, blurSizeY);\n _draw(DRAW.INTERMEDIATE);\n\n // Horizontal\n gl.uniform2f(program.uniform.px, blurSizeX, 0);\n _draw();\n };\n\n _filter.blur.SHADER = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform vec2 px;',\n\n 'void main(void) {',\n 'gl_FragColor = vec4(0.0);',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;',\n 'gl_FragColor += texture2D(texture, vUv )*0.159576912161;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x, 1.0*px.y))*0.147308056121;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x, 2.0*px.y))*0.115876621105;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x, 3.0*px.y))*0.0776744219933;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x, 4.0*px.y))*0.0443683338718;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x, 5.0*px.y))*0.0215963866053;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265;',\n '}',\n ].join('\\n');\n\n // -------------------------------------------------------------------------\n // Pixelate Filter\n\n _filter.pixelate = function (size) {\n const blurSizeX = (size) / _width;\n const blurSizeY = (size) / _height;\n\n const program = _compileShader(_filter.pixelate.SHADER);\n\n // Horizontal\n gl.uniform2f(program.uniform.size, blurSizeX, blurSizeY);\n _draw();\n };\n\n _filter.pixelate.SHADER = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform vec2 size;',\n 'uniform sampler2D texture;',\n\n 'vec2 pixelate(vec2 coord, vec2 size) {',\n 'return floor( coord / size ) * size;',\n '}',\n\n 'void main(void) {',\n 'gl_FragColor = vec4(0.0);',\n 'vec2 coord = pixelate(vUv, size);',\n 'gl_FragColor += texture2D(texture, coord);',\n '}',\n ].join('\\n');\n};\n\nexports.Canvas = WebGLImageFilter;\n", "import { log } from './log.js';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as fxImage from './imagefx.js';\n\n// internal temp canvases\nlet inCanvas = null;\nlet outCanvas = null;\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nfunction process(input, config) {\n let tensor;\n if (input instanceof tf.Tensor) {\n tensor = tf.clone(input);\n } else {\n const originalWidth = input.naturalWidth || input.videoWidth || input.width || (input.shape && (input.shape[1] > 0));\n const originalHeight = input.naturalHeight || input.videoHeight || input.height || (input.shape && (input.shape[2] > 0));\n let targetWidth = originalWidth;\n let targetHeight = originalHeight;\n if (config.filter.width > 0) targetWidth = config.filter.width;\n else if (config.filter.height > 0) targetWidth = originalWidth * (config.filter.height / originalHeight);\n if (config.filter.height > 0) targetHeight = config.filter.height;\n else if (config.filter.width > 0) targetHeight = originalHeight * (config.filter.width / originalWidth);\n if (!targetWidth || !targetHeight) {\n log('Human: invalid input', input);\n return null;\n }\n if (!inCanvas || (inCanvas.width !== targetWidth) || (inCanvas.height !== targetHeight)) {\n inCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n if (inCanvas.width !== targetWidth) inCanvas.width = targetWidth;\n if (inCanvas.height !== targetHeight) inCanvas.height = targetHeight;\n }\n const ctx = inCanvas.getContext('2d');\n if (input instanceof ImageData) ctx.putImageData(input, 0, 0);\n else ctx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);\n if (config.filter.enabled) {\n if (!this.fx || !outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas.height !== outCanvas.height)) {\n outCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(inCanvas.width, inCanvas.height) : document.createElement('canvas');\n if (outCanvas.width !== inCanvas.width) outCanvas.width = inCanvas.width;\n if (outCanvas.height !== inCanvas.height) outCanvas.height = inCanvas.height;\n this.fx = tf.ENV.flags.IS_BROWSER ? new fxImage.Canvas({ canvas: outCanvas }) : null; // && (typeof document !== 'undefined')\n }\n if (!this.fx) return inCanvas;\n this.fx.reset();\n this.fx.addFilter('brightness', config.filter.brightness); // must have at least one filter enabled\n if (config.filter.contrast !== 0) this.fx.addFilter('contrast', config.filter.contrast);\n if (config.filter.sharpness !== 0) this.fx.addFilter('sharpen', config.filter.sharpness);\n if (config.filter.blur !== 0) this.fx.addFilter('blur', config.filter.blur);\n if (config.filter.saturation !== 0) this.fx.addFilter('saturation', config.filter.saturation);\n if (config.filter.hue !== 0) this.fx.addFilter('hue', config.filter.hue);\n if (config.filter.negative) this.fx.addFilter('negative');\n if (config.filter.sepia) this.fx.addFilter('sepia');\n if (config.filter.vintage) this.fx.addFilter('brownie');\n if (config.filter.sepia) this.fx.addFilter('sepia');\n if (config.filter.kodachrome) this.fx.addFilter('kodachrome');\n if (config.filter.technicolor) this.fx.addFilter('technicolor');\n if (config.filter.polaroid) this.fx.addFilter('polaroid');\n if (config.filter.pixelate !== 0) this.fx.addFilter('pixelate', config.filter.pixelate);\n this.fx.apply(inCanvas);\n // read pixel data\n /*\n const gl = outCanvas.getContext('webgl');\n if (gl) {\n const glBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 4);\n const pixBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 3);\n gl.readPixels(0, 0, outCanvas.width, outCanvas.height, gl.RGBA, gl.UNSIGNED_BYTE, glBuffer);\n // gl returns rbga while we only need rgb, so discarding alpha channel\n // gl returns starting point as lower left, so need to invert vertical\n let i = 0;\n for (let y = outCanvas.height - 1; y >= 0; y--) {\n for (let x = 0; x < outCanvas.width; x++) {\n const index = (x + y * outCanvas.width) * 4;\n pixBuffer[i++] = glBuffer[index + 0];\n pixBuffer[i++] = glBuffer[index + 1];\n pixBuffer[i++] = glBuffer[index + 2];\n }\n }\n outCanvas.data = pixBuffer;\n }\n */\n } else {\n outCanvas = inCanvas;\n }\n let pixels;\n if (outCanvas.data) {\n const shape = [outCanvas.height, outCanvas.width, 3];\n pixels = tf.tensor3d(outCanvas.data, shape, 'int32');\n } else if ((config.backend === 'webgl') || (outCanvas instanceof ImageData)) {\n // tf kernel-optimized method to get imagedata, also if input is imagedata, just use it\n pixels = tf.browser.fromPixels(outCanvas);\n } else {\n // cpu and wasm kernel does not implement efficient fromPixels method nor we can use canvas as-is, so we do a silly one more canvas\n const tempCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n tempCanvas.width = targetWidth;\n tempCanvas.height = targetHeight;\n const tempCtx = tempCanvas.getContext('2d');\n tempCtx?.drawImage(outCanvas, 0, 0);\n const data = tempCtx?.getImageData(0, 0, targetWidth, targetHeight);\n pixels = tf.browser.fromPixels(data);\n }\n const casted = pixels.toFloat();\n tensor = casted.expandDims(0);\n pixels.dispose();\n casted.dispose();\n }\n return { tensor, canvas: config.filter.return ? outCanvas : null };\n}\n\nexports.process = process;\n", "import { log } from './log.js';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as backend from './tfjs/backend.js';\nimport * as facemesh from './face/facemesh.js';\nimport * as age from './age/age.js';\nimport * as gender from './gender/gender.js';\nimport * as emotion from './emotion/emotion.js';\nimport * as embedding from './embedding/embedding.js';\nimport * as posenet from './body/posenet.js';\nimport * as handpose from './hand/handpose.js';\nimport * as gesture from './gesture/gesture.js';\nimport * as image from './image.js';\nimport * as profile from './profile.js';\nimport * as config from '../config.js';\nimport * as sample from './sample.js';\nimport * as app from '../package.json';\n\n// helper function: gets elapsed time on both browser and nodejs\nconst now = () => {\n if (typeof performance !== 'undefined') return performance.now();\n // @ts-ignore\n return parseInt(Number(process.hrtime.bigint()) / 1000 / 1000);\n};\n\n// helper function: perform deep merge of multiple objects so it allows full inheriance with overrides\nfunction mergeDeep(...objects) {\n const isObject = (obj) => obj && typeof obj === 'object';\n return objects.reduce((prev, obj) => {\n Object.keys(obj || {}).forEach((key) => {\n const pVal = prev[key];\n const oVal = obj[key];\n if (Array.isArray(pVal) && Array.isArray(oVal)) {\n prev[key] = pVal.concat(...oVal);\n } else if (isObject(pVal) && isObject(oVal)) {\n prev[key] = mergeDeep(pVal, oVal);\n } else {\n prev[key] = oVal;\n }\n });\n return prev;\n }, {});\n}\n\nclass Human {\n constructor(userConfig = {}) {\n this.tf = tf;\n this.version = app.version;\n this.config = mergeDeep(config.default, userConfig);\n this.fx = null;\n this.state = 'idle';\n this.numTensors = 0;\n this.analyzeMemoryLeaks = false;\n this.checkSanity = false;\n this.firstRun = true;\n this.perf = {};\n // object that contains all initialized models\n this.models = {\n facemesh: null,\n posenet: null,\n handpose: null,\n iris: null,\n age: null,\n gender: null,\n emotion: null,\n };\n // export raw access to underlying models\n this.facemesh = facemesh;\n this.age = age;\n this.gender = gender;\n this.emotion = emotion;\n this.body = posenet;\n this.hand = handpose;\n }\n\n profile() {\n // @ts-ignore\n if (this.config.profile) return profile.data;\n return {};\n }\n\n // helper function: measure tensor leak\n analyze(...msg) {\n if (!this.analyzeMemoryLeaks) return;\n const current = tf.engine().state.numTensors;\n const previous = this.numTensors;\n this.numTensors = current;\n const leaked = current - previous;\n if (leaked !== 0) log(...msg, leaked);\n }\n\n // quick sanity check on inputs\n sanity(input) {\n if (!this.checkSanity) return null;\n if (!input) return 'input is not defined';\n if (tf.ENV.flags.IS_NODE && !(input instanceof tf.Tensor)) {\n return 'input must be a tensor';\n }\n try {\n tf.getBackend();\n } catch {\n return 'backend not loaded';\n }\n return null;\n }\n\n simmilarity(embedding1, embedding2) {\n // @ts-ignore\n if (this.config.face.embedding.enabled) return embedding.simmilarity(embedding1, embedding2);\n return 0;\n }\n\n // preload models, not explicitly required as it's done automatically on first use\n async load(userConfig) {\n this.state = 'load';\n const timeStamp = now();\n if (userConfig) this.config = mergeDeep(this.config, userConfig);\n\n if (this.firstRun) {\n log(`version: ${this.version} TensorFlow/JS version: ${tf.version_core}`);\n await this.checkBackend(true);\n if (tf.ENV.flags.IS_BROWSER) {\n log('configuration:', this.config);\n log('tf flags:', tf.ENV.flags);\n }\n this.firstRun = false;\n }\n\n if (this.config.async) {\n [\n this.models.facemesh,\n this.models.age,\n this.models.gender,\n this.models.emotion,\n this.models.embedding,\n this.models.posenet,\n this.models.handpose,\n ] = await Promise.all([\n // @ts-ignore\n this.models.facemesh || (this.config.face.enabled ? facemesh.load(this.config) : null),\n // @ts-ignore\n this.models.age || ((this.config.face.enabled && this.config.face.age.enabled) ? age.load(this.config) : null),\n // @ts-ignore\n this.models.gender || ((this.config.face.enabled && this.config.face.gender.enabled) ? gender.load(this.config) : null),\n // @ts-ignore\n this.models.emotion || ((this.config.face.enabled && this.config.face.emotion.enabled) ? emotion.load(this.config) : null),\n // @ts-ignore\n this.models.embedding || ((this.config.face.enabled && this.config.face.embedding.enabled) ? embedding.load(this.config) : null),\n // @ts-ignore\n this.models.posenet || (this.config.body.enabled ? posenet.load(this.config) : null),\n // @ts-ignore\n this.models.handpose || (this.config.hand.enabled ? handpose.load(this.config) : null),\n ]);\n } else {\n // @ts-ignore\n if (this.config.face.enabled && !this.models.facemesh) this.models.facemesh = await facemesh.load(this.config);\n // @ts-ignore\n if (this.config.face.enabled && this.config.face.age.enabled && !this.models.age) this.models.age = await age.load(this.config);\n // @ts-ignore\n if (this.config.face.enabled && this.config.face.gender.enabled && !this.models.gender) this.models.gender = await gender.load(this.config);\n // @ts-ignore\n if (this.config.face.enabled && this.config.face.emotion.enabled && !this.models.emotion) this.models.emotion = await emotion.load(this.config);\n // @ts-ignore\n if (this.config.face.enabled && this.config.face.embedding.enabled && !this.models.embedding) this.models.embedding = await embedding.load(this.config);\n // @ts-ignore\n if (this.config.body.enabled && !this.models.posenet) this.models.posenet = await posenet.load(this.config);\n // @ts-ignore\n if (this.config.hand.enabled && !this.models.handpose) this.models.handpose = await handpose.load(this.config);\n }\n const current = Math.trunc(now() - timeStamp);\n if (current > (this.perf.load || 0)) this.perf.load = current;\n }\n\n // check if backend needs initialization if it changed\n async checkBackend(force) {\n if (this.config.backend && (this.config.backend !== '') && force || (tf.getBackend() !== this.config.backend)) {\n const timeStamp = now();\n this.state = 'backend';\n /* force backend reload\n if (this.config.backend in tf.engine().registry) {\n const backendFactory = tf.findBackendFactory(this.config.backend);\n tf.removeBackend(this.config.backend);\n tf.registerBackend(this.config.backend, backendFactory);\n } else {\n log('Backend not registred:', this.config.backend);\n }\n */\n\n log('setting backend:', this.config.backend);\n\n if (this.config.backend === 'wasm') {\n log('settings wasm path:', this.config.wasmPath);\n tf.setWasmPaths(this.config.wasmPath);\n const simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n if (!simd) log('warning: wasm simd support is not enabled');\n }\n\n if (this.config.backend === 'humangl') {\n log('registering humangl backend');\n backend.register();\n }\n\n await tf.setBackend(this.config.backend);\n tf.enableProdMode();\n /* debug mode is really too mcuh\n tf.enableDebugMode();\n */\n if (tf.getBackend() === 'webgl') {\n if (this.config.deallocate) {\n log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', this.config.deallocate);\n tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', this.config.deallocate ? 0 : -1);\n }\n tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n const gl = await tf.backend().getGPGPUContext().gl;\n log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);\n }\n await tf.ready();\n this.perf.backend = Math.trunc(now() - timeStamp);\n }\n }\n\n async detectFace(input) {\n // run facemesh, includes blazeface and iris\n // eslint-disable-next-line no-async-promise-executor\n let timeStamp;\n let ageRes;\n let genderRes;\n let emotionRes;\n let embeddingRes;\n const faceRes = [];\n this.state = 'run:face';\n timeStamp = now();\n // @ts-ignore\n const faces = await this.models.facemesh?.estimateFaces(input, this.config);\n this.perf.face = Math.trunc(now() - timeStamp);\n for (const face of faces) {\n this.analyze('Get Face');\n\n // is something went wrong, skip the face\n if (!face.image || face.image.isDisposedInternal) {\n log('Face object is disposed:', face.image);\n continue;\n }\n\n // run age, inherits face from blazeface\n this.analyze('Start Age:');\n if (this.config.async) {\n // @ts-ignore\n ageRes = this.config.face.age.enabled ? age.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:age';\n timeStamp = now();\n // @ts-ignore\n ageRes = this.config.face.age.enabled ? await age.predict(face.image, this.config) : {};\n this.perf.age = Math.trunc(now() - timeStamp);\n }\n\n // run gender, inherits face from blazeface\n this.analyze('Start Gender:');\n if (this.config.async) {\n // @ts-ignore\n genderRes = this.config.face.gender.enabled ? gender.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:gender';\n timeStamp = now();\n // @ts-ignore\n genderRes = this.config.face.gender.enabled ? await gender.predict(face.image, this.config) : {};\n this.perf.gender = Math.trunc(now() - timeStamp);\n }\n\n // run emotion, inherits face from blazeface\n this.analyze('Start Emotion:');\n if (this.config.async) {\n // @ts-ignore\n emotionRes = this.config.face.emotion.enabled ? emotion.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:emotion';\n timeStamp = now();\n // @ts-ignore\n emotionRes = this.config.face.emotion.enabled ? await emotion.predict(face.image, this.config) : {};\n this.perf.emotion = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Emotion:');\n\n // run emotion, inherits face from blazeface\n this.analyze('Start Embedding:');\n if (this.config.async) {\n // @ts-ignore\n embeddingRes = this.config.face.embedding.enabled ? embedding.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:embedding';\n timeStamp = now();\n // @ts-ignore\n embeddingRes = this.config.face.embedding.enabled ? await embedding.predict(face.image, this.config) : {};\n this.perf.embedding = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Emotion:');\n\n // if async wait for results\n if (this.config.async) {\n [ageRes, genderRes, emotionRes, embeddingRes] = await Promise.all([ageRes, genderRes, emotionRes, embeddingRes]);\n }\n\n this.analyze('Finish Face:');\n // dont need face anymore\n face.image.dispose();\n\n // calculate iris distance\n // iris: array[ center, left, top, right, bottom]\n if (!this.config.face.iris.enabled) {\n delete face.annotations.leftEyeIris;\n delete face.annotations.rightEyeIris;\n }\n const irisSize = (face.annotations.leftEyeIris && face.annotations.rightEyeIris)\n /* average human iris size is 11.7mm */\n ? 11.7 * Math.max(Math.abs(face.annotations.leftEyeIris[3][0] - face.annotations.leftEyeIris[1][0]), Math.abs(face.annotations.rightEyeIris[4][1] - face.annotations.rightEyeIris[2][1]))\n : 0;\n\n // combine results\n faceRes.push({\n confidence: face.confidence,\n box: face.box,\n mesh: face.mesh,\n boxRaw: face.boxRaw,\n meshRaw: face.meshRaw,\n annotations: face.annotations,\n age: ageRes.age,\n gender: genderRes.gender,\n genderConfidence: genderRes.confidence,\n emotion: emotionRes,\n embedding: embeddingRes,\n iris: (irisSize !== 0) ? Math.trunc(irisSize) / 100 : 0,\n });\n this.analyze('End Face');\n }\n this.analyze('End FaceMesh:');\n if (this.config.async) {\n if (this.perf.face) delete this.perf.face;\n if (this.perf.age) delete this.perf.age;\n if (this.perf.gender) delete this.perf.gender;\n if (this.perf.emotion) delete this.perf.emotion;\n }\n return faceRes;\n }\n\n async image(input, userConfig = {}) {\n this.state = 'image';\n this.config = mergeDeep(this.config, userConfig);\n // @ts-ignore\n const process = image.process(input, this.config);\n process.tensor.dispose();\n return process.canvas;\n }\n\n // main detect function\n async detect(input, userConfig = {}) {\n // detection happens inside a promise\n return new Promise(async (resolve) => {\n this.state = 'config';\n let timeStamp;\n\n // update configuration\n this.config = mergeDeep(this.config, userConfig);\n\n // sanity checks\n this.state = 'check';\n const error = this.sanity(input);\n if (error) {\n log(error, input);\n resolve({ error });\n }\n\n let poseRes;\n let handRes;\n let faceRes;\n\n const timeStart = now();\n\n // configure backend\n await this.checkBackend();\n\n // load models if enabled\n await this.load();\n\n if (this.config.scoped) tf.engine().startScope();\n this.analyze('Start Scope:');\n\n timeStamp = now();\n // @ts-ignore\n const process = image.process(input, this.config);\n if (!process || !process.tensor) {\n log('could not convert input to tensor');\n resolve({ error: 'could not convert input to tensor' });\n return;\n }\n this.perf.image = Math.trunc(now() - timeStamp);\n this.analyze('Get Image:');\n\n // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n if (this.config.async) {\n faceRes = this.config.face.enabled ? this.detectFace(process.tensor) : [];\n if (this.perf.face) delete this.perf.face;\n } else {\n this.state = 'run:face';\n timeStamp = now();\n faceRes = this.config.face.enabled ? await this.detectFace(process.tensor) : [];\n this.perf.face = Math.trunc(now() - timeStamp);\n }\n\n // run posenet\n this.analyze('Start Body:');\n if (this.config.async) {\n // @ts-ignore\n poseRes = this.config.body.enabled ? this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n if (this.perf.body) delete this.perf.body;\n } else {\n this.state = 'run:body';\n timeStamp = now();\n // @ts-ignore\n poseRes = this.config.body.enabled ? await this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n this.perf.body = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Body:');\n\n // run handpose\n this.analyze('Start Hand:');\n if (this.config.async) {\n // @ts-ignore\n handRes = this.config.hand.enabled ? this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n if (this.perf.hand) delete this.perf.hand;\n } else {\n this.state = 'run:hand';\n timeStamp = now();\n // @ts-ignore\n handRes = this.config.hand.enabled ? await this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n this.perf.hand = Math.trunc(now() - timeStamp);\n }\n // this.analyze('End Hand:');\n\n // if async wait for results\n if (this.config.async) {\n [faceRes, poseRes, handRes] = await Promise.all([faceRes, poseRes, handRes]);\n }\n process.tensor.dispose();\n\n if (this.config.scoped) tf.engine().endScope();\n this.analyze('End Scope:');\n\n let gestureRes = [];\n if (this.config.gesture.enabled) {\n timeStamp = now();\n gestureRes = [...gesture.face(faceRes), ...gesture.body(poseRes), ...gesture.hand(handRes), ...gesture.iris(faceRes)];\n if (!this.config.async) this.perf.gesture = Math.trunc(now() - timeStamp);\n else if (this.perf.gesture) delete this.perf.gesture;\n }\n\n this.perf.total = Math.trunc(now() - timeStart);\n this.state = 'idle';\n resolve({ face: faceRes, body: poseRes, hand: handRes, gesture: gestureRes, performance: this.perf, canvas: process.canvas });\n });\n }\n\n async warmup(userConfig) {\n const b64toBlob = (base64, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n\n if (userConfig) this.config = mergeDeep(this.config, userConfig);\n const video = this.config.videoOptimized;\n this.config.videoOptimized = false;\n let blob;\n switch (this.config.warmup) {\n case 'face': blob = await b64toBlob(sample.face); break;\n case 'full': blob = await b64toBlob(sample.body); break;\n default: blob = null;\n }\n if (!blob) return null;\n const bitmap = await createImageBitmap(blob);\n const t0 = now();\n const warmup = await this.detect(bitmap, config);\n const t1 = now();\n bitmap.close();\n log('Warmup', this.config.warmup, (t1 - t0), warmup);\n this.config.videoOptimized = video;\n return warmup;\n }\n}\n\nexport { Human as default };\n", "// helper function: wrapper around console output\nexport function log(...msg) {\n const dt = new Date();\n const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n // eslint-disable-next-line no-console\n if (msg) console.log(ts, 'Human:', ...msg);\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport const config = {\n name: 'humangl',\n priority: 99,\n canvas: null,\n width: 1024,\n height: 1024,\n webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n alpha: false,\n antialias: false,\n premultipliedAlpha: false,\n preserveDrawingBuffer: false,\n depth: false,\n stencil: false,\n failIfMajorPerformanceCaveat: false,\n desynchronized: true,\n },\n};\n\nexport function register() {\n if (!tf.findBackend(config.name)) {\n // @ts-ignore\n config.canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(config.width, config.height) : document.createElement('canvas');\n // @ts-ignore\n const gl = config.canvas.getContext('webgl2', config.webGLattr);\n tf.setWebGLContext(2, gl);\n const ctx = new tf.GPGPUContext(gl);\n tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n const kernels = tf.getKernelsForBackend('webgl');\n kernels.forEach((kernelConfig) => {\n const newKernelConfig = { ...kernelConfig, backendName: config.name };\n tf.registerKernel(newKernelConfig);\n });\n tf.ENV.set('WEBGL_VERSION', 2);\n tf.ENV.set('WEBGL_MAX_TEXTURE_SIZE', gl.getParameter(gl.MAX_TEXTURE_SIZE));\n tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n }\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nfunction getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\nfunction getBoxCenter(box) {\n return [\n box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n ];\n}\nfunction cutBoxFromImageAndResize(box, image, cropSize) {\n const h = image.shape[1];\n const w = image.shape[2];\n const boxes = [[\n box.startPoint[1] / h,\n box.startPoint[0] / w,\n box.endPoint[1] / h,\n box.endPoint[0] / w,\n ]];\n return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\nfunction scaleBoxCoordinates(box, factor) {\n const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n const palmLandmarks = box.palmLandmarks.map((coord) => {\n const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n return scaledCoord;\n });\n return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\nfunction enlargeBox(box, factor = 1.5) {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\nfunction squarifyBox(box) {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const maxEdge = Math.max(...size);\n const halfSize = maxEdge / 2;\n const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\nfunction shiftBox(box, shiftFactor) {\n const boxSize = [\n box.endPoint[0] - box.startPoint[0],\n box.endPoint[1] - box.startPoint[1],\n ];\n const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]];\n const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\nexport {\n cutBoxFromImageAndResize,\n enlargeBox,\n getBoxCenter,\n getBoxSize,\n scaleBoxCoordinates,\n shiftBox,\n squarifyBox,\n};\n", "function normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\nfunction computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\nconst buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\nfunction dot(v1, v2) {\n let product = 0;\n for (let i = 0; i < v1.length; i++) {\n product += v1[i] * v2[i];\n }\n return product;\n}\nfunction getColumnFrom2DArr(arr, columnIndex) {\n const column = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\nfunction multiplyTransformMatrices(mat1, mat2) {\n const product = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) {\n // @ts-ignore\n product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n }\n return product;\n}\nfunction buildRotationMatrix(rotation, center) {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\nfunction invertTransformMatrix(matrix) {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [\n -dot(rotationComponent[0], translationComponent),\n -dot(rotationComponent[1], translationComponent),\n ];\n return [\n rotationComponent[0].concat(invertedTranslation[0]),\n rotationComponent[1].concat(invertedTranslation[1]),\n [0, 0, 1],\n ];\n}\nfunction rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\nexport {\n buildRotationMatrix,\n computeRotation,\n dot,\n getColumnFrom2DArr,\n invertTransformMatrix,\n normalizeRadians,\n rotatePoint,\n};\n", "/* eslint-disable indent */\n/* eslint-disable no-multi-spaces */\n\nexport default {\n backend: 'webgl', // select tfjs backend to use\n wasmPath: '../assets/', // path for wasm binaries\n // only used for backend: wasm\n async: true, // execute enabled models in parallel\n // this disables per-model performance data but\n // slightly increases performance\n // cannot be used if profiling is enabled\n profile: false, // enable tfjs profiling\n // this has significant performance impact\n // only enable for debugging purposes\n // currently only implemented for age,gender,emotion models\n deallocate: false, // aggresively deallocate gpu memory after each usage\n // only valid for webgl backend and only during first call\n // cannot be changed unless library is reloaded\n // this has significant performance impact\n // only enable on low-memory devices\n scoped: false, // enable scoped runs\n // some models *may* have memory leaks,\n // this wrapps everything in a local scope at a cost of performance\n // typically not needed\n videoOptimized: true, // perform additional optimizations when input is video,\n // must be disabled for images\n // basically this skips object box boundary detection for every n frames\n // while maintaining in-box detection since objects cannot move that fast\n warmup: 'face', // what to use for human.warmup(), can be 'none', 'face', 'full'\n // warmup pre-initializes all models for faster inference but can take\n // significant time on startup\n filter: {\n enabled: true, // enable image pre-processing filters\n width: 0, // resize input width\n height: 0, // resize input height\n // if both width and height are set to 0, there is no resizing\n // if just one is set, second one is scaled automatically\n // if both are set, values are used as-is\n return: true, // return processed canvas imagedata in result\n brightness: 0, // range: -1 (darken) to 1 (lighten)\n contrast: 0, // range: -1 (reduce contrast) to 1 (increase contrast)\n sharpness: 0, // range: 0 (no sharpening) to 1 (maximum sharpening)\n blur: 0, // range: 0 (no blur) to N (blur radius in pixels)\n saturation: 0, // range: -1 (reduce saturation) to 1 (increase saturation)\n hue: 0, // range: 0 (no change) to 360 (hue rotation in degrees)\n negative: false, // image negative\n sepia: false, // image sepia colors\n vintage: false, // image vintage colors\n kodachrome: false, // image kodachrome colors\n technicolor: false, // image technicolor colors\n polaroid: false, // image polaroid camera effect\n pixelate: 0, // range: 0 (no pixelate) to N (number of pixels to pixelate)\n },\n\n gesture: {\n enabled: true, // enable simple gesture recognition\n },\n\n face: {\n enabled: true, // controls if specified modul is enabled\n // face.enabled is required for all face models:\n // detector, mesh, iris, age, gender, emotion\n // (note: module is not loaded until it is required)\n detector: {\n modelPath: '../models/blazeface-back.json', // can be 'front' or 'back'.\n // 'front' is optimized for large faces\n // such as front-facing camera and\n // 'back' is optimized for distanct faces.\n inputSize: 256, // fixed value: 128 for front and 256 for 'back'\n rotation: false, // use best-guess rotated face image or just box with rotation as-is\n // false means higher performance, but incorrect mesh mapping if face angle is above 20 degrees\n maxFaces: 10, // maximum number of faces detected in the input\n // should be set to the minimum number for performance\n skipFrames: 11, // how many frames to go without re-running the face bounding box detector\n // only used for video inputs\n // e.g., if model is running st 25 FPS, we can re-use existing bounding\n // box for updated face analysis as the head probably hasn't moved much\n // in short time (10 * 1/25 = 0.25 sec)\n minConfidence: 0.5, // threshold for discarding a prediction\n iouThreshold: 0.2, // threshold for deciding whether boxes overlap too much in\n // non-maximum suppression (0.1 means drop if overlap 10%)\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on score\n // in non-maximum suppression,\n // this is applied on detection objects only and before minConfidence\n },\n\n mesh: {\n enabled: true,\n modelPath: '../models/facemesh.json',\n inputSize: 192, // fixed value\n returnRawData: false, // in addition to standard mesh and box values, return raw normalized values as well\n },\n\n iris: {\n enabled: true,\n modelPath: '../models/iris.json',\n inputSize: 64, // fixed value\n },\n\n age: {\n enabled: true,\n modelPath: '../models/age-ssrnet-imdb.json', // can be 'age-ssrnet-imdb' or 'age-ssrnet-wiki'\n // which determines training set for model\n inputSize: 64, // fixed value\n skipFrames: 31, // how many frames to go without re-running the detector\n // only used for video inputs\n },\n\n gender: {\n enabled: true,\n minConfidence: 0.1, // threshold for discarding a prediction\n modelPath: '../models/gender-ssrnet-imdb.json', // can be 'gender', 'gender-ssrnet-imdb' or 'gender-ssrnet-wiki'\n inputSize: 64, // fixed value\n skipFrames: 41, // how many frames to go without re-running the detector\n // only used for video inputs\n },\n\n emotion: {\n enabled: true,\n inputSize: 64, // fixed value\n minConfidence: 0.2, // threshold for discarding a prediction\n skipFrames: 21, // how many frames to go without re-running the detector\n modelPath: '../models/emotion-large.json', // can be 'mini', 'large'\n },\n\n embedding: {\n enabled: false,\n inputSize: 112, // fixed value\n modelPath: '../models/mobilefacenet.json',\n },\n },\n\n body: {\n enabled: true,\n modelPath: '../models/posenet.json',\n inputSize: 257, // fixed value\n maxDetections: 10, // maximum number of people detected in the input\n // should be set to the minimum number for performance\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on score\n // in non-maximum suppression\n nmsRadius: 20, // radius for deciding points are too close in non-maximum suppression\n outputStride: 16, // size of block in which to run point detectopn, smaller value means higher resolution\n // defined by model itself, can be 8, 16, or 32\n modelType: 'MobileNet', // Human includes MobileNet version, but you can switch to ResNet\n },\n\n hand: {\n enabled: true,\n rotation: false, // use best-guess rotated hand image or just box with rotation as-is\n // false means higher performance, but incorrect finger mapping if hand is inverted\n inputSize: 256, // fixed value\n skipFrames: 12, // how many frames to go without re-running the hand bounding box detector\n // only used for video inputs\n // e.g., if model is running st 25 FPS, we can re-use existing bounding\n // box for updated hand skeleton analysis as the hand probably\n // hasn't moved much in short time (10 * 1/25 = 0.25 sec)\n minConfidence: 0.1, // threshold for discarding a prediction\n iouThreshold: 0.1, // threshold for deciding whether boxes overlap too much\n // in non-maximum suppression\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on\n // score in non-maximum suppression\n maxHands: 1, // maximum number of hands detected in the input\n // should be set to the minimum number for performance\n landmarks: true, // detect hand landmarks or just hand boundary box\n detector: {\n modelPath: '../models/handdetect.json',\n },\n skeleton: {\n modelPath: '../models/handskeleton.json',\n },\n },\n};\n", "// data:image/jpeg;base64,\nexport const face = 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+ "sources": ["../src/tfjs/tf-node.js", "../src/blazeface/blazeface.js", "../src/blazeface/box.js", "../src/blazeface/util.js", "../src/blazeface/coords.js", "../src/blazeface/facepipeline.js", "../src/blazeface/facemesh.js", "../src/profile.js", "../src/age/age.js", "../src/gender/gender.js", "../src/emotion/emotion.js", "../src/embedding/embedding.js", "../src/posenet/modelBase.js", "../src/posenet/heapSort.js", "../src/posenet/buildParts.js", "../src/posenet/keypoints.js", "../src/posenet/vectors.js", "../src/posenet/decoders.js", "../src/posenet/decodePose.js", "../src/posenet/decodeMultiple.js", "../src/posenet/util.js", "../src/posenet/modelPoseNet.js", "../src/posenet/posenet.js", "../src/handpose/handdetector.js", "../src/handpose/handpipeline.js", "../src/handpose/anchors.js", "../src/handpose/handpose.js", "../src/gesture/gesture.js", "../src/imagefx.js", "../src/image.js", "../src/human.js", "../src/log.js", "../src/tfjs/backend.js", "../src/blazepose/blazepose.js", "../src/handpose/box.js", "../src/handpose/util.js", "../config.js", "../src/sample.js"], + "sourcesContent": ["export * from '@tensorflow/tfjs-node';\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\n\nconst NUM_LANDMARKS = 6;\n\nfunction generateAnchors(inputSize) {\n const spec = { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] };\n const anchors = [];\n for (let i = 0; i < spec.strides.length; i++) {\n const stride = spec.strides[i];\n const gridRows = Math.floor((inputSize + stride - 1) / stride);\n const gridCols = Math.floor((inputSize + stride - 1) / stride);\n const anchorsNum = spec.anchors[i];\n for (let gridY = 0; gridY < gridRows; gridY++) {\n const anchorY = stride * (gridY + 0.5);\n for (let gridX = 0; gridX < gridCols; gridX++) {\n const anchorX = stride * (gridX + 0.5);\n for (let n = 0; n < anchorsNum; n++) {\n anchors.push([anchorX, anchorY]);\n }\n }\n }\n }\n return anchors;\n}\n\nconst disposeBox = (box) => {\n box.startEndTensor.dispose();\n box.startPoint.dispose();\n box.endPoint.dispose();\n};\n\nconst createBox = (startEndTensor) => ({\n startEndTensor,\n startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]),\n endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]),\n});\n\nconst scaleBox = (box, factors) => {\n const starts = tf.mul(box.startPoint, factors);\n const ends = tf.mul(box.endPoint, factors);\n const newCoordinates = tf.concat2d([starts, ends], 1);\n return createBox(newCoordinates);\n};\n\nfunction decodeBounds(boxOutputs, anchors, inputSize) {\n const boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n const centers = tf.add(boxStarts, anchors);\n const boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n const boxSizesNormalized = tf.div(boxSizes, inputSize);\n const centersNormalized = tf.div(centers, inputSize);\n const halfBoxSize = tf.div(boxSizesNormalized, 2);\n const starts = tf.sub(centersNormalized, halfBoxSize);\n const ends = tf.add(centersNormalized, halfBoxSize);\n const startNormalized = tf.mul(starts, inputSize);\n const endNormalized = tf.mul(ends, inputSize);\n const concatAxis = 1;\n return tf.concat2d([startNormalized, endNormalized], concatAxis);\n}\n\nfunction scaleBoxFromPrediction(face, scaleFactor) {\n return tf.tidy(() => {\n const box = face['box'] ? face['box'] : face;\n return scaleBox(box, scaleFactor).startEndTensor.squeeze();\n });\n}\n\nclass BlazeFaceModel {\n constructor(model, config) {\n this.blazeFaceModel = model;\n this.width = config.face.detector.inputSize;\n this.height = config.face.detector.inputSize;\n this.anchorsData = generateAnchors(config.face.detector.inputSize);\n this.anchors = tf.tensor2d(this.anchorsData);\n this.inputSize = tf.tensor1d([this.width, this.height]);\n this.config = config;\n this.scaleFaces = 0.8;\n }\n\n async getBoundingBoxes(inputImage) {\n // sanity check on input\n if ((!inputImage) || (inputImage.isDisposedInternal) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return null;\n const [detectedOutputs, boxes, scores] = tf.tidy(() => {\n const resizedImage = inputImage.resizeBilinear([this.width, this.height]);\n // const normalizedImage = tf.mul(tf.sub(resizedImage.div(255), 0.5), 2);\n const normalizedImage = tf.sub(resizedImage.div(127.5), 1);\n const batchedPrediction = this.blazeFaceModel.predict(normalizedImage);\n let prediction;\n // are we using tfhub or pinto converted model?\n if (Array.isArray(batchedPrediction)) {\n const sorted = batchedPrediction.sort((a, b) => a.size - b.size);\n const concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n const concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n const concat = tf.concat([concat512, concat384], 1);\n prediction = concat.squeeze(0);\n } else {\n prediction = batchedPrediction.squeeze(); // when using tfhub model\n }\n const decodedBounds = decodeBounds(prediction, this.anchors, this.inputSize);\n const logits = tf.slice(prediction, [0, 0], [-1, 1]);\n const scoresOut = tf.sigmoid(logits).squeeze();\n return [prediction, decodedBounds, scoresOut];\n });\n const boxIndicesTensor = await tf.image.nonMaxSuppressionAsync(boxes, scores, this.config.face.detector.maxFaces, this.config.face.detector.iouThreshold, this.config.face.detector.scoreThreshold);\n const boxIndices = boxIndicesTensor.arraySync();\n boxIndicesTensor.dispose();\n const boundingBoxesMap = boxIndices.map((boxIndex) => tf.slice(boxes, [boxIndex, 0], [1, -1]));\n const boundingBoxes = boundingBoxesMap.map((boundingBox) => {\n const vals = boundingBox.arraySync();\n boundingBox.dispose();\n return vals;\n });\n\n const scoresVal = scores.dataSync();\n const annotatedBoxes = [];\n for (let i = 0; i < boundingBoxes.length; i++) {\n const boxIndex = boxIndices[i];\n const confidence = scoresVal[boxIndex];\n if (confidence > this.config.face.detector.minConfidence) {\n const box = createBox(boundingBoxes[i]);\n const anchor = this.anchorsData[boxIndex];\n const landmarks = tf.tidy(() => tf.slice(detectedOutputs, [boxIndex, NUM_LANDMARKS - 1], [1, -1]).squeeze().reshape([NUM_LANDMARKS, -1]));\n annotatedBoxes.push({ box, landmarks, anchor, confidence });\n }\n }\n detectedOutputs.dispose();\n boxes.dispose();\n scores.dispose();\n detectedOutputs.dispose();\n return {\n boxes: annotatedBoxes,\n scaleFactor: [inputImage.shape[2] / this.width, inputImage.shape[1] / this.height],\n };\n }\n\n async estimateFaces(input) {\n // @ts-ignore\n const { boxes, scaleFactor } = await this.getBoundingBoxes(input);\n const faces = [];\n for (const face of boxes) {\n const landmarkData = face.landmarks.arraySync();\n const scaledBox = scaleBoxFromPrediction(face, scaleFactor);\n // @ts-ignore\n const boxData = scaleBox.arraySync();\n const probabilityData = face.probability.arraySync();\n const anchor = face.anchor;\n const [scaleFactorX, scaleFactorY] = scaleFactor;\n const scaledLandmarks = landmarkData\n .map((landmark) => ([\n (landmark[0] + anchor[0]) * scaleFactorX,\n (landmark[1] + anchor[1]) * scaleFactorY,\n ]));\n const normalizedFace = {\n topLeft: boxData.slice(0, 2),\n bottomRight: boxData.slice(2),\n landmarks: scaledLandmarks,\n probability: probabilityData,\n };\n disposeBox(face.box);\n face.landmarks.dispose();\n face.probability.dispose();\n scaledBox.dispose();\n faces.push(normalizedFace);\n }\n return faces;\n }\n}\n\nasync function load(config) {\n const blazeface = await tf.loadGraphModel(config.face.detector.modelPath, { fromTFHub: config.face.detector.modelPath.includes('tfhub.dev') });\n const model = new BlazeFaceModel(blazeface, config);\n log(`load model: ${config.face.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n return model;\n}\n\nexports.load = load;\nexports.BlazeFaceModel = BlazeFaceModel;\nexports.disposeBox = disposeBox;\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nfunction scaleBoxCoordinates(box, factor) {\n const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n return { startPoint, endPoint };\n}\nexports.scaleBoxCoordinates = scaleBoxCoordinates;\n\nfunction getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\nexports.getBoxSize = getBoxSize;\n\nfunction getBoxCenter(box) {\n return [\n box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n ];\n}\nexports.getBoxCenter = getBoxCenter;\n\nfunction cutBoxFromImageAndResize(box, image, cropSize) {\n const h = image.shape[1];\n const w = image.shape[2];\n const boxes = [[\n box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h,\n box.endPoint[0] / w,\n ]];\n return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\nexports.cutBoxFromImageAndResize = cutBoxFromImageAndResize;\n\nfunction enlargeBox(box, factor = 1.5) {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n return { startPoint, endPoint, landmarks: box.landmarks };\n}\nexports.enlargeBox = enlargeBox;\n\nfunction squarifyBox(box) {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const maxEdge = Math.max(...size);\n const halfSize = maxEdge / 2;\n const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n return { startPoint, endPoint, landmarks: box.landmarks };\n}\nexports.squarifyBox = squarifyBox;\n", "exports.IDENTITY_MATRIX = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n/**\n * Normalizes the provided angle to the range -pi to pi.\n * @param angle The angle in radians to be normalized.\n */\nfunction normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\nexports.normalizeRadians = normalizeRadians;\n\n/**\n * Computes the angle of rotation between two anchor points.\n * @param point1 First anchor point\n * @param point2 Second anchor point\n */\nfunction computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\nexports.computeRotation = computeRotation;\n\nfunction radToDegrees(rad) {\n return rad * 180 / Math.PI;\n}\nexports.radToDegrees = radToDegrees;\n\nfunction buildTranslationMatrix(x, y) {\n return [[1, 0, x], [0, 1, y], [0, 0, 1]];\n}\n\nfunction dot(v1, v2) {\n let product = 0;\n for (let i = 0; i < v1.length; i++) {\n product += v1[i] * v2[i];\n }\n return product;\n}\nexports.dot = dot;\n\nfunction getColumnFrom2DArr(arr, columnIndex) {\n const column = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\nexports.getColumnFrom2DArr = getColumnFrom2DArr;\n\nfunction multiplyTransformMatrices(mat1, mat2) {\n const product = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) {\n product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n }\n return product;\n}\nfunction buildRotationMatrix(rotation, center) {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\nexports.buildRotationMatrix = buildRotationMatrix;\n\nfunction invertTransformMatrix(matrix) {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [\n -dot(rotationComponent[0], translationComponent),\n -dot(rotationComponent[1], translationComponent),\n ];\n return [\n rotationComponent[0].concat(invertedTranslation[0]),\n rotationComponent[1].concat(invertedTranslation[1]),\n [0, 0, 1],\n ];\n}\nexports.invertTransformMatrix = invertTransformMatrix;\n\nfunction rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\nexports.rotatePoint = rotatePoint;\n\nfunction xyDistanceBetweenPoints(a, b) {\n return Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n}\nexports.xyDistanceBetweenPoints = xyDistanceBetweenPoints;\n", "const MESH_ANNOTATIONS = {\n silhouette: [\n 10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n 397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n 172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n ],\n lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291],\n lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291],\n lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308],\n lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],\n rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173],\n rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133],\n rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190],\n rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243],\n rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189],\n rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244],\n rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245],\n rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193],\n rightEyebrowLower: [35, 124, 46, 53, 52, 65],\n rightEyeIris: [473, 474, 475, 476, 477],\n leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n leftEyeIris: [468, 469, 470, 471, 472],\n midwayBetweenEyes: [168],\n noseTip: [1],\n noseBottom: [2],\n noseRightCorner: [98],\n noseLeftCorner: [327],\n rightCheek: [205],\n leftCheek: [425],\n};\n\nconst MESH_TO_IRIS_INDICES_MAP = [ // A mapping from facemesh model keypoints to iris model keypoints.\n { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] },\n { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] },\n { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] },\n { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] },\n { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] },\n { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] },\n { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] },\n // { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] },\n // { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] },\n];\n\nconst UV468 = [\n [0.499976992607117, 0.652534008026123],\n [0.500025987625122, 0.547487020492554],\n [0.499974012374878, 0.602371990680695],\n [0.482113003730774, 0.471979022026062],\n [0.500150978565216, 0.527155995368958],\n [0.499909996986389, 0.498252987861633],\n [0.499523013830185, 0.40106201171875],\n [0.289712011814117, 0.380764007568359],\n [0.499954998493195, 0.312398016452789],\n [0.499987006187439, 0.269918978214264],\n [0.500023007392883, 0.107050001621246],\n [0.500023007392883, 0.666234016418457],\n [0.5000159740448, 0.679224014282227],\n [0.500023007392883, 0.692348003387451],\n [0.499976992607117, 0.695277988910675],\n [0.499976992607117, 0.70593398809433],\n [0.499976992607117, 0.719385027885437],\n [0.499976992607117, 0.737019002437592],\n [0.499967992305756, 0.781370997428894],\n [0.499816000461578, 0.562981009483337],\n [0.473773002624512, 0.573909997940063],\n [0.104906998574734, 0.254140973091125],\n [0.365929991006851, 0.409575998783112],\n [0.338757991790771, 0.41302502155304],\n [0.311120003461838, 0.409460008144379],\n [0.274657994508743, 0.389131009578705],\n [0.393361985683441, 0.403706014156342],\n [0.345234006643295, 0.344011008739471],\n [0.370094001293182, 0.346076011657715],\n [0.319321990013123, 0.347265005111694],\n [0.297903001308441, 0.353591024875641],\n [0.24779200553894, 0.410809993743896],\n [0.396889001131058, 0.842755019664764],\n [0.280097991228104, 0.375599980354309],\n [0.106310002505779, 0.399955987930298],\n [0.2099249958992, 0.391353011131287],\n [0.355807989835739, 0.534406006336212],\n [0.471751004457474, 0.65040397644043],\n [0.474155008792877, 0.680191993713379],\n [0.439785003662109, 0.657229006290436],\n [0.414617002010345, 0.66654098033905],\n [0.450374007225037, 0.680860996246338],\n [0.428770989179611, 0.682690978050232],\n [0.374971002340317, 0.727805018424988],\n [0.486716985702515, 0.547628998756409],\n [0.485300987958908, 0.527395009994507],\n [0.257764995098114, 0.314490020275116],\n [0.401223003864288, 0.455172002315521],\n [0.429818987846375, 0.548614978790283],\n [0.421351999044418, 0.533740997314453],\n [0.276895999908447, 0.532056987285614],\n [0.483370006084442, 0.499586999416351],\n [0.33721199631691, 0.282882988452911],\n [0.296391993761063, 0.293242990970612],\n [0.169294998049736, 0.193813979625702],\n [0.447580009698868, 0.302609980106354],\n [0.392390012741089, 0.353887975215912],\n [0.354490011930466, 0.696784019470215],\n [0.067304998636246, 0.730105042457581],\n [0.442739009857178, 0.572826027870178],\n [0.457098007202148, 0.584792017936707],\n [0.381974011659622, 0.694710969924927],\n [0.392388999462128, 0.694203019142151],\n [0.277076005935669, 0.271932005882263],\n [0.422551989555359, 0.563233017921448],\n [0.385919004678726, 0.281364023685455],\n [0.383103013038635, 0.255840003490448],\n [0.331431001424789, 0.119714021682739],\n [0.229923993349075, 0.232002973556519],\n [0.364500999450684, 0.189113974571228],\n [0.229622006416321, 0.299540996551514],\n [0.173287004232407, 0.278747975826263],\n [0.472878992557526, 0.666198015213013],\n [0.446828007698059, 0.668527007102966],\n [0.422762006521225, 0.673889994621277],\n [0.445307999849319, 0.580065965652466],\n [0.388103008270264, 0.693961024284363],\n [0.403039008378983, 0.706539988517761],\n [0.403629004955292, 0.693953037261963],\n [0.460041999816895, 0.557139039039612],\n [0.431158006191254, 0.692366003990173],\n 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[0.547226011753082, 0.420395016670227],\n [0.563543975353241, 0.359827995300293],\n [0.583841025829315, 0.368713974952698],\n [0.586614012718201, 0.692366003990173],\n [0.771915018558502, 0.683578014373779],\n [0.531597018241882, 0.352482974529266],\n [0.588370978832245, 0.804440975189209],\n [0.52079701423645, 0.442565023899078],\n [0.567984998226166, 0.493479013442993],\n [0.543282985687256, 0.819254994392395],\n [0.655317008495331, 0.745514988899231],\n [0.621008992195129, 0.574018001556396],\n [0.625559985637665, 0.78031200170517],\n [0.680198013782501, 0.570719003677368],\n [0.64276397228241, 0.604337990283966],\n [0.704662978649139, 0.621529996395111],\n [0.552012026309967, 0.862591981887817],\n [0.589071989059448, 0.508637011051178],\n [0.685944974422455, 0.775357007980347],\n [0.645735025405884, 0.812640011310577],\n [0.675342977046967, 0.703978002071381],\n [0.810858011245728, 0.646304965019226],\n [0.72012197971344, 0.714666962623596],\n [0.866151988506317, 0.682704985141754],\n [0.663187026977539, 0.644596993923187],\n [0.570082008838654, 0.466325998306274],\n [0.544561982154846, 0.548375964164734],\n [0.562758982181549, 0.558784961700439],\n [0.531987011432648, 0.530140042304993],\n [0.585271000862122, 0.335177004337311],\n [0.622952997684479, 0.32277899980545],\n [0.655896008014679, 0.320163011550903],\n [0.687132000923157, 0.322345972061157],\n [0.716481983661652, 0.333200991153717],\n [0.758756995201111, 0.382786989212036],\n [0.897013008594513, 0.468769013881683],\n [0.732392013072968, 0.424547016620636],\n [0.70211398601532, 0.433162987232208],\n [0.66652500629425, 0.433866024017334],\n [0.633504986763, 0.426087975502014],\n [0.603875994682312, 0.416586995124817],\n [0.579657971858978, 0.409945011138916],\n [0.992439985275269, 0.480777025222778],\n [0.567192018032074, 0.569419980049133],\n [0.54136598110199, 0.478899002075195],\n [0.526564002037048, 0.546118021011353],\n [0.523913025856018, 0.563830018043518],\n [0.531529009342194, 0.555056989192963],\n [0.566035985946655, 0.582329034805298],\n [0.51631098985672, 0.563053965568542],\n [0.5174720287323, 0.577877044677734],\n [0.573594987392426, 0.389806985855103],\n [0.560697972774506, 0.395331978797913],\n [0.549755990505219, 0.399751007556915],\n [0.710287988185883, 0.368252992630005],\n [0.723330020904541, 0.363372981548309],\n];\n\nconst TRI468 = [\n 127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n 151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n 157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n 181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n 228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n 170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n 155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n 158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n 102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n 90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n 196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n 246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n 117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n 153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n 63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n 135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n 76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n 166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n 190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n 112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n 110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n 348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n 314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n 321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n 466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n 429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n 293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n 427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n 431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n 434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n 348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n 318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n 373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n 340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n 351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n 164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n 327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n 405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n 438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n 449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n 444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n 464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n 243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n 166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n 119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n 77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n 201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n 103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n 217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n 78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n 57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n 142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n 90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n 178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n 224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n 143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n 153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n 63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n 241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n 186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n 141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n 223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n 128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n 451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n 344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n 424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n 321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n 448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n 281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n 283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n 432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n 268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n 6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n 317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n 272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n 336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n 441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n 373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n 425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n 403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n 383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n 254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n 259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n 453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nconst TRI68 = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n 8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n 45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n 23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n 39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n 35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n 48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n 66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\nconst TRI33 = [\n /* eyes */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n /* brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n /* 4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n /* nose */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n /* chin */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n /* cont */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\nconst TRI7 = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nconst VTX68 = [\n /* cont */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n /* nose */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n /* eyes */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n /* lip */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\nconst VTX33 = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\nconst VTX7 = [33, 133, 362, 263, 1, 78, 308];\n\nexports.MESH_ANNOTATIONS = MESH_ANNOTATIONS;\nexports.MESH_TO_IRIS_INDICES_MAP = MESH_TO_IRIS_INDICES_MAP;\n\nexports.TRI468 = TRI468;\nexports.TRI68 = TRI68;\nexports.TRI33 = TRI33;\nexports.TRI7 = TRI7;\n\nexports.UV468 = UV468;\nexports.UV68 = VTX68.map((x) => UV468[x]);\nexports.UV33 = VTX33.map((x) => UV468[x]);\nexports.UV7 = VTX7.map((x) => UV468[x]);\n", "/* eslint-disable class-methods-use-this */\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as bounding from './box';\nimport * as util from './util';\nimport * as coords from './coords.js';\n// eslint-disable-next-line no-unused-vars\nimport { log } from '../log.js';\n\nconst LANDMARKS_COUNT = 468;\nconst MESH_MOUTH_INDEX = 13;\nconst MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES = [MESH_MOUTH_INDEX, coords.MESH_ANNOTATIONS['midwayBetweenEyes'][0]];\nconst BLAZEFACE_MOUTH_INDEX = 3;\nconst BLAZEFACE_NOSE_INDEX = 2;\nconst BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES = [BLAZEFACE_MOUTH_INDEX, BLAZEFACE_NOSE_INDEX];\nconst LEFT_EYE_OUTLINE = coords.MESH_ANNOTATIONS['leftEyeLower0'];\nconst LEFT_EYE_BOUNDS = [LEFT_EYE_OUTLINE[0], LEFT_EYE_OUTLINE[LEFT_EYE_OUTLINE.length - 1]];\nconst RIGHT_EYE_OUTLINE = coords.MESH_ANNOTATIONS['rightEyeLower0'];\nconst RIGHT_EYE_BOUNDS = [RIGHT_EYE_OUTLINE[0], RIGHT_EYE_OUTLINE[RIGHT_EYE_OUTLINE.length - 1]];\nconst IRIS_UPPER_CENTER_INDEX = 3;\nconst IRIS_LOWER_CENTER_INDEX = 4;\nconst IRIS_IRIS_INDEX = 71;\nconst IRIS_NUM_COORDINATES = 76;\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates. Update the z coordinate to be an average of the original and the new. This produces the best visual effect.\nfunction replaceRawCoordinates(rawCoords, newCoords, prefix, keys) {\n for (let i = 0; i < coords.MESH_TO_IRIS_INDICES_MAP.length; i++) {\n const { key, indices } = coords.MESH_TO_IRIS_INDICES_MAP[i];\n const originalIndices = coords.MESH_ANNOTATIONS[`${prefix}${key}`];\n const shouldReplaceAllKeys = keys == null;\n if (shouldReplaceAllKeys || keys.includes(key)) {\n for (let j = 0; j < indices.length; j++) {\n const index = indices[j];\n rawCoords[originalIndices[j]] = [\n newCoords[index][0], newCoords[index][1],\n (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n ];\n }\n }\n }\n}\n// The Pipeline coordinates between the bounding box and skeleton models.\nclass Pipeline {\n constructor(boundingBoxDetector, meshDetector, irisModel, config) {\n // An array of facial bounding boxes.\n this.storedBoxes = [];\n this.runsWithoutFaceDetector = 0;\n this.boundingBoxDetector = boundingBoxDetector;\n this.meshDetector = meshDetector;\n this.irisModel = irisModel;\n this.meshWidth = config.face.mesh.inputSize;\n this.meshHeight = config.face.mesh.inputSize;\n this.irisSize = config.face.iris.inputSize;\n this.irisEnlarge = 2.3;\n this.skipped = 0;\n this.detectedFaces = 0;\n }\n\n transformRawCoords(rawCoords, box, angle, rotationMatrix) {\n // @ts-ignore\n const boxSize = bounding.getBoxSize({ startPoint: box.startPoint, endPoint: box.endPoint });\n const scaleFactor = [boxSize[0] / this.meshWidth, boxSize[1] / this.meshHeight];\n const coordsScaled = rawCoords.map((coord) => ([\n scaleFactor[0] * (coord[0] - this.meshWidth / 2),\n scaleFactor[1] * (coord[1] - this.meshHeight / 2), coord[2],\n ]));\n const coordsRotationMatrix = (angle !== 0) ? util.buildRotationMatrix(angle, [0, 0]) : util.IDENTITY_MATRIX;\n const coordsRotated = (angle !== 0) ? coordsScaled.map((coord) => ([...util.rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n const inverseRotationMatrix = (angle !== 0) ? util.invertTransformMatrix(rotationMatrix) : util.IDENTITY_MATRIX;\n // @ts-ignore\n const boxCenter = [...bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint }), 1];\n return coordsRotated.map((coord) => ([\n coord[0] + util.dot(boxCenter, inverseRotationMatrix[0]),\n coord[1] + util.dot(boxCenter, inverseRotationMatrix[1]),\n coord[2],\n ]));\n }\n\n getLeftToRightEyeDepthDifference(rawCoords) {\n const leftEyeZ = rawCoords[LEFT_EYE_BOUNDS[0]][2];\n const rightEyeZ = rawCoords[RIGHT_EYE_BOUNDS[0]][2];\n return leftEyeZ - rightEyeZ;\n }\n\n // Returns a box describing a cropped region around the eye fit for passing to the iris model.\n getEyeBox(rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, flip = false) {\n // @ts-ignore\n const box = bounding.squarifyBox(bounding.enlargeBox(this.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), this.irisEnlarge));\n // @ts-ignore\n const boxSize = bounding.getBoxSize(box);\n let crop = tf.image.cropAndResize(face, [[\n box.startPoint[1] / this.meshHeight,\n box.startPoint[0] / this.meshWidth, box.endPoint[1] / this.meshHeight,\n box.endPoint[0] / this.meshWidth,\n ]], [0], [this.irisSize, this.irisSize]);\n if (flip) {\n crop = tf.image.flipLeftRight(crop);\n }\n return { box, boxSize, crop };\n }\n\n // Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\n getEyeCoords(eyeData, eyeBox, eyeBoxSize, flip = false) {\n const eyeRawCoords = [];\n for (let i = 0; i < IRIS_NUM_COORDINATES; i++) {\n const x = eyeData[i * 3];\n const y = eyeData[i * 3 + 1];\n const z = eyeData[i * 3 + 2];\n eyeRawCoords.push([\n (flip\n ? (1 - (x / this.irisSize))\n : (x / this.irisSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n (y / this.irisSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n ]);\n }\n return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(IRIS_IRIS_INDEX) };\n }\n\n // The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\n getAdjustedIrisCoords(rawCoords, irisCoords, direction) {\n const upperCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeUpper0`][IRIS_UPPER_CENTER_INDEX]][2];\n const lowerCenterZ = rawCoords[coords.MESH_ANNOTATIONS[`${direction}EyeLower0`][IRIS_LOWER_CENTER_INDEX]][2];\n const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n return irisCoords.map((coord, i) => {\n let z = averageZ;\n if (i === 2) {\n z = upperCenterZ;\n } else if (i === 4) {\n z = lowerCenterZ;\n }\n return [coord[0], coord[1], z];\n });\n }\n\n async predict(input, config) {\n let useFreshBox = false;\n // run new detector every skipFrames unless we only want box to start with\n let detector;\n if ((this.skipped === 0) || (this.skipped > config.face.detector.skipFrames) || !config.face.mesh.enabled || !config.videoOptimized) {\n detector = await this.boundingBoxDetector.getBoundingBoxes(input);\n this.skipped = 0;\n }\n if (config.videoOptimized) this.skipped++;\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (detector && detector.boxes && (detector.boxes.length > 0) && (!config.face.mesh.enabled || (detector.boxes.length !== this.detectedFaces) && (this.detectedFaces !== config.face.detector.maxFaces))) {\n this.storedBoxes = [];\n this.detectedFaces = 0;\n for (const possible of detector.boxes) {\n this.storedBoxes.push({ startPoint: possible.box.startPoint.dataSync(), endPoint: possible.box.endPoint.dataSync(), landmarks: possible.landmarks, confidence: possible.confidence });\n }\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\n\n if (useFreshBox) {\n if (!detector || !detector.boxes || (detector.boxes.length === 0)) {\n this.storedBoxes = [];\n this.detectedFaces = 0;\n return null;\n }\n for (let i = 0; i < this.storedBoxes.length; i++) {\n // @ts-ignore\n const scaledBox = bounding.scaleBoxCoordinates({ startPoint: this.storedBoxes[i].startPoint, endPoint: this.storedBoxes[i].endPoint }, detector.scaleFactor);\n // @ts-ignore\n const enlargedBox = bounding.enlargeBox(scaledBox);\n // @ts-ignore\n const squarifiedBox = bounding.squarifyBox(enlargedBox);\n const landmarks = this.storedBoxes[i].landmarks.arraySync();\n const confidence = this.storedBoxes[i].confidence;\n this.storedBoxes[i] = { ...squarifiedBox, confidence, landmarks };\n }\n this.runsWithoutFaceDetector = 0;\n }\n if (detector && detector.boxes) {\n detector.boxes.forEach((prediction) => {\n prediction.box.startPoint.dispose();\n prediction.box.endPoint.dispose();\n prediction.landmarks.dispose();\n });\n }\n\n // log('face', `skipped: ${this.skipped} max: ${config.face.detector.maxFaces} detected: ${this.detectedFaces} stored: ${this.storedBoxes.length} new: ${detector?.boxes?.length}`);\n let results = tf.tidy(() => this.storedBoxes.map((box, i) => {\n // The facial bounding box landmarks could come either from blazeface (if we are using a fresh box), or from the mesh model (if we are reusing an old box).\n let face;\n let angle = 0;\n let rotationMatrix;\n if (config.face.detector.rotation) {\n const [indexOfMouth, indexOfForehead] = (box.landmarks.length >= LANDMARKS_COUNT) ? MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES : BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES;\n angle = util.computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]);\n // @ts-ignore\n const faceCenter = bounding.getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint });\n const faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]];\n const rotatedImage = tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized);\n rotationMatrix = util.buildRotationMatrix(-angle, faceCenter);\n // @ts-ignore\n face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, rotatedImage, [this.meshHeight, this.meshWidth]).div(255);\n } else {\n rotationMatrix = util.IDENTITY_MATRIX;\n const cloned = input.clone();\n // @ts-ignore\n face = bounding.cutBoxFromImageAndResize({ startPoint: box.startPoint, endPoint: box.endPoint }, cloned, [this.meshHeight, this.meshWidth]).div(255);\n }\n\n // if we're not going to produce mesh, don't spend time with further processing\n if (!config.face.mesh.enabled) {\n const prediction = {\n coords: null,\n box,\n faceConfidence: null,\n confidence: box.confidence,\n image: face,\n };\n return prediction;\n }\n\n const [, confidence, contourCoords] = this.meshDetector.predict(face); // The first returned tensor represents facial contours, which are included in the coordinates.\n const confidenceVal = confidence.dataSync()[0];\n if (confidenceVal < config.face.detector.minConfidence) return null; // if below confidence just exit\n const coordsReshaped = tf.reshape(contourCoords, [-1, 3]);\n let rawCoords = coordsReshaped.arraySync();\n\n if (config.face.iris.enabled) {\n const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = this.getEyeBox(rawCoords, face, LEFT_EYE_BOUNDS[0], LEFT_EYE_BOUNDS[1], true);\n const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = this.getEyeBox(rawCoords, face, RIGHT_EYE_BOUNDS[0], RIGHT_EYE_BOUNDS[1]);\n const eyePredictions = this.irisModel.predict(tf.concat([leftEyeCrop, rightEyeCrop]));\n const eyePredictionsData = eyePredictions.dataSync();\n const leftEyeData = eyePredictionsData.slice(0, IRIS_NUM_COORDINATES * 3);\n const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = this.getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n const rightEyeData = eyePredictionsData.slice(IRIS_NUM_COORDINATES * 3);\n const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = this.getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize);\n const leftToRightEyeDepthDifference = this.getLeftToRightEyeDepthDifference(rawCoords);\n if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left');\n replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right');\n // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged. So we only update a single contour line above and below the eye.\n } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n } else { // User is looking towards the left.\n replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n }\n const adjustedLeftIrisCoords = this.getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n const adjustedRightIrisCoords = this.getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n rawCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n }\n\n const transformedCoordsData = this.transformRawCoords(rawCoords, box, angle, rotationMatrix);\n // @ts-ignore\n const landmarksBox = bounding.enlargeBox(this.calculateLandmarksBoundingBox(transformedCoordsData));\n // @ts-ignore\n const squarifiedLandmarksBox = bounding.squarifyBox(landmarksBox);\n const transformedCoords = tf.tensor2d(transformedCoordsData);\n const prediction = {\n coords: transformedCoords,\n box: landmarksBox,\n faceConfidence: confidenceVal,\n confidence: box.confidence,\n image: face,\n };\n if (config.face.mesh.returnRawData) prediction.rawCoords = rawCoords;\n this.storedBoxes[i] = { ...squarifiedLandmarksBox, landmarks: transformedCoords.arraySync(), confidence: box.confidence, faceConfidence: confidenceVal };\n\n return prediction;\n }));\n results = results.filter((a) => a !== null);\n this.detectedFaces = results.length;\n return results;\n }\n\n calculateLandmarksBoundingBox(landmarks) {\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint, landmarks };\n }\n}\nexports.Pipeline = Pipeline;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as blazeface from './blazeface.js';\nimport * as facepipeline from './facepipeline.js';\nimport * as coords from './coords.js';\n\nclass MediaPipeFaceMesh {\n constructor(blazeFace, blazeMeshModel, irisModel, config) {\n // @ts-ignore\n this.facePipeline = new facepipeline.Pipeline(blazeFace, blazeMeshModel, irisModel, config);\n this.config = config;\n }\n\n async estimateFaces(input, config) {\n const predictions = await this.facePipeline.predict(input, config);\n const results = [];\n for (const prediction of (predictions || [])) {\n if (prediction.isDisposedInternal) continue; // guard against disposed tensors on long running operations such as pause in middle of processing\n const mesh = prediction.coords ? prediction.coords.arraySync() : null;\n const meshRaw = prediction.rawCoords;\n const annotations = {};\n if (mesh && mesh.length > 0) {\n for (const key of Object.keys(coords.MESH_ANNOTATIONS)) {\n annotations[key] = coords.MESH_ANNOTATIONS[key].map((index) => mesh[index]);\n }\n }\n const boxRaw = (config.face.mesh.returnRawData && prediction.box) ? { topLeft: prediction.box.startPoint, bottomRight: prediction.box.endPoint } : null;\n const box = prediction.box ? [\n Math.max(0, prediction.box.startPoint[0]),\n Math.max(0, prediction.box.startPoint[1]),\n Math.min(input.shape[2], prediction.box.endPoint[0]) - prediction.box.startPoint[0],\n Math.min(input.shape[1], prediction.box.endPoint[1]) - prediction.box.startPoint[1],\n ] : 0;\n results.push({\n confidence: prediction.confidence || 0,\n box,\n mesh,\n boxRaw,\n meshRaw,\n annotations,\n image: prediction.image ? tf.clone(prediction.image) : null,\n });\n if (prediction.coords) prediction.coords.dispose();\n if (prediction.image) prediction.image.dispose();\n }\n return results;\n }\n}\n\nlet faceModels = [null, null, null];\nasync function load(config) {\n faceModels = await Promise.all([\n // @ts-ignore\n (!faceModels[0] && config.face.enabled) ? blazeface.load(config) : null,\n (!faceModels[1] && config.face.mesh.enabled) ? tf.loadGraphModel(config.face.mesh.modelPath, { fromTFHub: config.face.mesh.modelPath.includes('tfhub.dev') }) : null,\n (!faceModels[2] && config.face.iris.enabled) ? tf.loadGraphModel(config.face.iris.modelPath, { fromTFHub: config.face.iris.modelPath.includes('tfhub.dev') }) : null,\n ]);\n const faceMesh = new MediaPipeFaceMesh(faceModels[0], faceModels[1], faceModels[2], config);\n if (config.face.mesh.enabled) log(`load model: ${config.face.mesh.modelPath.match(/\\/(.*)\\./)[1]}`);\n if (config.face.iris.enabled) log(`load model: ${config.face.iris.modelPath.match(/\\/(.*)\\./)[1]}`);\n return faceMesh;\n}\n\nexports.load = load;\nexports.MediaPipeFaceMesh = MediaPipeFaceMesh;\nexports.triangulation = coords.TRI468;\n", "import { log } from './log.js';\n\nconst profileData = {};\n\nfunction profile(name, data) {\n if (!data || !data.kernels) return;\n const maxResults = 5;\n const time = data.kernels\n .filter((a) => a.kernelTimeMs > 0)\n .reduce((a, b) => a += b.kernelTimeMs, 0);\n const slowest = data.kernels\n .map((a, i) => { a.id = i; return a; })\n .filter((a) => a.kernelTimeMs > 0)\n .sort((a, b) => b.kernelTimeMs - a.kernelTimeMs);\n const largest = data.kernels\n .map((a, i) => { a.id = i; return a; })\n .filter((a) => a.totalBytesSnapshot > 0)\n .sort((a, b) => b.totalBytesSnapshot - a.totalBytesSnapshot);\n if (slowest.length > maxResults) slowest.length = maxResults;\n if (largest.length > maxResults) largest.length = maxResults;\n const res = { newBytes: data.newBytes, newTensors: data.newTensors, peakBytes: data.peakBytes, numKernelOps: data.kernels.length, timeKernelOps: time, slowestKernelOps: slowest, largestKernelOps: largest };\n profileData[name] = res;\n log('Human profiler', name, res);\n}\n\nexports.run = profile;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile.js';\n\nconst models = {};\nlet last = { age: 0 };\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nasync function load(config) {\n if (!models.age) {\n models.age = await tf.loadGraphModel(config.face.age.modelPath);\n log(`load model: ${config.face.age.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.age;\n}\n\nasync function predict(image, config) {\n if (!models.age) return null;\n if ((skipped < config.face.age.skipFrames) && config.videoOptimized && last.age && (last.age > 0)) {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n /*\n const zoom = [0, 0]; // 0..1 meaning 0%..100%\n const box = [[\n (image.shape[1] * zoom[0]) / image.shape[1],\n (image.shape[2] * zoom[1]) / image.shape[2],\n (image.shape[1] - (image.shape[1] * zoom[0])) / image.shape[1],\n (image.shape[2] - (image.shape[2] * zoom[1])) / image.shape[2],\n ]];\n const resize = tf.image.cropAndResize(image, box, [0], [config.face.age.inputSize, config.face.age.inputSize]);\n */\n const resize = tf.image.resizeBilinear(image, [config.face.age.inputSize, config.face.age.inputSize], false);\n const enhance = tf.mul(resize, [255.0]);\n tf.dispose(resize);\n\n let ageT;\n const obj = {};\n\n if (!config.profile) {\n if (config.face.age.enabled) ageT = await models.age.predict(enhance);\n } else {\n const profileAge = config.face.age.enabled ? await tf.profile(() => models.age.predict(enhance)) : {};\n ageT = profileAge.result.clone();\n profileAge.result.dispose();\n // @ts-ignore\n profile.run('age', profileAge);\n }\n enhance.dispose();\n\n if (ageT) {\n const data = ageT.dataSync();\n obj.age = Math.trunc(10 * data[0]) / 10;\n }\n ageT.dispose();\n\n last = obj;\n resolve(obj);\n });\n}\n\nexports.predict = predict;\nexports.load = load;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile.js';\n\nconst models = {};\nlet last = { gender: '' };\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet alternative = false;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nasync function load(config) {\n if (!models.gender) {\n models.gender = await tf.loadGraphModel(config.face.gender.modelPath);\n alternative = models.gender.inputs[0].shape[3] === 1;\n log(`load model: ${config.face.gender.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.gender;\n}\n\nasync function predict(image, config) {\n if (!models.gender) return null;\n if ((skipped < config.face.gender.skipFrames) && config.videoOptimized && last.gender !== '') {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [config.face.gender.inputSize, config.face.gender.inputSize], false);\n let enhance;\n if (alternative) {\n enhance = tf.tidy(() => {\n const [red, green, blue] = tf.split(resize, 3, 3);\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n return grayscale.sub(0.5).mul(2);\n });\n } else {\n enhance = tf.mul(resize, [255.0]);\n }\n tf.dispose(resize);\n\n let genderT;\n const obj = {};\n\n if (!config.profile) {\n if (config.face.gender.enabled) genderT = await models.gender.predict(enhance);\n } else {\n const profileGender = config.face.gender.enabled ? await tf.profile(() => models.gender.predict(enhance)) : {};\n genderT = profileGender.result.clone();\n profileGender.result.dispose();\n // @ts-ignore\n profile.run('gender', profileGender);\n }\n enhance.dispose();\n\n if (genderT) {\n const data = genderT.dataSync();\n if (alternative) {\n // returns two values 0..1, bigger one is prediction\n const confidence = Math.trunc(100 * Math.abs(data[0] - data[1])) / 100;\n if (confidence > config.face.gender.minConfidence) {\n obj.gender = data[0] > data[1] ? 'female' : 'male';\n obj.confidence = confidence;\n }\n } else {\n // returns one value 0..1, .5 is prediction threshold\n const confidence = Math.trunc(200 * Math.abs((data[0] - 0.5))) / 100;\n if (confidence > config.face.gender.minConfidence) {\n obj.gender = data[0] <= 0.5 ? 'female' : 'male';\n obj.confidence = Math.min(0.99, confidence);\n }\n }\n }\n genderT.dispose();\n\n last = obj;\n resolve(obj);\n });\n}\n\nexports.predict = predict;\nexports.load = load;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile.js';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];\nconst models = {};\nlet last = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\nconst scale = 1; // score multiplication factor\n\nasync function load(config) {\n if (!models.emotion) {\n models.emotion = await tf.loadGraphModel(config.face.emotion.modelPath);\n log(`load model: ${config.face.emotion.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.emotion;\n}\n\nasync function predict(image, config) {\n if (!models.emotion) return null;\n if ((skipped < config.face.emotion.skipFrames) && config.videoOptimized && (last.length > 0)) {\n skipped++;\n return last;\n }\n if (config.videoOptimized) skipped = 0;\n else skipped = Number.MAX_SAFE_INTEGER;\n return new Promise(async (resolve) => {\n /*\n const zoom = [0, 0]; // 0..1 meaning 0%..100%\n const box = [[\n (image.shape[1] * zoom[0]) / image.shape[1],\n (image.shape[2] * zoom[1]) / image.shape[2],\n (image.shape[1] - (image.shape[1] * zoom[0])) / image.shape[1],\n (image.shape[2] - (image.shape[2] * zoom[1])) / image.shape[2],\n ]];\n const resize = tf.image.cropAndResize(image, box, [0], [config.face.emotion.inputSize, config.face.emotion.inputSize]);\n */\n const resize = tf.image.resizeBilinear(image, [config.face.emotion.inputSize, config.face.emotion.inputSize], false);\n const [red, green, blue] = tf.split(resize, 3, 3);\n resize.dispose();\n // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n red.dispose();\n green.dispose();\n blue.dispose();\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n redNorm.dispose();\n greenNorm.dispose();\n blueNorm.dispose();\n const normalize = tf.tidy(() => grayscale.sub(0.5).mul(2));\n grayscale.dispose();\n const obj = [];\n if (config.face.emotion.enabled) {\n let data;\n if (!config.profile) {\n const emotionT = await models.emotion.predict(normalize);\n data = emotionT.dataSync();\n tf.dispose(emotionT);\n } else {\n const profileData = await tf.profile(() => models.emotion.predict(normalize));\n data = profileData.result.dataSync();\n profileData.result.dispose();\n // @ts-ignore\n profile.run('emotion', profileData);\n }\n for (let i = 0; i < data.length; i++) {\n if (scale * data[i] > config.face.emotion.minConfidence) obj.push({ score: Math.min(0.99, Math.trunc(100 * scale * data[i]) / 100), emotion: annotations[i] });\n }\n obj.sort((a, b) => b.score - a.score);\n }\n normalize.dispose();\n last = obj;\n resolve(obj);\n });\n}\n\nexports.predict = predict;\nexports.load = load;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as profile from '../profile.js';\n\n// based on https://github.com/sirius-ai/MobileFaceNet_TF\n// model converted from https://github.com/sirius-ai/MobileFaceNet_TF/files/3551493/FaceMobileNet192_train_false.zip\n\nconst models = {};\n\nasync function load(config) {\n if (!models.embedding) {\n models.embedding = await tf.loadGraphModel(config.face.embedding.modelPath);\n log(`load model: ${config.face.embedding.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.embedding;\n}\n\nfunction simmilarity(embedding1, embedding2) {\n if (embedding1?.length !== embedding2?.length) return 0;\n // general minkowski distance\n // euclidean distance is limited case where order is 2\n const order = 2;\n const distance = 10.0 * ((embedding1.map((val, i) => (val - embedding2[i])).reduce((dist, diff) => dist + (diff ** order), 0) ** (1 / order)));\n return (Math.trunc(1000 * (1 - distance)) / 1000);\n}\n\nasync function predict(image, config) {\n if (!models.embedding) return null;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [config.face.embedding.inputSize, config.face.embedding.inputSize], false);\n // const normalize = tf.tidy(() => resize.div(127.5).sub(0.5)); // this is -0.5...0.5 ???\n let data = [];\n if (config.face.embedding.enabled) {\n if (!config.profile) {\n const embeddingT = await models.embedding.predict({ img_inputs: resize });\n data = [...embeddingT.dataSync()]; // convert object array to standard array\n tf.dispose(embeddingT);\n } else {\n const profileData = await tf.profile(() => models.embedding.predict({ img_inputs: resize }));\n data = [...profileData.result.dataSync()];\n profileData.result.dispose();\n // @ts-ignore\n profile.run('emotion', profileData);\n }\n }\n resize.dispose();\n // normalize.dispose();\n resolve(data);\n });\n}\n\nexports.predict = predict;\nexports.simmilarity = simmilarity;\nexports.load = load;\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nconst imageNetMean = [-123.15, -115.90, -103.06];\n\nfunction nameOutputResultsMobileNet(results) {\n const [offsets, heatmap, displacementFwd, displacementBwd] = results;\n return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nfunction nameOutputResultsResNet(results) {\n const [displacementFwd, displacementBwd, offsets, heatmap] = results;\n return { offsets, heatmap, displacementFwd, displacementBwd };\n}\n\nclass BaseModel {\n constructor(model) {\n this.model = model;\n }\n\n predict(input, config) {\n return tf.tidy(() => {\n const asFloat = (config.body.modelType === 'ResNet') ? input.toFloat().add(imageNetMean) : input.toFloat().div(127.5).sub(1.0);\n const asBatch = asFloat.expandDims(0);\n const results = this.model.predict(asBatch);\n const results3d = results.map((y) => y.squeeze([0]));\n const namedResults = (config.body.modelType === 'ResNet') ? nameOutputResultsResNet(results3d) : nameOutputResultsMobileNet(results3d);\n return {\n heatmapScores: namedResults.heatmap.sigmoid(),\n offsets: namedResults.offsets,\n displacementFwd: namedResults.displacementFwd,\n displacementBwd: namedResults.displacementBwd,\n };\n });\n }\n\n dispose() {\n this.model.dispose();\n }\n}\nexports.BaseModel = BaseModel;\n", "// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nfunction half(k) {\n return Math.floor(k / 2);\n}\nclass MaxHeap {\n constructor(maxSize, getElementValue) {\n this.priorityQueue = new Array(maxSize);\n this.numberOfElements = -1;\n this.getElementValue = getElementValue;\n }\n\n enqueue(x) {\n this.priorityQueue[++this.numberOfElements] = x;\n this.swim(this.numberOfElements);\n }\n\n dequeue() {\n const max = this.priorityQueue[0];\n this.exchange(0, this.numberOfElements--);\n this.sink(0);\n this.priorityQueue[this.numberOfElements + 1] = null;\n return max;\n }\n\n empty() {\n return this.numberOfElements === -1;\n }\n\n size() {\n return this.numberOfElements + 1;\n }\n\n all() {\n return this.priorityQueue.slice(0, this.numberOfElements + 1);\n }\n\n max() {\n return this.priorityQueue[0];\n }\n\n swim(k) {\n while (k > 0 && this.less(half(k), k)) {\n this.exchange(k, half(k));\n k = half(k);\n }\n }\n\n sink(k) {\n while (2 * k <= this.numberOfElements) {\n let j = 2 * k;\n if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n if (!this.less(k, j)) break;\n this.exchange(k, j);\n k = j;\n }\n }\n\n getValueAt(i) {\n return this.getElementValue(this.priorityQueue[i]);\n }\n\n less(i, j) {\n return this.getValueAt(i) < this.getValueAt(j);\n }\n\n exchange(i, j) {\n const t = this.priorityQueue[i];\n this.priorityQueue[i] = this.priorityQueue[j];\n this.priorityQueue[j] = t;\n }\n}\nexports.MaxHeap = MaxHeap;\n", "import * as heapSort from './heapSort';\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores) {\n const [height, width] = scores.shape;\n let localMaximum = true;\n const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n localMaximum = false;\n break;\n }\n }\n if (!localMaximum) break;\n }\n return localMaximum;\n}\n\nfunction buildPartWithScoreQueue(scoreThreshold, localMaximumRadius, scores) {\n const [height, width, numKeypoints] = scores.shape;\n const queue = new heapSort.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n const score = scores.get(heatmapY, heatmapX, keypointId);\n // Only consider parts with score greater or equal to threshold as root candidates.\n if (score < scoreThreshold) continue;\n // Only consider keypoints whose score is maximum in a local window.\n if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, localMaximumRadius, scores)) {\n queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n }\n }\n }\n }\n return queue;\n}\nexports.buildPartWithScoreQueue = buildPartWithScoreQueue;\n", "exports.partNames = [\n 'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n 'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n 'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexports.NUM_KEYPOINTS = exports.partNames.length;\n\nexports.partIds = exports.partNames.reduce((result, jointName, i) => {\n result[jointName] = i;\n return result;\n}, {});\n\nconst connectedPartNames = [\n ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexports.connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([exports.partIds[jointNameA], exports.partIds[jointNameB]]));\n\nexports.poseChain = [\n ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n ['rightKnee', 'rightAnkle'],\n];\n\nexports.partChannels = [\n 'left_face',\n 'right_face',\n 'right_upper_leg_front',\n 'right_lower_leg_back',\n 'right_upper_leg_back',\n 'left_lower_leg_front',\n 'left_upper_leg_front',\n 'left_upper_leg_back',\n 'left_lower_leg_back',\n 'right_feet',\n 'right_lower_leg_front',\n 'left_feet',\n 'torso_front',\n 'torso_back',\n 'right_upper_arm_front',\n 'right_upper_arm_back',\n 'right_lower_arm_back',\n 'left_lower_arm_front',\n 'left_upper_arm_front',\n 'left_upper_arm_back',\n 'left_lower_arm_back',\n 'right_hand',\n 'right_lower_arm_front',\n 'left_hand',\n];\n", "import * as kpt from './keypoints';\n\nfunction getOffsetPoint(y, x, keypoint, offsets) {\n return {\n y: offsets.get(y, x, keypoint),\n x: offsets.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n };\n}\nexports.getOffsetPoint = getOffsetPoint;\n\nfunction getImageCoords(part, outputStride, offsets) {\n const { heatmapY, heatmapX, id: keypoint } = part;\n const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n return {\n x: part.heatmapX * outputStride + x,\n y: part.heatmapY * outputStride + y,\n };\n}\nexports.getImageCoords = getImageCoords;\n\nfunction fillArray(element, size) {\n const result = new Array(size);\n for (let i = 0; i < size; i++) {\n result[i] = element;\n }\n return result;\n}\nexports.fillArray = fillArray;\n\nfunction clamp(a, min, max) {\n if (a < min) return min;\n if (a > max) return max;\n return a;\n}\nexports.clamp = clamp;\n\nfunction squaredDistance(y1, x1, y2, x2) {\n const dy = y2 - y1;\n const dx = x2 - x1;\n return dy * dy + dx * dx;\n}\nexports.squaredDistance = squaredDistance;\n\nfunction addVectors(a, b) {\n return { x: a.x + b.x, y: a.y + b.y };\n}\nexports.addVectors = addVectors;\n\nfunction clampVector(a, min, max) {\n return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\nexports.clampVector = clampVector;\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as kpt from './keypoints';\n\nfunction getPointsConfidence(heatmapScores, heatMapCoords) {\n const numKeypoints = heatMapCoords.shape[0];\n const result = new Float32Array(numKeypoints);\n for (let keypoint = 0; keypoint < numKeypoints; keypoint++) {\n const y = heatMapCoords.get(keypoint, 0);\n const x = heatMapCoords.get(keypoint, 1);\n result[keypoint] = heatmapScores.get(y, x, keypoint);\n }\n return result;\n}\nexports.getPointsConfidence = getPointsConfidence;\n\nfunction getOffsetPoint(y, x, keypoint, offsetsBuffer) {\n return {\n y: offsetsBuffer.get(y, x, keypoint),\n x: offsetsBuffer.get(y, x, keypoint + kpt.NUM_KEYPOINTS),\n };\n}\n\nfunction getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer) {\n const result = [];\n for (let keypoint = 0; keypoint < kpt.NUM_KEYPOINTS; keypoint++) {\n const heatmapY = heatMapCoordsBuffer.get(keypoint, 0).valueOf();\n const heatmapX = heatMapCoordsBuffer.get(keypoint, 1).valueOf();\n const { x, y } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsetsBuffer);\n result.push(y);\n result.push(x);\n }\n return tf.tensor2d(result, [kpt.NUM_KEYPOINTS, 2]);\n}\nexports.getOffsetVectors = getOffsetVectors;\n\nfunction getOffsetPoints(heatMapCoordsBuffer, outputStride, offsetsBuffer) {\n return tf.tidy(() => heatMapCoordsBuffer.toTensor().mul(tf.scalar(outputStride, 'int32')).toFloat().add(getOffsetVectors(heatMapCoordsBuffer, offsetsBuffer)));\n}\nexports.getOffsetPoints = getOffsetPoints;\n\nfunction mod(a, b) {\n return tf.tidy(() => {\n const floored = a.div(tf.scalar(b, 'int32'));\n return a.sub(floored.mul(tf.scalar(b, 'int32')));\n });\n}\n\nfunction argmax2d(inputs) {\n const [height, width, depth] = inputs.shape;\n return tf.tidy(() => {\n const reshaped = inputs.reshape([height * width, depth]);\n const coords = reshaped.argMax(0);\n const yCoords = coords.div(tf.scalar(width, 'int32')).expandDims(1);\n const xCoords = mod(coords, width).expandDims(1);\n return tf.concat([yCoords, xCoords], 1);\n });\n}\nexports.argmax2d = argmax2d;\n", "import * as keypoints from './keypoints';\nimport * as vectors from './vectors';\nimport * as decoders from './decoders';\n\nconst parentChildrenTuples = keypoints.poseChain.map(([parentJoinName, childJoinName]) => ([keypoints.partIds[parentJoinName], keypoints.partIds[childJoinName]]));\nconst parentToChildEdges = parentChildrenTuples.map(([, childJointId]) => childJointId);\nconst childToParentEdges = parentChildrenTuples.map(([parentJointId]) => parentJointId);\nfunction getDisplacement(edgeId, point, displacements) {\n const numEdges = displacements.shape[2] / 2;\n return {\n y: displacements.get(point.y, point.x, edgeId),\n x: displacements.get(point.y, point.x, numEdges + edgeId),\n };\n}\nfunction getStridedIndexNearPoint(point, outputStride, height, width) {\n return {\n // @ts-ignore\n y: vectors.clamp(Math.round(point.y / outputStride), 0, height - 1),\n // @ts-ignore\n x: vectors.clamp(Math.round(point.x / outputStride), 0, width - 1),\n };\n}\n\nfunction traverseToTargetKeypoint(edgeId, sourceKeypoint, targetKeypointId, scoresBuffer, offsets, outputStride, displacements, offsetRefineStep = 2) {\n const [height, width] = scoresBuffer.shape;\n // Nearest neighbor interpolation for the source->target displacements.\n const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, outputStride, height, width);\n const displacement = getDisplacement(edgeId, sourceKeypointIndices, displacements);\n // @ts-ignore\n const displacedPoint = vectors.addVectors(sourceKeypoint.position, displacement);\n let targetKeypoint = displacedPoint;\n for (let i = 0; i < offsetRefineStep; i++) {\n const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n // @ts-ignore\n const offsetPoint = vectors.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetKeypointId, offsets);\n // @ts-ignore\n targetKeypoint = vectors.addVectors({\n x: targetKeypointIndices.x * outputStride,\n y: targetKeypointIndices.y * outputStride,\n }, { x: offsetPoint.x, y: offsetPoint.y });\n }\n const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, outputStride, height, width);\n const score = scoresBuffer.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetKeypointId);\n return { position: targetKeypoint, part: keypoints.partNames[targetKeypointId], score };\n}\n\nfunction decodePose(root, scores, offsets, outputStride, displacementsFwd, displacementsBwd) {\n const numParts = scores.shape[2];\n const numEdges = parentToChildEdges.length;\n const instanceKeypoints = new Array(numParts);\n // Start a new detection instance at the position of the root.\n const { part: rootPart, score: rootScore } = root;\n // @ts-ignore\n const rootPoint = vectors.getImageCoords(rootPart, outputStride, offsets);\n instanceKeypoints[rootPart.id] = {\n score: rootScore,\n part: keypoints.partNames[rootPart.id],\n position: rootPoint,\n };\n // Decode the part positions upwards in the tree, following the backward displacements.\n for (let edge = numEdges - 1; edge >= 0; --edge) {\n const sourceKeypointId = parentToChildEdges[edge];\n const targetKeypointId = childToParentEdges[edge];\n if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsBwd);\n }\n }\n // Decode the part positions downwards in the tree, following the forward displacements.\n for (let edge = 0; edge < numEdges; ++edge) {\n const sourceKeypointId = childToParentEdges[edge];\n const targetKeypointId = parentToChildEdges[edge];\n if (instanceKeypoints[sourceKeypointId] && !instanceKeypoints[targetKeypointId]) {\n instanceKeypoints[targetKeypointId] = traverseToTargetKeypoint(edge, instanceKeypoints[sourceKeypointId], targetKeypointId, scores, offsets, outputStride, displacementsFwd);\n }\n }\n return instanceKeypoints;\n}\nexports.decodePose = decodePose;\n\nasync function decodeSinglePose(heatmapScores, offsets, config) {\n let totalScore = 0.0;\n // @ts-ignore\n const heatmapValues = decoders.argmax2d(heatmapScores);\n const allTensorBuffers = await Promise.all([heatmapScores.buffer(), offsets.buffer(), heatmapValues.buffer()]);\n const scoresBuffer = allTensorBuffers[0];\n const offsetsBuffer = allTensorBuffers[1];\n const heatmapValuesBuffer = allTensorBuffers[2];\n // @ts-ignore\n const offsetPoints = decoders.getOffsetPoints(heatmapValuesBuffer, config.body.outputStride, offsetsBuffer);\n const offsetPointsBuffer = await offsetPoints.buffer();\n // @ts-ignore\n const keypointConfidence = Array.from(decoders.getPointsConfidence(scoresBuffer, heatmapValuesBuffer));\n const instanceKeypoints = keypointConfidence.map((score, i) => {\n totalScore += score;\n return {\n position: {\n y: offsetPointsBuffer.get(i, 0),\n x: offsetPointsBuffer.get(i, 1),\n },\n part: keypoints.partNames[i],\n score,\n };\n });\n const filteredKeypoints = instanceKeypoints.filter((kpt) => kpt.score > config.body.scoreThreshold);\n heatmapValues.dispose();\n offsetPoints.dispose();\n return { keypoints: filteredKeypoints, score: totalScore / instanceKeypoints.length };\n}\nexports.decodeSinglePose = decodeSinglePose;\n", "import * as buildParts from './buildParts';\nimport * as decodePose from './decodePose';\nimport * as vectors from './vectors';\n\nconst kLocalMaximumRadius = 1;\n\nfunction withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, { x, y }, keypointId) {\n return poses.some(({ keypoints }) => {\n const correspondingKeypoint = keypoints[keypointId].position;\n // @ts-ignore\n return vectors.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n });\n}\n\nfunction getInstanceScore(existingPoses, squaredNmsRadius, instanceKeypoints) {\n const notOverlappedKeypointScores = instanceKeypoints.reduce((result, { position, score }, keypointId) => {\n if (!withinNmsRadiusOfCorrespondingPoint(existingPoses, squaredNmsRadius, position, keypointId)) result += score;\n return result;\n }, 0.0);\n return notOverlappedKeypointScores / instanceKeypoints.length;\n}\n\nfunction decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config) {\n const poses = [];\n // @ts-ignore\n const queue = buildParts.buildPartWithScoreQueue(config.body.scoreThreshold, kLocalMaximumRadius, scoresBuffer);\n const squaredNmsRadius = config.body.nmsRadius ^ 2;\n // Generate at most maxDetections object instances per image in decreasing root part score order.\n while (poses.length < config.body.maxDetections && !queue.empty()) {\n // The top element in the queue is the next root candidate.\n const root = queue.dequeue();\n // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n // @ts-ignore\n const rootImageCoords = vectors.getImageCoords(root.part, config.body.outputStride, offsetsBuffer);\n if (withinNmsRadiusOfCorrespondingPoint(poses, squaredNmsRadius, rootImageCoords, root.part.id)) continue;\n // Else start a new detection instance at the position of the root.\n // @ts-ignore\n const keypoints = decodePose.decodePose(root, scoresBuffer, offsetsBuffer, config.body.outputStride, displacementsFwdBuffer, displacementsBwdBuffer);\n const score = getInstanceScore(poses, squaredNmsRadius, keypoints);\n if (score > config.body.scoreThreshold) poses.push({ keypoints, score });\n }\n return poses;\n}\nexports.decodeMultiplePoses = decodeMultiplePoses;\n", "import * as kpt from './keypoints';\n\nfunction eitherPointDoesntMeetConfidence(a, b, minConfidence) {\n return (a < minConfidence || b < minConfidence);\n}\n\nfunction getAdjacentKeyPoints(keypoints, minConfidence) {\n return kpt.connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n return result;\n }\n result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n return result;\n }, []);\n}\nexports.getAdjacentKeyPoints = getAdjacentKeyPoints;\n\nconst { NEGATIVE_INFINITY, POSITIVE_INFINITY } = Number;\nfunction getBoundingBox(keypoints) {\n return keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n maxX: Math.max(maxX, x),\n maxY: Math.max(maxY, y),\n minX: Math.min(minX, x),\n minY: Math.min(minY, y),\n }), {\n maxX: NEGATIVE_INFINITY,\n maxY: NEGATIVE_INFINITY,\n minX: POSITIVE_INFINITY,\n minY: POSITIVE_INFINITY,\n });\n}\nexports.getBoundingBox = getBoundingBox;\n\nfunction getBoundingBoxPoints(keypoints) {\n const { minX, minY, maxX, maxY } = getBoundingBox(keypoints);\n return [{ x: minX, y: minY }, { x: maxX, y: minY }, { x: maxX, y: maxY }, { x: minX, y: maxY }];\n}\nexports.getBoundingBoxPoints = getBoundingBoxPoints;\n\nasync function toTensorBuffers3D(tensors) {\n return Promise.all(tensors.map((tensor) => tensor.buffer()));\n}\nexports.toTensorBuffers3D = toTensorBuffers3D;\n\nfunction scalePose(pose, scaleY, scaleX) {\n return {\n score: pose.score,\n keypoints: pose.keypoints.map(({ score, part, position }) => ({\n score,\n part,\n position: { x: position.x * scaleX, y: position.y * scaleY },\n })),\n };\n}\nexports.scalePose = scalePose;\n\nfunction resizeTo(image, [targetH, targetW]) {\n const input = image.squeeze(0);\n const resized = input.resizeBilinear([targetH, targetW]);\n input.dispose();\n return resized;\n}\nexports.resizeTo = resizeTo;\n\nfunction scaleAndFlipPoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) {\n const scaledPoses = poses.map((pose) => scalePose(pose, height / inputResolutionHeight, width / inputResolutionWidth));\n return scaledPoses;\n}\nexports.scaleAndFlipPoses = scaleAndFlipPoses;\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as modelBase from './modelBase';\nimport * as decodeMultiple from './decodeMultiple';\nimport * as decodePose from './decodePose';\nimport * as util from './util';\n\nasync function estimateMultiple(input, res, config) {\n return new Promise(async (resolve) => {\n const height = input.shape[1];\n const width = input.shape[2];\n // @ts-ignore\n const allTensorBuffers = await util.toTensorBuffers3D([res.heatmapScores, res.offsets, res.displacementFwd, res.displacementBwd]);\n const scoresBuffer = allTensorBuffers[0];\n const offsetsBuffer = allTensorBuffers[1];\n const displacementsFwdBuffer = allTensorBuffers[2];\n const displacementsBwdBuffer = allTensorBuffers[3];\n // @ts-ignore\n const poses = await decodeMultiple.decodeMultiplePoses(scoresBuffer, offsetsBuffer, displacementsFwdBuffer, displacementsBwdBuffer, config);\n // @ts-ignore\n const scaled = util.scaleAndFlipPoses(poses, [height, width], [config.body.inputSize, config.body.inputSize]);\n resolve(scaled);\n });\n}\n\nasync function estimateSingle(input, res, config) {\n return new Promise(async (resolve) => {\n const height = input.shape[1];\n const width = input.shape[2];\n // @ts-ignore\n const pose = await decodePose.decodeSinglePose(res.heatmapScores, res.offsets, config);\n const poses = [pose];\n // @ts-ignore\n const scaled = util.scaleAndFlipPoses(poses, [height, width], [config.body.inputSize, config.body.inputSize]);\n resolve(scaled);\n });\n}\n\nclass PoseNet {\n constructor(model) {\n this.baseModel = model;\n }\n\n async estimatePoses(input, config) {\n // @ts-ignore\n const resized = util.resizeTo(input, [config.body.inputSize, config.body.inputSize]);\n const res = this.baseModel.predict(resized, config);\n\n const poses = (config.body.maxDetections < 2) ? await estimateSingle(input, res, config) : await estimateMultiple(input, res, config);\n\n res.heatmapScores.dispose();\n res.offsets.dispose();\n res.displacementFwd.dispose();\n res.displacementBwd.dispose();\n resized.dispose();\n\n return poses;\n }\n\n dispose() {\n this.baseModel.dispose();\n }\n}\nexports.PoseNet = PoseNet;\n\nasync function load(config) {\n const model = await tf.loadGraphModel(config.body.modelPath);\n // @ts-ignore\n const mobilenet = new modelBase.BaseModel(model);\n log(`load model: ${config.body.modelPath.match(/\\/(.*)\\./)[1]}`);\n return new PoseNet(mobilenet);\n}\nexports.load = load;\n", "import * as modelPoseNet from './modelPoseNet';\nimport * as keypoints from './keypoints';\nimport * as util from './util';\n\n// @ts-ignore\nexports.load = modelPoseNet.load;\n// @ts-ignore\nexports.PoseNet = modelPoseNet.PoseNet;\n\nexports.partChannels = keypoints.partChannels;\nexports.partIds = keypoints.partIds;\nexports.partNames = keypoints.partNames;\nexports.poseChain = keypoints.poseChain;\n// @ts-ignore\nexports.getAdjacentKeyPoints = util.getAdjacentKeyPoints;\n// @ts-ignore\nexports.getBoundingBox = util.getBoundingBox;\n// @ts-ignore\nexports.getBoundingBoxPoints = util.getBoundingBoxPoints;\n// @ts-ignore\nexports.scaleAndFlipPoses = util.scaleAndFlipPoses;\n// @ts-ignore\nexports.scalePose = util.scalePose;\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\n\nclass HandDetector {\n constructor(model, inputSize, anchorsAnnotated) {\n this.model = model;\n this.anchors = anchorsAnnotated.map((anchor) => [anchor.x_center, anchor.y_center]);\n this.anchorsTensor = tf.tensor2d(this.anchors);\n this.inputSizeTensor = tf.tensor1d([inputSize, inputSize]);\n this.doubleInputSizeTensor = tf.tensor1d([inputSize * 2, inputSize * 2]);\n }\n\n normalizeBoxes(boxes) {\n return tf.tidy(() => {\n const boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n const boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n const boxCenterPoints = tf.add(tf.div(boxOffsets, this.inputSizeTensor), this.anchorsTensor);\n const halfBoxSizes = tf.div(boxSizes, this.doubleInputSizeTensor);\n const startPoints = tf.mul(tf.sub(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n const endPoints = tf.mul(tf.add(boxCenterPoints, halfBoxSizes), this.inputSizeTensor);\n return tf.concat2d([startPoints, endPoints], 1);\n });\n }\n\n normalizeLandmarks(rawPalmLandmarks, index) {\n return tf.tidy(() => {\n const landmarks = tf.add(tf.div(rawPalmLandmarks.reshape([-1, 7, 2]), this.inputSizeTensor), this.anchors[index]);\n return tf.mul(landmarks, this.inputSizeTensor);\n });\n }\n\n async getBoxes(input, config) {\n const batched = this.model.predict(input);\n const predictions = batched.squeeze();\n batched.dispose();\n const scoresT = tf.tidy(() => tf.sigmoid(tf.slice(predictions, [0, 0], [-1, 1])).squeeze());\n const scores = scoresT.dataSync();\n const rawBoxes = tf.slice(predictions, [0, 1], [-1, 4]);\n const boxes = this.normalizeBoxes(rawBoxes);\n rawBoxes.dispose();\n const filteredT = await tf.image.nonMaxSuppressionAsync(boxes, scores, config.hand.maxHands, config.hand.iouThreshold, config.hand.scoreThreshold);\n const filtered = filteredT.arraySync();\n\n scoresT.dispose();\n filteredT.dispose();\n const hands = [];\n for (const index of filtered) {\n if (scores[index] >= config.hand.minConfidence) {\n const matchingBox = tf.slice(boxes, [index, 0], [1, -1]);\n const rawPalmLandmarks = tf.slice(predictions, [index, 5], [1, 14]);\n const palmLandmarks = tf.tidy(() => this.normalizeLandmarks(rawPalmLandmarks, index).reshape([-1, 2]));\n rawPalmLandmarks.dispose();\n hands.push({ box: matchingBox, palmLandmarks, confidence: scores[index] });\n }\n }\n predictions.dispose();\n boxes.dispose();\n return hands;\n }\n\n async estimateHandBounds(input, config) {\n const inputHeight = input.shape[1];\n const inputWidth = input.shape[2];\n const image = tf.tidy(() => input.resizeBilinear([config.hand.inputSize, config.hand.inputSize]).div(127.5).sub(1));\n const predictions = await this.getBoxes(image, config);\n image.dispose();\n const hands = [];\n if (!predictions || predictions.length === 0) return hands;\n for (const prediction of predictions) {\n const boxes = prediction.box.dataSync();\n const startPoint = boxes.slice(0, 2);\n const endPoint = boxes.slice(2, 4);\n const palmLandmarks = prediction.palmLandmarks.arraySync();\n prediction.box.dispose();\n prediction.palmLandmarks.dispose();\n hands.push(box.scaleBoxCoordinates({ startPoint, endPoint, palmLandmarks, confidence: prediction.confidence }, [inputWidth / config.hand.inputSize, inputHeight / config.hand.inputSize]));\n }\n return hands;\n }\n}\nexports.HandDetector = HandDetector;\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as box from './box';\nimport * as util from './util';\n// eslint-disable-next-line no-unused-vars\nimport { log } from '../log.js';\n\n// const PALM_BOX_SHIFT_VECTOR = [0, -0.4];\nconst PALM_BOX_ENLARGE_FACTOR = 5; // default 3\n// const HAND_BOX_SHIFT_VECTOR = [0, -0.1]; // move detected hand box by x,y to ease landmark detection\nconst HAND_BOX_ENLARGE_FACTOR = 1.65; // default 1.65\nconst PALM_LANDMARK_IDS = [0, 5, 9, 13, 17, 1, 2];\nconst PALM_LANDMARKS_INDEX_OF_PALM_BASE = 0;\nconst PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE = 2;\n\nclass HandPipeline {\n constructor(handDetector, landmarkDetector, inputSize) {\n this.handDetector = handDetector;\n this.landmarkDetector = landmarkDetector;\n this.inputSize = inputSize;\n this.storedBoxes = [];\n this.skipped = 0;\n this.detectedHands = 0;\n }\n\n getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n // return box.enlargeBox(box.squarifyBox(box.shiftBox(boxAroundPalm, PALM_BOX_SHIFT_VECTOR)), PALM_BOX_ENLARGE_FACTOR);\n return box.enlargeBox(box.squarifyBox(boxAroundPalm), PALM_BOX_ENLARGE_FACTOR);\n }\n\n getBoxForHandLandmarks(landmarks) {\n const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n // const boxAroundHand = box.enlargeBox(box.squarifyBox(box.shiftBox(boundingBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n const boxAroundHand = box.enlargeBox(box.squarifyBox(boundingBox), HAND_BOX_ENLARGE_FACTOR);\n boxAroundHand.palmLandmarks = [];\n for (let i = 0; i < PALM_LANDMARK_IDS.length; i++) {\n boxAroundHand.palmLandmarks.push(landmarks[PALM_LANDMARK_IDS[i]].slice(0, 2));\n }\n return boxAroundHand;\n }\n\n transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n const boxSize = box.getBoxSize(box2);\n const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n const coordsScaled = rawCoords.map((coord) => [\n scaleFactor[0] * (coord[0] - this.inputSize / 2),\n scaleFactor[1] * (coord[1] - this.inputSize / 2),\n scaleFactor[2] * coord[2],\n ]);\n const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n const coordsRotated = coordsScaled.map((coord) => {\n const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n return [...rotated, coord[2]];\n });\n const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n const boxCenter = [...box.getBoxCenter(box2), 1];\n const originalBoxCenter = [\n util.dot(boxCenter, inverseRotationMatrix[0]),\n util.dot(boxCenter, inverseRotationMatrix[1]),\n ];\n return coordsRotated.map((coord) => [\n coord[0] + originalBoxCenter[0],\n coord[1] + originalBoxCenter[1],\n coord[2],\n ]);\n }\n\n async estimateHands(image, config) {\n let useFreshBox = false;\n\n // run new detector every skipFrames unless we only want box to start with\n let boxes;\n if ((this.skipped === 0) || (this.skipped > config.hand.skipFrames) || !config.hand.landmarks || !config.videoOptimized) {\n boxes = await this.handDetector.estimateHandBounds(image, config);\n this.skipped = 0;\n }\n if (config.videoOptimized) this.skipped++;\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxHands) || !config.hand.landmarks)) {\n this.detectedHands = 0;\n this.storedBoxes = [...boxes];\n // for (const possible of boxes) this.storedBoxes.push(possible);\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\n const hands = [];\n // log('hand', `skipped: ${this.skipped} max: ${config.hand.maxHands} detected: ${this.detectedHands} stored: ${this.storedBoxes.length} new: ${boxes?.length}`);\n\n // go through working set of boxes\n for (let i = 0; i < this.storedBoxes.length; i++) {\n const currentBox = this.storedBoxes[i];\n if (!currentBox) continue;\n if (config.hand.landmarks) {\n const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_PALM_BASE], currentBox.palmLandmarks[PALM_LANDMARKS_INDEX_OF_MIDDLE_FINGER_BASE]) : 0;\n const palmCenter = box.getBoxCenter(currentBox);\n const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n const rotatedImage = config.hand.rotation ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n const croppedInput = box.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n const handImage = croppedInput.div(255);\n croppedInput.dispose();\n rotatedImage.dispose();\n const [confidenceT, keypoints] = await this.landmarkDetector.predict(handImage);\n handImage.dispose();\n const confidence = confidenceT.dataSync()[0];\n confidenceT.dispose();\n if (confidence >= config.hand.minConfidence) {\n const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n const rawCoords = keypointsReshaped.arraySync();\n keypoints.dispose();\n keypointsReshaped.dispose();\n const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n this.storedBoxes[i] = nextBoundingBox;\n const result = {\n landmarks: coords,\n confidence,\n box: {\n topLeft: nextBoundingBox.startPoint,\n bottomRight: nextBoundingBox.endPoint,\n },\n };\n hands.push(result);\n } else {\n this.storedBoxes[i] = null;\n }\n keypoints.dispose();\n } else {\n // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), HAND_BOX_ENLARGE_FACTOR);\n const enlarged = box.enlargeBox(box.squarifyBox(currentBox), HAND_BOX_ENLARGE_FACTOR);\n const result = {\n confidence: currentBox.confidence,\n box: {\n topLeft: enlarged.startPoint,\n bottomRight: enlarged.endPoint,\n },\n };\n hands.push(result);\n }\n }\n this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n this.detectedHands = hands.length;\n return hands;\n }\n\n // eslint-disable-next-line class-methods-use-this\n calculateLandmarksBoundingBox(landmarks) {\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint };\n }\n}\n\nexports.HandPipeline = HandPipeline;\n", "exports.anchors = [\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.796875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.796875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.828125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.828125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.859375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.859375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.890625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.890625,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.921875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.921875,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.953125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.953125,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.984375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.984375,\n y_center: 0.015625,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.796875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.796875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.828125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.828125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.859375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.859375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.890625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.890625,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.921875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.921875,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.953125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.953125,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.984375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.984375,\n y_center: 0.046875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.234375,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.265625,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.296875,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.328125,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.359375,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.390625,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.421875,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.453125,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.484375,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.515625,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.546875,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.578125,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.609375,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.640625,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.671875,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.703125,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.734375,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.765625,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.796875,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.796875,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.828125,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.828125,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.859375,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.859375,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.890625,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.890625,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.921875,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.921875,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.953125,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.953125,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.984375,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.984375,\n y_center: 0.078125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.109375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.015625,\n y_center: 0.109375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.109375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.046875,\n y_center: 0.109375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.109375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.078125,\n y_center: 0.109375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.109375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.109375,\n y_center: 0.109375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.109375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.140625,\n y_center: 0.109375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.109375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.171875,\n y_center: 0.109375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.109375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.203125,\n y_center: 0.109375,\n },\n {\n w: 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0.9375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.6875,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.8125,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.0625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.1875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.3125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.4375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.5625,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.6875,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.8125,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n {\n w: 1,\n h: 1,\n x_center: 0.9375,\n y_center: 0.9375,\n },\n];\n", "// https://storage.googleapis.com/tfjs-models/demos/handpose/index.html\n\nimport { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as handdetector from './handdetector';\nimport * as handpipeline from './handpipeline';\nimport * as anchors from './anchors';\n\nconst MESH_ANNOTATIONS = {\n thumb: [1, 2, 3, 4],\n indexFinger: [5, 6, 7, 8],\n middleFinger: [9, 10, 11, 12],\n ringFinger: [13, 14, 15, 16],\n pinky: [17, 18, 19, 20],\n palmBase: [0],\n};\n\nclass HandPose {\n constructor(handPipeline) {\n this.handPipeline = handPipeline;\n }\n\n static getAnnotations() {\n return MESH_ANNOTATIONS;\n }\n\n async estimateHands(input, config) {\n const predictions = await this.handPipeline.estimateHands(input, config);\n if (!predictions) return [];\n const hands = [];\n for (const prediction of predictions) {\n const annotations = {};\n if (prediction.landmarks) {\n for (const key of Object.keys(MESH_ANNOTATIONS)) {\n annotations[key] = MESH_ANNOTATIONS[key].map((index) => prediction.landmarks[index]);\n }\n }\n const box = prediction.box ? [\n Math.max(0, prediction.box.topLeft[0]),\n Math.max(0, prediction.box.topLeft[1]),\n Math.min(input.shape[2], prediction.box.bottomRight[0]) - prediction.box.topLeft[0],\n Math.min(input.shape[1], prediction.box.bottomRight[1]) - prediction.box.topLeft[1],\n ] : 0;\n hands.push({\n confidence: prediction.confidence,\n box,\n landmarks: prediction.landmarks,\n annotations,\n });\n }\n return hands;\n }\n}\nexports.HandPose = HandPose;\n\nasync function load(config) {\n const [handDetectorModel, handPoseModel] = await Promise.all([\n config.hand.enabled ? tf.loadGraphModel(config.hand.detector.modelPath, { fromTFHub: config.hand.detector.modelPath.includes('tfhub.dev') }) : null,\n config.hand.landmarks ? tf.loadGraphModel(config.hand.skeleton.modelPath, { fromTFHub: config.hand.skeleton.modelPath.includes('tfhub.dev') }) : null,\n ]);\n // @ts-ignore\n const handDetector = new handdetector.HandDetector(handDetectorModel, config.hand.inputSize, anchors.anchors);\n // @ts-ignore\n const handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel, config.hand.inputSize);\n const handPose = new HandPose(handPipeline);\n if (config.hand.enabled) log(`load model: ${config.hand.detector.modelPath.match(/\\/(.*)\\./)[1]}`);\n if (config.hand.landmarks) log(`load model: ${config.hand.skeleton.modelPath.match(/\\/(.*)\\./)[1]}`);\n return handPose;\n}\nexports.load = load;\n", "exports.body = (res) => {\n if (!res) return [];\n const gestures = [];\n for (let i = 0; i < res.length; i++) {\n // raising hands\n const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n if (nose && leftWrist && rightWrist && (leftWrist.position.y < nose.position.y) && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'i give up' });\n else if (nose && leftWrist && (leftWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise left hand' });\n else if (nose && rightWrist && (rightWrist.position.y < nose.position.y)) gestures.push({ body: i, gesture: 'raise right hand' });\n\n // leaning\n const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n if (leftShoulder && rightShoulder) gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position.y > rightShoulder.position.y) ? 'left' : 'right'}` });\n }\n return gestures;\n};\n\nexports.face = (res) => {\n if (!res) return [];\n const gestures = [];\n for (let i = 0; i < res.length; i++) {\n if (res[i].mesh && res[i].mesh.length > 0) {\n const eyeFacing = res[i].mesh[35][2] - res[i].mesh[263][2];\n if (Math.abs(eyeFacing) < 10) gestures.push({ face: i, gesture: 'facing camera' });\n else gestures.push({ face: i, gesture: `facing ${eyeFacing < 0 ? 'right' : 'left'}` });\n const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n const chinDepth = res[i].mesh[152][2];\n if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n }\n }\n return gestures;\n};\n\nexports.iris = (res) => {\n if (!res) return [];\n const gestures = [];\n for (let i = 0; i < res.length; i++) {\n if (!res[i].annotations || !res[i].annotations.leftEyeIris || !res[i].annotations.rightEyeIris) continue;\n const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n if (difference < 0.25) gestures.push({ iris: i, gesture: 'looking at camera' });\n }\n return gestures;\n};\n\nexports.hand = (res) => {\n if (!res) return [];\n const gestures = [];\n for (let i = 0; i < res.length; i++) {\n const fingers = [];\n for (const [finger, pos] of Object.entries(res[i]['annotations'])) {\n if (finger !== 'palmBase') fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n }\n if (fingers && fingers.length > 0) {\n const closest = fingers.reduce((best, a) => (best.position[2] < a.position[2] ? best : a));\n const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n gestures.push({ hand: i, gesture: `${closest.name} forward ${highest.name} up` });\n }\n }\n return gestures;\n};\n", "/* eslint-disable no-use-before-define */\n/*\nWebGLImageFilter - MIT Licensed\n2013, Dominic Szablewski - phoboslab.org\n\n*/\n\nconst WebGLProgram = function (gl, vertexSource, fragmentSource) {\n const _collect = function (source, prefix, collection) {\n const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n source.replace(r, (match, name) => {\n collection[name] = 0;\n return match;\n });\n };\n\n const _compile = function (source, type) {\n const shader = gl.createShader(type);\n gl.shaderSource(shader, source);\n gl.compileShader(shader);\n\n if (!gl.getShaderParameter(shader, gl.COMPILE_STATUS)) {\n // @ts-ignore\n throw new Error('Filter: GL compile failed', gl.getShaderInfoLog(shader));\n }\n return shader;\n };\n\n this.uniform = {};\n this.attribute = {};\n\n const _vsh = _compile(vertexSource, gl.VERTEX_SHADER);\n const _fsh = _compile(fragmentSource, gl.FRAGMENT_SHADER);\n\n this.id = gl.createProgram();\n gl.attachShader(this.id, _vsh);\n gl.attachShader(this.id, _fsh);\n gl.linkProgram(this.id);\n\n if (!gl.getProgramParameter(this.id, gl.LINK_STATUS)) {\n // @ts-ignore\n throw new Error('Filter: GL link failed', gl.getProgramInfoLog(this.id));\n }\n\n gl.useProgram(this.id);\n\n // Collect attributes\n _collect(vertexSource, 'attribute', this.attribute);\n for (const a in this.attribute) {\n this.attribute[a] = gl.getAttribLocation(this.id, a);\n }\n\n // Collect uniforms\n _collect(vertexSource, 'uniform', this.uniform);\n _collect(fragmentSource, 'uniform', this.uniform);\n for (const u in this.uniform) {\n this.uniform[u] = gl.getUniformLocation(this.id, u);\n }\n};\n\nconst WebGLImageFilter = function (params) {\n if (!params) params = { };\n let _drawCount = 0;\n let _sourceTexture = null;\n let _lastInChain = false;\n let _currentFramebufferIndex = -1;\n let _tempFramebuffers = [null, null];\n let _filterChain = [];\n let _width = -1;\n let _height = -1;\n let _vertexBuffer = null;\n let _currentProgram = null;\n const _canvas = params.canvas || document.createElement('canvas');\n\n // key is the shader program source, value is the compiled program\n const _shaderProgramCache = { };\n\n const gl = _canvas.getContext('webgl');\n if (!gl) throw new Error('Filter: getContext() failed');\n\n this.addFilter = function (name) {\n // eslint-disable-next-line prefer-rest-params\n const args = Array.prototype.slice.call(arguments, 1);\n const filter = _filter[name];\n\n _filterChain.push({ func: filter, args });\n };\n\n this.reset = function () {\n _filterChain = [];\n };\n\n this.apply = function (image) {\n _resize(image.width, image.height);\n _drawCount = 0;\n\n // Create the texture for the input image if we haven't yet\n if (!_sourceTexture) _sourceTexture = gl.createTexture();\n gl.bindTexture(gl.TEXTURE_2D, _sourceTexture);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n\n // No filters? Just draw\n if (_filterChain.length === 0) {\n // const program = _compileShader(SHADER.FRAGMENT_IDENTITY);\n _draw();\n return _canvas;\n }\n\n for (let i = 0; i < _filterChain.length; i++) {\n _lastInChain = (i === _filterChain.length - 1);\n const f = _filterChain[i];\n f.func.apply(this, f.args || []);\n }\n\n return _canvas;\n };\n\n const _resize = function (width, height) {\n // Same width/height? Nothing to do here\n if (width === _width && height === _height) { return; }\n\n _canvas.width = width;\n _width = width;\n _canvas.height = height;\n _height = height;\n\n // Create the context if we don't have it yet\n if (!_vertexBuffer) {\n // Create the vertex buffer for the two triangles [x, y, u, v] * 6\n const vertices = new Float32Array([\n -1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0,\n -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0,\n ]);\n // eslint-disable-next-line no-unused-expressions\n (_vertexBuffer = gl.createBuffer(), gl.bindBuffer(gl.ARRAY_BUFFER, _vertexBuffer));\n gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);\n\n // Note sure if this is a good idea; at least it makes texture loading\n // in Ejecta instant.\n gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n }\n\n gl.viewport(0, 0, _width, _height);\n\n // Delete old temp framebuffers\n _tempFramebuffers = [null, null];\n };\n\n const _getTempFramebuffer = function (index) {\n // @ts-ignore\n _tempFramebuffers[index] = _tempFramebuffers[index] || _createFramebufferTexture(_width, _height);\n return _tempFramebuffers[index];\n };\n\n const _createFramebufferTexture = function (width, height) {\n const fbo = gl.createFramebuffer();\n gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n\n const renderbuffer = gl.createRenderbuffer();\n gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n\n const texture = gl.createTexture();\n gl.bindTexture(gl.TEXTURE_2D, texture);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n\n gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n\n gl.bindTexture(gl.TEXTURE_2D, null);\n gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n\n return { fbo, texture };\n };\n\n const _draw = function (flags) {\n let source = null;\n let target = null;\n let flipY = false;\n\n // Set up the source\n if (_drawCount === 0) {\n // First draw call - use the source texture\n source = _sourceTexture;\n } else {\n // All following draw calls use the temp buffer last drawn to\n // @ts-ignore\n source = _getTempFramebuffer(_currentFramebufferIndex)?.texture;\n }\n _drawCount++;\n\n // Set up the target\n if (_lastInChain && !(flags & DRAW.INTERMEDIATE)) {\n // Last filter in our chain - draw directly to the WebGL Canvas. We may\n // also have to flip the image vertically now\n target = null;\n flipY = _drawCount % 2 === 0;\n } else {\n // Intermediate draw call - get a temp buffer to draw to\n _currentFramebufferIndex = (_currentFramebufferIndex + 1) % 2;\n // @ts-ignore\n target = _getTempFramebuffer(_currentFramebufferIndex)?.fbo;\n }\n\n // Bind the source and target and draw the two triangles\n gl.bindTexture(gl.TEXTURE_2D, source);\n gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n\n gl.uniform1f(_currentProgram.uniform.flipY, (flipY ? -1 : 1));\n gl.drawArrays(gl.TRIANGLES, 0, 6);\n };\n\n const _compileShader = function (fragmentSource) {\n if (_shaderProgramCache[fragmentSource]) {\n _currentProgram = _shaderProgramCache[fragmentSource];\n gl.useProgram(_currentProgram.id);\n return _currentProgram;\n }\n\n // Compile shaders\n _currentProgram = new WebGLProgram(gl, SHADER.VERTEX_IDENTITY, fragmentSource);\n\n const floatSize = Float32Array.BYTES_PER_ELEMENT;\n const vertSize = 4 * floatSize;\n gl.enableVertexAttribArray(_currentProgram.attribute.pos);\n gl.vertexAttribPointer(_currentProgram.attribute.pos, 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n gl.enableVertexAttribArray(_currentProgram.attribute.uv);\n gl.vertexAttribPointer(_currentProgram.attribute.uv, 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n\n _shaderProgramCache[fragmentSource] = _currentProgram;\n return _currentProgram;\n };\n\n let DRAW = { INTERMEDIATE: 1 };\n\n let SHADER = {};\n SHADER.VERTEX_IDENTITY = [\n 'precision highp float;',\n 'attribute vec2 pos;',\n 'attribute vec2 uv;',\n 'varying vec2 vUv;',\n 'uniform float flipY;',\n\n 'void main(void) {',\n 'vUv = uv;',\n 'gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);',\n '}',\n ].join('\\n');\n\n SHADER.FRAGMENT_IDENTITY = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n\n 'void main(void) {',\n 'gl_FragColor = texture2D(texture, vUv);',\n '}',\n ].join('\\n');\n\n let _filter = {};\n\n // -------------------------------------------------------------------------\n // Color Matrix Filter\n\n _filter.colorMatrix = function (matrix) {\n // Create a Float32 Array and normalize the offset component to 0-1\n const m = new Float32Array(matrix);\n m[4] /= 255;\n m[9] /= 255;\n m[14] /= 255;\n m[19] /= 255;\n\n // Can we ignore the alpha value? Makes things a bit faster.\n const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0)\n ? _filter.colorMatrix.SHADER.WITHOUT_ALPHA\n : _filter.colorMatrix.SHADER.WITH_ALPHA;\n\n const program = _compileShader(shader);\n gl.uniform1fv(program.uniform.m, m);\n _draw();\n };\n\n _filter.colorMatrix.SHADER = {};\n _filter.colorMatrix.SHADER.WITH_ALPHA = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform float m[20];',\n\n 'void main(void) {',\n 'vec4 c = texture2D(texture, vUv);',\n 'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];',\n 'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];',\n 'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];',\n 'gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];',\n '}',\n ].join('\\n');\n _filter.colorMatrix.SHADER.WITHOUT_ALPHA = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform float m[20];',\n\n 'void main(void) {',\n 'vec4 c = texture2D(texture, vUv);',\n 'gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];',\n 'gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];',\n 'gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];',\n 'gl_FragColor.a = c.a;',\n '}',\n ].join('\\n');\n\n _filter.brightness = function (brightness) {\n const b = (brightness || 0) + 1;\n _filter.colorMatrix([\n b, 0, 0, 0, 0,\n 0, b, 0, 0, 0,\n 0, 0, b, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.saturation = function (amount) {\n const x = (amount || 0) * 2 / 3 + 1;\n const y = ((x - 1) * -0.5);\n _filter.colorMatrix([\n x, y, y, 0, 0,\n y, x, y, 0, 0,\n y, y, x, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.desaturate = function () {\n _filter.saturation(-1);\n };\n\n _filter.contrast = function (amount) {\n const v = (amount || 0) + 1;\n const o = -128 * (v - 1);\n\n _filter.colorMatrix([\n v, 0, 0, 0, o,\n 0, v, 0, 0, o,\n 0, 0, v, 0, o,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.negative = function () {\n _filter.contrast(-2);\n };\n\n _filter.hue = function (rotation) {\n rotation = (rotation || 0) / 180 * Math.PI;\n const cos = Math.cos(rotation);\n const sin = Math.sin(rotation);\n const lumR = 0.213;\n const lumG = 0.715;\n const lumB = 0.072;\n\n _filter.colorMatrix([\n lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.desaturateLuminance = function () {\n _filter.colorMatrix([\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.sepia = function () {\n _filter.colorMatrix([\n 0.393, 0.7689999, 0.18899999, 0, 0,\n 0.349, 0.6859999, 0.16799999, 0, 0,\n 0.272, 0.5339999, 0.13099999, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.brownie = function () {\n _filter.colorMatrix([\n 0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n 0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.vintagePinhole = function () {\n _filter.colorMatrix([\n 0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n 0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n 0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.kodachrome = function () {\n _filter.colorMatrix([\n 1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.technicolor = function () {\n _filter.colorMatrix([\n 1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.polaroid = function () {\n _filter.colorMatrix([\n 1.438, -0.062, -0.062, 0, 0,\n -0.122, 1.378, -0.122, 0, 0,\n -0.016, -0.016, 1.483, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n _filter.shiftToBGR = function () {\n _filter.colorMatrix([\n 0, 0, 1, 0, 0,\n 0, 1, 0, 0, 0,\n 1, 0, 0, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n };\n\n // -------------------------------------------------------------------------\n // Convolution Filter\n\n _filter.convolution = function (matrix) {\n const m = new Float32Array(matrix);\n const pixelSizeX = 1 / _width;\n const pixelSizeY = 1 / _height;\n\n const program = _compileShader(_filter.convolution.SHADER);\n gl.uniform1fv(program.uniform.m, m);\n gl.uniform2f(program.uniform.px, pixelSizeX, pixelSizeY);\n _draw();\n };\n\n _filter.convolution.SHADER = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform vec2 px;',\n 'uniform float m[9];',\n\n 'void main(void) {',\n 'vec4 c11 = texture2D(texture, vUv - px);', // top left\n 'vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y));', // top center\n 'vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y));', // top right\n\n 'vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) );', // mid left\n 'vec4 c22 = texture2D(texture, vUv);', // mid center\n 'vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) );', // mid right\n\n 'vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) );', // bottom left\n 'vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) );', // bottom center\n 'vec4 c33 = texture2D(texture, vUv + px );', // bottom right\n\n 'gl_FragColor = ',\n 'c11 * m[0] + c12 * m[1] + c22 * m[2] +',\n 'c21 * m[3] + c22 * m[4] + c23 * m[5] +',\n 'c31 * m[6] + c32 * m[7] + c33 * m[8];',\n 'gl_FragColor.a = c22.a;',\n '}',\n ].join('\\n');\n\n _filter.detectEdges = function () {\n _filter.convolution.call(this, [\n 0, 1, 0,\n 1, -4, 1,\n 0, 1, 0,\n ]);\n };\n\n _filter.sobelX = function () {\n _filter.convolution.call(this, [\n -1, 0, 1,\n -2, 0, 2,\n -1, 0, 1,\n ]);\n };\n\n _filter.sobelY = function () {\n _filter.convolution.call(this, [\n -1, -2, -1,\n 0, 0, 0,\n 1, 2, 1,\n ]);\n };\n\n _filter.sharpen = function (amount) {\n const a = amount || 1;\n _filter.convolution.call(this, [\n 0, -1 * a, 0,\n -1 * a, 1 + 4 * a, -1 * a,\n 0, -1 * a, 0,\n ]);\n };\n\n _filter.emboss = function (size) {\n const s = size || 1;\n _filter.convolution.call(this, [\n -2 * s, -1 * s, 0,\n -1 * s, 1, 1 * s,\n 0, 1 * s, 2 * s,\n ]);\n };\n\n // -------------------------------------------------------------------------\n // Blur Filter\n\n _filter.blur = function (size) {\n const blurSizeX = (size / 7) / _width;\n const blurSizeY = (size / 7) / _height;\n\n const program = _compileShader(_filter.blur.SHADER);\n\n // Vertical\n gl.uniform2f(program.uniform.px, 0, blurSizeY);\n _draw(DRAW.INTERMEDIATE);\n\n // Horizontal\n gl.uniform2f(program.uniform.px, blurSizeX, 0);\n _draw();\n };\n\n _filter.blur.SHADER = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform sampler2D texture;',\n 'uniform vec2 px;',\n\n 'void main(void) {',\n 'gl_FragColor = vec4(0.0);',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;',\n 'gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;',\n 'gl_FragColor += texture2D(texture, vUv )*0.159576912161;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x, 1.0*px.y))*0.147308056121;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x, 2.0*px.y))*0.115876621105;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x, 3.0*px.y))*0.0776744219933;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x, 4.0*px.y))*0.0443683338718;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x, 5.0*px.y))*0.0215963866053;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794;',\n 'gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265;',\n '}',\n ].join('\\n');\n\n // -------------------------------------------------------------------------\n // Pixelate Filter\n\n _filter.pixelate = function (size) {\n const blurSizeX = (size) / _width;\n const blurSizeY = (size) / _height;\n\n const program = _compileShader(_filter.pixelate.SHADER);\n\n // Horizontal\n gl.uniform2f(program.uniform.size, blurSizeX, blurSizeY);\n _draw();\n };\n\n _filter.pixelate.SHADER = [\n 'precision highp float;',\n 'varying vec2 vUv;',\n 'uniform vec2 size;',\n 'uniform sampler2D texture;',\n\n 'vec2 pixelate(vec2 coord, vec2 size) {',\n 'return floor( coord / size ) * size;',\n '}',\n\n 'void main(void) {',\n 'gl_FragColor = vec4(0.0);',\n 'vec2 coord = pixelate(vUv, size);',\n 'gl_FragColor += texture2D(texture, coord);',\n '}',\n ].join('\\n');\n};\n\nexports.Canvas = WebGLImageFilter;\n", "import { log } from './log.js';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as fxImage from './imagefx.js';\n\n// internal temp canvases\nlet inCanvas = null;\nlet outCanvas = null;\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nfunction process(input, config) {\n let tensor;\n if (input instanceof tf.Tensor) {\n tensor = tf.clone(input);\n } else {\n const originalWidth = input.naturalWidth || input.videoWidth || input.width || (input.shape && (input.shape[1] > 0));\n const originalHeight = input.naturalHeight || input.videoHeight || input.height || (input.shape && (input.shape[2] > 0));\n let targetWidth = originalWidth;\n let targetHeight = originalHeight;\n if (config.filter.width > 0) targetWidth = config.filter.width;\n else if (config.filter.height > 0) targetWidth = originalWidth * (config.filter.height / originalHeight);\n if (config.filter.height > 0) targetHeight = config.filter.height;\n else if (config.filter.width > 0) targetHeight = originalHeight * (config.filter.width / originalWidth);\n if (!targetWidth || !targetHeight) {\n log('Human: invalid input', input);\n return null;\n }\n if (!inCanvas || (inCanvas.width !== targetWidth) || (inCanvas.height !== targetHeight)) {\n inCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n if (inCanvas.width !== targetWidth) inCanvas.width = targetWidth;\n if (inCanvas.height !== targetHeight) inCanvas.height = targetHeight;\n }\n const ctx = inCanvas.getContext('2d');\n if (input instanceof ImageData) ctx.putImageData(input, 0, 0);\n else ctx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);\n if (config.filter.enabled) {\n if (!this.fx || !outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas.height !== outCanvas.height)) {\n outCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(inCanvas.width, inCanvas.height) : document.createElement('canvas');\n if (outCanvas.width !== inCanvas.width) outCanvas.width = inCanvas.width;\n if (outCanvas.height !== inCanvas.height) outCanvas.height = inCanvas.height;\n this.fx = tf.ENV.flags.IS_BROWSER ? new fxImage.Canvas({ canvas: outCanvas }) : null; // && (typeof document !== 'undefined')\n }\n if (!this.fx) return inCanvas;\n this.fx.reset();\n this.fx.addFilter('brightness', config.filter.brightness); // must have at least one filter enabled\n if (config.filter.contrast !== 0) this.fx.addFilter('contrast', config.filter.contrast);\n if (config.filter.sharpness !== 0) this.fx.addFilter('sharpen', config.filter.sharpness);\n if (config.filter.blur !== 0) this.fx.addFilter('blur', config.filter.blur);\n if (config.filter.saturation !== 0) this.fx.addFilter('saturation', config.filter.saturation);\n if (config.filter.hue !== 0) this.fx.addFilter('hue', config.filter.hue);\n if (config.filter.negative) this.fx.addFilter('negative');\n if (config.filter.sepia) this.fx.addFilter('sepia');\n if (config.filter.vintage) this.fx.addFilter('brownie');\n if (config.filter.sepia) this.fx.addFilter('sepia');\n if (config.filter.kodachrome) this.fx.addFilter('kodachrome');\n if (config.filter.technicolor) this.fx.addFilter('technicolor');\n if (config.filter.polaroid) this.fx.addFilter('polaroid');\n if (config.filter.pixelate !== 0) this.fx.addFilter('pixelate', config.filter.pixelate);\n this.fx.apply(inCanvas);\n // read pixel data\n /*\n const gl = outCanvas.getContext('webgl');\n if (gl) {\n const glBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 4);\n const pixBuffer = new Uint8Array(outCanvas.width * outCanvas.height * 3);\n gl.readPixels(0, 0, outCanvas.width, outCanvas.height, gl.RGBA, gl.UNSIGNED_BYTE, glBuffer);\n // gl returns rbga while we only need rgb, so discarding alpha channel\n // gl returns starting point as lower left, so need to invert vertical\n let i = 0;\n for (let y = outCanvas.height - 1; y >= 0; y--) {\n for (let x = 0; x < outCanvas.width; x++) {\n const index = (x + y * outCanvas.width) * 4;\n pixBuffer[i++] = glBuffer[index + 0];\n pixBuffer[i++] = glBuffer[index + 1];\n pixBuffer[i++] = glBuffer[index + 2];\n }\n }\n outCanvas.data = pixBuffer;\n }\n */\n } else {\n outCanvas = inCanvas;\n }\n let pixels;\n if (outCanvas.data) {\n const shape = [outCanvas.height, outCanvas.width, 3];\n pixels = tf.tensor3d(outCanvas.data, shape, 'int32');\n } else if ((config.backend === 'webgl') || (outCanvas instanceof ImageData)) {\n // tf kernel-optimized method to get imagedata, also if input is imagedata, just use it\n pixels = tf.browser.fromPixels(outCanvas);\n } else {\n // cpu and wasm kernel does not implement efficient fromPixels method nor we can use canvas as-is, so we do a silly one more canvas\n const tempCanvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(targetWidth, targetHeight) : document.createElement('canvas');\n tempCanvas.width = targetWidth;\n tempCanvas.height = targetHeight;\n const tempCtx = tempCanvas.getContext('2d');\n tempCtx?.drawImage(outCanvas, 0, 0);\n const data = tempCtx?.getImageData(0, 0, targetWidth, targetHeight);\n pixels = tf.browser.fromPixels(data);\n }\n const casted = pixels.toFloat();\n tensor = casted.expandDims(0);\n pixels.dispose();\n casted.dispose();\n }\n return { tensor, canvas: config.filter.return ? outCanvas : null };\n}\n\nexports.process = process;\n", "import { log } from './log.js';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as backend from './tfjs/backend.js';\nimport * as facemesh from './blazeface/facemesh.js';\nimport * as age from './age/age.js';\nimport * as gender from './gender/gender.js';\nimport * as emotion from './emotion/emotion.js';\nimport * as embedding from './embedding/embedding.js';\nimport * as posenet from './posenet/posenet.js';\nimport * as blazepose from './blazepose/blazepose.js';\nimport * as handpose from './handpose/handpose.js';\nimport * as gesture from './gesture/gesture.js';\nimport * as image from './image.js';\nimport * as profile from './profile.js';\nimport * as config from '../config.js';\nimport * as sample from './sample.js';\nimport * as app from '../package.json';\n\n// helper function: gets elapsed time on both browser and nodejs\nconst now = () => {\n if (typeof performance !== 'undefined') return performance.now();\n return parseInt(Number(process.hrtime.bigint()) / 1000 / 1000);\n};\n\n// helper function: perform deep merge of multiple objects so it allows full inheriance with overrides\nfunction mergeDeep(...objects) {\n const isObject = (obj) => obj && typeof obj === 'object';\n return objects.reduce((prev, obj) => {\n Object.keys(obj || {}).forEach((key) => {\n const pVal = prev[key];\n const oVal = obj[key];\n if (Array.isArray(pVal) && Array.isArray(oVal)) {\n prev[key] = pVal.concat(...oVal);\n } else if (isObject(pVal) && isObject(oVal)) {\n prev[key] = mergeDeep(pVal, oVal);\n } else {\n prev[key] = oVal;\n }\n });\n return prev;\n }, {});\n}\n\nclass Human {\n constructor(userConfig = {}) {\n this.tf = tf;\n this.version = app.version;\n this.config = mergeDeep(config.default, userConfig);\n this.fx = null;\n this.state = 'idle';\n this.numTensors = 0;\n this.analyzeMemoryLeaks = false;\n this.checkSanity = false;\n this.firstRun = true;\n this.perf = {};\n // object that contains all initialized models\n this.models = {\n facemesh: null,\n posenet: null,\n handpose: null,\n iris: null,\n age: null,\n gender: null,\n emotion: null,\n };\n // export raw access to underlying models\n this.facemesh = facemesh;\n this.age = age;\n this.gender = gender;\n this.emotion = emotion;\n this.body = posenet;\n this.hand = handpose;\n }\n\n profile() {\n if (this.config.profile) return profile.data;\n return {};\n }\n\n // helper function: measure tensor leak\n analyze(...msg) {\n if (!this.analyzeMemoryLeaks) return;\n const current = tf.engine().state.numTensors;\n const previous = this.numTensors;\n this.numTensors = current;\n const leaked = current - previous;\n if (leaked !== 0) log(...msg, leaked);\n }\n\n // quick sanity check on inputs\n sanity(input) {\n if (!this.checkSanity) return null;\n if (!input) return 'input is not defined';\n if (tf.ENV.flags.IS_NODE && !(input instanceof tf.Tensor)) {\n return 'input must be a tensor';\n }\n try {\n tf.getBackend();\n } catch {\n return 'backend not loaded';\n }\n return null;\n }\n\n simmilarity(embedding1, embedding2) {\n if (this.config.face.embedding.enabled) return embedding.simmilarity(embedding1, embedding2);\n return 0;\n }\n\n // preload models, not explicitly required as it's done automatically on first use\n async load(userConfig) {\n this.state = 'load';\n const timeStamp = now();\n if (userConfig) this.config = mergeDeep(this.config, userConfig);\n\n if (this.firstRun) {\n log(`version: ${this.version} TensorFlow/JS version: ${tf.version_core}`);\n await this.checkBackend(true);\n if (tf.ENV.flags.IS_BROWSER) {\n log('configuration:', this.config);\n log('tf flags:', tf.ENV.flags);\n }\n this.firstRun = false;\n }\n\n if (this.config.async) {\n [\n this.models.facemesh,\n this.models.age,\n this.models.gender,\n this.models.emotion,\n this.models.embedding,\n this.models.posenet,\n this.models.handpose,\n this.models.blazepose,\n ] = await Promise.all([\n this.models.facemesh || (this.config.face.enabled ? facemesh.load(this.config) : null),\n this.models.age || ((this.config.face.enabled && this.config.face.age.enabled) ? age.load(this.config) : null),\n this.models.gender || ((this.config.face.enabled && this.config.face.gender.enabled) ? gender.load(this.config) : null),\n this.models.emotion || ((this.config.face.enabled && this.config.face.emotion.enabled) ? emotion.load(this.config) : null),\n this.models.embedding || ((this.config.face.enabled && this.config.face.embedding.enabled) ? embedding.load(this.config) : null),\n this.models.posenet || (this.config.body.enabled ? posenet.load(this.config) : null),\n this.models.handpose || (this.config.hand.enabled ? handpose.load(this.config) : null),\n this.models.blazepose || (this.config.pose.enabled ? blazepose.load(this.config) : null),\n ]);\n } else {\n if (this.config.face.enabled && !this.models.facemesh) this.models.facemesh = await facemesh.load(this.config);\n if (this.config.face.enabled && this.config.face.age.enabled && !this.models.age) this.models.age = await age.load(this.config);\n if (this.config.face.enabled && this.config.face.gender.enabled && !this.models.gender) this.models.gender = await gender.load(this.config);\n if (this.config.face.enabled && this.config.face.emotion.enabled && !this.models.emotion) this.models.emotion = await emotion.load(this.config);\n if (this.config.face.enabled && this.config.face.embedding.enabled && !this.models.embedding) this.models.embedding = await embedding.load(this.config);\n if (this.config.body.enabled && !this.models.posenet) this.models.posenet = await posenet.load(this.config);\n if (this.config.hand.enabled && !this.models.handpose) this.models.handpose = await handpose.load(this.config);\n if (this.config.pose.enabled && !this.models.blazepose) this.models.blazepose = await blazepose.load(this.config);\n }\n const current = Math.trunc(now() - timeStamp);\n if (current > (this.perf.load || 0)) this.perf.load = current;\n }\n\n // check if backend needs initialization if it changed\n async checkBackend(force) {\n if (this.config.backend && (this.config.backend !== '') && force || (tf.getBackend() !== this.config.backend)) {\n const timeStamp = now();\n this.state = 'backend';\n /* force backend reload\n if (this.config.backend in tf.engine().registry) {\n const backendFactory = tf.findBackendFactory(this.config.backend);\n tf.removeBackend(this.config.backend);\n tf.registerBackend(this.config.backend, backendFactory);\n } else {\n log('Backend not registred:', this.config.backend);\n }\n */\n\n log('setting backend:', this.config.backend);\n\n if (this.config.backend === 'wasm') {\n log('settings wasm path:', this.config.wasmPath);\n tf.setWasmPaths(this.config.wasmPath);\n const simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n if (!simd) log('warning: wasm simd support is not enabled');\n }\n\n if (this.config.backend === 'humangl') {\n log('registering humangl backend');\n backend.register();\n }\n\n await tf.setBackend(this.config.backend);\n tf.enableProdMode();\n /* debug mode is really too mcuh\n tf.enableDebugMode();\n */\n if (tf.getBackend() === 'webgl') {\n if (this.config.deallocate) {\n log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', this.config.deallocate);\n tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', this.config.deallocate ? 0 : -1);\n }\n tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n const gl = await tf.backend().getGPGPUContext().gl;\n log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);\n }\n await tf.ready();\n this.perf.backend = Math.trunc(now() - timeStamp);\n }\n }\n\n async detectFace(input) {\n // run facemesh, includes blazeface and iris\n // eslint-disable-next-line no-async-promise-executor\n let timeStamp;\n let ageRes;\n let genderRes;\n let emotionRes;\n let embeddingRes;\n const faceRes = [];\n this.state = 'run:face';\n timeStamp = now();\n const faces = await this.models.facemesh?.estimateFaces(input, this.config);\n this.perf.face = Math.trunc(now() - timeStamp);\n for (const face of faces) {\n this.analyze('Get Face');\n\n // is something went wrong, skip the face\n if (!face.image || face.image.isDisposedInternal) {\n log('Face object is disposed:', face.image);\n continue;\n }\n\n // run age, inherits face from blazeface\n this.analyze('Start Age:');\n if (this.config.async) {\n ageRes = this.config.face.age.enabled ? age.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:age';\n timeStamp = now();\n ageRes = this.config.face.age.enabled ? await age.predict(face.image, this.config) : {};\n this.perf.age = Math.trunc(now() - timeStamp);\n }\n\n // run gender, inherits face from blazeface\n this.analyze('Start Gender:');\n if (this.config.async) {\n genderRes = this.config.face.gender.enabled ? gender.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:gender';\n timeStamp = now();\n genderRes = this.config.face.gender.enabled ? await gender.predict(face.image, this.config) : {};\n this.perf.gender = Math.trunc(now() - timeStamp);\n }\n\n // run emotion, inherits face from blazeface\n this.analyze('Start Emotion:');\n if (this.config.async) {\n emotionRes = this.config.face.emotion.enabled ? emotion.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:emotion';\n timeStamp = now();\n emotionRes = this.config.face.emotion.enabled ? await emotion.predict(face.image, this.config) : {};\n this.perf.emotion = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Emotion:');\n\n // run emotion, inherits face from blazeface\n this.analyze('Start Embedding:');\n if (this.config.async) {\n embeddingRes = this.config.face.embedding.enabled ? embedding.predict(face.image, this.config) : {};\n } else {\n this.state = 'run:embedding';\n timeStamp = now();\n embeddingRes = this.config.face.embedding.enabled ? await embedding.predict(face.image, this.config) : {};\n this.perf.embedding = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Emotion:');\n\n // if async wait for results\n if (this.config.async) {\n [ageRes, genderRes, emotionRes, embeddingRes] = await Promise.all([ageRes, genderRes, emotionRes, embeddingRes]);\n }\n\n this.analyze('Finish Face:');\n // dont need face anymore\n face.image.dispose();\n\n // calculate iris distance\n // iris: array[ center, left, top, right, bottom]\n if (!this.config.face.iris.enabled) {\n delete face.annotations.leftEyeIris;\n delete face.annotations.rightEyeIris;\n }\n const irisSize = (face.annotations.leftEyeIris && face.annotations.rightEyeIris)\n /* average human iris size is 11.7mm */\n ? 11.7 * Math.max(Math.abs(face.annotations.leftEyeIris[3][0] - face.annotations.leftEyeIris[1][0]), Math.abs(face.annotations.rightEyeIris[4][1] - face.annotations.rightEyeIris[2][1]))\n : 0;\n\n // combine results\n faceRes.push({\n confidence: face.confidence,\n box: face.box,\n mesh: face.mesh,\n boxRaw: face.boxRaw,\n meshRaw: face.meshRaw,\n annotations: face.annotations,\n age: ageRes.age,\n gender: genderRes.gender,\n genderConfidence: genderRes.confidence,\n emotion: emotionRes,\n embedding: embeddingRes,\n iris: (irisSize !== 0) ? Math.trunc(irisSize) / 100 : 0,\n });\n this.analyze('End Face');\n }\n this.analyze('End FaceMesh:');\n if (this.config.async) {\n if (this.perf.face) delete this.perf.face;\n if (this.perf.age) delete this.perf.age;\n if (this.perf.gender) delete this.perf.gender;\n if (this.perf.emotion) delete this.perf.emotion;\n }\n return faceRes;\n }\n\n async image(input, userConfig = {}) {\n this.state = 'image';\n this.config = mergeDeep(this.config, userConfig);\n const process = image.process(input, this.config);\n process.tensor.dispose();\n return process.canvas;\n }\n\n // main detect function\n async detect(input, userConfig = {}) {\n // detection happens inside a promise\n return new Promise(async (resolve) => {\n this.state = 'config';\n let timeStamp;\n\n // update configuration\n this.config = mergeDeep(this.config, userConfig);\n\n // sanity checks\n this.state = 'check';\n const error = this.sanity(input);\n if (error) {\n log(error, input);\n resolve({ error });\n }\n\n let poseRes;\n let blazeposeRes;\n let handRes;\n let faceRes;\n\n const timeStart = now();\n\n // configure backend\n await this.checkBackend();\n\n // load models if enabled\n await this.load();\n\n if (this.config.scoped) tf.engine().startScope();\n this.analyze('Start Scope:');\n\n timeStamp = now();\n const process = image.process(input, this.config);\n if (!process || !process.tensor) {\n log('could not convert input to tensor');\n resolve({ error: 'could not convert input to tensor' });\n return;\n }\n this.perf.image = Math.trunc(now() - timeStamp);\n this.analyze('Get Image:');\n\n // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n if (this.config.async) {\n faceRes = this.config.face.enabled ? this.detectFace(process.tensor) : [];\n if (this.perf.face) delete this.perf.face;\n } else {\n this.state = 'run:face';\n timeStamp = now();\n faceRes = this.config.face.enabled ? await this.detectFace(process.tensor) : [];\n this.perf.face = Math.trunc(now() - timeStamp);\n }\n\n // run posenet\n this.analyze('Start Body:');\n if (this.config.async) {\n poseRes = this.config.body.enabled ? this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n if (this.perf.body) delete this.perf.body;\n } else {\n this.state = 'run:body';\n timeStamp = now();\n poseRes = this.config.body.enabled ? await this.models.posenet?.estimatePoses(process.tensor, this.config) : [];\n this.perf.body = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Body:');\n\n // run posenet\n this.analyze('Start Pose:');\n if (this.config.async) {\n blazeposeRes = this.config.pose.enabled ? blazepose.predict(process.tensor, this.config) : [];\n if (this.perf.pose) delete this.perf.pose;\n } else {\n this.state = 'run:pose';\n timeStamp = now();\n blazeposeRes = this.config.pose.enabled ? await blazepose.predict(process.tensor, this.config) : [];\n this.perf.pose = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Pose:');\n\n // run handpose\n this.analyze('Start Hand:');\n if (this.config.async) {\n handRes = this.config.hand.enabled ? this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n if (this.perf.hand) delete this.perf.hand;\n } else {\n this.state = 'run:hand';\n timeStamp = now();\n handRes = this.config.hand.enabled ? await this.models.handpose?.estimateHands(process.tensor, this.config) : [];\n this.perf.hand = Math.trunc(now() - timeStamp);\n }\n this.analyze('End Hand:');\n\n // if async wait for results\n if (this.config.async) {\n [faceRes, poseRes, blazeposeRes, handRes] = await Promise.all([faceRes, poseRes, blazeposeRes, handRes]);\n }\n process.tensor.dispose();\n\n if (this.config.scoped) tf.engine().endScope();\n this.analyze('End Scope:');\n\n let gestureRes = [];\n if (this.config.gesture.enabled) {\n timeStamp = now();\n gestureRes = [...gesture.face(faceRes), ...gesture.body(poseRes), ...gesture.hand(handRes), ...gesture.iris(faceRes)];\n if (!this.config.async) this.perf.gesture = Math.trunc(now() - timeStamp);\n else if (this.perf.gesture) delete this.perf.gesture;\n }\n\n this.perf.total = Math.trunc(now() - timeStart);\n this.state = 'idle';\n resolve({ face: faceRes, body: poseRes, hand: handRes, pose: blazeposeRes, gesture: gestureRes, performance: this.perf, canvas: process.canvas });\n });\n }\n\n async warmupBitmap() {\n const b64toBlob = (base64, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n let blob;\n let res;\n switch (this.config.warmup) {\n case 'face': blob = await b64toBlob(sample.face); break;\n case 'full': blob = await b64toBlob(sample.body); break;\n default: blob = null;\n }\n if (blob) {\n const bitmap = await createImageBitmap(blob);\n res = await this.detect(bitmap, config);\n bitmap.close();\n }\n return res;\n }\n\n async warmupCanvas() {\n return new Promise((resolve) => {\n let src;\n let size = 0;\n switch (this.config.warmup) {\n case 'face':\n size = 256;\n src = 'data:image/jpeg;base64,' + sample.face;\n break;\n case 'full':\n size = 1200;\n src = 'data:image/jpeg;base64,' + sample.body;\n break;\n default:\n src = null;\n }\n const img = new Image(size, size);\n img.onload = () => {\n const canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(size, size) : document.createElement('canvas');\n canvas.width = size;\n canvas.height = size;\n const ctx = canvas.getContext('2d');\n ctx.drawImage(img, 0, 0);\n const data = ctx.getImageData(0, 0, size, size);\n this.detect(data, config).then((res) => resolve(res));\n };\n if (src) img.src = src;\n else resolve(null);\n });\n }\n\n async warmup(userConfig) {\n const t0 = now();\n if (userConfig) this.config = mergeDeep(this.config, userConfig);\n const video = this.config.videoOptimized;\n this.config.videoOptimized = false;\n let res;\n if (typeof createImageBitmap === 'function') res = await this.warmupBitmap();\n else res = await this.warmupCanvas();\n this.config.videoOptimized = video;\n const t1 = now();\n log('Warmup', this.config.warmup, (t1 - t0), res);\n return res;\n }\n}\n\nexport { Human as default };\n", "// helper function: wrapper around console output\nexport function log(...msg) {\n const dt = new Date();\n const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n // eslint-disable-next-line no-console\n if (msg) console.log(ts, 'Human:', ...msg);\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nexport const config = {\n name: 'humangl',\n priority: 99,\n canvas: null,\n width: 1024,\n height: 1024,\n webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n alpha: false,\n antialias: false,\n premultipliedAlpha: false,\n preserveDrawingBuffer: false,\n depth: false,\n stencil: false,\n failIfMajorPerformanceCaveat: false,\n desynchronized: true,\n },\n};\n\nexport function register() {\n if (!tf.findBackend(config.name)) {\n // @ts-ignore\n config.canvas = (typeof OffscreenCanvas !== 'undefined') ? new OffscreenCanvas(config.width, config.height) : document.createElement('canvas');\n // @ts-ignore\n const gl = config.canvas.getContext('webgl2', config.webGLattr);\n tf.setWebGLContext(2, gl);\n const ctx = new tf.GPGPUContext(gl);\n tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n const kernels = tf.getKernelsForBackend('webgl');\n kernels.forEach((kernelConfig) => {\n const newKernelConfig = { ...kernelConfig, backendName: config.name };\n tf.registerKernel(newKernelConfig);\n });\n tf.ENV.set('WEBGL_VERSION', 2);\n tf.ENV.set('WEBGL_MAX_TEXTURE_SIZE', gl.getParameter(gl.MAX_TEXTURE_SIZE));\n tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true);\n tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);\n }\n}\n", "import { log } from '../log.js';\nimport * as tf from '../../dist/tfjs.esm.js';\n// import * as profile from '../profile.js';\n\nconst models = {};\n\nexport async function load(config) {\n if (!models.blazepose) {\n models.blazepose = await tf.loadGraphModel(config.pose.modelPath);\n log(`load model: ${config.pose.modelPath.match(/\\/(.*)\\./)[1]}`);\n }\n return models.blazepose;\n}\n\nexport async function predict(image, config) {\n if (!models.blazepose) return null;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [config.pose.inputSize, config.pose.inputSize], false);\n const enhance = tf.div(resize, 127.5).sub(1);\n tf.dispose(resize);\n const logits = await models.blazepose.predict(enhance);\n //\n tf.dispose(enhance);\n logits.map((logit) => logit.dispose());\n resolve(logits);\n });\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\n\nfunction getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\nfunction getBoxCenter(box) {\n return [\n box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n ];\n}\nfunction cutBoxFromImageAndResize(box, image, cropSize) {\n const h = image.shape[1];\n const w = image.shape[2];\n const boxes = [[\n box.startPoint[1] / h,\n box.startPoint[0] / w,\n box.endPoint[1] / h,\n box.endPoint[0] / w,\n ]];\n return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\nfunction scaleBoxCoordinates(box, factor) {\n const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n const palmLandmarks = box.palmLandmarks.map((coord) => {\n const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n return scaledCoord;\n });\n return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\nfunction enlargeBox(box, factor = 1.5) {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];\n const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\nfunction squarifyBox(box) {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const maxEdge = Math.max(...size);\n const halfSize = maxEdge / 2;\n const startPoint = [centers[0] - halfSize, centers[1] - halfSize];\n const endPoint = [centers[0] + halfSize, centers[1] + halfSize];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\nfunction shiftBox(box, shiftFactor) {\n const boxSize = [\n box.endPoint[0] - box.startPoint[0],\n box.endPoint[1] - box.startPoint[1],\n ];\n const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]];\n const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]];\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\nexport {\n cutBoxFromImageAndResize,\n enlargeBox,\n getBoxCenter,\n getBoxSize,\n scaleBoxCoordinates,\n shiftBox,\n squarifyBox,\n};\n", "function normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\nfunction computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\nconst buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\nfunction dot(v1, v2) {\n let product = 0;\n for (let i = 0; i < v1.length; i++) {\n product += v1[i] * v2[i];\n }\n return product;\n}\nfunction getColumnFrom2DArr(arr, columnIndex) {\n const column = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\nfunction multiplyTransformMatrices(mat1, mat2) {\n const product = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) {\n // @ts-ignore\n product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n }\n return product;\n}\nfunction buildRotationMatrix(rotation, center) {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\nfunction invertTransformMatrix(matrix) {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [\n -dot(rotationComponent[0], translationComponent),\n -dot(rotationComponent[1], translationComponent),\n ];\n return [\n rotationComponent[0].concat(invertedTranslation[0]),\n rotationComponent[1].concat(invertedTranslation[1]),\n [0, 0, 1],\n ];\n}\nfunction rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\nexport {\n buildRotationMatrix,\n computeRotation,\n dot,\n getColumnFrom2DArr,\n invertTransformMatrix,\n normalizeRadians,\n rotatePoint,\n};\n", "/* eslint-disable indent */\n/* eslint-disable no-multi-spaces */\n\nexport default {\n backend: 'webgl', // select tfjs backend to use\n wasmPath: '../assets/', // path for wasm binaries\n // only used for backend: wasm\n async: true, // execute enabled models in parallel\n // this disables per-model performance data but\n // slightly increases performance\n // cannot be used if profiling is enabled\n profile: false, // enable tfjs profiling\n // this has significant performance impact\n // only enable for debugging purposes\n // currently only implemented for age,gender,emotion models\n deallocate: false, // aggresively deallocate gpu memory after each usage\n // only valid for webgl backend and only during first call\n // cannot be changed unless library is reloaded\n // this has significant performance impact\n // only enable on low-memory devices\n scoped: false, // enable scoped runs\n // some models *may* have memory leaks,\n // this wrapps everything in a local scope at a cost of performance\n // typically not needed\n videoOptimized: true, // perform additional optimizations when input is video,\n // must be disabled for images\n // basically this skips object box boundary detection for every n frames\n // while maintaining in-box detection since objects cannot move that fast\n warmup: 'face', // what to use for human.warmup(), can be 'none', 'face', 'full'\n // warmup pre-initializes all models for faster inference but can take\n // significant time on startup\n filter: {\n enabled: true, // enable image pre-processing filters\n width: 0, // resize input width\n height: 0, // resize input height\n // if both width and height are set to 0, there is no resizing\n // if just one is set, second one is scaled automatically\n // if both are set, values are used as-is\n return: true, // return processed canvas imagedata in result\n brightness: 0, // range: -1 (darken) to 1 (lighten)\n contrast: 0, // range: -1 (reduce contrast) to 1 (increase contrast)\n sharpness: 0, // range: 0 (no sharpening) to 1 (maximum sharpening)\n blur: 0, // range: 0 (no blur) to N (blur radius in pixels)\n saturation: 0, // range: -1 (reduce saturation) to 1 (increase saturation)\n hue: 0, // range: 0 (no change) to 360 (hue rotation in degrees)\n negative: false, // image negative\n sepia: false, // image sepia colors\n vintage: false, // image vintage colors\n kodachrome: false, // image kodachrome colors\n technicolor: false, // image technicolor colors\n polaroid: false, // image polaroid camera effect\n pixelate: 0, // range: 0 (no pixelate) to N (number of pixels to pixelate)\n },\n\n gesture: {\n enabled: true, // enable simple gesture recognition\n },\n\n face: {\n enabled: true, // controls if specified modul is enabled\n // face.enabled is required for all face models:\n // detector, mesh, iris, age, gender, emotion\n // (note: module is not loaded until it is required)\n detector: {\n modelPath: '../models/blazeface-back.json', // can be 'front' or 'back'.\n // 'front' is optimized for large faces\n // such as front-facing camera and\n // 'back' is optimized for distanct faces.\n inputSize: 256, // fixed value: 128 for front and 256 for 'back'\n rotation: false, // use best-guess rotated face image or just box with rotation as-is\n // false means higher performance, but incorrect mesh mapping if face angle is above 20 degrees\n maxFaces: 10, // maximum number of faces detected in the input\n // should be set to the minimum number for performance\n skipFrames: 11, // how many frames to go without re-running the face bounding box detector\n // only used for video inputs\n // e.g., if model is running st 25 FPS, we can re-use existing bounding\n // box for updated face analysis as the head probably hasn't moved much\n // in short time (10 * 1/25 = 0.25 sec)\n minConfidence: 0.5, // threshold for discarding a prediction\n iouThreshold: 0.2, // threshold for deciding whether boxes overlap too much in\n // non-maximum suppression (0.1 means drop if overlap 10%)\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on score\n // in non-maximum suppression,\n // this is applied on detection objects only and before minConfidence\n },\n\n mesh: {\n enabled: true,\n modelPath: '../models/facemesh.json',\n inputSize: 192, // fixed value\n returnRawData: false, // in addition to standard mesh and box values, return raw normalized values as well\n },\n\n iris: {\n enabled: true,\n modelPath: '../models/iris.json',\n inputSize: 64, // fixed value\n },\n\n age: {\n enabled: true,\n modelPath: '../models/age-ssrnet-imdb.json', // can be 'age-ssrnet-imdb' or 'age-ssrnet-wiki'\n // which determines training set for model\n inputSize: 64, // fixed value\n skipFrames: 31, // how many frames to go without re-running the detector\n // only used for video inputs\n },\n\n gender: {\n enabled: true,\n minConfidence: 0.1, // threshold for discarding a prediction\n modelPath: '../models/gender-ssrnet-imdb.json', // can be 'gender', 'gender-ssrnet-imdb' or 'gender-ssrnet-wiki'\n inputSize: 64, // fixed value\n skipFrames: 41, // how many frames to go without re-running the detector\n // only used for video inputs\n },\n\n emotion: {\n enabled: true,\n inputSize: 64, // fixed value\n minConfidence: 0.2, // threshold for discarding a prediction\n skipFrames: 21, // how many frames to go without re-running the detector\n modelPath: '../models/emotion-large.json', // can be 'mini', 'large'\n },\n\n embedding: {\n enabled: false,\n inputSize: 112, // fixed value\n modelPath: '../models/mobilefacenet.json',\n },\n },\n\n body: {\n enabled: true,\n modelPath: '../models/posenet.json',\n inputSize: 257, // fixed value\n maxDetections: 10, // maximum number of people detected in the input\n // should be set to the minimum number for performance\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on score\n // in non-maximum suppression\n nmsRadius: 20, // radius for deciding points are too close in non-maximum suppression\n outputStride: 16, // size of block in which to run point detectopn, smaller value means higher resolution\n // defined by model itself, can be 8, 16, or 32\n modelType: 'MobileNet', // Human includes MobileNet version, but you can switch to ResNet\n },\n\n pose: { // TBD: not currently in use\n enabled: false,\n scoreThreshold: 0.6, // threshold for deciding when to remove boxes based on score\n // in non-maximum suppression\n iouThreshold: 0.3, // threshold for deciding whether boxes overlap too much\n // in non-maximum suppression\n modelPath: '../models/blazepose.json',\n inputSize: 256, // fixed value\n },\n\n hand: {\n enabled: true,\n rotation: false, // use best-guess rotated hand image or just box with rotation as-is\n // false means higher performance, but incorrect finger mapping if hand is inverted\n inputSize: 256, // fixed value\n skipFrames: 12, // how many frames to go without re-running the hand bounding box detector\n // only used for video inputs\n // e.g., if model is running st 25 FPS, we can re-use existing bounding\n // box for updated hand skeleton analysis as the hand probably\n // hasn't moved much in short time (10 * 1/25 = 0.25 sec)\n minConfidence: 0.1, // threshold for discarding a prediction\n iouThreshold: 0.1, // threshold for deciding whether boxes overlap too much\n // in non-maximum suppression\n scoreThreshold: 0.5, // threshold for deciding when to remove boxes based on\n // score in non-maximum suppression\n maxHands: 1, // maximum number of hands detected in the input\n // should be set to the minimum number for performance\n landmarks: true, // detect hand landmarks or just hand boundary box\n detector: {\n modelPath: '../models/handdetect.json',\n },\n skeleton: {\n modelPath: '../models/handskeleton.json',\n },\n },\n};\n", "// data:image/jpeg;base64,\nexport const face = 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"names": [] } diff --git a/dist/human.node.json b/dist/human.node.json index ade06911..fdb25bc7 100644 --- a/dist/human.node.json +++ b/dist/human.node.json @@ -16,7 +16,7 @@ } ] }, - "src/face/blazeface.js": { + "src/blazeface/blazeface.js": { "bytes": 7024, "imports": [ { @@ -27,7 +27,7 @@ } ] }, - "src/face/box.js": { + "src/blazeface/box.js": { "bytes": 1935, "imports": [ { @@ -35,35 +35,35 @@ } ] }, - "src/face/util.js": { + "src/blazeface/util.js": { "bytes": 3087, "imports": [] }, - "src/face/coords.js": { + "src/blazeface/coords.js": { "bytes": 37915, "imports": [] }, - "src/face/facepipeline.js": { + "src/blazeface/facepipeline.js": { "bytes": 14306, "imports": [ { "path": "dist/tfjs.esm.js" }, { - "path": "src/face/box.js" + "path": "src/blazeface/box.js" }, { - "path": "src/face/util.js" + "path": "src/blazeface/util.js" }, { - "path": "src/face/coords.js" + "path": "src/blazeface/coords.js" }, { "path": "src/log.js" } ] }, - "src/face/facemesh.js": { + "src/blazeface/facemesh.js": { "bytes": 2991, "imports": [ { @@ -73,13 +73,13 @@ "path": "dist/tfjs.esm.js" }, { - "path": "src/face/blazeface.js" + "path": "src/blazeface/blazeface.js" }, { - "path": "src/face/facepipeline.js" + "path": "src/blazeface/facepipeline.js" }, { - "path": "src/face/coords.js" + "path": "src/blazeface/coords.js" } ] }, @@ -147,7 +147,7 @@ } ] }, - "src/body/modelBase.js": { + "src/posenet/modelBase.js": { "bytes": 1343, "imports": [ { @@ -155,78 +155,78 @@ } ] }, - "src/body/heapSort.js": { + "src/posenet/heapSort.js": { "bytes": 1590, "imports": [] }, - "src/body/buildParts.js": { + "src/posenet/buildParts.js": { "bytes": 1775, "imports": [ { - "path": "src/body/heapSort.js" + "path": "src/posenet/heapSort.js" } ] }, - "src/body/keypoints.js": { + "src/posenet/keypoints.js": { "bytes": 2011, "imports": [] }, - "src/body/vectors.js": { + "src/posenet/vectors.js": { "bytes": 1273, "imports": [ { - "path": "src/body/keypoints.js" + "path": "src/posenet/keypoints.js" } ] }, - "src/body/decoders.js": { + "src/posenet/decoders.js": { "bytes": 2083, "imports": [ { "path": "dist/tfjs.esm.js" }, { - "path": "src/body/keypoints.js" + "path": "src/posenet/keypoints.js" } ] }, - "src/body/decodePose.js": { + "src/posenet/decodePose.js": { "bytes": 5368, "imports": [ { - "path": "src/body/keypoints.js" + "path": "src/posenet/keypoints.js" }, { - "path": "src/body/vectors.js" + "path": "src/posenet/vectors.js" }, { - "path": "src/body/decoders.js" + "path": "src/posenet/decoders.js" } ] }, - "src/body/decodeMultiple.js": { + "src/posenet/decodeMultiple.js": { "bytes": 2373, "imports": [ { - "path": "src/body/buildParts.js" + "path": "src/posenet/buildParts.js" }, { - "path": "src/body/decodePose.js" + "path": "src/posenet/decodePose.js" }, { - "path": "src/body/vectors.js" + "path": "src/posenet/vectors.js" } ] }, - "src/body/util.js": { + "src/posenet/util.js": { "bytes": 2262, "imports": [ { - "path": "src/body/keypoints.js" + "path": "src/posenet/keypoints.js" } ] }, - "src/body/modelPoseNet.js": { + "src/posenet/modelPoseNet.js": { "bytes": 2519, "imports": [ { @@ -236,34 +236,45 @@ "path": "dist/tfjs.esm.js" }, { - "path": "src/body/modelBase.js" + "path": "src/posenet/modelBase.js" }, { - "path": "src/body/decodeMultiple.js" + "path": "src/posenet/decodeMultiple.js" }, { - "path": "src/body/decodePose.js" + "path": "src/posenet/decodePose.js" }, { - "path": "src/body/util.js" + "path": "src/posenet/util.js" } ] }, - "src/body/posenet.js": { + "src/posenet/posenet.js": { "bytes": 712, "imports": [ { - "path": "src/body/modelPoseNet.js" + "path": "src/posenet/modelPoseNet.js" }, { - "path": "src/body/keypoints.js" + "path": "src/posenet/keypoints.js" }, { - "path": "src/body/util.js" + "path": "src/posenet/util.js" } ] }, - "src/hand/box.js": { + "src/blazepose/blazepose.js": { + "bytes": 856, + "imports": [ + { + "path": "src/log.js" + }, + { + "path": "dist/tfjs.esm.js" + } + ] + }, + "src/handpose/box.js": { "bytes": 2522, "imports": [ { @@ -271,43 +282,43 @@ } ] }, - "src/hand/handdetector.js": { + "src/handpose/handdetector.js": { "bytes": 3548, "imports": [ { "path": "dist/tfjs.esm.js" }, { - "path": "src/hand/box.js" + "path": "src/handpose/box.js" } ] }, - "src/hand/util.js": { + "src/handpose/util.js": { "bytes": 2346, "imports": [] }, - "src/hand/handpipeline.js": { + "src/handpose/handpipeline.js": { "bytes": 7246, "imports": [ { "path": "dist/tfjs.esm.js" }, { - "path": "src/hand/box.js" + "path": "src/handpose/box.js" }, { - "path": "src/hand/util.js" + "path": "src/handpose/util.js" }, { "path": "src/log.js" } ] }, - "src/hand/anchors.js": { + "src/handpose/anchors.js": { "bytes": 224151, "imports": [] }, - "src/hand/handpose.js": { + "src/handpose/handpose.js": { "bytes": 2578, "imports": [ { @@ -317,13 +328,13 @@ "path": "dist/tfjs.esm.js" }, { - "path": "src/hand/handdetector.js" + "path": "src/handpose/handdetector.js" }, { - "path": "src/hand/handpipeline.js" + "path": "src/handpose/handpipeline.js" }, { - "path": "src/hand/anchors.js" + "path": "src/handpose/anchors.js" } ] }, @@ -350,7 +361,7 @@ ] }, "config.js": { - "bytes": 9644, + "bytes": 10110, "imports": [] }, "src/sample.js": { @@ -362,7 +373,7 @@ "imports": [] }, "src/human.js": { - "bytes": 17544, + "bytes": 18921, "imports": [ { "path": "src/log.js" @@ -374,7 +385,7 @@ "path": "src/tfjs/backend.js" }, { - "path": "src/face/facemesh.js" + "path": "src/blazeface/facemesh.js" }, { "path": "src/age/age.js" @@ -389,10 +400,13 @@ "path": "src/embedding/embedding.js" }, { - "path": "src/body/posenet.js" + "path": "src/posenet/posenet.js" }, { - "path": "src/hand/handpose.js" + "path": "src/blazepose/blazepose.js" + }, + { + "path": "src/handpose/handpose.js" }, { "path": "src/gesture/gesture.js" @@ -419,7 +433,7 @@ "dist/human.node-gpu.js.map": { "imports": [], "inputs": {}, - "bytes": 707547 + "bytes": 711946 }, "dist/human.node-gpu.js": { "imports": [], @@ 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